VerticaPy demo : Building a Prediction Churn Model Using Random Forest & Logistic Regression
VerticaPy demo : Building a Prediction Churn Model Using Random Forest & Logistic Regression

Abstract: 

Deep-diving VerticaPy – Building a prediction churn model using random forest and logistic regression VerticaPy is a Python library that exposes sci-kit like functionality to conduct data science projects on data stored in Vertica – taking advantage of the platform fast queries, built-in analytics and machine learning capabilities. In this session, we will demonstrate how simpler and quicker are data exploration and preparation. We will then demonstrate how in-database machine learning helps evaluate and deploy models easily while showcasing a prediction churn example using random forest and logistic regression

Bio: 

Badr Ouali is Vertica’s team point of reference for data Science projects since 2017. Prior to Vertica, Badr received both an undergraduate and a Master degree in Computer Science/Mathematics from the National School of Computer Science and Applied Mathematics in Grenoble, France. Badr enjoys sharing knowledge and insights related to data analytics with colleagues & peers and has a sweet spot for Python. He loves helping his customers finding the best value from their data and empower them to solve their use-cases.