Useful Python Packages for Cluster Analysis

Photo by on

Clustering is the process of separating different parts of data based on common characteristics. Disparate industries including retail, finance and healthcare use clustering techniques for various analytical tasks. In retail, clustering can help identify distinct consumer populations, which can then allow a company to create targeted advertising based on consumer…

Interpreting Model Feature Importance

Photo by on

For data scientists, a key part of interpreting machine learning models is understanding which factors impact predictions. In order to effectively use machine learning in their decision-making processes, companies need to know which factors are most important. For example, if a company wants to predict the likelihood of customer churn…

Tools for Time Series Analysis and Forecasting in Python

Photo by on

Across industries, organizations commonly use time series data, which means any information collected over a regular interval of time, in their operations. Examples include daily stock prices, energy consumption rates, social media engagement metrics and retail demand, among others. Analyze time series data yields insights like trends, seasonal patterns and…

Understanding model testing, feature selection, and model tuning

Photo by on

Building stable, accurate and interpretable machine learning models is an important task for many companies across industries. Machine learning model predictions have to be stable in time as the underlying training data is updated. Drastic changes in data due to unforeseen events can lead to significant deterioration in model performance…

Sadrach Pierre, Ph.D.

Data Scientist at WorldQuant Predictive. Writer for Built In & Towards Data Science. Cornell University Ph. D. in Chemical Physics.

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store