https://github.com/emna-chebbi/iris-dataset

The iris dataset contains 4 features which i used to classify the flowers into one of the three species based on the measurements.
https://github.com/emna-chebbi/iris-dataset

decision-tree-classifier decision-trees iris-dataset iris-flower-classification machine-learning

Last synced: 2 months ago
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The iris dataset contains 4 features which i used to classify the flowers into one of the three species based on the measurements.

• Host: GitHub
• URL: https://github.com/emna-chebbi/iris-dataset
• Owner: Emna-chebbi
• Created: 2024-11-10T13:06:00.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2024-11-10T13:20:11.000Z (over 1 year ago)
• Last Synced: 2025-01-11T07:54:10.328Z (over 1 year ago)
• Topics: decision-tree-classifier, decision-trees, iris-dataset, iris-flower-classification, machine-learning
• Language: Jupyter Notebook
• Homepage:
• Size: 208 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# Iris-dataset

The Iris dataset consists of 150 samples from three species of iris flowers: Setosa, Versicolor, and Virginica. 

For each sample, the dataset includes four features of the flowers:

Sepal length

Sepal width

Petal length

Petal width

Each feature is measured in centimeters.

The tasks I have done :

- Loaded the Iris dataset from scikit-learn using load_iris() and extracted the features (X) and target labels (y).

- Split the dataset into training and testing sets using train_test_split() to evaluate the model's performance.

- Trained a Decision Tree Classifier using DecisionTreeClassifier() from sklearn, fitting it to the training data (X_train, y_train).

- Predicted the class of a new flower by providing input features (e.g., sepal length, sepal width, petal length, petal width) to the trained model using clf.predict().

- Output the predicted iris species by mapping the predicted class index to the species name from iris.target_names.

- Calculated and displayed the model's accuracy using accuracy_score() by comparing the predicted labels on the test set (X_test) to the actual labels (y_test).

- Visualized the decision tree using plot_tree() to understand how the model splits the data.

- Customized the visualization with matplotlib by setting a larger figure size (figsize=(15,12)).

- Enhanced the tree plot by adding feature names ("Sepal Length", "Sepal Width", "Petal Length", "Petal Width") and class names ("Setosa", "Versicolor", "Virginica") for better clarity.