About NEEV

AI-Powered Predictive Analytics for National Infrastructure Monitoring

What is NEEV?

NEEV (meaning "foundation" in Hindi) is an AI-powered early warning and decision-support system built for the Infrastructure & Project Monitoring Division (IPMD) under the Ministry of Statistics and Programme Implementation (MoSPI), Government of India.

The PAIMANA portal currently tracks 1,981 ongoing infrastructure projects across 17 Central Ministries, with a combined revised cost of approximately ₹42.78 lakh crore. While PAIMANA provides robust descriptive monitoring, it does not predict which projects are likely to face cost escalation or schedule delays before those issues materialize.

NEEV bridges this gap by applying Machine Learning to the existing PAIMANA dataset, transforming it from a reporting tool into a predictive and prescriptive decision-support system.

How It Works
1

Data Ingestion

Raw project data (cost, expenditure, timelines, physical progress) is ingested from the official PAIMANA portal in CSV format. This data covers the April 2026 reporting period.

2

Feature Engineering

The raw data is transformed into predictive features such as the Financial-to-Physical Progress Ratio, Progress Gap, Project Age, and Budget Utilization Rate. These engineered features capture patterns that raw data alone cannot.

3

AI Risk Prediction

A Scikit-Learn Gradient Boosting model processes the engineered features and outputs three predictions for each project: a unified Risk Score (0-100), predicted Cost Overrun (₹ Crore), and predicted Schedule Delay (Months).

4

Dashboard Visualization

The predictions are rendered through an interactive Next.js dashboard featuring a Survey of India compliant geospatial heatmap, sector-wise analytics, and individual project risk cards with explainable AI-driven risk drivers.

Technology Stack

Frontend

  • Next.js 16.3 (App Router)
  • React 19
  • Tailwind CSS v4
  • Recharts (Data Visualization)
  • react-simple-maps (Geospatial)

Backend & ML

  • Python 3 (Pandas, NumPy)
  • Scikit-Learn (Gradient Boosting)
  • d3-scale (Color Mapping)
  • Cloudflare Pages (Deployment)
  • 100% Open-Source
Current MVP Statistics

50

Projects Monitored

15

Critical Risk

15

High Risk

10

Low Risk

Problem Statement

This project was developed for Smart India Hackathon (SIH) Problem Statement 26103, titled "Use case on web-based integrated project-monitoring platform", presented by the Ministry of Statistics and Programme Implementation (MoSPI), Data Informatics & Innovation Division (DIID).

The objective is to develop an AI-powered Predictive Analytics and Early Warning System that can analyze infrastructure project data to identify projects likely to experience cost escalation, schedule delays, and implementation risks before such issues materialise — enabling proactive interventions and evidence-based decision-making.

Theme: Smart Automation

Organization: MoSPI

Department: Data Informatics & Innovation Division (DIID)

Data Source: Official PAIMANA Portal (April 2026 Dataset)

Built with open-source tools for a Viksit Bharat