代写Business Analytics Assessment Portfolio代做Python编程

Business Analytics Assessment Portfolio

1. Task Description

This assessment has four parts aimed at guiding you through the CRISP-DM process: a research component, a descriptive statistics component, some analytics and a final conclusion. All four parts are to be answered.

2. Background

Researchers are interested in determining the spending habits of 1000 students across various demographic groups and academic backgrounds. A survey was undertaken to collect data on age, gender, major, monthly income, financial aid received, and expenses in different spending categories. Spending categories include tuition, housing, food, transportation, books & supplies, entertainment, personal care,   technology, health & wellness, and miscellaneous expenses. Additionally, the dataset includes the preferred payment method for each student.

3. Data Key

The definitions of each of the data columns is asfollows:

• Age: Age of the student (in years)

• Gender: Gender of the student (Male, Female, Non-binary)

• Major: Field of study or major

• Monthly Income: Monthly income of the student (in dollars)

• Financial Aid: Financial aid received by the student (in dollars)

• Tuition: Expenses for tuition (in dollars)

• Housing: Expenses for housing (in dollars)

• Food: Expenses for food (in dollars)

• Transportation: Expenses for transportation (in dollars)

• Books & Supplies: Expenses for books and supplies (in dollars)

• Entertainment: Expenses for entertainment (in dollars)

• Personal Care: Expenses for personal care items (in dollars)

• Technology: Expenses for technology (in dollars)

• Health & Wellness: Expenses for health and wellness (in dollars)

• Miscellaneous: Miscellaneous expenses (in dollars)

• Preferred Payment Method: Preferred payment method (Cash, Credit/Debit Card, Mobile Payment App)

Part A: Explain & Understand the Process of the CRISP-DM Model                        (15  marks)

1.   What  is  the  importance of defining business objectives at the beginning of a data mining project?

2. Why is data cleaning and pre-processing important in the data mining process?

3.   What are the key considerations when selecting a modelling technique?

4. Discuss the importance of model evaluation and validation.

5.   What are the challenges associated with deploying a data mining solution in a real- world scenario?

Part B: Data Preparation and Descriptive Statistics                                                      (20 marks)

Using the dataset supplied.

1.   What questions would the researchers have regarding this dataset?

2. Are there any issues with the data or other variables that could be collected to help answer the researchers’ questions?

3. Are there any unusual observations in the data set? Identify their values and any potential problems that they could cause  (Hint:  think of the results that could be affected)

4. Prepare a one-page dashboard (box plots,  time  series plots, bar charts and any relevant tables) that can be used to describe the main features of the dataset.

Part C: Statistical Analysis of the Data                         (30 marks)

1. What  is the relationship between  Age and  Monthly  Income? This  could help understand if there's a correlation between age and earning potential.

2. How does the major that a student is taking relate to Monthly Income? This could reveal whether there is a premium of income as a function of  the major that the student is undertaking.

3. Is there a relationship between Major and Monthly Income? Exploring whether certain fields of study lead to higher incomes.

4. What factors influence Tuition expenses? Analysing how variables like Financial Aid impact tuition costs.

5. Does the Preferred Payment Method relate to any other variables? Investigating whether payment preferences are associated with demographic or financial factors.

6. How do various expenses (e.g., Books & Supplies, Entertainment) relate to each other? Understanding spending patterns and potential trade-offs between different categories.

7. Can we predict Total Expenses based on individual spending categories? Building a model to estimate total expenses based on the breakdown of spending across different categories.

8. Does Financial Aid impact the relationship between Income and Expenses? Exploring whether financial aid mitigates the impact of income on expenditure patterns.

9. How do Health   &   Wellness expenses vary across different demographics? Investigating whether certain groups spend more on health-related items.

10. What is the impact of Age, Gender, and Major on Technology expenses? Exploring how demographic and academic factors influence technology spending habits.

Part D: Final Conclusions                                                                                              (35 marks)

Provide a comprehensive analysis of key insights derived from the dataset, spanning demographic information, academic standing, income, expenses, and payment preferences. Your response should encompass a detailed examination of the data trends, highlighting notable correlations, issues, and potential implications for any decision-making and strategic planning to better support students. (600 words)