In this ML Project I try to build a ML model which predicts the price of a used car. The ML model used is a Random Forest Regressor, which takes in features such as Seller Type, Transmission Type, Fuel Type, #Previous Owners, Distance Driven, Current Price of New Car, and Year. The model was implemented into a web app using Flask and deployed on Heroku.
The Jupyter Notebook used for data cleaning, EDA, and model training is also given.
The data is taken from this Kaggle dataset - Original Source: CarDekho.
NOTE: Due to recent changes in Heroku's free tier plan, the Heroku link is currently not available. However, one can easily run a local version of the web app by following the steps below.
Clone this repo and cd into the base directory.
Install the dependencies using requirements.txt.
pip install -r requirementsThen, run the app.py using Python.
python app.py
- Python
- Scikit Learn
- Pandas
- Flask
- HTML
- BootStrap
Sinjoy Saha
