The credit card has become the most popular payment method for both online and offline transactions. The necessity to create a fraud detection algorithm to precisely identify and stop fraudulent activity arises as a result of both the development of technology and the rise in fraud cases. This paper implements the random forest (RF) algorithm to solve the issue in the hand. A dataset of credit card transactions was used in this study. The main problem when dealing with credit card fraud detection is the imbalanced dataset in which most of the transaction are non-fraud ones. To overcome the problem of the imbalanced dataset, the synthetic minority over-sampling technique (SMOTE) was used. Implementing the hyperparameters technique to enhance the performance of the random forest classifier. The results showed that the RF classifier gained an accuracy of 98% and about 98% of F1-score value, which is promising. We also believe that our model is relatively easy to apply and can overcome the issue of imbalanced data for fraud detection applications.

Data scientists are critical to the success of many fintech firms, but the area is still relatively young and unproven. As technology advances, data scientists must stay up with new breakthroughs and adapt their skill sets accordingly. Real-time analytics, consumer analytics, algorithmic trading, Robo-advisors, financial planning, and other applications of data science in the FinTech industry are useful. Some tech-savvy entrepreneurs are attempting to establish wholly new information services firms of data captured, managed, and analyzed utilizing FinTech data science. Several variables are driving the "Big Data" (or "information services") opportunity in FinTech, but I'll focus on just two.

The post Best ML Project with Dataset and Source Code appeared first on finnstats. If you are interested to learn more about data science, you can find more articles here finnstats. Best ML Project with Dataset and Source Code, Understanding how machine learning algorithms are applied in practice in business requires an understanding of machine learning projects. These machine learning projects for students will also... If you are interested to learn more about data science, you can find more articles here finnstats. The post Best ML Project with Dataset and Source Code appeared first on finnstats.

We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. Birds, fish, insects and other creatures have long been known to swarm or "murmur," -- which means on occasion they will move and coordinate as a group, rather than as directed by a centralized leader. This age-old practice throughout the nature of protection-by-confusion has given rise to the emerging concept of "swarm learning" – what some are touting as the next era in AI innovation. When applied to intelligent devices operating in the real world, "swarm learning" refers to decentralization. This means that users can share learnings at the edge, or at distributed sites, without moving or exposing data.

With countries gradually opening up in baby steps and with a few more weeks to be in the "quarantine", take this time in isolation to learn new skills, read books, and improve yourself. While the intellectuals keep saying "it's not a race to be productive", for those interested in data analytics, data science or anything related to data, I thought let's make a list of top 9 data science projects to do during your spare time, in no particular order! The number of credit card owners is projected close to 1.2 billion by 2022. To ensure security of credit card transactions, it is essential to monitor fradualent activities. Credit card companies shall be able to recognize fraudulent credit card transactions so that customers are not charged for items that they did not purchase.

This article is about decoding some of the popular jargon used in data science. It is important to understand these concepts better. They are commonly asked in data science job interviews. Let's get into the topics. A dependent variable (target variable) is driven by the independent variables in the study.

Machine learning is a type of Artificial Intelligence that extracts patterns out of raw data by using an algorithm or method. The main focus of ML is to allow computer systems to learn from experience without explicitly programmed or human intervention. Human beings at this moment, are the most intelligent and advanced species on earth because they can think, evaluate and solve complex problems. On the other side, Artificial intelligence is in its initial stage and hasn't surpassed human intelligence. Due to growing volumes and varieties of available data, computational processing that is cheaper and more powerful, and affordable data storage, Machine Learning is essential for; *Producing models that can analyze bigger, more complex data and deliver faster and more accurate results. Scenarios where there is a lack of human expertise such as navigation in unknown territories or spatial planets need machine learning.

With the COVID-19 pandemic, a new digital normal has emerged from the disruption to regular routines and behaviors. Individuals and businesses have been driven by unforeseen circumstances to expedite digital transformation and adopt innovative approaches in response to a volatile and uncertain situation. As consumers, our shopping patterns have evolved with the boom in the e-commerce sector, thus making credit card payments for online grocery shopping and food deliveries, the new convenience. There has been a proliferation of real-time payments fraud, as new near-instant payment platforms, including person-to-person (P2P) transfers and mobile payment platforms grow across Asia Pacific. We recently conducted a survey with banks in the region and found that 4 out of 5 (78 percent) have seen their fraud losses increase. Further to this, almost a quarter (22 percent) say that fraud will rise significantly in the next 12 months, with an additional 58 percent saying they expect a moderate rise in fraud.

When observation in one class is higher than the observation in other classes then there exists a class imbalance. Example: To detect fraudulent credit card transactions. As you can see in the below graph fraudulent transaction is around 400 when compared with non-fraudulent transaction around 90000. Class Imbalance is a common problem in machine learning, especially in classification problems. Imbalance data can hamper our model accuracy big time.

In this Q&A on Explainable AI, Andrea Brennen speaks with In-Q-Tel's Peter Bronez about descriptive vs. prescriptive models, "white box" vs. "black box" explanation techniques, and why some models are easier to explain than others. Peter also discusses the reproducibility crisis in Psychology and why good experiment design is so important. Peter is a VP on the technical staff at IQT. Could you tell me about your experience with machine learning and AI? PETER: As an undergraduate, I studied econometrics and operations research, so my exposure to machine learning was in the context of designing models of the world that you could test mathematically -- basically, doing hypothesis testing using statistics. Afterwards, I worked at the Department of Defense and used a lot of the same techniques. From there, I went to the private sector and [worked on] social media and data mining in marketing applications, trying to create mathematical models to categorize people, activities, and messages in order to understand them better.