Movie Recommendation System

Overview

This Movie Recommendation System utilizes machine learning to recommend movies based on user input. It leverages the K-Nearest Neighbors algorithm to find movies similar to the one entered by the user. The recommendation is based on movie metadata, including titles, genres, and release dates.

Features

• User Interaction: Prompts users to input a movie title and guides them through the recommendation process.
• Data Preprocessing: Extracts and processes movie genres, converts release dates to timestamps, and applies one-hot encoding for genre categorization.
• Dynamic Matching: Uses regular expressions to match user input with movie titles in the dataset, accounting for case sensitivity and partial matches.
• Machine Learning Model: Implements a K-Nearest Neighbors classifier to find movies similar to the user's input.
• Recommendation Display: Shows titles and overviews of recommended movies based on the user's input.

Requirements

Before you run this system, ensure you have the following packages installed:

• Python 3.x
• Pandas
• NumPy
• scikit-learn
• ast
• re

You can install these packages using pip:

pip install pandas numpy scikit-learn

Dataset

This system uses the TMDB 5000 Movie Dataset. Ensure you have the tmdb_5000_movies.csv file in a directory named Datasets/IMDB/ relative to the script's location.

Usage

To run the Movie Recommendation System, execute the following command in your terminal:

python movie_recommendation_system.py

Follow the on-screen prompts to enter a movie title and navigate through the recommendations.

How It Works

1. Data Extraction and Transformation: The system reads the dataset, extracts relevant features, and preprocesses the data for the machine learning model.
2. User Interaction: Through the command line, the user is prompted to input a movie title.
3. Title Matching: The system matches the input with existing titles in the dataset using regular expressions.
4. Recommendation Generation: The K-Nearest Neighbors algorithm finds movies similar to the user's input based on genre and release date.
5. Output: The system displays recommended movies, including their titles and overviews.

Contributions

Contributions to this project are welcome! Please fork the repository and submit a pull request with your proposed changes.

License

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