Regressions

This project contains examples of Linear, Polynomial, and Logistic Regression models.

Technologies Used

• numpy
• pandas
• matplotlib.pyplot
• sklearn

Project Structure

The projects organized into the following directories:

• input: Contains the input data files.

• models: Stores the trained models.

• notebooks: Jupyter notebooks for exploratory data analysis and model development.

• output: Contains the output files generated by the models.

• src: Python scripts for training, predicting with the models and etc.

Examples

Linear Regression

In this example, we predict employees' salaries based on their years of experience. The dataset used is salary.csv, which contains two columns: YearsExperience and Salary.

Polynomial Regression

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Logistic Regression

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Installation

To get started with the project, follow these steps:

1. Clone the repository:

   git clone https://github.com/Anrsgrl/regressions
   cd regression_name

2. Install the required dependencies:

    pip install numpy pandas matplotlib scikit-learn

3. Run script

    cd folder_name
    python script_name

Detailed Documentation

More detailed documentation is available in the README files within each directory.