AI-powered resume classification system can accurately and efficiently analyze resumes, extract relevant information, and categorize them into predefined categories or job roles.
The process of reviewing and evaluating resumes for job applications is often time-consuming and labor-intensive. The task becomes even more complex when organizations receive an overwhelming number of applications. To address these challenges, the problem at hand is to develop an AI-powered resume classification system that can accurately and efficiently analyze resumes, extract relevant information, and categorize them into predefined categories or job roles.
The purpose of one-hot encoding is to represent categorical variables as binary vectors, enabling machine learning algorithms to effectively interpret and utilize these variables.
Hot encoded values.
Training model and appling different classifiers to check accuracy.
It is a type of supervised learning algorithm that builds a tree-like model to make predictions based on a set of input features.
A random forest algorithm works by creating multiple decision trees, each of which is based on a random subset of the data.
By using decision tree classifier accuracy is 0.5
Random Forest Classifier gives accuracy 0.29 with random state 7
XGB Classifier gives accuracy 0.39
KNN Classifier gives accuracy 0.33
So Decision Tree classifier gives highest accuracy 0.5.
Contributions to improve the AI Based Resume Classification System are welcome. Please follow these steps to contribute:
- Fork the repository.
- Create a new branch (
git checkout -b feature-branch). - Make your changes.
- Commit your changes (
git commit -am 'Add new feature'). - Push to the branch (
git push origin feature-branch). - Create a new Pull Request.