An implementation of multiple linear regression to predict salaries based on years of experience and age.
This project demonstrates how to build and use a Multiple Linear Regression model in Python. We utilize the Scikit-Learn library for regression analysis and Pandas and NumPy for data manipulation. Our goal is to predict salaries based on two independent variables: years of experience and age.
Make sure you have Python 3.8 or higher installed. You can install the required libraries using pip:
pip install scikit-learn pandas numpy- Clone the repository to your local machine.
- Navigate to the project directory.
- Execute the multiple_linear_regression.py script:
python multiple_linear_regression.py- View the intercept and coefficients, as well as sample salary predictions.
The dataset used for this project is provided in multiple_linear_regression_dataset.csv. Ensure that your data file is structured similarly for proper compatibility.
- The project uses Scikit-Learn's LinearRegression class to build and fit the model.
- Independent variables (X): Years of experience and age.
- Dependent variable (y): Salary.
This project is open-source and available under the MIT License.
This implementation is for educational purposes and serves as a beginner's guide to multiple linear regression.
For more information about this project, visit the repository or contact us.
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