Resume-Classifier

Overview

This project is a simple resume classifier that helps categorize resumes into predefined job categories. The classification is done using a K-Nearest Neighbors (KNN) classifier trained on a dataset of resumes.

Features

• Data Exploration: Explore the provided dataset to understand the distribution of resume categories using visualizations.

• Text Cleaning: Utilize the cleanResume function to preprocess resume text, removing unnecessary elements like URLs, mentions, and punctuations.

• Word Cloud: Generate a word cloud to visualize the most frequent words in the cleaned resume text using the wordcloud library.

• Feature Extraction: Use the TfidfVectorizer from scikit-learn to convert the cleaned text into numerical features suitable for machine learning.

• Model Training: Train a KNN classifier using the OneVsRestClassifier approach and evaluate its accuracy on training and test sets.

• Making Predictions: Allow users to input new resume text, and the model will predict its category, providing probability scores for each category.

• Category Descriptions: Provide brief descriptions of predicted categories based on the top predicted category.

Prerequisites

Ensure you have the necessary libraries installed. You can install them using:

pip install numpy pandas matplotlib seaborn scikit-learn nltk wordcloud

Dataset

The project uses the UpdatedResumeDataSet.csv dataset, containing labeled resumes for training and evaluation.