diff --git a/__init__.pyc b/__init__.pyc
index 9f1c574..dfb667a 100644
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diff --git a/q01_grid_search/__init__.pyc b/q01_grid_search/__init__.pyc
index 545de0a..487549e 100644
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diff --git a/q01_grid_search/build.py b/q01_grid_search/build.py
index 1438657..627d713 100644
--- a/q01_grid_search/build.py
+++ b/q01_grid_search/build.py
@@ -1,3 +1,4 @@
+# %load q01_grid_search/build.py
 # Default imports
 
 import warnings
@@ -6,6 +7,8 @@
 from sklearn.model_selection import train_test_split
 from sklearn.ensemble import RandomForestClassifier
 from sklearn.model_selection import GridSearchCV
+from sklearn.metrics import r2_score
+import numpy as np
 
 loan_data = pd.read_csv('data/loan_prediction.csv')
 X_bal = loan_data.iloc[:, :-1]
@@ -16,9 +19,14 @@
               "n_estimators": [10, 50, 120],
               "max_depth": [40, 20, 10],
               "max_leaf_nodes": [5, 10, 2]}
+classifier = RandomForestClassifier(random_state=9,oob_score=True)
 
-
-# Write your solution here :
+def grid_search(X_train, y_train, classifier,param_grid,cv=3):
 
 
+    grid_search = GridSearchCV(estimator=classifier, param_grid=param_grid,cv=3)
+    grid_search.fit(X_train,y_train)
+    y_prediction = grid_search.predict(X_test)
+    return grid_search, tuple(grid_search.cv_results_['params']), grid_search.cv_results_['mean_test_score']
 
+#grid_search(X_train, y_train, classifier, param_grid)
diff --git a/q01_grid_search/build.pyc b/q01_grid_search/build.pyc
index 4470232..ed91701 100644
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diff --git a/q01_grid_search/tests/__init__.pyc b/q01_grid_search/tests/__init__.pyc
index 426e901..af537e8 100644
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diff --git a/q01_grid_search/tests/test_q01_grid_search.pyc b/q01_grid_search/tests/test_q01_grid_search.pyc
index 782fd4f..632114f 100644
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diff --git a/q02_fit/__init__.pyc b/q02_fit/__init__.pyc
index 135dfa0..ad35ed1 100644
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diff --git a/q02_fit/build.py b/q02_fit/build.py
index 7a6602b..3d0f3c5 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
@@ -18,9 +19,10 @@
               "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):
+    grid, grid_param, grid_score = grid_search(X_train, y_train, rfc, param_grid, cv=3)
+    grid.fit(X_train,y_train)
+    y_pred = grid.predict(X_test)
+    return confusion_matrix(y_test,y_pred), classification_report(y_test,y_pred), accuracy_score(y_test,y_pred)
 
+#fit(X_test,y_test)
diff --git a/q02_fit/build.pyc b/q02_fit/build.pyc
index 2fc63d1..19441f7 100644
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diff --git a/q02_fit/tests/__init__.pyc b/q02_fit/tests/__init__.pyc
index c6d3bed..cb7739d 100644
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diff --git a/q02_fit/tests/test_q02_fit.pyc b/q02_fit/tests/test_q02_fit.pyc
index 1bdabb1..3a9dba2 100644
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