Using Logistic Regression in Machine Learning with Python

Recapping the primary step when conducting a model analysis, it is always important to import libraries and modules to access specific features. The standard imports are added, such as Matplotlib, pandas, NumPy and seaborn, as well as sklearn which allows us to split our sets into testing and training sets, along with the preprocessing of our data. On the 7th and 8th lines, we integrate TensorFlow with Keras. Keras is a high-level API of TensorFlow, so we must import them this way. The data being explored is a set consisting of patients' tumour data. Depending on the tumour size, our data will tell us if the patient is susceptible to cancer or not. Below is the data of the first 10 patients in the dataset. There are 101 patients in total.