Parkinson Detection From Voice Data Part1 iBest Workshop
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Begin your journey into Parkinson’s disease detection using voice data. Learn how to process and analyze voice recordings to identify early signs of Parkinson’s using AI and machine learning.
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.
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.
A Look at the Project Ahead
- Develop a machine learning model that can accurately predict the presence of Parkinson’s disease based on voice recordings.
- Implement different machine learning algorithms such as decision trees and support vector machines to analyze voice features and make predictions.
- Conduct a grid search to optimize the parameters of the model and improve its predictive power.
- Visualize the decision tree model to aid in interpreting the results and identifying important features.
What You’ll Need
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