Name : ADVAIT GURUNATH CHAVAN¶

Contact No : +91 70214 55852¶

Mail ID : advaitchavan135@gmail.com¶

Oasis Infobyte Data Science Internship¶

Task 1 : Iris Flower Classification¶

1. Importing the necessary dependencies¶

In [1]:

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn import tree 
from sklearn.metrics import accuracy_score, classification_report, confusion_matrix
from sklearn.tree import DecisionTreeClassifier 
from sklearn.model_selection import train_test_split 
from sklearn.linear_model import LogisticRegression
from sklearn.svm import LinearSVC
from sklearn.ensemble import RandomForestClassifier
from warnings import filterwarnings
filterwarnings(action='ignore')

2. Exploring the dataset¶

In [2]:

iris_data = pd.read_csv('iris.csv')
iris_data

Out[2]:

	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

In [3]:

iris_data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 150 entries, 0 to 149
Data columns (total 6 columns):
 #   Column         Non-Null Count  Dtype  
---  ------         --------------  -----  
 0   Id             150 non-null    int64  
 1   SepalLengthCm  150 non-null    float64
 2   SepalWidthCm   150 non-null    float64
 3   PetalLengthCm  150 non-null    float64
 4   PetalWidthCm   150 non-null    float64
 5   Species        150 non-null    object 
dtypes: float64(4), int64(1), object(1)
memory usage: 7.2+ KB

So, we have 150 rows and 6 columns and there are no null values or blank spaces in our dataset.¶

Hence, there is no need of imputing(filling up blank spaces with mean/median/mode values) the dataset¶

In [4]:

iris_data['SepalLengthCm'].max(), iris_data['SepalLengthCm'].min()

Out[4]:

(7.9, 4.3)

In [5]:

iris_data['SepalWidthCm'].max(), iris_data['SepalWidthCm'].min()

Out[5]:

(4.4, 2.0)

In [6]:

iris_data['PetalLengthCm'].max(), iris_data['PetalLengthCm'].min()

Out[6]:

(6.9, 1.0)

In [7]:

iris_data['PetalWidthCm'].max(), iris_data['PetalWidthCm'].min()

Out[7]:

(2.5, 0.1)

In [8]:

iris_data['Species'].describe()

Out[8]:

count             150
unique              3
top       Iris-setosa
freq               50
Name: Species, dtype: object

In [9]:

iris_data['Species'].value_counts()

Out[9]:

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

In [10]:

fig = plt.figure()
ax = fig.add_axes([0,0,1,1])
ax.axis('equal')
l = ['Versicolor', 'Setosa', 'Virginica']
s = [50,50,50]
ax.pie(s, labels = l,autopct='%1.2f%%')
plt.show()

In [11]:

plt.figure(1)
plt.boxplot([iris_data['PetalLengthCm']])
plt.title('Outliers if any in PetalLengthCm')
plt.figure(2)
plt.boxplot([iris_data['PetalWidthCm']])
plt.title('Outliers if any in PetalWidthCm')
plt.figure(3)
plt.boxplot([iris_data['SepalLengthCm']])
plt.title('Outliers if any in SepalLengthCm')
plt.figure(4)
plt.boxplot([iris_data['SepalWidthCm']])
plt.title('Outliers if any in SepalWidthCm')
plt.show()

In [12]:

plt.figure(1)
iris_data['PetalLengthCm'].hist()
plt.title('Histogram distribution of PetalLengthCm')

plt.figure(2)
iris_data['PetalWidthCm'].hist()
plt.title('Histogram distribution of PetalWidthCm')

plt.figure(3)
iris_data['SepalLengthCm'].hist()
plt.title('Histogram distribution of SepalLengthCm')

plt.figure(4)
iris_data['SepalWidthCm'].hist()
plt.title('Histogram distribution of SepalWidthCm')
plt.show()

In [13]:

plt.figure(1)
iris_data['PetalLengthCm'].plot(kind ='density')
plt.title('Density Plot of PetalLengthCm')

plt.figure(2)
iris_data['PetalWidthCm'].plot(kind ='density')
plt.title('Density Plot of PetalWidthCm')

plt.figure(3)
iris_data['SepalLengthCm'].plot(kind ='density')
plt.title('Density Plot of SepalLengthCm')

plt.figure(4)
iris_data['SepalWidthCm'].plot(kind ='density')
plt.title('Density Plot of SepalWidthCm')

plt.show()

In [14]:

plt.figure(figsize=(15,10))
plt.subplot(2,2,1)
sns.violinplot(x='Species',y='PetalLengthCm',data=iris_data)
plt.title('Species vs PetalLengthCm')

plt.subplot(2,2,2)
sns.violinplot(x='Species',y='PetalWidthCm',data=iris_data)
plt.title('Species vs PetalWidthCm')

plt.subplot(2,2,3)
sns.violinplot(x='Species',y='SepalLengthCm',data=iris_data)
plt.title('Species vs SepalLengthCm')

plt.subplot(2,2,4)
sns.violinplot(x='Species',y='SepalWidthCm',data=iris_data)
plt.title('Species vs SepalWidthCm')

Out[14]:

Text(0.5, 1.0, 'Species vs SepalWidthCm')

In [15]:

iris_data.iloc[:,1:-1].corr()

Out[15]:

	SepalLengthCm	SepalWidthCm	PetalLengthCm	PetalWidthCm
SepalLengthCm	1.000000	-0.109369	0.871754	0.817954
SepalWidthCm	-0.109369	1.000000	-0.420516	-0.356544
PetalLengthCm	0.871754	-0.420516	1.000000	0.962757
PetalWidthCm	0.817954	-0.356544	0.962757	1.000000

In [16]:

sns.heatmap(iris_data.iloc[:,1:-1].corr(), annot=True)

Out[16]:

<AxesSubplot:>

From the above heatmap of correlation of features that define the flower we can infer that PetalLengthCm has the more¶

influence on determining the species of flower¶

In [17]:

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

Out[17]:

<seaborn.axisgrid.PairGrid at 0x2690b753460>

3. Preparing the data for training and testing of model¶

In [18]:

x = iris_data.iloc[:, 1:-1] 
y = iris_data.Species
x_train, x_test, y_train, y_test = train_test_split(x, y,test_size=0.3)

4. Training using Decision Tree Model¶

In [19]:

model_1 = DecisionTreeClassifier() 
model_1.fit(x_train, y_train) 

Out[19]:

DecisionTreeClassifier()

5. Evaluating the performance by Decision Tree Model¶

In [20]:

y_pred = model_1.predict(x_test) 
score = accuracy_score(y_pred, y_test) 
score

Out[20]:

0.9555555555555556

In [21]:

y_pred

Out[21]:

array(['Iris-versicolor', 'Iris-virginica', 'Iris-virginica',
       'Iris-versicolor', 'Iris-virginica', 'Iris-virginica',
       'Iris-setosa', 'Iris-virginica', 'Iris-setosa', 'Iris-versicolor',
       'Iris-virginica', 'Iris-virginica', 'Iris-versicolor',
       'Iris-virginica', 'Iris-setosa', 'Iris-versicolor',
       'Iris-virginica', 'Iris-setosa', 'Iris-versicolor',
       'Iris-versicolor', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa',
       'Iris-virginica', 'Iris-virginica', 'Iris-virginica',
       'Iris-versicolor', 'Iris-setosa', 'Iris-setosa', 'Iris-versicolor',
       'Iris-versicolor', 'Iris-virginica', 'Iris-setosa',
       'Iris-virginica', 'Iris-setosa', 'Iris-versicolor', 'Iris-setosa',
       'Iris-versicolor', 'Iris-setosa', 'Iris-versicolor',
       'Iris-virginica', 'Iris-virginica', 'Iris-versicolor',
       'Iris-setosa', 'Iris-setosa'], dtype=object)

In [22]:

print(classification_report(y_pred, y_test)) 

                 precision    recall  f1-score   support

    Iris-setosa       1.00      1.00      1.00        15
Iris-versicolor       0.93      0.93      0.93        14
 Iris-virginica       0.94      0.94      0.94        16

       accuracy                           0.96        45
      macro avg       0.96      0.96      0.96        45
   weighted avg       0.96      0.96      0.96        45

In [23]:

plt.figure(figsize=(15, 10)) 
tree.plot_tree(model_1, filled=True) 

Out[23]:

[Text(0.3076923076923077, 0.9166666666666666, 'X[2] <= 2.6\ngini = 0.666\nsamples = 105\nvalue = [35, 36, 34]'),
 Text(0.23076923076923078, 0.75, 'gini = 0.0\nsamples = 35\nvalue = [35, 0, 0]'),
 Text(0.38461538461538464, 0.75, 'X[2] <= 4.75\ngini = 0.5\nsamples = 70\nvalue = [0, 36, 34]'),
 Text(0.15384615384615385, 0.5833333333333334, 'X[3] <= 1.65\ngini = 0.057\nsamples = 34\nvalue = [0, 33, 1]'),
 Text(0.07692307692307693, 0.4166666666666667, 'gini = 0.0\nsamples = 33\nvalue = [0, 33, 0]'),
 Text(0.23076923076923078, 0.4166666666666667, 'gini = 0.0\nsamples = 1\nvalue = [0, 0, 1]'),
 Text(0.6153846153846154, 0.5833333333333334, 'X[3] <= 1.75\ngini = 0.153\nsamples = 36\nvalue = [0, 3, 33]'),
 Text(0.38461538461538464, 0.4166666666666667, 'X[0] <= 6.2\ngini = 0.48\nsamples = 5\nvalue = [0, 2, 3]'),
 Text(0.3076923076923077, 0.25, 'gini = 0.0\nsamples = 2\nvalue = [0, 0, 2]'),
 Text(0.46153846153846156, 0.25, 'X[0] <= 6.95\ngini = 0.444\nsamples = 3\nvalue = [0, 2, 1]'),
 Text(0.38461538461538464, 0.08333333333333333, 'gini = 0.0\nsamples = 2\nvalue = [0, 2, 0]'),
 Text(0.5384615384615384, 0.08333333333333333, 'gini = 0.0\nsamples = 1\nvalue = [0, 0, 1]'),
 Text(0.8461538461538461, 0.4166666666666667, 'X[2] <= 4.85\ngini = 0.062\nsamples = 31\nvalue = [0, 1, 30]'),
 Text(0.7692307692307693, 0.25, 'X[1] <= 3.1\ngini = 0.444\nsamples = 3\nvalue = [0, 1, 2]'),
 Text(0.6923076923076923, 0.08333333333333333, 'gini = 0.0\nsamples = 2\nvalue = [0, 0, 2]'),
 Text(0.8461538461538461, 0.08333333333333333, 'gini = 0.0\nsamples = 1\nvalue = [0, 1, 0]'),
 Text(0.9230769230769231, 0.25, 'gini = 0.0\nsamples = 28\nvalue = [0, 0, 28]')]

In [24]:

color_class = {
    'Iris-setosa' : 'red',
    'Iris-versicolor' : 'green',
    'Iris-virginica' : 'blue'
}

colors = [color_class[label] for label in y_pred]

plt.scatter(x_test.SepalLengthCm, x_test.PetalLengthCm, c=colors)
plt.xlabel('Sepal Length')
plt.ylabel('Petal Length')
plt.title('Predicted Species using Decision Tree Model')
legend_entries = [
    plt.Line2D([0], [0], marker='o', color='w', markerfacecolor=color, markersize=10, label=label)
    for label, color in color_class.items()
]
plt.legend(handles=legend_entries, title='Species')
plt.show()

From the above Tree Plot and Predicted Species using Decision Tree Model we can infer the following:-¶

If Petal Length <= 2.45cm then the Species of the flower is Setosa.¶

If Petal Length > 2.45 cm then the Specis of the flower may be Versicolor or Virginica¶

For Petal Length > 3 and Petal Length < 5 most of the Species are Versicolor¶

In [25]:

iris_setosa_dataset = iris_data[iris_data.PetalLengthCm <= 2.45]
iris_setosa_dataset.to_excel('iris_setosa_dataset.xlsx',index=False)
iris_setosa_dataset.to_csv('iris_setosa_dataset.csv',index=False)
pd.read_excel('iris_setosa_dataset.xlsx')

Out[25]:

	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
5	6	5.4	3.9	1.7	0.4	Iris-setosa
6	7	4.6	3.4	1.4	0.3	Iris-setosa
7	8	5.0	3.4	1.5	0.2	Iris-setosa
8	9	4.4	2.9	1.4	0.2	Iris-setosa
9	10	4.9	3.1	1.5	0.1	Iris-setosa
10	11	5.4	3.7	1.5	0.2	Iris-setosa
11	12	4.8	3.4	1.6	0.2	Iris-setosa
12	13	4.8	3.0	1.4	0.1	Iris-setosa
13	14	4.3	3.0	1.1	0.1	Iris-setosa
14	15	5.8	4.0	1.2	0.2	Iris-setosa
15	16	5.7	4.4	1.5	0.4	Iris-setosa
16	17	5.4	3.9	1.3	0.4	Iris-setosa
17	18	5.1	3.5	1.4	0.3	Iris-setosa
18	19	5.7	3.8	1.7	0.3	Iris-setosa
19	20	5.1	3.8	1.5	0.3	Iris-setosa
20	21	5.4	3.4	1.7	0.2	Iris-setosa
21	22	5.1	3.7	1.5	0.4	Iris-setosa
22	23	4.6	3.6	1.0	0.2	Iris-setosa
23	24	5.1	3.3	1.7	0.5	Iris-setosa
24	25	4.8	3.4	1.9	0.2	Iris-setosa
25	26	5.0	3.0	1.6	0.2	Iris-setosa
26	27	5.0	3.4	1.6	0.4	Iris-setosa
27	28	5.2	3.5	1.5	0.2	Iris-setosa
28	29	5.2	3.4	1.4	0.2	Iris-setosa
29	30	4.7	3.2	1.6	0.2	Iris-setosa
30	31	4.8	3.1	1.6	0.2	Iris-setosa
31	32	5.4	3.4	1.5	0.4	Iris-setosa
32	33	5.2	4.1	1.5	0.1	Iris-setosa
33	34	5.5	4.2	1.4	0.2	Iris-setosa
34	35	4.9	3.1	1.5	0.1	Iris-setosa
35	36	5.0	3.2	1.2	0.2	Iris-setosa
36	37	5.5	3.5	1.3	0.2	Iris-setosa
37	38	4.9	3.1	1.5	0.1	Iris-setosa
38	39	4.4	3.0	1.3	0.2	Iris-setosa
39	40	5.1	3.4	1.5	0.2	Iris-setosa
40	41	5.0	3.5	1.3	0.3	Iris-setosa
41	42	4.5	2.3	1.3	0.3	Iris-setosa
42	43	4.4	3.2	1.3	0.2	Iris-setosa
43	44	5.0	3.5	1.6	0.6	Iris-setosa
44	45	5.1	3.8	1.9	0.4	Iris-setosa
45	46	4.8	3.0	1.4	0.3	Iris-setosa
46	47	5.1	3.8	1.6	0.2	Iris-setosa
47	48	4.6	3.2	1.4	0.2	Iris-setosa
48	49	5.3	3.7	1.5	0.2	Iris-setosa
49	50	5.0	3.3	1.4	0.2	Iris-setosa

In [26]:

iris_versicolor_dataset = iris_data[(iris_data['PetalLengthCm'] < 5) & (iris_data['PetalLengthCm'] > 3)]
iris_versicolor_dataset.to_excel('iris_versicolor_dataset.xlsx',index=False)
iris_versicolor_dataset.to_csv('iris_versicolor_dataset.csv',index=False)
pd.read_excel('iris_versicolor_dataset.xlsx')

Out[26]:

	Id	SepalLengthCm	SepalWidthCm	PetalLengthCm	PetalWidthCm	Species
0	51	7.0	3.2	4.7	1.4	Iris-versicolor
1	52	6.4	3.2	4.5	1.5	Iris-versicolor
2	53	6.9	3.1	4.9	1.5	Iris-versicolor
3	54	5.5	2.3	4.0	1.3	Iris-versicolor
4	55	6.5	2.8	4.6	1.5	Iris-versicolor
5	56	5.7	2.8	4.5	1.3	Iris-versicolor
6	57	6.3	3.3	4.7	1.6	Iris-versicolor
7	58	4.9	2.4	3.3	1.0	Iris-versicolor
8	59	6.6	2.9	4.6	1.3	Iris-versicolor
9	60	5.2	2.7	3.9	1.4	Iris-versicolor
10	61	5.0	2.0	3.5	1.0	Iris-versicolor
11	62	5.9	3.0	4.2	1.5	Iris-versicolor
12	63	6.0	2.2	4.0	1.0	Iris-versicolor
13	64	6.1	2.9	4.7	1.4	Iris-versicolor
14	65	5.6	2.9	3.6	1.3	Iris-versicolor
15	66	6.7	3.1	4.4	1.4	Iris-versicolor
16	67	5.6	3.0	4.5	1.5	Iris-versicolor
17	68	5.8	2.7	4.1	1.0	Iris-versicolor
18	69	6.2	2.2	4.5	1.5	Iris-versicolor
19	70	5.6	2.5	3.9	1.1	Iris-versicolor
20	71	5.9	3.2	4.8	1.8	Iris-versicolor
21	72	6.1	2.8	4.0	1.3	Iris-versicolor
22	73	6.3	2.5	4.9	1.5	Iris-versicolor
23	74	6.1	2.8	4.7	1.2	Iris-versicolor
24	75	6.4	2.9	4.3	1.3	Iris-versicolor
25	76	6.6	3.0	4.4	1.4	Iris-versicolor
26	77	6.8	2.8	4.8	1.4	Iris-versicolor
27	79	6.0	2.9	4.5	1.5	Iris-versicolor
28	80	5.7	2.6	3.5	1.0	Iris-versicolor
29	81	5.5	2.4	3.8	1.1	Iris-versicolor
30	82	5.5	2.4	3.7	1.0	Iris-versicolor
31	83	5.8	2.7	3.9	1.2	Iris-versicolor
32	85	5.4	3.0	4.5	1.5	Iris-versicolor
33	86	6.0	3.4	4.5	1.6	Iris-versicolor
34	87	6.7	3.1	4.7	1.5	Iris-versicolor
35	88	6.3	2.3	4.4	1.3	Iris-versicolor
36	89	5.6	3.0	4.1	1.3	Iris-versicolor
37	90	5.5	2.5	4.0	1.3	Iris-versicolor
38	91	5.5	2.6	4.4	1.2	Iris-versicolor
39	92	6.1	3.0	4.6	1.4	Iris-versicolor
40	93	5.8	2.6	4.0	1.2	Iris-versicolor
41	94	5.0	2.3	3.3	1.0	Iris-versicolor
42	95	5.6	2.7	4.2	1.3	Iris-versicolor
43	96	5.7	3.0	4.2	1.2	Iris-versicolor
44	97	5.7	2.9	4.2	1.3	Iris-versicolor
45	98	6.2	2.9	4.3	1.3	Iris-versicolor
46	100	5.7	2.8	4.1	1.3	Iris-versicolor
47	107	4.9	2.5	4.5	1.7	Iris-virginica
48	122	5.6	2.8	4.9	2.0	Iris-virginica
49	124	6.3	2.7	4.9	1.8	Iris-virginica
50	127	6.2	2.8	4.8	1.8	Iris-virginica
51	128	6.1	3.0	4.9	1.8	Iris-virginica
52	139	6.0	3.0	4.8	1.8	Iris-virginica

In [27]:

iris_virginica_dataset = iris_data[(iris_data['PetalLengthCm'] > 5)]
iris_virginica_dataset.to_csv('iris_virginica_dataset.csv',index=False)
pd.read_excel('iris_virginica_dataset.xlsx')

Out[27]:

	Id	SepalLengthCm	SepalWidthCm	PetalLengthCm	PetalWidthCm	Species
0	84	6.0	2.7	5.1	1.6	Iris-versicolor
1	101	6.3	3.3	6.0	2.5	Iris-virginica
2	102	5.8	2.7	5.1	1.9	Iris-virginica
3	103	7.1	3.0	5.9	2.1	Iris-virginica
4	104	6.3	2.9	5.6	1.8	Iris-virginica
5	105	6.5	3.0	5.8	2.2	Iris-virginica
6	106	7.6	3.0	6.6	2.1	Iris-virginica
7	108	7.3	2.9	6.3	1.8	Iris-virginica
8	109	6.7	2.5	5.8	1.8	Iris-virginica
9	110	7.2	3.6	6.1	2.5	Iris-virginica
10	111	6.5	3.2	5.1	2.0	Iris-virginica
11	112	6.4	2.7	5.3	1.9	Iris-virginica
12	113	6.8	3.0	5.5	2.1	Iris-virginica
13	115	5.8	2.8	5.1	2.4	Iris-virginica
14	116	6.4	3.2	5.3	2.3	Iris-virginica
15	117	6.5	3.0	5.5	1.8	Iris-virginica
16	118	7.7	3.8	6.7	2.2	Iris-virginica
17	119	7.7	2.6	6.9	2.3	Iris-virginica
18	121	6.9	3.2	5.7	2.3	Iris-virginica
19	123	7.7	2.8	6.7	2.0	Iris-virginica
20	125	6.7	3.3	5.7	2.1	Iris-virginica
21	126	7.2	3.2	6.0	1.8	Iris-virginica
22	129	6.4	2.8	5.6	2.1	Iris-virginica
23	130	7.2	3.0	5.8	1.6	Iris-virginica
24	131	7.4	2.8	6.1	1.9	Iris-virginica
25	132	7.9	3.8	6.4	2.0	Iris-virginica
26	133	6.4	2.8	5.6	2.2	Iris-virginica
27	134	6.3	2.8	5.1	1.5	Iris-virginica
28	135	6.1	2.6	5.6	1.4	Iris-virginica
29	136	7.7	3.0	6.1	2.3	Iris-virginica
30	137	6.3	3.4	5.6	2.4	Iris-virginica
31	138	6.4	3.1	5.5	1.8	Iris-virginica
32	140	6.9	3.1	5.4	2.1	Iris-virginica
33	141	6.7	3.1	5.6	2.4	Iris-virginica
34	142	6.9	3.1	5.1	2.3	Iris-virginica
35	143	5.8	2.7	5.1	1.9	Iris-virginica
36	144	6.8	3.2	5.9	2.3	Iris-virginica
37	145	6.7	3.3	5.7	2.5	Iris-virginica
38	146	6.7	3.0	5.2	2.3	Iris-virginica
39	148	6.5	3.0	5.2	2.0	Iris-virginica
40	149	6.2	3.4	5.4	2.3	Iris-virginica
41	150	5.9	3.0	5.1	1.8	Iris-virginica

6. Training using LinearRegressionModel¶

In [28]:

model_2 = LogisticRegression()

In [29]:

model_2.fit(x_train,y_train)

Out[29]:

LogisticRegression()

7. Evaluating the performance using LinearRegressionModel¶

In [30]:

y_pred_2 = model_2.predict(x_test)
y_pred_2

Out[30]:

array(['Iris-versicolor', 'Iris-virginica', 'Iris-virginica',
       'Iris-versicolor', 'Iris-virginica', 'Iris-virginica',
       'Iris-setosa', 'Iris-virginica', 'Iris-setosa', 'Iris-virginica',
       'Iris-virginica', 'Iris-virginica', 'Iris-versicolor',
       'Iris-virginica', 'Iris-setosa', 'Iris-versicolor',
       'Iris-virginica', 'Iris-setosa', 'Iris-versicolor',
       'Iris-versicolor', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa',
       'Iris-virginica', 'Iris-virginica', 'Iris-virginica',
       'Iris-versicolor', 'Iris-setosa', 'Iris-setosa', 'Iris-versicolor',
       'Iris-versicolor', 'Iris-virginica', 'Iris-setosa',
       'Iris-virginica', 'Iris-setosa', 'Iris-versicolor', 'Iris-setosa',
       'Iris-versicolor', 'Iris-setosa', 'Iris-versicolor',
       'Iris-virginica', 'Iris-virginica', 'Iris-versicolor',
       'Iris-setosa', 'Iris-setosa'], dtype=object)

In [31]:

confusion_matrix(y_test,y_pred_2)

Out[31]:

array([[15,  0,  0],
       [ 0, 13,  1],
       [ 0,  0, 16]], dtype=int64)

In [32]:

print(classification_report(y_test,y_pred_2))

                 precision    recall  f1-score   support

    Iris-setosa       1.00      1.00      1.00        15
Iris-versicolor       1.00      0.93      0.96        14
 Iris-virginica       0.94      1.00      0.97        16

       accuracy                           0.98        45
      macro avg       0.98      0.98      0.98        45
   weighted avg       0.98      0.98      0.98        45

In [33]:

accuracy_score(y_test,y_pred_2)

Out[33]:

0.9777777777777777

In [34]:

color_class = {
    'Iris-setosa' : 'red',
    'Iris-versicolor' : 'green',
    'Iris-virginica' : 'blue'
}

colors = [color_class[label] for label in y_pred_2]

plt.scatter(x_test.SepalLengthCm, x_test.PetalLengthCm, c=colors)
plt.xlabel('Sepal Length')
plt.ylabel('Petal Length')
plt.title('Predicted Species using Linear Regression Model')
legend_entries = [
    plt.Line2D([0], [0], marker='o', color='w', markerfacecolor=color, markersize=10, label=label)
    for label, color in color_class.items()
]
plt.legend(handles=legend_entries, title='Species')
plt.show()

8. Training using Linear Sub Vector Machine (SVM)¶

In [35]:

model_3 = LinearSVC()
model_3.fit(x_train, y_train)

Out[35]:

LinearSVC()

9. Evaluating the performance using the Linear Sub Vector Machine (SVM)¶

In [36]:

y_pred_3 = model_3.predict(x_test)
y_pred_3

Out[36]:

array(['Iris-versicolor', 'Iris-virginica', 'Iris-virginica',
       'Iris-versicolor', 'Iris-virginica', 'Iris-virginica',
       'Iris-setosa', 'Iris-virginica', 'Iris-setosa', 'Iris-versicolor',
       'Iris-virginica', 'Iris-virginica', 'Iris-versicolor',
       'Iris-virginica', 'Iris-setosa', 'Iris-versicolor',
       'Iris-virginica', 'Iris-setosa', 'Iris-versicolor',
       'Iris-versicolor', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa',
       'Iris-virginica', 'Iris-virginica', 'Iris-virginica',
       'Iris-versicolor', 'Iris-setosa', 'Iris-setosa', 'Iris-versicolor',
       'Iris-versicolor', 'Iris-virginica', 'Iris-setosa',
       'Iris-virginica', 'Iris-setosa', 'Iris-versicolor', 'Iris-setosa',
       'Iris-versicolor', 'Iris-setosa', 'Iris-versicolor',
       'Iris-virginica', 'Iris-virginica', 'Iris-versicolor',
       'Iris-setosa', 'Iris-setosa'], dtype=object)

In [37]:

print(confusion_matrix(y_test, y_pred_3))

[[15  0  0]
 [ 0 13  1]
 [ 0  1 15]]

In [38]:

print(classification_report(y_test, y_pred_3))

                 precision    recall  f1-score   support

    Iris-setosa       1.00      1.00      1.00        15
Iris-versicolor       0.93      0.93      0.93        14
 Iris-virginica       0.94      0.94      0.94        16

       accuracy                           0.96        45
      macro avg       0.96      0.96      0.96        45
   weighted avg       0.96      0.96      0.96        45

In [39]:

print(accuracy_score(y_test,y_pred_3))

0.9555555555555556

In [40]:

color_class = {
    'Iris-setosa' : 'red',
    'Iris-versicolor' : 'green',
    'Iris-virginica' : 'blue'
}

colors = [color_class[label] for label in y_pred_3]

plt.scatter(x_test.SepalLengthCm, x_test.PetalLengthCm, c=colors)
plt.xlabel('Sepal Length')
plt.ylabel('Petal Length')
plt.title('Predicted Species using Linear Sub-Vector Machine Model')
legend_entries = [
    plt.Line2D([0], [0], marker='o', color='w', markerfacecolor=color, markersize=10, label=label)
    for label, color in color_class.items()
]
plt.legend(handles=legend_entries, title='Species')
plt.show()

10. Training using Random Forest Classifier¶

In [41]:

classifier = RandomForestClassifier(n_estimators=100)
classifier.fit(x_train, y_train)

Out[41]:

RandomForestClassifier()

In [42]:

y_pred_4 = classifier.predict(x_test)
y_pred_4

Out[42]:

array(['Iris-versicolor', 'Iris-virginica', 'Iris-virginica',
       'Iris-versicolor', 'Iris-virginica', 'Iris-virginica',
       'Iris-setosa', 'Iris-virginica', 'Iris-setosa', 'Iris-virginica',
       'Iris-virginica', 'Iris-virginica', 'Iris-versicolor',
       'Iris-virginica', 'Iris-setosa', 'Iris-versicolor',
       'Iris-virginica', 'Iris-setosa', 'Iris-versicolor',
       'Iris-versicolor', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa',
       'Iris-virginica', 'Iris-virginica', 'Iris-virginica',
       'Iris-versicolor', 'Iris-setosa', 'Iris-setosa', 'Iris-versicolor',
       'Iris-versicolor', 'Iris-virginica', 'Iris-setosa',
       'Iris-virginica', 'Iris-setosa', 'Iris-versicolor', 'Iris-setosa',
       'Iris-versicolor', 'Iris-setosa', 'Iris-versicolor',
       'Iris-virginica', 'Iris-virginica', 'Iris-versicolor',
       'Iris-setosa', 'Iris-setosa'], dtype=object)

11. Evaluation using Random Forest Classifier¶

In [43]:

accuracy = classifier.score(x_test, y_test)
print(f"Accuracy: {accuracy}")

Accuracy: 0.9777777777777777

In [44]:

print(confusion_matrix(y_test, y_pred_4))

[[15  0  0]
 [ 0 13  1]
 [ 0  0 16]]

In [45]:

print(classification_report(y_test, y_pred_4))

                 precision    recall  f1-score   support

    Iris-setosa       1.00      1.00      1.00        15
Iris-versicolor       1.00      0.93      0.96        14
 Iris-virginica       0.94      1.00      0.97        16

       accuracy                           0.98        45
      macro avg       0.98      0.98      0.98        45
   weighted avg       0.98      0.98      0.98        45

In [46]:

print(accuracy_score(y_test,y_pred_4))

0.9777777777777777

In [47]:

color_class = {
    'Iris-setosa' : 'red',
    'Iris-versicolor' : 'green',
    'Iris-virginica' : 'blue'
}

colors = [color_class[label] for label in y_pred_4]

plt.scatter(x_test.SepalLengthCm, x_test.PetalLengthCm, c=colors)
plt.xlabel('Sepal Length')
plt.ylabel('Petal Length')
plt.title('Predicted Species using Random Forest Classifier')
legend_entries = [
    plt.Line2D([0], [0], marker='o', color='y', markerfacecolor=color, markersize=10, label=label)
    for label, color in color_class.items()
]
plt.legend(handles=legend_entries, title='Species')
plt.show()

12 Final Conclusion¶

Hence, we have classified the Iris Flower Dataset using four Classification Models namely Decision Tree Model, Linear Regression Model, Linear SVC Model and Random Forest Classifier¶

In, first three cases i.e. using Linear Regression Model and Random Forest Classifier Model we achieved an overall accuracy score of 0.98; incase of Sub-vector Machine Model and Decision Tree Model we achieved an accuracy of 0.96¶

From the Multi-class classification plot we can conclude that¶

1. For Flowers have Petal Length < 2.45 cm, belong to Setosa Species
¶

2. For Flowers having Petal Length > 3cm and Petal Length < 5cm, most of them belong Versicolor Species
¶

3. For Flowers having Petal Length > 5cm, belong to Virginca Species

In [ ]: