https://github.com/priyanshul28/ml_classification_eda_parkinsonsdisease

A guided Machine Learning Classification exercise on the Parkinson's Disease dataset demonstrating the use of Logistic Regression, Neural Network Classifiers, Decision Trees, Random Forests and XGBoost algorithms, as well as Data Preprocessing and Exploratory Data Analysis.
https://github.com/priyanshul28/ml_classification_eda_parkinsonsdisease

classification machine-learning pandas python scikit-learn statistics

Last synced: 13 days ago
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A guided Machine Learning Classification exercise on the Parkinson's Disease dataset demonstrating the use of Logistic Regression, Neural Network Classifiers, Decision Trees, Random Forests and XGBoost algorithms, as well as Data Preprocessing and Exploratory Data Analysis.

• Host: GitHub
• URL: https://github.com/priyanshul28/ml_classification_eda_parkinsonsdisease
• Owner: PriyanshuL28
• Created: 2025-01-18T05:04:17.000Z (20 days ago)
• Default Branch: main
• Last Pushed: 2025-01-18T05:05:28.000Z (20 days ago)
• Last Synced: 2025-01-25T14:13:31.981Z (13 days ago)
• Topics: classification, machine-learning, pandas, python, scikit-learn, statistics
• Language: Jupyter Notebook
• Homepage: https://www.kaggle.com/datasets/rabieelkharoua/parkinsons-disease-dataset-analysis/data
• Size: 4.14 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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# Project_Python_Parkinsons_Disease

A guided Machine Learning Classification exercise on the Parkinson's Disease dataset demonstrating the use of Logistic Regression, Neural Network Classifiers, Decision Trees, Random Forests and XGBoost algorithms, as well as Data Preprocessing and Exploratory Data Analysis.