This project develops a linear regression model to predict automobile sales prices based on various characteristics of the vehicles. The dataset is stored in otomobil.csv and includes features such as make, model, year, engine size, and more.
These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.
You need to have Python installed along with the following libraries:
- pandas
- statsmodels
You can install these packages using pip:
pip install pandas statsmodels
Installation
Clone this repository to your local machine:
git clone https://github.com/merttunayilmaz/AutomobileSalesPriceRegressionAnalysis.git
Navigate to the cloned repository:
bash
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cd AutomobileSalesPriceRegressionAnalysis
Install the required Python packages:
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pip install -r requirements.txt
Usage
Execute the script to perform the linear regression analysis:
bash
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python main.py
This will:
Load the automobile sales data from Data/otomobil.csv.
Separate the data into independent variables and the dependent variable SatisFiyati.
Add a constant term to the independent variables for the intercept.
Fit the linear regression model and print out the model's statistics.
Contributing
Contributions are welcome! For major changes, please open an issue first to discuss what you would like to change.
Please make sure to update tests as appropriate.
License
This project is licensed under the Apache License, Version 2.0 - see the LICENSE file for details.