{"id":24760298,"url":"https://github.com/tigureis/aula_deployment","last_synced_at":"2026-04-11T19:36:29.531Z","repository":{"id":266731231,"uuid":"898841684","full_name":"tigureis/Aula_deployment","owner":"tigureis","description":"This project not only demonstrates a machine learning pipeline but also serves as a template for deploying models into production. 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To be used as a base for others machininbg learning projects.\n\n## Overview\n\nThe project is divided into two main parts, implemented in separate Python files:\n\n1.  **Training (`train.py`):** This script extracts data from the Iris dataset, prepares features, trains a Decision Tree Classifier, and saves the trained model to a file (`trained_classifier.pkl`).\n2.  **Prediction (`predict.py`):** This script loads the trained model, loads new data, makes predictions on the new data using the loaded model, and prints the predictions.\n\n## How to Run\n\n1.  **Install Dependencies:** Make sure you have the following libraries installed:\n \n2.  **Training:** Execute the `train.py` script to train the model and save it:\n\n 3.  **Prediction:** Execute the `predict.py` script to load the trained model, make predictions on new data, and print the results:\n\n \n## File Structure\n\n-   `train.py`: Python script for training the model.\n-   `predict.py`: Python script for making predictions.\n-   `trained_classifier.pkl`: This file stores the trained Decision Tree Classifier model.\n\n## Functionality\n\n**`train.py`:**\n\n-   `extract_data()` : Loads the Iris dataset from scikit-learn.\n-   `preparing_features()` : Creates a Pandas DataFrame for features (X) and target (y).\n-   `train_model()` : Trains a Decision Tree Classifier with a maximum depth of 2.\n-   `serialize_object()` : Saves the trained model to a file using pickle.\n-   `run()` : Orchestrates the training pipeline.\n\n**`predict.py`:**\n\n-   `load_data()` : Loads new data for prediction.\n-   `load_model()` : Loads the trained model from the file.\n-   `make_predictions()` : Makes predictions on the new data using the loaded model.\n-   `write_results()` : Prints the predictions.\n-   `run()` : Orchestrates the prediction pipeline.\n\n\n2.  **Training:** Execute the `train.py` script to train the model and save it:\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftigureis%2Faula_deployment","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ftigureis%2Faula_deployment","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftigureis%2Faula_deployment/lists"}