Resume Matching Algorithm Python

Resume Matching Algorithm Python – I built a job recommender for people with non-technical backgrounds. This article covers the features of the app and the steps I took to build it.

I was inspired to build a job recommender due to recent layoffs at my old company. Many of my former colleagues who were let go were less than 3 years into their careers, and they wondered, based on their experiences, what roles they fit into and where they should start their job search. I remembered a similar struggle coming out of college where I also didn’t really know where to focus my search. I believe this issue is quite common for people with non-technical backgrounds.

Resume Matching Algorithm Python

Resume Matching Algorithm Python

I thus decided to build a job recommender for people in our positions – those with non-technical backgrounds like a Psychology major – that would use information based on our past experiences to help steer us on the right path.

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The app combines NLP techniques like topic modeling with classification-style machine learning to determine the best fit for you. You copy and paste your resume / LinkedIn into the text box, and the app analyzes the text and presents you with an ML-driven analysis of which jobs you’re a good fit for and why.

Feature 2: Return a chart of where your resume matches up with the other jobs based on subject matches. This chart hopefully provides some explanation for your Feature 1 results.

Feature 3: Select a drop-down menu of different job archetypes and see which keywords your resume both matches and doesn’t match.

In the rest of this article, I’ll walk through the steps I took to build the job recommender. The link to my code is here.

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The most important part of a data science project is scoping – that is, planning your project so that it fits your time and effort constraints, yet is still able to answer a valuable question. There is so much data and avenues for research that a certain domain can hold, the sheer amount can be overwhelming. You must therefore be clear about what issue you are trying to address, what specific data you will be looking for, and what the minimum threshold for success is.

They key question for my scope was figuring out what job types to analyze. So many different jobs exist in the non-technical business space. I felt that if I included too many job applicants, the project would not have its intended impact. The modeling might be less accurate, and the program might be too cluttered and unfocused to be helpful to the end user. So I thought back to my original intention and decided on 2 criteria to analyze jobs:

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After that, I thought of some broad job archetypes that made sense, but also sent out a survey to see what others thought of my ideas. I then asked my colleagues to rank the job types they were interested in so that I could limit the number of job types I had to analyze. Ideally, I wanted to analyze less than 10 job types.

Resume Matching Algorithm Python

With this information, I chose the 5 most popular jobs as the basis for my analysis but ended up adding more later when I was confident that my model could correctly distinguish between the job types. I ended up choosing 8 different job types in the non-technical business space and also added “Data Scientist” out of personal interest.

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The data I planned to use were jobs in the respective job types that fit the criteria described above. I first looked at LinkedIn and Indeed, but their sites proved too difficult to scrape due to dynamic loading.

I settled on scraping jobs from Glassdoor, which also used dynamic loading. But this time, I borrowed the code from a fellow data scientist and modified it to suit my needs. I am very grateful for this code because building my own scraper would have taken me much longer!

After modifying the scraper to suit my needs, I scraped 40 posts per job type and joined the files. Many of the jobs scraped clearly didn’t fit into the right job archetype – for example, I got a lot of jobs in construction when I scraped “Project Manager” roles. I also got a lot of jobs with the title “Analyst [Analyst]] or “Director of [Type]”, which were clearly unattainable roles for people with 0–3 years of experience. So I set up text filters to remove these job types and double checked to make sure the rest of the data fit what I was looking for.Finally, I ended up with the text descriptions of 149 job listings from 9 different job types as my data set.

The next step was to clean up the text descriptions. I used standard cleaning techniques like removing punctuation and capitalization, then tokenized and stripped the words to standardize semantically. Finally, I put the data into a table format using a vectorizer.

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Once the data was in an analyzable format, I performed topic modeling, trying several techniques but finally landing on TruncatedSVD. The optimization factor was how accurately I could predict job types with the classification model I built in Step 4. I came up with 20 different topics in total.

After topic modeling, I used the topic-document matrix and entered it into a classification algorithm. Optimizing for accuracy, I finally settled on a random forest classifier. The model returned ~90% accuracy on validation sets, showing strong competence in predicting correct job types for each job description.

Next, it was time to give the model a functional purpose. I used the topic model above to transform my resume, and then used the classification model above to predict which jobs the resume would be best suited for. I then extracted the percentage match by job type, giving a more nuanced view of one’s best job match – say, 60% project manager, 40% product manager – and giving the end user multiple career paths to explore.

Resume Matching Algorithm Python

They match those groups. I had quite a difficult time explaining the underlying mechanisms of the models to a non-technical crowd, and so I decided to show them a simplified diagram to explain these working compatibility results.

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First, I reduced the subject-document matrix into two dimensions using PCA. Then, I drew each work type according to the reduced dimensions. I also applied the dimensionality reduction to one’s resume and was able to plot where one’s resume held up against the other jobs on the chart. Interpreting the new PCA features, I saw that they were strongly oriented towards 2 topic types: Marketing related keywords, and project management related keywords.

They can improve their resumes for a better chance at whatever job they’re aiming for. I decided to make matching keywords work to make the app more useful.

In this feature, the user selects which job they are interested in from a drop-down menu, and the program returns which keywords match and are missing from their resume. People can see both where their resumes currently stand, and what words and experiences they could put in for a more targeted application.

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This feature was probably the easiest to do. I used the same topic model above to come up with the most significant words in each job type — say the top 20 — and used list comprehensions to see which words matched or missed.

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Now that I had created the features and models, I needed to deploy an app online for people to use.

For application writing, I used Streamlit. It is one of the easiest and most effective packages. I then used Heroku and git to upload it to the web, although in retrospect it would have been much easier to use streamlit’s newly released application features.

And there we have it! Job Recommender. I was quite proud of this project and I hope people find it useful. If you think the app can help someone, send it to them! If you have any criticisms, please comment below or message me. Thanks for reading. Recruitment agencies easily have to match hundreds of applicants with hundreds of job listings per month. It is a very time-consuming process to manually sift through such a mass of documents. There has to be a way to do this more efficiently, right? Well, luckily there are a bunch of NLP open source libraries for Python that can help us speed up this process and list multiple candidates for these job listings.

Resume Matching Algorithm Python

Natural Language Processing (NLP) is a branch of Artificial Intelligence/Computer Science that focuses on how computers can be programmed to understand, interpret and process huge volumes of human (natural) language. Human language is quite complex and ambiguous, which makes it difficult for computers to understand. Multiple techniques exist to process such language, from algorithmic approaches to statistical methods and machine learning.

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NLP can be used in a variety of ways, some well-known applications are sentiment analysis, chatbots, and text summarization and translation. Word processing can be quite difficult for machines. Similar words can mean completely different things or different words can have the same meaning. We can define some language features that make it possible

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