Use Machine Learning to predict good Civlization 6 start city locations.
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Updated
Dec 21, 2018 - Jupyter Notebook
Use Machine Learning to predict good Civlization 6 start city locations.
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Responsible datascience final project
A take on highly imbalanced fraud classification using permutation importance to select top features and explaining the model using SHAP.
A take on highly imbalanced fraud classification using permutation importance to select top features and explaining the model using SHAP.
Explaining blackbox predictions using python libraries.
Using LIME and SHAP for model interpretability of Machine Learning Black-box models.
Explaining complex ML models
Keras 101: A simple Neural Network for House Pricing regression
Contains a collection of my experimentations, explorations, and data analysis of random datasets
Interpretability of Image Keras Models
Explain variable influence in black-box model through pattern mining
Demo of shapely values for interpreting decision tree model
Enabling interactive plotting of the visualizations from the SHAP project.
Will They Pay? A machine learning solution to understand mobile app user payment behavior
Predicting whether or not a person deposits money after a marketing campaign. Gain insights to develop the best strategy in the next marketing campaign
Wine quality multi-class prediction neural net model implemented using pytorch with model exploration and explanation using shap.
Bayesian network implementation API inspired by SciKit-learn.
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