Cleaning and exploring your data is a crucial first step in any data project. In this assignment you will practice these skills on a real IoT dataset of electricity consumption data for a single household over four years. You will practice normalizing datatypes, handling missing data, visualizing and regularizing time series data, and identifying covariance in your data.
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Create a repository under your GitHub account from this template: https://github.com/amarbut/aai-iot-cleaning-and-eda. Instructions can be found here. Make your repository private and add your instructor’s Github account as a collaborator.
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Following the instructions in the jupyter notebook from the above Github template, perform basic data cleaning steps on the Household Electric Consumption Dataset which can be downloaded here .
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Also following the instructions in the jupyter notebook, perform an explanatory data analysis on the cleaned dataset, including visualizations typical for time series data and a consideration of covariance between variables in the dataset.
Assignment Materials:
Deliverables:
- When you have finished your code, print your notebook as a PDF and upload it to Blackboard.
- Commit your code and push the changes to GitHub so your instructor has access to the ipynb notebook files and any other code you create.