Regression Projects

This repository contains regression projects implemented in both Python and R, showcasing applications of simple linear and logistic regression. The projects are designed to demonstrate the concepts and techniques commonly used in regression analysis.

Table of Contents

• Overview
• Technologies Used
• Projects
• Setup
• Usage

Overview

Regression is one of the fundamental techniques in statistical modeling and machine learning. This repository includes:

• Simple Linear Regression
• Logistic Regression

The projects are implemented using Jupyter Notebooks for Python and R scripts, making it easy to follow along and understand the code.

Technologies Used

The repository is primarily composed of:

• Jupyter Notebook (99.2%)
• R (0.8%)

Projects

The repository includes the following projects:

1. Simple Linear Regression:

   • Demonstrates predicting a continuous variable using one or more explanatory variables.
   • Implemented in Python via Jupyter Notebooks.

2. Logistic Regression:

   • Shows binary classification using logistic regression.
   • Implemented in Python and R.

Setup

Python Environment

1. Clone the repository:

   git clone https://github.com/eyabesbes/Regression.git
   cd Regression

2. Install the required Python libraries using pip:

   pip install -r requirements.txt

3. Launch Jupyter Notebook:

   jupyter notebook

R Environment

1. Ensure R is installed on your system.
2. Install necessary R packages by running the provided scripts.

Usage

1. Open the relevant Jupyter Notebook or R script.
2. Follow the comments and instructions to understand and execute the regression models.
3. Modify datasets or parameters as needed to experiment with the models.