• The Intuitions Behind Bayesian Optimization with Gaussian Processes

    by Charles Brecque on KDnuggets
    In certain applications the objective function is expensive or difficult to evaluate. In these situations, a general approach consists in creating a simpler surrogate model of the objective function which is cheaper to evaluate and will be used instead to solve the optimization problem. Moreover, due to the high cost of evaluating the objective function, an iterative approach is often recommended. Iterative optimizers work by iteratively requesting […]
  • Optimize your Email Marketing strategy with automated Machine Learning
    by Charles Brecque on 10/12/2018 at 21:58

    (source)Optimizing Marketing spend for Sales is a billion dollar question for businesses across the world as over $1 trillion annually has been dedicated to the task since 2017. However, understanding the relationship between marketing budgets, actions and associated sales is challenging due to the many simultaneous and competing internal and external factors which influence sales (psychology, weather, mood, location, price, ads …).Machine Learning techniques have shown promising […]

  • Predicting international visitor spend in London
    by Charles Brecque on 10/12/2018 at 21:56

    (source)Last year, 39.2 million people from abroad visited London, making it one of the most popular destinations in the world. The UK Office for National Statistics have released quarterly international visitor data from 2002 to 2018 under the Open Government License which provides information including their countries of origin, duration and purpose of stay as well as their spend. In this short article we are going to use AuDaS to visualise the data and build a model which can predict the […]

  • Credit Card Clustering
    by Charles Brecque on 10/12/2018 at 21:55

    (source)Automated Clustering with AuDaSThe number one challenge faced by marketers is to understand who they are selling to. When you know your buyers’ personas you can tailor your targeting and offerings to increase their satisfaction and your revenue as a result. When you already have a pool of customers and enough data on them, it can be very useful to segment them. In this article, we are going to see how we can use clustering to segment some credit card customers. The data for […]

  • How to ace cold calling with Machine Learning
    by Charles Brecque on 10/12/2018 at 21:54

    (source)To Call or not to Call?Cold Caller: “Hello Luke, how are you doing today? I noticed you were up for car insurance renewal and I was wondering whether you would have a couple of minutes to hear about this amazing offer I can give you to reward you for your loyalty?”What would you do if you were Luke?’s actually very hard to tell, which is why cold calls have an extremely low success rate and […]

  • Solving the Kaggle Telco Customer Churn challenge in minutes with AuDaS
    by Charles Brecque on 10/12/2018 at 15:56

    Solving the Kaggle Telco Customer Churn challenge in minutes using AuDaSAuDaS is the automated Data Scientist developed by Mind Foundry which aims to allow anyone, with or without a background in Data Science to easily build and deploy quality controlled Machine Learning pipelines. AuDaS empowers Business Analysts and Data Scientists by allowing them to easily insert their domain expertise in the model building process and extract actionable insights.In this tutorial we are going to see […]

  • Detecting Data Leakage before it’s too late
    by Charles Brecque on 23/11/2018 at 15:14

    sourceSometimes it’s too good to be true.After reading Susan Li’s Expedia Case study article, I wanted to see if I could reproduce the results using AuDaS, Mind Foundry’s automated Machine Learning platform. The data is available on Kaggle and contains customer web analytics information for hotel bookings (true and false). The goal of this competition is to predict whether a customer will make a reservation or not. However, after cleaning the data and building my model […]

  • Warm Starting Bayesian Optimization
    by Charles Brecque on 15/10/2018 at 13:31

    (source)Hyper-parameter tuning is required whenever a Machine Learning model is trained on a new data-set. Nevertheless, it is often foregone as it lacks a theoretical framework which I have previously tried to demystify here:Demystifying Hyper-Parameter tuningOne approach which systematises intelligent and efficient hyper-parameter tuning is Bayesian Optimization which builds a probabilistic surrogate of the tunable problem to recommend optimal parameters. It gradually builds up its […]

  • Data Wrangling with Data Report (Part 1/3)
    by Charles Brecque on 12/10/2018 at 13:09

    A useful package for Wrangling large data setsThe story behind Data ReportBig Data does not always equate to quality data but its sheer size and the lack of appropriate tools often prevents us from making that judgement. At Mind Foundry, we built Data Report to efficiently profile large data sets and trim them down through cardinality and correlation analysis. There are many tools out there such as Seaborn and pandas profiling, but we have made Data Report easier to use whilst […]