Домашнее задание к уроку 27 в учебнике

An end-to-end predictive analytics pipeline built in R using 30-minute interval data from AEMO. This project utilizes a Fourier-expanded ARIMA model to capture extreme intraday solar generation volatility. By modeling these daily cycles, the pipeline drops the baseline forecasting error (MAPE) from 35.8% down to 4.73%. Includes an embedded interactive Tableau dashboard tracking the 48-hour forward projections.

Dataset: https://www.kaggle.com/datasets/dansbecker/nba-shot-logs

This project examines the growth of digital payments and e-commerce in India using publicly available data from RBI, NPCI, World Bank, and Our World in Data. Through a series of eight visualizations, the study analyzes trends in digital payment adoption, UPI transaction growth, internet penetration, and expansion of the e-commerce market. The findings indicate a strong relationship between increasing internet access and the adoption of digital payment systems. The rapid growth of UPI has played a significant role in accelerating India's transition toward a digital economy and has created new opportunities for retail businesses and consumers.