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The Data Science Workshop

The Data Science Workshop

By : Anthony So , Thomas Joseph, Robert Thas John, Andrew Worsley , Dr. Samuel Asare
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The Data Science Workshop

The Data Science Workshop

3 (2)
By: Anthony So , Thomas Joseph, Robert Thas John, Andrew Worsley , Dr. Samuel Asare

Overview of this book

Where there’s data, there’s insight. With so much data being generated, there is immense scope to extract meaningful information that’ll boost business productivity and profitability. By learning to convert raw data into game-changing insights, you’ll open new career paths and opportunities. The Data Science Workshop begins by introducing different types of projects and showing you how to incorporate machine learning algorithms in them. You’ll learn to select a relevant metric and even assess the performance of your model. To tune the hyperparameters of an algorithm and improve its accuracy, you’ll get hands-on with approaches such as grid search and random search. Next, you’ll learn dimensionality reduction techniques to easily handle many variables at once, before exploring how to use model ensembling techniques and create new features to enhance model performance. In a bid to help you automatically create new features that improve your model, the book demonstrates how to use the automated feature engineering tool. You’ll also understand how to use the orchestration and scheduling workflow to deploy machine learning models in batch. By the end of this book, you’ll have the skills to start working on data science projects confidently. By the end of this book, you’ll have the skills to start working on data science projects confidently.
Table of Contents (16 chapters)
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Preface
12
12. Feature Engineering

Converting Data Types

Another problem you may face in a project is incorrect data types being inferred for some columns. As we saw in Chapter 10, Analyzing a Dataset, the pandas package provides us with a very easy way to display the data type of each column using the .dtypes attribute. You may be wondering, when did pandas identify the type of each column? The types are detected when you load the dataset into a pandas DataFrame using methods such as read_csv(), read_excel(), and so on.

When you've done this, pandas will try its best to automatically find the best type according to the values contained in each column. Let's see how this works on the Online Retail dataset.

First, you must import pandas:

import pandas as pd

Then, you need to assign the URL to the dataset to a new variable:

file_url = 'https://github.com/PacktWorkshops/'\
           'The-Data-Science-Workshop/blob/'\
...

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