R Analytics Training Bangalore
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“The ability to take data – to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it’s going to be a hugely important skill in the next decades, not only at the professional level but even at the educational level for elementary school kids, for high school kids, for college kids. Because now we really do have essentially free and ubiquitous data. So the complimentary scarce factor is the ability to understand that data and extract value from it.
I think statisticians are part of it, but it’s just a part. You also want to be able to visualize the data, communicate the data, and utilize it effectively. But I do think those skills – of being able to access, understand, and communicate the insights you get from data analysis – are going to be extremely important. Managers need to be able to access and understand the data themselves.”
Daryl Pregibon, a research scientist at Google said- “R is really important to the point that it’s hard to overvalue it. It allows statisticians to do very intricate and complicated analyses without knowing the blood and guts of computing systems.”
Very rarely can we start analyzing data immediately after loading it. Often, we will need to preprocess the data to clean and transform it before embarking on analysis. This post provides recipes for some common cleaning and preprocessing steps.
Reading data from CSV files
CSV formats are best used to represent sets or sequences of records in which each record has an identical list of fields. This corresponds to a single relation in a relational database, or to data (though not calculations) in a typical spreadsheet.
Ensure that the auto-mpg.csv file is in your R working directory.
How to do it…
Reading data from .csv files can be done using the following commands:
- Read the data from auto-mpg.csv, which includes a header
- row:> auto <- read.csv(“auto-mpg.csv”, header=TRUE, sep = “,”)
- Verify the results:> names(auto)
Before getting around to applying some of the more advanced analytics and machine learning techniques, analysts face the challenge of becoming familiar with the large datasets that they often deal with. Increasingly, analysts rely on visualization techniques to tease apart hidden patterns.
Analysts often seek to classify or categorize items, for example, to predict whether a given person is a potential buyer or not.
Other examples include classifying—a product as defective or not, a tax return as fraudulent or not, a customer as likely to default on a payment or not, and a credit card transaction as genuine or fraudulent.
In many situations, data analysts seek to make numerical predictions and use regression techniques to do so. Some examples can be the future sales of a product, the amount of deposits that a bank will receive during the next month, the number of copies that a particular book will sell, and the expected selling price for a used car.
You may build a regression model and want to evaluate the model by comparing the model’s predictions with the actual outcomes.
You will generally evaluate a model’s performance on the training data, but will rely on the model’s performance on the hold out data to get an objective measure.
> plot(dat$price, dat$pred, xlab = “Actual”, ylab = “Predicted”)
> abline(0, 1)
How it works:
Step 1 computes the RMS error as defined—the square root of the mean squared errors.
The dat$price – dat$pred expression computes the vector of errors, and the code surrounding it computes the average of the squared errors and then finds the square root.
Step 2 generates the standard scatterplot and then adds on the 45 degree line.
Most projects in R start with loading at least some data into the running R session. As R supports a variety of file formats and database backend, there are several ways to do so.We will concentrate on the performance issue of loading larger datasets and dealing with special file formats.
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