Encoding is a technique of converting categorical variables into numerical values so that it could be easily fitted to a machine learning model. Before getting into the details, let’s understand about the different types of categorical variables. Nominal categorical variable: Nominal categorical variables are those for which we do not have to worry about the…Read more

# Exploratory Data Analysis

## Data Integration

Data Integration Data Integration is a technique of integrating the data which resides in different sources. The goal is to provide the users with a holistic view of the data. It can be viewed more as a practice of consolidating data from various disparate sources. This is viewed as one of the most important steps…Read more

## outlier detection

Outliers Another problem that we often face in data are the outliers. They are the one-off values which always stand out from the population. They may be very large or very small with respect to the entire population of the data. outlier detection is a very important and crucial step in Exploratory data analysis. outlier detection…Read more

## EDA using Probability Density Function and Cumulative Distribution Function

Introduction In this post, we will discuss about 2 very important topics and how it helps in Exploratory data analysis — Probability Density Function and Cumulative Density Function. A continuous random variable distribution can be characterized through itsÂ Probability Distribution Function. We will understand this statement in greater detail in the subsequent section. Cumulative Density Function…Read more

## 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…Read more