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Photo by Stephen Leonardi on Unsplash

At Fave, we in the data science team actively research and innovate new ways to contribute to helping more SMEs become digital. The transformed data team went through a courageous process to expand its impact from business intelligence and reporting to building data products. Personalisation stands as one of the essential data products in Fave today, spreading its integration into our consumers, merchants and the Fave app. This motivated us to share how we built a robust recommendations engine in-house, given the unique characteristics of Fave’s offerings and products.

Fave’s most known products are FavePay, a mobile payment method designed to allow users to transact with offline merchants easily, and Fave Deals. The hyper-growth of Fave as a platform and the ubiquity of FavePay in Malaysia and Singapore today brings with it new challenges. In the early days, products (deals/discounts) were listed via hand-crafted localisation logic that brings the highest selling products to the top of the list for each user in a specific neighbourhood (around the user location). It worked but it couldn’t scale as Fave’s inventory covered more and more categories. …

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Cute panda from pixabay

I have been using a high-performance laptop for some time and EC2 machine for when I needed high computing power. I never encountered issues of performance for as long as I remember with both tools giving me quite a comfortable processing power. Things started to seem awfully different when I downgraded my MacBook and cannot enjoy the privileges of high computing powers on my disposal.

This has forced me to dig down and learn more about Pandas library. So if you are trying to learn about tips to handle your multiple gigabytes files in Pandas this gonna help you. To my surprise batch processing wasn’t the easiest way around handling your file, well in most of the times you are looking into a way to reduce the files size without getting into batch processing. …

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image from the public domain

Among the most frequent questions I get asked about is what is next after We have prepared the data and trained a model and went further to test the performance? Usually in academia we stop at this point and it’s time to put together a research paper that will hopefully get accepted with less brutal comments from reviewers. Although lately more of the academic work have been made available in which the community benefited from this and brought more enthusiasts to ticker around with these model. In the other hand there are more software engineers who get stuck and often expressed the radical candor and call machine learning useless. …



Fares Hasan enjoys working with start-ups and developing Machine Learning & Data Science products.

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