import numpy as np
import pandas as pd
from sklearn import metrics
from sklearn.metrics import f1_score, precision_score, recall_score
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
from lightgbm import LGBMClassifier

# Convert target variable to numeric
df['Credit_Score'] = df['Credit_Score'].str.replace('Good', '3', n=-1)
df['Credit_Score'] = df['Credit_Score'].str.replace('Standard', '2', n=-1)
df['Credit_Score'] = df['Credit_Score'].str.replace('Poor', '1', n=-1)
df['Credit_Score'] = df[['Credit_Score']].apply(pd.to_numeric)

# Split the dataset
X=df.loc[:, df.columns != 'Credit_Score']
Y=df['Credit_Score']
x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=0.25)

# Train the LightGBM model
lgbm = LGBMClassifier()
lgbm.fit(x_train, y_train)
y_pred = lgbm.predict(x_test)

# Print performance metrics
print('F1 score: %.3f' % f1_score(y_test, y_pred, average='weighted'))
print('Precision: %.3f' % precision_score(y_test, y_pred, average='weighted'))
print('Recall: %.3f' % recall_score(y_test, y_pred, average='weighted'))