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May 15, 2024 · The Iris dataset is one of the most well-known and commonly used datasets in the field of machine learning and statistics. In this article, we will explore the Iris dataset in deep and learn about its uses and applications.
- Exploratory Data Analysis on Iris Dataset - GeeksforGeeks
The Iris dataset is one of the most well-known and commonly...
- Exploratory Data Analysis on Iris Dataset - GeeksforGeeks
- What Is Exploratory Data Analysis?
- Iris Dataset
- Getting Information About The Dataset
- Checking Missing Values
- Checking Duplicates
- Data Visualization
- Handling Outliers
Exploratory Data Analysis (EDA) is a technique to analyze data using some visual Techniques. With this technique, we can get detailed information about the statistical summary of the data. We will also be able to deal with the duplicates values, outliers, and also see some trends or patterns present in the dataset. Now let’s see a brief about the I...
If you are from a data science background you all must be familiar with the Iris Dataset. If you are not then don’t worry we will discuss this here. Iris Dataset is considered as the Hello World for data science. It contains five columns namely – Petal Length, Petal Width, Sepal Length, Sepal Width, and Species Type. Iris is a flowering plant, the ...
We will use the shape parameter to get the shape of the dataset. Example: Output: We can see that the dataframe contains 6 columns and 150 rows. Now, let’s also the columns and their data types. For this, we will use the info()method. Example: Output: We can see that only one column has categorical data and all the other columns are of the numeric ...
We will check if our data contains any missing values or not. Missing values can occur when no information is provided for one or more items or for a whole unit. We will use the isnull()method. Example: Output: We can see that no column as any missing value. Note: For more information, refer Working with Missing Data in Pandas.
Let’s see if our dataset contains any duplicates or not. Pandas drop_duplicates()method helps in removing duplicates from the data frame. Example: Output: We can see that there are only three unique species. Let’s see if the dataset is balanced or not i.e. all the species contain equal amounts of rows or not. We will use the Series.value_counts()fu...
Visualizing the target column
Our target column will be the Species column because at the end we will need the result according to the species only. Let’s see a countplot for species. Example: Output:
Relation between variables
We will see the relationship between the sepal length and sepal width and also between petal length and petal width. Example 1: Comparing Sepal Length and Sepal Width Output: From the above plot, we can infer that – 1. Species Setosa has smaller sepal lengths but larger sepal widths. 2. Versicolor Species lies in the middle of the other two species in terms of sepal length and width 3. Species Virginica has larger sepal lengths but smaller sepal widths. Example 2: Comparing Petal Length and P...
Histograms
Histograms allow seeing the distribution of data for various columns. It can be used for uni as well as bi-variate analysis. Example: Output: From the above plot, we can see that – 1. The highest frequency of the sepal length is between 30 and 35 which is between 5.5 and 6 2. The highest frequency of the sepal Width is around 70 which is between 3.0 and 3.5 3. The highest frequency of the petal length is around 50 which is between 1 and 2 4. The highest frequency of the petal width is between...
An Outlier is a data-item/object that deviates significantly from the rest of the (so-called normal)objects. They can be caused by measurement or execution errors. The analysis for outlier detection is referred to as outlier mining. There are many ways to detect the outliers, and the removal process is the data frame same as removing a data item fr...
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The data set consists of 50 samples from each of three species of Iris (Iris setosa, Iris virginica and Iris versicolor). Four features were measured from each sample: the length and the width of the sepals and petals, in centimeters.
Each point in the scatter plot refers to one of the 150 iris flowers in the dataset, with the color indicating their respective type (Setosa, Versicolour, and Virginica). You can already see a pattern regarding the Setosa type, which is easily identifiable based on its short and wide sepal.
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.
This is one of the earliest datasets used in the literature on classification methods and widely used in statistics and machine learning. The data set contains 3 classes of 50 instances each, where each class refers to a type of iris plant.
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Sep 30, 2023 · 1. About Iris dataset ¶. The iris dataset contains the following data. 50 samples of 3 different species of iris (150 samples total) Measurements: sepal length, sepal width, petal length, petal width. The format for the data: (sepal length, sepal width, petal length, petal width) 2. Display Iris Dataset ¶. In [1]:
