This project aims to build a classification model that predicts the species of iris flowers (Setosa, Versicolor, Virginica) based on the existing Iris dataset. This dataset can be accessed via the following link: https://scikit-learn.org/1.5/datasets/toy_dataset.html. The dataset consists of four main features (sepal length, sepal width, petal length, petal width) used to distinguish between the three flower classes.
The Random Forest algorithm is chosen for its ability to handle data efficiently, produce high accuracy, and overcome overfitting issues through the combination of multiple decision trees.
- Iris Species Classification: Build a model capable of classifying iris flowers into one of three species based on the available features.
- Model Evaluation: Evaluate the model's performance using a confusion matrix.
Random Forest Reliability: The Random Forest model provides consistent and accurate results compared to other algorithms like Decision Tree or Logistic Regression.
To run this project, you'll need several Python libraries:
- Scikit-learn: For implementing the Random Forest algorithm and model evaluation.
- NumPy: For numerical data processing.
- Pandas: For handling and manipulating tabular data.
- Experiment with Model Parameters: Try adjusting the n_estimators, max_depth, and min_samples_split parameters in Random Forest to improve model accuracy.
- Expand the Dataset: For real-world applications, consider using a larger or more complex dataset to test the model's robustness.