https://github.com/thatsabhishek/movie_recommendation_system_using_machine_learning_with_python

https://github.com/thatsabhishek/movie_recommendation_system_using_machine_learning_with_python

Last synced: 3 months ago
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• Host: GitHub
• URL: https://github.com/thatsabhishek/movie_recommendation_system_using_machine_learning_with_python
• Owner: thatsabhishek
• Created: 2023-07-25T16:33:24.000Z (almost 2 years ago)
• Default Branch: main
• Last Pushed: 2023-12-10T03:10:58.000Z (over 1 year ago)
• Last Synced: 2025-01-14T15:28:51.735Z (5 months ago)
• Language: Jupyter Notebook
• Size: 6.07 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Movie Recommendation System using Machine Learning with Python

## Overview

This project implements a movie recommendation system using machine learning techniques in Python. The system recommends movies based on their similarity to a user's favorite movie. The code utilizes the scikit-learn library for text vectorization and cosine similarity calculations.

## Code Structure

The code is organized into the following sections:

1. **Data Loading:**

   - The movie data is loaded from a CSV file (`movies.csv`) using pandas.

   - The first 5 rows of the dataset and its dimensions are printed for initial exploration.

2. **Feature Selection:**

   - Relevant features for recommendation, including 'genres', 'keywords', 'tagline', 'cast', and 'director', are selected.

   - Null values in these features are replaced with empty strings.

3. **Text Vectorization:**

   - The selected features are combined into a single text feature.

   - TF-IDF vectorization is applied to convert the text data into feature vectors.

4. **Cosine Similarity:**

   - Cosine similarity scores between movies are calculated based on their feature vectors.

5. **User Input:**

   - The user is prompted to input their favorite movie.

6. **Movie Recommendation:**

   - A list of movie titles from the dataset is created.

   - Using difflib, the closest match to the user's input is found.

   - The index of the matched movie is used to retrieve similarity scores with other movies.

   - The movies are sorted based on their similarity scores, and the top suggestions are presented to the user.

## How to Use

1. **Clone the Repository:**

   ```

   git clone https://github.com/your-username/Movie_Recommendation_System_using_Machine_Learning_with_Python.git

   cd Movie_Recommendation_System_using_Machine_Learning_with_Python

   ```

2. **Install Dependencies:**

   ```

   pip install pandas numpy scikit-learn

   ```

3. **Run the Code:**

   - Ensure that the `movies.csv` file is present in the same directory.

   ```

   python movie_recommendation_system.py

   ```

4. **Enter Favorite Movie:**

   - Follow the prompts to enter the name of your favorite movie.

5. **Review Recommendations:**

   - The code will display a list of movie recommendations based on the input.