Google Analytics Trick: See The Full Referring URL If you use Google Analytics to track your websites stats, you have probably realized that Google doesn’t let you see the full URL of the sites referring traffic to you. That’s a basic feature that somehow is not included in GA but fortunately for us, there is a simple trick you can implement to display the full URL of the referring site. Before I explain you this trick, let me give full credit to Ruben Yau for actually coming up with it. As stated above, Google Analytics doesn’t let you see from what specific page your visitors are coming. For example, instead of showing this as the traffic referrer: it will just show you this: Digg.com That’s not very useful information… The solution to view the full URL of the referring site is to implement a simple hack that uses filters to place the referrer into the User Defined Report. It should look like this: Now, if you look at your Traffic Sources, you usually see this:
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LingPipe Home How Can We Help You? Get the latest version: Free and Paid Licenses/DownloadsLearn how to use LingPipe: Tutorials Get expert help using LingPipe: Services Join us on Facebook What is LingPipe? LingPipe is tool kit for processing text using computational linguistics. Find the names of people, organizations or locations in newsAutomatically classify Twitter search results into categoriesSuggest correct spellings of queries To get a better idea of the range of possible LingPipe uses, visit our tutorials and sandbox. Architecture LingPipe's architecture is designed to be efficient, scalable, reusable, and robust. Java API with source code and unit tests; multi-lingual, multi-domain, multi-genre models; training with new data for new tasks; n-best output with statistical confidence estimates; online training (learn-a-little, tag-a-little); thread-safe models and decoders for concurrent-read exclusive-write (CREW) synchronization; and character encoding-sensitive I/O.
Do Faster Data Manipulation using These 7 R Packages Introduction Data Manipulation is an inevitable phase of predictive modeling. A robust predictive model can’t be just be built using machine learning algorithms. But, with an approach to understand the business problem, the underlying data, performing required data manipulations and then extracting business insights. Among these several phases of model building, most of the time is usually spent in understanding underlying data and performing required manipulations. What is Data Manipulation ? If you are still confused with this ‘term’, let me explain it to you. Actually, the data collection process can have many loopholes. At times, this stage is also known as data wrangling or data cleaning. Different Ways to Manipulate / Treat Data: There is no right or wrong way in manipulating data, as long as you understand the data and have taken the necessary actions by the end of the exercise. Usually, beginners on R find themselves comfortable manipulating data using inbuilt base R functions. #or
How Your Data Can Predict The Future Introduction to Principal Component Analysis (PCA) - Laura Diane Hamilton Principal Component Analysis (PCA) is a dimensionality-reduction technique that is often used to transform a high-dimensional dataset into a smaller-dimensional subspace prior to running a machine learning algorithm on the data. When should you use PCA? It is often helpful to use a dimensionality-reduction technique such as PCA prior to performing machine learning because: Reducing the dimensionality of the dataset reduces the size of the space on which k-nearest-neighbors (kNN) must calculate distance, which improve the performance of kNN. Reducing the dimensionality of the dataset reduces the number of degrees of freedom of the hypothesis, which reduces the risk of overfitting. What does PCA do? Principal Component Analysis does just what it advertises; it finds the principal components of the dataset. Can you ELI5? Let’s say your original dataset has two variables, x1 and x2: Now, we want to identify the first principal component that has explains the highest amount of variance.
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Downloadable Sample SPSS Data Files Downloadable Sample SPSS Data Files Data QualityEnsure that required fields contain data.Ensure that the required homicide (09A, 09B, 09C) offense segment data fields are complete.Ensure that the required homicide (09A, 09B, 09C) victim segment data fields are complete.Ensure that offenses coded as occurring at midnight are correctEnsure that victim variables are reported where required and are correct when reported but not required. Standardizing the Display of IBR Data: An Examination of NIBRS ElementsTime of Juvenile Firearm ViolenceTime of Day of Personal Robberies by Type of LocationIncidents on School Property by HourTemporal Distribution of Sexual Assault Within Victim Age CategoriesLocation of Juvenile and Adult Property Crime VictimizationsRobberies by LocationFrequency Distribution for Victim-Offender Relationship by Offender and Older Age Groups and Location Analysis ExamplesFBI's Analysis of RobberyFBI's Analysis of Motor Vehicle Theft Using Survival Model
Academia.edu Interactive Statistical Calculation Pages Zero Intelligence Agents This happens to be one of those rare instances where the benefit of hindsight does not make me regret something said flippantly on a panel. I deeply believe that in order to truly change the world we cannot simply "throw analytics at the problem." To that end, the medical and health industries are perhaps the most primed to be disrupted by data and analytics. To be successful, however, a deep respect for both the methodological and clinical contexts of the data are required. It is incredibly exciting to be at an organization that is both working within the current framework of health care and data to create new insight for people, but also pushing the envelope with respect to individuals' relationships with their own health. I feel lucky to have an opportunity to move into the health data space now. Sensor data The past decade of development in "big data" has -- in large part -- been built on top of the need to understand web logs files. Strength of team Here's to the next adventure!
The R Trader » Blog Archive » BERT: a newcomer in the R Excel connection A few months ago a reader point me out this new way of connecting R and Excel. I don’t know for how long this has been around, but I never came across it and I’ve never seen any blog post or article about it. So I decided to write a post as the tool is really worth it and before anyone asks, I’m not related to the company in any way. BERT stands for Basic Excel R Toolkit. In this post I’m not going to show you how R and Excel interact via BERT. How do I use BERT? My trading signals are generated using a long list of R files but I need the flexibility of Excel to display results quickly and efficiently. Use XML to build user defined menus and buttons in an Excel file.The above menus and buttons are essentially calls to VBA functions.Those VBA functions are wrapup around R functions defined using BERT. With this approach I can keep a clear distinction between the core of my code kept in R, SQL and Python and everything used to display and format results kept in Excel, VBA & XML.