ML Models Implementation from Scratch

This repository contains implementations of fundamental machine learning algorithms from scratch using Numpy and Python.

Structure

'collinearity-detection/' : Discusses methods of detecting collinearity of predictor variables in a dataset.
'feature-scaling/' : Implementation of 2 popular feature scaling methods from scratch.
'gradient-descent/' : Implementation of Gradient Descent from scratch.
'linear-regression/': Implementation of Linear Regression without using scikit-learn.
'outlier-detection/' : Implementation of 3 most commonly used outlier detection methods - n-Standard Deviation, Z-Score and IQR.
'regularisation/' : Implementation of regularisation techniques from scratch.

Name		Name	Last commit message	Last commit date
Latest commit History 35 Commits
collinearity-detection		collinearity-detection
decision-trees/classifiers		decision-trees/classifiers
feature-scaling		feature-scaling
gradient-descent		gradient-descent
k-nearest-neighbours		k-nearest-neighbours
linear-discriminant-analysis		linear-discriminant-analysis
linear-regression		linear-regression
logistic-regression		logistic-regression
naive-bayes		naive-bayes
outlier-detection		outlier-detection
perceptron-learning-algorithm		perceptron-learning-algorithm
regularisation		regularisation
support-vector-machines		support-vector-machines
README.md		README.md