Gain practical insights by exploiting data in your business to build advanced predictive modeling applicationsKey FeaturesA step-by-step guide to predictive modeling including lots of tips, tricks, and best practicesLearn how to use popular predictive modeling algorithms such as Linear Regression, Decision Trees, Logistic Regression, and ClusteringMaster open source Python tools to build sophisticated predictive modelsBook DescriptionSocial Media and the Internet of Things have resulted in an avalanche of data. Data is powerful but not in its raw form; it needs to be processed and modeled, and Python is one of the most robust tools out there to do so. It has an array of packages for predictive modeling and a suite of IDEs to choose from. Using the Python programming language, analysts can use these sophisticated methods to build scalable analytic applications. This book is your guide to getting started with predictive analytics using Python.You'll balance both statistical and mathematical concepts, and implement them in Python using libraries such as pandas, scikit-learn, and NumPy. Through case studies and code examples using popular open-source Python libraries, this book illustrates the complete development process for analytic applications. Covering a wide range of algorithms for classification, regression, clustering, as well as cutting-edge techniques such as deep learning, this book illustrates explains how these methods work. You will learn to choose the right approach for your problem and how to develop engaging visualizations to bring to life the insights of predictive modeling.Finally, you will learn best practices in predictive modeling, as well as the different applications of predictive modeling in the modern world. The course provides you with highly practical content from the following Packt books:1. Learning Predictive Analytics with Python2. Mastering Predictive Analytics with PythonWhat you will learnUnderstand the statistical and mathematical concepts behind predictive analytics algorithms and implement them using Python librariesGet to know various methods for importing, cleaning, sub-setting, merging, joining, concatenating, exploring, grouping, and plotting data with pandas and NumPyMaster the use of Python notebooks for exploratory data analysis and rapid prototypingGet to grips with applying regression, classification, clustering, and deep learning algorithmsDiscover advanced methods to analyze structured and unstructured dataVisualize the performance of models and the insights they produceEnsure the robustness of your analytic applications by mastering the best practices of predictive analysisTable of ContentsGetting Started with Predictive ModellingData CleaningData WranglingStatistical Concepts for Predictive ModellingLinear Regression with PythonLogistic Regression with PythonClustering with PythonTrees and Random Forests with PythonBest Practices for Predictive ModellingA List of LinksFrom Data to Decisions – Getting Started with Analytic ApplicationsExploratory Data Analysis and Visualization in PythonFinding Patterns in the Noise – Clustering and Unsupervised LearningConnecting the Dots with Models – Regression MethodsPutting Data in its Place – Classification Methods and AnalysisWords and Pixels – Working with Unstructured DataLearning from the Bottom Up – Deep Networks and Unsupervised FeaturesSharing Models with Prediction ServicesReporting and Testing – Iterating on Analytic SystemsBibliography
Author: Ashish Kumar
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