![]() | inputlookup car_data.csv | eval dummy_ = 1 ![]() Using the data loaded earlier, we can use eval to create dummy variables for the ‘Seller_Type’,’Transmission’,’Fuel_Type’ columns. It splits each field into separate fields based on the unique number of categorical values available in that column.ġ – Splunk: Creating dummy variables using Eval Command It’s an eval function that can help create dummy variables for our categorical fields. ![]() Now that we have loaded some data lets go over the first useful command. After onboarding the csv file use the ‘| inputlookup car_data.csv’ command to view the data. I added the csv file to my $SPLUNK_HOME/etc/apps/Splunk_MLTK/lookups/ directory. Click here to download the car_dataset.csv file to follow this blog. I downloaded this dataset about predicting used-car prices based on several factors km_driven, field_type, seller_type etc from kaggle. Before we begin – Let’s onboard some data in Splunk It provided metrics that are commonly used in the Data Science world for model verification and performance. This was a much awaited upgrade in Splunk MLTK when it was released. It was released with MLTK version 4.0.0 and is packed with statistics such as AUC (Area Under the Curve) for classification models for model validation and ANOVA (Analysis of Variance) for linear regression models. One of my favourite features in Splunk MLTK is the ‘Score’ command. I attempt to highlight commands that have helped in some data science or analytical use-cases in this blog. With each new release of the Splunk or Splunk MLTK a catalog of new commands are available. This blog sheds light on some features and commands in Splunk Machine Learning Toolkit (MLTK) or Core Splunk Enterprise that are lesser known and will assist you in various steps of your model creation or development. The Splunk Machine Learning Toolkit is packed with machine learning algorithms, new visualizations, web assistant and much more.
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