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| # Import Essential Library | |
| import streamlit as st | |
| import pandas as pd | |
| # Library for Visualization | |
| import matplotlib.pyplot as plt | |
| import seaborn as sns | |
| # Function to run EDA | |
| def run(): | |
| # Set Title | |
| st.title('Insurance Lead Prediction Model') | |
| # Sub Title | |
| st.subheader('Exploratory Data Analysis Section') | |
| st.markdown('---') | |
| # Insert Image | |
| st.image('https://www.startinsland.de/site/assets/files/4129/tk-logo_koop_official_health_partner_pos.800x0.png') | |
| # Markdown | |
| st.markdown('# Dataframe Insurance Lead') | |
| # Load Data | |
| data = pd.read_csv('data_eda.csv') | |
| # Display dataframe in StreamLit | |
| st.dataframe(data.head(20)) | |
| st.markdown('---') | |
| # EDA | |
| st.markdown('## EDA') | |
| # Convert Rate Balance Visualization | |
| st.markdown('### Convert Rate Balance') | |
| canvas = plt.figure(figsize=(10,5)) | |
| sns.barplot(x=data['Response'].value_counts().index, y=data['Response'].value_counts(), hue=data['Response'].value_counts().index) | |
| st.pyplot(canvas) | |
| st.markdown('Data is still slightly imbalanced (biased towards clients who will not likely convert)') | |
| # Holding Policy Duration Distribution Visualization | |
| st.markdown('### Holding Policy Duration Distribution') | |
| canvas = plt.figure(figsize=(10,5)) | |
| sns.histplot(data['Holding_Policy_Duration'], kde=True, bins=15) | |
| st.pyplot(canvas) | |
| # Holding Policy Type Distribution Visualization | |
| st.markdown('### Holding Policy Type Distribution') | |
| canvas = plt.figure(figsize=(10,5)) | |
| sns.barplot(x=data['Holding_Policy_Type'].value_counts().index, y=data['Holding_Policy_Type'].value_counts(), hue=data['Holding_Policy_Type'].value_counts().index) | |
| st.pyplot(canvas) | |
| # Recommended Policy Category Distribution Visualization | |
| st.markdown('### Recommended Policy Category Distribution') | |
| canvas = plt.figure(figsize=(15,5)) | |
| sns.barplot(x=data['Reco_Policy_Cat'].value_counts().index, y=data['Reco_Policy_Cat'].value_counts(), hue=data['Reco_Policy_Cat'].value_counts().index) | |
| st.pyplot(canvas) | |
| if __name__=='__main__': | |
| run() |