license:mit size_categories:n<1K format:csv modality:tabular library:datasets library:pandas library:mlcroissant library:polars region:us
---
license: mit
---


input_features = {
    "snoring range": "60",
    "respiration rate": "20",
    "body temperature": "96",
    "limb movement": "10",
    "blood oxygen ": "95",
    "eye movement": "85",
    "hours of sleep": "7",
    "heart rate ": "60",
}
These are the physiological parameters for which you want to predict the stress level.
Creating a Text Representation:
The features are converted into a text string that the model can process:
python


input_text = ", ".join([f"{key}: {value}" for key, value in input_features.items()])
This results in:
javascript


snoring range: 60, respiration rate: 20, body temperature: 96, limb movement: 10, blood oxygen : 95, eye movement: 85, hours of sleep: 7, heart rate : 60
Generating the Prediction:
The generate_prediction function uses the trained model to predict the stress level:
python


prediction = generate_prediction(model, tokenizer, input_text)
Inside this function:
The input text is tokenized and converted into tensors.
The model processes the input and outputs a numerical value representing the predicted stress level.
Displaying the Output:
The script then prints:
python


print("Input Features:")
print(input_text)
print("\nGenerated Prediction:")
print(prediction)
For example, the output might be:
javascript


Input Features:
snoring range: 60, respiration rate: 20, body temperature: 96, limb movement: 10, blood oxygen : 95, eye movement: 85, hours of sleep: 7, heart rate : 60

Generated Prediction:
2.5
Interpretation
Generated Prediction (2.5 in the example): This numerical value is the stress level predicted by your model for the given input features.
Scale of Stress Levels: The exact meaning depends on how stress levels are defined in your dataset. Assuming your stress levels are on a scale from 0 to 4:
0 could represent no stress.
4 could represent high stress.
Predicted Stress Level 2.5: This would indicate a moderate stress level according to your model's prediction.
What This Means
Personalized Assessment: The model provides an automated assessment of stress level based on physiological data.
Potential Applications:
Health Monitoring: Can be used in wellness apps to monitor user stress levels.
Stress Management: Helps in identifying stress triggers and managing stress proactively.
Important Considerations
Model Accuracy:
The reliability of the prediction depends on the quality and size of your training data.
If the dataset is small or not representative, predictions may not be accurate.
Data Consistency:
Ensure that the input features are in the same format and units as those used during training.
Any discrepancy can affect the prediction accuracy.
Model Evaluation:
Evaluate your model's performance using metrics like Mean Squared Error (MSE) or R-squared on a test dataset.
Cross-validation can help in assessing how well the model generalizes to unseen data.
Ethical and Privacy Concerns:
Be mindful of user privacy when collecting physiological data.
Ensure compliance with data protection regulations.
