Learning predictive analytics with python :: gain practical insights into predictive modelling by implementing Predictive Analytics algorithms on public datasets with Python /
Annotation
Gespeichert in:
| 1. Verfasser: | |
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Birmingham :
Packt Publishing,
[2016]
©2016 |
| Schriftenreihe: | Community experience distilled.
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | Annotation |
| Beschreibung: | Includes index. |
| Beschreibung: | 1 online resource |
| ISBN: | 9781783983278 1783983272 |
Internformat
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| 505 | 0 | |a Cover ; Copyright; Credits; Foreword; About the Author; Acknowledgments; About the Reviewer; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Getting Started with Predictive Modelling ; Introducing predictive modelling; Scope of predictive modelling; Ensemble of statistical algorithms; Statistical tools; Historical data; Mathematical function; Business context; Knowledge matrix for predictive modelling; Task matrix for predictive modelling; Applications and examples of predictive modelling; LinkedIn's ""People also viewed"" feature; What it does?; How is it done? | |
| 505 | 8 | |a Correct targeting of online adsHow is it done?; Santa Cruz predictive policing; How is it done?; Determining the activity of a smartphone user using accelerometer data; How is it done?; Sport and fantasy leagues; How was it done?; Python and its packages -- download and installation; Anaconda; Standalone Python; Installing a Python package; Installing pip; Installing Python packages with pip; Python and its packages for predictive modelling; IDEs for Python; Summary; Chapter 2: Data Cleaning ; Reading the data -- variations and examples; Data frames; Delimiters. | |
| 505 | 8 | |a Various methods of importing data in PythonCase 1 -- reading a dataset using the read_csv method; The read_csv method; Use cases of the read_csv method; Case 2 -- reading a dataset using the open method of Python; Reading a dataset line by line; Changing the delimiter of a dataset; Case 3 -- reading data from a URL; Case 4 -- miscellaneous cases; Reading from an .xls or .xlsx file; Writing to a CSV or Excel file; Basics -- summary, dimensions, structure; Handling missing values; Checking for missing values; What constitutes missing data?; How missing values are generated and propagated. | |
| 505 | 8 | |a Treating missing valuesDeletion; Imputation; Creating dummy variables; Visualizing a dataset by basic plotting; Scatter plots; Histograms; Boxplots; Summary; Chapter 3: Data Wrangling ; Subsetting a dataset; Selecting columns; Selecting rows; Selecting a combination of rows and columns; Creating new columns; Generating random numbers and their usage; Various methods for generating random numbers; Seeding a random number; Generating random numbers following probability distributions; Probability density function; Cumulative density function; Uniform distribution; Normal distribution. | |
| 505 | 8 | |a Using the Monte-Carlo simulation to find the value of piGeometry and mathematics behind the calculation of pi; Generating a dummy data frame; Grouping the data -- aggregation, filtering, and transformation; Aggregation; Filtering; Transformation; Miscellaneous operations; Random sampling -- splitting a dataset in training and testing datasets; Method 1 -- using the Customer Churn Model; Method 2 -- using sklearn; Method 3 -- using the shuffle function; Concatenating and appending data; Merging/joining datasets; Inner Join; Left Join; Right Join; An example of the Inner Join. | |
| 520 | 8 | |a Annotation |b Gain practical insights into predictive modelling by implementing Predictive Analytics algorithms on public datasets with PythonAbout 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 ClusteringWho This Book Is ForIf 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 themIn DetailSocial 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. Learning to predict who would win, lose, buy, lie, or die with Python is an indispensable skill set to have in this data age. This book is your guide to getting started with Predictive Analytics using Python. You will see how to process data and make predictive models from it. We balance both statistical and mathematical concepts, and implement them in Python using libraries such as pandas, scikit-learn, and numpy. You'll start by getting an understanding of the basics of predictive modeling, then you will see how to cleanse your data of impurities and get it ready it for predictive modeling. You will also learn more about the best predictive modeling algorithms such as Linear Regression, Decision Trees, and Logistic Regression. Finally, you will see the best practices in predictive modeling, as well as the different applications of predictive modeling in the modern world. Style and approachAll the concepts in this book been explained and illustrated using a dataset, and in a step-by-step manner. The Python code snippet to implement a method or concept is followed by the output, such as charts, dataset heads, pictures, and so on. The statistical concepts are explained in detail wherever required. | |
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| contents | Cover ; Copyright; Credits; Foreword; About the Author; Acknowledgments; About the Reviewer; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Getting Started with Predictive Modelling ; Introducing predictive modelling; Scope of predictive modelling; Ensemble of statistical algorithms; Statistical tools; Historical data; Mathematical function; Business context; Knowledge matrix for predictive modelling; Task matrix for predictive modelling; Applications and examples of predictive modelling; LinkedIn's ""People also viewed"" feature; What it does?; How is it done? Correct targeting of online adsHow is it done?; Santa Cruz predictive policing; How is it done?; Determining the activity of a smartphone user using accelerometer data; How is it done?; Sport and fantasy leagues; How was it done?; Python and its packages -- download and installation; Anaconda; Standalone Python; Installing a Python package; Installing pip; Installing Python packages with pip; Python and its packages for predictive modelling; IDEs for Python; Summary; Chapter 2: Data Cleaning ; Reading the data -- variations and examples; Data frames; Delimiters. Various methods of importing data in PythonCase 1 -- reading a dataset using the read_csv method; The read_csv method; Use cases of the read_csv method; Case 2 -- reading a dataset using the open method of Python; Reading a dataset line by line; Changing the delimiter of a dataset; Case 3 -- reading data from a URL; Case 4 -- miscellaneous cases; Reading from an .xls or .xlsx file; Writing to a CSV or Excel file; Basics -- summary, dimensions, structure; Handling missing values; Checking for missing values; What constitutes missing data?; How missing values are generated and propagated. Treating missing valuesDeletion; Imputation; Creating dummy variables; Visualizing a dataset by basic plotting; Scatter plots; Histograms; Boxplots; Summary; Chapter 3: Data Wrangling ; Subsetting a dataset; Selecting columns; Selecting rows; Selecting a combination of rows and columns; Creating new columns; Generating random numbers and their usage; Various methods for generating random numbers; Seeding a random number; Generating random numbers following probability distributions; Probability density function; Cumulative density function; Uniform distribution; Normal distribution. Using the Monte-Carlo simulation to find the value of piGeometry and mathematics behind the calculation of pi; Generating a dummy data frame; Grouping the data -- aggregation, filtering, and transformation; Aggregation; Filtering; Transformation; Miscellaneous operations; Random sampling -- splitting a dataset in training and testing datasets; Method 1 -- using the Customer Churn Model; Method 2 -- using sklearn; Method 3 -- using the shuffle function; Concatenating and appending data; Merging/joining datasets; Inner Join; Left Join; Right Join; An example of the Inner Join. |
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code="b">Gain practical insights into predictive modelling by implementing Predictive Analytics algorithms on public datasets with PythonAbout 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 ClusteringWho This Book Is ForIf 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 themIn DetailSocial 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. Learning to predict who would win, lose, buy, lie, or die with Python is an indispensable skill set to have in this data age. This book is your guide to getting started with Predictive Analytics using Python. You will see how to process data and make predictive models from it. We balance both statistical and mathematical concepts, and implement them in Python using libraries such as pandas, scikit-learn, and numpy. You'll start by getting an understanding of the basics of predictive modeling, then you will see how to cleanse your data of impurities and get it ready it for predictive modeling. You will also learn more about the best predictive modeling algorithms such as Linear Regression, Decision Trees, and Logistic Regression. Finally, you will see the best practices in predictive modeling, as well as the different applications of predictive modeling in the modern world. Style and approachAll the concepts in this book been explained and illustrated using a dataset, and in a step-by-step manner. The Python code snippet to implement a method or concept is followed by the output, such as charts, dataset heads, pictures, and so on. 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| id | ZDB-4-EBA-ocn938492639 |
| illustrated | Not Illustrated |
| indexdate | 2024-11-27T13:27:02Z |
| institution | BVB |
| isbn | 9781783983278 1783983272 |
| language | English |
| oclc_num | 938492639 |
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| publishDate | 2016 |
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| series | Community experience distilled. |
| series2 | Community experience distilled |
| spelling | Kumar, Ashish, author. Learning predictive analytics with python : gain practical insights into predictive modelling by implementing Predictive Analytics algorithms on public datasets with Python / Ashish Kumar. Birmingham : Packt Publishing, [2016] ©2016 1 online resource text txt rdacontent computer c rdamedia online resource cr rdacarrier text file Community experience distilled Online resource; title from PDF title page (EBSCO, viewed September 8, 2017). Includes index. Cover ; Copyright; Credits; Foreword; About the Author; Acknowledgments; About the Reviewer; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Getting Started with Predictive Modelling ; Introducing predictive modelling; Scope of predictive modelling; Ensemble of statistical algorithms; Statistical tools; Historical data; Mathematical function; Business context; Knowledge matrix for predictive modelling; Task matrix for predictive modelling; Applications and examples of predictive modelling; LinkedIn's ""People also viewed"" feature; What it does?; How is it done? Correct targeting of online adsHow is it done?; Santa Cruz predictive policing; How is it done?; Determining the activity of a smartphone user using accelerometer data; How is it done?; Sport and fantasy leagues; How was it done?; Python and its packages -- download and installation; Anaconda; Standalone Python; Installing a Python package; Installing pip; Installing Python packages with pip; Python and its packages for predictive modelling; IDEs for Python; Summary; Chapter 2: Data Cleaning ; Reading the data -- variations and examples; Data frames; Delimiters. Various methods of importing data in PythonCase 1 -- reading a dataset using the read_csv method; The read_csv method; Use cases of the read_csv method; Case 2 -- reading a dataset using the open method of Python; Reading a dataset line by line; Changing the delimiter of a dataset; Case 3 -- reading data from a URL; Case 4 -- miscellaneous cases; Reading from an .xls or .xlsx file; Writing to a CSV or Excel file; Basics -- summary, dimensions, structure; Handling missing values; Checking for missing values; What constitutes missing data?; How missing values are generated and propagated. Treating missing valuesDeletion; Imputation; Creating dummy variables; Visualizing a dataset by basic plotting; Scatter plots; Histograms; Boxplots; Summary; Chapter 3: Data Wrangling ; Subsetting a dataset; Selecting columns; Selecting rows; Selecting a combination of rows and columns; Creating new columns; Generating random numbers and their usage; Various methods for generating random numbers; Seeding a random number; Generating random numbers following probability distributions; Probability density function; Cumulative density function; Uniform distribution; Normal distribution. Using the Monte-Carlo simulation to find the value of piGeometry and mathematics behind the calculation of pi; Generating a dummy data frame; Grouping the data -- aggregation, filtering, and transformation; Aggregation; Filtering; Transformation; Miscellaneous operations; Random sampling -- splitting a dataset in training and testing datasets; Method 1 -- using the Customer Churn Model; Method 2 -- using sklearn; Method 3 -- using the shuffle function; Concatenating and appending data; Merging/joining datasets; Inner Join; Left Join; Right Join; An example of the Inner Join. Annotation Gain practical insights into predictive modelling by implementing Predictive Analytics algorithms on public datasets with PythonAbout 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 ClusteringWho This Book Is ForIf 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 themIn DetailSocial 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. Learning to predict who would win, lose, buy, lie, or die with Python is an indispensable skill set to have in this data age. This book is your guide to getting started with Predictive Analytics using Python. You will see how to process data and make predictive models from it. We balance both statistical and mathematical concepts, and implement them in Python using libraries such as pandas, scikit-learn, and numpy. You'll start by getting an understanding of the basics of predictive modeling, then you will see how to cleanse your data of impurities and get it ready it for predictive modeling. You will also learn more about the best predictive modeling algorithms such as Linear Regression, Decision Trees, and Logistic Regression. Finally, you will see the best practices in predictive modeling, as well as the different applications of predictive modeling in the modern world. Style and approachAll the concepts in this book been explained and illustrated using a dataset, and in a step-by-step manner. The Python code snippet to implement a method or concept is followed by the output, such as charts, dataset heads, pictures, and so on. The statistical concepts are explained in detail wherever required. English. Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 Python (Langage de programmation) COMPUTERS Programming Languages Python. bisacsh Python (Computer program language) fast has work: Learning predictive analytics with Python (Text) https://id.oclc.org/worldcat/entity/E39PD38KpFTqXgqbq8tckjRh73 https://id.oclc.org/worldcat/ontology/hasWork 1-78398-326-4 Community experience distilled. http://id.loc.gov/authorities/names/no2011030603 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1171944 Volltext |
| spellingShingle | Kumar, Ashish Learning predictive analytics with python : gain practical insights into predictive modelling by implementing Predictive Analytics algorithms on public datasets with Python / Community experience distilled. Cover ; Copyright; Credits; Foreword; About the Author; Acknowledgments; About the Reviewer; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Getting Started with Predictive Modelling ; Introducing predictive modelling; Scope of predictive modelling; Ensemble of statistical algorithms; Statistical tools; Historical data; Mathematical function; Business context; Knowledge matrix for predictive modelling; Task matrix for predictive modelling; Applications and examples of predictive modelling; LinkedIn's ""People also viewed"" feature; What it does?; How is it done? Correct targeting of online adsHow is it done?; Santa Cruz predictive policing; How is it done?; Determining the activity of a smartphone user using accelerometer data; How is it done?; Sport and fantasy leagues; How was it done?; Python and its packages -- download and installation; Anaconda; Standalone Python; Installing a Python package; Installing pip; Installing Python packages with pip; Python and its packages for predictive modelling; IDEs for Python; Summary; Chapter 2: Data Cleaning ; Reading the data -- variations and examples; Data frames; Delimiters. Various methods of importing data in PythonCase 1 -- reading a dataset using the read_csv method; The read_csv method; Use cases of the read_csv method; Case 2 -- reading a dataset using the open method of Python; Reading a dataset line by line; Changing the delimiter of a dataset; Case 3 -- reading data from a URL; Case 4 -- miscellaneous cases; Reading from an .xls or .xlsx file; Writing to a CSV or Excel file; Basics -- summary, dimensions, structure; Handling missing values; Checking for missing values; What constitutes missing data?; How missing values are generated and propagated. Treating missing valuesDeletion; Imputation; Creating dummy variables; Visualizing a dataset by basic plotting; Scatter plots; Histograms; Boxplots; Summary; Chapter 3: Data Wrangling ; Subsetting a dataset; Selecting columns; Selecting rows; Selecting a combination of rows and columns; Creating new columns; Generating random numbers and their usage; Various methods for generating random numbers; Seeding a random number; Generating random numbers following probability distributions; Probability density function; Cumulative density function; Uniform distribution; Normal distribution. Using the Monte-Carlo simulation to find the value of piGeometry and mathematics behind the calculation of pi; Generating a dummy data frame; Grouping the data -- aggregation, filtering, and transformation; Aggregation; Filtering; Transformation; Miscellaneous operations; Random sampling -- splitting a dataset in training and testing datasets; Method 1 -- using the Customer Churn Model; Method 2 -- using sklearn; Method 3 -- using the shuffle function; Concatenating and appending data; Merging/joining datasets; Inner Join; Left Join; Right Join; An example of the Inner Join. Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 Python (Langage de programmation) COMPUTERS Programming Languages Python. bisacsh Python (Computer program language) fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh96008834 |
| title | Learning predictive analytics with python : gain practical insights into predictive modelling by implementing Predictive Analytics algorithms on public datasets with Python / |
| title_auth | Learning predictive analytics with python : gain practical insights into predictive modelling by implementing Predictive Analytics algorithms on public datasets with Python / |
| title_exact_search | Learning predictive analytics with python : gain practical insights into predictive modelling by implementing Predictive Analytics algorithms on public datasets with Python / |
| title_full | Learning predictive analytics with python : gain practical insights into predictive modelling by implementing Predictive Analytics algorithms on public datasets with Python / Ashish Kumar. |
| title_fullStr | Learning predictive analytics with python : gain practical insights into predictive modelling by implementing Predictive Analytics algorithms on public datasets with Python / Ashish Kumar. |
| title_full_unstemmed | Learning predictive analytics with python : gain practical insights into predictive modelling by implementing Predictive Analytics algorithms on public datasets with Python / Ashish Kumar. |
| title_short | Learning predictive analytics with python : |
| title_sort | learning predictive analytics with python gain practical insights into predictive modelling by implementing predictive analytics algorithms on public datasets with python |
| title_sub | gain practical insights into predictive modelling by implementing Predictive Analytics algorithms on public datasets with Python / |
| topic | Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 Python (Langage de programmation) COMPUTERS Programming Languages Python. bisacsh Python (Computer program language) fast |
| topic_facet | Python (Computer program language) Python (Langage de programmation) COMPUTERS Programming Languages Python. |
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