![]() In the next step, we will read the data from our directory where the data is saved, and then we look at the first and last five rows of the data using head(), and tail() methods: #read the dataset ![]() The Python module pandas provide us with the functions to read data. Now we read the data and try to understand each feature's meaning. Let's import the necessary libraries: # Importing modules Let's install the requirements: $ pip install sklearn=0.24.2 imbalanced-learn numpy pandas matplotlib seaborn Once the dataset is downloaded, put it in the current working directory. I've also uploaded the dataset to Google Drive that you can access here. You will need to create a Kaggle account to download the dataset. The dataset is unbalanced, with the positive class (frauds) accounting for 0.172 percent of all transactions. This dataset contains 492 frauds out of 284,807 transactions over two days. The dataset utilized covers credit card transactions done by European cardholders in September 2013. ![]() ![]() We will be using the Credit Card Fraud Detection Dataset from Kaggle. Appendix: Outlier Detection and Removal.We always tried to provide a brief theoretical background regarding the methodologies used in this tutorial. However, the reader is expected to have prior experience with Python. For each code chunk, an appropriate description is given. This tutorial is entirely written in Python 3 version. Data Science can address such a challenge, and its significance, coupled with Machine Learning, cannot be emphasized. Credit card firms must detect fraudulent credit card transactions to prevent consumers from being charged for products they did not buy.
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