Diabetes.py

import numpy as np
import pandas as pd
import tensorflow as tf
from keras.layers import Dense,Dropout
from sklearn.model_selection import train_test_split
import matplotlib as mlp
import matplotlib.pyplot as plt
%matplotlib inline
from sklearn.preprocessing import StandardScaler

data=pd.read_csv("pima-indians-diabetes.csv")
data.head()

data = data.rename(index=str, columns={"6":"preg"})
data = data.rename(index=str, columns={"148":"gluco"})
data = data.rename(index=str, columns={"72":"bp"})
data = data.rename(index=str, columns={"35":"stinmm"})
data = data.rename(index=str, columns={"0":"insulin"})
data = data.rename(index=str, columns={"33.6":"mass"})
data =data.rename(index=str, columns={"0.627":"dpf"})
data = data.rename(index=str, columns={"50":"age"})
data = data.rename(index=str, columns={"1":"target"})

data.head()

X = data.iloc[:, :-1]
Y = data.iloc[:,8]


X_train_full, X_test, y_train_full, y_test = train_test_split(X, Y, random_state=42)
X_train, X_valid, y_train, y_valid = train_test_split(X_train_full, y_train_full, random_state=42)


np.random.seed(42)
tf.random.set_seed(42)
 
model=Sequential()
model.add(Dense(15,input_dim=8, activation='relu'))
model.add(Dense(10,activation='relu'))
model.add(Dense(8,activation='relu'))
model.add(Dropout(0.25))
model.add(Dense(1, activation='sigmoid'))

model.compile(loss="binary_crossentropy", optimizer="SGD", metrics=['accuracy'])
model_history = model.fit(X_train, y_train, epochs=200, validation_data=(X_valid, y_valid))