{"id":24117492,"url":"https://github.com/emna-chebbi/iris-dataset","last_synced_at":"2026-04-13T01:16:46.450Z","repository":{"id":262093142,"uuid":"886195558","full_name":"Emna-chebbi/Iris-dataset","owner":"Emna-chebbi","description":"The iris dataset contains 4 features which i used to classify the flowers into one of the three species based on the measurements.","archived":false,"fork":false,"pushed_at":"2024-11-10T13:20:11.000Z","size":213,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-01-11T07:54:10.328Z","etag":null,"topics":["decision-tree-classifier","decision-trees","iris-dataset","iris-flower-classification","machine-learning"],"latest_commit_sha":null,"homepage":"","language":"Jupyter 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Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Iris-dataset\nThe Iris dataset consists of 150 samples from three species of iris flowers: Setosa, Versicolor, and Virginica. \nFor each sample, the dataset includes four features of the flowers:\nSepal length\nSepal width\nPetal length\nPetal width\nEach feature is measured in centimeters.\n\nThe tasks I have done :\n- Loaded the Iris dataset from scikit-learn using load_iris() and extracted the features (X) and target labels (y).\n- Split the dataset into training and testing sets using train_test_split() to evaluate the model's performance.\n- Trained a Decision Tree Classifier using DecisionTreeClassifier() from sklearn, fitting it to the training data (X_train, y_train).\n- 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().\n- Output the predicted iris species by mapping the predicted class index to the species name from iris.target_names.\n- 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).\n- Visualized the decision tree using plot_tree() to understand how the model splits the data.\n- Customized the visualization with matplotlib by setting a larger figure size (figsize=(15,12)).\n- 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.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Femna-chebbi%2Firis-dataset","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Femna-chebbi%2Firis-dataset","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Femna-chebbi%2Firis-dataset/lists"}