model-stacking

🌳 Stacked Gradient Boosting Machines

Production-grade ensemble framework combining XGBoost, PyTorch & Sklearn - 70%+ test coverage with Optuna optimization for time-series prediction

"Predicting blood glucose levels using advanced machine learning techniques, including XGBoost, LightGBM, CatBoost, Random Forest, TabNet, and model stacking/ensembling with K-Fold cross-validation for improved accuracy."

Regression Modeling for Real Estate Price Predictions

ML system predicting Indian domestic airfares (R2 0.9165, 94.1% accuracy) and recommending cost-effective bookings, with validated ranking reliability

In this project we can see in action and in detail a big part of the ML pipeline (data wrangling,model building, model evaluation) that comprises different algorithms and approaches such as Decision Trees (RPART), Linear Discriminant Analysis (LDA), Gradient Boosting Machne (GBM), Random Forest (RF) Support Vector Machine (SVM) with or without M…

Air Travel Delay Study is a start-to-finish classification project with a goal of anticipating flight delay

Spatio-temporal traffic demand forecasting on a 15-min geohash grid. 42 leak-free features (target encodings, spatial-neighbour spillover, geo-clusters, cyclical time) + stacked ensemble of LightGBM, XGBoost, CatBoost, HGBR, ExtraTrees → 97.85 accuracy. Streamlit demo, geospatial viz, ablation study, error analysis, CI/CD, Docker, MLOps.

This Project is my Entry to a Kaggle Competition 'House Prices: Advanced Regression Techniques'. The aim was to rank in the Top 5% in the Leaderboard, I achieved this rank by Using Model Stacking with Meta-Modelling and my final score was RMSLE = 0.0726 and I Ranked 277th out of 5130 Submissions

Based on the data from Kaggle

CTD-squared BeatAML DREAM Challenge

King County Housing Price Predictions is a start-to-finish regression project which concludes with a stacked model.

Machine learning model predicting the survival on titanic.

Apply ensemble technique of model stacking to predict patient's readmission

A quantitative finance toolkit addressing model reliability through hyperparameter optimization (GridSearch/LassoCV), bias-variance tradeoff analysis, and ensemble learning (Random Forests and Stacking) applied to S&P 500 and Sector ETF data.

Code for classifying pet image dataset using a supervised learning approach: Stacking and Neural Network with freezing layer.

Based on customer visiting information to the site the customer sales revenue is predicted using machine learning models stacking

Diamond Prices is a small start-to-finish regression project that predicts diamond prices based on a selection of tabular data. Demonstrates model stacking.