** Restaurant_Analysis_ML_Project End-to-end machine learning project including regression, classification, recommendation systems, and geospatial analysis. Restaurant Analytics & ML Project
Cognifyz Data Science Internship**

This project includes four end-to-end machine learning tasks performed on a restaurant dataset.

Restaurant Rating Prediction (Regression)

• Built a Random Forest regression model to predict restaurant ratings.
• Evaluated using MSE and R² score.
• Analyzed feature importance.

Restaurant Recommendation System

• Implemented a content-based filtering system.
• Recommended restaurants based on cuisine, city, and price range.

Cuisine Classification (Multi-class Classification)

• Developed a Random Forest classifier.
• Handled class imbalance.
• Achieved ~40% accuracy across highly imbalanced cuisine categories.
• Accuracy significantly outperformed random baseline. ** Location-Based Analysis**
• Analyzed restaurant distribution by city.
• Computed average ratings by location.
• Visualized insights using matplotlib & seaborn.

Technologies Used

• Python
• Pandas
• NumPy
• Scikit-learn
• Matplotlib
• Seaborn

Key Insights

• Restaurant ratings are influenced by price range, votes, and city.
• Cuisine classification is challenging due to 119 highly imbalanced categories.
• Location plays a strong role in restaurant density and rating trends.