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@@ -7,14 +7,13 @@ This repository contains the supplementary material to my talk delivered at [PyD
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The idea behind the talk was to explain the ideology behind Exploratory Data Analysis and why it is essential for any Data Science project.
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- The *slides* can be viewed at http://bit.ly/SuriEDAPyData.
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- The *proposal* can be viewed at https://github.com/pydatadelhi/talks/issues/68.
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The talk included a lot of content from the following projects:
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-1. [Investigating Factors Affecting Wine Quality](): This project investigates a dataset using EDA to find chemical properties that affect red wine quality.
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+1. [Investigating Factors Affecting Wine Quality](https://github.com/pranavsuri/PyData-EDA/tree/master/Investigating%20Factors%20Affecting%20Red%20Wine%20Quality): This project investigates a dataset using EDA to find chemical properties that affect red wine quality.
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-2. [Identifying Fraud from Enron Email Dataset](): This project includes a section which uses EDA to remove outliers.
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+2. [Identifying Fraud from Enron Email Dataset](https://github.com/pranavsuri/PyData-EDA/tree/master/Identifying%20Fraud%20from%20Enron%20Email%20Dataset): This project includes a section which uses EDA to remove outliers.
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## Message
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The meet-up was a great experience as I got meet some amazing people. I certainly look forward to collaborating on a new project. One of the ideas I have is to explore datasets on different beverages (such as [white wine](https://archive.ics.uci.edu/ml/datasets/wine+quality)) or maybe some food items.
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