dennis_110319

python 中国综合社会调查CGSS幸福指数多元分类预测

项目源码请见：https://github.com/dennis0818/happiness_prediction

一数据来源

赛题使用的数据来自中国人民大学中国调查与数据中心主持之《中国综合社会调查（CGSS）》项目。赛题感谢此机构及其人员提供数据协助。中国综合社会调查为多阶分层抽样的截面面访调查。

happiness_train_complete.csv训练数据集
happiness_test_complete.csv测试数据集

二分析目标

赛题使用公开数据的问卷调查结果，选取其中多组变量，包括个体变量（性别、年龄、地域、职业、健康、婚姻与政治面貌等等）、家庭变量（父母、配偶、子女、家庭资本等等）、社会态度（公平、信用、公共服务等等），来预测其对幸福感的评价。

幸福感预测的准确性不是赛题的唯一目的，更希望选手对变量间的关系、变量群的意义有所探索与收获。

三数据分析

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

%matplotlib inline

train = pd.read_csv('d:/python/exercise/samples/happiness/happiness_train_complete.csv',parse_dates=['survey_time'],encoding='iso-8859-1')

test = pd.read_csv('d:/python/exercise/samples/happiness/happiness_test_complete.csv', parse_dates=['survey_time'], encoding = 'iso-8859-1')

pick_index = pd.read_excel('d:/python/exercise/samples/happiness/happiness_index_copy.xlsx', encoding = 'iso-8859-1', header = None)

pick_index = pick_index.iloc[:,0]
pick_index = list(pick_index.values)

train.shape

(8000, 140)

test.shape

(2968, 139)

train.head()

	id	happiness	survey_type	province	city	county	survey_time	gender	birth	nationality	religion	religion_freq	edu	edu_other	edu_status	edu_yr	income	political	join_party	floor_area	property_1	property_2	property_3	property_4	property_other	height_cm	weight_jin	health	health_problem	depression	hukou	hukou_loc	media_1	media_2	media_3	media_4	media_5	media_6	leisure_1	leisure_2	leisure_3	leisure_4	leisure_5	leisure_6	leisure_7	leisure_8	leisure_9	leisure_10	leisure_11	leisure_12	socialize	relax	learn	social_neighbor	social_friend	socia_outing	equity	class	class_10_before	class_10_after	class_14	work_exper	work_status	work_yr	work_type	work_manage	insur_1	insur_2	insur_3	insur_4	family_income	family_m	family_status	house	car	invest_1	invest_other	son	daughter	minor_child	marital	marital_1st	s_birth	marital_now	s_edu	s_political	s_hukou	s_income	s_work_exper	s_work_status	s_work_type	f_birth	f_edu	f_political	f_work_14	m_birth	m_edu	m_political	m_work_14	status_peer	status_3_before	view	inc_ability	inc_exp	trust_1	trust_2	trust_3	trust_4	trust_5	trust_6	trust_7	trust_8	trust_9	trust_10	trust_11	trust_12	trust_13	neighbor_familiarity	public_service_1	public_service_2	public_service_3	public_service_4	public_service_5	public_service_6	public_service_7	public_service_8	public_service_9
0	1	4	1	12	32	59	2015-08-04 14:18:00	1	1959	1	1	1	11	NaN	4.0	-2.0	20000	1	NaN	45.0	1	0	0	0	NaN	176	155	3	2	5	5	2.0	4	2	5	5	4	3	1	4	3	1	2	3	4	1	4	5	4	1	2	4	3	3.0	3.0	2	3	3	3	3	1	1	3.0	30.0	1.0	2.0	1	1	1	2	60000.0	2	2	1	2	1	NaN	1	0	0.0	3	1984.0	1958.0	1984.0	6.0	1.0	5.0	40000.0	5.0	NaN	NaN	-2	4	4	1	-2	4	1	1	3	2	4	3	50000.0	4	2	-8	-8	5	3	2	3	4	3	-8	4	1	4	50	60	50	50	30.0	30	50	50	50
1	2	4	2	18	52	85	2015-07-21 15:04:00	1	1992	1	1	1	12	NaN	4.0	2013.0	20000	1	NaN	110.0	0	0	0	1	NaN	170	110	5	4	3	1	1.0	2	2	1	3	5	1	2	3	4	3	5	4	3	2	3	4	5	1	2	4	3	6.0	2.0	1	3	6	4	8	5	1	3.0	2.0	1.0	3.0	1	1	1	1	40000.0	3	4	1	2	1	NaN	0	0	NaN	1	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	1972	3	1	2	1973	3	1	2	1	1	4	2	50000.0	5	4	4	3	5	3	3	3	2	3	3	3	2	3	90	70	70	80	85.0	70	90	60	60
2	3	4	2	29	83	126	2015-07-21 13:24:00	2	1967	1	0	3	4	NaN	4.0	-2.0	2000	1	NaN	120.0	1	1	0	0	NaN	160	122	4	4	5	1	1.0	2	2	2	5	1	3	1	4	4	3	5	4	4	2	3	5	5	5	3	4	2	2.0	5.0	2	4	5	4	6	3	2	NaN	NaN	NaN	NaN	1	1	2	2	8000.0	3	3	1	2	1	NaN	0	2	1.0	3	1990.0	1968.0	1990.0	3.0	1.0	1.0	6000.0	3.0	NaN	NaN	-2	1	1	2	-2	1	1	2	2	1	4	2	80000.0	3	3	3	3	4	3	3	3	3	3	-8	3	1	4	90	80	75	79	80.0	90	90	90	75
3	4	5	2	10	28	51	2015-07-25 17:33:00	2	1943	1	1	1	3	NaN	4.0	1959.0	6420	1	NaN	78.0	0	0	1	0	NaN	163	170	4	4	4	1	2.0	2	1	1	5	1	1	1	5	2	4	5	4	5	1	1	5	5	5	2	4	4	1.0	6.0	1	4	5	5	7	2	4	NaN	NaN	NaN	NaN	2	2	2	2	12000.0	3	3	1	1	1	NaN	1	4	0.0	7	1960.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	-2	14	1	2	-2	1	1	2	2	1	3	2	10000.0	3	3	4	3	5	3	3	5	4	3	3	3	2	3	100	90	70	80	80.0	90	90	80	80
4	5	4	1	7	18	36	2015-08-10 09:50:00	2	1994	1	1	1	12	NaN	1.0	2014.0	-1	2	NaN	70.0	0	0	0	1	NaN	165	110	5	5	3	2	3.0	1	3	4	2	5	5	3	3	3	2	4	4	3	5	2	5	5	1	4	3	4	7.0	5.0	3	2	1	1	1	4	6	NaN	NaN	NaN	NaN	1	2	2	2	-2.0	4	3	1	1	1	NaN	0	0	NaN	1	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	1970	6	1	10	1972	4	1	15	3	2	3	-8	200000.0	4	3	3	3	5	5	3	4	3	3	3	3	2	2	50	50	50	50	50.0	50	50	50	50

test.head()

	id	survey_type	province	city	county	survey_time	gender	birth	nationality	religion	religion_freq	edu	edu_other	edu_status	edu_yr	income	political	join_party	floor_area	property_0	property_1	property_2	property_3	property_other	height_cm	weight_jin	health	health_problem	depression	hukou	hukou_loc	media_1	media_2	media_3	media_4	media_5	media_6	leisure_1	leisure_2	leisure_3	leisure_4	leisure_5	leisure_6	leisure_7	leisure_8	leisure_9	leisure_10	leisure_11	leisure_12	socialize	relax	learn	social_neighbor	social_friend	socia_outing	equity	class	class_10_before	class_10_after	class_14	work_exper	work_status	work_yr	work_type	work_manage	insur_1	insur_2	insur_3	insur_4	family_income	family_m	family_status	house	car	invest_1	invest_other	son	daughter	minor_child	marital	marital_1st	s_birth	marital_now	s_edu	s_political	s_hukou	s_income	s_work_exper	s_work_status	s_work_type	f_birth	f_edu	f_political	f_work_14	m_birth	m_edu	m_political	m_work_14	status_peer	status_3_before	view	inc_ability	inc_exp	trust_1	trust_2	trust_3	trust_4	trust_5	trust_6	trust_7	trust_8	trust_9	trust_10	trust_11	trust_12	trust_13	neighbor_familiarity	public_service_1	public_service_2	public_service_3	public_service_4	public_service_5	public_service_6	public_service_7	public_service_8	public_service_9
0	8001	1	2	2	9	2015-07-24 10:30:00	2	1972	8	0	1	4	NaN	2.0	1989.0	18000	1	NaN	100.0	0	1	1	0	NaN	155	120	4	4	4	4	1	2	2	1	4	3	2	1	4	2	4	4	3	3	3	3	5	1	3	3	3	2	1.0	3.0	1	2	5	5	5	4	1	5.0	12.0	2.0	3.0	1	1	2	2	-2	5	3	1	2	1	NaN	1	0	1.0	3	1998.0	1972.0	1998.0	4.0	1.0	1.0	18000.0	1.0	5.0	2.0	1935	3	1	13	1936	3	1	13	3	1	4	2	30000.0	4	4	4	4	4	4	3	4	3	3	-8	3	2	4	80	80.0	60	80	80	80	80	80	80
1	8002	1	22	66	106	2015-07-12 15:38:00	2	1938	1	1	1	4	NaN	4.0	-2.0	20000	1	NaN	50.0	0	1	0	1	NaN	150	75	1	1	2	5	1	1	1	3	5	1	1	1	5	5	5	5	2	1	4	5	5	5	5	5	4	1	1.0	1.0	1	3	3	5	-8	4	5	NaN	NaN	NaN	NaN	1	1	2	2	20000	1	1	2	2	1	NaN	3	0	0.0	7	1959.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	-2	1	1	2	-2	1	1	2	3	3	4	2	50000.0	3	3	3	3	4	2	2	4	4	4	4	4	4	5	90	80.0	80	80	80	80	70	80	80
2	8003	2	9	22	44	2015-07-05 09:36:00	2	1935	1	1	1	1	NaN	NaN	NaN	500	1	NaN	60.0	0	0	0	1	NaN	155	85	4	2	2	1	1	1	1	1	1	1	1	5	5	4	5	5	4	5	5	5	5	5	5	2	3	1	2.0	6.0	1	4	1	1	1	2	4	NaN	NaN	NaN	NaN	1	1	2	2	-2	1	2	1	2	1	NaN	2	3	0.0	7	1954.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	-2	1	1	2	1914	1	1	2	3	1	3	4	2000.0	5	5	4	4	4	3	3	3	4	4	4	2	2	5	95	95.0	80	90	80	95	95	80	90
3	8004	2	18	52	86	2015-07-19 10:10:00	2	1992	1	1	1	4	NaN	2.0	-1.0	50000	1	NaN	220.0	0	0	1	0	NaN	160	126	5	5	5	1	1	2	2	1	5	4	1	1	5	4	4	5	3	4	4	3	3	3	3	2	2	2	1.0	1.0	1	4	5	4	6	5	5	NaN	NaN	NaN	NaN	2	2	2	2	100000	2	4	1	2	1	NaN	1	0	1.0	3	2014.0	1991.0	2014.0	4.0	1.0	1.0	50000.0	1.0	3.0	1.0	1967	4	1	2	1966	1	1	2	2	1	3	2	80000.0	4	4	3	3	4	3	3	3	3	3	3	3	3	4	80	80.0	70	90	80	80	70	60	50
4	8005	2	24	70	110	2015-08-03 11:41:00	1	1990	1	1	2	1	NaN	NaN	NaN	0	1	NaN	200.0	1	0	0	0	NaN	173	130	4	5	3	4	1	1	1	1	4	4	1	2	3	4	4	5	4	2	5	3	5	5	1	4	4	2	1.0	4.0	6	2	2	4	6	1	2	NaN	NaN	NaN	NaN	1	1	2	2	30000	3	2	1	2	1	NaN	0	0	NaN	1	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	1964	3	1	2	-2	-8	-8	-8	2	2	5	2	-2.0	4	3	4	4	5	4	3	-8	4	3	4	5	1	-8	60	50.0	0	30	40	50	60	-2	60

空值描述

def check_missing_data(df):
    flag = df.isnull().sum().any()
    if flag == True:
        total = df.isnull().sum()
        percent = (df.isnull().sum())/(df.isnull().count())
        output = pd.concat([total, percent], axis = 1, keys=['total','percent'])
        datatype = []
        for col in df.columns:
            col_type = str(df[col].dtype)
            datatype.append(col_type)
        output['dtype'] = datatype
        return np.transpose(output)
    else:
        return False
pd.set_option('max_columns', 141)
print(check_missing_data(train))

            id happiness survey_type province   city county     survey_time  \
total        0         0           0        0      0      0               0   
percent      0         0           0        0      0      0               0   
dtype    int64     int64       int64    int64  int64  int64  datetime64[ns]   

        gender  birth nationality religion religion_freq    edu edu_other  \
total        0      0           0        0             0      0      7997   
percent      0      0           0        0             0      0  0.999625   
dtype    int64  int64       int64    int64         int64  int64    object   

        edu_status   edu_yr income political join_party floor_area property_0  \
total         1120     1972      0         0       7176          0          0   
percent       0.14   0.2465      0         0      0.897          0          0   
dtype      float64  float64  int64     int64    float64    float64      int64   

        property_1 property_2 property_3 property_4 property_5 property_6  \
total            0          0          0          0          0          0   
percent          0          0          0          0          0          0   
dtype        int64      int64      int64      int64      int64      int64   

        property_7 property_8 property_other height_cm weight_jin health  \
total            0          0           7934         0          0      0   
percent          0          0        0.99175         0          0      0   
dtype        int64      int64         object     int64      int64  int64   

        health_problem depression  hukou hukou_loc media_1 media_2 media_3  \
total                0          0      0         4       0       0       0   
percent              0          0      0    0.0005       0       0       0   
dtype            int64      int64  int64   float64   int64   int64   int64   

        media_4 media_5 media_6 leisure_1 leisure_2 leisure_3 leisure_4  \
total         0       0       0         0         0         0         0   
percent       0       0       0         0         0         0         0   
dtype     int64   int64   int64     int64     int64     int64     int64   

        leisure_5 leisure_6 leisure_7 leisure_8 leisure_9 leisure_10  \
total           0         0         0         0         0          0   
percent         0         0         0         0         0          0   
dtype       int64     int64     int64     int64     int64      int64   

        leisure_11 leisure_12 socialize  relax  learn social_neighbor  \
total            0          0         0      0      0             796   
percent          0          0         0      0      0          0.0995   
dtype        int64      int64     int64  int64  int64         float64   

        social_friend socia_outing equity  class class_10_before  \
total             796            0      0      0               0   
percent        0.0995            0      0      0               0   
dtype         float64        int64  int64  int64           int64   

        class_10_after class_14 work_exper work_status   work_yr work_type  \
total                0        0          0        5049      5049      5049   
percent              0        0          0    0.631125  0.631125  0.631125   
dtype            int64    int64      int64     float64   float64   float64   

        work_manage insur_1 insur_2 insur_3 insur_4 family_income family_m  \
total          5049       0       0       0       0             1        0   
percent    0.631125       0       0       0       0      0.000125        0   
dtype       float64   int64   int64   int64   int64       float64    int64   

        family_status  house    car invest_0 invest_1 invest_2 invest_3  \
total               0      0      0        0        0        0        0   
percent             0      0      0        0        0        0        0   
dtype           int64  int64  int64    int64    int64    int64    int64   

        invest_4 invest_5 invest_6 invest_7 invest_8 invest_other    son  \
total          0        0        0        0        0         7971      0   
percent        0        0        0        0        0     0.996375      0   
dtype      int64    int64    int64    int64    int64       object  int64   

        daughter minor_child marital marital_1st  s_birth marital_now  \
total          0        1066       0         828     1718        1770   
percent        0     0.13325       0      0.1035  0.21475     0.22125   
dtype      int64     float64   int64     float64  float64     float64   

           s_edu s_political  s_hukou s_income s_work_exper s_work_status  \
total       1718        1718     1718     1718         1718          5435   
percent  0.21475     0.21475  0.21475  0.21475      0.21475      0.679375   
dtype    float64     float64  float64  float64      float64       float64   

        s_work_type f_birth  f_edu f_political f_work_14 m_birth  m_edu  \
total          5435       0      0           0         0       0      0   
percent    0.679375       0      0           0         0       0      0   
dtype       float64   int64  int64       int64     int64   int64  int64   

        m_political m_work_14 status_peer status_3_before   view inc_ability  \
total             0         0           0               0      0           0   
percent           0         0           0               0      0           0   
dtype         int64     int64       int64           int64  int64       int64   

         inc_exp trust_1 trust_2 trust_3 trust_4 trust_5 trust_6 trust_7  \
total          0       0       0       0       0       0       0       0   
percent        0       0       0       0       0       0       0       0   
dtype    float64   int64   int64   int64   int64   int64   int64   int64   

        trust_8 trust_9 trust_10 trust_11 trust_12 trust_13  \
total         0       0        0        0        0        0   
percent       0       0        0        0        0        0   
dtype     int64   int64    int64    int64    int64    int64   

        neighbor_familiarity public_service_1 public_service_2  \
total                      0                0                0   
percent                    0                0                0   
dtype                  int64            int64            int64   

        public_service_3 public_service_4 public_service_5 public_service_6  \
total                  0                0                0                0   
percent                0                0                0                0   
dtype              int64            int64          float64            int64   

        public_service_7 public_service_8 public_service_9  
total                  0                0                0  
percent                0                0                0  
dtype              int64            int64            int64

train = train[train['happiness'] != -8]

fig, ax = plt.subplots(1, 2, figsize = (14,6))

train['happiness'].value_counts().plot.pie(ax = ax[0],
                                           autopct='%1.1f%%',
                                           explode=[0.1,0,0,0,0],
                                           startangle = 90,
                                           shadow = True,
                                           colors = [ 'coral', 'yellow', 'chartreuse', 'paleturquoise', 'violet'],
                                           wedgeprops = {'linewidth': 1, 'edgecolor': 'gray'},
                                           textprops = {'fontsize': 13, 'color': 'black'})
#plt.title('happiness')
#plt.ylabel('')

color = ['violet', 'paleturquoise','chartreuse' ,'coral','yellow']
sns.countplot('happiness', data = train, ax = ax[1], palette = sns.color_palette(color))

train.groupby(['gender', 'happiness']).size()

gender  happiness
1       1              43
        2             216
        3             588
        4            2306
        5             599
2       1              61
        2             281
        3             571
        4            2512
        5             811
dtype: int64

trans_gender = {1:'male',2:'female'}
train['gender'] = train['gender'].map(trans_gender)

train.head()

	id	happiness	survey_type	province	city	county	survey_time	gender	birth	nationality	religion	religion_freq	edu	edu_other	edu_status	edu_yr	income	political	join_party	floor_area	property_1	property_2	property_3	property_4	property_other	height_cm	weight_jin	health	health_problem	depression	hukou	hukou_loc	media_1	media_2	media_3	media_4	media_5	media_6	leisure_1	leisure_2	leisure_3	leisure_4	leisure_5	leisure_6	leisure_7	leisure_8	leisure_9	leisure_10	leisure_11	leisure_12	socialize	relax	learn	social_neighbor	social_friend	socia_outing	equity	class	class_10_before	class_10_after	class_14	work_exper	work_status	work_yr	work_type	work_manage	insur_1	insur_2	insur_3	insur_4	family_income	family_m	family_status	house	car	invest_1	invest_other	son	daughter	minor_child	marital	marital_1st	s_birth	marital_now	s_edu	s_political	s_hukou	s_income	s_work_exper	s_work_status	s_work_type	f_birth	f_edu	f_political	f_work_14	m_birth	m_edu	m_political	m_work_14	status_peer	status_3_before	view	inc_ability	inc_exp	trust_1	trust_2	trust_3	trust_4	trust_5	trust_6	trust_7	trust_8	trust_9	trust_10	trust_11	trust_12	trust_13	neighbor_familiarity	public_service_1	public_service_2	public_service_3	public_service_4	public_service_5	public_service_6	public_service_7	public_service_8	public_service_9
0	1	4	1	12	32	59	2015-08-04 14:18:00	male	1959	1	1	1	11	NaN	4.0	-2.0	20000	1	NaN	45.0	1	0	0	0	NaN	176	155	3	2	5	5	2.0	4	2	5	5	4	3	1	4	3	1	2	3	4	1	4	5	4	1	2	4	3	3.0	3.0	2	3	3	3	3	1	1	3.0	30.0	1.0	2.0	1	1	1	2	60000.0	2	2	1	2	1	NaN	1	0	0.0	3	1984.0	1958.0	1984.0	6.0	1.0	5.0	40000.0	5.0	NaN	NaN	-2	4	4	1	-2	4	1	1	3	2	4	3	50000.0	4	2	-8	-8	5	3	2	3	4	3	-8	4	1	4	50	60	50	50	30.0	30	50	50	50
1	2	4	2	18	52	85	2015-07-21 15:04:00	male	1992	1	1	1	12	NaN	4.0	2013.0	20000	1	NaN	110.0	0	0	0	1	NaN	170	110	5	4	3	1	1.0	2	2	1	3	5	1	2	3	4	3	5	4	3	2	3	4	5	1	2	4	3	6.0	2.0	1	3	6	4	8	5	1	3.0	2.0	1.0	3.0	1	1	1	1	40000.0	3	4	1	2	1	NaN	0	0	NaN	1	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	1972	3	1	2	1973	3	1	2	1	1	4	2	50000.0	5	4	4	3	5	3	3	3	2	3	3	3	2	3	90	70	70	80	85.0	70	90	60	60
2	3	4	2	29	83	126	2015-07-21 13:24:00	female	1967	1	0	3	4	NaN	4.0	-2.0	2000	1	NaN	120.0	1	1	0	0	NaN	160	122	4	4	5	1	1.0	2	2	2	5	1	3	1	4	4	3	5	4	4	2	3	5	5	5	3	4	2	2.0	5.0	2	4	5	4	6	3	2	NaN	NaN	NaN	NaN	1	1	2	2	8000.0	3	3	1	2	1	NaN	0	2	1.0	3	1990.0	1968.0	1990.0	3.0	1.0	1.0	6000.0	3.0	NaN	NaN	-2	1	1	2	-2	1	1	2	2	1	4	2	80000.0	3	3	3	3	4	3	3	3	3	3	-8	3	1	4	90	80	75	79	80.0	90	90	90	75
3	4	5	2	10	28	51	2015-07-25 17:33:00	female	1943	1	1	1	3	NaN	4.0	1959.0	6420	1	NaN	78.0	0	0	1	0	NaN	163	170	4	4	4	1	2.0	2	1	1	5	1	1	1	5	2	4	5	4	5	1	1	5	5	5	2	4	4	1.0	6.0	1	4	5	5	7	2	4	NaN	NaN	NaN	NaN	2	2	2	2	12000.0	3	3	1	1	1	NaN	1	4	0.0	7	1960.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	-2	14	1	2	-2	1	1	2	2	1	3	2	10000.0	3	3	4	3	5	3	3	5	4	3	3	3	2	3	100	90	70	80	80.0	90	90	80	80
4	5	4	1	7	18	36	2015-08-10 09:50:00	female	1994	1	1	1	12	NaN	1.0	2014.0	-1	2	NaN	70.0	0	0	0	1	NaN	165	110	5	5	3	2	3.0	1	3	4	2	5	5	3	3	3	2	4	4	3	5	2	5	5	1	4	3	4	7.0	5.0	3	2	1	1	1	4	6	NaN	NaN	NaN	NaN	1	2	2	2	-2.0	4	3	1	1	1	NaN	0	0	NaN	1	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	1970	6	1	10	1972	4	1	15	3	2	3	-8	200000.0	4	3	3	3	5	5	3	4	3	3	3	3	2	2	50	50	50	50	50.0	50	50	50	50

trans_gender_op = {‘male’:1,‘female’:2}
copy = train[‘gender’].map(trans_gender_op).copy
train[‘gender’] = copy

train.shape

(7988, 140)

train.head()

	id	happiness	survey_type	province	city	county	survey_time	gender	birth	nationality	religion	religion_freq	edu	edu_other	edu_status	edu_yr	income	political	join_party	floor_area	property_1	property_2	property_3	property_4	property_other	height_cm	weight_jin	health	health_problem	depression	hukou	hukou_loc	media_1	media_2	media_3	media_4	media_5	media_6	leisure_1	leisure_2	leisure_3	leisure_4	leisure_5	leisure_6	leisure_7	leisure_8	leisure_9	leisure_10	leisure_11	leisure_12	socialize	relax	learn	social_neighbor	social_friend	socia_outing	equity	class	class_10_before	class_10_after	class_14	work_exper	work_status	work_yr	work_type	work_manage	insur_1	insur_2	insur_3	insur_4	family_income	family_m	family_status	house	car	invest_1	invest_other	son	daughter	minor_child	marital	marital_1st	s_birth	marital_now	s_edu	s_political	s_hukou	s_income	s_work_exper	s_work_status	s_work_type	f_birth	f_edu	f_political	f_work_14	m_birth	m_edu	m_political	m_work_14	status_peer	status_3_before	view	inc_ability	inc_exp	trust_1	trust_2	trust_3	trust_4	trust_5	trust_6	trust_7	trust_8	trust_9	trust_10	trust_11	trust_12	trust_13	neighbor_familiarity	public_service_1	public_service_2	public_service_3	public_service_4	public_service_5	public_service_6	public_service_7	public_service_8	public_service_9
0	1	4	1	12	32	59	2015-08-04 14:18:00	male	1959	1	1	1	11	NaN	4.0	-2.0	20000	1	NaN	45.0	1	0	0	0	NaN	176	155	3	2	5	5	2.0	4	2	5	5	4	3	1	4	3	1	2	3	4	1	4	5	4	1	2	4	3	3.0	3.0	2	3	3	3	3	1	1	3.0	30.0	1.0	2.0	1	1	1	2	60000.0	2	2	1	2	1	NaN	1	0	0.0	3	1984.0	1958.0	1984.0	6.0	1.0	5.0	40000.0	5.0	NaN	NaN	-2	4	4	1	-2	4	1	1	3	2	4	3	50000.0	4	2	-8	-8	5	3	2	3	4	3	-8	4	1	4	50	60	50	50	30.0	30	50	50	50
1	2	4	2	18	52	85	2015-07-21 15:04:00	male	1992	1	1	1	12	NaN	4.0	2013.0	20000	1	NaN	110.0	0	0	0	1	NaN	170	110	5	4	3	1	1.0	2	2	1	3	5	1	2	3	4	3	5	4	3	2	3	4	5	1	2	4	3	6.0	2.0	1	3	6	4	8	5	1	3.0	2.0	1.0	3.0	1	1	1	1	40000.0	3	4	1	2	1	NaN	0	0	NaN	1	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	1972	3	1	2	1973	3	1	2	1	1	4	2	50000.0	5	4	4	3	5	3	3	3	2	3	3	3	2	3	90	70	70	80	85.0	70	90	60	60
2	3	4	2	29	83	126	2015-07-21 13:24:00	female	1967	1	0	3	4	NaN	4.0	-2.0	2000	1	NaN	120.0	1	1	0	0	NaN	160	122	4	4	5	1	1.0	2	2	2	5	1	3	1	4	4	3	5	4	4	2	3	5	5	5	3	4	2	2.0	5.0	2	4	5	4	6	3	2	NaN	NaN	NaN	NaN	1	1	2	2	8000.0	3	3	1	2	1	NaN	0	2	1.0	3	1990.0	1968.0	1990.0	3.0	1.0	1.0	6000.0	3.0	NaN	NaN	-2	1	1	2	-2	1	1	2	2	1	4	2	80000.0	3	3	3	3	4	3	3	3	3	3	-8	3	1	4	90	80	75	79	80.0	90	90	90	75
3	4	5	2	10	28	51	2015-07-25 17:33:00	female	1943	1	1	1	3	NaN	4.0	1959.0	6420	1	NaN	78.0	0	0	1	0	NaN	163	170	4	4	4	1	2.0	2	1	1	5	1	1	1	5	2	4	5	4	5	1	1	5	5	5	2	4	4	1.0	6.0	1	4	5	5	7	2	4	NaN	NaN	NaN	NaN	2	2	2	2	12000.0	3	3	1	1	1	NaN	1	4	0.0	7	1960.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	-2	14	1	2	-2	1	1	2	2	1	3	2	10000.0	3	3	4	3	5	3	3	5	4	3	3	3	2	3	100	90	70	80	80.0	90	90	80	80
4	5	4	1	7	18	36	2015-08-10 09:50:00	female	1994	1	1	1	12	NaN	1.0	2014.0	-1	2	NaN	70.0	0	0	0	1	NaN	165	110	5	5	3	2	3.0	1	3	4	2	5	5	3	3	3	2	4	4	3	5	2	5	5	1	4	3	4	7.0	5.0	3	2	1	1	1	4	6	NaN	NaN	NaN	NaN	1	2	2	2	-2.0	4	3	1	1	1	NaN	0	0	NaN	1	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	1970	6	1	10	1972	4	1	15	3	2	3	-8	200000.0	4	3	3	3	5	5	3	4	3	3	3	3	2	2	50	50	50	50	50.0	50	50	50	50

sns.countplot('gender', data=train)

sns.countplot('gender', data = train, hue = 'happiness')

fm = train.groupby(['gender','happiness']).size()

fm.loc['female']

happiness
1      61
2     281
3     571
4    2512
5     811
dtype: int64

fig, ax = plt.subplots(1,2,figsize = (16,8))
fm.loc['male'].plot.pie(ax = ax[0],autopct='%1.1f%%',explode = [0,0,0,0.05,0])
ax[0].set_title('male')
ax[0].set_ylabel('')
fm.loc['female'].plot.pie(ax = ax[1], autopct = '%1.1f%%', explode = [0,0,0,0.05,0])
ax[1].set_title('female')
ax[1].set_ylabel('')

Text(0, 0.5, '')

用问卷提交时间减生日计算年龄

age = train['survey_time'].dt.year - train['birth']

train['age'] = age

train.head()

	id	happiness	survey_type	province	city	county	survey_time	gender	birth	nationality	religion	religion_freq	edu	edu_other	edu_status	edu_yr	income	political	join_party	floor_area	property_1	property_2	property_3	property_4	property_other	height_cm	weight_jin	health	health_problem	depression	hukou	hukou_loc	media_1	media_2	media_3	media_4	media_5	media_6	leisure_1	leisure_2	leisure_3	leisure_4	leisure_5	leisure_6	leisure_7	leisure_8	leisure_9	leisure_10	leisure_11	leisure_12	socialize	relax	learn	social_neighbor	social_friend	socia_outing	equity	class	class_10_before	class_10_after	class_14	work_exper	work_status	work_yr	work_type	work_manage	insur_1	insur_2	insur_3	insur_4	family_income	family_m	family_status	house	car	invest_1	invest_other	son	daughter	minor_child	marital	marital_1st	s_birth	marital_now	s_edu	s_political	s_hukou	s_income	s_work_exper	s_work_status	s_work_type	f_birth	f_edu	f_political	f_work_14	m_birth	m_edu	m_political	m_work_14	status_peer	status_3_before	view	inc_ability	inc_exp	trust_1	trust_2	trust_3	trust_4	trust_5	trust_6	trust_7	trust_8	trust_9	trust_10	trust_11	trust_12	trust_13	neighbor_familiarity	public_service_1	public_service_2	public_service_3	public_service_4	public_service_5	public_service_6	public_service_7	public_service_8	public_service_9	age
0	1	4	1	12	32	59	2015-08-04 14:18:00	male	1959	1	1	1	11	NaN	4.0	-2.0	20000	1	NaN	45.0	1	0	0	0	NaN	176	155	3	2	5	5	2.0	4	2	5	5	4	3	1	4	3	1	2	3	4	1	4	5	4	1	2	4	3	3.0	3.0	2	3	3	3	3	1	1	3.0	30.0	1.0	2.0	1	1	1	2	60000.0	2	2	1	2	1	NaN	1	0	0.0	3	1984.0	1958.0	1984.0	6.0	1.0	5.0	40000.0	5.0	NaN	NaN	-2	4	4	1	-2	4	1	1	3	2	4	3	50000.0	4	2	-8	-8	5	3	2	3	4	3	-8	4	1	4	50	60	50	50	30.0	30	50	50	50	56
1	2	4	2	18	52	85	2015-07-21 15:04:00	male	1992	1	1	1	12	NaN	4.0	2013.0	20000	1	NaN	110.0	0	0	0	1	NaN	170	110	5	4	3	1	1.0	2	2	1	3	5	1	2	3	4	3	5	4	3	2	3	4	5	1	2	4	3	6.0	2.0	1	3	6	4	8	5	1	3.0	2.0	1.0	3.0	1	1	1	1	40000.0	3	4	1	2	1	NaN	0	0	NaN	1	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	1972	3	1	2	1973	3	1	2	1	1	4	2	50000.0	5	4	4	3	5	3	3	3	2	3	3	3	2	3	90	70	70	80	85.0	70	90	60	60	23
2	3	4	2	29	83	126	2015-07-21 13:24:00	female	1967	1	0	3	4	NaN	4.0	-2.0	2000	1	NaN	120.0	1	1	0	0	NaN	160	122	4	4	5	1	1.0	2	2	2	5	1	3	1	4	4	3	5	4	4	2	3	5	5	5	3	4	2	2.0	5.0	2	4	5	4	6	3	2	NaN	NaN	NaN	NaN	1	1	2	2	8000.0	3	3	1	2	1	NaN	0	2	1.0	3	1990.0	1968.0	1990.0	3.0	1.0	1.0	6000.0	3.0	NaN	NaN	-2	1	1	2	-2	1	1	2	2	1	4	2	80000.0	3	3	3	3	4	3	3	3	3	3	-8	3	1	4	90	80	75	79	80.0	90	90	90	75	48
3	4	5	2	10	28	51	2015-07-25 17:33:00	female	1943	1	1	1	3	NaN	4.0	1959.0	6420	1	NaN	78.0	0	0	1	0	NaN	163	170	4	4	4	1	2.0	2	1	1	5	1	1	1	5	2	4	5	4	5	1	1	5	5	5	2	4	4	1.0	6.0	1	4	5	5	7	2	4	NaN	NaN	NaN	NaN	2	2	2	2	12000.0	3	3	1	1	1	NaN	1	4	0.0	7	1960.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	-2	14	1	2	-2	1	1	2	2	1	3	2	10000.0	3	3	4	3	5	3	3	5	4	3	3	3	2	3	100	90	70	80	80.0	90	90	80	80	72
4	5	4	1	7	18	36	2015-08-10 09:50:00	female	1994	1	1	1	12	NaN	1.0	2014.0	-1	2	NaN	70.0	0	0	0	1	NaN	165	110	5	5	3	2	3.0	1	3	4	2	5	5	3	3	3	2	4	4	3	5	2	5	5	1	4	3	4	7.0	5.0	3	2	1	1	1	4	6	NaN	NaN	NaN	NaN	1	2	2	2	-2.0	4	3	1	1	1	NaN	0	0	NaN	1	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	1970	6	1	10	1972	4	1	15	3	2	3	-8	200000.0	4	3	3	3	5	5	3	4	3	3	3	3	2	2	50	50	50	50	50.0	50	50	50	50	21

train1 = train.drop(['survey_time', 'birth'], axis=1)

train1.shape

(7988, 139)

train['age'].min()

train['age'].max()

bins = np.arange(16, 101, 16)
bins

array([16, 32, 48, 64, 80, 96])

年龄分布

fig = plt.figure()
plt.hist(train['age'], bins = bins)

(array([1337., 2177., 2560., 1591.,  323.]),
 array([16, 32, 48, 64, 80, 96]),
 )

年龄区间

cats = pd.cut(train['age'], bins, labels= ['Q1', 'Q2', 'Q3', 'Q4','Q5'])

train.groupby(cats).size()

age
Q1    1423
Q2    2265
Q3    2523
Q4    1511
Q5     266
dtype: int64

c_g = train.groupby([cats, 'happiness']).size()

c_g

age  happiness
Q1   1              14
     2              53
     3             201
     4             880
     5             275
Q2   1              30
     2             154
     3             352
     4            1386
     5             343
Q3   1              41
     2             182
     3             390
     4            1519
     5             391
Q4   1              18
     2              94
     3             178
     4             897
     5             324
Q5   1               1
     2              14
     3              38
     4             136
     5              77
dtype: int64

sns.countplot(cats, data = train, hue = 'happiness' )

挑选特征变量

features=['happiness','age','inc_ability','gender','status_peer','work_exper','family_status','health','equity','class','health_problem','family_m','house','depression','learn','relax','edu']

sns.heatmap(train[features].corr(), annot = True, cmap='RdYlGn',linewidths=0.2)
fig = plt.gcf()
fig.set_size_inches(12,12)

train['gender'] = train['gender'].map({'male':1,'female':0})

test['gender'] = test['gender'].map({2:0, 1:1})

test.head()

	id	survey_type	province	city	county	survey_time	gender	birth	nationality	religion	religion_freq	edu	edu_other	edu_status	edu_yr	income	political	join_party	floor_area	property_0	property_1	property_2	property_3	property_other	height_cm	weight_jin	health	health_problem	depression	hukou	hukou_loc	media_1	media_2	media_3	media_4	media_5	media_6	leisure_1	leisure_2	leisure_3	leisure_4	leisure_5	leisure_6	leisure_7	leisure_8	leisure_9	leisure_10	leisure_11	leisure_12	socialize	relax	learn	social_neighbor	social_friend	socia_outing	equity	class	class_10_before	class_10_after	class_14	work_exper	work_status	work_yr	work_type	work_manage	insur_1	insur_2	insur_3	insur_4	family_income	family_m	family_status	house	car	invest_1	invest_other	son	daughter	minor_child	marital	marital_1st	s_birth	marital_now	s_edu	s_political	s_hukou	s_income	s_work_exper	s_work_status	s_work_type	f_birth	f_edu	f_political	f_work_14	m_birth	m_edu	m_political	m_work_14	status_peer	status_3_before	view	inc_ability	inc_exp	trust_1	trust_2	trust_3	trust_4	trust_5	trust_6	trust_7	trust_8	trust_9	trust_10	trust_11	trust_12	trust_13	neighbor_familiarity	public_service_1	public_service_2	public_service_3	public_service_4	public_service_5	public_service_6	public_service_7	public_service_8	public_service_9
0	8001	1	2	2	9	2015-07-24 10:30:00	0	1972	8	0	1	4	NaN	2.0	1989.0	18000	1	NaN	100.0	0	1	1	0	NaN	155	120	4	4	4	4	1	2	2	1	4	3	2	1	4	2	4	4	3	3	3	3	5	1	3	3	3	2	1.0	3.0	1	2	5	5	5	4	1	5.0	12.0	2.0	3.0	1	1	2	2	-2	5	3	1	2	1	NaN	1	0	1.0	3	1998.0	1972.0	1998.0	4.0	1.0	1.0	18000.0	1.0	5.0	2.0	1935	3	1	13	1936	3	1	13	3	1	4	2	30000.0	4	4	4	4	4	4	3	4	3	3	-8	3	2	4	80	80.0	60	80	80	80	80	80	80
1	8002	1	22	66	106	2015-07-12 15:38:00	0	1938	1	1	1	4	NaN	4.0	-2.0	20000	1	NaN	50.0	0	1	0	1	NaN	150	75	1	1	2	5	1	1	1	3	5	1	1	1	5	5	5	5	2	1	4	5	5	5	5	5	4	1	1.0	1.0	1	3	3	5	-8	4	5	NaN	NaN	NaN	NaN	1	1	2	2	20000	1	1	2	2	1	NaN	3	0	0.0	7	1959.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	-2	1	1	2	-2	1	1	2	3	3	4	2	50000.0	3	3	3	3	4	2	2	4	4	4	4	4	4	5	90	80.0	80	80	80	80	70	80	80
2	8003	2	9	22	44	2015-07-05 09:36:00	0	1935	1	1	1	1	NaN	NaN	NaN	500	1	NaN	60.0	0	0	0	1	NaN	155	85	4	2	2	1	1	1	1	1	1	1	1	5	5	4	5	5	4	5	5	5	5	5	5	2	3	1	2.0	6.0	1	4	1	1	1	2	4	NaN	NaN	NaN	NaN	1	1	2	2	-2	1	2	1	2	1	NaN	2	3	0.0	7	1954.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	-2	1	1	2	1914	1	1	2	3	1	3	4	2000.0	5	5	4	4	4	3	3	3	4	4	4	2	2	5	95	95.0	80	90	80	95	95	80	90
3	8004	2	18	52	86	2015-07-19 10:10:00	0	1992	1	1	1	4	NaN	2.0	-1.0	50000	1	NaN	220.0	0	0	1	0	NaN	160	126	5	5	5	1	1	2	2	1	5	4	1	1	5	4	4	5	3	4	4	3	3	3	3	2	2	2	1.0	1.0	1	4	5	4	6	5	5	NaN	NaN	NaN	NaN	2	2	2	2	100000	2	4	1	2	1	NaN	1	0	1.0	3	2014.0	1991.0	2014.0	4.0	1.0	1.0	50000.0	1.0	3.0	1.0	1967	4	1	2	1966	1	1	2	2	1	3	2	80000.0	4	4	3	3	4	3	3	3	3	3	3	3	3	4	80	80.0	70	90	80	80	70	60	50
4	8005	2	24	70	110	2015-08-03 11:41:00	1	1990	1	1	2	1	NaN	NaN	NaN	0	1	NaN	200.0	1	0	0	0	NaN	173	130	4	5	3	4	1	1	1	1	4	4	1	2	3	4	4	5	4	2	5	3	5	5	1	4	4	2	1.0	4.0	6	2	2	4	6	1	2	NaN	NaN	NaN	NaN	1	1	2	2	30000	3	2	1	2	1	NaN	0	0	NaN	1	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	1964	3	1	2	-2	-8	-8	-8	2	2	5	2	-2.0	4	3	4	4	5	4	3	-8	4	3	4	5	1	-8	60	50.0	0	30	40	50	60	-2	60

age = test['survey_time'].dt.year - test['birth']
test['age'] = age
test = test.drop(['survey_time','birth'], axis = 1)
test.shape

(2968, 138)

features2 = ['age','inc_ability','gender','status_peer','work_exper','family_status','health','equity','class','health_problem','family_m','house','depression','learn','relax','edu']

x_test = test[features2].values

test.head()

	id	survey_type	province	city	county	gender	nationality	religion	religion_freq	edu	edu_other	edu_status	edu_yr	income	political	join_party	floor_area	property_0	property_1	property_2	property_3	property_other	height_cm	weight_jin	health	health_problem	depression	hukou	hukou_loc	media_1	media_2	media_3	media_4	media_5	media_6	leisure_1	leisure_2	leisure_3	leisure_4	leisure_5	leisure_6	leisure_7	leisure_8	leisure_9	leisure_10	leisure_11	leisure_12	socialize	relax	learn	social_neighbor	social_friend	socia_outing	equity	class	class_10_before	class_10_after	class_14	work_exper	work_status	work_yr	work_type	work_manage	insur_1	insur_2	insur_3	insur_4	family_income	family_m	family_status	house	car	invest_1	invest_other	son	daughter	minor_child	marital	marital_1st	s_birth	marital_now	s_edu	s_political	s_hukou	s_income	s_work_exper	s_work_status	s_work_type	f_birth	f_edu	f_political	f_work_14	m_birth	m_edu	m_political	m_work_14	status_peer	status_3_before	view	inc_ability	inc_exp	trust_1	trust_2	trust_3	trust_4	trust_5	trust_6	trust_7	trust_8	trust_9	trust_10	trust_11	trust_12	trust_13	neighbor_familiarity	public_service_1	public_service_2	public_service_3	public_service_4	public_service_5	public_service_6	public_service_7	public_service_8	public_service_9	age
0	8001	1	2	2	9	0	8	0	1	4	NaN	2.0	1989.0	18000	1	NaN	100.0	0	1	1	0	NaN	155	120	4	4	4	4	1	2	2	1	4	3	2	1	4	2	4	4	3	3	3	3	5	1	3	3	3	2	1.0	3.0	1	2	5	5	5	4	1	5.0	12.0	2.0	3.0	1	1	2	2	-2	5	3	1	2	1	NaN	1	0	1.0	3	1998.0	1972.0	1998.0	4.0	1.0	1.0	18000.0	1.0	5.0	2.0	1935	3	1	13	1936	3	1	13	3	1	4	2	30000.0	4	4	4	4	4	4	3	4	3	3	-8	3	2	4	80	80.0	60	80	80	80	80	80	80	43
1	8002	1	22	66	106	0	1	1	1	4	NaN	4.0	-2.0	20000	1	NaN	50.0	0	1	0	1	NaN	150	75	1	1	2	5	1	1	1	3	5	1	1	1	5	5	5	5	2	1	4	5	5	5	5	5	4	1	1.0	1.0	1	3	3	5	-8	4	5	NaN	NaN	NaN	NaN	1	1	2	2	20000	1	1	2	2	1	NaN	3	0	0.0	7	1959.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	-2	1	1	2	-2	1	1	2	3	3	4	2	50000.0	3	3	3	3	4	2	2	4	4	4	4	4	4	5	90	80.0	80	80	80	80	70	80	80	77
2	8003	2	9	22	44	0	1	1	1	1	NaN	NaN	NaN	500	1	NaN	60.0	0	0	0	1	NaN	155	85	4	2	2	1	1	1	1	1	1	1	1	5	5	4	5	5	4	5	5	5	5	5	5	2	3	1	2.0	6.0	1	4	1	1	1	2	4	NaN	NaN	NaN	NaN	1	1	2	2	-2	1	2	1	2	1	NaN	2	3	0.0	7	1954.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	-2	1	1	2	1914	1	1	2	3	1	3	4	2000.0	5	5	4	4	4	3	3	3	4	4	4	2	2	5	95	95.0	80	90	80	95	95	80	90	80
3	8004	2	18	52	86	0	1	1	1	4	NaN	2.0	-1.0	50000	1	NaN	220.0	0	0	1	0	NaN	160	126	5	5	5	1	1	2	2	1	5	4	1	1	5	4	4	5	3	4	4	3	3	3	3	2	2	2	1.0	1.0	1	4	5	4	6	5	5	NaN	NaN	NaN	NaN	2	2	2	2	100000	2	4	1	2	1	NaN	1	0	1.0	3	2014.0	1991.0	2014.0	4.0	1.0	1.0	50000.0	1.0	3.0	1.0	1967	4	1	2	1966	1	1	2	2	1	3	2	80000.0	4	4	3	3	4	3	3	3	3	3	3	3	3	4	80	80.0	70	90	80	80	70	60	50	23
4	8005	2	24	70	110	1	1	1	2	1	NaN	NaN	NaN	0	1	NaN	200.0	1	0	0	0	NaN	173	130	4	5	3	4	1	1	1	1	4	4	1	2	3	4	4	5	4	2	5	3	5	5	1	4	4	2	1.0	4.0	6	2	2	4	6	1	2	NaN	NaN	NaN	NaN	1	1	2	2	30000	3	2	1	2	1	NaN	0	0	NaN	1	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	1964	3	1	2	-2	-8	-8	-8	2	2	5	2	-2.0	4	3	4	4	5	4	3	-8	4	3	4	5	1	-8	60	50.0	0	30	40	50	60	-2	60	25

test[features2].isnull().sum()

age               0
inc_ability       0
gender            0
status_peer       0
work_exper        0
family_status     0
health            0
equity            0
class             0
health_problem    0
family_m          0
house             0
depression        0
learn             0
relax             0
edu               0
dtype: int64

data = train[features2]

data.isnull().sum()

age               0
inc_ability       0
gender            0
status_peer       0
work_exper        0
family_status     0
health            0
equity            0
class             0
health_problem    0
family_m          0
house             0
depression        0
learn             0
relax             0
edu               0
dtype: int64

采用逻辑回归模型进行预测，由于是多元分类且数据量不算小，所以不用默认的liblinear算法，采用newton-cg寻找参数最优解

x_train = train[features2].values
y_train = train['happiness'].values

from sklearn.linear_model import LogisticRegression
from sklearn.linear_model import LogisticRegressionCV

model = LogisticRegressionCV(random_state = 10, solver = 'newton-cg', multi_class = 'multinomial')

model.fit(x_train, y_train)

D:\ProgramData\lib\site-packages\sklearn\model_selection\_split.py:2053: FutureWarning: You should specify a value for 'cv' instead of relying on the default value. The default value will change from 3 to 5 in version 0.22.
  warnings.warn(CV_WARNING, FutureWarning)





LogisticRegressionCV(Cs=10, class_weight=None, cv='warn', dual=False,
           fit_intercept=True, intercept_scaling=1.0, max_iter=100,
           multi_class='multinomial', n_jobs=None, penalty='l2',
           random_state=10, refit=True, scoring=None, solver='newton-cg',
           tol=0.0001, verbose=0)

y_esti = model.predict(x_train)
(y_esti == y_train).mean()

0.6059088632949424

评测指标

用训练数据打分

分数计算公式： $\frac{1}{n}\sum_1^n(y_i-y^*)^2$

((y_esti-y_train)**2).mean()

0.6357035553329995

预测测试数据集

y_predict = model.predict(x_test)

test['happiness'] = y_predict
submit = test[['id', 'happiness']]

将此结果提交

submit.to_excel('d:/python/exercise/samples/happiness/submit_02.xlsx')

model.score(x_train, y_train)

0.6059088632949424

比赛排名如下：得分0.681

from IPython.display import Image
Image(filename = 'C:/Users/24866/happy_score.png')

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