binary-logistic-regression

Automating Assumption Checks for Regression Models (Work in Progress, Currently Paused)

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Welcome to the Logistic Regression Repository! This repository aims to provide a comprehensive collection of resources and code examples for logistic regression, a powerful statistical technique used for modeling and predicting categorical outcomes.

Implementation of Regression and Classification Algorithms

Logistic Regression is a supervised learning algorithm that is used when the target variable is categorical. In Logistic Regression the target variable is categorical where we have to strict the range of predicted values. Consider a classification problem, where we need to classify whether an email is a spam or not. So we have to predict either …

Pet-project repo: an attempt to independently reproduce basic classical machine learning algorithms using numpy

The project aimed to implement a digital processing algorithm of biomedical signals to estimate the chances of survival for patients admitted to the ICU. A classification model based on artificial intelligence was implemented to estimate the chances of survival.

Multi-Linear and Binary Logistic Regression in C.

Applying binary logistic regression to predict loan approval outcomes using applicant information such as marital status, credit history, and property area. The analysis focuses on identifying the most important predictors of loan approval while evaluating model performance and assumptions.

A collection of academic projects from my time at the University of Algiers 1, covering social network analysis, data visualization, formal languages, logistic regression, and statistical analysis. This repository excludes Machine Learning projects, which are available in a separate repo.

first business data analysis - factors affecting student satisfaction and bank clients

My Business Analytics Projects

Spatial and statistical analysis of Type 2 Diabetes using logistic regression and arcGIS.

Implementing binary / multiple logistic regression models, for the well known mnist dataset while also creating the support vector machine(SVM) models

Best suited Logistic Regression model for the readmission of patients undergoing diabetes related treatment in a hospital

Machine Learning project using Binary Logistic Regression to classify purchase decisions from demographic features in the Social Network Ads dataset. The process involves feature engineering, scaling, model training, evaluation, and decision boundary visualization. Achieved 86% accuracy, confirming strong generalization and predictive performance.

As part of a class team project, we analyzed Montgomery County traffic data to predict car crash injury severity. We built a binary logistic regression model (AUC=0.758) evaluating road, driver, and vehicle variables. Results proved vehicle body type, damage extent, and alcohol use are the strongest risk predictors, not just road conditions.