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Description Dear Participants, Please submit your project notes -1 here

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Description

Dear Participants,

Please submit your project notes -1 here.

 

Problem Statement:

Life Insurance Data 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.

 

Review Parameters	Review points
1) Introduction of the business problem	4
a) Defining problem statement
b) Need of the study/project
c) Understanding business/social opportunity
2)Data Report	2
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	10
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	4
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
Total	20

 

 Please note the following:

• You have to submit 2 files : 
   
  1. Business Report: In this, you should cover all the topics given in the rubric in a sequential manner. It should include a detailed explanation of the approach used, insights, inferences, all outputs of codes like graphs, tables, etc. and their business implications. Your report should not be filled with codes. You will be evaluated based on the business report.
  2. Python Notebook file: This is a must and will be used for reference while evaluating. Failing to do so shall lead to ZERO marks in all the sections where code file is necessary.
• Please note that the evaluation will be based on the Business Report. The Python-code/.ipynb file is only for reference. If you fail to submit the Business report ZERO Marks will be awarded.
• Any notes found copied/plagiarized from other sources will not be graded and marked as zero.
• Please ensure timely submission as the post-deadline assignment will not be accepted.

Standard Instructions for Business Report:

• All pages must be numbered.
• Tables/figures/charts/graphics (if any) must have number and title.
• Groups must make sure visualizations are clearly read at usual magnification and add value to the Report
  • All visualizations must be clearly labelled.
  • All axis labels and legends must be legible.
  • Tableau graphics default mode is not always conducive to normal copy-paste. A proper adjustment may be required. 

All raw codes and raw outputs must be in the Appendix. Illegible graphs and raw codes and raw outputs in the body of the report will mandate a heavy penalty.

Regards,

Program office.

 

Scoring guide (Rubric) - Project Note 1 (1)

Criteria	Points
1. Problem Understanding a) Defining problem statement b) Need of the study/project c) Understanding business/social opportunity	4
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)	2
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)	10
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	4
Points	20