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KNN and Decision Tree Model to Predict Values in Amount of One Pound Table

Stanley Ziweritin1 , Iduma Aka Ibiam2 , Taiwo Adisa Oyeniran3 , Godwin Epiahe Oko4

Section:Research Paper, Product Type: Journal Paper
Volume-9 , Issue-7 , Page no. 16-21, Jul-2021

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v9i7.1621

Online published on Jul 31, 2021

Copyright © Stanley Ziweritin, Iduma Aka Ibiam, Taiwo Adisa Oyeniran, Godwin Epiahe Oko . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: Stanley Ziweritin, Iduma Aka Ibiam, Taiwo Adisa Oyeniran, Godwin Epiahe Oko, “KNN and Decision Tree Model to Predict Values in Amount of One Pound Table,” International Journal of Computer Sciences and Engineering, Vol.9, Issue.7, pp.16-21, 2021.

MLA Style Citation: Stanley Ziweritin, Iduma Aka Ibiam, Taiwo Adisa Oyeniran, Godwin Epiahe Oko "KNN and Decision Tree Model to Predict Values in Amount of One Pound Table." International Journal of Computer Sciences and Engineering 9.7 (2021): 16-21.

APA Style Citation: Stanley Ziweritin, Iduma Aka Ibiam, Taiwo Adisa Oyeniran, Godwin Epiahe Oko, (2021). KNN and Decision Tree Model to Predict Values in Amount of One Pound Table. International Journal of Computer Sciences and Engineering, 9(7), 16-21.

BibTex Style Citation:
@article{Ziweritin_2021,
author = {Stanley Ziweritin, Iduma Aka Ibiam, Taiwo Adisa Oyeniran, Godwin Epiahe Oko},
title = {KNN and Decision Tree Model to Predict Values in Amount of One Pound Table},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {7 2021},
volume = {9},
Issue = {7},
month = {7},
year = {2021},
issn = {2347-2693},
pages = {16-21},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=5358},
doi = {https://doi.org/10.26438/ijcse/v9i7.1621}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v9i7.1621}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=5358
TI - KNN and Decision Tree Model to Predict Values in Amount of One Pound Table
T2 - International Journal of Computer Sciences and Engineering
AU - Stanley Ziweritin, Iduma Aka Ibiam, Taiwo Adisa Oyeniran, Godwin Epiahe Oko
PY - 2021
DA - 2021/07/31
PB - IJCSE, Indore, INDIA
SP - 16-21
IS - 7
VL - 9
SN - 2347-2693
ER -

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Abstract

Machine learning is one of the fast growing areas of interest in artificial intelligence adopted by professional in every spheres of life that uses algorithms with data to sytematically learn patterns and improve from experience. The increasing competitive and robust predicting methods of machine learning are becoming more interesting and popular. This is valuable to investors, surveyors and valuers against manually computed payment table values that depends on emperical results. There are tedious and rigorous processes in valuation practice that involves some aspects of financial analysis in computations for the one pound table values. The aim is to build K-nearest neigbr and decision tree model to predict the nemeric values in amount of one pound table at a give rate of interest and period of years.This model is useful to investors, accountants, data professionals, surveyors and valuers interested in financial analysis and its applications. A cross validation test was carried out with predicted R-squared test to detect overfitting and generalize model performance on testing dataset. We introduced noisy data with smoothing curve expeoneintial function to overcome the risk of overfitting in predicting target varaible. The K-nearest neighbor and decision tree techniques were trained, tested and resulted into 95.76% and 99.86% respectively.

Key-Words / Index Term

Artificial intelligence, Decision tree, K-nearest neighbor, Machine learning

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