# K-fold cross validation

The greatest headache for any machine learning engineer is the problem of overfitting. The model we trained works perfectly on the training dataset but when…

# Linear Regression using Gradient Descent Algorithm

Gradient descent is an optimization method used to find the minimum value of a function by iteratively updating the parameters of the function. Parameters refer…

# Understanding the Classification report through sklearn

A Classification report is used to measure the quality of predictions from a classification algorithm. How many predictions are True and how many are False.…

# K-Means on Iris Dataset

Read my previous post to understand how K-Means algorithm works. In this post I will try to run the K-Means on Iris dataset to classify…

# Mathematics behind K-Mean Clustering algorithm

K-Means is one of the simplest unsupervised clustering algorithm which is used to cluster our data into K number of clusters. The algorithm iteratively assigns…

# Understanding Support vector Machines using Python

Support Vector machines (SVM) can be used for both classification as well as regression tasks but they are mostly used in classification applications. Some of…

# Simple example of Polynomial regression using Python

Previously I wrote an article explaining the underlying maths behind polynomial regression. In this post I will use Python libraries to regress a simple dataset…

# Maths behind Polynomial regression

Polynomial regression is a process of finding a polynomial function that takes the form f( x ) = c0 + c1 x + c2 x2 ⋯ cn xn where n is the degree of the polynomial and c is a set of coefficients. Through…

# Multiple Linear Regression with Python on Framingham Heart Study data

Previously we built a simple linear regression model using a single explanatory variable to predict the price of pizza from its diameter. But in the…