dmittz/supervisedlearning

Making ML Models trained through Supervised Learning

General Description:

Introductory Projects:
Iris Data Analysis:
  ML Analysis of a the Iris Flowers' Data Set
Customer Segmentation:
  ML Model made trained to predict outcomes by comparing input data to clustered data points that have been assigned to seperate groups
Diabetes Dataset:
  ML Model that uses Decision Tree Classifier to make predictions and understand the given data

Spam Detector:
  ML Model that has been trained through Naive Bayes Neural Network to analyse 5000+ emails used as training data, until the neurons are sufficiently trained to independently predict & classify new incoming emails as 'spam' or 'not spam'.