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Project Overview

This data analysis project examines unemployment trends across Indian states during 2020, with a focus on understanding the impact of COVID-19 lockdown measures. Using Python's data science stack, I analyzed monthly unemployment rates, employment figures, and labor participation rates across different regions of India, revealing significant variations and the pandemic's effect on employment.

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Technical Specifications

Component	Technology
Programming Language	Python
Data Analysis	Pandas, NumPy
Data Visualization	Matplotlib, Seaborn, Plotly
Data Source	Unemployment_Rate_upto_11_2020.csv
Key Metrics	Unemployment Rate, Employed, Labor Participation

Key Features

Regional Analysis

Grouped statistics by region and state
Comparative analysis of unemployment rates
Labor participation rate trends

Geographic Visualization

Animated geo-scatter plots
Interactive regional comparisons
Lockdown impact visualization

Temporal Analysis

Monthly employment trends
Pre/post lockdown comparisons
Seasonal pattern identification

Statistical Insights

Correlation matrix analysis
Box plot distributions
Comparative regional statistics

Data Visualizations

Correlation Heatmap

This heatmap shows the correlation between unemployment rate, employment figures, labor participation rate, and geographic coordinates. The strongest relationships help identify which factors most influence unemployment trends.

State-wise Unemployment Distribution

Boxplot showing the distribution of unemployment rates across different Indian states. Highlights states with particularly high or low unemployment rates and shows variation within each state's data.

Geographic Unemployment Visualization

Animated map showing how unemployment rates changed month-by-month across different regions of India, particularly highlighting the impact of COVID-19 lockdown measures.

Monthly Employment Trends

Bar chart tracking estimated employed citizens each month from January to October 2020, showing the dramatic impact of lockdown measures on employment.

Technical Implementation

Data Preparation

# Import libraries
import pandas as pd
import numpy as np
import calendar
import datetime as dt
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px

# Load and prepare data
df = pd.read_csv("Unemployment_Rate_upto_11_2020.csv")
df.head()

# Clean and transform data
df = df.rename(columns={'Region': 'State', ' Date': 'Date', 
                       ' Estimated Employed': 'Estimated Employed',
                       ' Estimated Unemployment Rate (%)': 'Estimated Unemployment Rate',
                       ' Estimated Labour Participation Rate (%)': 'Estimated Labour Participation Rate',
                       'Region.1': 'Region'})

# Convert to proper data types
df['Date'] = pd.to_datetime(df['Date'], dayfirst=True)
df['Frequency'] = df['Frequency'].astype('category')
df['Region'] = df['Region'].astype('category')

# Extract month for temporal analysis
df['Month'] = df['Date'].dt.strftime('%B')

Geographic Visualization

# Impact of lockdown visualization
fig = px.scatter_geo(df, 'longitude', 'latitude', color="Region",
hover_name="State", size="Estimated Unemployment Rate",
animation_frame="Month", scope='asia',
template='seaborn',
title='Impact of lockdown on Employment across regions')

# Adjust map boundaries for India focus
fig.update_geos(lataxis_range=[5,35], lonaxis_range=[65, 100],
oceancolor="#3399FF", showocean=True)

Results & Findings

Regional Analysis

Highest Unemployment: Northern regions showed the highest average unemployment rates
Most Stable: Southern regions maintained relatively stable employment figures
Urban Impact: Metropolitan areas experienced sharper employment drops

Lockdown Impact

April 2020: Sharpest increase in unemployment following nationwide lockdown
Recovery: Gradual improvement from June onward
Sectoral Differences: Varied impact across different economic sectors

Challenges & Solutions

Challenge	Solution
Data normalization	Standardized column names and formats
Geographic visualization	Used Plotly with custom India-focused boundaries
Temporal analysis	Created month extraction for consistent time series
Outlier detection	Implemented boxplot visualization for distribution analysis

Policy Implications

Regional Focus: Targeted employment programs for high-unemployment regions
Sectoral Support: Industry-specific recovery initiatives
Skill Development: Enhanced training programs in affected areas
Monitoring: Improved real-time employment tracking systems

Future Enhancements

Integration with more recent employment data
District-level granularity for localized analysis
Economic sector breakdown of unemployment
Interactive dashboard for policy makers

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