28 lines
1.0 KiB
Python
28 lines
1.0 KiB
Python
# import pandas as pd
|
|
from sklearn.model_selection import train_test_split
|
|
from sklearn.preprocessing import MinMaxScaler
|
|
from datasets import load_dataset
|
|
|
|
dataset = load_dataset("mstz/liver")['train']
|
|
dataset = dataset.to_pandas()
|
|
|
|
train, test = train_test_split(dataset, test_size=0.2, random_state=42)
|
|
train, val = train_test_split(train, test_size=0.2, random_state=42)
|
|
|
|
numerical_features = ['age', 'total_bilirubin', 'direct_ribilubin', 'alkaline_phosphotase',
|
|
'alamine_aminotransferasi', 'aspartate_aminotransferase', 'total_proteins', 'albumin',
|
|
'albumin_to_globulin_ratio']
|
|
|
|
scaler = MinMaxScaler()
|
|
train[numerical_features] = scaler.fit_transform(train[numerical_features])
|
|
test[numerical_features] = scaler.fit_transform(test[numerical_features])
|
|
val[numerical_features] = scaler.fit_transform(val[numerical_features])
|
|
|
|
train.dropna(inplace=True)
|
|
test.dropna(inplace=True)
|
|
val.dropna(inplace=True)
|
|
|
|
train.to_csv('train.data')
|
|
test.to_csv('test.data')
|
|
val.to_csv('dev.data')
|