This repository contains Jupyter notebooks for four different tasks focused on analyzing restaurant data. Each task involves different machine learning techniques and exploratory data analysis methods.

• Task 1: Predict Restaurant Ratings
• Task 2: Restaurant Recommendation
• Task 3: Cuisine Classification
• Task 4: Location-based Analysis
• Installation
• Usage
• Contributing
• License

Objective: Build a machine learning model to predict the aggregate rating of a restaurant based on other features.

Steps:

1. Preprocess the dataset by handling missing values, encoding categorical variables, and splitting the data into training and testing sets.
2. Select a regression algorithm (e.g., linear regression, decision tree regression) and train it on the training data.
3. Evaluate the model's performance using appropriate regression metrics (e.g., mean squared error, R-squared) on the testing data.
4. Interpret the model's results and analyze the most influential features affecting restaurant ratings.

Notebook: task_1_predict_restaurant_ratings.ipynb

Objective: Create a restaurant recommendation system based on user preferences.

Steps:

1. Preprocess the dataset by handling missing values and encoding categorical variables.
2. Determine the criteria for restaurant recommendations (e.g., cuisine preference, price range).
3. Implement a content-based filtering approach where users are recommended restaurants similar to their preferred criteria.
4. Test the recommendation system by providing sample user preferences and evaluating the quality of recommendations.

Notebook: task_2_restaurant_recommendation.ipynb

Objective: Develop a machine learning model to classify restaurants based on their cuisines.

Steps:

1. Preprocess the dataset by handling missing values and encoding categorical variables.
2. Split the data into training and testing sets.
3. Select a classification algorithm (e.g., logistic regression, random forest) and train it on the training data.
4. Evaluate the model's performance using appropriate classification metrics (e.g., accuracy, precision, recall) on the testing data.
5. Analyze the model's performance across different cuisines and identify any challenges or biases.

Notebook: task_3_cuisine_classification.ipynb

Objective: Perform a geographical analysis of the restaurants in the dataset.

Steps:

1. Explore the latitude and longitude coordinates of the restaurants and visualize their distribution on a map.
2. Group the restaurants by city or locality and analyze the concentration of restaurants in different areas.
3. Calculate statistics such as the average ratings, cuisines, or price ranges by city or locality.
4. Identify any interesting insights or patterns related to the locations of the restaurants.

Notebook: task_4_location_based_analysis.ipynb

To run the notebooks in this repository, you need to have Python and Jupyter Notebook installed. You can install the necessary packages using the following command:

pip install -r requirements.txt

1. Clone the repository:

   git clone https://github.com/your-username/restaurant-analysis.git
   cd restaurant-analysis

2. Install the dependencies:

   pip install -r requirements.txt

3. Launch Jupyter Notebook:

   jupyter notebook

4. Open the notebook for the task you want to run.

Contributions are welcome! Please feel free to submit a Pull Request.

This project is licensed under the MIT License. See the LICENSE file for details.

Feel free to explore each notebook and use the provided code as a basis for further exploration and analysis of restaurant data. If you encounter any issues or have suggestions for improvement, please open an issue or submit a pull request.