Movie Recommendation System

This repository contains a movie recommendation system built using content-based and popularity-based approaches. The system utilizes a dataset comprising over 4800 movies, extracted from two CSV files: movies.csv and credits.csv.

Dataset Description

• movies.csv: Contains various attributes of movies such as budget, genres, keywords, popularity, release date, revenue, etc.
• credits.csv: Includes details about the cast and crew of each movie.

Steps Implemented

1. Data Extraction: Extracted data from the provided CSV files.
2. Data Transformation: Transformed raw data into a usable format for analysis.
3. Data Cleaning and Preprocessing: Cleaned the data to handle missing values, duplicates, etc.
4. Data Visualization: Visualized the dataset to gain insights and identify patterns.
5. Genre Analysis: Analyzed movie genres to understand their distribution and popularity.
6. Keyword Analysis: Explored keywords associated with movies to extract meaningful information.
7. Genre Word Cloud Visualization: Created word clouds based on movie genres for better visualization.
8. Correlation Analysis: Investigated correlations between different attributes of movies.
9. Feature Combination: Combined relevant features to enhance the recommendation system.
10. Index Mapping: Mapped movie indices for efficient retrieval and recommendation.
11. Movie Recommendation:
    • Based on Cosine Similarity with CountVectorizer: Utilized CountVectorizer to calculate similarity between movies.
    • Based on Cosine Similarity with TF-IDF: Employed TF-IDF (Term Frequency-Inverse Document Frequency) for improved recommendation.

Analysis

Technologies Used

How to Use

To use the movie recommendation system in your Jupyter Notebook, follow these steps:

1. Clone the Repository: Clone this repository to your local machine using Git.

   git clone https://github.com/MuskanRaisinghani23/Movie-Recommendation-System.git

2. Python: Ensure Python is installed on your system.

3. Virtual Environment: Set up a virtual environment to manage dependencies and isolate your project's environment from other Python projects. You can create a virtual environment using virtualenv or venv.

4. requirements.txt: Install the required Python dependencies by running the command:

pip install -r requirements.txt

5. Open Jupyter Notebook: Navigate to the directory where you cloned the repository and launch Jupyter Notebook.

   cd Movie-Recommendation-System
   jupyter notebook

6. Run the Notebook: Open the Jupyter Notebook file (movie_recommendation.ipynb) and run each cell sequentially. Make sure to follow the instructions provided within the notebook.

7. Explore and Enjoy: Once all the cells are executed, you can explore the movie recommendation system, analyze the dataset, and test the recommendation algorithms.

8. Customize and Extend: Feel free to customize the notebook, tweak parameters, or extend functionality based on your requirements.