Update README.md

README.md

@@ -139,7 +139,7 @@ In conclusion Duration and Activity Type are the dominant predictors, validating
 
 # **Part 4: Feature Engineering**
 
-While the baseline Linear Regression model performed well ($R^2 \approx 0.967$), the residual analysis revealed curved patterns, suggesting that the relationship between predictors and calorie burn is non-linear. To address this and capture complex workout behaviors better, I engineered 6 new features before training advanced models.
+While the baseline Linear Regression model performed well (R^2 = 0.967), the residual analysis revealed curved patterns, suggesting that the relationship between predictors and calorie burn is non-linear. To address this and capture complex workout behaviors better, I engineered 6 new features before training advanced models.
 
 **1. Heart_Range**
 
@@ -392,6 +392,6 @@ The trained Random Forest pipeline, including the scaler, clustering model, thre
 
 # **Conclusion**
 
-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.
+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, Weight, BMI and Height, suggests that this dataset might not be correct. 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 and heavily weighted toward Duration and Activity Type.