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  • in Recsys
  • 14 min read

Neural Collaborative Filtering

In this post well cover some of the basics of recommendation system approaches utilising neural networks. Collaborative filtering (CF) is a recommendation generation method that relies on user-item interactions to make predictions about what a user might like based on the historical data of user interaction with the item. We covered collaborative filtration using matrix factorisation is the notebook. Specifically we looked at methods such as SVD in order to generate the user and item matrices, these two matrices are then multiplied together to get the corresponding scores for each user, item pairing; a model-based approach. Neural Collaborative Filtering (NCF) bears some similarity to CF whilst leveraging the benefits of deep learning techniques to enhance recommendation performance.

Commonwealth Bank Internship

In today's we'll be going through one of internships provided by one of the leading banks in Australia; Commonwealth Bank. The internship is related to Data Science and is aimed to be a small project and interesting project in one's portfolio. The internship covers topics such a data wrangling (rearrangement and exploration of data), data annonymisation, as well as work with unstructured text data. Hopefully it will be interesting to read along!

Uplift Modeling Basics

Uplift modeling is a predictive modeling technique that aims to identify the individuals who are most likely to respond positively to a specific treatment or intervention. This technique is particularly useful in marketing and customer relationship management, where the goal is to target customers who are likely to be influenced by a marketing campaign or offer. By distinguishing between those who are positively influenced by the treatment and those who are not, uplift modeling helps organizations optimize their targeting strategies and maximize the return on investment of their marketing efforts.

  • in sql
  • 14 min read

SQL Interview Problem

An interview question related to SQL knowledge from a financial bank which I thought was interesting so decided to share. I was given a day to do the problems, which would be more than enough time to delve into into it. I really enjoyed going through the the interview process with this company, hopefully this will be helpful to someone.

  • in pyspark
  • 6 min read

PySpark Daily December Summary II

Continuing on where we left off last post, I'll be exploring pypspark on a daily basis, just to get more used to it. Here I will be posting summaries that cover roughtly 10 days worth of posts that I make on Kaggle, so that would equate to three posts a month

  • in pyspark
  • 11 min read

PySpark Daily December Summary I

Something I decided would be fun to do on a daily basis; write pyspark code everyday and post about it, this is mainly because I don't use it as often as I would like, so this is my motivation. If you too want to join in, just fork the notebook (on Kaggle) and practice various pyspark codings everyday! Visit my telegram channel if you have any questions or just post them here!

Here I will be posting summaries that cover roughtly 10 days worth of posts that I make on Kaggle, so that would equate to three posts a month

Coding Linear Regression

Посмотрим на некий обзор главных моментов которые дадут нам возможность реализовать линейные модели в python и numpy. Посмотрим как отличается линейная регрессия от логистической, и как можно добавлять регуляризацию для этих моделей, чтобы можно было контролировать обобщающию способность модели. В этом разделе фокус на линейной регрессии.

Coding Logistic Regression

Посмотрим на некий обзор главных моментов которые дадут нам возможность реализовать линейные модели в python и numpy. Посмотрим как отличается линейная регрессия от логистической, и как можно добавлять регуляризацию для этих моделей, чтобы можно было контролировать обобщающию способность модели. В этом разделе фокус на логистической регрессии.

Prediction of Product Stock Levels

In this project, we work with a client Gala Groceries, who has contacted Cognizant for logistics advice about product storage. Specifically, they are interested in wanting to know how better stock the items that they sell. Our role is to take on this project as a data scientist and understand what the client actually needs. This will result in the formulation/confirmation of a new project statement, in which we will be focusing on predicting stock levels of products. Such a model would enable the client to estimate their product stock levels at a given time & make subsequent business decisions in a more effective manner reducing understocking and overstocking losses.