EDA (Exploratory Data Analysis) -1: Loading the Datasets, Data type conversions,Removing duplicate entries, Dropping the column, Renaming the column, Outlier Detection, Missing Values and Imputation (Numerical and Categorical), Scatter plot and Correlation analysis, Transformations, Automatic EDA Methods (Pandas Profiling and Sweetviz).

Univariate, Bivariate and Multi-variate Analysis

In this lab you will practice functions. Functions that simply do something, functions that return values. Difficulties will range from beginner to advanced.

The data sets used in this exercise contain world population evolution (from 1960 to 2017) and the country's income classification for 264 observations. Firstly, the variables in the data sets have been carefully examined in order to get a proper understanding of the data sets. The two data sets have been merged using the common variable of Coun…