Create an application to predict your insurance premium cost with AutoAI

As shown above, this application leverages machine learning models to predict your insurance charges, and helps the customer understand how smoking or decreasing your BMI affects insurance premiums.

As we see the value of gross insurance premiums worldwide continue to skyrocket past 5 trillion dollars, we know that most of these costs are preventable. For example, just by eliminating smoking, and lowering your BMI by a few points could mean shaving thousands of dollars off of your premium charges. In this application, we study the effects of age, smoking, BMI, gender, and region to determine how much of a difference these factors can make on your insurance premium. By using our application, customers see the radical difference their lifestyle choices make on their insurance charges. By leveraging AI and machine learning, we help customers understand just how much smoking increases their premium, by predicting how much they will have to pay within seconds.

Description

Using IBM AutoAI, you automate all the tasks involved in building predictive models for different requirements. You see how AutoAI generates great models quickly which save time and effort and aid in faster decision-making process. You create a model that from a data set that includes the age, sex, BMI, number-of-children, smoking preferences, region and charges to predict the health insurance premium cost that an individual pays.

When you have completed this code pattern, you understand how to:

• Setup, quickly, the services on IBM Cloud for building the model.
• Ingest the data and initiate the AutoAI process.
• Build different models using AutoAI and evaluate the performance.
• Choose the best model and complete the deployment.
• Generate predictions using the deployed model by making REST calls.
• Compare the process of using AutoAI and building the model manually.
• Visualize the deployed model using a front-end application.

Architecture Components

Flow Description

1. The user creates an IBM Watson Studio Service on IBM Cloud.
2. The user creates an IBM Cloud Object Storage Service and adds that to Watson Studio.
3. The user uploads the insurance premium data file into Watson Studio.
4. The user creates an AutoAI Experiment to predict insurance premium on Watson Studio
5. AutoAI uses Watson Machine Learning to create several models, and the user deploys the best performing model.
6. The user uses the Flask web-application to connect to the deployed model and predict an insurance charge.

Included components

• IBM Watson Studio - IBM Watson® Studio helps data scientists and analysts prepare data and build models at scale across any cloud.
• IBM Watson Machine Learning - IBM Watson® Machine Learning helps data scientists and developers accelerate AI and machine-learning deployment.
• IBM Cloud Object Storage - IBM Cloud™ Object Storage makes it possible to store practically limitless amounts of data, simply and cost effectively.

Featured technologies

• artificial-intelligence - Build and train models, and create apps, with a trusted AI-infused platform.
• Python - Python is an interpreted, high-level, general-purpose programming language.

Watch the Video

IBM Watson AutoAI Part 1/3: Data exploration and visualization

IBM Watson AutoAI Part 2/3: Running AutoAI

IBM Watson AutoAI Part 3/3: Connecting model API to a web-app

Prerequisites

This Cloud pattern assumes you have an IBM Cloud account. Go to the link below to sign up for a no-charge trial account - no credit card required.

• IBM Cloud account
• Python 3.8.2

Steps

0. Download the data set
1. Clone the repo
2. Explore the data (optional)
3. Create IBM Cloud services
4. Create and Run AutoAI experiment
5. Create a deployment and test your model
6. Create a notebook from your model (optional)
7. Run the application

Step 0. Download the data set

We will use an insurance data set from Kaggle. You can find it here. Click on the Download button, and you should see that you will download a file named insurance-premium-prediction.zip. Once you unzip the file, you should see insurance.csv. This is the data set we will use for the remainder of the example. Remember that this example is purely educational, and you could use any data set you want - we just happened to choose this one.

Step 1. Clone the repo

Clone this repo onto your computer in the destination of your choice:

git clone https://github.com/IBM/predict-insurance-charges-with-ai

This gives you access to the notebooks in the notebooks directory. To explore the data before creating a model, you can look at the Claim Amount Exploratory notebook, and create a IBM Cloud Object Storage service, and paste your credentials in the notebook to run it. This step is purely optional.

Step 2. Explore the data (optional)

If you want to run the notebook that is explored below, go to notebooks/Claim Amount Exploratory.ipynb.

• Within Watson Studio, you explore the data before you create any machine learning models. You want to understand the data, and find any trends between what you are trying to predict (insurance premiums charges) and the data's features.

• Once you import, you see the data into a data frame, and call the df_claim.head() function, you see the first 5 rows of the data set. You see the features to be age, sex, bmi, children, smoker, and region.

• To check if there is a strong relationship between bmi and charges you create a scatter plot using the seaborn and matplotlib libraries. You see that there is no strong correlation between bmi and charges, as shown below.

• To check if there is a strong relationship between sex and charges you create a box plot. You see that the average claims for males and females are similar, whereas males have a bigger proportion of the higher claims.

• To check if there is a strong relationship between being a smoker and charges you create a box plot. You see that if you are a smoker, your claims are much higher on average.

• Let's see if the smoker group is well represented. As you see, below, it is. There are around 300 smokers, and around 1000 non-smokers.

• To check if there is a strong relationship between being a age and charges you create a scatter plot. You see that claim amounts increase with age, and tend to form groups around 12,000, 30,000, and 40,000.

If you want to see all of the code, and run the notebook yourself, check the data folder above.

Step 3. Create IBM Cloud services

First login to your IBM Cloud account. Use the video below for directions on how to create IBM Watson Studio Service.

• After logging into IBM Cloud, click Proceed to show that you have read your data rights.

• Click on IBM Cloud in the top left corner to ensure you are on the home page.

• Within your IBM Cloud account, click on the top search bar to search for cloud services and offerings. Type in Watson Studio and then click on Watson Studio under Catalog Results.

• This takes you to the Watson Studio service page. There you can name the service as you wish. For example, one may name it Watson-Studio-trial. You can also choose which data center to create your instance in. The gif above shows mine as being created in Dallas.

• For this guide, you choose the Lite service, which is no-charge. This has limited compute; it is enough to understand the main functionality of the service.

• Once you are satisfied with your service name, and location, and plan, click on create in the bottom-right corner. This creates your Watson Studio instance.

• To launch your Watson Studio service, go back to the home page by clicking on IBM Cloud in the top-left corner. There you see your services, and under there you should see your service name. This might take a minute or two to update.

• Once you see your service that you just created, click on your service name, and this takes you to your Watson Studio instance page, which says Welcome to Watson Studio. Let's get started!. Click on the Get Started button.

• This takes you to the Watson Studio tooling. There you see a heading that says Start by creating a project and a button that says Create Project. Click on Create a Project. Next click on Create an Empty project.

• On the create a new project page, name your project. One may name the project - insurance-demo. You also need to associate an IBM Cloud Object store instance, so that you store the data set.

• Under Select Storage service click on the Add button. This takes you to the IBM Cloud Object Store service page. Leave the service on the Lite tier and then click the Create button at the bottom of the page. You are prompted to name the service and choose the resource group. Once you select a name, click the resource group Confirm button.

• Once you've confirmed your IBM Cloud Object Store instance, you are taken back to the project page. Click on refresh and you should see your newly created Cloud Object Store instance under Storage. That's it! Now you can click Create at the bottom right of the page to create your first IBM Watson Studio project :)

• Once you have created your Watson Studio Project, you see a blue Add to Project button on the top-right corner of your screen. Click on Add to Project and then select Data. This brings up a column on the right-hand side that says Data.

• In the Data column, click on browse to add data from a file. Go into where you downloaded your dataset from Step 0 and then navigate to the data folder, and then select insurance.csv.

• Watson Studio takes a couple of seconds to load the data, and then you should see the import has completed. To make sure it has worked properly, you can click on Assets on the top of the page, and you should see your insurance file under Data Assets.

Step 4. Create and Run AutoAI experiment

• Once you've created your project, click on the Add to project at the top-right of your Watson Studio project page. This pops up an image with different assets you can choose to add to your project. Click on AutoAI experiment.

• This takes you to a page which says New AutoAI experiment at the top-left. Name your experiment as you want. One may name it auto-ai-insurance-demo.

• Next, you need to add a Watson Machine Learning instance before you create the Watson AutoAI experiment. On the right side of the screen click on Associate a Machine Learning instance.

• Same as before, select the Lite Tier, and click on the Create button at the bottom of the page. Name your instance as you wish. One may name it named mine machine-learning-free. Choose the location and the resource group and then click on Confirm when you are happy with your instance details.

• Once you create your machine learning service, you are taken back to the new AutoAI experiment page. Click on Reload on the right side of the screen. You should see your newly created machine learning instance. Great job! Click on Create on the bottom right part of your screen to create your first AutoAI experiment!

• After you create your experiment, you are taken to a page to add a data source to your project. Click on Select from project and then add the insurance.csv file. Click on Select asset to confirm your data source.

• Next, you see that AutoAI processes your data, and you see a What do you want to predict section. Select the charges as the Prediction column.

• Next, let's explore the AutoAI settings to see what you can customize when running your experiment. Click on Experiment settings. First, you see the data source tab, which lets you omit certain columns from your experiment. You choose to leave all columns. You can also select the training data split. It defaults to 85% training data. The data source tab also shows which metric you
  optimize for. For the regression, it is RMSE (Root Mean Squared Error), and for other types of experiments, such as Binary Classification, AutoAI defaults to Accuracy. Either way, you can change the metric from this tab depending on your use case.

• Click on the Prediction tab from within the Experiment settings. There you can select from Binary Classification, Regression, and Multiclass Classification.

• Lastly, you can see the Runtime tab from the Experiment settings this shows you other experiment details you may want to change depending on your use case.

• Once you are happy with your settings, ensure you are predicting for the charges column, and click on the run Run Experiment button on the bottom-right corner of the screen.

• Next, your AutoAI experiment runs on its own. You see a progress map on the right side of the screen which shows which stage of the experiment is running. This may be Hyper Parameter Optimization, feature engineering, or some other stage.

• You have different pipelines that are created, and you see the rankings of each model. Each model is ranked based on the metric that you selected. In the specific case that is the RMSE(Root mean squared error). Given that you want that number to be as small as possible, you can see that in the experiment, the model with the smallest RMSE is at the top of the leaderboard.

• Once the experiment is done, you see Experiment completed under the Progress map on the right hand side of the screen.

• Now that AutoAI has successfully generated eight different models, you can rank the models by different metrics, such as explained variance, root mean squared error, R-Squared, and mean absolute error. Each time you select a different metric, the models are re-ranked by that metric.

• Let's pick RMSE as the experiment's metric. You see the smallest RMSE value is 4514.389, from Pipeline 8. Click on Pipeline 8.

• On the left-hand side, you can see different Model Evaluation Measures. For this particular model, you can view the metrics, such as explained variance, RMSE, and other metrics.

• On the left-hand side, you can also see Feature Transformations, and Feature Importance.

• On the left-hand side, click on Feature Importance. You can see here that the most important predictor of the insurance premium is whether you are a smoker or not-smoker. This is by far the most important feature, with bmi coming in as the second most important. This makes sense, given that many companies offer discounts for employees who do not smoke.

Step 5. Create a deployment and test your model

• Once you are ready to deploy one of the models, click on Save As at the top-right corner of the model you want to deploy. Save it as a Model. You show you how to save it as a notebook in step 6.

• Name your model as you want, one may name it Insurance Premium Predictor - Pattern Demo.

• Once you have finished saving it as a deployment, you see a green notification at the top right of your screen saying that your model has been successfully saved. Click on View in Project on that notification at the top-right corner of your screen.

• Next, you are taken to a screen that has the name of the model you just saved. Click on Deployments from the Tab in the middle of the screen.

• Next, click on the Add Deployment button on the right-side of the screen. Name your deployment as you want. One may name it demo-deployment and then click Save.

• On your saved model overview page, you should see your new deployment demo-deployment being initialized.

• Click on demo-deployment or whatever you named your deployment.

• It takes a few minutes for the deployment to be complete. Once it is complete - you see that a Test tab appears in the top of the screen. Click on the Test tab.

• Here you can test your model. Enter input data such as age, bmi, children, smoker and region, and then click the Predict button at the bottom of the screen.

• As you can see, the model predicted a premium of 4655, when you enter age 27, bmi: 22, children: 0, smoker: no, region: southwest.

• To validate the prediction, you check the data file that you used to train the model, and see a row that has similar inputs to what was inputted. You can find a male, 26 year old, with 0 children, non-smoker to get a premium of 3,900. This is relatively close to the model's prediction, so we know the model is working properly.

Step 6. Create a notebook from your model (optional)

If you want to run the notebook that you explore below, go to [`https://github.com/IBM/predict-insurance-charges-with-autoai/blob/master/notebooks/Insurance%20Premium%20Predictor%20-%20P8%20notebook.ipynb).

With AutoAI's latest features, the code that is run to create these models is no more a black box. One or more of these models can be saved as a Jupyter notebook and the Python code can be run and enhanced from within.

6.1 Create notebook

• Click on Save As at the top-right corner of the model, and click Notebook.

• This opens a new tab (be sure to enable pop-up for this website) titled New Notebook where in you can edit the default name if you choose to and then click on Create. This might take a few minutes to load for the first time.

• Alternatively, you can also create the notebook from the Pipeline leaderboard view (shown above) by clicking on the Save as option against the model you want to save followed by selecting Notebook. The steps are very similar to the first method discussed above.

6.2 Run notebook

• Once the notebook has been created, it is listed under the Notebooks section within the Assets tab.
• Clicking on the notebook from the list opens the Jupyter notebook where the code in Python is available.
• If the notebook is locked, click on the pencil icon on the right tab to be able to run/edit the notebook.
• Select Cell option from the menu list and click Run All. This begins executing all steps in a sequence. Unless an error is encountered, the entire notebook content is executed.

6.3 Analyse notebook content

While understanding the content within the notebook requires prior knowledge of machine learning using python, we encourage you to browse through this tutorial to learn the basics of how regression models are built in python.

In this step, you do a high-level analyses of the notebook that is generated.

• AutoAI uses sckikit-learn for creating machine learning models and for executing the steps in pipelines.

• autoai-lib is used to transform data while being processed in the pipeline.

• Following snippet highlights sample code of how auto-ai is used in transforming numerical data and how scikit-learn is used in setting these transformations in a pipeline.

• Here you see the Python code that went into setting up Random Forest as the algorithm of choice for regression.

• Calling the fit method on the pipeline, returns an estimator which is then used to predict a value. The code below shows each of these steps.

• Finally, the Python code that was generated to validate the results and analyse the model performance is seen below. KFold-cross validation techniques have been applied to evaluate the model. The notebook can also be edited to apply other validation techniques and can be re-evaluated.

More information on the implementation considerations of AutoAI can be found here

Step 7. Run the application

The driver code to run the application can be found under the web-app folder within the git repository that was cloned from Step 1. To run and test your deployed model through this Python-based user-interface, you need to replace the following information within web-app/app.py:

1. Your Watson Machine Learning (which is associated with this deployed model) Instance ID and apikey.
2. Your deployed model's deployment URL, so you can make a POST request.
3. Your IBM Cloud IAM token, to authorize yourself.

Now, you go into detail on how to gather these credentials. If you already know how to do this, you can skip the steps below, and go straight to running the application.

7.1 Get Watson Machine Learning Instance ID and apikey

• To get your Watson Machine Learning Instance ID and apikey first go to https://cloud.ibm.com/resources and then under Services click on the Watson Machine Learning instance that is associated with your Watson Studio AutoAI experiment.

• Once the Watson Machine Learning service page loads, click on service credentials in the left sidebar.

• From there, expand the Key Name by clicking on the down arrow.

• There, you find your apikey, and Instance ID keep these handy.

7.2 Get model deployment URL

• From inside Watson Studio, click on your project that you created.

• From there, click on the deployments tab from the top of the screen. Mine is called Insurance-Premium-Predictor.

• Next, click on Implementation from the tab at the top of the screen.

• Scroll down to Code Snippets and click on Python.

• Copy the deploymnentID from /deployments/*******deploymentID*******/predictions section and paste it into web-app/app.py on line 49 - to complete the POST request URL.

7.3 Generate the access token

• From the command line, type curl -V to verify if cURL is installed in your system. If cURL is not installed, refer to this instructions to get it installed.
• Execute the following cURL command to generate your access token, but replace the apikey with the apikey you got from step 7.1 above.

curl -k -X POST \
--header "Content-Type: application/x-www-form-urlencoded" \
--header "Accept: application/json" \
--data-urlencode "grant_type=urn:ibm:params:oauth:grant-type:apikey" \
--data-urlencode "apikey=123456789" \
"https://iam.bluemix.net/identity/token"

7.3 (Windows Users only) - Using Windows 10 and Powershell to generate the access token

• Install python.org Windows distro 3.8.3 from http://python.org - make sure to add the /python38/scripts folder path to the $PATH environment, if you do not, you will get errors trying to run flask (flask.exe is installed to the scripts folder)

• Remove powershell alias for curl and install curl from python3.8

PS C:/> remove-item alias:curl

PS C:/> pip3 install curl

• 3. Execute curl to get secure token from IBM IAM. Please note that the token expires after 60 minutes. If you get an internal server error from the main query page (The server encountered an internal error and was unable to complete your request. Either the server is overloaded or there is an error in the application), it may be due to the token expiring. Also note that in powershell the continuation character is ‘

curl -X POST `
	--header "Content-Type: application/x-www-form-urlencoded" `
	--header "Accept: application/json" `
	--data-urlencode "grant_type=urn:ibm:params:oauth:grant-type:apikey" `
	--data-urlencode "apikey= API KEY" `
	"https://iam.bluemix.net/identity/token"

7.4 Modify the 'web-app/app.py' file

• Copy and paste the access token into the header in the web-app/app.py file. Replace the line " TODO: ADD YOUR IAM ACCESS TOKEN FROM IBM CLOUD HERE" with your token.

• Lastly, input your Watson Machine Learning Instance ID right under where you put your access token. Replace the line TODO: ADD YOUR ML INSTANCE ID HERE with your instance ID from step 7.1 above.

• Great job! You are ready to run the application!

7.5 Install dependencies, and run the app

Note, this app is tested on this version of Python 3.8.2

Within the web-app directory, run the following command:

pip3 install flask flask-wtf urllib3 requests

Next, run the following command to start the flask application.

flask run

7.5 (Windows Users only) - Running the app using Windows 10 and Powershell

• Install flask and dependencies

PS C:/> pip3 install flask flask-wtf urllib3 requests

Verify modules have been installed in the 'python38/scripts' folder

• Run 'web-ap/app.py' from the local directory using flask

PS C:/> set FLASK_APP=app.py

PS C:/> flask run

7.6 Run application from browser

• Go to 127.0.0.1:5000 in your browser to view the application. Go ahead and fill in the form, and click on the Predict button to see your predicted charges based on your data.

• As is expected, if you are a smoker, this drastically increase the insurance charges.

Bonus Section - Visualize the data and share your findings via Cognos Dashboard Embedded.

• You can add a Dashboard which is a lean version of Cognos Dashboard available on IBM cloud from "Add to Project" option in your Watson Studio project.

• You can start finding patterns in your data by easily visualizing various data points. This can get your exploration started within few minutes and with no coding involved

• From visualizing this data you can see the relation in the data points, how Gender, BMI, # of children and smoking might influence the insurance premium.

• Dashboards are very interactive and makes it easy to play with data.

• You can also pivot and summarize your measures to quickly look at all your measures

• Stop working in Silos and share your findings with your team in two clicks.

Related Links

• Fraud Prediction Using AutoAI
• Use AutoAI to predict Customer Churn tutorial
• Predict Loan Default with AutoAI tutorial

License

This code pattern is licensed under the Apache Software License, Version 2. Separate third-party code objects invoked within this code pattern are licensed by their respective providers pursuant to their own separate licenses. Contributions are subject to the Developer Certificate of Origin, Version 1.1 (DCO) and the Apache Software License, Version 2.

Apache Software License (ASL) FAQ