README.md

Author: rasmodev

README.md

@@ -12,15 +12,6 @@ This project aims to provide a streamlined tool for healthcare professionals to
 | ------------- | ------------- | -----------------
 |[Notebook with code and full analysis](https://github.com/rasmodev/Sepsis-Classification-ML-Project-with-FastAPI-Deployment/blob/main/dev/Sepsis_ML_Prediction_Deployment_With_FastAPI.ipynb)| [Published Article on Medium](https://medium.com/@rasmowanyama/fastapi-for-machine-learning-deployment-a-beginners-guide-ee74ee41316f) |[Link to working FastAPI](https://rasmodev-sepsis-prediction.hf.space/docs/)
 
-# Project Overview:
-**i. Data Collection and Preprocessing:** We collected and preprocessed a comprehensive dataset containing clinical and physiological data from patients to train and evaluate our sepsis classification model.
-
-**ii. Machine Learning Model:** We implemented a state-of-the-art machine learning model tailored for sepsis classification. This model has been fine-tuned to achieve high accuracy in detecting sepsis early, which is crucial for timely intervention.
-
-**iii. FAST API Integration:** We've seamlessly integrated the trained machine learning model into a web application using FAST API. This web application allows healthcare professionals to input patient data and receive instant predictions regarding sepsis risk.
-
-**iv. Usage and Deployment:** In the README file, you will find detailed instructions on how to use and deploy this web application, making it user-friendly for both developers and healthcare practitioners.
-
 # Repository Contents:
 - Machine Learning Model (Code and Weights)
 - FAST API Web Application Code
@@ -29,6 +20,26 @@ This project aims to provide a streamlined tool for healthcare professionals to
 - Example Usage
 - Dependencies and Installation Instructions
 
+- [Project Overview](#project-overview)
+- [How To Use](#how-to-use)
+- [Getting Started](#getting-started)
+- [Data](#data)
+- [Modeling](#modeling)
+- [Evaluation](#evaluation)
+- [Deployment](#deployment)
+- [Future Work](#future-work)
+- [Contact](#contact)
+
+
+# Project Overview:
+**i. Data Collection and Preprocessing:** I loaded and preprocessed a comprehensive dataset containing clinical and physiological data from patients to train and evaluate the sepsis classification model.
+
+**ii. Machine Learning Model:** I implemented a state-of-the-art machine learning model tailored for sepsis classification. This model has been fine-tuned to achieve high accuracy in detecting sepsis early, which is crucial for timely intervention.
+
+**iii. FAST API Integration:** I've seamlessly integrated the trained machine learning model into a web application using FAST API. This web application allows healthcare professionals to input patient data and receive instant predictions regarding sepsis risk.
+
+**iv. Usage and Deployment:** In this README file, you will find detailed instructions on how to use and deploy this web application, making it user-friendly for both developers and healthcare practitioners.
+
 # How to Use:
 - Clone this repository to your local machine.
 - Install the required dependencies.