Prediction_on_Salary_Data(SLR).ipynb

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        "<a href=\"https://colab.research.google.com/github/manasik29/Prediction-of-Salary-of-individuals-based-of-Years-of-Experience/blob/main/Prediction_on_Salary_Data(SLR).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "***2) Salary_hike -> Build a prediction model for Salary_hike***\n",
        "\n",
        "------------------------------------------------------------\n",
        "\n",
        "Build a simple linear regression model by performing EDA and do necessary transformations and select the best model using R or Python."
      ],
      "metadata": {
        "id": "oSKCSNIkKmGy"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "***Load Data & Libraries***"
      ],
      "metadata": {
        "id": "nx3BOfWFLCP3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "%matplotlib inline"
      ],
      "metadata": {
        "id": "qOuzWxUDm42Y"
      },
      "execution_count": 2,
      "outputs": []
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      "source": [
        "data_s = pd.read_csv('/content/Salary_Data.csv')"
      ],
      "metadata": {
        "id": "Tk48FLE9Fqw4"
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      "execution_count": 3,
      "outputs": []
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              "    YearsExperience    Salary\n",
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            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 30 entries, 0 to 29\n",
            "Data columns (total 2 columns):\n",
            " #   Column           Non-Null Count  Dtype  \n",
            "---  ------           --------------  -----  \n",
            " 0   YearsExperience  30 non-null     float64\n",
            " 1   Salary           30 non-null     float64\n",
            "dtypes: float64(2)\n",
            "memory usage: 608.0 bytes\n"
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              "   YearsExperience   Salary\n",
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              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_s.tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "4oFoE3FL1_sy",
        "outputId": "78422038-3ec4-4310-90fd-be5ec582d08a"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    YearsExperience    Salary\n",
              "25              9.0  105582.0\n",
              "26              9.5  116969.0\n",
              "27              9.6  112635.0\n",
              "28             10.3  122391.0\n",
              "29             10.5  121872.0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d1382a60-dec1-43c8-8903-8c719d4472d3\">\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>YearsExperience</th>\n",
              "      <th>Salary</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>9.0</td>\n",
              "      <td>105582.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>9.5</td>\n",
              "      <td>116969.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>9.6</td>\n",
              "      <td>112635.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>10.3</td>\n",
              "      <td>122391.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>10.5</td>\n",
              "      <td>121872.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d1382a60-dec1-43c8-8903-8c719d4472d3')\"\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-d1382a60-dec1-43c8-8903-8c719d4472d3 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-d1382a60-dec1-43c8-8903-8c719d4472d3');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_s.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ljFIrvba1FJY",
        "outputId": "56928fb8-0dba-4321-eeb4-85c702ca0385"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(30, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_s.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xfxSbPQL1Gue",
        "outputId": "afd615dd-5242-4eb8-8120-416895e0216b"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "YearsExperience    float64\n",
              "Salary             float64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "No need for dtype correction or transformation"
      ],
      "metadata": {
        "id": "0nZGRNHp2FdS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Check for missing values\n",
        "data_s.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uScfk29G1Ljq",
        "outputId": "f802c8b4-6bdb-4257-9ede-9e624c518475"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "YearsExperience    0\n",
              "Salary             0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#to genrate heat map\n",
        "import seaborn as sns\n",
        "cols = data_s.columns\n",
        "colors = ['#000099', '#ffff00']   #define colors, yellow for missing, blue for non missing\n",
        "sns.heatmap(data_s[cols].isnull(),\n",
        "               cmap= sns.color_palette(colors))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "id": "NPVrrPqE1idF",
        "outputId": "a5ad9d33-8acc-4495-a5f7-c1da400c282e"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f3625870f90>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Hence, we can conclude no missing values."
      ],
      "metadata": {
        "id": "9TTKJ29p1ppR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Check for duplicated values\n",
        "data_s[data_s.duplicated()].shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OBoJNiU41GjV",
        "outputId": "65f4a4c5-6f70-4b54-9c5b-95aa910fe796"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Hence, no duplicated rows."
      ],
      "metadata": {
        "id": "UzbLQqMf15UM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Check for Outliers\n",
        "data_s.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "spXlgZ0P2L7V",
        "outputId": "817c039c-c489-4473-a876-bba6f42e6c11"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       YearsExperience         Salary\n",
              "count        30.000000      30.000000\n",
              "mean          5.313333   76003.000000\n",
              "std           2.837888   27414.429785\n",
              "min           1.100000   37731.000000\n",
              "25%           3.200000   56720.750000\n",
              "50%           4.700000   65237.000000\n",
              "75%           7.700000  100544.750000\n",
              "max          10.500000  122391.000000"
            ],
            "text/html": [
              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>YearsExperience</th>\n",
              "      <th>Salary</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>30.000000</td>\n",
              "      <td>30.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>5.313333</td>\n",
              "      <td>76003.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>2.837888</td>\n",
              "      <td>27414.429785</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.100000</td>\n",
              "      <td>37731.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>3.200000</td>\n",
              "      <td>56720.750000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>4.700000</td>\n",
              "      <td>65237.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>7.700000</td>\n",
              "      <td>100544.750000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>10.500000</td>\n",
              "      <td>122391.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "    .colab-df-convert:hover {\n",
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              "    [theme=dark] .colab-df-convert {\n",
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              "          document.querySelector('#df-495c07b2-9890-422d-8235-8d09db94ed32 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-495c07b2-9890-422d-8235-8d09db94ed32');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#plot boxplot\n",
        "plt.boxplot(data_s.YearsExperience)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        },
        "id": "QGt4Rk0h2Uhm",
        "outputId": "d2381ab9-4aba-42ff-eac1-d1ef3074112e"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'whiskers': [<matplotlib.lines.Line2D at 0x7f3622ac1550>,\n",
              "  <matplotlib.lines.Line2D at 0x7f3622ac1a90>],\n",
              " 'caps': [<matplotlib.lines.Line2D at 0x7f3622ac1fd0>,\n",
              "  <matplotlib.lines.Line2D at 0x7f3622ac9550>],\n",
              " 'boxes': [<matplotlib.lines.Line2D at 0x7f3622ac1050>],\n",
              " 'medians': [<matplotlib.lines.Line2D at 0x7f3622ac9ad0>],\n",
              " 'fliers': [<matplotlib.lines.Line2D at 0x7f3622acd050>],\n",
              " 'means': []}"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#plot histogram\n",
        "sns.distplot(data_s.YearsExperience)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "K3CAs9Z42Tqh",
        "outputId": "8fee55af-5241-4ebd-aca4-dbe82d12ed45"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f3622a2ca10>"
            ]
          },
          "metadata": {},
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#plot boxplot\n",
        "plt.boxplot(data_s.Salary)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        },
        "id": "xzBd-gB-2hWa",
        "outputId": "ed519350-ccb9-4020-930d-3c13e428fb2a"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'whiskers': [<matplotlib.lines.Line2D at 0x7f3622986390>,\n",
              "  <matplotlib.lines.Line2D at 0x7f36229868d0>],\n",
              " 'caps': [<matplotlib.lines.Line2D at 0x7f3622986e10>,\n",
              "  <matplotlib.lines.Line2D at 0x7f362298e390>],\n",
              " 'boxes': [<matplotlib.lines.Line2D at 0x7f362297ee50>],\n",
              " 'medians': [<matplotlib.lines.Line2D at 0x7f362298e910>],\n",
              " 'fliers': [<matplotlib.lines.Line2D at 0x7f362298ee50>],\n",
              " 'means': []}"
            ]
          },
          "metadata": {},
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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py5CQJHUZEpKkLkNCktRlSEgrLMmDwL8Bb0syn2TPao9JerX8Wg5JUpdnEpKkLkNCktRlSEiSugwJSVKXISFJ6jIkJEldhoQkqet/AQZIzhcqQdADAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#plot dist plot\n",
        "sns.distplot(data_s.Salary)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 362
        },
        "id": "paJWOKVZ2mKv",
        "outputId": "53a26190-af45-4277-caaf-4b3f6d4d3cba"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f362293b350>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Hence, we can conclude there are no outliers in dataset."
      ],
      "metadata": {
        "id": "6U9lkeOw2st7"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Performing Transformations on each columns**"
      ],
      "metadata": {
        "id": "iD6lj2shnXFf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax=plt.subplots(2, figsize=(6,4), sharex= False, sharey = False)\n",
        "sns.boxplot(np.log(data_s.Salary), ax=ax[0])\n",
        "sns.boxplot(np.log(data_s.YearsExperience), ax=ax[1])\n",
        "plt.suptitle(\"Log Transformation on Continuous Variables\", fontsize= 17, y = 1.06)\n",
        "plt.tight_layout(pad=2.0)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 405
        },
        "id": "GgqaCZeWncZ7",
        "outputId": "23437b48-1515-4da3-b0a4-8350986e1136"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n",
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax=plt.subplots(2, figsize=(6,4), sharex= False, sharey = False)\n",
        "sns.boxplot(np.sqrt(data_s.Salary), ax=ax[0])\n",
        "sns.boxplot(np.sqrt(data_s.YearsExperience), ax=ax[1])\n",
        "plt.suptitle(\"Sqrt Transformation on Continuous Variables\", fontsize= 17, y = 1.06)\n",
        "plt.tight_layout(pad=2.0)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 405
        },
        "id": "3w_2mP9hnhL0",
        "outputId": "c09ab07d-6e88-44de-c201-e37394f2488d"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n",
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax=plt.subplots(2, figsize=(6,4), sharex= False, sharey = False)\n",
        "sns.boxplot(np.cbrt(data_s.Salary), ax=ax[0])\n",
        "sns.boxplot(np.cbrt(data_s.YearsExperience), ax=ax[1])\n",
        "plt.suptitle(\"Cbrt Transformation on Continuous Variables\", fontsize= 17, y = 1.06)\n",
        "plt.tight_layout(pad=2.0)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 405
        },
        "id": "qj830i8XnmCe",
        "outputId": "15b98d14-0528-441c-b917-c0cc36853530"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n",
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Correlation Coefficents\n",
        "data_s.corr()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "IBM8TNs1K8ts",
        "outputId": "3b8b87f2-1896-4c2a-d71b-22060443f17b"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 YearsExperience    Salary\n",
              "YearsExperience         1.000000  0.978242\n",
              "Salary                  0.978242  1.000000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-bec0ba37-d50b-49dd-921b-02307885435b\">\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>YearsExperience</th>\n",
              "      <th>Salary</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>YearsExperience</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.978242</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Salary</th>\n",
              "      <td>0.978242</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bec0ba37-d50b-49dd-921b-02307885435b')\"\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-bec0ba37-d50b-49dd-921b-02307885435b 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-bec0ba37-d50b-49dd-921b-02307885435b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.pairplot(data_s)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        },
        "id": "889OtaKTpUex",
        "outputId": "7d96caf9-0d2a-4dfb-c557-dc3a6a13cdb4"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.PairGrid at 0x7f3621bba990>"
            ]
          },
          "metadata": {},
          "execution_count": 28
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 6 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***Fitting a Regression model***"
      ],
      "metadata": {
        "id": "vPx71oUML5XB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import statsmodels.formula.api as smf"
      ],
      "metadata": {
        "id": "PY4zLNEFLv5P"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = smf.ols('Salary~YearsExperience', data = data_s).fit()"
      ],
      "metadata": {
        "id": "wQwTrUVoMA_b"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.regplot(x = 'YearsExperience', y = 'Salary', data = data_s)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "pf4r0Xe2MvoY",
        "outputId": "b3137bfc-d3f9-4309-cedc-cf3c0c450c2e"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f361cc789d0>"
            ]
          },
          "metadata": {},
          "execution_count": 31
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Parameters\n",
        "model.params"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9TOvUtPMMLGs",
        "outputId": "6cabc680-2a4d-483a-e833-4bc65ffb24d9"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Intercept          25792.200199\n",
              "YearsExperience     9449.962321\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#get t-values & p-values\n",
        "print(model.tvalues, '\\n' ,model.pvalues)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hbL21_d-MPDc",
        "outputId": "d828bbea-7cd1-44ea-e582-dbe2feb0dfb5"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Intercept          11.346940\n",
            "YearsExperience    24.950094\n",
            "dtype: float64 \n",
            " Intercept          5.511950e-12\n",
            "YearsExperience    1.143068e-20\n",
            "dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#get rsquared value\n",
        "(model.rsquared, model.rsquared_adj)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8wkam570MXw7",
        "outputId": "15165a9b-319a-4e1e-a4b3-021097945b1a"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.9569566641435086, 0.9554194021486339)"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "EGgSUIMM5WEM",
        "outputId": "f6add634-4056-4441-dcf4-b82e4a7884c6"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<class 'statsmodels.iolib.summary.Summary'>\n",
              "\"\"\"\n",
              "                            OLS Regression Results                            \n",
              "==============================================================================\n",
              "Dep. Variable:                 Salary   R-squared:                       0.957\n",
              "Model:                            OLS   Adj. R-squared:                  0.955\n",
              "Method:                 Least Squares   F-statistic:                     622.5\n",
              "Date:                Fri, 09 Sep 2022   Prob (F-statistic):           1.14e-20\n",
              "Time:                        14:27:16   Log-Likelihood:                -301.44\n",
              "No. Observations:                  30   AIC:                             606.9\n",
              "Df Residuals:                      28   BIC:                             609.7\n",
              "Df Model:                           1                                         \n",
              "Covariance Type:            nonrobust                                         \n",
              "===================================================================================\n",
              "                      coef    std err          t      P>|t|      [0.025      0.975]\n",
              "-----------------------------------------------------------------------------------\n",
              "Intercept        2.579e+04   2273.053     11.347      0.000    2.11e+04    3.04e+04\n",
              "YearsExperience  9449.9623    378.755     24.950      0.000    8674.119    1.02e+04\n",
              "==============================================================================\n",
              "Omnibus:                        2.140   Durbin-Watson:                   1.648\n",
              "Prob(Omnibus):                  0.343   Jarque-Bera (JB):                1.569\n",
              "Skew:                           0.363   Prob(JB):                        0.456\n",
              "Kurtosis:                       2.147   Cond. No.                         13.2\n",
              "==============================================================================\n",
              "\n",
              "Notes:\n",
              "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
              "\"\"\""
            ],
            "text/html": [
              "<table class=\"simpletable\">\n",
              "<caption>OLS Regression Results</caption>\n",
              "<tr>\n",
              "  <th>Dep. Variable:</th>         <td>Salary</td>      <th>  R-squared:         </th> <td>   0.957</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.955</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   622.5</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Date:</th>             <td>Fri, 09 Sep 2022</td> <th>  Prob (F-statistic):</th> <td>1.14e-20</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Time:</th>                 <td>14:27:16</td>     <th>  Log-Likelihood:    </th> <td> -301.44</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>No. Observations:</th>      <td>    30</td>      <th>  AIC:               </th> <td>   606.9</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Df Residuals:</th>          <td>    28</td>      <th>  BIC:               </th> <td>   609.7</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>   \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "         <td></td>            <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Intercept</th>       <td> 2.579e+04</td> <td> 2273.053</td> <td>   11.347</td> <td> 0.000</td> <td> 2.11e+04</td> <td> 3.04e+04</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>YearsExperience</th> <td> 9449.9623</td> <td>  378.755</td> <td>   24.950</td> <td> 0.000</td> <td> 8674.119</td> <td> 1.02e+04</td>\n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "  <th>Omnibus:</th>       <td> 2.140</td> <th>  Durbin-Watson:     </th> <td>   1.648</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Prob(Omnibus):</th> <td> 0.343</td> <th>  Jarque-Bera (JB):  </th> <td>   1.569</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Skew:</th>          <td> 0.363</td> <th>  Prob(JB):          </th> <td>   0.456</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Kurtosis:</th>      <td> 2.147</td> <th>  Cond. No.          </th> <td>    13.2</td>\n",
              "</tr>\n",
              "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***Residual Analysis***\n",
        "\n",
        "***Test normality of residual through QQ Plot ***"
      ],
      "metadata": {
        "id": "ootGbpiI2_Kl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import statsmodels.api as sm"
      ],
      "metadata": {
        "id": "2eYeK52ppkQ0"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#q line is the 45 degrees line\n",
        "qqplot = sm.qqplot(model.resid, line= 'q')\n",
        "plt.title('Normal Q-Q Plot of Residual')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "mrQClo8d202S",
        "outputId": "e3a46349-68fa-4987-b65f-baba07dcdded"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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y8cbwwANpOZjRo9MEyxYtCvzmDZ/7ZMysySjqcjER8PDDaa7L+PFwyCFw1VWw4Yb1/EaNm2syZtZkFG25mHfeSYtXHnlkWrjy1Vfh8cedYCrhJGNmTUbBl4uZMAGOPhq23RY+/RTuuAPKymCXXerpDZoeJxkzazIKtlzMjBnQq1fqd3nkEejRAz75JE2wdL9LtZxkzKzJqPflYubPTx09m2ySNg3bf38YORL69IFf/KLO8TYHTjJm1ijkM2qsXpeLGTwYdtgBfv97WH11eOONVItZf/06fpLmxaPLzKzBq82osTovF/PFF6k57IEHYM014e67Uz/MEv6bfHH4WzOzBq8oo8Z+/DE1iW2ySRopduGFab7Lscc6wdRBg/3mJI2VNEzSB5LKsrKVJQ2UNDq7Xykrl6TrJY2R9KGkrXJe55js/NGSjinV5zGzxVfQUWPz58P996dO/V694MAD04z9yy6D5Zarhzdo3hpsksnsGhGdI6JL9rwH8HJEtAdezp4D7Au0z24nAbdASkpAT2BbYBugZ3liMrPGo2CjxgYNgu23T81ha68N//kPPPRQ6syxelFjkpG0oaSls8e7SDpDUqnWqe4K3Js9vhc4KKf8vkgGAytKWgvYGxgYEVMj4ltgINA0Ns42a0bqfdTYuHFpIuWOO8KXX8K998Lbb6fnVq/yqck8DsyTtBHQF1gPeKCgUSUBvChpiKSsi481ImJi9vgrYI3s8TrAFznXfpmVVVW+CEknSSqTVDZp0qT6/AxmVg/qbdTYDz+k/Vw23TStjHzRRalpzB37BZPP6LL5ETFX0sHADRFxg6T3Cx0YsFNETJC0OjBQ0se5ByMiJEV9vFFE9CUlULp06VIvr2lm9atOo8bmz4f77oO//hUmToSjjkpzXerc3mY1ySd1z5F0JHAM8ExWtlThQkoiYkJ2/w3wBKlP5eusGYzs/pvs9AmkGla5dbOyqsrNrLl4803YZpu0DH+bNqkfpl8/J5giySfJHAdsD1weEZ9LWh+4v5BBSVpW0vLlj4G9gOFAf1KyI7t/KnvcHzg6G2W2HTA9a1Z7AdhL0kpZh/9eWZmZNXWff542Dtt5Z/j6a/j3vxd29FvR1NhcFhEfSboAaJM9/xy4ssBxrQE8IQlSjA9ExPOS3gUekXQ8MA44LDt/ALAfMAaYQUqMRMRUSb2Bd7PzekXE1ALHbmal9P33qSnsmmvSumKXXgrnnvvzkQNWFIqovgtC0gHAP4CWEbG+pM6kX9YHFiPAYuvSpUuUlZWVOgwzq6158+Cee9Ikyq+/TsvB9OkD665b6siaBUlDcqabLJBPc9klpP6QaQAR8QGwQb1GZ2ZWF6+/Dl26wAknwAYbpOHI99/vBNMA5NXxHxHTK5TNL0QwZma18tln8Nvfpv1cpk5NEynfeit19FuDkE+SGSHpKKCFpPaSbgAGFTguM7Oqffdd2va4Qwd44YW0BMzHH8Phh6eJNNZg5JNkTgc6AbOAB4HvgO6FDMrMrFLz5sHtt0P79nDVVWm+yyefpH6YZZYpdXRWiXxGl80ALsxuZmal8eqrcNZZMHQo7LQTDBgAW29d6qisBlXWZCQ9Lal/VbdiBmlmTVu1G5KNGQMHHwy77QbTpsHDD6cNxJxgGoXqajL/KFoUZtZsVbUh2VIzpnPYJ5fBddfB0kun4cjdu0OrVqUN2GqlyiQTEa8XMxAza54qbkjWgrn8fsad7HbyRRCT4Y9/TB37a65ZuiBtsVXXXPZIdj8s2whskVvxQjSzxqzapjAW3XhsN17mPbbiNk5mxPwOMGQI3HGHE0wjVl1z2ZnZ/W+KEYiZNT1VNYXBwhWV27SBluM+4WrOoyv9+Yz1OYTHea/NwYzd0sORG7sqazI5+7acEhHjcm/AKcUJz8was4pNYZCeX1g+VvXbb3muw9mMoBO78ioXcAUd+YgXWh/C5X93gmkK8pkns2clZfvWdyBm1vTkNoXlmjBuLtx8M7RvT4cX/sXYXY9jt3VHc7UuYM22rRZvQzJrkKpsLpP0Z1KNZYMKfTDLA28VOjAza/zatElNZLn24gVuWOpsOPWjtBzMtdfSvnNnvCxt01RdTeYB4ADSXi0H5Ny2jojfFyE2M2vkLr984Qr7m/Axz7A/L7APa600C554Al55BTp3Lm2QVlDVDWGeDkwHjpTUgrTHy5LAcpKWi4gqKsJmZkm3btDyh6n8cN6l/P77m5mp1rx3xNVsdffpae6LNXk1Lisj6TTScv9fs3D15QD+X+HCMrNGb84cuPVWDu3ZE36cDn86kaV69WKr1VcvdWRWRDUmGdJimJtExJRCB2NmTcRzz8HZZ6eVkXffHa69FjbfvNRRWQnkM7rsC1KzmZlZ9T76CPbdF/bbL62Y3L8/DBzoBNOM5VOT+Qx4TdKzpOX+AYiIawoWlZk1LlOmwCWXwC23wHLLwTXXwKmnQsuWpY7MSiyfJDM+u7XMbmZmyZw5ab7LJZekjcROPhkuvRRWXbXUkVkDkc9+MpcWIxAza0Qi4Nln4dxzYdQo2GuvVHvp1KnUkVkDU2OfjKTVJF0taYCkV8pvxQjOzBqg4cNh773hgAMAePXcZ2n38fMssXmnShfAtOYtn47/fsDHwPrApcBY4N0CxmRmDdGkSXDKKbDFFlBWBtddxwN/GcZvbt6PceNFxMIFMJ1orFw+SWaViLgTmBMRr0fEH4HdChyXmRVRtcvxz57NkG7X8N0a7Zl7S1/uXvZUHv37aDjjDP7ac6nqF8C0Zi+fjv852f1ESfsD/wNWLlxIZlZMVS7HH0G3XzzNdyedw9Zfj2EA+3Iu/2Dk9x1pfQ7MXr7qBTCrKrfmJ58kc5mkFYBzgBuAXwBnFTQqMyuaypbj33DGh7Q98Wz46WW+XqoDhzOA53MWXy+vrVS2ACakcjPIo7ksIp6JiOkRMTwido2IrSOifzGCM7PCy611rMY33MLJvM+WdPjpfbjhBjrNGbpIgsm9LncBzHKtW6dyM8hv7bK7SWuVLSLrmzGzRq5NG5g4bhZncD1/4zJaM4MbOJ27172YoaetzNr/qLq2Ur7ny4UXpqTTpk1KMN4Lxsrl0/H/DPBsdnuZ1Fz2QyGDMrP6V2nnfgT3HvQEI9WRqzmf/7ATmzOMC1v/i/OvSF2vNdVWunWDsWNh/vx07wRji4iIWt1IiWlQba9rLLett946zJqaf/87onXriDSLMt22a/V+TOywSwTEt+t0jD+s/nxIEW3bpvMrXt+2bVR53Awoi0p+pyody5+kTYBnI2Kj+k95pdelS5coK/Mefda0tGu3sMlrdb7mMv7G8dzJtCVWZuUbeqXhZEvmMw7IrHKShkREl4rl+cz4/17Sd+X3wNPABYUI0szqpqr5LuPHw9L8xPlcyWjacyz3cC1nsdH80WmCpROMFUg+a5ctX4xAzKxuqpvv8qdVHue8yeezAZ/TnwM4l38wmo1p27akIVszUG2SkbQM0A3omBWVAY9FxOxCB2ZmtVPZfJdNZrzHRid0p9usNxmhzdgjBvIyewAeamzFUWVzmaTNgY+AX5HWKxsL7A28JWlFSZcVI0Azy0/ufJc1mcid/JEyurDBrJFw660Mved9xrTdAwnatoW+fT0SzAqvuprM9cBJETEwt1DSHsBwYEQhAzOz2mnTBr4eN5OzuJa/8ndaMpt/cg73rfc3hv1pBY4Cjjq61FFac1Ndx/9aFRMMQES8RFrP7OCCRVWPJO0jaZSkMZJ6lDoes4KI4N8HPMzH6sDfuZCX2IOOfMQlra+mR58VSh2dNWPVJZklJC1dsVBSK9KKzDMquaZBkdQCuAnYl9SvdKSkjtVfZdbIvPsu/OpX7HTjESy/3oocucYrHKInmdt2IzeJWclVl2TuAx6XtGD8iaR2wCPA/YUNq95sA4yJiM+ywQoPAV1LHJNZnZQPU15XE3h8uWNgm21g9Gi4/XZW/mwID361q2ffW4NRZZKJiMuA54E3JU2WNBl4HRgYEb2LFWAdrQN8kfP8y6xsEZJOklQmqWzSpElFC86stvr1gzNPnMEfxvVmFBvzmx8f4p9LXsDDl42GE06AFi1KHaLZIqodwhwRNwI3Slo+e/59UaIqsojoC/SFNOO/xOGYVS6Ct7s/xHszL6ANX/A4h3AeV/P53A1oezkcfmKpAzT7uXwWyCQivm+kCWYCsF7O83WzMrPG5e23YccduX7yUUxmVX7Na/yOx/mcDQBvEmYNV15JphF7F2gvaX1JLYEjAO+FY43Hl1/CH/4A220Hn3/OeavcxS95lzf49SKneZMwa6iadJKJiLnAacALwEjgkYjw/B5r8B66awbXrngpM9bbmFn/fpThB/4VPvmEztcdR6vWi/a7eOa+NWT5bFrWmrT1cpuIOFFSe2CTiHim4NHVg4gYAAwodRxmeZk/n7dOe5Cdbu3BuvElD3MYF3Alk15qR9/+3iTMGp8al/qX9DAwBDg6IjbLks6giOhcjACLzUv9W8kMHgzdu8Pbb/MuXTiLa3mLnRYcbts2DUs2a4gWe6l/YMOIuIo0y59sEqbqOT6zZqd8vktbjeepZY+C7beH8eM5hnvZlrcXSTDgzn1rnPJJMrOz1ZgDQNKGwKyCRmXWxPXrB2ed+AN/HHcxH7MJe814gj5LXsTDvT/h9bZHE5X813TnvjVG+exU1JM0KXM9Sf2AHYFjCxmUWZM2fz5Dzvw378/8C+vwPx7kCC7gSr6Y24a2vVMfS+6+MODOfWu88tm0bKCk94DtSM1kZ0bE5IJHZtYUvfUWdO/ONVPKeJtt+B2PMZjtFxweP96d+9a0VLefzFblN6AtMBH4H9AmKzOzGpT3u6yvsTyz7OGw004wcSLdV7mf7fnvIgkGFjaJdeuWOvm9Bpk1dtXVZP5ZzbEAdqvnWMyalH794OwTv+eMmVdwDv9k/owluHzJnmx46Xn8stWyLOMmMWsGqkwyEbFrMQMxa1Lmz+eDM+/lg5l/ZS2+oh9H0YMr+HLuerTtvXAospvErKnLZ55MK+AUYCdSDeZN4NaI+Knw4RWf58lYnb3xBpx1Frz3HoPZlu78i7fZbsFhKTWDmTUldZkncx/QCbgBuDF73Fj2kzErns8/h0MPhV//Gr75hjNW7cf2/HeRBAMeimzNSz5JZrOIOD4iXs1uJ5ISjZkBfPcd9OgBm24KAwbApZfCqFFs+6+jaN160XnL7nex5iafJPOepAV/iknaFnB7ktm8eQw+4Q4mrdQerrySx5c6gv+74hO4+GJo3Zpu3aBv37QcjJTuvR2yNTf5TMbcGhgkqXxRizbAKEnDgIiI/1ew6MwaqtdeY+ox3dlu/FDeYgf252ne/XEbWveAmSsvTCTdujmpWPOWT5LZp+BRmDUWn34K550HTzzBjBZt+DMP8QiHUb6c34wZacSYE4tZUmNzWUSMA74DVgBWKb9FxLjsmFnTN306nH8+dOwIL74IvXuz8byPeYTDqbherBeyNFson/1kepPWKvuUbJFMPBnTmot58+DOO+Fvf4NJk+DYY1PP/dprs/odMK6SP7M8esxsoXyayw4jLfc/u9DBmDUor7yS5rt8+GFaDua552DrrRcc9kKWZjXLZ3TZcGDFQgdi1mCMHg0HHQS7756GJz/6aJpgmZNgAI8eM8tDPjWZPsD7koaTs49MRBxYsKjMSmHaNOjdG264AZZeGvr0STtVtmpV5SUePWZWvXxqMvcCVwJXkBbNLL+ZNQ1z58Itt0D79nDttXD00ak206MHtGq1YCXlJZZI9/36lTpgs8Yjn5rMjIi4vuCRmJXCwIGp32XEiLQczLXXwpZbLjjcr9+i/S7jxqXn4BqMWT7yqcm8KamPpO0r7DFj1niNGgUHHAB77QUzZ8Ljj8Orry6SYCDNecnt2IeFc2HMrGb51GTK/9flrvLnIczWOH37LfTqBTfeCMssA1ddBWeckfpgKlHVnBfPhTHLTz6TMXet5OYEY43L3Llw002w0UZw/fXwxz+mfpfzzqPfY0tX2edS1ZwXz4Uxy08+NRkk7U9aeXnBMJuI6FWooMzq1QsvwNlnw0cfwW67wTXXwBZbADX3uXgujFnd1FiTkXQrcDhwOmn9jEOBtgWOy6zuRjwN7+EAABRoSURBVI6E/faDffaB2bPhySfhpZcWJBiouc/Fc2HM6iafjv8dIuJo4NuIuBTYHti4sGGZ1cGUKamfZfPNYdAghhz1T9rPHsESB3el3fpapDksnz6Xbt3Sdsnz56d7Jxiz/OWTZGZm9zMkrQ3MAdYqXEhmi2nOnNTf0r596n856SQe6zOanZ88mzHjWxKxsDmsPNG4z8WssPJJMs9IWhG4GngPGAs8UMigzGqj37+DY9cYwMctN4czz2TiOlvD0KFw882ce+Vq1TaHXX556mPJ5T4Xs/qTz+iy3hExLSIeJ/XFbBoRFxc+NLOaPXPlCNY4Zh/u+WZ/RPAbnmajT1+k39DNgJqbw9znYlZYiojKD0i/BL6IiK+y50cDvwXGAZdExNSiRVlEXbp0ibIy7y7d4E2eDD17Mvfm2/ie5bmUntzMKcyhJZCSxdixaUhyZcvxlx83s/ohaUhEdKlYXl1N5jZgdnbxzqS1y+4DpgN9CxGkWY1mz05Lv2y0Edx2G7dxMu0ZzXV0X5BgYGFNxc1hZqVVXZJpkVNbORzoGxGPR8RFwEaFD80sRwQ8/TRstlma87LttjB0KFe3vZEprPqz08s77t0cZlZa1SYZSeWTNXcHXsk5ltckTrN6MWxYWmPswAPTtPxnn4Xnn4dOnfKqqXgIslnpVJdkHgRel/QUaRjzmwCSNiI1mZkV1qRJ8Oc/Q+fOMGQIXHddSjj77ZeqJbimYtbQVdnxDyBpO9KcmBcj4sesbGNguYh4rzghFpc7/huA2bPTxmG9esGPP8Ipp0DPnrDKKqWOzMyqsDgd/0TE4Ih4ojzBZGWfFDLBSLpE0gRJH2S3/XKO/UXSGEmjJO2dU75PVjZGUo+c8vUlvZ2VPyypZcX3swYkAp56Cjp1gnPPhZ12SjWX6693gjFrpPKZjFkK10ZE5+w2AEBSR+AI0kKd+wA3S2ohqQVwE7Av0BE4MjsX0o6e10bERsC3wPHF/iCWpw8/hD32gIMOgqWWgueeS30vHTqUOjIzq4OGmmQq0xV4KCJmRcTnwBhgm+w2JiI+i4jZwENAV0ki7XnzWHb9vcBBJYjbqvPNN/CnP6XNwj74IO3zMnRoWtQSvPWxWSPXUJPMaZI+lHSXpJWysnWAL3LO+TIrq6p8FWBaRMytUP4zkk6SVCapbNKkSfX5Oawqs2alDcM22gjuuistaDlmDJx6aqrJsHAZ/nHjqHTdMTNr+EqSZCS9JGl4JbeuwC3AhkBnYCLwz0LHExF9I6JLRHRZbbXVCv12zVsE/N//QceOcMEFsPPOMHx4mmC50kqLnOqtj80av5LMd4mIPfI5T9LtwDPZ0wnAejmH183KqKJ8CrCipCWz2kzu+VYK778PZ50Fr7+eOvdffBH23LPK0731sVnj1+CayyTlbiNwMDA8e9wfOELS0pLWB9oD7wDvAu2zkWQtSYMD+kcam/0q8Lvs+mOAp4rxGayCr76CE06ArbeGESPglltS/0s1CQa8DL9ZU9DgkgxwlaRhkj4EdgXOAoiIEcAjwEfA88CpETEvq6WcBrwAjAQeyc4FuAA4W9IYUh/NncX9KM3cTz9Bnz5pf5f77kvLwYweDSefDEvWXIn2umNmjV+1kzGbI0/GrAcR8NhjcP75aR2Xrl3h6qtTsqmlfv1SH8z48akGc/nlns1v1hBVNRnTa5BZ/RoyBLp3h//8J21//NJLsPvui/1y3bo5qZg1Zg2xucwao//9D447Dn75Sxg1Cm67LXX01yHBmFnj55qM1c3MmXDNNanvZfbstBzMhRfCCiuUOjIzawCcZGzxRMAjj6R+l/Hj4ZBD0uTKDTcsdWRm1oC4ucxq79130+KVRxyRJlC++io8/rgTjJn9jJOM5W/CBDjmGNhmG/j0U7jjjtTRv8sutX4pr0lm1jw4yVjNZsxIe7tsvDE89BD06AGffALHHw8tWiw4Ld/E4TXJzJoPz5OpwPNkckTAgw+mNca+/BJ+97vU77L++j87tTxx5K411rp15btUtmuXEktFbdumaTVm1vgs1qZl1owNHgw77JAyxOqrp/XGHn200gQDtVvM0muSmTUfTjK2qC++gN//HrbfPlUr7r47dfTvvHO1l9UmcXhNMrPmw0nGkh9/hEsugU02SUvCXHhhWmfs2GNTJ0sNapM4vCaZWfPhJNPczZ8P99+fksull8KBB6YZ+5ddRr+nlst7BFhtEke3bqmvpm1bkNJ9ZX03Ztb4eTJmczZoUFpn7N13oUsXePhh2HFH4Ocd+eUjwKDyZFBelu9ill6TzKx5cE2miat0WPG4cXDkkSmhTJgA994Lb7+9IMHA4u1K2a1b6saZPz/dO4mYmWsyTVjF2sjkcT/wxbFXMlf/YMkWwEUXpWVhllvuZ9d6BJiZ1QcnmSasvDYi5vMH7qcPf2HtuRN5svVRHDSyT7XDudq0qXwui0eAmVltuLmsAaqvJVfGj4cd+Q/vsA33cixfsB7bM4hDZvarMVt4BJiZ1QcnmRoUe42telty5fPP6b/MYfyHX7EmX/F77md7/stgts+rNuIRYGZWH7ysTAW5y8rUZqmU+lLnJVe+/z7t7XLNNcyNJbgiLqDPnHOZwbJA4eM3s+bJy8oshsUZYVVXi93hPm8e3HUXtG+fksyhh7Lkp5+w/t09Wa3tsq6NmFlJuOO/GqUYYbVYHe6vvw5nnZW2O95uO3jqKdh2W8DzUcystFyTqUYp1tiqVYf7Z5+llZF32QUmT04rJg8atCDBmJmVmpNMNUoxwiqvDvfvvkt7unToAM89B717w8cfp50qpcIFZ2ZWS24uq0Ztl0qpz/et9D3mzUurIl94IXzzTdql8u9/h7XXLmxAZmaLyUmmBg2mT+PVV1O/y9ChafmXZ59N642ZmTVgbi5r6D79FA45BHbbDaZNS4tYvvmmE4yZNQpOMg3V9OlpXbEOHWDgwNQs9vHHcNhh7ncxs0bDzWUNzdy5cOedafHKyZPhuOPgsstgrbVKHZmZWa25JtOQvPwybLUVnHxyqsGUlaWE4wRjZo2Uk0xDMHo0dO0Ke+wBP/yQtj9+7bWUcMzMGjEnmVKaNg3OOQc6dUqjx668Ej76CH77W/e7mFmT4D6ZUpg7F26/PfW7TJ0KJ5yQJlSusUapIzMzq1euyRTbiy9C585wyimw+ebw3ntpSr8TjJk1QU4yxTJqFPzmN7D33vDTT/DEE/DKKynhmJk1UU4yhTZ1KnTvDpttliZRXn01jBgBBx3kfhcza/LcJ1Moc+bAbbdBz56pg//EE6FXL1h99VJHZmZWNCWpyUg6VNIISfMldalw7C+SxkgaJWnvnPJ9srIxknrklK8v6e2s/GFJLbPypbPnY7Lj7Yr1+Xj+edhiCzj9dNhyS/jgA7j1VicYM2t2StVcNhw4BHgjt1BSR+AIoBOwD3CzpBaSWgA3AfsCHYEjs3MBrgSujYiNgG+B47Py44Fvs/Jrs/MKa+RI2G8/2HffNIKsf/+0JMzmmxf8rc3MGqKSJJmIGBkRoyo51BV4KCJmRcTnwBhgm+w2JiI+i4jZwENAV0kCdgMey66/Fzgo57XuzR4/BuyenV//pkyBM85IyWTQILjmGhg+HA44wP0uZtasNbSO/3WAL3Kef5mVVVW+CjAtIuZWKF/ktbLj07Pzf0bSSZLKJJVNmjQp/2jnzIHrroP27eGmm+BPf4IxY9KS/C1b5v86ZmZNVME6/iW9BKxZyaELI+KpQr3v4oiIvkBfgC5dukQeF8CAAWm2/qhRsOeeqfay2WaFDtXMrFEpWJKJiD0W47IJwHo5z9fNyqiifAqwoqQls9pK7vnlr/WlpCWBFbLz62bECDj77DSpcuON4ZlnUj+Mm8XMzH6moTWX9QeOyEaGrQ+0B94B3gXaZyPJWpIGB/SPiABeBX6XXX8M8FTOax2TPf4d8Ep2/uKZPBlOPTWNGnvnHfjXv1K/y/77O8GYmVWhJPNkJB0M3ACsBjwr6YOI2DsiRkh6BPgImAucGhHzsmtOA14AWgB3RcSI7OUuAB6SdBnwPnBnVn4ncL+kMcBUUmJaPE89Bccck1ZI/vOf4ZJLYJVKu3fMzCyH6vLHfVPUpUuXKCsrW7Rw5Eg47zy46iro2LHyC83MmjFJQyLiZ/vCe8Z/Pjp0SH0vZmZWKw2tT8bMzJoQJxkzMysYJxkzMysYJxkzMysYJxkzMysYJxkzMysYJxkzMysYJxkzMysYz/ivQNIkYFyp46hgVWByqYPIU2OKFRpXvI0pVmhc8TamWKFhxts2IlarWOgk0whIKqtsuYaGqDHFCo0r3sYUKzSueBtTrNC44nVzmZmZFYyTjJmZFYyTTOPQt9QB1EJjihUaV7yNKVZoXPE2plihEcXrPhkzMysY12TMzKxgnGTMzKxgnGQaIEmHShohab6kKocpShoraZikDySVVXVeIdUi1n0kjZI0RlKPYsZYIY6VJQ2UNDq7X6mK8+Zl3+sHkvoXOcZqvytJS0t6ODv+tqR2xYyvknhqivdYSZNyvs8TShFnFstdkr6RNLyK45J0ffZZPpS0VbFjzImlplh3kTQ953u9uNgx5iUifGtgN6ADsAnwGtClmvPGAqs29FiBFsCnwAZAS2Ao0LFE8V4F9Mge9wCurOK8H0oUX43fFXAKcGv2+Ajg4RL+++cT77HAjaWKsUIsOwNbAcOrOL4f8BwgYDvg7QYc6y7AM6X+Tmu6uSbTAEXEyIgYVeo48pFnrNsAYyLis4iYDTwEdC18dJXqCtybPb4XOKhEcVQln+8q9zM8BuwuSUWMMVdD+retUUS8AUyt5pSuwH2RDAZWlLRWcaJbVB6xNgpOMo1bAC9KGiLppFIHU411gC9ynn+ZlZXCGhExMXv8FbBGFee1klQmabCkYiaifL6rBedExFxgOrBKUaL7uXz/bX+bNT89Jmm94oS2WBrSz2o+tpc0VNJzkjqVOpjKLFnqAJorSS8Ba1Zy6MKIeCrPl9kpIiZIWh0YKOnj7K+felVPsRZNdfHmPomIkFTVGP622Xe7AfCKpGER8Wl9x9pMPA08GBGzJP2JVAvbrcQxNQXvkX5Of5C0H/Ak0L7EMf2Mk0yJRMQe9fAaE7L7byQ9QWq6qPckUw+xTgBy/3pdNysriOrilfS1pLUiYmLWDPJNFa9R/t1+Juk1YEtS30Oh5fNdlZ/zpaQlgRWAKUWIrTI1xhsRubHdQeoXa6iK+rNaFxHxXc7jAZJulrRqRDSohTPdXNZISVpW0vLlj4G9gEpHoTQA7wLtJa0vqSWps7qoI7Zy9AeOyR4fA/ysJiZpJUlLZ49XBXYEPipSfPl8V7mf4XfAK5H1BJdAjfFW6NM4EBhZxPhqqz9wdDbKbDtgek7zaoMiac3yvjhJ25B+n5fqj42qlXrkgW8/vwEHk9qCZwFfAy9k5WsDA7LHG5BG8gwFRpCarhpkrNnz/YBPSLWBksSaxbEK8DIwGngJWDkr7wLckT3eARiWfbfDgOOLHOPPviugF3Bg9rgV8CgwBngH2KDEP681xdsn+xkdCrwKbFrCWB8EJgJzsp/b44GTgZOz4wJuyj7LMKoZ3dkAYj0t53sdDOxQyp+Dqm5eVsbMzArGzWVmZlYwTjJmZlYwTjJmZlYwTjJmZlYwTjJmZlYwTjLWJElaJWd12q8kTcgeT5NUrDkv5bEcJKljzvNekmo9wVVSu2pW5O0k6ZVsNeRPJV0qqd7/f1f3WSS9pmpW4rbmyUnGmqSImBIRnSOiM3ArcG32uDMwv77fL5t5X5WDgAW/mCPi4oh4qR7fexnSJMIrImITYHPS6g9n1td75CjoZ7Gmx0nGmqMWkm5X2gfnxeyXNJI2lPR8tuDom5I2zcrbZbWEDyW9LKlNVn6PpFslvQ1cVdn1knYgzXK/OqtJbZhd97vsNX4paVC2yOE7kpbP3u9NSe9ltx1q+DxHAW9FxIsAETGDNFHvvOw9LpF0bvnJkoYr24NG0pNZvCNyF1mV9IOky7O4Bktao6bPkkvSXpL+m8X/qKTlsvIrJH2UfZf/qPW/nDU6TjLWHLUHboqITsA04LdZeV/g9IjYGjgXuDkrvwG4NyL+H9APuD7ntdYlzbQ+u7LrI2IQqZZxXlazWrD+WbYMy8PAmRGxBbAHMJO0ntqeEbEVcHiF96tMJ2BIbkH2PstIWrGGa/+YxdsFOENS+WrOywKDs7jeAE6s7rPkypbi+RuwR/YZyoCzs9c+GOiUfZeX1RCbNQFeINOao88j4oPs8RCgXfaX9g7Ao1q4NcvS2f32wCHZ4/tZdIHHRyNiXg3XV2UTYGJEvAsLFzzM1qK7UVJnYB6wce0/Yt7OkHRw9ng9UgKeAswGnsnKhwB71uI1tyM1qb2VfRctgf+StiT4CbhT0jM5r29NmJOMNUezch7PA5Yh1eqnZf02tfFjdr+411fmLNI6cFtkr/tTDed/RNpFcQGlLQqmRMQ0SXNZtNWiVXbOLqTa0/YRMUNptelW2TlzYuGaU/Oo3e8KAQMj4sifHUgLOe5OWtjzNLzkf5Pn5jIzFtQiPpd0KCzY632L7PAg0urCAN2AN2t5/ffA8pW87ShgLUm/zK5ZXguX7p8YEfOBP5C2OK5OP2CnnFFey5Ca2Hpmx8eStvFFac/69bPyFYBvswSzKakGUpOqPkuuwcCOkjbK3nNZSRtntb0VImIAKZFuUd2LWNPgJGO2UDfgeEnlK1uXbyN8OnCcpA9Jv/SrGrVV1fUPAedJel/ShuUnR9qu+HDghuyagaSaxM3AMVnZpiysLVUqImaSOuQvlPQJMJk0EKBfdsrjwMqSRpBqD59k5c8DS0oaCVxBSg41qfSzVIhnEnAs8GD2nf03+xzLA89kZf8Bzs7j/ayR8yrMZk2M0nbR1wC7RsS4UsdjzZuTjJmZFYyby8zMrGCcZMzMrGCcZMzMrGCcZMzMrGCcZMzMrGCcZMzMrGD+PyzJyY+YeeaqAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "list(np.where(model.resid>10000))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1ui0_SiI20wD",
        "outputId": "a6b5553d-9a45-4ce9-e2a9-eb5a27a63701"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[array([19, 23])]"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Residual Plot for Homoscedasticity"
      ],
      "metadata": {
        "id": "xje0gIzy3k9M"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def get_standardize_values( vals): \n",
        "  return (vals - vals.mean())/vals.std() # get standardized values of fitted values and residual"
      ],
      "metadata": {
        "id": "NVk5smqZ20pO"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(get_standardize_values(model.fittedvalues), get_standardize_values(model.resid))\n",
        "plt.ylabel('Standardize Residual values')\n",
        "plt.xlabel('Standardize Fitted values')\n",
        "plt.title('Residaul Plot')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "jn8eFKb-20jM",
        "outputId": "1dd60213-9075-4315-d9f0-d40cf6b030f7"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Residual vs Regressors"
      ],
      "metadata": {
        "id": "V0p0_8SZ36J7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig = plt.figure(figsize=(15,8))\n",
        "fig = sm.graphics.plot_regress_exog(model, 'YearsExperience', fig=fig)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 585
        },
        "id": "aogMsFRv20bN",
        "outputId": "2f1e0133-2f33-4d22-9d2c-194bab89b41a"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x576 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***Model Deletion using Cooks Distance and Leverage value***"
      ],
      "metadata": {
        "id": "Uajax-7y4Gw-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_influence = model.get_influence()\n",
        "(c, _) = model_influence.cooks_distance"
      ],
      "metadata": {
        "id": "FTssltFy20OV"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Plot the influencers values using stem plot\n",
        "fig = plt.subplots(figsize=(20,8))\n",
        "plt.stem(np.arange(len(data_s)), np.round(c,3))\n",
        "plt.xlabel('Row Index')\n",
        "plt.ylabel('Cooks Distance')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 552
        },
        "id": "rD1hctxR4Q9N",
        "outputId": "cf1474e3-6a58-4098-ffbf-af30e9e3c8a5"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:3: UserWarning: In Matplotlib 3.3 individual lines on a stem plot will be added as a LineCollection instead of individual lines. This significantly improves the performance of a stem plot. To remove this warning and switch to the new behaviour, set the \"use_line_collection\" keyword argument to True.\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#index and values of influencers where c is more than .5\n",
        "(np.argmax(c), np.max(c))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-Ce2JDiu4a7s",
        "outputId": "17ff1e97-c31c-4c83-93aa-cbdb5585afb7"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(23, 0.13175452313135258)"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "High Influence Points"
      ],
      "metadata": {
        "id": "cwDfJoVk4ew0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from statsmodels.graphics.regressionplots import influence_plot"
      ],
      "metadata": {
        "id": "iN89oR064hby"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "influence_plot(model)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 311
        },
        "id": "XYYfHZZ34iUU",
        "outputId": "5a136203-c5cf-4c19-a92a-ecf53ac128ce"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "k = data_s.shape[1]\n",
        "n = data_s.shape[0]\n",
        "leverage_cutoff = 3*(k+1)/n"
      ],
      "metadata": {
        "id": "4yf3pE5V4oZ3"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "leverage_cutoff"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yhntUxA-4pqo",
        "outputId": "0063d695-b1c1-4359-fbe4-ce3427a2ff48"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.3"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "From the above plots, we can conclude that there are no influencers or outliers in dataset. No deletion required"
      ],
      "metadata": {
        "id": "JXWN-n3D4yQT"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "***Prediction***"
      ],
      "metadata": {
        "id": "4UVXRQnfM_4e"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Get predicted value for YearsExperience = 5,10\n",
        "newdata = pd.Series([5,10])"
      ],
      "metadata": {
        "id": "8FJ6KkbUMrTf"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_pred = pd.DataFrame(newdata, columns = ['YearsExperience'])"
      ],
      "metadata": {
        "id": "MVeeAYMkNSaI"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "Fnb1yOkCNcst",
        "outputId": "93eae289-95b7-4181-920a-f1ef8c27b04f"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   YearsExperience\n",
              "0                5\n",
              "1               10"
            ],
            "text/html": [
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              "      <div>\n",
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              "      <th>YearsExperience</th>\n",
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              "  </thead>\n",
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              "      <th>0</th>\n",
              "      <td>5</td>\n",
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              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\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",
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              "\n",
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              "        const buttonEl =\n",
              "          document.querySelector('#df-c5cf4965-d58a-43c9-8798-276f9b82ea42 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-c5cf4965-d58a-43c9-8798-276f9b82ea42');\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",
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          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.predict(data_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eN3DMStYNkBO",
        "outputId": "4fba8a60-1d27-4a6f-ecab-1d8e1662b1fb"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0     73042.011806\n",
              "1    120291.823413\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Q_4tj_ZwNoTy"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}