Using Python and Keras to perform binary classification consists of the following steps:

1. Import the necessary packages.

   import numpy as np
   import pandas as pd
   from keras.models import Sequential
   from keras.layers import Dense
   from keras.wrappers.scikit_learn import KerasClassifier

2. Load the data and preprocess it.

   
   #load data
   dataset = pd.read_csv('data.csv')

split into input (X) and output (Y) variables

X = dataset.iloc[:,0:8].values Y = dataset.iloc[:,8].values

3. Define the model.

define the keras model

def create_model():

create model

model = Sequential()
model.add(Dense(12, input_dim=8, activation='relu'))
model.add(Dense(8, activation='relu'))
model.add(Dense(1, activation='sigmoid'))

#compile model
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
return model

4. Create the model.

model = create_model()

5. Fit the model to the data.

model.fit(X, Y, epochs=150, batch_size=10)

6. Evaluate the model.

scores = model.evaluate(X, Y) print("\n%s: %.2f%%" % (model.metrics_names[1], scores[1]*100))

7. Make predictions.

predictions = model.predict(X) rounded = [round(x[0]) for x in predictions] print(rounded)

## Output example

accuracy: 85.25% [0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0]

## Helpful links
- [Keras Documentation](https://keras.io/)
- [Using Keras for Binary Classification](https://machinelearningmastery.com/binary-classification-tutorial-with-the-keras-deep-learning-library/)

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