Car Price Prediction

This project is designed to predict car prices using an XGBoost model. It includes data preprocessing, model training, and prediction steps. The model is trained on a dataset containing various features of cars, and it can be used to estimate the price of a car based on user-provided input data.

Table of Contents

• [Prerequisites]
• [Getting Started]
• [Usage]
• [Model Evaluation]
• [Making Predictions]
• [Author]

Prerequisites

Before running the car price prediction script, you need to have the following prerequisites in place:

• Python: Make sure you have Python installed on your system.
• Libraries: Install the required Python libraries mentioned in the script. You can typically use pip to install them.

pip install pandas scikit-learn xgboost joblib

Getting Started

1. Clone the Repository: Clone this repository to your local machine:

   git clone https://github.com/yourusername/car_price_prediction.git

2. Data: Ensure that you have the dataset you want to use for car price prediction. In this example, the dataset is 'turboaz_27_09_2023.csv'.

3. Run the Script: Open a terminal, navigate to the project's root directory, and run the script:

   python main.py

   This script will preprocess the data, train the XGBoost model, and display model evaluation metrics.

Usage

Model Training

The script main.py performs the following tasks:

• Data preprocessing: It cleans and encodes the dataset, preparing it for training.
• Model training: It uses an XGBoost model to predict car prices.

Making Predictions

The script can also make predictions for a new data point. To do this:

1. Update the new_data_point dictionary in the script with the features of the car you want to predict the price for.
2. Run the script again to make predictions for the new data point.

Model Evaluation

After training the model, the script will display the following model evaluation metrics:

• R-squared (R²): A measure of the model's goodness-of-fit.
• Root Mean Squared Error (RMSE): A measure of the prediction error.
• Mean Absolute Error (MAE): A measure of the absolute prediction error.

Author

• Ismat Samadov
• Email: ismetsemedov@gmail.com