A machine learning project that predicts used car prices based on several features such as fuel type, seller type, transmission, year, and more.
This project uses Linear Regression and Lasso Regression models to analyze and predict prices.
The dataset contains information about used cars, including their technical specifications, configurations, and price.
Our goal is to build and evaluate regression models that can accurately predict the selling price of a car.
- Data preprocessing & cleaning
- Encoding categorical features
- Training Linear and Lasso Regression models
- Model performance evaluation using R² score
- Visualization of predicted vs actual prices
The dataset used in this project is [car data.csv](https://www.kaggle.com/datasets/nehalbirla/vehicle-dataset-from-cardekho).
Columns include:
| Column Name | Description |
|---|---|
| Car_Name | Name of the car |
| Year | Year of manufacture |
| Selling_Price | Price at which the car is being sold |
| Present_Price | Current ex-showroom price |
| Driven_kms | Distance the car has been driven (in km) |
| Fuel_Type | Type of fuel (Petrol/Diesel/CNG) |
| Seller_Type | Seller type (Dealer/Individual) |
| Transmission | Transmission type (Manual/Automatic) |
| Owner | Number of previous owners |
git clone https://github.com/yourusername/Car-Price-Prediction.git
cd Car-Price-Prediction
pip install -r requirements.txt
jupyter notebook "Car Price Prediction.ipynb"