Here’s a sample README for your movie recommendation system project:

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Movie Recommendation System Using Machine Learning

This project is a movie recommendation system that suggests movies based on user preferences. By analyzing features like genres, keywords, taglines, cast, and director, the system generates personalized movie recommendations using machine learning techniques such as TF-IDF vectorization and cosine similarity.

Features

• Personalized Recommendations: Recommends movies similar to the user’s input based on feature similarity.
• Machine Learning: Utilizes TF-IDF vectorization and cosine similarity to compute similarities among movies.
• NLP-based Analysis: Combines movie metadata like genres, keywords, and cast to enhance recommendation accuracy.

Project Overview

1. Data Loading and Preprocessing:

   • Loads movie data from a CSV file.
   • Handles missing values by replacing them with an empty string.

2. Feature Engineering:

   • Selects relevant features (genres, keywords, tagline, cast, director).
   • Combines selected features into a single text feature for each movie.

3. Vectorization and Similarity Calculation:

   • Converts combined text data into feature vectors using TF-IDF.
   • Calculates cosine similarity to identify similar movies.

4. Recommendation Generation:

   • Finds the closest matching movie based on user input.
   • Sorts and displays the top 20 recommended movies based on similarity.

Prerequisites

Install the following libraries:

pip install numpy pandas scikit-learn

Usage

1. Clone the repository and navigate to the project directory.
2. Place your movies.csv dataset in the directory.
3. Run the Python script:

   python movie_recommendation.py

4. Enter a movie title to receive personalized recommendations.

Example

Enter your favorite movie name: Inception
Movies suggested for you:

1. Interstellar
2. The Matrix
3. Shutter Island
...

Dataset

The movies.csv dataset should contain metadata for each movie, including columns like title, genres, keywords, tagline, cast, and director.

Technologies Used

• Python: Core programming language.
• Pandas: Data manipulation and cleaning.
• scikit-learn: Machine learning algorithms (TF-IDF vectorizer, cosine similarity).
• NLP Techniques: TF-IDF and cosine similarity for content-based filtering.

Future Improvements

• Integrate collaborative filtering for improved recommendations.
• Add a web interface for user interaction.
• Expand features for a more robust recommendation system.

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