ML_Basic

# Restaurant Data Analysis - Machine Learning Internship Projects

## 📌 Overview
This repository contains the complete implementation of three machine learning tasks completed during my Course. The project involves analyzing restaurant data to build predictive models and extract meaningful insights.

## 📊 Dataset
The dataset contains information about restaurants across multiple countries, including:
- Restaurant details (name, ID, location)
- Location information (city, locality, coordinates)
- Cuisines served
- Operational details (cost, currency, table booking, online delivery)
- Ratings and reviews (aggregate rating, votes, rating text)
- Price range and average cost

## 🎯 Tasks Completed

### Task 1: Restaurant Rating Prediction (Regression)
**Objective:** Build a regression model to predict restaurant aggregate ratings.

**Key Steps:**
- Data preprocessing and feature engineering
- Handling missing values and encoding categorical variables
- Training multiple regression models:
  - Linear Regression
  - Ridge/Lasso Regression
  - Decision Tree
  - Random Forest
  - Gradient Boosting
- Model evaluation using RMSE, R², and MAE metrics
- Feature importance analysis

**Results:**
- Best Model: Random Forest Regressor
- Test R² Score: [Your score]
- Test RMSE: [Your value]
- Most influential features: Votes, Price range, Average cost

### Task 2: Cuisine Classification (Classification)
**Objective:** Develop a classification model to categorize restaurants based on cuisine types.

**Key Steps:**
- Processing multi-cuisine data (selecting primary cuisine)
- Focusing on top 15 cuisine categories
- Training multiple classifiers:
  - Logistic Regression
  - Random Forest
  - Gradient Boosting
  - K-Nearest Neighbors
  - Support Vector Machine
- Performance evaluation using accuracy, precision, recall, and F1-score
- Per-class performance analysis

**Results:**
- Best Model: Random Forest Classifier
- Test Accuracy: [Your value]
- Test F1-Score: [Your value]
- Best performing cuisines: [List top 3-5 cuisines]

### Task 3: Location-based Analysis (Geographical Analysis)
**Objective:** Perform geographical analysis of restaurant distribution and characteristics.

**Key Steps:**
- Interactive map visualization using Folium
- Restaurant density heatmaps
- Rating distribution by location
- City-wise statistical analysis
- Cuisine distribution across top cities
- Price range analysis by location
- DBSCAN clustering for restaurant concentration identification

**Key Insights:**
- Top cities by restaurant count: [List top cities]
- Highest rated cities: [List top cities]
- Most expensive cities: [List top cities]
- Cuisine preferences vary significantly by location

## 🛠️ Technologies Used
- **Python 3.8+**
- **Data Manipulation:** Pandas, NumPy
- **Visualization:** Matplotlib, Seaborn, Plotly, Folium
- **Machine Learning:** Scikit-learn
- **Geospatial Analysis:** Geopandas, Shapely
- **Development Environment:** Jupyter Notebook

👨‍💻 Author
Anurag Yadav
M.Tech Bioinformatics
GitHub: https://github.com/ResidueRunner
LinkedIn: www.linkedin.com/in/anurag-yadav-631533159