Data Science Feature Engineering and Selection Tutorials

Allstate Kaggle Competition ML Capstone Project

A container for Deep Learning with Python 3

Python Scripts and Jupyter Notebooks

The complete code and notebooks used for the ACM Recommender Systems Challenge 2019

Kaggle Kernels (Python, R, Jupyter Notebooks)

Analytics labs notebooks for Statistics and Business School students

JupyterLab Notebook for Mesosphere DC/OS

Nvidia DLI workshop on AI-based anomaly detection techniques using GPU-accelerated XGBoost, deep learning-based autoencoders, and generative adversarial networks (GANs) and then implement and compare supervised and unsupervised learning techniques.

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Machine Learning Operator & Controller for Kubernetes

In this notebook, we will create an AI and time serie driven forecasting engine based on a set of 5 AI models and 5 time series models and employ several algorithms to perform feature engineering and selection on a multivariate time series dataset.

My contributions in Kaggle, mostly in a notebook format. Just for fun.

A jupyter notebook for binary classification of breast cancer using XGBoost with Bayesian optimization.

In this Amazon SageMaker tutorial, you'll find labs for setting up a notebook instance, feature engineering with XGBoost, regression modeling, hyperparameter tuning, bring your custom model etc.

I'm attempting the NYC Taxi Duration prediction Kaggle challenge. I'll by using a combination of Pandas, Matplotlib, and XGBoost as python libraries to help me understand and analyze the taxi dataset that Kaggle provides. The goal will be to build a predictive model for taxi duration time. I'll also be using Google Colab as my jupyter notebook.…

📓 📈 Functions from Abhishek Thakur's book Approaching (Almost) Any Machine Learning Problem.

XGBoost explainability show case with a Jupyter notebook including a Vagrant virtual machine definition to simplify reproduction of results.

Data & Scripts for the Memorial Sloan Kettering Cancer Center's (MSKCC) request for a machine learning algorithm that, using annotated information on genomic variants, automatically classifies genetic variations as either neutral or cancerous.

Collection of example Jupyter Notebooks and R Markdown files for predictive analytics