Data Every Day: ビデオゲームセールス

tldr

KggleのVideo Game SalesをVideo Game Sales Prediction - Data Every Day #028に沿ってやっていきます。

実行環境はGoogle Colaboratorです。

どんなデータ

ゲームのセールスを予測します。

インポート

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

import sklearn.preprocessing as sp
from sklearn.model_selection import train_test_split

import tensorflow as tf
from tensorflow_addons.metrics import RSquare

データのダウンロード

Google Driveをマウントします。

from google.colab import drive
drive.mount('/content/drive')

Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount("/content/drive", force_remount=True).

KaggleのAPIクライアントを初期化し、認証します。
認証情報はGoogle Drive内（/content/drive/My Drive/Colab Notebooks/Kaggle）にkaggle.jsonとして置いてあります。

import os
kaggle_path = "/content/drive/My Drive/Colab Notebooks/Kaggle"
os.environ['KAGGLE_CONFIG_DIR'] = kaggle_path

from kaggle.api.kaggle_api_extended import KaggleApi
api = KaggleApi()
api.authenticate() 

Kaggle APIを使ってデータをダウンロードします。

dataset_id = 'gregorut/videogamesales'
dataset = api.dataset_list_files(dataset_id)
file_name = dataset.files[0].name
file_path = os.path.join(api.get_default_download_dir(), file_name)
file_path

Warning: Looks like you're using an outdated API Version, please consider updating (server 1.5.10 / client 1.5.9)





'/content/vgsales.csv'

api.dataset_download_file(dataset_id, file_name, force=True, quiet=False)

100%|██████████| 381k/381k [00:00<00:00, 65.9MB/s]

Downloading vgsales.csv.zip to /content









True

import zipfile

zip_path = '/content/' + file_name + '.zip'
with zipfile.ZipFile(zip_path) as existing_zip:
    existing_zip.extractall('/content')

データの読み込み

Padasを使ってダウンロードしてきたCSVファイルを読み込みます。

data = pd.read_csv(file_path, index_col='Rank')

data

	Name	Platform	Year	Genre	Publisher	NA_Sales	EU_Sales	JP_Sales	Other_Sales	Global_Sales
Rank
1	Wii Sports	Wii	2006.0	Sports	Nintendo	41.49	29.02	3.77	8.46	82.74
2	Super Mario Bros.	NES	1985.0	Platform	Nintendo	29.08	3.58	6.81	0.77	40.24
3	Mario Kart Wii	Wii	2008.0	Racing	Nintendo	15.85	12.88	3.79	3.31	35.82
4	Wii Sports Resort	Wii	2009.0	Sports	Nintendo	15.75	11.01	3.28	2.96	33.00
5	Pokemon Red/Pokemon Blue	GB	1996.0	Role-Playing	Nintendo	11.27	8.89	10.22	1.00	31.37
...	...	...	...	...	...	...	...	...	...	...
16596	Woody Woodpecker in Crazy Castle 5	GBA	2002.0	Platform	Kemco	0.01	0.00	0.00	0.00	0.01
16597	Men in Black II: Alien Escape	GC	2003.0	Shooter	Infogrames	0.01	0.00	0.00	0.00	0.01
16598	SCORE International Baja 1000: The Official Game	PS2	2008.0	Racing	Activision	0.00	0.00	0.00	0.00	0.01
16599	Know How 2	DS	2010.0	Puzzle	7G//AMES	0.00	0.01	0.00	0.00	0.01
16600	Spirits & Spells	GBA	2003.0	Platform	Wanadoo	0.01	0.00	0.00	0.00	0.01

16598 rows × 10 columns

不要な列の削除

columns_to_drop = [
    'Name',
    'NA_Sales',
    'EU_Sales',
    'JP_Sales',
    'Other_Sales'
 ]

data = data.drop(columns_to_drop, axis=1)

data.isnull().sum()

Platform          0
Year            271
Genre             0
Publisher        58
Global_Sales      0
dtype: int64

欠損値の処理

data['Year'] =data['Year'].fillna(data['Year'].mean())

data = data.dropna(axis=0)

data.isnull().sum()

Platform        0
Year            0
Genre           0
Publisher       0
Global_Sales    0
dtype: int64

エンコード

Onehot Features

counts = data['Publisher'].value_counts()

data['Publisher'] = data['Publisher'].apply(lambda x: 'Small Publisher' if counts[x] < 50 else x)

def onehot_encode(data, columns):
    for column in columns:
        dummies = pd.get_dummies(data[column])
        data = pd.concat([data, dummies], axis=1)
        data = data.drop(column, axis=1)
    return data

onehot_columns = ['Platform', 'Genre', 'Publisher']

data = onehot_encode(data, onehot_columns)

data.head()

	Year	Global_Sales	2600	3DO	3DS	DC	DS	GB	GBA	GC	GEN	GG	N64	NES	NG	PC	PCFX	PS	PS2	PS3	PS4	PSP	PSV	SAT	SCD	SNES	TG16	WS	Wii	WiiU	X360	XB	XOne	Action	Adventure	Fighting	Misc	Platform	Puzzle	Racing	...	Bethesda Softworks	Capcom	Codemasters	Crave Entertainment	D3Publisher	Deep Silver	Disney Interactive Studios	Eidos Interactive	Electronic Arts	Empire Interactive	Focus Home Interactive	Hudson Soft	Idea Factory	Ignition Entertainment	Infogrames	Kadokawa Shoten	Konami Digital Entertainment	LucasArts	Majesco Entertainment	Marvelous Interactive	Microsoft Game Studios	Midway Games	Namco Bandai Games	Nintendo	Nippon Ichi Software	Rising Star Games	Sega	Small Publisher	Sony Computer Entertainment	Square Enix	SquareSoft	THQ	Take-Two Interactive	Tecmo Koei	Ubisoft	Unknown	Virgin Interactive	Vivendi Games	Warner Bros. Interactive Entertainment	Zoo Digital Publishing
Rank
1	2006.0	82.74	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
2	1985.0	40.24	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	...	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
3	2008.0	35.82	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	1	...	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
4	2009.0	33.00	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
5	1996.0	31.37	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0

5 rows × 92 columns

スケーリング

y = data['Global_Sales']
X = data.drop('Global_Sales', axis=1)

scaler = sp.StandardScaler()
X = scaler.fit_transform(X)

pd.DataFrame(X).head()

	0	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18	19	20	21	22	23	24	25	26	27	28	29	30	31	32	33	34	35	36	37	38	39	...	51	52	53	54	55	56	57	58	59	60	61	62	63	64	65	66	67	68	69	70	71	72	73	74	75	76	77	78	79	80	81	82	83	84	85	86	87	88	89	90
0	-0.070070	-0.090035	-0.013469	-0.177827	-0.056159	-0.387155	-0.077203	-0.224853	-0.186507	-0.040436	-0.007776	-0.140235	-0.077203	-0.026945	-0.247404	-0.007776	-0.27881	-0.387464	-0.295223	-0.143999	-0.28107	-0.159631	-0.102811	-0.01905	-0.121085	-0.010997	-0.01905	3.390051	-0.093387	-0.287283	-0.228977	-0.114219	-0.500094	-0.289865	-0.232177	-0.33979	-0.237621	-0.190803	-0.285677	-0.314184	...	-0.065659	-0.153552	-0.096307	-0.065659	-0.106065	-0.086202	-0.115569	-0.110073	-0.298238	-0.056159	-0.059321	-0.070152	-0.08866	-0.060841	-0.06134	-0.055065	-0.230145	-0.073967	-0.074789	-0.058286	-0.107512	-0.110073	-0.244362	4.746339	-0.07993	-0.072296	-0.200465	-0.50321	-0.207539	-0.119534	-0.056159	-0.21256	-0.160029	-0.144436	-0.24283	-0.111471	-0.06134	-0.100073	-0.119273	-0.079546
1	-3.698162	-0.090035	-0.013469	-0.177827	-0.056159	-0.387155	-0.077203	-0.224853	-0.186507	-0.040436	-0.007776	-0.140235	12.952819	-0.026945	-0.247404	-0.007776	-0.27881	-0.387464	-0.295223	-0.143999	-0.28107	-0.159631	-0.102811	-0.01905	-0.121085	-0.010997	-0.01905	-0.294981	-0.093387	-0.287283	-0.228977	-0.114219	-0.500094	-0.289865	-0.232177	-0.33979	4.208373	-0.190803	-0.285677	-0.314184	...	-0.065659	-0.153552	-0.096307	-0.065659	-0.106065	-0.086202	-0.115569	-0.110073	-0.298238	-0.056159	-0.059321	-0.070152	-0.08866	-0.060841	-0.06134	-0.055065	-0.230145	-0.073967	-0.074789	-0.058286	-0.107512	-0.110073	-0.244362	4.746339	-0.07993	-0.072296	-0.200465	-0.50321	-0.207539	-0.119534	-0.056159	-0.21256	-0.160029	-0.144436	-0.24283	-0.111471	-0.06134	-0.100073	-0.119273	-0.079546
2	0.275463	-0.090035	-0.013469	-0.177827	-0.056159	-0.387155	-0.077203	-0.224853	-0.186507	-0.040436	-0.007776	-0.140235	-0.077203	-0.026945	-0.247404	-0.007776	-0.27881	-0.387464	-0.295223	-0.143999	-0.28107	-0.159631	-0.102811	-0.01905	-0.121085	-0.010997	-0.01905	3.390051	-0.093387	-0.287283	-0.228977	-0.114219	-0.500094	-0.289865	-0.232177	-0.33979	-0.237621	-0.190803	3.500458	-0.314184	...	-0.065659	-0.153552	-0.096307	-0.065659	-0.106065	-0.086202	-0.115569	-0.110073	-0.298238	-0.056159	-0.059321	-0.070152	-0.08866	-0.060841	-0.06134	-0.055065	-0.230145	-0.073967	-0.074789	-0.058286	-0.107512	-0.110073	-0.244362	4.746339	-0.07993	-0.072296	-0.200465	-0.50321	-0.207539	-0.119534	-0.056159	-0.21256	-0.160029	-0.144436	-0.24283	-0.111471	-0.06134	-0.100073	-0.119273	-0.079546
3	0.448229	-0.090035	-0.013469	-0.177827	-0.056159	-0.387155	-0.077203	-0.224853	-0.186507	-0.040436	-0.007776	-0.140235	-0.077203	-0.026945	-0.247404	-0.007776	-0.27881	-0.387464	-0.295223	-0.143999	-0.28107	-0.159631	-0.102811	-0.01905	-0.121085	-0.010997	-0.01905	3.390051	-0.093387	-0.287283	-0.228977	-0.114219	-0.500094	-0.289865	-0.232177	-0.33979	-0.237621	-0.190803	-0.285677	-0.314184	...	-0.065659	-0.153552	-0.096307	-0.065659	-0.106065	-0.086202	-0.115569	-0.110073	-0.298238	-0.056159	-0.059321	-0.070152	-0.08866	-0.060841	-0.06134	-0.055065	-0.230145	-0.073967	-0.074789	-0.058286	-0.107512	-0.110073	-0.244362	4.746339	-0.07993	-0.072296	-0.200465	-0.50321	-0.207539	-0.119534	-0.056159	-0.21256	-0.160029	-0.144436	-0.24283	-0.111471	-0.06134	-0.100073	-0.119273	-0.079546
4	-1.797732	-0.090035	-0.013469	-0.177827	-0.056159	-0.387155	12.952819	-0.224853	-0.186507	-0.040436	-0.007776	-0.140235	-0.077203	-0.026945	-0.247404	-0.007776	-0.27881	-0.387464	-0.295223	-0.143999	-0.28107	-0.159631	-0.102811	-0.01905	-0.121085	-0.010997	-0.01905	-0.294981	-0.093387	-0.287283	-0.228977	-0.114219	-0.500094	-0.289865	-0.232177	-0.33979	-0.237621	-0.190803	-0.285677	3.182853	...	-0.065659	-0.153552	-0.096307	-0.065659	-0.106065	-0.086202	-0.115569	-0.110073	-0.298238	-0.056159	-0.059321	-0.070152	-0.08866	-0.060841	-0.06134	-0.055065	-0.230145	-0.073967	-0.074789	-0.058286	-0.107512	-0.110073	-0.244362	4.746339	-0.07993	-0.072296	-0.200465	-0.50321	-0.207539	-0.119534	-0.056159	-0.21256	-0.160029	-0.144436	-0.24283	-0.111471	-0.06134	-0.100073	-0.119273	-0.079546

5 rows × 91 columns

トレーニング

X.shape

(16540, 91)

X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.8)

model = tf.keras.Sequential([
    tf.keras.layers.Dense(128, activation='relu', input_shape=(91,)),
    tf.keras.layers.Dense(128, activation='relu'),
    tf.keras.layers.Dense(1),
])

model.summary()

Model: "sequential_3"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
dense_9 (Dense)              (None, 128)               11776     
_________________________________________________________________
dense_10 (Dense)             (None, 128)               16512     
_________________________________________________________________
dense_11 (Dense)             (None, 1)                 129       
=================================================================
Total params: 28,417
Trainable params: 28,417
Non-trainable params: 0
_________________________________________________________________

model.compile(
    optimizer=tf.keras.optimizers.RMSprop(0.001),
    loss='mse',
)

batch_size = 64
epochs = 100

history = model.fit(
    X_train, 
    y_train,
    validation_split=0.2,
    batch_size=batch_size,
    epochs=epochs,
    verbose=0,
)

結果

plt.figure(figsize=(14, 10))

epochs_range = range(1, epochs + 1)
train_loss = history.history['loss']
val_loss = history.history['val_loss']

plt.plot(epochs_range, train_loss, label='Training Loss')
plt.plot(epochs_range, val_loss, label='Validation Loss')

plt.xlabel('Epoch')
plt.xlabel('Loss')

plt.show()

y_pred = np.squeeze(model.predict(X_test))

result = RSquare()
result.update_state(y_test, y_pred)
print(f'R^2 Score:', result.result())

R^2 Score: tf.Tensor(0.0019521117, shape=(), dtype=float32)