MarthaK-Coder/Azubi-Africa-P6-Machine-Learning-API-using-FastAPI

# Predicting Sepsis Onset with FastAPI: A Dockerized Machine Learning Approach ## Table of Contents 1. Introduction 2. Business Understanding 3. Data Understanding - Loading Datasets and Exploratory Data Analysis (EDA) 4. Data Preparation - Data Splitting and Balancing Classes - Creating a Preprocessing Pipeline 5. Model Training and Evaluation - Selecting and Training Models - Model Evaluation - Hyperparameter Tuning - Model Persistence 6. FastAPI and Dockerization - Building a FastAPI - Docker Containerization - Pushing to Docker Hub 7. Conclusion ## Introduction Timely prediction of sepsis onset is crucial for enhancing patient outcomes in healthcare. This project explores the application of machine learning classification models to predict sepsis, encapsulating the solution within a Docker container and exposing it through FastAPI for seamless deployment. ## Business Understanding In shaping our project's trajectory, we center our attention on a critical healthcare challenge: predicting sepsis onset based on patient data. Our primary focus is to formulate hypotheses and questions that delve into the heart of this problem. The overarching hypothesis is rooted in the belief that early sepsis detection can significantly enhance patient outcomes. ## Data Understanding ### Loading Datasets and Exploratory Data Analysis (EDA) Initiating the project with loading datasets and conducting exploratory data analysis (EDA) to gain insights: - What features are available in the dataset? - How should missing values be handled? - What is the distribution of sepsis cases in the dataset? ## Data Preparation ### Data Splitting and Balancing Classes Addressing class imbalance by splitting data into training and testing sets and employing techniques such as oversampling or undersampling. ### Creating a Preprocessing Pipeline Developing a preprocessing pipeline to streamline tasks like imputing missing values, scaling features, and encoding categorical variables. ## Mod …