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.
Logistic Regression, Datasets, Principal component analysis, Variable Reduction
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