Прогнозирование спроса на такси

Applied Machine Learning Portfolio — End-to-end Python projects covering Linear Regression vs. CART Decision Trees (Diabetes progression modeling) and Logistic Regression & Employee Attrition Classification (HR analytics, feature engineering, ROC/AUC tuning, and statsmodels OLS/Logit analysis). Built with Scikit-Learn, Pandas, and Seaborn.

This repository contains a Phase 2 Project for the Data Science Flex Program at the Flatiron School. This project uses linear regression, pandas, numpy and exploratory data analysis using matplotlib and seaborn to predict and analyze home prices in the King County data set..

ARIMA vs Prophet sales forecasting comparison — trains both models on the same data, evaluates on a held-out test set, and benchmarks MAE/RMSE/MAPE.

Time Series for Predicting Air Quality (PM2.5/PM2) with analysis to show the PM2 changes