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README.md
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The analysis identified the Random Forest algorithm as the superior model, achieving near-perfect performance for both regression (R^2 = 0.9999) and classification (99.88% Accuracy).While these metrics demonstrate exceptional predictive power, the remarkably high accuracy, combined with the unexpectedly low feature importance of Heart Rate and Weight, suggests the underlying dataset is likely synthetic. In real-world physiology, heart rate and body mass are critical drivers of energy expenditure; their lower correlation here indicates the data was likely generated using a deterministic formula heavily weighted toward Duration and Activity Type.
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The analysis identified the Random Forest algorithm as the superior model, achieving near-perfect performance for both regression (R^2 = 0.9999) and classification (99.88% Accuracy).While these metrics demonstrate exceptional predictive power, the remarkably high accuracy, combined with the unexpectedly low feature importance of Heart Rate and Weight, suggests the underlying dataset is likely synthetic. In real-world physiology, heart rate and body mass are critical drivers of energy expenditure; their lower correlation here indicates the data was likely generated using a deterministic formula heavily weighted toward Duration and Activity Type.
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