Missing Value dengan Algoritma K-NN¶

Missing Values¶

Missing Values (nilai yang hilang) adalah kejadian umum, dimana nilai yang hilang dapat menandakan sejumlah hal berbeda dalam data. Dan mungkin data tidak tersedia atau tidak berlaku. Missing value biasanya disebabkan oleh orang yang memasukkan data dan tidak tahu nilai yang benar, atau tidak mengisinya. Metode penambangan data bervariasi dalam cara mereka memperlakukan nilai yang hilang. Biasanya, mereka mengabaikan nilai yang hilang, atau mengecualikan catatan yang berisi nilai yang hilang, atau mengganti nilai yang hilang dengan nilai tengah, atau menyimpulkan nilai yang hilang dari nilai yang ada.

Algoritma K-NN (k-Nearest Neighbors)¶

Algoritma K-Nearest Naighbors adalah suatu algoritma klasifikasi sederhana yang dapat digunakan untuk memprediksi klasifikasi dan regresi. Algoritma ini memiliki tujuan untuk mengklasifikasi objek baru berdasarkan atribut dan sample-sample data training.

langkah penyelesaian yang dilakukan oleh algoritma tersebut adalah:

Kita harus menentukan jumlah tetangga terdekat yang nantinya akan kita hitung. Misalnya : kita menentukan 2 tetangga terdekat (k=2).
Hitung jarak objek yang dipilih dengan seluruh tetangga yang ada. kemudian urutkan berdasarkan jarak yang diperoleh dari yang terkecil hingga ke terbesar.
Ambil 2 tetangga yang paling dekat atau nilai jarak yang terkecil, dan ambil rata-ratanya.

Mengatasi Missing Value Menggunakan Algoritma K-NN pada Python¶

Alat dan Bahan¶

Pada kasus kali ini, saya menggunakan dataset dari internet, yang bisa diunduh disini. Pada dataset tersebut terdapat 1 fitur bertipe binary dan 5 fitur bertipe numerikal. Pada fitur age dataset telah saya modif dengan mengubah dalam bentuk .csv dan memberikan missing value pada baris ke-257. Untuk mempermudah dalam penyelesaian kasus ini, perlu di siapkan library python untuk mempermudah dalam pengerjaan. Library ini dapat di unduh secara gratis dari internet.

Berikut merupakan library yang harus di persiapkan:

pandas, digunakan untuk data manajemen dan data analysis.
scipy, merupakan library berisi kumpulan algoritma dan fungsi matematika.

Pertama¶

Langkah pertama yang harus dilakukan adalah memasukkan library yang telah diunduh sebelumnya.

import pandas as pd
import math as mt
from sklearn.preprocessing import LabelEncoder

Kedua¶

Selanjutnya kita mengambil file csv tersebut.

data = pd.read_csv('Wong.csv', delimiter=';', decimal=',')
df = pd.DataFrame(data)
df.style.highlight_null(null_color='red').hide_index()

Maka akan tampil sebagai barikut:

no	id	days	duration	sex	age	piq	viq
1	3358	30	4	Male	20.6708	87	89
2	3535	16	17	Male	55.2882	95	77
3	3547	40	1	Male	55.9151	95	116
4	3592	13	10	Male	61.6646	59	73
5	3728	19	6	Male	30.1273	67	73
6	3790	13	3	Male	57.0623	76	69
7	3807	37	5	Male	24.6762	74	77
8	3808	31	7	Male	28.2683	91	110
9	4253	40	3	Male	22.6037	115	110
10	4356	31	7	Male	21.399	86	83
11	4384	35	8	Male	36.3806	76	90
12	4542	22	11	Female	21.9576	71	89
13	4705	18	1	Female	21.6838	127	109
14	4744	15	25	Male	57.566	82	85
15	4802	36	0	Male	62.475	88	97
16	4941	46	4	Female	19.0144	69	88
17	4983	33	5	Male	38.3929	102	117
18	5129	26	1	Male	25.0459	77	89
19	5154	35	5	Male	22.1903	82	95
20	5162	33	1	Male	25.0185	118	101
21	5174	38	4	Female	37.2704	87	99
22	5208	31	8	Female	21.3771	97	90
23	5253	29	1	Male	33.1335	104	105
24	5298	30	3	Male	22.9569	87	86
25	5640	34	7	Male	25.9986	93	113
26	5668	27	7	Male	40.9227	72	79
27	5680	17	1	Male	27.7563	84	90
28	5699	26	1	Female	34.2231	95	108
29	5713	36	8	Male	16.2683	89	97
30	5736	18	9	Male	16.1478	89	86
31	5754	36	1	Male	16.3368	87	86
32	5776	26	8	Male	17.128	71	88
33	6122	29	1	Male	56.2108	95	103
34	6163	21	1	Male	19.3593	112	106
35	6179	22	2	Male	38.0123	89	95
36	6671	30	7	Female	27.8056	71	82
37	6859	27	1	Male	34.2122	74	79
38	6870	22	0	Male	42.4832	84	95
39	6914	43	0	Male	61.5222	85	90
40	6937	18	0	Female	21.191	94	81
41	6977	30	1	Male	36.2108	97	94
42	7120	39	0	Male	69.7057	84	86
43	7309	31	0	Female	50.6667	85	95
44	7321	23	0	Male	26.0041	84	83
45	7548	31	0	Male	24.3669	108	106
46	2364	41	14	Male	25.8097	84	94
47	2600	3333	9	Male	43.9398	86	80
48	2761	40	3	Female	24.3696	98	112
49	3237	65	9	Male	49.8508	67	67
50	3277	51	1	Male	37.4702	104	96
51	3346	44	18	Female	57.2758	79	85
52	3359	59	9	Female	56.8953	84	91
53	3373	39	28	Female	26.308	87	91
54	3544	32	14	Male	54.5298	81	98
55	3655	57	5	Female	21.9055	90	103
56	3762	48	6	Male	20.3559	85	93
57	3919	58	1	Male	30.3655	99	95
58	4094	50	2	Male	19.7262	79	93
59	4133	34	14	Male	20	70	88
60	4183	42	3	Male	26.2341	98	116
61	4189	69	4	Female	29.462	75	86
62	4315	63	0	Male	38.141	107	130
63	4482	58	14	Female	18.2341	86	103
64	4638	20	17	Male	20.512	82	72
65	4678	63	7	Male	46.6448	96	95
66	4696	54	4	Male	46.9569	101	112
67	4755	24	18	Male	27.5127	105	102
68	4837	42	10	Male	19.6906	83	88
69	4996	51	12	Male	43.0281	77	78
70	5009	50	7	Male	24.3806	61	104
71	5014	46	7	Female	23.7618	75	90
72	5192	60	1	Male	58.6283	87	97
73	5204	71	0	Male	59.0746	97	107
74	5238	44	3	Male	45.1006	99	103
75	5280	83	1	Male	48.6434	78	88
76	5289	52	1	Male	48.5722	84	85
77	5456	48	14	Male	41.1636	80	101
78	5458	44	14	Male	34.4778	84	95
79	5474	65	2	Female	28.6598	95	86
80	5568	64	1	Female	51.9918	75	79
81	5580	56	7	Male	17.7933	86	95
82	5581	65	2	Male	26.3053	85	95
83	5628	51	3	Female	30.2642	81	85
84	6154	43	5	Female	22.6064	74	80
85	6180	59	12	Male	20.7201	67	84
86	6314	58	3	Male	16.6927	80	99
87	6340	71	0	Male	19.3238	76	72
88	6564	69	0	Male	34.4997	67	74
89	6614	57	0	Male	45.1116	80	101
90	6686	44	14	Female	38.3491	90	100
91	6795	55	0	Male	30.7159	87	104
92	7080	64	5	Female	76.6598	76	106
93	7084	54	2	Male	36.5722	87	93
94	7271	55	0	Male	41.7659	100	95
95	7371	55	1	Male	56.7858	80	88
96	2569	49	35	Male	18.7159	50	101
97	3058	56	28	Male	22.2533	65	75
98	3645	43	45	Male	27.4935	72	90
99	3844	73	9	Male	26.1164	79	94
100	4725	124	10	Male	32.9172	93	97
101	4744	65	25	Male	57.566	105	119
102	4807	64	14	Female	47.7974	74	74
103	4892	62	21	Male	22.0397	76	88
104	4962	63	1	Female	25.1964	69	67
105	5125	78	12	Male	17.5387	94	118
106	5222	63	30	Male	22.5298	77	85
107	5253	86	1	Male	33.1335	106	128
108	5386	78	21	Male	20.8761	78	93
109	5534	87	14	Male	29.2621	75	82
110	5712	88	14	Male	22.2697	70	68
111	5837	82	1	Female	33.3087	82	110
112	5879	75	21	Male	25.8453	80	105
113	5893	71	21	Male	22.8118	65	90
114	5916	84	0	Female	26.8556	93	73
115	6410	80	14	Male	32.1725	85	98
116	7173	84	4	Male	24.9801	72	75
117	7221	98	0	Male	63.5044	74	79
118	2453	120	10	Male	37.2758	63	99
119	2653	97	28	Male	30.0068	93	112
120	4218	82	28	Male	25.9904	74	92
121	4542	121	11	Female	21.9576	86	114
122	4902	102	8	Male	16.1424	87	77
123	4933	134	0	Male	18.4559	69	83
124	4941	131	4	Female	19.0144	96	96
125	5085	117	2	Male	49.0267	67	71
126	5111	107	7	Male	21.6947	71	80
127	5154	120	5	Male	22.1903	89	109
128	5222	93	30	Male	22.5298	77	91
129	5298	107	3	Male	22.9569	117	112
130	5339	119	7	Male	21.8152	87	82
131	5387	109	12	Male	21.7988	85	112
132	5414	105	10	Female	40.2765	93	104
133	5494	111	7	Male	54.6913	86	86
134	5896	126	4	Female	26.8775	50	74
135	5901	115	7	Male	22.1739	112	116
136	6135	96	18	Male	26.5626	66	105
137	6173	125	4	Male	35.3046	94	97
138	6214	112	0	Male	60.3176	65	74
139	6253	128	0	Female	46.4038	104	112
140	6433	120	4	Male	23.8604	100	103
141	6665	119	3	Female	23.0171	106	94
142	6834	123	0	Male	30.7488	72	75
143	1176	146	17	Female	19.729	65	98
144	2849	151	0	Male	20.0876	51	86
145	2882	141	18	Male	19.2334	84	85
146	3051	131	13	Male	37.2403	68	79
147	3728	151	6	Male	30.1273	96	105
148	3913	96	42	Female	23.9233	56	80
149	4133	133	14	Male	20	82	94
150	4661	135	17	Female	30.8419	84	93
151	4678	143	7	Male	46.6448	98	107
152	4696	150	4	Male	46.9569	120	120
153	4705	146	1	Female	21.6838	133	111
154	4802	142	0	Male	62.475	101	117
155	4807	139	14	Female	47.7974	80	78
156	4983	146	5	Male	38.3929	107	123
157	5014	151	7	Female	23.7618	97	110
158	5162	144	1	Male	25.0185	130	118
159	5238	150	3	Male	45.1006	117	126
160	5642	162	0	Male	65.87	89	103
161	5699	138	1	Female	34.2231	110	107
162	5713	144	8	Male	16.2683	100	99
163	5804	159	2	Female	28.8515	102	107
164	5818	125	14	Male	34.9268	72	91
165	6314	140	3	Male	16.6927	87	96
166	6664	164	2	Male	24.7337	66	73
167	1048	85	94	Male	20.115	63	82
168	1085	159	11	Male	30.7105	103	97
169	3237	189	9	Male	49.8508	79	82
170	3358	175	4	Male	20.6708	97	97
171	3808	165	7	Male	28.2683	94	111
172	4094	177	2	Male	19.7262	89	102
173	4253	175	3	Male	22.6037	114	118
174	4638	140	17	Male	20.512	89	78
175	4755	128	18	Male	27.5127	105	109
176	4865	142	35	Male	58.3354	84	103
177	4892	148	21	Male	22.0397	106	110
178	5009	174	7	Male	24.3806	77	103
179	5111	177	7	Male	21.6947	72	81
180	5125	173	12	Male	17.5387	106	119
181	5192	179	1	Male	58.6283	93	105
182	5505	171	1	Male	65.4784	95	93
183	5581	176	2	Male	26.3053	96	110
184	5599	148	21	Male	18.7488	72	81
185	5680	184	1	Male	27.7563	84	90
186	5782	108	68	Female	19.6715	69	85
187	6180	177	12	Male	20.7201	81	94
188	6671	184	7	Female	27.8056	91	92
189	2124	173	30	Male	30.7625	76	106
190	2646	187	14	Male	22.9158	97	97
191	2790	211	0	Male	48.8049	89	99
192	4189	202	4	Female	29.462	81	90
193	4775	180	28	Male	53.5551	70	86
194	4933	226	0	Male	18.4559	79	86
195	4962	210	1	Female	25.1964	71	70
196	5208	193	8	Female	21.3771	133	111
197	5456	193	14	Male	41.1636	87	110
198	5668	219	7	Male	40.9227	76	90
199	5712	192	14	Male	22.2697	87	85
200	5893	200	21	Male	22.8118	65	89
201	5916	205	0	Female	26.8556	92	76
202	6122	212	1	Male	56.2108	109	117
203	6136	216	1	Male	32.7912	92	89
204	6175	278	1	Male	51.1704	99	98
205	6228	174	3	Female	31.5537	114	108
206	7173	210	4	Male	24.9801	79	78
207	1176	216	17	Female	19.729	74	100
208	3467	186	42	Male	25.3936	53	69
209	4744	217	25	Male	57.566	108	118
210	5386	241	21	Male	20.8761	80	94
211	5837	242	1	Female	33.3087	93	105
212	6247	228	13	Male	42.3162	77	80
213	1892	276	2	Male	21.7796	87	107
214	2882	262	18	Male	19.2334	94	90
215	3058	236	28	Male	22.2533	85	88
216	4342	263	1	Male	44.063	79	91
217	4865	240	35	Male	58.3354	93	105
218	5085	269	2	Male	49.0267	65	77
219	5222	247	30	Male	22.5298	88	85
220	5339	271	7	Male	21.8152	94	89
221	5474	280	2	Female	28.6598	99	91
222	5600	232	0	Male	48.7885	75	81
223	2826	290	14	Male	23.2334	94	108
224	4725	286	10	Male	32.9172	105	94
225	5204	299	0	Male	59.0746	99	105
226	6498	270	28	Male	24.0767	82	101
227	2081	185	43	Male	17.6975	77	97
228	4678	340	7	Male	46.6448	108	119
229	5397	328	0	Female	62.7981	121	108
230	6214	318	0	Male	60.3176	78	82
231	7034	280	60	Male	23.1376	78	80
232	1493	453	60	Male	17.8042	59	81
233	1836	375	1	Male	47.0554	101	108
234	1939	295	130	Male	28.2738	67	117
235	2646	438	14	Male	22.9158	98	94
236	2653	352	28	Male	30.0068	105	126
237	3226	444	0	Male	27.4552	76	64
238	3467	333	42	Male	25.3936	68	74
239	4342	432	1	Male	44.063	92	107
240	4542	431	11	Female	21.9576	98	114
241	4661	374	17	Female	30.8419	93	95
242	4902	397	8	Male	16.1424	92	86
243	4983	398	5	Male	38.3929	121	132
244	5111	442	7	Male	21.6947	77	86
245	5125	510	12	Male	17.5387	112	125
246	5289	417	1	Male	48.5722	83	83
247	5386	436	21	Male	20.8761	90	103
248	5387	480	12	Male	21.7988	94	116
249	5505	527	1	Male	65.4784	104	87
250	5580	369	7	Male	17.7933	96	107
251	5581	378	2	Male	26.3053	95	95
252	5599	443	21	Male	18.7488	78	80
253	5668	390	7	Male	40.9227	92	92
254	5680	403	1	Male	27.7563	94	93
255	5712	365	14	Male	22.2697	98	86
256	5772	412	35	Male	26.2587	102	104
257	5804	354	2	Female	nan	122	105
258	5811	431	25	Male	80.0328	78	80
259	5841	415	8	Male	27.2279	82	83
260	6226	438	0	Male	36.8022	84	92
261	6247	389	13	Male	42.3162	82	80
262	6468	513	60	Male	43.4798	99	94
263	6614	362	0	Male	45.1116	88	106
264	6665	368	3	Female	23.0171	100	92
265	781	714	15	Male	29.8699	85	85
266	1048	576	94	Male	20.115	91	96
267	1157	810	23	Male	17.3881	97	84
268	1493	684	60	Male	17.8042	66	75
269	1611	511	60	Male	23.2799	69	107
270	1624	604	1	Male	19.5619	97	85
271	1939	562	130	Male	28.2738	85	111
272	2498	615	0	Female	17.4292	86	113
273	2826	636	14	Male	23.2334	111	101
274	2849	642	0	Male	20.0876	76	98
275	3032	525	20	Male	16.9391	79	87
276	3226	683	0	Male	27.4552	89	78
277	4218	814	28	Male	25.9904	99	96
278	4807	532	14	Female	47.7974	84	82
279	5014	637	7	Female	23.7618	101	114
280	5222	690	30	Male	22.5298	81	90
281	5253	591	1	Male	33.1335	114	124
282	5628	609	3	Female	30.2642	89	78
283	6059	794	1	Female	16.9801	71	76
284	6228	662	3	Female	31.5537	128	111
285	6247	616	13	Male	42.3162	85	82
286	405	986	0	Male	21.4702	66	116
287	626	870	55	Male	19.7536	80	85
288	1075	907	42	Female	27.2772	63	64
289	2849	1040	0	Male	20.0876	91	103
290	3032	884	20	Male	16.9391	87	93
291	3226	1123	0	Male	27.4552	88	81
292	4864	936	0	Female	53.9767	119	131
293	5474	1100	2	Female	28.6598	94	88
294	5568	1114	1	Female	51.9918	81	82
295	5580	1087	7	Male	17.7933	106	98
296	5581	1113	2	Male	26.3053	99	96
297	5617	1113	17	Male	19.7864	78	87
298	5642	1143	0	Male	65.87	104	109
299	5713	1016	8	Male	16.2683	126	106
300	5837	962	1	Female	33.3087	109	110
301	6140	1077	44	Female	21.4209	65	88
302	7061	923	0	Male	36.8816	74	81
303	651	1491	21	Male	22.0068	71	94
304	2527	1294	0	Male	16.9172	93	104
305	2638	1093	255	Male	16.5613	78	84
306	4865	1363	35	Male	58.3354	88	104
307	5009	1537	7	Male	24.3806	76	112
308	5014	1523	7	Female	23.7618	105	114
309	5085	1512	2	Male	49.0267	75	75
310	1939	1926	130	Male	28.2738	95	108
311	2662	1569	180	Male	28.0821	90	101
312	2826	1809	14	Male	23.2334	104	108
313	2882	1716	18	Male	19.2334	100	103
314	3768	1916	0	Male	19.1102	69	80
315	4356	2000	7	Male	21.399	104	91
316	4638	1779	17	Male	20.512	92	76
317	4696	1769	4	Male	46.9569	105	124
318	4744	1743	25	Male	57.566	97	118
319	6140	1742	44	Female	21.4209	67	87
320	1075	2259	42	Female	27.2772	78	79
321	1939	3111	130	Male	28.2738	88	111
322	2653	2191	28	Male	30.0068	117	129
323	3592	2569	10	Male	61.6646	76	93
324	3808	2434	7	Male	28.2683	105	111
325	651	3412	21	Male	22.0068	68	92
326	1939	3864	130	Male	28.2738	88	105
327	2600	3337	9	Male	43.9398	101	84
328	3835	4933	14	Male	25.9932	91	88
329	2773	7631	42	Male	6.51335	88	103
330	5142	11628	57	Male	16.4326	101	95
331	5964	11038	0	Male	12.8363	71	73

Ketiga¶

Pada langkah ini mengubah nilai dari fitur sex menjadi angak 0 / 1 dengan menggunakan fungsi dari sklearn, yaitu LabelEncode().

#encode fitur tipe biner
X = data.iloc[:,:].values
labelEncode_X = LabelEncoder()
X[:,4] = labelEncode_X.fit_transform(X[:,4])

Keempat¶

Selanjutnya kita mengimplementasikan rumus jarak ke dalam bentuk fungsi python. yaitu: eulidianDistance() dengan fungsi jarak tipe binary distanceSimetris().

def Zscore(x,mean,std):
    top = x - mean
    if top==0:
        return top
    else:
        return round(top / std, 2)
def normalisasi(num, col_x): 
    return Zscore(num, pd.Series(data[col_x].values).mean(), pd.Series(data[col_x].values).std())

#menghitung jarak tipe numerikal
def euclidianDistance(x,y):
    dis = 0
    for i in range(len(x)):
        dis += (x[i] - y[i]) ** 2
    return round(mt.sqrt(dis),2)

#Menghitung jarak tipe binary
def distanceSimetris(x,y):
    q=r=s=t=0
    for i in range(len(x)):
        if x[i]==1 and y[i]==1:
            q+=1
        elif x[i]==1 and y[i]==0:
            r+=1
        elif x[i]==0 and y[i]==1:
            s+=1
        elif x[i]==0 and y[i]==0:
            t+=1
    return ((r+s)/(q+r+s+t))

Kelima¶

Kemudian dari dataset tersebut, kita lakukan pengecekan dengan mencari baris yang missing values,.

c_j = 0
for j in df['age'].isna():
    if j == True:
        col_missing = c_j
    c_j+=1

Keenam¶

Pada langkah berikut, kita lakukan perhitungan jarak pada data yang missing dengan seluruh tetangganya dan menampungnya pada dapat dictionary yang ada.

missing_data = df.iloc[col_missing, [2,3,6,7]].values
missing_normal = [normalisasi(missing_data[0],data.columns[2]), normalisasi(missing_data[1],data.columns[3]), normalisasi(missing_data[2],data.columns[6]), normalisasi(missing_data[3],data.columns[7])]

for i in range(len(data[data.columns[0]])):
    if i==col_missing:
        continue;
    select_data = df.iloc[i, [2,3,6,7]].values
    normal_data = [normalisasi(select_data[0],data.columns[2]), normalisasi(select_data[1],data.columns[3]), normalisasi(select_data[2],data.columns[6]), normalisasi(select_data[3],data.columns[7])]
    data.loc[i, 'jarak'] =  euclidianDistance(missing_normal,normal_data) + distanceSimetris([X[col_missing, 4]],[X[i, 4]])

Ketujuh¶

Kemudian kita urutkan data tersebut berdasarkan jarak dari yang terkecil sampai ke terbesar. Selanjutnya kita mengisi data yang hilang dengan mengambil rata-rata dari 2 tetangga terdekat.

df = pd.DataFrame(data)
df.sort_values(by='jarak', axis=0, ascending=True, inplace=True) 
df.iloc[-1, [5]] = round(df.iloc[0:2,5].mean(), 2)
df.style.hide_index()

Berikut merupakan tampilan dari data yang telah di urutkan. pada baris terakhir telihat bahwa kolom age sudah terisi dengan angka 42.24 hasil dari rata-rata 2 tetangga terdekat.

no	id	days	duration	sex	age	piq	viq	jarak
229	5397	328	0	Female	62.7981	121	108	0.25
13	4705	18	1	Female	21.6838	127	109	0.53
205	6228	174	3	Female	31.5537	114	108	0.6
284	6228	662	3	Female	31.5537	128	111	0.64
161	5699	138	1	Female	34.2231	110	107	0.84
153	4705	146	1	Female	21.6838	133	111	0.86
196	5208	193	8	Female	21.3771	133	111	0.88
300	5837	962	1	Female	33.3087	109	110	1.07
139	6253	128	0	Female	46.4038	104	112	1.31
141	6665	119	3	Female	23.0171	106	94	1.33
163	5804	159	2	Female	28.8515	102	107	1.35
20	5162	33	1	Male	25.0185	118	101	1.48
279	5014	637	7	Female	23.7618	101	114	1.56
129	5298	107	3	Male	22.9569	117	112	1.64
9	4253	40	3	Male	22.6037	115	110	1.66
308	5014	1523	7	Female	23.7618	105	114	1.67
299	5713	1016	8	Male	16.2683	126	106	1.68
48	2761	40	3	Female	24.3696	98	112	1.69
157	5014	151	7	Female	23.7618	97	110	1.72
264	6665	368	3	Female	23.0171	100	92	1.73
34	6163	21	1	Male	19.3593	112	106	1.73
240	4542	431	11	Female	21.9576	98	114	1.75
221	5474	280	2	Female	28.6598	99	91	1.82
28	5699	26	1	Female	34.2231	95	108	1.83
124	4941	131	4	Female	19.0144	96	96	1.85
211	5837	242	1	Female	33.3087	93	105	1.92
292	4864	936	0	Female	53.9767	119	131	1.93
273	2826	636	14	Male	23.2334	111	101	1.94
132	5414	105	10	Female	40.2765	93	104	1.96
45	7548	31	0	Male	24.3669	108	106	1.98
22	5208	31	8	Female	21.3771	97	90	2
135	5901	115	7	Male	22.1739	112	116	2.07
173	4253	175	3	Male	22.6037	114	118	2.08
158	5162	144	1	Male	25.0185	130	118	2.09
152	4696	150	4	Male	46.9569	120	120	2.1
241	4661	374	17	Female	30.8419	93	95	2.13
55	3655	57	5	Female	21.9055	90	103	2.14
202	6122	212	1	Male	56.2108	109	117	2.22
90	6686	44	14	Female	38.3491	90	100	2.22
23	5253	29	1	Male	33.1335	104	105	2.23
79	5474	65	2	Female	28.6598	95	86	2.26
188	6671	184	7	Female	27.8056	91	92	2.26
293	5474	1100	2	Female	28.6598	94	88	2.3
67	4755	24	18	Male	27.5127	105	102	2.33
175	4755	128	18	Male	27.5127	105	109	2.33
177	4892	148	21	Male	22.0397	106	110	2.35
295	5580	1087	7	Male	17.7933	106	98	2.35
50	3277	51	1	Male	37.4702	104	96	2.38
21	5174	38	4	Female	37.2704	87	99	2.38
228	4678	340	7	Male	46.6448	108	119	2.38
298	5642	1143	0	Male	65.87	104	109	2.41
224	4725	286	10	Male	32.9172	105	94	2.41
233	1836	375	1	Male	47.0554	101	108	2.41
168	1085	159	11	Male	30.7105	103	97	2.43
63	4482	58	14	Female	18.2341	86	103	2.44
272	2498	615	0	Female	17.4292	86	113	2.46
140	6433	120	4	Male	23.8604	100	103	2.48
281	5253	591	1	Male	33.1335	114	124	2.48
121	4542	121	11	Female	21.9576	86	114	2.5
66	4696	54	4	Male	46.9569	101	112	2.5
180	5125	173	12	Male	17.5387	106	119	2.51
225	5204	299	0	Male	59.0746	99	105	2.52
40	6937	18	0	Female	21.191	94	81	2.53
162	5713	144	8	Male	16.2683	100	99	2.55
159	5238	150	3	Male	45.1006	117	126	2.55
74	5238	44	3	Male	45.1006	99	103	2.55
43	7309	31	0	Female	50.6667	85	95	2.57
209	4744	217	25	Male	57.566	108	118	2.59
204	6175	278	1	Male	51.1704	99	98	2.6
17	4983	33	5	Male	38.3929	102	117	2.61
151	4678	143	7	Male	46.6448	98	107	2.62
245	5125	510	12	Male	17.5387	112	125	2.63
156	4983	146	5	Male	38.3929	107	123	2.64
94	7271	55	0	Male	41.7659	100	95	2.65
154	4802	142	0	Male	62.475	101	117	2.65
111	5837	82	1	Female	33.3087	82	110	2.69
73	5204	71	0	Male	59.0746	97	107	2.69
57	3919	58	1	Male	30.3655	99	95	2.7
150	4661	135	17	Female	30.8419	84	93	2.72
52	3359	59	9	Female	56.8953	84	91	2.72
53	3373	39	28	Female	26.308	87	91	2.73
250	5580	369	7	Male	17.7933	96	107	2.74
147	3728	151	6	Male	30.1273	96	105	2.74
249	5505	527	1	Male	65.4784	104	87	2.75
183	5581	176	2	Male	26.3053	96	110	2.76
170	3358	175	4	Male	20.6708	97	97	2.76
101	4744	65	25	Male	57.566	105	119	2.77
296	5581	1113	2	Male	26.3053	99	96	2.78
60	4183	42	3	Male	26.2341	98	116	2.8
312	2826	1809	14	Male	23.2334	104	108	2.82
190	2646	187	14	Male	22.9158	97	97	2.82
33	6122	29	1	Male	56.2108	95	103	2.82
235	2646	438	14	Male	22.9158	98	94	2.83
256	5772	412	35	Male	26.2587	102	104	2.84
41	6977	30	1	Male	36.2108	97	94	2.86
201	5916	205	0	Female	26.8556	92	76	2.87
65	4678	63	7	Male	46.6448	96	95	2.89
282	5628	609	3	Female	30.2642	89	78	2.91
192	4189	202	4	Female	29.462	81	90	2.91
223	2826	290	14	Male	23.2334	94	108	2.92
171	3808	165	7	Male	28.2683	94	111	2.92
251	5581	378	2	Male	26.3053	95	95	2.93
243	4983	398	5	Male	38.3929	121	132	2.93
181	5192	179	1	Male	58.6283	93	105	2.93
137	6173	125	4	Male	35.3046	94	97	2.95
107	5253	86	1	Male	33.1335	106	128	2.97
277	4218	814	28	Male	25.9904	99	96	2.97
114	5916	84	0	Female	26.8556	93	73	2.98
3	3547	40	1	Male	55.9151	95	116	2.98
313	2882	1716	18	Male	19.2334	100	103	2.99
182	5505	171	1	Male	65.4784	95	93	2.99
239	4342	432	1	Male	44.063	92	107	3
278	4807	532	14	Female	47.7974	84	82	3.03
25	5640	34	7	Male	25.9986	93	113	3.03
100	4725	124	10	Male	32.9172	93	97	3.04
254	5680	403	1	Male	27.7563	94	93	3.04
248	5387	480	12	Male	21.7988	94	116	3.05
62	4315	63	0	Male	38.141	107	130	3.05
92	7080	64	5	Female	76.6598	76	106	3.05
83	5628	51	3	Female	30.2642	81	85	3.07
304	2527	1294	0	Male	16.9172	93	104	3.09
8	3808	31	7	Male	28.2683	91	110	3.11
105	5125	78	12	Male	17.5387	94	118	3.12
236	2653	352	28	Male	30.0068	105	126	3.13
315	4356	2000	7	Male	21.399	104	91	3.13
289	2849	1040	0	Male	20.0876	91	103	3.14
255	5712	365	14	Male	22.2697	98	86	3.14
317	4696	1769	4	Male	46.9569	105	124	3.16
220	5339	271	7	Male	21.8152	94	89	3.18
160	5642	162	0	Male	65.87	89	103	3.19
324	3808	2434	7	Male	28.2683	105	111	3.2
172	4094	177	2	Male	19.7262	89	102	3.2
270	1624	604	1	Male	19.5619	97	85	3.2
253	5668	390	7	Male	40.9227	92	92	3.2
127	5154	120	5	Male	22.1903	89	109	3.21
214	2882	262	18	Male	19.2334	94	90	3.22
119	2653	97	28	Male	30.0068	93	112	3.23
191	2790	211	0	Male	48.8049	89	99	3.23
294	5568	1114	1	Female	51.9918	81	82	3.23
51	3346	44	18	Female	57.2758	79	85	3.25
263	6614	362	0	Male	45.1116	88	106	3.25
207	1176	216	17	Female	19.729	74	100	3.25
247	5386	436	21	Male	20.8761	90	103	3.25
29	5713	36	8	Male	16.2683	89	97	3.28
203	6136	216	1	Male	32.7912	92	89	3.29
217	4865	240	35	Male	58.3354	93	105	3.3
71	5014	46	7	Female	23.7618	75	90	3.3
35	6179	22	2	Male	38.0123	89	95	3.31
213	1892	276	2	Male	21.7796	87	107	3.33
91	6795	55	0	Male	30.7159	87	104	3.34
15	4802	36	0	Male	62.475	88	97	3.34
72	5192	60	1	Male	58.6283	87	97	3.4
61	4189	69	4	Female	29.462	75	86	3.4
267	1157	810	23	Male	17.3881	97	84	3.4
197	5456	193	14	Male	41.1636	87	110	3.4
165	6314	140	3	Male	16.6927	87	96	3.41
242	4902	397	8	Male	16.1424	92	86	3.42
155	4807	139	14	Female	47.7974	80	78	3.42
318	4744	1743	25	Male	57.566	97	118	3.43
93	7084	54	2	Male	36.5722	87	93	3.49
81	5580	56	7	Male	17.7933	86	95	3.5
131	5387	109	12	Male	21.7988	85	112	3.54
115	6410	80	14	Male	32.1725	85	98	3.55
82	5581	65	2	Male	26.3053	85	95	3.56
322	2653	2191	28	Male	30.0068	117	129	3.57
12	4542	22	11	Female	21.9576	71	89	3.58
1	3358	30	4	Male	20.6708	87	89	3.6
30	5736	18	9	Male	16.1478	89	86	3.6
290	3032	884	20	Male	16.9391	87	93	3.61
56	3762	48	6	Male	20.3559	85	93	3.61
80	5568	64	1	Female	51.9918	75	79	3.63
38	6870	22	0	Male	42.4832	84	95	3.63
84	6154	43	5	Female	22.6064	74	80	3.65
78	5458	44	14	Male	34.4778	84	95	3.66
46	2364	41	14	Male	25.8097	84	94	3.68
260	6226	438	0	Male	36.8022	84	92	3.68
39	6914	43	0	Male	61.5222	85	90	3.69
24	5298	30	3	Male	22.9569	87	86	3.7
31	5754	36	1	Male	16.3368	87	86	3.7
16	4941	46	4	Female	19.0144	69	88	3.72
185	5680	184	1	Male	27.7563	84	90	3.73
306	4865	1363	35	Male	58.3354	88	104	3.73
27	5680	17	1	Male	27.7563	84	90	3.74
2	3535	16	17	Male	55.2882	95	77	3.75
133	5494	111	7	Male	54.6913	86	86	3.75
36	6671	30	7	Female	27.8056	71	82	3.76
19	5154	35	5	Male	22.1903	82	95	3.76
199	5712	192	14	Male	22.2697	87	85	3.76
262	6468	513	60	Male	43.4798	99	94	3.81
149	4133	133	14	Male	20	82	94	3.81
89	6614	57	0	Male	45.1116	80	101	3.81
54	3544	32	14	Male	54.5298	81	98	3.81
176	4865	142	35	Male	58.3354	84	103	3.82
86	6314	58	3	Male	16.6927	80	99	3.82
77	5456	48	14	Male	41.1636	80	101	3.84
130	5339	119	7	Male	21.8152	87	82	3.85
187	6180	177	12	Male	20.7201	81	94	3.85
226	6498	270	28	Male	24.0767	82	101	3.85
143	1176	146	17	Female	19.729	65	98	3.85
42	7120	39	0	Male	69.7057	84	86	3.86
219	5222	247	30	Male	22.5298	88	85	3.87
10	4356	31	7	Male	21.399	86	83	3.87
68	4837	42	10	Male	19.6906	83	88	3.88
112	5879	75	21	Male	25.8453	80	105	3.89
265	781	714	15	Male	29.8699	85	85	3.89
76	5289	52	1	Male	48.5722	84	85	3.9
102	4807	64	14	Female	47.7974	74	74	3.9
291	3226	1123	0	Male	27.4552	88	81	3.9
215	3058	236	28	Male	22.2533	85	88	3.91
276	3226	683	0	Male	27.4552	89	78	3.92
145	2882	141	18	Male	19.2334	84	85	3.95
44	7321	23	0	Male	26.0041	84	83	3.97
283	6059	794	1	Female	16.9801	71	76	3.97
174	4638	140	17	Male	20.512	89	78	3.97
210	5386	241	21	Male	20.8761	80	94	3.98
285	6247	616	13	Male	42.3162	85	82	3.98
99	3844	73	9	Male	26.1164	79	94	3.98
178	5009	174	7	Male	24.3806	77	103	3.99
58	4094	50	2	Male	19.7262	79	93	3.99
246	5289	417	1	Male	48.5722	83	83	4.02
216	4342	263	1	Male	44.063	79	91	4.02
95	7371	55	1	Male	56.7858	80	88	4.04
122	4902	102	8	Male	16.1424	87	77	4.07
259	5841	415	8	Male	27.2279	82	83	4.08
274	2849	642	0	Male	20.0876	76	98	4.09
280	5222	690	30	Male	22.5298	81	90	4.11
320	1075	2259	42	Female	27.2772	78	79	4.13
108	5386	78	21	Male	20.8761	78	93	4.13
14	4744	15	25	Male	57.566	82	85	4.15
75	5280	83	1	Male	48.6434	78	88	4.16
194	4933	226	0	Male	18.4559	79	86	4.16
316	4638	1779	17	Male	20.512	92	76	4.18
195	4962	210	1	Female	25.1964	71	70	4.19
18	5129	26	1	Male	25.0459	77	89	4.2
275	3032	525	20	Male	16.9391	79	87	4.2
261	6247	389	13	Male	42.3162	82	80	4.21
189	2124	173	30	Male	30.7625	76	106	4.23
198	5668	219	7	Male	40.9227	76	90	4.23
11	4384	35	8	Male	36.3806	76	90	4.24
307	5009	1537	7	Male	24.3806	76	112	4.26
244	5111	442	7	Male	21.6947	77	86	4.28
169	3237	189	9	Male	49.8508	79	82	4.3
297	5617	1113	17	Male	19.7864	78	87	4.3
301	6140	1077	44	Female	21.4209	65	88	4.32
327	2600	3337	9	Male	43.9398	101	84	4.33
128	5222	93	30	Male	22.5298	77	91	4.33
230	6214	318	0	Male	60.3176	78	82	4.34
103	4892	62	21	Male	22.0397	76	88	4.36
319	6140	1742	44	Female	21.4209	67	87	4.36
227	2081	185	43	Male	17.6975	77	97	4.42
206	7173	210	4	Male	24.9801	79	78	4.44
104	4962	63	1	Female	25.1964	69	67	4.44
120	4218	82	28	Male	25.9904	74	92	4.47
252	5599	443	21	Male	18.7488	78	80	4.48
106	5222	63	30	Male	22.5298	77	85	4.48
164	5818	125	14	Male	34.9268	72	91	4.49
212	6247	228	13	Male	42.3162	77	80	4.49
258	5811	431	25	Male	80.0328	78	80	4.52
222	5600	232	0	Male	48.7885	75	81	4.55
109	5534	87	14	Male	29.2621	75	82	4.55
186	5782	108	68	Female	19.6715	69	85	4.56
69	4996	51	12	Male	43.0281	77	78	4.58
64	4638	20	17	Male	20.512	82	72	4.59
32	5776	26	8	Male	17.128	71	88	4.6
302	7061	923	0	Male	36.8816	74	81	4.64
303	651	1491	21	Male	22.0068	71	94	4.67
59	4133	34	14	Male	20	70	88	4.69
117	7221	98	0	Male	63.5044	74	79	4.69
37	6859	27	1	Male	34.2122	74	79	4.69
323	3592	2569	10	Male	61.6646	76	93	4.72
179	5111	177	7	Male	21.6947	72	81	4.73
287	626	870	55	Male	19.7536	80	85	4.75
7	3807	37	5	Male	24.6762	74	77	4.76
136	6135	96	18	Male	26.5626	66	105	4.76
184	5599	148	21	Male	18.7488	72	81	4.8
26	5668	27	7	Male	40.9227	72	79	4.81
286	405	986	0	Male	21.4702	66	116	4.82
126	5111	107	7	Male	21.6947	71	80	4.82
193	4775	180	28	Male	53.5551	70	86	4.83
123	4933	134	0	Male	18.4559	69	83	4.85
87	6340	71	0	Male	19.3238	76	72	4.85
98	3645	43	45	Male	27.4935	72	90	4.86
309	5085	1512	2	Male	49.0267	75	75	4.91
118	2453	120	10	Male	37.2758	63	99	4.94
116	7173	84	4	Male	24.9801	72	75	4.94
142	6834	123	0	Male	30.7488	72	75	4.94
85	6180	59	12	Male	20.7201	67	84	4.96
148	3913	96	42	Female	23.9233	56	80	4.96
47	2600	3333	9	Male	43.9398	86	80	4.97
6	3790	13	3	Male	57.0623	76	69	4.99
113	5893	71	21	Male	22.8118	65	90	4.99
200	5893	200	21	Male	22.8118	65	89	5.01
70	5009	50	7	Male	24.3806	61	104	5.05
146	3051	131	13	Male	37.2403	68	79	5.05
231	7034	280	60	Male	23.1376	78	80	5.07
288	1075	907	42	Female	27.2772	63	64	5.12
266	1048	576	94	Male	20.115	91	96	5.14
269	1611	511	60	Male	23.2799	69	107	5.16
314	3768	1916	0	Male	19.1102	69	80	5.16
237	3226	444	0	Male	27.4552	76	64	5.21
134	5896	126	4	Female	26.8775	50	74	5.25
88	6564	69	0	Male	34.4997	67	74	5.26
218	5085	269	2	Male	49.0267	65	77	5.26
5	3728	19	6	Male	30.1273	67	73	5.3
166	6664	164	2	Male	24.7337	66	73	5.34
110	5712	88	14	Male	22.2697	70	68	5.36
138	6214	112	0	Male	60.3176	65	74	5.37
125	5085	117	2	Male	49.0267	67	71	5.38
97	3058	56	28	Male	22.2533	65	75	5.45
238	3467	333	42	Male	25.3936	68	74	5.46
49	3237	65	9	Male	49.8508	67	67	5.55
325	651	3412	21	Male	22.0068	68	92	5.62
328	3835	4933	14	Male	25.9932	91	88	5.69
4	3592	13	10	Male	61.6646	59	73	5.77
268	1493	684	60	Male	17.8042	66	75	5.83
144	2849	151	0	Male	20.0876	51	86	5.89
96	2569	49	35	Male	18.7159	50	101	5.94
232	1493	453	60	Male	17.8042	59	81	6.03
310	1939	1926	130	Male	28.2738	95	108	6.41
208	3467	186	42	Male	25.3936	53	69	6.45
167	1048	85	94	Male	20.115	63	82	6.51
271	1939	562	130	Male	28.2738	85	111	6.51
321	1939	3111	130	Male	28.2738	88	111	6.93
234	1939	295	130	Male	28.2738	67	117	7.17
326	1939	3864	130	Male	28.2738	88	105	7.22
329	2773	7631	42	Male	6.51335	88	103	7.96
311	2662	1569	180	Male	28.0821	90	101	8.24
331	5964	11038	0	Male	12.8363	71	73	11.23
330	5142	11628	57	Male	16.4326	101	95	11.25
305	2638	1093	255	Male	16.5613	78	84	11.27
257	5804	354	2	Female	42.24	122	105	nan

Seluruh file percobaan diatas dapat diunduh disini

Referensi¶

https://www.saedsayad.com/missing_values.htm

https://www.analyticsvidhya.com/blog/2018/03/introduction-k-neighbours-algorithm-clustering/