How mohammedghennai/rfc_based_resume_classifier works
This notebook project classifies resume text into job categories. The documented workflow loads resume data, preprocesses and cleans text, explores category and word distributions, trains a Random Forest with grid search, and evaluates it with classification metrics. All implemented stages map to the single notebook; no source excerpts were available, so relationships below rely only on the README and do not imply uninspected implementation details.
Main entry points: rfc_based_resume_classifier.ipynb Load data
Components 10
Resume data
- Load data
rfc_based_resume_classifier.ipynb - Text preprocessing
rfc_based_resume_classifier.ipynb - Data cleaning
rfc_based_resume_classifier.ipynb
Data exploration
- Exploratory analysis
rfc_based_resume_classifier.ipynb
Classification
- Random Forest training
rfc_based_resume_classifier.ipynb - Grid search
rfc_based_resume_classifier.ipynb - Model evaluation
rfc_based_resume_classifier.ipynb - Classification metrics
rfc_based_resume_classifier.ipynb
Other components
- Analyst
- Resume dataset
Connections 9
- Analyst → Load data: runs analysis
README.md - Resume dataset → Load data: loads resumes
README.md - Load data → Text preprocessing: prepares text
README.md - Load data → Exploratory analysis: analyzes data
README.md - Text preprocessing → Data cleaning: cleans text
README.md - Data cleaning → Random Forest training: trains on text
README.md - Random Forest training → Grid search: tunes parameters
README.md - Random Forest training → Model evaluation: evaluates model
README.md - Model evaluation → Classification metrics: reports metrics
README.md