This is a README file for the Iris Flower Classification project hosted on GitHub. The project aims to build a machine learning model to classify iris flowers into different species based on their sepal and petal measurements.
The dataset used in this project is the famous Iris dataset, which is widely used in machine learning and pattern recognition. It consists of 150 samples of iris flowers, each with four features: sepal length, sepal width, petal length, and petal width. The dataset is commonly used for classification tasks.
The dataset is included in the project repository under the file name iris_dataset.csv. It is a comma-separated values (CSV) file where each row represents a sample and each column represents a feature. The last column contains the target variable, which is the species of the iris flower.
The project repository contains the following files:
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iris_flower_classification.ipynb: This Jupyter Notebook file contains the code for the project. It includes the data preprocessing steps, model training, evaluation, and prediction. -
iris.csv: This CSV file contains the dataset used for training and testing the model. It is in a tabular format, with each row representing a sample and each column representing a feature or the target variable.
The iris_classification.ipynb notebook provides a step-by-step guide on how to preprocess the data, train the machine learning model, evaluate its performance, and make predictions on new data. You can modify the notebook as per your requirements, experiment with different machine learning algorithms, or try out different techniques to improve the model's performance.
Contributions to this project are welcome. If you have any ideas, suggestions, or bug fixes, please open an issue or submit a pull request.
Happy coding!