2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113 | """Importing the Dependencies
"""
import numpy as np
import pandas as pd
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from sklearn import svm
from sklearn.metrics import accuracy_score
"""Data Collection and Analysis
PIMA Diabetes Dataset
"""
# loading the diabetes dataset to a pandas DataFrame
diabetes_dataset = pd.read_csv('.../diabetes.csv')
pd.read_csv?
# printing the first 5 rows of the dataset
diabetes_dataset.head()
# number of rows and Columns in this dataset
diabetes_dataset.shape
# getting the statistical measures of the data
diabetes_dataset.describe()
diabetes_dataset['Outcome'].value_counts()
"""0 --> Non-Diabetic
1 --> Diabetic
"""
diabetes_dataset.groupby('Outcome').mean()
# separating the data and labels
X = diabetes_dataset.drop(columns = 'Outcome', axis=1)
Y = diabetes_dataset['Outcome']
print(X)
print(Y)
"""Data Standardization"""
scaler = StandardScaler()
scaler.fit(X)
standardized_data = scaler.transform(X)
print(standardized_data)
X = standardized_data
Y = diabetes_dataset['Outcome']
print(X)
print(Y)
"""Train Test Split"""
X_train, X_test, Y_train, Y_test = train_test_split(X,Y, test_size = 0.2, stratify=Y, random_state=2)
print(X.shape, X_train.shape, X_test.shape)
"""Training the Model"""
classifier = svm.SVC(kernel='linear')
#training the support vector Machine Classifier
classifier.fit(X_train, Y_train)
"""Model Evaluation
Accuracy Score
"""
# accuracy score on the training data
X_train_prediction = classifier.predict(X_train)
training_data_accuracy = accuracy_score(X_train_prediction, Y_train)
print('Accuracy score of the training data : ', training_data_accuracy)
# accuracy score on the test data
X_test_prediction = classifier.predict(X_test)
test_data_accuracy = accuracy_score(X_test_prediction, Y_test)
print('Accuracy score of the test data : ', test_data_accuracy)
"""Making a Predictive System"""
input_data = (5,166,72,19,175,25.8,0.587,51)
# changing the input_data to numpy array
input_data_as_numpy_array = np.asarray(input_data)
# reshape the array as we are predicting for one instance
input_data_reshaped = input_data_as_numpy_array.reshape(1,-1)
# standardize the input data
std_data = scaler.transform(input_data_reshaped)
print(std_data)
prediction = classifier.predict(std_data)
print(prediction)
if (prediction[0] == 0):
print('The person is not diabetic')
else:
print('The person is diabetic')
|
No comments:
Post a Comment