Illustration: Consider the sentiment-tagging task again. A Q1 resource uses an off-the-shelf model for movie reviews, and applies it to a new task (say, tweets about a customer service organization). Business is so blinded by spectacular charts  and anecdotal correlations (“Look at that spiteful tweet from a celebrity … so that’s why the sentiment is negative!”), that even questions about predictive accuracy are rarely asked until a few months down the road when the model is obviously floundering. Then too, there is rarely anyone to challenge the assumptions, biases and confidence intervals (Does the language in the tweets match the movie reviews? Do we have enough training data? Does the importance of tweets change over time?).
Overheard: “Survival analysis? Never heard of it … Wait … There is an R package for that!”
This is a really interesting article and I recommend reading it.
The sample included N = 262 individual orders for Interlocking Hearts Design Cake Knife/Server set with OrderItemSKU as 2401 from the period ranging from 1st March 2014 to 20th April 2016 with an ecommerce company which sells on Amazon.co.uk
The Profit response variable is measured as the product sale price on amazon.co.uk which includes amazon.co.uk commission and any applicable postage costs less the purchase price of the Hearts Design Cake Knife/Server set from the supplier.
Read the comments for a couple good critiques of the article.
With the emergence of the powerful forecasting methods based on Machine Learning, future predictions have become more accurate. In general, forecasting techniques can be grouped into two categories: qualitative and quantitative. Qualitative forecasts are applied when there is no data available and prediction is based only on expert judgement. Quantitative forecasts are based on time series modeling. This kind of models uses historical data and is especially efficient in forecasting some events that occur over periods of time: for example prices, sales figures, volume of production etc.
The existing models for time series prediction include the ARIMA models that are mainly used to model time series data without directly handling seasonality; VAR models, Holt-Winters seasonal methods, TAR modelsand other. Unfortunately, these algorithms may fail to deliver the required level of the prediction accuracy, as they can involve raw data that might be incomplete, inconsistent or contain some errors. As quality decisions are based only on quality data, it is crucial to perform preprocessing to prepare entry information for further processing.
Treating time series data as a set of waveform functions can generate some very interesting results.
The gaining popularity of self-service analytical tools such as Tableau increases the necessity of having curated data in your database. These tools aim to allow the end users to intuitively query data “at the speed of thought” from the data warehouse and visualize the results quickly. That type of capability allows users to go through several different iterations of the data to really explore the data and generate unique insights. These tools do not work well when the underlying database tables have not been curated properly.
This is a difficult and lengthy process, but it’s vital; data minus context is a lot less relevant than you’d hope.
While later seasons would focus on Homer, Bart was the lead character in most of the first three seasons
I’ve heard this argument before, that the show was originally about Bart before switching its focus to Homer, but the actual scripts only seem to partially support it.
Bart accounted for a significantly larger share of the show’s dialogue in season 1 than in any future season, but Homer’s share has always been higher than Bart’s. Dialogue share might not tell the whole story about a character’s prominence, but the fact is that Homer has always been the most talkative character on the show.
My reading is that it took a couple seasons for show writers to realize that Homer is the funniest character and that Bart’s character was too context-sensitive to be consistently funny. It took quite a bit more time before merchandisers figured that out, to the extent that they ever did.
If your application requires a precise LD value, this heuristic isn’t for you, but the estimates are typically within about 0.05 of the true distance, which is more than enough accuracy for such tasks as:
Confirming suspected near-duplication.
Estimating how much two document vary.
Filtering through large numbers of documents to look for a near-match to some substantial block of text.
The estimation process is pretty interesting. Worth a read.
Several people have approached these letters as a cryptographic cipher. The odd circumstances of death do sound like something out of a John Le Carré spy novel. Some of the best attempts, however, fail to produce anything but truly convoluted parsings.
Another possibility may already have occurred to you: Are they the first letters of words in a sentence (aninitialism)? Some suspect this death was a suicide, and that the message is merely some form of final note. With this morbid scenario in mind, it’s easy to imagine many phrases, like “My Life Is All But Over,” that fit the letters because indeed their frequency seems to match that of English text.
This lead has been picked up a few times. These writeups (example) present indications that the message is indeed an initialism. However, they don’t apply what is arguably the clear statistical tool for this job. And they don’t take advantage of big data. So, let’s do both.
Read on for Chi Square testing and book parsing examples using Spark. Spoiler alert: Sean doesn’t solve the mystery, but it’s still a fun read.
For any dataset to lend itself to the Chi Square test it has to fit the following conditions –
1 Both variables are categorical (in this case – exposure to smoking – yes/no, and health condition – sick/not sick are both categorical).
2 Researchers used a random sample to collect data.
3 Researchers had an adequate sample size.Generally the sample size should be at least 100.
4 The number of respondents in each cell should be at least 5.
This is an easy case for using R over T-SQL—the Chi Square test is built in, whereas you have to roll your own T-SQL code. Mala does show you how to do this from within SQL Server R Services as well.
In the case of telco customer churn, we collected a combination of the call detail record data and customer profile data from a mobile carrier, and then followed the data science process — data exploration and visualization, data pre-processing and feature engineering, model training, scoring and evaluation — in order to achieve the churn prediction. With a churn indicator in the dataset taking value 1 when the customer is churned and taking value 0 when the customer is non-churned, we addressed the problem as a binary classification problem and tried varioustree-based models along with methods like bagging, random forests and boosting. Because the number of churned customers is much less than that of non-churned customers (making the data set quite unbalanced), SMOTE (Synthetic Minority Oversampling Technique) was applied to adjust the proportion of majority class over minority class in the training data set, thus further improving model performance, especially precision and recall.
All the above data science procedures could be implemented with base R. Rather than moving the data out from the database to an external machine running R, we instead run R scripts directly on SQL Server data by leveraging the in-database analytics capability provided by SQL Server R Services, taking advantage of the rich and powerful CRAN R packages plus the parallel external memory algorithms in the RevoScaleR library. In what follows, we will describe the specific R packages and algorithms that we used to implement the data science solution for predicting telco customer churn.
They have provided the relevant materials in GitHub as well.