Parkinson Detection From Voice Data (Part1 iBest Workshop)
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Learn how to use Python and Scikit-Learn to accurately detect Parkinson’s Disease from voice patterns. Gain the skills to build your own AI-powered predictions and contribute to early detection and treatment of this neurological disorder. Explore machine learning techniques, grid search for parameter tuning, and visualize decision tree models for improved predictive power.
At a Glance
This Guided Project will provide an introduction to Artificial Intelligence and Machine Learning using Python and Scikit-Learn. Through it, learners will learn how to use Python and Scikit-Learn to build a Machine Learning model to accurately detect Parkinson’s Disease from voice patterns. By the end of this project, you will have gained the skills needed to start building your own AI-powered predictions.
This project aims to leverage machine learning techniques to analyze voice recordings and detect the presence of Parkinson’s disease, a neurological disorder that affects movement. The goal is to develop a model that can accurately predict the disease using voice data, which could help in the early detection and treatment of the condition.
In addition to implementing machine learning algorithms, the project will also involve conducting a grid search for tuning the parameters of the model. This step is essential for optimizing the performance of the model and improving its predictive power. Visualizing the decision tree model will also be part of the project, which can help in interpreting the results and identifying important features.
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