Spaces:
Sleeping
Sleeping
GitsSaikat
commited on
Add files via upload
Browse files- Survey Final.csv +370 -0
- logo.png +0 -0
- safetyapp.py +158 -0
Survey Final.csv
ADDED
@@ -0,0 +1,370 @@
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1 |
+
Overcrowding,Preference,Daytime Safety,Nighttime Safety,Taxi DSafety,Taxi NSafety,Reporting,Background Check,Percieved Safety
|
2 |
+
1,1,4,2,4,2,4,5,Uncomfortable
|
3 |
+
1,2,3,1,4,3,4,5,Unsafe
|
4 |
+
1,1,4,5,4,4,1,4,Safe
|
5 |
+
1,2,4,3,5,4,2,4,Comfortable
|
6 |
+
2,2,3,1,4,3,4,4,Unsafe
|
7 |
+
1,1,4,3,4,3,5,4,Uncomfortable
|
8 |
+
1,1,4,5,5,3,4,5,Comfortable
|
9 |
+
2,1,4,2,5,2,5,5,Uncomfortable
|
10 |
+
1,1,3,4,4,5,3,5,Comfortable
|
11 |
+
1,2,4,2,4,2,4,5,Uncomfortable
|
12 |
+
1,2,4,2,1,4,3,5,Uncomfortable
|
13 |
+
1,1,3,2,3,2,4,5,Vulnerable
|
14 |
+
1,2,4,1,4,2,3,5,Uncomfortable
|
15 |
+
1,3,4,2,4,2,3,5,Uncomfortable
|
16 |
+
1,1,5,2,5,2,1,5,Uncomfortable
|
17 |
+
1,3,3,1,5,2,4,5,Unsafe
|
18 |
+
2,2,5,4,5,5,4,5,Safe
|
19 |
+
1,1,5,4,5,5,2,5,Safe
|
20 |
+
1,1,4,3,4,4,3,5,Comfortable
|
21 |
+
2,3,4,4,3,3,3,4,Uncomfortable
|
22 |
+
1,3,4,2,3,2,4,5,Unsafe
|
23 |
+
2,3,4,2,4,2,4,5,Uncomfortable
|
24 |
+
1,1,4,4,3,2,3,5,Uncomfortable
|
25 |
+
2,3,4,3,4,3,4,5,Uncomfortable
|
26 |
+
2,4,4,2,3,3,3,4,Unsafe
|
27 |
+
1,1,3,2,4,4,2,5,Uncomfortable
|
28 |
+
2,2,3,2,4,3,4,5,Unsafe
|
29 |
+
2,1,4,3,4,3,4,5,Uncomfortable
|
30 |
+
1,5,5,1,5,5,5,5,Comfortable
|
31 |
+
1,1,5,3,5,3,4,4,Uncomfortable
|
32 |
+
2,3,4,3,4,3,4,4,Uncomfortable
|
33 |
+
2,4,4,3,4,3,3,5,Uncomfortable
|
34 |
+
2,3,4,2,4,2,4,4,Uncomfortable
|
35 |
+
2,3,4,2,3,3,3,3,Unsafe
|
36 |
+
2,2,4,2,2,2,4,4,Unsafe
|
37 |
+
1,3,4,1,3,1,4,4,Unsafe
|
38 |
+
2,2,4,3,3,3,2,4,Unsafe
|
39 |
+
1,3,4,3,5,3,5,5,Uncomfortable
|
40 |
+
3,3,4,2,4,4,4,4,Comfortable
|
41 |
+
1,2,4,1,3,1,1,5,Unsafe
|
42 |
+
1,3,4,3,4,3,5,4,Uncomfortable
|
43 |
+
2,3,4,3,3,3,3,4,Unsafe
|
44 |
+
1,3,3,1,3,1,3,5,Vulnerable
|
45 |
+
2,4,4,3,4,3,5,4,Uncomfortable
|
46 |
+
3,5,5,2,3,3,3,3,Unsafe
|
47 |
+
1,1,4,3,5,4,3,5,Comfortable
|
48 |
+
2,3,4,3,4,3,2,5,Uncomfortable
|
49 |
+
2,3,4,4,4,4,4,4,Safe
|
50 |
+
1,2,4,1,4,2,3,5,Uncomfortable
|
51 |
+
1,2,4,2,4,2,5,5,Uncomfortable
|
52 |
+
2,2,3,5,1,2,4,4,Unsafe
|
53 |
+
2,2,3,2,2,2,3,5,Vulnerable
|
54 |
+
1,2,4,2,2,2,4,4,Unsafe
|
55 |
+
1,3,4,3,4,3,3,4,Uncomfortable
|
56 |
+
2,2,4,2,4,2,3,5,Uncomfortable
|
57 |
+
2,3,4,3,4,3,2,5,Uncomfortable
|
58 |
+
1,2,4,3,4,3,1,5,Uncomfortable
|
59 |
+
1,3,3,3,4,4,3,4,Uncomfortable
|
60 |
+
1,5,5,1,5,5,1,5,Comfortable
|
61 |
+
2,1,5,5,5,5,4,4,Safe
|
62 |
+
1,3,5,2,5,2,4,5,Uncomfortable
|
63 |
+
3,2,5,2,4,2,4,5,Uncomfortable
|
64 |
+
3,3,4,4,4,4,3,4,Safe
|
65 |
+
2,5,2,2,4,4,4,5,Uncomfortable
|
66 |
+
1,3,4,3,4,3,3,5,Uncomfortable
|
67 |
+
1,3,3,3,3,3,3,3,Vulnerable
|
68 |
+
2,4,2,2,3,2,3,3,Vulnerable
|
69 |
+
2,2,4,2,4,4,2,4,Comfortable
|
70 |
+
1,3,4,3,4,4,5,4,Comfortable
|
71 |
+
2,4,3,3,4,3,3,4,Unsafe
|
72 |
+
1,2,4,2,4,2,3,5,Uncomfortable
|
73 |
+
2,2,4,2,4,4,4,5,Comfortable
|
74 |
+
2,2,4,3,4,3,3,5,Uncomfortable
|
75 |
+
1,1,3,2,3,3,4,5,Vulnerable
|
76 |
+
2,3,2,3,4,3,4,5,Unsafe
|
77 |
+
2,3,4,2,3,3,2,5,Unsafe
|
78 |
+
1,3,4,2,4,3,3,4,Uncomfortable
|
79 |
+
1,2,5,2,4,3,3,5,Uncomfortable
|
80 |
+
1,5,4,3,5,5,3,5,Comfortable
|
81 |
+
1,4,5,2,5,4,4,5,Comfortable
|
82 |
+
1,2,4,1,2,2,5,5,Unsafe
|
83 |
+
1,3,4,3,4,3,4,4,Uncomfortable
|
84 |
+
1,1,4,4,4,3,2,3,Comfortable
|
85 |
+
2,3,4,2,4,2,4,4,Uncomfortable
|
86 |
+
1,2,5,2,5,4,1,5,Comfortable
|
87 |
+
2,1,4,2,3,2,3,4,Unsafe
|
88 |
+
1,4,5,4,5,3,2,5,Comfortable
|
89 |
+
2,1,4,2,3,2,3,4,Unsafe
|
90 |
+
1,4,3,3,2,2,2,4,Vulnerable
|
91 |
+
1,3,5,4,4,4,5,5,Safe
|
92 |
+
2,2,3,2,4,4,3,5,Uncomfortable
|
93 |
+
1,4,2,2,2,2,2,4,Vulnerable
|
94 |
+
1,2,3,1,4,4,4,5,Uncomfortable
|
95 |
+
1,4,5,3,5,3,4,5,Uncomfortable
|
96 |
+
1,3,4,1,4,4,3,5,Comfortable
|
97 |
+
1,3,4,2,5,2,4,5,Uncomfortable
|
98 |
+
2,2,3,1,4,3,2,5,Unsafe
|
99 |
+
2,1,3,3,3,3,3,4,Vulnerable
|
100 |
+
2,3,4,3,4,4,4,4,Comfortable
|
101 |
+
1,3,3,3,3,3,2,4,Vulnerable
|
102 |
+
1,2,2,1,4,3,2,4,Unsafe
|
103 |
+
1,2,2,1,4,3,2,4,Unsafe
|
104 |
+
1,2,4,2,4,2,2,5,Uncomfortable
|
105 |
+
1,3,4,1,4,1,4,4,Uncomfortable
|
106 |
+
2,4,4,3,3,2,4,5,Unsafe
|
107 |
+
1,4,4,2,4,3,4,4,Uncomfortable
|
108 |
+
2,3,4,2,4,2,5,5,Uncomfortable
|
109 |
+
1,1,1,1,2,2,4,5,Vulnerable
|
110 |
+
2,4,3,2,3,2,4,4,Vulnerable
|
111 |
+
2,2,5,4,3,3,3,4,Uncomfortable
|
112 |
+
1,3,4,4,4,4,3,5,Safe
|
113 |
+
1,3,4,4,4,4,3,5,Safe
|
114 |
+
1,3,4,4,4,4,3,5,Safe
|
115 |
+
1,2,5,2,4,3,3,5,Uncomfortable
|
116 |
+
1,1,4,1,4,3,5,5,Uncomfortable
|
117 |
+
1,3,4,3,4,4,2,4,Comfortable
|
118 |
+
2,2,4,3,4,4,3,4,Comfortable
|
119 |
+
2,2,4,3,4,3,3,4,Uncomfortable
|
120 |
+
1,2,4,2,4,2,3,5,Uncomfortable
|
121 |
+
2,1,4,3,2,1,4,5,Unsafe
|
122 |
+
1,2,5,4,5,5,4,5,Safe
|
123 |
+
2,2,4,3,4,3,4,5,Uncomfortable
|
124 |
+
1,4,4,4,4,4,4,4,Safe
|
125 |
+
1,3,5,5,5,5,5,5,Safe
|
126 |
+
1,3,5,1,5,3,3,4,Uncomfortable
|
127 |
+
2,1,3,4,4,3,3,3,Uncomfortable
|
128 |
+
1,2,4,1,4,3,2,5,Uncomfortable
|
129 |
+
1,2,4,3,5,4,4,5,Comfortable
|
130 |
+
2,3,4,3,3,3,3,5,Unsafe
|
131 |
+
1,1,2,2,5,5,5,5,Uncomfortable
|
132 |
+
1,2,3,1,3,3,3,5,Vulnerable
|
133 |
+
1,3,4,2,4,4,4,5,Comfortable
|
134 |
+
2,2,1,1,2,2,2,3,Vulnerable
|
135 |
+
1,4,4,2,4,2,4,5,Uncomfortable
|
136 |
+
2,5,4,4,5,4,3,4,Safe
|
137 |
+
2,2,4,5,4,5,2,5,Safe
|
138 |
+
1,3,3,1,3,1,3,5,Vulnerable
|
139 |
+
1,2,4,2,4,4,3,5,Comfortable
|
140 |
+
2,3,4,2,3,2,4,4,Unsafe
|
141 |
+
2,3,4,3,3,2,3,5,Unsafe
|
142 |
+
2,2,4,2,4,4,4,5,Comfortable
|
143 |
+
2,1,4,3,4,3,4,5,Uncomfortable
|
144 |
+
2,3,4,3,4,3,4,5,Uncomfortable
|
145 |
+
2,3,4,3,4,3,3,5,Uncomfortable
|
146 |
+
3,1,4,3,4,2,3,5,Uncomfortable
|
147 |
+
2,3,4,5,5,4,4,5,Safe
|
148 |
+
2,1,2,1,5,2,4,5,Unsafe
|
149 |
+
1,2,3,3,4,4,2,5,Uncomfortable
|
150 |
+
1,4,5,4,4,4,1,5,Safe
|
151 |
+
2,4,5,4,4,5,4,5,Safe
|
152 |
+
1,3,5,3,4,3,3,5,Uncomfortable
|
153 |
+
2,3,5,4,4,3,4,5,Comfortable
|
154 |
+
1,3,4,4,5,4,4,5,Safe
|
155 |
+
1,2,3,2,4,4,3,5,Uncomfortable
|
156 |
+
1,2,4,2,5,4,4,5,Comfortable
|
157 |
+
2,1,4,4,5,3,3,4,Comfortable
|
158 |
+
2,1,2,2,4,4,4,5,Uncomfortable
|
159 |
+
2,3,4,2,4,2,4,5,Uncomfortable
|
160 |
+
1,3,3,3,3,3,4,5,Vulnerable
|
161 |
+
1,2,4,2,3,3,3,5,Unsafe
|
162 |
+
1,2,2,2,3,1,5,5,Vulnerable
|
163 |
+
3,3,4,3,4,3,2,4,Uncomfortable
|
164 |
+
1,3,4,3,4,4,3,5,Comfortable
|
165 |
+
2,4,5,2,5,5,3,5,Comfortable
|
166 |
+
2,3,4,4,4,5,4,5,Safe
|
167 |
+
1,3,4,2,3,3,4,5,Unsafe
|
168 |
+
1,4,5,3,4,3,2,5,Uncomfortable
|
169 |
+
1,3,4,3,5,3,5,5,Uncomfortable
|
170 |
+
1,3,5,4,5,4,4,4,Safe
|
171 |
+
2,4,4,3,4,3,3,4,Uncomfortable
|
172 |
+
2,3,3,3,4,3,3,4,Unsafe
|
173 |
+
2,3,4,2,4,1,3,5,Uncomfortable
|
174 |
+
1,1,4,1,2,1,4,5,Unsafe
|
175 |
+
1,4,4,2,4,4,4,4,Comfortable
|
176 |
+
1,3,3,2,3,3,3,4,Vulnerable
|
177 |
+
1,4,2,1,2,1,4,4,Vulnerable
|
178 |
+
2,1,4,5,3,5,3,5,Comfortable
|
179 |
+
3,4,3,3,3,3,3,3,Vulnerable
|
180 |
+
2,3,3,3,3,3,3,4,Vulnerable
|
181 |
+
2,2,3,3,3,3,3,4,Vulnerable
|
182 |
+
3,3,4,3,4,3,3,4,Uncomfortable
|
183 |
+
1,3,4,4,4,2,4,5,Comfortable
|
184 |
+
2,2,5,2,4,3,3,4,Uncomfortable
|
185 |
+
1,3,4,3,4,3,4,5,Uncomfortable
|
186 |
+
2,3,3,3,3,3,2,4,Vulnerable
|
187 |
+
1,2,4,2,4,2,3,4,Uncomfortable
|
188 |
+
2,2,5,4,5,3,4,5,Comfortable
|
189 |
+
1,3,3,5,2,5,4,3,Uncomfortable
|
190 |
+
1,3,4,3,4,3,2,5,Uncomfortable
|
191 |
+
2,2,4,3,4,3,2,5,Uncomfortable
|
192 |
+
1,3,5,2,5,4,5,5,Comfortable
|
193 |
+
3,4,4,2,4,2,4,4,Uncomfortable
|
194 |
+
2,4,4,2,3,2,4,5,Unsafe
|
195 |
+
2,3,4,2,4,2,4,4,Uncomfortable
|
196 |
+
2,3,4,3,4,3,2,5,Uncomfortable
|
197 |
+
1,3,5,2,5,2,2,5,Uncomfortable
|
198 |
+
2,3,4,2,4,2,4,4,Uncomfortable
|
199 |
+
1,2,4,2,4,1,2,5,Uncomfortable
|
200 |
+
2,4,2,2,4,3,4,3,Unsafe
|
201 |
+
2,3,4,2,4,2,4,5,Uncomfortable
|
202 |
+
2,3,4,1,3,3,4,4,Unsafe
|
203 |
+
2,3,4,2,4,2,4,5,Uncomfortable
|
204 |
+
2,2,4,3,3,3,3,4,Unsafe
|
205 |
+
2,2,4,3,4,3,3,5,Uncomfortable
|
206 |
+
2,3,4,2,4,2,2,4,Uncomfortable
|
207 |
+
2,3,4,3,3,4,3,3,Uncomfortable
|
208 |
+
3,3,3,3,3,3,3,4,Vulnerable
|
209 |
+
1,4,4,2,3,3,4,4,Unsafe
|
210 |
+
2,3,4,4,4,4,1,5,Safe
|
211 |
+
2,4,4,4,3,4,4,4,Comfortable
|
212 |
+
4,2,3,3,3,3,3,5,Vulnerable
|
213 |
+
1,4,4,3,3,3,3,3,Unsafe
|
214 |
+
1,3,4,2,4,2,2,5,Uncomfortable
|
215 |
+
2,3,3,3,3,3,3,3,Vulnerable
|
216 |
+
1,3,4,3,4,3,3,4,Uncomfortable
|
217 |
+
4,1,5,2,5,2,4,4,Uncomfortable
|
218 |
+
2,3,4,2,3,3,3,5,Unsafe
|
219 |
+
2,4,4,4,3,3,3,3,Uncomfortable
|
220 |
+
2,4,3,4,3,3,4,4,Unsafe
|
221 |
+
2,1,4,5,3,4,4,4,Comfortable
|
222 |
+
2,4,5,4,4,4,4,4,Safe
|
223 |
+
2,1,5,4,3,3,4,4,Uncomfortable
|
224 |
+
3,1,5,4,3,4,5,5,Comfortable
|
225 |
+
1,1,5,4,4,4,2,4,Safe
|
226 |
+
1,2,3,4,3,4,3,5,Uncomfortable
|
227 |
+
2,2,4,4,4,4,4,5,Safe
|
228 |
+
1,1,3,3,4,5,4,5,Uncomfortable
|
229 |
+
2,2,2,4,4,4,4,4,Comfortable
|
230 |
+
1,4,3,3,4,3,4,4,Unsafe
|
231 |
+
1,3,4,2,4,2,1,5,Uncomfortable
|
232 |
+
3,3,1,3,2,2,4,5,Vulnerable
|
233 |
+
2,2,3,4,4,5,4,5,Comfortable
|
234 |
+
2,2,5,2,4,5,3,4,Comfortable
|
235 |
+
1,4,3,4,2,5,3,4,Uncomfortable
|
236 |
+
1,4,5,5,5,5,1,5,Safe
|
237 |
+
2,4,2,4,4,4,4,5,Comfortable
|
238 |
+
1,3,4,2,4,3,4,5,Uncomfortable
|
239 |
+
2,2,2,2,3,3,2,4,Vulnerable
|
240 |
+
1,2,4,2,3,3,2,5,Unsafe
|
241 |
+
2,2,4,3,4,3,4,4,Uncomfortable
|
242 |
+
1,2,4,3,4,3,5,5,Uncomfortable
|
243 |
+
4,3,2,3,2,3,1,3,Vulnerable
|
244 |
+
3,2,3,2,2,1,2,5,Vulnerable
|
245 |
+
2,3,4,1,4,1,2,5,Uncomfortable
|
246 |
+
1,4,4,3,4,3,2,5,Uncomfortable
|
247 |
+
1,4,4,2,5,2,4,5,Uncomfortable
|
248 |
+
2,1,2,3,4,2,2,4,Unsafe
|
249 |
+
2,2,4,3,4,3,3,5,Uncomfortable
|
250 |
+
1,2,4,1,1,1,2,4,Unsafe
|
251 |
+
3,3,4,2,4,4,2,5,Comfortable
|
252 |
+
1,3,4,3,5,3,3,5,Uncomfortable
|
253 |
+
1,1,4,1,4,1,3,5,Uncomfortable
|
254 |
+
1,3,5,4,5,4,4,4,Safe
|
255 |
+
5,3,3,3,4,4,3,5,Uncomfortable
|
256 |
+
3,4,3,3,3,3,3,4,Vulnerable
|
257 |
+
1,1,5,3,4,3,2,4,Uncomfortable
|
258 |
+
2,3,3,3,3,3,2,3,Vulnerable
|
259 |
+
2,3,4,3,4,3,3,4,Uncomfortable
|
260 |
+
3,1,3,3,3,3,3,4,Vulnerable
|
261 |
+
1,4,4,3,4,2,3,5,Uncomfortable
|
262 |
+
1,3,4,4,5,4,4,4,Safe
|
263 |
+
1,4,3,2,4,3,2,5,Unsafe
|
264 |
+
2,4,5,4,5,5,5,5,Safe
|
265 |
+
2,3,4,2,4,3,2,5,Uncomfortable
|
266 |
+
1,3,3,2,4,3,4,5,Unsafe
|
267 |
+
1,3,4,3,4,3,4,5,Uncomfortable
|
268 |
+
2,2,4,3,4,3,2,4,Uncomfortable
|
269 |
+
1,2,4,4,4,4,4,5,Safe
|
270 |
+
2,4,4,4,4,4,2,5,Safe
|
271 |
+
2,1,5,4,4,3,2,5,Comfortable
|
272 |
+
1,4,4,2,4,2,4,5,Uncomfortable
|
273 |
+
1,3,4,2,4,2,4,5,Uncomfortable
|
274 |
+
2,3,5,4,3,2,1,4,Uncomfortable
|
275 |
+
1,1,3,1,2,2,3,5,Vulnerable
|
276 |
+
2,4,3,3,2,2,3,5,Vulnerable
|
277 |
+
1,3,4,3,4,3,5,5,Uncomfortable
|
278 |
+
1,2,4,2,4,3,4,4,Uncomfortable
|
279 |
+
4,4,4,3,4,3,5,5,Uncomfortable
|
280 |
+
1,4,3,3,3,3,3,5,Vulnerable
|
281 |
+
2,2,4,1,4,2,4,5,Uncomfortable
|
282 |
+
1,2,4,3,4,3,4,4,Uncomfortable
|
283 |
+
1,2,4,2,4,2,4,4,Uncomfortable
|
284 |
+
2,2,4,3,4,3,4,4,Uncomfortable
|
285 |
+
2,2,4,3,4,2,3,3,Uncomfortable
|
286 |
+
2,3,4,3,3,3,3,4,Unsafe
|
287 |
+
2,4,4,3,4,4,2,5,Comfortable
|
288 |
+
2,2,2,1,4,2,3,5,Unsafe
|
289 |
+
2,4,4,3,4,3,3,4,Uncomfortable
|
290 |
+
5,3,5,3,3,4,4,3,Uncomfortable
|
291 |
+
1,3,5,3,4,3,1,5,Uncomfortable
|
292 |
+
1,2,4,3,4,2,2,5,Uncomfortable
|
293 |
+
2,3,3,2,4,2,4,5,Unsafe
|
294 |
+
1,2,4,4,4,4,2,4,Safe
|
295 |
+
1,2,4,3,4,2,2,5,Uncomfortable
|
296 |
+
1,1,5,1,1,1,2,4,Unsafe
|
297 |
+
1,3,3,1,4,2,4,5,Unsafe
|
298 |
+
1,2,4,1,3,1,4,5,Unsafe
|
299 |
+
1,3,4,1,5,1,3,5,Uncomfortable
|
300 |
+
1,3,4,2,4,2,3,5,Uncomfortable
|
301 |
+
3,2,3,2,4,2,2,5,Unsafe
|
302 |
+
2,1,5,3,4,3,4,3,Uncomfortable
|
303 |
+
1,1,4,4,4,3,3,5,Comfortable
|
304 |
+
1,1,4,3,4,3,3,5,Uncomfortable
|
305 |
+
2,1,4,2,4,2,2,5,Uncomfortable
|
306 |
+
1,2,4,1,4,1,4,5,Uncomfortable
|
307 |
+
2,3,4,3,4,3,4,3,Uncomfortable
|
308 |
+
1,2,4,2,4,2,3,5,Uncomfortable
|
309 |
+
2,2,4,2,4,2,4,5,Uncomfortable
|
310 |
+
2,3,4,2,4,2,4,5,Uncomfortable
|
311 |
+
1,1,4,1,1,2,3,5,Unsafe
|
312 |
+
3,2,3,1,4,2,4,5,Unsafe
|
313 |
+
3,4,4,2,4,2,2,4,Uncomfortable
|
314 |
+
1,1,4,1,4,1,4,5,Uncomfortable
|
315 |
+
1,3,3,2,4,2,3,5,Unsafe
|
316 |
+
2,3,4,1,4,1,4,5,Uncomfortable
|
317 |
+
2,1,4,3,4,3,3,5,Uncomfortable
|
318 |
+
2,2,4,3,4,3,3,5,Uncomfortable
|
319 |
+
1,2,3,3,3,3,1,5,Vulnerable
|
320 |
+
1,1,4,2,4,2,3,5,Uncomfortable
|
321 |
+
1,2,3,2,3,2,4,3,Vulnerable
|
322 |
+
1,2,3,3,4,3,4,5,Unsafe
|
323 |
+
1,2,4,3,4,3,4,5,Uncomfortable
|
324 |
+
1,3,3,2,4,3,3,5,Unsafe
|
325 |
+
1,3,3,3,3,1,1,5,Vulnerable
|
326 |
+
1,1,2,1,3,1,3,5,Vulnerable
|
327 |
+
1,3,5,1,5,2,3,5,Uncomfortable
|
328 |
+
1,1,3,1,3,1,3,5,Vulnerable
|
329 |
+
1,1,4,3,3,2,3,5,Unsafe
|
330 |
+
2,2,4,3,3,3,4,5,Unsafe
|
331 |
+
2,3,4,3,4,3,4,5,Uncomfortable
|
332 |
+
1,3,5,1,5,2,3,5,Uncomfortable
|
333 |
+
1,1,4,1,5,1,3,5,Uncomfortable
|
334 |
+
1,1,4,2,5,1,4,5,Uncomfortable
|
335 |
+
1,3,4,3,4,3,3,4,Uncomfortable
|
336 |
+
2,3,4,2,4,2,4,4,Uncomfortable
|
337 |
+
1,2,3,2,4,2,2,5,Unsafe
|
338 |
+
1,2,3,2,4,2,3,5,Unsafe
|
339 |
+
1,3,5,2,3,3,4,5,Unsafe
|
340 |
+
1,3,4,3,4,4,3,4,Comfortable
|
341 |
+
1,1,4,2,2,3,4,5,Unsafe
|
342 |
+
1,3,3,3,3,3,3,4,Vulnerable
|
343 |
+
1,2,5,1,5,2,2,5,Uncomfortable
|
344 |
+
1,1,5,1,4,1,4,5,Uncomfortable
|
345 |
+
1,3,3,3,4,3,3,5,Unsafe
|
346 |
+
2,3,4,3,4,4,2,5,Comfortable
|
347 |
+
2,2,3,2,4,2,3,4,Unsafe
|
348 |
+
2,3,5,3,4,3,3,4,Uncomfortable
|
349 |
+
1,3,4,3,4,3,3,4,Uncomfortable
|
350 |
+
1,3,4,3,4,3,3,4,Uncomfortable
|
351 |
+
2,2,4,3,3,3,3,3,Unsafe
|
352 |
+
2,2,4,3,4,4,1,5,Comfortable
|
353 |
+
1,3,4,4,4,4,3,5,Safe
|
354 |
+
1,1,4,1,4,2,4,4,Uncomfortable
|
355 |
+
1,3,5,3,5,2,3,5,Uncomfortable
|
356 |
+
1,3,4,3,4,3,3,4,Uncomfortable
|
357 |
+
1,3,4,4,4,4,1,5,Safe
|
358 |
+
2,2,4,2,4,3,4,5,Uncomfortable
|
359 |
+
4,2,4,2,4,2,2,4,Uncomfortable
|
360 |
+
2,4,2,2,2,2,1,5,Vulnerable
|
361 |
+
2,2,4,4,4,4,2,4,Safe
|
362 |
+
2,3,4,4,4,4,3,5,Safe
|
363 |
+
1,5,4,4,1,1,5,5,Uncomfortable
|
364 |
+
2,1,5,3,4,3,5,5,Uncomfortable
|
365 |
+
1,1,4,2,4,4,5,5,Comfortable
|
366 |
+
1,4,4,2,5,4,4,5,Comfortable
|
367 |
+
2,1,4,2,3,2,3,5,Unsafe
|
368 |
+
2,4,4,2,4,4,4,4,Comfortable
|
369 |
+
2,3,4,3,4,4,2,4,Comfortable
|
370 |
+
2,4,4,4,4,4,2,4,Safe
|
logo.png
ADDED
![]() |
safetyapp.py
ADDED
@@ -0,0 +1,158 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
|
|
|
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|
|
|
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|
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|
|
|
|
1 |
+
import streamlit as st
|
2 |
+
import pandas as pd
|
3 |
+
import numpy as np
|
4 |
+
import matplotlib.pyplot as plt
|
5 |
+
import seaborn as sns
|
6 |
+
from sklearn.model_selection import train_test_split
|
7 |
+
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier, ExtraTreesClassifier, HistGradientBoostingClassifier
|
8 |
+
from sklearn.metrics import classification_report, accuracy_score, confusion_matrix
|
9 |
+
from sklearn.preprocessing import LabelEncoder
|
10 |
+
import shap
|
11 |
+
|
12 |
+
# Load Dataset
|
13 |
+
data_path = 'Survey Final.csv'
|
14 |
+
df = pd.read_csv(data_path)
|
15 |
+
|
16 |
+
# Encode Target Column
|
17 |
+
le = LabelEncoder()
|
18 |
+
df['Percieved Safety'] = le.fit_transform(df['Percieved Safety'])
|
19 |
+
|
20 |
+
# Data Splitting (Global for Use in All Sections)
|
21 |
+
test_size = 0.2 # Default test size (can be changed in data splitting section)
|
22 |
+
X = df.drop(columns=['Percieved Safety'])
|
23 |
+
y = df['Percieved Safety']
|
24 |
+
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size, random_state=42)
|
25 |
+
|
26 |
+
# Streamlit App
|
27 |
+
st.set_page_config(page_title="Evaluating Safety Perception on Commuting App", layout='wide')
|
28 |
+
st.title("Evaluating Safety Perception on Commuting ")
|
29 |
+
|
30 |
+
# Sidebar Navigation
|
31 |
+
selected = st.sidebar.selectbox(
|
32 |
+
"Navigation",
|
33 |
+
[
|
34 |
+
"๐ Data Overview",
|
35 |
+
"๐ Exploratory Data Analysis",
|
36 |
+
"๐ค Model Training, Evaluation & Explanations",
|
37 |
+
"๐ฎ Predict Percieved Safety"
|
38 |
+
]
|
39 |
+
)
|
40 |
+
|
41 |
+
# Data Overview
|
42 |
+
if selected == "๐ Data Overview":
|
43 |
+
st.header("๐ Data Overview")
|
44 |
+
if st.checkbox("Show Dataset"):
|
45 |
+
st.write(df.head())
|
46 |
+
st.write(f"Dataset Shape: {df.shape}")
|
47 |
+
st.write("Data Types:")
|
48 |
+
st.write(df.dtypes)
|
49 |
+
|
50 |
+
# Exploratory Data Analysis
|
51 |
+
if selected == "๐ Exploratory Data Analysis":
|
52 |
+
st.header("๐ Exploratory Data Analysis")
|
53 |
+
if st.checkbox("Correlation Heatmap"):
|
54 |
+
st.write("Correlation Heatmap")
|
55 |
+
fig, ax = plt.subplots(figsize=(10, 6))
|
56 |
+
sns.heatmap(df.corr(), annot=True, cmap='coolwarm', ax=ax)
|
57 |
+
st.pyplot(fig)
|
58 |
+
|
59 |
+
if st.checkbox("Histogram"):
|
60 |
+
st.write("Histograms of Numeric Columns")
|
61 |
+
numeric_columns = df.select_dtypes(include=['int64', 'float64']).columns.tolist()
|
62 |
+
selected_column = st.selectbox("Select Column for Histogram", numeric_columns)
|
63 |
+
fig, ax = plt.subplots()
|
64 |
+
sns.histplot(df[selected_column], kde=True, ax=ax)
|
65 |
+
st.pyplot(fig)
|
66 |
+
|
67 |
+
if st.checkbox("Boxplot for Numeric Columns"):
|
68 |
+
st.write("Boxplot of Numeric Columns")
|
69 |
+
numeric_columns = df.select_dtypes(include=['int64', 'float64']).columns.tolist()
|
70 |
+
selected_column = st.selectbox("Select Column for Boxplot", numeric_columns)
|
71 |
+
fig, ax = plt.subplots()
|
72 |
+
sns.boxplot(data=df, x=selected_column, ax=ax)
|
73 |
+
st.pyplot(fig)
|
74 |
+
|
75 |
+
if st.checkbox("Pairplot of Dataset"):
|
76 |
+
st.write("Pairplot of the Dataset")
|
77 |
+
fig = sns.pairplot(df)
|
78 |
+
st.pyplot(fig)
|
79 |
+
|
80 |
+
# Model Training, Evaluation & Explanations
|
81 |
+
if selected == "๐ค Model Training, Evaluation & Explanations":
|
82 |
+
st.header("๐ค Model Training, Evaluation & Explanations")
|
83 |
+
if st.checkbox("Train, Evaluate, and Explain Models"):
|
84 |
+
# Model Training
|
85 |
+
st.write("Training Tree-Based Models")
|
86 |
+
models = {
|
87 |
+
"Random Forest": RandomForestClassifier(random_state=42),
|
88 |
+
"Gradient Boosting": GradientBoostingClassifier(random_state=42),
|
89 |
+
"Extra Trees": ExtraTreesClassifier(random_state=42),
|
90 |
+
"Histogram Gradient Boosting": HistGradientBoostingClassifier(random_state=42)
|
91 |
+
}
|
92 |
+
|
93 |
+
model_preds = {}
|
94 |
+
model_accuracies = {}
|
95 |
+
for model_name, model in models.items():
|
96 |
+
model.fit(X_train, y_train)
|
97 |
+
preds = model.predict(X_test)
|
98 |
+
accuracy = accuracy_score(y_test, preds)
|
99 |
+
model_preds[model_name] = preds
|
100 |
+
model_accuracies[model_name] = accuracy
|
101 |
+
st.write(f"{model_name} Accuracy: {accuracy:.2f}")
|
102 |
+
|
103 |
+
# Model Evaluation
|
104 |
+
selected_model = st.selectbox("Select Model for Detailed Evaluation", list(models.keys()))
|
105 |
+
selected_model_instance = models[selected_model]
|
106 |
+
selected_preds = model_preds[selected_model]
|
107 |
+
st.write("Classification Report:")
|
108 |
+
st.text(classification_report(y_test, selected_preds))
|
109 |
+
st.write("Confusion Matrix:")
|
110 |
+
st.write(confusion_matrix(y_test, selected_preds))
|
111 |
+
|
112 |
+
# Feature Importance
|
113 |
+
if st.checkbox("Show Feature Importance"):
|
114 |
+
st.write(f"Feature Importance from {selected_model} Model")
|
115 |
+
if hasattr(selected_model_instance, 'feature_importances_'):
|
116 |
+
feature_importances = selected_model_instance.feature_importances_
|
117 |
+
importance_df = pd.DataFrame({"Feature": X_train.columns, "Importance": feature_importances})
|
118 |
+
importance_df = importance_df.sort_values(by="Importance", ascending=False)
|
119 |
+
st.bar_chart(importance_df.set_index("Feature"))
|
120 |
+
else:
|
121 |
+
st.write("The selected model does not support feature importances.")
|
122 |
+
|
123 |
+
# SHAP Explanations
|
124 |
+
if st.checkbox("Explain Predictions with SHAP"):
|
125 |
+
st.write(f"SHAP Explanation for {selected_model} Model")
|
126 |
+
explainer = shap.TreeExplainer(selected_model_instance)
|
127 |
+
shap_values = explainer.shap_values(X_test)
|
128 |
+
shap.summary_plot(shap_values, X_test, plot_type="bar")
|
129 |
+
st.pyplot()
|
130 |
+
|
131 |
+
# Predict Percieved Safety
|
132 |
+
if selected == "๐ฎ Predict Percieved Safety":
|
133 |
+
st.header("๐ฎ Predict Percieved Safety")
|
134 |
+
st.write("Please provide the following information to predict Percieved Safety for transport:")
|
135 |
+
|
136 |
+
# User Input for Prediction
|
137 |
+
overcrowding = st.selectbox("How overcrowded do you think the transport is on a scale from 0 (Not overcrowded) to 4 (Very overcrowded)?", [0, 1, 2, 3, 4])
|
138 |
+
preference = st.selectbox("How much do you prefer this mode of transport on a scale from 0 (Not preferred) to 4 (Highly preferred)?", [0, 1, 2, 3, 4])
|
139 |
+
daytime_safety = st.selectbox("How safe do you feel using this transport during the daytime on a scale from 0 (Not safe) to 4 (Very safe)?", [0, 1, 2, 3, 4])
|
140 |
+
nighttime_safety = st.selectbox("How safe do you feel using this transport during the nighttime on a scale from 0 (Not safe) to 4 (Very safe)?", [0, 1, 2, 3, 4])
|
141 |
+
taxi_dsafety = st.selectbox("How safe do you feel using a taxi during the day on a scale from 0 (Not safe) to 4 (Very safe)?", [0, 1, 2, 3, 4])
|
142 |
+
taxi_nsafety = st.selectbox("How safe do you feel using a taxi during the night on a scale from 0 (Not safe) to 4 (Very safe)?", [0, 1, 2, 3, 4])
|
143 |
+
reporting = st.selectbox("How comfortable are you with reporting incidents related to this transport on a scale from 0 (Not comfortable) to 4 (Very comfortable)?", [0, 1, 2, 3, 4])
|
144 |
+
background_check = st.selectbox("How effective do you think background checks are for transport personnel on a scale from 0 (Not effective) to 4 (Very effective)?", [0, 1, 2, 3, 4])
|
145 |
+
|
146 |
+
user_data = np.array([[
|
147 |
+
overcrowding, preference, daytime_safety, nighttime_safety,
|
148 |
+
taxi_dsafety, taxi_nsafety, reporting, background_check
|
149 |
+
]])
|
150 |
+
|
151 |
+
if st.button("Predict Percieved Safety"):
|
152 |
+
# Train the Model (Again) and Predict
|
153 |
+
model = RandomForestClassifier(random_state=42)
|
154 |
+
model.fit(X_train, y_train)
|
155 |
+
prediction = model.predict(user_data)
|
156 |
+
predicted_class = le.inverse_transform(prediction)
|
157 |
+
|
158 |
+
st.write(f"Predicted Percieved Safety Class: {predicted_class[0]}")
|