This article is about Machine Learning prediction using data collected from a website and google trend. It is a toy example that assumes a correlation between the keyword “coffee” search on Google and the historical price of coffee. The example shows how to combine different sources of data in a library called Prophet to make multivariate future predictions (Figure 1 shows the final output of the learning exercise). To get the data from this website I used Selenium and Pytrend to access the information from Google trend. The full code implementation can be found here on my GitHub account.
The k-nearest neighbours’ algorithm (kNN) is a non-parametric machine learning method used for classification and regression. It is non-parametric because the model does not learn any parameters to make correct predictions. Instead, it will look at closest training examples (the number of examples depends on the k selected by the user) in feature space. When used for classification, the output is a class group. An object is classified by a plurality vote of its neighbours, with the item being assigned to the class most common among its k nearest neighbours. When used for regression, the output is the average of…
The whole concept of machine learning is figuring out ways in which we can teach a computer to perform a task without needing to provide explicit instructions.
For example, we might want to instruct a machine to recognise the brand of a bottle of wine. The first step would be to enter a wine shop with some lab tools and write down wine characteristics for all the bottles of wine. …
Guido Salimbeni
Data Scientist
