Compare time series predictions of COVID-19 deaths
Preprocess time series data for various machine learning models
Visualize time series data
Compare the time series predictions of 4 machine learning models
In this 2-hour long project-based course, you will learn how to preprocess time series data, visualize time series data and compare the time series predictions of 4 machine learning models.You will create time series analysis models in the python programming language to predict the daily deaths due to SARS-CoV-19, or COVID-19. You will create and train the following models: SARIMAX, Prophet, neural networks and XGBOOST. You will visualize data using the matplotlib library, and extract features from a time series data set, perform data splitting and normalization. To successfully complete this project, learners should have prior Python programming experience, a basic understanding of machine learning, and a familiarity of the Pandas library. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
Skills you will develop
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:
Preprocess the data using pandas to be ready for machine learning, and visualize the data using matplotlib
Create a SARIMAX model, optimize the model hyperparameters, use the model for forecasting future COVID-19 deaths and visualize the results
Create a prophet model and use the model for forecasting future COVID-19 deaths and visualize the results
Create a function that extracts features for training the XGBOOST and a feedforward neural network models
Split time series feature dataset into training and test datasets and perform data normalization
Train an XGBOOST model and a feedforward neural network model, and finally compare the predictions of all the models covered in the project
How Guided Projects work
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In a split-screen video, your instructor guides you step-by-step
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