民泊サービスの宿泊料金予測

一泊の適正価格はいくら？

賞金: 100,000 円 参加ユーザー数: 333 約3年前に終了
Aws4 request&x amz signedheaders=host&x amz signature=4f2a363008215e7ad8240875430f7998c22c5a620bc39bb2e556b731d1e69bbd

yshr10ic


folder_special 0
EDA Python

EDA

EDA

  • train_data.csvとtest_data.csvを用いて簡単なEDAを実施してみました
  • 動作確認はローカル環境でしておりますが、複雑な処理はしておりませんので、必要なライブラリをインストールすればGoogle Colabでも問題なく動作すると思います
  • ご意見、ノートブックの不備などございましたら、コメントいただけると幸いです
!pip install matplotlib-venn > /dev/null
import datetime
import matplotlib.pyplot as plt
from matplotlib_venn import venn2
import numpy as np
import os
import pandas as pd
import seaborn as sns

pd.options.display.float_format = '{:.2f}'.format

import warnings
warnings.simplefilter('ignore')
data_dir = '../data/datasets'
train_df = pd.read_csv(os.path.join(data_dir, 'train_data.csv'))
test_df = pd.read_csv(os.path.join(data_dir, 'test_data.csv'))
station_df = pd.read_csv(os.path.join(data_dir, 'station_list.csv'))
submit_df = pd.read_csv(os.path.join(data_dir, 'submission.csv'))
print(f'{train_df.shape=}')
print(f'{test_df.shape=}')
print(f'{station_df.shape=}')
print(f'{submit_df.shape=}')
train_df.shape=(9990, 13)
test_df.shape=(4996, 12)
station_df.shape=(203, 3)
submit_df.shape=(4996, 2)

display(train_df.head())
id name host_id neighbourhood latitude longitude room_type minimum_nights number_of_reviews last_review reviews_per_month availability_365 y
0 1 KiyosumiShirakawa 3min|★SkyTree★|WIFI|Max4|Tre... 242899459 Koto Ku 35.68 139.80 Entire home/apt 1 55 2020-04-25 2.21 173 12008
1 2 Downtown Tokyo Iriya next to Ueno 308879948 Taito Ku 35.72 139.79 Entire home/apt 6 72 2020-03-25 2.11 9 6667
2 3 Japan Style,Private,Affordable,4min to Sta. 300877823 Katsushika Ku 35.75 139.82 Entire home/apt 1 18 2020-03-23 3.46 288 9923
3 4 4 min to Shinjuku Sta. by train / 2 ppl / Wi-fi 236935461 Shibuya Ku 35.68 139.68 Entire home/apt 1 2 2020-04-02 1.76 87 8109
4 5 LICENSED SHINJUKU HOUSE: Heart of the action! 243408889 Shinjuku Ku 35.70 139.70 Entire home/apt 1 86 2020-01-30 2.00 156 100390 
display(train_df.describe())
id host_id latitude longitude minimum_nights number_of_reviews reviews_per_month availability_365 y
count 9990.00 9990.00 9990.00 9990.00 9990.00 9990.00 8291.00 9990.00 9990.00
mean 4995.50 174101304.02 35.70 139.74 3.33 25.94 1.70 157.20 25104.64
std 2884.01 98387863.86 0.04 0.05 8.50 40.59 1.40 115.78 67049.85
min 1.00 151977.00 35.54 139.58 1.00 0.00 0.02 0.00 921.00
25% 2498.25 78803503.00 35.69 139.70 1.00 2.00 0.68 63.00 6960.75
50% 4995.50 188123234.00 35.70 139.73 1.00 11.00 1.48 154.00 11892.50
75% 7492.75 252666139.00 35.73 139.79 2.00 33.00 2.42 248.00 20085.00
max 9990.00 344270090.00 35.82 139.91 365.00 529.00 43.60 365.00 1000103.00 
display(train_df.info())
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 9990 entries, 0 to 9989
Data columns (total 13 columns):
 #   Column             Non-Null Count  Dtype  
---  ------             --------------  -----  
 0   id                 9990 non-null   int64  
 1   name               9990 non-null   object 
 2   host_id            9990 non-null   int64  
 3   neighbourhood      9990 non-null   object 
 4   latitude           9990 non-null   float64
 5   longitude          9990 non-null   float64
 6   room_type          9990 non-null   object 
 7   minimum_nights     9990 non-null   int64  
 8   number_of_reviews  9990 non-null   int64  
 9   last_review        8291 non-null   object 
 10  reviews_per_month  8291 non-null   float64
 11  availability_365   9990 non-null   int64  
 12  y                  9990 non-null   int64  
dtypes: float64(3), int64(6), object(4)
memory usage: 1014.7+ KB

None
display(test_df.head())
id name host_id neighbourhood latitude longitude room_type minimum_nights number_of_reviews last_review reviews_per_month availability_365
0 1 5-minute walk from Akasaka Sta, Superior double 184730720 Minato Ku 35.67 139.73 Private room 1 0 NaN NaN 183
1 2 7 min Sta.-Center of IKEBUKURO Cozy Room#503 20993205 Toshima Ku 35.73 139.72 Entire home/apt 2 21 2020-04-16 1.94 337
2 3 Designer'sApt 1min sta☆Shinjuku 7min☆Shibuya 4min 322521715 Setagaya Ku 35.66 139.67 Entire home/apt 1 14 2020-02-12 0.82 240
3 4 Komagome Station 2 minutes on foot 234477095 Toshima Ku 35.74 139.75 Entire home/apt 1 16 2020-02-17 1.19 0
4 5 Monthly/Metro1min/JR5min/Ueno,Asakusa,Akihabara 145453833 Taito Ku 35.72 139.78 Entire home/apt 30 2 2019-07-21 0.19 164 
display(test_df.describe())
id host_id latitude longitude minimum_nights number_of_reviews reviews_per_month availability_365
count 4996.00 4996.00 4996.00 4996.00 4996.00 4996.00 4164.00 4996.00
mean 2498.50 164873199.49 35.70 139.74 3.20 28.01 1.72 153.02
std 1442.37 104265915.85 0.04 0.06 10.35 41.05 1.31 115.44
min 1.00 771694.00 35.54 139.59 1.00 0.00 0.03 0.00
25% 1249.75 58261300.00 35.69 139.70 1.00 2.00 0.75 62.00
50% 2498.50 172626015.50 35.70 139.73 1.00 13.00 1.56 141.00
75% 3747.25 256411204.00 35.72 139.79 2.00 36.00 2.41 246.00
max 4996.00 344043769.00 35.80 139.91 365.00 407.00 19.70 365.00 
display(test_df.info())
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 4996 entries, 0 to 4995
Data columns (total 12 columns):
 #   Column             Non-Null Count  Dtype  
---  ------             --------------  -----  
 0   id                 4996 non-null   int64  
 1   name               4996 non-null   object 
 2   host_id            4996 non-null   int64  
 3   neighbourhood      4996 non-null   object 
 4   latitude           4996 non-null   float64
 5   longitude          4996 non-null   float64
 6   room_type          4996 non-null   object 
 7   minimum_nights     4996 non-null   int64  
 8   number_of_reviews  4996 non-null   int64  
 9   last_review        4164 non-null   object 
 10  reviews_per_month  4164 non-null   float64
 11  availability_365   4996 non-null   int64  
dtypes: float64(3), int64(5), object(4)
memory usage: 468.5+ KB

None
display(station_df.head())
station_name longitude latitude
0 東京 139.77 35.68
1 新橋 139.76 35.67
2 品川 139.74 35.63
3 大崎 139.73 35.62
4 五反田 139.72 35.63 
display(station_df.describe())
longitude latitude
count 203.00 203.00
mean 139.74 35.69
std 0.05 0.04
min 139.61 35.54
25% 139.70 35.67
50% 139.74 35.69
75% 139.77 35.73
max 139.87 35.78 
display(station_df.info())
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 203 entries, 0 to 202
Data columns (total 3 columns):
 #   Column        Non-Null Count  Dtype  
---  ------        --------------  -----  
 0   station_name  203 non-null    object 
 1   longitude     203 non-null    float64
 2   latitude      203 non-null    float64
dtypes: float64(2), object(1)
memory usage: 4.9+ KB

None

train_df, test_df

check null

  • last_reviewとreviews_per_monthで欠損値がある
display(
    pd.DataFrame({
        'train': train_df.isnull().sum(),
        'test': test_df.isnull().sum()
    }).reset_index()
)
index train test
0 availability_365 0 0.00
1 host_id 0 0.00
2 id 0 0.00
3 last_review 1699 832.00
4 latitude 0 0.00
5 longitude 0 0.00
6 minimum_nights 0 0.00
7 name 0 0.00
8 neighbourhood 0 0.00
9 number_of_reviews 0 0.00
10 reviews_per_month 1699 832.00
11 room_type 0 0.00
12 y 0 NaN

check duplicated

  • 重複はない
print(f"[tr] {train_df.duplicated().sum()}")
print(f"[te] {test_df.duplicated().sum()}")
[tr] 0
[te] 0

all_df = pd.concat([train_df, test_df])
all_df.drop(columns=['y'], inplace=True)

print(f"{all_df.duplicated().sum()}")
0

name

  • 一部重複あり
  • trainとtestでも重複あり
print(f"[tr] len: {len(train_df)}, nunique: {train_df['name'].nunique()}")
print(f"[te] len: {len(test_df)}, nunique: {test_df['name'].nunique()}")
[tr] len: 9990, nunique: 9114
[te] len: 4996, nunique: 4754

venn2([set(train_df['name']), set(test_df['name'])], set_labels=('train', 'test'));

host_id

  • 重複あり
  • trainとtestでは重複はない
print(f"[tr] len: {len(train_df)}, nunique: {train_df['host_id'].nunique()}")
print(f"[te] len: {len(test_df)}, nunique: {test_df['host_id'].nunique()}")
[tr] len: 9990, nunique: 2325
[te] len: 4996, nunique: 1254

venn2([set(train_df['host_id']), set(test_df['host_id'])], set_labels=('train', 'test'));

neighbourhood

  • ２３区のローマ字表記
  • trainとtestで同じ
  • trainとtestでレコード数の順番に大きな差はないが、testの方が新宿区の割合が高くなっている
print(f"[tr] len: {len(train_df)}, nunique: {train_df['neighbourhood'].nunique()}")
print(f"[te] len: {len(test_df)}, nunique: {test_df['neighbourhood'].nunique()}")
[tr] len: 9990, nunique: 23
[te] len: 4996, nunique: 23

venn2([set(train_df['neighbourhood']), set(test_df['neighbourhood'])], set_labels=('train', 'test'));
display(
    pd.DataFrame({
        'train': train_df['neighbourhood'].value_counts() / len(train_df),
        'test': test_df['neighbourhood'].value_counts() / len(test_df)
    }).reset_index().sort_values('train', ascending=False)
)
index train test
18 Shinjuku Ku 0.17 0.21
21 Taito Ku 0.16 0.11
22 Toshima Ku 0.11 0.10
20 Sumida Ku 0.08 0.11
16 Shibuya Ku 0.07 0.08
4 Chuo Ku 0.05 0.03
11 Minato Ku 0.04 0.04
14 Ota Ku 0.03 0.04
12 Nakano Ku 0.03 0.03
1 Arakawa Ku 0.03 0.02
7 Katsushika Ku 0.03 0.03
15 Setagaya Ku 0.03 0.02
8 Kita Ku 0.02 0.03
6 Itabashi Ku 0.02 0.02
19 Suginami Ku 0.02 0.02
9 Koto Ku 0.02 0.01
3 Chiyoda Ku 0.02 0.01
5 Edogawa Ku 0.02 0.02
17 Shinagawa Ku 0.02 0.02
0 Adachi Ku 0.01 0.01
2 Bunkyo Ku 0.01 0.01
13 Nerima Ku 0.01 0.01
10 Meguro Ku 0.00 0.01 
plt.figure(figsize=(25, 6))
sns.boxplot(x=train_df['neighbourhood'], y=train_df['y'])
plt.show()
  • yの値が50000以下のレコードで表示
  • 文京区だけ以上に高い
cond = train_df['y'] <= 50000
plt.figure(figsize=(25, 6))
sns.boxplot(x=train_df.loc[cond, 'neighbourhood'], y=train_df.loc[cond, 'y'])
plt.show()
train_df[['neighbourhood', 'y']].groupby('neighbourhood').agg(['min', 'median', 'mean', 'max', 'std'])
y
min median mean max std
neighbourhood
Adachi Ku 1138 5992.50 9229.71 113899 13974.48
Arakawa Ku 2375 8155.00 29292.74 300076 66255.71
Bunkyo Ku 2821 14899.50 27752.41 100023 23440.60
Chiyoda Ku 1583 11544.00 82929.30 900072 237491.24
Chuo Ku 1999 19884.50 42940.40 100179 42685.88
Edogawa Ku 1275 9934.00 54305.56 900077 167168.66
Itabashi Ku 1106 6952.00 21884.25 300075 57985.75
Katsushika Ku 1157 9485.00 25576.39 500058 61646.34
Kita Ku 2007 9490.50 14327.90 149977 17997.69
Koto Ku 1785 8992.00 15175.55 250056 25729.89
Meguro Ku 2461 10906.00 14005.31 37970 8688.90
Minato Ku 2683 14905.00 22049.52 999961 53838.95
Nakano Ku 1520 11958.00 18938.62 200067 23215.39
Nerima Ku 1176 5986.00 16594.46 500029 60913.03
Ota Ku 2162 12065.00 15485.92 100045 15858.06
Setagaya Ku 2502 8508.00 12588.77 79951 12077.33
Shibuya Ku 2046 13984.00 26797.93 1000103 59916.99
Shinagawa Ku 1483 11992.50 19234.70 149996 24353.24
Shinjuku Ku 1388 11906.00 19001.67 900132 29691.65
Suginami Ku 1944 7558.00 13412.81 100060 18831.11
Sumida Ku 921 12688.00 23264.12 300434 33305.88
Taito Ku 1374 15028.00 28606.30 900387 75333.16
Toshima Ku 1808 9905.00 25977.99 983382 88482.36

room_type

  • 4種類
  • trainとtestで割合もほとんど同じ
print(f"[tr] len: {len(train_df)}, nunique: {train_df['room_type'].nunique()}")
print(f"[te] len: {len(test_df)}, nunique: {test_df['room_type'].nunique()}")
[tr] len: 9990, nunique: 4
[te] len: 4996, nunique: 4

venn2([set(train_df['room_type']), set(test_df['room_type'])], set_labels=('train', 'test'));
display(
    pd.DataFrame({
        'train': train_df['room_type'].value_counts() / len(train_df),
        'test': test_df['room_type'].value_counts() / len(test_df)
    }).reset_index().sort_values('train', ascending=False)
)
index train test
0 Entire home/apt 0.69 0.70
2 Private room 0.21 0.22
1 Hotel room 0.05 0.04
3 Shared room 0.05 0.04 
plt.figure(figsize=(16, 6))
sns.boxplot(x=train_df['room_type'], y=train_df['y'])
plt.show()
  • yの値が50000以下のレコードで表示
  • シェアルームが他に比べて低いことが分かる
cond = train_df['y'] <= 50000
plt.figure(figsize=(25, 6))
sns.boxplot(x=train_df.loc[cond, 'room_type'], y=train_df.loc[cond, 'y'])
plt.show()
train_df[['room_type', 'y']].groupby('room_type').agg(['min', 'median', 'mean', 'max', 'std'])
y
min median mean max std
room_type
Entire home/apt 1423 13869.50 29799.55 1000103 73629.92
Hotel room 1374 10081.00 30752.15 900071 101631.14
Private room 1106 7547.00 12403.01 689471 24244.49
Shared room 921 3479.00 6052.31 99018 8679.72

minimum_nights

  • 最短宿泊日数
  • ほとんどの宿が1日もしくは2日
print(f"[tr] len: {len(train_df)}, nunique: {train_df['minimum_nights'].nunique()}")
print(f"[te] len: {len(test_df)}, nunique: {test_df['minimum_nights'].nunique()}")
[tr] len: 9990, nunique: 30
[te] len: 4996, nunique: 26

display(
    pd.DataFrame({
        'train': train_df['minimum_nights'].value_counts() / len(train_df),
        'test': test_df['minimum_nights'].value_counts() / len(test_df)
    }).reset_index().sort_values('train', ascending=False)
)
index train test
0 1 0.62 0.56
1 2 0.23 0.30
2 3 0.05 0.06
22 30 0.05 0.04
6 7 0.01 0.01
3 4 0.01 0.01
4 5 0.01 0.01
17 23 0.01 NaN
5 6 0.00 0.01
20 28 0.00 0.00
12 14 0.00 0.00
23 31 0.00 0.00
10 12 0.00 0.00
13 15 0.00 0.00
9 10 0.00 0.00
21 29 0.00 0.00
11 13 0.00 0.00
19 25 0.00 NaN
15 20 0.00 0.00
16 21 0.00 0.00
8 9 0.00 NaN
24 32 0.00 0.00
18 24 0.00 NaN
14 16 0.00 NaN
7 8 0.00 0.00
26 35 0.00 NaN
27 60 0.00 0.00
30 100 0.00 NaN
32 360 0.00 NaN
33 365 0.00 0.00
25 33 NaN 0.00
28 90 NaN 0.00
29 99 NaN 0.00
31 180 NaN 0.00
  • 最短宿泊日数が100日以上の宿を表示
  • 最短宿泊日数が年間の宿泊可能日数より多いので異常値かもしれない
display(train_df.loc[train_df['minimum_nights']>=100, :])
id name host_id neighbourhood latitude longitude room_type minimum_nights number_of_reviews last_review reviews_per_month availability_365 y
2103 2104 EarlyCheckIn/NewAprt./Legal/ShibuyaSta.8mins/WiFi 229764247 Shibuya Ku 35.65 139.71 Entire home/apt 100 62 2020-01-01 3.26 89 25086
4359 4360 0. ENTIRE APARTMENT 450ft 2LDK NEAR SHIBUYA/YO... 46674978 Nakano Ku 35.71 139.69 Entire home/apt 365 1 2019-08-05 0.11 0 15107
4738 4739 Etchujima - spacious, luxurious, family friendly 278445388 Koto Ku 35.67 139.80 Entire home/apt 360 3 2019-02-23 0.14 301 9945
  • 最短宿泊日数が年間の宿泊可能日数より多いのでレコードを表示
  • 1486レコードと全体の約15％となる
display(train_df.loc[train_df['minimum_nights']>train_df['availability_365'], :])
id name host_id neighbourhood latitude longitude room_type minimum_nights number_of_reviews last_review reviews_per_month availability_365 y
9 10 【2/F】Asakusabashi Building | for 6-9 ppl 260891041 Taito Ku 35.70 139.79 Entire home/apt 2 74 2020-01-26 1.48 0 24826
13 14 QY52 JR Train station 5-min walk, airport dire... 25523994 Taito Ku 35.70 139.78 Entire home/apt 1 0 NaN NaN 0 8208
30 31 VIP新宿/Higashi Shinjuku station 3min/Bed size 1.8m 38092840 Shinjuku Ku 35.70 139.71 Entire home/apt 2 24 2020-01-20 2.38 0 8799
34 35 Can enjoy cosplay ramen shop☆Ikebukuro8min☆AS485 38975201 Toshima Ku 35.73 139.70 Entire home/apt 2 61 2020-02-25 1.61 0 12259
40 41 Uhome Koiwa Apartment2, 503, 1mn walk from sta... 165921683 Edogawa Ku 35.74 139.89 Entire home/apt 1 6 2019-11-12 0.66 0 99990
... ... ... ... ... ... ... ... ... ... ... ... ... ...
9944 9945 New open ! Shinjuku 東新宿､歌舞伎町､徒歩4分 1204 191116680 Shinjuku Ku 35.70 139.71 Entire home/apt 1 26 2019-11-28 1.62 0 19617
9961 9962 My apartment is comfortable Located a 4-minute... 137060281 Shinjuku Ku 35.70 139.70 Private room 1 106 2020-03-10 2.56 0 5423
9975 9976 山手線池袋3分鐘新宿8分鐘目白清新公寓 49661789 Toshima Ku 35.72 139.71 Entire home/apt 1 0 NaN NaN 0 8644
9977 9978 匯家 KAIKE 浅草寺 Sensoji 10分可达 双人 背包客 短期促销开放3 213268738 Taito Ku 35.72 139.80 Private room 3 0 NaN NaN 0 3439
9988 9989 SHITARA HOUSE dormitory B 316273494 Katsushika Ku 35.75 139.83 Shared room 1 1 2020-01-01 0.25 0 3950

1486 rows × 13 columns

cond = train_df['minimum_nights'] < 50

plt.figure(figsize=(16, 8))
plt.scatter(train_df.loc[cond, 'minimum_nights'], train_df.loc[cond, 'y'])
plt.show()

number_of_reviews

train_df['number_of_reviews'].hist()
test_df['number_of_reviews'].hist();
print(f"[tr] len: {len(train_df)}, nunique: {train_df['number_of_reviews'].nunique()}")
print(f"[te] len: {len(test_df)}, nunique: {test_df['number_of_reviews'].nunique()}")
[tr] len: 9990, nunique: 261
[te] len: 4996, nunique: 225

print('[tr] min:', train_df.loc[~train_df['number_of_reviews'].isnull(), 'number_of_reviews'].min())
print('[tr] max:', train_df.loc[~train_df['number_of_reviews'].isnull(), 'number_of_reviews'].max())
print('[te] min:', test_df.loc[~test_df['number_of_reviews'].isnull(), 'number_of_reviews'].min())
print('[te] max:', test_df.loc[~test_df['number_of_reviews'].isnull(), 'number_of_reviews'].max())
[tr] min: 0
[tr] max: 529
[te] min: 0
[te] max: 407

plt.figure(figsize=(16, 8))
plt.scatter(train_df['number_of_reviews'], train_df['y'])
plt.show()

last_review

print('[tr] min:', train_df.loc[~train_df['last_review'].isnull(), 'last_review'].min())
print('[tr] max:', train_df.loc[~train_df['last_review'].isnull(), 'last_review'].max())
print('[te] min:', test_df.loc[~test_df['last_review'].isnull(), 'last_review'].min())
print('[te] max:', test_df.loc[~test_df['last_review'].isnull(), 'last_review'].max())
[tr] min: 2016-11-19
[tr] max: 2020-04-29
[te] min: 2015-05-25
[te] max: 2020-04-28
  • reviews_per_monthとlast_reviewで欠損はすべて同じレコードで発生している
print(train_df['reviews_per_month'].isnull().sum())
print(train_df['last_review'].isnull().sum())
print(train_df.loc[train_df['reviews_per_month'].isnull() | train_df['last_review'].isnull(), 'last_review'].isnull().sum())
1699
1699
1699

print(test_df['reviews_per_month'].isnull().sum())
print(test_df['last_review'].isnull().sum())
print(test_df.loc[test_df['reviews_per_month'].isnull() | test_df['last_review'].isnull(), 'last_review'].isnull().sum())
832
832
832
  • 最終レビューが2020-04-30から数えて何日前か
def get_last_review_days(df):
    days = datetime.date(2020, 4, 30) - pd.to_datetime(df['last_review']).dt.date
    days = days.astype('timedelta64[D]')
    days = days.astype('int')
    
    df['last_review_days'] = days
    
    return df
train_tmp_df = get_last_review_days(train_df.loc[~train_df['last_review'].isnull(), :])
test_tmp_df = get_last_review_days(test_df.loc[~test_df['last_review'].isnull(), :])
train_tmp_df['last_review_days'].hist()
test_tmp_df['last_review_days'].hist();
print('[tr] min:', train_tmp_df['last_review_days'].min())
print('[tr] max:', train_tmp_df['last_review_days'].max())
print('[te] min:', test_tmp_df['last_review_days'].min())
print('[te] max:', test_tmp_df['last_review_days'].max())
[tr] min: 1
[tr] max: 1258
[te] min: 2
[te] max: 1802

plt.figure(figsize=(16, 8))
plt.scatter(train_tmp_df['last_review_days'], train_tmp_df['y'])
plt.show()

reviews_per_month

train_df['reviews_per_month'].hist()
test_df['reviews_per_month'].hist();
print(f"[tr] len: {len(train_df)}, nunique: {train_df['reviews_per_month'].nunique()}")
print(f"[te] len: {len(test_df)}, nunique: {test_df['reviews_per_month'].nunique()}")
[tr] len: 9990, nunique: 594
[te] len: 4996, nunique: 501

print('[tr] min:', train_df['reviews_per_month'].min())
print('[tr] max:', train_df['reviews_per_month'].max())
print('[te] min:', test_df['reviews_per_month'].min())
print('[te] max:', test_df['reviews_per_month'].max())
[tr] min: 0.02
[tr] max: 43.6
[te] min: 0.03
[te] max: 19.7

plt.figure(figsize=(16, 8))
plt.scatter(train_df['reviews_per_month'], train_df['y'])
plt.show()

availability_365

  • 非常にいびつな形状をしている
  • 0〜365日まである
train_df['availability_365'].hist()
test_df['availability_365'].hist();
print(f"[tr] len: {len(train_df)}, nunique: {train_df['availability_365'].nunique()}")
print(f"[te] len: {len(test_df)}, nunique: {test_df['availability_365'].nunique()}")
[tr] len: 9990, nunique: 366
[te] len: 4996, nunique: 364

print('[tr] min:', train_df['availability_365'].min())
print('[tr] max:', train_df['availability_365'].max())
print('[te] min:', test_df['availability_365'].min())
print('[te] max:', test_df['availability_365'].max())
[tr] min: 0
[tr] max: 365
[te] min: 0
[te] max: 365

plt.figure(figsize=(16, 8))
plt.scatter(train_df['availability_365'], train_df['y'])
plt.show()

y

  • 対数を取れば比較的きれいな正規分布の形状となる
train_df['y'].hist();
np.log(train_df['y']).hist();

添付データ

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