The COVID-19 pandemic has left millions of people unemployed across the globe.

This study is all about predicting unemployment rate in the coming year, through the use of Machine Learning. It has made use of the open source data-set published in the European Union Open Data Portal.

Exploratory Data Analysis

Raw data from EU Open Data Portal

The raw dataset contained more than 2000 records which consisted data from various countries. However, the data had columns that contain multiple merged information, so first I needed to extract separate features from the composite features. Hence, a bit of data formatting was needed.

I took the first column from the dataset and split it based on commas (,) and then merged the processed columns to the original dataset.

For this experiment, I focused on last 10 years of data for every country. So, I did a manual selection of the columns as shown below.

In the dataframe, it can be observed that we have column named geo_time (renamed to Country_code later), having various country codes.

So, I put a little bit of extra effort in gathering the list of all the country codes along with their respective country names.

#unemployment #time-series-forecasting #machine-learning #covid19 #predictive-analytics