GitHub topics: stacked-ensemble
ReverendBayes/Telecom-Churn-Predictor
Predicts which telecom customers are likely to churn with 95% accuracy using real-world data features from usage, billing, and support data. Implements Sturges-based binning, one-hot encoding, stratified 80/20 train-test split, and a two-level ensemble pipeline with soft voting. Achieves 94.60% accuracy, 0.8968 AUC, 0.8675 precision, 0.7423 recall.
Language: Python - Size: 242 KB - Last synced at: 3 months ago - Pushed at: 3 months ago - Stars: 1 - Forks: 0
sheilateozy/cloudflight-ai-coding-contest
My solutions for the 2023 Cloudflight Coding Contest (AI Category)
Language: Jupyter Notebook - Size: 605 KB - Last synced at: over 1 year ago - Pushed at: over 1 year ago - Stars: 0 - Forks: 0
am-tropin/poland-apartment-prices
🇵🇱🏠 The project predicts an apartment price for Warsaw, Krakow and Poznan. Distributed apartments by districts using geopandas; built XGBoost model with MAPE = 9% (the best of others).
Language: Jupyter Notebook - Size: 78.9 MB - Last synced at: over 1 year ago - Pushed at: over 1 year ago - Stars: 0 - Forks: 0
GeorgiosEtsias/Stacked-ANN-ensemble
The current project introduces a script building a stacked learning ensemble, containing a single multilayered ANN (meta-learner) trained using the predictions of a number of ANNs (Level-0 learners).
Language: MATLAB - Size: 14.6 KB - Last synced at: almost 2 years ago - Pushed at: over 2 years ago - Stars: 1 - Forks: 0
taraponglab/enraqsar-skinirritation
Predict Skin Irritation based on pIC50 using command-line tool application
Language: Python - Size: 1.32 MB - Last synced at: over 1 year ago - Pushed at: over 1 year ago - Stars: 0 - Forks: 0
MariliaElia/sales-forecast-ml-models
Sales Time Series Forecasting using Machine Learning Techniques (Random Forest, XGBoost, and Stacked Ensemble Regressor)
Language: Jupyter Notebook - Size: 43.8 MB - Last synced at: about 2 years ago - Pushed at: about 2 years ago - Stars: 0 - Forks: 0
