This is a Machine Learning based project. It uses Regression technique to predict the price of the Used cars.
- XGBoost algorithm is used to make this project
- Flask framework is used to make web server
- Frontend is majorly based on Bootstrap and JS
- Responsive web design
- Deployed on Heroku cloud
- Refer the ipython notebook for Model building code.
Visit the link to access the application:
https://machineer-prediction.herokuapp.com/
You can also scan the below QR code to access the application:
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Clone the repository
Use git CLI, Zip or other. -
Create a New Conda Environment first
conda create -n used_Car_price_prediction python=3.6 -
Active the created environment
conda activate used_Car_price_prediction -
Navigate to the root folder of the project where app.py and requirements.txt files are present.
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Install requirements.txt
pip install -r requirements.txt
Incase of trouble installing refer this StackOverflow Answer -
Launch the Flask Application
python app.py
- Get the recommendation of cars for given Location and Price.
- Get the predicted price given the set of Attributes. Cool part is, no need to enter all the attributes it will be taken care by the algorithm.
- Along with the predicted price, it will also show the top 20 recommended vehicles within the price range for the given Location. Impressive right!
- Still not sure about the recommendations? No worries, take a look at more details for all suggestions.
Hope this will be helpful for implementing and deploying the Machine Learning models.