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#

logistic-regression-algorithm

Here are 157 public repositories matching this topic...

🫀 A machine learning project using logistic regression to predict heart disease risk from clinical data. Built with Python, scikit-learn, and Jupyter notebooks. Achieves 85%+ accuracy on 303-patient dataset with 13 medical features. Complete ML pipeline from data exploration to model evaluation.

  • UpdatedJul 25, 2025
  • Jupyter Notebook

This repository contains the Iris Classification Machine Learning Project. Which is a comprehensive exploration of machine learning techniques applied to the classification of iris flowers into different species based on their physical characteristics.

  • UpdatedOct 4, 2023
  • Jupyter Notebook

Machine learning project for predicting credit risk using Logistic Regression, Decision Tree, and Random Forest.

  • UpdatedAug 21, 2025
  • Python

It includes my work on Machine learning during Coursera Assignment. It includes Linear regression and Logistic regression working model .It also include Neural Network implementation and Backpropagation Algorithm .It also include SVM implementation and also a Spam Classifier using SVM.

  • UpdatedApr 11, 2021
  • MATLAB

A repo holding the implementation as well as some theoretical explanation of the important relevant concepts. It is going to be in development for a long long time. I'll keep adding things everytime I have something to add to it, and I have the time for it. One can use it to learn the basics of Machine Learning from kind of scratch.

  • UpdatedOct 2, 2021
  • Jupyter Notebook

This is repository about the MachineLaering Basics including all the Machine learning Algorithms

  • UpdatedOct 15, 2019
  • Jupyter Notebook

Fake Currency Detection using Logistic Regression Algorithm

  • UpdatedNov 8, 2022
  • Jupyter Notebook

Running a targetted marketing ads on facebook. The company wants to anaylze customer behaviour by predicting which customer clicks on the advertisement

  • UpdatedJun 27, 2019
  • Jupyter Notebook

Classification ML models for predicting customer outcomes (namely, whether they're likely to opt into email / catalog marketing) depending on customer demographics (age, proximity to store, gender, customer loyalty duration) as well as sales and shopping frequencies by department

  • UpdatedMay 4, 2021
  • Jupyter Notebook

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