The DVT project for PGP-Babi (2019-20) conducted by Saleesh Satheeshcahandran aims to perform visual data analysis to derive insights for an insurance company. Using Tableau, the project identifies various dimensions and measures from the given dataset and creates multiple dashboards for comprehensive analysis, including claim amounts, bluebook values, and income analysis. The target audience includes top management and CXOs within the insurance and related industries.

1. DVT Project
For PGP-BABI (2019-20)
Done By
Saleesh Satheeshcahandran (G6)
Executive Summary
Statement of Purpose
The purpose of this project is to do a visual analysis of the given data and to arrive at useful
insights that can be of use to the principal company. There is no clear set of instructions in
such open-ended problems and it is expected of the consultant to do a lot of exploration
first and formulate the problems themselves.
Target Audience
The target audience for this report are the top management and the CXOs of the insurance
company whose data we are analyzing.
It could also provide insights to any person from the same industry as well as industries who
work in close coordination with the insurance companies.
Tool Used
The tool used for this analysis is Tableau Desktop (Public edition) running on MacOS
Link to the Tableau Public File
https://public.tableau.com/views/DVTProject_15809190882690/CarInsuranceDataAnalysis?
:display_count=y&publish=yes&:origin=viz_share_link
Dimensions identified
When the given excel file was opened using Tableau, it automatically identified the
following Dimensions.
- BLUEBOOK What is the worth of the car
- CAR_TYPE Car type
- CAR_USE Purpose of using the car
- CLM_AMT Currentl Claimed amount
- EDUCATION Education level
- GENDER Gender
- HOME_VAL Value of the house owned
- ID Id of customer
- INCOME Income
- MSTATUS Marital status
- OCCUPATION Occupation level
- OLDCLAIM Previous claim
(Click the below link to see the analysis)

2. - PARENT1 If car owner has his parents
- URBANICITY Where the car is being driven primarily
Measures identified
When the given excel file was opened using Tableau, it automatically identified the
following Measures.
- CAR_AGE Age of car
- CLM_FREQ How often claimed
- HOMEKIDS No of kids the driver has at home
- KIDSDRIV Total No of kids the car driver has
- TRAVTIME Travel time taken taken in minutes(On an average)
- YOJ Year in which the car owner applied for the car insurance policy
Manual Conversion of Dimensions to Measures
The following dimensions where converted to Measures
- INCOME Income
- BLUEBOOK What is the worth of the car
- HOME_VAL Value of the house owned
- CLM_AMT Currently Claimed amount
- OLDCLAIM Previous claim
Hiding worksheets to declutter the story
After making the sheets and adding them to corresponding dashboards, the individual
sheets have been hidden. They can be viewed by opening from dashboards.
Creating a Parameter
A parameter named “MultiDimView” is created with the below variables
a) EDUCATION
b) GENDER
c) MSTATUS
And then the dimension “MultiSelectCriteria” is created
Story Creation
A story with the name “Car Insurance Data Analysis” is created with 6 Dashboards.
Dashboard -1 (Univariate Analysis against Claim amount)
1. Occupation
2. Car Age (Filtered to a maximum of 1500K)
3. Gender
4. M-Status
5. MultiSelectCriteria
Dashboard -2 (Claim amounts Analysis)
Two Dimensions are selected to analyze keeping “Claim amount” as the measure.
Dashboard -3 (Bluebook Analysis)

3. Two Dimensions are selected to analyze keeping “Bluebook” as the measure.
Dashboard -4 (Income Analysis)
Two Dimensions are selected to analyze keeping “Income” as the measure.
Dashboard - 5 (Age Analysis)
Time series analysis is done by selecting “Birth” as the dependent variable.
Dashboard - 6 (Regression Analysis)
Regression analysis is done by selecting different combination of variables.
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