from sklearn.cluster import KMeans
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import silhouette_score

Features = heart_data.drop(["DEATH_EVENT"], axis = 1).columns

X = heart_data[Features]

sc = []
for i in range(2, 25):
    kmeans = Pipeline([("scaling",StandardScaler()),("clustering",KMeans(n_clusters=i, init='k-means++', max_iter=300, n_init=10, random_state=0))]).fit(X)
    score = silhouette_score(X, kmeans["clustering"].labels_)
    sc.append(score)
plt.plot(range(2, 25), sc, marker = "o")
plt.title('Silhouette')
plt.xlabel('Number of clusters')
plt.ylabel('Score')
plt.show()


from sklearn.decomposition import PCA
import matplotlib.pyplot as plt
plt.style.use("seaborn-whitegrid")

pca = Pipeline([("scaling",StandardScaler()),("decompositioning",PCA(n_components = 2))]).fit(X)

X2D = pca.transform(X)

plt.scatter(X2D[:,0],X2D[:,1], c = kmeans["clustering"].labels_, cmap = "RdYlBu")
plt.colorbar();


kmeans = KMeans(n_clusters=2, random_state=0).fit(X)
cluster_result = kmeans.predict(X)
new_array = np.concatenate((heart_data, cluster_result[:,None]), axis = 1)
comparison = pd.DataFrame(new_array)
pd.set_option("display.max_rows", None, "display.max_columns", None) #to show all the rows
final_comp = comparison.iloc[:, 12:14]
final_comp

	12 		13
0 	1.0 	0.0
1 	1.0 	0.0
2 	1.0 	0.0
3 	1.0 	0.0
4 	1.0 	1.0
5 	1.0 	0.0
6 	1.0 	0.0
7 	1.0 	1.0
8 	1.0 	0.0
9 	1.0 	1.0
10 	1.0 	1.0
11 	1.0 	0.0
12 	1.0 	0.0
13 	1.0 	0.0
14 	0.0 	1.0
15 	1.0 	0.0
16 	1.0 	0.0
17 	1.0 	0.0
18 	1.0 	0.0
19 	1.0 	0.0
20 	0.0 	0.0
21 	1.0 	0.0
22 	1.0 	0.0
23 	0.0 	1.0
24 	1.0 	0.0
25 	1.0 	0.0
26 	1.0 	0.0
27 	1.0 	0.0
28 	1.0 	0.0
29 	1.0 	0.0
30 	1.0 	0.0
31 	1.0 	1.0
32 	1.0 	1.0
33 	0.0 	0.0
34 	1.0 	0.0
35 	1.0 	0.0
36 	1.0 	0.0
37 	1.0 	1.0
38 	0.0 	0.0
39 	1.0 	1.0
40 	1.0 	0.0
41 	1.0 	0.0
42 	1.0 	0.0
43 	0.0 	0.0
44 	1.0 	0.0
45 	1.0 	0.0
46 	1.0 	0.0
47 	1.0 	1.0
48 	1.0 	0.0
49 	1.0 	1.0
50 	1.0 	0.0
51 	1.0 	1.0
52 	1.0 	0.0
53 	1.0 	1.0
54 	1.0 	0.0
55 	1.0 	1.0
56 	0.0 	0.0
57 	0.0 	0.0
58 	1.0 	1.0
59 	1.0 	0.0
60 	1.0 	1.0
61 	1.0 	0.0
62 	0.0 	0.0
63 	1.0 	1.0
64 	0.0 	0.0
65 	1.0 	0.0
66 	1.0 	0.0
67 	1.0 	0.0
68 	1.0 	0.0
69 	1.0 	1.0
70 	0.0 	1.0
71 	0.0 	0.0
72 	1.0 	0.0
73 	0.0 	0.0
74 	1.0 	0.0
75 	1.0 	0.0
76 	0.0 	1.0
77 	0.0 	0.0
78 	0.0 	0.0
79 	0.0 	1.0
80 	0.0 	0.0
81 	0.0 	0.0
82 	1.0 	0.0
83 	0.0 	0.0
84 	1.0 	0.0
85 	0.0 	1.0
86 	0.0 	0.0
87 	0.0 	0.0
88 	0.0 	0.0
89 	0.0 	0.0
90 	0.0 	0.0
91 	0.0 	0.0
92 	0.0 	0.0
93 	1.0 	0.0
94 	0.0 	1.0
95 	0.0 	0.0
96 	0.0 	0.0
97 	0.0 	0.0
98 	0.0 	1.0
99 	0.0 	0.0
100 	0.0 	0.0
101 	0.0 	0.0
102 	0.0 	0.0
103 	0.0 	0.0
104 	0.0 	0.0
105 	1.0 	1.0
106 	0.0 	0.0
107 	0.0 	0.0
108 	0.0 	0.0
109 	0.0 	1.0
110 	1.0 	0.0
111 	0.0 	0.0
112 	0.0 	0.0
113 	1.0 	1.0
114 	0.0 	1.0
115 	0.0 	0.0
116 	0.0 	0.0
117 	0.0 	1.0
118 	0.0 	0.0
119 	1.0 	0.0
120 	0.0 	0.0
121 	0.0 	0.0
122 	0.0 	0.0
123 	0.0 	0.0
124 	1.0 	0.0
125 	0.0 	0.0
126 	1.0 	0.0
127 	0.0 	0.0
128 	0.0 	0.0
129 	0.0 	0.0
130 	0.0 	0.0
131 	0.0 	0.0
132 	0.0 	0.0
133 	0.0 	0.0
134 	0.0 	0.0
135 	0.0 	0.0
136 	0.0 	0.0
137 	0.0 	0.0
138 	0.0 	0.0
139 	0.0 	0.0
140 	1.0 	0.0
141 	0.0 	1.0
142 	0.0 	1.0
143 	0.0 	0.0
144 	1.0 	1.0
145 	0.0 	0.0
146 	0.0 	0.0
147 	0.0 	0.0
148 	1.0 	0.0
149 	0.0 	0.0
150 	1.0 	0.0
151 	0.0 	0.0
152 	0.0 	0.0
153 	0.0 	0.0
154 	0.0 	0.0
155 	0.0 	0.0
156 	0.0 	0.0
157 	0.0 	0.0
158 	0.0 	0.0
159 	0.0 	1.0
160 	0.0 	0.0
161 	0.0 	0.0
162 	0.0 	1.0
163 	1.0 	0.0
164 	1.0 	1.0
165 	1.0 	0.0
166 	0.0 	0.0
167 	1.0 	0.0
168 	0.0 	0.0
169 	0.0 	0.0
170 	0.0 	0.0
171 	0.0 	1.0
172 	0.0 	0.0
173 	0.0 	0.0
174 	0.0 	0.0
175 	0.0 	1.0
176 	0.0 	0.0
177 	0.0 	0.0
178 	0.0 	0.0
179 	0.0 	0.0
180 	0.0 	0.0
181 	1.0 	0.0
182 	1.0 	0.0
183 	1.0 	0.0
184 	1.0 	0.0
185 	1.0 	1.0
186 	1.0 	0.0
187 	1.0 	1.0
188 	0.0 	0.0
189 	0.0 	0.0
190 	0.0 	1.0
191 	0.0 	0.0
192 	0.0 	0.0
193 	0.0 	0.0
194 	1.0 	0.0
195 	1.0 	0.0
196 	0.0 	0.0
197 	0.0 	0.0
198 	0.0 	0.0
199 	0.0 	0.0
200 	0.0 	0.0
201 	0.0 	1.0
202 	0.0 	0.0
203 	0.0 	0.0
204 	0.0 	0.0
205 	0.0 	1.0
206 	0.0 	0.0
207 	0.0 	0.0
208 	0.0 	0.0
209 	0.0 	0.0
210 	0.0 	1.0
211 	0.0 	0.0
212 	0.0 	1.0
213 	1.0 	0.0
214 	0.0 	0.0
215 	0.0 	0.0
216 	0.0 	1.0
217 	1.0 	0.0
218 	0.0 	0.0
219 	0.0 	1.0
220 	1.0 	0.0
221 	0.0 	0.0
222 	0.0 	1.0
223 	0.0 	0.0
224 	0.0 	1.0
225 	0.0 	0.0
226 	0.0 	0.0
227 	0.0 	0.0
228 	0.0 	0.0
229 	0.0 	0.0
230 	1.0 	0.0
231 	0.0 	0.0
232 	0.0 	0.0
233 	0.0 	1.0
234 	0.0 	0.0
235 	0.0 	1.0
236 	0.0 	0.0
237 	0.0 	0.0
238 	0.0 	0.0
239 	0.0 	0.0
240 	0.0 	1.0
241 	0.0 	0.0
242 	0.0 	0.0
243 	0.0 	0.0
244 	0.0 	0.0
245 	0.0 	0.0
246 	1.0 	1.0
247 	0.0 	0.0
248 	0.0 	0.0
249 	0.0 	0.0
250 	0.0 	1.0
251 	0.0 	0.0
252 	0.0 	0.0
253 	0.0 	0.0
254 	0.0 	0.0
255 	0.0 	1.0
256 	0.0 	0.0
257 	0.0 	0.0
258 	0.0 	0.0
259 	0.0 	0.0
260 	0.0 	0.0
261 	0.0 	0.0
262 	1.0 	0.0
263 	0.0 	0.0
264 	0.0 	0.0
265 	0.0 	1.0
266 	1.0 	0.0
267 	0.0 	0.0
268 	0.0 	0.0
269 	0.0 	0.0
270 	0.0 	0.0
271 	0.0 	0.0
272 	0.0 	0.0
273 	0.0 	0.0
274 	0.0 	0.0
275 	0.0 	1.0
276 	0.0 	1.0
277 	0.0 	0.0
278 	0.0 	0.0
279 	0.0 	1.0
280 	0.0 	0.0
281 	0.0 	0.0
282 	0.0 	0.0
283 	0.0 	0.0
284 	0.0 	0.0
285 	0.0 	1.0
286 	0.0 	0.0
287 	0.0 	1.0
288 	0.0 	0.0
289 	0.0 	1.0
290 	0.0 	0.0
291 	0.0 	0.0
292 	0.0 	1.0
293 	0.0 	0.0
294 	0.0 	0.0
295 	0.0 	0.0
296 	0.0 	1.0
297 	0.0 	0.0
298 	0.0 	1.0