https://github.com/arif-miad/car-price-prediction-machine-learning-project

https://github.com/arif-miad/car-price-prediction-machine-learning-project

data-science data-visualization keras-tensorflow machine-learning python regression-models sklearn

Last synced: 3 days ago
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• Host: GitHub
• URL: https://github.com/arif-miad/car-price-prediction-machine-learning-project
• Owner: Arif-miad
• License: gpl-3.0
• Created: 2025-02-09T19:26:55.000Z (5 days ago)
• Default Branch: main
• Last Pushed: 2025-02-09T19:36:12.000Z (5 days ago)
• Last Synced: 2025-02-09T20:29:17.773Z (5 days ago)
• Topics: data-science, data-visualization, keras-tensorflow, machine-learning, python, regression-models, sklearn
• Homepage:
• Size: 17.6 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Car Price Prediction

## Overview

This project focuses on predicting car prices using machine learning techniques. The dataset contains various attributes like brand, model, engine size, fuel type, transmission type, mileage, number of doors, and owner count.

## Dataset

The dataset consists of 10,000 entries, each representing a car with the following columns:

- **Brand**: The brand of the car (e.g., Toyota, BMW, Ford).

- **Model**: The specific model of the car.

- **Year**: The production year of the car.

- **Engine_Size**: The engine size in liters.

- **Fuel_Type**: The type of fuel the car uses.

- **Transmission**: The transmission type.

- **Mileage**: The total distance the car has traveled.

- **Doors**: The number of doors in the car.

- **Owner_Count**: The number of previous owners.

- **Price**: The estimated selling price of the car.

## Workflow

1. **Data Preprocessing**

   - Handling missing values

   - Encoding categorical variables (One-Hot Encoding & Label Encoding)

   - Feature scaling

2. **Exploratory Data Analysis (EDA)**

   - Distribution plots (Histogram, KDE, Box Plot, etc.)

   - Correlation heatmap

   - Pair plots

   - Pie charts

3. **Feature Engineering**

   - Creating new features

   - Selecting important features

4. **Model Selection & Training**

   - Trying different regression models

   - Hyperparameter tuning

   - Evaluating models

5. **Model Evaluation**

   - RMSE, MAE, R² Score

   - Comparing model performances

6. **Deployment**

   - Saving the best model

   - Creating an API for predictions

## Implementation

The project includes:

- **One-Hot Encoding for categorical columns**

- **Label Encoding for categorical columns**

- **Top 10 Regression Models for car price prediction**

- **20 different visualizations using Seaborn & Matplotlib**

## Code Implementation

All code is available in my Kaggle Notebook:

[Kaggle Notebook](https://www.kaggle.com/code/arifmia/car-price-prediction-analyzing-key-factors-impact)

## Connect with Me

Feel free to reach out on LinkedIn:

[LinkedIn Profile](www.linkedin.com/in/arif-miah-8751bb217)

## License

This project is open-source and available for use and modification.