Our strategy for building products

Posted on

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 …

Read More

How to combine Visual Search and IM

Posted on

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 …

Read More

A closer view on Data Science Delivery

Posted on

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 …

Read More

Be one step ahead: Solver AI Suite short overview

Posted on

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 …

Read More

AI for eCommerce: How to use Visual Search

Posted on

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 …

Read More

Things Solver has joined forces with Asseco SEE Group

Posted on

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, …

Read More

Introduction to recommender systems

Posted on

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 …

Read More

How to model better recommendations in Covid time?

Posted on

Needs in the IT sector are constantly changing. After Coronavirus hit us unexpectedly, this is true, more than ever. In order to keep distance from each other, we were forced to limit all our activities that can’t be done online. At first, this was shocking and we weren’t prepared for such change. But when we …

Read More

Friday talks: EDA done right

Posted on

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 …

Read More

HOW TO START WITH DATA SCIENCE?

Posted on

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, …

Read More

Dash by Plotly

Posted on

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 …

Read More

FRIDAY TALKS: WOMEN IN DATA SCIENCE

Posted on

Hello, fellas. Cool down, I’m not going to talk about extreme feminism and gender (in)equality. 🙂 This post is going to be about extraordinary women I had a chance to meet at the Women in Data Science conference, held in Subotica, this April. I truly believe that these girls deserve to be heard of, as …

Read More

Hello Docker

Posted on

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 …

Read More

INTUITION vs. DATA

Posted on

Our company – Things Solver, helps clients make better decisions by analyzing relevant data. During analysis of the data, we often notice significant differences between what our clients would intuitively decide and what their data tells them to do. One striking example of a gap between our perception and reality is the state of the …

Read More

Friday talks: A Data Science Project

Posted on

This post is not going to be about another Data Science course you should enroll in. It’s not going to be about various skills you should build in order to develop a Data Science project, either. Considering the title of this post – A Data Science Project – I tried to create a pun. Your …

Read More

How the Big Data Won the Hearts in Telecommunications

Posted on

The fact that there is a deep connection between the telecommunications and Big Data is very clear – the main task for telecommunications is in exchanging data. Since the amount of data has enormously increased in the modern era, the experts in telecommunication companies needed some help from the specialised experts. The need for the …

Read More

Recommender Systems and Banks: Precious Recommendation

Posted on

The client’s path, from conceiving an idea to making it a project in the bank, used to be clear, but unpredictable. It involved a potentially noticed ad or the client’s own idea, a visit to the bank counter in person and the more or less successful deal with the bank. It was very time consuming, …

Read More

Data Exploration with Pandas (Part 2)

Posted on

In the previous article, I wrote about some introductory stuff and basic Pandas capabilities. In this part, the main focus will be on DateTime values. I am also going to introduce you to some grouping and merging possibilities in Pandas. For this purpose here is another dataset downloaded from UCI Repository, which contains date and time …

Read More

Big Data and Banking: How Our Data Protects Us

Posted on

Some of the main tasks for the bankers are, among others, keeping, preserving, deriving more from less, taking the best out of what the clients deposit to them, with great confidence and trust. Almost the same definition could be applied to the Data Scientists dealing with the big data. They also deal with keeping, preserving, …

Read More

Why the Telco Industry seems destined for Big Data

Posted on

A man is defined in numerous scientific ways, but one definition seems unchanged since the beginning of time. The man is the creature that communicates – from the first attempt to speak, till the last tale told to grandchildren. “The telecommunications are defined as the exchange of the information between the source and the destination, …

Read More

Success Formula: Science, Business and Programming

Posted on

In every business, the ultimate dream is the one about the magic success formula. The search for ingredients is very extensive, but they often live next door. So now the greater „magic” becomes the question of using, understanding and connecting those dots. That „magic” even has a name. „Data Science is actually the applied science …

Read More

Anomaly detection

Posted on

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 …

Read More

Data Exploration with Pandas (part 1)

Posted on

If you ever decide to become someone who is into big data, surely you can do it without having a clue about pandas. But that’s not the brightest solution, because why would you leave aside something that’s gonna make you a lot better. Pandas as well know library for manipulating datasets that contains numerical and …

Read More

Handling missing data

Posted on

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 …

Read More

Forecasting with VAR and Prophet

Posted on

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, …

Read More

Interactive log analysis with Apache Spark

Posted on

The Internet is becoming the largest global shop across markets, and anyone who is offering products and services of any kind prefers for web shops to become the primary outlets to supply customers. This leads to a reduction in the number of employees and traditional brick and mortar branches and reduction in costs, so it …

Read More

Forecasting with ARIMA

Posted on

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 …

Read More