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Car Price Predictor

Created by: techwizard
1 devlog
2h 33m 47s

A Machine Learning-powered web application that estimates used car prices based on features like brand, model, year, fuel type, and mileage. Built with a Python (Flask) backend and a responsive, interactive frontend, it utilizes regression algorit…

A Machine Learning-powered web application that estimates used car prices based on features like brand, model, year, fuel type, and mileage. Built with a Python (Flask) backend and a responsive, interactive frontend, it utilizes regression algorithms to provide real-time, data-driven valuations for buyers and sellers.

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techwizard
techwizard shipped Car Price Predictor
3 months ago

Shipped this project!

🚀 New Year, New Ship!

I kicked off 2026 by building a Car Price Predictor—a web application that estimates the fair market value of used cars.

How it works: Users simply input vehicle details (Company, Model, Year, Fuel, Km Driven), and the app sends this data to a Python Flask backend. There, a trained Linear Regression model processes the inputs and returns an estimated price instantly via AJAX, without reloading the page.

What I learned: This project was a deep dive into full-stack integration. I learned how to connect a Scikit-Learn ML pipeline to a live web interface, handle dynamic JavaScript events (like filtering car models based on the selected brand), and design a responsive UI using modern CSS variables.

techwizard
techwizard worked on Car Price Predictor
3 months ago
2h 33m logged

How I Built a Car Price Predictor to Start My 2026 Dev Journey

Date: January 1, 2026 By: Hardik Mittal

They say the best way to learn is to build. So, while everyone was making New Year’s resolutions, I decided to open my IDE and ship a project.

Here is the story behind my latest build: a Car Price Predictor.

  1. The Problem 🧐

Selling a used car is confusing. Prices vary wildly based on fuel type, kilometers driven, and the age of the car. I wanted to build a tool that uses actual data to give users a fair price estimate instantly.

  1. The Machine Learning Model 🤖

I started with a dataset of utilized cars. The data cleaning phase was crucial—handling missing values and cleaning up inconsistent car names. I used Scikit-Learn to train a Linear Regression model. To handle the categorical data (like “Petrol” vs “Diesel” or “Maruti” vs “Hyundai”), I implemented a pipeline using OneHotEncoder. The model achieved an accuracy (R² score) of approx 0.84, which is solid for real-world estimation.

  1. Building the Web App 💻

A model inside a Jupyter Notebook isn’t useful to anyone. I needed a frontend.

Flask was my choice for the backend API.

For the frontend, I wanted a clean, “tech” aesthetic. I built a responsive form using CSS Variables for easy theming (and a dark-mode-ready structure).

JavaScript handles the logic. I created dynamic dropdowns so that when a user selects “Honda,” the model list only shows Honda cars.

  1. The Result 🎉
    The app is now live! You can input your car’s details, click “Predict,” and get a price estimate in milliseconds without refreshing the page.

  2. What’s Next?
    I plan to deploy this soon and maybe add more features like current market trends graphs.
    Happy New Year and Happy Coding!

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Comments

Scutoid
Scutoid 3 months ago

ai devlog and frontend