## Monday, 29 February 2016

### NEAREST NEIGHBOURS

Hello, welcome to my blog. In my previous posts I have talked extensively about linear regression and how it can be implemented in Python. Now, I want to talk about another popular technique in Machine Learning – Nearest Neighbours.

## Tuesday, 23 February 2016

### POLYNOMIAL REGRESSION

Hello, welcome to my blog. I introduced the concept of linear regression in my previous posts by giving the basic intuition behind it and showing how it can be implemented in Python. In the last post, I gave a precaution to observe when applying linear regression to  a problem – Make sure the relationship between the dependent and independent variable is LINEAR i.e. it can be fitted with a straight line.

So, what do we do if a straight line cannot define the relationship between the two variables we are working with? Polynomial regression helps to solve this problem.

## Sunday, 14 February 2016

### LINEAR REGRESSION ROUNDUP

Hello, welcome to my blog. In my previous posts, I have been talking about linear regression which is a technique used to find the relationship between one or more explanatory variables (also called independent variable) and a response variable (also called dependent variable) using a straight line. Furthermore, I said that when we have more than one explanatory variable it is called multiple linear regression. Finally, I also implemented both types of regression using Python.

As a roundup I will just mention some precautions that should be taken when applying linear regression. Here are some tips to remember:

## Monday, 8 February 2016

### IMPLEMENTING MULTIPLE LINEAR REGRESSION USING PYTHON

Hello, welcome to my blog. In this post I will introduce the concept of multiple linear regression. First, let me do a brief recap. In the last two posts, I introduced the concept of regression which basically is a machine learning tool used to find the relationship between an explanatory (also called predictor, independent) variable and a response (or dependent) variable by modelling the relationship using the equation of a line i.e.

y = a + bx

Where a is the intercept, b is the slope and y is our prediction.

Up until now we have sort of used only one explanatory variable to predict the response variable. This really is not very accurate because if (for example) you are trying to predict the price of a house the square footage of the house is not the only feature that determines it price. Other attributes like number of bedrooms, bathrooms, location and many other features will contribute to the final price of the house.