ML Lab
Machine Learning Models
Eight end-to-end models, each applied to a different domain from my IIT Madras coursework and productivity workflows — classical classification, deep-learning sequence models, document intelligence, and real-time behavioral syncing.
Construction Cost-Overrun Predictor
Construction Economics
Predicts budget-overrun severity from project economic and contractual parameters. Every prediction is logged to MongoDB.
Tier 1 — Foundations
View →Credit Risk Classifier
DSAI in Finance
Classifies loan applicants by default risk using ensemble methods. Includes SMOTE for class imbalance.
Tier 2 — Ensembles
View →Construction Quality Defect Flagger
Construction Quality
Unsupervised anomaly detection on quality inspection data — no labelled defects required to train.
Tier 3 — Anomaly Detection
View →Hazard Image Classifier
Construction Safety
Detects safety-hazard categories in construction site images using a CNN trained on labelled examples.
Tier 4 — Deep Learning
View →Power Load Forecaster
Smart Power Grid
Forecasts electricity demand using an LSTM that learns the temporal patterns in historical load data.
Tier 5 — Sequence Models
View →SCADA Anomaly Detector
EMS SCADA
Detects deviations from normal SCADA operating behaviour using reconstruction error from a trained autoencoder.
Tier 6 — Unsupervised Deep
View →Database Indexer
Municipal Infrastructure
Automates the digitization of handwritten complaint and maintenance forms for the Karnataka Urban Water Supply Modernization Project.
Tier 7 — Document Intelligence
View →Habit Forge
Skill & Behavior Tracking
Tracks habits daily, syncs real-time with Firestore, and copies structured logs formatted for direct pasting into Google Sheets.
Tier 8 — Behavioral Intelligence
View →