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.

1. CREDIT CARD
FRAUD
DETECTION
USING
PYTHON (ML)
SANDEEP

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 is the 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 column name of data:

5. Screenshot
2. First 10 raw:

6. Screenshot
3. Dtype of column :

7. Screenshot
4. Statistics of Data:

8. Screenshot
5. Histogram of column v4:

9. Screenshot
6. Find outlier of column Time:

10. Screenshot
7. Find outlier of column V1:

11. Screenshot
8. Find outlier of column V2:

12. Screenshot
9. Find outlier of column V3:

13. Screenshot
10. Find outlier of column V4:

14. Screenshot
11. Count the values of time column:
1. 2.

15. Screenshot
12. Count value of column v5 Bar graph:

16. Screenshot
13. Count value of column Time Bar graph:

17. Screenshot
14. Replace Null by 0 in column v4: 1. 2.

18. Screenshot
15. Bivariate outlier detection of column v4 and Time:

19. Screenshot
16. Correlation of matrix of all column:

20. Screenshot
17. Find value in less then 0 in column v5:

21. Thank You