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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..7c8fffb 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 +13,22 @@
 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 :
+rfc = RandomForestClassifier(oob_score=True, random_state=9)
+
+def grid_search(X_train,y_train,model,param_grid,cv = 3):
+    grid = GridSearchCV(model,param_grid,scoring='accuracy',cv = 3)
+    grid.fit(X_train,y_train)
+    grid_param = grid.cv_results_['params']
+    grid_score = grid.cv_results_['mean_test_score']
+    return grid, grid_param, grid_score 
+
+# grid_search(X_bal,y_bal,rfc,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..5a17eb8 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 +14,23 @@
 
 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):
+    y_pred = grid.predict(X_test)
+    cm = confusion_matrix(y_test,y_pred)
+    cr = classification_report(y_test,y_pred)
+    acs = accuracy_score(y_test,y_pred)
+    return cm,cr,acs
+
+# fit(X_test, y_test)
 
 
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