akashkathole7/prankinsons-disease-prediction

prankinsons-disease-prediction

Welcome to the world of Prankinsons Prediction! This repository contains code and resources related to predicting and preventing pranks from your mischievous friends. With machine learning algorithms and data analysis techniques, we can predict when your friends are planning a prank and take preventive measures to save you from embarrassment and inconvenience.

The purpose of this project is to provide a fun and creative starting point for those interested in developing or implementing their own Prankinsons Prediction models. This project includes sample code and datasets to help individuals get started with building their own models and outsmart their prankster friends.

Datasets This project includes several datasets that can be used to train and test Prankinsons Prediction models. These datasets are generated from public sources, such as social media platforms, and contain information about prank-related keywords, phrases, and hashtags.

Code The code included in this project is written in Python and uses popular machine learning libraries such as scikit-learn and TensorFlow. The code provides examples of how to preprocess the data, build and evaluate different Prankinsons Prediction models, and visualize the output. Additionally, the code includes functions for hyperparameter tuning and model evaluation.

Resources This project includes a list of resources related to prank prediction and prevention, such as prank detection apps, videos, and blog posts. These resources can be helpful for individuals looking to outsmart their prankster friends or for those seeking inspiration for their own projects.

Contributing Contributions to this project are welcome. If you have suggestions for additional datasets, resources, or code examples, please submit a pull request. Additionally, please report any issues or bugs you encounter while using the code or datasets.

License This project is licensed under the MIT License. Please see the LICENSE file for more details.

Acknowledgments This project was inspired by the work of many individuals who have fallen victim to pranks and wish to outsmart their prankster friends. We would like to acknowledge their creativity and determination to stay ahead of the game. So, let's predict and prevent pranks with Prankinsons Prediction!