Have you ever wondered what it takes to predict the outcome of a thrilling boxing match? Look no further because I've been hard at work, harnessing the power of data to bring you closer to the answers.
In this exciting endeavor, I've embarked on a journey to predict whether a boxer will emerge victorious or face defeat in their upcoming match. The magic happens when I combine various data sources, such as web information, chat GPT interactions, and bard-like insights (all gathered through legitimate means).
My methodology is grounded in my knowledge and fueled by inspiration from the enlightening book, "Building ML Powered Applications" by Emmanuel Ameisen (O'Reilly). This invaluable resource has guided me in creating efficient workflows and pipelines for this iterative data science project.
Contained within this repository are an array of meticulously designed notebooks, each housing intricately crafted predictive models. Additionally, I've curated Py files hosting a collection of reusable functions tailored to streamline and optimize the notebooks' efficiency. I use google colab for cloud-based development and seamless testing on virtual machines. I've created a structured workflow for data processing, facilitating transformations, and preparing the data for machine learning models. This approach aims to simplify the intricate realm of data science, making it more accessible and navigable for comprehensive data processing, empowering the generation of informed predictions.
Dive into the notebooks, explore the code, and join me on this exhilarating journey. Whether you're a seasoned data scientist or a curious enthusiast, there's something here for everyone.
Got questions or eager to lend a hand? Don't hesitate to reach out to me at "seremm@outlook.com", via instagram "@sergemm.data.inv", or via "https://www.linkedin.com/in/sergio-emmanuel-ordaz-lópez-26b613123/" Together, we can unravel the mysteries of the boxing world!