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initial commit vers 2
Browse files- .gitattributes +1 -0
- app.py +78 -0
- dataset.csv +3 -0
- img.png +0 -0
.gitattributes
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@@ -32,3 +32,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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dataset.csv filter=lfs diff=lfs merge=lfs -text
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app.py
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# imports
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# ====================================
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import numpy as np
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import pandas as pd
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import seaborn as sns
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from random import randint
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import matplotlib.pyplot as plt
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import streamlit as st
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import streamlit.components.v1 as components
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#from sklearn.linear_model import LogisticRegression
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#from sklearn.svm import SVC
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#from sklearn.neighbors import KNeighborsClassifier
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#from sklearn.tree import DecisionTreeClassifier
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#from sklearn.ensemble import RandomForestClassifier
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#from sklearn.model_selection import train_test_split
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#from sklearn.model_selection import StratifiedKFold
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#from imblearn.pipeline import make_pipeline as imbalanced_make_pipeline
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#from imblearn.over_sampling import SMOTE
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#from sklearn.model_selection import RandomizedSearchCV
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#from sklearn.metrics import classification_report, confusion_matrix, f1_score,accuracy_score, precision_score, recall_score, roc_auc_score
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#from sklearn.feature_selection import SelectKBest
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#from sklearn.feature_selection import f_classif
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#import warnings
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#warnings.filterwarnings("ignore")
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# load upper
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# ==================================
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components.html(
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"""
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<a href="https://git.io/typing-svg"><img src="https://readme-typing-svg.herokuapp.com?font=Fira+Code&pause=1000&width=435&lines=Анализ+банкротства+компании" alt="Typing SVG" /></a>
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<a href="https://git.io/typing-svg"><img src="https://readme-typing-svg.herokuapp.com?font=Fira+Code&pause=1000&width=435&lines=методами+искуственного+интеллекта" alt="Typing SVG" /></a>
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"""
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)
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st.markdown("<h1 style='text-align: center;'>Применение методов машинного обучения в анализе банкротства</h1>", unsafe_allow_html=True)
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components.html(
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"""
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<img src="https://fincult.info/upload/als-property-editorblock/4a2/4a278980ab4958de5e75aa5290842d77.png" align="center">
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"""
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)
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#with open("D:\dev\to_git\test_task_ranhigs\Company_bankruptcy_prediction\for_web\img.png", "rb") as f:
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# st.image(f.read(), use_column_width=True)
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with st.expander("ℹ️ - О приложении", expanded=True):
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st.write(
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"""
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- Это приложение — это простой в использовании интерфейс, встроенный в специальную библиотеку Streamlit.
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- В том числе и сам алгоритм машинного обучения, который можно использовать через форму
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"""
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)
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st.write(
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"""
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# Краткое описание
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"""
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)
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# cleaning data
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# ==================================
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data = pd.read_csv("D:\dev\to_git\test_task_ranhigs\Company_bankruptcy_prediction\for_web\dataset.csv")
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data.columns = [i.title().strip() for i in list(data.columns)]
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row = data.shape[0]
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col = data.shape[1]
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text = print("The number of rows within the dataset are {} and the number of columns is {}".format(row,col))
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dataset.csv
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version https://git-lfs.github.com/spec/v1
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oid sha256:67bf2e7c75490f7ad3f76bbce57d49cdc25967cdab607527b94f944863fa14d8
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size 11456101
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img.png
ADDED
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