upura

[Salary Prediction] Simple EDA Notebook

import pandas as pd
import japanize_matplotlib
from IPython.core.display import display, HTML 
display(HTML("<style>.container { width:100% !important; }</style>"))


train = pd.read_csv('../datasets/data/train_data.csv')
test = pd.read_csv('../datasets/data/test_data.csv')

train.head()

	id	position	age	area	sex	partner	num_child	education	service_length	study_time	commute	overtime	salary
0	0	1	44	愛知県	2	1	2	1	24	2.0	1.6	9.2	428.074887
1	1	2	31	奈良県	1	0	0	0	13	9.0	0.7	12.4	317.930517
2	2	2	36	山口県	1	0	0	2	14	4.0	0.4	16.9	357.350316
3	3	0	22	東京都	2	0	0	0	4	3.0	0.4	6.1	201.310911
4	4	0	25	鹿児島県	2	0	0	1	5	3.0	0.2	4.9	178.067475

train.shape, test.shape

((21000, 13), (9000, 12))

check null

train.isnull().sum()

id                0
position          0
age               0
area              0
sex               0
partner           0
num_child         0
education         0
service_length    0
study_time        0
commute           0
overtime          0
salary            0
dtype: int64

test.isnull().sum()

id                0
position          0
age               0
area              0
sex               0
partner           0
num_child         0
education         0
service_length    0
study_time        0
commute           0
overtime          0
dtype: int64

position

train['position'].value_counts().plot.bar()

<matplotlib.axes._subplots.AxesSubplot at 0x7f54f46a15f8>

age

train['age'].hist()

<matplotlib.axes._subplots.AxesSubplot at 0x7f54f45dd978>

area

train['area'].value_counts().plot.bar(figsize=(20, 5))

<matplotlib.axes._subplots.AxesSubplot at 0x7f54f456b2e8>

sex

train['sex'].value_counts().plot.bar()

<matplotlib.axes._subplots.AxesSubplot at 0x7f54f44755c0>

partner

train['partner'].value_counts().plot.bar()

<matplotlib.axes._subplots.AxesSubplot at 0x7f54f4411f98>

num_child

train['num_child'].value_counts().plot.bar()

<matplotlib.axes._subplots.AxesSubplot at 0x7f54f43ebdd8>

education

train['education'].value_counts().plot.bar()

<matplotlib.axes._subplots.AxesSubplot at 0x7f54f4411c18>

service_length

train['service_length'].hist()

<matplotlib.axes._subplots.AxesSubplot at 0x7f54f434f320>

study_time

train['study_time'].hist()

<matplotlib.axes._subplots.AxesSubplot at 0x7f54f43b6320>

commute

train['commute'].hist()

<matplotlib.axes._subplots.AxesSubplot at 0x7f54ecfe7240>

overtime

train['overtime'].hist()

<matplotlib.axes._subplots.AxesSubplot at 0x7f54f4047d30>

salary

train['salary'].hist()

<matplotlib.axes._subplots.AxesSubplot at 0x7f54ecec51d0>

添付データ

salary-prediction-simple-eda.ipynb?X-Amz-Expires=10800&X-Amz-Date=20241231T035959Z&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIP7GCBGMWPMZ42PQ

[Salary Prediction] Simple EDA Notebook

check null

position

age

area

sex

partner

num_child

education

service_length

study_time

commute

overtime

salary

添付データ

new user