Resume Screening is necessary when companies receive thousands of applications for different roles and need to find suitable matches. For this project, the dataset originally consists of 2 columns -- Category and Resume, where the Category denotes the field (eg: Data Science, HR, Testing etc.). By using value_counts on Category, we can find the frequency-wise distribution of different categories present in our dataset. During pre-processing, we need to remove links, hashtags, urls etc. as these are irrelevant in the resume. Further, using nltk, we also remove stopwords (for eg words like'are', 'the', 'or') that provide no significance to the content.

Today's hot topic: Resume screening using artificial intelligence Problem: 75% - 88% of resumes received are unqualified Solution: Artificial intelligence that auto-screens thousands of resumes in minutes Results: Candidates are screened with near perfect accuracy and presented to the hiring manager in order of interview priority Outcome: This technology will free up time so talent acquisition can focus on what is most important: interviewing and building their best teams www.Ideal.com Ideal builds software that Talent Acquisition loves. Ideal uses artificial intelligence to help make precise and efficient high-volume hiring decisions. Companies use Ideal's Intelligent Screening technology to sift through the resume noise and instantly identify who to interview.