🎬 Movie Recommendation System

📌 Project Overview

The Movie Recommendation System suggests movies based on content similarity using TF-IDF vectorization and cosine similarity. It helps users find movies similar to their preferences.

🚀 Features

📂 Uses TF-IDF Vectorization to process movie features.

🔍 Finds the closest matching movie title from user input.

🧠 Computes cosine similarity to recommend similar movies.

🎭 Considers genres, keywords, taglines, cast, and director.

🎯 Returns top 20 recommended movies.

📁 Dataset

The system uses a dataset named movies.csv, which contains essential information about movies:

Title

Genres

Keywords

Tagline

Cast

Director

🛠️ Technologies Used

Python 🐍

NumPy 📊

Pandas 📑

Scikit-learn 🤖

difflib (for fuzzy matching)

📌 Code Workflow

Data Preprocessing:

Load movie dataset.

Fill missing values.

Combine relevant features into a single text column.

Feature Extraction & Similarity Calculation:

Apply TF-IDF Vectorization.

Compute cosine similarity between movies.

Movie Recommendation:

Get user input.

Find closest matching movie title.

Fetch similar movies based on similarity scores.

🤝 Contributing

Pull requests are welcome! If you want to enhance the system, feel free to fork the repository and submit a PR.

📄 License This project is MIT Licensed.

📌 Star the repository if you find it useful! ⭐