Resume Screening using Deep Learning on Cainvas

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.