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数据挖掘入门 - 二手车交易价格预测(二)

2021/4/15 10:49:55 人评论

二、 EDA-数据探索性分析 赛题:零基础入门数据挖掘 - 二手车交易价格预测 2.1 EDA目标 EDA的价值主要在于熟悉数据集,了解数据集,对数据集进行验证来确定所获得数据集可以用于接下来的机器学习或者深度学习使用。当了解了数据集之后我们下…

二、 EDA-数据探索性分析

赛题:零基础入门数据挖掘 - 二手车交易价格预测

2.1 EDA目标

  • EDA的价值主要在于熟悉数据集,了解数据集,对数据集进行验证来确定所获得数据集可以用于接下来的机器学习或者深度学习使用。
  • 当了解了数据集之后我们下一步就是要去了解变量间的相互关系以及变量与预测值之间的存在关系。
  • 引导数据科学从业者进行数据处理以及特征工程的步骤,使数据集的结构和特征集让接下来的预测问题更加可靠。
  • 完成对于数据的探索性分析,并对于数据进行一些图表或者文字总结。

2.2 内容介绍

  1. 载入各种数据科学以及可视化库:
    • 数据科学库 pandas、numpy、scipy;
    • 可视化库 matplotlib、seabon;
    • 其他;
  2. 载入数据:
    • 载入训练集和测试集;
    • 简略观察数据(head()+shape);
  3. 数据总览:
    • 通过describe()来熟悉数据的相关统计量
    • 通过info()来熟悉数据类型
  4. 判断数据缺失和异常
    • 查看每列的存在nan情况
    • 异常值检测
  5. 了解预测值的分布
    • 总体分布概况(无界约翰逊分布等)
    • 查看skewness and kurtosis
    • 查看预测值的具体频数
  6. 特征分为类别特征和数字特征,并对类别特征查看unique分布
  7. 数字特征分析
    • 相关性分析
    • 查看几个特征得 偏度和峰值
    • 每个数字特征得分布可视化
    • 数字特征相互之间的关系可视化
    • 多变量互相回归关系可视化
  8. 类型特征分析
    • unique分布
    • 类别特征箱形图可视化
    • 类别特征的小提琴图可视化
    • 类别特征的柱形图可视化类别
    • 特征的每个类别频数可视化(count_plot)
  9. 用pandas_profiling生成数据报告

2.3 代码示例

2.3.1 载入各种数据科学以及可视化库

#导入warnings包,利用过滤器来实现忽略警告语句。
import warnings
warnings.filterwarnings('ignore')

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

2.3.2 载入数据

## 1) 载入训练集和测试集;
Train_data = pd.read_csv('train.csv', sep=' ')
Test_data = pd.read_csv('testA.csv', sep=' ')

所有特征集均脱敏处理

  • name - 汽车编码
  • regDate - 汽车注册时间
  • model - 车型编码
  • brand - 品牌
  • bodyType - 车身类型
  • fuelType - 燃油类型
  • gearbox - 变速箱
  • power - 汽车功率
  • kilometer - 汽车行驶公里
  • notRepairedDamage - 汽车有尚未修复的损坏
  • regionCode - 看车地区编码
  • seller - 销售方
  • offerType - 报价类型
  • creatDate - 广告发布时间
  • price - 汽车价格
  • v_0’, ‘v_1’, ‘v_2’, ‘v_3’, ‘v_4’, ‘v_5’, ‘v_6’, ‘v_7’, ‘v_8’, ‘v_9’, ‘v_10’, ‘v_11’, ‘v_12’, ‘v_13’,‘v_14’(根据汽车的评论、标签等大量信息得到的embedding向量)【人工构造 匿名特征】
## 2) 简略观察数据(head()+shape)
Train_data.head().append(Train_data.tail())
SaleIDnameregDatemodelbrandbodyTypefuelTypegearboxpowerkilometer...v_5v_6v_7v_8v_9v_10v_11v_12v_13v_14
007362004040230.061.00.00.06012.5...0.2356760.1019880.1295490.0228160.097462-2.8818032.804097-2.4208210.7952920.914762
1122622003030140.012.00.00.0015.0...0.2647770.1210040.1357310.0265970.020582-4.9004822.096338-1.030483-1.7226740.245522
221487420040403115.0151.00.00.016312.5...0.2514100.1149120.1651470.0621730.027075-4.8467491.8035591.565330-0.832687-0.229963
337186519960908109.0100.00.01.019315.0...0.2742930.1103000.1219640.0333950.000000-4.5095991.285940-0.501868-2.438353-0.478699
4411108020120103110.051.00.00.0685.0...0.2280360.0732050.0918800.0788190.121534-1.8962400.9107830.9311102.8345181.923482
14999514999516397820000607121.0104.00.01.016315.0...0.2802640.0003100.0484410.0711580.0191741.988114-2.9839730.589167-1.304370-0.302592
14999614999618453520091102116.0110.00.00.012510.0...0.2532170.0007770.0840790.0996810.0793711.839166-2.7746152.5539940.924196-0.272160
1499971499971475872010100360.0111.01.00.0906.0...0.2333530.0007050.1188720.1001180.0979142.439812-1.6306772.2901971.8919220.414931
149998149998459072006031234.0103.01.00.015615.0...0.2563690.0002520.0814790.0835580.0814982.075380-2.6337191.4149370.431981-1.659014
1499991499991776721999020419.0286.00.01.019312.5...0.2844750.0000000.0400720.0625430.0258191.978453-3.1799130.031724-1.483350-0.342674

10 rows × 31 columns

Train_data.shape
(150000, 31)
Test_data.head().append(Test_data.tail())
SaleIDnameregDatemodelbrandbodyTypefuelTypegearboxpowerkilometer...v_5v_6v_7v_8v_9v_10v_11v_12v_13v_14
01500006693220111212222.045.01.01.031315.0...0.2644050.1218000.0708990.1065580.078867-7.050969-0.8546264.8001510.620011-3.664654
11500011749601999021119.0210.00.00.07512.5...0.2617450.0000000.0967330.0137050.0523833.679418-0.729039-3.796107-1.541230-0.757055
215000253562009030482.0210.00.00.01097.0...0.2602160.1120810.0780820.0620780.050540-4.9266901.0011060.8265620.1382260.754033
315000350688201004050.000.00.01.01607.0...0.2604660.1067270.0811460.0759710.048268-4.8646370.5054931.8703790.3660381.312775
41500041614281997070326.0142.00.00.07515.0...0.2509990.0000000.0778060.0286000.0817093.616475-0.673236-3.197685-0.025678-0.101290
4999519999520903199605034.044.00.00.011615.0...0.2846640.1300440.0498330.0288070.004616-5.9785111.303174-1.207191-1.981240-0.357695
49996199996708199910110.000.00.00.07515.0...0.2681010.1080950.0660390.0254680.025971-3.9138251.759524-2.075658-1.1548470.169073
4999719999766932004041249.010.01.01.022415.0...0.2694320.1057240.1176520.0574790.015669-4.6390650.6547131.137756-1.3905310.254420
49998199998969002002000827.010.00.01.033415.0...0.2611520.0004900.1373660.0862160.0513831.833504-2.8286872.465630-0.911682-2.057353
4999919999919338420041109166.061.0NaN1.0689.0...0.2287300.0003000.1035340.0806250.1242642.914571-1.1352700.5476282.094057-1.552150

10 rows × 30 columns

Test_data.shape
(50000, 30)

2.3.3 总览数据概况

  1. describe种有每列的统计量,个数count、平均值mean、方差std、最小值min、中位数25% 50% 75% 、以及最大值 看这个信息主要是瞬间掌握数据的大概的范围以及每个值的异常值的判断,比如有的时候会发现999 9999 -1 等值这些其实都是nan的另外一种表达方式,有的时候需要注意下
  2. info 通过info来了解数据每列的type,有助于了解是否存在除了nan以外的特殊符号异常
## 1) 通过describe()来熟悉数据的相关统计量
Train_data.describe()
SaleIDnameregDatemodelbrandbodyTypefuelTypegearboxpowerkilometer...v_5v_6v_7v_8v_9v_10v_11v_12v_13v_14
count150000.000000150000.0000001.500000e+05149999.000000150000.000000145494.000000141320.000000144019.000000150000.000000150000.000000...150000.000000150000.000000150000.000000150000.000000150000.000000150000.000000150000.000000150000.000000150000.000000150000.000000
mean74999.50000068349.1728732.003417e+0747.1290218.0527331.7923690.3758420.224943119.31654712.597160...0.2482040.0449230.1246920.0581440.061996-0.0010000.0090350.0048130.000313-0.000688
std43301.41452761103.8750955.364988e+0449.5360407.8649561.7606400.5486770.417546177.1684193.919576...0.0458040.0517430.2014100.0291860.0356923.7723863.2860712.5174781.2889881.038685
min0.0000000.0000001.991000e+070.0000000.0000000.0000000.0000000.0000000.0000000.500000...0.0000000.0000000.0000000.0000000.000000-9.168192-5.558207-9.639552-4.153899-6.546556
25%37499.75000011156.0000001.999091e+0710.0000001.0000000.0000000.0000000.00000075.00000012.500000...0.2436150.0000380.0624740.0353340.033930-3.722303-1.951543-1.871846-1.057789-0.437034
50%74999.50000051638.0000002.003091e+0730.0000006.0000001.0000000.0000000.000000110.00000015.000000...0.2577980.0008120.0958660.0570140.0584841.624076-0.358053-0.130753-0.0362450.141246
75%112499.250000118841.2500002.007111e+0766.00000013.0000003.0000001.0000000.000000150.00000015.000000...0.2652970.1020090.1252430.0793820.0874912.8443571.2550221.7769330.9428130.680378
max149999.000000196812.0000002.015121e+07247.00000039.0000007.0000006.0000001.00000019312.00000015.000000...0.2918380.1514201.4049360.1607910.22278712.35701118.81904213.84779211.1476698.658418

8 rows × 30 columns

Test_data.describe()
SaleIDnameregDatemodelbrandbodyTypefuelTypegearboxpowerkilometer...v_5v_6v_7v_8v_9v_10v_11v_12v_13v_14
count50000.00000050000.0000005.000000e+0450000.00000050000.00000048587.00000047107.00000048090.00000050000.00000050000.000000...50000.00000050000.00000050000.00000050000.00000050000.00000050000.00000050000.00000050000.00000050000.00000050000.000000
mean174999.50000068542.2232802.003393e+0746.8445208.0562401.7821850.3734050.224350119.88362012.595580...0.2486690.0450210.1227440.0579970.062000-0.017855-0.013742-0.013554-0.0031470.001516
std14433.90106761052.8081335.368870e+0449.4695487.8194771.7607360.5464420.417158185.0973873.908979...0.0446010.0517660.1959720.0292110.0356533.7479853.2312582.5159621.2865971.027360
min150000.0000000.0000001.991000e+070.0000000.0000000.0000000.0000000.0000000.0000000.500000...0.0000000.0000000.0000000.0000000.000000-9.160049-5.411964-8.916949-4.123333-6.112667
25%162499.75000011203.5000001.999091e+0710.0000001.0000000.0000000.0000000.00000075.00000012.500000...0.2437620.0000440.0626440.0350840.033714-3.700121-1.971325-1.876703-1.060428-0.437920
50%174999.50000052248.5000002.003091e+0729.0000006.0000001.0000000.0000000.000000109.00000015.000000...0.2578770.0008150.0958280.0570840.0587641.613212-0.355843-0.142779-0.0359560.138799
75%187499.250000118856.5000002.007110e+0765.00000013.0000003.0000001.0000000.000000150.00000015.000000...0.2653280.1020250.1254380.0790770.0874892.8327081.2629141.7643350.9414690.681163
max199999.000000196805.0000002.015121e+07246.00000039.0000007.0000006.0000001.00000020000.00000015.000000...0.2916180.1532651.3588130.1563550.21477512.33887218.85621812.9504985.9132732.624622

8 rows × 29 columns

## 2) 通过info()来熟悉数据类型
Train_data.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 150000 entries, 0 to 149999
Data columns (total 31 columns):
SaleID               150000 non-null int64
name                 150000 non-null int64
regDate              150000 non-null int64
model                149999 non-null float64
brand                150000 non-null int64
bodyType             145494 non-null float64
fuelType             141320 non-null float64
gearbox              144019 non-null float64
power                150000 non-null int64
kilometer            150000 non-null float64
notRepairedDamage    150000 non-null object
regionCode           150000 non-null int64
seller               150000 non-null int64
offerType            150000 non-null int64
creatDate            150000 non-null int64
price                150000 non-null int64
v_0                  150000 non-null float64
v_1                  150000 non-null float64
v_2                  150000 non-null float64
v_3                  150000 non-null float64
v_4                  150000 non-null float64
v_5                  150000 non-null float64
v_6                  150000 non-null float64
v_7                  150000 non-null float64
v_8                  150000 non-null float64
v_9                  150000 non-null float64
v_10                 150000 non-null float64
v_11                 150000 non-null float64
v_12                 150000 non-null float64
v_13                 150000 non-null float64
v_14                 150000 non-null float64
dtypes: float64(20), int64(10), object(1)
memory usage: 35.5+ MB
Test_data.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 50000 entries, 0 to 49999
Data columns (total 30 columns):
SaleID               50000 non-null int64
name                 50000 non-null int64
regDate              50000 non-null int64
model                50000 non-null float64
brand                50000 non-null int64
bodyType             48587 non-null float64
fuelType             47107 non-null float64
gearbox              48090 non-null float64
power                50000 non-null int64
kilometer            50000 non-null float64
notRepairedDamage    50000 non-null object
regionCode           50000 non-null int64
seller               50000 non-null int64
offerType            50000 non-null int64
creatDate            50000 non-null int64
v_0                  50000 non-null float64
v_1                  50000 non-null float64
v_2                  50000 non-null float64
v_3                  50000 non-null float64
v_4                  50000 non-null float64
v_5                  50000 non-null float64
v_6                  50000 non-null float64
v_7                  50000 non-null float64
v_8                  50000 non-null float64
v_9                  50000 non-null float64
v_10                 50000 non-null float64
v_11                 50000 non-null float64
v_12                 50000 non-null float64
v_13                 50000 non-null float64
v_14                 50000 non-null float64
dtypes: float64(20), int64(9), object(1)
memory usage: 11.4+ MB

2.3.4 判断数据缺失和异常

## 1) 查看每列的存在nan情况
Train_data.isnull().sum()
SaleID                  0
name                    0
regDate                 0
model                   1
brand                   0
bodyType             4506
fuelType             8680
gearbox              5981
power                   0
kilometer               0
notRepairedDamage       0
regionCode              0
seller                  0
offerType               0
creatDate               0
price                   0
v_0                     0
v_1                     0
v_2                     0
v_3                     0
v_4                     0
v_5                     0
v_6                     0
v_7                     0
v_8                     0
v_9                     0
v_10                    0
v_11                    0
v_12                    0
v_13                    0
v_14                    0
dtype: int64
Test_data.isnull().sum()
SaleID                  0
name                    0
regDate                 0
model                   0
brand                   0
bodyType             1413
fuelType             2893
gearbox              1910
power                   0
kilometer               0
notRepairedDamage       0
regionCode              0
seller                  0
offerType               0
creatDate               0
v_0                     0
v_1                     0
v_2                     0
v_3                     0
v_4                     0
v_5                     0
v_6                     0
v_7                     0
v_8                     0
v_9                     0
v_10                    0
v_11                    0
v_12                    0
v_13                    0
v_14                    0
dtype: int64
# nan可视化
missing = Train_data.isnull().sum()
missing = missing[missing > 0]
missing.sort_values(inplace=True)
missing.plot.bar()
<matplotlib.axes._subplots.AxesSubplot at 0x111b156dba8>


通过以上两句可以很直观的了解哪些列存在 “nan”, 并可以把nan的个数打印,主要的目的在于 nan存在的个数是否真的很大,如果很小一般选择填充,如果使用lgb等树模型可以直接空缺,让树自己去优化,但如果nan存在的过多、可以考虑删掉

# 可视化看下缺省值
msno.matrix(Train_data.sample(250))
<matplotlib.axes._subplots.AxesSubplot at 0x111b16e75f8>

msno.bar(Train_data.sample(1000))
<matplotlib.axes._subplots.AxesSubplot at 0x111b1936f98>

# 可视化看下缺省值
msno.matrix(Test_data.sample(250))
<matplotlib.axes._subplots.AxesSubplot at 0x111b1b4ba20>

msno.bar(Test_data.sample(1000))
<matplotlib.axes._subplots.AxesSubplot at 0x111b1bc5978>

27-1
测试集的缺省和训练集的差不多情况, 可视化有四列有缺省,notRepairedDamage缺省得最多

## 2) 查看异常值检测
Train_data.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 150000 entries, 0 to 149999
Data columns (total 31 columns):
SaleID               150000 non-null int64
name                 150000 non-null int64
regDate              150000 non-null int64
model                149999 non-null float64
brand                150000 non-null int64
bodyType             145494 non-null float64
fuelType             141320 non-null float64
gearbox              144019 non-null float64
power                150000 non-null int64
kilometer            150000 non-null float64
notRepairedDamage    150000 non-null object
regionCode           150000 non-null int64
seller               150000 non-null int64
offerType            150000 non-null int64
creatDate            150000 non-null int64
price                150000 non-null int64
v_0                  150000 non-null float64
v_1                  150000 non-null float64
v_2                  150000 non-null float64
v_3                  150000 non-null float64
v_4                  150000 non-null float64
v_5                  150000 non-null float64
v_6                  150000 non-null float64
v_7                  150000 non-null float64
v_8                  150000 non-null float64
v_9                  150000 non-null float64
v_10                 150000 non-null float64
v_11                 150000 non-null float64
v_12                 150000 non-null float64
v_13                 150000 non-null float64
v_14                 150000 non-null float64
dtypes: float64(20), int64(10), object(1)
memory usage: 35.5+ MB

可以发现除了notRepairedDamage 为object类型其他都为数字 这里我们把他的几个不同的值都进行显示就知道了

Train_data['notRepairedDamage'].value_counts()
0.0    111361
-       24324
1.0     14315
Name: notRepairedDamage, dtype: int64

可以看出来‘ - ’也为空缺值,因为很多模型对nan有直接的处理,这里我们先不做处理,先替换成nan

Train_data['notRepairedDamage'].replace('-', np.nan, inplace=True)
Train_data['notRepairedDamage'].value_counts()
0.0    111361
1.0     14315
Name: notRepairedDamage, dtype: int64
Train_data.isnull().sum()
SaleID                   0
name                     0
regDate                  0
model                    1
brand                    0
bodyType              4506
fuelType              8680
gearbox               5981
power                    0
kilometer                0
notRepairedDamage    24324
regionCode               0
seller                   0
offerType                0
creatDate                0
price                    0
v_0                      0
v_1                      0
v_2                      0
v_3                      0
v_4                      0
v_5                      0
v_6                      0
v_7                      0
v_8                      0
v_9                      0
v_10                     0
v_11                     0
v_12                     0
v_13                     0
v_14                     0
dtype: int64
Test_data['notRepairedDamage'].value_counts()
0.0    37249
-       8031
1.0     4720
Name: notRepairedDamage, dtype: int64
Test_data['notRepairedDamage'].replace('-', np.nan, inplace=True)

以下两个类别特征严重倾斜,一般不会对预测有什么帮助,故这边先删掉。当然也可以继续挖掘,但是一般意义不大

Train_data["seller"].value_counts()
0    149999
1         1
Name: seller, dtype: int64
Train_data["offerType"].value_counts()
0    150000
Name: offerType, dtype: int64
del Train_data["seller"]
del Train_data["offerType"]
del Test_data["seller"]
del Test_data["offerType"]

2.3.5 了解预测值的分布

Train_data['price']
0         1850
1         3600
2         6222
3         2400
4         5200
          ... 
149995    5900
149996    9500
149997    7500
149998    4999
149999    4700
Name: price, Length: 150000, dtype: int64
Train_data['price'].value_counts()
500      2337
1500     2158
1200     1922
1000     1850
2500     1821
         ... 
25321       1
8886        1
8801        1
37920       1
8188        1
Name: price, Length: 3763, dtype: int64
## 1) 总体分布概况(无界约翰逊分布等)
import scipy.stats as st
y = Train_data['price']
plt.figure(1); plt.title('Johnson SU')
sns.distplot(y, kde=False, fit=st.johnsonsu)
plt.figure(2); plt.title('Normal')
sns.distplot(y, kde=False, fit=st.norm)
plt.figure(3); plt.title('Log Normal')
sns.distplot(y, kde=False, fit=st.lognorm)
<matplotlib.axes._subplots.AxesSubplot at 0x111b1ee82b0>

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46-2
46-3
价格不服从正态分布,所以在进行回归之前,它必须进行转换。虽然对数变换做得很好,但最佳拟合是无界约翰逊分布

## 2) 查看skewness and kurtosis
sns.distplot(Train_data['price']);
print("Skewness: %f" % Train_data['price'].skew())
print("Kurtosis: %f" % Train_data['price'].kurt())
Skewness: 3.346487
Kurtosis: 18.995183

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Train_data.skew(), Train_data.kurt()
(SaleID               6.017846e-17
 name                 5.576058e-01
 regDate              2.849508e-02
 model                1.484388e+00
 brand                1.150760e+00
 bodyType             9.915299e-01
 fuelType             1.595486e+00
 gearbox              1.317514e+00
 power                6.586318e+01
 kilometer           -1.525921e+00
 notRepairedDamage    2.430640e+00
 regionCode           6.888812e-01
 creatDate           -7.901331e+01
 price                3.346487e+00
 v_0                 -1.316712e+00
 v_1                  3.594543e-01
 v_2                  4.842556e+00
 v_3                  1.062920e-01
 v_4                  3.679890e-01
 v_5                 -4.737094e+00
 v_6                  3.680730e-01
 v_7                  5.130233e+00
 v_8                  2.046133e-01
 v_9                  4.195007e-01
 v_10                 2.522046e-02
 v_11                 3.029146e+00
 v_12                 3.653576e-01
 v_13                 2.679152e-01
 v_14                -1.186355e+00
 dtype: float64, SaleID                 -1.200000
 name                   -1.039945
 regDate                -0.697308
 model                   1.740483
 brand                   1.076201
 bodyType                0.206937
 fuelType                5.880049
 gearbox                -0.264161
 power                5733.451054
 kilometer               1.141934
 notRepairedDamage       3.908072
 regionCode             -0.340832
 creatDate            6881.080328
 price                  18.995183
 v_0                     3.993841
 v_1                    -1.753017
 v_2                    23.860591
 v_3                    -0.418006
 v_4                    -0.197295
 v_5                    22.934081
 v_6                    -1.742567
 v_7                    25.845489
 v_8                    -0.636225
 v_9                    -0.321491
 v_10                   -0.577935
 v_11                   12.568731
 v_12                    0.268937
 v_13                   -0.438274
 v_14                    2.393526
 dtype: float64)
sns.distplot(Train_data.skew(),color='blue',axlabel ='Skewness')
<matplotlib.axes._subplots.AxesSubplot at 0x111b484ab38>

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sns.distplot(Train_data.kurt(),color='orange',axlabel ='Kurtness')
<matplotlib.axes._subplots.AxesSubplot at 0x111b4953940>

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skew、kurt说明参考https://www.cnblogs.com/wyy1480/p/10474046.html

## 3) 查看预测值的具体频数
plt.hist(Train_data['price'], orientation = 'vertical',histtype = 'bar', color ='red')
plt.show()

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查看频数, 大于20000得值极少,其实这里也可以把这些当作特殊得值(异常值)直接用填充或者删掉,再前面进行

# log变换 z之后的分布较均匀,可以进行log变换进行预测,这也是预测问题常用的trick
plt.hist(np.log(Train_data['price']), orientation = 'vertical',histtype = 'bar', color ='red') 
plt.show()

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2.3.6 特征分为类别特征和数字特征,并对类别特征查看unique分布

数据类型

  • name - 汽车编码
  • regDate - 汽车注册时间
  • model - 车型编码
  • brand - 品牌
  • bodyType - 车身类型
  • fuelType - 燃油类型
  • gearbox - 变速箱
  • power - 汽车功率
  • kilometer - 汽车行驶公里
  • notRepairedDamage - 汽车有尚未修复的损坏
  • regionCode - 看车地区编码
  • seller - 销售方 【以删】
  • offerType - 报价类型 【以删】
  • creatDate - 广告发布时间
  • price - 汽车价格
  • v_0’, ‘v_1’, ‘v_2’, ‘v_3’, ‘v_4’, ‘v_5’, ‘v_6’, ‘v_7’, ‘v_8’, ‘v_9’, ‘v_10’, ‘v_11’, ‘v_12’, ‘v_13’,‘v_14’(根据汽车的评论、标签等大量信息得到的embedding向量)【人工构造 匿名特征】
# 分离label即预测值
Y_train = Train_data['price']
# 这个区别方式适用于没有直接label coding的数据
# 这里不适用,需要人为根据实际含义来区分
# 数字特征
# numeric_features = Train_data.select_dtypes(include=[np.number])
# numeric_features.columns
# # 类型特征
# categorical_features = Train_data.select_dtypes(include=[np.object])
# categorical_features.columns
numeric_features = ['power', 'kilometer', 'v_0', 'v_1', 'v_2', 'v_3', 'v_4', 'v_5', 'v_6', 'v_7', 'v_8', 'v_9', 'v_10', 'v_11', 'v_12', 'v_13','v_14' ]

categorical_features = ['name', 'model', 'brand', 'bodyType', 'fuelType', 'gearbox', 'notRepairedDamage', 'regionCode',]
# 特征nunique分布
for cat_fea in categorical_features:
    print(cat_fea + "的特征分布如下:")
    print("{}特征有个{}不同的值".format(cat_fea, Train_data[cat_fea].nunique()))
    print(Train_data[cat_fea].value_counts())
name的特征分布如下:
name特征有个99662不同的值
708       282
387       282
55        280
1541      263
203       233
         ... 
5074        1
7123        1
11221       1
13270       1
174485      1
Name: name, Length: 99662, dtype: int64
model的特征分布如下:
model特征有个248不同的值
0.0      11762
19.0      9573
4.0       8445
1.0       6038
29.0      5186
         ...  
245.0        2
209.0        2
240.0        2
242.0        2
247.0        1
Name: model, Length: 248, dtype: int64
brand的特征分布如下:
brand特征有个40不同的值
0     31480
4     16737
14    16089
10    14249
1     13794
6     10217
9      7306
5      4665
13     3817
11     2945
3      2461
7      2361
16     2223
8      2077
25     2064
27     2053
21     1547
15     1458
19     1388
20     1236
12     1109
22     1085
26      966
30      940
17      913
24      772
28      649
32      592
29      406
37      333
2       321
31      318
18      316
36      228
34      227
33      218
23      186
35      180
38       65
39        9
Name: brand, dtype: int64
bodyType的特征分布如下:
bodyType特征有个8不同的值
0.0    41420
1.0    35272
2.0    30324
3.0    13491
4.0     9609
5.0     7607
6.0     6482
7.0     1289
Name: bodyType, dtype: int64
fuelType的特征分布如下:
fuelType特征有个7不同的值
0.0    91656
1.0    46991
2.0     2212
3.0      262
4.0      118
5.0       45
6.0       36
Name: fuelType, dtype: int64
gearbox的特征分布如下:
gearbox特征有个2不同的值
0.0    111623
1.0     32396
Name: gearbox, dtype: int64
notRepairedDamage的特征分布如下:
notRepairedDamage特征有个2不同的值
0.0    111361
1.0     14315
Name: notRepairedDamage, dtype: int64
regionCode的特征分布如下:
regionCode特征有个7905不同的值
419     369
764     258
125     137
176     136
462     134
       ... 
6414      1
7063      1
4239      1
5931      1
7267      1
Name: regionCode, Length: 7905, dtype: int64
# 特征nunique分布
for cat_fea in categorical_features:
    print(cat_fea + "的特征分布如下:")
    print("{}特征有个{}不同的值".format(cat_fea, Test_data[cat_fea].nunique()))
    print(Test_data[cat_fea].value_counts())
name的特征分布如下:
name特征有个37453不同的值
55       97
708      96
387      95
1541     88
713      74
         ..
22270     1
89855     1
42752     1
48899     1
11808     1
Name: name, Length: 37453, dtype: int64
model的特征分布如下:
model特征有个247不同的值
0.0      3896
19.0     3245
4.0      3007
1.0      1981
29.0     1742
         ... 
242.0       1
240.0       1
244.0       1
243.0       1
246.0       1
Name: model, Length: 247, dtype: int64
brand的特征分布如下:
brand特征有个40不同的值
0     10348
4      5763
14     5314
10     4766
1      4532
6      3502
9      2423
5      1569
13     1245
11      919
7       795
3       773
16      771
8       704
25      695
27      650
21      544
15      511
20      450
19      450
12      389
22      363
30      324
17      317
26      303
24      268
28      225
32      193
29      117
31      115
18      106
2       104
37       92
34       77
33       76
36       67
23       62
35       53
38       23
39        2
Name: brand, dtype: int64
bodyType的特征分布如下:
bodyType特征有个8不同的值
0.0    13985
1.0    11882
2.0     9900
3.0     4433
4.0     3303
5.0     2537
6.0     2116
7.0      431
Name: bodyType, dtype: int64
fuelType的特征分布如下:
fuelType特征有个7不同的值
0.0    30656
1.0    15544
2.0      774
3.0       72
4.0       37
6.0       14
5.0       10
Name: fuelType, dtype: int64
gearbox的特征分布如下:
gearbox特征有个2不同的值
0.0    37301
1.0    10789
Name: gearbox, dtype: int64
notRepairedDamage的特征分布如下:
notRepairedDamage特征有个2不同的值
0.0    37249
1.0     4720
Name: notRepairedDamage, dtype: int64
regionCode的特征分布如下:
regionCode特征有个6971不同的值
419     146
764      78
188      52
125      51
759      51
       ... 
7753      1
7463      1
7230      1
826       1
112       1
Name: regionCode, Length: 6971, dtype: int64

2.3.7 数字特征分析

numeric_features.append('price')
numeric_features
['power',
 'kilometer',
 'v_0',
 'v_1',
 'v_2',
 'v_3',
 'v_4',
 'v_5',
 'v_6',
 'v_7',
 'v_8',
 'v_9',
 'v_10',
 'v_11',
 'v_12',
 'v_13',
 'v_14',
 'price']
Train_data.head()
SaleIDnameregDatemodelbrandbodyTypefuelTypegearboxpowerkilometer...v_5v_6v_7v_8v_9v_10v_11v_12v_13v_14
007362004040230.061.00.00.06012.5...0.2356760.1019880.1295490.0228160.097462-2.8818032.804097-2.4208210.7952920.914762
1122622003030140.012.00.00.0015.0...0.2647770.1210040.1357310.0265970.020582-4.9004822.096338-1.030483-1.7226740.245522
221487420040403115.0151.00.00.016312.5...0.2514100.1149120.1651470.0621730.027075-4.8467491.8035591.565330-0.832687-0.229963
337186519960908109.0100.00.01.019315.0...0.2742930.1103000.1219640.0333950.000000-4.5095991.285940-0.501868-2.438353-0.478699
4411108020120103110.051.00.00.0685.0...0.2280360.0732050.0918800.0788190.121534-1.8962400.9107830.9311102.8345181.923482

5 rows × 29 columns

## 1) 相关性分析
price_numeric = Train_data[numeric_features]
correlation = price_numeric.corr()
print(correlation['price'].sort_values(ascending = False),'\n')
price        1.000000
v_12         0.692823
v_8          0.685798
v_0          0.628397
power        0.219834
v_5          0.164317
v_2          0.085322
v_6          0.068970
v_1          0.060914
v_14         0.035911
v_13        -0.013993
v_7         -0.053024
v_4         -0.147085
v_9         -0.206205
v_10        -0.246175
v_11        -0.275320
kilometer   -0.440519
v_3         -0.730946
Name: price, dtype: float64 
f , ax = plt.subplots(figsize = (7, 7))

plt.title('Correlation of Numeric Features with Price',y=1,size=16)

sns.heatmap(correlation,square = True,  vmax=0.8)
<matplotlib.axes._subplots.AxesSubplot at 0x111b6ed5b70>

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del price_numeric['price']
## 2) 查看几个特征得 偏度和峰值
for col in numeric_features:
    print('{:15}'.format(col), 
          'Skewness: {:05.2f}'.format(Train_data[col].skew()) , 
          '   ' ,
          'Kurtosis: {:06.2f}'.format(Train_data[col].kurt())  
         )
power           Skewness: 65.86     Kurtosis: 5733.45
kilometer       Skewness: -1.53     Kurtosis: 001.14
v_0             Skewness: -1.32     Kurtosis: 003.99
v_1             Skewness: 00.36     Kurtosis: -01.75
v_2             Skewness: 04.84     Kurtosis: 023.86
v_3             Skewness: 00.11     Kurtosis: -00.42
v_4             Skewness: 00.37     Kurtosis: -00.20
v_5             Skewness: -4.74     Kurtosis: 022.93
v_6             Skewness: 00.37     Kurtosis: -01.74
v_7             Skewness: 05.13     Kurtosis: 025.85
v_8             Skewness: 00.20     Kurtosis: -00.64
v_9             Skewness: 00.42     Kurtosis: -00.32
v_10            Skewness: 00.03     Kurtosis: -00.58
v_11            Skewness: 03.03     Kurtosis: 012.57
v_12            Skewness: 00.37     Kurtosis: 000.27
v_13            Skewness: 00.27     Kurtosis: -00.44
v_14            Skewness: -1.19     Kurtosis: 002.39
price           Skewness: 03.35     Kurtosis: 019.00
## 3) 每个数字特征得分布可视化
f = pd.melt(Train_data, value_vars=numeric_features)
g = sns.FacetGrid(f, col="variable",  col_wrap=2, sharex=False, sharey=False)
g = g.map(sns.distplot, "value")

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可以看出匿名特征相对分布均匀

## 4) 数字特征相互之间的关系可视化
sns.set()
columns = ['price', 'v_12', 'v_8' , 'v_0', 'power', 'v_5',  'v_2', 'v_6', 'v_1', 'v_14']
sns.pairplot(Train_data[columns],size = 2 ,kind ='scatter',diag_kind='kde')
plt.show()

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Train_data.columns
Index(['SaleID', 'name', 'regDate', 'model', 'brand', 'bodyType', 'fuelType',
       'gearbox', 'power', 'kilometer', 'notRepairedDamage', 'regionCode',
       'creatDate', 'price', 'v_0', 'v_1', 'v_2', 'v_3', 'v_4', 'v_5', 'v_6',
       'v_7', 'v_8', 'v_9', 'v_10', 'v_11', 'v_12', 'v_13', 'v_14'],
      dtype='object')
Y_train
0         1850
1         3600
2         6222
3         2400
4         5200
          ... 
149995    5900
149996    9500
149997    7500
149998    4999
149999    4700
Name: price, Length: 150000, dtype: int64

此处是多变量之间的关系可视化,可视化更多学习可参考很不错的文章https://www.jianshu.com/p/6e18d21a4cad

## 5) 多变量互相回归关系可视化
fig, ((ax1, ax2), (ax3, ax4), (ax5, ax6), (ax7, ax8), (ax9, ax10)) = plt.subplots(nrows=5, ncols=2, figsize=(24, 20))
# ['v_12', 'v_8' , 'v_0', 'power', 'v_5',  'v_2', 'v_6', 'v_1', 'v_14']
v_12_scatter_plot = pd.concat([Y_train,Train_data['v_12']],axis = 1)
sns.regplot(x='v_12',y = 'price', data = v_12_scatter_plot,scatter= True, fit_reg=True, ax=ax1)

v_8_scatter_plot = pd.concat([Y_train,Train_data['v_8']],axis = 1)
sns.regplot(x='v_8',y = 'price',data = v_8_scatter_plot,scatter= True, fit_reg=True, ax=ax2)

v_0_scatter_plot = pd.concat([Y_train,Train_data['v_0']],axis = 1)
sns.regplot(x='v_0',y = 'price',data = v_0_scatter_plot,scatter= True, fit_reg=True, ax=ax3)

power_scatter_plot = pd.concat([Y_train,Train_data['power']],axis = 1)
sns.regplot(x='power',y = 'price',data = power_scatter_plot,scatter= True, fit_reg=True, ax=ax4)

v_5_scatter_plot = pd.concat([Y_train,Train_data['v_5']],axis = 1)
sns.regplot(x='v_5',y = 'price',data = v_5_scatter_plot,scatter= True, fit_reg=True, ax=ax5)

v_2_scatter_plot = pd.concat([Y_train,Train_data['v_2']],axis = 1)
sns.regplot(x='v_2',y = 'price',data = v_2_scatter_plot,scatter= True, fit_reg=True, ax=ax6)

v_6_scatter_plot = pd.concat([Y_train,Train_data['v_6']],axis = 1)
sns.regplot(x='v_6',y = 'price',data = v_6_scatter_plot,scatter= True, fit_reg=True, ax=ax7)

v_1_scatter_plot = pd.concat([Y_train,Train_data['v_1']],axis = 1)
sns.regplot(x='v_1',y = 'price',data = v_1_scatter_plot,scatter= True, fit_reg=True, ax=ax8)

v_14_scatter_plot = pd.concat([Y_train,Train_data['v_14']],axis = 1)
sns.regplot(x='v_14',y = 'price',data = v_14_scatter_plot,scatter= True, fit_reg=True, ax=ax9)

v_13_scatter_plot = pd.concat([Y_train,Train_data['v_13']],axis = 1)
sns.regplot(x='v_13',y = 'price',data = v_13_scatter_plot,scatter= True, fit_reg=True, ax=ax10)

<matplotlib.axes._subplots.AxesSubplot at 0x111b47b2b38>

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2.3.8 类别特征分析

## 1) unique分布
for fea in categorical_features:
    print(Train_data[fea].nunique())
99662
248
40
8
7
2
2
7905
categorical_features
['name',
 'model',
 'brand',
 'bodyType',
 'fuelType',
 'gearbox',
 'notRepairedDamage',
 'regionCode']
## 2) 类别特征箱形图可视化

# 因为 name和 regionCode的类别太稀疏了,这里我们把不稀疏的几类画一下
categorical_features = ['model',
 'brand',
 'bodyType',
 'fuelType',
 'gearbox',
 'notRepairedDamage']
for c in categorical_features:
    Train_data[c] = Train_data[c].astype('category')
    if Train_data[c].isnull().any():
        Train_data[c] = Train_data[c].cat.add_categories(['MISSING'])
        Train_data[c] = Train_data[c].fillna('MISSING')

def boxplot(x, y, **kwargs):
    sns.boxplot(x=x, y=y)
    x=plt.xticks(rotation=90)

f = pd.melt(Train_data, id_vars=['price'], value_vars=categorical_features)
g = sns.FacetGrid(f, col="variable",  col_wrap=2, sharex=False, sharey=False, size=5)
g = g.map(boxplot, "value", "price")

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Train_data.columns
Index(['SaleID', 'name', 'regDate', 'model', 'brand', 'bodyType', 'fuelType',
       'gearbox', 'power', 'kilometer', 'notRepairedDamage', 'regionCode',
       'creatDate', 'price', 'v_0', 'v_1', 'v_2', 'v_3', 'v_4', 'v_5', 'v_6',
       'v_7', 'v_8', 'v_9', 'v_10', 'v_11', 'v_12', 'v_13', 'v_14'],
      dtype='object')
## 3) 类别特征的小提琴图可视化
catg_list = categorical_features
target = 'price'
for catg in catg_list :
    sns.violinplot(x=catg, y=target, data=Train_data)
    plt.show()

84-0
84-1
84-2
84-3
84-4
84-5

categorical_features = ['model',
 'brand',
 'bodyType',
 'fuelType',
 'gearbox',
 'notRepairedDamage']
## 4) 类别特征的柱形图可视化
def bar_plot(x, y, **kwargs):
    sns.barplot(x=x, y=y)
    x=plt.xticks(rotation=90)

f = pd.melt(Train_data, id_vars=['price'], value_vars=categorical_features)
g = sns.FacetGrid(f, col="variable",  col_wrap=2, sharex=False, sharey=False, size=5)
g = g.map(bar_plot, "value", "price")

86

##  5) 类别特征的每个类别频数可视化(count_plot)
def count_plot(x,  **kwargs):
    sns.countplot(x=x)
    x=plt.xticks(rotation=90)

f = pd.melt(Train_data,  value_vars=categorical_features)
g = sns.FacetGrid(f, col="variable",  col_wrap=2, sharex=False, sharey=False, size=5)
g = g.map(count_plot, "value")

87

2.3.9 用pandas_profiling生成数据报告

用pandas_profiling生成一个较为全面的可视化和数据报告(较为简单、方便) 最终打开html文件即可

import pandas_profiling
pfr = pandas_profiling.ProfileReport(Train_data)
pfr.to_file("./example.html")
HBox(children=(FloatProgress(value=0.0, description='variables', max=29.0, style=ProgressStyle(description_wid…
HBox(children=(FloatProgress(value=0.0, description='correlations', max=6.0, style=ProgressStyle(description_w…
HBox(children=(FloatProgress(value=0.0, description='interactions [continuous]', max=729.0, style=ProgressStyl…
HBox(children=(FloatProgress(value=0.0, description='table', max=1.0, style=ProgressStyle(description_width='i…
HBox(children=(FloatProgress(value=0.0, description='missing', max=4.0, style=ProgressStyle(description_width=…
HBox(children=(FloatProgress(value=0.0, description='warnings', max=3.0, style=ProgressStyle(description_width…
HBox(children=(FloatProgress(value=0.0, description='package', max=1.0, style=ProgressStyle(description_width=…
HBox(children=(FloatProgress(value=0.0, description='build report structure', max=1.0, style=ProgressStyle(des…

2.4 经验总结

所给出的EDA步骤为广为普遍的步骤,在实际的不管是工程还是比赛过程中,这只是最开始的一步,也是最基本的一步。
接下来一般要结合模型的效果以及特征工程等来分析数据的实际建模情况,根据自己的一些理解,查阅文献,对实际问题做出判断和深入的理解。
最后不断进行EDA与数据处理和挖掘,来到达更好的数据结构和分布以及较为强势相关的特征


数据探索在机器学习中我们一般称为EDA(Exploratory Data Analysis):

是指对已有的数据(特别是调查或观察得来的原始数据)在尽量少的先验假定下进行探索,通过作图、制表、方程拟合、计算特征量等手段探索数据的结构和规律的一种数据分析方法。

数据探索有利于我们发现数据的一些特性,数据之间的关联性,对于后续的特征构建是很有帮助的。

  1. 对于数据的初步分析(直接查看数据,或.sum(), .mean(),.descirbe()等统计函数)可以从:样本数量,训练集数量,是否有时间特征,是否是时许问题,特征所表示的含义(非匿名特征),特征类型(字符类似,int,float,time),特征的缺失情况(注意缺失的在数据中的表现形式,有些是空的有些是”NAN”符号等),特征的均值方差情况。
  2. 分析记录某些特征值缺失占比30%以上样本的缺失处理,有助于后续的模型验证和调节,分析特征应该是填充(填充方式是什么,均值填充,0填充,众数填充等),还是舍去,还是先做样本分类用不同的特征模型去预测。
  3. 对于异常值做专门的分析,分析特征异常的label是否为异常值(或者偏离均值较远或者事特殊符号),异常值是否应该剔除,还是用正常值填充,是记录异常,还是机器本身异常等。
  4. 对于Label做专门的分析,分析标签的分布情况等。
  5. 进步分析可以通过对特征作图,特征和label联合做图(统计图,离散图),直观了解特征的分布情况,通过这一步也可以发现数据之中的一些异常值等,通过箱型图分析一些特征值的偏离情况,对于特征和特征联合作图,对于特征和label联合作图,分析其中的一些关联性。

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