【手把手机器学习入门到放弃】SVM支持向量机_self-emp-not-inc

支持向量机

打仗的时候只有站最前面的人在打而已

支持向量机也是完成分类问题的一个工具，不同于逻辑回归，在支持向量机解决的分类问题中，只有最靠近对方阵营的样本对分界线的确定起到作用，而远离分界线的那些样本对分界线的确定没有作用。在这样的机制下，SVM拥有更好的鲁棒性，受离群点的影响几乎可忽略不计。

本次演示使用美国成人收入统计模型
数据说明如下：

• age: continuous.
• workclass: Private, Self-emp-not-inc, Self-emp-inc, Federal-gov, Local-gov, State-gov, Without-pay, Never-worked.
• fnlwgt: continuous.
• education: Bachelors, Some-college, 11th, HS-grad, Prof-school, Assoc-acdm, Assoc-voc, 9th, 7th-8th, 12th, Masters, 1st-4th, 10th, Doctorate, 5th-6th, Preschool.
• education-num: continuous.
• marital-status: Married-civ-spouse, Divorced, Never-married, Separated, Widowed, Married-spouse-absent, Married-AF-spouse.
• occupation: Tech-support, Craft-repair, Other-service, Sales, Exec-managerial, Prof-specialty, Handlers-cleaners, Machine-op-inspct, Adm-clerical, Farming-fishing, Transport-moving, Priv-house-serv, Protective-serv, Armed-Forces.
• relationship: Wife, Own-child, Husband, Not-in-family, Other-relative, Unmarried.
• race: White, Asian-Pac-Islander, Amer-Indian-Eskimo, Other, Black.
• sex: Female, Male.
• capital-gain: continuous.
• capital-loss: continuous.
• hours-per-week: continuous.
• native-country: United-States, Cambodia, England, Puerto-Rico, Canada, Germany, Outlying-US(Guam-USVI-etc), India, Japan, Greece, South, China, Cuba, Iran, Honduras, Philippines, Italy, Poland, Jamaica, Vietnam, Mexico, Portugal, Ireland, France, Dominican-Republic, Laos, Ecuador, Taiwan, Haiti, Columbia, Hungary, Guatemala, Nicaragua, Scotland, Thailand, Yugoslavia, El-Salvador, Trinadad&Tobago, Peru, Hong, Holand-Netherlands.

标签有两种：>50K, <=50K.

import pandas as pd 
import numpy as np
from sklearn import svm
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
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1. 数据初探

dataframe = pd.read_table('datasets/Adult/adult.data',sep=',',header=None)
dataframe.columns=["age", "workclass", "fnlwgt", "education", "education-num", "marital-status", 
                   "occupation", "relationship", "race", "sex", "capital-gain", "capital-loss", 
                   "hours-per-week", "native-country","salary"]
dataframe.head(3)
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/Users/yaochenli/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:1: FutureWarning: read_table is deprecated, use read_csv instead.
  """Entry point for launching an IPython kernel.
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2. 缺失值处理

dataframe.workclass.unique()

可以看到这个原始数据里面的缺失值是用“？”表示的

dataframe.shape
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(32561, 15)
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(dataframe==" ?").sum()
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/Users/yaochenli/anaconda3/lib/python3.7/site-packages/pandas/core/ops.py:1649: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison
  result = method(y)





age                  0
workclass         1836
fnlwgt               0
education            0
education-num        0
marital-status       0
occupation        1843
relationship         0
race                 0
sex                  0
capital-gain         0
capital-loss         0
hours-per-week       0
native-country     583
salary               0
dtype: int64
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对比样本量，缺失值不算很多，由于缺失的值都不是标量，而是标签变量，所以我们根据分布进行填充

• 工作类别 workclass

dataframe.workclass.value_counts()
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 Private             22696
 Self-emp-not-inc     2541
 Local-gov            2093
 ?                    1836
 State-gov            1298
 Self-emp-inc         1116
 Federal-gov           960
 Without-pay            14
 Never-worked            7
Name: workclass, dtype: int64
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这里private明显占了大多数，我们就把缺失值使用Private填充

dataframe.workclass.replace(" ?", " Private", inplace=True)
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dataframe.workclass.value_counts()
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 Private             24532
 Self-emp-not-inc     2541
 Local-gov            2093
 State-gov            1298
 Self-emp-inc         1116
 Federal-gov           960
 Without-pay            14
 Never-worked            7
Name: workclass, dtype: int64
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• 职业 occupation

dataframe.occupation.value_counts()
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 Prof-specialty       4140
 Craft-repair         4099
 Exec-managerial      4066
 Adm-clerical         3770
 Sales                3650
 Other-service        3295
 Machine-op-inspct    2002
 ?                    1843
 Transport-moving     1597
 Handlers-cleaners    1370
 Farming-fishing       994
 Tech-support          928
 Protective-serv       649
 Priv-house-serv       149
 Armed-Forces            9
Name: occupation, dtype: int64
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这里分布比较平均，我们把“？”单独以Others替代

dataframe.occupation.replace(" ?", " Other", inplace=True)
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dataframe.occupation.value_counts()
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 Prof-specialty       4140
 Craft-repair         4099
 Exec-managerial      4066
 Adm-clerical         3770
 Sales                3650
 Other-service        3295
 Machine-op-inspct    2002
 Other                1843
 Transport-moving     1597
 Handlers-cleaners    1370
 Farming-fishing       994
 Tech-support          928
 Protective-serv       649
 Priv-house-serv       149
 Armed-Forces            9
Name: occupation, dtype: int64
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• 原国籍 native-country

dataframe["native-country"].value_counts()
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 United-States                 29170
 Mexico                          643
 ?                               583
 Philippines                     198
 Germany                         137
 Canada                          121
 Puerto-Rico                     114
 El-Salvador                     106
 India                           100
 Cuba                             95
 England                          90
 Jamaica                          81
 South                            80
 China                            75
 Italy                            73
 Dominican-Republic               70
 Vietnam                          67
 Guatemala                        64
 Japan                            62
 Poland                           60
 Columbia                         59
 Taiwan                           51
 Haiti                            44
 Iran                             43
 Portugal                         37
 Nicaragua                        34
 Peru                             31
 Greece                           29
 France                           29
 Ecuador                          28
 Ireland                          24
 Hong                             20
 Trinadad&Tobago                  19
 Cambodia                         19
 Thailand                         18
 Laos                             18
 Yugoslavia                       16
 Outlying-US(Guam-USVI-etc)       14
 Hungary                          13
 Honduras                         13
 Scotland                         12
 Holand-Netherlands                1
Name: native-country, dtype: int64
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这里美国的样本量占了绝大多数，我们先使用美国来填充缺失值

dataframe["native-country"].replace(" ?", " United-States", inplace=True)
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dataframe["native-country"].value_counts()
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 United-States                 29753
 Mexico                          643
 Philippines                     198
 Germany                         137
 Canada                          121
 Puerto-Rico                     114
 El-Salvador                     106
 India                           100
 Cuba                             95
 England                          90
 Jamaica                          81
 South                            80
 China                            75
 Italy                            73
 Dominican-Republic               70
 Vietnam                          67
 Guatemala                        64
 Japan                            62
 Poland                           60
 Columbia                         59
 Taiwan                           51
 Haiti                            44
 Iran                             43
 Portugal                         37
 Nicaragua                        34
 Peru                             31
 France                           29
 Greece                           29
 Ecuador                          28
 Ireland                          24
 Hong                             20
 Cambodia                         19
 Trinadad&Tobago                  19
 Laos                             18
 Thailand                         18
 Yugoslavia                       16
 Outlying-US(Guam-USVI-etc)       14
 Honduras                         13
 Hungary                          13
 Scotland                         12
 Holand-Netherlands                1
Name: native-country, dtype: int64
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3. 数据分布初探

1. 探究正负样本的数量关系

plt.figure(figsize=(8,5))
sns.color_palette("Set3")
# sns.set(style="whitegrid")
sns.countplot(dataframe.salary, palette="rocket")
plt.title("distribution of salary")
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Text(0.5, 1.0, 'distribution of salary')
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可以看到样本标签不是很平衡，但我们用SVM问题不大

1. 探究学历与工资之间的关系

plt.figure(figsize