This repository contains a minor project for disease prediction using machine learning classifiers such as logistic regression, decision tree, random forest, and MLP (Multi-layer Perceptron). The project focuses on evaluating the performance of these classifiers based on accuracy, confusion matrices, and classification reports.

The dataset used in this project is provided in the following files:

• training_data.csv
• test_data.csv

You can replace it with your own dataset. Ensure that the dataset is in the CSV format.

The following classifiers are implemented in this project:

• Logistic Regression
• Decision Tree
• Random Forest
• MLP (Multi-layer Perceptron)

After running the main script, the following results are generated:

• Accuracy of each classifier
• Confusion matrices
• Classification reports

To use this project, follow these steps:

1. Clone this repository to your local machine.
2. Install the required dependencies by running pip install -r requirements.txt.
3. Ensure that your dataset is in the CSV format and replace the provided datasets (training_data.csv and testing.csv) with your own dataset if needed.

After installing the project, you can use it as follows:

1. Run the main script to train the machine learning models and evaluate their performance.
2. Examine the generated results, including accuracy, confusion matrices, and classification reports, to assess the performance of each classifier.

Contributions to this project are welcome! If you'd like to contribute, please follow these guidelines:

1. Fork the repository and create your branch from master.
2. Make your changes and ensure that the code passes any existing tests.
3. Submit a pull request with a clear description of your changes and their purpose.

This project is licensed under the MIT License.