Skip to content

ashleshanakti/Unemployment-Analysis-with-Python

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

3 Commits
 
 
 
 

Repository files navigation

Unemployment Analysis With Python

Unemployment is measured by the unemployment rate which is the number of people who are unemployed as a percentage of the total labour force. We will see that in this project the unemployment rate is calculated based on a particular region, so to analyze unemployment I will be using an unemployment dataset of India which is based on Covid-19 Period

Github, linkedIn - ashleshanakti

Appendix

In this project after uploading datset we have performed various EDA (Exploratory Data Analysis) Functions to check null values, region, groupby and unique values . It has different types of plot which are as follows: -

1)Heatmap - A heatmap is a graphical representation of data that uses a system of color-coding to represent different values.

2)Pairplot - A pairplot plot a pairwise relationships in a dataset. The pairplot function creates a grid of Axes such that each variable in data will by shared in the y-axis across a single row and in the x-axis across a single column.

3)Countplot - A univariate plot that shows the comparison of different groups in categorical variables. It shows the number of observations per category using bins.

4)Pieplot - A Pie Chart is a circle divided into sectors that each represent a proportion of the whole. It is one of the most common viz type, but also probably the most criticized.

5)Boxplot - A boxplot is a standardized way of displaying the distribution of data based on a five number summary (“minimum”, first quartile [Q1], median, third quartile [Q3] and “maximum”). It can tell you about your outliers and what their values are.

6)Histplot - A histogram is a traditional visualization tool that counts the number of data that fall into discrete bins to illustrate the distribution of one or more variables.

7)Density - A density plot is a representation of the distribution of a numeric variable.

Conclusion

So this is how you can analyze the unemployment rate by using the Python programming language. I hope that through this analysis it must be easy to understand in how many which state the unemployment rate is high and less . Thankyou for your valuable time & visting this project.

About

No description, website, or topics provided.

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published