Iris Flower Classifier

This project demonstrates a simple machine learning pipeline for classifying iris flowers using the famous Iris dataset and a Decision Tree Classifier. To be used as a base for others machininbg learning projects.

Overview

The project is divided into two main parts, implemented in separate Python files:

1. 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).
2. 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.

How to Run

1. Install Dependencies: Make sure you have the following libraries installed:

2. Training: Execute the train.py script to train the model and save it:

3. Prediction: Execute the predict.py script to load the trained model, make predictions on new data, and print the results:

File Structure

• train.py: Python script for training the model.
• predict.py: Python script for making predictions.
• trained_classifier.pkl: This file stores the trained Decision Tree Classifier model.

Functionality

train.py:

• extract_data() : Loads the Iris dataset from scikit-learn.
• preparing_features() : Creates a Pandas DataFrame for features (X) and target (y).
• train_model() : Trains a Decision Tree Classifier with a maximum depth of 2.
• serialize_object() : Saves the trained model to a file using pickle.
• run() : Orchestrates the training pipeline.

predict.py:

• load_data() : Loads new data for prediction.
• load_model() : Loads the trained model from the file.
• make_predictions() : Makes predictions on the new data using the loaded model.
• write_results() : Prints the predictions.
• run() : Orchestrates the prediction pipeline.

2. Training: Execute the train.py script to train the model and save it: