Misheck1992/english-to-chichewa

Eglish to chichewa translator # My Model Project ## Overview This project is designed for building and evaluating machine learning models. It includes data processing, feature engineering, model training, and evaluation components. ## Project Structure ``` my-model-project ├── data │ ├── raw │ ├── processed │ └── external ├── models │ └── saved_models ├── notebooks │ └── exploration.ipynb ├── src │ ├── data │ │ ├── make_dataset.py │ │ └── preprocess.py │ ├── features │ │ └── build_features.py │ ├── models │ │ ├── train_model.py │ │ ├── predict_model.py │ │ └── evaluate_model.py │ └── visualization │ └── visualize.py ├── tests │ └── test_model.py ├── requirements.txt ├── setup.py └── README.md ``` ## Installation 1. Clone the repository: ``` git clone ``` 2. Navigate to the project directory: ``` cd my-model-project ``` 3. Install the required packages: ``` pip install -r requirements.txt ``` ## Usage - To preprocess the data, run: ``` python src/data/preprocess.py ``` - To build features, execute: ``` python src/features/build_features.py ``` - To train the model, use: ``` python src/models/train_model.py ``` - For predictions, run: ``` python src/models/predict_model.py ``` - To evaluate the model, execute: ``` python src/models/evaluate_model.py ``` ## Notebooks The `notebooks/exploration.ipynb` file contains exploratory data analysis and is a great starting point for understanding the dataset. ## Testing To run the tests, use: ``` python -m unittest discover -s tests ``` ## License This project is licensed under the MIT License.