Features for predicting and classifying people, maybe will have to include demographic information, behavior patterns, interests, and historical data. The best library depends on the specific requirements and the programming language preferred, but popular choices, in my opinion, would include, scikit-learn for Python and TensorFlow for more complex neural network models.

Thanks Paulo Bolinhas ,

In this case Python is the language choosen.

I totally agree with you, demographic information would be the main feature.

What do you mean with behaviou patterns??

Thanks in advance,

No problem! It is a good question.

With behavior patterns i was referring to recurring actions or habits that the individuals exhibit over time. Those patterns can include online behavior such as browsing history, social media activity, purchase history and interactions with digital services. So, in the context of predicting and classifying people, behavior patterns can provide valuable insights into their preferences, tendencies, potential future actions,... You can use this information to personalize experiences or make predictions about individual behavior, for example.

Thank you very much Paulo Bolinhas ,

That is a fantastic and logical idea. I honestly didn't think to include these behavior patterns as features.

However, we have conducted an interview before and after the use of the skill integrated in Alexa with the patients, called the Technology Acceptance Model (TAM), about technology in general and the frequency of use they make of the internet, Facebook, mobile, television.

So your suggestion is quite appropriate with this data collected.

In fact, thanks to these suggestions I will add these responses as part of the behavior patterns.

Thanks again,

Predicting and classifying people can be a complex task depending on the desired outcome. Here's a breakdown of features and libraries to consider, but also some ethical considerations:

Features for Classification:

• Visual Features (if using images/videos): Age, gender, facial expressions, clothing, body language. (Requires libraries for computer vision)
• Demographic Data: Age, gender, occupation, location.
• Behavioral Data: Website clicks, purchase history, social media activity.
• Textual Data: Written content (emails, messages) for sentiment analysis or topic modeling. (Requires Natural Language Processing libraries)
• Sensor Data: Voice patterns, gait analysis (e.g., from wearables).

Libraries (consider ethical implications):

• Machine Learning Libraries: Scikit-learn, TensorFlow, PyTorch.These are powerful tools, but require a solid understanding of machine learning concepts and potential biases in the data.

Ethical Considerations:

• Privacy: Ensure data collection and usage comply with regulations and user consent.
• Bias: Training data can reflect societal biases, leading to discriminatory outcomes.
• Transparency: Be transparent about how classifications are made and the potential for errors.

Finding the "Best" Library:

There's no single "best" library - it depends on your specific task and data type. Here's a general approach:

Focus on Responsible Use:

Remember, responsible use of these technologies is crucial. Consider:

• Fairness: Ensure your classifications are not biased against certain groups.
• Explainability: Be able to explain how classifications are made.
• User Consent: Obtain user consent for data collection and usage.

Additional Resources:

• AI Now Institute: https://ainowinstitute.org/ (Promotes responsible AI development)

Thanks Adnan Majeed ,

There are important ethic considerations as you mention.

In our use case, the digitalized clinical tests, dementia and depression test, are administered. We use Alexa devices to pass these tests so that the information is digitized, obtaining the answer of the final score automatically.

Taking into account the data collected from our users, the features to clasify them will be:

• Demographic Data
• Behavior patterns
• Their answer to the clinical test
• Voice Data or voice patterns
• Gesture patterns

My proposal is to compare the digitized test with the classic one to check the sensitivity and specificity. And compare these two terms with the ones I get when I add the Machine Learning layer. Probably the library I use is Scikit-learn.

Do you think I'm on the right path? Are there any important suggestions to keep in mind?

Thanks in advance,