diff --git a/__init__.pyc b/__init__.pyc
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diff --git a/q01_myXGBoost/__init__.pyc b/q01_myXGBoost/__init__.pyc
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diff --git a/q01_myXGBoost/build.py b/q01_myXGBoost/build.py
index f000406..88ee1a4 100644
--- a/q01_myXGBoost/build.py
+++ b/q01_myXGBoost/build.py
@@ -1,25 +1,40 @@
-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
-
-# load data
-dataset = pd.read_csv('data/loan_clean_data.csv')
-# split data into X and y
-X = dataset.iloc[:, :-1]
-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]
-               }
-
-
-# Write your solution here :
-
-
-
-
+# %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
+
+# load data
+dataset = pd.read_csv('data/loan_clean_data.csv')
+# split data into X and y
+X = dataset.iloc[:, :-1]
+y = dataset.iloc[:, -1]
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=9)
+model=XGBClassifier(seed=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]
+               }
+
+
+# Write your solution here :
+
+def myXGBoost(X_train, X_test, y_train, y_test,model,param_grid,Kfold=3):
+    #acc_scorer = make_scorer(accuracy_score)
+    grid_obj = GridSearchCV(model, param_grid1,cv=Kfold)
+    grid_obj.fit(X_train,y_train)
+    bestParam= grid_obj.best_params_
+    y_pred=grid_obj.predict(X_test)
+    acc_score=accuracy_score(y_test,y_pred)
+    #print(grid_obj.best_score_)
+    return acc_score.item(),bestParam
+
+
+# accuracy, best_params =myXGBoost(X_train, X_test, y_train, y_test,model,param_grid1,Kfold=3)
+# print(type(accuracy))
+# print(type(best_params))
+# print(best_params)
+# print(accuracy)
diff --git a/q01_myXGBoost/build.pyc b/q01_myXGBoost/build.pyc
index 2b98a8a..8fd6ad6 100644
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diff --git a/q01_myXGBoost/tests/__init__.pyc b/q01_myXGBoost/tests/__init__.pyc
index 7411455..c704383 100644
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diff --git a/q01_myXGBoost/tests/test_q01_myXGBoost.pyc b/q01_myXGBoost/tests/test_q01_myXGBoost.pyc
index 54780c7..5abf134 100644
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diff --git a/q02_param2/__init__.pyc b/q02_param2/__init__.pyc
index fae1a21..7329b47 100644
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diff --git a/q02_param2/build.py b/q02_param2/build.py
index 156fe17..1f67c4c 100644
--- a/q02_param2/build.py
+++ b/q02_param2/build.py
@@ -1,3 +1,5 @@
+
+# %load q02_param2/build.py
 # Default imports
 from sklearn.model_selection import train_test_split
 from xgboost import XGBClassifier
@@ -16,6 +18,43 @@
                "reg_lambda": [0.05, 0.1, 0.5, 1.0]
                }
 
+model = XGBClassifier(seed=9)
 
 # Write your solution here :
+def param2(X_train, X_test, y_train, y_test,model,param_grid):
+
+    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]
+               }
+    cc_score,bestParam = myXGBoost(X_train, X_test, y_train, y_test,model,param_grid1,3)
+    #print(bestParam)
+    #dic ={}
+
+    #updatePara = {'subsample': 0.8, 'colsample_bytree': 0.7, 'max_depth': 2, 'min_child_weight': 4}
+
+    param_g={}
+    for k, v in bestParam.items():
+        #print(k,v)
+        param_g[k]=[v]
+
+    #print(param_g)
+
+    #updateParam = {'subsample': [0.8], 'colsample_bytree': [0.7], 'max_depth': [2], 'min_child_weight': [4]}
+
+    updateParam=param_g.copy()
+    updateParam.update(param_grid)
+    #print(updateParam)
+    cc_score2,bestParam2= myXGBoost(X_train, X_test, y_train, y_test,model,updateParam,3)
+
+    #print(cc_score2,bestParam2)
+    update_best_param={k: v for k, v in bestParam2.items() if k not in param_g}
+    return (cc_score2.item(),update_best_param)
+
 
+# accuracy1, best_params1 =param2(X_train, X_test, y_train, y_test,model,param_grid2)
+# print(type(accuracy1))
+# print( type(best_params1))
+# print(accuracy1)
+# print(best_params1)
diff --git a/q02_param2/build.pyc b/q02_param2/build.pyc
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diff --git a/q02_param2/tests/test_q02_param2.pyc b/q02_param2/tests/test_q02_param2.pyc
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diff --git a/q03_xgboost/__init__.pyc b/q03_xgboost/__init__.pyc
index 4fb1998..251ed47 100644
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diff --git a/q03_xgboost/build.py b/q03_xgboost/build.py
index fc75b96..8481d5c 100644
--- a/q03_xgboost/build.py
+++ b/q03_xgboost/build.py
@@ -1,3 +1,5 @@
+
+# %load q03_xgboost/build.py
 # Default imports
 from sklearn.model_selection import train_test_split
 from xgboost import XGBClassifier
@@ -13,4 +15,19 @@
 
 
 # Write your solution here :
+def xgboost(X_train, X_test, y_train, y_test,**kwargs):
+            model = XGBClassifier(seed=9,**kwargs)
+            #subsample=0.8,colsample_bytree=0.7, max_depth=2, min_child_weight=4, reg_alpha=0, reg_lambda=1.0,gamma=0,n_estimators=100,learning_rate=0.1)
+            model.fit(X_train,y_train)
+            y_pred=model.predict(X_test)
+            acc_score=accuracy_score(y_test,y_pred)
+            return acc_score.item()
+
+
 
+# accuracy=xgboost(X_train, X_test, y_train, y_test,subsample=0.8,
+#               colsample_bytree=0.7, max_depth=2,
+#               min_child_weight=4, reg_alpha=0, reg_lambda=1.0,
+#              gamma=0,n_estimators=100,learning_rate=0.1)
+# print(accuracy)
+# print(type(accuracy))
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