By harnessing existing city surveillance networks, a new AI system delivers minute-by-minute rainfall estimates with ...

In the classification part we have adopted a Random Forest (RF) classifier. The RF improves classification accuracy by combining multiple decision trees. We conducted experiments with five subjects ...

📘 This repository predicts OLA driver churn using ensemble methods—Bagging (Random Forest) and Boosting (XGBoost)—with KNN imputation and SMOTE. It reveals city-wise churn trends and key performance ...

A text‐classification pipeline for identifying human‐ versus ... A complete ML project that explores feature engineering, model training (Decision Tree, Random Forest, Gradient Boosting), and model ...