EMPLOYMENT_AND_UNEMPLOYMENT_DATA_ANALYSIS

EMPLOYMENT_AND_UNEMPLOYMENT_DATA_ANALYSIS Abstract

Analysis of employment and unemployment data during the COVID-19 pandemic using Python.The analysis includes the exploration of various data sets related to employment and unemployment, data cleaning, and visualization techniques to gain insights into the trends, patterns, and relationships within the data. Python libraries such as Pandas, NumPy, and Matplotlib are used extensively throughout the analysis. The data used in this analysis is obtained from various sources such as the Bureau of Labour Statistics and Kaggle. The data includes information on employment and unemployment rates, labour force participation rates, and industry-specific data. The analysis includes exploratory data analysis, hypothesis testing, and time-series analysis to understand the impact of the COVID-19 pandemic on employment and unemployment rates. The results of the analysis show that the COVID-19 pandemic has had a significant impact on employment and unemployment rates, with unprecedented levels of job losses and unemployment rates rising to record highs. The analysis also shows the impact of the pandemic on various industries, with some industries being hit harder than others. This analysis demonstrates the power of Python in analysing employment and unemployment data during the COVID-19 pandemic and provides insights into the trends and patterns within the data. The findings of this analysis can be used by policymakers and businesses to make informed decisions related to employment and labour market policies during the pandemic and beyond.