Task Estimation CLI

This command-line tool predicts task estimation time based on task descriptions using a pre-trained machine learning model. It also includes functionality for training the model with new data.

Prerequisites

Ensure you have Python installed (preferably Python 3.8+).

Installation

1. Clone or download this repository.

2. Set up the virtual environment and install dependencies:

   ./setup-env.sh

3. Ensure you have a dataset (tasks.csv) for training and a trained model saved as model.pkl in the project directory.

Training the Model

To train the model with a dataset (tasks.csv), run:

./train.sh

This will:

• Load the dataset from tasks.csv
• Train a machine learning model
• Save the trained model as model.pkl

Usage

Run the following command to estimate task duration based on a given description:

./estimate.sh "<task description>"

Example:

./estimate.sh "Implement user authentication system"

How It Works

1. The setup-env.sh script:

   • Creates and activates a Python virtual environment.
   • Installs dependencies from requirements.txt.

2. The train.sh script:

   • Activates the virtual environment.
   • Trains a model using script.py and tasks.csv.
   • Saves the trained model as model.pkl.
   • Deactivates the virtual environment.

3. The estimate.sh script:

   • Activates the Python virtual environment.
   • Loads the trained model (model.pkl).
   • Processes the given task description.
   • Outputs the estimated completion time.
   • Deactivates the virtual environment.

Dependencies

This project requires the following Python libraries:

• pandas==2.2.3
• joblib==1.4.2
• click==8.1.8
• scikit-learn==1.6.1

Notes

• The scripts assume that a virtual environment exists at venv/. Ensure you create one and install dependencies before running the scripts.

License

This project is licensed under the MIT License.