diff --git a/__init__.pyc b/__init__.pyc
index d1c94d3..c852fa2 100644
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diff --git a/q01_pipeline/__init__.pyc b/q01_pipeline/__init__.pyc
index b360a57..bf25e6c 100644
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diff --git a/q01_pipeline/build.py b/q01_pipeline/build.py
index 96beca7..c298155 100644
--- a/q01_pipeline/build.py
+++ b/q01_pipeline/build.py
@@ -9,5 +9,29 @@
 bank = pd.read_csv('data/Bank_data_to_class.csv', sep=',')
 
 # Write your solution here :
+y = bank['y']
+X = bank.drop(['y'], axis=1)
 
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=9)
+# Write your solution here :
+model = RandomForestClassifier(random_state=9,class_weight = 'balanced')
 
+def pipeline(X_train, X_test, y_train, y_test,model):
+    param_grid = {"max_depth": [2, 3, 5, 6, 8, 10, 15, 20, 30],
+              "max_leaf_nodes": [2, 5, 10, 15, 20],
+              "max_features": [8,10,12,14]}
+    grid = GridSearchCV(estimator=model,param_grid=param_grid)
+    label = LabelEncoder()
+    y_train=label.fit_transform(y_train)
+    for column in X_train.columns:
+        if X_train[column].dtype == type(object):
+            label = LabelEncoder()
+            X_train[column] = label.fit_transform(X_train[column])
+    y_test=label.fit_transform(y_test)
+    for column in X_test.columns:
+        if X_test[column].dtype == type(object):
+            label = LabelEncoder()
+            X_test[column] = label.fit_transform(X_test[column])
+    grid.fit(X_train, y_train)
+    auc= roc_auc_score(y_test, grid.predict(X_test))
+    return grid.fit(X_train, y_train),auc
diff --git a/q01_pipeline/build.pyc b/q01_pipeline/build.pyc
index 5a9b3ad..9a6152f 100644
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diff --git a/q01_pipeline/tests/__init__.pyc b/q01_pipeline/tests/__init__.pyc
index b2f2c5b..7db9671 100644
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diff --git a/q01_pipeline/tests/test_q01_pipeline.pyc b/q01_pipeline/tests/test_q01_pipeline.pyc
index 109bce3..cd8e108 100644
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