text_plain_output_1.png image_output_1.png
import matplotlib.pyplot as plt

import pandas as pd

import warnings

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.metrics import mean_squared_error
from sklearn.linear_model import LinearRegression, Ridge
from sklearn.model_selection import train_test_split, GroupKFold
from sklearn.preprocessing import OrdinalEncoder, OneHotEncoder, MinMaxScaler, StandardScaler
from sklearn.pipeline import make_pipeline
import dateutil.easter as easter
import holidays
from tqdm import tqdm
import warnings
warnings.filterwarnings('ignore')
pd.set_option('display.max_columns', 100)
pd.set_option('display.max_rows', 6)
pd.set_option('display.float_format', '{:.2f}'.format)
plt.style.use('seaborn-whitegrid')
plt.rc('figure', autolayout=True, titlesize=18, titleweight='bold')
plt.rc('axes', labelweight='bold', labelsize='large', titlesize=8, titlepad=2)

train_df = pd.read_csv('../input/tabular-playground-series-sep-2022/train.csv', parse_dates=['date'], index_col='row_id')
test_df = pd.read_csv('../input/tabular-playground-series-sep-2022/test.csv', parse_dates=['date'], index_col='row_id')
sub_df = pd.read_csv('../input/tabular-playground-series-sep-2022/sample_submission.csv')
print('shape of train data: ', train_df.shape)
display(train_df)
print('shape of test data', test_df.shape)
display(test_df)
print('shape of sample submission data: ', sub_df.shape)
display(sub_df)