This dataset documents the test scores, school rankings, and application strengths of a series of college applicants, and correlates each with the probability that application is accepted to the school of their choice. Each of these features is provided as a continuous, qualitative numerical value, which means that we have the ability to process this data to review applications and learn what makes an application strong or not.

Every application in the dataset contains the following features:

• Graduate Record Examinations ('GRE Score'): A score (out of 340) on the GREs.

• Test of English as a Foreign Language ('TOEFL Score'): A score (out of 120) on the TOEFL.

• University Rating ('University Rating'): A rank from 1 to 5 (with 5 being the best) of the university this entry describes application to.

• Statement of Purpose ('SOP'): A rank from 1 to 5 (in increments of 0.5) of the Statement of Purpose provided as part of the application.

• Letter of Recommendation ('LOR'): A rank from 1 to 5 (in increments of 0.5) of the Letter of Recommendation provided as part of the application.

• Undergraduate GPA ('CGPA'): Undergraduate college GPA, scaled here to be between 0 and 10, with 10 being the highest.

• Research Experience ('Research'): A boolean '0' or '1' value indicating whether the applicant has research experience.

• Chance of Admission ('Chance of Admit'): The chance (as a decimal probability between 0 and 1) that the application described in the previous data points will be accepted by the target university.

 

 

2

Your Task

Your task is to train machine learning models which can predict, based on the given features, the chances of application acceptance for a given student. First, we'll need to load the provided dataset into a Jupyter Notebook from the provided CSV file. The names of the columns will correspond to those given above.

Consider at least three features from the dataset and note their distributions, any extraneous/outlying values you might need to consider, and whether they seem correlated (on cursory look) to the chances of being accepted. Then, utilizing only the tools and processes outlined in this course, you'll train a linear regression or SVM model on 80% of the dataset (training set) to predict the chances of admission. Test your model on the remaining 20% of your data and report the mean square error of your model.

Next, use PCA to reduce the dimensionality of the dataset to two and then train a new model to predict the chances of admission using the new resulting dimensions as features. Generate a scatter plot showing the date points in blue and the decision boundary in black. Test your new model and report the mean square error. Compare the performance of this new model with the previous one. Which is more effective, and why?

 

3

Submission

A project submission consists of four files:

1. PDF of exactly one-page reporting:

(a) CWID / name of student

(b) Mean Square Error for training and test sets for both models

(c) salient project features (describe your design choices)

2. CSV files of predictions chance of admission for the test set for both models.

3. Working Jupyter Notebook that produces the CSV.

4. Jupyter Notebook downloaded as a python file.

 

College_Admissions.csv file

Serial No.	GRE Score	TOEFL Score	University Rating	SOP	LOR	CGPA	Research	Chance of Admit
1	337	118	4	4.5	4.5	9.65	1	0.92
2	324	107	4	4	4.5	8.87	1	0.76
3	316	104	3	3	3.5	8	1	0.72
4	322	110	3	3.5	2.5	8.67	1	0.8
5	314	103	2	2	3	8.21	0	0.65
6	330	115	5	4.5	3	9.34	1	0.9
7	321	109	3	3	4	8.2	1	0.75
8	308	101	2	3	4	7.9	0	0.68
9	302	102	1	2	1.5	8	0	0.5
10	323	108	3	3.5	3	8.6	0	0.45
11	325	106	3	3.5	4	8.4	1	0.52
12	327	111	4	4	4.5	9	1	0.84
13	328	112	4	4	4.5	9.1	1	0.78
14	307	109	3	4	3	8	1	0.62
15	311	104	3	3.5	2	8.2	1	0.61
16	314	105	3	3.5	2.5	8.3	0	0.54
17	317	107	3	4	3	8.7	0	0.66
18	319	106	3	4	3	8	1	0.65
19	318	110	3	4	3	8.8	0	0.63
20	303	102	3	3.5	3	8.5	0	0.62
21	312	107	3	3	2	7.9	1	0.64
22	325	114	4	3	2	8.4	0	0.7
23	328	116	5	5	5	9.5	1	0.94
24	334	119	5	5	4.5	9.7	1	0.95
25	336	119	5	4	3.5	9.8	1	0.97
26	340	120	5	4.5	4.5	9.6	1	0.94
27	322	109	5	4.5	3.5	8.8	0	0.76
28	298	98	2	1.5	2.5	7.5	1	0.44
29	295	93	1	2	2	7.2	0	0.46
30	310	99	2	1.5	2	7.3	0	0.54
31	300	97	2	3	3	8.1	1	0.65
32	327	103	3	4	4	8.3	1	0.74
33	338	118	4	3	4.5	9.4	1	0.91
34	340	114	5	4	4	9.6	1	0.9
35	331	112	5	4	5	9.8	1	0.94
36	320	110	5	5	5	9.2	1	0.88
37	299	106	2	4	4	8.4	0	0.64
38	300	105	1	1	2	7.8	0	0.58

1

39	304	105	1	3	1.5	7.5	0	0.52
40	307	108	2	4	3.5	7.7	0	0.48
41	308	110	3	3.5	3	8	1	0.46
42	316	105	2	2.5	2.5	8.2	1	0.49
43	313	107	2	2.5	2	8.5	1	0.53
44	332	117	4	4.5	4	9.1	0	0.87
45	326	113	5	4.5	4	9.4	1	0.91
46	322	110	5	5	4	9.1	1	0.88
47	329	114	5	4	5	9.3	1	0.86
48	339	119	5	4.5	4	9.7	0	0.89
49	321	110	3	3.5	5	8.85	1	0.82
50	327	111	4	3	4	8.4	1	0.78
51	313	98	3	2.5	4.5	8.3	1	0.76
52	312	100	2	1.5	3.5	7.9	1	0.56
53	334	116	4	4	3	8	1	0.78
54	324	112	4	4	2.5	8.1	1	0.72
55	322	110	3	3	3.5	8	0	0.7
56	320	103	3	3	3	7.7	0	0.64
57	316	102	3	2	3	7.4	0	0.64
58	298	99	2	4	2	7.6	0	0.46
59	300	99	1	3	2	6.8	1	0.36
60	311	104	2	2	2	8.3	0	0.42
61	309	100	2	3	3	8.1	0	0.48
62	307	101	3	4	3	8.2	0	0.47
63	304	105	2	3	3	8.2	1	0.54
64	315	107	2	4	3	8.5	1	0.56
65	325	111	3	3	3.5	8.7	0	0.52
66	325	112	4	3.5	3.5	8.92	0	0.55
67	327	114	3	3	3	9.02	0	0.61
68	316	107	2	3.5	3.5	8.64	1	0.57
69	318	109	3	3.5	4	9.22	1	0.68
70	328	115	4	4.5	4	9.16	1	0.78
71	332	118	5	5	5	9.64	1	0.94
72	336	112	5	5	5	9.76	1	0.96
73	321	111	5	5	5	9.45	1	0.93
74	314	108	4	4.5	4	9.04	1	0.84
75	314	106	3	3	5	8.9	0	0.74
76	329	114	2	2	4	8.56	1	0.72
77	327	112	3	3	3	8.72	1	0.74
78	301	99	2	3	2	8.22	0	0.64

2

79	296	95	2	3	2	7.54	1	0.44
80	294	93	1	1.5	2	7.36	0	0.46
81	312	105	3	2	3	8.02	1	0.5
82	340	120	4	5	5	9.5	1	0.96
83	320	110	5	5	4.5	9.22	1	0.92
84	322	115	5	4	4.5	9.36	1	0.92
85	340	115	5	4.5	4.5	9.45	1	0.94
86	319	103	4	4.5	3.5	8.66	0	0.76
87	315	106	3	4.5	3.5	8.42	0	0.72
88	317	107	2	3.5	3	8.28	0	0.66
89	314	108	3	4.5	3.5	8.14	0	0.64
90	316	109	4	4.5	3.5	8.76	1	0.74
91	318	106	2	4	4	7.92	1	0.64
92	299	97	3	5	3.5	7.66	0	0.38
93	298	98	2	4	3	8.03	0	0.34
94	301	97	2	3	3	7.88	1	0.44
95	303	99	3	2	2.5	7.66	0	0.36
96	304	100	4	1.5	2.5	7.84	0	0.42
97	306	100	2	3	3	8	0	0.48
98	331	120	3	4	4	8.96	1	0.86
99	332	119	4	5	4.5	9.24	1	0.9
100	323	113	3	4	4	8.88	1	0.79
101	322	107	3	3.5	3.5	8.46	1	0.71
102	312	105	2	2.5	3	8.12	0	0.64
103	314	106	2	4	3.5	8.25	0	0.62
104	317	104	2	4.5	4	8.47	0	0.57
105	326	112	3	3.5	3	9.05	1	0.74
106	316	110	3	4	4.5	8.78	1	0.69
107	329	111	4	4.5	4.5	9.18	1	0.87
108	338	117	4	3.5	4.5	9.46	1	0.91
109	331	116	5	5	5	9.38	1	0.93
110	304	103	5	5	4	8.64	0	0.68
111	305	108	5	3	3	8.48	0	0.61
112	321	109	4	4	4	8.68	1	0.69
113	301	107	3	3.5	3.5	8.34	1	0.62
114	320	110	2	4	3.5	8.56	0	0.72
115	311	105	3	3.5	3	8.45	1	0.59
116	310	106	4	4.5	4.5	9.04	1	0.66
117	299	102	3	4	3.5	8.62	0	0.56
118	290	104	4	2	2.5	7.46	0	0.45

3

119	296	99	2	3	3.5	7.28	0	0.47
120	327	104	5	3	3.5	8.84	1	0.71
121	335	117	5	5	5	9.56	1	0.94
122	334	119	5	4.5	4.5	9.48	1	0.94
123	310	106	4	1.5	2.5	8.36	0	0.57
124	308	108	3	3.5	3.5	8.22	0	0.61
125	301	106	4	2.5	3	8.47	0	0.57
126	300	100	3	2	3	8.66	1	0.64
127	323	113	3	4	3	9.32	1	0.85
128	319	112	3	2.5	2	8.71	1	0.78
129	326	112	3	3.5	3	9.1	1	0.84
130	333	118	5	5	5	9.35	1	0.92
131	339	114	5	4	4.5	9.76	1	0.96
132	303	105	5	5	4.5	8.65	0	0.77
133	309	105	5	3.5	3.5	8.56	0	0.71
134	323	112	5	4	4.5	8.78	0	0.79
135	333	113	5	4	4	9.28	1	0.89
136	314	109	4	3.5	4	8.77	1	0.82
137	312	103	3	5	4	8.45	0	0.76
138	316	100	2	1.5	3	8.16	1	0.71
139	326	116	2	4.5	3	9.08	1	0.8
140	318	109	1	3.5	3.5	9.12	0	0.78
141	329	110	2	4	3	9.15	1	0.84
142	332	118	2	4.5	3.5	9.36	1	0.9
143	331	115	5	4	3.5	9.44	1	0.92
144	340	120	4	4.5	4	9.92	1	0.97
145	325	112	2	3	3.5	8.96	1	0.8
146	320	113	2	2	2.5	8.64	1	0.81
147	315	105	3	2	2.5	8.48	0	0.75
148	326	114	3	3	3	9.11	1	0.83
149	339	116	4	4	3.5	9.8	1	0.96
150	311	106	2	3.5	3	8.26	1	0.79
151	334	114	4	4	4	9.43	1	0.93
152	332	116	5	5	5	9.28	1	0.94
153	321	112	5	5	5	9.06	1	0.86
154	324	105	3	3	4	8.75	0	0.79
155	326	108	3	3	3.5	8.89	0	0.8
156	312	109	3	3	3	8.69	0	0.77
157	315	105	3	2	2.5	8.34	0	0.7
158	309	104	2	2	2.5	8.26	0	0.65

4

159	306	106	2	2	2.5	8.14	0	0.61
160	297	100	1	1.5	2	7.9	0	0.52
161	315	103	1	1.5	2	7.86	0	0.57
162	298	99	1	1.5	3	7.46	0	0.53
163	318	109	3	3	3	8.5	0	0.67
164	317	105	3	3.5	3	8.56	0	0.68
165	329	111	4	4.5	4	9.01	1	0.81
166	322	110	5	4.5	4	8.97	0	0.78
167	302	102	3	3.5	5	8.33	0	0.65
168	313	102	3	2	3	8.27	0	0.64
169	293	97	2	2	4	7.8	1	0.64
170	311	99	2	2.5	3	7.98	0	0.65
171	312	101	2	2.5	3.5	8.04	1	0.68
172	334	117	5	4	4.5	9.07	1	0.89
173	322	110	4	4	5	9.13	1	0.86
174	323	113	4	4	4.5	9.23	1	0.89
175	321	111	4	4	4	8.97	1	0.87
176	320	111	4	4.5	3.5	8.87	1	0.85
177	329	119	4	4.5	4.5	9.16	1	0.9
178	319	110	3	3.5	3.5	9.04	0	0.82
179	309	108	3	2.5	3	8.12	0	0.72
180	307	102	3	3	3	8.27	0	0.73
181	300	104	3	3.5	3	8.16	0	0.71
182	305	107	2	2.5	2.5	8.42	0	0.71
183	299	100	2	3	3.5	7.88	0	0.68
184	314	110	3	4	4	8.8	0	0.75
185	316	106	2	2.5	4	8.32	0	0.72
186	327	113	4	4.5	4.5	9.11	1	0.89
187	317	107	3	3.5	3	8.68	1	0.84
188	335	118	5	4.5	3.5	9.44	1	0.93
189	331	115	5	4.5	3.5	9.36	1	0.93
190	324	112	5	5	5	9.08	1	0.88
191	324	111	5	4.5	4	9.16	1	0.9
192	323	110	5	4	5	8.98	1	0.87
193	322	114	5	4.5	4	8.94	1	0.86
194	336	118	5	4.5	5	9.53	1	0.94
195	316	109	3	3.5	3	8.76	0	0.77
196	307	107	2	3	3.5	8.52	1	0.78
197	306	105	2	3	2.5	8.26	0	0.73
198	310	106	2	3.5	2.5	8.33	0	0.73

5

199	311	104	3	4.5	4.5	8.43	0	0.7
200	313	107	3	4	4.5	8.69	0	0.72
201	317	103	3	2.5	3	8.54	1	0.73
202	315	110	2	3.5	3	8.46	1	0.72
203	340	120	5	4.5	4.5	9.91	1	0.97
204	334	120	5	4	5	9.87	1	0.97
205	298	105	3	3.5	4	8.54	0	0.69
206	295	99	2	2.5	3	7.65	0	0.57
207	315	99	2	3.5	3	7.89	0	0.63
208	310	102	3	3.5	4	8.02	1	0.66
209	305	106	2	3	3	8.16	0	0.64
210	301	104	3	3.5	4	8.12	1	0.68
211	325	108	4	4.5	4	9.06	1	0.79
212	328	110	4	5	4	9.14	1	0.82
213	338	120	4	5	5	9.66	1	0.95
214	333	119	5	5	4.5	9.78	1	0.96
215	331	117	4	4.5	5	9.42	1	0.94
216	330	116	5	5	4.5	9.36	1	0.93
217	322	112	4	4.5	4.5	9.26	1	0.91
218	321	109	4	4	4	9.13	1	0.85
219	324	110	4	3	3.5	8.97	1	0.84
220	312	104	3	3.5	3.5	8.42	0	0.74
221	313	103	3	4	4	8.75	0	0.76
222	316	110	3	3.5	4	8.56	0	0.75
223	324	113	4	4.5	4	8.79	0	0.76
224	308	109	2	3	4	8.45	0	0.71
225	305	105	2	3	2	8.23	0	0.67
226	296	99	2	2.5	2.5	8.03	0	0.61
227	306	110	2	3.5	4	8.45	0	0.63
228	312	110	2	3.5	3	8.53	0	0.64
229	318	112	3	4	3.5	8.67	0	0.71
230	324	111	4	3	3	9.01	1	0.82
231	313	104	3	4	4.5	8.65	0	0.73
232	319	106	3	3.5	2.5	8.33	1	0.74
233	312	107	2	2.5	3.5	8.27	0	0.69
234	304	100	2	2.5	3.5	8.07	0	0.64
235	330	113	5	5	4	9.31	1	0.91
236	326	111	5	4.5	4	9.23	1	0.88
237	325	112	4	4	4.5	9.17	1	0.85
238	329	114	5	4.5	5	9.19	1	0.86

6

239	310	104	3	2	3.5	8.37	0	0.7
240	299	100	1	1.5	2	7.89	0	0.59
241	296	101	1	2.5	3	7.68	0	0.6
242	317	103	2	2.5	2	8.15	0	0.65
243	324	115	3	3.5	3	8.76	1	0.7
244	325	114	3	3.5	3	9.04	1	0.76
245	314	107	2	2.5	4	8.56	0	0.63
246	328	110	4	4	2.5	9.02	1	0.81
247	316	105	3	3	3.5	8.73	0	0.72
248	311	104	2	2.5	3.5	8.48	0	0.71
249	324	110	3	3.5	4	8.87	1	0.8
250	321	111	3	3.5	4	8.83	1	0.77
251	320	104	3	3	2.5	8.57	1	0.74
252	316	99	2	2.5	3	9	0	0.7
253	318	100	2	2.5	3.5	8.54	1	0.71
254	335	115	4	4.5	4.5	9.68	1	0.93
255	321	114	4	4	5	9.12	0	0.85
256	307	110	4	4	4.5	8.37	0	0.79
257	309	99	3	4	4	8.56	0	0.76
258	324	100	3	4	5	8.64	1	0.78
259	326	102	4	5	5	8.76	1	0.77
260	331	119	4	5	4.5	9.34	1	0.9
261	327	108	5	5	3.5	9.13	1	0.87
262	312	104	3	3.5	4	8.09	0	0.71
263	308	103	2	2.5	4	8.36	1	0.7
264	324	111	3	2.5	1.5	8.79	1	0.7
265	325	110	2	3	2.5	8.76	1	0.75
266	313	102	3	2.5	2.5	8.68	0	0.71
267	312	105	2	2	2.5	8.45	0	0.72
268	314	107	3	3	3.5	8.17	1	0.73
269	327	113	4	4.5	5	9.14	0	0.83
270	308	108	4	4.5	5	8.34	0	0.77
271	306	105	2	2.5	3	8.22	1	0.72
272	299	96	2	1.5	2	7.86	0	0.54
273	294	95	1	1.5	1.5	7.64	0	0.49
274	312	99	1	1	1.5	8.01	1	0.52
275	315	100	1	2	2.5	7.95	0	0.58
276	322	110	3	3.5	3	8.96	1	0.78
277	329	113	5	5	4.5	9.45	1	0.89
278	320	101	2	2.5	3	8.62	0	0.7

7

279	308	103	2	3	3.5	8.49	0	0.66
280	304	102	2	3	4	8.73	0	0.67
281	311	102	3	4.5	4	8.64	1	0.68
282	317	110	3	4	4.5	9.11	1	0.8
283	312	106	3	4	3.5	8.79	1	0.81
284	321	111	3	2.5	3	8.9	1	0.8
285	340	112	4	5	4.5	9.66	1	0.94
286	331	116	5	4	4	9.26	1	0.93
287	336	118	5	4.5	4	9.19	1	0.92
288	324	114	5	5	4.5	9.08	1	0.89
289	314	104	4	5	5	9.02	0	0.82
290	313	109	3	4	3.5	9	0	0.79
291	307	105	2	2.5	3	7.65	0	0.58
292	300	102	2	1.5	2	7.87	0	0.56
293	302	99	2	1	2	7.97	0	0.56
294	312	98	1	3.5	3	8.18	1	0.64
295	316	101	2	2.5	2	8.32	1	0.61
296	317	100	2	3	2.5	8.57	0	0.68
297	310	107	3	3.5	3.5	8.67	0	0.76
298	320	120	3	4	4.5	9.11	0	0.86
299	330	114	3	4.5	4.5	9.24	1	0.9
300	305	112	3	3	3.5	8.65	0	0.71
301	309	106	2	2.5	2.5	8	0	0.62
302	319	108	2	2.5	3	8.76	0	0.66
303	322	105	2	3	3	8.45	1	0.65
304	323	107	3	3.5	3.5	8.55	1	0.73
305	313	106	2	2.5	2	8.43	0	0.62
306	321	109	3	3.5	3.5	8.8	1	0.74
307	323	110	3	4	3.5	9.1	1	0.79
308	325	112	4	4	4	9	1	0.8
309	312	108	3	3.5	3	8.53	0	0.69
310	308	110	4	3.5	3	8.6	0	0.7
311	320	104	3	3	3.5	8.74	1	0.76
312	328	108	4	4.5	4	9.18	1	0.84
313	311	107	4	4.5	4.5	9	1	0.78
314	301	100	3	3.5	3	8.04	0	0.67
315	305	105	2	3	4	8.13	0	0.66
316	308	104	2	2.5	3	8.07	0	0.65
317	298	101	2	1.5	2	7.86	0	0.54
318	300	99	1	1	2.5	8.01	0	0.58

8

319	324	111	3	2.5	2	8.8	1	0.79
320	327	113	4	3.5	3	8.69	1	0.8
321	317	106	3	4	3.5	8.5	1	0.75
322	323	104	3	4	4	8.44	1	0.73
323	314	107	2	2.5	4	8.27	0	0.72
324	305	102	2	2	2.5	8.18	0	0.62
325	315	104	3	3	2.5	8.33	0	0.67
326	326	116	3	3.5	4	9.14	1	0.81
327	299	100	3	2	2	8.02	0	0.63
328	295	101	2	2.5	2	7.86	0	0.69
329	324	112	4	4	3.5	8.77	1	0.8
330	297	96	2	2.5	1.5	7.89	0	0.43
331	327	113	3	3.5	3	8.66	1	0.8
332	311	105	2	3	2	8.12	1	0.73
333	308	106	3	3.5	2.5	8.21	1	0.75
334	319	108	3	3	3.5	8.54	1	0.71
335	312	107	4	4.5	4	8.65	1	0.73
336	325	111	4	4	4.5	9.11	1	0.83
337	319	110	3	3	2.5	8.79	0	0.72
338	332	118	5	5	5	9.47	1	0.94
339	323	108	5	4	4	8.74	1	0.81
340	324	107	5	3.5	4	8.66	1	0.81
341	312	107	3	3	3	8.46	1	0.75
342	326	110	3	3.5	3.5	8.76	1	0.79
343	308	106	3	3	3	8.24	0	0.58
344	305	103	2	2.5	3.5	8.13	0	0.59
345	295	96	2	1.5	2	7.34	0	0.47
346	316	98	1	1.5	2	7.43	0	0.49
347	304	97	2	1.5	2	7.64	0	0.47
348	299	94	1	1	1	7.34	0	0.42
349	302	99	1	2	2	7.25	0	0.57
350	313	101	3	2.5	3	8.04	0	0.62
351	318	107	3	3	3.5	8.27	1	0.74
352	325	110	4	3.5	4	8.67	1	0.73
353	303	100	2	3	3.5	8.06	1	0.64
354	300	102	3	3.5	2.5	8.17	0	0.63
355	297	98	2	2.5	3	7.67	0	0.59
356	317	106	2	2	3.5	8.12	0	0.73
357	327	109	3	3.5	4	8.77	1	0.79
358	301	104	2	3.5	3.5	7.89	1	0.68

9

359	314	105	2	2.5	2	7.64	0	0.7
360	321	107	2	2	1.5	8.44	0	0.81
361	322	110	3	4	5	8.64	1	0.85
362	334	116	4	4	3.5	9.54	1	0.93
363	338	115	5	4.5	5	9.23	1	0.91
364	306	103	2	2.5	3	8.36	0	0.69
365	313	102	3	3.5	4	8.9	1	0.77
366	330	114	4	4.5	3	9.17	1	0.86
367	320	104	3	3.5	4.5	8.34	1	0.74
368	311	98	1	1	2.5	7.46	0	0.57
369	298	92	1	2	2	7.88	0	0.51
370	301	98	1	2	3	8.03	1	0.67
371	310	103	2	2.5	2.5	8.24	0	0.72
372	324	110	3	3.5	3	9.22	1	0.89
373	336	119	4	4.5	4	9.62	1	0.95
374	321	109	3	3	3	8.54	1	0.79
375	315	105	2	2	2.5	7.65	0	0.39
376	304	101	2	2	2.5	7.66	0	0.38
377	297	96	2	2.5	2	7.43	0	0.34
378	290	100	1	1.5	2	7.56	0	0.47
379	303	98	1	2	2.5	7.65	0	0.56
380	311	99	1	2.5	3	8.43	1	0.71
381	322	104	3	3.5	4	8.84	1	0.78
382	319	105	3	3	3.5	8.67	1	0.73
383	324	110	4	4.5	4	9.15	1	0.82
384	300	100	3	3	3.5	8.26	0	0.62
385	340	113	4	5	5	9.74	1	0.96
386	335	117	5	5	5	9.82	1	0.96
387	302	101	2	2.5	3.5	7.96	0	0.46
388	307	105	2	2	3.5	8.1	0	0.53
389	296	97	2	1.5	2	7.8	0	0.49
390	320	108	3	3.5	4	8.44	1	0.76
391	314	102	2	2	2.5	8.24	0	0.64
392	318	106	3	2	3	8.65	0	0.71
393	326	112	4	4	3.5	9.12	1	0.84
394	317	104	2	3	3	8.76	0	0.77
395	329	111	4	4.5	4	9.23	1	0.89
396	324	110	3	3.5	3.5	9.04	1	0.82
397	325	107	3	3	3.5	9.11	1	0.84
398	330	116	4	5	4.5	9.45	1	0.91

10

399	312	103	3	3.5	4	8.78	0	0.67
400	333	117	4	5	4	9.66	1	0.95