Building Statistical Models in Python Develop useful models for regression, classification, time series, and survival analysis

Key FeaturesGain expertise in identifying and modeling patterns that generate successExplore the concepts with Python using important libraries such as stats modelsLearn how to build models on real-world data sets and find solutions to practical challengesBook Description
The ability to proficiently perform statistical modeling is a fundamental skill for data scientists and essential for businesses reliant on data insights. Building Statistical Models with Python is a comprehensive guide that will empower you to leverage mathematical and statistical principles in data assessment, understanding, and inference generation.
This book not only equips you with skills to navigate the complexities of statistical modeling, but also provides practical guidance for immediate implementation through illustrative examples. Through emphasis on application and code examples, you'll understand the concepts while gaining hands-on experience. With the help of Python and its essential libraries, you'll explore key statistical models, including hypothesis testing, regression, time series analysis, classification, and more.
By the end of this book, you'll gain fluency in statistical modeling while harnessing the full potential of Python's rich ecosystem for data analysis.
What you will learnExplore the use of statistics to make decisions under uncertaintyAnswer questions about data using hypothesis testsUnderstand the difference between regression and classification modelsBuild models with stats models in PythonAnalyze time series data and provide forecastsDiscover Survival Analysis and the problems it can solveWho this book is for
If you are looking to get started with building statistical models for your data sets, this book is for you! Building Statistical Models in Python bridges the gap between statistical theory and practical application of Python. Since you'll take a comprehensive journey through theory and application, no previous knowledge of statistics is required, but some experience with Python will be useful.
Table of ContentsSampling and GeneralizationDistributions of DataHypothesis TestingParametric TestsNon-Parametric TestsLinear RegressionMore Discussion on Model Selection & RegularizationLogistic RegressionDiscriminant AnalysisIntroduction to Time SeriesARIMA ModelsMultivariate Time Series MethodsTime to Event variables - An introductionModels with Survival Responses


Building Statistical Models in Python Develop useful models for regression, classification, time series, and survival analysis Fast Download
Building Statistical Models in Python Develop useful models for regression, classification, time series, and survival analysis Full Download