Data Visualisation and Predictive Model on IRIS dataset

Introduction¶

This notebook demos Data Visualisation and various Machine Learning Classification algorithms on IRIS dataset.

1) Loading Libraries¶

In [2]:

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from mpl_toolkits.basemap import Basemap
%matplotlib inline
import warnings
warnings.filterwarnings('ignore')
%config InlineBackend.figure_format = 'retina'

2) Data¶

In [3]:

iris = pd.read_csv("iris.csv")

In [4]:

iris.shape

Out[4]:

(150, 6)

In [5]:

iris.head()

Out[5]:

	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

In [6]:

iris = iris.drop("Id",axis = 1)

In [7]:

iris.head()

Out[7]:

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

In [8]:

iris.describe()

Out[8]:

	SepalLengthCm	SepalWidthCm	PetalLengthCm	PetalWidthCm
count	150.000000	150.000000	150.000000	150.000000
mean	5.843333	3.054000	3.758667	1.198667
std	0.828066	0.433594	1.764420	0.763161
min	4.300000	2.000000	1.000000	0.100000
25%	5.100000	2.800000	1.600000	0.300000
50%	5.800000	3.000000	4.350000	1.300000
75%	6.400000	3.300000	5.100000	1.800000
max	7.900000	4.400000	6.900000	2.500000

In [9]:

iris.groupby("Species").size()

Out[9]:

Species
Iris-setosa        50
Iris-versicolor    50
Iris-virginica     50
dtype: int64

3) Data Visualisation¶

In [10]:

iris.plot(kind = "box", subplots=True,layout=(2,2) ,sharex=False, sharey= False, figsize=(10,8))

Out[10]:

array([[<matplotlib.axes._subplots.AxesSubplot object at 0x1156b0588>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x1189c6b00>],
       [<matplotlib.axes._subplots.AxesSubplot object at 0x118a0a978>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x118a50eb8>]], dtype=object)

In [11]:

plt.figure(figsize=(12,10))
plt.subplot(2,2,1)
ax1 = sns.swarmplot(x='Species', y='SepalLengthCm', data=iris)
ax1.set_ylabel("Sepal Length (CM)")
plt.subplot(2,2,2)
ax2 = sns.swarmplot(x='Species', y='SepalWidthCm', data=iris)
ax2.set_ylabel("Sepal Width (CM)")
plt.subplot(2,2,3)
ax3 = sns.swarmplot(x='Species', y='PetalLengthCm', data=iris)
ax3.set_ylabel("Petal Length (CM)")
plt.subplot(2,2,4)
ax4 = sns.swarmplot(x='Species', y='PetalWidthCm', data=iris)
ax4.set_ylabel("Petal Width (CM)")

Out[11]:

<matplotlib.text.Text at 0x119b38e80>

In [12]:

iris.plot(kind = "density", figsize=(10,8))

Out[12]:

<matplotlib.axes._subplots.AxesSubplot at 0x119b3cc50>

In [13]:

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

Out[13]:

<seaborn.axisgrid.PairGrid at 0x11b086a20>

In [14]:

corr=iris[['SepalLengthCm','SepalWidthCm','PetalLengthCm','PetalWidthCm']].corr()

In [15]:

sns.heatmap(corr, vmax=1, square=True,annot=True)

Out[15]:

<matplotlib.axes._subplots.AxesSubplot at 0x11c4fb278>

Multi-class classification¶

In [16]:

from sklearn.model_selection import train_test_split

In [17]:

X = iris.iloc[:,0:4]
Y = iris.iloc[:,4]

In [18]:

X_train,X_test,Y_train,Y_test = train_test_split(X,Y, random_state = 22, test_size = 0.2)

In [19]:

X_train.head()

Out[19]:

	SepalLengthCm	SepalWidthCm	PetalLengthCm	PetalWidthCm
30	4.8	3.1	1.6	0.2
31	5.4	3.4	1.5	0.4
89	5.5	2.5	4.0	1.3
90	5.5	2.6	4.4	1.2
55	5.7	2.8	4.5	1.3

In [20]:

Y_train.head()

Out[20]:

30        Iris-setosa
31        Iris-setosa
89    Iris-versicolor
90    Iris-versicolor
55    Iris-versicolor
Name: Species, dtype: object

In [21]:

from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score
from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.svm import SVC

In [22]:

models = []
models.append(("LR",LogisticRegression()))
models.append(("NB",GaussianNB()))
models.append(("KNN",KNeighborsClassifier()))
models.append(("DT",DecisionTreeClassifier()))
models.append(("SVM",SVC()))

In [23]:

results = []
names = []
for name,model in models:
    kfold = KFold(n_splits=10, random_state=22)
    cv_result = cross_val_score(model,X_train,Y_train, cv = kfold,scoring = "accuracy")
    names.append(name)
    results.append(cv_result)
for i in range(len(names)):
    print(names[i],results[i].mean())

LR 0.95
NB 0.958333333333
KNN 0.958333333333
DT 0.941666666667
SVM 0.983333333333

Visualising the results¶

In [24]:

ax = sns.violinplot(data=results)
ax.set_xticklabels(names)

Out[24]:

[<matplotlib.text.Text at 0x11dadf588>,
 <matplotlib.text.Text at 0x11dacc080>,
 <matplotlib.text.Text at 0x11db479b0>,
 <matplotlib.text.Text at 0x11db4b4e0>,
 <matplotlib.text.Text at 0x11db4bfd0>]

SVM has the highest score and less variance.

Final Predictions using Test data¶

In [25]:

svm = SVC()
svm.fit(X_train,Y_train)
predictions = svm.predict(X_test)

In [26]:

from sklearn.metrics import accuracy_score
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix

In [27]:

print(accuracy_score(Y_test,predictions))

0.933333333333

In [28]:

print(classification_report(Y_test,predictions))

                 precision    recall  f1-score   support

    Iris-setosa       1.00      1.00      1.00         6
Iris-versicolor       0.83      1.00      0.91        10
 Iris-virginica       1.00      0.86      0.92        14

    avg / total       0.94      0.93      0.93        30

In [29]:

conf = confusion_matrix(Y_test,predictions)

In [30]:

label = ["Setosa","Versicolor","Virginica"]
sns.heatmap(conf, annot=True, xticklabels=label, yticklabels=label)

Out[30]:

<matplotlib.axes._subplots.AxesSubplot at 0x11dad6048>