{"id":63,"date":"2020-08-04T12:06:28","date_gmt":"2020-08-04T12:06:28","guid":{"rendered":"https:\/\/data-science.gotoauthority.com\/2020\/08\/04\/the-machine-learning-field-guide\/"},"modified":"2020-08-04T12:06:28","modified_gmt":"2020-08-04T12:06:28","slug":"the-machine-learning-field-guide","status":"publish","type":"post","link":"https:\/\/wealthrevelation.com\/data-science\/2020\/08\/04\/the-machine-learning-field-guide\/","title":{"rendered":"The Machine Learning Field Guide"},"content":{"rendered":"
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By Kamron Bhavnagri<\/a>, Up and coming machine learning engineer\/data scientist<\/b>.<\/p>\n

We all start with either a dataset or a goal in mind. Once\u00a0we’ve found, collected or scraped our data<\/a>, we pull it up, and witness the overwhelming sight of merciless cells of numbers, more numbers, categories, and maybe some words! A naive thought crosses our mind, to use our machine learning prowess to deal with this tangled mess… but a quick search reveals the host of tasks we’ll need to consider before\u00a0training a model<\/em>!<\/p>\n

Once we overcome the shock of our unruly data, we look for ways to battle our formidable nemesis. We start by trying to get our data into Python. It is relatively simple on paper, but the process can be slightly…\u00a0involved<\/em>. Nonetheless, a little effort was all that was needed (lucky us).<\/p>\n

Without wasting any time, we begin\u00a0data cleaning<\/em>\u00a0to get rid of the bogus and expose the beautiful. Our methods start simple – observe and remove. It works a few times, but then we realise… it really doesn’t do us justice! To deal with the mess, though, we find a powerful tool to add to our arsenal: charts! With our graphs, we can get a feel for our data, the patterns within it, and where things are missing. We can\u00a0interpolate<\/em>\u00a0(fill in) or remove missing data.<\/p>\n

Finally, we approach our highly anticipated challenge, data modelling! With a little research, we find out which tactics and models are commonly used. It is a little difficult to decipher which one we should use, but we still manage to get through it and figure it all out!<\/p>\n

We can’t finish a project without doing something impressive, though. So, a final product, website, app, or even a report will take us far! We know first impressions are important, so we fix up the GitHub repository and make sure everything’s well documented and explained. Now we are\u00a0finally able to show off our hard work to the rest of the world<\/em>!<\/p>\n

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Chapter 1 – Importing Data<\/h3>\n

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Data comes in all kinds of shapes and sizes, and so the process we use to get everything into code often varies.<\/p>\n

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Let’s be real, importing data seems easy, but sometimes… it’s a little pesky.<\/em><\/p>\n<\/blockquote>\n

The hard part about data cleaning isn’t the coding or theory, but instead our preparation! When we first start a new project and download our dataset, it can be tempting to open up a code editor and start typing… but this won’t do us any good. If we want to get a head start, we need to prepare ourselves for the best and worst parts of our data. To do this, we’ll need to start basic, by manually inspecting our spreadsheet\/s. Once we understand the basic format of the data (filetype along with any particularities) we can move onto getting it all into Python.<\/p>\n

When we’re lucky and just have one spreadsheet we can use the Pandas\u00a0read_csv<\/a>\u00a0function (letting it know where our data lies):<\/p>\n

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pd.read_csv(\"file_path.csv\")\r\n\r\n<\/pre>\n<\/div>\n
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In reality, we run into way more complex situations, so look out for:<\/p>\n