Regression and Classification

Regressionand Classification UnderstandingRegressionandClassification: -Startbyexplainingthefundamentalconceptsofregressionandclassification.Emphasize thatregressionisasupervisedlearningtechniqueusedforpredictingcontinuousnumerical values,whileclassificationisusedforpredictingdiscreteclasslabels.DataScienceCourse. Provideexamplesofregressiontasks,suchaspredictinghousepricesbasedonfeatureslike squarefootageandlocation,andclassificationtasks,suchasclassifyingemailsasspamor not spambasedontheircontent. RegressionTechniques: -Discusscommonregressiontechniquesusedinmachinelearning,suchaslinearregression, polynomialregression,andlogisticregression.Explaintheassumptions,principles,and limitationsofeachtechnique.Teachstudentshowtointerpretregressioncoefficients,assess modelperformanceusingmetricslikemeansquarederror(MSE)orR-squared(forregression), andvisualizeregressionmodelsusingscatterplotsandregressionlines. ClassificationAlgorithms: -Introducepopularclassificationalgorithms,includinglogisticregression,decisiontrees, randomforests,supportvectormachines(SVM),andk-nearestneighbors(k-NN).Explainthe principles,strengths,andweaknessesofeachalgorithm.Discusstechniquesformodel evaluationandselection,suchascross-validation,confusionmatrices,precision-recallcurves, andROCcurves. FeatureEngineeringandSelection: -Highlighttheimportanceoffeatureengineeringandselectioninregressionandclassification tasks.Teachstudentshowtopreprocessandtransforminputfeaturestoimprovemodel performance.Discusstechniquessuchasscaling,encodingcategoricalvariables,handling missingvalues,andcreatingnewfeaturesthroughfeatureengineering.Explainmethodsfor featureselection,suchascorrelationanalysis,forward/backwardselection,andregularization techniques. PracticalApplicationsandUseCases:

-Illustratepracticalapplicationsandusecasesofregressionandclassificationinvarious domains.Showexamplesofregressiontasks,suchaspredictingsalesrevenuebasedon marketingspend,andclassificationtasks,suchassentimentanalysisofcustomerreviews. Discusschallengesandconsiderationsspecifictoeachapplicationdomain,suchasdata quality,featureselection,andmodelinterpretability. Masteringthesepointersallowsstudentstoeffectivelyapplyregressionandclassification techniquestoanalyzedata,buildpredictivemodels,andsolvereal-worldproblems.DataScienceCourseinMumbai.Theywillgainasolidunderstandingoftheunderlyingconcepts, algorithms,evaluationmetrics,andbestpracticesinregressionandclassification,enabling themtotackleawiderangeofpredictivemodelingtasksintheircareers. Businessname:ExcelR-DataScience,DataAnalytics,BusinessAnalyticsCourseTraining Mumbai Address:304,3rdFloor,PratibhaBuilding.ThreePetrolpump,LalBahadurShastriRd, oppositeManasTower,Pakhdi,ThaneWest,Thane,Maharashtra400602 Phone:09108238354, Email:enquiry@excelr.com

Regression and Classification

Regression and Classification

Presentation Transcript

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Regression and Classification