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Challenge #158: Personality Quiz

12 - Quasar
Had a bit of fun with this challenge. Map interface tool for people to select where they want the next InspireChallenge 158 WF Image.png
11 - Bolide

Never worked with radio buttons so that was a pretty helpful challenge. Excited to look at others solutions and see how else they can be used to pull responses.


Screenshot 2023-08-17 093631.png


15 - Aurora
15 - Aurora



11 - Bolide

It was a fun challenge, thank you, @JoeM!

I followed a similar approach as @patrick_digan, thank you, Patrick!  I learned from you that Radio Buttons can be floating and do not need to be connected to the tools directly - this was neat, thanks much again!


5 - Atom

Sure, here are the five Alteryx exam questions using the generated datasets, along with the answers at the end.

### Question 1: Data Cleaning and Preparation
**Task:** You are given a dataset containing customer information. The dataset has missing values and some columns with incorrect data types.
1. Import the dataset into Alteryx.
2. Clean the dataset by handling missing values and correcting data types.
3. Filter out customers who have made purchases in the last 6 months.

**Multiple Choice Question:**
After cleaning the data, how many customers have made purchases in the last 6 months?
a) 50
b) 60
c) 70
d) 80

**Instructions:** Take a screenshot of your final cleaned dataset and upload it.

### Question 2: Data Transformation and Aggregation
**Task:** Using the same customer dataset, perform the following transformations:
1. Create a new column that calculates the total amount spent by each customer.
2. Aggregate the data to find the average amount spent per customer by city.

**Multiple Choice Question:**
What is the average amount spent per customer in the city with the highest total spending?
a) $400
b) $450
c) $500
d) $550

**Instructions:** Take a screenshot of the transformed dataset and the aggregated results, then upload them.

### Question 3: Data Visualization
**Task:** Visualize the distribution of total spending among different customer age groups.
1. Group customers into age ranges (e.g., 18-25, 26-35, etc.).
2. Create a bar chart showing the total spending for each age group.

**Multiple Choice Question:**
Which age group has the highest total spending?
a) 18-25
b) 26-35
c) 36-45
d) 46-55

**Instructions:** Take a screenshot of the bar chart and upload it.

### Question 4: Join and Blend Data
**Task:** You have a second dataset containing customer feedback. Join this dataset with the original customer dataset based on customer ID.
1. Perform an inner join on the customer ID.
2. Filter out customers who have not provided feedback.

**Multiple Choice Question:**
How many customers have provided feedback?
a) 80
b) 90
c) 100
d) 110

**Instructions:** Take a screenshot of the joined dataset and upload it.

### Question 5: Predictive Analysis
**Task:** Using the joined dataset, build a predictive model to predict customer churn.
1. Use the logistic regression tool to create a model.
2. Identify the top three predictors of customer churn.

**Multiple Choice Question:**
Which of the following is the top predictor of customer churn?
a) Age
b) Total Spent
c) Feedback Score
d) Total Purchases

**Instructions:** Take a screenshot of the model summary and upload it.

### Answers

1. **Question 1:** c) 70
2. **Question 2:** c) $500
3. **Question 3:** b) 26-35
4. **Question 4:** c) 100
5. **Question 5:** c) Feedback Score

These questions and answers are based on the assumption that typical distributions and transformations occur within the provided datasets. Adjustments might be necessary depending on the actual data values and results generated during the exam.

8 - Asteroid

Attached my app below.


Workflow and interface:

9 - Comet

Good practise with radio buttons!