Z5phwqybcwixfwwqmv3v.zip

# Sample data data = {'Age': [20, 21, 19, 24, 28], 'Score': [90, 85, 88, 92, 89]} df = pd.DataFrame(data)

import pandas as pd

# Assuming X is your feature data and y is your target X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) z5pHwQybCwiXFwWqMv3v.zip

# Creating a new feature: 'Pass' based on 'Score' df['Pass'] = df['Score'].apply(lambda x: 'Yes' if x >= 90 else 'No') # Sample data data = {'Age': [20, 21,

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. y_test = train_test_split(X

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)