Machine Learning Algorithms

This repository contains jupyter notebooks providing an implementation of basic Machine Learning models for regression and classification.

Notebook 1: Classification Algorithms

It contains an implementation of nearest neighbours methods for classification:

1. K-Nearest Neighbor method (k=1)
2. Conformal Predictor
   

Each algorithm is implemented on two datasets: Iris dataset from scikit-learn, Dataset imported from ionosphere.txt

Notebook 2: Regression Algorithms

It provides an implementation of an inductive conformal predictor for regression and the ability to use the Lasso method, data preprocessing, and tools for parameter selection in scikit-learn.

These algorithms are implemented on the diabetes dataset on the preprocessed scikit-version as well as the otiginal version found on: https://trevorhastie.github.io/data.html

It is a basic repository to have an understanding of ways to apply the ideas and algorithms of machine learning for classification and regression. All the code related details and relevant analysis are added in corresponding notebooks.