Presenting an all-in-one Capstone project to learn machine learning, a sample setup with SKLearn. This project aims to develop a predictive model to assess the risk of diabetes in patients using multiple health metrics. Diabetes is a significant global health issue, and early prediction can lead to better management and prevention strategies. The proposed approach employs machine learning algorithms to analyze patient data and predict diabetes risk, thereby contributing to preventive healthcare. Diabetes dataset ( https://www.kaggle.com/datasets/uciml/pima-indians-diabetes-database ). The primary task is to classify individuals as at-risk or not at-risk for diabetes using input features such as age, BMI, blood pressure, and glucose levels. Colab notebook: https://colab.research.google.com/drive/1rPYOV3d4HDtu_rQU__TWlEJRqlIGedcr?usp=sharing Project summary slides: https://docs.google.com/presentation/d/e/2PACX-1vSMsclezaCTE1rnmtjYe-fqPFl8ULvg9IelAoA0AL61NC772sGBTpA...
A Python application can be written as a standalone application or as part of a large AI project. As we learned in the previous chapter, AI is a very broad topic. Often, when we hear about an AI application, it is simply a client application that uses a large language model or a generative AI API from vendors like OpenAI, Gemini, and Anthropic. Some applications are Model Context Protocol (MCP) agents that can invoke APIs from commercial or open-source MCP frameworks such as LangChain or Llama. We do not need to know how these APIs are implemented or which AI algorithms are used under the hood. However, Python can do much more than that. We can leverage the vast ecosystem of community libraries to build our own AI or ML pipeline. A Pipeline is a series of steps or modules put together to handle every facet of data collection, extraction, processing, training, testing, and implementing various machine learning (ML), Natural Language Processing (NLP), Deep Learning, Neural Networks,...