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The Data Science Workshop

The Data Science Workshop - Second Edition

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

Ridge Regression

You just learned about lasso regression, which introduces a penalty and tries to eliminate certain features from the data. Ridge regression takes an alternative approach by introducing a penalty that penalizes large weights. As a result, the optimization process tries to reduce the magnitude of the coefficients without completely eliminating them.

Exercise 7.10: Fixing Model Overfitting Using Ridge Regression

The goal of this exercise is to teach you how to identify when your model starts overfitting, and to use ridge regression to fix overfitting in your model.

Note

You will be using the same dataset as in Exercise 7.09, Fixing Model Overfitting Using Lasso Regression.

The following steps will help you complete the exercise:

  1. Open a Colab notebook.
  2. Import the required libraries:
    import pandas as pd
    from sklearn.model_selection import train_test_split
    from sklearn.linear_model import LinearRegression, Ridge
    from sklearn.metrics import mean_squared_error...
CONTINUE READING
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Tech Concepts
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Programming languages
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The Data Science Workshop
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