# Exploratory Data Analysis

## Exploratory Data Analysis for Univariate, Bivarite and Multivariate data

Introduction In this post we will discuss about Exploratory Data Analysis and how we use it to analyze Univariate, Bivariate and Multivariate data sets.  Exploratory Data Analysis  involves initial investigation of the data before creating any kind of model. There are a lot of different techniques that can be employed while doing EDA.  It doesn’t have a set…

## Covariance and Correlation

Introduction In this post, we will discuss about Covariance and Correlation. This plays an important role while doing feature selection. Covariance, as the name suggests is the measure of variance of 2 variables when they are taken together. When we have one variable then we call it as variance, but in case of 2 variables we specify it as…

## Data Visualization using Functional/Object Oriented approach

Introduction Data Visualization is the graphical representation of data which helps us in storytelling. This can be seen mostly as an art and science. To communicate the results and findings in a better and comprehensive way we usually use plots, charts, statistical graphics and other tools. Data Visualization is a very powerful tool. It will enable…

## Data Visualization

This post will mostly cover ways of visualization of data using predefined statements and passing different parameters associated with the plots. However, if you want to learn the more functional and object oriented way of visualizing the data, then refer this post. Some of the visualizations that we will cover in this post are as…

## Data Preprocessing | Data Cleaning Python

Introduction Data Preprocessing is the most important step when we are building our model. In Data Preprocessing step, the data is transformed into a form where it becomes suitable for model ingestion. There are various steps involved in Data Preprocessing are shown below in the flowchart. In this post we will cover only the first step…

## Creating Feature and Target Matrix

Before creating any model, the first and foremost thing that we generally do is create the feature and target matrix. Let’s see how we will do that. Before that, let’s understand our dataset which was taken from Kaggle: Also refer this post to see how we implement an algorithm after selecting the Feature and Target…

## Regression Metrics

Introduction This post will be more theoretical and would explain in detail about the different Regression Metrics involved in Regression Models and what are their advantages and disadvantages. While we discuss about the different Regression Metrics in this post, take a while to also go through this post which discusses about the mathematical assumptions we…

## Simple Linear Regression

Introduction What is Simple Linear Regression Simple Linear Regression basically defines the relation between a one feature and the outcome variable. This can be specified using the formula y = α + βx which is similar to the slope-intercept form, where y is the value of the dependent variable, α is the intercept β denotes…