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Data Science Projects with Python
DataScienceProjectswithPythonisdesignedtogiveyoupracticalguidanceonindustry-standarddataanalysisandmachinelearningtoolsinPython,withthehelpofrealisticdata.ThebookwillhelpyouunderstandhowyoucanusepandasandMatplotlibtocriticallyexamineadatasetwithsummarystatisticsandgraphs,andextracttheinsightsyouseektoderive.Youwillcontinuetobuildonyourknowledgeasyoulearnhowtopreparedataandfeedittomachinelearningalgorithms,suchasregularizedlogisticregressionandrandomforest,usingthescikit-learnpackage.You’lldiscoverhowtotunethealgorithmstoprovidethebestpredictionsonnewand,unseendata.Asyoudelveintolaterchapters,you’llbeabletounderstandtheworkingandoutputofthesealgorithmsandgaininsightintonotonlythepredictivecapabilitiesofthemodelsbutalsotheirreasonsformakingthesepredictions.Bytheendofthisbook,youwillhavetheskillsyouneedtoconfidentlyusevariousmachinelearningalgorithmstoperformdetaileddataanalysisandextractmeaningfulinsightsfromunstructureddata.
目錄(47章)
倒序
- 封面
- 版權頁
- Preface
- About
- Chapter 1: Data Exploration and Cleaning
- Introduction
- Python and the Anaconda Package Management System
- Different Types of Data Science Problems
- Loading the Case Study Data with Jupyter and pandas
- Data Quality Assurance and Exploration
- Exploring the Financial History Features in the Dataset
- Summary
- Chapter 2: Introduction to Scikit-Learn and Model Evaluation
- Introduction
- Exploring the Response Variable and Concluding the Initial Exploration
- Introduction to Scikit-Learn
- Model Performance Metrics for Binary Classification
- Summary
- Chapter 3: Details of Logistic Regression and Feature Exploration
- Introduction
- Examining the Relationships between Features and the Response
- Univariate Feature Selection: What It Does and Doesn't Do
- Summary
- Chapter 4: The Bias-Variance Trade-off
- Introduction
- Estimating the Coefficients and Intercepts of Logistic Regression
- Cross Validation: Choosing the Regularization Parameter and Other Hyperparameters
- Summary
- Chapter 5: Decision Trees and Random Forests
- Introduction
- Decision trees
- Random Forests: Ensembles of Decision Trees
- Summary
- Chapter 6: Imputation of Missing Data Financial Analysis and Delivery to Client
- Introduction
- Review of Modeling Results
- Dealing with Missing Data: Imputation Strategies
- Final Thoughts on Delivering the Predictive Model to the Client
- Summary
- Appendix
- About
- Chapter 1: Data Exploration and Cleaning
- Chapter 2: Introduction to Scikit-Learn and Model Evaluation
- Chapter 3: Details of Logistic Regression and Feature Exploration
- Chapter 4: The Bias-Variance Trade-off
- Chapter 5: Decision Trees and Random Forests
- Chapter 6: Imputation of Missing Data Financial Analysis and Delivery to Client 更新時間：2021-06-11 13:29:22
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