Problem Statement: The problem is to analyze and gain insights from unemployment data, with a focus on the impact of the COVID-19 pandemic. Unemployment is a critical economic indicator, and the COVID-19 pandemic has caused a sharp increase in unemployment rates globally. Therefore, the goal of this data science project is to perform an Exploratory Data Analysis (EDA) on unemployment data using Python to achieve the following objectives:
Introduction: Unemployment is a critical economic indicator, and analyzing unemployment data can provide insights into the labor market's health. The COVID-19 pandemic has had a profound impact on unemployment rates worldwide, making it a relevant and interesting subject for data analysis. In this project, we'll use Python to conduct an EDA on unemployment data, aiming to gain a better understanding of unemployment trends, patterns, and their relationship with other factors.
Dataset : Unemployment_Rate_upto_11_2020.csv
Dataset Information :
States = states in India (Rename in data cleaning)
Date = date which the unemployment rate observed
Frequency = measuring frequency (Monthly)
Estimated Unemployment Rate (%) = percentage of people unemployed in each States of India
Estimated Employed = Number of people employed Estimated Labour Participation Rate (%) = The labour force participation rate is the portion of the working population in the 16-64 years' age group in the economy currently in employment or seeking employment. Region = East, West, north wise region location Longitude and latitude = for absolute location
In this analysis, we conducted a comprehensive examination of unemployment data to gain insights into the impact of the COVID-19 pandemic on unemployment rates. Unemployment, measured by the unemployment rate, signifies the percentage of individuals who are jobless within the total labor force. Given the significant increase in unemployment rates during the COVID-19 crisis, we undertook this data science project to delve into the factors contributing to this phenomenon.
The following key points summarize our analysis:
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Data Preparation: We started by loading the data from a CSV file and carried out essential data cleaning and formatting tasks. This included renaming columns, converting data types, and extracting relevant features such as months from date data.
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Exploratory Data Analysis (EDA): We conducted EDA to understand the structure and characteristics of the dataset. This involved examining basic statistics, plotting data distributions, and exploring unique values and frequency counts for categorical data. Our visualizations included line plots, bar charts, sunburst charts, and geographical scatter plots, allowing us to uncover patterns and trends in the data.
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Analysis and Insights: We posed questions about the highest and lowest unemployment rates, both by state and region, during the pandemic. By analyzing the data, we identified specific dates, states, and regions with the highest and lowest unemployment rates. These insights could be valuable for policymakers, businesses, and individuals seeking to understand the impact of the pandemic on employment.
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Region-Based Analysis: We extended our analysis to explore the average highest and lowest unemployment rates by region. This provided a broader perspective on how different regions were affected during the COVID-19 crisis.
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Conclusion: The analysis of unemployment data during the COVID-19 pandemic highlighted the significant challenges posed by the global health crisis. We observed disparities in unemployment rates across states and regions, with some experiencing more severe economic repercussions. This analysis can serve as a foundation for further research and policy initiatives aimed at addressing the economic impact of the pandemic and improving employment opportunities for affected individuals. Understanding the nuances of unemployment during challenging times is crucial for informed decision-making and resource allocation.
Overall, this data science project underscores the importance of data analysis in providing insights into socio-economic phenomena and can serve as a valuable reference for future research in the field of employment and labor economics.