├── Churn_Modelling.csv
├── Hyperparameter Optimization For Xgboost.ipynb
└── README.md


/Hyperparameter Optimization For Xgboost.ipynb:
--------------------------------------------------------------------------------
   1 | {
   2 |  "cells": [
   3 |   {
   4 |    "cell_type": "markdown",
   5 |    "metadata": {},
   6 |    "source": [
   7 |     "## Hyperparameter Optimization For Xgboost using RandomizedSearchCV"
   8 |    ]
   9 |   },
  10 |   {
  11 |    "cell_type": "markdown",
  12 |    "metadata": {},
  13 |    "source": [
  14 |     "### Implement K Fold Cross Validation and Stratified K Fold Cross Validation"
  15 |    ]
  16 |   },
  17 |   {
  18 |    "cell_type": "code",
  19 |    "execution_count": 1,
  20 |    "metadata": {},
  21 |    "outputs": [],
  22 |    "source": [
  23 |     "import pandas as pd"
  24 |    ]
  25 |   },
  26 |   {
  27 |    "cell_type": "code",
  28 |    "execution_count": 2,
  29 |    "metadata": {},
  30 |    "outputs": [],
  31 |    "source": [
  32 |     "## Read the Dataset\n",
  33 |     "\n",
  34 |     "df=pd.read_csv('Churn_Modelling.csv')"
  35 |    ]
  36 |   },
  37 |   {
  38 |    "cell_type": "code",
  39 |    "execution_count": 3,
  40 |    "metadata": {},
  41 |    "outputs": [
  42 |     {
  43 |      "data": {
  44 |       "text/html": [
  45 |        "<div>\n",
  46 |        "<style scoped>\n",
  47 |        "    .dataframe tbody tr th:only-of-type {\n",
  48 |        "        vertical-align: middle;\n",
  49 |        "    }\n",
  50 |        "\n",
  51 |        "    .dataframe tbody tr th {\n",
  52 |        "        vertical-align: top;\n",
  53 |        "    }\n",
  54 |        "\n",
  55 |        "    .dataframe thead th {\n",
  56 |        "        text-align: right;\n",
  57 |        "    }\n",
  58 |        "</style>\n",
  59 |        "<table border=\"1\" class=\"dataframe\">\n",
  60 |        "  <thead>\n",
  61 |        "    <tr style=\"text-align: right;\">\n",
  62 |        "      <th></th>\n",
  63 |        "      <th>RowNumber</th>\n",
  64 |        "      <th>CustomerId</th>\n",
  65 |        "      <th>Surname</th>\n",
  66 |        "      <th>CreditScore</th>\n",
  67 |        "      <th>Geography</th>\n",
  68 |        "      <th>Gender</th>\n",
  69 |        "      <th>Age</th>\n",
  70 |        "      <th>Tenure</th>\n",
  71 |        "      <th>Balance</th>\n",
  72 |        "      <th>NumOfProducts</th>\n",
  73 |        "      <th>HasCrCard</th>\n",
  74 |        "      <th>IsActiveMember</th>\n",
  75 |        "      <th>EstimatedSalary</th>\n",
  76 |        "      <th>Exited</th>\n",
  77 |        "    </tr>\n",
  78 |        "  </thead>\n",
  79 |        "  <tbody>\n",
  80 |        "    <tr>\n",
  81 |        "      <th>0</th>\n",
  82 |        "      <td>1</td>\n",
  83 |        "      <td>15634602</td>\n",
  84 |        "      <td>Hargrave</td>\n",
  85 |        "      <td>619</td>\n",
  86 |        "      <td>France</td>\n",
  87 |        "      <td>Female</td>\n",
  88 |        "      <td>42</td>\n",
  89 |        "      <td>2</td>\n",
  90 |        "      <td>0.00</td>\n",
  91 |        "      <td>1</td>\n",
  92 |        "      <td>1</td>\n",
  93 |        "      <td>1</td>\n",
  94 |        "      <td>101348.88</td>\n",
  95 |        "      <td>1</td>\n",
  96 |        "    </tr>\n",
  97 |        "    <tr>\n",
  98 |        "      <th>1</th>\n",
  99 |        "      <td>2</td>\n",
 100 |        "      <td>15647311</td>\n",
 101 |        "      <td>Hill</td>\n",
 102 |        "      <td>608</td>\n",
 103 |        "      <td>Spain</td>\n",
 104 |        "      <td>Female</td>\n",
 105 |        "      <td>41</td>\n",
 106 |        "      <td>1</td>\n",
 107 |        "      <td>83807.86</td>\n",
 108 |        "      <td>1</td>\n",
 109 |        "      <td>0</td>\n",
 110 |        "      <td>1</td>\n",
 111 |        "      <td>112542.58</td>\n",
 112 |        "      <td>0</td>\n",
 113 |        "    </tr>\n",
 114 |        "    <tr>\n",
 115 |        "      <th>2</th>\n",
 116 |        "      <td>3</td>\n",
 117 |        "      <td>15619304</td>\n",
 118 |        "      <td>Onio</td>\n",
 119 |        "      <td>502</td>\n",
 120 |        "      <td>France</td>\n",
 121 |        "      <td>Female</td>\n",
 122 |        "      <td>42</td>\n",
 123 |        "      <td>8</td>\n",
 124 |        "      <td>159660.80</td>\n",
 125 |        "      <td>3</td>\n",
 126 |        "      <td>1</td>\n",
 127 |        "      <td>0</td>\n",
 128 |        "      <td>113931.57</td>\n",
 129 |        "      <td>1</td>\n",
 130 |        "    </tr>\n",
 131 |        "    <tr>\n",
 132 |        "      <th>3</th>\n",
 133 |        "      <td>4</td>\n",
 134 |        "      <td>15701354</td>\n",
 135 |        "      <td>Boni</td>\n",
 136 |        "      <td>699</td>\n",
 137 |        "      <td>France</td>\n",
 138 |        "      <td>Female</td>\n",
 139 |        "      <td>39</td>\n",
 140 |        "      <td>1</td>\n",
 141 |        "      <td>0.00</td>\n",
 142 |        "      <td>2</td>\n",
 143 |        "      <td>0</td>\n",
 144 |        "      <td>0</td>\n",
 145 |        "      <td>93826.63</td>\n",
 146 |        "      <td>0</td>\n",
 147 |        "    </tr>\n",
 148 |        "    <tr>\n",
 149 |        "      <th>4</th>\n",
 150 |        "      <td>5</td>\n",
 151 |        "      <td>15737888</td>\n",
 152 |        "      <td>Mitchell</td>\n",
 153 |        "      <td>850</td>\n",
 154 |        "      <td>Spain</td>\n",
 155 |        "      <td>Female</td>\n",
 156 |        "      <td>43</td>\n",
 157 |        "      <td>2</td>\n",
 158 |        "      <td>125510.82</td>\n",
 159 |        "      <td>1</td>\n",
 160 |        "      <td>1</td>\n",
 161 |        "      <td>1</td>\n",
 162 |        "      <td>79084.10</td>\n",
 163 |        "      <td>0</td>\n",
 164 |        "    </tr>\n",
 165 |        "  </tbody>\n",
 166 |        "</table>\n",
 167 |        "</div>"
 168 |       ],
 169 |       "text/plain": [
 170 |        "   RowNumber  CustomerId   Surname  CreditScore Geography  Gender  Age  \\\n",
 171 |        "0          1    15634602  Hargrave          619    France  Female   42   \n",
 172 |        "1          2    15647311      Hill          608     Spain  Female   41   \n",
 173 |        "2          3    15619304      Onio          502    France  Female   42   \n",
 174 |        "3          4    15701354      Boni          699    France  Female   39   \n",
 175 |        "4          5    15737888  Mitchell          850     Spain  Female   43   \n",
 176 |        "\n",
 177 |        "   Tenure    Balance  NumOfProducts  HasCrCard  IsActiveMember  \\\n",
 178 |        "0       2       0.00              1          1               1   \n",
 179 |        "1       1   83807.86              1          0               1   \n",
 180 |        "2       8  159660.80              3          1               0   \n",
 181 |        "3       1       0.00              2          0               0   \n",
 182 |        "4       2  125510.82              1          1               1   \n",
 183 |        "\n",
 184 |        "   EstimatedSalary  Exited  \n",
 185 |        "0        101348.88       1  \n",
 186 |        "1        112542.58       0  \n",
 187 |        "2        113931.57       1  \n",
 188 |        "3         93826.63       0  \n",
 189 |        "4         79084.10       0  "
 190 |       ]
 191 |      },
 192 |      "execution_count": 3,
 193 |      "metadata": {},
 194 |      "output_type": "execute_result"
 195 |     }
 196 |    ],
 197 |    "source": [
 198 |     "df.head()"
 199 |    ]
 200 |   },
 201 |   {
 202 |    "cell_type": "code",
 203 |    "execution_count": 4,
 204 |    "metadata": {},
 205 |    "outputs": [
 206 |     {
 207 |      "data": {
 208 |       "image/png": 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\n",
 209 |       "text/plain": [
 210 |        "<Figure size 1440x1440 with 2 Axes>"
 211 |       ]
 212 |      },
 213 |      "metadata": {
 214 |       "needs_background": "light"
 215 |      },
 216 |      "output_type": "display_data"
 217 |     }
 218 |    ],
 219 |    "source": [
 220 |     "## Correlation\n",
 221 |     "import seaborn as sns\n",
 222 |     "import matplotlib.pyplot as plt\n",
 223 |     "#get correlations of each features in dataset\n",
 224 |     "corrmat = df.corr()\n",
 225 |     "top_corr_features = corrmat.index\n",
 226 |     "plt.figure(figsize=(20,20))\n",
 227 |     "#plot heat map\n",
 228 |     "g=sns.heatmap(df[top_corr_features].corr(),annot=True,cmap=\"RdYlGn\")"
 229 |    ]
 230 |   },
 231 |   {
 232 |    "cell_type": "code",
 233 |    "execution_count": 5,
 234 |    "metadata": {},
 235 |    "outputs": [],
 236 |    "source": [
 237 |     "#Get the Independent and Dependent Features\n",
 238 |     "X=df.iloc[:,3:13]\n",
 239 |     "Y=df.iloc[:,13]"
 240 |    ]
 241 |   },
 242 |   {
 243 |    "cell_type": "code",
 244 |    "execution_count": 6,
 245 |    "metadata": {},
 246 |    "outputs": [],
 247 |    "source": [
 248 |     "geography=pd.get_dummies(X['Geography'],drop_first=True)"
 249 |    ]
 250 |   },
 251 |   {
 252 |    "cell_type": "code",
 253 |    "execution_count": 7,
 254 |    "metadata": {},
 255 |    "outputs": [
 256 |     {
 257 |      "data": {
 258 |       "text/html": [
 259 |        "<div>\n",
 260 |        "<style scoped>\n",
 261 |        "    .dataframe tbody tr th:only-of-type {\n",
 262 |        "        vertical-align: middle;\n",
 263 |        "    }\n",
 264 |        "\n",
 265 |        "    .dataframe tbody tr th {\n",
 266 |        "        vertical-align: top;\n",
 267 |        "    }\n",
 268 |        "\n",
 269 |        "    .dataframe thead th {\n",
 270 |        "        text-align: right;\n",
 271 |        "    }\n",
 272 |        "</style>\n",
 273 |        "<table border=\"1\" class=\"dataframe\">\n",
 274 |        "  <thead>\n",
 275 |        "    <tr style=\"text-align: right;\">\n",
 276 |        "      <th></th>\n",
 277 |        "      <th>Germany</th>\n",
 278 |        "      <th>Spain</th>\n",
 279 |        "    </tr>\n",
 280 |        "  </thead>\n",
 281 |        "  <tbody>\n",
 282 |        "    <tr>\n",
 283 |        "      <th>0</th>\n",
 284 |        "      <td>0</td>\n",
 285 |        "      <td>0</td>\n",
 286 |        "    </tr>\n",
 287 |        "    <tr>\n",
 288 |        "      <th>1</th>\n",
 289 |        "      <td>0</td>\n",
 290 |        "      <td>1</td>\n",
 291 |        "    </tr>\n",
 292 |        "    <tr>\n",
 293 |        "      <th>2</th>\n",
 294 |        "      <td>0</td>\n",
 295 |        "      <td>0</td>\n",
 296 |        "    </tr>\n",
 297 |        "    <tr>\n",
 298 |        "      <th>3</th>\n",
 299 |        "      <td>0</td>\n",
 300 |        "      <td>0</td>\n",
 301 |        "    </tr>\n",
 302 |        "    <tr>\n",
 303 |        "      <th>4</th>\n",
 304 |        "      <td>0</td>\n",
 305 |        "      <td>1</td>\n",
 306 |        "    </tr>\n",
 307 |        "  </tbody>\n",
 308 |        "</table>\n",
 309 |        "</div>"
 310 |       ],
 311 |       "text/plain": [
 312 |        "   Germany  Spain\n",
 313 |        "0        0      0\n",
 314 |        "1        0      1\n",
 315 |        "2        0      0\n",
 316 |        "3        0      0\n",
 317 |        "4        0      1"
 318 |       ]
 319 |      },
 320 |      "execution_count": 7,
 321 |      "metadata": {},
 322 |      "output_type": "execute_result"
 323 |     }
 324 |    ],
 325 |    "source": [
 326 |     "geography.head()"
 327 |    ]
 328 |   },
 329 |   {
 330 |    "cell_type": "code",
 331 |    "execution_count": 8,
 332 |    "metadata": {},
 333 |    "outputs": [],
 334 |    "source": [
 335 |     "gender=pd.get_dummies(X['Gender'],drop_first=True)"
 336 |    ]
 337 |   },
 338 |   {
 339 |    "cell_type": "code",
 340 |    "execution_count": 9,
 341 |    "metadata": {},
 342 |    "outputs": [
 343 |     {
 344 |      "data": {
 345 |       "text/html": [
 346 |        "<div>\n",
 347 |        "<style scoped>\n",
 348 |        "    .dataframe tbody tr th:only-of-type {\n",
 349 |        "        vertical-align: middle;\n",
 350 |        "    }\n",
 351 |        "\n",
 352 |        "    .dataframe tbody tr th {\n",
 353 |        "        vertical-align: top;\n",
 354 |        "    }\n",
 355 |        "\n",
 356 |        "    .dataframe thead th {\n",
 357 |        "        text-align: right;\n",
 358 |        "    }\n",
 359 |        "</style>\n",
 360 |        "<table border=\"1\" class=\"dataframe\">\n",
 361 |        "  <thead>\n",
 362 |        "    <tr style=\"text-align: right;\">\n",
 363 |        "      <th></th>\n",
 364 |        "      <th>Male</th>\n",
 365 |        "    </tr>\n",
 366 |        "  </thead>\n",
 367 |        "  <tbody>\n",
 368 |        "    <tr>\n",
 369 |        "      <th>0</th>\n",
 370 |        "      <td>0</td>\n",
 371 |        "    </tr>\n",
 372 |        "    <tr>\n",
 373 |        "      <th>1</th>\n",
 374 |        "      <td>0</td>\n",
 375 |        "    </tr>\n",
 376 |        "    <tr>\n",
 377 |        "      <th>2</th>\n",
 378 |        "      <td>0</td>\n",
 379 |        "    </tr>\n",
 380 |        "    <tr>\n",
 381 |        "      <th>3</th>\n",
 382 |        "      <td>0</td>\n",
 383 |        "    </tr>\n",
 384 |        "    <tr>\n",
 385 |        "      <th>4</th>\n",
 386 |        "      <td>0</td>\n",
 387 |        "    </tr>\n",
 388 |        "  </tbody>\n",
 389 |        "</table>\n",
 390 |        "</div>"
 391 |       ],
 392 |       "text/plain": [
 393 |        "   Male\n",
 394 |        "0     0\n",
 395 |        "1     0\n",
 396 |        "2     0\n",
 397 |        "3     0\n",
 398 |        "4     0"
 399 |       ]
 400 |      },
 401 |      "execution_count": 9,
 402 |      "metadata": {},
 403 |      "output_type": "execute_result"
 404 |     }
 405 |    ],
 406 |    "source": [
 407 |     "gender.head()"
 408 |    ]
 409 |   },
 410 |   {
 411 |    "cell_type": "code",
 412 |    "execution_count": 10,
 413 |    "metadata": {},
 414 |    "outputs": [],
 415 |    "source": [
 416 |     "## Drop Categorical Features\n",
 417 |     "X=X.drop(['Geography','Gender'],axis=1)"
 418 |    ]
 419 |   },
 420 |   {
 421 |    "cell_type": "code",
 422 |    "execution_count": 11,
 423 |    "metadata": {},
 424 |    "outputs": [
 425 |     {
 426 |      "data": {
 427 |       "text/html": [
 428 |        "<div>\n",
 429 |        "<style scoped>\n",
 430 |        "    .dataframe tbody tr th:only-of-type {\n",
 431 |        "        vertical-align: middle;\n",
 432 |        "    }\n",
 433 |        "\n",
 434 |        "    .dataframe tbody tr th {\n",
 435 |        "        vertical-align: top;\n",
 436 |        "    }\n",
 437 |        "\n",
 438 |        "    .dataframe thead th {\n",
 439 |        "        text-align: right;\n",
 440 |        "    }\n",
 441 |        "</style>\n",
 442 |        "<table border=\"1\" class=\"dataframe\">\n",
 443 |        "  <thead>\n",
 444 |        "    <tr style=\"text-align: right;\">\n",
 445 |        "      <th></th>\n",
 446 |        "      <th>CreditScore</th>\n",
 447 |        "      <th>Age</th>\n",
 448 |        "      <th>Tenure</th>\n",
 449 |        "      <th>Balance</th>\n",
 450 |        "      <th>NumOfProducts</th>\n",
 451 |        "      <th>HasCrCard</th>\n",
 452 |        "      <th>IsActiveMember</th>\n",
 453 |        "      <th>EstimatedSalary</th>\n",
 454 |        "    </tr>\n",
 455 |        "  </thead>\n",
 456 |        "  <tbody>\n",
 457 |        "    <tr>\n",
 458 |        "      <th>0</th>\n",
 459 |        "      <td>619</td>\n",
 460 |        "      <td>42</td>\n",
 461 |        "      <td>2</td>\n",
 462 |        "      <td>0.00</td>\n",
 463 |        "      <td>1</td>\n",
 464 |        "      <td>1</td>\n",
 465 |        "      <td>1</td>\n",
 466 |        "      <td>101348.88</td>\n",
 467 |        "    </tr>\n",
 468 |        "    <tr>\n",
 469 |        "      <th>1</th>\n",
 470 |        "      <td>608</td>\n",
 471 |        "      <td>41</td>\n",
 472 |        "      <td>1</td>\n",
 473 |        "      <td>83807.86</td>\n",
 474 |        "      <td>1</td>\n",
 475 |        "      <td>0</td>\n",
 476 |        "      <td>1</td>\n",
 477 |        "      <td>112542.58</td>\n",
 478 |        "    </tr>\n",
 479 |        "    <tr>\n",
 480 |        "      <th>2</th>\n",
 481 |        "      <td>502</td>\n",
 482 |        "      <td>42</td>\n",
 483 |        "      <td>8</td>\n",
 484 |        "      <td>159660.80</td>\n",
 485 |        "      <td>3</td>\n",
 486 |        "      <td>1</td>\n",
 487 |        "      <td>0</td>\n",
 488 |        "      <td>113931.57</td>\n",
 489 |        "    </tr>\n",
 490 |        "    <tr>\n",
 491 |        "      <th>3</th>\n",
 492 |        "      <td>699</td>\n",
 493 |        "      <td>39</td>\n",
 494 |        "      <td>1</td>\n",
 495 |        "      <td>0.00</td>\n",
 496 |        "      <td>2</td>\n",
 497 |        "      <td>0</td>\n",
 498 |        "      <td>0</td>\n",
 499 |        "      <td>93826.63</td>\n",
 500 |        "    </tr>\n",
 501 |        "    <tr>\n",
 502 |        "      <th>4</th>\n",
 503 |        "      <td>850</td>\n",
 504 |        "      <td>43</td>\n",
 505 |        "      <td>2</td>\n",
 506 |        "      <td>125510.82</td>\n",
 507 |        "      <td>1</td>\n",
 508 |        "      <td>1</td>\n",
 509 |        "      <td>1</td>\n",
 510 |        "      <td>79084.10</td>\n",
 511 |        "    </tr>\n",
 512 |        "  </tbody>\n",
 513 |        "</table>\n",
 514 |        "</div>"
 515 |       ],
 516 |       "text/plain": [
 517 |        "   CreditScore  Age  Tenure    Balance  NumOfProducts  HasCrCard  \\\n",
 518 |        "0          619   42       2       0.00              1          1   \n",
 519 |        "1          608   41       1   83807.86              1          0   \n",
 520 |        "2          502   42       8  159660.80              3          1   \n",
 521 |        "3          699   39       1       0.00              2          0   \n",
 522 |        "4          850   43       2  125510.82              1          1   \n",
 523 |        "\n",
 524 |        "   IsActiveMember  EstimatedSalary  \n",
 525 |        "0               1        101348.88  \n",
 526 |        "1               1        112542.58  \n",
 527 |        "2               0        113931.57  \n",
 528 |        "3               0         93826.63  \n",
 529 |        "4               1         79084.10  "
 530 |       ]
 531 |      },
 532 |      "execution_count": 11,
 533 |      "metadata": {},
 534 |      "output_type": "execute_result"
 535 |     }
 536 |    ],
 537 |    "source": [
 538 |     "X.head()"
 539 |    ]
 540 |   },
 541 |   {
 542 |    "cell_type": "code",
 543 |    "execution_count": 12,
 544 |    "metadata": {},
 545 |    "outputs": [],
 546 |    "source": [
 547 |     "X=pd.concat([X,geography,gender],axis=1)"
 548 |    ]
 549 |   },
 550 |   {
 551 |    "cell_type": "code",
 552 |    "execution_count": 13,
 553 |    "metadata": {},
 554 |    "outputs": [
 555 |     {
 556 |      "data": {
 557 |       "text/html": [
 558 |        "<div>\n",
 559 |        "<style scoped>\n",
 560 |        "    .dataframe tbody tr th:only-of-type {\n",
 561 |        "        vertical-align: middle;\n",
 562 |        "    }\n",
 563 |        "\n",
 564 |        "    .dataframe tbody tr th {\n",
 565 |        "        vertical-align: top;\n",
 566 |        "    }\n",
 567 |        "\n",
 568 |        "    .dataframe thead th {\n",
 569 |        "        text-align: right;\n",
 570 |        "    }\n",
 571 |        "</style>\n",
 572 |        "<table border=\"1\" class=\"dataframe\">\n",
 573 |        "  <thead>\n",
 574 |        "    <tr style=\"text-align: right;\">\n",
 575 |        "      <th></th>\n",
 576 |        "      <th>CreditScore</th>\n",
 577 |        "      <th>Age</th>\n",
 578 |        "      <th>Tenure</th>\n",
 579 |        "      <th>Balance</th>\n",
 580 |        "      <th>NumOfProducts</th>\n",
 581 |        "      <th>HasCrCard</th>\n",
 582 |        "      <th>IsActiveMember</th>\n",
 583 |        "      <th>EstimatedSalary</th>\n",
 584 |        "      <th>Germany</th>\n",
 585 |        "      <th>Spain</th>\n",
 586 |        "      <th>Male</th>\n",
 587 |        "    </tr>\n",
 588 |        "  </thead>\n",
 589 |        "  <tbody>\n",
 590 |        "    <tr>\n",
 591 |        "      <th>0</th>\n",
 592 |        "      <td>619</td>\n",
 593 |        "      <td>42</td>\n",
 594 |        "      <td>2</td>\n",
 595 |        "      <td>0.00</td>\n",
 596 |        "      <td>1</td>\n",
 597 |        "      <td>1</td>\n",
 598 |        "      <td>1</td>\n",
 599 |        "      <td>101348.88</td>\n",
 600 |        "      <td>0</td>\n",
 601 |        "      <td>0</td>\n",
 602 |        "      <td>0</td>\n",
 603 |        "    </tr>\n",
 604 |        "    <tr>\n",
 605 |        "      <th>1</th>\n",
 606 |        "      <td>608</td>\n",
 607 |        "      <td>41</td>\n",
 608 |        "      <td>1</td>\n",
 609 |        "      <td>83807.86</td>\n",
 610 |        "      <td>1</td>\n",
 611 |        "      <td>0</td>\n",
 612 |        "      <td>1</td>\n",
 613 |        "      <td>112542.58</td>\n",
 614 |        "      <td>0</td>\n",
 615 |        "      <td>1</td>\n",
 616 |        "      <td>0</td>\n",
 617 |        "    </tr>\n",
 618 |        "    <tr>\n",
 619 |        "      <th>2</th>\n",
 620 |        "      <td>502</td>\n",
 621 |        "      <td>42</td>\n",
 622 |        "      <td>8</td>\n",
 623 |        "      <td>159660.80</td>\n",
 624 |        "      <td>3</td>\n",
 625 |        "      <td>1</td>\n",
 626 |        "      <td>0</td>\n",
 627 |        "      <td>113931.57</td>\n",
 628 |        "      <td>0</td>\n",
 629 |        "      <td>0</td>\n",
 630 |        "      <td>0</td>\n",
 631 |        "    </tr>\n",
 632 |        "    <tr>\n",
 633 |        "      <th>3</th>\n",
 634 |        "      <td>699</td>\n",
 635 |        "      <td>39</td>\n",
 636 |        "      <td>1</td>\n",
 637 |        "      <td>0.00</td>\n",
 638 |        "      <td>2</td>\n",
 639 |        "      <td>0</td>\n",
 640 |        "      <td>0</td>\n",
 641 |        "      <td>93826.63</td>\n",
 642 |        "      <td>0</td>\n",
 643 |        "      <td>0</td>\n",
 644 |        "      <td>0</td>\n",
 645 |        "    </tr>\n",
 646 |        "    <tr>\n",
 647 |        "      <th>4</th>\n",
 648 |        "      <td>850</td>\n",
 649 |        "      <td>43</td>\n",
 650 |        "      <td>2</td>\n",
 651 |        "      <td>125510.82</td>\n",
 652 |        "      <td>1</td>\n",
 653 |        "      <td>1</td>\n",
 654 |        "      <td>1</td>\n",
 655 |        "      <td>79084.10</td>\n",
 656 |        "      <td>0</td>\n",
 657 |        "      <td>1</td>\n",
 658 |        "      <td>0</td>\n",
 659 |        "    </tr>\n",
 660 |        "  </tbody>\n",
 661 |        "</table>\n",
 662 |        "</div>"
 663 |       ],
 664 |       "text/plain": [
 665 |        "   CreditScore  Age  Tenure    Balance  NumOfProducts  HasCrCard  \\\n",
 666 |        "0          619   42       2       0.00              1          1   \n",
 667 |        "1          608   41       1   83807.86              1          0   \n",
 668 |        "2          502   42       8  159660.80              3          1   \n",
 669 |        "3          699   39       1       0.00              2          0   \n",
 670 |        "4          850   43       2  125510.82              1          1   \n",
 671 |        "\n",
 672 |        "   IsActiveMember  EstimatedSalary  Germany  Spain  Male  \n",
 673 |        "0               1        101348.88        0      0     0  \n",
 674 |        "1               1        112542.58        0      1     0  \n",
 675 |        "2               0        113931.57        0      0     0  \n",
 676 |        "3               0         93826.63        0      0     0  \n",
 677 |        "4               1         79084.10        0      1     0  "
 678 |       ]
 679 |      },
 680 |      "execution_count": 13,
 681 |      "metadata": {},
 682 |      "output_type": "execute_result"
 683 |     }
 684 |    ],
 685 |    "source": [
 686 |     "X.head()"
 687 |    ]
 688 |   },
 689 |   {
 690 |    "cell_type": "code",
 691 |    "execution_count": 14,
 692 |    "metadata": {},
 693 |    "outputs": [],
 694 |    "source": [
 695 |     "## Hyper Parameter Optimization\n",
 696 |     "\n",
 697 |     "params={\n",
 698 |     " \"learning_rate\"    : [0.05, 0.10, 0.15, 0.20, 0.25, 0.30 ] ,\n",
 699 |     " \"max_depth\"        : [ 3, 4, 5, 6, 8, 10, 12, 15],\n",
 700 |     " \"min_child_weight\" : [ 1, 3, 5, 7 ],\n",
 701 |     " \"gamma\"            : [ 0.0, 0.1, 0.2 , 0.3, 0.4 ],\n",
 702 |     " \"colsample_bytree\" : [ 0.3, 0.4, 0.5 , 0.7 ]\n",
 703 |     "    \n",
 704 |     "}"
 705 |    ]
 706 |   },
 707 |   {
 708 |    "cell_type": "code",
 709 |    "execution_count": 15,
 710 |    "metadata": {},
 711 |    "outputs": [],
 712 |    "source": [
 713 |     "## Hyperparameter optimization using RandomizedSearchCV\n",
 714 |     "from sklearn.model_selection import RandomizedSearchCV, GridSearchCV\n",
 715 |     "import xgboost"
 716 |    ]
 717 |   },
 718 |   {
 719 |    "cell_type": "code",
 720 |    "execution_count": 16,
 721 |    "metadata": {},
 722 |    "outputs": [],
 723 |    "source": [
 724 |     "\n",
 725 |     "def timer(start_time=None):\n",
 726 |     "    if not start_time:\n",
 727 |     "        start_time = datetime.now()\n",
 728 |     "        return start_time\n",
 729 |     "    elif start_time:\n",
 730 |     "        thour, temp_sec = divmod((datetime.now() - start_time).total_seconds(), 3600)\n",
 731 |     "        tmin, tsec = divmod(temp_sec, 60)\n",
 732 |     "        print('\\n Time taken: %i hours %i minutes and %s seconds.' % (thour, tmin, round(tsec, 2)))"
 733 |    ]
 734 |   },
 735 |   {
 736 |    "cell_type": "code",
 737 |    "execution_count": 17,
 738 |    "metadata": {},
 739 |    "outputs": [],
 740 |    "source": [
 741 |     "classifier=xgboost.XGBClassifier()"
 742 |    ]
 743 |   },
 744 |   {
 745 |    "cell_type": "code",
 746 |    "execution_count": 18,
 747 |    "metadata": {},
 748 |    "outputs": [],
 749 |    "source": [
 750 |     "random_search=RandomizedSearchCV(classifier,param_distributions=params,n_iter=5,scoring='roc_auc',n_jobs=-1,cv=5,verbose=3)"
 751 |    ]
 752 |   },
 753 |   {
 754 |    "cell_type": "code",
 755 |    "execution_count": 19,
 756 |    "metadata": {},
 757 |    "outputs": [
 758 |     {
 759 |      "name": "stdout",
 760 |      "output_type": "stream",
 761 |      "text": [
 762 |       "Fitting 5 folds for each of 5 candidates, totalling 25 fits\n"
 763 |      ]
 764 |     },
 765 |     {
 766 |      "name": "stderr",
 767 |      "output_type": "stream",
 768 |      "text": [
 769 |       "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 12 concurrent workers.\n",
 770 |       "[Parallel(n_jobs=-1)]: Done  11 out of  25 | elapsed:    3.3s remaining:    4.2s\n",
 771 |       "[Parallel(n_jobs=-1)]: Done  20 out of  25 | elapsed:    3.8s remaining:    0.9s\n",
 772 |       "[Parallel(n_jobs=-1)]: Done  25 out of  25 | elapsed:    3.8s finished\n"
 773 |      ]
 774 |     },
 775 |     {
 776 |      "name": "stdout",
 777 |      "output_type": "stream",
 778 |      "text": [
 779 |       "\n",
 780 |       " Time taken: 0 hours 0 minutes and 4.69 seconds.\n"
 781 |      ]
 782 |     }
 783 |    ],
 784 |    "source": [
 785 |     "from datetime import datetime\n",
 786 |     "# Here we go\n",
 787 |     "start_time = timer(None) # timing starts from this point for \"start_time\" variable\n",
 788 |     "random_search.fit(X,Y)\n",
 789 |     "timer(start_time) # timing ends here for \"start_time\" variable"
 790 |    ]
 791 |   },
 792 |   {
 793 |    "cell_type": "code",
 794 |    "execution_count": 63,
 795 |    "metadata": {},
 796 |    "outputs": [
 797 |     {
 798 |      "data": {
 799 |       "text/plain": [
 800 |        "(10000, 11)"
 801 |       ]
 802 |      },
 803 |      "execution_count": 63,
 804 |      "metadata": {},
 805 |      "output_type": "execute_result"
 806 |     }
 807 |    ],
 808 |    "source": [
 809 |     "X.shape"
 810 |    ]
 811 |   },
 812 |   {
 813 |    "cell_type": "code",
 814 |    "execution_count": 21,
 815 |    "metadata": {},
 816 |    "outputs": [
 817 |     {
 818 |      "data": {
 819 |       "text/plain": [
 820 |        "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
 821 |        "       colsample_bytree=0.4, gamma=0.0, learning_rate=0.3,\n",
 822 |        "       max_delta_step=0, max_depth=3, min_child_weight=7, missing=None,\n",
 823 |        "       n_estimators=100, n_jobs=1, nthread=None,\n",
 824 |        "       objective='binary:logistic', random_state=0, reg_alpha=0,\n",
 825 |        "       reg_lambda=1, scale_pos_weight=1, seed=None, silent=True,\n",
 826 |        "       subsample=1)"
 827 |       ]
 828 |      },
 829 |      "execution_count": 21,
 830 |      "metadata": {},
 831 |      "output_type": "execute_result"
 832 |     }
 833 |    ],
 834 |    "source": [
 835 |     "random_search.best_estimator_"
 836 |    ]
 837 |   },
 838 |   {
 839 |    "cell_type": "code",
 840 |    "execution_count": 22,
 841 |    "metadata": {},
 842 |    "outputs": [
 843 |     {
 844 |      "data": {
 845 |       "text/plain": [
 846 |        "{'min_child_weight': 7,\n",
 847 |        " 'max_depth': 3,\n",
 848 |        " 'learning_rate': 0.3,\n",
 849 |        " 'gamma': 0.0,\n",
 850 |        " 'colsample_bytree': 0.4}"
 851 |       ]
 852 |      },
 853 |      "execution_count": 22,
 854 |      "metadata": {},
 855 |      "output_type": "execute_result"
 856 |     }
 857 |    ],
 858 |    "source": [
 859 |     "random_search.best_params_"
 860 |    ]
 861 |   },
 862 |   {
 863 |    "cell_type": "code",
 864 |    "execution_count": 23,
 865 |    "metadata": {},
 866 |    "outputs": [],
 867 |    "source": [
 868 |     "classifier=xgboost.XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
 869 |     "       colsample_bytree=0.5, gamma=0.4, learning_rate=0.1,\n",
 870 |     "       max_delta_step=0, max_depth=6, min_child_weight=7, missing=None,\n",
 871 |     "       n_estimators=100, n_jobs=1, nthread=None,\n",
 872 |     "       objective='binary:logistic', random_state=0, reg_alpha=0,\n",
 873 |     "       reg_lambda=1, scale_pos_weight=1, seed=None, silent=True,\n",
 874 |     "       subsample=1)"
 875 |    ]
 876 |   },
 877 |   {
 878 |    "cell_type": "code",
 879 |    "execution_count": 20,
 880 |    "metadata": {},
 881 |    "outputs": [],
 882 |    "source": [
 883 |     "from sklearn.model_selection import train_test_split\n",
 884 |     "\n",
 885 |     "X_train,X_test,y_train,y_test=train_test_split(X,Y,test_size=0.3,random_state=200)"
 886 |    ]
 887 |   },
 888 |   {
 889 |    "cell_type": "code",
 890 |    "execution_count": 21,
 891 |    "metadata": {},
 892 |    "outputs": [
 893 |     {
 894 |      "data": {
 895 |       "text/plain": [
 896 |        "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
 897 |        "              colsample_bynode=1, colsample_bytree=1, gamma=0, gpu_id=-1,\n",
 898 |        "              importance_type='gain', interaction_constraints='',\n",
 899 |        "              learning_rate=0.300000012, max_delta_step=0, max_depth=6,\n",
 900 |        "              min_child_weight=1, missing=nan, monotone_constraints='()',\n",
 901 |        "              n_estimators=100, n_jobs=0, num_parallel_tree=1, random_state=0,\n",
 902 |        "              reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=1,\n",
 903 |        "              tree_method='exact', validate_parameters=1, verbosity=None)"
 904 |       ]
 905 |      },
 906 |      "execution_count": 21,
 907 |      "metadata": {},
 908 |      "output_type": "execute_result"
 909 |     }
 910 |    ],
 911 |    "source": [
 912 |     "classifier.fit(X_train,y_train)"
 913 |    ]
 914 |   },
 915 |   {
 916 |    "cell_type": "code",
 917 |    "execution_count": 22,
 918 |    "metadata": {},
 919 |    "outputs": [],
 920 |    "source": [
 921 |     "prediction=classifier.predict(X_test)"
 922 |    ]
 923 |   },
 924 |   {
 925 |    "cell_type": "code",
 926 |    "execution_count": 23,
 927 |    "metadata": {},
 928 |    "outputs": [
 929 |     {
 930 |      "name": "stdout",
 931 |      "output_type": "stream",
 932 |      "text": [
 933 |       "[[2231  325]\n",
 934 |       " [ 139  305]]\n",
 935 |       "0.8453333333333334\n"
 936 |      ]
 937 |     }
 938 |    ],
 939 |    "source": [
 940 |     "from sklearn.metrics import confusion_matrix,accuracy_score\n",
 941 |     "cm=confusion_matrix(prediction,y_test)\n",
 942 |     "print(cm)\n",
 943 |     "acc_score=accuracy_score(prediction,y_test)\n",
 944 |     "print(acc_score)"
 945 |    ]
 946 |   },
 947 |   {
 948 |    "cell_type": "markdown",
 949 |    "metadata": {},
 950 |    "source": [
 951 |     "## Cross Validation\n",
 952 |     "\n",
 953 |     "### K Fold CV"
 954 |    ]
 955 |   },
 956 |   {
 957 |    "cell_type": "code",
 958 |    "execution_count": 29,
 959 |    "metadata": {},
 960 |    "outputs": [],
 961 |    "source": [
 962 |     "from sklearn.model_selection import cross_val_score\n",
 963 |     "score=cross_val_score(classifier,X,Y,cv=10)"
 964 |    ]
 965 |   },
 966 |   {
 967 |    "cell_type": "code",
 968 |    "execution_count": 30,
 969 |    "metadata": {},
 970 |    "outputs": [
 971 |     {
 972 |      "data": {
 973 |       "text/plain": [
 974 |        "array([0.861, 0.859, 0.858, 0.853, 0.849, 0.86 , 0.853, 0.866, 0.853,\n",
 975 |        "       0.845])"
 976 |       ]
 977 |      },
 978 |      "execution_count": 30,
 979 |      "metadata": {},
 980 |      "output_type": "execute_result"
 981 |     }
 982 |    ],
 983 |    "source": [
 984 |     "score"
 985 |    ]
 986 |   },
 987 |   {
 988 |    "cell_type": "code",
 989 |    "execution_count": 31,
 990 |    "metadata": {},
 991 |    "outputs": [
 992 |     {
 993 |      "data": {
 994 |       "text/plain": [
 995 |        "0.8557"
 996 |       ]
 997 |      },
 998 |      "execution_count": 31,
 999 |      "metadata": {},
1000 |      "output_type": "execute_result"
1001 |     }
1002 |    ],
1003 |    "source": [
1004 |     "score.mean()"
1005 |    ]
1006 |   },
1007 |   {
1008 |    "cell_type": "code",
1009 |    "execution_count": null,
1010 |    "metadata": {},
1011 |    "outputs": [],
1012 |    "source": []
1013 |   },
1014 |   {
1015 |    "cell_type": "markdown",
1016 |    "metadata": {},
1017 |    "source": [
1018 |     "### Stratified K fold Cross Validation"
1019 |    ]
1020 |   },
1021 |   {
1022 |    "cell_type": "code",
1023 |    "execution_count": 32,
1024 |    "metadata": {},
1025 |    "outputs": [
1026 |     {
1027 |      "data": {
1028 |       "text/plain": [
1029 |        "((10000, 11), (10000,))"
1030 |       ]
1031 |      },
1032 |      "execution_count": 32,
1033 |      "metadata": {},
1034 |      "output_type": "execute_result"
1035 |     }
1036 |    ],
1037 |    "source": [
1038 |     "X.shape,Y.shape"
1039 |    ]
1040 |   },
1041 |   {
1042 |    "cell_type": "code",
1043 |    "execution_count": 41,
1044 |    "metadata": {},
1045 |    "outputs": [],
1046 |    "source": [
1047 |     "from sklearn.model_selection import StratifiedKFold"
1048 |    ]
1049 |   },
1050 |   {
1051 |    "cell_type": "code",
1052 |    "execution_count": 42,
1053 |    "metadata": {},
1054 |    "outputs": [],
1055 |    "source": [
1056 |     "skf=StratifiedKFold(n_splits=10)"
1057 |    ]
1058 |   },
1059 |   {
1060 |    "cell_type": "code",
1061 |    "execution_count": 46,
1062 |    "metadata": {},
1063 |    "outputs": [
1064 |     {
1065 |      "data": {
1066 |       "text/plain": [
1067 |        "10"
1068 |       ]
1069 |      },
1070 |      "execution_count": 46,
1071 |      "metadata": {},
1072 |      "output_type": "execute_result"
1073 |     }
1074 |    ],
1075 |    "source": [
1076 |     "skf.get_n_splits(X, Y)"
1077 |    ]
1078 |   },
1079 |   {
1080 |    "cell_type": "code",
1081 |    "execution_count": 45,
1082 |    "metadata": {},
1083 |    "outputs": [
1084 |     {
1085 |      "data": {
1086 |       "text/plain": [
1087 |        "CreditScore           612.0\n",
1088 |        "Age                    33.0\n",
1089 |        "Tenure                  9.0\n",
1090 |        "Balance                 0.0\n",
1091 |        "NumOfProducts           1.0\n",
1092 |        "HasCrCard               0.0\n",
1093 |        "IsActiveMember          0.0\n",
1094 |        "EstimatedSalary    142797.5\n",
1095 |        "Germany                 0.0\n",
1096 |        "Spain                   0.0\n",
1097 |        "Male                    0.0\n",
1098 |        "Name: 1965, dtype: float64"
1099 |       ]
1100 |      },
1101 |      "execution_count": 45,
1102 |      "metadata": {},
1103 |      "output_type": "execute_result"
1104 |     }
1105 |    ],
1106 |    "source": [
1107 |     "X.iloc[1965]"
1108 |    ]
1109 |   },
1110 |   {
1111 |    "cell_type": "code",
1112 |    "execution_count": 47,
1113 |    "metadata": {},
1114 |    "outputs": [
1115 |     {
1116 |      "name": "stdout",
1117 |      "output_type": "stream",
1118 |      "text": [
1119 |       "Train: [1000 1001 1002 ... 9997 9998 9999] Validation: [  0   1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17\n",
1120 |       "  18  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35\n",
1121 |       "  36  37  38  39  40  41  42  43  44  45  46  47  48  49  50  51  52  53\n",
1122 |       "  54  55  56  57  58  59  60  61  62  63  64  65  66  67  68  69  70  71\n",
1123 |       "  72  73  74  75  76  77  78  79  80  81  82  83  84  85  86  87  88  89\n",
1124 |       "  90  91  92  93  94  95  96  97  98  99 100 101 102 103 104 105 106 107\n",
1125 |       " 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125\n",
1126 |       " 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143\n",
1127 |       " 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161\n",
1128 |       " 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179\n",
1129 |       " 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197\n",
1130 |       " 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215\n",
1131 |       " 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233\n",
1132 |       " 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251\n",
1133 |       " 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269\n",
1134 |       " 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287\n",
1135 |       " 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305\n",
1136 |       " 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323\n",
1137 |       " 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341\n",
1138 |       " 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359\n",
1139 |       " 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377\n",
1140 |       " 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395\n",
1141 |       " 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413\n",
1142 |       " 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431\n",
1143 |       " 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449\n",
1144 |       " 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467\n",
1145 |       " 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485\n",
1146 |       " 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503\n",
1147 |       " 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521\n",
1148 |       " 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539\n",
1149 |       " 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557\n",
1150 |       " 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575\n",
1151 |       " 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593\n",
1152 |       " 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611\n",
1153 |       " 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629\n",
1154 |       " 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647\n",
1155 |       " 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665\n",
1156 |       " 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683\n",
1157 |       " 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701\n",
1158 |       " 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719\n",
1159 |       " 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737\n",
1160 |       " 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755\n",
1161 |       " 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773\n",
1162 |       " 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791\n",
1163 |       " 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809\n",
1164 |       " 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827\n",
1165 |       " 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845\n",
1166 |       " 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863\n",
1167 |       " 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881\n",
1168 |       " 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899\n",
1169 |       " 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917\n",
1170 |       " 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935\n",
1171 |       " 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953\n",
1172 |       " 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971\n",
1173 |       " 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989\n",
1174 |       " 990 991 992 993 994 995 996 997 998 999]\n",
1175 |       "Train: [   0    1    2 ... 9997 9998 9999] Validation: [1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013\n",
1176 |       " 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027\n",
1177 |       " 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041\n",
1178 |       " 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055\n",
1179 |       " 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069\n",
1180 |       " 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083\n",
1181 |       " 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097\n",
1182 |       " 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111\n",
1183 |       " 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125\n",
1184 |       " 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139\n",
1185 |       " 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153\n",
1186 |       " 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167\n",
1187 |       " 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181\n",
1188 |       " 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195\n",
1189 |       " 1196 1197 1198 1199 1200 1201 1202 1203 1204 1205 1206 1207 1208 1209\n",
1190 |       " 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223\n",
1191 |       " 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237\n",
1192 |       " 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249 1250 1251\n",
1193 |       " 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264 1265\n",
1194 |       " 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279\n",
1195 |       " 1280 1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293\n",
1196 |       " 1294 1295 1296 1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307\n",
1197 |       " 1308 1309 1310 1311 1312 1313 1314 1315 1316 1317 1318 1319 1320 1321\n",
1198 |       " 1322 1323 1324 1325 1326 1327 1328 1329 1330 1331 1332 1333 1334 1335\n",
1199 |       " 1336 1337 1338 1339 1340 1341 1342 1343 1344 1345 1346 1347 1348 1349\n",
1200 |       " 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359 1360 1361 1362 1363\n",
1201 |       " 1364 1365 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376 1377\n",
1202 |       " 1378 1379 1380 1381 1382 1383 1384 1385 1386 1387 1388 1389 1390 1391\n",
1203 |       " 1392 1393 1394 1395 1396 1397 1398 1399 1400 1401 1402 1403 1404 1405\n",
1204 |       " 1406 1407 1408 1409 1410 1411 1412 1413 1414 1415 1416 1417 1418 1419\n",
1205 |       " 1420 1421 1422 1423 1424 1425 1426 1427 1428 1429 1430 1431 1432 1433\n",
1206 |       " 1434 1435 1436 1437 1438 1439 1440 1441 1442 1443 1444 1445 1446 1447\n",
1207 |       " 1448 1449 1450 1451 1452 1453 1454 1455 1456 1457 1458 1459 1460 1461\n",
1208 |       " 1462 1463 1464 1465 1466 1467 1468 1469 1470 1471 1472 1473 1474 1475\n",
1209 |       " 1476 1477 1478 1479 1480 1481 1482 1483 1484 1485 1486 1487 1488 1489\n",
1210 |       " 1490 1491 1492 1493 1494 1495 1496 1497 1498 1499 1500 1501 1502 1503\n",
1211 |       " 1504 1505 1506 1507 1508 1509 1510 1511 1512 1513 1514 1515 1516 1517\n",
1212 |       " 1518 1519 1520 1521 1522 1523 1524 1525 1526 1527 1528 1529 1530 1531\n",
1213 |       " 1532 1533 1534 1535 1536 1537 1538 1539 1540 1541 1542 1543 1544 1545\n",
1214 |       " 1546 1547 1548 1549 1550 1551 1552 1553 1554 1555 1556 1557 1558 1559\n",
1215 |       " 1560 1561 1562 1563 1564 1565 1566 1567 1568 1569 1570 1571 1572 1573\n",
1216 |       " 1574 1575 1576 1577 1578 1579 1580 1581 1582 1583 1584 1585 1586 1587\n",
1217 |       " 1588 1589 1590 1591 1592 1593 1594 1595 1596 1597 1598 1599 1600 1601\n",
1218 |       " 1602 1603 1604 1605 1606 1607 1608 1609 1610 1611 1612 1613 1614 1615\n",
1219 |       " 1616 1617 1618 1619 1620 1621 1622 1623 1624 1625 1626 1627 1628 1629\n",
1220 |       " 1630 1631 1632 1633 1634 1635 1636 1637 1638 1639 1640 1641 1642 1643\n",
1221 |       " 1644 1645 1646 1647 1648 1649 1650 1651 1652 1653 1654 1655 1656 1657\n",
1222 |       " 1658 1659 1660 1661 1662 1663 1664 1665 1666 1667 1668 1669 1670 1671\n",
1223 |       " 1672 1673 1674 1675 1676 1677 1678 1679 1680 1681 1682 1683 1684 1685\n",
1224 |       " 1686 1687 1688 1689 1690 1691 1692 1693 1694 1695 1696 1697 1698 1699\n",
1225 |       " 1700 1701 1702 1703 1704 1705 1706 1707 1708 1709 1710 1711 1712 1713\n",
1226 |       " 1714 1715 1716 1717 1718 1719 1720 1721 1722 1723 1724 1725 1726 1727\n",
1227 |       " 1728 1729 1730 1731 1732 1733 1734 1735 1736 1737 1738 1739 1740 1741\n",
1228 |       " 1742 1743 1744 1745 1746 1747 1748 1749 1750 1751 1752 1753 1754 1755\n",
1229 |       " 1756 1757 1758 1759 1760 1761 1762 1763 1764 1765 1766 1767 1768 1769\n",
1230 |       " 1770 1771 1772 1773 1774 1775 1776 1777 1778 1779 1780 1781 1782 1783\n",
1231 |       " 1784 1785 1786 1787 1788 1789 1790 1791 1792 1793 1794 1795 1796 1797\n",
1232 |       " 1798 1799 1800 1801 1802 1803 1804 1805 1806 1807 1808 1809 1810 1811\n",
1233 |       " 1812 1813 1814 1815 1816 1817 1818 1819 1820 1821 1822 1823 1824 1825\n",
1234 |       " 1826 1827 1828 1829 1830 1831 1832 1833 1834 1835 1836 1837 1838 1839\n",
1235 |       " 1840 1841 1842 1843 1844 1845 1846 1847 1848 1849 1850 1851 1852 1853\n",
1236 |       " 1854 1855 1856 1857 1858 1859 1860 1861 1862 1863 1864 1865 1866 1867\n",
1237 |       " 1868 1869 1870 1871 1872 1873 1874 1875 1876 1877 1878 1879 1880 1881\n",
1238 |       " 1882 1883 1884 1885 1886 1887 1888 1889 1890 1891 1892 1893 1894 1895\n",
1239 |       " 1896 1897 1898 1899 1900 1901 1902 1903 1904 1905 1906 1907 1908 1909\n",
1240 |       " 1910 1911 1912 1913 1914 1915 1916 1917 1918 1919 1920 1921 1922 1923\n",
1241 |       " 1924 1925 1926 1927 1928 1929 1930 1931 1932 1933 1934 1935 1936 1937\n",
1242 |       " 1938 1939 1940 1941 1942 1943 1944 1945 1946 1947 1948 1949 1950 1951\n",
1243 |       " 1952 1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1966\n",
1244 |       " 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1978 1979 1980 1981\n",
1245 |       " 1982 1983 1984 1986 1989 1990 1991 1992 1995 1997 1998 1999 2000 2001\n",
1246 |       " 2002 2003 2004 2006 2008 2010]\n"
1247 |      ]
1248 |     },
1249 |     {
1250 |      "name": "stdout",
1251 |      "output_type": "stream",
1252 |      "text": [
1253 |       "Train: [   0    1    2 ... 9997 9998 9999] Validation: [1965 1977 1985 1987 1988 1993 1994 1996 2005 2007 2009 2011 2012 2013\n",
1254 |       " 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027\n",
1255 |       " 2028 2029 2030 2031 2032 2033 2034 2035 2036 2037 2038 2039 2040 2041\n",
1256 |       " 2042 2043 2044 2045 2046 2047 2048 2049 2050 2051 2052 2053 2054 2055\n",
1257 |       " 2056 2057 2058 2059 2060 2061 2062 2063 2064 2065 2066 2067 2068 2069\n",
1258 |       " 2070 2071 2072 2073 2074 2075 2076 2077 2078 2079 2080 2081 2082 2083\n",
1259 |       " 2084 2085 2086 2087 2088 2089 2090 2091 2092 2093 2094 2095 2096 2097\n",
1260 |       " 2098 2099 2100 2101 2102 2103 2104 2105 2106 2107 2108 2109 2110 2111\n",
1261 |       " 2112 2113 2114 2115 2116 2117 2118 2119 2120 2121 2122 2123 2124 2125\n",
1262 |       " 2126 2127 2128 2129 2130 2131 2132 2133 2134 2135 2136 2137 2138 2139\n",
1263 |       " 2140 2141 2142 2143 2144 2145 2146 2147 2148 2149 2150 2151 2152 2153\n",
1264 |       " 2154 2155 2156 2157 2158 2159 2160 2161 2162 2163 2164 2165 2166 2167\n",
1265 |       " 2168 2169 2170 2171 2172 2173 2174 2175 2176 2177 2178 2179 2180 2181\n",
1266 |       " 2182 2183 2184 2185 2186 2187 2188 2189 2190 2191 2192 2193 2194 2195\n",
1267 |       " 2196 2197 2198 2199 2200 2201 2202 2203 2204 2205 2206 2207 2208 2209\n",
1268 |       " 2210 2211 2212 2213 2214 2215 2216 2217 2218 2219 2220 2221 2222 2223\n",
1269 |       " 2224 2225 2226 2227 2228 2229 2230 2231 2232 2233 2234 2235 2236 2237\n",
1270 |       " 2238 2239 2240 2241 2242 2243 2244 2245 2246 2247 2248 2249 2250 2251\n",
1271 |       " 2252 2253 2254 2255 2256 2257 2258 2259 2260 2261 2262 2263 2264 2265\n",
1272 |       " 2266 2267 2268 2269 2270 2271 2272 2273 2274 2275 2276 2277 2278 2279\n",
1273 |       " 2280 2281 2282 2283 2284 2285 2286 2287 2288 2289 2290 2291 2292 2293\n",
1274 |       " 2294 2295 2296 2297 2298 2299 2300 2301 2302 2303 2304 2305 2306 2307\n",
1275 |       " 2308 2309 2310 2311 2312 2313 2314 2315 2316 2317 2318 2319 2320 2321\n",
1276 |       " 2322 2323 2324 2325 2326 2327 2328 2329 2330 2331 2332 2333 2334 2335\n",
1277 |       " 2336 2337 2338 2339 2340 2341 2342 2343 2344 2345 2346 2347 2348 2349\n",
1278 |       " 2350 2351 2352 2353 2354 2355 2356 2357 2358 2359 2360 2361 2362 2363\n",
1279 |       " 2364 2365 2366 2367 2368 2369 2370 2371 2372 2373 2374 2375 2376 2377\n",
1280 |       " 2378 2379 2380 2381 2382 2383 2384 2385 2386 2387 2388 2389 2390 2391\n",
1281 |       " 2392 2393 2394 2395 2396 2397 2398 2399 2400 2401 2402 2403 2404 2405\n",
1282 |       " 2406 2407 2408 2409 2410 2411 2412 2413 2414 2415 2416 2417 2418 2419\n",
1283 |       " 2420 2421 2422 2423 2424 2425 2426 2427 2428 2429 2430 2431 2432 2433\n",
1284 |       " 2434 2435 2436 2437 2438 2439 2440 2441 2442 2443 2444 2445 2446 2447\n",
1285 |       " 2448 2449 2450 2451 2452 2453 2454 2455 2456 2457 2458 2459 2460 2461\n",
1286 |       " 2462 2463 2464 2465 2466 2467 2468 2469 2470 2471 2472 2473 2474 2475\n",
1287 |       " 2476 2477 2478 2479 2480 2481 2482 2483 2484 2485 2486 2487 2488 2489\n",
1288 |       " 2490 2491 2492 2493 2494 2495 2496 2497 2498 2499 2500 2501 2502 2503\n",
1289 |       " 2504 2505 2506 2507 2508 2509 2510 2511 2512 2513 2514 2515 2516 2517\n",
1290 |       " 2518 2519 2520 2521 2522 2523 2524 2525 2526 2527 2528 2529 2530 2531\n",
1291 |       " 2532 2533 2534 2535 2536 2537 2538 2539 2540 2541 2542 2543 2544 2545\n",
1292 |       " 2546 2547 2548 2549 2550 2551 2552 2553 2554 2555 2556 2557 2558 2559\n",
1293 |       " 2560 2561 2562 2563 2564 2565 2566 2567 2568 2569 2570 2571 2572 2573\n",
1294 |       " 2574 2575 2576 2577 2578 2579 2580 2581 2582 2583 2584 2585 2586 2587\n",
1295 |       " 2588 2589 2590 2591 2592 2593 2594 2595 2596 2597 2598 2599 2600 2601\n",
1296 |       " 2602 2603 2604 2605 2606 2607 2608 2609 2610 2611 2612 2613 2614 2615\n",
1297 |       " 2616 2617 2618 2619 2620 2621 2622 2623 2624 2625 2626 2627 2628 2629\n",
1298 |       " 2630 2631 2632 2633 2634 2635 2636 2637 2638 2639 2640 2641 2642 2643\n",
1299 |       " 2644 2645 2646 2647 2648 2649 2650 2651 2652 2653 2654 2655 2656 2657\n",
1300 |       " 2658 2659 2660 2661 2662 2663 2664 2665 2666 2667 2668 2669 2670 2671\n",
1301 |       " 2672 2673 2674 2675 2676 2677 2678 2679 2680 2681 2682 2683 2684 2685\n",
1302 |       " 2686 2687 2688 2689 2690 2691 2692 2693 2694 2695 2696 2697 2698 2699\n",
1303 |       " 2700 2701 2702 2703 2704 2705 2706 2707 2708 2709 2710 2711 2712 2713\n",
1304 |       " 2714 2715 2716 2717 2718 2719 2720 2721 2722 2723 2724 2725 2726 2727\n",
1305 |       " 2728 2729 2730 2731 2732 2733 2734 2735 2736 2737 2738 2739 2740 2741\n",
1306 |       " 2742 2743 2744 2745 2746 2747 2748 2749 2750 2751 2752 2753 2754 2755\n",
1307 |       " 2756 2757 2758 2759 2760 2761 2762 2763 2764 2765 2766 2767 2768 2769\n",
1308 |       " 2770 2771 2772 2773 2774 2775 2776 2777 2778 2779 2780 2781 2782 2783\n",
1309 |       " 2784 2785 2786 2787 2788 2789 2790 2791 2792 2793 2794 2795 2796 2797\n",
1310 |       " 2798 2799 2800 2801 2802 2803 2804 2805 2806 2807 2808 2809 2810 2811\n",
1311 |       " 2812 2813 2814 2815 2816 2817 2818 2819 2820 2821 2822 2823 2824 2825\n",
1312 |       " 2826 2827 2828 2829 2830 2831 2832 2833 2834 2835 2836 2837 2838 2839\n",
1313 |       " 2840 2841 2842 2843 2844 2845 2846 2847 2848 2849 2850 2851 2852 2853\n",
1314 |       " 2854 2855 2856 2857 2858 2859 2860 2861 2862 2863 2864 2865 2866 2867\n",
1315 |       " 2868 2869 2870 2871 2872 2873 2874 2875 2876 2877 2878 2879 2880 2881\n",
1316 |       " 2882 2883 2884 2885 2886 2887 2888 2889 2890 2891 2892 2893 2894 2895\n",
1317 |       " 2896 2897 2898 2899 2900 2901 2902 2903 2904 2905 2906 2907 2908 2909\n",
1318 |       " 2910 2911 2912 2913 2914 2919 2920 2921 2922 2923 2924 2925 2926 2927\n",
1319 |       " 2929 2930 2931 2933 2935 2936 2937 2938 2940 2941 2942 2943 2944 2946\n",
1320 |       " 2947 2948 2949 2950 2952 2953 2954 2955 2956 2958 2959 2960 2961 2963\n",
1321 |       " 2964 2965 2968 2969 2971 2972 2974 2975 2976 2978 2979 2980 2981 2982\n",
1322 |       " 2983 2984 2985 2986 2990 2991 2992 2993 2994 2995 2996 2997 2998 2999\n",
1323 |       " 3000 3001 3002 3003 3004 3005 3008 3009 3011 3012 3013 3014 3015 3016\n",
1324 |       " 3017 3018 3019 3020 3021 3022]\n",
1325 |       "Train: [   0    1    2 ... 9997 9998 9999] Validation: [2915 2916 2917 2918 2928 2932 2934 2939 2945 2951 2957 2962 2966 2967\n",
1326 |       " 2970 2973 2977 2987 2988 2989 3006 3007 3010 3023 3024 3025 3026 3027\n",
1327 |       " 3028 3029 3030 3031 3032 3033 3034 3035 3036 3037 3038 3039 3040 3041\n",
1328 |       " 3042 3043 3044 3045 3046 3047 3048 3049 3050 3051 3052 3053 3054 3055\n",
1329 |       " 3056 3057 3058 3059 3060 3061 3062 3063 3064 3065 3066 3067 3068 3069\n",
1330 |       " 3070 3071 3072 3073 3074 3075 3076 3077 3078 3079 3080 3081 3082 3083\n",
1331 |       " 3084 3085 3086 3087 3088 3089 3090 3091 3092 3093 3094 3095 3096 3097\n",
1332 |       " 3098 3099 3100 3101 3102 3103 3104 3105 3106 3107 3108 3109 3110 3111\n",
1333 |       " 3112 3113 3114 3115 3116 3117 3118 3119 3120 3121 3122 3123 3124 3125\n",
1334 |       " 3126 3127 3128 3129 3130 3131 3132 3133 3134 3135 3136 3137 3138 3139\n",
1335 |       " 3140 3141 3142 3143 3144 3145 3146 3147 3148 3149 3150 3151 3152 3153\n",
1336 |       " 3154 3155 3156 3157 3158 3159 3160 3161 3162 3163 3164 3165 3166 3167\n",
1337 |       " 3168 3169 3170 3171 3172 3173 3174 3175 3176 3177 3178 3179 3180 3181\n",
1338 |       " 3182 3183 3184 3185 3186 3187 3188 3189 3190 3191 3192 3193 3194 3195\n",
1339 |       " 3196 3197 3198 3199 3200 3201 3202 3203 3204 3205 3206 3207 3208 3209\n",
1340 |       " 3210 3211 3212 3213 3214 3215 3216 3217 3218 3219 3220 3221 3222 3223\n",
1341 |       " 3224 3225 3226 3227 3228 3229 3230 3231 3232 3233 3234 3235 3236 3237\n",
1342 |       " 3238 3239 3240 3241 3242 3243 3244 3245 3246 3247 3248 3249 3250 3251\n",
1343 |       " 3252 3253 3254 3255 3256 3257 3258 3259 3260 3261 3262 3263 3264 3265\n",
1344 |       " 3266 3267 3268 3269 3270 3271 3272 3273 3274 3275 3276 3277 3278 3279\n",
1345 |       " 3280 3281 3282 3283 3284 3285 3286 3287 3288 3289 3290 3291 3292 3293\n",
1346 |       " 3294 3295 3296 3297 3298 3299 3300 3301 3302 3303 3304 3305 3306 3307\n",
1347 |       " 3308 3309 3310 3311 3312 3313 3314 3315 3316 3317 3318 3319 3320 3321\n",
1348 |       " 3322 3323 3324 3325 3326 3327 3328 3329 3330 3331 3332 3333 3334 3335\n",
1349 |       " 3336 3337 3338 3339 3340 3341 3342 3343 3344 3345 3346 3347 3348 3349\n",
1350 |       " 3350 3351 3352 3353 3354 3355 3356 3357 3358 3359 3360 3361 3362 3363\n",
1351 |       " 3364 3365 3366 3367 3368 3369 3370 3371 3372 3373 3374 3375 3376 3377\n",
1352 |       " 3378 3379 3380 3381 3382 3383 3384 3385 3386 3387 3388 3389 3390 3391\n",
1353 |       " 3392 3393 3394 3395 3396 3397 3398 3399 3400 3401 3402 3403 3404 3405\n",
1354 |       " 3406 3407 3408 3409 3410 3411 3412 3413 3414 3415 3416 3417 3418 3419\n",
1355 |       " 3420 3421 3422 3423 3424 3425 3426 3427 3428 3429 3430 3431 3432 3433\n",
1356 |       " 3434 3435 3436 3437 3438 3439 3440 3441 3442 3443 3444 3445 3446 3447\n",
1357 |       " 3448 3449 3450 3451 3452 3453 3454 3455 3456 3457 3458 3459 3460 3461\n",
1358 |       " 3462 3463 3464 3465 3466 3467 3468 3469 3470 3471 3472 3473 3474 3475\n",
1359 |       " 3476 3477 3478 3479 3480 3481 3482 3483 3484 3485 3486 3487 3488 3489\n",
1360 |       " 3490 3491 3492 3493 3494 3495 3496 3497 3498 3499 3500 3501 3502 3503\n",
1361 |       " 3504 3505 3506 3507 3508 3509 3510 3511 3512 3513 3514 3515 3516 3517\n",
1362 |       " 3518 3519 3520 3521 3522 3523 3524 3525 3526 3527 3528 3529 3530 3531\n",
1363 |       " 3532 3533 3534 3535 3536 3537 3538 3539 3540 3541 3542 3543 3544 3545\n",
1364 |       " 3546 3547 3548 3549 3550 3551 3552 3553 3554 3555 3556 3557 3558 3559\n",
1365 |       " 3560 3561 3562 3563 3564 3565 3566 3567 3568 3569 3570 3571 3572 3573\n",
1366 |       " 3574 3575 3576 3577 3578 3579 3580 3581 3582 3583 3584 3585 3586 3587\n",
1367 |       " 3588 3589 3590 3591 3592 3593 3594 3595 3596 3597 3598 3599 3600 3601\n",
1368 |       " 3602 3603 3604 3605 3606 3607 3608 3609 3610 3611 3612 3613 3614 3615\n",
1369 |       " 3616 3617 3618 3619 3620 3621 3622 3623 3624 3625 3626 3627 3628 3629\n",
1370 |       " 3630 3631 3632 3633 3634 3635 3636 3637 3638 3639 3640 3641 3642 3643\n",
1371 |       " 3644 3645 3646 3647 3648 3649 3650 3651 3652 3653 3654 3655 3656 3657\n",
1372 |       " 3658 3659 3660 3661 3662 3663 3664 3665 3666 3667 3668 3669 3670 3671\n",
1373 |       " 3672 3673 3674 3675 3676 3677 3678 3679 3680 3681 3682 3683 3684 3685\n",
1374 |       " 3686 3687 3688 3689 3690 3691 3692 3693 3694 3695 3696 3697 3698 3699\n",
1375 |       " 3700 3701 3702 3703 3704 3705 3706 3707 3708 3709 3710 3711 3712 3713\n",
1376 |       " 3714 3715 3716 3717 3718 3719 3720 3721 3722 3723 3724 3725 3726 3727\n",
1377 |       " 3728 3729 3730 3731 3732 3733 3734 3735 3736 3737 3738 3739 3740 3741\n",
1378 |       " 3742 3743 3744 3745 3746 3747 3748 3749 3750 3751 3752 3753 3754 3755\n",
1379 |       " 3756 3757 3758 3759 3760 3761 3762 3763 3764 3765 3766 3767 3768 3769\n",
1380 |       " 3770 3771 3772 3773 3774 3775 3776 3777 3778 3779 3780 3781 3782 3783\n",
1381 |       " 3784 3785 3786 3787 3788 3789 3790 3791 3792 3793 3794 3795 3796 3797\n",
1382 |       " 3798 3799 3800 3801 3802 3803 3804 3805 3806 3807 3808 3809 3810 3811\n",
1383 |       " 3812 3813 3814 3815 3816 3817 3818 3819 3820 3821 3822 3823 3824 3825\n",
1384 |       " 3826 3827 3828 3829 3830 3831 3832 3833 3834 3835 3836 3837 3838 3839\n",
1385 |       " 3842 3844 3845 3846 3847 3848 3849 3850 3851 3852 3853 3854 3855 3856\n",
1386 |       " 3857 3858 3859 3860 3862 3863 3864 3865 3866 3867 3868 3869 3870 3871\n",
1387 |       " 3872 3873 3874 3875 3876 3878 3880 3881 3882 3883 3884 3886 3887 3888\n",
1388 |       " 3889 3890 3891 3892 3893 3894 3895 3896 3897 3899 3900 3901 3903 3904\n",
1389 |       " 3905 3906 3907 3908 3909 3911 3912 3914 3915 3916 3918 3919 3920 3922\n",
1390 |       " 3923 3924 3925 3926 3927 3928 3929 3931 3932 3933 3934 3936 3937 3939\n",
1391 |       " 3942 3943 3944 3945 3946 3947 3948 3949 3950 3952 3953 3954 3955 3956\n",
1392 |       " 3958 3960 3961 3962 3963 3965 3966 3967 3968 3970 3971 3972 3973 3974\n",
1393 |       " 3976 3977 3978 3980 3981 3984 3985 3986 3987 3989 3990 3992 3993 3994\n",
1394 |       " 3995 3996 3997 3999 4000 4002 4003 4004 4006 4007 4008 4009 4010 4011\n",
1395 |       " 4012 4015 4016 4018 4019 4020 4022 4023 4024 4025 4027 4028 4029 4030\n",
1396 |       " 4031 4032 4034 4035 4039 4040]\n"
1397 |      ]
1398 |     },
1399 |     {
1400 |      "name": "stdout",
1401 |      "output_type": "stream",
1402 |      "text": [
1403 |       "Train: [   0    1    2 ... 9997 9998 9999] Validation: [3840 3841 3843 3861 3877 3879 3885 3898 3902 3910 3913 3917 3921 3930\n",
1404 |       " 3935 3938 3940 3941 3951 3957 3959 3964 3969 3975 3979 3982 3983 3988\n",
1405 |       " 3991 3998 4001 4005 4013 4014 4017 4021 4026 4033 4036 4037 4038 4041\n",
1406 |       " 4042 4043 4044 4045 4046 4047 4048 4049 4050 4051 4052 4053 4054 4055\n",
1407 |       " 4056 4057 4058 4059 4060 4061 4062 4063 4064 4065 4066 4067 4068 4069\n",
1408 |       " 4070 4071 4072 4073 4074 4075 4076 4077 4078 4079 4080 4081 4082 4083\n",
1409 |       " 4084 4085 4086 4087 4088 4089 4090 4091 4092 4093 4094 4095 4096 4097\n",
1410 |       " 4098 4099 4100 4101 4102 4103 4104 4105 4106 4107 4108 4109 4110 4111\n",
1411 |       " 4112 4113 4114 4115 4116 4117 4118 4119 4120 4121 4122 4123 4124 4125\n",
1412 |       " 4126 4127 4128 4129 4130 4131 4132 4133 4134 4135 4136 4137 4138 4139\n",
1413 |       " 4140 4141 4142 4143 4144 4145 4146 4147 4148 4149 4150 4151 4152 4153\n",
1414 |       " 4154 4155 4156 4157 4158 4159 4160 4161 4162 4163 4164 4165 4166 4167\n",
1415 |       " 4168 4169 4170 4171 4172 4173 4174 4175 4176 4177 4178 4179 4180 4181\n",
1416 |       " 4182 4183 4184 4185 4186 4187 4188 4189 4190 4191 4192 4193 4194 4195\n",
1417 |       " 4196 4197 4198 4199 4200 4201 4202 4203 4204 4205 4206 4207 4208 4209\n",
1418 |       " 4210 4211 4212 4213 4214 4215 4216 4217 4218 4219 4220 4221 4222 4223\n",
1419 |       " 4224 4225 4226 4227 4228 4229 4230 4231 4232 4233 4234 4235 4236 4237\n",
1420 |       " 4238 4239 4240 4241 4242 4243 4244 4245 4246 4247 4248 4249 4250 4251\n",
1421 |       " 4252 4253 4254 4255 4256 4257 4258 4259 4260 4261 4262 4263 4264 4265\n",
1422 |       " 4266 4267 4268 4269 4270 4271 4272 4273 4274 4275 4276 4277 4278 4279\n",
1423 |       " 4280 4281 4282 4283 4284 4285 4286 4287 4288 4289 4290 4291 4292 4293\n",
1424 |       " 4294 4295 4296 4297 4298 4299 4300 4301 4302 4303 4304 4305 4306 4307\n",
1425 |       " 4308 4309 4310 4311 4312 4313 4314 4315 4316 4317 4318 4319 4320 4321\n",
1426 |       " 4322 4323 4324 4325 4326 4327 4328 4329 4330 4331 4332 4333 4334 4335\n",
1427 |       " 4336 4337 4338 4339 4340 4341 4342 4343 4344 4345 4346 4347 4348 4349\n",
1428 |       " 4350 4351 4352 4353 4354 4355 4356 4357 4358 4359 4360 4361 4362 4363\n",
1429 |       " 4364 4365 4366 4367 4368 4369 4370 4371 4372 4373 4374 4375 4376 4377\n",
1430 |       " 4378 4379 4380 4381 4382 4383 4384 4385 4386 4387 4388 4389 4390 4391\n",
1431 |       " 4392 4393 4394 4395 4396 4397 4398 4399 4400 4401 4402 4403 4404 4405\n",
1432 |       " 4406 4407 4408 4409 4410 4411 4412 4413 4414 4415 4416 4417 4418 4419\n",
1433 |       " 4420 4421 4422 4423 4424 4425 4426 4427 4428 4429 4430 4431 4432 4433\n",
1434 |       " 4434 4435 4436 4437 4438 4439 4440 4441 4442 4443 4444 4445 4446 4447\n",
1435 |       " 4448 4449 4450 4451 4452 4453 4454 4455 4456 4457 4458 4459 4460 4461\n",
1436 |       " 4462 4463 4464 4465 4466 4467 4468 4469 4470 4471 4472 4473 4474 4475\n",
1437 |       " 4476 4477 4478 4479 4480 4481 4482 4483 4484 4485 4486 4487 4488 4489\n",
1438 |       " 4490 4491 4492 4493 4494 4495 4496 4497 4498 4499 4500 4501 4502 4503\n",
1439 |       " 4504 4505 4506 4507 4508 4509 4510 4511 4512 4513 4514 4515 4516 4517\n",
1440 |       " 4518 4519 4520 4521 4522 4523 4524 4525 4526 4527 4528 4529 4530 4531\n",
1441 |       " 4532 4533 4534 4535 4536 4537 4538 4539 4540 4541 4542 4543 4544 4545\n",
1442 |       " 4546 4547 4548 4549 4550 4551 4552 4553 4554 4555 4556 4557 4558 4559\n",
1443 |       " 4560 4561 4562 4563 4564 4565 4566 4567 4568 4569 4570 4571 4572 4573\n",
1444 |       " 4574 4575 4576 4577 4578 4579 4580 4581 4582 4583 4584 4585 4586 4587\n",
1445 |       " 4588 4589 4590 4591 4592 4593 4594 4595 4596 4597 4598 4599 4600 4601\n",
1446 |       " 4602 4603 4604 4605 4606 4607 4608 4609 4610 4611 4612 4613 4614 4615\n",
1447 |       " 4616 4617 4618 4619 4620 4621 4622 4623 4624 4625 4626 4627 4628 4629\n",
1448 |       " 4630 4631 4632 4633 4634 4635 4636 4637 4638 4639 4640 4641 4642 4643\n",
1449 |       " 4644 4645 4646 4647 4648 4649 4650 4651 4652 4653 4654 4655 4656 4657\n",
1450 |       " 4658 4659 4660 4661 4662 4663 4664 4665 4666 4667 4668 4669 4670 4671\n",
1451 |       " 4672 4673 4674 4675 4676 4677 4678 4679 4680 4681 4682 4683 4684 4685\n",
1452 |       " 4686 4687 4688 4689 4690 4691 4692 4693 4694 4695 4696 4697 4698 4699\n",
1453 |       " 4700 4701 4702 4703 4704 4705 4706 4707 4708 4709 4710 4711 4712 4713\n",
1454 |       " 4714 4715 4716 4717 4718 4719 4720 4721 4722 4723 4724 4725 4726 4727\n",
1455 |       " 4728 4729 4730 4731 4732 4733 4734 4735 4736 4737 4738 4739 4740 4741\n",
1456 |       " 4742 4743 4744 4745 4746 4747 4748 4749 4750 4751 4752 4753 4754 4755\n",
1457 |       " 4756 4757 4758 4759 4760 4761 4762 4763 4764 4765 4766 4767 4768 4769\n",
1458 |       " 4770 4771 4772 4773 4774 4775 4776 4777 4778 4779 4780 4781 4782 4783\n",
1459 |       " 4784 4785 4786 4787 4788 4789 4790 4791 4792 4793 4794 4795 4796 4797\n",
1460 |       " 4798 4799 4800 4801 4802 4803 4804 4805 4806 4807 4808 4809 4810 4811\n",
1461 |       " 4812 4813 4814 4815 4816 4817 4818 4819 4820 4821 4822 4823 4824 4825\n",
1462 |       " 4826 4827 4828 4829 4830 4831 4832 4833 4834 4835 4836 4837 4838 4839\n",
1463 |       " 4840 4841 4842 4843 4844 4845 4846 4847 4848 4849 4850 4851 4852 4853\n",
1464 |       " 4854 4855 4856 4857 4858 4859 4860 4861 4862 4863 4864 4865 4866 4867\n",
1465 |       " 4868 4869 4870 4871 4872 4873 4874 4875 4876 4877 4878 4879 4880 4881\n",
1466 |       " 4882 4883 4885 4886 4887 4888 4890 4891 4893 4894 4895 4896 4897 4898\n",
1467 |       " 4900 4902 4903 4904 4905 4906 4907 4909 4910 4914 4915 4916 4917 4919\n",
1468 |       " 4920 4923 4924 4925 4926 4928 4929 4930 4931 4932 4933 4934 4935 4937\n",
1469 |       " 4938 4940 4942 4943 4944 4945 4947 4948 4949 4950 4951 4952 4953 4954\n",
1470 |       " 4955 4956 4957 4958 4959 4960 4961 4962 4963 4965 4966 4968 4969 4970\n",
1471 |       " 4971 4972 4973 4975 4976 4977 4978 4979 4982 4983 4984 4985 4986 4987\n",
1472 |       " 4989 4990 4991 4993 4995 4996 4998 4999 5000 5001 5003 5005 5006 5008\n",
1473 |       " 5009 5012 5014 5015 5016 5017 5021 5022 5023 5026 5027 5029 5030 5032\n",
1474 |       " 5034 5035 5036 5037 5038 5039]\n",
1475 |       "Train: [   0    1    2 ... 9997 9998 9999] Validation: [4884 4889 4892 4899 4901 4908 4911 4912 4913 4918 4921 4922 4927 4936\n",
1476 |       " 4939 4941 4946 4964 4967 4974 4980 4981 4988 4992 4994 4997 5002 5004\n",
1477 |       " 5007 5010 5011 5013 5018 5019 5020 5024 5025 5028 5031 5033 5040 5041\n",
1478 |       " 5042 5043 5044 5045 5046 5047 5048 5049 5050 5051 5052 5053 5054 5055\n",
1479 |       " 5056 5057 5058 5059 5060 5061 5062 5063 5064 5065 5066 5067 5068 5069\n",
1480 |       " 5070 5071 5072 5073 5074 5075 5076 5077 5078 5079 5080 5081 5082 5083\n",
1481 |       " 5084 5085 5086 5087 5088 5089 5090 5091 5092 5093 5094 5095 5096 5097\n",
1482 |       " 5098 5099 5100 5101 5102 5103 5104 5105 5106 5107 5108 5109 5110 5111\n",
1483 |       " 5112 5113 5114 5115 5116 5117 5118 5119 5120 5121 5122 5123 5124 5125\n",
1484 |       " 5126 5127 5128 5129 5130 5131 5132 5133 5134 5135 5136 5137 5138 5139\n",
1485 |       " 5140 5141 5142 5143 5144 5145 5146 5147 5148 5149 5150 5151 5152 5153\n",
1486 |       " 5154 5155 5156 5157 5158 5159 5160 5161 5162 5163 5164 5165 5166 5167\n",
1487 |       " 5168 5169 5170 5171 5172 5173 5174 5175 5176 5177 5178 5179 5180 5181\n",
1488 |       " 5182 5183 5184 5185 5186 5187 5188 5189 5190 5191 5192 5193 5194 5195\n",
1489 |       " 5196 5197 5198 5199 5200 5201 5202 5203 5204 5205 5206 5207 5208 5209\n",
1490 |       " 5210 5211 5212 5213 5214 5215 5216 5217 5218 5219 5220 5221 5222 5223\n",
1491 |       " 5224 5225 5226 5227 5228 5229 5230 5231 5232 5233 5234 5235 5236 5237\n",
1492 |       " 5238 5239 5240 5241 5242 5243 5244 5245 5246 5247 5248 5249 5250 5251\n",
1493 |       " 5252 5253 5254 5255 5256 5257 5258 5259 5260 5261 5262 5263 5264 5265\n",
1494 |       " 5266 5267 5268 5269 5270 5271 5272 5273 5274 5275 5276 5277 5278 5279\n",
1495 |       " 5280 5281 5282 5283 5284 5285 5286 5287 5288 5289 5290 5291 5292 5293\n",
1496 |       " 5294 5295 5296 5297 5298 5299 5300 5301 5302 5303 5304 5305 5306 5307\n",
1497 |       " 5308 5309 5310 5311 5312 5313 5314 5315 5316 5317 5318 5319 5320 5321\n",
1498 |       " 5322 5323 5324 5325 5326 5327 5328 5329 5330 5331 5332 5333 5334 5335\n",
1499 |       " 5336 5337 5338 5339 5340 5341 5342 5343 5344 5345 5346 5347 5348 5349\n",
1500 |       " 5350 5351 5352 5353 5354 5355 5356 5357 5358 5359 5360 5361 5362 5363\n",
1501 |       " 5364 5365 5366 5367 5368 5369 5370 5371 5372 5373 5374 5375 5376 5377\n",
1502 |       " 5378 5379 5380 5381 5382 5383 5384 5385 5386 5387 5388 5389 5390 5391\n",
1503 |       " 5392 5393 5394 5395 5396 5397 5398 5399 5400 5401 5402 5403 5404 5405\n",
1504 |       " 5406 5407 5408 5409 5410 5411 5412 5413 5414 5415 5416 5417 5418 5419\n",
1505 |       " 5420 5421 5422 5423 5424 5425 5426 5427 5428 5429 5430 5431 5432 5433\n",
1506 |       " 5434 5435 5436 5437 5438 5439 5440 5441 5442 5443 5444 5445 5446 5447\n",
1507 |       " 5448 5449 5450 5451 5452 5453 5454 5455 5456 5457 5458 5459 5460 5461\n",
1508 |       " 5462 5463 5464 5465 5466 5467 5468 5469 5470 5471 5472 5473 5474 5475\n",
1509 |       " 5476 5477 5478 5479 5480 5481 5482 5483 5484 5485 5486 5487 5488 5489\n",
1510 |       " 5490 5491 5492 5493 5494 5495 5496 5497 5498 5499 5500 5501 5502 5503\n",
1511 |       " 5504 5505 5506 5507 5508 5509 5510 5511 5512 5513 5514 5515 5516 5517\n",
1512 |       " 5518 5519 5520 5521 5522 5523 5524 5525 5526 5527 5528 5529 5530 5531\n",
1513 |       " 5532 5533 5534 5535 5536 5537 5538 5539 5540 5541 5542 5543 5544 5545\n",
1514 |       " 5546 5547 5548 5549 5550 5551 5552 5553 5554 5555 5556 5557 5558 5559\n",
1515 |       " 5560 5561 5562 5563 5564 5565 5566 5567 5568 5569 5570 5571 5572 5573\n",
1516 |       " 5574 5575 5576 5577 5578 5579 5580 5581 5582 5583 5584 5585 5586 5587\n",
1517 |       " 5588 5589 5590 5591 5592 5593 5594 5595 5596 5597 5598 5599 5600 5601\n",
1518 |       " 5602 5603 5604 5605 5606 5607 5608 5609 5610 5611 5612 5613 5614 5615\n",
1519 |       " 5616 5617 5618 5619 5620 5621 5622 5623 5624 5625 5626 5627 5628 5629\n",
1520 |       " 5630 5631 5632 5633 5634 5635 5636 5637 5638 5639 5640 5641 5642 5643\n",
1521 |       " 5644 5645 5646 5647 5648 5649 5650 5651 5652 5653 5654 5655 5656 5657\n",
1522 |       " 5658 5659 5660 5661 5662 5663 5664 5665 5666 5667 5668 5669 5670 5671\n",
1523 |       " 5672 5673 5674 5675 5676 5677 5678 5679 5680 5681 5682 5683 5684 5685\n",
1524 |       " 5686 5687 5688 5689 5690 5691 5692 5693 5694 5695 5696 5697 5698 5699\n",
1525 |       " 5700 5701 5702 5703 5704 5705 5706 5707 5708 5709 5710 5711 5712 5713\n",
1526 |       " 5714 5715 5716 5717 5718 5719 5720 5721 5722 5723 5724 5725 5726 5727\n",
1527 |       " 5728 5729 5730 5731 5732 5733 5734 5735 5736 5737 5738 5739 5740 5741\n",
1528 |       " 5742 5743 5744 5745 5746 5747 5748 5749 5750 5751 5752 5753 5754 5755\n",
1529 |       " 5756 5757 5758 5759 5760 5761 5762 5763 5764 5765 5766 5767 5768 5769\n",
1530 |       " 5770 5771 5772 5773 5774 5775 5776 5777 5778 5779 5780 5781 5782 5783\n",
1531 |       " 5784 5785 5786 5787 5788 5789 5790 5791 5792 5793 5794 5795 5796 5797\n",
1532 |       " 5798 5799 5800 5801 5802 5803 5804 5805 5806 5807 5808 5809 5810 5811\n",
1533 |       " 5812 5813 5814 5815 5816 5817 5818 5819 5820 5821 5822 5823 5824 5825\n",
1534 |       " 5826 5827 5828 5829 5830 5831 5832 5833 5834 5835 5836 5837 5838 5839\n",
1535 |       " 5840 5841 5842 5843 5844 5845 5846 5847 5848 5849 5850 5851 5852 5853\n",
1536 |       " 5854 5855 5856 5857 5858 5859 5860 5861 5862 5863 5864 5865 5866 5867\n",
1537 |       " 5868 5869 5870 5871 5872 5873 5874 5875 5876 5877 5878 5879 5880 5881\n",
1538 |       " 5882 5883 5884 5885 5886 5889 5890 5891 5892 5893 5894 5895 5896 5897\n",
1539 |       " 5898 5899 5900 5901 5902 5905 5906 5907 5909 5910 5911 5912 5913 5914\n",
1540 |       " 5916 5917 5918 5919 5920 5921 5924 5925 5928 5929 5930 5932 5934 5935\n",
1541 |       " 5937 5938 5939 5941 5943 5944 5945 5946 5947 5948 5951 5953 5954 5956\n",
1542 |       " 5957 5958 5959 5961 5963 5964 5965 5966 5967 5968 5969 5970 5971 5973\n",
1543 |       " 5974 5975 5977 5978 5979 5980 5981 5982 5983 5984 5985 5987 5988 5990\n",
1544 |       " 5991 5992 5993 5994 5995 5996 5997 5998 5999 6002 6003 6004 6005 6006\n",
1545 |       " 6007 6008 6009 6010 6012 6013 6015 6016 6017 6018 6019 6020 6021 6024\n",
1546 |       " 6025 6026 6027 6030 6031 6032]\n"
1547 |      ]
1548 |     },
1549 |     {
1550 |      "name": "stdout",
1551 |      "output_type": "stream",
1552 |      "text": [
1553 |       "Train: [   0    1    2 ... 9997 9998 9999] Validation: [5887 5888 5903 5904 5908 5915 5922 5923 5926 5927 5931 5933 5936 5940\n",
1554 |       " 5942 5949 5950 5952 5955 5960 5962 5972 5976 5986 5989 6000 6001 6011\n",
1555 |       " 6014 6022 6023 6028 6029 6033 6034 6035 6036 6037 6038 6039 6040 6041\n",
1556 |       " 6042 6043 6044 6045 6046 6047 6048 6049 6050 6051 6052 6053 6054 6055\n",
1557 |       " 6056 6057 6058 6059 6060 6061 6062 6063 6064 6065 6066 6067 6068 6069\n",
1558 |       " 6070 6071 6072 6073 6074 6075 6076 6077 6078 6079 6080 6081 6082 6083\n",
1559 |       " 6084 6085 6086 6087 6088 6089 6090 6091 6092 6093 6094 6095 6096 6097\n",
1560 |       " 6098 6099 6100 6101 6102 6103 6104 6105 6106 6107 6108 6109 6110 6111\n",
1561 |       " 6112 6113 6114 6115 6116 6117 6118 6119 6120 6121 6122 6123 6124 6125\n",
1562 |       " 6126 6127 6128 6129 6130 6131 6132 6133 6134 6135 6136 6137 6138 6139\n",
1563 |       " 6140 6141 6142 6143 6144 6145 6146 6147 6148 6149 6150 6151 6152 6153\n",
1564 |       " 6154 6155 6156 6157 6158 6159 6160 6161 6162 6163 6164 6165 6166 6167\n",
1565 |       " 6168 6169 6170 6171 6172 6173 6174 6175 6176 6177 6178 6179 6180 6181\n",
1566 |       " 6182 6183 6184 6185 6186 6187 6188 6189 6190 6191 6192 6193 6194 6195\n",
1567 |       " 6196 6197 6198 6199 6200 6201 6202 6203 6204 6205 6206 6207 6208 6209\n",
1568 |       " 6210 6211 6212 6213 6214 6215 6216 6217 6218 6219 6220 6221 6222 6223\n",
1569 |       " 6224 6225 6226 6227 6228 6229 6230 6231 6232 6233 6234 6235 6236 6237\n",
1570 |       " 6238 6239 6240 6241 6242 6243 6244 6245 6246 6247 6248 6249 6250 6251\n",
1571 |       " 6252 6253 6254 6255 6256 6257 6258 6259 6260 6261 6262 6263 6264 6265\n",
1572 |       " 6266 6267 6268 6269 6270 6271 6272 6273 6274 6275 6276 6277 6278 6279\n",
1573 |       " 6280 6281 6282 6283 6284 6285 6286 6287 6288 6289 6290 6291 6292 6293\n",
1574 |       " 6294 6295 6296 6297 6298 6299 6300 6301 6302 6303 6304 6305 6306 6307\n",
1575 |       " 6308 6309 6310 6311 6312 6313 6314 6315 6316 6317 6318 6319 6320 6321\n",
1576 |       " 6322 6323 6324 6325 6326 6327 6328 6329 6330 6331 6332 6333 6334 6335\n",
1577 |       " 6336 6337 6338 6339 6340 6341 6342 6343 6344 6345 6346 6347 6348 6349\n",
1578 |       " 6350 6351 6352 6353 6354 6355 6356 6357 6358 6359 6360 6361 6362 6363\n",
1579 |       " 6364 6365 6366 6367 6368 6369 6370 6371 6372 6373 6374 6375 6376 6377\n",
1580 |       " 6378 6379 6380 6381 6382 6383 6384 6385 6386 6387 6388 6389 6390 6391\n",
1581 |       " 6392 6393 6394 6395 6396 6397 6398 6399 6400 6401 6402 6403 6404 6405\n",
1582 |       " 6406 6407 6408 6409 6410 6411 6412 6413 6414 6415 6416 6417 6418 6419\n",
1583 |       " 6420 6421 6422 6423 6424 6425 6426 6427 6428 6429 6430 6431 6432 6433\n",
1584 |       " 6434 6435 6436 6437 6438 6439 6440 6441 6442 6443 6444 6445 6446 6447\n",
1585 |       " 6448 6449 6450 6451 6452 6453 6454 6455 6456 6457 6458 6459 6460 6461\n",
1586 |       " 6462 6463 6464 6465 6466 6467 6468 6469 6470 6471 6472 6473 6474 6475\n",
1587 |       " 6476 6477 6478 6479 6480 6481 6482 6483 6484 6485 6486 6487 6488 6489\n",
1588 |       " 6490 6491 6492 6493 6494 6495 6496 6497 6498 6499 6500 6501 6502 6503\n",
1589 |       " 6504 6505 6506 6507 6508 6509 6510 6511 6512 6513 6514 6515 6516 6517\n",
1590 |       " 6518 6519 6520 6521 6522 6523 6524 6525 6526 6527 6528 6529 6530 6531\n",
1591 |       " 6532 6533 6534 6535 6536 6537 6538 6539 6540 6541 6542 6543 6544 6545\n",
1592 |       " 6546 6547 6548 6549 6550 6551 6552 6553 6554 6555 6556 6557 6558 6559\n",
1593 |       " 6560 6561 6562 6563 6564 6565 6566 6567 6568 6569 6570 6571 6572 6573\n",
1594 |       " 6574 6575 6576 6577 6578 6579 6580 6581 6582 6583 6584 6585 6586 6587\n",
1595 |       " 6588 6589 6590 6591 6592 6593 6594 6595 6596 6597 6598 6599 6600 6601\n",
1596 |       " 6602 6603 6604 6605 6606 6607 6608 6609 6610 6611 6612 6613 6614 6615\n",
1597 |       " 6616 6617 6618 6619 6620 6621 6622 6623 6624 6625 6626 6627 6628 6629\n",
1598 |       " 6630 6631 6632 6633 6634 6635 6636 6637 6638 6639 6640 6641 6642 6643\n",
1599 |       " 6644 6645 6646 6647 6648 6649 6650 6651 6652 6653 6654 6655 6656 6657\n",
1600 |       " 6658 6659 6660 6661 6662 6663 6664 6665 6666 6667 6668 6669 6670 6671\n",
1601 |       " 6672 6673 6674 6675 6676 6677 6678 6679 6680 6681 6682 6683 6684 6685\n",
1602 |       " 6686 6687 6688 6689 6690 6691 6692 6693 6694 6695 6696 6697 6698 6699\n",
1603 |       " 6700 6701 6702 6703 6704 6705 6706 6707 6708 6709 6710 6711 6712 6713\n",
1604 |       " 6714 6715 6716 6717 6718 6719 6720 6721 6722 6723 6724 6725 6726 6727\n",
1605 |       " 6728 6729 6730 6731 6732 6733 6734 6735 6736 6737 6738 6739 6740 6741\n",
1606 |       " 6742 6743 6744 6745 6746 6747 6748 6749 6750 6751 6752 6753 6754 6755\n",
1607 |       " 6756 6757 6758 6759 6760 6761 6762 6763 6764 6765 6766 6767 6768 6769\n",
1608 |       " 6770 6771 6772 6773 6774 6775 6776 6777 6778 6779 6780 6781 6782 6783\n",
1609 |       " 6784 6785 6786 6787 6788 6789 6790 6791 6792 6793 6794 6795 6796 6797\n",
1610 |       " 6798 6799 6800 6801 6802 6803 6804 6805 6806 6807 6808 6809 6810 6811\n",
1611 |       " 6812 6813 6814 6815 6816 6817 6818 6819 6820 6821 6822 6823 6824 6825\n",
1612 |       " 6826 6827 6828 6829 6830 6831 6832 6833 6834 6835 6836 6837 6838 6839\n",
1613 |       " 6840 6841 6842 6843 6844 6845 6846 6847 6848 6849 6850 6851 6852 6853\n",
1614 |       " 6854 6855 6856 6857 6858 6859 6860 6861 6862 6863 6864 6865 6866 6867\n",
1615 |       " 6868 6869 6870 6871 6872 6873 6874 6875 6876 6877 6878 6879 6880 6881\n",
1616 |       " 6882 6883 6884 6885 6886 6887 6888 6889 6890 6891 6892 6893 6894 6895\n",
1617 |       " 6896 6897 6898 6899 6900 6901 6902 6903 6904 6905 6906 6907 6908 6909\n",
1618 |       " 6910 6914 6915 6916 6917 6918 6919 6920 6921 6922 6923 6924 6925 6926\n",
1619 |       " 6927 6930 6931 6932 6933 6934 6935 6936 6937 6938 6939 6940 6941 6942\n",
1620 |       " 6943 6944 6945 6946 6947 6949 6950 6952 6953 6954 6955 6956 6957 6958\n",
1621 |       " 6959 6960 6961 6962 6963 6965 6966 6967 6968 6969 6970 6971 6973 6974\n",
1622 |       " 6975 6976 6977 6978 6979 6980 6982 6983 6985 6986 6987 6988 6989 6990\n",
1623 |       " 6993 6994 6995 6996 6997 7000 7001 7002 7004 7005 7006 7007 7009 7010\n",
1624 |       " 7011 7012 7013 7014 7015 7016]\n",
1625 |       "Train: [   0    1    2 ... 9997 9998 9999] Validation: [6911 6912 6913 6928 6929 6948 6951 6964 6972 6981 6984 6991 6992 6998\n",
1626 |       " 6999 7003 7008 7017 7018 7019 7020 7021 7022 7023 7024 7025 7026 7027\n",
1627 |       " 7028 7029 7030 7031 7032 7033 7034 7035 7036 7037 7038 7039 7040 7041\n",
1628 |       " 7042 7043 7044 7045 7046 7047 7048 7049 7050 7051 7052 7053 7054 7055\n",
1629 |       " 7056 7057 7058 7059 7060 7061 7062 7063 7064 7065 7066 7067 7068 7069\n",
1630 |       " 7070 7071 7072 7073 7074 7075 7076 7077 7078 7079 7080 7081 7082 7083\n",
1631 |       " 7084 7085 7086 7087 7088 7089 7090 7091 7092 7093 7094 7095 7096 7097\n",
1632 |       " 7098 7099 7100 7101 7102 7103 7104 7105 7106 7107 7108 7109 7110 7111\n",
1633 |       " 7112 7113 7114 7115 7116 7117 7118 7119 7120 7121 7122 7123 7124 7125\n",
1634 |       " 7126 7127 7128 7129 7130 7131 7132 7133 7134 7135 7136 7137 7138 7139\n",
1635 |       " 7140 7141 7142 7143 7144 7145 7146 7147 7148 7149 7150 7151 7152 7153\n",
1636 |       " 7154 7155 7156 7157 7158 7159 7160 7161 7162 7163 7164 7165 7166 7167\n",
1637 |       " 7168 7169 7170 7171 7172 7173 7174 7175 7176 7177 7178 7179 7180 7181\n",
1638 |       " 7182 7183 7184 7185 7186 7187 7188 7189 7190 7191 7192 7193 7194 7195\n",
1639 |       " 7196 7197 7198 7199 7200 7201 7202 7203 7204 7205 7206 7207 7208 7209\n",
1640 |       " 7210 7211 7212 7213 7214 7215 7216 7217 7218 7219 7220 7221 7222 7223\n",
1641 |       " 7224 7225 7226 7227 7228 7229 7230 7231 7232 7233 7234 7235 7236 7237\n",
1642 |       " 7238 7239 7240 7241 7242 7243 7244 7245 7246 7247 7248 7249 7250 7251\n",
1643 |       " 7252 7253 7254 7255 7256 7257 7258 7259 7260 7261 7262 7263 7264 7265\n",
1644 |       " 7266 7267 7268 7269 7270 7271 7272 7273 7274 7275 7276 7277 7278 7279\n",
1645 |       " 7280 7281 7282 7283 7284 7285 7286 7287 7288 7289 7290 7291 7292 7293\n",
1646 |       " 7294 7295 7296 7297 7298 7299 7300 7301 7302 7303 7304 7305 7306 7307\n",
1647 |       " 7308 7309 7310 7311 7312 7313 7314 7315 7316 7317 7318 7319 7320 7321\n",
1648 |       " 7322 7323 7324 7325 7326 7327 7328 7329 7330 7331 7332 7333 7334 7335\n",
1649 |       " 7336 7337 7338 7339 7340 7341 7342 7343 7344 7345 7346 7347 7348 7349\n",
1650 |       " 7350 7351 7352 7353 7354 7355 7356 7357 7358 7359 7360 7361 7362 7363\n",
1651 |       " 7364 7365 7366 7367 7368 7369 7370 7371 7372 7373 7374 7375 7376 7377\n",
1652 |       " 7378 7379 7380 7381 7382 7383 7384 7385 7386 7387 7388 7389 7390 7391\n",
1653 |       " 7392 7393 7394 7395 7396 7397 7398 7399 7400 7401 7402 7403 7404 7405\n",
1654 |       " 7406 7407 7408 7409 7410 7411 7412 7413 7414 7415 7416 7417 7418 7419\n",
1655 |       " 7420 7421 7422 7423 7424 7425 7426 7427 7428 7429 7430 7431 7432 7433\n",
1656 |       " 7434 7435 7436 7437 7438 7439 7440 7441 7442 7443 7444 7445 7446 7447\n",
1657 |       " 7448 7449 7450 7451 7452 7453 7454 7455 7456 7457 7458 7459 7460 7461\n",
1658 |       " 7462 7463 7464 7465 7466 7467 7468 7469 7470 7471 7472 7473 7474 7475\n",
1659 |       " 7476 7477 7478 7479 7480 7481 7482 7483 7484 7485 7486 7487 7488 7489\n",
1660 |       " 7490 7491 7492 7493 7494 7495 7496 7497 7498 7499 7500 7501 7502 7503\n",
1661 |       " 7504 7505 7506 7507 7508 7509 7510 7511 7512 7513 7514 7515 7516 7517\n",
1662 |       " 7518 7519 7520 7521 7522 7523 7524 7525 7526 7527 7528 7529 7530 7531\n",
1663 |       " 7532 7533 7534 7535 7536 7537 7538 7539 7540 7541 7542 7543 7544 7545\n",
1664 |       " 7546 7547 7548 7549 7550 7551 7552 7553 7554 7555 7556 7557 7558 7559\n",
1665 |       " 7560 7561 7562 7563 7564 7565 7566 7567 7568 7569 7570 7571 7572 7573\n",
1666 |       " 7574 7575 7576 7577 7578 7579 7580 7581 7582 7583 7584 7585 7586 7587\n",
1667 |       " 7588 7589 7590 7591 7592 7593 7594 7595 7596 7597 7598 7599 7600 7601\n",
1668 |       " 7602 7603 7604 7605 7606 7607 7608 7609 7610 7611 7612 7613 7614 7615\n",
1669 |       " 7616 7617 7618 7619 7620 7621 7622 7623 7624 7625 7626 7627 7628 7629\n",
1670 |       " 7630 7631 7632 7633 7634 7635 7636 7637 7638 7639 7640 7641 7642 7643\n",
1671 |       " 7644 7645 7646 7647 7648 7649 7650 7651 7652 7653 7654 7655 7656 7657\n",
1672 |       " 7658 7659 7660 7661 7662 7663 7664 7665 7666 7667 7668 7669 7670 7671\n",
1673 |       " 7672 7673 7674 7675 7676 7677 7678 7679 7680 7681 7682 7683 7684 7685\n",
1674 |       " 7686 7687 7688 7689 7690 7691 7692 7693 7694 7695 7696 7697 7698 7699\n",
1675 |       " 7700 7701 7702 7703 7704 7705 7706 7707 7708 7709 7710 7711 7712 7713\n",
1676 |       " 7714 7715 7716 7717 7718 7719 7720 7721 7722 7723 7724 7725 7726 7727\n",
1677 |       " 7728 7729 7730 7731 7732 7733 7734 7735 7736 7737 7738 7739 7740 7741\n",
1678 |       " 7742 7743 7744 7745 7746 7747 7748 7749 7750 7751 7752 7753 7754 7755\n",
1679 |       " 7756 7757 7758 7759 7760 7761 7762 7763 7764 7765 7766 7767 7768 7769\n",
1680 |       " 7770 7771 7772 7773 7774 7775 7776 7777 7778 7779 7780 7781 7782 7783\n",
1681 |       " 7784 7785 7786 7787 7788 7789 7790 7791 7792 7793 7794 7795 7796 7797\n",
1682 |       " 7798 7799 7800 7801 7802 7803 7804 7805 7806 7807 7808 7809 7810 7811\n",
1683 |       " 7812 7813 7814 7815 7816 7817 7818 7819 7820 7821 7822 7823 7824 7825\n",
1684 |       " 7826 7827 7828 7829 7830 7831 7832 7833 7834 7835 7836 7837 7838 7839\n",
1685 |       " 7840 7841 7842 7843 7844 7845 7846 7847 7848 7849 7850 7851 7852 7853\n",
1686 |       " 7854 7855 7856 7857 7858 7859 7860 7861 7862 7863 7864 7865 7866 7867\n",
1687 |       " 7868 7869 7870 7871 7872 7873 7874 7875 7876 7877 7878 7879 7880 7881\n",
1688 |       " 7882 7883 7884 7885 7886 7887 7888 7889 7890 7891 7892 7893 7894 7895\n",
1689 |       " 7896 7897 7898 7899 7901 7902 7903 7904 7905 7906 7907 7908 7910 7911\n",
1690 |       " 7912 7913 7914 7916 7917 7918 7919 7920 7921 7922 7923 7924 7925 7928\n",
1691 |       " 7929 7930 7931 7932 7933 7934 7935 7936 7938 7939 7940 7941 7945 7946\n",
1692 |       " 7947 7948 7950 7951 7952 7953 7954 7955 7956 7957 7958 7959 7960 7961\n",
1693 |       " 7963 7964 7966 7967 7968 7970 7971 7972 7973 7975 7976 7977 7978 7979\n",
1694 |       " 7980 7981 7982 7983 7984 7985 7986 7987 7988 7989 7990 7991 7992 7993\n",
1695 |       " 7995 7996 7997 7999 8000 8001 8002 8004 8005 8006 8008 8009 8011 8012\n",
1696 |       " 8013 8014 8015 8016 8017 8019]\n"
1697 |      ]
1698 |     },
1699 |     {
1700 |      "name": "stdout",
1701 |      "output_type": "stream",
1702 |      "text": [
1703 |       "Train: [   0    1    2 ... 9997 9998 9999] Validation: [7900 7909 7915 7926 7927 7937 7942 7943 7944 7949 7962 7965 7969 7974\n",
1704 |       " 7994 7998 8003 8007 8010 8018 8020 8021 8022 8023 8024 8025 8026 8027\n",
1705 |       " 8028 8029 8030 8031 8032 8033 8034 8035 8036 8037 8038 8039 8040 8041\n",
1706 |       " 8042 8043 8044 8045 8046 8047 8048 8049 8050 8051 8052 8053 8054 8055\n",
1707 |       " 8056 8057 8058 8059 8060 8061 8062 8063 8064 8065 8066 8067 8068 8069\n",
1708 |       " 8070 8071 8072 8073 8074 8075 8076 8077 8078 8079 8080 8081 8082 8083\n",
1709 |       " 8084 8085 8086 8087 8088 8089 8090 8091 8092 8093 8094 8095 8096 8097\n",
1710 |       " 8098 8099 8100 8101 8102 8103 8104 8105 8106 8107 8108 8109 8110 8111\n",
1711 |       " 8112 8113 8114 8115 8116 8117 8118 8119 8120 8121 8122 8123 8124 8125\n",
1712 |       " 8126 8127 8128 8129 8130 8131 8132 8133 8134 8135 8136 8137 8138 8139\n",
1713 |       " 8140 8141 8142 8143 8144 8145 8146 8147 8148 8149 8150 8151 8152 8153\n",
1714 |       " 8154 8155 8156 8157 8158 8159 8160 8161 8162 8163 8164 8165 8166 8167\n",
1715 |       " 8168 8169 8170 8171 8172 8173 8174 8175 8176 8177 8178 8179 8180 8181\n",
1716 |       " 8182 8183 8184 8185 8186 8187 8188 8189 8190 8191 8192 8193 8194 8195\n",
1717 |       " 8196 8197 8198 8199 8200 8201 8202 8203 8204 8205 8206 8207 8208 8209\n",
1718 |       " 8210 8211 8212 8213 8214 8215 8216 8217 8218 8219 8220 8221 8222 8223\n",
1719 |       " 8224 8225 8226 8227 8228 8229 8230 8231 8232 8233 8234 8235 8236 8237\n",
1720 |       " 8238 8239 8240 8241 8242 8243 8244 8245 8246 8247 8248 8249 8250 8251\n",
1721 |       " 8252 8253 8254 8255 8256 8257 8258 8259 8260 8261 8262 8263 8264 8265\n",
1722 |       " 8266 8267 8268 8269 8270 8271 8272 8273 8274 8275 8276 8277 8278 8279\n",
1723 |       " 8280 8281 8282 8283 8284 8285 8286 8287 8288 8289 8290 8291 8292 8293\n",
1724 |       " 8294 8295 8296 8297 8298 8299 8300 8301 8302 8303 8304 8305 8306 8307\n",
1725 |       " 8308 8309 8310 8311 8312 8313 8314 8315 8316 8317 8318 8319 8320 8321\n",
1726 |       " 8322 8323 8324 8325 8326 8327 8328 8329 8330 8331 8332 8333 8334 8335\n",
1727 |       " 8336 8337 8338 8339 8340 8341 8342 8343 8344 8345 8346 8347 8348 8349\n",
1728 |       " 8350 8351 8352 8353 8354 8355 8356 8357 8358 8359 8360 8361 8362 8363\n",
1729 |       " 8364 8365 8366 8367 8368 8369 8370 8371 8372 8373 8374 8375 8376 8377\n",
1730 |       " 8378 8379 8380 8381 8382 8383 8384 8385 8386 8387 8388 8389 8390 8391\n",
1731 |       " 8392 8393 8394 8395 8396 8397 8398 8399 8400 8401 8402 8403 8404 8405\n",
1732 |       " 8406 8407 8408 8409 8410 8411 8412 8413 8414 8415 8416 8417 8418 8419\n",
1733 |       " 8420 8421 8422 8423 8424 8425 8426 8427 8428 8429 8430 8431 8432 8433\n",
1734 |       " 8434 8435 8436 8437 8438 8439 8440 8441 8442 8443 8444 8445 8446 8447\n",
1735 |       " 8448 8449 8450 8451 8452 8453 8454 8455 8456 8457 8458 8459 8460 8461\n",
1736 |       " 8462 8463 8464 8465 8466 8467 8468 8469 8470 8471 8472 8473 8474 8475\n",
1737 |       " 8476 8477 8478 8479 8480 8481 8482 8483 8484 8485 8486 8487 8488 8489\n",
1738 |       " 8490 8491 8492 8493 8494 8495 8496 8497 8498 8499 8500 8501 8502 8503\n",
1739 |       " 8504 8505 8506 8507 8508 8509 8510 8511 8512 8513 8514 8515 8516 8517\n",
1740 |       " 8518 8519 8520 8521 8522 8523 8524 8525 8526 8527 8528 8529 8530 8531\n",
1741 |       " 8532 8533 8534 8535 8536 8537 8538 8539 8540 8541 8542 8543 8544 8545\n",
1742 |       " 8546 8547 8548 8549 8550 8551 8552 8553 8554 8555 8556 8557 8558 8559\n",
1743 |       " 8560 8561 8562 8563 8564 8565 8566 8567 8568 8569 8570 8571 8572 8573\n",
1744 |       " 8574 8575 8576 8577 8578 8579 8580 8581 8582 8583 8584 8585 8586 8587\n",
1745 |       " 8588 8589 8590 8591 8592 8593 8594 8595 8596 8597 8598 8599 8600 8601\n",
1746 |       " 8602 8603 8604 8605 8606 8607 8608 8609 8610 8611 8612 8613 8614 8615\n",
1747 |       " 8616 8617 8618 8619 8620 8621 8622 8623 8624 8625 8626 8627 8628 8629\n",
1748 |       " 8630 8631 8632 8633 8634 8635 8636 8637 8638 8639 8640 8641 8642 8643\n",
1749 |       " 8644 8645 8646 8647 8648 8649 8650 8651 8652 8653 8654 8655 8656 8657\n",
1750 |       " 8658 8659 8660 8661 8662 8663 8664 8665 8666 8667 8668 8669 8670 8671\n",
1751 |       " 8672 8673 8674 8675 8676 8677 8678 8679 8680 8681 8682 8683 8684 8685\n",
1752 |       " 8686 8687 8688 8689 8690 8691 8692 8693 8694 8695 8696 8697 8698 8699\n",
1753 |       " 8700 8701 8702 8703 8704 8705 8706 8707 8708 8709 8710 8711 8712 8713\n",
1754 |       " 8714 8715 8716 8717 8718 8719 8720 8721 8722 8723 8724 8725 8726 8727\n",
1755 |       " 8728 8729 8730 8731 8732 8733 8734 8735 8736 8737 8738 8739 8740 8741\n",
1756 |       " 8742 8743 8744 8745 8746 8747 8748 8749 8750 8751 8752 8753 8754 8755\n",
1757 |       " 8756 8757 8758 8759 8760 8761 8762 8763 8764 8765 8766 8767 8768 8769\n",
1758 |       " 8770 8771 8772 8773 8774 8775 8776 8777 8778 8779 8780 8781 8782 8783\n",
1759 |       " 8784 8785 8786 8787 8788 8789 8790 8791 8792 8793 8794 8795 8796 8797\n",
1760 |       " 8798 8799 8800 8801 8802 8803 8804 8805 8806 8807 8808 8809 8810 8811\n",
1761 |       " 8812 8813 8814 8815 8816 8817 8818 8819 8820 8821 8822 8823 8824 8825\n",
1762 |       " 8826 8827 8828 8829 8830 8831 8832 8833 8834 8835 8836 8837 8838 8839\n",
1763 |       " 8840 8841 8842 8843 8844 8845 8846 8847 8848 8849 8850 8851 8852 8853\n",
1764 |       " 8854 8855 8856 8857 8858 8859 8860 8861 8862 8863 8864 8865 8866 8867\n",
1765 |       " 8868 8869 8870 8871 8872 8873 8874 8875 8877 8879 8881 8882 8883 8885\n",
1766 |       " 8886 8888 8889 8891 8892 8893 8894 8895 8896 8898 8899 8900 8901 8902\n",
1767 |       " 8903 8905 8906 8907 8908 8909 8911 8912 8913 8914 8916 8917 8919 8920\n",
1768 |       " 8921 8922 8924 8925 8926 8927 8928 8929 8930 8933 8934 8937 8938 8939\n",
1769 |       " 8940 8941 8942 8943 8944 8945 8946 8947 8949 8950 8951 8956 8957 8958\n",
1770 |       " 8959 8961 8962 8964 8965 8966 8967 8968 8969 8970 8971 8972 8973 8974\n",
1771 |       " 8975 8976 8977 8978 8979 8980 8981 8982 8983 8984 8985 8986 8987 8988\n",
1772 |       " 8989 8990 8992 8993 8994 8995 8996 8997 8998 8999 9000 9001 9002 9003\n",
1773 |       " 9004 9005 9006 9007 9009 9011 9012 9013 9014 9015 9016 9017 9018 9019\n",
1774 |       " 9020 9021 9022 9023 9025 9026]\n",
1775 |       "Train: [   0    1    2 ... 9023 9025 9026] Validation: [8876 8878 8880 8884 8887 8890 8897 8904 8910 8915 8918 8923 8931 8932\n",
1776 |       " 8935 8936 8948 8952 8953 8954 8955 8960 8963 8991 9008 9010 9024 9027\n",
1777 |       " 9028 9029 9030 9031 9032 9033 9034 9035 9036 9037 9038 9039 9040 9041\n",
1778 |       " 9042 9043 9044 9045 9046 9047 9048 9049 9050 9051 9052 9053 9054 9055\n",
1779 |       " 9056 9057 9058 9059 9060 9061 9062 9063 9064 9065 9066 9067 9068 9069\n",
1780 |       " 9070 9071 9072 9073 9074 9075 9076 9077 9078 9079 9080 9081 9082 9083\n",
1781 |       " 9084 9085 9086 9087 9088 9089 9090 9091 9092 9093 9094 9095 9096 9097\n",
1782 |       " 9098 9099 9100 9101 9102 9103 9104 9105 9106 9107 9108 9109 9110 9111\n",
1783 |       " 9112 9113 9114 9115 9116 9117 9118 9119 9120 9121 9122 9123 9124 9125\n",
1784 |       " 9126 9127 9128 9129 9130 9131 9132 9133 9134 9135 9136 9137 9138 9139\n",
1785 |       " 9140 9141 9142 9143 9144 9145 9146 9147 9148 9149 9150 9151 9152 9153\n",
1786 |       " 9154 9155 9156 9157 9158 9159 9160 9161 9162 9163 9164 9165 9166 9167\n",
1787 |       " 9168 9169 9170 9171 9172 9173 9174 9175 9176 9177 9178 9179 9180 9181\n",
1788 |       " 9182 9183 9184 9185 9186 9187 9188 9189 9190 9191 9192 9193 9194 9195\n",
1789 |       " 9196 9197 9198 9199 9200 9201 9202 9203 9204 9205 9206 9207 9208 9209\n",
1790 |       " 9210 9211 9212 9213 9214 9215 9216 9217 9218 9219 9220 9221 9222 9223\n",
1791 |       " 9224 9225 9226 9227 9228 9229 9230 9231 9232 9233 9234 9235 9236 9237\n",
1792 |       " 9238 9239 9240 9241 9242 9243 9244 9245 9246 9247 9248 9249 9250 9251\n",
1793 |       " 9252 9253 9254 9255 9256 9257 9258 9259 9260 9261 9262 9263 9264 9265\n",
1794 |       " 9266 9267 9268 9269 9270 9271 9272 9273 9274 9275 9276 9277 9278 9279\n",
1795 |       " 9280 9281 9282 9283 9284 9285 9286 9287 9288 9289 9290 9291 9292 9293\n",
1796 |       " 9294 9295 9296 9297 9298 9299 9300 9301 9302 9303 9304 9305 9306 9307\n",
1797 |       " 9308 9309 9310 9311 9312 9313 9314 9315 9316 9317 9318 9319 9320 9321\n",
1798 |       " 9322 9323 9324 9325 9326 9327 9328 9329 9330 9331 9332 9333 9334 9335\n",
1799 |       " 9336 9337 9338 9339 9340 9341 9342 9343 9344 9345 9346 9347 9348 9349\n",
1800 |       " 9350 9351 9352 9353 9354 9355 9356 9357 9358 9359 9360 9361 9362 9363\n",
1801 |       " 9364 9365 9366 9367 9368 9369 9370 9371 9372 9373 9374 9375 9376 9377\n",
1802 |       " 9378 9379 9380 9381 9382 9383 9384 9385 9386 9387 9388 9389 9390 9391\n",
1803 |       " 9392 9393 9394 9395 9396 9397 9398 9399 9400 9401 9402 9403 9404 9405\n",
1804 |       " 9406 9407 9408 9409 9410 9411 9412 9413 9414 9415 9416 9417 9418 9419\n",
1805 |       " 9420 9421 9422 9423 9424 9425 9426 9427 9428 9429 9430 9431 9432 9433\n",
1806 |       " 9434 9435 9436 9437 9438 9439 9440 9441 9442 9443 9444 9445 9446 9447\n",
1807 |       " 9448 9449 9450 9451 9452 9453 9454 9455 9456 9457 9458 9459 9460 9461\n",
1808 |       " 9462 9463 9464 9465 9466 9467 9468 9469 9470 9471 9472 9473 9474 9475\n",
1809 |       " 9476 9477 9478 9479 9480 9481 9482 9483 9484 9485 9486 9487 9488 9489\n",
1810 |       " 9490 9491 9492 9493 9494 9495 9496 9497 9498 9499 9500 9501 9502 9503\n",
1811 |       " 9504 9505 9506 9507 9508 9509 9510 9511 9512 9513 9514 9515 9516 9517\n",
1812 |       " 9518 9519 9520 9521 9522 9523 9524 9525 9526 9527 9528 9529 9530 9531\n",
1813 |       " 9532 9533 9534 9535 9536 9537 9538 9539 9540 9541 9542 9543 9544 9545\n",
1814 |       " 9546 9547 9548 9549 9550 9551 9552 9553 9554 9555 9556 9557 9558 9559\n",
1815 |       " 9560 9561 9562 9563 9564 9565 9566 9567 9568 9569 9570 9571 9572 9573\n",
1816 |       " 9574 9575 9576 9577 9578 9579 9580 9581 9582 9583 9584 9585 9586 9587\n",
1817 |       " 9588 9589 9590 9591 9592 9593 9594 9595 9596 9597 9598 9599 9600 9601\n",
1818 |       " 9602 9603 9604 9605 9606 9607 9608 9609 9610 9611 9612 9613 9614 9615\n",
1819 |       " 9616 9617 9618 9619 9620 9621 9622 9623 9624 9625 9626 9627 9628 9629\n",
1820 |       " 9630 9631 9632 9633 9634 9635 9636 9637 9638 9639 9640 9641 9642 9643\n",
1821 |       " 9644 9645 9646 9647 9648 9649 9650 9651 9652 9653 9654 9655 9656 9657\n",
1822 |       " 9658 9659 9660 9661 9662 9663 9664 9665 9666 9667 9668 9669 9670 9671\n",
1823 |       " 9672 9673 9674 9675 9676 9677 9678 9679 9680 9681 9682 9683 9684 9685\n",
1824 |       " 9686 9687 9688 9689 9690 9691 9692 9693 9694 9695 9696 9697 9698 9699\n",
1825 |       " 9700 9701 9702 9703 9704 9705 9706 9707 9708 9709 9710 9711 9712 9713\n",
1826 |       " 9714 9715 9716 9717 9718 9719 9720 9721 9722 9723 9724 9725 9726 9727\n",
1827 |       " 9728 9729 9730 9731 9732 9733 9734 9735 9736 9737 9738 9739 9740 9741\n",
1828 |       " 9742 9743 9744 9745 9746 9747 9748 9749 9750 9751 9752 9753 9754 9755\n",
1829 |       " 9756 9757 9758 9759 9760 9761 9762 9763 9764 9765 9766 9767 9768 9769\n",
1830 |       " 9770 9771 9772 9773 9774 9775 9776 9777 9778 9779 9780 9781 9782 9783\n",
1831 |       " 9784 9785 9786 9787 9788 9789 9790 9791 9792 9793 9794 9795 9796 9797\n",
1832 |       " 9798 9799 9800 9801 9802 9803 9804 9805 9806 9807 9808 9809 9810 9811\n",
1833 |       " 9812 9813 9814 9815 9816 9817 9818 9819 9820 9821 9822 9823 9824 9825\n",
1834 |       " 9826 9827 9828 9829 9830 9831 9832 9833 9834 9835 9836 9837 9838 9839\n",
1835 |       " 9840 9841 9842 9843 9844 9845 9846 9847 9848 9849 9850 9851 9852 9853\n",
1836 |       " 9854 9855 9856 9857 9858 9859 9860 9861 9862 9863 9864 9865 9866 9867\n",
1837 |       " 9868 9869 9870 9871 9872 9873 9874 9875 9876 9877 9878 9879 9880 9881\n",
1838 |       " 9882 9883 9884 9885 9886 9887 9888 9889 9890 9891 9892 9893 9894 9895\n",
1839 |       " 9896 9897 9898 9899 9900 9901 9902 9903 9904 9905 9906 9907 9908 9909\n",
1840 |       " 9910 9911 9912 9913 9914 9915 9916 9917 9918 9919 9920 9921 9922 9923\n",
1841 |       " 9924 9925 9926 9927 9928 9929 9930 9931 9932 9933 9934 9935 9936 9937\n",
1842 |       " 9938 9939 9940 9941 9942 9943 9944 9945 9946 9947 9948 9949 9950 9951\n",
1843 |       " 9952 9953 9954 9955 9956 9957 9958 9959 9960 9961 9962 9963 9964 9965\n",
1844 |       " 9966 9967 9968 9969 9970 9971 9972 9973 9974 9975 9976 9977 9978 9979\n",
1845 |       " 9980 9981 9982 9983 9984 9985 9986 9987 9988 9989 9990 9991 9992 9993\n",
1846 |       " 9994 9995 9996 9997 9998 9999]\n"
1847 |      ]
1848 |     },
1849 |     {
1850 |      "name": "stdout",
1851 |      "output_type": "stream",
1852 |      "text": [
1853 |       "[0.861, 0.859, 0.858, 0.853, 0.849, 0.86, 0.853, 0.866, 0.853, 0.845]\n"
1854 |      ]
1855 |     }
1856 |    ],
1857 |    "source": [
1858 |     "from sklearn.model_selection import StratifiedKFold\n",
1859 |     "\n",
1860 |     "accuracy=[]\n",
1861 |     "\n",
1862 |     "skf = StratifiedKFold(n_splits=10, random_state=None)\n",
1863 |     "skf.get_n_splits(X, Y)\n",
1864 |     "# X is the feature set and y is the target\n",
1865 |     "for train_index, test_index in skf.split(X,Y): \n",
1866 |     "    print(\"Train:\", train_index, \"Validation:\", test_index) \n",
1867 |     "    X1_train, X1_test = X.iloc[train_index], X.iloc[test_index] \n",
1868 |     "    y1_train, y1_test = Y.iloc[train_index], Y.iloc[test_index]\n",
1869 |     "    \n",
1870 |     "    classifier.fit(X1_train,y1_train)\n",
1871 |     "    prediction=classifier.predict(X1_test)\n",
1872 |     "    score=accuracy_score(prediction,y1_test)\n",
1873 |     "    accuracy.append(score)\n",
1874 |     "    \n",
1875 |     "print(accuracy)\n",
1876 |     "    "
1877 |    ]
1878 |   },
1879 |   {
1880 |    "cell_type": "code",
1881 |    "execution_count": 48,
1882 |    "metadata": {},
1883 |    "outputs": [
1884 |     {
1885 |      "data": {
1886 |       "text/plain": [
1887 |        "0.8557"
1888 |       ]
1889 |      },
1890 |      "execution_count": 48,
1891 |      "metadata": {},
1892 |      "output_type": "execute_result"
1893 |     }
1894 |    ],
1895 |    "source": [
1896 |     "import numpy as np\n",
1897 |     "np.array(accuracy).mean()"
1898 |    ]
1899 |   },
1900 |   {
1901 |    "cell_type": "code",
1902 |    "execution_count": null,
1903 |    "metadata": {},
1904 |    "outputs": [],
1905 |    "source": []
1906 |   }
1907 |  ],
1908 |  "metadata": {
1909 |   "kernelspec": {
1910 |    "display_name": "Python 3",
1911 |    "language": "python",
1912 |    "name": "python3"
1913 |   },
1914 |   "language_info": {
1915 |    "codemirror_mode": {
1916 |     "name": "ipython",
1917 |     "version": 3
1918 |    },
1919 |    "file_extension": ".py",
1920 |    "mimetype": "text/x-python",
1921 |    "name": "python",
1922 |    "nbconvert_exporter": "python",
1923 |    "pygments_lexer": "ipython3",
1924 |    "version": "3.7.7"
1925 |   }
1926 |  },
1927 |  "nbformat": 4,
1928 |  "nbformat_minor": 2
1929 | }
1930 | 


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/README.md:
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1 | # Types-of-Crossvalidation


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