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    <title>Home on DataXotic</title>
    <link>https://ejikeugba.github.io/DataXotic/</link>
    <description>Recent content in Home on DataXotic</description>
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    <item>
      <title>Feature Engineering</title>
      <link>https://ejikeugba.github.io/DataXotic/services/feature-eng/</link>
      <pubDate>Sun, 18 Nov 2018 12:33:46 +1000</pubDate>
      
      <guid>https://ejikeugba.github.io/DataXotic/services/feature-eng/</guid>
      <description>Scrambling for features!
To build a great machine learning model, it is very crucial to determine which features are the most important. Even after the data is cleaned and preprocessed, most algorithms perform better when fed with enginnered features that are specifically made to enhance the predictive properties of the dataset. A useful rule of thumb is that a fearture must have a relationship to the target in other to learn properly.</description>
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    <item>
      <title>Package Development</title>
      <link>https://ejikeugba.github.io/DataXotic/services/package-dev/</link>
      <pubDate>Sun, 18 Nov 2018 12:33:46 +1000</pubDate>
      
      <guid>https://ejikeugba.github.io/DataXotic/services/package-dev/</guid>
      <description>Bundling codes!
Software packages are an ideal way to bundle together and distribute codes and data for re-use in any given application. This can help improve productivity while also saving a lot of time.
We employ standard IDEs to build and maintain user-friendly packages that could be deployed to production and in empirical research. Debugging services for existing problematic codes are also provided. Any or all of the following are possible:</description>
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    <item>
      <title>About Us</title>
      <link>https://ejikeugba.github.io/DataXotic/about/</link>
      <pubDate>Thu, 06 Dec 2018 09:29:16 +1000</pubDate>
      
      <guid>https://ejikeugba.github.io/DataXotic/about/</guid>
      <description> </description>
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    <item>
      <title>Research</title>
      <link>https://ejikeugba.github.io/DataXotic/research/</link>
      <pubDate>Thu, 06 Dec 2018 09:29:16 +1000</pubDate>
      
      <guid>https://ejikeugba.github.io/DataXotic/research/</guid>
      <description>About Heading Smoothing in Ordinal Regression: An Application to Sensory Data The so-called proportional odds assumption is popular in cumulative, ordinal regression. In practice, however, such an assumption is sometimes too restrictive. For instance, when modeling the perception of boar taint on an individual level, it turns out that, at least for some subjects, the effects of predictors (androstenone and skatole) vary between response categories. For more flexible modeling, we consider the use of a ‘smooth-effects-on-response penalty’ (SERP) as a connecting link between proportional and fully non-proportional odds models, assuming that parameters of the latter vary smoothly over response categories.</description>
    </item>
    
    <item>
      <title>Web Scraping</title>
      <link>https://ejikeugba.github.io/DataXotic/services/data-scrap/</link>
      <pubDate>Wed, 28 Nov 2018 15:15:34 +1000</pubDate>
      
      <guid>https://ejikeugba.github.io/DataXotic/services/data-scrap/</guid>
      <description>Unlimited scraping!
Are you tired of looking for a Data Entry expert? Do you need a reliable and professional Data Scraper for Data Mining, Web Scraping, Data Collection, and Data Entry projects? Look no further! You have found one!
We provide an expert service on Data Mining, Data Scraping, Web Scraping, Data Collection, and Data Entry. We practice ethical web scraping/data mining. Our intent will be to collect the data you need without violating the TOS of a website, and without infringing on fair use web scraping practices.</description>
    </item>
    
    <item>
      <title>Machine Learning</title>
      <link>https://ejikeugba.github.io/DataXotic/services/machine-lean/</link>
      <pubDate>Wed, 28 Nov 2018 15:15:26 +1000</pubDate>
      
      <guid>https://ejikeugba.github.io/DataXotic/services/machine-lean/</guid>
      <description>Superb Algorithm!
Have you been searching for someone to support you with machine learning tasks in Python/R, you&amp;rsquo;ve arrived at the right place. We employ ML techniques to devise complex models and algorithms that lead to great prediction. our implemented algorithms enables you to produce reliable, and repeatable decisions, as well as uncover “hidden insights” through learning from historical relationships and trends in the data set.
We invest quality time to design pipelines to improve the quality of machine learning problems, use advanced techniques for model validation, and build state-of-the-art models that improve productivity and profits.</description>
    </item>
    
    <item>
      <title>A/B Testing</title>
      <link>https://ejikeugba.github.io/DataXotic/services/ab-test/</link>
      <pubDate>Wed, 28 Nov 2018 15:14:54 +1000</pubDate>
      
      <guid>https://ejikeugba.github.io/DataXotic/services/ab-test/</guid>
      <description>Track the clicks!
Do you deisre to improve your website&amp;rsquo;s conversion rate? Do you want to use a data-driven approach to make your desired change a dream come through? You have just arived at the right place!
We will create an A/B testing routine to increase your business performance with a detailed tracking of key changes in the analysis following the reports of the previous campaigns and the main target of interest.</description>
    </item>
    
    <item>
      <title>Data Visualization</title>
      <link>https://ejikeugba.github.io/DataXotic/services/data-viz/</link>
      <pubDate>Wed, 28 Nov 2018 15:14:39 +1000</pubDate>
      
      <guid>https://ejikeugba.github.io/DataXotic/services/data-viz/</guid>
      <description>DataViz!
Do you like to transform your raw data into stunning looking charts, plots, infographics, Dashboards etc, that will not only wow its end users but provide actionable Insights, communicate clear stories and reduce the time and effort spent on periodic reporting? You are at the right place.
We employ several dataViz techniques to discover and explain inherent patterns and trends in datasets. The end product include:
Basic Charts: Bar charts, Scatter plots, Area charts, Maps, Pie charts, Bubble charts Statistical Charts: Box plots, Histograms, Distplots etc.</description>
    </item>
    
    <item>
      <title>Amazon Kindle Store Reviews Sentiment Analysis</title>
      <link>https://ejikeugba.github.io/DataXotic/project/sentiment-analysis-kindle/</link>
      <pubDate>Sun, 18 Nov 2018 12:33:46 +1000</pubDate>
      
      <guid>https://ejikeugba.github.io/DataXotic/project/sentiment-analysis-kindle/</guid>
      <description>Amazon&#39;s e-commerce platform is one of the biggest online shopping platforms globally.
Considering its large customer base with an unprecedented amount of day-to-day data on user experience, trying to understand the main message being conveyed from the ocean of user reviews could be a very daunting task. Sentiment analysis or opinion mining could be of help, as it provides an analytical procedure for extracting the hidden polarities in a given body of text.</description>
    </item>
    
    <item>
      <title>Book-Crossing Recommender Engine with Python</title>
      <link>https://ejikeugba.github.io/DataXotic/project/recsys-book-crossing/</link>
      <pubDate>Sun, 18 Nov 2018 12:33:46 +1000</pubDate>
      
      <guid>https://ejikeugba.github.io/DataXotic/project/recsys-book-crossing/</guid>
      <description>Recommender engines are machine learning systems that facilitate a quick discovery of new products and services that might be of great interest.
In this era of e-commerce and several competing online services, one cannot but feel the impact of product recommendations in one’s day-to-day life. Every time we shop online, an underlying recommendation system guides us towards the most likely product that might be of interest to users. Such systems are generally grouped into two main categories: collaborative filtering and content-based systems.</description>
    </item>
    
    <item>
      <title>EDA on Web-Scraped UEFA Champions League Finals</title>
      <link>https://ejikeugba.github.io/DataXotic/project/web-scraping-uefa/</link>
      <pubDate>Sun, 18 Nov 2018 12:33:46 +1000</pubDate>
      
      <guid>https://ejikeugba.github.io/DataXotic/project/web-scraping-uefa/</guid>
      <description>The UEFA Champions League is an annual football competition established in 1955 by the Union of European Football Associations (UEFA).
The league features champions of all UEFA member associations, current title holders of the tournament, and clubs finishing from second to fourth position in the strongest leagues. There has been a total of 67 tournament seasons from the inaugural to the latest edition of the tournament. Documented details of the tournament seasons are available online from the official website, Wikipedia and other sources.</description>
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