[Kaggle] 런던 자전거 데이터 세트, 시계열 딥러닝 전 데이터 전처리해보기

https://www.kaggle.com/datasets/hmavrodiev/london-bike-sharing-dataset

London bike sharing dataset

 

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline

import os
for dirname, _, filenames in os.walk('/kaggle/input'):
    for filename in filenames:
        print(os.path.join(dirname, filename))

 

df = pd.read_csv('/kaggle/input/london-bike-sharing-dataset/london_merged.csv', parse_dates = ['timestamp'])
df.head()

 

df['timestamp']

 

df.shape

 

train = df.iloc[:17000, 1:2]
test = df.iloc[17000:17414, 1:2]

 

print(train.shape)
print(test.shape)

 

df['cnt'][:17000].plot(figsize = (15, 4), legend = True)
df['cnt'][17000:].plot(figsize = (15, 4), legend = True)
plt.legend(['train', 'test'])
plt.title('bike share demand')
plt.show()

 

from statsmodels.tsa.stattools import pacf

pacf = pacf(df['cnt'], nlags = 20, method = 'ols')
print(pacf)

 

from statsmodels.graphics.tsaplots import plot_pacf

plot_pacf(pacf, lags = 8, method = 'ols', title = 'pa').show

 

from sklearn.preprocessing import MinMaxScaler

sc = MinMaxScaler(feature_range = (0, 1))
train_scaled = sc.fit_transform(train)

 

train_scaled

 

X_train = []
y_train = []

for i in range(1, 17000):
    X_train.append(train_scaled[i-1:i, 0])
    y_train.append(train_scaled[i, 0])

X_train, y_train = np.array(X_train), np.array(y_train)

 

X_train.shape

 

X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1))
X_train.shape