The Step by Step Azure Machine Learning is good Idea Part–3

In the previous post , we discussed about different algorithm types which help us in Machine Learning. Now, In this post, we will see how to work with Azure Machine learning studio.

So, let’s start with Step by Step how to use Microsoft Azure Machine learning.

Step1 :- You have to login into Microsoft Azure portal with your credential if you have not created yet then below is the link by which you can create your Azure account.

Step1
Step 2:- Click on Portal option of Machine learning as shown below. Here you have to click on “Create AN ML WORKSPACE” when you click on this option you will find the below the screen. You need to add workplace name and select the server location.

Step2
Once you done with above steps, you will find the below screen

Step3

Step 4:- You can open Azure ML studio by clicking Open Studio icon or you can click directly login into https://studio.azureml.net 

Once you successfully logged in you will get the following screen.

Step4

In this screen, you will see different options like existing experiments created by you, samples by Microsoft etc.

Step 5:- You can check existing samples as well which are on different data analysis algorithms.

step5

Step 6:-  But, let’s not go with existing sample as we will try to create something from scratch.  For that when you click on new Experiment then you get the following screen.

There is 3 main part of the studio screen. In Left panel, you will find different data sources , filters, transformation,models,Options. You just need to drag-drop on the central panel which will be our working area where as the right side panel will show the properties.

Blank_Experiment_Template

for our test, we can use  already uploaded dataset or upload new dataset on which we want to create the model. In this example, we are using Adult Census income which is already available in dataset repository of Azure Studio.

Although, we can download same data from http://archive.ics.uci.edu/ml/datasets/Census+Income as well  and upload to Azure Studio back. If we open the link you will find different attributes like  age, sex, marital status, race country  etc.

Before going further, let’s understand  what we are going to do?

So, we are going to  predict the income and determine/predict  whether the income is more than $50,000  or not. The training model  is based on the known inputs like age,education, job type, marital status, race and  number of hours worked per week.

Just drag drop the data source (dataset) on the central panel.

Choose_Data_Source

You can visualize the data by right click on the central circle of each control. You can check data anytime in any step.

DataVisualizationtion

Step 7:- Now, this dataset  might content some missing data values and for better result or prediction we will clean these data. To achieve this we need to drag-drop the Missing data control and provide dataset inputs. Here from the property panel you can set any value and replace it with the missing data value.

In this example, we will set the value to 0 (as shown in the figure). Now the output of this missing value is a proper dataset on which we can do the analysis.

Indiandotnet_CleanMissing_Data

step 8: Once we have proper dataset then our first goal is to split the data using split control . Here splitting data divide our dataset into 2 part

Indiandotnet_Split_Data_ML

1) Training data :- With the help of this data we will train the Machine

2) Validation data :- This data is used to check the accuracy of prediction which we will apply.

We can set the ratio of these two parts by the properties.

Step 9:- Here  we need to apply the algorithm. Assuming the prediction result set we are going to use Two-class classification model. If you see in the left panel there are many classifications. For this example Two-class Boosted decision tree.

Indiandotnet_two_class_Boosted_Decision

Step 10: Now, we have data with us on this data, we have to apply the algorithm and teach the model how to evaluate. for this, we will drag drop the “Train Model” control from left panel to our designer panel .

Indiandotnet_Train_Model

Here we need to configure “Train Model” . For this just provide the output of Split data (Training data output) to the Train model and also output of two class Boosted decision control’s output to train model. Once you set both the control then your screen will look like as below

Indiandotnet_TrainModel_1

Step 10:- Now once we have provided the input data to Train Model next thing is to  provide the name of the column which we have to predict. for this click on Train Model. Check the right panel and select the column which you need to predict  by clicking Launch column selector.

Indiandotnet_Select_Column

Step 11:- So far we are good, we have created the trained model now it’s time to score the model and cross-check the accuracy of the algorithm which we have applied. To evaluate the model we will drag drop a Score Model from the left panel and connect with Train model and the other validation data output of split data. Once you are done with this just press save button and run the model. if everything works well then there would be a right checkbox appear on control.

Indiandotnet_Score_Model

Step 12:- Once everything with green checkbox check as shown in the figure .

Indiandotnet_Saved_Score_Model

We will check data visualization by right clicking on score model.

Indiandotnet_Score_Model_Visualization

You will find 2 additional columns which are Scored Label & Score probabilities. These two columns provide scored value and probability percentage

Indiandotnet_Score_Card_Visualization2

Although, we can have different methods as well to achieve the same task and we can evaluate the result of these different methods by one more control which is Evaluate Module.  We have to drag-drop this module from left panel to designer and provide the Score model’s  output to the evaluate module.

Although, we have  only one training model with two class method but we can create another train model as well as compare them by providing output of the model to Evaluate module.

Indiandotnet_Evaluate_Model

It provides a set of curves and metrics which we need to understand and choose the best train model if we are evaluating two train model.

Indiandotnet_Graph

Till now we have created a model. here are not going to save the model as a trained model and publish it we try to expose this model using a web service.

Indiandotnet_Web_Service_Input

Step 13- Add output of web service dragging and dropping the web service output control and configure as shown below figure.

Indiandotnet_Web_Service_Input_Output_ML

Step  14:- In the next step, we will publish the web service with the below button of the designer when you click on publish web service you will get the following screen after completion of the step.

Indiandotnet_Default_Api_Deployed

Here just click on Test operation and you are good to go with Azure Machine learning experience.

I hope you will try yourself. Please , keep sharing your inputs.

Enjoy

RJ

The Step by Step Azure Machine Learning is good Idea Part–1

Machine learning is not new in the market but nowadays  it is a buzz word everywhere. You might realize that there are lots of things happening in the Machine Learning.

Many big companies like Microsoft, Oracle, IBM,SAP and many other working in this area. They have provided Azure Machine Learning,Oracle Advanced Analytics, IBM SPS, SAP Predictive Analysis tools to work on it.

Before jumping into Azure Machine Learning directly let’s first understand basic of Machine Learning what exactly it is.

So, Machine learning is a way to understand the data pattern , recognize it and predict accordingly for future.  It helps in

1) Data Mining

2) Language Processing

3) Image recognition

and many other Artificial Intelligence  related stuff.

I know above statement is bit bookish so let me explain in Indiandotnet style .

Let’s say you are a teacher in  a school and you have quite experience in teaching. In each year you teach many students you also keep previous years data and some sort of basic detail of students.

When parent’s come to meet and wants to know the progress and whether he/she will pass in graduation or not. You simply do data analysis in your mind  whether that student doing study  or not (obvious), what his/her percentage in the last couple of exams or internal assessments , how he/she performed in previous class etc., then you give your prediction to the parents that their child does good or bad in the final exam or not.

Now, suppose instead of you there is a computer and parents asking the same question to the computer Now, a computer should provide the same answer as you give accurately or might be better.

For this, we need to feed enough data sample in the computer. If he has previous data samples by which he can analysis and predict accurately.

This overall exercise of processing data is part of Machine learning.

So, firstly you have to train the computer with providing the initial data which we can say training data. This is an iterative process.

 

Although, Machine learning Is more than this. Here, we are showing some more example where machine learning can help

1) Detecting  fraud credit card

2) Determine SPAM emails

3) Provide customer like to switch to competitor

4) Free text when typing etc. many more examples of machine learning.

There is 2 distinction in machine learning

 

1) Supervised Machine Learning :-

The Supervised learning means the value you want to predict is already exist in training data. Means the data already exist in the computer so data is labeled. The accuracy is high in such case.

 

2) Unsupervised machine Learning :-

So It is just opposite to Supervised Machine Learning. In this the predictive data not present in training data.

I hope now we have a basic understanding of Machine Learning. In next post, I will share step by step example of Azure machine learning.

 

Please, provide your inputs.