Resume Selector

Naive Bayes classifier

to automatically classify resumes as

or

. The project applied

natural language processing (NLP)

techniques to preprocess resume text, vectorize features, and evaluate model performance.

Stack: Python, scikit-learn, NLTK, Gensim, pandas, Matplotlib, Seaborn.

Key Contributions

• Data preprocessing: Cleaned raw resume text, removed stopwords, lemmatized/stemmed tokens, and applied custom preprocessing with gensim and nltk.
• Exploratory Data Analysis: Examined class imbalance (92 not-flagged vs. 33 flagged resumes), visualized distributions, and generated word clouds for each category.
• Feature engineering: Converted resume text into numerical features using CountVectorizer (bag-of-words).
• Model training: Trained a Multinomial Naive Bayes classifier on 125 labeled resumes.
• Evaluation: Achieved near-perfect results with:
  • Precision, Recall, F1-score = 1.00 (20% test split)
  • Accuracy = 97% (30% test split)
  • Visualized confusion matrices with seaborn heatmaps.

Skills Demonstrated

• Machine Learning (Naive Bayes classification)
• Natural Language Processing (text preprocessing, tokenization, stopword removal)
• Feature extraction (bag-of-words, CountVectorizer)
• Model evaluation (classification reports, confusion matrices)
• Python ML stack: scikit-learn, nltk, gensim, pandas, matplotlib, seaborn

Project Report PDF

Generated Wordclouds

Project Certificate