51 lines
1.7 KiB
Python
51 lines
1.7 KiB
Python
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import os
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import numpy as np
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import pandas as pd
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import wget
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from sklearn.preprocessing import MinMaxScaler
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from sklearn.model_selection import train_test_split
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def downloadCSV():
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url = 'https://git.wmi.amu.edu.pl/s434766/ium_434766/raw/branch/master/healthcare-dataset-stroke-data.csv'
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wget.download(url, out='healthcare-dataset-stroke-data.csv', bar=None)
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def dropNaN():
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data = pd.read_csv('healthcare-dataset-stroke-data.csv')
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data = data.dropna()
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return data
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def NormalizeData(data):
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data = data.astype({"age": np.int64})
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for col in data.columns:
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if data[col].dtype == object: # STRINGS TO LOWERCASE
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data[col] = data[col].str.lower()
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if data[col].dtype == np.float64: # FLOATS TO VALUES IN [ 0, 1]
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dataReshaped = data[col].values.reshape(-1,1)
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scaler = MinMaxScaler(feature_range=(0, 1))
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data[col] = scaler.fit_transform(dataReshaped)
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if col == 'ever_married': # YES/NO TO 1/0
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data[col] = data[col].map(dict(yes=1, no=0))
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if col == 'smoking_status':
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data[col] = data[col].str.replace(" ", "_")
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if col == 'work_type':
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data[col] = data[col].str.replace("-", "_")
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return data
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def saveToCSV(data1,data2,data3):
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data1.to_csv("data_train.csv", index=False)
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data2.to_csv("data_test.csv",index=False)
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data3.to_csv("data_val.csv",index=False)
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downloadCSV()
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data = dropNaN()
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data = NormalizeData(data)
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data_train, data_test = train_test_split(data, test_size=0.2, random_state=1)
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data_train, data_val = train_test_split(data_train, test_size=0.25, random_state=1) ## Twice to get 0.6, 0.2, 0.2
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saveToCSV(data_train,data_test,data_val)
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