Day_4_Supervised_Learning_Practical_Kgaugelo_Mmakola.ipynb

{
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/15kay/DSI/blob/main/Day_4_Supervised_Learning_Practical_Kgaugelo_Mmakola.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VRl1qPOJYU3_"
      },
      "source": [
        "# **DATA MODELLING**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YDB3_s_qXcDn"
      },
      "source": [
        "**Mount your google drive.**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2LEkOxu_KwwJ"
      },
      "outputs": [],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2Gp1e3l0R7h_"
      },
      "source": [
        "## SUPERVISED LEARNING - Regression"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "hDCPzKPaj5As"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lln25GIKXpAW"
      },
      "source": [
        "**Read in the processed housing data set from your drive folder using pandas.**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 244,
      "metadata": {
        "id": "cYe-y6LDOCg5"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "\n",
        "df = pd.read_csv('/content/drive/MyDrive/Shared with students/Practicals/Day 4 - Supervised Learning Prac/data_processed.csv')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FJR30tT_YuKI"
      },
      "source": [
        "**View the dataset using 'display' or 'head'**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "NAyHzsKwxv6T",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "71c8ea6a-b8b9-40d2-eaf3-634cb6735439"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "       Unnamed: 0     price  bedrooms  bathrooms  sqft_living  sqft_lot  \\\n",
              "0               0  0.098361  0.121212    0.18750     0.120755  0.003888   \n",
              "1               1  0.087213  0.151515    0.43750     0.183396  0.006623   \n",
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              "21612       21612  0.060984  0.121212    0.31250     0.143396  0.002020   \n",
              "\n",
              "       floors  waterfront  view  condition  ...  yr_built  yr_renovated  \\\n",
              "0         0.2         0.0   0.0       0.50  ...  0.165217      0.000000   \n",
              "1         0.4         0.0   0.0       0.50  ...  0.765217      0.000000   \n",
              "2         0.0         0.0   0.0       0.50  ...  0.765217      0.000000   \n",
              "3         0.2         0.0   0.0       0.25  ...  0.217391      0.000000   \n",
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              "...       ...         ...   ...        ...  ...       ...           ...   \n",
              "21608     0.0         1.0   1.0       0.75  ...  0.539130      0.000000   \n",
              "21609     0.0         0.0   0.0       0.50  ...  0.695652      0.000000   \n",
              "21610     0.4         0.0   0.0       0.75  ...  0.669565      0.000000   \n",
              "21611     0.4         0.0   0.0       0.50  ...  0.600000      0.993548   \n",
              "21612     0.4         0.0   0.0       0.50  ...  0.956522      0.000000   \n",
              "\n",
              "        zipcode       lat      long  sqft_living15  sqft_lot15  year  \\\n",
              "0      0.580808  0.697603  0.107973       0.223886    0.006432   0.0   \n",
              "1      0.257576  0.789287  0.332226       0.433832    0.012244   0.0   \n",
              "2      0.207071  0.327328  0.367940       0.220444    0.010372   1.0   \n",
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              "4      0.186869  0.322020  0.418605       0.246257    0.004689   0.0   \n",
              "...         ...       ...       ...            ...         ...   ...   \n",
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              "21609  0.020202  0.781567  0.300664       0.602478    0.041115   0.0   \n",
              "21610  0.161616  0.861670  0.274917       0.308209    0.011028   1.0   \n",
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              "21612  0.050505  0.988580  0.290698       0.308209    0.003388   1.0   \n",
              "\n",
              "          month       day  \n",
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              "      <th>Unnamed: 0</th>\n",
              "      <th>price</th>\n",
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              "      <th>bathrooms</th>\n",
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              "      <th>...</th>\n",
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              "    [theme=dark] .colab-df-convert {\n",
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              "    60% {\n",
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              "      border-right-color: var(--fill-color);\n",
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              "    80% {\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "\n",
              "  <div id=\"id_087fa7fd-6785-405e-8593-cf01ead871cb\">\n",
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              "      .colab-df-generate {\n",
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              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\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",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_087fa7fd-6785-405e-8593-cf01ead871cb button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "display(df)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FWuNO8QmpfN7"
      },
      "source": [
        "There seems to be a extra column due to exporting the dataframe to csv, so lets remove it."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FjH1ayvypr0I"
      },
      "source": [
        "**Drop the 0 column**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "LAZNdHczpxCf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "109b575b-09d8-4a94-fdbf-97130e9a127b"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "          price  bedrooms  bathrooms  sqft_living  sqft_lot  floors  \\\n",
              "0      0.098361  0.121212    0.18750     0.120755  0.003888     0.2   \n",
              "1      0.087213  0.151515    0.43750     0.183396  0.006623     0.4   \n",
              "2      0.027410  0.090909    0.21875     0.093585  0.005500     0.0   \n",
              "3      0.049836  0.060606    0.12500     0.086038  0.002714     0.2   \n",
              "4      0.029115  0.090909    0.31250     0.105660  0.002707     0.4   \n",
              "...         ...       ...        ...          ...       ...     ...   \n",
              "21608  0.114754  0.090909    0.40625     0.208302  0.011129     0.0   \n",
              "21609  0.113443  0.121212    0.31250     0.216604  0.023891     0.0   \n",
              "21610  0.085902  0.121212    0.28125     0.143396  0.005142     0.4   \n",
              "21611  0.235410  0.151515    0.50000     0.318491  0.008877     0.4   \n",
              "21612  0.060984  0.121212    0.31250     0.143396  0.002020     0.4   \n",
              "\n",
              "       waterfront  view  condition     grade  ...  yr_built  yr_renovated  \\\n",
              "0             0.0   0.0       0.50  0.583333  ...  0.165217      0.000000   \n",
              "1             0.0   0.0       0.50  0.666667  ...  0.765217      0.000000   \n",
              "2             0.0   0.0       0.50  0.500000  ...  0.765217      0.000000   \n",
              "3             0.0   0.0       0.25  0.500000  ...  0.217391      0.000000   \n",
              "4             0.0   0.0       0.50  0.500000  ...  0.886957      0.000000   \n",
              "...           ...   ...        ...       ...  ...       ...           ...   \n",
              "21608         1.0   1.0       0.75  0.583333  ...  0.539130      0.000000   \n",
              "21609         0.0   0.0       0.50  0.750000  ...  0.695652      0.000000   \n",
              "21610         0.0   0.0       0.75  0.583333  ...  0.669565      0.000000   \n",
              "21611         0.0   0.0       0.50  0.750000  ...  0.600000      0.993548   \n",
              "21612         0.0   0.0       0.50  0.583333  ...  0.956522      0.000000   \n",
              "\n",
              "        zipcode       lat      long  sqft_living15  sqft_lot15  year  \\\n",
              "0      0.580808  0.697603  0.107973       0.223886    0.006432   0.0   \n",
              "1      0.257576  0.789287  0.332226       0.433832    0.012244   0.0   \n",
              "2      0.207071  0.327328  0.367940       0.220444    0.010372   1.0   \n",
              "3      0.575758  0.831269  0.182724       0.180864    0.003560   1.0   \n",
              "4      0.186869  0.322020  0.418605       0.246257    0.004689   0.0   \n",
              "...         ...       ...       ...            ...         ...   ...   \n",
              "21608  0.994949  0.373492  0.160299       0.132679    0.080779   0.0   \n",
              "21609  0.020202  0.781567  0.300664       0.602478    0.041115   0.0   \n",
              "21610  0.161616  0.861670  0.274917       0.308209    0.011028   1.0   \n",
              "21611  0.196970  0.603185  0.241694       0.535364    0.014760   0.0   \n",
              "21612  0.050505  0.988580  0.290698       0.308209    0.003388   1.0   \n",
              "\n",
              "          month       day  \n",
              "0      0.454545  0.500000  \n",
              "1      0.545455  0.700000  \n",
              "2      0.181818  0.766667  \n",
              "3      0.000000  0.866667  \n",
              "4      0.818182  0.433333  \n",
              "...         ...       ...  \n",
              "21608  0.454545  0.633333  \n",
              "21609  0.545455  0.933333  \n",
              "21610  0.181818  0.333333  \n",
              "21611  0.363636  0.166667  \n",
              "21612  0.090909  0.533333  \n",
              "\n",
              "[21613 rows x 22 columns]"
            ],
            "text/html": [
              "\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>price</th>\n",
              "      <th>bedrooms</th>\n",
              "      <th>bathrooms</th>\n",
              "      <th>sqft_living</th>\n",
              "      <th>sqft_lot</th>\n",
              "      <th>floors</th>\n",
              "      <th>waterfront</th>\n",
              "      <th>view</th>\n",
              "      <th>condition</th>\n",
              "      <th>grade</th>\n",
              "      <th>...</th>\n",
              "      <th>yr_built</th>\n",
              "      <th>yr_renovated</th>\n",
              "      <th>zipcode</th>\n",
              "      <th>lat</th>\n",
              "      <th>long</th>\n",
              "      <th>sqft_living15</th>\n",
              "      <th>sqft_lot15</th>\n",
              "      <th>year</th>\n",
              "      <th>month</th>\n",
              "      <th>day</th>\n",
              "    </tr>\n",
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              "      <th>0</th>\n",
              "      <td>0.098361</td>\n",
              "      <td>0.121212</td>\n",
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              "      <td>0.003888</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.583333</td>\n",
              "      <td>...</td>\n",
              "      <td>0.165217</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.580808</td>\n",
              "      <td>0.697603</td>\n",
              "      <td>0.107973</td>\n",
              "      <td>0.223886</td>\n",
              "      <td>0.006432</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.454545</td>\n",
              "      <td>0.500000</td>\n",
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              "      <th>1</th>\n",
              "      <td>0.087213</td>\n",
              "      <td>0.151515</td>\n",
              "      <td>0.43750</td>\n",
              "      <td>0.183396</td>\n",
              "      <td>0.006623</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.666667</td>\n",
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              "      <td>0.000000</td>\n",
              "      <td>0.257576</td>\n",
              "      <td>0.789287</td>\n",
              "      <td>0.332226</td>\n",
              "      <td>0.433832</td>\n",
              "      <td>0.012244</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.090909</td>\n",
              "      <td>0.21875</td>\n",
              "      <td>0.093585</td>\n",
              "      <td>0.005500</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.765217</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.207071</td>\n",
              "      <td>0.327328</td>\n",
              "      <td>0.367940</td>\n",
              "      <td>0.220444</td>\n",
              "      <td>0.010372</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.181818</td>\n",
              "      <td>0.766667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.049836</td>\n",
              "      <td>0.060606</td>\n",
              "      <td>0.12500</td>\n",
              "      <td>0.086038</td>\n",
              "      <td>0.002714</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.217391</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.575758</td>\n",
              "      <td>0.831269</td>\n",
              "      <td>0.182724</td>\n",
              "      <td>0.180864</td>\n",
              "      <td>0.003560</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.866667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.029115</td>\n",
              "      <td>0.090909</td>\n",
              "      <td>0.31250</td>\n",
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              "      <td>...</td>\n",
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              "      <td>0.75</td>\n",
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              "      <td>...</td>\n",
              "      <td>0.669565</td>\n",
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              "    <tr>\n",
              "      <th>21611</th>\n",
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              "      <td>0.50</td>\n",
              "      <td>0.750000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.600000</td>\n",
              "      <td>0.993548</td>\n",
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              "      <th>21612</th>\n",
              "      <td>0.060984</td>\n",
              "      <td>0.121212</td>\n",
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              "      <td>0.143396</td>\n",
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              "      <td>0.4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.583333</td>\n",
              "      <td>...</td>\n",
              "      <td>0.956522</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.050505</td>\n",
              "      <td>0.988580</td>\n",
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              "      <td>0.308209</td>\n",
              "      <td>0.003388</td>\n",
              "      <td>1.0</td>\n",
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              "      <td>0.533333</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>21613 rows × 22 columns</p>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "df.drop(columns=['Unnamed: 0'],inplace=True)\n",
        "display(df)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GYtOxDoZCDtr"
      },
      "source": [
        "Now we have a dataset that is in the correct format and has already been explored a bit during a previous prac.\n",
        "\n",
        "In the excersie after the presentaion we talked about a data set about property sales. We concluded that to create a model to determine the sale price for the given input we would use regression."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**TEST/TRAIN split**"
      ],
      "metadata": {
        "id": "pBijq8YzCIEb"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6rYZbRDdD5A-"
      },
      "source": [
        "Since we want to predict the price, lets set our target or 'y' as the price and then remove it from the dataset (X).\n",
        "\n",
        "y = f(X)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "id": "5VUvmZmYECBM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "836f9e66-2ca2-47e3-a269-2ad492c54e95"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      price\n",
              "0  0.098361\n",
              "1  0.087213\n",
              "2  0.027410\n",
              "3  0.049836\n",
              "4  0.029115"
            ],
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>price</th>\n",
              "    </tr>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.098361</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.087213</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.027410</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.049836</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.029115</td>\n",
              "    </tr>\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-9f3ffc63-8eb8-4068-b563-d4eeba200107 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-9f3ffc63-8eb8-4068-b563-d4eeba200107');\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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              "        docLink.innerHTML = docLinkHtml;\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-d1df7e4b-6b32-4db8-96e9-7cbc6ef77e0e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "y",
              "summary": "{\n  \"name\": \"y\",\n  \"rows\": 21613,\n  \"fields\": [\n    {\n      \"column\": \"price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.04814782904691146,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 4028,\n        \"samples\": [\n          0.0441449180327868,\n          0.0185573770491803,\n          0.1026001311475409\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 37
        }
      ],
      "source": [
        "y = df[['price']]\n",
        "x = df.drop(['price'],axis=1)\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VKZjiW7dEjsV"
      },
      "source": [
        "Next we have to **split our dataset** (X) and target (y) into the training and testing data set using the sklearn.model_selection \"train_test_split\" function."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "id": "PpyaOurUE2yX"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "import numpy as np\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=1)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KNa24QyvB4Cu"
      },
      "source": [
        "**Linear Regression**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-1UGS9cRDAEa"
      },
      "source": [
        "If we think back on the visualistion that we did on this dataset in day 2, we saw some linear relationships between variables.\n",
        "\n",
        "So lets see how well a linear regression model will do."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YRfPwL9T3wrE"
      },
      "source": [
        "**Import the machine learning package LinearRegression from the library 'sklearn'**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "id": "Lopv7ta_DZwH"
      },
      "outputs": [],
      "source": [
        "from sklearn.linear_model import LinearRegression"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e-lrQzUPDmr5"
      },
      "source": [
        "**Set your model** (We are going to use LinearRegression.)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "id": "y_g2IODCDkFl"
      },
      "outputs": [],
      "source": [
        "model = LinearRegression()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PAo1saP6GaGt"
      },
      "source": [
        "**Fit the model** to the training data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "id": "qOJ0kTHWGcI0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "1bd10bf6-2a13-48c3-e877-c9da2ace6ded"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ],
      "source": [
        "model.fit(X_train,y_train)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xJAkk77jGlE6"
      },
      "source": [
        "Finaly use the fitted model to **predict the test price** and calculate the accuracy by useing \"score\"."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "id": "8Qkn0o0gGtxG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6309845f-e85d-44ba-ac23-3ad4aa827f25"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6845047741258912"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ],
      "source": [
        "model.score(X_test,y_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r0wi0iRf12lS"
      },
      "source": [
        "Grab a sample (row) from the test set so that you can do a prediction."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 61,
      "metadata": {
        "id": "75P6z_FG1mox",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bb65c919-2003-49e2-ab10-d6faaf2ea8f3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "price    0.055738\n",
              "Name: 3294, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ],
      "source": [
        "X_demo = X_test.iloc[5].values.reshape(1,-1)\n",
        "y_demo = y_test.iloc[5]\n",
        "\n",
        "y_demo"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7zgsaml2xnYo"
      },
      "source": [
        "Predict the house price for X_demo"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 65,
      "metadata": {
        "id": "viR1uoOp0NIM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "18a10347-2bd9-4d88-ffd7-7e1ee3c84c9e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0557377049180327"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ],
      "source": [
        "y_pred = model.predict(X_demo)\n",
        "y_pred[0][0]\n",
        "y_demo[0]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PgBjNso1QQmA"
      },
      "source": [
        "Lets see if we can try another algorithms what will let us do some parameter tuning, such as support vector regression."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AaLacPToQrNI"
      },
      "source": [
        "## Support vector regression"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QWjdGs93ROio"
      },
      "source": [
        "Since we want to do some parameter tuning, we do not want to tune the parameters using the test dataset."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2kazxFrtQhh2"
      },
      "source": [
        "Set up a SVR with parameters kernel='linear', C=0.1 and epsilon=0.01."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 94,
      "metadata": {
        "id": "vckv-uDgL8Y1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "90d823c7-bffb-4691-c39d-8c309ffc2e45"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8105235776051986"
            ]
          },
          "metadata": {},
          "execution_count": 94
        }
      ],
      "source": [
        "from sklearn.svm import SVR\n",
        "from sklearn.model_selection import cross_validate, KFold\n",
        "model = SVR(kernel='rbf', C=0.1, epsilon=0.01)\n",
        "scores = cross_validate(model, np.array(X_train), np.array(y_train), cv=5)\n",
        "result = np.mean(scores['test_score'])\n",
        "result\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QSMTE64EZnst"
      },
      "source": [
        "The next step would be to tune the parameters to get a better result. BUT this can't just be done by changing the values and looking at the test scores, since that may leed to overfitting.\n",
        "\n",
        "Hence cross validatoin should be employed, using the \"cross_validate\" sklearn.model_selection function. https://scikit-learn.org/stable/modules/cross_validation.html\n",
        "\n",
        "Once the cross validation is incorporated, more information on the parameters that can be tuned are in the link below\n",
        "https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html\n",
        "\n",
        "Change the parameters and see how the score changes.\n",
        "\n",
        "**TIP**: don't 'fine tune' the parameters, rather change the order of magnitude e.g. C=0.1, 1, 10, etc."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kA3rRUx-TtAj"
      },
      "outputs": [],
      "source": [
        "result = np.mean(scores['test_score'])\n",
        "result"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ziNA4wKDjmGu"
      },
      "source": [
        "Once you are satified with the validation test score, fit the model to the training data and view the score of the test set."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "J2UZUKtNVg1M"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KXB_O5MWJVU3"
      },
      "source": [
        "## SUPERVISED LEARNING - Classification"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_s9sNRH6QVUX"
      },
      "source": [
        "Lets look at a very simple example for classification."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Eb92kZ3DQAh_"
      },
      "source": [
        "**Download the (famous) iris dataset** using seaborn\n",
        "\n",
        "This dataset is already cleaned, so we don't have to check for missing values or outliers. But we will habe to\n",
        "1. look at our data so that we can better understand it,\n",
        "2. normalise (just to be safe - it's good practice),\n",
        "3. encode the strings,\n",
        "4. randomise and\n",
        "5. split the data into testing and training (4 and 5 can be done together by using 'random state')."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 245,
      "metadata": {
        "id": "Ba0WE0mVJbUS"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import seaborn as sb\n",
        "df = sb.load_dataset('iris')\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**1. Look at the data.** using print and describe()"
      ],
      "metadata": {
        "id": "_jzHSS6gvdr5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "display(df)"
      ],
      "metadata": {
        "id": "lHtL3pvzvd1q",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "225d28ec-a2f3-4282-e9d0-caf15f22c62d"
      },
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "     sepal_length  sepal_width  petal_length  petal_width    species\n",
              "0             5.1          3.5           1.4          0.2     setosa\n",
              "1             4.9          3.0           1.4          0.2     setosa\n",
              "2             4.7          3.2           1.3          0.2     setosa\n",
              "3             4.6          3.1           1.5          0.2     setosa\n",
              "4             5.0          3.6           1.4          0.2     setosa\n",
              "..            ...          ...           ...          ...        ...\n",
              "145           6.7          3.0           5.2          2.3  virginica\n",
              "146           6.3          2.5           5.0          1.9  virginica\n",
              "147           6.5          3.0           5.2          2.0  virginica\n",
              "148           6.2          3.4           5.4          2.3  virginica\n",
              "149           5.9          3.0           5.1          1.8  virginica\n",
              "\n",
              "[150 rows x 5 columns]"
            ],
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              "      <th></th>\n",
              "      <th>sepal_length</th>\n",
              "      <th>sepal_width</th>\n",
              "      <th>petal_length</th>\n",
              "      <th>petal_width</th>\n",
              "      <th>species</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.6</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>145</th>\n",
              "      <td>6.7</td>\n",
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              "      <td>5.2</td>\n",
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              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>146</th>\n",
              "      <td>6.3</td>\n",
              "      <td>2.5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.9</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>147</th>\n",
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              "      <td>3.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>virginica</td>\n",
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              "    <tr>\n",
              "      <th>148</th>\n",
              "      <td>6.2</td>\n",
              "      <td>3.4</td>\n",
              "      <td>5.4</td>\n",
              "      <td>2.3</td>\n",
              "      <td>virginica</td>\n",
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              "    <tr>\n",
              "      <th>149</th>\n",
              "      <td>5.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.1</td>\n",
              "      <td>1.8</td>\n",
              "      <td>virginica</td>\n",
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              "  </tbody>\n",
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              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
              "            title=\"Generate code using this dataframe.\"\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=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_2b8c0e27-e31f-4c71-99f9-ccc99bc5301e button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 150,\n  \"fields\": [\n    {\n      \"column\": \"sepal_length\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.828066127977863,\n        \"min\": 4.3,\n        \"max\": 7.9,\n        \"num_unique_values\": 35,\n        \"samples\": [\n          6.2,\n          4.5,\n          5.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sepal_width\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4358662849366982,\n        \"min\": 2.0,\n        \"max\": 4.4,\n        \"num_unique_values\": 23,\n        \"samples\": [\n          2.3,\n          4.0,\n          3.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"petal_length\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.7652982332594662,\n        \"min\": 1.0,\n        \"max\": 6.9,\n        \"num_unique_values\": 43,\n        \"samples\": [\n          6.7,\n          3.8,\n          3.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"petal_width\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7622376689603465,\n        \"min\": 0.1,\n        \"max\": 2.5,\n        \"num_unique_values\": 22,\n        \"samples\": [\n          0.2,\n          1.2,\n          1.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"species\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"setosa\",\n          \"versicolor\",\n          \"virginica\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BnOu6wplQFkF"
      },
      "source": [
        "**Visualise the data using a seaborn [swarm plot](https://seaborn.pydata.org/generated/seaborn.swarmplot.html)**\n",
        "\n",
        "\n",
        "Set x equal to 'species' and select any one of the other parameters (one of the feature vectors) as y in the plot."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 246,
      "metadata": {
        "id": "xetYkfFSK5FO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 480
        },
        "outputId": "6800899e-534f-42d8-a6bc-5f27dd17bac5"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Example data (replace with your actual data)\n",
        "data = sns.load_dataset(\"iris\")  # Load the Iris dataset for demonstration\n",
        "\n",
        "# Create a swarm plot\n",
        "sns.swarmplot(x=\"species\", y=\"sepal_length\",hue='species', data=data)\n",
        "\n",
        "# Customize the plot (add labels, title, etc.)\n",
        "plt.xlabel(\"Species\")\n",
        "plt.ylabel(\"Sepal Length\")\n",
        "plt.title(\"Swarm Plot of Sepal Length by Species\")\n",
        "\n",
        "# Show the plot\n",
        "plt.show()\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BD2UnRnTP70x"
      },
      "source": [
        "Let us now roughly follow the procedure from the previous excersise starting with **identifying and setting X and y**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 247,
      "metadata": {
        "id": "v2RXCyodJr1Y"
      },
      "outputs": [],
      "source": [
        "y = df['species']\n",
        "x = df.drop(columns=['species'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oaEppdQDOlBb"
      },
      "source": [
        "**2.** Next, we **normalise** X."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 248,
      "metadata": {
        "id": "7jlBuwc8KjnH"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "scaler =MinMaxScaler()\n",
        "scaler.fit(x)\n",
        "X_norm = scaler.transform(x)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**3.** This dataset contains strings (in the species column) and so have to be set to integers.\n",
        "Instead of doing this manualy, a Label **Encoder** can be used (https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.LabelEncoder.html)"
      ],
      "metadata": {
        "id": "wUFj_t8L0S86"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "label_encoder = LabelEncoder()\n",
        "y_transform = label_encoder.fit_transform(y)\n"
      ],
      "metadata": {
        "id": "r5B7rOhQ0TDa"
      },
      "execution_count": 249,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NYmcREDcPqXz"
      },
      "source": [
        "**4 & 5.** Divide the data into the **train and test** set using test_size=0.3."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 250,
      "metadata": {
        "id": "hXy-xpY6O6Hu"
      },
      "outputs": [],
      "source": [
        "X_train,X_test,y_train,y_test = train_test_split(X_norm, y_transform, test_size=0.3, random_state=15)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wUuA-tLaPnkg"
      },
      "source": [
        "**Select and set up a classifier (Ransom Forest)**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 251,
      "metadata": {
        "id": "5jfU1lIlKyAw"
      },
      "outputs": [],
      "source": [
        "from sklearn.ensemble import RandomForestClassifier"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Tune the hyperparameters on the training dataset using cross_validation."
      ],
      "metadata": {
        "id": "uxh5DNAK6s8x"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = RandomForestClassifier(min_samples_split=2, max_features='log2')\n",
        "model_trained = cross_validate(model,X_train,y_train,cv=3)\n",
        "result = np.mean(model_trained['test_score'])\n",
        "result"
      ],
      "metadata": {
        "id": "jTtHLNa86tEg",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9eaa7a72-8317-427a-a82c-4094b55e8085"
      },
      "execution_count": 252,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9333333333333332"
            ]
          },
          "metadata": {},
          "execution_count": 252
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "OtQv6V1r2gYg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Fit the model with the optimal parameters and\n",
        "\n",
        "predict the test set (X_test)."
      ],
      "metadata": {
        "id": "67p11_G28LXV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(X_train,y_train)\n",
        "prediction = model.predict(X_test)\n",
        "prediction"
      ],
      "metadata": {
        "id": "AzgaDyS48Le_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b433b741-d0ea-48c0-eac4-7022d05b58b1"
      },
      "execution_count": 253,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 1, 0, 0, 1, 2, 1, 1, 2, 2, 1, 1, 1, 2, 0, 1, 2, 0, 2, 1, 0,\n",
              "       1, 1, 0, 0, 2, 2, 2, 1, 0, 2, 1, 2, 0, 0, 2, 0, 0, 1, 2, 0, 0, 1,\n",
              "       2])"
            ]
          },
          "metadata": {},
          "execution_count": 253
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Calculate the [F1 score](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html)."
      ],
      "metadata": {
        "id": "iKxkHpyh8gJZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import f1_score\n",
        "f1 = f1_score(y_test,prediction, average='macro')\n",
        "f1"
      ],
      "metadata": {
        "id": "2Rwh1SDf8o3R",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fb0abdd6-4f4b-41b7-a111-4aed252003e5"
      },
      "execution_count": 261,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {},
          "execution_count": 261
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Lets plot the [confusion matrix](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.ConfusionMatrixDisplay.html) to get a visual on the model performance."
      ],
      "metadata": {
        "id": "0Qjs9pMr8D8H"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
        "\n",
        "\n",
        "cm = confusion_matrix(y_test, prediction, labels=model.classes_)\n",
        "disp = ConfusionMatrixDisplay(confusion_matrix=cm,display_labels=model.classes_)\n",
        "\n",
        "disp.plot()\n",
        "plt.grid(False)\n",
        "plt.title('Confusin Matxi with F1 Score ' + str(f1))\n",
        "plt.xticks([0,1,2], y.unique())\n",
        "plt.yticks([0,1,2], y.unique())\n",
        "plt.show()\n",
        ""
      ],
      "metadata": {
        "id": "F0NPnQKr8EIG",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 480
        },
        "outputId": "740d24f8-fc3c-4454-cd8f-e6b48469a3cc"
      },
      "execution_count": 263,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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Q4VLgJUuWmG23BB8fH2JiYswu075+/To//PCD2X6ZLYpXo0YNgHy9jLddu3YMGzaMN998857r82SWSCxfvjxDhSu9qpPZENlvv/3G4sWL6du3L/379+eFF17g888/f+AF8dKlz/VZsGABfn5+ponAAQEB/PLLLxw9ejRbQ2ElSpTI86uW8spvv/3G3bt3M7QfPnyYqKgo0y/9WrVq4eXlxbJlyzK81/SKloeHBzVq1ODrr7822+fPP/9k9+7d2fqetGnTBltbW+bMmZOhUmY0Grl9+/YDv8esXL9+ndOnT5OUlGRqa9euHTdv3jRbDf3WrVts2bKFli1b5ukaVFL0aThMigwfHx8++OADRo4cyRNPPGFaMToxMZHw8HC2bNliWgG4evXqdOnShTVr1hAdHU2jRo04cuQI69ato3Xr1mZXhuXWE088wQcffMCwYcMIDQ0lPj6eVatWUaVKFdOl4QBz587lwIEDhISEULFiRSIjI1m5ciXly5fP9RyWB1G6dGmGDx9+3/1atGjB+vXrcXR05JFHHuHQoUPs2bPHbAItpCVytra2LFiwgJiYGOzt7WncuDGOjo6my6bTlyUYPnw4P/74I6+99hrffPPNAw+VVKpUibJly3L27FmztX0aNWrEBx98AJCtSdG1atVi1apVfPzxx1SqVAk3NzeCg4MfKJbc2L9/v2mC961bt7h79y4ff/wxkPZeGjVqlOVr169fzzfffGNa/8rOzo7Tp0/z1Vdf4eDgYLoSy8bGhokTJzJkyBA6d+5M165dKVu2LGfOnOHUqVMsWrQIgLFjxzJgwAB69OjB008/TXx8PJ9//jmlS5fOsC5TZnx8fBgxYgTTp0/n0qVLtG7dmlKlShEREcHWrVt55plneOGFF+7Zx+eff050dLRpCOvHH3/k6tWrQNptX9KT3RkzZrBu3Tq2bdtmmpvUtm1b/P39ee211zh16hSurq6sWrWKlJSUbP07F+umJEiKlMcee4wNGzawaNEitm3bxqpVq7C3t8fPz4+wsDCeeeYZ077vvPMOXl5erFu3jq1bt+Lu7s6gQYOy9YP9Qbi6ujJnzhzef/99pk2bhpeXF6NGjeL8+fNmSVCrVq24dOkSX331Fbdv38bV1ZXAwECGDx+ercuu89sbb7yBjY0N33zzDQkJCTRo0IAlS5bw4osvmu1XtmxZJk2axLx583jjjTdISUlh2bJlbN++nQsXLrB69WrTPBJ7e3vef/99evTowdSpU5k4ceIDxxUQEMCWLVvM1ouqVasWJUqUIDk52ez2FFl56aWXuHz5MgsXLuSvv/4iMDAwX5OgvXv3ZrjHWPqiksOGDbtnEpR+f7y9e/eyfft2YmNjcXV1pWnTpgwaNMhsPtSjjz7K0qVLmTt3LosXL8ZoNOLt7W32PWnSpAkLFy5k1qxZzJo1i2LFitGoUSNeffVV05Da/QwcOJDKlSvz2WefMXfuXADKly9P06ZNM11I9J8WL15sdrHC999/b6rsPPXUU/f8ftja2jJ//nymTp3K8uXLSUhIoE6dOkyePJmqVatmK36xXgZjYZvpJyIiIpIPNCdIRERErJKGw0RERKTQOH/+PIsWLeK3337j5MmTVK1alY0bN2bYLzo6mlmzZrFlyxbu3LlDuXLleO6550yL4maHkiAREREpNE6ePMnOnTupV68eqampma7PdffuXUJDQ7G1teX111+nTJkynDt3znR3gezSnCAREREpNFJTU00LloaFhXH06NEMlaCZM2eyceNGNmzYkKsFOTUnSERERAqN+91GCODLL7+kW7duuV6RXEmQiIiIFBkRERHcuHEDV1dXBg8eTO3atQkMDGT8+PHZvr9kOs0JsgLh4eEYjUbs7OwKOhQREXlASUlJGAwG6tevn2fHOHHihEVXrj9+/Diffvppltu3bduW477T7xgwZcoU2rRpw4IFCzh37hzTp0/n7t27zJgxI9t9KQmyAkajEaMxiaTkKwUdiuSDmxeLF3QIImJBLuVKk5qSt9N3ExMTSUyMw9424y2JHrivFHecnZ0tEFXm0u8TV6VKFaZMmQJAcHAwxYoVY/z48YwcOTLbC30qCbICdnZ2JCVfoaT7gIIORfLBtICmBR2CiFjQq18Pzpfj2NvepEb5l3Pdzx9XZ+Hp6Z2ras+9pCdYQUFBZu3pt0M6efJktpMgzQkSERGRIsPb2/ueN8ZNSEjIdl+qBImIiAhGIMWYapF+DLnuJWv29vY0bdqUX375xax9z549QNq9BLNLSZCIiIgAkErBLx0YFxfHzp07Abh06RKxsbFs2bIFgMDAQNzc3Bg2bBjPPvsso0ePpkuXLpw/f57p06fz5JNP4uPjk+1jKQkSERERwEgqua8EkctEKjIykldeecWsLf35smXLCAoKonbt2ixYsIAPPviAIUOG4OzsTI8ePRg5cuQDHUtJkIiIiBQaXl5enDhx4r77BQcH89VXX+XqWEqCRERE5L9zgnI/HJbXc4IsSUmQiIiIAIVjTlB+0iXyIiIiYpVUCRIREREAUqysEqQkSERERDBimeGwopRGaThMRERErJIqQSIiIgJY5uqwokRJkIiIiABYZKnEokTDYSIiImKVVAkSERGRtMUSrWxitJIgERERASClKGUwFqAkSERERADNCRIRERGxCqoEiYiIyH/nBOX+1qdFaURNSZCIiIiAEVItkcEUoSxIw2EiIiJilVQJEhEREcAyw2FFiZIgERERsco5QRoOExEREaukSpCIiIgAkGrUcJiIiIhYIWubE6ThMBEREbFKqgSJiIgIRgykWqA2YihC1SQlQSIiIgJYZk6QrQXiyC9KgkRERASwzJygopQEaU6QiIiIWCVVgkRERCRtsURj7msjRWmxRCVBIiIiAmCRidFFiXW9WxEREZH/UiVIREREAIOFFkvUJfIiIiJShFjjnCANh4mIiIhVUiVIREREAEgtQkNZlqAkSERERNKGwywwQKThMBEREZEcOH/+PBMmTKBTp07UrFmTjh073nP/rVu34ufnd9/9MqNKkIiIiACWmRidWydPnmTnzp3Uq1eP1NRUjMasa0vx8fG89957uLu75+hYBf9uRUREpBBIu4t8bh+5vUS+VatW7Ny5k1mzZlGrVq177jtv3jwqVKjAo48+mqNjKQkSERGR/14ib8j1I7dzgmxsspeaXLhwgSVLljB+/PgcH0vDYSIiImJRV65cYcSIEVlu37ZtW66P8e6779KpUyeqV6+e4z6UBImIiAhgmavD8sP27dsJDw9ny5YtuepHSZCIiIiAEVItMTHaCJ6enhap9mQmISGB9957j+HDh+Pm5parvpQEiYiISJGxdOlSbGxs6NChA9HR0QAkJSWRmppKdHQ0xYsXx97ePlt9KQkSERERjBgstFhi3q46febMGc6fP09wcHCGbY0aNWLixIn07NkzW30pCRIREREg7eqwwm7AgAF06dLFrG3+/PmcPXuWyZMnU7ly5Wz3pSRIRERECo24uDh27twJwKVLl4iNjTVNgA4MDKRatWpUq1bN7DXr1q3j2rVrBAUFPdCxlASJiIgIwH8XOyxYkZGRvPLKK2Zt6c+XLVv2wInOvSgJEhERkf8ulljwN1D18vLixIkTD/Sa999/P0fHUhIkRVbCXzZsnVeR84dKc/6QI3fv2NHrg5M07n7dbL/lox/hP1+Wy/B6j2p3+b/t4fkVrliYnX0qfV69ymPdbuPonMLZP0qwdGp5fv2pdEGHJnlA51vygpIgKbJib9mx5SMfXCvGU7HGXU7udc5y32IOqTz3/imztuJOyXkdouSh0TMv8miHKNYtLMulMw606XGLt5efYWz3avz+H8eCDk8sTOc7f6Tm8ZVdhY2SoAcUHR3N0qVLad++PY888khBh2PVnDwSeXf/f3DySOLCYUemPVkvy31tbI006nojH6OTvOTnf5eWnaNY8JYnX37qAcDWL12Zv/0EL46/wsin/lXAEYol6XznF4OF7iJfdBKpgp8BVcRER0czZ84cTp06df+dJU/ZORhx8kjK9v6pKRAXY5uHEUl+adYxipRk2Px5GVNbUoINW1a5UbPhXcpWSCzA6MTSdL7zh5G022bk9pHbOUH5SZUgsQpJcTa8WqsxiXG2lHROIuCpm3R67RwOpVILOjTJgUdqxxFxxoG7seZJ7YlDJQGoWiuOG5ezt2KsFH4635JXrC4JOnnyJFOnTuXw4cPEx8dTvnx5nn76aQYMGABAeHg4H374IYcPH8bW1pYWLVrw+uuvU6ZMGSIiInjssccAzC7f27ZtG15eXkRFRTFlyhS2b99OXFwcNWvWZPTo0TRq1Mi078GDB5kxYwbHjx8nNTUVLy8v+vfvb1r4aceOHSxdupTjx4+TkJBAtWrVGD58OM2bN8/HT+nh4uyRxGODL+FdOxZjqoFjO134ebknl/4oxctrjmBrdd+Cos/NI5lb1+wytKe3lSmn+V4PE53v/JNaBBZLtCSr+/E/ePBg3N3deffdd3F0dOTChQtcvXoVSEuAQkNDCQkJ4cMPPyQuLo6ZM2cydOhQ1qxZg4eHB3PmzGHYsGGMGjXKtFaBh4cHKSkpDBgwgIsXLzJmzBjc3d1Zvnw5zz//PKtXr6Z27drExsYyaNAgAgICmDFjBvb29pw6dcp07xOAiIgIWrZsSf/+/bGxseGnn35i4MCBLF261KJrI1iTp8adN3se8NRNPKrEs3FaJQ5tdifgqZsFFJnklH3xVJISM/6wTkxIa3Morgrfw0TnO/8UlbvIW4pVJUG3bt0iIiKCN954g1atWgHQuHFj0/bp06dTu3Zt5syZg8GQ9uXy9fWlY8eO7Ny5k5CQEGrUqAFApUqV8Pf3N71227ZtHD58mIULF/Loo48C0KxZM9q0acO8efOYPXs2Z8+eJSYmhlGjRuHn5weQ4d4nvXv3Nv1/amoqQUFBnDp1ii+++EJJkAW1fPEym6b7cGKXi5KgIigx3gY7+4wzD+wd0toS4q3rB/nDTudb8opV/ctxdXWlYsWKzJgxg3Xr1pkqQJC2TPevv/5Ku3btSElJITk5meTkZCpXroynpydHjhy5Z98HDhzA0dHRlAAB2NnZ8fjjj3Pw4EEAfHx8cHR0ZOLEiWzevJlbt25l6Ofq1auMGzeORx99lJo1a1KrVi127drF2bNnLfQpCKT9ZVnKNYm/oqzq74CHxq3rxXArl3FSfHpb5DWd14eJznf+MGIg1WiT60de30DVkqwqCTIYDCxatIiqVavy1ltvERISQteuXdm/fz/R0dGkpKQwefJkatWqZfa4fPkyV65cuWff0dHRlClTJkO7u7s7d+7cAcDZ2ZklS5ZQqlQpxo4dS9OmTQkNDTWtjJmamsqQIUM4ePAgL7/8MsuWLePLL7+kefPmJCbq6gdLio+15a9bdpQuk/2ry6TwOP17CbyqJlDSMcWsvXr9uwCc+b1EQYQleUTnO/+kYMj1oyixuvS5SpUqzJo1i6SkJMLDw5kxYwaDBw9mx44dGAwGBg0aROvWrTO8ztXV9Z79Ojs7ExkZmaH95s2bODv/bxG/unXrsnDhQuLj49m3bx9TpkzhpZdeYuvWrZw/f55jx44xd+5csxji4+Nz8Y6tW1K8gZRkG4r/44fnllleGI0GaoTcLqDIJDd+3uhM9yE3eKJ3pGndGDv7VNr0uMUfB0vqSqGHjM635BWrS4LS2dnZERgYyMCBAxkyZAg3b97E39+fM2fOUKdOnXu+DiAhIcGsPSAggEWLFrFr1y6aNWsGQHJyMlu3biUgICBDP8WLFyckJIQLFy7w7rvvkpCQYOoz/RiQdgfd8PBwKleunNu3/FDa+Vl54qKLceda2g/Bo1tdibqS9v8h/a5w904xpjzhT8BTNyj3SBwAf+x04diPbtQIuU2dNhmHJKXwOxFeip82OPP8a1dwdk/m8lkHHn/mFuW8E5kx2rugwxML0/nOP6kWWSyx6LCqJOj48eNMmTKFJ554Am9vb2JjY5k3bx4VK1bEx8eHsWPH0rdvX0aMGEGHDh1wcnLi6tWr7Nmzh65duxIUFETZsmVxcnJi06ZNeHl5YW9vj5+fHy1atKBu3bq8+uqrjB492nR12PXr15k1axaQdvn7l19+SevWralQoQI3b97k888/p0GDBjg4OFC1alXKly/P9OnTSU1N5e7du8yaNQsPD48C/uQKr+0LKnIrorjp+W9b3PltizsAjbrcoIRTMrUfu8WJXS785ysPUlMNlK0Ux5Njz/HYwMvYWNf3/aEy9RUf+l5Ku5dUaecUzv5RnAl9qnB0n26h8DDS+c57aYsl5n44qygtlmgwGo1FKd5ciYyM5P333yc8PJxr165RunRpGjZsyKhRo0yVliNHjjB79mx+/fVXkpKSKF++PI0bN2bIkCGUL18egK1btzJjxgwuXrxIYmKiaZ2g27dvM3XqVLZv387du3epVasWo0aNIjAwEIAzZ84wc+ZMDh8+TGRkJC4uLjRr1oxRo0ZRtmxZAA4fPsxbb73Fn3/+iaenJ0OGDGHv3r0cPXqUjRs35uh9HzlyhKTkC5R0H5D7D1EKveGVmhZ0CCJiQa9+PRiANk89lmfHOHLkCFFJV9lV/MNc99UsfiQuduXvOapSWFhVEmStlARZFyVBIg+X/EyCfnL4KNd9NU94pcgkQVY1HCYiIiJZMFroBqpFaNVpJUEiIiKCEUi1sjlBmhYqIiIiVkmVIBEREQGwzHBYEaIkSERERADru4u8daV8IiIiIv+lSpCIiIj8d7HE3NdGitLEaCVBIiIiAhgsNBxWdIbUNBwmIiIiVkmVIBEREQEg1cpqI0qCREREJG1OkAWGw4rSnCDrSvlERERE/kuVIBEREQGsb50gJUEiIiICQKpWjBYRERFrY8RAikVuoFp0qknWlfKJiIiI/JcqQSIiIgJoTpCIiIhYI6PWCRIREREpMOfPn2fRokX89ttvnDx5kqpVq7Jx40bT9tjYWJYsWcLOnTs5d+4c9vb21K1bl5EjR+Ln5/dAx7KulE9ERESylIoh14/cOnnyJDt37qRSpUpUq1Ytw/bLly+zZs0amjZtysyZM3n77beJiYmhR48enD59+oGOpUqQiIiIWG7F6Fx20apVK1q3bg1AWFgYR48eNdvu5eXFDz/8QIkSJUxtjRs3plWrVqxcuZL/+7//y/axlASJiIhIoWFjc+9BqpIlS2ZoK1WqFD4+Ply/fv2BjqUkSERERAALLZZogCtXrjBixIgsd9m2bVvuj/M30dHRnDx5kiZNmjzQ65QEiYiICGCw0CXy+X+Z/bRp0zAYDPTs2fOBXqckSERERDCCRSY2GwFPT0+LV3uy8tVXX/HFF1/w/vvvU758+Qd6ra4OExERkSJp586dTJgwgaFDh9KlS5cHfr0qQSIiIgIUrRWjDx06xCuvvELnzp155ZVXctSHkiAREREBis5d5E+dOsWgQYNo3LgxkyZNynE/SoJERESk0IiLi2Pnzp0AXLp0idjYWLZs2QJAYGAgRqORF154AQcHB/r27Wu2jpCjoyOPPPJIto+lJEhERESAwjEcFhkZmWF4K/35smXLALh69SoA/fr1M9svMDCQ5cuXZ/tYSoJERETEoleH5YaXlxcnTpy45z73255dRWPwT0RERMTCVAkSERERMFposcRCMKSWXUqCREREBCgcc4Lyk4bDRERExCqpEiQiIiKA9VWClASJiIhI2tVhFkiCcnt1WH5SEiQiIiKAZS6RL0o0J0hERESskipBIiIiAmhOkIiIiFgha5wTpOEwERERsUqqBImIiAig4TARERGxSha6bUYRusJMw2EiIiJilVQJEhERETCC0SI3UM19F/lFSZCIiIgAWixRRERExCqoEiQiIiJWuU6QkiAREREBLDQnqAhREiQiIiKA9a0TpDlBIiIiYpVUCRIRERFAw2HykLp5sTjTApoWdBiSD767fKigQ5B81LaCf0GHIA8NrRgtIiIiYhVUCRIRERGMgNEC17frEnkREREpcrRitIiIiIgVUCVIREREdANVERERsV5aLFFERETECqgSJCIiIoBlrg4rSpQEiYiIyH8vkddd5EVERMQKWdttMzQnSERERAqN8+fPM2HCBDp16kTNmjXp2LFjpvv9+9//pm3bttSpU4ennnqKH3/88YGPpSRIREREgLSrw3L7yK2TJ0+yc+dOKlWqRLVq1TLdZ9OmTfzf//0f7du3Z8GCBfj7+zNs2DAOHTr0QMfScJiIiIgAhWNidKtWrWjdujUAYWFhHD16NMM+s2bNokOHDowYMQKAxo0b8+effzJ37lwWLFiQ7WOpEiQiIiKFho3NvVOTixcvcu7cOdq3b2/W/sQTT/DLL7+QmJiY7WOpEiQiIiKA5SZGX7lyxVSlycy2bdty3PeZM2cAqFKlill7tWrVSEpK4uLFi1kOo/2TkiAREREBo8FCt83I2yvM7ty5A4CTk5NZe/rz9O3ZoSRIRERELMrT0zNX1Z78ojlBIiIiAvx3wcRcPvKas7MzADExMWbt0dHRZtuzQ0mQiIiImFaMzvUjj+OsWrUq8L+5QenOnDmDnZ0d3t7e2e5LSZCIiIgUGd7e3lSuXJktW7aYtW/evJng4GDs7e2z3ZfmBImIiEiaQrBOUFxcHDt37gTg0qVLxMbGmhKewMBA3NzcGD58OGPGjMHHx4egoCA2b97M4cOH+fzzzx/oWEqCREREBCgc9w6LjIzklVdeMWtLf75s2TKCgoLo2LEjcXFxLFiwgPnz51OlShXmzJlD/fr1H+hYSoJEREQEKBwrRnt5eXHixIn77te9e3e6d++eq2NpTpCIiIhYpWxVgvbv35+jzhs1apSj14mIiEj+KwzDYfkpW0lQaGgoBkP2Pxij0YjBYOCPP/7IcWAiIiKSz5QEZbRs2bK8jkNEREQkX2UrCQoMDMzrOERERKQgGS00MboQTK7OrlxPjL5+/TrHjx/n7t27lohHRERECkpRuG+GBeU4Cdq6dSvt2rUjJCSELl268NtvvwFw69YtOnfuzNatWy0WpIiIiIil5SgJ2r59O8OHD8fV1ZWXXnoJ49/qZ25ubpQrV46vvvrKYkGKiIhI3rPEvcOKkhwlQXPnzqVhw4asWrWKXr16Zdju7++vK8NERESKGg2H3d/Jkydp3759ltvd3d2JjIzMcVAiIiIieS1Ht80oUaIEcXFxWW6/ePEiLi4uOY1JRERECkBRG87KrRxVgoKCgvj6669JTk7OsO3GjRt88cUXNGvWLNfBiYiISD6ysuGwHFWCRowYQY8ePXj66adp164dBoOBXbt2sXfvXtasWYPRaOSll16ydKwiIiKSp1QJuq+qVauycuVKXFxc+OijjzAajSxatIh58+bh6+vLypUr8fLysnSsIiIiIhaTo0oQwL/+9S8+++wz7ty5w/nz5zEajXh7e+Pm5mbJ+ERERCS/FLHhrNzKcRKUztnZmbp161oiFhERESlISoKy59atWyxYsICdO3dy6dIlACpWrEhISAgvvPAC7u7uFgtSRERExNJyvE7Qk08+yZIlSyhdujTt2rWjXbt2lC5dmiVLlvDUU0/x559/WjpWERERyStGg+UeRUSOKkFvvfUWKSkpfPHFFxmGwg4fPsyAAQN4++23Wb58uUWCFBERkbxlxDJ3kS9KI2o5qgQdPnyYPn36ZDoXqG7duvTp04fDhw/nOjgRERGRvJKjSlCZMmVwcHDIcruDgwNlypTJcVAiIiJSAIpSGccCclQJ6tOnD6tWreLGjRsZtl27do1Vq1bRp0+fXAcnIiIi+ciK5gNBNitBS5YsydBWsmRJ2rRpQ+vWralUqRIA586dY9u2bfj4+Fg2ShERERELy1YSNGXKlCy3ffPNNxnaTpw4wZQpU+jXr1+OAxMREZH8YwAMFhgOK0q1oGwlQdu2bcvrOERERKSgWdmcoGwlQRUrVszrOERERKSgFbE5PbmVo4nRIiIiIkVdjm+bcfz4cT7//HOOHTtGTEwMqampZtsNBgNbt27NdYAiIiKSD4xYZjisCA2p5agStG/fPrp3786OHTvw8PDg4sWLeHt74+HhweXLlylZsiSNGjWydKwiIiKSl4wWeBQhOUqCZs2ahbe3N1u2bOG9994DYNCgQaxatYrVq1dz7do12rVrZ9FARURERCwpR0nQsWPHePrpp3F0dMTW1hbANBxWr149evTowUcffWS5KEVERCTvWVklKEdzgmxtbSlVqhQATk5OFCtWjMjISNN2b29vTp8+bZkIRR6AnX0qfV69ymPdbuPonMLZP0qwdGp5fv2pdEGHJrkQ95cN//7Yg+PhJTlxqCSxUcUY/eEF2vS4lWHf1FTYtLwMm5e7E3HGAYcSqVStGcegiZeoViu+AKIXS9B3O5/o6rD78/Hx4dy5c0DaBOiqVauaTYLesWMH7u7uFglQ5EGMnnmRrgNvsH2dK59MqEhqKry9/Ay1AmMLOjTJhTu3irHiw/JcPFmcqjXj7rnvjFE+fPJ/Xvyr7l2Gvh1Br5FX8aiYRNTNHF8HIoWAvtuSF3L0UyEkJISvvvqK0aNHU6xYMZ5//nlee+012rRpA8CFCxcYNWqURQPND6GhoZQsWZJ58+ZZtN+wsDCOHj3Kxo0bLdqvmPPzv0vLzlEseMuTLz/1AGDrl67M336CF8dfYeRT/yrgCCWn3DySWHXoKG4eyfz5WwmGt/fLdL+dG1z44Qs3Jiw6S9P2d/I5Sskr+m7nH0usGJ1b27Zt49NPP+XUqVOUKlWKgIAAxowZg7e3t8WPlaMkaOjQofTp08c0H6hLly7Y2Njw/fffY2try+DBg+natatFA80Pb775JjY2WjqpqGrWMYqUZNj8eRlTW1KCDVtWudH/9auUrZDIjcv2BRih5JS9gxE3j+T77rd2fln86v9F0/Z3SE2FxHgbipdMve/rpHDTdzsfFXAStG/fPoYNG0bnzp0ZOXIkUVFRfPTRR/Tv359vvvmG4sWLW/R4OUqC7OzscHV1NWvr1KkTnTp1skhQlhYfH5+tD+6RRx7Jh2hyLrvvw1o9UjuOiDMO3I21NWs/cagkAFVrxekH5UPsrxgbToSXpGPfmyye7MmGxe7E/WVLeZ8E+r9+hZCnogo6RMkhfbetx6ZNm6hQoQLvvfceBkPa/CQ3Nzf69u3L0aNHadiwoUWPV2jKHmvXrqVmzZrcvHnTrD0qKoratWuzevVqAMLDw+nTpw/+/v4EBAQwevRos0nZERER+Pn5sXbtWsaPH09QUBDdu3cH4ODBg/Tq1YuAgADq16/Pk08+ybp160yvDQ0NZdCgQWbHP336NMOGDSMwMJB69erx1FNPmQ1rJSQkMHnyZJo1a0adOnXo1KkTP/zww33f74kTJ3jhhRdM7+Pll1/m8uXLZvv4+fkxf/58pk2bRtOmTQkODs7mp2md3DySuXXNLkN7eluZcvevJEjRdeWcA0ajgZ3rXfl+tRsvjL/MuDnncC6TzOQhldj/oybQFlX6bluP5ORkSpUqZUqAAEqXTvvuGo2WL1NlqxLUp0+fB+7YYDCwdOnSbO//+OOP8+abb7JlyxZ69+5tav/+++8BaNeuHeHh4YSGhhISEsKHH35IXFwcM2fOZOjQoaxZs8asvxkzZhASEsL06dNJTU0lNjaWQYMGERAQwIwZM7C3t+fUqVNER0dnGdO5c+fo0aMHnp6evPHGG5QtW5Y///zTLFkZM2YMP//8MyNGjKBq1aqsX7+e4cOHM3fuXB577LFM+71y5Qq9e/fG29ubadOmkZCQwIcffkjv3r3ZsGEDjo6Opn2XLVtGvXr1ePfdd0lO1hf9XuyLp5KUmPHKhsSEtDaH4hoWeZjF3U37my76djE+2vgn1RvcBSC4bTR9g2qyamY5GrWMKcgQJYf03c4/lpoTdOXKFUaMGJHl9qxuzN61a1fWr1/PihUreOqpp4iKimLGjBnUrFmTBg0aWCa4v8lWEpST7OtBX1O6dGlCQkLYuHGjWRK0ceNGmjZtiouLC9OnT6d27drMmTPHlCX6+vrSsWNHdu7cSUhIiOl11atX59133zU9P3LkCDExMYwaNQo/v7RJlferrMyePRs7OztWrVplSkyaNGli2n78+HG+//57Jk2axLPPPgtA8+bNuXTp0j2ToM8++4zk5GQWL16Mi4sLADVq1KBDhw6sW7eO0NBQ077Ozs5m71eylhhvg519xn939g5pbQnxhabwKXkg/RdheZ8EUwIEUKJUKkGP32H7WldSksFWF4kVOfpu56MCvkS+YcOGzJkzh9GjR/PWW28Bab8fFy5caJqHbEnZ+nGwfPlyix84Mx06dGDkyJFcvnyZChUqcP36dfbv38+UKVOIi4vj119/ZezYsaSkpJheU7lyZTw9PTly5IhZEtSiRQuzvn18fHB0dGTixImEhobSuHFj3Nzc7hnP3r17adu2rVll5u8OHjwIkGF17Pbt2zN58mTu3r1LyZIlM7zuwIEDBAUFmRIggGrVqlG9enUOHjxolgQ1b95cCVA23bpejDLlkzK0u5VLa4u8pt9+D7My/z3PrmUzVkxd3JNJTrIh/q4NpZxUNShq9N0uejw9PbOs9txL+u/5Z555hhYtWhAVFcXHH3/MwIEDWblypcXnxRaq9Llly5aUKFGCTZs2AfDtt9/i4OBA69atiY6OJiUlhcmTJ1OrVi2zx+XLl7ly5YpZX2XKlDF77uzszJIlSyhVqhRjx46ladOmhIaGcuLEiSzjiYqKwsPDI8vtd+7cwc7OziyZAXB3d8doNBITk3npPTo6OtN1lMqUKcOdO3cytEn2nP69BF5VEyjpmGLWXr1+WlXgzO8lCiIsySdlyifj5pHEzSsZ545EXrPDvngqJRyVABVF+m7nE0usFp3LVaPfeecdGjduTFhYGI0bN6Zdu3bMnz+fY8eOsX79+px3nIVClQQVL16c1q1bs3nzZgA2b95My5YtKVmyJKVLl8ZgMDB48GC+/PLLDI8hQ4aY9ZVZ9aRu3bosXLiQAwcO8OmnnxIZGclLL72UZTwuLi5cv349y+3Ozs4kJSVlSFxu3ryJwWAwTebK7HV/n8ydLjIyEmdn5/u+D8nczxudsS0GT/T+32drZ59Kmx63+ONgSV09YgWaPxXFjcv2HNz5v+rtnUhbfvnOGf+mMWgFjKJJ3+18VMC3zTh9+jTVq1c3aytfvjyurq5cuHAhd51notDVEDt27MjAgQP5+eefOXToEAMGDACgZMmS+Pv7c+bMGerUqZOrYxQvXpyQkBAuXLjAu+++S0JCAg4ODhn2Cw4O5rvvvmPMmDGZDokFBAQAsGXLFnr06GFq37JlCzVr1sx0KCz9dV988QV37twxJT1nzpzhxIkTdOvWLVfvzZqdCC/FTxucef61Kzi7J3P5rAOPP3OLct6JzBht+UW2JH+tX+zOX9G2RP73iqC9PziZqj6d+t+glFMqzw6/xk/fuPDOgCp0HXiDUk4pbFpWhpQkA/3CrtyreynE9N22HhUqVODYsWNmbZcuXeL27dtUrFjR4scrdElQkyZNcHFx4fXXX8fJyYnmzZubto0dO5a+ffsyYsQIOnTogJOTE1evXmXPnj107dqVoKCgLPvdsWMHX375Ja1bt6ZChQrcvHmTzz//nAYNGmSaAAEMGzaMHTt28Nxzz/Hiiy9StmxZTp8+TVxcHAMGDKB69eq0adOG999/n/j4eKpUqcKGDRsIDw/n448/zjKWfv36sXbtWvr378+QIUNISEhg5syZeHp60qVLl5x/eMLUV3zoeynt/kKlnVM4+0dxJvSpwtF9mc/rkqLjq089uBbxv7/4d292YfdmFwBadbtNKadEXMsmM+Prkyx4qwJrF5QlJclAjYZ/MXbOed03rIjTdzt/FPSK0c8++yzvvfce77zzDq1atSIqKopPPvmEMmXK0L59e4sfr9AlQXZ2drRt25Y1a9bw9NNPY2//vx96DRo0YOXKlcyePZvXXnuNpKQkypcvT+PGjalUqdI9+/Xx8cHGxoaZM2cSGRmJi4sLzZo1u+ftPSpXrszq1auZPn06kyZNIiUlhcqVKzNw4EDTPtOmTWPGjBksWLCAqKgoqlatyqxZs2jVqlWW/Xp6erJ8+XKmTp3KmDFjsLGxoWnTpoSFhWU5CVuyJynBhoVvV2Dh2xUKOhSxsGX/OXb/nQDPSolMWHQub4ORfKfvdj4p4CSoT58+2Nvbs2rVKr766itKlSqFv78/M2fOzLBIsyUYjHmx+pAUKkeOHOHK2etM6/xpQYci+eC7y4cKOgTJR20r+Bd0CJLHXv16MABtnsp82RVLOHLkCBfv3GH4vv/kuq/ZQYF4OzvneupKfshVJejatWvs37+fyMhI2rZtS/ny5UlJSSEmJobSpUvnyTX9IiIikkesrCySoyTIaDTy/vvvs2LFCpKTkzEYDPj6+lK+fHnu3r1Lq1atePnll+nXr5+FwxUREZG8UtBzgvJbji4YXbhwIcuWLaN///4sWbLEbHXo0qVL06ZNG9PtLkREREQKoxxVgv7973/TuXNnRo0axe3btzNs9/Pz46effsp1cCIiIpKPCvi2GfktR0nQlStXqF+/fpbbS5QoQWxsbI6DEhERkXxmgcUOTf0UETlKgsqUKZPhNhV/9/vvv+Pp6ZnjoERERCT/aU5QNjz++OOsXr2aixcvmtrSb++wa9cu1q1bl+GmoiIiIiKFSY4qQS+//DL79u2jU6dONGzYEIPBwIIFC/joo484dOgQNWrUYPDgwZaOVURERPKSKkH3V7p0ab744gtefPFFrl27hoODA/v37ycmJoaXXnqJlStXUqKE7uorIiJSlBiMuX8UJTleLLF48eIMHTqUoUOHWjIeERERkXxR6O4dJiIiIgWkiFVycitHSdBrr712330MBgPvvfdeTroXERGRgqAk6P727duXoS01NZUbN26QkpKCm5ub5gSJiIhIoZajJGj79u2ZticlJbFmzRqWLl3K4sWLcxWYiIiI5B8DlpnYXJTWnM7R1WFZsbOzo3fv3jRt2pS3337bkl2LiIiIWJRFk6B01atXZ//+/XnRtYiIiIhF5MnVYXv27NGcIBERkaJGE6Pvb86cOZm2x8TEsH//fo4dO8bAgQNzFZiIiIjkI0stdliEEimLJkHOzs54e3szadIknnnmmVwFJiIiIvmsCCUwlpCjJOj48eOWjkNEREQkXz3wxOj4+HgmT56c5WXyIiIiUkQZLfAoQh44CSpevDhr1qwhMjIyL+IRERGRAmJtN1DN0SXytWrV4s8//7R0LCIiIiL5JkdJ0Ouvv87mzZv597//TXJysqVjEhERkYJgZcNh2Z4YvX//fqpVq4abmxthYWEYDAYmTJjAO++8Q7ly5XBwcDDb32AwsGHDBosHLCIiInmjqA1n5Va2k6A+ffowbdo0OnbsiIuLCy4uLlSpUiUvYxMRERHJM9lOgoxGI0ZjWoq4fPnyPAtIRERECogqQSIiImKVrCwJeqCJ0QaDIa/iEBEREclXD1QJevXVV3n11Vezta/BYODYsWM5CkpERETyme4ddm9NmjShcuXKeRSKiIiIFKgilMBYwgMlQZ07d+bJJ5/Mq1hERESkIFlZEpSjxRJFREREijolQSIiIgIUnnuHrVu3js6dO1OnTh2CgoJ48cUXiY+Pt0znf6NL5EVERCRNIRgO++STT1iwYAGDBw/G39+f27dv88svv5CSkmLxY2U7CTp+/LjFDy4iIiKS7syZM8yZM4ePP/6YkJAQU3vbtm3z5HiqBImIiAhQ8PcOW7t2LV5eXmYJUF7SnCARERFJU8B3kf/tt9/w9fXl448/Jjg4mNq1a/Pss8/y22+/5a7jLKgSJCIiIhZ15coVRowYkeX2bdu2Zdp+48YNjh49yp9//smbb75JiRIl+PTTT+nfvz/ff/89ZcqUsWicqgSJiIiIZapAuawGGY1G7t69y0cffUS7du0ICQnhk08+wWg08vnnn+e84yyoEiQiIiIAWOoOoZ6enllWe+7FyckJFxcXqlevbmpzcXGhZs2anDp1ykLR/Y8qQSIiIlIoPPLII1luS0hIsPjxlASJiIhImgKeGN2yZUuioqL4448/TG23b9/m999/p1atWrnrPBMaDhMREREMWOYS+dwMqbVu3Zo6derw8ssvM3LkSBwcHJg/fz729vY899xzuQ/uH5QEiYiISJoCXifIxsaG+fPnM3nyZCZMmEBSUhINGzZkxYoVlC1b1uLHUxIkIiIihYabmxvTpk3Ll2MpCRIREZE0heDeYflJSZCIiIgABX/bjPymq8NERETEKqkSJCIiImmsrBKkJEhEREQADYeJiIiIWAVVgkRERMQiKz6b+ikilASJPGTaVvAv6BAkHw05afmbSkrh4hSdlG/H0nCYiIiIiBVQJUhERETSWFklSEmQiIiIpFESJCIiItZIc4JERERErIAqQSIiIpLGyipBSoJEREQEAIPRurIgDYeJiIiIVVIlSERERLRitIiIiFgvXR0mIiIiYgVUCRIREZE0VlYJUhIkIiIiGLDMcJgh913kGw2HiYiIiFVSJUhERETSaDhMRERErJG1XR2mJEhERETSWFkSpDlBIiIiYpVUCRIRERFAw2EiIiJijYyAJW6gWoQSKQ2HiYiIiFVSJUhEREQADYeJiIiItbKyJEjDYSIiImKVVAkSERERAAypBR1B/lISJCIiImk0HCYiIiJS8P766y+aN2+On58fR44csXj/qgSJiIgIUPiuDvv4449JSUnJs/5VCRIREZH/LZaY64dlwjl9+jQrV65k+PDhlukwE6oEiYiICFC4KkHvvPMOzz77LFWqVMmzYygJEhEREYu6cuUKI0aMyHL7tm3b7vn6LVu28OeffzJ79mx+//13C0f3P0qCREREJE0hqATFxcXx/vvvM3LkSBwdHfP0WEqCREREBLDccJinp+d9qz1Z+eSTTyhTpgzdunWzTDD3oCRIRERECoVLly6xePFi5s6dS0xMDAB37941/fevv/6iVKlSFjuekiARERFJYyzY8bCIiAiSkpIYOHBghm19+vShXr16fPHFFxY7npIgERERwYBlhsMMuXhtjRo1WLZsmVnbH3/8weTJk5k0aRJ16tTJXXD/oCRIRERECgUnJyeCgoIy3VarVi1q1apl0eMpCRIREZE0heDqsPykJEhERESAwrVYYrqgoCBOnDiRJ33rthkiIiJilVQJEhERkbShsFQLlIIKYTUpK0qCREREJE0RSmAsQUmQiIiIAIVzTlBe0pwgERERsUqqBImIiAhgtNCK0UWnnKQkSERERAANh4mIiIhYBVWCREREJI2VVYKUBImIiAgYwWCJOUFFKJHScJiIiIhYJVWCREREJE1qQQeQv5QEiYiICGCh4bAiRMNhIiIiYpVUCRIREZE01lUIUhIkDxc7+1T6vHqVx7rdxtE5hbN/lGDp1PL8+lPpgg5NLEzn+uGU9JeB8IWuXP/NgeuHi5Nwx5aW71+jereYLF+TkgT/ftKH26ftCR53E/8Xo/Iv4IeNhsNEiq7RMy/SdeANtq9z5ZMJFUlNhbeXn6FWYGxBhyYWpnP9cIq7bcvBOW7cPm1PmeoJ2XrN0eUuxFzR3/SWYDDm/lGUPJRJUFhYGB07drRYf7Nnz6Z+/foFHofcm5//XVp2jmLJZE8Wvl2Bb1eUYWz3alyPsOfF8VcKOjyxIJ3rh1epssn03XOW0J3nCR4Xed/970bacmCOK/UH3M6H6ORh81CmzkOHDuXu3bsW66979+6EhIQUeBxyb806RpGSDJs/L2NqS0qwYcsqN/q/fpWyFRK5cdm+ACMUS9G5fnjZOkDJsinZ3n/ftDK4VEnCt1MM+z8qc/8XyL1Z2XDYQ5kE+fj43Hef+Ph4ihcvnq3+ypcvT/ny5fMkDrGcR2rHEXHGgbuxtmbtJw6VBKBqrTj9YnxI6FwLwLXfHDixrjSdV18CQ0FH8xAwgsES6wQVoTyqyA2HrV27lpo1a3Lz5k2z9qioKGrXrs3q1aszDEOtXbsWPz8/wsPDef755/H392fq1KkAnDx5kl69elGnTh3atGnDhg0bGDp0KKGhoabX/3M4bN++ffj5+bF7925Gjx5N/fr1admyJQsWLDCLKbPhsGvXrjF27FiaNGlC3bp1adeuHUuXLjVt//rrr+nZsyeBgYE0atSI0NBQDh8+nPsPzgq4eSRz65pdhvb0tjLlkvM7JMkjOtdiNMKut8pS7YlYytePL+hwpIgqcpWgxx9/nDfffJMtW7bQu3dvU/v3338PQLt27Th06FCmrx09ejQ9evRg0KBBlChRgvj4ePr374+TkxPTpk0DYO7cuURHR2erivPmm2/SqVMn5s6dy9atW/nggw/w8/OjefPmme5/+/ZtevToAcDIkSPx8vLi/PnzXLhwwbRPREQEnTt3xsfHh8TERDZt2kSvXr3YsGEDVapUydZnZK3si6eSlJjxz8HEhLQ2h+JWthTqQ0znWk58VZpbf9rTds7Vgg7l4aLhsMKtdOnShISEsHHjRrMkaOPGjTRt2hQXF5csX/vss88ycOBA0/MVK1YQGRnJqlWr8PLyAqB27dq0adMmW0lQmzZtGD58OADBwcHs2LGD7777Lssk6LPPPiMyMpJvv/3WdLzg4GCzfYYNG2b6/9TUVJo2bcrhw4dZt24do0aNum9M1iwx3gY7+4xfYHuHtLaE+CJX+JQs6Fxbt8QYA3unl6Hei1E4eqrqZ1HWlQMVveEwgA4dOnDo0CEuX74MwPXr19m/fz8dOnS45+tatGhh9vzo0aP4+vqaEhIALy8vqlevnq04mjVrZvp/g8FAtWrVuHo1679KfvnlFxo3bmx2vH86ffo0L730Ek2aNKFGjRrUqlWLs2fPcu7cuWzFZM1uXS+GW7mkDO3pbZHXilzOL1nQubZuhxa5kppk4JEnYoiOKEZ0RDH+upp2zhOibYiOKEZKYgEHKUVCkfxJ0bJlS0qUKMGmTZsYMGAA3377LQ4ODrRu3fqer3N3dzd7fv36ddzc3DLs5+bmRkLC/denKF3afFE2Ozs7YmKyXtArKiqKf/3rX1luj42NpX///ri5uREWFkaFChVwcHBg/Pjx2YrH2p3+vQT1msRS0jHFbMJs9fppV+id+b1EQYUmFqZzbd1iLxcj4Y4ta56olGHbr5+48esnbnRffwH3msqEHoQBy9w7rCjNUS+SlaDixYvTunVrNm/eDMDmzZtp2bIlJUuWfKB+PDw8uHXrVob2zNoswcXFhevXr2e5/dChQ1y9epXJkyfz1FNP0bBhQ+rUqXPPxEr+5+eNztgWgyd6/29tETv7VNr0uMUfB0vqaqGHiM61davT9w7tPr5i9gh5O+1nq1/XaNp9fIXS3homyxGjMfePIqRIVoIAOnbsyMCBA/n55585dOgQAwYMeOA+ateuzddff83Fixfx9vYG0iYmHz9+nICAAEuHTHBwMIsXL+by5ctUqFAhw/b4+LQrHOzs/nfVy6+//sqlS5fuWUGSNCfCS/HTBmeef+0Kzu7JXD7rwOPP3KKcdyIzRnsXdHhiQTrXD7cjy51JjLbhr+tpv6LObS9lGu6q3ecOZWslULaWeXU8OiJtu9u/Eqny+F/5G7AUWUU2CWrSpAkuLi68/vrrODk5ZTkZ+V66devGp59+yuDBg00TnOfMmYO7uzsGg+ULev369WP9+vX07t2bIUOG4O3tzcWLFzl37hyvvvoq/v7+lCxZkkmTJjFw4ECuXbvG7NmzKVeunMVjeVhNfcWHvpfS7idV2jmFs38UZ0KfKhzd51jQoYmF6Vw/vH5b5ELMpf/9MXj2e0fOfp92Xv/VKQaH0rr6L89Y2UdbZJMgOzs72rZty5o1a3j66aext3/w8nfx4sVZvHgxb775JmPGjKFcuXIMHTqUr7/+OsN8H0twdXVl1apVTJ8+nQ8++IC4uDgqVqzIc889B6TNWfroo4+YOnUqQ4cOpXLlykyaNImFCxdaPJaHVVKCDQvfrsDCtzNW2uThonP98Oq94/wDv8bJK5khJ0/lQTRWxGi0yJygojQkZjAai1C0+SAqKorWrVvTr18/s8vVi7IjR45w5ex1pnX+tKBDEREL0y/+h1+F6MkANK7fLs+OceTIEa5eus0H47fnuq8x77SifEVX6tSpY4HI8laRrQRZyvz583F3d6dixYrcuHGDxYsXk5KSQrdu3Qo6NBEREclDVp8E2djY8Mknn3Dt2jVsbW2pV68eS5cuxdPTs6BDExERyV9WNjhk9UnQiy++yIsvvljQYYiIiBQ8TYwWERERyX/ffvstGzZs4Pfffyc6OppKlSoRGhpKt27d8uSqbSVBIiIiAlhmxejc+Oyzz6hYsSJhYWG4urqyZ88e/u///o+rV6/mycVKSoJEREQkTQEnQZ988onZ7ayCg4OJiopiyZIlDB06FBsby97ookjeNkNEREQePpndz7NGjRrExsZy9+5dix9PlSAREREBI5apBBnhypUrjBgxIstdtm3blu3uDh48SLly5XB0tPxq8KoEiYiISJpCdgPVAwcOsHnzZvr372/RftOpEiQiIiJpLHSJvKen5wNVezJz9epVRo4cSVBQEH369LFMYP+gSpCIiIgUKtHR0QwYMAAXFxdmz55t8QnR6VQJEhEREaDgL5EHiI+PZ9CgQcTExLBmzZo8uaF5OiVBIiIiAlhqTk/O+0hOTmbEiBGcOXOGFStWUK5cOQvEkzUlQSIiIlIoTJo0iR9//JGwsDBiY2M5dOiQaVvNmjWxt7e36PGUBImIiEia1IIdDtu9ezcA77//foZt27Ztw8vLy6LHUxIkIiIiFl0nKKe2b9+e++M/AF0dJiIiIlZJlSARERFJUwiuDstPSoJEREQkjZUlQRoOExEREaukSpCIiIikKeCrw/KbkiAREREhbbFES9w8rOgkUkqCREREJI3mBImIiIg8/FQJEhERkbRRLEvMCSpCxSQlQSIiIpJGw2EiIiIiDz9VgkRERCSNlVWClASJiIhIGitLgjQcJiIiIlZJlSARERFJk2qJxRKLDiVBIiIiQtqK0ZYYDis6Q2oaDhMRERGrpEqQiIiIpBVwLFEJKjqFICVBIiIi8l+6i7yIiIhYI6NF7iJfdGhOkIiIiFglVYJEREQkjYbDRERExCppxWgRERGRh58qQSIiIpJWBbLEitFFqJqkJEhERETSFKEExhI0HCYiIiJWSZUgERERAcCoG6iKiIiIVdJwmIiIiMjDT5UgERERSaPFEkVERMTqGI1giXuHFaEhNSVBIiIiAoDRyipBmhMkIiIihcbp06d5/vnn8ff3p2nTpkydOpXExMQ8OZYqQSIiIpLGEsNhuXDnzh369u1L5cqVmT17NteuXeP9998nPj6eCRMmWPx4SoJEREQEI5YZDstND6tXr+avv/5izpw5uLi4AJCSksKkSZMYNGgQ5cqVy3V8f6fhMBERESkUfvrpJ4KDg00JEED79u1JTU1l9+7dFj+eKkFWICkpCZdypXn168EFHYqIWJhTdFJBhyB5rFiqG0ZS8vw4LuWceHXdQIv0c+XKFUaMGJHlPtu2bcu0/cyZM3Tr1s2szcnJibJly3LmzJlcx/ZPSoKsgMFgoJhdMTyreBR0KCIi8oCSkpIwGOzy9Bj29vYAlKhS3CL9nb9wJ0evi46OxsnJKUO7s7Mzd+7krM97URJkBerXr1/QIYiISCHm5+dn0f7q1KlD9+7dLdpnXtCcIBERESkUnJyciImJydB+584dnJ2dLX48JUEiIiJSKFStWjXD3J+YmBhu3LhB1apVLX48JUEiIiJSKDRv3pw9e/YQHR1tatuyZQs2NjY0bdrU4sczGI1F6CYfIiIi8tC6c+cOHTp0oEqVKgwaNMi0WOKTTz6ZJ4slKgkSERGRQuP06dO8/fbbhIeHU6pUKTp16sTIkSNNV7BZkpIgERERsUqaEyQiIiJWSUmQiIiIWCUlQSIiImKVlASJiIiIVVISJCIiIlZJSZCIiIhYJSVBUqRFR0cze/ZsTp06VdChSD4LDQ1l0KBBFu83LCyMjh07Wrxfa2Ppz3H27Nk5uhm0zqfci+4iL0VadHQ0c+bM4V//+hePPPJIQYcj+ejNN9/ExkZ/xxVWQ4cO5e7duxbrr3v37oSEhBR4HPJwURIkIoVKfHw8xYsXv+9+hT3pze77eFj5+Pjcd58H+YzKly9P+fLl8yQOsV76M0oK1MmTJxkwYABBQUHUq1ePtm3bsmDBAtP28PBw+vTpg7+/PwEBAYwePZrIyEgAIiIieOyxxwB45ZVX8PPzw8/Pj4iICACioqJ47bXXCAoKom7dujz77LPs37/f7PgHDx6kV69eBAQEUL9+fZ588knWrVtn2r5jxw6ef/55goODadCgAd27d+enn37K64+l0Fu7di01a9bk5s2bZu1RUVHUrl2b1atXA/c+f5B2Dv38/Fi7di3jx48nKCiI7t27A/c/N5kNh50+fZphw4YRGBhIvXr1eOqpp9i4caNpe0JCApMnT6ZZs2bUqVOHTp068cMPP9z3/Z44cYIXXnjB9D5efvllLl++bLaPn58f8+fPZ9q0aTRt2pTg4OBsfppFT3bO/z+HodauXYufnx/h4eE8//zz+Pv7M3XqVCDt50CvXr2oU6cObdq0YcOGDQwdOpTQ0FDT6/85HLZv3z78/PzYvXs3o0ePpn79+rRs2dLs5wdkPhx27do1xo4dS5MmTahbty7t2rVj6dKlpu1ff/01PXv2JDAwkEaNGhEaGsrhw4dz/8FJoaNKkBSowYMH4+7uzrvvvoujoyMXLlzg6tWrQNov0NDQUEJCQvjwww+Ji4tj5syZDB06lDVr1uDh4cGcOXMYNmwYo0aNIigoCAAPDw9SUlIYMGAAFy9eZMyYMbi7u7N8+XKef/55Vq9eTe3atYmNjWXQoEEEBAQwY8YM7O3tOXXqlNndiyMiImjZsiX9+/fHxsaGn376iYEDB7J06VLT8azR448/zptvvsmWLVvo3bu3qf37778HoF27dvc9f383Y8YMQkJCmD59Oqmpqdk6N/907tw5evTogaenJ2+88QZly5blzz//NEtWxowZw88//8yIESOoWrUq69evZ/jw4cydO9eUUP/TlStX6N27N97e3kybNo2EhAQ+/PBDevfuzYYNG3B0dDTtu2zZMurVq8e7775LcnJyjj7boiA75//QoUOZvnb06NH06NGDQYMGUaJECeLj4+nfvz9OTk5MmzYNgLlz5xIdHZ2tKs6bb75Jp06dmDt3Llu3buWDDz7Az8+P5s2bZ7r/7du36dGjBwAjR47Ey8uL8+fPc+HCBdM+ERERdO7cGR8fHxITE9m0aRO9evViw4YNVKlSJVufkRQRRpECEhkZafT19TVu27Yt0+29evUy9ujRw5iammpqO3nypNHPz8+4Y8cOo9FoNF68eNHo6+tr/Pbbb81eu3XrVqOvr6/xp59+MrUlJiYaW7RoYRw2bJjRaDQaDx8+bPT19TUeP348W/GmpKQYk5KSjP379zeOGjXqgd7rw+ill14y9ujRw6wtNDTUOHDgQKPR+GDn74UXXjDrJzvnpnfv3qZjGY1G46hRo4yNGzc2xsTEZLr/H3/8YfT19TWuWrXKrL1Hjx7GLl26mJ6PGzfO2KFDB9Pz9957z+jv72+8ffu2qe3UqVNGPz8/47Jly0xtvr6+xieeeMLs/T7M7nf+//k5fvXVV0ZfX1/jvHnzzF7z+eefG2vUqGG8ePGiqe3ixYvGGjVqGHv37m1qmzVrltHf39/0fO/evUZfX1/jlClTTG2pqanGli1bGl9//XVT2z/jmDFjhrF27dpmx7uX9O9927ZtjdOnT8/Wa6To0HCYFBhXV1cqVqzIjBkzWLdunakCBBAXF8evv/5Ku3btSElJITk5meTkZCpXroynpydHjhy5Z98HDhzA0dGRRx991NRmZ2fH448/zsGDB4G0uQKOjo5MnDiRzZs3c+vWrQz9XL16lXHjxvHoo49Ss2ZNatWqxa5duzh79qyFPoWiq0OHDhw6dMhUabl+/Tr79++nQ4cOD3z+WrRoYfY8O+fmn/bu3Uvbtm3NKjN/l37e27VrZ9bevn17jh07luXk2QMHDhAUFISLi4uprVq1alSvXt3UZ7rmzZtjMBjuG+vD4F7n/17+ea6PHj2Kr68vXl5epjYvLy+qV6+erTiaNWtm+n+DwUC1atXMfpb80y+//ELjxo3NjvdPp0+f5qWXXqJJkybUqFGDWrVqcfbsWc6dO5etmKToUBIkBcZgMLBo0SKqVq3KW2+9RUhICF27dmX//v1ER0eTkpLC5MmTqVWrltnj8uXLXLly5Z59R0dHU6ZMmQzt7u7u3LlzBwBnZ2eWLFlCqVKlGDt2LE2bNiU0NJQTJ04AkJqaypAhQzh48CAvv/wyy5Yt48svv6R58+YkJiZa/gMpYlq2bEmJEiXYtGkTAN9++y0ODg60bt36gc/fP8/V/c5NZqKiovDw8Mhy+507d7CzszNLZiDt34TRaCQmJibT10VHR+Pu7p6hvUyZMqZ/S1m9j4fZvc7/vfzzs7x+/Tpubm4Z9susLTOlS5c2e25nZ3fP7+f9/p3ExsbSv39/Ll++TFhYGCtWrODLL7+kevXqJCQkZCsmKTo0J0gKVJUqVZg1axZJSUmEh4czY8YMBg8ezI4dOzAYDAwaNCjTH6qurq737NfZ2dlsAm66mzdv4uzsbHpet25dFi5cSHx8PPv27WPKlCm89NJLbN26lfPnz3Ps2DHmzp1rFkN8fHwu3vHDo3jx4rRu3ZrNmzczYMAANm/eTMuWLSlZsiTAA52/zKon9zo3mXFxceH69etZxuvs7ExSUhJ37twx+zdw8+ZNDAZDhl+mf39dZv+WIiMjqVy58n3fx8Pqfuc/uzw8PPjjjz8ytN+6dYtSpUpZKlyT+/07OXToEFevXmXevHlm1aiYmJgcXZ0mhZsqQVIo2NnZERgYyMCBA4mNjeXmzZv4+/tz5swZ6tSpk+GRXsq2s7MDyPAXWkBAALGxsezatcvUlpyczNatWwkICMhw/OLFixMSEkLPnj2JiIggISHB1Gf6MQAuXbpEeHi4xd9/UdWxY0eOHTvGzz//zKFDh0xDISVLlszW+cuOzM5NZoKDg/nuu++IjY3NdHv6ed+yZYtZ+5YtW6hZs2aWv7wDAgLYu3evWdXnzJkznDhxItN/S9Ykq/P/IGrXrs2JEye4ePGiqS0iIoLjx49bMlST4OBg9u7dm+HqvnTpf+T8/Xv/66+/cunSpTyJRwqWKkFSYI4fP86UKVN44okn8Pb2JjY2lnnz5lGxYkV8fHwYO3Ysffv2ZcSIEXTo0AEnJyeuXr3Knj176Nq1K0FBQZQtWxYnJyc2bdqEl5cX9vb2+Pn50aJFC+rWrcurr77K6NGjTVeHXb9+nVmzZgFpl79/+eWXtG7dmgoVKnDz5k0+//xzGjRogIODA1WrVqV8+fKmK5bu3r3LrFmz7llKtzZNmjTBxcWF119/HScnJ7MrcrJz/rJyv3OTmWHDhrFjxw6ee+45XnzxRcqWLcvp06eJi4tjwIABVK9enTZt2vD+++8THx9PlSpV2LBhA+Hh4Xz88cdZxtKvXz/Wrl1L//79GTJkCAkJCcycORNPT0+6dOmS8w/vIXCv859d3bp149NPP2Xw4MEMHz4cgDlz5uDu7p4nlbV+/fqxfv16evfuzZAhQ/D29ubixYucO3eOV199FX9/f0qWLMmkSZMYOHAg165dY/bs2ZQrV87isUjBUxIkBaZs2bK4u7szb948rl27RunSpWnYsCHTpk3D1taWBg0asHLlSmbPns1rr71GUlIS5cuXp3HjxlSqVAkAGxsbJk+ezIwZM+jXrx+JiYls27YNLy8v5s+fz9SpU5k2bRp3796lVq1aLF68mNq1awNpk29tbGyYOXMmkZGRuLi40KxZM0aNGgWAvb09s2fP5q233uKVV17B09OTIUOGsHfvXo4ePVpgn1thYmdnR9u2bVmzZg1PP/009vb2pm3ZOX9Zud+5yUzlypVZvXo106dPZ9KkSaSkpFC5cmUGDhxo2mfatGnMmDGDBQsWEBUVRdWqVZk1axatWrXKsl9PT0+WL1/O1KlTGTNmDDY2NjRt2pSwsLAsJ2Fbi3ud/+wqXrw4ixcv5s0332TMmDGUK1eOoUOH8vXXX2c5RJkbrq6urFq1iunTp/PBBx8QFxdHxYoVee6554C0OUsfffQRU6dOZejQoVSuXJlJkyaxcOFCi8ciBc9gNBqNBR2EiIhIuqioKFq3bk2/fv0YNmxYQYcjDzFVgkREpEDNnz8fd3d3KlasyI0bN1i8eDEpKSl069atoEOTh5ySIBERKVA2NjZ88sknXLt2DVtbW+rVq8fSpUvx9PQs6NDkIafhMBEREbFKukReRERErJKSIBEREbFKSoJERETEKikJEhEREaukJEhERESskpIgEckTrVq1IiwszPR83759+Pn5sW/fvgKMytw/Y8yKn58fs2fPfuD+165di5+fH0eOHMlJeJmaPXs2fn5+FutPxJopCRJ5CKX/8k1/1KlTh7Zt2/LWW29x8+bNgg7vgezcuTNHCYiIyP1osUSRh9jLL7+Ml5cXiYmJHDx4kFWrVrFz5042btxIiRIl8jWWRo0acfjwYbO7c2fHzp07WbFihenmmiIilqIkSOQh1rx5c+rUqQNA9+7dcXFxYcmSJWzbto2OHTtm+pq7d+9SsmRJi8diY2OT5R3gRUQKgobDRKxI48aNAYiIiAAgLCyM+vXrc+HCBQYMGED9+vUZM2YMAKmpqXz22Wd06NCBOnXq0KRJEyZMmMCdO3fM+jQajXz88cc0b96cevXqERoaysmTJzMcO6s5Qb/99hsDBgygUaNG+Pv78+STT7J06VJTfCtWrAAwG95LZ+kYs+vSpUtMnDiRtm3bUrduXYKCgnj55ZdNn+s/xcfHM2HCBIKCgmjQoAFjx47NECOkVb2ee+45/P39qV+/PgMHDsxVnCJyb6oEiViRCxcuAODi4mJqS05O5oUXXiAgIIBx48ZRvHhxACZMmMC6devo2rUroaGhREREsGLFCo4dO8aqVatMw1offfQRn3zyCSEhIYSEhPD777/Tv39/kpKS7hvP7t27GTRoEB4eHvTp0wd3d3dOnz7Njh076Nu3Lz169OD69evs3r2bqVOnZnh9fsSYmSNHjhAeHk6HDh0oX748ly5dYtWqVfTp04dNmzZlGGp86623cHJyYtiwYZw9e5ZVq1Zx+fJlli9fjsFgAODrr78mLCyMZs2aMWbMGOLi4li1ahXPPfcc69atw8vLK0exisg9GEXkofPVV18ZfX19jXv27DFGRkYar1y5Yty0aZMxMDDQWLduXePVq1eNRqPROG7cOKOvr6/xgw8+MHv9/v37jb6+vsYNGzaYtf/0009m7ZGRkcZatWoZBw4caExNTTXtN2PGDKOvr69x3Lhxpra9e/cafX19jXv37jUajUZjcnKysVWrVsaWLVsa79y5Y3acv/c1adIko6+vb4b3mBcxZsXX19c4a9Ys0/O4uLgM+4SHhxt9fX2N69atM7Wln4cuXboYExMTTe0LFiww+vr6Grdu3Wo0Go3G2NhYY8OGDY3jx4836/PGjRvGgIAAs/ZZs2Zl+nmIyIPTcJjIQ6xfv34EBwcTEhLCyJEjKVWqFHPmzKFcuXJm+/Xs2dPs+ZYtWyhdujRNmzbl1q1bpketWrUoWbKkaUhrz549JCUl0bt3b1NFA6Bv3773je3YsWNERETQp08fnJyczLb9va+s5EeMWUmvlgEkJSVx+/ZtfHx8cHJy4tixYxn279Gjh9mE8J49e1KsWDF27txpijE6OpoOHTqYvRcbGxvq1atXqJYVEHmYaDhM5CE2YcIEqlSpgq2tLe7u7lSpUgUbG/O/fYoVK0b58uXN2s6fP09MTAzBwcGZ9hsZGQnA5cuXAahcubLZdjc3N5ydne8Z28WLFwHw9fXN9vvJ7xizEh8fz7x581i7di3Xrl3DaDSatsXExGTYv1KlSmbPS5UqRdmyZbl06RIA586dA7JOzBwdHXMUp4jcm5IgkYdY3bp1TVeHZcXe3j5DYpSamkqZMmX44IMPMn2Nm5ubxWLMqYKM8e2332bt2rX07dsXf39/SpcujcFgYOTIkWYJUXalv2bq1KmULVs2w3ZbW9tcxywiGSkJEpEMfHx8+OWXX2jQoIHZ0M8/VahQAUirZHh7e5vab926lenVT3+Xvv+ff/5JkyZNstwvq6Gx/IgxK9999x2dO3c2W206ISEh0yoQpFWt0q/MA/jrr7+4ceMGzZs3B/73WZQpU+aen4WIWJbmBIlIBu3btyclJYWPP/44w7bk5GSio6MBaNKkCXZ2dnz++edmFZD0S9zvpVatWnh5ebFs2TJTf+n+3lf6lVb/3Cc/YsxKZpWZ5cuXk5KSkun+a9asMbsSbdWqVSQnJ5uSoEcffRRHR0fmzZuX6RVrt27dynGsIpI1VYJEJIPAwEB69OjBvHnz+OOPP2jatCl2dnacO3eOLVu28MYbb9CuXTvc3Nzo378/8+bNY9CgQYSEhHDs2DF++uknXF1d73kMGxsbJk6cyJAhQ+jcuTNdu3albNmynDlzhlOnTrFo0SIgLVkCeOedd2jWrBm2trZ06NAhX2LMSosWLVi/fj2Ojo488sgjHDp0iD179pgtPfB3SUlJ9OvXj/bt23P27FlWrlxJQEAAjz32GJA252fixImMHTuWrl278sQTT+Dm5sbly5fZuXMnDRo0YMKECTmKVUSypiRIRDL11ltvUbt2bVavXs2HH36Ira0tFStW5KmnnqJBgwam/UaMGIG9vT2rV69m37591K1bl8WLFzNo0KD7HuPRRx9l6dKlzJ07l8WLF2M0GvH29uaZZ54x7dOmTRtCQ0PZtGkTGzZswGg00qFDh3yLMTNvvPEGNjY2fPPNNyQkJNCgQQOWLFnCiy++mOn+EyZM4JtvvmHWrFkkJSXRoUMHxo8fbzbU9+STT+Lh4cH8+fNZtGgRiYmJlCtXjoYNG9K1a9ccxSki92Yw5mQWn4iIiEgRpzlBIiIiYpWUBImIiIhVUhIkIiIiVklJkIiIiFglJUEiIiJilZQEiYiIiFVSEiQiIiJWSUmQiIiIWCUlQSIiImKVlASJiIiIVVISJCIiIlZJSZCIiIhYpf8HqAWgh10NI84AAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}