FAANG Stock Data Visualization

Dynamic dashboards analyzing FAANG stocks with data retrieved via the Yahoo Finance API using Python. Includes interactive pivot tables and AI-generated insights, built with Google Sheets, Tableau, and Gemini for in-depth exploration.

import yfinance as yf
import pandas as pd
from datetime import datetime

faang_tickers = ['META', 'AAPL', 'AMZN', 'NFLX', 'GOOGL']
start_date = '2023-01-01'
end_date = datetime.today().strftime('%Y-%m-%d')

all_data = pd.DataFrame()

for ticker in faang_tickers:
    stock = yf.Ticker(ticker)
    hist = stock.history(start=start_date, end=end_date)
    hist['Ticker'] = ticker
    all_data = pd.concat([all_data, hist])

all_data.reset_index(inplace=True)
all_data.to_csv('faang_stocks_data.csv', index=False)

print(f"Data saved to faang_stocks_data.csv up to {end_date}")
        

Large Dataset Cleaning & Transformation

Performed extensive cleaning on a large NFL dataset containing missing and inconsistent values, utilizing SQL, Python, and R for data preprocessing and transformation. This involved handling missing data, standardizing formats, and ensuring accuracy across multiple platforms. I then analyzed the average yards gained per play type, focusing on 'pass' and 'run' plays.

Note: Excel was not used due to the size of the dataset exceeding its capacity.