Microsoft Azure Machine Learning Studio is one of the platforms for creating predictive analytics models using machine learning. Azures cloud platform allows users to develop and initiate modeling without the need for hefty onsite computers. Its ability to access much of the Azure ecosystem, as well as the ease of use of its web interface, make Azure ML Studio an excellent option for quickly setting up your first machine learning experiment.

ML studio provides users with the following components to create machine learning projects:

• Data Sources
• Experiments
• Trained Models
• Web Services
• Notebooks

A simple machine learning project can be made using these components and configured with a few clicks.

Data Sources

ML studio features numerous data source options, many of which integrate with Azure.

• Blob storage
• SQL Database
• Cosmos DB
• Web URL via HTTP
• Hive Query
• Local File

For this project, I needed past weather and climate data; I used data from the national centers for environmental information website. I will be using a .csv file with a file structure like the one below:

Sample rows of the dataset

The Experiment

In my free time, I enjoy running, but I don’t have a lot of time in my day, so I set up this experiment to look at my average runs per week and predict the likelihood that I will run in the future.

To build the experiment, users drag, drop, and connect machine learning operations into a working model. The experiment map I made below lets us look at the components I used to create this experiment.

The canvas of the entire experiment

RunData20182019.csv

This is my dataset, uploaded from my local computer. Includes data on weather by day, and if I ran, or not.

Select Columns in the dataset

If there are any columns in your dataset that you know will not help predict a result, you can remove them from future calculations. In the screenshot below, I am selecting precipitation, average wind speed, max and min temperature, and if I ran or not on a given day. I also added a calculated column, that indicates if I ran the preceding two days, as I usually don’t run three days in a row. This factor is unrelated to the weather but heavily influences my results. I excluded the name of the station and the date because those won’t yield much information about the weather itself.

Excluding columns Station and Date from model

Split Data

After the dataset is trimmed down to only the necessary features, I use the split data block to divide the data into a training set and a testing set. When the model runs, it will detect patterns in the training set, and then apply the patterns to the testing set, to see if there was a good fit of the model used.

Splitting the dataset into a training set and testing set

Two-Class Boosted Decision Tree

For this algorithm, I’ve chosen to use the Two-Class Boosted Decision tree. I elected to use this model as it is a fast and reliable algorithm for predicting on which day each class belongs. Since my dataset was small, I was not worried about it being one of the more memory intensive approaches. In setting up the model, I left the default properties as is.

Settings for the Two-Class Boosted Decision tree

Summary of ML algorithms provided by Microsoft

Train Model

After selecting the algorithm, the next step was training the model. Here, selecting the desired output column is the only configuration step needed. Since I was predicting, “will I run?” I chose the “Run” column.

Train model interface for selecting the output column

Score and Evaluate Model

Now that training and testing within the model are complete, let’s evaluate it! There 149 data points in the testing set, with 38 days that I went for a run. The model reported with 85.9% accuracy, with a tendency to say I won’t run more often than I will. For the first attempt with a simple model, I am happy with these results.

The Results

After creating an initial experiment, I can go a few ways with Azure ML. I can try to further fine tune my model, by testing other algorithms or even adding in different algorithms for improved feature selection. Once I am fully satisfied with the model, Azure makes it easy to create a web service that will allow a user to query and classify new data points instantly. Microsoft provides sample code for querying the service in C#, R, and python. Using ML Studio and the auto-generated web service, you can have a business ready machine learning model up and running in no time!

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To access the query editor, navigate to the home tab, and click on Edit Queries in the External Data section.

Renaming Columns

Renaming columns in Power BI is a best practice, as it makes working with the dataset easier. Many database columns often have longer names that only make sense within the context of the database. If so, right click on the column header, select Rename, and type in a user-friendly title! There is also a rename option under the Transform tab in the Any Column section.

Creating a Custom Column

If the data points aren’t in the exact format required, creating a custom column is a great option. With this feature, you can combine columns into one, and even conditionally create new ones based on data values. For instance, below I am combining the City and State columns into one. In the second image, I am creating a column that classifies the data point as either “Large Opportunity,” “Medium Opportunity,” or “Small Opportunity” based on its amount value. To access these features, click on the Add Column tab along the top menu, and create the column that best fits your needs.

Filtering Data

Depending on your needs, there may be data points that are irrelevant to the dashboard you are creating. Rather than filter them out on each visual, the Query Editor can filter them out as soon as the data gets to Power BI. Click on the arrow by the column header, and deselect the options that are not applicable to your situation.

Grouping Data

While Power BI is smart enough to group data in visuals across aggregations like Sum, Average, and Maximum, the Query Editor provides an option to group the data explicitly. Simply select the grouping column, and the aggregation details and the data will group as expected.

Combining Datasets

If you are pulling in similar data from multiple sources, Power BI allows you to append the tables together easily. If the tables already have the same columns, and you’re looking to operate similar SQL’s Union, you can use the append feature, seen below. If you are trying to imitate SQL’s Join, the Query Editor has an option to Merge Queries, which will work just as a Join operation would.

Pulling it all Together

As you perform each one of these steps, Power BI tracks the exact changes you’ve made. You can see these changes queuing on the left of the Query Editor. If you are a more technical user, you can also view all these query modifications in the advanced editor, where you can make modifications by changing the text of the query steps themselves.

After you’ve applied all your data transformations, you’ll have a dataset ready to be morphed into a shiny dashboard!

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SharePoint wikis have a wide range of uses. Here at DMC, we use wiki pages to host our Standard Operating Procedures (SOPs). 

Our SOPs often contain links to other SharePoint pages. Recently, we reorganized our SharePoint taxonomy, otherwise known as a scheme of classification. During this process, some site’s base URLs changed which caused some of the links in our wiki to become outdated.

With over 700 wiki pages, manually validating every link in each page was out of the question. To solve this problem, we developed a program using SharePoint’s client object model that replaced every outdated link with an updated one.

Client Object Model Background

The SharePoint client object model (CSOM) gives developers access to SharePoint data without requiring direct access to the server. CSOM is commonly used for updating lists and list items. In this demonstration, we will update a list of wiki pages.

Demonstration

Below is example code for updating the text of wiki pages using the client object model, which can be used for accessing SharePoint sites, lists, and items without direct access to the server. Explanations for the code are written in-line with comments.

1. Set up a connection to the SharePoint site, and load the list to edit.

using Microsoft.SharePoint.Client;

var site = new ClientContext("http://contoso.com/");

site.Load(site.Web);

List wikiList = site.Web.Lists.GetByTitle("Wiki List");
site.Load(wikiList);

site.ExecuteQuery();

using Microsoft.SharePoint.Client;

var site = new ClientContext("http://contoso.com/");

site.Load(site.Web);

List wikiList = site.Web.Lists.GetByTitle("Wiki List");
site.Load(wikiList);

site.ExecuteQuery();

2. Run a query for list items. Including fields in the VIewFields tag ensures that they are returned from the Caml Query. If you would like to filter your results before they come back, add parameters to the Wheresection.

CamlQuery camlQuery = new CamlQuery();

camlQuery.ViewXml = @"
<View>
    <Query>
        <Where>
        </Where>
    </Query>
    <ViewFields>
        <FieldRef Name='WikiField' />
    </ViewFields>
</View>";

ListItemCollection items = wikiList.GetItems(camlQuery);

site.Load(items);
site.ExecuteQuery();

CamlQuery camlQuery = new CamlQuery();

camlQuery.ViewXml = @"
<View>
    <Query>
        <Where>
        </Where>
    </Query>
    <ViewFields>
        <FieldRef Name='WikiField' />
    </ViewFields>
</View>";

ListItemCollection items = wikiList.GetItems(camlQuery);

site.Load(items);
site.ExecuteQuery();

3. Iterate through each wiki page and look for the text that will be replaced. If the text is found, replace it with the new text. Update the item and save the wiki page.

foreach (ListItem item in items)
            {
                if (item["WikiField"] != null)
                {
                    String wikiContent = item["WikiField"].ToString();
                    if (wikiContent.Contains("href=\"http://contoso.com/sites/OldSite/"))
                    {
                        wikiContent = wikiContent.Replace(
                            "href=\"http://contoso.com/sites/OldSite/",
                            "href=\"http://contoso.com/sites/NewSite/");

                        item["WikiField"] = wikiContent;
                        item.Update();
                        site.ExecuteQuery();

                    }
                }
            }

foreach (ListItem item in items)
            {
                if (item["WikiField"] != null)
                {
                    String wikiContent = item["WikiField"].ToString();
                    if (wikiContent.Contains("href=\"http://contoso.com/sites/OldSite/"))
                    {
                        wikiContent = wikiContent.Replace(
                            "href=\"http://contoso.com/sites/OldSite/",
                            "href=\"http://contoso.com/sites/NewSite/");

                        item["WikiField"] = wikiContent;
                        item.Update();
                        site.ExecuteQuery();

                    }
                }
            }

That’s it! After running this program, every wiki page in the list will have an updated URL, saving you the time and effort of manually replacing each one.

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