Sometime aback at Geektrust, we were up with our advocacy engine, advising kick-ass developers to our audience based on their profile, years of acquaintance and technology. It was congenital on Go & Elasticsearch. What we did was bout the user profiles for the requirements of the companies. In accession we akin the best developers who had a aing affinity to what the aggregation was attractive for. We took baby accomplish into the apple of Machine Acquirements at that time by accomplishing affinity matches.
But, we had a problem. We were relying on the technologies that our users entered, and bodies consistently entered Java. Not everyone, but at atomic 60–70% of them did that. This resulted in QA Engineers with some Java ability accepting recommended to a aggregation that was attractive for a Java Backend developer. This didn’t attending acceptable in avant-garde of the chump and we absitively to bind up our recommendations. What we bare to do was yze what affectionate of developers were registering on our site. And that’s area Machine Acquirements came to our help.
We went with supervised learning, which agency classifying abstracts based on avant-garde labelled training data.
As allotment of this, the aboriginal affair we did was to account bottomward the appearance we capital to yze in a developer to allocate him/her as a Backend or Frontend or Fullstack developer. Keeping that in apperception we came up with a set of appearance like calculation of backend technologies in a user and browse their resumes to accept what affectionate of technologies accept they formed on.
Once these were identified, we aerated out a book that apparent these appearance adjoin anniversary developer in our database. This was the aboriginal footfall in creating our training data.
In the aloft file, the columns announce the appearance and the aftermost cavalcade indicates the account we appetite to predict. In the training abstracts the absolute abstracts was bankrupt and apparent with a specific role. This can be generated with scripts, but after 100% accuracy. We had to adapt our training abstracts to characterization the cavalcade that needs to be predicted, which is role. 6 of us formed through about 7000 rows of abstracts and verified, authentic and adapted the data, if required.
Once the training abstracts was accessible we had to ysis the accurateness of this model. We approved altered classifiers like K Nearest Neighbours (KNN), Decision Trees and Support Vector Machines(SVM). We additionally had to go aback and accomplished tune the training data, abacus added features, removing some of them. All these decisions were based on the accurateness we got on these altered classifiers. SVM gave us the best accurateness and we went avant-garde with it application a beeline Kernel. The accomplishing was in Python. With able-bodied accustomed libraries like scikit-learn , NumPy, SciPy and pandas available, we had little agnosticism in selecting Python as our go-to accent for ML.
The aloft cipher shows how you can alternation & ysis your model. The accomplished abstracts in csv book is breach into ysis & alternation abstracts and again the ysis abstracts is acclimated to ysis if the anticipation is actual or not. It additionally gives you the anticipation allotment of the prediction. The predictions fabricated by the SVM were absolute adjoin the already accomplished abstracts and it was appealing accurate.
And that’s it. Now for every contour amend the developer does on Geektrust, we body the affection abstracts and adumbrate the role from it. Once we adumbrate what affectionate of a developer he/she is, we use that advice to acclaim them accurately to a specific claim of a company. Now our recommendations to companies are bigger than earlier.
However, this doesn’t agreement that the predicted role is actual all the time. Sometimes, we get predicted role with probabilities as low as 20–30%. In such cases we manually override the prediction, to what we anticipate is the role of the user. We additionally amend the absolute training abstracts at alternate intervals, which helps in convalescent approaching predictions.
At Geektrust, we abide to assignment on ML & AI accompanying being and this is an advancing activity. We accept started alive on our own resume parser and attending avant-garde to architecture an avant-garde cipher appraisal agent that checks for apple-pie code.
[About the author — Dhanush is co-founder at Geektrust and takes affliction of all things tech.
Geektrust is congenital for technologists to affix with arresting job opportunities.]
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