Implementation of ML Classification & Clustering algorithms
This repository contains the implementation of varioud ML Classification & Clustering algorithms along with the elements required to do the performance evaluaion such as Accuracy, Precision, Recall, F1-Score, ROC and AUC.
These algorithms are based on scikit learn package, so, you need to have it if you want to run them locally.
For the Feedforward Neural Network, the MNIST digit classifier is implemented using pytorch. The required packages descriptions are presented in the file itself.
Note: If you are not able to view the jupyter python notebook in the github itself, you can copy-paste the link in https://nbviewer.jupyter.org
The implementation are based on freely available datasets
kNN - iris dataset
ANN - MNIST digit (0-9) dataset
Decision Trees - Breast Cancer dataset
Naive Bayes - Play golf dataset (available in the repository)
KMeans & Hierarchial - Mall Customers dataset
PCA - Breast Cancer dataset