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diff --git a/q01_grid_search/build.py b/q01_grid_search/build.py
index 20c99a1..9a7ed38 100644
--- a/q01_grid_search/build.py
+++ b/q01_grid_search/build.py
@@ -1,22 +1,45 @@
+# %load q01_grid_search/build.py
 # Default imports
 
 import warnings
-warnings.filterwarnings("ignore")
+warnings.filterwarnings('ignore')
 import pandas as pd
 from sklearn.model_selection import train_test_split
 from sklearn.ensemble import RandomForestClassifier
 from sklearn.model_selection import GridSearchCV
+import numpy as np
+from sklearn.metrics import accuracy_score, roc_auc_score, make_scorer
 
 loan_data = pd.read_csv('data/loan_prediction.csv')
 X_bal = loan_data.iloc[:, :-1]
 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]}
+rfx = RandomForestClassifier(oob_score = True, random_state = 9)
 
 
 # Write your solution here :
+def grid_search(X_train, y_train,model, param_grid, cv =3):
+    np.random.seed(9)
+    acc_scorer = make_scorer(accuracy_score)
+    
+    #RUn the Grid
+    grid_obj = GridSearchCV(model, param_grid, scoring = acc_scorer)
+    grid_obj = grid_obj.fit(X_train, y_train)
+    var1 = list()
+    var2 =list()
+    for i in grid_obj.grid_scores_:
+        var1.append(i[0])
+        var2.append(i[1])
+    var3 = np.array(var2)
+    return grid_obj, var1, var3
+
+#grid_search(X_train, y_train, rfx, param_grid)
+    
+
+
 
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