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.
This project has four major parts :
- used_car_price_suggester.ipynb - This notebook contains the code for EDA, model creation, and training.
- 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.
- random_forest_regression_model.pkl - A serialized(
pickle
d) version of the Random Forrest Regressor model so we can simply load it and make suggestions. - templates - This folder contains the HTML template to allow the user to enter car details and get a price suggestion.
-
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 -
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.
- Navigate to URL http://localhost:5000 or http://127.0.0.1:5000/