Main Reference PaperAn effective method for classification with missing values, Data Mining and Knowledge Discovery, 2018 [Java / Python / R]
  • The classification accuracy is improved by imputing the missing value of data. The effective imputing method Model based Missing value Imputation using Correlation is proposed. It imputes the missing value using the correlation among the attributes. It also works effectively for both categorical and numeric data.

+ Description
  • The classification accuracy is improved by imputing the missing value of data. The effective imputing method Model based Missing value Imputation using Correlation is proposed. It imputes the missing value using the correlation among the attributes. It also works effectively for both categorical and numeric data.

  • To handle the missing values for classification.

  • To increase the classification accuracy.

+ Aim & Objectives
  • To handle the missing values for classification.

  • To increase the classification accuracy.

  • The proposed imputation method handles the missing values during classification is contributed because it will be suitable for real-time applications.

+ Contribution
  • The proposed imputation method handles the missing values during classification is contributed because it will be suitable for real-time applications.

  • OS: Ubuntu 12.04 LTS 64bit

  • Language: Java/Python/R

+ Software Tools & Technologies
  • OS: Ubuntu 12.04 LTS 64bit

  • Language: Java/Python/R

  • M.E / M.Tech/ MS / Ph.D.- Customized according to the client requirements.

+ Project Recommended For
  • M.E / M.Tech/ MS / Ph.D.- Customized according to the client requirements.

  • No Readymade Projects-project delivery Depending on the complexity of the project and requirements.

+ Order To Delivery
  • No Readymade Projects-project delivery Depending on the complexity of the project and requirements.

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