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diff --git a/q01_myXGBoost/__pycache__/__init__.cpython-36.pyc b/q01_myXGBoost/__pycache__/__init__.cpython-36.pyc
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diff --git a/q01_myXGBoost/__pycache__/build.cpython-36.pyc b/q01_myXGBoost/__pycache__/build.cpython-36.pyc
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diff --git a/q01_myXGBoost/build.py b/q01_myXGBoost/build.py
index db3654a..5f78d0c 100644
--- a/q01_myXGBoost/build.py
+++ b/q01_myXGBoost/build.py
@@ -1,8 +1,9 @@
+# %load q01_myXGBoost/build.py
 import pandas as pd
 from xgboost import XGBClassifier
 from sklearn.model_selection import train_test_split
 from sklearn.model_selection import GridSearchCV
-from sklearn.metrics import accuracy_score
+from sklearn.metrics import accuracy_score, make_scorer
 
 # load data
 dataset = pd.read_csv('data/loan_clean_data.csv')
@@ -11,13 +12,23 @@
 y = dataset.iloc[:, -1]
 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=9)
 
-param_grid1 = {"max_depth": [2, 3, 4, 5, 6, 7, 9, 11],
-               "min_child_weight": [4, 6, 7, 8],
-               "subsample": [0.6, .7, .8, .9, 1],
-               "colsample_bytree": [0.6, .7, .8, .9, 1]
+param_grid1 = {'max_depth': [2, 3, 4, 5, 6, 7, 9, 11],
+               'min_child_weight': [4, 6, 7, 8],
+               'subsample': [0.6, .7, .8, .9, 1],
+               'colsample_bytree': [0.6, .7, .8, .9, 1]
                }
+acc_scorer = make_scorer(accuracy_score)
+classifier = XGBClassifier()
 
+def myXGBoost(X_train, X_test,y_train,y_test,classifier,param_grid,KFold=3):
+    
+    grid = GridSearchCV(classifier, param_grid1, scoring=acc_scorer)
+    grid=grid.fit(X_train,y_train)
+    y_test1=grid.predict(X_test)
+    accuracy_clf_1 = accuracy_score(y_test, y_test1)
+    grid.grid_scores_
+    return(accuracy_clf_1,grid.best_params_)
 
-# Write your solution here :
+myXGBoost(X_train, X_test,y_train,y_test,classifier,param_grid1,KFold=3)
 
 
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