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初赛：
【2021 年 MathorCup 高校数学建模挑战赛—赛道A二手车估价问题】数据分析及可视化

【2021 年 MathorCup 高校数学建模挑战赛—赛道A二手车估价问题】问题一 Baseline 和数据

【2021 年 MathorCup 高校数学建模挑战赛—赛道A二手车估价问题】问题二 思路和python实现

【2021 年 MathorCup 高校数学建模挑战赛—赛道A二手车估价问题】4 问题三 思路和数据及参考资料

【Mathorcup杯大数据挑战赛复赛 B题 二手车估价】思路及Python实现

1 Txt转为CSV

其实可以不用转，直接可以读取txt文件

2 宏观查看数据

读取数据

train = pd.read_table('file1.txt')
test = pd.read_table('file2.txt')

train.info()

RangeIndex: 30000 entries, 0 to 29999
Data columns (total 36 columns):

Column Non-Null Count Dtype

0 carid 30000 non-null int64
1 tradeTime 30000 non-null object
2 brand 30000 non-null int64
3 serial 30000 non-null int64
4 model 30000 non-null int64
5 mileage 30000 non-null float64
6 color 30000 non-null int64
7 cityId 30000 non-null int64
8 carCode 29991 non-null float64
9 transferCount 30000 non-null int64
10 seatings 30000 non-null int64
11 registerDate 30000 non-null object
12 licenseDate 30000 non-null object
13 country 26243 non-null float64
14 maketype 26359 non-null float64
15 modelyear 29688 non-null float64
16 displacement 30000 non-null float64
17 gearbox 29999 non-null float64
18 oiltype 30000 non-null int64
19 newprice 30000 non-null float64
20 anonymousFeature1 28418 non-null float64
21 anonymousFeature2 30000 non-null int64
22 anonymousFeature3 30000 non-null int64
23 anonymousFeature4 17892 non-null float64
24 anonymousFeature5 30000 non-null int64
25 anonymousFeature6 30000 non-null int64
26 anonymousFeature7 11956 non-null object
27 anonymousFeature8 26225 non-null float64
28 anonymousFeature9 26256 non-null float64
29 anonymousFeature10 23759 non-null float64
30 anonymousFeature11 29539 non-null object
31 anonymousFeature12 30000 non-null object
32 anonymousFeature13 28381 non-null float64
33 anonymousFeature14 30000 non-null int64
34 anonymousFeature15 2420 non-null object
35 price 30000 non-null float64
dtypes: float64(15), int64(14), object(7)

训练集有3W行数据

test.info()

Data columns (total 35 columns):
#Column Non-Null Count Dtype

0 carid 5000 non-null int64
1 tradeTime 5000 non-null object
2 brand 5000 non-null int64
3 serial 5000 non-null int64
4 model 5000 non-null int64
5 mileage 5000 non-null float64
6 color 5000 non-null int64
7 cityId 5000 non-null int64
8 carCode 5000 non-null int64
9 transferCount 5000 non-null int64
10 seatings 5000 non-null int64
11 registerDate 5000 non-null object
12 licenseDate 5000 non-null object
13 country 4604 non-null float64
14 maketype 4625 non-null float64
15 modelyear 4894 non-null float64
16 displacement 5000 non-null float64
17 gearbox 5000 non-null int64
18 oiltype 5000 non-null int64
19 newprice 5000 non-null float64
20 anonymousFeature1 4660 non-null float64
21 anonymousFeature2 5000 non-null int64
22 anonymousFeature3 5000 non-null int64
23 anonymousFeature4 3137 non-null float64
24 anonymousFeature5 5000 non-null int64
25 anonymousFeature6 5000 non-null int64
26 anonymousFeature7 1685 non-null object
27 anonymousFeature8 4584 non-null float64
28 anonymousFeature9 4587 non-null float64
29 anonymousFeature10 3769 non-null float64
30 anonymousFeature11 4927 non-null object
31 anonymousFeature12 4999 non-null object
32 anonymousFeature13 4740 non-null float64
33 anonymousFeature14 5000 non-null int64
34 anonymousFeature15 281 non-null object
dtypes: float64(12), int64(16), object(7)

测试集有5000行数据

3 查看缺失值

msn.matrix(train)

msn.matrix(test)

carCode、modelyear、country、maketype、a1、a11缺失值较少，可以选择填充或者删除该行缺失值a4、a7、a8、a9、a10、a13、a15缺失值较多，可以直接不要这个字段的列

测试集缺失值和训练集相似，和训练集同样的处理方式

4 查看异常值

只取缺失值较少或没有的字段分析

column = [ "brand", "serial", "model", "mileage", "color", "cityId", "carCode", "transferCount", "seatings",
"country", "maketype", "modelyear", "displacement", "gearbox", "oiltype", "newprice", "anonymousFeature1", "anonymousFeature2",
                    "anonymousFeature3", "anonymousFeature5", "anonymousFeature6", "anonymousFeature14","price"]
fig = plt.figure(figsize=(80,60),dpi=75)
for i in range(len(column)):
    plt.subplot(8,4,i+1)
    sns.boxplot(train[column[i]],orient= 'v',width=0.5)
    plt.ylabel(column[i],fontsize = 40)
plt.show()

5 查看分布

（1）查看所有特征字段的数据分布

# 所有字段的分布
dist_cols = 6
dist_rows = len(test.columns)
plt.figure(figsize=(4*dist_cols,4*dist_rows))
i = 1
for col in column:
    if col =='price':
        continue
    ax = plt.subplot(dist_rows,dist_cols,i)
    ax = sns.kdeplot(train[col],color='Red',shade= True)
    ax  = sns.kdeplot(test[col],color='Blue',shade=True)
    ax.set_xlabel(col)
    ax.set_ylabel('Frequency')
    ax = ax.legend(['train','test'])
    i+=1
plt.show()

训练集和测试集的每个字段数据分布近似

（2）查看price字段的数据分布

train['price'].describe()

count 30000.000000
mean 18.062224
std 629.444049
min 0.050000
25% 6.100000
50% 10.479900
75% 18.000000
max 109000.000000
Name: price, dtype: float64

存在异常值，平均在20左右

# 价格分布
y_p = train[train['price']