Extended README

README.md

@@ -12,24 +12,76 @@ size_categories:
 ---
 license: cc0-1.0
 ---
-## Iris Species Dataset
 
-The Iris dataset was used in R.A. Fisher's classic 1936 paper, The Use of Multiple Measurements in Taxonomic Problems, and can also be found on the UCI Machine Learning Repository.
+# Iris Species Dataset
+The **Iris dataset** is a classic dataset in machine learning, originally published by Ronald Fisher. It contains 150 instances of iris flowers, each described by four features (sepal length, sepal width, petal length, and petal width), along with the corresponding species label (`setosa`, `versicolor`, or `virginica`). 
 
-It includes three iris species with 50 samples each as well as some properties about each flower. One flower species is linearly separable from the other two, but the other two are not linearly separable from each other.
-The dataset is taken from [UCI Machine Learning Repository's Kaggle](https://www.kaggle.com/datasets/uciml/iris).
-The following description is taken from UCI Machine Learning Repository. 
-This is perhaps the best known database to be found in the pattern recognition literature. Fisher's paper is a classic in the field and is referenced frequently to this day. (See Duda & Hart, for example.) The data set contains 3 classes of 50 instances each, where each class refers to a type of iris plant. One class is linearly separable from the other 2; the latter are NOT linearly separable from each other.
+It is commonly used as an introductory dataset for classification tasks and for demonstrating basic data exploration and model training workflows.
 
-**Predicted attribute**: class of iris plant
+**Supported Tasks and Leaderboards**
+- **Multi-class Classification**: Predict the species of the iris flower based on its four numeric features.
 
-**Features** in this dataset are the following:
+## Dataset Structure
+The features are:
+- **sepal_length** (`float`): Length of the sepal in centimeters  
+- **sepal_width** (`float`): Width of the sepal in centimeters  
+- **petal_length** (`float`): Length of the petal in centimeters  
+- **petal_width** (`float`): Width of the petal in centimeters
 
-- sepal length in cm 
-- sepal width in cm
-- petal length in cm
-- petal width in cm
-- class:
-  - Iris Setosa
-  - Iris Versicolour
-  - Iris Virginica
+The target variable is **species** (`string`). Labels ares:
+  - `setosa`
+  - `versicolor`
+  - `virginica`
+
+This dataset traditionally comes as a single collection of 150 rows.
+
+## Background
+The Iris dataset was first introduced by Ronald Fisher in 1936 and later became known through UCI Machine Learning Repository. This version was obtained from the [UCI Machine Learning Repository's Kaggle](https://www.kaggle.com/datasets/uciml/iris)
+
+Source Data:
+- **Repository**: [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/datasets/iris)  
+- **Original Paper**: R. A. Fisher (1936). “The Use of Multiple Measurements in Taxonomic Problems.” *Annals of Eugenics* 7 (2): 179–188.
+
+## Citation
+Fisher’s original publication:
+```
+@article{fisher1936use,
+  title={The use of multiple measurements in taxonomic problems},
+  author={Fisher, Ronald Aylmer},
+  journal={Annals of eugenics},
+  volume={7},
+  number={2},
+  pages={179--188},
+  year={1936},
+  publisher={Wiley Online Library}
+}
+```
+
+## Usage Example
+
+Here’s a minimal example in Python using the [datasets](https://github.com/huggingface/datasets) library:
+
+```python
+from datasets import load_dataset
+
+# If this dataset is hosted under "username/iris-dataset"
+dataset = load_dataset("username/iris-dataset")
+
+# The dataset might contain a default split or just one split
+df = dataset["train"].to_pandas()
+
+print(df.head())
+```
+
+Or using Scikit-Learn’s built-in functionality:
+
+```python
+from sklearn.datasets import load_iris
+
+iris = load_iris()
+X = iris.data  # shape (150, 4)
+y = iris.target  # shape (150,)
+```
+
+## Limitations and Potential Bias
+The dataset contains only three iris species from a limited geographic region (the Gaspe Peninsula in Canada). It is quite small (150 samples) and was originally collected for a statistical illustration rather than real-world machine learning applications. It serves primarily as a simple demonstration dataset and is not representative of broader botanical diversity.