Welcome to the Diseases Prediction and Classification repository! This repository contains Jupyter Notebook files for predicting and classifying diseases using various machine-learning models. Below is an overview of the available notebooks:
- Diabetes Prediction[ANN].ipynb
- Diabetes Prediction[LoR].ipynb
- Diabetes Prediction[KNN].ipynb
- Diabetes Prediction[SVM].ipynb
- Epilepsy Seizure Prediction[XGB and other models].ipynb
The `Diabetes Prediction[ANN] .ipynb' notebook focuses on predicting diabetes using an Artificial Neural Network (ANN) model. It covers data preprocessing, model training, and evaluation.
The Diabetes Prediction[LoR].ipynb notebook aims to predict diabetes using Logistic Regression (LoR). It covers data preparation, model fitting, and performance evaluation.
The Diabetes Prediction[KNN].ipynb notebook explores diabetes prediction using the k-Nearest Neighbors (KNN) algorithm. It includes data preprocessing, model training, and validation.
The Diabetes Prediction[SVM].ipynb notebook focuses on diabetes prediction using Support Vector Machines (SVM). It covers data preprocessing, SVM model fitting, and result evaluation.
The Epilepsy Seizure Prediction[XGB and other models].ipynb notebook is dedicated to the prediction of epilepsy seizures. It utilizes various models, including XGBoost, and explores different predictive approaches.
- Open the respective notebook you're interested in.
- Follow the instructions within the notebook to run and understand the disease prediction or classification process.
- Ensure you have the necessary libraries installed.
- Python
- Jupyter Notebook
- Relevant machine learning libraries (e.g., scikit-learn, XGBoost)
- Dataset (if not included in the repository)
This project is licensed under the MIT License - see the LICENSE file for details.
Feel free to contact me with any questions or suggestions related to this repository.
Happy disease prediction and classification!