Resume Classification Using Machine Learning and NLP (UpdatedResumeDataSet.csv – Kaggle)

🔍 Project Overview

In this project, I built a Resume Classification System using Machine Learning and Natural Language Processing (NLP) techniques. The goal was to automatically classify resumes into categories like IT, HR, Healthcare, etc., based on their content — a powerful tool for HR and recruitment automation.

The dataset used was the UpdatedResumeDataSet.csv from Kaggle.

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→Key Concepts Applied

• Text Preprocessing (Cleaning)

• Label Encoding (categorical conversion)

• TF-IDF Vectorization

• Train-test split

• Model Training (KNN, SVC, RandomForest)

• Resume Prediction Function

• Model Evaluation

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📂 Dataset Description

The dataset contains two columns:

• Resume: Free-form text data containing the full resume content.

• Category: The job field the resume falls under (e.g., IT, HR, Sales, etc.)

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→Step 1: Data Cleaning & Preprocessing

I created a cleanResume() function to:

• Remove URLs, punctuation, stopwords, and special characters

• Convert all text to lowercase

• Tokenize and lemmatize words

This step is critical to ensure that the data is clean and uniform for vectorization.

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→Step 2: Label Encoding

Used LabelEncoder() from sklearn.preprocessing to convert resume categories (strings) into numerical values that machine learning models can understand.

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📊 Step 3: Vectorization (TF-IDF)

Converted the cleaned text into a numerical format using TF-IDF (Term Frequency – Inverse Document Frequency), which helps identify important words in each resume without being biased toward common terms.

pythonCopyEditfrom sklearn.feature_extraction.text import TfidfVectorizer
tfidf = TfidfVectorizer(max_features=5000)
X = tfidf.fit_transform(cleaned_resumes)

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→Step 4: Splitting Dataset

Split the data into training and testing sets:

pythonCopyEditfrom sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, encoded_labels, test_size=0.2, random_state=42)

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🤖 Step 5: Model Training

I trained three different models:

1. KNeighborsClassifier

2. SVC (Support Vector Classifier)

3. RandomForestClassifier

Evaluated their accuracy and performance on the test set.

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→ Prediction Function

Here's the function that takes a resume and returns its predicted category:

pythonCopyEditdef pred(input_resume):
    cleaned_text = cleanResume(input_resume)
    vectorized_text = tfidf.transform([cleaned_text]).toarray()
    predicted_category = svc_model.predict(vectorized_text)
    predicted_category_name = le.inverse_transform(predicted_category)
    return predicted_category_name[0]

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→Final Result

I deployed a prediction system that classifies resumes into fields like:

• IT

• HR

• Health & Fitness

• Sales

• Teaching

• And many more...

Tested it using real resume samples, and it accurately returned categories using the SVC model!

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🔧 Tools & Technologies Used

• Python

• Scikit-learn

• Pandas, NumPy

• TF-IDF Vectorizer

• LabelEncoder

• ML Models: KNN, SVC, Random Forest

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→What I Learned

• How to clean and preprocess real-world text data

• Importance of choosing the right vectorization and ML model

• How to build an end-to-end NLP pipeline for classification

• Model saving and deployment using pickle

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→ What’s Next?

• Create a web app using Streamlit or Flask for interactive predictions

• Add more data and fine-tune hyperparameters for better accuracy

• Explore deep learning methods (e.g., BERT) for improved results

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→Let’s Connect

If you're working on a similar project or want to collaborate on NLP/ML, feel free to connect!

Linkedin

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#MachineLearning #NLP #ResumeClassifier #Python #TFIDF #Kaggle #Sklearn #AI #Hashnode #MLProjects