text_plain_output_1.png image_output_1.png
"""X = train_data.drop(['id', 'prognosis'], axis=1).reset_index(drop = True)
print(len(X.columns))
significant_features = [score[0] for score in sorted_scores if score[1] < 0.05]
X_significant = X[significant_features]
print(len(X_significant.columns))
X_scaled = scaler.fit_transform(X_significant)"""