Feature Engineering

A two-part agricultural decision-support project. Part 1 is a 22-class crop classifier that recommends what to grow given soil + climate features. Part 2 is the data pipeline that would feed the classifier from real-world sensors and weather services — built around the Penman-Monteith evapotranspiration model. Architecture flowchart LR subgraph DATA["Data Pipeline (Part 2)"] WB["WeatherBit APIsolar_rad, dewpt,DNI, DHI, GHI,wind_spd"] --> AGG["Multi-source averaging(temp, wind across APIs)"] OW["OpenWeather APItemp, wind"] --> AGG AGG --> PM["Penman-Monteith calces, ea, VPD, delta,alpha, net Rn, G"] PM --> DB["SQLite(CRUD wrapper)"] end subgraph ML["Crop Classifier (Part 1)"] DS["Kaggle Crop Recommendation2,200 samples × 22 crops"] --> FE["Feature engineering+ Crop_Type+ Sown_Season"] FE --> SPLIT["80/20 split"] SPLIT --> NB["Gaussian Naive Bayes"] SPLIT --> RF["Random Forest"] RF --> PKL["model.pkl"] end DB -.->|"intended runtime feed"| RF PKL --> APP["Flask + Bootstrapweb app"] Part 1 — Crop classifier Data: the standard Kaggle Crop Recommendation dataset — 2,200 samples balanced across 22 crops (rice, maize, jute, cotton, coconut, papaya, orange, apple, muskmelon, watermelon, grapes, mango, banana, pomegranate, lentil, blackgram, mungbean, mothbeans, pigeonpeas, kidneybeans, chickpea, coffee), 100 per class. Features: N / P / K (soil nutrients), temperature, humidity, pH, rainfall. ...

A two-notebook study on the Home Credit Default Risk dataset — the canonical real-world imbalanced classification problem (91% non-defaulters vs 9% defaulters). The interesting question wasn’t “can a model hit 99% accuracy” (a constant predict(0) already does that — and is useless). It was: for a problem where the minority class is the one that matters, which combination of techniques actually moves the needle? The work splits into a companion EDA notebook (missing-value handling, feature engineering, demographic-level defaulter analysis) and this modeling notebook. ...

Context At Bayer, I interned with the Solutions team under Engineering and Data Science, working on machine learning, data engineering, and operational analytics for seed processing and field equipment operations. What I Built I built XGBoost and LightGBM regression models to predict machine-level seed processing times from historical throughput and runtime data, achieving RMSE = 35. These models supported capacity planning and maintenance scheduling for upcoming planting cycles. I also engineered ETL pipelines over 10GB+ of agronomic and equipment data from BigQuery and SQL Server, consolidating multi-year data from multiple sources and reducing retrieval latency by 40%. ...