Predictive analytics

Predictive analytics is a branch of data analytics that uses statistical algorithms, machine learning techniques, and historical data to identify the likelihood of future outcomes. Essentially, it involves analyzing current and historical data to make predictions about future events. Here are some key elements of predictive analytics: 1. **Data Collection**: Gathering relevant data from various sources, which can include structured data (like databases) and unstructured data (like social media or sensor data).

Cambridge Analytica was a political consulting firm that gained notoriety for its use of data analytics and psychological profiling in political campaigns. Founded in 2013, the company was a subsidiary of the SCL Group, which specialized in military and behavioral psychology. Cambridge Analytica became widely known for its role in the 2016 U.S. presidential election, where it worked on behalf of Donald Trump's campaign.

Civis Analytics is a data science and analytics firm that focuses on helping organizations, particularly those in the public and nonprofit sectors, leverage data to drive decision-making and improve outcomes. Founded in 2013 by several former members of the Obama campaign's data team, Civis Analytics provides services that include data strategy, analytics, and consulting, as well as a software platform that allows clients to analyze data more effectively.

Convergent Cross Mapping (CCM) is a statistical technique used to infer causal relationships between time-series data based on observations of their interactions. It was introduced in the context of ecological and environmental sciences to determine whether one time series can effectively predict the behavior of another, which can provide insight into underlying causal structures. ### Key Concepts of Convergent Cross Mapping: 1. **Causal Inference**: CCM is particularly useful for distinguishing between correlation and direct causal effects.

Empirical Dynamic Modeling (EDM) is a framework used to analyze complex, nonlinear systems, particularly in the context of ecological and environmental data. Developed primarily in the field of ecology, EDM provides tools for understanding dynamic systems without requiring predefined models or assumptions about the underlying processes. It relies on data-driven approaches to capture the interplay between variables over time.

Logistic regression is a statistical method used for binary classification problems, where the goal is to model the relationship between one or more independent variables (features) and a binary dependent variable (outcome) that can take on two possible values, typically represented as 0 and 1. Unlike ordinary linear regression, which predicts continuous outcomes, logistic regression predicts the probability that a given input falls into one of the two categories.

Metaculus is an online platform that focuses on forecasting and prediction markets. It allows users to make predictions about future events and outcomes across various domains, including science, technology, politics, and economics. The community of forecasters contributes their insights, and the platform aggregates these predictions to provide collective forecasts and probabilities about specific events. Metaculus operates based on a points system, where users earn points for accurate predictions, encouraging participation and engagement.

PredPol, or Predictive Policing, is a technology and software system designed to assist law enforcement agencies in predicting and preventing crime. Developed through a collaboration of law enforcement professionals and data scientists, PredPol uses algorithms to analyze historical crime data, identifying patterns and trends that can indicate where crimes are likely to occur in the future. The system typically takes into account various factors, including: 1. **Historical Crime Data**: Past incidents of crime in a particular area.

Predictive Mean Matching (PMM) is a statistical technique used in the context of handling missing data, particularly within the framework of multiple imputation. The main goal of PMM is to generate plausible values for missing data based on observed data, while preserving the distributional characteristics of the original dataset. ### Key Features of Predictive Mean Matching: 1. **Model-Based Approach**: PMM begins by fitting a regression model to predict the variable with missing values using other observed variables in the dataset.

Predictive modeling is a statistical technique that uses historical data to forecast future outcomes. It involves the development of a mathematical model that can identify patterns in data and make predictions based on those patterns. Predictive modeling is widely used in various fields, including finance, healthcare, marketing, and business operations, to anticipate trends, behaviors, and events.