Add Random Forest classifier simulator

[Sakeeb91]

Summary

• add random forest training and evaluation helpers
• extend dataset and plotting utilities for classification workflows
• build a Streamlit page to explore metrics, confusion matrix, ROC curve, and feature importances

Testing

• python3 - <<'PY'
  from models.random_forest import train_random_forest, evaluate_random_forest
  from utils.data_helpers import generate_sample_classification
  from sklearn.model_selection import train_test_split

X, y = generate_sample_classification(n_samples=300, n_features=6, n_informative=4, n_redundant=1, random_state=42)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, stratify=y, random_state=42)
model = train_random_forest(X_train, y_train, n_estimators=100, max_depth=None, criterion='gini', random_state=42)
y_pred, y_proba, metrics = evaluate_random_forest(model, X_test, y_test)
print('Metrics:', metrics)
print('Proba available:', y_proba is not None)
PY

Closes #58.