Calling external web services from CRM Online Sandboxed plugin

I have seen this question many times – Can you call external endpoints from within a plugin running inside Sandbox of Dynamics CRM Online?

Recently I was riddled with the same situation where the sandbox did not allow me to call an external endpoint.

On the positive note, I was able to overcome this issue with a little tweak and I thought it might be useful to share with the community.



Say we need to call a JSON based web service from within a CRM plugin


Code that would not work

var client = new HttpClient();
client.DefaultRequestHeaders.Authorization = new AuthenticationHeaderValue( "Bearer", wsKey);
client.BaseAddress = new Uri("<your ws url>");
//Say jsonBody is typed object
HttpResponseMessage response = await client.PostJsonAsync("", jsonBody);

if (response.IsSuccessStatusCode)
        string result = await response.Content.ReadAsStringAsync();
        var typedResult= JsonConvert.DeserializeObject<Results>(result);      


Modified Code that will work

var client = new HttpClient();
client.DefaultRequestHeaders.Authorization = new AuthenticationHeaderValue( "Bearer", wsKey);
client.BaseAddress = new Uri("<your ws url>");
//Rather than using a typed object, construct the JSON object manually using strings
string jsonBody =  "{\"Inputs\": {\"input1\": {\"ColumnNames\": [\"AnnualReview\",\"Category\"],........";

//Rather than using PostJsonAsync use PostAsync
 HttpResponseMessage response = await client
                   .PostAsync("", new StringContent(jsonBody , Encoding.UTF8, "application/json"))

if (response.IsSuccessStatusCode)
        string result = await response.Content.ReadAsStringAsync();
		//Rather than using DeserializeObject, parse the json string manually
        var parsingResp = ((string)result).ParseWSResponse();



In nutshell, my experience has been that you can call external services as long as you stick to base .NET classes that come packaged with the framework out of the box.

Can Dynamics CRM understand images? Yes! Using deep learning.

Machine Learning is quite a buzzword these days and we have witnessed how quickly Microsoft and other vendors have made progress in this area. Just couple of years back Microsoft had no product or tool in this space and today they have closer to a dozen. Recently Microsoft has integrated Machine Learning into SQL Server and Dynamics CRM, it is slowly becoming core to its product line.

I would not be surprised if machine learning becomes a mandatory skill for most of the development jobs in the next decade.

How Image Recognition can help CRM?

Attaching documents is a common feature asked for in many CRM projects where customers can complete an application form and then upload scanned copies to support their application. Think of invoices, receipts, certificates, licenses, etc. As of now there is no way that Dynamics CRM can detect if the scan that a customer is uploading is a picture of a license, or beach or a car.

What if Dynamics CRM can detect and recognise the scanned image and tell the user that it is expecting a license not a Dilbert on the beach.


Source: Ol.v!er [H2vPk] – Flickr

Wouldn’t it be great?

Although there are some Image engines that can tell you what an uploaded picture contains but there isn’t any engine or tool (as per my knowledge) that can tell whether an upload document is a license or not. This is because there are only subtle differences between scanned copies of various documents.

In this blog series I will build and demonstrate an approach to have this kind of image recognition capability with our favourite Dynamics CRM and we will use a branch of machine learning called Deep Learning that is very good at tasks related to Computer Vision. I would not be delving into the concepts of Deep Learning (there are numerous posts and videos on the internet) but will try to cover the major building block in this whirlwind tour.

Australian Identity Documents

I will take a real business case which is ubiquitous in many online applications in Australia where a customer is asked to provide a scan of their Australian ID as a proof. For our blog we will use the following Australian IDs

1) Victoria Driver’s License



Courtesy: VicRoads


2) Australian Visa




3) Medicare card



Courtesy: Medicare

Note: Because of their sensitive nature I would only be exposing sample documents in this blog

The expectation is that the system can tell if the user is attaching a scanned copy of their Australian Visa when the record type is Australian Visa. So we will validate the image based on its content.

Good thing about deep learning based systems is that the detection algorithms do not rely on exact colour, resolution and placement but rather on pattern and feature matching. I got pretty good results when I built this system which I will share in later posts.

Technical Setup

Deep Learning based systems use a concept of neural networks to train themselves and to perform their tasks. There are many kinds of neural networks and the one that does the job for us is the Convolutional Neural Network. CNNs are good at image related tasks.

In order to train a CNN from scratch you need lot of hardware and computing power and I do not have that. So I will be using a partially trained network and customise it for our specific task i.e. to identify the images of those 3 types of Australian IDs.

Let us cover the building blocks of our solution

TensorFlow TM

TensorFlow is an open source framework for Deep Learning and we will be using it to train our engine.


TensorFlow comes in many platforms but we will use its Python version.

Dynamics 365

Once our model is trained we will deploy it online as web service and CRM can query that. I would not be posting the integration code here as I have already posted code to integrate Dynamics CRM with Machine Learning web services in my other blog


Let us start by training an image recognition model that can classify an image e.g. a scanned copy and tell if it is an Australian ID e.g. driving license or visa scan, etc.


We will use an approach called Transfer Learning. In this approach you take an existing Convolutional Neural Network and retrain its last few layers. Think of it this way that you have already got a network that can detect differences of aeroplane from a dog but you need to retrain it to pick more subtle differences i.e. the difference between a scanned invoice and a scanned passport.

TensorFlow is based on the concept of a tensor which is a mathematical vector that contains the features of an image. We will grab the penultimate layer of tensors and retrain it with some sample images of a Medicare card, an Australian Visa and Victoria’s Driver license.

Once the model is trained we will use a simple Support Vector Machine classify and predict the likelihood of the uploaded image to be an Australian ID. The output of the SVC classifier will a predicted class along with a likelihood probability e.g.

(Visa, 0.83)

Model thinks 83% the image is that of an Australian Visa

(Medicare, 0.89)

89%, it is a Medicare

(License, 0.45)

45% it is a license

If the confidence percentage is low it means that image is not in the class of our interest e.g. in the last example the uploaded image is most likely not a license. As a rule of thumb, a probability of 0.80 is good mark for the prediction to be reliable.

Training Pool

Below are the screenshots of the samples that I used as a training for my image classification model. As you can see images differ in terms of angles, positioning, colours, etc. system can still learn based on important properties and disregard irrelevant properties.

Australian Visa

Training Set



Training Set


Victoria Driver’s License

Training Set


Training Phase

The training procedure involves categorising all the training images into a folder which is a named after their class. As you can see in the screenshots above, the windows folders are named after the class i.e. DriversLicense, Medicare and Visa

We then iterate over all these images and pass them to the penultimate layer of TensorFlow which gives us a feature tensor (a 2048 dimensional array of that image), we then label the image with its respective class.

Support Vector Machine

Once we have the feature tensor and label of every image, our training dataset is complete and we feed it to a Support Vector Machine and train the model. To save time, I pickled the model so that it can be reused for all predictions.

I know some of this terminology may be new to you but in the next post I will explain the architecture and some sample code that generates the predictions. Then it will start falling in place. See you then.

Part 3

In the previous two instalments I have been explaining the image recognition system that I built to recognise Australian IDs and discussed how our traditional CRM can benefit from such intelligent capabilities.

In this post I will cover the Architecture and share some sample code



As you can see above there are basically two major pillars of the system

A) Python

B) CRM ecosystem

Python is used to build the model using TensorFlow, then the compiled version of the trained model is deployed to an online webservice that should be able to accept binary contents like image data.

On the CRM ecosystem side, user can upload the image in a web portal or directly from CRM based on the scenario, then we need to pass it to the model and get the score.

Source Code

Below is an excerpt of the source code from one of the unit tests that will give you glimpse of what happens under the hood on Python side of the fence. This is just one class for introductory purposes, not the entire source code.

import os

import pickle

import sklearn

import numpy as np

from sklearn.svm import SVC

import tensorflow as tf

import tensorflow.python.platform

from tensorflow.python.platform import gfile

model_dir = 'inception'

def CreateImageGraph():

#Get the tensorflow graph

with gfile.FastGFile(os.path.join(

model_dir, 'classify_image_graph_def.pb'), 'rb') as f:

graph_def = tf.GraphDef()


_ = tf.import_graph_def(graph_def, name='')

def ClassifyAustralianID(image):

nb_features = 2048

#Initialise the feature tensor

features = np.empty((1,nb_features))


with tf.Session() as sess:

next_to_last_tensor = sess.graph.get_tensor_by_name('pool_3:0')

print('Processing %s...' % (image))

if not gfile.Exists(image):

tf.logging.fatal('File does not exist %s', image)

image_data = gfile.FastGFile(image, 'rb').read()

#Get the feature tensor

predictions =,{'DecodeJpeg/contents:0': image_data})

features[0,:] = np.squeeze(predictions)

clear = '\n' * 20


return features

if __name__ == '__main__':

#Unpickle the trained model

trainedSVC = pickle.load(open('Trained SVC','rb'))

#Path to the image to be classified

unitTestImagePath = 'Test\\L5.jpg'

#Get feature tensor of the image

X_test = ClassifyAustralianID(unitTestImagePath)

print("Trying to match the image at path %s.....",unitTestImagePath)

#Get predicted probabilities of various classes


#Get predicted class


#Choose the item with the best probability

bestProb = y_predict_prob.argsort()[0][-1]

#Print the predicted class along with its probability

print("(%s, %s)" % (y_predict_class, y_predict_prob[0][bestProb]))

The purpose of the above stub is to test the prediction class ClassifyAustralianID with a sample image L5.jpg which is below. As we can see it is a driving license.


Running this image against the model gives us this output


It means the model says, it is 93% sure that the input image matches the Driving License class. In my testing I found anything above 80% was the correct prediction

i.e. the confidence percentage for the below images was low because they do not belong to one of our classes (Drivers License, Visa or Medicare), which is the expected output


Closing Notes

Image recognition is a field of budding research and getting a lot of attention these days because of driverless cars, robots, etc. This little proof of concept gave me a lot of insight into how things work behind the scenes and it was a great experience to create such a smart system. The world of machine learning is very interesting!!

Hope you enjoyed the blog.

Part 2 – Bot Framework

The recently released Bot Framework equips us with the basic plumbing that is required for chat sessions and making connections with services like LUIS. Some of the key features of Bot Builder SDK include

· Support for both C# and Node.js

· Open source on Github

· Conversation support – Prompts, Dialog and Rulesets for form flows

· Chat emulator – a client for testing

· Connector to Cognitive services like LUIS

Once you have the prerequisites discussed in the previous part, you can create a new bot project from Visual Studio by going

File > New > Project > Bot Application

The project setup is based on WebAPI / MVC style routing and you need to implement a message controller. Below is a screenshot of the source code for the bot


Handling messages

The main entry point of the bot framework is the MessagesController as shown below

public class MessagesController : ApiController
public virtual async Task<HttpResponseMessage> Post([FromBody] Activity
// check if activity is of type message
if (activity != null && activity.GetActivityType() ==
await Conversation.SendAsync(activity, () => new InsuranceDialog());
return new HttpResponseMessage(System.Net.HttpStatusCode.Accepted);

The controller is secured by the BotAuthentication decoration that secures the bot’s endpoint, then we are checking the incoming message to ensure it is of type message and initiate a dialog called InsuranceDialog. The dialog then passes the message to LUIS to determine the customer’s intent and generates a reply accordingly. We will dig in more details of LUIS in the next blog.


Replies from the bots are posted back on the chat screen using some of the common methods described below

context.PostAsync("Hi there. Welcome to BestPrice.");

Above line shows how to post a basic message back to the user

ResumeTypeOptionsAsync, options, "Let us know what are you interested in?");

Here we are using a dialog class which not only posts a message with options but also listens to the user’s input i.e. the option they chose.

PromptDialog.Confirm(context, HandleInsuranceOptions,"Do you want to know about 
our insurance?"
,"Didn't get that!");

This is an example of a confirm message where we expect a Yes or No from the user

Using the Channel Emulator

One of most useful application for such projects is the Channel Framework Emulator which is a client you use to unit test your bots. It can connect to both online and locally deployed bot apps. You need to ensure that AppId and Secret you use in this app are the ones your bot app uses i.e. the ones in its web.config. Below is a screenshot of our bot being tested locally. Let us meet in the next blog post where we explore LUIS.


Build a Chatbot for Dynamics CRM– Part 1

“Chatbots are about taking the power of human language and applying it more pervasively to our computing.”

Satya Nadella

We have seen an age of mobile phone apps, and guess what is coming next? Chatbots. To acknowledge their soaring growth and to leverage on this business opportunity, at this year’s Build conference, Microsoft has released a full framework to build bots. It is called the Bot Framework.

Microsoft is not alone in the game, Facebook and Amazon have released their bot platforms as well, and the developer base is growing at an astonishing pace. Technology is making huge leaps in Natural Language Processing, with Google just having open-sourced their NLP parser and Microsoft having enriched their Language platform LUIS (Language Understanding Intelligent Service). These advancements coupled with the capability to build chatbots presents an incredible opportunity for developers and the businesses alike. A proof of their popularity is this statistic that says since last year bots have outnumbered humans on the internet. So not only they are a raging trend but also a hot market.

But what does all this mean for businesses? Put simply, organisations will be able to leverage Conversation as a platform where they can deploy intelligent chatbots to serve their customers. The equation of return on investment is quite attractive too based on a survey finding that the average cost of a customer transaction via phone is around $2.50 and, the average cost of a digital transaction (online or on mobile) is only around $0.17.  It is not all doom and gloom though, there will still be lot of human element required to fill up what bots lack, at least for the foreseeable future.

I decided to give the Microsoft’s bot platforms a whirl to check how easy it is to build a basic chat-bot. Through this blog series, I will walk you through the process of building a chat-bot that may interact with Dynamics CRM and can optionally be deployed using Microsoft portals. We will use two spanking new platforms released recently as a part of Microsoft’s Cognitive Suite: Bot Framework and LUIS. Before we start building, let us first understand how bots fit into the ecosystem.


We will use the setup outlined in the above diagram. The bot will be primarily built on the Bot Framework using .NET (Node.js is also supported) and it will interact with LUIS to parse the natural language and try to understand what the customer means.

There will be three more parts to this series and I will also link the source code of the working bot in the last part

Part 1 – Introduction

Part 2 – Bot Framework

Part 3- LUIS

Part 4 – Chatbot Integration and Deployment

Let us layout the scenario to understand what we are building.


Say we are an insurance company called BestPrice and we are deploying a chatbot that customers can converse with to know about our products and to register their interest. The bot will pass some of the conversations to LUIS to determine customer’s intent. Three intents will be used for this demo

Greeting – Conversation is just a greeting like hi, hello, etc.

Enquire – The customer wants to enquire about our insurance products

Engage – Customers wants us to engage with them


In order to setup the project you need to have the following prerequisites

1. Bot Framework VS template

2. Bot Framework Channel Emulator

3. Bot Framework dlls (via Nuget)

4. A developer account with Bot Framework

5. A developer account with LUIS with subscription key

6. Once the bot is deployed online it needs to be registered with bot framework

You can read more about the above prerequisites here or search them online

In the next instalment we will start building the bot and go through some of key building blocks.

Use Machine Learning to predict customers you might lose – Part 4

So far we have seen how a Dynamics CRM integration can be connected to Azure ML to receive the predictions. Once we got the integration going there is no dearth of possibilities. You may like to build an alert / flagging functionality that can alert a Customer Service rep to contact a customer if their predictors are indicating that they might churn. You may incorporate predictions into exec reporting so that the execs are aware of the churn trends and make decisions to minimise churn.


One of the things I discussed at the start of this series was to be able to get some insights into the key drivers of customer churn e.g. how do you know which features are most likely to cause churn. Answering such questions begins with analysing your data, few starting points can be

1. From your data find out what fields change with respect to the Churn variable e.g. does the churn rate increase as the income of the customer goes up or is it dependent on their usage?

There are measures like correlation, covariance, entropy, etc that can help you answer such questions.

2. Find the distribution of your data and identify any outliers e.g. check if there is a skew in the data or if the classes are unbalanced. You may need to apply some statistical techniques like variance, standard deviation to have a better platform to delve into some of these insights.

Azure Machine Learning does provide some modules straight off the bat that can make the job easier e.g. it has the following modules

Compute Elementary Statistics

Compute Linear Correlation

Getting advanced insights can be tricky based on your algorithm or setup of the experiment (project). But there are ways e.g. with bit of a Python code you can produce a decision rule tree below. The last label in the box class= {LEAVE, STAY} tells us if the customer will churn based on what path they fall under


Above is the automatically generated insight that tells us that overage is most important variable in deciding customer churn. If overage exceeds 97.5 then a customer is more likely to churn, this does not mean that every customer whose overage is more than 97.5 will churn nor does it mean that whose overage is less than that will stay. It is just that Overage is the strongest indicator of churn based on our data.

We can even derive decision rules from insights like these e.g. customers with overage less than 97.5 and Leftover minutes less than 24.5 minutes are most likely to stay. On the contrary customers with overage more than 97.5 and average income more than $100059.5 are most likely to leave.

Here is another one that shows the impact of House Value, Handset Value and other features on the churn


Once decision rules have been identified based on the above insights, policies can be made to retain such customers who are at risk of churn e.g. give them discounts, offer them a change of plan, prize them with loyalty offerings, etc.

Where to from here?

Hopefully by now you appreciate the potential of machine learning and recognise the opportunity it provides when it is complemented with traditional information systems like CRMs, ERPs and Document Management systems. The field of machine learning is enormous and sometimes quite complex too as it based on scientific techniques and mathematics. You need to understand and lot of theory if you need to get into the black box i.e. how machine learning does what it does?

But great thing about using Azure Machine Learning suite is that it makes entry into machine learning easier by taking care of the complexities and giving you an easy-to-understand and easy-to-use environment. You have full control over the data structure and algorithms used in your project. It can be tuned as per the needs of your organisation to receive the best possible results.

For example you can tune the example I provided in the following different ways

1. Rather than going with Random Forest you can choose Support Vector Machines or Neural Networks and compare the results.

2. You are not restricted to Javascript, you can call the web services from a plugin. That way in a data migration scenario, while you are importing data you can set the prediction scores as the data is being imported

3. You can also change the threshold of confidence percentage to ignore the predictions score where confidence is less than a certain amount.

So there are lot of possibilities. Hope you enjoyed the series.

Happy CRM + ML!!

Use Machine Learning to predict customers you might lose – Part 3

Cruising through our machine learning journey and starting from where we left in the previous instalment, the next step is to expose our machine learning model as a Web service so that it can be invoked from within Dynamics CRM.

Azure Machine Learning has this fantastic concept of converting an experiment into a trained model. Trained model is like a compiled version of your experiment that can be exposed via a web service, all from the click of just one button i.e. Setup Web Service


Azure ML takes care of the rest by deploying the model. Once deployed you can inspect its configuration by going to the Web Services section as shown here


In order to connect to this web service from within Dynamics CRM, we can use the below JavaScript. We can pass CRM objects to this service in JSON format and get prediction results back

sendRequest: function (avgIncome,overAge,leftOver,houseVal,handsetVal,longCalls) {

var service = AzureScript.getRequestObject();

var url = "";

var jsonObject =


"Inputs": {

"input1": {

"ColumnNames": [














"Values": [


















"GlobalParameters": {}


var dataString = JSON.stringify(jsonObject);

if (service != null) {"POST", url, false);

service.setRequestHeader("X-Requested-Width", "XMLHttpRequest");

service.setRequestHeader("Authorization", "Bearer xxxxxxxxx==");

service.setRequestHeader("Accept", "application/json");

service.setRequestHeader("Content-Type", "application/json; charset=utf-8");

service.setRequestHeader("Content-Length", dataString.length);


//Recieve result

var requestResults = eval('(' + service.responseText + ')');

try {

resultOutput = requestResults.Results.output1.value.Values[0]

return resultOutput;


catch (err) {

console.log('Unable to interpret result');




Let us prepare Dynamics CRM to start consuming this web service. I have created an event onSave() of the Telecom Customer form which passes the relevant data to the Azure Service and gets the score. The Javascript for that is as below

function onFormSave() {

//Prepare data - only fields with high Information gain

var houseValue = Xrm.Page.getAttribute('manny_housevalue').getValue();

var income = Xrm.Page.getAttribute('manny_income').getValue();

var longcalls = Xrm.Page.getAttribute('manny_longcalls').getValue();

var overage = Xrm.Page.getAttribute('manny_overage').getValue();

var phonecost = Xrm.Page.getAttribute('manny_phonecost').getValue();

var leftOver = Xrm.Page.getAttribute('manny_leftover').getValue();

var valOutput = AzureScript.sendRequest(income, overage, leftOver, houseValue, phonecost, longcalls);

if (valOutput != null && valOutput[0]!=null && valOutput[1]!=null) {





var prob = parseFloat(valOutput[1]);

if(prob>=0 && prob<=1.0)




getRequestObject: function () {
 /// Get an instance of XMLHttpRequest for all browsers
 if (XMLHttpRequest) {
 // Chrome, Firefox, IE7+, Opera, Safari
 // ReSharper disable InconsistentNaming
 return new XMLHttpRequest();
 // ReSharper restore InconsistentNaming
 // IE6
 try {
 // The latest stable version. It has the best security, performance,
 // reliability, and W3C conformance. Ships with Vista, and available
 // with other OS's via downloads and updates.
 return new ActiveXObject('MSXML2.XMLHTTP.6.0');
 } catch (e) {
 try {
 // The fallback.
 return new ActiveXObject('MSXML2.XMLHTTP.3.0');
 } catch (e) {
 alertMessage('This browser is not AJAX enabled.');
 return null;


These scripts are trivial and should be self-explanatory. Basically we are passing the highly correlated features to the prediction service and getting two outputs

Prediction score -> assigned to-> manny_predictedchurnstatus

Prediction confidence -> assigned to -> manny_predictionconfidencepercentage

And they are displayed on the form like this, it’s integrated i.e. the moment you change the data the score is updated.


In the next blog post, we will touch upon the Insights that can be gained from a machine learning integration

Use Machine Learning to predict customers you might lose – Part 2

Continuing our journey from the previous post where we defined the issue of churn prediction, in this instalment, let us create the model in Azure Machine Learning. We are trying to predict the likelihood of customer’s churn based on certain features in the profile which are stored in the Telecom Customer entity. We will use a technique called Supervised Learning, where we train the model on our data first and let us understand the trends before it can start giving us some insights.

Obviously you need access to Azure Machine Learning, once you log into it, you can create a new Experiment. That gives you a workspace designer and a toolbox (somewhat like SSIS/Biztalk) where you can drag control and the feed into each other. So it is a flexible model and for most tasks you do not need to write code.

Below is a screenshot of my experiment with toolbox on the left


Now machine learning is something which is slightly atypical for a usual CRM audience, I would not be able to fit full details of each of these tools in this blog but I will touch on each of these steps so that you can understand at high-level that what is going on inside these boxes. Let us address them one by one

Dynamics CRM 2016 Telecom

This module is the input data module where we are reading the CRM customer information in the form of a dataset. At the moment of writing the blog, there is no direct connection available from Azure Machine Learning to CRM online. But where there is a will, there is a way i.e. I discovered that you can connect to CRM using the following

1. You schedule a daily export of CRM data into a location that Azure Machine Learning can read e.g. Azure blob storage, Web Url over Http

2. You can write a small Python based module that connects to Dynamics using Azure Directory Services, the module can the pass the data to the Azure using a DataFrame control

From my experience having an automatic sync is not important from Dynamics to Azure ML but it is important the other way round i.e. Azure ML to Dynamics.

Split Data

This module basically splits your data into a two sets

1. Training dataset – The data based on which the machine learning model will learn

2. Testing dataset – The data based on which the accuracy of the model will be determined

I have chosen stratified split which ensures that the Testing dataset is balanced when it comes to classes being predicted. The split ratio is 80/20 i.e. 80% of the records will be used for training and 20% for testing.

Two-class Decision Forest

This is main classifier i.e. the module that does the grunt of the work. The classifier of choice here is a random forest with bootstrap aggregation. Two-class makes sense for us because our prediction has two outcomes i.e. whether the customer will churn or not.

Random forests are fast classifiers and very difficult to overfit, rather than taking one path they learn your data from different angles (called ensembles). Then in the end the scores of various ensembles are combined to come up with an overall prediction score. You can read more about this classifier here.

Train Model

This module basically connects the classifier to the data. As you can see in the screenshot of the experiment I posted above there are two arrows coming out of Spilt Data, the one of the left is the 80% one i.e. the training dataset. The output of this module is trained model that is ready to make predictions.

Score Model

This step uses the trained model from the previous step and tests the accuracy of the model against our test data. Put simply, here we start feeding the data to the model that it has not seen before and count how many number of times the model gave the correct prediction Vs wrong prediction.

Evaluate Model

The scores (hit vs miss) generated from the previous modules are evaluated in this step. In Data Science there are standard metrics to measure this kind of performance e.g. Confusion Matrices, ROC curves and many more. Below is the screenshot of the Confusion matrix


I know there is a lot of confusing details here (hence the name Confusion Matrix) but as a rule of thumb we need to focus on AUC i.e. area under the curve. As shown in the results above we have a decent 72.9% of the area under the curve (which in layman terms means percentage of correct predictions). Higher percentage does not necessarily equate to a better model, more often than not a higher percentage (e.g. 90%) means overfitting i.e. a state where your model does very well on the sample data but not so well on the real-world data. So our model is good to go.

You can read more about the metrics and terms above here

In the next blog we will deploy and integrate the model with Dynamics CRM.