Data used in this project is airline_sentiment_analysis.csv containing 14000+ rows and 2 columns named test contaning test reviews and column airline sentiment contaning sentiment associated with each test respectively.

• python 3.x

• scikit-learn
• pandas
• numpy
• imblearn
• pyramid
• tensorflow (Optional)

To install the depenencies

$ pip install -r requirements.txt

• The ML class in ml.py file present in src folder implements different options to get started with model traning.
• A GridSearchCV traning can be perfomed with all the listed models in ml.py by calling the train_gridsearch() method provided the data after intializing the Sentiment_Analysis class in pipeline.py

import pipeline

SA = pipeline.Sentiment_Analysis()
SA.train_gridsearch(df,sample_data=True,sampling_ratio=(.4,.7),n_splits=5,test_size=.2)

• A custom model of choice can be trained with the same data processing pipeline by calling the train_model method and passing the model as argument after initializing the Sentiment_Analysis class in pipeline.py

import pipeline

model = "Compiled model of your choice"
SA = pipeline.Sentiment_Analysis()
SA.train_model(df,model,sample_data=True,sampling_ratio=(.4,.7),test_size=.2)

• Both traning methods accepts sampling_data as boolean, whether to perform SMOTE and Under sampling of majority class can be provied as a tuple to sampling_ratio and a test split size

$ cd src
$ python train.py

• Inference on models can be performed by calling the model_inference method in pipeline.py.
• When class Sentiment_Analysis initialized in inference mode providing targets are optional, targets can be provied incase there is requirement for scoring.

import pipeline

model = "pretrained model"
SA = Sentiment_Analysis(test=True)
SA.model_inference(model,df,targets=None)

$ cd src
$ python inference.py

API endpoints built using pyramid web framework along with Swagger UI API documentation is implemented to to serve the pretained ML models

$ cd src
$ python app.py

• pyramid server becomes available at http://localhost:5000
• Swagger UI can be accessed at http://localhost:5000/docs/