andreshenao/obesity-weight-regression

This model predicts a person’s weight (kg) from height (m) and age (years) using a Linear Regression model from scikit-learn. It is for educational demonstration in a URI data science course.

Feature	Type	Description
Height	Continuous	Height in meters
Age	Continuous	Age in years
Weight (target)	Continuous	Weight in kilograms

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Model Performance

Metric	Value
Mean Squared Error (MSE)	511.55
R² Score	0.2777

The model explains about 28% of weight variation — it recognizes the trend that taller and older people weigh more, but the predictions are not highly accurate.

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Evaluation Summary

• Clear positive correlation in predictions
• Spread increases for heavier individuals
• Indicates missing key predictors:
  • Diet
  • Muscle mass
  • Body composition
  • Lifestyle factors like exercise

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Strengths & Weaknesses

Strengths

• Simple and interpretable linear model
• Works as a learning tool for regression
• Fast and easy to run

Weaknesses

• Low predictive power
• Sensitive to outliers
• Not reliable for real-world medical prediction

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Training Details

• Model: LinearRegression()
• Train/Test: 75% / 25%
• Dataset: ObesityDataSet_raw_and_data_synthetic.csv
• Frameworks: Python, scikit-learn
• Random State: 42

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⚠ Limitations

• Should not be used for health decisions
• Trained on a limited synthetic dataset
• Does not model non-linear relationships important for weight

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How to Use

import skops.io as sio
model = sio.load("regression_model.skops", trusted=True)
prediction = model.predict([[1.75, 21]])
print(prediction)