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diff --git a/q01_grid_search/__pycache__/build.cpython-36.pyc b/q01_grid_search/__pycache__/build.cpython-36.pyc
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diff --git a/q01_grid_search/build.py b/q01_grid_search/build.py
index 20c99a1..255d523 100644
--- a/q01_grid_search/build.py
+++ b/q01_grid_search/build.py
@@ -1,8 +1,10 @@
+# %load q01_grid_search/build.py
 # Default imports
 
 import warnings
-warnings.filterwarnings("ignore")
+warnings.filterwarnings('ignore')
 import pandas as pd
+import numpy as np
 from sklearn.model_selection import train_test_split
 from sklearn.ensemble import RandomForestClassifier
 from sklearn.model_selection import GridSearchCV
@@ -12,11 +14,24 @@
 y_bal = loan_data.iloc[:, -1]
 
 X_train, X_test, y_train, y_test = train_test_split(X_bal, y_bal, test_size=0.33, random_state=9)
-param_grid = {"max_features": ['sqrt', 4, "log2"],
-              "n_estimators": [10, 50, 120],
-              "max_depth": [40, 20, 10],
-              "max_leaf_nodes": [5, 10, 2]}
-
+param_grid = {'max_features': ['sqrt', 4, 'log2'],
+              'n_estimators': [10, 50, 120],
+              'max_depth': [40, 20, 10],
+              'max_leaf_nodes': [5, 10, 2]}
 
+#acc_score = make_scorer(accuracy_score)
 # Write your solution here :
+clf = RandomForestClassifier(random_state=9,oob_score=True)
+model = GridSearchCV(clf,param_grid,cv=3)
+def grid_search(X_train,y_train,model,param_grid,cv=3):
+    grid = GridSearchCV(estimator=clf,param_grid=param_grid,cv=3)
+    grid.fit(X_train,y_train)
+    #test_score = grid_obj.cv_results_['mean_test_score']
+    #train_score = grid_obj.cv_results_['params']
+    #param_values = sorted([str(x) for x in list(grid_obj.param_grid.items())[0][1]])
+    #param_values.sort()
+    #x = np.arange(1,len(param_values)+1)
+    return grid,grid.cv_results_['params'],grid.cv_results_['mean_test_score']
+    
+grid_search(X_train,y_train,model,param_grid,cv=3)
 
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