Unit 6 Assignment: Wealth Management Analyst Project Part II- Regression Model

BUS330 – Business Analytics Unit 6 Assignment: Wealth Management Analyst Project Part II- Regression Model

© 2022 Post University, Waterbury, CT ALL RIGHTS RESERVED

Due Date: 11:59 pm EST Sunday of Unit 6 Points: 10 Overview: Throughout the course, you will be working on a Wealth Management Analyst Project due Thursday of Unit 8. For this project, imagine you are a new hire at a wealth management firm and tasked with determining the location of a brick-and-mortar office within Connecticut. Please use the data set attached in the Unit to complete this assignment. Your analysis must include:

1. Determine where accredited investors are located.

2. Analyze the structure of the investor household.

3. Analyze the retirement income mix of the investor.

4. Suggestion of an office location (zip code not county).

5. Suggestion of wealth management offerings.

Case Problem- Investment Banking:

• Play the role of Wealth Management Analyst and construct a regression model of Connecticut counties (through zip code) that are likely to have accredited investors.

• Please use the data set attached in the Unit to complete this assignment.

Project Assumptions:

• Accredited investor sample statistics are the same as zip code (population) statistics for family structure and retirement income.

• All… INCOME and BENEFITS…” columns are individual income.

© 2022 Post University, Waterbury, CT ALL RIGHTS RESERVED

Instructions: In Unit 6, appropriately partition the data set into income data, family structure, and retirement benefits. Experiment with various clustering methods and propose a final model for identifying counties/cities with a high level of accredited investors (investor). Also, suggest the investment products that should be offered to investors based on data. Your submission should be at least 3 pages in length. Requirements:

• Submit Part II for instructor feedback.

Be sure to read the criteria below by which your work will be evaluated before you write and again after you write.

Evaluation Rubric for Unit 6 Assignment CRITERIA Complete Incomplete

10 Points 0 Points Part II Submission

Part II was submitted.

Part II was not submitted.

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BUS3330-Business Analytics

Wealth Management Analyst Project- Instructor Notes

In this project, students are new hires at a wealth management firm and tasked with determining the location of a brick-and-mortar office within Connecticut. Also, the analyst must suggest what type of financial products the office should offer. Project Outcome:

determine where accredited investors (investor) are located. analyze the structure of the investor household. analyze the retirement income mix of the investor. suggest an office location. suggest wealth management offerings.

Project Assumptions:

accredited investor sample statistics is the same as zip code (population) statistics for family structure and retirement income. all "INCOME and BENEFITS…" columns are individual income.

The data is obtained from the United States Census Bureau (https://data.census.gov/cedsci/table? t=Earnings%20%28Individuals%29%3AFamily%20Size%20and%20Type%3AIncome %20%28Ho Instructions on how to generate tables from Census Bureau are given here (https://www2.census.gov/data/api-documentation/using-data-census-gov- download.pdf).

In [27]: 1 # Import Libraries 2 import pandas as pd 3 import numpy as np

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In [16]:

Out[16]:

Estimate!!EMPLOYMENT STATUS!!Population 16

years and over

Margin of Error!!EMPLOYMENT STATUS!!Population

16 years and over

Percent!!EMPLOYMENT STATUS!!Population 16

years and over

Percent Margin Error!!EMPLOYME STATUS!!Populati

16 years and ov

0 14637 271 14637 (

1

18732

259

18732

(

2

49643

611

49643

(

In [21]:

Out[21]: 0 189917

1 98798 2 90262 3 146608 4 100230 … 277 119937 278 266119 279 164613 280 113867 281 132479 Name: Estimate!!INCOME AND BENEFITS (IN 2019 INFLATION-ADJUSTED DOLLAR

S)!!Total households!!With earnings!!Mean earnings (dollars), Length: 282, dtype: object

1 data = pd.read_csv("BUS330_Project1.csv") 2 data

1 data['Estimate!!INCOME AND BENEFITS (IN 2019 INFLATION-ADJUSTED DOL 2

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In [35]:

Out[35]: Estimate!!EMPLOYMENT STATUS!!Population 16 years and over 276 Margin of Error!!EMPLOYMENT STATUS!!Population 16 years and over 231 Percent!!EMPLOYMENT STATUS!!Population 16 years and over 276 Percent Margin of Error!!EMPLOYMENT STATUS!!Population 16 years and ov er 1 Estimate!!EMPLOYMENT STATUS!!Population 16 years and over!!In labor fo rce 278

… Margin of Error!!PERCENTAGE OF FAMILIES AND PEOPLE WHOSE INCOME IN THE PAST 12 MONTHS IS BELOW THE POVERTY LEVEL!!All people!!Unrelated indiv iduals 15 years and over 1 Percent!!PERCENTAGE OF FAMILIES AND PEOPLE WHOSE INCOME IN THE PAST 12 MONTHS IS BELOW THE POVERTY LEVEL!!All people!!Unrelated individuals 1 5 years and over 170 Percent Margin of Error!!PERCENTAGE OF FAMILIES AND PEOPLE WHOSE INCOM E IN THE PAST 12 MONTHS IS BELOW THE POVERTY LEVEL!!All people!!Unrela ted individuals 15 years and over 122 id 282 Geographic Area Name 282 Length: 550, dtype: int64

Number of Unique Values per column

1 # Determine the number of unique values in each column 2 col_unique = data.nunique() 3 col_unique

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In [41]:

Out[41]: Text(0.5, 0, 'Number of Unique Values')

Remove All Columns with a Unique value There are 550 columns within the data, however 91 of these columns only have 1 unique value. These single value columns do not office any information with analysis and are removed.

1 # Library for plots 2 import matplotlib.pyplot as plt 3 col_unique.hist(bins=282) 4 plt.ylabel('Number of Occurrence') 5 plt.xlabel('Number of Unique Values')

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In [45]:

Out[45]:

Estimate!!EMPLOYMENT STATUS!!Population 16

years and over

Margin of Error!!EMPLOYMENT STATUS!!Population

16 years and over

Percent!!EMPLOYMENT STATUS!!Population 16

years and over

Estimate!!EMPLOYME STATUS!!Population years and over!!In la

fo

0

14637

271

14637

9

1

18732

259

18732

10

2

49643

611

49643

33

3

7703

148

7703

5

4

…

277

5577

…

56883

390

…

1369

5577

…

56883

4

42

278

11279

454

11279

7

279

16582

796

16582

11

280

7360

603

7360

5

281

6832

554

6832

5

282 rows × 459 columns

1 # Remove Columns with 1 unique value 2 col = data.columns 3 col_drop = [] 4 col_unique = data.nunique() 5 6 for i, unique_num in enumerate(col_unique): 7 if unique_num == 1: 8 col_drop.append(col[i]) 9 10 data_df = data.drop(columns=col_drop) 11 data_df

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In [48]:

1 # Fileout of Cleaned data for student analysis 2 data_df.to_csv('Wealth.Management.Analyst.csv',index=False)

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Conclusion The fairfield county in Connecticut is the richest and the analyst must provide it from the data. Office located within the fairfield county.