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Jayendran Frequent Visitor
Frequent Visitor

Introduction

Google Location Tracking

We are all used Google location for whenever we travel into the new areas or find exactly where we are. So we simply turn on the location in our mobile. As soon as we turn on the location google will track our location with exact Latitude and Longitude. Infact google will track our lat and lon for every 3-5 seconds. Considering this amount of data for every one across the world, it's pretty big. 

 

In this article we will see how we visualize our own location data from Google using the Microsoft PowerBI Tool. So, this article is going to combine the power of two big Shots

  1. Google for Data
  2. Microsoft for Technology

Before diving into this article further let see how the final report will look like,

 

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ghamers Occasional Visitor
Occasional Visitor

Data preparation can go a long way in improving the results of machine learning models. Before getting started with AutoML in Power BI, take a moment to explore some data cleaning techniques with your data. All of the necessary tools you’ll need already exist in the Power BI ecosystem. 

 

Standardization (Relabeling) 

 

Imagine you have a text column describing college degrees, i.e. “Master’s Degree”, “Bachelor’s Degree”, etc. Depending on how the data entry was done, you might end up with values such as “M.A.”, “Masters”, and “Master’s Degree”, all meaning the same thing. By default, a machine learning model will make no assumptions about these fields being synonyms and end up treating them as unique entries. In most cases, it would be ideal if the model analyzed these varying entries the same way. 

 

Once your data is available as an entity in Power Query Online, you can remedy this discrepancy using the “Replace values” feature. To discover this functionality, simply right click your desired column header and select “Replace values”. Use the “Advanced” mode to match the entire contents of your column’s cells rather than the default partial matching. 

 

 

Standardization.PNG


Discretizing and Binning (Bucketing) 

 

With AutoML in PowerBI, it is possible to create a powerful prediction model if your entity has a True/False column. This is a column containing two distinct values indicating separate states. numeric column is the easiest type of column to convert to a True/False column. In Power Query Online, this conversion can be achieved by using a Conditional ColumnIn this example, imagine you have a numeric column of scores ranging from 0 to 100We can narrow this column to a True/False column by separating values above sixty and those below. After adding the conditional column, remember to set the column type of the new column to True/False by clicking the type icon next to the column header. 

 

 

Bucketing.PNG

 

 

Removing Outliers 

 

There are times when a numeric column may have entries that are largely different from the rest of the values in a column. In most cases, the presence of these outlier values provides little benefit for a machine learning model. Let’s say we define an outlier for a numeric column as a value falling outside two standard deviations above or below the median value of a columnIn this section we build upon the concept of using conditional columns and enhance it with a little extra Power Query magic. For your table entity, open the advanced editor: 
 

 

 

Outliers.PNG

 

 

Next locate the column for which you wish to remove outliers. In this case the column header is “Fare”. We will create two new variables to store the values of the column’s standard deviation as well as the median. 
 

 

#"Two Standard Deviations" = List.StandardDeviation(#"Changed column type"[Fare]) * 2, 
#"Medium Value" = List.Median(#"Changed column type"[Fare]), 

 

 

Now we will use a conditional column to identify an outlier by comparing the Fare value to median value plus or minus two times the standard deviation.  If the value falls outside of that range, then we set the value to the overall median value. 

 

 

#"Outliers Replaced" = Table.AddColumn(#"Changed column type", "New column", each if [Fare] < #"Medium Value" - #"Two Standard Deviations" then #"Medium Value" else if [Fare] > #"Medium Value" + #"Two Standard Deviations" then #"Medium Value" else [Fare]) 

 

 

When using this code snippet, simply replace “Fare” with the header name of your desired column. An example section of Power Query code to perform the outlier replacement follows: 

 

 

let 
    Source = Csv.Document(Web.Contents("<YOUR_CSV_SOURCE"), [Delimiter = ",", Columns = 12, QuoteStyle = QuoteStyle.None]),  
    #"Promoted headers" = Table.PromoteHeaders(Source, [PromoteAllScalars = true]),  
    #"Changed column type" = Table.TransformColumnTypes(#"Promoted headers", {{"Fare", type number}}),  
    #"Two Standard Deviations" = List.StandardDeviation(#"Changed column type"[Fare]) * 2,  
    #"Medium Value" = List.Median(#"Changed column type"[Fare]),  
    #"Outliers Replaced" = Table.AddColumn(#"Changed column type", "New column", each if [Fare] < #"Medium Value" - #"Two Standard Deviations" then #"Medium Value" else if [Fare] > #"Medium Value" + #"Two Standard Deviations" then #"Medium Value" else [Fare]) 
in 
    #"Outliers Replaced" 

 

 

With only these simple techniques at your disposal, you can start to build powerful, narrowed down machine learning models in Power BIKeep a watch out for more advanced techniques that will be covered in future posts. In the meantime, get started building models in Power BI today and get a feel for how a bit of data preparation can have a positive impact on the resulting model reports. 

 

 

Yasin Shtiui | Software Engineer II at Microsoft Power BI (Artificial Intelligence) team

Garrett Hamers | Software Engineer at Microsoft Power BI (Artificial Intelligence) team

Administrator jfeil
Administrator

Flex your skills – and gain new ones – at Microsoft Business Applications Summit, coming to Atlanta, Georgia June 10 – 11, 2019.

 

This is the place to dive deep with the tools you use every day, see the future of business applications before anyone else, and connect with our vibrant community. Registration is open – secure your spot today!

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Yuchen Visitor
Visitor

Power BI, in the latest release, added support for supervised automated machine learning.This means that Power BI can help predict ‘unknowns’ once it learns from the ‘known’ values.

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augustindelaf Member
Member

Capture.PNG

 

In any reporting project, access to data is not enough.
You have to access instantly to its explanations, its insights. Moreover, these insights must be contextual, that means they have to be related to the data they explain. This link between data and insights must be clear, obvious for the human eye, unequivocal.

For that, the ideal chart in Power BI is called the Pulse Chart.

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