Movie Recommender System using Content-Based Filtering

Overview

This project implements a content-based movie recommender system using cosine similarity. The recommender system suggests movies similar to a given movie based on their content features such as genre, actors, directors, etc. The system utilizes a dataset obtained from Kaggle, containing information about movies including their titles, genres, actors, directors, and other relevant attributes.

Data Source

The dataset used in this project is sourced from Kaggle. You can find the dataset here. Ensure to download the dataset and place it in the appropriate directory before running the notebook.

Requirements

• Python 3.x
• Jupyter Notebook
• Pandas
• NumPy
• Scikit-learn
• Matplotlib
• Seaborn

Installation

1. Clone this repository to your local machine.
2. Download the dataset from the provided Kaggle link and place it in the same directory as the Jupyter Notebook.
3. Open the Jupyter Notebook Movie_Recommender_System.ipynb using Jupyter Notebook.
4. Execute the notebook cells sequentially to load the dataset, preprocess the data, and build the movie recommender system.
5. Follow the instructions provided in the notebook to interact with the recommender system and obtain movie recommendations.

Files

• Project1.ipynb: Jupyter Notebook containing the implementation of the movie recommender system.
• README.md: This file providing an overview of the project and instructions for usage.
• 'app.py'

Author

• Jatin Sharma

Feel free to reach out with any questions or suggestions!