Proceedings of the 2nd International Conference on ICT for Digital, Smart, and Sustainable Development, ICIDSSD 2020, 27-28 February 2020, Jamia Hamdard, New Delhi, India

Research Article

Predictive Modeling and Analysis of Logistic Regression and k-Nearest Neighbor for Personal Loan Campaign

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  • @INPROCEEDINGS{10.4108/eai.27-2-2020.2303232,
        author={Bhavya  Alankar and Iftikhar  Alam},
        title={Predictive Modeling and Analysis of Logistic Regression and k-Nearest Neighbor for Personal Loan Campaign},
        proceedings={Proceedings of the 2nd International Conference on ICT for Digital, Smart, and Sustainable Development, ICIDSSD 2020, 27-28 February 2020, Jamia Hamdard, New Delhi, India},
        publisher={EAI},
        proceedings_a={ICIDSSD},
        year={2021},
        month={3},
        keywords={personal loan management data science classification machine learning techniques logistic regression k-nearest neighbor},
        doi={10.4108/eai.27-2-2020.2303232}
    }
    
  • Bhavya Alankar
    Iftikhar Alam
    Year: 2021
    Predictive Modeling and Analysis of Logistic Regression and k-Nearest Neighbor for Personal Loan Campaign
    ICIDSSD
    EAI
    DOI: 10.4108/eai.27-2-2020.2303232
Bhavya Alankar1,*, Iftikhar Alam1
  • 1: School of Engineering Science & Technology, Jamia Hamdard, New Delhi
*Contact email: bhavya.alankar@gmail.com

Abstract

Science is the knowledge which a person understand and that can be taught to a computer. Extensive research has been made to develop appropriate machine learning algorithms for different classification or function approximation problems. Some of the machine learning methods depends on the characteristics of the data set and the requirements of the business domain. This case study provides the predictive performance of different classification methods for identifying the potential customers who have a higher probability of purchasing the loan. For this we will build two statistical model, a logistic regression model and a k-nearest neighbor model. The model is a way of showing the relationships between various factors in the real world and in the data set or raw data. It is also important to select the technique which performs best on the data set. This study works on the following two techniques to build the model and provides a guideline for similar comparison studies and to find the best one out.