Nothing Special   »   [go: up one dir, main page]
Scribd Logo
Upload

Welcome to Scribd!

  • 25 views

    Practical 4

    Uploaded by

    Vatsal
    This document analyzes the distribution of attributes in an Iris flower dataset using probability density functions, cumulative distribution functions, correlation, and box plots. It finds that there are 50 samples of each Iris species (setosa, versicolor, virginica) and explores the correlation between sepal/petal lengths and widths within and between species. Box plots show how attributes are distributed for each species.

    Copyright:

    © All Rights Reserved
    Download as PDF, TXT or read online from Scribd

    Practical 4

    Uploaded by

    Vatsal
    25 views4 pages
    This document analyzes the distribution of attributes in an Iris flower dataset using probability density functions, cumulative distribution functions, correlation, and box plots. It finds that there are 50 samples of each Iris species (setosa, versicolor, virginica) and explores the correlation between sepal/petal lengths and widths within and between species. Box plots show how attributes are distributed for each species.

    Original Description:

    Original Title

    PRACTICAL 4

    Copyright

    © © All Rights Reserved

    Available Formats

    PDF, TXT or read online from Scribd

    Did you find this document useful?

    Is this content inappropriate?

    This document analyzes the distribution of attributes in an Iris flower dataset using probability density functions, cumulative distribution functions, correlation, and box plots. It finds that there are 50 samples of each Iris species (setosa, versicolor, virginica) and explores the correlation between sepal/petal lengths and widths within and between species. Box plots show how attributes are distributed for each species.

    Copyright:

    © All Rights Reserved
    Download as PDF, TXT or read online from Scribd
    Download as pdf or txt
    25 views4 pages

    Practical 4

    Uploaded by

    Vatsal
    This document analyzes the distribution of attributes in an Iris flower dataset using probability density functions, cumulative distribution functions, correlation, and box plots. It finds that there are 50 samples of each Iris species (setosa, versicolor, virginica) and explores the correlation between sepal/petal lengths and widths within and between species. Box plots show how attributes are distributed for each species.

    Copyright:

    © All Rights Reserved
    Download as PDF, TXT or read online from Scribd
    Download as pdf or txt
    of 4

    PRACTICAL 4.

    ipynb - Colaboratory

    PRACTICAL 4

    Generate Probability Density Function (PDF) and Cumulative Distribution Function (CDF) for the given Iris data set to find the distribution of
    various attributes of the dataset.

    import numpy as np
    import pandas as pd
    import matplotlib.pyplot as plt
    import seaborn as sns
    #Load Dataset
    iris = pd.read_csv('/content/Iris.csv')

    iris.shape

    (150, 6)

    iris.describe()

    Id SepalLengthCm SepalWidthCm PetalLengthCm PetalWidthCm

    count 150.000000 150.000000 150.000000 150.000000 150.000000

    mean 75.500000 5.843333 3.054000 3.758667 1.198667

    std 43.445368 0.828066 0.433594 1.764420 0.763161

    min 1.000000 4.300000 2.000000 1.000000 0.100000

    25% 38.250000 5.100000 2.800000 1.600000 0.300000

    50% 75.500000 5.800000 3.000000 4.350000 1.300000

    75% 112.750000 6.400000 3.300000 5.100000 1.800000

    max 150.000000 7.900000 4.400000 6.900000 2.500000

    plt.title('Species Count')
    sns.countplot(iris['Species'])

    C:\Users\gcet\anaconda3\lib\site-packages\seaborn\_decorators.py:36: FutureWarning: Pass the following


    warnings.warn(
    <AxesSubplot:title={'center':'Species Count'}, xlabel='Species', ylabel='count'>

    Each species ( Iris virginica, setosa, versicolor) has 50 as it’s count

    plt.figure(figsize=(17,9))
    plt.title('Comparison between various species based on sapel length and width')
    sns.scatterplot(iris['SepalLengthCm'],iris['SepalWidthCm'],hue =iris['Species'],s=50)

    1/5
    PRACTICAL 4.ipynb - Colaboratory

    \Users\gcet\anaconda3\lib\site-packages\seaborn\_decorators.py:36: FutureWarning: Pass the following va


    warnings.warn(
    AxesSubplot:title={'center':'Comparison between various species based on sapel length and width'},
    abel='SepalLengthCm', ylabel='SepalWidthCm'>

    Correlation is a statistical method used to determine whether a linear relationship between variables exists and shows if one variable tends to
    occur with large or small values of another variable.

    #The correlation coefficients between measurement variables:
    iris.groupby("Species").corr()

    Id SepalLengthCm SepalWidthCm PetalLengthCm PetalWidthCm

    Species

    Iris-setosa Id 1.000000 -0.033561 -0.066688 0.053253 0.087492

    SepalLengthCm -0.033561 1.000000 0.746780 0.263874 0.279092

    SepalWidthCm -0.066688 0.746780 1.000000 0.176695 0.279973

    PetalLengthCm 0.053253 0.263874 0.176695 1.000000 0.306308

    PetalWidthCm 0.087492 0.279092 0.279973 0.306308 1.000000

    Iris-versicolor Id 1.000000 -0.269056 -0.081867 -0.189481 -0.168846

    SepalLengthCm -0.269056 1.000000 0.525911 0.754049 0.546461

    SepalWidthCm -0.081867 0.525911 1.000000 0.560522 0.663999

    PetalLengthCm -0.189481 0.754049 0.560522 1.000000 0.786668

    PetalWidthCm -0.168846 0.546461 0.663999 0.786668 1.000000

    Iris-virginica Id 1.000000 -0.012549 0.130884 -0.204204 0.036446

    SepalLengthCm -0.012549 1.000000 0.457228 0.864225 0.281108

    SepalWidthCm 0.130884 0.457228 1.000000 0.401045 0.537728

    PetalLengthCm -0.204204 0.864225 0.401045 1.000000 0.322108

    PetalWidthCm 0.036446 0.281108 0.537728 0.322108 1.000000

    Bi-variate Analysis

    sns.pairplot(iris,hue="Species",height=4)

    2/5
    PRACTICAL 4.ipynb - Colaboratory

    <seaborn.axisgrid.PairGrid at 0x7fceaa57d730>

    Checking Correlation

    plt.figure(figsize=(10,11))
    sns.heatmap(iris.corr(),annot=True)
    plt.plot()

    3/5
    PRACTICAL 4.ipynb - Colaboratory

    <ipython-input-9-decc81dd4f85>:2: FutureWarning: The default value of numeric_only in DataFrame.corr is


    sns.heatmap(iris.corr(),annot=True)
    []

    Box plots to know about distribution

    ig, axes = plt.subplots(2, 2, figsize=(16,9))
    sns.boxplot( y="PetalWidthCm", x= "Species", data=iris, orient='v' , ax=axes[0, 0])
    sns.boxplot( y="PetalLengthCm", x="Species", data=iris, orient='v' , ax=axes[0, 1])
    sns.boxplot( y="SepalLengthCm", x= "Species", data=iris, orient='v' , ax=axes[1, 0])
    sns.boxplot( y="SepalWidthCm", x= "Species", data=iris, orient='v' , ax=axes[1, 1])
    plt.show()

    4/5

    You might also like