Data Exploration | Intro to Azure ML Part 4

Learn how to explore a real-life data set. Now that Azure Machine Learning Studio is set up, let’s begin an end-to-end data science project in Azure Machine Learning. We’ll choose the flight delay data, and use it to predict whether or not a flight will be late on arrival based on the flight’s circumstances.
In this video, we will begin our preliminary exploration into the dataset using Azure Machine Learning’s dataset module.
In Part 4 we will cover:
introduction to projects
Exploring a data set using Azure ML
Building a data mining strategy
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[@cparker4486]

Great videos so far!

[@magomedm967]

Hi, thank you for the explanation!
I am trying to create a model where I can forecast/predict the outcome of an investment. Do you maybe know where I could get the right data to set it up? Unfortunately I could not find something on the UCI platform

[@Datasciencedojo]

Finding the right data for your investment forecasting model can be challenging, especially if you're looking for specific types of data that are not readily available in publicly accessible repositories such as UCI.
One option you may want to consider is using a data provider or financial data vendor that specializes in providing financial market data. These providers offer a wide range of historical and real-time financial data, including stock prices, company financial statements, economic indicators, and news and sentiment data.
Some popular data providers in the financial industry include Bloomberg, Refinitiv (formerly Thomson Reuters), FactSet, and Morningstar. These providers offer a wide range of data sets and APIs that you can use to access the data you need for your investment forecasting model.
Keep in mind that accessing this type of data can be expensive, and you'll need to ensure that you have the proper licenses and permissions to use the data in your model. It's also important to carefully consider the quality and accuracy of the data you're using, as errors or incomplete information can lead to inaccurate predictions.
Alternatively, you could try scraping financial data from publicly available sources such as financial news websites, regulatory filings, and social media platforms. However, this approach may be less reliable and may require significant data cleaning and processing before it can be used in your model.
In any case, it's important to carefully evaluate the data sources you use and to ensure that you're using the most accurate and relevant data possible to make your investment forecasts.

[@bjarke7886]

This series is great, thank you :)

[@andrewyzd7746]

Hi, may I know what does
mean?

[@akuakanwa7868]

Hi Data Dojo, I am working on a similar data for an online class. But my data does not include the DepTimeBlk or ArrtimeBlk so I have to create this time range. What do you suggest i do to create this, should I use an execute R script module or Convert to CSV? Or can this be done in Azure ML without an R code? Thanks

[@thamastersmooth]

I am trying to create one that will tell me who will win in a battle royal of Goku, Jiren, Shao Kahn and Hellraiser. What data should be considered?

[@contractorwolf]

where is part 5 of this series?

[@Datasciencedojo]

Hey James Wolf, this series is released weekly. Part 5 is here:
th-cam.com/video/ziLbKG51gLs/w-d-xo.html

[@contractorwolf]

thanks @data science dojo, I could not find this series in your playlists, maybe that playlist has not been defined yet?