Preprocessing of Low Response Data for Predictive Modeling

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. by 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: https://www.ijtsrd.com/papers/ijtsrd21667.pdf Paper URL: https://www.ijtsrd.com/engineering/computer-engineering/21667/preprocessing-of-low-response-data-for-predictive-modeling/farzana-naz

Authors and Affiliations

Keywords

Related Articles

Modeling and Simulation of Unified Series Shunt Compensator for Power Quality Improvement

Power Quality in the distribution system is the important issue for industrial, commercial and residential applications. An increasing demond for high quality, reliable electrical power and an increasing number of distor...

Role of Learning Process in Capability Development and Business Performance of East Java Manufacturing Firms: Resources as Moderator

The purpose of this research is to analyze how the influence of internal learning of employees, external learning obtained from suppliers and consumers, and ownership of processes and equipment, on the performance of the...

Performance Enhancement of DC Load and Batteries in Photovoltaic System

"To avoid the pollution and to save the non conventional resources, use of renewable energy sources such as wind energy, bio gas, hydro and solar potential has increased and become essential to adopt a low cost generatin...

Comprehensive Evaluation of Economic Development Level of Beijing, Tianjin and Hebei Cities Based on TOPSIS Method

The balance of regional economic development is very important for coordinated development. As the Capital Economic Circle of China, Beijing Tianjin Hebei region plays an important role in the national economic developme...

Job Satisfaction of Employees in Bharat Heavy Electricals Limited (BHEL), Tiruchirappalli

The Bharat Heavy Electricals Limited is one of the major industries for country's economic development. The main aim of this study is to assess the factors which are responsible for employee's job satisfaction. This pape...

Download PDF file
  • EP ID EP584949
  • DOI 10.31142/ijtsrd21667
  • Views 77
  • Downloads 0

How To Cite

(2019). Preprocessing of Low Response Data for Predictive Modeling. International Journal of Trend in Scientific Research and Development, 3(3), 157-160. https://europub.co.uk/articles/-A-584949