Bengaluru Restaurant EDA

This repository contains a comprehensive exploratory data analysis (EDA) on restaurant data from Bengaluru. The objective is to uncover patterns and insights related to ratings, cuisines, cost, location, votes, and dining preferences to better understand customer behavior and market trends.

🗂️ Repository Structure

├── 1_data_preprocessing.ipynb     # Data cleaning and preprocessing steps
├── 2_data_analysis.ipynb          # Data exploration and initial analysis
├── 3_eda.ipynb                    # Visualizations and final insights
├── Bengaluru_restaurant_eda.py    # Python script version of the EDA
├── Learn.py                       # Experimental or helper script
├── bengaluru_restaurant.docs      # Project documentation (DOCX or text)
├── cleaned_df.xls                 # Cleaned dataset used for EDA
├── zomato.xls                     # Raw dataset (1)
├── zomato_1.xls                   # Raw dataset (2)

🧪 Tools & Libraries Used

• Python
• Pandas
• NumPy
• Matplotlib
• Seaborn
• Jupyter Notebook

📊 Key Insights

• Popular restaurant types and locations in Bengaluru
• Relationship between ratings and cost
• Customer voting behavior and dining trends
• Cuisine distribution and popularity

🚀 How to Run

1. Clone the repo: git clone https://github.com/your-username/bengaluru-restaurant-eda.git
2. Open notebooks in JupyterLab or VS Code
3. Run notebooks in order: 1_data_preprocessing → 2_data_analysis → 3_eda

📌 License

This project is open-source and free to use for educational and non-commercial purposes.