Kacar Milan

Gymnasium Mrkonjic Grad, Bosnia and Herzegovina

Graz University of Technology, Austria

Graz University of Technology, Austria

• Data Science
• Statistics

Data Engineer / Scientist / Analyst

Working on various projects in Finance, Business Controlling & Performance Marketing topics.

Data Engineer / TL

Working on various projects in Finance (Startup Founding), Processes Automation, and Project Management.

Co-Founder

Designed, built, and implemented a Web Application that utilizes QR and NFC technologies. The app is based on PHP, JavaScrip, JQuery, and MySQL, and it enables you to order food/drinks from the menu and contact your bartender by simply tapping the NFC tag on the table with your smartphone. It is not only a preview of the menu but a full ordering system with the option to pay immediately. Together with Stefan Gajanovic.

Software Engineer

Working on various projects in Automation, Statistics & Geoinformatics in the Automotive industry.

Teaching Assistant

Indoor navigation, filtering, sensor fusion.

survey engineer

Tunnel survey | 3D Laser scanner measurements

Bosnian · Serbian · Croatian

German

English

Russian

Stock Prices Predictor (AI)

The project employs a hybrid methodology integrating Artificial Intelligence (AI) and Machine Learning (ML) techniques to discern fluctuations in stock prices. It focuses on monitoring and analyzing stock price dynamics, taking into account various indices such as the S&P 500, Dow Jones, DAX, among others. This multifaceted approach aims to enhance the precision and robustness of stock price prediction and decision-making processes.

Lotto Numbers Predictor (Python)

The project titled "Lotto Number Predictor" was designed with the objective of developing a computational model to forecast the numbers likely to emerge in the next lottery draw. Leveraging statistical and analytical methodologies, the project sought to derive patterns or trends from historical data in order to inform predictions. This endeavor represents an attempt to harness data-driven insights in the domain of lottery number forecasting, with the ultimate aim of enhancing the accuracy of predicting winning numbers.

Scientific paper miner - SPaRC (C++)

The project endeavors to establish a comprehensive digital platform akin to a Google-like search engine, designed specifically for scientific papers. It aims to facilitate efficient and comprehensive retrieval of scholarly content, offering a valuable resource for researchers, academicians, and professionals seeking access to a vast repository of scientific knowledge and literature.

Pollen tree detector (from satellite images) (AI/Image detection)

Building a tree map of an area is very intensive task, especially when the trees are covering large and inaccessible sites. Numerous tree maps are available, but they are incomplete. However, having a detailed tree map may help predicting seasonal pollen spread in cities. Why is this important? Airborne pollen causes seasonal allergies. The number of people having allergic reactions increases on yearly basis. Remote collection of such information can be made possible through the utilization of satellite imagery. This project presents an automated tree detecting and identification method based on image processing of satellite imagery.

Different Arduino projects

Mostly projects from youtube tutorials and combination of different projects.

Different signal processing projects (TSA/Python)

The project primarily focuses on the domain of Global Navigation Satellite System (GNSS) signal processing, with an emphasis on encoding and decoding techniques. Its core objectives encompass safeguarding against spoofing attacks and conducting in-depth analysis of GNSS time series data. This endeavor represents a multifaceted effort to enhance the reliability and security of GNSS signal utilization.

Gravity anomaly maps (Python/Physics)

The GRACE (Gravity Recovery and Climate Experiment) mission is a scientific endeavor aimed at creating highly precise and detailed gravity anomaly maps of Earth. This mission involves the use of twin satellites equipped with sensitive instruments to measure variations in Earth's gravitational field. By analyzing these data, the GRACE mission provides critical insights into changes in the distribution of mass on our planet, offering valuable information for climate and environmental studies, as well as geophysical research.

Kalman Filter · Adevanced Kalman Filter · Particle Filter (Python/Statistics)

The project is centered around the implementation of state estimation and sensor fusion techniques for indoor navigation. It incorporates the Kalman Filter and its advanced counterpart, the Extended Kalman Filter, along with the Particle Filter. These methodologies are integral components of a comprehensive framework aimed at improving the accuracy and robustness of indoor positioning and navigation systems, ensuring precise localization and tracking within complex indoor environments.

Dynamic pricing strategy and competitor prices crawler (Python/Proxy/WebCrawling)

The project involves the implementation of dynamic pricing strategies and the development of web crawlers to collect and analyze competitor pricing data. Through this initiative, the primary goal is to systematically identify and assess key market rivals. Subsequently, by leveraging the insights garnered from this analysis, the project seeks to optimize pricing strategies to strategically influence fluctuations in product prices, thereby enhancing sales performance and market competitiveness.

Demand prediction (AI)

The project involves the application of demand forecasting techniques to analyze and predict the demand for specific items, including both manufactured products and ordered articles, over the upcoming year. This analysis takes into account various factors, such as seasonality and the potential impact of public holidays, which may lead to fluctuations in demand. The aim is to provide accurate and data-driven insights to anticipate and plan for demand variations, thereby facilitating more effective inventory management and operational decision-making.

Tinder for Investors (AI) - TL

The project leverages the expertise and resources of our established cadre of prior investors to support and nurture startups within our purview. As a cornerstone of our operational strategy, this initiative entails the issuance of equity shares in the startups to these investors, commensurate with their contributions and assistance. This symbiotic relationship is particularly germane to startups, as it affords them a platform for articulating and elucidating their operational challenges, thereby facilitating the accrual of actionable insights and guidance from prospective investors to address and surmount these challenges effectively.

Datalake Creation (GCP) - TL

The amalgamation of diverse data sources, encompassing Pipedrive, Mixpanel, Google Analytics, and our proprietary backend dataset, was orchestrated to establish a comprehensive data lake. This data repository serves as an invaluable resource, enabling us to gain nuanced insights into the corporate landscape. It empowers us to discern the present positioning of our organization, proffers actionable guidance for potential improvements, and streamlines the process of making informed and astute business decisions.

Pitch Deck Analyzer (AI) - TL

Upon the submission of a startup's pitch deck, our initial imperative involves the meticulous analysis of its content. To expedite and systematize this endeavor, we have implemented an automated procedure. The pitch decks are subjected to a process of dissection, wherein they are deconstructed into constituent images and text components. An artificial intelligence (AI) framework is then deployed to perform data extraction, discerning and retrieving pertinent information. This AI-driven analysis furnishes us with an evaluative assessment of the deck's alignment with our specific search criteria, guiding our decision on its suitability for further consideration.

Startup Value Estimator (ML/AI) - TL

Employing the data submissions provided by the startup, our evaluation process encompasses an estimation of the intrinsic value of the venture. This assessment entails a comprehensive analysis of various determinants and variables. Subsequently, we undertake a judicious evaluation to ascertain the viability of investing in the startup, with the objective of ascertaining whether the opportunity aligns with our investment criteria and strategic objectives.