Machine Learning for Restaurants

Welcome to my Machine Learning for Restaurants repository! This repository contains various projects I worked on during my internship at Cognifyz Technologies, where I implemented machine learning solutions tailored for restaurant businesses.

Table of Contents

1. Cuisine Classification
2. Location-Based Analysis
3. Ratings Prediction
4. Recommendation System
5. Getting Started
6. Technologies Used
7. Acknowledgements
8. Contact

Cuisine Classification

This project involves developing a machine learning model to classify various cuisines based on dish ingredients and names. The goal is to accurately identify the cuisine category for a given dish using text data.

• Objective: Classify dishes into different cuisine categories.
• Techniques Used: Natural Language Processing (NLP), Classification Algorithms.
• Dataset: Datasets containing dish names and ingredients.

Location-Based Analysis

In this project, I conducted data analysis to understand restaurant performance and customer preferences based on location. This analysis helps in identifying key trends and insights that can drive business decisions.

• Objective: Analyze restaurant performance and customer preferences based on geographical data.
• Techniques Used: Data Analysis, Visualization, Geospatial Analysis.
• Dataset: Location-based restaurant data.

Ratings Prediction

The ratings prediction project involves building a predictive model to forecast restaurant ratings based on historical data and customer feedback. The model aims to provide accurate ratings predictions to help restaurants improve their services.

• Objective: Predict restaurant ratings using historical data.
• Techniques Used: Regression Analysis, Machine Learning Algorithms.
• Dataset: Historical ratings and customer feedback data.

Recommendation System

This project focuses on creating a recommendation system to suggest dishes and restaurants to customers based on their preferences and past behavior. The system enhances customer experience by providing personalized recommendations.

• Objective: Develop a recommendation system for dishes and restaurants.
• Techniques Used: Collaborative Filtering, Content-Based Filtering, Machine Learning Algorithms.
• Dataset: Customer preference and behavior data.

Getting Started

To get started with any of these projects, follow these steps:

1. Clone the repository:

   git clone https://github.com/instax-dutta/Machine-Learning-Restaurants.git

2. Navigate to the project directory of your choice:

   cd Machine-Learning-Restaurants/<project-name>

3. Install the required dependencies:

   pip install -r requirements.txt

4. Run the project scripts and explore the notebooks.

Technologies Used

• Python
• Jupyter Notebook
• Pandas
• NumPy
• Scikit-Learn
• Matplotlib
• Seaborn
• Natural Language Toolkit (NLTK)
• Geopandas

Acknowledgements

I would like to extend my gratitude to Cognifyz Technologies for providing me with the opportunity to work on these exciting projects. Special thanks to my mentors for their guidance and support.

Contact

For any questions or inquiries, please feel free to reach out:

• Name: Sai Dutta Abhishek Dash
• Email: contact@sdad.pro
• LinkedIn: [Sai Dutta Abhishek Dash ]