Data Science Feature Engineering and Selection Tutorials

A container for Deep Learning with Python 3

Machine Learning Operator & Controller for Kubernetes

Allstate Kaggle Competition ML Capstone Project

Python Scripts and Jupyter Notebooks

Kaggle Kernels (Python, R, Jupyter Notebooks)

Analytics labs notebooks for Statistics and Business School students

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

预测rossmann1115家商店未来的销售额

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.

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.

JupyterLab Notebook for Mesosphere DC/OS

Forecasting future sales of a product offers many advantages. Predicting future sales of a product helps a company manage the cost of manufacturing and marketing the product. In this notebook, I will try to you through the task of future sales prediction with machine learning using Python.

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

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.

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.…

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.

This notebook is ispired by the AIX360 HELOC Credit Approval Tutorial, which shows different explainability methods for a credit approval process. Here XGBoost is used for classification, achieving better accuracy than most of the models used in that notebook. Then, feature importance methods are shown, to be compared with the Data Scientist exp…

IEEE Fraud Detection with XGBoost and CatBoost