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| import streamlit as st | |
| import streamlit.components.v1 as components | |
| import pandas as pd | |
| import pickle | |
| import matplotlib.pyplot as plt | |
| from pybanking.churn_prediction import model_churn | |
| from pybanking.EDA import data_analysis | |
| import sklearn.metrics as metrics | |
| from mlxtend.plotting import plot_confusion_matrix | |
| import streamlit.components.v1 as components | |
| from PIL import Image | |
| st.set_page_config(page_title="Customer Churn Prediction Model", layout="wide") | |
| col1,col2 = st.columns([1,2]) | |
| with col1: | |
| image = Image.open('Shorthills.png') | |
| st.image(image) | |
| with col2: | |
| st.title('Customer Churn Prediction Model') | |
| df = model_churn.get_data() | |
| option2 = st.selectbox( | |
| 'Which dataset would you like to use for prediction?', | |
| ['Sample Dataset', 'Upload Custom'] | |
| ) | |
| if option2 == 'Upload Custom': | |
| file = st.file_uploader("Choose a file") | |
| if file is not None: | |
| #read csv | |
| df = pd.read_csv(file) | |
| else: | |
| st.warning("you need to upload a csv file.") | |
| st.subheader('This is the Selected Data') | |
| st.dataframe(df.head(5)) | |
| analysis_class = data_analysis.Analysis() | |
| option3 = st.selectbox( | |
| 'Select Exploratory Data Analysis type', | |
| ['None', 'DataPrep', 'SweetViz', 'PandasProfiling'] | |
| ) | |
| if option3 == 'SweetViz': | |
| res = analysis_class.sweetviz_analysis(df) | |
| res.show_html(filepath='SweetViz.html', open_browser=True, layout='widescreen', scale=None) | |
| HtmlFile = open('SweetViz.html', 'r', encoding='utf-8') | |
| source_code = HtmlFile.read() | |
| with st.expander("See Report"): | |
| components.html(source_code, height=600, scrolling=True) | |
| elif option3 == 'DataPrep': | |
| res = analysis_class.dataprep_analysis(df) | |
| # res.show_browser() | |
| res.save('DataPrep.html') | |
| HtmlFile = open('DataPrep.html', 'r', encoding='utf-8') | |
| source_code = HtmlFile.read() | |
| with st.expander("See Report"): | |
| components.html(source_code, height=600, scrolling=True) | |
| elif option3 == 'PandasProfiling': | |
| res = analysis_class.pandas_analysis(df) | |
| res.to_file("PandasProfiling.html") | |
| HtmlFile = open('PandasProfiling.html', 'r', encoding='utf-8') | |
| source_code = HtmlFile.read() | |
| with st.expander("See Report"): | |
| components.html(source_code, height=600, scrolling=True) | |
| model_names = [ | |
| "Logistic_Regression", | |
| "Support_Vector_Machine", | |
| "Support_Vector_Machine_Optimized", | |
| "Decision_Tree", | |
| "Neural_Network", | |
| "Random_Forest", | |
| "Pycaret_Best" | |
| ] | |
| option = st.selectbox( | |
| 'Select a model to be used', | |
| model_names | |
| ) | |
| model = pickle.load(open(option+'.pkl', 'rb')) | |
| st.write("Model Loaded : ", option) | |
| X, y = model_churn.preprocess_inputs(df, option) | |
| if option2 == 'Upload custom': | |
| model = model_churn.train(df, model) | |
| y_pred = model.predict(X) | |
| st.write("Accuracy:",metrics.accuracy_score(y, y_pred)) | |
| st.write("Precision:",metrics.precision_score(y, y_pred)) | |
| st.write("Recall:",metrics.recall_score(y, y_pred)) | |
| fig, ax = plot_confusion_matrix(conf_mat=metrics.confusion_matrix(y, y_pred), figsize=(6, 6), cmap=plt.cm.Reds, colorbar=True) | |
| plt.xlabel('Predictions', fontsize=18) | |
| plt.ylabel('Actuals', fontsize=18) | |
| plt.title('Confusion Matrix', fontsize=18) | |
| st.pyplot(fig) |