https://github.com/rootstrap/ai-job-title-area-classification

Classification of job titles into categories, using different ML techniques
https://github.com/rootstrap/ai-job-title-area-classification

hacktoberfest

Last synced: 10 months ago
JSON representation

Classification of job titles into categories, using different ML techniques

• Host: GitHub
• URL: https://github.com/rootstrap/ai-job-title-area-classification
• Owner: rootstrap
• Created: 2019-11-21T19:41:29.000Z (about 6 years ago)
• Default Branch: master
• Last Pushed: 2022-09-30T18:50:05.000Z (over 3 years ago)
• Last Synced: 2025-04-05T18:51:59.851Z (10 months ago)
• Topics: hacktoberfest
• Language: Python
• Homepage:
• Size: 45.9 MB
• Stars: 43
• Watchers: 8
• Forks: 6
• Open Issues: 8
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

          
# Classification of job positions by area

This is a project to classify job positions using machine learning, more specifically, supervised learning. The main goal is to get a classifier

that receives a job position in the form of a sentence, written in natural language, for example `CEO and Founder` and returns the job area for that position. The different areas (labels for the classification) are:  

* Business

* Technical

* Sales-Marketing

* Other

In the example, `CEO and Founder` would return `Business`.

There is an analogous project but it classifies according to the level of the position: [Classification of job positions by level](https://github.com/rootstrap/ai-job-title-level-classification).

## Implementation

This project is programed using the [Python language](https://www.python.org). The trained classifiers are implemented in the [Scikit Learn library](https://scikit-learn.org), a set of tools for machine learning in Python.

Two classification classes are studied:  

* [SGDClassifier](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDClassifier.html?highlight=sgdcl#sklearn.linear_model.SGDClassifier)

* [MLPClassifier](https://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPClassifier.html?highlight=mlpclassifier#sklearn.neural_network.MLPClassifier)

## Needed libraries

If you use pip and virtual environments, you can install easily the needed libraries, such as [pandas](https://pandas.pydata.org/) and scikit learn:  

`pip install -r requirements.txt`.

## Process data

Since both classifiers belong to supervised learning, they are trained using manually classified data, that you can see on `data_process/data_sets/classified_titles.tsv`. That is a tab-separated-values file, that has two columns in the form:  

` | `.  

The script `data_process/tsv_to_dataframe.py` takes the `tsv` file and:

1. Generates a dataframe that represents the job positions and corresponding classifications.

2. Split the dataframe into train(X) and test(y) set.

3. Normalizes the dataframe according to a defined criteria.

4. Stores X_train, X_test, y_train, y_test sets.

## Training and tuning

A classifier has:  

* parameters: values that corresponds to the mathematical model, that are adjusted after the training.

* hyper-parameters: values related to the way of training, that are adjusted using a selected part of the training set.  

It's possible to use a `fit` function to train and adjust the params. Besides, Scikit learn provides a tool named

 [`Pipeline`](https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html), and [`GridSearchCV`](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html) in order to make exhaustive search to achieve the hyperparams that optimize the results.

## Script execution

The steps are the same as the classification by level:

1. Run `data_process/tsv_file_to_dataframe.py` to extract the data from the tsv file and split the dataset.

2. Run `_fit_tune_classifier.py` to fit and tune the classifier. `fit` is to learn and fit the model to the train set. `tune` is to search for the optimal combination of the hyperparams, the ones that achieves better results (tuning may take a while).

3. Run `_test_classifier.py` to test the trained classifiers and show the results. Besides, a classified example set is stored in `test_data/_results.tsv`.

Notes:

* `` can be `mlp` or `sgd`, depending on the classifier.

* `mlp_fit_tune_classifier.py` can take days to complete the tuning.

## Results

These are the results of each classification class:  

### MLP

```

                  precision    recall  f1-score   support

       Business       0.91      0.92      0.91        63

          Other       0.79      0.86      0.83        44

Sales-Marketing       0.90      0.90      0.90        10

      Technical       0.86      0.73      0.79        33

       accuracy                           0.86       150

      macro avg       0.86      0.85      0.86       150

   weighted avg       0.86      0.86      0.86       150

```

### SGD

```

                  precision    recall  f1-score   support

       Business       0.97      0.97      0.97        63

          Other       0.95      0.95      0.95        44

Sales-Marketing       0.89      0.80      0.84        10

      Technical       0.97      1.00      0.99        33

       accuracy                           0.96       150

      macro avg       0.95      0.93      0.94       150

   weighted avg       0.96      0.96      0.96       150

```

SGD has better average than MLP. SGD uses SVM model, a good model for text classification.