Simple Keyword Clusterer
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Simple Keyword Clusterer

Description 💬

A weekend project that ends up to be quite useful for real-world tasks. It leverages TF-IDF vectorization to convert keywords of any context to vectors, which are fed to a KMeans clustering algorithm. The vectorization is then passed to a decomposition process through Principal Component Analysis for 2D representation.

You simply pass a list of keywords (for instance, a list of job roles) and the algorithm will return the clusters labelled by the most representative element of that group.

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The algorithm automatically finds the optimal number of clusters, but you can also tune it yourself by passing an additional argument in the constructor.

Goal of the project 🎯

Allow cluster extraction from unordered and unstructured textual data in a straightforward way.

Team 🤼

  • Me
  • Anyone who wants to contribute and improve the codebase 🙂

Features 🦓

The software offers the following features

  • preprocessing of keywords (stopword removal, string sanitization)
  • vectorization through TF-IDF to find the most relevant words across the whole corpus of keywords
  • KMeans clustering and PCA decomposition
  • Plotting
  • Outputs a Pandas DataFrame

Upcoming features

  • Annotations in plot to identify the clusters centers
  • More preprocessing options
  • GridSearch on the pipeline for ad-hoc tuning

Want to know more?

Drop me a line on my Twitter, LinkedIn or contact me through the form in the homepage.