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README.md

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 # Model description
 
-This model is a decision tree classifier trained to predict obesity levels based on demographic, lifestyle, and diet-related features. The dataset includes variables including age, height, weight, caloric food intake, physical activity, water consumption, smoking behavior, and transportation habits. The target label is the obesity category, which includes seven classes ranging from Insufficient_Weight to Obesity_Type_III. The decision tree originally had 12 layers which was cut down (pruned) to improve interpretability and reduce overfitting.
+This model is a decision tree classifier trained to predict obesity levels based on demographic, lifestyle, and diet-related features. The dataset includes variables including age, height, weight, caloric food intake, physical activity, water consumption, smoking behavior, and transportation habits. The target label is the obesity category, which includes seven classes ranging from Insufficient_Weight to Obesity_Type_III. The decision tree originally had 12 layers, which was cut down (pruned) to improve interpretability and reduce overfitting.
 
 ## Intended uses & limitations
 
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 # Citation
 
-The Estimation of Obesity Levels Based On Eating Habits and Physical Condition was found on the UCI Machin Learning Repo
+The Estimation of Obesity Levels Based on Eating Habits and Physical Condition was found on the UCI Machine Learning Repo.
 https://archive.ics.uci.edu/dataset/544/estimation+of+obesity+levels+based+on+eating+habits+and+physical+condition
 
 # Intended uses & limitations
 
-The intended use fot his model is educational use, possible tool for exploring health data, research, classification and interpretability techniques. The model performs very well for higher risk categories, but is less accurate for lower risk catergories. The model is Not intended for actual medical diagnosis or treatment decisions. Limitations include that normal-weight and neighboring overweight classes overlap, making them harder to classify. The data is also self-reported, which may lead to bias or inaccuracies.
+The intended use for this model is educational use, possible tool for exploring health data, research, classification, and interpretability techniques. The model performs very well for higher-risk categories but is less accurate for lower-risk categories. The model is not intended for actual medical diagnosis or treatment decisions. Limitations include that normal-weight and neighboring overweight classes overlap, making them harder to classify. The data is also self-reported, which may lead to bias or inaccuracies and not ready to be used for real production.
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+# Training Procedure
+
+I trained a Decision Tree Classifier on the obesity dataset using 16 features related to demographics, behavior, and lifestyle. The data was split into 80% training and 20% testing, but was then split into 75% training and 25% testing for validation. The decision tree was reduced to help with overfitting. The target variable was the 7-class obesity category. Evaluation metrics included overall accuracy and fairness metrics.
 
 # Evaluation Results
 
-The model achieves approximately 87% accuracy. Its performance is strongest on more distinct obesity categories and weaker on categories that are closer together. A more complex model could lead to higher accuracy, but it would be less interpretable and harder to present to medical professionals. I would trust this model as a decision-support tool, but not as the sole basis for medical diagnosis because it can make mistakes. This model as a supportive screening tool, and would be beneficial to flag individuals who need to change their lifestyle habits. 
+The model achieves approximately 87% accuracy. Its performance is strongest on more distinct obesity categories and weaker on categories that are closer together. A more complex model could lead to higher accuracy, but it would be less interpretable and harder to present to medical professionals. I would trust this model as a decision-support tool, but not as the sole basis for medical diagnosis because it can make mistakes. This model is a supportive screening tool and would be beneficial to flag individuals who need to change their lifestyle habits.