Project README

Python3 Development Environment Setup

This project is developed using Python3 and managed with Miniconda. Follow the steps below to set up the development environment:

1. Clone the Repository

git clone [repository_url]
cd [repository_directory]

Make sure to replace [repository_url] and [repository_directory] with your actual repository URL and directory.

2. Create and Activate Conda Environment

conda create --name your_environment_name python=input_python_version
conda activate your_environment_name

3. Install Dependencies

The dependencies for this project include:

Miniconda: Download and install Miniconda.

openpyxl: A library for reading and writing Excel files.

scikit-learn: A machine learning library for data processing, modeling, and evaluation.

pandas: A powerful data manipulation and analysis library.

google-auth-oauthlib: Google authentication library for OAuth.

pydrive: A Python library to interact with Google Drive.

conda install package_name

4. Project Structure

The project follows the structure commonly used in PyCharm and Visual Studio Code for Python projects. Below are key components:

• main.py: Main script for processing the MASSIVE Dataset and generating language-specific Excel files.
• functions.py: Module containing functions for creating Excel files.
• generator.sh: Shell script for running the Python script with specified input and output paths.

5. Running the Project

• Place the MASSIVE Dataset JSONL files in the specified input_jsonl_dir directory.
• Run the generator.sh script:

./generator.sh

This command executes the python scripts.