Parkinson's Disease Detection System

Technologies Used:

1. Frontend- HTML, CSS, Bootstrap
2. Backend- Flask
3. Machine Learning Algorithm- XGBoost Classifier

Description:

• Developed XGBoost Classifier-based Parkinson's Disease Detector using Machine Learning and Web Development techniques.

• Performed Data Cleaning and Data Standardization using MinMaxScaler.

• Trained the model on a labeled dataset from UCI.

• Achieved accurate classification of a person as healthy or having Parkinson's Disease with around 94.871% accuracy.

Dataset

https://archive.ics.uci.edu/dataset/174/parkinsons

Walkthrough

Demo Video

https://www.linkedin.com/posts/sanya-dureja-13960122a_i-am-thrilled-to-share-that-i-have-successfully-activity-7086478696378724352-QwEw?utm_source=share&utm_medium=member_desktop