diff --git a/__init__.pyc b/__init__.pyc
index d1c94d3..e6467dd 100644
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diff --git a/q01_pipeline/__init__.pyc b/q01_pipeline/__init__.pyc
index b360a57..6895574 100644
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diff --git a/q01_pipeline/build.py b/q01_pipeline/build.py
index 96beca7..15ff46e 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
@@ -7,7 +8,29 @@
 from sklearn.metrics import roc_auc_score
 
 bank = pd.read_csv('data/Bank_data_to_class.csv', sep=',')
-
+model=RandomForestClassifier(random_state=9,class_weight='balanced')
 # 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)
 
+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
 
+#pipeline(X_train, X_test, y_train, y_test,model)
diff --git a/q01_pipeline/build.pyc b/q01_pipeline/build.pyc
index 5a9b3ad..55c611a 100644
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diff --git a/q01_pipeline/tests/__init__.pyc b/q01_pipeline/tests/__init__.pyc
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diff --git a/q01_pipeline/tests/test_q01_pipeline.pyc b/q01_pipeline/tests/test_q01_pipeline.pyc
index 109bce3..68708a8 100644
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