The Iris Data Set is perhaps the best known database. The data set contains 3 classes of50 instances each, where each class refers to a type of iris plant.Get the data set from: https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.dataYou can use any text editor to open it.Attribute Information:1. sepal length in cm2. sepal width in cm3. petal length in cm4. petal width in cm5. class:-Iris Setosa-Iris VersicolourIris VirginicaWrite a program that reads the input file. (Hint: getline) And Print the summary statistics as follows:Summary StatisticsMinМаx| Sepal length | 4.3 (Setosa)| 7.9 (Virginica) || Sepal width | 2.0 (Versicolor)4.4 (Setosa)| Petal length | 1.0 (Setosa)| 6.9 (Virginica) |Petal width | 0.1 (Setosa)| 2.5 (Virginica) |

The Iris Data Set is perhaps the best known database. The data set contains 3 classes of 50 instances each, where each class refers to a type of iris plant. Get the data set from: https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data You can use any text editor to open it.

The Iris Data Set is perhaps the best known database. The data set contains 3 classes of 50 instances each, where each class refers to a type of iris plant. Get the data set from: https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data You can use any text editor to open it.

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

Problem 1PE

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Concept explainers

Intermediate SQL Concepts

A query is a request for data or information from one or more database tables. This information could be presented in the form of pictorials, graphs, or complicated findings which helps in easy interpretation. Structured Query Language (SQL) is the most widely used programming language for retrieving and organizing data from relational databases. Collection of tables with rows and columns is referred to as a database. SQL is the language of databases. It makes it easier to extract specific data from databases, which can then be used for analysis.

Question

The Iris Data Set is perhaps the best known database. The data set contains 3 classes of
50 instances each, where each class refers to a type of iris plant.
Get the data set from: https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data
You can use any text editor to open it.
Attribute Information:
1. sepal length in cm
2. sepal width in cm
3. petal length in cm
4. petal width in cm
5. class:
-Iris Setosa
-Iris Versicolour
Iris Virginica
Write a program that reads the input file. (Hint: getline) And Print the summary statistics as follows:
Summary Statistics
Min
Маx
| Sepal length | 4.3 (Setosa)
| 7.9 (Virginica) |
| Sepal width | 2.0 (Versicolor)
4.4 (Setosa)
| Petal length | 1.0 (Setosa)
| 6.9 (Virginica) |
Petal width | 0.1 (Setosa)
| 2.5 (Virginica) |

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Pls solve this question correctly in 5 min i will give u like for sure import seaborn as sns import pandas as pd import numpy as np from sklearn.cluster import KMeans from sklearn import datasets # download the iris datafile X,y = datasets.load_iris(return_X_y=True,) cols = ['s_length','s_width','p_length','p_width'] df = pd.DataFrame(X, columns=cols) df['class'] = y df sns.scatterplot(data=df, x='s_length', y='p_width', hue='class') from sklearn.model_selection import train_test_split train, test = train_test_split(df, test_size=0.4) X = train[['s_length', 'p_width']].to_numpy() y = train[['class']].to_numpy() k = 3 model = KMeans(n_clusters=k) model.fit_predict(X, y) s = model.score(X,y)
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