**Drishti Pulse | SIH 2026 | Problem Statement SIH26017 | Project Presentation & Prototype Demo

In this video, Team CodeCrafterz presents DrishtiPulse, our AI-powered solution for Smart India Hackathon (SIH) 2026 under Problem Statement SIH26017: Predictive Analytics System for Early Detection of Land Acquisition Delays (Theme: Smart Automation)[cite: 2, 3]. Land acquisition accounts for 35% of major project delays in India, far exceeding other bottlenecks[cite: 7]. DrishtiPulse transforms traditional reactive tracking portals (like LACRRIS and PRAGATI) into a proactive AI ecosystem that identifies, analyzes, and preempts delays before they impact physical progress[cite: 3]. VIDEO OUTLINE & TOPICS COVERED: Problem Statement & Overview (SIH26017 - Team CodeCrafterz)[cite: 2] Proposed Solution: DrishtiPulse Early Warning System[cite: 3] What Makes Us Unique? (Predictive ML vs Passive Tracking, SHAP, What-If Simulator)[cite: 3] Technical Architecture & Tech Stack (FastAPI, XGBoost, SHAP, LLMs)[cite: 4] Data Pipeline, Feature Engineering & ML Input Drivers[cite: 4] Feasibility, Security & Risk Mitigation Strategies[cite: 5] Phased Rollout Plan (Prototype, State Pilot, National Scale)[cite: 5] Impact & Key Benefits (Government Officers, Infra Ministries, Policy Reviewers)[cite: 6] Research & Statutory References (RFCTLARR Act 2013, MoSPI, SHAP Framework)[cite: 7] KEY TECHNICAL & FEATURE HIGHLIGHTS: Core Problem Solved: Replaces passive status portals with a continuous 0-100% delay risk score, enabling early intervention before delays materialize[cite: 3]. Explainable AI (SHAP): Uses SHAP drivers to eliminate black-box hesitation by showing officers the exact top risk drivers per case[cite: 3]. Tech Stack: Python, FastAPI backend; XGBoost, Random Forest, SHAP, e-Court Judgment NLP parser; React dashboard, PostgreSQL database; GPT/LLaMA LLM layer for RAG chatbot & auto-drafting administrative letters[cite: 4]. Key Features: Bottleneck heatmaps, role-based dashboards, What-If policy simulator, e-Court real-time litigation scanning[cite: 3, 4]. RESEARCH & BENCHMARK REFERENCES: Infrastructure Data: Based on PRAGATI & MoSPI reports showing 41.6% of central projects face time overruns[cite: 6, 7]. Statutory Framework: Compliant with RFCTLARR Act 2013 and Social Impact Assessment workflows[cite: 7]. AI Framework: Utilizes Lundberg & Lee (NeurIPS 2017) SHAP attribution methodology[cite: 7]. GITHUB REPO https://github.com/varunarastogi2-arc.... TAGS / KEYWORDS: #SmartIndiaHackathon2026 #SIH2026 #SIH26017 #DrishtiPulse #SmartAutomation #PredictiveAnalytics #MachineLearning #FastAPI #ReactJS #XGBoost #SHAP #TechPresentation #CodeCrafterz[cite: 2, 3, 4]