2주차 과제_당뇨

import os
os.environ['KAGGLE_USERNAME'] = 'spartasoommni' # username
os.environ['KAGGLE_KEY'] = 'cf783e19878a7a9e8284d2ea28c7517c' # key

!kaggle datasets download -d kandij/diabetes-dataset
!unzip diabetes-dataset.zip

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from tensorflow.keras.optimizers import Adam, SGD
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler

df = pd.read_csv('diabetes2.csv')

x_data = df.drop(columns=['Outcome'], axis=1)
x_data = x_data.astype(np.float32)

y_data = df[['Outcome']]
y_data = y_data.astype(np.float32)
print(x_train.shape, x_val.shape)
print(y_train.shape, y_val.shape)

scaler = StandardScaler()
x_data_scaled = scaler.fit_transform(x_data)
x_train, x_val, y_train, y_val = train_test_split(x_data_scaled, y_data, test_size=0.2, random_state=2021)

model = Sequential([
  Dense(1, activation='sigmoid')
])

model.compile(loss='binary_crossentropy', optimizer=Adam(lr=0.001), metrics=['acc'])

model.fit(
  x_train,
  y_train,
  validation_data=(x_val, y_val),
  epochs=24
)