Machine Learning Project on Resume Screening with Python
Companies often receive thousands of resumes for each job posting and employ dedicated screening officers to screen qualified candidates.
Hiring the right talent is a challenge for all businesses. This challenge is magnified by the high volume of applicants if the business is labour-intensive, growing, and facing high attrition rates.
IT departments are short of growing markets. In a typical service organization, professionals with a variety of technical skills and business domain expertise are hired and assigned to projects to resolve customer issues. This task of selecting the best talent among many others is known as Resume Screening.
Typically, large companies do not have enough time to open each CV, so they use machine learning algorithms for the Resume Screening task.
- Data was used from Kaggle which is a freely available platform for data scientists and machine learning enthusiasts. We are using jupyter-notebook to run resume screesing classification task.
- CLeaning the resume and convert the word into vector, by Countvectorizer
- Building the model
- predicting the model on test data
- Naive Bayes
- The machine learnig model accuracy is around 99%
- Clone or download the repo.
- Open command prompt in the downloaded folder.