https://github.com/ahmad-ali-rafique/logistic-regression-modeling

An in-depth exploration of logistic regression models, including data cleaning, model building, and performance evaluation on various datasets.
https://github.com/ahmad-ali-rafique/logistic-regression-modeling

accuracy confusion-matrix data dataanalytics logistic-regression logistic-regression-classifier machine-learning-algorithms mlmodels model modelling regression-models

Last synced: 4 days ago
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An in-depth exploration of logistic regression models, including data cleaning, model building, and performance evaluation on various datasets.

• Host: GitHub
• URL: https://github.com/ahmad-ali-rafique/logistic-regression-modeling
• Owner: Ahmad-Ali-Rafique
• Created: 2024-05-19T12:03:52.000Z (6 months ago)
• Default Branch: main
• Last Pushed: 2024-06-09T07:33:56.000Z (5 months ago)
• Last Synced: 2024-06-09T08:38:17.931Z (5 months ago)
• Topics: accuracy, confusion-matrix, data, dataanalytics, logistic-regression, logistic-regression-classifier, machine-learning-algorithms, mlmodels, model, modelling, regression-models
• Language: Jupyter Notebook
• Homepage:
• Size: 607 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Logistic-Regression-Modeling

An in-depth exploration of logistic regression models, including data cleaning, model building, and performance evaluation on various datasets.

## Contents

- [Introduction](#introduction)

- [Data Cleaning](#data-cleaning)

- [Model Building](#model-building)

- [Model Evaluation](#model-evaluation)

- [Future Work](#future-work)

- [About Me](#about-me)

## Introduction

Logistic regression is a powerful and widely used classification algorithm in machine learning. This repository showcases various aspects of logistic regression, from data preparation to model evaluation.

## Data Cleaning

Data cleaning is a critical step in the machine learning pipeline. In this section, I demonstrate techniques to preprocess and clean datasets to ensure high-quality inputs for the models.

## Model Building

This section covers the implementation of logistic regression models, highlighting different approaches and techniques used to build and refine the models.

## Model Evaluation

Evaluating the performance of a model is crucial. Here, I use various metrics such as accuracy, precision, recall, F1-score, and ROC-AUC to assess the effectiveness of the logistic regression models.

## Future Work

I plan to expand this repository with more advanced techniques and applications related to logistic regression, including regularization methods, multinomial logistic regression, and model optimization.

Thank you for exploring my logistic regression project. I hope you find it insightful and valuable!