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In this article, I’m going to analyze the Women’s Clothing E-Commerce dataset which contains numerical data, text reviews that are written by customers (available here).

The steps which we are going to follow are listed below

  • Data Description
  • Data Cleaning
  • Data Pre-Processing
  • Data Analysis
  • Data visualization
  • Data Modelling

Let’s start the fun with our first step Data Description

This dataset includes 23486 rows and 10 feature variables. Each row corresponds to a customer review, and includes the variables:

  • Clothing ID: Integer Categorical variable that refers to the specific piece being reviewed.
  • Age: Age of the reviewer’s age.
  • Title: The Title…

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Exploratory Data Analysis (EDA) is an approach for data analysis and data exploration that employs a variety of techniques (mostly graphical representation) on the data we are working on.

EDA helps in finding

  • uncover the different relationship between variables
  • extract important variables
  • detect outliers
  • maximize insight into a data set
  • test underlying assumptions
  • develop parsimonious models
  • determine optimal factor settings

According to a survey in Forbes, data scientists spend 80% of their time on data preparation.

But, what if I told you that python can automate the process of EDA with the help of some libraries? Won’t it make your…

Photo by Lukas Blazek on Unsplash

This article aims to discuss all the key features for the basics of the Python programming language. My target is to keep the information accurate, precise, graspable, short, and focus on the key points for better understanding.

After going through this blog, you will be able to implement Python very easily.

You don’t need any prior knowledge of python programming it will be easy to grasp the concept.

Why did I choose python?

  • Choosing python for me was an easy decision because I was bad at remembering the syntax. …

Kashish Rastogi

Data analyst| Pandas| Python

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