Gain practical insights into predictive modelling by implementing Predictive Analytics algorithms on public datasets with Python About This Book * A step-by-step guide to predictive modeling including lots of tips, tricks, and best practices * Get to grips with the basics of Predictive Analytics with Python * Learn how to use the popular predictive modeling algorithms such as Linear Regression, Decision Trees, Logistic Regression, and Clustering Who This Book Is For If you wish to learn how to implement Predictive Analytics algorithms using Python libraries, then this is the book for you. If you are familiar with coding in Python (or some other programming/statistical/scripting language) but have never used or read about Predictive Analytics algorithms, this book will also help you. The book will be beneficial to and can be read by any Data Science enthusiasts. Some familiarity with Python will be useful to get the most out of this book, but it is certainly not a prerequisite. What You Will Learn * Understand the statistical and mathematical concepts behind Predictive Analytics algorithms and implement Predictive Analytics algorithms using Python libraries * Analyze the result parameters arising from the implementation of Predictive Analytics algorithms * Write Python modules/functions from scratch to execute segments or the whole of these algorithms * Recognize and mitigate various contingencies and issues related to the implementation of Predictive Analytics algorithms * Get to know various methods of importing, cleaning, sub-setting, merging, joining, concatenating, exploring, grouping, and plotting data with pandas and numpy * Create dummy datasets and simple mathematical simulations using the Python numpy and pandas libraries * Understand the best practices while handling datasets in Python and creating predictive models out of them In Detail Social Media and the Internet of Things have resulted in an avalanche of data. Data is powerful but not in its r
ترست بايلوت
منذ شهر
منذ شهر
