app restarted

app.py

@@ -0,0 +1,291 @@
+import pandas as pd
+
+df=pd.read_csv("creditcardfrauddataset.csv")
+df.head()
+
+df.columns
+
+df.info()
+
+df.describe()
+
+df.describe().T
+
+from ydata_profiling import ProfileReport
+profile = ProfileReport(df, title = "Pandas Profiling Report")
+profile.to_file("pandas_profiling_report.html")
+
+df.isnull().sum()
+
+df.duplicated().sum()
+
+df.skew()
+
+x = df.drop(labels=['default payment next month'], axis = 1)
+y = df[['default payment next month']]
+
+x
+
+y
+
+import seaborn as sns
+sns.pairplot(df)
+
+import matplotlib.pyplot as plt
+plt.figure(figsize=(20,20))
+sns.heatmap(df.corr(), annot = True)
+plt.show()
+
+df.shape
+
+from sklearn.impute import SimpleImputer
+from sklearn.preprocessing import StandardScaler
+from sklearn.preprocessing import OrdinalEncoder
+from sklearn.pipeline import Pipeline
+from sklearn.compose import ColumnTransformer
+from sklearn.model_selection import train_test_split
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.svm import SVC
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.naive_bayes import GaussianNB
+from sklearn.metrics import classification_report, confusion_matrix, accuracy_score
+import warnings
+warnings.filterwarnings("ignore")
+
+pipeline=Pipeline(
+    steps=[
+      ('imputer',SimpleImputer(strategy='median')),
+      ('scaler',StandardScaler())
+    ]
+)
+
+preprocessor=ColumnTransformer([
+    ('num_pipeline',pipeline,x.columns)
+    ]
+)
+
+x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.30,random_state=30)
+
+x_train=pd.DataFrame(preprocessor.fit_transform(x_train),columns=preprocessor.get_feature_names_out())
+x_test=pd.DataFrame(preprocessor.transform(x_test),columns=preprocessor.get_feature_names_out())
+
+import numpy as np
+def evaluate_model(true, predicted):
+    ac = accuracy_score(true,predicted)
+    cm = confusion_matrix(true,predicted)
+    cr = classification_report(true,predicted)
+    return ac,cm,cr
+
+
+models={
+    'DecisionTreeClassifier':DecisionTreeClassifier(),
+    'SVC':SVC(),
+    'RandomForestClassifier':RandomForestClassifier(),
+    'Gaussian Naive Bayes':GaussianNB()
+}
+trained_model_list=[]
+model_list=[]
+ac_list=[]
+
+for i in range(len(list(models))):
+    model=list(models.values())[i]
+    model.fit(x_train,y_train)
+
+    #Make Predictions
+    y_pred=model.predict(x_test)
+
+    ac,cm,cr=evaluate_model(y_test,y_pred)
+
+    print(list(models.keys())[i])
+    model_list.append(list(models.keys())[i])
+
+    print('Model Training Performance')
+    print("Accuracy Score:",ac)
+    print("Confusion Matrix:",cm)
+    print("Classification Report:",cr)
+
+    ac_list.append(ac)
+
+    print('='*35)
+    print('\n')
+
+from sklearn.model_selection import GridSearchCV
+param_gridDTC = {
+    'criterion': ['gini', 'entropy'],
+    'max_depth': [5, 10, 20],
+    'min_samples_split': [2, 5, 10],
+    'min_samples_leaf': [1, 2, 4],
+    'max_features': ['auto', 'sqrt', 'log2']
+    }
+param_gridSVC = {
+    'C': [0.1, 1, 10],
+    'kernel': ['linear', 'rbf', 'poly'],
+    'gamma': ['scale', 'auto'],
+    'degree': [2, 3, 4]
+    }
+param_gridGNB = {
+    'var_smoothing': [1e-3, 1e-4, 1e-5, 1e-6]
+    }
+param_gridRFC = {
+    'n_estimators': [50, 100, 200],
+    'max_depth': [10, 20, 30],
+    'min_samples_split': [2, 5, 10],
+    'min_samples_leaf': [1, 2, 4],
+    'max_features': ['auto', 'sqrt', 'log2'],
+    'bootstrap': [True, False]
+    }
+
+gridDTC = GridSearchCV(estimator = DecisionTreeClassifier(), param_grid = param_gridDTC, cv = 5, verbose = 3)
+
+gridSVC = GridSearchCV(estimator = SVC(), param_grid = param_gridSVC, cv = 5, verbose = 3)
+
+gridGNB = GridSearchCV(estimator = GaussianNB(), param_grid = param_gridGNB, cv = 5, verbose = 3)
+
+gridRFC = GridSearchCV(estimator = RandomForestClassifier(), param_grid = param_gridRFC, cv = 5, verbose = 3)
+
+gridDTC.fit(x_train,y_train)
+
+gridSVC.fit(x_train,y_train)
+
+gridGNB.fit(x_train,y_train)
+
+gridRFC.fit(x_train,y_train)
+
+gridDTC.best_estimator_
+
+gridSVC.best_estimator_
+
+gridGNB.best_estimator_
+
+gridRFC.best_estimator_
+
+gridDTC.best_params_
+
+gridSVC.best_params_
+
+gridGNB.best_params_
+
+gridRFC.best_params_
+
+dtc=DecisionTreeClassifier(criterion='entropy', max_depth=5, max_features='sqrt',
+                       min_samples_leaf=2, min_samples_split=10)
+
+svc=SVC(C=1, degree=2, gamma='auto', kernel='rbf')
+
+gnb=GaussianNB(var_smoothing=0.001)
+
+rfc=RandomForestClassifier(bootstrap=False, max_depth=20, max_features='log2',
+                       min_samples_leaf=4, min_samples_split=10, n_estimators=200)
+
+import numpy as np
+def evaluate_model(true, predicted):
+    ac = accuracy_score(true,predicted)
+    cm = confusion_matrix(true,predicted)
+    cr = classification_report(true,predicted)
+    return ac,cm,cr
+
+
+models={
+    'DecisionTreeClassifier':dtc,
+    'SVC':svc,
+    'RandomForestClassifier':rfc,
+    'Gaussian Naive Bayes':gnb
+}
+trained_model_list=[]
+model_list=[]
+ac_list=[]
+
+for i in range(len(list(models))):
+    model=list(models.values())[i]
+    model.fit(x_train,y_train)
+
+    #Make Predictions
+    y_pred=model.predict(x_test)
+
+    ac,cm,cr=evaluate_model(y_test,y_pred)
+
+    print(list(models.keys())[i])
+    model_list.append(list(models.keys())[i])
+
+    print('Model Training Performance')
+    print("Accuracy Score:",ac)
+    print("Confusion Matrix:",cm)
+    print("Classification Report:",cr)
+
+    ac_list.append(ac)
+
+    print('='*35)
+    print('\n')
+
+import gradio as gr
+
+def default_prediction(LIMIT_BAL,SEX,EDUCATION,MARRIAGE,AGE,PAY_0,PAY_2,PAY_3,PAY_4,PAY_5,PAY_6,BILL_AMT1,BILL_AMT2,BILL_AMT3,BILL_AMT4,BILL_AMT5,BILL_AMT6,PAY_AMT1,PAY_AMT2,PAY_AMT3,PAY_AMT4,PAY_AMT5,PAY_AMT6):
+    data = {
+        'LIMIT_BAL': [LIMIT_BAL],
+        'SEX': [SEX],
+        'EDUCATION': [EDUCATION],
+        'MARRIAGE': [MARRIAGE],
+        'AGE': [AGE],
+        'PAY_0': [PAY_0],
+        'PAY_2': [PAY_2],
+        'PAY_3': [PAY_3],
+        'PAY_4': [PAY_4],
+        'PAY_5': [PAY_5],
+        'PAY_6': [PAY_6],
+        'BILL_AMT1': [BILL_AMT1],
+        'BILL_AMT2': [BILL_AMT2],
+        'BILL_AMT3': [BILL_AMT3],
+        'BILL_AMT4': [BILL_AMT4],
+        'BILL_AMT5': [BILL_AMT5],
+        'BILL_AMT6': [BILL_AMT6],
+        'PAY_AMT1': [PAY_AMT1],
+        'PAY_AMT2': [PAY_AMT2],
+        'PAY_AMT3': [PAY_AMT3],
+        'PAY_AMT4': [PAY_AMT4],
+        'PAY_AMT5': [PAY_AMT5],
+        'PAY_AMT6': [PAY_AMT6]
+    }
+
+    data_df = pd.DataFrame(data)
+
+    processed_data = preprocessor.transform(data_df)
+
+    default_predict = rfc.predict(processed_data)
+    if default_predict[0]==0:
+      return "The person will not default"
+    else:
+      return "The person will default"
+
+iface = gr.Interface(
+    fn=default_prediction,
+    inputs=[
+        gr.Number(label='Balance Limit(Maximum Credit Limit for the User)'),
+        gr.Dropdown(choices=['1','2'],label='Sex(1 for Male & 2 for Female)'),
+        gr.Dropdown(choices=['1', '2', '3', '4', '5', '6'],label='Education(1(Graduate School),2(University),3(High School), 4(Middle School), 5(Elementary School), 6(Illiterate))'),
+        gr.Dropdown(choices=['0','1','2','3'],label='Marriage(0(Others),1(Married),2(Single),3(Divorced))'),
+        gr.Number(label='Age(Enter your Age in Years)'),
+        gr.Dropdown(choices=['-2','-1','0','1','2','3','4','5','6','7','8'],label='Pay_0(Payment status for last month)'),
+        gr.Dropdown(choices=['-2','-1','0','1','2','3','4','5','6','7','8'],label='Pay_2(Payment status for 2 months ago)'),
+        gr.Dropdown(choices=['-2','-1','0','1','2','3','4','5','6','7','8'],label='Pay_3(Payment status for 3 months ago'),
+        gr.Dropdown(choices=['-2','-1','0','1','2','3','4','5','6','7','8'],label='Pay_4(Payment status for 4 months ago)'),
+        gr.Dropdown(choices=['-2','-1','0','1','2','3','4','5','6','7','8'],label='Pay_5(Payment status for 5 months ago)'),
+        gr.Dropdown(choices=['-2','-1','0','1','2','3','4','5','6','7','8'],label='Pay_6(Payment status for 6 months ago)'),
+        gr.Number(label='Bill Amount 1(Bill Amount for last month)'),
+        gr.Number(label='Bill Amount 2(Bill Amount for 2 months ago)'),
+        gr.Number(label='Bill Amount 3(Bill Amount for 3 months ago)'),
+        gr.Number(label='Bill Amount 4(Bill Amount for 4 months ago)'),
+        gr.Number(label='Bill Amount 5(Bill Amount for 5 months ago)'),
+        gr.Number(label='Bill Amount 6(Bill Amount for 6 months ago)'),
+        gr.Number(label='Pay Amount 1(Amount paid for last month)'),
+        gr.Number(label='Pay Amount 2(Amount paid for 2 months ago)'),
+        gr.Number(label='Pay Amount 3(Amount paid for 3 months ago)'),
+        gr.Number(label='Pay Amount 4(Amount paid for 4 months ago)'),
+        gr.Number(label='Pay Amount 5(Amount paid for 5 months ago)'),
+        gr.Number(label='Pay Amount 6(Amount paid for 6 months ago)')
+    ],
+    outputs="text",
+    title="Credit Card Default Predictor",
+    description="Enter credit card related details to predict default"
+)
+
+iface.launch(debug=True,share=True)

creditcardfrauddataset.csv

@@ -0,0 +1,1002 @@
+LIMIT_BAL,SEX,EDUCATION,MARRIAGE,AGE,PAY_0,PAY_2,PAY_3,PAY_4,PAY_5,PAY_6,BILL_AMT1,BILL_AMT2,BILL_AMT3,BILL_AMT4,BILL_AMT5,BILL_AMT6,PAY_AMT1,PAY_AMT2,PAY_AMT3,PAY_AMT4,PAY_AMT5,PAY_AMT6,default payment next month
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requirements.txt

@@ -0,0 +1,10 @@
+pandas
+numpy
+ydata-profiling
+matplotlib
+seaborn
+scikit-learn
+pydantic
+visions
+numba
+gradio