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@@ -33,3 +33,9 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+data_test_overview_panel_\#total_approved_accounts_appsflyer.xlsx filter=lfs diff=lfs merge=lfs -text
+Pickle_files/main_df filter=lfs diff=lfs merge=lfs -text
+raw_data_nov7_combined1.xlsx filter=lfs diff=lfs merge=lfs -text
+upf_data_converted_old.xlsx filter=lfs diff=lfs merge=lfs -text
+upf_data_converted_randomized_resp_metrics.xlsx filter=lfs diff=lfs merge=lfs -text
+upf_data_converted.xlsx filter=lfs diff=lfs merge=lfs -text

4_Model_Build_persistant_data.py

@@ -0,0 +1,714 @@
+'''
+MMO Build Sprint 3
+additions : adding more variables to session state for saved model : random effect, predicted train & test
+
+MMO Build Sprint 4
+additions : ability to run models for different response metrics
+'''
+
+import streamlit as st
+import pandas as pd
+import plotly.express as px
+import plotly.graph_objects as go
+from Eda_functions import format_numbers
+import numpy as np
+import pickle
+from st_aggrid import AgGrid
+from st_aggrid import GridOptionsBuilder, GridUpdateMode
+from utilities import set_header, load_local_css
+from st_aggrid import GridOptionsBuilder
+import time
+import itertools
+import statsmodels.api as sm
+import numpy as npc
+import re
+import itertools
+from sklearn.metrics import mean_absolute_error, r2_score, mean_absolute_percentage_error
+from sklearn.preprocessing import MinMaxScaler
+import os
+import matplotlib.pyplot as plt
+from statsmodels.stats.outliers_influence import variance_inflation_factor
+
+st.set_option('deprecation.showPyplotGlobalUse', False)
+import statsmodels.api as sm
+import statsmodels.formula.api as smf
+
+from datetime import datetime
+import seaborn as sns
+from Data_prep_functions import *
+
+
+@st.cache_resource(show_spinner=False)
+def save_to_pickle(file_path, final_df):
+    # Open the file in write-binary mode and dump the objects
+    with open(file_path, "wb") as f:
+        pickle.dump({"final_df_transformed": final_df}, f)
+
+def get_random_effects(media_data, panel_col, mdf):
+    random_eff_df = pd.DataFrame(columns=[panel_col, "random_effect"])
+
+    for i, market in enumerate(media_data[panel_col].unique()):
+        print(i, end='\r')
+        intercept = mdf.random_effects[market].values[0]
+        random_eff_df.loc[i, 'random_effect'] = intercept
+        random_eff_df.loc[i, panel_col] = market
+
+    return random_eff_df
+
+
+def mdf_predict(X_df, mdf, random_eff_df):
+    X = X_df.copy()
+    X['fixed_effect'] = mdf.predict(X)
+    X = pd.merge(X, random_eff_df, on=panel_col, how='left')
+    X['pred'] = X['fixed_effect'] + X['random_effect']
+    # X.to_csv('Test/megred_df.csv',index=False)
+    X.drop(columns=['fixed_effect', 'random_effect'], inplace=True)
+    return X['pred']
+
+
+st.set_page_config(
+    page_title="Model Build",
+    page_icon=":shark:",
+    layout="wide",
+    initial_sidebar_state='collapsed'
+)
+
+load_local_css('styles.css')
+set_header()
+
+st.header(pd.__version__)
+st.title('1. Build Your Model')
+with open("data_import.pkl", "rb") as f:
+    data = pickle.load(f)
+
+    st.session_state['bin_dict'] = data["bin_dict"]
+
+#st.write(data["bin_dict"])
+
+with open("final_df_transformed.pkl", "rb") as f:
+    data = pickle.load(f)
+
+# Accessing the loaded objects
+    media_data = data["final_df_transformed"]
+# Sprint4 - available response metrics is a list of all reponse metrics in the data
+## these will be put in a drop down
+
+    st.session_state['media_data']=media_data
+
+if 'available_response_metrics' not in st.session_state:
+    # st.session_state['available_response_metrics'] = ['Total Approved Accounts - Revenue',
+    #                                                   'Total Approved Accounts - Appsflyer',
+    #                                                   'Account Requests - Appsflyer',
+    #                                                   'App Installs - Appsflyer']
+
+    st.session_state['available_response_metrics']=st.session_state['bin_dict']["Response Metrics"]
+# Sprint4
+if "is_tuned_model" not in st.session_state:
+    st.session_state["is_tuned_model"] = {}
+for resp_metric in st.session_state['available_response_metrics'] :
+    resp_metric=resp_metric.lower().replace(" ", "_").replace('-', '').replace(':', '').replace("__", "_")
+    st.session_state["is_tuned_model"][resp_metric] = False
+
+# Sprint4 - used_response_metrics is a list of resp metrics for which user has created & saved a model
+if 'used_response_metrics' not in st.session_state:
+    st.session_state['used_response_metrics'] = []
+
+# Sprint4 - saved_model_names
+if 'saved_model_names' not in st.session_state:
+    st.session_state['saved_model_names'] = []
+
+
+if 'Model' not in st.session_state:
+    if "session_state_saved" in st.session_state["project_dct"]["model_build"].keys() and \
+            st.session_state["project_dct"]["model_build"]['session_state_saved'] is not None and \
+            'Model' in st.session_state["project_dct"]["model_build"]["session_state_saved"].keys():
+        st.session_state['Model'] = st.session_state["project_dct"]["model_build"]["session_state_saved"]['Model']
+    else:
+        st.session_state['Model'] = {}
+
+# Sprint4 - select a response metric
+default_target_idx = st.session_state["project_dct"]["model_build"].get("sel_target_col", None) if st.session_state["project_dct"]["model_build"].get("sel_target_col", None) is not None else st.session_state['available_response_metrics'][0]
+
+sel_target_col = st.selectbox("Select the response metric",
+                              st.session_state['available_response_metrics'],
+                              index=st.session_state['available_response_metrics'].index(default_target_idx))
+ # , on_change=reset_save())
+st.session_state["project_dct"]["model_build"]["sel_target_col"] = sel_target_col
+
+target_col = sel_target_col.lower().replace(" ", "_").replace('-', '').replace(':', '').replace("__", "_")
+new_name_dct={col:col.lower().replace('.','_').lower().replace('@','_').replace(" ", "_").replace('-', '').replace(':', '').replace("__", "_") for col in media_data.columns}
+media_data.columns=[col.lower().replace('.','_').replace('@','_').replace(" ", "_").replace('-', '').replace(':', '').replace("__", "_") for col in media_data.columns]
+panel_col = [col.lower().replace('.','_').replace('@','_').replace(" ", "_").replace('-', '').replace(':', '').replace("__", "_") for col in  st.session_state['bin_dict']['Panel Level 1']][0]# set the panel column
+date_col = 'date'
+
+is_panel = True if len(panel_col)>0 else False
+
+if 'is_panel' not in st.session_state:
+    st.session_state['is_panel']=is_panel
+
+
+if is_panel :
+    media_data.sort_values([date_col, panel_col], inplace=True)
+else :
+    media_data.sort_values(date_col, inplace=True)
+
+media_data.reset_index(drop=True, inplace=True)
+
+date = media_data[date_col]
+st.session_state['date'] = date
+y = media_data[target_col]
+
+if is_panel:
+    spends_data = media_data[
+        [c for c in media_data.columns if "_cost" in c.lower() or "_spend" in c.lower()] + [date_col, panel_col]]
+    # Sprint3 - spends for resp curves
+else:
+    spends_data = media_data[
+        [c for c in media_data.columns if "_cost" in c.lower() or "_spend" in c.lower()] + [date_col]]
+
+y = media_data[target_col]
+media_data.drop([date_col], axis=1, inplace=True)
+media_data.reset_index(drop=True, inplace=True)
+
+
+columns = st.columns(2)
+
+old_shape = media_data.shape
+
+if "old_shape" not in st.session_state:
+    st.session_state['old_shape'] = old_shape
+
+if 'media_data' not in st.session_state:
+    st.session_state['media_data'] = pd.DataFrame()
+
+# Sprint3
+if "orig_media_data" not in st.session_state:
+    st.session_state['orig_media_data'] = pd.DataFrame()
+
+# Sprint3 additions
+if 'random_effects' not in st.session_state:
+    st.session_state['random_effects'] = pd.DataFrame()
+if 'pred_train' not in st.session_state:
+    st.session_state['pred_train'] = []
+if 'pred_test' not in st.session_state:
+    st.session_state['pred_test'] = []
+# end of Sprint3 additions
+
+# Section 3 - Create combinations
+
+# bucket=['paid_search', 'kwai','indicacao','infleux', 'influencer','FB: Level Achieved - Tier 1 Impressions',
+#       ' FB: Level Achieved - Tier 2 Impressions','paid_social_others',
+#         ' GA App: Will And Cid Pequena Baixo Risco Clicks',
+#       'digital_tactic_others',"programmatic"
+#       ]
+
+# srishti - bucket names changed
+bucket = ['paid_search', 'kwai', 'indicacao', 'infleux', 'influencer', 'fb_level_achieved_tier_2',
+          'fb_level_achieved_tier_1', 'paid_social_others',
+          'ga_app',
+          'digital_tactic_others', "programmatic"
+          ]
+
+with columns[0]:
+    if st.button('Create Combinations of Variables'):
+
+        top_3_correlated_features = []
+        # # for col in st.session_state['media_data'].columns[:19]:
+        # original_cols = [c for c in st.session_state['media_data'].columns if
+        #                  "_clicks" in c.lower() or "_impressions" in c.lower()]
+        #original_cols = [c for c in original_cols if "_lag" not in c.lower() and "_adstock" not in c.lower()]
+
+        original_cols=st.session_state['bin_dict']['Media'] + st.session_state['bin_dict']['Internal']
+
+        original_cols=[col.lower().replace('.','_').replace('@','_').replace(" ", "_").replace('-', '').replace(':', '').replace("__", "_") for col in original_cols]
+        original_cols = [col for col in original_cols if "_cost" not in col]
+        # for col in st.session_state['media_data'].columns[:19]:
+        for col in original_cols:  # srishti - new
+            corr_df = pd.concat([st.session_state['media_data'].filter(regex=col),
+                                 y], axis=1).corr()[target_col].iloc[:-1]
+            top_3_correlated_features.append(list(corr_df.sort_values(ascending=False).head(2).index))
+        flattened_list = [item for sublist in top_3_correlated_features for item in sublist]
+        # all_features_set={var:[col for col in flattened_list if var in col] for var in bucket}
+        all_features_set = {var: [col for col in flattened_list if var in col] for var in bucket if
+                            len([col for col in flattened_list if var in col]) > 0}  # srishti
+        channels_all = [values for values in all_features_set.values()]
+        st.session_state['combinations'] = list(itertools.product(*channels_all))
+        # if 'combinations' not in st.session_state:
+        #   st.session_state['combinations']=combinations_all
+
+        st.session_state['final_selection'] = st.session_state['combinations']
+        st.success('Done')
+
+        # revenue.reset_index(drop=True,inplace=True)
+    y.reset_index(drop=True, inplace=True)
+    if 'Model_results' not in st.session_state:
+        st.session_state['Model_results'] = {'Model_object': [],
+                                             'Model_iteration': [],
+                                             'Feature_set': [],
+                                             'MAPE': [],
+                                             'R2': [],
+                                             'ADJR2': [],
+                                             'pos_count': []
+                                             }
+
+
+    def reset_model_result_dct():
+        st.session_state['Model_results'] = {'Model_object': [],
+                                             'Model_iteration': [],
+                                             'Feature_set': [],
+                                             'MAPE': [],
+                                             'R2': [],
+                                             'ADJR2': [],
+                                             'pos_count': []
+                                             }
+
+        # if st.button('Build Model'):
+
+
+    if 'iterations' not in st.session_state:
+        st.session_state['iterations'] = 0
+
+    if 'final_selection' not in st.session_state:
+        st.session_state['final_selection'] = False
+
+save_path = r"Model/"
+with columns[1]:
+    if st.session_state['final_selection']:
+        st.write(f'Total combinations created {format_numbers(len(st.session_state["final_selection"]))}')
+
+# st.session_state["project_dct"]["model_build"]["all_iters_check"] = False
+
+checkbox_default = st.session_state["project_dct"]["model_build"]["all_iters_check"] if st.session_state["project_dct"]["model_build"]['all_iters_check'] is not None else False
+
+if st.checkbox('Build all iterations', value=checkbox_default):
+    # st.session_state["project_dct"]["model_build"]["all_iters_check"]
+    iterations = len(st.session_state['final_selection'])
+    st.session_state["project_dct"]["model_build"]["all_iters_check"] = True
+
+else:
+    iterations = st.number_input('Select the number of iterations to perform', min_value=0, step=100,
+                                 value=st.session_state['iterations'], on_change=reset_model_result_dct)
+    st.session_state["project_dct"]["model_build"]["all_iters_check"] = False
+    st.session_state["project_dct"]["model_build"]["iterations"] = iterations
+    if iterations <1:
+        st.error('Please enter a number greater than 0')
+        # st.stop()
+
+# build_button = st.session_state["project_dct"]["model_build"]["build_button"] if \
+#     "build_button" in st.session_state["project_dct"]["model_build"].keys() else False
+
+if st.button('Build Model', on_click=reset_model_result_dct):
+    if len(st.session_state["final_selection"]) < 1 :
+        st.error('Please create combinations')
+    st.session_state["project_dct"]["model_build"]["build_button"]=True
+    st.session_state['iterations'] = iterations
+
+    # Section 4 - Model
+    # st.session_state['media_data'] = st.session_state['media_data'].fillna(method='ffill')
+    st.session_state['media_data'] = st.session_state['media_data'].ffill()
+    st.markdown(
+        'Data Split -- Training Period: May 9th, 2023 - October 5th,2023 , Testing Period: October 6th, 2023 - November 7th, 2023 ')
+    progress_bar = st.progress(0)  # Initialize the progress bar
+    # time_remaining_text = st.empty()  # Create an empty space for time remaining text
+    start_time = time.time()  # Record the start time
+    progress_text = st.empty()
+
+    # time_elapsed_text = st.empty()
+    # for i, selected_features in enumerate(st.session_state["final_selection"][40000:40000 + int(iterations)]):
+    # st.write(st.session_state["final_selection"])
+    # for i, selected_features in enumerate(st.session_state["final_selection"]):
+
+    if is_panel == True:
+        for i, selected_features in enumerate(st.session_state["final_selection"][0:int(iterations)]):  # srishti
+            df = st.session_state['media_data']
+
+            fet = [var for var in selected_features if len(var) > 0]
+            inp_vars_str = " + ".join(fet)  # new
+
+            X = df[fet]
+            y = df[target_col]
+            ss = MinMaxScaler()
+            X = pd.DataFrame(ss.fit_transform(X), columns=X.columns)
+
+            X[target_col] = y  # Sprint2
+            X[panel_col] = df[panel_col]  # Sprint2
+
+            X_train = X.iloc[:8000]
+            X_test = X.iloc[8000:]
+            y_train = y.iloc[:8000]
+            y_test = y.iloc[8000:]
+
+            print(X_train.shape)
+            # model = sm.OLS(y_train, X_train).fit()
+            md_str = target_col + " ~ " + inp_vars_str
+            # md = smf.mixedlm("total_approved_accounts_revenue ~ {}".format(inp_vars_str),
+            #                 data=X_train[[target_col] + fet],
+            #                 groups=X_train[panel_col])
+            md = smf.mixedlm(md_str,
+                             data=X_train[[target_col] + fet],
+                             groups=X_train[panel_col])
+            mdf = md.fit()
+            predicted_values = mdf.fittedvalues
+
+            coefficients = mdf.fe_params.to_dict()
+            model_positive = [col for col in coefficients.keys() if coefficients[col] > 0]
+
+            pvalues = [var for var in list(mdf.pvalues) if var <= 0.06]
+
+            if (len(model_positive) / len(selected_features)) > 0 and (
+                    len(pvalues) / len(selected_features)) >= 0:  # srishti - changed just for testing, revert later
+                # predicted_values = model.predict(X_train)
+                mape = mean_absolute_percentage_error(y_train, predicted_values)
+                r2 = r2_score(y_train, predicted_values)
+                adjr2 = 1 - (1 - r2) * (len(y_train) - 1) / (len(y_train) - len(selected_features) - 1)
+
+                filename = os.path.join(save_path, f"model_{i}.pkl")
+                with open(filename, "wb") as f:
+                    pickle.dump(mdf, f)
+                # with open(r"C:\Users\ManojP\Documents\MMM\simopt\Model\model.pkl", 'rb') as file:
+                #   model = pickle.load(file)
+
+                st.session_state['Model_results']['Model_object'].append(filename)
+                st.session_state['Model_results']['Model_iteration'].append(i)
+                st.session_state['Model_results']['Feature_set'].append(fet)
+                st.session_state['Model_results']['MAPE'].append(mape)
+                st.session_state['Model_results']['R2'].append(r2)
+                st.session_state['Model_results']['pos_count'].append(len(model_positive))
+                st.session_state['Model_results']['ADJR2'].append(adjr2)
+
+            current_time = time.time()
+            time_taken = current_time - start_time
+            time_elapsed_minutes = time_taken / 60
+            completed_iterations_text = f"{i + 1}/{iterations}"
+            progress_bar.progress((i + 1) / int(iterations))
+            progress_text.text(
+                f'Completed iterations: {completed_iterations_text},Time Elapsed (min): {time_elapsed_minutes:.2f}')
+        st.write(
+            f'Out of {st.session_state["iterations"]} iterations : {len(st.session_state["Model_results"]["Model_object"])} valid models')
+
+    else:
+
+        for i, selected_features in enumerate(st.session_state["final_selection"][0:int(iterations)]):  # srishti
+            df = st.session_state['media_data']
+
+            fet = [var for var in selected_features if len(var) > 0]
+            inp_vars_str = " + ".join(fet)
+
+            X = df[fet]
+            y = df[target_col]
+            ss = MinMaxScaler()
+            X = pd.DataFrame(ss.fit_transform(X), columns=X.columns)
+            X = sm.add_constant(X)
+            X_train = X.iloc[:130]
+            X_test = X.iloc[130:]
+            y_train = y.iloc[:130]
+            y_test = y.iloc[130:]
+
+            model = sm.OLS(y_train, X_train).fit()
+
+
+            coefficients = model.params.to_list()
+            model_positive = [coef for coef in coefficients if coef > 0]
+            predicted_values = model.predict(X_train)
+            pvalues = [var for var in list(model.pvalues) if var <= 0.06]
+
+            # if (len(model_possitive) / len(selected_features)) > 0.9 and (len(pvalues) / len(selected_features)) >= 0.8:
+            if (len(model_positive) / len(selected_features)) > 0 and (len(pvalues) / len(
+                    selected_features)) >= 0.5:  # srishti - changed just for testing, revert later VALID MODEL CRITERIA
+                # predicted_values = model.predict(X_train)
+                mape = mean_absolute_percentage_error(y_train, predicted_values)
+                adjr2 = model.rsquared_adj
+                r2 = model.rsquared
+
+                filename = os.path.join(save_path, f"model_{i}.pkl")
+                with open(filename, "wb") as f:
+                    pickle.dump(model, f)
+                # with open(r"C:\Users\ManojP\Documents\MMM\simopt\Model\model.pkl", 'rb') as file:
+                #   model = pickle.load(file)
+
+                st.session_state['Model_results']['Model_object'].append(filename)
+                st.session_state['Model_results']['Model_iteration'].append(i)
+                st.session_state['Model_results']['Feature_set'].append(fet)
+                st.session_state['Model_results']['MAPE'].append(mape)
+                st.session_state['Model_results']['R2'].append(r2)
+                st.session_state['Model_results']['ADJR2'].append(adjr2)
+                st.session_state['Model_results']['pos_count'].append(len(model_positive))
+
+            current_time = time.time()
+            time_taken = current_time - start_time
+            time_elapsed_minutes = time_taken / 60
+            completed_iterations_text = f"{i + 1}/{iterations}"
+            progress_bar.progress((i + 1) / int(iterations))
+            progress_text.text(
+                f'Completed iterations: {completed_iterations_text},Time Elapsed (min): {time_elapsed_minutes:.2f}')
+        st.write(
+            f'Out of {st.session_state["iterations"]} iterations : {len(st.session_state["Model_results"]["Model_object"])} valid models')
+
+    pd.DataFrame(st.session_state['Model_results']).to_csv('model_output.csv')
+
+
+    def to_percentage(value):
+        return f'{value * 100:.1f}%'
+
+## Section 5 - Select Model
+st.title('2. Select Models')
+show_results_defualt = st.session_state["project_dct"]["model_build"]["show_results_check"] if st.session_state["project_dct"]["model_build"]['show_results_check'] is not None else False
+if 'tick' not in st.session_state:
+    st.session_state['tick'] = False
+if st.checkbox('Show results of top 10 models (based on MAPE and Adj. R2)', value=show_results_defualt):
+    st.session_state["project_dct"]["model_build"]["show_results_check"] = True
+    st.session_state['tick'] = True
+    st.write('Select one model iteration to generate performance metrics for it:')
+    data = pd.DataFrame(st.session_state['Model_results'])
+    data = data[data['pos_count']==data['pos_count'].max()].reset_index(drop=True) # Sprint4 -- Srishti -- only show models with the lowest num of neg coeffs
+    data.sort_values(by=['ADJR2'], ascending=False, inplace=True)
+    data.drop_duplicates(subset='Model_iteration', inplace=True)
+    top_10 = data.head(10)
+    top_10['Rank'] = np.arange(1, len(top_10) + 1, 1)
+    top_10[['MAPE', 'R2', 'ADJR2']] = np.round(top_10[['MAPE', 'R2', 'ADJR2']], 4).applymap(to_percentage)
+    top_10_table = top_10[['Rank', 'Model_iteration', 'MAPE', 'ADJR2', 'R2']]
+    # top_10_table.columns=[['Rank','Model Iteration Index','MAPE','Adjusted R2','R2']]
+    gd = GridOptionsBuilder.from_dataframe(top_10_table)
+    gd.configure_pagination(enabled=True)
+    
+    gd.configure_selection(
+        use_checkbox=True,
+        selection_mode="single",
+        pre_select_all_rows=False,
+        pre_selected_rows=[1],
+    )
+
+    gridoptions = gd.build()
+
+    table = AgGrid(top_10, gridOptions=gridoptions, update_mode=GridUpdateMode.SELECTION_CHANGED)
+
+    selected_rows = table.selected_rows
+    # if st.session_state["selected_rows"] != selected_rows:
+    #   st.session_state["build_rc_cb"] = False
+    st.session_state["selected_rows"] = selected_rows
+
+
+
+    # Section 6 - Display Results
+
+    if len(selected_rows) > 0:
+        st.header('2.1 Results Summary')
+
+        model_object = data[data['Model_iteration'] == selected_rows[0]['Model_iteration']]['Model_object']
+        features_set = data[data['Model_iteration'] == selected_rows[0]['Model_iteration']]['Feature_set']
+
+        with open(str(model_object.values[0]), 'rb') as file:
+            # print(file)
+            model = pickle.load(file)
+        st.write(model.summary())
+        st.header('2.2 Actual vs. Predicted Plot')
+
+        if is_panel :
+            df = st.session_state['media_data']
+            X = df[features_set.values[0]]
+            y = df[target_col]
+
+            ss = MinMaxScaler()
+            X = pd.DataFrame(ss.fit_transform(X), columns=X.columns)
+
+            # Sprint2 changes
+            X[target_col] = y  # new
+            X[panel_col] = df[panel_col]
+            X[date_col] = date
+
+            X_train = X.iloc[:8000]
+            X_test = X.iloc[8000:].reset_index(drop=True)
+            y_train = y.iloc[:8000]
+            y_test = y.iloc[8000:].reset_index(drop=True)
+
+            test_spends = spends_data[8000:]  # Sprint3 - test spends for resp curves
+            random_eff_df = get_random_effects(media_data, panel_col, model)
+            train_pred = model.fittedvalues
+            test_pred = mdf_predict(X_test, model, random_eff_df)
+            print("__" * 20, test_pred.isna().sum())
+
+        else :
+            df = st.session_state['media_data']
+            X = df[features_set.values[0]]
+            y = df[target_col]
+
+            ss = MinMaxScaler()
+            X = pd.DataFrame(ss.fit_transform(X), columns=X.columns)
+            X = sm.add_constant(X)
+
+            X[date_col] = date
+
+            X_train = X.iloc[:130]
+            X_test = X.iloc[130:].reset_index(drop=True)
+            y_train = y.iloc[:130]
+            y_test = y.iloc[130:].reset_index(drop=True)
+
+            test_spends = spends_data[130:]  # Sprint3 - test spends for resp curves
+            train_pred = model.predict(X_train[features_set.values[0]+['const']])
+            test_pred = model.predict(X_test[features_set.values[0]+['const']])
+
+
+        # save x test to test - srishti
+        # x_test_to_save = X_test.copy()
+        # x_test_to_save['Actuals'] = y_test
+        # x_test_to_save['Predictions'] = test_pred
+        #
+        # x_train_to_save = X_train.copy()
+        # x_train_to_save['Actuals'] = y_train
+        # x_train_to_save['Predictions'] = train_pred
+        #
+        # x_train_to_save.to_csv('Test/x_train_to_save.csv', index=False)
+        # x_test_to_save.to_csv('Test/x_test_to_save.csv', index=False)
+
+        st.session_state['X'] = X_train
+        st.session_state['features_set'] = features_set.values[0]
+        print("**" * 20, "selected model features : ", features_set.values[0])
+        metrics_table, line, actual_vs_predicted_plot = plot_actual_vs_predicted(X_train[date_col], y_train, train_pred,
+                                                                                 model, target_column=sel_target_col,
+                                                                                 is_panel=is_panel)  # Sprint2
+
+        st.plotly_chart(actual_vs_predicted_plot, use_container_width=True)
+
+        st.markdown('## 2.3 Residual Analysis')
+        columns = st.columns(2)
+        with columns[0]:
+            fig = plot_residual_predicted(y_train, train_pred, X_train)  # Sprint2
+            st.plotly_chart(fig)
+
+        with columns[1]:
+            st.empty()
+            fig = qqplot(y_train, train_pred)  # Sprint2
+            st.plotly_chart(fig)
+
+        with columns[0]:
+            fig = residual_distribution(y_train, train_pred)  # Sprint2
+            st.pyplot(fig)
+
+        vif_data = pd.DataFrame()
+        # X=X.drop('const',axis=1)
+        X_train_orig = X_train.copy()  # Sprint2 -- creating a copy of xtrain. Later deleting panel, target & date from xtrain
+        del_col_list = list(set([target_col, panel_col, date_col]).intersection(set(X_train.columns)))
+        X_train.drop(columns=del_col_list, inplace=True)  # Sprint2
+
+        vif_data["Variable"] = X_train.columns
+        vif_data["VIF"] = [variance_inflation_factor(X_train.values, i) for i in range(X_train.shape[1])]
+        vif_data.sort_values(by=['VIF'], ascending=False, inplace=True)
+        vif_data = np.round(vif_data)
+        vif_data['VIF'] = vif_data['VIF'].astype(float)
+        st.header('2.4 Variance Inflation Factor (VIF)')
+        # st.dataframe(vif_data)
+        color_mapping = {
+            'darkgreen': (vif_data['VIF'] < 3),
+            'orange': (vif_data['VIF'] >= 3) & (vif_data['VIF'] <= 10),
+            'darkred': (vif_data['VIF'] > 10)
+        }
+
+        # Create a horizontal bar plot
+        fig, ax = plt.subplots()
+        fig.set_figwidth(10)  # Adjust the width of the figure as needed
+
+        # Sort the bars by descending VIF values
+        vif_data = vif_data.sort_values(by='VIF', ascending=False)
+
+        # Iterate through the color mapping and plot bars with corresponding colors
+        for color, condition in color_mapping.items():
+            subset = vif_data[condition]
+            bars = ax.barh(subset["Variable"], subset["VIF"], color=color, label=color)
+
+            # Add text annotations on top of the bars
+            for bar in bars:
+                width = bar.get_width()
+                ax.annotate(f'{width:}', xy=(width, bar.get_y() + bar.get_height() / 2), xytext=(5, 0),
+                            textcoords='offset points', va='center')
+
+        # Customize the plot
+        ax.set_xlabel('VIF Values')
+        # ax.set_title('2.4 Variance Inflation Factor (VIF)')
+        # ax.legend(loc='upper right')
+
+        # Display the plot in Streamlit
+        st.pyplot(fig)
+
+        with st.expander('Results Summary Test data'):
+            # ss = MinMaxScaler()
+            # X_test = pd.DataFrame(ss.fit_transform(X_test), columns=X_test.columns)
+            st.header('2.2 Actual vs. Predicted Plot')
+
+            metrics_table, line, actual_vs_predicted_plot = plot_actual_vs_predicted(X_test[date_col], y_test,
+                                                                                     test_pred, model,
+                                                                                     target_column=sel_target_col,
+                                                                                     is_panel=is_panel)  # Sprint2
+
+            st.plotly_chart(actual_vs_predicted_plot, use_container_width=True)
+
+            st.markdown('## 2.3 Residual Analysis')
+            columns = st.columns(2)
+            with columns[0]:
+                fig = plot_residual_predicted(y, test_pred, X_test)  # Sprint2
+                st.plotly_chart(fig)
+
+            with columns[1]:
+                st.empty()
+                fig = qqplot(y, test_pred)  # Sprint2
+                st.plotly_chart(fig)
+
+            with columns[0]:
+                fig = residual_distribution(y, test_pred)  # Sprint2
+                st.pyplot(fig)
+
+        value = False
+        save_button_model = st.checkbox('Save this model to tune', key='build_rc_cb')  # , on_click=set_save())
+
+        if save_button_model:
+            mod_name = st.text_input('Enter model name')
+            if len(mod_name) > 0:
+                mod_name = mod_name + "__" + target_col  # Sprint4 - adding target col to model name
+                if is_panel :
+                    pred_train= model.fittedvalues
+                    pred_test= mdf_predict(X_test, model, random_eff_df)
+                else :
+                    st.session_state['features_set'] = st.session_state['features_set'] + ['const']
+                    pred_train= model.predict(X_train_orig[st.session_state['features_set']])
+                    pred_test= model.predict(X_test[st.session_state['features_set']])
+
+                st.session_state['Model'][mod_name] = {"Model_object": model,
+                                                       'feature_set': st.session_state['features_set'],
+                                                       'X_train': X_train_orig,
+                                                       'X_test': X_test,
+                                                       'y_train': y_train,
+                                                       'y_test': y_test,
+                                                       'pred_train':pred_train,
+                                                       'pred_test': pred_test
+                                                       }
+                st.session_state['X_train'] = X_train_orig
+                st.session_state['X_test_spends'] = test_spends
+                st.session_state['saved_model_names'].append(mod_name)
+                # Sprint3 additions
+                if is_panel :
+                    random_eff_df = get_random_effects(media_data, panel_col, model)
+                    st.session_state['random_effects'] = random_eff_df
+
+
+
+                with open("best_models.pkl", "wb") as f:
+                    pickle.dump(st.session_state['Model'], f)
+                    st.success(mod_name + ' model saved! Proceed to the next page to tune the model')
+
+                    urm = st.session_state['used_response_metrics']
+                    urm.append(sel_target_col)
+                    st.session_state['used_response_metrics'] = list(set(urm))
+                    mod_name = ""
+                    # Sprint4 - add the formatted name of the target col to used resp metrics
+                value = False
+
+                st.session_state["project_dct"]["model_build"]["session_state_saved"] = {}
+                for key in ['Model', 'bin_dict', 'used_response_metrics', 'date', 'saved_model_names', 'media_data', 'X_test_spends']:
+                    st.session_state["project_dct"]["model_build"]["session_state_saved"][key] = st.session_state[key]
+
+                project_dct_path = os.path.join(st.session_state['project_path'], "project_dct.pkl")
+                with open(project_dct_path, 'wb') as f:
+                    pickle.dump(st.session_state["project_dct"], f)
+
+                st.toast("💾 Saved Successfully!")
+else :
+    st.session_state["project_dct"]["model_build"]["show_results_check"] = False

7_Build_Response_Curves.py

@@ -0,0 +1,185 @@
+import streamlit as st
+import plotly.express as px
+import numpy as np
+import plotly.graph_objects as go
+from utilities_with_panel import channel_name_formating, load_authenticator, initialize_data
+from sklearn.metrics import r2_score
+from collections import OrderedDict
+from classes import class_from_dict,class_to_dict
+import pickle
+import json
+from utilities import (
+    load_local_css,
+    set_header,
+    channel_name_formating,
+)
+
+for k, v in st.session_state.items():
+    if k not in ['logout', 'login','config'] and not k.startswith('FormSubmitter'):
+        st.session_state[k] = v
+
+def s_curve(x,K,b,a,x0):
+    return K / (1 + b*np.exp(-a*(x-x0)))
+
+def save_scenario(scenario_name):
+    """
+    Save the current scenario with the mentioned name in the session state
+    
+    Parameters
+    ----------
+    scenario_name
+        Name of the scenario to be saved
+    """
+    if 'saved_scenarios' not in st.session_state:
+        st.session_state = OrderedDict()
+        
+    #st.session_state['saved_scenarios'][scenario_name] = st.session_state['scenario'].save()
+    st.session_state['saved_scenarios'][scenario_name] = class_to_dict(st.session_state['scenario'])
+    st.session_state['scenario_input'] = ""
+    print(type(st.session_state['saved_scenarios']))
+    with open('../saved_scenarios.pkl', 'wb') as f:
+        pickle.dump(st.session_state['saved_scenarios'],f)
+
+
+def reset_curve_parameters():
+    del st.session_state['K']
+    del st.session_state['b']
+    del st.session_state['a']
+    del st.session_state['x0']
+    
+def update_response_curve():
+    # st.session_state['rcs'][selected_channel_name]['K'] = st.session_state['K']
+    # st.session_state['rcs'][selected_channel_name]['b'] = st.session_state['b']
+    # st.session_state['rcs'][selected_channel_name]['a'] = st.session_state['a']
+    # st.session_state['rcs'][selected_channel_name]['x0'] = st.session_state['x0']
+    # rcs = st.session_state['rcs']
+    _channel_class = st.session_state['scenario'].channels[selected_channel_name]
+    _channel_class.update_response_curves({
+                           'K'  : st.session_state['K'], 
+                           'b'  : st.session_state['b'], 
+                           'a'  : st.session_state['a'],
+                           'x0' : st.session_state['x0']})
+    
+
+# authenticator = st.session_state.get('authenticator')
+# if authenticator is None:
+#     authenticator = load_authenticator()
+
+# name, authentication_status, username = authenticator.login('Login', 'main')
+# auth_status = st.session_state.get('authentication_status')
+
+# if auth_status == True:
+#     is_state_initiaized = st.session_state.get('initialized',False)
+#     if not is_state_initiaized:
+#         print("Scenario page state reloaded")
+
+# Sprint4 - if used_response_metrics is not blank, then select one of the used_response_metrics, else target is revenue by default
+st.set_page_config(layout='wide')
+load_local_css('styles.css')
+set_header()
+
+if "used_response_metrics" in st.session_state and st.session_state['used_response_metrics']!=[]:
+    sel_target_col = st.selectbox("Select the response metric", st.session_state['used_response_metrics'])
+    target_col = sel_target_col.lower().replace(" ", "_").replace('-', '').replace(':', '').replace("__", "_")
+else :
+    sel_target_col = 'Total Approved Accounts - Revenue'
+    target_col = 'total_approved_accounts_revenue'
+
+initialize_data(target_col)
+
+st.subheader("Build response curves")
+    
+channels_list = st.session_state['channels_list']
+selected_channel_name = st.selectbox('Channel', st.session_state['channels_list'] + ['Others'], format_func=channel_name_formating,on_change=reset_curve_parameters)
+
+rcs = {}
+for channel_name in channels_list:
+    rcs[channel_name] = st.session_state['scenario'].channels[channel_name].response_curve_params
+# rcs = st.session_state['rcs']
+
+
+if 'K' not in st.session_state:
+    st.session_state['K'] = rcs[selected_channel_name]['K']
+if 'b' not in st.session_state:
+    st.session_state['b'] = rcs[selected_channel_name]['b']
+if 'a' not in st.session_state:
+    st.session_state['a'] = rcs[selected_channel_name]['a']
+if 'x0' not in st.session_state:
+    st.session_state['x0'] = rcs[selected_channel_name]['x0']
+    
+x = st.session_state['actual_input_df'][selected_channel_name].values
+y = st.session_state['actual_contribution_df'][selected_channel_name].values
+
+power = (np.ceil(np.log(x.max()) / np.log(10) )- 3)
+
+# fig = px.scatter(x, s_curve(x/10**power,
+#                             st.session_state['K'],
+#                             st.session_state['b'],
+#                             st.session_state['a'],
+#                             st.session_state['x0']))
+
+fig = px.scatter(x=x, y=y)
+fig.add_trace(go.Scatter(x=sorted(x), y=s_curve(sorted(x)/10**power,st.session_state['K'],
+                                    st.session_state['b'],
+                                    st.session_state['a'],
+                                    st.session_state['x0']),
+                        line=dict(color='red')))
+
+fig.update_layout(title_text="Response Curve",showlegend=False)
+fig.update_annotations(font_size=10)
+fig.update_xaxes(title='Spends')
+fig.update_yaxes(title=sel_target_col)
+
+st.plotly_chart(fig,use_container_width=True)
+
+r2 = r2_score(y, s_curve(x / 10**power, 
+                        st.session_state['K'],
+                        st.session_state['b'],
+                        st.session_state['a'],
+                        st.session_state['x0']))
+
+st.metric('R2',round(r2,2))
+columns = st.columns(4)
+
+with columns[0]:
+    st.number_input('K',key='K',format="%0.5f")
+with columns[1]:
+    st.number_input('b',key='b',format="%0.5f")
+with columns[2]:
+    st.number_input('a',key='a',step=0.0001,format="%0.5f")
+with columns[3]:
+    st.number_input('x0',key='x0',format="%0.5f")
+    
+
+st.button('Update parameters',on_click=update_response_curve)
+st.button('Reset parameters',on_click=reset_curve_parameters)
+scenario_name = st.text_input('Scenario name', key='scenario_input',placeholder='Scenario name',label_visibility='collapsed')
+st.button('Save', on_click=lambda  : save_scenario(scenario_name),disabled=len(st.session_state['scenario_input']) == 0)
+
+file_name = st.text_input('rcs download file name', key='file_name_input',placeholder='file name',label_visibility='collapsed')
+st.download_button(
+                    label="Download response curves",
+                    data=json.dumps(rcs),
+                    file_name=f"{file_name}.json",
+                    mime="application/json",
+                    disabled= len(file_name) == 0,
+                )
+
+
+def s_curve_derivative(x, K, b, a, x0):
+    # Derivative of the S-curve function
+    return a * b * K * np.exp(-a * (x - x0)) / ((1 + b * np.exp(-a * (x - x0))) ** 2)
+
+# Parameters of the S-curve
+K = st.session_state['K']
+b = st.session_state['b']
+a = st.session_state['a']
+x0 = st.session_state['x0']
+
+# Optimized spend value obtained from the tool
+optimized_spend = st.number_input('value of x')  # Replace this with your optimized spend value
+
+# Calculate the slope at the optimized spend value
+slope_at_optimized_spend = s_curve_derivative(optimized_spend, K, b, a, x0)
+
+st.write("Slope ", slope_at_optimized_spend)

8_Scenario_Planner.py

@@ -0,0 +1,464 @@
+import streamlit as st
+from numerize.numerize import numerize
+import numpy as np
+from functools import partial
+from collections import OrderedDict
+from plotly.subplots import make_subplots
+import plotly.graph_objects as go
+from utilities import format_numbers,load_local_css,set_header,initialize_data,load_authenticator,send_email,channel_name_formating
+from classes import class_from_dict,class_to_dict
+import pickle
+import streamlit_authenticator as stauth
+import yaml
+from yaml import SafeLoader
+import re
+import pandas as pd
+import plotly.express as px
+target='Revenue'
+st.set_page_config(layout='wide')
+load_local_css('styles.css')
+set_header()
+
+for k, v in st.session_state.items():
+    if k not in ['logout', 'login','config'] and not k.startswith('FormSubmitter'):
+        st.session_state[k] = v
+# ======================================================== #
+# ======================= Functions ====================== #
+# ======================================================== #
+
+
+def optimize():
+    """
+    Optimize the spends for the sales    
+    """
+    
+    channel_list = [key for key,value in st.session_state['optimization_channels'].items() if value]
+    print('channel_list')
+    print(channel_list)
+    print('@@@@@@@@')
+    if len(channel_list) > 0 :
+        scenario = st.session_state['scenario']
+        result = st.session_state['scenario'].optimize(st.session_state['total_spends_change'],channel_list)
+        for channel_name, modified_spends in result:
+            st.session_state[channel_name] = numerize(modified_spends * scenario.channels[channel_name].conversion_rate,1)
+            prev_spends = st.session_state['scenario'].channels[channel_name].actual_total_spends 
+            st.session_state[f'{channel_name}_change'] = round(100*(modified_spends - prev_spends) / prev_spends,2)
+            
+
+def save_scenario(scenario_name):
+    """
+    Save the current scenario with the mentioned name in the session state
+    
+    Parameters
+    ----------
+    scenario_name
+        Name of the scenario to be saved
+    """
+    if 'saved_scenarios' not in st.session_state:
+        st.session_state = OrderedDict()
+        
+    #st.session_state['saved_scenarios'][scenario_name] = st.session_state['scenario'].save()
+    st.session_state['saved_scenarios'][scenario_name] = class_to_dict(st.session_state['scenario'])
+    st.session_state['scenario_input'] = ""
+    print(type(st.session_state['saved_scenarios']))
+    with open('../saved_scenarios.pkl', 'wb') as f:
+        pickle.dump(st.session_state['saved_scenarios'],f)
+        
+def update_all_spends():
+    """
+    Updates spends for all the channels with the given overall spends change
+    """
+    percent_change = st.session_state['total_spends_change']
+    for channel_name in st.session_state['channels_list']:
+        channel = st.session_state['scenario'].channels[channel_name]
+        current_spends =  channel.actual_total_spends
+        modified_spends = (1 + percent_change/100) * current_spends
+        st.session_state['scenario'].update(channel_name, modified_spends)
+        st.session_state[channel_name] = numerize(modified_spends*channel.conversion_rate,1)
+        st.session_state[f'{channel_name}_change'] = percent_change
+
+def extract_number_for_string(string_input):
+    string_input = string_input.upper()
+    if string_input.endswith('K'):
+        return float(string_input[:-1])*10**3
+    elif string_input.endswith('M'):
+        return float(string_input[:-1])*10**6
+    elif string_input.endswith('B'):
+        return float(string_input[:-1])*10**9
+    
+def validate_input(string_input):
+    pattern = r'\d+\.?\d*[K|M|B]$'
+    match = re.match(pattern, string_input)
+    if match is None:
+        return False
+    return True
+
+def update_data_by_percent(channel_name):
+    prev_spends = st.session_state['scenario'].channels[channel_name].actual_total_spends * st.session_state['scenario'].channels[channel_name].conversion_rate
+    modified_spends = prev_spends * (1 + st.session_state[f'{channel_name}_change']/100)
+    st.session_state[channel_name] = numerize(modified_spends,1)
+    st.session_state['scenario'].update(channel_name, modified_spends/st.session_state['scenario'].channels[channel_name].conversion_rate)
+    
+def update_data(channel_name):
+    """
+    Updates the spends for the given channel
+    """
+    
+    if validate_input(st.session_state[channel_name]):
+        modified_spends = extract_number_for_string(st.session_state[channel_name])
+        prev_spends = st.session_state['scenario'].channels[channel_name].actual_total_spends * st.session_state['scenario'].channels[channel_name].conversion_rate
+        st.session_state[f'{channel_name}_change'] = round(100*(modified_spends - prev_spends) / prev_spends,2)
+        st.session_state['scenario'].update(channel_name, modified_spends/st.session_state['scenario'].channels[channel_name].conversion_rate)
+    # st.session_state['scenario'].update(channel_name, modified_spends)
+    # else:
+    #     try:
+    #         modified_spends = float(st.session_state[channel_name])
+    #         prev_spends = st.session_state['scenario'].channels[channel_name].actual_total_spends * st.session_state['scenario'].channels[channel_name].conversion_rate
+    #         st.session_state[f'{channel_name}_change'] = round(100*(modified_spends - prev_spends) / prev_spends,2)
+    #         st.session_state['scenario'].update(channel_name, modified_spends/st.session_state['scenario'].channels[channel_name].conversion_rate)
+    #         st.session_state[f'{channel_name}'] = numerize(modified_spends,1)
+    #     except ValueError:
+    #         st.write('Invalid input')
+
+def select_channel_for_optimization(channel_name):
+    """
+    Marks the given channel for optimization
+    """
+    st.session_state['optimization_channels'][channel_name] = st.session_state[f'{channel_name}_selected']
+
+def select_all_channels_for_optimization():
+    """
+    Marks all the channel for optimization
+    """
+    for channel_name in st.session_state['optimization_channels'].keys():
+        st.session_state[f'{channel_name}_selected' ] = st.session_state['optimze_all_channels']
+        st.session_state['optimization_channels'][channel_name] = st.session_state['optimze_all_channels']
+
+def update_penalty():
+    """
+    Updates the penalty flag for sales calculation
+    """
+    st.session_state['scenario'].update_penalty(st.session_state['apply_penalty'])
+  
+def reset_scenario():
+    # print(st.session_state['default_scenario_dict'])
+    # st.session_state['scenario']  = class_from_dict(st.session_state['default_scenario_dict'])
+    # for channel in st.session_state['scenario'].channels.values():
+    #     st.session_state[channel.name] = float(channel.actual_total_spends * channel.conversion_rate)
+    initialize_data()
+    for channel_name in  st.session_state['channels_list']:
+        st.session_state[f'{channel_name}_selected'] = False
+        st.session_state[f'{channel_name}_change'] = 0
+    st.session_state['optimze_all_channels'] = False
+
+def format_number(num):
+    if num >= 1_000_000:
+        return f"{num / 1_000_000:.2f}M"
+    elif num >= 1_000:
+        return f"{num / 1_000:.0f}K"
+    else:
+        return f"{num:.2f}"
+
+def summary_plot(data, x, y, title, text_column):
+    fig = px.bar(data, x=x, y=y, orientation='h',
+                 title=title, text=text_column, color='Channel_name')
+
+    # Convert text_column to numeric values
+    data[text_column] = pd.to_numeric(data[text_column], errors='coerce')
+    
+    # Update the format of the displayed text based on magnitude
+    fig.update_traces(texttemplate='%{text:.2s}', textposition='outside', hovertemplate='%{x:.2s}')
+    
+    fig.update_layout(xaxis_title=x, yaxis_title='Channel Name', showlegend=False)
+    return fig
+
+def s_curve(x,K,b,a,x0):
+    return K / (1 + b*np.exp(-a*(x-x0)))
+
+@st.cache
+def plot_response_curves():
+    cols=4
+    rcs = st.session_state['rcs']
+    shapes = []
+    fig = make_subplots(rows=6, cols=cols,subplot_titles=channels_list)
+    for i in range(0, len(channels_list)):
+        col = channels_list[i]
+        x = st.session_state['actual_df'][col].values
+        spends = x.sum()
+        power = (np.ceil(np.log(x.max()) / np.log(10) )- 3)
+        x = np.linspace(0,3*x.max(),200)
+
+        K = rcs[col]['K']
+        b = rcs[col]['b']
+        a = rcs[col]['a']
+        x0 = rcs[col]['x0']
+        
+        y = s_curve(x/10**power,K,b,a,x0)
+        roi = y/x
+        marginal_roi = a * (y)*(1-y/K)
+        fig.add_trace(
+            go.Scatter(x=52*x*st.session_state['scenario'].channels[col].conversion_rate, 
+                       y=52*y,
+                       name=col, 
+                       customdata = np.stack((roi, marginal_roi),axis=-1),
+                       hovertemplate="Spend:%{x:$.2s}<br>Sale:%{y:$.2s}<br>ROI:%{customdata[0]:.3f}<br>MROI:%{customdata[1]:.3f}"),
+            row=1+(i)//cols , col=i%cols + 1
+        )
+        
+        fig.add_trace(go.Scatter(x=[spends*st.session_state['scenario'].channels[col].conversion_rate], 
+                                 y=[52*s_curve(spends/(10**power*52),K,b,a,x0)],
+                                 name=col,
+                                 legendgroup=col,
+                                 showlegend=False,
+                                 marker=dict(color=['black'])),
+                      row=1+(i)//cols , col=i%cols + 1)
+        
+        shapes.append(go.layout.Shape(type="line", 
+                                      x0=0, 
+                                      y0=52*s_curve(spends/(10**power*52),K,b,a,x0), 
+                                      x1=spends*st.session_state['scenario'].channels[col].conversion_rate, 
+                                      y1=52*s_curve(spends/(10**power*52),K,b,a,x0), 
+                                      line_width=1, 
+                                      line_dash="dash", 
+                                      line_color="black",
+                                      xref= f'x{i+1}',
+                                      yref= f'y{i+1}'))
+        
+        shapes.append(go.layout.Shape(type="line", 
+                                      x0=spends*st.session_state['scenario'].channels[col].conversion_rate, 
+                                      y0=0, 
+                                      x1=spends*st.session_state['scenario'].channels[col].conversion_rate, 
+                                      y1=52*s_curve(spends/(10**power*52),K,b,a,x0), 
+                                      line_width=1, 
+                                      line_dash="dash", 
+                                      line_color="black",
+                                      xref= f'x{i+1}',
+                                      yref= f'y{i+1}'))
+        
+        
+        
+    fig.update_layout(height=1500, width=1000, title_text="Response Curves",showlegend=False,shapes=shapes)
+    fig.update_annotations(font_size=10)
+    fig.update_xaxes(title='Spends')
+    fig.update_yaxes(title=target)
+    return fig
+
+
+
+# ======================================================== #
+# ==================== HTML Components =================== #
+# ======================================================== #
+
+def generate_spending_header(heading):
+    return st.markdown(f"""<h2 class="spends-header">{heading}</h2>""",unsafe_allow_html=True)
+
+
+# ======================================================== #
+# =================== Session variables ================== #
+# ======================================================== #
+
+with open('config.yaml') as file:
+    config = yaml.load(file, Loader=SafeLoader)
+    st.session_state['config'] = config
+    
+authenticator = stauth.Authenticate(
+    config['credentials'],
+    config['cookie']['name'],
+    config['cookie']['key'],
+    config['cookie']['expiry_days'],
+    config['preauthorized']
+)
+st.session_state['authenticator'] = authenticator
+name, authentication_status, username = authenticator.login('Login', 'main')
+auth_status = st.session_state.get('authentication_status')
+if auth_status == True:
+    authenticator.logout('Logout', 'main')
+    is_state_initiaized = st.session_state.get('initialized',False)
+    if not is_state_initiaized:
+        initialize_data()
+
+
+    channels_list = st.session_state['channels_list']
+        
+
+    # ======================================================== #
+    # ========================== UI ========================== #
+    # ======================================================== #
+    
+    print(list(st.session_state.keys()))
+
+    st.header('Simulation')
+    main_header = st.columns((2,2))
+    sub_header = st.columns((1,1,1,1))
+    _scenario = st.session_state['scenario']
+
+    with main_header[0]:
+        st.subheader('Actual')
+
+    with main_header[-1]:
+        st.subheader('Simulated')
+
+    with sub_header[0]:
+        st.metric(label = 'Spends', value=format_numbers(_scenario.actual_total_spends))
+
+    with sub_header[1]:
+        st.metric(label = target, value=format_numbers(float(_scenario.actual_total_sales),include_indicator=False))
+        
+    with sub_header[2]:
+        st.metric(label = 'Spends', 
+                    value=format_numbers(_scenario.modified_total_spends),
+                    delta=numerize(_scenario.delta_spends,1))
+        
+    with sub_header[3]:
+        st.metric(label = target, 
+                    value=format_numbers(float(_scenario.modified_total_sales),include_indicator=False), 
+                    delta=numerize(_scenario.delta_sales,1))
+
+
+
+    with st.expander("Channel Spends Simulator"):
+        _columns = st.columns((2,4,1,1))
+        with _columns[0]:
+            st.checkbox(label='Optimize all Channels',
+                            key=f'optimze_all_channels',
+                            value=False,
+                            on_change=select_all_channels_for_optimization,
+                            )
+            st.number_input('Percent change of total spends',
+                            key=f'total_spends_change',
+                            step= 1,
+                            on_change=update_all_spends)
+        with _columns[2]:
+            st.button('Optimize',on_click=optimize)
+        with _columns[3]:
+            st.button('Reset',on_click=reset_scenario)
+            
+        
+        
+        st.markdown("""<hr class="spends-heading-seperator">""", unsafe_allow_html=True)
+        _columns = st.columns((2.5,2,1.5,1.5,1))
+        with _columns[0]:
+            generate_spending_header('Channel')
+        with _columns[1]:
+            generate_spending_header('Spends Input')
+        with _columns[2]:
+            generate_spending_header('Spends')
+        with _columns[3]:
+            generate_spending_header(target)
+        with _columns[4]:
+            generate_spending_header('Optimize')
+            
+        st.markdown("""<hr class="spends-heading-seperator">""", unsafe_allow_html=True)
+        
+        if 'acutual_predicted' not in st.session_state:
+            st.session_state['acutual_predicted']={'Channel_name':[],
+                                                   'Actual_spend':[],
+                                                   'Optimized_spend':[],
+                                                   'Delta':[]
+                                                   }
+        for i,channel_name in enumerate(channels_list):
+            _channel_class = st.session_state['scenario'].channels[channel_name]
+            _columns = st.columns((2.5,1.5,1.5,1.5,1))
+            with _columns[0]:
+                st.write(channel_name_formating(channel_name))
+            with _columns[1]:
+                channel_bounds = _channel_class.bounds
+                channel_spends = float(_channel_class.actual_total_spends )
+                min_value = float((1+channel_bounds[0]/100) * channel_spends )
+                max_value = float((1+channel_bounds[1]/100) * channel_spends )
+                #print(st.session_state[channel_name])
+                spend_input = st.text_input(channel_name,
+                              key=channel_name,
+                              label_visibility='collapsed',
+                              on_change=partial(update_data,channel_name))
+                if not validate_input(spend_input):
+                    st.error('Invalid input')
+                    
+                st.number_input('Percent change',
+                            key=f'{channel_name}_change',
+                            step= 1,
+                            on_change=partial(update_data_by_percent,channel_name))
+            
+            with _columns[2]:
+                # spends
+                current_channel_spends = float(_channel_class.modified_total_spends *  _channel_class.conversion_rate)
+                actual_channel_spends = float(_channel_class.actual_total_spends * _channel_class.conversion_rate)
+                spends_delta = float(_channel_class.delta_spends * _channel_class.conversion_rate)
+                st.session_state['acutual_predicted']['Channel_name'].append(channel_name)
+                st.session_state['acutual_predicted']['Actual_spend'].append(actual_channel_spends)
+                st.session_state['acutual_predicted']['Optimized_spend'].append(current_channel_spends)
+                st.session_state['acutual_predicted']['Delta'].append(spends_delta)
+                ## REMOVE 
+                st.metric('Spends',
+                            format_numbers(current_channel_spends),
+                            delta=numerize(spends_delta,1),
+                            label_visibility='collapsed')
+
+            with _columns[3]:
+                # sales
+                current_channel_sales = float(_channel_class.modified_total_sales)
+                actual_channel_sales = float(_channel_class.actual_total_sales)
+                sales_delta = float(_channel_class.delta_sales)
+                st.metric(target,
+                            format_numbers(current_channel_sales,include_indicator=False),
+                            delta=numerize(sales_delta,1),
+                            label_visibility='collapsed')
+                
+            with _columns[4]:
+                
+                st.checkbox(label='select for optimization',
+                            key=f'{channel_name}_selected',
+                            value=False,
+                            on_change=partial(select_channel_for_optimization,channel_name),
+                            label_visibility='collapsed')
+
+
+            st.markdown("""<hr class="spends-child-seperator">""",unsafe_allow_html=True)
+
+
+    with st.expander("See Response Curves"):
+        fig = plot_response_curves()
+        st.plotly_chart(fig,use_container_width=True)
+
+    _columns = st.columns(2)
+    with _columns[0]:
+        st.subheader('Save Scenario')
+        scenario_name = st.text_input('Scenario name', key='scenario_input',placeholder='Scenario name',label_visibility='collapsed')
+        st.button('Save', on_click=lambda  : save_scenario(scenario_name),disabled=len(st.session_state['scenario_input']) == 0)
+        
+    summary_df=pd.DataFrame(st.session_state['acutual_predicted'])
+    summary_df.drop_duplicates(subset='Channel_name',keep='last',inplace=True)
+    
+    summary_df_sorted = summary_df.sort_values(by='Delta', ascending=False)
+    summary_df_sorted['Delta_percent'] = np.round(((summary_df_sorted['Optimized_spend'] / summary_df_sorted['Actual_spend'])-1) * 100, 2)
+    
+    with open("summary_df.pkl", "wb") as f:
+        pickle.dump(summary_df_sorted, f)
+        #st.dataframe(summary_df_sorted)
+        # ___columns=st.columns(3)
+        # with ___columns[2]:
+        #     fig=summary_plot(summary_df_sorted, x='Delta_percent', y='Channel_name', title='Delta', text_column='Delta_percent')
+        #     st.plotly_chart(fig,use_container_width=True)
+        # with ___columns[0]:
+        #     fig=summary_plot(summary_df_sorted, x='Actual_spend', y='Channel_name', title='Actual Spend', text_column='Actual_spend')
+        #     st.plotly_chart(fig,use_container_width=True)         
+        # with ___columns[1]:
+        #     fig=summary_plot(summary_df_sorted, x='Optimized_spend', y='Channel_name', title='Planned Spend', text_column='Optimized_spend')
+        #     st.plotly_chart(fig,use_container_width=True)  
+
+elif auth_status == False:
+    st.error('Username/Password is incorrect')
+    
+if auth_status != True:
+    try:
+        username_forgot_pw, email_forgot_password, random_password = authenticator.forgot_password('Forgot password')
+        if username_forgot_pw:
+            st.session_state['config']['credentials']['usernames'][username_forgot_pw]['password'] = stauth.Hasher([random_password]).generate()[0]
+            send_email(email_forgot_password, random_password)
+            st.success('New password sent securely')
+            # Random password to be transferred to user securely
+        elif username_forgot_pw == False:
+            st.error('Username not found')
+    except Exception as e:
+        st.error(e)
+

Data_prep_functions.py

@@ -0,0 +1,236 @@
+import streamlit as st
+import pandas as pd
+import plotly.express as px
+import plotly.graph_objects as go
+import numpy as np
+import pickle
+import statsmodels.api as sm
+import numpy as np
+from sklearn.metrics import mean_absolute_error, r2_score,mean_absolute_percentage_error  
+from sklearn.preprocessing import MinMaxScaler
+import matplotlib.pyplot as plt
+from statsmodels.stats.outliers_influence import variance_inflation_factor
+from plotly.subplots import make_subplots
+
+st.set_option('deprecation.showPyplotGlobalUse', False)
+from datetime import datetime
+import seaborn as sns
+
+def calculate_discount(promo_price_series, non_promo_price_series):
+    # Calculate the 4-week moving average of non-promo price
+    window_size = 4
+    base_price = non_promo_price_series.rolling(window=window_size).mean()
+    
+    # Calculate discount_raw
+    discount_raw_series = (1 - promo_price_series / base_price) * 100
+    
+    # Calculate discount_final
+    discount_final_series = discount_raw_series.where(discount_raw_series >= 5, 0)
+    
+    return base_price, discount_raw_series, discount_final_series
+
+
+def create_dual_axis_line_chart(date_series, promo_price_series, non_promo_price_series, base_price_series, discount_series):
+    # Create traces for the primary axis (price vars)
+    trace1 = go.Scatter(
+        x=date_series,
+        y=promo_price_series,
+        name='Promo Price',
+        yaxis='y1'
+    )
+    
+    trace2 = go.Scatter(
+        x=date_series,
+        y=non_promo_price_series,
+        name='Non-Promo Price',
+        yaxis='y1'
+    )
+
+    trace3 = go.Scatter(
+        x=date_series,
+        y=base_price_series,
+        name='Base Price',
+        yaxis='y1'
+    )
+    
+    # Create a trace for the secondary axis (discount)
+    trace4 = go.Scatter(
+        x=date_series,
+        y=discount_series,
+        name='Discount',
+        yaxis='y2'
+    )
+
+    # Create the layout with dual axes
+    layout = go.Layout(
+        title='Price and Discount Over Time',
+        yaxis=dict(
+            title='Price',
+            side='left'
+        ),
+        yaxis2=dict(
+            title='Discount',
+            side='right',
+            overlaying='y',
+            showgrid=False
+        ),
+        xaxis=dict(title='Date'),
+    )
+
+    # Create the figure with the defined traces and layout
+    fig = go.Figure(data=[trace1, trace2, trace3, trace4], layout=layout)
+
+    return fig
+
+
+def to_percentage(value):
+  return f'{value * 100:.1f}%'   
+
+def plot_actual_vs_predicted(date, y, predicted_values, model,target_column=None, flag=None, repeat_all_years=False, is_panel=False):
+    if flag is not None :
+      fig = make_subplots(specs=[[{"secondary_y": True}]])
+    else :
+      fig = go.Figure()
+
+    if is_panel :
+      df=pd.DataFrame()
+      df['date'] = date
+      df['Actual'] = y
+      df['Predicted'] = predicted_values
+      df_agg = df.groupby('date').agg({'Actual':'sum', 'Predicted':'sum'}).reset_index()
+      df_agg.columns = ['date', 'Actual', 'Predicted']
+      assert len(df_agg) == pd.Series(date).nunique()
+      # date = df_agg['date']
+      # y = df_agg['Actual']
+      # predicted_values = df_agg['Predicted']
+      # ymax = df_agg['Actual'].max() # Sprint3 - ymax to set y value for flag
+
+      fig.add_trace(go.Scatter(x=df_agg['date'], y=df_agg['Actual'], mode='lines', name='Actual', line=dict(color='#08083B')))
+      fig.add_trace(go.Scatter(x=df_agg['date'], y=df_agg['Predicted'], mode='lines', name='Predicted', line=dict(color='#11B6BD')))
+
+    else :
+      fig.add_trace(go.Scatter(x=date, y=y, mode='lines', name='Actual', line=dict(color='#08083B')))
+      fig.add_trace(go.Scatter(x=date, y=predicted_values, mode='lines', name='Predicted', line=dict(color='#11B6BD')))
+
+    line_values=[]
+    if flag:
+      min_date, max_date = flag[0], flag[1]
+      min_week = datetime.strptime(str(min_date), "%Y-%m-%d").strftime("%U")
+      max_week = datetime.strptime(str(max_date), "%Y-%m-%d").strftime("%U")
+      month=pd.to_datetime(min_date).month
+      day=pd.to_datetime(min_date).day
+      #st.write(pd.to_datetime(min_date).week)
+      #st.write(min_week)
+      # Initialize an empty list to store line values
+
+      # Sprint3 change : put flags to secondary axis, & made their y value to 1 instead of 5M
+      if repeat_all_years:
+        #line_values=list(pd.to_datetime((pd.Series(date)).dt.week).map(lambda x: 10000 if x==min_week else 0  ))
+        #st.write(pd.Series(date).map(lambda x: pd.Timestamp(x).week))
+        line_values=list(pd.Series(date).map(lambda x: 1 if (pd.Timestamp(x).week >=int(min_week)) & (pd.Timestamp(x).week <=int(max_week)) else 0))
+        assert len(line_values) == len(date)
+        #st.write(line_values)
+        fig.add_trace(go.Scatter(x=date, y=line_values, mode='lines', name='Flag', line=dict(color='#FF5733')),secondary_y=True)
+      else:
+        line_values = []
+
+        line_values = list(pd.Series(date).map(lambda x: 1 if (pd.Timestamp(x) >= pd.Timestamp(min_date)) and (pd.Timestamp(x) <= pd.Timestamp(max_date)) else 0))
+
+        #st.write(line_values)
+        fig.add_trace(go.Scatter(x=date, y=line_values, mode='lines', name='Flag', line=dict(color='#FF5733')),secondary_y=True)
+
+
+    # Calculate MAPE
+    mape = mean_absolute_percentage_error(y, predicted_values)
+
+    # Calculate AdjR2 # Assuming X is your feature matrix
+    r2 = r2_score(y, predicted_values)
+    adjr2 = 1 - (1 - r2) * (len(y) - 1) / (len(y) - len(model.fe_params) - 1)
+
+    # Create a table to display the metrics
+    metrics_table = pd.DataFrame({
+      'Metric': ['MAPE', 'R-squared', 'AdjR-squared'],
+      'Value': [mape, r2, adjr2]
+    })
+    # st.write(metrics_table)
+    fig.update_layout(
+      xaxis=dict(title='Date'),
+      yaxis=dict(title=target_column),
+      xaxis_tickangle=-30
+    )
+    fig.add_annotation(
+    text=f"MAPE: {mape*100:0.1f}%,  Adjr2: {adjr2 *100:.1f}%",
+    xref="paper",
+    yref="paper",
+    x=0.95,  # Adjust these values to position the annotation
+    y=1.2,
+    showarrow=False,
+    )
+    # print("{}{}"*20, len(line_values))
+  #metrics_table.set_index(['Metric'],inplace=True)
+    return metrics_table,line_values, fig
+
+def plot_residual_predicted(actual, predicted, df):
+        df_=df.copy()
+        df_['Residuals'] = actual - pd.Series(predicted)
+        df_['StdResidual'] = (df_['Residuals'] - df_['Residuals'].mean()) / df_['Residuals'].std()
+        
+        # Create a Plotly scatter plot
+        fig = px.scatter(df_, x=predicted, y='StdResidual', opacity=0.5,color_discrete_sequence=["#11B6BD"])
+        
+        # Add horizontal lines
+        fig.add_hline(y=0, line_dash="dash", line_color="darkorange")
+        fig.add_hline(y=2, line_color="red")
+        fig.add_hline(y=-2, line_color="red")
+        
+        fig.update_xaxes(title='Predicted')
+        fig.update_yaxes(title='Standardized Residuals (Actual - Predicted)')
+        
+        # Set the same width and height for both figures
+        fig.update_layout(title='2.3.1 Residuals over Predicted Values', autosize=False, width=600, height=400)
+        
+        return fig
+
+def residual_distribution(actual, predicted):
+        Residuals = actual - pd.Series(predicted)
+        
+        # Create a Seaborn distribution plot
+        sns.set(style="whitegrid")
+        plt.figure(figsize=(6, 4))
+        sns.histplot(Residuals, kde=True, color="#11B6BD")
+        
+        plt.title('2.3.3 Distribution of Residuals')
+        plt.xlabel('Residuals')
+        plt.ylabel('Probability Density')
+        
+        return plt
+
+
+def qqplot(actual, predicted):
+        Residuals = actual - pd.Series(predicted)
+        Residuals = pd.Series(Residuals)
+        Resud_std = (Residuals - Residuals.mean()) / Residuals.std()
+        
+        # Create a QQ plot using Plotly with custom colors
+        fig = go.Figure()
+        fig.add_trace(go.Scatter(x=sm.ProbPlot(Resud_std).theoretical_quantiles,
+                                y=sm.ProbPlot(Resud_std).sample_quantiles,
+                                mode='markers',
+                                marker=dict(size=5, color="#11B6BD"),
+                                name='QQ Plot'))
+        
+        # Add the 45-degree reference line
+        diagonal_line = go.Scatter(
+            x=[-2, 2],  # Adjust the x values as needed to fit the range of your data
+            y=[-2, 2],  # Adjust the y values accordingly
+            mode='lines',
+            line=dict(color='red'),  # Customize the line color and style
+            name=' '
+        )
+        fig.add_trace(diagonal_line)
+        
+        # Customize the layout
+        fig.update_layout(title='2.3.2 QQ Plot of Residuals',title_x=0.5, autosize=False, width=600, height=400,
+                          xaxis_title='Theoretical Quantiles', yaxis_title='Sample Quantiles')
+        
+        return fig

Eda_functions.py

@@ -0,0 +1,172 @@
+import streamlit as st
+import plotly.express as px
+import numpy as np
+import plotly.graph_objects as go
+from sklearn.metrics import r2_score
+from collections import OrderedDict
+import plotly.express as px
+import plotly.graph_objects as go
+import pandas as pd
+import seaborn as sns
+import matplotlib.pyplot as plt
+import streamlit as st
+import re
+from matplotlib.colors import ListedColormap
+# from st_aggrid import AgGrid, GridOptionsBuilder
+# from src.agstyler import PINLEFT, PRECISION_TWO, draw_grid
+
+
+def format_numbers(x):
+    if abs(x) >= 1e6:
+        # Format as millions with one decimal place and commas
+        return f'{x/1e6:,.1f}M'
+    elif abs(x) >= 1e3:
+        # Format as thousands with one decimal place and commas
+        return f'{x/1e3:,.1f}K'
+    else:
+        # Format with one decimal place and commas for values less than 1000
+        return f'{x:,.1f}'
+
+    
+
+def line_plot(data, x_col, y1_cols, y2_cols, title):
+    fig = go.Figure()
+      
+    for y1_col in y1_cols:
+        fig.add_trace(go.Scatter(x=data[x_col], y=data[y1_col], mode='lines', name=y1_col,line=dict(color='#11B6BD')))
+
+    for y2_col in y2_cols:
+        fig.add_trace(go.Scatter(x=data[x_col], y=data[y2_col], mode='lines', name=y2_col, yaxis='y2',line=dict(color='#739FAE')))
+    if len(y2_cols)!=0:
+        fig.update_layout(yaxis=dict(), yaxis2=dict(overlaying='y', side='right'))
+    else:
+        fig.update_layout(yaxis=dict(), yaxis2=dict(overlaying='y', side='right'))
+    if title:
+        fig.update_layout(title=title)
+    fig.update_xaxes(showgrid=False)
+    fig.update_yaxes(showgrid=False)
+    fig.update_layout(legend=dict(
+        orientation="h",
+        yanchor="top",
+        y=1.1,
+        xanchor="center",
+        x=0.5
+    ))
+
+    return fig
+
+
+def line_plot_target(df,target,title):
+
+    coefficients = np.polyfit(df['date'].view('int64'), df[target], 1)
+    trendline = np.poly1d(coefficients)
+    fig = go.Figure()
+
+    fig.add_trace(go.Scatter(x=df['date'], y=df[target], mode='lines', name=target,line=dict(color='#11B6BD')))
+    trendline_x = df['date']
+    trendline_y = trendline(df['date'].view('int64'))
+
+
+    fig.add_trace(go.Scatter(x=trendline_x, y=trendline_y, mode='lines', name='Trendline', line=dict(color='#739FAE')))
+
+    fig.update_layout(
+        title=title,
+        xaxis=dict(type='date')
+    )
+
+    for year in df['date'].dt.year.unique()[1:]:
+
+        january_1 = pd.Timestamp(year=year, month=1, day=1)
+        fig.add_shape(
+            go.layout.Shape(
+                type="line",
+                x0=january_1,
+                x1=january_1,
+                y0=0,
+                y1=1,
+                xref="x",
+                yref="paper",
+                line=dict(color="grey", width=1.5, dash="dash"),
+            )
+        )
+    fig.update_layout(legend=dict(
+        orientation="h",
+        yanchor="top",
+        y=1.1,
+        xanchor="center",
+        x=0.5
+    ))
+    return fig
+
+def correlation_plot(df,selected_features,target):
+    custom_cmap = ListedColormap(['#08083B', "#11B6BD"])  
+    corr_df=df[selected_features]
+    corr_df=pd.concat([corr_df,df[target]],axis=1)
+    fig, ax = plt.subplots(figsize=(16, 12))
+    sns.heatmap(corr_df.corr(),annot=True, cmap='Blues', fmt=".2f", linewidths=0.5,mask=np.triu(corr_df.corr()))
+    #plt.title('Correlation Plot')
+    plt.xticks(rotation=45)
+    plt.yticks(rotation=0)
+    return fig
+
+def summary(data,selected_feature,spends,Target=None):
+        
+        if Target:
+            sum_df = data[selected_feature]
+            sum_df['Year']=data['date'].dt.year
+            sum_df=sum_df.groupby('Year')[selected_feature].sum()
+            sum_df=sum_df.reset_index()
+            total_sum = sum_df.sum(numeric_only=True)
+            total_sum['Year'] = 'Total'
+            #sum_df = pd.concat([sum_df, total_sum.to_frame().T],axis=0,ignore_index=True).copy()
+            sum_df = sum_df.append(total_sum, ignore_index=True)
+            #st.write(sum_df)
+            sum_df.set_index(['Year'],inplace=True)
+            sum_df=sum_df.applymap(format_numbers)
+            spends_col=[col for col in sum_df.columns if any(keyword in col for keyword in ['spends', 'cost'])]
+            for col in spends_col:
+                sum_df[col]=sum_df[col].map(lambda x: f'${x}')
+            # st.write(spends_col)
+            # sum_df = sum_df.reindex(sorted(sum_df.columns), axis=1)
+
+            return sum_df
+        else:
+            #selected_feature=list(selected_feature)
+            selected_feature.append(spends) 
+            selected_feature=list(set(selected_feature))
+            if len(selected_feature)>1:
+                sum_df = data[selected_feature]
+                sum_df['Year']=data['date'].dt.year
+                sum_df=sum_df.groupby('Year')[selected_feature].agg('sum')
+                sum_df['CPM/CPC']=(sum_df.iloc[:, 1] / sum_df.iloc[:, 0])*1000
+                sum_df.loc['Grand Total']=sum_df.sum()
+            
+                sum_df=sum_df.applymap(format_numbers)
+                sum_df.fillna('-',inplace=True)
+                sum_df=sum_df.replace({"0.0":'-','nan':'-'})
+                spends_col=[col for col in sum_df.columns if any(keyword in col for keyword in ['spends', 'cost'])]
+                for col in spends_col:
+                    sum_df[col]=sum_df[col].map(lambda x: f'${x}')
+                return sum_df
+            else:
+                sum_df = data[selected_feature]
+                sum_df['Year']=data['date'].dt.year
+                sum_df=sum_df.groupby('Year')[selected_feature].agg('sum')
+                sum_df.loc['Grand Total']=sum_df.sum()
+                sum_df=sum_df.applymap(format_numbers)
+                sum_df.fillna('-',inplace=True)
+                sum_df=sum_df.replace({"0.0":'-','nan':'-'})
+                spends_col=[col for col in sum_df.columns if any(keyword in col for keyword in ['spends', 'cost'])]
+                for col in spends_col:
+                    sum_df[col]=sum_df[col].map(lambda x: f'${x}')
+                return sum_df
+
+
+def sanitize_key(key, prefix=""):
+    # Use regular expressions to remove non-alphanumeric characters and spaces
+    key = re.sub(r'[^a-zA-Z0-9]', '', key)
+    return f"{prefix}{key}"
+
+
+
+

Home.py

@@ -0,0 +1,631 @@
+import sqlite3
+import uuid
+import streamlit as st
+from utilities import (
+    load_local_css,
+    set_header,
+    load_authenticator,
+    send_email,
+)
+import streamlit_authenticator as stauth
+import yaml
+from yaml import SafeLoader
+import os
+import datetime
+import shutil
+import pandas as pd
+from st_aggrid import AgGrid
+from st_aggrid import GridOptionsBuilder, GridUpdateMode
+import pickle
+from pathlib import Path
+
+st.set_page_config(layout="wide")
+load_local_css("styles.css")
+set_header()
+
+# def authenticator():
+for k, v in st.session_state.items():
+    if k not in ["logout", "login", "config"] and not k.startswith(
+        "FormSubmitter"
+    ):
+        st.session_state[k] = v
+with open("config.yaml") as file:
+    config = yaml.load(file, Loader=SafeLoader)
+    st.session_state["config"] = config
+authenticator = stauth.Authenticate(
+    config["credentials"],
+    config["cookie"]["name"],
+    config["cookie"]["key"],
+    config["cookie"]["expiry_days"],
+    config["preauthorized"],
+)
+st.session_state["authenticator"] = authenticator
+name, authentication_status, username = authenticator.login("Login", "main")
+auth_status = st.session_state.get("authentication_status")
+
+st.session_state["name"] = name
+if auth_status == True:
+    authenticator.logout("Logout", "main")
+    is_state_initiaized = st.session_state.get("initialized", False)
+
+    if not is_state_initiaized:
+
+        if "session_name" not in st.session_state:
+            st.session_state["session_name"] = None
+
+        cols1 = st.columns([2, 1])
+
+        with cols1[0]:
+            st.markdown(f"**Welcome {name}**")
+        with cols1[1]:
+            st.markdown(
+                f"**Current Session: {st.session_state['session_name']}**"
+            )
+
+        # relative_path = Path('DB_Sample','..' ,'DB', 'User.db')
+        # absolute_path = Path.cwd() / relative_path
+        # st.write(absolute_path)
+        # database_file=Path(__file__).parent / relative_path
+
+    database_file = r"DB/User.db"
+
+    conn = sqlite3.connect(
+        database_file, check_same_thread=False
+    )  # connection with sql db
+    c = conn.cursor()
+
+    # c.execute('DELETE FROM sessions')
+    # conn.commit()
+    # c.executemany("INSERT INTO users (username, email) VALUES (?, ?)",
+    #         [("Geetha Krishna", "geetha1732@gmail.com"),
+    #         ("Samkeet Sangai", "samkeet.sangai@blend360.com"),
+    #         ('Manoj P','manojp1732@gmcail.com'),
+    #         ('Srishti Verma','srishti.verma@blend360.com'),
+    #         ('Ismail mohammed',"mohammed.ismail@blend360.com"),
+    #         ('Sharon Sheng','sharon.sheng@mastercard.com'),
+    #         ('Ioannis Papadopoulos','ioannis.papadopoulos@mastercard.com'),
+    #         ('Herman Kwong',"herman.kwong@mastercard.com")
+    #         ])
+
+    # conn.commit()
+
+    # c.execute(f"PRAGMA table_info({'sessions'})")
+    # conn.commit()
+
+    # st.write(c.fetchall())
+
+    # c.execute("Select * from users")
+    # st.write(c.fetchall())
+
+    page_name = "Home Page"
+
+    c.execute(
+        "SELECT email, user_id, user_type FROM users WHERE username = ?",
+        (name,),
+    )
+    user_data = c.fetchone()
+
+    email, user_id, user_type = user_data
+
+    # st.write(user_type)
+    # with st.sidebar:
+    #     # if user_type != 'technical':
+    #     st.page_link("home.py", label="Home123")
+    #     st.page_link('pages/1_Data_Import.py',label='Data Import')
+    #     st.page_link('pages/2_Data_Validation.py',label="Data Validation")
+    #     st.page_link('pages/3_Transformations.py',label='Transformations')
+    #     st.page_link("pages/4_Model_Build.py")
+    #     st.page_link('pages/5_Model_Tuning_with_panel.py',label='Model Tuning')
+
+    #     st.page_link('pages/5_Saved_Model_Results.py',label="Saved Model Results")
+
+    # st.write(pd.to_datetime(created_time))
+    # c.execute("DELETE FROM sessions")
+    # # c.execute('select * from sessions')
+    # conn.commit()
+    # output = c.fetchall()
+
+    # st.write(output)
+
+    # if emails is not None:
+    #     email = emails[0]
+
+    folder_path = r"Users"
+    user_folder_path = os.path.join(folder_path, email)
+
+    # project_dct = {
+    #         'data_import': {
+    #                         "granularity_selection":0,
+    #                         'cat_dct':{},
+    #                         "merged_df":None,
+    #                         'edited_df':None,
+    #                         "numeric_columns":None,
+    #                         "files_dict":None,
+    #                         'formatted_panel1_values':None,
+    #                         'formatted_panel2_values':None,
+    #                         "missing_stats_df":None,
+    #                         'edited_stats_df':None
+
+    #                         },
+
+    #         'data_validation': {"target_column":0,
+    #                         'selected_panels':None,
+    #                         "selected_feature":0,
+    #                         "validated_variables":[],
+    #                         "Non_media_variables":0
+
+    #         },
+    #         'transformations': {},
+    #         'model_build': {},
+    #         'model_tuning':{},
+    #         'saved_model_results': {},
+    #         'model_result_overview': {},
+    #         'build_response_curves': {},
+    #         'scenario_planner': {},
+    #         'saved_scenarios': {},
+    #         'optimized_result_analysis': {}
+    #     }
+    # st.session_state['project_dct']=project_dct
+
+    # st.write(project_dct)
+
+    def dump_session_details_db(allowed_users, session_name):
+
+        created_time = datetime.datetime.now().strftime("%Y-%m-%d %H:%M")
+
+        session_id = str(uuid.uuid4())
+
+        if len(allowed_users) == 0:
+            c.execute(
+                "INSERT INTO sessions VALUES (?, ?, ?, ?, ?, ?, ?,?)",
+                (
+                    user_id,
+                    name,
+                    session_id,
+                    session_name,
+                    page_name,
+                    created_time,
+                    created_time,
+                    None,
+                ),
+            )
+            conn.commit()
+        else:
+            for allowed_user in allowed_users:
+                c.execute(
+                    "INSERT INTO sessions VALUES (?, ?, ?, ?, ?, ?, ?,?)",
+                    (
+                        user_id,
+                        name,
+                        session_id,
+                        session_name,
+                        "1_Home.py",
+                        created_time,
+                        created_time,
+                        allowed_user,
+                    ),
+                )
+            conn.commit()
+
+    def update_project_name_box():
+        st.session_state["project_name_box"] = ""
+
+        # st.success('Project created')
+
+    if "session_path" not in st.session_state:
+        st.session_state["session_path"] = None
+
+        # creating dir for user
+
+    if not os.path.exists(user_folder_path):
+        os.makedirs(user_folder_path)
+
+    c.execute("SELECT DISTINCT username FROM users ")
+    allowed_users_db = [user[0] for user in c.fetchall() if user[0] != name]
+
+    c.execute(
+        "SELECT session_name from sessions WHERE allowed_users = ?", (name,)
+    )
+    available_sessions = c.fetchall()  # all sessions available for user
+
+    c.execute(
+        "SELECT Distinct Session_name, status, updated_time as last_updated FROM sessions WHERE owner=?",
+        (name,),
+    )
+
+    session_summary = c.fetchall()
+
+    session_summary_df = pd.DataFrame(
+        session_summary,
+        columns=["Project Name", "Last Page Edited", "Modified Date"],
+    )
+    session_summary_df["Modified Date"] = session_summary_df[
+        "Modified Date"
+    ].map(lambda x: pd.to_datetime(x))
+
+    session_summary_df = session_summary_df.sort_values(
+        by=["Modified Date"], ascending=False
+    )
+
+    session_summary_df["Last Page Modified"] = session_summary_df[
+        "Last Page Edited"
+    ].map(lambda x: x[2:].replace("_", " ").replace(".py", ""))
+
+    st.header("Manage Projects")
+
+    st.markdown(
+        """
+    * **Load Existing Project:** Select the project you want and click 'Load Project'.
+    * **Delete Project:** If you wish to delete a project, select it and click 'Delete Project'.
+    * **Modify User Access:** Make changes to user access permissions as needed.
+    
+    """
+    )
+
+    # session_col=st.columns([5,5])
+    # with session_col[0]:
+    gd = GridOptionsBuilder.from_dataframe(session_summary_df)
+    gd.configure_pagination(
+        enabled=True, paginationAutoPageSize=False, paginationPageSize=10
+    )
+    gd.configure_selection(use_checkbox=True)
+
+    gridoptions = gd.build()
+
+    column_defs = gridoptions["columnDefs"]
+    columns_to_hide = ["Last Page Edited"]
+    for col in column_defs:
+        if col["headerName"] in columns_to_hide:
+            col["hide"] = True
+
+    if session_summary_df.shape[0] < 5:
+        height = (session_summary_df.shape[0]) * 20 + 100
+
+    else:
+        height = None
+
+    table = AgGrid(
+        session_summary_df,
+        gridOptions=gridoptions,
+        update_mode=GridUpdateMode.SELECTION_CHANGED,
+        height=height,
+        fit_columns_on_grid_load=True,
+    )
+
+    if len(table.selected_rows) > 0:
+
+        selected_rows = table.selected_rows
+
+        project_name = selected_rows[0]["Project Name"]
+
+        st.session_state["project_name"] = project_name
+
+        project_col = st.columns(2)
+
+        with project_col[0]:
+
+            project_path = os.path.join(user_folder_path, project_name)
+
+            st.session_state["project_path"] = project_path  # load project dct
+
+            project_dct_path = os.path.join(project_path, "project_dct.pkl")
+
+            with open(project_dct_path, "rb") as f:
+                st.session_state["project_dct"] = pickle.load(f)
+
+                # st.write(st.session_state['project_dct'])
+
+            with st.spinner("Redirecting to last Saved Page"):
+
+                try:
+                    page_link = st.page_link(
+                        f"pages/{selected_rows[0]['Last Page Edited']}",
+                        label=f"Load Project - **{project_name}**",
+                    )
+                except Exception as e:
+                    try:
+                        pag_link = st.page_link(
+                            "pages/1_Data_Import.py",
+                            label=f"Load Project - **{project_name}**",
+                        )
+                    except Exception as e:
+                        st.error("Something went wrong")
+
+        with project_col[1]:
+
+            if st.button(
+                f"Delete Project - **{selected_rows[0]['Project Name']}**"):
+
+                project_name_to_delete = selected_rows[0]["Project Name"]
+                st.warning(
+                    f"{project_name_to_delete} will be deleted permanentaly and all the information regarding the project will be lost"
+                )
+                c.execute(
+                "Delete FROM sessions WHERE session_name =? AND owner =?",
+                (
+                    project_name_to_delete,
+                    st.session_state["name"],
+                ),
+            )
+                if os.path.exists(project_path):
+                        shutil.rmtree(project_path)
+
+                conn.commit()
+                st.rerun()
+
+
+        with st.expander("Modify user access for selected project"):
+
+            c.execute(
+                "SELECT DISTINCT allowed_users FROM sessions WHERE session_name = ?",
+                (project_name,),
+            )
+
+            present_users = c.fetchall()
+
+            present_users = [
+                user[0]
+                for user in present_users
+                if user[0] != name and user[0] is not None
+            ]
+
+            present_users = None if len(present_users) == 0 else present_users
+
+            allowed_users = st.multiselect(
+                "Modify other users access",
+                allowed_users_db,
+                default=present_users,
+            )
+
+            if st.button("Save Changes", use_container_width=True):
+                pass
+
+    c.execute("SELECT  Session_name FROM sessions WHERE owner=?", (name,))
+
+    user_projects = [
+        project[0] for project in c.fetchall()
+    ]  # user owned sessions
+
+    with st.expander("Create New Project"):
+        st.markdown(
+            "To create a new project, Enter Project name below, select user who you want to give access of this project and click **Create New Project**"
+        )
+
+        project_col1 = st.columns(2)
+        with project_col1[0]:
+            project_name = st.text_input("Enter Project Name",key="project_name_box")
+
+        if project_name in user_projects:
+            st.warning("Project already exists please enter new name")
+
+        with project_col1[1]:
+
+            allowed_users = st.multiselect(
+                "Select Users who can access to this Project", allowed_users_db
+            )
+            allowed_users = list(allowed_users)
+
+        Create = st.button("Create New Project", use_container_width=True)
+
+        if Create:
+            if len(project_name) == 0:
+                st.error("Plase enter a valid project name")
+                st.stop()
+            allowed_users.append(name)
+
+            if project_name in user_projects:
+
+                st.warning("Project already exists please enter new name")
+                st.stop()
+
+            project_path = os.path.join(user_folder_path, project_name)
+
+            os.makedirs(project_path)
+
+            dump_session_details_db(allowed_users, project_name)
+
+            project_dct = {
+                "data_import": {
+                    "granularity_selection": 0,
+                    "cat_dct": {},
+                    "merged_df": None,
+                    "edited_df": None,
+                    "numeric_columns": None,
+                    "files_dict": None,
+                    "formatted_panel1_values": None,
+                    "formatted_panel2_values": None,
+                    "missing_stats_df": None,
+                    "edited_stats_df": None,
+                    "default_df": None,
+                    "final_df": None,
+                    "edited_df": None,
+                },
+                "data_validation": {
+                    "target_column": 0,
+                    "selected_panels": None,
+                    "selected_feature": 0,
+                    "validated_variables": [],
+                    "Non_media_variables": 0,
+                },
+                "transformations": {"Media": {}, "Exogenous": {}},
+                "model_build": {
+                    "sel_target_col": None,
+                    "all_iters_check": False,
+                    "iterations": 0,
+                    "build_button": False,
+                    "show_results_check": False,
+                    "session_state_saved": {},
+                },
+                "model_tuning": {
+                    "sel_target_col": None,
+                    "sel_model": {},
+                    "flag_expander": False,
+                    "start_date_default": None,
+                    "end_date_default": None,
+                    "repeat_default": "No",
+                    "flags": {},
+                    "select_all_flags_check": {},
+                    "selected_flags": {},
+                    "trend_check": False,
+                    "week_num_check": False,
+                    "sine_cosine_check": False,
+                    "session_state_saved": {},
+                },
+                "saved_model_results": {
+                    "selected_options": None,
+                    "model_grid_sel": [1],
+                },
+                "model_result_overview": {},
+                "build_response_curves": {
+                    "response_metrics_selectbox": 0,
+                    "panel_selected_selectbox": 0,
+                    "selected_channel_name_selectbox": 0,
+                    "K_number_input": "default",
+                    "b_number_input": "default",
+                    "a_number_input": "default",
+                    "x0_number_input": "default",
+                },
+                "scenario_planner": {
+                    "panel_selected": 0,
+                    "metrics_selected": 0,
+                    "scenario": None,
+                    "optimization_key_value": None,
+                    "total_spends_change": None,
+                    "optimze_all_channels": False,
+                },
+                "saved_scenarios": {
+                    "selected_scenario_selectbox_key": 0,
+                },
+                "optimized_result_analysis": {
+                    "selected_scenario_selectbox_visualize": 0,
+                    "metric_selectbox_visualize": 0,
+                },
+            }
+
+            st.session_state["project_dct"] = project_dct
+
+            st.session_state["project_path"] = project_path
+
+            project_dct_path = os.path.join(project_path, "project_dct.pkl")
+
+            with open(project_dct_path, "wb") as f:
+                pickle.dump(project_dct, f)
+
+            st.success("Project Created")
+            st.rerun()
+            # st.session_state['project_name_box']=''
+            # st.rerun()
+
+    # st.header('Clone Project')
+
+    with st.expander("**Clone saved projects**"):
+
+        c.execute(
+            "SELECT DISTINCT owner FROM sessions WHERE allowed_users=?",
+            (name,),
+        )  # owner
+        owners = c.fetchall()
+
+        owners = [owner[0] for owner in owners]
+
+        if len(owners) == 0:
+
+            st.warning("You dont have any shared project yet!")
+
+            st.stop()
+
+        cols = st.columns(2)
+
+        with cols[0]:
+
+            owner = st.selectbox("Select Owner", owners)
+
+            c.execute("SELECT email FROM users WHERE username=?", (owner,))
+
+            owner_email = c.fetchone()[0]
+
+            owner_folder_path = os.path.join(folder_path, owner_email)
+
+        with cols[1]:
+
+            c.execute(
+                "SELECT session_name FROM sessions WHERE  owner=? AND allowed_users = ?",
+                (owner, name),
+            )  # available sessions for user
+            project_names = c.fetchall()
+
+            project_name_owner = st.selectbox(
+                "Select a saved Project available for you",
+                [project_name[0] for project_name in project_names],
+            )
+            owner_project_path = os.path.join(owner_folder_path, project_name)
+
+        with cols[0]:
+            project_name_user = st.text_input(
+                "Enter Project Name", value=project_name_owner
+            )
+
+            if project_name in user_projects:
+
+                st.warning(
+                    "This Project name already exists in your directory Please enter a different name"
+                )
+
+                # st.stop()
+
+            project_path = os.path.join(user_folder_path, project_name_user)
+
+            owner_project_path = os.path.join(
+                owner_folder_path, project_name_owner
+            )
+
+        with cols[1]:
+
+            allowed_users = st.multiselect(
+                "Select Users who can access this session", allowed_users_db
+            )
+            allowed_users = list(allowed_users)
+
+        if st.button("Load Project", use_container_width=True):
+
+            if os.path.exists(project_path):
+
+                st.warning(
+                    "This Project name already exists in your directory Please enter a different name"
+                )
+
+                st.stop()
+
+            shutil.copytree(owner_project_path, project_path)
+
+            project_dct_path = os.path.join(project_path, "project_dct.pkl")
+
+            with open(project_dct_path, "rb") as f:
+                st.session_state["project_dct"] = pickle.load(f)
+
+            st.session_state["project_path"] = project_path
+
+            # st.write(st.session_state['project_dct'])
+
+            dump_session_details_db(allowed_users, project_name_user)
+            st.success("Project Cloned")
+
+elif auth_status == False:
+    st.error("Username/Password is incorrect")
+
+if auth_status != True:
+    try:
+        username_forgot_pw, email_forgot_password, random_password = (
+            authenticator.forgot_password("Forgot password")
+        )
+        if username_forgot_pw:
+            st.session_state["config"]["credentials"]["usernames"][
+                username_forgot_pw
+            ]["password"] = stauth.Hasher([random_password]).generate()[0]
+            send_email(email_forgot_password, random_password)
+            st.success("New password sent securely")
+            # Random password to be transferred to user securely
+        elif username_forgot_pw == False:
+            st.error("Username not found")
+    except Exception as e:
+        st.error(e)

Home_old_version.py

@@ -0,0 +1,538 @@
+import sqlite3
+import uuid
+import json
+import streamlit as st
+from utilities import (
+    load_local_css,
+    set_header,
+    load_authenticator,
+    send_email,
+)
+import streamlit_authenticator as stauth
+import yaml
+from yaml import SafeLoader
+import os
+import datetime
+import subprocess
+import shutil
+import pandas as pd
+from st_aggrid import AgGrid
+from st_aggrid import GridOptionsBuilder, GridUpdateMode
+import pickle
+from pathlib import Path
+
+st.set_page_config(layout="wide")
+load_local_css("styles.css")
+set_header()
+
+# def authenticator():
+for k, v in st.session_state.items():
+    if k not in ["logout", "login", "config"] and not k.startswith(
+        "FormSubmitter"
+    ):
+        st.session_state[k] = v
+with open("config.yaml") as file:
+    config = yaml.load(file, Loader=SafeLoader)
+    st.session_state["config"] = config
+authenticator = stauth.Authenticate(
+    config["credentials"],
+    config["cookie"]["name"],
+    config["cookie"]["key"],
+    config["cookie"]["expiry_days"],
+    config["preauthorized"],
+)
+st.session_state["authenticator"] = authenticator
+name, authentication_status, username = authenticator.login("Login", "main")
+auth_status = st.session_state.get("authentication_status")
+
+if auth_status == True:
+    authenticator.logout("Logout", "main")
+    is_state_initiaized = st.session_state.get("initialized", False)
+
+    if not is_state_initiaized:
+
+        if "session_name" not in st.session_state:
+            st.session_state["session_name"] = None
+
+        cols1 = st.columns([2, 1])
+
+        with cols1[0]:
+            st.markdown(f"**Welcome {name}**")
+        with cols1[1]:
+            st.markdown(
+                f"**Current Session: {st.session_state['session_name']}**"
+            )
+
+        # relative_path = Path('DB_Sample','..' ,'DB', 'User.db')
+        # absolute_path = Path.cwd() / relative_path
+        # st.write(absolute_path)
+        # database_file=Path(__file__).parent / relative_path
+
+        database_file = r"C:\Users\ManojP\Documents\Mastercard\Build\DB_Sample\V6_persistant_data_home_page_connected_pages\DB\User.db"
+
+    conn = sqlite3.connect(database_file)  # connection with sql db
+    c = conn.cursor()
+    c.execute("SELECT * from sessions")
+    st.write(c.fetchall())
+    # c.executemany("INSERT INTO users (username, email) VALUES (?, ?)",
+    #         [("Geetha Krishna", "geetha1732@gmail.com"),
+    #         ("Samkeet Sangai", "samkeet.sangai@blend360.com"),
+    #         ('Manoj P','manojp1732@gmail.com'),
+    #         ('Srishti Verma','srishti.verma@blend360.com'),
+    #         ('Ismail mohammed',"mohammed.ismail@blend360.com"),
+    #         ('Sharon Sheng','sharon.sheng@mastercard.com'),
+    #         ('Ioannis Papadopoulos','ioannis.papadopoulos@mastercard.com'),
+    #         ('Herman Kwong',"herman.kwong@mastercard.com")
+    #         ])
+
+    # conn.commit()
+
+    # c.execute("DELETE from sessions")
+    # conn.commit()
+    # st.write(c.fetchall())
+
+    page_name = "Home Page"
+
+    c.execute(
+        "SELECT email, user_id, user_type FROM users WHERE username = ?",
+        (name,),
+    )
+    user_data = c.fetchone()
+    email, user_id, user_type = user_data
+
+    # st.write(user_type)
+    # with st.sidebar:
+    #     # if user_type != 'technical':
+    #     st.page_link("home.py", label="Home123")
+    #     st.page_link('pages/1_Data_Import.py',label='Data Import')
+    #     st.page_link('pages/2_Data_Validation.py',label="Data Validation")
+    #     st.page_link('pages/3_Transformations.py',label='Transformations')
+    #     st.page_link("pages/4_Model_Build.py")
+    #     st.page_link('pages/5_Model_Tuning_with_panel.py',label='Model Tuning')
+
+    #     st.page_link('pages/5_Saved_Model_Results.py',label="Saved Model Results")
+
+    # st.write(pd.to_datetime(created_time))
+    # c.execute("DELETE FROM sessions")
+    # c.execute('select * from sessions')
+    # conn.commit()
+    # output = c.fetchall()
+
+    # st.write(output)
+
+    # if emails is not None:
+    #     email = emails[0]
+
+    folder_path = r"C:\Users\ManojP\Documents\Mastercard\Build\DB_Sample\V6_persistant_data_home_page_connected_pages\Users"
+    user_folder_path = os.path.join(folder_path, email)
+
+    # project_dct = {
+    #         'data_import': {
+    #                         "granularity_selection":0,
+    #                         'cat_dct':{},
+    #                         "merged_df":None,
+    #                         'edited_df':None,
+    #                         "numeric_columns":None,
+    #                         "files_dict":None,
+    #                         'formatted_panel1_values':None,
+    #                         'formatted_panel2_values':None,
+    #                         "missing_stats_df":None,
+    #                         'edited_stats_df':None
+
+    #                         },
+
+    #         'data_validation': {"target_column":0,
+    #                         'selected_panels':None,
+    #                         "selected_feature":0,
+    #                         "validated_variables":[],
+    #                         "Non_media_variables":0
+
+    #         },
+    #         'transformations': {},
+    #         'model_build': {},
+    #         'model_tuning':{},
+    #         'saved_model_results': {},
+    #         'model_result_overview': {},
+    #         'build_response_curves': {},
+    #         'scenario_planner': {},
+    #         'saved_scenarios': {},
+    #         'optimized_result_analysis': {}
+    #     }
+    # st.session_state['project_dct']=project_dct
+
+    # st.write(project_dct)
+
+    def dump_session_details_db(allowed_users, session_name):
+        created_time = datetime.datetime.now().strftime("%Y-%m-%d %H:%M")
+
+        session_id = str(uuid.uuid4())
+
+        if len(allowed_users) == 0:
+            c.execute(
+                "INSERT INTO sessions VALUES (?, ?, ?, ?, ?, ?, ?,?)",
+                (
+                    user_id,
+                    name,
+                    session_id,
+                    session_name,
+                    page_name,
+                    created_time,
+                    created_time,
+                    None,
+                ),
+            )
+            conn.commit()
+        else:
+            for allowed_user in allowed_users:
+                c.execute(
+                    "INSERT INTO sessions VALUES (?, ?, ?, ?, ?, ?, ?,?)",
+                    (
+                        user_id,
+                        name,
+                        session_id,
+                        session_name,
+                        page_name,
+                        created_time,
+                        created_time,
+                        allowed_user,
+                    ),
+                )
+            conn.commit()
+
+        # st.success('Project created')
+
+    if "session_path" not in st.session_state:
+        st.session_state["session_path"] = None
+
+        # creating dir for user
+
+    if not os.path.exists(user_folder_path):
+        os.makedirs(user_folder_path)
+
+    c.execute("SELECT DISTINCT username FROM users ")
+    allowed_users_db = [user[0] for user in c.fetchall() if user[0] != name]
+
+    c.execute(
+        "SELECT session_name from sessions WHERE allowed_users = ?", (name,)
+    )
+    available_sessions = c.fetchall()  # all sessions available for user
+
+    c.execute(
+        "SELECT Distinct Session_name, status, updated_time as last_updated FROM sessions WHERE owner=?",
+        (name,),
+    )
+
+    session_summary = c.fetchall()
+
+    session_summary_df = pd.DataFrame(
+        session_summary,
+        columns=["Project Name", "Last Page Edited", "Modified Date"],
+    )
+    session_summary_df["Modified Date"] = session_summary_df[
+        "Modified Date"
+    ].map(lambda x: pd.to_datetime(x))
+
+    session_summary_df = session_summary_df.sort_values(
+        by=["Modified Date"], ascending=False
+    )
+
+    st.header("Manage Projects")
+
+    st.markdown(
+        """
+    * **Load Existing Project:** Select the project you want and click 'Load Project'.
+    * **Delete Project:** If you wish to delete a project, select it and click 'Delete Project'.
+    * **Modify User Access:** Make changes to user access permissions as needed.
+    
+    """
+    )
+
+    # session_col=st.columns([5,5])
+    # with session_col[0]:
+    gd = GridOptionsBuilder.from_dataframe(session_summary_df)
+    gd.configure_pagination(
+        enabled=True, paginationAutoPageSize=False, paginationPageSize=10
+    )
+    gd.configure_selection(use_checkbox=True)
+
+    gridoptions = gd.build()
+
+    if session_summary_df.shape[0] < 5:
+        height = (session_summary_df.shape[0]) * 20 + 100
+
+    else:
+        height = None
+
+    table = AgGrid(
+        session_summary_df,
+        gridOptions=gridoptions,
+        update_mode=GridUpdateMode.SELECTION_CHANGED,
+        height=height,
+        fit_columns_on_grid_load=True,
+    )
+
+    if len(table.selected_rows) > 0:
+
+        selected_rows = table.selected_rows
+
+        project_name = selected_rows[0]["Project Name"]
+
+        project_col = st.columns(2)
+
+        with project_col[0]:
+
+            project_path = os.path.join(user_folder_path, project_name)
+
+            st.session_state["project_path"] = project_path  # load project dct
+
+            project_dct_path = os.path.join(project_path, "project_dct.pkl")
+
+            with open(project_dct_path, "rb") as f:
+                st.session_state["project_dct"] = pickle.load(f)
+
+                # st.write(st.session_state['project_dct'])
+
+            with st.spinner("Redirecting to last Saved Page"):
+
+                page_link = st.page_link(
+                    "pages/1_Data_Import.py",
+                    label=f"Load Project - **{project_name}**",
+                )
+
+        with project_col[1]:
+
+            if st.button(
+                f"Delete Project - **{selected_rows[0]['Project Name']}**"
+            ):
+
+                project_name_to_delete = selected_rows[0]["Project Name"]
+                st.warning(
+                    f"{project_name_to_delete} will be deleted permanentaly and all the information regarding the project will be lost"
+                )
+
+        with st.expander("Modify user access for selected project"):
+
+            c.execute(
+                "SELECT DISTINCT allowed_users FROM sessions WHERE session_name = ?",
+                (project_name,),
+            )
+
+            present_users = c.fetchall()
+
+            present_users = [
+                user[0]
+                for user in present_users
+                if user[0] != name and user[0] is not None
+            ]
+
+            present_users = None if len(present_users) == 0 else present_users
+
+            allowed_users = st.multiselect(
+                "Modify other users access",
+                allowed_users_db,
+                default=present_users,
+            )
+
+            if st.button("Save Changes", use_container_width=True):
+                pass
+
+    c.execute("SELECT  Session_name FROM sessions WHERE owner=?", (name,))
+    user_projects = [
+        project[0] for project in c.fetchall()
+    ]  # user owned sessions
+
+    with st.expander("Create New Project"):
+        st.markdown(
+            "To create a new project, Enter Project name below, select user who you want to give access of this project and click **Create New Project**"
+        )
+        project_col1 = st.columns(2)
+        with project_col1[0]:
+            project_name = st.text_input("Enter Project Name")
+
+        if project_name in user_projects:
+            st.warning("Project already exists please enter new name")
+
+        with project_col1[1]:
+
+            allowed_users = st.multiselect(
+                "Select Users who can access to this Project", allowed_users_db
+            )
+            allowed_users = list(allowed_users)
+
+        Create = st.button("Create New Project", use_container_width=True)
+        # st.button("Label", use_container_width=True)
+
+        if Create:
+
+            allowed_users.append(name)
+
+            if project_name in user_projects:
+
+                st.warning("Project already exists please enter new name")
+                st.stop()
+
+            project_path = os.path.join(user_folder_path, project_name)
+
+            os.makedirs(project_path)
+
+            dump_session_details_db(allowed_users, project_name)
+
+            project_dct = {
+                "data_import": {
+                    "granularity_selection": 0,
+                    "cat_dct": {},
+                    "merged_df": None,
+                    "edited_df": None,
+                    "numeric_columns": None,
+                    "files_dict": None,
+                    "formatted_panel1_values": None,
+                    "formatted_panel2_values": None,
+                    "missing_stats_df": None,
+                    "edited_stats_df": None,
+                },
+                "data_validation": {
+                    "target_column": 0,
+                    "selected_panels": None,
+                    "selected_feature": 0,
+                    "validated_variables": [],
+                    "Non_media_variables": 0,
+                },
+                "transformations": {"Media": {}, "Exogenous": {}},
+                "model_build": {
+                    "sel_target_col": None,
+                    "all_iters_check": False,
+                    "iterations": 0,
+                    "build_button": False,
+                    "show_results_check": False,
+                    "session_state_saved": {},
+                },
+                "model_tuning": {
+                    "sel_target_col": None,
+                    "sel_model": {},
+                    "flag_expander": False,
+                    "start_date_default": None,
+                    "end_date_default": None,
+                    "repeat_default": "No",
+                    "flags": None,
+                    "select_all_flags_check": {},
+                    "selected_flags": {},
+                    "trend_check": False,
+                    "week_num_check": False,
+                    "sine_cosine_check": False,
+                    "session_state_saved": {},
+                },
+                "saved_model_results": {
+                    "selected_options": None,
+                    "model_grid_sel": [1],
+                },
+                "model_result_overview": {},
+                "build_response_curves": {},
+                "scenario_planner": {},
+                "saved_scenarios": {},
+                "optimized_result_analysis": {},
+            }
+            st.session_state["project_dct"] = project_dct
+
+            st.session_state["project_path"] = project_path
+
+            project_dct_path = os.path.join(project_path, "project_dct.pkl")
+            st.session_state["session_path"] = project_path
+
+            with open(project_dct_path, "wb") as f:
+                pickle.dump(project_dct, f)
+
+            st.success("Project Created")
+
+    # st.header('Clone Project')
+
+    with st.expander("**Clone saved projects**"):
+
+        c.execute(
+            "SELECT DISTINCT owner FROM sessions WHERE allowed_users=?",
+            (name,),
+        )  # owner
+        owners = c.fetchall()
+
+        owners = [owner[0] for owner in owners]
+
+        if len(owners) == 0:
+
+            st.warning("You dont have any shared project yet!")
+
+            st.stop()
+
+        cols = st.columns(2)
+
+        with cols[0]:
+
+            owner = st.selectbox("Select Owner", owners)
+
+            c.execute("SELECT email FROM users WHERE username=?", (owner,))
+
+            owner_email = c.fetchone()[0]
+
+            owner_folder_path = os.path.join(folder_path, owner_email)
+
+        with cols[1]:
+
+            c.execute(
+                "SELECT session_name FROM sessions WHERE  owner=? AND allowed_users = ?",
+                (owner, name),
+            )  # available sessions for user
+            project_names = c.fetchall()
+
+            project_name_owner = st.selectbox(
+                "Select a saved Project available for you",
+                [project_name[0] for project_name in project_names],
+            )
+            owner_project_path = os.path.join(owner_folder_path, project_name)
+
+        with cols[0]:
+            project_name_user = st.text_input(
+                "Enter Project Name", value=project_name_owner
+            )
+
+            if project_name in user_projects:
+
+                st.warning(
+                    "This Project name already exists in your directory Please enter a different name"
+                )
+
+                # st.stop()
+
+            project_path = os.path.join(user_folder_path, project_name_user)
+
+            owner_project_path = os.path.join(
+                owner_folder_path, project_name_owner
+            )
+
+        with cols[1]:
+
+            allowed_users = st.multiselect(
+                "Select Users who can access this session", allowed_users_db
+            )
+            allowed_users = list(allowed_users)
+
+        if st.button("Load Project", use_container_width=True):
+
+            if os.path.exists(project_path):
+
+                st.warning(
+                    "This Project name already exists in your directory Please enter a different name"
+                )
+
+                st.stop()
+
+            shutil.copytree(owner_project_path, project_path)
+
+            project_dct_path = os.path.join(project_path, "project_dct.pkl")
+
+            with open(project_dct_path, "rb") as f:
+                st.session_state["project_dct"] = pickle.load(f)
+            st.session_state["session_path"] = project_path
+            st.session_state["project_path"] = project_path
+
+            # st.write(st.session_state['project_dct'])
+
+            dump_session_details_db(allowed_users, project_name_user)
+            st.success("Project Cloned")

Home_redirecting.py

@@ -0,0 +1,536 @@
+def home():
+    import sqlite3
+    import uuid
+    import json
+    import streamlit as st
+    from utilities import (
+        load_local_css,
+        set_header,
+        load_authenticator,
+        send_email,
+    )
+    import streamlit_authenticator as stauth
+    import yaml
+    from yaml import SafeLoader
+    import os
+    import datetime
+    import subprocess
+    import shutil
+    import pandas as pd
+    from st_aggrid import AgGrid
+    from st_aggrid import GridOptionsBuilder, GridUpdateMode
+    import pickle
+    from pathlib import Path
+
+    # st.set_page_config(layout="wide")
+    load_local_css("styles.css")
+    # set_header()
+
+    # def authenticator():
+    for k, v in st.session_state.items():
+        if k not in ["logout", "login", "config"] and not k.startswith(
+            "FormSubmitter"
+        ):
+            st.session_state[k] = v
+    with open("config.yaml") as file:
+        config = yaml.load(file, Loader=SafeLoader)
+        st.session_state["config"] = config
+    authenticator = stauth.Authenticate(
+        config["credentials"],
+        config["cookie"]["name"],
+        config["cookie"]["key"],
+        config["cookie"]["expiry_days"],
+        config["preauthorized"],
+    )
+    st.session_state["authenticator"] = authenticator
+    name, authentication_status, username = authenticator.login(
+        "Login", "main"
+    )
+    auth_status = st.session_state.get("authentication_status")
+
+    if auth_status == True:
+        authenticator.logout("Logout", "main")
+        is_state_initiaized = st.session_state.get("initialized", False)
+
+        if not is_state_initiaized:
+
+            if "session_name" not in st.session_state:
+                st.session_state["session_name"] = None
+
+            cols1 = st.columns([2, 1])
+
+            with cols1[0]:
+                st.markdown(f"**Welcome {name}**")
+            with cols1[1]:
+                st.markdown(
+                    f"**Current Session: {st.session_state['session_name']}**"
+                )
+
+            # relative_path = Path('DB_Sample','..' ,'DB', 'User.db')
+            # absolute_path = Path.cwd() / relative_path
+            # st.write(absolute_path)
+            # database_file=Path(__file__).parent / relative_path
+
+            database_file = r"C:\Users\ManojP\Documents\Mastercard\Build\DB_Sample\DB\User.db"
+
+        conn = sqlite3.connect(database_file)  # connection with sql db
+        c = conn.cursor()
+
+        # c.executemany("INSERT INTO users (username, email) VALUES (?, ?)",
+        #         [("Geetha Krishna", "geetha1732@gmail.com"),
+        #         ("Samkeet Sangai", "samkeet.sangai@blend360.com"),
+        #         ('Manoj P','manojp1732@gmail.com'),
+        #         ('Srishti Verma','srishti.verma@blend360.com'),
+        #         ('Ismail mohammed',"mohammed.ismail@blend360.com"),
+        #         ('Sharon Sheng','sharon.sheng@mastercard.com'),
+        #         ('Ioannis Papadopoulos','ioannis.papadopoulos@mastercard.com'),
+        #         ('Herman Kwong',"herman.kwong@mastercard.com")
+        #         ])
+
+        # conn.commit()
+
+        # c.execute("DELETE from sessions")
+        # conn.commit()
+        # st.write(c.fetchall())
+
+        page_name = "Home Page"
+        c.execute(
+            "SELECT email, user_id, user_type FROM users WHERE username = ?",
+            (name,),
+        )
+        user_data = c.fetchone()
+        email, user_id, user_type = user_data
+
+        # st.write(user_type)
+        # with st.sidebar:
+        #     # if user_type != 'technical':
+        #     st.page_link("home.py", label="Home123")
+        #     st.page_link('pages/1_Data_Import.py',label='Data Import')
+        #     st.page_link('pages/2_Data_Validation.py',label="Data Validation")
+        #     st.page_link('pages/3_Transformations.py',label='Transformations')
+        #     st.page_link("pages/4_Model_Build.py")
+        #     st.page_link('pages/5_Model_Tuning_with_panel.py',label='Model Tuning')
+
+        #     st.page_link('pages/5_Saved_Model_Results.py',label="Saved Model Results")
+
+        # st.write(pd.to_datetime(created_time))
+        # c.execute("DELETE FROM sessions")
+        # c.execute('select * from sessions')
+        # conn.commit()
+        # output = c.fetchall()
+
+        # st.write(output)
+
+        # if emails is not None:
+        #     email = emails[0]
+
+        folder_path = (
+            r"C:\Users\ManojP\Documents\Mastercard\Build\DB_Sample\Users"
+        )
+        user_folder_path = os.path.join(folder_path, email)
+
+        # project_dct = {
+        #         'data_import': {
+        #                         "granularity_selection":0,
+        #                         'cat_dct':{},
+        #                         "merged_df":None,
+        #                         'edited_df':None,
+        #                         "numeric_columns":None,
+        #                         "files_dict":None,
+        #                         'formatted_panel1_values':None,
+        #                         'formatted_panel2_values':None,
+        #                         "missing_stats_df":None,
+        #                         'edited_stats_df':None
+
+        #                         },
+
+        #         'data_validation': {"target_column":0,
+        #                         'selected_panels':None,
+        #                         "selected_feature":0,
+        #                         "validated_variables":[],
+        #                         "Non_media_variables":0
+
+        #         },
+        #         'transformations': {},
+        #         'model_build': {},
+        #         'model_tuning':{},
+        #         'saved_model_results': {},
+        #         'model_result_overview': {},
+        #         'build_response_curves': {},
+        #         'scenario_planner': {},
+        #         'saved_scenarios': {},
+        #         'optimized_result_analysis': {}
+        #     }
+        # st.session_state['project_dct']=project_dct
+
+        # st.write(project_dct)
+
+        def dump_session_details_db(allowed_users, session_name):
+            created_time = datetime.datetime.now().strftime("%Y-%m-%d %H:%M")
+
+            session_id = str(uuid.uuid4())
+
+            if len(allowed_users) == 0:
+                c.execute(
+                    "INSERT INTO sessions VALUES (?, ?, ?, ?, ?, ?, ?,?)",
+                    (
+                        user_id,
+                        name,
+                        session_id,
+                        session_name,
+                        page_name,
+                        created_time,
+                        created_time,
+                        None,
+                    ),
+                )
+                conn.commit()
+            else:
+                for allowed_user in allowed_users:
+                    c.execute(
+                        "INSERT INTO sessions VALUES (?, ?, ?, ?, ?, ?, ?,?)",
+                        (
+                            user_id,
+                            name,
+                            session_id,
+                            session_name,
+                            page_name,
+                            created_time,
+                            created_time,
+                            allowed_user,
+                        ),
+                    )
+                conn.commit()
+
+            # st.success('Project created')
+
+        if "session_path" not in st.session_state:
+            st.session_state["session_path"] = None
+
+            # creating dir for user
+
+        if not os.path.exists(user_folder_path):
+            os.makedirs(user_folder_path)
+
+        c.execute("SELECT DISTINCT username FROM users ")
+        allowed_users_db = [
+            user[0] for user in c.fetchall() if user[0] != name
+        ]
+
+        c.execute(
+            "SELECT session_name from sessions WHERE allowed_users = ?",
+            (name,),
+        )
+        available_sessions = c.fetchall()  # all sessions available for user
+
+        c.execute(
+            "SELECT Distinct Session_name, status, updated_time as last_updated FROM sessions WHERE owner=?",
+            (name,),
+        )
+
+        session_summary = c.fetchall()
+
+        session_summary_df = pd.DataFrame(
+            session_summary,
+            columns=["Project Name", "Last Page Edited", "Modified Date"],
+        )
+        session_summary_df["Modified Date"] = session_summary_df[
+            "Modified Date"
+        ].map(lambda x: pd.to_datetime(x))
+
+        session_summary_df = session_summary_df.sort_values(
+            by=["Modified Date"], ascending=False
+        )
+
+        st.header("Manage Projects")
+
+        st.markdown(
+            """
+        * **Load Existing Project:** Select the project you want and click 'Load Project'.
+        * **Delete Project:** If you wish to delete a project, select it and click 'Delete Project'.
+        * **Modify User Access:** Make changes to user access permissions as needed.
+        
+        """
+        )
+
+        # session_col=st.columns([5,5])
+        # with session_col[0]:
+        gd = GridOptionsBuilder.from_dataframe(session_summary_df)
+        gd.configure_pagination(
+            enabled=True, paginationAutoPageSize=False, paginationPageSize=10
+        )
+        gd.configure_selection(use_checkbox=True)
+
+        gridoptions = gd.build()
+
+        if session_summary_df.shape[0] < 5:
+            height = (session_summary_df.shape[0]) * 20 + 100
+
+        else:
+            height = None
+
+        table = AgGrid(
+            session_summary_df,
+            gridOptions=gridoptions,
+            update_mode=GridUpdateMode.SELECTION_CHANGED,
+            height=height,
+            fit_columns_on_grid_load=True,
+        )
+
+        if len(table.selected_rows) > 0:
+
+            selected_rows = table.selected_rows
+
+            project_name = selected_rows[0]["Project Name"]
+
+            project_col = st.columns(2)
+
+            with project_col[0]:
+
+                project_path = os.path.join(user_folder_path, project_name)
+
+                st.session_state["project_path"] = (
+                    project_path  # load project dct
+                )
+
+                project_dct_path = os.path.join(
+                    project_path, "project_dct.pkl"
+                )
+
+                with open(project_dct_path, "rb") as f:
+                    st.session_state["project_dct"] = pickle.load(f)
+
+                    st.write(st.session_state["project_dct"])
+
+                with st.spinner("Redirecting to last Saved Page"):
+
+                    page_link = st.page_link(
+                        "pages/1_Data_Import.py",
+                        label=f"Load Project - **{project_name}**",
+                    )
+
+            with project_col[1]:
+
+                if st.button(
+                    f"Delete Project - **{selected_rows[0]['Project Name']}**"
+                ):
+
+                    project_name_to_delete = selected_rows[0]["Project Name"]
+                    st.warning(
+                        f"{project_name_to_delete} will be deleted permanentaly and all the information regarding the project will be lost"
+                    )
+
+            with st.expander("Modify user access for selected project"):
+
+                c.execute(
+                    "SELECT DISTINCT allowed_users FROM sessions WHERE session_name = ?",
+                    (project_name,),
+                )
+
+                present_users = c.fetchall()
+
+                present_users = [
+                    user[0]
+                    for user in present_users
+                    if user[0] != name and user[0] is not None
+                ]
+
+                present_users = (
+                    None if len(present_users) == 0 else present_users
+                )
+
+                allowed_users = st.multiselect(
+                    "Modify other users access",
+                    allowed_users_db,
+                    default=present_users,
+                )
+
+                if st.button("Save Changes", use_container_width=True):
+                    pass
+
+        c.execute("SELECT  Session_name FROM sessions WHERE owner=?", (name,))
+
+        user_projects = [
+            project[0] for project in c.fetchall()
+        ]  # user owned sessions
+
+        with st.expander("Create New Project"):
+            st.markdown(
+                "To create a new project, Enter Project name below, select user who you want to give access of this project and click **Create New Project**"
+            )
+
+            project_col1 = st.columns(2)
+            with project_col1[0]:
+                project_name = st.text_input("Enter Project Name")
+
+            if project_name in user_projects:
+                st.warning("Project already exists please enter new name")
+
+            with project_col1[1]:
+
+                allowed_users = st.multiselect(
+                    "Select Users who can access to this Project",
+                    allowed_users_db,
+                )
+                allowed_users = list(allowed_users)
+
+            Create = st.button("Create New Project", use_container_width=True)
+
+            if Create:
+
+                allowed_users.append(name)
+
+                if project_name in user_projects:
+
+                    st.warning("Project already exists please enter new name")
+                    st.stop()
+
+                project_path = os.path.join(user_folder_path, project_name)
+
+                os.makedirs(project_path)
+
+                dump_session_details_db(allowed_users, project_name)
+
+                project_dct = {
+                    "data_import": {
+                        "granularity_selection": 0,
+                        "cat_dct": {},
+                        "merged_df": None,
+                        "edited_df": None,
+                        "numeric_columns": None,
+                        "files_dict": None,
+                        "formatted_panel1_values": None,
+                        "formatted_panel2_values": None,
+                        "missing_stats_df": None,
+                        "edited_stats_df": None,
+                    },
+                    "data_validation": {
+                        "target_column": 0,
+                        "selected_panels": None,
+                        "selected_feature": 0,
+                        "validated_variables": [],
+                        "Non_media_variables": 0,
+                    },
+                    "transformations": {},
+                    "model_build": {},
+                    "model_tuning": {},
+                    "saved_model_results": {},
+                    "model_result_overview": {},
+                    "build_response_curves": {},
+                    "scenario_planner": {},
+                    "saved_scenarios": {},
+                    "optimized_result_analysis": {},
+                }
+                st.session_state["project_dct"] = project_dct
+
+                # st.session_state['project_path']=project_path
+
+                project_dct_path = os.path.join(
+                    project_path, "project_dct.pkl"
+                )
+
+                with open(project_dct_path, "wb") as f:
+                    pickle.dump(project_dct, f)
+
+                st.success("Project Created")
+
+        # st.header('Clone Project')
+
+        with st.expander("**Clone saved projects**"):
+
+            c.execute(
+                "SELECT DISTINCT owner FROM sessions WHERE allowed_users=?",
+                (name,),
+            )  # owner
+            owners = c.fetchall()
+
+            owners = [owner[0] for owner in owners]
+
+            if len(owners) == 0:
+
+                st.warning("You dont have any shared project yet!")
+
+                st.stop()
+
+            cols = st.columns(2)
+
+            with cols[0]:
+
+                owner = st.selectbox("Select Owner", owners)
+
+                c.execute("SELECT email FROM users WHERE username=?", (owner,))
+
+                owner_email = c.fetchone()[0]
+
+                owner_folder_path = os.path.join(folder_path, owner_email)
+
+            with cols[1]:
+
+                c.execute(
+                    "SELECT session_name FROM sessions WHERE  owner=? AND allowed_users = ?",
+                    (owner, name),
+                )  # available sessions for user
+                project_names = c.fetchall()
+
+                project_name_owner = st.selectbox(
+                    "Select a saved Project available for you",
+                    [project_name[0] for project_name in project_names],
+                )
+                owner_project_path = os.path.join(
+                    owner_folder_path, project_name
+                )
+
+            with cols[0]:
+                project_name_user = st.text_input(
+                    "Enter Project Name", value=project_name_owner
+                )
+
+                if project_name in user_projects:
+
+                    st.warning(
+                        "This Project name already exists in your directory Please enter a different name"
+                    )
+
+                    # st.stop()
+
+                project_path = os.path.join(
+                    user_folder_path, project_name_user
+                )
+
+                owner_project_path = os.path.join(
+                    owner_folder_path, project_name_owner
+                )
+
+            with cols[1]:
+
+                allowed_users = st.multiselect(
+                    "Select Users who can access this session",
+                    allowed_users_db,
+                )
+                allowed_users = list(allowed_users)
+
+            if st.button("Load Project", use_container_width=True):
+
+                if os.path.exists(project_path):
+
+                    st.warning(
+                        "This Project name already exists in your directory Please enter a different name"
+                    )
+
+                    st.stop()
+
+                shutil.copytree(owner_project_path, project_path)
+
+                project_dct_path = os.path.join(
+                    project_path, "project_dct.pkl"
+                )
+
+                with open(project_dct_path, "rb") as f:
+                    st.session_state["project_dct"] = pickle.load(f)
+
+                st.session_state["project_path"] = project_path
+
+                # st.write(st.session_state['project_dct'])
+
+                dump_session_details_db(allowed_users, project_name_user)
+                st.success("Project Cloned")

Media_data_for_model.csv

@@ -0,0 +1,182 @@
+Date,paid_search_impressions,paid_search_clicks,kwai_impressions,kwai_clicks,programmaticimpressions,programmaticclicks,affiliates_impressions,affiliates_clicks,indicacao_impressions,indicacao_clicks,infleux_impressions,infleux_clicks,influencer_impressions,influencer_clicks,Total Approved Accounts - Revenue,FB: Level Achieved - Tier 1 Impressions, FB: Level Achieved - Tier 2 Impressions,paid_social_others, GA App: Will And Cid Pequena Baixo Risco Clicks,digital_tactic_others
+2023-05-09,6111,1916,1365036.0,5044.0,104781,31371909,0,3341,0,11190,0,61956,0,457,5066400,2371841.0,1021599.0,2302543.0,34816.0,19205.0
+2023-05-10,6233,1888,1234034.0,3899.0,140810,32973036,0,3214,0,9988,0,52049,0,705,5480000,2100238.0,943808.0,2336369.0,19716.0,17415.0
+2023-05-11,5568,1816,1016155.0,2788.0,102248,50729517,0,3203,0,10869,0,8042,0,381,4133100,2461265.0,1127717.0,1110415.0,21547.0,11051.0
+2023-05-12,5109,1769,1228032.0,3101.0,100246,63142114,0,2492,0,7096,0,10596,0,299,3573910,2313368.0,1107256.0,1191901.0,31966.0,11081.0
+2023-05-13,3712,1231,1344557.0,3399.0,100714,59509032,0,3986,0,4282,0,9753,0,366,2776120,3067797.0,1388882.0,1403486.0,38518.0,10762.0
+2023-05-14,3719,1241,1520157.0,3491.0,120162,49538293,0,1891,0,3002,0,7363,0,278,2611960,3140882.0,1429620.0,2518831.0,44744.0,12151.0
+2023-05-15,7735,2663,2102264.0,5175.0,106903,46609819,0,2518,0,4548,0,16201,0,880,3951760,2916228.0,1288902.0,2456845.0,36269.0,15290.0
+2023-05-16,9409,3206,2134290.0,5636.0,88201,9662393,0,2247,0,6690,0,15031,0,1588,4150900,3161940.0,1370882.0,2403330.0,37393.0,14187.0
+2023-05-17,8409,2785,1473128.0,4336.0,56382,2232239,0,2557,0,6401,0,8946,0,322,3788540,3199527.0,1379566.0,2608845.0,39190.0,12591.0
+2023-05-18,8364,2873,1733275.0,5009.0,38145,7321146,0,2912,0,7286,0,14366,0,660,3652210,2623727.0,1115471.0,1723470.0,36020.0,12100.0
+2023-05-19,6432,2050,1784426.0,5063.0,23340,8715910,0,3934,0,6035,0,20378,0,362,3777590,2995998.0,1287313.0,1959870.0,36885.0,12848.0
+2023-05-20,5428,1724,1635604.0,4408.0,34693,8783612,0,3318,0,4714,0,21030,0,236,3437270,2996479.0,1326416.0,1903323.0,31048.0,12256.0
+2023-05-21,5657,1807,1788487.0,4492.0,24812,5015214,0,2253,0,4227,0,11656,0,494,3020020,3167634.0,1309450.0,3651254.0,33361.0,13073.0
+2023-05-22,5768,2036,2176947.0,5688.0,25298,3002995,0,2739,0,8313,0,25663,0,1147,3643240,3573865.0,1548365.0,3939226.0,33410.0,14092.0
+2023-05-23,5051,1720,2359219.0,6966.0,24773,3005057,0,4738,0,13827,0,47900,0,965,5146270,3248157.0,1376975.0,3631390.0,35016.0,13025.0
+2023-05-24,6078,1977,1612918.0,4924.0,24591,2833280,0,4816,0,12417,0,94489,0,1254,5832220,3572793.0,1550315.0,3532105.0,37491.0,12546.0
+2023-05-25,6547,2075,1468456.0,3624.0,19705,2771412,0,5070,0,7395,0,70016,0,762,5217860,3164337.0,1353382.0,3253308.0,34658.0,13154.0
+2023-05-26,3719,1189,1770048.0,4874.0,16879,2875657,0,2855,0,6964,0,29015,0,627,4123930,2989794.0,1248779.0,3345390.0,38267.0,12788.0
+2023-05-27,3620,1145,1900387.0,5061.0,14156,2663378,0,3295,0,4472,0,5625,0,1473,2668440,3576647.0,1527545.0,3694843.0,40685.0,12844.0
+2023-05-28,4195,1302,2026053.0,5703.0,12334,2609966,0,2190,0,3737,0,5030,0,1401,2630380,3376177.0,1447089.0,2563297.0,42359.0,13543.0
+2023-05-29,5265,1798,2328823.0,6483.0,14783,2537637,0,3954,0,5211,0,221,0,1575,3057510,3765997.0,1720747.0,2865333.0,39579.0,8116.0
+2023-05-30,3879,1366,2294654.0,6008.0,15979,2489630,0,4465,0,6041,0,6,0,1192,3360270,3790830.0,1751416.0,2822819.0,37234.0,8830.0
+2023-05-31,3933,1348,1645187.0,4081.0,14208,2337652,0,3797,0,4794,0,6,0,888,3158130,4151434.0,1953620.0,2714074.0,45856.0,6861.0
+2023-06-01,4817,1530,1862175.0,4841.0,48192,3241822,0,3060,0,4802,0,12820,0,1137,3322330,4151797.0,1903421.0,2255850.0,51175.0,7095.0
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Media_data_for_model_dma_level.csv

@@ -0,0 +1,538 @@
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+28/08/2023,2031,760,7120359,17304,592505,571748,0,44816,0,8728,0,19999,0,3813,2357294,420574,1878047,50335,13442,D1,P1,20994,42982,1970,3877550,642,64200,2612,3941750,5610.5,4325.83
+29/08/2023,1560,550,6349650,18074,395464,276869,0,217642,0,8742,0,36555,0,2778,2437012,455532,1707585,52913,12648,D1,P1,25919,51217,2270,4323380,803,80300,3073,4403680,6223.41,4701.59
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+29/09/2023,1513,641,4477584,8505,211297,372199,0,443,0,4039,0,4466,0,1774,2429135,1379398,1425850,31700,7184,D1,P1,12276,26946,1488,2459350,613,61300,2101,2520650,5148.56,3726.34
+30/09/2023,1127,478,4089760,9316,222854,364180,0,517,0,3251,0,13297,0,1425,1049942,435407,548780,10869,4505,D1,P1,12905,27091,1533,2701900,640,64000,2173,2765900,5022.4,3685.32
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+23/09/2023,1190,492,4948823,10035,2432739,154849,0,702,0,8285,0,4369,0,806,2740252,1531672,1407980,51747,8665,D2,P2,12641,27336,1443,2653190,652,65200,2095,2718390,5094.46,3633.56
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+21/10/2023,559,184,1405730,3216,207981,276329,0,2723,0,3213,0,44899,0,1893,1184901,485051,850250,14780,6181,D2,P2,17236,31207,1638,3121900,793,82100,2431,3204000,4493.8,3159.39
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+23/10/2023,625,231,2018062,5517,333114,436011,0,2839,0,3772,0,11121,0,1351,1208181,428535,1111507,11684,6220,D2,P2,12048,23419,1256,2226550,585,61300,1840,2287750,4042.32,2864.75
+24/10/2023,574,226,1889784,5099,188275,228582,0,2709,0,2462,0,1109,0,1918,1083131,378488,1161439,11452,5728,D2,P2,10595,21221,1092,1909130,407,43350,1499,1952480,3725.66,2756.39
+25/10/2023,536,184,2276229,5661,77308,332105,0,2708,0,2679,0,525,0,2402,905535,310989,865636,11200,5884,D2,P2,10143,20535,887,1832160,331,37050,1218,1869210,3037.38,2236.69
+26/10/2023,609,200,1753696,4367,85971,236204,0,2136,0,2300,0,10,0,3842,968078,332008,771447,10098,5558,D2,P2,9900,19640,858,1671270,347,38950,1205,1710220,2995.78,2165.99
+27/10/2023,563,209,1636932,3338,246909,285904,0,1992,0,2323,0,5,0,2851,1063329,352124,929257,9507,5001,D2,P2,8240,17233,780,1422510,309,35500,1089,1458010,2635.69,1927
+28/10/2023,450,155,1588245,4276,235960,324079,0,2716,0,1753,0,1,0,2585,1191854,384343,1028724,8578,4958,D2,P2,7529,15744,725,1476840,300,33850,1025,1510690,2588.52,1884.51
+29/10/2023,309,117,1731474,5065,70210,331208,0,2241,0,1708,0,5,0,3120,1137463,385520,764681,10186,5650,D2,P2,7535,14889,747,1514990,287,32300,1034,1547290,2594.6,1909.86
+31/10/2023,486,182,2220653,5950,41641,213812,0,149,0,2404,0,15,0,1380,913362,318222,1020094,10416,3703,D2,P2,8678,18059,784,1613510,309,34550,1093,1648060,2844.9,2076.88
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+9/5/2023,6111,1916,1365036,5044,104781,31371909,0,3341,0,11190,0,61956,0,457,2371841,1021599,2302543,34816,19205,D3,P3,35411,86251,2786,4926900,1395,139500,4181,5066400,6110.89,4301.97
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+12/5/2023,5109,1769,1228032,3101,100246,63142114,0,2492,0,7096,0,10596,0,299,2313368,1107256,1191901,31966,11081,D3,P3,21030,75112,2052,3462310,1116,111600,3168,3573910,5091.46,3601.62
+13/05/2023,3712,1231,1344557,3399,100714,59509032,0,3986,0,4282,0,9753,0,366,3067797,1388882,1403486,38518,10762,D3,P3,16294,64652,1611,2693420,827,82700,2438,2776120,3925.27,2780.04
+14/05/2023,3719,1241,1520157,3491,120162,49538293,0,1891,0,3002,0,7363,0,278,3140882,1429620,2518831,44744,12151,D3,P3,13378,55706,1428,2535460,765,76500,2193,2611960,3658.41,2543.11
+15/05/2023,7735,2663,2102264,5175,106903,46609819,0,2518,0,4548,0,16201,0,880,2916228,1288902,2456845,36269,15290,D3,P3,21857,67301,2149,3844360,1075,107500,3223,3951760,5540.69,3945.07
+16/05/2023,9409,3206,2134290,5636,88201,9662393,0,2247,0,6690,0,15031,0,1588,3161940,1370882,2403330,37393,14187,D3,P3,26562,53380,2486,4026650,1251,125100,3736,4150900,6839.28,4817.99
+17/05/2023,8409,2785,1473128,4336,56382,2232239,0,2557,0,6401,0,8946,0,322,3199527,1379566,2608845,39190,12591,D3,P3,21930,41033,2100,3675940,1126,112600,3226,3788540,6156.6,4185.47
+18/05/2023,8364,2873,1733275,5009,38145,7321146,0,2912,0,7286,0,14366,0,660,2623727,1115471,1723470,36020,12100,D3,P3,21813,40251,1987,3528210,1240,124000,3227,3652210,6388.27,4150.71
+19/05/2023,6432,2050,1784426,5063,23340,8715910,0,3934,0,6035,0,20378,0,362,2995998,1287313,1959870,36885,12848,D3,P3,19874,38360,1888,3663690,1140,114000,3027,3777590,5981.25,3891.85
+20/05/2023,5428,1724,1635604,4408,34693,8783612,0,3318,0,4714,0,21030,0,236,2996479,1326416,1903323,31048,12256,D3,P3,17568,33060,1691,3342720,931,93100,2623,3437270,5113.91,3453.26
+21/05/2023,5657,1807,1788487,4492,24812,5015214,0,2253,0,4227,0,11656,0,494,3167634,1309450,3651254,33361,13073,D3,P3,14766,28367,1461,2940020,800,80000,2261,3020020,4874.35,3300.96
+22/05/2023,5768,2036,2176947,5688,25298,3002995,0,2739,0,8313,0,25663,0,1147,3573865,1548365,3939226,33410,14092,D3,P3,21520,40205,1854,3533540,1097,109700,2951,3643240,6425.41,4211.67
+23/05/2023,5051,1720,2359219,6966,24773,3005057,0,4738,0,13827,0,47900,0,965,3248157,1376975,3631390,35016,13025,D3,P3,29860,51811,2527,5012370,1339,133900,3866,5146270,7978.08,5238.68
+24/05/2023,6078,1977,1612918,4924,24591,2833280,0,4816,0,12417,0,94489,0,1254,3572793,1550315,3532105,37491,12546,D3,P3,41297,68099,2993,5666720,1655,165500,4648,5832220,8715.48,5761.11
+25/05/2023,6547,2075,1468456,3624,19705,2771412,0,5070,0,7395,0,70016,0,762,3164337,1353382,3253308,34658,13154,D3,P3,33436,56714,2588,5066960,1509,150900,4097,5217860,7939.09,5062.98
+26/05/2023,3719,1189,1770048,4874,16879,2875657,0,2855,0,6964,0,29015,0,627,2989794,1248779,3345390,38267,12788,D3,P3,22185,40261,2101,4010930,1130,113000,3231,4123930,6785.57,4495.67
+27/05/2023,3620,1145,1900387,5061,14156,2663378,0,3295,0,4472,0,5625,0,1473,3576647,1527545,3694843,40685,12844,D3,P3,13490,26751,1407,2592840,756,75600,2163,2668440,5325.41,3549.17
+28/05/2023,4195,1302,2026053,5703,12334,2609966,0,2190,0,3737,0,5030,0,1401,3376177,1447089,2563297,42359,13543,D3,P3,13124,25607,1374,2558180,722,72200,2096,2630380,5282.85,3554.29
+29/05/2023,5265,1798,2328823,6483,14783,2537637,0,3954,0,5211,0,221,0,1575,3765997,1720747,2865333,39579,8116,D3,P3,15619,30688,1585,2979010,785,78500,2370,3057510,5961.63,4097.14
+30/05/2023,3879,1366,2294654,6008,15979,2489630,0,4465,0,6041,0,6,0,1192,3790830,1751416,2822819,37234,8830,D3,P3,17258,32693,1773,3270270,900,90000,2673,3360270,6752.47,4596.75
+31/05/2023,3933,1348,1645187,4081,14208,2337652,0,3797,0,4794,0,6,0,888,4151434,1953620,2714074,45856,6861,D3,P3,16458,31379,1688,3065730,924,92400,2612,3158130,6598.66,4373.22
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+2/6/2023,5733,1800,966546,2646,43573,4582872,0,1563,0,10678,0,46810,0,1309,4313201,2009602,2074692,47378,6120,D3,P3,25710,47869,2271,4131170,1130,113000,3401,4244170,6992.72,4747.36
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+7/6/2023,5072,1648,2143372,5356,22553,21321547,0,1576,0,7213,0,21215,0,628,4178339,1811963,2354753,51632,6259,D3,P3,22242,56660,2125,3900920,1184,118400,3309,4019320,6611.5,4370.78
+8/6/2023,4444,1465,3190766,8024,53653,10254268,0,2046,0,10491,0,19549,0,769,3941272,1738344,2283350,59291,6775,D3,P3,23293,50105,2238,4145770,1270,127000,3508,4272770,6851.85,4515.66
+9/6/2023,4818,1605,3278715,9328,18347,4890758,0,1925,0,8360,0,32385,0,1732,3969227,1777864,2353376,52000,6026,D3,P3,25950,50611,2404,4657210,1315,131500,3719,4788710,6881.12,4639.8
+10/6/2023,3465,1207,2887842,8529,725,5489947,0,1230,0,5401,0,37954,0,2136,4458593,2061762,2535928,66567,5554,D3,P3,24413,47973,2370,4584570,1225,122500,3595,4707070,6334.68,4283.38
+11/6/2023,4727,1501,3149290,8114,738,5313957,0,1839,0,8198,0,32493,0,1533,4442610,2006438,2183963,47655,6008,D3,P3,23656,46275,2220,4441870,1183,118300,3403,4560170,6134.11,4098
+12/6/2023,6437,2208,4416005,12345,149561,5298884,0,1905,0,8542,0,101079,0,472,4645531,1995891,3301882,38760,4966,D3,P3,44382,76997,3520,6853780,1782,178200,5302,7031980,8549.02,5779.3
+13/06/2023,3556,1254,4626697,12984,258088,5952266,0,2095,0,10415,0,59770,0,1016,4508060,1912958,3440789,47281,4630,D3,P3,35764,67060,2737,5184020,1530,153000,4266,5335600,7908.1,5200.7
+14/06/2023,3178,1060,3389530,10298,685692,10454400,0,2258,0,24457,0,16016,0,1101,4573214,1920050,3160905,41549,5083,D3,P3,27677,56158,2257,4257990,1244,124400,3501,4382390,7187.71,4826.11
+15/06/2023,2981,999,3131350,10791,1072645,11631302,0,2265,0,17304,0,10395,0,1188,4075106,1690702,3267810,50496,5037,D3,P3,23775,50354,2201,4212820,1215,121500,3416,4334320,7339.75,4890.1
+16/06/2023,2705,947,2923279,11124,1166424,11840950,0,1780,0,8938,0,24339,0,966,4533368,1939737,2881833,41872,4604,D3,P3,22957,49677,2225,4445430,1154,115400,3379,4560830,6663.77,4416.03
+17/06/2023,3697,1154,2955836,10440,807683,9748201,0,2139,0,5741,0,54129,0,766,4958344,2059487,3183051,52618,3675,D3,P3,26623,53187,2434,4755560,1286,128600,3723,4890110,6983.5,4694.41
+18/06/2023,3229,1080,3280006,12373,116340,8176712,0,1481,0,4741,0,16724,0,864,4270249,1735486,3251229,39780,3696,D3,P3,16690,36522,1715,3294460,936,93600,2651,3388060,5614.57,3749.6
+19/06/2023,3082,1003,6545797,24462,55763,4841897,0,2098,0,10520,0,26558,0,2211,4137846,1743715,2680413,43156,4347,D3,P3,25736,50759,2343,4515000,1244,124400,3587,4639400,7090.09,4789.96
+20/06/2023,2422,857,6734594,28910,52166,4718912,0,2205,0,10284,0,30610,0,1002,4218772,1771102,2058734,42288,4260,D3,P3,27941,52107,2478,4829920,1398,139800,3876,4969720,7584.9,4974.28
+21/06/2023,3366,1132,4784180,17247,52817,5971594,0,3387,0,9277,0,41697,0,645,4113884,1743016,2111350,44159,4193,D3,P3,28338,53853,2376,4353550,1357,135700,3733,4489250,7214.21,4702.88
+22/06/2023,2841,924,3300680,13360,29784,6803330,0,4064,0,7068,0,68638,0,481,3738171,1533407,1597072,35381,4173,D3,P3,34683,62182,2532,4863520,1434,143400,3966,5006920,7223.75,4679.68
+23/06/2023,2474,805,2284446,9012,80066,6833289,0,3274,0,7379,0,13501,0,721,4479743,1889155,1647740,39089,3640,D3,P3,16506,35549,1530,2980550,888,88800,2418,3069350,5295.75,3457.41
+24/06/2023,2462,814,1947190,7247,50309,6526903,0,2767,0,4703,0,8438,0,616,3758421,1565736,1648519,46332,3834,D3,P3,13804,31588,1381,2698000,788,78800,2169,2776800,4822.39,3136
+25/06/2023,2082,679,3560248,14850,50806,6368664,0,2767,0,4414,0,5346,0,628,4038846,1700182,2514456,43065,4201,D3,P3,13435,30121,1424,2782640,778,77800,2202,2860440,5082.66,3353.86
+26/06/2023,2399,839,5999950,28401,23209,10788275,0,3699,0,13383,0,13592,0,790,3427918,1403888,3598236,33883,4642,D3,P3,21114,49622,1959,3810990,1175,117500,3134,3928490,6965.39,4504.75
+27/06/2023,2307,804,5005495,18260,81344,14103220,0,7082,0,8898,0,40917,0,945,3819654,1523667,3556028,35326,4628,D3,P3,32019,65348,2877,5691820,1595,159500,4472,5851320,9589,6333.48
+28/06/2023,2215,759,3721084,11248,20153,10547995,0,8387,0,7120,0,39693,0,944,3671994,1568555,1397196,33212,2998,D3,P3,30267,63086,2863,5931970,1516,151600,4379,6083570,8919.81,5942.75
+29/06/2023,2013,706,3918049,10226,155296,8525871,0,10096,0,5693,0,24049,0,1512,3937747,1585655,3393043,30700,2519,D3,P3,22893,54097,2125,4215060,1132,113200,3257,4328260,7178.85,4806.94
+30/06/2023,1258,454,3088874,7943,902115,10945715,0,8904,0,9611,0,62404,0,1029,4945464,1946944,1835310,52445,2839,D3,P3,30707,67849,2616,5112840,1397,139700,4013,5252540,7664.09,5203.16
+1/7/2023,1641,539,3872657,12034,191537,12141356,0,4956,0,6049,0,31194,0,923,5328149,2224200,2123805,56724,2513,D3,P3,22229,54353,2266,4505840,1205,120500,3471,4626340,6983.83,4746.37
+2/7/2023,1336,485,5799582,17238,576858,12180985,0,4148,0,4670,0,4766,0,617,4527404,1997256,2038953,50510,21201,D3,P3,15205,43684,1672,3328290,886,88600,2559,3416990,5952.59,4038.91
+3/7/2023,2712,924,9986061,28191,442261,14059535,0,5347,0,7408,0,19028,0,1044,4179823,1854231,2234940,57543,14473,D3,P3,22798,57392,2425,4796940,1308,130800,3732,4925290,7701.44,5182.84
+4/7/2023,4137,1419,4717456,14519,2137830,14463201,0,6164,0,8277,0,12283,0,1531,4449073,1959412,2350308,49085,9854,D3,P3,23585,60143,2220,4262060,1327,132700,3547,4394760,7689.38,5021.95
+5/7/2023,4166,1422,4779589,12676,2354716,18574154,0,7967,0,11552,0,6628,0,1420,4464681,1969744,2390838,41411,12751,D3,P3,22847,61021,2348,4359400,1422,142200,3769,4497600,7976.49,5170.89
+6/7/2023,4182,1444,4939385,12222,2364811,15879491,0,6575,0,8461,0,18225,0,997,4490815,1942923,2239375,44808,16216,D3,P3,23716,60282,2602,4960450,1543,154300,4145,5114750,8225.73,5423.76
+7/7/2023,3497,1181,3121447,7534,1063284,17341347,0,6025,0,8113,0,25962,0,659,4891573,2128787,1976413,57373,12464,D3,P3,23336,58252,2335,4783910,1269,126900,3604,4910810,7182.26,4846.37
+8/7/2023,2760,856,3295227,9788,1119268,19207341,0,4102,0,6195,0,40506,0,832,5039604,2277255,2330435,44052,15163,D3,P3,22278,62471,2128,4352160,1191,119100,3319,4471260,6706.12,4485.38
+9/7/2023,2809,875,3913741,11815,749310,25182206,0,6420,0,5222,0,49626,0,718,4669470,2144473,3908306,49659,11716,D3,P3,24736,69003,2620,5573560,1325,132500,3945,5706060,7673.85,5278.59
+10/7/2023,4312,1489,5972974,18402,1511035,25950979,0,9842,0,10638,0,36204,0,935,4584106,2019220,4391654,52303,12983,D3,P3,28497,78251,2578,5157430,1419,141900,3997,5299330,8174.81,5453.8
+11/7/2023,4579,1550,4999618,16469,559119,23938153,0,11688,0,36570,0,25216,0,1289,4458364,1932300,4150666,47979,12292,D3,P3,28688,77223,2707,5384090,1483,148300,4190,5532390,8594.12,5787.78
+12/7/2023,4079,1418,4465722,13191,583520,25196511,0,4610,0,9813,0,20388,0,1210,4558876,2000168,4109583,54631,12366,D3,P3,25749,73523,2403,4926480,1410,141000,3813,5067480,8000.73,5259.61
+13/07/2023,3719,1260,4635033,12302,903614,25720336,0,7867,0,6792,0,22248,0,857,4596184,1957206,3729970,48474,11017,D3,P3,22447,69283,2134,4261960,1312,131200,3447,4393760,7385.89,4769.15
+14/07/2023,3632,1224,3441594,9800,1566300,28606996,0,6726,0,6172,0,14670,0,432,4683387,2007387,3912229,52588,10079,D3,P3,19225,67928,2002,3875450,1077,107700,3079,3983150,6615.15,4505.74
+15/07/2023,2909,941,6025085,21326,1836196,28705476,0,5705,0,4369,0,31202,0,595,5008167,2251661,3727627,58143,10214,D3,P3,19533,64001,2026,4112810,1066,106600,3093,4220310,6115.73,4122.89
+16/07/2023,2818,853,7339565,26586,4043959,26752554,0,7733,0,3961,0,27180,0,1082,4716541,2092258,2114014,59204,11281,D3,P3,18871,60797,1956,3715100,1126,112600,3084,3832400,6216.99,4066.5
+17/07/2023,4420,1486,9638491,32269,819444,29437537,0,11485,0,5220,0,66236,0,2418,4359325,1937825,1989872,55815,11896,D3,P3,35493,89662,2771,5539890,1521,152100,4292,5691990,8025.2,5388.69
+18/07/2023,4574,1551,9498457,31230,1206114,29164369,0,5012,0,7146,0,47074,0,2358,4882304,2163458,2157773,69573,12604,D3,P3,32238,84091,2687,5214920,1329,132900,4016,5347820,7833.63,5336.47
+19/07/2023,4632,1537,9742535,26935,1491736,30394328,0,6147,0,7028,0,11807,0,1476,4613422,2080215,1981362,67495,12116,D3,P3,23193,69138,2342,4581910,1084,108400,3426,4690310,7118.68,4991.3
+20/07/2023,4891,1632,7630122,20720,2370192,23939153,0,6261,0,5635,0,17220,0,1338,4484291,1922090,1663127,75312,12252,D3,P3,23193,63445,2209,4471970,995,99500,3255,4576570,6700.79,4599.1
+21/07/2023,3978,1378,7284968,21477,2456715,17869335,0,6360,0,10877,0,11431,0,769,4519305,1975730,1686467,68931,10065,D3,P3,21478,57996,2115,4257360,1006,100600,3121,4357960,6467.01,4497.43
+22/07/2023,3151,978,5638955,16181,2015345,12808440,0,7097,0,5145,0,13811,0,699,4689346,2086993,1861883,71898,9081,D3,P3,17602,50209,1897,3876970,830,83000,2727,3959970,5812.35,4126.62
+23/07/2023,2905,986,6133144,16089,881691,11267535,0,5593,0,4746,0,68021,0,514,4242878,1843843,1681215,61164,10797,D3,P3,25804,58279,2442,5168300,1161,116100,3603,5284400,6622.56,4639.27
+24/07/2023,4606,1651,8830736,21931,189913,35658281,0,8836,0,6040,0,64066,0,1626,3943001,1708849,1900126,59494,10236,D3,P3,31953,73505,2898,6111230,1424,142400,4321,6253530,8030.22,5492.47
+25/07/2023,4414,1597,7750251,15384,3348941,25011847,0,10262,0,9572,0,15072,0,916,3976490,1729916,1911109,68826,11468,D3,P3,26162,65005,2440,4822810,1154,115400,3594,4938210,7092.81,4965.39
+26/07/2023,4488,1530,8125332,16391,1040452,24380635,0,9947,0,16453,0,38777,0,1551,3837786,1680967,1856885,74924,13290,D3,P3,31894,69746,2797,5552460,1348,134800,4147,5687460,7359.12,5027.76
+27/07/2023,4105,1494,8054962,14724,220302,13070502,0,6758,0,8841,0,20622,0,1313,3636297,1544742,1772602,63935,11680,D3,P3,24634,57255,2498,5018670,1196,119600,3694,5138270,7106.66,4875.99
+28/07/2023,3743,1318,6955526,11566,3991586,5347413,0,10451,0,8496,0,20184,0,1563,3890784,1680538,1608577,73120,11390,D3,P3,22265,50660,2204,4515740,1054,105400,3258,4621140,6441.87,4432.07
+29/07/2023,3395,1192,5132501,9837,1349895,4441709,0,7115,0,5449,0,18983,0,1129,4295602,1813637,1824777,80511,11911,D3,P3,17626,42341,1930,3807270,860,86000,2790,3893270,5519.71,3922.54
+30/07/2023,2746,903,4903153,11018,710000,4431986,0,8491,0,4599,0,24834,0,1109,3924352,1733790,1740605,72221,11440,D3,P3,17619,43309,1942,3937090,834,83400,2776,4020490,5556.3,3945.89
+31/07/2023,4208,1476,6832832,19648,1721973,4079656,0,7754,0,7818,0,27918,0,2050,3760475,1501134,1957900,62124,11605,D3,P3,22728,51399,2256,4604950,1046,104600,3302,4709550,6481.79,4556.75
+1/8/2023,4203,1489,6398210,15890,2250090,5457683,0,7588,0,7948,0,61894,0,1248,3687038,1439092,1774701,61448,11492,D3,P3,31009,67515,2609,5471270,1309,130900,3918,5602170,6825.97,4671.87
+2/8/2023,4285,1515,5402871,14825,785167,6582085,0,6079,0,7236,0,79041,0,1345,3807266,1493340,2025308,54506,11515,D3,P3,34034,74123,2858,5856910,1360,136000,4218,5992910,7171.23,4989.3
+3/8/2023,4667,1744,4724924,8903,1111815,10407793,0,9077,0,7117,0,31714,0,1983,3797879,1479623,1718831,52587,11408,D3,P3,23978,62504,2404,4832400,1095,109500,3498,4941800,6670.71,4627.48
+4/8/2023,4201,1562,3732952,9116,4574481,10660977,0,8436,0,6970,0,25097,0,1055,3814852,1466211,1374674,57482,10785,D3,P3,21714,62922,2226,4561560,975,97500,3201,4659060,6133.71,4366.77
+5/8/2023,3080,1110,3310732,9884,3460283,11580456,0,5735,0,5049,0,5911,0,704,4048945,1601234,1442690,65158,9942,D3,P3,13792,52995,1578,3188640,720,72000,2298,3260640,4760.04,3375.44
+6/8/2023,2809,979,4153998,11663,5103054,8381689,0,4868,0,4184,0,28658,0,637,4040770,1250223,1844909,47698,10816,D3,P3,17706,56827,1841,3921270,842,84200,2683,4005470,5062.91,3520.39
+7/8/2023,3522,1186,3164171,10000,4137349,8709017,0,6112,0,6640,0,43866,0,1285,3928811,1288638,1861166,44623,11648,D3,P3,26772,71580,2456,4923260,1132,113200,3587,5034960,6580.87,4648.84
+8/8/2023,4111,1495,6020074,15008,2923076,5012245,0,6684,0,6961,0,12257,0,3038,3921429,1094791,2059462,48047,10379,D3,P3,22363,60212,2075,4138870,971,97100,3046,4235970,6286.57,4418.71
+9/8/2023,3609,1285,5616011,13195,2483117,3900778,0,8177,0,6447,0,12774,0,1309,3322165,452000,2262292,52219,11047,D3,P3,21099,53059,2067,4030270,1010,101000,3077,4131270,6309.32,4273.88
+10/8/2023,3872,1330,5270940,11342,1305507,3614496,0,6927,0,6602,0,21830,0,986,2868345,452896,1541791,46720,10556,D3,P3,22515,52055,2216,4848640,974,97400,3190,4946040,6281.59,4498.26
+11/8/2023,3673,1257,4485834,10445,3562053,5521177,0,5810,0,13751,0,5797,0,751,3242607,511016,1679989,47262,10344,D3,P3,17086,46491,1551,3242370,671,67100,2222,3309470,4608.96,3355.58
+12/8/2023,2744,960,3946512,10052,4015316,5813616,0,6205,0,10379,0,9890,0,532,3761072,595191,1931989,54787,9210,D3,P3,15949,46498,1376,3113130,639,63900,2016,3177130,4121.65,2907.82
+13/08/2023,2418,775,5051792,11809,1869057,6348414,0,4093,0,5187,0,21320,0,399,3279816,503805,2171335,55136,10546,D3,P3,14745,44224,1327,2888900,648,64800,1975,2953700,3905.29,2752.8
+14/08/2023,3551,1196,4839009,11805,1765326,3758610,0,6170,0,7226,0,19575,0,706,2974893,427940,1822890,49127,11042,D3,P3,20336,49993,1770,3798710,748,74800,2518,3873510,5195.83,3786.85
+15/08/2023,3430,1312,6520709,24857,1792924,1852314,0,6063,0,9302,0,34681,0,778,3080012,462344,1935145,57900,11083,D3,P3,24449,48710,1946,4332210,807,80700,2752,4412810,5367.05,3930.18
+16/08/2023,3253,1175,4844378,27290,3478033,1293346,0,5884,0,8280,0,29077,0,1055,3150093,468159,2094524,58937,11823,D3,P3,23892,46995,1911,3996580,780,78000,2691,4074580,5452.88,3979.2
+17/08/2023,3714,1417,4702754,23408,4058942,1186576,0,11301,0,10504,0,15462,0,1017,3071572,444690,1961293,58681,10987,D3,P3,21265,42100,1700,3517380,704,70400,2404,3587780,5150.22,3718.85
+18/08/2023,1936,710,4370177,18919,3789636,1020973,0,9525,0,8958,0,32346,0,909,3252966,461174,2031390,61098,10354,D3,P3,20983,41383,1778,3654630,679,67900,2457,3722530,5019.58,3716.93
+19/08/2023,1998,723,3683868,14860,5187185,1336224,0,8305,0,6265,0,13396,0,1267,3460234,494225,2037289,68988,9709,D3,P3,14640,31119,1331,2887610,528,52800,1860,2943410,4059.2,3012.38
+20/08/2023,2458,839,5558511,16917,6444084,1333600,0,8083,0,5668,0,9425,0,593,3233047,427550,2093935,58698,11658,D3,P3,13616,29557,1227,2592690,476,47600,1703,2640290,3842.86,2850.38
+21/08/2023,2316,892,5802540,21997,1578062,1099659,0,9057,0,8434,0,11204,0,597,3110596,442523,1991499,71910,13059,D3,P3,19336,38812,1656,3445610,663,66300,2319,3510210,5100.16,3789.37
+22/08/2023,2208,845,5006504,13886,598218,939529,0,18631,0,11119,0,18304,0,666,3020862,419659,1960342,66769,12591,D3,P3,21348,42166,1844,3824050,762,76200,2582,3877380,5610.58,4088.17
+23/08/2023,2104,821,5240143,16309,1941212,2081327,0,17073,0,8077,0,6042,0,709,2634348,409468,1726842,53998,13783,D3,P3,18864,38949,1513,3153720,655,65500,2165,3219310,4984.78,3596.01
+24/08/2023,2011,685,5623870,14314,380971,2132605,0,13223,0,7340,0,11449,0,1774,2244344,385012,1409286,55699,13185,D3,P3,18821,38282,1665,3378840,678,67800,2358,3474730,5209.76,3795.85
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+27/08/2023,1333,486,5591938,13138,956722,732493,0,12952,0,4227,0,19562,0,2354,2470188,417919,1980888,53707,14151,D3,P3,14834,30563,1572,2979370,536,53600,2108,3032970,4232.88,3241.64
+28/08/2023,2031,760,7120359,17304,592505,571748,0,44816,0,8728,0,19999,0,3813,2357294,420574,1878047,50335,13442,D3,P3,20994,42982,1970,3877550,642,64200,2612,3941750,5610.5,4325.83
+29/08/2023,1560,550,6349650,18074,395464,276869,0,217642,0,8742,0,36555,0,2778,2437012,455532,1707585,52913,12648,D3,P3,25919,51217,2270,4323380,803,80300,3073,4403680,6223.41,4701.59
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+17/09/2023,2467,802,4503316,11119,581720,236009,0,637,0,4814,0,10024,0,2464,2935930,1503370,1649587,48796,10073,D3,P3,12357,25262,1396,2560250,455,45500,1851,2605750,4246.67,3287.31
+18/09/2023,2910,1024,5568066,14302,184276,143660,0,888,0,6922,0,10381,0,1767,2373681,1330212,1479501,51224,10488,D3,P3,16441,33582,1727,3152630,572,57200,2300,3209930,5491.87,4171.85
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Model/summary_df.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:acd8580c5c2bddd6a5d6cedbf29320b52812abe71afb1d1a81bb6eb086cb944b
+size 1482

Model_Results_Pretrained.py

@@ -0,0 +1,349 @@
+import streamlit as st
+import plotly.express as px
+import numpy as np
+import plotly.graph_objects as go
+from sklearn.metrics import r2_score
+from collections import OrderedDict
+import pickle
+import json
+import streamlit as st
+import plotly.express as px
+import numpy as np
+import plotly.graph_objects as go
+from sklearn.metrics import r2_score
+import pickle
+import json
+import pandas as pd
+import statsmodels.api as sm
+from sklearn.metrics import mean_absolute_percentage_error
+import sys
+import os
+from utilities import (set_header, 
+                       initialize_data,
+                       load_local_css,
+                       create_channel_summary,
+                       create_contribution_pie,
+                       create_contribuion_stacked_plot,
+                       create_channel_spends_sales_plot,
+                       format_numbers,
+                       channel_name_formating,
+                       load_authenticator)
+import seaborn as sns
+import matplotlib.pyplot as plt
+import sweetviz as sv
+import tempfile
+
+original_stdout = sys.stdout
+sys.stdout = open('temp_stdout.txt', 'w')
+sys.stdout.close()
+sys.stdout = original_stdout
+
+st.set_page_config(layout='wide')
+load_local_css('styles.css')
+set_header()
+
+for k, v in st.session_state.items():
+    if k not in ['logout', 'login','config'] and not k.startswith('FormSubmitter'):
+        st.session_state[k] = v
+
+authenticator = st.session_state.get('authenticator')
+if authenticator is None:
+    authenticator = load_authenticator()
+
+name, authentication_status, username = authenticator.login('Login', 'main')
+auth_status = st.session_state.get('authentication_status')
+
+if auth_status == True:
+    is_state_initiaized = st.session_state.get('initialized',False)
+    if not is_state_initiaized:
+        a=1
+      
+
+    def plot_residual_predicted(actual, predicted, df_):
+            df_['Residuals'] = actual - pd.Series(predicted)
+            df_['StdResidual'] = (df_['Residuals'] - df_['Residuals'].mean()) / df_['Residuals'].std()
+            
+            # Create a Plotly scatter plot
+            fig = px.scatter(df_, x=predicted, y='StdResidual', opacity=0.5,color_discrete_sequence=["#11B6BD"])
+            
+            # Add horizontal lines
+            fig.add_hline(y=0, line_dash="dash", line_color="darkorange")
+            fig.add_hline(y=2, line_color="red")
+            fig.add_hline(y=-2, line_color="red")
+            
+            fig.update_xaxes(title='Predicted')
+            fig.update_yaxes(title='Standardized Residuals (Actual - Predicted)')
+            
+            # Set the same width and height for both figures
+            fig.update_layout(title='Residuals over Predicted Values', autosize=False, width=600, height=400)
+            
+            return fig
+
+    def residual_distribution(actual, predicted):
+            Residuals = actual - pd.Series(predicted)
+            
+            # Create a Seaborn distribution plot
+            sns.set(style="whitegrid")
+            plt.figure(figsize=(6, 4))
+            sns.histplot(Residuals, kde=True, color="#11B6BD")
+            
+            plt.title(' Distribution of Residuals')
+            plt.xlabel('Residuals')
+            plt.ylabel('Probability Density')
+            
+            return plt
+
+    
+    def qqplot(actual, predicted):
+            Residuals = actual - pd.Series(predicted)
+            Residuals = pd.Series(Residuals)
+            Resud_std = (Residuals - Residuals.mean()) / Residuals.std()
+            
+            # Create a QQ plot using Plotly with custom colors
+            fig = go.Figure()
+            fig.add_trace(go.Scatter(x=sm.ProbPlot(Resud_std).theoretical_quantiles,
+                                    y=sm.ProbPlot(Resud_std).sample_quantiles,
+                                    mode='markers',
+                                    marker=dict(size=5, color="#11B6BD"),
+                                    name='QQ Plot'))
+            
+            # Add the 45-degree reference line
+            diagonal_line = go.Scatter(
+                x=[-2, 2],  # Adjust the x values as needed to fit the range of your data
+                y=[-2, 2],  # Adjust the y values accordingly
+                mode='lines',
+                line=dict(color='red'),  # Customize the line color and style
+                name=' '
+            )
+            fig.add_trace(diagonal_line)
+            
+            # Customize the layout
+            fig.update_layout(title='QQ Plot of Residuals',title_x=0.5, autosize=False, width=600, height=400,
+                            xaxis_title='Theoretical Quantiles', yaxis_title='Sample Quantiles')
+            
+            return fig
+
+
+    def plot_actual_vs_predicted(date, y, predicted_values, model):
+        fig = go.Figure()
+
+        fig.add_trace(go.Scatter(x=date, y=y, mode='lines', name='Actual', line=dict(color='blue')))
+        fig.add_trace(go.Scatter(x=date, y=predicted_values, mode='lines', name='Predicted', line=dict(color='orange')))
+        
+        # Calculate MAPE
+        mape = mean_absolute_percentage_error(y, predicted_values)*100
+        
+        # Calculate R-squared
+        rss = np.sum((y - predicted_values) ** 2)
+        tss = np.sum((y - np.mean(y)) ** 2)
+        r_squared = 1 - (rss / tss)
+        
+        # Get the number of predictors
+        num_predictors = model.df_model
+        
+        # Get the number of samples
+        num_samples = len(y)
+        
+        # Calculate Adjusted R-squared
+        adj_r_squared = 1 - ((1 - r_squared) * ((num_samples - 1) / (num_samples - num_predictors - 1)))
+        metrics_table = pd.DataFrame({
+        'Metric': ['MAPE', 'R-squared', 'AdjR-squared'],
+        'Value': [mape, r_squared, adj_r_squared]})
+        fig.update_layout(
+            xaxis=dict(title='Date'),
+            yaxis=dict(title='Value'),
+            title=f'MAPE : {mape:.2f}%, AdjR2: {adj_r_squared:.2f}',
+            xaxis_tickangle=-30
+        )
+
+        return metrics_table,fig
+
+
+
+
+    # # Perform linear regression
+    # model = sm.OLS(y, X).fit()
+    eda_columns=st.columns(3)
+    with eda_columns[0]:
+        tactic=st.checkbox('Tactic Level Model')
+    if tactic:
+        with open('mastercard_mmm_model.pkl', 'rb') as file:
+            model = pickle.load(file)
+        train=pd.read_csv('train_mastercard.csv')
+        test=pd.read_csv('test_mastercard.csv')
+        train['Date']=pd.to_datetime(train['Date'])
+        test['Date']=pd.to_datetime(test['Date'])
+        train.set_index('Date',inplace=True)
+        test.set_index('Date',inplace=True)
+        test.dropna(inplace=True)
+        X_train=train.drop(["total_approved_accounts_revenue"],axis=1)
+        y_train=train['total_approved_accounts_revenue']
+        X_test=test.drop(["total_approved_accounts_revenue"],axis=1)
+        X_train=sm.add_constant(X_train)
+        X_test=sm.add_constant(X_test)
+        y_test=test['total_approved_accounts_revenue']
+
+        # sys.stdout.close()
+        # sys.stdout = original_stdout
+
+        # st.set_page_config(layout='wide')
+        # load_local_css('styles.css')
+        # set_header()
+
+        channel_data=pd.read_excel("Channel_wise_imp_click_spends_new.xlsx",sheet_name='Sheet3')
+        target_column='Total Approved Accounts - Revenue'
+
+  
+        with eda_columns[1]:
+          if st.button('Generate EDA Report'):
+              def generate_report_with_target(channel_data, target_feature):
+                  report = sv.analyze([channel_data, "Dataset"], target_feat=target_feature,verbose=False)
+                  temp_dir = tempfile.mkdtemp()
+                  report_path = os.path.join(temp_dir, "report.html")
+                  report.show_html(filepath=report_path, open_browser=False)  # Generate the report as an HTML file
+                  return report_path
+          
+              report_file = generate_report_with_target(channel_data, target_column)
+          
+              if os.path.exists(report_file):
+                  with open(report_file, 'rb') as f:
+                      st.download_button(
+                          label="Download EDA Report",
+                          data=f.read(),
+                          file_name="report.html",
+                          mime="text/html"
+                      )
+              else:
+                  st.warning("Report generation failed. Unable to find the report file.")
+
+
+        st.title('Analysis of Result')
+
+        st.write(model.summary(yname='Revenue'))
+
+        metrics_table_train,fig_train= plot_actual_vs_predicted(X_train.index, y_train, model.predict(X_train), model)
+        metrics_table_test,fig_test= plot_actual_vs_predicted(X_test.index, y_test, model.predict(X_test), model)
+
+        metrics_table_train=metrics_table_train.set_index('Metric').transpose()
+        metrics_table_train.index=['Train']
+        metrics_table_test=metrics_table_test.set_index('Metric').transpose()
+        metrics_table_test.index=['test']
+        metrics_table=np.round(pd.concat([metrics_table_train,metrics_table_test]),2)
+
+        st.markdown('Result Overview')
+        st.dataframe(np.round(metrics_table,2),use_container_width=True)
+
+        st.subheader('Actual vs Predicted Plot Train')
+
+        st.plotly_chart(fig_train,use_container_width=True)
+        st.subheader('Actual vs Predicted Plot Test')
+        st.plotly_chart(fig_test,use_container_width=True)
+
+        st.markdown('## Residual Analysis')
+        columns=st.columns(2)
+        Xtrain1=X_train.copy()
+        with columns[0]:
+            fig=plot_residual_predicted(y_train,model.predict(Xtrain1),Xtrain1)
+            st.plotly_chart(fig)
+
+        with columns[1]:
+            st.empty()
+            fig = qqplot(y_train,model.predict(X_train))
+            st.plotly_chart(fig)
+
+        with columns[0]:
+            fig=residual_distribution(y_train,model.predict(X_train))
+            st.pyplot(fig)
+    else:
+        with open('mastercard_mmm_model_channel.pkl', 'rb') as file:
+            model = pickle.load(file)
+        train=pd.read_csv('train_mastercard_channel.csv')
+        test=pd.read_csv('test_mastercard_channel.csv')
+        # train['Date']=pd.to_datetime(train['Date'])
+        # test['Date']=pd.to_datetime(test['Date'])
+        # train.set_index('Date',inplace=True)
+        # test.set_index('Date',inplace=True)
+        test.dropna(inplace=True)
+        X_train=train.drop(["total_approved_accounts_revenue"],axis=1)
+        y_train=train['total_approved_accounts_revenue']
+        X_test=test.drop(["total_approved_accounts_revenue"],axis=1)
+        X_train=sm.add_constant(X_train)
+        X_test=sm.add_constant(X_test)
+        y_test=test['total_approved_accounts_revenue']
+
+    
+
+        channel_data=pd.read_excel("Channel_wise_imp_click_spends_new.xlsx",sheet_name='Sheet3')
+        target_column='Total Approved Accounts - Revenue'
+        with eda_columns[1]:
+          if st.button('Generate EDA Report'):
+              def generate_report_with_target(channel_data, target_feature):
+                  report = sv.analyze([channel_data, "Dataset"], target_feat=target_feature)
+                  temp_dir = tempfile.mkdtemp()
+                  report_path = os.path.join(temp_dir, "report.html")
+                  report.show_html(filepath=report_path, open_browser=False)  # Generate the report as an HTML file
+                  return report_path
+          
+              report_file = generate_report_with_target(channel_data, target_column)
+          
+              # Provide a link to download the generated report
+              with open(report_file, 'rb') as f:
+                  st.download_button(
+                      label="Download EDA Report",
+                      data=f.read(),
+                      file_name="report.html",
+                      mime="text/html"
+                  )
+
+
+        st.title('Analysis of Result')
+
+        st.write(model.summary(yname='Revenue'))
+
+        metrics_table_train,fig_train= plot_actual_vs_predicted(X_train.index, y_train, model.predict(X_train), model)
+        metrics_table_test,fig_test= plot_actual_vs_predicted(X_test.index, y_test, model.predict(X_test), model)
+
+        metrics_table_train=metrics_table_train.set_index('Metric').transpose()
+        metrics_table_train.index=['Train']
+        metrics_table_test=metrics_table_test.set_index('Metric').transpose()
+        metrics_table_test.index=['test']
+        metrics_table=np.round(pd.concat([metrics_table_train,metrics_table_test]),2)
+
+        st.markdown('Result Overview')
+        st.dataframe(np.round(metrics_table,2),use_container_width=True)
+
+        st.subheader('Actual vs Predicted Plot Train')
+
+        st.plotly_chart(fig_train,use_container_width=True)
+        st.subheader('Actual vs Predicted Plot Test')
+        st.plotly_chart(fig_test,use_container_width=True)
+
+        st.markdown('## Residual Analysis')
+        columns=st.columns(2)
+        Xtrain1=X_train.copy()
+        with columns[0]:
+            fig=plot_residual_predicted(y_train,model.predict(Xtrain1),Xtrain1)
+            st.plotly_chart(fig)
+
+        with columns[1]:
+            st.empty()
+            fig = qqplot(y_train,model.predict(X_train))
+            st.plotly_chart(fig)
+
+        with columns[0]:
+            fig=residual_distribution(y_train,model.predict(X_train))
+            st.pyplot(fig)
+
+elif auth_status == False:
+    st.error('Username/Password is incorrect')
+    
+if auth_status != True:
+    try:
+        username_forgot_pw, email_forgot_password, random_password = authenticator.forgot_password('Forgot password')
+        if username_forgot_pw:
+            st.success('New password sent securely')
+            # Random password to be transferred to user securely
+        elif username_forgot_pw == False:
+            st.error('Username not found')
+    except Exception as e:
+        st.error(e)

Pickle_files/main_df

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0ed723abf96a19138a886dc00961bef6d05391483deeca9bea4a50f05045d48e
+size 2221488

Scenario.py

@@ -0,0 +1,338 @@
+import streamlit as st
+import pandas as pd
+import plotly.express as px
+import plotly.graph_objects as go
+import numpy as np
+import plotly.express as px
+import plotly.graph_objects as go
+import pandas as pd
+import seaborn as sns
+import matplotlib.pyplot as plt
+import datetime
+from utilities import set_header,initialize_data,load_local_css
+from scipy.optimize import curve_fit
+import statsmodels.api as sm
+from plotly.subplots import make_subplots
+
+st.set_page_config(
+  page_title="Data Validation",
+  page_icon=":shark:",
+  layout="wide",
+  initial_sidebar_state='collapsed'
+)
+load_local_css('styles.css')
+set_header()
+
+def format_numbers(x):
+    if abs(x) >= 1e6:
+        # Format as millions with one decimal place and commas
+        return f'{x/1e6:,.1f}M'
+    elif abs(x) >= 1e3:
+        # Format as thousands with one decimal place and commas
+        return f'{x/1e3:,.1f}K'
+    else:
+        # Format with one decimal place and commas for values less than 1000
+        return f'{x:,.1f}'
+
+def format_axis(x):
+    if isinstance(x, tuple):
+        x = x[0]  # Extract the numeric value from the tuple
+    if abs(x) >= 1e6:
+        return f'{x / 1e6:.0f}M'
+    elif abs(x) >= 1e3:
+        return f'{x / 1e3:.0f}k'
+    else:
+        return f'{x:.0f}'
+
+
+attributred_app_installs=pd.read_csv("attributed_app_installs.csv")
+attributred_app_installs_tactic=pd.read_excel('attributed_app_installs_tactic.xlsx')
+data=pd.read_excel('Channel_wise_imp_click_spends.xlsx')
+data['Date']=pd.to_datetime(data['Date'])
+st.header('Saturation Curves')
+
+# st.dataframe(data.head(2))
+st.markdown('Data QC')
+
+st.markdown('Channel wise summary')
+summary_df=data.groupby(data['Date'].dt.strftime('%B %Y')).sum()
+summary_df=summary_df.sort_index(key=lambda x: pd.to_datetime(x, format='%B %Y'))
+st.dataframe(summary_df.applymap(format_numbers))
+
+
+
+def line_plot_target(df,target,title):
+    df=df
+    df['Date_unix'] = df['Date'].apply(lambda x: x.timestamp())
+
+# Perform polynomial fitting
+    coefficients = np.polyfit(df['Date_unix'], df[target], 1)
+    # st.dataframe(df)
+    coefficients = np.polyfit(df['Date'].view('int64'), df[target], 1)
+    trendline = np.poly1d(coefficients)
+    fig = go.Figure()
+
+    fig.add_trace(go.Scatter(x=df['Date'], y=df[target], mode='lines', name=target,line=dict(color='#11B6BD')))
+    trendline_x = df['Date']
+    trendline_y = trendline(df['Date'].view('int64'))
+
+
+    fig.add_trace(go.Scatter(x=trendline_x, y=trendline_y, mode='lines', name='Trendline', line=dict(color='#739FAE')))
+
+    fig.update_layout(
+        title=title,
+        xaxis=dict(type='date')
+    )
+
+    for year in df['Date'].dt.year.unique()[1:]:
+
+        january_1 = pd.Timestamp(year=year, month=1, day=1)
+        fig.add_shape(
+            go.layout.Shape(
+                type="line",
+                x0=january_1,
+                x1=january_1,
+                y0=0,
+                y1=1,
+                xref="x",
+                yref="paper",
+                line=dict(color="grey", width=1.5, dash="dash"),
+            )
+        )
+
+    return fig
+channels_d= data.columns[:28]
+channels=list(set([col.replace('_impressions','').replace('_clicks','').replace('_spend','') for col in channels_d if col.lower()!='date']))
+channel= st.selectbox('Select Channel_name',channels)
+target_column = st.selectbox('Select Channel)',[col for col in data.columns if col.startswith(channel)])
+fig=line_plot_target(data, target=str(target_column), title=f'{str(target_column)} Over Time')
+st.plotly_chart(fig, use_container_width=True)
+
+# st.markdown('## Saturation Curve')
+
+
+st.header('Build saturation curve')
+
+# Your data
+# st.write(len(attributred_app_installs))
+# st.write(len(data))
+# col=st.columns(3)
+# with col[0]:
+col=st.columns(2)
+with col[0]:
+    if st.checkbox('Cap Outliers'):
+        x = data[target_column]
+        x.index=data['Date']
+        # st.write(x)
+        result = sm.tsa.seasonal_decompose(x, model='additive')
+        x_resid=result.resid
+        # fig = make_subplots(rows=1, cols=1, shared_xaxes=True, vertical_spacing=0.02)
+        # trace_x = go.Scatter(x=data['Date'], y=x, mode='lines', name='x')
+        # fig.add_trace(trace_x)
+        # trace_x_resid = go.Scatter(x=data['Date'], y=x_resid, mode='lines', name='x_resid', yaxis='y2',line=dict(color='orange'))
+
+        # fig.add_trace(trace_x_resid)
+        # fig.update_layout(title='',
+        #               xaxis=dict(title='Date'),
+        #               yaxis=dict(title='x', side='left'),
+        #               yaxis2=dict(title='x_resid', side='right'))
+        # st.title('')
+        # st.plotly_chart(fig)
+
+        # x=result.resid
+        # x=x.fillna(0)
+        x_mean = np.mean(x)
+        x_std = np.std(x)
+        x_scaled = (x - x_mean) / x_std
+        lower_threshold = -2.0 
+        upper_threshold = 2.0   
+        x_scaled = np.clip(x_scaled, lower_threshold, upper_threshold)
+    else:
+        x = data[target_column]
+        x_mean = np.mean(x)
+        x_std = np.std(x)
+        x_scaled = (x - x_mean) / x_std
+with col[1]:
+    if st.checkbox('Attributed'):
+        column=[col for col in attributred_app_installs.columns if col in target_column]
+        data['app_installs_appsflyer']=attributred_app_installs[column]
+        y=data['app_installs_appsflyer']
+        title='Attributed-App_installs_appsflyer'
+        # st.dataframe(y)
+        # st.dataframe(x)
+        # st.dataframe(x_scaled)
+    else:
+        y=data["app_installs_appsflyer"]
+        title='App_installs_appsflyer'
+        # st.write(len(y))
+    # Curve fitting function
+def sigmoid(x, K, a, x0):
+    return K / (1 + np.exp(-a * (x - x0)))
+
+initial_K = np.max(y)
+initial_a = 1
+initial_x0 = 0
+columns=st.columns(3)
+
+
+with columns[0]:
+    K = st.number_input('K (Amplitude)', min_value=0.01, max_value=2.0 * np.max(y), value=float(initial_K), step=5.0)
+with columns[1]:
+    a = st.number_input('a (Slope)', min_value=0.01, max_value=5.0, value=float(initial_a), step=0.5)
+with columns[2]:
+    x0 = st.number_input('x0 (Center)', min_value=float(min(x_scaled)), max_value=float(max(x_scaled)), value=float(initial_x0), step=2.0)
+params, _ = curve_fit(sigmoid, x_scaled, y, p0=[K, a, x0], maxfev=20000)
+
+
+x_slider = st.slider('X Value', min_value=float(min(x)), max_value=float(max(x))+1, value=float(x_mean), step=1.)
+
+# Calculate the corresponding value on the fitted curve
+x_slider_scaled = (x_slider - x_mean) / x_std
+y_slider_fit = sigmoid(x_slider_scaled, *params)
+
+# Display the corresponding value
+st.write(f'{target_column}: {format_numbers(x_slider)}')
+st.write(f'Corresponding  App_installs: {format_numbers(y_slider_fit)}')
+
+# Scatter plot of your data
+fig = px.scatter(data_frame=data, x=x_scaled, y=y, labels={'x': f'{target_column}', 'y': 'App Installs'}, title=title)
+
+# Add the fitted sigmoid curve to the plot
+x_fit = np.linspace(min(x_scaled), max(x_scaled), 100)  # Generate x values for the curve
+y_fit = sigmoid(x_fit, *params)
+fig.add_trace(px.line(x=x_fit, y=y_fit).data[0])
+fig.data[1].update(line=dict(color='orange'))
+fig.add_vline(x=x_slider_scaled, line_dash='dash', line_color='red', annotation_text=f'{format_numbers(x_slider)}')
+
+x_tick_labels = {format_axis(x_scaled[i]): format_axis(x[i]) for i in range(len(x_scaled))}
+num_points = 30  # Number of points you want to select
+keys = list(x_tick_labels.keys())
+values = list(x_tick_labels.values())
+spacing = len(keys) // num_points  # Calculate the spacing
+if spacing==0:
+    spacing=15
+    selected_keys = keys[::spacing]
+    selected_values = values[::spacing]
+else:
+    selected_keys = keys[::spacing]
+    selected_values = values[::spacing]
+
+# Update the x-axis ticks with the selected keys and values
+fig.update_xaxes(tickvals=selected_keys, ticktext=selected_values)
+fig.update_xaxes(tickvals=list(x_tick_labels.keys()), ticktext=list(x_tick_labels.values()))
+# Show the plot using st.plotly_chart
+
+fig.update_xaxes(showgrid=False)
+fig.update_yaxes(showgrid=False)
+fig.update_layout(
+    width=600,  # Adjust the width as needed
+    height=600  # Adjust the height as needed
+)
+st.plotly_chart(fig)
+
+
+
+
+st.markdown('Tactic level')
+if channel=='paid_social':
+
+    tactic_data=pd.read_excel("Tatcic_paid.xlsx",sheet_name='paid_social_impressions')
+else:
+    tactic_data=pd.read_excel("Tatcic_paid.xlsx",sheet_name='digital_app_display_impressions')  
+target_column = st.selectbox('Select Channel)',[col for col in tactic_data.columns if col!='Date' and col!='app_installs_appsflyer'])
+fig=line_plot_target(tactic_data, target=str(target_column), title=f'{str(target_column)} Over Time')
+st.plotly_chart(fig, use_container_width=True)
+
+if st.checkbox('Cap Outliers',key='tactic1'):
+    x = tactic_data[target_column]
+    x_mean = np.mean(x)
+    x_std = np.std(x)
+    x_scaled = (x - x_mean) / x_std
+    lower_threshold = -2.0 
+    upper_threshold = 2.0   
+    x_scaled = np.clip(x_scaled, lower_threshold, upper_threshold)
+else:
+    x = tactic_data[target_column]
+    x_mean = np.mean(x)
+    x_std = np.std(x)
+    x_scaled = (x - x_mean) / x_std
+
+if st.checkbox('Attributed',key='tactic2'):
+    column=[col for col in attributred_app_installs_tactic.columns if col in target_column]
+    tactic_data['app_installs_appsflyer']=attributred_app_installs_tactic[column]
+    y=tactic_data['app_installs_appsflyer']
+    title='Attributed-App_installs_appsflyer'
+    # st.dataframe(y)
+    # st.dataframe(x)
+    # st.dataframe(x_scaled)
+else:
+    y=data["app_installs_appsflyer"]
+    title='App_installs_appsflyer'
+    # st.write(len(y))
+# Curve fitting function
+def sigmoid(x, K, a, x0):
+    return K / (1 + np.exp(-a * (x - x0)))
+
+# Curve fitting
+# st.dataframe(x_scaled.head(3))
+# # y=y.astype(float)
+# st.dataframe(y.head(3))
+initial_K = np.max(y)
+initial_a = 1
+initial_x0 = 0
+K = st.number_input('K (Amplitude)', min_value=0.01, max_value=2.0 * np.max(y), value=float(initial_K), step=5.0,key='tactic3')
+a = st.number_input('a (Slope)', min_value=0.01, max_value=5.0, value=float(initial_a), step=2.0,key='tactic41')
+x0 = st.number_input('x0 (Center)', min_value=float(min(x_scaled)), max_value=float(max(x_scaled)), value=float(initial_x0), step=2.0,key='tactic4')
+params, _ = curve_fit(sigmoid, x_scaled, y, p0=[K, a, x0], maxfev=20000)
+
+# Slider to vary x
+x_slider = st.slider('X Value', min_value=float(min(x)), max_value=float(max(x)), value=float(x_mean), step=1.,key='tactic7')
+
+# Calculate the corresponding value on the fitted curve
+x_slider_scaled = (x_slider - x_mean) / x_std
+y_slider_fit = sigmoid(x_slider_scaled, *params)
+
+# Display the corresponding value
+st.write(f'{target_column}: {format_axis(x_slider)}')
+st.write(f'Corresponding  App_installs: {format_axis(y_slider_fit)}')
+
+# Scatter plot of your data
+fig = px.scatter(data_frame=data, x=x_scaled, y=y, labels={'x': f'{target_column}', 'y': 'App Installs'}, title=title)
+
+# Add the fitted sigmoid curve to the plot
+x_fit = np.linspace(min(x_scaled), max(x_scaled), 100)  # Generate x values for the curve
+y_fit = sigmoid(x_fit, *params)
+fig.add_trace(px.line(x=x_fit, y=y_fit).data[0])
+fig.data[1].update(line=dict(color='orange'))
+fig.add_vline(x=x_slider_scaled, line_dash='dash', line_color='red', annotation_text=f'{format_numbers(x_slider)}')
+
+
+
+x_tick_labels = {format_axis((x_scaled[i],0)): format_axis(x[i]) for i in range(len(x_scaled))}
+num_points = 50  # Number of points you want to select
+keys = list(x_tick_labels.keys())
+values = list(x_tick_labels.values())
+spacing = len(keys) // num_points  # Calculate the spacing
+if spacing==0:
+    spacing=2
+    selected_keys = keys[::spacing]
+    selected_values = values[::spacing]
+else:
+    selected_keys = keys[::spacing]
+    selected_values = values[::spacing]
+
+# Update the x-axis ticks with the selected keys and values
+fig.update_xaxes(tickvals=selected_keys, ticktext=selected_values)
+
+# Round the x-axis and y-axis tick values to zero decimal places
+fig.update_xaxes(tickformat=".f")  # Format x-axis ticks to zero decimal places
+fig.update_yaxes(tickformat=".f")  # Format y-axis ticks to zero decimal places
+
+# Show the plot using st.plotly_chart
+fig.update_xaxes(showgrid=False)
+fig.update_yaxes(showgrid=False)
+fig.update_layout(
+    width=600,  # Adjust the width as needed
+    height=600  # Adjust the height as needed
+)
+st.plotly_chart(fig)

Test/X_test_tuned_trend.csv

@@ -0,0 +1,971 @@
+paid_search_clicks,kwai_clicks,fb_level_achieved_tier_2_clicks_lag_2,fb_level_achieved_tier_1_impressions,ga_app_clicks,digital_tactic_others_impressions_lag_2,programmatic_clicks_lag_3,total_approved_accounts_revenue,date,dma,Trend
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+0.0,0.0,0.0,0.0,0.0,0.0,0.07086223984142716,30.899,8/7/2023,Birmingham/Anniston/Tuscaloosa SMM Food,124
+0.0,0.0015968856587974965,0.0,0.0,0.006559212363940131,0.0,0.0,21.32,8/8/2022,Birmingham/Anniston/Tuscaloosa SMM Food,125
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+0.0,0.0,0.0,0.021250793330832705,0.0,0.0,0.0,9.71,9/19/2022,Birmingham/Anniston/Tuscaloosa SMM Food,131
+0.0,0.0,0.0,0.0,0.002048671383380772,0.0,0.0,12.55,9/20/2021,Birmingham/Anniston/Tuscaloosa SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.055996035678889985,9.499,9/25/2023,Birmingham/Anniston/Tuscaloosa SMM Food,133
+0.0,0.0,0.0,0.02414759149407838,0.0,0.0,0.0,9.26,9/26/2022,Birmingham/Anniston/Tuscaloosa SMM Food,134
+0.0,0.0,0.0,0.0,0.001227223437387516,0.0,0.0,11.65,9/27/2021,Birmingham/Anniston/Tuscaloosa SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.051040634291377604,14.16,9/4/2023,Birmingham/Anniston/Tuscaloosa SMM Food,136
+0.0,0.0,0.0,0.021329020533966504,0.0,0.0,0.0,13.7,9/5/2022,Birmingham/Anniston/Tuscaloosa SMM Food,137
+0.0,0.0,0.0,0.0,0.001038562576297197,0.0,0.0,12.02,9/6/2021,Birmingham/Anniston/Tuscaloosa SMM Food,138
+0.0,0.0,0.0,0.0,0.0,0.0,0.20416253716551042,142.058,8/7/2023,Boston/Manchester SMM Food,124
+0.0,0.006427977432121013,0.0,0.0,0.02215311501340549,0.0,0.0,133.68,8/8/2022,Boston/Manchester SMM Food,125
+0.0,0.0,0.0,0.0,0.006543129798732431,0.0,0.0,118.91,8/9/2021,Boston/Manchester SMM Food,126
+0.0,0.0,0.04895274042536989,0.0,0.0,0.16361847104901653,0.1798810703666997,172.275,9/11/2023,Boston/Manchester SMM Food,127
+0.0,0.0,0.0,0.06149452980004475,0.0,0.0,0.0,167.04,9/12/2022,Boston/Manchester SMM Food,128
+0.0,0.0,0.0,0.0,0.0038660012518507932,0.0,0.0,117.31,9/13/2021,Boston/Manchester SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.19425173439048563,178.545,9/18/2023,Boston/Manchester SMM Food,130
+0.0,0.0,0.0,0.06410174720660182,0.0,0.0,0.0,145.96,9/19/2022,Boston/Manchester SMM Food,131
+0.0,0.0,0.0,0.0,0.0032907402655753953,0.0,0.0,113.79,9/20/2021,Boston/Manchester SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.22398414271555994,156.549,9/25/2023,Boston/Manchester SMM Food,133
+0.0,0.0,0.0,0.07283976560910188,0.0,0.0,0.0,157.5,9/26/2022,Boston/Manchester SMM Food,134
+0.0,0.0,0.0,0.0,0.0044307467147211566,0.0,0.0,114.52,9/27/2021,Boston/Manchester SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.19573835480673935,149.121,9/4/2023,Boston/Manchester SMM Food,136
+0.0,0.0,0.0,0.06433771488842611,0.0,0.0,0.0,138.78,9/5/2022,Boston/Manchester SMM Food,137
+0.0,0.0,0.0,0.0,0.004624356057413845,0.0,0.0,116.39,9/6/2021,Boston/Manchester SMM Food,138
+0.0,0.0,0.0,0.0,0.0,0.0,0.07036669970267592,21.059,8/7/2023,Buffalo SMM Food,124
+0.0,0.001065745719110646,0.0,0.0,0.006125601663532545,0.0,0.0,14.52,8/8/2022,Buffalo SMM Food,125
+0.0,0.0,0.0,0.0,0.002417333262757264,0.0,0.0,16.9,8/9/2021,Buffalo SMM Food,126
+0.0,0.0,0.007447706411528032,0.0,0.0,0.05334496751978329,0.06987115956392467,21.384,9/11/2023,Buffalo SMM Food,127
+0.0,0.0,0.0,0.016659126610822184,0.0,0.0,0.0,16.16,9/12/2022,Buffalo SMM Food,128
+0.0,0.0,0.0,0.0,0.0018525877998869001,0.0,0.0,17.94,9/13/2021,Buffalo SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.07333994053518335,19.469,9/18/2023,Buffalo SMM Food,130
+0.0,0.0,0.0,0.017365432764953035,0.0,0.0,0.0,15.79,9/19/2022,Buffalo SMM Food,131
+0.0,0.0,0.0,0.0,0.0014635134339006364,0.0,0.0,13.96,9/20/2021,Buffalo SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.07284440039643211,19.007,9/25/2023,Buffalo SMM Food,133
+0.0,0.0,0.0,0.019732598682000637,0.0,0.0,0.0,17.25,9/26/2022,Buffalo SMM Food,134
+0.0,0.0,0.0,0.0,0.0011474291715493155,0.0,0.0,16.09,9/27/2021,Buffalo SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.05698711595639247,24.686,9/4/2023,Buffalo SMM Food,136
+0.0,0.0,0.0,0.01742935740030342,0.0,0.0,0.0,24.05,9/5/2022,Buffalo SMM Food,137
+0.0,0.0,0.0,0.0,0.002140218293024599,0.0,0.0,18.49,9/6/2021,Buffalo SMM Food,138
+0.0,0.0,0.0,0.0,0.0,0.0,0.11248761149653122,91.731,8/7/2023,Charlotte SMM Food,124
+0.0,0.00275216557111256,0.0,0.0,0.01447925716853174,0.0,0.0,105.22,8/8/2022,Charlotte SMM Food,125
+0.0,0.0,0.0,0.0,0.00312867749309781,0.0,0.0,68.53,8/9/2021,Charlotte SMM Food,126
+0.0,0.0,0.019428597524418424,0.0,0.0,0.14467607804531682,0.10257680872150644,109.118,9/11/2023,Charlotte SMM Food,127
+0.0,0.0,0.0,0.0334917711067763,0.0,0.0,0.0,90.2,9/12/2022,Charlotte SMM Food,128
+0.0,0.0,0.0,0.0,0.0033154826735872405,0.0,0.0,71.22,9/13/2021,Charlotte SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.09712586719524281,95.013,9/18/2023,Charlotte SMM Food,130
+0.0,0.0,0.0,0.03491174014852976,0.0,0.0,0.0,80.4,9/19/2022,Charlotte SMM Food,131
+0.0,0.0,0.0,0.0,0.0025410453028164894,0.0,0.0,67.35,9/20/2021,Charlotte SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.10009910802775024,115.348,9/25/2023,Charlotte SMM Food,133
+0.0,0.0,0.0,0.03967072787903239,0.0,0.0,0.0,69.61,9/26/2022,Charlotte SMM Food,134
+0.0,0.0,0.0,0.0,0.0018940313333067407,0.0,0.0,67.22,9/27/2021,Charlotte SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.09514370664023786,64.194,9/4/2023,Charlotte SMM Food,136
+0.0,0.0,0.0,0.035040255247413665,0.0,0.0,0.0,69.04,9/5/2022,Charlotte SMM Food,137
+0.0,0.0,0.0,0.0,0.0029257897474006802,0.0,0.0,95.05,9/6/2021,Charlotte SMM Food,138
+0.0,0.0,0.0,0.0,0.0,0.0,0.2259663032705649,127.164,8/7/2023,Chicago SMM Food,124
+0.0,0.021210939190104566,0.0,0.0,0.029393362157871656,0.0,0.0,128.34,8/8/2022,Chicago SMM Food,125
+0.0,0.0,0.0,0.0,0.007107256701402499,0.0,0.0,131.14,8/9/2021,Chicago SMM Food,126
+0.0,0.0,0.15988812078214787,0.0,0.0,0.2559436219550531,0.21110009910802774,139.546,9/11/2023,Chicago SMM Food,127
+0.0,0.0,0.0,0.07186262484115848,0.0,0.0,0.0,115.04,9/12/2022,Chicago SMM Food,128
+0.0,0.0,0.0,0.0,0.004144971902184347,0.0,0.0,126.47,9/13/2021,Chicago SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.2423191278493558,150.534,9/18/2023,Chicago SMM Food,130
+0.0,0.0,0.0,0.07490942409399909,0.0,0.0,0.0,112.06,9/19/2022,Chicago SMM Food,131
+0.0,0.0,0.0,0.0,0.004689304878444938,0.0,0.0,105.95,9/20/2021,Chicago SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.17443012884043607,140.18,9/25/2023,Chicago SMM Food,133
+0.0,0.0,0.0,0.0851206890876872,0.0,0.0,0.0,113.37,9/26/2022,Chicago SMM Food,134
+0.0,0.0,0.0,0.0,0.005144565185862888,0.0,0.0,112.47,9/27/2021,Chicago SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.17938553022794845,134.87,9/4/2023,Chicago SMM Food,136
+0.0,0.0,0.0,0.07518517635856978,0.0,0.0,0.0,128.32,9/5/2022,Chicago SMM Food,137
+0.0,0.0,0.0,0.0,0.005288071152331588,0.0,0.0,132.52,9/6/2021,Chicago SMM Food,138
+0.0,0.0,0.0,0.0,0.0,0.0,0.1442021803766105,72.288,8/7/2023,Cleveland/Akron/Canton SMM Food,124
+0.0,0.0,0.0,0.0,0.010643565366495456,0.0,0.0,88.44,8/8/2022,Cleveland/Akron/Canton SMM Food,125
+0.0,0.0,0.0,0.0,0.005229307933303457,0.0,0.0,81.13,8/9/2021,Cleveland/Akron/Canton SMM Food,126
+0.0,0.0,0.018360600644668993,0.0,0.0,0.003847068303229848,0.13082259663032705,77.361,9/11/2023,Cleveland/Akron/Canton SMM Food,127
+0.0,0.0,0.0,0.04319655098784653,0.0,0.0,0.0,83.99,9/12/2022,Cleveland/Akron/Canton SMM Food,128
+0.0,0.0,0.0,0.0,0.0038678569324516817,0.0,0.0,81.0,9/13/2021,Cleveland/Akron/Canton SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.1367690782953419,74.588,9/18/2023,Cleveland/Akron/Canton SMM Food,130
+0.0,0.0,0.0,0.045027978918762056,0.0,0.0,0.0,68.98,9/19/2022,Cleveland/Akron/Canton SMM Food,131
+0.0,0.0,0.0,0.0,0.004016929940723048,0.0,0.0,79.08,9/20/2021,Cleveland/Akron/Canton SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.1337958374628345,75.304,9/25/2023,Cleveland/Akron/Canton SMM Food,133
+0.0,0.0,0.0,0.05116595995981537,0.0,0.0,0.0,73.72,9/26/2022,Cleveland/Akron/Canton SMM Food,134
+0.0,0.0,0.0,0.0,0.003246203931154074,0.0,0.0,72.7,9/27/2021,Cleveland/Akron/Canton SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.12983151635282458,85.988,9/4/2023,Cleveland/Akron/Canton SMM Food,136
+0.0,0.0,0.0,0.04519373333208909,0.0,0.0,0.0,95.79,9/5/2022,Cleveland/Akron/Canton SMM Food,137
+0.0,0.0,0.0,0.0,0.0032332141669478556,0.0,0.0,84.14,9/6/2021,Cleveland/Akron/Canton SMM Food,138
+0.0,0.0,0.0,0.0,0.0,0.0,0.10555004955401387,54.72,8/7/2023,Columbus OH SMM Food,124
+0.0,0.0016355875358600512,0.0,0.0,0.010457378746206322,0.0,0.0,58.12,8/8/2022,Columbus OH SMM Food,125
+0.0,0.0,0.0,0.0,0.003066821473068197,0.0,0.0,54.05,8/9/2021,Columbus OH SMM Food,126
+0.0,0.0,0.012976309777184706,0.0,0.0,0.09535695517348619,0.09563924677898909,56.575,9/11/2023,Columbus OH SMM Food,127
+0.0,0.0,0.0,0.026403905702080413,0.0,0.0,0.0,54.07,9/12/2022,Columbus OH SMM Food,128
+0.0,0.0,0.0,0.0,0.002041867221177515,0.0,0.0,53.22,9/13/2021,Columbus OH SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.0931615460852329,59.875,9/18/2023,Columbus OH SMM Food,130
+0.0,0.0,0.0,0.0275233666153899,0.0,0.0,0.0,51.71,9/19/2022,Columbus OH SMM Food,131
+0.0,0.0,0.0,0.0,0.0023913537343448264,0.0,0.0,47.83,9/20/2021,Columbus OH SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.0753221010901883,61.444,9/25/2023,Columbus OH SMM Food,133
+0.0,0.0,0.0,0.031275209501753144,0.0,0.0,0.0,56.01,9/26/2022,Columbus OH SMM Food,134
+0.0,0.0,0.0,0.0,0.0023177450705095877,0.0,0.0,51.58,9/27/2021,Columbus OH SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.0817641228939544,60.308,9/4/2023,Columbus OH SMM Food,136
+0.0,0.0,0.0,0.027624684053467907,0.0,0.0,0.0,61.28,9/5/2022,Columbus OH SMM Food,137
+0.0,0.0,0.0,0.0,0.0019162995005174012,0.0,0.0,56.04,9/6/2021,Columbus OH SMM Food,138
+0.0,0.0,0.0,0.0,0.0,0.0,0.22150644202180375,73.064,8/7/2023,Dallas/Ft. Worth SMM Food,124
+0.0,0.007823266746219531,0.0,0.0,0.02227744561366501,0.0,0.0,55.67,8/8/2022,Dallas/Ft. Worth SMM Food,125
+0.0,0.0,0.0,0.0,0.00542044303519496,0.0,0.0,58.56,8/9/2021,Dallas/Ft. Worth SMM Food,126
+0.0,0.0,0.053580445593371474,0.0,0.0,0.2960835671223029,0.20366699702675917,82.586,9/11/2023,Dallas/Ft. Worth SMM Food,127
+0.0,0.0,0.0,0.055526458478242974,0.0,0.0,0.0,62.13,9/12/2022,Dallas/Ft. Worth SMM Food,128
+0.0,0.0,0.0,0.0,0.004551365953778902,0.0,0.0,54.28,9/13/2021,Dallas/Ft. Worth SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.20862239841427155,77.309,9/18/2023,Dallas/Ft. Worth SMM Food,130
+0.0,0.0,0.0,0.05788064427948714,0.0,0.0,0.0,58.16,9/19/2022,Dallas/Ft. Worth SMM Food,131
+0.0,0.0,0.0,0.0,0.005119822777851043,0.0,0.0,55.82,9/20/2021,Dallas/Ft. Worth SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.21258671952428146,75.709,9/25/2023,Dallas/Ft. Worth SMM Food,133
+0.0,0.0,0.0,0.06577063414997705,0.0,0.0,0.0,62.33,9/26/2022,Dallas/Ft. Worth SMM Food,134
+0.0,0.0,0.0,0.0,0.003531360183490589,0.0,0.0,56.4,9/27/2021,Dallas/Ft. Worth SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.1734390485629336,80.36,9/4/2023,Dallas/Ft. Worth SMM Food,136
+0.0,0.0,0.0,0.058093711196037776,0.0,0.0,0.0,60.65,9/5/2022,Dallas/Ft. Worth SMM Food,137
+0.0,0.0,0.0,0.0,0.004822913881708902,0.0,0.0,60.16,9/6/2021,Dallas/Ft. Worth SMM Food,138
+0.0,0.0,0.0,0.0,0.0,0.0,0.06095143706640238,19.057,8/7/2023,Des Moines/Ames SMM Food,124
+0.0,0.001062279879373701,0.0,0.0,0.002908470061792389,0.0,0.0,21.69,8/8/2022,Des Moines/Ames SMM Food,125
+0.0,0.0,0.0,0.0,0.0009030978924323453,0.0,0.0,15.48,8/9/2021,Des Moines/Ames SMM Food,126
+0.0,0.0,0.007034601336350359,0.0,0.0,0.038076904404842446,0.0639246778989098,17.536,9/11/2023,Des Moines/Ames SMM Food,127
+0.0,0.0,0.0,0.009818746375673355,0.0,0.0,0.0,19.26,9/12/2022,Des Moines/Ames SMM Food,128
+0.0,0.0,0.0,0.0,0.0007459836015571291,0.0,0.0,16.86,9/13/2021,Des Moines/Ames SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.05401387512388503,19.413,9/18/2023,Des Moines/Ames SMM Food,130
+0.0,0.0,0.0,0.010235037169468615,0.0,0.0,0.0,17.91,9/19/2022,Des Moines/Ames SMM Food,131
+0.0,0.0,0.0,0.0,0.0005560856200662181,0.0,0.0,16.58,9/20/2021,Des Moines/Ames SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.05797819623389494,20.201,9/25/2023,Des Moines/Ames SMM Food,133
+0.0,0.0,0.0,0.011630224459597573,0.0,0.0,0.0,17.11,9/26/2022,Des Moines/Ames SMM Food,134
+0.0,0.0,0.0,0.0,0.000921036138240933,0.0,0.0,12.87,9/27/2021,Des Moines/Ames SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.04707631318136769,20.614,9/4/2023,Des Moines/Ames SMM Food,136
+0.0,0.0,0.0,0.010272713801757724,0.0,0.0,0.0,19.45,9/5/2022,Des Moines/Ames SMM Food,137
+0.0,0.0,0.0,0.0,0.0008585615580110242,0.0,0.0,16.73,9/6/2021,Des Moines/Ames SMM Food,138
+0.0,0.0,0.0,0.0,0.0,0.0,0.14073339940535184,124.957,8/7/2023,Detroit SMM Food,124
+0.0,0.004029038694198784,0.0,0.0,0.01633679345002101,0.0,0.0,129.16,8/8/2022,Detroit SMM Food,125
+0.0,0.0,0.0,0.0,0.005138998144060223,0.0,0.0,98.0,8/9/2021,Detroit SMM Food,126
+0.0,0.0,0.027197842309067383,0.0,0.0,0.16679364832597332,0.1367690782953419,114.674,9/11/2023,Detroit SMM Food,127
+0.0,0.0,0.0,0.05264923653159416,0.0,0.0,0.0,99.78,9/12/2022,Detroit SMM Food,128
+0.0,0.0,0.0,0.0,0.003737959290389495,0.0,0.0,105.22,9/13/2021,Detroit SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.13478691774033696,132.01,9/18/2023,Detroit SMM Food,130
+0.0,0.0,0.0,0.05488143517450875,0.0,0.0,0.0,98.35,9/19/2022,Detroit SMM Food,131
+0.0,0.0,0.0,0.0,0.004616314774809995,0.0,0.0,76.97,9/20/2021,Detroit SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.12041625371655104,134.155,9/25/2023,Detroit SMM Food,133
+0.0,0.0,0.0,0.062362588370685694,0.0,0.0,0.0,111.04,9/26/2022,Detroit SMM Food,134
+0.0,0.0,0.0,0.0,0.004344766846879995,0.0,0.0,88.84,9/27/2021,Detroit SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.11397423191278494,147.194,9/4/2023,Detroit SMM Food,136
+0.0,0.0,0.0,0.05508346156574187,0.0,0.0,0.0,125.93,9/5/2022,Detroit SMM Food,137
+0.0,0.0,0.0,0.0,0.00365569078375011,0.0,0.0,109.54,9/6/2021,Detroit SMM Food,138
+0.0,0.0,0.0,0.0,0.0,0.0,0.09365708622398414,71.874,8/7/2023,Grand Rapids SMM Food,124
+0.0,0.0,0.0,0.0,0.008291799484969583,0.0,0.0,89.36,8/8/2022,Grand Rapids SMM Food,125
+0.0,0.0,0.0,0.0,0.0016558856561927318,0.0,0.0,60.59,8/9/2021,Grand Rapids SMM Food,126
+0.0,0.0,0.010334378341344649,0.0,0.0,0.004593258733825526,0.06937561942517344,59.484,9/11/2023,Grand Rapids SMM Food,127
+0.0,0.0,0.0,0.022162738793584366,0.0,0.0,0.0,51.84,9/12/2022,Grand Rapids SMM Food,128
+0.0,0.0,0.0,0.0,0.0015989781177654881,0.0,0.0,62.76,9/13/2021,Grand Rapids SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.10852329038652131,84.959,9/18/2023,Grand Rapids SMM Food,130
+0.0,0.0,0.0,0.023102384621302353,0.0,0.0,0.0,53.77,9/19/2022,Grand Rapids SMM Food,131
+0.0,0.0,0.0,0.0,0.001827226831674759,0.0,0.0,45.22,9/20/2021,Grand Rapids SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.08572844400396432,79.122,9/25/2023,Grand Rapids SMM Food,133
+0.0,0.0,0.0,0.02625158212085696,0.0,0.0,0.0,53.48,9/26/2022,Grand Rapids SMM Food,134
+0.0,0.0,0.0,0.0,0.0012488730443978803,0.0,0.0,45.61,9/27/2021,Grand Rapids SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.06689791873141725,97.272,9/4/2023,Grand Rapids SMM Food,136
+0.0,0.0,0.0,0.023187427795256017,0.0,0.0,0.0,79.71,9/5/2022,Grand Rapids SMM Food,137
+0.0,0.0,0.0,0.0,0.001495678564316035,0.0,0.0,69.21,9/6/2021,Grand Rapids SMM Food,138
+0.0,0.0,0.0,0.0,0.0,0.0,0.05847373637264618,34.23,8/7/2023,Greensboro SMM Food,124
+0.0,0.0,0.0,0.0,0.008662935605147259,0.0,0.0,41.28,8/8/2022,Greensboro SMM Food,125
+0.0,0.0,0.0,0.0,0.002148878135828745,0.0,0.0,33.05,8/9/2021,Greensboro SMM Food,126
+0.0,0.0,0.0,0.0,0.0,0.004162733342605559,0.05302279484638256,40.267,9/11/2023,Greensboro SMM Food,127
+0.0,0.0,0.0,0.01938722492394592,0.0,0.0,0.0,38.46,9/12/2022,Greensboro SMM Food,128
+0.0,0.0,0.0,0.0,0.0019855782429505676,0.0,0.0,34.42,9/13/2021,Greensboro SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.059960356788899896,39.972,9/18/2023,Greensboro SMM Food,130
+0.0,0.0,0.0,0.02020919576536063,0.0,0.0,0.0,37.05,9/19/2022,Greensboro SMM Food,131
+0.0,0.0,0.0,0.0,0.0019132066995159206,0.0,0.0,29.9,9/20/2021,Greensboro SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.05698711595639247,58.21,9/25/2023,Greensboro SMM Food,133
+0.0,0.0,0.0,0.022964008737958168,0.0,0.0,0.0,31.18,9/26/2022,Greensboro SMM Food,134
+0.0,0.0,0.0,0.0,0.0017647522514448503,0.0,0.0,32.39,9/27/2021,Greensboro SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.05004955401387512,28.032,9/4/2023,Greensboro SMM Food,136
+0.0,0.0,0.0,0.020283588694575232,0.0,0.0,0.0,31.02,9/5/2022,Greensboro SMM Food,137
+0.0,0.0,0.0,0.0,0.0016861951060072422,0.0,0.0,55.3,9/6/2021,Greensboro SMM Food,138
+0.0,0.0,0.0,0.0,0.0,0.0,0.08622398414271557,34.307,8/7/2023,Harrisburg/Lancaster SMM Food,124
+0.0,0.00118647246994757,0.0,0.0,0.010951608346242928,0.0,0.0,37.54,8/8/2022,Harrisburg/Lancaster SMM Food,125
+0.0,0.0,0.0,0.0,0.0027989849063399744,0.0,0.0,31.88,8/9/2021,Harrisburg/Lancaster SMM Food,126
+0.0,0.0,0.009303514502060626,0.0,0.0,0.05938927543516645,0.06838453914767095,47.335,9/11/2023,Harrisburg/Lancaster SMM Food,127
+0.0,0.0,0.0,0.01722270730805324,0.0,0.0,0.0,47.54,9/12/2022,Harrisburg/Lancaster SMM Food,128
+0.0,0.0,0.0,0.0,0.0017567109688410004,0.0,0.0,37.65,9/13/2021,Harrisburg/Lancaster SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.06838453914767095,47.204,9/18/2023,Harrisburg/Lancaster SMM Food,130
+0.0,0.0,0.0,0.017952907903006455,0.0,0.0,0.0,47.12,9/19/2022,Harrisburg/Lancaster SMM Food,131
+0.0,0.0,0.0,0.0,0.0023622814049309086,0.0,0.0,39.6,9/20/2021,Harrisburg/Lancaster SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.06689791873141725,42.773,9/25/2023,Harrisburg/Lancaster SMM Food,133
+0.0,0.0,0.0,0.02040015539180732,0.0,0.0,0.0,39.34,9/26/2022,Harrisburg/Lancaster SMM Food,134
+0.0,0.0,0.0,0.0,0.0015365035375355792,0.0,0.0,36.2,9/27/2021,Harrisburg/Lancaster SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.06987115956392467,49.172,9/4/2023,Harrisburg/Lancaster SMM Food,136
+0.0,0.0,0.0,0.018018995117875386,0.0,0.0,0.0,46.11,9/5/2022,Harrisburg/Lancaster SMM Food,137
+0.0,0.0,0.0,0.0,0.0018253711510738705,0.0,0.0,41.15,9/6/2021,Harrisburg/Lancaster SMM Food,138
+0.0,0.0018337180408220845,0.0,0.0,0.010148098646058258,0.0,0.0,57.79,8/8/2022,Hartford/New Haven SMM Food,125
+0.0,0.0,0.0,0.0,0.0037509490545957138,0.0,0.0,69.72,8/9/2021,Hartford/New Haven SMM Food,126
+0.0,0.0,0.014994996886122389,0.0,0.0,0.08363767112400147,0.0882061446977205,74.813,9/11/2023,Hartford/New Haven SMM Food,127
+0.0,0.0,0.0,0.025926611998646588,0.0,0.0,0.0,80.45,9/12/2022,Hartford/New Haven SMM Food,128
+0.0,0.0,0.0,0.0,0.0030495017874599055,0.0,0.0,68.05,9/13/2021,Hartford/New Haven SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.10059464816650149,103.87,9/18/2023,Hartford/New Haven SMM Food,130
+0.0,0.0,0.0,0.027025836833104906,0.0,0.0,0.0,76.63,9/19/2022,Hartford/New Haven SMM Food,131
+0.0,0.0,0.0,0.0,0.0023901166139442343,0.0,0.0,59.62,9/20/2021,Hartford/New Haven SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.08721506442021804,89.9,9/25/2023,Hartford/New Haven SMM Food,133
+0.0,0.0,0.0,0.030709859033967845,0.0,0.0,0.0,79.63,9/26/2022,Hartford/New Haven SMM Food,134
+0.0,0.0,0.0,0.0,0.0023709412477350544,0.0,0.0,64.74,9/27/2021,Hartford/New Haven SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.09910802775024777,67.473,9/4/2023,Hartford/New Haven SMM Food,136
+0.0,0.0,0.0,0.027125322790759788,0.0,0.0,0.0,68.92,9/5/2022,Hartford/New Haven SMM Food,137
+0.0,0.0,0.0,0.0,0.0025515608262215235,0.0,0.0,67.33,9/6/2021,Hartford/New Haven SMM Food,138
+0.0,0.0,0.0,0.0,0.0,0.0,0.1962338949454906,133.105,8/7/2023,Houston SMM Food,124
+0.0,0.0046540451267612325,0.0,0.0,0.022832912673530933,0.0,0.0,118.76,8/8/2022,Houston SMM Food,125
+0.0,0.0,0.0,0.0,0.005281885550328628,0.0,0.0,103.5,8/9/2021,Houston SMM Food,126
+0.0,0.0,0.01913533089801548,0.0,0.0,0.21760885851354209,0.1684836471754212,140.942,9/11/2023,Houston SMM Food,127
+0.0,0.0,0.0,0.04769953315392536,0.0,0.0,0.0,140.87,9/12/2022,Houston SMM Food,128
+0.0,0.0,0.0,0.0,0.004650335585826283,0.0,0.0,105.88,9/13/2021,Houston SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.1620416253716551,129.531,9/18/2023,Houston SMM Food,130
+0.0,0.0,0.0,0.049721876494397986,0.0,0.0,0.0,145.85,9/19/2022,Houston SMM Food,131
+0.0,0.0,0.0,0.0,0.003903114863868561,0.0,0.0,111.54,9/20/2021,Houston SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.15510406342913777,135.992,9/25/2023,Houston SMM Food,133
+0.0,0.0,0.0,0.05649970535943224,0.0,0.0,0.0,150.89,9/26/2022,Houston SMM Food,134
+0.0,0.0,0.0,0.0,0.003332802359195532,0.0,0.0,100.18,9/27/2021,Houston SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.15857284440039643,134.53,9/4/2023,Houston SMM Food,136
+0.0,0.0,0.0,0.04990490981700915,0.0,0.0,0.0,130.92,9/5/2022,Houston SMM Food,137
+0.0,0.0,0.0,0.0,0.0032511524127564434,0.0,0.0,108.11,9/6/2021,Houston SMM Food,138
+0.0,0.0,0.0,0.0,0.0,0.0,0.14172447968285432,48.376,8/7/2023,Indianapolis SMM Food,124
+0.0,0.0024260878158616334,0.0,0.0,0.012808526067531899,0.0,0.0,46.59,8/8/2022,Indianapolis SMM Food,125
+0.0,0.0,0.0,0.0,0.003151564220508767,0.0,0.0,38.95,8/9/2021,Indianapolis SMM Food,126
+0.0,0.0,0.016644041455881685,0.0,0.0,0.11700205142709444,0.11446977205153618,49.036,9/11/2023,Indianapolis SMM Food,127
+0.0,0.0,0.0,0.023691657521331348,0.0,0.0,0.0,35.97,9/12/2022,Indianapolis SMM Food,128
+0.0,0.0,0.0,0.0,0.0022490848882767175,0.0,0.0,37.35,9/13/2021,Indianapolis SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.11645193260654113,51.603,9/18/2023,Indianapolis SMM Food,130
+0.0,0.0,0.0,0.0246961257538268,0.0,0.0,0.0,38.52,9/19/2022,Indianapolis SMM Food,131
+0.0,0.0,0.0,0.0,0.0029072329413917966,0.0,0.0,31.83,9/20/2021,Indianapolis SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.10009910802775024,53.418,9/25/2023,Indianapolis SMM Food,133
+0.0,0.0,0.0,0.028062573805723218,0.0,0.0,0.0,39.19,9/26/2022,Indianapolis SMM Food,134
+0.0,0.0,0.0,0.0,0.002647437657267423,0.0,0.0,31.38,9/27/2021,Indianapolis SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.10505450941526263,55.713,9/4/2023,Indianapolis SMM Food,136
+0.0,0.0,0.0,0.024787035718660904,0.0,0.0,0.0,46.84,9/5/2022,Indianapolis SMM Food,137
+0.0,0.0,0.0,0.0,0.0030024912122374,0.0,0.0,37.3,9/6/2021,Indianapolis SMM Food,138
+0.0,0.0,0.0,0.0,0.0,0.0,0.07383548067393458,96.535,8/7/2023,Jacksonville SMM Food,124
+0.0,0.0008658822942801401,0.0,0.0,0.007301484604295482,0.0,0.0,62.66,8/8/2022,Jacksonville SMM Food,125
+0.0,0.0,0.0,0.0,0.0019435161493304308,0.0,0.0,34.35,8/9/2021,Jacksonville SMM Food,126
+0.0,0.0,0.0069561155917302895,0.0,0.0,0.08053507618258654,0.059960356788899896,27.967,9/11/2023,Jacksonville SMM Food,127
+0.0,0.0,0.0,0.02082072414146757,0.0,0.0,0.0,27.95,9/12/2022,Jacksonville SMM Food,128
+0.0,0.0,0.0,0.0,0.0015767099505548275,0.0,0.0,32.77,9/13/2021,Jacksonville SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.06491575817641229,29.339,9/18/2023,Jacksonville SMM Food,130
+0.0,0.0,0.0,0.02170347183093534,0.0,0.0,0.0,26.19,9/19/2022,Jacksonville SMM Food,131
+0.0,0.0,0.0,0.0,0.001496297124516331,0.0,0.0,28.8,9/20/2021,Jacksonville SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.049554013875123884,28.801,9/25/2023,Jacksonville SMM Food,133
+0.0,0.0,0.0,0.024661976783877985,0.0,0.0,0.0,25.24,9/26/2022,Jacksonville SMM Food,134
+0.0,0.0,0.0,0.0,0.0016119678819717068,0.0,0.0,25.69,9/27/2021,Jacksonville SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.07433102081268583,51.144,9/4/2023,Jacksonville SMM Food,136
+0.0,0.0,0.0,0.02178336541288693,0.0,0.0,0.0,43.45,9/5/2022,Jacksonville SMM Food,137
+0.0,0.0,0.0,0.0,0.0012927908186189051,0.0,0.0,31.77,9/6/2021,Jacksonville SMM Food,138
+0.0,0.0,0.0,0.0,0.0,0.0,0.11248761149653122,29.172,8/7/2023,Kansas City SMM Food,124
+0.0,0.0018331404008659269,0.0,0.0,0.0055076600234367145,0.0,0.0,35.15,8/8/2022,Kansas City SMM Food,125
+0.0,0.0,0.0,0.0,0.0016793909438039844,0.0,0.0,32.17,8/9/2021,Kansas City SMM Food,126
+0.0,0.0,0.0,0.0,0.0,0.08131822285155027,0.11050545094152626,33.379,9/11/2023,Kansas City SMM Food,127
+0.0,0.0,0.0,0.020919710852413544,0.0,0.0,0.0,31.54,9/12/2022,Kansas City SMM Food,128
+0.0,0.0,0.0,0.0,0.00130639914302542,0.0,0.0,35.16,9/13/2021,Kansas City SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.10555004955401387,30.958,9/18/2023,Kansas City SMM Food,130
+0.0,0.0,0.0,0.021806655337224045,0.0,0.0,0.0,30.39,9/19/2022,Kansas City SMM Food,131
+0.0,0.0,0.0,0.0,0.0012995949808221627,0.0,0.0,33.17,9/20/2021,Kansas City SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.10009910802775024,32.027,9/25/2023,Kansas City SMM Food,133
+0.0,0.0,0.0,0.024779225733846975,0.0,0.0,0.0,30.72,9/26/2022,Kansas City SMM Food,134
+0.0,0.0,0.0,0.0,0.0017282571996273786,0.0,0.0,30.48,9/27/2021,Kansas City SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.0882061446977205,31.622,9/4/2023,Kansas City SMM Food,136
+0.0,0.0,0.0,0.021886928747765332,0.0,0.0,0.0,33.43,9/5/2022,Kansas City SMM Food,137
+0.0,0.0,0.0,0.0,0.0012779453738117983,0.0,0.0,34.17,9/6/2021,Kansas City SMM Food,138
+0.0,0.0,0.0,0.0,0.0,0.0,0.059960356788899896,28.985,8/7/2023,Knoxville SMM Food,124
+0.0,0.0009172922503781605,0.0,0.0,0.004641057182821841,0.0,0.0,24.48,8/8/2022,Knoxville SMM Food,125
+0.0,0.0,0.0,0.0,0.00217362054384059,0.0,0.0,22.62,8/9/2021,Knoxville SMM Food,126
+0.0,0.0,0.007657423696883808,0.0,0.0,0.05601546016597905,0.06095143706640238,34.993,9/11/2023,Knoxville SMM Food,127
+0.0,0.0,0.0,0.012082336514998791,0.0,0.0,0.0,21.57,9/12/2022,Knoxville SMM Food,128
+0.0,0.0,0.0,0.0,0.0016899064672090188,0.0,0.0,22.64,9/13/2021,Knoxville SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.05153617443012884,23.653,9/18/2023,Knoxville SMM Food,130
+0.0,0.0,0.0,0.012594597987226747,0.0,0.0,0.0,25.34,9/19/2022,Knoxville SMM Food,131
+0.0,0.0,0.0,0.0,0.0013614510008517755,0.0,0.0,23.4,9/20/2021,Knoxville SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.055996035678889985,23.93,9/25/2023,Knoxville SMM Food,133
+0.0,0.0,0.0,0.014311428395501457,0.0,0.0,0.0,26.36,9/26/2022,Knoxville SMM Food,134
+0.0,0.0,0.0,0.0,0.0009154690964382679,0.0,0.0,25.75,9/27/2021,Knoxville SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.05302279484638256,27.206,9/4/2023,Knoxville SMM Food,136
+0.0,0.0,0.0,0.01264096049426039,0.0,0.0,0.0,23.09,9/5/2022,Knoxville SMM Food,137
+0.0,0.0,0.0,0.0,0.0013441313152434838,0.0,0.0,25.4,9/6/2021,Knoxville SMM Food,138
+0.0,0.0,0.0,0.0,0.0,0.0,0.05946481665014866,31.532,8/7/2023,Las Vegas SMM Food,124
+0.0,0.002099721240632628,0.0,0.0,0.003961259522696397,0.0,0.0,24.92,8/8/2022,Las Vegas SMM Food,125
+0.0,0.0,0.0,0.0,0.0010515523405034157,0.0,0.0,17.85,8/9/2021,Las Vegas SMM Food,126
+0.0,0.0,0.009369763221981868,0.0,0.0,0.08034252733962806,0.035678889990089196,34.959,9/11/2023,Las Vegas SMM Food,127
+0.0,0.0,0.0,0.0185917027305958,0.0,0.0,0.0,28.18,9/12/2022,Las Vegas SMM Food,128
+0.0,0.0,0.0,0.0,0.0012927908186189051,0.0,0.0,24.83,9/13/2021,Las Vegas SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.048562933597621406,33.115,9/18/2023,Las Vegas SMM Food,130
+0.0,0.0,0.0,0.019379945382538788,0.0,0.0,0.0,26.13,9/19/2022,Las Vegas SMM Food,131
+0.0,0.0,0.0,0.0,0.001155470454153165,0.0,0.0,24.17,9/20/2021,Las Vegas SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.05302279484638256,30.334,9/25/2023,Las Vegas SMM Food,133
+0.0,0.0,0.0,0.022021719233974155,0.0,0.0,0.0,25.26,9/26/2022,Las Vegas SMM Food,134
+0.0,0.0,0.0,0.0,0.0013317601112375612,0.0,0.0,24.46,9/27/2021,Las Vegas SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.04707631318136769,30.934,9/4/2023,Las Vegas SMM Food,136
+0.0,0.0,0.0,0.019451285727142276,0.0,0.0,0.0,26.34,9/5/2022,Las Vegas SMM Food,137
+0.0,0.0,0.0,0.0,0.0016472258133885859,0.0,0.0,23.98,9/6/2021,Las Vegas SMM Food,138
+0.0,0.0,0.0,0.0,0.0,0.0,0.0639246778989098,10.063,8/7/2023,Little Rock/Pine Bluff SMM Food,124
+0.0,0.0014186837323228979,0.0,0.0,0.006373025743650996,0.0,0.0,8.89,8/8/2022,Little Rock/Pine Bluff SMM Food,125
+0.0,0.0,0.0,0.0,0.001933619186125693,0.0,0.0,10.01,8/9/2021,Little Rock/Pine Bluff SMM Food,126
+0.0,0.0,0.0067615890956558185,0.0,0.0,0.04907078091276023,0.055004955401387515,10.863,9/11/2023,Little Rock/Pine Bluff SMM Food,127
+0.0,0.0,0.0,0.015617921237149364,0.0,0.0,0.0,9.69,9/12/2022,Little Rock/Pine Bluff SMM Food,128
+0.0,0.0,0.0,0.0,0.0014356782248873107,0.0,0.0,11.67,9/13/2021,Little Rock/Pine Bluff SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.062438057482656094,9.718,9/18/2023,Little Rock/Pine Bluff SMM Food,130
+0.0,0.0,0.0,0.01628008283607892,0.0,0.0,0.0,10.38,9/19/2022,Little Rock/Pine Bluff SMM Food,131
+0.0,0.0,0.0,0.0,0.0015482561813412057,0.0,0.0,9.39,9/20/2021,Little Rock/Pine Bluff SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.05252725470763132,9.803,9/25/2023,Little Rock/Pine Bluff SMM Food,133
+0.0,0.0,0.0,0.018499299465241616,0.0,0.0,0.0,9.19,9/26/2022,Little Rock/Pine Bluff SMM Food,134
+0.0,0.0,0.0,0.0,0.0015946481963634153,0.0,0.0,9.27,9/27/2021,Little Rock/Pine Bluff SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.04013875123885034,11.034,9/4/2023,Little Rock/Pine Bluff SMM Food,136
+0.0,0.0,0.0,0.016340012143976613,0.0,0.0,0.0,9.59,9/5/2022,Little Rock/Pine Bluff SMM Food,137
+0.0,0.0,0.0,0.0,0.0017777420156510687,0.0,0.0,11.85,9/6/2021,Little Rock/Pine Bluff SMM Food,138
+0.0,0.0,0.0,0.0,0.0,0.0,0.3062438057482656,114.624,8/7/2023,Los Angeles SMM Food,124
+0.0,0.019228478860571916,0.0,0.0,0.03378946950137623,0.0,0.0,106.07,8/8/2022,Los Angeles SMM Food,125
+0.0,0.0,0.0,0.0,0.010010778281592518,0.0,0.0,93.99,8/9/2021,Los Angeles SMM Food,126
+0.0,0.0,0.13303797876107476,0.0,0.0,0.3747884838535892,0.2522299306243806,131.228,9/11/2023,Los Angeles SMM Food,127
+0.0,0.0,0.0,0.10969678061239888,0.0,0.0,0.0,115.82,9/12/2022,Los Angeles SMM Food,128
+0.0,0.0,0.0,0.0,0.009888921922134182,0.0,0.0,119.97,9/13/2021,Los Angeles SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.24727452923686818,119.603,9/18/2023,Los Angeles SMM Food,130
+0.0,0.0,0.0,0.11434765534026742,0.0,0.0,0.0,109.13,9/19/2022,Los Angeles SMM Food,131
+0.0,0.0,0.0,0.0,0.00820272681612694,0.0,0.0,99.09,9/20/2021,Los Angeles SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.25966303270564917,114.482,9/25/2023,Los Angeles SMM Food,133
+0.0,0.0,0.0,0.12993493592533384,0.0,0.0,0.0,115.06,9/26/2022,Los Angeles SMM Food,134
+0.0,0.0,0.0,0.0,0.00794355009220286,0.0,0.0,98.9,9/27/2021,Los Angeles SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.2477700693756194,135.601,9/4/2023,Los Angeles SMM Food,136
+0.0,0.0,0.0,0.11476858539692965,0.0,0.0,0.0,107.08,9/5/2022,Los Angeles SMM Food,137
+0.0,0.0,0.0,0.0,0.008441491053441243,0.0,0.0,109.3,9/6/2021,Los Angeles SMM Food,138
+0.0,0.0,0.0,0.0,0.0,0.0,0.049554013875123884,5.978,8/7/2023,Madison WI SMM Food,124
+0.0,0.000593236234973785,0.0,0.0,0.0031558941419108397,0.0,0.0,5.08,8/8/2022,Madison WI SMM Food,125
+0.0,0.0,0.0,0.0,0.0008400047520021403,0.0,0.0,5.58,8/9/2021,Madison WI SMM Food,126
+0.0,0.0,0.004571583640934544,0.0,0.0,0.034073713711936085,0.037165510406342916,7.98,9/11/2023,Madison WI SMM Food,127
+0.0,0.0,0.0,0.00930365997973997,0.0,0.0,0.0,6.03,9/12/2022,Madison WI SMM Food,128
+0.0,0.0,0.0,0.0,0.0004911367990351248,0.0,0.0,8.3,9/13/2021,Madison WI SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.044598612487611496,7.618,9/18/2023,Madison WI SMM Food,130
+0.0,0.0,0.0,0.009698112368307879,0.0,0.0,0.0,6.79,9/19/2022,Madison WI SMM Food,131
+0.0,0.0,0.0,0.0,0.0004218580566019586,0.0,0.0,5.87,9/20/2021,Madison WI SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.03815659068384539,7.501,9/25/2023,Madison WI SMM Food,133
+0.0,0.0,0.0,0.011020108853925868,0.0,0.0,0.0,6.51,9/26/2022,Madison WI SMM Food,134
+0.0,0.0,0.0,0.0,0.0006612408541165596,0.0,0.0,5.48,9/27/2021,Madison WI SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.04509415262636274,8.164,9/4/2023,Madison WI SMM Food,136
+0.0,0.0,0.0,0.009733812499659315,0.0,0.0,0.0,5.28,9/5/2022,Madison WI SMM Food,137
+0.0,0.0,0.0,0.0,0.00041381677399810894,0.0,0.0,5.27,9/6/2021,Madison WI SMM Food,138
+0.0,0.005007849599907721,0.0,0.0,0.011139032086932654,0.0,0.0,265.02,8/8/2022,Miami/West Palm Beach SMM Food,125
+0.0,0.0,0.0,0.0,0.0028466140417627763,0.0,0.0,109.22,8/9/2021,Miami/West Palm Beach SMM Food,126
+0.0,0.0,0.018040328170654842,0.0,0.0,0.135383570276492,0.0867195242814668,117.213,9/11/2023,Miami/West Palm Beach SMM Food,127
+0.0,0.0,0.0,0.040042460444501625,0.0,0.0,0.0,103.86,9/12/2022,Miami/West Palm Beach SMM Food,128
+0.0,0.0,0.0,0.0,0.0025509422660212277,0.0,0.0,106.45,9/13/2021,Miami/West Palm Beach SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.08225966303270565,115.904,9/18/2023,Miami/West Palm Beach SMM Food,130
+0.0,0.0,0.0,0.04174016266482536,0.0,0.0,0.0,95.09,9/19/2022,Miami/West Palm Beach SMM Food,131
+0.0,0.0,0.0,0.0,0.002568261951629519,0.0,0.0,104.62,9/20/2021,Miami/West Palm Beach SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.07036669970267592,103.499,9/25/2023,Miami/West Palm Beach SMM Food,133
+0.0,0.0,0.0,0.04742996560650661,0.0,0.0,0.0,111.66,9/26/2022,Miami/West Palm Beach SMM Food,134
+0.0,0.0,0.0,0.0,0.0022886727410956695,0.0,0.0,102.39,9/27/2021,Miami/West Palm Beach SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.10158572844400396,146.302,9/4/2023,Miami/West Palm Beach SMM Food,136
+0.0,0.0,0.0,0.04189381415960767,0.0,0.0,0.0,147.87,9/5/2022,Miami/West Palm Beach SMM Food,137
+0.0,0.0,0.0,0.0,0.0027909436237361245,0.0,0.0,104.06,9/6/2021,Miami/West Palm Beach SMM Food,138
+0.0,0.0,0.0,0.0,0.0,0.0,0.0931615460852329,24.866,8/7/2023,Milwaukee SMM Food,124
+0.0,0.001717034769678263,0.0,0.0,0.009219639785413772,0.0,0.0,23.28,8/8/2022,Milwaukee SMM Food,125
+0.0,0.0,0.0,0.0,0.0018352681142786086,0.0,0.0,19.3,8/9/2021,Milwaukee SMM Food,126
+0.0,0.0,0.01275815316445043,0.0,0.0,0.08156726192538118,0.062438057482656094,25.297,9/11/2023,Milwaukee SMM Food,127
+0.0,0.0,0.0,0.021252502340492276,0.0,0.0,0.0,18.62,9/12/2022,Milwaukee SMM Food,128
+0.0,0.0,0.0,0.0,0.0014635134339006364,0.0,0.0,20.78,9/13/2021,Milwaukee SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.06689791873141725,28.503,9/18/2023,Milwaukee SMM Food,130
+0.0,0.0,0.0,0.02215355637305754,0.0,0.0,0.0,20.56,9/19/2022,Milwaukee SMM Food,131
+0.0,0.0,0.0,0.0,0.001509905448922846,0.0,0.0,18.05,9/20/2021,Milwaukee SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.08275520317145689,24.272,9/25/2023,Milwaukee SMM Food,133
+0.0,0.0,0.0,0.02517341451994022,0.0,0.0,0.0,19.51,9/26/2022,Milwaukee SMM Food,134
+0.0,0.0,0.0,0.0,0.0013719665242568095,0.0,0.0,20.33,9/27/2021,Milwaukee SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.07284440039643211,28.938,9/4/2023,Milwaukee SMM Food,136
+0.0,0.0,0.0,0.02223510677772291,0.0,0.0,0.0,25.25,9/5/2022,Milwaukee SMM Food,137
+0.0,0.0,0.0,0.0,0.001925577903521843,0.0,0.0,24.08,9/6/2021,Milwaukee SMM Food,138
+0.0,0.0,0.0,0.0,0.0,0.0,0.1709613478691774,37.06,8/7/2023,Minneapolis/St. Paul SMM Food,124
+0.0,0.0038046255712315823,0.0,0.0,0.00971510650585097,0.0,0.0,46.84,8/8/2022,Minneapolis/St. Paul SMM Food,125
+0.0,0.0,0.0,0.0,0.002077125152594394,0.0,0.0,41.5,8/9/2021,Minneapolis/St. Paul SMM Food,126
+0.0,0.0,0.03094870136244261,0.0,0.0,0.1252957809638225,0.17393458870168482,38.324,9/11/2023,Minneapolis/St. Paul SMM Food,127
+0.0,0.0,0.0,0.038681592342424166,0.0,0.0,0.0,51.68,9/12/2022,Minneapolis/St. Paul SMM Food,128
+0.0,0.0,0.0,0.0,0.001091758753522664,0.0,0.0,45.98,9/13/2021,Minneapolis/St. Paul SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.1570862239841427,40.978,9/18/2023,Minneapolis/St. Paul SMM Food,130
+0.0,0.0,0.0,0.0403215970895781,0.0,0.0,0.0,40.48,9/19/2022,Minneapolis/St. Paul SMM Food,131
+0.0,0.0,0.0,0.0,0.002432178707564371,0.0,0.0,49.56,9/20/2021,Minneapolis/St. Paul SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.15262636273538155,38.141,9/25/2023,Minneapolis/St. Paul SMM Food,133
+0.0,0.0,0.0,0.04581802852379454,0.0,0.0,0.0,38.38,9/26/2022,Minneapolis/St. Paul SMM Food,134
+0.0,0.0,0.0,0.0,0.0014257812616825726,0.0,0.0,47.81,9/27/2021,Minneapolis/St. Paul SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.1645193260654113,40.776,9/4/2023,Minneapolis/St. Paul SMM Food,136
+0.0,0.0,0.0,0.04047002664468756,0.0,0.0,0.0,44.99,9/5/2022,Minneapolis/St. Paul SMM Food,137
+0.0,0.0,0.0,0.0,0.0022967140236995194,0.0,0.0,43.4,9/6/2021,Minneapolis/St. Paul SMM Food,138
+0.0,0.0,0.0,0.0,0.0,0.0,0.06194251734390485,48.944,8/7/2023,Mobile/Pensacola SMM Food,124
+0.0,0.0007766369210538014,0.0,0.0,0.006063745643502932,0.0,0.0,31.02,8/8/2022,Mobile/Pensacola SMM Food,125
+0.0,0.0,0.0,0.0,0.0015507304221423902,0.0,0.0,18.44,8/9/2021,Mobile/Pensacola SMM Food,126
+0.0,0.0,0.007016034816117654,0.0,0.0,0.06153590867568924,0.05153617443012884,18.764,9/11/2023,Mobile/Pensacola SMM Food,127
+0.0,0.0,0.0,0.016447031490323782,0.0,0.0,0.0,17.84,9/12/2022,Mobile/Pensacola SMM Food,128
+0.0,0.0,0.0,0.0,0.001671349661200135,0.0,0.0,18.48,9/13/2021,Mobile/Pensacola SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.05054509415262636,17.263,9/18/2023,Mobile/Pensacola SMM Food,130
+0.0,0.0,0.0,0.01714434533067197,0.0,0.0,0.0,15.41,9/19/2022,Mobile/Pensacola SMM Food,131
+0.0,0.0,0.0,0.0,0.0012228935159854428,0.0,0.0,17.3,9/20/2021,Mobile/Pensacola SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.030723488602576808,16.84,9/25/2023,Mobile/Pensacola SMM Food,133
+0.0,0.0,0.0,0.019481373757693706,0.0,0.0,0.0,14.96,9/26/2022,Mobile/Pensacola SMM Food,134
+0.0,0.0,0.0,0.0,0.0012754711330106138,0.0,0.0,15.57,9/27/2021,Mobile/Pensacola SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.053518334985133795,21.946,9/4/2023,Mobile/Pensacola SMM Food,136
+0.0,0.0,0.0,0.017207456111133626,0.0,0.0,0.0,23.31,9/5/2022,Mobile/Pensacola SMM Food,137
+0.0,0.0,0.0,0.0,0.0013367085928399302,0.0,0.0,17.9,9/6/2021,Mobile/Pensacola SMM Food,138
+0.0,0.0,0.0,0.0,0.0,0.0,0.08572844400396432,77.43,8/7/2023,Nashville SMM Food,124
+0.0,0.0024515039739325646,0.0,0.0,0.011818829747058097,0.0,0.0,56.6,8/8/2022,Nashville SMM Food,125
+0.0,0.0,0.0,0.0,0.002948676474811637,0.0,0.0,41.75,8/9/2021,Nashville SMM Food,126
+0.0,0.0,0.018863584556427716,0.0,0.0,0.1291559227775106,0.09613478691774033,54.687,9/11/2023,Nashville SMM Food,127
+0.0,0.0,0.0,0.023131705316610268,0.0,0.0,0.0,43.39,9/12/2022,Nashville SMM Food,128
+0.0,0.0,0.0,0.0,0.0023907351741445306,0.0,0.0,39.43,9/13/2021,Nashville SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.10951437066402378,55.368,9/18/2023,Nashville SMM Food,130
+0.0,0.0,0.0,0.02411243295492175,0.0,0.0,0.0,41.52,9/19/2022,Nashville SMM Food,131
+0.0,0.0,0.0,0.0,0.0027346546455091774,0.0,0.0,42.92,9/20/2021,Nashville SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.09217046580773042,58.347,9/25/2023,Nashville SMM Food,133
+0.0,0.0,0.0,0.0273993150200572,0.0,0.0,0.0,48.74,9/26/2022,Nashville SMM Food,134
+0.0,0.0,0.0,0.0,0.0031305331736986982,0.0,0.0,43.99,9/27/2021,Nashville SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.08225966303270565,54.91,9/4/2023,Nashville SMM Food,136
+0.0,0.0,0.0,0.024201194256313775,0.0,0.0,0.0,43.7,9/5/2022,Nashville SMM Food,137
+0.0,0.0,0.0,0.0,0.002930119668802753,0.0,0.0,44.1,9/6/2021,Nashville SMM Food,138
+0.0,0.0,0.0,0.0,0.0,0.0,0.05004955401387512,10.449,8/7/2023,New Orleans SMM Food,124
+0.0,0.0011893606697283578,0.0,0.0,0.006126220223732841,0.0,0.0,13.23,8/8/2022,New Orleans SMM Food,125
+0.0,0.0,0.0,0.0,0.0021148573248124577,0.0,0.0,14.12,8/9/2021,New Orleans SMM Food,126
+0.0,0.0,0.007795828665891241,0.0,0.0,0.0697753770970809,0.040634291377601585,12.088,9/11/2023,New Orleans SMM Food,127
+0.0,0.0,0.0,0.01804932909865379,0.0,0.0,0.0,9.64,9/12/2022,New Orleans SMM Food,128
+0.0,0.0,0.0,0.0,0.0012334090393904772,0.0,0.0,24.18,9/13/2021,New Orleans SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.05698711595639247,10.331,9/18/2023,New Orleans SMM Food,130
+0.0,0.0,0.0,0.01881457643298289,0.0,0.0,0.0,9.9,9/19/2022,New Orleans SMM Food,131
+0.0,0.0,0.0,0.0,0.001212996552780705,0.0,0.0,34.03,9/20/2021,New Orleans SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.04162537165510406,11.492,9/25/2023,New Orleans SMM Food,133
+0.0,0.0,0.0,0.02137928211724966,0.0,0.0,0.0,8.96,9/26/2022,New Orleans SMM Food,134
+0.0,0.0,0.0,0.0,0.0007688703289680858,0.0,0.0,17.31,9/27/2021,New Orleans SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.04162537165510406,18.667,9/4/2023,New Orleans SMM Food,136
+0.0,0.0,0.0,0.018883835570947957,0.0,0.0,0.0,19.88,9/5/2022,New Orleans SMM Food,137
+0.0,0.0,0.0,0.0,0.001574235709753643,0.0,0.0,7.57,9/6/2021,New Orleans SMM Food,138
+0.0,0.0,0.0,0.0,0.0,0.0,0.4534192269573835,246.856,8/7/2023,New York SMM Food,124
+0.0,0.039101892732193715,0.0,0.0,0.0494452281708712,0.0,0.0,205.05,8/8/2022,New York SMM Food,125
+0.0,0.0,0.0,0.0,0.015521531106030714,0.0,0.0,234.8,8/9/2021,New York SMM Food,126
+0.0,0.0,0.24068159947386805,0.0,0.0,0.520680218910273,0.410802775024777,288.514,9/11/2023,New York SMM Food,127
+0.0,0.0,0.0,0.17120571402939075,0.0,0.0,0.0,254.52,9/12/2022,New York SMM Food,128
+0.0,0.0,0.0,0.0,0.013122136089082036,0.0,0.0,230.57,9/13/2021,New York SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.41873141724479684,509.862,9/18/2023,New York SMM Food,130
+0.0,0.0,0.0,0.1784644168917692,0.0,0.0,0.0,247.62,9/19/2022,New York SMM Food,131
+0.0,0.0,0.0,0.0,0.012023573173356115,0.0,0.0,230.26,9/20/2021,New York SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.42913776015857286,303.344,9/25/2023,New York SMM Food,133
+0.0,0.0,0.0,0.20279176256454842,0.0,0.0,0.0,260.65,9/26/2022,New York SMM Food,134
+0.0,0.0,0.0,0.0,0.011220682033371742,0.0,0.0,237.2,9/27/2021,New York SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.4474727452923687,274.76,9/4/2023,New York SMM Food,136
+0.0,0.0,0.0,0.17912136984330115,0.0,0.0,0.0,223.46,9/5/2022,New York SMM Food,137
+0.0,0.0,0.0,0.0,0.01324461100874067,0.0,0.0,236.95,9/6/2021,New York SMM Food,138
+0.0,0.0,0.0,0.0,0.0,0.0,0.07482656095143707,53.749,8/7/2023,Norfolk/Portsmouth/Newport News SMM Food,124
+0.0,0.0011087798958443822,0.0,0.0,0.008415511525028807,0.0,0.0,62.84,8/8/2022,Norfolk/Portsmouth/Newport News SMM Food,125
+0.0,0.0,0.0,0.0,0.00208578499539854,0.0,0.0,53.72,8/9/2021,Norfolk/Portsmouth/Newport News SMM Food,126
+0.0,0.0,0.010047019244106654,0.0,0.0,0.08559141854246288,0.062438057482656094,66.739,9/11/2023,Norfolk/Portsmouth/Newport News SMM Food,127
+0.0,0.0,0.0,0.015531940702298817,0.0,0.0,0.0,56.23,9/12/2022,Norfolk/Portsmouth/Newport News SMM Food,128
+0.0,0.0,0.0,0.0,0.0010960886749247368,0.0,0.0,57.58,9/13/2021,Norfolk/Portsmouth/Newport News SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.06045589692765114,57.374,9/18/2023,Norfolk/Portsmouth/Newport News SMM Food,130
+0.0,0.0,0.0,0.016190456939540337,0.0,0.0,0.0,55.17,9/19/2022,Norfolk/Portsmouth/Newport News SMM Food,131
+0.0,0.0,0.0,0.0,0.0014499051094941215,0.0,0.0,47.98,9/20/2021,Norfolk/Portsmouth/Newport News SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.06442021803766104,71.472,9/25/2023,Norfolk/Portsmouth/Newport News SMM Food,133
+0.0,0.0,0.0,0.01839745623572133,0.0,0.0,0.0,47.55,9/26/2022,Norfolk/Portsmouth/Newport News SMM Food,134
+0.0,0.0,0.0,0.0,0.0011863984641679714,0.0,0.0,51.74,9/27/2021,Norfolk/Portsmouth/Newport News SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.05797819623389494,49.663,9/4/2023,Norfolk/Portsmouth/Newport News SMM Food,136
+0.0,0.0,0.0,0.01625005632294706,0.0,0.0,0.0,49.62,9/5/2022,Norfolk/Portsmouth/Newport News SMM Food,137
+0.0,0.0,0.0,0.0,0.0014202142198799074,0.0,0.0,80.86,9/6/2021,Norfolk/Portsmouth/Newport News SMM Food,138
+0.0,0.0012719631834588849,0.0,0.0,0.004641057182821841,0.0,0.0,2.43,8/8/2022,Oklahoma City SMM Food,125
+0.0,0.0,0.0,0.0,0.001920629421919474,0.0,0.0,2.79,8/9/2021,Oklahoma City SMM Food,126
+0.0,0.0,0.008075170402119658,0.0,0.0,0.05973936106606689,0.0639246778989098,5.321,9/11/2023,Oklahoma City SMM Food,127
+0.0,0.0,0.0,0.017613566913413724,0.0,0.0,0.0,4.4,9/12/2022,Oklahoma City SMM Food,128
+0.0,0.0,0.0,0.0,0.0016657826193974697,0.0,0.0,5.31,9/13/2021,Oklahoma City SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.06739345887016848,4.506,9/18/2023,Oklahoma City SMM Food,130
+0.0,0.0,0.0,0.01836033899387075,0.0,0.0,0.0,3.98,9/19/2022,Oklahoma City SMM Food,131
+0.0,0.0,0.0,0.0,0.0015340292967343948,0.0,0.0,3.66,9/20/2021,Oklahoma City SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.06095143706640238,6.145,9/25/2023,Oklahoma City SMM Food,133
+0.0,0.0,0.0,0.02086312538457761,0.0,0.0,0.0,3.72,9/26/2022,Oklahoma City SMM Food,134
+0.0,0.0,0.0,0.0,0.0010830989107185184,0.0,0.0,4.46,9/27/2021,Oklahoma City SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.06541129831516353,8.982,9/4/2023,Oklahoma City SMM Food,136
+0.0,0.0,0.0,0.018427926025991807,0.0,0.0,0.0,2.67,9/5/2022,Oklahoma City SMM Food,137
+0.0,0.0,0.0,0.0,0.002021454734567743,0.0,0.0,2.99,9/6/2021,Oklahoma City SMM Food,138
+0.0,0.0,0.0,0.0,0.0,0.0,0.04311199207135778,12.325,8/7/2023,Omaha SMM Food,124
+0.0,0.0009476183480764309,0.0,0.0,0.003898166382266192,0.0,0.0,15.49,8/8/2022,Omaha SMM Food,125
+0.0,0.0,0.0,0.0,0.000536291693656742,0.0,0.0,12.09,8/9/2021,Omaha SMM Food,126
+0.0,0.0,0.00745909950348901,0.0,0.0,0.04438741589312105,0.049554013875123884,13.324,9/11/2023,Omaha SMM Food,127
+0.0,0.0,0.0,0.009233480542505387,0.0,0.0,0.0,11.97,9/12/2022,Omaha SMM Food,128
+0.0,0.0,0.0,0.0,0.0005041265632413435,0.0,0.0,13.54,9/13/2021,Omaha SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.06045589692765114,12.73,9/18/2023,Omaha SMM Food,130
+0.0,0.0,0.0,0.0096249574987297,0.0,0.0,0.0,11.64,9/19/2022,Omaha SMM Food,131
+0.0,0.0,0.0,0.0,0.0006804162203257396,0.0,0.0,14.6,9/20/2021,Omaha SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.04558969276511397,13.377,9/25/2023,Omaha SMM Food,133
+0.0,0.0,0.0,0.010936981891184227,0.0,0.0,0.0,11.62,9/26/2022,Omaha SMM Food,134
+0.0,0.0,0.0,0.0,0.0004911367990351248,0.0,0.0,12.9,9/27/2021,Omaha SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.05004955401387512,14.921,9/4/2023,Omaha SMM Food,136
+0.0,0.0,0.0,0.00966038834039735,0.0,0.0,0.0,12.01,9/5/2022,Omaha SMM Food,137
+0.0,0.0,0.0,0.0,0.000555467059865922,0.0,0.0,13.82,9/6/2021,Omaha SMM Food,138
+0.0,0.0,0.0,0.0,0.0,0.0,0.15807730426164518,296.409,8/7/2023,Orlando/Daytona Beach/Melborne SMM Food,124
+0.0,0.0035978304669271864,0.0,0.0,0.014912249308739028,0.0,0.0,177.13,8/8/2022,Orlando/Daytona Beach/Melborne SMM Food,125
+0.0,0.0,0.0,0.0,0.005805187479779151,0.0,0.0,65.31,8/9/2021,Orlando/Daytona Beach/Melborne SMM Food,126
+0.0,0.0,0.024174453275719036,0.0,0.0,0.18581466537428856,0.1238850346878097,77.936,9/11/2023,Orlando/Daytona Beach/Melborne SMM Food,127
+0.0,0.0,0.0,0.04488872730475567,0.0,0.0,0.0,68.78,9/12/2022,Orlando/Daytona Beach/Melborne SMM Food,128
+0.0,0.0,0.0,0.0,0.003562906753705691,0.0,0.0,66.88,9/13/2021,Orlando/Daytona Beach/Melborne SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.13627353815659068,75.008,9/18/2023,Orlando/Daytona Beach/Melborne SMM Food,130
+0.0,0.0,0.0,0.046791899365486236,0.0,0.0,0.0,58.18,9/19/2022,Orlando/Daytona Beach/Melborne SMM Food,131
+0.0,0.0,0.0,0.0,0.003940847036086625,0.0,0.0,59.08,9/20/2021,Orlando/Daytona Beach/Melborne SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.11149653121902874,68.939,9/25/2023,Orlando/Daytona Beach/Melborne SMM Food,133
+0.0,0.0,0.0,0.053170328912353564,0.0,0.0,0.0,66.79,9/26/2022,Orlando/Daytona Beach/Melborne SMM Food,134
+0.0,0.0,0.0,0.0,0.004718377207858856,0.0,0.0,57.95,9/27/2021,Orlando/Daytona Beach/Melborne SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.17244796828543113,108.309,9/4/2023,Orlando/Daytona Beach/Melborne SMM Food,136
+0.0,0.0,0.0,0.0469641470254011,0.0,0.0,0.0,84.61,9/5/2022,Orlando/Daytona Beach/Melborne SMM Food,137
+0.0,0.0,0.0,0.0,0.004595902288200223,0.0,0.0,60.35,9/6/2021,Orlando/Daytona Beach/Melborne SMM Food,138
+0.0,0.0,0.0,0.0,0.0,0.0,0.028245787908820614,6.148,8/7/2023,Paducah KY/Cape Girardeau MO SMM Food,124
+0.0,0.00036420199235732373,0.0,0.0,0.0035888862821181287,0.0,0.0,5.95,8/8/2022,Paducah KY/Cape Girardeau MO SMM Food,125
+0.0,0.0,0.0,0.0,0.0016602155775948047,0.0,0.0,6.97,8/9/2021,Paducah KY/Cape Girardeau MO SMM Food,126
+0.0,0.0,0.0033689794894980066,0.0,0.0,0.02940059048808853,0.036669970267591674,5.015,9/11/2023,Paducah KY/Cape Girardeau MO SMM Food,127
+0.0,0.0,0.0,0.008585794855104294,0.0,0.0,0.0,5.9,9/12/2022,Paducah KY/Cape Girardeau MO SMM Food,128
+0.0,0.0,0.0,0.0,0.0006470139695097488,0.0,0.0,5.93,9/13/2021,Paducah KY/Cape Girardeau MO SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.0421209117938553,5.725,9/18/2023,Paducah KY/Cape Girardeau MO SMM Food,130
+0.0,0.0,0.0,0.008949811523566999,0.0,0.0,0.0,4.53,9/19/2022,Paducah KY/Cape Girardeau MO SMM Food,131
+0.0,0.0,0.0,0.0,0.001383719168062436,0.0,0.0,5.28,9/20/2021,Paducah KY/Cape Girardeau MO SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.03815659068384539,4.811,9/25/2023,Paducah KY/Cape Girardeau MO SMM Food,133
+0.0,0.0,0.0,0.010169803507137157,0.0,0.0,0.0,6.14,9/26/2022,Paducah KY/Cape Girardeau MO SMM Food,134
+0.0,0.0,0.0,0.0,0.0010738205077140764,0.0,0.0,5.48,9/27/2021,Paducah KY/Cape Girardeau MO SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.024777006937561942,6.809,9/4/2023,Paducah KY/Cape Girardeau MO SMM Food,136
+0.0,0.0,0.0,0.008982757054847872,0.0,0.0,0.0,7.88,9/5/2022,Paducah KY/Cape Girardeau MO SMM Food,137
+0.0,0.0,0.0,0.0,0.0006903131835304776,0.0,0.0,7.07,9/6/2021,Paducah KY/Cape Girardeau MO SMM Food,138
+0.0,0.0,0.0,0.0,0.0,0.0,0.2522299306243806,136.614,8/7/2023,Philadelphia SMM Food,124
+0.0,0.006726039649498299,0.0,0.0,0.0256807638356943,0.0,0.0,135.86,8/8/2022,Philadelphia SMM Food,125
+0.0,0.0,0.0,0.0,0.007499423868390242,0.0,0.0,130.86,8/9/2021,Philadelphia SMM Food,126
+0.0,0.0,0.04194430100389289,0.0,0.0,0.2407575070389005,0.21754212091179384,175.883,9/11/2023,Philadelphia SMM Food,127
+0.0,0.0,0.0,0.06751901436081623,0.0,0.0,0.0,157.21,9/12/2022,Philadelphia SMM Food,128
+0.0,0.0,0.0,0.0,0.006012405146878353,0.0,0.0,149.83,9/13/2021,Philadelphia SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.20416253716551042,235.367,9/18/2023,Philadelphia SMM Food,130
+0.0,0.0,0.0,0.07038165514606878,0.0,0.0,0.0,153.26,9/19/2022,Philadelphia SMM Food,131
+0.0,0.0,0.0,0.0,0.0061806535213589,0.0,0.0,146.17,9/20/2021,Philadelphia SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.2358771060455897,157.971,9/25/2023,Philadelphia SMM Food,133
+0.0,0.0,0.0,0.07997571812552183,0.0,0.0,0.0,135.14,9/26/2022,Philadelphia SMM Food,134
+0.0,0.0,0.0,0.0,0.00549714450003168,0.0,0.0,145.33,9/27/2021,Philadelphia SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.2309217046580773,165.02,9/4/2023,Philadelphia SMM Food,136
+0.0,0.0,0.0,0.07064074003892257,0.0,0.0,0.0,151.89,9/5/2022,Philadelphia SMM Food,137
+0.0,0.0,0.0,0.0,0.0056214751002912015,0.0,0.0,155.51,9/6/2021,Philadelphia SMM Food,138
+0.0,0.0,0.0,0.0,0.0,0.0,0.17195242814667988,79.849,8/7/2023,Phoenix/Prescott SMM Food,124
+0.0,0.0,0.0,0.0,0.014418019708702422,0.0,0.0,66.86,8/8/2022,Phoenix/Prescott SMM Food,125
+0.0,0.0,0.0,0.0,0.005271370026923594,0.0,0.0,46.12,8/9/2021,Phoenix/Prescott SMM Food,126
+0.0,0.0,0.0,0.0,0.0,0.001808959402470655,0.13627353815659068,80.322,9/11/2023,Phoenix/Prescott SMM Food,127
+0.0,0.0,0.0,0.036806299803701564,0.0,0.0,0.0,73.57,9/12/2022,Phoenix/Prescott SMM Food,128
+0.0,0.0,0.0,0.0,0.0034243492688393585,0.0,0.0,56.9,9/13/2021,Phoenix/Prescott SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.1630327056491576,72.968,9/18/2023,Phoenix/Prescott SMM Food,130
+0.0,0.0,0.0,0.0383667967368777,0.0,0.0,0.0,67.09,9/19/2022,Phoenix/Prescott SMM Food,131
+0.0,0.0,0.0,0.0,0.002687025510086375,0.0,0.0,54.17,9/20/2021,Phoenix/Prescott SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.1367690782953419,71.044,9/25/2023,Phoenix/Prescott SMM Food,133
+0.0,0.0,0.0,0.04359675990047249,0.0,0.0,0.0,75.15,9/26/2022,Phoenix/Prescott SMM Food,134
+0.0,0.0,0.0,0.0,0.0027371288863103616,0.0,0.0,54.23,9/27/2021,Phoenix/Prescott SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.14866204162537167,80.716,9/4/2023,Phoenix/Prescott SMM Food,136
+0.0,0.0,0.0,0.038508030393034846,0.0,0.0,0.0,67.72,9/5/2022,Phoenix/Prescott SMM Food,137
+0.0,0.0,0.0,0.0,0.0037181653639800187,0.0,0.0,56.91,9/6/2021,Phoenix/Prescott SMM Food,138
+0.0,0.0022663703679840757,0.0,0.0,0.00748705266438432,0.0,0.0,50.92,8/8/2022,Pittsburgh SMM Food,125
+0.0,0.0,0.0,0.0,0.003074244195471751,0.0,0.0,59.65,8/9/2021,Pittsburgh SMM Food,126
+0.0,0.0,0.009616613547803052,0.0,0.0,0.0779249674907126,0.11050545094152626,57.215,9/11/2023,Pittsburgh SMM Food,127
+0.0,0.0,0.0,0.025851136933559334,0.0,0.0,0.0,57.81,9/12/2022,Pittsburgh SMM Food,128
+0.0,0.0,0.0,0.0,0.002534859700813528,0.0,0.0,64.84,9/13/2021,Pittsburgh SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.10604558969276512,50.418,9/18/2023,Pittsburgh SMM Food,130
+0.0,0.0,0.0,0.026947161800410718,0.0,0.0,0.0,49.22,9/19/2022,Pittsburgh SMM Food,131
+0.0,0.0,0.0,0.0,0.002456921115576216,0.0,0.0,55.17,9/20/2021,Pittsburgh SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.10059464816650149,54.89,9/25/2023,Pittsburgh SMM Food,133
+0.0,0.0,0.0,0.030620459431281187,0.0,0.0,0.0,46.36,9/26/2022,Pittsburgh SMM Food,134
+0.0,0.0,0.0,0.0,0.002446405592171182,0.0,0.0,58.95,9/27/2021,Pittsburgh SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.08870168483647176,59.663,9/4/2023,Pittsburgh SMM Food,136
+0.0,0.0,0.0,0.027046358148388426,0.0,0.0,0.0,56.47,9/5/2022,Pittsburgh SMM Food,137
+0.0,0.0,0.0,0.0,0.0023140337093078105,0.0,0.0,57.77,9/6/2021,Pittsburgh SMM Food,138
+0.0,0.0,0.0,0.0,0.0,0.0,0.10257680872150644,31.759,8/7/2023,Portland OR SMM Food,124
+0.0,0.002691513375716019,0.0,0.0,0.007488289784784913,0.0,0.0,32.69,8/8/2022,Portland OR SMM Food,125
+0.0,0.0,0.0,0.0,0.0017870204186555107,0.0,0.0,36.91,8/9/2021,Portland OR SMM Food,126
+0.0,0.0,0.020675086178223175,0.0,0.0,0.10859833173904791,0.09613478691774033,41.43,9/11/2023,Portland OR SMM Food,127
+0.0,0.0,0.0,0.025917320120911743,0.0,0.0,0.0,37.08,9/12/2022,Portland OR SMM Food,128
+0.0,0.0,0.0,0.0,0.0024290859065628904,0.0,0.0,40.37,9/13/2021,Portland OR SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.11050545094152626,36.716,9/18/2023,Portland OR SMM Food,130
+0.0,0.0,0.0,0.02701615099834603,0.0,0.0,0.0,34.13,9/19/2022,Portland OR SMM Food,131
+0.0,0.0,0.0,0.0,0.0006643336551180404,0.0,0.0,39.78,9/20/2021,Portland OR SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.099603567888999,39.153,9/25/2023,Portland OR SMM Food,133
+0.0,0.0,0.0,0.030698852877443856,0.0,0.0,0.0,34.55,9/26/2022,Portland OR SMM Food,134
+0.0,0.0,0.0,0.0,0.0015080497683219573,0.0,0.0,36.01,9/27/2021,Portland OR SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.10356788899900891,45.084,9/4/2023,Portland OR SMM Food,136
+0.0,0.0,0.0,0.027115601306424007,0.0,0.0,0.0,32.45,9/5/2022,Portland OR SMM Food,137
+0.0,0.0,0.0,0.0,0.0023134151491075146,0.0,0.0,41.15,9/6/2021,Portland OR SMM Food,138
+0.0,0.0,0.0,0.0,0.0,0.0,0.05153617443012884,35.059,8/7/2023,Providence RI/New Bedford MA SMM Food,124
+0.0,0.0014989756862287948,0.0,0.0,0.006621068383969743,0.0,0.0,36.29,8/8/2022,Providence RI/New Bedford MA SMM Food,125
+0.0,0.0,0.0,0.0,0.0017084632732179026,0.0,0.0,35.9,8/9/2021,Providence RI/New Bedford MA SMM Food,126
+0.0,0.0,0.005980529346775456,0.0,0.0,0.05103785298168852,0.05004955401387512,39.575,9/11/2023,Providence RI/New Bedford MA SMM Food,127
+0.0,0.0,0.0,0.016572692519385028,0.0,0.0,0.0,47.61,9/12/2022,Providence RI/New Bedford MA SMM Food,128
+0.0,0.0,0.0,0.0,0.0012748525728103176,0.0,0.0,36.32,9/13/2021,Providence RI/New Bedford MA SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.05450941526263627,46.736,9/18/2023,Providence RI/New Bedford MA SMM Food,130
+0.0,0.0,0.0,0.01727533407353234,0.0,0.0,0.0,43.68,9/19/2022,Providence RI/New Bedford MA SMM Food,131
+0.0,0.0,0.0,0.0,0.0017511439270383351,0.0,0.0,32.15,9/20/2021,Providence RI/New Bedford MA SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.053518334985133795,44.89,9/25/2023,Providence RI/New Bedford MA SMM Food,133
+0.0,0.0,0.0,0.01963021820698563,0.0,0.0,0.0,43.55,9/26/2022,Providence RI/New Bedford MA SMM Food,134
+0.0,0.0,0.0,0.0,0.0016719682214004312,0.0,0.0,32.14,9/27/2021,Providence RI/New Bedford MA SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.052031714568880075,36.566,9/4/2023,Providence RI/New Bedford MA SMM Food,136
+0.0,0.0,0.0,0.017338927045711716,0.0,0.0,0.0,40.72,9/5/2022,Providence RI/New Bedford MA SMM Food,137
+0.0,0.0,0.0,0.0,0.0022255796006654645,0.0,0.0,34.4,9/6/2021,Providence RI/New Bedford MA SMM Food,138
+0.0,0.0,0.0,0.0,0.0,0.0,0.10208126858275521,74.844,8/7/2023,Raleigh/Durham/Fayetteville SMM Food,124
+0.0,0.002229401410789994,0.0,0.0,0.014169977068383676,0.0,0.0,95.13,8/8/2022,Raleigh/Durham/Fayetteville SMM Food,125
+0.0,0.0,0.0,0.0,0.0034323905514432084,0.0,0.0,67.86,8/9/2021,Raleigh/Durham/Fayetteville SMM Food,126
+0.0,0.0,0.017138164073892976,0.0,0.0,0.12866985361914052,0.09217046580773042,92.348,9/11/2023,Raleigh/Durham/Fayetteville SMM Food,127
+0.0,0.0,0.0,0.03276343729674159,0.0,0.0,0.0,84.3,9/12/2022,Raleigh/Durham/Fayetteville SMM Food,128
+0.0,0.0,0.0,0.0,0.0024866120051904306,0.0,0.0,74.59,9/13/2021,Raleigh/Durham/Fayetteville SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.09464816650148662,87.201,9/18/2023,Raleigh/Durham/Fayetteville SMM Food,130
+0.0,0.0,0.0,0.034152526763903276,0.0,0.0,0.0,76.26,9/19/2022,Raleigh/Durham/Fayetteville SMM Food,131
+0.0,0.0,0.0,0.0,0.0025843445168372186,0.0,0.0,62.15,9/20/2021,Raleigh/Durham/Fayetteville SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.09266600594648167,108.314,9/25/2023,Raleigh/Durham/Fayetteville SMM Food,133
+0.0,0.0,0.0,0.03880802245868132,0.0,0.0,0.0,60.35,9/26/2022,Raleigh/Durham/Fayetteville SMM Food,134
+0.0,0.0,0.0,0.0,0.00243712718916674,0.0,0.0,64.26,9/27/2021,Raleigh/Durham/Fayetteville SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.07730426164519326,60.95,9/4/2023,Raleigh/Durham/Fayetteville SMM Food,136
+0.0,0.0,0.0,0.03427824709389885,0.0,0.0,0.0,65.5,9/5/2022,Raleigh/Durham/Fayetteville SMM Food,137
+0.0,0.0,0.0,0.0,0.0019868153633511593,0.0,0.0,98.44,9/6/2021,Raleigh/Durham/Fayetteville SMM Food,138
+0.0,0.0,0.0,0.0,0.0,0.0,0.6694747274529237,288.005,8/7/2023,Rem US East North Central SMM Food,124
+0.0,0.010814575259181308,0.0,0.0,0.07214515040133847,0.0,0.0,308.06,8/8/2022,Rem US East North Central SMM Food,125
+0.0,0.0,0.0,0.0,0.02009021674541791,0.0,0.0,247.25,8/9/2021,Rem US East North Central SMM Food,126
+0.0,0.0,0.07424624851147829,0.0,0.0,0.5637364309077629,0.6283448959365708,263.553,9/11/2023,Rem US East North Central SMM Food,127
+0.0,0.0,0.0,0.1610144344327281,0.0,0.0,0.0,240.58,9/12/2022,Rem US East North Central SMM Food,128
+0.0,0.0,0.0,0.0,0.016926281320903215,0.0,0.0,240.68,9/13/2021,Rem US East North Central SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.5926660059464817,298.802,9/18/2023,Rem US East North Central SMM Food,130
+0.0,0.0,0.0,0.16784105194697213,0.0,0.0,0.0,230.68,9/19/2022,Rem US East North Central SMM Food,131
+0.0,0.0,0.0,0.0,0.016729579177209047,0.0,0.0,202.81,9/20/2021,Rem US East North Central SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.5812685827552032,297.755,9/25/2023,Rem US East North Central SMM Food,133
+0.0,0.0,0.0,0.1907202755058764,0.0,0.0,0.0,247.8,9/26/2022,Rem US East North Central SMM Food,134
+0.0,0.0,0.0,0.0,0.014327709919459187,0.0,0.0,202.26,9/27/2021,Rem US East North Central SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.5827552031714569,348.214,9/4/2023,Rem US East North Central SMM Food,136
+0.0,0.0,0.0,0.16845889879914241,0.0,0.0,0.0,298.88,9/5/2022,Rem US East North Central SMM Food,137
+0.0,0.0,0.0,0.0,0.015420087233182148,0.0,0.0,258.8,9/6/2021,Rem US East North Central SMM Food,138
+0.0,0.0,0.0,0.0,0.0,0.0,0.28344895936570863,84.899,8/7/2023,Rem US Middle Atlantic SMM Food,124
+0.0,0.004143700225496054,0.0,0.0,0.0231415742134787,0.0,0.0,71.81,8/8/2022,Rem US Middle Atlantic SMM Food,125
+0.0,0.0,0.0,0.0,0.006617357022767966,0.0,0.0,78.23,8/9/2021,Rem US Middle Atlantic SMM Food,126
+0.0,0.0,0.028037555383228337,0.0,0.0,0.20463726833171583,0.267591674925669,91.266,9/11/2023,Rem US Middle Atlantic SMM Food,127
+0.0,0.0,0.0,0.05392961586570856,0.0,0.0,0.0,79.8,9/12/2022,Rem US Middle Atlantic SMM Food,128
+0.0,0.0,0.0,0.0,0.005358587015165347,0.0,0.0,82.84,9/13/2021,Rem US Middle Atlantic SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.2968285431119921,95.654,9/18/2023,Rem US Middle Atlantic SMM Food,130
+0.0,0.0,0.0,0.056216099457233604,0.0,0.0,0.0,79.34,9/19/2022,Rem US Middle Atlantic SMM Food,131
+0.0,0.0,0.0,0.0,0.005923332478035712,0.0,0.0,79.18,9/20/2021,Rem US Middle Atlantic SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.288404360753221,92.07,9/25/2023,Rem US Middle Atlantic SMM Food,133
+0.0,0.0,0.0,0.06387918718332888,0.0,0.0,0.0,76.85,9/26/2022,Rem US Middle Atlantic SMM Food,134
+0.0,0.0,0.0,0.0,0.004886007022139106,0.0,0.0,78.62,9/27/2021,Rem US Middle Atlantic SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.25421209117938554,92.244,9/4/2023,Rem US Middle Atlantic SMM Food,136
+0.0,0.0,0.0,0.05642303894016692,0.0,0.0,0.0,90.03,9/5/2022,Rem US Middle Atlantic SMM Food,137
+0.0,0.0,0.0,0.0,0.005541680834453002,0.0,0.0,88.71,9/6/2021,Rem US Middle Atlantic SMM Food,138
+0.0,0.0,0.0,0.0,0.0,0.0,0.3865213082259663,129.148,8/7/2023,Rem US Mountain SMM Food,124
+0.0,0.00753329148822845,0.0,0.0,0.02747273273595218,0.0,0.0,113.3,8/8/2022,Rem US Mountain SMM Food,125
+0.0,0.0,0.0,0.0,0.007616950306446507,0.0,0.0,115.63,8/9/2021,Rem US Mountain SMM Food,126
+0.0,0.0,0.023499729051807798,0.0,0.0,0.3230963384624684,0.3295341922695738,135.595,9/11/2023,Rem US Mountain SMM Food,127
+0.0,0.0,0.0,0.08969502904489553,0.0,0.0,0.0,124.27,9/12/2022,Rem US Mountain SMM Food,128
+0.0,0.0,0.0,0.0,0.005267040105521521,0.0,0.0,115.0,9/13/2021,Rem US Mountain SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.3637264618434093,137.112,9/18/2023,Rem US Mountain SMM Food,130
+0.0,0.0,0.0,0.09349787851239232,0.0,0.0,0.0,126.58,9/19/2022,Rem US Mountain SMM Food,131
+0.0,0.0,0.0,0.0,0.004318168758267262,0.0,0.0,118.66,9/20/2021,Rem US Mountain SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.3399405351833498,137.733,9/25/2023,Rem US Mountain SMM Food,133
+0.0,0.0,0.0,0.10624302537960408,0.0,0.0,0.0,125.46,9/26/2022,Rem US Mountain SMM Food,134
+0.0,0.0,0.0,0.0,0.0057761151503652325,0.0,0.0,115.09,9/27/2021,Rem US Mountain SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.31714568880079286,153.818,9/4/2023,Rem US Mountain SMM Food,136
+0.0,0.0,0.0,0.09384205752517928,0.0,0.0,0.0,123.49,9/5/2022,Rem US Mountain SMM Food,137
+0.0,0.0,0.0,0.0,0.005829929887790996,0.0,0.0,119.33,9/6/2021,Rem US Mountain SMM Food,138
+0.0,0.0,0.0,0.0,0.0,0.0,0.14816650148662042,95.632,8/7/2023,Rem US New England SMM Food,124
+0.0,0.0027053767346638,0.0,0.0,0.012870382087561513,0.0,0.0,92.11,8/8/2022,Rem US New England SMM Food,125
+0.0,0.0,0.0,0.0,0.0037107426415764655,0.0,0.0,93.54,8/9/2021,Rem US New England SMM Food,126
+0.0,0.0,0.016692145621939145,0.0,0.0,0.10162023020001978,0.14717542120911795,96.91,9/11/2023,Rem US New England SMM Food,127
+0.0,0.0,0.0,0.02729342543083433,0.0,0.0,0.0,108.12,9/12/2022,Rem US New England SMM Food,128
+0.0,0.0,0.0,0.0,0.003017336657044507,0.0,0.0,105.63,9/13/2021,Rem US New England SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.14568880079286423,104.654,9/18/2023,Rem US New England SMM Food,130
+0.0,0.0,0.0,0.028450599800614283,0.0,0.0,0.0,107.99,9/19/2022,Rem US New England SMM Food,131
+0.0,0.0,0.0,0.0,0.001866196124293415,0.0,0.0,103.16,9/20/2021,Rem US New England SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.1377601585728444,103.237,9/25/2023,Rem US New England SMM Food,133
+0.0,0.0,0.0,0.032328838321626044,0.0,0.0,0.0,110.46,9/26/2022,Rem US New England SMM Food,134
+0.0,0.0,0.0,0.0,0.002290528421696558,0.0,0.0,93.03,9/27/2021,Rem US New England SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.13181367690782952,95.746,9/4/2023,Rem US New England SMM Food,136
+0.0,0.0,0.0,0.028555330519776528,0.0,0.0,0.0,94.74,9/5/2022,Rem US New England SMM Food,137
+0.0,0.0,0.0,0.0,0.0022682602544858974,0.0,0.0,92.71,9/6/2021,Rem US New England SMM Food,138
+0.0,0.006359527097316345,0.0,0.0,0.02227744561366501,0.0,0.0,56.31,8/8/2022,Rem US Pacific SMM Food,125
+0.0,0.0,0.0,0.0,0.006462098412493638,0.0,0.0,52.58,8/9/2021,Rem US Pacific SMM Food,126
+0.0,0.0,0.04350177887159544,0.0,0.0,0.2505022822201761,0.26957383548067393,68.969,9/11/2023,Rem US Pacific SMM Food,127
+0.0,0.0,0.0,0.07303394106363369,0.0,0.0,0.0,64.99,9/12/2022,Rem US Pacific SMM Food,128
+0.0,0.0,0.0,0.0,0.005756939784156053,0.0,0.0,60.43,9/13/2021,Rem US Pacific SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.2755203171456888,59.672,9/18/2023,Rem US Pacific SMM Food,130
+0.0,0.0,0.0,0.07613040119907764,0.0,0.0,0.0,59.55,9/19/2022,Rem US Pacific SMM Food,131
+0.0,0.0,0.0,0.0,0.006201684568168969,0.0,0.0,50.8,9/20/2021,Rem US Pacific SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.25173439048562934,59.2,9/25/2023,Rem US Pacific SMM Food,133
+0.0,0.0,0.0,0.08650810352908421,0.0,0.0,0.0,58.94,9/26/2022,Rem US Pacific SMM Food,134
+0.0,0.0,0.0,0.0,0.00586766206000906,0.0,0.0,56.23,9/27/2021,Rem US Pacific SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.2512388503468781,68.417,9/4/2023,Rem US Pacific SMM Food,136
+0.0,0.0,0.0,0.07641064808426322,0.0,0.0,0.0,64.25,9/5/2022,Rem US Pacific SMM Food,137
+0.0,0.0,0.0,0.0,0.006398386711863137,0.0,0.0,56.78,9/6/2021,Rem US Pacific SMM Food,138
+0.0,0.0,0.0,0.0,0.0,0.0,0.6902874132804757,361.601,8/7/2023,Rem US South Atlantic SMM Food,124
+0.0,0.012989967334070573,0.0,0.0,0.07146720842181392,0.0,0.0,306.04,8/8/2022,Rem US South Atlantic SMM Food,125
+0.0,0.0,0.0,0.0,0.021449812065668792,0.0,0.0,216.74,8/9/2021,Rem US South Atlantic SMM Food,126
+0.0,0.0,0.07827180767102376,0.0,0.0,0.7032422127774433,0.5802775024777007,251.083,9/11/2023,Rem US South Atlantic SMM Food,127
+0.0,0.0,0.0,0.17196903338655478,0.0,0.0,0.0,233.99,9/12/2022,Rem US South Atlantic SMM Food,128
+0.0,0.0,0.0,0.0,0.015825244164376112,0.0,0.0,221.21,9/13/2021,Rem US South Atlantic SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.6040634291377601,232.236,9/18/2023,Rem US South Atlantic SMM Food,130
+0.0,0.0,0.0,0.17926009905695495,0.0,0.0,0.0,215.08,9/19/2022,Rem US South Atlantic SMM Food,131
+0.0,0.0,0.0,0.0,0.017987112064411077,0.0,0.0,197.7,9/20/2021,Rem US South Atlantic SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.5708622398414271,309.873,9/25/2023,Rem US South Atlantic SMM Food,133
+0.0,0.0,0.0,0.20369590803657825,0.0,0.0,0.0,210.58,9/26/2022,Rem US South Atlantic SMM Food,134
+0.0,0.0,0.0,0.0,0.014305441752248528,0.0,0.0,213.12,9/27/2021,Rem US South Atlantic SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.5802775024777007,234.028,9/4/2023,Rem US South Atlantic SMM Food,136
+0.0,0.0,0.0,0.17991998108708984,0.0,0.0,0.0,215.63,9/5/2022,Rem US South Atlantic SMM Food,137
+0.0,0.0,0.0,0.0,0.01699123014193431,0.0,0.0,279.36,9/6/2021,Rem US South Atlantic SMM Food,138
+0.0,0.0,0.0,0.0,0.0,0.0,0.9940535183349851,402.383,8/7/2023,Rem US South Central SMM Food,124
+0.0,0.014466126242031146,0.0,0.0,0.09628013229649275,0.0,0.0,349.19,8/8/2022,Rem US South Central SMM Food,125
+0.0,0.0,0.0,0.0,0.026810254761435028,0.0,0.0,321.57,8/9/2021,Rem US South Central SMM Food,126
+0.0,0.0,0.0887623135688707,0.0,0.0,0.8841922317358683,0.8275520317145688,406.505,9/11/2023,Rem US South Central SMM Food,127
+0.0,0.0,0.0,0.23375662511916412,0.0,0.0,0.0,389.86,9/12/2022,Rem US South Central SMM Food,128
+0.0,0.0,0.0,0.0,0.023796629465592297,0.0,0.0,338.41,9/13/2021,Rem US South Central SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.9340931615460852,374.323,9/18/2023,Rem US South Central SMM Food,130
+0.0,0.0,0.0,0.24366733338763769,0.0,0.0,0.0,410.27,9/19/2022,Rem US South Central SMM Food,131
+0.0,0.0,0.0,0.0,0.023312915388960728,0.0,0.0,322.93,9/20/2021,Rem US South Central SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.8875123885034688,376.732,9/25/2023,Rem US South Central SMM Food,133
+0.0,0.0,0.0,0.2768828031055804,0.0,0.0,0.0,415.53,9/26/2022,Rem US South Central SMM Food,134
+0.0,0.0,0.0,0.0,0.021042799453873943,0.0,0.0,312.75,9/27/2021,Rem US South Central SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.7715559960356789,385.963,9/4/2023,Rem US South Central SMM Food,136
+0.0,0.0,0.0,0.24456430760086398,0.0,0.0,0.0,384.02,9/5/2022,Rem US South Central SMM Food,137
+0.0,0.0,0.0,0.0,0.021850639075460684,0.0,0.0,348.91,9/6/2021,Rem US South Central SMM Food,138
+0.0,0.0,0.0,0.0,0.0,0.0,0.4821605550049554,81.439,8/7/2023,Rem US West North Central SMM Food,124
+0.0,0.005221576383686008,0.0,0.0,0.032486163159352294,0.0,0.0,85.9,8/8/2022,Rem US West North Central SMM Food,125
+0.0,0.0,0.0,0.0,0.010447481783001583,0.0,0.0,67.85,8/9/2021,Rem US West North Central SMM Food,126
+0.0,0.0,0.04495249924795993,0.0,0.0,0.32640646345165913,0.42170465807730423,81.258,9/11/2023,Rem US West North Central SMM Food,127
+0.0,0.0,0.0,0.08643118883692774,0.0,0.0,0.0,90.01,9/12/2022,Rem US West North Central SMM Food,128
+0.0,0.0,0.0,0.0,0.006995915865349195,0.0,0.0,80.77,9/13/2021,Rem US West North Central SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.4405351833498513,77.838,9/18/2023,Rem US West North Central SMM Food,130
+0.0,0.0,0.0,0.09009565954761295,0.0,0.0,0.0,77.07,9/19/2022,Rem US West North Central SMM Food,131
+0.0,0.0,0.0,0.0,0.007515506433597942,0.0,0.0,73.56,9/20/2021,Rem US West North Central SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.4400396432111001,78.46,9/25/2023,Rem US West North Central SMM Food,133
+0.0,0.0,0.0,0.10237703355668529,0.0,0.0,0.0,82.7,9/26/2022,Rem US West North Central SMM Food,134
+0.0,0.0,0.0,0.0,0.007104782460601314,0.0,0.0,74.24,9/27/2021,Rem US West North Central SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.38503468780971256,87.813,9/4/2023,Rem US West North Central SMM Food,136
+0.0,0.0,0.0,0.09042731448975182,0.0,0.0,0.0,85.04,9/5/2022,Rem US West North Central SMM Food,137
+0.0,0.0,0.0,0.0,0.007015091231558375,0.0,0.0,87.6,9/6/2021,Rem US West North Central SMM Food,138
+0.0,0.0,0.0,0.0,0.0,0.0,0.04112983151635283,43.858,8/7/2023,Richmond/Petersburg SMM Food,124
+0.0,0.0009487736279887459,0.0,0.0,0.007115916544206644,0.0,0.0,41.87,8/8/2022,Richmond/Petersburg SMM Food,125
+0.0,0.0,0.0,0.0,0.0012606256882035066,0.0,0.0,34.56,8/9/2021,Richmond/Petersburg SMM Food,126
+0.0,0.0,0.005540418423986577,0.0,0.0,0.04781667750388989,0.03964321110009911,41.396,9/11/2023,Richmond/Petersburg SMM Food,127
+0.0,0.0,0.0,0.01266418225172223,0.0,0.0,0.0,36.0,9/12/2022,Richmond/Petersburg SMM Food,128
+0.0,0.0,0.0,0.0,0.0013101105042271969,0.0,0.0,35.96,9/13/2021,Richmond/Petersburg SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.04162537165510406,37.841,9/18/2023,Richmond/Petersburg SMM Food,130
+0.0,0.0,0.0,0.013201112554278954,0.0,0.0,0.0,34.82,9/19/2022,Richmond/Petersburg SMM Food,131
+0.0,0.0,0.0,0.0,0.00111155267993214,0.0,0.0,33.78,9/20/2021,Richmond/Petersburg SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.0421209117938553,52.209,9/25/2023,Richmond/Petersburg SMM Food,133
+0.0,0.0,0.0,0.015000619892522049,0.0,0.0,0.0,37.32,9/26/2022,Richmond/Petersburg SMM Food,134
+0.0,0.0,0.0,0.0,0.0012204192751842583,0.0,0.0,33.61,9/27/2021,Richmond/Petersburg SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.03617443012884043,37.615,9/4/2023,Richmond/Petersburg SMM Food,136
+0.0,0.0,0.0,0.013249707732199589,0.0,0.0,0.0,38.38,9/5/2022,Richmond/Petersburg SMM Food,137
+0.0,0.0,0.0,0.0,0.0012822752952138712,0.0,0.0,47.08,9/6/2021,Richmond/Petersburg SMM Food,138
+0.0,0.0,0.0,0.0,0.0,0.0,0.11793855302279485,26.418,8/7/2023,Sacramento/Stockton/Modesto SMM Food,124
+0.0,0.0036908304998685493,0.0,0.0,0.006869111024288489,0.0,0.0,24.42,8/8/2022,Sacramento/Stockton/Modesto SMM Food,125
+0.0,0.0,0.0,0.0,0.00287321213037551,0.0,0.0,23.32,8/9/2021,Sacramento/Stockton/Modesto SMM Food,126
+0.0,0.0,0.017499367285692864,0.0,0.0,0.09899835674878281,0.08275520317145689,27.508,9/11/2023,Sacramento/Stockton/Modesto SMM Food,127
+0.0,0.0,0.0,0.029183182904838144,0.0,0.0,0.0,25.44,9/12/2022,Sacramento/Stockton/Modesto SMM Food,128
+0.0,0.0,0.0,0.0,0.0019026911761108865,0.0,0.0,24.91,9/13/2021,Sacramento/Stockton/Modesto SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.11298315163528246,22.085,9/18/2023,Sacramento/Stockton/Modesto SMM Food,130
+0.0,0.0,0.0,0.030420478364843156,0.0,0.0,0.0,24.71,9/19/2022,Sacramento/Stockton/Modesto SMM Food,131
+0.0,0.0,0.0,0.0,0.0023127965889072188,0.0,0.0,23.73,9/20/2021,Sacramento/Stockton/Modesto SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.07879088206144698,27.12,9/25/2023,Sacramento/Stockton/Modesto SMM Food,133
+0.0,0.0,0.0,0.034567240528453246,0.0,0.0,0.0,26.42,9/26/2022,Sacramento/Stockton/Modesto SMM Food,134
+0.0,0.0,0.0,0.0,0.0016187720441749643,0.0,0.0,22.87,9/27/2021,Sacramento/Stockton/Modesto SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.10257680872150644,30.477,9/4/2023,Sacramento/Stockton/Modesto SMM Food,136
+0.0,0.0,0.0,0.030532460487995166,0.0,0.0,0.0,25.38,9/5/2022,Sacramento/Stockton/Modesto SMM Food,137
+0.0,0.0,0.0,0.0,0.0018699074854951917,0.0,0.0,25.66,9/6/2021,Sacramento/Stockton/Modesto SMM Food,138
+0.0,0.0,0.0,0.0,0.0,0.0,0.13280475718533202,31.866,8/7/2023,Salt Lake City SMM Food,124
+0.0,0.0,0.0,0.0,0.005755702663755461,0.0,0.0,30.89,8/8/2022,Salt Lake City SMM Food,125
+0.0,0.0,0.0,0.0,0.002216919757861319,0.0,0.0,30.55,8/9/2021,Salt Lake City SMM Food,126
+0.0,0.0,0.0,0.0,0.0,0.0020036799212335816,0.12338949454905847,35.521,9/11/2023,Salt Lake City SMM Food,127
+0.0,0.0,0.0,0.024569078193799245,0.0,0.0,0.0,28.16,9/12/2022,Salt Lake City SMM Food,128
+0.0,0.0,0.0,0.0,0.0018668146844937111,0.0,0.0,34.69,9/13/2021,Salt Lake City SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.12091179385530228,39.734,9/18/2023,Salt Lake City SMM Food,130
+0.0,0.0,0.0,0.0256107469124291,0.0,0.0,0.0,20.08,9/19/2022,Salt Lake City SMM Food,131
+0.0,0.0,0.0,0.0,0.002090114916800613,0.0,0.0,30.04,9/20/2021,Salt Lake City SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.11446977205153618,36.76,9/25/2023,Salt Lake City SMM Food,133
+0.0,0.0,0.0,0.02910187138286449,0.0,0.0,0.0,28.2,9/26/2022,Salt Lake City SMM Food,134
+0.0,0.0,0.0,0.0,0.0013169146664304542,0.0,0.0,31.92,9/27/2021,Salt Lake City SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.12933597621407333,38.673,9/4/2023,Salt Lake City SMM Food,136
+0.0,0.0,0.0,0.025705023731608795,0.0,0.0,0.0,29.76,9/5/2022,Salt Lake City SMM Food,137
+0.0,0.0,0.0,0.0,0.0014486679890935294,0.0,0.0,30.71,9/6/2021,Salt Lake City SMM Food,138
+0.0,0.0,0.0,0.0,0.0,0.0,0.06739345887016848,27.155,8/7/2023,San Diego SMM Food,124
+0.0,0.0020376249453456933,0.0,0.0,0.006930348484117806,0.0,0.0,23.05,8/8/2022,San Diego SMM Food,125
+0.0,0.0,0.0,0.0,0.0011183568421353973,0.0,0.0,18.52,8/9/2021,San Diego SMM Food,126
+0.0,0.0,0.008977334498881523,0.0,0.0,0.06158408920762952,0.058969276511397425,31.97,9/11/2023,San Diego SMM Food,127
+0.0,0.0,0.0,0.020400456588260608,0.0,0.0,0.0,26.77,9/12/2022,San Diego SMM Food,128
+0.0,0.0,0.0,0.0,0.0014214513402804997,0.0,0.0,22.06,9/13/2021,San Diego SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.055996035678889985,29.921,9/18/2023,San Diego SMM Food,130
+0.0,0.0,0.0,0.021265385972826126,0.0,0.0,0.0,23.46,9/19/2022,San Diego SMM Food,131
+0.0,0.0,0.0,0.0,0.0010942329943238486,0.0,0.0,18.64,9/20/2021,San Diego SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.059960356788899896,29.193,9/25/2023,San Diego SMM Food,133
+0.0,0.0,0.0,0.02416417332539202,0.0,0.0,0.0,27.29,9/26/2022,San Diego SMM Food,134
+0.0,0.0,0.0,0.0,0.001024335691690386,0.0,0.0,21.74,9/27/2021,San Diego SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.06739345887016848,34.927,9/4/2023,San Diego SMM Food,136
+0.0,0.0,0.0,0.021343666889227886,0.0,0.0,0.0,24.65,9/5/2022,San Diego SMM Food,137
+0.0,0.0,0.0,0.0,0.0013292858704363766,0.0,0.0,22.7,9/6/2021,San Diego SMM Food,138
+0.0,0.0,0.0,0.0,0.0,0.0,0.10852329038652131,37.539,8/7/2023,San Francisco/Oakland/San Jose SMM Food,124
+0.0,0.007054716784551935,0.0,0.0,0.010025623726399625,0.0,0.0,43.76,8/8/2022,San Francisco/Oakland/San Jose SMM Food,125
+0.0,0.0,0.0,0.0,0.0026325922124603163,0.0,0.0,35.86,8/9/2021,San Francisco/Oakland/San Jose SMM Food,126
+0.0,0.0,0.03269606409616145,0.0,0.0,0.08548204542111204,0.08919722497522299,39.91,9/11/2023,San Francisco/Oakland/San Jose SMM Food,127
+0.0,0.0,0.0,0.047694445908865776,0.0,0.0,0.0,39.0,9/12/2022,San Francisco/Oakland/San Jose SMM Food,128
+0.0,0.0,0.0,0.0,0.0020171248131656697,0.0,0.0,39.68,9/13/2021,San Francisco/Oakland/San Jose SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.09712586719524281,39.834,9/18/2023,San Francisco/Oakland/San Jose SMM Food,130
+0.0,0.0,0.0,0.04971657356674364,0.0,0.0,0.0,41.01,9/19/2022,San Francisco/Oakland/San Jose SMM Food,131
+0.0,0.0,0.0,0.0,0.002183517507045328,0.0,0.0,37.16,9/20/2021,San Francisco/Oakland/San Jose SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.0817641228939544,38.973,9/25/2023,San Francisco/Oakland/San Jose SMM Food,133
+0.0,0.0,0.0,0.056493679565598955,0.0,0.0,0.0,40.14,9/26/2022,San Francisco/Oakland/San Jose SMM Food,134
+0.0,0.0,0.0,0.0,0.0021674349418376285,0.0,0.0,36.73,9/27/2021,San Francisco/Oakland/San Jose SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.09068384539147671,45.739,9/4/2023,San Francisco/Oakland/San Jose SMM Food,136
+0.0,0.0,0.0,0.049899587365703414,0.0,0.0,0.0,42.29,9/5/2022,San Francisco/Oakland/San Jose SMM Food,137
+0.0,0.0,0.0,0.0,0.0014294926228843492,0.0,0.0,39.55,9/6/2021,San Francisco/Oakland/San Jose SMM Food,138
+0.0,0.0,0.0,0.0,0.0,0.0,0.176412289395441,54.498,8/7/2023,Seattle/Tacoma SMM Food,124
+0.0,0.006289632662621285,0.0,0.0,0.014541731748761649,0.0,0.0,38.02,8/8/2022,Seattle/Tacoma SMM Food,125
+0.0,0.0,0.0,0.0,0.0025020760101978337,0.0,0.0,44.59,8/9/2021,Seattle/Tacoma SMM Food,126
+0.0,0.0,0.04408198262886745,0.0,0.0,0.13501876631890186,0.14073339940535184,56.797,9/11/2023,Seattle/Tacoma SMM Food,127
+0.0,0.0,0.0,0.03795459470136038,0.0,0.0,0.0,45.0,9/12/2022,Seattle/Tacoma SMM Food,128
+0.0,0.0,0.0,0.0,0.0019701142379431645,0.0,0.0,47.26,9/13/2021,Seattle/Tacoma SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.15163528245787908,53.988,9/18/2023,Seattle/Tacoma SMM Food,130
+0.0,0.0,0.0,0.03956377652701973,0.0,0.0,0.0,42.63,9/19/2022,Seattle/Tacoma SMM Food,131
+0.0,0.0,0.0,0.0,0.0010663977853105227,0.0,0.0,44.68,9/20/2021,Seattle/Tacoma SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.14767096134786917,57.516,9/25/2023,Seattle/Tacoma SMM Food,133
+0.0,0.0,0.0,0.04495690578008078,0.0,0.0,0.0,43.4,9/26/2022,Seattle/Tacoma SMM Food,134
+0.0,0.0,0.0,0.0,0.002235476563870203,0.0,0.0,47.9,9/27/2021,Seattle/Tacoma SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.13429137760158572,69.688,9/4/2023,Seattle/Tacoma SMM Food,136
+0.0,0.0,0.0,0.039709416436374406,0.0,0.0,0.0,41.17,9/5/2022,Seattle/Tacoma SMM Food,137
+0.0,0.0,0.0,0.0,0.0016051637197684492,0.0,0.0,42.94,9/6/2021,Seattle/Tacoma SMM Food,138
+0.0,0.0,0.0,0.0,0.0,0.0,0.13875123885034688,41.124,8/7/2023,St. Louis SMM Food,124
+0.0,0.0016459850550708866,0.0,0.0,0.010519234766235935,0.0,0.0,33.68,8/8/2022,St. Louis SMM Food,125
+0.0,0.0,0.0,0.0,0.0028880575751826162,0.0,0.0,35.24,8/9/2021,St. Louis SMM Food,126
+0.0,0.0,0.013647236303775618,0.0,0.0,0.11378140992305427,0.09266600594648167,41.021,9/11/2023,St. Louis SMM Food,127
+0.0,0.0,0.0,0.028725686548902455,0.0,0.0,0.0,46.76,9/12/2022,St. Louis SMM Food,128
+0.0,0.0,0.0,0.0,0.002591148679040476,0.0,0.0,37.37,9/13/2021,St. Louis SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.09266600594648167,38.672,9/18/2023,St. Louis SMM Food,130
+0.0,0.0,0.0,0.029943585281161426,0.0,0.0,0.0,41.99,9/19/2022,St. Louis SMM Food,131
+0.0,0.0,0.0,0.0,0.0026987781538920018,0.0,0.0,33.82,9/20/2021,St. Louis SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.09217046580773042,52.498,9/25/2023,St. Louis SMM Food,133
+0.0,0.0,0.0,0.034025339850661744,0.0,0.0,0.0,38.58,9/26/2022,St. Louis SMM Food,134
+0.0,0.0,0.0,0.0,0.003129296053298106,0.0,0.0,35.39,9/27/2021,St. Louis SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.09514370664023786,44.922,9/4/2023,St. Louis SMM Food,136
+0.0,0.0,0.0,0.03005381188828102,0.0,0.0,0.0,51.7,9/5/2022,St. Louis SMM Food,137
+0.0,0.0,0.0,0.0,0.0020622797077872873,0.0,0.0,45.12,9/6/2021,St. Louis SMM Food,138
+0.0,0.0,0.0,0.0,0.0,0.0,0.18434093161546083,392.534,8/7/2023,Tampa/Ft. Myers SMM Food,124
+0.0,0.002718951273633502,0.0,0.0,0.014108739608554358,0.0,0.0,249.67,8/8/2022,Tampa/Ft. Myers SMM Food,125
+0.0,0.0,0.0,0.0,0.005458175207413023,0.0,0.0,94.56,8/9/2021,Tampa/Ft. Myers SMM Food,126
+0.0,0.0,0.018081680874809502,0.0,0.0,0.19101996581778166,0.1377601585728444,112.223,9/11/2023,Tampa/Ft. Myers SMM Food,127
+0.0,0.0,0.0,0.05247086431152936,0.0,0.0,0.0,92.25,9/12/2022,Tampa/Ft. Myers SMM Food,128
+0.0,0.0,0.0,0.0,0.004128889336976647,0.0,0.0,93.22,9/13/2021,Tampa/Ft. Myers SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.13181367690782952,105.58,9/18/2023,Tampa/Ft. Myers SMM Food,130
+0.0,0.0,0.0,0.05469550040636205,0.0,0.0,0.0,85.97,9/19/2022,Tampa/Ft. Myers SMM Food,131
+0.0,0.0,0.0,0.0,0.004280436586049198,0.0,0.0,86.54,9/20/2021,Tampa/Ft. Myers SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.13875123885034688,98.576,9/25/2023,Tampa/Ft. Myers SMM Food,133
+0.0,0.0,0.0,0.062151307947871305,0.0,0.0,0.0,92.27,9/26/2022,Tampa/Ft. Myers SMM Food,134
+0.0,0.0,0.0,0.0,0.004065796196546442,0.0,0.0,83.16,9/27/2021,Tampa/Ft. Myers SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.18533201189296333,155.563,9/4/2023,Tampa/Ft. Myers SMM Food,136
+0.0,0.0,0.0,0.0548968423482816,0.0,0.0,0.0,131.88,9/5/2022,Tampa/Ft. Myers SMM Food,137
+0.0,0.0,0.0,0.0,0.004370127815092137,0.0,0.0,85.36,9/6/2021,Tampa/Ft. Myers SMM Food,138
+0.0,0.0,0.0,0.0,0.0,0.0,0.033201189296333006,14.745,8/7/2023,Tucson/Sierra Vista SMM Food,124
+0.0,0.0008540406751789106,0.0,0.0,0.0032796061819700653,0.0,0.0,11.07,8/8/2022,Tucson/Sierra Vista SMM Food,125
+0.0,0.0,0.0,0.0,0.0014684619155030051,0.0,0.0,10.23,8/9/2021,Tucson/Sierra Vista SMM Food,126
+0.0,0.0,0.004325155281482285,0.0,0.0,0.03514997428852929,0.033201189296333006,13.086,9/11/2023,Tucson/Sierra Vista SMM Food,127
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+0.0,0.0,0.0,0.0,0.0,0.0,0.04558969276511397,13.845,9/18/2023,Tucson/Sierra Vista SMM Food,130
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+0.0,0.0,0.0,0.0,0.0,0.0,0.17443012884043607,117.859,8/7/2023,Washington DC/Hagerstown SMM Food,124
+0.0,0.008450294918628531,0.0,0.0,0.01769886301107308,0.0,0.0,121.38,8/8/2022,Washington DC/Hagerstown SMM Food,125
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+0.0,0.0,0.0,0.0,0.0,0.0,0.024281466798810703,3.773,8/7/2023,Yakima/Pasco/Richland/Kennewick SMM Food,124
+0.0,0.00041792250827997425,0.0,0.0,0.0016707311009998389,0.0,0.0,3.86,8/8/2022,Yakima/Pasco/Richland/Kennewick SMM Food,125
+0.0,0.0,0.0,0.0,0.00020907334770009088,0.0,0.0,3.64,8/9/2021,Yakima/Pasco/Richland/Kennewick SMM Food,126
+0.0,0.0,0.0024254626885814802,0.0,0.0,0.023424539263820505,0.022794846382556987,4.202,9/11/2023,Yakima/Pasco/Richland/Kennewick SMM Food,127
+0.0,0.0,0.0,0.0051309930988511126,0.0,0.0,0.0,3.78,9/12/2022,Yakima/Pasco/Richland/Kennewick SMM Food,128
+0.0,0.0,0.0,0.0,0.00041010541279633213,0.0,0.0,3.71,9/13/2021,Yakima/Pasco/Richland/Kennewick SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.018334985133795837,4.43,9/18/2023,Yakima/Pasco/Richland/Kennewick SMM Food,130
+0.0,0.0,0.0,0.005348534635223674,0.0,0.0,0.0,3.76,9/19/2022,Yakima/Pasco/Richland/Kennewick SMM Food,131
+0.0,0.0,0.0,0.0,0.0005270132906523001,0.0,0.0,3.12,9/20/2021,Yakima/Pasco/Richland/Kennewick SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.02180376610505451,3.573,9/25/2023,Yakima/Pasco/Richland/Kennewick SMM Food,133
+0.0,0.0,0.0,0.006077619195352498,0.0,0.0,0.0,2.75,9/26/2022,Yakima/Pasco/Richland/Kennewick SMM Food,134
+0.0,0.0,0.0,0.0,0.000404538370993667,0.0,0.0,3.74,9/27/2021,Yakima/Pasco/Richland/Kennewick SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.015857284440039643,4.901,9/4/2023,Yakima/Pasco/Richland/Kennewick SMM Food,136
+0.0,0.0,0.0,0.005368223354373092,0.0,0.0,0.0,3.93,9/5/2022,Yakima/Pasco/Richland/Kennewick SMM Food,137
+0.0,0.0,0.0,0.0,0.0004614459094209107,0.0,0.0,3.81,9/6/2021,Yakima/Pasco/Richland/Kennewick SMM Food,138

Test/output_df.csv

@@ -0,0 +1,29 @@
+Date,paid_search,kwai,fb_level_achieved_tier_2,fb_level_achieved_tier_1,ga_app,digital_tactic_others,programmatic,f1,const
+2023-05-29,7.761772068985416,-0.11831412496677594,29.26262853674282,43.02259197416761,34.850430143372286,31.805310964227356,49.945005056325215,-151.2233076306245,3208.7414522702875
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+2023-07-03,11.898849705377968,-0.21171921819363215,46.9589343899434,62.3806139692273,117.01657513265482,61.827996998986066,89.18225343091929,-334.28310107822256,4764.608039630399
+2023-07-10,11.400078866984943,-0.20451126846569176,51.51273512856333,62.14094623079805,119.16568176464439,58.064920572392516,85.76433103185752,-342.242222532466,4772.288544720711
+2023-07-17,12.240551031341669,-0.20879227519812163,53.406074306806204,66.62714284775411,120.08780443602339,53.92649980095306,84.74701307508934,-358.16046544095275,4765.362259286595
+2023-07-24,12.472994192760371,-0.20201100495743263,51.138519266662804,68.11537266284667,135.7641063558982,53.40467326346821,85.38679362668982,-374.0787083494395,4772.94893420709
+2023-07-31,12.216919598346994,-0.20118792897095253,52.36907023775602,69.38935909702018,136.58185949073408,51.25674449278376,81.80915243758835,-389.99695125792636,4780.3159616991115
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Test/scenario_test_df.csv

@@ -0,0 +1,29 @@
+other_contributions,correction,sales
+3092.2502606580683,0.018289272297352,161.77901932815132
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Transformation_functions.py

@@ -0,0 +1,133 @@
+import streamlit as st
+import pandas as pd
+import plotly.express as px
+import plotly.graph_objects as go
+from Eda_functions import format_numbers,line_plot,summary
+import numpy as np
+import re
+
+def sanitize_key(key, prefix=""):
+    # Use regular expressions to remove non-alphanumeric characters and spaces
+    key = re.sub(r'[^a-zA-Z0-9]', '', key)
+    return f"{prefix}{key}"
+
+
+def check_box(options, ad_stock_value,lag_value,num_columns=4, prefix=""):
+    num_rows = -(-len(options) // num_columns)  # Ceiling division to calculate rows
+
+    selected_options = []
+    adstock_info = {}  # Store adstock and lag info for each selected option
+    if ad_stock_value!=0:
+        for row in range(num_rows):
+            cols = st.columns(num_columns)
+            for col in cols:
+                if options:
+                    option = options.pop(0)
+                    key = sanitize_key(f"{option}_{row}", prefix=prefix)
+                    selected = col.checkbox(option, key=key)
+                    if selected:
+                        selected_options.append(option)
+                        
+                        # Input minimum and maximum adstock values
+                        adstock = col.slider('Select Adstock Range', 0.0, 1.0, ad_stock_value, step=0.05, format="%.2f",key= f"adstock_{key}" )
+
+                        # Input minimum and maximum lag values
+                        lag = col.slider('Select Lag Range', 0, 7, lag_value, step=1,key=f"lag_{key}" )
+
+                        # Create a dictionary to store adstock and lag info for the option
+                        option_info = {
+                            'adstock': adstock,
+                            'lag': lag}
+                        # Append the dictionary to the adstock_info list
+                        adstock_info[option]=option_info
+
+                    else:adstock_info[option]={
+                            'adstock': ad_stock_value,
+                            'lag': lag_value}
+
+        return selected_options, adstock_info   
+    else:
+        for row in range(num_rows):
+            cols = st.columns(num_columns)
+            for col in cols:
+                if options:
+                    option = options.pop(0)
+                    key = sanitize_key(f"{option}_{row}", prefix=prefix)
+                    selected = col.checkbox(option, key=key)
+                    if selected:
+                        selected_options.append(option)
+                        
+                        # Input minimum and maximum lag values
+                        lag = col.slider('Select Lag Range', 0, 7, lag_value, step=1,key=f"lag_{key}" )
+
+                        # dictionary to store adstock and lag info for the option
+                        option_info = {
+                            'lag': lag}
+                        # Append the dictionary to the adstock_info list
+                        adstock_info[option]=option_info
+
+                    else:adstock_info[option]={
+                            'lag': lag_value}
+                        
+        return selected_options, adstock_info 
+
+def apply_lag(X, features,lag_dict):
+    #lag_data=pd.DataFrame()
+    for col in features:
+        for lag in range(lag_dict[col]['lag'][0], lag_dict[col]['lag'][1] + 1):
+            if lag>0:
+                X[f'{col}_lag{lag}'] = X[col].shift(periods=lag, fill_value=0)
+    return X
+
+def apply_adstock(X, variable_name, decay):
+    values = X[variable_name].values
+    adstock = np.zeros(len(values))
+    
+    for row in range(len(values)):
+        if row == 0:
+            adstock[row] = values[row]
+        else:
+            adstock[row] = values[row] + adstock[row - 1] * decay
+    
+    return adstock
+
+def top_correlated_features(df,target,media_data):
+    corr_df=df.drop(target,axis=1)
+    #corr_df[target]=df[target]
+    #st.dataframe(corr_df)
+    for i in media_data:
+        #st.write(media_data[2])
+        #st.dataframe(corr_df.filter(like=media_data[2]))
+        d=(pd.concat([corr_df.filter(like=i),df[target]],axis=1)).corr()[target]
+        d=d.sort_values(ascending=False)
+        d=d.drop(target,axis=0)
+        corr=pd.DataFrame({'Feature_name':d.index,"Correlation":d.values})
+        corr.columns = pd.MultiIndex.from_product([[i], ['Feature_name', 'Correlation']])
+
+    return corr
+
+def top_correlated_features(df,variables,target):
+    correlation_df=pd.DataFrame()
+    for col in variables:
+        d=pd.concat([df.filter(like=col),df[target]],axis=1).corr()[target]
+        #st.dataframe(d)
+        d=d.sort_values(ascending=False).iloc[1:]
+        corr_df=pd.DataFrame({'Media_channel':d.index,'Correlation':d.values})
+        corr_df.columns=pd.MultiIndex.from_tuples([(col, 'Variable'), (col, 'Correlation')])
+        correlation_df=pd.concat([corr_df,correlation_df],axis=1)
+    return correlation_df
+
+def top_correlated_feature(df,variable,target):
+    d=pd.concat([df.filter(like=variable),df[target]],axis=1).corr()[target]
+    # st.dataframe(d)
+    d=d.sort_values(ascending=False).iloc[1:]
+    # st.dataframe(d)
+    corr_df=pd.DataFrame({'Media_channel':d.index,'Correlation':d.values})
+    corr_df['Adstock']=corr_df['Media_channel'].map(lambda x:x.split('_adst')[1] if len(x.split('_adst'))>1 else '-')
+    corr_df['Lag']=corr_df['Media_channel'].map(lambda x:x.split('_lag')[1][0] if len(x.split('_lag'))>1 else '-' )
+    corr_df.drop(['Correlation'],axis=1,inplace=True)
+    corr_df['Correlation']=np.round(d.values,2)
+    sorted_corr_df= corr_df.loc[corr_df['Correlation'].abs().sort_values(ascending=False).index]
+    #corr_df.columns=pd.MultiIndex.from_tuples([(variable, 'Variable'), (variable, 'Correlation')])
+    #correlation_df=pd.concat([corr_df,correlation_df],axis=1)
+    return sorted_corr_df

Users/saved_scenarios.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b09928ce441321b24be8466d1d76e7bd4427a35d5ea9cf31e49fcdfc0f4e8454
+size 13213

classes-gk.py

@@ -0,0 +1,541 @@
+import numpy as np
+from scipy.optimize import minimize, LinearConstraint, NonlinearConstraint
+from collections import OrderedDict
+import pandas as pd
+from numerize.numerize import numerize
+
+
+def class_to_dict(class_instance):
+    attr_dict = {}
+    if isinstance(class_instance, Channel):
+        attr_dict["type"] = "Channel"
+        attr_dict["name"] = class_instance.name
+        attr_dict["dates"] = class_instance.dates
+        attr_dict["spends"] = class_instance.actual_spends
+        attr_dict["conversion_rate"] = class_instance.conversion_rate
+        attr_dict["modified_spends"] = class_instance.modified_spends
+        attr_dict["modified_sales"] = class_instance.modified_sales
+        attr_dict["response_curve_type"] = class_instance.response_curve_type
+        attr_dict["response_curve_params"] = (
+            class_instance.response_curve_params
+        )
+        attr_dict["penalty"] = class_instance.penalty
+        attr_dict["bounds"] = class_instance.bounds
+        attr_dict["actual_total_spends"] = class_instance.actual_total_spends
+        attr_dict["actual_total_sales"] = class_instance.actual_total_sales
+        attr_dict["modified_total_spends"] = (
+            class_instance.modified_total_spends
+        )
+        attr_dict["modified_total_sales"] = class_instance.modified_total_sales
+        attr_dict["actual_mroi"] = class_instance.get_marginal_roi("actual")
+        attr_dict["modified_mroi"] = class_instance.get_marginal_roi(
+            "modified"
+        )
+
+    elif isinstance(class_instance, Scenario):
+        attr_dict["type"] = "Scenario"
+        attr_dict["name"] = class_instance.name
+        channels = []
+        for channel in class_instance.channels.values():
+            channels.append(class_to_dict(channel))
+        attr_dict["channels"] = channels
+        attr_dict["constant"] = class_instance.constant
+        attr_dict["correction"] = class_instance.correction
+        attr_dict["actual_total_spends"] = class_instance.actual_total_spends
+        attr_dict["actual_total_sales"] = class_instance.actual_total_sales
+        attr_dict["modified_total_spends"] = (
+            class_instance.modified_total_spends
+        )
+        attr_dict["modified_total_sales"] = class_instance.modified_total_sales
+
+    return attr_dict
+
+
+def class_from_dict(attr_dict):
+    if attr_dict["type"] == "Channel":
+        return Channel.from_dict(attr_dict)
+    elif attr_dict["type"] == "Scenario":
+        return Scenario.from_dict(attr_dict)
+
+
+class Channel:
+    def __init__(
+        self,
+        name,
+        dates,
+        spends,
+        response_curve_type,
+        response_curve_params,
+        bounds,
+        conversion_rate=1,
+        modified_spends=None,
+        penalty=True,
+    ):
+        self.name = name
+        self.dates = dates
+        self.conversion_rate = conversion_rate
+        self.actual_spends = spends.copy()
+
+        if modified_spends is None:
+            self.modified_spends = self.actual_spends.copy()
+        else:
+            self.modified_spends = modified_spends
+
+        self.response_curve_type = response_curve_type
+        self.response_curve_params = response_curve_params
+        self.bounds = bounds
+        self.penalty = penalty
+
+        self.upper_limit = self.actual_spends.max() + self.actual_spends.std()
+        self.power = np.ceil(np.log(self.actual_spends.max()) / np.log(10)) - 3
+        # self.actual_sales = None
+        # self.actual_sales = self.response_curve(self.actual_spends)
+        self.actual_total_spends = self.actual_spends.sum()
+        self.actual_total_sales = self.actual_sales.sum()
+        self.modified_sales = self.calculate_sales()
+        self.modified_total_spends = self.modified_spends.sum()
+        self.modified_total_sales = self.modified_sales.sum()
+        self.delta_spends = (
+            self.modified_total_spends - self.actual_total_spends
+        )
+        self.delta_sales = self.modified_total_sales - self.actual_total_sales
+
+    @property
+    def actual_sales(self):
+        return self.response_curve(self.actual_spends)
+
+    def update_penalty(self, penalty):
+        self.penalty = penalty
+
+    def _modify_spends(self, spends_array, total_spends):
+        return spends_array * total_spends / spends_array.sum()
+
+    def modify_spends(self, total_spends):
+        self.modified_spends = (
+            self.modified_spends * total_spends / self.modified_spends.sum()
+        )
+
+    def calculate_sales(self):
+        return self.response_curve(self.modified_spends)
+
+    def response_curve(self, x):
+        if self.penalty:
+            x = np.where(
+                x < self.upper_limit,
+                x,
+                self.upper_limit
+                + (x - self.upper_limit) * self.upper_limit / x,
+            )
+        if self.response_curve_type == "s-curve":
+            if self.power >= 0:
+                x = x / 10**self.power
+            x = x.astype("float64")
+            K = self.response_curve_params["K"]
+            b = self.response_curve_params["b"]
+            a = self.response_curve_params["a"]
+            x0 = self.response_curve_params["x0"]
+            sales = K / (1 + b * np.exp(-a * (x - x0)))
+        if self.response_curve_type == "linear":
+            beta = self.response_curve_params["beta"]
+            sales = beta * x
+
+        return sales
+
+    def get_marginal_roi(self, flag):
+        K = self.response_curve_params["K"]
+        a = self.response_curve_params["a"]
+        # x = self.modified_total_spends
+        # if self.power >= 0 :
+        #     x = x / 10**self.power
+        # x = x.astype('float64')
+        # return K*b*a*np.exp(-a*(x-x0)) / (1 + b * np.exp(-a*(x - x0)))**2
+        if flag == "actual":
+            y = self.response_curve(self.actual_spends)
+            # spends_array = self.actual_spends
+            # total_spends = self.actual_total_spends
+            # total_sales = self.actual_total_sales
+
+        else:
+            y = self.response_curve(self.modified_spends)
+            # spends_array = self.modified_spends
+            # total_spends = self.modified_total_spends
+            # total_sales = self.modified_total_sales
+
+        # spends_inc_1 = self._modify_spends(spends_array, total_spends+1)
+        mroi = a * (y) * (1 - y / K)
+        return mroi.sum() / len(self.modified_spends)
+        # spends_inc_1 = self.spends_array + 1
+        # new_total_sales = self.response_curve(spends_inc_1).sum()
+        # return (new_total_sales - total_sales) / len(self.modified_spends)
+
+    def update(self, total_spends):
+        self.modify_spends(total_spends)
+        self.modified_sales = self.calculate_sales()
+        self.modified_total_spends = self.modified_spends.sum()
+        self.modified_total_sales = self.modified_sales.sum()
+        self.delta_spends = (
+            self.modified_total_spends - self.actual_total_spends
+        )
+        self.delta_sales = self.modified_total_sales - self.actual_total_sales
+
+    def intialize(self):
+        self.new_spends = self.old_spends
+
+    def __str__(self):
+        return f"{self.name},{self.actual_total_sales}, {self.modified_total_spends}"
+
+    @classmethod
+    def from_dict(cls, attr_dict):
+        return Channel(
+            name=attr_dict["name"],
+            dates=attr_dict["dates"],
+            spends=attr_dict["spends"],
+            bounds=attr_dict["bounds"],
+            modified_spends=attr_dict["modified_spends"],
+            response_curve_type=attr_dict["response_curve_type"],
+            response_curve_params=attr_dict["response_curve_params"],
+            penalty=attr_dict["penalty"],
+        )
+
+    def update_response_curves(self, response_curve_params):
+        self.response_curve_params = response_curve_params
+
+
+class Scenario:
+    def __init__(self, name, channels, constant, correction):
+        self.name = name
+        self.channels = channels
+        self.constant = constant
+        self.correction = correction
+
+        self.actual_total_spends = self.calculate_modified_total_spends()
+        self.actual_total_sales = self.calculate_actual_total_sales()
+        self.modified_total_sales = self.calculate_modified_total_sales()
+        self.modified_total_spends = self.calculate_modified_total_spends()
+        self.delta_spends = (
+            self.modified_total_spends - self.actual_total_spends
+        )
+        self.delta_sales = self.modified_total_sales - self.actual_total_sales
+
+    def update_penalty(self, value):
+        for channel in self.channels.values():
+            channel.update_penalty(value)
+
+    def calculate_modified_total_spends(self):
+        total_actual_spends = 0.0
+        for channel in self.channels.values():
+            total_actual_spends += (
+                channel.actual_total_spends * channel.conversion_rate
+            )
+        return total_actual_spends
+
+    def calculate_modified_total_spends(self):
+        total_modified_spends = 0.0
+        for channel in self.channels.values():
+            # import streamlit as st
+            # st.write(channel.modified_total_spends )
+            total_modified_spends += (
+                channel.modified_total_spends * channel.conversion_rate
+            )
+        return total_modified_spends
+
+    def calculate_actual_total_sales(self):
+        total_actual_sales = self.constant.sum() + self.correction.sum()
+        for channel in self.channels.values():
+            total_actual_sales += channel.actual_total_sales
+        return total_actual_sales
+
+    def calculate_modified_total_sales(self):
+        total_modified_sales = self.constant.sum() + self.correction.sum()
+        for channel in self.channels.values():
+            total_modified_sales += channel.modified_total_sales
+        return total_modified_sales
+
+    def update(self, channel_name, modified_spends):
+        self.channels[channel_name].update(modified_spends)
+        self.modified_total_sales = self.calculate_modified_total_sales()
+        self.modified_total_spends = self.calculate_modified_total_spends()
+        self.delta_spends = (
+            self.modified_total_spends - self.actual_total_spends
+        )
+        self.delta_sales = self.modified_total_sales - self.actual_total_sales
+
+    # def optimize_spends(self, sales_percent, channels_list, algo="COBYLA"):
+    #     desired_sales = self.actual_total_sales * (1 + sales_percent / 100.0)
+
+    #     def constraint(x):
+    #         for ch, spends in zip(channels_list, x):
+    #             self.update(ch, spends)
+    #         return self.modified_total_sales - desired_sales
+
+    #     bounds = []
+    #     for ch in channels_list:
+    #         bounds.append(
+    #             (1 + np.array([-50.0, 100.0]) / 100.0)
+    #             * self.channels[ch].actual_total_spends
+    #         )
+
+    #     initial_point = []
+    #     for bound in bounds:
+    #         initial_point.append(bound[0])
+
+    #     power = np.ceil(np.log(sum(initial_point)) / np.log(10))
+
+    #     constraints = [NonlinearConstraint(constraint, -1.0, 1.0)]
+
+    #     res = minimize(
+    #         lambda x: sum(x) / 10 ** (power),
+    #         bounds=bounds,
+    #         x0=initial_point,
+    #         constraints=constraints,
+    #         method=algo,
+    #         options={"maxiter": int(2e7), "catol": 1},
+    #     )
+
+    #     for channel_name, modified_spends in zip(channels_list, res.x):
+    #         self.update(channel_name, modified_spends)
+
+    #     return zip(channels_list, res.x)
+
+    def optimize_spends(
+        self, sales_percent, channels_list, algo="trust-constr"
+    ):
+        desired_sales = self.actual_total_sales * (1 + sales_percent / 100.0)
+
+        def constraint(x):
+            for ch, spends in zip(channels_list, x):
+                self.update(ch, spends)
+            return self.modified_total_sales - desired_sales
+
+        bounds = []
+        for ch in channels_list:
+            bounds.append(
+                (1 + np.array([-50.0, 100.0]) / 100.0)
+                * self.channels[ch].actual_total_spends
+            )
+
+        initial_point = []
+        for bound in bounds:
+            initial_point.append(bound[0])
+
+        power = np.ceil(np.log(sum(initial_point)) / np.log(10))
+
+        constraints = [NonlinearConstraint(constraint, -1.0, 1.0)]
+
+        res = minimize(
+            lambda x: sum(x) / 10 ** (power),
+            bounds=bounds,
+            x0=initial_point,
+            constraints=constraints,
+            method=algo,
+            options={"maxiter": int(2e7), "xtol": 100},
+        )
+
+        for channel_name, modified_spends in zip(channels_list, res.x):
+            self.update(channel_name, modified_spends)
+
+        return zip(channels_list, res.x)
+
+    def optimize(self, spends_percent, channels_list):
+        # channels_list = self.channels.keys()
+        num_channels = len(channels_list)
+        spends_constant = []
+        spends_constraint = 0.0
+        for channel_name in channels_list:
+            # spends_constraint += self.channels[channel_name].modified_total_spends
+            spends_constant.append(self.channels[channel_name].conversion_rate)
+            spends_constraint += (
+                self.channels[channel_name].actual_total_spends
+                * self.channels[channel_name].conversion_rate
+            )
+        spends_constraint = spends_constraint * (1 + spends_percent / 100)
+        # constraint= LinearConstraint(np.ones((num_channels,)), lb = spends_constraint, ub = spends_constraint)
+        constraint = LinearConstraint(
+            np.array(spends_constant),
+            lb=spends_constraint,
+            ub=spends_constraint,
+        )
+        bounds = []
+        old_spends = []
+        for channel_name in channels_list:
+            _channel_class = self.channels[channel_name]
+            channel_bounds = _channel_class.bounds
+            channel_actual_total_spends = (
+                _channel_class.actual_total_spends
+                * ((1 + spends_percent / 100))
+            )
+            old_spends.append(channel_actual_total_spends)
+            bounds.append(
+                (1 + channel_bounds / 100) * channel_actual_total_spends
+            )
+
+        def objective_function(x):
+            for channel_name, modified_spends in zip(channels_list, x):
+                self.update(channel_name, modified_spends)
+            return -1 * self.modified_total_sales
+
+        res = minimize(
+            lambda x: objective_function(x) / 1e8,
+            method="trust-constr",
+            x0=old_spends,
+            constraints=constraint,
+            bounds=bounds,
+            options={"maxiter": int(1e7), "xtol": 100},
+        )
+        # res = dual_annealing(
+        # objective_function,
+        # x0=old_spends,
+        # mi
+        # constraints=constraint,
+        # bounds=bounds,
+        # tol=1e-16
+        # )
+        print(res)
+        for channel_name, modified_spends in zip(channels_list, res.x):
+            self.update(channel_name, modified_spends)
+
+        return zip(channels_list, res.x)
+
+    def save(self):
+        details = {}
+        actual_list = []
+        modified_list = []
+        data = {}
+        channel_data = []
+
+        summary_rows = []
+        actual_list.append(
+            {
+                "name": "Total",
+                "Spends": self.actual_total_spends,
+                "Sales": self.actual_total_sales,
+            }
+        )
+        modified_list.append(
+            {
+                "name": "Total",
+                "Spends": self.modified_total_spends,
+                "Sales": self.modified_total_sales,
+            }
+        )
+        for channel in self.channels.values():
+            name_mod = channel.name.replace("_", " ")
+            if name_mod.lower().endswith(" imp"):
+                name_mod = name_mod.replace("Imp", " Impressions")
+            summary_rows.append(
+                [
+                    name_mod,
+                    channel.actual_total_spends,
+                    channel.modified_total_spends,
+                    channel.actual_total_sales,
+                    channel.modified_total_sales,
+                    round(
+                        channel.actual_total_sales
+                        / channel.actual_total_spends,
+                        2,
+                    ),
+                    round(
+                        channel.modified_total_sales
+                        / channel.modified_total_spends,
+                        2,
+                    ),
+                    channel.get_marginal_roi("actual"),
+                    channel.get_marginal_roi("modified"),
+                ]
+            )
+            data[channel.name] = channel.modified_spends
+            data["Date"] = channel.dates
+            data["Sales"] = (
+                data.get("Sales", np.zeros((len(channel.dates),)))
+                + channel.modified_sales
+            )
+            actual_list.append(
+                {
+                    "name": channel.name,
+                    "Spends": channel.actual_total_spends,
+                    "Sales": channel.actual_total_sales,
+                    "ROI": round(
+                        channel.actual_total_sales
+                        / channel.actual_total_spends,
+                        2,
+                    ),
+                }
+            )
+            modified_list.append(
+                {
+                    "name": channel.name,
+                    "Spends": channel.modified_total_spends,
+                    "Sales": channel.modified_total_sales,
+                    "ROI": round(
+                        channel.modified_total_sales
+                        / channel.modified_total_spends,
+                        2,
+                    ),
+                    "Marginal ROI": channel.get_marginal_roi("modified"),
+                }
+            )
+
+            channel_data.append(
+                {
+                    "channel": channel.name,
+                    "spends_act": channel.actual_total_spends,
+                    "spends_mod": channel.modified_total_spends,
+                    "sales_act": channel.actual_total_sales,
+                    "sales_mod": channel.modified_total_sales,
+                }
+            )
+        summary_rows.append(
+            [
+                "Total",
+                self.actual_total_spends,
+                self.modified_total_spends,
+                self.actual_total_sales,
+                self.modified_total_sales,
+                round(self.actual_total_sales / self.actual_total_spends, 2),
+                round(
+                    self.modified_total_sales / self.modified_total_spends, 2
+                ),
+                0.0,
+                0.0,
+            ]
+        )
+        details["Actual"] = actual_list
+        details["Modified"] = modified_list
+        columns_index = pd.MultiIndex.from_product(
+            [[""], ["Channel"]], names=["first", "second"]
+        )
+        columns_index = columns_index.append(
+            pd.MultiIndex.from_product(
+                [["Spends", "NRPU", "ROI", "MROI"], ["Actual", "Simulated"]],
+                names=["first", "second"],
+            )
+        )
+        details["Summary"] = pd.DataFrame(summary_rows, columns=columns_index)
+        data_df = pd.DataFrame(data)
+        channel_list = list(self.channels.keys())
+        data_df = data_df[["Date", *channel_list, "Sales"]]
+
+        details["download"] = {
+            "data_df": data_df,
+            "channels_df": pd.DataFrame(channel_data),
+            "total_spends_act": self.actual_total_spends,
+            "total_sales_act": self.actual_total_sales,
+            "total_spends_mod": self.modified_total_spends,
+            "total_sales_mod": self.modified_total_sales,
+        }
+
+        return details
+
+    @classmethod
+    def from_dict(cls, attr_dict):
+        channels_list = attr_dict["channels"]
+        channels = {
+            channel["name"]: class_from_dict(channel)
+            for channel in channels_list
+        }
+        return Scenario(
+            name=attr_dict["name"],
+            channels=channels,
+            constant=attr_dict["constant"],
+            correction=attr_dict["correction"],
+        )

classes.py

@@ -0,0 +1,541 @@
+import numpy as np
+from scipy.optimize import minimize, LinearConstraint, NonlinearConstraint
+from collections import OrderedDict
+import pandas as pd
+from numerize.numerize import numerize
+
+
+def class_to_dict(class_instance):
+    attr_dict = {}
+    if isinstance(class_instance, Channel):
+        attr_dict["type"] = "Channel"
+        attr_dict["name"] = class_instance.name
+        attr_dict["dates"] = class_instance.dates
+        attr_dict["spends"] = class_instance.actual_spends
+        attr_dict["conversion_rate"] = class_instance.conversion_rate
+        attr_dict["modified_spends"] = class_instance.modified_spends
+        attr_dict["modified_sales"] = class_instance.modified_sales
+        attr_dict["response_curve_type"] = class_instance.response_curve_type
+        attr_dict["response_curve_params"] = (
+            class_instance.response_curve_params
+        )
+        attr_dict["penalty"] = class_instance.penalty
+        attr_dict["bounds"] = class_instance.bounds
+        attr_dict["actual_total_spends"] = class_instance.actual_total_spends
+        attr_dict["actual_total_sales"] = class_instance.actual_total_sales
+        attr_dict["modified_total_spends"] = (
+            class_instance.modified_total_spends
+        )
+        attr_dict["modified_total_sales"] = class_instance.modified_total_sales
+        attr_dict["actual_mroi"] = class_instance.get_marginal_roi("actual")
+        attr_dict["modified_mroi"] = class_instance.get_marginal_roi(
+            "modified"
+        )
+
+    elif isinstance(class_instance, Scenario):
+        attr_dict["type"] = "Scenario"
+        attr_dict["name"] = class_instance.name
+        channels = []
+        for channel in class_instance.channels.values():
+            channels.append(class_to_dict(channel))
+        attr_dict["channels"] = channels
+        attr_dict["constant"] = class_instance.constant
+        attr_dict["correction"] = class_instance.correction
+        attr_dict["actual_total_spends"] = class_instance.actual_total_spends
+        attr_dict["actual_total_sales"] = class_instance.actual_total_sales
+        attr_dict["modified_total_spends"] = (
+            class_instance.modified_total_spends
+        )
+        attr_dict["modified_total_sales"] = class_instance.modified_total_sales
+
+    return attr_dict
+
+
+def class_from_dict(attr_dict):
+    if attr_dict["type"] == "Channel":
+        return Channel.from_dict(attr_dict)
+    elif attr_dict["type"] == "Scenario":
+        return Scenario.from_dict(attr_dict)
+
+
+class Channel:
+    def __init__(
+        self,
+        name,
+        dates,
+        spends,
+        response_curve_type,
+        response_curve_params,
+        bounds,
+        conversion_rate=1,
+        modified_spends=None,
+        penalty=True,
+    ):
+        self.name = name
+        self.dates = dates
+        self.conversion_rate = conversion_rate
+        self.actual_spends = spends.copy()
+
+        if modified_spends is None:
+            self.modified_spends = self.actual_spends.copy()
+        else:
+            self.modified_spends = modified_spends
+
+        self.response_curve_type = response_curve_type
+        self.response_curve_params = response_curve_params
+        self.bounds = bounds
+        self.penalty = penalty
+
+        self.upper_limit = self.actual_spends.max() + self.actual_spends.std()
+        self.power = np.ceil(np.log(self.actual_spends.max()) / np.log(10)) - 3
+        # self.actual_sales = None
+        # self.actual_sales = self.response_curve(self.actual_spends)
+        self.actual_total_spends = self.actual_spends.sum()
+        self.actual_total_sales = self.actual_sales.sum()
+        self.modified_sales = self.calculate_sales()
+        self.modified_total_spends = self.modified_spends.sum()
+        self.modified_total_sales = self.modified_sales.sum()
+        self.delta_spends = (
+            self.modified_total_spends - self.actual_total_spends
+        )
+        self.delta_sales = self.modified_total_sales - self.actual_total_sales
+
+    @property
+    def actual_sales(self):
+        return self.response_curve(self.actual_spends)
+
+    def update_penalty(self, penalty):
+        self.penalty = penalty
+
+    def _modify_spends(self, spends_array, total_spends):
+        return spends_array * total_spends / spends_array.sum()
+
+    def modify_spends(self, total_spends):
+        self.modified_spends = (
+            self.modified_spends * total_spends / self.modified_spends.sum()
+        )
+
+    def calculate_sales(self):
+        return self.response_curve(self.modified_spends)
+
+    def response_curve(self, x):
+        if self.penalty:
+            x = np.where(
+                x < self.upper_limit,
+                x,
+                self.upper_limit
+                + (x - self.upper_limit) * self.upper_limit / x,
+            )
+        if self.response_curve_type == "s-curve":
+            if self.power >= 0:
+                x = x / 10**self.power
+            x = x.astype("float64")
+            K = self.response_curve_params["K"]
+            b = self.response_curve_params["b"]
+            a = self.response_curve_params["a"]
+            x0 = self.response_curve_params["x0"]
+            sales = K / (1 + b * np.exp(-a * (x - x0)))
+        if self.response_curve_type == "linear":
+            beta = self.response_curve_params["beta"]
+            sales = beta * x
+
+        return sales
+
+    def get_marginal_roi(self, flag):
+        K = self.response_curve_params["K"]
+        a = self.response_curve_params["a"]
+        # x = self.modified_total_spends
+        # if self.power >= 0 :
+        #     x = x / 10**self.power
+        # x = x.astype('float64')
+        # return K*b*a*np.exp(-a*(x-x0)) / (1 + b * np.exp(-a*(x - x0)))**2
+        if flag == "actual":
+            y = self.response_curve(self.actual_spends)
+            # spends_array = self.actual_spends
+            # total_spends = self.actual_total_spends
+            # total_sales = self.actual_total_sales
+
+        else:
+            y = self.response_curve(self.modified_spends)
+            # spends_array = self.modified_spends
+            # total_spends = self.modified_total_spends
+            # total_sales = self.modified_total_sales
+
+        # spends_inc_1 = self._modify_spends(spends_array, total_spends+1)
+        mroi = a * (y) * (1 - y / K)
+        return mroi.sum() / len(self.modified_spends)
+        # spends_inc_1 = self.spends_array + 1
+        # new_total_sales = self.response_curve(spends_inc_1).sum()
+        # return (new_total_sales - total_sales) / len(self.modified_spends)
+
+    def update(self, total_spends):
+        self.modify_spends(total_spends)
+        self.modified_sales = self.calculate_sales()
+        self.modified_total_spends = self.modified_spends.sum()
+        self.modified_total_sales = self.modified_sales.sum()
+        self.delta_spends = (
+            self.modified_total_spends - self.actual_total_spends
+        )
+        self.delta_sales = self.modified_total_sales - self.actual_total_sales
+
+    def intialize(self):
+        self.new_spends = self.old_spends
+
+    def __str__(self):
+        return f"{self.name},{self.actual_total_sales}, {self.modified_total_spends}"
+
+    @classmethod
+    def from_dict(cls, attr_dict):
+        return Channel(
+            name=attr_dict["name"],
+            dates=attr_dict["dates"],
+            spends=attr_dict["spends"],
+            bounds=attr_dict["bounds"],
+            modified_spends=attr_dict["modified_spends"],
+            response_curve_type=attr_dict["response_curve_type"],
+            response_curve_params=attr_dict["response_curve_params"],
+            penalty=attr_dict["penalty"],
+        )
+
+    def update_response_curves(self, response_curve_params):
+        self.response_curve_params = response_curve_params
+
+
+class Scenario:
+    def __init__(self, name, channels, constant, correction):
+        self.name = name
+        self.channels = channels
+        self.constant = constant
+        self.correction = correction
+
+        self.actual_total_spends = self.calculate_modified_total_spends()
+        self.actual_total_sales = self.calculate_actual_total_sales()
+        self.modified_total_sales = self.calculate_modified_total_sales()
+        self.modified_total_spends = self.calculate_modified_total_spends()
+        self.delta_spends = (
+            self.modified_total_spends - self.actual_total_spends
+        )
+        self.delta_sales = self.modified_total_sales - self.actual_total_sales
+
+    def update_penalty(self, value):
+        for channel in self.channels.values():
+            channel.update_penalty(value)
+
+    def calculate_modified_total_spends(self):
+        total_actual_spends = 0.0
+        for channel in self.channels.values():
+            total_actual_spends += (
+                channel.actual_total_spends * channel.conversion_rate
+            )
+        return total_actual_spends
+
+    def calculate_modified_total_spends(self):
+        total_modified_spends = 0.0
+        for channel in self.channels.values():
+            # import streamlit as st
+            # st.write(channel.modified_total_spends )
+            total_modified_spends += (
+                channel.modified_total_spends * channel.conversion_rate
+            )
+        return total_modified_spends
+
+    def calculate_actual_total_sales(self):
+        total_actual_sales = self.constant.sum() + self.correction.sum()
+        for channel in self.channels.values():
+            total_actual_sales += channel.actual_total_sales
+        return total_actual_sales
+
+    def calculate_modified_total_sales(self):
+        total_modified_sales = self.constant.sum() + self.correction.sum()
+        for channel in self.channels.values():
+            total_modified_sales += channel.modified_total_sales
+        return total_modified_sales
+
+    def update(self, channel_name, modified_spends):
+        self.channels[channel_name].update(modified_spends)
+        self.modified_total_sales = self.calculate_modified_total_sales()
+        self.modified_total_spends = self.calculate_modified_total_spends()
+        self.delta_spends = (
+            self.modified_total_spends - self.actual_total_spends
+        )
+        self.delta_sales = self.modified_total_sales - self.actual_total_sales
+
+    # def optimize_spends(self, sales_percent, channels_list, algo="COBYLA"):
+    #     desired_sales = self.actual_total_sales * (1 + sales_percent / 100.0)
+
+    #     def constraint(x):
+    #         for ch, spends in zip(channels_list, x):
+    #             self.update(ch, spends)
+    #         return self.modified_total_sales - desired_sales
+
+    #     bounds = []
+    #     for ch in channels_list:
+    #         bounds.append(
+    #             (1 + np.array([-50.0, 100.0]) / 100.0)
+    #             * self.channels[ch].actual_total_spends
+    #         )
+
+    #     initial_point = []
+    #     for bound in bounds:
+    #         initial_point.append(bound[0])
+
+    #     power = np.ceil(np.log(sum(initial_point)) / np.log(10))
+
+    #     constraints = [NonlinearConstraint(constraint, -1.0, 1.0)]
+
+    #     res = minimize(
+    #         lambda x: sum(x) / 10 ** (power),
+    #         bounds=bounds,
+    #         x0=initial_point,
+    #         constraints=constraints,
+    #         method=algo,
+    #         options={"maxiter": int(2e7), "catol": 1},
+    #     )
+
+    #     for channel_name, modified_spends in zip(channels_list, res.x):
+    #         self.update(channel_name, modified_spends)
+
+    #     return zip(channels_list, res.x)
+
+    def optimize_spends(
+        self, sales_percent, channels_list, algo="trust-constr"
+    ):
+        desired_sales = self.actual_total_sales * (1 + sales_percent / 100.0)
+
+        def constraint(x):
+            for ch, spends in zip(channels_list, x):
+                self.update(ch, spends)
+            return self.modified_total_sales - desired_sales
+
+        bounds = []
+        for ch in channels_list:
+            bounds.append(
+                (1 + np.array([-50.0, 100.0]) / 100.0)
+                * self.channels[ch].actual_total_spends
+            )
+
+        initial_point = []
+        for bound in bounds:
+            initial_point.append(bound[0])
+
+        power = np.ceil(np.log(sum(initial_point)) / np.log(10))
+
+        constraints = [NonlinearConstraint(constraint, -1.0, 1.0)]
+
+        res = minimize(
+            lambda x: sum(x) / 10 ** (power),
+            bounds=bounds,
+            x0=initial_point,
+            constraints=constraints,
+            method=algo,
+            options={"maxiter": int(2e7), "xtol": 100},
+        )
+
+        for channel_name, modified_spends in zip(channels_list, res.x):
+            self.update(channel_name, modified_spends)
+
+        return zip(channels_list, res.x)
+
+    def optimize(self, spends_percent, channels_list):
+        # channels_list = self.channels.keys()
+        num_channels = len(channels_list)
+        spends_constant = []
+        spends_constraint = 0.0
+        for channel_name in channels_list:
+            # spends_constraint += self.channels[channel_name].modified_total_spends
+            spends_constant.append(self.channels[channel_name].conversion_rate)
+            spends_constraint += (
+                self.channels[channel_name].actual_total_spends
+                * self.channels[channel_name].conversion_rate
+            )
+        spends_constraint = spends_constraint * (1 + spends_percent / 100)
+        # constraint= LinearConstraint(np.ones((num_channels,)), lb = spends_constraint, ub = spends_constraint)
+        constraint = LinearConstraint(
+            np.array(spends_constant),
+            lb=spends_constraint,
+            ub=spends_constraint,
+        )
+        bounds = []
+        old_spends = []
+        for channel_name in channels_list:
+            _channel_class = self.channels[channel_name]
+            channel_bounds = _channel_class.bounds
+            channel_actual_total_spends = (
+                _channel_class.actual_total_spends
+                * ((1 + spends_percent / 100))
+            )
+            old_spends.append(channel_actual_total_spends)
+            bounds.append(
+                (1 + channel_bounds / 100) * channel_actual_total_spends
+            )
+
+        def objective_function(x):
+            for channel_name, modified_spends in zip(channels_list, x):
+                self.update(channel_name, modified_spends)
+            return -1 * self.modified_total_sales
+
+        res = minimize(
+            lambda x: objective_function(x) / 1e8,
+            method="trust-constr",
+            x0=old_spends,
+            constraints=constraint,
+            bounds=bounds,
+            options={"maxiter": int(1e7), "xtol": 100},
+        )
+        # res = dual_annealing(
+        # objective_function,
+        # x0=old_spends,
+        # mi
+        # constraints=constraint,
+        # bounds=bounds,
+        # tol=1e-16
+        # )
+        print(res)
+        for channel_name, modified_spends in zip(channels_list, res.x):
+            self.update(channel_name, modified_spends)
+
+        return zip(channels_list, res.x)
+
+    def save(self):
+        details = {}
+        actual_list = []
+        modified_list = []
+        data = {}
+        channel_data = []
+
+        summary_rows = []
+        actual_list.append(
+            {
+                "name": "Total",
+                "Spends": self.actual_total_spends,
+                "Sales": self.actual_total_sales,
+            }
+        )
+        modified_list.append(
+            {
+                "name": "Total",
+                "Spends": self.modified_total_spends,
+                "Sales": self.modified_total_sales,
+            }
+        )
+        for channel in self.channels.values():
+            name_mod = channel.name.replace("_", " ")
+            if name_mod.lower().endswith(" imp"):
+                name_mod = name_mod.replace("Imp", " Impressions")
+            summary_rows.append(
+                [
+                    name_mod,
+                    channel.actual_total_spends,
+                    channel.modified_total_spends,
+                    channel.actual_total_sales,
+                    channel.modified_total_sales,
+                    round(
+                        channel.actual_total_sales
+                        / channel.actual_total_spends,
+                        2,
+                    ),
+                    round(
+                        channel.modified_total_sales
+                        / channel.modified_total_spends,
+                        2,
+                    ),
+                    channel.get_marginal_roi("actual"),
+                    channel.get_marginal_roi("modified"),
+                ]
+            )
+            data[channel.name] = channel.modified_spends
+            data["Date"] = channel.dates
+            data["Sales"] = (
+                data.get("Sales", np.zeros((len(channel.dates),)))
+                + channel.modified_sales
+            )
+            actual_list.append(
+                {
+                    "name": channel.name,
+                    "Spends": channel.actual_total_spends,
+                    "Sales": channel.actual_total_sales,
+                    "ROI": round(
+                        channel.actual_total_sales
+                        / channel.actual_total_spends,
+                        2,
+                    ),
+                }
+            )
+            modified_list.append(
+                {
+                    "name": channel.name,
+                    "Spends": channel.modified_total_spends,
+                    "Sales": channel.modified_total_sales,
+                    "ROI": round(
+                        channel.modified_total_sales
+                        / channel.modified_total_spends,
+                        2,
+                    ),
+                    "Marginal ROI": channel.get_marginal_roi("modified"),
+                }
+            )
+
+            channel_data.append(
+                {
+                    "channel": channel.name,
+                    "spends_act": channel.actual_total_spends,
+                    "spends_mod": channel.modified_total_spends,
+                    "sales_act": channel.actual_total_sales,
+                    "sales_mod": channel.modified_total_sales,
+                }
+            )
+        summary_rows.append(
+            [
+                "Total",
+                self.actual_total_spends,
+                self.modified_total_spends,
+                self.actual_total_sales,
+                self.modified_total_sales,
+                round(self.actual_total_sales / self.actual_total_spends, 2),
+                round(
+                    self.modified_total_sales / self.modified_total_spends, 2
+                ),
+                0.0,
+                0.0,
+            ]
+        )
+        details["Actual"] = actual_list
+        details["Modified"] = modified_list
+        columns_index = pd.MultiIndex.from_product(
+            [[""], ["Channel"]], names=["first", "second"]
+        )
+        columns_index = columns_index.append(
+            pd.MultiIndex.from_product(
+                [["Spends", "NRPU", "ROI", "MROI"], ["Actual", "Simulated"]],
+                names=["first", "second"],
+            )
+        )
+        details["Summary"] = pd.DataFrame(summary_rows, columns=columns_index)
+        data_df = pd.DataFrame(data)
+        channel_list = list(self.channels.keys())
+        data_df = data_df[["Date", *channel_list, "Sales"]]
+
+        details["download"] = {
+            "data_df": data_df,
+            "channels_df": pd.DataFrame(channel_data),
+            "total_spends_act": self.actual_total_spends,
+            "total_sales_act": self.actual_total_sales,
+            "total_spends_mod": self.modified_total_spends,
+            "total_sales_mod": self.modified_total_sales,
+        }
+
+        return details
+
+    @classmethod
+    def from_dict(cls, attr_dict):
+        channels_list = attr_dict["channels"]
+        channels = {
+            channel["name"]: class_from_dict(channel)
+            for channel in channels_list
+        }
+        return Scenario(
+            name=attr_dict["name"],
+            channels=channels,
+            constant=attr_dict["constant"],
+            correction=attr_dict["correction"],
+        )

config.yaml

@@ -0,0 +1,32 @@
+credentials:
+  usernames:
+    geetha:
+      email: geethu4444@gmail.com
+      name: Geetha Krishna
+      password: '$2b$12$r.KJDzrp6kFErWwh/n7vh.eSvXNU60HBDjrQrNQqkqOH8KSlVacMu'
+    manoj:
+      email: manojp1732@gmail.com
+      name: Manoj P
+      password: '$2b$12$r.KJDzrp6kFErWwh/n7vh.eSvXNU60HBDjrQrNQqkqOH8KSlVacMu'
+    samkeet:
+      email: samkeet.sangai@blend360.com
+      name: 'Samkeet Sangai'
+      password: '$2b$12$r.KJDzrp6kFErWwh/n7vh.eSvXNU60HBDjrQrNQqkqOH8KSlVacMu'
+    srishti:
+      email: srishti.verma@blend360.com
+      name: 'Srishti Verma'
+      password: '$2b$12$r.KJDzrp6kFErWwh/n7vh.eSvXNU60HBDjrQrNQqkqOH8KSlVacMu'
+    Ismail:
+      email: mohammed.ismail@blend360.com
+      name: 'Ismail mohammed'
+      password: '$2b$12$r.KJDzrp6kFErWwh/n7vh.eSvXNU60HBDjrQrNQqkqOH8KSlVacMu'
+
+cookie:
+  expiry_days: 1
+  key: some_signature_key
+  name: some_cookie_name
+preauthorized:
+  emails:
+  - geethu4444@gmail.com
+  - manojp1732@gmail.com
+  - samkeet@gmail.com

data_test_overview_panel_#total_approved_accounts_appsflyer.xlsx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:31cebbaf2ba1e644eb5cf98e537f5d01171be4d3ea6b7a298800d4be28da0b92
+size 1634822

db_creation.py

@@ -0,0 +1,98 @@
+import sqlite3
+import uuid
+import json
+import streamlit as st
+from utilities import (
+    load_local_css,
+    set_header,
+    load_authenticator,
+    send_email,
+)
+import streamlit_authenticator as stauth
+import yaml
+from yaml import SafeLoader
+
+
+
+# st.set_page_config(layout="wide")
+# load_local_css("styles.css")
+# set_header()
+# for k, v in st.session_state.items():
+#     if k not in ["logout", "login", "config"] and not k.startswith(
+#         "FormSubmitter"
+#     ):
+#         st.session_state[k] = v
+# with open("config.yaml") as file:
+#     config = yaml.load(file, Loader=SafeLoader)
+#     st.session_state["config"] = config
+# authenticator = stauth.Authenticate(
+#     config["credentials"],
+#     config["cookie"]["name"],
+#     config["cookie"]["key"],
+#     config["cookie"]["expiry_days"],
+#     config["preauthorized"],
+# )
+# st.session_state["authenticator"] = authenticator
+# name, authentication_status, username = authenticator.login("Login", "main")
+# auth_status = st.session_state.get("authentication_status")
+# if auth_status == True:
+#     authenticator.logout("Logout", "main")
+#     is_state_initiaized = st.session_state.get("initialized", False)
+#     if not is_state_initiaized:
+database_file = r'C:\Users\ManojP\Documents\Mastercard\Build\DB_Sample\DB\User.db'
+
+conn = sqlite3.connect(database_file)
+c = conn.cursor()
+
+
+#c.execute('DROP TABLE  IF EXISTS users  ')
+# c.execute('DROP TABLE  IF EXISTS sessions ')
+# conn.commit()
+
+#output = c.fetchall()
+
+#st.write(output)
+
+c.execute('''CREATE TABLE IF NOT EXISTS users
+            (user_id INTEGER PRIMARY KEY,
+            username TEXT,
+            email TEXT,
+            user_type TEXT )''')
+
+c.execute('''CREATE TABLE IF NOT EXISTS sessions
+            (user_id INTEGER,
+             owner TEXT,
+            session_id INTEGER,
+            session_name TEXT,
+            status TEXT,
+            created_time TIMESTAMP,
+            updated_time TIMESTAMP,
+            allowed_users TEXT)''')
+
+#c.execute("DELETE FROM sessions")
+
+
+ 
+user_id = str(uuid.uuid4())
+
+
+
+
+# c.executemany("INSERT INTO users (username, email,user_type) VALUES (?, ?,?)",
+#         [("Geetha Krishna", "geetha1732@gmail.com","technical"),
+#         ("Samkeet Sangai", "samkeet.sangai@blend360.com","technical"),
+#         ('Manoj P','manojp1732@gmail.com',"technical"),
+#         ('Srishti Verma','srishti.verma@blend360.com',"technical"),
+#         ('Ismail mohammed',"mohammed.ismail@blend360.com","technical"),
+#         ('Sharon Sheng','sharon.sheng@mastercard.com',"technical"),
+#         ('Ioannis Papadopoulos','ioannis.papadopoulos@mastercard.com',"business"),
+#         ('Herman Kwong',"herman.kwong@mastercard.com",'technical'),
+
+#         ])
+
+
+
+
+conn.commit()
+
+

model_output.csv

@@ -0,0 +1,6 @@
+,Model_object,Model_iteration,Feature_set,MAPE,R2,ADJR2,pos_count
+0,Model/model_0.pkl,0,"['paid_search_clicks_lag_1_moving_average_1_saturation_20_power_2_adstock_0_7', 'kwai_clicks_lag_2_moving_average_1_saturation_10_power_4_adstock_0_7', 'fb_level_achieved_tier_2_clicks_lag_2_moving_average_2_saturation_20_power_2_adstock_0_7', 'fb_level_achieved_tier_1_impressions_lag_2_moving_average_2_saturation_10_power_2_adstock_0_7', 'ga_app_clicks', 'digital_tactic_others_clicks_lag_2_moving_average_2_saturation_20_power_2_adstock_0_7', 'programmatic_clicks_lag_2_moving_average_1_saturation_10_power_2_adstock_0_7']",0.2246671699826956,0.8695595237589726,0.8694321404813935,8
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