diff --git a/__init__.pyc b/__init__.pyc
index d1c94d3..5c9e459 100644
Binary files a/__init__.pyc and b/__init__.pyc differ
diff --git a/q01_pipeline/__init__.pyc b/q01_pipeline/__init__.pyc
index b360a57..43a111b 100644
Binary files a/q01_pipeline/__init__.pyc and b/q01_pipeline/__init__.pyc differ
diff --git a/q01_pipeline/build.py b/q01_pipeline/build.py
index 96beca7..f0e31fa 100644
--- a/q01_pipeline/build.py
+++ b/q01_pipeline/build.py
@@ -1,3 +1,4 @@
+# %load q01_pipeline/build.py
 import pandas as pd
 import numpy as np
 from sklearn.model_selection import train_test_split, GridSearchCV
@@ -5,9 +6,50 @@
 from sklearn.ensemble import RandomForestClassifier
 from sklearn.utils.class_weight import compute_class_weight
 from sklearn.metrics import roc_auc_score
+from sklearn.metrics import precision_score, recall_score
+from sklearn.metrics import f1_score, confusion_matrix
+#from imblearn.over_sampling import SMOTE
 
 bank = pd.read_csv('data/Bank_data_to_class.csv', sep=',')
+label_enc = LabelEncoder()
+for column in bank.select_dtypes(include=["object"]).columns.values:
+    bank[column] = label_enc.fit_transform(bank[column])
 
+#bank.replace({0:1, 1:0}, inplace=True)
 # Write your solution here :
+y=bank['y']
+X=bank.iloc[:,:-1]
+#print(y)
 
+X_train,X_test,y_train,y_test = train_test_split(X,y,test_size = 0.3, random_state=9)
 
+model = RandomForestClassifier(random_state=9,class_weight='balanced')
+
+
+def pipeline(X_train,X_test,y_train,y_test,model):
+
+    param_grid = {
+              "n_estimators": [10, 50, 120],
+              "max_depth": [40, 20, 10],
+              "max_leaf_nodes": [5, 10, 2]}
+
+    grid_obj = GridSearchCV(model, param_grid,cv=5)
+    grid_obj.fit(X_train,y_train)
+    y_pred=grid_obj.predict(X_test)
+    acc_score= roc_auc_score(y_test,y_pred)
+    return grid_obj, acc_score.item()
+
+
+
+
+# grid_model,  auc_score= pipeline(X_train,X_test,y_train,y_test,model)
+# print(auc_score)
+# bank_test = pd.read_csv('data/Bank_data_to_test.csv')
+# y = bank_test['y']
+# X = bank_test.drop(['y'], axis=1)
+# prediction = grid_model.predict_proba(X)[:, 1]
+# #y = bank_test['y']
+# print(type(auc_score))
+
+# auc_score_test = roc_auc_score(y, prediction)
+# print(auc_score_test)
diff --git a/q01_pipeline/build.pyc b/q01_pipeline/build.pyc
index 5a9b3ad..60f257e 100644
Binary files a/q01_pipeline/build.pyc and b/q01_pipeline/build.pyc differ
diff --git a/q01_pipeline/tests/__init__.pyc b/q01_pipeline/tests/__init__.pyc
index b2f2c5b..7295fe1 100644
Binary files a/q01_pipeline/tests/__init__.pyc and b/q01_pipeline/tests/__init__.pyc differ
diff --git a/q01_pipeline/tests/test_q01_pipeline.pyc b/q01_pipeline/tests/test_q01_pipeline.pyc
index 109bce3..e6ea413 100644
Binary files a/q01_pipeline/tests/test_q01_pipeline.pyc and b/q01_pipeline/tests/test_q01_pipeline.pyc differ