Enhancement to predict function

labs06/task02.py

@@ -39,33 +39,18 @@ def find_13(dane):
 def find_best_flats(dane):
     return dane.loc[(dane['Borough'] == 'Winogrady') & (dane['Rooms'] == 3) & (dane['Floor'] == 1)]
 
-def predict(dane, col_name):
+def predict(dane, rooms, sqrMeters):
     from sklearn import linear_model
-    from sklearn.metrics import mean_squared_error, r2_score
-    d_X = pd.DataFrame(dane[col_name])
-    d_X_train = d_X[4000:]
-    d_X_test = d_X[:4000]
-    d_y = pd.DataFrame(dane['Expected'])
-    d_y_train = d_y[4000:]
-    d_y_test = d_y[:4000]
-    regr = linear_model.LinearRegression()
-    regr.fit(d_X_train, d_y_train)
-    y_pred = regr.predict(d_X_test)
-    print('MODEL(%s): pred_y = %f * x + %f' % (col_name, regr.coef_[0], regr.intercept_) )
-    print('Mean squared error: %.2f' % mean_squared_error(d_y_test, y_pred))
-
-    import matplotlib.pyplot as plt
-
-    plt.clf()
-    dataLine, =  plt.plot(d_X_test, d_y_test, 'ro', label='collected data')
-    predLine, = plt.plot(d_X_test, y_pred, color='blue', linestyle='--', linewidth = 2, label='predictions')
-    ax = plt.gca().add_artist(plt.legend(handles=[dataLine], loc=1))
-    plt.legend(handles=[predLine], loc=4)
-    plt.xticks(())
-    plt.yticks(())
-    plt.xlabel(col_name)
-    plt.ylabel('Price')
-    plt.show()
+    import numpy as np
+    data = dane
+    df = pd.DataFrame(data, columns=np.array(['Rooms','SqrMeters']))
+    target = pd.DataFrame(data, columns=["Expected"])
+    X = df
+    y = target["Expected"]
+    lm = linear_model.LinearRegression()
+    model = lm.fit(X, y)
+    inData = pd.DataFrame.from_records([(rooms, sqrMeters)], columns=['Rooms', 'SqrMeters'])
+    return lm.predict(inData)[0]
 
 def main():
     dane = wczytaj_dane()
@@ -87,8 +72,7 @@ def main():
     print('"Najlepsze" mieszkania: ')
     print(find_best_flats(dane))
 
-    predict(dane, 'Rooms')
-    predict(dane, 'SqrMeters')
+    print('Predicted price(actual 146000): ', predict(dane,1,31.21))
 
 
 if __name__ == "__main__":