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A solution to last week's challenge can be found here.
We’re celebrating the launch of a new Alteryx podcast, Top Shelf Data Science, in our Weekly Challenges today and next week!
Top Shelf will feature a variety of expert guests who are driving innovation in the field, in conversation with @SusanCS, data science journalist for the Community. You’ll hear technical details of their work and their personal stories of doing “top shelf” data science.
Join in the fun and figure out the details of our “data-driven” cocktail recipe in this challenge, and get the rest of the components and instructions next week. You’ll use a range of tools for these challenges, including some predictive tools. But don’t worry, we’ll be sticking to their default settings. It’s like a wine or whiskey tasting — just a sampling — that we hope both predictive newbies and connoisseurs will enjoy.
Both challenges use a Kaggle dataset of cocktail recipes. You’ll need this PDF of the recipe to fill in as you work. Next week we’ll give you the full answers so you can make sure your cocktail is the perfect blend.
For this week’s challenge, you’ll need to use the market basket (“MB”) tools and the Naive Bayes classifier tool, among others. Here are some resources that might help if these tools are new to you or you’d like a refresher:
- Market Basket: MB Rules and MB Inspect tools help documentation; Market Basket 101 and 102 on the Data Science Blog
- Naive Bayes Classifier: help documentation
Remember, we’re not trying to tune and refine models here; we’re just getting a taste for how the predictive tools work. So you can leave the tools’ settings on their default options, unless otherwise directed, and use the results you obtain to complete the recipe.
Finally, if you haven’t yet done so, you’ll need to install the R predictive tools for this challenge and next week’s challenge.
Prep that cocktail shaker, fire up your favorite podcast app, and get ready for an educational, enlightening happy hour!
A solution to last week's challenge can be found here.
We’re celebrating the launch of a new Alteryx podcast, Top Shelf Data Science, in our Weekly Challenges today and next week!
Top Shelf will feature a variety of expert guests who are driving innovation in the field, in conversation with @SusanCS, data science journalist for the Community. You’ll hear technical details of their work and their personal stories of doing “top shelf” data science.
Join in the fun and figure out the details of our “data-driven” cocktail recipe in this challenge, and get the rest of the components and instructions next week. You’ll use a range of tools for these challenges, including some predictive tools. But don’t worry, we’ll be sticking to their default settings. It’s like a wine or whiskey tasting — just a sampling — that we hope both predictive newbies and connoisseurs will enjoy.
Both challenges use a Kaggle dataset of cocktail recipes. You’ll need this PDF of the recipe to fill in as you work. Next week we’ll give you the full answers so you can make sure your cocktail is the perfect blend.
For this week’s challenge, you’ll need to use the market basket (“MB”) tools and the Naive Bayes classifier tool, among others. Here are some resources that might help if these tools are new to you or you’d like a refresher:
- Market Basket: MB Rules and MB Inspect tools help documentation; Market Basket 101 and 102 on the Data Science Blog
- Naive Bayes Classifier: help documentation
Remember, we’re not trying to tune and refine models here; we’re just getting a taste for how the predictive tools work. So you can leave the tools’ settings on their default options, unless otherwise directed, and use the results you obtain to complete the recipe.
Finally, if you haven’t yet done so, you’ll need to install the R predictive tools for this challenge and next week’s challenge.
Prep that cocktail shaker, fire up your favorite podcast app, and get ready for an educational, enlightening happy hour!
I think I am ready for Part 2... (however this is certainly new territory)
Spoiler
1. {Powder sugar}->{Lemon}
2. Confusion matrix for the lemon model
3. Mesure questions
2. Confusion matrix for the lemon model
3. Mesure questions
