Preprocessing of Low Response Data for Predictive Modeling

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Original Article

Author :

Farzana Naz | Imaad Shafi | Md Kamre Alam

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Abstract :

For training a model, the raw data have to go through various preprocessing phases like Cleaning, Missing Values Imputation, Dimension Variable reduction, and Sampling. These steps are data and problem specific and affect the accuracy of the model at a very large extent. For the current scenario, we have 2.2M records with 511 variables. This data was used in a Direct Mail Campaign of some Life Insurance Products and now we know which record had a positive response for the campaign. Rows records 2,259,747 Columns 511 Rows with positive response 2,739, i.e. Response Rate 0.1212 . The dataset is not complete, i.e. we have to take care of missing values. Farzana Naz | Imaad Shafi | Md Kamre Alam "Preprocessing of Low Response Data for Predictive Modeling" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: Paper URL:

Keyword :

LogisticRegression, Datasets, Principalcomponentanalysis, VariableReduction
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