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Andrzej Preibisz 2022-03-20 18:28:35 +01:00
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heart_2020_cleaned.csv Normal file
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import csv
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
dataset = pd.read_csv("heart_2020_cleaned.csv")
print(dataset.describe(include='all'))
dataset = dataset.dropna()
print(dataset.describe(include='all'))
dataset_train, dataset_test = train_test_split(dataset, test_size=.2, train_size=.8, random_state=1)
print(dataset_train.describe(include='all'))
print("Wielkości:")
print("Zbiór uczący:", dataset_train.shape[0])
print("Zbiór testowy:", dataset_test.shape[0])
print("Łącznie: ", dataset.shape[0])
print(dataset["GenHealth"].value_counts())
print(dataset_train["GenHealth"].value_counts())
print("Średnia BMI -łącznie: ", dataset["BMI"].mean())
print("Odchylenie standardowe BMI - uczący:", dataset_train["BMI"].std())
print("Odchylenie standardowe BMI - łącznie:", dataset["BMI"].std())
print("Mediana BMI:", dataset_test["BMI"].median())
max_bmi = dataset_train["BMI"].max()
print(max_bmi)
dataset_train["BMI"] = dataset_train["BMI"].apply(lambda x: x/max_bmi)
dataset_test["BMI"] = dataset_test["BMI"].apply(lambda x: x/max_bmi)
print(dataset_train["BMI"])