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diff --git a/q01_grid_search/__pycache__/__init__.cpython-36.pyc b/q01_grid_search/__pycache__/__init__.cpython-36.pyc
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diff --git a/q01_grid_search/__pycache__/build.cpython-36.pyc b/q01_grid_search/__pycache__/build.cpython-36.pyc
index dbd3e7a..46a5309 100644
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diff --git a/q01_grid_search/build.py b/q01_grid_search/build.py
index 20c99a1..a04eb7d 100644
--- a/q01_grid_search/build.py
+++ b/q01_grid_search/build.py
@@ -1,7 +1,8 @@
+# %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
@@ -12,11 +13,18 @@
 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]}
 
 
 # Write your solution here :
+def grid_search(X_train, y_train, model, param_grid, cv=3):
+    grid = GridSearchCV(estimator=model, param_grid=param_grid, cv=cv)
+    grid.fit(X_train, y_train)
+    return grid, grid.cv_results_['params'], grid.cv_results_['mean_test_score']
+
+
+
 
diff --git a/q01_grid_search/tests/__pycache__/__init__.cpython-36.pyc b/q01_grid_search/tests/__pycache__/__init__.cpython-36.pyc
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diff --git a/q02_fit/build.py b/q02_fit/build.py
index fbafb1a..e71d66d 100644
--- a/q02_fit/build.py
+++ b/q02_fit/build.py
@@ -22,6 +22,17 @@
 
 
 # Write your solution here :
+def fit(X_test, y_test):
+    print(grid.best_params_)
+    predicted = grid.predict(X_test)
+    predict = pd.DataFrame(predicted)
+    expected = y_test
 
+    matrix = confusion_matrix(expected, predict)
+    clr = classification_report(expected, predict)
+    accuracy = accuracy_score(expected, predict)
 
+    return matrix, clr, accuracy
 
+
+fit(X_test, y_test)