The importance of Machine Learning and Data Science cannot be overstated. If you are interested in studying past trends and training machines to learn with time how to define scenarios, identify and label events, or predict a value in the present or future, data science is of the essence. It is essential to study the underlying data and model it by selecting an appropriate algorithm to approach any such use case. The various control parameters of the algorithm need to be tweaked to fit the data set. As a result, the developed application improves and becomes more efficient in solving the problem.
In this blog, we have attempted to illustrate the modeling of a data set using a machine learning paradigm classification, with Credit Card Fraud Detection being the base. Classification is a machine learning paradigm that involves deriving a function that will separate data into categories, or classes, characterized by a training set of data containing observations (instances) whose category membership is known. This function is then used in identifying in which of the categories a new observation belongs.
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Problem Statement:
The Credit Card Fraud Detection Problem includes modeling past credit card transactions with the knowledge of the ones that turned out to be fraud. This model is then used to identify whether a new transaction is fraudulent or not. Our aim here is to detect 100% of the fraudulent transactions while minimizing the incorrect fraud classifications.
Data Set Analysis:
This problem has been picked from Kaggle.
Observations
Inferences drawn:
Theory:
Credit Card Fraud Detection is a typical example of classification. In this process, we have focused more on analyzing the feature modeling and possible business use cases of the algorithm’s output than on the algorithm itself. We used the implementation of Binomial Logistic Regression Algorithm in the ‘ROCR’ package on the PCA transformed Credit Card Fraud data.
Some Definitions:
The following are essential definitions – in the current problem’s context – needed to understand the approaches mentioned later:
Incorrect Measures of Efficiency of a Data Model:
Let’s look at the various measures of efficiency that fail at analyzing the correctness of the underlying data model.
This first part of three-part blog series provides an insight into the analysis of the data and the pitfalls in handling a skewed data set. In the subsequent posts, we shall try to fit a model to this data set, analyze the results and look into the various measures of efficiency that can be resorted to as the metric for defining the utility (correctness) of the modeling. So, stay tuned for our next blog for continued work on Credit Card Fraud Detection.
Where can I find the blog articles in continuation to this topic?
Can you please help me to access the rest of this blog for credit card fraud detection?
Thanks much in advance
Regards
Srini
Work in fraud detection
Where can I find the blog articles in continuation to this topic?
good
Thanks
Where can i find next part?
where can i find the remaining part of CREDIT CARD FRAUD DETECTION
Excellent explanation. Need help to go through the rest of the blog please.
Need articles on credit cards fraud detectiom
Interesting read
Nice first entry
Where can I find the blog articles in continuation to this topic?
Nice Article and would like to know about other parts, I am a data engineer by profession
Very nice explanation. Can I get access to continuation of the article “credit-card-fraud-detection”..
It’s very comprehensive and insightful.
🙂
I would like to understand the data pre processing steps in the data and the feature selection
Would like to.get a deep understanding in the data preprocessing and feature selection of the data
please guide me to other parts
Would like to understand the data analysis that happens in creditcard data