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In this project, we will see in a hands-on training jupyter notebook how to effectively diagnose and deal with missing data in Python.

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🧹 Exploratory Data Analysis in Python :

  • 🎯 We will see in this hands-on training notebook how to effectively diagnose and treat missing data in Python.
  • 📊 The majority of data science work often revolves around pre-processing data, and making sure it's ready for analysis. However, we will be covering how transform our raw data into accurate insights. In this notebook, we will see:
    • Import data into pandas, and use simple functions to diagnose problems in our data.
    • Visualize missing and out of range data using missingno and seaborn.
    • Apply a range of data cleaning tasks that will ensure the delivery of accurate insights.
    • Make sure we have a clean dataset ready for data analysis.
  • 📫 Feel free to contact me if anything is wrong or if anything needs to be changed 😎! labrijisaad@gmail.com

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In this project, we will see in a hands-on training jupyter notebook how to effectively diagnose and deal with missing data in Python.

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