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Hands-On Predictive Analytics with Python
Predictiveanalyticsisanappliedfieldthatemploysavarietyofquantitativemethodsusingdatatomakepredictions.Itinvolvesmuchmorethanjustthrowingdataontoacomputertobuildamodel.Thisbookprovidespracticalcoveragetohelpyouunderstandthemostimportantconceptsofpredictiveanalytics.Usingpractical,step-by-stepexamples,webuildpredictiveanalyticssolutionswhileusingcutting-edgePythontoolsandpackages.Thebook'sstep-by-stepapproachstartsbydefiningtheproblemandmovesontoidentifyingrelevantdata.Wewillalsobeperformingdatapreparation,exploringandvisualizingrelationships,buildingmodels,tuning,evaluating,anddeployingmodel.EachstagehasrelevantpracticalexamplesandefficientPythoncode.YouwillworkwithmodelssuchasKNN,RandomForests,andneuralnetworksusingthemostimportantlibrariesinPython'sdatasciencestack:NumPy,Pandas,Matplotlib,Seaborn,Keras,Dash,andsoon.Inadditiontohands-oncodeexamples,youwillfindintuitiveexplanationsoftheinnerworkingsofthemaintechniquesandalgorithmsusedinpredictiveanalytics.Bytheendofthisbook,youwillbeallsettobuildhigh-performancepredictiveanalyticssolutionsusingPythonprogramming.
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- Leave a review - let other readers know what you think
- Other Books You May Enjoy
- Further reading
- Summary
- Building the web application
- Producing the predictive model objects
品牌：中圖公司
上架時間：2021-07-02 12:35:25
出版社：Packt Publishing
本書數字版權由中圖公司提供，并由其授權上海閱文信息技術有限公司制作發行
- Leave a review - let other readers know what you think 更新時間：2021-07-02 14:22:37
- Other Books You May Enjoy
- Further reading
- Summary
- Building the web application
- Producing the predictive model objects
- Implementing a predictive model as a web application
- Building a basic interactive app
- Building a basic static app
- The application layout
- Installation
- Plotly
- What is Dash?
- Introducing Dash
- Using an analytic application
- A feature of an existing product
- Using a technical report
- Model communication and/or deployment phase
- Technical requirements
- Implementing a Model with Dash
- Summary
- Not only a technical problem but a business problem
- Analyzing the results
- Transforming the target
- Fitting a neural network
- Improving our diamond price predictions
- Improving performance
- Optimizing more than one parameter
- Optimizing a single hyperparameter
- Hyperparameter tuning
- Technical requirements
- Model Tuning and Improving Performance
- Further reading
- Summary
- The k-fold cross-validation
- Defining a custom metric for classification
- Receiver Operating Characteristic (ROC) and precision-recall curves
- Visualizing probabilities
- Visualization methods for evaluating classification models
- Confusion matrix and related metrics
- Evaluation for classification models
- Visualization methods for evaluating regression models
- Defining a custom metric
- R-squared (R2)
- MAE
- MSE and Root Mean Squared Error (RMSE)
- Metrics for regression models
- Evaluation of regression models
- Technical requirements
- Model Evaluation
- Further reading
- Summary
- Practical advice on training neural networks
- Dropout
- Early stopping
- Using a validation set
- Regularization for neural networks
- So many decisions; so little time
- The dark art of training neural networks
- Evaluating predictions
- Building the MLP for predicting credit card default
- Classification with neural networks
- Making predictions with the neural network
- Training the MLP
- Building the MLP for predicting diamond prices
- Regressing with neural networks
- Keras – deep learning for humans
- TensorFlow
- Introducing TensorFlow and Keras
- How MLPs learn
- Anatomy of an MLP – elements of a neural network model
- Deep learning
- Introducing neural network models
- Technical requirements
- Introducing Neural Nets for Predictive Analytics
- Further reading
- Summary
- Gaussian Naive Bayes with scikit-learn
- Gaussian Naive Bayes
- Back to the classification problem
- Using Bayesian terms
- Bayes' theorem
- Conditional probability
- Naive Bayes classifiers
- Multiclass classification
- Training versus testing error
- Random forests
- Training a larger classification tree
- The good and the bad of trees
- How trees work
- Classification trees
- A complete logistic regression model
- A simple logistic regression model
- Logistic regression
- Credit card default dataset
- Predicting categories and probabilities
- Classification tasks
- Technical requirements
- Predicting Categories with Machine Learning
- Further reading
- Summary
- Training versus testing error
- KNN
- Lasso regression
- MLR
- Standardization – centering and scaling
- Dimensionality reduction using PCA
- Train-test split
- Further feature transformations
- Introducing scikit-learn
- Practical considerations before modeling
- Evaluation function and optimization
- Overfitting
- The goal of ML models – generalization
- Creating your first ML model
- Tasks in supervised learning
- Introduction to ML
- Technical requirements
- Predicting Numerical Values with Machine Learning
- Further reading
- Summary
- Introduction to graphical multivariate EDA
- One numerical feature and one categorical feature
- Barplots for two categorical variables
- Cross tables
- Two categorical features
- The Pearson correlation coefficient
- Scatter plots
- Two numerical features
- Bivariate EDA
- Univariate EDA for categorical features
- Univariate EDA for numerical features
- Univariate EDA
- What is EDA?
- Technical requirements
- Dataset Understanding – Exploratory Data Analysis
- Further reading
- Summary
- A brief introduction to feature engineering
- One-hot encoding with pandas
- Near collinearity
- Low variance features
- Encoding categorical features
- Credit card default – numerical features
- Credit card default – data collection and preparation
- Deliverables of the project
- Metrics for the model
- Methodology
- Goal
- Credit card default – proposing a solution
- Credit card default – problem understanding and definition
- Practical project – credit card default
- Dealing with missing values
- Diamond prices – data collection and preparation
- Deliverables for the project
- Metrics for the model
- Methodology
- Goal
- Diamond prices – proposing a solution at a high level
- Getting more context
- Diamond prices – problem understanding and definition
- Practical project – diamond prices
- Define the deliverables of the project
- Define key metrics of model performance
- Define your methodology
- Proposing a solution
- Think about access to the data
- Make explicit the data that will be required
- Define what is going to be predicted
- Context is everything
- Understanding the business problem and proposing a solution
- Technical requirements
- Problem Understanding and Data Preparation
- Further reading
- Summary
- Dash
- TensorFlow and Keras
- Scikit-learn
- Seaborn
- Matplotlib
- pandas
- SciPy
- A mini NumPy tutorial
- NumPy
- Jupyter
- Anaconda
- A quick tour of Python's data science stack
- CRISP-DM and other approaches
- Communication and/or deployment
- Model evaluation
- Model building
- Dataset understanding using EDA
- Data collection and preparation
- Problem understanding and definition
- The predictive analytics process
- Reviewing important concepts of predictive analytics
- What is predictive analytics?
- Technical requirements
- The Predictive Analytics Process
- Reviews
- Get in touch
- Conventions used
- Download the color images
- Download the example code files
- To get the most out of this book
- What this book covers
- Who this book is for
- Preface
- Packt is searching for authors like you
- About the reviewer
- About the author
- Contributors
- Packt.com
- Why subscribe?
- About Packt
- Hands-On Predictive Analytics with Python
- Copyright and Credits
- Title Page
- coverpage
- coverpage
- Title Page
- Copyright and Credits
- Hands-On Predictive Analytics with Python
- About Packt
- Why subscribe?
- Packt.com
- Contributors
- About the author
- About the reviewer
- Packt is searching for authors like you
- Preface
- Who this book is for
- What this book covers
- To get the most out of this book
- Download the example code files
- Download the color images
- Conventions used
- Get in touch
- Reviews
- The Predictive Analytics Process
- Technical requirements
- What is predictive analytics?
- Reviewing important concepts of predictive analytics
- The predictive analytics process
- Problem understanding and definition
- Data collection and preparation
- Dataset understanding using EDA
- Model building
- Model evaluation
- Communication and/or deployment
- CRISP-DM and other approaches
- A quick tour of Python's data science stack
- Anaconda
- Jupyter
- NumPy
- A mini NumPy tutorial
- SciPy
- pandas
- Matplotlib
- Seaborn
- Scikit-learn
- TensorFlow and Keras
- Dash
- Summary
- Further reading
- Problem Understanding and Data Preparation
- Technical requirements
- Understanding the business problem and proposing a solution
- Context is everything
- Define what is going to be predicted
- Make explicit the data that will be required
- Think about access to the data
- Proposing a solution
- Define your methodology
- Define key metrics of model performance
- Define the deliverables of the project
- Practical project – diamond prices
- Diamond prices – problem understanding and definition
- Getting more context
- Diamond prices – proposing a solution at a high level
- Goal
- Methodology
- Metrics for the model
- Deliverables for the project
- Diamond prices – data collection and preparation
- Dealing with missing values
- Practical project – credit card default
- Credit card default – problem understanding and definition
- Credit card default – proposing a solution
- Goal
- Methodology
- Metrics for the model
- Deliverables of the project
- Credit card default – data collection and preparation
- Credit card default – numerical features
- Encoding categorical features
- Low variance features
- Near collinearity
- One-hot encoding with pandas
- A brief introduction to feature engineering
- Summary
- Further reading
- Dataset Understanding – Exploratory Data Analysis
- Technical requirements
- What is EDA?
- Univariate EDA
- Univariate EDA for numerical features
- Univariate EDA for categorical features
- Bivariate EDA
- Two numerical features
- Scatter plots
- The Pearson correlation coefficient
- Two categorical features
- Cross tables
- Barplots for two categorical variables
- One numerical feature and one categorical feature
- Introduction to graphical multivariate EDA
- Summary
- Further reading
- Predicting Numerical Values with Machine Learning
- Technical requirements
- Introduction to ML
- Tasks in supervised learning
- Creating your first ML model
- The goal of ML models – generalization
- Overfitting
- Evaluation function and optimization
- Practical considerations before modeling
- Introducing scikit-learn
- Further feature transformations
- Train-test split
- Dimensionality reduction using PCA
- Standardization – centering and scaling
- MLR
- Lasso regression
- KNN
- Training versus testing error
- Summary
- Further reading
- Predicting Categories with Machine Learning
- Technical requirements
- Classification tasks
- Predicting categories and probabilities
- Credit card default dataset
- Logistic regression
- A simple logistic regression model
- A complete logistic regression model
- Classification trees
- How trees work
- The good and the bad of trees
- Training a larger classification tree
- Random forests
- Training versus testing error
- Multiclass classification
- Naive Bayes classifiers
- Conditional probability
- Bayes' theorem
- Using Bayesian terms
- Back to the classification problem
- Gaussian Naive Bayes
- Gaussian Naive Bayes with scikit-learn
- Summary
- Further reading
- Introducing Neural Nets for Predictive Analytics
- Technical requirements
- Introducing neural network models
- Deep learning
- Anatomy of an MLP – elements of a neural network model
- How MLPs learn
- Introducing TensorFlow and Keras
- TensorFlow
- Keras – deep learning for humans
- Regressing with neural networks
- Building the MLP for predicting diamond prices
- Training the MLP
- Making predictions with the neural network
- Classification with neural networks
- Building the MLP for predicting credit card default
- Evaluating predictions
- The dark art of training neural networks
- So many decisions; so little time
- Regularization for neural networks
- Using a validation set
- Early stopping
- Dropout
- Practical advice on training neural networks
- Summary
- Further reading
- Model Evaluation
- Technical requirements
- Evaluation of regression models
- Metrics for regression models
- MSE and Root Mean Squared Error (RMSE)
- MAE
- R-squared (R2)
- Defining a custom metric
- Visualization methods for evaluating regression models
- Evaluation for classification models
- Confusion matrix and related metrics
- Visualization methods for evaluating classification models
- Visualizing probabilities
- Receiver Operating Characteristic (ROC) and precision-recall curves
- Defining a custom metric for classification
- The k-fold cross-validation
- Summary
- Further reading
- Model Tuning and Improving Performance
- Technical requirements
- Hyperparameter tuning
- Optimizing a single hyperparameter
- Optimizing more than one parameter
- Improving performance
- Improving our diamond price predictions
- Fitting a neural network
- Transforming the target
- Analyzing the results
- Not only a technical problem but a business problem
- Summary
- Implementing a Model with Dash
- Technical requirements
- Model communication and/or deployment phase
- Using a technical report
- A feature of an existing product
- Using an analytic application
- Introducing Dash
- What is Dash?
- Plotly
- Installation
- The application layout
- Building a basic static app
- Building a basic interactive app
- Implementing a predictive model as a web application
- Producing the predictive model objects
- Building the web application
- Summary
- Further reading
- Other Books You May Enjoy
- Leave a review - let other readers know what you think 更新時間：2021-07-02 14:22:37
