For my CS 9637 Data Science Course, I proposed, built and tuned a random-forest multi-classifier that can predict the probability of someone falling into a class of obesity given that they maintain their current lifestyle choices and habits. My model achieved 87% accuracy in predicting obesity. Such a model could theoretically be deployed in health clinics where patients receive physical check ups; a health professional could intake answers to questions about the patient's lifestyle choices, input those answers into my model, and help health professionals to make recommendations to their patients based on the model's predictions.

Background: Obesity is an ever rising concern across numerous counties and, in the US, contributes to an estimated 112,000 deaths per year, all of which were preventable had they taken the necessary steps to reduce obesity onset. These increasing rates of obesity are also reported on a global scale by the World Health Organization, leaving many health professionals to view obesity as a very serious epidemic. Being able to predict the probability of succumbing to obesity based on one’s current lifestyle choices may allow individuals to take action and correct their habits in order to avoid obesity and all of its potential health ailments that may come with it. Objective: To develop a predictive model that can show the probability of someone falling into a class of obesity given that they maintain their current lifestyle choices and habits. Our motivation behind building this model is to hopefully provide incentives for someone using this model to correct their lifestyle choices if the model predicts that they may fall into a class of obesity given their inputed lifestyle. Methods: Obesity data used in training our models came from data containing estimations of obesity levels in people, ages from 14 to 61, from the countries of Mexico, Peru and Colombia, with diverse eating habits and physical condition. The data consisted of 2111 rows and 16 features. The target variable had 7 possible classes: Obesity I, Obesity II, Obesity III, Overweight I, Overweight II, Normal Weight, and Insufficient Weight. Data was split into training and testing data. Three supervised machine learning models were trained and evaluated (Logistic Regression, Random Forest, XGBoosting). Automated hyper-parameter tuning and feature-space tuning were employed to improve accuracy of our prediction models. Results:Selected model was a Random Forest-based Multi-Classifier that was hyper-parameter tuned and feature-tuned. Selected model achieved an accuracy of 86.55% on the test set. Averaged Precision score — averaged across the 7 classes — was 0.8; Recall score was 0.87, and F1-score was 0.86. Averaged Sensitivity and Specificity scores were 0.87 and 0.98. Averaged Balanced Accuracy was 0.92. Macro-Averaged AUROC was 0.98. Conclusion: We built a moderately accurate prediction model that classifies people’s risk of falling into a specific class of obesity (along with a normal weight class and underweight class) given that they maintain their existing lifestyle choices. Tuning strategies provided a decent improvement in performance. We achieved decent sensitivity and high specificity. Performance of our model is believed to be held back by the relatively small size of our dataset (especially when considering we have 7 classes to learn from) and, for any future work, acquisition of a larger dataset would be utmost priority to improve our model. Some limitations of our model are also discussed.

Final Grade: 98/100. Feedback from my professor are copied & pasted below:
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Overall: Overall project goal is clearly stated and the importance is justified.
‍Writing Quality: Clear, concise, simple. Great!
‍Visualization: Clean!
‍Introduction: Clearly explained the problem you are addressing.
‍Methods: Clear, great job! Good work dummy-coding the categorical variables. That is an easy oversight. Although arguments could be made that it's unfair to compare models with different features (since you permute across feature combos and choose the best combo), it is clear from your motivation that this is a non-issue in your project.
‍Reproducibility:  Sufficient explanation to reproduce the analysis.
‍Results: Ideally you would have included more results (confusion matrix, ROC, etc.) for the XGBoost model in your figures. Clearly for you, the Random forest was better. But to be most convincing, you would demonstrate this in your figures. For example, a figure with the ROCs for the Random forst and XGBoost models on the same plot so we can appreciate the differences (if any). Was the XGBoost model still good at predicting obese vs. not obese, but more easily confused the varying levels of obesity (like the random forest model)?
‍Discussion: Good discussion of limitations (especially geopgraphic restriction of the dataset)
‍Interpretation: Interpretation is most important for the discussion - great!