Welcome to the first release of our Car Price Prediction and Estimation Project, scheduled for April 15, 2024! This project leverages machine learning models to predict and estimate car prices with high accuracy, achieving a prediction error coefficient of less than 1 cent. The model demonstrates exceptional accuracy and reliability, making it a valuable tool for car price estimation.
This project aims to assist users in predicting car prices based on various features such as manufacturing year, mileage, fuel type, engine power, and more. By utilizing machine learning algorithms like Regression, Random Forest, or Gradient Boosting, we've developed a robust model that offers precise car price predictions.
Understanding and predicting car prices can be essential for both buyers and sellers in the automotive industry. Factors such as the car's age, mileage, fuel efficiency, and engine power significantly influence its market value. Our project harnesses these insights to provide accurate estimates of car prices, empowering users with valuable information.
This is the initial release of our project, scheduled for April 15, 2024. In this release, you'll find the first version of our car price prediction model, which demonstrates outstanding accuracy and performance. We encourage users to provide feedback and suggestions to further enhance the project in future releases.
- Jupyter Notebook: Interactive development environment for data analysis and model training.
- Matplotlib: Plotting library for creating visualizations from data.
- Pandas: Data manipulation and analysis library.
- NumPy: Library for numerical computations in Python.
- scikit-learn: Machine learning library for model training and evaluation.
- XGBoost: Implementation of gradient boosting for model training.
- PyCharm: Integrated Development Environment (IDE) used for coding.
- Ensure to protect your data and adhere to valid licenses for its use.
- Present the project in a documented and understandable manner for others to use or provide feedback.
- scikit-learn Documentation: For training and using machine learning models
- Matplotlib Documentation: Learn more about plotting with Matplotlib
- Pandas Documentation: Explore Pandas for data manipulation and analysis
- NumPy Documentation: Get details on numerical computations with NumPy
- XGBoost Documentation: Dive into the XGBoost library for gradient boosting
| Column Name | Description |
|---|---|
| name | The name or model of the car |
| year | The manufacturing year of the car |
| selling_price | The selling price of the car (in currency units) |
| km_driven | The total kilometers driven by the car |
| fuel | The type of fuel used by the car (e.g., petrol, diesel) |
| seller_type | The type of seller (e.g., individual, dealer) |
| transmission | The type of transmission (e.g., manual, automatic) |
| owner | The number of previous owners of the car |
| mileage | The mileage of the car (kilometers per liter or kilogram) |
| engine | The engine displacement of the car (in cc) |
| max_power | The maximum power output of the engine |
| seats | The number of seats in the car |
If Anaconda is not already installed on your system, you can easily download and install it from the Anaconda Official Website. Anaconda provides a comprehensive Python distribution with powerful package management and environment management capabilities.
To get started with our Car Price Prediction project, follow these steps to clone the project repository and set up the required environment:
git clone https://github.com/AM-mirzanejad/Car-Price-Prediction.git
cd Car-Price-Prediction
pip install -r requirements.txt
conda create --name car-price-prediction-env python=3.8
conda activate car-price-prediction-env
pip install -r requirements.txt