DataWhale-賽題二手車交易價格預測-Task1&Task2

二手車交易價格預測

大家好，我是一個數據挖掘小白，通過看官方提供的內容我發現很多專業詞彙我都沒有接觸過，我想通過這個平臺來鍛鍊提高自己，感謝大家批評指正。

Task1-賽題理解

1.1 賽題概況

賽題以預測二手車的交易價格爲任務，數據集來自某交易平臺的二手車交易記錄，總數據量超過40w，包含31列變量信息，其中15列爲匿名變量。爲了保證比賽的公平性，將會從中抽取15萬條作爲訓練集，5萬條作爲測試集A，5萬條作爲測試集B，同時會對name、model、brand和regionCode等信息進行脫敏。

1.2 數據概況

train.csv

SaleID - 銷售樣本ID

name - 汽車編碼

regDate - 汽車註冊時間

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 - 汽車行駛公里

notRepairedDamage - 汽車有尚未修復的損壞

regionCode - 看車地區編碼

seller - 銷售方

offerType - 報價類型

creatDate - 廣告發布時間

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’ 【匿名特徵，包含v0-14在內15個匿名特徵】

數字全都脫敏處理，都爲label encoding形式，即數字形式

1.3 預測指標

本賽題的評價標準爲MAE(Mean Absolute Error)。

1.3 數據讀取pandas

import pandas as pd
import numpy as np

## 1 載入訓練集和測試集
path = './tianchi/'
Train_data = pd.read_csv(path+'used_car_train_20200313.csv', sep=' ')
Test_data = pd.read_csv(path+'used_car_testA_20200313.csv', sep=' ')

print('Train data shape:',Train_data.shape)
print('TestA data shape:',Test_data.shape)

Train data shape: (150000, 31)
TestA data shape: (50000, 30)

Train_data.head()

	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
0	0	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	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 × 31 columns

1.4 分類指標評價計算

## accuracy
import numpy as np 
from sklearn.metrics import accuracy_score
y_pred = [0,1,0,1]
y_true = [0,1,1,1]
print('ACC:',accuracy_score(y_true,y_pred))

ACC: 0.75

## Precision,Recall,F1-score
from sklearn import metrics
y_pred = [0,1,0,0]
y_true = [0,1,0,1]
print('Precision:',metrics.precision_score(y_true,y_pred))
print('Recall',metrics.recall_score(y_true,y_pred))
print('F1-score:',metrics.f1_score(y_true,y_pred))

Precision: 1.0
Recall 0.5
F1-score: 0.6666666666666666

# AUC
import numpy as np
from sklearn.metrics import roc_auc_score
y_true = np.array([0,0,1,1])
y_scores=np.array([0.1,0.4,0.35,0.8])
print('AUC score:',roc_auc_score(y_true,y_scores))

AUC score: 0.75

1.5 迴歸指標評價計算

# coding=utf-8
import numpy as np
from sklearn import metrics

def mape(y_true,y_pred):
    return np.mean(np.abs((y_pred - y_true)/y_true))
y_true = np.array([1.0,5.0,4.0,3.0,2.0,5.0,-3.0])
y_pred = np.array([1.0,4.5,3.8,3.2,3.0,4.8,-2.2])

#Mse
print('MSE:',metrics.mean_squared_error(y_true,y_pred))
print('RMSE:',np.sqrt(metrics.mean_squared_error(y_true,y_pred)))
print('MAE:',metrics.mean_absolute_error(y_true,y_pred))
print('MAPE:',mape(y_true,y_pred))

MSE: 0.2871428571428571
RMSE: 0.5358571238146014
MAE: 0.4142857142857143
MAPE: 0.1461904761904762

## R2-score
from sklearn.metrics import r2_score
y_true =[3,-0.5,2,7]
y_pred =[2.5,0.0,2,8]
print('R2-score:',r2_score(y_true,y_pred))

R2-score: 0.9486081370449679

Task2-數據分析

2.1 目標

1.熟悉數據集，瞭解數據集
2.瞭解變量間的互相關係以及變量與預測值之間的存在關係
3.引導我們進行數據吹以及特徵工程的步驟
4.對數據進行一些圖表或者文字總結。

2.2 內容步驟

2.2.1 載入各種數據科學以及可視化庫

#coding:utf-8
#導入warnings包，利用過濾器來實現忽略警告語句。
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

2.2.2 載入數據

## 1載入訓練集和測試集
path = './tianchi/'
Train_data = pd.read_csv(path+'used_car_train_20200313.csv', sep=' ')
Test_data = pd.read_csv(path+'used_car_testA_20200313.csv', sep=' ')

## 2觀察數據（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
0	0	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	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)

Test_data.head().append(Test_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
0	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	0.0	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	0.0	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)

2.2.3 總覽數據概況

##1通過describe（）來熟悉數據的相關統計量
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

Test_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	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（）來熟悉數據類型
Train_data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 150000 entries, 0 to 149999
Data columns (total 31 columns):
 #   Column             Non-Null Count   Dtype  
---  ------             --------------   -----  
 0   SaleID             150000 non-null  int64  
 1   name               150000 non-null  int64  
 2   regDate            150000 non-null  int64  
 3   model              149999 non-null  float64
 4   brand              150000 non-null  int64  
 5   bodyType           145494 non-null  float64
 6   fuelType           141320 non-null  float64
 7   gearbox            144019 non-null  float64
 8   power              150000 non-null  int64  
 9   kilometer          150000 non-null  float64
 10  notRepairedDamage  150000 non-null  object 
 11  regionCode         150000 non-null  int64  
 12  seller             150000 non-null  int64  
 13  offerType          150000 non-null  int64  
 14  creatDate          150000 non-null  int64  
 15  price              150000 non-null  int64  
 16  v_0                150000 non-null  float64
 17  v_1                150000 non-null  float64
 18  v_2                150000 non-null  float64
 19  v_3                150000 non-null  float64
 20  v_4                150000 non-null  float64
 21  v_5                150000 non-null  float64
 22  v_6                150000 non-null  float64
 23  v_7                150000 non-null  float64
 24  v_8                150000 non-null  float64
 25  v_9                150000 non-null  float64
 26  v_10               150000 non-null  float64
 27  v_11               150000 non-null  float64
 28  v_12               150000 non-null  float64
 29  v_13               150000 non-null  float64
 30  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):
 #   Column             Non-Null Count  Dtype  
---  ------             --------------  -----  
 0   SaleID             50000 non-null  int64  
 1   name               50000 non-null  int64  
 2   regDate            50000 non-null  int64  
 3   model              50000 non-null  float64
 4   brand              50000 non-null  int64  
 5   bodyType           48587 non-null  float64
 6   fuelType           47107 non-null  float64
 7   gearbox            48090 non-null  float64
 8   power              50000 non-null  int64  
 9   kilometer          50000 non-null  float64
 10  notRepairedDamage  50000 non-null  object 
 11  regionCode         50000 non-null  int64  
 12  seller             50000 non-null  int64  
 13  offerType          50000 non-null  int64  
 14  creatDate          50000 non-null  int64  
 15  v_0                50000 non-null  float64
 16  v_1                50000 non-null  float64
 17  v_2                50000 non-null  float64
 18  v_3                50000 non-null  float64
 19  v_4                50000 non-null  float64
 20  v_5                50000 non-null  float64
 21  v_6                50000 non-null  float64
 22  v_7                50000 non-null  float64
 23  v_8                50000 non-null  float64
 24  v_9                50000 non-null  float64
 25  v_10               50000 non-null  float64
 26  v_11               50000 non-null  float64
 27  v_12               50000 non-null  float64
 28  v_13               50000 non-null  float64
 29  v_14               50000 non-null  float64
dtypes: float64(20), int64(9), object(1)
memory usage: 11.4+ MB

2.2.4 判斷數據缺失和異常

##1查看每列的存在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

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可視化
missing = Train_data.isnull().sum()
missing = missing[missing > 0]
missing.sort_values(inplace = True)
missing.plot.bar()

<matplotlib.axes._subplots.AxesSubplot at 0x118cbff10>

#可視化看下缺省值
msno.matrix(Train_data.sample(250))

<matplotlib.axes._subplots.AxesSubplot at 0x118b63910>

msno.bar(Train_data.sample(1000))

<matplotlib.axes._subplots.AxesSubplot at 0x106d80290>

msno.matrix(Test_data.sample(250))

<matplotlib.axes._subplots.AxesSubplot at 0x11f6e36d0>

msno.bar(Test_data.sample(1000))

<matplotlib.axes._subplots.AxesSubplot at 0x11880d6d0>

##2查看異常值檢測
Train_data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 150000 entries, 0 to 149999
Data columns (total 31 columns):
 #   Column             Non-Null Count   Dtype  
---  ------             --------------   -----  
 0   SaleID             150000 non-null  int64  
 1   name               150000 non-null  int64  
 2   regDate            150000 non-null  int64  
 3   model              149999 non-null  float64
 4   brand              150000 non-null  int64  
 5   bodyType           145494 non-null  float64
 6   fuelType           141320 non-null  float64
 7   gearbox            144019 non-null  float64
 8   power              150000 non-null  int64  
 9   kilometer          150000 non-null  float64
 10  notRepairedDamage  150000 non-null  object 
 11  regionCode         150000 non-null  int64  
 12  seller             150000 non-null  int64  
 13  offerType          150000 non-null  int64  
 14  creatDate          150000 non-null  int64  
 15  price              150000 non-null  int64  
 16  v_0                150000 non-null  float64
 17  v_1                150000 non-null  float64
 18  v_2                150000 non-null  float64
 19  v_3                150000 non-null  float64
 20  v_4                150000 non-null  float64
 21  v_5                150000 non-null  float64
 22  v_6                150000 non-null  float64
 23  v_7                150000 non-null  float64
 24  v_8                150000 non-null  float64
 25  v_9                150000 non-null  float64
 26  v_10               150000 non-null  float64
 27  v_11               150000 non-null  float64
 28  v_12               150000 non-null  float64
 29  v_13               150000 non-null  float64
 30  v_14               150000 non-null  float64
dtypes: float64(20), int64(10), object(1)
memory usage: 35.5+ MB

Train_data['notRepairedDamage'].value_counts()

0.0    111361
-       24324
1.0     14315
Name: notRepairedDamage, dtype: int64

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)

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    8031
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

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"]
##刪除完畢
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
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

2.2.5 瞭解預測值的分佈

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總體分佈概況
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 0x119904b50>

##2查看skewness 和 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

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 0x1197c88d0>

sns.distplot(Train_data.kurt(),color='orange',axlabel='Kurtness')

<matplotlib.axes._subplots.AxesSubplot at 0x1a301f4c10>

##3查看預測值的具體頻數
plt.hist(Train_data['price'],orientation = 'vertical',histtype='bar',color='red')
plt.show()

#log變換
plt.hist(np.log(Train_data['price']),orientation = 'vertical',histtype='bar',color='red')
plt.show()

2.2.6 特徵分爲類別特徵和數字特徵，並對類別特徵查看unique分佈

#分離預測值
Y_train = Train_data['price']

number_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']

#特徵nunique分佈
for cat_fea in categorical_features:
    print(cat_fea + "的特徵分佈如下：")
    print("{}特徵有個{}不同的值".format(cat_fea,Train_data[cat_fea].nunique()))
    print(Train_data[cat_fea].value_counts())

name的特徵分佈如下：
name特徵有個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的特徵分佈如下：
model特徵有個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的特徵分佈如下：
brand特徵有個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的特徵分佈如下：
bodyType特徵有個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的特徵分佈如下：
fuelType特徵有個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的特徵分佈如下：
gearbox特徵有個2不同的值
0.0    111623
1.0     32396
Name: gearbox, dtype: int64
notRepairedDamage的特徵分佈如下：
notRepairedDamage特徵有個2不同的值
0.0    111361
1.0     14315
Name: notRepairedDamage, dtype: int64
regionCode的特徵分佈如下：
regionCode特徵有個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

#特徵nunique分佈
for cat_fea in categorical_features:
    print(cat_fea + "的特徵分佈如下：")
    print("{}特徵有個{}不同的值".format(cat_fea,Test_data[cat_fea].nunique()))
    print(Test_data[cat_fea].value_counts())

name的特徵分佈如下：
name特徵有個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的特徵分佈如下：
model特徵有個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的特徵分佈如下：
brand特徵有個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的特徵分佈如下：
bodyType特徵有個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的特徵分佈如下：
fuelType特徵有個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的特徵分佈如下：
gearbox特徵有個2不同的值
0.0    37301
1.0    10789
Name: gearbox, dtype: int64
notRepairedDamage的特徵分佈如下：
notRepairedDamage特徵有個2不同的值
0.0    37249
1.0     4720
Name: notRepairedDamage, dtype: int64
regionCode的特徵分佈如下：
regionCode特徵有個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

2.2.7 數字特徵分析

number_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']
number_features.append('price')

number_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()

	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
0	0	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	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[number_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 0x11979ed50>

## 2查看幾個特徵的偏度和峯值
for col in number_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每個數字特徵的分佈可視化
f = pd.melt(Train_data,value_vars=number_features)
g = sns.FacetGrid(f,col="variable",col_wrap=2,sharex=False,sharey= False)
g=g.map(sns.distplot,"value")

##4數字特徵相互之間的關係可視化
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

##5多變量互相迴歸關係可視化
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 0x1a35942d10>

2.2.8 類別特徵分析

##1unique分佈
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類別特徵箱形圖可視化
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類別特徵的小提琴圖可視化
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類別特徵的柱形圖可視化
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類別特徵的每個類別頻數可視化(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")

2.2.9 用pandas_profiling生成數據報告

import pandas_profiling
###剛開始這個地方一直報錯，終於解決了！！！！原因是我安裝命令寫錯了
##錯誤的寫成了pip install pandas profilling 正確的應該是pip install pandas_profiling

pfr = pandas_profiling.ProfileReport(Train_data)
pfr.to_file("./tianchi/example.html")

總結

別人分享的，感謝🙏。