## Linear Regression Analysis using R

One of the most frequent used techniques in statistics is linear regression where we investigate the potential relationship between a variable of interest (often called the response variable but there are many other names in use) and a set of one of more variables (known as the independent variables or some other term). Unsurprisingly there are flexible facilities in (more…)

## Python Learning Paths

Why Python Python is a general purpose language that is easy and intuitive. This gives it a relatively flat learning curve, and it increases the speed at which you can…

## Analysis Of Variance (ANOVA)

So today, I will tell you about ANOVA(ANalysis Of VAriance ).

ANOVA: is a parametric method appropriate for comparing the means for 2 or more independent or dependent groups.

There are 3 types of ANOVA: (more…)

## Data Wrangling Using NumPy / SciPy / Pandas in Python

import numpy as num  # Importing numpy import scipy as sci         # Importing scipy

## Loops and Control Structures in R

In this section we learn about control structures loops used in R. Control structures in R contains conditionals, loop statements like any other programming languages. Loops are very important and…

## R FUNCTIONS FOR REGRESSION ANALYSIS

R FUNCTIONS FOR REGRESSION ANALYSIS Here are some helpful R functions for regression analysis grouped by their goal. The name of package is in parentheses.   Linear model Anova: Anova…

## Sentiment Analysis on Twitter Data

Analyzing Twitter data Extract tweets and followers from the Twitter website with R and the twitteR package 1. With the tm package, clean text by removing punctuation, 2. numbers, hyperlinks and…

## Feature selection in R (i.e. pick important variables) using Boruta Package

Introduction Variable selection is an important aspect of model building which every analyst must learn. After all, it helps in building predictive models free from correlated variables, biases and unwanted…