UsedCarPrice-Predictor

This is a Supervised Machine Learning based model. Algorithms used in this model are:

Linear Regression
SVR
KNN
Decision Tree
Random Forest for Regression
GridSearch CV on Random Forest

Here, Selling Price of a used car is predicted on the basis of given features like Year, Present Price, Kms Driven, Transmission mode, Seller Type, Fuel Type and owner.

The R2 score and RMSE value from different models are found to be:

The R2 Score,Bias & RMSE value for  Linear Regression is 0.6576364489340596 , 9.0 & 3.05629557865939 respectively.
The R2 Score,Bias & RMSE value for  SVR is -0.06230577764424439 , 29.0 & 5.383644370018745 respectively.
The R2 Score,Bias & RMSE value for  KNN is 0.002453284381575438 , 27.0 & 5.216968488886138 respectively.
The R2 Score,Bias & RMSE value for  Decision Tree Regressor is 0.7438270965512692 , 7.0 & 2.643736623291612 respectively.
The R2 Score,Bias & RMSE value for  Random Forest is 0.8733907611265234 , 3.0 & 1.8585929737237548 respectively.
The R2 Score,Bias & RMSE value for  GridSearch CV on Random Forest is 0.9033470958982315 , 3.0 & 1.6238991176931048 respectively.

Dataset

Load the file car.csv from the model folder.

Requirements

Python
FastAPI
Sklearn
Uvicorn
Pandas
Heroku Cli

Structure

├── model/CarPricePrediction        # machine learning model is here
├── model/model.pkl     # pickle file for GridSearch Cv on Random Forest Regressor
├── main.py            # API is here for ML Model
├── schemas.py       # schema of model
├── requirements.txt  # packages needed to install