資料探索性分析
#導入warnings包，利用過濾器來實作忽略警告語句。
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
載入資料
## 1) 載入訓練集和測試集；
Train_data = pd.read_csv('train.csv', sep=' ')
Test_data = pd.read_csv('testA.csv', sep=' ')
- name - 汽車編碼
- regDate - 汽車注冊時間
- model - 車型編碼
- brand - 品牌
- bodyType - 車身類型
- fuelType - 燃油類型
- gearbox - 變速箱
- power - 汽車功率
- kilometer - 汽車行駛公裡
- notRepairedDamage - 汽車有尚未修複的損壞
- regionCode - 看車地區編碼
- seller - 銷售方
- offerType - 報價類型
- creatDate - 廣告釋出時間
- price - 汽車價格
- 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’（根據汽車的評論、标簽等大量資訊得到的embedding向量）【人工構造 匿名特征】
## 2) 簡略觀察資料(head()+shape)
Train_data.head().append(Train_data.tail())
| SaleID | name | regDate | model | brand | bodyType | fuelType | gearbox | power | kilometer | ... | v_5 | v_6 | v_7 | v_8 | v_9 | v_10 | v_11 | v_12 | v_13 | v_14 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 736 | 20040402 | 30.0 | 6 | 1.0 | 0.0 | 0.0 | 60 | 12.5 | ... | 0.235676 | 0.101988 | 0.129549 | 0.022816 | 0.097462 | -2.881803 | 2.804097 | -2.420821 | 0.795292 | 0.914762 | ||
| 1 | 1 | 2262 | 20030301 | 40.0 | 1 | 2.0 | 0.0 | 0.0 | 15.0 | ... | 0.264777 | 0.121004 | 0.135731 | 0.026597 | 0.020582 | -4.900482 | 2.096338 | -1.030483 | -1.722674 | 0.245522 | |
| 2 | 2 | 14874 | 20040403 | 115.0 | 15 | 1.0 | 0.0 | 0.0 | 163 | 12.5 | ... | 0.251410 | 0.114912 | 0.165147 | 0.062173 | 0.027075 | -4.846749 | 1.803559 | 1.565330 | -0.832687 | -0.229963 |
| 3 | 3 | 71865 | 19960908 | 109.0 | 10 | 0.0 | 0.0 | 1.0 | 193 | 15.0 | ... | 0.274293 | 0.110300 | 0.121964 | 0.033395 | 0.000000 | -4.509599 | 1.285940 | -0.501868 | -2.438353 | -0.478699 |
| 4 | 4 | 111080 | 20120103 | 110.0 | 5 | 1.0 | 0.0 | 0.0 | 68 | 5.0 | ... | 0.228036 | 0.073205 | 0.091880 | 0.078819 | 0.121534 | -1.896240 | 0.910783 | 0.931110 | 2.834518 | 1.923482 |
| 149995 | 149995 | 163978 | 20000607 | 121.0 | 10 | 4.0 | 0.0 | 1.0 | 163 | 15.0 | ... | 0.280264 | 0.000310 | 0.048441 | 0.071158 | 0.019174 | 1.988114 | -2.983973 | 0.589167 | -1.304370 | -0.302592 |
| 149996 | 149996 | 184535 | 20091102 | 116.0 | 11 | 0.0 | 0.0 | 0.0 | 125 | 10.0 | ... | 0.253217 | 0.000777 | 0.084079 | 0.099681 | 0.079371 | 1.839166 | -2.774615 | 2.553994 | 0.924196 | -0.272160 |
| 149997 | 149997 | 147587 | 20101003 | 60.0 | 11 | 1.0 | 1.0 | 0.0 | 90 | 6.0 | ... | 0.233353 | 0.000705 | 0.118872 | 0.100118 | 0.097914 | 2.439812 | -1.630677 | 2.290197 | 1.891922 | 0.414931 |
| 149998 | 149998 | 45907 | 20060312 | 34.0 | 10 | 3.0 | 1.0 | 0.0 | 156 | 15.0 | ... | 0.256369 | 0.000252 | 0.081479 | 0.083558 | 0.081498 | 2.075380 | -2.633719 | 1.414937 | 0.431981 | -1.659014 |
| 149999 | 149999 | 177672 | 19990204 | 19.0 | 28 | 6.0 | 0.0 | 1.0 | 193 | 12.5 | ... | 0.284475 | 0.000000 | 0.040072 | 0.062543 | 0.025819 | 1.978453 | -3.179913 | 0.031724 | -1.483350 | -0.342674 |
10 rows × 31 columns
Train_data.shape
(150000, 31)
| SaleID | name | regDate | model | brand | bodyType | fuelType | gearbox | power | kilometer | ... | v_5 | v_6 | v_7 | v_8 | v_9 | v_10 | v_11 | v_12 | v_13 | v_14 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 150000 | 66932 | 20111212 | 222.0 | 4 | 5.0 | 1.0 | 1.0 | 313 | 15.0 | ... | 0.264405 | 0.121800 | 0.070899 | 0.106558 | 0.078867 | -7.050969 | -0.854626 | 4.800151 | 0.620011 | -3.664654 | |
| 1 | 150001 | 174960 | 19990211 | 19.0 | 21 | 0.0 | 0.0 | 0.0 | 75 | 12.5 | ... | 0.261745 | 0.000000 | 0.096733 | 0.013705 | 0.052383 | 3.679418 | -0.729039 | -3.796107 | -1.541230 | -0.757055 |
| 2 | 150002 | 5356 | 20090304 | 82.0 | 21 | 0.0 | 0.0 | 0.0 | 109 | 7.0 | ... | 0.260216 | 0.112081 | 0.078082 | 0.062078 | 0.050540 | -4.926690 | 1.001106 | 0.826562 | 0.138226 | 0.754033 |
| 3 | 150003 | 50688 | 20100405 | 0.0 | 0.0 | 0.0 | 1.0 | 160 | 7.0 | ... | 0.260466 | 0.106727 | 0.081146 | 0.075971 | 0.048268 | -4.864637 | 0.505493 | 1.870379 | 0.366038 | 1.312775 | |
| 4 | 150004 | 161428 | 19970703 | 26.0 | 14 | 2.0 | 0.0 | 0.0 | 75 | 15.0 | ... | 0.250999 | 0.000000 | 0.077806 | 0.028600 | 0.081709 | 3.616475 | -0.673236 | -3.197685 | -0.025678 | -0.101290 |
| 49995 | 199995 | 20903 | 19960503 | 4.0 | 4 | 4.0 | 0.0 | 0.0 | 116 | 15.0 | ... | 0.284664 | 0.130044 | 0.049833 | 0.028807 | 0.004616 | -5.978511 | 1.303174 | -1.207191 | -1.981240 | -0.357695 |
| 49996 | 199996 | 708 | 19991011 | 0.0 | 0.0 | 0.0 | 0.0 | 75 | 15.0 | ... | 0.268101 | 0.108095 | 0.066039 | 0.025468 | 0.025971 | -3.913825 | 1.759524 | -2.075658 | -1.154847 | 0.169073 | |
| 49997 | 199997 | 6693 | 20040412 | 49.0 | 1 | 0.0 | 1.0 | 1.0 | 224 | 15.0 | ... | 0.269432 | 0.105724 | 0.117652 | 0.057479 | 0.015669 | -4.639065 | 0.654713 | 1.137756 | -1.390531 | 0.254420 |
| 49998 | 199998 | 96900 | 20020008 | 27.0 | 1 | 0.0 | 0.0 | 1.0 | 334 | 15.0 | ... | 0.261152 | 0.000490 | 0.137366 | 0.086216 | 0.051383 | 1.833504 | -2.828687 | 2.465630 | -0.911682 | -2.057353 |
| 49999 | 199999 | 193384 | 20041109 | 166.0 | 6 | 1.0 | NaN | 1.0 | 68 | 9.0 | ... | 0.228730 | 0.000300 | 0.103534 | 0.080625 | 0.124264 | 2.914571 | -1.135270 | 0.547628 | 2.094057 | -1.552150 |
10 rows × 30 columns
Test_data.shape
(50000, 30)
資料概況
## 1) 通過describe()來熟悉資料的相關統計量
Train_data.describe()
| SaleID | name | regDate | model | brand | bodyType | fuelType | gearbox | power | kilometer | ... | v_5 | v_6 | v_7 | v_8 | v_9 | v_10 | v_11 | v_12 | v_13 | v_14 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 150000.000000 | 150000.000000 | 1.500000e+05 | 149999.000000 | 150000.000000 | 145494.000000 | 141320.000000 | 144019.000000 | 150000.000000 | 150000.000000 | ... | 150000.000000 | 150000.000000 | 150000.000000 | 150000.000000 | 150000.000000 | 150000.000000 | 150000.000000 | 150000.000000 | 150000.000000 | 150000.000000 |
| mean | 74999.500000 | 68349.172873 | 2.003417e+07 | 47.129021 | 8.052733 | 1.792369 | 0.375842 | 0.224943 | 119.316547 | 12.597160 | ... | 0.248204 | 0.044923 | 0.124692 | 0.058144 | 0.061996 | -0.001000 | 0.009035 | 0.004813 | 0.000313 | -0.000688 |
| std | 43301.414527 | 61103.875095 | 5.364988e+04 | 49.536040 | 7.864956 | 1.760640 | 0.548677 | 0.417546 | 177.168419 | 3.919576 | ... | 0.045804 | 0.051743 | 0.201410 | 0.029186 | 0.035692 | 3.772386 | 3.286071 | 2.517478 | 1.288988 | 1.038685 |
| min | 0.000000 | 0.000000 | 1.991000e+07 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.500000 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | -9.168192 | -5.558207 | -9.639552 | -4.153899 | -6.546556 |
| 25% | 37499.750000 | 11156.000000 | 1.999091e+07 | 10.000000 | 1.000000 | 0.000000 | 0.000000 | 0.000000 | 75.000000 | 12.500000 | ... | 0.243615 | 0.000038 | 0.062474 | 0.035334 | 0.033930 | -3.722303 | -1.951543 | -1.871846 | -1.057789 | -0.437034 |
| 50% | 74999.500000 | 51638.000000 | 2.003091e+07 | 30.000000 | 6.000000 | 1.000000 | 0.000000 | 0.000000 | 110.000000 | 15.000000 | ... | 0.257798 | 0.000812 | 0.095866 | 0.057014 | 0.058484 | 1.624076 | -0.358053 | -0.130753 | -0.036245 | 0.141246 |
| 75% | 112499.250000 | 118841.250000 | 2.007111e+07 | 66.000000 | 13.000000 | 3.000000 | 1.000000 | 0.000000 | 150.000000 | 15.000000 | ... | 0.265297 | 0.102009 | 0.125243 | 0.079382 | 0.087491 | 2.844357 | 1.255022 | 1.776933 | 0.942813 | 0.680378 |
| max | 149999.000000 | 196812.000000 | 2.015121e+07 | 247.000000 | 39.000000 | 7.000000 | 6.000000 | 1.000000 | 19312.000000 | 15.000000 | ... | 0.291838 | 0.151420 | 1.404936 | 0.160791 | 0.222787 | 12.357011 | 18.819042 | 13.847792 | 11.147669 | 8.658418 |
8 rows × 30 columns
| SaleID | name | regDate | model | brand | bodyType | fuelType | gearbox | power | kilometer | ... | v_5 | v_6 | v_7 | v_8 | v_9 | v_10 | v_11 | v_12 | v_13 | v_14 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 50000.000000 | 50000.000000 | 5.000000e+04 | 50000.000000 | 50000.000000 | 48587.000000 | 47107.000000 | 48090.000000 | 50000.000000 | 50000.000000 | ... | 50000.000000 | 50000.000000 | 50000.000000 | 50000.000000 | 50000.000000 | 50000.000000 | 50000.000000 | 50000.000000 | 50000.000000 | 50000.000000 |
| mean | 174999.500000 | 68542.223280 | 2.003393e+07 | 46.844520 | 8.056240 | 1.782185 | 0.373405 | 0.224350 | 119.883620 | 12.595580 | ... | 0.248669 | 0.045021 | 0.122744 | 0.057997 | 0.062000 | -0.017855 | -0.013742 | -0.013554 | -0.003147 | 0.001516 |
| std | 14433.901067 | 61052.808133 | 5.368870e+04 | 49.469548 | 7.819477 | 1.760736 | 0.546442 | 0.417158 | 185.097387 | 3.908979 | ... | 0.044601 | 0.051766 | 0.195972 | 0.029211 | 0.035653 | 3.747985 | 3.231258 | 2.515962 | 1.286597 | 1.027360 |
| min | 150000.000000 | 0.000000 | 1.991000e+07 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.500000 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | -9.160049 | -5.411964 | -8.916949 | -4.123333 | -6.112667 |
| 25% | 162499.750000 | 11203.500000 | 1.999091e+07 | 10.000000 | 1.000000 | 0.000000 | 0.000000 | 0.000000 | 75.000000 | 12.500000 | ... | 0.243762 | 0.000044 | 0.062644 | 0.035084 | 0.033714 | -3.700121 | -1.971325 | -1.876703 | -1.060428 | -0.437920 |
| 50% | 174999.500000 | 52248.500000 | 2.003091e+07 | 29.000000 | 6.000000 | 1.000000 | 0.000000 | 0.000000 | 109.000000 | 15.000000 | ... | 0.257877 | 0.000815 | 0.095828 | 0.057084 | 0.058764 | 1.613212 | -0.355843 | -0.142779 | -0.035956 | 0.138799 |
| 75% | 187499.250000 | 118856.500000 | 2.007110e+07 | 65.000000 | 13.000000 | 3.000000 | 1.000000 | 0.000000 | 150.000000 | 15.000000 | ... | 0.265328 | 0.102025 | 0.125438 | 0.079077 | 0.087489 | 2.832708 | 1.262914 | 1.764335 | 0.941469 | 0.681163 |
| max | 199999.000000 | 196805.000000 | 2.015121e+07 | 246.000000 | 39.000000 | 7.000000 | 6.000000 | 1.000000 | 20000.000000 | 15.000000 | ... | 0.291618 | 0.153265 | 1.358813 | 0.156355 | 0.214775 | 12.338872 | 18.856218 | 12.950498 | 5.913273 | 2.624622 |
8 rows × 29 columns
## 2) 通過info()來熟悉資料類型
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
<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
判斷資料缺失和異常
## 1) 檢視每列的存在nan情況
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
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可視化
missing = Train_data.isnull().sum()
missing = missing[missing > 0]
missing.sort_values(inplace=True)
missing.plot.bar()
<matplotlib.axes._subplots.AxesSubplot at 0x111b156dba8>
# 可視化看下預設值
msno.matrix(Train_data.sample(250))
<matplotlib.axes._subplots.AxesSubplot at 0x111b16e75f8>
<matplotlib.axes._subplots.AxesSubplot at 0x111b1936f98>
# 可視化看下預設值
msno.matrix(Test_data.sample(250))
<matplotlib.axes._subplots.AxesSubplot at 0x111b1b4ba20>
<matplotlib.axes._subplots.AxesSubplot at 0x111b1bc5978>
測試集的預設和訓練集的差不多情況, 可視化有四列有預設，notRepairedDamage預設得最多
<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
0.0 111361
- 24324
1.0 14315
Name: notRepairedDamage, dtype: int64
‘ - ’替換成nan
0.0 111361
1.0 14315
Name: notRepairedDamage, dtype: int64
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
0.0 37249
- 8031
1.0 4720
Name: notRepairedDamage, dtype: int64
先删掉類别特征嚴重傾斜的變量，當然你也可以繼續挖掘，但是一般意義不大
0 149999
1 1
Name: seller, dtype: int64
0 150000
Name: offerType, dtype: int64
del Train_data["seller"]
del Train_data["offerType"]
del Test_data["seller"]
del Test_data["offerType"]
了解預測值的分布
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
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) 總體分布概況（無界約翰遜分布等）
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>
價格不服從正态分布，是以在進行回歸之前，它必須進行轉換。雖然對數變換做得很好，但最佳拟合是無界約翰遜分布
## 2) 檢視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
(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)
<matplotlib.axes._subplots.AxesSubplot at 0x111b484ab38>
<matplotlib.axes._subplots.AxesSubplot at 0x111b4953940>
skew、kurt說明參考https://www.cnblogs.com/wyy1480/p/10474046.html
## 3) 檢視預測值的具體頻數
plt.hist(Train_data['price'], orientation = 'vertical',histtype = 'bar', color ='red')
plt.show()
檢視頻數, 大于20000得值極少，其實這裡也可以把這些當作特殊得值（異常值）直接用填充或者删掉，再前面進行
# log變換 z之後的分布較均勻，可以進行log變換進行預測，這也是預測問題常用的trick
plt.hist(np.log(Train_data['price']), orientation = 'vertical',histtype = 'bar', color ='red')
plt.show()
特征分為類别特征和數字特征，并對類别特征檢視unique分布
資料類型
列
- name - 汽車編碼
- regDate - 汽車注冊時間
- model - 車型編碼
- brand - 品牌
- bodyType - 車身類型
- fuelType - 燃油類型
- gearbox - 變速箱
- power - 汽車功率
- kilometer - 汽車行駛公裡
- notRepairedDamage - 汽車有尚未修複的損壞
- regionCode - 看車地區編碼
- seller - 銷售方 【以删】
- offerType - 報價類型 【以删】
- creatDate - 廣告釋出時間
- price - 汽車價格
- 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’（根據汽車的評論、标簽等大量資訊得到的embedding向量）【人工構造 匿名特征】
# 分離label即預測值
Y_train = Train_data['price']
# 這個差別方式适用于沒有直接label coding的資料
# 這裡不适用，需要人為根據實際含義來區分
# 數字特征
# numeric_features = Train_data.select_dtypes(include=[np.number])
# numeric_features.columns
# # 類型特征
# 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("{}特征有個{}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特征有個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特征有個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特征有個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特征有個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特征有個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特征有個2不同的值
0.0 111623
1.0 32396
Name: gearbox, dtype: int64
notRepairedDamage的特征分布如下：
notRepairedDamage特征有個2不同的值
0.0 111361
1.0 14315
Name: notRepairedDamage, dtype: int64
regionCode的特征分布如下：
regionCode特征有個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("{}特征有個{}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特征有個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特征有個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特征有個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特征有個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特征有個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特征有個2不同的值
0.0 37301
1.0 10789
Name: gearbox, dtype: int64
notRepairedDamage的特征分布如下：
notRepairedDamage特征有個2不同的值
0.0 37249
1.0 4720
Name: notRepairedDamage, dtype: int64
regionCode的特征分布如下：
regionCode特征有個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
數字特征分析
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']
| SaleID | name | regDate | model | brand | bodyType | fuelType | gearbox | power | kilometer | ... | v_5 | v_6 | v_7 | v_8 | v_9 | v_10 | v_11 | v_12 | v_13 | v_14 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 736 | 20040402 | 30.0 | 6 | 1.0 | 0.0 | 0.0 | 60 | 12.5 | ... | 0.235676 | 0.101988 | 0.129549 | 0.022816 | 0.097462 | -2.881803 | 2.804097 | -2.420821 | 0.795292 | 0.914762 | ||
| 1 | 1 | 2262 | 20030301 | 40.0 | 1 | 2.0 | 0.0 | 0.0 | 15.0 | ... | 0.264777 | 0.121004 | 0.135731 | 0.026597 | 0.020582 | -4.900482 | 2.096338 | -1.030483 | -1.722674 | 0.245522 | |
| 2 | 2 | 14874 | 20040403 | 115.0 | 15 | 1.0 | 0.0 | 0.0 | 163 | 12.5 | ... | 0.251410 | 0.114912 | 0.165147 | 0.062173 | 0.027075 | -4.846749 | 1.803559 | 1.565330 | -0.832687 | -0.229963 |
| 3 | 3 | 71865 | 19960908 | 109.0 | 10 | 0.0 | 0.0 | 1.0 | 193 | 15.0 | ... | 0.274293 | 0.110300 | 0.121964 | 0.033395 | 0.000000 | -4.509599 | 1.285940 | -0.501868 | -2.438353 | -0.478699 |
| 4 | 4 | 111080 | 20120103 | 110.0 | 5 | 1.0 | 0.0 | 0.0 | 68 | 5.0 | ... | 0.228036 | 0.073205 | 0.091880 | 0.078819 | 0.121534 | -1.896240 | 0.910783 | 0.931110 | 2.834518 | 1.923482 |
5 rows × 29 columns
## 1) 相關性分析
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>
## 2) 檢視幾個特征得 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) 每個數字特征得分布可視化
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")
可以看出匿名特征相對分布均勻
## 4) 數字特征互相之間的關系可視化
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()
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
多變量之間的關系可視化
## 5) 多變量互相回歸關系可視化
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>
類别特征分析
## 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) 類别特征箱形圖可視化
# 因為 name和 regionCode的類别太稀疏了，這裡我們把不稀疏的幾類畫一下
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")
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) 類别特征的小提琴圖可視化
catg_list = categorical_features
target = 'price'
for catg in catg_list :
sns.violinplot(x=catg, y=target, data=Train_data)
plt.show()
categorical_features = ['model',
'brand',
'bodyType',
'fuelType',
'gearbox',
'notRepairedDamage']
## 4) 類别特征的柱形圖可視化
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")
## 5) 類别特征的每個類别頻數可視化(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")
用pandas_profiling生成資料報告
用pandas_profiling生成一個較為全面的可視化和資料報告(較為簡單、友善) 最終打開html檔案即可
import pandas_profiling
pfr = pandas_profiling.ProfileReport(Train_data)
pfr.to_file("./example.html")