https://github.com/lasithaamarasinghe/movie-recommender-system

This ML model recommends movies that may align with the user's preferences based on TF-IDF matrix.
https://github.com/lasithaamarasinghe/movie-recommender-system

jupyter-notebook machine-learning movie-recommendation movielens-dataset numpy pandas python regex scikit-learn tf-idf-vectorizer

Last synced: 6 days ago
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This ML model recommends movies that may align with the user's preferences based on TF-IDF matrix.

• Host: GitHub
• URL: https://github.com/lasithaamarasinghe/movie-recommender-system
• Owner: LasithaAmarasinghe
• Created: 2024-05-23T06:47:16.000Z (6 months ago)
• Default Branch: main
• Last Pushed: 2024-06-16T06:19:50.000Z (5 months ago)
• Last Synced: 2024-06-17T22:57:11.022Z (5 months ago)
• Topics: jupyter-notebook, machine-learning, movie-recommendation, movielens-dataset, numpy, pandas, python, regex, scikit-learn, tf-idf-vectorizer
• Language: Jupyter Notebook
• Homepage:
• Size: 29.3 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Movie-Recommender-System

![](https://github.com/LasithaAmarasinghe/Movie-Recommendation-System/assets/106037441/67844ce1-e550-43c6-a98d-f0801243a1ea)

## Overview

- When a user enters a movie title into the input box, this recommendation system swiftly generates suggestions for other movies that may align with the user's preferences.

- This repository contains all the codes and resources used to build and utilize the recommendation system.

## Key Features

- **Data Exploration**: Comprehensive analysis to understand the MovieLens 25M dataset's structure and distribution.

- **Search Engine**: Building a search engine to find a specific movie title in the dataset. 

- **Recommendation Engine**: Creating a recommendation engine to suggest specific movies based on user preferences and movie ratings.

## Steps

* Reading in movie data with pandas.

* Cleaning movie titles with regex.

* Creating a [TF-IDF](https://www.geeksforgeeks.org/understanding-tf-idf-term-frequency-inverse-document-frequency/) matrix. (Time Frequency-Inverse Document Frequency)

* Creating a search function.

* Building an interactive search box with Jupyter.

* Reading in movie ratings data.

* Finding users who liked the same movie.

* Finding how much all users like movies.

* Creating a recommendation score.

* Building a recommendation function.

* Creating an interactive recommendation widget.

## Code

You can find the code for this project here:

- [Movie-Recommendation.ipynb](https://github.com/LasithaAmarasinghe/Movie-Recommendation/blob/main/Movie%20Recommendation.ipynb)

  

## Technologies/Tools 

* Jupyter Notebook / [Google Colab](https://colab.research.google.com/)

* Python 3.10.12

* Python packages

  * Pandas - `pip install pandas`

  * Numpy - `pip install numpy`

  * Scikit-learn - `pip install scikit-learn`

  * Regex - `pip install regex`

![Python](https://img.shields.io/badge/python-3670A0?logo=python&logoColor=FFFF00)

![Jupyter Notebook](https://img.shields.io/badge/jupyter-%23FA0F00.svg?logo=jupyter&logoColor=white)

![Pandas](https://img.shields.io/badge/pandas_-%20green?logo=pandas)

![NumPy](https://img.shields.io/badge/numpy-%23013243.svg?logo=numpy&logoColor=white)

![scikit-learn](https://img.shields.io/badge/scikit--learn-F7931E?logo=scikit-learn&logoColor=FFFFFF)

![regex](https://img.shields.io/badge/regex_-%20purple)

## Data

You can download the dataset files used in this project here:

* [MovieLens 25M dataset.](https://grouplens.org/datasets/movielens/25m/)