kaggle预测房价

kaggle房价预测比赛官方地址：https://www.kaggle.com/c/house-prices-advanced-regression-techniques

kaggle数据集描述：https://www.kaggle.com/c/house-prices-advanced-regression-techniques/data

Step 1：引入相关的包

1. # coding:utf-8

2. # 注意读取文件时，Windows系统的\\和Linux系统的/的区别

3.  

4. import numpy as np

5. import pandas as pd

6. import matplotlib.pyplot as plt

7. from sklearn.linear_model import Ridge

8. from sklearn.model_selection import cross_val_score

9. from sklearn.ensemble import RandomForestRegressor

Step 2：读取数据 

文件的组织形式是house price文件夹下面放house_price.py和input文件夹。input文件夹下面放的是从https://www.kaggle.com/c/house-prices-advanced-regression-techniques/data

下载的train.csv test.csv sample_submission.csv 和 data_description.txt 四个文件。

1. # 将csv数据转换为DataFrame数据，方便用pandas进行数据预处理

2. # 注意将print的注释打开，可以查看输出结果

1. #不要让pandas自己给数据加编号，这样ID就成为index了

2. train_df = pd.read_csv(".\\input\\train.csv",index_col = 0)

3. test_df = pd.read_csv('.\\input\\test.csv',index_col = 0)

4. # print train_df.shape

5. # print test_df.shape

6. # print train_df.head() # 默认展示前五行 这里是5行,80列

7. # print test_df.head() # 这里是5行,79列

Step 3：合并数据 ：特征工程的工作！！！

这么做主要是为了用DF进行数据预处理的时候更加方便。等所有的需要的预处理进行完之后，我们再把他们分隔开。实际项目中，不会这样做。首先，SalePrice作为我们的训练目标，只会出现在训练集中，不会在测试集中。所以，我们先把SalePrice这一列给拿出来，不让它碍事儿。

[code]# 看SalePrice的形状和用log1p处理后的形状

1. %matplotlib inline

2. prices = pd.DataFrame({'price':train_df['SalePrice'],'log(price+1)':np.log1p(train_df['SalePrice'])})

3. ps = prices.hist()

4. # plt.plot()

5. # plt.show()

6.  

7. # log1p即log(1+x)，可以让label平滑化，将数据变为正态分布，目的在于使数据的呈现方式接近我们所希望的前提假设，从而进行更好的统计推断

8. y_train = np.log1p(train_df.pop('SalePrice')) #提出和test数据不一致的price，马上进行train和test的合并

9. all_df = pd.concat((train_df,test_df),axis = 0) #合并

10. # print all_df.shape #查看all_df (2919,79)

11. # print y_train.head() #查看处理后的标记预测值

Step 4：变量转化：特征工程和数据清洗的工作！！！

正确化变量属性：MSSubClass 的值其实应该是一个category（等级的划分），虽然是数字，但是代表多类别，Pandas是不会懂这些。使用DF的时候，这类数字符号会被默认记成数字。这种东西就很有误导性，我们需要把它变回成string

1. print all_df['MSSubClass'].dtypes #dtype('int64')

2. all_df['MSSubClass'] = all_df['MSSubClass'].astype(str) #转为string，便于查看他的分布情况

3. print all_df['MSSubClass'].dtypes

4. print all_df['MSSubClass'].value_counts()

把category的变量转变成numerical表达形式：当我们用numerical来表达categorical的时候，要注意，数字本身有大小的含义，所以乱用数字会给之后的模型学习带来麻烦。于是我们可以用One-Hot的方法来表达category。pandas自带的get_dummies方法，可以帮你一键做到One-Hot。

[code]#不能被计算机理解的变量（字符串，离散型变量等）

1. print pd.get_dummies(all_df['MSSubClass'],prefix = 'MSSubClass'#处理离散型变量的方法get_dummies,即就是one-hot).head()

2. all_dummy_df = pd.get_dummies(all_df) #pandas自动选择那些事离散型变量，省去了我们做选择

3. print all_dummy_df.head()

清洗第二步：处理numerical变量：

比如，有一些数据是缺失的

1. print all_dummy_df.isnull().sum().sort_values(ascending = False).head(11) #查看缺失情况，按照缺失情况排序

2. # 注意：处理缺失情况时要看数据描述，确实值得处理方式工具意义和缺失情况有很大不同，有时确实本身就有意义，我们要把他当

#做一个类型，其他时候要将其补上或者删除这个特征

1. #我们这里用mean填充

2. mean_cols = all_dummy_df.mean()

3. print mean_cols.head(10)

4. all_dummy_df = all_dummy_df.fillna(mean_cols) #fillna填充

5. print all_dummy_df.isnull().sum().sum() #输出0

标准化numerical数据：

这一步并不是必要，但是得看你想要用的分类器是什么。一般来说，regression的分类器都需要这一步，最好是把源数据给放在一个标准分布内，不要让数据间的差距太大。我们不需要把One-Hot的那些0/1数据给标准化，因为只有0和1，我们的目标应该是那些本来就是numerical的数据型的特征。

1. numeric_cols = all_df.columns[all_df.dtypes != 'object'] #查看那些是numerical数据，本来就是数字化的数据

2. print numeric_cols

1. #标准化numerical数据，让数据更加平滑，更加便于计算：如z-score标准化：（x-x’）/s 【x：原数据；x'：平均数；s：标准差】

2. numeric_col_means = all_dummy_df.loc[:,numeric_cols].mean() #均值

3. numeric_col_std = all_dummy_df.loc[:,numeric_cols].std() #标准差

4. all_dummy_df.loc[:,numeric_cols] = (all_dummy_df.loc[:,numeric_cols] - numeric_col_means) / numeric_col_std

Step 5-1: 建立模型【房价预测/Ridge/RandomForest/cross_validation】

1. # 把数据处理之后，分回训练集和测试集（起初在数据处理时将train和test数据结合了）

2. dummy_train_df = all_dummy_df.loc[train_df.index]

3. dummy_test_df = all_dummy_df.loc[test_df.index]

4. print dummy_train_df.shape,dummy_test_df.shape #输出（（1460,303），（1459,303））

5.  

6. # 将DF数据转换成Numpy Array的形式，更好地配合sklearn

7. X_train = dummy_train_df.values

8. X_test = dummy_test_df.values

Ridge Regression（回归模型的一种：对于多因子的数据集，可以直接把所有的特征都放进去，不用考虑特征提取）

[code]from sklearn.linear_model import Ridge

from sklearn.model_selection import cross_val_score  #交叉验证来测试模型

1.  
2.  

#不是很必要，知识吧DataFrame转换成Numpy Array格式数据

X_train = dummy_train_df.values

X_test = dummy_test_df.values

1.  
2.  

#用Sklearn自带的cross_calidation来测试模型

1. alphas = np.logspace(-3,2,50) #创建等比梳理与，如：10^-3至10^2其中的50个数

2. test_scores = [] #交叉验证的得分，最后找到最好的参数

3. for alpha in alphas:

4. clf = Ridge(alpha)

5. test_score = np.sqrt(-cross_val_score(clf,X_train,y_train,cv = 10,scoring = 'neg_mean_squared_error'))

6. test_scores.append(np.mean(test_score))

plt.plot(alphas,test_scores) #可视化参数与分数

plt.title('Alpha vs CV Error')

1. plt.show()

2.  

1.  
2.  

• 存下所有的cv值，看看那个alpha值更好【调参数】
• 

大概alpha=10~20的时候，可以把score达到0.135左右。

Random Forest

[code]from sklearn.ensemble import RandomForestRegressorRF

1. max_features = [.1,.3,.5,.7,.9,.99]

2. test_scores = []

3. for max_feat in max_features:

4. clf = RandomForestRegressor(n_estimators = 200,max_features = max_feat)

5. test_score = np.sqrt(-cross_val_score(clf,X_train,y_train,cv = 5,scoring = 'neg_mean_squared_error'))

6. test_scores.append(np.mean(test_score))

7. plt.plot(max_features,test_scores)

8. plt.title('Max Features vs CV Error')

9. plt.show()

max_features=0.3时，RF达到了最优0.137

Step 5-2: 建立模型 【进阶版/bagging/boosting/AdaBoost/XGBoost】

从模型的角度考虑，用了bagging、boosting（AdaBoost）、XGBoost三个模型（模型框架）。 

把数据集分回 训练/测试集

 

1. dummy_train_df = all_dummy_df.loc[train_df.index]

2. dummy_test_df = all_dummy_df.loc[test_df.index]

3. print dummy_train_df.shape,dummy_test_df.shape

4.  

5. # 将DF数据转换成Numpy Array的形式，更好地配合sklearn

6. X_train = dummy_train_df.values

7. X_test = dummy_test_df.values

1、bagging： 

单个分类器的效果真的是很有限。我们会倾向于把N多的分类器合在一起，做一个“综合分类器”以达到最好的效果。我们从刚刚的试验中得知，Ridge(alpha=15)给了我们最好的结果

1. ridge = Ridge(alpha=15)

2. # bagging 把很多小的分类器放在一起，每个train随机的一部分数据，然后把它们的最终结果综合起来（多数投票）

3. # bagging 算是一种算法框架

4. params = [1, 10, 15, 20, 25, 30, 40] # 多少个弱分类器

5. test_scores = []

6. for param in params:

7. clf = BaggingRegressor(n_estimators=param，base_estimator = ridge) # #base_estimator = ridge是弱分类器0.132（params=25时）

8. #clf = BaggingRegressor(n_estimators = param)#用Bagging自带的DecisionTree，最好0.140

9. test_score = np.sqrt(-cross_val_score(clf, X_train, y_train, cv=10, scoring='neg_mean_squared_error'))

10. test_scores.append(np.mean(test_score))

11.  

12. plt.plot(params, test_scores)

13. plt.title('n_estimators vs CV Error')

14. plt.show()

15.  

16. br = BaggingRegressor(base_estimator=ridge, n_estimators=25)

17. br.fit(X_train, y_train)

18. y_final = np.expm1(br.predict(X_test))

2、boosting 

Boosting比Bagging理论上更高级点，它也是揽来一把的分类器。但是把他们线性排列。下一个分类器把上一个分类器分类得不好的地方加上更高的权重，这样下一个分类器就能在这个部分学得更加“深刻”。

1. from sklearn.ensemble import AdaBoostRegressor

2. ms = [10,15,20,25,30,35,40,45,50]

3. test_scores = []

4. for param in params:

5. clf = AdaBoostRegressor(base_estimator = ridge,n_estimators = param) #ms=25时，0.132，但是不稳定，需要更多的参数或者更多小分类器

6. test_score = np.sqrt(-cross_val_score(clf,X_train,y_train,cv = 10,scoring = 'neg_mean_squared_error'))

7. test_scores.append(np.mean(test_score))

8. plt.plot(params,test_scores)

9. plt.title('n_estimators vs CV Error')

10. plt.show()

3、XGBoost （kaggle神器）

这依旧是一款Boosting框架的模型，但是却做了很多的改进。 
 

1. from xgboost import XGBRegressor

2. params = [1,2,3,4,5,6]

3. test_scores = []

4. for param in params:

5. clf = XGBRegressor(max_depth = param) #深度params=5时，错误率达到0.127

6. test_score = np.sqrt(-cross_val_score(clf,X_train,y_train,cv = 10,scoring = 'neg_mean_squared_error'))

7. test_scores.append(np.mean(test_score))

8. plt.plot(params,test_scores)

9. plt.title('max_depth vs CV Error')

10. plt.show()

11.  

12. xgb = XGBRegressor(max_depth = 5)

13. xgb.fit(X_train, y_train)

14. y_final = np.expm1(xgb.predict(X_test))

Step 6: Ensemble 

这里我们用一个Stacking的思维来汲取两种或者多种模型的优点 ;

首先，我们把最好的parameter拿出来，做成我们最终的model;

1. ridge = Ridge(alpha = 15)

2. rf = RandomForestRegressor(n_estimators = 500,max_features = .3)

3. ridge.fit(X_train,y_train)

4. rf.fit(X_train,y_train)

5.  
6.  

#最前面个label做了一个log(1+x),这里需要把predit的值给exp回去，并且戒掉那个‘1’

1. y_ridge = np.expm1(ridge.predict(X_test))

2. y_rf = np.expm1(rf.predict(X_test))

3. #把所有的model的预测结果作为新的输入，最简单的就是不下直接【平均化】

4. y_final = (y_ridge + y_rf) / 2

Step 7: 提交结果 

注意提交的格式！包括大小写、索引、列头等小细节。

1. submission_df = pd.DataFrame(data = {'Id':test_df.index,'SalePrice':y_final})

2. print submission_df.head(10)

3. submission_df.to_csv('.\\input\\submission.csv',columns = ['Id','SalePrice'],index =False)

Step5-1版完整练习代码：

1. # coding:utf-8

2. # 注意Windows系统的\\和Linux系统的/的区别

3.  

4. import numpy as np

5. import pandas as pd

6. import matplotlib.pyplot as plt

7. from sklearn.linear_model import Ridge

8. from sklearn.model_selection import cross_val_score

9. from sklearn.ensemble import RandomForestRegressor

10.  

11. # 文件的组织形式是house price文件夹下面放house_price.py和input文件夹

12. # input文件夹下面放的是从https://www.kaggle.com/c/house-prices-advanced-regression-techniques/data下载的train.csv test.csv sample_submission.csv 和 data_description.txt 四个文件

13.  

14. # step1 检查源数据集，读入数据，将csv数据转换为DataFrame数据

15. train_df = pd.read_csv(".\\input\\train.csv",index_col = 0)

16. test_df = pd.read_csv('.\\input\\test.csv',index_col = 0)

17. # print train_df.shape

18. # print test_df.shape

19. # print train_df.head() # 默认展示前五行 这里是5行,80列

20. # print test_df.head() # 这里是5行,79列

21.  

22. # step2 合并数据，进行数据预处理

23. prices = pd.DataFrame({'price':train_df['SalePrice'],'log(price+1)':np.log1p(train_df['SalePrice'])})

24. # ps = prices.hist()

25. # plt.plot()

26. # plt.show()

27.  

28. y_train = np.log1p(train_df.pop('SalePrice'))

29. all_df = pd.concat((train_df,test_df),axis = 0)

30. # print all_df.shape

31. # print y_train.head()

32.  

33. # step3 变量转化

34. print all_df['MSSubClass'].dtypes

35. all_df['MSSubClass'] = all_df['MSSubClass'].astype(str)

36. print all_df['MSSubClass'].dtypes

37. print all_df['MSSubClass'].value_counts()

38. # 把category的变量转变成numerical表达形式

39. # get_dummies方法可以帮你一键one-hot

40. print pd.get_dummies(all_df['MSSubClass'],prefix = 'MSSubClass').head()

41. all_dummy_df = pd.get_dummies(all_df)

42. print all_dummy_df.head()

43.  

44. # 处理好numerical变量

45. print all_dummy_df.isnull().sum().sort_values(ascending = False).head(11)

46. # 我们这里用mean填充

47. mean_cols = all_dummy_df.mean()

48. print mean_cols.head(10)

49. all_dummy_df = all_dummy_df.fillna(mean_cols)

50. print all_dummy_df.isnull().sum().sum()

51.  

52. # 标准化numerical数据

53. numeric_cols = all_df.columns[all_df.dtypes != 'object']

54. print numeric_cols

55. numeric_col_means = all_dummy_df.loc[:,numeric_cols].mean()

56. numeric_col_std = all_dummy_df.loc[:,numeric_cols].std()

57. all_dummy_df.loc[:,numeric_cols] = (all_dummy_df.loc[:,numeric_cols] - numeric_col_means) / numeric_col_std

58.  

59. # step4 建立模型

60. # 把数据处理之后，送回训练集和测试集

61. dummy_train_df = all_dummy_df.loc[train_df.index]

62. dummy_test_df = all_dummy_df.loc[test_df.index]

63. print dummy_train_df.shape,dummy_test_df.shape

64.  

65. # 将DF数据转换成Numpy Array的形式，更好地配合sklearn

66.  

67. X_train = dummy_train_df.values

68. X_test = dummy_test_df.values

69.  

70. # Ridge Regression

71. # alphas = np.logspace(-3,2,50)

72. # test_scores = []

73. # for alpha in alphas:

74. # clf = Ridge(alpha)

75. # test_score = np.sqrt(-cross_val_score(clf,X_train,y_train,cv = 10,scoring = 'neg_mean_squared_error'))

76. # test_scores.append(np.mean(test_score))

77. # plt.plot(alphas,test_scores)

78. # plt.title('Alpha vs CV Error')

79. # plt.show()

80.  

81. # random forest

82. # max_features = [.1,.3,.5,.7,.9,.99]

83. # test_scores = []

84. # for max_feat in max_features:

85. # clf = RandomForestRegressor(n_estimators = 200,max_features = max_feat)

86. # test_score = np.sqrt(-cross_val_score(clf,X_train,y_train,cv = 5,scoring = 'neg_mean_squared_error'))

87. # test_scores.append(np.mean(test_score))

88. # plt.plot(max_features,test_scores)

89. # plt.title('Max Features vs CV Error')

90. # plt.show()

91.  

92. # Step 5: ensemble

93. # 用stacking的思维来汲取两种或者多种模型的优点

94.  

95. ridge = Ridge(alpha = 15)

96. rf = RandomForestRegressor(n_estimators = 500,max_features = .3)

97. ridge.fit(X_train,y_train)

98. rf.fit(X_train,y_train)

99.  

100. y_ridge = np.expm1(ridge.predict(X_test))

101. y_rf = np.expm1(rf.predict(X_test))

102.  

103. y_final = (y_ridge + y_rf) / 2

104.  

105. # Step 6: 提交结果

106. submission_df = pd.DataFrame(data = {'Id':test_df.index,'SalePrice':y_final})

107. print submission_df.head(10)

108. submission_df.to_csv('.\\input\\submission.csv',columns = ['Id','SalePrice'],index =

Step5-2版完整练习代码：

1. # coding:utf-8

2. # 注意Windows系统的\\和Linux系统的/的区别

3.  

4. import numpy as np

5. import pandas as pd

6. import matplotlib.pyplot as plt

7. from sklearn.linear_model import Ridge

8. from sklearn.model_selection import cross_val_score

9. from sklearn.ensemble import RandomForestRegressor

10. from sklearn.ensemble import BaggingRegressor

11. from sklearn.ensemble import AdaBoostRegressor

12. from xgboost import XGBRegressor

13.  

14. # 文件的组织形式是house price文件夹下面放house_price.py和input文件夹

15. # input文件夹下面放的是从https://www.kaggle.com/c/house-prices-advanced-regression-techniques/data下载的train.csv test.csv sample_submission.csv 和 data_description.txt 四个文件

16.  

17. # step1 检查源数据集，读入数据，将csv数据转换为DataFrame数据

18. train_df = pd.read_csv("./input/train.csv",index_col = 0)

19. test_df = pd.read_csv('./input/test.csv',index_col = 0)

20. # print train_df.shape

21. # print test_df.shape

22. # print train_df.head() # 默认展示前五行 这里是5行,80列

23. # print test_df.head() # 这里是5行,79列

24.  

25. # step2 合并数据，进行数据预处理

26. prices = pd.DataFrame({'price':train_df['SalePrice'],'log(price+1)':np.log1p(train_df['SalePrice'])})

27. # ps = prices.hist()

28. # plt.plot()

29. # plt.show()

30.  

31. y_train = np.log1p(train_df.pop('SalePrice'))

32. all_df = pd.concat((train_df,test_df),axis = 0)

33. # print all_df.shape

34. # print y_train.head()

35.  

36. # step3 变量转化

37. print all_df['MSSubClass'].dtypes

38. all_df['MSSubClass'] = all_df['MSSubClass'].astype(str)

39. print all_df['MSSubClass'].dtypes

40. print all_df['MSSubClass'].value_counts()

41. # 把category的变量转变成numerical表达形式

42. # get_dummies方法可以帮你一键one-hot

43. print pd.get_dummies(all_df['MSSubClass'],prefix = 'MSSubClass').head()

44. all_dummy_df = pd.get_dummies(all_df)

45. print all_dummy_df.head()

46.  

47. # 处理好numerical变量

48. print all_dummy_df.isnull().sum().sort_values(ascending = False).head(11)

49. # 我们这里用mean填充

50. mean_cols = all_dummy_df.mean()

51. print mean_cols.head(10)

52. all_dummy_df = all_dummy_df.fillna(mean_cols)

53. print all_dummy_df.isnull().sum().sum()

54.  

55. # 标准化numerical数据

56. numeric_cols = all_df.columns[all_df.dtypes != 'object']

57. print numeric_cols

58. numeric_col_means = all_dummy_df.loc[:,numeric_cols].mean()

59. numeric_col_std = all_dummy_df.loc[:,numeric_cols].std()

60. all_dummy_df.loc[:,numeric_cols] = (all_dummy_df.loc[:,numeric_cols] - numeric_col_means) / numeric_col_std

61.  

62. # step4 建立模型

63. # 把数据处理之后，送回训练集和测试集

64. dummy_train_df = all_dummy_df.loc[train_df.index]

65. dummy_test_df = all_dummy_df.loc[test_df.index]

66. print dummy_train_df.shape,dummy_test_df.shape

67.  

68. # 将DF数据转换成Numpy Array的形式，更好地配合sklearn

69.  

70. X_train = dummy_train_df.values

71. X_test = dummy_test_df.values

72.  

73. # Ridge Regression

74. # alphas = np.logspace(-3,2,50)

75. # test_scores = []

76. # for alpha in alphas:

77. # clf = Ridge(alpha)

78. # test_score = np.sqrt(-cross_val_score(clf,X_train,y_train,cv = 10,scoring = 'neg_mean_squared_error'))

79. # test_scores.append(np.mean(test_score))

80. # plt.plot(alphas,test_scores)

81. # plt.title('Alpha vs CV Error')

82. # plt.show()

83.  

84. # random forest

85. # max_features = [.1,.3,.5,.7,.9,.99]

86. # test_scores = []

87. # for max_feat in max_features:

88. # clf = RandomForestRegressor(n_estimators = 200,max_features = max_feat)

89. # test_score = np.sqrt(-cross_val_score(clf,X_train,y_train,cv = 5,scoring = 'neg_mean_squared_error'))

90. # test_scores.append(np.mean(test_score))

91. # plt.plot(max_features,test_scores)

92. # plt.title('Max Features vs CV Error')

93. # plt.show()

94.  

95. # ensemble

96. # 用stacking的思维来汲取两种或者多种模型的优点

97.  

98. # ridge = Ridge(alpha = 15)

99. # rf = RandomForestRegressor(n_estimators = 500,max_features = .3)

100. # ridge.fit(X_train,y_train)

101. # rf.fit(X_train,y_train)

102. # y_ridge = np.expm1(ridge.predict(X_test))

103. # y_rf = np.expm1(rf.predict(X_test))

104. # y_final = (y_ridge + y_rf) / 2

105.  

106. # 做一点高级的ensemble

107. ridge = Ridge(alpha = 15)

108. # bagging 把很多小的分类器放在一起，每个train随机的一部分数据，然后把它们的最终结果综合起来（多数投票）

109. # bagging 算是一种算法框架

110. # params = [1,10,15,20,25,30,40]

111. # test_scores = []

112. # for param in params:

113. # clf = BaggingRegressor(base_estimator = ridge,n_estimators = param)

114. # test_score = np.sqrt(-cross_val_score(clf,X_train,y_train,cv = 10,scoring = 'neg_mean_squared_error'))

115. # test_scores.append(np.mean(test_score))

116. # plt.plot(params,test_scores)

117. # plt.title('n_estimators vs CV Error')

118. # plt.show()

119.  

120. # br = BaggingRegressor(base_estimator = ridge,n_estimators = 25)

121. # br.fit(X_train,y_train)

122. # y_final = np.expm1(br.predict(X_test))

123.  

124. # boosting 比bagging更高级，它是弄来一把分类器，把它们线性排列，下一个分类器把上一个分类器分类不好的地方加上更高的权重，这样，下一个分类器在这部分就能学习得更深刻

125. # params = [10,15,20,25,30,35,40,45,50]

126. # test_scores = []

127. # for param in params:

128. # clf = AdaBoostRegressor(base_estimator = ridge,n_estimators = param)

129. # test_score = np.sqrt(-cross_val_score(clf,X_train,y_train,cv = 10,scoring = 'neg_mean_squared_error'))

130. # test_scores.append(np.mean(test_score))

131. # plt.plot(params,test_scores)

132. # plt.title('n_estimators vs CV Error')

133. # plt.show()

134.  

135. # xgboost

136. params = [1,2,3,4,5,6]

137. test_scores = []

138. for param in params:

139. clf = XGBRegressor(max_depth = param)

140. test_score = np.sqrt(-cross_val_score(clf,X_train,y_train,cv = 10,scoring = 'neg_mean_squared_error'))

141. test_scores.append(np.mean(test_score))

142. plt.plot(params,test_scores)

143. plt.title('max_depth vs CV Error')

144. plt.show()

145.  

146. xgb = XGBRegressor(max_depth = 5)

147. xgb.fit(X_train, y_train)

148. y_final = np.expm1(xgb.predict(X_test))

149.  

150. # 提交结果

151. submission_df = pd.DataFrame(data = {'Id':test_df.index,'SalePrice':y_final})

152. print submission_df.head(10)

153. submission_df.to_csv('./input/submission_xgboosting.csv',columns = ['Id','SalePrice'

总结：

并不是所有的数据源都是整齐划一的x=[var1,var2,var3...]

方法：

【非标准-现实生活数据】-->降维、取特征、数字化表达（特征工程）-->【高维数据】

【文本数据】-->单词出现次数、单词出现频率、语义网络等（特征工程）-->【数据】

【图片数据】-->RGB点阵-->【数组】

【视频数据】-->分为【音轨】and【视频轨】-->【音轨：声波/语音识别】【视频轨：一维图片/图片识别】

 

可参考：

https://www.cnblogs.com/irenelin/p/7400388.html

http://blog.csdn.net/qilixuening/article/details/75153131

http://blog.csdn.net/qilixuening/article/details/75151026

http://blog.csdn.net/chris_lee_hehe/article/details/78700140

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