Prepare Time Series Data

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prerequisites
intermediate Python • intermediate data science • basic Google Colab
skills learned
build baseline models with Naive and sNaive methods • set up and optimize a Prophet model as an alternative model for comparison • evaluate various models using weighted MAE and MAPE
Jiahao Weng
1 week · 8-10 hours per week · INTERMEDIATE
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In this liveProject, you’ll create baseline models with Naive and sNaive methods for time series forecasts that you can use as a point of comparison for other models. Taking on the role of a data scientist for a large retail company, you’ll go hands-on to prepare sales data, create the baseline models, and optimize a Prophet model to compare against your baseline.

This project is designed for learning purposes and is not a complete, production-ready application or solution.

project author

Jiahao Weng
Jiahao Weng is a machine learning practitioner and a senior data scientist at a multinational company where he delivers projects ranging from proof-of-concept to production machine learning systems. As a freelance writer, he also contributes data science Medium articles to share his knowledge with the community.

prerequisites

The liveProject series is for intermediate data scientists interested in tackling their first end-to-end machine learning project. To begin this liveProject, you will need to be familiar with the following:


TOOLS
  • Intermediate Python
  • Basic Google Colab
  • Basic Jupyter Notebook
TECHNIQUES
  • Basics of data visualization
  • Basics of data science

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