二手车价格预测task02:数据探索性分析

• task02学习了数据的分析画图
  • 1. 学习了sns.pairplot()用法
  • 2. 学习了sns.distplot()方法的使用
  • 3. 敲了一遍task数据分析,加了些注释说明
  • 4. 删除了两个类别特征异常的列和是三个和price相关性非常的列后进行预测,结果如图,效果并没有提高.应该做进一步的处理和特征工程(task03)

以下是按照教程进行数据分析的过程

# 导包
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

• 读取数据

Train_data = pd.read_csv('car_train_0110.csv', sep=' ')
Test_data = pd.read_csv('car_testA_0110.csv', sep=' ')

Train_data.head().append(Train_data.tail())

	SaleID	name	regDate	model	brand	bodyType	fuelType	gearbox	power	kilometer	...	v_14	v_15	v_16	v_17	v_18	v_19	v_20	v_21	v_22	v_23
0	134890	734	20160002	13.0	9	NaN	0.0	1.0	0	15.0	...	0.092139	0.000000	18.763832	-1.512063	-1.008718	-12.100623	-0.947052	9.077297	0.581214	3.945923
1	306648	196973	20080307	72.0	9	7.0	5.0	1.0	173	15.0	...	0.001070	0.122335	-5.685612	-0.489963	-2.223693	-0.226865	-0.658246	-3.949621	4.593618	-1.145653
2	340675	25347	20020312	18.0	12	3.0	0.0	1.0	50	12.5	...	0.064410	0.003345	-3.295700	1.816499	3.554439	-0.683675	0.971495	2.625318	-0.851922	-1.246135
3	57332	5382	20000611	38.0	8	7.0	0.0	1.0	54	15.0	...	0.069231	0.000000	-3.405521	1.497826	4.782636	0.039101	1.227646	3.040629	-0.801854	-1.251894
4	265235	173174	20030109	87.0	0	5.0	5.0	1.0	131	3.0	...	0.000099	0.001655	-4.475429	0.124138	1.364567	-0.319848	-1.131568	-3.303424	-1.998466	-1.279368
249995	10556	9332	20170003	13.0	9	NaN	NaN	1.0	58	15.0	...	0.079119	0.001447	11.782508	20.402576	-2.722772	0.462388	-4.429385	7.883413	0.698405	-1.082013
249996	146710	102110	20030511	29.0	17	3.0	0.0	0.0	61	15.0	...	0.000000	0.002342	-2.988272	1.500532	3.502201	-0.761715	-2.484556	-2.532968	-0.940266	-1.106426
249997	116066	82802	20130312	124.0	16	6.0	0.0	1.0	122	3.0	...	0.003358	0.100760	-6.939560	-1.144959	-5.337949	0.896026	-0.592565	-3.872725	2.135984	3.807554
249998	90082	65971	20121212	111.0	4	7.0	5.0	0.0	184	9.0	...	0.002974	0.008251	-7.222167	-1.383696	-5.402794	-0.409451	-1.891556	-3.104789	-3.777374	3.186218
249999	76453	56954	20051111	13.0	9	3.0	0.0	1.0	58	12.5	...	0.000000	0.009071	10.491312	-11.270043	-0.272595	-0.026478	-2.168249	-0.980042	-0.955164	-1.169593

10 rows × 40 columns

• 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

Train_data.columns

Index(['SaleID', 'name', 'regDate', 'model', 'brand', 'bodyType', 'fuelType',
       'gearbox', 'power', 'kilometer', 'notRepairedDamage', 'regionCode',
       'seller', 'offerType', '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', 'v_15', 'v_16', 'v_17', 'v_18', 'v_19', 'v_20', 'v_21',
       'v_22', 'v_23'],
      dtype='object')

Train_data_part = Train_data.cloumns=['SaleID', 'name', 'regDate', 'model', 'brand', 'bodyType', 'fuelType','gearbox', 'power', 'kilometer', 'notRepairedDamage', 'regionCode','seller', 'offerType', 'creatDate', 'price']

Train_data_part

['SaleID',
 'name',
 'regDate',
 'model',
 'brand',
 'bodyType',
 'fuelType',
 'gearbox',
 'power',
 'kilometer',
 'notRepairedDamage',
 'regionCode',
 'seller',
 'offerType',
 'creatDate',
 'price']

Train_data.describe()

	SaleID	name	regDate	model	brand	bodyType	fuelType	gearbox	power	kilometer	...	v_14	v_15	v_16	v_17	v_18	v_19	v_20	v_21	v_22	v_23
count	250000.000000	250000.000000	2.500000e+05	250000.000000	250000.000000	224620.000000	227510.000000	236487.000000	250000.000000	250000.000000	...	250000.000000	250000.000000	250000.000000	250000.000000	250000.000000	250000.000000	250000.000000	250000.000000	250000.000000	250000.000000
mean	185351.790768	83153.362172	2.003401e+07	44.911480	7.785236	4.563271	1.665008	0.780783	115.528412	12.577418	...	0.032489	0.030408	0.014725	0.000915	0.006273	0.006604	-0.001374	0.000609