Pandas

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. ...

A self-directed exploration of the IPL 2022 season — partly to dig into a sport I follow closely, partly to get hands-on with Plotly specifically (subplots, hover-aware box plots, scatter-quadrant layouts) rather than my usual Matplotlib / Seaborn defaults. Published as a Medium article so the interactive charts actually render for readers. Analytical lens The notebook works through seven dimensions, organized so the per-tournament patterns set the context before drilling into per-team specifics: ...