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This project is aimed to use data munging techniques, such as assessing the quality of the data for validity, accuracy, completeness, consistency and uniformity, to clean the OpenStreetMap data for a part of the world.

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Ashish25/Wrangling

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OpenStreet Data Wrangling

My Report 🔍

What’s so hard about retrieving data from databases or various files formats? You grab some data from this file and that database, clean it up, merge it, and then feed it into your state of the art, deep learning algorithm … Right?

But the reality is this -- anyone who has worked with data extensively knows it is an absolute nightmare to get data from different data sources to play well with each other.

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And In this project I have played and tried all of the skills required to deal with even the most nightmarish data wrangling scenarios such as:

  • Assess the quality of the data for validity, accuracy, completeness, consistency and uniformity.
  • Parse and gather data from popular file formats such as .csv, .json, .xml, and .html
  • Process data from multiple files or very large files that can be cleaned programmatically.
  • Learn how to store, query, and aggregate data using database querying.

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This project is aimed to use data munging techniques, such as assessing the quality of the data for validity, accuracy, completeness, consistency and uniformity, to clean the OpenStreetMap data for a part of the world.

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