SMS spam classification data from Kaggle (https://www.kaggle.com/uciml/sms-spam-collection-dataset) using naive bayesian model.

This algorithm performs the following steps:

1. Load and read .csv file with SMS Spam Collection Dataset | Kaggle data from disk.
2. Parse this file and extract v1 value as label (spam or ham) and v2 value as message.
3. Transform messages to tokens and then to lemmas, replace all numbers with constant token __NUMBER__.
4. Shuffle all messages.
5. Split messages into train and test sets.
6. Fit bayesian model with train set.
7. Predict labels on test set.
8. Calculate the following metrics:

• accuracy,
• precision,
• recall,
• F1-score,
• Matthews correlation.

1. Node JS library and NPM package manager.
2. Libraries installed from package.json file.

1. Go to the project root directory.
2. Run npm i or npm install command. This command installs necessary libraries.
3. Open .env file and configure the following parameters:

• SMS_COLLECTION_PATH: string value, that specifies .csv file path to the SMS Spam collection data from Kaggle (absolute or relative path).
• TRAIN_SIZE: float value, that specifies the size of train set.
• COUNT_EXPERIMENTS: integer value, that specifies the number of experiments.

In the project root directory execute npm start command.

RESULTS:

• Count experiments: 100
• Train set size: 0.8
• Avg accuracy: 0.9768671454219029
• Avg precision (spam): 0.8840480938416516
• Avg recall (spam): 0.9494494826142801
• Avg F1-score (spam): 0.9153065782697162
• Matthews correlation: 0.9028739302029823

• csv-parser (version 2.3.2) is used for parsing .csv files.
• natural (version 0.6.3) is used for tokenizing input texts from corpus to words.
• lemmatizer (version 0.0.1) is used for creating lemmas from words.