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| import streamlit as st | |
| import pandas as pd | |
| import seaborn as sns | |
| import matplotlib.pyplot as plt | |
| import pickle | |
| import numpy as np | |
| import base64 | |
| from pycaret.classification import load_model, predict_model | |
| def load_data(file): | |
| data = pd.read_csv(file) | |
| return data | |
| st.set_option('deprecation.showPyplotGlobalUse', False) | |
| def load_data(dataset): | |
| df = pd.read_csv(dataset) | |
| return df | |
| st.sidebar.image("poyo.jpg",width=200) | |
| def main(): | |
| st.markdown("<h1 style='text-align:center;color: brown;'>Application de Credit</h1>",unsafe_allow_html=True) | |
| st.markdown("<h2 style='text-align:center;color: black;'>Welcome to my App</h2>",unsafe_allow_html=True) | |
| menu=["Home","Data Analysis","Data Visualisation","Machine Learning"] | |
| choice = st.sidebar.selectbox("select Menu",menu) | |
| if choice == "Home": | |
| left,middle,right = st.columns((2,3,2)) | |
| with middle: | |
| st.image("poo.jpg",width=400) | |
| st.write("Nous travaillons sur le Dataset Loan+Prediction ") | |
| st.subheader("Information de Crédit") | |
| st.write("Nous effectuerons une classification sur ce Dataset (Loan+Approval+Prediction.csv)") | |
| st.write("Visualisation du Dataset") | |
| data = load_data("Loan+Approval+Prediction.csv") | |
| st.write(data.head()) | |
| if choice == "Data Analysis": | |
| st.subheader("Credit DataFrame") | |
| data = load_data("Loan+Approval+Prediction.csv") | |
| st.write(data.head(5)) | |
| if st.checkbox("Summary"): | |
| st.write(data.describe().head()) | |
| elif st.checkbox("Corrélation"): | |
| plt.figure(figsize=(15,15)) | |
| st.write(sns.heatmap(data.corr(),annot=True)) | |
| st.pyplot() | |
| if choice == "Data Visualisation": | |
| if st.checkbox("Countplot"): | |
| fig = plt.figure(figsize = (5,5)) | |
| data = load_data('Loan+Approval+Prediction.csv') | |
| sns.countplot(x="Credit_History",data=data) | |
| st.pyplot(fig) | |
| if st.checkbox("Scatter"): | |
| fig = plt.figure(figsize = (8,8)) | |
| data = load_data('Loan+Approval+Prediction.csv') | |
| sns.scatterplot(x="ApplicantIncome",y="CoapplicantIncome",data=data,hue="Loan_Status") | |
| st.pyplot(fig) | |
| if choice == "Machine Learning": | |
| tab1, tab2, tab3 = st.tabs([":clipboard: Data",":bar_chart: Visualisation", ":mask: :smile: Prediction"]) | |
| uploaded_file = st.sidebar.file_uploader("Upload your input CSV file", type=["csv"]) | |
| if uploaded_file is not None: | |
| df = load_data(uploaded_file) | |
| with tab1: | |
| st.subheader("Loaded dataset") | |
| st.write(df) | |
| with tab2: | |
| st.subheader("Histplot") | |
| fig = plt.figure(figsize = (8,8)) | |
| data = load_data(uploaded_file) | |
| sns.histplot(x = "ApplicantIncome",data=data) | |
| st.pyplot(fig) | |
| with tab3: | |
| data =load_model("class_loan_model") | |
| prediction = data.predict(df) | |
| st.subheader('Prediction') | |
| pp = pd.DataFrame(prediction,columns=["Prediction"]) | |
| ndf = pd.concat([df,pp],axis=1) | |
| ndf.Prediction.replace(0, "Pas crédit ", inplace = True) | |
| ndf.Prediction.replace(1, "Crédit accordé", inplace = True) | |
| st.write(ndf) | |
| def filedownload(df): | |
| csv = df.to_csv(index=False) | |
| b64 = base64.b64encode(csv.encode()).decode() # strings <-> bytes conversions | |
| href = f'<a href="data:file/csv;base64,{b64}" download="pret_predictions.csv">Download CSV File</a>' | |
| return href | |
| button = st.button("Download") | |
| if button: | |
| st.markdown(filedownload(ndf), unsafe_allow_html=True) | |
| if __name__=='__main__': | |
| main() |