diff --git a/__init__.pyc b/__init__.pyc
index d1c94d3..67d5d98 100644
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diff --git a/q01_pipeline/__init__.pyc b/q01_pipeline/__init__.pyc
index b360a57..f67279c 100644
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diff --git a/q01_pipeline/build.py b/q01_pipeline/build.py
index 96beca7..26f41a2 100644
--- a/q01_pipeline/build.py
+++ b/q01_pipeline/build.py
@@ -9,5 +9,21 @@
 bank = pd.read_csv('data/Bank_data_to_class.csv', sep=',')
 
 # Write your solution here :
+le = LabelEncoder()
 
 
+
+model = RandomForestClassifier(random_state=9,class_weight = 'balanced',n_estimators=50)
+
+for column in bank.select_dtypes(include=['object']).columns.values:
+    bank[column] = le.fit_transform(bank[column])
+X_train,X_test,y_train,y_test = train_test_split(bank.iloc[:,:-1],bank['y'],test_size =0.3,random_state=9)
+
+
+def pipeline(X_train,X_test,y_train,y_test,model):
+
+    param_grid = {"max_depth":[2,3,4,5,6,10,15,20,30],"max_features":[8,10,12,14],"max_leaf_nodes":[2,5,10,15,20]}
+    gs = GridSearchCV(estimator=model,param_grid=param_grid,verbose=1)
+    gs.fit(X_train,y_train)
+    auc = roc_auc_score(y_test,gs.predict(X_test))
+    return gs.fit(X_train,y_train),auc
diff --git a/q01_pipeline/build.pyc b/q01_pipeline/build.pyc
index 5a9b3ad..fac097d 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..f8066b5 100644
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