import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import GaussianNB
from sklearn.metrics import classification_report, accuracy_score
from sklearn.datasets import load_breast_cancer # As example gene expression-like dataset
# Load example dataset
data = load_breast_cancer()
X = data.data # Gene expression features (e.g., gene1, gene2, ...)
y = data.target # Labels (e.g., cancer: 1, normal: 0)
# Train/test split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
# Gaussian Naive Bayes
model = GaussianNB()
model.fit(X_train, y_train)
# Predictions
y_pred = model.predict(X_test)
# Evaluation
print("Accuracy:", accuracy_score(y_test, y_pred))
print("Report:\n", classification_report(y_test, y_pred))
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