Category: Python

Kislay Keshari explains the Naive Bayes algorithm and shows an implementation in Python:

Naive Bayes in the Industry

Now that you have an idea of what exactly Naive Bayes is and how it works, let’s see where it is used in the industry.

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Our first industrial use case is News Categorization, or we can use the term ‘text classification’ to broaden the spectrum of this algorithm. News on the web is rapidly growing where each news site has its own different layout and categorization for grouping news. Companies use a web crawler to extract useful text from HTML pages of news articles to construct a Full Text RSS. The contents of each news article is tokenized (categorized). In order to achieve better classification results, we remove the less significant words, i.e. stop, from the document. We apply the naive Bayes classifier for classification of news content based on news code.

It’s a good overview of the topic and a particular implementation in Python.  Naive Bayes is a technique which you want in the bag:  there are a lot of techniques which tend to be better in specific domains, but Naive Bayes is easy to implement and usually provides acceptable performance.

Nikolai Janakiev explains the concept of the covariance matrix using a bit of Python and some graphs:

In this article we saw the relationship of the covariance matrix with linear transformation which is an important building block for understanding and using PCA, SVD, the Bayes Classifier, the Mahalanobis distance and other topics in statistics and pattern recognition. I found the covariance matrix to be a helpful cornerstone in the understanding of the many concepts and methods in pattern recognition and statistics.

Many of the matrix identities can be found in The Matrix Cookbook. The relationship between SVD, PCA and the covariance matrix are elegantly shown in this question.

Understanding covariance is critical for a number of statistical techniques, and this is a good way of describing it.

The authors at Knoyd have a post on exploratory data analysis of a time series data set:

From the plot above we can clearly see that time-series has strong seasonal and trend components. To estimate the trend component we can use a function from the pandas library called rolling_mean and plot the results. If we want to make the plot more fancy and reusable for another time-series it is a good idea to make a function. We can call this function plot_moving_average.

The second part of the series promises to use Box-Jenkins to forecast future values.

Atul Harsha gives us a demo on k nearest neighbors in Python:

In order to make any predictions, you have to calculate the distance between the new point and the existing points, as you will be needing k closest points.

In this case for calculating the distance, we will use the Euclidean distance. This is defined as the square root of the sum of the squared differences between the two arrays of numbers

Specifically, we need only first 4 attributes(features) for distance calculation as the last attribute is a class label. So for one of the approach is to limit the Euclidean distance to a fixed length, thereby ignoring the final dimension.

Check it out.