Predictive Analytics Solutions

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Predictive Analytics

Predictive Analytics techniques take advantage of the generality feature of Machine Learning Tools, which makes them suitable to solve a variety of different problems.

Such techniques are indeed exploitable by any business that produces data, which can use their databases to gain valuable information.

Our Predictive Analytics Solutions follow the CRISP-DM methodology, which is a particularly efficient method to follow in building models to forecast the future.

The main reason behind the use of CRISP-DM is the willingness to make the Predictive Analysis as reliable as possible, and to give even non-experts the possibility to understand their firm’s data asset.

Methodology

CRISP-DM methodology proposes a 6-steps framework, which can be repeated cyclically with the aim of reviewing and perfectioning the provisional model:

  • Business Understanding
  • Data Understanding
  • Data Preparation
  • Modeling
  • Evaluation
  • Deployment

PREDICTIVE ANALYTICS USE CASES

The use of machine learning techniques within Predictive Analytics projects permits to determine, in a probabilistic way, what will happen in the future. Examples are expected purchasing behaviors, customers loyalty, demand and energy forecasts, future demand for goods and services, optimal resource allocation, expected machinery breakdowns, and much more.

Some real-life examples of Predictive Analytics Projects are:

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