Project Overview: The "Logistic Regression Scenario 1" project aims to explore and implement logistic regression techniques in a specific scenario. Leveraging Jupyter Notebook, the project provides an interactive platform for data analysis, model development, and evaluation.

Objective: The primary objective of this project is to demonstrate the application of logistic regression in a real-world scenario. Through careful data preprocessing, feature engineering, model training, and validation, the project seeks to achieve accurate predictions and insights.

Key Features:

• Data Exploration: Comprehensive exploration of the dataset to understand the underlying patterns and relationships.
• Data Preprocessing: Cleaning and preprocessing of the dataset to prepare it for model training.
• Model Development: Implementation of logistic regression model using Python's scikit-learn library.
• Model Evaluation: Rigorous evaluation of the model's performance using appropriate metrics and techniques.
• Interactive Analysis: Utilization of Jupyter Notebook for interactive analysis, code execution, and visualization.

Project Structure:

• Logistic Regression Scenario 1 Completed-checkpoint.ipynb: Jupyter Notebook containing the complete project code, including data exploration, preprocessing, model development, and evaluation.
• README.md: Project README file providing an overview of the project, installation instructions, and usage guidelines.

Contributors:

• Caleb-sage

Repository Link: Regression_Model/Logistic Regression Scenario 1 Completed-checkpoint.ipynb