https://github.com/jasmeetsingh7314/recommendation-system
Recommendation System for games done using python and written in jupyter notebook.
https://github.com/jasmeetsingh7314/recommendation-system
cosine-similarity countvectorizer machine-learning python
Last synced: 7 months ago
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Recommendation System for games done using python and written in jupyter notebook.
- Host: GitHub
- URL: https://github.com/jasmeetsingh7314/recommendation-system
- Owner: JasmeetSingh7314
- Created: 2024-04-13T08:55:04.000Z (almost 2 years ago)
- Default Branch: master
- Last Pushed: 2024-04-13T09:01:51.000Z (almost 2 years ago)
- Last Synced: 2025-07-13T12:51:56.715Z (7 months ago)
- Topics: cosine-similarity, countvectorizer, machine-learning, python
- Language: Jupyter Notebook
- Homepage:
- Size: 373 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Recommendation System for Games using IGDB API
This project implements a recommendation system for games using data collected from the IGDB API. The system utilizes the CountVectorizer and Cosine Similarity algorithms to suggest games based on user preferences and game attributes.
## Algorithms
### CountVectorizer
The CountVectorizer algorithm is used to convert a collection of text documents into a matrix of token counts. It works by tokenizing the input text and building a vocabulary of known words. Each document is then converted into a vector of token counts, where each element in the vector represents the frequency of a word in the vocabulary within the document.
### Cosine Similarity
Cosine Similarity is a metric used to measure the similarity between two vectors by calculating the cosine of the angle between them. In the context of the recommendation system, it is used to measure the similarity between the user's preferences (input vector) and the game attributes (vectors of game descriptions or features). A higher cosine similarity indicates a higher degree of similarity between the two vectors.
## Usage
To use the recommendation system, follow these steps:
- Get the files and run the Jupyter Notebook
- Input user preferences.
- Use the model to recommend games based on the user's preferences.