NLP_classifiers

Contains algorithms to train machine learning models using a variety of classifier strategies.

These steps can be followed as a reference:

1. The data to train your model can be found in data\sentiment_competition_train.csv
2. Pre-process dataset
3. Split dataset into a training and validation set
4. Vectorize data
5. Train model using classification algorithm
6. Validate trained model using validation dataset
7. Improve model/pre-processing/vectorizer etc.
8. Evaluate with the test set and hope for the best!

Getting started

Before we can make a new Python project, we need to make sure that you have a (recent) Python version installed on your device. Python 3.8+ is required. If you have a recent version of Python installed, a version of the tool pip is automatically installed.

Check Python version

python --version

Note: When the command is not found or the version is 2.x, try python3 --version and use python3 instead of python in the commands below.

Project setup

When the right python version is installed, we can open our project and install all the required packages for this project.

• Create a virtual environment:

  python -m venv venv

• Activate the virtual environment:

  • See this link how to activate the venv for your operating system.

• Update pip to get its latest version:

  python -m pip install -U pip

• Install wheel:

  python -m pip install wheel

• Install required packages:

  python -m pip install -r requirements.txt

Dataset

The sentiment_competition_train.csv is a subset of the IMBD sentiment review dataset.

See link: https://www.kaggle.com/datasets/lakshmi25npathi/imdb-dataset-of-50k-movie-reviews