Neural network model for creditworthiness prediction using Python and the Keras library.md

Here is an example of building a neural network model for creditworthiness prediction using Python and the Keras library:

import pandas as pd
import numpy as np
from keras.models import Sequential
from keras.layers import Dense
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler

# Load the data
data = pd.read_csv('credit_data.csv')

# Preprocess the data
X = data.iloc[:, :-1].values
y = data.iloc[:, -1].values
sc = StandardScaler()
X = sc.fit_transform(X)

# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Define the neural network architecture
model = Sequential()
model.add(Dense(12, input_dim=X.shape[1], activation='relu'))
model.add(Dense(8, activation='relu'))
model.add(Dense(1, activation='sigmoid'))

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

# Train the model
model.fit(X_train, y_train, epochs=100, batch_size=10)

# Evaluate the model
scores = model.evaluate(X_test, y_test)
print("Accuracy: %.2f%%" % (scores[1]*100))

In this example, we load the credit data from a CSV file and preprocess it using StandardScaler to normalize the features. We then split the data into training and testing sets, define the neural network architecture using the Keras Sequential API, and compile the model with binary_crossentropy loss and adam optimizer. We train the model using the fit() function and evaluate its performance on the testing set using the evaluate() function. The output of the code is the accuracy of the neural network model in predicting the creditworthiness of loan applicants.