Metadata-Version: 1.1
Name: scikit-data
Version: 0.1.3
Summary: The propose of this library is to allow the data analysis process more easy and automatic.
Home-page: https://github.com/OpenDataScienceLab/skdata
Author: Ivan Ogasawara
Author-email: ivan.ogasawara@gmail.com
License: MIT license
Download-URL: https://github.com/OpenDataScienceLab/skdata/archive/master.tar.gz
Description-Content-Type: UNKNOWN
Description: ===============================
        SciKit Data
        ===============================
        
        
        .. image:: https://img.shields.io/pypi/v/scikit-data.svg
                :target: https://pypi.python.org/pypi/scikit-data
        
        .. image:: https://img.shields.io/travis/OpenDataScienceLab/skdata.svg
                :target: https://travis-ci.org/OpenDataScienceLab/skdata
        
        .. image:: https://readthedocs.org/projects/skdata/badge/?version=latest
                :target: https://skdata.readthedocs.io/en/latest/?badge=latest
                :alt: Documentation Status
        
        
        Conda package current release info
        ==================================
        
        .. image:: https://anaconda.org/conda-forge/scikit-data/badges/version.svg
                :target: https://anaconda.org/conda-forge/scikit-data
                :alt: Anaconda-Server Badge
        
        .. image:: https://anaconda.org/conda-forge/scikit-data/badges/downloads.svg
                :target: https://anaconda.org/conda-forge/scikit-data
                :alt: Anaconda-Server Badge
        
        
        About SciKit Data
        =================
        
        The propose of this library is to allow the data analysis process more easy and automatic.
        
            The data analysis process is composed of following steps:
        
            * The statement of problem
            * Collecting your data
            * Cleaning the data
            * Normalizing the data
            * Transforming the data
            * Exploratory statistics
            * Exploratory visualization
            * Predictive modeling
            * Validating your model
            * Visualizing and interpreting your results
            * Deploying your solution
        
            (Cuesta, Hector and Kumar, Sampath; 2016)
        
        This project contemplates the follow features:
        
        * Data Preparation
        * Data Exploration
        * Prepare data to Predictive modeling
        * Visualizing results
        * Reproducible data analysis
        
        
        Data Preparation
        ----------------
        
            Data preparation is about how to obtain, clean, normalize, and transform the data into an
            optimal dataset, trying to avoid any possible data quality issues such as invalid, ambiguous,
            out-of-range, or missing values.
        
            (...)
        
            Scrubbing data, also called data cleansing, is the process of correcting or
            removing data in a dataset that is incorrect, inaccurate, incomplete,
            improperly formatted, or duplicated.
        
            (...)
        
            In order to avoid dirty data, our dataset should possess the following characteristics:
        
            * Correct
            * Completeness
            * Accuracy
            * Consistency
            * Uniformity
        
            (...)
        
            **Data transformation**
        
            Data transformation is usually related to databases and data warehouses where values from
            a source format are extract, transform, and load in a destination format.
        
            Extract, Transform, and Load (ETL) obtains data from various data sources, performs some
            transformation functions depending on our data model, and loads the resulting data into
            the destination.
        
            (...)
        
            Some important transformations:
        
            * Text facet and Clustering
            * Numeric fact
            * Replace
        
            **Data reduction methods**
        
            Data reduction is the transformation of numerical or alphabetical digital information
            derived empirically or experimentally into a corrected, ordered, and simplified form.
            Reduced data size is very small in volume and comparatively original, hence, the storage
            efficiency will increase and at the same time we can minimize the data handling costs and
            will minimize the analysis time also.
        
            We can use several types of data reduction methods, which are listed as follows:
        
            * Filtering and sampling
            * Binned algorithm
            * Dimensionality reduction
        
            (Cuesta, Hector and Kumar, Sampath; 2016)
        
        
        Data exploration
        ----------------
        
            Data exploration is essentially looking at the processed data in a graphical or statistical form
            and trying to find patterns, connections, and relations in the data. Visualization is used to
            provide overviews in which meaningful patterns may be found.
        
            (...)
        
            The goals of exploratory data analysis (EDA) are as follows:
        
            * Detection of data errors
            * Checking of assumptions
            * Finding hidden patters (like tendency)
            * Preliminary selection of appropriate models
            * Determining relationships between the variables
        
            (...)
        
            The four types of EDA are univariate nongraphical, multivariate nongraphical, univariate
            graphical, and multivariate graphical. The nongraphical methods refer to the calculation of
            summary statistics or the outlier detection. In this book, we will focus on the univariate and
        
            (Cuesta, Hector and Kumar, Sampath; 2016)
        
        **Outlier Detection**
        
        Two outlier detection method should be used, initially, for SkData are:
        
        * IQR;
        * Chauvenet.
        
        Another methods should be implemented soon [1].
        
        
        Prepare data to Predictive modeling
        -----------------------------------
        
            From the galaxy of information we have to extract usable hidden patterns and trends using
            relevant algorithms. To extract the future behavior of these hidden patterns, we can use
            predictive modeling. Predictive modeling is a statistical technique to predict future
            behavior by analyzing existing information, that is, historical data. We have to use proper
            statistical models that best forecast the hidden patterns of the data or
            information (Cuesta, Hector and Kumar, Sampath; 2016).
        
        SkData, should allow you to format your data to send it to some predictive library
        as scikit-learn.
        
        
        Visualizing results
        -------------------
        
            In an explanatory data analysis process, simple visualization techniques are very useful for
            discovering patterns, since the human eye plays an important role. Sometimes, we have to
            generate a three-dimensional plot for finding the visual pattern. But, for getting better
            visual patterns, we can also use a scatter plot matrix, instead of a three-dimensional plot. In
            practice, the hypothesis of the study, dimensionality of the feature space, and data all play
            important roles in ensuring a good visualization technique (Cuesta, Hector and Kumar, Sampath; 2016).
        
        
        Quantitative and Qualitative data analysis
        ------------------------------------------
        
            Quantitative data are numerical measurements expressed in terms of numbers.
        
            Qualitative data are categorical measurements expressed in terms of natural language
            descriptions.
        
            Quantitative analytics involves analysis of numerical data. The type of the analysis will
            depend on the level of measurement. There are four kinds of measurements:
        
            * Nominal data has no logical order and is used as classification data.
            * Ordinal data has a logical order and differences between values are not constant.
            * Interval data is continuous and depends on logical order. The data has standardized differences between values, but do not include zero.
            * Ratio data is continuous with logical order as well as regular intervals differences between values and may include zero.
        
            Qualitative analysis can explore the complexity and meaning of social phenomena. Data for
            qualitative study may include written texts (for example, documents or e-mail) and/or
            audible and visual data (digital images or sounds).
        
            (Cuesta, Hector and Kumar, Sampath; 2016)
        
        
        Reproducibility for Data Analysis
        ---------------------------------
        
        A good way to promote reproducibility for data analysis is store the
        operation history. This history can be used to prepare another dataset
        with the same steps (operations).
        
        
        Books used as reference to guide this project:
        ----------------------------------------------
        
        - https://www.packtpub.com/big-data-and-business-intelligence/clean-data
        - https://www.packtpub.com/big-data-and-business-intelligence/python-data-analysis
        - https://www.packtpub.com/big-data-and-business-intelligence/mastering-machine-learning-scikit-learn
        - https://www.packtpub.com/big-data-and-business-intelligence/practical-data-analysis-second-edition
        
        Some other materials used as reference:
        ---------------------------------------
        
        - https://github.com/rsouza/MMD/blob/master/notebooks/3.1_Kaggle_Titanic.ipynb
        - https://github.com/agconti/kaggle-titanic/blob/master/Titanic.ipynb
        - https://github.com/donnemartin/data-science-ipython-notebooks/blob/master/kaggle/titanic.ipynb
        
        
        Installing scikit-data
        ======================
        
        Using conda
        -----------
        
        Installing `scikit-data` from the `conda-forge` channel can be achieved by adding `conda-forge` to your channels with:
        
        .. code-block:: console
        
           $ conda config --add channels conda-forge
        
        
        Once the `conda-forge` channel has been enabled, `scikit-data` can be installed with:
        
        .. code-block:: console
        
           $ conda install scikit-data
        
        
        It is possible to list all of the versions of `scikit-data` available on your platform with:
        
        .. code-block:: console
        
           $ conda search scikit-data --channel conda-forge
        
        
        Using pip
        ---------
        
        To install scikit-data, run this command in your terminal:
        
        .. code-block:: console
        
            $ pip install skdata
        
        If you don't have `pip`_ installed, this `Python installation guide`_ can guide
        you through the process.
        
        .. _pip: https://pip.pypa.io
        .. _Python installation guide: http://docs.python-guide.org/en/latest/starting/installation/
        
        
        More Information
        ----------------
        
        * License: MIT
        * Documentation: https://skdata.readthedocs.io
        
        
        References
        ----------
        
        * CUESTA, Hector; KUMAR, Sampath. Practical Data Analysis. Packt Publishing Ltd, 2016.
        
        
        **Electronic materials**
        
        * [1] http://www.datasciencecentral.com/profiles/blogs/introduction-to-outlier-detection-methods
        
        
        =======
        History
        =======
        
        0.1.0 (2016-08-14)
        ------------------
        
        * First release on PyPI.
        
Keywords: scikit data analysis
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
