Resume Screening and Job Role Classification

This project focuses on classifying resumes into job categories using Natural Language Processing (NLP) and Machine Learning techniques.

📁 Dataset

The dataset contains resumes with the following columns:

• Category: The job role (e.g., Data Scientist, Web Developer, Java Developer, Sales, Mechanical, etc.)
• Resume: Text summary of the candidate’s resume

🧪 Exploratory Data Analysis

• Checked the number of resumes and unique job categories
• Performed basic text analysis on the resume data

🔢 Preprocessing

Label Encoding

• Converted job role categories (text) into numeric labels using Label Encoding to make them suitable for machine learning models.

Text Vectorization (TF-IDF)

• Applied TF-IDF (Term Frequency–Inverse Document Frequency) using TfidfVectorizer from scikit-learn
• Transformed textual resume data into numerical vectors representing the importance of words

🧠 Model Used

OneVsRestClassifier with K-Nearest Neighbors (KNN)

• Used the OneVsRestClassifier wrapper to handle multi-class classification
• Trained the model using K-Nearest Neighbors (KNN) algorithm
• Split the data into training and testing sets

✅ Results

• Achieved an impressive accuracy of around 98%
• The model successfully predicted job categories based on resume content

📊 Features

• Independent Feature: Resume text
• Dependent Feature: Job category

💻 Technologies Used

• Python
• Pandas
• Scikit-learn
• Natural Language Processing (TF-IDF)

Live Demo:- https://huggingface.co/spaces/ghavateankita/Resume-Analyzer

🚀 How to Run

1. Clone the repository:

   git clone <repository_url>
   cd resume-screening
   

2. Install dependencies & Run the script:

  pip install -r requirements.txt
  python resume_classifier.py