Step-by-step guide to build high performing predictive applications
- Master how to use Python and its data analytics ecosystem to implement end-to-end Predictive Analytics projects
- Explore advanced predictive modeling algorithms such as support vector machine, apriori, and neural networks
- Find the right balance between theory, intuition and practice to be a useful resource for practitioners
Predictive Analytics involves meticulous usage of data, statistical algorithms and techniques of machine learning in establishing the future outcomes based on historical data.
This book provides practical coverage in understanding important concepts of predictive analytics, how to process massive data sets, building predictive models along with usage of cutting-edge python tools and packages. The book walks you step-by-step right from defining the problem statement, identifying relevant data, performing data treatment, synthesizing analytics model and optimizing them with effective Python codes. You will understand each and every phase of predictive analytics process with the help of successful demonstrated applications. You will work with predictive algorithms such as SVM, k-NN, Apriori algorithm, NLP process and neural networks. You will learn to code with various python libraries such as TensorFlow, NumPy, SciPy, pandas and build machine learning models, intuitive visualizations, performing complex calculations and achieving actionable predictive results.
With this book, you will be all set to build the predictive analytic application with practical examples and best practices using robust tools of python programming.
What you will learn
- Understand the main concepts and principles of Predictive Analytics and how to use them when building real-world predictive models.
- Properly use scikit-learn, the main Python library for Predictive Analytics and Machine Learning.
- Learn the types of Predictive Analytics problem and how to apply the main models and algorithms to solve real world problems.
- Build, evaluate, and interpret classification and regression models on real-world datasets.
- Prepare a dataset for modelling and extract information using Exploratory Data Analysis
- Explore how to implement a predictive model as a web application
Who This Book Is For
This book is for Python programmers who wants to learn predictive modeling and aspire to enter data science and machine learning areas. All you need is basic familiarity with linear algebra and statistical knowledge.