Random Forest Classification of Mushrooms

Random Forest Classi...

There is a plethora of classification algorithms available to people who have a bit of coding experience and a set of data. A common machine learning method is the random forest, which is a good place to start. This is a use case in R of the randomForest package used on a data set from UCI’s Machine Learning Data Repository. […]

Exploring handwritten digit classification: a tidy analysis of the MNIST dataset

Exploring handwritte...

In a recent post, I offered a definition of the distinction between data science and machine learning: that data science is focused on extracting insights, while machine learning is interested in making predictions. I also noted that the two fields greatly overlap: I use both machine learning and data science in my work: I might fit […]

Named Entity Recognition: Milestone Models, Papers and Technologies

Named Entity Recogni...

Named Entity Recognition: Extracting named entities from text Named Entity Recognition (NER), or entity extraction is an NLP technique which locates and classifies the named entities present in the text. Named Entity Recognition classifies the named entities into pre-defined categories such as the names of persons, organizations, locations, quantities, monetary values, specialized terms, product terminology and […]

Classification and Clustering Algorithms

Classification and C...

A famous dialogue you could listen from the data science people. It could be true if we add it’s so challenging at the end of the dialogue. The foremost challenge starts from  categorising the problem itself. The first level of categorising could be whether supervised or unsupervised learning. The next level is what kind of algorithms to get […]

Practical Naive Bayes — Classification of Amazon Reviews

Practical Naive Baye...

If you search around the internet looking for applying Naive Bayes classification on text, you’ll find a ton of articles that talk about the intuition behind the algorithm, maybe some slides from a lecture about the math and some notation behind it, and a bunch of articles I’m not going to link here that pretty much just […]

2 Ways to Implement Multinomial Logistic Regression in Python

2 Ways to Implement ...

Logistic regression is one of the most popular supervised classification algorithm. This classification algorithm mostly used for solving binary classification problems. People follow the myth that logistic regression is only useful for the binary classification problems. Which is not true. Logistic regression algorithm can also use to solve the multi-classification problems. So in this article, your are going to […]

A Gentle Introduction to Recommender Systems with Implicit Feedback

A Gentle Introductio...

Recommender systems have become a very important part of the retail, social networking, and entertainment industries. From providing advice on songs for you to try, suggesting books for you to read, or finding clothes to buy, recommender systems have greatly improved the ability of customers to make choices more easily. Why is it so important […]

Introduction to Evaluating Classification Models

Introduction to Eval...

In this post we will describe how to evaluate a predictive model. Why bother creating complex predictive models if 5% of the customers will churn anyway? Because a predictive model will rank our clients based on the probability that they  will abandon the company. It helps answer these two questions: 1. How should we optimise our resources? 2.  What […]

How to visualize decision trees in Python

How to visualize dec...

Decision tree classifier is the most popularly used supervised learning algorithm. Unlike other classification algorithms, decision tree classifier in not a black box in the modeling phase.  What that’s means, we can visualize the trained decision tree to understand how the decision tree gonna work for the give input features. So in this article, you […]