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Venkadesh_Ponnu
Partner - Contributor III
Partner - Contributor III

Possible Machine Line learning types we can perform in Qlik Auto ML?

Here I am using Qlik Auto ML to create and deploy ML model.

In demo/Training videos we are using Supervised Learning i.e. passing the historical data and train the model. then we will predict the target column mostly yes/no type. (Categorial)

Now my question 1 is - what are the Machine learning types we can use it in Qlik Auto ML other than Supervised Categorial type? (please answer as per features available on 1st sept 2022)

I am trying to predict the stock price i.e. Close Price. I am unable to complete it due to insufficient features in the dataset. 

Question 2 : Can I predict Stock prediction in Qlik ML?
Sample Data:

Date Symbol Series Prev Close Open High Low Last Close VWAP Volume Turnover Trades Deliverable Volume %Deliverble
######## MUNDRAPORT EQ 440 770 1050 770 959 962.9 984.72 27294366 2.69E+15   9859619 0.3612


I have derived the below columns in addition to create the ML dataset. But it didn't work yet.

Date Symbol Series Prev Close Open High Low Last Close VWAP Volume Turnover Trades Deliverable Volume %Deliverble Avg CP 5 days Avg CP Last month Avg CP LY Avg CP 5 days/ Avg CP Last Month (P/Q) Avg CP 5 days/ Avg CP LY (P/R) Avg CP Last month / Avg CP LY (Q/R) Avg Vol 5 days Avg Vol Last month Avg Vol LY Avg Vol 5 days/ Avg Vol Last Month (P/Q) Avg Vol 5 days/ Avg Vol LY (P/R) Avg Vol Last month / Avg Vol LY (Q/R) Median Median - CS (Median - CS)^2 Sum(SD)/Count Daily Volatility Avg SD 5 days Avg SD Last month Avg SD LY Avg SD 5 days/ Avg SD Last Month (AG/AH) Avg SD 5 days/ Avg SD LY (AG/AI) Avg SD Last month / Avg SD LY (AH/AI)
######## MUNDRAPORT EQ 1074.95 1091 1116 1046.3 1078 1066.9 1082.93 845666 9.16E+13   344171 0.41 1085.31 1030.93 1030.93 1.05 1.05 1 1523206 3909740 3909740 0.39 0.39 1 1030.93 -35.97 1293.67 4765.84 69.04 71.82 54.47 54.47 1.32 1.32 1


Thanks in advance.
Qlik AutoML 
@KellyHobson 

Labels (2)
1 Solution

Accepted Solutions
KellyHobson
Former Employee
Former Employee

Hey @Venkadesh_Ponnu ,

Thank you for reaching out in the AutoML forum!

For question #1, AutoML on Qlik Cloud supports binary classification, multi class classification, and regression problems.

The Target you select is used to determine what kind of algorithms to use in the analysis process.

If you select a Target that has only two unique values, algorithms will be used that work best with binary classification problems. For example: customer retention (will my customer leave, yes/no), employee retention (will my employee leave, yes/no), etc.

If you select a Target that is a string value and has more than two unique values, algorithms will be used that work best with multi class classification problems. For example: campaign mix, product recommendation, up-sell opportunity, etc.

If you select a Target that is a number, algorithms will be used that work best with regression problems. For example: how much will this customer purchase, what will be the value of this sale, etc.

Additional information in the User Guide -> https://help.qlik.com/en-US/cloud-services/Subsystems/Hub/Content/Sense_Hub/AutoML/machine-learning-...

For question #2, it depends on how you are defining you target variable and what question you are asking

Which data attribute/variable are you trying to predict?

For stock data, a more common application is time series forecasting. This capability is built natively in the Qlik Sense line chart.

https://help.qlik.com/en-US/cloud-services/Subsystems/Hub/Content/Sense_Hub/Visualizations/LineChart...

ref-> https://towardsdatascience.com/predicting-stock-prices-using-a-keras-lstm-model-4225457f0233

Let me know if you have any additional questions.

Best,

Kelly

 

Qlik AutoML 

View solution in original post

2 Replies
KellyHobson
Former Employee
Former Employee

Hey @Venkadesh_Ponnu ,

Thank you for reaching out in the AutoML forum!

For question #1, AutoML on Qlik Cloud supports binary classification, multi class classification, and regression problems.

The Target you select is used to determine what kind of algorithms to use in the analysis process.

If you select a Target that has only two unique values, algorithms will be used that work best with binary classification problems. For example: customer retention (will my customer leave, yes/no), employee retention (will my employee leave, yes/no), etc.

If you select a Target that is a string value and has more than two unique values, algorithms will be used that work best with multi class classification problems. For example: campaign mix, product recommendation, up-sell opportunity, etc.

If you select a Target that is a number, algorithms will be used that work best with regression problems. For example: how much will this customer purchase, what will be the value of this sale, etc.

Additional information in the User Guide -> https://help.qlik.com/en-US/cloud-services/Subsystems/Hub/Content/Sense_Hub/AutoML/machine-learning-...

For question #2, it depends on how you are defining you target variable and what question you are asking

Which data attribute/variable are you trying to predict?

For stock data, a more common application is time series forecasting. This capability is built natively in the Qlik Sense line chart.

https://help.qlik.com/en-US/cloud-services/Subsystems/Hub/Content/Sense_Hub/Visualizations/LineChart...

ref-> https://towardsdatascience.com/predicting-stock-prices-using-a-keras-lstm-model-4225457f0233

Let me know if you have any additional questions.

Best,

Kelly

 

Qlik AutoML 

Venkadesh_Ponnu
Partner - Contributor III
Partner - Contributor III
Author

@KellyHobson Thank you very much for your detailed information. It's very nice to reach out to you.