https://github.com/djdhairya/resume-categorization
The Resume Categorization App helps automate the process of categorizing resumes into different job domains using machine learning. It provides a user-friendly interface for uploading resumes, categorizing them, and downloading the results.
https://github.com/djdhairya/resume-categorization
accuracy logistic-regression machine-learning matplotlib pandas pkl pypdf2 sickit-learn streamlit
Last synced: about 1 month ago
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The Resume Categorization App helps automate the process of categorizing resumes into different job domains using machine learning. It provides a user-friendly interface for uploading resumes, categorizing them, and downloading the results.
- Host: GitHub
- URL: https://github.com/djdhairya/resume-categorization
- Owner: djdhairya
- License: mit
- Created: 2024-12-16T21:00:17.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2024-12-16T21:01:25.000Z (5 months ago)
- Last Synced: 2025-04-08T13:21:59.516Z (about 1 month ago)
- Topics: accuracy, logistic-regression, machine-learning, matplotlib, pandas, pkl, pypdf2, sickit-learn, streamlit
- Language: Jupyter Notebook
- Homepage:
- Size: 209 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE.txt
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README
# Resume Categorization App
## Overview
This is a **Resume Categorization** app built using **Streamlit** and **Machine Learning**. The app allows users to upload resumes in PDF format, which are then categorized into various job domains using a **Logistic Regression** model. The app stores categorized resumes in folders and generates a CSV file containing the filename and corresponding category.
## Project Structure
```
Resume-Categorization/
│
├── app.py
├── application.ipynb
├── model/
│ ├── model.pkl
│ └── tfidf.pkl
├── data/
│ └── ResumeDataSet.csv
```
## Requirements
To run this app, you will need to install the following dependencies:
- Python
- Streamlit
- scikit-learn
- PyPDF2
- pandas
- pickle
## How to Use
### 1. Launch the App
Run the Streamlit app by executing:
```bash
streamlit run app.py
```
This will start the Streamlit app and open it in your browser.
### 2. Upload Resumes
- Click the file uploader to select multiple PDF resumes for processing.
- The app will process the resumes and categorize them based on their content.
### 3. Categorize the Resumes
- After uploading the resumes, click the "Categorize Resumes" button.
- The app will display a table with the filename and its assigned category.
- The categorized resumes will be stored in folders named after the predicted categories.
### 4. Download Results
- After categorization, you can download the results as a CSV file that contains the filenames and their respective categories.
- The CSV file will be available for download directly from the app.
### 5. Directory Structure
- The categorized resumes will be stored in the output directory, organized into folders for each category.
- Example: `categorized_resumes/Java Developer/Resume1.pdf`
## File Descriptions
### `app.py`
This file contains the Streamlit application code. It handles:
- Uploading resumes
- Categorizing the resumes using the pre-trained model
- Saving the categorized resumes in respective folders
- Generating and providing a downloadable CSV file with the results
### `application.ipynb`
This Jupyter Notebook is used for:
- Training the machine learning model using a dataset of resumes (`ResumeDataSet.csv`).
- Evaluating the model's performance.
- Saving the trained model (`model.pkl`) and the TF-IDF vectorizer (`tfidf.pkl`).
### `model/model.pkl`
This is the trained **Logistic Regression** model used to categorize resumes. It is used by the `app.py` file to predict the category of the uploaded resume.
### `model/tfidf.pkl`
This file contains the **TF-IDF vectorizer** that is used to transform the text data (cleaned resume) into numerical features that can be fed into the model for categorization.
### `data/ResumeDataSet.csv`
This CSV file contains the dataset of resumes used to train the machine learning model. Each row represents a resume with its associated category.
## Category Mapping
The app categorizes resumes into the following job domains:
- **0**: Advocate
- **1**: Arts
- **2**: Automation Testing
- **3**: Blockchain
- **4**: Business Analyst
- **5**: Civil Engineer
- **6**: Data Science
- **7**: Database
- **8**: DevOps Engineer
- **9**: DotNet Developer
- **10**: ETL Developer
- **11**: Electrical Engineering
- **12**: HR
- **13**: Hadoop
- **14**: Health and Fitness
- **15**: Java Developer
- **16**: Mechanical Engineer
- **17**: Network Security Engineer
- **18**: Operations Manager
- **19**: PMO
- **20**: Python Developer
- **21**: SAP Developer
- **22**: Sales
- **23**: Testing
- **24**: Web Designing
## Conclusion
The Resume Categorization App helps automate the process of categorizing resumes into different job domains using machine learning. It provides a user-friendly interface for uploading resumes, categorizing them, and downloading the results.
