Make a classifier which takes in a job description and gives the department name for it.
- Use a neural network model
- Make use of a pre-trained Word Embeddings (example: Word2Vec, GloVe, etc.)
- Calculate the accuracy on a test set (data not used to train the model)
All the data required can be found in the data folder in the root of the project
There are two sources for loading your training/test data
-
For Job Description:
docs folder contains around 1000 json files, each of which is a single job posting. You have to use the value ofdescription
field inside thejd_information
field. -
For Job Department:
document_departments.csv file contains the mapping of document id to department name where document id is the name of the corresponding file in docs folder.
Mandatory
- Write your solution code in a jupyter notebook & save it in format YourName_PhoneNumber.ipynb.
- Make sure to run all cells in notebook before submission.
- Write clean code & mention comments, steps followed in markdown cells.
- Update the requirements.txt with all the packages used in your code base.
Preferable
- Adhere to PEP8 formatting
- Use python 3
- You can use notebook(YourName_PhoneNumber.ipynb) given in repo as a template.