This project is a collection of recent research in areas such as new infrastructure and urban computing, including white papers, academic papers, AI lab and dataset etc.
In tune with conventional big data and data science practitioners’ line of thought, currently causal analysis was the only approach considered for our demand forecasting effort which was applicable across the product portfolio. Experience dictates that not all data are same. Each group of data has different data patterns based on how they were sold and supported over the product life cycle. One-methodology-fits-all is very pleasing from an implementation of view. On a practical ground, one must consider solutions for varying needs of different product types in our product portfolio like new products both evolutionary and revolutionary, niche products, high growth products and more. With this backdrop, we have evolved a solution which segments the product portfolio into quadrants and then match a series of algorithms for each quadrant instead of one methodology for all. And technology stack would be simulated/mocked data(Hadoop Ecosystem) > AzureML with R/Python > Zeppelin.
Model ogólny mobilności miejskiej dla miast małych i średnich - do celów dydaktycznych, badawczych i innych (c) Rafal Kucharski, Politechnika Krakowska, 2018
Developed multiple predictive models with 90% accuracy for forecasting the daily-hourly bike rental count using Python & Machine Learning techniques like Regression, Clustering, Ensemble, Neural Network to achieve maximum accuracy
Big Data Inventory Management on AWS (Demand Forecasting, Machine Learning, Dashboarding) : Presented at Carlson School of Management during the Trends Marketplace event to professors, alumni and working professionals from various companies.
In tune with conventional big data and data science practitioners’ line of thought, currently causal analysis was the only approach considered for our demand forecasting effort which was applicable across the product portfolio. Experience dictates that not all data are same. Each group of data has different data patterns based on how they were sold and supported over the product life cycle. One-methodology-fits-all is very pleasing from an implementation of view. On a practical ground, one must consider solutions for varying needs of different product types in our product portfolio like new products both evolutionary and revolutionary, niche products, high growth products and more. With this backdrop, we have evolved a solution which segments the product portfolio into quadrants and then match a series of algorithms for each quadrant instead of one methodology for all. And technology stack would be simulated/mocked data(Hadoop Ecosystem) > AzureML with R/Python > Zeppelin.
