kryukovaeks Goto Github PK

2020

Materials for Applied Data Analysis CS-401, fall 2020

ai_friend

AI friend

audio_to_txt

bagelbot

A Slackbot for arranging random pairings for "Coffee & Bagel" meetings

calculator_for_investors

clip

CLIP Openai

coursera_ml_da_specialization

Coursera Specialization: Machine Learning and Data Analysis (Yandex & MIPT)

crypto-reading-list

data-science-for-business-epfl-course

Data Science for business, EPFL course

econ

The elements of statistical learning book

econometrics

email-and-phone-scraper

A Python based scraper that scrapes for email addresses and phone numbers and put the emails into an excel spreadsheet

fakenewscorpus

A dataset of millions of news articles scraped from a curated list of data sources.

hackathon

hackathon-1

kaggle_optiver

Kaggle competition Optiver - Trading at the Close

kryukovaeks

Config files for my GitHub profile.

langchain-experiments

Building Apps with LLMs

langchain_2

langchain_3

A Langchain app that allows you to chat with multiple PDFs

ml

ML course on Coursera

ml_system_design

newspaper

News, full-text, and article metadata extraction in Python 3. Advanced docs:

nlp-financial-text-processing-dataset

The key arguments for the low utilization of statistical techniques in financial sentiment analysis have been the difficulty of implementation for practical applications and the lack of high quality training data for building such models. Especially in the case of finance and economic texts, annotated collections are a scarce resource and many are reserved for proprietary use only. To resolve the missing training data problem, we present a collection of ∼ 5000 sentences to establish human-annotated standards for benchmarking alternative modeling techniques. The objective of the phrase level annotation task was to classify each example sentence into a positive, negative or neutral category by considering only the information explicitly available in the given sentence. Since the study is focused only on financial and economic domains, the annotators were asked to consider the sentences from the view point of an investor only; i.e. whether the news may have positive, negative or neutral influence on the stock price. As a result, sentences which have a sentiment that is not relevant from an economic or financial perspective are considered neutral.

python_intro

Jupyter notebooks in Russian. Introduction to Python, basic algorithms and data structures

stocks

Stocks analysis

text-to-img

Text to img

thematic_companies

Find companies to invest for certain theme

trading_bot_advisor

trading_bot_advisor

tsne_gplvm

Aml project to compare T-sne and gplvm