Discover your next movie! This recommender system (Python, Pandas, scikit-learn) suggests similar films based on cast, crew, genre & sequels (IMDB 5000 data incl.). UI with Tailwind CSS. Run in Google Colab & find your cinematic match!
This project implements a movie recommendation system using the IMDB 5000 Movies dataset (included in this repository). It recommends six similar movies based on your chosen film, considering factors like:
Actor cast and crew Director Movie similarity (genre, theme, etc.) Part 1, Part 2, etc. relationships (sequels/prequels) Features:
Leverages Python libraries like Pandas, scikit-learn, and others for data analysis and recommendation algorithms. Employs Google Colab for cloud-based development and execution. Provides a basic user interface (UI) built with HTML, CSS (Tailwind CSS), for a user-friendly experience. Installation:
Clone the repository:
Bash git clone https://github.com/gourab9817/IMDB-movie-recommendation.git Use code with caution. Install dependencies (if not already installed):
Bash pip install pandas scikit-learn [other required libraries] Use code with caution. Replace [other required libraries] with any additional dependencies specific to your project. Consider creating a requirements.txt file to manage dependencies more efficiently.
Usage:
Run the Jupyter Notebook (or Python script):
Locate the main script or Jupyter Notebook file (e.g., main.ipynb or app.py). Open it in Google Colab or a local Jupyter Notebook environment. Follow the instructions within the code to provide movie input and interact with the recommendation system. (Optional) Deploy the UI:
If you've built a separate UI component, follow the deployment instructions specific to your framework/server setup. This might involve building the UI using Tailwind CSS, and serving it with a web server (e.g., Flask, Django).
Data:
The project utilizes the IMDB 5000 Movies dataset, which is included in the data directory of this repository. Libraries:
Pandas: Data manipulation and analysis. scikit-learn: Machine learning algorithms for recommendations (e.g., cosine similarity, collaborative filtering).