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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..af5f9b7 100644
--- a/q01_grid_search/build.py
+++ b/q01_grid_search/build.py
@@ -1,22 +1,41 @@
+# %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
+from sklearn.metrics import make_scorer,accuracy_score
 
 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]}
+
+model = RandomForestClassifier(oob_score=True, random_state=9)
 
 
 # Write your solution here :
+def grid_search(X_train, y_train, model, param_grid, cv=3):
+    clf = GridSearchCV(model,param_grid,cv=cv)
+    clf.fit(X_train,y_train)
+
+    
+    results = pd.DataFrame(clf.cv_results_)
+    model1 = clf
+    variable1 = list(results['params'])
+    variable2 = np.array(results['mean_test_score'])
+    return model1,variable1,variable2
+    
+
+
+#print(grid_search(X_train,y_train,model,param_grid,cv = 3))
 
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diff --git a/q02_fit/__pycache__/__init__.cpython-36.pyc b/q02_fit/__pycache__/__init__.cpython-36.pyc
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diff --git a/q02_fit/build.py b/q02_fit/build.py
index fbafb1a..45cba7c 100644
--- a/q02_fit/build.py
+++ b/q02_fit/build.py
@@ -1,3 +1,4 @@
+# %load q02_fit/build.py
 # Default imports
 
 import pandas as pd
@@ -13,15 +14,27 @@
 
 X_train, X_test, y_train, y_test = train_test_split(X_bal, y_bal, test_size=0.33, random_state=9)
 rfc = RandomForestClassifier(oob_score=True, 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]}
 
 grid, grid_param, grid_score = grid_search(X_train, y_train, rfc, param_grid, cv=3)
 
 
 # Write your solution here :
+def fit(X_test,y_test):
+    clf =  grid
+    clf.fit(X_train,y_train)
+    y_pred = clf.predict(X_test)
+    variable1 = confusion_matrix(y_test, y_pred)
+    variable2 = classification_report(y_test, y_pred)
+    variable3 = accuracy_score(y_test, y_pred)
+
+    return variable1, variable2, variable3
+
+
+
 
 
 
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