US economy forecasting

•

0 likes•57 views

This project was completed during the Lviv Data Science Summer School 2018 (http://cs.ucu.edu.ua/en/summerschool/summer-school-2018/).

Education

A SIMPLE US ECONOMY
FORECASTING MODEL
Svitlana Markova
Carla Murteira
Pablo Zarate

IDEA
• Using past values of important macroeconomic variables as inputs, we can
predict the future state of an Economy.
• Difficulties:
• Choosing RightVariables
• Transforming them Appropiately (Non-Stationarity, non-linear Relations)
• Choosing the right model Specification
• Difficult Benchmark due to Economic Incentives

PIPELINE
ChooseVariables Find Data Clean andTransform
Select Appropiate
Specification
Train Model with
PastValues
Predict FutureValues
(10 Q)

DATA
• Source: Federal Reserve (FRED)
• Time Span: 1980-2015 (Training) 2016-2018 (Prediction)
• Time Unit: Year`s Quarter
• Variables: quarterly (averaged) and seasonally adjusted

VARIABLES
• Inflation: Implicit Price Deflator, Index 2012=100
• GDP: Real Gross Domestic Product in Billions of Dollars
• Wages: Mean usual weekly real earnings
• Consumption: Real Personal Consumption Expenditures in Billions of Dollars.
• Investment: Real Gross Private Domestic Investment in Billions of Dollars.
• Interest Rate: Effective Federal Funds Rate in Percent.
• Unemployment: Civilian Unemployment Rate in Percent.

RESULTS
Real GDP Unemploment
Interest rate
Inflation
Investment Wages
Consumption

RESULTS
Time Period P. GDP Actual GDP P. Investment Actual Investment P. Unemployment Actual Unemployment P. Consumption Actual Consumption
Q1 2016 9.778.420 9,771290926 8.039.071 8,020562672 4.915.796 4,9 9.403.743 9,401933481
Q2 2016 9.785.485 9,776930979 8.046.045 8,018029304 4.764.169 4,9 9.410.226 9,410346137
Q3 2016 9.792.638 9,78169866 8.053.049 8,01705657 4.628.676 4,9 9.416.291 9,416891263
Q4 2016 9.798.676 9,786064858 8.056.419 8,036463766 4.513.520 4,7 9.421.543 9,423250136
Q1 2017 9.803.790 9,790488101 8.055.886 8,048349849 4.434.365 4,7 9.426.120 9,427678867
Q2 2017 9.807.993 9,797857556 8.051.660 8,062137444 4.395.442 4,3 9.430.017 9,434751919
Q3 2017 9.811.405 9,804818102 8.044.258 8,083259471 4.396.913 4,3 9.433.294 9,440253438
Q4 2017 9.814.136 9,810481406 8.034.384 8,085189839 4.435.518 4,1 9.435.992 9,449858643
Q1 2018 9.816.288 9,815964936 8.022.712 8,108029653 4.506.563 4,1 9.438.157 9,451154004
Q2 2018 9.817.950 9,825915125 8.009.846 8,10683262 4.604.795 3,9 9.439.831 9,460929426

Similar to US economy forecasting

Index Numbers class 12 economic works.pptx

SoumitMondal7

Lec-3 Forecasting.pdf Data science college

msherazmalik1

Alessandra Garbero: Challenges of impact evaluation

IFAD International Fund for Agricultural Development

O M Unit 3 Forecasting

RASHMIPANWAR10

Measuring the Impact of Subsidy Reforms (EN)

Paul Mithun

Bridging marke- credit risk-Modelling the Incremental Risk Charge.pptx

Garima Singh Makhija

Inflation Houston 2010

blnoeide

National income

Mithila Gadhire

Inclusive growth - Fiona Ross, New-Zealand

OECD Governance

Forecasting Exchange Rates

Dr. Hesniati S.E., M.M.

The "Forecasting Sales Data Using Statistical Modeling and Machine Learning" project revolves around harnessing US sales data from 1992 to 2020 to predict future sales trends and analyze the impact of significant crises. Sourced meticulously from the US Census Bureau, the data underwent rigorous monitoring to ensure quality and accuracy in the estimates representing various sectors of the US economy. Through the application of Time-series Analysis techniques, the project aimed to unveil valuable insights buried within the sales data and produce reliable sales forecasts for the coming years. A comprehensive set of tasks was undertaken, encompassing data collection, rigorous data cleaning, and compelling data visualizations to understand the trends and patterns better. The project's core entailed statistical methods and advanced Machine Learning models to perform robust sales predictions. Leveraging the power of predictive analytics, the team sought to equip businesses with the knowledge to make informed decisions and plan effectively for the future. Moreover, the project's unique feature was the in-depth exploration of the impact of significant crises, such as the 2008 Economic crisis and the recent COVID-19 pandemic, on the retail sector. By examining sales patterns during these periods, the team aimed to gain critical insights into the sector's resilience and adaptability during times of upheaval. The project's findings were presented in a detailed and comprehensive report, offering a complete overview of the methodologies employed, insights gained, and forecasts generated. As a result, this project contributes valuable knowledge to sales forecasting and crisis impact analysis, providing a solid foundation for making strategic decisions in the retail industry.

Usa Retail Sales Analysis.pdf

Vishwas Saini

Module4.pdf

NazrulIslam978716

Relationship Profitability Is Back!

Baker Hill

Dive deep into the world of insurance churn prediction with this captivating data analysis project presented by Boston Institute of Analytics. Our talented students embark on a journey to unravel the mysteries behind customer churn in the insurance industry, leveraging advanced data analysis techniques to forecast and anticipate customer behavior. From analyzing historical data and customer demographics to identifying predictive indicators and developing churn prediction models, this project offers a comprehensive exploration of the factors influencing insurance churn dynamics. Gain valuable insights and actionable recommendations derived from rigorous data analysis, presented in an engaging and informative format. Don't miss this opportunity to delve into the fascinating realm of data analysis and unlock new perspectives on insurance churn prediction. Explore the project now and embark on a journey of discovery with Boston Institute of Analytics. To learn more about our data science and artificial intelligence programs, visit https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/.

Insurance Churn Prediction Data Analysis Project

Boston Institute of Analytics

Survival_Analysis

Rushil Goyal

Chapter 2 Lecrure Slides.pdf

K60LngcVinh

Financial cycle presentation, March 2015

Benjamin Huston

Fiat value in the theory of value, by Edward C Prescott (Arizona State Univer...

ADEMU_Project

2014.05.19 - OECD-ECLAC Workshop_Session 2_Doyne FARMER

OECD_NAEC

Explore our students' cutting-edge project on predicting bank customer churn using advanced analytics techniques. This project employs machine learning algorithms to analyze customer data and forecast the likelihood of churn, offering valuable insights for financial institutions. Gain insights into customer retention strategies, predictive modeling, and the potential impact on banking operations. To learn more, do check out https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/

Bank Customer Churn Prediction- Saurav Singh.pptx

Boston Institute of Analytics

Similar to US economy forecasting (20)

Index Numbers class 12 economic works.pptx

Lec-3 Forecasting.pdf Data science college

Alessandra Garbero: Challenges of impact evaluation

O M Unit 3 Forecasting

Measuring the Impact of Subsidy Reforms (EN)

Bridging marke- credit risk-Modelling the Incremental Risk Charge.pptx

Inflation Houston 2010

National income

Inclusive growth - Fiona Ross, New-Zealand

Forecasting Exchange Rates

Usa Retail Sales Analysis.pdf

Module4.pdf

Relationship Profitability Is Back!

Insurance Churn Prediction Data Analysis Project

Survival_Analysis

Chapter 2 Lecrure Slides.pdf

Financial cycle presentation, March 2015

Fiat value in the theory of value, by Edward C Prescott (Arizona State Univer...

2014.05.19 - OECD-ECLAC Workshop_Session 2_Doyne FARMER

Bank Customer Churn Prediction- Saurav Singh.pptx

More from Lviv Data Science Summer School

Ukrainian Catholic University Faculty of Applied Sciences Data Science Master Program January 24th Abstract. In digital marketing, memes have become an attractive tool for engaging an online audience. Memes have an impact on buyers’ and sellers’ online behavior and information spreading processes. Thus, the technology of generating memes is a significant tool for social media engagement. In this study, we collected a new memes dataset of ∼650K meme instances, applied state of the art Deep Learning technique – GPT-2 model [1] towards meme generation, and compared machine-generated memes with human-created. We justified that MTurk workers can be used for the approximate estimating of users’ behavior in a social network, more precisely to measure engagement. Generated memes cause the same engagement as human memes, which didn’t collect engagement in the social network (historically). Still, generated memes are less engaging then random memes created by humans.

Master defence 2020 - Andrew Kurochkin - Meme Generation for Social Media Aud...

Lviv Data Science Summer School

Master defence 2020 - Andrew Kurochkin - Meme Generation for Social Media Aud...

Lviv Data Science Summer School

Ukrainian Catholic University Faculty of Applied Sciences Data Science Master Program January 23d Abstract. In modern days synthesis of human images and videos is arguably one of the most popular topics in the Data Science community. The synthesis of human speech is less trendy but deeply bonded to the mentioned topic. Since the publication of WaveNet paper by Google researchers in 2016, the state-of-the-art approach transferred from parametric and concatenative systems to deep learning models. Most of the work on the area focuses on improving the intelligibility and naturalness of the speech. However, almost every significant study also mentions ways to generate speech with the voices of different speakers. Usually, such an enhancement requires the model’s re-training in case of generating audio with the voice of a speaker that was not present in the training set. Additionally, studies focused on highly modular speech generation are rare. Therefore there is a room left for research on ways to add new parameters for other aspects of the speech, like sentiment, prosody, and melody. In this work, we aimed to implement a competitive text-to-speech solution with the ability to specify the speaker without model re-training and explore possibilities for adding emotions to the generated speech. Our approach generates good quality speech with the mean opinion score of 3,78 (out of 5) points and the ability to mimic speaker voice in real-time, which is a big improvement over the baseline that merely obtains 2,08. On top of that, we researched sentiment representation possibilities. We built an emotion classifier that performs on the level of the current state of the art solutions by giving an accuracy of more than eighty percent.

Master defence 2020 - Nazariy Perepichka - Parameterizing of Human Speech Gen...

Lviv Data Science Summer School

Master defence 2020 - Anastasiia Khaburska - Statistical and Neural Language ...

Lviv Data Science Summer School

Ukrainian Catholic University Faculty of Applied Sciences Data Science Master Program January 24th Abstract. This work presents a context-based question answering model for the Ukrainian language based on Wikipedia articles using Bidirectional Encoder Representations from Transformers (BERT) (Devlin et al., 2018) model, which takes a context (Wikipedia article) and a question to the context. The result of the model is an answer to the question. The model consists of two parts. The first one is a pre-trained multilingual BERT model, which is trained on the top-100, the most popular languages on Wikipedia articles. The second part is the fine-tuned model, which is trained on the data set of questions and answers to the Wikipedia articles. The training and validation data is Stanford Question Answering Dataset (SQuAD) (Rajpurkar et al., 2016). There are no question answering datasets for the Ukrainian language. The plan is to build an appropriate dataset with machine translation and use it for the fine-tuning training stage and compare the result with models which were fine-tuned on the other languages. The next experiment is to train a model on the Slavic language datasets before fine-tuning on the Ukrainian language and compare the results.

Master defence 2020 - Serhii Tiutiunnyk - Context-based Question-answering Sy...

Lviv Data Science Summer School

Ukrainian Catholic University Faculty of Applied Sciences Data Science Master Program January 24th Abstract. This work tackles the problem of matching Wikipedia red links with existing articles. Links in Wikipedia pages are considered red when leading to nonexistent articles. In other Wikipedia, editions could exist articles that correspond to such red links. In our work, we propose a way to match red links in one Wikipedia edition to existent pages in another edition. We solve this task in the context of Ukrainian red links and English existing pages. We created a dataset of 3 171 most frequent Ukrainian red links and a dataset of 2 957 927 pairs of red links and the most probable candidates for the corresponding pages in English Wikipedia. This dataset is publicly released. We defined the task as a Named Entity Linking problem. Red links are named entities and we link Ukrainian red links to English Wikipedia pages. In this work, we provide a thorough analysis of the data and define its conceptual characteristics to exploit in entity resolution. These characteristics are graph properties (connections with the pages where red links occur and connections with the pages which occur in the same pages with red links) and word properties (title names). BabelNet knowledge base was applied to this task. We evaluated its powers in terms of F1 score (29 %) and regarded it as a baseline for our approach. To improve the results we introduced several similarity metrics based on mentioned red links characteristics. Combined in a linear model they resulted in F1 score 85 % which is our best result. In our thesis, we also discuss the bottlenecks and limitations of the current approach and outline the ideas for future improvements. To the best of our knowledge, we are the first to state the problem and propose a solution for red links in the Ukrainian Wikipedia edition. All the code for this project is publicly released on github.

Master defence 2020 - Kateryna Liubonko - Matching Red Links to Wikidata Items

Lviv Data Science Summer School

Ukrainian Catholic University Faculty of Applied Sciences Data Science Master Program January 24th Abstract. Every day a lot of visitors leave countless reviews about hotels, restaurants, cafes, attractions or other services. In most cases, they set the rate about this service, sometimes they also set the rate about the specific topic if service provides this possibility. However, the main information about user opinion is hidden inside the body of review text. Thereby, in this work, we propose a solution to analyze one or several user reviews, determine sentiments and acquire important characteristics for these reviews. We determine which characteristics were influenced by such reviews. In this case, the proposed solution can detect sentiments from text and classify for pos-itive and negative. Then it acquires top positive and negative phrases, which can explain why the user left such review. Besides, we analyze all reviews about one hotel or just several reviews and summarize the most important positive and negative properties for a specific hotel.

Master defence 2020 - Dmytro Babenko - Determining Sentiment and Important Pr...

Lviv Data Science Summer School

Ukrainian Catholic University Faculty of Applied Sciences Data Science Master Program January 23rd Abstract. Advances in the demand response for energy imbalance management (EIM) ancillary services can change the future power systems. These changes are subject to research in academia and industry. Although an important/promising part of this research is the application of Machine Learning methods to shape future power systems domain, the domain has not fully benefited from this application yet. Thus, the main objective of the presented project is to investigate and assess opportunities for applying reinforcement learning (RL) to achieve such advances by developing an intelligent voltage control-based ancillary service that uses thermostatically controlled loads (TCLs). Two stages of the project are presented: a proof of concept (PoC) and extensions. The PoC includes modeling and training of a voltage controller utilizing Q-learning, chosen due to its efficiency that is achieved without unnecessary sophistication. Simplest relevant for demand response power system of 20 TCLs is considered in the experiments to provide ancillary service. The power system model is developed with Modelica tools. Extensions aim to exceed PoC performance by applying advanced RL methods: Q-learning modification that uses a window of environment states as an input (WIQL), smart discretization strategies for environment’s continuous state space and a deep Q-network (DQN) with experience replay. To investigate particularities of the developed controller, modifications in an experimental setup such as controller testing longer than training, different simulation start time is considered. The improvement of 4% in median performance is achieved compared to the competing analytical approach – optimal constant control chosen using whole time interval simulation for the same voltage controller design. The presented results and corresponding discussions can be useful for both further works on the RL-driven voltage controllers for EIM and other applications of RL in the power system domain using Modelica models.

Master defence 2020 - Oleh Lukianykhin - Reinforcement Learning for Voltage C...

Lviv Data Science Summer School

Ukrainian Catholic University Faculty of Applied Sciences Data Science Master Program January 23rd Abstract. Speaker classification is an essential task in the machine learning domain, with many practical applications in identification and natural language processing. This work concentrates on speaker classification as a subtask of general speaker diarization for real-world conversation scenarios. We research the domain of modern speech processing and present the original speaker classification approach based on the recent developments in convolutional neural networks. Our method uses a spectrogram as input to the CNN classifier model, allowing it to capture spatial information about voice frequencies distribution. Presented results show beyond human ability performance and give strong prospects for future development.

Master defence 2020 - Borys Olshanetskyi -Context Independent Speaker Classif...

Lviv Data Science Summer School

Ukrainian Catholic University Faculty of Applied Sciences Data Science Master Program January 23rd Abstract. Currently, the active development of image processing methods requires large amounts of correctly labeled data. The lack of quality data makes it impossible to use various machine learning methods. In case of limited possibilities for collecting real data, used methods for their synthetic generation. In practice, we can formulate the task of the high-quality generation of synthetic images as an efficient generation of complex data distributions, which is the object of study of this work. Generating high-quality synthetic data is an expensive and complicated process in terms of existing methods. We can distinguish two main approaches that are used to generate synthetic data: image generation based on rendered 3-D scenes and the use of GANs for simple images. These methods have some drawbacks, such as a narrow range of applicability and insufficient distribution complexity of the obtained data. When using GANs to generate complex distributions, in practice, we face a visible increase in the complexity of the model architecture and training procedure. A deep understanding of the real data complex distributions can be used to improve the quality of synthetic generation. Minimizing the differences in the real and synthetic data distributions can improve not only the generation process but also develop tools for solving the problem of data lack in the field of image processing.

Master defence 2020 - Philipp Kofman - Efficient Generation of Complex Data D...

Lviv Data Science Summer School

Ukrainian Catholic University Faculty of Applied Sciences Data Science Master Program January 23rd Abstract. Customer Lifetime Value (CLV) is a present value of the future cash flows attributed to a customer during their entire relationship with the company (Farris et al., 2010). CLV represents a 360-degree view of the client’s business situation (McKinsey, Customer Lifecycle Management), which takes into account the probability of customer churn and their future purchases. The modeling of CLV in retail is a complicated task due to the lack of access to historical data of purchases, the difficulty of customer identification, and building the historical reference with a particular customer. In this research, historical transactional data were taken from twelve North American brick-and-mortar grocery stores to compare different approaches to CLV modeling in terms of segmentation and forecast. The research has resulted in the suggestions on CLV estimation for the offline retail business case with given advantages and limitations of each approach.

Master defence 2020 - Anastasiia Kasprova - Customer Lifetime Value for Retai...

Lviv Data Science Summer School

Ukrainian Catholic University Faculty of Applied Sciences Data Science Master Program January 23rd Abstract. In this project (Glusco and Maksymenko, 2019), we treat the Reinforcement Learning problem of Exploration vs. Exploitation. The problem can be rephrased in terms of generalization and overfitting or efficient learning. To face the problem we decided to combine the techniques from different researches: we introduce noise as an environment’s characteristics (Packer et al., 2018); create multiple Reinforcement Learning agents and environments setup to train in parallel and interact within each other (Jaderberg et al., 2017); use parallel tempering approach to initialize environments with different temperatures (noises) and perform exchanges using Metropolis-Hastings criterion (Pushkarov et al., 2019). We implemented multi-agent architecture with a parallel tempering approach based on two different Reinforcement Learning agent algorithms – Deep Q Network and Advantage Actor-Critic – and environment wrapper of the OpenAI Gym (Gym: A toolkit for developing and comparing reinforcement learning algorithms) environment for noise addition. We used the CartPole environment to run multiple experiments with three different types of exchanges: no exchange, random exchange, smart exchange according to Metropolis-Hastings rule. We implemented aggregation functionality to gather the results of all the experiments and visualize them with charts for analysis. Experiments showed that a parallel tempering approach with multiple environments with different noise level can improve the performance of the agent under specific circumstances. At the same time, results raised new questions that should be addressed to fully understand the picture of the implemented approach.

Master defence 2020 - Dmitri Glusco - Replica Exchange For Multiple-Environme...

Lviv Data Science Summer School

Ukrainian Catholic University Faculty of Applied Sciences Data Science Master Program January 22nd Abstract. The thesis introduces the reader to the concepts of edge computing in terms of person re-identification and tracking problem. It describes the challenges, limitations, and current state-of-the-art solutions. The author proposed a pipeline for the task, launched several experiments on validating different parts of the system, and provided a theoretical explanation of the person re-identification process in the overlapping multi-camera environment.

Master defence 2020 - Ivan Prodaiko - Person Re-identification in a Top-view ...

Lviv Data Science Summer School

Ukrainian Catholic University Faculty of Applied Sciences Data Science Master Program January 22nd Abstract. Generative Adversarial Networks (GANs) in recent years has certainly become one of the biggest trends in the computer vision domain. GANs are used for generating face images and computer game scenes, transferring artwork style, visualizing designs, creating super-resolution images, translating text to images, etc. We want to present a model to solve an image problem: generate new outfits onto people’s images. This task seems to be extremely important for the offline/online trade and fashion industry.Changing clothing on people’s images isn’t a trivial task. The generated part of the image should have high quality without blurring. Another problem is generating long sleeves on the images with T-shirts, for example. As a result, well-known models are not suitable for this task. In the master project, we are going to reproduce the model for clothing hanging on people’s images based on the existing approaches and improve it in order to get better quality of the image.

Master defence 2020 - Yevhen Pozdniakov - Changing Clothing on People Images...

Lviv Data Science Summer School

Ukrainian Catholic University Faculty of Applied Sciences Data Science Master Program January 22nd Abstract. Generative adversarial networks (GANs) are one of the most popular models capable of producing high-quality images. However, most of the works generate images from the vector of random values, without explicit control of desired output properties. We study the ways of introducing such control for the user-selected region of interest (RoI). First, we overview and analyze the existing works in areas of image completion (inpainting) and controllable generation. Second, we propose our model based on GANs, which united approaches from the two mentioned areas, for the controllable local content generation. Third, we evaluate the controllability of our model on three accessible datasets – Celeba, Cats, and Cars – and give numerical and visual results of our method.

Master defence 2020 - Oleh Onyshchak - Image Recommendation for Wikipedia Ar...

Lviv Data Science Summer School

Master defence 2020 - Oleh Misko - Ensembling and Transfer Learning for Multi...

Lviv Data Science Summer School

Ukrainian Catholic University Faculty of Applied Sciences Data Science Master Program January 22nd Abstract. Today virtual and augmented reality applications become more and more popular. Such a trend creates a demand for 3D processing algorithms which may be applied to many areas. This work is focused on sigh language video sequences. There are a lot of prerecorded photos and video dictionaries that can be transformed into 3D and unified in one place. We research nuances of hand pose video sequence analysis as well as the influence of results refinement for 2D and 3D keypoint detection. Besides that, we designed a solution for the parametrization of hand shape and engineered system for 3D hand pose reconstruction. Model show good results on train data but lack generalization. Retraining on multiple datasets and usage of various data augmentation techniques will improve performance.

Master defence 2020 - Roman Riazantsev - 3D Reconstruction of Video Sign Lan...

Lviv Data Science Summer School

Master defence 2020 - Vadym Korshunov - Region-Selected Image Generation with...

Lviv Data Science Summer School

Ukrainian Catholic University Faculty of Applied Sciences Data Science Master Program January 21st Abstract. Novelty is an inherent part of innovations and discoveries. Such processes may be considered as the appearance of new ideas or as the emergence of atypical connections between existing ones. The importance of such connections hints for investigation of innovations through network or graph representation in the space of ideas. In such representation, a graph node corresponds to the relevant notion (idea), whereas an edge between two nodes means that the corresponding notions have been used in a common context. The question addressed in this research is the possibility to identify the edges between existing concepts where the innovations may emerge. To this end, a well-documented scientific knowledge landscape has been used. Namely, we downloaded 1.2M arXiv.org manuscripts dated starting from April 2007 and until September 2019; and extracted relevant concepts for them using ScienceWISE.info platform. Combining approaches developed in complex networks science and graph embedding the predictability of edges (links) on the scientific knowledge landscape where the innovations may appear is investigated. We argue that the conclusions drawn from this analysis may be used not only to the scientific knowledge analysis but are rather generic and may be applied to any domain that involves creativity within.

Master defence 2020 -Roman Moiseiev - Stock Market Prediction Utilizing Centr...

Lviv Data Science Summer School

Ukrainian Catholic University Faculty of Applied Sciences Data Science Master Program January 21st Abstract. Human navigation in information spaces has increasing importance in ever-growing data sources we possess. Therefore, an efficient navigation strategy would give a huge benefit to the satisfaction of human information needs. Often, the search space can be understood as a network and navigation can be seen as a walk on this network. Previous studies have shown that despite not knowing the global network structure people tend to be efficient at finding what they need. This is usually explained by the fact that people possess some background knowledge. In this work, we explore an adapted version of the network consisting of Wikipedia pages and links between them as well as human trails on it. The goal of our research is to find a procedure to label articles that are similar to a given one. Among others, this would lay a foundation for a recommender system for Wikipedia editors, which will suggest links from the given page to the related articles. Our work is, therefore, providing a basement for enhancing the Wikipedia navigation process making it more user-friendly.

Master defence 2020 - Maksym Opirskyi -Topological Approach to Wikipedia Arti...

Lviv Data Science Summer School

More from Lviv Data Science Summer School (20)