Alright. Let's start with step 1.

Step 1: Import necessary libraries and load data into a pandas DataFrame.
```python
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from statsmodels.tsa.stattools import adfuller
from statsmodels.graphics.tsaplots import plot_acf, plot_pacf
from statsmodels.tsa.statespace.sarimax import SARIMAX
from sklearn.metrics import mean_squared_error, mean_absolute_error
from datetime import datetime, timedelta
%matplotlib inline

# load the data into a pandas DataFrame
df = pd.DataFrame({
    'date': ['2019-02-01', '2019-03-01', '2019-04-01', '2019-05-01', '2019-06-01', '2019-07-01', '2019-08-01', '2019-09-01', '2019-10-01', '2019-11-01', '2019-12-01', '2020-01-01', '2020-02-01', '2020-03-01', '2020-04-01', '2020-05-01', '2020-06-01', '2020-07-01', '2020-08-01', '2020-09-01', '2020-10-01', '2020-11-01', '2020-12-01', '2021-01-01', '2021-02-01', '2021-03-01', '2021-04-01', '2021-05-01', '2021-06-01', '2021-07-01', '2021-08-01', '2021-09-01', '2021-10-01', '2021-11-01', '2021-12-01', '2022-01-01', '2022-02-01', '2022-03-01', '2022-04-01', '2022-05-01', '2022-06-01', '2022-07-01', '2022-08-01', '2022-09-01', '2022-10-01', '2022-11-01', '2022-12-01', '2023-01-01'],
    'search_volume': [2900, 2900, 5400, 4400, 4400, 4400, 6600, 8100, 4400, 3600, 2900, 4400, 4400, 6600, 14800, 49500, 12100, 9900, 8100, 6600, 12100, 9900, 5400, 9900, 8100, 14800, 14800, 14800, 14800, 9900, 9900, 8100, 8100, 6600, 5400, 14800, 9900, 9900, 9900, 9900, 12100, 9900, 9900, 6600, 6600, 5400, 5400, 8100]
})

# set the date column as the index and convert it to a datetime format
df['date'] = pd.to_datetime(df['date'])
df.set_index('date', inplace=True)

# sort the DataFrame by date
df.sort_index(inplace=True)
```
Let me know if you
좋아요. 1단계부터 시작하겠습니다.1단계: 필요한 라이브러리를 가져와서 판다 데이터프레임에 데이터를 로드합니다.```pythonimport pandas as pdimport numpy as npmatplotlib.pyplot을 plt로 가져옵니다.statsmodels.tsa.stattools에서 adfuller를 가져옵니다.statsmodels.graphics.tsaplots에서 plot_acf, plot_pacf를 가져옵니다.statsmodels.tsa.statespace.sarimax에서 SARIMAX를 가져옵니다.sklearn.metrics에서 mean_squared_error, mean_absolute_error를 가져옵니다.datetime에서 datetime, timedelta를 가져옵니다.matplotlib 인라인# 판다 데이터 프레임에 데이터를 로드합니다.df = pd.DataFrame({    'date': ['2019-02-01', '2019-03-01', '2019-04-01', '2019-05-01', '2019-06-01', '2019-07-01', '2019-08-01', '2019-09-01', '2019-10-01', '2019-11-01', '2019-12-01', '2020-01-01', '2020-02-01', '2020-03-01', '2020-04-01', '2020-05-01', '2020-06-01', '2020-07-01', '2020-08-01', '2020-09-01', '2020-10-01', '2020-11-01', '2020-12-01', '2021-01-01', '2021-02-01', '2021-03-01', '2021-04-01', '2021-05-01', '2021-06-01', '2021-07-01', '2021-08-01', '2021-09-01', '2021-10-01', '2021-11-01', '2021-12-01', '2022-01-01', '2022-02-01', '2022-03-01', '2022-04-01', '2022-05-01', '2022-06-01', '2022-07-01', '2022-08-01', '2022-09-01', '2022-10-01', '2022-11-01', '2022-12-01', '2023-01-01'],    'search_volume': [2900, 2900, 5400, 4400, 4400, 4400, 6600, 8100, 4400, 3600, 2900, 4400, 4400, 6600, 14800, 49500, 12100, 9900, 8100, 6600, 12100, 9900, 5400, 9900, 8100, 14800, 14800, 14800, 14800, 9900, 9900, 8100, 8100, 6600, 5400, 14800, 9900, 9900, 9900, 9900, 12100, 9900, 9900, 6600, 6600, 5400, 5400, 8100]})# 날짜 열을 인덱스로 설정하고 날짜/시간 형식으로 변환합니다.df['date'] = pd.to_datetime(df['date'])df.set_index('date', inplace=True)# 날짜별로 데이터프레임 정렬df.sort_index(inplace=True)```다음과 같은 경우 알려주세요.