Create 4.Final Summary and Insights.py

pages/4.Final Summary and Insights.py

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+import streamlit as st
+
+# Conclusion Page
+st.title("Conclusion")
+st.markdown("""
+### Summary of Analysis
+- **EDA and Insights**: Explored key trends in hotel data, including pricing, ratings, discounts, and cashback.  
+- **Feature Engineering**: Prepared the dataset by handling missing values and selecting important features.  
+- **Model Creation**: Used Optuna to optimize hyperparameters and build an effective Random Forest model.
+
+### Key Findings
+- Higher-priced hotels tend to have better ratings but offer fewer discounts and cashback.  
+- Budget hotels rely on promotional offers to attract customers.  
+- Regional variations and customer preferences influence hotel distribution and reviews.
+
+### Closing Note
+This analysis highlights the value of data-driven decision-making in the hospitality industry.  
+Thank you for using the Hotel Data Analysis App!
+""")