Exploratory-Data-Analysis---Attrition

Problem Statement: The attached data is relates to an attrition problem of a company. Study the data very carefully. The variables are self-explanatory. Start by identiyfing the variable types of the variables. This will help you to understand the kind of data exploration techniques you must use. Each row in the dataset represent an employee. Every employee corresponds to a value of the variable "Attrition". A value of attirion = 1 indicate that the employee has left the company. In the same way, attirion = 0 would indicate that the employee is still working in the company. Your task will be to find 5 most important factors which you feel affects attiion.

Python-Functions-For-EDA-

Imagine how lengthy the process of data exploration may become especially when you are dealing with datasets that contains a large number of variables! I hope you have already started experiencing thistedious process. For every numerical variable you identify in your dataset, you generate histogram and boxplot. For every categorical variable you identify in your dataset, you create bar chart and maybe pie chart. This is how the first few steps of your data exploration look like. Generating graphs one at a time may be okay if you have a dataset with less number of columns. However, this may be an extremely tedious task when you are working on a large dataset with lots of columns in it. Not only that this process can also become extremely time consuming. In this assignment, you will create functions to reduce this everyday task of yours to a considerable extent and save hours and hours of your time.