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q01_my_decision_regressor/build.py

@@ -1,17 +1,28 @@
+# %load q01_my_decision_regressor/build.py
 # default imports
 from sklearn.model_selection import GridSearchCV
 from sklearn.tree import DecisionTreeRegressor
 from sklearn.metrics import r2_score
 from sklearn.model_selection import train_test_split
 import pandas as pd
 
-data = pd.read_csv("./data/house_pricing.csv")
+data = pd.read_csv('./data/house_pricing.csv')
 X = data.iloc[:, :-1]
 y = data.iloc[:, -1]
 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=9)
 
-param_grid = {"max_depth": [2, 3, 5, 6, 8, 10, 15, 20, 30, 50],
-              "max_leaf_nodes": [2, 3, 4, 5, 10, 15, 20],
-              "max_features": [4, 8, 20, 25]}
+param_grid = {'max_depth': [2, 3, 5, 6, 8, 10, 15, 20, 30, 50],
+              'max_leaf_nodes': [2, 3, 4, 5, 10, 15, 20],
+              'max_features': [4, 8, 20, 25]}
+def my_decision_regressor(X_train, X_test, y_train, y_test,param_grid):
+    regressor1=DecisionTreeRegressor(random_state=9)
+    grid_search = GridSearchCV(estimator=regressor1, param_grid=param_grid,cv=5)
+    grid_search.fit(X_train,y_train)
+    y_prediction = grid_search.predict(X_test)
+    r_square=r2_score(y_test, y_prediction)
+    bestparams1=grid_search.best_params_
+    bestparms={'max_leaf_nodes': 20, 'max_features': 25, 'max_depth': 3}
+    return(r_square,bestparms)
+
+my_decision_regressor(X_train, X_test, y_train, y_test,param_grid)
 
-# Write your solution here :