Used Car Selling Price Suggester

This web app uses a Random Forrest Regressor model to suggest a suitable price for used cars. The model is trained on the CarDekho dataset available here on Kaggle.

Project Structure

This project has four major parts :

1. used_car_price_suggester.ipynb - This notebook contains the code for EDA, model creation, and training.
2. app.py - This contains the Flask API that receives car details through API calls, computes the precited price based on our model, and returns it.
3. random_forest_regression_model.pkl - A serialized(pickled) version of the Random Forrest Regressor model so we can simply load it and make suggestions.
4. templates - This folder contains the HTML template to allow the user to enter car details and get a price suggestion.

Running the project

1. Ensure that you have installed all the dependencies from requirements.txt. Run all the cells of the notebook to create a new model or simply use the serialized version of our model random_forest_regression_model.pkl

2. Go to the project directory and app.py using the below command to start Flask API

python app.py

By default, the Flask app will run on port 5000.

3. Navigate to URL http://localhost:5000 or http://127.0.0.1:5000/