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Resume Screening App

Summary

This app uses Natural Language Processing (NLP) to determine the most suitable job role for a user based on their resume. It employs various machine learning and data analysis libraries to achieve an accuracy of 99.48%.

Features

  • Natural Language Processing (NLP): The app processes and analyzes resumes to extract relevant information.
  • Job Role Prediction: Based on the resume analysis, the app suggests the most suitable job role for the user.
  • High Accuracy: The app achieves an impressive accuracy rate of 99.48%.
  • Interactive Interface: The app uses Streamlit to provide an interactive and user-friendly web interface.

Technologies Used

  • K-Nearest Neighbors (KNN): This algorithm is used for classifying the job roles based on the features extracted from the resumes.
  • scikit-learn: A powerful Python library for machine learning, used for implementing the KNN algorithm and other ML tasks.
  • pandas: A data manipulation and analysis library, used for handling and processing the resume data.
  • numpy: A fundamental package for scientific computing with Python, used for numerical operations.
  • seaborn: A statistical data visualization library, used for creating informative and attractive graphs.
  • matplotlib: A plotting library, used for creating static, animated, and interactive visualizations in Python.
  • Streamlit: An open-source app framework used to create a beautiful web application for the app.

Explanation of Subtopics

  • Natural Language Processing (NLP):

    • Used to extract meaningful information from the text in resumes.
    • Helps in understanding the context and relevance of the content.

  • Job Role Prediction:

    • Utilizes the extracted features from resumes to predict the most suitable job role.
    • Aims to provide accurate and relevant job suggestions based on the user's skills and experience.

  • K-Nearest Neighbors (KNN):

    • A simple, instance-based learning algorithm.
    • Classifies job roles by comparing the similarity of resume features with labeled examples.

  • scikit-learn:

    • Provides simple and efficient tools for data mining and data analysis.
    • Contains various machine learning algorithms and is used for implementing KNN in this app.

  • pandas:

    • Offers data structures and operations for manipulating numerical tables and time series.
    • Facilitates easy handling and processing of resume datasets.

  • numpy:

    • Supports large, multi-dimensional arrays and matrices.
    • Includes a large collection of high-level mathematical functions to operate on these arrays.

  • seaborn:

    • Based on matplotlib, it provides a high-level interface for drawing attractive and informative statistical graphics.
    • Used to visualize the patterns and correlations in the resume data.

  • matplotlib:

    • A comprehensive library for creating static, animated, and interactive visualizations in Python.
    • Used for plotting the data and results for better understanding and presentation.

  • Streamlit:

    • An open-source app framework that turns data scripts into shareable web apps in minutes.
    • Used to create an interactive and user-friendly interface for users to input their resumes and view the predicted job roles.

Accuracy

  • The app achieves an accuracy of 99.48%, indicating its high reliability and effectiveness in predicting the most suitable job role based on the resume analysis.
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