Movie Recommendation System

Overview

This project is a movie recommendation system designed to suggest movies based on a user's previous search history and interests. The system leverages collaborative filtering and content-based filtering techniques to provide personalized movie recommendations.

Features

• Collaborative Filtering: Recommends movies by finding users with similar tastes and suggesting movies they have liked.
• Content-Based Filtering: Suggests movies similar to those the user has previously enjoyed, based on movie attributes like genre, cast, and keywords.
• Hybrid Approach: Combines collaborative and content-based filtering to improve recommendation accuracy and address the cold-start problem.

Libraries and Tools

• Python: The primary programming language used for the project.
• Pandas: For data manipulation and analysis.
• NumPy: For numerical operations.
• Scikit-learn: For machine learning algorithms.
• Surprise: For building and evaluating collaborative filtering models.
• NLTK: For natural language processing tasks.

Installation

1. Clone the repository: bash git clone https://github.com/yourusername/movierecommender.git cd movierecommender

2. Install the required libraries: bash pip install numpy pandas scikit-learn scikit-surprise nltk

Usage

1. Data Preparation: Load and preprocess the movie and ratings datasets. python import pandas as pd from surprise import Dataset

   Load datasets

   movies = pd.read_csv('movies.csv') ratings = pd.read_csv('ratings.csv')

   Merge datasets

   data = Dataset.load_builtin('ml-100k')

2. Model Training: Train the recommendation models using collaborative filtering and content-based filtering. python from surprise import SVD from surprise.model_selection import train_test_split

   Split data into training and testing sets

   trainset, testset = train_test_split(data, test_size=0.25)

   Train the SVD model

   algo = SVD() algo.fit(trainset)

3. Making Recommendations: Generate movie recommendations for a user. python from surprise import accuracy

   Make predictions

   predictions = algo.test(testset)

   Evaluate the model

   accuracy.rmse(predictions)

Contributing

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