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Accredited Investor Clustering
Unit 6 Assignment: Wealth Management Analyst Project Part II- Regression Model
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(zipcode not county).
5. Suggestion of wealth management offerings.
Accredited Investor Clustering
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.
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.
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Accredited Investor Clustering
Accredited Investor Clustering is the process of grouping geographic areas—such as ZIP codes—based on similarities in income, family structure, and retirement income data to identify where high-net-worth individuals (accredited investors) are most likely located. This method supports wealth management firms in targeting potential clients and determining optimal office locations.