Pinned Repositories
Advanced_Seminar_in_Empirical_International-Trade
Seminar Paper
Airbnb
Tool for scraping and analysing housing and Rbnb Data
automation-framework-bookit
Behavioral_Economics_Theory
Seminar Paper
BlackFridayScrape
BMW-Anonymization-API
This repository allows you to anonymize sensitive information in images/videos. The solution is fully compatible with the DL-based training/inference solutions that we already published/will publish for Object Detection and Semantic Segmentation.
Causal_Inference_and_Policy_Evaluation
Current working on Causal Inference
dl_cmu_content
Course content repository
Publication_Analysis
rtemis
Advanced Machine Learning and Visualization in R
bakaibaiazbekov's Repositories
bakaibaiazbekov/dl_cmu_content
Course content repository
bakaibaiazbekov/Publication_Analysis
bakaibaiazbekov/rtemis
Advanced Machine Learning and Visualization in R
bakaibaiazbekov/Advanced_Seminar_in_Empirical_International-Trade
Seminar Paper
bakaibaiazbekov/Behavioral_Economics_Theory
Seminar Paper
bakaibaiazbekov/BlackFridayScrape
bakaibaiazbekov/BMW-Anonymization-API
This repository allows you to anonymize sensitive information in images/videos. The solution is fully compatible with the DL-based training/inference solutions that we already published/will publish for Object Detection and Semantic Segmentation.
bakaibaiazbekov/Causal_Inference_and_Policy_Evaluation
Current working on Causal Inference
bakaibaiazbekov/Causal_Inference_Presentation
bakaibaiazbekov/Criminal_Recidivism_withR
bakaibaiazbekov/Data-Science--Cheat-Sheet
Cheat Sheets
bakaibaiazbekov/Data-Science-Olympics-2019
My submission to the DSO2019 competition https://www.datascience-olympics.com/
bakaibaiazbekov/dowhy
DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.
bakaibaiazbekov/EconML
ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.
bakaibaiazbekov/Econometrics_script
bakaibaiazbekov/Essemble
bakaibaiazbekov/git_practice
bakaibaiazbekov/holiday-scraper
bakaibaiazbekov/Kaggle_HSE24
R script abt HSE24 kaggle competition
bakaibaiazbekov/LearningX
Deep & Classical Reinforcement Learning + Machine Learning Examples in Python
bakaibaiazbekov/Master-Statistics-Live-Series
bakaibaiazbekov/microeconometrics_script
bakaibaiazbekov/ML_Research_Proposal
bakaibaiazbekov/portfo
bakaibaiazbekov/QC_bot
bakaibaiazbekov/text_classifier
bakaibaiazbekov/Time_Series_Analysis_in_R
bakaibaiazbekov/ts
bakaibaiazbekov/tweet-conf-dash
A shiny twitter conference dashboard
bakaibaiazbekov/unsorted
classify unsorted items based on model created with sorted items