This repository contains a machine-learning project focused on classifying diseases based on a pool of over 130 symptoms. This project aims to develop a model that can accurately predict an individual's disease based on various combinations of symptoms.
The dataset used for this project consists of 4920 samples. The dataset was obtained from Kaggle.
In this project, I employed several different machine learning models from Scikit-learn to train and evaluate the predictive model. The steps involved in the process include:
- Importing data: Cleaning the dataset, handling missing values, and scaling features.
- Model training: Training the selected algorithm on the preprocessed dataset.
- Model evaluation: Assessing the performance of the trained model using appropriate metrics such as accuracy, precision, recall, and F1-score.
Currently, the trained model achieves an accuracy score of 99.4% using Decision Trees.
To run this project locally, follow these steps:
- Open this link to access the Google Colab file and make a copy
- Download dataset files from this repository
- Run the Google Colab to preprocess the data, train the model, make predictions, and save the model.
Programming languages: Python
Libraries: Scikit-learn Pandas NumPy Matplotlib Seaborn
Some potential enhancements for this project include:
- Exploring how the order of severity can affect the accuracy
- Tuning hyperparameters to optimize the model's performance.
- Deploying the trained model as a web application or API for real-time predictions.
Special thanks to Pranay Patil for providing the dataset used in this project.