This is an example of text classification on job description dataset, using different classifiers with tuning techniques.
For solving this problem we follow the next steps:
- Exploratory data analysis
- Data preprocessing
- Modeling
- Show job position based on description
The classifier is built on dataset, which contains > 70k job descriptions belong to 30 job positions.
Example contains training 4 different classifiers with comparing results on validation dataset:
| Classifier | Accuracy |
|---|---|
| Naive Bayes classifier | 0.87 |
| LogisticRegression | 0.94 |
| DecisionTreeClassifier | 0.93 |
| LinearSVC classifier | 0.95 |
Example
Input text (job description):
Our client is a leading innovator in manufacturing thousands of products that affect the lives of millions of people every day; and a strategic player in the Home Health, Personal Care, Electronics, Industrial and Transportation Markets. Specifics:
- Prepare support for and record required journal entries related to general ledger accounts
- Prepare monthly account reconciliations
- Support and actively participate in department initiatives
- Assist with Ad-hoc analysis and projects
Output (job position):
Staff Accountant
With Kaggle API download dataset:
kaggle datasets download -d bman93/datasetDependencies:
pip install -r requirements.txtOptional:
python -m spacy download en_core_web_sm