/DiabetesPrediction

A machine learning model using svm to predict if a patient has diabetes

Primary LanguageJupyter NotebookMIT LicenseMIT

README.md for Diabetes Prediction Notebook

Overview

This notebook aims to predict diabetes using the PIMA Diabetes dataset. It takes you through the entire machine learning pipeline, from data preprocessing to model evaluation.

Table of Contents

  1. Import Data
  2. Split Data for Training
  3. Train the Model
  4. Model Evaluation
  5. Build the Predictive System

Prerequisites

  • Python 3.x
  • scikit-learn
  • pandas
  • matplotlib (for data visualization)

Usage

  1. Import Data: The notebook starts by importing the PIMA Diabetes dataset. This dataset is commonly used for machine learning tasks related to healthcare.

  2. Split Data for Training: The data is split into training and testing sets to evaluate the performance of the model.

  3. Train the Model: The notebook covers how to train a machine learning model using the training data. It may explore different algorithms and techniques to fit the model.

  4. Model Evaluation: After training, the notebook evaluates the model using various metrics such as accuracy, precision, and recall. This step helps in understanding how well the model will perform on unseen data.

  5. Build the Predictive System: Finally, the notebook shows how to use the trained model to make predictions on new data.

How to Run

  1. Clone the repository to your local machine.
  2. Open the diabetesprediction.ipynb notebook in Jupyter Notebook or Jupyter Lab.
  3. Run the notebook cells in sequence to go through the machine learning pipeline.

License

This project is open-source and available to anyone who wishes to learn about machine learning applied to healthcare.