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The dataset belongs to a leading life insurance company. The company wants to predict the bonus for its agents so that it may design appropriate engagement activity for their high performing agents and upskill programs for low-performing agents. 1. Problem Understanding
a) Defining problem statement b) Need of the study/project c) Understanding business/social opportunity
2. Data Report
a) Understanding how data was collected in terms of time, frequency and methodology b) Visual inspection of data (rows, columns, descriptive details) c) Understanding of attributes (variable info, renaming if required)
3. Exploratory Data Analysis
a) Univariate analysis (distribution and spread for every continuous attribute, distribution of data in categories for categorical ones)
b) Bivariate analysis (relationship between different variables , correlations) a) Removal of unwanted variables (if applicable) b) Missing Value treatment (if applicable) d) Outlier treatment (if required) e) Variable transformation (if applicable) f) Addition of new variables (if required)
4. Business insights from EDA
a) Is the data unbalanced? If so, what can be done? Please explain in the context of the business b) Any business insights using clustering (if applicable) c) Any other business insights

Sagot :

a.  Defining problem statement b) Need of the study/project.

b. Visual inspection of data (rows, columns, descriptive details).

c. Univariate analysis (distribution and spread for every continuous attribute, distribution of data in categories for categorical ones).

d. Any business insights using clustering (if applicable).

To manage their connections, claims, and underwriting more effectively, insurance company for property and liability are gathering data from telematics, agent interactions, client interactions, smart homes, and even social media. Insurance A common industrial data warehouse model that may be used for both life and non-life insurances is the Data Warehouse Data Model. All common insurance reporting and analytical Data Marts can be produced using the data modelled in the model.

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