def Ionosphere(X):
binaryCols = {"signal": {"g": 1, "b": 0}}
X = X.copy()
X = Processor.removeMissing(X)
X = X.drop(columns=['col0', 'col1', "col13"])
X = Processor.toBinaryCol(X, binaryCols)
Y = X["signal"]
X = X.iloc[:, :-1]
return [X, Y]
def adult(X):
binaryCols = {
"sex": {
"Male": 0,
"Female": 1
},
"salary": {
">50K": 0,
"<=50K": 1
}
}
X = X.copy()
X = Processor.removeMissing(X)
X = Processor.toBinaryCol(X, binaryCols)
X = Processor.normalize(X, ["fnlwgt", "hours-per-week"])
Y = X["salary"]
X = X.iloc[:, :-1]
X = Processor.OHE(X)
countryCols = [
"native-country_Cambodia", "native-country_England",
"native-country_Puerto-Rico", "native-country_Canada",
"native-country_Outlying-US(Guam-USVI-etc)",
"native-country_India", "native-country_Japan",
"native-country_Greece", "native-country_South",
"native-country_China", "native-country_Cuba",
"native-country_Iran", "native-country_Honduras",
"native-country_Italy", "native-country_Poland",
"native-country_Jamaica", "native-country_Vietnam",
"native-country_Portugal", "native-country_Ireland",
"native-country_France", "native-country_Dominican-Republic",
"native-country_Laos", "native-country_Ecuador",
"native-country_Taiwan", "native-country_Haiti",
"native-country_Columbia", "native-country_Hungary",
"native-country_Guatemala", "native-country_Nicaragua",
"native-country_Scotland", "native-country_Thailand",
"native-country_Yugoslavia", "native-country_El-Salvador",
"native-country_Trinadad&Tobago", "native-country_Peru",
"native-country_Hong", "native-country_Holand-Netherlands"
]
X = X.drop(columns=(["capital-gain", "capital-loss", "education-num"] +
countryCols))
return [X, Y]