Salary_by_Education_Saudi_Arabia.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CKlsPlh---xG"
      },
      "outputs": [],
      "source": [
        "# we trying in this project to understand the relationship between \n",
        "# saleries and other variables such that provided in this dataset eduction and gender \n",
        "# the dataset was provided by Suadi General Authority for Statistics"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "cdDKWHwt_Frb"
      },
      "outputs": [],
      "source": [
        "#import libraries \n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import plotly.express as px\n",
        "import scipy as sp\n",
        "from google.colab import files\n",
        "import io\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73,
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
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        },
        "id": "YbgSmG3mTomK",
        "outputId": "f3f15f71-1575-4561-bc35-fb84baba91ca"
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      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "\n",
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              "      Upload widget is only available when the cell has been executed in the\n",
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          "text": [
            "Saving Salary by Education KSA.csv to Salary by Education KSA.csv\n"
          ]
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        "uploaded = files.upload()"
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    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "L5Rc1wu6Tq_T"
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      "outputs": [],
      "source": [
        "df = pd.read_csv(io.BytesIO(uploaded['Salary by Education KSA.csv']))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6bfe97f4-b5ec-40d8-bb96-56f3f9c918f1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 504 entries, 0 to 503\n",
            "Data columns (total 6 columns):\n",
            " #   Column        Non-Null Count  Dtype \n",
            "---  ------        --------------  ----- \n",
            " 0   Degree Level  504 non-null    object\n",
            " 1   Nationality   504 non-null    object\n",
            " 2   Gender        504 non-null    object\n",
            " 3   Year Quarter  504 non-null    object\n",
            " 4   Salary        504 non-null    int64 \n",
            " 5   Currency      504 non-null    object\n",
            "dtypes: int64(1), object(5)\n",
            "memory usage: 23.8+ KB\n"
          ]
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      "source": [
        "#EDA\n",
        "#Explor the data \n",
        "df.info()\n"
      ]
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      "execution_count": 5,
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        {
          "output_type": "execute_result",
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              "      <th></th>\n",
              "      <th>Degree Level</th>\n",
              "      <th>Nationality</th>\n",
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              "      <td>Primary</td>\n",
              "      <td>NonSaudi</td>\n",
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              "      <td>2017 Q1</td>\n",
              "      <td>1590</td>\n",
              "      <td>SAR</td>\n",
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              "      <td>SAR</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Primary</td>\n",
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              "      <td>2017 Q1</td>\n",
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              "      <td>2017 Q2</td>\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "      background-color: #434B5C;\n",
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              "\n",
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              "          document.querySelector('#df-1789fc96-afde-4b69-b7ad-9a4f8b9688ab 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-1789fc96-afde-4b69-b7ad-9a4f8b9688ab');\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",
              "  "
            ],
            "text/plain": [
              "  Degree Level Nationality  Gender Year Quarter  Salary Currency\n",
              "0      Primary    NonSaudi  Female      2017 Q1    1590      SAR\n",
              "1      Primary    NonSaudi    Male      2017 Q1    2173      SAR\n",
              "2      Primary       Saudi  Female      2017 Q1    4172      SAR\n",
              "3      Primary       Saudi    Male      2017 Q1    6172      SAR\n",
              "4      Primary    NonSaudi  Female      2017 Q2    1597      SAR"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "eCWallmELZNP",
        "outputId": "efa3df0d-a27a-4b24-f352-6121539011e8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "\n",
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              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Salary</th>\n",
              "      <td>504.0</td>\n",
              "      <td>8950.031746</td>\n",
              "      <td>6372.635277</td>\n",
              "      <td>1331.0</td>\n",
              "      <td>4194.0</td>\n",
              "      <td>7890.5</td>\n",
              "      <td>12448.75</td>\n",
              "      <td>35622.0</td>\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-54e233c4-befc-4830-8c53-4564c213045c');\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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              "  "
            ],
            "text/plain": [
              "        count         mean          std  ...     50%       75%      max\n",
              "Salary  504.0  8950.031746  6372.635277  ...  7890.5  12448.75  35622.0\n",
              "\n",
              "[1 rows x 8 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "df.describe().transpose()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "zwasQYt7-mY6"
      },
      "outputs": [],
      "source": [
        "#reorder Degree Level column acconding to the level of degrees\n",
        "# set Degree Level as index\n",
        "df.set_index('Degree Level',inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "dc3DikUEI_sy"
      },
      "outputs": [],
      "source": [
        "#reorder Degree Level column\n",
        "df = df.loc[['Primary','Secondary','Intermediate','Diploma','Bachelor','Master Degree','Doctorate'], :]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "S_v7kfpS5PKC"
      },
      "outputs": [],
      "source": [
        "#reverse setting index\n",
        "df.reset_index(inplace = True)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xPgNjJHEB-QW",
        "outputId": "4dabf3d5-f443-4e25-f344-8183511b05ab"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Primary', 'Secondary', 'Intermediate', 'Diploma', 'Bachelor',\n",
              "       'Master Degree', 'Doctorate'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "df['Degree Level'].unique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eDAdz7liz4dd",
        "outputId": "f5f4a2fb-a05a-4512-f2ac-784f24f8b6ab"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 504 entries, 0 to 503\n",
            "Data columns (total 6 columns):\n",
            " #   Column        Non-Null Count  Dtype \n",
            "---  ------        --------------  ----- \n",
            " 0   Degree Level  504 non-null    object\n",
            " 1   Nationality   504 non-null    object\n",
            " 2   Gender        504 non-null    object\n",
            " 3   Year Quarter  504 non-null    object\n",
            " 4   Salary        504 non-null    int64 \n",
            " 5   Currency      504 non-null    object\n",
            "dtypes: int64(1), object(5)\n",
            "memory usage: 23.8+ KB\n"
          ]
        }
      ],
      "source": [
        "df.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q6N5Y7PGFJNl",
        "outputId": "5d374136-a532-4d90-8a80-b1649dcb0c22"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Degree Level    0\n",
              "Nationality     0\n",
              "Gender          0\n",
              "Year Quarter    0\n",
              "Salary          0\n",
              "Currency        0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "df.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#get_dummies\n",
        "df_D = pd.get_dummies(df, columns=['Nationality','Degree Level', 'Gender']).corr().copy()"
      ],
      "metadata": {
        "id": "2H64VwVxAEV1"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_D"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 646
        },
        "id": "IKSis_6JAEae",
        "outputId": "809ca601-9180-4639-ddc1-a695fddef8dc"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>-0.166667</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Gender_Female</th>\n",
              "      <td>-0.201918</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Gender_Male</th>\n",
              "      <td>0.201918</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0fbbaf5f-d550-4b61-9593-b0a8bbfbcd9c')\"\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",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </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",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [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-0fbbaf5f-d550-4b61-9593-b0a8bbfbcd9c 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-0fbbaf5f-d550-4b61-9593-b0a8bbfbcd9c');\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",
              "  "
            ],
            "text/plain": [
              "                              Salary  ...  Gender_Male\n",
              "Salary                      1.000000  ...     0.201918\n",
              "Nationality_NonSaudi       -0.359148  ...     0.000000\n",
              "Nationality_Saudi           0.359148  ...     0.000000\n",
              "Degree Level_Bachelor       0.037548  ...     0.000000\n",
              "Degree Level_Diploma       -0.122695  ...     0.000000\n",
              "Degree Level_Doctorate      0.698043  ...     0.000000\n",
              "Degree Level_Intermediate  -0.310917  ...     0.000000\n",
              "Degree Level_Master Degree  0.310875  ...     0.000000\n",
              "Degree Level_Primary       -0.345063  ...     0.000000\n",
              "Degree Level_Secondary     -0.267789  ...     0.000000\n",
              "Gender_Female              -0.201918  ...    -1.000000\n",
              "Gender_Male                 0.201918  ...     1.000000\n",
              "\n",
              "[12 rows x 12 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_D['Salary'].sort_values(ascending=False)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x2mAtHLEAEe8",
        "outputId": "be8b337d-2954-4c37-bfa8-17a1b6afe351"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Salary                        1.000000\n",
              "Degree Level_Doctorate        0.698043\n",
              "Nationality_Saudi             0.359148\n",
              "Degree Level_Master Degree    0.310875\n",
              "Gender_Male                   0.201918\n",
              "Degree Level_Bachelor         0.037548\n",
              "Degree Level_Diploma         -0.122695\n",
              "Gender_Female                -0.201918\n",
              "Degree Level_Secondary       -0.267789\n",
              "Degree Level_Intermediate    -0.310917\n",
              "Degree Level_Primary         -0.345063\n",
              "Nationality_NonSaudi         -0.359148\n",
              "Name: Salary, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(8,4),dpi=100)\n",
        "sns.barplot(x=df_D['Salary'].sort_values().index,y=df_D['Salary'].sort_values())\n",
        "plt.title(\"Features Correlation to Salary\")\n",
        "plt.xticks(rotation=90);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 572
        },
        "id": "8wnB2CBWAiVK",
        "outputId": "393f37df-0d36-4880-c5aa-f171746b1a37"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KgKh9nIfA6Ed",
        "outputId": "07cbeb46-8e51-4f33-852a-fa993cbc2999"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 504 entries, 0 to 503\n",
            "Data columns (total 6 columns):\n",
            " #   Column        Non-Null Count  Dtype \n",
            "---  ------        --------------  ----- \n",
            " 0   Degree Level  504 non-null    object\n",
            " 1   Nationality   504 non-null    object\n",
            " 2   Gender        504 non-null    object\n",
            " 3   Year Quarter  504 non-null    object\n",
            " 4   Salary        504 non-null    int64 \n",
            " 5   Currency      504 non-null    object\n",
            "dtypes: int64(1), object(5)\n",
            "memory usage: 23.8+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 474
        },
        "id": "WS5lNjxjU_wf",
        "outputId": "e09769e7-2ad3-44a8-ea32-7214e5392732"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb8879d1890>"
            ]
          },
          "metadata": {},
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "\n",
        "plt.figure(figsize=(10,5),dpi=100)\n",
        "sns.countplot(df['Degree Level'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 474
        },
        "id": "xk1TIumEVGQN",
        "outputId": "411a20bb-60fd-48d7-a84b-1ea5b95915f8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb887894650>"
            ]
          },
          "metadata": {},
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.figure(figsize=(10,5),dpi=100)\n",
        "sns.scatterplot(data = df, x = 'Salary', y = 'Degree Level')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 474
        },
        "id": "s8TvDj8XV8IT",
        "outputId": "7a3a1620-d8ce-47c6-d99b-af660cb4e439"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb88618cd90>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.figure(figsize=(10,5),dpi=100)\n",
        "sns.boxplot(data = df, x = 'Degree Level', y = 'Salary', hue='Nationality')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4BBUZpKZWH9j",
        "outputId": "c5a6587d-1232-4ec6-a5c7-df7d7c2678ec"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Degree Level     Primary\n",
              "Nationality     NonSaudi\n",
              "Gender            Female\n",
              "Year Quarter     2020 Q4\n",
              "Salary              1331\n",
              "Currency             SAR\n",
              "Name: 60, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        " # min salary\n",
        " df.loc[df['Salary'].idxmin()]\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Hwbv7jF0Xqdu",
        "outputId": "89aaa049-da88-4a02-ea3e-562c0a7a8c18"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Degree Level    Doctorate\n",
              "Nationality         Saudi\n",
              "Gender               Male\n",
              "Year Quarter      2021 Q1\n",
              "Salary              35622\n",
              "Currency              SAR\n",
              "Name: 499, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        " # Max salary\n",
        " df.loc[df['Salary'].idxmax()]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 474
        },
        "id": "oxSrxGAdaQay",
        "outputId": "3c5a0988-fbc5-4680-f97d-67dd5dc12532"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb885fcfdd0>"
            ]
          },
          "metadata": {},
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.figure(figsize=(10,5),dpi=100)\n",
        "sns.boxplot(data = df, x = 'Degree Level', y = 'Salary', hue='Gender')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "YUFIXehEbJEX",
        "outputId": "b432b59e-5de9-4430-c703-96bd04ba4287"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb885e0a2d0>"
            ]
          },
          "metadata": {},
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "sns.countplot(data = df, x='Nationality')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "X50H1XCTftjy",
        "outputId": "5902c752-191c-4dff-8f9c-dd20af5363df"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb885dbecd0>"
            ]
          },
          "metadata": {},
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.figure(figsize=(10,5),dpi=100)\n",
        "\n",
        "sns.kdeplot(data=df, x='Salary', hue= 'Nationality')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "GrkyNiT4v1s3",
        "outputId": "8e32ff2c-a9c5-479c-902f-e15135564a87"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "sns.countplot(data = df, x = 'Gender');"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "wNSmfO1iLGrg",
        "outputId": "274fdca0-b090-46fc-c34a-80f78923a71b"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.figure(figsize=(10,5),dpi=100)\n",
        "\n",
        "sns.kdeplot(data=df, x='Salary', hue= 'Gender',  color='red');"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "kiW2fI5_sQUT",
        "outputId": "05141c87-92ee-4606-e3db-d2cacf030135"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-730e3459-10e3-4314-a590-6b652428f178\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Salary</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Nationality</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>NonSaudi</th>\n",
              "      <td>6663.587302</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Saudi</th>\n",
              "      <td>11236.476190</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-730e3459-10e3-4314-a590-6b652428f178')\"\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",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\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",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [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-730e3459-10e3-4314-a590-6b652428f178 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-730e3459-10e3-4314-a590-6b652428f178');\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",
              "  "
            ],
            "text/plain": [
              "                   Salary\n",
              "Nationality              \n",
              "NonSaudi      6663.587302\n",
              "Saudi        11236.476190"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ],
      "source": [
        "df.groupby('Nationality').mean('Salary')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "Ezew74n1uLuJ",
        "outputId": "a1056ed4-ed05-4ff9-8b05-ee128c442f46"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-226bed24-b235-4576-aea9-db8109cd257c\">\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Salary</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Gender</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Female</th>\n",
              "      <td>7664.559524</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Male</th>\n",
              "      <td>10235.503968</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "\n",
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              "          const element = document.querySelector('#df-226bed24-b235-4576-aea9-db8109cd257c');\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",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "              Salary\n",
              "Gender              \n",
              "Female   7664.559524\n",
              "Male    10235.503968"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ],
      "source": [
        "df.groupby('Gender').mean('Salary')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "-zKe67gfO5nK"
      },
      "outputs": [],
      "source": [
        "# convert to period\n",
        "df['Date'] = pd.to_datetime([f'{x[:4]}{x[-2:]}'for x in df['Year Quarter']])\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YYuM02OItQjM",
        "outputId": "f1dbd643-470b-4a65-bba7-e89672b56e5b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0     2017-01-01\n",
              "1     2017-01-01\n",
              "2     2017-01-01\n",
              "3     2017-01-01\n",
              "4     2017-04-01\n",
              "         ...    \n",
              "499   2021-01-01\n",
              "500   2021-04-01\n",
              "501   2021-04-01\n",
              "502   2021-04-01\n",
              "503   2021-04-01\n",
              "Name: Date, Length: 504, dtype: datetime64[ns]"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ],
      "source": [
        "pd.to_datetime(df['Date'], format='%Y%m')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "hVAbRa_-P2w9",
        "outputId": "6477ca5e-bbe8-4110-bc1c-d0522a4a6c58"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Degree Level</th>\n",
              "      <th>Nationality</th>\n",
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              "      <th>Year Quarter</th>\n",
              "      <th>Salary</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Primary</td>\n",
              "      <td>NonSaudi</td>\n",
              "      <td>Female</td>\n",
              "      <td>2017 Q1</td>\n",
              "      <td>1590</td>\n",
              "      <td>SAR</td>\n",
              "      <td>2017-01-01</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Primary</td>\n",
              "      <td>NonSaudi</td>\n",
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              "      <td>2017 Q1</td>\n",
              "      <td>2173</td>\n",
              "      <td>SAR</td>\n",
              "      <td>2017-01-01</td>\n",
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              "      <th>2</th>\n",
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              "      <td>Female</td>\n",
              "      <td>2017 Q1</td>\n",
              "      <td>4172</td>\n",
              "      <td>SAR</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Primary</td>\n",
              "      <td>Saudi</td>\n",
              "      <td>Male</td>\n",
              "      <td>2017 Q1</td>\n",
              "      <td>6172</td>\n",
              "      <td>SAR</td>\n",
              "      <td>2017-01-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Primary</td>\n",
              "      <td>NonSaudi</td>\n",
              "      <td>Female</td>\n",
              "      <td>2017 Q2</td>\n",
              "      <td>1597</td>\n",
              "      <td>SAR</td>\n",
              "      <td>2017-04-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>499</th>\n",
              "      <td>Doctorate</td>\n",
              "      <td>Saudi</td>\n",
              "      <td>Male</td>\n",
              "      <td>2021 Q1</td>\n",
              "      <td>35622</td>\n",
              "      <td>SAR</td>\n",
              "      <td>2021-01-01</td>\n",
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              "    <tr>\n",
              "      <th>500</th>\n",
              "      <td>Doctorate</td>\n",
              "      <td>NonSaudi</td>\n",
              "      <td>Female</td>\n",
              "      <td>2021 Q2</td>\n",
              "      <td>16727</td>\n",
              "      <td>SAR</td>\n",
              "      <td>2021-04-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>501</th>\n",
              "      <td>Doctorate</td>\n",
              "      <td>NonSaudi</td>\n",
              "      <td>Male</td>\n",
              "      <td>2021 Q2</td>\n",
              "      <td>23036</td>\n",
              "      <td>SAR</td>\n",
              "      <td>2021-04-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>502</th>\n",
              "      <td>Doctorate</td>\n",
              "      <td>Saudi</td>\n",
              "      <td>Female</td>\n",
              "      <td>2021 Q2</td>\n",
              "      <td>24632</td>\n",
              "      <td>SAR</td>\n",
              "      <td>2021-04-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>503</th>\n",
              "      <td>Doctorate</td>\n",
              "      <td>Saudi</td>\n",
              "      <td>Male</td>\n",
              "      <td>2021 Q2</td>\n",
              "      <td>28380</td>\n",
              "      <td>SAR</td>\n",
              "      <td>2021-04-01</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>504 rows × 7 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f1404e5d-8014-4077-a40f-7e428da15cd3')\"\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-f1404e5d-8014-4077-a40f-7e428da15cd3 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-f1404e5d-8014-4077-a40f-7e428da15cd3');\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",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "    Degree Level Nationality  Gender Year Quarter  Salary Currency       Date\n",
              "0        Primary    NonSaudi  Female      2017 Q1    1590      SAR 2017-01-01\n",
              "1        Primary    NonSaudi    Male      2017 Q1    2173      SAR 2017-01-01\n",
              "2        Primary       Saudi  Female      2017 Q1    4172      SAR 2017-01-01\n",
              "3        Primary       Saudi    Male      2017 Q1    6172      SAR 2017-01-01\n",
              "4        Primary    NonSaudi  Female      2017 Q2    1597      SAR 2017-04-01\n",
              "..           ...         ...     ...          ...     ...      ...        ...\n",
              "499    Doctorate       Saudi    Male      2021 Q1   35622      SAR 2021-01-01\n",
              "500    Doctorate    NonSaudi  Female      2021 Q2   16727      SAR 2021-04-01\n",
              "501    Doctorate    NonSaudi    Male      2021 Q2   23036      SAR 2021-04-01\n",
              "502    Doctorate       Saudi  Female      2021 Q2   24632      SAR 2021-04-01\n",
              "503    Doctorate       Saudi    Male      2021 Q2   28380      SAR 2021-04-01\n",
              "\n",
              "[504 rows x 7 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ],
      "source": [
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 559
        },
        "id": "rsI6b0QywLvt",
        "outputId": "b1886f0e-67db-4d22-e0b6-cecf3448290e"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          "data": {
            "text/plain": [
              "<Figure size 800x500 with 0 Axes>"
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      ],
      "source": [
        "# mean salaries over time with gender color\n",
        "plt.figure(figsize=(8,5),dpi=100)\n",
        "gender_dff = df.groupby(['Date', 'Gender']).mean().reset_index()\n",
        "fig = px.line(gender_dff, x=\"Date\", y=\"Salary\",color='Gender')\n",
        "fig.show()"
      ]
    },
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        "id": "TeIBgF0wrb0o",
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              "<Figure size 800x500 with 0 Axes>"
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      "source": [
        "# mean salaries over time with Nationality color\n",
        "plt.figure(figsize=(8,5),dpi=100)\n",
        "nationality_diff = df.groupby(['Date', 'Nationality']).mean().reset_index()\n",
        "fig = px.line(nationality_diff, x=\"Date\", y=\"Salary\",color='Nationality')\n",
        "fig.show()"
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    },
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      "execution_count": 37,
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\"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"xaxis\": {\"anchor\": \"y\", \"domain\": [0.0, 1.0], \"title\": {\"text\": \"Date\"}}, \"yaxis\": {\"anchor\": \"x\", \"domain\": [0.0, 1.0], \"title\": {\"text\": \"Salary\"}}},\n",
              "                        {\"responsive\": true}\n",
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              "        if (!display || display === 'none') {{\n",
              "            console.log([gd, 'removed!']);\n",
              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
              "        }}\n",
              "}});\n",
              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
              "if (notebookContainer) {{\n",
              "    x.observe(notebookContainer, {childList: true});\n",
              "}}\n",
              "\n",
              "// Listen for the clearing of the current output cell\n",
              "var outputEl = gd.closest('.output');\n",
              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
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              "\n",
              "                        })\n",
              "                };\n",
              "                \n",
              "            </script>\n",
              "        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 0 Axes>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "# salaries over time with Degree Level color\n",
        "plt.figure(figsize=(8,5),dpi=100)\n",
        "fig = px.box(df, x=df['Date'], y=\"Salary\", color = 'Degree Level')\n",
        "fig.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        },
        "id": "zn_lO9DCvX6w",
        "outputId": "7273646f-119d-4c7f-d06a-d4ded8feacbe"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "ax = df.groupby('Degree Level').mean('Salary').sort_values('Salary')\n",
        "fig = ax.plot(figsize=(15, 5), color = 'Green')\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MvT4QtEUvd7M",
        "outputId": "c3780980-11a0-4779-d0c7-cf7b01d8a205"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 504 entries, 0 to 503\n",
            "Data columns (total 7 columns):\n",
            " #   Column        Non-Null Count  Dtype         \n",
            "---  ------        --------------  -----         \n",
            " 0   Degree Level  504 non-null    object        \n",
            " 1   Nationality   504 non-null    object        \n",
            " 2   Gender        504 non-null    object        \n",
            " 3   Year Quarter  504 non-null    object        \n",
            " 4   Salary        504 non-null    int64         \n",
            " 5   Currency      504 non-null    object        \n",
            " 6   Date          504 non-null    datetime64[ns]\n",
            "dtypes: datetime64[ns](1), int64(1), object(5)\n",
            "memory usage: 27.7+ KB\n"
          ]
        }
      ],
      "source": [
        "df.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**In the next section I will use multiples machine learning algorithms to predict salaries based on the features given bold text**\n",
        "\n",
        "\n",
        "since the dataset is small I will ues ba\n",
        "\n",
        "algorithms:\n",
        "*Linear Regression \n",
        "*Polynomal Regression\n",
        "*SVM\n",
        "*K-neabor\n",
        "*decsion tree\n",
        "*Random Forseet\n",
        "*"
      ],
      "metadata": {
        "id": "GHkbeFQLQHye"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "id": "dlStMNopbDLn"
      },
      "outputs": [],
      "source": [
        "#drop year quarter & currency \n",
        "df.drop(['Currency', 'Year Quarter', 'Date'], axis=1, inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "id": "axcmGN4lbUjS"
      },
      "outputs": [],
      "source": [
        "#get_dummies\n",
        "df_d = pd.get_dummies(df, columns=['Nationality','Degree Level', 'Gender'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yAJ2PYBFFlTq",
        "outputId": "5ed1e23a-d0e4-4fbd-cb4a-e6ee324059e2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 504 entries, 0 to 503\n",
            "Data columns (total 12 columns):\n",
            " #   Column                      Non-Null Count  Dtype\n",
            "---  ------                      --------------  -----\n",
            " 0   Salary                      504 non-null    int64\n",
            " 1   Nationality_NonSaudi        504 non-null    uint8\n",
            " 2   Nationality_Saudi           504 non-null    uint8\n",
            " 3   Degree Level_Bachelor       504 non-null    uint8\n",
            " 4   Degree Level_Diploma        504 non-null    uint8\n",
            " 5   Degree Level_Doctorate      504 non-null    uint8\n",
            " 6   Degree Level_Intermediate   504 non-null    uint8\n",
            " 7   Degree Level_Master Degree  504 non-null    uint8\n",
            " 8   Degree Level_Primary        504 non-null    uint8\n",
            " 9   Degree Level_Secondary      504 non-null    uint8\n",
            " 10  Gender_Female               504 non-null    uint8\n",
            " 11  Gender_Male                 504 non-null    uint8\n",
            "dtypes: int64(1), uint8(11)\n",
            "memory usage: 9.5 KB\n"
          ]
        }
      ],
      "source": [
        "df_d.info()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_d.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 305
        },
        "id": "TBcrlVmDBaxI",
        "outputId": "fc1904e0-dbce-472c-bc84-9989e4d4c791"
      },
      "execution_count": 43,
      "outputs": [
        {
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              "   Salary  Nationality_NonSaudi  ...  Gender_Female  Gender_Male\n",
              "0    1590                     1  ...              1            0\n",
              "1    2173                     1  ...              0            1\n",
              "2    4172                     0  ...              1            0\n",
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              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "id": "8-_sbBeDdJhs"
      },
      "outputs": [],
      "source": [
        "X = df_d.drop('Salary', axis=1)\n",
        "y= df_d['Salary']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
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              "\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "          document.querySelector('#df-ed5bb446-64a4-4e19-8bf4-e2b3d01363b0 button.colab-df-convert');\n",
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              "\n",
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              "          const element = document.querySelector('#df-ed5bb446-64a4-4e19-8bf4-e2b3d01363b0');\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",
              "  "
            ],
            "text/plain": [
              "     Nationality_NonSaudi  Nationality_Saudi  ...  Gender_Female  Gender_Male\n",
              "0                       1                  0  ...              1            0\n",
              "1                       1                  0  ...              0            1\n",
              "2                       0                  1  ...              1            0\n",
              "3                       0                  1  ...              0            1\n",
              "4                       1                  0  ...              1            0\n",
              "..                    ...                ...  ...            ...          ...\n",
              "499                     0                  1  ...              0            1\n",
              "500                     1                  0  ...              1            0\n",
              "501                     1                  0  ...              0            1\n",
              "502                     0                  1  ...              1            0\n",
              "503                     0                  1  ...              0            1\n",
              "\n",
              "[504 rows x 11 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ],
      "source": [
        "X"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1V2VXoFdE359",
        "outputId": "880d1439-acec-4a6f-c5ba-5eda6a995a19"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       1590\n",
              "1       2173\n",
              "2       4172\n",
              "3       6172\n",
              "4       1597\n",
              "       ...  \n",
              "499    35622\n",
              "500    16727\n",
              "501    23036\n",
              "502    24632\n",
              "503    28380\n",
              "Name: Salary, Length: 504, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ],
      "source": [
        "y"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "id": "dcXARBbKGSBX"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {
        "id": "II559BRMGab0"
      },
      "outputs": [],
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.10, random_state=101)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 522
        },
        "id": "8hsK-PQxGpqP",
        "outputId": "59ea3f03-8eee-4adb-99e8-71079f0d4299"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "          const element = document.querySelector('#df-1a341d16-3e74-4c75-96d0-1a3e0d75ac68');\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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              "          dataTable['output_type'] = 'display_data';\n",
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              "  "
            ],
            "text/plain": [
              "     Nationality_NonSaudi  Nationality_Saudi  ...  Gender_Female  Gender_Male\n",
              "130                     0                  1  ...              1            0\n",
              "427                     0                  1  ...              0            1\n",
              "2                       0                  1  ...              1            0\n",
              "372                     1                  0  ...              1            0\n",
              "443                     0                  1  ...              0            1\n",
              "..                    ...                ...  ...            ...          ...\n",
              "63                      0                  1  ...              0            1\n",
              "326                     0                  1  ...              1            0\n",
              "337                     1                  0  ...              0            1\n",
              "11                      0                  1  ...              0            1\n",
              "351                     0                  1  ...              0            1\n",
              "\n",
              "[453 rows x 11 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ],
      "source": [
        "X_train"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tSku-z2YGsv-",
        "outputId": "04a424ab-5d9b-4cf4-e369-9f2d101251a1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "130     5313\n",
              "427    20431\n",
              "2       4172\n",
              "372     8779\n",
              "443    23079\n",
              "       ...  \n",
              "63      6518\n",
              "326    10467\n",
              "337     8653\n",
              "11      5958\n",
              "351    13257\n",
              "Name: Salary, Length: 453, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ],
      "source": [
        "y_train"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###**First algorithm will be Linear Regression**###\n"
      ],
      "metadata": {
        "id": "vT2aiuRHb5UY"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {
        "id": "iC6uWFzNFnhK"
      },
      "outputs": [],
      "source": [
        "from sklearn.linear_model import LinearRegression "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "metadata": {
        "id": "djjYPfjAFxuB"
      },
      "outputs": [],
      "source": [
        "model_Lr = LinearRegression()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KzZRrw8nGMPQ",
        "outputId": "baedf6a7-c174-4abf-c76d-2ec45376e473"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ],
      "source": [
        "model_Lr.fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 522
        },
        "id": "iomQ0aNGGyHA",
        "outputId": "7be84560-b762-46f4-f234-f5eddb400d0c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Nationality_NonSaudi</th>\n",
              "      <th>Nationality_Saudi</th>\n",
              "      <th>Degree Level_Bachelor</th>\n",
              "      <th>Degree Level_Diploma</th>\n",
              "      <th>Degree Level_Doctorate</th>\n",
              "      <th>Degree Level_Intermediate</th>\n",
              "      <th>Degree Level_Master Degree</th>\n",
              "      <th>Degree Level_Primary</th>\n",
              "      <th>Degree Level_Secondary</th>\n",
              "      <th>Gender_Female</th>\n",
              "      <th>Gender_Male</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>130</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>427</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>372</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>443</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>63</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>326</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>337</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>351</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>453 rows × 11 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8f12da7a-30d7-4471-a877-33f5fe5c738d')\"\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",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\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",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [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-8f12da7a-30d7-4471-a877-33f5fe5c738d 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-8f12da7a-30d7-4471-a877-33f5fe5c738d');\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",
              "  "
            ],
            "text/plain": [
              "     Nationality_NonSaudi  Nationality_Saudi  ...  Gender_Female  Gender_Male\n",
              "130                     0                  1  ...              1            0\n",
              "427                     0                  1  ...              0            1\n",
              "2                       0                  1  ...              1            0\n",
              "372                     1                  0  ...              1            0\n",
              "443                     0                  1  ...              0            1\n",
              "..                    ...                ...  ...            ...          ...\n",
              "63                      0                  1  ...              0            1\n",
              "326                     0                  1  ...              1            0\n",
              "337                     1                  0  ...              0            1\n",
              "11                      0                  1  ...              0            1\n",
              "351                     0                  1  ...              0            1\n",
              "\n",
              "[453 rows x 11 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ],
      "source": [
        "X_train"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iCcF53-ZHbrf",
        "outputId": "416884bd-9c00-4a4a-9037-eeadc69d54fe"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "130     5313\n",
              "427    20431\n",
              "2       4172\n",
              "372     8779\n",
              "443    23079\n",
              "       ...  \n",
              "63      6518\n",
              "326    10467\n",
              "337     8653\n",
              "11      5958\n",
              "351    13257\n",
              "Name: Salary, Length: 453, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ],
      "source": [
        "y_train"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 56,
      "metadata": {
        "id": "_lrjdU-aMp2e"
      },
      "outputs": [],
      "source": [
        "#check overfitting vs underfitting on training data\n",
        "trin_p = model_Lr.predict(X_train)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#evaluation \n",
        "from sklearn.metrics import mean_squared_error, mean_absolute_error\n",
        "np.sqrt(mean_squared_error(y_train,trin_p))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JU1HdME9UqRl",
        "outputId": "f1540766-e7c3-48c7-c007-fdac19f7a774"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1667.9215106122092"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mean_absolute_error(y_train, trin_p)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S3hq7ZCeVO2q",
        "outputId": "654b7a86-15c8-418c-8a72-1eaf29e75080"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1109.9889624724062"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# plot Actual Value vs Fitted Values\\\n",
        "plt.figure(figsize=(8,5),dpi=100)\n",
        "ax = sns.distplot(y_train, hist=False, color=\"r\", label=\"Actual Value\")\n",
        "sns.distplot(trin_p, hist=False, color=\"b\", label=\"Fitted Values\" , ax=ax);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "90pybA1GVm9z",
        "outputId": "eb8249c7-95c5-4784-ecf4-278981506372"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "metadata": {
        "id": "i4wCegrTIRKZ"
      },
      "outputs": [],
      "source": [
        "pred_Lr = model_Lr.predict(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 61,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-Jt7zl4kOAGr",
        "outputId": "48885b92-1005-4b81-c979-b87772be2f95"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 4544., 10528.,  1200.,   -48.,   496., 14800.,   -48.,  5936.,\n",
              "        3760., 10672., 16640., 21232.,  8352., 10208.,  2512.,  7104.,\n",
              "       16640.,  6080., 16640., 19200.,   -48.,   496., 10528.,  5936.,\n",
              "        6080., 13088., 16640.,  8496.,   -48.,  5936.,  7104.,  6080.,\n",
              "       13088.,  3056.,  7648., 17360.,  7104., 16640., 16640.,  3520.,\n",
              "        8496., 10528., 12768.,  3760.,  5936.,  1200., 16640., 13088.,\n",
              "        8496.,  3760.,  7648.])"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ],
      "source": [
        "pred_Lr"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 62,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u2fy2gX9IePo",
        "outputId": "8c21fbda-39ef-4649-cfb0-69a54c191370"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "18      4165\n",
              "306    10035\n",
              "104     2045\n",
              "4       1597\n",
              "156     1664\n",
              "370    11836\n",
              "32      1566\n",
              "344     6790\n",
              "81      3071\n",
              "227    10081\n",
              "492    14349\n",
              "482    21528\n",
              "95      8873\n",
              "368    11022\n",
              "65      1869\n",
              "39      6632\n",
              "432    12847\n",
              "237     4455\n",
              "452    13526\n",
              "437    17074\n",
              "12      1576\n",
              "212     1433\n",
              "290    10180\n",
              "296     6988\n",
              "269     4084\n",
              "359    13373\n",
              "460    12763\n",
              "321     8378\n",
              "64      1384\n",
              "336     6470\n",
              "19      5978\n",
              "261     4822\n",
              "315    13181\n",
              "205     2300\n",
              "179     7956\n",
              "415    17276\n",
              "31      6448\n",
              "456    14305\n",
              "444    11862\n",
              "228     3960\n",
              "333     8205\n",
              "302     9993\n",
              "405    15829\n",
              "89      3019\n",
              "312     7751\n",
              "100     1980\n",
              "496    10774\n",
              "347    12475\n",
              "289     7787\n",
              "141     2753\n",
              "215     7523\n",
              "Name: Salary, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ],
      "source": [
        "y_test"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 63,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "TIIZxVq9fW75",
        "outputId": "4cfdd8b5-9c60-43e6-abf5-39b71eddf2a2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "\n",
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              "<style scoped>\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Actual</th>\n",
              "      <th>Predicted</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>4165</td>\n",
              "      <td>4544.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>306</th>\n",
              "      <td>10035</td>\n",
              "      <td>10528.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>104</th>\n",
              "      <td>2045</td>\n",
              "      <td>1200.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1597</td>\n",
              "      <td>-48.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>156</th>\n",
              "      <td>1664</td>\n",
              "      <td>496.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>370</th>\n",
              "      <td>11836</td>\n",
              "      <td>14800.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>1566</td>\n",
              "      <td>-48.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>344</th>\n",
              "      <td>6790</td>\n",
              "      <td>5936.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>81</th>\n",
              "      <td>3071</td>\n",
              "      <td>3760.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>227</th>\n",
              "      <td>10081</td>\n",
              "      <td>10672.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>492</th>\n",
              "      <td>14349</td>\n",
              "      <td>16640.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>482</th>\n",
              "      <td>21528</td>\n",
              "      <td>21232.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>95</th>\n",
              "      <td>8873</td>\n",
              "      <td>8352.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>368</th>\n",
              "      <td>11022</td>\n",
              "      <td>10208.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>65</th>\n",
              "      <td>1869</td>\n",
              "      <td>2512.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>6632</td>\n",
              "      <td>7104.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>432</th>\n",
              "      <td>12847</td>\n",
              "      <td>16640.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>237</th>\n",
              "      <td>4455</td>\n",
              "      <td>6080.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>452</th>\n",
              "      <td>13526</td>\n",
              "      <td>16640.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>437</th>\n",
              "      <td>17074</td>\n",
              "      <td>19200.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>1576</td>\n",
              "      <td>-48.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>212</th>\n",
              "      <td>1433</td>\n",
              "      <td>496.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>290</th>\n",
              "      <td>10180</td>\n",
              "      <td>10528.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>296</th>\n",
              "      <td>6988</td>\n",
              "      <td>5936.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>269</th>\n",
              "      <td>4084</td>\n",
              "      <td>6080.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>359</th>\n",
              "      <td>13373</td>\n",
              "      <td>13088.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>460</th>\n",
              "      <td>12763</td>\n",
              "      <td>16640.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>321</th>\n",
              "      <td>8378</td>\n",
              "      <td>8496.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>64</th>\n",
              "      <td>1384</td>\n",
              "      <td>-48.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>336</th>\n",
              "      <td>6470</td>\n",
              "      <td>5936.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>5978</td>\n",
              "      <td>7104.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>261</th>\n",
              "      <td>4822</td>\n",
              "      <td>6080.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>315</th>\n",
              "      <td>13181</td>\n",
              "      <td>13088.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>205</th>\n",
              "      <td>2300</td>\n",
              "      <td>3056.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>179</th>\n",
              "      <td>7956</td>\n",
              "      <td>7648.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>415</th>\n",
              "      <td>17276</td>\n",
              "      <td>17360.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>6448</td>\n",
              "      <td>7104.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>456</th>\n",
              "      <td>14305</td>\n",
              "      <td>16640.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>444</th>\n",
              "      <td>11862</td>\n",
              "      <td>16640.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>228</th>\n",
              "      <td>3960</td>\n",
              "      <td>3520.0</td>\n",
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              "    <tr>\n",
              "      <th>333</th>\n",
              "      <td>8205</td>\n",
              "      <td>8496.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>302</th>\n",
              "      <td>9993</td>\n",
              "      <td>10528.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>405</th>\n",
              "      <td>15829</td>\n",
              "      <td>12768.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>89</th>\n",
              "      <td>3019</td>\n",
              "      <td>3760.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>312</th>\n",
              "      <td>7751</td>\n",
              "      <td>5936.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>100</th>\n",
              "      <td>1980</td>\n",
              "      <td>1200.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>496</th>\n",
              "      <td>10774</td>\n",
              "      <td>16640.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>347</th>\n",
              "      <td>12475</td>\n",
              "      <td>13088.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>289</th>\n",
              "      <td>7787</td>\n",
              "      <td>8496.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>141</th>\n",
              "      <td>2753</td>\n",
              "      <td>3760.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>215</th>\n",
              "      <td>7523</td>\n",
              "      <td>7648.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0066d928-50e1-409a-8f77-01d36dc75b5c')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "    [theme=dark] .colab-df-convert {\n",
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              "        buttonEl.style.display =\n",
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              "\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "  "
            ],
            "text/plain": [
              "     Actual  Predicted\n",
              "18     4165     4544.0\n",
              "306   10035    10528.0\n",
              "104    2045     1200.0\n",
              "4      1597      -48.0\n",
              "156    1664      496.0\n",
              "370   11836    14800.0\n",
              "32     1566      -48.0\n",
              "344    6790     5936.0\n",
              "81     3071     3760.0\n",
              "227   10081    10672.0\n",
              "492   14349    16640.0\n",
              "482   21528    21232.0\n",
              "95     8873     8352.0\n",
              "368   11022    10208.0\n",
              "65     1869     2512.0\n",
              "39     6632     7104.0\n",
              "432   12847    16640.0\n",
              "237    4455     6080.0\n",
              "452   13526    16640.0\n",
              "437   17074    19200.0\n",
              "12     1576      -48.0\n",
              "212    1433      496.0\n",
              "290   10180    10528.0\n",
              "296    6988     5936.0\n",
              "269    4084     6080.0\n",
              "359   13373    13088.0\n",
              "460   12763    16640.0\n",
              "321    8378     8496.0\n",
              "64     1384      -48.0\n",
              "336    6470     5936.0\n",
              "19     5978     7104.0\n",
              "261    4822     6080.0\n",
              "315   13181    13088.0\n",
              "205    2300     3056.0\n",
              "179    7956     7648.0\n",
              "415   17276    17360.0\n",
              "31     6448     7104.0\n",
              "456   14305    16640.0\n",
              "444   11862    16640.0\n",
              "228    3960     3520.0\n",
              "333    8205     8496.0\n",
              "302    9993    10528.0\n",
              "405   15829    12768.0\n",
              "89     3019     3760.0\n",
              "312    7751     5936.0\n",
              "100    1980     1200.0\n",
              "496   10774    16640.0\n",
              "347   12475    13088.0\n",
              "289    7787     8496.0\n",
              "141    2753     3760.0\n",
              "215    7523     7648.0"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ],
      "source": [
        "# dataframe of y_test vs y_preds\n",
        "df_T=pd.DataFrame({'Actual':y_test, 'Predicted':pred_Lr})\n",
        "df_T"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 64,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SFYwb9tX-nfy",
        "outputId": "ac77616d-4ea7-4507-829f-c4642a0a6891"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1800.8882176717743"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ],
      "source": [
        "np.sqrt(mean_squared_error(y_test,pred_Lr))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 65,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ShaDElqZOC8O",
        "outputId": "350a92df-8cef-4f80-82cd-00995e651375"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1304.2549019607843"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ],
      "source": [
        "mean_absolute_error(y_test, pred_Lr)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 66,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9R3E7YJwOLX9",
        "outputId": "f9a18704-cab4-4c47-a56e-658da74469c0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "8950.031746031746"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ],
      "source": [
        "df['Salary'].mean()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Kde for y_test vs y_preds\n",
        "plt.figure(figsize=(8,5), dpi=100)\n",
        "ax = sns.distplot(y_test, hist=False, color=\"r\", label=\"Actual Value\")\n",
        "sns.distplot(pred_Lr, hist=False, color=\"b\", label=\"Fitted Values\" , ax=ax);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "J9BQjRxxVawF",
        "outputId": "6270b2ea-5c81-4ccc-8b31-e78508409067"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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CiYiIklWRyFi4ML6916NH2FhyePVVe2QOMHZsinuTpsDdd1tL219/hdtuCxhIyZLw9NNW/pGdDWedFf/hRUSkCFOyKhIVzzxjjeMPPxwaNgwdDQBLlkCvXra+8ko49dSw8SRD+fLxYWHDhtkOcjDFi9tPBOefb38WzjkHxowJGJCISHhKVkWi4tln7dqzZ9g4tvLeHpEvXQoHHghDhoSOKHlOPtmevGdnWxvU7OyAwWRlWW+tvn3tP0Lv3jZ2S0SkiFKyKhIFv/5qbYucg06dQkcD2EbvCy/YZt+4cZHropVww4dDpUrw2Wd24CqoYsUsiKuusl/37RtvDisiUsQoWRWJgtjp7yOPhOrVw8aCDVS69FJb33qrjSjNdHvtBYMH2/rWW60EIijn4L774Npr7dcXXwzPPRc2JhGRAJSsikTBCy/YNQLNS72HCy+0kfUtW1ongKLiwgvh4INh5Uq48cbQ0WAJ6913Q58+9h+mZ0947bXQUYmIpJSSVZHQFi+G996z9emnh40FO9/z2mv22P/JJ6FEidARpU5WFowcaesxYyIyUMo5Kwno3h02b7YfaKZNCx2ViEjKKFkVCe3f/7Zds0MOsR5KAf3yS7xM8p//hKZNg4YTxOGHW/co7+Gyy+xQfnDFitl821NPhfXr7frZZ6GjEhFJCSWrIqH96192DXywassWO3i+erUlbNdcEzScoIYMsZZW06fDU0+FjmarEiVsUMAxx9h/pA4d4LvvQkclIpJ0SlZFQvrzT/jvf20dOFkdNQqmToUyZWwTLysraDhB7bUX3HKLrW++GdatCxvP/5QubTvxrVvD8uVw0klWRiIiksGUrIqENGWK1SE2bQqNGwcLY8ECuOEGWw8eDPvsEyyUyLjiCqhdG+bPhxEjQkeTQ4UK8Mor0KgR/PwznHJKwDmxIiLJp2RVJKRXX7Vr4NFQV1xhT5Zbt463rCrqSpeGAQNsPWgQLFsWNp5t/O1v9oNO1apWu9q9e+BJBiIiyaNkVSSU7Gz4z39sfdJJwcKYPNnKZrOy4NFHi/bj/+316AEHHWStrO66K3Q022nUyP7jlS4NL78cr1sQEckwSlZFQvnkE6s7rFwZDjssSAh//WUn3gH69YNmzYKEEVlZWdbmFOCBB2DevLDx7OCww2D0aFsPGmRjx0REMoySVZFQYiUAHTrYTNMAbr3VJr3Wqwe33RYkhMg7/nho3x42bYKbbgodzU507x6f3HD++WppJSIZR8mqSCixSUQnnxzk9jNmwPDhtn74YShbNkgYaWHIELs+80xEc8EBA+yg1fr10LlzxApsRUQKR8mqSAi//w5ffGHTiU44IeW337wZLrrIeqt26xYkhLTSooVNOgW4/nobGBApWVkwfrzVsf7yi001iMQ0AxGRwlOyKhJCbFf10ENhzz1TfvsHHoDPP7dy2fvvT/nt09KAAVCyJLz9dvxcXKRUqgTPP28HrqZMieCJMBGRglGyKhJCwBKA336zRvdgj7dr1Eh5CGmpbl1r8QW2uxrJTlEHHQSPPGLr226Dd94JGo6ISCIoWRVJtU2bbFQUwIknpvz2/fpZD/kjjoALLkj57dPajTfabvTXX8Ozz4aOZhfOPdcOWnlvtQuqXxWRNKdkVSTVPv3UOvDvsQccfHBKbz11KkyaBMWKwYMP2lXyrkoVuO46W99xh9X+RtKIEdCkidVGX3BBBItsRUTyTt+qRFLtzTft2q5dSrPFjRvh8sttfckl9sRY8u+KK2yA1I8/wlNPhY5mF8qVs9YFJUvCSy/BqFGhIxIRKTAlqyKp9tZbdm3fPqW3HTkSvvvOznP9858pvXVGKV8ebrjB1nfeaT8ERFKLFjB4sK379YOffgobj4hIASlZFUml1avh449tfdxxKbvtggVw++22HjzY6i6l4C65BKpXh59/hrFjQ0eTiyuvhGOOgbVr4bzzInoqTEQkd0pWRVJp2jQrdGzQAOrXT9ltr7/eRqu2bm05ixRO2bLQv7+tBwywXvyRVKwYjBlj28Hvvx+fAiEikkaUrIqkUoASgHffhaeftvkDDzygQ1WJcvHFsPfeMH8+PPZY6GhyUa8e3HuvrW+8EX74IWg4IiL5pW9bIqkUO1yVohKAzZvhsstsfdFF0KpVSm5bJJQuDTfdZOuBA+1Je2RdeCEcfzxs2GBZtroDiEgaUbIqkioLFsC339oW57HHpuSWDz0EX31lXbI00Cjxeve2YQGLFsV78UeScxZg2bJWihLpQlsRkW0pWRVJldgggJYtLXtMsqVL4dZbbX3XXVC1atJvWeSULAm33GLrwYOtLjiy6te39gUA114LixeHjUdEJI+UrIqkSmz0ZYp2VW+7DVauhObN7SmwJMc550DDhvbDwQMPhI5mN6680lpa/fknXH116GhERPJEyapIqkybZtejj076rb76Kt4HftgwyMpK+i2LrBIl7AcDgHvuse5kkVW8uJ0GK1bMhgbE/kyKiESYklWRVJg/35qyFysGRxyR1Ft5b5tmW7bAGWfAUUcl9XYCdO8OjRvD8uU2fCHSWra003ZgI80iOzNWRMQoWRVJhdgO1sEHQ8WKSb3V5MlWHluypO30SfJlZcXrg++9F1atChvPbg0YYHXTX30FDz8cOhoRkVwpWRVJhVi9apJLADZutMmaANdck9K5A0Vet27x3dXI165WrRpvD3HrrbBkSdh4RERyoWRVJBViO6tJfiY/ciTMmWOjQG+8Mam3ku2k3e7qhRfaYasVK+KzeEVEIkjJqkiyLVgAP/5ovS6PPDJpt1m6NN6ZaOBAqFAhabeSXejWDZo0SZPa1awsuP9+Wz/6KHz3Xdh4RER2QcmqSLLFdlVbtIDKlZN2m1tusd28Fi3gvPOSdhvJRdrtrh51FPz975CdDTfcEDoaEZGdUrIqkmwpKAGYNSs+n374cGs6IGF07Qr77mutTCO/uwowZIhl2ZMnx2urRUQiRN/SRJItlgAkKVn13g5VbdkCXbrA//1fUm4jeZR2u6tNmkCfPra+9lr7gyQiEiFKVkWSackS+OEHWycpi3z9dXjrLWtVNWRIUm4h+XTmmfHd1REjQkeTB7fdZkXOn38OL7wQOhoRkW0ETVadc7c75/x2r+9DxiSSUB98YNemTa2vZYJlZ8N119n68svVqioqcu6u3nefjb2NtD33jI9fvfVW+4MlIhIRUdhZ/QbYK8crecelRVItlqwmqQvAE0/A119DlSpw001JuYUU0Jlnwn77pVHt6jXX2B+k776Dp58OHY2IyP9EIVnd7L1flOP1x64+0TlXyjlXMfYC1JxHou399+2ahBGra9ZYBwCwa5UqCb+FFELa7a5WqhTvCHDbbTZhQkQkAqKQrO7jnFvgnJvrnHvaOVcnl8/tD6zM8ZqfkghFCmLtWpgxw9ZJ2Fm9915YuNAe/V9yScLfXhKgS5f47mpa1K5edplNlPj5Zxg9OnQ0IiJA+GT1E+A84ASgL1AfeM85t6sd00FApRyvWimIUaRgPv0UNm2CvfaCevUS+taLFsHdd9t60CAoVSqhby8Jkna7q+XKxetJBg3S7qqIRELQZNV7P8V7P8l7P8t7/zpwElAZOHMXn7/Be78q9gJWpzJekXzJWa/qXELf+rbbrAygdWurjZToiu2urliRJrurF1wANWrAb7/BuHGhoxERCb6zug3v/QpgNtAodCwihRarV01wCcA338Djj9t66NCE58GSYFlZ9sMF2O7qihVh49mtMmXg+uttPXCgPR0QEQkoUsmqc6480BBYGDoWkULZsgU++sjWCT5cdcMN9vann560JgOSYJ07w/77p9Hu6sUXWzurefNgwoTQ0YhIERe6z+pQ59xRzrl6zrnDgReBbOCZkHGJFNq331pmUq4cHHRQwt72nXfg1VeheHEYPDhhbytJlrN29f7702B3tWxZm2YFcNdd6rsqIkGF3lmthSWmPwATgWVAG+/90qBRiRRWrASgTRvLLBPA+3hnoYsugsaNE/K2kiJduqTZ7uoll9ggix9/1FQrEQkq9AGrbt77mt77Ut77Wlt//VPImEQS4sMP7ZrAEoCXXoLp023TK9ZfVdJHsWJpVrtavryNRQOb4+t92HhEpMgKvbMqkpk+/tiuhx2WkLfbvDneUejqq+2wtqSfWO3qypUwfHjoaPLg0kvtwNXnn8Pbb4eORkSKKCWrIon2xx/26BSst1QCjBtnUzD32AOuuy4hbykB5NxdTYva1T33hPPPt/WQIWFjEZEiS8mqSKJ98old9903ITNQ16+PJzg33mhTMSV9de4MTZum0e5qv36WZb/xBnzxRehoRKQIUrIqkmixllVt2iTk7R56CObPh1q17KmspLe0212tXz8+eeKee8LGIiJFkpJVkURLYL3qypXWOQjgjjugdOlCv6VEwBlnwAEH2H/fYcNCR5MHsdqTiRPh99/DxiIiRY6SVZFEys6OlwEkYGd16FBYvtwqCs45p9BvJxGRc3d12LA02F09+GBo29ZO+j34YOhoRKSIUbIqkkjffgt//WVtf5o2LdRbLV5sLY7AdlcT1K5VIqJTpzTbXb3qKruOGgVr14aNRUSKFCWrIokUKwE49FAbW1QIAwZYTnDooTZaVTLL9rWrf/4ZNp7dOu00q19dvhzGjw8djYgUIUpWRRIpQYerfvnFNrAABg0C5woZl0RSp05w4IGwalUa7K5mZcEVV9h62DANCRCRlFGyKpJIsZ3VQiarAwbApk3Qrh0ce2wC4pJI2r52NfK7q+efDxUqWNPfN98MHY2IFBFKVkUSZcUK+yYOhUpWf/oJxo619Z13JiAuibTTT4/vrt5/f+hodqNiRTjvPFs//HDQUESk6FCyKpIon35q1wYNbPJPAd15pzUVOOEEOPzwBMUmkZVzd3X4cCsJjbQ+few6ebI1ABYRSTIlqyKJEktWDzmkwG/xww/xsyvaVS06Tj8dmjVLk93V/fe3NlbZ2fD446GjEZEiQMmqSKIkIFm94w7YsgVOPbVQbyNpZvvOAIsXh41nt/r2tetjj1lxtYhIEilZFUmUQiarX38Nzz5ra+2qFj2nn25tytasSYP//p06QbVqsGCBlQOIiCSRklWRRFi40MZQFitm034K4I47rBvQGWdA8+YJjk8izzkYMsTWjz4KP/4YNp5clSwJvXvb+pFHwsYiIhlPyapIIsR2Vffbz6ZX5dMXX8Dzz1vCcvvtiQ1N0sfRR8NJJ9lU05tuCh3Nblx0kf2BffPNiGfWIpLulKyKJEIhSwBi9Ypdu9oITim6YkMgJk2C6dNDR5OLevXgxBNtHZtgISKSBEpWRRKhEMnqZ5/Byy9ve8hGiq5mzeCcc2x9/fURHxQVO2g1diysXx82FhHJWEpWRQrL+0Ilq3fcYdcePWDffRMYl6StO++EUqVg2jSYMiV0NLk48USoU8eaw06aFDoaEclQSlZFCmvePPtmXaKEbYvlw8yZ8Mortqt6881Jik/STp06cPnltr7hBmtpGklZWVa7CppoJSJJo2RVpLBiu6oHHWTbYfkwcKBdu3aFxo0THJektf79oXJla2kWGxQRSb17Q/Hi8NFH8OWXoaMRkQykZFWksApYAvDtt/Cvf9n6xhsTHJOkvT32iP+5uOWWCJeE1qhhTWJBE61EJCmUrIoUVgGT1YEDrdy1Uyd1AJCdu+wyqFULfvsNhg0LHU0uYj1XJ0yADRvCxiIiGUfJqkhhZGfD55/bOh/J6pw58Mwzto58P00JpkwZuOsuW991FyxaFDaeXTruOMuqly+31hYiIgmkZFWkML7/3uZjlitnAwHyaPBg2LLFGsAXcOCVFBE9e9oY1r/+inC5SFZWvN/W2LFhYxGRjKNkVaQwYiUABx9s37Dz4Ndf4cknba0OALI7xYrFSwCeeCK+kR85551n19dft9HDIiIJomRVpDAKUK969902TvPYY+Gww5IUl2SUww6zPrzew5VXRnRQwD77wJFH2iODp54KHY2IZBAlqyKFkc9kdeHC+IFp7apKfgweDGXLwgcf2DmmSIrtro4dG9GMWkTSkZJVkYLauDHeVzKPyeq999ph6SOOgKOPTl5oknlq1YrXrPbrBytXho1npyr5NxMAACAASURBVM480zLq2bOt76qISAIoWRUpqFmzLGHdYw9o0GC3n/7HH/EhPzffDM4lOT7JONdea8MjFi+GW28NHc1OVKgAnTvbWgetRCRBlKyKFFSsBKBVqzxlng88AGvX2lmsDh2SHJtkpFKl7M8R2PWLL8LGs1O9etn1ueesU4aISCEpWRUpqHzUq65dG08ybrhBu6pScO3b29P2LVvgkkvsGilt20L9+rB6NbzwQuhoRCQDKFkVKah8JKtjx8KyZfY9vFOnJMclGe+++6B8eSsLffTR0NFsp1ixbQ9aiYgUkpJVkYJYswa+/dbWu0lWs7MtuQA7GFO8eJJjk4y39942rhfg+uth/vyw8ezg3HPt8cHbb8O8eaGjEZE0p2RVpCBmzrTnrzVr2isXL7wAc+dC1arxcj6RwrrkEuu/unq1rSPVKapuXWskDDBuXNhYRCTtKVkVKYjYGKGWLXP9NO/hnntsfeml1tVHJBGysuCxx6BECZg8GSZNCh3RdmKlAOPHRyyTFpF0o2RVpCBmzLDrbpLVadOstLV0abjsshTEJUVK06bx3quXXQZLloSNZxsdO9pPZ3PmwPTpoaMRkTSmZFWkIGLJ6sEH5/ppsV3VXr1gzz2THJMUSf37w4EHwtKl0KdPhDYxy5e3hBXg6afDxiIiaU3Jqkh+rVsH331n61yS1a+/htdes8PR11yTotikyClVyspCS5SAF1+Ep54KHVEOPXrY9dlnYdOmsLGISNpSsiqSX199ZUf8q1XL9XDV0KF27dQJGjVKUWxSJDVvDnfcYevLL4dffw0bz/+0b2+PFJYuhbfeCh2NiKQpJasi+ZWzBGAX3f1//x0mTLD1ddelKC4p0q67Dtq0gVWrrHNUdnboiLDt3q5dba1SABEpICWrIvmVh3rV4cPtqedRR8Ghh6YoLinSihe3coBy5eCdd2DAgNARbRUrBXjxRfjrr7CxiEhaUrIqkl+7SVZXr4ZRo2ytXVVJpX32gUcesfWdd1rSGlzr1tCwoc0c/ve/Q0cjImlIyapIfmzcaDWrsMtkddw4exTbpAmceGIKYxMBeva07hNbtkD37lYuGpRz8d3V8ePDxiIiaSkyyapz7h/OOe+cGxY6FpFd+vZbS1grV4Z69Xb47S1bYMQIW19+uXUCEEm1kSNhv/1g4ULo1g02bw4cUCxZffPNiDWDFZF0EIlvpc65Q4CLgVmhYxHJ1W4OV73xBsyeDRUr2iEXkRDKlbOJVuXKwX//C9dfHzigxo3hkEPs1NdzzwUORkTSTfBk1TlXHngauBD4M3A4IrnbTb3q8OF27d3beqKLhNK0qZWkANx/f3wdTM+edlUpgIjkU/BkFXgQeNV7v9smfM65Us65irEXUCH54YnkkEuy+sMP8J//2IarRqtKFHTqBLfcYuuLLoJPPgkYTNeukJVlo1d//DFgICKSboImq865bsDBQP88fkl/YGWO1/wkhSayo+xs+OILW7doscNvjxxp11NPhQYNUhiXSC5uv93+TG7YAKecAnPmBAqkenU47jhbq+eqiORDsGTVOVcbGA708N6vz+OXDQIq5XjVSlJ4Ijv64QcbtVqunPUIymHlSnjiCVtfcUXqQxPZlWLFbEBFy5bwxx9wwgkBzzjFSgGeeQa8DxSEiKSbkDurLYFqwAzn3Gbn3GbgKOCKrb/O2v4LvPcbvPerYi9gdYpjlqIsVgLQvLk9zsxhzBhYs8bqBI89NkBsIrkoXx5efRXq14effrId1tUh/vb8+9+hdGk7hRh7SiEishshk9WpwIFA8xyvz7DDVs2991EYFigSt4t61exseOABW19xxS4nsIoEVb261VRXrQqffgonnxxgoFSFCnZjUFcAEcmzYMmq93619/7rnC9gDbBs61okWnaRrL72GsydC1WqxJ9yikRR48aWsFaqBO+9Zzusa9akOIiuXe363HMqBRCRPIlCNwCR6NuyBWbOtPV2yWqsXdWFF0LZsimOSySfWrWC11+3XsDTpgUoCTj5ZKv7/vln6wwgIrIbkUpWvfdHe++vCh2HyA7mzrUZqqVK2Wigrb75BqZOtUMsl1wSMD6RfGjd2nZYy5eHd96Bo4+GxYtTdPOyZeG002z97LMpuqmIpLNIJasikRUrAWjWDEqU+N+HY7WqHTtC3boB4hIpoMMOs+lWe+5pf7wPPzyFba26dbPrxIn21EJEJBdKVkXyYif1qqtXx4fxaAiApKNDDoEPPrAuAXPnQps28OabKbhxhw5WOLtgAbz/fgpuKCLpTMmqSF7spF51wgQ7Td2kiT1GFUlH++wDH35otazLllkf1sGDk3z2qVQpOP10W6srgIjshpJVkd3xfoedVe/h4YftQxdfrHZVkt5q1LDuAOefb0/l+/e3stJFi5J401hXgEmTYPPmJN5IRNKdklWR3Zk/30b/FC8OBxwA2CHmL7+0DaJzzw0cn0gClC4Njz8Oo0ZByZLwyis25GLixCTdsF07a/q6dKmd8hIR2QUlqyK7E9tVbdrUvqMDjzxiH+raFfbYI1BcIgnmHFx0EXz2mQ1qW77c/oyfdhr8+GOCb1aiBHTubGt1BRCRXChZFdmd7UoA/vwz/r21T59AMYkk0YEHwiefwK232gOFyZPtZ7XrrrO61oSJlQK88AJs3JjANxaRTFKgZNU51yDRgYhE1nbJ6rhxsH69dbFq0yZgXCJJVLIk3HEHfPUVnHgibNoEQ4dai7brroOFCxNwk7ZtrWD2zz9T1IZARNJRQXdW5zjn3nbO9XTOlU5oRCJRkyNZ9T5eAtCnjw5WSebbd18bKfzqq1YasGZNPGnt2tVyzAK3Ss3Kgi5dbK2uACKyC84XoD+Jc6450As4CygJPAeM9t6ndHaec64isHLlypVUrFgxlbeWomLRIthrL8tKV69m2mflOPpomxa5YIGNrBQpKryHKVPgrrus3VVMrVrWiapjR9ssLV48H2/64YdwxBFQoQIsWfK/unARyRyrVq2iUqVKAJW896vy+/UF2ln13n/hvb8SqAmcD+wFvO+c+9o5d41zbs+CvK9I5MT6q+67L5Qr979d1R49lKhK0eMcnHSSDRKYMcNGDFeqZA0zRo60A/5VqljZwODB8Pbb9oQ/V23aQO3aNmVjypSU/HOISHop0M7qDm/iXCngEmAQttO6EZgI3OC9T0Rl067uq51VSa677oKbb4YePVhy33hq1bLavRkzoEWL0MGJhLduHbz1Frz4Irz88s4PYNWrZ8MzGja0V6NGdm3QAMqUwYpghw6FM89UOYBIBirszmp+HtbswDnXCttZ7QasAYYCo4FawG3Av4FDC3MPkaBy1KuOHWuJ6qGHKlEViSlTBk491V7Z2XYga9o0ePdd+9/n55/jr52pXh3q7Xkb9WhJvRd+p+6wDdRrUop69awutmzZ1P2ziEg0FbRm9RqsZrUJ8BrwOPCa935Ljs+pBfzsvS9UQrybOLSzKslVvz78/DNbpr5NowuOZt48GDMGevUKHZhIeli+3BLYOXPs9dNP9pozB1blYX+lTh1rpXXggdC6NRx+OFSrlvy4RSRxCruzWtBk9UdgDPDErh7zO+dKAmd575/M9w3yHoeSVUme5cttwg7wxr9W0+GM8lSqZAertNsjUjje2/9iv/xir58fepWf35rDzzXa8Ev11sybt+tktnFjq4s95RQ70FWyZGpjF5H8CVUG0B74NedOKoBzzgG1vfe/eu83AklLVEWSLna4qkEDxkwsD0DPnkpURRLBOftZsGrVrS2MG9WBZqfA8pLw/RKoVInly+Gbb2xnduZM+Ogj+/Xs2fYaPtwOeHXrBuefD4cconZyIpmooH1WfwL+tpOP7wHMK3g4IhGytV51WdO2vPiifah374DxiGSyAw6A/fe3SVYvvQTYKOP/+z/rOvDYY/D117Yb+8ILlpxWrw4rV8KoUVYi0Lw5TJgAmzcH/mcRkYQqaLK6q59dywPrC/ieItGyNVmd4M9i40b7RqiDVSJJ4lx8/OrEibv8tCpVrKfr6NFWkvPWW9ZKrnRpmDXL1k2awNixhRhWICKRkq+aVefcfVuXVwKPAWtz/HYW0BrI9t4fkbAIc49HNauSPE2a4GfPpkWDlXw5tyIjR8Jll4UOSiSDff897LefTRVYvNi2VvPozz/hoYdg2DD44w/7WMuWMGKEHcoSkXBSPRSgxdaXAw7M8esWwL7Al8B5+Q1CJHJWrYLZs5lJC76cW5GSJaF799BBiWS4ffeFZs3sOX6s9iaPqlSBm26yFln33GNDOz7/3IZjXXihzRwQkfSUr2TVe3+M9/4Y7ODUibFfb3118N5f7L3/MTmhiqTQl18CMKbcFYA9dszHJo+IFFQeSgFyU64cXHutHcDq3duqCx5/3Mp4co6IFZH0UdBxq70Kso0rkjZmzmQdpXl6Y2dAB6tEUubMM+06dSosXVrgt6le3ZLUt9+2Xq1z59phrUGDrG2WiKSPPCerzrkXttaIxta7fCUvXJEUmTGDl+jIik3lqVPHZp6LSAo0amS9rLKz7dh/IR11lB28OvtsO3B14412CGvdugTEKiIpkZ+d1ZWAz7HO7SWS3mbMYDS2ndqrFxQraN8MEcm/QpYCbK9SJRg3Dh5+2M5uPfOMDRNYtCghby8iSVagCVZRoW4AkhTr1vFz+QOov+UnAObNg3r1woYkUqTMmwcNGthPiQsW2DP9BHnnHejcGZYts03cqVOtTEBEkifV3QAAcM6Vcc6VzfHrus65q5xzxxfk/UQi5auvGLvlHADatfNKVEVSrX59OPRQe27//PMJfeujj4ZPPrEfQOfMsTrWH3UsWCTSCvpw89/AOQDOucrAdKAf8G/nXN8ExSYSRPZnMxlLLwB699bsRpEgElwKkFPDhvDeezY84NdfrSRACatIdBU0WT0YeG/rujOwCKiLJbBXJCAukWCmvrKO36hD5dLr6NgxdDQiRVSXLnZ97z0rBUiwWrXg3XetreuiRdC+Pfz+e8JvIyIJUNBktSwQa7F8PPCC934L8DGWtIqkrTEf7w9Aj6MXUKZM4GBEiqratW30lPcwaVJSblGtGrz5JuyzD/zyC3ToAMuXJ+VWIlIIBU1W5wAdnXO1gQ7AG1s/Xg1Q/1VJW38u3shLf7YFoFff0oGjESniklgKEFOtGrzxBtSsCd98AyefrLZWIlFT0GT1TmAo8DPwiff+o60fPx6YmYC4RIKY9MBiNlCaA4p9y8Gn1AwdjkjR1rmzjaD68EP47bek3aZePUtYq1SBjz+28axp3ChHJOMUdILV80AdoBVwQo7fmgpcnYC4RIIY92xJAM6p/x6umA5XiQRVs6Yd14ek7q4CNG0K//oXZGXB00/DPfck9XYikg8FbnXuvV/kvZ+5tVY19rHp3vvvExOaSGr99BN8MKc6xcimR/slocMREUhJKUDMMcfAiBG2/sc/4JVXkn5LEcmDgvZZLeec+6dz7kPn3Bzn3Nycr0QHKZIKTz1l1/a8Sc22jcIGIyLmjDNsOMD06TYsIMn69oWLL7YygJ49U3JLEdmNgu6sPg70xtpXPQAM3+4lkla8h3HjrEjtHMbZbHIRCa96devkDynZXXXOdlcPOwxWroRu3WDjxqTfVkRyUaBxq865FcDJ3vsPEh9SvuLQuFVJiPfft9K48qxmcbmGlF21yHZzRCS8Rx+17c6DD4bPP0/JLX/5BZo3hxUr4NprVcMqUhhBxq0CfwLqRicZY9w4u3ZhEmVbNFGiKhIlnTrZyacZM2xGagrUrQtjx9p66FB47bWU3FZEdqKg35FvAe50zpVNZDAiIaxbF3+6eA7joEWLsAGJyLb+9jdo187Wzz2Xstt27AiXX27r88+HZctSdmsRyaGgyWo/bBjAYufcV865GTlfCYxPJOkmT7batDqlFtGWd1WvKhJFKewKkNPdd8P++8PixXDppSm9tYhsVbyAX/dSQqMQCShWAnC2f4pieCWrIlHUsaPVrc6aBd9/D/vum5Lbli5tf0e0bm2bup06wZlnpuTWIrJVgQ5YRYUOWElhLV4Me+8N2dnwPU1oUuoXWL0aSpQIHZqIbO/kk6149I474NZbU3rrW2+Ff/4Tqla1sazVq6f09iJpLdQBK5xzlZ1zFzjnBjnn9tj6sYOdc3sX9D1FUm3CBEtUW++zjCbMhmbNlKiKRFWsFCCFdasxN99s3QGWLYPLLkv57UWKtIIOBWgGzAZuAK4FKm/9rU7AoMSEJpJ8sRKAc+q/bwuVAIhE19//DiVLwrffwtdfp/TWJUtad4CsLHj+eXUHEEmlgu6s3gc84b3fB1if4+OvAW0LHZVICsyaBV98YRupXTduHV+lZFUkuipVghNOsHWKD1qB7axedZWtL7kE1qxJeQgiRVJBk9VDgFE7+fjvQI2ChyOSOrHxqqec4qn69TT7hZJVkWjLWQoQ4MzF7bdDnTo2NODOO1N+e5EiqaDJ6gZgZyeaGgNLCx6OSGps2QLPPGPrnh3+gD/+gOLF4YADwgYmIrk79VQ7oj97Nnz5ZcpvX748PPigre+9157QiEhyFTRZfRm41TkXO4ninXN1gCHAv/L6Js65vs65Wc65VVtfHznnTixgTCJ59u678Pvv9lTxpMof2gebNrVvgiISXRUqwEkn2TpAKQDAKadYC6vsbLjiiiAbvCJFSmGGApTHdlHLANOAOcBq4KZ8vM984B9AS6AV8F/g3865pgWMSyRPJkywa+fOUPqrT+0XLVuGC0hE8i5wKQDAfffZz7bTptmBKxFJngIlq977ld779sDJwBXAA8BJ3vujvPd5Ljn33k/23r/mvf/Rez/be38T8BfQZmef75wr5ZyrGHsBFQoSvxRtGzbApEm27t4d+Pxz+0WrVsFiEpF8OPlkKFsW5s6N//+bYnXrwg032Praa2Ht2iBhiBQJ+U5WnXPFnHPnO+dewQ5Z9QWOBGo651xBA3HOZTnnugHlgI928Wn9gZU5XvMLej8puqZMgRUroGZNOKqtj3+z086qSHooV86exUOwUgCA66+H2rXh11/hnnuChSGS8fKVrG5NRl8GHgf2Br4CvgHqAk8AL+Y3AOfcgc65v7BDW48Ap3vvv93Fpw8CKuV41crv/URiJQBnnQVZC36DpUvtcFWzZmEDE5G8i5UCTJwYrBSgbFkYOtTWgwdb0ioiiZffndXzsD6q7bz3Lbz3Z3nvu3nvDwKOA451zp2Tz/f8AWgOtAYeBp50zu2/s0/03m/w3q+KvbAaWZE8W7UKJk+29TYlADpcJZJeTjzRjub/8gt88kmwMLp0gbZtYf16uOWWYGGIZLT8JqtnAQO9929v/xve+/8Cg4Ee+XlD7/1G7/0c7/3n3vv+wJfAlfmMSyRPXnzRvqnsuy+0aIFKAETSVZkycNpptg5YCuBcfHf1qads0IiIJFZ+k9VmwH9y+f0pwEEFDwewmEoV8j1Edurpp+3avbt9k9HhKpE0duaZdp040ZonB3LIIdCtm1UjXH99sDBEMlZ+k9U9gMW5/P5ioEpe38w5N8g519Y5V29r7eog4Gjg6XzGJbJbixbB1Km27t4d+87y2Wf2Ae2siqSfE06AihWtafKHHwYNZeBAG9385pvwxhtBQxHJOPlNVrOAzbn8fjZQPB/vVw0Yh9WtTsXGuHbw3r+Zz7hEduu552zzpU0baNgQ+O23+OQqHa4SST+lSsHpp9s6NpIukPr14bLLbH3ddUE3ekUyTn4SSwAHPOGc27CL38/X43vvfe983l+kwHKWAAA6XCWSCbp3hyeftFKAYcNsezOQm26CMWNsBOvEiVYaICKFl9+d1SeBJWzb6zTnawm2UyoSKT/+CJ9+CllZ8TI3Ha4SyQDHHgvVqtlTkrfeChpK1arQr5+tb7/dxrGKSOHla2fVe98rWYGIJFOst+pxx0H16ls/GKtX1eEqkfRVvLj1XB050h6fnHhi0HCuvNI2eH/4wf7eOfvsoOGIZIQCjVsVSSfex5PVHj1yfFA7qyKZIVbb89JLsCbPE7+TomJFq1kFuOMO2LQpaDgiGUHJqmS8zz+H2bOtLWPHjls/qMNVIpmjdWs74bRmTXzqR0CXXQZ77gk//QTjVBgnUmhKViXjxQ5WnXYaVKiw9YM6XCWSOZyL767GHqMEVL48/OMftv7nP2HjxrDxiKQ7JauS0bKz4dlnbd0j52w19VcVySyxZHXKFFi2LGwsQN++UKOGTYMdMyZ0NCLpTcmqZLS337ZhAHvsAR065PgNTa4SySz77w/Nm8PmzfCvf4WOhjJl4MYbbT1ggI15FpGCUbIqGS32RLBLFyhZcusHdbhKJDNFqBQA4MILoVYtG7D16KOhoxFJX0pWJWOtXx/fYNmmBECHq0QyU6wL/7vv2v/ngZUuDTffbOuBA2Ht2rDxiKQrJauSsV59FVatgtq14YgjcvyGDleJZKbataFtW3t6EitWD6xXL6hXDxYvhoceCh2NSHpSsioZK+d41WI5/6TrcJVI5opYKUDJknDLLbYeOhTWrQsbj0g6UrIqGWnFCttZhfj3rv/R4SqRzNW5s5X4fPEFfPtt6GgAm2JVp47trqozgEj+KVmVjPTCC9bbsGnT7cpSdbhKJLNVrQonnGDr2OOVwEqUgOuvt/Xdd2uqlUh+KVmVjLTDeNUYHa4SyXw9e9p1/HjYsiVsLFudfz5Urw6//hqZCgWRtKFkVTLOwoXw3//aOnY4+H9i9ao6XCWSuU47DSpVsszw3XdDRwNY39VrrrH1oEE2sERE8kbJqmSciRPtaf9hh9m48G1Mn27XQw9NeVwikiJlysCZZ9p63LiwseTQpw9Urgw//AAvvhg6GpH0oWRVMk7sEdsOB6sAPvnErq1bpyweEQngnHPsOmlSZBqcVqwIl19u64ED7YdqEdk9JauSUebMsc3TYsVsatU2srPjZQDaWRXJbEccYY9W/voLXnopdDT/c+WVUK4czJwJr78eOhqR9KBkVTJKrA/4ccfZYYZtfP+9feMqV87miItI5nIuvrsaoVKAqlXh4ottPXBg2FhE0oWSVckY3m87CGAHsRKAVq0gKytlcYlIIGefbdc334QFC8LGkkO/fjYs4L337CUiuVOyKhnjyy9t87RUKTj99J18gg5XiRQtDRtaOcCWLZHqF1Wzpo1hBe2uiuSFklXJGLHvRaecYgcZdqBkVaToiZUCPPlkpE40XX+91db/5z8wY0boaESiTcmqZIQtW+L1qjstAVi7FmbNsrU6AYgUHV262OOWr7+2xy8R0aBBvA/03XeHjUUk6pSsSkb44AMbTlWxIpx00k4+YeZM6wZQowbUqpXy+EQkkCpVbEgAROqgFcRHsE6aBPPmhY1FJMqUrEpGiJUAdOq0i8FUOUsAnEtZXCISAbFSgKefhs2bw8aSw0EHwfHH25Oh++8PHY1IdClZlbS3aZPtTMAuSgBAwwBEirIOHWDPPWHJEnjjjdDRbOO66+w6ejQsWxY2FpGoUrIqae/NN+0v+erV4ZhjdvFJOlwlUnSVKBH/SXbs2LCxbKddO2je3MrqH3oodDQi0aRkVdJerATgzDOhePGdfMLSpfGCsFatUhaXiERIrFfUv/9tfydEhHPx2tWRI2HdurDxiESRklVJa2vXxicp7rIEILaruu++ULlySuISkYg56CBo2dLqhsaPDx3NNrp0gbp1LYeO2BkwkUhQsippbfJkWLPGRoDvshxVJQAiAtC7t11Hj45Uz9XixeHqq209dKg1LhGROCWrktZiJQBnnZXLIf9YsqrDVSJF21lnQZky8M038UOXEdG7t3XZmjPHKhVEJE7JqqSt5cthyhRb77IEwHvtrIqIqVwZOne29ejRYWPZTvnycMkltr7nnkht/IoEp2RV0tYLL1j5WbNm0LTpLj7pp58sqy1Vyj5RRIq2WCnAs8/CX3+FjWU7l19uf1V9/LENOhERo2RV0lbOEoBdiu2qtmgBJUsmPSYRibi2baFRI0tUJ04MHc02qlePzy/QCFaROCWrkpZ+/x3eecfWsfnaOxWrS1MJgIiAFbfnPGgVMf36WYiTJ8N334WORiQalKxKWnruOavpOuIIqFcvl09UvaqIbO/ccyErCz78MHIZYZMm8Pe/2/ree8PGIhIVSlYlLT3zjF1zLQHYuBFmzrS1OgGISMxee8HJJ9s6grursRGsTz0FCxeGjUUkCpSsStqZPRs++8w2Rrp0yeUTZ82CDRusH0zDhimLT0TSQKwUYNw4+8E2Qg4/3F4bN8KIEaGjEQlPyaqkndiuavv2UK1aLp+YswRgl01YRaRIOukkqFHDxka98kroaHYQ2119+GFYvTpsLCKhKVmVtOJ9PFndZW/VmI8+suthhyU1JhFJQ8WLw3nn2fqxx4KGsjOnnQaNG8PKlfD446GjEQlLyaqklZkz4YcfoHRp6NhxN5/84Yd2VbIqIjsTKwV4/XXryRwhxYrBtdfa+v77rae0SFGlZFXSSqy36qmnQoUKuXziokUwd649/tfhKhHZmUaNoEMHe2QzalToaHZw9tnWe/W336wDikhRpWRV0saWLTZ0BvJRAnDAAVCpUlLjEpE0FptxOno0rFsXNpbtlC4NV1xha41glaJMyaqkjffes2EAlSrBiSfu5pNjJQCHH570uEQkjZ18MtSpY2OZJ00KHc0O+vaFcuWsuckbb4SORiQMJauSNp56yq5nnGHzs3OlZFVE8iIrC/r0sfVDD4WNZSeqVIELL7T1PfeEjUUklKDJqnOuv3PuU+fcaufcEufcS865JiFjkmhavz6+6XH22bv55A0b4PPPba1kVUR2p3dvKFHCxjPH/u6IkKuuspx66lSYMSN0NCKpF3pn9SjgQaAN0B4oAbzhnCsXNCqJnMmTYdUqqF0b2rbdzSfPnGkJ6557ahiAiOxetWrxCSMRQ4+A8gAAIABJREFU3F2tWxe6dbO1dlelKAqarHrvT/DeP+G9/8Z7/yVwHlAHaBkyLomeWAlAjx7W0iVXOVtWaRiAiORF7KDVhAnw559hY9mJ2JCAiRNh3rywsYikWuid1e3Fjm0v39lvOudKOecqxl5Abs2LJEMsXQpTpth6tyUAoHpVEcm/ww+HZs2s5uiJJ0JHs4ODDoLjj7euKPffHzoakdSKTLLqnCsGDAM+8N5/vYtP6w+szPGan6LwJKDnnoPNm+Hgg2H//Xfzyd4rWRWR/HMOLr3U1g8/bFlhxMR2V0ePhmXLwsYikkqRSVax2tUDgG65fM4gbPc19qqVgrgksPHj7ZqnXdV582DhQjss0apVUuMSkQzTvTtUrAg//minmSKmXTto0QLWro1kaa1I0kQiWXXOPQCcAhzjvd/lbqn3foP3flXsBaxOWZASxOzZdkA3KwvOOisPX/D++3Zt1QrKlElqbCKSYcqXh3PPtfWDD4aNZSeci++ujhwZuRkGIkkTunWV25qong4c671X2bhsI7ar2r69jR3crffes+v//V/SYhKRDNa3r11ffhl++ilsLDvRpYt1B1i6FJ58MnQ0IqkRemf1QaAn0B1Y7ZyrsfWlLTHB+3yWAEA8WT3yyKTEJCIZbr/94IQT7C+gESNCR7OD4sXhmmtsPXQoZGeHjUckFZwPOGzYOberm/fy3j+Rh6+vCKxcuXIlFStWTGhsEt7779sGafnysHgxlC27my9YsiS+/bpsGeyxR9JjFJEM9OabdvS+XDn47TcbIxUha9bEJ8Q+/7xN9ROJslWrVlGpUiWASlvLOPMldJ9Vt4vXEyHjkmiI7aqecUYeElWI16secIASVREpuOOOgwMPtKzwscdCR7ODcuXibWHvvts2gUUyWegyAJGd2rDBml9DAUoAVK8qIoXhXPxZ+4gRsGlT2Hh24vLLoVQpmD49/lefSKZSsiqR9OqrNkRm773h6KPz+EWxnVUlqyJSWGedBTVqwO+/x39yjpBq1eC882ytEayS6ZSsSiTFxqt2725tq3brr79g5kxb63CViBRWqVJw2WW2vu++SD5r79fPNoFfeQW++SZ0NCLJo2RVImfZMttZhXyUAHz0kR2LrVsXatdOWmwiUoT06WP9mmfMgHffDR3NDvbZBzp1svWQIWFjEUkmJasSORMmWIlYixZ2xiFPVK8qIolWtWr8Wfu99wYNZVf+8Q+7TphgA/xEMpGSVYmcMWPsev75+fiiadPsqmRVRBLpqqvsOnmyjdSLmFatrMtWdrZqVyVzKVmVSJk5E774AkqWtHrVPFm3Dj7+2NbHHJO02ESkCGrcGE491dbDhoWNZRduvNGuY8bAwoVhYxFJBiWrEiljx9q1Y8d8tEr96CPYuNFaBzRqlLTYRKSI6tfPrk88YcNHIqZtWzj8cGv5d//9oaMRSTwlqxIZGzbA00/bulevfHzh22/b9eij7WisiEgitW0LhxxiT3EimA06F99dffhhm2wlkkmUrEpkvPyy/SW7997Qvn0+vvCdd+yqEgARSQbn4Oabbf3AA5HMBk86CQ46yLr4jRwZOhqRxFKyKpERO1h17rl57K0KsHYtfPKJrfM8PUBEJJ9OPTWeDQ4fHjqaHeTcXR0+HFavDhuPSCIpWZVImD8f3njD1vkqAfjwQ+tzVbs2NGiQlNhERLbZXR0xAlauDBvPTpxxhvVe/fNPePTR0NGIJI6SVYmEceNgyxbrPJWvM1KqVxWRVOnUCfbbD1asgAcfDB3NDrKy4n1Xhw6F9evDxiOSKEpWJTjv410A8tVbFVSvKiKpU6wY3HSTre+7z0oCIqZnT3vQtGiRNS8QyQRKViW499+HOXOgXDno3DkfX7hmDUyfbmvVq4pIKnTtao9/li2DUaNCR7ODkiXhuutsPWSIVUmJpDslqxJcbFe1a1coXz4fX/jee7B5M9StC/XrJyU2EZFtFC8O/fvb+p57rJ1VxPTuDdWqwc8/w1NPhY5GpPCUrEpQf/0FEyfaOt8lAG++adfjjktoTCIiuTr7bPshefFiePzx0NHsoGxZuP56W//zn9pdlfSnZFWCmjTJnuY3bmwTWPIllqzmqymriEghlSgRP8k0ZIhNNImYvn2henXbXVXtqqQ7JasSVGxTolevfB7mX7QIvvrK1u3aJTwuEZFc9eoFNWvC77/DY4+FjmYHZcvCDTfYesAAm0gtkq6UrEows2ZZm9TixW0QQL5MnWrXFi3gb39LeGwiIrkqVSred3XAAHtEFDF9+kCNGvDrr/GzASLpSMmqBPPII3bt2BH22iufX6wSABEJrXdvO9y5eLGNYY2YMmXiZ8HuuiuS1QoieaJkVYJYvTp+SrVv33x+sfdKVkUkvJIl4Y47bD1kiA0LiJiLLrJqhd9+i4+0Fkk3SlYliAkTrBNA48YF6Of/3XewYAGULg1HHpmU+ERE8qR7d9h/f5txOnRo6Gh2ULp0fHd14EDtrkp6UrIqKec9PPywrfv0KcCU1Niu6pFH2t/EIiKhZGVZzSrA/ffbD9IRc8EFsPfeMH9+JDttieyWklVJuY8/hi+/tDwz3werQCUAIhItHTvCYYfB2rVw++2ho9lB6dJw4422HjgQ1q8PG49IfilZlZSLHazq2hX22COfX7x+Pfz3v7bu0CGhcYmIFIhzNs0KYPRo+OabsPHsRO/eULu2bfxGsNOWSK6UrEpKLVsGzz1n63wfrAKYNs3GG9asCc2aJTQ2EZECO+IIOP102LIl3uA0Qv6/vfuOk6o6/zj+eWBZivQuUYqCiohBsUYFBexGxaD4019iiQ2DNSrCT6Niwdg1VuwlitggWCABoxJRVFAQSwRRbDTpRVjYPb8/nrvuMC7Llpm9Mzvf9+s1r7kzc+fOmbP33n3m3HOeU7cu/N//+fJ11/mYAZFsoWBVqtVjj3kH/x49YK+9KrGB117z+yOOqERnVxGRNLrxRu/D+sorJVeAMsjpp0PnzrBoEdx6a9ylESk/BatSbYqK4IEHfHnQoErGmq++6vdHHJGycomIpMQOO/ioUYALL4SNG+MtT5I6dbzPKnivhYUL4y2PSHkpWJVq8/rrMHs2NGrk2V4qbPZsv9WpoylWRSQzXXMNNGvm00GPHBl3aX5hwAC/qrVmDQwfHndpRMpHwapUm+J0Vb//PTRsWIkNFHcBOOAAaNw4ZeUSEUmZFi3g2mt9+corYenSeMuTxMznLwCPpWfPjrc8IuWhYFWqxfffw9ixvlypgVVQ0gXg8MNTUiYRkbQ4+2zYZRcPVK+6Ku7S/MKBB3pPqo0bSwZdiWQyBatSLUaOhMJCHzC7yy6V2MDatfDGG76s/qoiksny8uDOO3353nvhww/jLU8pbrzRW1mfew7eey/u0oiUTcGqpN26dSVdAM4/v5IbmTTJ0wh06ABdu6asbCIiadGnjyeTLiryy0lFRXGXaBPdu8Mf/uDLl13mMwuKZCoFq5J2zzwDixd7QurjjqvkRor7EBx1lFJWiUh2uO02H1E6dWpGZuIfPtzzr775ZsmQAJFMpGBV0ioEuOMOXx482K+OVVhhIYwb58vHHpuysomIpFW7dp6BH+Dyyz3BaQZp377kateQIX6qFclEClYlrd58E2bOhAYN4IwzKrmRd9/1k3yTJtC7d0rLJyKSVuee67OgLF/uuVczzOWXQ9OmMGsWPP543KURKZ2CVUmr4lbVU06B5s0ruZExY/z+qKM8x6qISLbIy/MuALVqeZ+ol1+Ou0SbaN4crrjCl4cNg5Ur4y2PSGkUrErazJkD//iHL1d6YFUIJcGqugCISDbaYw/48599+ZxzYMWKeMuT5LzzoEsXn9Hq+uvjLo3ILylYlbS57TaPNQ8/HHbaqZIb+ewzj3rz8+HQQ1NaPhGRanP11dC5syedvvTSuEuzifx8uP12X779dk0UIJlHwaqkxeLF8OijvnzZZVXYUHGrar9+PqpWRCQbNWgADz/syw8+CBMmxFueJEccAYcdBhs2wCWXxF0akU0pWJW0uPtuz6+6555VHBOlLgAiUlP06uXX3AFOOy2jpmI186theXnefWv8+LhLJFJCwaqk3Jo1HqyCX+2qdFrUr7+G99/3gQlHH52q4omIxOfGG2HHHWH+fJ8sIIOy8XftWjK+4LzzvMFBJBMoWJWUe/RRbzDYbrsqTAIAMHq03/fuDW3apKRsIiKxatAAnnrKmzBHj4ann467RJu46irYemsfKnDzzXGXRsQpWJWU2rgRbr3Vly+5BGrXrsLGnn3W7wcOrHK5REQyxh57wF/+4suDBnlkmCEaN/buAAA33ABffRVveURAwaqk2NNP+9X7Vq3g1FOrsKE5c2D6dI92q9Q8KyKSgYYO9T6sq1b5D/L16+Mu0c8GDoQ+fbwbwPnnZ1RPBclRsQarZtbLzMaZ2Q9mFsxMo2iyWGGh/xIHTylYv34VNlbcBaBPH498RURqkrw8/3XfooX/MB8yJO4S/cwM7rnH52B5+WV48cW4SyS5Lu6W1a2AGcCfYi6HpMDzz8N//wvNmvkMg1WiLgAiUtP96lclc5zeeSc891y85Umw004+FSvA4ME+W6xIXGINVkMIr4UQrgghvFSe9c2srpk1Lr4BSryZIYqK4LrrfPnCC6uYEvXzz2HmTG956N8/JeUTEclIRx5Zkoz6tNNg1qx4y5Ng2DBPXLBgQUY1/EoOirtltaKGAisSbt/FWxwpNnasn2MbN67C1KrFnnnG7w8+2CeuFhGpya6/Hvr29bx//ftnTDNmvXowcqQvjxwJb70Vb3kkd2VbsDoCaJJw2ybe4gh45/viVtXBg6Fp0ypsrKgInnjCl//3f6tcNhGRjJeXB6NGQfv2Prj0pJM8tUoG6NULzjzTl888E376Kd7ySG7KqmA1hLA+hLCy+AasirtMAi+95OMDGjaEiy6q4sb+8x9PJ9CokWatEpHc0bKlj2SqVw9ee81HqWaIm26Cdu3giy/gyivjLo3koqwKViXzFBaWpAu88EI/31ZJ8WCDE07w5NkiIrmiZ0948klfvuuukqkAY9a0aUl3gNtug7ffjrc8knsUrEqVjBoFn3ziJ7MqNwSsXVsyGvaUU6pcNhGRrDNgAIwY4csXXADjxsVbnsiRR/r4rxA8h/aaNXGXSHJJ3HlWG5pZDzPrET3VKXrcPs5ySfls2ABXX+3Ll15axb6q4P0JVq2CTp1gv/2qWjwRkew0ZAicfrr34T/hBJg8Oe4SAd6qus023q22OK2VSHWIu2V1D+DD6AZwW7Q8PLYSSbk9/riftFq1SkEGgOINAvzhD1Ar7l1TRCQmZnD//XDUUT6N1G9/CzNmxF0qmjaFhx7y5bvv9q61ItXBQhbPoxblWl2xYsUKGjduHHdxcsratdClC/zwg//arvLAqnnzvEU1BPjyS9huu5SUU0Qka/30ExxyiA88bd0a3ngDunaNu1Scfz787W/Qpo2nxG7dOu4SSaZbuXIlTZo0AWgSDZCvEDVfSaXcdpsHqh07wqBBKdjggw96oNq3rwJVERHwOavHjYMePWDRIjjoIJ80JWZ//St06wYLF8If/+inbpF0UrAqFbZwoZ+sAG64wTOtVElBQcm1pZREviIiNUTTpjBxIvz6137yzYCAtX59ePppyM+Hl1+Ge++NtTiSAxSsSoVdfTWsXg177gkDB6Zgg2PG+El4663h6KNTsEERkRqkRQsPWLt397lPDzjAk1vHaNddSxotLr449uJIDadgVSrk00/9ij3ALbekaBzU/ff7/RlnQJ06KdigiEgN07IlvP6652L98UdvYY15/tMLLvD2hYICOP54WLEi1uJIDaZgVcotBB9IVVjok0v16pWCjX7+Ofz73x71nnFGCjYoIlJDFQesvXrBypVw6KElualjYAaPPQYdOsDcuX4KV/9VSQcFq1JuY8bAP//p/ZRuvjlFG73vPr8/8kifF1tERDavcWMYP97TWa1b53lYb7optiixWTMYPdovij3/PNxxRyzFkBpOwaqUy9q1Pp0q+AQAnTunYKPLlsHDD/vy4MEp2KCISA6oX98nUSlOcF08icC6dbEUZ6+94NZbffnSS73xVySVFKxKuYwYAd98442fw4alaKMPPOBz9nXvDgcfnKKNiojkgNq14c47/Varll+PP+AAP1HHYPBgn8+lsNAbe7/+OpZiSA2lYFW2aM4cv8oEcPvt0KBBCja6fj3cdZcvX3KJd34SEZGKOf98mDABmjeHDz7wAVgxTC1VPOlWz56wZAn07+9tESKpoGBVyhQCnH22j/Y85BA/AaXEM8/A/PnQrh2ceGKKNioikoP69YNp02C33TxTwBFHeCNAQUG1FqN+fXjxRZ+C+6OP4Pe/h6Kiai2C1FAKVqVMjzzi/Y/q1/fEzylpAA2hpIPTBRf4iC0REam8jh1hypSS/v+33gp77w0fflitxWjf3gPW/HzvVnv55dX68VJDKViVzZo/H/78Z18ePhy23z5FG375ZZg1Cxo2hLPOStFGRURyXL168Le/eeqW5s29eXPPPX2gQTUOvtp/f3j0UV+++eaS3NwilaVgVTZr8GBP8tyzZ0kmgCoLAa66ypf/9CefSlBERFLnmGN8Bpfjj/cRTyNGeBeBKVOqrQgnnQTXXOPLgwZ5G4VIZSlYlVKNGuWXcvLyPLtUXl6KNjx2rF+WatjQ+1SJiEjqtWnjCVBffBHatvUJWPbf3xsJliypliJceSWcckpJhoBqjJWlhlGwKr/w7bf+Sxj86tGvf52iDRcVwdVX+/L55/tsLCIikj79+3sr62mn+ZWte+/1RNm33572AVhm3gXgyCPhp5/8ftastH6k1FAKVmUTRUX+S3j5ck/0fMUVKdz4mDEwYwY0alTSGVZERNKrWTMfLTtpEuy6q5/gL74YunXz83IaZ7+qU8cbePfd1z/2kENg9uy0fZzUUApWZRN33AH//rfnUn3qKT/RpMTGjX5NCDwDQPPmKdqwiIiUS58+MH26N3e2aeNJtPv3hwMPhLfeStvHNmjgfVZ32cUH7vbpA199lbaPkxpIwar87IMPYOhQX779dujSJYUbHznSL0W1aKFWVRGRuNSuDWec4c2bw4ZB3boeqPbuDX37wn/+k5aPbd7cG3a7doXvvoODDoJ589LyUVIDKVgVAJYuhQEDvAvTscfCmWemcOPLlsFf/uLLw4crA4CISNwaNYLrr4cvvoBzzvHLaK+/7lO29uuXlqC1dWsPWLt08UC1d2/48suUf4zUQApWhaIin9N53jzPpfrooyme/fS663z06c47K6+qiEgmad8e7rvPW1rPPtuD1kmTPGjdbz/P7F9YmLKP23prj4k7d/b/Ob16eaICkbIoWBVuvBFeecXzST//fIobPr/4wpNUg8+okrIcWCIikjIdOsD993vQetZZPgXVlClw3HF+7f7++31Ifwpss433POjWDX74wQPWap5oS7KMgtUcN3ZsyYj/e+6BHj1SuPEQ/Jf6hg1w+OFw2GEp3LiIiKRchw7wwAPe7DlsmGcSmD3b8xm2b+/pBxcvrvLHbL01vPEG7L67b653b/jXv6q8WamhFKzmsA8/9FlGQvDz0Omnp/gDHnnEz0YNGngkLCIi2aFtW+/T+s03cNdd0LEj/PijT0vVvr03RFQxaWrLlt4l4KCDYNUqOOIIz0IjkkzBao6aPx+OPhrWroWDD4Y770zDBxTPUHXttdCpU4o/QERE0q5hQzjvPG9dHT0a9twT1q3zDC/du3uk+dJLnp6wEpo0gddegxNP9E38/vceDxcVpfh7SFazkMZkwOlmZo2BFStWrKBx48ZxFydrrFjhafU++gh22gneeSfF/VRD8NQCL74Ie+zhH6C+qiIi2S8EmDzZxyIkDr5q3x7OPdfTYrVoUeHNFhXBkCFwyy3+eMAAeOwx2Gqr1BVd4rNy5UqaNGkC0CSEsLKi71ewmmPWrvWuo5MnexqRt9/2UZkp9fDDfsLKy/PkrSmbr1VERDLGt996JoEHH/QuAuAjdU86yVtjKzEI4pFHPJPWhg2w227wwgu6MFcTVDVYVTeAHLJhAxx/vAeqTZrAhAlpCFQ/+wzOP9+Xr79egaqISE217bZwww0etD76qI+WWrfOI87ddvP0V6NH+z+fcjr9dO/H2qqVj6vYfXcYNy6N30GygoLVHFFQ4H2CXn0V6tf3VFUpHfkPfpL6n//x5tt+/Ur6rIqISM1Vrx6ceqpfSXv7bf9nk5fnEwsMHOhNo9ddB4sWlWtz++/vm9p7b1i+3MdXDBni/8ckN6kbQA5Yt877/7zyis+sN2ZMGrJIheCX/h95xH8Sz5jhuUlERCT3/PCDp8B64AFYuNCfy8/34PW883yg1hYUFMCll3oyAoCePeHvf4cdd0xjuSUt1A1AyrRmDfz2tx6o1q/vl1PSku709ts9UK1VC558UoGqiEgua9fOh/XPm+f5qPbe26PPJ5+Evfbyx48/7q0pm5Gf75lqnn/e071Om+bdAu6/X9kCco1aVmuw+fM9UJ02zbOPvPyyJ15OuVdf9Q8qKvKg9cIL0/AhIiKS1d5/37MIPPtsyTX9Fi28o+qgQWWOpPruOzjlFO/PCp7R5qGHfIpwyXxqWZVSzZoF++zjgWrLlj4zSFoC1ffe88s6RUXeDeCCC9LwISIikvX23BOeeMIHZI0Y4emuliyBm2/2qPPII73xo5Rm02228f9jd9zh88y88Yaneb3pJvVlzQVqWa2BxozxX6ArV8IOO/ixn5ZfnzNm+M/b5cuhTx/P7Jyfn4YPEhGRGqew0Puo3Xuvp6cp1qlTybSKpeRsnTsXzjyzpJW1a1e4+27/NySZSS2r8rONG+Gyy6B/fw9Ue/XyfPxpCVQ/+8ynvlq+HPbdF8aOVaAqIiLlV7u2D/UfPx6++AIuvthnqPnqK/9n9qtfeZaB997b5G3bbQcTJ3q2rFat/N9R377wu9/5RFtS86hltYb46itvTZ082R9fdBH89a9Qp04aPmzqVL9cs2SJ93afNCnFU2CJiEhOWrsWRo2Ce+6B6dNLnu/Z05tTTzzRE4VHli2DK6/0uQmKijxj1jnnwLBhGuebSTSDVY4HqyH4hFEXXQSrV0OjRj4of8CANH3gq6/6zAJr13r/o1df9U6xIiIiqRKCN4zce++mA7Lq1/f/QX/8o086YAbAJ594LtZXXvHV6tb1VS67DDp0iOk7yM8UrOZwsDpnDgweXNLVZ//9fS7ltFz2DwFuu83PBoWFnv/quec8zYCIiEi6LF7sA7Meftiv+Rfr0sUj0lNOgbZtAe/H+pe/+NwE4C2tf/gDXH65ry7xULCag8Hq2rU+kLJ4FGR+vs9setFF3gUo5VauhNNOgxdf9MenngojR6apj4GIiEgpQoB33/WgddQoTyQO/o/v8MPh5JPh6KMJ9Rvw1ls+adbEib5KrVrep3XQIB8XHDXISjVRsJpDweqGDX6MDh/uOVQBDj3UZ/fYYYc0feikSZ6S6uuvPTi9807vEKQjXURE4rJ6NYwe7f8Up0wpeX6rrXyU8cknQ79+vPtBHtdf73nGi3XpAmed5e0u6sVWPRSs5kCwum6dTwBy443w5Zf+XMeOflX+2GPTFDcuXQpDh3oLKninn2ef9VlHREREMsXnn/s/yaef9tHGxVq2hGOOgeOOY2brftz3cD5PPeVxLvhVyeOO824CffsqoU06KVitwcHqokX+o/Guu2DBAn+uTRu44gofFFm3bho+tKDAh1Vec40PswQ491yPlBs1SsMHioiIpEBxN4G//90bV378seS1Ro3gqKNYfdgAnll2GA882YBp00pebtrU49oBAzwrY1r+v+YwBas1LFgtLPQO4g89BC+95Jf+wWfvuOgiOPtsv8qRcuvWedK6m28u+WW6yy4+Nd6BB6bhA0VERNJk40Z46y0fa/HSS/DDDyWv1a4N++zDtO6n8siPR/PC5FYsXFhyibJxYzjqKO9m17evp3uVqlGwWgOC1Y0bvcvNc8/5beHCktf22stH/J94YprGM82b5823I0eWfHCbNnDttT6oKi8vDR8qIiJSTYqK4P33PXAdN27TjAJAYYvWvN3tLJ4Pv+OFz3fmh8Wb9gfYeWfo189vvXptkuZVyknBapYGq/Pm+dzGEyb45B3FV9wBmjeHgQO9A3iPHmn48B9/9DlZR4/2oZLF+0D79nDppT7FXYMGafhgERGRmM2bV/LPd+JEWLXq55eKMN5t0Jd/tD2TSev2Y9r8doSw6cCQnXby4Rt77eW3XXdVf9ctUbCaBcHqsmXw4Yc+Gcf06d6lJrEPOHiAetRR3oLar1+KW1ELCvyD//Uv+Oc/fQ7WwsKS1/v29U6wxx2ndFQiIpI7Nmzw6VwnT/ZuA2+/7ekaI0tpxr85iIkNjmGi9WPOmna/2ETdutC1K3Tr5q2w3br5rVOnNKWTzEI1Ilg1sz8BlwJtgRnAeSGE98p+V2YFq0VF8P33Plp/zhy//+IL+OgjmDv3l+vn5fkEUAcd5DOX7r13inbq5ct9Ko9PPoFZszxKfv99WL9+0/V2281nATnhhDTNIiAiIpJlCgth5kz4z3/ggw9g2jTvNlBUBMAiWvE+e/Iee/mt1j4sLWpW6qbq1g106GB06uSBa8eOft++vU8F27Zt7gzkyvpg1cwGAk8A5wBTgQuB44EdQwiLtvDeag1Wp0yB2bN9ZP78+SX38+fDN9/8Mh5MtN12sPvufuvZE37zmxRO/vTTTz6M8ZNPNu1EnqhFC+jd23uMH3KIHzUiIiJStjVrvOVp2jT49FO/ffIJLF1KAOayHZ/QjU/oxqfszCd04zO6so76W9x088YbaNuqkK3bQovWtWneOo9mzY3mzaFZM78VLzdp4gOst9rKZ53NpnTnNSFYnQq8H0IYHD2uBXwL/C2EcOM1e3FyAAANtElEQVQW3lutwWq/fp4jf3Py8jwG7NzZb9tv731ZdtvNd7S0CQFatYIlS/zxttuWXIfo3t0j486ds2vPFhERyVQh+DSwn37ql1LnzvX+fdGtcOFivmVbvqLTL27fsQ0LaEsBVWtWbZC3nq3qFPgtfwMNWjekbvOG5Oezya1OHTb73FVXVc846qwOVs0sH1gLDAghjEl4/nGgaQjhmKT168Imf91GwHfffvtttQSrw4d71882bfzWti20bu337dp5035sg+fHj/fW0x139LwbIiIiEo+CAg9mFy70pOnF94sWwYIFhGXLWbYMFizLZ+GqBixc3ZBlRY1YTjOW0YxlNGUZzVhOU5ZHyytpTAH1UlrMJUuqL1jddtttoZLBatx5iVoCtYGFSc8vBHYqZf2hwFXJT0YVICIiIiLl1KJFtX9kIyDrgtWKGgHclvRcc2BpDGXJJo2A74BtgFVbWFdUXxWl+io/1VXFqL7KT3VVMaqviklFfTUCNjOwpmxxB6s/AoVAm6Tn2wALklcOIawHkocxVThCzzVW0ld1VWWa33ON6qtiVF/lp7qqGNVX+amuKkb1VTEpqq9K13Otyr4xFUIIBcA0oG/xc9EAq77AO3GVS0REREQyQ9wtq+CX9R83sw+A9/DUVVsBj8ZaKhERERGJXezBagjhWTNrBQzHJwX4CDgshJA86Eoqbz1wDb/sQiGlU31VjOqr/FRXFaP6Kj/VVcWoviom1vqKPc+qiIiIiMjmxNpnVURERESkLApWRURERCRjKVgVERERkYylYFVEREREMpaC1SxnZl+bWUi6XZ60zq5mNtnM1pnZt2Z2WSnbOd7MPo/W+djMjkh63cxsuJnNN7OfzGyimXVJ9/erbmb2p6hO15nZVDPbK+4ypZuZXV3KPvR5wuv1zOweM1tiZqvN7AUza5O0jfZm9oqZrTWzRWZ2s5nlJa1zoJlNN7P1ZjbHzE6tpq9YJWbWy8zGmdkPUd0cm/T6Fo8NM2tuZn83s5VmttzMHjazhknrVPk4jVs56uqxUva18Unr5ERdAZjZUDN738xWRcfNGDPbMWmdajv+Mvn8V866eqOU/ev+pHVqfF0BmNkgM5sZHUcrzewdMzs84fXs2q9CCLpl8Q34GrgST/tVfNsq4fXG+GxgTwHdgBOBtcBZCev8BtgIXAp0Ba4FCoBdEtYZAiwHjgF2BcYCc4F6cddBCutyIJ6W4zRgZ2AksAxoHXfZ0vy9rwZmJe1DLRNevw/4BugD9MQn7Hg74fXawMfAv4AewOHAYuCGhHU6AWuAW6N9bHC0zx0a9/cvR/0cDlwH9AcCcGzS61s8NoDX8LR8ewP7A7OBpxNeT8lxGvetHHX1WFQXiftas6R1cqKuonKOB06NvsevgVeAeWx6Dq+W448MP/+Vs67eiMqduH81zrW6isr4W+AIoAuwA3B9dAx0y8b9KvYK1a3KO+TXwIVlvD4IWArkJzx3I/B5wuNngZeT3vcucH+0bMB84JKE15sA64AT466DFNblVODuhMe1gO+By+MuW5q/99XAR5t5rUl0ghuQ8NxOeCCyT/T4cKJpkxPWOQdYUbzfAX8FZiVtexQwPu7vX8G62iQAK8+xEZ3EA7BHwjqHAUVAu+hxlY/TTLsl11X03GPAmDLek5N1lVDGVtH375WwL1XL8Zdt57/kuoqeewO4o4z35GRdJZRzKfDHbNyv1A2gZrg8asr/0MwuTWqm3xd4K/jUtsUmADuaWbOEdSYmbXNC9Dz4r6e2ieuEEFbgO+G+1ABmlo//ukz8jkXR4xrxHbegS3Tpdm50CbZ99HxPoA6b1svn+C/y4nrZF/g4bDqRxwS8Baxbwjpl7WPZqjzHxr7A8hDCBwnvm4gHYHsnrFPV4zRbHBhdUvyvmd1nZi0SXsv1umoS3S+N7qvl+MvS819yXRU72cx+NLNZZjbCzBokvJaTdWVmtc3sRHx20HfIwv0q9hmspMruAqbjB+xvgBHA1sDF0ettga+S3rMw4bVl0X3yjGELo+dJuC9rnWzXEr/sUdp33Kn6i1OtpuKX1/6L7ztXAZPNbBf871sQQlie9J7k/aO0eqMc6zQ2s/ohhJ+q+iViUp5joy2wKPHFEMJGM1uatE5Vj9NsMB54Ef+u2wM3AK+Z2b4hhEJyuK7MrBZwB34pdlb0dLUcf0Azsuj8t5m6Anga7xrwA94l56/AjsBx0es5VVdm1h0PTusBq4H+IYRPzawHWbZfKVjNQGZ2I94PrixdQwifhxBuS3huppkVAA+Y2dAQgqaRky0KIbyW8HCmmU3FT/gnANkaREoGCiGMSnj4sZnNBL4EDgQmxVKozHEPsAveT1fKVmpdhRBGJjz82MzmA5PMbPsQwpfVWcAM8V+8v2kTYADwuJn1jrdIlaNuAJmpuLNyWbe5m3nvVPxHSMfo8QKgTdI6bRJeK2udBUnrlbVOtvuRqH9O0vM16TuWS/Rr+wugM/7d882sadJqyftHZfexlVncqgrlOzYWAK0TX4y66jQnNXWYtftnCGEufux1jp7Kyboys7uBo4CDQgjfJbxUXcdf1pz/yqir0kyN7hP3r5ypqxBCQQhhTghhWghhKDADuIAs3K8UrGagEMLiqNW0rFvBZt7eA+/fVXwp7R2gl5nVSVjnYOC/IYRlCev0TdrOwdHz4JfcFiSuY2aN8T5k71ADRPU5jU2/Y63ocY34juVlniZoe3zg0DRgA5vWy45Ae0rq5R2gu5klBhkHAyuBTxPWKWsfy1blOTbeAZqaWc+E9/XBz79TE9ap6nGadcxsG6AFvq9BjtWVubvx7Al9QgjJ3Ruq5fjLhvNfOeqqND2i+8T9q8bXVRlqAXXJxv0q7tFpulVpZN++wIV4Go/tgJPxIPXxhHWa4P9Mn8A7RQ/EU00kp3nZAPwZ70dyNaWnrloGHA10B8ZQM1NXrQNOwVuvH4i+c5u4y5bm730L0Btvjf8NnqpkMdAqev0+vFvAQXhn+SnAlIT3F6c4mRDti4dG+2FpKU5uivaxc8me1FUN8X96PfDRshdFy+2j17d4bODpmKYDewH74S3XiemYUnKcxn0rq66i124G9on2tb74P7IvgLq5VldROe/F0571ZtN0S/UT1qmW448MP/9tqa7wH9hXRnXUMToevwTezLW6iso4AugV1UX36HERcHA27lexV6huVdoZd8fTsSzH+xZ+Cgwl4cQfrbcrMDnaYb4DhpSyrePx/i3r8ZybRyS9bsBw/J/EOnw03w5x10Ea6nRwdACvx1ty9o67TNXwnUfhAxLWR/vHKGD7hNfr4X3ElkYnpheBtknb6AC8iue7XIwHwHlJ6xwIfBh9zpfAqXF/93LWz4F44JV8eyx6fYvHBn4Z+2lgFZ765RGgYdI6VT5O476VVVdA/egf3yI8cPwaz7nYJmkbOVFXURlLq6uQeGxU5/FHBp//tlRXwLbAm8CSaL+YjQdRjZO2U+PrKirfw9Extj465iYSBarZuF9ZtCERERERkYyjPqsiIiIikrEUrIqIiIhIxlKwKiIiIiIZS8GqiIiIiGQsBasiIiIikrEUrIqIiIhIxlKwKiIiIiIZS8GqiIiIiGQsBasiIjEzs1PNbHnc5RARyUQKVkVEqsjMWpnZfWb2jZmtN7MFZjbBzPaLu2wiItkuL+4CiIjUAC8A+cApwFygDdAXaFFdBTCz/BBCQXV9nohIdVHLqohIFZhZU+AAYEgI4d8hhHkhhPdCCCNCCP+I1rnYzD42szVm9q2Z3WtmDcvY5vZmNtbMFprZajN738z6Ja3ztZldaWZPmNlKYKSZvW5mdyet18rMCsysbxq+vohI2ilYFRGpmtXR7Vgzq7uZdYqA84FueOtrH+CmMrbZEHgVb53dDRgPjDOz9knrXQLMiNa5FngIOCmpHP8LfA+8XoHvJCKSMSyEEHcZRESympn9DngQqA9MB94ERoUQZm5m/QHA/SGEltHjU4E7QghNy/iMWdF77o4efw18GELon7BOPeAH4JwQwujouRnAiyGEa6r6PUVE4qCWVRGRKgohvAC0A47GW0EPBKZHQShm1s/MJpnZ92a2CngSaGFmDUrbnpk1NLNbzOwzM1tuZquBrkByy+oHSeVYF2379Gg7uwO7AI+l5IuKiMRAwaqISAqEENaFEP4VQrg2hPAbPEC8xsw6Ai8DM4HfAT2BP0Vvy9/M5m4B+gPD8P6wPYCPS1l/TSnvfQg42My2AU4DXg8hzKvk1xIRiZ2yAYiIpMenwLF4cFoL+HMIoQjAzE7Ywnv3Ax4LIbwUrd8Q6FieDw0hfGxmHwBnAicBgytVehGRDKFgVUSkCsysBfAc8AjeeroK2AO4DBgLzAHqAOeZ2Tg8ED1nC5udDRwXrR/wwVMVuRL2EHA33vL6UgXeJyKScdQNQESkalYDU4GLgLeAWXhw+SAwOIQwA7gYGBK9djIwdAvbvBhYBkwBxgET8IFb5fUMsBF4JurHKiKStZQNQESkhon6yX4J7BlCqEiQKyKScRSsiojUEGZWB5816xagUwhB072KSNZTNwARkZpjP2A+sCdb7hcrIpIV1LIqIiIiIhlLLasiIiIikrEUrIqIiIhIxlKwKiIiIiIZS8GqiIiIiGQsBasiIiIikrEUrIqIiIhIxlKwKiIiIiIZS8GqiIiIiGSs/weLuJfwG+AuVwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import r2_score\n"
      ],
      "metadata": {
        "id": "KZUAHaroYYnc"
      },
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "r2_score(y_test, pred_Lr)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wN1rO0TBYbfa",
        "outputId": "cb903daf-8770-4f23-e83e-6ff869133c48"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8688823252083054"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 70,
      "metadata": {
        "id": "BpVh20OTOPMA"
      },
      "outputs": [],
      "source": [
        "# residuals\n",
        "test_res = y_test - pred_Lr"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_res"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x6gMq7z8a7aB",
        "outputId": "4bcbcbd2-6932-44dc-a478-47a88d598492"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "18     -379.0\n",
              "306    -493.0\n",
              "104     845.0\n",
              "4      1645.0\n",
              "156    1168.0\n",
              "370   -2964.0\n",
              "32     1614.0\n",
              "344     854.0\n",
              "81     -689.0\n",
              "227    -591.0\n",
              "492   -2291.0\n",
              "482     296.0\n",
              "95      521.0\n",
              "368     814.0\n",
              "65     -643.0\n",
              "39     -472.0\n",
              "432   -3793.0\n",
              "237   -1625.0\n",
              "452   -3114.0\n",
              "437   -2126.0\n",
              "12     1624.0\n",
              "212     937.0\n",
              "290    -348.0\n",
              "296    1052.0\n",
              "269   -1996.0\n",
              "359     285.0\n",
              "460   -3877.0\n",
              "321    -118.0\n",
              "64     1432.0\n",
              "336     534.0\n",
              "19    -1126.0\n",
              "261   -1258.0\n",
              "315      93.0\n",
              "205    -756.0\n",
              "179     308.0\n",
              "415     -84.0\n",
              "31     -656.0\n",
              "456   -2335.0\n",
              "444   -4778.0\n",
              "228     440.0\n",
              "333    -291.0\n",
              "302    -535.0\n",
              "405    3061.0\n",
              "89     -741.0\n",
              "312    1815.0\n",
              "100     780.0\n",
              "496   -5866.0\n",
              "347    -613.0\n",
              "289    -709.0\n",
              "141   -1007.0\n",
              "215    -125.0\n",
              "Name: Salary, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 72,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 474
        },
        "id": "5O4l9WmsR_WU",
        "outputId": "562c07f7-8d0d-408d-b780-ca813603e7fc"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.lines.Line2D at 0x7fb88277d150>"
            ]
          },
          "metadata": {},
          "execution_count": 72
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# plot residuls\n",
        "plt.figure(figsize=(8,5), dpi=100)\n",
        "sns.scatterplot(x=y_test,y=test_res)\n",
        "plt.axhline(y=0, color='r', linestyle='--')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 73,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 706
        },
        "id": "Ax4S85q_rarM",
        "outputId": "83fac464-f9b9-4dfb-94fe-8098a0972dac"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 600x800 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Create a figure and axis to plot on\n",
        "fig, ax = plt.subplots(figsize=(6,8),dpi=100)\n",
        "# probplot returns the raw values if needed\n",
        "# we just want to see the plot, so we assign these values to _\n",
        "_ = sp.stats.probplot(test_res,plot=ax)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 74,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w9NUcOOtTJAY",
        "outputId": "84a8c190-886a-4354-e9c9-ae00e8e9f816"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([7.14359103e+16, 7.14359103e+16, 7.08948672e+15, 7.08948672e+15,\n",
              "       7.08948672e+15, 7.08948672e+15, 7.08948672e+15, 7.08948672e+15,\n",
              "       7.08948672e+15, 6.43370192e+15, 6.43370192e+15])"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ],
      "source": [
        "model_Lr.coef_"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 75,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xDE4yX1fsL6a",
        "outputId": "2cebaf83-56bb-4bca-f4d4-53515fa2a80e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-8.495909890571899e+16"
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ],
      "source": [
        "model_Lr.intercept_"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 76,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UNQAebwosTXI",
        "outputId": "bce5a88e-4825-479d-f204-b6c40be2a428"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'copy_X': True,\n",
              " 'fit_intercept': True,\n",
              " 'n_jobs': None,\n",
              " 'normalize': 'deprecated',\n",
              " 'positive': False}"
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ],
      "source": [
        "model_Lr.get_params()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# creating fuction to perform other algorithms\n",
        "# I will use the base model since the data is small  "
      ],
      "metadata": {
        "id": "3e9bUlfTvsuH"
      },
      "execution_count": 77,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 78,
      "metadata": {
        "id": "M9PMZdX1mNrC"
      },
      "outputs": [],
      "source": [
        "def run_model(model,X_train,y_train,X_test,y_test):\n",
        "    \n",
        "    # Fit Model\n",
        "    model.fit(X_train,y_train)\n",
        "    \n",
        "    # Get Metrics\n",
        "    preds = model.predict(X_test)\n",
        "    \n",
        "    MAE = mean_absolute_error(y_test, preds)\n",
        "    print(f'MAE : {MAE}')\n",
        "    rmse = np.sqrt(mean_squared_error(y_test,preds))\n",
        "    print(f'RMSE : {rmse}')\n",
        "    \n",
        "    \n",
        "    plt.figure(figsize=(8,5), dpi=100)\n",
        "    ax = sns.distplot(y_test, hist= False, color=\"r\", label=\"Actual Value\")\n",
        "    sns.distplot(preds, hist=False, color=\"b\", label=\"Predicted Values\" , ax=ax);"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 79,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "id": "SlO8MSl5mN0h",
        "outputId": "11e31949-2b8a-4a9a-d541-e875c55ff24b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MAE : 1304.2549019607843\n",
            "RMSE : 1800.8882176717743\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "run_model(model_Lr,X_train,y_train,X_test,y_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Poly_Regression"
      ],
      "metadata": {
        "id": "BU9fE8ctxXTa"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 80,
      "metadata": {
        "id": "n2ZAPakpr7RW"
      },
      "outputs": [],
      "source": [
        "from sklearn.pipeline import make_pipeline"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 81,
      "metadata": {
        "id": "ltygs6IbmN3x"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import PolynomialFeatures"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 82,
      "metadata": {
        "id": "AUb1RCqZmN6u"
      },
      "outputs": [],
      "source": [
        "pipe = make_pipeline(PolynomialFeatures(6),LinearRegression())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 83,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "id": "Ft11vM09rsRl",
        "outputId": "c0be914b-ca44-412d-99ca-9f1782c6f251"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MAE : 410.1372549019608\n",
            "RMSE : 624.5587775915624\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "run_model(pipe,X_train,y_train,X_test,y_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###K_Neighbors Regression"
      ],
      "metadata": {
        "id": "dbE6fQYjx39w"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.neighbors import KNeighborsRegressor"
      ],
      "metadata": {
        "id": "BJklE9mFyBPF"
      },
      "execution_count": 86,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "    \n",
        "knr = KNeighborsRegressor()\n",
        "run_model(knr,X_train,y_train,X_test,y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "id": "0m35JYIGyBWN",
        "outputId": "e978a6a7-9ccc-4cbe-f926-4dc9ef002d1d"
      },
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MAE : 436.5019607843137\n",
            "RMSE : 727.4550696994095\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##SVM Regression"
      ],
      "metadata": {
        "id": "1L8NfRsT0exi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.svm import SVR"
      ],
      "metadata": {
        "id": "y7kHcvE00h0g"
      },
      "execution_count": 88,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "svr = SVR()"
      ],
      "metadata": {
        "id": "PKo2AFC90h_W"
      },
      "execution_count": 89,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "svr.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YkuZZzdL0iFY",
        "outputId": "c957c189-ea3c-4952-a51b-942047ec9713"
      },
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVR()"
            ]
          },
          "metadata": {},
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "run_model(svr,X_train,y_train,X_test,y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 492
        },
        "id": "wPJmxFZS1Faz",
        "outputId": "092f6ac3-edb8-40f8-ba57-edf0c12cc32a"
      },
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MAE : 4130.892122617115\n",
            "RMSE : 4956.449051188314\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Decision Tree Regression"
      ],
      "metadata": {
        "id": "hRrblHXJ2P1w"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import tree"
      ],
      "metadata": {
        "id": "y-9oC7q1yGui"
      },
      "execution_count": 93,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 94,
      "metadata": {
        "id": "Uaw6mlRHbi3F"
      },
      "outputs": [],
      "source": [
        "dtr = tree.DecisionTreeRegressor()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 95,
      "metadata": {
        "id": "ohIqh0tpbunK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e5e7bdb1-0807-4211-84d1-eadbcfd48bca"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeRegressor()"
            ]
          },
          "metadata": {},
          "execution_count": 95
        }
      ],
      "source": [
        "dtr.fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "run_model(dtr,X_train,y_train,X_test,y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "id": "lFfJOOY4wHXB",
        "outputId": "51ba92c3-c1b3-4222-abb8-91e9e289877c"
      },
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MAE : 385.2138045057595\n",
            "RMSE : 627.3265883985509\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 97,
      "metadata": {
        "id": "9NdUX9kxg-Tc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "88a07301-d34f-4138-d011-efeea9988164"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.07774022, 0.05747013, 0.05665313, 0.02383341, 0.52868697,\n",
              "       0.0005661 , 0.19367019, 0.00232895, 0.00078203, 0.03016021,\n",
              "       0.02810866])"
            ]
          },
          "metadata": {},
          "execution_count": 97
        }
      ],
      "source": [
        "dtr.feature_importances_"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 98,
      "metadata": {
        "id": "l8WfxL-TjgID",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b221199b-c3b2-40c4-f589-a8228344cc47"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Nationality_NonSaudi', 'Nationality_Saudi', 'Degree Level_Bachelor',\n",
              "       'Degree Level_Diploma', 'Degree Level_Doctorate',\n",
              "       'Degree Level_Intermediate', 'Degree Level_Master Degree',\n",
              "       'Degree Level_Primary', 'Degree Level_Secondary', 'Gender_Female',\n",
              "       'Gender_Male'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 98
        }
      ],
      "source": [
        "X_train.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 99,
      "metadata": {
        "id": "l5E2MdXZjzHb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e92ae4de-b2c4-48ef-e5f3-34d9101c31b6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4"
            ]
          },
          "metadata": {},
          "execution_count": 99
        }
      ],
      "source": [
        "dtr.feature_importances_.argmax()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 100,
      "metadata": {
        "id": "kulAAX4dk9PL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "e1928f5a-5d9a-4169-a093-36c2443d82cc"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'Degree Level_Doctorate'"
            ]
          },
          "metadata": {},
          "execution_count": 100
        }
      ],
      "source": [
        "X.columns[4]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 101,
      "metadata": {
        "id": "HtRGmnO3lITS"
      },
      "outputs": [],
      "source": [
        "df_import = pd.DataFrame(index=X.columns,data=dtr.feature_importances_,columns=['Feature Importance']).sort_values('Feature Importance', ascending = False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 102,
      "metadata": {
        "id": "uNNRvzGrl6VV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "outputId": "54dcfbfa-171a-46b5-e993-1c5064207d46"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-09180ae2-b462-47f8-b623-1fc5e167ce91\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Feature Importance</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Degree Level_Doctorate</th>\n",
              "      <td>0.528687</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Degree Level_Master Degree</th>\n",
              "      <td>0.193670</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Nationality_NonSaudi</th>\n",
              "      <td>0.077740</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Nationality_Saudi</th>\n",
              "      <td>0.057470</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Degree Level_Bachelor</th>\n",
              "      <td>0.056653</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Gender_Female</th>\n",
              "      <td>0.030160</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Gender_Male</th>\n",
              "      <td>0.028109</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Degree Level_Diploma</th>\n",
              "      <td>0.023833</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Degree Level_Primary</th>\n",
              "      <td>0.002329</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Degree Level_Secondary</th>\n",
              "      <td>0.000782</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Degree Level_Intermediate</th>\n",
              "      <td>0.000566</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-09180ae2-b462-47f8-b623-1fc5e167ce91')\"\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",
              "       width=\"24px\">\n",
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              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </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",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [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-09180ae2-b462-47f8-b623-1fc5e167ce91 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-09180ae2-b462-47f8-b623-1fc5e167ce91');\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",
              "  "
            ],
            "text/plain": [
              "                            Feature Importance\n",
              "Degree Level_Doctorate                0.528687\n",
              "Degree Level_Master Degree            0.193670\n",
              "Nationality_NonSaudi                  0.077740\n",
              "Nationality_Saudi                     0.057470\n",
              "Degree Level_Bachelor                 0.056653\n",
              "Gender_Female                         0.030160\n",
              "Gender_Male                           0.028109\n",
              "Degree Level_Diploma                  0.023833\n",
              "Degree Level_Primary                  0.002329\n",
              "Degree Level_Secondary                0.000782\n",
              "Degree Level_Intermediate             0.000566"
            ]
          },
          "metadata": {},
          "execution_count": 102
        }
      ],
      "source": [
        "df_import"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Random Forest Regression"
      ],
      "metadata": {
        "id": "K993U93K4VOb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestRegressor"
      ],
      "metadata": {
        "id": "c8tKV7Gf4a24"
      },
      "execution_count": 103,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rfr = RandomForestRegressor()"
      ],
      "metadata": {
        "id": "gvb6skRp4mER"
      },
      "execution_count": 104,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "run_model(rfr,X_train,y_train,X_test,y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "id": "OZs8tZo35LvQ",
        "outputId": "6febd965-14d2-45db-be81-842731ddc136"
      },
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MAE : 385.52504894504835\n",
            "RMSE : 623.5860930059577\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import_rfr = pd.DataFrame(index=X.columns,data=rfr.feature_importances_,columns=['Feature Importance']).sort_values('Feature Importance', ascending = False)"
      ],
      "metadata": {
        "id": "cXypJW8E6COL"
      },
      "execution_count": 106,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import_rfr"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "yc9mzUXN5bfZ",
        "outputId": "fe2a06e1-ad75-4369-d53c-7429d29c0eec"
      },
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-6ea5b8af-e52e-47ce-9d8f-6abb3a77232b\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Feature Importance</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Degree Level_Doctorate</th>\n",
              "      <td>0.522254</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Degree Level_Master Degree</th>\n",
              "      <td>0.198346</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Nationality_NonSaudi</th>\n",
              "      <td>0.068966</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Nationality_Saudi</th>\n",
              "      <td>0.065701</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Degree Level_Bachelor</th>\n",
              "      <td>0.057925</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Gender_Female</th>\n",
              "      <td>0.032005</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Gender_Male</th>\n",
              "      <td>0.027066</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Degree Level_Diploma</th>\n",
              "      <td>0.024016</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Degree Level_Primary</th>\n",
              "      <td>0.002445</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Degree Level_Secondary</th>\n",
              "      <td>0.000988</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Degree Level_Intermediate</th>\n",
              "      <td>0.000289</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6ea5b8af-e52e-47ce-9d8f-6abb3a77232b')\"\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",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </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",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [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-6ea5b8af-e52e-47ce-9d8f-6abb3a77232b 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-6ea5b8af-e52e-47ce-9d8f-6abb3a77232b');\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",
              "  "
            ],
            "text/plain": [
              "                            Feature Importance\n",
              "Degree Level_Doctorate                0.522254\n",
              "Degree Level_Master Degree            0.198346\n",
              "Nationality_NonSaudi                  0.068966\n",
              "Nationality_Saudi                     0.065701\n",
              "Degree Level_Bachelor                 0.057925\n",
              "Gender_Female                         0.032005\n",
              "Gender_Male                           0.027066\n",
              "Degree Level_Diploma                  0.024016\n",
              "Degree Level_Primary                  0.002445\n",
              "Degree Level_Secondary                0.000988\n",
              "Degree Level_Intermediate             0.000289"
            ]
          },
          "metadata": {},
          "execution_count": 107
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##AdaBoost Regression"
      ],
      "metadata": {
        "id": "pgbDkwpy8y0X"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import AdaBoostRegressor"
      ],
      "metadata": {
        "id": "rF2x7wml84h5"
      },
      "execution_count": 108,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "AdB = AdaBoostRegressor()"
      ],
      "metadata": {
        "id": "Wjk72kUu9E9U"
      },
      "execution_count": 109,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "AdB.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1hXFW-sl9MOI",
        "outputId": "4936ffff-fe06-48b9-86cf-6544b3a06fc8"
      },
      "execution_count": 110,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "AdaBoostRegressor()"
            ]
          },
          "metadata": {},
          "execution_count": 110
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "run_model(AdB,X_train,y_train,X_test,y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 492
        },
        "id": "WtOWIKQY9QwH",
        "outputId": "240859c1-abf8-40f4-f6cf-fe32dab82951"
      },
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MAE : 1811.2751426397915\n",
            "RMSE : 2195.3665557521444\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import_AdB = pd.DataFrame(index=X.columns,data=AdB.feature_importances_,columns=['Feature Importance']).sort_values('Feature Importance', ascending = False)"
      ],
      "metadata": {
        "id": "PC6LnXW19YZi"
      },
      "execution_count": 112,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import_AdB"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "UbCQzS_n9ge5",
        "outputId": "a79863c0-9864-4016-e19c-d0743c5bcf54"
      },
      "execution_count": 113,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-c60437b8-283e-4b0a-8127-0d77ca4e2eb8\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Feature Importance</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Degree Level_Doctorate</th>\n",
              "      <td>0.670070</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Degree Level_Master Degree</th>\n",
              "      <td>0.146888</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Nationality_NonSaudi</th>\n",
              "      <td>0.057546</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Nationality_Saudi</th>\n",
              "      <td>0.050011</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Degree Level_Bachelor</th>\n",
              "      <td>0.026525</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Gender_Female</th>\n",
              "      <td>0.026267</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Gender_Male</th>\n",
              "      <td>0.022692</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Degree Level_Diploma</th>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Degree Level_Intermediate</th>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Degree Level_Primary</th>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Degree Level_Secondary</th>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c60437b8-283e-4b0a-8127-0d77ca4e2eb8')\"\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",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </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",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [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-c60437b8-283e-4b0a-8127-0d77ca4e2eb8 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-c60437b8-283e-4b0a-8127-0d77ca4e2eb8');\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",
              "  "
            ],
            "text/plain": [
              "                            Feature Importance\n",
              "Degree Level_Doctorate                0.670070\n",
              "Degree Level_Master Degree            0.146888\n",
              "Nationality_NonSaudi                  0.057546\n",
              "Nationality_Saudi                     0.050011\n",
              "Degree Level_Bachelor                 0.026525\n",
              "Gender_Female                         0.026267\n",
              "Gender_Male                           0.022692\n",
              "Degree Level_Diploma                  0.000000\n",
              "Degree Level_Intermediate             0.000000\n",
              "Degree Level_Primary                  0.000000\n",
              "Degree Level_Secondary                0.000000"
            ]
          },
          "metadata": {},
          "execution_count": 113
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Gradient Boosting Regression"
      ],
      "metadata": {
        "id": "6jKOnEca9trT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import GradientBoostingRegressor"
      ],
      "metadata": {
        "id": "L1Vjhi_99yY0"
      },
      "execution_count": 114,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "gbr = GradientBoostingRegressor()"
      ],
      "metadata": {
        "id": "RDPhNqBB99H_"
      },
      "execution_count": 115,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "gbr.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ovfEyEwK-Jz0",
        "outputId": "006a96d5-0f01-457e-c287-8701bd66bd2a"
      },
      "execution_count": 116,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GradientBoostingRegressor()"
            ]
          },
          "metadata": {},
          "execution_count": 116
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "run_model(gbr,X_train,y_train,X_test,y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "id": "Q3BFGhV3-ODL",
        "outputId": "f08539c5-bf7e-464c-bbfd-a5fc9005c684"
      },
      "execution_count": 117,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MAE : 382.3856603941374\n",
            "RMSE : 619.0976042361268\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import_gbr = pd.DataFrame(index=X.columns,data=gbr.feature_importances_,columns=['Feature Importance']).sort_values('Feature Importance', ascending = False)"
      ],
      "metadata": {
        "id": "JiNG3to4-v9V"
      },
      "execution_count": 118,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import_gbr"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "rzHlTYdn-04O",
        "outputId": "c8e1e83a-2649-4c48-fffd-dab433a75b3a"
      },
      "execution_count": 119,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-a7be6837-9518-4d2a-9105-5caa3a01759f\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Feature Importance</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Degree Level_Doctorate</th>\n",
              "      <td>0.521292</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Degree Level_Master Degree</th>\n",
              "      <td>0.183323</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Nationality_NonSaudi</th>\n",
              "      <td>0.077820</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Nationality_Saudi</th>\n",
              "      <td>0.054977</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Degree Level_Bachelor</th>\n",
              "      <td>0.041847</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Gender_Female</th>\n",
              "      <td>0.031554</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Gender_Male</th>\n",
              "      <td>0.026925</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Degree Level_Primary</th>\n",
              "      <td>0.023414</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Degree Level_Intermediate</th>\n",
              "      <td>0.020856</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Degree Level_Secondary</th>\n",
              "      <td>0.016057</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Degree Level_Diploma</th>\n",
              "      <td>0.001936</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a7be6837-9518-4d2a-9105-5caa3a01759f')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
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              "      \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",
              "      cursor: pointer;\n",
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              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-a7be6837-9518-4d2a-9105-5caa3a01759f');\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",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                            Feature Importance\n",
              "Degree Level_Doctorate                0.521292\n",
              "Degree Level_Master Degree            0.183323\n",
              "Nationality_NonSaudi                  0.077820\n",
              "Nationality_Saudi                     0.054977\n",
              "Degree Level_Bachelor                 0.041847\n",
              "Gender_Female                         0.031554\n",
              "Gender_Male                           0.026925\n",
              "Degree Level_Primary                  0.023414\n",
              "Degree Level_Intermediate             0.020856\n",
              "Degree Level_Secondary                0.016057\n",
              "Degree Level_Diploma                  0.001936"
            ]
          },
          "metadata": {},
          "execution_count": 119
        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "name": "Salary_by_Education_KSA _Final.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}