Compare time series predictions of COVID-19 deaths

4.3
estrellas
23 calificaciones
ofrecido por
Coursera Project Network
En este proyecto guiado, tú:

Preprocess time series data for various machine learning models

Visualize time series data

Compare the time series predictions of 4 machine learning models

Clock2 hours
IntermediateIntermedio
CloudNo se necesita descarga
VideoVideo de pantalla dividida
Comment DotsInglés (English)
LaptopSolo escritorio

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.

Habilidades que desarrollarás

  • Time Series Forecasting
  • Machine Learning
  • Feature Engineering
  • Python Programming
  • Time Series Models

Aprende paso a paso

En un video que se reproduce en una pantalla dividida con tu área de trabajo, tu instructor te guiará en cada paso:

  1. Preprocess the data using pandas to be ready for machine learning, and visualize the data using matplotlib

  2. Create a SARIMAX model, optimize the model hyperparameters, use the model for forecasting future COVID-19 deaths and visualize the results

  3. Create a prophet model and use the model for forecasting future COVID-19 deaths and visualize the results

  4. Create a function that extracts features for training the XGBOOST and a feedforward neural network models

  5. Split time series feature dataset into training and test datasets and perform data normalization

  6. Train an XGBOOST model and a feedforward neural network model, and finally compare the predictions of all the models covered in the project

Cómo funcionan los proyectos guiados

Tu espacio de trabajo es un escritorio virtual directamente en tu navegador, no requiere descarga.

En un video de pantalla dividida, tu instructor te guía paso a paso

Reseñas

Principales reseñas sobre COMPARE TIME SERIES PREDICTIONS OF COVID-19 DEATHS

Ver todas las reseñas

Preguntas Frecuentes

Preguntas Frecuentes

¿Tienes más preguntas? Visita el Centro de Ayuda al Alumno.