給与推定

給与推定により人事の赤池くんの窮地を救おう

賞金: 100,000 参加チーム数: 281 終了まで: 8日

[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
  • Favicon
    new user
    コメントするには 新規登録 もしくは ログイン が必要です。