In today’s data-driven business landscape, effective Master Data Management is key. The amount of data enterprises generate and collect is growing at unprecedented speed. Everyone agrees that data can deliver the edge a business needs to be successful. However, the requirement to be data-driven has pushed many businesses to frantically acquire as much data as …
Research by renowned analysts is also clear – as many as 43% of web shop visitors first go to “search”, and as many as 39% of all online customers admit that “search” significantly influenced their purchase. Even if your website is organized perfectly, customers will still get confused and some simply prefer search mechanics. Yet, …
When we started building our product, we focused on the Recommender engine and Segmentation. As a startup, we embraced agility and focus on the market. At the beginning of 2020, we decided that our product must become a reflection of our team. Then we said it must be a “no bullshit” product, agile or, in …
In this text we will try to briefly explain IM applications integration with Visual Search. You can read more about Visual Search implementation in our last blog post. Instant Messaging (IM) and Chatbots Among all other benefits, The Internet has transformed and simplified how people communicate with each other. In addition to email, IM has …
Bonuses and monetary incentives are touted as a cure-all solution for instilling a culture in a company. Fortunately, it will take a touch more than financial incentives to get the culture right. Giving monetary incentives can lead to poor results; look no further than what happened to the Brits in colonial India. The British governor …
Segmentation is a widely known term, especially within the marketing sector. It is an insightful, effective and actionable approach to better understanding a customer or client base. What is so attractive about leveraging AI specifically within the banking sector is the amount and variety of data. Segmentation per se is not the final outcome of …
According to Gartner, only 15% to 20% of data science projects get completed. Of those projects that did complete, CEOs say that only about 8% of them generate value. Despite these facts, data science is still considered as an opportunity for business growth. These facts are something that always lingers in the back of everyone’s …
Intro and motivation In the beginning, it started as the three separate projects. The first one for managing machine learning models known as MoMa, the second one for forecasting using multiple models to give the best results (Fibi), and the third one was a business solution for segmentation and recommender system with personalized view and …
Time series is a term that you must or would have faced in your Data Science career. If you are completely new to this, don’t worry, it is really intuitive. What is actually a Time Series? It is a collection of data points collected at constant time intervals. So, we have a history data about …
In the field of software development, different teams have different approaches to how things are being done. When the team is small, introducing processes may be overwhelming, and the biggest challenge is to determine when is the right time to define one. From the early days at Things Solver, we didn’t quite think about defining …
I didn’t want to start this post by quoting the percentage of data science projects failing to deliver commercial value or business utilization. Many data science projects fail – and that’s a fact. What’s important is to make sure that you are not spending hundreds of Ks of euros/dollars? and other resources and fail. Being …
Lately we’ve been hearing different kinds of buzzwords all the time – ‘deep learning’, ‘neural networks’, ‘computer vision’, ‘artificial intelligence’… But the explanation of each one of them is actually not so simple. Still, it’s not hard to understand the main concepts of them in order to use it in some real-world applications. One of …
This confirms Things Solver’s strategy to continuously enhance its solutions and offer the most advanced products and services to customers Joining forces with the established IT partner ASEE, together we will accelerate the development of existing and new products based on advanced analytics and data processing. We are happy to embark on this new journey, …
After watching Udemy online course Building Recommender Systems with Machine Learning and AI, I came up with the idea to write a text that can help beginners to understand the basic ideas of the recommender systems. A recommender system, or a recommendation system is a subclass of information filtering system that seeks to predict the …
Main challenges Although EDA is often observed as an initial step which should be straightforward, there are some challenges that could slow down and make this process poor and painful. Some of the challenges I have encountered so far are listed below. Poorly defined business problem (and not having the understanding of it). Not having …
Marketing is one of the most fertile fields for Data Science. There are many different techniques and directions which marketing teams are striving to realize, but the two most frequently discussed are – up sell and cross sell. For those of you who are less familiar with the terms, “up sell” stands for the action …
We live in the era of information technology, when we generate more data in 10 minutes than the amount of total data generated from prehistoric times by the year of 2003. Having the processing power and memory resources to handle all this data being generated from different actors within different processes – organizations strive to …
After participating in a meetup at the end of March, subjected “Data Science – what is it?”, a lot of people contacted me to send them some introductory materials to help them get started with learning. It took me a long time to sit down and start compiling a list, because there are many sources, …
Let’s say you have been working on a project for clients segmentation. You have your client segments well separated and your final task is to present findings and results to the project stakeholders. Usual situation is that none of them have that level of technical expertise to understand your code so you need to visualize …
Retail industry. In the glory of Data Science, it’s all about the data and tailor-made targeting. If you want to brag about it, you would say – I’ve got the unique, omni-channel, 360-something, that can perfectly model customer’s behaviour and even go to Mars. What really happens is that you literally feel lost. There are …
Pedro Domingos is a professor of computer science and engineering at the University of Washington. He is a winner of the SIGKDD Innovation Award, which FYI is the highest honor in Data Science. They say that approximately seven years is the time period needed to become an expert in the field of Data Science. This …
Having spent couple of weeks on data preparation and developing that particular machine learning model, you are finally ready to show off with some really good results to your boss. You have your notebooks with lines of code doing magic, maybe some reports in Excel, amazing visualizations in Plotly etc. It’s 5 minutes till your presentation …
When dealing with anomalies in the data, there are lots of challenges that should be solved. We’ve already had a couple of articles regarding this topic, and if you haven’t already read it, I suggest you to take a look at this one, aaand this one, from my fellow colleague Miloš. In this post, I’m …
The problem of anomaly detection is a very challenging problem often faced in data analysis. Whether it is about clustering, classification or some other machine learning problem, it is of great importance to identify anomalies and handle them in some way, in order to achieve optimal model performances. Furthermore, anomalies could often influence the analysis …
Hi, everyone. Although I planned for my next post to be about anomaly detection and their treatment, I faced some other type of problem that quickly escalated into huge issue affecting the modelling and results accuracy, and couldn’t resist to share my experience as soon as possible. In this post, I will be talking about …
In my previous post, I tried to present the ARIMA model for forecasting. It was based on the use of autoregression and moving average concepts, combining the regression of variable based on its lagged values and calculation of error based on the linear combination of error terms occurred in the past, respectively. In this post, …
One of the most challenging machine learning problems is predicting some output based on the history of its previous values. The complexity of the problem multiplies as new features and constraints are added to analysis. Thus, in time series analysis it is not always enough to use previous values only, there often are many features …
