Week: 08/04/2020
Overview of Things Learned:
Technical Area:
- Multi-label classification
- Support Vector Machine; Linear/Gaussian kernels
- Naive Bayes classifier
Tools:
- NumPy
- pandas
- scikit-learn
- NLTK (Natural Language Toolkit)
Soft Skills:
- Research
- Presentation
- Communication
- Teamwork
Achievement Highlights
- Researched on SVM and Naive Bayes learning algorithms and how they apply to our classification problem
- Implemented a draft version of the Python code responsible for text pre-processing, model training, and prediction
- Shared my findings with the team and set our goals for the following week
Meetings attended
- Weekly meetings on Tuesdays & Fridays
- Project party on Monday
Goals for the Upcoming Week
- Finish designing the Annotator (TagPredictor) class
- Evaluate the performance of the learning algorithms/models under consideration
- Fit the hyperparameters for the learning algorithms/models
Tasks Done
- Decided on which pipeline to use for the project
- Researched on learning algorithms for multi-label classification
- Implemented prototypes of the Annotator and Classifier classes