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Used Cars Price Prediction

Machine learning project for analyzing vehicle attributes and predicting used-car prices.

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Conceptual visual representing the Used Cars Price Prediction machine learning project
  • Python
  • Pandas
  • Scikit-learn
  • Machine Learning
  • Matplotlib
Read the full case study: Used Cars Price Prediction

Overview

A machine learning project that analyses second-hand vehicle attributes and predicts listing prices. The emphasis is on a clean dataset, understandable features and a reproducible evaluation method.

Problem / Objective

Objective: predict used-car prices from listing attributes using a documented, reproducible modelling process.

Vehicle pricing depends on many interacting attributes, which makes manual pricing inconsistent between listings.

Solution

A cleaned dataset, analysed features and a regression model, evaluated with a documented methodology rather than a single headline score.

Key capabilities

  • Data preprocessing
  • Feature analysis
  • Model training
  • Price prediction
  • Evaluation methodology

Technology

  • AI / ML

    • Machine Learning
    • Scikit-learn

  • Development

    • Python

  • Data

    • Pandas
    • Matplotlib

Implementation

Pandas is used for cleaning and feature preparation, scikit-learn for regression modelling, and Matplotlib for diagnostic plots. Evaluation is performed on a held-out split so results can be reproduced.

How it flows

  1. Listing data
  2. Preprocessing
  3. Feature analysis
  4. Model training
  5. Price prediction

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