📈 Do Tech Stocks Move Together? – Time Series & ML Analysis (Python)

Time Series Forecasting (ARIMA) & Machine Learning (XGBoost) using Python

(Team Project–Focused on data pipeline, cleaning, EDA and modeling, Report produced independently)

Analyzed 10 years of daily adjusted closing prices for AAPL, MSFT, NVDA, and AMZN. Built ARIMA, SARIMA, and XGBoost models to forecast equity returns. Applied log return transformation, ADF stationarity testing, and exploratory data analysis to understand co-movement and volatility patterns across major tech stocks.

Tools: Python, yfinance, pandas, numpy, statsmodels, XGBoost, scikit-learn, matplotlib, seaborn

Output: Reproducible forecasting pipeline + comparative model evaluation