9: Univariate_Outliers_Detection_Methods.ipynb

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    "colab": {
      "name": "9: Univariate_Outliers_Detection.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/gulabpatel/Anomaly_Detection_Python/blob/main/9%3A%20Univariate_Outliers_Detection.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LKcZd_nrZ675"
      },
      "source": [
        "### Discussion Related With Outliers And Impact On Machine Learning!!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SkRBfxXgZ68J"
      },
      "source": [
        "##### Which Machine Learning Models Are Sensitive To Outliers?\n",
        "1. Naivye Bayes Classifier--- Not Sensitive To Outliers\n",
        "2. SVM--------                Not Sensitive To Outliers          \n",
        "3. Linear Regression----------  Sensitive To Outliers\n",
        "4. Logistic Regression-------   Sensitive To Outliers\n",
        "5. Decision Tree Regressor or Classifier---- Not Sensitive\n",
        "6. Ensemble(RF,XGboost,GB)------- Not Sensitive\n",
        "7. KNN--------------------------- Not Sensitive \n",
        "8. Kmeans------------------------ Sensitive\n",
        "9. Hierarichal------------------- Sensitive \n",
        "10. PCA-------------------------- Sensitive \n",
        "11. Neural Networks-------------- Sensitive\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GLwzLjIEZ68O"
      },
      "source": [
        "import pandas as pd"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        },
        "id": "EG6pzsIF05j7",
        "outputId": "ba1b68d5-5478-4e79-f04c-581ad09c179f"
      },
      "source": [
        "df = pd.read_csv('https://raw.githubusercontent.com/gulabpatel/ML_Data_and_Resources/master/titanic_train.csv')\n",
        "df.head()"
      ],
      "execution_count": 2,
      "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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              "\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>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Braund, Mr. Owen Harris</td>\n",
              "      <td>male</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>A/5 21171</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
              "      <td>female</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17599</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C85</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Heikkinen, Miss. Laina</td>\n",
              "      <td>female</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>STON/O2. 3101282</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
              "      <td>female</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>113803</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>C123</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Allen, Mr. William Henry</td>\n",
              "      <td>male</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>373450</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            ],
            "text/plain": [
              "   PassengerId  Survived  Pclass  ...     Fare Cabin  Embarked\n",
              "0            1         0       3  ...   7.2500   NaN         S\n",
              "1            2         1       1  ...  71.2833   C85         C\n",
              "2            3         1       3  ...   7.9250   NaN         S\n",
              "3            4         1       1  ...  53.1000  C123         S\n",
              "4            5         0       3  ...   8.0500   NaN         S\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7eBnr5ttZ68S",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6881e0bc-5429-4733-eee2-ec0364750c64"
      },
      "source": [
        "df['Age'].isnull().sum()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "177"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IFAASjFfeO8l"
      },
      "source": [
        "#1. Histogram"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LvzsniBsZ68U"
      },
      "source": [
        "import seaborn as sns"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dD4NLUT_Z68U",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "outputId": "9b6bf8cd-5f3b-4c49-ee07-2b090b160626"
      },
      "source": [
        "sns.distplot(df['Age'].dropna())"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fbd6966f290>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7YA_6IVHZ68V",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "outputId": "d33a27bd-4b19-4cd1-efa8-f45414408bf0"
      },
      "source": [
        "sns.distplot(df['Age'].fillna(100))"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fbd67295910>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yyA63NSa2TST"
      },
      "source": [
        "We can delete the the point far away from peak."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ce2xLaNkZ68W"
      },
      "source": [
        "#2. IQR \n",
        "\n",
        "When data is Gaussian Distributed"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yjt870MqZ68X",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "outputId": "b76f88b7-5e72-4eaf-d455-519f454cb62c"
      },
      "source": [
        "figure=df.Age.hist(bins=50)\n",
        "figure.set_title('Age')\n",
        "figure.set_xlabel('Age')\n",
        "figure.set_ylabel('No of passenger')"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'No of passenger')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dDRIVtlRZ68Y",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "ec3ba555-860f-4a3a-e6dd-6d851829aafd"
      },
      "source": [
        "figure=df.boxplot(column=\"Age\")"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "M-YZVyZKZ68Z",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "900390cf-4a99-4dc7-fe18-69de19079c03"
      },
      "source": [
        "df['Age'].describe()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    714.000000\n",
              "mean      29.699118\n",
              "std       14.526497\n",
              "min        0.420000\n",
              "25%       20.125000\n",
              "50%       28.000000\n",
              "75%       38.000000\n",
              "max       80.000000\n",
              "Name: Age, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VHogLCmeZ68c"
      },
      "source": [
        "If The Data Is Normally Distributed We use IQR method"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ymywgQZPZ68c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9232c786-b2d6-4f31-ad79-60a03d19b7e1"
      },
      "source": [
        "##### Assuming Age follows A Gaussian Distribution we will calculate the boundaries which differentiates the outliers\n",
        "\n",
        "uppper_boundary=df['Age'].mean() + 3* df['Age'].std()\n",
        "lower_boundary=df['Age'].mean() - 3* df['Age'].std()\n",
        "print(lower_boundary), print(uppper_boundary),print(df['Age'].mean())"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "-13.88037434994331\n",
            "73.27860964406095\n",
            "29.69911764705882\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(None, None, None)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3TZgLiyDZ68d"
      },
      "source": [
        "#3. Boxplot\n",
        "\n",
        "If Features Are Skewed We Use the below Technique "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gBsCTCpGZ68e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "outputId": "f786858c-306d-425d-fb72-851508edb002"
      },
      "source": [
        "figure=df.Fare.hist(bins=50)\n",
        "figure.set_title('Fare')\n",
        "figure.set_xlabel('Fare')\n",
        "figure.set_ylabel('No of passenger')"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'No of passenger')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rQHbbu4fZ68f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "a530ce9c-2f33-4b4c-88f7-9315c1a029f5"
      },
      "source": [
        "df.boxplot(column=\"Fare\")"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fbd66bd0dd0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4UW8X1ycZ68g",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e94066ef-63cf-4e27-8fba-67fb7ba28d70"
      },
      "source": [
        "df['Fare'].describe()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    891.000000\n",
              "mean      32.204208\n",
              "std       49.693429\n",
              "min        0.000000\n",
              "25%        7.910400\n",
              "50%       14.454200\n",
              "75%       31.000000\n",
              "max      512.329200\n",
              "Name: Fare, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "utC4TE7hZ68g"
      },
      "source": [
        "#### Lets compute the Interquantile range to calculate the boundaries\n",
        "IQR=df.Fare.quantile(0.75)-df.Fare.quantile(0.25)"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4a7thKdDZ68h",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c8a8e2f4-f4cd-4935-f8c0-272ddef4d087"
      },
      "source": [
        "lower_bridge=df['Fare'].quantile(0.25)-(IQR*1.5)\n",
        "upper_bridge=df['Fare'].quantile(0.75)+(IQR*1.5)\n",
        "print(lower_bridge), print(upper_bridge)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "-26.724\n",
            "65.6344\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(None, None)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "deyUsUoBZ68i",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3d25f5a0-8fa9-4d1b-89a5-32a781ed9467"
      },
      "source": [
        "#### Extreme outliers\n",
        "lower_bridge=df['Fare'].quantile(0.25)-(IQR*3)\n",
        "upper_bridge=df['Fare'].quantile(0.75)+(IQR*3)\n",
        "print(lower_bridge), print(upper_bridge)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "-61.358399999999996\n",
            "100.2688\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(None, None)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9ICbk79TZ68i"
      },
      "source": [
        "data=df.copy()"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BJIypZiEZ68j"
      },
      "source": [
        "data.loc[data['Age']>=73,'Age']=73"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z_mZh-iaZ68j"
      },
      "source": [
        "data.loc[data['Fare']>=100,'Fare']=100"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xx3VUNBlZ68k",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "outputId": "1caf8874-4035-4a4f-cacc-007857ba1654"
      },
      "source": [
        "figure=data.Age.hist(bins=50)\n",
        "figure.set_title('Fare')\n",
        "figure.set_xlabel('Fare')\n",
        "figure.set_ylabel('No of passenger')"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'No of passenger')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0MB2pl2jZ68l",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "outputId": "d18b3b2c-7933-47af-8f84-f3588275831f"
      },
      "source": [
        "figure=data.Fare.hist(bins=50)\n",
        "figure.set_title('Fare')\n",
        "figure.set_xlabel('Fare')\n",
        "figure.set_ylabel('No of passenger')"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'No of passenger')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q4zmrGqUefJU"
      },
      "source": [
        "#4. Z-Score\n",
        "\n",
        "$ Z-score = \\frac{x - mean}{Standard Deviation}$\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 360
        },
        "id": "joE8g9Lyehwm",
        "outputId": "882aeebd-f898-4c81-e4ba-0b2fbac51b51"
      },
      "source": [
        "df['zscore'] = (df.Age-df.Age.mean())/df.Age.std()\n",
        "df.sample(5)"
      ],
      "execution_count": 24,
      "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>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "      <th>zscore</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>381</th>\n",
              "      <td>382</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Nakid, Miss. Maria (\"Mary\")</td>\n",
              "      <td>female</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2653</td>\n",
              "      <td>15.7417</td>\n",
              "      <td>NaN</td>\n",
              "      <td>C</td>\n",
              "      <td>-1.975639</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>156</th>\n",
              "      <td>157</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Gilnagh, Miss. Katherine \"Katie\"</td>\n",
              "      <td>female</td>\n",
              "      <td>16.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>35851</td>\n",
              "      <td>7.7333</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Q</td>\n",
              "      <td>-0.943043</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>843</th>\n",
              "      <td>844</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Lemberopolous, Mr. Peter L</td>\n",
              "      <td>male</td>\n",
              "      <td>34.5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2683</td>\n",
              "      <td>6.4375</td>\n",
              "      <td>NaN</td>\n",
              "      <td>C</td>\n",
              "      <td>0.330491</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>279</th>\n",
              "      <td>280</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Abbott, Mrs. Stanton (Rosa Hunt)</td>\n",
              "      <td>female</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>C.A. 2673</td>\n",
              "      <td>20.2500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>0.364911</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>382</th>\n",
              "      <td>383</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Tikkanen, Mr. Juho</td>\n",
              "      <td>male</td>\n",
              "      <td>32.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>STON/O 2. 3101293</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>0.158392</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     PassengerId  Survived  Pclass  ... Cabin Embarked    zscore\n",
              "381          382         1       3  ...   NaN        C -1.975639\n",
              "156          157         1       3  ...   NaN        Q -0.943043\n",
              "843          844         0       3  ...   NaN        C  0.330491\n",
              "279          280         1       3  ...   NaN        S  0.364911\n",
              "382          383         0       3  ...   NaN        S  0.158392\n",
              "\n",
              "[5 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r30u4_Je6JyZ",
        "outputId": "135402ba-0a79-474f-e11b-d178f487303b"
      },
      "source": [
        "df.zscore.min(), df.zscore.max()"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(-2.0155662426542023, 3.462698625977656)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0nkQo_ot6EDE",
        "outputId": "17839038-e062-4aba-b5d9-1cdda37f16c0"
      },
      "source": [
        "outliers_z = df[(df.zscore < -1.5) | (df.zscore > 3)]\n",
        "outliers_z.sample(5), outliers_z.shape"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(     PassengerId  Survived  Pclass  ... Cabin Embarked    zscore\n",
              " 831          832         1       2  ...   NaN        S -1.987342\n",
              " 7              8         0       3  ...   NaN        S -1.906799\n",
              " 530          531         1       2  ...   NaN        S -1.906799\n",
              " 827          828         1       2  ...   NaN        C -1.975639\n",
              " 183          184         1       2  ...    F4        S -1.975639\n",
              " \n",
              " [5 rows x 13 columns], (52, 13))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "34IURUlBfChX"
      },
      "source": [
        "#5. Modified Z-Score\n",
        "\n",
        "Z-score is easy to calculate using mean and standard deviation, but it has its own limitations. There are a few situations when it’s not an ideal technique, for example:\n",
        "* the data are not normally distributed\n",
        "* data/sample size is small\n",
        "\n",
        "In addition to these, Z-score is sensitive to extreme values (because one of its parameters — mean — itself is sensitive to extreme values), so it may fail to screen outliers appropriately.\n",
        "To overcome these shortcomings, several modifications are made to the standard Z-score:\n",
        "* median is used instead of the mean as a parameter, since the median is less sensitive to outliers\n",
        "* Median Absolute Deviation (MAD) is used in lieu of standard deviation\n",
        "* The values are multiplied by a constant so that it is approximately equal to SD (for normally distributed data, MAD is approximately equal to 0.6745*SD)\n",
        "\n",
        "\n",
        "Putting them all together, the equation looks like the following:\n",
        "\n",
        "$ Modified Z-score = \\frac{0.6745(x - mean)}{Standard Deviation}$"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "4Vy2RZwLfMkf",
        "outputId": "654d1271-735f-4757-96b5-4a63bfee7843"
      },
      "source": [
        "df['mod_zscore'] = (0.6745*(df.Age-df.Age.mean()))/df.Age.std()\n",
        "df.sample(5)"
      ],
      "execution_count": 29,
      "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>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "      <th>zscore</th>\n",
              "      <th>mod_zscore</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>511</th>\n",
              "      <td>512</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Webber, Mr. James</td>\n",
              "      <td>male</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>SOTON/OQ 3101316</td>\n",
              "      <td>8.050</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>773</th>\n",
              "      <td>774</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Elias, Mr. Dibo</td>\n",
              "      <td>male</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2674</td>\n",
              "      <td>7.225</td>\n",
              "      <td>NaN</td>\n",
              "      <td>C</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59</th>\n",
              "      <td>60</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Goodwin, Master. William Frederick</td>\n",
              "      <td>male</td>\n",
              "      <td>11.0</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>CA 2144</td>\n",
              "      <td>46.900</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>-1.287242</td>\n",
              "      <td>-0.868245</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>386</th>\n",
              "      <td>387</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Goodwin, Master. Sidney Leonard</td>\n",
              "      <td>male</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>CA 2144</td>\n",
              "      <td>46.900</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>-1.975639</td>\n",
              "      <td>-1.332569</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>696</th>\n",
              "      <td>697</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Kelly, Mr. James</td>\n",
              "      <td>male</td>\n",
              "      <td>44.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>363592</td>\n",
              "      <td>8.050</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>0.984469</td>\n",
              "      <td>0.664024</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     PassengerId  Survived  Pclass  ... Embarked    zscore  mod_zscore\n",
              "511          512         0       3  ...        S       NaN         NaN\n",
              "773          774         0       3  ...        C       NaN         NaN\n",
              "59            60         0       3  ...        S -1.287242   -0.868245\n",
              "386          387         0       3  ...        S -1.975639   -1.332569\n",
              "696          697         0       3  ...        S  0.984469    0.664024\n",
              "\n",
              "[5 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S87QxVgk7AMR",
        "outputId": "91edbcb7-1716-48dd-edce-77883a60e448"
      },
      "source": [
        "df.mod_zscore.min(), df.mod_zscore.max()"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(-1.3594994306702592, 2.335590223221929)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kcdFURKi7EdI",
        "outputId": "59640f79-ad1c-4a84-f8fb-ea203ecfa38e"
      },
      "source": [
        "outliers_z = df[(df.zscore < -1) | (df.zscore > 2)]\n",
        "outliers_z.sample(5), outliers_z.shape"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(     PassengerId  Survived  Pclass  ... Embarked    zscore  mod_zscore\n",
              " 644          645         1       3  ...        C -1.992849   -1.344177\n",
              " 570          571         1       2  ...        S  2.223584    1.499807\n",
              " 33            34         0       2  ...        S  2.498943    1.685537\n",
              " 489          490         1       3  ...        S -1.424921   -0.961110\n",
              " 435          436         1       1  ...        S -1.080723   -0.728948\n",
              " \n",
              " [5 rows x 14 columns], (111, 14))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    }
  ]
}