Skills: Python (Pandas, Numpy, Matplotlib, Seaborn, Sklearn, Statsmodels)
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Updated
Jun 4, 2024 - HTML
Skills: Python (Pandas, Numpy, Matplotlib, Seaborn, Sklearn, Statsmodels)
I practiced hypothesis testing with Python.
Проекты, выполненные в рамках образовательной программы "Специалист по Data Science" АНО ДПО "Образовательные технологии Яндекса"
Python port of "Common statistical tests are linear models" by Jonas Kristoffer Lindeløv.
The Currency Exchange Rate Prediction System is a Python application designed to assist users in analyzing historical currency exchange rates. It predicts future rates and makes recommendations on buying or selling currencies.
Multivariate Time series interpolation using hierarchical mixed effects models.
Undergraduate thesis, Seoul National University Dept. of Economics — "Modeling Volatility and Risk Spillover Between the Financial Markets of US and China Using GARCH Value-at-Risk Forecasting and Granger Causality."
Build a machine learning/deep learning approach to forecast the total energy demand on an hourly basis for the next 3 years based on past trends.
Training a linear regression model using English Premier League (EPL) Soccer Data.
Sports Analytics in Python
Traditional Regression problem project in Python
Logistic regression analysis to identify factors that predict odds of Kickstarter campaign success.
Analysis of the results of an A/B test for an e-commerce website.
Collection of end-to-end regression problems (in-depth: linear regression, logistic regression, poisson regression) 📈
This repository contains all practices from Time Series Forecasting topic of the MSc in Data Science
[Python] Building a demand model and correcting for reverse causation with 2-stage least squares regression (OLS in statsmodels, IV2SLS in linearmodels)
Data Manipulation, Descriptive Statistics, and Visualizations in Python.
how to perform t-testing & ANOVA
This repository uses Machine Learning models to gather insights on Customer Retention rate in Telecom Industry.
A Data Science approach to predict house sales from given dataset. All steps from CRISP-DM Framework are accounted for and Linear Regression is used for modelling.
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