ni3choudhary / Movie-Recommeder-System-Deployment

Movie-Recommeder-System-Deployment

The aim of this project is to generate a content-based movie recommender system for 5000 movies on tmdb dataset obtained from kaggle.

Dataset

You can find the dataset here.

• This repository consists of files required for end to end implementation of Movie recommender system Machine Learning Web App created with Streamlit on Heroku platform.

setup

• Clone the repository : https://github.com/ni3choudhary/Movie-Recommeder-System-Deployment.git
• Inside the project root directory, Create Python Virtual Environment and activate it using below commands

$ python3 -m venv env

Activate Virtual Environment

$ .env/bin/activate 
          OR
$ .\env\Scripts\activate

Install Libraries using below command

$ pip install -r requirements.txt

Generate an API key from tmdb website and then create a .env file in root directory with below details.

API_KEY = "Your-API-Key"

• Run jupyter notebook to get the pickle files

• Run app.py on terminal to start local server.

$ streamlit run app.py

• If you want to view the deployed model, click on the following link: Deployed at: https://movie-recommeder-ni3.herokuapp.com/

• Please do ⭐ the repository, if it helped you in anyway.