Restaurant Data Analysis and Prediction

This project performs exploratory data analysis (EDA), geospatial analysis, feature engineering, and predictive modeling on a restaurant dataset to uncover valuable insights and make predictions regarding restaurant ratings.

Libraries Used

• pandas: Data manipulation and analysis.
• numpy: Numerical operations.
• matplotlib: Data visualization.
• seaborn: Statistical data visualization.
• folium: Geospatial mapping.
• scikit-learn: Machine learning and model evaluation.

Dataset

The dataset contains various restaurant details such as:

• Restaurant Name
• City
• Cuisines
• Latitude, Longitude
• Price Range
• Table Booking and Online Delivery Options
• Ratings (Aggregate)

The data is loaded from a CSV file, and the analysis is performed to derive useful insights and develop prediction models.

Analysis and Tasks

1. Descriptive Analysis

• Statistical summary for numerical columns.
• Exploration of categorical variables like city, cuisine, and country.
• Insights on the most popular cuisines and cities.

2. Geospatial Analysis

• Visualizing restaurant locations on a map using Folium.
• Distribution of restaurants across cities.
• Correlation between restaurant location (latitude/longitude) and ratings.

3. Table Booking and Online Delivery Analysis

• Percentage of restaurants offering table booking and online delivery.
• Comparing ratings for restaurants that offer table booking.
• Analysis of online delivery options by price range.

4. Price Range and Feature Engineering

• Analysis of restaurant price ranges and average ratings.
• Feature engineering, including creating new features based on restaurant name length.

5. Predictive Modeling

• Predicting restaurant ratings using Linear Regression and Random Forest models.
• Evaluation metrics like RMSE and R² score for model performance.

6. Customer Preference and Data Visualization

• Analyzing ratings and popularity of cuisines.
• Visualizing the distribution of ratings and relationship between votes and ratings.

Business Growth Insights

By analyzing the dataset, businesses in the restaurant industry can derive actionable insights to enhance their growth and customer satisfaction:

1. Customer Preferences

• Cuisines: Understanding the most popular cuisines can help businesses optimize their menu offerings to cater to customer preferences and increase sales.
• Locations: Identifying top cities with the highest number of restaurants allows businesses to target growth in areas with less competition or explore underserved regions.

2. Service Offerings

• Table Booking & Online Delivery: Knowing the percentage of customers who prefer table booking and online delivery helps businesses enhance their services, offer promotions, and make strategic decisions for better customer engagement.
• Price Range: Analyzing customer ratings based on different price ranges helps restaurants set competitive pricing strategies and optimize their value proposition to attract more customers.

3. Improved Customer Experience

• Rating Insights: By predicting restaurant ratings, businesses can evaluate their performance based on key factors (such as price range, table booking, and customer votes) and focus on areas that require improvement.
• Personalized Offers: Customer ratings and preferences can be used to design personalized promotions for specific cuisines or services, helping restaurants increase customer retention.

4. Geospatial Optimization

• Restaurant Locations: Visualizing restaurant locations on a map enables businesses to spot areas with high competition or discover new locations that have high potential but are currently underserved.

5. Data-Driven Decision Making

• The combination of statistical analysis, predictive modeling, and customer preference insights empowers restaurants to make data-driven decisions, resulting in higher customer satisfaction, improved services, and business growth.

Files

• restaurants_map.html: A map displaying the locations of restaurants.
• The script performs all the tasks mentioned above and outputs key metrics and visualizations.

How to Run

1. Clone the repository or download the script.

2. Install the required libraries:

   pip install pandas numpy matplotlib seaborn folium scikit-learn

3. Place your dataset CSV file in the appropriate path.

4. Run the Notebook in Jupyter Lab:

   • Open your terminal or command prompt and navigate to the directory where the Jupyter Notebook is saved.
   • Launch Jupyter Lab by running:

     jupyter lab

   • In Jupyter Lab, open the notebook (e.g., restaurant_data_analysis.ipynb).
   • Run the cells in the notebook one by one to perform the analysis.

5. View the Output:

   • The analysis results and visualizations (e.g., charts) will appear within the notebook.
   • A map will be saved as restaurants_map.html in the same directory.

6. Optional: Visual Studio Code (VS)

   • If you prefer, you can also open the Jupyter notebook in Visual Studio Code by installing the Jupyter extension.
   • Run the cells just as you would in Jupyter Lab.

Conclusion

This project demonstrates how data analysis and predictive modeling can provide valuable insights to the restaurant industry. Through exploratory data analysis (EDA), geospatial visualization, and predictive models, we identified key trends in restaurant ratings, customer preferences, and service offerings.

The findings highlight how businesses can leverage data to optimize their menu offerings, expand to high-demand areas, and improve customer experience. By understanding what drives customer satisfaction and the factors influencing restaurant ratings, businesses can take strategic actions to increase growth and retention.

Future Directions:

• Model Refinement: Implement hyperparameter tuning and model optimization to improve prediction accuracy.
• Expanded Data: Integrate additional features, such as customer reviews or seasonal trends, to enhance model performance.
• Advanced Modeling: Explore more complex algorithms, such as Gradient Boosting or XGBoost, to further improve the prediction of restaurant ratings.

Overall, this project highlights the power of data science in transforming business strategies and supporting informed decision-making within the restaurant industry.