A project showcasing the various steps involved in carrying out a basic linear regression task for the prediction of a target variable.
This repository contains a project that focuses on linear regression, a powerful statistical modeling technique used to analyze and predict relationships between variables. The project explores various aspects of linear regression, including data preprocessing, model training, evaluation, and interpretation.
- Implements linear regression algorithm from scratch
- Utilizes popular libraries like NumPy, Pandas, and Scikit-learn
- Includes data preprocessing techniques such as feature scaling and handling missing values
- Demonstrates model evaluation using metrics like Mean Squared Error (MSE) and R-squared
- Provides visualizations to aid in interpreting the results
- Python 3.x
- NumPy
- Pandas
- Scikit-learn
- Matplotlib
- Clone the repository:
git clone https://github.com/your-username/Linear_Regression_Project.git - Navigate to the project directory:
cd Linear_Regression_Project - Install the required dependencies:
pip install -r requirements.txt
- Prepare your dataset in a suitable format, ensuring it is compatible with linear regression.
- Open the Jupyter Notebook or Python script containing the project code.
- Customize the code as needed, such as updating the data file path or adjusting model parameters.
- Execute the code to perform data preprocessing, model training, evaluation, and interpretation.
- Analyze the results, interpret the coefficients, and assess the model's performance.
Contributions are welcome! If you encounter any issues or have suggestions for improvement, please feel free to submit a pull request or open an issue.
This project is licensed under the MIT License.
This project was inspired by the importance of linear regression in statistical modeling and its wide range of applications. I appreciate the contributions of the open-source community and various libraries used in this project.