-Machine Learning Project on Resume Screening using Python-

Why do we need Resume Screening?

• For each recruitment, companies take out the resume, referrals and go through them manually.
• Companies often received thousands of resumes for every job posting.
• When companies collect resumes then they categorize those resumes according to their requirements and then they send the collected resumes to the Hiring Team's.
• It becomes very difficult for the hiring teams to read the resume and select the resume according to the requirement, there is no problem if there are one or two resumes but it is very difficult to go through 1000’s resumes and select the best one.
• To solve this problem, we will screen the resume using machine learning and Nlp using Python so that we can complete days of work in few minutes.

Introduction :-

• Resume screening is the process of determining whether a candidate is qualified for a role based on his or her education, experience, and other information captured on their resume.
• It’s a form of pattern matching between a job’s requirements and the qualifications of a candidate based on their resume.
• The goal of screening resumes is to decide whether to move a candidate forward – usually onto an interview – or to reject them.

Modules & Libraries Description

Modules :-

• KNN

- It's supervised technique, used for classification. "K" in the KNN repersent the number of nearest neighbours used to classify or predict in case of continuous variable.

• NLP

- NLP is a field in machine learning with the ability of a computer to understand, analyze, manipulate, and potentially generate human language.

Libraries :-

• NumPy

- NumPy is one of the fundamental packages for Python providing support for large multidimensional arrays and matrices

• Pandas

- It is an open-source, Python library. Pandas enable the provision of easy data structure and quicker data analysis for Python. For operations like data analysis and modelling,

• Matplotlib

- This open-source library in Python is widely used for publication of quality figures in a variety of hard copy formats and interactive environments across platforms. You can design charts, graphs, pie charts, scatterplots, histograms, error charts, etc. with just a few lines of code.

• Seaborn

- When it comes to visualisation of statistical models like heat maps, Seaborn is among the reliable sources. This Python library is derived from Matplotlib and closely integrated with Pandas data structures.

• Scipy

- This is yet another open-source software used for scientific computing in Python. Apart from that, Scipy is also used for Data Computation, productivity, and high- performance computing and quality assurance.

• Scikit-learn

- It is a free software machine learning library for the Python programming language and can be effectively used for a variety of applications which include classification, regression, clustering, model selection, naive Bayes’, grade boosting, K-means, and preprocessing.

• Nltk

- Natural Language toolkit or NLTK is said to be one among the popular Python NLP Libraries. It contains a set of processing libraries that provide processing solutions for numerical and symbolic language processing in English only.

Functionality of Application

Screening resumes usually involves a three-step process based on the role’s minimum and preferred qualifications. Both types of qualifications should be related to on-the- job performance and are ideally captured in the job description.

These qualifications can include:

• Work experience
• Education
• Skills and knowledge
• Personality traits
• Competencies

Tools & Technologies used

• Machine Learning and Artificial intelligence, along with text mining and natural language processing algorithms, can be applied for the development of programs (i.e. Applicant Tracking Systems) capable of screening objectively thousands of resumes in few minutes without bias to identify the best fit for a job opening based on thresholds, specific criteria or scores.

Tech innovations in resume screening

• Designed to meet the needs of recruiters that current technology can’t solve, a new class of recruiting technology called AI for recruitment has arrived.
• AI for recruiting is an emerging category of HR technology designed to reduce — or even remove — time-consuming, administrative activities like manually screening resumes.
• The best AI software is designed to integrate seamlessly with your current recruiting stack so it doesn’t disrupt your workflow nor the candidate workflow.
• Industry experts predict this type of automation technology will transform the recruiting function.