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Practical data analysis /
About This BookLearn to use various data analysis tools and algorithms to classify, cluster, visualize, simulate, and forecast your dataApply Machine Learning algorithms to different kinds of data such as social networks, time series, and imagesA hands-on guide to understanding the nature of data an...
| Clasificación: | Libro Electrónico |
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| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham :
Packt,
2016.
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| Edición: | Second edition. |
| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Cover ; Copyright; Credits; About the Authors; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Getting Started; [Computer science]; Computer science; Artificial intelligence; Machine learning; Statistics; Mathematics; Knowledge domain; Data, information, and knowledge; Inter-relationship between data, information, and knowledge; The nature of data; The data analysis process; The problem; Data preparation; Data exploration; Predictive modeling; Visualization of results; Quantitative versus qualitative data analysis; Importance of data visualization.
- What about big data?Quantified self; Sensors and cameras; Social network analysis; Tools and toys for this book; Why Python?; Why mlpy?; Why D3.js?; Why MongoDB?; Summary; Chapter 2: Preprocessing Data; Data sources; Open data; Text files; Excel files; SQL databases; NoSQL databases; Multimedia; Web scraping; Data scrubbing; Statistical methods; Text parsing; Data transformation; Data formats; Parsing a CSV file with the CSV module; Parsing CSV file using NumPy; JSON; Parsing JSON file using the JSON module; XML; Parsing XML in Python using the XML module; YAML; Data reduction methods.
- Filtering and samplingBinned algorithm; Dimensionality reduction; Getting started with OpenRefine; Text facet; Clustering; Text filters; Numeric facets; Transforming data; Exporting data; Operation history; Summary; Chapter 3: Getting to Grips with Visualization; What is visualization?; Working with web-based visualization; Exploring scientific visualization; Visualization in art; The visualization life cycle; Visualizing different types of data; HTML; DOM; CSS; JavaScript; SVG; Getting started with D3.js; Bar chart; Pie chart; Scatter plots; Single line chart; Multiple line chart.
- Interaction and animationData from social networks; An overview of visual analytics; Summary; Chapter 4: Text Classification; Learning and classification; Bayesian classification; NaÃv̄e Bayes; E-mail subject line tester; The data; The algorithm; Classifier accuracy; Summary; Chapter 5: Similarity-Based Image Retrieval; Image similarity search; Dynamic time warping; Processing the image dataset; Implementing DTW; Analyzing the results; Summary; Chapter 6: Simulation of Stock Prices; Financial time series; Random Walk simulation; Monte Carlo methods; Generating random numbers.
- Implementation in D3jsQuantitative analyst; Summary; Chapter 7: Predicting Gold Prices; Working with time series data; Components of a time series; Smoothing time series; Lineal regression; The data
- historical gold prices; Nonlinear regressions; Kernel Ridge Regressions; Smoothing the gold prices time series; Predicting in the smoothed time series; Contrasting the predicted value; Summary; Chapter 8: Working with Support Vector Machines; Understanding the multivariate dataset; Dimensionality reduction; Linear Discriminant Analysis (LDA); Principal Component Analysis (PCA).