from keras.models import Sequential
from keras.layers import Dense
classifier = Sequential()
classifier.add(Dense(units = 20, kernel_initializer = 'uniform', activation =
'relu'))
classifier.add(Dense(units=2, kernel_initializer='normal',
activation='softmax'))
classifier.compile(optimizer = 'rmsprop', loss = 'binary_crossentropy',
metrics = ['accuracy'])
classifier.fit(X_train, y_train, batch_size = 1, epochs = 100)

import pandas as pd
import numpy as np
import seaborn as sns
from sklearn.preprocessing import LabelEncoder
import matplotlib.pyplot as plt
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split

from sklearn import metrics
from sklearn.metrics import confusion_matrix

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

X=df.iloc[:, :-1]
X1=df.drop(df.columns[[0,2,3,20]],axis=1)
Y=df.iloc[:, -1:]
gender_encoder = LabelEncoder()
y = gender_encoder.fit_transform(Y)

scaler = StandardScaler()
scaler.fit(X1)
X = scaler.transform(X1)

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2,
random_state=1)


https://www.kaggle.com/javapocalypse/breast-cancer-classification-in-keras-
using-ann