https://github.com/oegedijk/explainerdashboard

Quickly build Explainable AI dashboards that show the inner workings of so-called "blackbox" machine learning models.

dash dashboard data-scientists explainer inner-workings interactive-dashboards interactive-plots model-predictions permutation-importances plotly shap shap-values xai xai-library

Last synced: 14 May 2025

https://github.com/archd3sai/Customer-Survival-Analysis-and-Churn-Prediction

In this project, I have utilized survival analysis models to see how the likelihood of the customer churn changes over time and to calculate customer LTV. I have also implemented the Random Forest model to predict if a customer is going to churn and deployed a model using the flask web app.

customer-churn-prediction customer-survival-analysis data-analysis explainable-ai flask-application hazard partial-dependence-plot random-forest shap-values survival-analysis

Last synced: 08 Apr 2025

https://github.com/oegedijk/explainingtitanic

A demonstration of the explainerdashboard package that that displays model quality, permutation importances, SHAP values and interactions, and individual trees for sklearn RandomForestClassifiers, etc

explainable explainerdashboard individual-trees permutation-importances shap-values xai

Last synced: 12 Apr 2025

https://github.com/csfelix/data-science-mental-maps

🐍 Mental Maps Related to Contents in Data Science 🐍

computer-vision cross-validation data-preparation data-science data-transformation deep-learning encoder feature-engineering imputation machine-learning normalization one-hot-encoder ordinal-encoder pickle pipelines python scale shap-values standardization xgboost

Last synced: 28 Apr 2025

https://github.com/pranav2092/intrustion-detection-using-modified-tree-shap

artificial-intelligence explainable-ai explainable-artificial-intelligence explainable-machine-learning explainable-ml google-colab intrusion-detection jupyter-notebook machine-learning modified-tree-shap python3 random-forest-classifier shap-values shapley-value tree-shap unsw-nb15

Last synced: 06 Apr 2025

https://github.com/kamal-shirupa/solar-power-forecasting

This project predicts solar panel energy output based on weather conditions and irradiance levels using a hybrid model of Gradient Boosting (for feature importance) and LSTM (for time-series forecasting). By leveraging real-world irradiance, weather, and panel data, the model enhances solar energy utilization and power grid efficiency.

deep-learning gradient-boosting knn-imputation lstm machine-learning pca-analysis shap-values

Last synced: 09 Apr 2025