Unemployment surged during Covid-19, presenting a compelling case for data analysis. Join me as I break down my approach to this critical issue
🎯 Objective:
Analyze India's unemployment trends to uncover insights for policymakers and businesses.
🛠️ Tools:
• Python for data processing • Pandas for manipulation • Matplotlib/Seaborn for visualization • Jupyter Notebooks for documentation
🪜 Steps:
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Data Collection: Gather unemployment rates from reliable sources
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Preprocessing: Clean and structure the dataset
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Exploratory Analysis: Identify patterns and anomalies
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Time Series Modeling: Forecast future trends
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Correlation Analysis: Explore factors influencing unemployment
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Visualization: Create compelling charts to communicate findings
📊 Expected Results:
• Historical unemployment patterns • Covid-19 impact quantification • Regional disparities identification • Sector-wise unemployment breakdown
🧗♂️ Potential Hurdles:
• Data quality and consistency issues • Accounting for informal sector employment • Interpreting seasonal variations • Factoring in demographic shifts