shamonia/recommendation-system

Recommendation System using Machine Learning Algorithms and Python

This project aims to develop a recommendation system using machine learning algorithms and Python. The project uses a sample dataset provided in the data.csv file, which includes information about various items such as their names, descriptions, features, and ratings.

Installation

To run the code, you will need to install Python 3 and the following libraries:

pandas scikit-learn nltk To install these libraries, you can use pip, a package installer for Python:

pip install pandas scikit-learn nltk

You will also need to download the nltk data by running the following code in Python:

import nltk
nltk.download('stopwords')

Usage

To run the recommendation system, you can execute the recommendation_system.py script:

python recommendation_system.py

This script reads the data.csv file, preprocesses the data, and creates a similarity matrix using cosine similarity. It then defines a function to recommend items based on the similarity scores and tests the recommendation system by calling the function with a test item id.

You can modify the test item id and the number of recommended items to be returned in the recommend_items() function.

Contributing

This project is open to contributions. If you have any suggestions or improvements, please feel free to create a pull request.

License

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