<add>pd practice script

Author: PapyrusNotes

PandasPractice/datasets/housing_data.csv renamed to PandasPractice/housing/datasets/housing_data.csv

@@ -4,8 +4,12 @@
 from sklearn.preprocessing import MinMaxScaler
 
 from sklearn.linear_model import LinearRegression
+from sklearn.svm import SVR
+from sklearn.ensemble import RandomForestRegressor
 
-data = pd.read_csv("./datasets/housing_data.csv", header=None, sep=',')  # if no columns , header = None
+from sklearn.metrics import mean_absolute_error, mean_squared_error, mean_absolute_percentage_error
+
+data = pd.read_csv("datasets/housing_data.csv", header=None, sep=',')  # if no columns , header = None
 column_names = ['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX', 'PTRATIO', 'B', 'LSTAT',
                 'MEDV', 'isHighValue']
 
@@ -23,7 +27,6 @@
 med_val = data['CRIM'].median()
 data1['CRIM'] = data1['CRIM'].fillna(med_val)
 
-
 # print(data1.describe())
 
 # 결측치 처리 - CRIM column의 행들 중 결측치가 존재하는 행들 제거
@@ -81,3 +84,29 @@ def get_iqr_outlier_prop(x):
 model_lr.fit(X_train_r_scaled, y_train_r)
 print(model_lr.coef_)
 print(model_lr.intercept_)
+
+# SVM
+model_svr = SVR()
+model_svr.fit(X_train_r_scaled, y_train_r)
+
+# RandomForest
+model_rfr = RandomForestRegressor(random_state=123)
+model_rfr.fit(X_train_r_scaled, y_train_r)
+for x, val in zip(X_cols, model_rfr.feature_importances_):
+    print(f'{x} : %.3f' % val)
+
+# 모형별 평가
+y_pred_lr = model_lr.predict(X_test_r_scaled)
+y_pred_svr = model_svr.predict(X_test_r_scaled)
+y_pred_rfr = model_rfr.predict(X_test_r_scaled)
+
+print('-'*30)
+print("선형 회귀 결과")
+print('MAE : %.3f' %mean_absolute_error())
+print('MSE : %.3f' %mean_squared_error())
+print('MAPE : %.3f' %mean_absolute_percentage_error())
+print('-'*30)
+print('-'*30)
+ a= [1,2,4,5,6]
+ for x in a:
+     printa)

PandasPractice/housing.py renamed to PandasPractice/housing/housing.py

@@ -0,0 +1,38 @@
+import pandas as pd
+'''
+# python dictionary obj to pd.Series obj
+dict_data = {'song_name': 'STAY', 'artist': 'Bustin Jieber', 'release_date': 20220808}
+
+sr = pd.Series(dict_data)
+print(type(sr))
+print('\n')
+print(sr)  ## dtype: object
+
+dict_data2 = {'a': 1, 'b': 2, '3': 20220808}
+sr = pd.Series(dict_data2)
+print(type(sr))
+print('\n')
+print(sr)  ## dtype: int64
+
+
+dict_data3 = {'a': 'apple', 'b': 'bile', 'c': 'cyclone'}
+sr = pd.Series(dict_data3)
+print(type(sr))
+print('\n')
+print(sr)  ## dtype: object
+idx = sr.index
+val = sr.values
+print(idx)
+print(val)
+print(type(val))  ## numpy.ndarray
+'''
+
+# python tuple obj to pd.Series obj
+tuple_data = ('음성군', '진천읍', True, 4412)
+sr = pd.Series(tuple_data, ['first_loc', 'second_loc', 'including', '_id'])
+print(sr)
+
+# slicing , choosing elements.
+
+
+