Feature Selection Techniques on Parkinson’s Dataset

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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Видео Feature Selection Techniques on Parkinson’s Dataset канала EduMentor Deepti