Iris is a flowering plant with showy flowers.It’s a classification problem where we will predict the flower class based on its petal length, petal width, sepal length, and sepal width.

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This data set consists of the physical parameters of three species of flower — Versicolor, Setosa and Virginica. The numeric parameters which the dataset contains are Sepal width, Sepal length, Petal width and Petal length. In this data we will be predicting the classes of the flowers based on these parameters.The data consists of continuous numeric values which describe the dimensions of the respective features. We will be training the model based on these features.

The Iris Dataset contains four features (length and width of sepals and petals) of 50 samples of three species of Iris (Iris setosa, Iris virginica and Iris versicolor). These measures were used to create a linear discriminant model to classify the species. The dataset is often used in data mining, classification and clustering examples and to test algorithms.

• sepal length in cm

• sepal width in cm

• petal length in cm

• petal width in cm

-- Iris Setosa

-- Iris Versicolour

-- Iris Virginica

1. Linear Regression
2. Logistic Regression
3. K-Nearest Neighbours
4. Support Vector Machine
5. K-Means Clustering
6. Decision Tree
7. Random Forest Classifier

Therefore, KNN has the highest accuracy with 97% and then SVM with 94% accuracy. We have just implemented some of the common Machine Learning. Since the dataset is small with very few features.

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