Question_1_Financial_Transaction_ETL_Pipeline.ipynb

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      "name": "python3",
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    "language_info": {
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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Summary\n",
        "\n",
        "In this notebook, we intend to build a ETL pipeline to process the raw transaction data. The specific goals are outlined as following:\n",
        "\n",
        "- Cleanses and validates transaction data\n",
        "- Performs necessary transformations (currency conversion, timestamp standardization)\n",
        "- Aggregates data at different time intervals\n",
        "- Implement data quality checks and validation rules\n",
        "- Create summary statistics and data profiling reports\n",
        "\n",
        "Data source:\n",
        "https://www.kaggle.com/datasets/ealaxi/paysim1/data\n",
        "\n",
        "Coding environment:\n",
        "Google colab, Python 3 + CPU\n",
        "\n"
      ],
      "metadata": {
        "id": "zxO2Yz8a6xVt"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## packages and dependencies"
      ],
      "metadata": {
        "id": "ZPmcBGVZEx8j"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install kaggle"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2YLy_mDhFYmT",
        "outputId": "a2373358-05c5-4b1e-f3c1-fa0cf0476786"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: kaggle in /usr/local/lib/python3.10/dist-packages (1.6.17)\n",
            "Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.10/dist-packages (from kaggle) (1.16.0)\n",
            "Requirement already satisfied: certifi>=2023.7.22 in /usr/local/lib/python3.10/dist-packages (from kaggle) (2024.8.30)\n",
            "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.10/dist-packages (from kaggle) (2.8.2)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from kaggle) (2.32.3)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from kaggle) (4.66.6)\n",
            "Requirement already satisfied: python-slugify in /usr/local/lib/python3.10/dist-packages (from kaggle) (8.0.4)\n",
            "Requirement already satisfied: urllib3 in /usr/local/lib/python3.10/dist-packages (from kaggle) (2.2.3)\n",
            "Requirement already satisfied: bleach in /usr/local/lib/python3.10/dist-packages (from kaggle) (6.2.0)\n",
            "Requirement already satisfied: webencodings in /usr/local/lib/python3.10/dist-packages (from bleach->kaggle) (0.5.1)\n",
            "Requirement already satisfied: text-unidecode>=1.3 in /usr/local/lib/python3.10/dist-packages (from python-slugify->kaggle) (1.3)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->kaggle) (3.4.0)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->kaggle) (3.10)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "\n",
        "# Create the .kaggle directory\n",
        "os.makedirs('/root/.config/kaggle', exist_ok=True)\n",
        "\n",
        "# Move kaggle.json to the .kaggle directory\n",
        "!mv kaggle.json /root/.config/kaggle/"
      ],
      "metadata": {
        "id": "8YFt5dMgSM4p"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# security concern on API key\n",
        "!chmod 600 /root/.config/kaggle/kaggle.json"
      ],
      "metadata": {
        "id": "cBZcOAGrSWkS"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "import kaggle\n",
        "\n",
        "import zipfile\n",
        "\n",
        "from sklearn.metrics import confusion_matrix\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "hKbvp1UlEgvV"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## dataset"
      ],
      "metadata": {
        "id": "44MfouvaSjDk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!kaggle datasets download -d ealaxi/paysim1"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z1WLtTY2Sm3x",
        "outputId": "4faf50f4-979d-459e-b950-684d185be631"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
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          "text": [
            "Dataset URL: https://www.kaggle.com/datasets/ealaxi/paysim1\n",
            "License(s): CC-BY-SA-4.0\n",
            "Downloading paysim1.zip to /content\n",
            " 98% 175M/178M [00:07<00:00, 27.1MB/s]\n",
            "100% 178M/178M [00:07<00:00, 24.7MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "with zipfile.ZipFile(\"paysim1.zip\", 'r') as zip_ref:\n",
        "    # Extract to a folder named \"data\"\n",
        "    zip_ref.extractall(\"data\")"
      ],
      "metadata": {
        "id": "-z3k-cf_TuQ_"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# if we work this one on databricks, we may use databricks widgets to make this notebook/whole process reusable easily\n",
        "\n",
        "df = pd.read_csv(\"data/PS_20174392719_1491204439457_log.csv\")\n",
        "\n",
        "# Display the first few rows\n",
        "df.head(10)"
      ],
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      "outputs": [
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              "   step      type    amount     nameOrig  oldbalanceOrg  newbalanceOrig  \\\n",
              "0     1   PAYMENT   9839.64  C1231006815      170136.00       160296.36   \n",
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              "3     1  CASH_OUT    181.00   C840083671         181.00            0.00   \n",
              "4     1   PAYMENT  11668.14  C2048537720       41554.00        29885.86   \n",
              "5     1   PAYMENT   7817.71    C90045638       53860.00        46042.29   \n",
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              "7     1   PAYMENT   7861.64  C1912850431      176087.23       168225.59   \n",
              "8     1   PAYMENT   4024.36  C1265012928        2671.00            0.00   \n",
              "9     1     DEBIT   5337.77   C712410124       41720.00        36382.23   \n",
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              "    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-a3a23865-609b-4070-ae5d-4bab577a25c2 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## data understanding & basic preprocessing"
      ],
      "metadata": {
        "id": "OjfKl5iKB7cg"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### schema"
      ],
      "metadata": {
        "id": "8aMuAVQ1UWm4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# review the schema\n",
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IK669AIxCBQH",
        "outputId": "81df65e7-3df0-4bbd-e35f-34b9845a892e"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 6362620 entries, 0 to 6362619\n",
            "Data columns (total 11 columns):\n",
            " #   Column          Dtype  \n",
            "---  ------          -----  \n",
            " 0   step            int64  \n",
            " 1   type            object \n",
            " 2   amount          float64\n",
            " 3   nameOrig        object \n",
            " 4   oldbalanceOrg   float64\n",
            " 5   newbalanceOrig  float64\n",
            " 6   nameDest        object \n",
            " 7   oldbalanceDest  float64\n",
            " 8   newbalanceDest  float64\n",
            " 9   isFraud         int64  \n",
            " 10  isFlaggedFraud  int64  \n",
            "dtypes: float64(5), int64(3), object(3)\n",
            "memory usage: 534.0+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Shown from our current knowledge, there is no unique identifier for each transaction. This step will be skipped."
      ],
      "metadata": {
        "id": "1MdCNNpk2Ze8"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### timestamp"
      ],
      "metadata": {
        "id": "aCdiVbejUb-j"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# check time stamp\n",
        "df['step'].unique()"
      ],
      "metadata": {
        "id": "ipgrRtMrCqvm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "53ccb23f-f96c-48cd-eb89-4c3646a3f720"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([  1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,\n",
              "        14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,\n",
              "        27,  28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,\n",
              "        40,  41,  42,  43,  44,  45,  46,  47,  48,  49,  50,  51,  52,\n",
              "        53,  54,  55,  56,  57,  58,  59,  60,  61,  62,  63,  64,  65,\n",
              "        66,  67,  68,  69,  70,  71,  72,  73,  74,  75,  76,  77,  78,\n",
              "        79,  80,  81,  82,  83,  84,  85,  86,  87,  88,  89,  90,  91,\n",
              "        92,  93,  94,  95,  96,  97,  98,  99, 100, 101, 102, 103, 104,\n",
              "       105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117,\n",
              "       118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130,\n",
              "       131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143,\n",
              "       144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156,\n",
              "       157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169,\n",
              "       170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182,\n",
              "       183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195,\n",
              "       196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208,\n",
              "       209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221,\n",
              "       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,\n",
              "       248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260,\n",
              "       261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273,\n",
              "       274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286,\n",
              "       287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299,\n",
              "       300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312,\n",
              "       313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325,\n",
              "       326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338,\n",
              "       339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351,\n",
              "       352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364,\n",
              "       365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377,\n",
              "       378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390,\n",
              "       391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403,\n",
              "       404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416,\n",
              "       417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429,\n",
              "       430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442,\n",
              "       443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455,\n",
              "       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,\n",
              "       482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494,\n",
              "       495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507,\n",
              "       508, 509, 510, 511, 512, 513, 514, 515, 516, 517, 518, 519, 520,\n",
              "       521, 522, 523, 524, 525, 526, 527, 528, 529, 530, 531, 532, 533,\n",
              "       534, 535, 536, 537, 538, 539, 540, 541, 542, 543, 544, 545, 546,\n",
              "       547, 548, 549, 550, 551, 552, 553, 554, 555, 556, 557, 558, 559,\n",
              "       560, 561, 562, 563, 564, 565, 566, 567, 568, 569, 570, 571, 572,\n",
              "       573, 574, 575, 576, 577, 578, 579, 580, 581, 582, 583, 584, 585,\n",
              "       586, 587, 588, 589, 590, 591, 592, 593, 594, 595, 596, 597, 598,\n",
              "       599, 600, 601, 602, 603, 604, 605, 606, 607, 608, 609, 610, 611,\n",
              "       612, 613, 614, 615, 616, 617, 618, 619, 620, 621, 622, 623, 624,\n",
              "       625, 626, 627, 628, 629, 630, 631, 632, 633, 634, 635, 636, 637,\n",
              "       638, 639, 640, 641, 642, 643, 644, 645, 646, 647, 648, 649, 650,\n",
              "       651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663,\n",
              "       664, 665, 666, 667, 668, 669, 670, 671, 672, 673, 674, 675, 676,\n",
              "       677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688, 689,\n",
              "       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,\n",
              "       716, 717, 718, 719, 720, 721, 722, 723, 724, 725, 726, 727, 728,\n",
              "       729, 730, 731, 732, 733, 734, 735, 736, 737, 738, 739, 740, 741,\n",
              "       742, 743])"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# plot the distribution of time stamp\n",
        "# Value counts for the column\n",
        "value_counts = df['step'].value_counts()\n",
        "\n",
        "# Plot\n",
        "plt.figure(figsize=(8, 5))\n",
        "value_counts.plot(kind='bar', color='skyblue', edgecolor='skyblue')\n",
        "plt.title('Value Counts of steps', fontsize=16)\n",
        "plt.xlabel('Steps', fontsize=14)\n",
        "plt.ylabel('Count', fontsize=14)\n",
        "# Set blank xticks to allow quicker plot\n",
        "plt.xticks([], [])\n",
        "plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 470
        },
        "id": "t_mBlv2t3f_d",
        "outputId": "4a5c3231-351f-403c-9f2e-38f8729a1583"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### null check & data type validate\n"
      ],
      "metadata": {
        "id": "LDjf_MSvUknZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# identify missing data\n",
        "\n",
        "print(df.isnull().sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i6hxv1m0UorP",
        "outputId": "e32f3239-5c8f-425c-c2f9-26d526574d00"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['type'] = df['type'].astype(str)\n",
        "df['nameOrig'] = df['nameOrig'].astype(str)\n",
        "df['nameDest'] = df['nameDest'].astype(str)"
      ],
      "metadata": {
        "id": "eZBB9VwfaOzf"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(df['type'].apply(type).value_counts())\n",
        "print(df['nameOrig'].apply(type).value_counts())\n",
        "print(df['nameDest'].apply(type).value_counts())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CWgAZATkbdQD",
        "outputId": "64bff0a9-ce40-4bf9-d8b6-dfa628321048"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "type\n",
            "<class 'str'>    6362620\n",
            "Name: count, dtype: int64\n",
            "nameOrig\n",
            "<class 'str'>    6362620\n",
            "Name: count, dtype: int64\n",
            "nameDest\n",
            "<class 'str'>    6362620\n",
            "Name: count, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### duplicates check"
      ],
      "metadata": {
        "id": "sm0sKARIcQIy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(df.duplicated().sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DngkmXi6cR0J",
        "outputId": "848f48ae-9e46-4ed6-e62e-46220bbbc7f5"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### standardize"
      ],
      "metadata": {
        "id": "FfgzS0Ecf9e3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# delete minus transaction\n",
        "df = df[df['amount'] > 0]"
      ],
      "metadata": {
        "id": "Yt_WJOtCgfj2"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# check type: no need to standard the case here\n",
        "df['type'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HGp5KQNigqzw",
        "outputId": "78391a90-ecf2-4f57-dca8-b54f0bfd1d86"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['PAYMENT', 'TRANSFER', 'CASH_OUT', 'DEBIT', 'CASH_IN'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### transaction preview"
      ],
      "metadata": {
        "id": "gUzOcFAjh4ju"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "max(df['amount'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xg6NexSIjyWL",
        "outputId": "9a2d0220-12c9-4558-d7a2-2408e61e5df8"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "92445516.64"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Plot the histogram\n",
        "plt.hist(df['amount'], bins=list(range(0, 1000001, 10000)), edgecolor='black')  # Adjust `bins` as needed\n",
        "plt.xlabel('Transaction Amount')\n",
        "plt.ylabel('Number of Transactions')\n",
        "plt.title('Transaction Amount Distribution')\n",
        "plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "TaxxLYN6h8wR",
        "outputId": "b92ef60e-87dc-48a1-fc2d-2c4803a37326"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## aggregation pre-processing\n",
        "\n",
        "For data with datetime type index, we could use df.resample() function to do time-series analysis. Here, we separate the data based on the step column and use df.groupby() function to do aggregate analysis."
      ],
      "metadata": {
        "id": "uDFeQTUFmiVi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['day'] = (df['step'] - 1) // 24\n",
        "df['week'] = df['day'] // 7\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "kVImm7u3nWu5",
        "outputId": "2f454f67-be20-4d1d-ec14-40f685fdc636"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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  day  \\\n",
              "0  M1979787155             0.0             0.0        0               0    0   \n",
              "1  M2044282225             0.0             0.0        0               0    0   \n",
              "2   C553264065             0.0             0.0        1               0    0   \n",
              "3    C38997010         21182.0             0.0        1               0    0   \n",
              "4  M1230701703             0.0             0.0        0               0    0   \n",
              "\n",
              "   week  \n",
              "0     0  \n",
              "1     0  \n",
              "2     0  \n",
              "3     0  \n",
              "4     0  "
            ],
            "text/html": [
              "\n",
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              "<style scoped>\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",
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              "      <td>PAYMENT</td>\n",
              "      <td>9839.64</td>\n",
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              "      <td>170136.0</td>\n",
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              "      <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",
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              "      <td>0.0</td>\n",
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              "      <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",
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              "    </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",
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              "    <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",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "  </style>\n",
              "\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-64c0d987-d409-42f5-8fbf-66fdfbac8436');\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",
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              "        element.appendChild(docLink);\n",
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              "\n",
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              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "\n",
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              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\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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              "  }\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",
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              "      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-6d2c8455-a9ec-431c-8164-6a2f06278e32 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## summary statistics and data profiling reports\n",
        "\n"
      ],
      "metadata": {
        "id": "YfnwMAvCo-8s"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### general profiling"
      ],
      "metadata": {
        "id": "L4YJGMIupJlL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# data types & schema\n",
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e_qajxn2pH67",
        "outputId": "bc38f545-5455-4011-bf4c-e5030a75425f"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Index: 6362604 entries, 0 to 6362619\n",
            "Data columns (total 13 columns):\n",
            " #   Column          Dtype  \n",
            "---  ------          -----  \n",
            " 0   step            int64  \n",
            " 1   type            object \n",
            " 2   amount          float64\n",
            " 3   nameOrig        object \n",
            " 4   oldbalanceOrg   float64\n",
            " 5   newbalanceOrig  float64\n",
            " 6   nameDest        object \n",
            " 7   oldbalanceDest  float64\n",
            " 8   newbalanceDest  float64\n",
            " 9   isFraud         int64  \n",
            " 10  isFlaggedFraud  int64  \n",
            " 11  day             int64  \n",
            " 12  week            int64  \n",
            "dtypes: float64(5), int64(5), object(3)\n",
            "memory usage: 679.6+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# missing data\n",
        "print(df.isnull().sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ellQhx_rpTLp",
        "outputId": "fb2ded62-c820-4058-a17e-22863df0eace"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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",
            "day               0\n",
            "week              0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# distribution\n",
        "\n",
        "# Set the display format to avoid scientific notation\n",
        "pd.set_option('display.float_format', '{:,.2f}'.format)\n",
        "\n",
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "EdWsc9rupgO7",
        "outputId": "e3e144cf-af4e-49b8-d8ed-899d63912b47"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              step        amount  oldbalanceOrg  newbalanceOrig  \\\n",
              "count 6,362,604.00  6,362,604.00   6,362,604.00    6,362,604.00   \n",
              "mean        243.40    179,862.36     833,885.20      855,115.82   \n",
              "std         142.33    603,858.92   2,888,246.00    2,924,051.87   \n",
              "min           1.00          0.01           0.00            0.00   \n",
              "25%         156.00     13,389.75           0.00            0.00   \n",
              "50%         239.00     74,872.88      14,208.58            0.00   \n",
              "75%         335.00    208,721.82     107,316.00      144,258.52   \n",
              "max         743.00 92,445,516.64  59,585,040.37   49,585,040.37   \n",
              "\n",
              "       oldbalanceDest  newbalanceDest      isFraud  isFlaggedFraud  \\\n",
              "count    6,362,604.00    6,362,604.00 6,362,604.00    6,362,604.00   \n",
              "mean     1,100,702.16    1,224,997.20         0.00            0.00   \n",
              "std      3,399,182.94    3,674,132.20         0.04            0.00   \n",
              "min              0.00            0.00         0.00            0.00   \n",
              "25%              0.00            0.00         0.00            0.00   \n",
              "50%        132,705.84      214,661.65         0.00            0.00   \n",
              "75%        943,036.71    1,111,909.52         0.00            0.00   \n",
              "max    356,015,889.35  356,179,278.92         1.00            1.00   \n",
              "\n",
              "               day         week  \n",
              "count 6,362,604.00 6,362,604.00  \n",
              "mean          9.49         1.00  \n",
              "std           5.92         0.86  \n",
              "min           0.00         0.00  \n",
              "25%           6.00         0.00  \n",
              "50%           9.00         1.00  \n",
              "75%          13.00         1.00  \n",
              "max          30.00         4.00  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ca5fa750-96f5-405b-aabd-ae2cc33d56d3\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <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>isFraud</th>\n",
              "      <th>isFlaggedFraud</th>\n",
              "      <th>day</th>\n",
              "      <th>week</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>6,362,604.00</td>\n",
              "      <td>6,362,604.00</td>\n",
              "      <td>6,362,604.00</td>\n",
              "      <td>6,362,604.00</td>\n",
              "      <td>6,362,604.00</td>\n",
              "      <td>6,362,604.00</td>\n",
              "      <td>6,362,604.00</td>\n",
              "      <td>6,362,604.00</td>\n",
              "      <td>6,362,604.00</td>\n",
              "      <td>6,362,604.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>243.40</td>\n",
              "      <td>179,862.36</td>\n",
              "      <td>833,885.20</td>\n",
              "      <td>855,115.82</td>\n",
              "      <td>1,100,702.16</td>\n",
              "      <td>1,224,997.20</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>9.49</td>\n",
              "      <td>1.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>142.33</td>\n",
              "      <td>603,858.92</td>\n",
              "      <td>2,888,246.00</td>\n",
              "      <td>2,924,051.87</td>\n",
              "      <td>3,399,182.94</td>\n",
              "      <td>3,674,132.20</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.00</td>\n",
              "      <td>5.92</td>\n",
              "      <td>0.86</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.00</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>156.00</td>\n",
              "      <td>13,389.75</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>6.00</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>239.00</td>\n",
              "      <td>74,872.88</td>\n",
              "      <td>14,208.58</td>\n",
              "      <td>0.00</td>\n",
              "      <td>132,705.84</td>\n",
              "      <td>214,661.65</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>9.00</td>\n",
              "      <td>1.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>335.00</td>\n",
              "      <td>208,721.82</td>\n",
              "      <td>107,316.00</td>\n",
              "      <td>144,258.52</td>\n",
              "      <td>943,036.71</td>\n",
              "      <td>1,111,909.52</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>13.00</td>\n",
              "      <td>1.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>743.00</td>\n",
              "      <td>92,445,516.64</td>\n",
              "      <td>59,585,040.37</td>\n",
              "      <td>49,585,040.37</td>\n",
              "      <td>356,015,889.35</td>\n",
              "      <td>356,179,278.92</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>30.00</td>\n",
              "      <td>4.00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"step\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2249426.2972883997,\n        \"min\": 1.0,\n        \"max\": 6362604.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          243.39650589601365,\n          239.0,\n          6362604.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"amount\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 32381388.875079695,\n        \"min\": 0.01,\n        \"max\": 92445516.64,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          179862.35584672098,\n          74872.875,\n          6362604.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"oldbalanceOrg\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 20671082.688246123,\n        \"min\": 0.0,\n        \"max\": 59585040.37,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          6362604.0,\n          833885.2010350499,\n          107316.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"newbalanceOrig\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17156393.787847705,\n        \"min\": 0.0,\n        \"max\": 49585040.37,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          6362604.0,\n          855115.8189275174,\n          49585040.37\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"oldbalanceDest\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 125286921.9739444,\n        \"min\": 0.0,\n        \"max\": 356015889.35,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          6362604.0,\n          1100702.1589297643,\n          943036.7075\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"newbalanceDest\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 125312040.47738518,\n        \"min\": 0.0,\n        \"max\": 356179278.92,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          6362604.0,\n          1224997.203175218,\n          1111909.5225\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"isFraud\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2249520.164817953,\n        \"min\": 0.0,\n        \"max\": 6362604.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.0012883090005287143,\n          1.0,\n          0.0358698963301808\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"isFlaggedFraud\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2249520.166614505,\n        \"min\": 0.0,\n        \"max\": 6362604.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2.5146936694472893e-06,\n          1.0,\n          0.0015857766996200341\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"day\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2249516.5092701484,\n        \"min\": 0.0,\n        \"max\": 6362604.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          9.491875810595788,\n          9.0,\n          6362604.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"week\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2249519.8199731987,\n        \"min\": 0.0,\n        \"max\": 6362604.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          6362604.0,\n          1.001991480217848,\n          4.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "According to the summary above, outliers happen mostly in balance/amount-related columns. There are vast amounts of money as outliers."
      ],
      "metadata": {
        "id": "k6nYtQvfrScz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# confusion matrix of isFraud and isFlaggedFraud\n",
        "# it helps us understand the flag system's performance\n",
        "\n",
        "# Generate confusion matrix\n",
        "cm = confusion_matrix(df['isFraud'], df['isFlaggedFraud'])\n",
        "\n",
        "# Print confusion matrix\n",
        "print(\"Confusion Matrix:\")\n",
        "print(cm)\n",
        "\n",
        "# Plot confusion matrix as a heatmap\n",
        "plt.figure(figsize=(6, 4))\n",
        "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=['Flagged 0', 'Flagged 1'], yticklabels=['Actual 0', 'Actual 1'])\n",
        "plt.title(\"Confusion Matrix\")\n",
        "plt.xlabel(\"Predicted Labels\")\n",
        "plt.ylabel(\"Actual Labels\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 462
        },
        "id": "hpnRsqZRs5pb",
        "outputId": "7dece852-13ae-4487-dd84-10c61066cd32"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion Matrix:\n",
            "[[6354407       0]\n",
            " [   8181      16]]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x400 with 2 Axes>"
            ],
            "image/png": 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r49nDJiIi42WgHvazZ89Qv359+Pr6Ys+ePXB0dMTNmzdhb2+vdRtM2EREZLwMdA/7119/RcmSJbFy5Up1WenSpXVqg0PiREREOaBQKJCcnKyxKRSKLOvu2LEDNWvWRIcOHeDk5ITq1atj6dKlOp2PCZuIiIxXLu5hh4WFwdbWVmMLCwvL8jR37tzBwoULUa5cOezbtw/9+/fH4MGDsWrVKu1DFQRByKvrzi8sqw8UOwQivXsWNU/sEIj0zkLPN24tfSfm+Njne3/I1KOWy+WQy+WZ6pqbm6NmzZo4efKkumzw4MGIiorCqVOntDof72ETEZHxysU97OySc1acnZ1RqVIljbKKFSvizz//1Pp8TNhERGS8DDRLvH79+rh+/bpG2Y0bN+Dq6qp1G0zYRERkvAw0S3zYsGGoV68epkyZgsDAQJw5cwZLlizBkiVLtG6Dk86IiIj0rFatWti6dSvWr18PT09PTJw4EbNnz0bXrl21boM9bCIiMl4GXJq0VatWaNWqVY6PZ8ImIiLjxZd/EBERSYCEXv7BhE1ERMaLPWwiIiIJkFAPWzp/WhARERkx9rCJiMh4cUiciIhIAiQ0JM6ETURExos9bCIiIglgwiYiIpIACQ2JS+dPCyIiIiPGHjYRERkvDokTERFJgISGxJmwiYjIeLGHTUREJAHsYRMREeV/MgklbOmMBRARERkx9rCJiMhoSamHzYRNRETGSzr5mgmbiIiMF3vYREREEsCETUREJAFSSticJU5ERCQB7GETEZHRklIPmwmbiIiMl3TyNRM2EREZL/awiYiIJIAJW0uJiYlYsWIFTp06hfj4eABA8eLFUa9ePQQHB8PR0VHM8IiIqICTUsIWbZZ4VFQUypcvjzlz5sDW1hYNGzZEw4YNYWtrizlz5qBChQo4e/asWOERERHlK6L1sAcNGoQOHTpg0aJFmf7CEQQB/fr1w6BBg3Dq1CmRIiQiooJOSj1s0RL2xYsXERERkeWXJZPJMGzYMFSvXl2EyIiIyGhIJ1+LNyRevHhxnDlzJtv9Z86cwWeffWbAiIiIyNjIZLIcb4YmWg975MiR6NOnD86dO4cmTZqok/N///2HQ4cOYenSpZg+fbpY4RERkRHgkLgWBgwYgGLFimHWrFlYsGABlEolAMDU1BTe3t6IiIhAYGCgWOEREZERYMLWUseOHdGxY0dkZGQgMTERAFCsWDGYmZmJGRYREVG+ky8WTjEzM4Ozs7PYYRARkbGRTgc7fyRsIiIiMUhpSJyv1yQiIqNlqFniISEhmY6vUKGCTm2wh01EREbLkD3sypUr4+DBg+rPhQrploKZsImIyGgZMmEXKlQIxYsXz/nxeRiL1nbs2KF13datW+sxEiIiopxRKBRQKBQaZXK5HHK5PMv6N2/ehIuLCywsLFC3bl2EhYWhVKlSWp9PJgiCkKuIc8DERLtb5zKZTP18ti4sqw/U+RgiqXkWNU/sEIj0zkLP3UqXfltyfGyf4pcQGhqqUTZ+/HiEhIRkqrtnzx6kpqbCw8MDcXFxCA0NxaNHj3DlyhUUKVJEq/OJkrD1jQmbjAETNhkDfSfsEv235vjYO7Nb6NTDft/z58/h6uqKmTNnomfPnlqdj/ewiYjIaOXmHra2yTkrdnZ2KF++PG7duqX1MfkiYaelpeHYsWOIjY1Fenq6xr7BgweLFBURERV0Yj2HnZqaitu3b+O7777T+hjRE/aFCxfQokULvHz5EmlpaXBwcEBiYiIKFy4MJycnJmwiIpK8kSNHwt/fH66urnj8+DHGjx8PU1NTdO7cWes2RF84ZdiwYfD398ezZ89gaWmJ06dP4/79+/D29ubbuoiISL9kudh08PDhQ3Tu3BkeHh4IDAxE0aJFcfr0aTg6Omrdhug97OjoaCxevBgmJiYwNTWFQqFAmTJlEB4ejqCgILRr107sECXJxdEWk4a0wVf1K6OwhRluP0hE35Dfcf5qLADg574t0KF5DXxe3B7pGUpciIlFyLydiLpyX93Gtb9C4epSVKPdsXO2Y/rKA5nOV6ZkMZxe/yOUKhWcG/6QZUwdmntj9dTu2HnkIgKHL9Vst39LdG9bD3ZFLHHq4h0MnrIRt2MTAAANvMth/7IhWbb5ZddwnPvfNRHp04Z1a7Fq5XIkJiagvEcF/PjTWFSpWlXssCiXDDUkvmHDhly3IXrCNjMzUz/m5eTkhNjYWFSsWBG2trZ48OCByNFJk10RSxyOGI5jUTcRMHABEp6lwr2UI54lv1TXuXX/CYb9ugl3HybCUm6GQd82xs4FA+HZJhSJz1LV9UIX7MLKLSfUn1PSNGdEAkChQiZYHdYdJy7cxhdepbOMqZSzA8KGBeD4+cwTLEYEN8X3nX3Qe9wa3HuUhHHft8LO+QNQvf0kKNLf4PTFO3BrOkbjmHHft4JvbQ8mazKIvXt2Y3p4GH4ZH4oqVbywds0q9O/bE9t37UXRokU/3QDlW1xLXAfVq1dHVFQUAMDHxwfjxo3D2rVrMXToUHh6eoocnTSN6N4MD+OfoW/I7zj7733cf5yEQ6ev4e7DRHWdjXvP4sg/13HvURJi7sRj9IwtsC1iCc9yLhptpaa9xn9JKert5ev0D0+HkO/9cf3uf/hz//ks4zExkSFiShAmLtqtEcM7A7r44tel+7Dr6GVcufkYvcauhrOjLVr7egEAMt4oNWJIepGGVo2qYvWO07n5moi0tmbVSrT7JhABbdujrLs7fhkfCgsLC2zb8qfYoVEuGWot8bwgesKeMmWK+tWakydPhr29Pfr374+EhAQsWbJE5OikqaVPFZy/Gou14T1w/1AYTq0fje5t62Vb36yQKXq2q4/nKS9x+cYjjX0jun+Fh0d+xan1ozGsWxOYmmr+ZHxqlUe7ZtUxdOof2bb/Ux8/JDxNxaptpzLtcytRFM6Otjj8zzV1WXLqa0RduYc6Vd2ybK+VT1UUtbXCmu1M2KR/GenpiLn6L76o+///DpmYmOCLL+rh0sULIkZGeUFKCVv0IfGaNWuq/9nJyQl79+4VMZqCoXSJYujdoQHm/H4Y4cv3w7uyK2b88A3S3yixduc/6np+DTyxemp3FLYwQ3xiMlr1m4ek52nq/QvWH8OFmAd4lpyGL7zKYMKg1ijuaIvRM96uDORga4Wlod+i+y+rkJL2OstY6lUrg+CAuqjTaWqW+4sXswEAPHmaolH+JCkFnxW1yfKYoIC6OHAqBo+ePNf6OyHKqWfPn0GpVGYa+i5atCju3r0jUlRkjERP2LmV1VqugkoJmYmpSBGJz8REhvNXYzF+3k4AwMXrD1HZ3Rm9v/lSI2Efi7qBOp3CUMzOGt3b1cPv4T3Q8LvpSPjfPew5vx9W171y8zHSM95g3s+dMXbODqRnvMGCsZ2xce9ZnDh/O8s4rAvLsXxSN3w/cb3GHwK5UcLJDs3qVsS3o1fkSXtEZOSkcwtb/IRdunTpjw4t3Lnz8b9gw8LCMq3lavpZLZg5186T+KQoPjEZMXfiNcqu3Y1HQJNqGmUvX6fjzoNE3HmQiDOX7+Hy9nEIalsP01fsz7LdqMv3YGZmClcXB9y8/wQ+tcujpU8VDP2uCYC3Q0umpiZIifoNAyatR3TMA7iVKIY/Z/dVt2Fi8vb/65So31C17UTEJyYDAJwciqj/GQCcihbBpesPM8XwXZsvkPQiDbuOXdL9iyHKAXs7e5iamiIpKUmjPCkpCcWKFRMpKsorUpp0JnrCHjp0qMbnjIwMXLhwAXv37sWoUaM+efyYMWMwfPhwjTKnBqPzMkTJORV9B+VdnTTKypVyQmzc048eZyKTQW6W/U/Cy+NzKJUqJPxv+LpR0AyYvvcil1aNqmJEcFP4Bs/E4yfP8UqRAe9vJmu0ETKgFawLW2DktM14GP8MGW+UiEt4Ad86Hrj0v/vnRawsUMvTDUs3Hc8UQ7fWX2DdrjN480b18S+BKI+YmZujYqXK+Of0KTRu0hQAoFKp8M8/p9Cp87ciR0e5xYStgyFDsn6+dv78+Th79uwnj89qLVdjHg4HgLm/H8aRiBEY1eMr/HngPGpVdkOP9vUxcOJ6AEBhC3OM7tUcfx27jPjEFyhqZ42+gQ3h4mSHLQfezvSuU7U0anm64tjZm0hJe40vqpbGryPbY/3uKDxPeQUAuH73P43z1qhUCipBwNXbceqy9/8ZgPrY98vnrzuC0b2+xq3YBNx7lITx37dEXMIL7DhyUePYRrXLo/TnxbBy68k8+qaItPNdUHeM/Wk0Klf2hGeVqvh9zSq8evUKAW25ToTUSShfi5+ws+Pn54cxY8Zg5cqVYociOeeuxqLjiKWYMKg1furjh3uPkjBq2p/YsOftH0BKlQoebp/hW/86KGpnhacvXuLsv/fRtMcs9VC6Ij0DHZp74+d+LSA3K4R7j5Mwd+0RzFlz+GOnzpEZEQdR2FKOeb90hl0RS5yMvo3WAxZAkf5Go15wQD2cir6NG/f+y6YlIv342q8Fnj19igXz5iAxMQEeFSpiweJlKMohccmTUg87375eMzw8HAsWLMC9e/d0Ppav1yRjwNdrkjHQ9+s1y43K+ZNJN6d9nYeRfJroPezq1atr/IUjCALi4+ORkJCABQsWiBgZEREVdBLqYIufsNu0aaORsE1MTODo6IhGjRqhQoUKIkZGREQFnZSGxEVP2CEhIWKHQERERkpC+Vr8pUlNTU3x5MmTTOVJSUkwNTXu2d5ERKRfJiayHG+GJnoPO7s5bwqFAubm5gaOhoiIjImUetiiJew5c+YAeHv/YNmyZbC2tlbvUyqViIyM5D1sIiKi/xEtYc+aNQvA2x72okWLNIa/zc3N4ebmhkWLFokVHhERGQFOOtPC3bt3AQC+vr7YsmUL7O3txQqFiIiMlITytfj3sI8cOSJ2CEREZKSk1MMWfZZ4+/bt8euvv2YqDw8PR4cOHUSIiIiIjIVMJsvxZmiiJ+zIyEi0aNEiU7mfnx8iIyNFiIiIiIyFTJbzzdBET9ipqalZPr5lZmaG5OTkLI4gIiIyPqIn7CpVqmDjxo2Zyjds2IBKlSqJEBERERkLKQ2Jiz7pbOzYsWjXrh1u376Nxo0bAwAOHTqE9evXY9OmTSJHR0REBZmE5pyJn7D9/f2xbds2TJkyBZs3b4alpSWqVq2KgwcPwsfHR+zwiIioAJPSLHHREzYAtGzZEi1btsxUfuXKFXh6eooQERERGQMJ5Wvx72F/KCUlBUuWLEHt2rXh5eUldjhERFSASekedr5J2JGRkejWrRucnZ0xffp0NG7cGKdPnxY7LCIionxB1CHx+Ph4REREYPny5UhOTkZgYCAUCgW2bdvGGeJERKR3HBLXgr+/Pzw8PHDp0iXMnj0bjx8/xty5c8UKh4iIjJCUhsRF62Hv2bMHgwcPRv/+/VGuXDmxwiAiIiPGHrYWjh8/jpSUFHh7e6NOnTqYN28eEhMTxQqHiIiMkJR62KIl7C+++AJLly5FXFwc+vbtiw0bNsDFxQUqlQoHDhxASkqKWKEREZGR4FriOrCyskKPHj1w/PhxXL58GSNGjMDUqVPh5OSE1q1bix0eERFRviB6wn6fh4cHwsPD8fDhQ6xfv17scIiIqICT0pB4vljp7EOmpqYICAhAQECA2KEQEVEBJqVJZ/kyYRMRERmClNYSz5Mh8efPn+dFM0RERAYlpSFxnRP2r7/+qvH+6sDAQBQtWhQlSpTAxYsX8zQ4IiIifRJjlvjUqVMhk8kwdOhQnY7TOWEvWrQIJUuWBAAcOHAABw4cwJ49e+Dn54dRo0bp2hwREZHRiIqKwuLFi1G1alWdj9U5YcfHx6sT9q5duxAYGIivvvoKP/zwA6KionQOgIiISCyGHBJPTU1F165dsXTpUtjb2+t8vM4J297eHg8ePAAA7N27F02bNgUACIIApVKpcwBERERiyc2QuEKhQHJyssamUCiyPdeAAQPQsmVLdd7Ulc4Ju127dujSpQuaNWuGpKQk+Pn5AQAuXLgAd3f3HAVBREQkhtz0sMPCwmBra6uxhYWFZXmeDRs24Pz589nu14bOj3XNmjULbm5uePDgAcLDw2FtbQ0AiIuLw/fff5/jQIiIiAwtN5PHxowZg+HDh2uUyeXyTPUePHiAIUOG4MCBA7CwsMjx+WSCIAg5Pjqfsqw+UOwQiPTuWdQ8sUMg0jsLPa8W0mze6Rwfe2DgF1rV27ZtG9q2bQtTU1N1mVKphEwmg4mJCRQKhca+7Gj1VezYsUOroABw/W8iIqL3NGnSBJcvX9Yo6969OypUqIDRo0drlawBLRO2tkuEymQyTjwjIiLJMMT6J0WKFIGnp6dGmZWVFYoWLZqp/GO0StgqlUq36IiIiCRASkuT5uruwOvXr3N1A52IiEhMJiLl66NHj+p8jM6PdSmVSkycOBElSpSAtbU17ty5AwAYO3Ysli9frnMAREREYinQa4lPnjwZERERCA8Ph7m5ubrc09MTy5Yty9PgiIiI9EmMtcRzSueEvXr1aixZsgRdu3bVmNnm5eWFa9eu5WlwRERE9JbO97AfPXqU5YpmKpUKGRkZeRIUERGRIcggnUlnOvewK1WqhL///jtT+ebNm1G9evU8CYqIiMgQTGQ53wxN5x72uHHjEBQUhEePHkGlUmHLli24fv06Vq9ejV27dukjRiIiIr2Q0mNdOvew27Rpg507d+LgwYOwsrLCuHHjEBMTg507d6JZs2b6iJGIiEgvpDTpLEfPYTdo0AAHDhzI61iIiIgMykRCPewcL5xy9uxZxMTEAHh7X9vb2zvPgiIiIiJNOifshw8fonPnzjhx4gTs7OwAAM+fP0e9evWwYcMGfP7553kdIxERkV5IqIOt+z3sXr16ISMjAzExMXj69CmePn2KmJgYqFQq9OrVSx8xEhER6YWUVjrTuYd97NgxnDx5Eh4eHuoyDw8PzJ07Fw0aNMjT4IiIiPRJSj1snRN2yZIls1wgRalUwsXFJU+CIiIiMgQpTTrTeUh82rRpGDRoEM6ePasuO3v2LIYMGYLp06fnaXBERET6JMvFZmha9bDt7e01xuvT0tJQp04dFCr09vA3b96gUKFC6NGjBwICAvQSKBERkTHTKmHPnj1bz2EQEREZnpRWOtMqYQcFBek7DiIiIoMTY03wnMrxwikA8Pr1a6Snp2uU2djY5CogIiIiQ5FSD1vnSWdpaWkYOHAgnJycYGVlBXt7e42NiIhIKqS0lrjOCfuHH37A4cOHsXDhQsjlcixbtgyhoaFwcXHB6tWr9REjERGRXhTohVN27tyJ1atXo1GjRujevTsaNGgAd3d3uLq6Yu3atejatas+4iQiIjJqOvewnz59ijJlygB4e7/66dOnAIAvv/wSkZGReRsdERGRHpnIcr4ZPFZdDyhTpgzu3r0LAKhQoQL++OMPAG973u9eBkJERCQFUhoS1zlhd+/eHRcvXgQA/Pjjj5g/fz4sLCwwbNgwjBo1Ks8DJCIi0pcCt9LZ+4YNG6b+56ZNm+LatWs4d+4c3N3dUbVq1TwNjoiISJ8K9FriH3J1dUW7du3g4OCAPn365EVMRERE9IFcJ+x3kpKSsHz58rxqjoiISO+k9Bx2rlY6IyIikjIprXTGhE1EREZLQvmaCZuIiIyXlCadaZ2w27Vr99H9z58/z20sREREBiWhfK19wra1tf3k/m7duuU6ICIiIspM64S9cuVKfcZBRERkcJx0JrJnUfPEDoGIiCQgz55tNoACmbCJiIi0wR42ERGRBIjx1q2cYsImIiKjJaWELaXheyIiIklauHAhqlatChsbG9jY2KBu3brYs2ePTm1o1cPesWOH1g22bt1apwCIiIjEYqh72J9//jmmTp2KcuXKQRAErFq1Cm3atMGFCxdQuXJlrdqQCYIgfKqSiYl2HXGZTAalUqlVXX16/UbsCIiIKC9Y6PnG7ahd13N87LRWHrk6t4ODA6ZNm4aePXtqVV+rr0KlUuUqKCIiovwoNx1shUIBhUKhUSaXyyGXyz96nFKpxKZNm5CWloa6detqfT7ewyYiIqNlIpPleAsLC4Otra3GFhYWlu25Ll++DGtra8jlcvTr1w9bt25FpUqVtI5VqyHxD6WlpeHYsWOIjY1Fenq6xr7Bgwfr2lye45A4EVHBoO8h8Z9238jxseObuOrUw05PT0dsbCxevHiBzZs3Y9myZTh27JjWSVvnhH3hwgW0aNECL1++RFpaGhwcHJCYmIjChQvDyckJd+7c0aU5vWDCJiIqGPJzwp7Sonyuzt20aVOULVsWixcv1qq+zkPiw4YNg7+/P549ewZLS0ucPn0a9+/fh7e3N6ZPn65zwERERGKRyXK+5ZZKpcrUQ/8Ynf92iY6OxuLFi2FiYgJTU1MoFAqUKVMG4eHhCAoK+uRrOImIiPILQ70Pe8yYMfDz80OpUqWQkpKCdevW4ejRo9i3b5/WbeicsM3MzNSPeTk5OSE2NhYVK1aEra0tHjx4oGtzREREojHUUuJPnjxBt27dEBcXB1tbW1StWhX79u1Ds2bNtG5D54RdvXp1REVFoVy5cvDx8cG4ceOQmJiINWvWwNPTU9fmiIiIRGOopUmXL1+e6zZ0voc9ZcoUODs7AwAmT54Me3t79O/fHwkJCViyZEmuAyIiIjKU3DzWZWg697Br1qyp/mcnJyfs3bs3TwMiIiKizPi2LiIiMloSeh227gm7dOnSH10sPT88h01ERKQNKb1eU+eEPXToUI3PGRkZuHDhAvbu3YtRo0blVVxERER6J4N0MrbOCXvIkCFZls+fPx9nz57NdUBERESGIqUedp69/MPPzw9//vlnXjVHRESkdyaynG8GjzWvGtq8eTMcHBzyqjkiIiJ6T44WTnl/0pkgCIiPj0dCQgIWLFiQp8ERERHp08cmUec3OifsNm3aaFygiYkJHB0d0ahRI1SoUCFPgyMiItInKd3D1jlhh4SE6CEMIiIiw5NQB1v3e9impqZ48uRJpvKkpCSYmprmSVBERESGUKCXJhUEIctyhUIBc3PzXAdERERkKAVySHzOnDkA3t6gX7ZsGaytrdX7lEolIiMjeQ+biIhIT7RO2LNmzQLwtoe9aNEijeFvc3NzuLm5YdGiRXkfIRERkZ5I6R621gn77t27AABfX19s2bIF9vb2eguKiIjIEEwK8tKkR44c0UccREREBielHrbOs8Tbt2+PX3/9NVN5eHg4OnTokCdBERERGUKBXpo0MjISLVq0yFTu5+eHyMjIPAmKiIjIEKT0WJfOCTs1NTXLx7fMzMyQnJycJ0ERERGRJp0TdpUqVbBx48ZM5Rs2bEClSpXyJCgiIiJDkMlyvhmazpPOxo4di3bt2uH27dto3LgxAODQoUNYv349Nm3alOcBEhER6YsYQ9s5pXPC9vf3x7Zt2zBlyhRs3rwZlpaWqFq1Kg4ePAgfHx99xEhERKQXEsrXkAnZrTWaA1euXIGnp2deNZdjr9+IHQEREeUFC527lbqJiIrN8bHBtUrlYSSfpvM97A+lpKRgyZIlqF27Nry8vPIiJiIiIoOQyWQ53gwtxwk7MjIS3bp1g7OzM6ZPn47GjRvj9OnTeRkbERER/Y9Ogw3x8fGIiIjA8uXLkZycjMDAQCgUCmzbto0zxImISHIkdAtb+x62v78/PDw8cOnSJcyePRuPHz/G3Llz9RkbERGRXklp4RSte9h79uzB4MGD0b9/f5QrV06fMRERERlEgexhHz9+HCkpKfD29kadOnUwb948JCYm6jM2IiIivZLSwilaJ+wvvvgCS5cuRVxcHPr27YsNGzbAxcUFKpUKBw4cQEpKij7jJCIiynNSmiWeq+ewr1+/juXLl2PNmjV4/vw5mjVrhh07duRlfDnC57CJiAoGfT+Hvf7Coxwf27l6iTyM5NNy9Ry2h4cHwsPD8fDhQ6xfvz6vYiIiIjIIk1xshpanK53lF+xhExEVDPruYf8R/TjHxwZWc8nDSD5Nz18FERFR/iWlWeJM2EREZLTEmDyWU2IMw2vlwYMH6NGjh9hhEBFRASale9j5NmE/ffoUq1atEjsMIiKifEG0IfFPPf51584dA0VCRETGylBD4mFhYdiyZQuuXbsGS0tL1KtXD7/++is8PDy0bkO0WeImJiaQyWT42OllMhmUSqXObXOWOBFRwaDvWeLbLsXn+NiAqsW1rvv111+jU6dOqFWrFt68eYOffvoJV65cwdWrV2FlZaVVG6Il7BIlSmDBggVo06ZNlvujo6Ph7e3NhE1EZMT0nbC3X855wm5TRfuE/aGEhAQ4OTnh2LFjaNiwoVbHiHYP29vbG+fOnct2/6d630RERLllAlmON4VCgeTkZI1NoVBodd4XL14AABwcHHSIVSSjRo1CvXr1st3v7u6OI0eOGDAiIiIyNrl5+UdYWBhsbW01trCwsE+eU6VSYejQoahfvz48PT21j5UrnRERUX6l7yHxXVf+y/GxzcrZZepRy+VyyOXyjx7Xv39/7NmzB8ePH8fnn3+u9fm4cAoRERktWS7WOtMmOX9o4MCB2LVrFyIjI3VK1gATNhERGTFDLXQmCAIGDRqErVu34ujRoyhdurTObTBhExGR0TIx0GriAwYMwLp167B9+3YUKVIE8fFvZ6fb2trC0tJSqzZ4D5uIiPItfd/D3nc1IcfHNq/kqHXd7BZoWblyJYKDg7Vqgz1sIiIyWoYcEs8tURL2p5YlfV/r1q31GAkREZE0iJKwAwICtKqX06VJiYiItJGbWeKGJkrCVqlUYpyWiIhIg4l08jXvYRMRkfFiD1tHaWlpOHbsGGJjY5Genq6xb/DgwSJFRUREBZ2hJp3lBdET9oULF9CiRQu8fPkSaWlpcHBwQGJiIgoXLgwnJycmbCIiIoj48o93hg0bBn9/fzx79gyWlpY4ffo07t+/D29vb0yfPl3s8IiIqACT5eJ/hiZ6wo6OjsaIESNgYmICU1NTKBQKlCxZEuHh4fjpp5/EDs9oKJVKzJszG35fNUbtGlXR8uumWLxwvsazgwcP7Eff3j3QsF4deFX2wLWYmEztJCYk4KcfR6Fxw/qoU7MaOn7TFgf379Oos3TxQnTr2gl1vL3w5Rc19X5tRLo4dzYKg77vh6aNvoRXZQ8cPnQwU507t29j8IB+qF/HG3VqVkOXwPaIe/xYhGgpt0xkOd8MHqvhT6nJzMwMJiZvw3ByckJsbCyAt8u1PXjwQMzQjMrK5UuxaeN6jPl5HLbu3I2hw0YiYsUyrFu7Rl3n1auXqF69BoYOH5ltOz//NBr37t7Fb/MW4s+tO9GkaTOMGjEUMTFX1XUyMjLQ7Kuv0aFjZ71eE1FOvHr1Eh4eHhjzy/gs9z+IjUXwd11QunQZLItYg81bdqBPv+9hruNLICh/kFIPW/R72NWrV0dUVBTKlSsHHx8fjBs3DomJiVizZo1O7wml3ImOvoBGjZugoU8jAECJEp9jz+6/cOXyJXUd/9YBAIBHjx5m287FCxfw87jxqFK1KgCgT7/v8fvqVYj5919UrFgJAPD9wLfzErZv3aKHKyHKnS8b+ODLBj7Z7p87Zxa+bNgQw0b+oC4rWaqUIUIjPZDSpDPRe9hTpkyBs7MzAGDy5Mmwt7dH//79kZCQgCVLlogcnfGoVq06zpw+jXv37gIArl+7hgsXzuHLBg11aserenXs27sHL54/h0qlwp7df0GRrkDNWrX1ETaRQalUKvx97ChcXd3Qr3dPNGpQF107dchy2JykQZaLzdBE72HXrPn/9zCdnJywd+9eEaMxXj169UFqaioCWvnB1NQUSqUSg4YMQ8tWui0NO23GbPwwYhga1q+DQoUKwcLCArN+m4dSrq56ipzIcJ4mJeHly5dYsXwpBg4aiqHDR+LE8b8xfMhALFu5mn+Ykl6JnrBzS6FQQKFQaJQJprq/VNzY7du7B7v/2omw8Blwd3fHtWsxmDY1DI6OTmgd0FbrdubP/Q0pKclYsjwCdnb2OHL4IH4YMRQrV69FufIeerwCIv1TCW9XafT1bYLvgoIBABUqVsTF6PPYtHEDE7YEmUhoTFz0hF26dOlsXzsGAHfu3Pno8WFhYQgNDdUo+3nsePwyLiQvwjMas2aEo0fPPvBr0RIAUK68B+IeP8byZYu1TtgPYmOxYd3v+HP7Lri7lwMAeFSogPPnzmLD+rUYO36C3uInMgR7O3sUKlQIZcqW1SgvXaYsos+fEykqyg3ppOt8kLCHDh2q8TkjIwMXLlzA3r17MWrUqE8eP2bMGAwfPlyjTDBl71pXr1+9hskHzymYmppCpdL+lXCvX78CAJjINKdGmJiYQtChHaL8yszcHJU9q6jnerxz//49OLuUECkqyhUJZWzRE/aQIUOyLJ8/fz7Onj37yePl8szD36/f5EloRsWnkS+WLlmE4s4uKOvujmsxMVizaiXatG2vrvPi+XPExcUhIeEJAKj/o1WsWDEUc3SEW+kyKFXKFRNDx2H4yNGws7PD4cMHcfrUCcxdsFjdTtzjx3jx4gXi4h5DqVSqn+cuVaoUCltZGfCqiTJ7mZamfrwUAB49fIhrMTGwtbWFs4sLgrr3xA8jhsHbuxZq1a6DE8f/RuTRI1i2crWIUVNOSWktcZmQF2/V1oM7d+6gWrVqSE5O1vlYJmzdpaWlYv6c33D40EE8fZoERycn+Pm1RN/+A2Bmbg7g7WNY434Zk+nYft8PRP8BgwC87Wn8NnMGLlw4h5cvX6JUyVLo1r2H+pEwABj704/YsX1rpnaWrVyNWrXr6OcCibQUdeYf9OreLVN56zZtMXHKVADA1i2bsWLpEvz3Xzzc3Eqj/8BB8G3c1NChGgULPXcrz9x5keNja5exzcNIPi3fJuzw8HAsWLAA9+7d0/lYJmwiooKBCfv/iT4kXr16dY1JZ4IgID4+HgkJCViwYIGIkRERUUEnnQHxfJCw27Rpo5GwTUxM4OjoiEaNGqFChQoiRkZERAWehDJ2vh0Szw0OiRMRFQz6HhI/e1f3eVLv1Cxtk4eRfJroS5OampriyZMnmcqTkpJgamoqQkRERGQsZLKcb4Ym+pB4dh18hUIB8//NTiYiItIHCY2Ii5ew58yZAwCQyWRYtmwZrK2t1fuUSiUiIyN5D5uIiOh/REvYs2bNAvC2h71o0SKN4W9zc3O4ublh0aJFYoVHRETGQEJdbNES9t27b1fJ8vX1xZYtW2Bvby9WKEREZKSktNKZ6Pewjxw5InYIRERkpCT0si7xZ4m3b98ev/76a6by8PBwdOjQQYSIiIjIWMhysRma6Ak7MjISLVq0yFTu5+eHyMhIESIiIiKjIaGMLXrCTk1NzfLxLTMzsxy9+IOIiKggEj1hV6lSBRs3bsxUvmHDBlSqVEmEiIiIyFjIcvE/QxN90tnYsWPRrl073L59G40bNwYAHDp0COvXr8emTZtEjo6IiAoyKU06Ez1h+/v7Y9u2bZgyZQo2b94MS0tLVK1aFQcPHoSPj4/Y4RERUQEmoXydv1/+ceXKFXh6eup8HF/+QURUMOj75R9XHqXm+FjPEtafrpSHRL+H/aGUlBQsWbIEtWvXhpeXl9jhEBFRASale9j5JmFHRkaiW7ducHZ2xvTp09G4cWOcPn1a7LCIiIjyBVHvYcfHxyMiIgLLly9HcnIyAgMDoVAosG3bNs4QJyIivZPSpDPRetj+/v7w8PDApUuXMHv2bDx+/Bhz584VKxwiIjJChlo3JTIyEv7+/nBxcYFMJsO2bdt0jlW0hL1nzx707NkToaGhaNmypcbbuoiIiAzCQBk7LS0NXl5emD9/fo5DFW1I/Pjx41i+fDm8vb1RsWJFfPfdd+jUqZNY4RARkREy1OQxPz8/+Pn55aoN0XrYX3zxBZYuXYq4uDj07dsXGzZsgIuLC1QqFQ4cOICUlBSxQiMiIiMhk+V8UygUSE5O1tgUCoXeYhV9lriVlRV69OiB48eP4/LlyxgxYgSmTp0KJycntG7dWuzwiIiIshQWFgZbW1uNLSwsTG/ny5cLpyiVSuzcuRMrVqzAjh07dD6eC6cQERUM+l445Ub8yxwf62pvmqlHLZfLIZfLP3qcTCbD1q1bERAQoNP5RF+aNCumpqYICAjQ+WKIiIh0kotb2Nok57yULxM2ERGRIYixYllOMWETEZHRMtTCKampqbh165b68927dxEdHQ0HBweUKlVKqzby5T3s3OI9bCKigkHf97BvP3mV42PLOllqXffo0aPw9fXNVB4UFISIiAit2mDCJiKifKugJOy8wCFxIiIyXtK5hc2ETURExouTzoiIiCRASm/rYsImIiKjJaF8zYRNRERGTEIZW/S1xImIiOjT2MMmIiKjxUlnREREEsBJZ0RERBIgoXzNhE1ERMaLPWwiIiJJkE7G5ixxIiIiCWAPm4iIjBaHxImIiCRAQvmaCZuIiIwXe9hEREQSwIVTiIiIpEA6+ZqzxImIiKSAPWwiIjJaEupgM2ETEZHx4qQzIiIiCeCkMyIiIimQTr5mwiYiIuMloXzNWeJERERSwB42EREZLU46IyIikgBOOiMiIpIAKfWweQ+biIhIAtjDJiIio8UeNhEREeUp9rCJiMhocdIZERGRBEhpSJwJm4iIjJaE8jUTNhERGTEJZWxOOiMiIpIA9rCJiMhocdIZERGRBHDSGRERkQRIKF/zHjYRERkxWS62HJg/fz7c3NxgYWGBOnXq4MyZM1ofy4RNRERGS5aL/+lq48aNGD58OMaPH4/z58/Dy8sLzZs3x5MnT7SLVRAEQeez5nOv34gdARER5QULPd+4fZWR82MtzXSrX6dOHdSqVQvz5s0DAKhUKpQsWRKDBg3Cjz/++Mnj2cMmIiKjJZPlfFMoFEhOTtbYFApFludJT0/HuXPn0LRpU3WZiYkJmjZtilOnTmkVa4GcdKbvv8hIk0KhQFhYGMaMGQO5XC52OER6wd95wZSbfBEyKQyhoaEaZePHj0dISEimuomJiVAqlfjss880yj/77DNcu3ZNq/MVyCFxMqzk5GTY2trixYsXsLGxETscIr3g75w+pFAoMvWo5XJ5ln/QPX78GCVKlMDJkydRt25ddfkPP/yAY8eO4Z9//vnk+dgXJSIiyoHsknNWihUrBlNTU/z3338a5f/99x+KFy+uVRu8h01ERKRn5ubm8Pb2xqFDh9RlKpUKhw4d0uhxfwx72ERERAYwfPhwBAUFoWbNmqhduzZmz56NtLQ0dO/eXavjmbAp1+RyOcaPH8+JOFSg8XdOudWxY0ckJCRg3LhxiI+PR7Vq1bB3795ME9Gyw0lnREREEsB72ERERBLAhE1ERCQBTNhEREQSwIRtBBo1aoShQ4eKHYbWZDIZtm3bJnYYJDH8nVNBx4RdAAQHB0Mmk2Xabt26JXZoepObV9SRNBnb7zwyMhL+/v5wcXFhcicATNgFxtdff424uDiNrXTp0mKHpRe5fUUdSZcx/c7T0tLg5eWF+fPnix0K5RNM2AWEXC5H8eLFNTZTU9Ms665ZswY1a9ZEkSJFULx4cXTp0iVTstuxYwfKlSsHCwsL+Pr6YtWqVZDJZHj+/Lm6ztKlS1GyZEkULlwYbdu2xcyZM2FnZ6fRzvbt21GjRg1YWFigTJkyCA0NxZs3///+05s3b6Jhw4awsLBApUqVcODAgU9e68yZM9G7d290794dlSpVwqJFi1C4cGGsWLFC+y+MJMmYfud+fn6YNGkS2rZtq/0XRAUaE7YRysjIwMSJE3Hx4kVs27YN9+7dQ3BwsHr/3bt38c033yAgIAAXL15E37598fPPP2u0ceLECfTr1w9DhgxBdHQ0mjVrhsmTJ2vU+fvvv9GtWzcMGTIEV69exeLFixEREaGup1Kp0K5dO5ibm+Off/7BokWLMHr06I/GnhevqCPjIOXfOVGWBJK8oKAgwdTUVLCyslJv33zzjXq/j4+PMGTIkGyPj4qKEgAIKSkpgiAIwujRowVPT0+NOj///LMAQHj27JkgCILQsWNHoWXLlhp1unbtKtja2qo/N2nSRJgyZYpGnTVr1gjOzs6CIAjCvn37hEKFCgmPHj1S79+zZ48AQNi6dWuWsT569EgAIJw8eVKjfNSoUULt2rWzvUaSPmP6nX9Il7pUcHFp0gLC19cXCxcuVH+2srLKtu65c+cQEhKCixcv4tmzZ1CpVACA2NhYVKpUCdevX0etWrU0jqldu7bG5+vXr2caqqtduzZ27dql/nzx4kWcOHFCo0eiVCrx+vVrvHz5EjExMShZsiRcXFzU+7VdBJ+ME3/nZMyYsAsIKysruLu7f7JeWloamjdvjubNm2Pt2rVwdHREbGwsmjdvjvT09DyNKTU1FaGhoWjXrl2mfRYWFjlqMy9eUUfSZSy/c6KsMGEbmWvXriEpKQlTp05FyZIlAQBnz57VqOPh4YHdu3drlEVFRWWq82HZh59r1KiB69evZ/sf2IoVK+LBgweIi4uDs7MzAOD06dMfjf/9V9QFBAQA+P9X1A0cOPCjx5LxkPrvnCgrnHRmZEqVKgVzc3PMnTsXd+7cwY4dOzBx4kSNOn379sW1a9cwevRo3LhxA3/88QciIiIAvF3sAQAGDRqE3bt3Y+bMmbh58yYWL16MPXv2qPcDwLhx47B69WqEhobi33//RUxMDDZs2IBffvkFANC0aVOUL18eQUFBuHjxIv7+++9Mk36yMnz4cCxduhSrVq1CTEwM+vfvr9Mr6qjgKwi/89TUVERHRyM6OhrA20ly0dHRiI2NzYNviCRJ7JvolHtBQUFCmzZtst3/4WScdevWCW5uboJcLhfq1q0r7NixQwAgXLhwQV1n+/btgru7uyCXy4VGjRoJCxcuFAAIr169UtdZsmSJUKJECcHS0lIICAgQJk2aJBQvXlzj3Hv37hXq1asnWFpaCjY2NkLt2rWFJUuWqPdfv35d+PLLLwVzc3OhfPnywt69e7WaYDN37lyhVKlSgrm5uVC7dm3h9OnTWn1XJF3G9js/cuSIACDTFhQUpO1XRgUMX69JWpk8eTIWLVqEBw8eZFund+/euHbtGv7++28DRkaUd/g7p/yM97ApSwsWLECtWrVQtGhRnDhxAtOmTct0j3j69Olo1qwZrKyssGfPHqxatQoLFiwQKWIi3fF3TlLChE1ZunnzJiZNmoSnT5+iVKlSGDFiBMaMGaNR58yZMwgPD0dKSgrKlCmDOXPmoFevXiJFTKQ7/s5JSjgkTkREJAGcJU5ERCQBTNhEREQSwIRNREQkAUzYREREEsCETUREJAFM2ER5IDg4WL22OQA0atQIQ4cONXgcR48ehUwmw/Pnz/V2jg+vNScMESdRQcOETQVWcHAwZDIZZDIZzM3N4e7ujgkTJuDNmzd6P/eWLVsyrV2dHUMnLzc3N8yePdsg5yKivMOFU6hA+/rrr7Fy5UooFArs3r0bAwYMgJmZWabFMQAgPT0d5ubmeXJeBweHPGmHiOgd9rCpQJPL5ShevDhcXV3Rv39/NG3aFDt27ADw/0O7kydPhouLCzw8PAAADx48QGBgIOzs7ODg4IA2bdrg3r176jaVSiWGDx8OOzs7FC1aFD/88AM+XH/owyFxhUKB0aNHo2TJkpDL5XB3d8fy5ctx7949+Pr6AgDs7e0hk8kQHBwM4O1rQ8PCwlC6dGlYWlrCy8sLmzdv1jjP7t27Ub58eVhaWsLX11cjzpxQKpXo2bOn+pweHh747bffsqwbGhoKR0dH2NjYoF+/fhrvmdYm9vfdv38f/v7+sLe3h5WVFSpXrpzp1ZdExo49bDIqlpaWSEpKUn8+dOgQbGxscODAAQBARkYGmjdvjrp16+Lvv/9GoUKFMGnSJHz99de4dOkSzM3NMWPGDERERGDFihWoWLEiZsyYga1bt6Jx48bZnrdbt244deoU5syZAy8vL9y9exeJiYkoWbIk/vzzT7Rv3x7Xr1+HjY0NLC0tAQBhYWH4/fffsWjRIpQrVw6RkZH49ttv4ejoCB8fHzx48ADt2rXDgAED0KdPH5w9exYjRozI1fejUqnw+eefY9OmTShatChOnjyJPn36wNnZGYGBgRrfm4WFBY4ePYp79+6he/fuKFq0KCZPnqxV7B8aMGAA0tPTERkZCSsrK1y9ehXW1ta5uhaiAkfUd4UR6dH7r2NUqVTCgQMHBLlcLowcOVK9/7PPPhMUCoX6mDVr1ggeHh6CSqVSlykUCsHS0lLYt2+fIAiC4OzsLISHh6v3Z2RkCJ9//rnGqx/ff9Xj9evXBQDCgQMHsozz3WsUnz17pi57/fq1ULhwYeHkyZMadXv27Cl07txZEARBGDNmjFCpUiWN/aNHj87U1odcXV2FWbNmZbv/QwMGDBDat2+v/hwUFCQ4ODgIaWlp6rKFCxcK1tbWglKp1Cr2D6+5SpUqQkhIiNYxERkj9rCpQNu1axesra2RkZEBlUqFLl26ICQkRL2/SpUqGvetL168iFu3bqFIkSIa7bx+/Rq3b9/GixcvEBcXhzp16qj3FSpUCDVr1sw0LP5OdHQ0TE1Ns+xZZufWrVt4+fIlmjVrplGenp6O6tWrAwBiYmI04gCAunXran2O7MyfPx8rVqxAbGwsXr16hfT0dFSrVk2jjpeXFwoXLqxx3tTUVDx48ACpqamfjP1DgwcPRv/+/bF//340bdoU7du3R9WqVXN9LUQFCRM2FWi+vr5YuHAhzM3N4eLigkKFNH/yVlZWGp9TU1Ph7e2NtWvXZmrL0dExRzG8G+LWRWpqKgDgr7/+QokSJTT2yeXyHMWhjQ0bNmDkyJGYMWMG6tatiyJFimDatGn4559/tG4jJ7H36tULzZs3x19//YX9+/cjLCwMM2bMwKBBg3J+MUQFDBM2FWhWVlZwd3fXun6NGjWwceNGODk5wcbGJss6zs7O+Oeff9CwYUMAwJs3b3Du3DnUqFEjy/pVqlSBSqXCsWPH0LRp00z73/XwlUqluqxSpUqQy+WIjY3NtmdesWJF9QS6d06fPv3pi/yIEydOoF69evj+++/VZbdv385U7+LFi3j16pX6j5HTp0/D2toaJUuWhIODwydjz0rJkiXRr18/9OvXD2PGjMHSpUuZsInew1niRO/p2rUrihUrhjZt2uDvv//G3bt3cfToUQwePBgPHz4EAAwZMgRTp07Ftm3bcO3aNXz//fcffYbazc0NQUFB6NGjB7Zt26Zu848//gAAuLq6QiaTYdeuXUhISEBqaiqKFCmCkSNHYtiwYVi1ahVu376N8+fPY+7cuVi1ahUAoF+/frh58yZGjRqF69evY926dYiIiNDqOh89eoTo6GiN7dmzZyhXrhzOnj2Lffv24caNGxg7diyioqIyHZ+eno6ePXvi6tWr2L17N8aPH4+BAwfCxMREq9g/NHToUOzbtw93797F+fPnceTIEVSsWFGrayEyGmLfRCfSl/cnnemyPy4uTujWrZtQrFgxQS6XC2XKlBF69+4tvHjxQhCEt5PMhgwZItjY2Ah2dnbC8OHDhW7dumU76UwQBOHVq1fCsGHDBGdnZ8Hc3Fxwd3cXVqxYod4/YcIEoXjx4oJMJhOCgoIEQXg7UW727NmCh4eHYGZmJjg6OgrNmzcXjh07pj5u586dgru7uyCXy4UGDRoIK1as0GrSGYBM25o1a4TXr18LwcHBgq2trWBnZyf0799f+PHHHwUvL69M39u4ceOEokWLCtbW1kLv3r2F169fq+t8KvYPJ50NHDhQKFu2rCCXywVHR0fhu+++ExITE7O9BiJjJBOEbGbKEBERUb7BIXEiIiIJYMImIiKSACZsIiIiCWDCJiIikgAmbCIiIglgwiYiIpIAJmwiIiIJYMImIiKSACZsIiIiCWDCJiIikgAmbCIiIgn4P6CSMYXtAz1vAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### time interval based statistics\n"
      ],
      "metadata": {
        "id": "D3azRa8QrkYx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# plot daily average amount\n",
        "daily_average = df.groupby('day')['amount'].mean()\n",
        "x = range(31)\n",
        "\n",
        "# Create the plot\n",
        "plt.plot(x, daily_average)\n",
        "\n",
        "# Set the x-axis range from 0 to 30\n",
        "plt.xlim(0, 30)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 445
        },
        "id": "R7aAbleqrivW",
        "outputId": "1e3848ac-a58c-456c-de50-08c91c5d523e"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We may see from the above plot that a lot of abnormal transaction happened on the last day, which requires our attention. and the 2 and 12 day also requires attention.\n",
        "\n",
        "We can do a few similar analyses on other columns for further study.\n",
        "\n",
        "For weekly data, as we only have 5 weeks (0-4 indexed) on records, it's feasible to have a numerical description for analysis."
      ],
      "metadata": {
        "id": "rknKapWBs4_d"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cols = ['amount', 'oldbalanceOrg', 'newbalanceOrig',\n",
        "       'oldbalanceDest', 'newbalanceDest']\n",
        "df.groupby(\"week\")[cols].describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 318
        },
        "id": "QDN1XZ1As5sW",
        "outputId": "c9dd4bfe-f141-4202-9804-4255898b5b09"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           amount                                                            \\\n",
              "            count       mean        std  min       25%       50%        75%   \n",
              "week                                                                          \n",
              "0    1,930,180.00 160,323.93 266,919.80 0.06 13,049.50 77,864.54 215,805.31   \n",
              "1    2,854,592.00 192,782.82 809,928.83 0.01 13,043.51 73,327.99 204,182.46   \n",
              "2    1,279,258.00 182,682.67 438,823.28 0.03 15,142.20 75,310.83 210,198.46   \n",
              "3      232,129.00 168,538.69 436,830.92 0.26 11,402.43 68,795.35 199,962.50   \n",
              "4       66,445.00 177,614.92 522,208.81 0.36 13,464.88 74,678.58 207,442.83   \n",
              "\n",
              "                   oldbalanceOrg             ... oldbalanceDest  \\\n",
              "               max         count       mean  ...            75%   \n",
              "week                                         ...                  \n",
              "0    10,000,000.00  1,930,180.00 855,773.88  ...     945,658.22   \n",
              "1    92,445,516.64  2,854,592.00 830,641.59  ...     940,580.53   \n",
              "2    19,953,778.06  1,279,258.00 847,428.85  ...     960,460.94   \n",
              "3    15,415,938.31    232,129.00 688,025.28  ...     886,082.60   \n",
              "4    15,116,218.46     66,445.00 586,200.53  ...     824,138.00   \n",
              "\n",
              "                    newbalanceDest                                      \\\n",
              "                max          count         mean          std  min  25%   \n",
              "week                                                                     \n",
              "0     42,207,404.59   1,930,180.00 1,113,361.63 2,399,685.10 0.00 0.00   \n",
              "1    355,553,416.30   2,854,592.00 1,235,454.93 3,933,771.70 0.00 0.00   \n",
              "2    356,015,889.35   1,279,258.00 1,361,094.29 4,449,148.10 0.00 0.00   \n",
              "3    328,194,464.92     232,129.00 1,271,900.63 4,239,453.61 0.00 0.00   \n",
              "4    172,319,019.71      66,445.00 1,234,528.21 3,991,334.77 0.00 0.00   \n",
              "\n",
              "                                             \n",
              "            50%          75%            max  \n",
              "week                                         \n",
              "0    226,546.74 1,148,935.17  42,283,775.08  \n",
              "1    212,418.71 1,091,515.05 355,553,416.30  \n",
              "2    214,281.10 1,122,808.71 356,179,278.92  \n",
              "3    170,453.40 1,020,071.11 328,431,698.23  \n",
              "4    176,377.29   974,360.33 172,321,214.32  \n",
              "\n",
              "[5 rows x 40 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-34462060-8c65-479f-a8fe-d06e333ed602\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr th {\n",
              "        text-align: left;\n",
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              "\n",
              "    .dataframe thead tr:last-of-type th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th colspan=\"8\" halign=\"left\">amount</th>\n",
              "      <th colspan=\"2\" halign=\"left\">oldbalanceOrg</th>\n",
              "      <th>...</th>\n",
              "      <th colspan=\"2\" halign=\"left\">oldbalanceDest</th>\n",
              "      <th colspan=\"8\" halign=\"left\">newbalanceDest</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>...</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>week</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1,930,180.00</td>\n",
              "      <td>160,323.93</td>\n",
              "      <td>266,919.80</td>\n",
              "      <td>0.06</td>\n",
              "      <td>13,049.50</td>\n",
              "      <td>77,864.54</td>\n",
              "      <td>215,805.31</td>\n",
              "      <td>10,000,000.00</td>\n",
              "      <td>1,930,180.00</td>\n",
              "      <td>855,773.88</td>\n",
              "      <td>...</td>\n",
              "      <td>945,658.22</td>\n",
              "      <td>42,207,404.59</td>\n",
              "      <td>1,930,180.00</td>\n",
              "      <td>1,113,361.63</td>\n",
              "      <td>2,399,685.10</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>226,546.74</td>\n",
              "      <td>1,148,935.17</td>\n",
              "      <td>42,283,775.08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2,854,592.00</td>\n",
              "      <td>192,782.82</td>\n",
              "      <td>809,928.83</td>\n",
              "      <td>0.01</td>\n",
              "      <td>13,043.51</td>\n",
              "      <td>73,327.99</td>\n",
              "      <td>204,182.46</td>\n",
              "      <td>92,445,516.64</td>\n",
              "      <td>2,854,592.00</td>\n",
              "      <td>830,641.59</td>\n",
              "      <td>...</td>\n",
              "      <td>940,580.53</td>\n",
              "      <td>355,553,416.30</td>\n",
              "      <td>2,854,592.00</td>\n",
              "      <td>1,235,454.93</td>\n",
              "      <td>3,933,771.70</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>212,418.71</td>\n",
              "      <td>1,091,515.05</td>\n",
              "      <td>355,553,416.30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1,279,258.00</td>\n",
              "      <td>182,682.67</td>\n",
              "      <td>438,823.28</td>\n",
              "      <td>0.03</td>\n",
              "      <td>15,142.20</td>\n",
              "      <td>75,310.83</td>\n",
              "      <td>210,198.46</td>\n",
              "      <td>19,953,778.06</td>\n",
              "      <td>1,279,258.00</td>\n",
              "      <td>847,428.85</td>\n",
              "      <td>...</td>\n",
              "      <td>960,460.94</td>\n",
              "      <td>356,015,889.35</td>\n",
              "      <td>1,279,258.00</td>\n",
              "      <td>1,361,094.29</td>\n",
              "      <td>4,449,148.10</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>214,281.10</td>\n",
              "      <td>1,122,808.71</td>\n",
              "      <td>356,179,278.92</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>232,129.00</td>\n",
              "      <td>168,538.69</td>\n",
              "      <td>436,830.92</td>\n",
              "      <td>0.26</td>\n",
              "      <td>11,402.43</td>\n",
              "      <td>68,795.35</td>\n",
              "      <td>199,962.50</td>\n",
              "      <td>15,415,938.31</td>\n",
              "      <td>232,129.00</td>\n",
              "      <td>688,025.28</td>\n",
              "      <td>...</td>\n",
              "      <td>886,082.60</td>\n",
              "      <td>328,194,464.92</td>\n",
              "      <td>232,129.00</td>\n",
              "      <td>1,271,900.63</td>\n",
              "      <td>4,239,453.61</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>170,453.40</td>\n",
              "      <td>1,020,071.11</td>\n",
              "      <td>328,431,698.23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>66,445.00</td>\n",
              "      <td>177,614.92</td>\n",
              "      <td>522,208.81</td>\n",
              "      <td>0.36</td>\n",
              "      <td>13,464.88</td>\n",
              "      <td>74,678.58</td>\n",
              "      <td>207,442.83</td>\n",
              "      <td>15,116,218.46</td>\n",
              "      <td>66,445.00</td>\n",
              "      <td>586,200.53</td>\n",
              "      <td>...</td>\n",
              "      <td>824,138.00</td>\n",
              "      <td>172,319,019.71</td>\n",
              "      <td>66,445.00</td>\n",
              "      <td>1,234,528.21</td>\n",
              "      <td>3,991,334.77</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>176,377.29</td>\n",
              "      <td>974,360.33</td>\n",
              "      <td>172,321,214.32</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 40 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-34462060-8c65-479f-a8fe-d06e333ed602')\"\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",
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              "  </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-34462060-8c65-479f-a8fe-d06e333ed602 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-34462060-8c65-479f-a8fe-d06e333ed602');\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-5d362e5d-e791-43ac-b1e4-a30a8009ff90\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-5d362e5d-e791-43ac-b1e4-a30a8009ff90')\"\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-5d362e5d-e791-43ac-b1e4-a30a8009ff90 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe"
            }
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    }
  ]
}