import numpy as np
import pandas as pd
from sklearn.model_selection import KFold, cross_val_score, GridSearchCV
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.ensemble import RandomForestClassifier
from category_encoders import TargetEncoder
# Sample dataset
data = {
'category': ['A', 'B', 'A', 'C', 'B', 'A', 'C', 'C', 'B', 'A'],
'feature': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
'target': [0, 1, 0, 1, 0, 1, 0, 1, 0, 1]
}
df = pd.DataFrame(data)
X = df[['category', 'feature']]
y = df['target']