Merge pull request #233965 from s-polly/docs-editor/samples-designer-1681155899

Fix broken links to KDD Cup

Author: prmerger-automator[bot]

articles/machine-learning/samples-designer.md

@@ -12,6 +12,7 @@ ms.author: keli19
 ms.date: 10/21/2021
 ms.custom: designer
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 # Example pipelines & datasets for Azure Machine Learning designer
 
 Use the built-in examples in Azure Machine Learning designer to quickly get started building your own machine learning pipelines. The Azure Machine Learning designer [GitHub repository](https://github.com/Azure/MachineLearningDesigner) contains detailed documentation to help you understand some common  machine learning scenarios.
@@ -116,10 +117,10 @@ The sample datasets are available under **Datasets**-**Samples** category. You c
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 | Adult Census Income Binary Classification dataset | A subset of the 1994 Census database, using working adults over the age of 16 with an adjusted income index of > 100.<br/>**Usage**: Classify people using demographics to predict whether a person earns over 50K a year.<br/> **Related Research**: Kohavi, R., Becker, B., (1996). [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml). Irvine, CA: University of California, School of Information and Computer Science|
 |Automobile price data (Raw)|Information about automobiles by make and model, including the price, features such as the number of cylinders and MPG, as well as an insurance risk score.<br/> The risk score is initially associated with auto price. It is then adjusted for actual risk in a process known to actuaries as symboling. A value of +3 indicates that the auto is risky, and a value of -3 that it is probably safe.<br/>**Usage**: Predict the risk score by features, using regression or multivariate classification.<br/>**Related Research**: Schlimmer, J.C. (1987). [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml). Irvine, CA: University of California, School of Information and Computer Science. |
-| CRM Appetency Labels Shared |Labels from the KDD Cup 2009 customer relationship prediction challenge ([orange_small_train_appetency.labels](http://www.sigkdd.org/site/2009/files/orange_small_train_appetency.labels)).|
-|CRM Churn Labels Shared|Labels from the KDD Cup 2009 customer relationship prediction challenge ([orange_small_train_churn.labels](http://www.sigkdd.org/site/2009/files/orange_small_train_churn.labels)).|
-|CRM Dataset Shared | This data comes from the KDD Cup 2009 customer relationship prediction challenge ([orange_small_train.data.zip](http://www.sigkdd.org/site/2009/files/orange_small_train.data.zip)). <br/>The dataset contains 50K customers from the French Telecom company Orange. Each customer has 230 anonymized features, 190 of which are numeric and 40 are categorical. The features are very sparse. |
-|CRM Upselling Labels Shared|Labels from the KDD Cup 2009 customer relationship prediction challenge ([orange_large_train_upselling.labels](http://www.sigkdd.org/site/2009/files/orange_large_train_upselling.labels)|
+| CRM Appetency Labels Shared |Labels from the KDD Cup 2009 customer relationship prediction challenge ([orange_small_train_appetency.labels](https://kdd.org/cupfiles/KDDCupData/2009/orange_small_train_appetency.labels)).|
+|CRM Churn Labels Shared|Labels from the KDD Cup 2009 customer relationship prediction challenge ([orange_small_train_churn.labels](https://kdd.org/cupfiles/KDDCupData/2009/files/orange_small_train_churn.labels)).|
+|CRM Dataset Shared | This data comes from the KDD Cup 2009 customer relationship prediction challenge ([orange_small_train.data.zip](https://kdd.org/cupfiles/KDDCupData/2009/orange_small_train.data.zip)). <br/>The dataset contains 50K customers from the French Telecom company Orange. Each customer has 230 anonymized features, 190 of which are numeric and 40 are categorical. The features are very sparse. |
+|CRM Upselling Labels Shared|Labels from the KDD Cup 2009 customer relationship prediction challenge ([orange_large_train_upselling.labels](https://kdd.org/cupfiles/KDDCupData/2009/orange_small_train_upselling.labels)|
 |Flight Delays Data|Passenger flight on-time performance data taken from the TranStats data collection of the U.S. Department of Transportation ([On-Time](https://www.transtats.bts.gov/DL_SelectFields.asp?Table_ID=236&DB_Short_Name=On-Time)).<br/>The dataset covers the time period April-October 2013. Before uploading to the designer, the dataset was processed as follows: <br/>-    The dataset was filtered to cover only the 70 busiest airports in the continental US <br/>- Canceled flights were labeled as delayed by more than 15 minutes <br/>- Diverted flights were filtered out <br/>- The following columns were selected: Year, Month, DayofMonth, DayOfWeek, Carrier, OriginAirportID, DestAirportID, CRSDepTime, DepDelay, DepDel15, CRSArrTime, ArrDelay, ArrDel15, Canceled|
 |German Credit Card UCI dataset|The UCI Statlog (German Credit Card) dataset ([Statlog+German+Credit+Data](https://archive.ics.uci.edu/ml/datasets/Statlog+(German+Credit+Data))), using the german.data file.<br/>The dataset classifies people, described by a set of attributes, as low or high credit risks. Each example represents a person. There are 20 features, both numerical and categorical, and a binary label (the credit risk value). High credit risk entries have label = 2, low credit risk entries have label = 1. The cost of misclassifying a low risk example as high is 1, whereas the cost of misclassifying a high risk example as low is 5.|
 |IMDB Movie Titles|The dataset contains information about movies that were rated in Twitter tweets: IMDB movie ID, movie name, genre, and production year. There are 17K movies in the dataset. The dataset was introduced in the paper "S. Dooms, T. De Pessemier and L. Martens. MovieTweetings: a Movie Rating Dataset Collected From Twitter. Workshop on Crowdsourcing and Human Computation for Recommender Systems, CrowdRec at RecSys 2013."|
@@ -137,3 +138,4 @@ The sample datasets are available under **Datasets**-**Samples** category. You c
 ## Next steps
 
 Learn the fundamentals of predictive analytics and machine learning with [Tutorial: Predict automobile price with the designer](tutorial-designer-automobile-price-train-score.md)
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