from sklearn.model_selection import train_test_split from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import accuracy_score
model = RandomForestClassifier() model.fit(X_train, y_train) z5pHwQybCwiXFwWqMv3v.zip
# Creating a new feature: 'Pass' based on 'Score' df['Pass'] = df['Score'].apply(lambda x: 'Yes' if x >= 90 else 'No') from sklearn
y_pred = model.predict(X_test) print("Accuracy:", accuracy_score(y_test, y_pred)) This process can vary widely depending on your specific data and goals. If you have more details about the zip file's contents and what you're trying to achieve, I could provide more targeted advice. z5pHwQybCwiXFwWqMv3v.zip
import zipfile