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Data Science Projects with Python - A case study approach to gaining valuable insights from real data with machine learning, 2nd Edition (Paperback, 2nd Revised edition)
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Data Science Projects with Python - A case study approach to gaining valuable insights from real data with machine learning, 2nd Edition (Paperback, 2nd Revised edition)
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Gain hands-on experience of Python programming with
industry-standard machine learning techniques using pandas,
scikit-learn, and XGBoost Key Features Think critically about data
and use it to form and test a hypothesis Choose an appropriate
machine learning model and train it on your data Communicate
data-driven insights with confidence and clarity Book DescriptionIf
data is the new oil, then machine learning is the drill. As
companies gain access to ever-increasing quantities of raw data,
the ability to deliver state-of-the-art predictive models that
support business decision-making becomes more and more valuable. In
this book, you'll work on an end-to-end project based around a
realistic data set and split up into bite-sized practical
exercises. This creates a case-study approach that simulates the
working conditions you'll experience in real-world data science
projects. You'll learn how to use key Python packages, including
pandas, Matplotlib, and scikit-learn, and master the process of
data exploration and data processing, before moving on to fitting,
evaluating, and tuning algorithms such as regularized logistic
regression and random forest. Now in its second edition, this book
will take you through the end-to-end process of exploring data and
delivering machine learning models. Updated for 2021, this edition
includes brand new content on XGBoost, SHAP values, algorithmic
fairness, and the ethical concerns of deploying a model in the real
world. By the end of this data science book, you'll have the
skills, understanding, and confidence to build your own machine
learning models and gain insights from real data. What you will
learn Load, explore, and process data using the pandas Python
package Use Matplotlib to create compelling data visualizations
Implement predictive machine learning models with scikit-learn Use
lasso and ridge regression to reduce model overfitting Evaluate
random forest and logistic regression model performance Deliver
business insights by presenting clear, convincing conclusions Who
this book is forData Science Projects with Python - Second Edition
is for anyone who wants to get started with data science and
machine learning. If you're keen to advance your career by using
data analysis and predictive modeling to generate business
insights, then this book is the perfect place to begin. To quickly
grasp the concepts covered, it is recommended that you have basic
experience of programming with Python or another similar language,
and a general interest in statistics.
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