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Offered By: IBMSkillsNetwork

Predict the Stock Market using Weather Data

Employ machine learning to perform time series analysis! Leveraging Python, random forest, linear regression, and ARIMA-like models, this project walks you through the process of predicting the Dow Jones Industrial Average index using past prices and weather data in New York City. Discover the art of conducting seasonal adjustments, ensuring stationarity, performing time series feature engineering, building effective time series models, and evaluating performance through time series cross-validation. If you want to learn time series analysis, this project is for you!

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Guided Project

Data Science

387 Enrolled
4.6
(37 Reviews)

At a Glance

Employ machine learning to perform time series analysis! Leveraging Python, random forest, linear regression, and ARIMA-like models, this project walks you through the process of predicting the Dow Jones Industrial Average index using past prices and weather data in New York City. Discover the art of conducting seasonal adjustments, ensuring stationarity, performing time series feature engineering, building effective time series models, and evaluating performance through time series cross-validation. If you want to learn time series analysis, this project is for you!


A Look at the Project Ahead

Join our guided project and unravel the secrets of predicting the Dow Jones Industrial Average index 🚀. Your mission? Craft a prediction model using past prices and weather data from the concrete jungle of New York City. Will adding New York City weather to your model enhance your forecast? Get ready to decode the mysteries of time series analysis, conquer seasonality, and flex your Python muscles in the realm of linear regression, random forest, and ARIMA-like models. Time to turn data into money – let the project begin!
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In this project, you will learn:
  • The unique features and challenges associated with analyzing time series data
  • Strategies for dealing with seasonality
  • Feature engineering for time series analysis
  • Methods for ensuring stationarity
  • Linear regression and ARIMA models using Python
  • Random forest and decision tree model strategies for time series analysis

What You'll Need

You'll need a current versions of Chrome, Edge, Firefox, Internet Explorer or Safari, and an intermediate knowledge of Python. Prior knowledge of time series analysis, linear regression, and tree-based models like random forest are useful but not strictly necessary.

Disclaimer

The project presented herein is for educational purposes only; no financial advice is provided or implied. If you decide to engage in any financial activity on the basis of this project, you do so at your own risk.

Estimated Effort

2 Hours

Level

Advanced

Industries

Financial Services

Skills You Will Learn

Machine Learning, Pandas, Python, Random forest, Scikit-learn, time series

Language

English

Course Code

GPXX0K1YEN

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