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Titanic - ML Project

Titanic - ML Project
(Titanic Data - Notes)-Dimension (1309 rows, 12 columns)-High skewness for variables Sibsp,Parch & Fare-Missing value in variable Age, Cabin &Embarked.-Zero value in varibale Fare. (Outlier Treatment)Try using the same workflow shownbelow after outlier treament & check anymodel performance improvement (Gray Nodes)Gray color nodes are components, whichmeans two or more nodes are combined toform one component. Open each to view thesub workflows in each component nodes (Sample File Submission)Export the sample submission csv tothe local path. Upload the file in Kaggleto see the model score. Train DataJoining Train & TestTest DataCorrelation CheckTrain & TestTrain & ValidationSample SubmissionSelect Columns CSV Reader Concatenate CSV Reader Scatter Plot(JFreeChart) Outlier Treatment Data Cleaning Data EDA Partitioning Variable Encoding Normalization Partitioning Modelling (FindingBest Model) CSV Writer Best Model (LogisticsRegression) Column Filter (Titanic Data - Notes)-Dimension (1309 rows, 12 columns)-High skewness for variables Sibsp,Parch & Fare-Missing value in variable Age, Cabin &Embarked.-Zero value in varibale Fare. (Outlier Treatment)Try using the same workflow shownbelow after outlier treament & check anymodel performance improvement (Gray Nodes)Gray color nodes are components, whichmeans two or more nodes are combined toform one component. Open each to view thesub workflows in each component nodes (Sample File Submission)Export the sample submission csv tothe local path. Upload the file in Kaggleto see the model score. Train DataJoining Train & TestTest DataCorrelation CheckTrain & TestTrain & ValidationSample SubmissionSelect ColumnsCSV Reader Concatenate CSV Reader Scatter Plot(JFreeChart) Outlier Treatment Data Cleaning Data EDA Partitioning Variable Encoding Normalization Partitioning Modelling (FindingBest Model) CSV Writer Best Model (LogisticsRegression) Column Filter

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