├── Concrete_Prediction
├── Concrete.csv
├── multiple-regression-on-cement-data.ipynb
└── readme.md
├── House-Price-Predictions
├── DATA BASE.docx
├── Design.jpeg
├── OIG.jpeg
├── OIG2.jpeg
├── Real_Estate.csv
├── Real_Estate_Prediction 2.0.ipynb
├── Real_Estate_Prediction 3.0.ipynb
├── database_setup1.mysql-notebook
├── database_setup2.mysql-notebook
└── readme.md
├── Iris-Species-Prediction
├── Iris2023.csv
├── Iris_Classification.ipynb
├── database_setup.mysql-notebook
├── iris1.jpg
├── iris2.jpg
└── readme.md
├── Outlier_Treatment
├── Outlier treat mine.ipynb
└── readme.md
└── README.md
/Concrete_Prediction/Concrete.csv:
--------------------------------------------------------------------------------
1 | cement,slag,flyash,water,superplasticizer,coarseaggregate,fineaggregate,age,csMPa
2 | 540,0,0,162,2.5,1040,676,28,79.99
3 | 540,0,0,162,2.5,1055,676,28,61.89
4 | 332.5,142.5,0,228,0,932,594,270,40.27
5 | 332.5,142.5,0,228,0,932,594,365,41.05
6 | 198.6,132.4,0,192,0,978.4,825.5,360,44.3
7 | 266,114,0,228,0,932,670,90,47.03
8 | 380,95,0,228,0,932,594,365,43.7
9 | 380,95,0,228,0,932,594,28,36.45
10 | 266,114,0,228,0,932,670,28,45.85
11 | 475,0,0,228,0,932,594,28,39.29
12 | 198.6,132.4,0,192,0,978.4,825.5,90,38.07
13 | 198.6,132.4,0,192,0,978.4,825.5,28,28.02
14 | 427.5,47.5,0,228,0,932,594,270,43.01
15 | 190,190,0,228,0,932,670,90,42.33
16 | 304,76,0,228,0,932,670,28,47.81
17 | 380,0,0,228,0,932,670,90,52.91
18 | 139.6,209.4,0,192,0,1047,806.9,90,39.36
19 | 342,38,0,228,0,932,670,365,56.14
20 | 380,95,0,228,0,932,594,90,40.56
21 | 475,0,0,228,0,932,594,180,42.62
22 | 427.5,47.5,0,228,0,932,594,180,41.84
23 | 139.6,209.4,0,192,0,1047,806.9,28,28.24
24 | 139.6,209.4,0,192,0,1047,806.9,3,8.06
25 | 139.6,209.4,0,192,0,1047,806.9,180,44.21
26 | 380,0,0,228,0,932,670,365,52.52
27 | 380,0,0,228,0,932,670,270,53.3
28 | 380,95,0,228,0,932,594,270,41.15
29 | 342,38,0,228,0,932,670,180,52.12
30 | 427.5,47.5,0,228,0,932,594,28,37.43
31 | 475,0,0,228,0,932,594,7,38.6
32 | 304,76,0,228,0,932,670,365,55.26
33 | 266,114,0,228,0,932,670,365,52.91
34 | 198.6,132.4,0,192,0,978.4,825.5,180,41.72
35 | 475,0,0,228,0,932,594,270,42.13
36 | 190,190,0,228,0,932,670,365,53.69
37 | 237.5,237.5,0,228,0,932,594,270,38.41
38 | 237.5,237.5,0,228,0,932,594,28,30.08
39 | 332.5,142.5,0,228,0,932,594,90,37.72
40 | 475,0,0,228,0,932,594,90,42.23
41 | 237.5,237.5,0,228,0,932,594,180,36.25
42 | 342,38,0,228,0,932,670,90,50.46
43 | 427.5,47.5,0,228,0,932,594,365,43.7
44 | 237.5,237.5,0,228,0,932,594,365,39
45 | 380,0,0,228,0,932,670,180,53.1
46 | 427.5,47.5,0,228,0,932,594,90,41.54
47 | 427.5,47.5,0,228,0,932,594,7,35.08
48 | 349,0,0,192,0,1047,806.9,3,15.05
49 | 380,95,0,228,0,932,594,180,40.76
50 | 237.5,237.5,0,228,0,932,594,7,26.26
51 | 380,95,0,228,0,932,594,7,32.82
52 | 332.5,142.5,0,228,0,932,594,180,39.78
53 | 190,190,0,228,0,932,670,180,46.93
54 | 237.5,237.5,0,228,0,932,594,90,33.12
55 | 304,76,0,228,0,932,670,90,49.19
56 | 139.6,209.4,0,192,0,1047,806.9,7,14.59
57 | 198.6,132.4,0,192,0,978.4,825.5,7,14.64
58 | 475,0,0,228,0,932,594,365,41.93
59 | 198.6,132.4,0,192,0,978.4,825.5,3,9.13
60 | 304,76,0,228,0,932,670,180,50.95
61 | 332.5,142.5,0,228,0,932,594,28,33.02
62 | 304,76,0,228,0,932,670,270,54.38
63 | 266,114,0,228,0,932,670,270,51.73
64 | 310,0,0,192,0,971,850.6,3,9.87
65 | 190,190,0,228,0,932,670,270,50.66
66 | 266,114,0,228,0,932,670,180,48.7
67 | 342,38,0,228,0,932,670,270,55.06
68 | 139.6,209.4,0,192,0,1047,806.9,360,44.7
69 | 332.5,142.5,0,228,0,932,594,7,30.28
70 | 190,190,0,228,0,932,670,28,40.86
71 | 485,0,0,146,0,1120,800,28,71.99
72 | 374,189.2,0,170.1,10.1,926.1,756.7,3,34.4
73 | 313.3,262.2,0,175.5,8.6,1046.9,611.8,3,28.8
74 | 425,106.3,0,153.5,16.5,852.1,887.1,3,33.4
75 | 425,106.3,0,151.4,18.6,936,803.7,3,36.3
76 | 375,93.8,0,126.6,23.4,852.1,992.6,3,29
77 | 475,118.8,0,181.1,8.9,852.1,781.5,3,37.8
78 | 469,117.2,0,137.8,32.2,852.1,840.5,3,40.2
79 | 425,106.3,0,153.5,16.5,852.1,887.1,3,33.4
80 | 388.6,97.1,0,157.9,12.1,852.1,925.7,3,28.1
81 | 531.3,0,0,141.8,28.2,852.1,893.7,3,41.3
82 | 425,106.3,0,153.5,16.5,852.1,887.1,3,33.4
83 | 318.8,212.5,0,155.7,14.3,852.1,880.4,3,25.2
84 | 401.8,94.7,0,147.4,11.4,946.8,852.1,3,41.1
85 | 362.6,189,0,164.9,11.6,944.7,755.8,3,35.3
86 | 323.7,282.8,0,183.8,10.3,942.7,659.9,3,28.3
87 | 379.5,151.2,0,153.9,15.9,1134.3,605,3,28.6
88 | 362.6,189,0,164.9,11.6,944.7,755.8,3,35.3
89 | 286.3,200.9,0,144.7,11.2,1004.6,803.7,3,24.4
90 | 362.6,189,0,164.9,11.6,944.7,755.8,3,35.3
91 | 439,177,0,186,11.1,884.9,707.9,3,39.3
92 | 389.9,189,0,145.9,22,944.7,755.8,3,40.6
93 | 362.6,189,0,164.9,11.6,944.7,755.8,3,35.3
94 | 337.9,189,0,174.9,9.5,944.7,755.8,3,24.1
95 | 374,189.2,0,170.1,10.1,926.1,756.7,7,46.2
96 | 313.3,262.2,0,175.5,8.6,1046.9,611.8,7,42.8
97 | 425,106.3,0,153.5,16.5,852.1,887.1,7,49.2
98 | 425,106.3,0,151.4,18.6,936,803.7,7,46.8
99 | 375,93.8,0,126.6,23.4,852.1,992.6,7,45.7
100 | 475,118.8,0,181.1,8.9,852.1,781.5,7,55.6
101 | 469,117.2,0,137.8,32.2,852.1,840.5,7,54.9
102 | 425,106.3,0,153.5,16.5,852.1,887.1,7,49.2
103 | 388.6,97.1,0,157.9,12.1,852.1,925.7,7,34.9
104 | 531.3,0,0,141.8,28.2,852.1,893.7,7,46.9
105 | 425,106.3,0,153.5,16.5,852.1,887.1,7,49.2
106 | 318.8,212.5,0,155.7,14.3,852.1,880.4,7,33.4
107 | 401.8,94.7,0,147.4,11.4,946.8,852.1,7,54.1
108 | 362.6,189,0,164.9,11.6,944.7,755.8,7,55.9
109 | 323.7,282.8,0,183.8,10.3,942.7,659.9,7,49.8
110 | 379.5,151.2,0,153.9,15.9,1134.3,605,7,47.1
111 | 362.6,189,0,164.9,11.6,944.7,755.8,7,55.9
112 | 286.3,200.9,0,144.7,11.2,1004.6,803.7,7,38
113 | 362.6,189,0,164.9,11.6,944.7,755.8,7,55.9
114 | 439,177,0,186,11.1,884.9,707.9,7,56.1
115 | 389.9,189,0,145.9,22,944.7,755.8,7,59.09
116 | 362.6,189,0,164.9,11.6,944.7,755.8,7,22.9
117 | 337.9,189,0,174.9,9.5,944.7,755.8,7,35.1
118 | 374,189.2,0,170.1,10.1,926.1,756.7,28,61.09
119 | 313.3,262.2,0,175.5,8.6,1046.9,611.8,28,59.8
120 | 425,106.3,0,153.5,16.5,852.1,887.1,28,60.29
121 | 425,106.3,0,151.4,18.6,936,803.7,28,61.8
122 | 375,93.8,0,126.6,23.4,852.1,992.6,28,56.7
123 | 475,118.8,0,181.1,8.9,852.1,781.5,28,68.3
124 | 469,117.2,0,137.8,32.2,852.1,840.5,28,66.9
125 | 425,106.3,0,153.5,16.5,852.1,887.1,28,60.29
126 | 388.6,97.1,0,157.9,12.1,852.1,925.7,28,50.7
127 | 531.3,0,0,141.8,28.2,852.1,893.7,28,56.4
128 | 425,106.3,0,153.5,16.5,852.1,887.1,28,60.29
129 | 318.8,212.5,0,155.7,14.3,852.1,880.4,28,55.5
130 | 401.8,94.7,0,147.4,11.4,946.8,852.1,28,68.5
131 | 362.6,189,0,164.9,11.6,944.7,755.8,28,71.3
132 | 323.7,282.8,0,183.8,10.3,942.7,659.9,28,74.7
133 | 379.5,151.2,0,153.9,15.9,1134.3,605,28,52.2
134 | 362.6,189,0,164.9,11.6,944.7,755.8,28,71.3
135 | 286.3,200.9,0,144.7,11.2,1004.6,803.7,28,67.7
136 | 362.6,189,0,164.9,11.6,944.7,755.8,28,71.3
137 | 439,177,0,186,11.1,884.9,707.9,28,66
138 | 389.9,189,0,145.9,22,944.7,755.8,28,74.5
139 | 362.6,189,0,164.9,11.6,944.7,755.8,28,71.3
140 | 337.9,189,0,174.9,9.5,944.7,755.8,28,49.9
141 | 374,189.2,0,170.1,10.1,926.1,756.7,56,63.4
142 | 313.3,262.2,0,175.5,8.6,1046.9,611.8,56,64.9
143 | 425,106.3,0,153.5,16.5,852.1,887.1,56,64.3
144 | 425,106.3,0,151.4,18.6,936,803.7,56,64.9
145 | 375,93.8,0,126.6,23.4,852.1,992.6,56,60.2
146 | 475,118.8,0,181.1,8.9,852.1,781.5,56,72.3
147 | 469,117.2,0,137.8,32.2,852.1,840.5,56,69.3
148 | 425,106.3,0,153.5,16.5,852.1,887.1,56,64.3
149 | 388.6,97.1,0,157.9,12.1,852.1,925.7,56,55.2
150 | 531.3,0,0,141.8,28.2,852.1,893.7,56,58.8
151 | 425,106.3,0,153.5,16.5,852.1,887.1,56,64.3
152 | 318.8,212.5,0,155.7,14.3,852.1,880.4,56,66.1
153 | 401.8,94.7,0,147.4,11.4,946.8,852.1,56,73.7
154 | 362.6,189,0,164.9,11.6,944.7,755.8,56,77.3
155 | 323.7,282.8,0,183.8,10.3,942.7,659.9,56,80.2
156 | 379.5,151.2,0,153.9,15.9,1134.3,605,56,54.9
157 | 362.6,189,0,164.9,11.6,944.7,755.8,56,77.3
158 | 286.3,200.9,0,144.7,11.2,1004.6,803.7,56,72.99
159 | 362.6,189,0,164.9,11.6,944.7,755.8,56,77.3
160 | 439,177,0,186,11.1,884.9,707.9,56,71.7
161 | 389.9,189,0,145.9,22,944.7,755.8,56,79.4
162 | 362.6,189,0,164.9,11.6,944.7,755.8,56,77.3
163 | 337.9,189,0,174.9,9.5,944.7,755.8,56,59.89
164 | 374,189.2,0,170.1,10.1,926.1,756.7,91,64.9
165 | 313.3,262.2,0,175.5,8.6,1046.9,611.8,91,66.6
166 | 425,106.3,0,153.5,16.5,852.1,887.1,91,65.2
167 | 425,106.3,0,151.4,18.6,936,803.7,91,66.7
168 | 375,93.8,0,126.6,23.4,852.1,992.6,91,62.5
169 | 475,118.8,0,181.1,8.9,852.1,781.5,91,74.19
170 | 469,117.2,0,137.8,32.2,852.1,840.5,91,70.7
171 | 425,106.3,0,153.5,16.5,852.1,887.1,91,65.2
172 | 388.6,97.1,0,157.9,12.1,852.1,925.7,91,57.6
173 | 531.3,0,0,141.8,28.2,852.1,893.7,91,59.2
174 | 425,106.3,0,153.5,16.5,852.1,887.1,91,65.2
175 | 318.8,212.5,0,155.7,14.3,852.1,880.4,91,68.1
176 | 401.8,94.7,0,147.4,11.4,946.8,852.1,91,75.5
177 | 362.6,189,0,164.9,11.6,944.7,755.8,91,79.3
178 | 379.5,151.2,0,153.9,15.9,1134.3,605,91,56.5
179 | 362.6,189,0,164.9,11.6,944.7,755.8,91,79.3
180 | 286.3,200.9,0,144.7,11.2,1004.6,803.7,91,76.8
181 | 362.6,189,0,164.9,11.6,944.7,755.8,91,79.3
182 | 439,177,0,186,11.1,884.9,707.9,91,73.3
183 | 389.9,189,0,145.9,22,944.7,755.8,91,82.6
184 | 362.6,189,0,164.9,11.6,944.7,755.8,91,79.3
185 | 337.9,189,0,174.9,9.5,944.7,755.8,91,67.8
186 | 222.4,0,96.7,189.3,4.5,967.1,870.3,3,11.58
187 | 222.4,0,96.7,189.3,4.5,967.1,870.3,14,24.45
188 | 222.4,0,96.7,189.3,4.5,967.1,870.3,28,24.89
189 | 222.4,0,96.7,189.3,4.5,967.1,870.3,56,29.45
190 | 222.4,0,96.7,189.3,4.5,967.1,870.3,100,40.71
191 | 233.8,0,94.6,197.9,4.6,947,852.2,3,10.38
192 | 233.8,0,94.6,197.9,4.6,947,852.2,14,22.14
193 | 233.8,0,94.6,197.9,4.6,947,852.2,28,22.84
194 | 233.8,0,94.6,197.9,4.6,947,852.2,56,27.66
195 | 233.8,0,94.6,197.9,4.6,947,852.2,100,34.56
196 | 194.7,0,100.5,165.6,7.5,1006.4,905.9,3,12.45
197 | 194.7,0,100.5,165.6,7.5,1006.4,905.9,14,24.99
198 | 194.7,0,100.5,165.6,7.5,1006.4,905.9,28,25.72
199 | 194.7,0,100.5,165.6,7.5,1006.4,905.9,56,33.96
200 | 194.7,0,100.5,165.6,7.5,1006.4,905.9,100,37.34
201 | 190.7,0,125.4,162.1,7.8,1090,804,3,15.04
202 | 190.7,0,125.4,162.1,7.8,1090,804,14,21.06
203 | 190.7,0,125.4,162.1,7.8,1090,804,28,26.4
204 | 190.7,0,125.4,162.1,7.8,1090,804,56,35.34
205 | 190.7,0,125.4,162.1,7.8,1090,804,100,40.57
206 | 212.1,0,121.6,180.3,5.7,1057.6,779.3,3,12.47
207 | 212.1,0,121.6,180.3,5.7,1057.6,779.3,14,20.92
208 | 212.1,0,121.6,180.3,5.7,1057.6,779.3,28,24.9
209 | 212.1,0,121.6,180.3,5.7,1057.6,779.3,56,34.2
210 | 212.1,0,121.6,180.3,5.7,1057.6,779.3,100,39.61
211 | 230,0,118.3,195.5,4.6,1029.4,758.6,3,10.03
212 | 230,0,118.3,195.5,4.6,1029.4,758.6,14,20.08
213 | 230,0,118.3,195.5,4.6,1029.4,758.6,28,24.48
214 | 230,0,118.3,195.5,4.6,1029.4,758.6,56,31.54
215 | 230,0,118.3,195.5,4.6,1029.4,758.6,100,35.34
216 | 190.3,0,125.2,161.9,9.9,1088.1,802.6,3,9.45
217 | 190.3,0,125.2,161.9,9.9,1088.1,802.6,14,22.72
218 | 190.3,0,125.2,161.9,9.9,1088.1,802.6,28,28.47
219 | 190.3,0,125.2,161.9,9.9,1088.1,802.6,56,38.56
220 | 190.3,0,125.2,161.9,9.9,1088.1,802.6,100,40.39
221 | 166.1,0,163.3,176.5,4.5,1058.6,780.1,3,10.76
222 | 166.1,0,163.3,176.5,4.5,1058.6,780.1,14,25.48
223 | 166.1,0,163.3,176.5,4.5,1058.6,780.1,28,21.54
224 | 166.1,0,163.3,176.5,4.5,1058.6,780.1,56,28.63
225 | 166.1,0,163.3,176.5,4.5,1058.6,780.1,100,33.54
226 | 168,42.1,163.8,121.8,5.7,1058.7,780.1,3,7.75
227 | 168,42.1,163.8,121.8,5.7,1058.7,780.1,14,17.82
228 | 168,42.1,163.8,121.8,5.7,1058.7,780.1,28,24.24
229 | 168,42.1,163.8,121.8,5.7,1058.7,780.1,56,32.85
230 | 168,42.1,163.8,121.8,5.7,1058.7,780.1,100,39.23
231 | 213.7,98.1,24.5,181.7,6.9,1065.8,785.4,3,18
232 | 213.7,98.1,24.5,181.7,6.9,1065.8,785.4,14,30.39
233 | 213.7,98.1,24.5,181.7,6.9,1065.8,785.4,28,45.71
234 | 213.7,98.1,24.5,181.7,6.9,1065.8,785.4,56,50.77
235 | 213.7,98.1,24.5,181.7,6.9,1065.8,785.4,100,53.9
236 | 213.8,98.1,24.5,181.7,6.7,1066,785.5,3,13.18
237 | 213.8,98.1,24.5,181.7,6.7,1066,785.5,14,17.84
238 | 213.8,98.1,24.5,181.7,6.7,1066,785.5,28,40.23
239 | 213.8,98.1,24.5,181.7,6.7,1066,785.5,56,47.13
240 | 213.8,98.1,24.5,181.7,6.7,1066,785.5,100,49.97
241 | 229.7,0,118.2,195.2,6.1,1028.1,757.6,3,13.36
242 | 229.7,0,118.2,195.2,6.1,1028.1,757.6,14,22.32
243 | 229.7,0,118.2,195.2,6.1,1028.1,757.6,28,24.54
244 | 229.7,0,118.2,195.2,6.1,1028.1,757.6,56,31.35
245 | 229.7,0,118.2,195.2,6.1,1028.1,757.6,100,40.86
246 | 238.1,0,94.1,186.7,7,949.9,847,3,19.93
247 | 238.1,0,94.1,186.7,7,949.9,847,14,25.69
248 | 238.1,0,94.1,186.7,7,949.9,847,28,30.23
249 | 238.1,0,94.1,186.7,7,949.9,847,56,39.59
250 | 238.1,0,94.1,186.7,7,949.9,847,100,44.3
251 | 250,0,95.7,187.4,5.5,956.9,861.2,3,13.82
252 | 250,0,95.7,187.4,5.5,956.9,861.2,14,24.92
253 | 250,0,95.7,187.4,5.5,956.9,861.2,28,29.22
254 | 250,0,95.7,187.4,5.5,956.9,861.2,56,38.33
255 | 250,0,95.7,187.4,5.5,956.9,861.2,100,42.35
256 | 212.5,0,100.4,159.3,8.7,1007.8,903.6,3,13.54
257 | 212.5,0,100.4,159.3,8.7,1007.8,903.6,14,26.31
258 | 212.5,0,100.4,159.3,8.7,1007.8,903.6,28,31.64
259 | 212.5,0,100.4,159.3,8.7,1007.8,903.6,56,42.55
260 | 212.5,0,100.4,159.3,8.7,1007.8,903.6,100,42.92
261 | 212.6,0,100.4,159.4,10.4,1003.8,903.8,3,13.33
262 | 212.6,0,100.4,159.4,10.4,1003.8,903.8,14,25.37
263 | 212.6,0,100.4,159.4,10.4,1003.8,903.8,28,37.4
264 | 212.6,0,100.4,159.4,10.4,1003.8,903.8,56,44.4
265 | 212.6,0,100.4,159.4,10.4,1003.8,903.8,100,47.74
266 | 212,0,124.8,159,7.8,1085.4,799.5,3,19.52
267 | 212,0,124.8,159,7.8,1085.4,799.5,14,31.35
268 | 212,0,124.8,159,7.8,1085.4,799.5,28,38.5
269 | 212,0,124.8,159,7.8,1085.4,799.5,56,45.08
270 | 212,0,124.8,159,7.8,1085.4,799.5,100,47.82
271 | 231.8,0,121.6,174,6.7,1056.4,778.5,3,15.44
272 | 231.8,0,121.6,174,6.7,1056.4,778.5,14,26.77
273 | 231.8,0,121.6,174,6.7,1056.4,778.5,28,33.73
274 | 231.8,0,121.6,174,6.7,1056.4,778.5,56,42.7
275 | 231.8,0,121.6,174,6.7,1056.4,778.5,100,45.84
276 | 251.4,0,118.3,188.5,5.8,1028.4,757.7,3,17.22
277 | 251.4,0,118.3,188.5,5.8,1028.4,757.7,14,29.93
278 | 251.4,0,118.3,188.5,5.8,1028.4,757.7,28,29.65
279 | 251.4,0,118.3,188.5,5.8,1028.4,757.7,56,36.97
280 | 251.4,0,118.3,188.5,5.8,1028.4,757.7,100,43.58
281 | 251.4,0,118.3,188.5,6.4,1028.4,757.7,3,13.12
282 | 251.4,0,118.3,188.5,6.4,1028.4,757.7,14,24.43
283 | 251.4,0,118.3,188.5,6.4,1028.4,757.7,28,32.66
284 | 251.4,0,118.3,188.5,6.4,1028.4,757.7,56,36.64
285 | 251.4,0,118.3,188.5,6.4,1028.4,757.7,100,44.21
286 | 181.4,0,167,169.6,7.6,1055.6,777.8,3,13.62
287 | 181.4,0,167,169.6,7.6,1055.6,777.8,14,21.6
288 | 181.4,0,167,169.6,7.6,1055.6,777.8,28,27.77
289 | 181.4,0,167,169.6,7.6,1055.6,777.8,56,35.57
290 | 181.4,0,167,169.6,7.6,1055.6,777.8,100,45.37
291 | 182,45.2,122,170.2,8.2,1059.4,780.7,3,7.32
292 | 182,45.2,122,170.2,8.2,1059.4,780.7,14,21.5
293 | 182,45.2,122,170.2,8.2,1059.4,780.7,28,31.27
294 | 182,45.2,122,170.2,8.2,1059.4,780.7,56,43.5
295 | 182,45.2,122,170.2,8.2,1059.4,780.7,100,48.67
296 | 168.9,42.2,124.3,158.3,10.8,1080.8,796.2,3,7.4
297 | 168.9,42.2,124.3,158.3,10.8,1080.8,796.2,14,23.51
298 | 168.9,42.2,124.3,158.3,10.8,1080.8,796.2,28,31.12
299 | 168.9,42.2,124.3,158.3,10.8,1080.8,796.2,56,39.15
300 | 168.9,42.2,124.3,158.3,10.8,1080.8,796.2,100,48.15
301 | 290.4,0,96.2,168.1,9.4,961.2,865,3,22.5
302 | 290.4,0,96.2,168.1,9.4,961.2,865,14,34.67
303 | 290.4,0,96.2,168.1,9.4,961.2,865,28,34.74
304 | 290.4,0,96.2,168.1,9.4,961.2,865,56,45.08
305 | 290.4,0,96.2,168.1,9.4,961.2,865,100,48.97
306 | 277.1,0,97.4,160.6,11.8,973.9,875.6,3,23.14
307 | 277.1,0,97.4,160.6,11.8,973.9,875.6,14,41.89
308 | 277.1,0,97.4,160.6,11.8,973.9,875.6,28,48.28
309 | 277.1,0,97.4,160.6,11.8,973.9,875.6,56,51.04
310 | 277.1,0,97.4,160.6,11.8,973.9,875.6,100,55.64
311 | 295.7,0,95.6,171.5,8.9,955.1,859.2,3,22.95
312 | 295.7,0,95.6,171.5,8.9,955.1,859.2,14,35.23
313 | 295.7,0,95.6,171.5,8.9,955.1,859.2,28,39.94
314 | 295.7,0,95.6,171.5,8.9,955.1,859.2,56,48.72
315 | 295.7,0,95.6,171.5,8.9,955.1,859.2,100,52.04
316 | 251.8,0,99.9,146.1,12.4,1006,899.8,3,21.02
317 | 251.8,0,99.9,146.1,12.4,1006,899.8,14,33.36
318 | 251.8,0,99.9,146.1,12.4,1006,899.8,28,33.94
319 | 251.8,0,99.9,146.1,12.4,1006,899.8,56,44.14
320 | 251.8,0,99.9,146.1,12.4,1006,899.8,100,45.37
321 | 249.1,0,98.8,158.1,12.8,987.8,889,3,15.36
322 | 249.1,0,98.8,158.1,12.8,987.8,889,14,28.68
323 | 249.1,0,98.8,158.1,12.8,987.8,889,28,30.85
324 | 249.1,0,98.8,158.1,12.8,987.8,889,56,42.03
325 | 249.1,0,98.8,158.1,12.8,987.8,889,100,51.06
326 | 252.3,0,98.8,146.3,14.2,987.8,889,3,21.78
327 | 252.3,0,98.8,146.3,14.2,987.8,889,14,42.29
328 | 252.3,0,98.8,146.3,14.2,987.8,889,28,50.6
329 | 252.3,0,98.8,146.3,14.2,987.8,889,56,55.83
330 | 252.3,0,98.8,146.3,14.2,987.8,889,100,60.95
331 | 246.8,0,125.1,143.3,12,1086.8,800.9,3,23.52
332 | 246.8,0,125.1,143.3,12,1086.8,800.9,14,42.22
333 | 246.8,0,125.1,143.3,12,1086.8,800.9,28,52.5
334 | 246.8,0,125.1,143.3,12,1086.8,800.9,56,60.32
335 | 246.8,0,125.1,143.3,12,1086.8,800.9,100,66.42
336 | 275.1,0,121.4,159.5,9.9,1053.6,777.5,3,23.8
337 | 275.1,0,121.4,159.5,9.9,1053.6,777.5,14,38.77
338 | 275.1,0,121.4,159.5,9.9,1053.6,777.5,28,51.33
339 | 275.1,0,121.4,159.5,9.9,1053.6,777.5,56,56.85
340 | 275.1,0,121.4,159.5,9.9,1053.6,777.5,100,58.61
341 | 297.2,0,117.5,174.8,9.5,1022.8,753.5,3,21.91
342 | 297.2,0,117.5,174.8,9.5,1022.8,753.5,14,36.99
343 | 297.2,0,117.5,174.8,9.5,1022.8,753.5,28,47.4
344 | 297.2,0,117.5,174.8,9.5,1022.8,753.5,56,51.96
345 | 297.2,0,117.5,174.8,9.5,1022.8,753.5,100,56.74
346 | 213.7,0,174.7,154.8,10.2,1053.5,776.4,3,17.57
347 | 213.7,0,174.7,154.8,10.2,1053.5,776.4,14,33.73
348 | 213.7,0,174.7,154.8,10.2,1053.5,776.4,28,40.15
349 | 213.7,0,174.7,154.8,10.2,1053.5,776.4,56,46.64
350 | 213.7,0,174.7,154.8,10.2,1053.5,776.4,100,50.08
351 | 213.5,0,174.2,154.6,11.7,1052.3,775.5,3,17.37
352 | 213.5,0,174.2,154.6,11.7,1052.3,775.5,14,33.7
353 | 213.5,0,174.2,154.6,11.7,1052.3,775.5,28,45.94
354 | 213.5,0,174.2,154.6,11.7,1052.3,775.5,56,51.43
355 | 213.5,0,174.2,154.6,11.7,1052.3,775.5,100,59.3
356 | 277.2,97.8,24.5,160.7,11.2,1061.7,782.5,3,30.45
357 | 277.2,97.8,24.5,160.7,11.2,1061.7,782.5,14,47.71
358 | 277.2,97.8,24.5,160.7,11.2,1061.7,782.5,28,63.14
359 | 277.2,97.8,24.5,160.7,11.2,1061.7,782.5,56,66.82
360 | 277.2,97.8,24.5,160.7,11.2,1061.7,782.5,100,66.95
361 | 218.2,54.6,123.8,140.8,11.9,1075.7,792.7,3,27.42
362 | 218.2,54.6,123.8,140.8,11.9,1075.7,792.7,14,35.96
363 | 218.2,54.6,123.8,140.8,11.9,1075.7,792.7,28,55.51
364 | 218.2,54.6,123.8,140.8,11.9,1075.7,792.7,56,61.99
365 | 218.2,54.6,123.8,140.8,11.9,1075.7,792.7,100,63.53
366 | 214.9,53.8,121.9,155.6,9.6,1014.3,780.6,3,18.02
367 | 214.9,53.8,121.9,155.6,9.6,1014.3,780.6,14,38.6
368 | 214.9,53.8,121.9,155.6,9.6,1014.3,780.6,28,52.2
369 | 214.9,53.8,121.9,155.6,9.6,1014.3,780.6,56,53.96
370 | 214.9,53.8,121.9,155.6,9.6,1014.3,780.6,100,56.63
371 | 218.9,0,124.1,158.5,11.3,1078.7,794.9,3,15.34
372 | 218.9,0,124.1,158.5,11.3,1078.7,794.9,14,26.05
373 | 218.9,0,124.1,158.5,11.3,1078.7,794.9,28,30.22
374 | 218.9,0,124.1,158.5,11.3,1078.7,794.9,56,37.27
375 | 218.9,0,124.1,158.5,11.3,1078.7,794.9,100,46.23
376 | 376,0,0,214.6,0,1003.5,762.4,3,16.28
377 | 376,0,0,214.6,0,1003.5,762.4,14,25.62
378 | 376,0,0,214.6,0,1003.5,762.4,28,31.97
379 | 376,0,0,214.6,0,1003.5,762.4,56,36.3
380 | 376,0,0,214.6,0,1003.5,762.4,100,43.06
381 | 500,0,0,140,4,966,853,28,67.57
382 | 475,0,59,142,1.9,1098,641,28,57.23
383 | 315,137,0,145,5.9,1130,745,28,81.75
384 | 505,0,60,195,0,1030,630,28,64.02
385 | 451,0,0,165,11.3,1030,745,28,78.8
386 | 516,0,0,162,8.2,801,802,28,41.37
387 | 520,0,0,170,5.2,855,855,28,60.28
388 | 528,0,0,185,6.9,920,720,28,56.83
389 | 520,0,0,175,5.2,870,805,28,51.02
390 | 385,0,136,158,20,903,768,28,55.55
391 | 500.1,0,0,200,3,1124.4,613.2,28,44.13
392 | 450.1,50,0,200,3,1124.4,613.2,28,39.38
393 | 397,17.2,158,167,20.8,967,633,28,55.65
394 | 333,17.5,163,167,17.9,996,652,28,47.28
395 | 334,17.6,158,189,15.3,967,633,28,44.33
396 | 405,0,0,175,0,1120,695,28,52.3
397 | 200,200,0,190,0,1145,660,28,49.25
398 | 516,0,0,162,8.3,801,802,28,41.37
399 | 145,116,119,184,5.7,833,880,28,29.16
400 | 160,128,122,182,6.4,824,879,28,39.4
401 | 234,156,0,189,5.9,981,760,28,39.3
402 | 250,180,95,159,9.5,860,800,28,67.87
403 | 475,0,0,162,9.5,1044,662,28,58.52
404 | 285,190,0,163,7.6,1031,685,28,53.58
405 | 356,119,0,160,9,1061,657,28,59
406 | 275,180,120,162,10.4,830,765,28,76.24
407 | 500,0,0,151,9,1033,655,28,69.84
408 | 165,0,143.6,163.8,0,1005.6,900.9,3,14.4
409 | 165,128.5,132.1,175.1,8.1,1005.8,746.6,3,19.42
410 | 178,129.8,118.6,179.9,3.6,1007.3,746.8,3,20.73
411 | 167.4,129.9,128.6,175.5,7.8,1006.3,746.6,3,14.94
412 | 172.4,13.6,172.4,156.8,4.1,1006.3,856.4,3,21.29
413 | 173.5,50.1,173.5,164.8,6.5,1006.2,793.5,3,23.08
414 | 167,75.4,167,164,7.9,1007.3,770.1,3,15.52
415 | 173.8,93.4,159.9,172.3,9.7,1007.2,746.6,3,15.82
416 | 190.3,0,125.2,166.6,9.9,1079,798.9,3,12.55
417 | 250,0,95.7,191.8,5.3,948.9,857.2,3,8.49
418 | 213.5,0,174.2,159.2,11.7,1043.6,771.9,3,15.61
419 | 194.7,0,100.5,170.2,7.5,998,901.8,3,12.18
420 | 251.4,0,118.3,192.9,5.8,1043.6,754.3,3,11.98
421 | 165,0,143.6,163.8,0,1005.6,900.9,14,16.88
422 | 165,128.5,132.1,175.1,8.1,1005.8,746.6,14,33.09
423 | 178,129.8,118.6,179.9,3.6,1007.3,746.8,14,34.24
424 | 167.4,129.9,128.6,175.5,7.8,1006.3,746.6,14,31.81
425 | 172.4,13.6,172.4,156.8,4.1,1006.3,856.4,14,29.75
426 | 173.5,50.1,173.5,164.8,6.5,1006.2,793.5,14,33.01
427 | 167,75.4,167,164,7.9,1007.3,770.1,14,32.9
428 | 173.8,93.4,159.9,172.3,9.7,1007.2,746.6,14,29.55
429 | 190.3,0,125.2,166.6,9.9,1079,798.9,14,19.42
430 | 250,0,95.7,191.8,5.3,948.9,857.2,14,24.66
431 | 213.5,0,174.2,159.2,11.7,1043.6,771.9,14,29.59
432 | 194.7,0,100.5,170.2,7.5,998,901.8,14,24.28
433 | 251.4,0,118.3,192.9,5.8,1043.6,754.3,14,20.73
434 | 165,0,143.6,163.8,0,1005.6,900.9,28,26.2
435 | 165,128.5,132.1,175.1,8.1,1005.8,746.6,28,46.39
436 | 178,129.8,118.6,179.9,3.6,1007.3,746.8,28,39.16
437 | 167.4,129.9,128.6,175.5,7.8,1006.3,746.6,28,41.2
438 | 172.4,13.6,172.4,156.8,4.1,1006.3,856.4,28,33.69
439 | 173.5,50.1,173.5,164.8,6.5,1006.2,793.5,28,38.2
440 | 167,75.4,167,164,7.9,1007.3,770.1,28,41.41
441 | 173.8,93.4,159.9,172.3,9.7,1007.2,746.6,28,37.81
442 | 190.3,0,125.2,166.6,9.9,1079,798.9,28,24.85
443 | 250,0,95.7,191.8,5.3,948.9,857.2,28,27.22
444 | 213.5,0,174.2,159.2,11.7,1043.6,771.9,28,44.64
445 | 194.7,0,100.5,170.2,7.5,998,901.8,28,37.27
446 | 251.4,0,118.3,192.9,5.8,1043.6,754.3,28,33.27
447 | 165,0,143.6,163.8,0,1005.6,900.9,56,36.56
448 | 165,128.5,132.1,175.1,8.1,1005.8,746.6,56,53.72
449 | 178,129.8,118.6,179.9,3.6,1007.3,746.8,56,48.59
450 | 167.4,129.9,128.6,175.5,7.8,1006.3,746.6,56,51.72
451 | 172.4,13.6,172.4,156.8,4.1,1006.3,856.4,56,35.85
452 | 173.5,50.1,173.5,164.8,6.5,1006.2,793.5,56,53.77
453 | 167,75.4,167,164,7.9,1007.3,770.1,56,53.46
454 | 173.8,93.4,159.9,172.3,9.7,1007.2,746.6,56,48.99
455 | 190.3,0,125.2,166.6,9.9,1079,798.9,56,31.72
456 | 250,0,95.7,191.8,5.3,948.9,857.2,56,39.64
457 | 213.5,0,174.2,159.2,11.7,1043.6,771.9,56,51.26
458 | 194.7,0,100.5,170.2,7.5,998,901.8,56,43.39
459 | 251.4,0,118.3,192.9,5.8,1043.6,754.3,56,39.27
460 | 165,0,143.6,163.8,0,1005.6,900.9,100,37.96
461 | 165,128.5,132.1,175.1,8.1,1005.8,746.6,100,55.02
462 | 178,129.8,118.6,179.9,3.6,1007.3,746.8,100,49.99
463 | 167.4,129.9,128.6,175.5,7.8,1006.3,746.6,100,53.66
464 | 172.4,13.6,172.4,156.8,4.1,1006.3,856.4,100,37.68
465 | 173.5,50.1,173.5,164.8,6.5,1006.2,793.5,100,56.06
466 | 167,75.4,167,164,7.9,1007.3,770.1,100,56.81
467 | 173.8,93.4,159.9,172.3,9.7,1007.2,746.6,100,50.94
468 | 190.3,0,125.2,166.6,9.9,1079,798.9,100,33.56
469 | 250,0,95.7,191.8,5.3,948.9,857.2,100,41.16
470 | 213.5,0,174.2,159.2,11.7,1043.6,771.9,100,52.96
471 | 194.7,0,100.5,170.2,7.5,998,901.8,100,44.28
472 | 251.4,0,118.3,192.9,5.8,1043.6,754.3,100,40.15
473 | 446,24,79,162,11.6,967,712,28,57.03
474 | 446,24,79,162,11.6,967,712,28,44.42
475 | 446,24,79,162,11.6,967,712,28,51.02
476 | 446,24,79,162,10.3,967,712,28,53.39
477 | 446,24,79,162,11.6,967,712,3,35.36
478 | 446,24,79,162,11.6,967,712,3,25.02
479 | 446,24,79,162,11.6,967,712,3,23.35
480 | 446,24,79,162,11.6,967,712,7,52.01
481 | 446,24,79,162,11.6,967,712,7,38.02
482 | 446,24,79,162,11.6,967,712,7,39.3
483 | 446,24,79,162,11.6,967,712,56,61.07
484 | 446,24,79,162,11.6,967,712,56,56.14
485 | 446,24,79,162,11.6,967,712,56,55.25
486 | 446,24,79,162,10.3,967,712,56,54.77
487 | 387,20,94,157,14.3,938,845,28,50.24
488 | 387,20,94,157,13.9,938,845,28,46.68
489 | 387,20,94,157,11.6,938,845,28,46.68
490 | 387,20,94,157,14.3,938,845,3,22.75
491 | 387,20,94,157,13.9,938,845,3,25.51
492 | 387,20,94,157,11.6,938,845,3,34.77
493 | 387,20,94,157,14.3,938,845,7,36.84
494 | 387,20,94,157,13.9,938,845,7,45.9
495 | 387,20,94,157,11.6,938,845,7,41.67
496 | 387,20,94,157,14.3,938,845,56,56.34
497 | 387,20,94,157,13.9,938,845,56,47.97
498 | 387,20,94,157,11.6,938,845,56,61.46
499 | 355,19,97,145,13.1,967,871,28,44.03
500 | 355,19,97,145,12.3,967,871,28,55.45
501 | 491,26,123,210,3.9,882,699,28,55.55
502 | 491,26,123,201,3.9,822,699,28,57.92
503 | 491,26,123,210,3.9,882,699,3,25.61
504 | 491,26,123,210,3.9,882,699,7,33.49
505 | 491,26,123,210,3.9,882,699,56,59.59
506 | 491,26,123,201,3.9,822,699,3,29.55
507 | 491,26,123,201,3.9,822,699,7,37.92
508 | 491,26,123,201,3.9,822,699,56,61.86
509 | 424,22,132,178,8.5,822,750,28,62.05
510 | 424,22,132,178,8.5,882,750,3,32.01
511 | 424,22,132,168,8.9,822,750,28,72.1
512 | 424,22,132,178,8.5,822,750,7,39
513 | 424,22,132,178,8.5,822,750,56,65.7
514 | 424,22,132,168,8.9,822,750,3,32.11
515 | 424,22,132,168,8.9,822,750,7,40.29
516 | 424,22,132,168,8.9,822,750,56,74.36
517 | 202,11,141,206,1.7,942,801,28,21.97
518 | 202,11,141,206,1.7,942,801,3,9.85
519 | 202,11,141,206,1.7,942,801,7,15.07
520 | 202,11,141,206,1.7,942,801,56,23.25
521 | 284,15,141,179,5.5,842,801,28,43.73
522 | 284,15,141,179,5.5,842,801,3,13.4
523 | 284,15,141,179,5.5,842,801,7,24.13
524 | 284,15,141,179,5.5,842,801,56,44.52
525 | 359,19,141,154,10.9,942,801,28,62.94
526 | 359,19,141,154,10.9,942,801,28,59.49
527 | 359,19,141,154,10.9,942,801,3,25.12
528 | 359,19,141,154,10.9,942,801,3,23.64
529 | 359,19,141,154,10.9,942,801,7,35.75
530 | 359,19,141,154,10.9,942,801,7,38.61
531 | 359,19,141,154,10.9,942,801,56,68.75
532 | 359,19,141,154,10.9,942,801,56,66.78
533 | 436,0,0,218,0,838.4,719.7,28,23.85
534 | 289,0,0,192,0,913.2,895.3,90,32.07
535 | 289,0,0,192,0,913.2,895.3,3,11.65
536 | 393,0,0,192,0,940.6,785.6,3,19.2
537 | 393,0,0,192,0,940.6,785.6,90,48.85
538 | 393,0,0,192,0,940.6,785.6,28,39.6
539 | 480,0,0,192,0,936.2,712.2,28,43.94
540 | 480,0,0,192,0,936.2,712.2,7,34.57
541 | 480,0,0,192,0,936.2,712.2,90,54.32
542 | 480,0,0,192,0,936.2,712.2,3,24.4
543 | 333,0,0,192,0,931.2,842.6,3,15.62
544 | 255,0,0,192,0,889.8,945,90,21.86
545 | 255,0,0,192,0,889.8,945,7,10.22
546 | 289,0,0,192,0,913.2,895.3,7,14.6
547 | 255,0,0,192,0,889.8,945,28,18.75
548 | 333,0,0,192,0,931.2,842.6,28,31.97
549 | 333,0,0,192,0,931.2,842.6,7,23.4
550 | 289,0,0,192,0,913.2,895.3,28,25.57
551 | 333,0,0,192,0,931.2,842.6,90,41.68
552 | 393,0,0,192,0,940.6,785.6,7,27.74
553 | 255,0,0,192,0,889.8,945,3,8.2
554 | 158.8,238.2,0,185.7,0,1040.6,734.3,7,9.62
555 | 239.6,359.4,0,185.7,0,941.6,664.3,7,25.42
556 | 238.2,158.8,0,185.7,0,1040.6,734.3,7,15.69
557 | 181.9,272.8,0,185.7,0,1012.4,714.3,28,27.94
558 | 193.5,290.2,0,185.7,0,998.2,704.3,28,32.63
559 | 255.5,170.3,0,185.7,0,1026.6,724.3,7,17.24
560 | 272.8,181.9,0,185.7,0,1012.4,714.3,7,19.77
561 | 239.6,359.4,0,185.7,0,941.6,664.3,28,39.44
562 | 220.8,147.2,0,185.7,0,1055,744.3,28,25.75
563 | 397,0,0,185.7,0,1040.6,734.3,28,33.08
564 | 382.5,0,0,185.7,0,1047.8,739.3,7,24.07
565 | 210.7,316.1,0,185.7,0,977,689.3,7,21.82
566 | 158.8,238.2,0,185.7,0,1040.6,734.3,28,21.07
567 | 295.8,0,0,185.7,0,1091.4,769.3,7,14.84
568 | 255.5,170.3,0,185.7,0,1026.6,724.3,28,32.05
569 | 203.5,135.7,0,185.7,0,1076.2,759.3,7,11.96
570 | 397,0,0,185.7,0,1040.6,734.3,7,25.45
571 | 381.4,0,0,185.7,0,1104.6,784.3,28,22.49
572 | 295.8,0,0,185.7,0,1091.4,769.3,28,25.22
573 | 228,342.1,0,185.7,0,955.8,674.3,28,39.7
574 | 220.8,147.2,0,185.7,0,1055,744.3,7,13.09
575 | 316.1,210.7,0,185.7,0,977,689.3,28,38.7
576 | 135.7,203.5,0,185.7,0,1076.2,759.3,7,7.51
577 | 238.1,0,0,185.7,0,1118.8,789.3,28,17.58
578 | 339.2,0,0,185.7,0,1069.2,754.3,7,21.18
579 | 135.7,203.5,0,185.7,0,1076.2,759.3,28,18.2
580 | 193.5,290.2,0,185.7,0,998.2,704.3,7,17.2
581 | 203.5,135.7,0,185.7,0,1076.2,759.3,28,22.63
582 | 290.2,193.5,0,185.7,0,998.2,704.3,7,21.86
583 | 181.9,272.8,0,185.7,0,1012.4,714.3,7,12.37
584 | 170.3,155.5,0,185.7,0,1026.6,724.3,28,25.73
585 | 210.7,316.1,0,185.7,0,977,689.3,28,37.81
586 | 228,342.1,0,185.7,0,955.8,674.3,7,21.92
587 | 290.2,193.5,0,185.7,0,998.2,704.3,28,33.04
588 | 381.4,0,0,185.7,0,1104.6,784.3,7,14.54
589 | 238.2,158.8,0,185.7,0,1040.6,734.3,28,26.91
590 | 186.2,124.1,0,185.7,0,1083.4,764.3,7,8
591 | 339.2,0,0,185.7,0,1069.2,754.3,28,31.9
592 | 238.1,0,0,185.7,0,1118.8,789.3,7,10.34
593 | 252.5,0,0,185.7,0,1111.6,784.3,28,19.77
594 | 382.5,0,0,185.7,0,1047.8,739.3,28,37.44
595 | 252.5,0,0,185.7,0,1111.6,784.3,7,11.48
596 | 316.1,210.7,0,185.7,0,977,689.3,7,24.44
597 | 186.2,124.1,0,185.7,0,1083.4,764.3,28,17.6
598 | 170.3,155.5,0,185.7,0,1026.6,724.3,7,10.73
599 | 272.8,181.9,0,185.7,0,1012.4,714.3,28,31.38
600 | 339,0,0,197,0,968,781,3,13.22
601 | 339,0,0,197,0,968,781,7,20.97
602 | 339,0,0,197,0,968,781,14,27.04
603 | 339,0,0,197,0,968,781,28,32.04
604 | 339,0,0,197,0,968,781,90,35.17
605 | 339,0,0,197,0,968,781,180,36.45
606 | 339,0,0,197,0,968,781,365,38.89
607 | 236,0,0,194,0,968,885,3,6.47
608 | 236,0,0,194,0,968,885,14,12.84
609 | 236,0,0,194,0,968,885,28,18.42
610 | 236,0,0,194,0,968,885,90,21.95
611 | 236,0,0,193,0,968,885,180,24.1
612 | 236,0,0,193,0,968,885,365,25.08
613 | 277,0,0,191,0,968,856,14,21.26
614 | 277,0,0,191,0,968,856,28,25.97
615 | 277,0,0,191,0,968,856,3,11.36
616 | 277,0,0,191,0,968,856,90,31.25
617 | 277,0,0,191,0,968,856,180,32.33
618 | 277,0,0,191,0,968,856,360,33.7
619 | 254,0,0,198,0,968,863,3,9.31
620 | 254,0,0,198,0,968,863,90,26.94
621 | 254,0,0,198,0,968,863,180,27.63
622 | 254,0,0,198,0,968,863,365,29.79
623 | 307,0,0,193,0,968,812,180,34.49
624 | 307,0,0,193,0,968,812,365,36.15
625 | 307,0,0,193,0,968,812,3,12.54
626 | 307,0,0,193,0,968,812,28,27.53
627 | 307,0,0,193,0,968,812,90,32.92
628 | 236,0,0,193,0,968,885,7,9.99
629 | 200,0,0,180,0,1125,845,7,7.84
630 | 200,0,0,180,0,1125,845,28,12.25
631 | 225,0,0,181,0,1113,833,7,11.17
632 | 225,0,0,181,0,1113,833,28,17.34
633 | 325,0,0,184,0,1063,783,7,17.54
634 | 325,0,0,184,0,1063,783,28,30.57
635 | 275,0,0,183,0,1088,808,7,14.2
636 | 275,0,0,183,0,1088,808,28,24.5
637 | 300,0,0,184,0,1075,795,7,15.58
638 | 300,0,0,184,0,1075,795,28,26.85
639 | 375,0,0,186,0,1038,758,7,26.06
640 | 375,0,0,186,0,1038,758,28,38.21
641 | 400,0,0,187,0,1025,745,28,43.7
642 | 400,0,0,187,0,1025,745,7,30.14
643 | 250,0,0,182,0,1100,820,7,12.73
644 | 250,0,0,182,0,1100,820,28,20.87
645 | 350,0,0,186,0,1050,770,7,20.28
646 | 350,0,0,186,0,1050,770,28,34.29
647 | 203.5,305.3,0,203.5,0,963.4,630,7,19.54
648 | 250.2,166.8,0,203.5,0,977.6,694.1,90,47.71
649 | 157,236,0,192,0,935.4,781.2,90,43.38
650 | 141.3,212,0,203.5,0,971.8,748.5,28,29.89
651 | 166.8,250.2,0,203.5,0,975.6,692.6,3,6.9
652 | 122.6,183.9,0,203.5,0,958.2,800.1,90,33.19
653 | 183.9,122.6,0,203.5,0,959.2,800,3,4.9
654 | 102,153,0,192,0,887,942,3,4.57
655 | 102,153,0,192,0,887,942,90,25.46
656 | 122.6,183.9,0,203.5,0,958.2,800.1,28,24.29
657 | 166.8,250.2,0,203.5,0,975.6,692.6,28,33.95
658 | 200,133,0,192,0,965.4,806.2,3,11.41
659 | 108.3,162.4,0,203.5,0,938.2,849,28,20.59
660 | 305.3,203.5,0,203.5,0,965.4,631,7,25.89
661 | 108.3,162.4,0,203.5,0,938.2,849,90,29.23
662 | 116,173,0,192,0,909.8,891.9,90,31.02
663 | 141.3,212,0,203.5,0,971.8,748.5,7,10.39
664 | 157,236,0,192,0,935.4,781.2,28,33.66
665 | 133,200,0,192,0,927.4,839.2,28,27.87
666 | 250.2,166.8,0,203.5,0,977.6,694.1,7,19.35
667 | 173,116,0,192,0,946.8,856.8,7,11.39
668 | 192,288,0,192,0,929.8,716.1,3,12.79
669 | 192,288,0,192,0,929.8,716.1,28,39.32
670 | 153,102,0,192,0,888,943.1,3,4.78
671 | 288,192,0,192,0,932,717.8,3,16.11
672 | 305.3,203.5,0,203.5,0,965.4,631,28,43.38
673 | 236,157,0,192,0,972.6,749.1,7,20.42
674 | 173,116,0,192,0,946.8,856.8,3,6.94
675 | 212,141.3,0,203.5,0,973.4,750,7,15.03
676 | 236,157,0,192,0,972.6,749.1,3,13.57
677 | 183.9,122.6,0,203.5,0,959.2,800,90,32.53
678 | 166.8,250.2,0,203.5,0,975.6,692.6,7,15.75
679 | 102,153,0,192,0,887,942,7,7.68
680 | 288,192,0,192,0,932,717.8,28,38.8
681 | 212,141.3,0,203.5,0,973.4,750,28,33
682 | 102,153,0,192,0,887,942,28,17.28
683 | 173,116,0,192,0,946.8,856.8,28,24.28
684 | 183.9,122.6,0,203.5,0,959.2,800,28,24.05
685 | 133,200,0,192,0,927.4,839.2,90,36.59
686 | 192,288,0,192,0,929.8,716.1,90,50.73
687 | 133,200,0,192,0,927.4,839.2,7,13.66
688 | 305.3,203.5,0,203.5,0,965.4,631,3,14.14
689 | 236,157,0,192,0,972.6,749.1,90,47.78
690 | 108.3,162.4,0,203.5,0,938.2,849,3,2.33
691 | 157,236,0,192,0,935.4,781.2,7,16.89
692 | 288,192,0,192,0,932,717.8,7,23.52
693 | 212,141.3,0,203.5,0,973.4,750,3,6.81
694 | 212,141.3,0,203.5,0,973.4,750,90,39.7
695 | 153,102,0,192,0,888,943.1,28,17.96
696 | 236,157,0,192,0,972.6,749.1,28,32.88
697 | 116,173,0,192,0,909.8,891.9,28,22.35
698 | 183.9,122.6,0,203.5,0,959.2,800,7,10.79
699 | 108.3,162.4,0,203.5,0,938.2,849,7,7.72
700 | 203.5,305.3,0,203.5,0,963.4,630,28,41.68
701 | 203.5,305.3,0,203.5,0,963.4,630,3,9.56
702 | 133,200,0,192,0,927.4,839.2,3,6.88
703 | 288,192,0,192,0,932,717.8,90,50.53
704 | 200,133,0,192,0,965.4,806.2,7,17.17
705 | 200,133,0,192,0,965.4,806.2,28,30.44
706 | 250.2,166.8,0,203.5,0,977.6,694.1,3,9.73
707 | 122.6,183.9,0,203.5,0,958.2,800.1,3,3.32
708 | 153,102,0,192,0,888,943.1,90,26.32
709 | 200,133,0,192,0,965.4,806.2,90,43.25
710 | 116,173,0,192,0,909.8,891.9,3,6.28
711 | 173,116,0,192,0,946.8,856.8,90,32.1
712 | 250.2,166.8,0,203.5,0,977.6,694.1,28,36.96
713 | 305.3,203.5,0,203.5,0,965.4,631,90,54.6
714 | 192,288,0,192,0,929.8,716.1,7,21.48
715 | 157,236,0,192,0,935.4,781.2,3,9.69
716 | 153,102,0,192,0,888,943.1,7,8.37
717 | 141.3,212,0,203.5,0,971.8,748.5,90,39.66
718 | 116,173,0,192,0,909.8,891.9,7,10.09
719 | 141.3,212,0,203.5,0,971.8,748.5,3,4.83
720 | 122.6,183.9,0,203.5,0,958.2,800.1,7,10.35
721 | 166.8,250.2,0,203.5,0,975.6,692.6,90,43.57
722 | 203.5,305.3,0,203.5,0,963.4,630,90,51.86
723 | 310,0,0,192,0,1012,830,3,11.85
724 | 310,0,0,192,0,1012,830,7,17.24
725 | 310,0,0,192,0,1012,830,28,27.83
726 | 310,0,0,192,0,1012,830,90,35.76
727 | 310,0,0,192,0,1012,830,120,38.7
728 | 331,0,0,192,0,1025,821,3,14.31
729 | 331,0,0,192,0,1025,821,7,17.44
730 | 331,0,0,192,0,1025,821,28,31.74
731 | 331,0,0,192,0,1025,821,90,37.91
732 | 331,0,0,192,0,1025,821,120,39.38
733 | 349,0,0,192,0,1056,809,3,15.87
734 | 349,0,0,192,0,1056,809,7,9.01
735 | 349,0,0,192,0,1056,809,28,33.61
736 | 349,0,0,192,0,1056,809,90,40.66
737 | 349,0,0,192,0,1056,809,120,40.86
738 | 238,0,0,186,0,1119,789,7,12.05
739 | 238,0,0,186,0,1119,789,28,17.54
740 | 296,0,0,186,0,1090,769,7,18.91
741 | 296,0,0,186,0,1090,769,28,25.18
742 | 297,0,0,186,0,1040,734,7,30.96
743 | 480,0,0,192,0,936,721,28,43.89
744 | 480,0,0,192,0,936,721,90,54.28
745 | 397,0,0,186,0,1040,734,28,36.94
746 | 281,0,0,186,0,1104,774,7,14.5
747 | 281,0,0,185,0,1104,774,28,22.44
748 | 500,0,0,200,0,1125,613,1,12.64
749 | 500,0,0,200,0,1125,613,3,26.06
750 | 500,0,0,200,0,1125,613,7,33.21
751 | 500,0,0,200,0,1125,613,14,36.94
752 | 500,0,0,200,0,1125,613,28,44.09
753 | 540,0,0,173,0,1125,613,7,52.61
754 | 540,0,0,173,0,1125,613,14,59.76
755 | 540,0,0,173,0,1125,613,28,67.31
756 | 540,0,0,173,0,1125,613,90,69.66
757 | 540,0,0,173,0,1125,613,180,71.62
758 | 540,0,0,173,0,1125,613,270,74.17
759 | 350,0,0,203,0,974,775,7,18.13
760 | 350,0,0,203,0,974,775,14,22.53
761 | 350,0,0,203,0,974,775,28,27.34
762 | 350,0,0,203,0,974,775,56,29.98
763 | 350,0,0,203,0,974,775,90,31.35
764 | 350,0,0,203,0,974,775,180,32.72
765 | 385,0,0,186,0,966,763,1,6.27
766 | 385,0,0,186,0,966,763,3,14.7
767 | 385,0,0,186,0,966,763,7,23.22
768 | 385,0,0,186,0,966,763,14,27.92
769 | 385,0,0,186,0,966,763,28,31.35
770 | 331,0,0,192,0,978,825,180,39
771 | 331,0,0,192,0,978,825,360,41.24
772 | 349,0,0,192,0,1047,806,3,14.99
773 | 331,0,0,192,0,978,825,3,13.52
774 | 382,0,0,186,0,1047,739,7,24
775 | 382,0,0,186,0,1047,739,28,37.42
776 | 382,0,0,186,0,1111,784,7,11.47
777 | 281,0,0,186,0,1104,774,28,22.44
778 | 339,0,0,185,0,1069,754,7,21.16
779 | 339,0,0,185,0,1069,754,28,31.84
780 | 295,0,0,185,0,1069,769,7,14.8
781 | 295,0,0,185,0,1069,769,28,25.18
782 | 238,0,0,185,0,1118,789,28,17.54
783 | 296,0,0,192,0,1085,765,7,14.2
784 | 296,0,0,192,0,1085,765,28,21.65
785 | 296,0,0,192,0,1085,765,90,29.39
786 | 331,0,0,192,0,879,825,3,13.52
787 | 331,0,0,192,0,978,825,7,16.26
788 | 331,0,0,192,0,978,825,28,31.45
789 | 331,0,0,192,0,978,825,90,37.23
790 | 349,0,0,192,0,1047,806,7,18.13
791 | 349,0,0,192,0,1047,806,28,32.72
792 | 349,0,0,192,0,1047,806,90,39.49
793 | 349,0,0,192,0,1047,806,180,41.05
794 | 349,0,0,192,0,1047,806,360,42.13
795 | 302,0,0,203,0,974,817,14,18.13
796 | 302,0,0,203,0,974,817,180,26.74
797 | 525,0,0,189,0,1125,613,180,61.92
798 | 500,0,0,200,0,1125,613,90,47.22
799 | 500,0,0,200,0,1125,613,180,51.04
800 | 500,0,0,200,0,1125,613,270,55.16
801 | 540,0,0,173,0,1125,613,3,41.64
802 | 252,0,0,185,0,1111,784,7,13.71
803 | 252,0,0,185,0,1111,784,28,19.69
804 | 339,0,0,185,0,1060,754,28,31.65
805 | 393,0,0,192,0,940,758,3,19.11
806 | 393,0,0,192,0,940,758,28,39.58
807 | 393,0,0,192,0,940,758,90,48.79
808 | 382,0,0,185,0,1047,739,7,24
809 | 382,0,0,185,0,1047,739,28,37.42
810 | 252,0,0,186,0,1111,784,7,11.47
811 | 252,0,0,185,0,1111,784,28,19.69
812 | 310,0,0,192,0,970,850,7,14.99
813 | 310,0,0,192,0,970,850,28,27.92
814 | 310,0,0,192,0,970,850,90,34.68
815 | 310,0,0,192,0,970,850,180,37.33
816 | 310,0,0,192,0,970,850,360,38.11
817 | 525,0,0,189,0,1125,613,3,33.8
818 | 525,0,0,189,0,1125,613,7,42.42
819 | 525,0,0,189,0,1125,613,14,48.4
820 | 525,0,0,189,0,1125,613,28,55.94
821 | 525,0,0,189,0,1125,613,90,58.78
822 | 525,0,0,189,0,1125,613,270,67.11
823 | 322,0,0,203,0,974,800,14,20.77
824 | 322,0,0,203,0,974,800,28,25.18
825 | 322,0,0,203,0,974,800,180,29.59
826 | 302,0,0,203,0,974,817,28,21.75
827 | 397,0,0,185,0,1040,734,28,39.09
828 | 480,0,0,192,0,936,721,3,24.39
829 | 522,0,0,146,0,896,896,7,50.51
830 | 522,0,0,146,0,896,896,28,74.99
831 | 273,105,82,210,9,904,680,28,37.17
832 | 162,190,148,179,19,838,741,28,33.76
833 | 154,144,112,220,10,923,658,28,16.5
834 | 147,115,89,202,9,860,829,28,19.99
835 | 152,178,139,168,18,944,695,28,36.35
836 | 310,143,111,168,22,914,651,28,33.69
837 | 144,0,175,158,18,943,844,28,15.42
838 | 304,140,0,214,6,895,722,28,33.42
839 | 374,0,0,190,7,1013,730,28,39.05
840 | 159,149,116,175,15,953,720,28,27.68
841 | 153,239,0,200,6,1002,684,28,26.86
842 | 310,143,0,168,10,914,804,28,45.3
843 | 305,0,100,196,10,959,705,28,30.12
844 | 151,0,184,167,12,991,772,28,15.57
845 | 142,167,130,174,11,883,785,28,44.61
846 | 298,137,107,201,6,878,655,28,53.52
847 | 321,164,0,190,5,870,774,28,57.21
848 | 366,187,0,191,7,824,757,28,65.91
849 | 280,129,100,172,9,825,805,28,52.82
850 | 252,97,76,194,8,835,821,28,33.4
851 | 165,0,150,182,12,1023,729,28,18.03
852 | 156,243,0,180,11,1022,698,28,37.36
853 | 160,188,146,203,11,829,710,28,32.84
854 | 298,0,107,186,6,879,815,28,42.64
855 | 318,0,126,210,6,861,737,28,40.06
856 | 287,121,94,188,9,904,696,28,41.94
857 | 326,166,0,174,9,882,790,28,61.23
858 | 356,0,142,193,11,801,778,28,40.87
859 | 132,207,161,179,5,867,736,28,33.3
860 | 322,149,0,186,8,951,709,28,52.42
861 | 164,0,200,181,13,849,846,28,15.09
862 | 314,0,113,170,10,925,783,28,38.46
863 | 321,0,128,182,11,870,780,28,37.26
864 | 140,164,128,237,6,869,656,28,35.23
865 | 288,121,0,177,7,908,829,28,42.13
866 | 298,0,107,210,11,880,744,28,31.87
867 | 265,111,86,195,6,833,790,28,41.54
868 | 160,250,0,168,12,1049,688,28,39.45
869 | 166,260,0,183,13,859,827,28,37.91
870 | 276,116,90,180,9,870,768,28,44.28
871 | 322,0,116,196,10,818,813,28,31.18
872 | 149,139,109,193,6,892,780,28,23.69
873 | 159,187,0,176,11,990,789,28,32.76
874 | 261,100,78,201,9,864,761,28,32.4
875 | 237,92,71,247,6,853,695,28,28.63
876 | 313,0,113,178,8,1002,689,28,36.8
877 | 155,183,0,193,9,1047,697,28,18.28
878 | 146,230,0,202,3,827,872,28,33.06
879 | 296,0,107,221,11,819,778,28,31.42
880 | 133,210,0,196,3,949,795,28,31.03
881 | 313,145,0,178,8,867,824,28,44.39
882 | 152,0,112,184,8,992,816,28,12.18
883 | 153,145,113,178,8,1002,689,28,25.56
884 | 140,133,103,200,7,916,753,28,36.44
885 | 149,236,0,176,13,847,893,28,32.96
886 | 300,0,120,212,10,878,728,28,23.84
887 | 153,145,113,178,8,867,824,28,26.23
888 | 148,0,137,158,16,1002,830,28,17.95
889 | 326,0,138,199,11,801,792,28,40.68
890 | 153,145,0,178,8,1000,822,28,19.01
891 | 262,111,86,195,5,895,733,28,33.72
892 | 158,0,195,220,11,898,713,28,8.54
893 | 151,0,185,167,16,1074,678,28,13.46
894 | 273,0,90,199,11,931,762,28,32.24
895 | 149,118,92,183,7,953,780,28,23.52
896 | 143,169,143,191,8,967,643,28,29.72
897 | 260,101,78,171,10,936,763,28,49.77
898 | 313,161,0,178,10,917,759,28,52.44
899 | 284,120,0,168,7,970,794,28,40.93
900 | 336,0,0,182,3,986,817,28,44.86
901 | 145,0,134,181,11,979,812,28,13.2
902 | 150,237,0,174,12,1069,675,28,37.43
903 | 144,170,133,192,8,814,805,28,29.87
904 | 331,170,0,195,8,811,802,28,56.61
905 | 155,0,143,193,9,1047,697,28,12.46
906 | 155,183,0,193,9,877,868,28,23.79
907 | 135,0,166,180,10,961,805,28,13.29
908 | 266,112,87,178,10,910,745,28,39.42
909 | 314,145,113,179,8,869,690,28,46.23
910 | 313,145,0,127,8,1000,822,28,44.52
911 | 146,173,0,182,3,986,817,28,23.74
912 | 144,136,106,178,7,941,774,28,26.14
913 | 148,0,182,181,15,839,884,28,15.52
914 | 277,117,91,191,7,946,666,28,43.57
915 | 298,0,107,164,13,953,784,28,35.86
916 | 313,145,0,178,8,1002,689,28,41.05
917 | 155,184,143,194,9,880,699,28,28.99
918 | 289,134,0,195,6,924,760,28,46.24
919 | 148,175,0,171,2,1000,828,28,26.92
920 | 145,0,179,202,8,824,869,28,10.54
921 | 313,0,0,178,8,1000,822,28,25.1
922 | 136,162,126,172,10,923,764,28,29.07
923 | 155,0,143,193,9,877,868,28,9.74
924 | 255,99,77,189,6,919,749,28,33.8
925 | 162,207,172,216,10,822,638,28,39.84
926 | 136,196,98,199,6,847,783,28,26.97
927 | 164,163,128,197,8,961,641,28,27.23
928 | 162,214,164,202,10,820,680,28,30.65
929 | 157,214,152,200,9,819,704,28,33.05
930 | 149,153,194,192,8,935,623,28,24.58
931 | 135,105,193,196,6,965,643,28,21.91
932 | 159,209,161,201,7,848,669,28,30.88
933 | 144,15,195,176,6,1021,709,28,15.34
934 | 154,174,185,228,7,845,612,28,24.34
935 | 167,187,195,185,7,898,636,28,23.89
936 | 184,86,190,213,6,923,623,28,22.93
937 | 156,178,187,221,7,854,614,28,29.41
938 | 236.9,91.7,71.5,246.9,6,852.9,695.4,28,28.63
939 | 313.3,0,113,178.5,8,1001.9,688.7,28,36.8
940 | 154.8,183.4,0,193.3,9.1,1047.4,696.7,28,18.29
941 | 145.9,230.5,0,202.5,3.4,827,871.8,28,32.72
942 | 296,0,106.7,221.4,10.5,819.2,778.4,28,31.42
943 | 133.1,210.2,0,195.7,3.1,949.4,795.3,28,28.94
944 | 313.3,145,0,178.5,8,867.2,824,28,40.93
945 | 151.6,0,111.9,184.4,7.9,992,815.9,28,12.18
946 | 153.1,145,113,178.5,8,1001.9,688.7,28,25.56
947 | 139.9,132.6,103.3,200.3,7.4,916,753.4,28,36.44
948 | 149.5,236,0,175.8,12.6,846.8,892.7,28,32.96
949 | 299.8,0,119.8,211.5,9.9,878.2,727.6,28,23.84
950 | 153.1,145,113,178.5,8,867.2,824,28,26.23
951 | 148.1,0,136.6,158.1,16.1,1001.8,830.1,28,17.96
952 | 326.5,0,137.9,199,10.8,801.1,792.5,28,38.63
953 | 152.7,144.7,0,178.1,8,999.7,822.2,28,19.01
954 | 261.9,110.5,86.1,195.4,5,895.2,732.6,28,33.72
955 | 158.4,0,194.9,219.7,11,897.7,712.9,28,8.54
956 | 150.7,0,185.3,166.7,15.6,1074.5,678,28,13.46
957 | 272.6,0,89.6,198.7,10.6,931.3,762.2,28,32.25
958 | 149,117.6,91.7,182.9,7.1,953.4,780.3,28,23.52
959 | 143,169.4,142.7,190.7,8.4,967.4,643.5,28,29.73
960 | 259.9,100.6,78.4,170.6,10.4,935.7,762.9,28,49.77
961 | 312.9,160.5,0,177.6,9.6,916.6,759.5,28,52.45
962 | 284,119.7,0,168.3,7.2,970.4,794.2,28,40.93
963 | 336.5,0,0,181.9,3.4,985.8,816.8,28,44.87
964 | 144.8,0,133.6,180.8,11.1,979.5,811.5,28,13.2
965 | 150,236.8,0,173.8,11.9,1069.3,674.8,28,37.43
966 | 143.7,170.2,132.6,191.6,8.5,814.1,805.3,28,29.87
967 | 330.5,169.6,0,194.9,8.1,811,802.3,28,56.62
968 | 154.8,0,142.8,193.3,9.1,1047.4,696.7,28,12.46
969 | 154.8,183.4,0,193.3,9.1,877.2,867.7,28,23.79
970 | 134.7,0,165.7,180.2,10,961,804.9,28,13.29
971 | 266.2,112.3,87.5,177.9,10.4,909.7,744.5,28,39.42
972 | 314,145.3,113.2,178.9,8,869.1,690.2,28,46.23
973 | 312.7,144.7,0,127.3,8,999.7,822.2,28,44.52
974 | 145.7,172.6,0,181.9,3.4,985.8,816.8,28,23.74
975 | 143.8,136.3,106.2,178.1,7.5,941.5,774.3,28,26.15
976 | 148.1,0,182.1,181.4,15,838.9,884.3,28,15.53
977 | 277,116.8,91,190.6,7,946.5,665.6,28,43.58
978 | 298.1,0,107.5,163.6,12.8,953.2,784,28,35.87
979 | 313.3,145,0,178.5,8,1001.9,688.7,28,41.05
980 | 155.2,183.9,143.2,193.8,9.2,879.6,698.5,28,28.99
981 | 289,133.7,0,194.9,5.5,924.1,760.1,28,46.25
982 | 147.8,175.1,0,171.2,2.2,1000,828.5,28,26.92
983 | 145.4,0,178.9,201.7,7.8,824,868.7,28,10.54
984 | 312.7,0,0,178.1,8,999.7,822.2,28,25.1
985 | 136.4,161.6,125.8,171.6,10.4,922.6,764.4,28,29.07
986 | 154.8,0,142.8,193.3,9.1,877.2,867.7,28,9.74
987 | 255.3,98.8,77,188.6,6.5,919,749.3,28,33.8
988 | 272.8,105.1,81.8,209.7,9,904,679.7,28,37.17
989 | 162,190.1,148.1,178.8,18.8,838.1,741.4,28,33.76
990 | 153.6,144.2,112.3,220.1,10.1,923.2,657.9,28,16.5
991 | 146.5,114.6,89.3,201.9,8.8,860,829.5,28,19.99
992 | 151.8,178.1,138.7,167.5,18.3,944,694.6,28,36.35
993 | 309.9,142.8,111.2,167.8,22.1,913.9,651.2,28,38.22
994 | 143.6,0,174.9,158.4,17.9,942.7,844.5,28,15.42
995 | 303.6,139.9,0,213.5,6.2,895.5,722.5,28,33.42
996 | 374.3,0,0,190.2,6.7,1013.2,730.4,28,39.06
997 | 158.6,148.9,116,175.1,15,953.3,719.7,28,27.68
998 | 152.6,238.7,0,200,6.3,1001.8,683.9,28,26.86
999 | 310,142.8,0,167.9,10,914.3,804,28,45.3
1000 | 304.8,0,99.6,196,9.8,959.4,705.2,28,30.12
1001 | 150.9,0,183.9,166.6,11.6,991.2,772.2,28,15.57
1002 | 141.9,166.6,129.7,173.5,10.9,882.6,785.3,28,44.61
1003 | 297.8,137.2,106.9,201.3,6,878.4,655.3,28,53.52
1004 | 321.3,164.2,0,190.5,4.6,870,774,28,57.22
1005 | 366,187,0,191.3,6.6,824.3,756.9,28,65.91
1006 | 279.8,128.9,100.4,172.4,9.5,825.1,804.9,28,52.83
1007 | 252.1,97.1,75.6,193.8,8.3,835.5,821.4,28,33.4
1008 | 164.6,0,150.4,181.6,11.7,1023.3,728.9,28,18.03
1009 | 155.6,243.5,0,180.3,10.7,1022,697.7,28,37.36
1010 | 160.2,188,146.4,203.2,11.3,828.7,709.7,28,35.31
1011 | 298.1,0,107,186.4,6.1,879,815.2,28,42.64
1012 | 317.9,0,126.5,209.7,5.7,860.5,736.6,28,40.06
1013 | 287.3,120.5,93.9,187.6,9.2,904.4,695.9,28,43.8
1014 | 325.6,166.4,0,174,8.9,881.6,790,28,61.24
1015 | 355.9,0,141.6,193.3,11,801.4,778.4,28,40.87
1016 | 132,206.5,160.9,178.9,5.5,866.9,735.6,28,33.31
1017 | 322.5,148.6,0,185.8,8.5,951,709.5,28,52.43
1018 | 164.2,0,200.1,181.2,12.6,849.3,846,28,15.09
1019 | 313.8,0,112.6,169.9,10.1,925.3,782.9,28,38.46
1020 | 321.4,0,127.9,182.5,11.5,870.1,779.7,28,37.27
1021 | 139.7,163.9,127.7,236.7,5.8,868.6,655.6,28,35.23
1022 | 288.4,121,0,177.4,7,907.9,829.5,28,42.14
1023 | 298.2,0,107,209.7,11.1,879.6,744.2,28,31.88
1024 | 264.5,111,86.5,195.5,5.9,832.6,790.4,28,41.54
1025 | 159.8,250,0,168.4,12.2,1049.3,688.2,28,39.46
1026 | 166,259.7,0,183.2,12.7,858.8,826.8,28,37.92
1027 | 276.4,116,90.3,179.6,8.9,870.1,768.3,28,44.28
1028 | 322.2,0,115.6,196,10.4,817.9,813.4,28,31.18
1029 | 148.5,139.4,108.6,192.7,6.1,892.4,780,28,23.7
1030 | 159.1,186.7,0,175.6,11.3,989.6,788.9,28,32.77
1031 | 260.9,100.5,78.3,200.6,8.6,864.5,761.5,28,32.4
1032 |
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/Concrete_Prediction/readme.md:
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1 | # Multiple Regression on Concrete Data
2 |
3 | 
4 |
5 | ## Project Description
6 |
7 | This GitHub repository contains a data analysis project on multiple regression using the "Concrete Data" dataset. The dataset consists of various input features, such as cement, slag, fly ash, water, superplasticizer, coarse aggregate, fine aggregate, and age, to predict the compressive strength (csMPa) of concrete. The project aims to explore and implement various regression models to predict concrete strength.
8 |
9 | ## Introduction
10 | This project explores the application of multiple regression techniques to predict the compressive strength of concrete based on various input features. The dataset used for this analysis includes the following columns:
11 |
12 | - `cement`: Amount of cement in the concrete mix (kg/m³)
13 | - `slag`: Amount of blast furnace slag in the concrete mix (kg/m³)
14 | - `flyash`: Amount of fly ash in the concrete mix (kg/m³)
15 | - `water`: Amount of water in the concrete mix (kg/m³)
16 | - `superplasticizer`: Amount of superplasticizer in the concrete mix (kg/m³)
17 | - `coarseaggregate`: Amount of coarse aggregate in the concrete mix (kg/m³)
18 | - `fineaggregate`: Amount of fine aggregate in the concrete mix (kg/m³)
19 | - `age`: Age of the concrete (days)
20 |
21 | ## Dataset
22 |
23 | The dataset used in this project can be accessed from the following link:
24 | [Concrete Data on Kaggle](https://www.kaggle.com/datasets/kushalvala/concrete/code)
25 |
26 | ## Models
27 |
28 | The project includes the following regression models for predicting concrete compressive strength (csMPa):
29 |
30 | 1. ADA Booster
31 | 2. GradientBoostingRegressor
32 | 3. DecisionTreeRegressor
33 | 4. ElasticNet
34 | 5. RandomForestRegressor
35 | 6. LinearRegression
36 | 7. Support Vector Regression (SVR)
37 | 8. KNeighborsRegressor
38 | 9. MLPRegressor
39 |
40 | ## Project Structure
41 |
42 | The project is structured as follows:
43 |
44 | - **data**: This directory contains the dataset file(s).
45 |
46 | - **notebooks**: Jupyter notebooks or other relevant documents for data exploration and modeling.
47 |
48 | - **models**: Saved model files, if applicable.
49 |
50 | - **results**: This directory may include visualizations, reports, or any other output from the analysis.
51 |
52 | - **README.md**: The main project documentation you are currently reading.
53 |
54 | ## Getting Started
55 |
56 | To get started with this project, follow these steps:
57 |
58 | 1. Clone the repository to your local machine:
59 |
60 |
61 | git clone https://github.com/hiranvjoseph/multiple-regression-concrete-data.git
62 |
63 |
64 | 2. Navigate to the project directory:
65 |
66 |
67 | cd multiple-regression-concrete-data
68 |
69 |
70 | 3. Install the required dependencies. You may want to set up a virtual environment before installing dependencies.
71 |
72 |
73 | pip install -r requirements.txt
74 |
75 |
76 | 4. Download the dataset from the provided [Kaggle link](https://www.kaggle.com/datasets/kushalvala/concrete/code) and place it in the `data` directory.
77 |
78 | 5. Run the Jupyter notebooks or Python scripts in the `notebooks` and `src` directories to explore the data, train the regression models, and evaluate their performance.
79 |
80 | ## Contact Information
81 |
82 | - **Username**: hiranvjoseph
83 | - **Name**: Hiran Joseph
84 | - **Email**: hiranvjoseph@gmail.com
85 |
86 | Feel free to reach out if you have any questions or suggestions regarding this project. Happy coding!
87 |
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/House-Price-Predictions/Real_Estate.csv:
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1 | Transaction date,House age,distance_to_mrt,stores,latitude,longitude,house_price_of_unit_area
2 | 42:30.5,13.3,4082.015,8,25.00705868,121.5616942,6.48867314
3 | 52:29.9,35.5,274.0144,2,25.01214826,121.5469898,24.97072465
4 | 10:52.3,1.1,1978.671,10,25.00384986,121.5283365,26.69426677
5 | 26:01.2,22.2,1055.067,5,24.962887,121.4821784,38.09163849
6 | 29:47.9,8.5,967.4,6,25.01103682,121.4799462,21.6547096
7 | 18:34.1,13.3,279.1726,2,24.99499354,121.5438234,36.97237579
8 | 07:23.4,38.5,377.7956,3,25.00989486,121.5589553,27.6373821
9 | 57:25.3,15.2,552.4371,5,24.99710934,121.5443775,44.11658501
10 | 06:48.4,24,617.4424,3,24.98762168,121.5278412,49.07124675
11 | 21:33.3,13,323.655,8,24.97866304,121.4834571,43.11435275
12 | 42:58.4,13.2,750.0704,5,24.93489597,121.5532889,32.74736266
13 | 36:29.3,6.5,289.3248,8,24.99309418,121.5014782,60.46035153
14 | 05:20.1,27.5,49.66105,9,24.96979399,121.5447769,58.16330444
15 | 29:47.8,12.9,373.8389,2,25.01166143,121.5106716,43.77117419
16 | 24:50.1,8,185.4296,5,24.93719703,121.5450593,40.25533345
17 | 06:31.0,4.9,639.6198,8,24.97546864,121.4765644,49.15705382
18 | 03:03.3,3.1,1360.139,5,24.95754044,121.5505921,13.67353159
19 | 54:06.6,10.3,4079.418,0,24.96029045,121.5482248,0
20 | 27:33.3,1.1,143.8383,4,24.97215779,121.4770057,56.25135518
21 | 14:19.0,13.7,193.5845,1,24.94355128,121.5154104,16.44401694
22 | 53:06.0,8.4,330.0854,6,24.96058183,121.4973468,47.35555099
23 | 48:38.9,31.3,1236.564,0,24.95706351,121.5447337,8.468854747
24 | 08:21.4,17.3,837.7233,6,24.95279903,121.4771533,52.76115275
25 | 28:39.9,14.1,193.5845,6,24.94856402,121.4880756,50.61249953
26 | 20:08.9,1.5,197.1338,0,24.98777578,121.4774391,12.5702765
27 | 50:20.3,16.1,3771.895,9,24.9707859,121.5452953,0
28 | 30:38.9,34.4,2408.993,3,24.99444419,121.5093054,22.91559838
29 | 31:31.6,18.2,519.4617,0,24.93804633,121.5522142,9.426271813
30 | 31:16.6,11.9,1978.671,7,24.99480823,121.5204076,25.88302273
31 | 04:04.8,5.4,1939.749,1,24.98419941,121.4755396,4.266377177
32 | 01:41.6,9.7,170.7311,3,24.99087701,121.5128241,27.20664522
33 | 39:42.5,38.5,1828.319,4,24.9970651,121.512329,15.5643239
34 | 27:20.2,32.8,258.186,3,24.99477474,121.510337,30.85539137
35 | 29:39.7,31,289.3248,2,24.9446364,121.476227,34.06650015
36 | 07:50.4,34.8,1156.777,0,24.9643233,121.5597265,18.41025026
37 | 46:26.9,9.9,4510.359,10,24.98221114,121.5490572,1.102852363
38 | 28:53.3,5.9,482.7581,5,24.94829725,121.5130819,31.43092068
39 | 09:24.9,37.1,461.1016,8,24.95407645,121.4900054,47.02921199
40 | 58:58.4,26.8,279.1726,4,24.98297993,121.4957039,29.48443087
41 | 53:12.9,30.4,1447.286,8,24.96259751,121.5358766,19.5472047
42 | 47:12.8,4,492.2313,9,24.95994863,121.5523771,26.33933576
43 | 08:58.7,5.4,90.45606,2,24.96891904,121.4767749,51.41138556
44 | 13:07.9,25.3,537.7971,4,25.01338189,121.4813469,48.54988028
45 | 11:55.7,16.4,1449.722,1,24.97434583,121.5469264,10.36893366
46 | 26:38.7,26.4,2615.465,7,24.94893492,121.4918407,20.58773654
47 | 22:04.7,8,405.2134,4,24.9936867,121.5299532,34.21865577
48 | 54:27.0,19.2,488.5727,1,24.94387683,121.5553412,39.28268043
49 | 59:42.9,30.3,1013.341,6,24.93467554,121.4944715,13.06073818
50 | 01:20.2,12.9,90.45606,9,24.93668959,121.4937013,36.4262787
51 | 36:22.5,13.5,2175.03,0,24.98582906,121.4953206,0
52 | 34:51.8,13.2,707.9067,6,24.99785939,121.4985328,29.76962852
53 | 13:42.0,8.1,373.8389,3,24.96410171,121.5646385,43.07206862
54 | 02:48.6,32.3,379.5575,4,24.98360353,121.5367396,36.17835788
55 | 12:05.1,38,4197.349,8,24.98514676,121.5593931,22.21030128
56 | 45:31.2,13,1159.454,6,24.94830167,121.5381399,38.90638549
57 | 17:15.2,34.4,414.9476,0,24.9567116,121.4886711,31.59628454
58 | 51:27.1,34.4,90.45606,1,24.94667072,121.555846,16.89875516
59 | 23:33.3,24.2,3078.176,5,24.93681248,121.4823517,0
60 | 34:01.2,13.2,204.1705,1,24.96757303,121.5505179,47.25603168
61 | 56:33.3,16.1,1867.233,4,24.99717954,121.5599689,26.53102136
62 | 58:18.6,5.6,2707.392,6,24.97085388,121.4966967,0.439332089
63 | 26:50.3,14.2,4082.015,6,24.93825252,121.4811714,10.60066495
64 | 46:04.5,3.8,393.2606,9,24.97452344,121.5288017,33.66850037
65 | 22:09.1,38.2,4082.015,1,24.99638681,121.4778317,0
66 | 23:38.1,25.3,1712.632,7,24.97384577,121.4982257,39.70830599
67 | 16:54.7,15.6,124.9912,4,24.99387525,121.4841221,54.33071172
68 | 53:26.5,26.8,918.6357,0,24.96011385,121.5206302,37.56244494
69 | 24:38.1,0,279.1726,5,24.98562809,121.5247019,23.10016713
70 | 01:12.6,18.9,6306.153,5,25.00618212,121.5605288,0
71 | 35:59.0,16.6,394.0173,5,24.9697705,121.5286527,34.91191452
72 | 38:47.3,7.6,3771.895,3,24.98937138,121.494687,8.784265127
73 | 50:51.4,12,2408.993,9,24.93284364,121.5510656,25.69646295
74 | 22:10.4,6.6,461.1016,4,24.93558667,121.4743979,45.2974607
75 | 04:51.2,37.1,1402.016,8,24.97320899,121.5210472,38.67655041
76 | 53:06.4,25.3,577.9615,5,24.98591548,121.5334037,20.16895031
77 | 02:42.2,11.6,186.9686,3,24.94076534,121.5012944,33.01791658
78 | 29:41.0,25.9,1931.207,9,25.01274015,121.5011039,38.01299433
79 | 54:43.5,29.4,201.8939,8,25.01368493,121.5336716,29.71119022
80 | 37:26.2,17.5,492.2313,5,25.00834258,121.5316479,21.01262993
81 | 20:02.3,32.6,289.3248,10,25.01282857,121.5458438,38.30368728
82 | 34:53.5,24.2,506.1144,3,24.96756562,121.549018,27.69035178
83 | 03:49.5,16.3,4510.359,7,24.93328972,121.494506,0
84 | 36:36.4,16.1,489.8821,5,24.94228562,121.4831658,28.19168613
85 | 28:17.9,0,1805.665,0,24.97965571,121.4783986,29.95679839
86 | 39:28.8,31.3,512.5487,7,24.93212707,121.5237032,47.74342784
87 | 27:18.4,39.2,371.2495,4,24.94440662,121.5453004,19.13224459
88 | 07:43.8,0,187.4823,0,24.9588108,121.4985449,18.39107727
89 | 15:43.6,18.4,390.5684,9,24.99901508,121.5246806,42.19357532
90 | 34:49.6,33,4449.27,9,25.00642117,121.5432131,3.957364719
91 | 21:39.7,13,421.479,0,24.93212916,121.4763012,32.12698068
92 | 41:47.0,17.7,1712.632,8,24.98334732,121.4739871,32.43492491
93 | 16:00.8,13.3,718.2937,2,24.98712844,121.5302675,11.24922184
94 | 56:01.7,11.6,170.1289,8,24.97090782,121.5614025,33.20955519
95 | 06:44.5,35.9,179.4538,2,24.9965586,121.5074983,38.40267729
96 | 52:29.8,5.6,383.2805,8,25.00884895,121.5092619,41.26128764
97 | 39:29.8,16.2,640.7391,4,24.93880207,121.5349301,30.47123455
98 | 48:54.4,11.6,2469.645,7,24.93573949,121.5156555,17.56245591
99 | 44:19.8,0,4066.587,6,24.96329585,121.5604822,0
100 | 13:57.5,35.4,318.5292,5,24.98383819,121.5210145,47.37204258
101 | 31:02.5,37.7,1935.009,7,25.00562019,121.528879,28.28137335
102 | 37:14.8,40.9,577.9615,3,24.9712575,121.5502236,41.58516426
103 | 02:52.4,17.3,4066.587,9,24.94827479,121.5331225,25.58971056
104 | 09:56.5,34.9,2185.128,6,25.00888597,121.4856729,30.85092896
105 | 40:15.8,32,186.9686,8,24.9386951,121.4981342,50.91810727
106 | 36:41.4,13.7,193.5845,9,25.00516591,121.5283525,57.80803669
107 | 11:16.5,12.7,512.5487,3,24.94504886,121.5105191,18.70607451
108 | 25:53.8,4.5,579.2083,7,25.01400301,121.5191006,42.82919278
109 | 04:53.3,33.6,1978.671,7,24.97461692,121.5592983,38.3613258
110 | 56:55.3,12.7,2147.376,5,24.99670579,121.4760711,27.74371289
111 | 10:44.5,12,167.5989,3,24.97682393,121.515875,23.69789473
112 | 31:00.8,39.8,1712.632,8,24.98165905,121.4973241,24.87151519
113 | 20:14.1,13.5,718.2937,3,24.94428211,121.5443766,9.21356696
114 | 16:45.5,6.8,323.655,1,24.99144059,121.4798598,34.79054681
115 | 58:06.1,18.3,90.45606,6,24.96978682,121.4786685,59.33493772
116 | 39:27.9,13.3,274.0144,0,24.99204975,121.5394968,38.83127557
117 | 34:50.0,38,161.942,0,25.00031825,121.4902145,46.87699098
118 | 17:24.2,17,482.7581,7,24.98236897,121.4959743,50.02525314
119 | 27:03.7,15.1,512.5487,5,24.95401458,121.5490746,21.71190524
120 | 01:30.9,34.8,279.1726,2,24.97166559,121.473888,34.94289478
121 | 05:32.3,13.3,390.9696,6,24.94244337,121.503169,39.0209329
122 | 53:11.8,34.5,1159.454,0,24.94609304,121.5166428,7.255057854
123 | 30:22.2,37.8,444.1334,0,24.93216211,121.5248138,42.75258978
124 | 46:06.8,32.6,438.8513,5,24.99510445,121.5369168,23.04599311
125 | 46:08.4,5.3,383.2805,0,24.93814132,121.5051919,35.49622988
126 | 26:00.8,4.3,512.5487,0,24.98995289,121.5491698,12.52701523
127 | 09:41.3,4,252.5822,8,24.98454181,121.5398238,24.99064473
128 | 31:52.0,14,187.4823,5,24.97134978,121.5459403,32.88421106
129 | 56:03.1,28.2,1497.713,3,25.00937842,121.49382,12.05637208
130 | 35:20.7,6.5,964.7496,4,25.01457794,121.513938,20.07341746
131 | 36:41.1,35.3,196.6172,5,24.95240312,121.523599,46.94012461
132 | 42:52.8,32.1,150.9347,2,24.93500621,121.5111629,13.35872546
133 | 08:54.7,15.7,964.7496,8,25.00816119,121.5066201,38.62841343
134 | 57:02.0,6.4,2077.39,6,24.96459616,121.4744727,4.466027389
135 | 26:32.6,32.7,90.45606,1,24.94245194,121.5194145,31.86071601
136 | 38:02.6,17.5,1935.009,8,24.97953047,121.5187721,40.76480979
137 | 33:58.5,14,250.631,6,24.9415406,121.4860343,20.82663171
138 | 40:57.2,20.6,2275.877,5,24.94012887,121.5105875,1.909460277
139 | 52:56.5,25.6,482.7581,0,24.97095398,121.5078486,30.38383626
140 | 54:35.8,13.2,90.45606,6,24.97010424,121.5381538,37.97072484
141 | 27:59.2,17.5,561.9845,4,24.97792463,121.5344733,28.08291593
142 | 10:46.8,12.6,208.3905,2,24.95219694,121.5361995,17.37338976
143 | 49:55.5,20.4,156.2442,7,24.9934262,121.5550663,45.66512995
144 | 36:37.2,0,377.7956,4,24.97182023,121.4830066,28.95693856
145 | 48:48.8,33.4,967.4,3,24.99421468,121.5078619,10.43617775
146 | 27:42.7,30,1157.988,3,24.95573062,121.50475,17.00908233
147 | 48:13.0,31,104.8101,6,24.97909272,121.4879391,36.32604829
148 | 40:38.5,17.7,4082.015,7,24.974418,121.5246831,7.468033918
149 | 49:08.0,10.4,287.6025,10,24.99574508,121.5452509,34.18072546
150 | 16:36.9,16.9,23.38284,1,24.96972976,121.5496603,47.25492942
151 | 14:21.5,16.2,1783.18,1,24.96476727,121.5151152,8.404123066
152 | 02:34.8,23,482.7581,6,24.95135562,121.5376014,39.83015889
153 | 34:49.2,1.1,577.9615,5,24.99621922,121.4871389,27.53145886
154 | 21:48.0,4.5,590.9292,1,24.94902779,121.4877087,44.71972693
155 | 23:36.3,12,292.9978,3,24.98857963,121.494622,32.15400137
156 | 17:42.9,32.5,2707.392,1,25.00807584,121.5110082,12.74237413
157 | 30:06.7,17.4,1402.016,5,24.99230981,121.5065692,44.80793563
158 | 37:47.2,16.1,1449.722,8,24.97525805,121.5112282,23.57740163
159 | 42:07.9,31,3171.329,9,25.01236987,121.5459015,3.398304762
160 | 27:23.1,0,639.6198,1,24.96396715,121.5133918,40.50698971
161 | 19:11.2,16.6,250.631,5,25.00647419,121.5284111,43.66163259
162 | 03:40.3,30.9,4197.349,4,24.99917721,121.4994669,7.608042222
163 | 30:52.9,35.8,451.2438,4,24.97189644,121.5523966,29.09677876
164 | 42:58.0,39.2,482.7581,1,24.9952665,121.5425095,45.34845422
165 | 53:44.3,30.6,90.45606,5,25.00355638,121.4826113,48.4698805
166 | 48:44.8,3.8,292.9978,0,24.95576492,121.532449,39.55300214
167 | 57:59.1,30.9,167.5989,7,24.9339128,121.563924,31.81233353
168 | 32:43.8,33.9,482.7581,3,24.94014838,121.5250364,39.84421932
169 | 18:34.3,15.9,557.478,5,24.9863846,121.4917531,50.76868745
170 | 01:12.9,24.2,201.8939,6,24.9957264,121.5073138,59.25370444
171 | 45:46.9,15,289.3248,2,24.95468383,121.4909726,32.96835265
172 | 41:25.6,17.8,383.8624,2,24.98435824,121.5432099,22.51603555
173 | 13:26.1,15.2,109.9455,8,24.94474363,121.4892008,35.46345475
174 | 41:17.9,0,292.9978,10,24.93280476,121.5103726,65.57171606
175 | 01:57.2,39.8,1236.564,8,24.96148342,121.5437778,22.5686886
176 | 14:01.5,18.2,732.8528,5,24.95250342,121.5069753,33.94583548
177 | 23:45.5,1.5,193.5845,7,24.93518629,121.5030631,32.29772613
178 | 23:38.0,15.5,167.5989,0,24.94188429,121.5527427,13.79480847
179 | 00:02.6,0,292.9978,7,24.95409837,121.4964938,35.25220099
180 | 25:21.5,18.1,535.527,1,24.98739389,121.5022454,26.86704988
181 | 39:50.9,11.5,289.3248,1,24.95570815,121.5254586,26.722181
182 | 25:43.2,13.9,189.5181,6,24.96098394,121.5401037,58.81516219
183 | 38:43.7,8.9,4082.015,6,24.98702875,121.553908,17.3475391
184 | 32:16.9,40.9,492.2313,2,25.01136611,121.5452423,15.46684727
185 | 41:26.0,30.6,461.1016,5,25.01113618,121.5029632,17.8779668
186 | 44:54.6,17.9,170.1289,5,24.9919231,121.5343821,33.81895543
187 | 22:11.6,27.5,451.6419,2,24.95193223,121.4843683,26.75930501
188 | 44:03.6,22.2,1758.406,8,24.94391398,121.5156229,12.23420462
189 | 11:23.3,13.9,329.9747,0,24.96134984,121.5432054,13.84893727
190 | 19:43.4,36.6,49.66105,3,24.98547843,121.5293248,38.98826969
191 | 43:32.1,26.9,90.45606,5,24.95241754,121.485552,28.72889336
192 | 13:08.6,3.8,372.1386,2,24.97748387,121.5036704,37.24170556
193 | 27:21.9,18,379.5575,5,25.00519246,121.5090848,49.48980857
194 | 11:30.3,13.3,2147.376,3,24.93373206,121.56445,0.365175618
195 | 03:53.6,30.4,143.8383,5,24.99132918,121.5488259,56.92159098
196 | 08:35.7,35.9,90.45606,5,24.95875412,121.5644754,26.78236924
197 | 15:01.3,17.4,289.3248,8,24.99569265,121.4910376,50.30015495
198 | 45:14.9,18.1,377.7956,6,24.97082041,121.5284403,56.50331702
199 | 05:57.3,8.1,1449.722,6,25.00001208,121.5065904,41.4221676
200 | 49:53.6,16.9,1455.798,6,24.97345066,121.5310783,12.44817753
201 | 52:37.9,31.9,2185.128,7,24.9897957,121.5603826,26.70263374
202 | 54:15.3,34,4082.015,2,24.99196167,121.5226182,0
203 | 43:24.2,17.2,1009.235,6,24.98506713,121.4937857,48.56706595
204 | 40:20.1,4.1,438.8513,0,24.93709483,121.5041317,14.62680374
205 | 27:08.0,37.7,4197.349,7,25.00441103,121.5612283,0
206 | 26:23.6,3.5,1805.665,8,24.95755375,121.5213053,23.37239442
207 | 40:24.7,18.2,1758.406,1,24.96544293,121.5522337,32.7927237
208 | 09:42.1,3.8,170.1289,0,24.94396765,121.4928792,45.48583821
209 | 26:23.8,10.3,1360.139,1,24.9944335,121.5364721,36.55305112
210 | 04:04.4,7.1,250.631,6,24.97390481,121.5435709,28.60194589
211 | 37:37.4,17.5,82.88643,5,24.97765738,121.5302841,37.16632244
212 | 22:36.6,12.8,211.4473,5,24.9838692,121.5154328,47.96156312
213 | 49:31.2,12.2,2147.376,5,24.95811916,121.4945025,26.98910401
214 | 27:09.9,6.5,515.1122,3,24.95988593,121.5641964,46.46906819
215 | 02:43.6,14.8,193.5845,0,24.97517763,121.5274137,19.23345158
216 | 08:10.5,34.8,451.2438,1,25.01222661,121.4953096,45.80993752
217 | 48:03.9,41.3,590.9292,5,24.97767434,121.4742645,50.6280124
218 | 18:51.9,32.5,617.4424,6,25.0021065,121.4761217,35.30334306
219 | 32:46.0,3.9,90.45606,5,24.94865244,121.4967426,49.5873159
220 | 51:26.9,1.1,377.7956,5,24.96204324,121.502779,22.44097284
221 | 55:23.9,8,104.8101,5,24.96671665,121.5161057,24.13315504
222 | 17:38.4,13.7,185.4296,4,24.98083914,121.5353246,27.640863
223 | 31:02.8,17.2,4066.587,1,25.003312,121.5528676,0
224 | 30:21.4,2.6,390.5684,7,24.99112314,121.5190613,45.10944319
225 | 08:59.4,23,480.6977,7,25.00618883,121.5242554,40.65124852
226 | 41:14.5,17.5,289.3248,5,25.0052952,121.5074865,42.79792519
227 | 48:02.6,18.9,4082.015,0,24.9957005,121.4826721,0
228 | 20:10.9,32.3,4573.779,3,24.96447994,121.4831001,0.566048677
229 | 57:03.9,40.9,90.45606,0,24.9976161,121.5167219,11.9655155
230 | 25:14.6,0,552.4371,0,24.98540508,121.5611869,5.962874681
231 | 00:01.6,4.7,4066.587,9,24.95243366,121.4953686,0
232 | 07:29.8,8.9,1487.868,9,24.98774483,121.5125722,43.57453104
233 | 28:31.4,10,2674.961,1,24.95029242,121.5522533,4.843244817
234 | 24:34.7,28,2408.993,3,24.95998498,121.5506031,20.05608383
235 | 16:12.4,4,373.3937,7,24.99591234,121.5520824,52.8640473
236 | 45:39.1,19.2,90.45606,5,24.98641801,121.4781166,26.48498095
237 | 23:51.7,34.6,170.7311,6,25.00357017,121.5085561,35.13161908
238 | 50:22.0,17.3,1264.73,5,24.97453685,121.4867785,26.20531812
239 | 24:24.6,24.2,1931.207,10,24.95709442,121.561199,43.92938556
240 | 05:55.1,11.5,2147.376,2,24.97333177,121.5281049,16.0679269
241 | 41:40.4,3.1,451.6419,1,24.97361463,121.4749118,12.30087069
242 | 13:01.3,12.5,90.45606,0,24.93480089,121.5088129,18.52797606
243 | 08:47.5,39.7,2408.993,6,24.94675276,121.4771512,5.978499096
244 | 50:58.2,26.9,482.7581,5,24.97566697,121.5208087,33.24944233
245 | 22:08.4,28.6,289.3248,2,25.01144114,121.5096643,45.33261286
246 | 11:37.1,35.5,383.2805,7,24.99393536,121.4852627,54.91633808
247 | 22:17.3,37.1,1559.827,4,24.95785841,121.564699,16.34467537
248 | 54:48.6,18.1,390.5684,7,24.99474312,121.5485203,38.56842027
249 | 40:14.3,20.6,567.0349,6,25.00860646,121.5458508,40.75218082
250 | 27:07.3,15.9,104.8101,8,24.97474286,121.5382245,46.54003257
251 | 10:39.3,14.4,1712.632,2,24.96231192,121.5517727,22.75869286
252 | 04:17.4,5.4,329.9747,1,24.96362085,121.5599593,24.74358956
253 | 33:33.1,36.1,189.5181,8,25.01430455,121.5266929,32.95758377
254 | 35:23.2,7.8,482.7581,1,24.93704887,121.4988836,29.86204123
255 | 17:50.0,16.9,1447.286,6,24.99571282,121.4837544,46.25856067
256 | 42:58.2,34.8,323.655,6,25.00609518,121.4849829,45.47558781
257 | 28:26.5,2,461.7848,0,24.932075,121.4788161,11.87222441
258 | 28:51.8,34.5,90.45606,7,24.93901638,121.5181664,47.58495975
259 | 28:59.2,16.2,519.4617,3,24.96803907,121.4931462,34.65070379
260 | 16:06.4,0,1867.233,3,24.97739306,121.5568249,0
261 | 17:49.6,8.3,289.3248,1,24.98547776,121.5404083,13.6939915
262 | 55:33.8,10,2180.245,5,24.9982804,121.5283552,4.226155753
263 | 51:41.2,4,336.0532,1,25.00882274,121.5332665,40.43652071
264 | 58:28.9,37.3,1449.722,1,24.9628587,121.5246111,6.332630455
265 | 45:43.7,40.9,533.4762,0,25.00864512,121.5212549,29.89509778
266 | 18:06.1,2,815.9314,3,24.9723232,121.5241743,19.59263558
267 | 58:15.4,0,2408.993,9,24.97171438,121.5515236,23.81788412
268 | 44:06.4,0,506.1144,0,24.97436054,121.5561409,36.90829304
269 | 46:32.4,12.7,383.2805,9,24.93924556,121.4847187,63.99400275
270 | 08:59.2,2.6,1360.139,6,24.95037137,121.4843937,39.74017781
271 | 44:55.2,6.8,196.6172,5,25.00670437,121.4977509,41.40176678
272 | 24:01.4,30.4,444.1334,0,24.95081496,121.5445012,11.2778173
273 | 04:44.2,7.1,184.3302,9,24.99973605,121.543042,59.0363953
274 | 40:39.7,30.8,185.4296,3,24.98121756,121.4771656,41.00843703
275 | 30:03.5,19.1,1156.412,0,24.93353627,121.5502071,9.617077735
276 | 33:56.5,19.2,390.5684,5,24.94051922,121.5237967,48.74103951
277 | 18:28.4,5.2,995.7554,0,24.98939896,121.5605427,3.113895827
278 | 24:49.1,21.7,4082.015,3,25.01054288,121.4775842,0
279 | 15:29.1,1.1,1144.436,8,24.94898754,121.5256559,40.48227489
280 | 33:56.1,34.9,490.3446,3,24.97908214,121.5648481,32.41133383
281 | 09:46.1,20.9,492.2313,5,24.96036782,121.4798635,31.27934661
282 | 19:39.6,30.9,750.0704,6,24.95107482,121.5584471,21.15534385
283 | 26:15.2,5.9,390.5684,5,24.93577761,121.5345645,41.78517079
284 | 13:32.0,10.4,837.7233,8,24.98188315,121.5468229,44.29544499
285 | 32:20.9,20.4,815.9314,0,24.98031448,121.4765661,15.59324741
286 | 49:42.0,6.8,443.802,5,24.99278327,121.5293412,47.93045608
287 | 28:34.7,33.2,1414.837,5,24.97531002,121.5247491,21.17947401
288 | 31:21.1,16.3,2469.645,0,25.00080449,121.5394202,0
289 | 57:18.6,16.2,3085.17,0,24.97971814,121.5415791,6.987268739
290 | 40:43.5,16.1,489.8821,0,24.97028089,121.5436483,33.3560109
291 | 26:09.3,5.6,1712.632,5,24.97590422,121.4747242,30.22499709
292 | 47:28.2,28,444.1334,1,24.99582724,121.533512,37.6754012
293 | 25:37.5,13.8,718.2937,1,24.97024233,121.5300246,36.94282621
294 | 54:15.4,14.1,1712.632,2,24.94481023,121.5354475,11.06078997
295 | 43:47.6,40.9,4079.418,3,24.95742894,121.5495543,0
296 | 29:29.6,40.9,292.9978,3,24.94176541,121.5390348,20.72704362
297 | 44:14.6,17,718.2937,6,25.01354781,121.5636251,36.50674061
298 | 12:00.2,29.3,1447.286,6,24.99748512,121.5155596,33.055904
299 | 49:27.6,31.5,292.9978,7,24.95693401,121.5103626,46.26609679
300 | 39:13.0,1.1,1828.319,0,25.00658382,121.4739703,25.96419362
301 | 47:57.5,0,1783.18,1,24.99578013,121.5196663,18.45922061
302 | 02:04.6,16.2,401.8807,5,24.94255723,121.5220431,44.4321919
303 | 15:13.9,16.5,250.631,0,24.95124076,121.4899762,29.38105374
304 | 06:23.3,15,1867.233,8,24.97415687,121.489834,46.62552973
305 | 42:50.1,13.5,815.9314,7,24.9325086,121.5079563,36.49521186
306 | 50:38.8,4,216.8329,2,25.01033552,121.5185035,33.16993857
307 | 07:00.4,35.8,279.1726,1,25.00215723,121.5631773,49.17659401
308 | 48:34.8,30.9,451.6419,5,24.97599751,121.5130862,28.39451475
309 | 45:25.1,12.6,312.8963,8,24.94194974,121.5629228,52.35345485
310 | 28:35.0,0,732.8528,0,24.96844874,121.4813394,32.41967627
311 | 12:19.0,8.5,431.1114,5,24.98528112,121.5508701,25.98841826
312 | 50:22.2,18.4,157.6052,0,24.9446743,121.4839531,36.37768525
313 | 28:51.9,3.6,104.8101,5,24.96712884,121.5640816,57.75577802
314 | 32:09.5,1.1,390.5684,0,24.9854184,121.5204792,40.7916612
315 | 25:26.1,13.6,170.1289,7,24.97678118,121.5319782,59.11426283
316 | 14:04.1,16.9,274.0144,2,24.94407135,121.5650999,45.88692038
317 | 53:21.3,9.9,104.8101,7,24.95503769,121.5633661,37.07228268
318 | 11:38.1,13.1,56.47425,8,24.9827127,121.49747,33.90075795
319 | 19:00.0,13.7,4082.015,0,25.0116121,121.4789519,0
320 | 25:58.5,37.8,323.6912,8,24.96546117,121.4878067,39.52934393
321 | 16:18.0,0,506.1144,5,24.99826885,121.5271412,37.95501636
322 | 00:44.2,16.9,2147.376,5,24.9421989,121.552601,30.40430372
323 | 10:59.6,17.3,56.47425,5,24.996894,121.5618092,22.79513539
324 | 59:58.7,6.3,1447.286,0,24.94043539,121.5584005,0
325 | 29:42.0,18,350.8515,3,24.99140032,121.5200639,39.52966962
326 | 57:45.6,17.1,2147.376,2,24.95291183,121.4946213,0
327 | 58:27.4,13.7,587.8877,3,25.00898521,121.5130203,47.16677813
328 | 17:47.2,16.4,4082.015,4,25.00848297,121.5019106,0
329 | 00:39.6,37.1,964.7496,1,24.95510725,121.5312667,12.76260554
330 | 06:45.3,4,639.6198,7,25.00306708,121.4792334,41.11505105
331 | 20:07.3,31.5,600.8604,1,24.95251576,121.4988142,43.61694052
332 | 50:54.2,42.7,1164.838,8,25.001585,121.5096325,20.30996605
333 | 40:38.5,1.1,461.1016,1,24.94547543,121.4820866,14.407178
334 | 17:33.4,18.2,186.5101,6,24.99629051,121.5526033,57.02314443
335 | 34:31.7,17.5,1626.083,5,24.93530662,121.5627837,21.22293983
336 | 29:46.3,40.9,1559.827,0,24.99288602,121.5053819,28.10885047
337 | 50:58.0,2.1,2185.128,5,24.96985056,121.538104,26.24541361
338 | 36:36.5,13.6,2216.612,0,24.94749606,121.5277872,0
339 | 56:58.4,18.5,918.6357,6,25.0126724,121.5517709,35.43147307
340 | 58:35.4,32,169.9803,4,24.95465387,121.4777661,28.72611053
341 | 57:50.1,0,617.7134,1,24.98470458,121.4960224,42.08379518
342 | 48:49.0,6.2,579.2083,0,24.94088366,121.5238677,13.39726056
343 | 53:12.1,21.2,157.6052,2,24.98521263,121.5230275,32.00781321
344 | 42:03.7,13,587.8877,6,24.96347673,121.5619635,35.99089994
345 | 43:27.2,20.3,84.87882,3,24.97772957,121.4875798,39.54575641
346 | 28:02.7,30.1,590.9292,3,24.99771841,121.5308501,24.03903076
347 | 23:08.8,3.6,6306.153,4,24.93319539,121.534005,0
348 | 59:41.5,16.6,4527.687,7,24.99016124,121.5298817,0
349 | 43:26.4,16.5,1406.43,3,24.95357374,121.5617352,41.43015538
350 | 38:19.0,20,552.4371,1,24.95806562,121.4773608,29.72640084
351 | 28:25.4,16.4,216.8329,0,24.97914134,121.4958455,34.33224998
352 | 58:44.1,18,1360.139,8,24.95380135,121.5653208,31.74366223
353 | 44:21.2,6.4,2175.03,0,24.9882756,121.5151222,0
354 | 01:22.6,2,757.3377,8,25.01327218,121.4772412,33.4417429
355 | 25:25.4,30.9,640.7391,0,25.00920814,121.5180726,19.59839536
356 | 07:13.1,32.6,492.2313,0,24.9447888,121.5022657,14.59856881
357 | 16:57.7,11.8,1236.564,8,24.93733687,121.5371775,21.18257465
358 | 38:08.2,31.9,4082.015,3,24.94495123,121.5594182,0
359 | 54:37.5,16,170.1289,5,24.96186851,121.502395,19.2173606
360 | 54:08.6,15.2,431.1114,7,24.98880987,121.5622015,35.76266672
361 | 50:06.3,13.6,461.1016,6,24.9730476,121.4973002,27.79083786
362 | 02:12.0,29.1,451.2438,7,24.9817234,121.5350223,21.83025828
363 | 12:30.5,6.2,390.9696,5,25.00775338,121.5550677,54.39069622
364 | 20:47.5,17.9,3529.564,6,25.00704258,121.4942776,0
365 | 08:18.0,30.9,1455.798,5,24.99079243,121.4744368,8.112291946
366 | 01:58.9,35.3,387.7721,3,24.95360038,121.5592157,48.13977011
367 | 06:20.2,0,482.7581,3,24.98323682,121.5138966,34.66588299
368 | 33:41.0,16.1,2185.128,2,24.93958926,121.502423,29.36461716
369 | 38:57.3,13,2103.555,0,24.93485683,121.5462163,22.49146982
370 | 51:20.5,13.7,3079.89,7,24.98888196,121.5117835,29.99029892
371 | 57:12.0,30.7,1414.837,2,25.00003116,121.5290064,39.41648222
372 | 57:14.1,39.8,515.1122,5,24.99605822,121.5322287,47.66577608
373 | 30:42.4,25.3,718.2937,1,24.95710832,121.5003461,38.85517961
374 | 37:54.8,40.1,424.7132,4,25.00301447,121.5437748,19.28513785
375 | 07:00.7,5.6,250.631,1,25.00803359,121.5565594,27.78689427
376 | 15:58.9,30.6,464.223,0,24.95393434,121.516533,44.48092654
377 | 37:38.7,6.3,319.0708,4,24.95842851,121.4826933,15.4729221
378 | 59:44.2,0,292.9978,2,24.95208368,121.5069047,44.12914007
379 | 03:19.9,10.5,472.1745,1,24.9728011,121.5480736,13.27087514
380 | 59:34.0,25.3,156.2442,6,24.95033413,121.514014,27.62109721
381 | 42:10.9,16.2,2261.432,5,24.943163,121.5098915,17.34588971
382 | 44:29.0,35.9,185.4296,8,25.01297342,121.5079639,31.02739054
383 | 36:43.1,32,289.3248,1,24.9495066,121.5204378,27.67835252
384 | 12:09.3,17.8,2185.128,8,24.9343045,121.5070436,9.78540164
385 | 05:47.0,4.6,451.2438,3,24.94436341,121.4967726,49.98740884
386 | 41:07.2,24.2,1264.73,8,25.01008597,121.4869215,52.54092548
387 | 42:20.1,0,3947.945,7,24.98759681,121.5455353,0
388 | 42:54.6,15.9,563.2854,0,24.99831158,121.5311967,23.04163671
389 | 05:40.3,7.1,482.7581,3,25.00216108,121.5498282,21.89934295
390 | 10:14.7,17.5,3771.895,0,24.96339966,121.5634999,0
391 | 46:23.5,40.9,837.7233,8,24.98966845,121.4971461,40.09707168
392 | 24:22.0,7.1,718.2937,3,24.98631048,121.5298222,16.6927426
393 | 54:47.2,0,451.6419,4,24.94989098,121.5503868,43.78332661
394 | 55:49.8,33.5,577.9615,8,24.98516495,121.5126919,34.47409439
395 | 16:21.7,13.6,942.4664,0,24.96765702,121.5541292,24.03759221
396 | 41:17.0,0,1360.139,8,24.95087389,121.5077602,28.88585628
397 | 16:21.2,28.2,187.4823,5,24.96893472,121.5367171,36.3156345
398 | 57:02.9,31.5,1717.193,5,25.0018065,121.5469372,27.03164301
399 | 25:34.6,34.4,123.7429,10,24.99739403,121.4962621,57.93798973
400 | 33:59.9,13.3,4510.359,2,24.99776558,121.5579614,0
401 | 48:39.7,35.9,208.3905,9,25.010412,121.5650267,54.83166378
402 | 42:46.0,31.5,5512.038,4,24.94456234,121.5085079,1.443337713
403 | 39:05.1,11.8,1712.632,0,25.00410918,121.5528041,31.35847071
404 | 40:29.4,21.7,600.8604,7,24.94808367,121.5465583,41.40918783
405 | 30:15.0,32.8,2185.128,1,24.93890894,121.5101367,0
406 | 46:53.9,13.8,259.6607,1,25.00205216,121.5280183,44.35062301
407 | 39:38.3,21.7,557.478,9,24.93832915,121.5222392,37.51299282
408 | 11:13.1,16.6,1712.632,4,24.93988442,121.5235117,31.69233586
409 | 36:26.1,20.6,312.8963,9,24.93516733,121.5163531,43.58892098
410 | 29:50.6,15.1,552.4371,7,24.93744358,121.5486092,29.72105262
411 | 30:36.6,18.3,170.1289,6,24.98118569,121.4867975,29.09630995
412 | 16:34.0,11.9,323.6912,2,24.95006992,121.4839182,33.87134651
413 | 47:23.3,0,451.6419,8,24.96390119,121.5433874,25.25510512
414 | 33:29.4,35.9,292.9978,5,24.99786261,121.5582859,25.2856203
415 | 49:41.5,12,90.45606,6,24.95290409,121.5263954,37.58055383
416 |
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/House-Price-Predictions/Real_Estate_Prediction 2.0.ipynb:
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2 | "cells": [
3 | {
4 | "cell_type": "code",
5 | "execution_count": 11,
6 | "metadata": {},
7 | "outputs": [
8 | {
9 | "data": {
10 | "text/html": [
11 | "\n",
12 | " \n",
20 | " "
21 | ],
22 | "text/plain": [
23 | ""
24 | ]
25 | },
26 | "metadata": {},
27 | "output_type": "display_data"
28 | }
29 | ],
30 | "source": [
31 | "import pandas as pd\n",
32 | "from sklearn.model_selection import train_test_split\n",
33 | "from sklearn.linear_model import LinearRegression\n",
34 | "import dash\n",
35 | "from dash import html, dcc, Input, Output, State\n",
36 | "import mysql.connector\n",
37 | "\n",
38 | "# Load real estate data\n",
39 | "real_estate_data = pd.read_csv(\"Real_Estate.csv\") # Replace the path with your path\n",
40 | "\n",
41 | "# Define features and target\n",
42 | "features = ['distance_to_mrt', 'stores', 'latitude', 'longitude']\n",
43 | "target = 'house_price_of_unit_area'\n",
44 | "X = real_estate_data[features]\n",
45 | "y = real_estate_data[target]\n",
46 | "\n",
47 | "# Split the data into training and testing sets\n",
48 | "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
49 | "\n",
50 | "# Train the Linear Regression model\n",
51 | "model = LinearRegression()\n",
52 | "model.fit(X_train, y_train)\n",
53 | "\n",
54 | "# Database setup\n",
55 | "conn = mysql.connector.connect(\n",
56 | " host='localhost',\n",
57 | " user='root',\n",
58 | " password='root',\n",
59 | " database='house'\n",
60 | ")\n",
61 | "cursor = conn.cursor()\n",
62 | "\n",
63 | "cursor.execute('''\n",
64 | " CREATE TABLE IF NOT EXISTS real_estate_predictions (\n",
65 | " id INT AUTO_INCREMENT PRIMARY KEY,\n",
66 | " distance_to_mrt DOUBLE,\n",
67 | " stores DOUBLE,\n",
68 | " latitude DOUBLE,\n",
69 | " longitude DOUBLE,\n",
70 | " prediction DOUBLE,\n",
71 | " prediction_score DOUBLE\n",
72 | " )\n",
73 | "''')\n",
74 | "conn.commit()\n",
75 | "\n",
76 | "# Dash app setup\n",
77 | "app = dash.Dash(__name__)\n",
78 | "\n",
79 | "app.layout = html.Div([\n",
80 | "\n",
81 | " html.Div([\n",
82 | "\n",
83 | " html.H1(\"Real Estate Price Prediction\", style={'text-align': 'center'}),\n",
84 | "\n",
85 | " html.Div([\n",
86 | "\n",
87 | " dcc.Input(id='distance_to_mrt', type='number', placeholder='Distance to MRT Station (meters)',\n",
88 | " style={'margin': '10px', 'padding': '10px'}),\n",
89 | "\n",
90 | " dcc.Input(id='stores', type='number', placeholder='Number of Convenience Stores',\n",
91 | " style={'margin': '10px', 'padding': '10px'}),\n",
92 | "\n",
93 | " dcc.Input(id='latitude', type='number', placeholder='Latitude',\n",
94 | " style={'margin': '10px', 'padding': '10px'}),\n",
95 | "\n",
96 | " dcc.Input(id='longitude', type='number', placeholder='Longitude',\n",
97 | " style={'margin': '10px', 'padding': '10px'}),\n",
98 | "\n",
99 | " html.Button('Predict Price', id='predict_button', n_clicks=0,\n",
100 | " style={'margin': '10px', 'padding': '10px', 'background-color': '#007BFF', 'color': 'white'}),\n",
101 | "\n",
102 | " ], style={'text-align': 'center'}),\n",
103 | "\n",
104 | " html.Div(id='prediction_output', style={'text-align': 'center', 'font-size': '20px', 'margin-top': '20px'})\n",
105 | "\n",
106 | " ], style={'width': '50%', 'margin': '0 auto', 'border': '2px solid #007BFF', 'padding': '20px', 'border-radius': '10px'})\n",
107 | "\n",
108 | "])\n",
109 | "\n",
110 | "@app.callback(\n",
111 | " Output('prediction_output', 'children'),\n",
112 | " [Input('predict_button', 'n_clicks')],\n",
113 | " [\n",
114 | " State('distance_to_mrt', 'value'),\n",
115 | " State('stores', 'value'),\n",
116 | " State('latitude', 'value'),\n",
117 | " State('longitude', 'value')\n",
118 | " ]\n",
119 | ")\n",
120 | "def update_output(n_clicks, distance_to_mrt, stores, latitude, longitude):\n",
121 | " if n_clicks > 0 and all(v is not None for v in [distance_to_mrt, stores, latitude, longitude]):\n",
122 | "\n",
123 | " # Prepare the feature vector\n",
124 | " features = pd.DataFrame([[distance_to_mrt, stores, latitude, longitude]],\n",
125 | " columns=['distance_to_mrt', 'stores', 'latitude', 'longitude'])\n",
126 | "\n",
127 | " # Predict\n",
128 | " prediction = model.predict(features)[0]\n",
129 | "\n",
130 | " # Prediction score\n",
131 | " prediction_score = model.score(X_test, y_test)\n",
132 | "\n",
133 | " # Store input values and prediction score in the database\n",
134 | " cursor.execute('''\n",
135 | " INSERT INTO real_estate_predictions (distance_to_mrt, stores, latitude, longitude, prediction, prediction_score)\n",
136 | " VALUES (%s, %s, %s, %s, %s, %s)\n",
137 | " ''', (distance_to_mrt, stores, latitude, longitude, prediction, prediction_score))\n",
138 | " conn.commit()\n",
139 | "\n",
140 | " return f'Predicted House Price of Unit Area: {prediction:.2f}, Prediction Score: {prediction_score:.4f}'\n",
141 | "\n",
142 | " elif n_clicks > 0:\n",
143 | " return 'Please enter all values to get a prediction'\n",
144 | "\n",
145 | " return ''\n",
146 | "\n",
147 | "if __name__ == '__main__':\n",
148 | " app.run_server(port=8058)\n"
149 | ]
150 | },
151 | {
152 | "cell_type": "markdown",
153 | "metadata": {},
154 | "source": [
155 | "# WITH BG IMAGE"
156 | ]
157 | },
158 | {
159 | "cell_type": "code",
160 | "execution_count": 18,
161 | "metadata": {},
162 | "outputs": [
163 | {
164 | "data": {
165 | "text/html": [
166 | "\n",
167 | " \n",
175 | " "
176 | ],
177 | "text/plain": [
178 | ""
179 | ]
180 | },
181 | "metadata": {},
182 | "output_type": "display_data"
183 | }
184 | ],
185 | "source": [
186 | "import base64\n",
187 | "import dash\n",
188 | "from dash import html, dcc, Input, Output, State\n",
189 | "import mysql.connector\n",
190 | "import pandas as pd\n",
191 | "from sklearn.model_selection import train_test_split\n",
192 | "from sklearn.linear_model import LinearRegression\n",
193 | "\n",
194 | "# Load real estate data\n",
195 | "real_estate_data = pd.read_csv(\"Real_Estate.csv\") # Replace the path with your path\n",
196 | "\n",
197 | "# Define features and target\n",
198 | "features = ['distance_to_mrt', 'stores', 'latitude', 'longitude']\n",
199 | "target = 'house_price_of_unit_area'\n",
200 | "X = real_estate_data[features]\n",
201 | "y = real_estate_data[target]\n",
202 | "\n",
203 | "# Split the data into training and testing sets\n",
204 | "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
205 | "\n",
206 | "# Train the Linear Regression model\n",
207 | "model = LinearRegression()\n",
208 | "model.fit(X_train, y_train)\n",
209 | "\n",
210 | "# Database setup\n",
211 | "conn = mysql.connector.connect(\n",
212 | " host='localhost',\n",
213 | " user='root',\n",
214 | " password='root',\n",
215 | " database='house'\n",
216 | ")\n",
217 | "cursor = conn.cursor()\n",
218 | "\n",
219 | "cursor.execute('''\n",
220 | " CREATE TABLE IF NOT EXISTS real_estate_predictions (\n",
221 | " id INT AUTO_INCREMENT PRIMARY KEY,\n",
222 | " distance_to_mrt DOUBLE,\n",
223 | " stores DOUBLE,\n",
224 | " latitude DOUBLE,\n",
225 | " longitude DOUBLE,\n",
226 | " prediction DOUBLE,\n",
227 | " prediction_score DOUBLE\n",
228 | " )\n",
229 | "''')\n",
230 | "conn.commit()\n",
231 | "\n",
232 | "# Dash app setup\n",
233 | "app = dash.Dash(__name__)\n",
234 | "\n",
235 | "# Encode background image to base64\n",
236 | "image_filename = 'OIG2.jpeg' # Replace with the path to your image file\n",
237 | "encoded_image = base64.b64encode(open(image_filename, 'rb').read())\n",
238 | "\n",
239 | "app.layout = html.Div([\n",
240 | "\n",
241 | " html.Div([\n",
242 | "\n",
243 | " html.H1(\"Real Estate Price Prediction\", style={'text-align': 'center'}),\n",
244 | "\n",
245 | " html.Div([\n",
246 | "\n",
247 | " dcc.Input(id='distance_to_mrt', type='number', placeholder='Distance to MRT Station (meters)',\n",
248 | " style={'margin': '10px', 'padding': '10px'}),\n",
249 | "\n",
250 | " dcc.Input(id='stores', type='number', placeholder='Number of Convenience Stores',\n",
251 | " style={'margin': '10px', 'padding': '10px'}),\n",
252 | "\n",
253 | " dcc.Input(id='latitude', type='number', placeholder='Latitude',\n",
254 | " style={'margin': '10px', 'padding': '10px'}),\n",
255 | "\n",
256 | " dcc.Input(id='longitude', type='number', placeholder='Longitude',\n",
257 | " style={'margin': '10px', 'padding': '10px'}),\n",
258 | "\n",
259 | " html.Button('Predict Price', id='predict_button', n_clicks=0,\n",
260 | " style={'margin': '10px', 'padding': '10px', 'background-color': '#007BFF', 'color': 'white'}),\n",
261 | "\n",
262 | " ], style={'text-align': 'center'}),\n",
263 | "\n",
264 | " html.Div(id='prediction_output', style={'text-align': 'center', 'font-size': '20px', 'margin-top': '20px'})\n",
265 | "\n",
266 | " ], style={'width': '50%', 'margin': '0 auto', 'border': '2px solid #007BFF', 'padding': '20px', 'border-radius': '10px',\n",
267 | " 'background-image': f'url(\"data:image/png;base64,{encoded_image.decode()}\")'})\n",
268 | "\n",
269 | "])\n",
270 | "\n",
271 | "@app.callback(\n",
272 | " Output('prediction_output', 'children'),\n",
273 | " [Input('predict_button', 'n_clicks')],\n",
274 | " [\n",
275 | " State('distance_to_mrt', 'value'),\n",
276 | " State('stores', 'value'),\n",
277 | " State('latitude', 'value'),\n",
278 | " State('longitude', 'value')\n",
279 | " ]\n",
280 | ")\n",
281 | "def update_output(n_clicks, distance_to_mrt, stores, latitude, longitude):\n",
282 | " if n_clicks > 0 and all(v is not None for v in [distance_to_mrt, stores, latitude, longitude]):\n",
283 | "\n",
284 | " # Prepare the feature vector\n",
285 | " features = pd.DataFrame([[distance_to_mrt, stores, latitude, longitude]],\n",
286 | " columns=['distance_to_mrt', 'stores', 'latitude', 'longitude'])\n",
287 | "\n",
288 | " # Predict\n",
289 | " prediction = model.predict(features)[0]\n",
290 | "\n",
291 | " # Prediction score\n",
292 | " prediction_score = model.score(X_test, y_test)\n",
293 | "\n",
294 | " # Store input values and prediction score in the database\n",
295 | " cursor.execute('''\n",
296 | " INSERT INTO real_estate_predictions (distance_to_mrt, stores, latitude, longitude, prediction, prediction_score)\n",
297 | " VALUES (%s, %s, %s, %s, %s, %s)\n",
298 | " ''', (distance_to_mrt, stores, latitude, longitude, prediction, prediction_score))\n",
299 | " conn.commit()\n",
300 | "\n",
301 | " return f'Predicted House Price of Unit Area: {prediction:.2f}, Prediction Score: {prediction_score:.4f}'\n",
302 | "\n",
303 | " elif n_clicks > 0:\n",
304 | " return 'Please enter all values to get a prediction'\n",
305 | "\n",
306 | " return ''\n",
307 | "\n",
308 | "if __name__ == '__main__':\n",
309 | " app.run_server(port=8058)\n"
310 | ]
311 | },
312 | {
313 | "cell_type": "code",
314 | "execution_count": null,
315 | "metadata": {},
316 | "outputs": [],
317 | "source": []
318 | }
319 | ],
320 | "metadata": {
321 | "kernelspec": {
322 | "display_name": "Python 3",
323 | "language": "python",
324 | "name": "python3"
325 | },
326 | "language_info": {
327 | "codemirror_mode": {
328 | "name": "ipython",
329 | "version": 3
330 | },
331 | "file_extension": ".py",
332 | "mimetype": "text/x-python",
333 | "name": "python",
334 | "nbconvert_exporter": "python",
335 | "pygments_lexer": "ipython3",
336 | "version": "3.12.0"
337 | }
338 | },
339 | "nbformat": 4,
340 | "nbformat_minor": 2
341 | }
342 |
--------------------------------------------------------------------------------
/House-Price-Predictions/Real_Estate_Prediction 3.0.ipynb:
--------------------------------------------------------------------------------
1 | {
2 | "cells": [
3 | {
4 | "cell_type": "code",
5 | "execution_count": 7,
6 | "metadata": {},
7 | "outputs": [
8 | {
9 | "data": {
10 | "text/html": [
11 | "\n",
12 | " \n",
20 | " "
21 | ],
22 | "text/plain": [
23 | ""
24 | ]
25 | },
26 | "metadata": {},
27 | "output_type": "display_data"
28 | }
29 | ],
30 | "source": [
31 | "import atexit\n",
32 | "import base64\n",
33 | "import dash\n",
34 | "from dash import html, dcc, Input, Output, State\n",
35 | "import mysql.connector\n",
36 | "import pandas as pd\n",
37 | "from sklearn.model_selection import train_test_split\n",
38 | "from sklearn.linear_model import LinearRegression\n",
39 | "\n",
40 | "# Load real estate data\n",
41 | "real_estate_data = pd.read_csv(\"Real_Estate.csv\") # Replace the path with your path\n",
42 | "\n",
43 | "# Define features and target\n",
44 | "features = ['distance_to_mrt', 'stores', 'latitude', 'longitude']\n",
45 | "target = 'house_price_of_unit_area'\n",
46 | "X = real_estate_data[features]\n",
47 | "y = real_estate_data[target]\n",
48 | "\n",
49 | "# Split the data into training and testing sets\n",
50 | "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
51 | "\n",
52 | "# Train the Linear Regression model\n",
53 | "model = LinearRegression()\n",
54 | "model.fit(X_train, y_train)\n",
55 | "\n",
56 | "# Database setup\n",
57 | "conn = mysql.connector.connect(\n",
58 | " host='localhost',\n",
59 | " user='root',\n",
60 | " password='root',\n",
61 | " database='house'\n",
62 | ")\n",
63 | "cursor = conn.cursor()\n",
64 | "\n",
65 | "cursor.execute('''\n",
66 | " CREATE TABLE IF NOT EXISTS real_estate_predictions (\n",
67 | " id INT AUTO_INCREMENT PRIMARY KEY,\n",
68 | " distance_to_mrt DOUBLE,\n",
69 | " stores DOUBLE,\n",
70 | " latitude DOUBLE,\n",
71 | " longitude DOUBLE,\n",
72 | " prediction DOUBLE,\n",
73 | " prediction_score DOUBLE\n",
74 | " )\n",
75 | "''')\n",
76 | "conn.commit()\n",
77 | "\n",
78 | "# Register function to close the database connection on exit\n",
79 | "atexit.register(lambda: (cursor.close() if cursor else None, conn.close() if conn else None))\n",
80 | "\n",
81 | "# Dash app setup\n",
82 | "app = dash.Dash(__name__)\n",
83 | "\n",
84 | "# Encode background image to base64\n",
85 | "image_filename = 'OIG.jpeg' # Replace with the path to your image file\n",
86 | "encoded_image = base64.b64encode(open(image_filename, 'rb').read())\n",
87 | "\n",
88 | "app.layout = html.Div([\n",
89 | " html.Div([\n",
90 | " html.H1(\"Real Estate Price Prediction\", style={'text-align': 'center'}),\n",
91 | " html.Div([\n",
92 | " dcc.Input(id='distance_to_mrt', type='number', placeholder='Distance to MRT Station (meters)',\n",
93 | " style={'margin': '10px', 'padding': '10px'}),\n",
94 | " dcc.Input(id='stores', type='number', placeholder='Number of Convenience Stores',\n",
95 | " style={'margin': '10px', 'padding': '10px'}),\n",
96 | " dcc.Input(id='latitude', type='number', placeholder='Latitude',\n",
97 | " style={'margin': '10px', 'padding': '10px'}),\n",
98 | " dcc.Input(id='longitude', type='number', placeholder='Longitude',\n",
99 | " style={'margin': '10px', 'padding': '10px'}),\n",
100 | " html.Button('Predict Price', id='predict_button', n_clicks=0,\n",
101 | " style={'margin': '10px', 'padding': '10px', 'background-color': '#007BFF', 'color': 'white'}),\n",
102 | " ], style={'text-align': 'center'}),\n",
103 | " html.Div(id='prediction_output', style={'text-align': 'center', 'font-size': '20px', 'margin-top': '20px'}),\n",
104 | " ], style={'width': '50%', 'margin': '0 auto', 'border': '2px solid #007BFF', 'padding': '20px',\n",
105 | " 'border-radius': '10px', 'background-image': f'url(\"data:image/png;base64,{encoded_image.decode()}\")'})\n",
106 | "])\n",
107 | "\n",
108 | "def predict_house_price(distance_to_mrt, stores, latitude, longitude):\n",
109 | " # Prepare the feature vector\n",
110 | " features = pd.DataFrame([[distance_to_mrt, stores, latitude, longitude]],\n",
111 | " columns=['distance_to_mrt', 'stores', 'latitude', 'longitude'])\n",
112 | "\n",
113 | " # Predict\n",
114 | " prediction = model.predict(features)[0]\n",
115 | "\n",
116 | " # Prediction score\n",
117 | " prediction_score = model.score(X_test, y_test)\n",
118 | "\n",
119 | " return prediction, prediction_score\n",
120 | "\n",
121 | "def save_prediction_to_database(distance_to_mrt, stores, latitude, longitude, prediction, prediction_score):\n",
122 | " cursor.execute('''\n",
123 | " INSERT INTO real_estate_predictions (distance_to_mrt, stores, latitude, longitude, prediction, prediction_score)\n",
124 | " VALUES (%s, %s, %s, %s, %s, %s)\n",
125 | " ''', (distance_to_mrt, stores, latitude, longitude, prediction, prediction_score))\n",
126 | " conn.commit()\n",
127 | "\n",
128 | "@app.callback(\n",
129 | " Output('prediction_output', 'children'),\n",
130 | " [Input('predict_button', 'n_clicks')],\n",
131 | " [\n",
132 | " State('distance_to_mrt', 'value'),\n",
133 | " State('stores', 'value'),\n",
134 | " State('latitude', 'value'),\n",
135 | " State('longitude', 'value')\n",
136 | " ]\n",
137 | ")\n",
138 | "def update_output(n_clicks, distance_to_mrt, stores, latitude, longitude):\n",
139 | " if n_clicks > 0 and all(v is not None for v in [distance_to_mrt, stores, latitude, longitude]):\n",
140 | " prediction, prediction_score = predict_house_price(distance_to_mrt, stores, latitude, longitude)\n",
141 | "\n",
142 | " # Store input values and prediction score in the database\n",
143 | " save_prediction_to_database(distance_to_mrt, stores, latitude, longitude, prediction, prediction_score)\n",
144 | "\n",
145 | " return f'Predicted House Price of Unit Area: {prediction:.2f}, Prediction Score: {prediction_score:.4f}'\n",
146 | " elif n_clicks > 0:\n",
147 | " return 'Please enter all values to get a prediction'\n",
148 | " return ''\n",
149 | "\n",
150 | "if __name__ == '__main__':\n",
151 | " app.run_server(port=8058)\n"
152 | ]
153 | }
154 | ],
155 | "metadata": {
156 | "kernelspec": {
157 | "display_name": "Python 3",
158 | "language": "python",
159 | "name": "python3"
160 | },
161 | "language_info": {
162 | "codemirror_mode": {
163 | "name": "ipython",
164 | "version": 3
165 | },
166 | "file_extension": ".py",
167 | "mimetype": "text/x-python",
168 | "name": "python",
169 | "nbconvert_exporter": "python",
170 | "pygments_lexer": "ipython3",
171 | "version": "3.12.0"
172 | }
173 | },
174 | "nbformat": 4,
175 | "nbformat_minor": 2
176 | }
177 |
--------------------------------------------------------------------------------
/House-Price-Predictions/database_setup1.mysql-notebook:
--------------------------------------------------------------------------------
1 | {
2 | "type": "MySQLNotebook",
3 | "version": "1.0",
4 | "caption": "DB Notebook",
5 | "content": "\\about\nCREATE DATABASE house;\nuse house;\nSELECT * from real_estate_predictions;\n",
6 | "options": {
7 | "tabSize": 4,
8 | "indentSize": 4,
9 | "insertSpaces": true,
10 | "defaultEOL": "LF",
11 | "trimAutoWhitespace": true
12 | },
13 | "viewState": {
14 | "cursorState": [
15 | {
16 | "inSelectionMode": false,
17 | "selectionStart": {
18 | "lineNumber": 3,
19 | "column": 11
20 | },
21 | "position": {
22 | "lineNumber": 3,
23 | "column": 11
24 | }
25 | }
26 | ],
27 | "viewState": {
28 | "scrollLeft": 0,
29 | "firstPosition": {
30 | "lineNumber": 1,
31 | "column": 1
32 | },
33 | "firstPositionDeltaTop": 0
34 | },
35 | "contributionsState": {
36 | "editor.contrib.folding": {},
37 | "editor.contrib.wordHighlighter": false
38 | }
39 | },
40 | "contexts": [
41 | {
42 | "state": {
43 | "start": 1,
44 | "end": 1,
45 | "language": "mysql",
46 | "result": {
47 | "type": "text",
48 | "text": [
49 | {
50 | "type": 2,
51 | "content": "Welcome to the MySQL Shell - DB Notebook.\n\nPress Ctrl+Enter to execute the code block.\n\nExecute \\sql to switch to SQL, \\js to JavaScript and \\ts to TypeScript mode.\nExecute \\help or \\? for help;",
52 | "language": "ansi"
53 | }
54 | ]
55 | },
56 | "currentHeight": 120,
57 | "statements": [
58 | {
59 | "delimiter": ";",
60 | "span": {
61 | "start": 0,
62 | "length": 6
63 | },
64 | "contentStart": 0,
65 | "state": 0
66 | }
67 | ]
68 | },
69 | "data": []
70 | },
71 | {
72 | "state": {
73 | "start": 2,
74 | "end": 5,
75 | "language": "mysql",
76 | "result": {
77 | "type": "resultIds",
78 | "list": [
79 | "6e8ffc7e-4625-4d9e-9f32-122d08f806ab"
80 | ]
81 | },
82 | "currentHeight": 185.328125,
83 | "statements": [
84 | {
85 | "delimiter": ";",
86 | "span": {
87 | "start": 0,
88 | "length": 22
89 | },
90 | "contentStart": 0,
91 | "state": 0
92 | },
93 | {
94 | "delimiter": ";",
95 | "span": {
96 | "start": 22,
97 | "length": 11
98 | },
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/House-Price-Predictions/readme.md:
--------------------------------------------------------------------------------
1 | # Real Estate Price Prediction
2 | 
3 |
4 | Real Estate Price Prediction is a machine learning project that leverages Linear Regression to predict house prices based on various features. This project is valuable for investors, developers, and homeowners, providing insights for informed decision-making and investment planning.
5 |
6 | ## Dataset
7 |
8 | The dataset contains 414 entries with detailed information on real estate transactions. Each entry includes the following features:
9 |
10 | - **Transaction Date**: Date of the property transaction.
11 | - **House Age**: Age of the property in years.
12 | - **Distance to the Nearest MRT Station**: Proximity to the nearest Mass Rapid Transit station in meters, indicating convenience and accessibility.
13 | - **Number of Convenience Stores**: Count of convenience stores in the vicinity, reflecting the property’s accessibility to basic amenities.
14 | - **Latitude and Longitude**: Geographical coordinates of the property, defining its location.
15 | - **House Price of Unit Area**: The target variable representing the house price per unit area.
16 |
17 | ## Project Overview
18 |
19 | The project involves using a Linear Regression model to predict house prices based on the provided dataset. The model is trained on historical real estate transactions, learning the relationships between various features and the house price of unit area. This trained model can then be used to make predictions on new data.
20 |
21 | ## File Structure
22 |
23 | The repository is organized as follows:
24 |
25 | - **`Real_Estate.csv`**: The dataset file containing real estate transaction information.
26 | - **`Real_Estate_Prediction.ipynb`**: Jupyter Notebook containing the Python code for data exploration, model training, and predictions.
27 | - **`images/`**: Images used in the project, such as bg or results.
28 | - **`database_setup.sql`**: SQL file with the database setup for storing prediction results.
29 | - **`README.md`**: This file, providing an overview of the project, dataset, and file structure.
30 |
31 | ## Getting Started
32 |
33 | 1. **Clone the Repository:**
34 | ```bash
35 | git clone https://github.com/hiranvjoseph/Machine-Learning-Prediction-Projects-Repository/real-estate-price-prediction.git
36 | cd real-estate-price-prediction
37 | ```
38 |
39 | 2. **Install Dependencies:**
40 | ```bash
41 | pip install -r requirements.txt
42 | ```
43 |
44 | 3. **Run the Jupyter Notebook:**
45 | Open and run the `Real_Estate_Prediction.ipynb` notebook to explore the data, train the model, and make predictions.
46 |
47 | ## Usage
48 |
49 | The project demonstrates how to use Linear Regression for real estate price prediction. The Jupyter Notebook provides step-by-step guidance on data preprocessing, model training, and prediction.
50 |
51 | ## Database Setup
52 |
53 | The `database_setup.sql` file contains the SQL code for setting up a database to store prediction results. Make sure to run this script before making predictions if you want to store results in a database.
54 |
55 | ```bash
56 | mysql -u root -p house < database_setup.sql
57 | ```
58 |
59 | ## Demo
60 |
61 | For a quick demonstration, please refer to the `demo/README.md` file.
62 |
63 | ## License
64 |
65 | This project is licensed under the [MIT License](LICENSE).
66 |
67 |
68 |
69 | ## Acknowledgments
70 |
71 | Special thanks to AMAN KHARWAL for his valuable contributions and insights throughout the development of this project.
72 | visit [thecleverprogrammer](https://thecleverprogrammer.com/)
73 |
74 |
75 | ## Contact
76 |
77 | For inquiries, suggestions, or collaboration opportunities, feel free to reach out:
78 |
79 | - **Hiran Joseph**
80 | - Email: [Hiran](hiranvjoseph@gmail.com)
81 | - LinkedIn: [Hiran Joseph](https://www.linkedin.com/in/hiranjoe/)
82 | - Kaggle: [@Hiran Joe ](https://www.kaggle.com/hiranjoseph)
83 |
--------------------------------------------------------------------------------
/Iris-Species-Prediction/Iris2023.csv:
--------------------------------------------------------------------------------
1 | sepal_length,sepal_width,petal_length,petal_width,species
2 | 5.1,3.5,1.4,0.2,Iris-setosa
3 | 4.9,3,1.4,0.2,Iris-setosa
4 | 4.7,3.2,1.3,0.2,Iris-setosa
5 | 4.6,3.1,1.5,0.2,Iris-setosa
6 | 5,3.6,1.4,0.2,Iris-setosa
7 | 5.4,3.9,1.7,0.4,Iris-setosa
8 | 4.6,3.4,1.4,0.3,Iris-setosa
9 | 5,3.4,1.5,0.2,Iris-setosa
10 | 4.4,2.9,1.4,0.2,Iris-setosa
11 | 4.9,3.1,1.5,0.1,Iris-setosa
12 | 5.4,3.7,1.5,0.2,Iris-setosa
13 | 4.8,3.4,1.6,0.2,Iris-setosa
14 | 4.8,3,1.4,0.1,Iris-setosa
15 | 4.3,3,1.1,0.1,Iris-setosa
16 | 5.8,4,1.2,0.2,Iris-setosa
17 | 5.7,4.4,1.5,0.4,Iris-setosa
18 | 5.4,3.9,1.3,0.4,Iris-setosa
19 | 5.1,3.5,1.4,0.3,Iris-setosa
20 | 5.7,3.8,1.7,0.3,Iris-setosa
21 | 5.1,3.8,1.5,0.3,Iris-setosa
22 | 5.4,3.4,1.7,0.2,Iris-setosa
23 | 5.1,3.7,1.5,0.4,Iris-setosa
24 | 4.6,3.6,1,0.2,Iris-setosa
25 | 5.1,3.3,1.7,0.5,Iris-setosa
26 | 4.8,3.4,1.9,0.2,Iris-setosa
27 | 5,3,1.6,0.2,Iris-setosa
28 | 5,3.4,1.6,0.4,Iris-setosa
29 | 5.2,3.5,1.5,0.2,Iris-setosa
30 | 5.2,3.4,1.4,0.2,Iris-setosa
31 | 4.7,3.2,1.6,0.2,Iris-setosa
32 | 4.8,3.1,1.6,0.2,Iris-setosa
33 | 5.4,3.4,1.5,0.4,Iris-setosa
34 | 5.2,4.1,1.5,0.1,Iris-setosa
35 | 5.5,4.2,1.4,0.2,Iris-setosa
36 | 4.9,3.1,1.5,0.1,Iris-setosa
37 | 5,3.2,1.2,0.2,Iris-setosa
38 | 5.5,3.5,1.3,0.2,Iris-setosa
39 | 4.9,3.1,1.5,0.1,Iris-setosa
40 | 4.4,3,1.3,0.2,Iris-setosa
41 | 5.1,3.4,1.5,0.2,Iris-setosa
42 | 5,3.5,1.3,0.3,Iris-setosa
43 | 4.5,2.3,1.3,0.3,Iris-setosa
44 | 4.4,3.2,1.3,0.2,Iris-setosa
45 | 5,3.5,1.6,0.6,Iris-setosa
46 | 5.1,3.8,1.9,0.4,Iris-setosa
47 | 4.8,3,1.4,0.3,Iris-setosa
48 | 5.1,3.8,1.6,0.2,Iris-setosa
49 | 4.6,3.2,1.4,0.2,Iris-setosa
50 | 5.3,3.7,1.5,0.2,Iris-setosa
51 | 5,3.3,1.4,0.2,Iris-setosa
52 | 7,3.2,4.7,1.4,Iris-versicolor
53 | 6.4,3.2,4.5,1.5,Iris-versicolor
54 | 6.9,3.1,4.9,1.5,Iris-versicolor
55 | 5.5,2.3,4,1.3,Iris-versicolor
56 | 6.5,2.8,4.6,1.5,Iris-versicolor
57 | 5.7,2.8,4.5,1.3,Iris-versicolor
58 | 6.3,3.3,4.7,1.6,Iris-versicolor
59 | 4.9,2.4,3.3,1,Iris-versicolor
60 | 6.6,2.9,4.6,1.3,Iris-versicolor
61 | 5.2,2.7,3.9,1.4,Iris-versicolor
62 | 5,2,3.5,1,Iris-versicolor
63 | 5.9,3,4.2,1.5,Iris-versicolor
64 | 6,2.2,4,1,Iris-versicolor
65 | 6.1,2.9,4.7,1.4,Iris-versicolor
66 | 5.6,2.9,3.6,1.3,Iris-versicolor
67 | 6.7,3.1,4.4,1.4,Iris-versicolor
68 | 5.6,3,4.5,1.5,Iris-versicolor
69 | 5.8,2.7,4.1,1,Iris-versicolor
70 | 6.2,2.2,4.5,1.5,Iris-versicolor
71 | 5.6,2.5,3.9,1.1,Iris-versicolor
72 | 5.9,3.2,4.8,1.8,Iris-versicolor
73 | 6.1,2.8,4,1.3,Iris-versicolor
74 | 6.3,2.5,4.9,1.5,Iris-versicolor
75 | 6.1,2.8,4.7,1.2,Iris-versicolor
76 | 6.4,2.9,4.3,1.3,Iris-versicolor
77 | 6.6,3,4.4,1.4,Iris-versicolor
78 | 6.8,2.8,4.8,1.4,Iris-versicolor
79 | 6.7,3,5,1.7,Iris-versicolor
80 | 6,2.9,4.5,1.5,Iris-versicolor
81 | 5.7,2.6,3.5,1,Iris-versicolor
82 | 5.5,2.4,3.8,1.1,Iris-versicolor
83 | 5.5,2.4,3.7,1,Iris-versicolor
84 | 5.8,2.7,3.9,1.2,Iris-versicolor
85 | 6,2.7,5.1,1.6,Iris-versicolor
86 | 5.4,3,4.5,1.5,Iris-versicolor
87 | 6,3.4,4.5,1.6,Iris-versicolor
88 | 6.7,3.1,4.7,1.5,Iris-versicolor
89 | 6.3,2.3,4.4,1.3,Iris-versicolor
90 | 5.6,3,4.1,1.3,Iris-versicolor
91 | 5.5,2.5,4,1.3,Iris-versicolor
92 | 5.5,2.6,4.4,1.2,Iris-versicolor
93 | 6.1,3,4.6,1.4,Iris-versicolor
94 | 5.8,2.6,4,1.2,Iris-versicolor
95 | 5,2.3,3.3,1,Iris-versicolor
96 | 5.6,2.7,4.2,1.3,Iris-versicolor
97 | 5.7,3,4.2,1.2,Iris-versicolor
98 | 5.7,2.9,4.2,1.3,Iris-versicolor
99 | 6.2,2.9,4.3,1.3,Iris-versicolor
100 | 5.1,2.5,3,1.1,Iris-versicolor
101 | 5.7,2.8,4.1,1.3,Iris-versicolor
102 | 6.3,3.3,6,2.5,Iris-virginica
103 | 5.8,2.7,5.1,1.9,Iris-virginica
104 | 7.1,3,5.9,2.1,Iris-virginica
105 | 6.3,2.9,5.6,1.8,Iris-virginica
106 | 6.5,3,5.8,2.2,Iris-virginica
107 | 7.6,3,6.6,2.1,Iris-virginica
108 | 4.9,2.5,4.5,1.7,Iris-virginica
109 | 7.3,2.9,6.3,1.8,Iris-virginica
110 | 6.7,2.5,5.8,1.8,Iris-virginica
111 | 7.2,3.6,6.1,2.5,Iris-virginica
112 | 6.5,3.2,5.1,2,Iris-virginica
113 | 6.4,2.7,5.3,1.9,Iris-virginica
114 | 6.8,3,5.5,2.1,Iris-virginica
115 | 5.7,2.5,5,2,Iris-virginica
116 | 5.8,2.8,5.1,2.4,Iris-virginica
117 | 6.4,3.2,5.3,2.3,Iris-virginica
118 | 6.5,3,5.5,1.8,Iris-virginica
119 | 7.7,3.8,6.7,2.2,Iris-virginica
120 | 7.7,2.6,6.9,2.3,Iris-virginica
121 | 6,2.2,5,1.5,Iris-virginica
122 | 6.9,3.2,5.7,2.3,Iris-virginica
123 | 5.6,2.8,4.9,2,Iris-virginica
124 | 7.7,2.8,6.7,2,Iris-virginica
125 | 6.3,2.7,4.9,1.8,Iris-virginica
126 | 6.7,3.3,5.7,2.1,Iris-virginica
127 | 7.2,3.2,6,1.8,Iris-virginica
128 | 6.2,2.8,4.8,1.8,Iris-virginica
129 | 6.1,3,4.9,1.8,Iris-virginica
130 | 6.4,2.8,5.6,2.1,Iris-virginica
131 | 7.2,3,5.8,1.6,Iris-virginica
132 | 7.4,2.8,6.1,1.9,Iris-virginica
133 | 7.9,3.8,6.4,2,Iris-virginica
134 | 6.4,2.8,5.6,2.2,Iris-virginica
135 | 6.3,2.8,5.1,1.5,Iris-virginica
136 | 6.1,2.6,5.6,1.4,Iris-virginica
137 | 7.7,3,6.1,2.3,Iris-virginica
138 | 6.3,3.4,5.6,2.4,Iris-virginica
139 | 6.4,3.1,5.5,1.8,Iris-virginica
140 | 6,3,4.8,1.8,Iris-virginica
141 | 6.9,3.1,5.4,2.1,Iris-virginica
142 | 6.7,3.1,5.6,2.4,Iris-virginica
143 | 6.9,3.1,5.1,2.3,Iris-virginica
144 | 5.8,2.7,5.1,1.9,Iris-virginica
145 | 6.8,3.2,5.9,2.3,Iris-virginica
146 | 6.7,3.3,5.7,2.5,Iris-virginica
147 | 6.7,3,5.2,2.3,Iris-virginica
148 | 6.3,2.5,5,1.9,Iris-virginica
149 | 6.5,3,5.2,2,Iris-virginica
150 | 6.2,3.4,5.4,2.3,Iris-virginica
151 | 5.9,3,5.1,1.8,Iris-virginica
152 |
--------------------------------------------------------------------------------
/Iris-Species-Prediction/Iris_Classification.ipynb:
--------------------------------------------------------------------------------
1 | {
2 | "cells": [
3 | {
4 | "cell_type": "markdown",
5 | "metadata": {},
6 | "source": [
7 | "# 1"
8 | ]
9 | },
10 | {
11 | "cell_type": "markdown",
12 | "metadata": {},
13 | "source": [
14 | "pip install mysql-connector-python\n"
15 | ]
16 | },
17 | {
18 | "cell_type": "code",
19 | "execution_count": 1,
20 | "metadata": {},
21 | "outputs": [
22 | {
23 | "data": {
24 | "text/html": [
25 | "\n",
26 | " \n",
34 | " "
35 | ],
36 | "text/plain": [
37 | ""
38 | ]
39 | },
40 | "metadata": {},
41 | "output_type": "display_data"
42 | }
43 | ],
44 | "source": [
45 | "import dash\n",
46 | "from dash import dcc, html\n",
47 | "from dash.dependencies import Input, Output, State\n",
48 | "import mysql.connector\n",
49 | "import numpy as np\n",
50 | "import pandas as pd\n",
51 | "from sklearn.neighbors import KNeighborsClassifier\n",
52 | "from sklearn.model_selection import train_test_split\n",
53 | "\n",
54 | "# Load Iris dataset\n",
55 | "iris = pd.read_csv(\"Iris2023.csv\")\n",
56 | "x = iris.drop(\"species\", axis=1)\n",
57 | "y = iris[\"species\"]\n",
58 | "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=0)\n",
59 | "\n",
60 | "app = dash.Dash(__name__)\n",
61 | "\n",
62 | "# Database setup\n",
63 | "conn = mysql.connector.connect(\n",
64 | " host='localhost',\n",
65 | " user='root',\n",
66 | " password='root',\n",
67 | " database='iris'\n",
68 | ")\n",
69 | "cursor = conn.cursor()\n",
70 | "\n",
71 | "# Create and fit the KNN model\n",
72 | "knn = KNeighborsClassifier(n_neighbors=1)\n",
73 | "knn.fit(x_train, y_train)\n",
74 | "\n",
75 | "# Create a table if it doesn't exist\n",
76 | "cursor.execute('''\n",
77 | " CREATE TABLE IF NOT EXISTS user_inputs (\n",
78 | " id INT AUTO_INCREMENT PRIMARY KEY,\n",
79 | " sepal_length DOUBLE,\n",
80 | " sepal_width DOUBLE,\n",
81 | " petal_length DOUBLE,\n",
82 | " petal_width DOUBLE\n",
83 | " )\n",
84 | "''')\n",
85 | "conn.commit()\n",
86 | "\n",
87 | "app.layout = html.Div([\n",
88 | " html.H1(\"Iris Flower Prediction\", style={'textAlign': 'center', 'marginBottom': 20, 'color': '#333'}),\n",
89 | "\n",
90 | " html.Label(\"Enter Sepal Length:\"),\n",
91 | " dcc.Input(id='sepal_length', type='number', value=5.1),\n",
92 | "\n",
93 | " html.Label(\"Enter Sepal Width:\"),\n",
94 | " dcc.Input(id='sepal_width', type='number', value=3.5),\n",
95 | "\n",
96 | " html.Label(\"Enter Petal Length:\"),\n",
97 | " dcc.Input(id='petal_length', type='number', value=1.4),\n",
98 | "\n",
99 | " html.Label(\"Enter Petal Width:\"),\n",
100 | " dcc.Input(id='petal_width', type='number', value=0.2),\n",
101 | "\n",
102 | " html.Button(id='store_button', n_clicks=0, children='Store Input'),\n",
103 | "\n",
104 | " html.Div(id='output_message', style={'marginTop': 20, 'fontSize': 18}),\n",
105 | "])\n",
106 | "\n",
107 | "@app.callback(\n",
108 | " Output('output_message', 'children'),\n",
109 | " [Input('store_button', 'n_clicks')],\n",
110 | " [\n",
111 | " State('sepal_length', 'value'),\n",
112 | " State('sepal_width', 'value'),\n",
113 | " State('petal_length', 'value'),\n",
114 | " State('petal_width', 'value')\n",
115 | " ]\n",
116 | ")\n",
117 | "def predict_and_store(n_clicks, sepal_length, sepal_width, petal_length, petal_width):\n",
118 | " if n_clicks > 0 and all(v is not None for v in [sepal_length, sepal_width, petal_length, petal_width]):\n",
119 | " # Store input values in the database\n",
120 | " cursor.execute('''\n",
121 | " INSERT INTO user_inputs (sepal_length, sepal_width, petal_length, petal_width)\n",
122 | " VALUES (%s, %s, %s, %s)\n",
123 | " ''', (sepal_length, sepal_width, petal_length, petal_width))\n",
124 | " conn.commit()\n",
125 | "\n",
126 | " # Predict\n",
127 | " x_new = np.array([[sepal_length, sepal_width, petal_length, petal_width]])\n",
128 | " prediction = knn.predict(x_new)[0]\n",
129 | "\n",
130 | " # Prediction probability scores\n",
131 | " prob_scores = knn.predict_proba(x_new)\n",
132 | "\n",
133 | " return f'Predicted Species: {prediction}, Prediction Score: {prob_scores.max():.2f}, Input values stored in the Iris database'\n",
134 | "\n",
135 | " elif n_clicks > 0:\n",
136 | " return 'Please enter all values to get a prediction'\n",
137 | "\n",
138 | " return ''\n",
139 | "\n",
140 | "if __name__ == '__main__':\n",
141 | " app.run_server(port=8056, debug=True)\n"
142 | ]
143 | },
144 | {
145 | "cell_type": "markdown",
146 | "metadata": {},
147 | "source": [
148 | "# 4"
149 | ]
150 | },
151 | {
152 | "cell_type": "code",
153 | "execution_count": 16,
154 | "metadata": {},
155 | "outputs": [
156 | {
157 | "data": {
158 | "text/html": [
159 | "\n",
160 | " \n",
168 | " "
169 | ],
170 | "text/plain": [
171 | ""
172 | ]
173 | },
174 | "metadata": {},
175 | "output_type": "display_data"
176 | }
177 | ],
178 | "source": [
179 | "import dash\n",
180 | "from dash import dcc, html\n",
181 | "from dash.dependencies import Input, Output, State\n",
182 | "import mysql.connector\n",
183 | "import numpy as np\n",
184 | "import pandas as pd\n",
185 | "from sklearn.neighbors import KNeighborsClassifier\n",
186 | "from sklearn.model_selection import train_test_split\n",
187 | "import warnings\n",
188 | "\n",
189 | "warnings.filterwarnings(\"ignore\")\n",
190 | "\n",
191 | "# Load Iris dataset\n",
192 | "iris = pd.read_csv(\"Iris2023.csv\")\n",
193 | "x = iris.drop(\"species\", axis=1)\n",
194 | "y = iris[\"species\"]\n",
195 | "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=0)\n",
196 | "\n",
197 | "app = dash.Dash(__name__)\n",
198 | "\n",
199 | "# Database setup\n",
200 | "conn = mysql.connector.connect(\n",
201 | " host='localhost',\n",
202 | " user='root',\n",
203 | " password='root',\n",
204 | " database='iris'\n",
205 | ")\n",
206 | "cursor = conn.cursor()\n",
207 | "\n",
208 | "# Create and fit the KNN model\n",
209 | "knn = KNeighborsClassifier(n_neighbors=1)\n",
210 | "knn.fit(x_train, y_train)\n",
211 | "\n",
212 | "# Create a table if it doesn't exist\n",
213 | "cursor.execute('''\n",
214 | " CREATE TABLE IF NOT EXISTS iris_predictions (\n",
215 | " id INT AUTO_INCREMENT PRIMARY KEY,\n",
216 | " sepal_length DOUBLE,\n",
217 | " sepal_width DOUBLE,\n",
218 | " petal_length DOUBLE,\n",
219 | " petal_width DOUBLE,\n",
220 | " predicted_species VARCHAR(255),\n",
221 | " prediction_score DOUBLE\n",
222 | " )\n",
223 | "''')\n",
224 | "conn.commit()\n",
225 | "\n",
226 | "app.layout = html.Div([\n",
227 | " html.H1(\"Iris Flower Prediction\", style={'textAlign': 'center', 'color': 'darkviolet', 'fontFamily': 'Arial'}),\n",
228 | "\n",
229 | " html.Div([\n",
230 | " html.Label(\"Enter Sepal Length:\", style={'marginRight': '10px', 'textAlign': 'center'}),\n",
231 | " dcc.Input(id='sepal_length', type='number', value=5.1, style={'marginRight': '10px', 'textAlign': 'center'}),\n",
232 | " ], style={'marginBottom': '10px', 'display': 'flex', 'justifyContent': 'center'}),\n",
233 | "\n",
234 | " html.Div([\n",
235 | " html.Label(\"Enter Sepal Width:\", style={'marginRight': '10px', 'textAlign': 'center'}),\n",
236 | " dcc.Input(id='sepal_width', type='number', value=3.5, style={'marginRight': '10px', 'textAlign': 'center'}),\n",
237 | " ], style={'marginBottom': '10px', 'display': 'flex', 'justifyContent': 'center'}),\n",
238 | "\n",
239 | " html.Div([\n",
240 | " html.Label(\"Enter Petal Length:\", style={'marginRight': '10px', 'textAlign': 'center'}),\n",
241 | " dcc.Input(id='petal_length', type='number', value=1.4, style={'marginRight': '10px', 'textAlign': 'center'}),\n",
242 | " ], style={'marginBottom': '10px', 'display': 'flex', 'justifyContent': 'center'}),\n",
243 | "\n",
244 | " html.Div([\n",
245 | " html.Label(\"Enter Petal Width:\", style={'marginRight': '10px', 'textAlign': 'center'}),\n",
246 | " dcc.Input(id='petal_width', type='number', value=0.2, style={'marginRight': '10px', 'textAlign': 'center'}),\n",
247 | " ], style={'marginBottom': '20px', 'display': 'flex', 'justifyContent': 'center'}),\n",
248 | "\n",
249 | " html.Button(id='store_button', n_clicks=0, children='Species', style={'marginBottom': '20px','font-weight': 'bold', 'background-color': '#007BFF', 'color': 'white', 'align-self': 'center'}),\n",
250 | "\n",
251 | " html.Div(id='output_message', style={'marginTop': '20px', 'fontSize': '18px', 'color': 'red','font-weight': 'bold', 'fontFamily': 'Arial','textAlign': 'center'}),\n",
252 | "], style={'backgroundImage': 'url(\"https://wallpapercave.com/wp/qqGZC6u.jpg\")', 'backgroundSize': 'cover'})\n",
253 | "\n",
254 | "@app.callback(\n",
255 | " Output('output_message', 'children'),\n",
256 | " [Input('store_button', 'n_clicks')],\n",
257 | " [\n",
258 | " State('sepal_length', 'value'),\n",
259 | " State('sepal_width', 'value'),\n",
260 | " State('petal_length', 'value'),\n",
261 | " State('petal_width', 'value')\n",
262 | " ]\n",
263 | ")\n",
264 | "def predict_and_store(n_clicks, sepal_length, sepal_width, petal_length, petal_width):\n",
265 | " if n_clicks > 0 and all(v is not None for v in [sepal_length, sepal_width, petal_length, petal_width]):\n",
266 | " # Store input values in the database\n",
267 | " cursor.execute('''\n",
268 | " INSERT INTO iris_predictions (sepal_length, sepal_width, petal_length, petal_width)\n",
269 | " VALUES (%s, %s, %s, %s)\n",
270 | " ''', (sepal_length, sepal_width, petal_length, petal_width))\n",
271 | " conn.commit()\n",
272 | "\n",
273 | " # Predict\n",
274 | " x_new = np.array([[sepal_length, sepal_width, petal_length, petal_width]])\n",
275 | " predicted_species = knn.predict(x_new)[0]\n",
276 | "\n",
277 | " # Prediction probability scores\n",
278 | " prediction_score = knn.predict_proba(x_new).max()\n",
279 | "\n",
280 | " # Update the prediction in the database\n",
281 | " cursor.execute('''\n",
282 | " UPDATE iris_predictions\n",
283 | " SET predicted_species = %s, prediction_score = %s\n",
284 | " ORDER BY id DESC\n",
285 | " LIMIT 1\n",
286 | " ''', (predicted_species, prediction_score))\n",
287 | " conn.commit()\n",
288 | "\n",
289 | " return f'Predicted Species: {predicted_species}, Prediction Score: {prediction_score:.2f}, Input values stored in the database!'\n",
290 | "\n",
291 | " elif n_clicks > 0:\n",
292 | " return 'Please enter all values to get a prediction'\n",
293 | "\n",
294 | " return ''\n",
295 | "\n",
296 | "if __name__ == '__main__':\n",
297 | " app.run_server(port=8057, debug=True, external_stylesheets=external_stylesheets)\n"
298 | ]
299 | },
300 | {
301 | "cell_type": "markdown",
302 | "metadata": {},
303 | "source": [
304 | "# 5"
305 | ]
306 | },
307 | {
308 | "cell_type": "code",
309 | "execution_count": null,
310 | "metadata": {},
311 | "outputs": [],
312 | "source": []
313 | }
314 | ],
315 | "metadata": {
316 | "kernelspec": {
317 | "display_name": "Python 3",
318 | "language": "python",
319 | "name": "python3"
320 | },
321 | "language_info": {
322 | "codemirror_mode": {
323 | "name": "ipython",
324 | "version": 3
325 | },
326 | "file_extension": ".py",
327 | "mimetype": "text/x-python",
328 | "name": "python",
329 | "nbconvert_exporter": "python",
330 | "pygments_lexer": "ipython3",
331 | "version": "3.12.0"
332 | }
333 | },
334 | "nbformat": 4,
335 | "nbformat_minor": 2
336 | }
337 |
--------------------------------------------------------------------------------
/Iris-Species-Prediction/database_setup.mysql-notebook:
--------------------------------------------------------------------------------
1 | {
2 | "type": "MySQLNotebook",
3 | "version": "1.0",
4 | "caption": "DB Notebook",
5 | "content": "\\about\nUSE iris;\nSELECT * FROM iris_predictions;",
6 | "options": {
7 | "tabSize": 4,
8 | "indentSize": 4,
9 | "insertSpaces": true,
10 | "defaultEOL": "LF",
11 | "trimAutoWhitespace": true
12 | },
13 | "viewState": {
14 | "cursorState": [
15 | {
16 | "inSelectionMode": false,
17 | "selectionStart": {
18 | "lineNumber": 2,
19 | "column": 10
20 | },
21 | "position": {
22 | "lineNumber": 2,
23 | "column": 10
24 | }
25 | }
26 | ],
27 | "viewState": {
28 | "scrollLeft": 0,
29 | "firstPosition": {
30 | "lineNumber": 1,
31 | "column": 1
32 | },
33 | "firstPositionDeltaTop": 0
34 | },
35 | "contributionsState": {
36 | "editor.contrib.folding": {},
37 | "editor.contrib.wordHighlighter": false
38 | }
39 | },
40 | "contexts": [
41 | {
42 | "state": {
43 | "start": 1,
44 | "end": 1,
45 | "language": "mysql",
46 | "result": {
47 | "type": "text",
48 | "text": [
49 | {
50 | "type": 2,
51 | "content": "Welcome to the MySQL Shell - DB Notebook.\n\nPress Ctrl+Enter to execute the code block.\n\nExecute \\sql to switch to SQL, \\js to JavaScript and \\ts to TypeScript mode.\nExecute \\help or \\? for help;",
52 | "language": "ansi"
53 | }
54 | ]
55 | },
56 | "currentHeight": 120,
57 | "statements": [
58 | {
59 | "delimiter": ";",
60 | "span": {
61 | "start": 0,
62 | "length": 6
63 | },
64 | "contentStart": 0,
65 | "state": 0
66 | }
67 | ]
68 | },
69 | "data": []
70 | },
71 | {
72 | "state": {
73 | "start": 2,
74 | "end": 3,
75 | "language": "mysql",
76 | "result": {
77 | "type": "resultIds",
78 | "list": [
79 | "f9d60b57-9cb8-4943-916c-1596078e3021"
80 | ]
81 | },
82 | "currentHeight": 352,
83 | "statements": [
84 | {
85 | "delimiter": ";",
86 | "span": {
87 | "start": 0,
88 | "length": 9
89 | },
90 | "contentStart": 0,
91 | "state": 0
92 | },
93 | {
94 | "delimiter": ";",
95 | "span": {
96 | "start": 9,
97 | "length": 32
98 | },
99 | "contentStart": 10,
100 | "state": 0
101 | }
102 | ]
103 | },
104 | "data": [
105 | {
106 | "tabId": "ff32b13a-a267-4838-f0c6-c9f1b99a4550",
107 | "resultId": "f9d60b57-9cb8-4943-916c-1596078e3021",
108 | "rows": [
109 | {
110 | "0": 1,
111 | "1": 5.1,
112 | "2": 3.5,
113 | "3": 1.4,
114 | "4": 0.2,
115 | "5": "Iris-setosa",
116 | "6": 1
117 | },
118 | {
119 | "0": 2,
120 | "1": 5.1,
121 | "2": 3.5,
122 | "3": 1.4,
123 | "4": 0.2,
124 | "5": "Iris-setosa",
125 | "6": 1
126 | },
127 | {
128 | "0": 3,
129 | "1": 5.1,
130 | "2": 3.5,
131 | "3": 1.4,
132 | "4": 0.2,
133 | "5": "Iris-setosa",
134 | "6": 1
135 | },
136 | {
137 | "0": 4,
138 | "1": 5.1,
139 | "2": 3.5,
140 | "3": 1.4,
141 | "4": 0.2,
142 | "5": "Iris-setosa",
143 | "6": 1
144 | },
145 | {
146 | "0": 5,
147 | "1": 5.1,
148 | "2": 3.5,
149 | "3": 1.4,
150 | "4": 0.2,
151 | "5": "Iris-setosa",
152 | "6": 1
153 | },
154 | {
155 | "0": 6,
156 | "1": 5.1,
157 | "2": 3.5,
158 | "3": 1.4,
159 | "4": 0.2,
160 | "5": "Iris-setosa",
161 | "6": 1
162 | },
163 | {
164 | "0": 7,
165 | "1": 5.1,
166 | "2": 3.5,
167 | "3": 1.4,
168 | "4": 0.2,
169 | "5": "Iris-setosa",
170 | "6": 1
171 | },
172 | {
173 | "0": 8,
174 | "1": 5.1,
175 | "2": 3.5,
176 | "3": 1.4,
177 | "4": 0.2,
178 | "5": "Iris-setosa",
179 | "6": 1
180 | },
181 | {
182 | "0": 9,
183 | "1": 5.1,
184 | "2": 3.5,
185 | "3": 1.4,
186 | "4": 0.2,
187 | "5": "Iris-setosa",
188 | "6": 1
189 | },
190 | {
191 | "0": 10,
192 | "1": 5.1,
193 | "2": 3.5,
194 | "3": 1.4,
195 | "4": 2.2,
196 | "5": "Iris-setosa",
197 | "6": 1
198 | },
199 | {
200 | "0": 11,
201 | "1": 5.1,
202 | "2": 3.5,
203 | "3": 14.4,
204 | "4": 2.2,
205 | "5": "Iris-virginica",
206 | "6": 1
207 | },
208 | {
209 | "0": 12,
210 | "1": 5.1,
211 | "2": 3.5,
212 | "3": 1.4,
213 | "4": 0.2,
214 | "5": "Iris-setosa",
215 | "6": 1
216 | },
217 | {
218 | "0": 13,
219 | "1": 5.1,
220 | "2": 3.5,
221 | "3": 1.4,
222 | "4": 0.2,
223 | "5": "Iris-setosa",
224 | "6": 1
225 | },
226 | {
227 | "0": 14,
228 | "1": 5.1,
229 | "2": 3.5,
230 | "3": 1.4,
231 | "4": 0.2,
232 | "5": "Iris-setosa",
233 | "6": 1
234 | },
235 | {
236 | "0": 15,
237 | "1": 5.1,
238 | "2": 3.5,
239 | "3": 1.4,
240 | "4": 0.2,
241 | "5": "Iris-setosa",
242 | "6": 1
243 | },
244 | {
245 | "0": 16,
246 | "1": 5.1,
247 | "2": 3.5,
248 | "3": 1.4,
249 | "4": 3.2,
250 | "5": "Iris-setosa",
251 | "6": 1
252 | },
253 | {
254 | "0": 17,
255 | "1": 5.1,
256 | "2": 3.5,
257 | "3": 17.4,
258 | "4": 3.2,
259 | "5": "Iris-virginica",
260 | "6": 1
261 | },
262 | {
263 | "0": 18,
264 | "1": 5.1,
265 | "2": 3.5,
266 | "3": 17.4,
267 | "4": 3.2,
268 | "5": "Iris-virginica",
269 | "6": 1
270 | },
271 | {
272 | "0": 19,
273 | "1": 5.1,
274 | "2": 3.5,
275 | "3": 2.4,
276 | "4": 3.2,
277 | "5": "Iris-versicolor",
278 | "6": 1
279 | },
280 | {
281 | "0": 20,
282 | "1": 5.1,
283 | "2": 3.5,
284 | "3": 1.4,
285 | "4": 0.2,
286 | "5": "Iris-setosa",
287 | "6": 1
288 | },
289 | {
290 | "0": 21,
291 | "1": 5.1,
292 | "2": 3.5,
293 | "3": 1.4,
294 | "4": 0.2,
295 | "5": "Iris-setosa",
296 | "6": 1
297 | },
298 | {
299 | "0": 22,
300 | "1": 5.1,
301 | "2": 3.5,
302 | "3": 1.4,
303 | "4": 0.2,
304 | "5": "Iris-setosa",
305 | "6": 1
306 | },
307 | {
308 | "0": 23,
309 | "1": 5.1,
310 | "2": 3.5,
311 | "3": 1.4,
312 | "4": 0.2,
313 | "5": "Iris-setosa",
314 | "6": 1
315 | },
316 | {
317 | "0": 24,
318 | "1": 5.1,
319 | "2": 3.5,
320 | "3": 1.4,
321 | "4": 0.2,
322 | "5": "Iris-setosa",
323 | "6": 1
324 | },
325 | {
326 | "0": 25,
327 | "1": 5.1,
328 | "2": 3.5,
329 | "3": 1.4,
330 | "4": 0.2,
331 | "5": "Iris-setosa",
332 | "6": 1
333 | },
334 | {
335 | "0": 26,
336 | "1": 5.1,
337 | "2": 3.5,
338 | "3": 1.4,
339 | "4": 0.2,
340 | "5": "Iris-setosa",
341 | "6": 1
342 | },
343 | {
344 | "0": 27,
345 | "1": 5.1,
346 | "2": 3.5,
347 | "3": 1.4,
348 | "4": 0.2,
349 | "5": "Iris-setosa",
350 | "6": 1
351 | },
352 | {
353 | "0": 28,
354 | "1": 5.1,
355 | "2": 3.5,
356 | "3": 1.4,
357 | "4": 0.2,
358 | "5": "Iris-setosa",
359 | "6": 1
360 | },
361 | {
362 | "0": 29,
363 | "1": 5.1,
364 | "2": 3.5,
365 | "3": 1.4,
366 | "4": 0.2,
367 | "5": "Iris-setosa",
368 | "6": 1
369 | },
370 | {
371 | "0": 30,
372 | "1": 5.1,
373 | "2": 3.5,
374 | "3": 7.4,
375 | "4": 0.2,
376 | "5": "Iris-virginica",
377 | "6": 1
378 | },
379 | {
380 | "0": 31,
381 | "1": 5.1,
382 | "2": 3.5,
383 | "3": 1.4,
384 | "4": 0.2,
385 | "5": "Iris-setosa",
386 | "6": 1
387 | },
388 | {
389 | "0": 32,
390 | "1": 5.1,
391 | "2": 3.5,
392 | "3": 1.4,
393 | "4": 5.2,
394 | "5": "Iris-versicolor",
395 | "6": 1
396 | }
397 | ],
398 | "columns": [
399 | {
400 | "title": "id",
401 | "field": "0",
402 | "dataType": {
403 | "type": 4
404 | }
405 | },
406 | {
407 | "title": "sepal_length",
408 | "field": "1",
409 | "dataType": {
410 | "type": 9
411 | }
412 | },
413 | {
414 | "title": "sepal_width",
415 | "field": "2",
416 | "dataType": {
417 | "type": 9
418 | }
419 | },
420 | {
421 | "title": "petal_length",
422 | "field": "3",
423 | "dataType": {
424 | "type": 9
425 | }
426 | },
427 | {
428 | "title": "petal_width",
429 | "field": "4",
430 | "dataType": {
431 | "type": 9
432 | }
433 | },
434 | {
435 | "title": "predicted_species",
436 | "field": "5",
437 | "dataType": {
438 | "type": 17
439 | }
440 | },
441 | {
442 | "title": "prediction_score",
443 | "field": "6",
444 | "dataType": {
445 | "type": 9
446 | }
447 | }
448 | ],
449 | "executionInfo": {
450 | "text": "OK, 32 records retrieved in 0s"
451 | },
452 | "totalRowCount": 32,
453 | "hasMoreRows": false,
454 | "currentPage": 0,
455 | "index": 0,
456 | "sql": "\nSELECT * FROM iris_predictions;"
457 | }
458 | ]
459 | }
460 | ]
461 | }
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https://raw.githubusercontent.com/hiranvjoseph/Machine-Learning-Prediction-Projects-Repository/f3cacf9f9d92d883957375e2c04bb18b4e237491/Iris-Species-Prediction/iris1.jpg
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/Iris-Species-Prediction/iris2.jpg:
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https://raw.githubusercontent.com/hiranvjoseph/Machine-Learning-Prediction-Projects-Repository/f3cacf9f9d92d883957375e2c04bb18b4e237491/Iris-Species-Prediction/iris2.jpg
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/Iris-Species-Prediction/readme.md:
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1 | # Iris Classification
2 | 
3 |
4 | Iris Classification is a machine learning project that utilizes the K-Nearest Neighbors (KNN) model to predict the species of Iris flowers based on their sepal and petal characteristics.
5 |
6 | ## Dataset
7 |
8 | The dataset contains 151 entries, each with the following features:
9 |
10 | - **Sepal Length**: Length of the iris flower's sepal.
11 | - **Sepal Width**: Width of the iris flower's sepal.
12 | - **Petal Length**: Length of the iris flower's petal.
13 | - **Petal Width**: Width of the iris flower's petal.
14 | - **Species**: Target variable representing the species of the iris flower (Iris-setosa, Iris-versicolor, Iris-virginica).
15 |
16 | ## Project Overview
17 |
18 | The project involves using the K-Nearest Neighbors (KNN) algorithm to classify iris flowers into three species based on their sepal and petal characteristics. The model is trained on the provided dataset and can make predictions for new data.
19 |
20 | ## File Structure
21 |
22 | The repository is organized as follows:
23 |
24 | - **`Iris_Dataset.csv`**: The dataset file containing iris flower characteristics.
25 | - **`Iris_Classification.ipynb`**: Jupyter Notebook containing the Python code for data exploration, model training, and predictions.
26 | - **`images/`**: Folder containing images used in the project, such as visualizations or plots.
27 | - **`database_setup.sql`**: SQL file with the database setup for storing input values.
28 | - **`README.md`**: This file, providing an overview of the project, dataset, and file structure.
29 |
30 | ## Getting Started
31 |
32 | 1. **Clone the Repository:**
33 | ```bash
34 | git clone https://github.com/hiranvjoseph/Machine-Learning-Prediction-Projects-Repository/iris-classification.git
35 | cd iris-classification
36 | ```
37 |
38 | 2. **Install Dependencies:**
39 | ```bash
40 | pip install -r requirements.txt
41 | ```
42 |
43 | 3. **Run the Jupyter Notebook:**
44 | Open and run the `Iris_Classification.ipynb` notebook to explore the data, train the model, and make predictions.
45 |
46 | ## Usage
47 |
48 | The project demonstrates how to use the K-Nearest Neighbors (KNN) algorithm for iris flower species classification. The Jupyter Notebook provides step-by-step guidance on data preprocessing, model training, and prediction.
49 |
50 | ## Database Setup
51 |
52 | The `database_setup.sql` file contains the SQL code for setting up a database to store input values. Run this script if you want to store input values in a database.
53 |
54 | ```bash
55 | mysql -u root -p house < database_setup.sql
56 | ```
57 |
58 | ## Demo
59 |
60 | For a quick demonstration, please refer to the `demo/README.md` file.
61 |
62 | ## Contact
63 |
64 | For inquiries, suggestions, or collaboration opportunities, feel free to reach out:
65 |
66 | **Hiran Joseph**
67 | - Email: [Hiran](hiranvjoseph@gmail.com)
68 | - LinkedIn: [Hiran Joseph](https://www.linkedin.com/in/hiranjoe/)
69 | - Kaggle: [@Hiran Joe ](https://www.kaggle.com/hiranjoseph)
70 |
71 |
72 | We appreciate your interest and look forward to hearing from you!
73 |
74 |
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/Outlier_Treatment/readme.md:
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1 | # Outlier Treatment in Data Analysis on Car Crashes Data
2 |
3 | 
4 |
5 | ## Introduction
6 | This project focuses on the detection and treatment of outliers in a dataset related to car crashes. The dataset contains various columns, including:
7 |
8 | - `total`: Total number of car crashes.
9 | - `speeding`: Number of car crashes attributed to speeding.
10 | - `alcohol`: Number of car crashes related to alcohol consumption.
11 | - `not_distracted`: Number of car crashes where distractions were not involved.
12 | - `no_previous`: Number of car crashes with no previous history of accidents.
13 | - `ins_premium`: Insurance premium total.
14 | - `ins_losses`: Insurance losses total.
15 | - `abbrev`: State abbreviations.
16 |
17 | The primary goal of this project is to identify and handle outliers in this dataset using the boxplot method, which is a powerful visualization technique for detecting data points that deviate significantly from the majority.
18 |
19 | ## Outlier Detection
20 | Outliers are extreme data points that deviate from the overall pattern of the dataset. In this project, we will use the boxplot method to identify potential outliers for each of the columns mentioned above. The boxplot visually represents the distribution of data and highlights data points that fall outside the "whiskers" of the plot.
21 |
22 | ## Outlier Treatment
23 | Once we have identified potential outliers, we will explore various methods to handle them. Outlier treatment methods may include:
24 |
25 | - **Removal**: Removing the identified outliers from the dataset.
26 | - **Transformation**: Applying mathematical transformations to the data to make the distribution more normal.
27 | - **Imputation**: Replacing outliers with more reasonable values.
28 | - **Winsorization**: Capping the extreme values to a predefined percentile.
29 |
30 | ## Getting Started
31 | Follow these steps to get started with this project:
32 |
33 | 1. Clone the repository to your local machine:
34 |
35 | ```
36 | git clone https://github.com/hiranvjoseph/outlier-treatment-car-crashes.git
37 | ```
38 |
39 | 2. Install the required dependencies. You can use a virtual environment for this:
40 |
41 | ```
42 | pip install -r requirements.txt
43 | ```
44 |
45 | 3. Run the Jupyter notebooks or Python scripts to detect and treat outliers in the car crashes dataset.
46 |
47 | 4. Analyze the results and evaluate the impact of outlier treatment on your data analysis.
48 |
49 | ## Data Source
50 | The dataset used in this project can be found at [insert_dataset_link_here].
51 |
52 | ## License
53 | This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.
54 |
55 | ## Contributing
56 | If you'd like to contribute to this project, please open an issue or submit a pull request. We welcome contributions and improvements.
57 |
58 | ## Contact
59 | For any questions or feedback, please contact [Hiran Joseph] at [hiranvjoseph@gmail.com].
60 |
61 | Thank you for exploring the techniques and best practices for handling outliers in data analysis on car crashes data!
62 |
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/README.md:
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1 | # Machine Learning Projects Repository
2 | 
3 |
4 | Welcome to the Machine Learning Projects Repository! This repository hosts a collection of predictive modeling projects, each focusing on different aspects of data analysis and machine learning. These projects cover a wide range of topics, including linear regression, multiple regression, outlier treatment, and more.
5 |
6 | ## Table of Contents
7 | - [Project Descriptions](#project-descriptions)
8 | - [Getting Started](#getting-started)
9 | - [Folder Structure](#folder-structure)
10 | - [Data Sources](#data-sources)
11 | - [Results and Insights](#results-and-insights)
12 | - [License](#license)
13 | - [Contributing](#contributing)
14 | - [Contact](#contact)
15 |
16 | ## Project Descriptions
17 | Explore our machine learning projects to gain insights and hands-on experience in different areas of predictive modeling:
18 |
19 | 1. **Linear Regression**: Learn about simple linear regression techniques where we model the relationship between a single input feature and the target variable.
20 |
21 | 2. **Multiple Regression**: Dive deeper into multiple regression, where we predict a target variable using multiple input features.
22 |
23 | 3. **Outlier Treatment in Data Analysis**: Discover the methods and techniques used to identify and manage outliers in your datasets, ensuring data quality and model accuracy.
24 |
25 | [Include brief descriptions and links to each project in your repository.]
26 |
27 | ## Getting Started
28 | To get started with any of the projects in this repository, follow these general steps:
29 |
30 | 1. Clone the repository to your local machine:
31 |
32 |
33 | git clone https://github.com/hiranvjoseph/Machine-Learning-Prediction-Projects-Repository/tree/main
34 |
35 | 2. Navigate to the specific project folder of your choice.
36 |
37 | 3. Refer to the project's README and Jupyter notebooks to understand the problem, dataset, and the steps taken in the analysis.
38 |
39 | 4. Run the Jupyter notebooks or Python scripts to explore, model, and evaluate the data.
40 |
41 | ## Folder Structure
42 | The repository is organized into project-specific folders, making it easy to find and explore each project. The folder structure for each project typically includes:
43 |
44 | - `data/`: Contains the dataset(s) used for the analysis.
45 | - `notebooks/`: Jupyter notebooks with code, explanations, and results.
46 | - `results/`: Results of the program.
47 |
48 | ## Data Sources
49 | Where available, we include links to the data sources used in the projects. Make sure to check the project-specific README for details.
50 |
51 | ## Results and Insights
52 | Each project provides insights, results, and the performance of the machine learning models used. We encourage you to analyze and interpret the findings to gain a deeper understanding of the topics covered.
53 |
54 | ## License
55 | This repository is open-source and available under the MIT License. See the [LICENSE](LICENSE) file for more details.
56 |
57 | ## Contributing
58 | We welcome contributions and improvements. If you have ideas for new projects, enhancements to existing projects, or general improvements to the repository, please feel free to open an issue or submit a pull request.
59 |
60 | ## Contact
61 | - **Hiran Joseph**
62 | - **Email**: [hiranvjoseph@gmail.com](mailto:hiranvjoseph@gmail.com)
63 |
64 | We hope you find these machine learning projects informative and instructive. Enjoy exploring the world of predictive modeling and data analysis!
65 | Explore, learn, and experiment with various machine learning prediction projects to gain valuable insights into data analysis and predictive modeling. Happy coding!
66 |
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