Cite this DOI

N.Adityasundar, T.SaiAbhigna, & B.Lakshman (2020). Credit Card Fraud Detection Using Machine Learning Classification Algorithms over Highly Imbalanced Data. *Journal of Science & Technology*, *5*(3), 138–146. https://doi.org/10.46243/jst.2020.v5.i3.pp138-146

@article{nadityasundar2020credit,
  author    = {N.Adityasundar and T.SaiAbhigna and B.Lakshman},
  title     = {{Credit Card Fraud Detection Using Machine Learning Classification Algorithms over Highly Imbalanced Data}},
  journal   = {Journal of Science \& Technology},
  year      = {2020},
  month     = {may},
  volume    = {5},
  number    = {3},
  pages     = {138--146},
  publisher = {Longman Publishers},
  issn      = {2456-5660},
  doi       = {10.46243/jst.2020.v5.i3.pp138-146},
  url       = {https://doi.org/10.46243/jst.2020.v5.i3.pp138-146},
  language  = {en},
  abstract  = {:Most online customers use cards to pay for their purchases. As charge cards become the most mainstream strategy for installment, instances of misrepresentation relationship with it too increases. The primary goal of this venture is to be ready to perceive false exchanges from non-fake exchanges. In request to do so,primarily,data mining methods are utilized to examine the examples and attributes of deceitful and non-fake transactions.Then,machine learning systems are utilized to foresee the fake and non-fake exchanges automatically. Algorithms LR (Logistic Regression) is used. Therefore, the blend of AI and information mining procedures are utilized to distinguish the fake and non-fake exchanges by learning the examples of the information. Models are made utilizing these calculations and afterward precision,accuracy,recall are determined and an examination is made.}
}

TY  - JOUR
TI  - Credit Card Fraud Detection Using Machine Learning Classification Algorithms over Highly Imbalanced Data
AU  - N.Adityasundar
AU  - T.SaiAbhigna
AU  - B.Lakshman
JO  - Journal of Science & Technology
PY  - 2020
DA  - 2020/05/29/
VL  - 5
IS  - 3
SP  - 138
EP  - 146
PB  - Longman Publishers
SN  - 2456-5660
LA  - en
AB  - :Most online customers use cards to pay for their purchases. As charge cards become the most mainstream strategy for installment, instances of misrepresentation relationship with it too increases. The primary goal of this venture is to be ready to perceive false exchanges from non-fake exchanges. In request to do so,primarily,data mining methods are utilized to examine the examples and attributes of deceitful and non-fake transactions.Then,machine learning systems are utilized to foresee the fake and non-fake exchanges automatically. Algorithms LR (Logistic Regression) is used. Therefore, the blend of AI and information mining procedures are utilized to distinguish the fake and non-fake exchanges by learning the examples of the information. Models are made utilizing these calculations and afterward precision,accuracy,recall are determined and an examination is made.
DO  - 10.46243/jst.2020.v5.i3.pp138-146
UR  - https://doi.org/10.46243/jst.2020.v5.i3.pp138-146
ER  -

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    "id": "10.46243/jst.2020.v5.i3.pp138-146",
    "DOI": "10.46243/jst.2020.v5.i3.pp138-146",
    "URL": "https://doi.org/10.46243/jst.2020.v5.i3.pp138-146",
    "title": "Credit Card Fraud Detection Using Machine Learning Classification Algorithms over Highly Imbalanced Data",
    "source": "Smart Scholars DOI Registry",
    "container-title": "Journal of Science & Technology",
    "author": [
        {
            "family": "N.Adityasundar"
        },
        {
            "family": "T.SaiAbhigna"
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        {
            "family": "B.Lakshman"
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        "date-parts": [
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                2020,
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    "volume": "5",
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    "page": "138-146",
    "publisher": "Longman Publishers",
    "language": "en",
    "abstract": ":Most online customers use cards to pay for their purchases. As charge cards become the most mainstream strategy for installment, instances of misrepresentation relationship with it too increases. The primary goal of this venture is to be ready to perceive false exchanges from non-fake exchanges. In request to do so,primarily,data mining methods are utilized to examine the examples and attributes of deceitful and non-fake transactions.Then,machine learning systems are utilized to foresee the fake and non-fake exchanges automatically. Algorithms LR (Logistic Regression) is used. Therefore, the blend of AI and information mining procedures are utilized to distinguish the fake and non-fake exchanges by learning the examples of the information. Models are made utilizing these calculations and afterward precision,accuracy,recall are determined and an examination is made.",
    "ISSN": "2456-5660"
}

From the record as registered (version 2) — the record and its history. Programs: https://registry.smartscholars.in/api.php?action=cite&doi=10.46243%2Fjst.2020.v5.i3.pp138-146 gives all four in one JSON answer.