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Subject Code – MIS772
Subject Name – Predictive Analytics
University Name – Deakin Business School, Australia

Predictive Analytics MIS772 is a statistical subject offered at Deakin Business School, Australia to introduce students to the main concepts and techniques of Predictive Analytics which is considered to be one of the most exciting as well as a preferred area of study among students. This subject is a combination of data science and business analytics.

This subject Predictive Analytics MIS772 will offer students an understanding of predictive analytics principles and theories, as well as hands-on experience using predictive analytics approaches to organizational data to support business choices and actions. Statistical modeling (such as linear regression), machine learning (such as data clustering), and advanced analytic methods (such as text mining) are all covered in this unit as predictive analytics techniques, and they are all becoming indispensable elements of the successful business analyst’s toolbox.

At the end of this course, students have to attempt an online examination that will assist their subject knowledge and understanding. Here at Assignment Global, our main motto is to make your tough exam simple and easy for you. So, if you feel difficulty in your Predictive Analytics MIS772 exam, you can approach us for the best exam help online.

Let’s Know about the Subject Predictive Analytics MIS772 in Brief:

Predictive Analytics = Data Mining + Data Modelling

Predictive Analytics is a form of advanced analytics that uses historical data, statistical modeling, data mining techniques, and machine learning to create predictions about future outcomes. It focuses on building models from data –mathematical, machine learning, or visual.

Predictive Data Mining is data mining that is done with the goal of forecasting or predicting trends using business intelligence or other data. This form of data mining can assist corporate leaders in making better decisions while also adding value to the analytics team’s work.

Predictive Data Modelling is a mathematical procedure that analyses patterns in a set of input data to anticipate future events or outcomes. It’s an important part of predictive analytics, a sort of data analytics that forecasts activity, behavior, and trends using current and past data.

List of Tools For Predictive Analytics  

Open Source Tools

R with R Studio

Python / Anaconda with Spyder

WEKA

Community Tools

RapidMiner Studio

KNIME Analytics Platform

Machine Learning Tools

Tensorflow

Sagemaker

PyTorch

MXNet

Commercial Tools

SAS Viya

IBM SPSS Modeler

Microsoft Azure ML

MathWorks Matlab

4 Different Predictive Analytics Techniques and Methods

Linear Regression

Linear Regression is a statistical method that is used to predict the value of one variable that is based on the value of another variable. In the Linear Regression Model,  the variable you want to predict is called the dependent variable whereas the variable you are using to predict the others’ variable value is known as the independent variable.

Data Clustering

Data Clustering is a prominent data mining technique that employs machine learning to divide a huge dataset into smaller subsets by grouping objects into groups based on their similarities. There are two types of clustering- Hard and Soft clustering.

K means clustering is one of the most popular unsupervised machine learning algorithms. This method is used to find a fixed number of clusters in a set of data points based on the target number ‘K’.

Logistic Regression

One of the most important tools for predictive analytics is logistic regression modeling. Regression techniques are used to detect correlations between inputs and outputs in the form of a linear expression that defines the strength of the relationship as a mathematical formula. The outputs are expressed as a function of the inputs plus a constant in the formula. This linear relationship is then utilized to forecast a variable’s numerical value in the future.

Time Series Analysis

A method for examining time-series data is time series analysis. Based on previously observed values, time series models anticipate future values. A time series is a collection of data points that appear over time. The four components of Time Series Analysis are-

Secular Trend

Seasonal Variations

Cyclical Fluctuations

Irregular Variations

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