-
Notifications
You must be signed in to change notification settings - Fork 7
/
app.py
80 lines (64 loc) · 2.57 KB
/
app.py
1
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
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sat Apr 25 17:15:47 2020
@author: pranjal27bhardwaj
"""
#Importing all the required libraries for this project
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
from sklearn.datasets import load_breast_cancer #Using the dataset via scikitlearn.datasets
from sklearn.model_selection import train_test_split
#Loading the datasets
cancer = load_breast_cancer()
df_cancer = pd.DataFrame(np.c_[cancer['data'], cancer['target']], columns= np.append(cancer['feature_names'],['target']))
#Training the data and splitting
x = df_cancer.drop(['target'],axis =1)
y= df_cancer['target']
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=5)
#logistic regression model
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report , confusion_matrix
logistic_model = LogisticRegression(random_state = 0)
logistic_model.fit(x_train, y_train)
y_predict =logistic_model.predict(x_test)
cm = confusion_matrix(y_test,y_predict)
print(classification_report(y_test,y_predict))
#K- nearest neighbours model
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import classification_report , confusion_matrix
knn_model = KNeighborsClassifier(n_neighbors = 5, metric = 'minkowski', p = 2)
knn_model.fit(x_train, y_train)
y_predict =knn_model.predict(x_test)
cm = confusion_matrix(y_test,y_predict)
from sklearn import metrics
print(classification_report(y_test,y_predict))
Ks = 10
mean_acc = np.zeros((Ks-1))
std_acc = np.zeros((Ks-1))
ConfustionMx = [];
for n in range(1,Ks):
#Train Model and Predict
neigh = KNeighborsClassifier(n_neighbors = n).fit(x_train,y_train)
yhat=neigh.predict(x_test)
mean_acc[n-1] = metrics.accuracy_score(y_test, yhat)
std_acc[n-1]=np.std(yhat==y_test)/np.sqrt(yhat.shape[0])
print( "The best accuracy was with", mean_acc.max(), "with k=", mean_acc.argmax()+1)
#Using SVM Model
from sklearn.svm import SVC
from sklearn.metrics import classification_report , confusion_matrix
from sklearn.svm import SVC
svm_model = SVC(kernel = 'linear', random_state = 0)
svm_model.fit(x_train, y_train)
y_predict =svm_model.predict(x_test)
cm = confusion_matrix(y_test,y_predict)
#model improvisation
min_train =x_train.min()
range_train =(x_train - min_train).max()
x_train_scaled =(x_train-min_train)/range_train
from sklearn.metrics import f1_score
f1_score(y_test, yhat, average='weighted')
from sklearn.metrics import jaccard_similarity_score
jaccard_similarity_score(y_test, yhat)