Creating a Bernoulli Naive Bayes' Classifier.

.gitignore

@@ -0,0 +1 @@
+__pycache__

app.py

@@ -1 +1,45 @@
+import marimo
 
+__generated_with = "0.11.4"
+app = marimo.App(width="medium")
+
+
+@app.cell
+def _(mo):
+    mo.md("""# Exploration on Smartphone Consumer Trends in India""")
+    return
+
+
+@app.cell(hide_code=True)
+def _():
+    import marimo as mo
+    import polars as pl
+
+    dataset_raw = pl.read_csv("dataset/diabetes_binary_health_indicators_BRFSS2015.csv")
+    dataset_raw.head()
+    return dataset_raw, mo, pl
+
+
+@app.cell
+def _(dataset_raw):
+    dataset_priors = dataset_raw.select(["Diabetes_binary", "HighBP", "HighChol", "Stroke", "HeartDiseaseorAttack"])
+    dataset_priors.head()
+    return (dataset_priors,)
+
+
+@app.cell
+def _():
+    from sklearn.naive_bayes import BernoulliNB
+    from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
+    from sklearn.model_selection import train_test_split
+    return (
+        BernoulliNB,
+        accuracy_score,
+        classification_report,
+        confusion_matrix,
+        train_test_split,
+    )
+
+
+if __name__ == "__main__":
+    app.run()

dataset/app.py

@@ -0,0 +1,140 @@
+import marimo
+
+__generated_with = "0.11.4"
+app = marimo.App(width="medium")
+
+
+@app.cell(hide_code=True)
+def _(mo):
+    mo.md(r"""# Diabetes Dataset Analysis""")
+    return
+
+
+@app.cell(hide_code=True)
+def _():
+    import marimo as mo
+    import polars as pl
+    return mo, pl
+
+
+@app.cell(hide_code=True)
+def _(mo):
+    mo.accordion(
+        {"Notes": """
+    ## Dataset Column Notes
+
+    > Only highlighted columns of interest 
+
+    * Diabetes_binary: [ 0 (No diabetes) | 1 (Pre/diabetes) ]
+    * HighBP: [ 0 (No High BP) | 1 (High BP) ]
+    * HighChol: [ 0 (No High BP) | 1 (High BP) ]
+    * Stroke: [ 0 (Never) | 1 (Had) ]
+    * HeartDiseaseorAttack: [ 0 (No) | 1 (Yes) ]
+    * Smoker: [ 0 (<100 cigs lifetime) | 1 (>100 cigs lifetime) 
+    * HvyAlcohol: [ 0 (<14 🍺/week for men, <7 🍺/week for women) | 1 (otherwise) ]
+        """}
+    )
+    return
+
+
+@app.cell(hide_code=True)
+def _(pl):
+    dataset_raw = pl.read_csv("dataset/diabetes_binary_health_indicators_BRFSS2015.csv")
+    dataset_prior_conditions = dataset_raw.select(["Diabetes_binary", "HighBP", "HighChol", "Stroke", "HeartDiseaseorAttack"])
+    dataset_prior_conditions.head()
+    return dataset_prior_conditions, dataset_raw
+
+
+@app.cell
+def _(mo):
+    mo.md("""## Naive Bayes' Classifier""")
+    return
+
+
+@app.cell
+def _(dataset_prior_conditions, mo, pl):
+    from sklearn.naive_bayes import BernoulliNB
+    from sklearn.model_selection import train_test_split
+    from sklearn.metrics import accuracy_score, classification_report, confusion_matrix
+
+    X_priors_NB, y_priors_NB = dataset_prior_conditions.select(pl.exclude("Diabetes_binary")), dataset_prior_conditions.select("Diabetes_binary")
+    X_train_priors, X_test_priors, y_train_priors, y_test_priors = train_test_split(
+        X_priors_NB, y_priors_NB, random_state=33, test_size=0.25
+    )
+
+    bnb = BernoulliNB()
+    y_pred_priors = bnb.fit(X_train_priors, y_train_priors).predict(X_test_priors)
+    mo.md(f"""
+        Accuracy             : {accuracy_score(y_test_priors, y_pred_priors)}
+
+        Confusion Matrix:
+
+    ```
+        {confusion_matrix(y_test_priors, y_pred_priors)}
+    ```
+
+        Classification Report: 
+
+    ```
+        {classification_report(y_test_priors, y_pred_priors)}
+    ```
+        """)
+    return (
+        BernoulliNB,
+        X_priors_NB,
+        X_test_priors,
+        X_train_priors,
+        accuracy_score,
+        bnb,
+        classification_report,
+        confusion_matrix,
+        train_test_split,
+        y_pred_priors,
+        y_priors_NB,
+        y_test_priors,
+        y_train_priors,
+    )
+
+
+@app.cell
+def _(X_test_priors, pl, y_pred_priors, y_test_priors):
+    import altair as alt
+    alt.data_transformers.enable("vegafusion")
+
+    # X_test_priors, y_pred_priors, y_test_priors
+    dataset_result_priors = pl.concat([X_test_priors, y_test_priors, pl.DataFrame({"Predicted Diabetes_binary": y_pred_priors})], how="horizontal")
+    dataset_result_priors1 = dataset_result_priors.select(
+        (pl.col("HighBP") * 8),
+        (pl.col("HighChol") * 4),
+        (pl.col("Stroke") * 2),
+        pl.exclude(["HighBP", "HighChol", "Stroke"])
+    )
+    dataset_result_priors1 = dataset_result_priors1.select(
+        pl.sum_horizontal(pl.col("HighBP", "HighChol", "Stroke", "HeartDiseaseorAttack")),
+        pl.col("Diabetes_binary", "Predicted Diabetes_binary")
+    )
+    dataset_result_priors2 = dataset_result_priors.select(
+        pl.exclude(["Diabetes_binary", "Predicted Diabetes_binary"]),
+        (pl.col("Diabetes_binary") * 2),
+        pl.col("Predicted Diabetes_binary")
+    )
+    dataset_result_priors2 = dataset_result_priors2.select(
+        pl.col("HighBP", "HighChol", "Stroke", "HeartDiseaseorAttack"),
+        pl.sum_horizontal(pl.col("Diabetes_binary", "Predicted Diabetes_binary"))
+    )
+    dataset_result_priors2.head(10)
+    return (
+        alt,
+        dataset_result_priors,
+        dataset_result_priors1,
+        dataset_result_priors2,
+    )
+
+
+@app.cell
+def _():
+    return
+
+
+if __name__ == "__main__":
+    app.run()

pyproject.toml

@@ -11,4 +11,7 @@ dependencies = [
     "marimo>=0.11.4",
     "polars>=1.22.0",
     "pyarrow>=19.0.0",
+    "scikit-learn>=1.6.1",
+    "vegafusion[embed]>=2.0.1",
+    "vl-convert-python>=1.7.0",
 ]

uv.lock

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@@ -48,6 +68,9 @@ dependencies = [
     { name = "marimo" },
     { name = "polars" },
     { name = "pyarrow" },
+    { name = "scikit-learn" },
+    { name = "vegafusion" },
+    { name = "vl-convert-python" },
 ]
 
 [package.metadata]
@@ -56,6 +79,9 @@ requires-dist = [
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     { name = "polars", specifier = ">=1.22.0" },
     { name = "pyarrow", specifier = ">=19.0.0" },
+    { name = "scikit-learn", specifier = ">=1.6.1" },
+    { name = "vegafusion", extras = ["embed"], specifier = ">=2.0.1" },
+    { name = "vl-convert-python", specifier = ">=1.7.0" },
 ]
 
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