data.loc[index, 'video_memory_value'] = re.sub('\D', '', data.loc[index, 'video_memory'])
 if data.loc[index, 'video_memory_value'] == '':
     data.loc[index, 'video_memory_value'] = float(0)
 else:
     data.loc[index, 'video_memory_value'] = float(data.loc[index, 'video_memory_value'])

cpu_mapping = {'i3':3, 'i5':5, 'i7':7, 'i9':9, 'AMD':6}
data['cpu'] = data['cpu'].map(cpu_mapping)

data = data.join(pd.get_dummies(data[['brand', 'video_card']]))

data = data.drop_duplicates()
data = data.dropna(axis=0, how='any')

 screen_pixels = re.findall(r'\d+', data.loc[index, 'screen_pixels'])
 if len(screen_pixels) >= 2:
     data.loc[index, 'screen_pixels_x'] = float(screen_pixels[0])
     data.loc[index, 'screen_pixels_y'] = float(screen_pixels[1])

 ssd_value = re.findall('\d+', str(data.loc[index, 'ssd']))
 if len(ssd_value) > 0:
     data.loc[index, 'ssd_value'] = float(ssd_value[0])
 else:
     data.loc[index, 'ssd_value'] = 0

 def ridge_model(train_data, test_data, train_target, test_target):
     param_alpha = {'alpha':[1.0, 0.1, 0.01, 0.001]}
     estimator = Ridge()
     estimator = GridSearchCV(estimator, param_grid=param_alpha)
     estimator.fit(train_data, train_target)
     print('RIDGE最佳参数alpha:', estimator.best_params_)
     print('RIDGE最佳得分：', estimator.best_score_)
     estimator = estimator.best_estimator_
     print('RIDGE损失函数值MAE：%.2f' % 
     np.divide(np.sum(np.absolute(estimator.predict(test_data) - test_target)), len(test_target)))
     print('RIDGE预测性能得分：%.2f' % estimator.score(test_data, test_target))


 RIDGE最佳参数alpha: {'alpha': 0.1}
 RIDGE最佳得分： 0.36026660370026786
 RIDGE损失函数值MAE：27.46
 RIDGE预测性能得分：0.54


 data_columns = data.columns
 min_max_scaler = MinMaxScaler()
 data = min_max_scaler.fit_transform(data)
 data = pd.DataFrame(data)
 data.columns = data_columns


 def xgb_model(train_data, test_data, train_target, test_target):
     param_learning_rate = {'learning_rate':[1.0, 0.1, 0.01, 0.001], 'max_depth':[3, 4, 5, 6, 7, 8], 'n_estimators':[10, 20, 30, 50, 70, 90, 100, 120, 140, 160, 200, 230, 260, 300]}
     estimator = xgb.XGBRegressor(silent=False, objective='reg:gamma')
     estimator = GridSearchCV(estimator, param_grid=param_learning_rate)
     estimator.fit(train_data, train_target)
     print('XGB最佳参数learning_rate:', estimator.best_params_)
     print('XGB最佳得分：', estimator.best_score_)

     estimator = estimator.best_estimator_
     predict_target = estimator.predict(test_data)

     actual_target = []
     for index, row in test_target.iterrows():
         comment_count = row['comment_count']
         actual_target.append(comment_count)

     mae_value = np.divide(np.sum(np.absolute(predict_target - actual_target)), len(actual_target))
     print('XGB损失函数值MAE：%.2f' % mae_value )
     print('XGB预测性能得分：%.2f' % estimator.score(test_data, test_target))