GOAL
Develop a model classify resumes into predefined categories. Note: Text classification is an example of supervised machine learning since we train the model with labelled data.
DATASET
https://www.kaggle.com/datasets/dhainjeamita/updatedresumedataset?resource=download
CONTENT
Data for the case is available in CSV format having 963 rows and 2 columns.
STEPS TAKEN
All the required libraries and packages were imported and then the required dataset for the project was loaded.
EDA was carried out to visualize various parameters and the most corelated unigrams and bigrams.
Data was cleaned also known as Text Preprocessing. Text Preprocessing was done using the re function of python and the nltk library which is used for NLP based models.
Model building was then implemented using different algorithms. 9 different models were used to train and valuate the results. 5 of the used models gave a very high accuracy whereas Dummy Classifier gave the least accuracy of less than 0.1.
TEXT PREPROCESSING
The text needs to be transformed to vectors so as the algorithms will be able make predictions. In this case, the Term Frequency – Inverse Document Frequency (TFIDF) weight will be used to evaluate how important a word is to a document in a collection of documents.
After removing punctuation and lower casing the words, importance of a word is determined in terms of its frequency.
TF-IDF is a measure of originality of a word.
TF is the number of times a term appears in a particular document.
IDF is a measure of how common or rare a term is across the entire corpus of documents.
MODELS USED
The classification models used are:
- K Nearest Neighbor
- Dummy Classifier
- Linear Support Vector Classifier
- Stochastic Gradient Descent
- Random Forest
- Decision Tree
- Multinomial Naive Bayes Classifier
- Gradient Boost
- AdaBoost
LIBRARIES REQUIRED
- Pandas - for data analysis
- Numpy - for data analysis
- matplotlib - for data visualization
- seaborn - for data visualization
- scikit-learn - for data analysis
VISUALIZATION
By viewing Confusion Matrix it is easily deduced that SGD model is the best model for this project.
ACCURACIES
| Model | Architecture | Accuracy in % (on testing data) |
|---|---|---|
| Model 1 | K Nearest Neighbor Model | 97.92 |
| Model 2 | Dummy Classifier Model | 9.84 |
| Model 3 | Linear Support Vector Model | 100.00 |
| Model 4 | Stochastic Gradient Descent Model | 100.00 |
| Model 5 | Random Forest Classifier Model | 100.00 |
| Model 6 | Decision Tree Classifier Model | 100.00 |
| Model 7 | Multinomial Naive Bayes Model | 96.37 |
| Model 8 | Gradient Boost Classifier Model | 100.00 |
| Model 9 | AdaBoost Model | 30.05 |
CONCLUSION
The most succesful model was found to be Stochastic Gradient Descent Classifier for Role classification based on their resume.
Connect with me on Linkedin: https://www.linkedin.com/in/prajwal-uday-1b9678229/
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