An Introduction to Artificial Neural Networks for Predictive Modeling in Python

An Introduction to Artificial Neural Networks for Predictive Modeling in Python

Artificial Neural Networks (ANNs) are a subset of machine learning that are inspired by the operation of the human brain. ANNs are designed to recognize patterns in data and can be used for predictive modeling.

In this article, we will cover the basics of ANNs for predictive modeling using Python.

What is an Artificial Neural Network?

An Artificial Neural Network is composed of interconnected nodes or neurons that work together to recognize complex patterns in data. The inputs to the network are fed into the neurons through weighted connections, and the output is calculated by applying an activation function to the sum of the weighted inputs.

Creating an Artificial Neural Network in Python

To create an Artificial Neural Network in Python, we will use the popular machine learning library, scikit-learn. We will begin by importing the necessary libraries:

import pandas as pd
import numpy as np
from sklearn.neural_network import MLPClassifier
from sklearn.model_selection import train_test_split

Next, we will load our data into a Pandas DataFrame:

data = pd.read_csv('data.csv')

After loading the data, we will split it into training and test sets:

X = data.drop('target_variable', axis=1)
y = data['target_variable']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

We will then create the Artificial Neural Network:

model = MLPClassifier(hidden_layer_sizes=(10,10,10), max_iter=1000)
model.fit(X_train, y_train)

The hidden_layer_sizes parameter specifies the number of neurons in each hidden layer of the network. In this example, we have three hidden layers, each with ten neurons. The max_iter parameter specifies the maximum number of iterations for the training process.

Finally, we will evaluate the performance of the network on the test set:

y_pred = model.predict(X_test)
accuracy = np.mean(y_pred == y_test)

Conclusion

Artificial Neural Networks are a powerful tool for predictive modeling. In this article, we have covered the basics of ANNs and demonstrated how to create an ANN in Python using scikit-learn. With this knowledge, you can begin to apply ANNs to your own predictive modeling problems.