coffee-sleep-predictor

Coffee Health Predictor: Advanced Modeling of Sleep and Stress

1. Executive Summary and Final Model Declaration

This project implemented a complete ML pipeline on the Global Coffee Health Dataset (~10,000 records). The final models are selected based on optimal accuracy, simplicity, and ethical considerations for health risk identification.

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Part 2: Data Analysis, Cleaning, and Preparation

The initial EDA revealed a weak negative correlation ( -0.19) between the primary numerical predictor ({Caffeine\mg}) and the target ({Sleep\Hours}), proving that prediction relied on complex, hidden factors.

• Cleaning Action: Imputed approx 60% missing values in the {Health\Issues} column with a new category, 'None', to preserve data integrity.
• Feature Engineering Insight: The high {R}^2 score was primarily driven by thesimple encoding of self-reported categorical features.

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Part 3: Define and Train a Baseline Model

Feature Importance Insight

The Feature Importance analysis revealed that Sleep\Quality and Stress\Level were the dominant predictors in the baseline Linear Regression model.

• Visualization: The Feature Importance graph visually confirms that {Sleep\Quality} and {Stress\Level} coefficients dominate, while the numerical features have negligible impact.

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Part 4: Feature Engineering (Advanced)

Applying Clustering (Unsupervised Learning)

Complex FE was performed to challenge the linear baseline and capture non-linear interactions, including the creation of the {Physical\Health\Cluster} feature.

• Health Profile Clustering (K-Means, K=4): A critical feature was generated based on {BMI}, {Heart\Rate}, and {Physical\Activity\Hours}.
• Interpretation: The clustering successfully segmented the population into four distinct health profiles (e.g., Cluster 0: High Risk/Sedentary and Cluster 1: Healthy/Athlete).
• Visualization: The PCA plot confirmed clear segmentation of the physiological profiles.

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Part 5: Train and Evaluate Three Improved Models (Regression)

Evaluation Summary

The comparison showed that the complex FE features did not improve the accuracy of the Linear Regression baseline.

Conclusion

The Linear Regression Baseline was declared the winner; the sophisticated modeling was unable to justify its complexity by improving on the initial linear result.

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Part 7: Target Reframing (Regression-to-Classification)

• Strategy:* The continuous {Sleep\Hours} variable was converted into three discrete classification classes (Suboptimal, Average, Optimal) using Quantile Binning (Quartiles).
• Critical Metric: Recall was confirmed as the primary metric for the subsequent classification task, as minimizing False Negatives (FN) (missing a critical health risk) is the central ethical goal (as per the conclusion of Part 6).

Part 8: Train & Eval Classification Models

A. Critical Recall Performance

• Classification Winner: Logistic Regression. Selected for its superior interpretability, speed, and identical macro performance ({F1} \approx 0.849).
• Critical Recall Performance: The model achieved a Recall of {1.00} for the critical Suboptimal Sleep (Class 0) category. This signifies that the model did not miss a single case in the highest risk category, perfectly fulfilling the primary ethical mandate.
• Visualization: The Confusion Matrix visually confirms False Negatives in the critical risk class.

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https://youtu.be/5uNY_zuJmko