Artificial Intelligence in Healthcare has shown its impact in various fields, including diagnostics and disease prediction. For example, IBM Watson Health (now Merative) has made significant strides in disease diagnosis and treatment. Other research, like that from PSG College, has achieved over 70% accuracy in predicting heart disease using Naive Bayes and Support Vector Machine (SVM), two common Machine Learning algorithms.
In this article, we explore a public dataset on mental health in the tech industry, creating a classification model with K-means, an unsupervised learning algorithm for grouping data. The dataset is hosted on Kaggle, and you can download it here.
We use Google Colab as the development environment and rely on the Pandas library for data manipulation. To implement the model, we utilize PyCaret, an open-source library designed to simplify the usage of Machine Learning algorithms. Other popular options like TensorFlow and PyTorch are also mentioned as potential alternatives.
For those interested in the full implementation and code, you can find it here.