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I have extensive experience in data management and data processing. Over the past few years I saw the data management technology transition into the Big Data ecosystem and I needed to follow suit. I... Read More
My Interaction was very short but left a positive impression. I enrolled and asked for a refund since I could not find the time. What happened next: They initiated Refund immediately. Their... Read More
The Credit Card Fraud detection Dataset contains transactions made by credit cards in September 2013 by European cardholders. This dataset present transactions that occurred in two days, where we have 492 frauds out of 284,807 transactions. The dataset is highly unbalanced, the positive class (frauds) account for 0.172% of all transactions.
The dataset has been collected and analyzed during a research collaboration of Worldline and the Machine Learning Group (http://mlg.ulb.ac.be) of ULB (Université Libre de Bruxelles) on big data mining and fraud detection. More details on current and past projects on related topics are available on http://mlg.ulb.ac.be/BruFence and http://mlg.ulb.ac.be/ARTML
In this data science project, we will look at few examples where we can apply various time series forecasting techniques.
Machine Learning Project - Work with KKBOX's Music Recommendation System dataset to build the best music recommendation engine.
The goal of this machine learning project is to predict which products existing customers will use next month based on their past behaviour and that of similar customers.