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Uplift Modeling

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 & 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

  • in business
  • 27 min read

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.

  • in finance
  • 15 min read

Prediction of customer stable funds volume

Твоей сегодняшней задачей как стажера нашего отдела будет научиться прогнозировать объем стабильных средств клиентов без сроков погашения, в данном конкретном случае это расчетные счета клиентов.

Почему это важно? Номинально, все средства на расчетных счетах клиенты могут в любой момент забрать из Банка, а в ожидании этого Банк не может их использовать в долгосрочном / среднесрочном плане (например, для выдачи кредитов). Получается, что в такой ситуации Банк ничего не зарабатывает, но платит клиентам проценты по средствам на их счетах, пусть и не высокие, но в масштабах бизнеса Банка эти убытки могут быть значительны.

  • in PySpark
  • 15 min read

Utilising Prophet with PySpark

In this notebook, we look at how to use a popular machine learning library prophet with the pyspark architecture. pyspark itself unfortunatelly does not contain such an additive regression model, however we can utilise user defined functions, UDF, which allows us to utilise different functionality of different libraries that is not available in pyspark

  • in EDA
  • 8 min read

Comparison of Subsets

An important concept in machine learning is model generalisation & performance deterioration. When we train a model, we perform an optimisation step, using metrics and/or loss values we can understand how well our model is understanding the relation between all data points and features in the input data we feed it. Going through this process, we can tune a model so that it performs well on the data that we use to train it.