This repository contains Jupyter notebooks showcasing machine learning models for restaurant recommendations and performing exploratory data analysis (EDA) using Python. The aim is to provide valuable insights and practical implementations of various data analysis and machine learning techniques.
This repository is designed to demonstrate the application of machine learning models in the context of restaurant recommendations and to showcase exploratory data analysis techniques. Whether you are new to machine learning and data analysis or looking to expand your skillset, these notebooks provide practical examples and insights.
Restaurant_Recommendations.ipynb: Implementation of a machine learning model for recommending restaurants based on user preferences and historical data.
restaurant_data.csv: Dataset used for restaurant recommendations and exploratory data analysis.
Contributions are welcome! If you have improvements or new notebooks to add, please fork this repository and submit a pull request with your changes.
This repository is licensed under the MIT License. See the LICENSE file for details.
Special thanks to the open-source community for providing valuable resources and tools. Gratitude to the contributors and reviewers who helped enhance this project.