Project Overview: Diabetes Prediction
Developed by a team comprising Ravi, Vishal, and Sanaullah, this Python-based project focuses on predicting diabetes using machine learning techniques. The project utilizes essential libraries such as Pandas, NumPy, sklearn, and Streamlit, employing Support Vector Machine and Random Forest Classifier algorithms.
Local Setup Guide:
Python Installation: Ensure Python is installed on your operating system.
Anaconda Environment: Install Anaconda for managing the project environment.
Accessing Project: Launch Anaconda Navigator via the terminal with anaconda-navigator.
Project Execution: Run the project within Jupyter Notebook.
For environment updates, execute the following commands:
bash conda update spyder conda install pyopengl spyder
Running as a Local Web Application:
Execute the command below to launch the web application locally:
bash
streamlit run "/home/bakru_k78/VsCodeProject/Final-Year-Project/Diabetes Prediction web app.py"
Deployment:
The project is deployed and hosted online using GitHub in conjunction with Render.