from sklearn.ensemble import RandomForestClassifier

model = RandomForestClassifier()

def train_model(df):
    """Train the AI model."""
    df["target"] = (df["close"].pct_change() > 0.05).astype(int)  # Label: 1 if price increased by >5%
    features = df[["close", "volume"]].dropna()
    target = df["target"].dropna()
    model.fit(features[:-1], target)

def predict_growth(latest_data):
    """Predict explosive growth."""
    return model.predict([latest_data])