├── house-price-prediction.ipynb
├── pima-indians-diabetes-database-svm-accuracy-79.ipynb
└── winequality-prediction.ipynb


/house-price-prediction.ipynb:
--------------------------------------------------------------------------------
   1 | {
   2 |  "cells": [
   3 |   {
   4 |    "cell_type": "code",
   5 |    "execution_count": 3,
   6 |    "id": "6dac32b6",
   7 |    "metadata": {
   8 |     "execution": {
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  19 |      "status": "completed"
  20 |     },
  21 |     "tags": []
  22 |    },
  23 |    "outputs": [],
  24 |    "source": [
  25 |     "import pandas as pd \n",
  26 |     "import numpy as np \n",
  27 |     "import matplotlib.pyplot as plt\n",
  28 |     "import seaborn as sns\n",
  29 |     "from sklearn.preprocessing import LabelEncoder\n",
  30 |     "from sklearn.linear_model import LinearRegression\n",
  31 |     "from sklearn.ensemble import RandomForestRegressor\n",
  32 |     "from sklearn.model_selection import train_test_split \n",
  33 |     "from sklearn.metrics import mean_squared_error,mean_absolute_percentage_error"
  34 |    ]
  35 |   },
  36 |   {
  37 |    "cell_type": "code",
  38 |    "execution_count": 4,
  39 |    "id": "c9020f95",
  40 |    "metadata": {
  41 |     "execution": {
  42 |      "iopub.execute_input": "2022-11-05T08:25:28.860843Z",
  43 |      "iopub.status.busy": "2022-11-05T08:25:28.859652Z",
  44 |      "iopub.status.idle": "2022-11-05T08:25:28.913453Z",
  45 |      "shell.execute_reply": "2022-11-05T08:25:28.912083Z"
  46 |     },
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  52 |      "status": "completed"
  53 |     },
  54 |     "tags": []
  55 |    },
  56 |    "outputs": [],
  57 |    "source": [
  58 |     "data =pd.read_csv(\"D:\\House pricece\\data.csv\")"
  59 |    ]
  60 |   },
  61 |   {
  62 |    "cell_type": "code",
  63 |    "execution_count": 5,
  64 |    "id": "819a0220",
  65 |    "metadata": {
  66 |     "execution": {
  67 |      "iopub.execute_input": "2022-11-05T08:25:28.958510Z",
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  77 |      "status": "completed"
  78 |     },
  79 |     "tags": []
  80 |    },
  81 |    "outputs": [
  82 |     {
  83 |      "data": {
  84 |       "text/html": [
  85 |        "<div>\n",
  86 |        "<style scoped>\n",
  87 |        "    .dataframe tbody tr th:only-of-type {\n",
  88 |        "        vertical-align: middle;\n",
  89 |        "    }\n",
  90 |        "\n",
  91 |        "    .dataframe tbody tr th {\n",
  92 |        "        vertical-align: top;\n",
  93 |        "    }\n",
  94 |        "\n",
  95 |        "    .dataframe thead th {\n",
  96 |        "        text-align: right;\n",
  97 |        "    }\n",
  98 |        "</style>\n",
  99 |        "<table border=\"1\" class=\"dataframe\">\n",
 100 |        "  <thead>\n",
 101 |        "    <tr style=\"text-align: right;\">\n",
 102 |        "      <th></th>\n",
 103 |        "      <th>price</th>\n",
 104 |        "      <th>bedrooms</th>\n",
 105 |        "      <th>bathrooms</th>\n",
 106 |        "      <th>sqft_living</th>\n",
 107 |        "      <th>sqft_lot</th>\n",
 108 |        "      <th>floors</th>\n",
 109 |        "      <th>waterfront</th>\n",
 110 |        "      <th>view</th>\n",
 111 |        "      <th>condition</th>\n",
 112 |        "      <th>sqft_above</th>\n",
 113 |        "      <th>sqft_basement</th>\n",
 114 |        "      <th>city</th>\n",
 115 |        "    </tr>\n",
 116 |        "  </thead>\n",
 117 |        "  <tbody>\n",
 118 |        "    <tr>\n",
 119 |        "      <th>0</th>\n",
 120 |        "      <td>313000.0</td>\n",
 121 |        "      <td>3.0</td>\n",
 122 |        "      <td>1.50</td>\n",
 123 |        "      <td>1340</td>\n",
 124 |        "      <td>7912</td>\n",
 125 |        "      <td>1.5</td>\n",
 126 |        "      <td>0</td>\n",
 127 |        "      <td>0</td>\n",
 128 |        "      <td>3</td>\n",
 129 |        "      <td>1340</td>\n",
 130 |        "      <td>0</td>\n",
 131 |        "      <td>Shoreline</td>\n",
 132 |        "    </tr>\n",
 133 |        "    <tr>\n",
 134 |        "      <th>1</th>\n",
 135 |        "      <td>2384000.0</td>\n",
 136 |        "      <td>5.0</td>\n",
 137 |        "      <td>2.50</td>\n",
 138 |        "      <td>3650</td>\n",
 139 |        "      <td>9050</td>\n",
 140 |        "      <td>2.0</td>\n",
 141 |        "      <td>0</td>\n",
 142 |        "      <td>4</td>\n",
 143 |        "      <td>5</td>\n",
 144 |        "      <td>3370</td>\n",
 145 |        "      <td>280</td>\n",
 146 |        "      <td>Seattle</td>\n",
 147 |        "    </tr>\n",
 148 |        "    <tr>\n",
 149 |        "      <th>2</th>\n",
 150 |        "      <td>342000.0</td>\n",
 151 |        "      <td>3.0</td>\n",
 152 |        "      <td>2.00</td>\n",
 153 |        "      <td>1930</td>\n",
 154 |        "      <td>11947</td>\n",
 155 |        "      <td>1.0</td>\n",
 156 |        "      <td>0</td>\n",
 157 |        "      <td>0</td>\n",
 158 |        "      <td>4</td>\n",
 159 |        "      <td>1930</td>\n",
 160 |        "      <td>0</td>\n",
 161 |        "      <td>Kent</td>\n",
 162 |        "    </tr>\n",
 163 |        "    <tr>\n",
 164 |        "      <th>3</th>\n",
 165 |        "      <td>420000.0</td>\n",
 166 |        "      <td>3.0</td>\n",
 167 |        "      <td>2.25</td>\n",
 168 |        "      <td>2000</td>\n",
 169 |        "      <td>8030</td>\n",
 170 |        "      <td>1.0</td>\n",
 171 |        "      <td>0</td>\n",
 172 |        "      <td>0</td>\n",
 173 |        "      <td>4</td>\n",
 174 |        "      <td>1000</td>\n",
 175 |        "      <td>1000</td>\n",
 176 |        "      <td>Bellevue</td>\n",
 177 |        "    </tr>\n",
 178 |        "    <tr>\n",
 179 |        "      <th>4</th>\n",
 180 |        "      <td>550000.0</td>\n",
 181 |        "      <td>4.0</td>\n",
 182 |        "      <td>2.50</td>\n",
 183 |        "      <td>1940</td>\n",
 184 |        "      <td>10500</td>\n",
 185 |        "      <td>1.0</td>\n",
 186 |        "      <td>0</td>\n",
 187 |        "      <td>0</td>\n",
 188 |        "      <td>4</td>\n",
 189 |        "      <td>1140</td>\n",
 190 |        "      <td>800</td>\n",
 191 |        "      <td>Redmond</td>\n",
 192 |        "    </tr>\n",
 193 |        "  </tbody>\n",
 194 |        "</table>\n",
 195 |        "</div>"
 196 |       ],
 197 |       "text/plain": [
 198 |        "       price  bedrooms  bathrooms  sqft_living  sqft_lot  floors  waterfront  \\\n",
 199 |        "0   313000.0       3.0       1.50         1340      7912     1.5           0   \n",
 200 |        "1  2384000.0       5.0       2.50         3650      9050     2.0           0   \n",
 201 |        "2   342000.0       3.0       2.00         1930     11947     1.0           0   \n",
 202 |        "3   420000.0       3.0       2.25         2000      8030     1.0           0   \n",
 203 |        "4   550000.0       4.0       2.50         1940     10500     1.0           0   \n",
 204 |        "\n",
 205 |        "   view  condition  sqft_above  sqft_basement       city  \n",
 206 |        "0     0          3        1340              0  Shoreline  \n",
 207 |        "1     4          5        3370            280    Seattle  \n",
 208 |        "2     0          4        1930              0       Kent  \n",
 209 |        "3     0          4        1000           1000   Bellevue  \n",
 210 |        "4     0          4        1140            800    Redmond  "
 211 |       ]
 212 |      },
 213 |      "execution_count": 5,
 214 |      "metadata": {},
 215 |      "output_type": "execute_result"
 216 |     }
 217 |    ],
 218 |    "source": [
 219 |     "\n",
 220 |     "data  = data.drop(['date','country',\"street\",\"statezip\",\"yr_built\",\"yr_renovated\"],axis=1)\n",
 221 |     "data.head()"
 222 |    ]
 223 |   },
 224 |   {
 225 |    "cell_type": "code",
 226 |    "execution_count": 6,
 227 |    "id": "87b54c71",
 228 |    "metadata": {
 229 |     "execution": {
 230 |      "iopub.execute_input": "2022-11-05T08:25:29.020509Z",
 231 |      "iopub.status.busy": "2022-11-05T08:25:29.019655Z",
 232 |      "iopub.status.idle": "2022-11-05T08:25:29.025233Z",
 233 |      "shell.execute_reply": "2022-11-05T08:25:29.023946Z"
 234 |     },
 235 |     "papermill": {
 236 |      "duration": 0.01951,
 237 |      "end_time": "2022-11-05T08:25:29.027633",
 238 |      "exception": false,
 239 |      "start_time": "2022-11-05T08:25:29.008123",
 240 |      "status": "completed"
 241 |     },
 242 |     "tags": []
 243 |    },
 244 |    "outputs": [],
 245 |    "source": [
 246 |     "le = LabelEncoder()"
 247 |    ]
 248 |   },
 249 |   {
 250 |    "cell_type": "code",
 251 |    "execution_count": 7,
 252 |    "id": "79e08a06",
 253 |    "metadata": {
 254 |     "execution": {
 255 |      "iopub.execute_input": "2022-11-05T08:25:29.050061Z",
 256 |      "iopub.status.busy": "2022-11-05T08:25:29.049622Z",
 257 |      "iopub.status.idle": "2022-11-05T08:25:29.058985Z",
 258 |      "shell.execute_reply": "2022-11-05T08:25:29.057952Z"
 259 |     },
 260 |     "papermill": {
 261 |      "duration": 0.023815,
 262 |      "end_time": "2022-11-05T08:25:29.061671",
 263 |      "exception": false,
 264 |      "start_time": "2022-11-05T08:25:29.037856",
 265 |      "status": "completed"
 266 |     },
 267 |     "tags": []
 268 |    },
 269 |    "outputs": [],
 270 |    "source": [
 271 |     "data['city_new'] = le.fit_transform(data['city'])\n"
 272 |    ]
 273 |   },
 274 |   {
 275 |    "cell_type": "code",
 276 |    "execution_count": 8,
 277 |    "id": "58495df7",
 278 |    "metadata": {
 279 |     "execution": {
 280 |      "iopub.execute_input": "2022-11-05T08:25:29.085329Z",
 281 |      "iopub.status.busy": "2022-11-05T08:25:29.084522Z",
 282 |      "iopub.status.idle": "2022-11-05T08:25:29.094941Z",
 283 |      "shell.execute_reply": "2022-11-05T08:25:29.093411Z"
 284 |     },
 285 |     "papermill": {
 286 |      "duration": 0.026067,
 287 |      "end_time": "2022-11-05T08:25:29.098176",
 288 |      "exception": false,
 289 |      "start_time": "2022-11-05T08:25:29.072109",
 290 |      "status": "completed"
 291 |     },
 292 |     "tags": []
 293 |    },
 294 |    "outputs": [],
 295 |    "source": [
 296 |     "cols  = ['bedrooms',\"bathrooms\",\"floors\",\"price\"]\n",
 297 |     "\n",
 298 |     "for  col in cols :\n",
 299 |     "    data[col] = data[col].astype(int)\n",
 300 |     "\n",
 301 |     "# Because how can 1.50 bathroom exists"
 302 |    ]
 303 |   },
 304 |   {
 305 |    "cell_type": "code",
 306 |    "execution_count": 9,
 307 |    "id": "101ec19d",
 308 |    "metadata": {
 309 |     "execution": {
 310 |      "iopub.execute_input": "2022-11-05T08:25:29.120976Z",
 311 |      "iopub.status.busy": "2022-11-05T08:25:29.120502Z",
 312 |      "iopub.status.idle": "2022-11-05T08:25:29.136886Z",
 313 |      "shell.execute_reply": "2022-11-05T08:25:29.135439Z"
 314 |     },
 315 |     "papermill": {
 316 |      "duration": 0.031037,
 317 |      "end_time": "2022-11-05T08:25:29.139498",
 318 |      "exception": false,
 319 |      "start_time": "2022-11-05T08:25:29.108461",
 320 |      "status": "completed"
 321 |     },
 322 |     "tags": []
 323 |    },
 324 |    "outputs": [
 325 |     {
 326 |      "data": {
 327 |       "text/html": [
 328 |        "<div>\n",
 329 |        "<style scoped>\n",
 330 |        "    .dataframe tbody tr th:only-of-type {\n",
 331 |        "        vertical-align: middle;\n",
 332 |        "    }\n",
 333 |        "\n",
 334 |        "    .dataframe tbody tr th {\n",
 335 |        "        vertical-align: top;\n",
 336 |        "    }\n",
 337 |        "\n",
 338 |        "    .dataframe thead th {\n",
 339 |        "        text-align: right;\n",
 340 |        "    }\n",
 341 |        "</style>\n",
 342 |        "<table border=\"1\" class=\"dataframe\">\n",
 343 |        "  <thead>\n",
 344 |        "    <tr style=\"text-align: right;\">\n",
 345 |        "      <th></th>\n",
 346 |        "      <th>price</th>\n",
 347 |        "      <th>bedrooms</th>\n",
 348 |        "      <th>bathrooms</th>\n",
 349 |        "      <th>sqft_living</th>\n",
 350 |        "      <th>sqft_lot</th>\n",
 351 |        "      <th>floors</th>\n",
 352 |        "      <th>waterfront</th>\n",
 353 |        "      <th>view</th>\n",
 354 |        "      <th>condition</th>\n",
 355 |        "      <th>sqft_above</th>\n",
 356 |        "      <th>sqft_basement</th>\n",
 357 |        "      <th>city</th>\n",
 358 |        "      <th>city_new</th>\n",
 359 |        "    </tr>\n",
 360 |        "  </thead>\n",
 361 |        "  <tbody>\n",
 362 |        "    <tr>\n",
 363 |        "      <th>0</th>\n",
 364 |        "      <td>313000</td>\n",
 365 |        "      <td>3</td>\n",
 366 |        "      <td>1</td>\n",
 367 |        "      <td>1340</td>\n",
 368 |        "      <td>7912</td>\n",
 369 |        "      <td>1</td>\n",
 370 |        "      <td>0</td>\n",
 371 |        "      <td>0</td>\n",
 372 |        "      <td>3</td>\n",
 373 |        "      <td>1340</td>\n",
 374 |        "      <td>0</td>\n",
 375 |        "      <td>Shoreline</td>\n",
 376 |        "      <td>36</td>\n",
 377 |        "    </tr>\n",
 378 |        "    <tr>\n",
 379 |        "      <th>1</th>\n",
 380 |        "      <td>2384000</td>\n",
 381 |        "      <td>5</td>\n",
 382 |        "      <td>2</td>\n",
 383 |        "      <td>3650</td>\n",
 384 |        "      <td>9050</td>\n",
 385 |        "      <td>2</td>\n",
 386 |        "      <td>0</td>\n",
 387 |        "      <td>4</td>\n",
 388 |        "      <td>5</td>\n",
 389 |        "      <td>3370</td>\n",
 390 |        "      <td>280</td>\n",
 391 |        "      <td>Seattle</td>\n",
 392 |        "      <td>35</td>\n",
 393 |        "    </tr>\n",
 394 |        "    <tr>\n",
 395 |        "      <th>2</th>\n",
 396 |        "      <td>342000</td>\n",
 397 |        "      <td>3</td>\n",
 398 |        "      <td>2</td>\n",
 399 |        "      <td>1930</td>\n",
 400 |        "      <td>11947</td>\n",
 401 |        "      <td>1</td>\n",
 402 |        "      <td>0</td>\n",
 403 |        "      <td>0</td>\n",
 404 |        "      <td>4</td>\n",
 405 |        "      <td>1930</td>\n",
 406 |        "      <td>0</td>\n",
 407 |        "      <td>Kent</td>\n",
 408 |        "      <td>18</td>\n",
 409 |        "    </tr>\n",
 410 |        "    <tr>\n",
 411 |        "      <th>3</th>\n",
 412 |        "      <td>420000</td>\n",
 413 |        "      <td>3</td>\n",
 414 |        "      <td>2</td>\n",
 415 |        "      <td>2000</td>\n",
 416 |        "      <td>8030</td>\n",
 417 |        "      <td>1</td>\n",
 418 |        "      <td>0</td>\n",
 419 |        "      <td>0</td>\n",
 420 |        "      <td>4</td>\n",
 421 |        "      <td>1000</td>\n",
 422 |        "      <td>1000</td>\n",
 423 |        "      <td>Bellevue</td>\n",
 424 |        "      <td>3</td>\n",
 425 |        "    </tr>\n",
 426 |        "    <tr>\n",
 427 |        "      <th>4</th>\n",
 428 |        "      <td>550000</td>\n",
 429 |        "      <td>4</td>\n",
 430 |        "      <td>2</td>\n",
 431 |        "      <td>1940</td>\n",
 432 |        "      <td>10500</td>\n",
 433 |        "      <td>1</td>\n",
 434 |        "      <td>0</td>\n",
 435 |        "      <td>0</td>\n",
 436 |        "      <td>4</td>\n",
 437 |        "      <td>1140</td>\n",
 438 |        "      <td>800</td>\n",
 439 |        "      <td>Redmond</td>\n",
 440 |        "      <td>31</td>\n",
 441 |        "    </tr>\n",
 442 |        "  </tbody>\n",
 443 |        "</table>\n",
 444 |        "</div>"
 445 |       ],
 446 |       "text/plain": [
 447 |        "     price  bedrooms  bathrooms  sqft_living  sqft_lot  floors  waterfront  \\\n",
 448 |        "0   313000         3          1         1340      7912       1           0   \n",
 449 |        "1  2384000         5          2         3650      9050       2           0   \n",
 450 |        "2   342000         3          2         1930     11947       1           0   \n",
 451 |        "3   420000         3          2         2000      8030       1           0   \n",
 452 |        "4   550000         4          2         1940     10500       1           0   \n",
 453 |        "\n",
 454 |        "   view  condition  sqft_above  sqft_basement       city  city_new  \n",
 455 |        "0     0          3        1340              0  Shoreline        36  \n",
 456 |        "1     4          5        3370            280    Seattle        35  \n",
 457 |        "2     0          4        1930              0       Kent        18  \n",
 458 |        "3     0          4        1000           1000   Bellevue         3  \n",
 459 |        "4     0          4        1140            800    Redmond        31  "
 460 |       ]
 461 |      },
 462 |      "execution_count": 9,
 463 |      "metadata": {},
 464 |      "output_type": "execute_result"
 465 |     }
 466 |    ],
 467 |    "source": [
 468 |     "data.head()"
 469 |    ]
 470 |   },
 471 |   {
 472 |    "cell_type": "code",
 473 |    "execution_count": 10,
 474 |    "id": "ea52dc4b",
 475 |    "metadata": {
 476 |     "execution": {
 477 |      "iopub.execute_input": "2022-11-05T08:25:29.479804Z",
 478 |      "iopub.status.busy": "2022-11-05T08:25:29.478912Z",
 479 |      "iopub.status.idle": "2022-11-05T08:25:29.695123Z",
 480 |      "shell.execute_reply": "2022-11-05T08:25:29.693698Z"
 481 |     },
 482 |     "papermill": {
 483 |      "duration": 0.231745,
 484 |      "end_time": "2022-11-05T08:25:29.698138",
 485 |      "exception": false,
 486 |      "start_time": "2022-11-05T08:25:29.466393",
 487 |      "status": "completed"
 488 |     },
 489 |     "tags": []
 490 |    },
 491 |    "outputs": [
 492 |     {
 493 |      "data": {
 494 |       "text/plain": [
 495 |        "<Axes: xlabel='condition', ylabel='count'>"
 496 |       ]
 497 |      },
 498 |      "execution_count": 10,
 499 |      "metadata": {},
 500 |      "output_type": "execute_result"
 501 |     },
 502 |     {
 503 |      "data": {
 504 |       "image/png": 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",
 505 |       "text/plain": [
 506 |        "<Figure size 640x480 with 1 Axes>"
 507 |       ]
 508 |      },
 509 |      "metadata": {},
 510 |      "output_type": "display_data"
 511 |     }
 512 |    ],
 513 |    "source": [
 514 |     "sns.countplot(data=data,x=data['condition'])"
 515 |    ]
 516 |   },
 517 |   {
 518 |    "cell_type": "code",
 519 |    "execution_count": 11,
 520 |    "id": "41e4d100",
 521 |    "metadata": {
 522 |     "execution": {
 523 |      "iopub.execute_input": "2022-11-05T08:25:30.328068Z",
 524 |      "iopub.status.busy": "2022-11-05T08:25:30.327436Z",
 525 |      "iopub.status.idle": "2022-11-05T08:25:30.337400Z",
 526 |      "shell.execute_reply": "2022-11-05T08:25:30.336508Z"
 527 |     },
 528 |     "papermill": {
 529 |      "duration": 0.025507,
 530 |      "end_time": "2022-11-05T08:25:30.339545",
 531 |      "exception": false,
 532 |      "start_time": "2022-11-05T08:25:30.314038",
 533 |      "status": "completed"
 534 |     },
 535 |     "tags": []
 536 |    },
 537 |    "outputs": [
 538 |     {
 539 |      "data": {
 540 |       "text/plain": [
 541 |        "city\n",
 542 |        "Seattle                1573\n",
 543 |        "Renton                  293\n",
 544 |        "Bellevue                286\n",
 545 |        "Redmond                 235\n",
 546 |        "Issaquah                187\n",
 547 |        "Kirkland                187\n",
 548 |        "Kent                    185\n",
 549 |        "Auburn                  176\n",
 550 |        "Sammamish               175\n",
 551 |        "Federal Way             148\n",
 552 |        "Shoreline               123\n",
 553 |        "Woodinville             115\n",
 554 |        "Maple Valley             96\n",
 555 |        "Mercer Island            86\n",
 556 |        "Burien                   74\n",
 557 |        "Snoqualmie               71\n",
 558 |        "Kenmore                  66\n",
 559 |        "Des Moines               58\n",
 560 |        "North Bend               50\n",
 561 |        "Covington                43\n",
 562 |        "Duvall                   42\n",
 563 |        "Lake Forest Park         36\n",
 564 |        "Bothell                  33\n",
 565 |        "Newcastle                33\n",
 566 |        "SeaTac                   29\n",
 567 |        "Tukwila                  29\n",
 568 |        "Vashon                   29\n",
 569 |        "Enumclaw                 28\n",
 570 |        "Carnation                22\n",
 571 |        "Normandy Park            18\n",
 572 |        "Clyde Hill               11\n",
 573 |        "Medina                   11\n",
 574 |        "Fall City                11\n",
 575 |        "Black Diamond             9\n",
 576 |        "Ravensdale                7\n",
 577 |        "Pacific                   6\n",
 578 |        "Algona                    5\n",
 579 |        "Yarrow Point              4\n",
 580 |        "Skykomish                 3\n",
 581 |        "Preston                   2\n",
 582 |        "Milton                    2\n",
 583 |        "Inglewood-Finn Hill       1\n",
 584 |        "Snoqualmie Pass           1\n",
 585 |        "Beaux Arts Village        1\n",
 586 |        "Name: count, dtype: int64"
 587 |       ]
 588 |      },
 589 |      "execution_count": 11,
 590 |      "metadata": {},
 591 |      "output_type": "execute_result"
 592 |     }
 593 |    ],
 594 |    "source": [
 595 |     "data['city'].value_counts()"
 596 |    ]
 597 |   },
 598 |   {
 599 |    "cell_type": "code",
 600 |    "execution_count": 12,
 601 |    "id": "8f160844",
 602 |    "metadata": {
 603 |     "execution": {
 604 |      "iopub.execute_input": "2022-11-05T08:25:30.365700Z",
 605 |      "iopub.status.busy": "2022-11-05T08:25:30.365038Z",
 606 |      "iopub.status.idle": "2022-11-05T08:25:30.602652Z",
 607 |      "shell.execute_reply": "2022-11-05T08:25:30.601799Z"
 608 |     },
 609 |     "papermill": {
 610 |      "duration": 0.253522,
 611 |      "end_time": "2022-11-05T08:25:30.605014",
 612 |      "exception": false,
 613 |      "start_time": "2022-11-05T08:25:30.351492",
 614 |      "status": "completed"
 615 |     },
 616 |     "tags": []
 617 |    },
 618 |    "outputs": [
 619 |     {
 620 |      "data": {
 621 |       "text/plain": [
 622 |        "<Axes: title={'center': 'price Range'}>"
 623 |       ]
 624 |      },
 625 |      "execution_count": 12,
 626 |      "metadata": {},
 627 |      "output_type": "execute_result"
 628 |     },
 629 |     {
 630 |      "data": {
 631 |       "image/png": 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KI19++aUkqXfv3k7Tp02bpiFDhkiS4uPj5ed3ocHlzJkzevLJJ5WQkKB69eqpS5cuWrt2rdq1a1e6mgMAgIuCT2HEm76uK1ascHr86aef6tNPP/WpUgAA4NLBvWkAAICpCCMAAMBUhBEAAGAqwggAADAVYQQAAJiKMAIAAExFGAEAAKYijAAAAFMRRgAAgKkIIwAAwFSEEQAAYCrCCAAAMBVhBAAAmIowAgAATEUYAQAApiKMAAAAUxFGAACAqQgjAADAVIQRAABgKsIIAAAwFWEEAACYijACAABMRRgBAACmIowAAABTEUYAAICpCCMAAFRShgyzq1AhCCMAAMBUhBEAACopiyxmV6FCEEYAAICpCCMAAMBUhBEAAGAqwggAADAVYQQAAJiKMAIAAExFGAEAAKYijAAAAFMRRgAAgKkIIwAAwFSEEQAAYCrCCAAAMBVhBAAAmIowAgAATEUYAQAApiKMAAAAUxFGAACAqQgjAADAVIQRAABgKsIIAAAwFWEEAACYijACAABMRRgBAACmIowAAABT+RRGIiIidMMNNygwMFCNGzfWgAEDtGfPnmLnmzt3rtq2bauAgABdd911WrhwYYkrDADApcKQYXYVKoRPYWTlypUaNmyY1q1bp8jISOXk5Oj2229XRkaG23nWrl2rhx9+WI8//ri2bNmiAQMGaMCAAdqxY0epKw8AAKo+i2EYJY5dSUlJaty4sVauXKmbb77ZZZmHHnpIGRkZ+u233+zTevTooU6dOmny5MlercdqtSo4OFipqakKCgoqaXUBAKhS3v11l6auiZMkHRrf3+Ta+M7b43ep+oykpqZKkurXr++2THR0tPr06eM0rW/fvoqOjnY7T1ZWlqxWq9MfAAC4OJU4jNhsNo0YMUI33nij2rdv77ZcQkKCQkJCnKaFhIQoISHB7TwREREKDg62/4WFhZW0mgAAoJIrcRgZNmyYduzYodmzZ5dlfSRJo0aNUmpqqv3vyJEjZb4OAABQOVQvyUzDhw/Xb7/9plWrVql58+Yey4aGhioxMdFpWmJiokJDQ93O4+/vL39//5JUDQAAVDE+tYwYhqHhw4dr3rx5WrZsmVq1alXsPOHh4YqKinKaFhkZqfDwcN9qCgAALko+tYwMGzZMM2fO1IIFCxQYGGjv9xEcHKxatWpJkgYNGqRmzZopIiJCkvTCCy+oV69e+vjjj9W/f3/Nnj1bMTEx+vrrr8v4pQAAgKrIp5aRL7/8Uqmpqerdu7eaNGli/5szZ469THx8vE6cOGF/3LNnT82cOVNff/21OnbsqB9//FHz58/32OkVAABcOnxqGfFmSJIVK1YUmfbAAw/ogQce8GVVAADgEsG9aQAAgKkIIwAAwFSEEQAAKilulAcAAFABCCMAAFRSFlnMrkKFIIwAAABTEUYAAICpCCMAAMBUhBEAAGAqwggAADAVYQQAAJiKMAIAAExFGAEAAKYijAAAAFMRRgAAgKkIIwAAwFSEEQAAYCrCCAAAMBVhBAAAmIowAgAATEUYAQAApiKMAAAAUxFGAACAqQgjAADAVIQRAABgKsIIAAAwFWEEAACYijACAABMRRgBAACmIowAAFBJGTLMrkKFIIwAAABTEUYAAKikLLKYXYUKQRgBAACmIowAAABTEUYAAICpCCMAAMBUhBEAAGAqwggAADAVYQQAAJiKMAIAAExFGAEAAKYijAAAAFMRRgAAqKS4UR4AAEAFIIwAAFBJcaM8AACACkAYAQAApiKMAAAAUxFGAACAqQgjAADAVIQRAABgKsIIAAAwlc9hZNWqVbr77rvVtGlTWSwWzZ8/32P5FStWyGKxFPlLSEgoaZ0BAMBFxOcwkpGRoY4dO2rixIk+zbdnzx6dOHHC/te4cWNfVw0AAC5C1X2doV+/furXr5/PK2rcuLHq1q3r83wAAODiVmF9Rjp16qQmTZrotttu05o1azyWzcrKktVqdfoDAAAXp3IPI02aNNHkyZP1008/6aefflJYWJh69+6tzZs3u50nIiJCwcHB9r+wsLDyriYAAJXOpXLXXp9P0/iqTZs2atOmjf1xz549deDAAX366af69ttvXc4zatQojRw50v7YarUSSAAAuEiVexhxpVu3blq9erXb5/39/eXv71+BNQIAoPLhrr3lKDY2Vk2aNDFj1QAAoJLxuWUkPT1d+/fvtz+Oi4tTbGys6tevrxYtWmjUqFE6duyYZsyYIUmaMGGCWrVqpWuvvVaZmZmaMmWKli1bpiVLlpTdqwAAAFWWz2EkJiZGt9xyi/1xQd+OwYMHa/r06Tpx4oTi4+Ptz2dnZ+ull17SsWPHVLt2bXXo0EFLly51WgYAALh0WQzDqPRdda1Wq4KDg5WamqqgoCCzqwMAQIV499ddmromTpJ0aHx/k2vjO2+P39ybBgAAmIowAgAATEUYAQAApiKMAAAAUxFGAACAqQgjAADAVIQRAAAqqUvlRnmEEQAAYCrCCAAAlRQ3ygMAAKgAhBEAAGAqwggAADAVYQQAAJiKMAIAAExFGAEAAKYijAAAAFMRRgAAgKkIIwAAwFSEEQAAKqn40xlmV6FCEEYAAKiklv510uwqVAjCCAAAMBVhBAAAmIowAgAATEUYAQAApiKMAAAAUxFGAACAqQgjAADAVIQRAABgKsIIAAAwFWEEAACYijACAABMRRgBAACmIowAAABTEUYAAICpCCMAAMBUhBEAAGAqwggAADAVYQQAAJiKMAIAAExFGAEAAKYijAAAAFMRRgAAgKkIIwAAwFSEEQAAYCrCCAAAMBVhBAAAmIowAgAATEUYAQAApiKMAAAAUxFGAACAqQgjAADAVIQRAABgKp/DyKpVq3T33XeradOmslgsmj9/frHzrFixQtdff738/f3VunVrTZ8+vQRVBQAAFyOfw0hGRoY6duyoiRMnelU+Li5O/fv31y233KLY2FiNGDFCTzzxhP744w+fKwsAAC4+1X2doV+/furXr5/X5SdPnqxWrVrp448/liRdc801Wr16tT799FP17dvX19UDAICLTLn3GYmOjlafPn2cpvXt21fR0dFu58nKypLVanX6AwAAF6dyDyMJCQkKCQlxmhYSEiKr1apz5865nCciIkLBwcH2v7CwsPKuJgAAMEmlvJpm1KhRSk1Ntf8dOXLE7CoBAIBy4nOfEV+FhoYqMTHRaVpiYqKCgoJUq1Ytl/P4+/vL39+/vKsGAAAqgXJvGQkPD1dUVJTTtMjISIWHh5f3qgEAQBXgcxhJT09XbGysYmNjJeVfuhsbG6v4+HhJ+adYBg0aZC//zDPP6ODBg3r11Ve1e/duTZo0ST/88INefPHFsnkFAACgSvM5jMTExKhz587q3LmzJGnkyJHq3LmzRo8eLUk6ceKEPZhIUqtWrfT7778rMjJSHTt21Mcff6wpU6ZwWS8AAJAkWQzDMMyuRHGsVquCg4OVmpqqoKAgs6sDAECFaPna7/b/Hxrf38SalIy3x+9KeTUNAAC4dBBGAACAqQgjAADAVIQRAABgKsIIAAAwFWEEAACYijACAABMRRgBAACmIowAAABTEUYAAICpCCMAAMBUhBEAAGAqwggAADAVYQQAAJiKMAIAAExFGAEAAKYijAAAAFMRRgAAgKkIIwAAwFSEEQAAYCrCCAAAMBVhBAAAmIowAgAATEUYAQAApiKMAAAAUxFGAACAqQgjAADAVIQRAABgKsIIAAAwFWEEgCmiD5zSY9M36sjps2ZXBYDJqptdAQCXpof/s06SlHouRz8929Pk2gAwEy0jAEx1IuWc2VUAYDLCCAAAMBVhBAAAmIowAgAATEUYAWAqw+wKADAdYQQAAJiKMAIAAExFGAFgKoPzNMAljzACAABMRRgBAACmIowAAABTEUYAAICpCCMATGUw0ghwySOMAAAAUxFGAJiKS3sBEEYAAICpCCMAAMBUhBEAAGAqwggAADAVYQSAqei/CoAwAgAATFWiMDJx4kS1bNlSAQEB6t69uzZs2OC27PTp02WxWJz+AgICSlxhAABwcfE5jMyZM0cjR47UmDFjtHnzZnXs2FF9+/bVyZMn3c4TFBSkEydO2P8OHz5cqkoDAICLh89h5JNPPtGTTz6poUOHql27dpo8ebJq166tqVOnup3HYrEoNDTU/hcSElKqSgO4eDDoGQCfwkh2drY2bdqkPn36XFiAn5/69Omj6Ohot/Olp6fr8ssvV1hYmO69917t3LnT43qysrJktVqd/gAAwMXJpzCSnJysvLy8Ii0bISEhSkhIcDlPmzZtNHXqVC1YsEDfffedbDabevbsqaNHj7pdT0REhIKDg+1/YWFhvlQTAABUIeV+NU14eLgGDRqkTp06qVevXvr555/VqFEjffXVV27nGTVqlFJTU+1/R44cKe9qAjAN52mAS111Xwo3bNhQ1apVU2JiotP0xMREhYaGerWMGjVqqHPnztq/f7/bMv7+/vL39/elagAAoIryqWWkZs2a6tKli6KiouzTbDaboqKiFB4e7tUy8vLytH37djVp0sS3mgJAOTqZlqn/rDqo0xnZZlcFuOT41DIiSSNHjtTgwYPVtWtXdevWTRMmTFBGRoaGDh0qSRo0aJCaNWumiIgISdK7776rHj16qHXr1kpJSdFHH32kw4cP64knnijbVwIApfDY9I3accyqFXtP6vsnephdHeCS4nMYeeihh5SUlKTRo0crISFBnTp10uLFi+2dWuPj4+Xnd6HB5cyZM3ryySeVkJCgevXqqUuXLlq7dq3atWtXdq8CAEppx7H8q/bW7D9VbNnjKedUvZpFjQMZwBEoCz6HEUkaPny4hg8f7vK5FStWOD3+9NNP9emnn5ZkNQAuAVVtnJGz2bnqOX6ZJOnguDvl52cxuUZA1ce9aQDAB4nWLPv/86pakgIqKcIIAFNxOAdAGAEAAKYijACADwxOzQBljjACAABMRRgBAACmIowAMFVVPu1RhasOVCqEEQBAlfH9+sNavS/Z7GqgjBFGAJMYhqE/diboyOmzZlcFJWRwYXKF2hJ/Rm/M26FHvllvdlVQxko0AiuA0luyK1FPf7tJknRofH+TawNUfsdTMs2uAsoJLSOASTbEnTa7CpVCVW5boM8IUDYIIwCAKoHTYhcvwghgEm6vBgD5CCOASfiNl49THfCWhQh/0SKMAEAJEaSAskEYKUeGYeihr6L1+PSNZlcFlRAHMgDIx6W95ejI6XNaf/6KicycPAXUqGZyjVDRDMNQxKLdurxBbQ3sfrnZ1UEZo0NlxeL9vnjRMlKOHL84Fk51XpJij6To61UH9ca8HUWe4zNR9TESKFA2CCNAObJm5rp9jtM0+RzvTZNnq1pvylPfblJSWpbZ1QCqPMIIUI5o/PDe0l2JunbMYv2+7YTZVfHJqQzCCFBahBGgHDmeiil8d1rOf+creBeemBGjzBybhs3cbGp9AFQ8wkgFoUm+bK3Zn6x3f92lrNw8s6vikeO4CIt3JJhYk9KLP3VWb83fwY39gDK041iqNh3m1hCEEVRJA6es19Q1cZq25pDZVfHa/NhjZlehVP4xZZ2+XXdYj3LHVKBMGIahuz5frfu/jFbK2Wyzq2MqwshFbtaGeA2aukEZWe47UlZllf1X+sV0xczRM+ckSYdOlfF7TqshLlGO/bWT08smjPzrx216+tuYIqeFKzvCSAUx63Mx6uftWrU3Sd+sjjOnAuWssn7dUs5m68sVB5SQeuGW51Vs34Dzvlkdp6dmxCgnz+by+co+RPmi7Sf0xrztbuuPsnMuO08fL9mjHcdSvSrvHBhKv4PIsxmaE3NEf+xM1MHkjFIvryJd0mFk65EUvTV/h85klH/zmK+dFXPzbBoybYM+XrKnTNZvPZdTJsuBd16cE6sPFu/WS3O32qcZkiYs3atBUzcoJ89WbuHkz31JuveL1frrhLV8VnCJGfvbLi3Zlahftx4v1/Ws3pese79YrV3Hy3a7Pfv9Zn2/Pl4/xBwp0+VWNqv3JZeq70Vunk27jltL1aLw76h9+nzZft31+WqvyjtFkTLeH9AyUoXcO3GNvl13WG//utOr8ifTMrX+4KlyrlW+FXuStGJPkj5ftr9C1ldVVdbv28q9SUWmGYY0Yek+rdqbpMhdieW27ke/2aCtR1P1xH9jym0dl6L0cj7V+cg367X1aKoeK6fbR7gbDyXm0Gl9Grm3SrecTFy+X498s173fxnt8vlT6VnFjgfz0tytuvOzP/XVqoMlrscuH38AOO6/HvwqutSn06taAHF0SYeRAvsS070q131clB76ep3+3Ff0QFMcXz8jWbmudwz7EtP04FfRij7gWyiquh/Ri8mFrZDtZvuWpdMV0OJXnp7+NkYPTF4rWyUZCM2X73Dq2RytO3iqRAeH0xXckfH/TY7Wv6P2aeb6+Apdb1lJTs/SR3+4b0HOzbOpy3tLdcP7S5WZ4/7quwWx+S1fk5ZX3A9AxxbzM2dzNCP6cCmXV3URRnxQsF9Zvd/3IaDL6kPyxIwYbYg7rYf/s8639ZeiApX98tnCcvNsenP+9ko3eFZFH1Or8jgmhmHoj52J2njojPYnefdjobzZfPgS3fnZn/rfr9eV7AoqkzZbXBXrY1CgcGtC4QB41iGAeHPa21KKXueOc/7ixWm9wh+pc9mlbRkp1eymIoxUMYnWzOILlaH3ftulNm8u9rpDVsUr+u37afNRfbcu3tTBs1zt0Hw5mJWFqrJjclVNx7pX9PtWFo6l5F95tGi772PLZLs5XfKfVQf11vwdVbopvqSm/HlQ87Yc9aqsp7fnP3/GKS3Tc/+50lwB5zjvP2dtKbZ84bqW9gdLVf4BQhipIGbvQEr6IZ1y/iqcTyL3ui2z67hVA6es05b4MyVaR1nz5l4h2bk2PTUjRv9de6hM1plozdQvW497PO/u7Y5m+9FU7UlIK3WdHFd3KDlD93yxWou2V67WInfKs2NfSZVVPfJsRonuZ/P+wr/07brD2nIkpWwqUkhZXoY+fU2cJpbR6Y645Ay99/tfenHO1uILq2i4LbzdcvI8b8jSvA2+zlt4v1zaMFFZvislQRgpByfTMl0M/V02vP2wpWfl6rt1F84/lvZD6ilMPfLNeq3Zf0r3TVpbupVUoPmxx7RkV6LG/OJd5+Xi3P7pKv1z1haPl1AXfg+3u2htSj2bo7u/WK2+E1aVKMAeP/+LPH+FF/776o/btO1oqp79vmxaiyJ3JWpBOQ7i5vjay2MHm2cz9M9ZW/T1qgPe18nNdF8P4gOnrNMN7y8tcXg/l125T5vm5tn09q+79NEfe5wubffFN6vj9ND5Dp2pPl4J6O33JtGa6XLZpTpN4+O8VTk8lDXCSBmbEX1I3d6P0uBpG5VrK98dqidvzNuuN+cXvW19SXmqfmk7Su5NTCvx6IOu3ldv3mtXO/SMrNwS7zwLdmrLd590W8bxdEN6Vq42HS56MEpKz3Qo73s9HPsSOf7K8rRD35uYptSzvu3wn5wRoxdmx3p12tAwDMUlZ7g9SLia7jz6Qtl/eVbsOalfth7XuIW7vZ6nJOGw4Ni09UiKvVVq3cH8y0+raofR4jh+bs8W6gMReyRF/117qNj3cuxvu7Q+7rRmRB8uQWtD8c5kZKv7uCh1fGdJkecqtmWk0ONS/2gs3fxmIoyUsdEL8n9pr9qbpLd8CANLdibow8W7i71ywNtbrC/ZWbaXjpZX58vdCVbd/ukqdX1vafmswI0a1Yp+9Lu9v1Q9IqKcWxfOO5OR7dXByFMRm8MZHHfhwPGXlbfb2tFhh9FRvZl9x7FU3f7pKnUbV7L335tfrf+O2qdb/m+Fxi/25cDv+v/FWbIzQVvPn8bIzMnTDxuP6GShwJRozSzRZbol2dEXzHPvxDV69vvN2nn8QmtYeR03Snr1kavB20qyLE99fAZMXKMxv+zUQi/70uxLTCt0s8ni5/GmzF8JFy7B/X698xUsp0rx48rXRpWybkGnzwhcWut4+W0xn5Gnvt2kSSsO6I+dCeeLF51hzf5kp9YWT4qcizQMzdoQrxfnxCq3BOMJlFefl9X78q9MyrUZuuvzP4scOErCmx1C9WpFC2Wcby2JWOR80Fy0/YQ6j43U2N/+Kna5nnYGeV68h461qoiOmwXjobi7lLwsTFi6T5L01Urvx29w9T6mns3R/V+u1bfrXF/+uDcxTU99u0n3Tlyjv05Y9dEfe/TqT9s0YOIae5lfth5X93FRGvXzdh9fRdns6B2vWCmPzTtnY7w6vrukRIN/Ff7ebD2Sok7vLnE63esNx9dVEK4L7z/2nfSuT9TPW445hSTH70Svj5Zr0or9RfpXFdlOhR8ahtMy35i3w2Nfr4ysXJ1M83a/5H0aOZiUXmRMmdJ+52kZgSRpqYeBrLzdkZ3wcJrg+UK9s3/efNTr86mG8oeGn7flmFeXnFUUxy/PjmNWfbC46KV387YcdXlKQ5JmbzyiU+nedQbck5Cm/p/9qai/ElXdz3mn4Xia6Netx7UnIU0z18fr5blbNfa3XZKkqWvc9wcp4Ckregp0S3Ym6K35O5xaQ0q/Y6oae6birqYp+P+XKw9o0+EzblscDzkc6F+cE6ulf+V/H487fKc+OX9p59kS9LsYt3C3IhYVH0gr2oLYY/bw8a+ftistM1fDvi/+So7CCh9GX5wTK2tmrs+nex0/t4Zh6KQ1U93GRelDh5YxxzAwbU2cXp+33e3n1V3LyOFTZ/Xh4j166ttNTuULL8abfW+39923DF4/NlLd3o/yqtOxY12L+1H09LebtPGQ836t1IOelWpucxFGysiZjGw9McPziJebDp/Wr1uPa/vRVN3+6UqX/QtSfOisNfKHrXr2u00unyvc5Oo4mE5JhoavqOPauZzc8+sz9J9VB/X1qgN6cc5W3f/lhc6xhcPHe797d4B47vtN2nncqsf/G1NkR/E/hTrfxiVn6PV52/XjpqNOB7PiFOxQXe2HPAWVp77dpG/XHdasDReG7C7JaRqnupRq7sqj4GDiy47aXUuPL+9Jdq5N/f79p9O0r1Ye9PiD4cdNRzV02ga3zzuFrDLYQtuPpuqF2bFFRh4tybILfydKWrvCLYCTVhxQUlqWJq1w3Vn4nV93aeb6eEW7Gd3aMQR4850oEkZczFL4tZ7x0Geq4LO02YsOxxY3/y/g2CpdcONJR9+ti7dfFl4SzoGuct8zqTDCiKRDpzwP9pOelevUEevrVQeLDARmLebadcOQ7v8yWs/P2qK7v1itvYnpGupi2OfPovbZyxfm6qO11s1IrN7sjE6lZ3nd+dSXX+l5NkNb4s+UaJTRgtUs3J6g9xf+5bKD4fI9ziPgetvpNPXchW3oV2hvVPSmUsW/3nPZeTp6xvkOttZM951g3bXuONqbeKHJudRjDjg1l3s3z/I9J7Xfyyb08vTnvqIDC/pyPj6/Kb50Vu1Ncnl/n8KjeBasJys3Ty/P3er0+VyyK1EH3A3a5rB9dieU7H40h0+73neV5MdDaa4icVq3w9c+PSvXZYuHq1WlZ7oOm44twt6c6ix6uWzhdZfs1oZPf7tJB30YgK/w+/lDzBG1eWuxx07ukvRLbOVpua5IhBHlN9n+ts31ByArN0/tx/yha8f8YZ9mGEXPfxf38S7NceXz8wHFkzybockrD2j7Ue8GJ8vOzR8i+fqxkV7dk8KXndvHS/bovklr9eqPxY8L4C40xSW7/9IX7lTnzT7UZjOU7OZ0jqud5TPfFX8J7N8/XqG/fbDcKUDsP5muHhFRxfbtcTd8tWPorehh0LceSdHQaRvV55NVkqS5MUf04FfRpgwr73jZsKfP3up9yS7Dk80ofcuQuwDurj6FW9cKDPrGfUtJgTfnOZ8KOXwqw6vt726/U5LX7rik/SfTnPq3/LkvqdiRmBOtmZq9IV4/bb4wONk9X6xxWdZVrd3V2bGzsTenHjcfTnF6XHg7bog7XWzwcvfe//3jlR7nczpNI+nb6EP2y/1f/XGb8mxGsS3opcmEZdsdtmIRRs6b6mZ8iMTU/ANY4e/AtqMpPi3fl/P3+xKdd64fexhwTMo/RTTlz4Mav2i37v5itdYeSC42PDj2NSn8i6Sgro7Nhd42+2bm5NmbY+e7SfhZuXm6899/qtdHy326tLJA4V9HRZqXXVT1v9GHnB47toz4csz/PGqfRi/IHwWzoLm+oG9CWdgcn2L/vze/AiXp2e82afDUDR5bNAovauOh0+oxLsreYVpyvsnXlD8P6pUft+XfeuDrdRr5Q6yHZRtFPt9ZuXkyDEPxp866mct93STpN4eh/Auedgx4yelZ2nT4jB75Zr09PDkt083n1ZdQ7a6ou5Cy083ddp2/R66XX3iJvT5aoX/Odt3vw+lUgJsDl6sqTli6T2mZOYo+cMr1wdZhWYVDxKPfbNBj0zdqxOwtLluLsnLz1H1clF77ebvePd/Hyl4X11V0UWdDeTbD44+j17zoePzIN+udHheu74a44jv3njnfh8zVWDCeBg50DIe5NkNvLdipsb/tcjq1XNyppoLubIZh+HzJveN2/6OYKyp3Hbfq4a/XedViWxEII+c5HgQc+bl5hwp/2Uf/UnZjetz2adGdq+R+x7Px0GltPHThC/aP/6x3XdCBu531lysOqEdElI6cPqsbxy+zT/f2gN32rcUup6/el6xvVsfJMAwt352kXSesTpehesOamaM+n6zUB4UuEfWzWJSbZ9Oy3YmaujrO5fnfaWsOOT12bGr35RTUx5F7NSP6sPadvNBy4+rcb1nwpl5ns3O1aEeCVu5NcnlQdueJ/8YowZqpbW5a0hz74exJTNPPm10PcGYY0n2T1uqhr9bZA8nKvUlq8+Zi9YiI0s0fLXcqH7kr0enyVm+czsjSqz9udRqXo+t7S536ERVmK+XFQQ9MXut1v5OjJTzH7xjgXH21nQKZF58Fx+9rQfnCB74Hv1qnh/+zTt9vKDrGiePpA1cdfNfsP6X5scedrk4qMGaBb4MH7kksGpxnRB/Wla8v1A0eOpOW5PTTi3NinR7bDKPYcY0+PN+R3tVAjo4DBzqeil61N0mLd7q+ZDnTh1PWBYFm5A9b1fHdouOgeOTw/sQc8hy6Hv1mvaIPnvL4PapI1c2uQGVXzc99M+gvW4/rP6sOatLA67Vij+c7+S7a4fs9Khx5ujeMqybHYpv6HD60exPT9NPmo/rnrVfZD/T/V+iGUsdKecAt+LXSNjTQ40HW085m5vp47T/p+vTN/y3Zq8kr3Y+mWXg7OpYtSUfRVXsvbO/yGryq8AF1d0J+gLu9XYjDJZOel7H9aKquax5cZHppO8cWOJF6TrHnx/VIy8pVUEANvTI3//RcorXoabEnzzdRHxrf/0JdDMPj8PfPfrfZq0uPHTtA5h+MLU6P/9yX7PW9nTYeOuP29FThYDB02kaNu+86r5brLlTEuPl1uunwaXW5vL7TjwHHJTh+rF21wBRuFSh4/PPmo3q0x+U67NBfbq+Xdy93tS1mbzziouT5urhp+friH87TCvq/pfjYGuBKZk6eAmpUO/9/5/pOX3tI04u5DUT8ac8/lKyZOZryZ5w+i9qnzx/urJuuaqhBUz11XPb++1aw7563xfcRjh1/ZBZ3Kqo046mUB8JIMQp3dCxgGIb9RkhvLSi+VcTd5XG7jluLDLojFf31NcvFL5kCrm6LXfgL6Gn5D32dP2rnDzEXzvUW/u4cSzmnpLQsNQr097jc4up39MxZBQbUKHaelXuLhjt346NYLBa3QSQtM0f/nLWlyB1JHa+IKMmoq8uK6YRWFnYcS1VocID98R0T8q/sqF2zmna9e4dXy7j7i9U6NL5/kQBYw8U4K6Xt9FjQ9H/Si0sg73X4dZ2da1PfCe5bdbwdA8Wx9cuQ8+tZsitRT3/r+sozdw4kue4c6irHFQ7v7mw94tugZ/d/Ga0D4+70cDBzv3/ypGBbZWSVbGj5CUv3qmlwLXUIC1bb0CCPZd21wt768QpFvdS7ROsvTtu3Fuvhbi30XO8rS3SJfHGnSDu8faHFovCQC64UXlzModM652K/LXkOEYeSM7Q7IU19rw1xWc75knhDE5fv1xUNL1O/65robHauatesvIf8yluzSsLdZ9JxcmmuDb/zsz9dTi98x0dPP2S9+TI4Sk7PLnZ8ElcNQjuPp6p7qwYKqOHnU8/768dG2v9vkaXYq2xmb4gvcv29lN/64Uqyh4PfdW+7buas5lD/wue4veHuKqay9MSMGG18o48aBfo7BSbHJnRvxowZv2i302klSaruYgTaknA86HV6N1Ijb7vaq/m2lvHN3gpf4l74AORrEPHkORf39/H2l6/jWDULYo8rIytPdfyreZznXE6e/Ktf2F5e9Rk5/6+7Tqdbj6aW6h43BQPZSc6tXK64OxV4ICnD532XL2ZtiFfModMlCiNl3Xl8caGW8f83OdpNSc8X5Pb+vxWSpKlDuurvbUOKPO9Y602Hz9iv7AquVUOp53L0QJfm+uiBjt5Wu0IRRgrJzrUpz2Zo5/FU/b/J0bqxdQOX5RxPy5TVJXGeGIah5PSyaVb7Yvl+fVHMHTVdjXey9UiqnvhvjAZ0bqYP7++gL1ceUNfL63lczks/bHU+/2yRRhQ6h+to8c4Et+dd3dnlokNdcRw3WUW0cpTUDe8v1Z+v3qKbPnTue7EnIU1b4s94NZJo4Vaj2RviXQ7gVFzTtCu5he6A6unuzuWp8GlQV6eI3Pl92wlNWFq6ensap8ITbzo/9/5ohda+9nf746xcW/6lyx4uUU05m6M3529329dHkp6ftVm7CnW6nbh8vy6r6Tkc+cpdGJHyBxgsT4VDuLdiDp9RZ1/7a3jw/kLvB8v7ZnWcUx9AV7bEp7gOIw7By+pwYULBj8+5m45qUHhLl6duzUYYcfDz5qMa+YPz5ahr9hf/C9ib3tml5emcbHlw1Qfm0/M77B83HVXTurXsY6J44niZn6QiOz+zWN2MaVAZFQ4ikvTVqgMeDzSeuLsiwVOfG3cq02i+JTVsZtncybi8JKdn6RWHy+SHTt+onlc20J3XNdHXq9wPsf/dOs99mZb+VTSEu7vk3BNPd6quykoaMEvrWMo5nwc+y8rN0/fr4r0KGcdSzhFGKrvCQQTueRNEXCmu4xi8U9IgUtYuhjBSFSwodJn82gOnKuRUoTfGluA0J0rHMPI7p7duXEc1qln01cqDXrdKZuXmleoUXXkhjAAAUIU4nmqvUc2inDzv+7i8MDu2yLRz2XmqVcan53zFOCMAAFRRvgQRd75ZfdCHOxOXD8IIAACXsP9bslfd3o8ytQ6EEQAAYCrCCAAAKLORmUuCMAIAAJzukFzRShRGJk6cqJYtWyogIEDdu3fXhg2eb5E9d+5ctW3bVgEBAbruuuu0cOHCElW2rL11VzuPz//vDWEVVBMAuGD0Xe204uXe2j3Wu2H/kW9Uv7almn/svdeWUU1K5vOHO5u6/jkby+c+W97wOYzMmTNHI0eO1JgxY7R582Z17NhRffv21cmTrkexXLt2rR5++GE9/vjj2rJliwYMGKABAwZox46yu8ttST3+t1Y6OO5OjbvvOs17rqc2v3WbBodfrrWv/V2HxvfX+Ps76POHO+uLf3TWoz0ut893eYPaWvFybz3+t1a6tqnzfRlev/PCl6H/dU30j+4t1LJBbc0fdqM6htX1WJ/wKxqocaC/hvRsqad7XeH03PdPdNetbRtLktqEBGrsgPbq36GJy+W82f8aLfznTUWmz3qyhx67sZXHOhRn2Uu9NKRnS6f3Q5L+1rqhJj/SRT2vdD1iraPOLepq8iPXKzQoQFeH1CnyfHEh0RtP33xF8YVc+NcdbXVofH9tfKOP3hvQXktH3qwD4+7U8pd7q881jb1axog+VxWZVvh9v7xBbfv/P3mwo74ceL3e7H+N/nr3Ds18orv2vtdP296+XVc0vExDerZ0e1DqcUV9j3X5dfjf9P0T3Z2mrX/9Vu157w61DQ0sMuz/tKE3aNe7fRUXcWeR52JH36bY0bfp93/+TePuu05j7m6n29uFqGlwgG5oeWEk3uLep8aB/prxWDd99WgX+7QmDvfgkaR7OjbVn6/eorahgfZpy17qpZ3v9NU9HZvqno5NPa5DkmY81k2SdFXjOnrjzmuKPH9f52aaOqRrscvp0DxYD3Ztro1v9NHA7i30n0Fd1a5J0fuxzHyyuwZ2b+FyGTdd1VAHxt2pmU921/N/b+12Xc3q1tLt7UI09MaWatnwMgXUqKad7/R1GhX1rg5NNKRnS3VuUVcv3Fr0s+bo3k5N9Wb/a4p8fgL980d1CAy4MLrD8Fta66rGzt/HxSNuUuzo21wuu2PzYPW/roneueda7R57h+Ii7tTQG1t6rE+BmtX8VP+ymvbHfhbp28e7uSw756keTq9z9F3tNPeZcL3Z33mbxrzZR0/3ulKLR9zk9LreuedaHRx3p6YO6arY0bdp+cu9dV/nZmpwWU1NHdJVD3Rpbi/b5fIL36dOYXWLjMBdeL/3St82Rer7XO8r7f//1x3uw9GKl3urYR3/8+utp++f6K67OzbVlrdu04JhN2rVK7e4nbe8DApvWeHrLGAxfLmdoKTu3bvrhhtu0BdffCFJstlsCgsL0/PPP6/XXnutSPmHHnpIGRkZ+u233+zTevTooU6dOmny5Mku15GVlaWsrAvDOVutVoWFhSk1NVVBQZ5vymSGuOQMvf/7LvW4ooGeuOkKZefaVN3PIr9Ce/MVe05qyLSNkqTlL/dWzKHT+mNngj57uLPLGxjZbIYsFs/DzVszcxRQvZq2H0vR/V9Gy2KRYt+6XcG1a+hMRrb+HbXPPtBYwT0kzmXn6Z1fd+qBrmEKrlVDfT5ZqcmPdNEd7UMl5Q8dbLHk77AK1r3/ZLrq1q5h//JI0umMbB0+laHQ4AA1Ca7lVK88m6F3ft2p+VuOyZqZq3/+vbUOJGVo3P9cp+BaRW+UF7HwLy3Zlah5z/VU3do1ZRiGzuXknX9tqWoTGqiAGtW09UiKov5K1HO3tFaezVBaZq4aBfrri2X71aRugHLybLr5qkbaeTxVz3yXP7LmP7q30Ig+V2n3iTRNXnnAPljUpjf7qG7tmoo9ckbV/fy072S6/qdzsyLbrbDlu09q38k0DQpvqRrV/LTu4Cm9++su7UlM05+v3qKw+rW19UiKftp8VD2vbCDruVy1axqkuz5fLUnaPfYO+Vf30+b4M2pwmb9aNrzM4/oc5eTZlJtnaNKK/bq9Xaiuax4swzC09Wiq2oQEKqCGn1qNym95fP7vrfXS7fk7y7G/7dK0NXHa9nZf1fF3/qydycjW6v3JuvnqRkW2zemMbK3Zn6xWDS9T+2bej9qYmZOntm8tliQ9dfMVGnZLa82NOaIW9Wvr9mtDi5RPSM3U0Okb9dcJq1O9z2RkK/70WbdB/mx2rqatOaS+14Yo/vRZ/Ttqv57tdYXahAapVcPLdNKaqQZ1/FXNz6LDpzL0Q8wR9biiga5vUU+XnX8f0jJzVMe/unJthtYfPK3rL6+r9Kxc/d8fe/SP7perk5t1b4g7rdRzOWoTEqgWDuFSyh+We+6mo3pr/g799GzPIu9dWmaOjpw+p3ZNy25/tu7gKdXxr66cPJtW70vW5Q0vcwptkbsS9fu24xo7oL3TzSoLhpYvsPZAsrbEp+i53lfap6/am6Ttx1L1XO8rZRjS7oQ0tQkNdHlX8+/XH1ZgQA37un/adFQxh8/ovQHtVc3PIpvNUK7NUHU/S5H925gFO/Tf6MN6s/816taqvurVrqmw+rWVnWtT1F+J6n5FA6cQs3JvkgZP3aArGl2mZYVuupeelaujZ866vZFfwes2DMP+ndn5Tl/VqlGtSL2OnD6r1HM5at8sWNm5Nu04nqqT1kzd0b6JDMPQgElrtfVIiobf0lov3X61Jq04oKtDAnVbuxAdSzmncQv/UkhggB7p0UIfLt6jwT1bKvzKBvbbj3ga46Pla7+7fa4sTRnUVX3aFR1ivrSsVquCg4OLPX77FEays7NVu3Zt/fjjjxowYIB9+uDBg5WSkqIFCxYUmadFixYaOXKkRowYYZ82ZswYzZ8/X1u3uh7x9O2339Y777xTZHplDSO+OJScoaZ1a6lm9bLvrmOzGcrOs9lvnV1g29EUBQbUUCsfDnplyYwBdQzD0E+bj+m6ZsFq4/DrWpIOJqXrbHaeTwfXsrB0V6Ka1q1VpgcgdwofYMyQmZOnk9asIgdqTyr7nUV9VRm2Q1VisxmKO5WhKxpe5vX7dtKaqbq1a5Zqn5pyNlvZeTY1DgwovrAbeTbDZTgrreW7T2ro9I1lvtzClo68Wa0bBxZf0EfehhGftl5ycrLy8vIUEuKcnkJCQpSQ4PrmZgkJCT6Vl6RRo0YpNTXV/nfkSMXel6U8tWx4WbkEEUny87MUCSKS1KF5XdOCiCRTRvazWCz6f12aFwkiknRFozoVHkQkqU+7kAoJIlLF3LyxOAE1qvkURCRdVEFEqhzboSrx87PoykZ1fHrfGgcFlHqfWrd2zVIFEUnlEkQk6Za2jfXE3/JP8/697YVToH9r3VCSdPPVjbTmtb/rhVuvUmhQgL56tIue7nWFrmkSpIe7hSn8igZqUb+27urQRAE1/HSXw+n9q0PqaMzd7TTxH9eXSxDxRaX85vv7+8vf37/4ggAAXOTevKud3iymL92Lt12tF2+7WpLU99pQjernvuwX/yjL2pUNn+Jkw4YNVa1aNSUmOt/2OjExUaGhRc8DS1JoaKhP5QEAwKXFpzBSs2ZNdenSRVFRF4aNtdlsioqKUnh4uMt5wsPDncpLUmRkpNvyAADg0uLzaZqRI0dq8ODB6tq1q7p166YJEyYoIyNDQ4cOlSQNGjRIzZo1U0REhCTphRdeUK9evfTxxx+rf//+mj17tmJiYvT111+X7SsBAABVks9h5KGHHlJSUpJGjx6thIQEderUSYsXL7Z3Uo2Pj5ef34UGl549e2rmzJl688039frrr+uqq67S/Pnz1b59+7J7FQAAoMryeZwRM3h7aRAAAKg8yuXSXgAAgLJGGAEAAKYijAAAAFMRRgAAgKkIIwAAwFSEEQAAYCrCCAAAMBVhBAAAmKpS3rW3sIJx2axWq8k1AQAA3io4bhc3vmqVCCNpaWmSpLCwMJNrAgAAfJWWlqbg4GC3z1eJ4eBtNpuOHz+uwMBAWSyWMluu1WpVWFiYjhw5wjDzJmI7VA5sh8qB7VA5sB3KhmEYSktLU9OmTZ3uW1dYlWgZ8fPzU/Pmzctt+UFBQXzYKgG2Q+XAdqgc2A6VA9uh9Dy1iBSgAysAADAVYQQAAJjqkg4j/v7+GjNmjPz9/c2uyiWN7VA5sB0qB7ZD5cB2qFhVogMrAAC4eF3SLSMAAMB8hBEAAGAqwggAADAVYQQAAJiKMAIAAEx1SYeRiRMnqmXLlgoICFD37t21YcMGs6tUZa1atUp33323mjZtKovFovnz5zs9bxiGRo8erSZNmqhWrVrq06eP9u3b51Tm9OnTGjhwoIKCglS3bl09/vjjSk9Pdyqzbds23XTTTQoICFBYWJg+/PDD8n5pVUpERIRuuOEGBQYGqnHjxhowYID27NnjVCYzM1PDhg1TgwYNVKdOHd1///1KTEx0KhMfH6/+/furdu3aaty4sV555RXl5uY6lVmxYoWuv/56+fv7q3Xr1po+fXp5v7wq48svv1SHDh3so3eGh4dr0aJF9ufZBuYYP368LBaLRowYYZ/GtqgkjEvU7NmzjZo1axpTp041du7caTz55JNG3bp1jcTERLOrViUtXLjQeOONN4yff/7ZkGTMmzfP6fnx48cbwcHBxvz5842tW7ca99xzj9GqVSvj3Llz9jJ33HGH0bFjR2PdunXGn3/+abRu3dp4+OGH7c+npqYaISEhxsCBA40dO3YYs2bNMmrVqmV89dVXFfUyK72+ffsa06ZNM3bs2GHExsYad955p9GiRQsjPT3dXuaZZ54xwsLCjKioKCMmJsbo0aOH0bNnT/vzubm5Rvv27Y0+ffoYW7ZsMRYuXGg0bNjQGDVqlL3MwYMHjdq1axsjR440du3aZXz++edGtWrVjMWLF1fo662sfvnlF+P333839u7da+zZs8d4/fXXjRo1ahg7duwwDINtYIYNGzYYLVu2NDp06GC88MIL9ulsi8rhkg0j3bp1M4YNG2Z/nJeXZzRt2tSIiIgwsVYXh8JhxGazGaGhocZHH31kn5aSkmL4+/sbs2bNMgzDMHbt2mVIMjZu3Ggvs2jRIsNisRjHjh0zDMMwJk2aZNSrV8/Iysqyl/nXv/5ltGnTppxfUdV18uRJQ5KxcuVKwzDy3/caNWoYc+fOtZf566+/DElGdHS0YRj5wdLPz89ISEiwl/nyyy+NoKAg+3v/6quvGtdee63Tuh566CGjb9++5f2Sqqx69eoZU6ZMYRuYIC0tzbjqqquMyMhIo1evXvYwwraoPC7J0zTZ2dnatGmT+vTpY5/m5+enPn36KDo62sSaXZzi4uKUkJDg9H4HBwere/fu9vc7OjpadevWVdeuXe1l+vTpIz8/P61fv95e5uabb1bNmjXtZfr27as9e/bozJkzFfRqqpbU1FRJUv369SVJmzZtUk5OjtO2aNu2rVq0aOG0La677jqFhITYy/Tt21dWq1U7d+60l3FcRkEZvj9F5eXlafbs2crIyFB4eDjbwATDhg1T//79i7xfbIvKo0rctbesJScnKy8vz+nDJUkhISHavXu3SbW6eCUkJEiSy/e74LmEhAQ1btzY6fnq1aurfv36TmVatWpVZBkFz9WrV69c6l9V2Ww2jRgxQjfeeKPat28vKf99qlmzpurWretUtvC2cLWtCp7zVMZqtercuXOqVatWebykKmX79u0KDw9XZmam6tSpo3nz5qldu3aKjY1lG1Sg2bNna/Pmzdq4cWOR5/g+VB6XZBgBLgXDhg3Tjh07tHr1arOrcklq06aNYmNjlZqaqh9//FGDBw/WypUrza7WJeXIkSN64YUXFBkZqYCAALOrAw8uydM0DRs2VLVq1Yr0mE5MTFRoaKhJtbp4Fbynnt7v0NBQnTx50un53NxcnT592qmMq2U4rgP5hg8frt9++03Lly9X8+bN7dNDQ0OVnZ2tlJQUp/KFt0Vx77O7MkFBQfwKPK9mzZpq3bq1unTpooiICHXs2FH//ve/2QYVaNOmTTp58qSuv/56Va9eXdWrV9fKlSv12WefqXr16goJCWFbVBKXZBipWbOmunTpoqioKPs0m82mqKgohYeHm1izi1OrVq0UGhrq9H5brVatX7/e/n6Hh4crJSVFmzZtspdZtmyZbDabunfvbi+zatUq5eTk2MtERkaqTZs2nKI5zzAMDR8+XPPmzdOyZcuKnNbq0qWLatSo4bQt9uzZo/j4eKdtsX37dqdwGBkZqaCgILVr185exnEZBWX4/rhns9mUlZXFNqhAt956q7Zv367Y2Fj7X9euXTVw4ED7/9kWlYTZPWjNMnv2bMPf39+YPn26sWvXLuOpp54y6tat69RjGt5LS0sztmzZYmzZssWQZHzyySfGli1bjMOHDxuGkX9pb926dY0FCxYY27ZtM+69916Xl/Z27tzZWL9+vbF69Wrjqquucrq0NyUlxQgJCTEeffRRY8eOHcbs2bON2rVrc2mvg2effdYIDg42VqxYYZw4ccL+d/bsWXuZZ555xmjRooWxbNkyIyYmxggPDzfCw8Ptzxdcynj77bcbsbGxxuLFi41GjRq5vJTxlVdeMf766y9j4sSJXMro4LXXXjNWrlxpxMXFGdu2bTNee+01w2KxGEuWLDEMg21gJseraQyDbVFZXLJhxDAM4/PPPzdatGhh1KxZ0+jWrZuxbt06s6tUZS1fvtyQVORv8ODBhmHkX9771ltvGSEhIYa/v79x6623Gnv27HFaxqlTp4yHH37YqFOnjhEUFGQMHTrUSEtLcyqzdetW429/+5vh7+9vNGvWzBg/fnxFvcQqwdU2kGRMmzbNXubcuXPGc889Z9SrV8+oXbu2cd999xknTpxwWs6hQ4eMfv36GbVq1TIaNmxovPTSS0ZOTo5TmeXLlxudOnUyatasaVxxxRVO67jUPfbYY8bll19u1KxZ02jUqJFx66232oOIYbANzFQ4jLAtKgeLYRiGOW0yAAAAl2ifEQAAUHkQRgAAgKkIIwAAwFSEEQAAYCrCCAAAMBVhBAAAmIowAgAATEUYAQAApiKMAAAAUxFGAACAqQgjAADAVP8fq6hDPWW3sxAAAAAASUVORK5CYII=",
 632 |       "text/plain": [
 633 |        "<Figure size 640x480 with 1 Axes>"
 634 |       ]
 635 |      },
 636 |      "metadata": {},
 637 |      "output_type": "display_data"
 638 |     }
 639 |    ],
 640 |    "source": [
 641 |     "data['price'].plot(title='price Range')"
 642 |    ]
 643 |   },
 644 |   {
 645 |    "cell_type": "code",
 646 |    "execution_count": 13,
 647 |    "id": "a67df3de",
 648 |    "metadata": {
 649 |     "execution": {
 650 |      "iopub.execute_input": "2022-11-05T08:25:30.632224Z",
 651 |      "iopub.status.busy": "2022-11-05T08:25:30.631444Z",
 652 |      "iopub.status.idle": "2022-11-05T08:25:30.638410Z",
 653 |      "shell.execute_reply": "2022-11-05T08:25:30.637266Z"
 654 |     },
 655 |     "papermill": {
 656 |      "duration": 0.023171,
 657 |      "end_time": "2022-11-05T08:25:30.640841",
 658 |      "exception": false,
 659 |      "start_time": "2022-11-05T08:25:30.617670",
 660 |      "status": "completed"
 661 |     },
 662 |     "tags": []
 663 |    },
 664 |    "outputs": [
 665 |     {
 666 |      "data": {
 667 |       "text/plain": [
 668 |        "551962.9754347826"
 669 |       ]
 670 |      },
 671 |      "execution_count": 13,
 672 |      "metadata": {},
 673 |      "output_type": "execute_result"
 674 |     }
 675 |    ],
 676 |    "source": [
 677 |     "np.mean(data['price'])"
 678 |    ]
 679 |   },
 680 |   {
 681 |    "cell_type": "code",
 682 |    "execution_count": 14,
 683 |    "id": "e93dadf9",
 684 |    "metadata": {
 685 |     "execution": {
 686 |      "iopub.execute_input": "2022-11-05T08:25:30.667111Z",
 687 |      "iopub.status.busy": "2022-11-05T08:25:30.666605Z",
 688 |      "iopub.status.idle": "2022-11-05T08:25:30.861896Z",
 689 |      "shell.execute_reply": "2022-11-05T08:25:30.860676Z"
 690 |     },
 691 |     "papermill": {
 692 |      "duration": 0.21127,
 693 |      "end_time": "2022-11-05T08:25:30.864446",
 694 |      "exception": false,
 695 |      "start_time": "2022-11-05T08:25:30.653176",
 696 |      "status": "completed"
 697 |     },
 698 |     "tags": []
 699 |    },
 700 |    "outputs": [
 701 |     {
 702 |      "data": {
 703 |       "text/plain": [
 704 |        "<Axes: xlabel='floors', ylabel='count'>"
 705 |       ]
 706 |      },
 707 |      "execution_count": 14,
 708 |      "metadata": {},
 709 |      "output_type": "execute_result"
 710 |     },
 711 |     {
 712 |      "data": {
 713 |       "image/png": 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",
 714 |       "text/plain": [
 715 |        "<Figure size 640x480 with 1 Axes>"
 716 |       ]
 717 |      },
 718 |      "metadata": {},
 719 |      "output_type": "display_data"
 720 |     }
 721 |    ],
 722 |    "source": [
 723 |     "sns.countplot(data=data,x=data['floors'])"
 724 |    ]
 725 |   },
 726 |   {
 727 |    "cell_type": "code",
 728 |    "execution_count": 15,
 729 |    "id": "9b40aea2",
 730 |    "metadata": {
 731 |     "execution": {
 732 |      "iopub.execute_input": "2022-11-05T08:25:30.891036Z",
 733 |      "iopub.status.busy": "2022-11-05T08:25:30.889937Z",
 734 |      "iopub.status.idle": "2022-11-05T08:25:31.110151Z",
 735 |      "shell.execute_reply": "2022-11-05T08:25:31.109298Z"
 736 |     },
 737 |     "papermill": {
 738 |      "duration": 0.236117,
 739 |      "end_time": "2022-11-05T08:25:31.112606",
 740 |      "exception": false,
 741 |      "start_time": "2022-11-05T08:25:30.876489",
 742 |      "status": "completed"
 743 |     },
 744 |     "tags": []
 745 |    },
 746 |    "outputs": [
 747 |     {
 748 |      "data": {
 749 |       "text/plain": [
 750 |        "<Axes: xlabel='bathrooms', ylabel='count'>"
 751 |       ]
 752 |      },
 753 |      "execution_count": 15,
 754 |      "metadata": {},
 755 |      "output_type": "execute_result"
 756 |     },
 757 |     {
 758 |      "data": {
 759 |       "image/png": 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",
 760 |       "text/plain": [
 761 |        "<Figure size 640x480 with 1 Axes>"
 762 |       ]
 763 |      },
 764 |      "metadata": {},
 765 |      "output_type": "display_data"
 766 |     }
 767 |    ],
 768 |    "source": [
 769 |     "sns.countplot(data=data,x=data['bathrooms'])"
 770 |    ]
 771 |   },
 772 |   {
 773 |    "cell_type": "code",
 774 |    "execution_count": 16,
 775 |    "id": "ecc0c0fa",
 776 |    "metadata": {
 777 |     "execution": {
 778 |      "iopub.execute_input": "2022-11-05T08:25:31.140448Z",
 779 |      "iopub.status.busy": "2022-11-05T08:25:31.139323Z",
 780 |      "iopub.status.idle": "2022-11-05T08:25:31.391756Z",
 781 |      "shell.execute_reply": "2022-11-05T08:25:31.390457Z"
 782 |     },
 783 |     "papermill": {
 784 |      "duration": 0.26891,
 785 |      "end_time": "2022-11-05T08:25:31.394121",
 786 |      "exception": false,
 787 |      "start_time": "2022-11-05T08:25:31.125211",
 788 |      "status": "completed"
 789 |     },
 790 |     "tags": []
 791 |    },
 792 |    "outputs": [
 793 |     {
 794 |      "data": {
 795 |       "text/plain": [
 796 |        "<Axes: xlabel='bedrooms', ylabel='count'>"
 797 |       ]
 798 |      },
 799 |      "execution_count": 16,
 800 |      "metadata": {},
 801 |      "output_type": "execute_result"
 802 |     },
 803 |     {
 804 |      "data": {
 805 |       "image/png": 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",
 806 |       "text/plain": [
 807 |        "<Figure size 640x480 with 1 Axes>"
 808 |       ]
 809 |      },
 810 |      "metadata": {},
 811 |      "output_type": "display_data"
 812 |     }
 813 |    ],
 814 |    "source": [
 815 |     "sns.countplot(data=data,x=data['bedrooms'])"
 816 |    ]
 817 |   },
 818 |   {
 819 |    "cell_type": "code",
 820 |    "execution_count": 17,
 821 |    "id": "d491175d",
 822 |    "metadata": {
 823 |     "execution": {
 824 |      "iopub.execute_input": "2022-11-05T08:25:31.421492Z",
 825 |      "iopub.status.busy": "2022-11-05T08:25:31.421059Z",
 826 |      "iopub.status.idle": "2022-11-05T08:25:31.604739Z",
 827 |      "shell.execute_reply": "2022-11-05T08:25:31.603899Z"
 828 |     },
 829 |     "papermill": {
 830 |      "duration": 0.200317,
 831 |      "end_time": "2022-11-05T08:25:31.607467",
 832 |      "exception": false,
 833 |      "start_time": "2022-11-05T08:25:31.407150",
 834 |      "status": "completed"
 835 |     },
 836 |     "tags": []
 837 |    },
 838 |    "outputs": [
 839 |     {
 840 |      "data": {
 841 |       "text/plain": [
 842 |        "<Axes: xlabel='waterfront', ylabel='count'>"
 843 |       ]
 844 |      },
 845 |      "execution_count": 17,
 846 |      "metadata": {},
 847 |      "output_type": "execute_result"
 848 |     },
 849 |     {
 850 |      "data": {
 851 |       "image/png": 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",
 852 |       "text/plain": [
 853 |        "<Figure size 640x480 with 1 Axes>"
 854 |       ]
 855 |      },
 856 |      "metadata": {},
 857 |      "output_type": "display_data"
 858 |     }
 859 |    ],
 860 |    "source": [
 861 |     "sns.countplot(data=data,x=data['waterfront'])"
 862 |    ]
 863 |   },
 864 |   {
 865 |    "cell_type": "markdown",
 866 |    "id": "160e09e0",
 867 |    "metadata": {
 868 |     "papermill": {
 869 |      "duration": 0.013553,
 870 |      "end_time": "2022-11-05T08:25:31.660822",
 871 |      "exception": false,
 872 |      "start_time": "2022-11-05T08:25:31.647269",
 873 |      "status": "completed"
 874 |     },
 875 |     "tags": []
 876 |    },
 877 |    "source": [
 878 |     "## Model Creation"
 879 |    ]
 880 |   },
 881 |   {
 882 |    "cell_type": "code",
 883 |    "execution_count": 18,
 884 |    "id": "a00ed84d",
 885 |    "metadata": {
 886 |     "execution": {
 887 |      "iopub.execute_input": "2022-11-05T08:25:31.689713Z",
 888 |      "iopub.status.busy": "2022-11-05T08:25:31.689309Z",
 889 |      "iopub.status.idle": "2022-11-05T08:25:31.699095Z",
 890 |      "shell.execute_reply": "2022-11-05T08:25:31.698235Z"
 891 |     },
 892 |     "papermill": {
 893 |      "duration": 0.027012,
 894 |      "end_time": "2022-11-05T08:25:31.701749",
 895 |      "exception": false,
 896 |      "start_time": "2022-11-05T08:25:31.674737",
 897 |      "status": "completed"
 898 |     },
 899 |     "tags": []
 900 |    },
 901 |    "outputs": [
 902 |     {
 903 |      "data": {
 904 |       "text/plain": [
 905 |        "sqft_basement\n",
 906 |        "0       2745\n",
 907 |        "500       53\n",
 908 |        "600       45\n",
 909 |        "800       43\n",
 910 |        "900       41\n",
 911 |        "        ... \n",
 912 |        "2300       1\n",
 913 |        "265        1\n",
 914 |        "1610       1\n",
 915 |        "862        1\n",
 916 |        "1640       1\n",
 917 |        "Name: count, Length: 207, dtype: int64"
 918 |       ]
 919 |      },
 920 |      "execution_count": 18,
 921 |      "metadata": {},
 922 |      "output_type": "execute_result"
 923 |     }
 924 |    ],
 925 |    "source": [
 926 |     "data['sqft_basement'].value_counts()"
 927 |    ]
 928 |   },
 929 |   {
 930 |    "cell_type": "code",
 931 |    "execution_count": 19,
 932 |    "id": "cf79255f",
 933 |    "metadata": {
 934 |     "execution": {
 935 |      "iopub.execute_input": "2022-11-05T08:25:31.731019Z",
 936 |      "iopub.status.busy": "2022-11-05T08:25:31.730556Z",
 937 |      "iopub.status.idle": "2022-11-05T08:25:31.738304Z",
 938 |      "shell.execute_reply": "2022-11-05T08:25:31.736852Z"
 939 |     },
 940 |     "papermill": {
 941 |      "duration": 0.025349,
 942 |      "end_time": "2022-11-05T08:25:31.740891",
 943 |      "exception": false,
 944 |      "start_time": "2022-11-05T08:25:31.715542",
 945 |      "status": "completed"
 946 |     },
 947 |     "tags": []
 948 |    },
 949 |    "outputs": [],
 950 |    "source": [
 951 |     "data = data.drop([\"city\",\"view\",\"waterfront\",\"sqft_basement\"],axis=1)"
 952 |    ]
 953 |   },
 954 |   {
 955 |    "cell_type": "code",
 956 |    "execution_count": 20,
 957 |    "id": "40dce89c",
 958 |    "metadata": {
 959 |     "execution": {
 960 |      "iopub.execute_input": "2022-11-05T08:25:31.822281Z",
 961 |      "iopub.status.busy": "2022-11-05T08:25:31.821767Z",
 962 |      "iopub.status.idle": "2022-11-05T08:25:31.829605Z",
 963 |      "shell.execute_reply": "2022-11-05T08:25:31.828190Z"
 964 |     },
 965 |     "papermill": {
 966 |      "duration": 0.025326,
 967 |      "end_time": "2022-11-05T08:25:31.832232",
 968 |      "exception": false,
 969 |      "start_time": "2022-11-05T08:25:31.806906",
 970 |      "status": "completed"
 971 |     },
 972 |     "tags": []
 973 |    },
 974 |    "outputs": [],
 975 |    "source": [
 976 |     "x  = np.array(data.loc[:,data.columns != \"price\"].values)\n",
 977 |     "y = np.array(data[\"price\"].values)"
 978 |    ]
 979 |   },
 980 |   {
 981 |    "cell_type": "code",
 982 |    "execution_count": 21,
 983 |    "id": "2cada5be",
 984 |    "metadata": {
 985 |     "execution": {
 986 |      "iopub.execute_input": "2022-11-05T08:25:31.860997Z",
 987 |      "iopub.status.busy": "2022-11-05T08:25:31.860507Z",
 988 |      "iopub.status.idle": "2022-11-05T08:25:31.868697Z",
 989 |      "shell.execute_reply": "2022-11-05T08:25:31.867705Z"
 990 |     },
 991 |     "papermill": {
 992 |      "duration": 0.02536,
 993 |      "end_time": "2022-11-05T08:25:31.871073",
 994 |      "exception": false,
 995 |      "start_time": "2022-11-05T08:25:31.845713",
 996 |      "status": "completed"
 997 |     },
 998 |     "tags": []
 999 |    },
1000 |    "outputs": [
1001 |     {
1002 |      "data": {
1003 |       "text/plain": [
1004 |        "array([[   3,    1, 1340, ...,    3, 1340,   36],\n",
1005 |        "       [   5,    2, 3650, ...,    5, 3370,   35],\n",
1006 |        "       [   3,    2, 1930, ...,    4, 1930,   18],\n",
1007 |        "       ...,\n",
1008 |        "       [   3,    2, 3010, ...,    3, 3010,   32],\n",
1009 |        "       [   4,    2, 2090, ...,    3, 1070,   35],\n",
1010 |        "       [   3,    2, 1490, ...,    4, 1490,    9]], dtype=int64)"
1011 |       ]
1012 |      },
1013 |      "execution_count": 21,
1014 |      "metadata": {},
1015 |      "output_type": "execute_result"
1016 |     }
1017 |    ],
1018 |    "source": [
1019 |     "x"
1020 |    ]
1021 |   },
1022 |   {
1023 |    "cell_type": "code",
1024 |    "execution_count": 22,
1025 |    "id": "2dba4b6c",
1026 |    "metadata": {
1027 |     "execution": {
1028 |      "iopub.execute_input": "2022-11-05T08:25:31.899555Z",
1029 |      "iopub.status.busy": "2022-11-05T08:25:31.899146Z",
1030 |      "iopub.status.idle": "2022-11-05T08:25:31.905957Z",
1031 |      "shell.execute_reply": "2022-11-05T08:25:31.904792Z"
1032 |     },
1033 |     "papermill": {
1034 |      "duration": 0.024129,
1035 |      "end_time": "2022-11-05T08:25:31.908496",
1036 |      "exception": false,
1037 |      "start_time": "2022-11-05T08:25:31.884367",
1038 |      "status": "completed"
1039 |     },
1040 |     "tags": []
1041 |    },
1042 |    "outputs": [
1043 |     {
1044 |      "data": {
1045 |       "text/plain": [
1046 |        "array([ 313000, 2384000,  342000, ...,  416904,  203400,  220600])"
1047 |       ]
1048 |      },
1049 |      "execution_count": 22,
1050 |      "metadata": {},
1051 |      "output_type": "execute_result"
1052 |     }
1053 |    ],
1054 |    "source": [
1055 |     "y"
1056 |    ]
1057 |   },
1058 |   {
1059 |    "cell_type": "code",
1060 |    "execution_count": 23,
1061 |    "id": "03618f50",
1062 |    "metadata": {
1063 |     "execution": {
1064 |      "iopub.execute_input": "2022-11-05T08:25:31.937320Z",
1065 |      "iopub.status.busy": "2022-11-05T08:25:31.936881Z",
1066 |      "iopub.status.idle": "2022-11-05T08:25:31.944616Z",
1067 |      "shell.execute_reply": "2022-11-05T08:25:31.943245Z"
1068 |     },
1069 |     "papermill": {
1070 |      "duration": 0.024941,
1071 |      "end_time": "2022-11-05T08:25:31.947081",
1072 |      "exception": false,
1073 |      "start_time": "2022-11-05T08:25:31.922140",
1074 |      "status": "completed"
1075 |     },
1076 |     "tags": []
1077 |    },
1078 |    "outputs": [],
1079 |    "source": [
1080 |     "x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.1,random_state=42)"
1081 |    ]
1082 |   },
1083 |   {
1084 |    "cell_type": "code",
1085 |    "execution_count": 24,
1086 |    "id": "17dbd7a0",
1087 |    "metadata": {
1088 |     "execution": {
1089 |      "iopub.execute_input": "2022-11-05T08:25:31.975637Z",
1090 |      "iopub.status.busy": "2022-11-05T08:25:31.975207Z",
1091 |      "iopub.status.idle": "2022-11-05T08:25:31.982824Z",
1092 |      "shell.execute_reply": "2022-11-05T08:25:31.981979Z"
1093 |     },
1094 |     "papermill": {
1095 |      "duration": 0.024459,
1096 |      "end_time": "2022-11-05T08:25:31.984999",
1097 |      "exception": false,
1098 |      "start_time": "2022-11-05T08:25:31.960540",
1099 |      "status": "completed"
1100 |     },
1101 |     "tags": []
1102 |    },
1103 |    "outputs": [
1104 |     {
1105 |      "data": {
1106 |       "text/plain": [
1107 |        "array([[   3,    1, 1340, ...,    3, 1340,   36],\n",
1108 |        "       [   5,    2, 3650, ...,    5, 3370,   35],\n",
1109 |        "       [   3,    2, 1930, ...,    4, 1930,   18],\n",
1110 |        "       ...,\n",
1111 |        "       [   3,    2, 3010, ...,    3, 3010,   32],\n",
1112 |        "       [   4,    2, 2090, ...,    3, 1070,   35],\n",
1113 |        "       [   3,    2, 1490, ...,    4, 1490,    9]], dtype=int64)"
1114 |       ]
1115 |      },
1116 |      "execution_count": 24,
1117 |      "metadata": {},
1118 |      "output_type": "execute_result"
1119 |     }
1120 |    ],
1121 |    "source": [
1122 |     "x"
1123 |    ]
1124 |   },
1125 |   {
1126 |    "cell_type": "code",
1127 |    "execution_count": 25,
1128 |    "id": "4b754402",
1129 |    "metadata": {
1130 |     "execution": {
1131 |      "iopub.execute_input": "2022-11-05T08:25:32.040456Z",
1132 |      "iopub.status.busy": "2022-11-05T08:25:32.040032Z",
1133 |      "iopub.status.idle": "2022-11-05T08:25:32.067598Z",
1134 |      "shell.execute_reply": "2022-11-05T08:25:32.066056Z"
1135 |     },
1136 |     "papermill": {
1137 |      "duration": 0.044743,
1138 |      "end_time": "2022-11-05T08:25:32.070063",
1139 |      "exception": false,
1140 |      "start_time": "2022-11-05T08:25:32.025320",
1141 |      "status": "completed"
1142 |     },
1143 |     "tags": []
1144 |    },
1145 |    "outputs": [
1146 |     {
1147 |      "data": {
1148 |       "text/html": [
1149 |        "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
1150 |       ],
1151 |       "text/plain": [
1152 |        "LinearRegression()"
1153 |       ]
1154 |      },
1155 |      "execution_count": 25,
1156 |      "metadata": {},
1157 |      "output_type": "execute_result"
1158 |     }
1159 |    ],
1160 |    "source": [
1161 |     "model_lr = LinearRegression()\n",
1162 |     "model_lr.fit(x_train,y_train)"
1163 |    ]
1164 |   },
1165 |   {
1166 |    "cell_type": "code",
1167 |    "execution_count": 26,
1168 |    "id": "4154b24a",
1169 |    "metadata": {
1170 |     "execution": {
1171 |      "iopub.execute_input": "2022-11-05T08:25:32.099871Z",
1172 |      "iopub.status.busy": "2022-11-05T08:25:32.099154Z",
1173 |      "iopub.status.idle": "2022-11-05T08:25:32.108611Z",
1174 |      "shell.execute_reply": "2022-11-05T08:25:32.107058Z"
1175 |     },
1176 |     "papermill": {
1177 |      "duration": 0.027656,
1178 |      "end_time": "2022-11-05T08:25:32.111250",
1179 |      "exception": false,
1180 |      "start_time": "2022-11-05T08:25:32.083594",
1181 |      "status": "completed"
1182 |     },
1183 |     "tags": []
1184 |    },
1185 |    "outputs": [],
1186 |    "source": [
1187 |     "predictions_lr = model_lr.predict(x_test)"
1188 |    ]
1189 |   },
1190 |   {
1191 |    "cell_type": "code",
1192 |    "execution_count": 27,
1193 |    "id": "e7210f04",
1194 |    "metadata": {
1195 |     "execution": {
1196 |      "iopub.execute_input": "2022-11-05T08:25:32.143266Z",
1197 |      "iopub.status.busy": "2022-11-05T08:25:32.141898Z",
1198 |      "iopub.status.idle": "2022-11-05T08:25:32.154013Z",
1199 |      "shell.execute_reply": "2022-11-05T08:25:32.152405Z"
1200 |     },
1201 |     "papermill": {
1202 |      "duration": 0.03151,
1203 |      "end_time": "2022-11-05T08:25:32.156761",
1204 |      "exception": false,
1205 |      "start_time": "2022-11-05T08:25:32.125251",
1206 |      "status": "completed"
1207 |     },
1208 |     "tags": []
1209 |    },
1210 |    "outputs": [
1211 |     {
1212 |      "data": {
1213 |       "text/plain": [
1214 |        "0.3183144703701215"
1215 |       ]
1216 |      },
1217 |      "execution_count": 27,
1218 |      "metadata": {},
1219 |      "output_type": "execute_result"
1220 |     }
1221 |    ],
1222 |    "source": [
1223 |     "mean_absolute_percentage_error(predictions_lr,y_test)"
1224 |    ]
1225 |   },
1226 |   {
1227 |    "cell_type": "code",
1228 |    "execution_count": 28,
1229 |    "id": "ac1f2b3e",
1230 |    "metadata": {
1231 |     "execution": {
1232 |      "iopub.execute_input": "2022-11-05T08:25:32.216071Z",
1233 |      "iopub.status.busy": "2022-11-05T08:25:32.215312Z",
1234 |      "iopub.status.idle": "2022-11-05T08:25:32.369227Z",
1235 |      "shell.execute_reply": "2022-11-05T08:25:32.367811Z"
1236 |     },
1237 |     "papermill": {
1238 |      "duration": 0.172599,
1239 |      "end_time": "2022-11-05T08:25:32.372327",
1240 |      "exception": false,
1241 |      "start_time": "2022-11-05T08:25:32.199728",
1242 |      "status": "completed"
1243 |     },
1244 |     "tags": []
1245 |    },
1246 |    "outputs": [],
1247 |    "source": [
1248 |     "model = RandomForestRegressor(n_estimators=10)\n",
1249 |     "model.fit(x_train,y_train)\n",
1250 |     "predictions = model.predict(x_test)"
1251 |    ]
1252 |   },
1253 |   {
1254 |    "cell_type": "code",
1255 |    "execution_count": 29,
1256 |    "id": "4bbf64d2",
1257 |    "metadata": {
1258 |     "execution": {
1259 |      "iopub.execute_input": "2022-11-05T08:25:32.401682Z",
1260 |      "iopub.status.busy": "2022-11-05T08:25:32.401256Z",
1261 |      "iopub.status.idle": "2022-11-05T08:25:32.409150Z",
1262 |      "shell.execute_reply": "2022-11-05T08:25:32.408030Z"
1263 |     },
1264 |     "papermill": {
1265 |      "duration": 0.025347,
1266 |      "end_time": "2022-11-05T08:25:32.411577",
1267 |      "exception": false,
1268 |      "start_time": "2022-11-05T08:25:32.386230",
1269 |      "status": "completed"
1270 |     },
1271 |     "tags": []
1272 |    },
1273 |    "outputs": [
1274 |     {
1275 |      "data": {
1276 |       "text/plain": [
1277 |        "0.24210417557737207"
1278 |       ]
1279 |      },
1280 |      "execution_count": 29,
1281 |      "metadata": {},
1282 |      "output_type": "execute_result"
1283 |     }
1284 |    ],
1285 |    "source": [
1286 |     "mean_absolute_percentage_error(predictions,y_test)"
1287 |    ]
1288 |   },
1289 |   {
1290 |    "cell_type": "code",
1291 |    "execution_count": 32,
1292 |    "id": "0a16e9cb",
1293 |    "metadata": {},
1294 |    "outputs": [
1295 |     {
1296 |      "data": {
1297 |       "text/html": [
1298 |        "<div>\n",
1299 |        "<style scoped>\n",
1300 |        "    .dataframe tbody tr th:only-of-type {\n",
1301 |        "        vertical-align: middle;\n",
1302 |        "    }\n",
1303 |        "\n",
1304 |        "    .dataframe tbody tr th {\n",
1305 |        "        vertical-align: top;\n",
1306 |        "    }\n",
1307 |        "\n",
1308 |        "    .dataframe thead th {\n",
1309 |        "        text-align: right;\n",
1310 |        "    }\n",
1311 |        "</style>\n",
1312 |        "<table border=\"1\" class=\"dataframe\">\n",
1313 |        "  <thead>\n",
1314 |        "    <tr style=\"text-align: right;\">\n",
1315 |        "      <th></th>\n",
1316 |        "      <th>price</th>\n",
1317 |        "      <th>bedrooms</th>\n",
1318 |        "      <th>bathrooms</th>\n",
1319 |        "      <th>sqft_living</th>\n",
1320 |        "      <th>sqft_lot</th>\n",
1321 |        "      <th>floors</th>\n",
1322 |        "      <th>condition</th>\n",
1323 |        "      <th>sqft_above</th>\n",
1324 |        "      <th>city_new</th>\n",
1325 |        "    </tr>\n",
1326 |        "  </thead>\n",
1327 |        "  <tbody>\n",
1328 |        "    <tr>\n",
1329 |        "      <th>0</th>\n",
1330 |        "      <td>313000</td>\n",
1331 |        "      <td>3</td>\n",
1332 |        "      <td>1</td>\n",
1333 |        "      <td>1340</td>\n",
1334 |        "      <td>7912</td>\n",
1335 |        "      <td>1</td>\n",
1336 |        "      <td>3</td>\n",
1337 |        "      <td>1340</td>\n",
1338 |        "      <td>36</td>\n",
1339 |        "    </tr>\n",
1340 |        "    <tr>\n",
1341 |        "      <th>1</th>\n",
1342 |        "      <td>2384000</td>\n",
1343 |        "      <td>5</td>\n",
1344 |        "      <td>2</td>\n",
1345 |        "      <td>3650</td>\n",
1346 |        "      <td>9050</td>\n",
1347 |        "      <td>2</td>\n",
1348 |        "      <td>5</td>\n",
1349 |        "      <td>3370</td>\n",
1350 |        "      <td>35</td>\n",
1351 |        "    </tr>\n",
1352 |        "    <tr>\n",
1353 |        "      <th>2</th>\n",
1354 |        "      <td>342000</td>\n",
1355 |        "      <td>3</td>\n",
1356 |        "      <td>2</td>\n",
1357 |        "      <td>1930</td>\n",
1358 |        "      <td>11947</td>\n",
1359 |        "      <td>1</td>\n",
1360 |        "      <td>4</td>\n",
1361 |        "      <td>1930</td>\n",
1362 |        "      <td>18</td>\n",
1363 |        "    </tr>\n",
1364 |        "    <tr>\n",
1365 |        "      <th>3</th>\n",
1366 |        "      <td>420000</td>\n",
1367 |        "      <td>3</td>\n",
1368 |        "      <td>2</td>\n",
1369 |        "      <td>2000</td>\n",
1370 |        "      <td>8030</td>\n",
1371 |        "      <td>1</td>\n",
1372 |        "      <td>4</td>\n",
1373 |        "      <td>1000</td>\n",
1374 |        "      <td>3</td>\n",
1375 |        "    </tr>\n",
1376 |        "    <tr>\n",
1377 |        "      <th>4</th>\n",
1378 |        "      <td>550000</td>\n",
1379 |        "      <td>4</td>\n",
1380 |        "      <td>2</td>\n",
1381 |        "      <td>1940</td>\n",
1382 |        "      <td>10500</td>\n",
1383 |        "      <td>1</td>\n",
1384 |        "      <td>4</td>\n",
1385 |        "      <td>1140</td>\n",
1386 |        "      <td>31</td>\n",
1387 |        "    </tr>\n",
1388 |        "  </tbody>\n",
1389 |        "</table>\n",
1390 |        "</div>"
1391 |       ],
1392 |       "text/plain": [
1393 |        "     price  bedrooms  bathrooms  sqft_living  sqft_lot  floors  condition  \\\n",
1394 |        "0   313000         3          1         1340      7912       1          3   \n",
1395 |        "1  2384000         5          2         3650      9050       2          5   \n",
1396 |        "2   342000         3          2         1930     11947       1          4   \n",
1397 |        "3   420000         3          2         2000      8030       1          4   \n",
1398 |        "4   550000         4          2         1940     10500       1          4   \n",
1399 |        "\n",
1400 |        "   sqft_above  city_new  \n",
1401 |        "0        1340        36  \n",
1402 |        "1        3370        35  \n",
1403 |        "2        1930        18  \n",
1404 |        "3        1000         3  \n",
1405 |        "4        1140        31  "
1406 |       ]
1407 |      },
1408 |      "execution_count": 32,
1409 |      "metadata": {},
1410 |      "output_type": "execute_result"
1411 |     }
1412 |    ],
1413 |    "source": [
1414 |     "data.head()"
1415 |    ]
1416 |   },
1417 |   {
1418 |    "cell_type": "code",
1419 |    "execution_count": 33,
1420 |    "id": "32b3dfde",
1421 |    "metadata": {
1422 |     "execution": {
1423 |      "iopub.execute_input": "2022-11-05T08:25:32.495627Z",
1424 |      "iopub.status.busy": "2022-11-05T08:25:32.495195Z",
1425 |      "iopub.status.idle": "2022-11-05T08:25:32.504061Z",
1426 |      "shell.execute_reply": "2022-11-05T08:25:32.503047Z"
1427 |     },
1428 |     "papermill": {
1429 |      "duration": 0.026607,
1430 |      "end_time": "2022-11-05T08:25:32.506128",
1431 |      "exception": false,
1432 |      "start_time": "2022-11-05T08:25:32.479521",
1433 |      "status": "completed"
1434 |     },
1435 |     "tags": []
1436 |    },
1437 |    "outputs": [
1438 |     {
1439 |      "data": {
1440 |       "text/plain": [
1441 |        "array([284900.])"
1442 |       ]
1443 |      },
1444 |      "execution_count": 33,
1445 |      "metadata": {},
1446 |      "output_type": "execute_result"
1447 |     }
1448 |    ],
1449 |    "source": [
1450 |     "a  = [1,1,200,200,1,2,200,36]\n",
1451 |     "model.predict([a])"
1452 |    ]
1453 |   },
1454 |   {
1455 |    "cell_type": "code",
1456 |    "execution_count": 35,
1457 |    "id": "76fa023e",
1458 |    "metadata": {},
1459 |    "outputs": [
1460 |     {
1461 |      "data": {
1462 |       "text/plain": [
1463 |        "array([314950.])"
1464 |       ]
1465 |      },
1466 |      "execution_count": 35,
1467 |      "metadata": {},
1468 |      "output_type": "execute_result"
1469 |     }
1470 |    ],
1471 |    "source": [
1472 |     "b =[3,1,1340,7912,1,3,1340,36]\n",
1473 |     "model.predict([b])"
1474 |    ]
1475 |   },
1476 |   {
1477 |    "cell_type": "code",
1478 |    "execution_count": null,
1479 |    "id": "7b7c018b",
1480 |    "metadata": {},
1481 |    "outputs": [],
1482 |    "source": []
1483 |   }
1484 |  ],
1485 |  "metadata": {
1486 |   "kernelspec": {
1487 |    "display_name": "Python 3",
1488 |    "language": "python",
1489 |    "name": "python3"
1490 |   },
1491 |   "language_info": {
1492 |    "codemirror_mode": {
1493 |     "name": "ipython",
1494 |     "version": 3
1495 |    },
1496 |    "file_extension": ".py",
1497 |    "mimetype": "text/x-python",
1498 |    "name": "python",
1499 |    "nbconvert_exporter": "python",
1500 |    "pygments_lexer": "ipython3",
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1502 |   },
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1506 |    "end_time": "2022-11-05T08:25:33.493433",
1507 |    "environment_variables": {},
1508 |    "exception": null,
1509 |    "input_path": "__notebook__.ipynb",
1510 |    "output_path": "__notebook__.ipynb",
1511 |    "parameters": {},
1512 |    "start_time": "2022-11-05T08:25:16.574810",
1513 |    "version": "2.3.4"
1514 |   }
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1516 |  "nbformat": 4,
1517 |  "nbformat_minor": 5
1518 | }
1519 | 


--------------------------------------------------------------------------------
/pima-indians-diabetes-database-svm-accuracy-79.ipynb:
--------------------------------------------------------------------------------
  1 | {
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 10 |      "iopub.status.busy": "2023-05-17T05:30:26.965827Z",
 11 |      "iopub.status.idle": "2023-05-17T05:30:28.438333Z",
 12 |      "shell.execute_reply": "2023-05-17T05:30:28.437064Z"
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 19 |      "status": "completed"
 20 |     },
 21 |     "tags": []
 22 |    },
 23 |    "outputs": [],
 24 |    "source": [
 25 |     "import pandas as pd\n",
 26 |     "import numpy as np\n",
 27 |     "import matplotlib.pyplot as plt\n",
 28 |     "import sklearn \n",
 29 |     "from sklearn import svm\n",
 30 |     "from sklearn import metrics"
 31 |    ]
 32 |   },
 33 |   {
 34 |    "cell_type": "code",
 35 |    "execution_count": 2,
 36 |    "id": "49a07ff9",
 37 |    "metadata": {
 38 |     "execution": {
 39 |      "iopub.execute_input": "2023-05-17T05:30:28.452774Z",
 40 |      "iopub.status.busy": "2023-05-17T05:30:28.452446Z",
 41 |      "iopub.status.idle": "2023-05-17T05:30:28.478510Z",
 42 |      "shell.execute_reply": "2023-05-17T05:30:28.477000Z"
 43 |     },
 44 |     "papermill": {
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 46 |      "end_time": "2023-05-17T05:30:28.481010",
 47 |      "exception": false,
 48 |      "start_time": "2023-05-17T05:30:28.446780",
 49 |      "status": "completed"
 50 |     },
 51 |     "tags": []
 52 |    },
 53 |    "outputs": [],
 54 |    "source": [
 55 |     "data = pd.read_csv(\"C:/Users/harin/Downloads/diabetes.csv\")"
 56 |    ]
 57 |   },
 58 |   {
 59 |    "cell_type": "code",
 60 |    "execution_count": 3,
 61 |    "id": "e9fabbaf",
 62 |    "metadata": {
 63 |     "execution": {
 64 |      "iopub.execute_input": "2023-05-17T05:30:28.490963Z",
 65 |      "iopub.status.busy": "2023-05-17T05:30:28.490629Z",
 66 |      "iopub.status.idle": "2023-05-17T05:30:28.524254Z",
 67 |      "shell.execute_reply": "2023-05-17T05:30:28.523300Z"
 68 |     },
 69 |     "papermill": {
 70 |      "duration": 0.041305,
 71 |      "end_time": "2023-05-17T05:30:28.526516",
 72 |      "exception": false,
 73 |      "start_time": "2023-05-17T05:30:28.485211",
 74 |      "status": "completed"
 75 |     },
 76 |     "tags": []
 77 |    },
 78 |    "outputs": [
 79 |     {
 80 |      "data": {
 81 |       "text/html": [
 82 |        "<div>\n",
 83 |        "<style scoped>\n",
 84 |        "    .dataframe tbody tr th:only-of-type {\n",
 85 |        "        vertical-align: middle;\n",
 86 |        "    }\n",
 87 |        "\n",
 88 |        "    .dataframe tbody tr th {\n",
 89 |        "        vertical-align: top;\n",
 90 |        "    }\n",
 91 |        "\n",
 92 |        "    .dataframe thead th {\n",
 93 |        "        text-align: right;\n",
 94 |        "    }\n",
 95 |        "</style>\n",
 96 |        "<table border=\"1\" class=\"dataframe\">\n",
 97 |        "  <thead>\n",
 98 |        "    <tr style=\"text-align: right;\">\n",
 99 |        "      <th></th>\n",
100 |        "      <th>Pregnancies</th>\n",
101 |        "      <th>Glucose</th>\n",
102 |        "      <th>BloodPressure</th>\n",
103 |        "      <th>SkinThickness</th>\n",
104 |        "      <th>Insulin</th>\n",
105 |        "      <th>BMI</th>\n",
106 |        "      <th>DiabetesPedigreeFunction</th>\n",
107 |        "      <th>Age</th>\n",
108 |        "      <th>Outcome</th>\n",
109 |        "    </tr>\n",
110 |        "  </thead>\n",
111 |        "  <tbody>\n",
112 |        "    <tr>\n",
113 |        "      <th>0</th>\n",
114 |        "      <td>6</td>\n",
115 |        "      <td>148</td>\n",
116 |        "      <td>72</td>\n",
117 |        "      <td>35</td>\n",
118 |        "      <td>0</td>\n",
119 |        "      <td>33.6</td>\n",
120 |        "      <td>0.627</td>\n",
121 |        "      <td>50</td>\n",
122 |        "      <td>1</td>\n",
123 |        "    </tr>\n",
124 |        "    <tr>\n",
125 |        "      <th>1</th>\n",
126 |        "      <td>1</td>\n",
127 |        "      <td>85</td>\n",
128 |        "      <td>66</td>\n",
129 |        "      <td>29</td>\n",
130 |        "      <td>0</td>\n",
131 |        "      <td>26.6</td>\n",
132 |        "      <td>0.351</td>\n",
133 |        "      <td>31</td>\n",
134 |        "      <td>0</td>\n",
135 |        "    </tr>\n",
136 |        "    <tr>\n",
137 |        "      <th>2</th>\n",
138 |        "      <td>8</td>\n",
139 |        "      <td>183</td>\n",
140 |        "      <td>64</td>\n",
141 |        "      <td>0</td>\n",
142 |        "      <td>0</td>\n",
143 |        "      <td>23.3</td>\n",
144 |        "      <td>0.672</td>\n",
145 |        "      <td>32</td>\n",
146 |        "      <td>1</td>\n",
147 |        "    </tr>\n",
148 |        "    <tr>\n",
149 |        "      <th>3</th>\n",
150 |        "      <td>1</td>\n",
151 |        "      <td>89</td>\n",
152 |        "      <td>66</td>\n",
153 |        "      <td>23</td>\n",
154 |        "      <td>94</td>\n",
155 |        "      <td>28.1</td>\n",
156 |        "      <td>0.167</td>\n",
157 |        "      <td>21</td>\n",
158 |        "      <td>0</td>\n",
159 |        "    </tr>\n",
160 |        "    <tr>\n",
161 |        "      <th>4</th>\n",
162 |        "      <td>0</td>\n",
163 |        "      <td>137</td>\n",
164 |        "      <td>40</td>\n",
165 |        "      <td>35</td>\n",
166 |        "      <td>168</td>\n",
167 |        "      <td>43.1</td>\n",
168 |        "      <td>2.288</td>\n",
169 |        "      <td>33</td>\n",
170 |        "      <td>1</td>\n",
171 |        "    </tr>\n",
172 |        "    <tr>\n",
173 |        "      <th>5</th>\n",
174 |        "      <td>5</td>\n",
175 |        "      <td>116</td>\n",
176 |        "      <td>74</td>\n",
177 |        "      <td>0</td>\n",
178 |        "      <td>0</td>\n",
179 |        "      <td>25.6</td>\n",
180 |        "      <td>0.201</td>\n",
181 |        "      <td>30</td>\n",
182 |        "      <td>0</td>\n",
183 |        "    </tr>\n",
184 |        "    <tr>\n",
185 |        "      <th>6</th>\n",
186 |        "      <td>3</td>\n",
187 |        "      <td>78</td>\n",
188 |        "      <td>50</td>\n",
189 |        "      <td>32</td>\n",
190 |        "      <td>88</td>\n",
191 |        "      <td>31.0</td>\n",
192 |        "      <td>0.248</td>\n",
193 |        "      <td>26</td>\n",
194 |        "      <td>1</td>\n",
195 |        "    </tr>\n",
196 |        "    <tr>\n",
197 |        "      <th>7</th>\n",
198 |        "      <td>10</td>\n",
199 |        "      <td>115</td>\n",
200 |        "      <td>0</td>\n",
201 |        "      <td>0</td>\n",
202 |        "      <td>0</td>\n",
203 |        "      <td>35.3</td>\n",
204 |        "      <td>0.134</td>\n",
205 |        "      <td>29</td>\n",
206 |        "      <td>0</td>\n",
207 |        "    </tr>\n",
208 |        "    <tr>\n",
209 |        "      <th>8</th>\n",
210 |        "      <td>2</td>\n",
211 |        "      <td>197</td>\n",
212 |        "      <td>70</td>\n",
213 |        "      <td>45</td>\n",
214 |        "      <td>543</td>\n",
215 |        "      <td>30.5</td>\n",
216 |        "      <td>0.158</td>\n",
217 |        "      <td>53</td>\n",
218 |        "      <td>1</td>\n",
219 |        "    </tr>\n",
220 |        "    <tr>\n",
221 |        "      <th>9</th>\n",
222 |        "      <td>8</td>\n",
223 |        "      <td>125</td>\n",
224 |        "      <td>96</td>\n",
225 |        "      <td>0</td>\n",
226 |        "      <td>0</td>\n",
227 |        "      <td>0.0</td>\n",
228 |        "      <td>0.232</td>\n",
229 |        "      <td>54</td>\n",
230 |        "      <td>1</td>\n",
231 |        "    </tr>\n",
232 |        "  </tbody>\n",
233 |        "</table>\n",
234 |        "</div>"
235 |       ],
236 |       "text/plain": [
237 |        "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
238 |        "0            6      148             72             35        0  33.6   \n",
239 |        "1            1       85             66             29        0  26.6   \n",
240 |        "2            8      183             64              0        0  23.3   \n",
241 |        "3            1       89             66             23       94  28.1   \n",
242 |        "4            0      137             40             35      168  43.1   \n",
243 |        "5            5      116             74              0        0  25.6   \n",
244 |        "6            3       78             50             32       88  31.0   \n",
245 |        "7           10      115              0              0        0  35.3   \n",
246 |        "8            2      197             70             45      543  30.5   \n",
247 |        "9            8      125             96              0        0   0.0   \n",
248 |        "\n",
249 |        "   DiabetesPedigreeFunction  Age  Outcome  \n",
250 |        "0                     0.627   50        1  \n",
251 |        "1                     0.351   31        0  \n",
252 |        "2                     0.672   32        1  \n",
253 |        "3                     0.167   21        0  \n",
254 |        "4                     2.288   33        1  \n",
255 |        "5                     0.201   30        0  \n",
256 |        "6                     0.248   26        1  \n",
257 |        "7                     0.134   29        0  \n",
258 |        "8                     0.158   53        1  \n",
259 |        "9                     0.232   54        1  "
260 |       ]
261 |      },
262 |      "execution_count": 3,
263 |      "metadata": {},
264 |      "output_type": "execute_result"
265 |     }
266 |    ],
267 |    "source": [
268 |     "data.head(10)"
269 |    ]
270 |   },
271 |   {
272 |    "cell_type": "code",
273 |    "execution_count": 5,
274 |    "id": "abd6d66a",
275 |    "metadata": {
276 |     "execution": {
277 |      "iopub.execute_input": "2023-05-17T05:30:28.609497Z",
278 |      "iopub.status.busy": "2023-05-17T05:30:28.609183Z",
279 |      "iopub.status.idle": "2023-05-17T05:30:28.616414Z",
280 |      "shell.execute_reply": "2023-05-17T05:30:28.615121Z"
281 |     },
282 |     "papermill": {
283 |      "duration": 0.015147,
284 |      "end_time": "2023-05-17T05:30:28.618308",
285 |      "exception": false,
286 |      "start_time": "2023-05-17T05:30:28.603161",
287 |      "status": "completed"
288 |     },
289 |     "tags": []
290 |    },
291 |    "outputs": [
292 |     {
293 |      "data": {
294 |       "text/plain": [
295 |        "(768, 9)"
296 |       ]
297 |      },
298 |      "execution_count": 5,
299 |      "metadata": {},
300 |      "output_type": "execute_result"
301 |     }
302 |    ],
303 |    "source": [
304 |     "data.shape"
305 |    ]
306 |   },
307 |   {
308 |    "cell_type": "code",
309 |    "execution_count": 6,
310 |    "id": "bf342298",
311 |    "metadata": {
312 |     "execution": {
313 |      "iopub.execute_input": "2023-05-17T05:30:28.629370Z",
314 |      "iopub.status.busy": "2023-05-17T05:30:28.629046Z",
315 |      "iopub.status.idle": "2023-05-17T05:30:28.663119Z",
316 |      "shell.execute_reply": "2023-05-17T05:30:28.662087Z"
317 |     },
318 |     "papermill": {
319 |      "duration": 0.041756,
320 |      "end_time": "2023-05-17T05:30:28.664927",
321 |      "exception": false,
322 |      "start_time": "2023-05-17T05:30:28.623171",
323 |      "status": "completed"
324 |     },
325 |     "tags": []
326 |    },
327 |    "outputs": [
328 |     {
329 |      "data": {
330 |       "text/html": [
331 |        "<div>\n",
332 |        "<style scoped>\n",
333 |        "    .dataframe tbody tr th:only-of-type {\n",
334 |        "        vertical-align: middle;\n",
335 |        "    }\n",
336 |        "\n",
337 |        "    .dataframe tbody tr th {\n",
338 |        "        vertical-align: top;\n",
339 |        "    }\n",
340 |        "\n",
341 |        "    .dataframe thead th {\n",
342 |        "        text-align: right;\n",
343 |        "    }\n",
344 |        "</style>\n",
345 |        "<table border=\"1\" class=\"dataframe\">\n",
346 |        "  <thead>\n",
347 |        "    <tr style=\"text-align: right;\">\n",
348 |        "      <th></th>\n",
349 |        "      <th>Pregnancies</th>\n",
350 |        "      <th>Glucose</th>\n",
351 |        "      <th>BloodPressure</th>\n",
352 |        "      <th>SkinThickness</th>\n",
353 |        "      <th>Insulin</th>\n",
354 |        "      <th>BMI</th>\n",
355 |        "      <th>DiabetesPedigreeFunction</th>\n",
356 |        "      <th>Age</th>\n",
357 |        "      <th>Outcome</th>\n",
358 |        "    </tr>\n",
359 |        "  </thead>\n",
360 |        "  <tbody>\n",
361 |        "    <tr>\n",
362 |        "      <th>count</th>\n",
363 |        "      <td>768.000000</td>\n",
364 |        "      <td>768.000000</td>\n",
365 |        "      <td>768.000000</td>\n",
366 |        "      <td>768.000000</td>\n",
367 |        "      <td>768.000000</td>\n",
368 |        "      <td>768.000000</td>\n",
369 |        "      <td>768.000000</td>\n",
370 |        "      <td>768.000000</td>\n",
371 |        "      <td>768.000000</td>\n",
372 |        "    </tr>\n",
373 |        "    <tr>\n",
374 |        "      <th>mean</th>\n",
375 |        "      <td>3.845052</td>\n",
376 |        "      <td>120.894531</td>\n",
377 |        "      <td>69.105469</td>\n",
378 |        "      <td>20.536458</td>\n",
379 |        "      <td>79.799479</td>\n",
380 |        "      <td>31.992578</td>\n",
381 |        "      <td>0.471876</td>\n",
382 |        "      <td>33.240885</td>\n",
383 |        "      <td>0.348958</td>\n",
384 |        "    </tr>\n",
385 |        "    <tr>\n",
386 |        "      <th>std</th>\n",
387 |        "      <td>3.369578</td>\n",
388 |        "      <td>31.972618</td>\n",
389 |        "      <td>19.355807</td>\n",
390 |        "      <td>15.952218</td>\n",
391 |        "      <td>115.244002</td>\n",
392 |        "      <td>7.884160</td>\n",
393 |        "      <td>0.331329</td>\n",
394 |        "      <td>11.760232</td>\n",
395 |        "      <td>0.476951</td>\n",
396 |        "    </tr>\n",
397 |        "    <tr>\n",
398 |        "      <th>min</th>\n",
399 |        "      <td>0.000000</td>\n",
400 |        "      <td>0.000000</td>\n",
401 |        "      <td>0.000000</td>\n",
402 |        "      <td>0.000000</td>\n",
403 |        "      <td>0.000000</td>\n",
404 |        "      <td>0.000000</td>\n",
405 |        "      <td>0.078000</td>\n",
406 |        "      <td>21.000000</td>\n",
407 |        "      <td>0.000000</td>\n",
408 |        "    </tr>\n",
409 |        "    <tr>\n",
410 |        "      <th>25%</th>\n",
411 |        "      <td>1.000000</td>\n",
412 |        "      <td>99.000000</td>\n",
413 |        "      <td>62.000000</td>\n",
414 |        "      <td>0.000000</td>\n",
415 |        "      <td>0.000000</td>\n",
416 |        "      <td>27.300000</td>\n",
417 |        "      <td>0.243750</td>\n",
418 |        "      <td>24.000000</td>\n",
419 |        "      <td>0.000000</td>\n",
420 |        "    </tr>\n",
421 |        "    <tr>\n",
422 |        "      <th>50%</th>\n",
423 |        "      <td>3.000000</td>\n",
424 |        "      <td>117.000000</td>\n",
425 |        "      <td>72.000000</td>\n",
426 |        "      <td>23.000000</td>\n",
427 |        "      <td>30.500000</td>\n",
428 |        "      <td>32.000000</td>\n",
429 |        "      <td>0.372500</td>\n",
430 |        "      <td>29.000000</td>\n",
431 |        "      <td>0.000000</td>\n",
432 |        "    </tr>\n",
433 |        "    <tr>\n",
434 |        "      <th>75%</th>\n",
435 |        "      <td>6.000000</td>\n",
436 |        "      <td>140.250000</td>\n",
437 |        "      <td>80.000000</td>\n",
438 |        "      <td>32.000000</td>\n",
439 |        "      <td>127.250000</td>\n",
440 |        "      <td>36.600000</td>\n",
441 |        "      <td>0.626250</td>\n",
442 |        "      <td>41.000000</td>\n",
443 |        "      <td>1.000000</td>\n",
444 |        "    </tr>\n",
445 |        "    <tr>\n",
446 |        "      <th>max</th>\n",
447 |        "      <td>17.000000</td>\n",
448 |        "      <td>199.000000</td>\n",
449 |        "      <td>122.000000</td>\n",
450 |        "      <td>99.000000</td>\n",
451 |        "      <td>846.000000</td>\n",
452 |        "      <td>67.100000</td>\n",
453 |        "      <td>2.420000</td>\n",
454 |        "      <td>81.000000</td>\n",
455 |        "      <td>1.000000</td>\n",
456 |        "    </tr>\n",
457 |        "  </tbody>\n",
458 |        "</table>\n",
459 |        "</div>"
460 |       ],
461 |       "text/plain": [
462 |        "       Pregnancies     Glucose  BloodPressure  SkinThickness     Insulin  \\\n",
463 |        "count   768.000000  768.000000     768.000000     768.000000  768.000000   \n",
464 |        "mean      3.845052  120.894531      69.105469      20.536458   79.799479   \n",
465 |        "std       3.369578   31.972618      19.355807      15.952218  115.244002   \n",
466 |        "min       0.000000    0.000000       0.000000       0.000000    0.000000   \n",
467 |        "25%       1.000000   99.000000      62.000000       0.000000    0.000000   \n",
468 |        "50%       3.000000  117.000000      72.000000      23.000000   30.500000   \n",
469 |        "75%       6.000000  140.250000      80.000000      32.000000  127.250000   \n",
470 |        "max      17.000000  199.000000     122.000000      99.000000  846.000000   \n",
471 |        "\n",
472 |        "              BMI  DiabetesPedigreeFunction         Age     Outcome  \n",
473 |        "count  768.000000                768.000000  768.000000  768.000000  \n",
474 |        "mean    31.992578                  0.471876   33.240885    0.348958  \n",
475 |        "std      7.884160                  0.331329   11.760232    0.476951  \n",
476 |        "min      0.000000                  0.078000   21.000000    0.000000  \n",
477 |        "25%     27.300000                  0.243750   24.000000    0.000000  \n",
478 |        "50%     32.000000                  0.372500   29.000000    0.000000  \n",
479 |        "75%     36.600000                  0.626250   41.000000    1.000000  \n",
480 |        "max     67.100000                  2.420000   81.000000    1.000000  "
481 |       ]
482 |      },
483 |      "execution_count": 6,
484 |      "metadata": {},
485 |      "output_type": "execute_result"
486 |     }
487 |    ],
488 |    "source": [
489 |     "data.describe()"
490 |    ]
491 |   },
492 |   {
493 |    "cell_type": "code",
494 |    "execution_count": 7,
495 |    "id": "ce6226fb",
496 |    "metadata": {
497 |     "execution": {
498 |      "iopub.execute_input": "2023-05-17T05:30:28.677344Z",
499 |      "iopub.status.busy": "2023-05-17T05:30:28.675884Z",
500 |      "iopub.status.idle": "2023-05-17T05:30:28.688803Z",
501 |      "shell.execute_reply": "2023-05-17T05:30:28.686983Z"
502 |     },
503 |     "papermill": {
504 |      "duration": 0.021206,
505 |      "end_time": "2023-05-17T05:30:28.691089",
506 |      "exception": false,
507 |      "start_time": "2023-05-17T05:30:28.669883",
508 |      "status": "completed"
509 |     },
510 |     "tags": []
511 |    },
512 |    "outputs": [],
513 |    "source": [
514 |     "label = data[[\"Outcome\"]]"
515 |    ]
516 |   },
517 |   {
518 |    "cell_type": "code",
519 |    "execution_count": 8,
520 |    "id": "d3ee4364",
521 |    "metadata": {
522 |     "execution": {
523 |      "iopub.execute_input": "2023-05-17T05:30:28.702419Z",
524 |      "iopub.status.busy": "2023-05-17T05:30:28.702124Z",
525 |      "iopub.status.idle": "2023-05-17T05:30:28.707547Z",
526 |      "shell.execute_reply": "2023-05-17T05:30:28.706698Z"
527 |     },
528 |     "papermill": {
529 |      "duration": 0.013589,
530 |      "end_time": "2023-05-17T05:30:28.709617",
531 |      "exception": false,
532 |      "start_time": "2023-05-17T05:30:28.696028",
533 |      "status": "completed"
534 |     },
535 |     "tags": []
536 |    },
537 |    "outputs": [],
538 |    "source": [
539 |     "feature = data.drop(data[['Outcome',\"Age\",\"SkinThickness\",\"Pregnancies\"]],axis=1)"
540 |    ]
541 |   },
542 |   {
543 |    "cell_type": "code",
544 |    "execution_count": 9,
545 |    "id": "2ce00ebd",
546 |    "metadata": {
547 |     "execution": {
548 |      "iopub.execute_input": "2023-05-17T05:30:28.720429Z",
549 |      "iopub.status.busy": "2023-05-17T05:30:28.720118Z",
550 |      "iopub.status.idle": "2023-05-17T05:30:28.732930Z",
551 |      "shell.execute_reply": "2023-05-17T05:30:28.731499Z"
552 |     },
553 |     "papermill": {
554 |      "duration": 0.021368,
555 |      "end_time": "2023-05-17T05:30:28.735697",
556 |      "exception": false,
557 |      "start_time": "2023-05-17T05:30:28.714329",
558 |      "status": "completed"
559 |     },
560 |     "tags": []
561 |    },
562 |    "outputs": [
563 |     {
564 |      "data": {
565 |       "text/html": [
566 |        "<div>\n",
567 |        "<style scoped>\n",
568 |        "    .dataframe tbody tr th:only-of-type {\n",
569 |        "        vertical-align: middle;\n",
570 |        "    }\n",
571 |        "\n",
572 |        "    .dataframe tbody tr th {\n",
573 |        "        vertical-align: top;\n",
574 |        "    }\n",
575 |        "\n",
576 |        "    .dataframe thead th {\n",
577 |        "        text-align: right;\n",
578 |        "    }\n",
579 |        "</style>\n",
580 |        "<table border=\"1\" class=\"dataframe\">\n",
581 |        "  <thead>\n",
582 |        "    <tr style=\"text-align: right;\">\n",
583 |        "      <th></th>\n",
584 |        "      <th>Glucose</th>\n",
585 |        "      <th>BloodPressure</th>\n",
586 |        "      <th>Insulin</th>\n",
587 |        "      <th>BMI</th>\n",
588 |        "      <th>DiabetesPedigreeFunction</th>\n",
589 |        "    </tr>\n",
590 |        "  </thead>\n",
591 |        "  <tbody>\n",
592 |        "    <tr>\n",
593 |        "      <th>0</th>\n",
594 |        "      <td>148</td>\n",
595 |        "      <td>72</td>\n",
596 |        "      <td>0</td>\n",
597 |        "      <td>33.6</td>\n",
598 |        "      <td>0.627</td>\n",
599 |        "    </tr>\n",
600 |        "    <tr>\n",
601 |        "      <th>1</th>\n",
602 |        "      <td>85</td>\n",
603 |        "      <td>66</td>\n",
604 |        "      <td>0</td>\n",
605 |        "      <td>26.6</td>\n",
606 |        "      <td>0.351</td>\n",
607 |        "    </tr>\n",
608 |        "    <tr>\n",
609 |        "      <th>2</th>\n",
610 |        "      <td>183</td>\n",
611 |        "      <td>64</td>\n",
612 |        "      <td>0</td>\n",
613 |        "      <td>23.3</td>\n",
614 |        "      <td>0.672</td>\n",
615 |        "    </tr>\n",
616 |        "    <tr>\n",
617 |        "      <th>3</th>\n",
618 |        "      <td>89</td>\n",
619 |        "      <td>66</td>\n",
620 |        "      <td>94</td>\n",
621 |        "      <td>28.1</td>\n",
622 |        "      <td>0.167</td>\n",
623 |        "    </tr>\n",
624 |        "    <tr>\n",
625 |        "      <th>4</th>\n",
626 |        "      <td>137</td>\n",
627 |        "      <td>40</td>\n",
628 |        "      <td>168</td>\n",
629 |        "      <td>43.1</td>\n",
630 |        "      <td>2.288</td>\n",
631 |        "    </tr>\n",
632 |        "    <tr>\n",
633 |        "      <th>...</th>\n",
634 |        "      <td>...</td>\n",
635 |        "      <td>...</td>\n",
636 |        "      <td>...</td>\n",
637 |        "      <td>...</td>\n",
638 |        "      <td>...</td>\n",
639 |        "    </tr>\n",
640 |        "    <tr>\n",
641 |        "      <th>763</th>\n",
642 |        "      <td>101</td>\n",
643 |        "      <td>76</td>\n",
644 |        "      <td>180</td>\n",
645 |        "      <td>32.9</td>\n",
646 |        "      <td>0.171</td>\n",
647 |        "    </tr>\n",
648 |        "    <tr>\n",
649 |        "      <th>764</th>\n",
650 |        "      <td>122</td>\n",
651 |        "      <td>70</td>\n",
652 |        "      <td>0</td>\n",
653 |        "      <td>36.8</td>\n",
654 |        "      <td>0.340</td>\n",
655 |        "    </tr>\n",
656 |        "    <tr>\n",
657 |        "      <th>765</th>\n",
658 |        "      <td>121</td>\n",
659 |        "      <td>72</td>\n",
660 |        "      <td>112</td>\n",
661 |        "      <td>26.2</td>\n",
662 |        "      <td>0.245</td>\n",
663 |        "    </tr>\n",
664 |        "    <tr>\n",
665 |        "      <th>766</th>\n",
666 |        "      <td>126</td>\n",
667 |        "      <td>60</td>\n",
668 |        "      <td>0</td>\n",
669 |        "      <td>30.1</td>\n",
670 |        "      <td>0.349</td>\n",
671 |        "    </tr>\n",
672 |        "    <tr>\n",
673 |        "      <th>767</th>\n",
674 |        "      <td>93</td>\n",
675 |        "      <td>70</td>\n",
676 |        "      <td>0</td>\n",
677 |        "      <td>30.4</td>\n",
678 |        "      <td>0.315</td>\n",
679 |        "    </tr>\n",
680 |        "  </tbody>\n",
681 |        "</table>\n",
682 |        "<p>768 rows × 5 columns</p>\n",
683 |        "</div>"
684 |       ],
685 |       "text/plain": [
686 |        "     Glucose  BloodPressure  Insulin   BMI  DiabetesPedigreeFunction\n",
687 |        "0        148             72        0  33.6                     0.627\n",
688 |        "1         85             66        0  26.6                     0.351\n",
689 |        "2        183             64        0  23.3                     0.672\n",
690 |        "3         89             66       94  28.1                     0.167\n",
691 |        "4        137             40      168  43.1                     2.288\n",
692 |        "..       ...            ...      ...   ...                       ...\n",
693 |        "763      101             76      180  32.9                     0.171\n",
694 |        "764      122             70        0  36.8                     0.340\n",
695 |        "765      121             72      112  26.2                     0.245\n",
696 |        "766      126             60        0  30.1                     0.349\n",
697 |        "767       93             70        0  30.4                     0.315\n",
698 |        "\n",
699 |        "[768 rows x 5 columns]"
700 |       ]
701 |      },
702 |      "execution_count": 9,
703 |      "metadata": {},
704 |      "output_type": "execute_result"
705 |     }
706 |    ],
707 |    "source": [
708 |     "feature"
709 |    ]
710 |   },
711 |   {
712 |    "cell_type": "code",
713 |    "execution_count": 10,
714 |    "id": "8f76abae",
715 |    "metadata": {
716 |     "execution": {
717 |      "iopub.execute_input": "2023-05-17T05:30:28.747604Z",
718 |      "iopub.status.busy": "2023-05-17T05:30:28.747263Z",
719 |      "iopub.status.idle": "2023-05-17T05:30:28.752607Z",
720 |      "shell.execute_reply": "2023-05-17T05:30:28.751452Z"
721 |     },
722 |     "papermill": {
723 |      "duration": 0.013865,
724 |      "end_time": "2023-05-17T05:30:28.754867",
725 |      "exception": false,
726 |      "start_time": "2023-05-17T05:30:28.741002",
727 |      "status": "completed"
728 |     },
729 |     "tags": []
730 |    },
731 |    "outputs": [],
732 |    "source": [
733 |     "data['BMI'] = data['BMI'].astype(\"int\")"
734 |    ]
735 |   },
736 |   {
737 |    "cell_type": "code",
738 |    "execution_count": 11,
739 |    "id": "831d83b6",
740 |    "metadata": {
741 |     "execution": {
742 |      "iopub.execute_input": "2023-05-17T05:30:28.766969Z",
743 |      "iopub.status.busy": "2023-05-17T05:30:28.766669Z",
744 |      "iopub.status.idle": "2023-05-17T05:30:34.068993Z",
745 |      "shell.execute_reply": "2023-05-17T05:30:34.067738Z"
746 |     },
747 |     "papermill": {
748 |      "duration": 5.310809,
749 |      "end_time": "2023-05-17T05:30:34.071057",
750 |      "exception": false,
751 |      "start_time": "2023-05-17T05:30:28.760248",
752 |      "status": "completed"
753 |     },
754 |     "tags": []
755 |    },
756 |    "outputs": [
757 |     {
758 |      "name": "stderr",
759 |      "output_type": "stream",
760 |      "text": [
761 |       "c:\\Users\\harin\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\utils\\validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
762 |       "  y = column_or_1d(y, warn=True)\n"
763 |      ]
764 |     },
765 |     {
766 |      "name": "stdout",
767 |      "output_type": "stream",
768 |      "text": [
769 |       "The accuracy of kernel: linear is 80%\n",
770 |       "The accuracy of kernel: rbf is 77%\n",
771 |       "The accuracy of kernel: poly is 75%\n"
772 |      ]
773 |     },
774 |     {
775 |      "name": "stderr",
776 |      "output_type": "stream",
777 |      "text": [
778 |       "c:\\Users\\harin\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\utils\\validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
779 |       "  y = column_or_1d(y, warn=True)\n",
780 |       "c:\\Users\\harin\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\utils\\validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
781 |       "  y = column_or_1d(y, warn=True)\n"
782 |      ]
783 |     }
784 |    ],
785 |    "source": [
786 |     "li = []\n",
787 |     "kernels = [\"linear\",\"rbf\",\"poly\"]\n",
788 |     "for i in range(0,len(kernels)):\n",
789 |     "    model = svm.SVC(kernel=kernels[i])\n",
790 |     "    x_train,x_test,y_train,y_test = sklearn.model_selection.train_test_split(feature,label,test_size=0.2)\n",
791 |     "    model.fit(x_train,y_train)\n",
792 |     "    prediction = model.predict(x_test)\n",
793 |     "    accuracy = metrics.accuracy_score(y_test,prediction)\n",
794 |     "    print(f\"The accuracy of kernel: {kernels[i]} is {int(accuracy*100)}%\")\n",
795 |     "    \n",
796 |     "    #li.append(accuracy)\n",
797 |     "#print(\"Average Accuracy is:\",f\"{int(np.mean(li)*100)}%\")"
798 |    ]
799 |   },
800 |   {
801 |    "cell_type": "code",
802 |    "execution_count": 12,
803 |    "id": "70fef5f2",
804 |    "metadata": {
805 |     "execution": {
806 |      "iopub.execute_input": "2023-05-17T05:30:34.092791Z",
807 |      "iopub.status.busy": "2023-05-17T05:30:34.092474Z",
808 |      "iopub.status.idle": "2023-05-17T05:32:37.351981Z",
809 |      "shell.execute_reply": "2023-05-17T05:32:37.351110Z"
810 |     },
811 |     "papermill": {
812 |      "duration": 123.273413,
813 |      "end_time": "2023-05-17T05:32:37.359547",
814 |      "exception": false,
815 |      "start_time": "2023-05-17T05:30:34.086134",
816 |      "status": "completed"
817 |     },
818 |     "tags": []
819 |    },
820 |    "outputs": [
821 |     {
822 |      "name": "stderr",
823 |      "output_type": "stream",
824 |      "text": [
825 |       "c:\\Users\\harin\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\utils\\validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
826 |       "  y = column_or_1d(y, warn=True)\n"
827 |      ]
828 |     },
829 |     {
830 |      "name": "stdout",
831 |      "output_type": "stream",
832 |      "text": [
833 |       "The accuracy of kernel: Linear is 74% C: 1\n"
834 |      ]
835 |     },
836 |     {
837 |      "name": "stderr",
838 |      "output_type": "stream",
839 |      "text": [
840 |       "c:\\Users\\harin\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\utils\\validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
841 |       "  y = column_or_1d(y, warn=True)\n"
842 |      ]
843 |     },
844 |     {
845 |      "name": "stdout",
846 |      "output_type": "stream",
847 |      "text": [
848 |       "The accuracy of kernel: Linear is 77% C: 2\n"
849 |      ]
850 |     },
851 |     {
852 |      "name": "stderr",
853 |      "output_type": "stream",
854 |      "text": [
855 |       "c:\\Users\\harin\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\utils\\validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
856 |       "  y = column_or_1d(y, warn=True)\n"
857 |      ]
858 |     },
859 |     {
860 |      "name": "stdout",
861 |      "output_type": "stream",
862 |      "text": [
863 |       "The accuracy of kernel: Linear is 75% C: 3\n"
864 |      ]
865 |     },
866 |     {
867 |      "name": "stderr",
868 |      "output_type": "stream",
869 |      "text": [
870 |       "c:\\Users\\harin\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\utils\\validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
871 |       "  y = column_or_1d(y, warn=True)\n"
872 |      ]
873 |     },
874 |     {
875 |      "name": "stdout",
876 |      "output_type": "stream",
877 |      "text": [
878 |       "The accuracy of kernel: Linear is 77% C: 4\n"
879 |      ]
880 |     },
881 |     {
882 |      "name": "stderr",
883 |      "output_type": "stream",
884 |      "text": [
885 |       "c:\\Users\\harin\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\utils\\validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
886 |       "  y = column_or_1d(y, warn=True)\n"
887 |      ]
888 |     },
889 |     {
890 |      "name": "stdout",
891 |      "output_type": "stream",
892 |      "text": [
893 |       "The accuracy of kernel: Linear is 78% C: 5\n"
894 |      ]
895 |     },
896 |     {
897 |      "name": "stderr",
898 |      "output_type": "stream",
899 |      "text": [
900 |       "c:\\Users\\harin\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\utils\\validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
901 |       "  y = column_or_1d(y, warn=True)\n"
902 |      ]
903 |     }
904 |    ],
905 |    "source": [
906 |     "for i in range(1,10):\n",
907 |     "    model = svm.SVC(kernel=\"linear\",C=i)\n",
908 |     "    x_train,x_test,y_train,y_test = sklearn.model_selection.train_test_split(feature,label,test_size=0.2)\n",
909 |     "    model.fit(x_train,y_train)\n",
910 |     "    prediction = model.predict(x_test)\n",
911 |     "    accuracy = metrics.accuracy_score(y_test,prediction)\n",
912 |     "    print(f\"The accuracy of kernel: Linear is {int(accuracy*100)}% C: {i}\")"
913 |    ]
914 |   }
915 |  ],
916 |  "metadata": {
917 |   "kernelspec": {
918 |    "display_name": "Python 3",
919 |    "language": "python",
920 |    "name": "python3"
921 |   },
922 |   "language_info": {
923 |    "codemirror_mode": {
924 |     "name": "ipython",
925 |     "version": 3
926 |    },
927 |    "file_extension": ".py",
928 |    "mimetype": "text/x-python",
929 |    "name": "python",
930 |    "nbconvert_exporter": "python",
931 |    "pygments_lexer": "ipython3",
932 |    "version": "3.10.0"
933 |   },
934 |   "papermill": {
935 |    "default_parameters": {},
936 |    "duration": 142.857483,
937 |    "end_time": "2023-05-17T05:32:38.300466",
938 |    "environment_variables": {},
939 |    "exception": null,
940 |    "input_path": "__notebook__.ipynb",
941 |    "output_path": "__notebook__.ipynb",
942 |    "parameters": {},
943 |    "start_time": "2023-05-17T05:30:15.442983",
944 |    "version": "2.4.0"
945 |   }
946 |  },
947 |  "nbformat": 4,
948 |  "nbformat_minor": 5
949 | }
950 | 


--------------------------------------------------------------------------------
/winequality-prediction.ipynb:
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   1 | {
   2 |  "cells": [
   3 |   {
   4 |    "cell_type": "code",
   5 |    "execution_count": 3,
   6 |    "id": "a9c11110",
   7 |    "metadata": {
   8 |     "execution": {
   9 |      "iopub.execute_input": "2023-08-07T07:07:49.414411Z",
  10 |      "iopub.status.busy": "2023-08-07T07:07:49.413913Z",
  11 |      "iopub.status.idle": "2023-08-07T07:07:49.426948Z",
  12 |      "shell.execute_reply": "2023-08-07T07:07:49.425982Z"
  13 |     },
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  19 |      "status": "completed"
  20 |     },
  21 |     "tags": []
  22 |    },
  23 |    "outputs": [],
  24 |    "source": [
  25 |     "import pandas as pd\n",
  26 |     "import numpy as np\n",
  27 |     "import matplotlib.pyplot as plt\n",
  28 |     "%matplotlib inline"
  29 |    ]
  30 |   },
  31 |   {
  32 |    "cell_type": "code",
  33 |    "execution_count": 4,
  34 |    "id": "8431d5c0",
  35 |    "metadata": {
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  37 |      "iopub.execute_input": "2023-08-07T07:07:49.441953Z",
  38 |      "iopub.status.busy": "2023-08-07T07:07:49.441587Z",
  39 |      "iopub.status.idle": "2023-08-07T07:07:49.499514Z",
  40 |      "shell.execute_reply": "2023-08-07T07:07:49.498507Z"
  41 |     },
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  43 |      "duration": 0.066537,
  44 |      "end_time": "2023-08-07T07:07:49.501606",
  45 |      "exception": false,
  46 |      "start_time": "2023-08-07T07:07:49.435069",
  47 |      "status": "completed"
  48 |     },
  49 |     "tags": []
  50 |    },
  51 |    "outputs": [
  52 |     {
  53 |      "data": {
  54 |       "text/html": [
  55 |        "<div>\n",
  56 |        "<style scoped>\n",
  57 |        "    .dataframe tbody tr th:only-of-type {\n",
  58 |        "        vertical-align: middle;\n",
  59 |        "    }\n",
  60 |        "\n",
  61 |        "    .dataframe tbody tr th {\n",
  62 |        "        vertical-align: top;\n",
  63 |        "    }\n",
  64 |        "\n",
  65 |        "    .dataframe thead th {\n",
  66 |        "        text-align: right;\n",
  67 |        "    }\n",
  68 |        "</style>\n",
  69 |        "<table border=\"1\" class=\"dataframe\">\n",
  70 |        "  <thead>\n",
  71 |        "    <tr style=\"text-align: right;\">\n",
  72 |        "      <th></th>\n",
  73 |        "      <th>fixed acidity</th>\n",
  74 |        "      <th>volatile acidity</th>\n",
  75 |        "      <th>citric acid</th>\n",
  76 |        "      <th>residual sugar</th>\n",
  77 |        "      <th>chlorides</th>\n",
  78 |        "      <th>free sulfur dioxide</th>\n",
  79 |        "      <th>total sulfur dioxide</th>\n",
  80 |        "      <th>density</th>\n",
  81 |        "      <th>pH</th>\n",
  82 |        "      <th>sulphates</th>\n",
  83 |        "      <th>alcohol</th>\n",
  84 |        "      <th>quality</th>\n",
  85 |        "    </tr>\n",
  86 |        "  </thead>\n",
  87 |        "  <tbody>\n",
  88 |        "    <tr>\n",
  89 |        "      <th>0</th>\n",
  90 |        "      <td>7.4</td>\n",
  91 |        "      <td>0.700</td>\n",
  92 |        "      <td>0.00</td>\n",
  93 |        "      <td>1.9</td>\n",
  94 |        "      <td>0.076</td>\n",
  95 |        "      <td>11.0</td>\n",
  96 |        "      <td>34.0</td>\n",
  97 |        "      <td>0.99780</td>\n",
  98 |        "      <td>3.51</td>\n",
  99 |        "      <td>0.56</td>\n",
 100 |        "      <td>9.4</td>\n",
 101 |        "      <td>5</td>\n",
 102 |        "    </tr>\n",
 103 |        "    <tr>\n",
 104 |        "      <th>1</th>\n",
 105 |        "      <td>7.8</td>\n",
 106 |        "      <td>0.880</td>\n",
 107 |        "      <td>0.00</td>\n",
 108 |        "      <td>2.6</td>\n",
 109 |        "      <td>0.098</td>\n",
 110 |        "      <td>25.0</td>\n",
 111 |        "      <td>67.0</td>\n",
 112 |        "      <td>0.99680</td>\n",
 113 |        "      <td>3.20</td>\n",
 114 |        "      <td>0.68</td>\n",
 115 |        "      <td>9.8</td>\n",
 116 |        "      <td>5</td>\n",
 117 |        "    </tr>\n",
 118 |        "    <tr>\n",
 119 |        "      <th>2</th>\n",
 120 |        "      <td>7.8</td>\n",
 121 |        "      <td>0.760</td>\n",
 122 |        "      <td>0.04</td>\n",
 123 |        "      <td>2.3</td>\n",
 124 |        "      <td>0.092</td>\n",
 125 |        "      <td>15.0</td>\n",
 126 |        "      <td>54.0</td>\n",
 127 |        "      <td>0.99700</td>\n",
 128 |        "      <td>3.26</td>\n",
 129 |        "      <td>0.65</td>\n",
 130 |        "      <td>9.8</td>\n",
 131 |        "      <td>5</td>\n",
 132 |        "    </tr>\n",
 133 |        "    <tr>\n",
 134 |        "      <th>3</th>\n",
 135 |        "      <td>11.2</td>\n",
 136 |        "      <td>0.280</td>\n",
 137 |        "      <td>0.56</td>\n",
 138 |        "      <td>1.9</td>\n",
 139 |        "      <td>0.075</td>\n",
 140 |        "      <td>17.0</td>\n",
 141 |        "      <td>60.0</td>\n",
 142 |        "      <td>0.99800</td>\n",
 143 |        "      <td>3.16</td>\n",
 144 |        "      <td>0.58</td>\n",
 145 |        "      <td>9.8</td>\n",
 146 |        "      <td>6</td>\n",
 147 |        "    </tr>\n",
 148 |        "    <tr>\n",
 149 |        "      <th>4</th>\n",
 150 |        "      <td>7.4</td>\n",
 151 |        "      <td>0.700</td>\n",
 152 |        "      <td>0.00</td>\n",
 153 |        "      <td>1.9</td>\n",
 154 |        "      <td>0.076</td>\n",
 155 |        "      <td>11.0</td>\n",
 156 |        "      <td>34.0</td>\n",
 157 |        "      <td>0.99780</td>\n",
 158 |        "      <td>3.51</td>\n",
 159 |        "      <td>0.56</td>\n",
 160 |        "      <td>9.4</td>\n",
 161 |        "      <td>5</td>\n",
 162 |        "    </tr>\n",
 163 |        "    <tr>\n",
 164 |        "      <th>...</th>\n",
 165 |        "      <td>...</td>\n",
 166 |        "      <td>...</td>\n",
 167 |        "      <td>...</td>\n",
 168 |        "      <td>...</td>\n",
 169 |        "      <td>...</td>\n",
 170 |        "      <td>...</td>\n",
 171 |        "      <td>...</td>\n",
 172 |        "      <td>...</td>\n",
 173 |        "      <td>...</td>\n",
 174 |        "      <td>...</td>\n",
 175 |        "      <td>...</td>\n",
 176 |        "      <td>...</td>\n",
 177 |        "    </tr>\n",
 178 |        "    <tr>\n",
 179 |        "      <th>1594</th>\n",
 180 |        "      <td>6.2</td>\n",
 181 |        "      <td>0.600</td>\n",
 182 |        "      <td>0.08</td>\n",
 183 |        "      <td>2.0</td>\n",
 184 |        "      <td>0.090</td>\n",
 185 |        "      <td>32.0</td>\n",
 186 |        "      <td>44.0</td>\n",
 187 |        "      <td>0.99490</td>\n",
 188 |        "      <td>3.45</td>\n",
 189 |        "      <td>0.58</td>\n",
 190 |        "      <td>10.5</td>\n",
 191 |        "      <td>5</td>\n",
 192 |        "    </tr>\n",
 193 |        "    <tr>\n",
 194 |        "      <th>1595</th>\n",
 195 |        "      <td>5.9</td>\n",
 196 |        "      <td>0.550</td>\n",
 197 |        "      <td>0.10</td>\n",
 198 |        "      <td>2.2</td>\n",
 199 |        "      <td>0.062</td>\n",
 200 |        "      <td>39.0</td>\n",
 201 |        "      <td>51.0</td>\n",
 202 |        "      <td>0.99512</td>\n",
 203 |        "      <td>3.52</td>\n",
 204 |        "      <td>0.76</td>\n",
 205 |        "      <td>11.2</td>\n",
 206 |        "      <td>6</td>\n",
 207 |        "    </tr>\n",
 208 |        "    <tr>\n",
 209 |        "      <th>1596</th>\n",
 210 |        "      <td>6.3</td>\n",
 211 |        "      <td>0.510</td>\n",
 212 |        "      <td>0.13</td>\n",
 213 |        "      <td>2.3</td>\n",
 214 |        "      <td>0.076</td>\n",
 215 |        "      <td>29.0</td>\n",
 216 |        "      <td>40.0</td>\n",
 217 |        "      <td>0.99574</td>\n",
 218 |        "      <td>3.42</td>\n",
 219 |        "      <td>0.75</td>\n",
 220 |        "      <td>11.0</td>\n",
 221 |        "      <td>6</td>\n",
 222 |        "    </tr>\n",
 223 |        "    <tr>\n",
 224 |        "      <th>1597</th>\n",
 225 |        "      <td>5.9</td>\n",
 226 |        "      <td>0.645</td>\n",
 227 |        "      <td>0.12</td>\n",
 228 |        "      <td>2.0</td>\n",
 229 |        "      <td>0.075</td>\n",
 230 |        "      <td>32.0</td>\n",
 231 |        "      <td>44.0</td>\n",
 232 |        "      <td>0.99547</td>\n",
 233 |        "      <td>3.57</td>\n",
 234 |        "      <td>0.71</td>\n",
 235 |        "      <td>10.2</td>\n",
 236 |        "      <td>5</td>\n",
 237 |        "    </tr>\n",
 238 |        "    <tr>\n",
 239 |        "      <th>1598</th>\n",
 240 |        "      <td>6.0</td>\n",
 241 |        "      <td>0.310</td>\n",
 242 |        "      <td>0.47</td>\n",
 243 |        "      <td>3.6</td>\n",
 244 |        "      <td>0.067</td>\n",
 245 |        "      <td>18.0</td>\n",
 246 |        "      <td>42.0</td>\n",
 247 |        "      <td>0.99549</td>\n",
 248 |        "      <td>3.39</td>\n",
 249 |        "      <td>0.66</td>\n",
 250 |        "      <td>11.0</td>\n",
 251 |        "      <td>6</td>\n",
 252 |        "    </tr>\n",
 253 |        "  </tbody>\n",
 254 |        "</table>\n",
 255 |        "<p>1599 rows × 12 columns</p>\n",
 256 |        "</div>"
 257 |       ],
 258 |       "text/plain": [
 259 |        "      fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
 260 |        "0               7.4             0.700         0.00             1.9      0.076   \n",
 261 |        "1               7.8             0.880         0.00             2.6      0.098   \n",
 262 |        "2               7.8             0.760         0.04             2.3      0.092   \n",
 263 |        "3              11.2             0.280         0.56             1.9      0.075   \n",
 264 |        "4               7.4             0.700         0.00             1.9      0.076   \n",
 265 |        "...             ...               ...          ...             ...        ...   \n",
 266 |        "1594            6.2             0.600         0.08             2.0      0.090   \n",
 267 |        "1595            5.9             0.550         0.10             2.2      0.062   \n",
 268 |        "1596            6.3             0.510         0.13             2.3      0.076   \n",
 269 |        "1597            5.9             0.645         0.12             2.0      0.075   \n",
 270 |        "1598            6.0             0.310         0.47             3.6      0.067   \n",
 271 |        "\n",
 272 |        "      free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
 273 |        "0                    11.0                  34.0  0.99780  3.51       0.56   \n",
 274 |        "1                    25.0                  67.0  0.99680  3.20       0.68   \n",
 275 |        "2                    15.0                  54.0  0.99700  3.26       0.65   \n",
 276 |        "3                    17.0                  60.0  0.99800  3.16       0.58   \n",
 277 |        "4                    11.0                  34.0  0.99780  3.51       0.56   \n",
 278 |        "...                   ...                   ...      ...   ...        ...   \n",
 279 |        "1594                 32.0                  44.0  0.99490  3.45       0.58   \n",
 280 |        "1595                 39.0                  51.0  0.99512  3.52       0.76   \n",
 281 |        "1596                 29.0                  40.0  0.99574  3.42       0.75   \n",
 282 |        "1597                 32.0                  44.0  0.99547  3.57       0.71   \n",
 283 |        "1598                 18.0                  42.0  0.99549  3.39       0.66   \n",
 284 |        "\n",
 285 |        "      alcohol  quality  \n",
 286 |        "0         9.4        5  \n",
 287 |        "1         9.8        5  \n",
 288 |        "2         9.8        5  \n",
 289 |        "3         9.8        6  \n",
 290 |        "4         9.4        5  \n",
 291 |        "...       ...      ...  \n",
 292 |        "1594     10.5        5  \n",
 293 |        "1595     11.2        6  \n",
 294 |        "1596     11.0        6  \n",
 295 |        "1597     10.2        5  \n",
 296 |        "1598     11.0        6  \n",
 297 |        "\n",
 298 |        "[1599 rows x 12 columns]"
 299 |       ]
 300 |      },
 301 |      "execution_count": 4,
 302 |      "metadata": {},
 303 |      "output_type": "execute_result"
 304 |     }
 305 |    ],
 306 |    "source": [
 307 |     "df= pd.read_csv(\"C:/Users/User/Downloads/winequality-red.csv\")\n",
 308 |     "df"
 309 |    ]
 310 |   },
 311 |   {
 312 |    "cell_type": "code",
 313 |    "execution_count": 5,
 314 |    "id": "7bab339d",
 315 |    "metadata": {
 316 |     "execution": {
 317 |      "iopub.execute_input": "2023-08-07T07:07:49.514590Z",
 318 |      "iopub.status.busy": "2023-08-07T07:07:49.514173Z",
 319 |      "iopub.status.idle": "2023-08-07T07:07:49.544718Z",
 320 |      "shell.execute_reply": "2023-08-07T07:07:49.543381Z"
 321 |     },
 322 |     "papermill": {
 323 |      "duration": 0.03933,
 324 |      "end_time": "2023-08-07T07:07:49.546661",
 325 |      "exception": false,
 326 |      "start_time": "2023-08-07T07:07:49.507331",
 327 |      "status": "completed"
 328 |     },
 329 |     "tags": []
 330 |    },
 331 |    "outputs": [
 332 |     {
 333 |      "data": {
 334 |       "text/html": [
 335 |        "<div>\n",
 336 |        "<style scoped>\n",
 337 |        "    .dataframe tbody tr th:only-of-type {\n",
 338 |        "        vertical-align: middle;\n",
 339 |        "    }\n",
 340 |        "\n",
 341 |        "    .dataframe tbody tr th {\n",
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 348 |        "</style>\n",
 349 |        "<table border=\"1\" class=\"dataframe\">\n",
 350 |        "  <thead>\n",
 351 |        "    <tr style=\"text-align: right;\">\n",
 352 |        "      <th></th>\n",
 353 |        "      <th>fixed acidity</th>\n",
 354 |        "      <th>volatile acidity</th>\n",
 355 |        "      <th>citric acid</th>\n",
 356 |        "      <th>residual sugar</th>\n",
 357 |        "      <th>chlorides</th>\n",
 358 |        "      <th>free sulfur dioxide</th>\n",
 359 |        "      <th>total sulfur dioxide</th>\n",
 360 |        "      <th>density</th>\n",
 361 |        "      <th>pH</th>\n",
 362 |        "      <th>sulphates</th>\n",
 363 |        "      <th>alcohol</th>\n",
 364 |        "    </tr>\n",
 365 |        "  </thead>\n",
 366 |        "  <tbody>\n",
 367 |        "    <tr>\n",
 368 |        "      <th>0</th>\n",
 369 |        "      <td>7.4</td>\n",
 370 |        "      <td>0.700</td>\n",
 371 |        "      <td>0.00</td>\n",
 372 |        "      <td>1.9</td>\n",
 373 |        "      <td>0.076</td>\n",
 374 |        "      <td>11.0</td>\n",
 375 |        "      <td>34.0</td>\n",
 376 |        "      <td>0.99780</td>\n",
 377 |        "      <td>3.51</td>\n",
 378 |        "      <td>0.56</td>\n",
 379 |        "      <td>9.4</td>\n",
 380 |        "    </tr>\n",
 381 |        "    <tr>\n",
 382 |        "      <th>1</th>\n",
 383 |        "      <td>7.8</td>\n",
 384 |        "      <td>0.880</td>\n",
 385 |        "      <td>0.00</td>\n",
 386 |        "      <td>2.6</td>\n",
 387 |        "      <td>0.098</td>\n",
 388 |        "      <td>25.0</td>\n",
 389 |        "      <td>67.0</td>\n",
 390 |        "      <td>0.99680</td>\n",
 391 |        "      <td>3.20</td>\n",
 392 |        "      <td>0.68</td>\n",
 393 |        "      <td>9.8</td>\n",
 394 |        "    </tr>\n",
 395 |        "    <tr>\n",
 396 |        "      <th>2</th>\n",
 397 |        "      <td>7.8</td>\n",
 398 |        "      <td>0.760</td>\n",
 399 |        "      <td>0.04</td>\n",
 400 |        "      <td>2.3</td>\n",
 401 |        "      <td>0.092</td>\n",
 402 |        "      <td>15.0</td>\n",
 403 |        "      <td>54.0</td>\n",
 404 |        "      <td>0.99700</td>\n",
 405 |        "      <td>3.26</td>\n",
 406 |        "      <td>0.65</td>\n",
 407 |        "      <td>9.8</td>\n",
 408 |        "    </tr>\n",
 409 |        "    <tr>\n",
 410 |        "      <th>3</th>\n",
 411 |        "      <td>11.2</td>\n",
 412 |        "      <td>0.280</td>\n",
 413 |        "      <td>0.56</td>\n",
 414 |        "      <td>1.9</td>\n",
 415 |        "      <td>0.075</td>\n",
 416 |        "      <td>17.0</td>\n",
 417 |        "      <td>60.0</td>\n",
 418 |        "      <td>0.99800</td>\n",
 419 |        "      <td>3.16</td>\n",
 420 |        "      <td>0.58</td>\n",
 421 |        "      <td>9.8</td>\n",
 422 |        "    </tr>\n",
 423 |        "    <tr>\n",
 424 |        "      <th>4</th>\n",
 425 |        "      <td>7.4</td>\n",
 426 |        "      <td>0.700</td>\n",
 427 |        "      <td>0.00</td>\n",
 428 |        "      <td>1.9</td>\n",
 429 |        "      <td>0.076</td>\n",
 430 |        "      <td>11.0</td>\n",
 431 |        "      <td>34.0</td>\n",
 432 |        "      <td>0.99780</td>\n",
 433 |        "      <td>3.51</td>\n",
 434 |        "      <td>0.56</td>\n",
 435 |        "      <td>9.4</td>\n",
 436 |        "    </tr>\n",
 437 |        "    <tr>\n",
 438 |        "      <th>...</th>\n",
 439 |        "      <td>...</td>\n",
 440 |        "      <td>...</td>\n",
 441 |        "      <td>...</td>\n",
 442 |        "      <td>...</td>\n",
 443 |        "      <td>...</td>\n",
 444 |        "      <td>...</td>\n",
 445 |        "      <td>...</td>\n",
 446 |        "      <td>...</td>\n",
 447 |        "      <td>...</td>\n",
 448 |        "      <td>...</td>\n",
 449 |        "      <td>...</td>\n",
 450 |        "    </tr>\n",
 451 |        "    <tr>\n",
 452 |        "      <th>1594</th>\n",
 453 |        "      <td>6.2</td>\n",
 454 |        "      <td>0.600</td>\n",
 455 |        "      <td>0.08</td>\n",
 456 |        "      <td>2.0</td>\n",
 457 |        "      <td>0.090</td>\n",
 458 |        "      <td>32.0</td>\n",
 459 |        "      <td>44.0</td>\n",
 460 |        "      <td>0.99490</td>\n",
 461 |        "      <td>3.45</td>\n",
 462 |        "      <td>0.58</td>\n",
 463 |        "      <td>10.5</td>\n",
 464 |        "    </tr>\n",
 465 |        "    <tr>\n",
 466 |        "      <th>1595</th>\n",
 467 |        "      <td>5.9</td>\n",
 468 |        "      <td>0.550</td>\n",
 469 |        "      <td>0.10</td>\n",
 470 |        "      <td>2.2</td>\n",
 471 |        "      <td>0.062</td>\n",
 472 |        "      <td>39.0</td>\n",
 473 |        "      <td>51.0</td>\n",
 474 |        "      <td>0.99512</td>\n",
 475 |        "      <td>3.52</td>\n",
 476 |        "      <td>0.76</td>\n",
 477 |        "      <td>11.2</td>\n",
 478 |        "    </tr>\n",
 479 |        "    <tr>\n",
 480 |        "      <th>1596</th>\n",
 481 |        "      <td>6.3</td>\n",
 482 |        "      <td>0.510</td>\n",
 483 |        "      <td>0.13</td>\n",
 484 |        "      <td>2.3</td>\n",
 485 |        "      <td>0.076</td>\n",
 486 |        "      <td>29.0</td>\n",
 487 |        "      <td>40.0</td>\n",
 488 |        "      <td>0.99574</td>\n",
 489 |        "      <td>3.42</td>\n",
 490 |        "      <td>0.75</td>\n",
 491 |        "      <td>11.0</td>\n",
 492 |        "    </tr>\n",
 493 |        "    <tr>\n",
 494 |        "      <th>1597</th>\n",
 495 |        "      <td>5.9</td>\n",
 496 |        "      <td>0.645</td>\n",
 497 |        "      <td>0.12</td>\n",
 498 |        "      <td>2.0</td>\n",
 499 |        "      <td>0.075</td>\n",
 500 |        "      <td>32.0</td>\n",
 501 |        "      <td>44.0</td>\n",
 502 |        "      <td>0.99547</td>\n",
 503 |        "      <td>3.57</td>\n",
 504 |        "      <td>0.71</td>\n",
 505 |        "      <td>10.2</td>\n",
 506 |        "    </tr>\n",
 507 |        "    <tr>\n",
 508 |        "      <th>1598</th>\n",
 509 |        "      <td>6.0</td>\n",
 510 |        "      <td>0.310</td>\n",
 511 |        "      <td>0.47</td>\n",
 512 |        "      <td>3.6</td>\n",
 513 |        "      <td>0.067</td>\n",
 514 |        "      <td>18.0</td>\n",
 515 |        "      <td>42.0</td>\n",
 516 |        "      <td>0.99549</td>\n",
 517 |        "      <td>3.39</td>\n",
 518 |        "      <td>0.66</td>\n",
 519 |        "      <td>11.0</td>\n",
 520 |        "    </tr>\n",
 521 |        "  </tbody>\n",
 522 |        "</table>\n",
 523 |        "<p>1599 rows × 11 columns</p>\n",
 524 |        "</div>"
 525 |       ],
 526 |       "text/plain": [
 527 |        "      fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
 528 |        "0               7.4             0.700         0.00             1.9      0.076   \n",
 529 |        "1               7.8             0.880         0.00             2.6      0.098   \n",
 530 |        "2               7.8             0.760         0.04             2.3      0.092   \n",
 531 |        "3              11.2             0.280         0.56             1.9      0.075   \n",
 532 |        "4               7.4             0.700         0.00             1.9      0.076   \n",
 533 |        "...             ...               ...          ...             ...        ...   \n",
 534 |        "1594            6.2             0.600         0.08             2.0      0.090   \n",
 535 |        "1595            5.9             0.550         0.10             2.2      0.062   \n",
 536 |        "1596            6.3             0.510         0.13             2.3      0.076   \n",
 537 |        "1597            5.9             0.645         0.12             2.0      0.075   \n",
 538 |        "1598            6.0             0.310         0.47             3.6      0.067   \n",
 539 |        "\n",
 540 |        "      free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
 541 |        "0                    11.0                  34.0  0.99780  3.51       0.56   \n",
 542 |        "1                    25.0                  67.0  0.99680  3.20       0.68   \n",
 543 |        "2                    15.0                  54.0  0.99700  3.26       0.65   \n",
 544 |        "3                    17.0                  60.0  0.99800  3.16       0.58   \n",
 545 |        "4                    11.0                  34.0  0.99780  3.51       0.56   \n",
 546 |        "...                   ...                   ...      ...   ...        ...   \n",
 547 |        "1594                 32.0                  44.0  0.99490  3.45       0.58   \n",
 548 |        "1595                 39.0                  51.0  0.99512  3.52       0.76   \n",
 549 |        "1596                 29.0                  40.0  0.99574  3.42       0.75   \n",
 550 |        "1597                 32.0                  44.0  0.99547  3.57       0.71   \n",
 551 |        "1598                 18.0                  42.0  0.99549  3.39       0.66   \n",
 552 |        "\n",
 553 |        "      alcohol  \n",
 554 |        "0         9.4  \n",
 555 |        "1         9.8  \n",
 556 |        "2         9.8  \n",
 557 |        "3         9.8  \n",
 558 |        "4         9.4  \n",
 559 |        "...       ...  \n",
 560 |        "1594     10.5  \n",
 561 |        "1595     11.2  \n",
 562 |        "1596     11.0  \n",
 563 |        "1597     10.2  \n",
 564 |        "1598     11.0  \n",
 565 |        "\n",
 566 |        "[1599 rows x 11 columns]"
 567 |       ]
 568 |      },
 569 |      "execution_count": 5,
 570 |      "metadata": {},
 571 |      "output_type": "execute_result"
 572 |     }
 573 |    ],
 574 |    "source": [
 575 |     "#splitting input and output features\n",
 576 |     "X=df.drop(\"quality\",axis=1)\n",
 577 |     "Y=df[\"quality\"]\n",
 578 |     "X\n"
 579 |    ]
 580 |   },
 581 |   {
 582 |    "cell_type": "code",
 583 |    "execution_count": 6,
 584 |    "id": "37c1eedc",
 585 |    "metadata": {
 586 |     "execution": {
 587 |      "iopub.execute_input": "2023-08-07T07:07:49.560673Z",
 588 |      "iopub.status.busy": "2023-08-07T07:07:49.560279Z",
 589 |      "iopub.status.idle": "2023-08-07T07:07:49.567578Z",
 590 |      "shell.execute_reply": "2023-08-07T07:07:49.566745Z"
 591 |     },
 592 |     "papermill": {
 593 |      "duration": 0.016584,
 594 |      "end_time": "2023-08-07T07:07:49.569561",
 595 |      "exception": false,
 596 |      "start_time": "2023-08-07T07:07:49.552977",
 597 |      "status": "completed"
 598 |     },
 599 |     "tags": []
 600 |    },
 601 |    "outputs": [
 602 |     {
 603 |      "data": {
 604 |       "text/plain": [
 605 |        "0       5\n",
 606 |        "1       5\n",
 607 |        "2       5\n",
 608 |        "3       6\n",
 609 |        "4       5\n",
 610 |        "       ..\n",
 611 |        "1594    5\n",
 612 |        "1595    6\n",
 613 |        "1596    6\n",
 614 |        "1597    5\n",
 615 |        "1598    6\n",
 616 |        "Name: quality, Length: 1599, dtype: int64"
 617 |       ]
 618 |      },
 619 |      "execution_count": 6,
 620 |      "metadata": {},
 621 |      "output_type": "execute_result"
 622 |     }
 623 |    ],
 624 |    "source": [
 625 |     "Y"
 626 |    ]
 627 |   },
 628 |   {
 629 |    "cell_type": "code",
 630 |    "execution_count": 7,
 631 |    "id": "22c404f5",
 632 |    "metadata": {
 633 |     "execution": {
 634 |      "iopub.execute_input": "2023-08-07T07:07:49.584536Z",
 635 |      "iopub.status.busy": "2023-08-07T07:07:49.583774Z",
 636 |      "iopub.status.idle": "2023-08-07T07:07:50.921198Z",
 637 |      "shell.execute_reply": "2023-08-07T07:07:50.919600Z"
 638 |     },
 639 |     "papermill": {
 640 |      "duration": 1.348708,
 641 |      "end_time": "2023-08-07T07:07:50.924625",
 642 |      "exception": false,
 643 |      "start_time": "2023-08-07T07:07:49.575917",
 644 |      "status": "completed"
 645 |     },
 646 |     "tags": []
 647 |    },
 648 |    "outputs": [],
 649 |    "source": [
 650 |     "#splitting the data into training and testing sets (70% training and 30% testing)\n",
 651 |     "from sklearn.model_selection import train_test_split,cross_val_score\n",
 652 |     "X_train,X_test,Y_train,Y_test=train_test_split(X,Y,test_size=0.3,random_state=42)\n"
 653 |    ]
 654 |   },
 655 |   {
 656 |    "cell_type": "code",
 657 |    "execution_count": 8,
 658 |    "id": "3610bf65",
 659 |    "metadata": {
 660 |     "execution": {
 661 |      "iopub.execute_input": "2023-08-07T07:07:50.941093Z",
 662 |      "iopub.status.busy": "2023-08-07T07:07:50.940199Z",
 663 |      "iopub.status.idle": "2023-08-07T07:07:50.947492Z",
 664 |      "shell.execute_reply": "2023-08-07T07:07:50.946150Z"
 665 |     },
 666 |     "papermill": {
 667 |      "duration": 0.018691,
 668 |      "end_time": "2023-08-07T07:07:50.950277",
 669 |      "exception": false,
 670 |      "start_time": "2023-08-07T07:07:50.931586",
 671 |      "status": "completed"
 672 |     },
 673 |     "tags": []
 674 |    },
 675 |    "outputs": [
 676 |     {
 677 |      "name": "stdout",
 678 |      "output_type": "stream",
 679 |      "text": [
 680 |       "Shape of X_train: (1119, 11)\n",
 681 |       "Shape of X_test: (480, 11)\n",
 682 |       "Shape of Y_train: (1119,)\n",
 683 |       "Shape of Y_test: (480,)\n"
 684 |      ]
 685 |     }
 686 |    ],
 687 |    "source": [
 688 |     "import numpy as np\n",
 689 |     "\n",
 690 |     "# Assuming you have split the data into X_train, X_test, y_train, y_test\n",
 691 |     "print(\"Shape of X_train:\", X_train.shape)\n",
 692 |     "print(\"Shape of X_test:\", X_test.shape)\n",
 693 |     "print(\"Shape of Y_train:\", Y_train.shape)\n",
 694 |     "print(\"Shape of Y_test:\", Y_test.shape)"
 695 |    ]
 696 |   },
 697 |   {
 698 |    "cell_type": "code",
 699 |    "execution_count": 9,
 700 |    "id": "bae513aa",
 701 |    "metadata": {
 702 |     "execution": {
 703 |      "iopub.execute_input": "2023-08-07T07:07:50.966761Z",
 704 |      "iopub.status.busy": "2023-08-07T07:07:50.966234Z",
 705 |      "iopub.status.idle": "2023-08-07T07:07:51.130001Z",
 706 |      "shell.execute_reply": "2023-08-07T07:07:51.128498Z"
 707 |     },
 708 |     "papermill": {
 709 |      "duration": 0.176061,
 710 |      "end_time": "2023-08-07T07:07:51.133273",
 711 |      "exception": false,
 712 |      "start_time": "2023-08-07T07:07:50.957212",
 713 |      "status": "completed"
 714 |     },
 715 |     "tags": []
 716 |    },
 717 |    "outputs": [
 718 |     {
 719 |      "data": {
 720 |       "text/html": [
 721 |        "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
 722 |       ],
 723 |       "text/plain": [
 724 |        "LinearRegression()"
 725 |       ]
 726 |      },
 727 |      "execution_count": 9,
 728 |      "metadata": {},
 729 |      "output_type": "execute_result"
 730 |     }
 731 |    ],
 732 |    "source": [
 733 |     "#creating LogisticRegression model\n",
 734 |     "from sklearn.linear_model import LinearRegression\n",
 735 |     "model = LinearRegression()\n",
 736 |     "\n",
 737 |     "#train the model using training data\n",
 738 |     "model.fit(X_train,Y_train)"
 739 |    ]
 740 |   },
 741 |   {
 742 |    "cell_type": "code",
 743 |    "execution_count": 10,
 744 |    "id": "ebde7927",
 745 |    "metadata": {
 746 |     "execution": {
 747 |      "iopub.execute_input": "2023-08-07T07:07:51.162479Z",
 748 |      "iopub.status.busy": "2023-08-07T07:07:51.161422Z",
 749 |      "iopub.status.idle": "2023-08-07T07:07:51.172331Z",
 750 |      "shell.execute_reply": "2023-08-07T07:07:51.171039Z"
 751 |     },
 752 |     "papermill": {
 753 |      "duration": 0.032875,
 754 |      "end_time": "2023-08-07T07:07:51.177836",
 755 |      "exception": false,
 756 |      "start_time": "2023-08-07T07:07:51.144961",
 757 |      "status": "completed"
 758 |     },
 759 |     "tags": []
 760 |    },
 761 |    "outputs": [],
 762 |    "source": [
 763 |     "#make predictions on train data\n",
 764 |     "\n",
 765 |     "train_pred=model.predict(X_train)\n",
 766 |     "#train_pred=train_pred.astype(int)"
 767 |    ]
 768 |   },
 769 |   {
 770 |    "cell_type": "code",
 771 |    "execution_count": 11,
 772 |    "id": "a97b86a7",
 773 |    "metadata": {
 774 |     "execution": {
 775 |      "iopub.execute_input": "2023-08-07T07:07:51.265837Z",
 776 |      "iopub.status.busy": "2023-08-07T07:07:51.265163Z",
 777 |      "iopub.status.idle": "2023-08-07T07:07:51.282683Z",
 778 |      "shell.execute_reply": "2023-08-07T07:07:51.281095Z"
 779 |     },
 780 |     "papermill": {
 781 |      "duration": 0.035588,
 782 |      "end_time": "2023-08-07T07:07:51.286915",
 783 |      "exception": false,
 784 |      "start_time": "2023-08-07T07:07:51.251327",
 785 |      "status": "completed"
 786 |     },
 787 |     "tags": []
 788 |    },
 789 |    "outputs": [
 790 |     {
 791 |      "name": "stdout",
 792 |      "output_type": "stream",
 793 |      "text": [
 794 |       "MAE: 0.49518748493865494\n",
 795 |       "RMSE: 0.6486806989670354\n",
 796 |       "R-Squared: 0.36119824413213175\n"
 797 |      ]
 798 |     }
 799 |    ],
 800 |    "source": [
 801 |     "from sklearn import metrics\n",
 802 |     "\n",
 803 |     "# evaluating the model on the training data\n",
 804 |     "\n",
 805 |     "print(\"MAE:\",metrics.mean_absolute_error(Y_train,train_pred))\n",
 806 |     "print(\"RMSE:\",np.sqrt(metrics.mean_squared_error(Y_train,train_pred)))\n",
 807 |     "print(\"R-Squared:\",metrics.r2_score(Y_train,train_pred))"
 808 |    ]
 809 |   },
 810 |   {
 811 |    "cell_type": "code",
 812 |    "execution_count": 12,
 813 |    "id": "8a002e4e",
 814 |    "metadata": {
 815 |     "execution": {
 816 |      "iopub.execute_input": "2023-08-07T07:07:51.305107Z",
 817 |      "iopub.status.busy": "2023-08-07T07:07:51.304634Z",
 818 |      "iopub.status.idle": "2023-08-07T07:07:51.312423Z",
 819 |      "shell.execute_reply": "2023-08-07T07:07:51.311247Z"
 820 |     },
 821 |     "papermill": {
 822 |      "duration": 0.018979,
 823 |      "end_time": "2023-08-07T07:07:51.314834",
 824 |      "exception": false,
 825 |      "start_time": "2023-08-07T07:07:51.295855",
 826 |      "status": "completed"
 827 |     },
 828 |     "tags": []
 829 |    },
 830 |    "outputs": [],
 831 |    "source": [
 832 |     "# Make predictions on test data\n",
 833 |     "\n",
 834 |     "test_pred = model.predict(X_test)"
 835 |    ]
 836 |   },
 837 |   {
 838 |    "cell_type": "code",
 839 |    "execution_count": 13,
 840 |    "id": "1630eb92",
 841 |    "metadata": {
 842 |     "execution": {
 843 |      "iopub.execute_input": "2023-08-07T07:07:51.331995Z",
 844 |      "iopub.status.busy": "2023-08-07T07:07:51.331108Z",
 845 |      "iopub.status.idle": "2023-08-07T07:07:51.339214Z",
 846 |      "shell.execute_reply": "2023-08-07T07:07:51.337915Z"
 847 |     },
 848 |     "papermill": {
 849 |      "duration": 0.019394,
 850 |      "end_time": "2023-08-07T07:07:51.341607",
 851 |      "exception": false,
 852 |      "start_time": "2023-08-07T07:07:51.322213",
 853 |      "status": "completed"
 854 |     },
 855 |     "tags": []
 856 |    },
 857 |    "outputs": [
 858 |     {
 859 |      "name": "stdout",
 860 |      "output_type": "stream",
 861 |      "text": [
 862 |       "MAE: 0.513395608245112\n",
 863 |       "RMSE: 0.6412759715991394\n",
 864 |       "R-Squared: 0.3513885332505232\n"
 865 |      ]
 866 |     }
 867 |    ],
 868 |    "source": [
 869 |     "# evaluating the model on the testing data\n",
 870 |     "\n",
 871 |     "print(\"MAE:\",metrics.mean_absolute_error(Y_test,test_pred))\n",
 872 |     "print(\"RMSE:\",np.sqrt(metrics.mean_squared_error(Y_test,test_pred)))\n",
 873 |     "print(\"R-Squared:\",metrics.r2_score(Y_test,test_pred))"
 874 |    ]
 875 |   },
 876 |   {
 877 |    "cell_type": "code",
 878 |    "execution_count": 14,
 879 |    "id": "ceda0c78",
 880 |    "metadata": {
 881 |     "execution": {
 882 |      "iopub.execute_input": "2023-08-07T07:07:51.386950Z",
 883 |      "iopub.status.busy": "2023-08-07T07:07:51.386011Z",
 884 |      "iopub.status.idle": "2023-08-07T07:07:51.444225Z",
 885 |      "shell.execute_reply": "2023-08-07T07:07:51.442615Z"
 886 |     },
 887 |     "papermill": {
 888 |      "duration": 0.070712,
 889 |      "end_time": "2023-08-07T07:07:51.447748",
 890 |      "exception": false,
 891 |      "start_time": "2023-08-07T07:07:51.377036",
 892 |      "status": "completed"
 893 |     },
 894 |     "tags": []
 895 |    },
 896 |    "outputs": [
 897 |     {
 898 |      "name": "stdout",
 899 |      "output_type": "stream",
 900 |      "text": [
 901 |       "The r2 using 5-fold cross-validation on training data: 0.334985992062552\n"
 902 |      ]
 903 |     }
 904 |    ],
 905 |    "source": [
 906 |     "# Perform k-fold cross-validation on the training data \n",
 907 |     "\n",
 908 |     "from sklearn.model_selection import  cross_val_score\n",
 909 |     "\n",
 910 |     "r2_cv_scores = cross_val_score(model,X_train,Y_train,cv=5,scoring='r2')\n",
 911 |     "mean_r2train_cv = r2_cv_scores.mean()\n",
 912 |     "print(\"The r2 using 5-fold cross-validation on training data:\", mean_r2train_cv)\n"
 913 |    ]
 914 |   },
 915 |   {
 916 |    "cell_type": "code",
 917 |    "execution_count": 15,
 918 |    "id": "eade077a",
 919 |    "metadata": {
 920 |     "execution": {
 921 |      "iopub.execute_input": "2023-08-07T07:07:51.483401Z",
 922 |      "iopub.status.busy": "2023-08-07T07:07:51.482683Z",
 923 |      "iopub.status.idle": "2023-08-07T07:07:51.556137Z",
 924 |      "shell.execute_reply": "2023-08-07T07:07:51.554781Z"
 925 |     },
 926 |     "papermill": {
 927 |      "duration": 0.094352,
 928 |      "end_time": "2023-08-07T07:07:51.559680",
 929 |      "exception": false,
 930 |      "start_time": "2023-08-07T07:07:51.465328",
 931 |      "status": "completed"
 932 |     },
 933 |     "tags": []
 934 |    },
 935 |    "outputs": [
 936 |     {
 937 |      "name": "stdout",
 938 |      "output_type": "stream",
 939 |      "text": [
 940 |       "The r2 using 5-fold cross-validation on testing data: 0.326678061098226\n"
 941 |      ]
 942 |     }
 943 |    ],
 944 |    "source": [
 945 |     "# Perform k-fold cross-validation on the testing data \n",
 946 |     "\n",
 947 |     "r2test_cv_scores = cross_val_score(model,X_test,Y_test,cv=5,scoring='r2')\n",
 948 |     "mean_r2test_cv= r2test_cv_scores.mean()\n",
 949 |     "print(\"The r2 using 5-fold cross-validation on testing data:\", mean_r2test_cv)\n"
 950 |    ]
 951 |   },
 952 |   {
 953 |    "cell_type": "code",
 954 |    "execution_count": 16,
 955 |    "id": "f2e8f598",
 956 |    "metadata": {
 957 |     "execution": {
 958 |      "iopub.execute_input": "2023-08-07T07:07:51.591703Z",
 959 |      "iopub.status.busy": "2023-08-07T07:07:51.591246Z",
 960 |      "iopub.status.idle": "2023-08-07T07:07:51.644925Z",
 961 |      "shell.execute_reply": "2023-08-07T07:07:51.643797Z"
 962 |     },
 963 |     "papermill": {
 964 |      "duration": 0.06555,
 965 |      "end_time": "2023-08-07T07:07:51.648211",
 966 |      "exception": false,
 967 |      "start_time": "2023-08-07T07:07:51.582661",
 968 |      "status": "completed"
 969 |     },
 970 |     "tags": []
 971 |    },
 972 |    "outputs": [
 973 |     {
 974 |      "name": "stdout",
 975 |      "output_type": "stream",
 976 |      "text": [
 977 |       "The mean squared error  using 5-fold cross-validation on training data: -0.43652212341343855\n"
 978 |      ]
 979 |     }
 980 |    ],
 981 |    "source": [
 982 |     "# Perform k-fold cross-validation on the training data \n",
 983 |     "msetrain_cv_scores = cross_val_score(model,X_train,Y_train,cv=5,scoring='neg_mean_squared_error')\n",
 984 |     "mean_msetrain_cv = msetrain_cv_scores.mean()\n",
 985 |     "print(\"The mean squared error  using 5-fold cross-validation on training data:\", mean_msetrain_cv)"
 986 |    ]
 987 |   },
 988 |   {
 989 |    "cell_type": "code",
 990 |    "execution_count": 17,
 991 |    "id": "d8eca687",
 992 |    "metadata": {
 993 |     "execution": {
 994 |      "iopub.execute_input": "2023-08-07T07:07:51.675607Z",
 995 |      "iopub.status.busy": "2023-08-07T07:07:51.674949Z",
 996 |      "iopub.status.idle": "2023-08-07T07:07:51.721929Z",
 997 |      "shell.execute_reply": "2023-08-07T07:07:51.720700Z"
 998 |     },
 999 |     "papermill": {
1000 |      "duration": 0.064823,
1001 |      "end_time": "2023-08-07T07:07:51.725314",
1002 |      "exception": false,
1003 |      "start_time": "2023-08-07T07:07:51.660491",
1004 |      "status": "completed"
1005 |     },
1006 |     "tags": []
1007 |    },
1008 |    "outputs": [
1009 |     {
1010 |      "name": "stdout",
1011 |      "output_type": "stream",
1012 |      "text": [
1013 |       "The mean squared error  using 5-fold cross-validation on testing data: -0.4235962818050936\n"
1014 |      ]
1015 |     }
1016 |    ],
1017 |    "source": [
1018 |     "# Perform k-fold cross-validation on the training data \n",
1019 |     "msetest_cv_scores = cross_val_score(model,X_test,Y_test,cv=5,scoring='neg_mean_squared_error')\n",
1020 |     "mean_msetest_cv = msetest_cv_scores.mean()\n",
1021 |     "print(\"The mean squared error  using 5-fold cross-validation on testing data:\", mean_msetest_cv)"
1022 |    ]
1023 |   },
1024 |   {
1025 |    "cell_type": "code",
1026 |    "execution_count": 18,
1027 |    "id": "f2be066f",
1028 |    "metadata": {
1029 |     "execution": {
1030 |      "iopub.execute_input": "2023-08-07T07:07:51.782441Z",
1031 |      "iopub.status.busy": "2023-08-07T07:07:51.781493Z",
1032 |      "iopub.status.idle": "2023-08-07T07:07:51.836107Z",
1033 |      "shell.execute_reply": "2023-08-07T07:07:51.834934Z"
1034 |     },
1035 |     "papermill": {
1036 |      "duration": 0.067354,
1037 |      "end_time": "2023-08-07T07:07:51.839451",
1038 |      "exception": false,
1039 |      "start_time": "2023-08-07T07:07:51.772097",
1040 |      "status": "completed"
1041 |     },
1042 |     "tags": []
1043 |    },
1044 |    "outputs": [
1045 |     {
1046 |      "name": "stdout",
1047 |      "output_type": "stream",
1048 |      "text": [
1049 |       "The root mean squared error  using 5-fold cross-validation on training data: -0.6592628863743302\n"
1050 |      ]
1051 |     }
1052 |    ],
1053 |    "source": [
1054 |     "rmsetrain_cv_scores = cross_val_score(model,X_train,Y_train,cv=5,scoring='neg_root_mean_squared_error')\n",
1055 |     "mean_rmsetrain_cv= rmsetrain_cv_scores.mean()\n",
1056 |     "print(\"The root mean squared error  using 5-fold cross-validation on training data:\", mean_rmsetrain_cv)\n"
1057 |    ]
1058 |   },
1059 |   {
1060 |    "cell_type": "code",
1061 |    "execution_count": 19,
1062 |    "id": "aaeadaf6",
1063 |    "metadata": {
1064 |     "execution": {
1065 |      "iopub.execute_input": "2023-08-07T07:07:51.867554Z",
1066 |      "iopub.status.busy": "2023-08-07T07:07:51.866990Z",
1067 |      "iopub.status.idle": "2023-08-07T07:07:51.917291Z",
1068 |      "shell.execute_reply": "2023-08-07T07:07:51.915704Z"
1069 |     },
1070 |     "papermill": {
1071 |      "duration": 0.06848,
1072 |      "end_time": "2023-08-07T07:07:51.920600",
1073 |      "exception": false,
1074 |      "start_time": "2023-08-07T07:07:51.852120",
1075 |      "status": "completed"
1076 |     },
1077 |     "tags": []
1078 |    },
1079 |    "outputs": [
1080 |     {
1081 |      "name": "stdout",
1082 |      "output_type": "stream",
1083 |      "text": [
1084 |       "The root mean squared error  using 5-fold cross-validation on testing data: -0.6496133904328669\n"
1085 |      ]
1086 |     }
1087 |    ],
1088 |    "source": [
1089 |     "rmsetest_cv_scores = cross_val_score(model,X_test,Y_test,cv=5,scoring='neg_root_mean_squared_error')\n",
1090 |     "mean_rmsetest_cv= rmsetest_cv_scores.mean()\n",
1091 |     "print(\"The root mean squared error  using 5-fold cross-validation on testing data:\", mean_rmsetest_cv)\n"
1092 |    ]
1093 |   }
1094 |  ],
1095 |  "metadata": {
1096 |   "kernelspec": {
1097 |    "display_name": "Python 3",
1098 |    "language": "python",
1099 |    "name": "python3"
1100 |   },
1101 |   "language_info": {
1102 |    "codemirror_mode": {
1103 |     "name": "ipython",
1104 |     "version": 3
1105 |    },
1106 |    "file_extension": ".py",
1107 |    "mimetype": "text/x-python",
1108 |    "name": "python",
1109 |    "nbconvert_exporter": "python",
1110 |    "pygments_lexer": "ipython3",
1111 |    "version": "3.10.0"
1112 |   },
1113 |   "papermill": {
1114 |    "default_parameters": {},
1115 |    "duration": 13.529599,
1116 |    "end_time": "2023-08-07T07:07:52.877766",
1117 |    "environment_variables": {},
1118 |    "exception": null,
1119 |    "input_path": "__notebook__.ipynb",
1120 |    "output_path": "__notebook__.ipynb",
1121 |    "parameters": {},
1122 |    "start_time": "2023-08-07T07:07:39.348167",
1123 |    "version": "2.4.0"
1124 |   }
1125 |  },
1126 |  "nbformat": 4,
1127 |  "nbformat_minor": 5
1128 | }
1129 | 


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