Predictive Analysis

Assistance with predictive statistical analysis

Predictive analysis is the process of obtaining data from prevailing data collected. The general direction and recognizable patterns of data are identified and used for predictive purposes.The main objective of predictive analysis is cultivating predictive intelligence. It is one of the branches of advanced analysis. The working procedure of analysis is data collection, statistics and assignment. The examples of employing predictive analysis are:

Collection analytics
Fraud detection
Direct marketing
Cross sells
Risk management
Customer relationship management

Gradient Boosted Model (GBM)

The Gradient Boosted Model generates a prediction model which has an ensemble of different decision trees even before generalizing it.

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Generalized Linear Model (GLM) for Two Values

The Generalized Linear Model (GLM) is a complex alternative of the General Linear Model. It uses the latter model’s comparison of the results of various

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Random Forest

Random Forest is possibly the most famous classification algorithm, competent in both regression and classification. It can perfectly analyse considerable volumes of data.

Time Series Modelling

The most widely used predictive models are the forecast models that deal in metric value prediction, evaluating the numeric values from the historical data for new data based on the data that's available.

Clustering Model

An extremely common, high-speed algorithm, K-means requires placing unlabeled data objects in separate groups based on similarities of the data character.

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Neural networks

These are complex techniques competent in creating extremely complicated relationships. They are popular because they are powerful and resilient. The power

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