Car Price Predictor using Python

Car Price Predictor Using Python

Introduction

A simple project based on Car Price Prediction is designed using Python to estimate the selling price of used cars by analyzing different features like brand, year, mileage, fuel type, and overall condition. In today’s fast-moving automotive market, pricing a used car fairly is one of the most critical decisions for both buyers and sellers. Buyers want value for money, while sellers want the best return without losing potential customers.

This project bridges that gap by applying data science and machine learning techniques. It not only performs detailed data analysis but also builds a predictive model capable of estimating future car prices. With clean coding practices and real-world datasets, it is both an academic learning resource and a practical tool for business use.

Project Overview

The Car Price Prediction project is built around a dataset that contains crucial attributes influencing car prices. Below are the major features considered:

• Make & Model – Identifies the manufacturer and car model.

• Year of Production – Indicates when the car was manufactured.

• Condition – Reflects the physical and mechanical state of the car.

• Mileage – Distance driven (in kilometers).

• Fuel Type – Petrol, diesel, hybrid, or electric.

• Engine Volume – Engine capacity measured in cubic centimeters (cm³).

• Transmission Type – Automatic or manual transmission system.

• Drive Unit – Specifies front-wheel, rear-wheel, or all-wheel drive.

• Segment – Categorizes cars into economic, luxury, or specialty segments.

By preprocessing and analyzing this dataset, the system identifies patterns that strongly affect car values and uses them to predict prices for new entries.

Methodology

1. Data Collection & Cleaning – The dataset is imported, missing values are handled, and irrelevant columns are removed.

2. Exploratory Data Analysis (EDA) – Statistical insights and visualizations such as box plots, scatter plots, and correlation heatmaps are generated.

3. Feature Engineering – Categorical features like fuel type and transmission are encoded, while numerical values like mileage are scaled.

4. Model Building – Machine learning algorithms such as Linear Regression, Decision Tree, and Random Forest are applied.

5. Model Evaluation – Metrics such as R², RMSE, and MAE are used to determine prediction accuracy.

6. Prediction Interface – A simple Python-based interface (or web app with Streamlit/Flask) is created for users to input details and get real-time predictions.

Key Findings from Data Analysis

• Car prices rose significantly after the year 2000.

• Petrol cars with automatic transmission are usually priced higher than diesel cars with manual transmission.

• Electric cars form the most expensive category overall.

• All-wheel drive cars command higher prices compared to front-wheel or rear-wheel drive.

• Luxury and specialty segment cars (especially European brands) consistently hold premium value.

Applications

• Buyers & Sellers – Get fair and transparent pricing before making transactions.

• Dealerships – Integrate price prediction into used car sales platforms.

• Students & Learners – Gain hands-on experience with end-to-end data science workflows.

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Predictive Modeling Approach

To predict car prices, a Decision Tree Regressor was employed. This model is effective in capturing non-linear relationships and feature importance within the dataset.

Model Accuracy: 85.29%
Most Influential Factors:

• Year of Production – Newer cars typically have higher prices.
• Engine Volume – Larger engines contribute to higher price points.

Impact of This Project

For Buyers – Helps in identifying fair prices for vehicles based on their features.
For Sellers – Assists in setting competitive prices in the Belarusian car market.
For Dealers – Provides insights into pricing trends for better inventory management.

By leveraging machine learning and data-driven insights, this project provides accurate car price predictions for the Belarusian automotive market. With an 85.29% accuracy rate, it serves as a valuable tool for anyone looking to buy or sell a car in Belarus.