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Recruiters and HR teams in companies have a tough time scanning thousands of qualified resumes. Either they need many people to do this or they miss out on qualified candidates. This is a waste of time, money and productivity for the company.
To solve this, our resume parser application can take in millions of resumes, parse the needed fields and categorise them. This resume parser uses the popular python library - Spacy for OCR and text classifications. First we train our model with these fields, then the application can pick out the values of these fields from new resumes being input.
The dataset of resumes has the following fields:
This project analyzes a dataset containing ecommerce product reviews. The goal is to use machine learning models to perform sentiment analysis on product reviews and rank them based on relevance. Reviews play a key role in product recommendation systems.
In this machine learning project, we will implement Back-propagation Algorithm from scratch for classification problems.
In this machine learning project, we will use binary leaf images and extracted features, including shape, margin, and texture to accurately identify plant species using different benchmark classification techniques.