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from sklearn import preprocessing

import pandas as pd

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

import pandas as pd
data = pd.read_csv('../input/hotel-booking-demand/hotel_bookings.csv')

data = data.drop(['company'], axis=1)
data = data.dropna(axis=0)

data1 = data.copy()

from sklearn import preprocessing
label_encoder = preprocessing.LabelEncoder()
data['customer_type'] = label_encoder.fit_transform(data['customer_type'])
data['assigned_room_type'] = label_encoder.fit_transform(data['assigned_room_type'])
data['deposit_type'] = label_encoder.fit_transform(data['deposit_type'])
data['reservation_status'] = label_encoder.fit_transform(data['reservation_status'])
data['meal'] = label_encoder.fit_transform(data['meal'])
data['country'] = label_encoder.fit_transform(data['country'])
data['distribution_channel'] = label_encoder.fit_transform(data['distribution_channel'])
data['market_segment'] = label_encoder.fit_transform(data['market_segment'])
data['reserved_room_type'] = label_encoder.fit_transform(data['reserved_room_type'])
data['reservation_status_date'] = label_encoder.fit_transform(data['reservation_status_date'])
print('customer_type:', data['customer_type'].unique())
print('reservation_status', data['reservation_status'].unique())
print('deposit_type', data['deposit_type'].unique())
print('assigned_room_type', data['assigned_room_type'].unique())
print('meal', data['meal'].unique())
print('Country:', data['country'].unique())
print('Dist_Channel:', data['distribution_channel'].unique())
print('Market_seg:', data['market_segment'].unique())
print('reserved_room_type:', data['reserved_room_type'].unique())