This document outlines a 3-step approach to data analytics: 1) data exploration to understand the properties, limitations, and transformations needed for the given datasets, 2) model development including formulating hypotheses, statistical testing, and model performance testing, and 3) interpretation and reporting of results including visual presentation of main variables and coefficients from a commercial perspective to support arguments.

1. Sprocket Central
Pty Ltd
Task – 2 Data Insights
Data analytics approach
Vidhiya S B
]
Note: The data and informationin this document is reflectiveof a hypotheticalsituation and client. This document is to be used for KPMG Virtual Internship purposes only.

2. Agenda
1. Data Exploration
2. Model Development
3. Interpretation
Note: The data and informationin this document is reflectiveof a hypotheticalsituation and client. This document is to be used for KPMG Virtual Internship purposes only.

3. Data Exploration
 Understand in detail the underlying data's properties for each field, such
as variable distributions, whether a certain demographic is being
emphasized, and the fields' applicability.
 There are some limitations in the given datasets like some values are
missing and some datatypes are different according to their value.
 Additionally, the necessary data must be transformed such that it is in an
analysis-friendly manner. Making sure the data types are appropriate and
rolling data up to an aggregate level are two examples of actions that may
be included in this. Or, adding previously compiled ABS data at a
geographic level to provide more variables.

4.  Record the data's assumptions, constraints, and exclusions as well as how you
might advance in the subsequent step if more time were available to address
assumptions and eliminate constraints.

5. Model Development
 In order to use existing data to solve the business question, we must first
formulate a hypothesis connected to it. To ascertain whether or not the
hypothesis is true, do statistical tests.
 Create calculated fields based on existing data, for example, convert the D.O.B
into an age bracket.
 Test the performance of the model using factors like residual deviance, AIC,
ROC curves, R Squared) appropriately according to the model performance,
assumptions and limitations.

6. Interpretation and Report
 Presentation of the results visually. This can entail analyzing the main
variables and co - efficients from a commercial standpoint.
 This presentation gives us a general understanding of the business
problem, and we use it to support our arguments with both quantitative
and qualitative evidence.

7. Thank You