Unemployment Analysis with Python

By Debajyoti Talukder

I have created an Application using Python and its Libraries to implement data science and machine learning concepts and analyse the unemployment rate of different states in the country of India.

Unemployment Analysis using Python:

I have created an Application using Python and its Libraries to implement data science and machine learning concepts and analyse the unemployment rate of different states in the country of India. I have used Python Pandas, Numpy, Matplotlib, and Seaborn Libraries to implement this project.

Technologies:

Language: Python
Platform: Google Colab Python3 Runtime Engine
Libraries: Pandas, Numpy, Matplotlib, and Seaborn
Dataset used: Unemployment_Rate_upto_11_2020.csv (Source: Kaggle)

Installation of Libraries

I have implemented the project using Google Collaboration, which is an open-source, cloud-based platform for the implementation of machine learning and data science projects.
As all the required libraries are already pre-installed in the Google Colab Python3 runtime engine, we do not need to install them separately using the pip command, which we generally do when using a Jupyter notebook on our PC.

How to use:

Just open the Google Collaboratory using your Google account and import the jupyter source file (UNEMPLOYMENT_ANALYSIS_WITH_PYTHON.ipnyb) and the dataset (Unemployment_Rate_upto_11_2020.csv) to Google Collaboratory virtual storage (Google Collaboratory File Upload Method).

Then run the project using the Google Colab Python 3 runtime engine. I have also attached here the html file and corresponding pdf file to see the outcome of the project.

 

Output: Jupyter Source file (.ipnyb), HTML file, and pdf file attached already.