Anomaly_Detection_for_Dummies.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Anomaly Detection for Dummies.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "gxBgvhA4mowO",
        "colab_type": "code",
        "outputId": "9d831709-2f9c-4bf7-a790-0972691dd4fb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 260
        }
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from numpy import percentile\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import matplotlib\n",
        "\n",
        "from sklearn.ensemble import IsolationForest\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "\n",
        "from pyod.models.abod import ABOD\n",
        "from pyod.models.cblof import CBLOF\n",
        "from pyod.models.feature_bagging import FeatureBagging\n",
        "from pyod.models.hbos import HBOS\n",
        "from pyod.models.iforest import IForest\n",
        "from pyod.models.knn import KNN\n",
        "from pyod.models.lof import LOF\n",
        "from scipy import stats\n",
        "from sklearn.preprocessing import MinMaxScaler"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: pyod in /usr/local/lib/python3.6/dist-packages (0.7.3)\n",
            "Requirement already satisfied: scipy>=0.19.1 in /usr/local/lib/python3.6/dist-packages (from pyod) (1.3.0)\n",
            "Requirement already satisfied: numba>=0.35 in /usr/local/lib/python3.6/dist-packages (from pyod) (0.40.1)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.6/dist-packages (from pyod) (3.0.3)\n",
            "Requirement already satisfied: numpy>=1.13 in /usr/local/lib/python3.6/dist-packages (from pyod) (1.16.4)\n",
            "Requirement already satisfied: scikit-learn>=0.19.1 in /usr/local/lib/python3.6/dist-packages (from pyod) (0.21.2)\n",
            "Requirement already satisfied: llvmlite>=0.25.0dev0 in /usr/local/lib/python3.6/dist-packages (from numba>=0.35->pyod) (0.29.0)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->pyod) (2.5.3)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->pyod) (1.1.0)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib->pyod) (0.10.0)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->pyod) (2.4.0)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.6/dist-packages (from scikit-learn>=0.19.1->pyod) (0.13.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.6/dist-packages (from python-dateutil>=2.1->matplotlib->pyod) (1.12.0)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from kiwisolver>=1.0.1->matplotlib->pyod) (41.0.1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "usk8MvW8nHUW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = pd.read_excel(\"Superstore.xls\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "chl7XcjqmvpO",
        "colab_type": "code",
        "outputId": "d62ced8d-4a18-4ff3-e4f8-7edce6ebdcf8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        }
      },
      "source": [
        "df.head(3)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Row ID</th>\n",
              "      <th>Order ID</th>\n",
              "      <th>Order Date</th>\n",
              "      <th>Ship Date</th>\n",
              "      <th>Ship Mode</th>\n",
              "      <th>Customer ID</th>\n",
              "      <th>Customer Name</th>\n",
              "      <th>Segment</th>\n",
              "      <th>Country</th>\n",
              "      <th>City</th>\n",
              "      <th>State</th>\n",
              "      <th>Postal Code</th>\n",
              "      <th>Region</th>\n",
              "      <th>Product ID</th>\n",
              "      <th>Category</th>\n",
              "      <th>Sub-Category</th>\n",
              "      <th>Product Name</th>\n",
              "      <th>Sales</th>\n",
              "      <th>Quantity</th>\n",
              "      <th>Discount</th>\n",
              "      <th>Profit</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>CA-2016-152156</td>\n",
              "      <td>2016-11-08</td>\n",
              "      <td>2016-11-11</td>\n",
              "      <td>Second Class</td>\n",
              "      <td>CG-12520</td>\n",
              "      <td>Claire Gute</td>\n",
              "      <td>Consumer</td>\n",
              "      <td>United States</td>\n",
              "      <td>Henderson</td>\n",
              "      <td>Kentucky</td>\n",
              "      <td>42420</td>\n",
              "      <td>South</td>\n",
              "      <td>FUR-BO-10001798</td>\n",
              "      <td>Furniture</td>\n",
              "      <td>Bookcases</td>\n",
              "      <td>Bush Somerset Collection Bookcase</td>\n",
              "      <td>261.96</td>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>41.9136</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>CA-2016-152156</td>\n",
              "      <td>2016-11-08</td>\n",
              "      <td>2016-11-11</td>\n",
              "      <td>Second Class</td>\n",
              "      <td>CG-12520</td>\n",
              "      <td>Claire Gute</td>\n",
              "      <td>Consumer</td>\n",
              "      <td>United States</td>\n",
              "      <td>Henderson</td>\n",
              "      <td>Kentucky</td>\n",
              "      <td>42420</td>\n",
              "      <td>South</td>\n",
              "      <td>FUR-CH-10000454</td>\n",
              "      <td>Furniture</td>\n",
              "      <td>Chairs</td>\n",
              "      <td>Hon Deluxe Fabric Upholstered Stacking Chairs,...</td>\n",
              "      <td>731.94</td>\n",
              "      <td>3</td>\n",
              "      <td>0.0</td>\n",
              "      <td>219.5820</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>CA-2016-138688</td>\n",
              "      <td>2016-06-12</td>\n",
              "      <td>2016-06-16</td>\n",
              "      <td>Second Class</td>\n",
              "      <td>DV-13045</td>\n",
              "      <td>Darrin Van Huff</td>\n",
              "      <td>Corporate</td>\n",
              "      <td>United States</td>\n",
              "      <td>Los Angeles</td>\n",
              "      <td>California</td>\n",
              "      <td>90036</td>\n",
              "      <td>West</td>\n",
              "      <td>OFF-LA-10000240</td>\n",
              "      <td>Office Supplies</td>\n",
              "      <td>Labels</td>\n",
              "      <td>Self-Adhesive Address Labels for Typewriters b...</td>\n",
              "      <td>14.62</td>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.8714</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Row ID        Order ID Order Date  ... Quantity Discount    Profit\n",
              "0       1  CA-2016-152156 2016-11-08  ...        2      0.0   41.9136\n",
              "1       2  CA-2016-152156 2016-11-08  ...        3      0.0  219.5820\n",
              "2       3  CA-2016-138688 2016-06-12  ...        2      0.0    6.8714\n",
              "\n",
              "[3 rows x 21 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 154
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fCqj102y9Kfo",
        "colab_type": "code",
        "outputId": "e9afbcbb-6a9f-4aaa-d6d1-287cc82a5d9e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        }
      },
      "source": [
        "df.Sales.describe()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count     9994.000000\n",
              "mean       229.858001\n",
              "std        623.245101\n",
              "min          0.444000\n",
              "25%         17.280000\n",
              "50%         54.490000\n",
              "75%        209.940000\n",
              "max      22638.480000\n",
              "Name: Sales, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 155
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SMcvIb1K_69n",
        "colab_type": "code",
        "outputId": "28d1ae16-8c89-4f23-a7b9-5e336b330d6f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "plt.scatter(range(df.shape[0]), np.sort(df['Sales'].values))\n",
        "plt.xlabel('index')\n",
        "plt.ylabel('Sales')\n",
        "plt.title(\"Sales distribution\")\n",
        "sns.despine()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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zgacj4jzgeOCVpbXKzMxGbU+jv8pSa6hUrvQ8I2k68BxwcDlNMjOzHOox+qvWULlOUjvw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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Th6A8C8xB2ua",
        "colab_type": "code",
        "outputId": "91be5603-6b28-43da-8574-9d68aece779c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "sns.distplot(df['Sales'])\n",
        "plt.title(\"Distribution of Sales\")\n",
        "sns.despine()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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cmR5X5uuimZkdCnnOQGYCrRGxOiL2AAuBORV15gC3puW7gfMlKZUvjIj2iHgGaE3xiIiH\ngc2VO4uIH0dEKT19BJjcxz6ZmdkAyJNAJgFry56vS2VV66Q3/23AhJzb9uSjwA/Lnk+T9EtJD0l6\nZ7UNJF0hqUVSS1tbWx92ZWZmfTFkB9ElfQ4oAXekoheAqRFxFvAp4E5Jx1ZuFxE3R0RjRDQ2NDQM\nXIPNzI4yeRLIemBK2fPJqaxqHUl1wFhgU85tX0XSR4D3An8UEQGQLoNtSsuPAauA1+Vov5mZHQJ5\nEshSYLqkaZKGkw2KN1fUaQbmpuWLgAfTG38z0JTu0poGTAce7WlnkmYDnwH+ICJeKStv6BqAl3RK\nirU6R/vNzOwQqOutQkSUJF0N3AfUAgsiYrmk64CWiGgG5gO3S2olGxhvStsul7QYWEF2OeqqiOgA\nkHQXcB4wUdI64AsRMR/4JlAP3J+Nw/NIuuPqXcB1kvYCncCVEfGqQXgzMxsYSleIjkiNjY3R0tJy\nUDHuXLKmavml50w9qLhmZkOY8lQasoPoZmY2tDmBmJlZIU4gZmZWiBOImZkV4gRiZmaFOIGYmVkh\nTiBmZlaIE4iZmRXiBGJmZoU4gZiZWSFOIGZmVogTiJmZFeIEYmZmhTiBmJlZIU4gZmZWiBOImZkV\n4gRiZmaFOIGYmVkhuRKIpNmSVkpqlXRNlfX1khal9UsknVy2bl4qXylpVln5AkkbJD1ZEWu8pPsl\n/Sb9PC6VS9LXU6wnJJ1dtNNmZnbwek0gkmqBG4ELgBnAJZJmVFS7HNgSEacBNwDXp21nAE3A6cBs\n4KYUD+CWVFbpGuAnETEd+El6Ttr/9PS4AvhWvi6amdmhkOcMZCbQGhGrI2IPsBCYU1FnDnBrWr4b\nOF+SUvnCiGiPiGeA1hSPiHgY2Fxlf+WxbgXeX1Z+W2QeAcZJOiFPJ83MrP/lSSCTgLVlz9elsqp1\nIqIEbAMm5Ny20vER8UJafhE4vg/tQNIVkloktbS1tfWyKzMzK2pID6JHRADRx21ujojGiGhsaGg4\nRC0zM7M8CWQ9MKXs+eRUVrWOpDpgLLAp57aVXuq6NJV+buhDO8zMbIDkSSBLgemSpkkaTjYo3lxR\npxmYm5YvAh5MZw/NQFO6S2sa2QD4o73srzzWXOD7ZeUfTndjnQtsK7vUZWZmA6yutwoRUZJ0NXAf\nUAssiIjlkq4DWiKiGZgP3C6plWxgvCltu1zSYmAFUAKuiogOAEl3AecBEyWtA74QEfOBLwOLJV0O\nPAd8MDXlXuBCsoH4V4DL+uMAmJlZMcpOFI5MjY2N0dLSclAx7lyypmr5pedMPai4ZmZDmPJUGtKD\n6GZmNnQ5gZiZWSFOIGZmVogTiJmZFeIEYmZmhTiBmJlZIU4gZmZWiBOImZkV4gRiZmaFOIGYmVkh\nTiBmZlaIE4iZmRXiBGJmZoU4gZiZWSFOIGZmVogTiJmZFeIEYmZmhTiBmJlZIbkSiKTZklZKapV0\nTZX19ZIWpfVLJJ1ctm5eKl8paVZvMSX9XNKy9Hhe0vdS+XmStpWt+/zBdNzMzA5OXW8VJNUCNwK/\nB6wDlkpqjogVZdUuB7ZExGmSmoDrgYslzQCagNOBE4EHJL0ubVM1ZkS8s2zf9wDfL9vPzyPivUU7\na2Zm/SfPGchMoDUiVkfEHmAhMKeizhzg1rR8N3C+JKXyhRHRHhHPAK0pXq8xJR0L/C7wvWJdMzOz\nQylPApkErC17vi6VVa0TESVgGzChh23zxHw/8JOI2F5W9nZJj0v6oaTTqzVW0hWSWiS1tLW15eie\nmZkVMZQH0S8B7ip7/gvgpIg4A/gG3ZyZRMTNEdEYEY0NDQ0D0Ewzs6NTngSyHphS9nxyKqtaR1Id\nMBbY1MO2PcaUNJHsMte/d5VFxPaI2JmW7wWGpXpmZjYI8iSQpcB0SdMkDScbFG+uqNMMzE3LFwEP\nRkSk8qZ0l9Y0YDrwaI6YFwH/FhG7uwokvTaNqyBpZmr7pr5118zM+kuvd2FFREnS1cB9QC2wICKW\nS7oOaImIZmA+cLukVmAzWUIg1VsMrABKwFUR0QFQLWbZbpuAL1c05SLgY5JKwC6gKSUpMzMbBDqS\n34MbGxujpaXloGLcuWRN1fJLz5l6UHHNzIYw5ak0lAfRzcxsCHMCMTOzQpxAzMysECcQMzMrxAnE\nzMwKcQIxM7NCnEDMzKwQJxAzMyvECcTMzApxAjEzs0KcQMzMrBAnEDMzK8QJxMzMCnECMTOzQpxA\nzMysECcQMzMrxAnEzMwKcQIxM7NCciUQSbMlrZTUKumaKuvrJS1K65dIOrls3bxUvlLSrN5iSrpF\n0jOSlqXHmalckr6e6j8h6eyD6biZmR2cXhOIpFrgRuACYAZwiaQZFdUuB7ZExGnADcD1adsZQBNw\nOjAbuElSbY6YfxkRZ6bHslR2ATA9Pa4AvlWkw2Zm1j/ynIHMBFojYnVE7AEWAnMq6swBbk3LdwPn\nS1IqXxgR7RHxDNCa4uWJWWkOcFtkHgHGSTohR/vNzOwQyJNAJgFry56vS2VV60RECdgGTOhh295i\nfildprpBUn0f2oGkKyS1SGppa2vL0T0zMytiKA6izwPeALwNGA98ti8bR8TNEdEYEY0NDQ2Hon1m\nZka+BLIemFL2fHIqq1pHUh0wFtjUw7bdxoyIF9Jlqnbg/5Fd7srbDjMzGyB5EshSYLqkaZKGkw2K\nN1fUaQbmpuWLgAcjIlJ5U7pLaxrZAPijPcXsGtdIYyjvB54s28eH091Y5wLbIuKFQr02M7ODVtdb\nhYgoSboauA+oBRZExHJJ1wEtEdEMzAdul9QKbCZLCKR6i4EVQAm4KiI6AKrFTLu8Q1IDIGAZcGUq\nvxe4kGwg/hXgsoPuvZmZFabsROHI1NjYGC0tLQcV484la6qWX3rO1IOKa2Y2hClPpaE4iG5mZocB\nJxAzMyvECcTMzApxAjEzs0KcQMzMrBAnEDMzK8QJxMzMCnECMTOzQpxAzMysECcQMzMrxAnEzMwK\ncQIxM7NCnEDMzKwQJ5ACtryyh/ZSx2A3w8xsUDmB9NGO3Xu54f6nufW/nh3sppiZDSonkD5atnYr\npc5g+fPbB7spZmaDygmkDyKCx57bAsCqtp2D3Bozs8HlBNIH67bsYsOOdkbV17Fqw8t0dh653+Zo\nZtabXAlE0mxJKyW1Srqmyvp6SYvS+iWSTi5bNy+Vr5Q0q7eYku5I5U9KWiBpWCo/T9I2ScvS4/MH\n0/EiHluzhWG14p2nTWTX3g5e3L57oJtgZjZk9JpAJNUCNwIXADOASyTNqKh2ObAlIk4DbgCuT9vO\nAJqA04HZwE2SanuJeQfwBuDNwDHAn5Tt5+cRcWZ6XFekw0Xt7ejkiXVbOf3EsUwefwzgy1hmdnTL\ncwYyE2iNiNURsQdYCMypqDMHuDUt3w2cL0mpfGFEtEfEM0BritdtzIi4NxLgUWDywXWxf/z6xR3s\n3tvJW086jobR9QCs2uAEYmZHrzwJZBKwtuz5ulRWtU5ElIBtwIQetu01Zrp09SHgR2XFb5f0uKQf\nSjq9WmMlXSGpRVJLW1tbju7ls3FnOwBTx49kdH0dY0bUsart5X6Lb2Z2uBnKg+g3AQ9HxM/T818A\nJ0XEGcA3gO9V2ygibo6IxohobGho6LfG7NxdYsSwGobV1iCJUxtG+xKWmR3V8iSQ9cCUsueTU1nV\nOpLqgLHAph627TGmpC8ADcCnusoiYntE7EzL9wLDJE3M0f5+sbO9xKjhdfueO4GY2dEuTwJZCkyX\nNE3ScLJB8eaKOs3A3LR8EfBgGsNoBprSXVrTgOlk4xrdxpT0J8As4JKI6OzagaTXpnEVJM1Mbd9U\npNNF7GwvMXpEWQJ5zShe2t7Ojt17B6oJZmZDSl1vFSKiJOlq4D6gFlgQEcslXQe0REQzMB+4XVIr\nsJksIZDqLQZWACXgqojoAKgWM+3y28BzwH+nfPHddMfVRcDHJJWAXUBTSlIDYmd7ideMqd/3/NSG\n0QCsbnuZM6aMG6hmmJkNGb0mENh3yejeirLPly3vBv5nN9t+CfhSnpipvGqbIuKbwDfztPdQ2Lm7\nxCkTR+173pVAVrXtdAIxs6PSUB5EHzI6OoNdezsYXb8/t500YSR1NfI4iJkdtZxAcni5vQRwwBjI\nsNoapk4YyaoNvpXXzI5OuS5hHe12diWQsjOQO5esob6ulpbntnDnkjX7yi89Z+qAt8/MbDD4DCSH\nagkE4JSJo9i4s50NnhPLzI5CTiA5dJdA3jJ5LCL7jhAzs6ONE0gOO3dXTyBjRgxj+vGjWbZuK50D\nd0exmdmQ4ASSw872EsNqxfC6Vx+uM6eMY+sre3lu0yuD0DIzs8HjBJLDzvYSo+rrSB9sPMCME8Yy\nvLaGZWu3DELLzMwGjxNIDi+3l151+arL8LoaTj/xWH61fht7Ozqr1jEzOxI5geSws4cEAnDW1OPY\nvbeT/2zdOICtMjMbXE4gOezc3XMCObVhFG+eNJYHnnqJpc9uHsCWmZkNHieQXnRG8PKenhOIJD5w\n1iSOGzmcj9/5SzalL58yMzuSOYH0YteeDjrjwGlMqhkxrJZLZk5l8yt7+Ow9TzCAEwWbmQ0KJ5Be\ndPchwmpOHHcMn5n1eh54agPNjz9/qJtmZjaonEB60ZVARuVIIACX/dY0zpo6jmubl+/7HnUzsyOR\nJ1PsRXefQu/OoqVredf0Bp5Yt42mmx/hDa8dA8C8C9/IpHHHHLJ2mpkNNCeQXnSdgYzJmUAAjj92\nBLNmHM+9T75I64bs+0IeerqNv/vAm3nfGSceknaamQ00J5AqIoLvL3ue951xIjvbS9QIRgyv7VOM\nd0xv4K0njScIdraXeOjpNj5+1y+55xfrOPeUCZwxeRxnnzSO+rq+xTUzGypyJRBJs4F/JPv+8n+O\niC9XrK8HbgPeCmwCLo6IZ9O6ecDlQAfwiYi4r6eYkqYBC4EJwGPAhyJiT0/76G//2bqJTy5axoO/\n3sD2XXsZVV9HTZVpTHpzTEo6I4fX8YdnTWbCqOE89twWfrayDcg+xX7e6xp4+6kTePOkscw48VhG\nDt//X9J1J1e1KVTMzAabervdVFIt8DTwe8A6YClwSUSsKKvzZ8BbIuJKSU3AByLiYkkzgLuAmcCJ\nwAPA69JmVWNKWgx8NyIWSvo28HhEfKu7ffTU9sbGxmhpaenbEUm+9bNVXP+jXyPgtWNH8PHfnV4o\nTjUvt5dYs/kVVr60g3WbX+H5bdn3idQo+671N55wLBt3tvPrF3ewa08H0yaOYvJxx7Bjd4mNO9sZ\nObyWyeNHMjU9Jh93DMcM238mI0GNxGuOHcHxY+rpDNj0cjs7dpeorRF1NWJPqZPdezvZXepg994O\n9nZ0csywOsaMqGNUfR2j6+sYXlfDrj0d7NrbwcjhtYw9ZhgjhtUSEURkn5EJsn3VaPASXURQ6gz2\nlDrZ29GJJOrrahheW0NNzf42ldK62lQWEbSXOqmrEXW12f0knZ3Bno5OhtXW7KuXtw17O4LOCOpq\nsn048R967aUO2kudjBxWu+//sD9V+yOu6/Vf/trq6Mzq9fTa2tvRya69HYyoq2V4XQ0R2dWJ3Xs7\nGTOijhHDamkvdbBp5x5KHcGE0cMZObyWTS/vYe3mV6irqWHq+JHUD6thxQvbWf78dl4zpp6zpoxj\n66693LlkDQ8/3cY7pk/k0nOm8obXHnswXc/14s2TQN4OXBsRs9LzeekA/X1ZnftSnf+WVAe8CDQA\n15TX7aqXNntVTODLQBvw2ogole+7u31EDx04mAQC8M8/X80X//0ppr9mNJf91rTCcXoSEWzfXeL5\nrbtYv3UXz2/dxYvbdjOqvo4Txo6gvq6Gtp3tbH1lL8cMq2X0iDr2lDrZ/PIetryyh85ePm4iwUB9\nJKU2vXHWSvv2m6WYruWkm/KIoLypIvvF7UpOIktWUvYL25kSR0/9q6sRw2pr2NvRSSkdrOyXWrSX\nOvdtO6xW1Cgr6zK8roZaiSDbR3l/ytsD2RtZ5f9F+bGoVLWsyu9sX3JQb//PBx7dvm9fTWX7KvtQ\nrf1d++k6rtkyByyUt1WI9A8pey5lb8h7O/bXK///6gz2vc4648BEUKP9MWvK4nXG/rqdXX8gpfBd\nr5nOTtiT5ryr9toaVpv9Duzeu/91NKw2+2NiT+nAsuw1zAFl5f3p2kep4oXV3e/08Noa3nrScTy2\nZgt7Sp38j7Mn89UPnvHqivkXSWUcAAAGjklEQVTkeuXluYQ1CVhb9nwdcE53ddIb/zayS1CTgEcq\ntp2UlqvFnABsjYhSlfrd7eOACagkXQFckZ7ulLQyRx+7MxHY+BzZqdNRbiIVx/oo5mOxn4/FfoN+\nLH5Ttvw14Gs9XqPp0Y8iYnZvlY64QfSIuBm4uT9iSWqJiMb+iHW487HYz8diPx+L/Y7GY5HnouF6\nYErZ88mprGqddHlpLNlAd3fbdle+CRiXYlTuq7t9mJnZIMiTQJYC0yVNkzQcaAKaK+o0A3PT8kXA\ng2lsohloklSf7q6aDjzaXcy0zU9TDFLM7/eyDzMzGwS9XsJK4w1XA/eR3XK7ICKWS7oOaImIZmA+\ncLukVmAzWUIg1VsMrABKwFUR0QFQLWba5WeBhZK+CPwyxaa7fRxi/XIp7AjhY7Gfj8V+Phb7HXXH\note7sMzMzKrxZIpmZlaIE4iZmRXiBNINSbMlrZTUKumawW7PoSLpWUm/krRMUksqGy/pfkm/ST+P\nS+WS9PV0TJ6QdHZZnLmp/m8kze1uf0OJpAWSNkh6sqys3/ou6a3p2LambYfkR9O7OQ7XSlqfXhfL\nJF1Ytm5e6tNKSbPKyqv+zqSbZZak8kXpxpkhSdIUST+VtELSckl/nsqPutdFLtnH8v0of5AN7K8C\nTgGGA48DMwa7XYeor88CEyvKvgJck5avAa5PyxcCPyT7lOq5wJJUPh5YnX4el5aPG+y+5ej7u4Cz\ngScPRd/J7jg8N23zQ+CCwe5zH47DtcBfVKk7I/0+1APT0u9JbU+/M8BioCktfxv42GD3uYdjcQJw\ndloeQzbl0oyj8XWR5+EzkOpmAq0RsToi9pBN7jhnkNs0kOYAt6blW4H3l5XfFplHyD6zcwIwC7g/\nIjZHxBbgfqDXT7EOtoh4mOyOvnL90ve07tiIeCSyd43bymINKd0ch+7MARZGRHtEPAO0kv2+VP2d\nSX9d/y5wd9q+/JgOORHxQkT8Ii3vAJ4imwXjqHtd5OEEUl216VsmdVP3cBfAjyU9pmwaGIDjI+KF\ntPwicHxa7u64HEnHq7/6PiktV5YfTq5Ol2UWdF2yoe/HoafpiYY0SScDZwFL8OuiKicQe0dEnA1c\nAFwl6V3lK9NfSUflvd5Hc9+BbwGnAmcCLwBfHdzmDCxJo4F7gE9GxPbydUf56+IATiDV5Zm+5YgQ\nEevTzw3Av5JdingpnWqTfm5I1fs6Nc3hqL/6vj4tV5YfFiLipYjoiIhO4J/IXhfQv9MTDUmShpEl\njzsi4rup2K+LKpxAqsszfcthT9IoSWO6loH3AE9y4LQxldPJfDjdeXIusC2d1t8HvEfScelSx3tS\n2eGoX/qe1m2XdG4aB/hwWawhr+vNMvkA2esC+nd6oiEn/V/NB56KiK+VrfLroprBHsUfqg+yuyue\nJruz5HOD3Z5D1MdTyO6WeRxY3tVPsuvWPyGbHfoBYHwqF3BjOia/AhrLYn2UbEC1FbhssPuWs/93\nkV2e2Ut2Lfry/uw70Ej2xrsK+CZp5oeh9ujmONye+vkE2ZvkCWX1P5f6tJKyO4i6+51Jr7NH0/H5\nF6B+sPvcw7F4B9nlqSeAZelx4dH4usjz8FQmZmZWiC9hmZlZIU4gZmZWiBOImZkV4gRiZmaFOIGY\nmVkhTiBm/UTS59IMrk+kGWzP6aHuLZIu6m692eGg16+0NbPeSXo78F6ymVzbJU0km5XW7IjlMxCz\n/nECsDEi2gEiYmNEPC/p85KWSnpS0s3VvvshfT/EQ2lCy/vKpsz4RPpeiickLRzg/pj1yh8kNOsH\nafK9/wBGkn1SeVFEPCRpfERsTnVuBxZHxA8k3QL8G9k0Fg8BcyKiTdLFwKyI+Kik54Fp6YxmXERs\nHYy+mXXHl7DM+kFE7JT0VuCdwO8Ai5R9K98OSZ8hSyzjyaaM+UHZpq8H3gTcn05OasmmFYFsOo07\nJH0P+N6AdMSsD5xAzPpJRHQAPwN+JulXwJ8CbyGbH2mtpGuBERWbCVgeEW+vEvL3yb4t8H3A5yS9\nOfZ/r4bZoPMYiFk/kPR6SdPLis4km2wQYGO6xFXtrquVQEMahEfSMEmnS6oBpkTET4HPAmOB0Yeu\nB2Z95zMQs/4xGviGpHFAiWwG1iuArWQzr75INuX5ASJiT7qd9+uSxpL9Tv5fslltv5PKBHzdYyA2\n1HgQ3czMCvElLDMzK8QJxMzMCnECMTOzQpxAzMysECcQMzMrxAnEzMwKcQIxM7NC/j90wwiEvFzJ\nWwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oHg3nyjUTiRu",
        "colab_type": "code",
        "outputId": "26be45fb-530e-4495-a787-8bd9a9013a30",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "print(\"Skewness: %f\" % df['Sales'].skew())\n",
        "print(\"Kurtosis: %f\" % df['Sales'].kurt())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Skewness: 12.972752\n",
            "Kurtosis: 305.311753\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iFvD-4nsTzY6",
        "colab_type": "text"
      },
      "source": [
        "Profit Distribution"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0nnFS8vi_rOe",
        "colab_type": "code",
        "outputId": "a869b730-d211-491c-c652-c37048771440",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        }
      },
      "source": [
        "df.Profit.describe()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    9994.000000\n",
              "mean       28.656896\n",
              "std       234.260108\n",
              "min     -6599.978000\n",
              "25%         1.728750\n",
              "50%         8.666500\n",
              "75%        29.364000\n",
              "max      8399.976000\n",
              "Name: Profit, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 159
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h9ZmvO1b_4sF",
        "colab_type": "code",
        "outputId": "851972ba-c7ae-4efa-9186-30b50c42f719",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "plt.scatter(range(df.shape[0]), np.sort(df['Profit'].values))\n",
        "plt.xlabel('index')\n",
        "plt.ylabel('Profit')\n",
        "plt.title(\"Profit distribution\")\n",
        "sns.despine()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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SO/Xf9qXtRtsH2D/9oVW/etVu5xQum9IfzHFpOzdX43YGpvULl1y2a5p2V0m9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\nAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GIAYrDJyCT6r",
        "colab_type": "code",
        "outputId": "83148977-aa51-4b1b-b2d3-db0050916dfe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "sns.distplot(df['Profit'])\n",
        "plt.title(\"Distribution of Profit\")\n",
        "sns.despine()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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iUNDANHe/H7g/J+2qjOW9wIf3s+81wDV50l8EZvclsyL9JZme3M7Vy0gk0EhliaVEUlNX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\nAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "USy48-H2UXqB",
        "colab_type": "code",
        "outputId": "96a80a11-bd32-4b44-8c6c-54c155adf532",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "print(\"Skewness: %f\" % df['Profit'].skew())\n",
        "print(\"Kurtosis: %f\" % df['Profit'].kurt())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Skewness: 7.561432\n",
            "Kurtosis: 397.188515\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Nt0PQIjW-wXd",
        "colab_type": "text"
      },
      "source": [
        "## Isolation Forest\n",
        "\n",
        "### Anomaly Detection on Sales"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uFuAUh5S778M",
        "colab_type": "code",
        "outputId": "bc047c0e-35f6-4a7a-9e73-6c12a20801be",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        }
      },
      "source": [
        "isolation_forest = IsolationForest(n_estimators=100)\n",
        "isolation_forest.fit(df['Sales'].values.reshape(-1, 1))\n",
        "xx = np.linspace(df['Sales'].min(), df['Sales'].max(), len(df)).reshape(-1,1)\n",
        "anomaly_score = isolation_forest.decision_function(xx)\n",
        "outlier = isolation_forest.predict(xx)\n",
        "plt.figure(figsize=(10,4))\n",
        "plt.plot(xx, anomaly_score, label='anomaly score')\n",
        "plt.fill_between(xx.T[0], np.min(anomaly_score), np.max(anomaly_score), \n",
        "                 where=outlier==-1, color='r', \n",
        "                 alpha=.4, label='outlier region')\n",
        "plt.legend()\n",
        "plt.ylabel('anomaly score')\n",
        "plt.xlabel('Sales')\n",
        "plt.show();"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/ensemble/iforest.py:237: FutureWarning: default contamination parameter 0.1 will change in version 0.22 to \"auto\". This will change the predict method behavior.\n",
            "  FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/ensemble/iforest.py:247: FutureWarning: behaviour=\"old\" is deprecated and will be removed in version 0.22. Please use behaviour=\"new\", which makes the decision_function change to match other anomaly detection algorithm API.\n",
            "  FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/ensemble/iforest.py:415: DeprecationWarning: threshold_ attribute is deprecated in 0.20 and will be removed in 0.22.\n",
            "  \" be removed in 0.22.\", DeprecationWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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vdhuAmZWa2R9Ciqmp2rQ5Vlqdnl960/NLb3p+6U3PLyTmXtfrZCIi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\nAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 720x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FkhRwo1cgYtK",
        "colab_type": "code",
        "outputId": "f72e1ebe-edb4-4282-c810-151b504f0894",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 517
        }
      },
      "source": [
        "df.loc[df['Sales'] > 1500].head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Row ID</th>\n",
              "      <th>Order ID</th>\n",
              "      <th>Order Date</th>\n",
              "      <th>Ship Date</th>\n",
              "      <th>Ship Mode</th>\n",
              "      <th>Customer ID</th>\n",
              "      <th>Customer Name</th>\n",
              "      <th>Segment</th>\n",
              "      <th>Country</th>\n",
              "      <th>City</th>\n",
              "      <th>State</th>\n",
              "      <th>Postal Code</th>\n",
              "      <th>Region</th>\n",
              "      <th>Product ID</th>\n",
              "      <th>Category</th>\n",
              "      <th>Sub-Category</th>\n",
              "      <th>Product Name</th>\n",
              "      <th>Sales</th>\n",
              "      <th>Quantity</th>\n",
              "      <th>Discount</th>\n",
              "      <th>Profit</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>11</td>\n",
              "      <td>CA-2014-115812</td>\n",
              "      <td>2014-06-09</td>\n",
              "      <td>2014-06-14</td>\n",
              "      <td>Standard Class</td>\n",
              "      <td>BH-11710</td>\n",
              "      <td>Brosina Hoffman</td>\n",
              "      <td>Consumer</td>\n",
              "      <td>United States</td>\n",
              "      <td>Los Angeles</td>\n",
              "      <td>California</td>\n",
              "      <td>90032</td>\n",
              "      <td>West</td>\n",
              "      <td>FUR-TA-10001539</td>\n",
              "      <td>Furniture</td>\n",
              "      <td>Tables</td>\n",
              "      <td>Chromcraft Rectangular Conference Tables</td>\n",
              "      <td>1706.184</td>\n",
              "      <td>9</td>\n",
              "      <td>0.2</td>\n",
              "      <td>85.3092</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>28</td>\n",
              "      <td>US-2015-150630</td>\n",
              "      <td>2015-09-17</td>\n",
              "      <td>2015-09-21</td>\n",
              "      <td>Standard Class</td>\n",
              "      <td>TB-21520</td>\n",
              "      <td>Tracy Blumstein</td>\n",
              "      <td>Consumer</td>\n",
              "      <td>United States</td>\n",
              "      <td>Philadelphia</td>\n",
              "      <td>Pennsylvania</td>\n",
              "      <td>19140</td>\n",
              "      <td>East</td>\n",
              "      <td>FUR-BO-10004834</td>\n",
              "      <td>Furniture</td>\n",
              "      <td>Bookcases</td>\n",
              "      <td>Riverside Palais Royal Lawyers Bookcase, Royal...</td>\n",
              "      <td>3083.430</td>\n",
              "      <td>7</td>\n",
              "      <td>0.5</td>\n",
              "      <td>-1665.0522</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>149</th>\n",
              "      <td>150</td>\n",
              "      <td>CA-2016-114489</td>\n",
              "      <td>2016-12-05</td>\n",
              "      <td>2016-12-09</td>\n",
              "      <td>Standard Class</td>\n",
              "      <td>JE-16165</td>\n",
              "      <td>Justin Ellison</td>\n",
              "      <td>Corporate</td>\n",
              "      <td>United States</td>\n",
              "      <td>Franklin</td>\n",
              "      <td>Wisconsin</td>\n",
              "      <td>53132</td>\n",
              "      <td>Central</td>\n",
              "      <td>FUR-CH-10000454</td>\n",
              "      <td>Furniture</td>\n",
              "      <td>Chairs</td>\n",
              "      <td>Hon Deluxe Fabric Upholstered Stacking Chairs,...</td>\n",
              "      <td>1951.840</td>\n",
              "      <td>8</td>\n",
              "      <td>0.0</td>\n",
              "      <td>585.5520</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>165</th>\n",
              "      <td>166</td>\n",
              "      <td>CA-2014-139892</td>\n",
              "      <td>2014-09-08</td>\n",
              "      <td>2014-09-12</td>\n",
              "      <td>Standard Class</td>\n",
              "      <td>BM-11140</td>\n",
              "      <td>Becky Martin</td>\n",
              "      <td>Consumer</td>\n",
              "      <td>United States</td>\n",
              "      <td>San Antonio</td>\n",
              "      <td>Texas</td>\n",
              "      <td>78207</td>\n",
              "      <td>Central</td>\n",
              "      <td>TEC-MA-10000822</td>\n",
              "      <td>Technology</td>\n",
              "      <td>Machines</td>\n",
              "      <td>Lexmark MX611dhe Monochrome Laser Printer</td>\n",
              "      <td>8159.952</td>\n",
              "      <td>8</td>\n",
              "      <td>0.4</td>\n",
              "      <td>-1359.9920</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>167</th>\n",
              "      <td>168</td>\n",
              "      <td>CA-2014-139892</td>\n",
              "      <td>2014-09-08</td>\n",
              "      <td>2014-09-12</td>\n",
              "      <td>Standard Class</td>\n",
              "      <td>BM-11140</td>\n",
              "      <td>Becky Martin</td>\n",
              "      <td>Consumer</td>\n",
              "      <td>United States</td>\n",
              "      <td>San Antonio</td>\n",
              "      <td>Texas</td>\n",
              "      <td>78207</td>\n",
              "      <td>Central</td>\n",
              "      <td>FUR-CH-10004287</td>\n",
              "      <td>Furniture</td>\n",
              "      <td>Chairs</td>\n",
              "      <td>SAFCO Arco Folding Chair</td>\n",
              "      <td>1740.060</td>\n",
              "      <td>9</td>\n",
              "      <td>0.3</td>\n",
              "      <td>-24.8580</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     Row ID        Order ID Order Date  ... Quantity Discount     Profit\n",
              "10       11  CA-2014-115812 2014-06-09  ...        9      0.2    85.3092\n",
              "27       28  US-2015-150630 2015-09-17  ...        7      0.5 -1665.0522\n",
              "149     150  CA-2016-114489 2016-12-05  ...        8      0.0   585.5520\n",
              "165     166  CA-2014-139892 2014-09-08  ...        8      0.4 -1359.9920\n",
              "167     168  CA-2014-139892 2014-09-08  ...        9      0.3   -24.8580\n",
              "\n",
              "[5 rows x 21 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 114
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XFbRlmrYgtTS",
        "colab_type": "code",
        "outputId": "04338a31-652c-4ee9-d7ed-5279bc7aa74e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 399
        }
      },
      "source": [
        "df.iloc[10]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Row ID                                                 11\n",
              "Order ID                                   CA-2014-115812\n",
              "Order Date                            2014-06-09 00:00:00\n",
              "Ship Date                             2014-06-14 00:00:00\n",
              "Ship Mode                                  Standard Class\n",
              "Customer ID                                      BH-11710\n",
              "Customer Name                             Brosina Hoffman\n",
              "Segment                                          Consumer\n",
              "Country                                     United States\n",
              "City                                          Los Angeles\n",
              "State                                          California\n",
              "Postal Code                                         90032\n",
              "Region                                               West\n",
              "Product ID                                FUR-TA-10001539\n",
              "Category                                        Furniture\n",
              "Sub-Category                                       Tables\n",
              "Product Name     Chromcraft Rectangular Conference Tables\n",
              "Sales                                             1706.18\n",
              "Quantity                                                9\n",
              "Discount                                              0.2\n",
              "Profit                                            85.3092\n",
              "Name: 10, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 115
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bH4o-CL-N9Np",
        "colab_type": "text"
      },
      "source": [
        "The region where the data has low probability to appear is on the right side of the distribution."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7HK9cBvwGOqG",
        "colab_type": "text"
      },
      "source": [
        "### Anomaly detection on Profit"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PoDzs4DTFSY-",
        "colab_type": "code",
        "outputId": "84e7df8f-2013-4c12-8b54-7bf0f1d4fc7b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        }
      },
      "source": [
        "isolation_forest = IsolationForest(n_estimators=100)\n",
        "isolation_forest.fit(df['Profit'].values.reshape(-1, 1))\n",
        "xx = np.linspace(df['Profit'].min(), df['Profit'].max(), len(df)).reshape(-1,1)\n",
        "anomaly_score = isolation_forest.decision_function(xx)\n",
        "outlier = isolation_forest.predict(xx)\n",
        "plt.figure(figsize=(10,4))\n",
        "plt.plot(xx, anomaly_score, label='anomaly score')\n",
        "plt.fill_between(xx.T[0], np.min(anomaly_score), np.max(anomaly_score), \n",
        "                 where=outlier==-1, color='r', \n",
        "                 alpha=.4, label='outlier region')\n",
        "plt.legend()\n",
        "plt.ylabel('anomaly score')\n",
        "plt.xlabel('Profit')\n",
        "plt.show();"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/ensemble/iforest.py:237: FutureWarning: default contamination parameter 0.1 will change in version 0.22 to \"auto\". This will change the predict method behavior.\n",
            "  FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/ensemble/iforest.py:247: FutureWarning: behaviour=\"old\" is deprecated and will be removed in version 0.22. Please use behaviour=\"new\", which makes the decision_function change to match other anomaly detection algorithm API.\n",
            "  FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/ensemble/iforest.py:415: DeprecationWarning: threshold_ attribute is deprecated in 0.20 and will be removed in 0.22.\n",
            "  \" be removed in 0.22.\", DeprecationWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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4EPhi9iMUkcGkoyNCUSREcSRES7uG/RCRvtt/33154a9/zXUY/ZLLkrYZwBvu/iaAmd0F\nzAc6kzZ3fzvYp7lrRIa4jpK2okiIkmhYSZsIyZ6WZhr6Jh+kozlaLnuPjge6dgdZG2wTEdlN15K2\nhtYYqzbU5zgikdwqaWpic319Vkfkl/5xdzZv3jzg4UcKoveomX0W+CzAxIkTcxyNiGRCZ9IWDvHG\nxj0PGSAyFEx4803WArVdxiaTNIrHoaQk+UqDkpKSzkF5+yuXSds6oGv/3QnBtj5z91uAWyDZe3Tg\noYnIYNNRPRoNhzjp4LE8tGJDjiMSya1oLMbk117LdRiF6913YfZsmDs315F0ymX16LPA/mY22cyK\ngHOB+3MYj4gMYh3jtBVHQkweXU5Uk8aLyBCTs6TN3WPAvwIPAyuB37n7CjP7lpmdBmBm081sLfBx\n4KdmtiJX8YpIbnVt01YaDdMed9rj6qMkIkNHTtu0uftiYPEu277WZflZktWmIjLEde09WlaUnHuw\nuT1OtOdpCEVECormHhWRvNC1I0JpkLQ1tmZvomYRkVxT0iYieWHRG8khPiLhUOek8RvrWnMZkohI\nVilpE5G88E7DjlK1xS+vB2D+TUtzFY6ISNYpaRORvDCjpoSicPKRdfT7RuU4GhGR7FPSJiJ5oShk\nHDK+CoDzj0oOon3pnPflMiQRkaxS0iYieaEl7pQEXUXNjJHlRaze3JTjqEREskdJm4jkhZZ4guLI\njkfWuOoSNjeqI4KIDB1K2kQkL7R2KWkDGDeslG1N7TmMSEQku5S0iUheaIs7RV1K2qpLo2xvVtIm\nIkOHkjYRyQuxBERCOx5Zw0qjKmkTkSFFSZuI5IWYO5HQjkniq8uiNLfHaY1p/lERGRqUtIlIXogl\nnEh4R9I2rKwIQFWkIjJkKGkTkbyQrB7tUtIWTGWlpE1EhgolbSKSF5IlbV06IpQlk7ZtStpEZIhQ\n0iYieSHmu5a0JatHF9z+fK5CEhHJKiVtIpIXdm3TVlkSAaCpLZ6rkEREskpJm4gMeu4Q952H/Bg7\nrCSHEYmIZF8k1wGIiPQmRrKErWv1aEk0zOz9R9HQ0g4v5SoyEZHsUUmbiAx68Y6kLbzzIysSMl54\nZ3suQhIRyTolbSIy6LV3U9IG8PyabQBsjJRlPSYRkWxT0iYig15n9Wh456Tt6lMOAqAuXJz1mERE\nsk1Jm4gMet21aQOoCTojbFfSJiJDgJI2ERn02oKkLbpLm7aqYNgPJW0iMhQoaRORQa8+6OheFUxd\n1WFYx1RWStpEZAhQ0iYig14dYWDHgLodlLSJyFCipE1EBr16TyZtVSU7l7R1lLypI4KIDAVK2kRk\n0KsLqkd3LWmLhkMUhY3GULS7t4mIFBQlbSIy6NUH1aO7tmkDKCuK0BzS5C4iUviUtInIoNdTSRtA\nWVFYJW0iMiQoaRORQa/ewxSHjeJIeLd9ZUVhmpW0icgQkNOkzcxOMrNXzewNM7uqm/3FZrYw2P+0\nmU3KfpQikmt1RKiMdv+4KldJm4gMEb0mbZb0CTP7WrA+0cxmDPSDzSwM3AScDEwBzjOzKbsc9hlg\nq7vvB9wAXDfQzxWR/FNHmKqi7h9Xw0qjbImUZjkiEZHsS6Wk7b+BDwLnBev1JJOtgZoBvOHub7p7\nG3AXMH+XY+YDtwXLi4C5ZmaIyJBSR6THpK2qJEpjWCVtIlL4UknajnL3fwFaANx9K1CUhs8eD7zT\nZX1tsK3bY9w9BmwHRu56IjP7rJktM7NltbW1aQhNRAaTeg9T2UPSVlYUZm20MssRiYhkXypJW3tQ\nlekAZjYaSGQ0qj5y91vcfZq7Txs9enSuwxGRNNtTSdu9y9+lLRThrfbdOymIiBSSVJK2HwH3AmPM\n7DvAE8B30/DZ64C9u6xPCLZ1e4yZRYBhwOY0fLaI5JH6PbRpa487AOtjStpEpLD1mrS5+53Al4Dv\nAeuB09397jR89rPA/mY22cyKgHOB+3c55n5gQbB8FvBXd/c0fLaI5JFkSVv3SdntC44AoDmhEYxE\npLDtcRjxoFp0hbsfCKxK5we7e8zM/hV4GAgDv3D3FWb2LWCZu98P3Ar82szeALaQTOxEZAhpdWgl\n1OOQH5NHlgOwRUmbiBS4PSZt7h4PxlGb6O5r0v3h7r4YWLzLtq91WW4BPp7uzxWR/FEfJGM9VY+O\nKEv2HN0aV9ImIoUtlQn7hgMrzOwZoLFjo7uflrGoREQC9YnkKD976j0KsKpNw36ISGFLJWm7JuNR\niIj0oC6+55I2MyPicd5W71ERKXCpdER4nGR7tsrgtTLYJiKScR3Voz2VtAG8v2ULUQ27LSIFLpVp\nrM4GniHZtuxs4GkzOyvTgYmIANQF1aM9lbQBVMdaiatfuYgUuFSqR68Gprv7RugcXPcvJKeVEhHJ\nqB0dEXqu/izyOA2oqE1EClsq3a1CHQlbYHOK7xMRGbC6jurRHob8ACj2GK0JJW0iUthSKWl7yMwe\nBn4brJ8DPJi5kEREdqhLGCGc8j00WitOxGlxJW0iUth6Tdrc/UozOwP4ULDpFne/N7NhiYgk/XRb\nJQmMkPWclI2It7BZ47SJSIHrNWkzs8nAYne/J1gvNbNJ7v52poMTEWlLoa3a2PYGGjxEQ8KoCKlH\ngogUplT+NL0bSHRZjwfbREQybt9oO4dTv8djxrYnx/3eoEnjRaSApZK0Rdy9rWMlWC7KXEgiIjub\nYK173F/T3gDApzeMzEY4IiI5kUrSVmtmnVNWmdl8YFPmQhIR2aEuEaJ8p8L+3UU8WSW6JpZK3yoR\nkfyUyhPuEuBOM/sJYMA7wAUZjUpEBHCHTfEwYdtzO7UjmjZkKSIRkdxJpffoP4CZZlYRrDdkPCoR\nEaAxGMZjb2vZ43FhdiR1b7eHmRSNZzQuEZFcSGUaq8vNrApoBH5oZs+b2bzMhyYiQ93zLcnms+96\nccrv2ajOCCJSoFJp0/Zpd68D5gEjgU8C12Y0KhERYElzMlk7xBp7PfYHo7cC8HhzSUZjEhHJlVSS\nto5Bkk4Bbnf3FV22iYhkzMhwsgPCyba512NPr2gC6KXLgohI/kolaXvOzP5EMml72Mwq0XNRRLLg\nui3DAKii9zZqUYNR4Th1cf1NKSKFKZXeo58BpgJvunuTmY0EPpXZsEREdtjDDFY7GRZKsC2h6axE\npDCl0ns0ATzfZX0z0HtdhYjIAJVYgk9WNSa7QaWgOpRgu5I2ESlQerqJyKDU6tDiIYaFUm+NUR1O\nsFUTx4tIgdLTTUQGpcagxKy8DxPADwu5qkdFpGClMk7bf5rZwdkIRkSkw9pgvLW6PiRh1aEE21XS\nJiIFKpWn20rgFjN72swuMbNhmQ5KRGRdezJpmxyNpfye6nCCBg/RnnrhnIhI3ug1aXP3n7v7LJLz\njU4CXjKz35jZcZkOTkSGrreCyd+PKd3zFFZdVQft39QZQUQKUUpPNjMLAwcGr03AcuALZnZXBmMT\nkSFsYyxMZShBdbgPbdqCwXi3qYpURApQr0N+mNkNwEeBR4Dvuvszwa7rzOzVTAYnIkPXtkSos+Qs\nVSppE5FClsrgui8BX3X37kZKmpHmeEREgGRpWX+TNpW0iUgh6jFpM7MjgsXlwAG2y5Dk7v68u2/P\nYGwiMoRtTYSoDvcxaeuoHk1oKisRKTx7Kmn7zz3sc+D4/n6omY0AFpLs2PA2cLa7b+3muIeAmcAT\n7v6R/n6eiOSf2liI95Wm3nMUupS0qXpURApQj0mbu2eyd+hVwCPufq2ZXRWs/0c3x/0AKAMuzmAs\nIjIINXqIyj5Wj1aGnBDO5ng4Q1GJiOROKm3aMLNDgClAScc2d799AJ87H5gTLN8GPEY3SZu7P2Jm\nc3bdLiKFb3siRKn1bcC1kMHekThvtKX0aBMRySup9B79OskEawqwGDgZeAIYSNJW4+7rg+UNQM0A\nziUiBWZbPNkmLd6P9+4djbFRJW0iUoBS+XP0LOBw4AV3/5SZ1QB39PYmM/sLMLabXVd3XXF3N+vj\nn9O7f9Zngc8CTJw4cSCnEpFBYH0whdXU4rY+v3dMOMFb7SppE5HCk8qTrdndE2YWM7MqYCOwd29v\ncvcTetpnZu+Z2Th3X29m44Jz9pu73wLcAjBt2jRNYCOS594LSsrGRvrWpg1gazzEuliEhoRR0YfJ\n5kVEBrtUulgtM7Nq4GfAc8DzwJMD/Nz7gQXB8gLgvgGeT0QKyPLWIgBqwn2vID2uLDnt1WqVtolI\ngen1qebulwaLNwdDcFS5+0sD/Nxrgd+Z2WeA1cDZAGY2DbjE3S8K1v9GcuqsCjNbC3zG3R8e4GeL\nyCDXGIyztlek70nbQcXtAGzSALsiUmBS7T16GMkx1SLB+n7ufk9/P9TdNwNzu9m+DLioy/rs/n6G\niOSvxoQxIhQn1I8xcocHw4Q81lTCsWWtaY5MRCR3Uuk9+gvgMGAF0NHAxIF+J20iInvS7CHK+tke\nbViQtP2yroKvj9KkLSJSOFIpaZvp7lMyHomISKApYZT1s1P5iD5OfSUiki9SafTxpJkpaRORrGl0\n63dJW9jg36rrAGjQHKQiUkBSSdpuJ5m4vWpmL5nZy2Y20I4IIiI92hoPDajELBrkaieuHZOmiERE\nci+V6tFbgU8CL7OjTZuISMbUJ0K8L9S3yeK7mlKU7EG6LqZhP0SkcKTyRKt19/szHomISKA+YVRY\n//9GnFueHKttRKg/E2GJiAxOqSRtL5jZb4A/Ap395wcy5IeISE/ckyVtlWmYzWBLQnOQikjhSCVp\nKyWZrM3rsk1DfohIRrS40Y5RGRpYa4xPVDXw27ryNEUlIpJ7qcyI8KlsBCIiArC8NQqADbDj516R\nOHGM7XFjWFhzkIpI/uu196iZTTCze81sY/D6vZlNyEZwIjL0rI8lqzSPKR3YbAajgt6n56wfPeCY\nREQGg1SG/PglyQne9wpefwy2iYik3Z1BleaofkwW39Xs0mRnhFVt0QHHJCIyGKSStI1291+6eyx4\n/QrQn64ikhH7FyWH+hgbGVibtnEDfL+IyGCTStK22cw+YWbh4PUJYHOmAxORoanJjX0i/R+jrauD\ni9rSch4RkcEglaTt08DZwAZgPXAWoM4JIpIRW+MhqtM0f+jRpa2UDmC8NxGRwSSV3qOrgdOyEIuI\nCLXxEOMj6RkUtzLkNHuIdt8xtZWISL7qNWkzs9HAPwOTuh7v7p/OXFgiMhQlHFa2FTE52pyW89UF\nE8avi4WZFNXsCCKS31IZXPc+4G/AXwA99UQkY24Leo4ubixNy/kOCuYg/fn2Cr49antazikikiup\nJG1l7v4fGY9ERIa8mCdLxu7eqzYt5zulvIUrauGOOiVtIpL/UumI8ICZnZLxSERkyFvaXAzAwUEJ\n2UCVhpyacJxTytNT3SoikkupJG2Xk0zcms2szszqzawu04GJyNCzKR5iv2g7ZWmYLL7D6HCcFlcv\nBBHJf6n0Hq3MRiAiIvWJEIcXp3dstRJzWjTqh4gUgFTatGFmw4H9gZKObe6+JFNBicjQ05KA9+Kh\nzjlD06U05DQkUqlUEBEZ3FIZ8uMiklWkE4AXgZnAk8DxmQ1NRIaSp1qKafEQx5S1pPW8xeb8rbUo\nrecUEcmFVNu0TQdWu/txwAeAbRmNSkSGnAs3jAJgcjQ9U1h1WBlMGL+qLaWKBRGRQSuVpK3F3VsA\nzKzY3VcBB2Q2LBEZqvZK02wIHW4aswWAx5tKejlSRGRwSyVpW2tm1cAfgD+b2X3A6syGJSJDSSzo\nLHp5dV3ap5uaWpIcPuR7W4al98QiIlnWa9Lm7h9z923u/g3gGuBW4PRMByYiQ8e2oKPAiDR3Qugw\nPpKscr1xqzrDi0j+6lOXKnd/3N3vd/f09skXkSFtQywMZC5pu2vcJgBu2FqVkfOLiGSD+sGLSM7d\n21AGwPSS1oycf+9onLMrGwE0ZpuI5K2cJG1mNsLM/mxmrwf/Du/mmKlm9qSZrTCzl8zsnFzEKiKZ\n914sRARnbCRzGdWHSpMJ4Qsa/kNE8lSuStquAh5x9/2BR4L1XTUBF7j7wcBJwA+DDhEiUmDeiUU4\noiSzrS6mBPOZboyHM/o5IiKZkqukbT5wW7B8G910bHD319z99WD5XWAjMDprEYpIVrQ6LG8toiqU\n2XrLyuD8DQnNQyoi+SlXSVuNu68PljcANXs62MxmAEXAPzIdmIhk16ag5Gtqmucc3VV5MAn9iy2q\nHhWR/JSxpM3M/mJmr3Tzmt+SdLB2AAAVbElEQVT1OHd3wPdwnnHAr4FPuXu3f4qb2WfNbJmZLaut\nrU3r9xCRzPm/5iJmrRkLwKHF7Rn9rDJLPmbubijP6OeIiGRKxuZ1cfcTetpnZu+Z2Th3Xx8kZRt7\nOK4K+F/gand/ag+fdQtwC8C0adN6TABFZHB5qLG0c3lWaWZ6jnYIGRxa1Ma6mNq0iUh+ylX16P3A\ngmB5AXDfrgeYWRFwL3C7uy/KYmwikiUNiRCVluCNyeuIZKGp2emVTWxJhNkU12hHIpJ/cvXkuhb4\nsJm9DpwQrGNm08zs58ExZwPHABea2YvBa2puwhWRdGtKGPc0lHFwcXtWEjaAA4LJ6KetHsc/NIG8\niOSZnDy13H0zMLeb7cuAi4LlO4A7shyaiGTJP60fBcAZFU1Z+8xpXQbv/d6WKn4+dkvWPltEZKBU\nRyAiWVcbC/FiMMjtWZXZS9pKQvDKpHeJ4rS5hv4QkfyipE1Esm55kLB9a+Q2QlnOnSpCzjFlLSxp\nLqFJY7aJSB5R0iYiWbcqaE82r7w5J5//Vnvy86+o3W0GPRGRQUtJm4hk3dtB0jQqnJvZ2389bjMA\nDzaWktAgQSKSJ5S0iUjWvdEeZUZJa9Z6je5qfCTOVSO2A/C0ZkgQkTyhpE1Esuqt9jAvthZxYFFm\nZ0DozSeqGgFYVF+W0zhERFKlpE1EsuoPQZJ0So7as3WoCDkTIzHWa4YEEckTStpEJKsWNZQxLJTg\nqJLMThCfimklbSxvLaJV7dpEJA8oaRORrKlPGOtiEU4oa8YGwWgbc8uaafQQB7w1HlfiJiKDnJI2\nEcmaPzQkq0bnlrXkOJKkD5fviOOn2ytyGImISO+UtIlI1nQ0+p9akttOCB2KLDnAL8CNWytzHI2I\nyJ4paRORrHi5Ncry1iJOLW9ir0g81+F0umBYIzNKWmn2EL/cXp7rcEREeqSkTUSy4snmYgAWBENt\nDCbfG5Usbfvm5moNtisig5aSNhHJinsbSgnjTB8EvUZ39b6iGDXhZOnfxrgeiyIyOOnpJCIZl3BY\n2VbE+4vaB0Wv0e58I2jb9mYwxZaIyGCjpE1EMq42KL06YZD0Gu3O/kUxAH64tSrHkYiIdE9Jm4hk\njDssayliwYZRAMwqbc1xRD3bryjG4cVtPNNSzC/UIUFEBiElbSKSMQ80lnLWu6NZ1Ral1BLMLB18\n7dm6WjiuljHhON/aXM0bbaomFZHBRUmbiGSEO1y2cQQAPx6zhYcmbMxxRL0rCcG3gp6k/71N47aJ\nyOCipE1EMuIfQYP+Myqa+GhFM/tEB8/YbHtyUnkLhxS1cU9DGf/XXJTrcEREOilpE5G0a0nAnXXJ\ndmHnDcJx2XqzTzTZKeGf1o8mrnHbRGSQUNImIml15cZqDnp7L35ZV0EE5/Diwd2OrTvfHrW9c/nx\nYFBgEZFcU9ImIgPWnDDOWDeKSW+O5+6GckaEEnx5xHYenrCRokE6LtueDA8neGGf9QB8b/OwHEcj\nIpKk7lEiMiB/b41wyrqazvV/qa7nomENDA8nchjVwA0PJxgfifF6e5SNsRBjIvn9fUQk/6mkTUQG\nZElzCQD/PKyeVZPWceWIurxP2DpcObwOgC/WDs9xJCIiStpEZIDeaItQbgmuHllHSYE9UU6vbGb/\naDtLmkvYrDlJRSTH9BQSkQFZ1RYtmJK17nw+KG07cvU47qgrw9WbVERyREmbiPRLzOHVtgh1iRAT\nI7Fch5Mxp1S08MuxmwD46qbhXKOOCSKSI0raRKTPljQVc/SasZy4toY1sQiHFLfnOqSMOq6slVtq\nNgNwR10FCZW2iUgOKGkTkT5Z0lTMBRtGsTEe5oBoOzfXbOZfqutzHVbGzStv4eJhye959aZqmhJ5\nOJaJiOS1nCRtZjbCzP5sZq8H/+7WNcvM9jGz583sRTNbYWaX5CJWEdkh4XBbMNPB7/eq5eG9N3JS\neQvDwkOj6Ok/RiTbt/22vpwpb++lzgkiklW5euJcBTzi7vsDjwTru1oPfNDdpwJHAVeZ2V5ZjFFE\nAg0JY8H6kez71ngeaSrlQ6UtHFmSfzMdDFTI4KmJ6/loeRMAs9bU8ESTZkwQkezIVdI2H7gtWL4N\nOH3XA9y9zd1bg9ViVJUrkhPucGXtcB4PxmP7t+o6vjVyey/vKlxjIwl+XLOVG8dsocVDfGLDKLbH\nVVUqIpmXqxkRatx9fbC8Aajp7iAz2xv4X2A/4Ep3f7eH4z4LfBZg4sSJ6Y9WZIhJeHLC9xVtUR5v\nKmZ9PEJNOM5TEzdgyk8AmF/RzOttdfxkWxX/aI9wRLiwO2OISO5lLGkzs78AY7vZdXXXFXd3M+u2\nQYy7vwMcFlSL/sHMFrn7e90cdwtwC8C0adOGRuMakTR7vS3CFbXDeam1aKft48IxZpW2cN2obUrY\ndnFaRTM/2VbF2e+O5qaaLZxY3pLrkESkgGUsaXP3E3raZ2bvmdk4d19vZuOAjb2c610zewWYDSxK\nc6giQ9aGWIg768qJYfzPtsrO7Z+oamBcOM5F1Q0UK1Hr0f7RGF8dsZ1vbxnGxe+N5MHx77FPNE5Z\nSH87ikj65ap69H5gAXBt8O99ux5gZhOAze7eHPQu/RBwQ1ajFCkwzQmjoxKvMWEc/04NzR4iilOE\n85nqBr4wvI6oErWUmMFF1Q2sj4e5dXsFJ69LtvSYHI1x57hN7BWJ5zhCESkkuUrargV+Z2afAVYD\nZwOY2TTgEne/CDgI+M+g6tSA69395RzFK5LXfl1Xzv82lPJUy+49HU8tb+Kmmq05iKpwXDNyO8eX\ntfB6W4TlrUXc21DGFRuHM6u0hX8d3pDr8ESkQOQkaXP3zcDcbrYvAy4Klv8MHJbl0EQK0n9tqSTm\nxj6RGB+vbKQkaEY6PJzgjIrmHEdXGGaVtjKrtJWWRCNr2sM82VLMky3FXL91GJcMq+fMyibGhONU\nhpyQSjJFpB9yVdImIrtwh9p4iFfaovxiewXpbBW1NRHm36rr+MKIwp+5INdKQvD78ZtoThjHvVPD\nhniYm7dXcvP2ZJvBOaUtnFfVCMAHitsYE0nkMlwRySNK2kQGiR9tq+SGrVWd6+MjMcaF09Mm6qiS\nVo4vU8/GbCoNOU9M3ECrG+/GwtzfUMqPt1XxWHMJjwVj3kEycZta3EZN0P4tgmMG7b6jOO6AonaO\nK2vd7TNEZGhR0iaSBb+rK+O5XYbS2NVTzcXUhONcNWI7hxW3876iWJaik0yJGETM2b8oxhUj6rlw\nWCMbYmEAlrUUsbS5mOWtRbzQWtHruc6saCRiyQa+Z1c2ckSJxoUTGWqUtMmQ0ZwwljQXE+9HvePv\nG8qoT/Q8KUdzwnirPUIb3TdWagtKTcb2UnJ2ekUTH6tUG7NCNTKcYGQ4WR16cHE7C4Ylq0lbEuDB\nvfNme4SwOfsEJW9PtxRx9aZqnmguwYAN8TB31ZdTZE6lJXhfUYyjSlo5ryrZZi6i9nIiBUtJWz80\nxwE3SAyep2MCWFRfRqOnN6Ynm4t5tS2a1nPmysZ4eEDvrwolmFLUfelGeciZVdrK5Gj3pWNhg3Mr\nG9k7qiEgZHclISBoxXhw8c732JyyVpZO3DGm+P81F7GkqYQWN95sj7CkuYRnWor58bYqSizBv1Q3\ncGRJsir1sOJ2KjRmnEjBUNLWDzNeKqE+fhgMkTbd1aEEJ5cXRunPqHCcj/Sjt2QY2DcaU68/ybmj\nS9s4urStc31DLMSSpmQ7ucWNpfxnl3aRAAdEd/9D46LqBj5e2ZTxWEUkvZS09cPn92qnbeMmKC/P\ndSg7KTfnrMomLK39DqHY0PRFIoPU2EiCs6uaOLuqic3xEK+3JR/rjzaVsLp990f8Q02lXFk7nGs2\nDQOgxGC/aPuAqlVHhuNM7Gcp8qhwnAk9DEI8KRrjALXtFOmkpK0fPl0Th+21UK1qBxEZPEaGE4wM\nSuFmdimN6+qZ5iIeaUr2Xo1hvNEWodmN/g48Up8wlreWEO9H04ye2oB2tVc41u0fjUYyqRsWyvyQ\nKSXmXD2yjhFhDc8iuaWkTURkCJlR2saMHhK6bHOH19ojxLpJ+Fa3h3m0qaTHeoN3Y2E2xMJsYGBt\nVXsTc+PtWITN8TATe2izemBRO/9UpepmyTwlbSIikhNm9Fj9eXBxO6dU5H5swVaHj60bzfLWKMtb\nd++UtTWRTBoXN5ZSGF22emdBx6qjS/s2dmCpOWE1tRkQJW394Q7r10OT/rISyYotW/a83x3WrMlO\nLDKkFAOLWQM9jPizwsq4Jr4vdc1DJxt5iQr+2lTS+4G7KCHO3uTRINHxKia+0Matu026mTtK2vrj\n2GNhypRcRyEytEyc2P32aBT++Z/hgAOyG48IcDBwT66DyLIXaltZVtu3UtD69gRv1cWIJ/KrLfjY\n0cNyHcJOlLT1x5gxyZeIDA5nnJHrCESGjA8EL8m+nod4FxEREZFBQ0mbiIiISB5Q0iYiIiKSB5S0\niYiIiOQBJW0iIiIieUBJm4iIiEgeUNImIiIikgeUtImIiIjkAXPPr9GJe2NmtcDqXMfRD6OATbkO\nYhDR9dhB12Jnuh470/XYQddiZ7oeOxus12Mfdx+dyoEFl7TlKzNb5u7Tch3HYKHrsYOuxc50PXam\n67GDrsXOdD12VgjXQ9WjIiIiInlASZuIiIhIHlDSNnjckusABhldjx10LXam67EzXY8ddC12puux\ns7y/HmrTJiIiIpIHVNImIiIikgeUtGWJmV1mZqvMbIWZfb/L9i+b2Rtm9qqZndhl+0nBtjfM7Kou\n2yeb2dPB9oVmVpTt75IuZnaFmbmZjQrWzcx+FHy3l8zsiC7HLjCz14PXgi7bjzSzl4P3/MjMLBff\nZSDM7AfBvfGSmd1rZtVd9g3Z+6M7PX3vQmJme5vZo2b29+B5cXmwfYSZ/Tn4HfizmQ0Ptvf59ybf\nmFnYzF4wsweC9W7vczMrDtbfCPZP6nKObn+X8o2ZVZvZouCZsdLMPjjE743PB78nr5jZb82spKDv\nD3fXK8Mv4DjgL0BxsD4m+HcKsBwoBiYD/wDCwesfwL5AUXDMlOA9vwPODZZvBj6X6+/Xz2uyN/Aw\nyTH1RgXbTgEeBAyYCTwdbB8BvBn8OzxYHh7seyY41oL3npzr79aPazEPiATL1wHXDfX7o4fr1OP3\nLqQXMA44IliuBF4L7oXvA1cF26/qcp/0+fcm317AF4DfAA8E693e58ClwM3B8rnAwmC529+lXH+v\nfl6L24CLguUioHqo3hvAeOAtoLTLfXFhId8fKmnLjs8B17p7K4C7bwy2zwfucvdWd38LeAOYEbze\ncPc33b0NuAuYH5QiHQ8sCt5/G3B6Fr9HOt0AfAno2qhyPnC7Jz0FVJvZOOBE4M/uvsXdtwJ/Bk4K\n9lW5+1Oe/M27nTy8Hu7+J3ePBatPAROC5aF8f3Sn2++d45jSzt3Xu/vzwXI9sJLkf07zSf5MYeef\nbZ9+b7L4VdLCzCYApwI/D9b3dJ93vUaLgLnB8T39LuUVMxsGHAPcCuDube6+jSF6bwQiQKmZRYAy\nYD0FfH8oacuO9wOzg+LYx81serB9PPBOl+PWBtt62j4S2NblP/iO7XnFzOYD69x9+S67+no9xgfL\nu27PZ58m+ZcxDNH7Yw96+t4FK6i++QDwNFDj7uuDXRuAmmC5r/dJvvkhyT/wEsH6nu7zzu8c7N8e\nHF8o12IyUAv8Mqgu/rmZlTNE7w13XwdcD6whmaxtB56jgO+PSK4DKBRm9hdgbDe7riZ5nUeQLJ6e\nDvzOzPbNYnhZ18v1+ArJKsEhY0/Xw93vC465GogBd2YzNhmczKwC+D3w7+5eZ12aa7q7m1nBd/03\ns48AG939OTObk+t4BoEIcARwmbs/bWY3kqwO7TRU7g2AoO3efJLJ7DbgbvK3xDAlStrSxN1P6Gmf\nmX0OuCeownvGzBIk50BbR7JtV4cJwTZ62L6ZZPF2JPgroevxg0pP18PMDiX5C7Y8+E9oAvC8mc2g\n5+uxDpizy/bHgu0Tujl+0NnT/QFgZhcCHwHmBvcJFPD90U97uh4FxcyiJBO2O939nmDze2Y2zt3X\nB1VcHc0s+vp7k09mAaeZ2SlACVAF3EjP93nHtVgbVJcNI/l7USj3zlpgrbs/HawvIpm0DcV7A+AE\n4C13rwUws3tI3jMFe3+oejQ7/kCyMwJm9n6SjUc3AfcD5wY9WiYD+5NsWP8ssH/QA6aIZIPJ+4P/\nzB8FzgrOuwC4L6vfZIDc/WV3H+Puk9x9EsmH0BHuvoHk9bgg6PE0E9geFPk/DMwzs+HBX1bzgIeD\nfXVmNjNol3ABeXY9INkjkmT1z2nu3tRl15C7P3rR7ffOcUxpF9zLtwIr3f2/uuy6n+TPFHb+2fbp\n9yYrXyJN3P3L7j4heFacC/zV3c+n5/u86zU6Kzje6fl3Ka8Ez8l3zOyAYNNc4O8MwXsjsAaYaWZl\nwe9Nx/Uo3PsjUz0c9Nqph0sRcAfwCvA8cHyXfVeT7KnyKl16PpLs9fNasO/qLtv3JXkzvUGyKLg4\n199vgNfmbXb0HjXgpuA7vwxM63Lcp4Pv/AbwqS7bpwXX9R/ATwgGjM6nV/Cd3gFeDF436/7o8Vp1\n+70L6QV8iGQHnZe63BOnkGx78wjwOsne6COC4/v8e5OPL5IlQx29R7u9z0mWxt0dbH8G2LfL+7v9\nXcq3FzAVWBbcH38g2ftzyN4bwDeBVcH/A78m2QO0YO8PzYggIiIikgdUPSoiIiKSB5S0iYiIiOQB\nJW0iIiIieUBJm4iIiEgeUNImIiIikgeUtInIkGRmcTN70cxeMbO7zaysj+//uJmtNLNHzWyamf0o\n2D7HzI7OTNQiMpQpaRORoarZ3ae6+yFAG3BJ153BgKR7ekZ+Bvhndz/O3Ze5+78F2+cAStpEJO2U\ntImIwN+A/cxskpm9ama3kxysc28zO8/MXg5K5K4DMLOvkRwE91Yz+0FQuvZAMMH7JcDng1K82Tn6\nPiJSgDT3qIgMacEchCcDDwWb9gcWuPtTZrYXcB1wJLAV+JOZne7u3zKz44EvuvuyjsnM3f1tM7sZ\naHD367P+ZUSkoKmkTUSGqlIze5HklEBrSM73CbDa3Z8KlqcDj7l7rScnn74TOCb7oYqIqKRNRIau\nZnef2nVDcs5pGnMTjojInqmkTUSkZ88Ax5rZKDMLA+cBj/fynnqgMuORiciQo6RNRKQH7r4euAp4\nFFgOPOfu9/Xytj8CH1NHBBFJN3P3XMcgIiIiIr1QSZuIiIhIHlDSJiIiIpIHlLSJiIiI5AElbSIi\nIiJ5QEmbiIiISB5Q0iYiIiKSB5S0iYiIiOQBJW0iIiIieeD/A1K8ek6fHvLIAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 720x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GivF2cSFS208",
        "colab_type": "text"
      },
      "source": [
        "There were two regions that the data has low probability to appear: one on the left side of the distribution, another one on the right side of the distribution."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XtizVySOlPUT",
        "colab_type": "code",
        "outputId": "a6dd1bda-0de3-4e50-fa89-5feb6edabac2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 569
        }
      },
      "source": [
        "df.loc[df['Profit'] < -100].head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Row ID</th>\n",
              "      <th>Order ID</th>\n",
              "      <th>Order Date</th>\n",
              "      <th>Ship Date</th>\n",
              "      <th>Ship Mode</th>\n",
              "      <th>Customer ID</th>\n",
              "      <th>Customer Name</th>\n",
              "      <th>Segment</th>\n",
              "      <th>Country</th>\n",
              "      <th>City</th>\n",
              "      <th>State</th>\n",
              "      <th>Postal Code</th>\n",
              "      <th>Region</th>\n",
              "      <th>Product ID</th>\n",
              "      <th>Category</th>\n",
              "      <th>Sub-Category</th>\n",
              "      <th>Product Name</th>\n",
              "      <th>Sales</th>\n",
              "      <th>Quantity</th>\n",
              "      <th>Discount</th>\n",
              "      <th>Profit</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>US-2015-108966</td>\n",
              "      <td>2015-10-11</td>\n",
              "      <td>2015-10-18</td>\n",
              "      <td>Standard Class</td>\n",
              "      <td>SO-20335</td>\n",
              "      <td>Sean O'Donnell</td>\n",
              "      <td>Consumer</td>\n",
              "      <td>United States</td>\n",
              "      <td>Fort Lauderdale</td>\n",
              "      <td>Florida</td>\n",
              "      <td>33311</td>\n",
              "      <td>South</td>\n",
              "      <td>FUR-TA-10000577</td>\n",
              "      <td>Furniture</td>\n",
              "      <td>Tables</td>\n",
              "      <td>Bretford CR4500 Series Slim Rectangular Table</td>\n",
              "      <td>957.5775</td>\n",
              "      <td>5</td>\n",
              "      <td>0.45</td>\n",
              "      <td>-383.0310</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>15</td>\n",
              "      <td>US-2015-118983</td>\n",
              "      <td>2015-11-22</td>\n",
              "      <td>2015-11-26</td>\n",
              "      <td>Standard Class</td>\n",
              "      <td>HP-14815</td>\n",
              "      <td>Harold Pawlan</td>\n",
              "      <td>Home Office</td>\n",
              "      <td>United States</td>\n",
              "      <td>Fort Worth</td>\n",
              "      <td>Texas</td>\n",
              "      <td>76106</td>\n",
              "      <td>Central</td>\n",
              "      <td>OFF-AP-10002311</td>\n",
              "      <td>Office Supplies</td>\n",
              "      <td>Appliances</td>\n",
              "      <td>Holmes Replacement Filter for HEPA Air Cleaner...</td>\n",
              "      <td>68.8100</td>\n",
              "      <td>5</td>\n",
              "      <td>0.80</td>\n",
              "      <td>-123.8580</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>28</td>\n",
              "      <td>US-2015-150630</td>\n",
              "      <td>2015-09-17</td>\n",
              "      <td>2015-09-21</td>\n",
              "      <td>Standard Class</td>\n",
              "      <td>TB-21520</td>\n",
              "      <td>Tracy Blumstein</td>\n",
              "      <td>Consumer</td>\n",
              "      <td>United States</td>\n",
              "      <td>Philadelphia</td>\n",
              "      <td>Pennsylvania</td>\n",
              "      <td>19140</td>\n",
              "      <td>East</td>\n",
              "      <td>FUR-BO-10004834</td>\n",
              "      <td>Furniture</td>\n",
              "      <td>Bookcases</td>\n",
              "      <td>Riverside Palais Royal Lawyers Bookcase, Royal...</td>\n",
              "      <td>3083.4300</td>\n",
              "      <td>7</td>\n",
              "      <td>0.50</td>\n",
              "      <td>-1665.0522</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>37</td>\n",
              "      <td>CA-2016-117590</td>\n",
              "      <td>2016-12-08</td>\n",
              "      <td>2016-12-10</td>\n",
              "      <td>First Class</td>\n",
              "      <td>GH-14485</td>\n",
              "      <td>Gene Hale</td>\n",
              "      <td>Corporate</td>\n",
              "      <td>United States</td>\n",
              "      <td>Richardson</td>\n",
              "      <td>Texas</td>\n",
              "      <td>75080</td>\n",
              "      <td>Central</td>\n",
              "      <td>FUR-FU-10003664</td>\n",
              "      <td>Furniture</td>\n",
              "      <td>Furnishings</td>\n",
              "      <td>Electrix Architect's Clamp-On Swing Arm Lamp, ...</td>\n",
              "      <td>190.9200</td>\n",
              "      <td>5</td>\n",
              "      <td>0.60</td>\n",
              "      <td>-147.9630</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>72</th>\n",
              "      <td>73</td>\n",
              "      <td>US-2015-134026</td>\n",
              "      <td>2015-04-26</td>\n",
              "      <td>2015-05-02</td>\n",
              "      <td>Standard Class</td>\n",
              "      <td>JE-15745</td>\n",
              "      <td>Joel Eaton</td>\n",
              "      <td>Consumer</td>\n",
              "      <td>United States</td>\n",
              "      <td>Memphis</td>\n",
              "      <td>Tennessee</td>\n",
              "      <td>38109</td>\n",
              "      <td>South</td>\n",
              "      <td>FUR-CH-10000513</td>\n",
              "      <td>Furniture</td>\n",
              "      <td>Chairs</td>\n",
              "      <td>High-Back Leather Manager's Chair</td>\n",
              "      <td>831.9360</td>\n",
              "      <td>8</td>\n",
              "      <td>0.20</td>\n",
              "      <td>-114.3912</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Row ID        Order ID Order Date  ... Quantity Discount     Profit\n",
              "3        4  US-2015-108966 2015-10-11  ...        5     0.45  -383.0310\n",
              "14      15  US-2015-118983 2015-11-22  ...        5     0.80  -123.8580\n",
              "27      28  US-2015-150630 2015-09-17  ...        7     0.50 -1665.0522\n",
              "36      37  CA-2016-117590 2016-12-08  ...        5     0.60  -147.9630\n",
              "72      73  US-2015-134026 2015-04-26  ...        8     0.20  -114.3912\n",
              "\n",
              "[5 rows x 21 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 117
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YGnZlzDDlyZO",
        "colab_type": "code",
        "outputId": "e3338db2-65f0-4323-dff7-6c151538efa5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 416
        }
      },
      "source": [
        "df.iloc[3]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Row ID                                                       4\n",
              "Order ID                                        US-2015-108966\n",
              "Order Date                                 2015-10-11 00:00:00\n",
              "Ship Date                                  2015-10-18 00:00:00\n",
              "Ship Mode                                       Standard Class\n",
              "Customer ID                                           SO-20335\n",
              "Customer Name                                   Sean O'Donnell\n",
              "Segment                                               Consumer\n",
              "Country                                          United States\n",
              "City                                           Fort Lauderdale\n",
              "State                                                  Florida\n",
              "Postal Code                                              33311\n",
              "Region                                                   South\n",
              "Product ID                                     FUR-TA-10000577\n",
              "Category                                             Furniture\n",
              "Sub-Category                                            Tables\n",
              "Product Name     Bretford CR4500 Series Slim Rectangular Table\n",
              "Sales                                                  957.577\n",
              "Quantity                                                     5\n",
              "Discount                                                  0.45\n",
              "Profit                                                -383.031\n",
              "outlier                                                      0\n",
              "Name: 3, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YVhBJua_sG-u",
        "colab_type": "code",
        "outputId": "470db9c5-dbce-4b58-b013-2bb1f689aa01",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 569
        }
      },
      "source": [
        "df.loc[df['Profit'] > 200].head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
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              "        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>Row ID</th>\n",
              "      <th>Order ID</th>\n",
              "      <th>Order Date</th>\n",
              "      <th>Ship Date</th>\n",
              "      <th>Ship Mode</th>\n",
              "      <th>Customer ID</th>\n",
              "      <th>Customer Name</th>\n",
              "      <th>Segment</th>\n",
              "      <th>Country</th>\n",
              "      <th>City</th>\n",
              "      <th>State</th>\n",
              "      <th>Postal Code</th>\n",
              "      <th>Region</th>\n",
              "      <th>Product ID</th>\n",
              "      <th>Category</th>\n",
              "      <th>Sub-Category</th>\n",
              "      <th>Product Name</th>\n",
              "      <th>Sales</th>\n",
              "      <th>Quantity</th>\n",
              "      <th>Discount</th>\n",
              "      <th>Profit</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>CA-2016-152156</td>\n",
              "      <td>2016-11-08</td>\n",
              "      <td>2016-11-11</td>\n",
              "      <td>Second Class</td>\n",
              "      <td>CG-12520</td>\n",
              "      <td>Claire Gute</td>\n",
              "      <td>Consumer</td>\n",
              "      <td>United States</td>\n",
              "      <td>Henderson</td>\n",
              "      <td>Kentucky</td>\n",
              "      <td>42420</td>\n",
              "      <td>South</td>\n",
              "      <td>FUR-CH-10000454</td>\n",
              "      <td>Furniture</td>\n",
              "      <td>Chairs</td>\n",
              "      <td>Hon Deluxe Fabric Upholstered Stacking Chairs,...</td>\n",
              "      <td>731.94</td>\n",
              "      <td>3</td>\n",
              "      <td>0.0</td>\n",
              "      <td>219.5820</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>25</td>\n",
              "      <td>CA-2015-106320</td>\n",
              "      <td>2015-09-25</td>\n",
              "      <td>2015-09-30</td>\n",
              "      <td>Standard Class</td>\n",
              "      <td>EB-13870</td>\n",
              "      <td>Emily Burns</td>\n",
              "      <td>Consumer</td>\n",
              "      <td>United States</td>\n",
              "      <td>Orem</td>\n",
              "      <td>Utah</td>\n",
              "      <td>84057</td>\n",
              "      <td>West</td>\n",
              "      <td>FUR-TA-10000577</td>\n",
              "      <td>Furniture</td>\n",
              "      <td>Tables</td>\n",
              "      <td>Bretford CR4500 Series Slim Rectangular Table</td>\n",
              "      <td>1044.63</td>\n",
              "      <td>3</td>\n",
              "      <td>0.0</td>\n",
              "      <td>240.2649</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>54</th>\n",
              "      <td>55</td>\n",
              "      <td>CA-2016-105816</td>\n",
              "      <td>2016-12-11</td>\n",
              "      <td>2016-12-17</td>\n",
              "      <td>Standard Class</td>\n",
              "      <td>JM-15265</td>\n",
              "      <td>Janet Molinari</td>\n",
              "      <td>Corporate</td>\n",
              "      <td>United States</td>\n",
              "      <td>New York City</td>\n",
              "      <td>New York</td>\n",
              "      <td>10024</td>\n",
              "      <td>East</td>\n",
              "      <td>TEC-PH-10002447</td>\n",
              "      <td>Technology</td>\n",
              "      <td>Phones</td>\n",
              "      <td>AT&amp;T CL83451 4-Handset Telephone</td>\n",
              "      <td>1029.95</td>\n",
              "      <td>5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>298.6855</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>144</th>\n",
              "      <td>145</td>\n",
              "      <td>CA-2017-155376</td>\n",
              "      <td>2017-12-22</td>\n",
              "      <td>2017-12-27</td>\n",
              "      <td>Standard Class</td>\n",
              "      <td>SG-20080</td>\n",
              "      <td>Sandra Glassco</td>\n",
              "      <td>Consumer</td>\n",
              "      <td>United States</td>\n",
              "      <td>Independence</td>\n",
              "      <td>Missouri</td>\n",
              "      <td>64055</td>\n",
              "      <td>Central</td>\n",
              "      <td>OFF-AP-10001058</td>\n",
              "      <td>Office Supplies</td>\n",
              "      <td>Appliances</td>\n",
              "      <td>Sanyo 2.5 Cubic Foot Mid-Size Office Refrigera...</td>\n",
              "      <td>839.43</td>\n",
              "      <td>3</td>\n",
              "      <td>0.0</td>\n",
              "      <td>218.2518</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>149</th>\n",
              "      <td>150</td>\n",
              "      <td>CA-2016-114489</td>\n",
              "      <td>2016-12-05</td>\n",
              "      <td>2016-12-09</td>\n",
              "      <td>Standard Class</td>\n",
              "      <td>JE-16165</td>\n",
              "      <td>Justin Ellison</td>\n",
              "      <td>Corporate</td>\n",
              "      <td>United States</td>\n",
              "      <td>Franklin</td>\n",
              "      <td>Wisconsin</td>\n",
              "      <td>53132</td>\n",
              "      <td>Central</td>\n",
              "      <td>FUR-CH-10000454</td>\n",
              "      <td>Furniture</td>\n",
              "      <td>Chairs</td>\n",
              "      <td>Hon Deluxe Fabric Upholstered Stacking Chairs,...</td>\n",
              "      <td>1951.84</td>\n",
              "      <td>8</td>\n",
              "      <td>0.0</td>\n",
              "      <td>585.5520</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     Row ID        Order ID Order Date  ... Quantity Discount    Profit\n",
              "1         2  CA-2016-152156 2016-11-08  ...        3      0.0  219.5820\n",
              "24       25  CA-2015-106320 2015-09-25  ...        3      0.0  240.2649\n",
              "54       55  CA-2016-105816 2016-12-11  ...        5      0.0  298.6855\n",
              "144     145  CA-2017-155376 2017-12-22  ...        3      0.0  218.2518\n",
              "149     150  CA-2016-114489 2016-12-05  ...        8      0.0  585.5520\n",
              "\n",
              "[5 rows x 21 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 121
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LRkUWSletcq-",
        "colab_type": "code",
        "outputId": "713376f0-4ac1-45ef-80fd-32f2f29bd9f3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 399
        }
      },
      "source": [
        "df.iloc[1]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Row ID                                                           2\n",
              "Order ID                                            CA-2016-152156\n",
              "Order Date                                     2016-11-08 00:00:00\n",
              "Ship Date                                      2016-11-11 00:00:00\n",
              "Ship Mode                                             Second Class\n",
              "Customer ID                                               CG-12520\n",
              "Customer Name                                          Claire Gute\n",
              "Segment                                                   Consumer\n",
              "Country                                              United States\n",
              "City                                                     Henderson\n",
              "State                                                     Kentucky\n",
              "Postal Code                                                  42420\n",
              "Region                                                       South\n",
              "Product ID                                         FUR-CH-10000454\n",
              "Category                                                 Furniture\n",
              "Sub-Category                                                Chairs\n",
              "Product Name     Hon Deluxe Fabric Upholstered Stacking Chairs,...\n",
              "Sales                                                       731.94\n",
              "Quantity                                                         3\n",
              "Discount                                                         0\n",
              "Profit                                                     219.582\n",
              "Name: 1, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 122
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JQKECo0BSefE",
        "colab_type": "code",
        "outputId": "c9313dc2-650c-4e9e-e7f1-b33f2f79d931",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        }
      },
      "source": [
        "sns.regplot(x=\"Sales\", y=\"Profit\", data=df)\n",
        "sns.despine();"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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bXdGVniMXyuoZSF9Yua6UlpSAgotMy879XaSzOU73ZUnn8tTFY7Q0JNi5v6tsv7Pc8zjF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\nRK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AChZpGY4Ghc9",
        "colab_type": "code",
        "outputId": "bc0069d5-4ded-4401-c7c1-77f503ef919f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        }
      },
      "source": [
        "cols = ['Sales', 'Profit']\n",
        "df[cols].head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Sales</th>\n",
              "      <th>Profit</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>261.9600</td>\n",
              "      <td>41.9136</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>731.9400</td>\n",
              "      <td>219.5820</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>14.6200</td>\n",
              "      <td>6.8714</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>957.5775</td>\n",
              "      <td>-383.0310</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>22.3680</td>\n",
              "      <td>2.5164</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      Sales    Profit\n",
              "0  261.9600   41.9136\n",
              "1  731.9400  219.5820\n",
              "2   14.6200    6.8714\n",
              "3  957.5775 -383.0310\n",
              "4   22.3680    2.5164"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 164
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lhPyayrkG9Hu",
        "colab_type": "code",
        "outputId": "5909803d-ef1d-4ea3-fa03-16642b284723",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        }
      },
      "source": [
        "minmax = MinMaxScaler(feature_range=(0, 1))\n",
        "df[['Sales','Profit']] = minmax.fit_transform(df[['Sales','Profit']])\n",
        "df[['Sales','Profit']].head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Sales</th>\n",
              "      <th>Profit</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.011552</td>\n",
              "      <td>0.442794</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.032313</td>\n",
              "      <td>0.454639</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.000626</td>\n",
              "      <td>0.440458</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.042280</td>\n",
              "      <td>0.414464</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.000968</td>\n",
              "      <td>0.440168</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      Sales    Profit\n",
              "0  0.011552  0.442794\n",
              "1  0.032313  0.454639\n",
              "2  0.000626  0.440458\n",
              "3  0.042280  0.414464\n",
              "4  0.000968  0.440168"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 165
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "il0LFdCFJEsw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X1 = df['Sales'].values.reshape(-1,1)\n",
        "X2 = df['Profit'].values.reshape(-1,1)\n",
        "\n",
        "X = np.concatenate((X1,X2),axis=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s2tddgHcUiAF",
        "colab_type": "text"
      },
      "source": [
        "Cluster-based Local Outlier Factor (CBLOF)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QtBn0u7CKlS6",
        "colab_type": "code",
        "outputId": "e24373b2-fc43-46f2-d25c-45843d30f5b3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 517
        }
      },
      "source": [
        "outliers_fraction = 0.01\n",
        "xx , yy = np.meshgrid(np.linspace(0, 1, 100), np.linspace(0, 1, 100))\n",
        "clf = CBLOF(contamination=outliers_fraction,check_estimator=False, random_state=0)\n",
        "clf.fit(X)\n",
        "# predict raw anomaly score\n",
        "scores_pred = clf.decision_function(X) * -1\n",
        "        \n",
        "# prediction of a datapoint category outlier or inlier\n",
        "y_pred = clf.predict(X)\n",
        "n_inliers = len(y_pred) - np.count_nonzero(y_pred)\n",
        "n_outliers = np.count_nonzero(y_pred == 1)\n",
        "\n",
        "plt.figure(figsize=(8, 8))\n",
        "\n",
        "df1 = df\n",
        "df1['outlier'] = y_pred.tolist()\n",
        "    \n",
        "# sales - inlier feature 1,  profit - inlier feature 2\n",
        "inliers_sales = np.array(df1['Sales'][df1['outlier'] == 0]).reshape(-1,1)\n",
        "inliers_profit = np.array(df1['Profit'][df1['outlier'] == 0]).reshape(-1,1)\n",
        "    \n",
        "# sales - outlier feature 1, profit - outlier feature 2\n",
        "outliers_sales = df1['Sales'][df1['outlier'] == 1].values.reshape(-1,1)\n",
        "outliers_profit = df1['Profit'][df1['outlier'] == 1].values.reshape(-1,1)\n",
        "         \n",
        "print('OUTLIERS:',n_outliers,'INLIERS:',n_inliers)\n",
        "        \n",
        "# Use threshold value to consider a datapoint inlier or outlier\n",
        "# threshold = stats.scoreatpercentile(scores_pred,100 * outliers_fraction)\n",
        "threshold = percentile(scores_pred, 100 * outliers_fraction)\n",
        "        \n",
        "# decision function calculates the raw anomaly score for every point\n",
        "Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()]) * -1\n",
        "Z = Z.reshape(xx.shape)\n",
        "# fill blue map colormap from minimum anomaly score to threshold value\n",
        "plt.contourf(xx, yy, Z, levels=np.linspace(Z.min(), threshold, 7),cmap=plt.cm.Blues_r)\n",
        "        \n",
        "# draw red contour line where anomaly score is equal to thresold\n",
        "a = plt.contour(xx, yy, Z, levels=[threshold],linewidths=2, colors='red')\n",
        "        \n",
        "# fill orange contour lines where range of anomaly score is from threshold to maximum anomaly score\n",
        "plt.contourf(xx, yy, Z, levels=[threshold, Z.max()],colors='orange')\n",
        "b = plt.scatter(inliers_sales, inliers_profit, c='white',s=20, edgecolor='k')\n",
        "    \n",
        "c = plt.scatter(outliers_sales, outliers_profit, c='black',s=20, edgecolor='k')\n",
        "       \n",
        "plt.axis('tight')   \n",
        "plt.legend([a.collections[0], b,c], ['learned decision function', 'inliers','outliers'],\n",
        "           prop=matplotlib.font_manager.FontProperties(size=20),loc='lower right')\n",
        "      \n",
        "plt.xlim((0, 1))\n",
        "plt.ylim((0, 1))\n",
        "plt.title('Cluster-based Local Outlier Factor (CBLOF)')\n",
        "plt.show();"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "OUTLIERS: 100 INLIERS: 9894\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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aBxpf2eRDusKFp8kkM9/iqUVRzMAwIAtIAlYrpcZqrQ86P0gpdRNwE0BisneX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\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q5Hh5iMEXuhM",
        "colab_type": "code",
        "outputId": "3e03b88b-3733-4450-b10d-85bd0a347f88",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 517
        }
      },
      "source": [
        "outliers_fraction = 0.01\n",
        "xx , yy = np.meshgrid(np.linspace(0, 1, 100), np.linspace(0, 1, 100))\n",
        "clf = HBOS(contamination=outliers_fraction)\n",
        "clf.fit(X)\n",
        "# predict raw anomaly score\n",
        "scores_pred = clf.decision_function(X) * -1\n",
        "        \n",
        "# prediction of a datapoint category outlier or inlier\n",
        "y_pred = clf.predict(X)\n",
        "n_inliers = len(y_pred) - np.count_nonzero(y_pred)\n",
        "n_outliers = np.count_nonzero(y_pred == 1)\n",
        "plt.figure(figsize=(8, 8))\n",
        "# copy of dataframe\n",
        "df1 = df\n",
        "df1['outlier'] = y_pred.tolist()\n",
        "    \n",
        "# sales - inlier feature 1,  profit - inlier feature 2\n",
        "inliers_sales = np.array(df1['Sales'][df1['outlier'] == 0]).reshape(-1,1)\n",
        "inliers_profit = np.array(df1['Profit'][df1['outlier'] == 0]).reshape(-1,1)\n",
        "    \n",
        "# sales - outlier feature 1, profit - outlier feature 2\n",
        "outliers_sales = df1['Sales'][df1['outlier'] == 1].values.reshape(-1,1)\n",
        "outliers_profit = df1['Profit'][df1['outlier'] == 1].values.reshape(-1,1)\n",
        "         \n",
        "print('OUTLIERS:',n_outliers,'INLIERS:',n_inliers)\n",
        "        \n",
        "# threshold value to consider a datapoint inlier or outlier\n",
        "threshold = percentile(scores_pred, 100 * outliers_fraction)\n",
        "        \n",
        "# decision function calculates the raw anomaly score for every point\n",
        "Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()]) * -1\n",
        "Z = Z.reshape(xx.shape)\n",
        "# fill blue map colormap from minimum anomaly score to threshold value\n",
        "plt.contourf(xx, yy, Z, levels=np.linspace(Z.min(), threshold, 7),cmap=plt.cm.Blues_r)\n",
        "        \n",
        "# draw red contour line where anomaly score is equal to thresold\n",
        "a = plt.contour(xx, yy, Z, levels=[threshold],linewidths=2, colors='red')\n",
        "        \n",
        "# fill orange contour lines where range of anomaly score is from threshold to maximum anomaly score\n",
        "plt.contourf(xx, yy, Z, levels=[threshold, Z.max()],colors='orange')\n",
        "b = plt.scatter(inliers_sales, inliers_profit, c='white',s=20, edgecolor='k')\n",
        "    \n",
        "c = plt.scatter(outliers_sales, outliers_profit, c='black',s=20, edgecolor='k')\n",
        "       \n",
        "plt.axis('tight')  \n",
        "     \n",
        "plt.legend([a.collections[0], b,c], ['learned decision function', 'inliers','outliers'],\n",
        "           prop=matplotlib.font_manager.FontProperties(size=20),loc='lower right')\n",
        "      \n",
        "plt.xlim((0, 1))\n",
        "plt.ylim((0, 1))\n",
        "plt.title('Histogram-base Outlier Detection (HBOS)')\n",
        "plt.show();"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "OUTLIERS: 90 INLIERS: 9904\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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XYGsr2rsoIvuNp70LICKyr7zFS/EuewWMG6wPz5Dz8aQMb+9iibQ5ZdYi0iHZ2gonUPvr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\nhBDCzv0/x5NR0JIxCi0AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lrx85bG0YOqM",
        "colab_type": "code",
        "outputId": "dd23022c-4fd5-4eb7-96fd-5a9c3f989033",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 571
        }
      },
      "source": [
        "outliers_fraction = 0.01\n",
        "xx , yy = np.meshgrid(np.linspace(0, 1, 100), np.linspace(0, 1, 100))\n",
        "clf = IForest(contamination=outliers_fraction,random_state=0)\n",
        "clf.fit(X)\n",
        "# predict raw anomaly score\n",
        "scores_pred = clf.decision_function(X) * -1\n",
        "        \n",
        "# prediction of a datapoint category outlier or inlier\n",
        "y_pred = clf.predict(X)\n",
        "n_inliers = len(y_pred) - np.count_nonzero(y_pred)\n",
        "n_outliers = np.count_nonzero(y_pred == 1)\n",
        "plt.figure(figsize=(8, 8))\n",
        "# copy of dataframe\n",
        "df1 = df\n",
        "df1['outlier'] = y_pred.tolist()\n",
        "    \n",
        "# sales - inlier feature 1,  profit - inlier feature 2\n",
        "inliers_sales = np.array(df1['Sales'][df1['outlier'] == 0]).reshape(-1,1)\n",
        "inliers_profit = np.array(df1['Profit'][df1['outlier'] == 0]).reshape(-1,1)\n",
        "    \n",
        "# sales - outlier feature 1, profit - outlier feature 2\n",
        "outliers_sales = df1['Sales'][df1['outlier'] == 1].values.reshape(-1,1)\n",
        "outliers_profit = df1['Profit'][df1['outlier'] == 1].values.reshape(-1,1)\n",
        "         \n",
        "print('OUTLIERS: ',n_outliers,'INLIERS: ',n_inliers)\n",
        "        \n",
        "# threshold value to consider a datapoint inlier or outlier\n",
        "threshold = percentile(scores_pred, 100 * outliers_fraction)\n",
        "        \n",
        "# decision function calculates the raw anomaly score for every point\n",
        "Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()]) * -1\n",
        "Z = Z.reshape(xx.shape)\n",
        "# fill blue map colormap from minimum anomaly score to threshold value\n",
        "plt.contourf(xx, yy, Z, levels=np.linspace(Z.min(), threshold, 7),cmap=plt.cm.Blues_r)\n",
        "        \n",
        "# draw red contour line where anomaly score is equal to thresold\n",
        "a = plt.contour(xx, yy, Z, levels=[threshold],linewidths=2, colors='red')\n",
        "        \n",
        "# fill orange contour lines where range of anomaly score is from threshold to maximum anomaly score\n",
        "plt.contourf(xx, yy, Z, levels=[threshold, Z.max()],colors='orange')\n",
        "b = plt.scatter(inliers_sales, inliers_profit, c='white',s=20, edgecolor='k')\n",
        "    \n",
        "c = plt.scatter(outliers_sales, outliers_profit, c='black',s=20, edgecolor='k')\n",
        "       \n",
        "plt.axis('tight')\n",
        "plt.legend([a.collections[0], b,c], ['learned decision function', 'inliers','outliers'],\n",
        "           prop=matplotlib.font_manager.FontProperties(size=20),loc='lower right')\n",
        "      \n",
        "plt.xlim((0, 1))\n",
        "plt.ylim((0, 1))\n",
        "plt.title('Isolation Forest')\n",
        "plt.show();"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/ensemble/iforest.py:247: FutureWarning: behaviour=\"old\" is deprecated and will be removed in version 0.22. Please use behaviour=\"new\", which makes the decision_function change to match other anomaly detection algorithm API.\n",
            "  FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "OUTLIERS:  100 INLIERS:  9894\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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cLREREel20RnWDwHXguuL5eFuiYiISLeLzrAWERHpRRTWIiIiEU5hLSIiEuEU1iK9gK2v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\nN2CdoiinFEUxHiefj+TJU/UN44UMTNS21je3pPYrmWUHoyLC0ORpes9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QIjf7qVkLIYQQ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\nD1NzNAfDEDBoAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6gtIWWbRYxEj",
        "colab_type": "code",
        "outputId": "5c3d373d-f758-4be9-f01e-24f7ef7dc24b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 517
        }
      },
      "source": [
        "outliers_fraction = 0.01\n",
        "xx , yy = np.meshgrid(np.linspace(0, 1, 100), np.linspace(0, 1, 100))\n",
        "clf = KNN(contamination=outliers_fraction)\n",
        "clf.fit(X)\n",
        "# predict raw anomaly score\n",
        "scores_pred = clf.decision_function(X) * -1\n",
        "        \n",
        "# prediction of a datapoint category outlier or inlier\n",
        "y_pred = clf.predict(X)\n",
        "n_inliers = len(y_pred) - np.count_nonzero(y_pred)\n",
        "n_outliers = np.count_nonzero(y_pred == 1)\n",
        "plt.figure(figsize=(8, 8))\n",
        "# copy of dataframe\n",
        "df1 = df\n",
        "df1['outlier'] = y_pred.tolist()\n",
        "    \n",
        "# sales - inlier feature 1,  profit - inlier feature 2\n",
        "inliers_sales = np.array(df1['Sales'][df1['outlier'] == 0]).reshape(-1,1)\n",
        "inliers_profit = np.array(df1['Profit'][df1['outlier'] == 0]).reshape(-1,1)\n",
        "    \n",
        "# sales - outlier feature 1, profit - outlier feature 2\n",
        "outliers_sales = df1['Sales'][df1['outlier'] == 1].values.reshape(-1,1)\n",
        "outliers_profit = df1['Profit'][df1['outlier'] == 1].values.reshape(-1,1)\n",
        "         \n",
        "print('OUTLIERS: ',n_outliers,'INLIERS: ',n_inliers)\n",
        "        \n",
        "# threshold value to consider a datapoint inlier or outlier\n",
        "threshold = percentile(scores_pred, 100 * outliers_fraction)\n",
        "        \n",
        "# decision function calculates the raw anomaly score for every point\n",
        "Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()]) * -1\n",
        "Z = Z.reshape(xx.shape)\n",
        "# fill blue map colormap from minimum anomaly score to threshold value\n",
        "plt.contourf(xx, yy, Z, levels=np.linspace(Z.min(), threshold, 7),cmap=plt.cm.Blues_r)\n",
        "        \n",
        "# draw red contour line where anomaly score is equal to thresold\n",
        "a = plt.contour(xx, yy, Z, levels=[threshold],linewidths=2, colors='red')\n",
        "        \n",
        "# fill orange contour lines where range of anomaly score is from threshold to maximum anomaly score\n",
        "plt.contourf(xx, yy, Z, levels=[threshold, Z.max()],colors='orange')\n",
        "b = plt.scatter(inliers_sales, inliers_profit, c='white',s=20, edgecolor='k')\n",
        "    \n",
        "c = plt.scatter(outliers_sales, outliers_profit, c='black',s=20, edgecolor='k')\n",
        "       \n",
        "plt.axis('tight')  \n",
        "   \n",
        "plt.legend([a.collections[0], b,c], ['learned decision function', 'inliers','outliers'],\n",
        "           prop=matplotlib.font_manager.FontProperties(size=20),loc='lower right')\n",
        "      \n",
        "plt.xlim((0, 1))\n",
        "plt.ylim((0, 1))\n",
        "plt.title('K Nearest Neighbors (KNN)')\n",
        "plt.show();"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "OUTLIERS:  91 INLIERS:  9903\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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TWXzEP+QUlcoa4R4gC6KIlvJ1EzhIZW1o3lgYRBYfMTYJac+QoG4eWWrUeCSs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\noWtq4BoLdGdZiMTd7qctVbX9SGDbhgS1cPiwDg2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Sb94kuHQZHmodyTPDR9Gg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            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gvXGH0BHBBNN",
        "colab_type": "code",
        "outputId": "0c20f0a6-99be-4766-b83a-cd918869f782",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "df1.loc[df1['outlier'] == 1].tail(10)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Row ID</th>\n",
              "      <th>Order ID</th>\n",
              "      <th>Order Date</th>\n",
              "      <th>Ship Date</th>\n",
              "      <th>Ship Mode</th>\n",
              "      <th>Customer ID</th>\n",
              "      <th>Customer Name</th>\n",
              "      <th>Segment</th>\n",
              "      <th>Country</th>\n",
              "      <th>City</th>\n",
              "      <th>State</th>\n",
              "      <th>Postal Code</th>\n",
              "      <th>Region</th>\n",
              "      <th>Product ID</th>\n",
              "      <th>Category</th>\n",
              "      <th>Sub-Category</th>\n",
              "      <th>Product Name</th>\n",
              "      <th>Sales</th>\n",
              "      <th>Quantity</th>\n",
              "      <th>Discount</th>\n",
              "      <th>Profit</th>\n",
              "      <th>outlier</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>9231</th>\n",
              "      <td>9232</td>\n",
              "      <td>CA-2014-148383</td>\n",
              "      <td>2014-12-27</td>\n",
              "      <td>2014-12-31</td>\n",
              "      <td>Standard Class</td>\n",
              "      <td>RP-19390</td>\n",
              "      <td>Resi Pölking</td>\n",
              "      <td>Consumer</td>\n",
              "      <td>United States</td>\n",
              "      <td>Phoenix</td>\n",
              "      <td>Arizona</td>\n",
              "      <td>85023</td>\n",
              "      <td>West</td>\n",
              "      <td>OFF-BI-10003650</td>\n",
              "      <td>Office Supplies</td>\n",
              "      <td>Binders</td>\n",
              "      <td>GBC DocuBind 300 Electric Binding Machine</td>\n",
              "      <td>0.041802</td>\n",
              "      <td>6</td>\n",
              "      <td>0.70</td>\n",
              "      <td>0.393714</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9270</th>\n",
              "      <td>9271</td>\n",
              "      <td>US-2017-102183</td>\n",
              "      <td>2017-08-21</td>\n",
              "      <td>2017-08-28</td>\n",
              "      <td>Standard Class</td>\n",
              "      <td>PK-19075</td>\n",
              "      <td>Pete Kriz</td>\n",
              "      <td>Consumer</td>\n",
              "      <td>United States</td>\n",
              "      <td>New York City</td>\n",
              "      <td>New York</td>\n",
              "      <td>10035</td>\n",
              "      <td>East</td>\n",
              "      <td>OFF-BI-10001359</td>\n",
              "      <td>Office Supplies</td>\n",
              "      <td>Binders</td>\n",
              "      <td>GBC DocuBind TL300 Electric Binding System</td>\n",
              "      <td>0.190171</td>\n",
              "      <td>6</td>\n",
              "      <td>0.20</td>\n",
              "      <td>0.536875</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9412</th>\n",
              "      <td>9413</td>\n",
              "      <td>CA-2015-164777</td>\n",
              "      <td>2015-01-27</td>\n",
              "      <td>2015-01-29</td>\n",
              "      <td>First Class</td>\n",
              "      <td>SC-20305</td>\n",
              "      <td>Sean Christensen</td>\n",
              "      <td>Consumer</td>\n",
              "      <td>United States</td>\n",
              "      <td>Los Angeles</td>\n",
              "      <td>California</td>\n",
              "      <td>90036</td>\n",
              "      <td>West</td>\n",
              "      <td>FUR-CH-10002024</td>\n",
              "      <td>Furniture</td>\n",
              "      <td>Chairs</td>\n",
              "      <td>HON 5400 Series Task Chairs for Big and Tall</td>\n",
              "      <td>0.123839</td>\n",
              "      <td>5</td>\n",
              "      <td>0.20</td>\n",
              "      <td>0.440000</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9425</th>\n",
              "      <td>9426</td>\n",
              "      <td>CA-2014-116246</td>\n",
              "      <td>2014-09-12</td>\n",
              "      <td>2014-09-17</td>\n",
              "      <td>Second Class</td>\n",
              "      <td>LW-17215</td>\n",
              "      <td>Luke Weiss</td>\n",
              "      <td>Consumer</td>\n",
              "      <td>United States</td>\n",
              "      <td>New York City</td>\n",
              "      <td>New York</td>\n",
              "      <td>10035</td>\n",
              "      <td>East</td>\n",
              "      <td>FUR-CH-10002024</td>\n",
              "      <td>Furniture</td>\n",
              "      <td>Chairs</td>\n",
              "      <td>HON 5400 Series Task Chairs for Big and Tall</td>\n",
              "      <td>0.167190</td>\n",
              "      <td>6</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.468039</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9639</th>\n",
              "      <td>9640</td>\n",
              "      <td>CA-2015-116638</td>\n",
              "      <td>2015-01-28</td>\n",
              "      <td>2015-01-31</td>\n",
              "      <td>Second Class</td>\n",
              "      <td>JH-15985</td>\n",
              "      <td>Joseph Holt</td>\n",
              "      <td>Consumer</td>\n",
              "      <td>United States</td>\n",
              "      <td>Concord</td>\n",
              "      <td>North Carolina</td>\n",
              "      <td>28027</td>\n",
              "      <td>South</td>\n",
              "      <td>FUR-TA-10000198</td>\n",
              "      <td>Furniture</td>\n",
              "      <td>Tables</td>\n",
              "      <td>Chromcraft Bull-Nose Wood Oval Conference Tabl...</td>\n",
              "      <td>0.189822</td>\n",
              "      <td>13</td>\n",
              "      <td>0.40</td>\n",
              "      <td>0.315845</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9649</th>\n",
              "      <td>9650</td>\n",
              "      <td>CA-2016-107104</td>\n",
              "      <td>2016-11-26</td>\n",
              "      <td>2016-11-30</td>\n",
              "      <td>Standard Class</td>\n",
              "      <td>MS-17365</td>\n",
              "      <td>Maribeth Schnelling</td>\n",
              "      <td>Consumer</td>\n",
              "      <td>United States</td>\n",
              "      <td>Los Angeles</td>\n",
              "      <td>California</td>\n",
              "      <td>90045</td>\n",
              "      <td>West</td>\n",
              "      <td>FUR-BO-10002213</td>\n",
              "      <td>Furniture</td>\n",
              "      <td>Bookcases</td>\n",
              "      <td>DMI Eclipse Executive Suite Bookcases</td>\n",
              "      <td>0.150464</td>\n",
              "      <td>8</td>\n",
              "      <td>0.15</td>\n",
              "      <td>0.450687</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9660</th>\n",
              "      <td>9661</td>\n",
              "      <td>CA-2016-160717</td>\n",
              "      <td>2016-06-06</td>\n",
              "      <td>2016-06-11</td>\n",
              "      <td>Standard Class</td>\n",
              "      <td>ME-17320</td>\n",
              "      <td>Maria Etezadi</td>\n",
              "      <td>Home Office</td>\n",
              "      <td>United States</td>\n",
              "      <td>Santa Barbara</td>\n",
              "      <td>California</td>\n",
              "      <td>93101</td>\n",
              "      <td>West</td>\n",
              "      <td>TEC-PH-10001459</td>\n",
              "      <td>Technology</td>\n",
              "      <td>Phones</td>\n",
              "      <td>Samsung Galaxy Mega 6.3</td>\n",
              "      <td>0.133558</td>\n",
              "      <td>9</td>\n",
              "      <td>0.20</td>\n",
              "      <td>0.455120</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9741</th>\n",
              "      <td>9742</td>\n",
              "      <td>CA-2015-117086</td>\n",
              "      <td>2015-11-08</td>\n",
              "      <td>2015-11-12</td>\n",
              "      <td>Standard Class</td>\n",
              "      <td>QJ-19255</td>\n",
              "      <td>Quincy Jones</td>\n",
              "      <td>Corporate</td>\n",
              "      <td>United States</td>\n",
              "      <td>Burlington</td>\n",
              "      <td>Vermont</td>\n",
              "      <td>5408</td>\n",
              "      <td>East</td>\n",
              "      <td>FUR-BO-10004834</td>\n",
              "      <td>Furniture</td>\n",
              "      <td>Bookcases</td>\n",
              "      <td>Riverside Palais Royal Lawyers Bookcase, Royal...</td>\n",
              "      <td>0.194560</td>\n",
              "      <td>5</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.507542</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9774</th>\n",
              "      <td>9775</td>\n",
              "      <td>CA-2014-169019</td>\n",
              "      <td>2014-07-26</td>\n",
              "      <td>2014-07-30</td>\n",
              "      <td>Standard Class</td>\n",
              "      <td>LF-17185</td>\n",
              "      <td>Luke Foster</td>\n",
              "      <td>Consumer</td>\n",
              "      <td>United States</td>\n",
              "      <td>San Antonio</td>\n",
              "      <td>Texas</td>\n",
              "      <td>78207</td>\n",
              "      <td>Central</td>\n",
              "      <td>OFF-BI-10004995</td>\n",
              "      <td>Office Supplies</td>\n",
              "      <td>Binders</td>\n",
              "      <td>GBC DocuBind P400 Electric Binding System</td>\n",
              "      <td>0.096172</td>\n",
              "      <td>8</td>\n",
              "      <td>0.80</td>\n",
              "      <td>0.193206</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9857</th>\n",
              "      <td>9858</td>\n",
              "      <td>CA-2015-164301</td>\n",
              "      <td>2015-03-26</td>\n",
              "      <td>2015-03-30</td>\n",
              "      <td>Standard Class</td>\n",
              "      <td>EB-13840</td>\n",
              "      <td>Ellis Ballard</td>\n",
              "      <td>Corporate</td>\n",
              "      <td>United States</td>\n",
              "      <td>Seattle</td>\n",
              "      <td>Washington</td>\n",
              "      <td>98103</td>\n",
              "      <td>West</td>\n",
              "      <td>FUR-TA-10001889</td>\n",
              "      <td>Furniture</td>\n",
              "      <td>Tables</td>\n",
              "      <td>Bush Advantage Collection Racetrack Conference...</td>\n",
              "      <td>0.149891</td>\n",
              "      <td>8</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.480724</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      Row ID        Order ID Order Date  ... Discount    Profit outlier\n",
              "9231    9232  CA-2014-148383 2014-12-27  ...     0.70  0.393714       1\n",
              "9270    9271  US-2017-102183 2017-08-21  ...     0.20  0.536875       1\n",
              "9412    9413  CA-2015-164777 2015-01-27  ...     0.20  0.440000       1\n",
              "9425    9426  CA-2014-116246 2014-09-12  ...     0.10  0.468039       1\n",
              "9639    9640  CA-2015-116638 2015-01-28  ...     0.40  0.315845       1\n",
              "9649    9650  CA-2016-107104 2016-11-26  ...     0.15  0.450687       1\n",
              "9660    9661  CA-2016-160717 2016-06-06  ...     0.20  0.455120       1\n",
              "9741    9742  CA-2015-117086 2015-11-08  ...     0.00  0.507542       1\n",
              "9774    9775  CA-2014-169019 2014-07-26  ...     0.80  0.193206       1\n",
              "9857    9858  CA-2015-164301 2015-03-26  ...     0.00  0.480724       1\n",
              "\n",
              "[10 rows x 22 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 180
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eb8xqyIMBmRO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "df = pd.read_excel(\"Superstore.xls\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6VTJuQnV4ZsZ",
        "colab_type": "code",
        "outputId": "0575d6d4-8195-4245-918f-9dbe56ed36bf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 399
        }
      },
      "source": [
        "df.iloc[1995]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Row ID                                                        1996\n",
              "Order ID                                            US-2017-147221\n",
              "Order Date                                     2017-12-02 00:00:00\n",
              "Ship Date                                      2017-12-04 00:00:00\n",
              "Ship Mode                                             Second Class\n",
              "Customer ID                                               JS-16030\n",
              "Customer Name                                            Joy Smith\n",
              "Segment                                                   Consumer\n",
              "Country                                              United States\n",
              "City                                                       Houston\n",
              "State                                                        Texas\n",
              "Postal Code                                                  77036\n",
              "Region                                                     Central\n",
              "Product ID                                         OFF-AP-10002534\n",
              "Category                                           Office Supplies\n",
              "Sub-Category                                            Appliances\n",
              "Product Name     3.6 Cubic Foot Counter Height Office Refrigerator\n",
              "Sales                                                       294.62\n",
              "Quantity                                                         5\n",
              "Discount                                                       0.8\n",
              "Profit                                                    -766.012\n",
              "Name: 1995, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 191
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LST1aO-nZhb8",
        "colab_type": "code",
        "outputId": "837be56f-3053-48ff-f724-164b9ccf17d5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 399
        }
      },
      "source": [
        "df.iloc[9649]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Row ID                                            9650\n",
              "Order ID                                CA-2016-107104\n",
              "Order Date                         2016-11-26 00:00:00\n",
              "Ship Date                          2016-11-30 00:00:00\n",
              "Ship Mode                               Standard Class\n",
              "Customer ID                                   MS-17365\n",
              "Customer Name                      Maribeth Schnelling\n",
              "Segment                                       Consumer\n",
              "Country                                  United States\n",
              "City                                       Los Angeles\n",
              "State                                       California\n",
              "Postal Code                                      90045\n",
              "Region                                            West\n",
              "Product ID                             FUR-BO-10002213\n",
              "Category                                     Furniture\n",
              "Sub-Category                                 Bookcases\n",
              "Product Name     DMI Eclipse Executive Suite Bookcases\n",
              "Sales                                          3406.66\n",
              "Quantity                                             8\n",
              "Discount                                          0.15\n",
              "Profit                                         160.314\n",
              "Name: 9649, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 192
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5fX46THBDRVH",
        "colab_type": "code",
        "outputId": "926b8163-d31d-415e-d080-256fea5c22cf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 399
        }
      },
      "source": [
        "df.iloc[9270]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Row ID                                                 9271\n",
              "Order ID                                     US-2017-102183\n",
              "Order Date                              2017-08-21 00:00:00\n",
              "Ship Date                               2017-08-28 00:00:00\n",
              "Ship Mode                                    Standard Class\n",
              "Customer ID                                        PK-19075\n",
              "Customer Name                                     Pete Kriz\n",
              "Segment                                            Consumer\n",
              "Country                                       United States\n",
              "City                                          New York City\n",
              "State                                              New York\n",
              "Postal Code                                           10035\n",
              "Region                                                 East\n",
              "Product ID                                  OFF-BI-10001359\n",
              "Category                                    Office Supplies\n",
              "Sub-Category                                        Binders\n",
              "Product Name     GBC DocuBind TL300 Electric Binding System\n",
              "Sales                                               4305.55\n",
              "Quantity                                                  6\n",
              "Discount                                                0.2\n",
              "Profit                                              1453.12\n",
              "Name: 9270, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 196
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MYbNaaO7D3NY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}