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import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)

pd.set_option('display.max_columns', None)
import datetime
from sklearn.impute import SimpleImputer
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import Ridge

df = pd.read_csv('/kaggle/input/hackerearth-machine-learning-exhibit-art/dataset/train.csv')
df_test = pd.read_csv('/kaggle/input/hackerearth-machine-learning-exhibit-art/dataset/test.csv')

def summary(df):
    summary = pd.DataFrame(df.dtypes, columns=['dtypes'])
    summary = summary.reset_index()
    summary['Feature Name'] = summary['index']
    summary = summary[['Feature Name', 'dtypes']]
    summary['missing'] = df.isnull().sum().values
    summary['Uniques'] = df.nunique().values
    return summary
summary(df)

df.drop(['Customer Id', 'Artist Name'], axis=1, inplace=True)

df['duration'].value_counts(sort=True)