A Complete Machine Learning Project in Credit Card Detection with Exceeding Accuracy!

Introduction

Machine Learning is a subset of AI (artificial intelligence) and also perhaps one of the most popular concepts. It allows machines or systems to learn tasks on their own, without needing too much of human assistance. It focuses on the development of computer programs that can access data on its own and use it to train itself independently. These lessons can be learned by the system by repeated patterns of the same problem, or with instructions. Machine learning enables huge quantities of data. It is accurate and fast but also requires a lot of time and resources to be trained properly. With exciting career options, a machine learning expert can earn salaries as high as $120,000.

Machine Learning has numerous applications in today’s technological world. One of its key applications comes in detecting credit card frauds. Credit card fraud has become quite common with time. Detecting it can often prove to be a strenuous task and sometimes it can take a long time after the act is committed. But with machine learning, the process becomes faster and more accurate.

Contents of the Tutorial

The video starts with an introduction to credit card fraud detection, where the tutor details more about fraudulent credit card transactions is and the challenges associated with it. This helps the student get a general overview of what the topic is all about.

After this, the students are taken to the next phase of downloading the data-set from a well-known resource. After downloading the database, the video will guide you to take a closer look at the variables. The next step is the part which consumes most of the time for data scientists- Data Cleaning. But here, the topic is explained fairly quickly compared to other tutorials. Next comes the part of handling class imbalance or detection of fraudulent transactions. With the techniques explained in this video, this step also takes a fairly short amount of time to understand.

Decision Tree, which is the next step, might be one of the key aspects of the whole project. It is the simplest and yet the most effective models for classification and here it gives results of 89% accuracy. This is impressive because when the total cost of fraud lies in billions of USD, just an accuracy of 0.1% can save 1 million USD. Because of its interpretability, it is seen as the most important step in the entire project.

After that, the video follows into building a random forest model which increases the accuracy rate to 90.3%. The results obtained in this video tutorial has an accuracy level exceeding that of most other models. The results are globally competitive exceeding others by 3–7%.

Conclusion

With so many tutorials out there, it might be a little hard to pick the course you want to take. Having a video tutorial that explains the functions of machine learning with a live example helps in comprehending the topic a lot better. And the accuracy rate in this video gives strong testimony to the quality of the tutorial and content involved in this course.

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