What is Agile? According to Agile Alliance : Agile is the ability to create and respond to change. It is a way of dealing with and ultimately suceeding in, an uncertain and turbulent environment. Instead of betting everything on a “big bang” launch, an agile team delivers work in small, but consumable, increments…

On May 2021, LinkedIn releases a time-series forecasting library, Greykite to simplify prediction process for its data scientists. Introduction to GreyKite The Greykite library is an open source Python library developed to support LinkedIn’s forecasting needs. LinkedIn developed GreyKite to support its team make effective decisions based on the time-series forecasting models. …

Prophet is a procedure for forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects. It works best with time series that have strong seasonal effects and several seasons of historical data. Prophet is open source software…

Heart disease is the leading cause of death worldwide, accounting for one third of deaths in 2019. Heart disease cases nearly doubled over the period, from 271 million in 1990 to 523 million in 2019, and the number of heart disease deaths rose from 12.1 million to 18.6 million. Coronary…

Logistic Regression is a Machine Learning classification algorithm that is used to predict the probability of a categorical dependent variable. It’s an extension of the linear regression model for classification problems. Unlike linear regression which outputs continuous number values, logistic regression transforms its output using the logistic sigmoid function to…

Identification of customers based on their choices is an important strategy in any organization. This identification may help in approaching customers with specific offers. An organization with a large number of customers may experience difficulty in identifying and placing into a record each customer. A huge amount of data processing…

The CausalImpact R package implements an approach to estimating the causal effect of a designed intervention on a time series. Basically, it builds a Bayesian structural time series model based on multiple comparable control groups (or markets) and uses the model to project (or forecast) a series of the baseline…

Causal inference is branch of statistics that’s concerned about the effects with the consequences of our actions and that’s really important because identifying one causal law in our data can be more powerful than dozens of correlational patterns that we might find. The main standard method for estimating causal effects…

A stock (also known as company’s ‘equity’) is a financial instrument that represents ownership in a company or corporation and represents a proportionate claim on its assets (what it owns) and earnings (what it generates in profits) — Investopedia The stock market is a market that enables the seamless exchange…