├── Screenshot 2025-04-17 070725.png ├── Screenshot 2025-04-17 071941.png ├── Screenshot 2025-04-17 072821.png ├── Screenshot 2025-04-17 074111.png ├── Screenshot 2025-04-17 074410.png └── README.md /Screenshot 2025-04-17 070725.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Jaslenek/DistDataAnalysis/HEAD/Screenshot 2025-04-17 070725.png -------------------------------------------------------------------------------- /Screenshot 2025-04-17 071941.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Jaslenek/DistDataAnalysis/HEAD/Screenshot 2025-04-17 071941.png -------------------------------------------------------------------------------- /Screenshot 2025-04-17 072821.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Jaslenek/DistDataAnalysis/HEAD/Screenshot 2025-04-17 072821.png -------------------------------------------------------------------------------- /Screenshot 2025-04-17 074111.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Jaslenek/DistDataAnalysis/HEAD/Screenshot 2025-04-17 074111.png -------------------------------------------------------------------------------- /Screenshot 2025-04-17 074410.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Jaslenek/DistDataAnalysis/HEAD/Screenshot 2025-04-17 074410.png -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # DistDataAnalysis 2 | # 📊 MGNREGA Data Analysis & Visualization Project 3 | 4 | This project involves a detailed **Exploratory Data Analysis (EDA)** of the MGNREGA (Mahatma Gandhi National Rural Employment Guarantee Act) dataset. The aim is to understand key employment trends across different states and years using visualizations and statistical summaries. 5 | 6 | --- 7 | 8 | ## 📁 Contents 9 | 10 | ### ✅ Key Objectives 11 | - Analyze trends in average wage rates and employment across years. 12 | - Understand the relationship between various economic and employment indicators. 13 | - Visualize state-wise contributions to total employment, especially focusing on SC, ST, and Women categories. 14 | - Examine the distribution and spread of key metrics through box plots and scatter plots. 15 | - Compare gender-based employment distribution across top-performing states. 16 | 17 | --- 18 | 19 | ## 📊 Visualizations Created 20 | 21 | ### 1. **Histogram + KDE Plots** 22 | Analyzed the distribution of: 23 | - Average Wage Rate per Day per Person 24 | - Average Days of Employment per Household 25 | - Total Expenditure 26 | 27 | ### 2. **Trend Line Plot** 28 | - Year-wise average wage trends using line charts for visual time series analysis. 29 | 30 | ### 3. **Correlation Heatmap** 31 | - Identified relationships among numerical features such as wages, employment days, and expenditures. 32 | 33 | ### 4. **Stacked Bar Plot** 34 | - Top 10 states based on total individuals worked. 35 | - Breakdown of employment into SC, ST, and Women Persondays. 36 | 37 | ### 5. **Box Plots** 38 | - Visualized the spread and outliers in: 39 | - Average Wage Rate per Day 40 | - Total Individuals Worked 41 | 42 | ### 6. **Scatter Plot** 43 | - Wage vs Employment visualized by district and colored by state. 44 | 45 | ### 7. **Gender-based Employment Plot** 46 | - Compared women vs men employment across top 10 states using a stacked bar chart. 47 | 48 | --- 49 | 50 | ## 🛠️ Tools and Libraries Used 51 | 52 | - **Python** 53 | - **Pandas** – for data manipulation 54 | - **Seaborn** and **Matplotlib** – for plotting 55 | - **NumPy** – for numerical operations 56 | 57 | 58 | 59 | --------------------------------------------------------------------------------