Welcome to the Disease Prediction System! This repository contains a machine learning-based system that predicts diseases based on a set of input symptoms. 🚑💻 The system utilizes three powerful classification models: Support Vector Machine (SVM), Naive Bayes, and Random Forest Classifier. The final prediction is determined using a majority voting approach, combining the predictions of all three models. 🗳️
The goal of this project is to predict potential diseases based on the input symptoms using machine learning algorithms. This project leverages popular classification models in scikit-learn to train and evaluate the system.
- SVM (Support Vector Machine): A robust classifier that performs well on high-dimensional datasets. 🔍
- Naive Bayes: A probabilistic classifier based on Bayes' theorem, ideal for categorical data. 📊
- Random Forest Classifier: An ensemble method that builds multiple decision trees to improve accuracy and reduce overfitting. 🌳
- Train models on a labeled dataset containing diseases and their corresponding symptoms.
- Make predictions for diseases based on the input symptoms.
- Combine predictions from SVM, Naive Bayes, and Random Forest using majority voting. ✔️
- Visualize the model's performance with confusion matrices to better understand the accuracy of predictions. 🔍
The dataset used in this project is a CSV file containing various diseases and their associated symptoms. Each row corresponds to a unique combination of symptoms and the disease it represents.
The dataset is pre-processed by removing missing values and encoding categorical features, making it ready for training and testing the machine learning models. ⚙️
To run this project, you'll need to install the following Python libraries:
numpy🍀pandas📊scipy🧮matplotlib📈seaborn🎨scikit-learn🤖
You can install all the required libraries with the following command:
pip install numpy pandas scipy matplotlib seaborn scikit-learn