https://github.com/rafay-imraan/recommendation-system

A machine learning model that outputs personalized similar movie recommendations for people based on the ones they have rated positively.
https://github.com/rafay-imraan/recommendation-system

machine-learning pandas python scikit-learn

Last synced: about 2 months ago
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A machine learning model that outputs personalized similar movie recommendations for people based on the ones they have rated positively.

• Host: GitHub
• URL: https://github.com/rafay-imraan/recommendation-system
• Owner: rafay-imraan
• Created: 2025-06-23T13:05:39.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2025-06-23T13:27:09.000Z (about 1 year ago)
• Last Synced: 2025-06-23T14:27:31.273Z (about 1 year ago)
• Topics: machine-learning, pandas, python, scikit-learn
• Language: Jupyter Notebook
• Homepage:
• Size: 815 KB
• Stars: 0
• Watchers: 0
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# Recommendation-System

An AI agent that asks the user the name of a movie and recommends similar movies that it has learnt from a dataset downloaded from [Kaggle](https://www.kaggle.com/). The underlying model was trained using the K-Nearest Neighbors algorithm provided by the [scikit-learn](https://scikit-learn.org/stable/index.html) library. Other scikit-learn functions used are [cosine_similarity](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.cosine_similarity.html), [OneHotEncoder](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OneHotEncoder.html), [TfidfVectorizer](https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html). [sparce.hstack](https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.hstack.html) from [Scipy](https://scipy.org/). [Pandas](https://pandas.pydata.org/) and [numpy](https://numpy.org/) was also used in the project.

How it works is that it detects if it has learnt about the mentioned movie. If it has, it looks for its genre and recommends movies from the same genres.

**MAKE SURE THE AFOREMENTIONED LIBRARIES ARE INSTALLED BEFORE EXECUTING THE CODE**