Upload app.py

app.py

@@ -1,30 +1,89 @@
+# Import necessary libraries
 import streamlit as st
-
-# Import the other scripts
-import predict
 import eda
+import predict
+
+# Set up the Streamlit page configuration
+st.set_page_config(
+    page_title="Customer Churn Predictor",
+    page_icon="🏃",
+    layout="wide",
+    initial_sidebar_state="expanded"
+)
+
+def main():
+    # Navigation sidebar
+    st.sidebar.title("🧭 Navigation")
+    page = st.sidebar.radio("Go to", ["🏠 Home", "📊 EDA", "🔍 Prediction"])
 
-# Set the title of the app
-st.title("Customer Churn Analysis App")
+    if page == "🏠 Home":
+        # Sidebar content for Home page
+        st.sidebar.markdown("---")
+        st.sidebar.subheader("📊 About the Model")
+        recall = 0.89  # You may want to update this value based on your model's performance
+        st.sidebar.write("🎯 Model Recall:")
+        st.sidebar.progress(recall)
+        st.sidebar.write(f"{recall:.2%}")
+        st.sidebar.write("**🤔 What is Recall?**")
+        st.sidebar.write("Recall measures how well our model identifies customers who are likely to churn.")
+        st.sidebar.write("**💡 What does this mean?**")
+        st.sidebar.write("Out of all the customers who are likely to churn, our model correctly identifies 89% of them.")
+        st.sidebar.write("This helps us catch most cases, *reducing the chance of missing someone who needs attention*")
 
-# Create a sidebar for navigation
-st.sidebar.title("Navigation")
-page = st.sidebar.radio("Go to", ["Home", "Predict Churn", "EDA"])
+        st.sidebar.markdown("---")
+        st.sidebar.subheader("📚 Fun Fact")
+        st.sidebar.info("It costs 5-25 times more to acquire a new customer than it does to retain an existing one.")
 
-# Define the home page
-if page == "Home":
-    st.write("## Welcome to the Customer Churn Analysis App")
-    st.write("""
-    This application allows you to:
-    - Predict customer churn based on input data.
-    - Perform exploratory data analysis (EDA) on customer churn data.
-    Use the sidebar to navigate between the pages.
-    """)
+        # Main content for Home page
+        st.title("🏃 Welcome to Customer Churn Prediction Tool")
+        st.write("""
+        This application provides functionalities for Exploratory Data Analysis and 
+        Prediction regarding customer churn risk. Use the navigation pane on the left to 
+        select the module you wish to utilize.
+        """)
+        
+        # Display image
+        col1, col2, col3 = st.columns([1,2,1])
+        with col2:
+            st.image("predictix.jpg", caption="Customer Churn Prediction", use_column_width=True)
+        
+        st.markdown("---")
+        
+        # Dataset information
+        st.write("#### 📊 Dataset")
+        st.info("""
+        The dataset contains customer information including tenure, contract type, payment method,
+        monthly charges, total charges, and feedback. It's used to predict customer churn.
+        """)
+        
+        # Problem Statement
+        st.write("#### ⚠️ Problem Statement")
+        st.warning("""
+        Customer churn is a significant challenge for businesses, leading to revenue loss and increased 
+        acquisition costs. Early identification of customers likely to churn is crucial for implementing 
+        effective retention strategies. As a data scientist, your task is to develop a machine learning 
+        model that can predict customer churn based on historical data and customer behavior patterns.
+        
+        The goal is to develop a model with high recall to identify potential churners, allowing the 
+        business to take proactive measures to retain these customers.
+        """)
+        
+        # Project Objective
+        st.write("#### 🎯 Objective")
+        st.success("""
+        This project aims to create a classification model to predict customer churn by evaluating 
+        various algorithms. Model performance will be primarily assessed using Recall to measure 
+        effectiveness in identifying potential churners, minimizing the risk of missing customers 
+        who are likely to leave.
+        """)
 
-# Navigate to the Predict Churn page
-elif page == "Predict Churn":
-    predict.run()
+    elif page == "📊 EDA":
+        # Run the EDA module
+        eda.run()
+    
+    elif page == "🔍 Prediction":
+        # Run the Prediction module
+        predict.run()
 
-# Navigate to the EDA page
-elif page == "EDA":
-    eda.run()
+if __name__ == "__main__":
+    main()