import pandas as ps
 from sklearn.ensemble import RandomForestClassifier
 from sklearn.model_selection import train_test_split
 from sklearn.preprocessing import LabelEncoder
 from sklearn.metrics import accuracy_score,confusion_matrix,classification_report,precision_score
 df=pd.read_csv("loan_data_set.csv")
 print(df.head())
 x=df.iloc[:614]
 y=df.Loan_Status
 encoder=LabelEncoder()
 x_train_enc=x.apply(encoder.fit_transform)
 x_train,x_test,y_train,y_test=train_test_split(x_train_enc,y,test_size=0.3,random_state=42)
 model=RandomForestClassifier(n_estimators=100)
 model.fit(x_train,y_train)
 y_pred=model.predict(x_test)
 print(accuracy_score(y_test,y_pred))
 print(precision_score(y_test,y_pred,average='weighted',pos_label='Y'))
 print(confusion_matrix(y_test,y_pred))
 print(classification_report(y_test,y_pred))