Every model starts
messy.
So did I.
I'm Nitya — a B.Tech CS (AI/ML) student, two-time published researcher, and someone who has broken far more pipelines than she's shipped. This is the log of how the pipelines started working.
Where the trail starts
Third-year B.Tech Computer Science (AI/ML) student at Dronacharya Group of Institutions, under AKTU — graduating 2027. Somewhere between semester exams and 2 a.m. debugging sessions, "I should learn ML" turned into two published papers and a shelf of half-finished side projects that mostly finished anyway.
Machine learning wasn't a fall — it was curiosity about how computers could learn, quietly moving in and never signing a lease to leave.
What's in the pack
No hero travels empty-handed. These are the tools picked up along the way — some forged in coursework, most forged in production bugs at 1 a.m.
Every project was a boss fight
Twelve build logs. Some shipped clean, most fought back — protobuf conflicts, MySQL errors, a PowerShell that refused to cooperate. All of them taught something the tutorials didn't.
Emotion Detection — Wellness Chatbot
★ flagshipA full-stack emotional wellness chatbot: a fine-tuned BERT classifier scores PHQ-9 signals, a Qwen2.5-based layer holds the conversation, deployed on FastAPI + Streamlit + Firebase.
WayzUp
civic techA civic-tech mapping tool built to surface and route around local issues, combining geospatial data with a simple, usable interface.
Solar Panel Condition Classifier
computer visionTransfer-learning model on EfficientNetB0 classifying solar panel condition across 6 classes from ~900 images — catching degradation before it costs output.
Crop Production Prediction
data scienceStreamlit app forecasting crop yield using FAOSTAT data, benchmarked across Random Forest, Gradient Boosting, and Linear Regression.
Tourism Attraction Analytics
multi-task mlA multi-task system predicting, classifying, and recommending tourism attractions in one pipeline, using LightGBM and XGBoost.
Insurance Claim Prediction
classificationPredictive model estimating the likelihood of insurance claims from policyholder data.
Agricultural Anomaly Detection
data scienceSystem for flagging irregular patterns in agricultural data — built to catch the outliers that skew every downstream decision.
Tennis Analysis
computer visionVideo-based analysis project tracking player and ball movement to pull structured stats out of raw match footage.
Bird Observation
data analysisExploratory analysis of bird observation data — patterns, species distribution, and trends over time.
Food App
full-stackA food ordering / discovery application built end-to-end as a full-stack exercise.
The proof left behind
Two papers, two rooms full of reviewers, two "yes, publish this."
View ICOTET 2024 paperCustomer Segmentation
Conference paper on customer segmentation techniques — presented at ICOTET 2024.
View paperBlockchain–AI for Cybersecurity
Random Forest–based anomaly detection across 14,133 scenarios, hitting 95.68% accuracy — combining blockchain integrity guarantees with AI-driven threat detection.
View paper