🚗 Car Price Prediction using Machine Learning

This project implements an end-to-end Machine Learning pipeline to predict the price of a car based on multiple technical and performance-related features. The project demonstrates data preprocessing, feature scaling, model training, and evaluation using real-world data.

📌 Project Overview

The objective of this project is to build a predictive model that estimates car prices using historical data. This helps understand how various factors like engine size, horsepower, mileage, and weight affect car pricing.

📂 Dataset

The dataset includes the following features:

Wheelbase

Curb Weight

Engine Size

Horsepower

City MPG

Highway MPG

Other numerical car attributes

Target Variable:

Price

🛠 Technologies Used

Python

Pandas & NumPy – Data manipulation

Scikit-learn – ML models & preprocessing

Matplotlib / Seaborn – Data visualization

Jupyter Notebook

⚙️ Workflow

Load and explore dataset

Check for missing values and duplicates

Feature selection and target separation

Train-test split (80%-20%)

Feature scaling using StandardScaler

Train ML models:

Linear Regression (for price prediction)

Classification model (price categories)

Evaluate using:

R² Score

Mean Squared Error (MSE)

Accuracy Score

Confusion Matrix

📊 Results

The model learns patterns between car attributes and price and performs well on unseen data. Evaluation metrics show reliable prediction accuracy.

🎯 Key Learnings

Understanding supervised learning

Practical experience with regression & classification

Importance of data preprocessing

Model evaluation techniques

Real-world ML pipeline implementation