Project: Movie Recommendation System Using Machine Learning!

This project implements a Movie Recommendation System that suggests movies similar to the one selected by the user. The system uses content-based filtering, leveraging movie metadata like genres, cast, crew, and plot descriptions to calculate similarity scores.

Key Features:

• Content-Based Recommendations: Uses cosine similarity to find movies with similar content.
• Customizable Suggestions: Users receive personalized recommendations based on the input movie.
• Efficient Preprocessing: Combines key features into a unified "tags" column for improved similarity computation.

How It Works:

1. Preprocessed movie metadata is converted into numerical vectors using CountVectorizer.
2. A similarity matrix is generated using cosine similarity.
3. Given a movie title, the system identifies and ranks the most similar movies.
4. Top 5 recommendations are presented to the user.

This simple and effective system demonstrates how machine learning and natural language processing can enhance user experiences in entertainment applications.

Demo:

Dataset used:

• Dataset link

How to run?

STEPS:

Clone the repository

https://github.com/entbappy/Movie-Recommendation-System-Using-ML.git

STEP 01- Create a conda environment after opening the repository

conda create -n movie python=3.11 -y

conda activate movie

STEP 02- install the requirements

pip install -r requirements.txt

#run this file to generate the models

Preprocessing.ipynb

Now run,

streamlit run app.py

Author: Ibraheem
ML Engineer
Email: [email protected]