machine-learning/week-1/Week1_ML_Anav.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F5bxOfYK5vKs",
        "outputId": "dcd03517-6f7a-4f2a-d4bf-4d3a867edcf6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.0\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.1.1\u001b[0m\n",
            "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
            "cp: cannot stat 'kaggle.json': No such file or directory\n",
            "chmod: cannot access '/home/darkwinter/.kaggle/kaggle.json': No such file or directory\n",
            "Dataset URL: https://www.kaggle.com/datasets/rupakroy/online-payments-fraud-detection-dataset\n",
            "License(s): CC-BY-NC-SA-4.0\n",
            "Downloading online-payments-fraud-detection-dataset.zip to /mnt/c/Users/Anav Agrawal/Desktop/Anav/Github/summer-of-code-2024/machine-learning/week-1\n",
            "100%|████████████████████████████████████████| 178M/178M [01:00<00:00, 3.12MB/s]\n",
            "100%|████████████████████████████████████████| 178M/178M [01:00<00:00, 3.07MB/s]\n"
          ]
        }
      ],
      "source": [
        "! pip install -q kaggle\n",
        "! mkdir ~/.kaggle\n",
        "! cp kaggle.json ~/.kaggle/\n",
        "! chmod 600 ~/.kaggle/kaggle.json\n",
        "! kaggle datasets download -d rupakroy/online-payments-fraud-detection-dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TrL0ijH4dyMm",
        "outputId": "05fd46d0-0b7f-4704-a577-ecea0f5705bc"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Archive:  online-payments-fraud-detection-dataset.zip\n",
            "  inflating: PS_20174392719_1491204439457_log.csv  \n"
          ]
        }
      ],
      "source": [
        "! unzip online-payments-fraud-detection-dataset.zip"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "Vs2bL_sUeF43"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "2024-07-04 02:32:23.360811: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
            "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
            "2024-07-04 02:32:24.085110: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import tensorflow as tf\n",
        "import sklearn as sk\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "nHDByc7fd3oO"
      },
      "outputs": [],
      "source": [
        "df = pd.read_csv('PS_20174392719_1491204439457_log.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "9l0ETHw7d-cn",
        "outputId": "4cc0c9fd-6db9-4fc8-a6df-3cdbf12732cb"
      },
      "outputs": [
        {
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>step</th>\n",
              "      <th>type</th>\n",
              "      <th>amount</th>\n",
              "      <th>nameOrig</th>\n",
              "      <th>oldbalanceOrg</th>\n",
              "      <th>newbalanceOrig</th>\n",
              "      <th>nameDest</th>\n",
              "      <th>oldbalanceDest</th>\n",
              "      <th>newbalanceDest</th>\n",
              "      <th>isFraud</th>\n",
              "      <th>isFlaggedFraud</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>PAYMENT</td>\n",
              "      <td>9839.64</td>\n",
              "      <td>C1231006815</td>\n",
              "      <td>170136.0</td>\n",
              "      <td>160296.36</td>\n",
              "      <td>M1979787155</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>PAYMENT</td>\n",
              "      <td>1864.28</td>\n",
              "      <td>C1666544295</td>\n",
              "      <td>21249.0</td>\n",
              "      <td>19384.72</td>\n",
              "      <td>M2044282225</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>TRANSFER</td>\n",
              "      <td>181.00</td>\n",
              "      <td>C1305486145</td>\n",
              "      <td>181.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>C553264065</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>CASH_OUT</td>\n",
              "      <td>181.00</td>\n",
              "      <td>C840083671</td>\n",
              "      <td>181.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>C38997010</td>\n",
              "      <td>21182.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>PAYMENT</td>\n",
              "      <td>11668.14</td>\n",
              "      <td>C2048537720</td>\n",
              "      <td>41554.0</td>\n",
              "      <td>29885.86</td>\n",
              "      <td>M1230701703</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   step      type    amount     nameOrig  oldbalanceOrg  newbalanceOrig  \\\n",
              "0     1   PAYMENT   9839.64  C1231006815       170136.0       160296.36   \n",
              "1     1   PAYMENT   1864.28  C1666544295        21249.0        19384.72   \n",
              "2     1  TRANSFER    181.00  C1305486145          181.0            0.00   \n",
              "3     1  CASH_OUT    181.00   C840083671          181.0            0.00   \n",
              "4     1   PAYMENT  11668.14  C2048537720        41554.0        29885.86   \n",
              "\n",
              "      nameDest  oldbalanceDest  newbalanceDest  isFraud  isFlaggedFraud  \n",
              "0  M1979787155             0.0             0.0        0               0  \n",
              "1  M2044282225             0.0             0.0        0               0  \n",
              "2   C553264065             0.0             0.0        1               0  \n",
              "3    C38997010         21182.0             0.0        1               0  \n",
              "4  M1230701703             0.0             0.0        0               0  "
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "Eg7t_ROtes2Y",
        "outputId": "46a2e97a-62fe-4ea6-f4fb-6b7d38255251"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Column: step\n",
            "Number of unique values: 743\n",
            "Unique values: [  1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17  18\n",
            "  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35  36\n",
            "  37  38  39  40  41  42  43  44  45  46  47  48  49  50  51  52  53  54\n",
            "  55  56  57  58  59  60  61  62  63  64  65  66  67  68  69  70  71  72\n",
            "  73  74  75  76  77  78  79  80  81  82  83  84  85  86  87  88  89  90\n",
            "  91  92  93  94  95  96  97  98  99 100 101 102 103 104 105 106 107 108\n",
            " 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126\n",
            " 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144\n",
            " 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162\n",
            " 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180\n",
            " 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198\n",
            " 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216\n",
            " 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234\n",
            " 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252\n",
            " 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270\n",
            " 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288\n",
            " 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306\n",
            " 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324\n",
            " 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342\n",
            " 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360\n",
            " 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378\n",
            " 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396\n",
            " 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414\n",
            " 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432\n",
            " 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450\n",
            " 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468\n",
            " 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486\n",
            " 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504\n",
            " 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522\n",
            " 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540\n",
            " 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558\n",
            " 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576\n",
            " 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594\n",
            " 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612\n",
            " 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630\n",
            " 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648\n",
            " 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666\n",
            " 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684\n",
            " 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702\n",
            " 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720\n",
            " 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738\n",
            " 739 740 741 742 743]\n",
            "\n",
            "Column: type\n",
            "Number of unique values: 5\n",
            "Unique values: ['PAYMENT' 'TRANSFER' 'CASH_OUT' 'DEBIT' 'CASH_IN']\n",
            "\n",
            "Column: amount\n",
            "Number of unique values: 5316900\n",
            "Unique values: [9.83964000e+03 1.86428000e+03 1.81000000e+02 ... 3.39682130e+05\n",
            " 6.31140928e+06 8.50002520e+05]\n",
            "\n",
            "Column: nameOrig\n",
            "Number of unique values: 6353307\n",
            "Unique values: ['C1231006815' 'C1666544295' 'C1305486145' ... 'C1162922333' 'C1685995037'\n",
            " 'C1280323807']\n",
            "\n",
            "Column: oldbalanceOrg\n",
            "Number of unique values: 1845844\n",
            "Unique values: [1.70136000e+05 2.12490000e+04 1.81000000e+02 ... 3.39682130e+05\n",
            " 6.31140928e+06 8.50002520e+05]\n",
            "\n",
            "Column: newbalanceOrig\n",
            "Number of unique values: 2682586\n",
            "Unique values: [1.60296360e+05 1.93847200e+04 0.00000000e+00 ... 2.56745479e+07\n",
            " 1.56745479e+07 5.67454789e+06]\n",
            "\n",
            "Column: nameDest\n",
            "Number of unique values: 2722362\n",
            "Unique values: ['M1979787155' 'M2044282225' 'C553264065' ... 'C1850423904' 'C1881841831'\n",
            " 'C2080388513']\n",
            "\n",
            "Column: oldbalanceDest\n",
            "Number of unique values: 3614697\n",
            "Unique values: [      0.     21182.     41898.   ...  503464.5    68488.84 6510099.11]\n",
            "\n",
            "Column: newbalanceDest\n",
            "Number of unique values: 3555499\n",
            "Unique values: [      0.     40348.79  157982.12 ... 1762283.33 6379898.11 7360101.63]\n",
            "\n",
            "Column: isFraud\n",
            "Number of unique values: 2\n",
            "Unique values: [0 1]\n",
            "\n",
            "Column: isFlaggedFraud\n",
            "Number of unique values: 2\n",
            "Unique values: [0 1]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "for col in df.columns:\n",
        "  unique_values = df[col].unique()\n",
        "  num_unique_values = len(unique_values)\n",
        "  print(f\"Column: {col}\")\n",
        "  print(f\"Number of unique values: {num_unique_values}\")\n",
        "  print(f\"Unique values: {unique_values}\")\n",
        "  print()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "orUgYrnIiSen",
        "outputId": "2de877e9-adcc-4e70-a9ec-ad31c7b08ede"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "step              0\n",
            "type              0\n",
            "amount            0\n",
            "nameOrig          0\n",
            "oldbalanceOrg     0\n",
            "newbalanceOrig    0\n",
            "nameDest          0\n",
            "oldbalanceDest    0\n",
            "newbalanceDest    0\n",
            "isFraud           0\n",
            "isFlaggedFraud    0\n",
            "dtype: int64\n"
          ]
        }
      ],
      "source": [
        "print(df.isna().sum())\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7zR8EL7DiabV"
      },
      "source": [
        "No null values, that's great."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 571
        },
        "id": "wg60-hEFe-PO",
        "outputId": "d69af03c-c317-4302-9a6d-0460520d1d31"
      },
      "outputs": [
        {
          "data": {
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              "summary": "{\n  \"name\": \"df[df[\\\"isFlaggedFraud\\\"] == 1]\",\n  \"rows\": 16,\n  \"fields\": [\n    {\n      \"column\": \"step\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 181,\n        \"min\": 212,\n        \"max\": 741,\n        \"num_unique_values\": 13,\n        \"samples\": [\n          730,\n          671,\n          212\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"TRANSFER\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"amount\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3572499.102346531,\n        \"min\": 353874.22,\n        \"max\": 10000000.0,\n        \"num_unique_values\": 14,\n        \"samples\": [\n          399045.08\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"nameOrig\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 16,\n        \"samples\": [\n          \"C728984460\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"oldbalanceOrg\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6972668.5331245465,\n        \"min\": 353874.22,\n        \"max\": 19585040.37,\n        \"num_unique_values\": 13,\n        \"samples\": [\n          17316255.05\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"newbalanceOrig\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6972668.5331245465,\n        \"min\": 353874.22,\n        \"max\": 19585040.37,\n        \"num_unique_values\": 13,\n        \"samples\": [\n          17316255.05\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"nameDest\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 16,\n        \"samples\": [\n          \"C639921569\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"oldbalanceDest\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 0.0,\n        \"max\": 0.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"newbalanceDest\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 0.0,\n        \"max\": 0.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"isFraud\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 1,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"isFlaggedFraud\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 1,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>step</th>\n",
              "      <th>type</th>\n",
              "      <th>amount</th>\n",
              "      <th>nameOrig</th>\n",
              "      <th>oldbalanceOrg</th>\n",
              "      <th>newbalanceOrig</th>\n",
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              "      <th>newbalanceDest</th>\n",
              "      <th>isFraud</th>\n",
              "      <th>isFlaggedFraud</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2736446</th>\n",
              "      <td>212</td>\n",
              "      <td>TRANSFER</td>\n",
              "      <td>4953893.08</td>\n",
              "      <td>C728984460</td>\n",
              "      <td>4953893.08</td>\n",
              "      <td>4953893.08</td>\n",
              "      <td>C639921569</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3247297</th>\n",
              "      <td>250</td>\n",
              "      <td>TRANSFER</td>\n",
              "      <td>1343002.08</td>\n",
              "      <td>C1100582606</td>\n",
              "      <td>1343002.08</td>\n",
              "      <td>1343002.08</td>\n",
              "      <td>C1147517658</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3760288</th>\n",
              "      <td>279</td>\n",
              "      <td>TRANSFER</td>\n",
              "      <td>536624.41</td>\n",
              "      <td>C1035541766</td>\n",
              "      <td>536624.41</td>\n",
              "      <td>536624.41</td>\n",
              "      <td>C1100697970</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5563713</th>\n",
              "      <td>387</td>\n",
              "      <td>TRANSFER</td>\n",
              "      <td>4892193.09</td>\n",
              "      <td>C908544136</td>\n",
              "      <td>4892193.09</td>\n",
              "      <td>4892193.09</td>\n",
              "      <td>C891140444</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5996407</th>\n",
              "      <td>425</td>\n",
              "      <td>TRANSFER</td>\n",
              "      <td>10000000.00</td>\n",
              "      <td>C689608084</td>\n",
              "      <td>19585040.37</td>\n",
              "      <td>19585040.37</td>\n",
              "      <td>C1392803603</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5996409</th>\n",
              "      <td>425</td>\n",
              "      <td>TRANSFER</td>\n",
              "      <td>9585040.37</td>\n",
              "      <td>C452586515</td>\n",
              "      <td>19585040.37</td>\n",
              "      <td>19585040.37</td>\n",
              "      <td>C1109166882</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6168499</th>\n",
              "      <td>554</td>\n",
              "      <td>TRANSFER</td>\n",
              "      <td>3576297.10</td>\n",
              "      <td>C193696150</td>\n",
              "      <td>3576297.10</td>\n",
              "      <td>3576297.10</td>\n",
              "      <td>C484597480</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6205439</th>\n",
              "      <td>586</td>\n",
              "      <td>TRANSFER</td>\n",
              "      <td>353874.22</td>\n",
              "      <td>C1684585475</td>\n",
              "      <td>353874.22</td>\n",
              "      <td>353874.22</td>\n",
              "      <td>C1770418982</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6266413</th>\n",
              "      <td>617</td>\n",
              "      <td>TRANSFER</td>\n",
              "      <td>2542664.27</td>\n",
              "      <td>C786455622</td>\n",
              "      <td>2542664.27</td>\n",
              "      <td>2542664.27</td>\n",
              "      <td>C661958277</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6281482</th>\n",
              "      <td>646</td>\n",
              "      <td>TRANSFER</td>\n",
              "      <td>10000000.00</td>\n",
              "      <td>C19004745</td>\n",
              "      <td>10399045.08</td>\n",
              "      <td>10399045.08</td>\n",
              "      <td>C1806199534</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6281484</th>\n",
              "      <td>646</td>\n",
              "      <td>TRANSFER</td>\n",
              "      <td>399045.08</td>\n",
              "      <td>C724693370</td>\n",
              "      <td>10399045.08</td>\n",
              "      <td>10399045.08</td>\n",
              "      <td>C1909486199</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6296014</th>\n",
              "      <td>671</td>\n",
              "      <td>TRANSFER</td>\n",
              "      <td>3441041.46</td>\n",
              "      <td>C917414431</td>\n",
              "      <td>3441041.46</td>\n",
              "      <td>3441041.46</td>\n",
              "      <td>C1082139865</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6351225</th>\n",
              "      <td>702</td>\n",
              "      <td>TRANSFER</td>\n",
              "      <td>3171085.59</td>\n",
              "      <td>C1892216157</td>\n",
              "      <td>3171085.59</td>\n",
              "      <td>3171085.59</td>\n",
              "      <td>C1308068787</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6362460</th>\n",
              "      <td>730</td>\n",
              "      <td>TRANSFER</td>\n",
              "      <td>10000000.00</td>\n",
              "      <td>C2140038573</td>\n",
              "      <td>17316255.05</td>\n",
              "      <td>17316255.05</td>\n",
              "      <td>C1395467927</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6362462</th>\n",
              "      <td>730</td>\n",
              "      <td>TRANSFER</td>\n",
              "      <td>7316255.05</td>\n",
              "      <td>C1869569059</td>\n",
              "      <td>17316255.05</td>\n",
              "      <td>17316255.05</td>\n",
              "      <td>C1861208726</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6362584</th>\n",
              "      <td>741</td>\n",
              "      <td>TRANSFER</td>\n",
              "      <td>5674547.89</td>\n",
              "      <td>C992223106</td>\n",
              "      <td>5674547.89</td>\n",
              "      <td>5674547.89</td>\n",
              "      <td>C1366804249</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a6e2e58f-b964-4bec-987b-656ecaadd860')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-a6e2e58f-b964-4bec-987b-656ecaadd860 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-a6e2e58f-b964-4bec-987b-656ecaadd860');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-c69833e9-27d4-4119-a60f-34e6d3c6f96b\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c69833e9-27d4-4119-a60f-34e6d3c6f96b')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-c69833e9-27d4-4119-a60f-34e6d3c6f96b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "         step      type       amount     nameOrig  oldbalanceOrg  \\\n",
              "2736446   212  TRANSFER   4953893.08   C728984460     4953893.08   \n",
              "3247297   250  TRANSFER   1343002.08  C1100582606     1343002.08   \n",
              "3760288   279  TRANSFER    536624.41  C1035541766      536624.41   \n",
              "5563713   387  TRANSFER   4892193.09   C908544136     4892193.09   \n",
              "5996407   425  TRANSFER  10000000.00   C689608084    19585040.37   \n",
              "5996409   425  TRANSFER   9585040.37   C452586515    19585040.37   \n",
              "6168499   554  TRANSFER   3576297.10   C193696150     3576297.10   \n",
              "6205439   586  TRANSFER    353874.22  C1684585475      353874.22   \n",
              "6266413   617  TRANSFER   2542664.27   C786455622     2542664.27   \n",
              "6281482   646  TRANSFER  10000000.00    C19004745    10399045.08   \n",
              "6281484   646  TRANSFER    399045.08   C724693370    10399045.08   \n",
              "6296014   671  TRANSFER   3441041.46   C917414431     3441041.46   \n",
              "6351225   702  TRANSFER   3171085.59  C1892216157     3171085.59   \n",
              "6362460   730  TRANSFER  10000000.00  C2140038573    17316255.05   \n",
              "6362462   730  TRANSFER   7316255.05  C1869569059    17316255.05   \n",
              "6362584   741  TRANSFER   5674547.89   C992223106     5674547.89   \n",
              "\n",
              "         newbalanceOrig     nameDest  oldbalanceDest  newbalanceDest  isFraud  \\\n",
              "2736446      4953893.08   C639921569             0.0             0.0        1   \n",
              "3247297      1343002.08  C1147517658             0.0             0.0        1   \n",
              "3760288       536624.41  C1100697970             0.0             0.0        1   \n",
              "5563713      4892193.09   C891140444             0.0             0.0        1   \n",
              "5996407     19585040.37  C1392803603             0.0             0.0        1   \n",
              "5996409     19585040.37  C1109166882             0.0             0.0        1   \n",
              "6168499      3576297.10   C484597480             0.0             0.0        1   \n",
              "6205439       353874.22  C1770418982             0.0             0.0        1   \n",
              "6266413      2542664.27   C661958277             0.0             0.0        1   \n",
              "6281482     10399045.08  C1806199534             0.0             0.0        1   \n",
              "6281484     10399045.08  C1909486199             0.0             0.0        1   \n",
              "6296014      3441041.46  C1082139865             0.0             0.0        1   \n",
              "6351225      3171085.59  C1308068787             0.0             0.0        1   \n",
              "6362460     17316255.05  C1395467927             0.0             0.0        1   \n",
              "6362462     17316255.05  C1861208726             0.0             0.0        1   \n",
              "6362584      5674547.89  C1366804249             0.0             0.0        1   \n",
              "\n",
              "         isFlaggedFraud  \n",
              "2736446               1  \n",
              "3247297               1  \n",
              "3760288               1  \n",
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              "6362462               1  \n",
              "6362584               1  "
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df[df[\"isFlaggedFraud\"] == 1]\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J_IkiPAKf9af"
      },
      "source": [
        "We can see the whenever the transactions were marked as fraud, there were actually fraud. So isFlaggedFraud is an extremely important feature"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        },
        "id": "8_FbxH52fgui",
        "outputId": "055ac6ea-a23f-4c9a-a20e-6384c155ef1b"
      },
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            ],
            "text/plain": [
              "   step      type    amount  oldbalanceOrg  newbalanceOrig  oldbalanceDest  \\\n",
              "0     1   PAYMENT   9839.64       170136.0       160296.36             0.0   \n",
              "1     1   PAYMENT   1864.28        21249.0        19384.72             0.0   \n",
              "2     1  TRANSFER    181.00          181.0            0.00             0.0   \n",
              "3     1  CASH_OUT    181.00          181.0            0.00         21182.0   \n",
              "4     1   PAYMENT  11668.14        41554.0        29885.86             0.0   \n",
              "\n",
              "   newbalanceDest  isFraud  isFlaggedFraud  isNameDestC  \n",
              "0             0.0        0               0        False  \n",
              "1             0.0        0               0        False  \n",
              "2             0.0        1               0         True  \n",
              "3             0.0        1               0         True  \n",
              "4             0.0        0               0        False  "
            ]
          },
          "execution_count": 9,
          "metadata": {},
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        }
      ],
      "source": [
        "# Removing the ids\n",
        "df[\"isNameDestC\"] = df[\"nameDest\"].str[0] == \"C\"\n",
        "df.drop(\"nameDest\", axis=1, inplace=True)\n",
        "df.drop('nameOrig', axis=1, inplace=True)\n",
        "df.head()\n"
      ]
    },
    {
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        },
        "id": "c-biNldDgzHe",
        "outputId": "00adb3b8-25cf-46ff-859d-30fb38a7ab48"
      },
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "      --bg-color: #E8F0FE;\n",
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              "\n",
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              "    display: none;\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "    (() => {\n",
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              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "   step    amount  oldbalanceOrg  newbalanceOrig  oldbalanceDest  \\\n",
              "0     1   9839.64       170136.0       160296.36             0.0   \n",
              "1     1   1864.28        21249.0        19384.72             0.0   \n",
              "2     1    181.00          181.0            0.00             0.0   \n",
              "3     1    181.00          181.0            0.00         21182.0   \n",
              "4     1  11668.14        41554.0        29885.86             0.0   \n",
              "\n",
              "   newbalanceDest  isFraud  isFlaggedFraud  isNameDestC  CASH_IN  CASH_OUT  \\\n",
              "0             0.0        0               0        False    False     False   \n",
              "1             0.0        0               0        False    False     False   \n",
              "2             0.0        1               0         True    False     False   \n",
              "3             0.0        1               0         True    False      True   \n",
              "4             0.0        0               0        False    False     False   \n",
              "\n",
              "   DEBIT  PAYMENT  TRANSFER  \n",
              "0  False     True     False  \n",
              "1  False     True     False  \n",
              "2  False    False      True  \n",
              "3  False    False     False  \n",
              "4  False     True     False  "
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Converting payment type to one hot features\n",
        "\n",
        "one_hot_paymenttype = pd.get_dummies(df['type'])\n",
        "df = pd.concat([df, one_hot_paymenttype], axis=1)\n",
        "df.drop('type', axis=1, inplace=True)\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Yzq-Z8_dhtQg"
      },
      "source": [
        "Now the original data looks pretty clean, let's add some new features."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "id": "pDWXikyVg8c3",
        "outputId": "99e56e67-1add-4900-d89e-f5f0d65906fa"
      },
      "outputs": [
        {
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              "      <th></th>\n",
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              "<p>5 rows × 27 columns</p>\n",
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              "\n",
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              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "      --hover-bg-color: #E2EBFA;\n",
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              "\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "    fill: var(--button-hover-fill-color);\n",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
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              "</div>\n",
              "\n",
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              "  </div>\n"
            ],
            "text/plain": [
              "   step    amount  oldbalanceOrg  newbalanceOrig  oldbalanceDest  \\\n",
              "0     1   9839.64       170136.0       160296.36             0.0   \n",
              "1     1   1864.28        21249.0        19384.72             0.0   \n",
              "2     1    181.00          181.0            0.00             0.0   \n",
              "3     1    181.00          181.0            0.00         21182.0   \n",
              "4     1  11668.14        41554.0        29885.86             0.0   \n",
              "\n",
              "   newbalanceDest  isFraud  isFlaggedFraud  isNameDestC  CASH_IN  ...  \\\n",
              "0             0.0        0               0        False    False  ...   \n",
              "1             0.0        0               0        False    False  ...   \n",
              "2             0.0        1               0         True    False  ...   \n",
              "3             0.0        1               0         True    False  ...   \n",
              "4             0.0        0               0        False    False  ...   \n",
              "\n",
              "   amount_by_newbalDest  oldbalOrig_by_amount  newbalOrig_by_amount  \\\n",
              "0               9839.64             17.289119             16.289221   \n",
              "1               1864.28             11.391855             10.392391   \n",
              "2                181.00              0.994505              0.000000   \n",
              "3                181.00              0.994505              0.000000   \n",
              "4              11668.14              3.561016              2.561102   \n",
              "\n",
              "   oldbalDest_by_amount  newbalDest_by_amount  log_amount  log_oldbalanceOrg  \\\n",
              "0              0.000000                   0.0    9.194276          12.044359   \n",
              "1              0.000000                   0.0    7.531166           9.964112   \n",
              "2              0.000000                   0.0    5.204007           5.204007   \n",
              "3            116.384615                   0.0    5.204007           5.204007   \n",
              "4              0.000000                   0.0    9.364703          10.634773   \n",
              "\n",
              "   log_newbalanceOrig  log_newbalanceDest  log_oldbalanceDest  \n",
              "0           11.984786                 0.0            0.000000  \n",
              "1            9.872292                 0.0            0.000000  \n",
              "2            0.000000                 0.0            0.000000  \n",
              "3            0.000000                 0.0            9.960954  \n",
              "4           10.305174                 0.0            0.000000  \n",
              "\n",
              "[5 rows x 27 columns]"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Adding the relative value of the amount to the balances seems like a valuable feature\n",
        "df['amount_by_oldbalOrig'] = df['amount'] / (df['oldbalanceOrg']+1)   # We are adding 1 everywhere to avoid division by zero\n",
        "df['amount_by_newbalOrig'] = df['amount'] / (df['newbalanceOrig']+1)\n",
        "df['amount_by_oldbalDest'] = df['amount'] / (df['oldbalanceDest']+1)\n",
        "df['amount_by_newbalDest'] = df['amount'] / (df['newbalanceDest']+1)\n",
        "\n",
        "# Inverse of the above four columns\n",
        "df['oldbalOrig_by_amount'] = df['oldbalanceOrg'] / (df['amount']+1)\n",
        "df['newbalOrig_by_amount'] = df['newbalanceOrig'] / (df['amount']+1)\n",
        "df['oldbalDest_by_amount'] = df['oldbalanceDest'] / (df['amount']+1)\n",
        "df['newbalDest_by_amount'] = df['newbalanceDest'] / (df['amount']+1)\n",
        "\n",
        "# Since the amount has a large range of values, the log of the values will be a good feature to add\n",
        "df['log_amount'] = np.log(df['amount']+1)\n",
        "df['log_oldbalanceOrg'] = np.log(df['oldbalanceOrg']+1)\n",
        "df['log_newbalanceOrig'] = np.log(df['newbalanceOrig']+1)\n",
        "df['log_newbalanceDest'] = np.log(df['newbalanceDest']+1)\n",
        "df['log_oldbalanceDest'] = np.log(df['oldbalanceDest']+1)\n",
        "\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "h4RGD4JIsaoI"
      },
      "outputs": [],
      "source": [
        "y = df['isFraud']\n",
        "X = df.drop('isFraud', axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "id": "2dyJydqbq3Wn",
        "outputId": "ec1e3033-aefc-42d1-ae96-c0df15f1d61c"
      },
      "outputs": [
        {
          "data": {
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              "      <th></th>\n",
              "      <th>step</th>\n",
              "      <th>amount</th>\n",
              "      <th>oldbalanceOrg</th>\n",
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              "      <th>oldbalDest_by_amount</th>\n",
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              "      <td>-0.285812</td>\n",
              "      <td>-0.323814</td>\n",
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              "      <td>-0.323814</td>\n",
              "      <td>-0.333411</td>\n",
              "      <td>-0.001586</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
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              "      <td>-0.159675</td>\n",
              "      <td>-0.014153</td>\n",
              "      <td>-0.014440</td>\n",
              "      <td>-0.008909</td>\n",
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              "      <td>-0.847721</td>\n",
              "      <td>-1.248013</td>\n",
              "      <td>-1.144463</td>\n",
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              "      <td>-0.292442</td>\n",
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              "      <td>-0.014440</td>\n",
              "      <td>0.014758</td>\n",
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              "      <td>-0.389887</td>\n",
              "      <td>-0.847721</td>\n",
              "      <td>-1.248013</td>\n",
              "      <td>0.331751</td>\n",
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              "      <td>-0.282221</td>\n",
              "      <td>-0.323814</td>\n",
              "      <td>-0.333411</td>\n",
              "      <td>-0.001586</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
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              "      <td>-0.028125</td>\n",
              "      <td>-0.013375</td>\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-c96af2b9-db97-4de2-b645-30c9a0a3a81f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "       step    amount  oldbalanceOrg  newbalanceOrig  oldbalanceDest  \\\n",
              "0 -1.703041 -0.281560      -0.229810       -0.237622       -0.323814   \n",
              "1 -1.703041 -0.294767      -0.281359       -0.285812       -0.323814   \n",
              "2 -1.703041 -0.297555      -0.288654       -0.292442       -0.323814   \n",
              "3 -1.703041 -0.297555      -0.288654       -0.292442       -0.317582   \n",
              "4 -1.703041 -0.278532      -0.274329       -0.282221       -0.323814   \n",
              "\n",
              "   newbalanceDest  isFlaggedFraud  isNameDestC  CASH_IN  CASH_OUT  ...  \\\n",
              "0       -0.333411       -0.001586        False    False     False  ...   \n",
              "1       -0.333411       -0.001586        False    False     False  ...   \n",
              "2       -0.333411       -0.001586         True    False     False  ...   \n",
              "3       -0.333411       -0.001586         True    False      True  ...   \n",
              "4       -0.333411       -0.001586        False    False     False  ...   \n",
              "\n",
              "   amount_by_newbalDest  oldbalOrig_by_amount  newbalOrig_by_amount  \\\n",
              "0             -0.049065             -0.009214             -0.009502   \n",
              "1             -0.140398             -0.011002             -0.011289   \n",
              "2             -0.159675             -0.014153             -0.014440   \n",
              "3             -0.159675             -0.014153             -0.014440   \n",
              "4             -0.028125             -0.013375             -0.013663   \n",
              "\n",
              "   oldbalDest_by_amount  newbalDest_by_amount  log_amount  log_oldbalanceOrg  \\\n",
              "0             -0.008909              -0.00906   -0.907462           0.816576   \n",
              "1             -0.008909              -0.00906   -1.824023           0.449673   \n",
              "2             -0.008909              -0.00906   -3.106552          -0.389887   \n",
              "3              0.014758              -0.00906   -3.106552          -0.389887   \n",
              "4             -0.008909              -0.00906   -0.813537           0.567961   \n",
              "\n",
              "   log_newbalanceOrig  log_newbalanceDest  log_oldbalanceDest  \n",
              "0            1.045603           -1.248013           -1.144463  \n",
              "1            0.711877           -1.248013           -1.144463  \n",
              "2           -0.847721           -1.248013           -1.144463  \n",
              "3           -0.847721           -1.248013            0.331751  \n",
              "4            0.780263           -1.248013           -1.144463  \n",
              "\n",
              "[5 rows x 26 columns]"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "def normalize_column(column):\n",
        "  \"\"\"\n",
        "  Normalizes a column by subtracting the mean and dividing by the standard deviation.\n",
        "  \"\"\"\n",
        "  mean = column.mean()\n",
        "  std = column.std()\n",
        "  return (column - mean) / std\n",
        "\n",
        "for col in X.columns:\n",
        "  if X[col].dtype == np.float64 or X[col].dtype == np.int64:\n",
        "    X[col] = normalize_column(X[col])\n",
        "\n",
        "X.head()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2cEMMZWfpu5f"
      },
      "source": [
        "Now that we have our dataset ready, lets make it ready for model training"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ww9E_8s2qL3_"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "id": "XyMKjsmLqO_2",
        "outputId": "106ee08a-355b-4429-f7d2-5966c8b12dbb"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "X_train"
            },
            "text/html": [
              "\n",
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              "    <div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>step</th>\n",
              "      <th>amount</th>\n",
              "      <th>oldbalanceOrg</th>\n",
              "      <th>newbalanceOrig</th>\n",
              "      <th>oldbalanceDest</th>\n",
              "      <th>newbalanceDest</th>\n",
              "      <th>isFlaggedFraud</th>\n",
              "      <th>isNameDestC</th>\n",
              "      <th>CASH_IN</th>\n",
              "      <th>CASH_OUT</th>\n",
              "      <th>...</th>\n",
              "      <th>amount_by_newbalDest</th>\n",
              "      <th>oldbalOrig_by_amount</th>\n",
              "      <th>newbalOrig_by_amount</th>\n",
              "      <th>oldbalDest_by_amount</th>\n",
              "      <th>newbalDest_by_amount</th>\n",
              "      <th>log_amount</th>\n",
              "      <th>log_oldbalanceOrg</th>\n",
              "      <th>log_newbalanceOrig</th>\n",
              "      <th>log_newbalanceDest</th>\n",
              "      <th>log_oldbalanceDest</th>\n",
              "    </tr>\n",
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              "      <th>6271196</th>\n",
              "      <td>2.638920</td>\n",
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              "      <td>-0.001586</td>\n",
              "      <td>True</td>\n",
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              "      <td>-0.161710</td>\n",
              "      <td>-0.010792</td>\n",
              "      <td>-0.010473</td>\n",
              "      <td>-0.008643</td>\n",
              "      <td>-0.008997</td>\n",
              "      <td>1.115298</td>\n",
              "      <td>1.400766</td>\n",
              "      <td>1.590829</td>\n",
              "      <td>0.503623</td>\n",
              "      <td>0.802050</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1888243</th>\n",
              "      <td>-0.557831</td>\n",
              "      <td>0.053981</td>\n",
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              "      <td>-0.139539</td>\n",
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              "      <td>-0.171855</td>\n",
              "      <td>-0.001586</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
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              "      <td>-0.161743</td>\n",
              "      <td>-0.014120</td>\n",
              "      <td>-0.013802</td>\n",
              "      <td>-0.008137</td>\n",
              "      <td>-0.008492</td>\n",
              "      <td>0.785686</td>\n",
              "      <td>0.873268</td>\n",
              "      <td>1.207647</td>\n",
              "      <td>0.743558</td>\n",
              "      <td>0.871041</td>\n",
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              "    <tr>\n",
              "      <th>5549957</th>\n",
              "      <td>0.973799</td>\n",
              "      <td>-0.264788</td>\n",
              "      <td>-0.287458</td>\n",
              "      <td>-0.292442</td>\n",
              "      <td>-0.323814</td>\n",
              "      <td>-0.333411</td>\n",
              "      <td>-0.001586</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>...</td>\n",
              "      <td>0.066919</td>\n",
              "      <td>-0.014400</td>\n",
              "      <td>-0.014440</td>\n",
              "      <td>-0.008909</td>\n",
              "      <td>-0.009060</td>\n",
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              "      <td>0.138241</td>\n",
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              "      <td>-1.248013</td>\n",
              "      <td>-1.144463</td>\n",
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              "    <tr>\n",
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              "      <td>-0.292442</td>\n",
              "      <td>-0.296702</td>\n",
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              "      <td>-0.161738</td>\n",
              "      <td>-0.014351</td>\n",
              "      <td>-0.014440</td>\n",
              "      <td>-0.008873</td>\n",
              "      <td>-0.008821</td>\n",
              "      <td>1.286989</td>\n",
              "      <td>0.826728</td>\n",
              "      <td>-0.847721</td>\n",
              "      <td>0.750028</td>\n",
              "      <td>0.549650</td>\n",
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              "    <tr>\n",
              "      <th>682342</th>\n",
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              "      <td>0.043398</td>\n",
              "      <td>-0.288716</td>\n",
              "      <td>-0.292442</td>\n",
              "      <td>0.303247</td>\n",
              "      <td>0.302811</td>\n",
              "      <td>-0.001586</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
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              "      <td>-0.161746</td>\n",
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              "      <td>-0.006805</td>\n",
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              "      <td>0.768853</td>\n",
              "      <td>-1.307741</td>\n",
              "      <td>-0.847721</td>\n",
              "      <td>0.948903</td>\n",
              "      <td>1.015157</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 26 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4a91bf43-964b-4886-8dbe-abf43f3714f6')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-4a91bf43-964b-4886-8dbe-abf43f3714f6 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-4a91bf43-964b-4886-8dbe-abf43f3714f6');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
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              "\n",
              "\n",
              "<div id=\"df-9eef6ebc-593b-4902-8d94-88f26a72d339\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-9eef6ebc-593b-4902-8d94-88f26a72d339')\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
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              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-9eef6ebc-593b-4902-8d94-88f26a72d339 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "             step    amount  oldbalanceOrg  newbalanceOrig  oldbalanceDest  \\\n",
              "6271196  2.638920  0.342006       1.328034        1.436652       -0.174944   \n",
              "1888243 -0.557831  0.053981      -0.207478       -0.139539       -0.086687   \n",
              "5549957  0.973799 -0.264788      -0.287458       -0.292442       -0.323814   \n",
              "2025342 -0.445418  0.575888      -0.226320       -0.292442       -0.296702   \n",
              "682342  -1.457137  0.043398      -0.288716       -0.292442        0.303247   \n",
              "\n",
              "         newbalanceDest  isFlaggedFraud  isNameDestC  CASH_IN  CASH_OUT  ...  \\\n",
              "6271196       -0.300846       -0.001586         True     True     False  ...   \n",
              "1888243       -0.171855       -0.001586         True     True     False  ...   \n",
              "5549957       -0.333411       -0.001586        False    False     False  ...   \n",
              "2025342       -0.164726       -0.001586         True    False      True  ...   \n",
              "682342         0.302811       -0.001586         True    False     False  ...   \n",
              "\n",
              "         amount_by_newbalDest  oldbalOrig_by_amount  newbalOrig_by_amount  \\\n",
              "6271196             -0.161710             -0.010792             -0.010473   \n",
              "1888243             -0.161743             -0.014120             -0.013802   \n",
              "5549957              0.066919             -0.014400             -0.014440   \n",
              "2025342             -0.161738             -0.014351             -0.014440   \n",
              "682342              -0.161746             -0.014455             -0.014440   \n",
              "\n",
              "         oldbalDest_by_amount  newbalDest_by_amount  log_amount  \\\n",
              "6271196             -0.008643             -0.008997    1.115298   \n",
              "1888243             -0.008137             -0.008492    0.785686   \n",
              "5549957             -0.008909             -0.009060   -0.517472   \n",
              "2025342             -0.008873             -0.008821    1.286989   \n",
              "682342              -0.006805             -0.006756    0.768853   \n",
              "\n",
              "         log_oldbalanceOrg  log_newbalanceOrig  log_newbalanceDest  \\\n",
              "6271196           1.400766            1.590829            0.503623   \n",
              "1888243           0.873268            1.207647            0.743558   \n",
              "5549957           0.138241           -0.847721           -1.248013   \n",
              "2025342           0.826728           -0.847721            0.750028   \n",
              "682342           -1.307741           -0.847721            0.948903   \n",
              "\n",
              "         log_oldbalanceDest  \n",
              "6271196            0.802050  \n",
              "1888243            0.871041  \n",
              "5549957           -1.144463  \n",
              "2025342            0.549650  \n",
              "682342             1.015157  \n",
              "\n",
              "[5 rows x 26 columns]"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X_train.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jdagnU2nvqSH"
      },
      "source": [
        "Now since there are only some 8000 fraud examples out of 6 million, we will use undersampling to balance the dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IVwuXhqyvMKg",
        "outputId": "a99f41f2-97f7-416f-9a97-6190e5aee3c0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Number of fraud examples in undersampled data set: 8213\n",
            "Number of non-fraud examples in undersampled data set: 8213\n"
          ]
        }
      ],
      "source": [
        "fraud_indices = df[df['isFraud'] == 1].index\n",
        "non_fraud_indices = df[df['isFraud'] == 0].index\n",
        "\n",
        "# Randomly select the same number of non-fraud indices as there are fraud indices\n",
        "random_non_fraud_indices = np.random.choice(non_fraud_indices, len(fraud_indices), replace=False)\n",
        "\n",
        "# Combine the fraud and non-fraud indices\n",
        "undersampled_indices = np.concatenate([fraud_indices, random_non_fraud_indices])\n",
        "\n",
        "# Create the undersampled data sets\n",
        "X_train_small = X.loc[undersampled_indices]\n",
        "y_train_small = y.loc[undersampled_indices]\n",
        "\n",
        "# Print the number of fraud and non-fraud examples in the undersampled data sets\n",
        "print(\"Number of fraud examples in undersampled data set:\", len(X_train_small[y_train_small == 1]))\n",
        "print(\"Number of non-fraud examples in undersampled data set:\", len(X_train_small[y_train_small == 0]))\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8MGHXWIG38RP"
      },
      "source": [
        "We are finally ready to start training the models"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KAT724h2xONI"
      },
      "outputs": [],
      "source": [
        "from sklearn.metrics import f1_score\n",
        "from sklearn.metrics import roc_auc_score\n",
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "def calculate_metrics(y_true, y_pred):\n",
        "  accuracy = accuracy_score(y_test, y_pred)\n",
        "  f1 = f1_score(y_test, y_pred)\n",
        "  auc = roc_auc_score(y_test, y_pred)\n",
        "\n",
        "  print(f\"Accuracy: {accuracy}\")\n",
        "  print(f\"F1 Score: {f1}\")\n",
        "  print(f\"ROC AUC Score: {auc}\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Vt6TJf0F4CxI"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "from sklearn.metrics import confusion_matrix\n",
        "import seaborn as sns\n",
        "\n",
        "def plot_confusion_matrix(y_true, y_pred):\n",
        "  cm = confusion_matrix(y_test, y_pred)\n",
        "\n",
        "  plt.figure(figsize=(8, 6))\n",
        "  sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\")\n",
        "  plt.xlabel(\"Predicted\")\n",
        "  plt.ylabel(\"Actual\")\n",
        "  plt.title(\"Confusion Matrix\")\n",
        "  plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KfcoJzW342cA"
      },
      "source": [
        "Model 1 : XGBoost"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v3hPcJ0osHCG",
        "outputId": "c07139a1-de98-4f67-b07f-d09d5dc85777"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy: 0.9996133668205864\n",
            "F1 Score: 0.8681672025723473\n",
            "ROC AUC Score: 0.9998064369928806\n"
          ]
        }
      ],
      "source": [
        "import xgboost as xgb\n",
        "\n",
        "model_xgb = xgb.XGBClassifier(\n",
        "    n_estimators=500,\n",
        "    objective='binary:logistic',\n",
        "    max_depth=8,\n",
        "    learning_rate=0.1,\n",
        "    subsample=0.8,\n",
        "    colsample_bytree=0.8,\n",
        "    seed=42,\n",
        ")\n",
        "\n",
        "model_xgb.fit(X_train_small, y_train_small)\n",
        "y_pred_xgb = model_xgb.predict(X_test)\n",
        "calculate_metrics(y_test, y_pred_xgb)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "Ww37_gM0w2G3",
        "outputId": "bdb97c9a-4699-427c-c042-c12b779fd221"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plot_confusion_matrix(y_test, y_pred)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dN5uq9SS476_"
      },
      "source": [
        "Model 2 : Random Forest"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 618
        },
        "id": "JKTuJC_RujAt",
        "outputId": "988a09e8-15e3-4164-f4ea-97bf706a5ffd"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy: 0.9992848857860441\n",
            "F1 Score: 0.7807228915662651\n",
            "ROC AUC Score: 0.9996419871209785\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "model_rf = RandomForestClassifier(n_estimators=300, criterion='entropy', random_state=0)\n",
        "model_rf.fit(X_train_small, y_train_small)\n",
        "y_pred_rf = model_rf.predict(X_test)\n",
        "\n",
        "calculate_metrics(y_test, y_pred_rf)\n",
        "plot_confusion_matrix(y_test, y_pred_rf)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "a178ht8EuRyu"
      },
      "outputs": [],
      "source": [
        "# import keras\n",
        "# from keras import layers\n",
        "\n",
        "# keras_model = keras.Sequential([\n",
        "#         layers.Dense(64, activation='relu', input_shape=(X_train.shape[1],)),\n",
        "#         layers.Dense(32, activation='relu'),\n",
        "#         layers.Dense(1, activation='sigmoid'),\n",
        "#     ])\n",
        "\n",
        "# early_stopping = keras.callbacks.EarlyStopping(monitor='val_loss', patience=3)\n",
        "\n",
        "# keras_model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
        "\n",
        "# history = keras_model.fit(X_train_small, y_train_small, epochs=10, batch_size=32,\n",
        "#                           validation_data=(X_test, y_test), callbacks=[early_stopping])\n",
        "\n",
        "# # We will have to deal with na values before using keras"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MgsQXgWa5nsY"
      },
      "source": [
        "Model 3 : SVM"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Sjdnzr124YAe",
        "outputId": "872f8346-7f27-4fbe-d109-59c3d96493bd"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Fitting 2 folds for each of 18 candidates, totalling 36 fits\n",
            "[CV] END .........................C=1, gamma=0.1, kernel=rbf; total time=   0.7s\n",
            "[CV] END .........................C=1, gamma=0.1, kernel=rbf; total time=   1.4s\n",
            "[CV] END ......................C=1, gamma=0.1, kernel=linear; total time=   1.3s\n",
            "[CV] END ......................C=1, gamma=0.1, kernel=linear; total time=   1.8s\n",
            "[CV] END ........................C=1, gamma=0.01, kernel=rbf; total time=   1.4s\n",
            "[CV] END ........................C=1, gamma=0.01, kernel=rbf; total time=   2.8s\n",
            "[CV] END .....................C=1, gamma=0.01, kernel=linear; total time=   0.2s\n",
            "[CV] END .....................C=1, gamma=0.01, kernel=linear; total time=   1.4s\n",
            "[CV] END ........................C=1, gamma=0.01, kernel=rbf; total time=   0.9s\n",
            "[CV] END ........................C=1, gamma=0.01, kernel=rbf; total time=   1.6s\n",
            "[CV] END .....................C=1, gamma=0.01, kernel=linear; total time=   0.2s\n",
            "[CV] END .....................C=1, gamma=0.01, kernel=linear; total time=   1.1s\n",
            "[CV] END ........................C=10, gamma=0.1, kernel=rbf; total time=   0.5s\n",
            "[CV] END ........................C=10, gamma=0.1, kernel=rbf; total time=   1.5s\n",
            "[CV] END .....................C=10, gamma=0.1, kernel=linear; total time=   0.2s\n",
            "[CV] END .....................C=10, gamma=0.1, kernel=linear; total time=   9.2s\n",
            "[CV] END .......................C=10, gamma=0.01, kernel=rbf; total time=   0.9s\n",
            "[CV] END .......................C=10, gamma=0.01, kernel=rbf; total time=   2.1s\n",
            "[CV] END ....................C=10, gamma=0.01, kernel=linear; total time=   0.2s\n",
            "[CV] END ....................C=10, gamma=0.01, kernel=linear; total time=   3.9s\n",
            "[CV] END .......................C=10, gamma=0.01, kernel=rbf; total time=   0.4s\n",
            "[CV] END .......................C=10, gamma=0.01, kernel=rbf; total time=   1.0s\n",
            "[CV] END ....................C=10, gamma=0.01, kernel=linear; total time=   0.2s\n",
            "[CV] END ....................C=10, gamma=0.01, kernel=linear; total time=   6.0s\n",
            "[CV] END .......................C=100, gamma=0.1, kernel=rbf; total time=   0.9s\n",
            "[CV] END .......................C=100, gamma=0.1, kernel=rbf; total time=   1.4s\n",
            "[CV] END ....................C=100, gamma=0.1, kernel=linear; total time=   0.2s\n",
            "[CV] END ....................C=100, gamma=0.1, kernel=linear; total time=  18.5s\n",
            "[CV] END ......................C=100, gamma=0.01, kernel=rbf; total time=   0.3s\n",
            "[CV] END ......................C=100, gamma=0.01, kernel=rbf; total time=   0.9s\n",
            "[CV] END ...................C=100, gamma=0.01, kernel=linear; total time=   0.2s\n",
            "[CV] END ...................C=100, gamma=0.01, kernel=linear; total time=  14.8s\n",
            "[CV] END ......................C=100, gamma=0.01, kernel=rbf; total time=   0.3s\n",
            "[CV] END ......................C=100, gamma=0.01, kernel=rbf; total time=   0.9s\n",
            "[CV] END ...................C=100, gamma=0.01, kernel=linear; total time=   0.2s\n",
            "[CV] END ...................C=100, gamma=0.01, kernel=linear; total time=  16.3s\n",
            "Best parameters: {'C': 10, 'gamma': 0.1, 'kernel': 'linear'}\n"
          ]
        }
      ],
      "source": [
        "from sklearn.svm import SVC\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "# Define the parameter grid for hyperparameter tuning\n",
        "param_grid = {\n",
        "    'C': [1,10,100],\n",
        "    'gamma': [0.1 ,0.01,0.01],\n",
        "    'kernel': ['rbf', 'linear']\n",
        "}\n",
        "\n",
        "# Initialize the grid search object with the SVM model and parameter grid\n",
        "grid_search = GridSearchCV(SVC(), param_grid, cv=2, scoring='f1',verbose=2)\n",
        "\n",
        "# Fit the grid search object to the training data\n",
        "grid_search.fit(X_train_small, y_train_small)\n",
        "\n",
        "# Print the best parameters\n",
        "print(f\"Best parameters: {grid_search.best_params_}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 618
        },
        "id": "CrvYcTzK_-tl",
        "outputId": "75a0409a-2388-4e22-8344-9fc0543e1633"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy: 0.9782896039681767\n",
            "F1 Score: 0.10473443727923781\n",
            "ROC AUC Score: 0.9878979708271036\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Train the SVM model with the best parameters on the entire training data\n",
        "best_model = SVC(**grid_search.best_params_)\n",
        "best_model.fit(X_train_small, y_train_small)\n",
        "\n",
        "# Evaluate the best model on the test data\n",
        "y_pred_svm = best_model.predict(X_test)\n",
        "calculate_metrics(y_test, y_pred_svm)\n",
        "plot_confusion_matrix(y_test, y_pred_svm)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BcX1MKBnASEW"
      },
      "source": [
        "Turns out SVMs are terrible for these type of tasks."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 618
        },
        "id": "7XFIkJ4YAOFt",
        "outputId": "09eb37cf-bfd9-487c-db12-0cee1be5479a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy: 0.9925675272136322\n",
            "F1 Score: 0.25480617711944537\n",
            "ROC AUC Score: 0.9953542809189654\n"
          ]
        },
        {
          "data": {
            "image/png": 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AwPKxWD4GAACASAoBAABYPhZJIQAAAERSCAAAwJ5CkRQCAABAFIUAAADX9hTa62WjLVu2KCwsTGXLlpXBYNCqVatu2n/FihVq0aKFSpUqpZIlSyo4OFgbNmyw+b4UhQAAAAVIenq6AgICNHPmzFz137Jli1q0aKF169Zp586dCgkJUVhYmHbv3m3TfdlTCAAAUID2FLZp00Zt2rTJdf+pU6da/Txu3DitXr1an332mQIDA3M9DkUhAACAHY+kycjIUEZGhlWb0WiU0Wi0y/1MJpPOnz8vb29vm64rOGUxAADAXSg6OloeHh5Wr+joaLvdb+LEibpw4YK6du1q03UkhQAAAHZcPo6MjFRERIRVm71SwsWLF2vkyJFavXq1SpcubdO1FIUAAAB2ZM+l4n9asmSJevfurU8++UShoaE2X09RCAAAUIAeNMmLjz/+WM8995yWLFmitm3b5mkMikIAAIAC5MKFCzp8+LDl52PHjikxMVHe3t6qUKGCIiMjdeLECS1cuFDStSXj8PBwTZs2TUFBQUpOTpYkubu7y8PDI9f3LdxlMQAAQH5wMtjvZaMdO3YoMDDQcpxMRESEAgMDNXz4cEnSyZMnlZSUZOn//vvv6+rVq+rbt6/KlCljeQ0cONCm+5IUAgAAFCDNmzeX2Wy+4fuxsbFWP8fHx+fLfSkKAQAACvmewvxAUQgAAGDHw6sLC8piAAAAkBQCAACwfExSCAAAAJEUAgAAsKdQJIUAAAAQSSEAAAB7CkVSCAAAAJEUAgAAsKdQFIUAAAAsH4vlYwAAAIikEAAAgOVjkRQCAABAJIUAAADsKRRJIQAAAERSCAAAwJ5CkRQCAABAJIUAAADsKRRFIQAAAEWhWD4GAACASAoBAAB40EQkhQAAABBJIQAAAHsKRVIIAAAAkRQCAACwp1AkhQAAABBJIQAAAHsKRVEIAADA8rFYPgYAAIBICgEAAGQgKSQpBAAAAEkhAAAASaFICgEAACCSQgAAAImgkKQQAAAAJIUAAADsKRRFIQAAAEWhWD4GAACASAoBAABICkVSCAAAAJEUAgAAkBSKpBAAAAAiKQQAAODwapEUAgAAQCSFAAAA7CkUSSEAAABEUggAAEBSKIpCAAAAikKxfAwAAACRFAIAAJAUiqQQAAAAIikEAADg8GqRFAIAAEAkhQAAAOwpFEkhAAAARFIIAABAUiiKQgAAAIpCsXwMAAAAkRQCAABwJI1ICgEAACCSQgAAAPYUiqQQAAAAoigEAACQwWCw28tWW7ZsUVhYmMqWLSuDwaBVq1bd8pr4+HjVr19fRqNRVatWVWxsrM33pSgEAAAoQNLT0xUQEKCZM2fmqv+xY8fUtm1bhYSEKDExUa+++qp69+6tDRs22HRf9hQCAIB7nj33FGZkZCgjI8OqzWg0ymg05ti/TZs2atOmTa7Hj4mJUaVKlTRp0iRJUs2aNbV161ZNmTJFrVq1yvU4JIUAAOCeZ8/l4+joaHl4eFi9oqOj823uCQkJCg0NtWpr1aqVEhISbBqHpBAAAMCOIiMjFRERYdV2o5QwL5KTk+Xr62vV5uvrq3PnzunSpUtyd3fP1TgUhQAAAHY8keZmS8UFCcvHAAAAhZifn59SUlKs2lJSUlSyZMlcp4QSSSEAAEChPrw6ODhY69ats2rbtGmTgoODbRqHpBAAAKAAuXDhghITE5WYmCjp2pEziYmJSkpKknRtj2KPHj0s/V9++WUdPXpUr7/+ug4cOKBZs2Zp2bJlGjRokE33JSkEAAD3vIKUFO7YsUMhISGWn68/pBIeHq7Y2FidPHnSUiBKUqVKlbR27VoNGjRI06ZN03/+8x/NnTvXpuNoJMlgNpvN+fMRCg73wH6OngIAOzm9bYajpwDATtxdHHfv/7yyym5j/z6ro93Gzk8khQAA4J5XkJJCR6EoBAAAoCbkQRMAAACQFAIAALB8LJJCAAAAiKQQAACApFAkhQAAABBFIfJZ4/pVtHzqSzq6cawu7Z6hsOZ1Le8VKeKkMQM6aPuyN5X23SQd3ThWc0c/qzKlPLKN0/rh/2rLwsE6nTBZf3w9Xssmv2D1/qTXu+jbj17XmR+m6PslQ286p8rlfXRq60Sd3DL+hn2eaNVAl3bPyHaf0t4l9P7IZ3R041j9+d1krZ7xiqpUKJWbrwLA/0tPv6Dxb49VmxYhCmpQVz2eflI/7tubY98xI4erXu3q+nBRrFV7m5aPqF7t6lavD+a+b3n/xInfs71fr3Z17d2TaMdPhruJwWCw26uwYPkY+aqYu1H7fjmhhasTtHTyi1bvFXVzVb2a5fX2nC+095cT8ipZVBOHdNEnU1/Sw0//XbB1fLSeZg57SlEzPlP8tl9UpIiT/lulTLZ7LVz9vRrVqaja95e74XyKFHHSwuhe+nb3ET0YUCnHPhXKeCt6UEdt3XU423vLpryoK1ez9MSr7+lc+mUNeOYRrYvpr8DOY3TxcmZuvxbgnjZy+Fs6fPiQxkSPV6nSpbX2szV6+YVe+nT1Ovn6+lr6bf5yk/bu3aNSpUvnOM4r/Qaoc5eulp+LFS2Wrc97c2NVpWpVy88eHp7590GAuxxFIfLVxm9/1sZvf87xvXMXLqtdH+vfRjHo7WXa+tHrKu/npd+S/5Kzs5MmDnlcb05dpQWrEiz9DhxNtrrutfHLJUk+Xo/dtCgc8UqYDh5L0VfbDuZYFDo5GRQ7LlyjY9apcWAVeZZwt7xXtUJpBdWtpPqPj9H+/7//gHFLdfzLcerapoFiVyZkGw+AtcuXLyvuy42aMn2WGjRsJEnq07e/tnz9lT5Zulj9Blz73awpKSl6O3q0Zr03T/1feSnHsYoWKyYfn5sn9R6enrfsA+SkMCV69uLQ5eO0tDSNHz9enTp1UnBwsIKDg9WpUydNmDBBqampjpwa7pCSJdxlMpl05vwlSVJgjfIq5+slk8mshI/f0NGNY7VqRh/VyiEpvJVmjaqpc4tAvfr2shv2efPFNko9fcGqAL3O6Hrtv5kuZ161tJnNZmVmXtVD9arYPB/gXpSVdVVZWVkyGo1W7UajUbt37ZIkmUwmvRU5ROE9n1fVqvffcKz5c+eoWeMgdevSUbEfzNXVq1ez9Xm1Xx+FNA1Wz2efUvxXcfn7YXB3M9jxVUg4rCjcvn27qlWrpunTp8vDw0NNmzZV06ZN5eHhoenTp6tGjRrasWPHLcfJyMjQuXPnrF5mU9Yd+AS4XUbXIhozoIOWrd+p8+mXJUmV/uMjSXrr5cf0ztwNenxgjM6cu6QNcwbKq2TRXI/t7VFMc0Y+oxeiFlnG/reH6lVWz47BemX04hzfP3g8WUknT2t0//byLOEulyLOeq1nqP7j5yU/n+z7IAFkV6xYcdUNCNT7MbN06lSKsrKytPaz1dq7J1FpaackSfPnzZGzcxF1f6bHDcfp/vSzenvCZM35YIG6PNFN8+a+p6mTJ1jeL1q0qF4bMlQTJk/TuzPfU2D9Bho0oC+FIWADhy0f9+/fX0888YRiYmKyRbZms1kvv/yy+vfvr4SEmy/RRUdHa+TIkVZtzr6N5FLmgXyfM/JPkSJO+nD88zIYDBowbqml3en//154Z+4GrYpLlCS9GPWhDm8Yrc4tAjXv029zNf6sYU9p6fod+nbXkRzfL17UqHljeuiV0R/rzzPpOfa5etWkJ1+bo9lRT+vklgm6ejVLm384qPVbfxKrDEDujY0erxHD31TLR5rK2dlZNWrWUus2bbX/55/0808/avGHC/XxJytuunz3bHgvy19Xq15DLi4uGjMqSgNefU2urq7y8vK26lO7Tl2lnjqlBfPnqXnIo3b9fLg7sHzswKJwz549io2NzfH/BIPBoEGDBikwMPCW40RGRioiIsKqrXSTN/Jtnsh/RYo46aN3nleFMl5q8+K7VkneybSzkqQDR09a2jKvXNXx3/9UeT/vXN+j2QPV1LZZHb367LV/GRgMBjk7O+n89mnqO+ZjJe7/Tf7lfPTp1L/3Ljk5Xft78fz2aarbabSO/Z6m3ft/04NPvq2Sxd3k6lJEaX9d0JaFg7Xz56Tb+g6Ae0n5ChU0L/ZDXbp4URfSL6hUqdJ6/bVXVe4/5bVr1w6dPv2n2rQIsfTPysrS5Anv6KNFC/XFxs05jlm7boCuXr2qP078Lv9KlW/Y5/uE7+zymYC7kcOKQj8/P23btk01atTI8f1t27ZZPZV2I0ajMdteFYOTc77MEfnvekFYpUIptX5xuk6ftU7pdu//TZczruh+f199l3jUck2Fst5KOnk61/dpHj5Jzk5/745o17yuXusZqpCek/XHqTO6lHFFDbqMtbpmRN92Kl7UTYMnLNfvyX9ZvXfuwrXCtUqFUqpfq4JGzvrcps8NQHIvWlTuRYvq3Nmz+u67rXo1YohCW7TUgw8+ZNWvz0vPq11YB3Xo2PmGYx08sF9OTk7y9r7vpn18SvHQCXKHpNCBReHgwYP14osvaufOnXr00UctBWBKSori4uI0Z84cTZw40VHTQx4Vc3dVlfJ//yHsX+4+1a1WTn+du6iTaWe1eEJvBdYor84DY+TsZJDvfSUkSafPXtSVq1k6n35Zc5dv1bCXH9PvyX8p6eRpDQoPlSSt2LTLMm7l8j4q7m6Ur09JuRtdVLfatSeQ9x9N1pWrWTp4LMVqXvVrVZDJbNbPR/5OIP/515IsD7v8s71zaKBS/7qg35JPq/b9ZTVxSBd9Fr9Xcd8fyI+vC7gnfPftNzKbzfL3r6SkpCRNmTRelSpVVoeOneXi4iJPTy+r/kWKuOg+Hx9LArgncbf27dujRo0eVLFixbRnz25NHB+tx9q1V0mPa/t716xeKRcXF9WoUVOSFPflJq1e+amGjxxzZz8sUIg5rCjs27evfHx8NGXKFM2aNUtZWdceDnF2dlaDBg0UGxurrl273mIUFDT1a1XUxrkDLT+PH/y4JGnRmu81Jmad5TDrbUsjra5r2Xuavtl5SJIUOXWlrmaZNG9MD7kbXbT9x1/V5sXplqJNkmYPf1pNG/79lOIP/z9e9ceG25Qo3opfqZJ657XOKn1fCSWnndNHn/+g6PfX59v4wL3g/PnzenfqZKWkJMvDw1OPtmipfgMGycXFJVfXu7q6asMX6xQza4auZGaqXLn/6Jlne1rtIZSkOTGz9MfJP1TE2Vn+lSrrnYlT1KJla3t8JNyFCAolg9lsNjt6EleuXFFaWpokycfHJ9d/UNyIe2C//JgWgALo9LYZt+4EoFByv71//d+WqoO/sNvYhye2sdvY+alAHF7t4uKiMmVsP4cOAAAgP7CnsIAUhQAAAI5ETejg32gCAACAgoGkEAAA3PNYPiYpBAAAgEgKAQAA2FMokkIAAACIpBAAAEBOTkSFJIUAAAAgKQQAAGBPIUUhAAAAR9KI5WMAAACIpBAAAIDlY5EUAgAAQCSFAAAA7CkUSSEAAABEUggAAEBSKJJCAAAAiKQQAACAp49FUQgAAMDysVg+BgAAgEgKAQAAWD4WSSEAAABEUggAAMCeQpEUAgAAQCSFAAAA7CkUSSEAAABEUggAAMCeQpEUAgAAQCSFAAAA7CkURSEAAADLx2L5GAAAACIpBAAAYPlYJIUAAAAQSSEAAAB7CkVSCAAAAJEUAgAAsKdQJIUAAAAQSSEAAAB7CkVRCAAAwPKxWD4GAACASAoBAABYPhZJIQAAAERSCAAAQFIokkIAAIACZ+bMmfL395ebm5uCgoK0bdu2m/afOnWqqlevLnd3d5UvX16DBg3S5cuXbbonRSEAALjnGQz2e9lq6dKlioiIUFRUlHbt2qWAgAC1atVKp06dyrH/4sWLNXToUEVFRWn//v2aN2+eli5dqjfffNOm+1IUAgAAFCCTJ0/WCy+8oF69eqlWrVqKiYlR0aJF9cEHH+TY/7vvvlPjxo3VvXt3+fv7q2XLlnrqqadumS7+G0UhAAC45xkMBru9MjIydO7cOatXRkZGjvPIzMzUzp07FRoaamlzcnJSaGioEhIScrzmoYce0s6dOy1F4NGjR7Vu3To99thjNn0HFIUAAOCeZ8/l4+joaHl4eFi9oqOjc5xHWlqasrKy5Ovra9Xu6+ur5OTkHK/p3r27Ro0apYcfflguLi6qUqWKmjdvzvIxAABAQRIZGamzZ89avSIjI/Nt/Pj4eI0bN06zZs3Srl27tGLFCq1du1ajR4+2aRyOpAEAAPc8ex5JYzQaZTQac9XXx8dHzs7OSklJsWpPSUmRn59fjtcMGzZMzz77rHr37i1JqlOnjtLT0/Xiiy/qf//7n5yccpcBkhQCAAAUEK6urmrQoIHi4uIsbSaTSXFxcQoODs7xmosXL2Yr/JydnSVJZrM51/cmKQQAAPe8gnR2dUREhMLDw9WwYUM98MADmjp1qtLT09WrVy9JUo8ePVSuXDnLvsSwsDBNnjxZgYGBCgoK0uHDhzVs2DCFhYVZisPcoCgEAAAoQLp166bU1FQNHz5cycnJqlevntavX295+CQpKckqGXzrrbdkMBj01ltv6cSJEypVqpTCwsI0duxYm+5rMNuSKxYS7oH9HD0FAHZyetsMR08BgJ24uzju3i1mfG+3sTf1e9BuY+cn9hQCAACA5WMAAICCtKfQUSgKAQDAPc+eR9IUFiwfAwAAgKQQAADAiaCQpBAAAAAkhQAAAOwpFEkhAAAARFIIAADAkTQiKQQAAIBICgEAAGQQUSFFIQAAuOdxJA3LxwAAABBJIQAAAEfSiKQQAAAAIikEAADgSBqRFAIAAEAkhQAAAHIiKiQpBAAAAEkhAAAAewpFUQgAAMCRNGL5GAAAACIpBAAAYPlYJIUAAAAQSSEAAABH0oikEAAAACIpBAAAEDkhSSEAAABEUggAAMA5haIoBAAAkBM1IcvHAAAAICkEAABg+VgkhQAAABBJIQAAAL/mTiSFAAAAEEkhAAAAewqVy6JwzZo1uR6wffv2eZ4MAAAAHCNXRWHHjh1zNZjBYFBWVtbtzAcAAOCO45zCXBaFJpPJ3vMAAABwGJaPedAEAAAAyuODJunp6fr666+VlJSkzMxMq/cGDBiQLxMDAAC4U8gJ81AU7t69W4899pguXryo9PR0eXt7Ky0tTUWLFlXp0qUpCgEAAAohm5ePBw0apLCwMP31119yd3fX999/r19//VUNGjTQxIkT7TFHAAAAu3IyGOz2KixsLgoTExP12muvycnJSc7OzsrIyFD58uU1fvx4vfnmm/aYIwAAAOzM5qLQxcVFTk7XLitdurSSkpIkSR4eHvrtt9/yd3YAAAB3gMFgv1dhYfOewsDAQG3fvl3333+/mjVrpuHDhystLU2LFi1S7dq17TFHAAAA2JnNSeG4ceNUpkwZSdLYsWPl5eWlPn36KDU1Ve+//36+TxAAAMDeDAaD3V6Fhc1JYcOGDS1/Xbp0aa1fvz5fJwQAAIA7L0/nFAIAANxNClGgZzc2F4WVKlW6aRR69OjR25oQAADAnVaYjo6xF5uLwldffdXq5ytXrmj37t1av369hgwZkl/zAgAAwB1kc1E4cODAHNtnzpypHTt23PaEAAAA7jSCwjw8fXwjbdq00aeffppfwwEAAOAOyrcHTZYvXy5vb+/8Gg4AAOCOKUxHx9hLng6v/ucXZzablZycrNTUVM2aNStfJwcAAIA7w+aisEOHDlZFoZOTk0qVKqXmzZurRo0a+Tq5vPpr+wxHTwEAABQi+bafrhCzuSgcMWKEHaYBAAAAR7K5MHZ2dtapU6eytf/5559ydnbOl0kBAADcSfyauzwkhWazOcf2jIwMubq63vaEAAAA7jSnwlO72U2ui8Lp06dLulZJz507V8WLF7e8l5WVpS1bthSYPYUAAACwTa6LwilTpki6lhTGxMRYLRW7urrK399fMTEx+T9DAAAAOyMptKEoPHbsmCQpJCREK1askJeXl90mBQAAgDvL5gdNvvrqKwpCAABwVyloD5rMnDlT/v7+cnNzU1BQkLZt23bT/mfOnFHfvn1VpkwZGY1GVatWTevWrbPpnjYXhY8//rjeeeedbO3jx4/XE088YetwAAAA+IelS5cqIiJCUVFR2rVrlwICAtSqVascT3+RpMzMTLVo0ULHjx/X8uXLdfDgQc2ZM0flypWz6b4G840eJ76BUqVKafPmzapTp45V+759+xQaGqqUlBSbJmAPl686egYAAMBWbvn2y3dtN+Tzg3Ybe0K76jb1DwoKUqNGjTRjxrVfxmEymVS+fHn1799fQ4cOzdY/JiZGEyZM0IEDB+Ti4pLnedqcFF64cCHHo2dcXFx07ty5PE8EAADgbpSRkaFz585ZvTIyMnLsm5mZqZ07dyo0NNTS5uTkpNDQUCUkJOR4zZo1axQcHKy+ffvK19dXtWvX1rhx45SVlWXTPG0uCuvUqaOlS5dma1+yZIlq1apl63AAAAAOZzDY7xUdHS0PDw+rV3R0dI7zSEtLU1ZWlnx9fa3afX19lZycnOM1R48e1fLly5WVlaV169Zp2LBhmjRpksaMGWPTd2BzUDts2DB17txZR44c0SOPPCJJiouL0+LFi7V8+XJbhwMAAHA4Jzv+5pHIyEhFRERYtRmNxnwb32QyqXTp0nr//ffl7OysBg0a6MSJE5owYYKioqJyPY7NRWFYWJhWrVqlcePGafny5XJ3d1dAQIA2b94sb29vW4cDAAC4qxmNxlwXgT4+PnJ2ds72jEZKSor8/PxyvKZMmTJycXGxOkO6Zs2aSk5OVmZmZq5/45zNy8eS1LZtW3377bdKT0/X0aNH1bVrVw0ePFgBAQF5GQ4AAMChnOz4soWrq6saNGiguLg4S5vJZFJcXJyCg4NzvKZx48Y6fPiwTCaTpe2XX35RmTJlbPoVxHkqCiVpy5YtCg8PV9myZTVp0iQ98sgj+v777/M6HAAAACRFRERozpw5WrBggfbv368+ffooPT1dvXr1kiT16NFDkZGRlv59+vTR6dOnNXDgQP3yyy9au3atxo0bp759+9p0X5uWj5OTkxUbG6t58+bp3Llz6tq1qzIyMrRq1SoeMgEAAIWWHbcU2qxbt25KTU3V8OHDlZycrHr16mn9+vWWh0+SkpLk5PR3rle+fHlt2LBBgwYNUt26dVWuXDkNHDhQb7zxhk33zfU5hWFhYdqyZYvatm2rp59+Wq1bt5azs7NcXFy0Z8+eAlUUck4hAACFjyPPKfzfF7/YbeyxbarZbez8lOuv/4svvtCAAQPUp08f3X///facEwAAwB1lz6ePC4tc7yncunWrzp8/rwYNGigoKEgzZsxQWlqaPecGAACAOyTXReGDDz6oOXPm6OTJk3rppZe0ZMkSlS1bViaTSZs2bdL58+ftOU8AAAC7sefh1YWFzb/7+J8OHjyoefPmadGiRTpz5oxatGihNWvW5Of88oQ9hQAAFD6O3FM4YuMh+43dsnBsu8vzkTSSVL16dY0fP16///67Pv744/yaEwAAAO6w20oKCyqSQgAACh9HJoWjNh2229jDW1S129j56baSQgAAANwdHFiTAwAAFAyF6YEQeyEpBAAAAEkhAACAE0khSSEAAABICgEAAGQQUSFFIQAAuOexfMzyMQAAAERSCAAAQFIokkIAAACIpBAAAEAGTq8mKQQAAABJIQAAAHsKRVIIAAAAkRQCAACILYUUhQAAAHKiKmT5GAAAACSFAAAAPGgikkIAAACIpBAAAIAHTURSCAAAAJEUAgAAyElEhSSFAAAAICkEAABgTyFFIQAAAEfSiOVjAAAAiKQQAACAX3MnkkIAAACIpBAAAIAHTURSCAAAAJEUAgAAsKdQJIUAAAAQSSEAAAB7CkVRCAAAwNKp+A4AAAAgkkIAAAAZWD8mKQQAAABJIQAAgMgJSQoBAAAgkkIAAAAOrxZJIQAAAERSCAAAwJ5CURQCAADwG03E8jEAAABEUggAAMDh1SIpBAAAgEgKAQAASMnEdwAAAACRFAIAALCnUCSFAAAAEEkhAAAAh1eLpBAAAAAiKQQAAGBPoSgKAQAAWDoV3wEAAABEUggAAMDysUgKAQAACpyZM2fK399fbm5uCgoK0rZt23J13ZIlS2QwGNSxY0eb70lRCAAA7nkGO75stXTpUkVERCgqKkq7du1SQECAWrVqpVOnTt30uuPHj2vw4MFq0qRJHu5KUQgAAFCgTJ48WS+88IJ69eqlWrVqKSYmRkWLFtUHH3xww2uysrL09NNPa+TIkapcuXKe7ktRCAAA7nkGg/1eGRkZOnfunNUrIyMjx3lkZmZq586dCg0NtbQ5OTkpNDRUCQkJN5z/qFGjVLp0aT3//PN5/g4oCgEAAOwoOjpaHh4eVq/o6Ogc+6alpSkrK0u+vr5W7b6+vkpOTs7xmq1bt2revHmaM2fObc2Tp48BAMA9z8mOv+guMjJSERERVm1GozFfxj5//ryeffZZzZkzRz4+Prc1FkUhAAC459nzRBqj0ZjrItDHx0fOzs5KSUmxak9JSZGfn1+2/keOHNHx48cVFhZmaTOZTJKkIkWK6ODBg6pSpUqu7s3yMQAAQAHh6uqqBg0aKC4uztJmMpkUFxen4ODgbP1r1Kihffv2KTEx0fJq3769QkJClJiYqPLly+f63iSFAADgnmew4/KxrSIiIhQeHq6GDRvqgQce0NSpU5Wenq5evXpJknr06KFy5copOjpabm5uql27ttX1np6ekpSt/VYoCgEAAAqQbt26KTU1VcOHD1dycrLq1aun9evXWx4+SUpKkpNT/i/2GsxmsznfR3Wwy1cdPQMAAGArNwdGVet+uvnB0Lfjsf+WttvY+Yk9hQAAAGD5GAAAwJ5H0hQWJIUAAAAgKQQAALDnOYWFBUUhAAC451EUsnwMAAAAkRQCAAAUqMOrHYWkEAAAACSFAAAATgSFJIUAAAAgKQQAAGBPoUgKAQAAIJJCAAAAzikURSEAAADLx2L5GAAAACIpBAAA4EgakRQCAABAJIUAAADsKRRJIQAAAERSiEJq2ZLFWrb0Y/1x4oQkqUrV+/VSn1f0cJNmDp4ZgH/auWO7Yj+Yp/0//6jU1FRNmT5TjzwaatXn6JEjmjp5gnbu2K6rWVmqUrmKJk19V2XKlpUkLV+2VF+s+1z7f/5J6enp+iZhu0qWLGm5fvu2H9S7V48c7//Rkk9Uu05d+31A3DU4koaiEIVUaV8/DRw0WBUqVpTZbNZnq1dpYL++WvrpSlWter+jpwfg/126dFHVq1dXx86PK2Jgv2zv/5aUpJ7Pdlenzo+rT78BKl6suI4cPiRXo9HS5/LlS3qocRM91LiJpk+dlG2MevUCFRe/1apt5rvT9MMPCfpv7Tr5/6GAuxRFIQql5iGPWP3cf+AgLVvysfbuSaQoBAqQh5s0u2mC/+70KXq4aVMNGvy6pa18hQpWfZ7p0VPStUQwJy6urvIpVcry85UrV/TVV3F6qvszMhD/IJf4O4U9hbgLZGVl6Yt1a3Xp0kUFBAQ6ejoAcslkMumbr+NVsaK/Xn7heTVvEqynn3xCm+O+vK1xv/5qs86eOaOOnR7Pp5niXuBkMNjtVVgU6KLwt99+03PPPXfTPhkZGTp37pzVKyMj4w7NEI506JeDerBhoBoF1tHYUVGaMn2mqlSt6uhpAcil03/+qYsXL+qDeXPU+OEminn/Az3yaAtFDOynHdu35XnclSuW66HGD8vXzy8fZwvc/Qp0UXj69GktWLDgpn2io6Pl4eFh9ZrwTvQdmiEcyd+/kpZ9ukoffrxMT3R7SsPefENHDh929LQA5JLJbJIkhYQ8qmfDe6pGzZp6/oUX1bRZc32ydEmexkxJTtZ3325Vp85d8nOquAcY7PgqLBy6p3DNmjU3ff/o0aO3HCMyMlIRERFWbWZn4w16427i4uqqChUrSpJq/be2fvpxnz76cKGGjxjl4JkByA0vTy8VKVJElatUsWqvVLmKEnftzNOYq1Z+Kg9PTzX7175jALfm0KKwY8eOMhgMMpvNN+xzq03CRqNRRqN1EXj5ar5MD4WMyWTSlcxMR08DQC65uLrqv7Xr6PjxY1btv/56XGXKlrN5PLPZrNWrViisfUe5uLjk1zRxryhMkZ6dOHT5uEyZMlqxYoVMJlOOr127djlyeijApk2ZpJ07tuvEid916JeDmjZlknZs36bH2oU5emoA/uFieroO7N+vA/v3S5JO/P67Duzfr5N//CFJCu/1vDZ88YU+/WSZkn79VR9/9KG2xH+lrk8+ZRkjLTVVB/bv129JSZKkw4d+0YH9+3X2zBmre2374Xud+P13dX6cpWMgLwzmm8V0dta+fXvVq1dPo0blvNy3Z88eBQYGymQy2TQuSeHdL2rYm9r2/fdKTT2l4iVKqFq16ur1/AsKfqixo6cG4B9udLB0+w6dNHrc25KuPRjywZz3lZKSLH//SurTr79CHvn7gOvZM99VzKwZ2cYYNSZaHTp1tvw8dMhrOvnHCS34KG/7EeF4bg5cv/zhyFm7jR1UxcNuY+cnhxaF33zzjdLT09W6desc309PT9eOHTvUrJltv6WCohAAgMKHotCxHFoU2gtFIQAAhY8ji8JtR+1XFD5QuXAUhfxGEwAAcM/jOZMCfk4hAAAA7gySQgAAAKJCkkIAAACQFAIAAMhAVEhSCAAAAJJCAAAA3eK36t4TSAoBAABAUggAAEBQSFEIAABAVSiWjwEAACCSQgAAAI6kEUkhAAAARFIIAADAkTQiKQQAAIBICgEAANhRKJJCAAAAiKQQAACAqFAUhQAAABxJI5aPAQAAIJJCAAAAjqQRSSEAAABEUggAAMCOQpEUAgAAQCSFAAAARIUiKQQAAIBICgEAADinUCSFAAAAEEkhAAAA5xSKohAAAIDFY7F8DAAAAJEUAgAAEBWKpBAAAKDAmTlzpvz9/eXm5qagoCBt27bthn3nzJmjJk2ayMvLS15eXgoNDb1p/xuhKAQAAPc8gx3/Z6ulS5cqIiJCUVFR2rVrlwICAtSqVSudOnUqx/7x8fF66qmn9NVXXykhIUHly5dXy5YtdeLECdu+A7PZbLZ5tgXc5auOngEAALCVmwM3tR04edFuY1fydlZGRoZVm9FolNFozLF/UFCQGjVqpBkzZkiSTCaTypcvr/79+2vo0KG3vF9WVpa8vLw0Y8YM9ejRI9fzJCkEAAD3PIPBfq/o6Gh5eHhYvaKjo3OcR2Zmpnbu3KnQ0FBLm5OTk0JDQ5WQkJCrz3Lx4kVduXJF3t7eNn0HPGgCAABgR5GRkYqIiLBqu1FKmJaWpqysLPn6+lq1+/r66sCBA7m63xtvvKGyZctaFZa5QVEIAADuefZ8+PhmS8X57e2339aSJUsUHx8vNzc3m66lKAQAACggR9L4+PjI2dlZKSkpVu0pKSny8/O76bUTJ07U22+/rS+//FJ169a1+d7sKQQAACggXF1d1aBBA8XFxVnaTCaT4uLiFBwcfMPrxo8fr9GjR2v9+vVq2LBhnu5NUggAAO55eTk6xl4iIiIUHh6uhg0b6oEHHtDUqVOVnp6uXr16SZJ69OihcuXKWR5WeeeddzR8+HAtXrxY/v7+Sk5OliQVL15cxYsXz/V9KQoBAAAKkG7duik1NVXDhw9XcnKy6tWrp/Xr11sePklKSpKT09+LvbNnz1ZmZqa6dOliNU5UVJRGjBiR6/tyTiEAACgQHHlO4eFTl+w2dtXS7nYbOz+xpxAAAAAsHwMAABScHYWOQ1IIAAAAkkIAAACiQopCAACAAnUkjaOwfAwAAACSQgAAAANBIUkhAAAASAoBAADYUSiSQgAAAIikEAAAgKhQJIUAAAAQSSEAAADnFIqiEAAAgCNpxPIxAAAARFIIAADA4rFICgEAACCSQgAAAPYUiqQQAAAAIikEAAAQuwpJCgEAACCSQgAAAPYUiqIQAACAxWOxfAwAAACRFAIAALB8LJJCAAAAiKQQAABABnYVkhQCAACApBAAAIDHj0VSCAAAAJEUAgAAEBSKohAAAIAjacTyMQAAAERSCAAAwJE0IikEAACASAoBAAB40kQkhQAAABBJIQAAAEGhSAoBAAAgkkIAAADOKRRFIQAAAEfSiOVjAAAAiKQQAACA5WORFAIAAEAUhQAAABBFIQAAAMSeQgAAAPYUiqQQAAAAIikEAADgnEJRFAIAALB8LJaPAQAAIJJCAAAAFo9FUggAAACRFAIAABAViqQQAAAAIikEAADgSBqRFAIAAEAkhQAAAJxTKJJCAAAAiKQQAACAHYWiKAQAAKAqFMvHAAAAEEkhAAAAR9KIpBAAAAAiKQQAAOBIGpEUAgAAQJLBbDabHT0JIK8yMjIUHR2tyMhIGY1GR08HQD7in2/gzqIoRKF27tw5eXh46OzZsypZsqSjpwMgH/HPN3BnsXwMAAAAikIAAABQFAIAAEAUhSjkjEajoqKi2IQO3IX45xu4s3jQBAAAACSFAAAAoCgEAACAKAoBAAAgikIAAACIohCF3MyZM+Xv7y83NzcFBQVp27Ztjp4SgNu0ZcsWhYWFqWzZsjIYDFq1apWjpwTcEygKUWgtXbpUERERioqK0q5duxQQEKBWrVrp1KlTjp4agNuQnp6ugIAAzZw509FTAe4pHEmDQisoKEiNGjXSjBkzJEkmk0nly5dX//79NXToUAfPDkB+MBgMWrlypTp27OjoqQB3PZJCFEqZmZnauXOnQkNDLW1OTk4KDQ1VQkKCA2cGAEDhRFGIQiktLU1ZWVny9fW1avf19VVycrKDZgUAQOFFUQgAAACKQhROPj4+cnZ2VkpKilV7SkqK/Pz8HDQrAAAKL4pCFEqurq5q0KCB4uLiLG0mk0lxcXEKDg524MwAACicijh6AkBeRUREKDw8XA0bNtQDDzygqVOnKj09Xb169XL01ADchgsXLujw4cOWn48dO6bExER5e3urQoUKDpwZcHfjSBoUajNmzNCECROUnJysevXqafr06QoKCnL0tADchvj4eIWEhGRrDw8PV2xs7J2fEHCPoCgEAAAAewoBAABAUQgAAABRFAIAAEAUhQAAABBFIQAAAERRCAAAAFEUAgAAQBSFAAAAEEUhgAKsZ8+e6tixo+Xn5s2b69VXX73j84iPj5fBYNCZM2fu+L0B4E6hKARgs549e8pgMMhgMMjV1VVVq1bVqFGjdPXqVbved8WKFRo9enSu+lLIAYBtijh6AgAKp9atW2v+/PnKyMjQunXr1LdvX7m4uCgyMtKqX2ZmplxdXfPlnt7e3vkyDgAgO5JCAHliNBrl5+enihUrqk+fPgoNDdWaNWssS75jx45V2bJlVb16dUnSb7/9pq5du8rT01Pe3t7q0KGDjh8/bhkvKytLERER8vT01H333afXX39d//7V7P9ePs7IyNAbb7yh8uXLy2g0qmrVqpo3b56OHz+ukJAQSZKXl5cMBoN69uwpSTKZTIqOjlalSpXk7u6ugIAALV++3Oo+69atU7Vq1eTu7q6QkBCreQLA3YqiEEC+cHd3V2ZmpiQpLi5OBw8e1KZNm/T555/rypUratWqlUqUKKFvvvlG3377rYoXL67WrVtbrpk0aZJiY2P1wQcfaOvWrTp9+rRWrlx503v26NFDH3/8saZPn679+/frvffeU/HixVW+fHl9+umnkqSDBw/q5MmTmjZtmiQpOjpaCxcuVExMjH766ScNGjRIzzzzjL7++mtJ14rXzp07KywsTImJierdu7eGDh1qr68NAAoMlo8B3Baz2ay4uDht2LBB/fv3V2pqqooVK6a5c+dalo0//PBDmUwmzZ07VwaDQZI0f/58eXp6Kj4+Xi1bttTUqVMVGRmpzp07S5JiYmK0YcOGG973l19+0bJly7Rp0yaFhoZKkipXrmx5//pSc+nSpeXp6SnpWrI4btw4ffnllwoODrZcs3XrVr333ntq1qyZZs+erSpVqmjSpEmSpOrVq2vfvn1655138vFbA4CCh6IQQJ58/vnnKl68uK5cuSKTyaTu3btrxIgR6tu3r+rUqWO1j3DPnj06fPiwSpQoYTXG5cuXdeTIEZ09e1YnT55UUFCQ5b0iRYqoYcOG2ZaQr0tMTJSzs7OaNWuW6zkfPnxYFy9eVIsWLazaMzMzFRgYKEnav3+/1TwkWQpIALibURQCyJOQkBDNnj1brq6uKlu2rIoU+fuPk2LFiln1vXDhgho0aKCPPvoo2zilSpXK0/3d3d1tvubChQuSpLVr16pcuXJW7xmNxjzNAwDuFhSFAPKkWLFiqlq1aq761q9fX0uXLlXp0qVVsmTJHPuUKVNGP/zwg5o2bSpJunr1qnbu3Kn69evn2L9OnToymUz6+uuvLcvH/3Q9qczKyrK01apVS0ajUUlJSTdMGGvWrKk1a9ZYtX3//fe3/pAAUMjxoAkAu3v66afl4+OjDh066JtvvtGxY8cUHx+vAQMG6Pfff5ckDRw4UG+//bZWrVqlAwcO6JVXXrnpGYP+/v4KDw/Xc889p1WrVlnGXLZsmSSpYsWKMhgM+vzzz5WamqoLFy6oRIkSGjx4sAYNGqQFCxboyJEj2rVrl959910tWLBAkvTyyy/r0KFDGjJkiA4ePKjFixcrNjbW3l8RADgcRSEAuytatKi2bNmiChUqqHPnzqpZs6aef/55Xb582ZIcvvbaa3r22WcVHh6u4OBglShRQp06dbrpuLNnz1aXLl30yiuvqEaNGnrhhReUnp4uSSpXrpxGjhypoUOHytfXV/369ZMkjR49WsOGDVN0dLRq1qyp1q1ba+3atapUqZIkqUKFCvr000+1atUqBQQEKCYmRuPGjbPjtwMABYPBfKNd3AAAALhnkBQCAACAohAAAAAUhQAAABBFIQAAAERRCAAAAFEUAgAAQBSFAAAAEEUhAAAARFEIAAAAURQCAABAFIUAAACQ9H//lInJAcjefgAAAABJRU5ErkJggg==",
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from sklearn.neural_network import MLPClassifier\n",
        "\n",
        "# Create a simple neural network model\n",
        "model_nn = MLPClassifier(hidden_layer_sizes=(100,), activation='relu', solver='adam', max_iter=1000)\n",
        "\n",
        "# Train the model on the training data\n",
        "model_nn.fit(X_train_small, y_train_small)\n",
        "\n",
        "# Evaluate the model on the test data\n",
        "y_pred_nn = model_nn.predict(X_test)\n",
        "calculate_metrics(y_test, y_pred_nn)\n",
        "plot_confusion_matrix(y_test, y_pred_nn)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2dGqiaCmCGN_"
      },
      "source": [
        "Again, the accuracy might seem fine but there are too many false positives for this to be a good model."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 618
        },
        "id": "zuguj5z_BNr-",
        "outputId": "9a56bef1-0f38-4e2d-c256-31793c37d3e7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy: 0.9630788888853963\n",
            "F1 Score: 0.06432597136199789\n",
            "ROC AUC Score: 0.9799746703095291\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "model_log = LogisticRegression(max_iter=3000)\n",
        "model_log.fit(X_train_small, y_train_small)\n",
        "y_pred_log = model_log.predict(X_test)\n",
        "\n",
        "calculate_metrics(y_test, y_pred_log)\n",
        "plot_confusion_matrix(y_test, y_pred_log)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xUMmSvG9HWkw"
      },
      "source": [
        "### Results"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0eaocNTLD2iU",
        "outputId": "043c0355-6ff0-4829-f3d4-c402ab839319"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                 Model  Accuracy  F1 Score  ROC AUC Score\n",
            "0              XGBoost    0.9996      0.87         0.9998\n",
            "1        Random Forest    0.9992      0.78         0.9996\n",
            "2                  SVM    0.9782      0.10         0.9878\n",
            "3       Neural Network    0.9925      0.25         0.9953\n",
            "4  Logistic Regression    0.9630      0.06         0.9799\n"
          ]
        }
      ],
      "source": [
        "models = [\"XGBoost\", \"Random Forest\", \"SVM\", \"Neural Network\", \"Logistic Regression\"]\n",
        "accuracies = [0.9996, 0.9992, 0.9782, 0.9925, 0.9630]\n",
        "f1_scores = [0.87, 0.78, 0.10, 0.25, 0.06]\n",
        "auc_scores = [0.9998, 0.9996, 0.9878, 0.9953, 0.9799]\n",
        "\n",
        "results = pd.DataFrame({\n",
        "    \"Model\": models,\n",
        "    \"Accuracy\": accuracies,\n",
        "    \"F1 Score\": f1_scores,\n",
        "    \"ROC AUC Score\": auc_scores\n",
        "})\n",
        "\n",
        "print(results.to_string())\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        },
        "id": "3PEOK08QFLLB",
        "outputId": "5c761838-8dd1-4f58-ff69-845f569eaa57"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 900x400 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Bar chart of the model F1 scores\n",
        "plt.figure(figsize=(9, 4))\n",
        "plt.bar(models, f1_scores)\n",
        "plt.title(\"Model F1 Scores\")\n",
        "plt.xlabel(\"Model\")\n",
        "plt.ylabel(\"F1 Score\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D6cVea2KHtgo"
      },
      "source": [
        "Overall, The XGBoost model performed the best with the highest accuracy (99.96%) and F1 score (87%).\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UAgY9uuSHrOe"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "gpuType": "T4",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.10.12"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}