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Car Price Prediction

Posted
By Tark S Patel
2 min read
Car Price Prediction

🚗 Car Price Prediction: Predicting Used Car Prices with Machine Learning

🔍 Project Overview

Buying or selling a used car can be challenging, especially when it comes to determining the right price. A car’s price depends on multiple factors such as its brand, model, year of manufacture, fuel type, transmission, mileage, and condition.

This project aims to solve this problem by building a Machine Learning-powered Car Price Prediction System that provides accurate price estimates based on historical data.

This web-based application helps both car buyers and sellers make informed decisions by predicting the fair market value of a used vehicle. Instead of relying on guesswork or inconsistent price listings, users can simply enter a few details about the car and get an instant price prediction backed by data.

Car Price Prediction

🔗 Project Links

🚀 Live Demo: Try the App
📂 GitHub Repository: View Code

🏗 How Does It Work?

The project uses Supervised Machine Learning Algorithms to analyze past sales data and learn the pricing patterns. Here’s the complete workflow:

1️⃣ Data Collection & Cleaning

  • I scraped a large dataset from a website using Python and Selenium. It contain features like brand, model, manufacturing year, mileage, fuel type, engine size, location, and selling price.
  • The dataset was cleaned by handling missing values, removing duplicates, and normalizing text data.

2️⃣ Model Selection & Training

  • Various regression models were trained and tested, including:
    • ✅ Linear Regression – For a simple price trend analysis.
    • ✅ Random Forest Regressor – To capture complex relationships between features.
    • ✅ Gradient Boosting Regressor – To improve prediction accuracy.
  • After evaluating different models, Gradient Boosting was chosen for deployment due to its high accuracy and stability.

3️⃣ Web Application Development

  • A user-friendly web interface was built using Flask.
  • Users can enter car details in a simple form and instantly get a predicted price.

4️⃣ Deployment

  • The model and web application were deployed on Render for easy access.
  • Now, users can predict car prices from any device with an internet connection.

🌟 Why is This Project Useful?

✅ For Car Buyers: Helps them check if the seller’s price is reasonable.
✅ For Car Sellers: Provides an estimated price to maximize profit while staying competitive.
✅ For Dealers: Assists dealerships in setting fair and competitive prices for their inventory.
✅ For Auto Enthusiasts: A great tool for analyzing car price trends and making informed investment decisions.

🛠 Technologies & Skills Used

  • Machine Learning (Supervised Learning, Regression)
  • Data Processing (Pandas, NumPy, Scikit-Learn)
  • Model Evaluation & Optimization (Grid Search, Hyperparameter Tuning)
  • Flask (Web Application)
  • Deployment on Render

🎯 Future Enhancements

🔹 Add more features like car location, insurance history, and accident records to improve accuracy.
🔹 Implement a real-time car price API that updates with the latest market trends.
🔹 Integrate with web scraping to pull live data from online car marketplaces.
🔹 Develop a mobile-friendly version for better accessibility.

🔥 This project bridges the gap between data science and the automobile industry, helping users make smarter buying and selling decisions.

Whether you’re a first-time car buyer, a seasoned seller, or a data enthusiast, this tool brings the power of AI-driven pricing predictions to your fingertips! 🚗💨

This post is licensed under CC BY 4.0 by the author.