Update Home.py


Home.py

@@ -3,12 +3,12 @@ import pandas as pd
 import numpy as np
 
 # Page configuration
-st.set_page_config(page_title="Electronics Sales Analysis", layout="wide")
+st.set_page_config(page_title="Customer Chrun Prediction", layout="wide")
 
 # Title with centered alignment
 st.markdown(
     """
-    <h1 style="text-align: center; color: white;">📱 Consumer Electronics Sales Analysis and ML Model 💻</h1>
+    <h1 style="text-align: center; color: white;">📱 Customer Churn Prediction and ML Model 💻</h1>
     """, 
     unsafe_allow_html=True
 )
@@ -17,7 +17,7 @@ st.markdown(
 st.markdown(
     """
     <div style="text-align: center;">
-        <img src="https://cdn-uploads.huggingface.co/production/uploads/67441c51a784a9d15cb12871/dV0WXaXfOUrNjQmNQkspQ.jpeg" width="90%" />
+        <img src="https://cdn-uploads.huggingface.co/production/uploads/67445925102349e867c92342/qVlbdupofJ-3eN5WxJqQ5.jpeg" width="90%" />
     </div>
     """, 
     unsafe_allow_html=True
@@ -26,7 +26,7 @@ st.markdown(
 # Project description
 st.markdown(
     """
-    ## Project Title: 📱Consumer Electronics Sales | EDA + Model 💻:
+    ## Project Title: 📱Customer Churn Prediction | EDA + Model 💻:
     
     ##### 📊 Data Exploration and Preprocessing:
     - Preparing data by encoding categorical features like "ProductCategory" and "ProductBrand" and scaling numerical data such as "price" and "rating", as the dataset has minimal outliers or missing values.
@@ -41,7 +41,7 @@ st.markdown(
     - Comparing model performance using metrics like **accuracy**, **F1 score**, or **Log-loss score**, depending on the task.
     - Employing techniques like **hyperparameter tuning** and **cross-validation** for optimization.
     
-    ##### By integrating **machine learning** with **data analysis**, this project empowers the Electronics market to enhance customer satisfaction, optimize pricing strategies according to purchase intent, and maximize profitability.
+    ##### Customer churn prediction helps businesses identify customers at risk of leaving, enabling proactive retention strategies. By analyzing patterns like purchase behavior and customer feedback, it aids in tailoring personalized offers to reduce churn. This ultimately enhances customer satisfaction, loyalty, and profitability .
     """, 
     unsafe_allow_html=True
 )
@@ -107,4 +107,5 @@ st.markdown(
 
 # Center-aligned button with emoji and functionality
 if st.button("Next ⏭️"):
-    st.switch_page("pages/0_Problem-Statement_and_Aim.py")
+    st.switch_page("pages/0_Problem-Statement_and_Aim.py")
+