Car Price Prediction System

Live App: https://car-price-predictor-cl9kwcrxtnvzvkhsnxq6ev.streamlit.app/

A machine learning–based web application that predicts the selling price of used cars using historical data and user inputs. The application is built using Python, Scikit-learn, and Streamlit.

Project Overview

Buying or selling a used car often involves uncertainty in pricing. This project aims to predict the fair selling price of a used car based on important features such as year, fuel type, transmission, ownership, and kilometers driven using machine learning techniques.

Features

• Predicts used car selling price
• Simple and interactive Streamlit interface
• Multiple machine learning models compared
• Fast prediction using pre-trained model
• Clean UI with background image

Tech Stack

• Python
• Streamlit
• Pandas
• NumPy
• Scikit-learn
• pickle

Project Structure

Car-Price-Predictor/ │ ├── app.py # Streamlit web app ├── model_training.ipynb # Model training & evaluation ├── best_model.pkl # Trained ML model ├── requirements.txt # Python dependencies ├── README.md # Project documentation │ ├── dataset/ │ └── Car_details_v3_CLEANED # Dataset │ └── assets/ |── background.jpg # Background image └──background.mp4 # Background video

---

Dataset Information

• Downloaded from kaggle and Cleaned

Target Variable

• Selling Price

Important Features

• Year
• Kms Driven
• Fuel Type
• Seller Type
• Transmission
• Owner
• Engine
• Max Power
• Seats
• Mileage Value
• Mileage Unit

Machine Learning Models Used

• Decision Tree Regressor
• Random Forest Regressor
• Gradient Boost Regressor The best-performing model is saved as best_model.pkl and used in the application.

Install required dependencies:

bash

• pip install -r requirements.txt

Run the Application

• streamlit run app.py

Usage

• Enter car details such as year, fuel type, transmission, and kilometers driven...
• Click on Predict
• The predicted selling price will be displayed instantly