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from sklearn.preprocessing import MinMaxScaler

import pandas as pd

import pandas as pd

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

all_data = pd.read_csv('/kaggle/input/data-science-salaries-2023/ds_salaries.csv')

all_data = all_data.drop('salary', axis=1)
all_data = all_data.drop('salary_currency', axis=1)
all_data = all_data.drop('employee_residence', axis=1)

all_data.nunique()

all_data = all_data.drop('salary_in_usd', axis=1)

df_reg = pd.get_dummies(all_data, drop_first=True, columns=['experience_level', 'employment_type', 'company_size', 'job_title', 'company_location'])
df_reg

aux = df_reg
scaler = MinMaxScaler()
aux = pd.DataFrame(scaler.fit_transform(aux), columns=aux.columns)

target = 'salary_in_euro'
X = aux.loc[:, aux.columns != target]
y = aux.loc[:, aux.columns == target]

y