Skip to content

Commit

Permalink
Quarterly QA LWMS ID 6601
Browse files Browse the repository at this point in the history
  • Loading branch information
vltabacaru committed Aug 17, 2023
1 parent 1f7366b commit 7e196a8
Show file tree
Hide file tree
Showing 3 changed files with 6 additions and 6 deletions.
Original file line number Diff line number Diff line change
Expand Up @@ -7,7 +7,7 @@ This lab will show you how launch Jupyter Notebooks in your NoVNC instance.

Watch the video below for a quick walk through of the lab.

[](youtube:HI9iczwKwJ4)
[OML4Py Workshop Walk-through](youtube:HI9iczwKwJ4)

### Objectives
* Get familiar with the lab Instance
Expand Down
2 changes: 1 addition & 1 deletion oml4py/introduction/introduction.md
Original file line number Diff line number Diff line change
Expand Up @@ -14,7 +14,7 @@ Oracle Machine Learning for Python (OML4Py) is a component of Oracle Database, t

Watch the video below on introduction to Oracle Machine Learning for Python.

[] (youtube:P861m__PEMQ)
[Oracle Machine Learning for Python](youtube:P861m__PEMQ)

### Objectives

Expand Down
8 changes: 4 additions & 4 deletions use-case/use-case.md
Original file line number Diff line number Diff line change
Expand Up @@ -26,21 +26,21 @@ In this lab, you will:
## Task 1: Review the Customer Insurance Data

1. Review the historical customer data. Note the Buy Insurance column.
![customer-data-1](./images/customer-data-1.png)
![historical customer data](./images/customer-data-1.png)

2. Note the LTV and LTV\_BIN columns. LTV gives us a numerical score of the life time value of the customer, and LTV\_BIN is just a readable category derived from the score with VERY HIGH, HIGH, MEDIUM, and LOW business value.

![customer-data-2](./images/customer-data-2.png)
![LTV and LTV_BIN columns](./images/customer-data-2.png)

Since the data already has the buy insurance and LTV information and the other data, we can use all this information in machine learning to "train" and build a model that can predict new customer outcomes for buy insurance and LTV.

3. Consider the following new customer records with the buy insurance column empty.

![customer-data-3](./images/customer-data-3.png)
![new customer records](./images/customer-data-3.png)

4. And with the LTV and LTV_BIN columns empty.

![customer-data-4](./images/customer-data-4.png)
![LTV and LTV_BIN columns empty](./images/customer-data-4.png)

5. In this workshop, you will pass new records to machine learning models to predict whether the customer will buy the insurance and automatically determine the LTV without humans. Have fun.

Expand Down

0 comments on commit 7e196a8

Please sign in to comment.