hadryan/movies_recommendation_system

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. To see the demo visit https://mrs-prashantkr.herokuapp.com/

Movies_Recommendation_System

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:

1. Python - To build the Machine Learning model.
2. Jupyter Notebook (You can use any other IDE like Google Colab) - To train the model.
3. Streamlit Framework - To create Web Application for our model.
4. Heroku - To deploy our model.

Python libraries Used:

1. Pandas
2. NumPy
3. SciKit Learn
4. Streamlit
5. Requests
6. Pickle

Tools Needed to build this Web Application:

1. Jupyter Notebook
2. Pycharm (Any other IDE can work)
3. Any Browser

Steps to Run this in your local system:

1. Dowload the Datasets
2. Copy and paste the Jupyter Notebook ipynb code in a New Notebook in your system
3. Generate the pickle file
4. Create a python project in Pycharm (or any Python IDE)