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Required Machine Learning freelancer for Credit Card Fraud Detection - Case Study School Project job

Posted at - Mar 11, 2021

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I have sample transaction records that are manually classified as either 'fraudulent' or 'non-fraudulent'.

I want to use these records to train a supervised machine learning algorithm.

After building a model using this training data, the algorithm should be able to classify new records as either fraudulent or non-fraudulent.

Please let me know which of the following algorithms you can leverage to accomplish said task and what software you'll use.

Linear Regression
Logistic Regression
Decision Tree
SVM
Naive Bayes
kNN
K-Means
Random Forest
Dimensionality Reduction Algorithms
Gradient Boosting algorithms

Example Records:
Name,Type,Amount,Where Issued,Where Used,Age,IsFradulent
John,Visa,$2000,USA,RUS,29,Yes
Smith,MasterCard,$2000,USA,FRA,45,No
etc
etc

Thank you!

About the recuiterMember since Aug 29, 2017 Charles
from Canterbury, New Zealand

Skills & Expertise Required

Machine Learning 

Candidate shortlisted and hiredHiring open till - Mar 11, 2022

Work from Anywhere
40 hrs / week
Hourly Type
Remote Job
$12.53
Cost

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