Overview:
This project focuses on predicting Iris flower varieties based on specific features using a Decision Tree model. The goal is to classify Iris flowers into one of three species: Setosa, Versicolor, or Virginica. The model is trained on a well-known dataset that includes measurements like petal length and width. The model’s performance is evaluated based on accuracy.
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Key Features:
1. Decision Tree Model:
The Decision Tree algorithm is used for classifying the Iris flower species. This model is interpretable and visually represents the decision-making process, making it suitable for distinguishing between the three Iris varieties.
2. Feature Extraction:
The model uses important features like petal length, petal width, sepal length, and sepal width to classify Iris flowers into Setosa, Versicolor, and Virginica species.
3. Accuracy Validation:
The performance of the model is validated using accuracy, which measures the proportion of correctly classified Iris flowers, ensuring reliable predictions.
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