pmgsy-classification-using-machine-learning

Intelligent classification of PMGSY projects using XGBoost, trained & deployed on IBM Cloud Watsonx.

Intelligent Classification of Rural Infrastructure Projects

This project classifies road and bridge projects under the Pradhan Mantri Gram Sadak Yojana (PMGSY) into their respective schemes (PMGSY-I, PMGSY-II, PMGSY-III, RCPLWEA) using XGBoost Classifier, trained and deployed on IBM Cloud Watsonx.ai.

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📌 Problem Statement

Manual classification of thousands of PMGSY projects is time-consuming, error-prone, and inefficient.
This project proposes an AI-based solution that automatically classifies projects into schemes based on physical and financial attributes.

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⚙️ Solution Approach

• Data sourced from AI Kosh PMGSY dataset.
• Attributes used: road length, sanctioned works, completed works, bridges, expenditure, etc.
• Trained a supervised learning model (XGBoost Classifier).
• Deployed on IBM Cloud Lite with Watsonx.ai (no external stack used).

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📊 Results

The model accurately predicts the correct PMGSY scheme with probability scores.
Sample outputs and visuals are included in the PPT file.

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🧑‍💻 Technology Used

• IBM Cloud Lite
• Watsonx.ai
• XGBoost Classifier

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📄 Files in this Repository

• Project_Presentation.pptx → Full project presentation with theory, algorithm, results, and conclusions.

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🔮 Future Scope

• Integration with government project databases for real-time use.
• Use of geospatial features for improved accuracy.
• Development of dashboards for policymakers.

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✍️ Author: Kamal Sharma – Chandigarh University – CSE AIML