- Demo
- Synopsis
- Appendix
- Links
- Directory Tree
- Color Reference
- Features
- Run Locally
- License
- Technology Used
Car price prediction program is executed with the help of car dekhko dataset, where based on the given features, such as the year the car bought and how much kilometers driven and the fuel type of the car, the seller type based on which the seller sells the car, either the seller may be individual or a dealer, and the owner of a car indicates a first owner, a second owner or a third and more owner which alters the selling price of the car, and the type by which car differs is the transmission of the car, which is either manual or automatic driven cars. This app is very much useful for the people who want to know how much the amount the car can be sold or bought.
So based on the given data,
Based on the given data we can identify this as a regression problem, so we can use various machine learning problems to solve this problem which are as follows:
- Linear regression
- Lasso regression
- Ridge regression
- Decision tree regressor
- Random forest regressor
Machine learning model : Random forest regressor (sklearn)
Data preprocessing : Pandas
Data visualization : Matplotlib, Seaborn
Web framework : Flask
Model deployment : Heroku platform
The requirement for developing this model is present in the requirements.txt file.
The development of the model is present in the main.ipynb file.
The pickle file of the model for deployment is present in car price prediction folder.
The flask framework for the web app development is made in the app.py file.
The templates for the framework is done in html and css and the file is located in the templates folder.
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Github link : https://github.com/Vedakeerthi/CAR_PRICE_PREDICTION
├── template
│ ├── home.html
├── Procfile
├── README.md
├── CAR DETAILS FROM CAR DEKHO.csv
├── model-gif.gif
├── app.py
├── main.ipynb
|── Car_prediciton.pkl
├── requirements.txt
Color | Hex |
---|---|
Body of the web page | |
Border of the web page |
- Live prediction analysis.
- Fullscreen mode supports in mobile, pc.
- Cross platform can be used on multiple operating system.
Clone the project
git clone https://github.com/Vedakeerthi/CAR_PRICE_PREDICTION.git
Install dependencies
pip install -r requirements.txt
Start the server
python app.py
Run the app on server by the local link provided