Vedakeerthi / CAR_PRICE_PREDICTION

Based on the dataset from the car dekho, the price of the car is predicted based on some real world features.

Car Price prediction

Table of Content

• Demo
• Synopsis
• Appendix
• Links
• Directory Tree
• Color Reference
• Features
• Run Locally
• License
• Technology Used

Demo

Synopsis

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,

Independent variable (X) : Name, year, km_driven, fuel, seller_type, transmission, owner

Dependent variable (Y) : selling_price

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

Appendix

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.

Links

• Dataset link : https://www.kaggle.com/datasets/mohamedsamir3/car-price-details

• Github link : https://github.com/Vedakeerthi/CAR_PRICE_PREDICTION

Directory Tree

├── 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 Reference

Color	Hex
Body of the web page	#A09B97
Border of the web page	#A40000

Features

• Live prediction analysis.
• Fullscreen mode supports in mobile, pc.
• Cross platform can be used on multiple operating system.

Run Locally

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

License

Technology Used