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Preprocess time series data for various machine learning models
Visualize time series data
Compare the time series predictions of 4 machine learning models
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.
Time Series Forecasting
Machine Learning
Feature Engineering
Python Programming
Time Series Models
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
Your workspace is a cloud desktop right in your browser, no download required
In a split-screen video, your instructor guides you step-by-step
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