https://github.com/kathrin-92/exploring-supervised-learning

A collection of different exercises in various ML Supervised Learning Methods.
https://github.com/kathrin-92/exploring-supervised-learning

gradient-descent knn linear-regression logistic-regression naive-bayes random-forest supervised-machine-learning svm-classifier

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A collection of different exercises in various ML Supervised Learning Methods.

• Host: GitHub
• URL: https://github.com/kathrin-92/exploring-supervised-learning
• Owner: Kathrin-92
• Created: 2023-01-10T15:02:20.000Z (over 3 years ago)
• Default Branch: main
• Last Pushed: 2023-01-10T15:11:36.000Z (over 3 years ago)
• Last Synced: 2025-05-19T09:12:23.666Z (about 1 year ago)
• Topics: gradient-descent, knn, linear-regression, logistic-regression, naive-bayes, random-forest, supervised-machine-learning, svm-classifier
• Language: Python
• Homepage:
• Size: 9.77 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# Exploring Supervised Learning

## A collection of different exercises in various ML Supervised Learning Methods. 

![confusion_matrix_breast-cancer](https://user-images.githubusercontent.com/71875232/211586392-b0ed2188-e1dd-4aa5-a6f2-133cdf2357d3.png)

## General Info

This repository is a collection of different exercises on supervised machine learning methods. I used different standard datasets, including the iris flower and breast cancer dataset.  

I was able to get a basic exposure to the scikit-learn library and also learn about different performance metrics, including MAE, MSE, RMSE, Coefficient of Determination, the Confusion Matrix, and more. 

ML methods I was able to familiarize with: 

* Linear Regression

* Logistic Regression

* Lasso, Ridge, Elastic Net Regression and GLM 

* KNN

* Naive Bayes

* SVM

* Random Froest and Gradient Descent 

![histplot_iris_flowers](https://user-images.githubusercontent.com/71875232/211586414-80ce8418-a5cf-4dea-842d-958c6f0ff4f6.png)