📌 Project Title

Used Car Price Valuation AI — Machine Learning Resale Estimator

💡 Overview

The secondhand automotive market faces significant price opacity, making accurate valuation challenging for individual sellers, buyers, and dealership networks.

To solve this problem, I built an end-to-end Machine Learning web application that automates vehicle market price discovery. The application ingests 11 core vehicle specifications — including brand model variant, city location, registration year, driven distance, fuel type, transmission type, owner history, fuel efficiency, engine displacement (CC), and horsepower (BHP) — to deliver instant, reliable market price predictions in Lakhs INR alongside an estimated fair-market value range.

⚡ Key Features & Deliverables

High-Accuracy ML Regression Engine: Powered by Scikit-Learn HistGradientBoostingRegressor trained on thousands of vehicle transactions with custom label encodings for 200+ car models across 11 metropolitan markets.

Real-Time Interactive Valuation Dashboard: Built with Streamlit featuring live input sliders, numerical controls, and instant price calculations.

Interactive Resale Value Range & Gauge Visualization: Integrated dynamic Plotly gauge charts providing a visual market valuation indicator and a condition-based fair market range (+/- 8% variance band).

Figma-Grade Human Design Theme: Designed a custom CSS UI system (Obsidian Slate & Indigo Emerald theme: #0B0F19 background, #6366F1 indigo accents) with Plus Jakarta Sans typography and translucent glassmorphic cards.

🛠️ Tech Stack

Python 3.14 • Scikit-Learn • Streamlit • Plotly • Pandas • NumPy • Joblib • CSS3 Glassmorphism