In this tutorial, we are going to use the K-Nearest Neighbors (KNN) algorithm to solve a classification problem. Firstly, what exactly do we mean by classification?
Classification across a variable means that results are categorised into a particular group. e.g. classifying a fruit as either an apple or an orange.
Continue reading “K-Nearest Neighbors (KNN): Solving Classification Problems”
Let’s take a look at how we can construct decision trees in Python.
A decision tree is a model used to solve classification and regression tasks. As we saw in our example for R, the model allows us to generate various outcomes using the model, allowing us to make a decision with the data.
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The statsmodels and sklearn libraries are frequently used when it comes to generating regression output. While these libraries are frequently used in regression analysis, it is often the case that a user needs to work with different libraries depending on the extent of the analysis.
Continue reading “Linear and Logistic Regression Modelling in Python”
A K-Means Clustering algorithm allows us to group observations in close proximity to the mean. This allows us to create greater efficiency in categorising the data into specific segments.
In this instance, K-Means is used to analyse traffic clusters across the City of London.
Continue reading “K-Means Clustering: Analysing City of London Traffic”