Add the following lines at the end: Remember to replace {YOUR_SPARK_DIRECTORY} with the directory where you unpacked Spark above. For example, breaking up your code into code cells that you can run independently will allow you to iterate faster and be done sooner. Sparkmagic works with a remote REST server for Spark, called livy, running inside the Hops cluster. #Add the label column , that basically corresponds to the has_subscribed column, #Show number of customers that have signed up term deposit vs those that did not. It will pick the default branch youve set in GitHub and set it as base and head branch. More options will appear as shown in figure VectorAssembler is used to assemble the feature vectors. We will create a dataframe and then display it. 'Customers which has subscribed to term deposit', "SELECT age, job, marital FROM campaign WHERE has_subscribed = 'yes'", # split into training(60%), validation(20%) and test(20%) datasets, #convert the categorical attributes to binary features. However, if you are proficient in Python/Jupyter and machine learning tasks, it makes perfect sense to start by spinning up a single cluster on your local machine. Java 8 works with UBUNTU 18.04 LTS/SPARK-2.3.1-BIN-HADOOP2.7, so we will go with that version. Clicking Start as shown There is another and more generalized way to use PySpark in a Jupyter Notebook: usefindSparkpackage to make a Spark Context available in your code. Below are some of the issues you might experience as you go through these that I also experienced. #start by the feature transformer of one hot encoder for building the categorical features, string indexer and one hot encoders transformers", # Combine all the feature columns into a single column in the dataframe, # Extract the "features" from the training set into vector format, Calculate accuracy for a given label and prediction RDD, labelsAndPredictionsRdd : RDD consisting of tuples (label, prediction), #map the training features data frame to the predicted labels list by index, # Predict training set with GMM cluster model, "==========================================", "GMM accuracy against unfiltered training set(%) = ", "GMM accuracy against validation set(%) = ", # Configure an machine learning pipeline, which consists of the, # an estimator (classification) (Logistic regression), # Fit the pipeline to create a model from the training data, #perform prediction using the featuresdf and pipelineModel, #compute the accuracy in percentage float, "LogisticRegression Model training accuracy (%) = ", "LogisticRegression Model test accuracy (%) = ", "LogisticRegression Model validation accuracy (%) = ", #you can create a pipeline combining multiple pipelines, #(e.g feature extraction pipeline, and classification pipeline), # Run the prediction with our trained model on test data (which has not been used in training). As with ordinary source code files, we should version them Spark is an open-source extremely fast data processing engine that can handle your most complex data processing logic and massive datasets. With Spark ready and accepting connections and a Jupyter notebook opened you now run through the usual stuff.
In this exercise we will be using the CrossValidator class provided by Spark within the pyspark.ml.tuning package: If replacement of missing values is required, we could use the Dataframe.fillna function (similar to pandas). # Set aspect ratio to be equal so that pie is drawn as a circle. Remember, Spark is not a new programming language you have to learn; it is a framework working on top of HDFS. When receiving the REST request, livy executes the code on the Spark driver in the cluster. You can view a list of all commands by executing a cell with %%help: Printing a list of all sparkmagic commands.
Our goal is to create model that can generalized well to the dataset and avoid overfitting. You will need the pyspark package we previously install. If the code includes a spark command, using the spark session, a spark job will be launched on the cluster from the Spark driver. We will create a logistic regression model where the model makes predictions by applying the logistic function. La bonne gestion et le dploiement dalgorithmes au niveau de votre organisation permettra dactionner des gains de productivit. - A model is trained using k-1 of the folds as training data Keep in mind that once youve enabled Git, you will no longer be able to see notebooks stored in HDFS and vice versa. The steps to do plotting using a pyspark notebook are illustrated below. Install Apache Spark; go to theSpark download pageand choose the latest (default) version. Livy is an interface that Jupyter-on-Hopsworks uses to interact with the Hops cluster. Copyright 2020 Logical Clocks AB. Regardless of the mode, Git options are the same. ','blue-collar','entrepreneur','housemaid', 'management','retired','self-employed','services','student','technician','unemployed','unknown', 'basic.4y','basic.6y','basic.9y','high.school', 'illiterate','professional.course','university.degree','unknown'. If you already have spark installed, continue reading. Click on the JupyterLab button to start the jupyter notebook server. MLlib offers standardScaler aspart of the pyspark.mllib. Then click on Generate new token. When you run a notebook, the jupyter configuration used is stored and attached to the notebook as an xattribute. This means that if a dependency of Jupyter is removed or an incorrect version is installed it may not work properly.
For example if Jupyter cannot start, simply click the Finally hit the Generate token button. MLLib supports the use of Spark dataframe for building the machine learning pipeline. Thanks toPierre-Henri Cumenge,Antoine Toubhans,Adil Baaj,Vincent Quagliaro, andAdrien Lina. Python 3.4+ is required for the latest version of PySpark, so make sure you have it installed before continuing.
When you create a Jupyter notebook on Hopsworks, you first select a kernel. Fortunately, Spark provides a wonderful Python API called PySpark. I also encourage you to set up avirtualenv. So far throughout this tutorial, the Jupyter notebook have behaved more or less identical to how it does if you start the notebook server locally on your machine using a python kernel, without access to a Hadoop cluster. # A CrossValidator requires an Estimator, a set of Estimator ParamMaps, and an Evaluator. Click on the plus button to create a new branch to commit your changes and Spark, a kernel for executing scala code and interacting with the cluster through spark-scala, PySpark, a kernel for executing python code and interacting with the cluster through pyspark, SparkR, a kernel for executing R code and interacting with the cluster through spark-R. You can also easily interface with SparkSQL and MLlib for database manipulation and machine learning. you previously run is selected, you will see options to view the previously run configuration or start jupyter server The following procedure is followed for each of the k folds: It will be much easier to start working with real-life large clusters if you have internalized these concepts beforehand. When the python/scala/R or spark execution is finished, the results are sent back from livy to the pyspark kernel/sparkmagic. your modifications will be lost. While using Spark, most data engineers recommends to develop either in Scala (which is the native Spark language) or in Python through completePySpark API. Soyou are all set to go now! Update PySpark driver environment variables: add these lines to your~/.bashrc(or~/.zshrc) file.
If you are doing Machine Learning you should pick the Experiments tab.
Expand the Advanced configuration and enable Git by choosing GITHUB or GITLAB, here we use GitHub. Other dimensionality reduction techniques available include PCA and SVD. Just make sure that you have your plotting libraries (e.g matplotlib or seaborn) installed on the Jupyter machine, contact a system administrator if this is not already installed. #Build a list of pipelist stages for the machine learning pipeline. List of supporting clustering techniques out of the box by Spark currently are: Lets train a Gaussian mixture model and see how it performs with our current featureset. To avoid overfitting, it is common practice when training a (supervised) machine learning model to split the available data into training, test and validation sets. Thosecluster nodes probably run Linux. Jupyter Notebookis a popular application that enables you to edit, run and share Python code into a web view. Learn the most important concepts, Learn how to use Python Virtual Environments, Fire up Jupyter Notebook and get ready to code, Start your local/remote Spark Cluster and grab the IP of your spark cluster. Navigate into a Project and head over to Jupyter from the left panel. Before installing pySpark, you must have Python and Spark installed. with the Jupyter logs as shown in the The advantage of the pipeline API is that it bundles and chains the transformers (feature encoders, feature selectors etc) and estimators (trained model) together and make it easier for reusability. For instance, as of this writing python 3.8 does not support pyspark version 2.3.2. For Restart your terminal and launch PySpark again: Now, this command should start a Jupyter Notebook in your web browser. For example, if I created a directory ~/Spark/PySpark_work and work from there, I can launch Jupyter. If you wish to, you can share the same secret API key with However like many developers, I love Python because its flexible, robust, easy to learn, and benefits from all my favoriteslibraries. This is because: Spark is implemented on Hadoop/HDFS and written mostly in Scala, a functional programming language that runs on a Java virtual machine (JVM). set up an Ubuntu distro on a Windows machine, there are cereal brands in a modern American store, Turn your Python script into a command-line application, Analyze web pages with Python requests and Beautiful Soup, It offers robust, distributed, fault-tolerant data objects (called, It is fast (up to 100x faster than traditional, It integrates beautifully with the world of machine learning and graph analytics through supplementary packages like. Create a new Python [default] notebook and write the following script: I hope this 3-minutes guide will help you easily getting started with Python and Spark.
Notebooks
To install Spark, make sure you haveJava 8 or higher installed on your computer. By default all files and folders created by Spark are group writable (i.e umask=007). However for CSV, it requires to use additional Spark Package. all the members of a Project. Thats why Jupyter is a great tool to test and prototype programs. I am working on a detailed introductory guide to PySpark DataFrame operations. You can To learn more about Python vs. Scala pro and cons for Spark context, please refer to this interesting article:Scala vs. Python for Apache Spark. The two last lines of code print the version of spark we are using. store encrypted information accessible only to the owner of the secret. We will be using the out-of-the-box MLlib featurization technique named one hot encoding to transform such categorical features into a feature vectors consist of binary 0s and 1s. If you havent yet, no need to worry. However, unlike most Python libraries, starting with PySpark is not as straightforward as pip installand import. Give a name to the secret, paste the API token from the previous step and finally click Add. Take a backup of .bashrc before proceeding. First copy the web URL of your repository from GitHub or GitLab. If the variables in the feature vectors has too huge of scale difference, you might like to normalize it with feature scaling. After you have started the Jupyter notebook server, you can create a pyspark notebook from the Jupyter dashboard: When you execute the first cell in a pyspark notebook, the spark session is automatically created, referring to the Hops cluster. #whether the customer signed up for the term deposit. findSpark package is not specific to Jupyter Notebook, you can use this trick in your favorite IDE too. https://spark.apache.org/docs/latest/api/python/_modules/pyspark/ml/classification.html. The label column currently is in string format. Import the libraries first. However, there is one main difference from a user-standpoint when using pyspark notebooks instead of regular python notebooks, this is related to plotting. As noted above, the "duration" column should not be used as part of the features as it wont be known till phone calls. brevity, here we use Python mode. You can do this either by taking one of the built-in tours on Hopsworks, or by uploading one of the example notebooks to your project and run it through the Jupyter service.
Run: It seems to be a good start! The profile setup for this IPyThon notebook allows PySpark API to be called directly from the code cells below. This exercise will go through the building of a machine learning pipeline with MLlib for classification purpose. # Make predictions on test observations and print results.
If you havent install spark yet, go to my article install spark on windows laptop for development to help you install spark on your computer. To do this, use the sparkmagic %%local to access the local pandas dataframe and then you can plot like usual. It is wise to get comfortable with a Linux command-line-based setup process for running and learning Spark. Jupyter notebooks have become the lingua franca for data scientists. Hopsworks Enterprise Edition comes with a feature to allow users to version their notebooks with Git and interact with remote repositories such as It looks something like this. To do so, configure your $PATH variables by adding the following lines in your ~/.bashrc(or~/.zshrc) file: You can run a regular jupyter notebook by typing: Lets check if PySpark is properly installed without using Jupyter Notebook first. You can select to start with classic Jupyter by clicking on You distribute (and replicate) your large dataset in small, fixed chunks over many nodes, then bring the compute engine close to them to make the whole operation parallelized, fault-tolerant, and scalable.
The below articles will get you going quickly. It has has visualization libraries such as matplotlib, ggplot, seaborn etc For first time users of IPython notebook, the code cells below can be run directly from this note book either by pressing the "play" icon on the top , or by hitting CTRL+ Enter key.
For example, the attribute of "marital", is a categorical feature with 4 possible values of 'divorced','married','single','unknown'. Start a new spark session using the spark IP and create a SqlContext.
When using Jupyter on Hopsworks, a library called sparkmagic is used to interact with the Hops cluster. # We use a ParamGridBuilder to construct a grid of parameters to search over. Here you can see which version of Spark you haveand which versions of Java and Scala it is using. https://spark.apache.org/docs/latest/api/python/pyspark.ml.html. Next, the kernel kernel sends the code as a HTTP REST request to livy. I am using Python 3 in the following examples but you can easily adapt them to Python 2. Some of the headings has been renamed for clarity purpose. What you can do however, is to use sparkmagic to download your remote spark dataframe as a local pandas dataframe and plot it using matplotlib, seaborn, or sparkmagics built in visualization. Apache Sparkis a must for Big datas lovers. For the purpose of this guide it will be GitHub. In addition to having access to a regular python interpreter as well as the spark cluster, you also have access to magic commands provided by sparkmagic. That's it! Click on the Notebook Configuration button to view the previous used configuration. https://spark.apache.org/docs/latest/api/python/pyspark.ml.html#pyspark.ml.tuning.CrossValidator
Downloading the spark dataframe to a pandas dataframe using %%sql, Downloading the spark dataframe to a pandas dataframe using %%spark.
These will set environment variables to launch PySpark with Python 3and enable it tobe called from Jupyter Notebook. From within JupyterLab you can perform all the common git operations such as diff a file, commit your changes, see the history of your branch, Now, add a long set of commands to your .bashrc shell script. Currently theres the following options of spark-csv by Databricks guys, and pyspark_csv. It is widely used in data science and data engineering today. Jupyter is provided as a micro-service on Hopsworks and can be found in the main UI inside a project. By working with PySpark and Jupyter Notebook, you can learn all these concepts without spending anything. number of contacts performed during this campaign and for this client (numeric, includes last contact), number of days that passed by after the client was last contacted from a previous campaign (numeric; 999 means client was not previously contacted), number of contacts performed before this campaign and for this client (numeric), outcome of the previous marketing campaign, employment variation rate - quarterly indicator (numeric), consumer price index - monthly indicator (numeric), consumer confidence index - monthly indicator (numeric), euribor 3 month rate - daily indicator (numeric), number of employees - quarterly indicator (numeric), has the client subscribed a term deposit? If you're usingWindows, you canset up an Ubuntu distro on a Windows machine using Oracle Virtual Box.
In this exercise we will be using the CrossValidator class provided by Spark within the pyspark.ml.tuning package: If replacement of missing values is required, we could use the Dataframe.fillna function (similar to pandas). # Set aspect ratio to be equal so that pie is drawn as a circle. Remember, Spark is not a new programming language you have to learn; it is a framework working on top of HDFS. When receiving the REST request, livy executes the code on the Spark driver in the cluster. You can view a list of all commands by executing a cell with %%help: Printing a list of all sparkmagic commands.
Our goal is to create model that can generalized well to the dataset and avoid overfitting. You will need the pyspark package we previously install. If the code includes a spark command, using the spark session, a spark job will be launched on the cluster from the Spark driver. We will create a logistic regression model where the model makes predictions by applying the logistic function. La bonne gestion et le dploiement dalgorithmes au niveau de votre organisation permettra dactionner des gains de productivit. - A model is trained using k-1 of the folds as training data Keep in mind that once youve enabled Git, you will no longer be able to see notebooks stored in HDFS and vice versa. The steps to do plotting using a pyspark notebook are illustrated below. Install Apache Spark; go to theSpark download pageand choose the latest (default) version. Livy is an interface that Jupyter-on-Hopsworks uses to interact with the Hops cluster. Copyright 2020 Logical Clocks AB. Regardless of the mode, Git options are the same. ','blue-collar','entrepreneur','housemaid', 'management','retired','self-employed','services','student','technician','unemployed','unknown', 'basic.4y','basic.6y','basic.9y','high.school', 'illiterate','professional.course','university.degree','unknown'. If you already have spark installed, continue reading. Click on the JupyterLab button to start the jupyter notebook server. MLlib offers standardScaler aspart of the pyspark.mllib. Then click on Generate new token. When you run a notebook, the jupyter configuration used is stored and attached to the notebook as an xattribute. This means that if a dependency of Jupyter is removed or an incorrect version is installed it may not work properly.
For example if Jupyter cannot start, simply click the Finally hit the Generate token button. MLLib supports the use of Spark dataframe for building the machine learning pipeline. Thanks toPierre-Henri Cumenge,Antoine Toubhans,Adil Baaj,Vincent Quagliaro, andAdrien Lina. Python 3.4+ is required for the latest version of PySpark, so make sure you have it installed before continuing.
When you create a Jupyter notebook on Hopsworks, you first select a kernel. Fortunately, Spark provides a wonderful Python API called PySpark. I also encourage you to set up avirtualenv. So far throughout this tutorial, the Jupyter notebook have behaved more or less identical to how it does if you start the notebook server locally on your machine using a python kernel, without access to a Hadoop cluster. # A CrossValidator requires an Estimator, a set of Estimator ParamMaps, and an Evaluator. Click on the plus button to create a new branch to commit your changes and Spark, a kernel for executing scala code and interacting with the cluster through spark-scala, PySpark, a kernel for executing python code and interacting with the cluster through pyspark, SparkR, a kernel for executing R code and interacting with the cluster through spark-R. You can also easily interface with SparkSQL and MLlib for database manipulation and machine learning. you previously run is selected, you will see options to view the previously run configuration or start jupyter server The following procedure is followed for each of the k folds: It will be much easier to start working with real-life large clusters if you have internalized these concepts beforehand. When the python/scala/R or spark execution is finished, the results are sent back from livy to the pyspark kernel/sparkmagic. your modifications will be lost. While using Spark, most data engineers recommends to develop either in Scala (which is the native Spark language) or in Python through completePySpark API. Soyou are all set to go now! Update PySpark driver environment variables: add these lines to your~/.bashrc(or~/.zshrc) file.
If you are doing Machine Learning you should pick the Experiments tab. Expand the Advanced configuration and enable Git by choosing GITHUB or GITLAB, here we use GitHub. Other dimensionality reduction techniques available include PCA and SVD. Just make sure that you have your plotting libraries (e.g matplotlib or seaborn) installed on the Jupyter machine, contact a system administrator if this is not already installed. #Build a list of pipelist stages for the machine learning pipeline. List of supporting clustering techniques out of the box by Spark currently are: Lets train a Gaussian mixture model and see how it performs with our current featureset. To avoid overfitting, it is common practice when training a (supervised) machine learning model to split the available data into training, test and validation sets. Thosecluster nodes probably run Linux. Jupyter Notebookis a popular application that enables you to edit, run and share Python code into a web view. Learn the most important concepts, Learn how to use Python Virtual Environments, Fire up Jupyter Notebook and get ready to code, Start your local/remote Spark Cluster and grab the IP of your spark cluster. Navigate into a Project and head over to Jupyter from the left panel. Before installing pySpark, you must have Python and Spark installed. with the Jupyter logs as shown in the The advantage of the pipeline API is that it bundles and chains the transformers (feature encoders, feature selectors etc) and estimators (trained model) together and make it easier for reusability. For instance, as of this writing python 3.8 does not support pyspark version 2.3.2. For Restart your terminal and launch PySpark again: Now, this command should start a Jupyter Notebook in your web browser. For example, if I created a directory ~/Spark/PySpark_work and work from there, I can launch Jupyter. If you wish to, you can share the same secret API key with However like many developers, I love Python because its flexible, robust, easy to learn, and benefits from all my favoriteslibraries. This is because: Spark is implemented on Hadoop/HDFS and written mostly in Scala, a functional programming language that runs on a Java virtual machine (JVM). set up an Ubuntu distro on a Windows machine, there are cereal brands in a modern American store, Turn your Python script into a command-line application, Analyze web pages with Python requests and Beautiful Soup, It offers robust, distributed, fault-tolerant data objects (called, It is fast (up to 100x faster than traditional, It integrates beautifully with the world of machine learning and graph analytics through supplementary packages like. Create a new Python [default] notebook and write the following script: I hope this 3-minutes guide will help you easily getting started with Python and Spark.
Notebooks To install Spark, make sure you haveJava 8 or higher installed on your computer. By default all files and folders created by Spark are group writable (i.e umask=007). However for CSV, it requires to use additional Spark Package. all the members of a Project. Thats why Jupyter is a great tool to test and prototype programs. I am working on a detailed introductory guide to PySpark DataFrame operations. You can To learn more about Python vs. Scala pro and cons for Spark context, please refer to this interesting article:Scala vs. Python for Apache Spark. The two last lines of code print the version of spark we are using. store encrypted information accessible only to the owner of the secret. We will be using the out-of-the-box MLlib featurization technique named one hot encoding to transform such categorical features into a feature vectors consist of binary 0s and 1s. If you havent yet, no need to worry. However, unlike most Python libraries, starting with PySpark is not as straightforward as pip installand import. Give a name to the secret, paste the API token from the previous step and finally click Add. Take a backup of .bashrc before proceeding. First copy the web URL of your repository from GitHub or GitLab. If the variables in the feature vectors has too huge of scale difference, you might like to normalize it with feature scaling. After you have started the Jupyter notebook server, you can create a pyspark notebook from the Jupyter dashboard: When you execute the first cell in a pyspark notebook, the spark session is automatically created, referring to the Hops cluster. #whether the customer signed up for the term deposit. findSpark package is not specific to Jupyter Notebook, you can use this trick in your favorite IDE too. https://spark.apache.org/docs/latest/api/python/_modules/pyspark/ml/classification.html. The label column currently is in string format. Import the libraries first. However, there is one main difference from a user-standpoint when using pyspark notebooks instead of regular python notebooks, this is related to plotting. As noted above, the "duration" column should not be used as part of the features as it wont be known till phone calls. brevity, here we use Python mode. You can do this either by taking one of the built-in tours on Hopsworks, or by uploading one of the example notebooks to your project and run it through the Jupyter service.
Run: It seems to be a good start! The profile setup for this IPyThon notebook allows PySpark API to be called directly from the code cells below. This exercise will go through the building of a machine learning pipeline with MLlib for classification purpose. # Make predictions on test observations and print results.
If you havent install spark yet, go to my article install spark on windows laptop for development to help you install spark on your computer. To do this, use the sparkmagic %%local to access the local pandas dataframe and then you can plot like usual. It is wise to get comfortable with a Linux command-line-based setup process for running and learning Spark. Jupyter notebooks have become the lingua franca for data scientists. Hopsworks Enterprise Edition comes with a feature to allow users to version their notebooks with Git and interact with remote repositories such as It looks something like this. To do so, configure your $PATH variables by adding the following lines in your ~/.bashrc(or~/.zshrc) file: You can run a regular jupyter notebook by typing: Lets check if PySpark is properly installed without using Jupyter Notebook first. You can select to start with classic Jupyter by clicking on You distribute (and replicate) your large dataset in small, fixed chunks over many nodes, then bring the compute engine close to them to make the whole operation parallelized, fault-tolerant, and scalable.
The below articles will get you going quickly. It has has visualization libraries such as matplotlib, ggplot, seaborn etc For first time users of IPython notebook, the code cells below can be run directly from this note book either by pressing the "play" icon on the top , or by hitting CTRL+ Enter key.
For example, the attribute of "marital", is a categorical feature with 4 possible values of 'divorced','married','single','unknown'. Start a new spark session using the spark IP and create a SqlContext.
When using Jupyter on Hopsworks, a library called sparkmagic is used to interact with the Hops cluster. # We use a ParamGridBuilder to construct a grid of parameters to search over. Here you can see which version of Spark you haveand which versions of Java and Scala it is using. https://spark.apache.org/docs/latest/api/python/pyspark.ml.html. Next, the kernel kernel sends the code as a HTTP REST request to livy. I am using Python 3 in the following examples but you can easily adapt them to Python 2. Some of the headings has been renamed for clarity purpose. What you can do however, is to use sparkmagic to download your remote spark dataframe as a local pandas dataframe and plot it using matplotlib, seaborn, or sparkmagics built in visualization. Apache Sparkis a must for Big datas lovers. For the purpose of this guide it will be GitHub. In addition to having access to a regular python interpreter as well as the spark cluster, you also have access to magic commands provided by sparkmagic. That's it! Click on the Notebook Configuration button to view the previous used configuration. https://spark.apache.org/docs/latest/api/python/pyspark.ml.html#pyspark.ml.tuning.CrossValidator
Downloading the spark dataframe to a pandas dataframe using %%sql, Downloading the spark dataframe to a pandas dataframe using %%spark.
These will set environment variables to launch PySpark with Python 3and enable it tobe called from Jupyter Notebook. From within JupyterLab you can perform all the common git operations such as diff a file, commit your changes, see the history of your branch, Now, add a long set of commands to your .bashrc shell script. Currently theres the following options of spark-csv by Databricks guys, and pyspark_csv. It is widely used in data science and data engineering today. Jupyter is provided as a micro-service on Hopsworks and can be found in the main UI inside a project. By working with PySpark and Jupyter Notebook, you can learn all these concepts without spending anything. number of contacts performed during this campaign and for this client (numeric, includes last contact), number of days that passed by after the client was last contacted from a previous campaign (numeric; 999 means client was not previously contacted), number of contacts performed before this campaign and for this client (numeric), outcome of the previous marketing campaign, employment variation rate - quarterly indicator (numeric), consumer price index - monthly indicator (numeric), consumer confidence index - monthly indicator (numeric), euribor 3 month rate - daily indicator (numeric), number of employees - quarterly indicator (numeric), has the client subscribed a term deposit? If you're usingWindows, you canset up an Ubuntu distro on a Windows machine using Oracle Virtual Box.