Intelligent Data Analysis

15SNAP: Social Network Analysis Using Predictive Modeling

Samridhi Seth and Rahul Johari

USIC&T, GGSIP University, New Delhi, India

15.1 Introduction

Predictive analytics uses archive data, machine learning, and artificial intelligence for prediction. Predictive analytics has two steps: first, archive data is fed into algorithms and patterns are created, and second, current data is used on same algorithms and patterns for predictions. Digitization of every resource has aided the predictive analytics. Currently, hundreds of tools have been invented that can be used to predict probabilities in an automated way and decrease human labor.

Predictive analytics involves identifying “what is” needed from archive data, then to study whether the archive data used meets our needs. Then the algorithm is modified to learn from a data set to predict appropriate outcomes. It is important to use an accurate and authentic data set and update the algorithm regularly.

To the Best of Our Knowledge and Wisdom, Predictive Analytics Process Can be as Detailed as: [1]

1. Define project: All the minute details like outcomes, scope, deliverables, objectives, and data sets to be used are defined.
2. Data collection: It involves data mining with complete customer interaction.
3. Data analysis: Extracting important information via inspection, cleaning, and modeling.
4. Statistics. Validation and testing.
5. Modeling: Accurate predictive models are prepared.
6. Deployment: Models prepared are deployed in everyday decision-making ...