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Credit card fraud detection using python machine learning
COMPANY_NAME provides data-driven business transformation services using advanced analytics and artificial intelligence. It helps businesses contextualize data, generate insights from complex problems, and make data-driven decisions. The document then discusses using machine learning for credit card fraud detection. It explains supervised learning as inferring a function from labeled training and test data to map inputs to outputs with minimal error. Screenshots are provided of exploring and preprocessing a credit card transaction dataset for outlier detection, correlation, and preparing the data for machine learning models.
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Credit card fraud detection using python machine learning
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- 2. COMPANY PROFILE ABOUT COMPANY_NAME:- COMPANY_NAME has been a trusted partner for enabling data-driven business transformation in enterprises by leveraging advanced analytics, domain expertise, and artificial intelligence-powered technology accelerators. It enables businesses to contextualize data, generate actionable insights from complex business problems, and make data-driven decisions across pan-enterprise processes to create sustainable business impact. COMPANY_NAME aims to transform enterprise to digital enterprise, reimagine their business goals in multiple dimensions with analytical decision supports. Company works 24*7 and involves with enterprise and agencies work culture for seamlessness delivery, Quality of our service is perfectly consistent and coherent.
- 3. Introduction • It isthe Credit Card Fraud Detection to most interesting careers in data analytics today. • Machine Learning can be thought of as the study of a list of sub- problems, like a: decision making, clustering, classification, forecasting, data analyzing, Supervised learning, or classification is the machine learning task of inferring a function from a labeled data. In Supervised learning, we have a training set, and a test set. • The training and test set consists of a set of examples consisting of input and output vectors, and the goal of the supervised learning algorithm is to infer a function that maps the input vector to the output vector with minimal error. In layman’s terms, supervised learning can be termed as the process of concept learning, where a brain is exposed to a set of inputs and result vectors and the brain learns the concept that relates said inputs to outputs.
- 4. Screenshot 1. All columnname of data:
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- 6. Screenshot 3. Dtype ofcolumn :
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- 8. Screenshot 5. Histogram ofcolumn v4:
- 9. Screenshot 6. Find outlierof column Time:
- 10. Screenshot 7. Find outlierof column V1:
- 11. Screenshot 8. Find outlierof column V2:
- 12. Screenshot 9. Find outlierof column V3:
- 13. Screenshot 10. Find outlierof column V4:
- 14. Screenshot 11. Count thevalues of time column: 1. 2.
- 15. Screenshot 12. Count valueof column v5 Bar graph:
- 16. Screenshot 13. Count valueof column Time Bar graph:
- 17. Screenshot 14. Replace Nullby 0 in column v4: 1. 2.
- 18. Screenshot 15. Bivariate outlierdetection of column v4 and Time:
- 19. Screenshot 16. Correlation ofmatrix of all column:
- 20. Screenshot 17. Find valuein less then 0 in column v5:
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