Mastering predictive analytics with Python exploit the power of data in your business by building advanced predictive modeling applications with Python
Exploit the power of data in your business by building advanced predictive modeling applications with Python About This Book Master open source Python tools to build sophisticated predictive models Learn to identify the right machine learning algorithm for your problem with this forward-thinking gui...
| Otros Autores: | |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Birmingham, England :
Packt Publishing
2016.
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| Edición: | 1st edition |
| Colección: | Community experience distilled.
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| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009630298406719 |
Tabla de Contenidos:
- Cover
- Copyright
- Credits
- About the Author
- About the Reviewer
- www.PacktPub.com
- Table of Contents
- Preface
- Chapter 1: From Data to Decisions - Getting Started with Analytic Applications
- Designing an advanced analytic solution
- Data layer: warehouses, lakes, and streams
- Modeling layer
- Deployment layer
- Reporting layer
- Case study: sentiment analysis of social media feeds
- Data input and transformation
- Sanity checking
- Model development
- Scoring
- Visualization and reporting
- Case study: targeted e-mail campaigns
- Data input and transformation
- Sanity checking
- Model development
- Scoring
- Visualization and reporting
- Summary
- Chapter 2: Exploratory Data Analysis and Visualization in Python
- Exploring categorical and numerical data in IPython
- Installing IPython notebook
- The notebook interface
- Loading and inspecting data
- Basic manipulations - grouping, filtering, mapping, and pivoting
- Charting with Matplotlib
- Time series analysis
- Cleaning and converting
- Time series diagnostics
- Joining signals and correlation
- Working with geospatial data
- Loading geospatial data
- Working in the cloud
- Introduction to PySpark
- Creating the SparkContext
- Creating an RDD
- Creating a Spark DataFrame
- Summary
- Chapter 3: Finding Patterns in the Noise - Clustering and Unsupervised Learning
- Similarity and distance metrics
- Numerical distance metrics
- Correlation similarity metrics and time series
- Similarity metrics for categorical data
- K-means clustering
- Affinity propagation - automatically choosing cluster numbers
- k-medoids
- Agglomerative clustering
- Where agglomerative clustering fails
- Streaming clustering in Spark
- Summary
- Chapter 4: Connecting the Dots with Models - Regression Methods
- Linear regression
- Data preparation.
- Model fitting and evaluation
- Statistical significance of regression outputs
- Generalize estimating equations
- Mixed effects models
- Time series data
- Generalized linear models
- Applying regularization to linear models
- Tree methods
- Decision trees
- Random forest
- Scaling out with PySpark - predicting year of song release
- Summary
- Chapter 5: Putting Data in its Place - Classification Methods and Analysis
- Logistic regression
- Multiclass logistic classifiers: multinomial regression
- Formatting a dataset for classification problems
- Learning pointwise updates with stochastic gradient descent
- Jointly optimizing all parameters with second-order methods
- Fitting the model
- Evaluating classification models
- Strategies for improving classification models
- Separating Nonlinear boundaries with Support vector machines
- Fitting and SVM to the census data
- Boosting: combining small models to improve accuracy
- Gradient boosted decision trees
- Comparing classification methods
- Case study: fitting classifier models in pyspark
- Summary
- Chapter 6: Words and Pixels - Working with Unstructured Data
- Working with textual data
- Cleaning textual data
- Extracting features from textual data
- Using dimensionality reduction to simplify datasets
- Principal component analysis
- Latent Dirichlet Allocation
- Using dimensionality reduction in predictive modeling
- Images
- Cleaning image data
- Thresholding images to highlight objects
- Dimensionality reduction for image analysis
- Case Study: Training a Recommender System in PySpark
- Summary
- Chapter 7: Learning from the Bottom Up - Deep Networks and Unsupervised Features
- Learning patterns with neural networks
- A network of one - the perceptron
- Combining perceptrons - a single-layer neural network
- Parameter fitting with back-propagation.
- Discriminative versus generative models
- Vanishing gradients and explaining away
- Pretraining belief networks
- Using dropout to regularize networks
- Convolutional networks and rectified units
- Compressing Data with autoencoder networks
- Optimizing the learning rate
- The TensorFlow library and digit recognition
- The MNIST data
- Constructing the network
- Summary
- Chapter 8: Sharing Models with Prediction Services
- The architecture of a prediction service
- Clients and making requests
- The GET requests
- The POST request
- The HEAD request
- The PUT request
- The DELETE request
- Server - the web traffic controller
- Application - the engine of the predictive services
- Persisting information with database systems
- Case study - logistic regression service
- Setting up the database
- The web server
- The web application
- The flow of a prediction service - training a model
- On-demand and bulk prediction
- Summary
- Chapter 9: Reporting and Testing - Iterating on Analytic Systems
- Checking the health of models with diagnostics
- Evaluating changes in model performance
- Changes in feature importance
- Changes in unsupervised model performance
- Iterating on models through A/B testing
- Experimental allocation - assigning customers to experiments
- Deciding a sample size
- Multiple hypothesis testing
- Guidelines for communication
- Translate terms to business values
- Visualizing results
- Case Study: building a reporting service
- The report server
- The report application
- The visualization layer
- Summary
- Index.
