GenderPayGap2 (2).ipynb

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      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn import preprocessing\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.metrics import mean_squared_error\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import pandas as pd\n",
        "from sklearn.neighbors import KNeighborsRegressor"
      ]
    },
    {
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      "outputs": [],
      "source": [
        "df = pd.read_csv(\"GenderPayGap2.csv\")"
      ]
    },
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      "source": [
        "Preview of Dataset"
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            "text/plain": [
              "              JobTitle  Gender  Age  PerfEval Education            Dept  \\\n",
              "0     Graphic Designer  Female   18         5   College      Operations   \n",
              "1    Software Engineer    Male   21         5   College      Management   \n",
              "2  Warehouse Associate  Female   19         4       PhD  Administration   \n",
              "3    Software Engineer    Male   20         5   Masters           Sales   \n",
              "4     Graphic Designer    Male   26         5   Masters     Engineering   \n",
              "\n",
              "   Seniority  BasePay  Bonus  \n",
              "0          2    42363   9938  \n",
              "1          5   108476  11128  \n",
              "2          5    90208   9268  \n",
              "3          4   108080  10154  \n",
              "4          5    99464   9319  "
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              "      <th>4</th>\n",
              "      <td>Graphic Designer</td>\n",
              "      <td>Male</td>\n",
              "      <td>26</td>\n",
              "      <td>5</td>\n",
              "      <td>Masters</td>\n",
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        "df.head()"
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      "id": "4385bf47-9494-4f98-8bfc-b972d8a8687e",
      "metadata": {
        "id": "4385bf47-9494-4f98-8bfc-b972d8a8687e"
      },
      "source": [
        "There are 11 different types of jobs in this dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "id": "bca731b5-9010-45f5-8db6-2d32afaf5288",
      "metadata": {
        "colab": {
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        "outputId": "cd5ebe91-d0a7-475a-b353-e9486dd9c9b6"
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Graphic Designer' 'Software Engineer' 'Warehouse Associate' 'IT'\n",
            " 'Sales Associate' 'Driver' 'Financial Analyst' 'Marketing Associate'\n",
            " 'Data Scientist' 'Manager']\n"
          ]
        }
      ],
      "source": [
        "print(df['JobTitle'].unique())"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "6d13e3a6-3a50-4907-b3ca-f213f98cdd60",
      "metadata": {
        "id": "6d13e3a6-3a50-4907-b3ca-f213f98cdd60"
      },
      "source": [
        "The spread of individuals in each job is pretty even."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "id": "5354803a-a0ef-43ac-a830-3532ef1f4a2f",
      "metadata": {
        "colab": {
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        },
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        "outputId": "1514617b-0d16-4a5c-d1e7-357db1fb02aa"
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        {
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          "text": [
            "98\n",
            "109\n",
            "90\n",
            "96\n",
            "94\n",
            "91\n",
            "107\n",
            "118\n",
            "107\n",
            "90\n"
          ]
        }
      ],
      "source": [
        "print(df['JobTitle'].value_counts()['Graphic Designer'])\n",
        "print(df['JobTitle'].value_counts()['Software Engineer'])\n",
        "print(df['JobTitle'].value_counts()['Warehouse Associate'])\n",
        "print(df['JobTitle'].value_counts()['IT'])\n",
        "print(df['JobTitle'].value_counts()['Sales Associate'])\n",
        "print(df['JobTitle'].value_counts()['Driver'])\n",
        "print(df['JobTitle'].value_counts()['Financial Analyst'])\n",
        "print(df['JobTitle'].value_counts()['Marketing Associate'])\n",
        "print(df['JobTitle'].value_counts()['Data Scientist'])\n",
        "print(df['JobTitle'].value_counts()['Manager'])"
      ]
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      "id": "205ec46a-c011-4405-8e4c-8ddb15a83c32",
      "metadata": {
        "id": "205ec46a-c011-4405-8e4c-8ddb15a83c32"
      },
      "source": [
        "There are 5 different types of departments in this dataset."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "id": "2927a4f8-20cc-46f1-885e-7d4b6c0b08bb",
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Operations' 'Management' 'Administration' 'Sales' 'Engineering']\n"
          ]
        }
      ],
      "source": [
        "print(df['Dept'].unique())"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f3d885db-92be-4c7c-a85f-858de4a4be6f",
      "metadata": {
        "id": "f3d885db-92be-4c7c-a85f-858de4a4be6f"
      },
      "source": [
        "The spread of individuals in each department is pretty even."
      ]
    },
    {
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      "execution_count": 9,
      "id": "56f91b90-9748-41a8-9dd1-c687aa8c6a13",
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          "text": [
            "210\n",
            "198\n",
            "193\n",
            "207\n",
            "192\n"
          ]
        }
      ],
      "source": [
        "print(df['Dept'].value_counts()['Operations'])\n",
        "print(df['Dept'].value_counts()['Management'])\n",
        "print(df['Dept'].value_counts()['Administration'])\n",
        "print(df['Dept'].value_counts()['Sales'])\n",
        "print(df['Dept'].value_counts()['Engineering'])"
      ]
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    {
      "cell_type": "markdown",
      "id": "f01f341e-1a63-41bc-8035-136dbbf8bfd4",
      "metadata": {
        "id": "f01f341e-1a63-41bc-8035-136dbbf8bfd4"
      },
      "source": [
        "Gender, Education, Department, and JobTitle are all categorical variables. In order to be able to use these features for our models we must integer encode them so that they\n",
        "can be numerical features."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "id": "0304e805-c76e-46ad-92b9-aa090f4dc5e1",
      "metadata": {
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      },
      "outputs": [],
      "source": [
        "newGender = {'Gender':{'Male':0,'Female':1}}\n",
        "df = df.replace(newGender)\n",
        "newEducation = {'Education':{'High School':0,'College':1,'Masters':2,'PhD':3}}\n",
        "df = df.replace(newEducation)\n",
        "newJobTitle = {'JobTitle':{'Graphic Designer':0,'Software Engineer':1,'Warehouse Associate':2,'IT':3, 'Sales Associate':4,'Driver':5,'Financial Analyst':6,'Marketing Associate':7,'Data Scientist':8,'Manager':9}}\n",
        "df = df.replace(newJobTitle)\n",
        "newDept = {'Dept':{'Operations':0,'Management':1,'Administration':2,'Sales':3, 'Engineering':4}}\n",
        "df = df.replace(newDept)"
      ]
    },
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      "source": [
        "Preview of data with integer encoding in place."
      ]
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              "   JobTitle  Gender  Age  PerfEval  Education  Dept  Seniority  BasePay  Bonus\n",
              "0         0       1   18         5          1     0          2    42363   9938\n",
              "1         1       0   21         5          1     1          5   108476  11128\n",
              "2         2       1   19         4          3     2          5    90208   9268\n",
              "3         1       0   20         5          2     3          4   108080  10154\n",
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              "      <td>99464</td>\n",
              "      <td>9319</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-476db9cf-adaf-4d7b-bfe2-1a22e9da17a8')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "\n",
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              "          document.querySelector('#df-476db9cf-adaf-4d7b-bfe2-1a22e9da17a8 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-476db9cf-adaf-4d7b-bfe2-1a22e9da17a8');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e90e6c44-4e4d-4b94-98ea-c510baa0e5f4",
      "metadata": {
        "id": "e90e6c44-4e4d-4b94-98ea-c510baa0e5f4"
      },
      "source": [
        "The Bonus feature is dropped because we only want to focus on one type of income. Basepay is a much more telling feature, which is why we will keep Basepay instead of Bonus."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "id": "717e1481-9943-4e56-b059-b17c7599e456",
      "metadata": {
        "id": "717e1481-9943-4e56-b059-b17c7599e456"
      },
      "outputs": [],
      "source": [
        "df = df.drop(columns='Bonus')"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "bba27848-d289-4548-bef2-4feb0a785c10",
      "metadata": {
        "id": "bba27848-d289-4548-bef2-4feb0a785c10"
      },
      "source": [
        "In order to see which variables we should use for our models we will see how each variable correlates with BasePay (target). Variables with higher correlations are good indicators that those variables will do well in predicting the target variable. PerfEval, JobTitle, and Dept have very low correlations with BasePay so we will not use them in our models."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "id": "87fab4ad-f2f1-4a0d-ae1e-e29799920c22",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "87fab4ad-f2f1-4a0d-ae1e-e29799920c22",
        "outputId": "d3541207-38f0-43dc-82ee-c03bc343d06c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           JobTitle    Gender       Age  PerfEval  Education      Dept  \\\n",
              "JobTitle   1.000000  0.108362  0.031353 -0.067511   0.013636 -0.024136   \n",
              "Gender     0.108362  1.000000  0.028625 -0.066627  -0.056454  0.017852   \n",
              "Age        0.031353  0.028625  1.000000 -0.056875   0.019835 -0.001154   \n",
              "PerfEval  -0.067511 -0.066627 -0.056875  1.000000   0.060037  0.016840   \n",
              "Education  0.013636 -0.056454  0.019835  0.060037   1.000000  0.010452   \n",
              "Dept      -0.024136  0.017852 -0.001154  0.016840   0.010452  1.000000   \n",
              "Seniority -0.014549  0.028131 -0.021414 -0.021127   0.049004  0.063307   \n",
              "BasePay    0.074094 -0.167766  0.562681 -0.053622   0.172541  0.080623   \n",
              "\n",
              "           Seniority   BasePay  \n",
              "JobTitle   -0.014549  0.074094  \n",
              "Gender      0.028131 -0.167766  \n",
              "Age        -0.021414  0.562681  \n",
              "PerfEval   -0.021127 -0.053622  \n",
              "Education   0.049004  0.172541  \n",
              "Dept        0.063307  0.080623  \n",
              "Seniority   1.000000  0.511096  \n",
              "BasePay     0.511096  1.000000  "
            ],
            "text/html": [
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              "      <th>JobTitle</th>\n",
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              "      <th>PerfEval</th>\n",
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              "      <th>Dept</th>\n",
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              "      <td>0.016840</td>\n",
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              "      <td>0.063307</td>\n",
              "      <td>0.080623</td>\n",
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              "      <th>Seniority</th>\n",
              "      <td>-0.014549</td>\n",
              "      <td>0.028131</td>\n",
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              "      <td>-0.021127</td>\n",
              "      <td>0.049004</td>\n",
              "      <td>0.063307</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.511096</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BasePay</th>\n",
              "      <td>0.074094</td>\n",
              "      <td>-0.167766</td>\n",
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              "      <td>-0.053622</td>\n",
              "      <td>0.172541</td>\n",
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              "      <td>0.511096</td>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-14fd1a37-6723-4855-8c10-707800c4943a')\"\n",
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              "\n",
              "      <script>\n",
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              "          document.querySelector('#df-14fd1a37-6723-4855-8c10-707800c4943a button.colab-df-convert');\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-14fd1a37-6723-4855-8c10-707800c4943a');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "df.corr()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "id": "95c1b481-bae3-455f-b2a6-1ebc1e100aec",
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        "outputId": "8edabee0-3066-46cb-b98f-a54d4b355f52"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<function matplotlib.pyplot.show(close=None, block=None)>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x432 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.figure(figsize = (6,6))\n",
        "sns.heatmap(df.corr(), center=0, annot=True)\n",
        "plt.title('Correlation Map')\n",
        "plt.show"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "0c643751-31f7-47da-8bbe-2c5b8310c694",
      "metadata": {
        "id": "0c643751-31f7-47da-8bbe-2c5b8310c694"
      },
      "source": [
        "The first model we will use is linear regression. To prepare the model we train and test the model using a test, train split."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "id": "0b51453f-2768-4f3d-b630-d3681074b411",
      "metadata": {
        "id": "0b51453f-2768-4f3d-b630-d3681074b411"
      },
      "outputs": [],
      "source": [
        "train, test = train_test_split(df, test_size=0.2, random_state=1)\n",
        "model = LinearRegression()\n",
        "model.fit(\n",
        "    X=train[[\"Age\", \"Gender\", \"Education\", \"Seniority\"]],\n",
        "    y=train[\"BasePay\"]\n",
        ")\n",
        "predicted = model.predict(\n",
        "    X=test[[\"Age\", \"Gender\", \"Education\", \"Seniority\"]]\n",
        ")\n",
        "expected = test[\"BasePay\"]"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "0d091a74-e07e-4b7d-b4d2-3adfb729ef26",
      "metadata": {
        "id": "0d091a74-e07e-4b7d-b4d2-3adfb729ef26"
      },
      "source": [
        "To measure the accuracy of the model we look at the mean squared error and the root mean squared error. In our opinion, the error is somewhat high because there are only 1000 rows of data in this dataset. With a small training and test set, the model will have higher variation."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "id": "0bedb46c-e34f-4de8-91d3-e6ab4a21e760",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0bedb46c-e34f-4de8-91d3-e6ab4a21e760",
        "outputId": "1cb1aaa3-9f43-4e66-f85f-a2929e6bdb4a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "236749224.74826562\n",
            "15386.657361112115\n"
          ]
        }
      ],
      "source": [
        "lr_mse = mean_squared_error(expected, predicted)\n",
        "print(lr_mse)\n",
        "lr_rmse = mean_squared_error(expected, predicted, squared = False)\n",
        "print(lr_rmse)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b142b013-52c3-4759-a918-8fb41c223924",
      "metadata": {
        "id": "b142b013-52c3-4759-a918-8fb41c223924"
      },
      "source": [
        "To see which variables held more weight in the model we used the SelectKbest function to be able to see which variables had the highest importance scores. In the figure we see that Age and Seniority had a high importance, while Gender and Education didn't have much importance."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "id": "5dc41db8-faa9-4348-8119-1a83d4b7f81f",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 336
        },
        "id": "5dc41db8-faa9-4348-8119-1a83d4b7f81f",
        "outputId": "d61b06b5-5390-4bc2-9be9-7f359ffcde8f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Feature 0: 370.368230\n",
            "Feature 1: 18.045034\n",
            "Feature 2: 21.372117\n",
            "Feature 3: 280.806801\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "from sklearn.datasets import make_regression\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.feature_selection import SelectKBest\n",
        "from sklearn.feature_selection import f_regression\n",
        "from matplotlib import pyplot\n",
        "\n",
        "# feature selection\n",
        "def select_features(X_train, y_train, X_test):\n",
        " # configure to select all features\n",
        " fs = SelectKBest(score_func=f_regression, k='all')\n",
        " # learn relationship from training data\n",
        " fs.fit(X_train, y_train)\n",
        " # transform train input data\n",
        " X_train_fs = fs.transform(X_train)\n",
        " # transform test input data\n",
        " X_test_fs = fs.transform(X_test)\n",
        " return X_train_fs, X_test_fs, fs\n",
        " \n",
        "# feature selection\n",
        "X_train_fs, X_test_fs, fs = select_features(train[[\"Age\", \"Gender\", \"Education\", \"Seniority\"]], train[\"BasePay\"], test[[\"Age\", \"Gender\",\"Education\", \"Seniority\"]])\n",
        "# what are scores for the features\n",
        "for i in range(len(fs.scores_)):\n",
        " print('Feature %d: %f' % (i, fs.scores_[i]))\n",
        "# plot the scores\n",
        "pyplot.bar([i for i in range(len(fs.scores_))], fs.scores_)\n",
        "pyplot.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1aca7c6e-48de-413b-a088-cf0a8c7cad7c",
      "metadata": {
        "id": "1aca7c6e-48de-413b-a088-cf0a8c7cad7c"
      },
      "source": [
        "Below is a preview of the predicted BasePay on the actual data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "id": "4efb1c83-f636-4d2c-b1d9-ac48703965b0",
      "metadata": {
        "id": "4efb1c83-f636-4d2c-b1d9-ac48703965b0"
      },
      "outputs": [],
      "source": [
        "df[\"Predicted BasePay Linear\"] = model.predict(\n",
        "    X=df[[\"Age\", \"Gender\",\"Education\", \"Seniority\"]]\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "id": "623ee542-6d1b-46ab-9e06-3c2d67027aa1",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "623ee542-6d1b-46ab-9e06-3c2d67027aa1",
        "outputId": "056209fb-f945-4810-ee87-b5b80d2f4da7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   JobTitle  Gender  Age  PerfEval  Education  Dept  Seniority  BasePay  \\\n",
              "0         0       1   18         5          1     0          2    42363   \n",
              "1         1       0   21         5          1     1          5   108476   \n",
              "2         2       1   19         4          3     2          5    90208   \n",
              "3         1       0   20         5          2     3          4   108080   \n",
              "4         0       0   26         5          2     4          5    99464   \n",
              "\n",
              "   Predicted BasePay Linear  \n",
              "0              55391.632648  \n",
              "1              95615.580662  \n",
              "2              90655.960673  \n",
              "3              87923.944313  \n",
              "4             103602.804080  "
            ],
            "text/html": [
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              "      <div>\n",
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              "      <th>JobTitle</th>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "90fb99b1-fab8-4a74-99d5-8fbae943f3cc",
      "metadata": {
        "id": "90fb99b1-fab8-4a74-99d5-8fbae943f3cc"
      },
      "source": [
        "The second model we will use is K-nearest neighbors regression. To prepare the model we train and test the model using a test, train split."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "id": "a343be6d-9d0e-4335-8330-2cdddc57d47e",
      "metadata": {
        "id": "a343be6d-9d0e-4335-8330-2cdddc57d47e"
      },
      "outputs": [],
      "source": [
        "x_data = df[[\"Age\", \"Gender\",\"Education\", \"Seniority\"]]\n",
        "y_data = df[\"BasePay\"]\n",
        "X_train, X_test, y_train, y_test = train_test_split(x_data, y_data,test_size=0.2, random_state = 1)\n",
        "knn_model = KNeighborsRegressor()\n",
        "knn_model.fit(X_train, y_train)\n",
        "predicted = knn_model.predict(X_train)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c2088b40-9264-4eb3-af77-9b28d1dd7c50",
      "metadata": {
        "id": "c2088b40-9264-4eb3-af77-9b28d1dd7c50"
      },
      "source": [
        "To measure the accuracy of the model we look at the mean squared error and the root mean squared error. In our opinion, the error is somewhat high because there are only 1000 rows of data in this dataset. With a small training and test set, the model will have higher variation."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "id": "df41756c-c621-4b88-bfb0-54a17b1aa9b6",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "df41756c-c621-4b88-bfb0-54a17b1aa9b6",
        "outputId": "1797e9c6-164d-4f72-8c69-2e810b730b3e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "191576384.13995004\n",
            "13841.112099103526\n"
          ]
        }
      ],
      "source": [
        "mse = mean_squared_error(y_train, predicted)\n",
        "print(mse)\n",
        "rmse = mean_squared_error(y_train, predicted,squared = False)\n",
        "print(rmse)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4108648a-5378-4a4c-a126-11c6aa352192",
      "metadata": {
        "id": "4108648a-5378-4a4c-a126-11c6aa352192"
      },
      "source": [
        "Below is a preview of the predicted BasePay on the actual data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "id": "f54ed95c-c68a-45dc-97df-3dcd6cc9d44c",
      "metadata": {
        "id": "f54ed95c-c68a-45dc-97df-3dcd6cc9d44c"
      },
      "outputs": [],
      "source": [
        "df[\"Predicted BasePay KNN\"] = knn_model.predict(\n",
        "    X=df[[\"Age\", \"Gender\",\"Education\", \"Seniority\"]]\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "id": "98977494-dd9d-424b-8fbf-fb932b319315",
      "metadata": {
        "colab": {
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          "height": 268
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        "outputId": "bc7d1bc7-fc69-4c9f-fe3a-3e4d9dc40e89"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   JobTitle  Gender  Age  PerfEval  Education  Dept  Seniority  BasePay  \\\n",
              "0         0       1   18         5          1     0          2    42363   \n",
              "1         1       0   21         5          1     1          5   108476   \n",
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              "3         1       0   20         5          2     3          4   108080   \n",
              "4         0       0   26         5          2     4          5    99464   \n",
              "\n",
              "   Predicted BasePay Linear  Predicted BasePay KNN  \n",
              "0              55391.632648                58590.2  \n",
              "1              95615.580662                99977.6  \n",
              "2              90655.960673                90283.6  \n",
              "3              87923.944313                90789.4  \n",
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              "            + ' to learn more about interactive tables.';\n",
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            ]
          },
          "metadata": {},
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        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Created \"Age_Group\" column for better visuals for Bias later on."
      ],
      "metadata": {
        "id": "YNiNOLKe_F7A"
      },
      "id": "YNiNOLKe_F7A"
    },
    {
      "cell_type": "code",
      "source": [
        "df.loc[df['Age']<=20, 'Age_Group'] = '0-20'\n",
        "df.loc[df['Age'].between(20,30), 'Age_Group'] = '20-30'\n",
        "df.loc[df['Age'].between(30,40), 'Age_Group'] = '30-40'\n",
        "df.loc[df['Age'].between(40,50), 'Age_Group'] = '40-50'\n",
        "df.loc[df['Age']>50, 'Age_Group'] = '50+'\n",
        "\n"
      ],
      "metadata": {
        "id": "lSBWh8LC1GW9"
      },
      "id": "lSBWh8LC1GW9",
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Created \"is_rich\" column with binary values where if the base pay is higher than the median, then they are considered higher income."
      ],
      "metadata": {
        "id": "jaQqzrUP_ju1"
      },
      "id": "jaQqzrUP_ju1"
    },
    {
      "cell_type": "code",
      "source": [
        "df['is_rich'] = pd.cut(df[\"BasePay\"], bins=[0, df[\"BasePay\"].median(), float('Inf')], labels=[0, 1])\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
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          "height": 305
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      "id": "FYZIcw_W2TaB",
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   JobTitle  Gender  Age  PerfEval  Education  Dept  Seniority  BasePay  \\\n",
              "0         0       1   18         5          1     0          2    42363   \n",
              "1         1       0   21         5          1     1          5   108476   \n",
              "2         2       1   19         4          3     2          5    90208   \n",
              "3         1       0   20         5          2     3          4   108080   \n",
              "4         0       0   26         5          2     4          5    99464   \n",
              "\n",
              "   Predicted BasePay Linear  Predicted BasePay KNN Age_Group is_rich  \n",
              "0              55391.632648                58590.2      0-20       0  \n",
              "1              95615.580662                99977.6     20-30       1  \n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "\n",
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              "\n",
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              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
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              "          document.querySelector('#df-6b1f8cfd-3ed0-4eb1-9cfb-c8a03cefb6e5 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-6b1f8cfd-3ed0-4eb1-9cfb-c8a03cefb6e5');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Preprocessed data and added \"Age_Group\" to knn trained model."
      ],
      "metadata": {
        "id": "Dq_pjHm1AIuT"
      },
      "id": "Dq_pjHm1AIuT"
    },
    {
      "cell_type": "code",
      "source": [
        "x_data = df[[\"Age\", \"Gender\",\"Education\", \"Seniority\", \"Age_Group\",]]\n",
        "y_data = df[[\"BasePay\", \"is_rich\"]]\n",
        "X_train_cat, X_test_cat, y_train_cat, y_test_cat = train_test_split(x_data, y_data,test_size=0.2, random_state = 1)\n",
        "X_train = X_train_cat[[\"Age\", \"Gender\",\"Education\", \"Seniority\"]]\n",
        "X_test = X_test_cat[[\"Age\", \"Gender\",\"Education\", \"Seniority\"]]\n",
        "y_train = y_train_cat[\"BasePay\"]\n",
        "y_test = y_test_cat[\"BasePay\"]\n",
        "knn_model = KNeighborsRegressor()\n",
        "knn_model.fit(X_train, y_train)\n",
        "predicted = knn_model.predict(X_test)\n",
        "knn_score = knn_model.score(X_test,y_test)\n",
        "print(predicted)\n",
        "print(knn_score)\n",
        "x_data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 671
        },
        "id": "oBhhCMYg2p9z",
        "outputId": "2399e508-412b-4f93-abed-0942bd7deb4e"
      },
      "id": "oBhhCMYg2p9z",
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[120734.8 128454.2  97306.8 109223.   69802.8 135800.2  84679.8 121814.\n",
            " 103076.8  78327.  142496.8  83553.8 114664.8  72569.2 109090.2  75289.6\n",
            "  86544.6  73091.8 103171.8  84735.6  68184.   65772.8 116225.8  97296.8\n",
            "  97077.   94061.8  86431.8 120127.4  58735.4 100338.6  57239.2  80611.2\n",
            "  91975.4  73222.4  74761.6 128297.4  82165.8 104837.8  65254.2 101585.2\n",
            " 115761.4  80556.6  75889.8 115402.2  68950.   59462.8  98173.2  95286.6\n",
            "  67089.  101304.8 100416.8 119509.4  77324.2  91947.6  71707.6  57084.8\n",
            "  95758.8  87180.6 139027.  120867.2  93035.6  92322.  131018.2  92987.4\n",
            " 110636.8  63473.2  77437.  107937.8 119701.4  95005.8  95433.4  75492.6\n",
            " 105515.8  95889.2 107588.   69218.2  88612.2 108401.  131133.4  90269.6\n",
            "  60855.   91708.2 119866.4  54831.4  94710.6 105243.8 114328.2  91572.4\n",
            "  68281.2  82584.   86707.8  79858.2  86364.8 105031.8  62462.   98796.8\n",
            " 106438.2 106387.2 123696.4  83629.4  90927.8  97134.4  82292.6  97072.2\n",
            "  88367.2  59916.8  76630.   72684.   81304.4 107352.2 106848.8  57752.\n",
            " 119701.4  72944.6 120236.8 124260.2 105103.4  79858.2  63166.  100421.2\n",
            " 112435.2 104717.4  90789.4  62809.   74845.6 103129.4 127514.2 104740.2\n",
            "  68184.   97627.2  87980.   90838.2 118136.6  90283.6  75395.6  89594.8\n",
            " 118188.4  71160.8  84610.8 109183.8 108735.8  93909.6 113008.6  70126.\n",
            "  96453.4  88749.8 120758.8 131342.6  83098.8  60429.2 112134.2 116745.\n",
            "  88880.   99123.6  66808.   79519.8 129398.4  75289.6 105850.   76621.\n",
            "  75434.4  87202.   68281.2  86793.  120947.2  92556.8  74845.6 130585.8\n",
            "  55099.8 126327.2  64558.8  86544.6  93487.6 111522.6 105715.4 104804.4\n",
            "  96471.   81387.2  99365.2 142022.   80892.8  92632.8  91947.6  71457.2\n",
            " 101371.   78302.2  86123.6  55569.6 135467.4 112574.8 102707.   81981.2\n",
            " 128190.6  74656.6  90269.6 129283.  153073.8  91637.2  86175.4 102559.6]\n",
            "0.5500551237069016\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Age  Gender  Education  Seniority Age_Group\n",
              "0   18       1          1          2      0-20\n",
              "1   21       0          1          5     20-30\n",
              "2   19       1          3          5      0-20\n",
              "3   20       0          2          4     20-30\n",
              "4   26       0          2          5     20-30"
            ],
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              "\n",
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              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th></th>\n",
              "      <th>Age</th>\n",
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              "  </svg>\n",
              "      </button>\n",
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              "    .colab-df-convert {\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "  </style>\n",
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              "          document.querySelector('#df-d641f756-4d72-45a9-a1b1-e0f4efb745ec button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d641f756-4d72-45a9-a1b1-e0f4efb745ec');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Recalculated MSE"
      ],
      "metadata": {
        "id": "j8Dn_KQTAmPZ"
      },
      "id": "j8Dn_KQTAmPZ"
    },
    {
      "cell_type": "code",
      "source": [
        "mse = mean_squared_error(y_test, predicted)\n",
        "print(mse)\n",
        "rmse = mean_squared_error(y_test, predicted,squared = False)\n",
        "print(rmse)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YYUgLEQy7EbF",
        "outputId": "08e3fbab-7943-465a-8c11-b0526960d110"
      },
      "id": "YYUgLEQy7EbF",
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "304428359.2842\n",
            "17447.875494861833\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Installed aequitas toolkit"
      ],
      "metadata": {
        "id": "ydh61zPYBO52"
      },
      "id": "ydh61zPYBO52"
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -q aequitas"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aW98Xhsk7ICR",
        "outputId": "9c4f954a-51fb-4c46-891d-05f8febb1531"
      },
      "id": "aW98Xhsk7ICR",
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.2/2.2 MB\u001b[0m \u001b[31m22.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m97.3/97.3 KB\u001b[0m \u001b[31m9.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m456.4/456.4 KB\u001b[0m \u001b[31m34.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m161.9/161.9 KB\u001b[0m \u001b[31m15.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m232.6/232.6 KB\u001b[0m \u001b[31m24.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.8/2.8 MB\u001b[0m \u001b[31m70.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.4/3.4 MB\u001b[0m \u001b[31m75.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Building wheel for Flask-Bootstrap (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for markdown2 (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for millify (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for tabulate (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for xhtml2pdf (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for visitor (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Created \"score\" which is the predicted value of \"is_rich\" and renamed \"is_rich\" to label_value. A score of 1 means the person if of higher income."
      ],
      "metadata": {
        "id": "VQU7ibeTBXsN"
      },
      "id": "VQU7ibeTBXsN"
    },
    {
      "cell_type": "code",
      "source": [
        "import aequitas \n",
        "from aequitas.preprocessing import preprocess_input_df\n",
        "    \n",
        "# double-check that categorical columns are of type 'string'\n",
        "df = pd.DataFrame(X_test_cat)\n",
        "df[[\"Gender\",\"Education\", \"Seniority\", \"Age_Group\"]] = df[[\"Gender\",\"Education\", \"Seniority\", \"Age_Group\"]].astype(str)\n",
        "df[\"score\"] = predicted > y_test.median()\n",
        "df[\"label_value\"] = y_test_cat[\"is_rich\"]\n",
        "print(type(y_test.iloc[0]))\n",
        "df[\"score\"] = df[\"score\"].astype(int)\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "id": "jHqd9ahT7NBx",
        "outputId": "b7718e28-3a0a-4c09-f486-06c64a300edb"
      },
      "id": "jHqd9ahT7NBx",
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'numpy.int64'>\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Age Gender Education Seniority Age_Group  score label_value\n",
              "507   63      1         1         5       50+      1           1\n",
              "818   56      1         0         5       50+      1           1\n",
              "452   21      1         1         5     20-30      1           0\n",
              "368   49      1         1         4     40-50      1           1\n",
              "242   19      0         1         3      0-20      0           0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c76cab1e-aa3a-475a-8d91-c8a014cfb202\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Education</th>\n",
              "      <th>Seniority</th>\n",
              "      <th>Age_Group</th>\n",
              "      <th>score</th>\n",
              "      <th>label_value</th>\n",
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              "      <th>507</th>\n",
              "      <td>63</td>\n",
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              "      <td>5</td>\n",
              "      <td>50+</td>\n",
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              "      <th>818</th>\n",
              "      <td>56</td>\n",
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              "      <td>5</td>\n",
              "      <td>50+</td>\n",
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              "    </tr>\n",
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              "      <th>452</th>\n",
              "      <td>21</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>20-30</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>368</th>\n",
              "      <td>49</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>40-50</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>242</th>\n",
              "      <td>19</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0-20</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c76cab1e-aa3a-475a-8d91-c8a014cfb202')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-c76cab1e-aa3a-475a-8d91-c8a014cfb202 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-c76cab1e-aa3a-475a-8d91-c8a014cfb202');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.drop(columns=\"Age\")\n",
        "df, _ = preprocess_input_df(df)"
      ],
      "metadata": {
        "id": "mjjKAwqf7oxw"
      },
      "id": "mjjKAwqf7oxw",
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from aequitas.group import Group\n",
        "    \n",
        "g = Group()\n",
        "xtab, _ = g.get_crosstabs(df)"
      ],
      "metadata": {
        "id": "aQnk2hIE7tfD"
      },
      "id": "aQnk2hIE7tfD",
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from aequitas.plotting import Plot\n",
        "    \n",
        "aqp = Plot()\n",
        "fpr_plot = aqp.plot_group_metric(xtab, 'fpr')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 355
        },
        "id": "jzBLu2zM7vvq",
        "outputId": "c00c085f-7a33-46ae-f715-96c483a2d74c"
      },
      "id": "jzBLu2zM7vvq",
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Dropped age column and created a False Positive Rate plot . The decimal represents the likelihood of the prediction to be wrong. The prediction of Males is 0.17 times more likely to be wrong about being higher income earners."
      ],
      "metadata": {
        "id": "fHiaGmbRBzWD"
      },
      "id": "fHiaGmbRBzWD"
    },
    {
      "cell_type": "markdown",
      "source": [
        "Created reference group to be Males with a masters degree and a seniority level of 3."
      ],
      "metadata": {
        "id": "87LQuArWC-8e"
      },
      "id": "87LQuArWC-8e"
    },
    {
      "cell_type": "code",
      "source": [
        "from aequitas.bias import Bias\n",
        "b = Bias()\n",
        "bdf = b.get_disparity_predefined_groups(xtab, original_df=df, ref_groups_dict={'Gender':'0', 'Education':'2', 'Age_Group':'30-40', 'Seniority': '3'}, alpha=0.05, mask_significance=True)\n",
        "bdf.style"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 589
        },
        "id": "B4bwNqSd7y9q",
        "outputId": "8d88343e-c951-4bde-e09f-12ade4727e53"
      },
      "id": "B4bwNqSd7y9q",
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "get_disparity_predefined_group()\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7f601d7540a0>"
            ],
            "text/html": [
              "<style type=\"text/css\">\n",
              "</style>\n",
              "<table id=\"T_80ff5\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_80ff5_level0_col0\" class=\"col_heading level0 col0\" >model_id</th>\n",
              "      <th id=\"T_80ff5_level0_col1\" class=\"col_heading level0 col1\" >score_threshold</th>\n",
              "      <th id=\"T_80ff5_level0_col2\" class=\"col_heading level0 col2\" >k</th>\n",
              "      <th id=\"T_80ff5_level0_col3\" class=\"col_heading level0 col3\" >attribute_name</th>\n",
              "      <th id=\"T_80ff5_level0_col4\" class=\"col_heading level0 col4\" >attribute_value</th>\n",
              "      <th id=\"T_80ff5_level0_col5\" class=\"col_heading level0 col5\" >tpr</th>\n",
              "      <th id=\"T_80ff5_level0_col6\" class=\"col_heading level0 col6\" >tnr</th>\n",
              "      <th id=\"T_80ff5_level0_col7\" class=\"col_heading level0 col7\" >for</th>\n",
              "      <th id=\"T_80ff5_level0_col8\" class=\"col_heading level0 col8\" >fdr</th>\n",
              "      <th id=\"T_80ff5_level0_col9\" class=\"col_heading level0 col9\" >fpr</th>\n",
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              "      <th id=\"T_80ff5_level0_col11\" class=\"col_heading level0 col11\" >npv</th>\n",
              "      <th id=\"T_80ff5_level0_col12\" class=\"col_heading level0 col12\" >precision</th>\n",
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              "      <th id=\"T_80ff5_level0_col25\" class=\"col_heading level0 col25\" >prev</th>\n",
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              "      <th id=\"T_80ff5_level0_col35\" class=\"col_heading level0 col35\" >npv_disparity</th>\n",
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              "      <th id=\"T_80ff5_level0_col39\" class=\"col_heading level0 col39\" >fdr_ref_group_value</th>\n",
              "      <th id=\"T_80ff5_level0_col40\" class=\"col_heading level0 col40\" >for_ref_group_value</th>\n",
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              "      <th id=\"T_80ff5_level0_col43\" class=\"col_heading level0 col43\" >tpr_ref_group_value</th>\n",
              "      <th id=\"T_80ff5_level0_col44\" class=\"col_heading level0 col44\" >tnr_ref_group_value</th>\n",
              "      <th id=\"T_80ff5_level0_col45\" class=\"col_heading level0 col45\" >npv_ref_group_value</th>\n",
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              "      <td id=\"T_80ff5_row0_col3\" class=\"data row0 col3\" >Gender</td>\n",
              "      <td id=\"T_80ff5_row0_col4\" class=\"data row0 col4\" >0</td>\n",
              "      <td id=\"T_80ff5_row0_col5\" class=\"data row0 col5\" >0.750000</td>\n",
              "      <td id=\"T_80ff5_row0_col6\" class=\"data row0 col6\" >0.829787</td>\n",
              "      <td id=\"T_80ff5_row0_col7\" class=\"data row0 col7\" >0.290909</td>\n",
              "      <td id=\"T_80ff5_row0_col8\" class=\"data row0 col8\" >0.142857</td>\n",
              "      <td id=\"T_80ff5_row0_col9\" class=\"data row0 col9\" >0.170213</td>\n",
              "      <td id=\"T_80ff5_row0_col10\" class=\"data row0 col10\" >0.250000</td>\n",
              "      <td id=\"T_80ff5_row0_col11\" class=\"data row0 col11\" >0.709091</td>\n",
              "      <td id=\"T_80ff5_row0_col12\" class=\"data row0 col12\" >0.857143</td>\n",
              "      <td id=\"T_80ff5_row0_col13\" class=\"data row0 col13\" >56</td>\n",
              "      <td id=\"T_80ff5_row0_col14\" class=\"data row0 col14\" >55</td>\n",
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              "      <td id=\"T_80ff5_row0_col29\" class=\"data row0 col29\" >1.000000</td>\n",
              "      <td id=\"T_80ff5_row0_col30\" class=\"data row0 col30\" >1.000000</td>\n",
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              "      <td id=\"T_80ff5_row0_col32\" class=\"data row0 col32\" >1.000000</td>\n",
              "      <td id=\"T_80ff5_row0_col33\" class=\"data row0 col33\" >1.000000</td>\n",
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              "      <td id=\"T_80ff5_row0_col45\" class=\"data row0 col45\" >0</td>\n",
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              "      <td id=\"T_80ff5_row1_col3\" class=\"data row1 col3\" >Gender</td>\n",
              "      <td id=\"T_80ff5_row1_col4\" class=\"data row1 col4\" >1</td>\n",
              "      <td id=\"T_80ff5_row1_col5\" class=\"data row1 col5\" >0.800000</td>\n",
              "      <td id=\"T_80ff5_row1_col6\" class=\"data row1 col6\" >0.759259</td>\n",
              "      <td id=\"T_80ff5_row1_col7\" class=\"data row1 col7\" >0.145833</td>\n",
              "      <td id=\"T_80ff5_row1_col8\" class=\"data row1 col8\" >0.317073</td>\n",
              "      <td id=\"T_80ff5_row1_col9\" class=\"data row1 col9\" >0.240741</td>\n",
              "      <td id=\"T_80ff5_row1_col10\" class=\"data row1 col10\" >0.200000</td>\n",
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              "      <td id=\"T_80ff5_row1_col15\" class=\"data row1 col15\" >0.422680</td>\n",
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              "      <td id=\"T_80ff5_row1_col17\" class=\"data row1 col17\" >13</td>\n",
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              "      <td id=\"T_80ff5_row1_col45\" class=\"data row1 col45\" >0</td>\n",
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              "      <td id=\"T_80ff5_row2_col3\" class=\"data row2 col3\" >Education</td>\n",
              "      <td id=\"T_80ff5_row2_col4\" class=\"data row2 col4\" >0</td>\n",
              "      <td id=\"T_80ff5_row2_col5\" class=\"data row2 col5\" >0.652174</td>\n",
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              "      <td id=\"T_80ff5_row2_col32\" class=\"data row2 col32\" >1.333333</td>\n",
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              "      <td id=\"T_80ff5_row2_col36\" class=\"data row2 col36\" >2</td>\n",
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              "      <td id=\"T_80ff5_row2_col45\" class=\"data row2 col45\" >2</td>\n",
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              "    <tr>\n",
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              "      <td id=\"T_80ff5_row3_col3\" class=\"data row3 col3\" >Education</td>\n",
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              "      <td id=\"T_80ff5_row3_col31\" class=\"data row3 col31\" >0.880000</td>\n",
              "      <td id=\"T_80ff5_row3_col32\" class=\"data row3 col32\" >0.547619</td>\n",
              "      <td id=\"T_80ff5_row3_col33\" class=\"data row3 col33\" >1.159664</td>\n",
              "      <td id=\"T_80ff5_row3_col34\" class=\"data row3 col34\" >1.035294</td>\n",
              "      <td id=\"T_80ff5_row3_col35\" class=\"data row3 col35\" >1.127451</td>\n",
              "      <td id=\"T_80ff5_row3_col36\" class=\"data row3 col36\" >2</td>\n",
              "      <td id=\"T_80ff5_row3_col37\" class=\"data row3 col37\" >2</td>\n",
              "      <td id=\"T_80ff5_row3_col38\" class=\"data row3 col38\" >2</td>\n",
              "      <td id=\"T_80ff5_row3_col39\" class=\"data row3 col39\" >2</td>\n",
              "      <td id=\"T_80ff5_row3_col40\" class=\"data row3 col40\" >2</td>\n",
              "      <td id=\"T_80ff5_row3_col41\" class=\"data row3 col41\" >2</td>\n",
              "      <td id=\"T_80ff5_row3_col42\" class=\"data row3 col42\" >2</td>\n",
              "      <td id=\"T_80ff5_row3_col43\" class=\"data row3 col43\" >2</td>\n",
              "      <td id=\"T_80ff5_row3_col44\" class=\"data row3 col44\" >2</td>\n",
              "      <td id=\"T_80ff5_row3_col45\" class=\"data row3 col45\" >2</td>\n",
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              "    <tr>\n",
              "      <th id=\"T_80ff5_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
              "      <td id=\"T_80ff5_row4_col0\" class=\"data row4 col0\" >0</td>\n",
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              "      <td id=\"T_80ff5_row4_col3\" class=\"data row4 col3\" >Education</td>\n",
              "      <td id=\"T_80ff5_row4_col4\" class=\"data row4 col4\" >2</td>\n",
              "      <td id=\"T_80ff5_row4_col5\" class=\"data row4 col5\" >0.739130</td>\n",
              "      <td id=\"T_80ff5_row4_col6\" class=\"data row4 col6\" >0.772727</td>\n",
              "      <td id=\"T_80ff5_row4_col7\" class=\"data row4 col7\" >0.260870</td>\n",
              "      <td id=\"T_80ff5_row4_col8\" class=\"data row4 col8\" >0.227273</td>\n",
              "      <td id=\"T_80ff5_row4_col9\" class=\"data row4 col9\" >0.227273</td>\n",
              "      <td id=\"T_80ff5_row4_col10\" class=\"data row4 col10\" >0.260870</td>\n",
              "      <td id=\"T_80ff5_row4_col11\" class=\"data row4 col11\" >0.739130</td>\n",
              "      <td id=\"T_80ff5_row4_col12\" class=\"data row4 col12\" >0.772727</td>\n",
              "      <td id=\"T_80ff5_row4_col13\" class=\"data row4 col13\" >22</td>\n",
              "      <td id=\"T_80ff5_row4_col14\" class=\"data row4 col14\" >23</td>\n",
              "      <td id=\"T_80ff5_row4_col15\" class=\"data row4 col15\" >0.226804</td>\n",
              "      <td id=\"T_80ff5_row4_col16\" class=\"data row4 col16\" >0.488889</td>\n",
              "      <td id=\"T_80ff5_row4_col17\" class=\"data row4 col17\" >5</td>\n",
              "      <td id=\"T_80ff5_row4_col18\" class=\"data row4 col18\" >6</td>\n",
              "      <td id=\"T_80ff5_row4_col19\" class=\"data row4 col19\" >17</td>\n",
              "      <td id=\"T_80ff5_row4_col20\" class=\"data row4 col20\" >17</td>\n",
              "      <td id=\"T_80ff5_row4_col21\" class=\"data row4 col21\" >23</td>\n",
              "      <td id=\"T_80ff5_row4_col22\" class=\"data row4 col22\" >22</td>\n",
              "      <td id=\"T_80ff5_row4_col23\" class=\"data row4 col23\" >45</td>\n",
              "      <td id=\"T_80ff5_row4_col24\" class=\"data row4 col24\" >200</td>\n",
              "      <td id=\"T_80ff5_row4_col25\" class=\"data row4 col25\" >0.511111</td>\n",
              "      <td id=\"T_80ff5_row4_col26\" class=\"data row4 col26\" >1.000000</td>\n",
              "      <td id=\"T_80ff5_row4_col27\" class=\"data row4 col27\" >1.000000</td>\n",
              "      <td id=\"T_80ff5_row4_col28\" class=\"data row4 col28\" >1.000000</td>\n",
              "      <td id=\"T_80ff5_row4_col29\" class=\"data row4 col29\" >1.000000</td>\n",
              "      <td id=\"T_80ff5_row4_col30\" class=\"data row4 col30\" >1.000000</td>\n",
              "      <td id=\"T_80ff5_row4_col31\" class=\"data row4 col31\" >1.000000</td>\n",
              "      <td id=\"T_80ff5_row4_col32\" class=\"data row4 col32\" >1.000000</td>\n",
              "      <td id=\"T_80ff5_row4_col33\" class=\"data row4 col33\" >1.000000</td>\n",
              "      <td id=\"T_80ff5_row4_col34\" class=\"data row4 col34\" >1.000000</td>\n",
              "      <td id=\"T_80ff5_row4_col35\" class=\"data row4 col35\" >1.000000</td>\n",
              "      <td id=\"T_80ff5_row4_col36\" class=\"data row4 col36\" >2</td>\n",
              "      <td id=\"T_80ff5_row4_col37\" class=\"data row4 col37\" >2</td>\n",
              "      <td id=\"T_80ff5_row4_col38\" class=\"data row4 col38\" >2</td>\n",
              "      <td id=\"T_80ff5_row4_col39\" class=\"data row4 col39\" >2</td>\n",
              "      <td id=\"T_80ff5_row4_col40\" class=\"data row4 col40\" >2</td>\n",
              "      <td id=\"T_80ff5_row4_col41\" class=\"data row4 col41\" >2</td>\n",
              "      <td id=\"T_80ff5_row4_col42\" class=\"data row4 col42\" >2</td>\n",
              "      <td id=\"T_80ff5_row4_col43\" class=\"data row4 col43\" >2</td>\n",
              "      <td id=\"T_80ff5_row4_col44\" class=\"data row4 col44\" >2</td>\n",
              "      <td id=\"T_80ff5_row4_col45\" class=\"data row4 col45\" >2</td>\n",
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              "    <tr>\n",
              "      <th id=\"T_80ff5_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
              "      <td id=\"T_80ff5_row5_col0\" class=\"data row5 col0\" >0</td>\n",
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              "      <td id=\"T_80ff5_row5_col2\" class=\"data row5 col2\" >97</td>\n",
              "      <td id=\"T_80ff5_row5_col3\" class=\"data row5 col3\" >Education</td>\n",
              "      <td id=\"T_80ff5_row5_col4\" class=\"data row5 col4\" >3</td>\n",
              "      <td id=\"T_80ff5_row5_col5\" class=\"data row5 col5\" >0.800000</td>\n",
              "      <td id=\"T_80ff5_row5_col6\" class=\"data row5 col6\" >0.750000</td>\n",
              "      <td id=\"T_80ff5_row5_col7\" class=\"data row5 col7\" >0.294118</td>\n",
              "      <td id=\"T_80ff5_row5_col8\" class=\"data row5 col8\" >0.166667</td>\n",
              "      <td id=\"T_80ff5_row5_col9\" class=\"data row5 col9\" >0.250000</td>\n",
              "      <td id=\"T_80ff5_row5_col10\" class=\"data row5 col10\" >0.200000</td>\n",
              "      <td id=\"T_80ff5_row5_col11\" class=\"data row5 col11\" >0.705882</td>\n",
              "      <td id=\"T_80ff5_row5_col12\" class=\"data row5 col12\" >0.833333</td>\n",
              "      <td id=\"T_80ff5_row5_col13\" class=\"data row5 col13\" >24</td>\n",
              "      <td id=\"T_80ff5_row5_col14\" class=\"data row5 col14\" >17</td>\n",
              "      <td id=\"T_80ff5_row5_col15\" class=\"data row5 col15\" >0.247423</td>\n",
              "      <td id=\"T_80ff5_row5_col16\" class=\"data row5 col16\" >0.585366</td>\n",
              "      <td id=\"T_80ff5_row5_col17\" class=\"data row5 col17\" >4</td>\n",
              "      <td id=\"T_80ff5_row5_col18\" class=\"data row5 col18\" >5</td>\n",
              "      <td id=\"T_80ff5_row5_col19\" class=\"data row5 col19\" >12</td>\n",
              "      <td id=\"T_80ff5_row5_col20\" class=\"data row5 col20\" >20</td>\n",
              "      <td id=\"T_80ff5_row5_col21\" class=\"data row5 col21\" >25</td>\n",
              "      <td id=\"T_80ff5_row5_col22\" class=\"data row5 col22\" >16</td>\n",
              "      <td id=\"T_80ff5_row5_col23\" class=\"data row5 col23\" >41</td>\n",
              "      <td id=\"T_80ff5_row5_col24\" class=\"data row5 col24\" >200</td>\n",
              "      <td id=\"T_80ff5_row5_col25\" class=\"data row5 col25\" >0.609756</td>\n",
              "      <td id=\"T_80ff5_row5_col26\" class=\"data row5 col26\" >1.090909</td>\n",
              "      <td id=\"T_80ff5_row5_col27\" class=\"data row5 col27\" >1.197339</td>\n",
              "      <td id=\"T_80ff5_row5_col28\" class=\"data row5 col28\" >1.078431</td>\n",
              "      <td id=\"T_80ff5_row5_col29\" class=\"data row5 col29\" >0.733333</td>\n",
              "      <td id=\"T_80ff5_row5_col30\" class=\"data row5 col30\" >1.127451</td>\n",
              "      <td id=\"T_80ff5_row5_col31\" class=\"data row5 col31\" >1.100000</td>\n",
              "      <td id=\"T_80ff5_row5_col32\" class=\"data row5 col32\" >0.766667</td>\n",
              "      <td id=\"T_80ff5_row5_col33\" class=\"data row5 col33\" >1.082353</td>\n",
              "      <td id=\"T_80ff5_row5_col34\" class=\"data row5 col34\" >0.970588</td>\n",
              "      <td id=\"T_80ff5_row5_col35\" class=\"data row5 col35\" >0.955017</td>\n",
              "      <td id=\"T_80ff5_row5_col36\" class=\"data row5 col36\" >2</td>\n",
              "      <td id=\"T_80ff5_row5_col37\" class=\"data row5 col37\" >2</td>\n",
              "      <td id=\"T_80ff5_row5_col38\" class=\"data row5 col38\" >2</td>\n",
              "      <td id=\"T_80ff5_row5_col39\" class=\"data row5 col39\" >2</td>\n",
              "      <td id=\"T_80ff5_row5_col40\" class=\"data row5 col40\" >2</td>\n",
              "      <td id=\"T_80ff5_row5_col41\" class=\"data row5 col41\" >2</td>\n",
              "      <td id=\"T_80ff5_row5_col42\" class=\"data row5 col42\" >2</td>\n",
              "      <td id=\"T_80ff5_row5_col43\" class=\"data row5 col43\" >2</td>\n",
              "      <td id=\"T_80ff5_row5_col44\" class=\"data row5 col44\" >2</td>\n",
              "      <td id=\"T_80ff5_row5_col45\" class=\"data row5 col45\" >2</td>\n",
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              "    <tr>\n",
              "      <th id=\"T_80ff5_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
              "      <td id=\"T_80ff5_row6_col0\" class=\"data row6 col0\" >0</td>\n",
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              "      <td id=\"T_80ff5_row6_col2\" class=\"data row6 col2\" >97</td>\n",
              "      <td id=\"T_80ff5_row6_col3\" class=\"data row6 col3\" >Seniority</td>\n",
              "      <td id=\"T_80ff5_row6_col4\" class=\"data row6 col4\" >1</td>\n",
              "      <td id=\"T_80ff5_row6_col5\" class=\"data row6 col5\" >0.428571</td>\n",
              "      <td id=\"T_80ff5_row6_col6\" class=\"data row6 col6\" >0.769231</td>\n",
              "      <td id=\"T_80ff5_row6_col7\" class=\"data row6 col7\" >0.166667</td>\n",
              "      <td id=\"T_80ff5_row6_col8\" class=\"data row6 col8\" >0.666667</td>\n",
              "      <td id=\"T_80ff5_row6_col9\" class=\"data row6 col9\" >0.230769</td>\n",
              "      <td id=\"T_80ff5_row6_col10\" class=\"data row6 col10\" >0.571429</td>\n",
              "      <td id=\"T_80ff5_row6_col11\" class=\"data row6 col11\" >0.833333</td>\n",
              "      <td id=\"T_80ff5_row6_col12\" class=\"data row6 col12\" >0.333333</td>\n",
              "      <td id=\"T_80ff5_row6_col13\" class=\"data row6 col13\" >9</td>\n",
              "      <td id=\"T_80ff5_row6_col14\" class=\"data row6 col14\" >24</td>\n",
              "      <td id=\"T_80ff5_row6_col15\" class=\"data row6 col15\" >0.092784</td>\n",
              "      <td id=\"T_80ff5_row6_col16\" class=\"data row6 col16\" >0.272727</td>\n",
              "      <td id=\"T_80ff5_row6_col17\" class=\"data row6 col17\" >6</td>\n",
              "      <td id=\"T_80ff5_row6_col18\" class=\"data row6 col18\" >4</td>\n",
              "      <td id=\"T_80ff5_row6_col19\" class=\"data row6 col19\" >20</td>\n",
              "      <td id=\"T_80ff5_row6_col20\" class=\"data row6 col20\" >3</td>\n",
              "      <td id=\"T_80ff5_row6_col21\" class=\"data row6 col21\" >7</td>\n",
              "      <td id=\"T_80ff5_row6_col22\" class=\"data row6 col22\" >26</td>\n",
              "      <td id=\"T_80ff5_row6_col23\" class=\"data row6 col23\" >33</td>\n",
              "      <td id=\"T_80ff5_row6_col24\" class=\"data row6 col24\" >200</td>\n",
              "      <td id=\"T_80ff5_row6_col25\" class=\"data row6 col25\" >0.212121</td>\n",
              "      <td id=\"T_80ff5_row6_col26\" class=\"data row6 col26\" >0.450000</td>\n",
              "      <td id=\"T_80ff5_row6_col27\" class=\"data row6 col27\" >0.668182</td>\n",
              "      <td id=\"T_80ff5_row6_col28\" class=\"data row6 col28\" >0.416667</td>\n",
              "      <td id=\"T_80ff5_row6_col29\" class=\"data row6 col29\" >3.333333</td>\n",
              "      <td id=\"T_80ff5_row6_col30\" class=\"data row6 col30\" >0.604167</td>\n",
              "      <td id=\"T_80ff5_row6_col31\" class=\"data row6 col31\" >1.442308</td>\n",
              "      <td id=\"T_80ff5_row6_col32\" class=\"data row6 col32\" >1.714286</td>\n",
              "      <td id=\"T_80ff5_row6_col33\" class=\"data row6 col33\" >0.642857</td>\n",
              "      <td id=\"T_80ff5_row6_col34\" class=\"data row6 col34\" >0.915751</td>\n",
              "      <td id=\"T_80ff5_row6_col35\" class=\"data row6 col35\" >1.150794</td>\n",
              "      <td id=\"T_80ff5_row6_col36\" class=\"data row6 col36\" >3</td>\n",
              "      <td id=\"T_80ff5_row6_col37\" class=\"data row6 col37\" >3</td>\n",
              "      <td id=\"T_80ff5_row6_col38\" class=\"data row6 col38\" >3</td>\n",
              "      <td id=\"T_80ff5_row6_col39\" class=\"data row6 col39\" >3</td>\n",
              "      <td id=\"T_80ff5_row6_col40\" class=\"data row6 col40\" >3</td>\n",
              "      <td id=\"T_80ff5_row6_col41\" class=\"data row6 col41\" >3</td>\n",
              "      <td id=\"T_80ff5_row6_col42\" class=\"data row6 col42\" >3</td>\n",
              "      <td id=\"T_80ff5_row6_col43\" class=\"data row6 col43\" >3</td>\n",
              "      <td id=\"T_80ff5_row6_col44\" class=\"data row6 col44\" >3</td>\n",
              "      <td id=\"T_80ff5_row6_col45\" class=\"data row6 col45\" >3</td>\n",
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              "    <tr>\n",
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              "      <td id=\"T_80ff5_row7_col3\" class=\"data row7 col3\" >Seniority</td>\n",
              "      <td id=\"T_80ff5_row7_col4\" class=\"data row7 col4\" >2</td>\n",
              "      <td id=\"T_80ff5_row7_col5\" class=\"data row7 col5\" >0.700000</td>\n",
              "      <td id=\"T_80ff5_row7_col6\" class=\"data row7 col6\" >0.892857</td>\n",
              "      <td id=\"T_80ff5_row7_col7\" class=\"data row7 col7\" >0.107143</td>\n",
              "      <td id=\"T_80ff5_row7_col8\" class=\"data row7 col8\" >0.300000</td>\n",
              "      <td id=\"T_80ff5_row7_col9\" class=\"data row7 col9\" >0.107143</td>\n",
              "      <td id=\"T_80ff5_row7_col10\" class=\"data row7 col10\" >0.300000</td>\n",
              "      <td id=\"T_80ff5_row7_col11\" class=\"data row7 col11\" >0.892857</td>\n",
              "      <td id=\"T_80ff5_row7_col12\" class=\"data row7 col12\" >0.700000</td>\n",
              "      <td id=\"T_80ff5_row7_col13\" class=\"data row7 col13\" >10</td>\n",
              "      <td id=\"T_80ff5_row7_col14\" class=\"data row7 col14\" >28</td>\n",
              "      <td id=\"T_80ff5_row7_col15\" class=\"data row7 col15\" >0.103093</td>\n",
              "      <td id=\"T_80ff5_row7_col16\" class=\"data row7 col16\" >0.263158</td>\n",
              "      <td id=\"T_80ff5_row7_col17\" class=\"data row7 col17\" >3</td>\n",
              "      <td id=\"T_80ff5_row7_col18\" class=\"data row7 col18\" >3</td>\n",
              "      <td id=\"T_80ff5_row7_col19\" class=\"data row7 col19\" >25</td>\n",
              "      <td id=\"T_80ff5_row7_col20\" class=\"data row7 col20\" >7</td>\n",
              "      <td id=\"T_80ff5_row7_col21\" class=\"data row7 col21\" >10</td>\n",
              "      <td id=\"T_80ff5_row7_col22\" class=\"data row7 col22\" >28</td>\n",
              "      <td id=\"T_80ff5_row7_col23\" class=\"data row7 col23\" >38</td>\n",
              "      <td id=\"T_80ff5_row7_col24\" class=\"data row7 col24\" >200</td>\n",
              "      <td id=\"T_80ff5_row7_col25\" class=\"data row7 col25\" >0.263158</td>\n",
              "      <td id=\"T_80ff5_row7_col26\" class=\"data row7 col26\" >0.500000</td>\n",
              "      <td id=\"T_80ff5_row7_col27\" class=\"data row7 col27\" >0.644737</td>\n",
              "      <td id=\"T_80ff5_row7_col28\" class=\"data row7 col28\" >0.875000</td>\n",
              "      <td id=\"T_80ff5_row7_col29\" class=\"data row7 col29\" >1.500000</td>\n",
              "      <td id=\"T_80ff5_row7_col30\" class=\"data row7 col30\" >0.388393</td>\n",
              "      <td id=\"T_80ff5_row7_col31\" class=\"data row7 col31\" >0.669643</td>\n",
              "      <td id=\"T_80ff5_row7_col32\" class=\"data row7 col32\" >0.900000</td>\n",
              "      <td id=\"T_80ff5_row7_col33\" class=\"data row7 col33\" >1.050000</td>\n",
              "      <td id=\"T_80ff5_row7_col34\" class=\"data row7 col34\" >1.062925</td>\n",
              "      <td id=\"T_80ff5_row7_col35\" class=\"data row7 col35\" >1.232993</td>\n",
              "      <td id=\"T_80ff5_row7_col36\" class=\"data row7 col36\" >3</td>\n",
              "      <td id=\"T_80ff5_row7_col37\" class=\"data row7 col37\" >3</td>\n",
              "      <td id=\"T_80ff5_row7_col38\" class=\"data row7 col38\" >3</td>\n",
              "      <td id=\"T_80ff5_row7_col39\" class=\"data row7 col39\" >3</td>\n",
              "      <td id=\"T_80ff5_row7_col40\" class=\"data row7 col40\" >3</td>\n",
              "      <td id=\"T_80ff5_row7_col41\" class=\"data row7 col41\" >3</td>\n",
              "      <td id=\"T_80ff5_row7_col42\" class=\"data row7 col42\" >3</td>\n",
              "      <td id=\"T_80ff5_row7_col43\" class=\"data row7 col43\" >3</td>\n",
              "      <td id=\"T_80ff5_row7_col44\" class=\"data row7 col44\" >3</td>\n",
              "      <td id=\"T_80ff5_row7_col45\" class=\"data row7 col45\" >3</td>\n",
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              "    <tr>\n",
              "      <th id=\"T_80ff5_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
              "      <td id=\"T_80ff5_row8_col0\" class=\"data row8 col0\" >0</td>\n",
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              "      <td id=\"T_80ff5_row8_col3\" class=\"data row8 col3\" >Seniority</td>\n",
              "      <td id=\"T_80ff5_row8_col4\" class=\"data row8 col4\" >3</td>\n",
              "      <td id=\"T_80ff5_row8_col5\" class=\"data row8 col5\" >0.666667</td>\n",
              "      <td id=\"T_80ff5_row8_col6\" class=\"data row8 col6\" >0.840000</td>\n",
              "      <td id=\"T_80ff5_row8_col7\" class=\"data row8 col7\" >0.275862</td>\n",
              "      <td id=\"T_80ff5_row8_col8\" class=\"data row8 col8\" >0.200000</td>\n",
              "      <td id=\"T_80ff5_row8_col9\" class=\"data row8 col9\" >0.160000</td>\n",
              "      <td id=\"T_80ff5_row8_col10\" class=\"data row8 col10\" >0.333333</td>\n",
              "      <td id=\"T_80ff5_row8_col11\" class=\"data row8 col11\" >0.724138</td>\n",
              "      <td id=\"T_80ff5_row8_col12\" class=\"data row8 col12\" >0.800000</td>\n",
              "      <td id=\"T_80ff5_row8_col13\" class=\"data row8 col13\" >20</td>\n",
              "      <td id=\"T_80ff5_row8_col14\" class=\"data row8 col14\" >29</td>\n",
              "      <td id=\"T_80ff5_row8_col15\" class=\"data row8 col15\" >0.206186</td>\n",
              "      <td id=\"T_80ff5_row8_col16\" class=\"data row8 col16\" >0.408163</td>\n",
              "      <td id=\"T_80ff5_row8_col17\" class=\"data row8 col17\" >4</td>\n",
              "      <td id=\"T_80ff5_row8_col18\" class=\"data row8 col18\" >8</td>\n",
              "      <td id=\"T_80ff5_row8_col19\" class=\"data row8 col19\" >21</td>\n",
              "      <td id=\"T_80ff5_row8_col20\" class=\"data row8 col20\" >16</td>\n",
              "      <td id=\"T_80ff5_row8_col21\" class=\"data row8 col21\" >24</td>\n",
              "      <td id=\"T_80ff5_row8_col22\" class=\"data row8 col22\" >25</td>\n",
              "      <td id=\"T_80ff5_row8_col23\" class=\"data row8 col23\" >49</td>\n",
              "      <td id=\"T_80ff5_row8_col24\" class=\"data row8 col24\" >200</td>\n",
              "      <td id=\"T_80ff5_row8_col25\" class=\"data row8 col25\" >0.489796</td>\n",
              "      <td id=\"T_80ff5_row8_col26\" class=\"data row8 col26\" >1.000000</td>\n",
              "      <td id=\"T_80ff5_row8_col27\" class=\"data row8 col27\" >1.000000</td>\n",
              "      <td id=\"T_80ff5_row8_col28\" class=\"data row8 col28\" >1.000000</td>\n",
              "      <td id=\"T_80ff5_row8_col29\" class=\"data row8 col29\" >1.000000</td>\n",
              "      <td id=\"T_80ff5_row8_col30\" class=\"data row8 col30\" >1.000000</td>\n",
              "      <td id=\"T_80ff5_row8_col31\" class=\"data row8 col31\" >1.000000</td>\n",
              "      <td id=\"T_80ff5_row8_col32\" class=\"data row8 col32\" >1.000000</td>\n",
              "      <td id=\"T_80ff5_row8_col33\" class=\"data row8 col33\" >1.000000</td>\n",
              "      <td id=\"T_80ff5_row8_col34\" class=\"data row8 col34\" >1.000000</td>\n",
              "      <td id=\"T_80ff5_row8_col35\" class=\"data row8 col35\" >1.000000</td>\n",
              "      <td id=\"T_80ff5_row8_col36\" class=\"data row8 col36\" >3</td>\n",
              "      <td id=\"T_80ff5_row8_col37\" class=\"data row8 col37\" >3</td>\n",
              "      <td id=\"T_80ff5_row8_col38\" class=\"data row8 col38\" >3</td>\n",
              "      <td id=\"T_80ff5_row8_col39\" class=\"data row8 col39\" >3</td>\n",
              "      <td id=\"T_80ff5_row8_col40\" class=\"data row8 col40\" >3</td>\n",
              "      <td id=\"T_80ff5_row8_col41\" class=\"data row8 col41\" >3</td>\n",
              "      <td id=\"T_80ff5_row8_col42\" class=\"data row8 col42\" >3</td>\n",
              "      <td id=\"T_80ff5_row8_col43\" class=\"data row8 col43\" >3</td>\n",
              "      <td id=\"T_80ff5_row8_col44\" class=\"data row8 col44\" >3</td>\n",
              "      <td id=\"T_80ff5_row8_col45\" class=\"data row8 col45\" >3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_80ff5_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
              "      <td id=\"T_80ff5_row9_col0\" class=\"data row9 col0\" >0</td>\n",
              "      <td id=\"T_80ff5_row9_col1\" class=\"data row9 col1\" >binary 0/1</td>\n",
              "      <td id=\"T_80ff5_row9_col2\" class=\"data row9 col2\" >97</td>\n",
              "      <td id=\"T_80ff5_row9_col3\" class=\"data row9 col3\" >Seniority</td>\n",
              "      <td id=\"T_80ff5_row9_col4\" class=\"data row9 col4\" >4</td>\n",
              "      <td id=\"T_80ff5_row9_col5\" class=\"data row9 col5\" >0.760000</td>\n",
              "      <td id=\"T_80ff5_row9_col6\" class=\"data row9 col6\" >0.800000</td>\n",
              "      <td id=\"T_80ff5_row9_col7\" class=\"data row9 col7\" >0.333333</td>\n",
              "      <td id=\"T_80ff5_row9_col8\" class=\"data row9 col8\" >0.136364</td>\n",
              "      <td id=\"T_80ff5_row9_col9\" class=\"data row9 col9\" >0.200000</td>\n",
              "      <td id=\"T_80ff5_row9_col10\" class=\"data row9 col10\" >0.240000</td>\n",
              "      <td id=\"T_80ff5_row9_col11\" class=\"data row9 col11\" >0.666667</td>\n",
              "      <td id=\"T_80ff5_row9_col12\" class=\"data row9 col12\" >0.863636</td>\n",
              "      <td id=\"T_80ff5_row9_col13\" class=\"data row9 col13\" >22</td>\n",
              "      <td id=\"T_80ff5_row9_col14\" class=\"data row9 col14\" >18</td>\n",
              "      <td id=\"T_80ff5_row9_col15\" class=\"data row9 col15\" >0.226804</td>\n",
              "      <td id=\"T_80ff5_row9_col16\" class=\"data row9 col16\" >0.550000</td>\n",
              "      <td id=\"T_80ff5_row9_col17\" class=\"data row9 col17\" >3</td>\n",
              "      <td id=\"T_80ff5_row9_col18\" class=\"data row9 col18\" >6</td>\n",
              "      <td id=\"T_80ff5_row9_col19\" class=\"data row9 col19\" >12</td>\n",
              "      <td id=\"T_80ff5_row9_col20\" class=\"data row9 col20\" >19</td>\n",
              "      <td id=\"T_80ff5_row9_col21\" class=\"data row9 col21\" >25</td>\n",
              "      <td id=\"T_80ff5_row9_col22\" class=\"data row9 col22\" >15</td>\n",
              "      <td id=\"T_80ff5_row9_col23\" class=\"data row9 col23\" >40</td>\n",
              "      <td id=\"T_80ff5_row9_col24\" class=\"data row9 col24\" >200</td>\n",
              "      <td id=\"T_80ff5_row9_col25\" class=\"data row9 col25\" >0.625000</td>\n",
              "      <td id=\"T_80ff5_row9_col26\" class=\"data row9 col26\" >1.100000</td>\n",
              "      <td id=\"T_80ff5_row9_col27\" class=\"data row9 col27\" >1.347500</td>\n",
              "      <td id=\"T_80ff5_row9_col28\" class=\"data row9 col28\" >1.079545</td>\n",
              "      <td id=\"T_80ff5_row9_col29\" class=\"data row9 col29\" >0.681818</td>\n",
              "      <td id=\"T_80ff5_row9_col30\" class=\"data row9 col30\" >1.208333</td>\n",
              "      <td id=\"T_80ff5_row9_col31\" class=\"data row9 col31\" >1.250000</td>\n",
              "      <td id=\"T_80ff5_row9_col32\" class=\"data row9 col32\" >0.720000</td>\n",
              "      <td id=\"T_80ff5_row9_col33\" class=\"data row9 col33\" >1.140000</td>\n",
              "      <td id=\"T_80ff5_row9_col34\" class=\"data row9 col34\" >0.952381</td>\n",
              "      <td id=\"T_80ff5_row9_col35\" class=\"data row9 col35\" >0.920635</td>\n",
              "      <td id=\"T_80ff5_row9_col36\" class=\"data row9 col36\" >3</td>\n",
              "      <td id=\"T_80ff5_row9_col37\" class=\"data row9 col37\" >3</td>\n",
              "      <td id=\"T_80ff5_row9_col38\" class=\"data row9 col38\" >3</td>\n",
              "      <td id=\"T_80ff5_row9_col39\" class=\"data row9 col39\" >3</td>\n",
              "      <td id=\"T_80ff5_row9_col40\" class=\"data row9 col40\" >3</td>\n",
              "      <td id=\"T_80ff5_row9_col41\" class=\"data row9 col41\" >3</td>\n",
              "      <td id=\"T_80ff5_row9_col42\" class=\"data row9 col42\" >3</td>\n",
              "      <td id=\"T_80ff5_row9_col43\" class=\"data row9 col43\" >3</td>\n",
              "      <td id=\"T_80ff5_row9_col44\" class=\"data row9 col44\" >3</td>\n",
              "      <td id=\"T_80ff5_row9_col45\" class=\"data row9 col45\" >3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_80ff5_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
              "      <td id=\"T_80ff5_row10_col0\" class=\"data row10 col0\" >0</td>\n",
              "      <td id=\"T_80ff5_row10_col1\" class=\"data row10 col1\" >binary 0/1</td>\n",
              "      <td id=\"T_80ff5_row10_col2\" class=\"data row10 col2\" >97</td>\n",
              "      <td id=\"T_80ff5_row10_col3\" class=\"data row10 col3\" >Seniority</td>\n",
              "      <td id=\"T_80ff5_row10_col4\" class=\"data row10 col4\" >5</td>\n",
              "      <td id=\"T_80ff5_row10_col5\" class=\"data row10 col5\" >0.939394</td>\n",
              "      <td id=\"T_80ff5_row10_col6\" class=\"data row10 col6\" >0.285714</td>\n",
              "      <td id=\"T_80ff5_row10_col7\" class=\"data row10 col7\" >0.500000</td>\n",
              "      <td id=\"T_80ff5_row10_col8\" class=\"data row10 col8\" >0.138889</td>\n",
              "      <td id=\"T_80ff5_row10_col9\" class=\"data row10 col9\" >0.714286</td>\n",
              "      <td id=\"T_80ff5_row10_col10\" class=\"data row10 col10\" >0.060606</td>\n",
              "      <td id=\"T_80ff5_row10_col11\" class=\"data row10 col11\" >0.500000</td>\n",
              "      <td id=\"T_80ff5_row10_col12\" class=\"data row10 col12\" >0.861111</td>\n",
              "      <td id=\"T_80ff5_row10_col13\" class=\"data row10 col13\" >36</td>\n",
              "      <td id=\"T_80ff5_row10_col14\" class=\"data row10 col14\" >4</td>\n",
              "      <td id=\"T_80ff5_row10_col15\" class=\"data row10 col15\" >0.371134</td>\n",
              "      <td id=\"T_80ff5_row10_col16\" class=\"data row10 col16\" >0.900000</td>\n",
              "      <td id=\"T_80ff5_row10_col17\" class=\"data row10 col17\" >5</td>\n",
              "      <td id=\"T_80ff5_row10_col18\" class=\"data row10 col18\" >2</td>\n",
              "      <td id=\"T_80ff5_row10_col19\" class=\"data row10 col19\" >2</td>\n",
              "      <td id=\"T_80ff5_row10_col20\" class=\"data row10 col20\" >31</td>\n",
              "      <td id=\"T_80ff5_row10_col21\" class=\"data row10 col21\" >33</td>\n",
              "      <td id=\"T_80ff5_row10_col22\" class=\"data row10 col22\" >7</td>\n",
              "      <td id=\"T_80ff5_row10_col23\" class=\"data row10 col23\" >40</td>\n",
              "      <td id=\"T_80ff5_row10_col24\" class=\"data row10 col24\" >200</td>\n",
              "      <td id=\"T_80ff5_row10_col25\" class=\"data row10 col25\" >0.825000</td>\n",
              "      <td id=\"T_80ff5_row10_col26\" class=\"data row10 col26\" >1.800000</td>\n",
              "      <td id=\"T_80ff5_row10_col27\" class=\"data row10 col27\" >2.205000</td>\n",
              "      <td id=\"T_80ff5_row10_col28\" class=\"data row10 col28\" >1.076389</td>\n",
              "      <td id=\"T_80ff5_row10_col29\" class=\"data row10 col29\" >0.694444</td>\n",
              "      <td id=\"T_80ff5_row10_col30\" class=\"data row10 col30\" >1.812500</td>\n",
              "      <td id=\"T_80ff5_row10_col31\" class=\"data row10 col31\" >4.464286</td>\n",
              "      <td id=\"T_80ff5_row10_col32\" class=\"data row10 col32\" >0.181818</td>\n",
              "      <td id=\"T_80ff5_row10_col33\" class=\"data row10 col33\" >1.409091</td>\n",
              "      <td id=\"T_80ff5_row10_col34\" class=\"data row10 col34\" >0.340136</td>\n",
              "      <td id=\"T_80ff5_row10_col35\" class=\"data row10 col35\" >0.690476</td>\n",
              "      <td id=\"T_80ff5_row10_col36\" class=\"data row10 col36\" >3</td>\n",
              "      <td id=\"T_80ff5_row10_col37\" class=\"data row10 col37\" >3</td>\n",
              "      <td id=\"T_80ff5_row10_col38\" class=\"data row10 col38\" >3</td>\n",
              "      <td id=\"T_80ff5_row10_col39\" class=\"data row10 col39\" >3</td>\n",
              "      <td id=\"T_80ff5_row10_col40\" class=\"data row10 col40\" >3</td>\n",
              "      <td id=\"T_80ff5_row10_col41\" class=\"data row10 col41\" >3</td>\n",
              "      <td id=\"T_80ff5_row10_col42\" class=\"data row10 col42\" >3</td>\n",
              "      <td id=\"T_80ff5_row10_col43\" class=\"data row10 col43\" >3</td>\n",
              "      <td id=\"T_80ff5_row10_col44\" class=\"data row10 col44\" >3</td>\n",
              "      <td id=\"T_80ff5_row10_col45\" class=\"data row10 col45\" >3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_80ff5_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
              "      <td id=\"T_80ff5_row11_col0\" class=\"data row11 col0\" >0</td>\n",
              "      <td id=\"T_80ff5_row11_col1\" class=\"data row11 col1\" >binary 0/1</td>\n",
              "      <td id=\"T_80ff5_row11_col2\" class=\"data row11 col2\" >97</td>\n",
              "      <td id=\"T_80ff5_row11_col3\" class=\"data row11 col3\" >Age_Group</td>\n",
              "      <td id=\"T_80ff5_row11_col4\" class=\"data row11 col4\" >0-20</td>\n",
              "      <td id=\"T_80ff5_row11_col5\" class=\"data row11 col5\" >nan</td>\n",
              "      <td id=\"T_80ff5_row11_col6\" class=\"data row11 col6\" >1.000000</td>\n",
              "      <td id=\"T_80ff5_row11_col7\" class=\"data row11 col7\" >0.000000</td>\n",
              "      <td id=\"T_80ff5_row11_col8\" class=\"data row11 col8\" >nan</td>\n",
              "      <td id=\"T_80ff5_row11_col9\" class=\"data row11 col9\" >0.000000</td>\n",
              "      <td id=\"T_80ff5_row11_col10\" class=\"data row11 col10\" >nan</td>\n",
              "      <td id=\"T_80ff5_row11_col11\" class=\"data row11 col11\" >1.000000</td>\n",
              "      <td id=\"T_80ff5_row11_col12\" class=\"data row11 col12\" >nan</td>\n",
              "      <td id=\"T_80ff5_row11_col13\" class=\"data row11 col13\" >0</td>\n",
              "      <td id=\"T_80ff5_row11_col14\" class=\"data row11 col14\" >11</td>\n",
              "      <td id=\"T_80ff5_row11_col15\" class=\"data row11 col15\" >0.000000</td>\n",
              "      <td id=\"T_80ff5_row11_col16\" class=\"data row11 col16\" >0.000000</td>\n",
              "      <td id=\"T_80ff5_row11_col17\" class=\"data row11 col17\" >0</td>\n",
              "      <td id=\"T_80ff5_row11_col18\" class=\"data row11 col18\" >0</td>\n",
              "      <td id=\"T_80ff5_row11_col19\" class=\"data row11 col19\" >11</td>\n",
              "      <td id=\"T_80ff5_row11_col20\" class=\"data row11 col20\" >0</td>\n",
              "      <td id=\"T_80ff5_row11_col21\" class=\"data row11 col21\" >0</td>\n",
              "      <td id=\"T_80ff5_row11_col22\" class=\"data row11 col22\" >11</td>\n",
              "      <td id=\"T_80ff5_row11_col23\" class=\"data row11 col23\" >11</td>\n",
              "      <td id=\"T_80ff5_row11_col24\" class=\"data row11 col24\" >200</td>\n",
              "      <td id=\"T_80ff5_row11_col25\" class=\"data row11 col25\" >0.000000</td>\n",
              "      <td id=\"T_80ff5_row11_col26\" class=\"data row11 col26\" >0.000000</td>\n",
              "      <td id=\"T_80ff5_row11_col27\" class=\"data row11 col27\" >0.000000</td>\n",
              "      <td id=\"T_80ff5_row11_col28\" class=\"data row11 col28\" >nan</td>\n",
              "      <td id=\"T_80ff5_row11_col29\" class=\"data row11 col29\" >nan</td>\n",
              "      <td id=\"T_80ff5_row11_col30\" class=\"data row11 col30\" >0.000000</td>\n",
              "      <td id=\"T_80ff5_row11_col31\" class=\"data row11 col31\" >0.000000</td>\n",
              "      <td id=\"T_80ff5_row11_col32\" class=\"data row11 col32\" >nan</td>\n",
              "      <td id=\"T_80ff5_row11_col33\" class=\"data row11 col33\" >nan</td>\n",
              "      <td id=\"T_80ff5_row11_col34\" class=\"data row11 col34\" >1.200000</td>\n",
              "      <td id=\"T_80ff5_row11_col35\" class=\"data row11 col35\" >1.250000</td>\n",
              "      <td id=\"T_80ff5_row11_col36\" class=\"data row11 col36\" >30-40</td>\n",
              "      <td id=\"T_80ff5_row11_col37\" class=\"data row11 col37\" >30-40</td>\n",
              "      <td id=\"T_80ff5_row11_col38\" class=\"data row11 col38\" >30-40</td>\n",
              "      <td id=\"T_80ff5_row11_col39\" class=\"data row11 col39\" >30-40</td>\n",
              "      <td id=\"T_80ff5_row11_col40\" class=\"data row11 col40\" >30-40</td>\n",
              "      <td id=\"T_80ff5_row11_col41\" class=\"data row11 col41\" >30-40</td>\n",
              "      <td id=\"T_80ff5_row11_col42\" class=\"data row11 col42\" >30-40</td>\n",
              "      <td id=\"T_80ff5_row11_col43\" class=\"data row11 col43\" >30-40</td>\n",
              "      <td id=\"T_80ff5_row11_col44\" class=\"data row11 col44\" >30-40</td>\n",
              "      <td id=\"T_80ff5_row11_col45\" class=\"data row11 col45\" >30-40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_80ff5_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
              "      <td id=\"T_80ff5_row12_col0\" class=\"data row12 col0\" >0</td>\n",
              "      <td id=\"T_80ff5_row12_col1\" class=\"data row12 col1\" >binary 0/1</td>\n",
              "      <td id=\"T_80ff5_row12_col2\" class=\"data row12 col2\" >97</td>\n",
              "      <td id=\"T_80ff5_row12_col3\" class=\"data row12 col3\" >Age_Group</td>\n",
              "      <td id=\"T_80ff5_row12_col4\" class=\"data row12 col4\" >20-30</td>\n",
              "      <td id=\"T_80ff5_row12_col5\" class=\"data row12 col5\" >0.250000</td>\n",
              "      <td id=\"T_80ff5_row12_col6\" class=\"data row12 col6\" >0.894737</td>\n",
              "      <td id=\"T_80ff5_row12_col7\" class=\"data row12 col7\" >0.209302</td>\n",
              "      <td id=\"T_80ff5_row12_col8\" class=\"data row12 col8\" >0.571429</td>\n",
              "      <td id=\"T_80ff5_row12_col9\" class=\"data row12 col9\" >0.105263</td>\n",
              "      <td id=\"T_80ff5_row12_col10\" class=\"data row12 col10\" >0.750000</td>\n",
              "      <td id=\"T_80ff5_row12_col11\" class=\"data row12 col11\" >0.790698</td>\n",
              "      <td id=\"T_80ff5_row12_col12\" class=\"data row12 col12\" >0.428571</td>\n",
              "      <td id=\"T_80ff5_row12_col13\" class=\"data row12 col13\" >7</td>\n",
              "      <td id=\"T_80ff5_row12_col14\" class=\"data row12 col14\" >43</td>\n",
              "      <td id=\"T_80ff5_row12_col15\" class=\"data row12 col15\" >0.072165</td>\n",
              "      <td id=\"T_80ff5_row12_col16\" class=\"data row12 col16\" >0.140000</td>\n",
              "      <td id=\"T_80ff5_row12_col17\" class=\"data row12 col17\" >4</td>\n",
              "      <td id=\"T_80ff5_row12_col18\" class=\"data row12 col18\" >9</td>\n",
              "      <td id=\"T_80ff5_row12_col19\" class=\"data row12 col19\" >34</td>\n",
              "      <td id=\"T_80ff5_row12_col20\" class=\"data row12 col20\" >3</td>\n",
              "      <td id=\"T_80ff5_row12_col21\" class=\"data row12 col21\" >12</td>\n",
              "      <td id=\"T_80ff5_row12_col22\" class=\"data row12 col22\" >38</td>\n",
              "      <td id=\"T_80ff5_row12_col23\" class=\"data row12 col23\" >50</td>\n",
              "      <td id=\"T_80ff5_row12_col24\" class=\"data row12 col24\" >200</td>\n",
              "      <td id=\"T_80ff5_row12_col25\" class=\"data row12 col25\" >0.240000</td>\n",
              "      <td id=\"T_80ff5_row12_col26\" class=\"data row12 col26\" >0.777778</td>\n",
              "      <td id=\"T_80ff5_row12_col27\" class=\"data row12 col27\" >0.528889</td>\n",
              "      <td id=\"T_80ff5_row12_col28\" class=\"data row12 col28\" >0.771429</td>\n",
              "      <td id=\"T_80ff5_row12_col29\" class=\"data row12 col29\" >1.285714</td>\n",
              "      <td id=\"T_80ff5_row12_col30\" class=\"data row12 col30\" >1.046512</td>\n",
              "      <td id=\"T_80ff5_row12_col31\" class=\"data row12 col31\" >0.631579</td>\n",
              "      <td id=\"T_80ff5_row12_col32\" class=\"data row12 col32\" >1.500000</td>\n",
              "      <td id=\"T_80ff5_row12_col33\" class=\"data row12 col33\" >0.500000</td>\n",
              "      <td id=\"T_80ff5_row12_col34\" class=\"data row12 col34\" >1.073684</td>\n",
              "      <td id=\"T_80ff5_row12_col35\" class=\"data row12 col35\" >0.988372</td>\n",
              "      <td id=\"T_80ff5_row12_col36\" class=\"data row12 col36\" >30-40</td>\n",
              "      <td id=\"T_80ff5_row12_col37\" class=\"data row12 col37\" >30-40</td>\n",
              "      <td id=\"T_80ff5_row12_col38\" class=\"data row12 col38\" >30-40</td>\n",
              "      <td id=\"T_80ff5_row12_col39\" class=\"data row12 col39\" >30-40</td>\n",
              "      <td id=\"T_80ff5_row12_col40\" class=\"data row12 col40\" >30-40</td>\n",
              "      <td id=\"T_80ff5_row12_col41\" class=\"data row12 col41\" >30-40</td>\n",
              "      <td id=\"T_80ff5_row12_col42\" class=\"data row12 col42\" >30-40</td>\n",
              "      <td id=\"T_80ff5_row12_col43\" class=\"data row12 col43\" >30-40</td>\n",
              "      <td id=\"T_80ff5_row12_col44\" class=\"data row12 col44\" >30-40</td>\n",
              "      <td id=\"T_80ff5_row12_col45\" class=\"data row12 col45\" >30-40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_80ff5_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
              "      <td id=\"T_80ff5_row13_col0\" class=\"data row13 col0\" >0</td>\n",
              "      <td id=\"T_80ff5_row13_col1\" class=\"data row13 col1\" >binary 0/1</td>\n",
              "      <td id=\"T_80ff5_row13_col2\" class=\"data row13 col2\" >97</td>\n",
              "      <td id=\"T_80ff5_row13_col3\" class=\"data row13 col3\" >Age_Group</td>\n",
              "      <td id=\"T_80ff5_row13_col4\" class=\"data row13 col4\" >30-40</td>\n",
              "      <td id=\"T_80ff5_row13_col5\" class=\"data row13 col5\" >0.500000</td>\n",
              "      <td id=\"T_80ff5_row13_col6\" class=\"data row13 col6\" >0.833333</td>\n",
              "      <td id=\"T_80ff5_row13_col7\" class=\"data row13 col7\" >0.200000</td>\n",
              "      <td id=\"T_80ff5_row13_col8\" class=\"data row13 col8\" >0.444444</td>\n",
              "      <td id=\"T_80ff5_row13_col9\" class=\"data row13 col9\" >0.166667</td>\n",
              "      <td id=\"T_80ff5_row13_col10\" class=\"data row13 col10\" >0.500000</td>\n",
              "      <td id=\"T_80ff5_row13_col11\" class=\"data row13 col11\" >0.800000</td>\n",
              "      <td id=\"T_80ff5_row13_col12\" class=\"data row13 col12\" >0.555556</td>\n",
              "      <td id=\"T_80ff5_row13_col13\" class=\"data row13 col13\" >9</td>\n",
              "      <td id=\"T_80ff5_row13_col14\" class=\"data row13 col14\" >25</td>\n",
              "      <td id=\"T_80ff5_row13_col15\" class=\"data row13 col15\" >0.092784</td>\n",
              "      <td id=\"T_80ff5_row13_col16\" class=\"data row13 col16\" >0.264706</td>\n",
              "      <td id=\"T_80ff5_row13_col17\" class=\"data row13 col17\" >4</td>\n",
              "      <td id=\"T_80ff5_row13_col18\" class=\"data row13 col18\" >5</td>\n",
              "      <td id=\"T_80ff5_row13_col19\" class=\"data row13 col19\" >20</td>\n",
              "      <td id=\"T_80ff5_row13_col20\" class=\"data row13 col20\" >5</td>\n",
              "      <td id=\"T_80ff5_row13_col21\" class=\"data row13 col21\" >10</td>\n",
              "      <td id=\"T_80ff5_row13_col22\" class=\"data row13 col22\" >24</td>\n",
              "      <td id=\"T_80ff5_row13_col23\" class=\"data row13 col23\" >34</td>\n",
              "      <td id=\"T_80ff5_row13_col24\" class=\"data row13 col24\" >200</td>\n",
              "      <td id=\"T_80ff5_row13_col25\" class=\"data row13 col25\" >0.294118</td>\n",
              "      <td id=\"T_80ff5_row13_col26\" class=\"data row13 col26\" >1.000000</td>\n",
              "      <td id=\"T_80ff5_row13_col27\" class=\"data row13 col27\" >1.000000</td>\n",
              "      <td id=\"T_80ff5_row13_col28\" class=\"data row13 col28\" >1.000000</td>\n",
              "      <td id=\"T_80ff5_row13_col29\" class=\"data row13 col29\" >1.000000</td>\n",
              "      <td id=\"T_80ff5_row13_col30\" class=\"data row13 col30\" >1.000000</td>\n",
              "      <td id=\"T_80ff5_row13_col31\" class=\"data row13 col31\" >1.000000</td>\n",
              "      <td id=\"T_80ff5_row13_col32\" class=\"data row13 col32\" >1.000000</td>\n",
              "      <td id=\"T_80ff5_row13_col33\" class=\"data row13 col33\" >1.000000</td>\n",
              "      <td id=\"T_80ff5_row13_col34\" class=\"data row13 col34\" >1.000000</td>\n",
              "      <td id=\"T_80ff5_row13_col35\" class=\"data row13 col35\" >1.000000</td>\n",
              "      <td id=\"T_80ff5_row13_col36\" class=\"data row13 col36\" >30-40</td>\n",
              "      <td id=\"T_80ff5_row13_col37\" class=\"data row13 col37\" >30-40</td>\n",
              "      <td id=\"T_80ff5_row13_col38\" class=\"data row13 col38\" >30-40</td>\n",
              "      <td id=\"T_80ff5_row13_col39\" class=\"data row13 col39\" >30-40</td>\n",
              "      <td id=\"T_80ff5_row13_col40\" class=\"data row13 col40\" >30-40</td>\n",
              "      <td id=\"T_80ff5_row13_col41\" class=\"data row13 col41\" >30-40</td>\n",
              "      <td id=\"T_80ff5_row13_col42\" class=\"data row13 col42\" >30-40</td>\n",
              "      <td id=\"T_80ff5_row13_col43\" class=\"data row13 col43\" >30-40</td>\n",
              "      <td id=\"T_80ff5_row13_col44\" class=\"data row13 col44\" >30-40</td>\n",
              "      <td id=\"T_80ff5_row13_col45\" class=\"data row13 col45\" >30-40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_80ff5_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
              "      <td id=\"T_80ff5_row14_col0\" class=\"data row14 col0\" >0</td>\n",
              "      <td id=\"T_80ff5_row14_col1\" class=\"data row14 col1\" >binary 0/1</td>\n",
              "      <td id=\"T_80ff5_row14_col2\" class=\"data row14 col2\" >97</td>\n",
              "      <td id=\"T_80ff5_row14_col3\" class=\"data row14 col3\" >Age_Group</td>\n",
              "      <td id=\"T_80ff5_row14_col4\" class=\"data row14 col4\" >40-50</td>\n",
              "      <td id=\"T_80ff5_row14_col5\" class=\"data row14 col5\" >0.730769</td>\n",
              "      <td id=\"T_80ff5_row14_col6\" class=\"data row14 col6\" >0.684211</td>\n",
              "      <td id=\"T_80ff5_row14_col7\" class=\"data row14 col7\" >0.350000</td>\n",
              "      <td id=\"T_80ff5_row14_col8\" class=\"data row14 col8\" >0.240000</td>\n",
              "      <td id=\"T_80ff5_row14_col9\" class=\"data row14 col9\" >0.315789</td>\n",
              "      <td id=\"T_80ff5_row14_col10\" class=\"data row14 col10\" >0.269231</td>\n",
              "      <td id=\"T_80ff5_row14_col11\" class=\"data row14 col11\" >0.650000</td>\n",
              "      <td id=\"T_80ff5_row14_col12\" class=\"data row14 col12\" >0.760000</td>\n",
              "      <td id=\"T_80ff5_row14_col13\" class=\"data row14 col13\" >25</td>\n",
              "      <td id=\"T_80ff5_row14_col14\" class=\"data row14 col14\" >20</td>\n",
              "      <td id=\"T_80ff5_row14_col15\" class=\"data row14 col15\" >0.257732</td>\n",
              "      <td id=\"T_80ff5_row14_col16\" class=\"data row14 col16\" >0.555556</td>\n",
              "      <td id=\"T_80ff5_row14_col17\" class=\"data row14 col17\" >6</td>\n",
              "      <td id=\"T_80ff5_row14_col18\" class=\"data row14 col18\" >7</td>\n",
              "      <td id=\"T_80ff5_row14_col19\" class=\"data row14 col19\" >13</td>\n",
              "      <td id=\"T_80ff5_row14_col20\" class=\"data row14 col20\" >19</td>\n",
              "      <td id=\"T_80ff5_row14_col21\" class=\"data row14 col21\" >26</td>\n",
              "      <td id=\"T_80ff5_row14_col22\" class=\"data row14 col22\" >19</td>\n",
              "      <td id=\"T_80ff5_row14_col23\" class=\"data row14 col23\" >45</td>\n",
              "      <td id=\"T_80ff5_row14_col24\" class=\"data row14 col24\" >200</td>\n",
              "      <td id=\"T_80ff5_row14_col25\" class=\"data row14 col25\" >0.577778</td>\n",
              "      <td id=\"T_80ff5_row14_col26\" class=\"data row14 col26\" >2.777778</td>\n",
              "      <td id=\"T_80ff5_row14_col27\" class=\"data row14 col27\" >2.098765</td>\n",
              "      <td id=\"T_80ff5_row14_col28\" class=\"data row14 col28\" >1.368000</td>\n",
              "      <td id=\"T_80ff5_row14_col29\" class=\"data row14 col29\" >0.540000</td>\n",
              "      <td id=\"T_80ff5_row14_col30\" class=\"data row14 col30\" >1.750000</td>\n",
              "      <td id=\"T_80ff5_row14_col31\" class=\"data row14 col31\" >1.894737</td>\n",
              "      <td id=\"T_80ff5_row14_col32\" class=\"data row14 col32\" >0.538462</td>\n",
              "      <td id=\"T_80ff5_row14_col33\" class=\"data row14 col33\" >1.461538</td>\n",
              "      <td id=\"T_80ff5_row14_col34\" class=\"data row14 col34\" >0.821053</td>\n",
              "      <td id=\"T_80ff5_row14_col35\" class=\"data row14 col35\" >0.812500</td>\n",
              "      <td id=\"T_80ff5_row14_col36\" class=\"data row14 col36\" >30-40</td>\n",
              "      <td id=\"T_80ff5_row14_col37\" class=\"data row14 col37\" >30-40</td>\n",
              "      <td id=\"T_80ff5_row14_col38\" class=\"data row14 col38\" >30-40</td>\n",
              "      <td id=\"T_80ff5_row14_col39\" class=\"data row14 col39\" >30-40</td>\n",
              "      <td id=\"T_80ff5_row14_col40\" class=\"data row14 col40\" >30-40</td>\n",
              "      <td id=\"T_80ff5_row14_col41\" class=\"data row14 col41\" >30-40</td>\n",
              "      <td id=\"T_80ff5_row14_col42\" class=\"data row14 col42\" >30-40</td>\n",
              "      <td id=\"T_80ff5_row14_col43\" class=\"data row14 col43\" >30-40</td>\n",
              "      <td id=\"T_80ff5_row14_col44\" class=\"data row14 col44\" >30-40</td>\n",
              "      <td id=\"T_80ff5_row14_col45\" class=\"data row14 col45\" >30-40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_80ff5_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
              "      <td id=\"T_80ff5_row15_col0\" class=\"data row15 col0\" >0</td>\n",
              "      <td id=\"T_80ff5_row15_col1\" class=\"data row15 col1\" >binary 0/1</td>\n",
              "      <td id=\"T_80ff5_row15_col2\" class=\"data row15 col2\" >97</td>\n",
              "      <td id=\"T_80ff5_row15_col3\" class=\"data row15 col3\" >Age_Group</td>\n",
              "      <td id=\"T_80ff5_row15_col4\" class=\"data row15 col4\" >50+</td>\n",
              "      <td id=\"T_80ff5_row15_col5\" class=\"data row15 col5\" >0.960784</td>\n",
              "      <td id=\"T_80ff5_row15_col6\" class=\"data row15 col6\" >0.222222</td>\n",
              "      <td id=\"T_80ff5_row15_col7\" class=\"data row15 col7\" >0.500000</td>\n",
              "      <td id=\"T_80ff5_row15_col8\" class=\"data row15 col8\" >0.125000</td>\n",
              "      <td id=\"T_80ff5_row15_col9\" class=\"data row15 col9\" >0.777778</td>\n",
              "      <td id=\"T_80ff5_row15_col10\" class=\"data row15 col10\" >0.039216</td>\n",
              "      <td id=\"T_80ff5_row15_col11\" class=\"data row15 col11\" >0.500000</td>\n",
              "      <td id=\"T_80ff5_row15_col12\" class=\"data row15 col12\" >0.875000</td>\n",
              "      <td id=\"T_80ff5_row15_col13\" class=\"data row15 col13\" >56</td>\n",
              "      <td id=\"T_80ff5_row15_col14\" class=\"data row15 col14\" >4</td>\n",
              "      <td id=\"T_80ff5_row15_col15\" class=\"data row15 col15\" >0.577320</td>\n",
              "      <td id=\"T_80ff5_row15_col16\" class=\"data row15 col16\" >0.933333</td>\n",
              "      <td id=\"T_80ff5_row15_col17\" class=\"data row15 col17\" >7</td>\n",
              "      <td id=\"T_80ff5_row15_col18\" class=\"data row15 col18\" >2</td>\n",
              "      <td id=\"T_80ff5_row15_col19\" class=\"data row15 col19\" >2</td>\n",
              "      <td id=\"T_80ff5_row15_col20\" class=\"data row15 col20\" >49</td>\n",
              "      <td id=\"T_80ff5_row15_col21\" class=\"data row15 col21\" >51</td>\n",
              "      <td id=\"T_80ff5_row15_col22\" class=\"data row15 col22\" >9</td>\n",
              "      <td id=\"T_80ff5_row15_col23\" class=\"data row15 col23\" >60</td>\n",
              "      <td id=\"T_80ff5_row15_col24\" class=\"data row15 col24\" >200</td>\n",
              "      <td id=\"T_80ff5_row15_col25\" class=\"data row15 col25\" >0.850000</td>\n",
              "      <td id=\"T_80ff5_row15_col26\" class=\"data row15 col26\" >6.222222</td>\n",
              "      <td id=\"T_80ff5_row15_col27\" class=\"data row15 col27\" >3.525926</td>\n",
              "      <td id=\"T_80ff5_row15_col28\" class=\"data row15 col28\" >1.575000</td>\n",
              "      <td id=\"T_80ff5_row15_col29\" class=\"data row15 col29\" >0.281250</td>\n",
              "      <td id=\"T_80ff5_row15_col30\" class=\"data row15 col30\" >2.500000</td>\n",
              "      <td id=\"T_80ff5_row15_col31\" class=\"data row15 col31\" >4.666667</td>\n",
              "      <td id=\"T_80ff5_row15_col32\" class=\"data row15 col32\" >0.078431</td>\n",
              "      <td id=\"T_80ff5_row15_col33\" class=\"data row15 col33\" >1.921569</td>\n",
              "      <td id=\"T_80ff5_row15_col34\" class=\"data row15 col34\" >0.266667</td>\n",
              "      <td id=\"T_80ff5_row15_col35\" class=\"data row15 col35\" >0.625000</td>\n",
              "      <td id=\"T_80ff5_row15_col36\" class=\"data row15 col36\" >30-40</td>\n",
              "      <td id=\"T_80ff5_row15_col37\" class=\"data row15 col37\" >30-40</td>\n",
              "      <td id=\"T_80ff5_row15_col38\" class=\"data row15 col38\" >30-40</td>\n",
              "      <td id=\"T_80ff5_row15_col39\" class=\"data row15 col39\" >30-40</td>\n",
              "      <td id=\"T_80ff5_row15_col40\" class=\"data row15 col40\" >30-40</td>\n",
              "      <td id=\"T_80ff5_row15_col41\" class=\"data row15 col41\" >30-40</td>\n",
              "      <td id=\"T_80ff5_row15_col42\" class=\"data row15 col42\" >30-40</td>\n",
              "      <td id=\"T_80ff5_row15_col43\" class=\"data row15 col43\" >30-40</td>\n",
              "      <td id=\"T_80ff5_row15_col44\" class=\"data row15 col44\" >30-40</td>\n",
              "      <td id=\"T_80ff5_row15_col45\" class=\"data row15 col45\" >30-40</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "calculated_disparities = b.list_disparities(bdf)\n",
        "disparity_significance = b.list_significance(bdf)\n",
        "disparity_significance"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KVfhLLMjFa8U",
        "outputId": "b3fd8cbb-0d52-4453-be4b-5bf228f056bf"
      },
      "id": "KVfhLLMjFa8U",
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[]"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "bdf[['attribute_name', 'attribute_value'] +  calculated_disparities + disparity_significance]\n",
        "\n",
        "aqp.plot_disparity(bdf, group_metric='fpr_disparity', attribute_name='Gender', significance_alpha=0.05)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 274
        },
        "id": "333gJu6DFdiu",
        "outputId": "fd027918-baba-4b7f-eadd-4b593fc1747a"
      },
      "id": "333gJu6DFdiu",
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "bdf[['attribute_name', 'attribute_value'] +  calculated_disparities + disparity_significance]\n",
        "\n",
        "aqp.plot_disparity(bdf, group_metric='fpr_disparity', attribute_name='Age_Group', significance_alpha=0.05)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 274
        },
        "id": "Gm-5YGx7FkyZ",
        "outputId": "cd225ee6-5c26-4aa1-82be-5613f115caf7"
      },
      "id": "Gm-5YGx7FkyZ",
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "bdf[['attribute_name', 'attribute_value'] +  calculated_disparities + disparity_significance]\n",
        "\n",
        "aqp.plot_disparity(bdf, group_metric='fpr_disparity', attribute_name='Seniority' , significance_alpha=0.05)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 274
        },
        "id": "c5hCi3XrFx-K",
        "outputId": "12fd7abb-dfa8-4e77-f70e-58442daba2e0"
      },
      "id": "c5hCi3XrFx-K",
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Created 3 FPR plots based on gender, age, and seniority. Age has the most bias and gender has the least bias."
      ],
      "metadata": {
        "id": "U6PjewO6DlEU"
      },
      "id": "U6PjewO6DlEU"
    },
    {
      "cell_type": "code",
      "source": [
        "from aequitas.fairness import Fairness\n",
        "f = Fairness()\n",
        "fdf = f.get_group_value_fairness(bdf)\n",
        "fg = aqp.plot_fairness_group_all(fdf, ncols=2, metrics = \"all\")\n",
        "m = aqp.plot_fairness_disparity(fdf, group_metric='fdr', attribute_name='Gender')\n",
        "m = aqp.plot_fairness_disparity(fdf, group_metric='fdr', attribute_name='Age_Group')\n",
        "m = aqp.plot_fairness_disparity(fdf, group_metric='fdr', attribute_name='Seniority')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "SM3K_SwjDaxl",
        "outputId": "a82220aa-257b-407b-f0ec-bacf1e26f74a"
      },
      "id": "SM3K_SwjDaxl",
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x2160 with 10 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Created 3 FDR plots and since all three are majority red, it is safe to say that gender, age, and experience exhibit levels of unfairness when comparing income."
      ],
      "metadata": {
        "id": "ixv9sJQ8EErh"
      },
      "id": "ixv9sJQ8EErh"
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.13"
    },
    "colab": {
      "provenance": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}