Parkinson's disease detector using XGBoost
https://archive.ics.uci.edu/ml/machine-learning-databases/parkinsons/
- Importing libraries
- Dealing with missing values and duplicated values
- Exploratory Data Analysis
- Detecting Outliers
- Training the model
- Prediction
XGBoost is a supervised learning algorithm that implements a process called boosting to yield accurate models. Boosting refers to the ensemble learning technique of building many models sequentially, with each new model attempting to correct for the deficiencies in the previous model. In tree boosting, each new model that is added to the ensemble is a decision tree. XGBoost provides parallel tree boosting (also known as GBDT, GBM) that solves many data science problems in a fast and accurate way. For many problems, XGBoost is one of the best gradient boosting machine (GBM) frameworks today.
Despite the dataset is not so large it's easy enough to move through. This project can be classified as a classification problem: the response is categorical. It's possible to make the model perform better: feel free to modify it.
'Exploiting Nonlinear Recurrence and Fractal Scaling Properties for Voice Disorder Detection', Little MA, McSharry PE, Roberts SJ, Costello DAE, Moroz IM. BioMedical Engineering OnLine 2007, 6:23 (26 June 2007)