Technical requirements

We will be using scikit-learn, a popular Python library specially designed for machine learning tasks. It offers algorithms and tools for data preprocessing, feature selection, model selection, and model evaluation.

If you have not worked with scikit-learn before, it can be installed by using the following code snippet:

pip install scikit-learn

The documentation for scikit-learn can be found at https://scikit-learn.org/stable/.

For deep learning, we have the option to use TensorFlow or Keras. TensorFlow is a powerful open source library for numerical computation that provides solutions to train, test, and deploy a variety of deep learning neural networks. It serves as the infrastructure layer, which enables low-level tensor operations on the CPU, TPU, and GPU. On the other hand, Keras is a high-level Python API built on top of TensorFlow. It is specially prepared to enable fast experimentation and provides informative feedback when an error is discovered...