Data Science Report

Executive summary

The objective was to classify patients into risk categories. The agent identified this as a classification problem predicting Outcome, trained 6 models, and selected Decision Tree Classifier, which scored 0.7462 F1 on data it had never seen.

Dataset overview

Data quality findings

Quality score: 100/100

• No significant data quality problems detected.

Cleaning performed

Remaining missing values are imputed inside the modelling pipeline (numeric columns with the median, categorical columns with the most frequent value). The imputers are fitted on the training split only, so no information from the test set leaks into training.

Problem type and target variable

• Problem type: Classification — The target has exactly two distinct values, so this is binary classification.
• Target variable: Outcome
• Confidence in the target: 85%
• Evaluation metric: F1 — Using F1 as recommended by the AI analyst for this objective.
• Split: Stratified 80/20 split with random_state=42, so both splits keep the same class balance. 614 training rows, 154 test rows.

Preprocessing

Preprocessing runs inside a scikit-learn pipeline fitted on the training split only, so no information from the test rows can influence it.

• Numeric columns: missing values filled with the training median, then scaled for the linear and distance-based models that need it.
• Categorical columns: missing values filled with the most frequent training value, then one-hot encoded with rare categories grouped together.
• Categories never seen during training are ignored at prediction time rather than causing an error.

Feature engineering

14 feature(s) were derived from the original columns.

Feature selection

17 feature(s) were kept and 5 removed.

Kept: Pregnancies Glucose BloodPressure SkinThickness BMI Age Insulin_log DiabetesPedigreeFunction_log Glucose_per_BMI Glucose_per_Age Glucose_per_Insulin BMI_per_Age BMI_per_Insulin Age_per_Insulin Glucose_minus_Age BMI_minus_Age BMI_minus_Insulin

Models evaluated

Best model

• Model: Decision Tree Classifier
• Headline score: 0.7462 (F1)
• Why this model: Decision Tree Classifier achieved the best F1 of 0.7462, ahead of Extra Trees Classifier (0.7290) by 0.0172. F1 was chosen because it matches what the objective is asking for.
• Naive baseline: 0.5113 — the score to beat before a model means anything.
• Cross-validated: 0.6956 ± 0.0417 over 5 folds.

Performance on each class

What drives the predictions

Impurity-based importance: How much each feature reduced prediction error across all the splits in the trees. Higher means the model relied on it more.

Did the analysis meet the objective?

Model performance and objective fulfilment are separate questions. A model can score well and still fail the objective, so each check below was applied independently.

Key insights

• The winning model's decisions are driven mainly by Glucose (35%), BMI (14%), Glucose_per_Age (7%).
• The objective was partially met. F1 of 0.7462 is usable but leaves real room to improve. Success criterion: A model that clearly beats the naive baseline on F1 and behaves sensibly on the outcome the objective cares about.

Limitations

• Scores are measured on a single random hold-out split of this dataset. Performance on genuinely new data collected later can differ.
• No hyperparameter search was performed; every model used sensible defaults.
• The agent applies general statistical rules and has no domain knowledge about what these columns mean in your business.
• Gemini was not used for this run: google-genai package is not installed.

Recommended next steps

1. Collect more data. 768 rows is a small sample, and the score above could move considerably on a larger one.
2. Try adjusting the decision threshold rather than the model. Moving it away from 0.5 trades precision for recall and often fixes a model that is accurate but never flags the class you care about.
3. If the classes are imbalanced, resampling the training data or class weighting is usually a bigger win than switching algorithms.
4. Tune Decision Tree Classifier with a grid or randomised search. This run used sensible defaults with no hyperparameter search, so there is headroom left.
5. Review the 5 removed feature(s) with a domain expert. Automated selection is statistical; it does not know which columns matter to the business.
6. Before deploying, re-test on data from a later time period than the training data. A random split cannot tell you whether the pattern holds next quarter.

AI analyst commentary

'Outcome' scored highest as a target candidate because column name is exactly 'outcome'; last column in the file; two distinct values (binary outcome). The objective wording suggests a classification task. Identifier and constant columns were excluded because they cannot generalise. This recommendation was produced by the built-in rule-based analyst rather than Gemini.

Preprocessing suggested by the AI layer:

• Impute missing numeric values with the median and categorical values with the mode.
• One-hot encode categorical columns, grouping rare categories first.
• Scale numeric features for the distance-based and linear models only.

This report was produced automatically. Review the findings with someone who knows the data before making decisions based on them.