analysis.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/maxwn04/USACO/blob/master/analysis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ocdCoe-IS4og",
        "outputId": "931a28ab-6285-4594-9b5a-4a0be2f01a58"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import statsmodels as sm\n",
        "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
        "from sklearn.model_selection import train_test_split,GridSearchCV,RandomizedSearchCV\n",
        "from sklearn.linear_model import LinearRegression,Ridge,Lasso\n",
        "from sklearn.tree import DecisionTreeRegressor\n",
        "from sklearn.ensemble import RandomForestRegressor,GradientBoostingRegressor\n",
        "from sklearn.metrics import r2_score,mean_squared_error\n",
        "from sklearn import preprocessing"
      ],
      "metadata": {
        "id": "KtDATDAuWFQQ"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hc13n4PjSxwf",
        "outputId": "4c1e459e-0d23-4438-bd54-af192382c446"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Index(['Time', 'V1', 'V2', 'V3', 'V4', 'V5', 'V6', 'V7', 'V8', 'V9', 'V10',\n",
            "       'V11', 'V12', 'V13', 'V14', 'V15', 'V16', 'V17', 'V18', 'V19', 'V20',\n",
            "       'V21', 'V22', 'V23', 'V24', 'V25', 'V26', 'V27', 'V28', 'Amount',\n",
            "       'Fraud'],\n",
            "      dtype='object')\n"
          ]
        }
      ],
      "source": [
        "df = pd.read_csv('/content/drive/MyDrive/Colab Notebooks/creditcard.csv')\n",
        "df = df.rename(columns={'Class': 'Fraud'})\n",
        "\n",
        "print(df.columns)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "pmwM3zaySxwt",
        "outputId": "fb90bd3a-0406-4f7c-e4cf-da3115151b52"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "        Time        V1        V2        V3        V4        V5        V6  \\\n",
              "541    406.0 -2.312227  1.951992 -1.609851  3.997906 -0.522188 -1.426545   \n",
              "623    472.0 -3.043541 -3.157307  1.088463  2.288644  1.359805 -1.064823   \n",
              "4920  4462.0 -2.303350  1.759247 -0.359745  2.330243 -0.821628 -0.075788   \n",
              "6108  6986.0 -4.397974  1.358367 -2.592844  2.679787 -1.128131 -1.706536   \n",
              "6329  7519.0  1.234235  3.019740 -4.304597  4.732795  3.624201 -1.357746   \n",
              "\n",
              "            V7        V8        V9  ...       V21       V22       V23  \\\n",
              "541  -2.537387  1.391657 -2.770089  ...  0.517232 -0.035049 -0.465211   \n",
              "623   0.325574 -0.067794 -0.270953  ...  0.661696  0.435477  1.375966   \n",
              "4920  0.562320 -0.399147 -0.238253  ... -0.294166 -0.932391  0.172726   \n",
              "6108 -3.496197 -0.248778 -0.247768  ...  0.573574  0.176968 -0.436207   \n",
              "6329  1.713445 -0.496358 -1.282858  ... -0.379068 -0.704181 -0.656805   \n",
              "\n",
              "           V24       V25       V26       V27       V28  Amount  Fraud  \n",
              "541   0.320198  0.044519  0.177840  0.261145 -0.143276    0.00      1  \n",
              "623  -0.293803  0.279798 -0.145362 -0.252773  0.035764  529.00      1  \n",
              "4920 -0.087330 -0.156114 -0.542628  0.039566 -0.153029  239.93      1  \n",
              "6108 -0.053502  0.252405 -0.657488 -0.827136  0.849573   59.00      1  \n",
              "6329 -1.632653  1.488901  0.566797 -0.010016  0.146793    1.00      1  \n",
              "\n",
              "[5 rows x 31 columns]"
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              "      <th>Time</th>\n",
              "      <th>V1</th>\n",
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              "      <th>V23</th>\n",
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              "      <th>541</th>\n",
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              "      <td>-0.932391</td>\n",
              "      <td>0.172726</td>\n",
              "      <td>-0.087330</td>\n",
              "      <td>-0.156114</td>\n",
              "      <td>-0.542628</td>\n",
              "      <td>0.039566</td>\n",
              "      <td>-0.153029</td>\n",
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              "<p>5 rows × 31 columns</p>\n",
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              "\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",
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            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "fraud = df.loc[df['Fraud'] == 1]\n",
        "fraud.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "62tmb7pUSxww",
        "outputId": "37d6902e-4c6c-4e3c-db83-1c0a98424b29"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 284807 entries, 0 to 284806\n",
            "Data columns (total 31 columns):\n",
            " #   Column  Non-Null Count   Dtype  \n",
            "---  ------  --------------   -----  \n",
            " 0   Time    284807 non-null  float64\n",
            " 1   V1      284807 non-null  float64\n",
            " 2   V2      284807 non-null  float64\n",
            " 3   V3      284807 non-null  float64\n",
            " 4   V4      284807 non-null  float64\n",
            " 5   V5      284807 non-null  float64\n",
            " 6   V6      284807 non-null  float64\n",
            " 7   V7      284807 non-null  float64\n",
            " 8   V8      284807 non-null  float64\n",
            " 9   V9      284807 non-null  float64\n",
            " 10  V10     284807 non-null  float64\n",
            " 11  V11     284807 non-null  float64\n",
            " 12  V12     284807 non-null  float64\n",
            " 13  V13     284807 non-null  float64\n",
            " 14  V14     284807 non-null  float64\n",
            " 15  V15     284807 non-null  float64\n",
            " 16  V16     284807 non-null  float64\n",
            " 17  V17     284807 non-null  float64\n",
            " 18  V18     284807 non-null  float64\n",
            " 19  V19     284807 non-null  float64\n",
            " 20  V20     284807 non-null  float64\n",
            " 21  V21     284807 non-null  float64\n",
            " 22  V22     284807 non-null  float64\n",
            " 23  V23     284807 non-null  float64\n",
            " 24  V24     284807 non-null  float64\n",
            " 25  V25     284807 non-null  float64\n",
            " 26  V26     284807 non-null  float64\n",
            " 27  V27     284807 non-null  float64\n",
            " 28  V28     284807 non-null  float64\n",
            " 29  Amount  284807 non-null  float64\n",
            " 30  Fraud   284807 non-null  int64  \n",
            "dtypes: float64(30), int64(1)\n",
            "memory usage: 67.4 MB\n"
          ]
        }
      ],
      "source": [
        "df.info()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Skewness: %f\" %df['Amount'].skew())\n",
        "print(\"Kurtosis: %f\" %df['Amount'].kurt())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qvxXULpsTQxg",
        "outputId": "ee80a8d8-8d42-4357-e1cd-c0d44b12f12e"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Skewness: 16.977724\n",
            "Kurtosis: 845.092646\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Correlation matrix\n",
        "corrmat = df.corr()\n",
        "fig = plt.figure(figsize = (12, 9))\n",
        "sns.heatmap(corrmat, vmax = .8, square = True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 577
        },
        "id": "J95wq-nBXK6h",
        "outputId": "0b24de6a-a267-46de-96b8-88181fda9fbb"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x648 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.set(rc={'figure.figsize':(25,5)})\n",
        "df['Fraud'] = df['Fraud'].astype(str)\n",
        "\n",
        "sns.boxplot(data = df, x = \"Amount\", y = \"Fraud\", showfliers = True)\n",
        "#sns.boxplot(data = fraud, x = \"Amount\", showfliers = False)\n",
        "\n",
        "#sns.boxplot(fraud[\"Amount\"], x = \"Amount\", showfliers = False)\n",
        "\n",
        "#sns.boxplot(data=df[[\"Amount\"]], x=\"Amount\", hue=\"Class\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "ST7MBW29V4UC",
        "outputId": "b3832dde-0ef6-4330-beec-60de661e3a39"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc080319290>"
            ]
          },
          "metadata": {},
          "execution_count": 27
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(data = df, x = \"V2\", y = \"Fraud\", showfliers = True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "b1Z4Ha25lxSN",
        "outputId": "428cdd9d-39d0-469e-ec83-a0b7e0fc94bb"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc08013d350>"
            ]
          },
          "metadata": {},
          "execution_count": 28
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x360 with 1 Axes>"
            ],
            "image/png": 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/2aqP/v0H/PE5ua37+MMfXi4YRrfUU2gu+XOqrq6O9evrCga5+3oOFto3H/7wMR3Wsb9qasZ0OIf2UgWvh/nHZ/+kon///sJoICIi/u3fvpZo//PnXxWvv7W5W/qqSB8WR4wdnnjNER7NcTAq5Z6yo+VPPPFY3s/w9j+nu1tv+H2jszo6ui8uVm9vmRf7p0e+ET169Og//kK4908bmpub45133onRo0f3xPAHpOnTz4qKig/+pe3TT/9kmSuiJ/TW495b66L8yvnZ6GzsnqhtX8boDefT/tYwe/acdstK7efss8/e57Hb1n3xxf9UsI7Zs+cWHKPQvIvti67up/z2+fUUGj+dTkdlZbrd6xbpdLpL46XT6UinK9u9znfRRXNLrjWi/XHO375QLfljttTf0T7c2771nC+++J8K9lHo85ZfT6G55M9p9uy57fZv/vp9OQcL7ZtideyPysp0zJ49p8M5tFXos9P2+KRS+15nOp2Oiy++ZJ+3B4ADQUVFZZv3xe8pO1o+ffpZrX6Gl3KPt796w+8bndXR0X1xsXp7y7zYPz0SRA8bNiyOOeaYePDBByMi4sEHH4xjjjnG86GLqKoaEhMmTI5UKhUTJkyKwYOryl0SPaC3HvfeWhflV87PRmdj90Rt+zJGbzif9reGI444Mvdt1P79B3Spn6FDh+7z2G3rPvbY/1WwjiOOGFdwjELzLrYvurqf8tvX1IwpOv7EiZNj4sQPXk+adFKrviZOPKlL402YMDkmTjyp3ev+/QdExN5v1I4dO67kWiNaH+e22xeqZeLED+YwYcLkovu3bfuamjFx7LH/q2Af+XXkt2+pp9Bc8ud0xBHjYuLEye22359zsNC+aVtH2/FatByT/PeFtmkxadJJccQRR7abQ/488redOHFyweth/r496aRTWv1SnL99Tc2YovVOnDg5jj32L1qdey0Kh+XtQ++WujvSsn7gwIEdtulObed4sIwFSbvjjnvLXUKvZd+UV6GfRx1ff1Nt/ru37eTJHy/pnq6Ue838n+GFfk53t97w+0ZndXR0X1ys3t4yL/ZP5de//vWv98RAH/3oR+O73/1u3H777fGHP/whvvWtb8WQIUNK3n7nzj0F/+Gdg9m4cUfG6tVvxDnnfC4OO+ywcpdDD+mtx71YXQMG9IsdO/aUqTLKrZyf2c7G7ona9mWM3nCe728NRx/9Z/Hb3/465sz5UmzevKnkfgYM6BfDh9fs89ht626p44tfbF1HR/MrtLzYvujqfmpp/7nPfT7q6tYVHf/P/uwjrV6/8spLsWvXzvjQh8bGeedd2KXxCvW3evUb8X/+z+di1apnY968y9rdrH9Q6+yoq3u74Bxb9m+h7QvV8tprr8bQoUPj3HNndbp/x407Ml555aXYvXtXzJv3rzF4cFWHfRx99J/Fb36zPIYPHxFNTU259h3Npe3+b+l30KDDY9iw4TFr1kUFj09XFNo3+cf/zTff+OPxPCIuvPDiWL36jdi9e1d88YuXxsaN62PAgAGxZ8+e+OIXL43NmzfF5z43O2pr18agQYfHwIGDYs+e3TFmzNj43OcubDeHqqqqGD78g3m0bDtkyNCYOXNWh9fD/H17xBHj4tlnn4pRo0bHRRddkqtv3rx/jf/9v4+P3/xmeYwZMzbOO++C+N3vnolRo0bF8OEjc/3nn3sbN26IoUOHxj/+42dj5cqno0+fPjFyZHU0NTXFZz87M154YVX06dMnamrGxPDhI2PWrC9EXd3bMXnyKfHyyy/GtGlnRm3tmqiuHhUjRnyw/uKLvxArVqyIIUOGxY4d22P06JoYNGhQbN/eGOl0n6isrIyKiorIZDLRp0+fOPzwwbFr16749Kc/G9u3N8b27Y2RyWRi5MjqGDTo8Ni+fXuMGDEympqaoqKiItLp9B/neGGsXPl0VFRUREVFZWQymYiIGDmyOvbseT8ymeYYObI6du/eHZlMJiorKyOdTuf6fv/99yOb3fsnxRUVlbk/v06lUpFKpaKycu+yvn37xZw586K2dk289962Vsdn+PARf3w0TcSQIUNi165duXWHHz44stmWx+5kc/t3586dJT2upaKiomi7tv9ToLP20OKMMz6V+BjLly+NLdt2RJ+qozps8/7W1RERRdu0tBs6uH9MmND+f+wl4ec//389Ms6hrOX6mu+006bGKad8Ip599qncsr59+8acOZdGbe2aaGx8L0aNGh3Dho2IYcOGx7RpZ8Tzz6+KmTNnxebNm6Kx8b340pf+JcaP/+ui90ldvdd87bVXi/6c7m694feNzuro6D62WL37Oy/ZRc/Y+4WYvoXXZQ+Qu4x3321M5F8iB/bfiBGDYtOm98pdBnAAcL0ASuV6Ab3P/PlXRUTyz4bOH+/1tzZH/3GndNhmx9rHIiKKtmlpd3QPPSM6ouf3FdA59xY9o6IiFcOGFf7Lth55NAcAAAAAAIcuQTQAAAAAAIkSRAMAAAAAkChBNAAAAAAAiRJEAwAAAACQKEE0AAAAAACJEkQDAAAAAJAoQTQAAAAAAIkSRAMAAAAAkChBNAAAAAAAiRJEAwAAAACQKEE0AAAAAACJEkQDAAAAAJAoQTQAAAAAAIkSRAMAAAAAkChBNAAAAAAAiRJEAwAAAACQKEE0AAAAAACJEkQDAAAAAJAoQTQAAAAAAIkSRAMAAAAAkChBNAAAAAAAiUqXuwAAAACg95swYXK5Szhg2FcA7QmiAQAAgE6deOKkcpdwwLCvANrzaA4AAAAAABIliAYAAAAAIFGCaAAAAAAAEiWIBgAAAAAgUYJoAAAAAAASJYgGAAAAACBRgmgAAAAAABIliAYAAAAAIFGCaAAAAAAAEiWIBgAAAAAgUYJoAAAAAAASJYgGAAAAACBRgmgAAAAAABIliAYAAAAAIFGCaAAAAAAAEiWIBgAAAAAgUYJoAAAAAAASJYgGAAAAACBRgmgAAAAAABIliAYAAAAAIFGCaAAAAAAAEiWIBgAAAAAgUYJoAAAAAAASJYgGAAAAACBR6XIXAAAAAFBI866G2LH2saLrI6Jomw/aDe/O0gDoIkE0AAAA0OuMHTuu0zaNjX2juTkTgwdXddJyeEn9AZCcokH0DTfcUFInX/rSl7qlGAAAAICIiBkzZnbaZsSIQbFp03s9UA0A+6toEL1hw4bc6927d8cjjzwSxx13XIwZMybq6urihRdeiE984hOJFwkAAAAAwIGraBB9zTXX5F5feuml8Z3vfCemTJmSW/bII4/Eww8/nFx1AAAAAAAc8CpKbbh06dI49dRTWy07+eSTY8mSJd1eFAAAAAAAB4+Sg+hx48bFPffc02rZD3/4wzjiiCO6vSgAAAAAAA4eRR/Nke/qq6+OuXPnxm233RbV1dWxcePGSKfTceONNyZZHwAAAAAAB7iSg+g///M/j8WLF8eqVati06ZNMWLEiPjYxz4Wffr0SbI+AAAAAAAOcCUH0RERffr0ieOPPz6pWgAAAAAAOAiVHERPnjw5UqlUwXVPPPFEd9UDAAAAAMBBpuQg+rrrrmv1ftOmTXH33XfHP/zDP3R7UQAAAAAAHDxKDqL/+q//uuCyCy+8MM4777xuLQoAAAAAgINHxf5s3Ldv33j77be7qxYAAAAAAA5CJX8j+oYbbmj1fteuXbFkyZKYNGlStxcFAAAAAMDBo+QgesOGDa3e/8mf/Emcf/75ccYZZ3R7UQAAAAAAHDxKDqKvueaaJOsAAAAAAOAgVXIQHRGxZ8+eWL16ddTX10c2m80tP+GEE7q9MAAAAAAADg4lB9HPPPNMzJs3L/bs2RONjY0xcODA2L59e4waNSoee+yxJGsEAAAAAOAAVlFqw2uuuSYuvPDCeOqpp2LAgAHx1FNPxcUXXxwzZsxIsj4AAAAAAA5wJQfRa9asiZkzZ7ZaNnv27Ljrrru6uyYAAAAAAA4iJQfRgwYNisbGxoiIGDFiRLz++uuxbdu22LFjR2LFAQAAAABw4Cv5GdGnnXZaLFmyJKZPnx6f+tSnYubMmZFOp2PKlClJ1gcAAAAAwAGu5CD63//933OvL7jggvjoRz8a27dvj4kTJyZSGAAAAAAAB4eSHs3R3Nwcp556auzZsye3bPz48TF58uSoqCj56R4AAAAAAByCSkqRKysro7KyMnbv3p10PQAAAAAAHGRKfjTHzJkzY968eXHRRRfFqFGjIpVK5daNHTs2keIAAAAAADjwdRpEb9q0KUaMGBFXXXVVRET8+te/jmw2m1ufSqXi5ZdfTq5CAAAAgG527713x1tvre3WPrdubYiIiMGDq7q131KMHTsuZsyY2ePjApSq0yB6ypQpsXLlynjllVciImLOnDlx8803J14YAAAAQFLeemttrHn9lRg1sOQ/Fu9UfWNTRET027m52/osxYY/jgvQm3V6tc3/9nNExNNPP51YMQAAAAA9ZdTAdJz/F0O7rb87n98SEdGtfXZlXIDerNN/rDD/WdAR7YNpAAAAAAAoptNvRDc3N8dvfvObXADd9n1ExAknnJBchQAAAAAAHNA6DaKHDRsWX/3qV3Pvq6qqWr1PpVLx2GOPJVMdAAAAAAAHvE6D6F/96lc9UQcAAAAAAAepTp8RDQAAAAAA+0MQDQAAAABAogTRAAAAAAAkShANAAAAAECiBNEAAAAAACRKEA0AAAAAQKIE0QAAAAAAJEoQDQAAAABAogTRAAAAAAAkShANAAAAAECiBNEAAAAAACRKEA0AAAAAQKIE0QAAAAAAJEoQDQAAAABAogTRAAAAAAAkShANAAAAAECiBNEAAAAAACRKEA0AAAAAQKIE0QAAAAAAJEoQDQAAAABAogTRAAAAcJBbvnxpLF++tNxlQFk5D6C80uUuAAAAAEjWk08uiYiIE0+cVOZKoHycB1BevhENAAAAAECiBNEAAAAAACRKEA0AAAAAQKIE0QAAAAAAJEoQDQAAAABAogTRAAAAAAAkShANAAAAAECiBNEAAAAAACRKEA0AAAAAQKIE0QAAAAAAJEoQDQAAAABAogTRAAAAAAAkShANAAAAAECiBNEAAAAAACRKEA0AAAAAQKIE0QAAAAAAJEoQDQAAAABAogTRAAAAAAAkShANAAAAAECiBNEAAAAAACRKEA0AAAAAQKIE0QAAAAAAJEoQDQAAwCGpoaE+rr32m7F1a8M+ta+tXRNz5lwQb721tst9dUetSY8JB7tSz6GGhvq4+uor4lvfuiJqa9e02ia/j5Z2V199RdTWrs29/slPfhSzZs2If/mXS+Lqq68ouG2hvjpa19H7lhqLzadlu/z6Sh3/q1/955g1a0Y8/fRvWl3/yqVc18DOxnVt7liPBNHz58+Pk08+OT784Q/Hq6++2hNDAgAAQFGLFt0fr732h1i48Gf71H7Bgptj586dceutN3W5r+6oNekx4WBX6jm0aNH98eabr8cbb7weCxbc3Gqb/D5a2r355uuxYMFNudcPPfTziIjYsuXdePPN1wtuW6ivjtZ19L6lxmLzadkuv75Sx9+wYX1ERCxY8N+trn/lUq5rYGfjujZ3rEeC6FNOOSXuueeeGDNmTE8MBwAAAEU1NNTHk08uiWw2G08+ubSkb0Tmt3/xxeejrm5dRETU1a2LpUsfL7mv7qi1q/UDrZV6DjU01MeyZU/k3tfVrcttU1u7NtfHsmVL2rXryLJlS1ptW7ivwus6ep8/9pNPLik4n/w559fXtp5C4z/xxGO59s3NTa2uf+X4VnS5roGdjevaXFy6JwYZP358TwwDAAAAJVm06P7IZLIREZHJZGLhwp/FuefOKrn99753Y6v1zc3NJffVHbXufV16/Vu3NsTWrVtj/vyruq2u3qBPn8p4//3mfdq2tnZt9I9MN1dUHo17MvFO7dqD7vh2t9ratTF48OCIKP0asGjR/dHU1P4zlslkYsGCm3J9NDU1RUS2pDqamppabVuor1Sq8Dgdvc+vsampqeB88udcrJ5C42ezHc/t1ltviquvvq6kuXeXrl7De2rcctV1oPCMaAAAAA45K1Ysj+bmpojY++2+FSuWd6n9jh3bC7Yrpa/uqLWr9QOtlXoO7V3ePoRt+VZwSx+lhtAtbfO3LdRXS9TeA0oAAA4GSURBVPDbdl1H7/PHz2azBeeTP+di9RQav5jO1iehXNfAzsZ1bS6uR74RDQAAAL3JCSecGEuXPhHNzU1RWZmOE044sUvt+/XrVzCMLqWv7qq1K/UPHlwVgwdXxb/929e6tbZyGzFiUGza9N4+bTt//lWxa8Pr3VxReQzsWxHDR4076I5vd8v/xnip14ATTjgxHn/8sWgbNFdWpqO6ujo2btz4x+Ax1a5Nx1JRU1OT27ZQX6nU3kC57bqO3tfV1eXGT6VSBeeTP+di9RQav1jYXFPT84/i7eo1vKfGLVddBwrfiAYAAOCQM336WVFRsfdvzysqKuL00z/ZpfYXX3xJq/WVlZUl99UdtXa1fqC1Us+h6dPPinS6st3yioqKmD17bq6PdDod6XRp3/dMp9Otti3UV2VluuC6jt7n15hOpwvOJ3/OxeopNH4q1X67FhddNLekeXencl0DOxvXtbk4QTQAAACHnKqqITFhwuRIpVIxYcKkGDy4qkvtjz32L3LfAqypGROTJn285L66o9au1g+0Vuo5VFU1JCZOPCn3vqZmTG6bI44Yl+tj4sTJ7dp1ZOLEya22LdxX4XUdvc8fe8KEyQXnkz/n/Pra1lNo/JNOOiXXvrIy3er6N3bsuM52d7cr1zWws3Fdm4vrkUdzXH311fHII4/E5s2b4/zzz4+qqqr4xS9+0RNDAwAAQEHTp58V69a9XfI31tq2nz17Tsyff1VcdNHcGDTo8C711R21drV+oLVSz6Hp08+KtWvXRCoVce65s+Lee+/ObZPfRzabjbVr10RExMyZF8Tdd98eEREf/vCx8dBDP4+hQ4dFVdWQgtsW6qujdR29b6mx2Hxatpsx47xcfaWO//LLL8aGDetj9uwvRnX1qNz1r1zKdQ3sbFzX5o6lssX+2cte5N13Gwv+y55A+e3Pc9mAQ4vrBVAq1wvoXi3Pxj3YniHcHc+IPv8vhnZbPXc+vyUiolv7LHXcw0YdfdAd3+52sJ4HlMa9Rc+oqEjFsGEDC6/r4VoAAAAAADjECKIBAAAAAEiUIBoAAAAAgEQJogEAAAAASJQgGgAAAACARAmiAQAAAABIlCAaAAAAAIBECaIBAAAAAEiUIBoAAAAAgEQJogEAAAAASJQgGgAAAACARAmiAQAAAABIlCAaAAAAAIBECaIBAAAAAEiUIBoAAAAAgEQJogEAAAAASJQgGgAAAACARAmiAQAAAABIlCAaAAAAAIBECaIBAAAAAEiUIBoAAAAAgEQJogEAAAAASFS63AUAAAAAyZowYXK5S4Cycx5AeQmiAQAA4CB34omTyl0ClJ3zAMrLozkAAAAAAEiUIBoAAAAAgEQJogEAAAAASJQgGgAAAACARAmiAQAAAABIlCAaAAAAAIBECaIBAAAAAEiUIBoAAAAAgEQJogEAAAAASJQgGgAAAACARAmiAQAAAABIlCAaAAAAAIBECaIBAAAAAEiUIBoAAAAAgEQJogEAAAAASJQgGgAAAACARAmiAQAAAABIlCAaAAAAAIBECaIBAAAAAEiUIBoAAAAAgEQJogEAAAAASJQgGgAAAACARAmiAQAAAABIlCAaAAAAAIBEpctdAAAAAEA5bGhsijuf39Kt/UVEt/ZZ6rhH9uiIAF0niAYAAAAOOWPHjuv2PodsbYiIiMMGV3V738UcGcnMB6A7CaIBAACAQ86MGTPLXQLAIcUzogEAAAAASJQgGgAAAACARAmiAQAAAABIlCAaAAAAAIBECaIBAAAAAEiUIBoAAAAAgEQJogEAAAAASJQgGgAAAACARAmiAQAAAABIlCAaAAAAAIBEpctdQKkqKlLlLgEowjkKlMr1AiiV6wVQCtcKoFSuF8krto9T2Ww224O1AAAAAABwiPFoDgAAAAAAEiWIBgAAAAAgUYJoAAAAAAASJYgGAAAAACBRgmgAAAAAABIliAYAAAAAIFGCaAAAAAAAEiWIBgAAAAAgUYJoAAAAAAASJYgG9sv3v//9mDp1akyfPj3OOOOM3PKdO3fGvHnz4rTTToupU6fG448/XsYqgd7gt7/9bRxzzDHxgx/8ILds8+bNMWvWrJgyZUqcfvrp8dxzz5WxQqDcvvGNb8TUqVPj9NNPj7PPPjteeOGF3DrXCyDf6tWr4zOf+UxMmTIlPvOZz8SaNWvKXRLQS9TX18fnP//5mDJlSkyfPj3mzp0bW7ZsiYiIVatWxemnnx5TpkyJWbNmxbvvvlvmag8tgmhgnz3yyCPx8MMPx09/+tNYtGhR3H777bl1t99+ewwcODAeffTRuOWWW+I//uM/Yvv27WWsFiinxsbG+Pa3vx2TJk1qtfw73/lOjB8/PhYvXhxXXHFFXHbZZZHNZstUJVBukyZNikWLFsXChQvjoosuiksvvTS3zvUCyHfllVfGjBkzYvHixTFjxoy44ooryl0S0EukUqm48MILY/HixbFo0aIYO3ZsfPvb345MJhOXXXZZXHHFFbF48eIYP358fPvb3y53uYcUQTSwz+64446YO3duDBw4MCIihg8fnlv30EMPxWc+85mIiDjyyCPjuOOOi6VLl5alTqD8rr322rjgggtiyJAhrZY//PDDcfbZZ0dExPjx46Nv376tvgEJHFo+/vGPR58+fSIi4mMf+1hs2LAhMplMRLheAB94991346WXXopp06ZFRMS0adPipZdeyn3jETi0VVVVxd/8zd/k3n/sYx+Lurq6+P3vfx/9+vWL8ePHR0TE2WefHQ8//HC5yjwkCaKBffbGG2/Ec889F2effXZ88pOfjB//+Me5dXV1dTFmzJjc+9GjR8eGDRvKUSZQZkuWLIn33nsvpk6d2mp5fX19ZLPZGDp0aG6ZawXQ4p577omTTjopKioqXC+AVtavXx/V1dVRWVkZERGVlZUxcuTIWL9+fZkrA3qbTCYTP/zhD+Pkk0+O9evXR01NTW7d0KFDI5PJRENDQxkrPLSky10A0HudddZZUVdXV3Ddr3/962hubo7169fHvffeG/X19fHZz342/vRP/zSOP/74Hq4UKKdi14qHH344vvOd78Sdd97Zw1UBvVFn9xYtodIvfvGLWLRoUdxzzz09WR4AcJC56qqron///nHOOefEo48+Wu5yDnmCaKBD999/f9H1NTU1MW3atKioqIhhw4bF3/3d38Xzzz8fxx9/fNTU1MS6dety31xav359qz+NAQ4exa4VzzzzTGzatCk+/elPR8Teb0E//vjj0dDQEHPnzo2IiC1btrS6VowaNSr5ooGy6OzeIiLi0Ucfjeuvvz7uuuuu3GO/Wh7r43oBROz9i4iNGzdGc3NzVFZWRnNzc7zzzjsxevTocpcG9CLz58+PtWvXxi233BIVFRUxevToVv9DfMuWLVFRURFVVVVlrPLQ4tEcwD6bNm1aLFu2LCIiduzYEc8++2x85CMfiYiIqVOnxo9+9KOIiFizZk288MILMXHixLLVCpTH+PHjY8WKFfGrX/0qfvWrX8WUKVPikksuyYXQU6dOjfvuuy8i9obWu3btiuOOO66cJQNl9Pjjj8c111wTt99+e3zoQx9qtc71AmgxbNiwOOaYY+LBBx+MiIgHH3wwjjnmmFaP7wEObd/97nfj97//fdx8883Rt2/fiIg47rjjYteuXfHMM89ERMR9993X7vGBJCuV9U9NA/to165d8bWvfS1eeumliIg444wzYvbs2RGxN5i+/PLL4+WXX46Kioq47LLL4tRTTy1nuUAvcPnll8dxxx0X55xzTkREbNq0KS677LKoq6uLfv36xTe+8Y34q7/6qzJXCZTL3/7t30afPn1ahUl33XVXDBkyxPUCaOWNN96Iyy+/PLZt2xaHH354zJ8/P4466qhylwX0Aq+99lpMmzYtjjzyyDjssMMiIuJDH/pQ3HzzzbFy5cq48sorY/fu3TFmzJi47rrrcn+BRfIE0QAAAAAAJMqjOQAAAAAASJQgGgAAAACARAmiAQAAAABIlCAaAAAAAIBECaIBAAAAAEiUIBoAAAAAgEQJogEAIAEXXHBB3HDDDe2W//KXv4wTTzwxbrvttpg2bVr85V/+ZZx88slx2223laFKAADoGYJoAABIwFlnnRULFy6MbDbbavnChQtj+vTpkc1mY/78+fH000/HbbfdFvfcc0/84he/KFO1AACQLEE0AAAk4NRTT42GhoZ45plncsu2bt0ajz/+eJx55pnx+c9/Po499thIp9Nx1FFHxSmnnBIrV64sY8UAAJAcQTQAACTgsMMOi7//+7+PBx54ILfsoYceiqOOOio+8pGPtGqbzWbjmWeeiaOPPrqnywQAgB4hiAYAgISceeaZsXjx4ti9e3dERDzwwANx1llntWt34403RiaTiU996lM9XSIAAPQIQTQAACRk/PjxMWTIkPjlL38ZtbW18cILL8S0adNatfnBD34QDzzwQCxYsCD69u1bpkoBACBZ6XIXAAAAB7MzzjgjHnjggVi9enVMmDAhhg8fnlv305/+NBYsWBD33HNPjBo1qoxVAgBAsnwjGgAAEnTmmWfGihUr4sc//nGceeaZueULFy6M66+/Pu68884YO3ZsGSsEAIDkpbLZbLbcRQAAwMHs3HPPjVdeeSWWL1+ee/zGySefHBs3bmz1OI7p06fHN7/5zXKVCQAAiRFEAwAAAACQKI/mAAAAAAAgUYJoAAAAAAASJYgGAAAAACBRgmgAAAAAABIliAYAAAAAIFGCaAAAAAAAEiWIBgAAAAAgUYJoAAAAAAASJYgGAAAAACBR/x8vM958GV65uAAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(data = df, x = \"V5\", y = \"Fraud\", showfliers = True)"
      ],
      "metadata": {
        "id": "VXTWgWdJoDcq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "outputId": "295fb117-3dae-4805-a65e-b26938f6abfd"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc08015b550>"
            ]
          },
          "metadata": {},
          "execution_count": 29
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x360 with 1 Axes>"
            ],
            "image/png": 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525bH07K/6urqZvW1pbKyxz7KCOhD2ps/21NdXZ3q6va3qa6uLv1lf1suvXRGq/k/aT3Xn3feec1qa5wH25ofd+X8t+M4qpr13d65p2W9jXV15hzaVm27e/3QdPu2dOU56Uwfnamxs+27+5pmb9EjZ/jhw4fnyCOPzMyZM5MkM2fOzJFHHplhw4b1RPdlaciQoZkw4cRUVFRkwoRJOeCAIb1dEgDAXmN3rsXK8Tqup2pur59d7X9n27Vcf/DBh7Rqf/DBh5buahs7dlwOOuiQLh9XR/voyrENGTI0Eyee2GzZ2LHj2q3/qKPeU+p34MBBHR7n2/s/qUWvO35Yrqqqzgc/eEqzNRMnnpgDDhiSo4764wwcOKhVvU2XTZhwYpvPf8v+Jkw4sVl9bQVHJ554crPjaqqt9m3Vxp7nW9+6q7dL6NCeXl9fs7PQuDOv66Ztxo4dl0mTTupUn43z5cSJJ7aao5ruc+LEkzJp0gfbXN841zed/5vOw03nw2HDhjWb2ydO3DEPtjU/7tr5b8dxNO27vXNPy/PVpEkndfoc2lZtu3v90HT7xvNd078qajwP7Y6u1tjZ9t19TbO36JEPK0ySY445Jl/72tdyxx135De/+U2+/OUvZ+jQoZ3efm/7sMIkOeSQQ7No0e/yiU/8ZfbZZ5/eLodu0tfGKX2XsUq5MFYpyu5ci7W17Z4+Vnvq2rO9fna1/51t13J9W+0PP/yI/OpXv8yVV161yz8wdrSPrhzbIYccmldeWZj99ts/I0aMyIUXXpqlS19vt/7Gfi+77Mq8+ebKDo+z6f733/+ADB8+Ih/5yJQ8++zT+dSnZuT44ydk0aLfZvDg/TN8+IhMm3ZhadtDDjk0//M/8zJq1Khs37491dXVufzyK7NixfIMGzYs559/YbvP/47+dhxPY7vG+j72sXPz1FOP5+MfPy8vvfRC3vGOg3LBBZfk6KP/+K3j+uusWLG89Hx8/OPn5cknf50RI0ampqYm/fv3z+WXfzavvfZq1q9fn5EjR6W2tvatzyUane3bt2XMmAMzePDgbNy4If369S8dw5AhQ7N5c01GjhyVkSNHpX///qmp2ZTKyso0NDSkqqoqDQ0Nqa6uzujRY1JTU9PhB5cNGDCg9FfAnTVo0KCdfhBkXzFlyse6vM28eXOzen1Nsw8lTNr+sMLt6xZl2AEDM2FC81/mdMWPf/yfu7xtXzVo0OBs376t9LXxdTFq1Jjst99+2bhxY6tthg8fkWHDhmXbtq0ZOXLH623QoMHZunVrTjvt9Cxb9nouvPDSPPPMgtTX16eqqir9+vXLmDEHZtiwYaW5YsGCJzJ69Jjsv//+2bZta2n94ME7arnssivz+uu/z9atW3Lllf8373nPsXnllYUZOHBgtmzZnPr6+rfmhB1z1kUXfSovvPBcLrvsr0vz5RFHvKu0TeM+V6xYnqFDh2XatAtL6xvnzPPPvzBPP/1ks7m+vXn47edwQEaMGJtXXllY2m/jPNjW/NhZTef5I454V6s5v73zQNPz1Xvec2yXzqE7q2NXrh8at//Lv/yrLF36ev7yLy/JkiWLmz1Xu6urNXa2fXdf0/S2nviwwoqGMvlI4lWrNpY+BbQ9I0ful5UrN3TYBnqbcUq5MFYpF8Yq5cJYpRwYp3SnG2+8LknX3xu66fa/fe3NDDyk+d36NYsfTpJmy2sWP5zDd+M9ohv7S3a9XmiLeZVy0V1jtbKyIsOHD2573W7vHQAAAAAAOiCIBgAAAACgUIJoAAAAAAAKJYgGAAAAAKBQgmgAAAAAAAoliAYAAAAAoFCCaAAAAAAACiWIBgAAAACgUIJoAAAAAAAKJYgGAAAAAKBQgmgAAAAAAAoliAYAAAAAoFCCaAAAAAAACiWIBgAAAACgUIJoAAAAAAAKJYgGAAAAAKBQgmgAAAAAAAoliAYAAAAAoFCCaAAAAAAACiWIBgAAAACgUIJoAAAAAAAKJYgGAAAAAKBQ1b1dAAAAAND3TJhwYm+X0CXlVi9AuRFEAwAAAN3uhBMm9XYJXVJu9QKUG2/NAQAAAABAoQTRAAAAAAAUShANAAAAAEChBNEAAAAAABRKEA0AAAAAQKEE0QAAAAAAFEoQDQAAAABAoQTRAAAAAAAUShANAAAAAEChBNEAAAAAABRKEA0AAAAAQKEE0QAAAAAAFEoQDQAAAABAoQTRAAAAAAAUShANAAAAAEChBNEAAAAAABRKEA0AAAAAQKEE0QAAAAAAFEoQDQAAAABAoQTRAAAAAAAUShANAAAAAEChBNEAAAAAABRKEA0AAAAAQKEE0QAAAAAAFKq6twsAAAAAaEvdlrWpWfxwq2VJmi3fsWxET5YGQBcJogEAAIA9zkEHHdLm8nXrqlNVVZnBg/dvsnREu+0B2DN0GETfeuutndrJX//1X3dLMQAAAABJMnXqtHbXjRy5X1au3NCD1QCwuzoMopcvX176fuvWrXnwwQdz9NFHZ9y4cVm6dGmee+65/Nmf/VnhRQIAAAAAUL46DKJvuOGG0vef+cxn8tWvfjWTJ08uLXvwwQfzwAMPFFcdAAAAAABlr7KzDefOnZtTTz212bKTTz45c+bM6faiAAAAAADoOzodRB9yyCG58847my27++67c/DBB3d7UQAAAAAA9B0dvjVHU9dff31mzJiRb37zmxk9enRWrFiR6urqfP3rXy+yPgAAAAAAylyng+h3v/vdmTVrVp5++umsXLkyI0eOzLHHHpt+/foVWR8AAAAAAGWu00F0kvTr1y/HHXdcUbUAAAAAANAHdTqIPvHEE1NRUdHmukceeaS76gEAAAAAoI/pdBB90003NXu8cuXKfPe7382HP/zhbi8KAAAAAIC+o9NB9Pvf//42l1188cX55Cc/2a1FAQAAAADQd1Tuzsb9+/fP73//++6qBQAAAACAPqjTd0TfeuutzR5v2bIlc+bMyaRJk7q9KAAAAAAA+o5OB9HLly9v9njffffNBRdckClTpnR7UQAAAAAA9B2dDqJvuOGGIusAAAAAAKCP6nQQnSTbtm3LokWLsmbNmjQ0NJSWH3/88d1eGAAAAAAAfUOng+gnnngiV155ZbZt25aNGzdm8ODB2bRpU8aMGZOHH364yBoBAAAAAChjlZ1teMMNN+Tiiy/Or3/96wwaNCi//vWvM3369EydOrXI+gAAAAAAKHOdDqJfffXVTJs2rdmySy65JN/5zne6uyYAAAAAAPqQTgfR++23XzZu3JgkGTlyZH77299m/fr1qampKaw4AAAAAADKX6ffI/q0007LnDlzcuaZZ+ZjH/tYpk2blurq6kyePLnI+gAAAAAAKHOdDqL/7u/+rvT9RRddlGOOOSabNm3KxIkTCykMAAAAAIC+oVNvzVFXV5dTTz0127ZtKy0bP358TjzxxFRWdvrdPQAAAAAA2At1KkWuqqpKVVVVtm7dWnQ9AAAAAAD0MZ1+a45p06blyiuvzKWXXpoxY8akoqKitO6ggw4qpDgAAAAAAMrfToPolStXZuTIkbnuuuuSJL/85S/T0NBQWl9RUZGXXnqpuAoBAACAHnHXXd/Na68t7rH+1q1bmyQ54IAhXdquX7+qbN9el4MOOiRTp04rojQAutlOg+jJkyfnqaeeyssvv5wkufzyy3PbbbcVXhgAAADQs157bXFe/e3LGTO4039AvVvWbKxNkgzY/GaXttuSZPlb2wJQHnZ6Zml693OSPP7444UVAwAAAPSuMYOrc8EfD+uRvr797Ook2aX+GrcFoDzs9MMKm74XdNI6mAYAAAAAgI7s9I7ourq6/M///E8pgG75OEmOP/744ioEAAAAAKCs7TSIHj58eD7/+c+XHg8ZMqTZ44qKijz88MPFVAcAAAAAQNnbaRD9i1/8oifqAAAAAACgj9rpe0QDAAAAAMDuEEQDAAAAAFAoQTQAAAAAAIUSRAMAAAAAUChBNAAAAAAAhRJEAwAAAABQKEE0AAAAAACFEkQDAAAAAFAoQTQAAAAAAIUSRAMAAAAAUChBNAAAAAAAhRJEAwAAAABQKEE0AAAAAACFEkQDAAAAAFAoQTQAAAAAAIUSRAMAAAAAUChBNAAAAAAAhRJEAwAAAABQKEE0AAAAAACFEkQDAAAAAFAoQTQAAAC0Yd68uZk3b25vl8FewFgD9gbVvV0AAAAA7Ikee2xOkuSEEyb1ciX0dcYasDdwRzQAAAAAAIUSRAMAAAAAUChBNAAAAAAAhRJEAwAAAABQKEE0AAAAAACFEkQDAAAAAFAoQTQAAAAAAIUSRAMAAAAAUChBNAAAAAAAhRJEAwAAAABQKEE0AAAAAACFEkQDAAAAAFAoQTQAAAAAAIUSRAMAAAAAUChBNAAAAAAAhRJEAwAAAABQKEE0AAAAAACFEkQDAAAAAFAoQTQAAAAAAIUSRAMAAAAAUChBNAAAAAAAhRJEAwAAAABQKEE0AABAGVu7dk2+8pUvZd26tV1+3PL7q6++utSurX6uv/6afPnL17Tatul+Z878SS68cGo+97krd1rDkiWL3/r6aunx9ddfk+uvv6bUdsmSV3P55RfltdcWN3v84ovPlba99trPZ/r0C3LttZ9vts/Gfb3wwrOZPv2CTJ9+QWk/jcfzxS9+Pl/+8o42TfuBvUnL1+jutgNoS9W11157bdGd3Hjjjbnmmmvyj//4j5k8eXKGDx/e5X1s3rwtDQ0dtxk0aEBqarbtYpXQM4xTyoWxSrkwVikXxipFueeeu/PUU49n69atOeaY93bp8cKFLzf7/vHHf11q11Y/CxY8kTVrVmfbtubbNt3vSy89nySpqdmUbds6rmHhwpeyePGrWbjw5be+vpQlSxaX+jjmmPfmppu+nDVrVmfhwpdz8sl/Vnr89NMLsmLF8ixc+HJ+//vXUldXl3Xr1jbZ18ulfT3zzIJs2bI5dXV1pf00Hs+6dWtLbTZvrimtT5J58+YmSSZMOLHn/kN72bx5c1O7cXXeO3rfHunv6RWbk2SX+nt6xeZUDx7WJ/5/enustXyN7m473ub8T7norrFaUVGRgQP7t7muR+6IPuWUU3LnnXdm3LhxPdEdAADAXmHt2jV57LE5aWhoyGOPzc2SJa924fGcPProI218P7fV3Y5r167Jo48+Unr86KOPNGvfdL9NzZ79cIc1LF36equvb/cxJy+88Gxp2dKlr+dXv5pfelxTs6nVNo3tWi6vqdnUbP0LLzzX7Hiatlm69HV3RbNXaTmPdPRXEZ1pB9Ce6p7oZPz48T3RDQAAwF7l/vvvTX39jvC3vr4+//Zvt3X6cW1tbemvTpt+X19fn5/85L9y/vkXNuuntrau9Li2tjZJRZv7ba6hwxo6Ultbm3/5l683W/bNb/7LTrfrjH/5l/+/2fG09K//+o1cf/1NWbdubdatW5cbb7yuW/otB0uWLM7A1Pd2GZ2ycVt93liyuE/8/yxZsjgHHHBAr/Tdch5p+frvajuA9niPaAAAgDI1f/681NXVJknq6mqzdOnrnX684+7lhlbf19XVZv78ea36aVz/trfbN91vSx3V0LGGZncyN27fHXbst/0wvOVd1tCXtZxHWr7+u9oOoD09ckc0AAAA3e/440/I3LmPpK6uNlVV1Rk9enRWrFjRqccVFRVv3QXd0Oz7qqrqHH/8Ca36mT374TQPbytK7Zvut6WxY8e1W0PHKjJw4MBmYXRVVXW3hNEDBw5KTU1N2gujx47d8baSBxwwJAccMCSf+9w/7Haf5eLGG6/LluW/7e0yOmVw/8qMGHNIn/j/6c27ulvOIy1f/11tB9Aed0QDAACUqTPPPCeVlTveIqOysjKXXHJ5px9XV1enurqq1feVlZU566yPtuqncX1b7Zvut7mKDmvoSHV1daZPv6LZsosvnr7T7Tpj+vRPNzueli69dEa39APloOU80vL139V2AO0RRAMAAJSpIUOGZsKEE1NRUZEJEybl4IMP7cLjEzNx4kltfD8pBxwwpFU/EyeeVHo8ceJJzdo33W9TH/zgKR3WMHbsuFZf3+7jxBx11B+Xlo0dOy5/8ifHlx4PHDio1TaN7VouHzhwULP1Rx31nmbH07TN2LHjctBBh3TxfwLKV2j/lFwAAA46SURBVMt5pOXrv6vtANrTI0H09ddfn0mTJmX58uW54IIL8pGPfKQnugUAAOjzzjzznLzznX9UujuxK49bfv/ud7+7w7shDzvs8PzhHx7eatum+/3oR89LkowcOWqnNVxyyYy3vl5eenzYYYfnsMMOL7W95JLLs++++5buUm58fNllny5te/DBh2bAgAE5+OBDm+2zcV/Tp1+RAQMGZMCAAaX9NB7PIYccmj/8wx1tmvYDe5OWr9HdbQfQloqGhoadf1zxHmDVqo07/WTlkSP3y8qVG3qoItg1xinlwlilXBirlAtjlXJgnDbX+L69feE9iDur8T2iL/jjYT3S37efXZ0ku9Tft59dnX3GHN4n/n/2xrG2tzCvUi66a6xWVlZk+PDBba/b7b0DAAAAAEAHBNEAAAAAABRKEA0AAAAAQKEE0QAAAAAAFEoQDQAAAABAoQTRAAAAAAAUShANAAAAAEChBNEAAAAAABRKEA0AAAAAQKEE0QAAAAAAFEoQDQAAAABAoQTRAAAAAAAUShANAAAAAEChBNEAAAAAABRKEA0AAAAAQKEE0QAAAAAAFEoQDQAAAABAoQTRAAAAAAAUShANAAAAAEChBNEAAAAAABRKEA0AAAAAQKEE0QAAAAAAFKq6twsAAACAPdGECSf2dgnsJYw1YG8giAYAAIA2nHDCpN4ugb2EsQbsDbw1BwAAAAAAhRJEAwAAAABQKEE0AAAAAACFEkQDAAAAAFAoQTQAAAAAAIUSRAMAAAAAUChBNAAAAAAAhRJEAwAAAABQKEE0AAAAAACFEkQDAAAAAFAoQTQAAAAAAIUSRAMAAAAAUChBNAAAAAAAhRJEAwAAAABQKEE0AAAAAACFEkQDAAAAAFAoQTQAAAAAAIUSRAMAAAAAUChBNAAAAAAAhRJEAwAAAABQKEE0AAAAAACFEkQDAAAAAFAoQTQAAAAAAIUSRAMAAAAAUKjq3i4AAAAA2HMs31ibbz+7usf6SrJL/S3fWJtDu7keAIojiAYAAACSJAcddEiP9jd03dokyT4HDOnSdv36VWWf7XU9Xi8Au04QDQAAACRJpk6d1tsldMrIkftl5coNvV0GAF3gPaIBAAAAACiUIBoAAAAAgEIJogEAAAAAKJQgGgAAAACAQgmiAQAAAAAolCAaAAAAAIBCCaIBAAAAACiUIBoAAAAAgEIJogEAAAAAKJQgGgAAAACAQlX3dgGdVVlZ0a3toDcZp5QLY5VyYaxSLoxVyoFxSrkwVikXxirlojvGakf7qGhoaGjY7R4AAAAAAKAd3poDAAAAAIBCCaIBAAAAACiUIBoAAAAAgEIJogEAAAAAKJQgGgAAAACAQgmiAQAAAAAolCAaAAAAAIBCCaIBAAAAACiUIBoAAAAAgEKVZRD94x//OGeeeWbe/e535z/+4z+arfviF7+Y008/PWeddVbOO++8PPfcc6V1b775Zi688MJMnjw5Z511Vp555pmeLp29SEfjdPPmzbnyyitz2mmn5fTTT8/s2bM7tQ56wqJFi3L++ednypQp+dCHPpSvf/3rpXXGJ3uS733vezn99NNz5plnZsqUKaXlxil7ol/96lc58sgjm10TuDZlT+LnKMrFokWLcu6552by5Mk599xz8+qrr/Z2SZAkWbNmTf7qr/4qkydPzplnnpkZM2Zk9erVSZKnn346Z511ViZPnpwLL7wwq1at6uVqIfnGN76RP/qjP8rChQuT9Mw4Lcsg+sgjj8wtt9ySM844o9W6SZMm5f77789PfvKTXHrppfnMZz5TWvfVr34148ePz6xZs3LNNdfkqquuSkNDQ0+Wzl6ko3F6xx13ZPDgwfn5z3+e22+/PX//93+fTZs27XQd9ISbbropkydPzo9//OP86Ec/yn/913/l2WefTWJ8sud48MEH88ADD+RHP/pR7r///txxxx2ldcYpe5qNGzfm5ptvzqRJk5otd23KnsTPUZSLL3zhC5k6dWpmzZqVqVOn5pprruntkiBJUlFRkYsvvjizZs3K/fffn4MOOig333xz6uvrc9VVV+Waa67JrFmzMn78+Nx88829XS57uRdeeCFPP/10xo0blyQ9Nk7LMog+4ogjcvjhh6eysnX5H/zgB9OvX78kybHHHpvly5envr4+SfLAAw/kvPPOS5KMHz8+/fv3b/abfuhOHY3Tn/3sZzn33HOTJIceemiOPvrozJ07d6froCdUVFRkw4YNSZItW7akoqIiw4YNS2J8suf41re+lRkzZmTw4MFJkhEjRpTWGafsab7yla/koosuytChQ5std23KnsTPUZSDVatW5cUXXyzd7HPGGWfkxRdfLN11Cr1pyJAh+ZM/+ZPS42OPPTZLly7N888/nwEDBmT8+PFJkvPOOy8PPPBAb5UJ2bZtW770pS/l2muvLS3rqXFalkF0Z91555056aSTUllZmTVr1qShoaEUpiTJgQcemOXLl/diheytli5dWvqtU9J8LHa0DnrC5z//+fz0pz/NxIkTc/LJJ+eiiy7KO97xjiTGJ3uO3/3ud3nmmWdy3nnn5aMf/Wh++MMfltYZp+xJ5syZkw0bNuT0009vtty1KXsyP0exp1q2bFlGjx6dqqqqJElVVVVGjRqVZcuW9XJl0Fx9fX3uvvvunHzyyVm2bFnGjh1bWjds2LDU19dn7dq1vVghe7Nbb701Z511Vunn/CQ9Nk6ru3Vv3eScc87J0qVL21z3y1/+snTS6ch///d/5/7778+dd97Z3eVBku4Zp9AbdjZ2f/CDH2TKlCm5+OKL88Ybb+T888/P0UcfnWOOOaaHK2VvtrNxWldXl2XLluWuu+7KmjVr8hd/8Rf5gz/4gxx33HE9XCl7u47G6gMPPJCvfvWr+fa3v93DVUFrnb129XMUwO677rrrMnDgwHziE5/Iz3/+894uB0oWLFiQ559/Pn/zN3/TK/3vkUH0vffeu1vb//znP88tt9yS73znO6U/1W38U8jVq1eXfpu/bNmyjBkzZveKZa+1O+N07Nixef3115uNxcY/4eloHXSHnY3d733ve3nooYeSJKNGjcoHPvCBPP744znmmGOMT3rMzsbp2LFjc8YZZ6SysjLDhw/Pn/7pn+bZZ5/NcccdZ5zSozoaq0888URWrlyZP//zP0+y4y7o2bNnZ+3atZkxY0YS16b0nM5cu/o5ij3dgQcemBUrVqSuri5VVVWpq6vLG2+8kQMPPLC3S4OSG2+8MYsXL87tt9+eysrKHHjggc1+Ebh69epUVlZmyJAhvVgle6vHH388v/vd73LKKackSZYvX56LLroo559/fo+M0z731hyzZ8/ODTfckDvuuKPZLeZJcvrpp+f73/9+kh0/GGzZsiVHH310b5TJXu7000/PD37wgyTJq6++mueeey4TJ07c6TroCe94xzvy6KOPJtnxAVtPPvlk3vnOdyYxPtlznHHGGaVxWlNTkyeffDLvete7khin7DnGjx+f+fPn5xe/+EV+8YtfZPLkybniiitKIbRrU/Ykfo6iHAwfPjxHHnlkZs6cmSSZOXNmjjzyyGZvHQO96Wtf+1qef/753Hbbbenfv3+S5Oijj86WLVvyxBNPJEm+//3vt3rLLugpl1xySR577LHS9emYMWNyxx135OKLL+6RcVrRUIYfdzxz5sz80z/9U9avX59+/fpl3333zbe+9a0cfvjh+cAHPpB+/fo1OxF95zvfydChQ7Ny5cpcddVVWbp0aQYMGJAvfvGLed/73teLR0Jf1tE4rampydVXX52XXnoplZWVueqqq3LqqacmSYfroCc8//zzuf7661NTU5Pa2tp8+MMfLoUmxid7ii1btuQf/uEf8uKLLyZJpkyZkksuuSSJccqe6+qrr87RRx+dT3ziE0ni2pQ9ip+jKBe/+93vcvXVV2f9+vXZf//9c+ONN+awww7r7bIgr7zySs4444wceuih2WeffZLsuMnntttuy1NPPZUvfOEL2bp1a8aNG5ebbrqp2YdtQ285+eSTc/vtt+eII47okXFalkE0AAAAAADlo8+9NQcAAAAAAHsWQTQAAAAAAIUSRAMAAAAAUChBNAAAAAAAhRJEAwAAAABQKEE0AAAAAACFEkQDAEABLrrootx6662tlj/00EM54YQTcsstt+Soo47Ke9/73tK/1157rRcqBQCA4lU0NDQ09HYRAADQ18ycOTO33HJLHnrooVRUVJSWf/rTn87YsWMzaNCgLF68ODfffHMvVgkAAD3DHdEAAFCAU089NWvXrs0TTzxRWrZu3brMnj07Z599di9WBgAAPU8QDQAABdhnn33yoQ99KPfdd19p2c9+9rMcdthhede73pUkmT17dt7//vfnIx/5SO66667eKhUAAAoniAYAgIKcffbZmTVrVrZu3Zokue+++3LOOeckST70oQ/lpz/9aebPn5/rrrsu//zP/5yZM2f2ZrkAAFAYQTQAABRk/PjxGTp0aB566KEsWbIkzz33XM4444wkyeGHH57Ro0enqqoq73vf+zJt2rTMmjWrlysGAIBiVPd2AQAA0JdNmTIl9913XxYtWpQJEyZkxIgR7bb1OeIAAPRV7ogGAIACnX322Zk/f35++MMfNvuQwoceeijr1q1LQ0NDnn322Xzve9/LKaec0ouVAgBAcSoa3HYBAACFOv/88/Pyyy9n3rx56d+/f5Lks5/9bObNm5dt27Zl9OjRmTp1aqZNm9bLlQIAQDEE0QAAAAAAFMpbcwAAAAAAUChBNAAAAAAAhRJEAwAAAABQKEE0AAAAAACFEkQDAAAAAFAoQTQAAAAAAIUSRAMAAAAAUChBNAAAAAAAhRJEAwAAAABQqP8HpjAOxBCfC1EAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.datasets import load_boston\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.decomposition import PCA,KernelPCA\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.model_selection import cross_val_score,GridSearchCV\n",
        "from sklearn.metrics import mean_squared_error\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.pipeline import FeatureUnion"
      ],
      "metadata": {
        "id": "t-_Ji-b53mq0"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pipe=[] \n",
        "pipe.append(('SC',StandardScaler()))"
      ],
      "metadata": {
        "id": "Acp8Gl343nVU"
      },
      "execution_count": 31,
      "outputs": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3.10.0 64-bit",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.10.0"
    },
    "orig_nbformat": 4,
    "vscode": {
      "interpreter": {
        "hash": "677b76c3314f1a18399c50471b2653eadc5e3bab58d25e3b0fee81ab4af4f8a7"
      }
    },
    "colab": {
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}