Model Deployment using Pickle and Joblib

Overview

This repository demonstrates the process of saving and loading machine learning models using both pickle and joblib in Python. The example uses a logistic regression model trained on the Iris dataset.

Contents

• model_training.py: Python script for training a logistic regression model on the Iris dataset and saving it using both pickle and joblib.

• model_evaluation.py: Python script for loading the saved models and making predictions on new data.

• requirements.txt: File containing the required Python libraries and their versions.

Instructions

1. Clone the Repository:

   git clone <repository-url>
   cd <repository-directory>

2. Install Dependencies:

   pip install -r requirements.txt

3. Train and Save the Model:

   python model_training.py

4. Evaluate the Model:

   python model_evaluation.py

Project Structure

• model.pkl: Pickle file containing the trained logistic regression model.

• model.joblib: Joblib file containing the trained logistic regression model.

Notes

• The model_training.ipynb script trains a logistic regression model on the Iris dataset, evaluates its accuracy, and saves it using both pickle and joblib.

• The model_evaluation.py script loads the saved models and makes predictions on new data.

• The requirements.txt file specifies the required Python libraries and their versions.

References

• scikit-learn Documentation
• Pickle Documentation
• Joblib Documentation

Feel free to customize this structure and documentation to fit your specific needs.