It is time to take what you have learned about unsupervised learning and the AWS services and apply it to new situations. For this assignment, you will need to complete one of two (not both) challenges. Which challenge you take on is your choice. Just be sure to give it your all -- as the skills you hone will become powerful tools in your FinTech tool belt.

1. Create a new repository for this project called unit13-challenge. Do not add this homework to an existing repository.

2. Clone the new repository to your computer.

3. Inside your local git repository, create a directory for the challenge assignment you choose. Use folder names corresponding to the challenges: RoboAdvisor or ClusteringCrypto.

4. Add your solution files to this folder.

5. Push the above changes to GitHub or GitLab.

Photo by Alex Knight from Pexels | Free License

You were hired as a digital transformation consultant by one of the most prominent retirement plan providers in the country; they want to increase their client portfolio, especially by engaging young people. Since machine learning and NLP are disrupting finance to improve customer experience, you decide to create a robo advisor that could be used by customers or potential new customers to get investment portfolio recommendations for retirement.

In this homework assignment, you will combine your new Amazon Web Services skills with your already mastered Python superpowers, to create a bot that will recommend an investment portfolio for a retirement plan.

You are asked to accomplish the following main tasks:

1. Initial Robo Advisor Configuration: Define an Amazon Lex bot with a single intent that establishes a conversation about the requirements to suggest an investment portfolio for retirement.

2. Build and Test the Robo Advisor: Make sure that your bot is working and responding accurately along with the conversation with the user, by building and testing it.

3. Enhance the Robo Advisor with an Amazon Lambda Function: Create an Amazon Lambda function that validates the user's input and returns the investment portfolio recommendation. This task includes testing the Amazon Lambda function and making the integration with the bot.

• lambda_function.py
• correct_dialog.txt
• age_error.txt
• incorrect_amount_error.txt
• negative_age_error.txt

In this section, you will create the RoboAdvisor bot and add an intent with its corresponding slots.

Sign in into your AWS Management Console and create a new custom Amazon Lex bot. Use the following parameters:

• Bot name: RoboAdvisor
• Output voice: Salli
• Session timeout: 5 minutes
• Sentiment analysis: No
• COPPA: No
• Advanced options: No
• Leave default values for all other options.

Create the RecommendPortfolio intent, and configure some sample utterances as follows (you can add more utterances as you wish):

• I want to save money for my retirement
• I'm ​{age}​ and I would like to invest for my retirement
• I'm ​{age}​ and I want to invest for my retirement
• I want the best option to invest for my retirement
• I'm worried about my retirement
• I want to invest for my retirement
• I would like to invest for my retirement

This bot will use four slots, three using built-in types and one custom slot named riskLevel. Define the three initial slots as follows:

The riskLevel custom slot will be used to retrieve the risk level the user is willing to take on the investment portfolio. Create this custom slot as follows:

• Select the + icon next to 'Slot Types' in the 'Editor' on the left side of the screen.
• Choose create custom slot from the resulting display window.
• For Slot type name, type: riskLevel
• Select the radial dial button next to Restrict to Slot values and synonyms, then fill in the appropriate values and synonums. Example: Low, Minimal; High, Maximum.
• Click Add slot to intent when finished.

To format the response cards for the intent, click on the gear icon next to the intent as seen in the image below:

Next, input the following data in the resulting display window:

• Prompt: What level of investment risk would you like to take?
• Maximum number of retries: 2
• Prompt response cards: 4

Configure the response cards for the riskLevel slot as is shown bellow:

Card 1	Card 2

Card 3	Card 4

Note: You can download free icons from this website or you can use the icons provided in the Icons directory.

Move to the Confirmation Prompt section, and set the following messages:

• Confirm: Thanks, now I will look for the best investment portfolio for you.
• Cancel: I will be pleased to assist you in the future.

Leave the error handling configuration for the RecommendPortfolio bot with the default values.

In this section, you will test your Robo Advisor. To build your bot, click on the Build button in the upper right hand corner. Once the build is complete, test it in the chatbot window. You should see a conversation like the one below.

In this section, you will create an Amazon Lambda function that will validate the data provided by the user on the Robo Advisor. Start by creating a new lambda function from scratch and name it recommendPortfolio. Select Python 3.7 as runtime.

In the Lambda function, start by deleting the AWS generated default lines of code, then paste in the starter code provided in lambda_function.py and complete the recommend_portfolio() function by following these guidelines:

• The age should be greater than zero and less than 65.
• the investment_amount should be equal to or greater than 5000.

Once the intent is fulfilled, the bot should response with an investment recommendation based on the selected risk level as follows:

• none: "100% bonds (AGG), 0% equities (SPY)"
• very low: "80% bonds (AGG), 20% equities (SPY)"
• low: "60% bonds (AGG), 40% equities (SPY)"
• medium: "40% bonds (AGG), 60% equities (SPY)"
• high: "20% bonds (AGG), 80% equities (SPY)"
• very high: "0% bonds (AGG), 100% equities (SPY)"

Be creative while coding your solution, you can have all the code on the recommend_portfolio() function, or you can split the functionality across different functions, put your Python coding skills in action!

Once you finish coding your lambda function, test it using the sample test cases provided for this homework.

After successfully testing your code, open the Amazon Lex Console and navigate to the RecommendPortfolio bot configuration, integrate your new lambda function by selecting it in the Lambda initialization and validation and Fulfillment sections. Build your bot, and you should have a conversation as follows.

You should create a brand new repository in GitHub and upload the following files to your repo.

• A python script with your final lambda function.

• From the Amazon Lex Console, export your bot, intent, and slot using Amazon Lex as the target platform, and upload the ZIP files to your repo.

• Create a short video or animated GIF showing a demo of your Robo Advisor in action from the test window. Upload the video or animated GIF file to your repo.

Once you have uploaded all the files into the repo, post a link to your homework's repository in BootCamp Spot.

• Make sure your intent and slot names are named correctly in your Lambda code. The names in Lex should match the names in Lambda exactly:

• You may have to refresh the Lex intent page after creating the custom slot and the lambda function in order to see them in the options.

• If you are using a Mac, you can create a screen-recording using the built-in QuickTime player. Follow this link to learn more.

• If you are using Windows 10, you can create a screen-recording using the built-in Xbox Game Bar. Follow this link to learn more.

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