Machine Learning Project
Team:
- David Balaban - Team Leader
- Daniel Lichter - Techsmith
- Asma Sadia - Specification Leader
- Maitri Patel - Quality Assurance Specialist
Keystone Strategy's recruiting team often receives large "resume books" containing hundreds of resumes from universities and their student organizations, which they then have to manually parse to catalog attributes about applicants such as education, work experience, skills, etc, before doing a more detailed review for fit with the organization. Keystone would like to automate this process using machine learning. A machine learning-based resume parsing will save recruiting team from hours of daily work by eliminating manual processing and analysis for every resume they receive.
Our custom dataset consists of resumes from three resume books from graduate business schools such as Tuck School of Business at Dartmouth, Haas School of Business at Berekely, and Standard Graduate School of Business. In total, there are 841 resumes of MBA candidates.
The modeling approach that we took to create the resume parsing model was to use MaskRCNN and EasyOCR to parse through the resumes and extract the information.
MaskRCNN is a pre-trained model generally used for object detection. We trained this model on the resumes available to us and used it to classify different portions of a resume using bounding boxes for each section of the resume, the weights that we used prior to training came from COCO dataset and are pretrained with 80 different classes on about 330K images. Then for the text extraction part we used EasyOCR model which is an Optical Character Recognition model that is already trained on multiple languages (including english), has very high accuracy and it's very easy to use.
We used mean Average Precision (mAP) as the metric, which is standard for evaluating an object detection model. It measures the average precision (AUC of a precision-recall curve) of a model across all object classes, and ranges between 0 and 1. Based on the mAP for each IoU Threshold on the 50 testing resumes, so for 75% IoU we got almost a 95% mAP which is very good, but again might be too good to be true since there might be some overfitting involved, then we can see that the 85% IoU Threshold had a mAP score of 73%, which is very good since we believe an 85% IoU threshold is enough for the model to be able to correctly find the sections and that even though there might be some overlapping it's been a very rare prediction. Finally, for the 95% IoU Threshold we see the abismal score of 0.167%, which again is understandable due to the ammount of training this model has gone through (Only about 850 resumes)
Selecting the right candidates from a pool of applicants can be one of the toughest jobs for the talent acquisition leaders. Moreover, going through each resume manually for every hiring season can be tiresome and time consuming. The machine learning resume parser tool can be a life saver for the entire company. It can provide unbiased solutions while overcoming possible manual errors.
To be able to run our app clone this repository and the first step is to make sure to run the requirements.txt file to install all necessary dependecies:
pip install -r requirements.txt
Then it's necessary to install poppler as well to handle the pdfs and there are two ways of doing this:
pip install python-poppler
or
conda install -c conda-forge poppler
Then once all the necessaries packages have been installed we will run the application using the framework Streamlit by running the following code:
python -m streamlit run Resume_Parser.py
Here is a screenshot of our application:
