Framing and supplementing churn analysis using GPT-3 API — GPT-3 is a large language model (LLM) that stands for Generative pre-trained transformer. It is a deep-learning model that has over 175 billion parameters. GPT-3 works by finding the next word given a sequence of words. GPT-3 does a good job of generating human-like text. This makes GPT-3 a great…

Exploring data science use cases with the GPT-3 API — GPT-3 is a language machine-learning model that was released by Open AI late last year. It has gained widespread media attention for its ability to write essays, songs, poetry, and even code! The tool is free to use and simply requires an email to sign up. GPT-3 is a type…

Object Orienting Programming for Time Series Analysis — Time series analysis is one of the most common data science tasks. It involves analyzing trends in data points that are ordered temporally. There are a wide variety of time series data including stock market data, weather data, consumer demand data, and much more. …

Understanding P-values and ML use cases — A p-value is a statistical metric that helps statisticians decide whether they should accept or reject the null hypothesis. The p-value measures the probability there is no relationship between variables. A low p-value gives evidence against the null hypothesis. P-values are often misinterpreted. For example, it often leads people to…

Python Helper Classes for EDA, Feature Engineering and Machine Learning — In computer programming, classes are a useful way to organize data (attributes) and functions (methods). For example, you can define a class that defines attributes and methods related to a machine learning model. An instance of this type of class may have attributes such as training data file name, model…

Python List Derivation for Data Science — Effective Python is a book by Brett Slatkin that covers 59 specific ways to write better python. The book is written in a random-access fashion where each topic has self-contained source code. …

Accessing Toy Data with Scikit-learn and Keras — Toy datasets can be used to teach important concepts in machine learning without having to deal with the challenges of data engineering. While data engineering is a very important part of the machine learning pipeline, using toy datasets avoids issues around missing value treatment, outlier treatment, file formats, and more. …

Using Function Wrappers for Machine Learning — Function wrappers are useful tools for modifying the behavior of functions. In Python, they’re called decorators. Decorators allow us to extend the behavior of a function or a class without changing the original implementation of the wrapped function. A particularly useful application of decorators is for monitoring the runtime of…

Augmenting Imbalanced Healthcare Readmission Data with GANs — Generative adversarial networks (GANs) are a class of deep learning models developed by Ian Goodfellow and colleagues in 2014. At a high level, GANs are made up of two competing neural networks that make up a zero-sum game. This means that the gains of one neural network agent corresponds to…