siriamarlapudi/iris-flower-classification

Iris Flower Classification Project using Machine Learning

The aim of the iris flower classification is to predict flowers based on their specific features.

Dataset

1. The iris dataset contains three classes of flowers, Versicolor, Setosa, Virginica, and each class contains 4 features, ‘Sepal length’, ‘Sepal width’, ‘Petal length’, ‘Petal width’.
2. The dataset contains 150 samples with 3 classes of iris flowers (50 samples for each class).

Prerequisites:

1. Numpy- 1.19.3
2. Matplotlib- 3.3.2
3. Seaborn – 0.11.1
4. Pandas – 1.2.4
5. Scikit-learn – 0.24.2

Steps to Classify Iris Flower:

1. Load the data
2. Analyze and visualize the dataset
3. Model training.
4. Model Evaluation.
5. Testing the model.

Usage

1. Download iris.csv and save it in the project directory.
2. Run the Iris flower classification.ipynb file, which contains the code for data preprocessing, model training, and evaluation.
3. The script will load the dataset, preprocess the data, split it into training and testing sets, train a linear regression model, and evaluate its performance.
4. After the model training is completed, it will predict flowers based on their specific features.

Contributing

Contributions to this project are welcome. If you find any issues or have suggestions for improvements, please feel free to open an issue or submit a pull request.

Contact

For any questions or inquiries, please contact [[email protected]].