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Data-Driven Infrastructure Analytics 🚧📊

A data-driven project analyzing road and bridge construction under the PMGSY (Pradhan Mantri Gram Sadak Yojana)** scheme across Indian states and UTs. This project leverages Python and data visualization tools to uncover insights, monitor progress, and support data-backed decision-making for rural infrastructure development.

📌 Objective

To perform exploratory data analysis (EDA) and generate meaningful visualizations that highlight the status of road and bridge works (sanctioned, completed, pending), identify regions needing attention, and understand development trends.

🔧 Tools & Technologies

  • Python
  • Pandas
  • Matplotlib
  • Seaborn
  • Jupyter Notebook

📊 Key Insights

  • Comparison between sanctioned vs. completed road and bridge projects
  • Identification of top pending regions for focused infrastructure push
  • Detection of outliers and performance disparities
  • Correlation analysis to explore relationships among key metrics
  • Color-rich, clear visualizations using custom palettes and charts
  • Geo-mapping visualizations with Excel for state-wise profit analysis

Visualizations

  • Bar Charts for sanctioned/completed projects
  • Pie Charts for completed vs. pending lengths
  • Correlation heatmaps
  • State-wise project status
  • Histogram distributions for road metrics
  • India Map visualization (via Excel Maps)

Key Takeaways

  • Data storytelling helps transform government records into actionable insights
  • EDA helps identify gaps and strengths in implementation
  • Visualization is powerful for comparing regional infrastructure statuses
  • Python is a flexible tool for public infrastructure analysis and reporting

About

This project presents an exploratory data analysis (EDA) of road and bridge construction data under the Pradhan Mantri Gram Sadak Yojana (PMGSY) scheme. Using Python libraries like Pandas, Seaborn, and Matplotlib, the project transforms raw CSV data into actionable insights and visual stories.

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