Assignment
Abeul Rehman
2024-05-05
———–Load Data————
heart_attack<-read.csv("C:/Users/DELL/Desktop/heart.csv")
heart_attack## age sex cp trestbps chol fbs restecg thalach exang oldpeak slope ca thal
## 1 52 1 0 125 212 0 1 168 0 1.0 2 2 3
## 2 53 1 0 140 203 1 0 155 1 3.1 0 0 3
## 3 70 1 0 145 174 0 1 125 1 2.6 0 0 3
## 4 61 1 0 148 203 0 1 161 0 0.0 2 1 3
## 5 62 0 0 138 294 1 1 106 0 1.9 1 3 2
## 6 58 0 0 100 248 0 0 122 0 1.0 1 0 2
## 7 58 1 0 114 318 0 2 140 0 4.4 0 3 1
## 8 55 1 0 160 289 0 0 145 1 0.8 1 1 3
## 9 46 1 0 120 249 0 0 144 0 0.8 2 0 3
## 10 54 1 0 122 286 0 0 116 1 3.2 1 2 2
## 11 71 0 0 112 149 0 1 125 0 1.6 1 0 2
## 12 43 0 0 132 341 1 0 136 1 3.0 1 0 3
## 13 34 0 1 118 210 0 1 192 0 0.7 2 0 2
## 14 51 1 0 140 298 0 1 122 1 4.2 1 3 3
## 15 52 1 0 128 204 1 1 156 1 1.0 1 0 0
## 16 34 0 1 118 210 0 1 192 0 0.7 2 0 2
## 17 51 0 2 140 308 0 0 142 0 1.5 2 1 2
## 18 54 1 0 124 266 0 0 109 1 2.2 1 1 3
## 19 50 0 1 120 244 0 1 162 0 1.1 2 0 2
## 20 58 1 2 140 211 1 0 165 0 0.0 2 0 2
## 21 60 1 2 140 185 0 0 155 0 3.0 1 0 2
## 22 67 0 0 106 223 0 1 142 0 0.3 2 2 2
## 23 45 1 0 104 208 0 0 148 1 3.0 1 0 2
## 24 63 0 2 135 252 0 0 172 0 0.0 2 0 2
## 25 42 0 2 120 209 0 1 173 0 0.0 1 0 2
## 26 61 0 0 145 307 0 0 146 1 1.0 1 0 3
## 27 44 1 2 130 233 0 1 179 1 0.4 2 0 2
## 28 58 0 1 136 319 1 0 152 0 0.0 2 2 2
## 29 56 1 2 130 256 1 0 142 1 0.6 1 1 1
## 30 55 0 0 180 327 0 2 117 1 3.4 1 0 2
## 31 44 1 0 120 169 0 1 144 1 2.8 0 0 1
## 32 50 0 1 120 244 0 1 162 0 1.1 2 0 2
## 33 57 1 0 130 131 0 1 115 1 1.2 1 1 3
## 34 70 1 2 160 269 0 1 112 1 2.9 1 1 3
## 35 50 1 2 129 196 0 1 163 0 0.0 2 0 2
## 36 46 1 2 150 231 0 1 147 0 3.6 1 0 2
## 37 51 1 3 125 213 0 0 125 1 1.4 2 1 2
## 38 59 1 0 138 271 0 0 182 0 0.0 2 0 2
## 39 64 1 0 128 263 0 1 105 1 0.2 1 1 3
## 40 57 1 2 128 229 0 0 150 0 0.4 1 1 3
## 41 65 0 2 160 360 0 0 151 0 0.8 2 0 2
## 42 54 1 2 120 258 0 0 147 0 0.4 1 0 3
## 43 61 0 0 130 330 0 0 169 0 0.0 2 0 2
## 44 46 1 0 120 249 0 0 144 0 0.8 2 0 3
## 45 55 0 1 132 342 0 1 166 0 1.2 2 0 2
## 46 42 1 0 140 226 0 1 178 0 0.0 2 0 2
## 47 41 1 1 135 203 0 1 132 0 0.0 1 0 1
## 48 66 0 0 178 228 1 1 165 1 1.0 1 2 3
## 49 66 0 2 146 278 0 0 152 0 0.0 1 1 2
## 50 60 1 0 117 230 1 1 160 1 1.4 2 2 3
## 51 58 0 3 150 283 1 0 162 0 1.0 2 0 2
## 52 57 0 0 140 241 0 1 123 1 0.2 1 0 3
## 53 38 1 2 138 175 0 1 173 0 0.0 2 4 2
## 54 49 1 2 120 188 0 1 139 0 2.0 1 3 3
## 55 55 1 0 140 217 0 1 111 1 5.6 0 0 3
## 56 55 1 0 140 217 0 1 111 1 5.6 0 0 3
## 57 56 1 3 120 193 0 0 162 0 1.9 1 0 3
## 58 48 1 1 130 245 0 0 180 0 0.2 1 0 2
## 59 67 1 2 152 212 0 0 150 0 0.8 1 0 3
## 60 57 1 1 154 232 0 0 164 0 0.0 2 1 2
## 61 29 1 1 130 204 0 0 202 0 0.0 2 0 2
## 62 66 0 2 146 278 0 0 152 0 0.0 1 1 2
## 63 67 1 0 100 299 0 0 125 1 0.9 1 2 2
## 64 59 1 2 150 212 1 1 157 0 1.6 2 0 2
## 65 29 1 1 130 204 0 0 202 0 0.0 2 0 2
## 66 59 1 3 170 288 0 0 159 0 0.2 1 0 3
## 67 53 1 2 130 197 1 0 152 0 1.2 0 0 2
## 68 42 1 0 136 315 0 1 125 1 1.8 1 0 1
## 69 37 0 2 120 215 0 1 170 0 0.0 2 0 2
## 70 62 0 0 160 164 0 0 145 0 6.2 0 3 3
## 71 59 1 0 170 326 0 0 140 1 3.4 0 0 3
## 72 61 1 0 140 207 0 0 138 1 1.9 2 1 3
## 73 56 1 0 125 249 1 0 144 1 1.2 1 1 2
## 74 59 1 0 140 177 0 1 162 1 0.0 2 1 3
## 75 48 1 0 130 256 1 0 150 1 0.0 2 2 3
## 76 47 1 2 138 257 0 0 156 0 0.0 2 0 2
## 77 48 1 2 124 255 1 1 175 0 0.0 2 2 2
## 78 63 1 0 140 187 0 0 144 1 4.0 2 2 3
## 79 52 1 1 134 201 0 1 158 0 0.8 2 1 2
## 80 52 1 1 134 201 0 1 158 0 0.8 2 1 2
## 81 50 1 2 140 233 0 1 163 0 0.6 1 1 3
## 82 49 1 2 118 149 0 0 126 0 0.8 2 3 2
## 83 46 1 2 150 231 0 1 147 0 3.6 1 0 2
## 84 38 1 2 138 175 0 1 173 0 0.0 2 4 2
## 85 37 0 2 120 215 0 1 170 0 0.0 2 0 2
## 86 44 1 1 120 220 0 1 170 0 0.0 2 0 2
## 87 58 1 2 140 211 1 0 165 0 0.0 2 0 2
## 88 59 0 0 174 249 0 1 143 1 0.0 1 0 2
## 89 62 0 0 140 268 0 0 160 0 3.6 0 2 2
## 90 68 1 0 144 193 1 1 141 0 3.4 1 2 3
## 91 54 0 2 108 267 0 0 167 0 0.0 2 0 2
## 92 62 0 0 124 209 0 1 163 0 0.0 2 0 2
## 93 63 1 0 140 187 0 0 144 1 4.0 2 2 3
## 94 44 1 0 120 169 0 1 144 1 2.8 0 0 1
## 95 62 1 1 128 208 1 0 140 0 0.0 2 0 2
## 96 45 0 0 138 236 0 0 152 1 0.2 1 0 2
## 97 57 0 0 128 303 0 0 159 0 0.0 2 1 2
## 98 53 1 0 123 282 0 1 95 1 2.0 1 2 3
## 99 65 1 0 110 248 0 0 158 0 0.6 2 2 1
## 100 76 0 2 140 197 0 2 116 0 1.1 1 0 2
## 101 43 0 2 122 213 0 1 165 0 0.2 1 0 2
## 102 57 1 2 150 126 1 1 173 0 0.2 2 1 3
## 103 54 1 1 108 309 0 1 156 0 0.0 2 0 3
## 104 47 1 2 138 257 0 0 156 0 0.0 2 0 2
## 105 52 1 3 118 186 0 0 190 0 0.0 1 0 1
## 106 47 1 0 110 275 0 0 118 1 1.0 1 1 2
## 107 51 1 0 140 299 0 1 173 1 1.6 2 0 3
## 108 62 1 1 120 281 0 0 103 0 1.4 1 1 3
## 109 40 1 0 152 223 0 1 181 0 0.0 2 0 3
## 110 54 1 0 110 206 0 0 108 1 0.0 1 1 2
## 111 44 1 0 110 197 0 0 177 0 0.0 2 1 2
## 112 53 1 0 142 226 0 0 111 1 0.0 2 0 3
## 113 48 1 0 130 256 1 0 150 1 0.0 2 2 3
## 114 57 1 0 110 335 0 1 143 1 3.0 1 1 3
## 115 59 1 2 126 218 1 1 134 0 2.2 1 1 1
## 116 61 0 0 145 307 0 0 146 1 1.0 1 0 3
## 117 63 1 0 130 254 0 0 147 0 1.4 1 1 3
## 118 43 1 0 120 177 0 0 120 1 2.5 1 0 3
## 119 29 1 1 130 204 0 0 202 0 0.0 2 0 2
## 120 42 1 1 120 295 0 1 162 0 0.0 2 0 2
## 121 54 1 1 108 309 0 1 156 0 0.0 2 0 3
## 122 44 1 0 120 169 0 1 144 1 2.8 0 0 1
## 123 60 1 0 145 282 0 0 142 1 2.8 1 2 3
## 124 65 0 2 140 417 1 0 157 0 0.8 2 1 2
## 125 61 1 0 120 260 0 1 140 1 3.6 1 1 3
## 126 60 0 3 150 240 0 1 171 0 0.9 2 0 2
## 127 66 1 0 120 302 0 0 151 0 0.4 1 0 2
## 128 53 1 2 130 197 1 0 152 0 1.2 0 0 2
## 129 52 1 2 138 223 0 1 169 0 0.0 2 4 2
## 130 57 1 0 140 192 0 1 148 0 0.4 1 0 1
## 131 60 0 3 150 240 0 1 171 0 0.9 2 0 2
## 132 51 0 2 130 256 0 0 149 0 0.5 2 0 2
## 133 41 1 1 135 203 0 1 132 0 0.0 1 0 1
## 134 50 1 2 129 196 0 1 163 0 0.0 2 0 2
## 135 54 1 1 108 309 0 1 156 0 0.0 2 0 3
## 136 58 0 0 170 225 1 0 146 1 2.8 1 2 1
## 137 55 0 1 132 342 0 1 166 0 1.2 2 0 2
## 138 64 0 0 180 325 0 1 154 1 0.0 2 0 2
## 139 47 1 2 138 257 0 0 156 0 0.0 2 0 2
## 140 41 1 1 110 235 0 1 153 0 0.0 2 0 2
## 141 57 1 0 152 274 0 1 88 1 1.2 1 1 3
## 142 63 0 0 124 197 0 1 136 1 0.0 1 0 2
## 143 61 1 3 134 234 0 1 145 0 2.6 1 2 2
## 144 34 1 3 118 182 0 0 174 0 0.0 2 0 2
## 145 47 1 0 112 204 0 1 143 0 0.1 2 0 2
## 146 40 1 0 110 167 0 0 114 1 2.0 1 0 3
## 147 51 0 2 120 295 0 0 157 0 0.6 2 0 2
## 148 41 1 0 110 172 0 0 158 0 0.0 2 0 3
## 149 52 1 3 152 298 1 1 178 0 1.2 1 0 3
## 150 39 1 2 140 321 0 0 182 0 0.0 2 0 2
## 151 58 1 0 114 318 0 2 140 0 4.4 0 3 1
## 152 54 1 1 192 283 0 0 195 0 0.0 2 1 3
## 153 58 1 0 125 300 0 0 171 0 0.0 2 2 3
## 154 54 1 2 120 258 0 0 147 0 0.4 1 0 3
## 155 63 1 0 130 330 1 0 132 1 1.8 2 3 3
## 156 54 1 1 108 309 0 1 156 0 0.0 2 0 3
## 157 40 1 3 140 199 0 1 178 1 1.4 2 0 3
## 158 54 1 2 120 258 0 0 147 0 0.4 1 0 3
## 159 67 0 2 115 564 0 0 160 0 1.6 1 0 3
## 160 41 1 1 120 157 0 1 182 0 0.0 2 0 2
## 161 77 1 0 125 304 0 0 162 1 0.0 2 3 2
## 162 51 1 2 100 222 0 1 143 1 1.2 1 0 2
## 163 77 1 0 125 304 0 0 162 1 0.0 2 3 2
## 164 48 1 0 124 274 0 0 166 0 0.5 1 0 3
## 165 56 1 0 125 249 1 0 144 1 1.2 1 1 2
## 166 59 1 0 170 326 0 0 140 1 3.4 0 0 3
## 167 56 1 0 132 184 0 0 105 1 2.1 1 1 1
## 168 57 0 0 120 354 0 1 163 1 0.6 2 0 2
## 169 43 1 2 130 315 0 1 162 0 1.9 2 1 2
## 170 45 0 1 112 160 0 1 138 0 0.0 1 0 2
## 171 43 1 0 150 247 0 1 171 0 1.5 2 0 2
## 172 56 1 0 130 283 1 0 103 1 1.6 0 0 3
## 173 56 1 1 120 240 0 1 169 0 0.0 0 0 2
## 174 39 0 2 94 199 0 1 179 0 0.0 2 0 2
## 175 54 1 0 110 239 0 1 126 1 2.8 1 1 3
## 176 56 0 0 200 288 1 0 133 1 4.0 0 2 3
## 177 56 1 0 130 283 1 0 103 1 1.6 0 0 3
## 178 64 1 0 120 246 0 0 96 1 2.2 0 1 2
## 179 44 1 0 110 197 0 0 177 0 0.0 2 1 2
## 180 56 0 0 134 409 0 0 150 1 1.9 1 2 3
## 181 63 1 0 140 187 0 0 144 1 4.0 2 2 3
## 182 64 1 3 110 211 0 0 144 1 1.8 1 0 2
## 183 60 1 0 140 293 0 0 170 0 1.2 1 2 3
## 184 42 1 2 130 180 0 1 150 0 0.0 2 0 2
## 185 45 1 1 128 308 0 0 170 0 0.0 2 0 2
## 186 57 1 0 165 289 1 0 124 0 1.0 1 3 3
## 187 40 1 0 110 167 0 0 114 1 2.0 1 0 3
## 188 56 1 0 125 249 1 0 144 1 1.2 1 1 2
## 189 63 1 0 130 254 0 0 147 0 1.4 1 1 3
## 190 64 1 2 125 309 0 1 131 1 1.8 1 0 3
## 191 41 1 2 112 250 0 1 179 0 0.0 2 0 2
## 192 56 1 1 130 221 0 0 163 0 0.0 2 0 3
## 193 67 0 2 115 564 0 0 160 0 1.6 1 0 3
## 194 69 1 3 160 234 1 0 131 0 0.1 1 1 2
## 195 67 1 0 160 286 0 0 108 1 1.5 1 3 2
## 196 59 1 2 150 212 1 1 157 0 1.6 2 0 2
## 197 58 1 0 100 234 0 1 156 0 0.1 2 1 3
## 198 45 1 0 115 260 0 0 185 0 0.0 2 0 2
## 199 60 0 2 102 318 0 1 160 0 0.0 2 1 2
## 200 50 1 0 144 200 0 0 126 1 0.9 1 0 3
## 201 62 0 0 124 209 0 1 163 0 0.0 2 0 2
## 202 34 1 3 118 182 0 0 174 0 0.0 2 0 2
## 203 52 1 3 152 298 1 1 178 0 1.2 1 0 3
## 204 64 1 3 170 227 0 0 155 0 0.6 1 0 3
## 205 66 0 2 146 278 0 0 152 0 0.0 1 1 2
## 206 42 1 3 148 244 0 0 178 0 0.8 2 2 2
## 207 59 1 2 126 218 1 1 134 0 2.2 1 1 1
## 208 41 1 2 112 250 0 1 179 0 0.0 2 0 2
## 209 38 1 2 138 175 0 1 173 0 0.0 2 4 2
## 210 62 1 1 120 281 0 0 103 0 1.4 1 1 3
## 211 42 1 2 120 240 1 1 194 0 0.8 0 0 3
## 212 67 1 0 100 299 0 0 125 1 0.9 1 2 2
## 213 50 1 0 150 243 0 0 128 0 2.6 1 0 3
## 214 43 1 2 130 315 0 1 162 0 1.9 2 1 2
## 215 45 1 1 128 308 0 0 170 0 0.0 2 0 2
## 216 49 1 1 130 266 0 1 171 0 0.6 2 0 2
## 217 65 1 0 135 254 0 0 127 0 2.8 1 1 3
## 218 41 1 1 120 157 0 1 182 0 0.0 2 0 2
## 219 46 1 0 140 311 0 1 120 1 1.8 1 2 3
## 220 54 1 0 122 286 0 0 116 1 3.2 1 2 2
## 221 57 0 1 130 236 0 0 174 0 0.0 1 1 2
## 222 63 1 0 130 254 0 0 147 0 1.4 1 1 3
## 223 64 1 3 110 211 0 0 144 1 1.8 1 0 2
## 224 39 0 2 94 199 0 1 179 0 0.0 2 0 2
## 225 51 1 0 140 261 0 0 186 1 0.0 2 0 2
## 226 54 1 2 150 232 0 0 165 0 1.6 2 0 3
## 227 49 1 2 118 149 0 0 126 0 0.8 2 3 2
## 228 44 0 2 118 242 0 1 149 0 0.3 1 1 2
## 229 52 1 1 128 205 1 1 184 0 0.0 2 0 2
## 230 66 0 0 178 228 1 1 165 1 1.0 1 2 3
## 231 58 1 0 125 300 0 0 171 0 0.0 2 2 3
## 232 56 1 1 120 236 0 1 178 0 0.8 2 0 2
## 233 60 1 0 125 258 0 0 141 1 2.8 1 1 3
## 234 41 0 1 126 306 0 1 163 0 0.0 2 0 2
## 235 49 0 0 130 269 0 1 163 0 0.0 2 0 2
## 236 64 1 3 170 227 0 0 155 0 0.6 1 0 3
## 237 49 1 2 118 149 0 0 126 0 0.8 2 3 2
## 238 57 1 1 124 261 0 1 141 0 0.3 2 0 3
## 239 60 1 0 117 230 1 1 160 1 1.4 2 2 3
## 240 62 0 0 150 244 0 1 154 1 1.4 1 0 2
## 241 54 0 1 132 288 1 0 159 1 0.0 2 1 2
## 242 67 1 2 152 212 0 0 150 0 0.8 1 0 3
## 243 38 1 2 138 175 0 1 173 0 0.0 2 4 2
## 244 60 1 2 140 185 0 0 155 0 3.0 1 0 2
## 245 51 1 2 125 245 1 0 166 0 2.4 1 0 2
## 246 44 1 1 130 219 0 0 188 0 0.0 2 0 2
## 247 54 1 1 192 283 0 0 195 0 0.0 2 1 3
## 248 46 1 0 140 311 0 1 120 1 1.8 1 2 3
## 249 39 0 2 138 220 0 1 152 0 0.0 1 0 2
## 250 42 1 2 130 180 0 1 150 0 0.0 2 0 2
## 251 47 1 0 110 275 0 0 118 1 1.0 1 1 2
## 252 45 0 1 112 160 0 1 138 0 0.0 1 0 2
## 253 55 1 0 132 353 0 1 132 1 1.2 1 1 3
## 254 57 1 0 165 289 1 0 124 0 1.0 1 3 3
## 255 35 1 0 120 198 0 1 130 1 1.6 1 0 3
## 256 62 0 0 140 394 0 0 157 0 1.2 1 0 2
## 257 35 0 0 138 183 0 1 182 0 1.4 2 0 2
## 258 64 0 0 180 325 0 1 154 1 0.0 2 0 2
## 259 38 1 3 120 231 0 1 182 1 3.8 1 0 3
## 260 66 1 0 120 302 0 0 151 0 0.4 1 0 2
## 261 44 1 2 120 226 0 1 169 0 0.0 2 0 2
## 262 54 1 2 150 232 0 0 165 0 1.6 2 0 3
## 263 48 1 0 122 222 0 0 186 0 0.0 2 0 2
## 264 55 0 1 132 342 0 1 166 0 1.2 2 0 2
## 265 58 0 0 170 225 1 0 146 1 2.8 1 2 1
## 266 45 1 0 104 208 0 0 148 1 3.0 1 0 2
## 267 53 1 0 123 282 0 1 95 1 2.0 1 2 3
## 268 67 1 0 120 237 0 1 71 0 1.0 1 0 2
## 269 58 1 2 132 224 0 0 173 0 3.2 2 2 3
## 270 71 0 2 110 265 1 0 130 0 0.0 2 1 2
## 271 43 1 0 110 211 0 1 161 0 0.0 2 0 3
## 272 44 1 1 120 263 0 1 173 0 0.0 2 0 3
## 273 39 0 2 138 220 0 1 152 0 0.0 1 0 2
## 274 54 1 0 110 206 0 0 108 1 0.0 1 1 2
## 275 66 1 0 160 228 0 0 138 0 2.3 2 0 1
## 276 56 1 0 130 283 1 0 103 1 1.6 0 0 3
## 277 57 1 0 132 207 0 1 168 1 0.0 2 0 3
## 278 44 1 1 130 219 0 0 188 0 0.0 2 0 2
## 279 55 1 0 160 289 0 0 145 1 0.8 1 1 3
## 280 41 0 1 105 198 0 1 168 0 0.0 2 1 2
## 281 45 0 1 130 234 0 0 175 0 0.6 1 0 2
## 282 35 1 1 122 192 0 1 174 0 0.0 2 0 2
## 283 41 0 1 130 204 0 0 172 0 1.4 2 0 2
## 284 64 1 3 110 211 0 0 144 1 1.8 1 0 2
## 285 58 1 2 132 224 0 0 173 0 3.2 2 2 3
## 286 71 0 2 110 265 1 0 130 0 0.0 2 1 2
## 287 64 0 2 140 313 0 1 133 0 0.2 2 0 3
## 288 71 0 1 160 302 0 1 162 0 0.4 2 2 2
## 289 58 0 2 120 340 0 1 172 0 0.0 2 0 2
## 290 40 1 0 152 223 0 1 181 0 0.0 2 0 3
## 291 52 1 2 138 223 0 1 169 0 0.0 2 4 2
## 292 58 1 0 128 259 0 0 130 1 3.0 1 2 3
## 293 61 1 2 150 243 1 1 137 1 1.0 1 0 2
## 294 59 1 2 150 212 1 1 157 0 1.6 2 0 2
## 295 56 0 0 200 288 1 0 133 1 4.0 0 2 3
## 296 67 1 0 100 299 0 0 125 1 0.9 1 2 2
## 297 67 1 0 120 237 0 1 71 0 1.0 1 0 2
## 298 58 1 0 150 270 0 0 111 1 0.8 2 0 3
## 299 35 1 1 122 192 0 1 174 0 0.0 2 0 2
## 300 52 1 1 120 325 0 1 172 0 0.2 2 0 2
## 301 46 0 1 105 204 0 1 172 0 0.0 2 0 2
## 302 51 1 2 94 227 0 1 154 1 0.0 2 1 3
## 303 55 0 1 132 342 0 1 166 0 1.2 2 0 2
## 304 60 1 0 145 282 0 0 142 1 2.8 1 2 3
## 305 52 0 2 136 196 0 0 169 0 0.1 1 0 2
## 306 62 1 0 120 267 0 1 99 1 1.8 1 2 3
## 307 44 0 2 118 242 0 1 149 0 0.3 1 1 2
## 308 44 1 1 120 220 0 1 170 0 0.0 2 0 2
## 309 59 1 2 126 218 1 1 134 0 2.2 1 1 1
## 310 56 0 1 140 294 0 0 153 0 1.3 1 0 2
## 311 61 1 0 120 260 0 1 140 1 3.6 1 1 3
## 312 48 1 0 130 256 1 0 150 1 0.0 2 2 3
## 313 70 1 2 160 269 0 1 112 1 2.9 1 1 3
## 314 74 0 1 120 269 0 0 121 1 0.2 2 1 2
## 315 40 1 3 140 199 0 1 178 1 1.4 2 0 3
## 316 42 1 3 148 244 0 0 178 0 0.8 2 2 2
## 317 64 0 2 140 313 0 1 133 0 0.2 2 0 3
## 318 63 0 2 135 252 0 0 172 0 0.0 2 0 2
## 319 59 1 0 140 177 0 1 162 1 0.0 2 1 3
## 320 53 0 2 128 216 0 0 115 0 0.0 2 0 0
## 321 53 0 0 130 264 0 0 143 0 0.4 1 0 2
## 322 48 0 2 130 275 0 1 139 0 0.2 2 0 2
## 323 45 1 0 142 309 0 0 147 1 0.0 1 3 3
## 324 66 1 1 160 246 0 1 120 1 0.0 1 3 1
## 325 48 1 1 130 245 0 0 180 0 0.2 1 0 2
## 326 56 0 1 140 294 0 0 153 0 1.3 1 0 2
## 327 54 1 1 192 283 0 0 195 0 0.0 2 1 3
## 328 57 1 0 150 276 0 0 112 1 0.6 1 1 1
## 329 70 1 0 130 322 0 0 109 0 2.4 1 3 2
## 330 53 0 2 128 216 0 0 115 0 0.0 2 0 0
## 331 37 0 2 120 215 0 1 170 0 0.0 2 0 2
## 332 63 0 0 108 269 0 1 169 1 1.8 1 2 2
## 333 37 1 2 130 250 0 1 187 0 3.5 0 0 2
## 334 54 0 2 110 214 0 1 158 0 1.6 1 0 2
## 335 60 1 0 130 206 0 0 132 1 2.4 1 2 3
## 336 58 1 0 150 270 0 0 111 1 0.8 2 0 3
## 337 57 1 2 150 126 1 1 173 0 0.2 2 1 3
## 338 54 1 2 125 273 0 0 152 0 0.5 0 1 2
## 339 56 1 2 130 256 1 0 142 1 0.6 1 1 1
## 340 60 1 0 130 253 0 1 144 1 1.4 2 1 3
## 341 38 1 2 138 175 0 1 173 0 0.0 2 4 2
## 342 44 1 2 120 226 0 1 169 0 0.0 2 0 2
## 343 65 0 2 155 269 0 1 148 0 0.8 2 0 2
## 344 52 1 2 172 199 1 1 162 0 0.5 2 0 3
## 345 41 1 1 120 157 0 1 182 0 0.0 2 0 2
## 346 66 1 1 160 246 0 1 120 1 0.0 1 3 1
## 347 50 1 0 150 243 0 0 128 0 2.6 1 0 3
## 348 54 0 2 108 267 0 0 167 0 0.0 2 0 2
## 349 43 1 0 132 247 1 0 143 1 0.1 1 4 3
## 350 62 0 2 130 263 0 1 97 0 1.2 1 1 3
## 351 66 1 0 120 302 0 0 151 0 0.4 1 0 2
## 352 50 1 0 144 200 0 0 126 1 0.9 1 0 3
## 353 57 1 0 110 335 0 1 143 1 3.0 1 1 3
## 354 57 1 0 110 201 0 1 126 1 1.5 1 0 1
## 355 57 1 1 124 261 0 1 141 0 0.3 2 0 3
## 356 46 0 0 138 243 0 0 152 1 0.0 1 0 2
## 357 59 1 0 164 176 1 0 90 0 1.0 1 2 1
## 358 67 1 0 160 286 0 0 108 1 1.5 1 3 2
## 359 59 1 3 134 204 0 1 162 0 0.8 2 2 2
## 360 53 0 2 128 216 0 0 115 0 0.0 2 0 0
## 361 48 1 0 122 222 0 0 186 0 0.0 2 0 2
## 362 62 1 2 130 231 0 1 146 0 1.8 1 3 3
## 363 43 0 2 122 213 0 1 165 0 0.2 1 0 2
## 364 53 1 2 130 246 1 0 173 0 0.0 2 3 2
## 365 57 0 1 130 236 0 0 174 0 0.0 1 1 2
## 366 53 1 2 130 246 1 0 173 0 0.0 2 3 2
## 367 58 1 2 112 230 0 0 165 0 2.5 1 1 3
## 368 48 1 1 110 229 0 1 168 0 1.0 0 0 3
## 369 58 1 2 105 240 0 0 154 1 0.6 1 0 3
## 370 51 1 2 110 175 0 1 123 0 0.6 2 0 2
## 371 43 0 0 132 341 1 0 136 1 3.0 1 0 3
## 372 55 1 0 132 353 0 1 132 1 1.2 1 1 3
## 373 54 0 2 110 214 0 1 158 0 1.6 1 0 2
## 374 58 1 1 120 284 0 0 160 0 1.8 1 0 2
## 375 46 0 2 142 177 0 0 160 1 1.4 0 0 2
## 376 66 1 0 160 228 0 0 138 0 2.3 2 0 1
## 377 59 1 1 140 221 0 1 164 1 0.0 2 0 2
## 378 64 0 0 130 303 0 1 122 0 2.0 1 2 2
## 379 67 1 0 120 237 0 1 71 0 1.0 1 0 2
## 380 52 1 3 118 186 0 0 190 0 0.0 1 0 1
## 381 58 1 0 146 218 0 1 105 0 2.0 1 1 3
## 382 58 1 2 132 224 0 0 173 0 3.2 2 2 3
## 383 59 1 0 110 239 0 0 142 1 1.2 1 1 3
## 384 58 1 0 150 270 0 0 111 1 0.8 2 0 3
## 385 35 1 0 126 282 0 0 156 1 0.0 2 0 3
## 386 51 1 2 110 175 0 1 123 0 0.6 2 0 2
## 387 42 0 2 120 209 0 1 173 0 0.0 1 0 2
## 388 77 1 0 125 304 0 0 162 1 0.0 2 3 2
## 389 64 1 0 120 246 0 0 96 1 2.2 0 1 2
## 390 63 1 3 145 233 1 0 150 0 2.3 0 0 1
## 391 58 0 1 136 319 1 0 152 0 0.0 2 2 2
## 392 45 1 3 110 264 0 1 132 0 1.2 1 0 3
## 393 51 1 2 110 175 0 1 123 0 0.6 2 0 2
## 394 62 0 0 160 164 0 0 145 0 6.2 0 3 3
## 395 63 1 0 130 330 1 0 132 1 1.8 2 3 3
## 396 66 0 2 146 278 0 0 152 0 0.0 1 1 2
## 397 68 1 2 180 274 1 0 150 1 1.6 1 0 3
## 398 40 1 0 110 167 0 0 114 1 2.0 1 0 3
## 399 66 1 0 160 228 0 0 138 0 2.3 2 0 1
## 400 63 1 3 145 233 1 0 150 0 2.3 0 0 1
## 401 49 1 2 120 188 0 1 139 0 2.0 1 3 3
## 402 71 0 0 112 149 0 1 125 0 1.6 1 0 2
## 403 70 1 1 156 245 0 0 143 0 0.0 2 0 2
## 404 46 0 1 105 204 0 1 172 0 0.0 2 0 2
## 405 61 1 0 140 207 0 0 138 1 1.9 2 1 3
## 406 56 1 2 130 256 1 0 142 1 0.6 1 1 1
## 407 58 1 2 140 211 1 0 165 0 0.0 2 0 2
## 408 58 1 0 100 234 0 1 156 0 0.1 2 1 3
## 409 46 0 0 138 243 0 0 152 1 0.0 1 0 2
## 410 46 1 2 150 231 0 1 147 0 3.6 1 0 2
## 411 41 0 1 105 198 0 1 168 0 0.0 2 1 2
## 412 56 1 0 125 249 1 0 144 1 1.2 1 1 2
## 413 57 1 0 150 276 0 0 112 1 0.6 1 1 1
## 414 70 1 0 130 322 0 0 109 0 2.4 1 3 2
## 415 59 1 3 170 288 0 0 159 0 0.2 1 0 3
## 416 41 0 1 130 204 0 0 172 0 1.4 2 0 2
## 417 54 1 2 125 273 0 0 152 0 0.5 0 1 2
## 418 52 1 2 138 223 0 1 169 0 0.0 2 4 2
## 419 62 0 0 124 209 0 1 163 0 0.0 2 0 2
## 420 65 0 2 160 360 0 0 151 0 0.8 2 0 2
## 421 57 0 0 128 303 0 0 159 0 0.0 2 1 2
## 422 42 0 0 102 265 0 0 122 0 0.6 1 0 2
## 423 57 0 0 120 354 0 1 163 1 0.6 2 0 2
## 424 58 0 1 136 319 1 0 152 0 0.0 2 2 2
## 425 45 1 0 142 309 0 0 147 1 0.0 1 3 3
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## 1025 0take a quick view of the data
View(heart_attack)NOW, we’ll follow the steps for pre-processing for this we’ll check for missing values there are no missing values in this data set but to make sure we’ll use the function of sum()
is.na(heart_attack)## age sex cp trestbps chol fbs restecg thalach exang oldpeak
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## 0 1
## 499 526prop.table(table(heart_attack$target))##
## 0 1
## 0.4868293 0.5131707pie(table(heart_attack$target), main="Class Distribution")
class(heart_attack$target)## [1] "integer"if (class(heart_attack$target) == "factor") {
heart_attack$target <- as.numeric(as.character(heart_attack$target))
}
model1 <- lm(target ~ ., data = heart_attack)
model1##
## Call:
## lm(formula = target ~ ., data = heart_attack)
##
## Coefficients:
## (Intercept) age sex cp trestbps chol
## 0.879266 -0.001430 -0.210721 0.111820 -0.001818 -0.000458
## fbs restecg thalach exang oldpeak slope
## 0.004225 0.044308 0.002878 -0.144644 -0.061020 0.076220
## ca thal
## -0.095612 -0.115237summary(model1)##
## Call:
## lm(formula = target ~ ., data = heart_attack)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.92108 -0.21388 0.04493 0.26507 0.95158
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.8792656 0.1578856 5.569 3.28e-08 ***
## age -0.0014296 0.0014432 -0.991 0.322151
## sex -0.2107215 0.0255809 -8.237 5.40e-16 ***
## cp 0.1118197 0.0120981 9.243 < 2e-16 ***
## trestbps -0.0018183 0.0006758 -2.691 0.007247 **
## chol -0.0004580 0.0002273 -2.015 0.044173 *
## fbs 0.0042250 0.0320768 0.132 0.895237
## restecg 0.0443080 0.0214084 2.070 0.038739 *
## thalach 0.0028781 0.0006051 4.756 2.26e-06 ***
## exang -0.1446437 0.0275997 -5.241 1.95e-07 ***
## oldpeak -0.0610202 0.0121847 -5.008 6.49e-07 ***
## slope 0.0762203 0.0227286 3.354 0.000828 ***
## ca -0.0956120 0.0116263 -8.224 6.02e-16 ***
## thal -0.1152366 0.0188241 -6.122 1.32e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3505 on 1011 degrees of freedom
## Multiple R-squared: 0.5149, Adjusted R-squared: 0.5087
## F-statistic: 82.56 on 13 and 1011 DF, p-value: < 2.2e-16Interpretation: Positive coefficients (e.g., cp, slope, thalach) indicate that an increase in the corresponding independent variable is associated with an increase in the estimated value of the dependent variable. Negative coefficients (e.g., age, sex, exang) indicate that an increase in the corresponding independent variable is associated with a decrease in the estimated value of the dependent variable. The magnitude of the coefficient represents the strength of the relationship between the independent and dependent variables. For example, larger absolute values indicate stronger effects.
Interpretation: This logistic regression model suggests that the predictor variables age, sex, cp (chest pain type), trestbps (resting blood pressure), chol (serum cholesterol), fbs (fasting blood sugar), restecg (resting electrocardiographic results), thalach (maximum heart rate achieved), exang (exercise-induced angina), oldpeak (ST depression induced by exercise relative to rest), slope (slope of the peak exercise ST segment), ca (number of major vessels colored by fluoroscopy), and thal (thalassemia type) are associated with the target variable, each with varying degrees of impact, as indicated by their coefficients. apply regrssion
model2<-glm(model_glm <- glm(target ~ ., data = heart_attack, family = binomial))
model2##
## Call: glm(formula = model_glm <- glm(target ~ ., data = heart_attack,
## family = binomial))
##
## Coefficients:
## (Intercept) age sex cp trestbps chol
## 0.879266 -0.001430 -0.210721 0.111820 -0.001818 -0.000458
## fbs restecg thalach exang oldpeak slope
## 0.004225 0.044308 0.002878 -0.144644 -0.061020 0.076220
## ca thal
## -0.095612 -0.115237
##
## Degrees of Freedom: 1024 Total (i.e. Null); 1011 Residual
## Null Deviance: 256.1
## Residual Deviance: 124.2 AIC: 775.6—————-apply KNN—————-
1-fold cross validation
set.seed(123)
index <- sample(1:nrow(heart_attack), nrow(heart_attack))
# Split the data into training and testing sets (1-fold cross-validation)
train_data <- heart_attack[index <= nrow(heart_attack) * 0.8, ] # 80% for training
test_data <- heart_attack[index > nrow(heart_attack) * 0.8, ] # 20% for testing
# Here you would typically fit your model using the training data,
# but since you don't want to fit a model, we'll proceed to evaluation
# For example, let's calculate the mean of the target variable in the training set
mean_target_training <- mean(heart_attack$target)This line calculates the mean of the target variable (target) in the training set (heart_attack$target). However, it’s worth noting that this line doesn’t fit any model to the training data; it simply calculates a summary statistic (mean) as an example. Typically, after splitting the data, you would fit a model using the training data and then evaluate its performance on the testing data. In this case, model fitting and evaluation are not included in the provided code snippet.
Overall, this code sets up a data splitting procedure using a 1-fold cross-validation approach and randomly divides the dataset into training and testing sets for further analysis or model building. 2-fold cross validation
# Set a seed for reproducibility
set.seed(123)
# Create an index for splitting the data into two folds
index <- sample(1:nrow(heart_attack))
# Split the data into two folds
fold1 <- heart_attack[index %% 2 == 0, ] # Fold 1
fold2 <- heart_attack[index %% 2 != 0, ] # Fold 2
# Perform 2-fold cross-validation
# Fold 1 as training, Fold 2 as testing
train_data_fold1 <- fold1
test_data_fold1 <- fold2
# Fit your model (or use a prediction method) using train_data_fold1
# For example, let's calculate the mean of the target variable in train_data_fold1
mean_target_fold1 <- mean(train_data_fold1$target)
# Use the mean as a baseline prediction for test_data_fold1
predicted_mean_fold1 <- rep(mean_target_fold1, nrow(test_data_fold1))
# Calculate some metric (e.g., mean squared error) for evaluation
MSE_fold1 <- mean((predicted_mean_fold1 - test_data_fold1$target)^2)
# Print the mean squared error for Fold 1
print(paste("Mean Squared Error for Fold 1:", MSE_fold1))## [1] "Mean Squared Error for Fold 1: 0.249908633167051"# Fold 2 as training, Fold 1 as testing
train_data_fold2 <- fold2
test_data_fold2 <- fold1
# Fit your model (or use a prediction method) using train_data_fold2
# For example, let's calculate the mean of the target variable in train_data_fold2
mean_target_fold2 <- mean(train_data_fold2$target)
# Use the mean as a baseline prediction for test_data_fold2
predicted_mean_fold2 <- rep(mean_target_fold2, nrow(test_data_fold2))
# Calculate some metric (e.g., mean squared error) for evaluation
MSE_fold2 <- mean((predicted_mean_fold2 - test_data_fold2$target)^2)
# Print the mean squared error for Fold 2
print(paste("Mean Squared Error for Fold 2:", MSE_fold2))## [1] "Mean Squared Error for Fold 2: 0.250498543414593"# Define the control parameters for 3-fold cross-validation
library(caret)## Warning: package 'caret' was built under R version 4.3.3## Loading required package: ggplot2## Loading required package: latticeheart_attack$target <- factor(heart_attack$target)
ctrl <- trainControl(method = "cv", number = 3)
# Train your model using 3-fold cross-validation with linear regression (lm)
model_3fold <- train(target ~ ., data = heart_attack, method = "glm", trControl = ctrl)
# Print the results
print(model_3fold)## Generalized Linear Model
##
## 1025 samples
## 13 predictor
## 2 classes: '0', '1'
##
## No pre-processing
## Resampling: Cross-Validated (3 fold)
## Summary of sample sizes: 682, 684, 684
## Resampling results:
##
## Accuracy Kappa
## 0.8429275 0.6846486Interpretation: The code demonstrates two different approaches to cross-validation: manual implementation of 2-fold cross-validation and utilizing the train() function from the caret package for 3-fold cross-validation. Cross-validation helps evaluate the performance of a model and assess its generalization ability by partitioning the dataset into multiple subsets for training and testing iteratively. 3-fold cross validation
# Define the control parameters for 3-fold cross-validation
library(caret)
heart_attack$target <- factor(heart_attack$target)
ctrl <- trainControl(method = "cv", number = 3)
# Train your model using 3-fold cross-validation with linear regression (lm)
model_3fold <- train(target ~ ., data = heart_attack, method = "glm", trControl = ctrl)
# Print the results
print(model_3fold)## Generalized Linear Model
##
## 1025 samples
## 13 predictor
## 2 classes: '0', '1'
##
## No pre-processing
## Resampling: Cross-Validated (3 fold)
## Summary of sample sizes: 684, 684, 682
## Resampling results:
##
## Accuracy Kappa
## 0.8292366 0.65739Interpretation: The code performs model training and evaluation using 3-fold cross-validation with linear regression. Cross-validation helps assess the performance and generalization ability of the model by partitioning the dataset into multiple subsets for training and testing iteratively. The results printed provide insights into the model’s performance metrics obtained from cross-validation. These metrics help evaluate how well the model generalizes to unseen data.
Interpretation: The first part of the code calculates the optimal cutoff value based on the binary prediction (predicted) and the true binary labels (test$sex). The second part trains a KNN model using cross-validation and evaluates it using a confusion matrix to assess its performance. Overall, the code performs cutoff optimization and evaluates a KNN model using a confusion matrix to understand its classification performance.
library(caret)
set.seed(1234)
# Train the KNN model
model_knn <- train(target ~ .,
data = heart_attack,
method = "knn",
trControl=trainControl(method = "cv", number = 5))
head(model_knn)## $method
## [1] "knn"
##
## $modelInfo
## $modelInfo$label
## [1] "k-Nearest Neighbors"
##
## $modelInfo$library
## NULL
##
## $modelInfo$loop
## NULL
##
## $modelInfo$type
## [1] "Classification" "Regression"
##
## $modelInfo$parameters
## parameter class label
## 1 k numeric #Neighbors
##
## $modelInfo$grid
## function (x, y, len = NULL, search = "grid")
## {
## if (search == "grid") {
## out <- data.frame(k = (5:((2 * len) + 4))[(5:((2 * len) +
## 4))%%2 > 0])
## }
## else {
## by_val <- if (is.factor(y))
## length(levels(y))
## else 1
## out <- data.frame(k = sample(seq(1, floor(nrow(x)/3),
## by = by_val), size = len, replace = TRUE))
## }
## }
##
## $modelInfo$fit
## function (x, y, wts, param, lev, last, classProbs, ...)
## {
## if (is.factor(y)) {
## knn3(as.matrix(x), y, k = param$k, ...)
## }
## else {
## knnreg(as.matrix(x), y, k = param$k, ...)
## }
## }
## <bytecode: 0x000002169a3ef5e0>
##
## $modelInfo$predict
## function (modelFit, newdata, submodels = NULL)
## {
## if (modelFit$problemType == "Classification") {
## out <- predict(modelFit, newdata, type = "class")
## }
## else {
## out <- predict(modelFit, newdata)
## }
## out
## }
## <bytecode: 0x000002169a56eba8>
##
## $modelInfo$predictors
## function (x, ...)
## colnames(x$learn$X)
##
## $modelInfo$tags
## [1] "Prototype Models"
##
## $modelInfo$prob
## function (modelFit, newdata, submodels = NULL)
## predict(modelFit, newdata, type = "prob")
##
## $modelInfo$levels
## function (x)
## levels(x$learn$y)
##
## $modelInfo$sort
## function (x)
## x[order(-x[, 1]), ]
##
##
## $modelType
## [1] "Classification"
##
## $results
## k Accuracy Kappa AccuracySD KappaSD
## 1 5 0.6809916 0.3617139 0.01059727 0.02212386
## 2 7 0.7287732 0.4575272 0.01722177 0.03389509
## 3 9 0.7307435 0.4614475 0.03020486 0.06045208
##
## $pred
## NULL
##
## $bestTune
## k
## 3 9# Make predictions
predictions <- predict(model_knn, data = heart_attack)
predictions## [1] 1 0 0 1 0 1 0 0 0 0 0 0 1 0 1 1 0 0 1 1 0 1 1 1 1 1 1 0 0 0 1 1 0 0 1 1 0
## [38] 1 0 0 0 0 1 0 1 1 0 0 1 0 1 0 1 0 0 0 1 1 0 1 1 1 0 0 1 0 1 0 1 0 0 0 0 0
## [75] 1 1 1 0 0 0 1 1 1 1 1 1 1 1 0 0 1 1 0 1 0 1 1 0 1 1 1 1 1 1 1 0 0 0 1 1 1
## [112] 0 1 0 0 1 0 0 1 1 1 1 0 0 0 1 1 1 1 0 1 0 0 1 1 1 1 1 1 1 0 0 0 1 1 0 1 0
## [149] 0 1 0 0 1 0 0 1 1 0 1 1 1 1 1 1 0 0 0 1 1 0 1 0 1 1 0 0 0 0 1 0 0 1 0 1 1
## [186] 0 0 0 0 1 1 1 1 1 0 0 1 1 1 1 1 1 0 0 1 1 0 1 1 0 1 0 0 1 1 1 0 1 0 0 1 0
## [223] 1 1 1 0 1 0 1 0 1 1 0 1 1 0 1 0 0 1 1 0 1 0 1 1 0 0 1 1 0 0 0 0 0 0 1 1 1
## [260] 1 1 0 1 1 1 1 0 0 1 1 1 1 1 1 1 0 1 1 0 1 1 1 1 1 1 1 0 0 1 1 1 0 1 0 0 0
## [297] 0 0 1 1 1 1 1 0 1 0 0 1 0 1 0 1 0 1 1 1 0 1 0 1 0 1 0 1 1 1 0 0 0 1 1 0 1
## [334] 1 0 0 1 1 0 0 1 1 1 1 1 1 0 1 0 0 1 1 0 0 0 1 0 0 1 1 1 0 1 1 1 1 0 0 0 0
## [371] 0 0 1 1 1 1 1 0 0 1 0 1 1 0 0 0 1 1 0 1 0 0 0 0 0 1 0 0 1 1 0 0 1 1 0 0 1
## [408] 1 1 1 1 0 0 0 0 1 1 1 1 0 1 0 1 0 0 0 1 1 0 1 0 0 1 1 1 1 1 0 1 1 1 0 0 1
## [445] 1 1 1 0 1 0 1 1 0 1 0 1 1 1 1 1 0 1 1 1 1 1 1 0 1 0 1 0 0 1 0 1 0 0 1 0 0
## [482] 1 0 1 1 0 0 0 1 1 1 1 1 0 1 1 0 1 1 1 0 1 1 1 1 0 0 1 0 0 1 0 1 1 1 0 0 1
## [519] 0 1 0 0 1 1 0 0 0 1 0 1 0 1 0 1 1 1 1 1 0 0 0 1 0 1 1 0 0 1 1 1 0 0 0 1 1
## [556] 0 0 1 1 0 0 1 1 1 0 1 1 1 1 1 0 0 1 1 0 1 0 0 0 0 1 1 1 0 0 1 1 0 1 0 1 0
## [593] 0 0 0 1 1 1 1 1 0 0 1 1 1 1 1 0 0 0 0 0 1 0 0 1 1 1 1 1 0 1 0 0 0 0 1 1 1
## [630] 0 0 1 1 0 1 0 1 1 0 0 1 0 0 0 1 0 1 0 0 1 0 1 0 0 1 1 1 1 1 1 0 0 1 1 0 0
## [667] 1 0 1 1 1 1 1 0 0 1 0 0 1 0 1 0 0 0 0 1 1 1 0 0 0 1 0 1 1 0 1 0 1 0 1 0 0
## [704] 0 1 0 0 1 0 0 1 0 0 0 0 1 0 0 1 1 0 1 1 1 1 0 1 1 0 1 1 0 1 1 1 1 1 0 1 1
## [741] 1 1 0 0 1 1 0 0 1 0 1 0 1 0 0 1 0 1 1 1 0 1 1 1 0 0 0 0 1 1 0 1 1 0 1 1 1
## [778] 0 1 1 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 1 1 1 0 0 0 0
## [815] 0 0 1 1 1 1 0 0 0 1 0 1 1 0 1 0 1 0 1 0 0 1 1 1 0 1 0 1 0 0 0 0 1 0 1 1 1
## [852] 1 1 0 0 1 1 1 1 0 0 0 0 1 0 1 1 1 1 1 1 1 0 0 0 0 0 1 0 1 1 1 0 1 0 0 0 0
## [889] 0 1 0 1 0 1 0 0 0 0 0 1 0 0 0 0 1 0 1 1 0 1 1 0 0 1 1 0 0 1 1 1 1 0 1 1 1
## [926] 0 1 1 0 0 0 1 0 1 1 1 1 0 0 1 0 1 1 0 0 1 1 1 0 1 0 0 0 1 0 0 1 1 0 1 1 1
## [963] 1 0 1 1 0 0 0 1 1 1 1 1 1 1 0 1 1 0 0 1 1 0 1 0 0 1 0 0 1 0 1 0 1 1 0 0 0
## [1000] 0 0 1 1 1 0 0 1 1 1 1 0 1 0 0 1 0 0 0 0 1 1 0 0 1 0
## Levels: 0 1# Convert predictions and target to factors with the same levels
predictions <- factor(predictions, levels = levels(heart_attack$target))
# Create the confusion matrix
#conf_matrix <- confusionMatrix(predictions, heart_attack$target)
#conf_matrix
# Print confusion matrix
#print(conf_matrix)Interpretation: The code first checks the lengths and missing values of the true and predicted labels to ensure data consistency and integrity. It then converts the true and predicted labels to factors with appropriate levels for creating the confusion matrix. Finally, it attempts to create a confusion matrix, but there seems to be a mistake in using test\(age instead of test\)sex for the true labels, which might result in an error or incorrect evaluation. To correct the code, ensure that the correct variables are used for comparison in the confusion matrix creation. If the intention is to compare the true and predicted binary sex labels, replace test\(age with test\)sex.
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