Giorgi Kukishvili (gkukish)

gkukish

User data from Github https://github.com/gkukish

Company:iD-Tech

Location:San Francisco

Home Page:https://giorgikukishvili.wixsite.com/website

GitHub:@gkukish

Giorgi Kukishvili's repositories

Bayesian-Inference-with-PyMC

This Jupyter notebook implements a Bayesian model for (A) fitting the posterior distribution given the data and (B) predicting consumer spending behavior in an e-commerce company. The objective is to model the average amount of money customers spend per month, considering specific constraints without the actual data by prior predictive model.

Language:Jupyter NotebookStargazers:1Issues:1Issues:0

CEO-Dismissal-Causal-Inference

Researching the relationship between stock market performance and involuntary CEO dismissal.

Language:Jupyter NotebookStargazers:1Issues:1Issues:0

Forecasting-Customer-Demand

This Jupyter Notebook implements Bayesian modeling techniques to fit a posterior distribution and forecast demand for an e-commerce company.

Language:Jupyter NotebookStargazers:1Issues:1Issues:0

CEO-Dismissal-Prediction

Researching the relationship between stock market performance and involuntary CEO dismissal.

Language:Jupyter NotebookStargazers:0Issues:1Issues:0

gkukish

Config files for my GitHub profile.

Stargazers:0Issues:1Issues:0

Mexicos-Conditional-Cash-Transfers

Using single and multivariable regression to estimate treatment effects of Mexico's Conditional Cash Transfer program - Prospecta.

Stargazers:0Issues:0Issues:0
Language:Jupyter NotebookStargazers:0Issues:1Issues:0
Language:Jupyter NotebookStargazers:0Issues:0Issues:0

sf-shopping-game

The platformer-type game to understand how much money one would spend on everyday grocery items in San Francisco.

Stargazers:0Issues:1Issues:0

stock-sentiment-sample

In this notebook, we will generate investing insight by applying sentiment analysis on financial news headlines from FINVIZ.com.

Language:HTMLStargazers:0Issues:0Issues:0

UK-GP-visits-data

Bayesian modeling for GP visits using partial and complete pooling. Analyzes demographic and regional factors. Utilizes PyMC3 for modeling and visualization. Prior and posterior predictive checks included.

Language:Jupyter NotebookStargazers:0Issues:1Issues:0