여러 회사의 주가 예측하기¶

실습목표
1. 주식 데이터셋을 야후로부터 불러와서 모델에 입력가능한 형태로 변환합니다.
2. 시계열 데이터의 특징을 이해하고 EDA 및 F.E 를 진행합니다.
3. Cross Validation 을 통해 Hyper Parameter Tunning 을 진행하여 성능을 높여봅니다.

실습단계
1. 라이브러리 임포트
2. 데이터셋 로드 및 전처리
3. EDA 및 F.E
4. 모델 로드 및 학습
5. 모델 평가 및 예측
6. 모델 결과 시각화

Mission : 아래 코드를 모두 수행하여 AAPL 의 주가를 예측합니다.¶

(선택) ALL Stock 데이터에 대해서도 각각 종목별로 주가를 예측합니다.
(선택2) 기존에 사용했던 WandB 를 활용하여 CV 시에 파라미터 별 성능을 기록합니다.

1.라이브러리 임포트¶

In [ ]:

import yfinance as yf
import pandas as pd

from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import GridSearchCV

2.데이터셋 로드 및 전처리¶

In [ ]:

# Define the stock symbol and the date range
stock_symbol = 'AAPL'  # Example: Apple Inc.
start_date = '2000-01-01'
end_date = '2023-12-31'

# Fetch the stock data
stock_data_AAPL = yf.download(stock_symbol, start=start_date, end=end_date)

# Display the first few rows of the data
print(stock_data_AAPL.head())

# Save to a CSV file if needed
stock_data_AAPL.to_csv('stock_data_AAPL.csv')

[*********************100%%**********************]  1 of 1 completed

                Open      High       Low     Close  Adj Close     Volume
Date                                                                    
2000-01-03  0.936384  1.004464  0.907924  0.999442   0.844981  535796800
2000-01-04  0.966518  0.987723  0.903460  0.915179   0.773741  512377600
2000-01-05  0.926339  0.987165  0.919643  0.928571   0.785063  778321600
2000-01-06  0.947545  0.955357  0.848214  0.848214   0.717125  767972800
2000-01-07  0.861607  0.901786  0.852679  0.888393   0.751094  460734400

In [ ]:

stock_data_AAPL

Out[ ]:

	Open	High	Low	Close	Adj Close	Volume
Date
2000-01-03	0.936384	1.004464	0.907924	0.999442	0.844981	535796800
2000-01-04	0.966518	0.987723	0.903460	0.915179	0.773741	512377600
2000-01-05	0.926339	0.987165	0.919643	0.928571	0.785063	778321600
2000-01-06	0.947545	0.955357	0.848214	0.848214	0.717125	767972800
2000-01-07	0.861607	0.901786	0.852679	0.888393	0.751094	460734400
...	...	...	...	...	...	...
2023-12-22	195.179993	195.410004	192.970001	193.600006	193.091385	37122800
2023-12-26	193.610001	193.889999	192.830002	193.050003	192.542831	28919300
2023-12-27	192.490005	193.500000	191.089996	193.149994	192.642548	48087700
2023-12-28	194.139999	194.660004	193.169998	193.580002	193.071426	34049900
2023-12-29	193.899994	194.399994	191.729996	192.529999	192.024185	42628800

6037 rows × 6 columns

In [ ]:

# @title Close (only APPLE)

from matplotlib import pyplot as plt
stock_data_AAPL['Close'].plot(kind='line', figsize=(8, 4), title='Close')
plt.gca().spines[['top', 'right']].set_visible(False)

Out[ ]:

<Axes: title={'center': 'Close'}, xlabel='Date'>

(Opt) 2-1.AAPL 외에 다른 주식들도 한번에 불러올 수는 없을까?¶

In [ ]:

# Define the list of stock symbols
stock_symbols = ['AAPL', 'GOOGL', 'MSFT', 'AMZN', 'TSLA']  # Example symbols

# Define the date range
start_date = '2000-01-01'
end_date = '2023-12-31'

# Initialize an empty DataFrame to hold all stock data
all_stock_data = pd.DataFrame()

# Fetch and concatenate data for each stock symbol
for symbol in stock_symbols:
    # Fetch the stock data
    stock_data = yf.download(symbol, start=start_date, end=end_date)

    # Add a column for the stock symbol
    stock_data['Symbol'] = symbol

    # Append to the all_stock_data DataFrame
    all_stock_data = pd.concat([all_stock_data, stock_data])

# Reset the index to handle the concatenation
all_stock_data.reset_index(inplace=True)

# Display the first few rows of the combined data
print(all_stock_data.head())

# Save to a CSV file if needed
all_stock_data.to_csv('multiple_stocks_data.csv', index=False)

[*********************100%%**********************]  1 of 1 completed
[*********************100%%**********************]  1 of 1 completed
[*********************100%%**********************]  1 of 1 completed
[*********************100%%**********************]  1 of 1 completed
[*********************100%%**********************]  1 of 1 completed

        Date      Open      High       Low     Close  Adj Close     Volume  \
0 2000-01-03  0.936384  1.004464  0.907924  0.999442   0.844981  535796800   
1 2000-01-04  0.966518  0.987723  0.903460  0.915179   0.773741  512377600   
2 2000-01-05  0.926339  0.987165  0.919643  0.928571   0.785063  778321600   
3 2000-01-06  0.947545  0.955357  0.848214  0.848214   0.717125  767972800   
4 2000-01-07  0.861607  0.901786  0.852679  0.888393   0.751094  460734400   

  Symbol  
0   AAPL  
1   AAPL  
2   AAPL  
3   AAPL  
4   AAPL

In [ ]:

all_stock_data

Out[ ]:

	Date	Open	High	Low	Close	Adj Close	Volume	Symbol
0	2000-01-03	0.936384	1.004464	0.907924	0.999442	0.844981	535796800	AAPL
1	2000-01-04	0.966518	0.987723	0.903460	0.915179	0.773741	512377600	AAPL
2	2000-01-05	0.926339	0.987165	0.919643	0.928571	0.785063	778321600	AAPL
3	2000-01-06	0.947545	0.955357	0.848214	0.848214	0.717125	767972800	AAPL
4	2000-01-07	0.861607	0.901786	0.852679	0.888393	0.751094	460734400	AAPL
...	...	...	...	...	...	...	...	...
26381	2023-12-22	256.760010	258.220001	251.369995	252.539993	252.539993	93249800	TSLA
26382	2023-12-26	254.490005	257.970001	252.910004	256.609985	256.609985	86892400	TSLA
26383	2023-12-27	258.350006	263.339996	257.519989	261.440002	261.440002	106494400	TSLA
26384	2023-12-28	263.660004	265.130005	252.710007	253.179993	253.179993	113619900	TSLA
26385	2023-12-29	255.100006	255.190002	247.429993	248.479996	248.479996	100615300	TSLA

26386 rows × 8 columns

In [ ]:

# @title Close (for all stocks)

from matplotlib import pyplot as plt
import seaborn as sns
def _plot_series(series, series_name, series_index=0):
  palette = list(sns.palettes.mpl_palette('Dark2'))
  xs = series['Date']
  ys = series['Close']

  plt.plot(xs, ys, label=series_name, color=palette[series_index % len(palette)])

fig, ax = plt.subplots(figsize=(10, 5.2), layout='constrained')
df_sorted = all_stock_data.sort_values('Date', ascending=True)
for i, (series_name, series) in enumerate(df_sorted.groupby('Symbol')):
  _plot_series(series, series_name, i)
  fig.legend(title='Symbol', bbox_to_anchor=(1, 1), loc='upper left')
sns.despine(fig=fig, ax=ax)
plt.xlabel('Date')
_ = plt.ylabel('Close')

3.EDA 및 F.E¶

In [ ]:

# Load the dataset
data = stock_data_AAPL.reset_index()  # Reset index to get 'Date' as a column

In [ ]:

data

Out[ ]:

	Date	Open	High	Low	Close	Adj Close	Volume
0	2000-01-03	0.936384	1.004464	0.907924	0.999442	0.844981	535796800
1	2000-01-04	0.966518	0.987723	0.903460	0.915179	0.773741	512377600
2	2000-01-05	0.926339	0.987165	0.919643	0.928571	0.785063	778321600
3	2000-01-06	0.947545	0.955357	0.848214	0.848214	0.717125	767972800
4	2000-01-07	0.861607	0.901786	0.852679	0.888393	0.751094	460734400
...	...	...	...	...	...	...	...
6032	2023-12-22	195.179993	195.410004	192.970001	193.600006	193.091385	37122800
6033	2023-12-26	193.610001	193.889999	192.830002	193.050003	192.542831	28919300
6034	2023-12-27	192.490005	193.500000	191.089996	193.149994	192.642548	48087700
6035	2023-12-28	194.139999	194.660004	193.169998	193.580002	193.071426	34049900
6036	2023-12-29	193.899994	194.399994	191.729996	192.529999	192.024185	42628800

6037 rows × 7 columns

In [ ]:

data['Date'].dtype

Out[ ]:

dtype('<M8[ns]')

In [ ]:

# Convert the 'Date' column to datetime
data['Date'] = pd.to_datetime(data['Date'])
data['Date']

Out[ ]:

0      2000-01-03
1      2000-01-04
2      2000-01-05
3      2000-01-06
4      2000-01-07
          ...    
6032   2023-12-22
6033   2023-12-26
6034   2023-12-27
6035   2023-12-28
6036   2023-12-29
Name: Date, Length: 6037, dtype: datetime64[ns]

In [ ]:

data['Date'].dtype

Out[ ]:

dtype('<M8[ns]')

F.E(Feature Engineering) , 시계열 데이터의 종가기준 10, 50일 선 계산

In [ ]:

# Example feature engineering: Adding moving averages
data['MA10'] = data['Close'].rolling(10).mean()
data['MA50'] = data['Close'].rolling(50).mean()

In [ ]:

data.describe()

Out[ ]:

	Date	Open	High	Low	Close	Adj Close	Volume	MA10	MA50
count	6037	6037.000000	6037.000000	6037.000000	6037.000000	6037.000000	6.037000e+03	6028.000000	5988.000000
mean	2012-01-02 03:12:00.954116096	35.836839	36.227569	35.461397	35.861158	34.463051	3.996452e+08	35.769486	35.367676
min	2000-01-03 00:00:00	0.231964	0.235536	0.227143	0.234286	0.198078	2.404830e+07	0.238482	0.255500
25%	2006-01-04 00:00:00	2.173214	2.202500	2.130000	2.170000	1.834633	1.290580e+08	2.190375	2.255037
50%	2011-12-30 00:00:00	14.465357	14.616071	14.329286	14.480714	12.332440	2.810528e+08	14.535911	14.693614
75%	2017-12-29 00:00:00	41.345001	41.862499	40.869999	41.312500	39.168839	5.333888e+08	41.292250	41.092112
max	2023-12-29 00:00:00	198.020004	199.619995	197.000000	198.110001	197.589523	7.421641e+09	195.958002	187.442600
std	NaN	51.002750	51.568311	50.478962	51.050850	50.717337	3.855627e+08	50.878698	50.133817

In [ ]:

data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 6037 entries, 0 to 6036
Data columns (total 9 columns):
 #   Column     Non-Null Count  Dtype         
---  ------     --------------  -----         
 0   Date       6037 non-null   datetime64[ns]
 1   Open       6037 non-null   float64       
 2   High       6037 non-null   float64       
 3   Low        6037 non-null   float64       
 4   Close      6037 non-null   float64       
 5   Adj Close  6037 non-null   float64       
 6   Volume     6037 non-null   int64         
 7   MA10       6028 non-null   float64       
 8   MA50       5988 non-null   float64       
dtypes: datetime64[ns](1), float64(7), int64(1)
memory usage: 424.6 KB

In [ ]:

# Drop rows with NaN values created by rolling windows
data.dropna(inplace=True)

In [ ]:

data.info()

<class 'pandas.core.frame.DataFrame'>
Index: 5988 entries, 49 to 6036
Data columns (total 9 columns):
 #   Column     Non-Null Count  Dtype         
---  ------     --------------  -----         
 0   Date       5988 non-null   datetime64[ns]
 1   Open       5988 non-null   float64       
 2   High       5988 non-null   float64       
 3   Low        5988 non-null   float64       
 4   Close      5988 non-null   float64       
 5   Adj Close  5988 non-null   float64       
 6   Volume     5988 non-null   int64         
 7   MA10       5988 non-null   float64       
 8   MA50       5988 non-null   float64       
dtypes: datetime64[ns](1), float64(7), int64(1)
memory usage: 467.8 KB

열 이름	의미
Date	거래 날짜. 주식이 거래된 날짜를 나타냅니다.
Open	시가. 해당 거래일의 시작 시점에서 주식의 가격입니다.
High	고가. 해당 거래일 동안 주식의 최고 가격입니다.
Low	저가. 해당 거래일 동안 주식의 최저 가격입니다.
Close	종가. 해당 거래일의 마지막 거래 가격입니다.
Adj Close	수정 종가. 주식 배당, 주식 분할 등의 요인을 반영한 종가입니다.
Volume	거래량. 해당 거래일 동안 거래된 주식의 수량입니다.
Symbol	주식 종목 코드. 특정 주식 종목을 식별하는 코드입니다.
MA10	10일 이동 평균. 최근 10일 동안의 종가의 평균입니다.
MA50	50일 이동 평균. 최근 50일 동안의 종가의 평균입니다.
Lag1	1일 전 종가. 이전 거래일의 종가를 나타냅니다.
Lag2	2일 전 종가. 이전 두 거래일 전의 종가를 나타냅니다.
Volatility	변동성. 최근 10일 동안의 종가의 표준 편차를 나타냅니다.

(Opt) 3-1.ALL Stocks 에서 F.E¶

In [ ]:

# Feature Engineering: Moving Averages
all_stock_data['MA10'] = all_stock_data.groupby('Symbol')['Close'].transform(lambda x: x.rolling(10).mean())
all_stock_data['MA50'] = all_stock_data.groupby('Symbol')['Close'].transform(lambda x: x.rolling(50).mean())

# Feature Engineering: Lag Features , 전날 , 전전날 종가 표시
all_stock_data['Lag1'] = all_stock_data.groupby('Symbol')['Close'].shift(1)
all_stock_data['Lag2'] = all_stock_data.groupby('Symbol')['Close'].shift(2)
# Feature Engineering: Volatility (Standard Deviation of the closing prices over a window)
all_stock_data['Volatility'] = all_stock_data.groupby('Symbol')['Close'].transform(lambda x: x.rolling(window=10).std())

# Drop rows with NaN values created by rolling windows and lag features
all_stock_data.dropna(inplace=True)

# Display the first few rows of the enhanced data
print(all_stock_data.head())

# Save to a CSV file if needed
all_stock_data.to_csv('enhanced_multiple_stocks_data.csv', index=False)

         Date      Open      High       Low     Close  Adj Close     Volume  \
49 2000-03-14  1.082310  1.109375  1.017857  1.020089   0.862437  428579200   
50 2000-03-15  1.032366  1.073661  1.018973  1.037946   0.877534  443609600   
51 2000-03-16  1.047433  1.089286  1.022321  1.085379   0.917637  378100800   
52 2000-03-17  1.072545  1.116071  1.068080  1.116071   0.943585  305043200   
53 2000-03-20  1.102679  1.127232  1.092634  1.098214   0.928488  204489600   

   Symbol      MA10      MA50      Lag1      Lag2  Volatility  
49   AAPL  1.102176  0.988284  1.083147  1.122768    0.039177  
50   AAPL  1.089621  0.989054  1.020089  1.083147    0.037419  
51   AAPL  1.089230  0.992458  1.037946  1.020089    0.037443  
52   AAPL  1.086551  0.996208  1.085379  1.037946    0.033978  
53   AAPL  1.084152  1.001208  1.116071  1.085379    0.031968

3-2.특성 선택 및 데이터 분할¶

In [ ]:

data.info()

<class 'pandas.core.frame.DataFrame'>
Index: 5988 entries, 49 to 6036
Data columns (total 9 columns):
 #   Column     Non-Null Count  Dtype         
---  ------     --------------  -----         
 0   Date       5988 non-null   datetime64[ns]
 1   Open       5988 non-null   float64       
 2   High       5988 non-null   float64       
 3   Low        5988 non-null   float64       
 4   Close      5988 non-null   float64       
 5   Adj Close  5988 non-null   float64       
 6   Volume     5988 non-null   int64         
 7   MA10       5988 non-null   float64       
 8   MA50       5988 non-null   float64       
dtypes: datetime64[ns](1), float64(7), int64(1)
memory usage: 467.8 KB

학습에 사용할 특성(features)들만 선택한다.

In [ ]:

# Preparing features and target variable
# Assuming 'Close' is the target variable
features = data.columns.drop(['Date','Close'])
X = data[features]
y = data['Close']

train, test 데이터로 분리한다.

In [ ]:

# Splitting data into train and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

4.모델 로드 및 학습¶

In [ ]:

# Training the XGBoost model
xgb_model = XGBRegressor(objective='reg:squarederror', random_state=42)
xgb_model.fit(X_train, y_train)

Out[ ]:

XGBRegressor(base_score=None, booster=None, callbacks=None,
             colsample_bylevel=None, colsample_bynode=None,
             colsample_bytree=None, device=None, early_stopping_rounds=None,
             enable_categorical=False, eval_metric=None, feature_types=None,
             gamma=None, grow_policy=None, importance_type=None,
             interaction_constraints=None, learning_rate=None, max_bin=None,
             max_cat_threshold=None, max_cat_to_onehot=None,
             max_delta_step=None, max_depth=None, max_leaves=None,
             min_child_weight=None, missing=nan, monotone_constraints=None,
             multi_strategy=None, n_estimators=None, n_jobs=None,
             num_parallel_tree=None, random_state=42, ...)

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5.모델 평가 및 예측¶

In [ ]:

# Making predictions and evaluating the model
y_pred = xgb_model.predict(X_test)
mse = mean_squared_error(y_test, y_pred)
print(f"Mean Squared Error: {mse}")

Mean Squared Error: 0.22851586618060776

5-1.모델 고도화하기¶

Cross Validation 을 통해 Hyper Parameter Tunning 해주기

In [ ]:

# Hyperparameter tuning using GridSearchCV
param_grid = {
    'n_estimators': [100, 200, 300],
    'learning_rate': [0.01, 0.1, 0.2],
    'max_depth': [3, 5, 7],
    'subsample': [0.8, 1.0],
    'colsample_bytree': [0.8, 1.0]
}

grid_search = GridSearchCV(estimator=XGBRegressor(objective='reg:squarederror', random_state=42),
                           param_grid=param_grid,
                           cv=3,
                           n_jobs=-1,
                           scoring='neg_mean_squared_error')

grid_search.fit(X_train, y_train)
best_params = grid_search.best_params_
print(f"Best parameters: {best_params}")

Best parameters: {'colsample_bytree': 0.8, 'learning_rate': 0.1, 'max_depth': 7, 'n_estimators': 100, 'subsample': 0.8}

In [ ]:

# Training the final model with the best parameters
final_model = XGBRegressor(objective='reg:squarederror', random_state=42, **best_params)
final_model.fit(X_train, y_train)

Out[ ]:

XGBRegressor(base_score=None, booster=None, callbacks=None,
             colsample_bylevel=None, colsample_bynode=None,
             colsample_bytree=0.8, device=None, early_stopping_rounds=None,
             enable_categorical=False, eval_metric=None, feature_types=None,
             gamma=None, grow_policy=None, importance_type=None,
             interaction_constraints=None, learning_rate=0.1, max_bin=None,
             max_cat_threshold=None, max_cat_to_onehot=None,
             max_delta_step=None, max_depth=7, max_leaves=None,
             min_child_weight=None, missing=nan, monotone_constraints=None,
             multi_strategy=None, n_estimators=100, n_jobs=None,
             num_parallel_tree=None, random_state=42, ...)

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In [ ]:

final_pred = final_model.predict(X_test)
final_mse = mean_squared_error(y_test, final_pred)
print(f"Final Mean Squared Error: {final_mse}")

Final Mean Squared Error: 0.19201722289941325

6.모델 결과 시각화¶

In [ ]:

# Create a DataFrame for visualization
visualization_df = X_test.copy()
visualization_df['Actual'] = y_test
visualization_df['Predicted'] = final_pred
visualization_df = visualization_df.sort_index()  # Ensure the data is sorted by date

# Plotting the actual vs predicted prices
plt.figure(figsize=(14, 7))
plt.plot(visualization_df.index, visualization_df['Actual'], label='Actual Price', color='blue')
plt.plot(visualization_df.index, visualization_df['Predicted'], label='Predicted Price', color='red')
plt.fill_between(visualization_df.index, visualization_df['Actual'], visualization_df['Predicted'], color='gray', alpha=0.2)

plt.xlabel('Date')
plt.ylabel('Stock Price')
plt.title(f'Stock Price Prediction for AAPL')
plt.legend()
plt.show()

In [5]:

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).

In [6]:

jupyter nbconvert --to html '/content/drive/MyDrive/Colab Notebooks/[3강]_회귀문제_주가예측하기_실습3.ipynb'

[NbConvertApp] WARNING | pattern '/content/drive/MyDrive/Colab Notebooks/[3강]_회귀문제_주가예측하기_실습3.ipynb' matched no files
This application is used to convert notebook files (*.ipynb)
        to various other formats.

        WARNING: THE COMMANDLINE INTERFACE MAY CHANGE IN FUTURE RELEASES.

Options
=======
The options below are convenience aliases to configurable class-options,
as listed in the "Equivalent to" description-line of the aliases.
To see all configurable class-options for some <cmd>, use:
    <cmd> --help-all

--debug
    set log level to logging.DEBUG (maximize logging output)
    Equivalent to: [--Application.log_level=10]
--show-config
    Show the application's configuration (human-readable format)
    Equivalent to: [--Application.show_config=True]
--show-config-json
    Show the application's configuration (json format)
    Equivalent to: [--Application.show_config_json=True]
--generate-config
    generate default config file
    Equivalent to: [--JupyterApp.generate_config=True]
-y
    Answer yes to any questions instead of prompting.
    Equivalent to: [--JupyterApp.answer_yes=True]
--execute
    Execute the notebook prior to export.
    Equivalent to: [--ExecutePreprocessor.enabled=True]
--allow-errors
    Continue notebook execution even if one of the cells throws an error and include the error message in the cell output (the default behaviour is to abort conversion). This flag is only relevant if '--execute' was specified, too.
    Equivalent to: [--ExecutePreprocessor.allow_errors=True]
--stdin
    read a single notebook file from stdin. Write the resulting notebook with default basename 'notebook.*'
    Equivalent to: [--NbConvertApp.from_stdin=True]
--stdout
    Write notebook output to stdout instead of files.
    Equivalent to: [--NbConvertApp.writer_class=StdoutWriter]
--inplace
    Run nbconvert in place, overwriting the existing notebook (only
            relevant when converting to notebook format)
    Equivalent to: [--NbConvertApp.use_output_suffix=False --NbConvertApp.export_format=notebook --FilesWriter.build_directory=]
--clear-output
    Clear output of current file and save in place,
            overwriting the existing notebook.
    Equivalent to: [--NbConvertApp.use_output_suffix=False --NbConvertApp.export_format=notebook --FilesWriter.build_directory= --ClearOutputPreprocessor.enabled=True]
--no-prompt
    Exclude input and output prompts from converted document.
    Equivalent to: [--TemplateExporter.exclude_input_prompt=True --TemplateExporter.exclude_output_prompt=True]
--no-input
    Exclude input cells and output prompts from converted document.
            This mode is ideal for generating code-free reports.
    Equivalent to: [--TemplateExporter.exclude_output_prompt=True --TemplateExporter.exclude_input=True --TemplateExporter.exclude_input_prompt=True]
--allow-chromium-download
    Whether to allow downloading chromium if no suitable version is found on the system.
    Equivalent to: [--WebPDFExporter.allow_chromium_download=True]
--disable-chromium-sandbox
    Disable chromium security sandbox when converting to PDF..
    Equivalent to: [--WebPDFExporter.disable_sandbox=True]
--show-input
    Shows code input. This flag is only useful for dejavu users.
    Equivalent to: [--TemplateExporter.exclude_input=False]
--embed-images
    Embed the images as base64 dataurls in the output. This flag is only useful for the HTML/WebPDF/Slides exports.
    Equivalent to: [--HTMLExporter.embed_images=True]
--sanitize-html
    Whether the HTML in Markdown cells and cell outputs should be sanitized..
    Equivalent to: [--HTMLExporter.sanitize_html=True]
--log-level=<Enum>
    Set the log level by value or name.
    Choices: any of [0, 10, 20, 30, 40, 50, 'DEBUG', 'INFO', 'WARN', 'ERROR', 'CRITICAL']
    Default: 30
    Equivalent to: [--Application.log_level]
--config=<Unicode>
    Full path of a config file.
    Default: ''
    Equivalent to: [--JupyterApp.config_file]
--to=<Unicode>
    The export format to be used, either one of the built-in formats
            ['asciidoc', 'custom', 'html', 'latex', 'markdown', 'notebook', 'pdf', 'python', 'rst', 'script', 'slides', 'webpdf']
            or a dotted object name that represents the import path for an
            ``Exporter`` class
    Default: ''
    Equivalent to: [--NbConvertApp.export_format]
--template=<Unicode>
    Name of the template to use
    Default: ''
    Equivalent to: [--TemplateExporter.template_name]
--template-file=<Unicode>
    Name of the template file to use
    Default: None
    Equivalent to: [--TemplateExporter.template_file]
--theme=<Unicode>
    Template specific theme(e.g. the name of a JupyterLab CSS theme distributed
    as prebuilt extension for the lab template)
    Default: 'light'
    Equivalent to: [--HTMLExporter.theme]
--sanitize_html=<Bool>
    Whether the HTML in Markdown cells and cell outputs should be sanitized.This
    should be set to True by nbviewer or similar tools.
    Default: False
    Equivalent to: [--HTMLExporter.sanitize_html]
--writer=<DottedObjectName>
    Writer class used to write the
                                        results of the conversion
    Default: 'FilesWriter'
    Equivalent to: [--NbConvertApp.writer_class]
--post=<DottedOrNone>
    PostProcessor class used to write the
                                        results of the conversion
    Default: ''
    Equivalent to: [--NbConvertApp.postprocessor_class]
--output=<Unicode>
    overwrite base name use for output files.
                can only be used when converting one notebook at a time.
    Default: ''
    Equivalent to: [--NbConvertApp.output_base]
--output-dir=<Unicode>
    Directory to write output(s) to. Defaults
                                  to output to the directory of each notebook. To recover
                                  previous default behaviour (outputting to the current
                                  working directory) use . as the flag value.
    Default: ''
    Equivalent to: [--FilesWriter.build_directory]
--reveal-prefix=<Unicode>
    The URL prefix for reveal.js (version 3.x).
            This defaults to the reveal CDN, but can be any url pointing to a copy
            of reveal.js.
            For speaker notes to work, this must be a relative path to a local
            copy of reveal.js: e.g., "reveal.js".
            If a relative path is given, it must be a subdirectory of the
            current directory (from which the server is run).
            See the usage documentation
            (https://nbconvert.readthedocs.io/en/latest/usage.html#reveal-js-html-slideshow)
            for more details.
    Default: ''
    Equivalent to: [--SlidesExporter.reveal_url_prefix]
--nbformat=<Enum>
    The nbformat version to write.
            Use this to downgrade notebooks.
    Choices: any of [1, 2, 3, 4]
    Default: 4
    Equivalent to: [--NotebookExporter.nbformat_version]

Examples
--------

    The simplest way to use nbconvert is

            > jupyter nbconvert mynotebook.ipynb --to html

            Options include ['asciidoc', 'custom', 'html', 'latex', 'markdown', 'notebook', 'pdf', 'python', 'rst', 'script', 'slides', 'webpdf'].

            > jupyter nbconvert --to latex mynotebook.ipynb

            Both HTML and LaTeX support multiple output templates. LaTeX includes
            'base', 'article' and 'report'.  HTML includes 'basic', 'lab' and
            'classic'. You can specify the flavor of the format used.

            > jupyter nbconvert --to html --template lab mynotebook.ipynb

            You can also pipe the output to stdout, rather than a file

            > jupyter nbconvert mynotebook.ipynb --stdout

            PDF is generated via latex

            > jupyter nbconvert mynotebook.ipynb --to pdf

            You can get (and serve) a Reveal.js-powered slideshow

            > jupyter nbconvert myslides.ipynb --to slides --post serve

            Multiple notebooks can be given at the command line in a couple of
            different ways:

            > jupyter nbconvert notebook*.ipynb
            > jupyter nbconvert notebook1.ipynb notebook2.ipynb

            or you can specify the notebooks list in a config file, containing::

                c.NbConvertApp.notebooks = ["my_notebook.ipynb"]

            > jupyter nbconvert --config mycfg.py

To see all available configurables, use `--help-all`.

---------------------------------------------------------------------------
CalledProcessError                        Traceback (most recent call last)
<ipython-input-6-f4ed6f94c225> in <cell line: 1>()
----> 1 get_ipython().run_cell_magic('shell', '', "jupyter nbconvert --to html '/content/drive/MyDrive/Colab Notebooks/[3강]_회귀문제_주가예측하기_실습3.ipynb'\n")

/usr/local/lib/python3.10/dist-packages/google/colab/_shell.py in run_cell_magic(self, magic_name, line, cell)
    332     if line and not cell:
    333       cell = ' '
--> 334     return super().run_cell_magic(magic_name, line, cell)
    335 
    336 

/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py in run_cell_magic(self, magic_name, line, cell)
   2471             with self.builtin_trap:
   2472                 args = (magic_arg_s, cell)
-> 2473                 result = fn(*args, **kwargs)
   2474             return result
   2475 

/usr/local/lib/python3.10/dist-packages/google/colab/_system_commands.py in _shell_cell_magic(args, cmd)
    110   result = _run_command(cmd, clear_streamed_output=False)
    111   if not parsed_args.ignore_errors:
--> 112     result.check_returncode()
    113   return result
    114 

/usr/local/lib/python3.10/dist-packages/google/colab/_system_commands.py in check_returncode(self)
    135   def check_returncode(self):
    136     if self.returncode:
--> 137       raise subprocess.CalledProcessError(
    138           returncode=self.returncode, cmd=self.args, output=self.output
    139       )

CalledProcessError: Command 'jupyter nbconvert --to html '/content/drive/MyDrive/Colab Notebooks/[3강]_회귀문제_주가예측하기_실습3.ipynb'
' returned non-zero exit status 255.

Regression 경진대회 최종 2등 (0)	2024.07.22
Pytorch Environment Settings - Apple Silicon Mac (0)	2024.07.03
코딩테스트를 위한 자료구조 및 알고리즘 개론 (0)	2024.05.20
Python EDA - Pandas (0)	2024.04.30
Python EDA - Numpy (0)	2024.04.30

=)

ML Regression 주가 예측

여러 회사의 주가 예측하기¶

Mission : 아래 코드를 모두 수행하여 AAPL 의 주가를 예측합니다.¶

1.라이브러리 임포트¶

2.데이터셋 로드 및 전처리¶

(Opt) 2-1.AAPL 외에 다른 주식들도 한번에 불러올 수는 없을까?¶

3.EDA 및 F.E¶

(Opt) 3-1.ALL Stocks 에서 F.E¶

3-2.특성 선택 및 데이터 분할¶

4.모델 로드 및 학습¶

5.모델 평가 및 예측¶

5-1.모델 고도화하기¶

6.모델 결과 시각화¶

'Upstage AI Lab 3기' 카테고리의 다른 글

티스토리툴바

ML Regression 주가 예측

여러 회사의 주가 예측하기¶

Mission : 아래 코드를 모두 수행하여 AAPL 의 주가를 예측합니다.¶

1.라이브러리 임포트¶

2.데이터셋 로드 및 전처리¶

(Opt) 2-1.AAPL 외에 다른 주식들도 한번에 불러올 수는 없을까?¶

3.EDA 및 F.E¶

(Opt) 3-1.ALL Stocks 에서 F.E¶

3-2.특성 선택 및 데이터 분할¶

4.모델 로드 및 학습¶

5.모델 평가 및 예측¶

5-1.모델 고도화하기¶

6.모델 결과 시각화¶

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