Find the correlation matrix using python on Iris Dataset

Q. Find the correlation matrix

Correlation: Correlation is Statistical Measure which finds the extent to which two or more variable related with each other.

Correlation is a statistical measure that describes the degree to which two variables change together. correlation is denoted by 'r'

Type of Correlations:

Positive Correlation(r>0): If the value of one variable increases then value of another variable also increases

Negative Correlation(r<0):If the value of one variable increases then value of another variable decreases

No Correlation(r=0):There is no any linear relationship between the two variables.

Program using Python

Import Libraries:

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

Read CSV File: Download csv file Iris Dataset

then use pd.read_csv() function

df=pd.read_csv('/Iris.csv')

Id	SepalLengthCm	SepalWidthCm	PetalLengthCm	PetalWidthCm	Species
0	1	5.1	3.5	1.4	0.2	Iris-setosa
1	2	4.9	3.0	1.4	0.2	Iris-setosa
2	3	4.7	3.2	1.3	0.2	Iris-setosa
3	4	4.6	3.1	1.5	0.2	Iris-setosa
4	5	5.0	3.6	1.4	0.2	Iris-setosa
...	...	...	...	...	...	...
145	146	6.7	3.0	5.2	2.3	Iris-virginica
146	147	6.3	2.5	5.0	1.9	Iris-virginica
147	148	6.5	3.0	5.2	2.0	Iris-virginica
148	149	6.2	3.4	5.4	2.3	Iris-virginica
149	150	5.9	3.0	5.1	1.8	Iris-virginica

150 rows × 6 columns

Information of Top 5 rows

df.head()

	Id	SepalLengthCm	SepalWidthCm	PetalLengthCm	PetalWidthCm	Species
0	1	5.1	3.5	1.4	0.2	Iris-setosa
1	2	4.9	3.0	1.4	0.2	Iris-setosa
2	3	4.7	3.2	1.3	0.2	Iris-setosa
3	4	4.6	3.1	1.5	0.2	Iris-setosa
4	5	5.0	3.6	1.4	0.2	Iris-setosa

Correlation using function corr()

cor=df.corr() cor

	Id	SepalLengthCm	SepalWidthCm	PetalLengthCm	PetalWidthCm
Id	1.000000	0.716676	-0.397729	0.882747	0.899759
SepalLengthCm	0.716676	1.000000	-0.109369	0.871754	0.817954
SepalWidthCm	-0.397729	-0.109369	1.000000	-0.420516	-0.356544
PetalLengthCm	0.882747	0.871754	-0.420516	1.000000	0.962757
PetalWidthCm	0.899759	0.817954	-0.356544	0.962757	1.000000

Correlation map using heatmap

sns.heatmap(data=cor,annot=True)

<matplotlib.axes._subplots.AxesSubplot at 0x7f2be4bd6610>

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