By the end of this project, you will learn how to perform the entire time series analysis workflow for the daily COVID-19 deaths. This workflow includes the following steps: how to examine time series data, prepare the data for analysis, train different models and test their performance, and finally use the models to forecast into the future. You will learn how to visualize data using the matplotlib library, extract features from a time series data set, and perform data splitting and normalization. You will create time series analysis models using the python programming language. You will create and train four time series models: SARIMAX, Facebook prophet, neural networks and XGBOOST.
Compare time series predictions of COVID-19 deaths
Taught in English
Instructor: Sherif A. Tawfik Abbas
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(37 reviews)
What you'll learn
Preprocess time series data for various machine learning models
Visualize time series data
Compare the time series predictions of 4 machine learning models
Skills you'll practice
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Guided Project
Recommended experience
(37 reviews)
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About this Guided Project
Learn step-by-step
In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:
Understand the basics of time series analysis
Explore and prepare the data
Forecasting using SARIMAX
Forecasting using Facebook's Prophet model
Preparing the dataset for XGBOOST and NN
Splitting the dataset, and data normalization
Train the XGBOOST and NN models
Forecast the future
Recommended experience
Basic knowledge of python and basic experience with the pandas library.
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How you'll learn
Skill-based, hands-on learning
Practice new skills by completing job-related tasks.
Expert guidance
Follow along with pre-recorded videos from experts using a unique side-by-side interface.
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Access the tools and resources you need in a pre-configured cloud workspace.
Available only on desktop
This Guided Project is designed for laptops or desktop computers with a reliable Internet connection, not mobile devices.
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Reviewed on Mar 6, 2021
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