Feature Selection Techniques on Parkinson’s Dataset

Описание: Applying multiple Feature Selection techniques on the Parkinson’s Disease dataset to identify the most important features for accurate disease prediction. Methods like Variance Threshold, Correlation, Chi-Square, Mutual Information, Relief help remove irrelevant and redundant features, improving model performance.
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⏱️Chapter/TimeStamp⏱️
00:00 - intro
01:01 - how to import whole libraries?
03:40 - how to understand parkinson's disease dataset?
07:00 - how to preprocess categorical columns?
08:42 - how to get independent and target variables?
09:55 - how to get model accuracy without feature selection methods?
12:10 - how to implement variance threshold?
16:08 - how to implement correlation coefficient?
21:28 - how to implement chi-squared test method?
23:28 - how to implement mutual information method?
24:20 - how to implement relief algorithm?
26:02 - how to compare all methods together?
26:53 - how to get final accuracy of model after feature selection?
28:19 - outro
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#FeatureSelection #MachineLearning #DataScience #ParkinsonsDisease #FeatureEngineering #DimensionalityReduction #VarianceThreshold #Correlation #ChiSquareTest #MutualInformation #ReliefAlgorithm #RFE #MLProject #PythonMachineLearning #ScikitLearn #AIInHealthcare #DataScienceTutorial #PredictiveModeling #MLAlgorithms #DataAnalytics #edumentordeepti #edumentor #deepti
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