A movie recommendation system, or a movie recommender system, is an ML-based approach to filtering or predicting the users’ film preferences based on their past choices and behavior.
Use this link to run the live application visit https://mrs-prashantkr.herokuapp.com/
Refer the below screenshots from the application.
Dataset: Used TMDB Movie Dataset form Kaggel https://www.kaggle.com/datasets/tmdb/tmdb-movie-metadata
Technologies used:
- Python - To build the Machine Learning model.
- Jupyter Notebook (You can use any other IDE like Google Colab) - To train the model.
- Streamlit Framework - To create Web Application for our model.
- Heroku - To deploy our model.
Python libraries Used:
- Pandas
- NumPy
- SciKit Learn
- Streamlit
- Requests
- Pickle
Tools Needed to build this Web Application:
- Jupyter Notebook
- Pycharm (Any other IDE can work)
- Any Browser
Steps to Run this in your local system:
- Dowload the Datasets
- Copy and paste the Jupyter Notebook ipynb code in a New Notebook in your system
- Generate the pickle file
- Create a python project in Pycharm (or any Python IDE)