Dataset: Multiple Stock Prices By Industry

About this Dataset

Multiple Stock Prices By Industry

Description 📑

This dataset provides comprehensive from 2014 to September 2024 for a variety of industries, such as Food, Technology, Pharmaceutical and Finance, allowing for in-depth time series analysis of market trends, forecasting, research, investment analysis, and SOTA machine learning model.

What's inside? 🤔

The main dataset stock_prices.csv is a multi-level index columns dataframe which allows easy to access by industry name as follow,

Technology: Microsoft "MSFT", Google "GOOGL", Meta "META", Nvidia "NVDA"
Finance: JPMorgan Chase & Co. "JPM", Bank of America "BAC", MasterCard "MA", Visa "V"
Pharmaceutical: Johnson & Johnson "JNJ", Pfizer "PFE",
Merck & Co "MRK", AstraZeneca "AZN"
Food: Yum! "YUM", McDonald's "MCD", Starbucks "SBUX", Domino's Pizza "DPZ",
Education: Udemy "UDMY", Duolingo "DUOL", Coursera "COUR", Chegg Inc "CHGG",
Energy: Exxon Mobil "XOM", Chevron "CVX", Shell "SHEL", NextEra "NEE"

ML Use cases 🧙🏻‍♂️

Univariate Time Series Forecasting: ARIMA, RNN, LSTM
Multivariate Time Series Forecasting: Vector AutoRegressive (VAR)
Exploratory Data Analysis

Getting Started 👩🏻‍💻

Don't know where to begin?? Kick start your analysis with this notebook to gain a better understanding of the data!

HAPPY KAGGLING

Tables

Full Ticker Names

@kaggle.chayanonc_multiple_stock_prices_by_industry_updated_daily.full_ticker_names

303.86 KB
11133 rows
2 columns


CREATE TABLE full_ticker_names (
  "symbol" VARCHAR,
  "security_name" VARCHAR
);

Stock Prices

@kaggle.chayanonc_multiple_stock_prices_by_industry_updated_daily.stock_prices

485.06 KB
2720 rows
25 columns


CREATE TABLE stock_prices (
  "unnamed_0" VARCHAR,
  "tech" VARCHAR,
  "tech_1" VARCHAR,
  "tech_2" VARCHAR,
  "tech_3" VARCHAR,
  "finance" VARCHAR,
  "finance_1" VARCHAR,
  "finance_2" VARCHAR,
  "finance_3" VARCHAR,
  "pharma" VARCHAR,
  "pharma_1" VARCHAR,
  "pharma_2" VARCHAR,
  "pharma_3" VARCHAR,
  "food" VARCHAR,
  "food_1" VARCHAR,
  "food_2" VARCHAR,
  "food_3" VARCHAR,
  "education" VARCHAR,
  "education_1" VARCHAR,
  "education_2" VARCHAR,
  "education_3" VARCHAR,
  "energy" VARCHAR,
  "energy_1" VARCHAR,
  "energy_2" VARCHAR,
  "energy_3" VARCHAR
);