Code used in Kaggle's Santander Product Recommendation competition.
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Updated
Dec 28, 2016 - Python
Code used in Kaggle's Santander Product Recommendation competition.
A python 2 container runtime for processing data science tasks and workloads (used by https://github.com/jay-johnson/sci-pype for distributed analysis)
Consumer Spending Analytics
A dashboard that supports fleet managers and decision makers to gain insights into their automotive fleets and optimize them
Microbial Phenotype Prediction, successor to PICA, implemented with Python 3.7 and scikit-learn
This is a Machine Learning web app developed using Python and StreamLit. Uses algorithms like Logistic Regression, KNN, SVM, Random Forest, Gradient Boosting, and XGBoost to build powerful and accurate models to predict the status of the user (High Risk / Low Risk) with respect to Heart Attack and Breast Cancer.
A Machine Learning API with native redis caching and export + import using S3. Analyze entire datasets using an API for building, training, testing, analyzing, extracting, importing, and archiving. This repository can run from a docker container or from the repository.
comparison of different machine learning models such as GB, XGB and NN to see which performs better at real time SYN flood detection
PSO Fuzzy XGBoost Classifier Boosted with Neural Gas Features on EEG Signals in Emotion Recognition
XGB model trainer to classify an observation as an exoplanet, candidate or false positive
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