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This project demonstrates the use of a Decision Tree classifier to predict the species of Iris flowers based on their features. The project includes data exploration, model training, evaluation, visualization, and making predictions on new data.

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Shekoder/DesicionTree

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Decision Tree Classifier for Iris Dataset

This repository contains a Python project that uses a Decision Tree Classifier to classify Iris species based on sepal and petal measurements. The project includes data exploration, model training, evaluation, and visualization.

Project Structure

  • Iris.csv: The dataset used for training and testing the model.
  • decision_tree_model.pkl: The saved Decision Tree model.
  • decision_tree.png: The visualization of the trained Decision Tree.
  • decision_tree.py: The main script for loading data, training the model, making predictions, and visualizing results.

Installation

To run this project, you need to have Python installed. You also need to install the following packages:

pip install pandas scikit-learn matplotlib seaborn pydotplus pillow joblib

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This project demonstrates the use of a Decision Tree classifier to predict the species of Iris flowers based on their features. The project includes data exploration, model training, evaluation, visualization, and making predictions on new data.

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