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.

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 columns in this dataset are:

• Id
• SepalLengthCm
• SepalWidthCm
• PetalLengthCm
• PetalWidthCm
• Species

The Iris flower data set or Fisher's Iris data set is a multivariate data set used and made famous by the British statistician and biologist Ronald Fisher in his 1936 paper The use of multiple measurements in taxonomic problems as an example of linear discriminant analysis. Please use this data set to clustering the iris flowers data. You can use k-means clustering algorithm.

The Iris dataset is one of the most famous datasets used in machine learning and statistics. It was introduced by the British biologist and statistician Ronald A. Fisher in 1936. The dataset consists of 150 observations of iris flowers, with each observation belonging to one of three species (classes) of the Iris flower. The dataset is widely used for classification purposes and is often used as a beginner's dataset for learning machine learning techniques.

Features The Iris dataset contains four features (attributes), which are:

Sepal Length: The length of the sepal in centimeters. Sepal Width: The width of the sepal in centimeters. Petal Length: The length of the petal in centimeters. Petal Width: The width of the petal in centimeters. Each of these features is a continuous numerical value. These features are measured for each of the 150 iris flowers in the dataset.

Classes The dataset contains three classes, which correspond to three different species of the Iris flower:

Iris Setosa: This class is often linearly separable from the other two classes, making it easy to classify. Iris Versicolor: This class is somewhat more challenging to distinguish from the Iris Virginica class. Iris Virginica: The third class, which can sometimes be difficult to distinguish from Iris Versicolor based on the given features. Each class has 50 observations, making the dataset balanced with equal representation of each class. The goal when using this dataset is typically to build a model that can predict the species of an iris flower based on its measurements.

In summary, the Iris dataset is a small, well-structured dataset that includes:

150 samples (observations) of iris flowers. 4 features (sepal length, sepal width, petal length, petal width). 3 classes (Iris Setosa, Iris Versicolor, Iris Virginica), each with 50 samples. The dataset's simplicity and clear structure make it ideal for demonstrating basic cl

Context: 🌼 The Iris flower dataset, an iconic multivariate set, was first introduced by the renowned British statistician and biologist, Ronald Fisher in 1936 📝. Commonly known as Anderson's Iris dataset, it was curated by Edgar Anderson to measure the morphologic variation of three Iris species 🌸: Iris Setosa, Iris Virginica, and Iris Versicolor.

📊 The set comprises 100 samples from each species, with four features - sepal length, sepal width, petal length, and petal width, measured in centimetres.

🔬 This dataset has since served as a standard test case for various statistical classification techniques in machine learning, including the widely used support vector machines (SVM).

So, whether you're a newbie dipping your toes into the ML pond or a seasoned data scientist testing out a new classification method, the Iris dataset is a classic starting point! 🎯🚀

Columns:

1. Sepal Length: The length of the sepal of the iris flower, is measured in centimetres.
2. Sepal Width: The width of the sepal of the iris flower, measured in centimetres.
3. Petal Length: The length of the petal of the iris flower, is measured in centimetres.
4. Petal Width: The width of the petal of the iris flower, measured in centimetres.
5. Species:The specific species of the iris flower, categorized into Sentosa, Virginica, and Versicolor.

Problem Statement:

1.🎯 Classification Challenge: Can you accurately predict the species of an Iris flower based on the four given measurements: sepal length, sepal width, petal length, and petal width?

2.💡 Feature Importance: Which feature (sepal length, sepal width, petal length, or petal width) is the most significant in distinguishing between the species of Iris flowers?

3.📈 Data Scaling: How does standardization (or normalization) of the features affect the performance of your classification models?

4.🧪 Model Experimentation: Can simpler models such as Logistic Regression perform as well as more complex models like Support Vector Machines or Neural Networks on the Iris dataset? Compare the performance of various models.

5.🤖 AutoML Challenge: Use AutoML tools (like Google's AutoML or H2O's AutoML) to build a classification model. How does its performance compare with your handcrafted models?

Kindly, upvote if you find the dataset interesting

Dataset

This dataset was created by NotePub

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Dataset

This dataset was created by Taylan Torres

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This dataset provides a refined version of the popular Iris dataset, tailored for enhanced usability in machine learning and data science applications. Key improvements include:
- Data Quality: Removal of duplicate and inconsistent entries.
- Feature Consistency: Verified feature distributions to ensure better modeling accuracy.
- Enhanced Labeling: Clear and intuitive labels for easier interpretability.

This dataset is ideal for beginners and professionals alike, offering a robust foundation for testing classification algorithms and exploring supervised learning workflows.

Tags

Classification, Machine Learning, Data Cleaning, Iris, Clean Data, Data Analysis

File Details

File Name: Iris_clean_dataset.csv
- Size: 5.11 KB
- Rows: 150
- Columns: 6
- Columns:
1. Id
2. SepalLengthCm
3. SepalWidthCm
4. PetalLengthCm
5. PetalWidthCm
6. Species

Each row corresponds to a single observation of Iris flower measurements, including species classifications (Iris-setosa, Iris-versicolor, Iris-virginica).

Usability

Usability Score: 1.76
This score reflects the dataset's ease of use for various machine learning and data analysis tasks.

License

License Type: CC BY 4.0
You are free to use, modify, and distribute this dataset, provided appropriate credit is given to the original author.

Expected Update Frequency

Frequency: This dataset will not receive regular updates. However, feedback is welcomed for future revisions.

Provenance

Source: Original Iris dataset with modifications.
Methodology: Data cleaning involved removing anomalies, revalidating measurements, and restructuring for compatibility with modern ML workflows.

Encourage interaction:
"_Your engagement improves this dataset’s visibility. Feel free to comment or share your use case._"

Example Use Cases

• Learning: Ideal for beginners experimenting with machine learning models like Logistic Regression, Random Forest, and KNN.
• Research: Test your novel classification techniques on this cleaned version.
• Application: Use it in practical ML projects for training supervised learning models.

Notes to Users

If you find this dataset helpful, consider leaving feedback or sharing your implementation in the Kaggle discussions section. Collaboration and suggestions are always welcome!

Let me know if you'd like further refinements or adjustments!

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This dataset was created by Jaymin151617

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The Iris dataset originated from a seminal paper by British statistician and biologist Ronald Fisher titled "The Use of Multiple Measurements in Taxonomic Problems," published in 1936. Fisher collected and measured samples of iris flowers from three different species: Setosa, Versicolor, and Virginica.

The dataset comprises 150 samples, with each sample having four features measured: 1. Sepal Length 2. Sepal Width 3. Petal Length 4. Petal Width

Additionally, each sample is labeled with its corresponding species, making it a supervised learning problem with three target variables: 1. Setosa 2. Versicolor 3. Virginia

The Iris dataset is often used as a benchmark in machine learning and pattern recognition for tasks like classification and clustering. Its simplicity and clarity make it an excellent starting point for learning various algorithms and techniques.

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