Drug_Performance_Evaluation_.ipynb

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        "Importing Packages and Data:"
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import datetime as dt\n",
        "from matplotlib import pyplot as plt\n",
        "from sklearn.metrics import mean_absolute_error"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('/content/Drug_clean.csv')"
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      "execution_count": 3,
      "outputs": []
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        "df.head(5)"
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          "data": {
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              "                   Condition                         Drug  EaseOfUse  \\\n",
              "0  Acute Bacterial Sinusitis                  Amoxicillin   3.852353   \n",
              "1  Acute Bacterial Sinusitis  Amoxicillin-Pot Clavulanate   3.470000   \n",
              "2  Acute Bacterial Sinusitis  Amoxicillin-Pot Clavulanate   3.121429   \n",
              "3  Acute Bacterial Sinusitis                   Ampicillin   2.000000   \n",
              "4  Acute Bacterial Sinusitis                   Ampicillin   3.250000   \n",
              "\n",
              "   Effective            Form Indication       Price     Reviews  Satisfaction  \\\n",
              "0   3.655882         Capsule   On Label   12.590000   86.294118      3.197647   \n",
              "1   3.290000  Liquid (Drink)  Off Label  287.370000   43.000000      2.590000   \n",
              "2   2.962857          Tablet   On Label   70.608571  267.285714      2.248571   \n",
              "3   3.000000         Capsule   On Label   12.590000    1.000000      1.000000   \n",
              "4   3.000000          Tablet   On Label  125.240000   15.000000      3.000000   \n",
              "\n",
              "  Type  \n",
              "0   RX  \n",
              "1   RX  \n",
              "2   RX  \n",
              "3   RX  \n",
              "4   RX  "
            ],
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              "      <th></th>\n",
              "      <th>Condition</th>\n",
              "      <th>Drug</th>\n",
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              "      <td>Acute Bacterial Sinusitis</td>\n",
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              "      <td>3.655882</td>\n",
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              "      <th>1</th>\n",
              "      <td>Acute Bacterial Sinusitis</td>\n",
              "      <td>Amoxicillin-Pot Clavulanate</td>\n",
              "      <td>3.470000</td>\n",
              "      <td>3.290000</td>\n",
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              "      <td>Amoxicillin-Pot Clavulanate</td>\n",
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              "      <td>2.962857</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Acute Bacterial Sinusitis</td>\n",
              "      <td>Ampicillin</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>Capsule</td>\n",
              "      <td>On Label</td>\n",
              "      <td>12.590000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>RX</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
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              "      <td>Ampicillin</td>\n",
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      "source": [
        "df.shape"
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        "id": "XqsGTcjWiyxZ",
        "outputId": "07d4f53c-cd46-4a7b-bd92-df959612ec7a"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(628, 20)"
            ]
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          "metadata": {},
          "execution_count": 43
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        "df.info()"
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        "outputId": "2a70f0a5-0631-4721-8da8-4375bcb342b9"
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      "execution_count": 7,
      "outputs": [
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          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 685 entries, 0 to 684\n",
            "Data columns (total 10 columns):\n",
            " #   Column        Non-Null Count  Dtype  \n",
            "---  ------        --------------  -----  \n",
            " 0   Condition     685 non-null    object \n",
            " 1   Drug          685 non-null    object \n",
            " 2   EaseOfUse     685 non-null    float64\n",
            " 3   Effective     685 non-null    float64\n",
            " 4   Form          685 non-null    object \n",
            " 5   Indication    685 non-null    object \n",
            " 6   Price         685 non-null    float64\n",
            " 7   Reviews       685 non-null    float64\n",
            " 8   Satisfaction  685 non-null    float64\n",
            " 9   Type          685 non-null    object \n",
            "dtypes: float64(5), object(5)\n",
            "memory usage: 53.6+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Checking missing values:\n",
        "df.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A7evMV7ni8rR",
        "outputId": "2fdd84cc-fa42-4068-caaf-df871891a37c"
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      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Condition       0\n",
              "Drug            0\n",
              "EaseOfUse       0\n",
              "Effective       0\n",
              "Form            0\n",
              "Indication      0\n",
              "Price           0\n",
              "Reviews         0\n",
              "Satisfaction    0\n",
              "Type            0\n",
              "dtype: int64"
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          "metadata": {},
          "execution_count": 9
        }
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      "source": [
        "# Drop all duplicates:\n",
        "df = df.drop_duplicates()"
      ],
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      "execution_count": 10,
      "outputs": []
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      "cell_type": "code",
      "source": [
        "# Checking if there's any negative value within each numerical column:\n",
        "num_cols = ['EaseOfUse', 'Effective', 'Price', 'Reviews', 'Satisfaction']\n",
        "(df[num_cols] < 0).any().any()"
      ],
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        "outputId": "bcb56212-a3ba-4271-d686-10f79353c5ba"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Inspecting the distribution of the numerical variables"
      ],
      "metadata": {
        "id": "_Hdm2nZQjLOy"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Frequency of the categorical variables:"
      ],
      "metadata": {
        "id": "Hp_hbyKcjMR3"
      }
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    {
      "cell_type": "code",
      "source": [
        "df.replace('\\r\\r\\n', np.nan, inplace=True)\n",
        "df.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hG45K92hjPlB",
        "outputId": "3f7d16c3-950b-43a3-982c-5c9f16b776a6"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Condition       0\n",
              "Drug            0\n",
              "EaseOfUse       0\n",
              "Effective       0\n",
              "Form            0\n",
              "Indication      8\n",
              "Price           0\n",
              "Reviews         0\n",
              "Satisfaction    0\n",
              "Type            5\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.impute import SimpleImputer\n",
        "\n",
        "imputer = SimpleImputer(strategy='most_frequent')\n",
        "imputer.fit(df)\n",
        "df = pd.DataFrame( imputer.transform(df),columns=df.columns)\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 399
        },
        "id": "MsgRjSc4jbqn",
        "outputId": "85b063c3-3904-44d5-a0c5-eca3b594a9e5"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   Condition                         Drug EaseOfUse Effective  \\\n",
              "0  Acute Bacterial Sinusitis                  Amoxicillin  3.852353  3.655882   \n",
              "1  Acute Bacterial Sinusitis  Amoxicillin-Pot Clavulanate      3.47      3.29   \n",
              "2  Acute Bacterial Sinusitis  Amoxicillin-Pot Clavulanate  3.121429  2.962857   \n",
              "3  Acute Bacterial Sinusitis                   Ampicillin       2.0       3.0   \n",
              "4  Acute Bacterial Sinusitis                   Ampicillin      3.25       3.0   \n",
              "\n",
              "             Form Indication      Price     Reviews Satisfaction Type  \n",
              "0         Capsule   On Label      12.59   86.294118     3.197647   RX  \n",
              "1  Liquid (Drink)  Off Label     287.37        43.0         2.59   RX  \n",
              "2          Tablet   On Label  70.608571  267.285714     2.248571   RX  \n",
              "3         Capsule   On Label      12.59         1.0          1.0   RX  \n",
              "4          Tablet   On Label     125.24        15.0          3.0   RX  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9b15a0d9-a6c7-4e55-96d7-edf942109760\" class=\"colab-df-container\">\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Condition</th>\n",
              "      <th>Drug</th>\n",
              "      <th>EaseOfUse</th>\n",
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              "    </tr>\n",
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              "      <th>0</th>\n",
              "      <td>Acute Bacterial Sinusitis</td>\n",
              "      <td>Amoxicillin</td>\n",
              "      <td>3.852353</td>\n",
              "      <td>3.655882</td>\n",
              "      <td>Capsule</td>\n",
              "      <td>On Label</td>\n",
              "      <td>12.59</td>\n",
              "      <td>86.294118</td>\n",
              "      <td>3.197647</td>\n",
              "      <td>RX</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Acute Bacterial Sinusitis</td>\n",
              "      <td>Amoxicillin-Pot Clavulanate</td>\n",
              "      <td>3.47</td>\n",
              "      <td>3.29</td>\n",
              "      <td>Liquid (Drink)</td>\n",
              "      <td>Off Label</td>\n",
              "      <td>287.37</td>\n",
              "      <td>43.0</td>\n",
              "      <td>2.59</td>\n",
              "      <td>RX</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Acute Bacterial Sinusitis</td>\n",
              "      <td>Amoxicillin-Pot Clavulanate</td>\n",
              "      <td>3.121429</td>\n",
              "      <td>2.962857</td>\n",
              "      <td>Tablet</td>\n",
              "      <td>On Label</td>\n",
              "      <td>70.608571</td>\n",
              "      <td>267.285714</td>\n",
              "      <td>2.248571</td>\n",
              "      <td>RX</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Acute Bacterial Sinusitis</td>\n",
              "      <td>Ampicillin</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>Capsule</td>\n",
              "      <td>On Label</td>\n",
              "      <td>12.59</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>RX</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Acute Bacterial Sinusitis</td>\n",
              "      <td>Ampicillin</td>\n",
              "      <td>3.25</td>\n",
              "      <td>3.0</td>\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-9b15a0d9-a6c7-4e55-96d7-edf942109760 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-9b15a0d9-a6c7-4e55-96d7-edf942109760');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8590d539-9c51-4b8d-bb8c-c1d2dc18eec6')\"\n",
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              "            style=\"display:none;\">\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-8590d539-9c51-4b8d-bb8c-c1d2dc18eec6 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TCUxsLjQjnHl",
        "outputId": "e245f494-1e7f-4a60-f935-dbefd8dfbbc5"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Condition       0\n",
              "Drug            0\n",
              "EaseOfUse       0\n",
              "Effective       0\n",
              "Form            0\n",
              "Indication      0\n",
              "Price           0\n",
              "Reviews         0\n",
              "Satisfaction    0\n",
              "Type            0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "importantConditions = df.Condition.value_counts()[df.Condition.value_counts()>9].index"
      ],
      "metadata": {
        "id": "4iHCCllFjp6M"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#keeping only the frequent conditions\n",
        "df = df[df.Condition.isin(importantConditions)]"
      ],
      "metadata": {
        "id": "VlDwHMayjp-L"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cat_cols = df.dtypes[df.dtypes==\"object\"].index\n",
        "for col in cat_cols:\n",
        "    factorize = pd.factorize(df[\"Condition\"])\n",
        "    df[f\"{col}_Encode\"] = factorize[0]\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 399
        },
        "id": "RDS-8ph5juJB",
        "outputId": "a335df47-f6d0-4ee1-d7fd-691745a11b6a"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   Condition                         Drug EaseOfUse Effective  \\\n",
              "0  Acute Bacterial Sinusitis                  Amoxicillin  3.852353  3.655882   \n",
              "1  Acute Bacterial Sinusitis  Amoxicillin-Pot Clavulanate      3.47      3.29   \n",
              "2  Acute Bacterial Sinusitis  Amoxicillin-Pot Clavulanate  3.121429  2.962857   \n",
              "3  Acute Bacterial Sinusitis                   Ampicillin       2.0       3.0   \n",
              "4  Acute Bacterial Sinusitis                   Ampicillin      3.25       3.0   \n",
              "\n",
              "             Form Indication      Price     Reviews Satisfaction Type  \\\n",
              "0         Capsule   On Label      12.59   86.294118     3.197647   RX   \n",
              "1  Liquid (Drink)  Off Label     287.37        43.0         2.59   RX   \n",
              "2          Tablet   On Label  70.608571  267.285714     2.248571   RX   \n",
              "3         Capsule   On Label      12.59         1.0          1.0   RX   \n",
              "4          Tablet   On Label     125.24        15.0          3.0   RX   \n",
              "\n",
              "   Condition_Encode  Drug_Encode  EaseOfUse_Encode  Effective_Encode  \\\n",
              "0                 0            0                 0                 0   \n",
              "1                 0            0                 0                 0   \n",
              "2                 0            0                 0                 0   \n",
              "3                 0            0                 0                 0   \n",
              "4                 0            0                 0                 0   \n",
              "\n",
              "   Form_Encode  Indication_Encode  Price_Encode  Reviews_Encode  \\\n",
              "0            0                  0             0               0   \n",
              "1            0                  0             0               0   \n",
              "2            0                  0             0               0   \n",
              "3            0                  0             0               0   \n",
              "4            0                  0             0               0   \n",
              "\n",
              "   Satisfaction_Encode  Type_Encode  \n",
              "0                    0            0  \n",
              "1                    0            0  \n",
              "2                    0            0  \n",
              "3                    0            0  \n",
              "4                    0            0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2eccf1a4-5e59-444f-b053-6209549dca1d\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Condition</th>\n",
              "      <th>Drug</th>\n",
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              "      <th>Effective_Encode</th>\n",
              "      <th>Form_Encode</th>\n",
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              "      <th>Price_Encode</th>\n",
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              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Acute Bacterial Sinusitis</td>\n",
              "      <td>Amoxicillin</td>\n",
              "      <td>3.852353</td>\n",
              "      <td>3.655882</td>\n",
              "      <td>Capsule</td>\n",
              "      <td>On Label</td>\n",
              "      <td>12.59</td>\n",
              "      <td>86.294118</td>\n",
              "      <td>3.197647</td>\n",
              "      <td>RX</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Acute Bacterial Sinusitis</td>\n",
              "      <td>Amoxicillin-Pot Clavulanate</td>\n",
              "      <td>3.47</td>\n",
              "      <td>3.29</td>\n",
              "      <td>Liquid (Drink)</td>\n",
              "      <td>Off Label</td>\n",
              "      <td>287.37</td>\n",
              "      <td>43.0</td>\n",
              "      <td>2.59</td>\n",
              "      <td>RX</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Acute Bacterial Sinusitis</td>\n",
              "      <td>Amoxicillin-Pot Clavulanate</td>\n",
              "      <td>3.121429</td>\n",
              "      <td>2.962857</td>\n",
              "      <td>Tablet</td>\n",
              "      <td>On Label</td>\n",
              "      <td>70.608571</td>\n",
              "      <td>267.285714</td>\n",
              "      <td>2.248571</td>\n",
              "      <td>RX</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Acute Bacterial Sinusitis</td>\n",
              "      <td>Ampicillin</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>Capsule</td>\n",
              "      <td>On Label</td>\n",
              "      <td>12.59</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>RX</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Acute Bacterial Sinusitis</td>\n",
              "      <td>Ampicillin</td>\n",
              "      <td>3.25</td>\n",
              "      <td>3.0</td>\n",
              "      <td>Tablet</td>\n",
              "      <td>On Label</td>\n",
              "      <td>125.24</td>\n",
              "      <td>15.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>RX</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "  </style>\n",
              "\n",
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              "        document.querySelector('#df-2eccf1a4-5e59-444f-b053-6209549dca1d button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "\n",
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              "    </g>\n",
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              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
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              "      --disabled-fill-color: #AAA;\n",
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              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\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",
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              "    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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              "    40% {\n",
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              "      border-top-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "    }\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",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-1cb6ecdb-f150-4368-8c2a-81afe34c1c7c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = df[[\"Condition_Encode\",\"Type_Encode\",\"Price\",\"Form_Encode\",\"Indication_Encode\",\"Drug_Encode\",\"Reviews\",\"EaseOfUse\"]]\n",
        "y = df[\"Effective\"]"
      ],
      "metadata": {
        "id": "USRRHeDijuMB"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "df_combined = pd.concat([X[[\"Drug_Encode\"]], y], axis=1)\n",
        "\n",
        "# Create a pair plot\n",
        "sns.pairplot(df_combined, kind='scatter', hue='Effective')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "id": "jLuRj7KSjyNG",
        "outputId": "cc97845c-a736-436e-9a1d-1bd0d7ef4ab0"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 322.111x250 with 2 Axes>"
            ],
            "image/png": 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+++wzRo8ezdSpU6mrq+vw/Hg8zplnnsm2bdv45z//yfr163n88cepqKjo6UeR9qFmdz35eyVFgLLyErZs3Nb+/Oo6rFYLVpul2+/VOoBbknKpx0lx4MCBHe7F8tZbbzFw4MAu3+fhhx/mmmuuYdasWYwaNYr58+djs9lYsGBBh+cvWLAAr9fLiy++yCmnnEJlZSXf+MY3GD16dE8/itQJIQR1NfXk5XvavVZWUcLmDpJi7e468gvzevR+NocVn0yKUo71uPf5tttu46abbmLlypWcfPLJACxdupSFCxfy29/+tkv3iMfjrFixgjlz5mSOqarKlClTWLZsWYfXvPzyy0yaNIkbbriBl156iaKiIi6++GJuv/12NE1rd34sFiMWi2WeBwLyP7quCgXDRKOxDjtNyipKqKtpIBQMZ8YYAtTsrmtX3e4qu90mtySQcq7HSfG6666jtLSUhx56iH/84x9Aepzic889x3nnndelezQ0NJBKpSgpyZ5DW1JSkhn7uLctW7bw9ttvc8kll/Dqq6+yadMmrr/+ehKJBHPnzm13/rx587jnnnu6+ekkgLqaBoAOB2KXDygFYOvmHRw/5pjM8aqd1ZnXusvusFHT0iYpSblyQOMUZ8yYwYwZM3orli7RdZ3i4mL+9Kc/oWka48aNo6qqiv/7v//rMCnOmTOH2bNnZ54HAoFuVe+PZPV16aTY0ZjDsvJiADZv2JqVFKurajl+7KgevZ/DYZfT/KSc63FS/OSTT9B1nYkTJ2Yd/+ijj9A0jfHjx+/3HoWFhWialjV/GtJzqEtLOy5tlJWVYTQas6rKxxxzDDU1NcTj8XZbrprNZszmri9hJe3RUJdeF9HdwYo3ZouZ4tJCNq3f06nm9wUIBsMUlRT26P3sDhsBf1CulCPlVI87Wm644QZ27my/KGhVVRU33HBDl+5hMpkYN25cVoeNrussXryYSZMmdXjNKaecwqZNm9B1PXNsw4YNlJWVtUuI0oFpqPditVkxmzv+XisGlLFh3ZbM89YpgcU9TYpOO8lkkuZwpEfXS1Jv6HFS/OqrrzjxxBPbHR87dixfffVVl+8ze/ZsHn/8cf7yl7+wdu1arrvuOsLhMLNmzQLgsssuy+qIue666/B6vdx8881s2LCBV155hfvvv7/LiVjquoa6Rjz7mK43YFAZ69duyiw4u31L+keyuLSHSTEz/1lWoaXc6XH12Ww2U1tby9ChQ7OOV1dX73NTo73NnDmT+vp67r77bmpqahgzZgyLFi3KdL7s2LEDVd2TuwcOHMjrr7/OrbfeygknnEBFRQU333wzt99+e08/itSJxoYmnO7ON7QfVDmApn+/QV1tAyWlRWzZtJ38wjys1u6PUYTslXJ62lkjSQeqx0nxrLPOYs6cObz00ku43eneSZ/Px5133smZZ57ZrXvdeOON3HjjjR2+tmTJknbHJk2axPLly7sds9Q9jQ1NOF2dJ8XKoekOq6++WE9JaREb1m2mvJPVuLvC7pAlRSn3elx9fvDBB9m5cyeDBw9m8uTJTJ48mSFDhlBTU8NDDz3UmzFKOdJY793njnx5BR48+W5WffYlQgjWrt7AoMqezyxqTYpyq1Mpl3qcFCsqKvjiiy944IEHGDVqFOPGjeO3v/0tq1evlkNeDhPexn2XFBVF4eiRQ1n+wQqqq2qpr2tkaMt2BT1htVpQVVWWFKWcOqBxina7nR/84Ae9FYt0ENF1HZ8vgGsfSRHg+DHH8MQfnub5p15GVVWOGjl0n+fvi6IoOJxyUQgptw4oKW7cuJF33nmHurq6rCEyAHffffcBBSblVjAQIpVM7XevlbEnnYDN/gJP/OEpRp94LA6HfZ/n74/Daccvlw+TcqjHSfHxxx/nuuuuo7CwkNLS0qzBtoqiyKR4iPM2NAHss/oM6Srv1dd/nw/f+4TvXHzOAb+vzW7F75clRSl3epwUf/nLX3LffffJoTCHqaaW0prTtf+S3+hxxzJ63LG98r52h00uNCvlVI87Wpqamvjud7/bm7FIB5GmRh8ATue+S4q9zeGwy5VypJzqcVL87ne/yxtvvNGbsUgHkaYmP4qiZIbJ9Jd0SVH2Pku5c0BbnP7sZz9j+fLlHH/88RiNxqzXb7rppgMOTsodn9ePw2nPmk3UG3at3cVH//kIZ76T0y48DYsje/aLw2En4JdJUcqdHifFP/3pTzgcDt59913efffdrNcURZFJ8RDn8/r328nSXSFfiHefeRd3kRvvbi9L/7mUM644I+scu9NGwB9C1/VeT8iS1BU9Tord2YdFOvQ0Nflx9HLVec2SNWgGjTFnjqFhZwNfvP0FDTsbKBy4ZwEJh8OOruuEguF9zqaRpL4if4qlDvm8fuzOAxtz2FYinmDL51sYcMwADCYDJUNLsDqtbPh4Q9Z5rYtCyB5oKVe6nRRHjRqF1+vNPL/++utpaGjIPK+rq8Nm69/Gean3eRt9BzwQu62qr3ZRYrdSbrOSagqjKAplw8vYvno7emrPwH+7ozUpynZFKTe6nRTXrVtHMpnMPP/73/+etRmUEIJoNNo70Uk542vyZ21IdSDi3hCJVTuoLM5DCUSJr9lFYmcjxZXFJGIJarftWXnd4WxZKUcmRSlHDrj63LrAaFtyKflDn98X6JWkmAhGqH5jFYlEEq9RwXh0KWqRi+S2BuxGAyarid0bdmfOby2d+nyy+izlhmxTlNpJJJKEguEDHqMoUjp1S75EoLClthFHoQtFUdBKXCgWI4ltDeSX51OzpSZzjdFkxGw2yQHcUs50OykqitKuJChLhoeXQMvSXQdaUvSt3kG8qZm4x4IuwJ6XHuKjKApasQsRiFBY5MZb5SURS2Suc7ocsk1RypluD8kRQnDGGWdkthyIRCJMmzYts2lU2/ZG6dDUup7hgXS0JPzN+FbvwD6kiG07G7C6bGiGPTswKi4rGDXcqooQgsZdjZQOS29BYHfYZe+zlDPdTop7763c0cb3F1xwQc8jknLO15IU7c6eV58bP92MZjbiGFqCb8VGnAXZG2ApioLqtoI/gsFooGFXQyYpOpxyUQgpdw44Ke7P0qVLGT9+vNx7+RDS2vPb05JipMZHZJcX9wmDSCaSRIIRyoaVtTtPddnQG0IUl3ho2LlnWJfDYadJtilKOdLnHS3f+ta3qKqq6uu3kXpRa/XZZu9+SVEIQdNnWzG6rVhKPQQaWu7lbn8vxWYCTaXAZaexqjFz3OGy0+T19Sx46YDV1NRw5plnYrfb8Xg8nR7rCwsXLuzT+3dFnyfFjobsSAc3X1MAq82KoU0bYFdFqrzE6gM4hpehKAqBhgCapmG2t9/2VFEUFLsZh6YR9oWJR+JAuoOnqVGWFPvKFVdckekwbfs4++yzAfjNb35DdXU1K1euZMOGDZ0eO1CVlZU88sgjWcdmzpzZa/fvqQPajkA6PPV0jKIQgqbPt2LKs2MqSPc0BxqC2Ny2TkcoqHYzWjCCooC32kvp0FIcTgd+fwAhhBzZ0EfOPvtsnnzyyaxjrU1cmzdvZty4cRx11FGZ1zo61hesVitWq7VP32N/5DhFqR2/L9CjMYrNOxqIe8M4hu/ZniLYGMDq6vxeit2MIsBpNeOr8QHgdNpJJVOEguEexS/tn9lsprS0NOuRl5dHZWUl//rXv/jrX/+KoihcccUVHR6D9D7vV199NUVFRbhcLk4//XRWrVqV9T7/+c9/OOmkk7BYLBQWFjJjxgwAvvnNb7J9+3ZuvfXWrGF+bavPGzZsQFEU1q1bl3XP3/zmNwwbNizzfM2aNXzrW9/C4XBQUlLCpZdemjX1uLtkUpTa8TV1PykKPd2WaCpwYMpPlxKTiSTNgWZsrs5/+RWLEVSFwnwXTbXZ+8LIHuj+98knn3D22Wfzve99j+rqan772992eAzSC03X1dXx2muvsWLFCk488UTOOOOMzNoIr7zyCjNmzODb3/42n3/+OYsXL2bChAkA/Pvf/2bAgAHce++9VFdXU11d3S6Wo48+mvHjx/PUU09lHX/qqae4+OKLgXRiPv300xk7diyffvopixYtora2lu9973s9/g76PCl2pfrz6KOPUllZicViYeLEiXz88cdduvezzz6LoihMnz79AKOU2vL7/Njt3avChDbXkAhEcB61p5c53JQu6Vmd+0iKioJiMeKy7SkptlbdvS1bIki977///S8OhyPrcf/991NUVITZbMZqtVJaWorb7e7w2AcffMDHH3/M888/z/jx4znqqKN48MEH8Xg8/POf/wTgvvvu48ILL+See+7hmGOOYfTo0cyZMweA/Px8NE3D6XRmSqodueSSS3jmmWcyzzds2MCKFSu45JJLAPj973/P2LFjuf/++xk5ciRjx45lwYIFvPPOOz1um8x5R8tzzz3H7NmzmTt3Lp999hmjR49m6tSp1NXV7fO6bdu28aMf/Yivf/3rvRmuBPi8gW4Nx9ETSZo+24ql1IOxTS9zyJvelW9fSRFAsZqwahq+Wh9CiExSlD3QfWfy5MmsXLky63Httdd2+fpVq1YRCoUoKCjISqxbt25l8+bNAKxcuZIzzjhjP3fatwsvvJBt27axfPlyIF1KPPHEExk5cmQmjnfeeScrhtbXWuPorj7vaAkG971d5cMPP8w111zDrFmzAJg/fz6vvPIKCxYs4I477ujwmlQqxSWXXMI999zD+++/j8/n6+2wj2h+f4CRjuGZ5woCqwbRFOi0L/k3rdyOHk/iPDp7LGLQG8Rit6Jq++7FVqwmDCKdXCPBCI6WzbKaZEmxz9jtdoYPH77/EzsRCoUoKytjyZIl7V5rbRPsjQ6T0tJSTj/9dJ5++mm+9rWv8fTTT3PddddlxTFt2jR+/etft7u2rKz92Niu6HFSHDt2bIdVY0VRsFgsDB8+nCuuuILJkyd3eo94PM6KFSsyRWoAVVWZMmUKy5Yt6/S6e++9l+LiYq666iref//9nn4EqRMBXzCzrqGCoMKiY9YgocOuiJqVGGP1AQJrd+EYXoZmNWXdJ+gNYXG2H4qzN8Wa3t/Hbjbhr/NTNrwMu92W2WZVOviceOKJ1NTUYDAYqKys7PCcE044gcWLF2cKPHszmUykUqn9vtcll1zCT37yEy666CK2bNnChRdemBXHv/71LyorKzNTjw9Uj6vPZ599Nlu2bMFutzN58mQmT56Mw+Fg8+bNnHTSSVRXVzNlyhReeumlTu/R0NBAKpWipKQk63hJSQk1NTUdXvPBBx/wxBNP8Pjjj3cpzlgsRiAQyHpInYvH4kSjMeyO9K+8yyAwqdAYU1AVKDDtWRBWjyepe38tRpcNe2VRu3uFvKH9Vp0BFLMRFHBYTfjr04nQ4bLjbfD1zoeS2onFYtTU1GQ9utNjO2XKFCZNmsT06dN544032LZtGx9++CE//elP+fTTT4H07LdnnnmGuXPnsnbtWlavXp1VoqusrOS9996jqqpqn+99/vnnEwwGue6665g8eTLl5eWZ12644Qa8Xi8XXXQRn3zyCZs3b+b1119n1qxZXUq4HelxUmxoaOC2227j/fff56GHHuKhhx7ivffe40c/+hHhcJg33niDu+66i1/84hc9fYt2gsEgl156KY8//jiFhYX7vwCYN28ebrc78xg4cGCvxXM4ysx7dtgBgdsoiKQgqisEEwpOAxgUgdAFde+vJRWJ4z5+EIqaXWuIRWIkYnFsXUmKioJiNuJ22AjUp9/f6XLKNsU+tGjRIsrKyrIep556apevVxSFV199ldNOO41Zs2Zx9NFHc+GFF7J9+/ZMIeeb3/wmzz//PC+//DJjxozh9NNPz+pEvffee9m2bRvDhg2jqKj9j2orp9PJtGnTWLVqVaaDpVV5eTlLly4llUpx1llncfzxx3PLLbfg8Xh6vPGZIno45cTtdrNixYp27RKbNm1i3Lhx+P1+1q1bx0knndRpu2I8Hsdms/HPf/4zqwf58ssvx+fztStlrly5krFjx6K1aaPS9XTJRVVV1q9fnzV+CdK/iLFYLPM8EAgwcOBA/H4/Llf2IgUSbFy/hQvOmsWdv7iFY0YMZqBVpyGmENMVFASlFoE/ARve20xwYzV5Y4dgLmr/PTZWNbLi1U857hvHYXHsPzEmdzYS9gbZLXSmXDmF3z34ZxwOO3/4ywN98TElqVM9LilaLBY+/PDDdsc//PBDLJZ0O5Ku65l/d8RkMjFu3DgWL16cOabrOosXL2bSpEntzh85ciSrV6/O6jE799xzMz1pHZUCzWYzLpcr6yF1rnUxCLvdhk0T6AJiLTVmgUJzChykCG2sxnXswA4TIkDYG0JRVcz2ri0EopiNmFUtU312uZx4G5sO/ANJUjf1uGXyhz/8Iddeey0rVqzgpJNOAtIDP//85z9z5513AvD6668zZsyYfd5n9uzZXH755YwfP54JEybwyCOPEA6HM42zl112GRUVFcybNw+LxcJxxx2XdX1rT9fex6We8WeqzzasmiCuA206Vry7/Qwa5Kbia0NJujrfgjTkC2F1WFCUrv3uKhYjKpAIRUklUjjdDjas7dmQCkk6ED1OinfddRdDhgzh97//PX/7298AGDFiBI8//nhmtPm1116b1X3ekZkzZ1JfX8/dd99NTU0NY8aMYdGiRZl2iR07dshN0ftR64rXNrsViwrB5J6EmAhGaFy5g5LCEZQMK6KqvvMNyoKNwUwni6JAYYENVVVoaGwmlepgXx9zugfaZjIS9AZxuRx4vT45/1nqdwfUh33JJZe0a/hsq6vjlG688UZuvPHGDl/raBxUWwsXLuzSe0hd4/cHsNttWA0qqqITCsbYsr6K4kHFRNfvRrOYaE6Cy2FgdwN01CIthCDUFKJsWBlms8bxx5VgsxkRQjA47uGLL2qJRvdaod2kgQI2s5FAQwCny0E8Fqc5HDngvWIkqTtkEUzKEvAFcTjtmDWBEIIPXv6YTZ9sYv2bq0gGotgGFxGOpNBUBaet49/UaDhKKpnCmWfn+ONK0DSFdevrWbuuHgQcO6qIvQt/rT3QdouJYGMQZ0vVvLHB28E7SFLf6XFSVFUVTdM6fUiHJl+TH7vDhkmF5kiCaHOcY087lkKHjbgCRoeFRFIQi+u4HR0nxZA3BMDokwZhNmts2dpELJYikdDZvsOHzWakvLx9e6RiNuCwmgk0BnB50q/LsYpSf+tx9fmFF17Iep5IJPj888/5y1/+wj333HPAgUm54fcFsTlsmBVBXY2Pgop8zKhoBo0qb5D8WAKT2UioOUm+24imQkrPvkfIG2ToMeWUV7jZsdNPPL5nEG00msTrjTBwgJvq6hC63qb+bTZiMRqobgzidsuSopQbPU6KHW1Y9Z3vfIdjjz2W5557jquuuuqAApNyw9fkx263YVR0GmsD5Jfnk2oKgVEjlkzi3d1I6ZBSQpF0UvQ4jTT6E1n3aA6E+fb5YwkGYzQ1Rdq9R119mPx8K0VFNmpr96yZqJgMGBSFsDeE3WFDVVUaG+SwHKl/9Xqb4te+9rWscYfSocXX5Ke0JA+DpuL3N2N32RGBCKrTgt1jp2l3OknpOoQiKQrcpnb3GDmqDJvdzK6qjqdUxuMpgsE4paWOrOOtPdAiEkfoApfbIccqSv2uV5NiJBLh//2//0dFRUVv3lbqR35fkAHl6SlXyRSIcBREeoVsR76DkC9EvGXjen8wgcmokucyZq63GOGECZVs39KYVW3eW1NTBLfLgsWyp7KimNP/tpoMhJpCuD0uGupk9VnqXz2uPufl5WWNHxNCEAwGsdls/P3vf++V4KT+JYQg4AtQWuQhldRRzCZEMAomDcWo4fCkS3b+Oh9FA4tIJAXBcJLSAjORaDoBDi61ULfbR31DaJ/T+/yBKKmUTlGRjZ070yVKRVNBU7GajC090A4a62VSlPpXj5Pi3rtwqapKUVEREydOJC8v70DjknIgEokSjycocDoI+MLY3Tb02iCKIz1VTzNqWBxW/HV+igamS5ON/jgmo4VhA9KbU4VCMZa/9RWDRw/Z53sJAcFgjMLCPUkR0qXFzABut5P6usZ93EWSel+PkmIymWT79u1ceeWVDBgwoLdjknKkdd6z227G72vGajGRSuqobdZJdLhtNNX4ELqOoqoIAdX1UexWDQF8+dFmFE1D6cIsJH8gxuBBHswmjVhLVVsxG7FZTIQa09XnbVt29slnlaTO9KhN0WAw8H//938kk8n9nywdMpq8LYsxOK00R+IQSaSnPZv3JEV7np1UKkXIF8ocE6Q7XcKRFKGmUId7PHckEIghhCA/f081WzEZsBgNBL1B3B4XjQ1Ncu9wqV/1uKPl9NNP59133+3NWKQc8/v8mIwGHA4zsZiOHoqB2UjbNR3MdiuaQSNQ175nOZVMEQ1FurRUGICuC8LhBHltk6LZgKYoNHtDuD1OYtGY3OpU6lc9blP81re+xR133MHq1asZN24cdnv2RkfnnnvuAQcn9S9fU4DysgIURSGpC9TmWKY9sZWigN1jx1fno2JkdtNJqCmEYP8bVbUVDMYoLrajKC3zqE3pP8lUOIbLk16WrKGuMbPtqST1tR4nxeuvvx5Ibzy1N0VRerwUuJQ7/qYAwwall3oXQoWkjmpuPw7R4bGze1M18UgMk3VP0gx5g2gGDZO1/TWdCYZilJU5cTrNBAIxlJakaNZUrKb0vetqGxkyfPCBfDRJ6rIeV591Xe/0IRPiocnn8zO8cgCJeBKttRnP0v530+axowBNLfs0t/LXB7C5bO0We9iXSCRJMqmT50m3QyqaitBULCYDWip9o/raru8dIkkHqkdJUdd1FixYwDnnnMNxxx3H8ccfz3nnncdf//pX2Sh+CPM1BRhQXkQwEEFNpsCopscO7kUzaNjcdry794whjMcShP1h7Hld3y+6VSgcx+PZ0zmjmA1YjUZioRhWm1UOy5H6VbeTohCCc889l6uvvpqqqiqOP/54jj32WLZt28YVV1zBjBkz+iJOqR/4vH5Kiz00N8fRI/FMVbYj7iIXIV+IaCi90GzTbi8KZAZ4d0coFMfpNKO2bH6lmo3YLOmxinn5bpkUpX7V7TbFhQsX8t5777F48eJ2ezq//fbbTJ8+nb/+9a9cdtllvRak1D+avD4KCl14vc0QiaN4Oi/1OfIdaAaN6i3VVB4/mLrt9djzHWjG7i8bFwrFUFUXLpcZny+aGZZT0zIsp66m/kA+liR1S7dLis888wx33nlnh5vcn3766dxxxx089dRTvRKc1L/8TUE8HjvJuA76nrnIHVFUlYKKfBp2NrDxk01EwxEKyvN79L7ptRZTeNwtVWiTAYOqEm4M4slzUVstk6LUf7qdFL/44gvOPvvsTl//1re+xapVqw4oKCk3TAqomkoq3rJA4j6qzwCe0nw8xW7CvjDFg4u6PD6xI+FwArc73dvcmoyToSh5+R6ZFKV+1e3qs9frzWwq1ZGSkhKamuRyT4eifEe6upyKJsDQcSdLW4oCJUNL6fyvoetC4TjlZU5UVUG0JGMtpeN2u6ivb0TXdbmBmdQvuv1XlkqlMBg6z6Wapsnpf4egSCRKWXEeuq6TaI7vt5TY28LhOKqq4HKa08NyVAWr0YjdbCGVTOGVi81K/aTbf/lCCK644grM5o43OY/FYgcclNT/fF4/FaWFhIMxiCZQuriJfW+JRtPjFd1uMz5/ND0sx2TAoKT/RGuq6ygsLujXmKQjU7eT4uWXX77fc2TP86GnyeujtCSf5uYYJHQUk3H/F/WycDiO220B/ChmI1aziWTLPICa3fUcN/qYfo9JOvJ0Oyk++eSTfRGHlGONDU0UF3uIt+zHvK8xin0lFE5vUaAo6bGKVpOBxlAco8lITXVdv8cjHZlky7UEpHfNyy90koyn0suF9WC84YEKh+NoqorTaUYxtwzL8QYpKMyjpqq23+ORjkwyKUoAeHc3YLObSUaTYNS6NX+5t0QiSVIpHbfLnOnoSQQi5BV4qN4tk6LUPw6KpPjoo49SWVmJxWJh4sSJfPzxx52e+/jjj/P1r3+dvLw88vLymDJlyj7Pl7omHmwGINm87+l9fS09XtGSGauoxJIU5HvYvasmZzFJR5acJ8XnnnuO2bNnM3fuXD777DNGjx7N1KlTqavruA1pyZIlXHTRRbzzzjssW7aMgQMHctZZZ1FVVdXPkR9eDCI9YDvmi6AYs5Oi0HWSzXHUaAIt3H4f594UCsdxOdNLjwlNwWI04HG5ZFKU+k3Ok+LDDz/MNddcw6xZsxg1ahTz58/HZrOxYMGCDs9/6qmnuP766xkzZgwjR47kz3/+M7quy72mD5DDbCIeS5CKpdqVFJtr/Pg21+FKxHCho8QTfRZHY40fg1EjEQijmNLDchxmO01eP5FItM/eV5Ja5TQpxuNxVqxYwZQpUzLHVFVlypQpLFu2rEv3aG5uJpFIkJ/f8bzbWCxGIBDIekjtuV02wsGWpGPK7mSJh2KoSvp/GwUFc7TvxqLW7fKRTKRwOU2oVhM2sxGTmk7Su3fK0qLU93KaFBsaGkilUu2mDZaUlFBT07X/AG6//XbKy8uzEmtb8+bNw+12Zx4DBw484LgPRwUFDqLN8fRfhGFPUhQCUrEkLsee1bS1PlwyM9YcpbE2QHGZKz1W0WSElrnYsgot9YecV58PxK9+9SueffZZXnjhBSyWjneQmzNnDn6/P/PYuVNumbm3RDxBQaGLeCQBRkNWz7OeSI9btFjSg7kFAu0Ae6bj0Tjb1u/gi+Vf8eWn62nY3Ujr2sTJaILGugBlgwsQRhVVUdDDcQwGjV07dh/YG0tSF+SumxEoLCxE0zRqa7OHW9TW1lJaWrrPax988EF+9atf8dZbb3HCCSd0ep7ZbO50SqKUtmPDDtx5drw7mtq1J4pECkUBm8OIwaSSSgmEvv/tJhT2/HG1bYEMNgXZ9OVWUBRcHgeJeJJtG3YSaY4yYFgFyUQKvzeC2WLE5rYQApLBCIXFBTIpSv0ipyVFk8nEuHHjsjpJWjtNJk2a1Ol1DzzwAL/4xS9YtGgR48eP749QD2u7Nu9AVRWS4QTK3oO2UzrDjyrA5bFiNBuwOkzYXGbsit7hvUyKIF9NUaalKDGkH+VaCqeiE2wKsmH1ZkwWM0NGDKK4vJCKylKKBxRSu6uemq92YVI1wuEYyUQKd54VASjxFIVFBezcLpOi1PdyWlIEmD17Npdffjnjx49nwoQJPPLII4TDYWbNmgWk51FXVFQwb948AH79619z99138/TTT1NZWZlpe3Q4HDgcchvMngg1NMEQJ/FwHMVty3qtxKFhVCESimeOGc0aHoPASYpmXSGFgobAqgqMCiQFNOuQIF3PNisCl6qTMOhUOW2UDypFUffUwfMK3CRDMUQsiaYqKDo01gbIL7Cz06hhMxspMuWxffuu/vlCpCNazpPizJkzqa+v5+6776ampoYxY8awaNGiTOfLjh07stbRe+yxx4jH43znO9/Jus/cuXP5+c9/3p+hHzZEPE4qmSIeSaAV7SkpOhQdi1GlaneQfKcZRQU9BclYioBqwKoI7KpAUwS6gLiAsK60VJf3JL1ILMmWqnpGDili0uhKdvjb917bzBZi4RiaqqAnUtTt9nHM2EFoVhM2kxGX3cGuHdVyXUWpz+U8KQLceOON3HjjjR2+tmTJkqzn27Zt6/uAjjAWk5oejqMqoKWTogK4VEE4kiQeT7croqc7WkAhmRSEDCoIoOVYR5LJFLs27yaV1KkLJSh1mSl2GKkLZY91FLEkqkFBJAWpWJKGmgAGo4anwk2iKYxFTRGPxamtrqesojeWtZWkjsmfXAmXy0okFAPTnjnPdkVP7+0ciKeX2FYUBKC3dBOrWXt7d5IQ40l2btpNIpmkqKKAuABfNEmB1YjdtOdPLxVPInSBwWxEFwKR0gk2NZOIJymo8GDQVAwt6xZv3yqr0FLfkknxCBcJRykocJLYa86zQxXERHqldUVVUEiPWaRl6EzEH2L31mrqqhoINIVIJvckSSEEQV+I7et3kkwkKCkrwtAydTAQSxFJpChzmDIJOBVryXiaQqrlDVRVwdsQIq84vUWC0hxH0zS2b5VDqqS+dVBUn6XcqdtRizvfjm+nL5MULYrAoEBQVxAp0aZTRJBIpjAZNRRdEI8niYSjeOt9ABhNBgwGLT1dMKVjtZnJK8pDM2T/9jY2Jyl3mSi0GqhvTkI8hc1qIhJNYFQ1BAKhQ0N1gFEnDkQzadCQoKikgG1bZFKU+pZMikc47+46nEUK8VAMxZyetWJTdJICkoDQRWYDK10IUnq6JGezmimypwfMp5IpYpEY8VgCXRfYXSasNjMmi6nD90wKQSCaIt9mpCmaQtXBYNQwqgqNiZaphrqgrtrHscogPIPzsNb7KSspZuum7X37hUhHPFl9PsKFvT4AosEYmAwogEWBqA6gIHQdY8sUFqGD0lLnbTOiBs2gYXPa8BS6yS/24M53dpoQWwXiSXRdUGw1YFAVIvEkBk3FYtIQgIpCqClCNJqkqLIAh9lEkTuPLRtlUpT6lkyKR7h4OEQsmiCWTLcdWhWBqkCM1kQoEC2dKgIy7YDqAU710wX4YylcVgMGo0o0kSSeSGE1GNJtmAokE0mCoRiFFW6sZiMOs42a6jrCoeYDe3NJ2geZFI9wJgM0ByKZRSCsik5CgI6CECKdCfX07BUh0m19AEonPc7dEYynSCZ18gqtCAGxZBKTUcOgaSgo6CmdYCCGxWbCmW/Hmkr/uW6RVWipD8mkeASLRWLkFziIh+JoFiMq6apzrCXxiZb2Q0NLwtT1PcvjHGhJsVUoEMPltmK1Gojr6TZLU0vHjKYoNNQFSaV0CivzMERTKIrCpvVbeufNJakDMikewWq21VBY7EonRasZsyJQ2lSd483pmSfGliSltwyXEUBvbOKi6ILmcIJEPEVZmQuETjyZwtwygFxVFRrqvIRCcQorCzCkdErLitiwTiZFqe/IpHgEq99ejcGoEQlEwaxlVZ0BfA3pBXm1liQlMmtAiPQZKUGiqZm4txmSHS8QsS9Ky9hGb1OE/DwrVrORZCqFpioYDCqqouKr8xEIxPCUOinIszOgtIwNazcd4CeXpM7JpHgEC3mbAAj4wyiaikXJrOdKPJagOdS6H0u6hKioLf8U6YJivKkZkdJB14n7ur93i0joCF3g90dIJnUGlLuxGo3ousDcsqNgPJqgttoHwOARpRS58lj35aZ0e6ck9QGZFI9gIh4l2hwnEktiUcnqdfbWNaG1DMVRWxoQ0/0tIj2zRQFSOgaHGc1mgpSOHk127/0TerrzRoDfF8GdZ03PfwbMBg0FMBkN1O72EgrGKBlWSJ5qJhgIUSW3JpD6iEyKRyg9pWO3GYj4I+gGBWvLgO0UCslkioA3iMqeDg9Bele/PRRUqxFFVVANKmgqqUi8w/cCQAhELEkqGCPpbSZZH0YjXfDUVIVIKAFC4Mm3kkrpKIqCyaBhNZvx1jXhD8TIH+DBoaXnG3y1en1ffTXSEU4mxSNU7fYaygbkEfFHMVhNWBWItdRIfQ1+oE0P816dKkKk2xRV854JUapZQ8RT6QGIrXSBiCZJ+aMkG5pJ+aOIWBIdiCUTqAqkdJEZ3hPwxygosKEjEEJgMRowoBKPxqnZ3YSiKgwbXkJhYT5rVq3to29GOtLJpHiEqtq0E7vDQrgpgivPjqpAXCjouo6/3o/TYsFuNEFLAtxTZ95DD0RJBePozQloKUQmA1FSgWi6NNgQJhWIIlI6itmA6jKTMmn4AyGMqgaKQkrXURUFUPD50+2SRUUOhACDpqIAmlGjemcDQV+EoceVMWzAQFavlElR6hsyKR6hArX1ANTX+XCbDZm5zsGGAG6zBatmQChAy+ySPesm7lk+TDFqoIBICkikxxCSSCGSenpWitWI5rKgOUyoFo1kIkXAG8BqNmWNc1TU9L1TSUEgEKOgyArp1crQAJfLQWNdE76mCAUD8hhZXsGaVetIJLrXhilJXSGT4hFKScaJhmIEI1GsarrqnAjH0QMxVFUlGI9D6wrXLW2KrSXF1o5fk1lDsRhQbAYUmxHFqKLrgqQuiDbHiQajRP0REtEkqUQKvzeAyWTEoGhomoamKpiNBgyqmk60Cvh8ERRFxeWxoCgKNrMJAwqpRIqdOxsRQjB61EBi0Rhr12zIxVcnHeZkUjwCBZuClJQ4CXubcXqsqAqEmhM01/pJ6joJLb2BGKT/f+taiqrSMr2vJSkadAGxZOY5LavpiEQSg8WI0WYETSERihH1RzBoGhbNgNWs4bSbMBhUrGYDVosBkymdJBEQCkYpKLKBAkZNxZQCs9VE9a4GvDUBjjq+AqvFwqfLV/b7dycd/mRSPAJtXrWRkgoPgYYQxQVO4imdYLWfhK4TFUlI6FiN6WE2mYTHng4RPZVOmKqqoKYgFYoRD8ZIhNMzYDSjAc2okdJFSxVXYDFqeCxWbGYjDquJVEonFk8RjSVJJFMYDRrmlp0E/U1RVFXFk2dFVRUMikqhzYHfG6CuJogz386Ms05j+Qcr+vmbk44EMikegRq3V6EoCrXVXtw2E/7GZjCoROIxLIoBRYBQQFFV1NbSnxAt47ZFZrqfyaCBCkZNy3TB6IBI6STDMYxJHYfRhM1kQlM0VBWsZgPJlE4qJTL18FRKkEikF681qCopXRD0R8krsKIZVFJCYBBQYHOwfWc9kUCUM78xms8+XkUkEu3/L1A6rMmkeITRdR0jCSKBaHrhBQHhaIpINI7FYEIxqCT1FBaTAaOmYDW1LgYBRoOC2WTA2DLtz2TUyHOacTlM2M0GFFVFI13lNRsMaKqKpqbnTpuMKpaWITypVHqOtd4yM0YISKZ0kikdizl974A/newKCm0YNZVIMonTbCbqbaZ6SwOVw0soyHPy0VJZWpR6l0yKR5htX25j2NEl+GsDlJe4CYXiRKJxVKGgGFU0TcVttWBQFHQdzC1bFGiagtGQbvdTWv9qFAVVUzGaNNwuC6X5NpwOE6qqIBAt56voQiel6xg0lXgi1bIjYHrQtqKkz0VAIqlDy6DtlC7w+6K48iw4HWb0lI5uUnGZzOzaUk8ynuSmK2eweNF7OfompcOVTIpHmK2fr8VqN9PsDWM0aTQ0htGTKZKKwGwyYkIhqacwmDXy3WaslnRSFC1bESSTOm0ntsRjSeKxFKlkClVVsFlN5Hus5LstGI3pUqcu0sk1XQUXqEo6sapKuoNbU9X0VEIBqZSOqbVtMRAlmdApq3BhNhiIplJERZJEc5LqTfVMnDiSLz9bQyzafh9pSeopmRSPIPFoHLuWJBKIYjWpBAJRovEE0VQSh9WMmky3Fua7rbispvQGUq3LhYnWbaHT4xYF6YRmMhswmTU0g7anLiwEqqpgtRrJ99jwOM0YDenEZzUbMJs0TAYNk0nDZNSwmDWsFgM2qwFNU1EUsFvSQ3W8jc1YrAYGVLiJBSMoViOBaJRd62pJxJJcffFU3npNlhal3iOT4hHkg+ffYfioMoJ1AYwmjUZflOZYHKfVjEEI7FYjBR5rusqb0tEULWtPFkVRUFQFTW1ZLkdR0LS9HgZ1z6PlmNGoYjZrGI0aakv1Wxekh/8ksx+tC9tqmorNYsCoKkTCCUrLnFSW5qMH4ugK1PnD7FpTzdcmjGLNsk/kqjlSrzkokuKjjz5KZWUlFouFiRMn8vHHH+/z/Oeff56RI0disVg4/vjjefXVV/sp0kPXqjdWYNebSSV1lHiCBm+YWCRGod2O02LG47RgNhvS0/pa2gpVbc8KOSgKrQMWdaGDnn4ajyeJx1Mk4ikScZ1kXCeV0Ekl0z3Mekqg6y0lTRWMRhWTUcNqNWC3mXA4TDjsRhx2I1arEZNZy4wZT6XSibI5GCeZ0Bk8xMPQCg8VHid2k5Edm+tp2u3n0vO/ydv/eitXX610mMl5UnzuueeYPXs2c+fO5bPPPmP06NFMnTqVurq6Ds//8MMPueiii7jqqqv4/PPPmT59OtOnT2fNmjX9HPmhIegN8PTPFvLl68s5auxAIvUBkrEk8VCSPIcVj9uC3WZMjzlsqR5rBiU9Drul8CWEIJVqeejpxWZbmxU1RUVTW5Oogqq1TNtTQGlZ2EGIdHJMJtPX6zp77tcmaWoqGDQFk9mA2WLAZksnS5NZozkYQ9cFhWUO8jwWKoqc5Lss7FhVhSIEQzwGXn7wHzT7Qzn6pqXDhSJyXO+YOHEiJ510Er///e+B9JCRgQMH8sMf/pA77rij3fkzZ84kHA7z3//+N3Psa1/7GmPGjGH+/Pn7fb9AIIDb7cbv9+NyuXrvgxwkmsMRNq3dypqPvmTbsvXYolBc4eaMS8ajJ1IEdzYSCcfRNBXVoKCqWjqZqUpmnrPS0iuMqqRnmahKeuC2kk54rf9uXT1H6cbWBJnNsFrHPbZuhtWSPIXesl5jy791XYAu0FueG4waCEGsOUEkFCeZSKEaNQaNH0wilmT5y6upawgz4pujGX3GWAoqCrsVnyQZ9n9K34nH46xYsYI5c+ZkjqmqypQpU1i2bFmH1yxbtozZs2dnHZs6dSovvvhiX4baL4Sup4eeJJOIZAo9kSQRixMLRYj6g0QDzcSDzUQCIeLhCMlYDHQdTUlXS41GAwajhsmoclKBkUnfGYMlz4HJbUekUqRCcRxFDhxFe7+vQAi9JTm1JC493cmi6yK94EPLsJnW4TNtf0tFy8stzzr4ZMqeBXba/lNpWWyiZZUcRQFUWnqn0ytCtJ7TmqRbE5w1Dzxt3iEVjGBx25j8/ZOIByJEm0I0LHmf2niKZCJFIpHeOTCVEghFQTWaUE1GDBYTRpsNi9OO1WPH7LJjslow2swYLSZUkwFV01ANGoqa84qV1A9ymhQbGhpIpVKUlJRkHS8pKWHdunUdXlNTU9Ph+TU1Ha/EHIvFiMX2DNnw+9NrBQYCgU7jCm2vYecrS7v0GQCsHiv5gzxdPr+nLIDFCK4CDQocgKPTc1WTAUvxnpj0uI7Bbs5sJ5DZgupQ7J9Q2pZW9xwUuo6qGDC7bZjdtqxLks0x4t5gJzeMgx4HbxMJLyT6IORwYxhfVed/c50pP3MC7qMG7vMcp9MpS8O9KKdJsT/MmzePe+65p93xgQM7/0M7Z9yp3PWdK7v8Hpqxf0sQekrPVC1FSmfvBhAF0FICYzxBa7JIv7CnmKa0+b+HCwEko+nVvzWTIatkJ3RBLBxH1ZSW6YtKZgpjfzCYe/af2h233sbT77++z3MO16agXMlpUiwsLETTNGpra7OO19bWUlpa2uE1paWl3Tp/zpw5WdVtXdfxer0UFBT0+a9rIBBg4MCB7Ny585D5oz0UY4b9x20ArMf1f1xt7dVq0aXv+rHrL+Cx/dzX6XT2SnxSWk6ToslkYty4cSxevJjp06cD6aS1ePFibrzxxg6vmTRpEosXL+aWW27JHHvzzTeZNGlSh+ebzWbMZnPWMY/H0xvhd5nL5TqkEgwcmjHDoRn3oRjz4Szn1efZs2dz+eWXM378eCZMmMAjjzxCOBxm1qxZAFx22WVUVFQwb948AG6++Wa+8Y1v8NBDD/E///M/PPvss3z66af86U9/yuXHkCTpMJHzpDhz5kzq6+u5++67qampYcyYMSxatCjTmbJjxw7UNm1DJ598Mk8//TR33XUXd955J0cddRQvvvgixx2X47qRJEmHByH1mWg0KubOnSui0WiuQ+myQzFmIQ7NuA/FmI8EOR+8LUmSdDCRo1ElSZLakElRkiSpDZkUJUmS2pBJsQ9s27aNq666iiFDhmC1Whk2bBhz584lHo9nnaNk5vbueSxfvjyHkXd/Gbf+NG/ePE466SScTifFxcVMnz6d9evXZ53zzW9+s913eu211+YoYvj5z3/eLp6RI0dmXo9Go9xwww0UFBTgcDi44IIL2k1OkPqXTIp9YN26dei6zh//+Ee+/PJLfvOb3zB//nzuvPPOdue+9dZbVFdXZx7jxo3LQcRp3V3Grb+9++673HDDDSxfvpw333yTRCLBWWedRTgczjrvmmuuyfpOH3jggRxFnHbsscdmxfPBBx9kXrv11lv5z3/+w/PPP8+7777L7t27Of/883MYrSSH5PSTBx54QAwZMiTzfOvWrQIQn3/+ee6C2suECRPEDTfckHmeSqVEeXm5mDdvXg6j6lxdXZ0AxLvvvps59o1vfEPcfPPNuQtqL3PnzhWjR4/u8DWfzyeMRqN4/vnnM8fWrl0rALFs2bJ+ilDamywp9hO/309+fn674+eeey7FxcWceuqpvPzyyzmILK11GbcpU6Zkju1vGbdca13xaO/v9amnnqKwsJDjjjuOOXPm0NzcnIvwMjZu3Eh5eTlDhw7lkksuYceOHQCsWLGCRCKR9Z2PHDmSQYMGHbTf+ZEg5zNajgSbNm3id7/7HQ8++GDmmMPh4KGHHuKUU05BVVX+9a9/MX36dF588UXOPffcfo+xJ8u45ZKu69xyyy2ccsopWbOZLr74YgYPHkx5eTlffPEFt99+O+vXr+ff//53TuKcOHEiCxcuZMSIEVRXV3PPPffw9a9/nTVr1lBTU4PJZGo3F39fS+FJ/SDXRdVDye233966nGqnj7Vr12Zds2vXLjFs2DBx1VVX7ff+l156qTj11FP7Kvx9qqqqEoD48MMPs47/+Mc/FhMmTMhJTPty7bXXisGDB4udO3fu87zFixcLQGzatKmfItu3pqYm4XK5xJ///Gfx1FNPCZPJ1O6ck046SfzkJz/JQXSSEELIkmI33HbbbVxxxRX7PGfo0KGZf+/evZvJkydz8sknd2nBiokTJ/Lmm28eaJg90pNl3HLlxhtv5L///S/vvfceAwYM2Oe5EydOBNKl9WHDhvVHePvk8Xg4+uij2bRpE2eeeSbxeByfz5dVWjwYv/MjiUyK3VBUVERR0d6r4nWsqqqKyZMnM27cOJ588smsRS06s3LlSsrKyg40zB7pyTJu/U0IwQ9/+ENeeOEFlixZwpAhQ/Z7zcqVKwFy9r3uLRQKsXnzZi699FLGjRuH0Whk8eLFXHDBBQCsX7+eHTt2dLoUntQPcl1UPRzt2rVLDB8+XJxxxhli165dorq6OvNotXDhQvH000+LtWvXirVr14r77rtPqKoqFixYkLO4n332WWE2m8XChQvFV199JX7wgx8Ij8cjampqchZTW9ddd51wu91iyZIlWd9pc3OzEEKITZs2iXvvvVd8+umnYuvWreKll14SQ4cOFaeddlrOYr7tttvEkiVLxNatW8XSpUvFlClTRGFhoairqxNCpJsBBg0aJN5++23x6aefikmTJolJkyblLF4pvYOa1MuefPLJTtscWy1cuFAcc8wxwmazCZfLJSZMmJA1NCNXfve734lBgwYJk8kkJkyYIJYvX57rkDI6+06ffPJJIYQQO3bsEKeddprIz88XZrNZDB8+XPz4xz8Wfr8/ZzHPnDlTlJWVCZPJJCoqKsTMmTOz2jcjkYi4/vrrRV5enrDZbGLGjBlZP55S/5Or5EiSJLUhxylKkiS1IZOiJElSGzIpSpIktSGToiRJUhsyKUqSJLUhk6IkSVIbMilKkiS1IZOiJElSGzIpSoc9RVF48cUXcx2GdIiQSfEQccUVV2T2+DAajZSUlHDmmWeyYMECdF3PdXgd7kWy934kknQokEnxEHL22WdTXV3Ntm3beO2115g8eTI333wz55xzDslkssNrEolEv8W3914ke+9HIkmHApkUDyFms5nS0lIqKio48cQTufPOO3nppZd47bXXWLhwIZCuKj722GOce+652O127rvvPhYuXNhudecXX3wRRVGyjv3yl7+kuLgYp9PJ1VdfzR133MGYMWO6HJ/BYKC0tDTrUVhYmHm9srKS+++/nyuvvBKn08mgQYParTO5a9cuLrroIvLz87Hb7YwfP56PPvoo8/pjjz3GsGHDMJlMjBgxgr/97W9Z12/cuJHTTjsNi8XCqFGjOlyfcufOnXzve9/D4/GQn5/Peeedx7Zt27r8OaXDm0yKh7jTTz+d0aNHZy23//Of/5wZM2awevVqrrzyyi7d56mnnuK+++7j17/+NStWrGDQoEE89thjvR7vQw89xPjx4/n888+5/vrrue666zLblIZCIb7xjW9QVVXFyy+/zKpVq/jJT36SaR544YUXuPnmm7nttttYs2YN//u//8usWbN45513gPT6j+effz4mk4mPPvqI+fPnc/vtt2e9fyKRYOrUqTidTt5//32WLl2Kw+Hg7LPPztqCVjqC5XqZHqlrLr/8cnHeeed1+NrMmTPFMcccI4RIL691yy23ZL3+5JNPCrfbnXXshRdeyFrKbOLEiVk7+QkhxCmnnNLpTnR7mzt3rlBVVdjt9qzH//7v/2bOGTx4sPj+97+fea7ruiguLhaPPfaYEEKIP/7xj8LpdIrGxsYO3+Pkk08W11xzTdax7373u+Lb3/62EEKI119/XRgMBlFVVZV5/bXXXhOAeOGFF4QQQvztb38TI0aMELquZ86JxWLCarWK119/vUufVTq8yZW3DwNCiKyq8Pjx47t9j/Xr13P99ddnHZswYQJvv/12l+8xYsSIdjsSulyurOcnnHBC5t+KolBaWprZV3rlypWMHTu2w10PAdauXcsPfvCDrGOnnHIKv/3tbzOvDxw4kPLy8szre69gvWrVKjZt2oTT6cw6Ho1G2bx5c1c+pnSYk0nxMLB27dqspfntdnvW66qqIvZaNrMvOmBMJhPDhw/f5zlGozHruaIomeqx1Wrt9Zj2FgqFGDduHE899VS717q61YR0eJNtioe4t99+m9WrV2f2+OhIUVERwWCQcDicOda6d0mrESNG8Mknn2Qd2/t5XzvhhBNYuXIlXq+3w9ePOeYYli5dmnVs6dKljBo1KvP6zp07qa6uzry+fPnyrPNPPPFENm7cSHFxMcOHD896uN3uXv5E0qFIJsVDSCwWo6amhqqqKj777DPuv/9+zjvvPM455xwuu+yyTq+bOHEiNpuNO++8k82bN/P0009neqtb/fCHP+SJJ57gL3/5Cxs3buSXv/wlX3zxRbse6n1JJpPU1NRkPfbeHXBfLrroIkpLS5k+fTpLly5ly5Yt/Otf/8psDP/jH/+YhQsX8thjj7Fx40Yefvhh/v3vf/OjH/0IgClTpnD00Udz+eWXs2rVKt5//31++tOfZr3HJZdcQmFhIeeddx7vv/8+W7duZcmSJdx0003s2rWry7FKh7FcN2pKXXP55Zdn9iQxGAyiqKhITJkyRSxYsECkUqnMebTpVGjrhRdeEMOHDxdWq1Wcc8454k9/+pPY+3/+e++9VxQWFgqHwyGuvPJKcdNNN4mvfe1rXYpv7ty5He6fYjabM+cMHjxY/OY3v8m6bvTo0WLu3LmZ59u2bRMXXHCBcLlcwmazifHjx4uPPvoo8/of/vAHMXToUGE0GsXRRx8t/vrXv2bdb/369eLUU08VJpNJHH300WLRokXtvpPq6mpx2WWXicLCQmE2m8XQoUPFNddck9O9XKSDh9yjRerUmWeeSWlpabuxgJJ0OJMdLRIAzc3NzJ8/n6lTp6JpGs888wxvvfVWh4OfJelwJkuKEgCRSIRp06bx+eefE41GGTFiBHfddRfnn38+AA6Ho9NrX3vtNb7+9a/3V6iS1KdkUpS6ZNOmTZ2+VlFR0S/DaSSpP8ikKEmS1IYckiNJktSGTIqSJEltyKQoSZLUhkyKkiRJbcikKEmS1IZMipIkSW3IpChJktSGTIqSJElt/H/uQACQxic0pgAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.3,random_state=0)"
      ],
      "metadata": {
        "id": "EDeb-KCrjyQX"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "GhNJ16r3j4jB",
        "outputId": "b312d0cd-5dd1-46ca-8caa-25a6b6c73924"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Condition_Encode  Type_Encode   Price  Form_Encode  Indication_Encode  \\\n",
              "217                 6            6  419.99            6                  6   \n",
              "196                 5            5    7.95            5                  5   \n",
              "194                 5            5    7.95            5                  5   \n",
              "530                17           17  143.99           17                 17   \n",
              "26                  1            1  397.03            1                  1   \n",
              "\n",
              "     Drug_Encode    Reviews EaseOfUse  \n",
              "217            6       15.0       4.0  \n",
              "196            5        1.0       5.0  \n",
              "194            5   1.434783      4.21  \n",
              "530           17      133.0      4.14  \n",
              "26             1  30.333333     4.495  "
            ],
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Condition_Encode</th>\n",
              "      <th>Type_Encode</th>\n",
              "      <th>Price</th>\n",
              "      <th>Form_Encode</th>\n",
              "      <th>Indication_Encode</th>\n",
              "      <th>Drug_Encode</th>\n",
              "      <th>Reviews</th>\n",
              "      <th>EaseOfUse</th>\n",
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              "  <tbody>\n",
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              "      <th>217</th>\n",
              "      <td>6</td>\n",
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              "      <td>419.99</td>\n",
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              "      <td>15.0</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>196</th>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>7.95</td>\n",
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              "    <tr>\n",
              "      <th>194</th>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>7.95</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>1.434783</td>\n",
              "      <td>4.21</td>\n",
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              "    <tr>\n",
              "      <th>530</th>\n",
              "      <td>17</td>\n",
              "      <td>17</td>\n",
              "      <td>143.99</td>\n",
              "      <td>17</td>\n",
              "      <td>17</td>\n",
              "      <td>17</td>\n",
              "      <td>133.0</td>\n",
              "      <td>4.14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>397.03</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>30.333333</td>\n",
              "      <td>4.495</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-25bccd33-fc93-4a2f-a628-90cc6b4c9359')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
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              "    }\n",
              "\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-25bccd33-fc93-4a2f-a628-90cc6b4c9359 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-25bccd33-fc93-4a2f-a628-90cc6b4c9359');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "\n",
              "\n",
              "<div id=\"df-6d2c0efd-5834-4757-a55f-dc3e3a293b0e\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-6d2c0efd-5834-4757-a55f-dc3e3a293b0e')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </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",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --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",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      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-6d2c0efd-5834-4757-a55f-dc3e3a293b0e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "scaler = MinMaxScaler()\n",
        "scaler.fit(X_train[[\"Price\",\"Reviews\"]])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "Qns70iX1j8bA",
        "outputId": "87595507-35e6-457a-e448-57c941cbc79e"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MinMaxScaler()"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 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-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-1 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-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-1 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-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 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-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 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-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 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-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MinMaxScaler()</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-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MinMaxScaler</label><div class=\"sk-toggleable__content\"><pre>MinMaxScaler()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "s4SKAxpXj_S1",
        "outputId": "c6f29377-0d70-4810-f1b2-4ab83fb9b892"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Condition_Encode  Type_Encode   Price  Form_Encode  Indication_Encode  \\\n",
              "217                 6            6  419.99            6                  6   \n",
              "196                 5            5    7.95            5                  5   \n",
              "194                 5            5    7.95            5                  5   \n",
              "530                17           17  143.99           17                 17   \n",
              "26                  1            1  397.03            1                  1   \n",
              "\n",
              "     Drug_Encode    Reviews EaseOfUse  \n",
              "217            6       15.0       4.0  \n",
              "196            5        1.0       5.0  \n",
              "194            5   1.434783      4.21  \n",
              "530           17      133.0      4.14  \n",
              "26             1  30.333333     4.495  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2cd2ea78-96e3-42b2-bce3-891b441d35a1\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Condition_Encode</th>\n",
              "      <th>Type_Encode</th>\n",
              "      <th>Price</th>\n",
              "      <th>Form_Encode</th>\n",
              "      <th>Indication_Encode</th>\n",
              "      <th>Drug_Encode</th>\n",
              "      <th>Reviews</th>\n",
              "      <th>EaseOfUse</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>217</th>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>419.99</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>15.0</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>196</th>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>7.95</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>194</th>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>7.95</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>1.434783</td>\n",
              "      <td>4.21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>530</th>\n",
              "      <td>17</td>\n",
              "      <td>17</td>\n",
              "      <td>143.99</td>\n",
              "      <td>17</td>\n",
              "      <td>17</td>\n",
              "      <td>17</td>\n",
              "      <td>133.0</td>\n",
              "      <td>4.14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>397.03</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>30.333333</td>\n",
              "      <td>4.495</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2cd2ea78-96e3-42b2-bce3-891b441d35a1')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-2cd2ea78-96e3-42b2-bce3-891b441d35a1 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-2cd2ea78-96e3-42b2-bce3-891b441d35a1');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-329dc372-bb0e-410c-9cf9-f9b07a0273a8\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-329dc372-bb0e-410c-9cf9-f9b07a0273a8')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </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",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --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",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      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-329dc372-bb0e-410c-9cf9-f9b07a0273a8 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.model_selection import cross_val_score\n",
        "def get_score(n):\n",
        "    print(\"test : \",n, end=\", \")\n",
        "    model = RandomForestRegressor(n_estimators=n)\n",
        "    cv_scores =-1* cross_val_score(model, X, y, cv=5, scoring='neg_mean_absolute_error')\n",
        "    return cv_scores.mean()"
      ],
      "metadata": {
        "id": "vJ1oKoLTj_VV"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "n_values = [10,20,30,35,40,45,50,55,60,65,70,80,100,120]\n",
        "scores = [ get_score(n) for n in n_values]\n",
        "n= n_values[scores.index(min(scores))]\n",
        "n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jgWyXaKUj_X4",
        "outputId": "4c68a778-9db5-42cf-90a9-36cc7f382f60"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "test :  10, test :  20, test :  30, test :  35, test :  40, test :  45, test :  50, test :  55, test :  60, test :  65, test :  70, test :  80, test :  100, test :  120, "
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "80"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "scores = np.array(scores) *100\n",
        "scores"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O3MuQsb8kKau",
        "outputId": "0b28395b-390e-47fa-d9ab-491a9e0ac15d"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([54.67888385, 53.74158602, 54.37018226, 53.0237591 , 54.1007707 ,\n",
              "       53.62595982, 54.19959369, 52.99746733, 52.98413363, 53.52404273,\n",
              "       53.3625315 , 52.54895146, 53.98368972, 53.07487113])"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "n_values = np.array(n_values)"
      ],
      "metadata": {
        "id": "lLBdHeUGkPYt"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "plt.figure(figsize=(8, 6))\n",
        "plt.scatter(n_values,scores)\n",
        "plt.xlabel('N Estimators')\n",
        "plt.ylabel('Mean Absolute Error x 100')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 501
        },
        "id": "b3tjyuRtkQYA",
        "outputId": "f6412625-e850-46b6-bad2-05521c40a6bd"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = RandomForestRegressor(n_estimators = n)\n",
        "model.fit(X_train,y_train)\n",
        "preds= model.predict(X_test)\n",
        "mean_absolute_error(y_test,preds)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SN5rn9TkkUNW",
        "outputId": "cb38e622-c9fa-46dd-f844-dd2e18b14150"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.46165987744968245"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "plt.figure(figsize=(8, 6))\n",
        "plt.scatter(y_test, preds)\n",
        "plt.plot([min(y_test), max(y_test)], [min(y_test), max(y_test)], linestyle='--', color='red', linewidth=2)\n",
        "plt.title('Actual vs Predicted Values')\n",
        "plt.xlabel('Actual Values')\n",
        "plt.ylabel('Predicted Values')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 527
        },
        "id": "l7dkzXD1kWUB",
        "outputId": "1dc06404-f6c5-45d0-b142-6d41c273d375"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(8, 6))\n",
        "sns.kdeplot(y_test, label='Actual Values', fill=True)\n",
        "sns.kdeplot(preds, label='Predicted Values', fill=True)\n",
        "plt.title('Distribution of Actual vs Predicted Values')\n",
        "plt.xlabel('Values')\n",
        "plt.ylabel('Density')\n",
        "plt.legend()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "id": "1HgjSUEekeSU",
        "outputId": "31bc2f82-8151-4d42-a751-2bd60a62d6e5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 527
        }
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Linear Regression"
      ],
      "metadata": {
        "id": "QI6GJ6iFaCz1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "model = LinearRegression()\n",
        "model.fit(X_train,y_train)\n",
        "y_preds = model.predict(X_test)\n",
        "mean_absolute_error(y_test,y_preds)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fmgiAhiPaHc4",
        "outputId": "677093dc-bacb-4bf6-fb9a-d2c8adab6c24"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.4467375570670194"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(8, 6))\n",
        "sns.kdeplot(y_test, label='Actual Values', fill=True)\n",
        "sns.kdeplot(y_preds, label='Predicted Values', fill=True)\n",
        "plt.title('Distribution of Actual vs Predicted Values')\n",
        "plt.xlabel('Values')\n",
        "plt.ylabel('Density')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 527
        },
        "id": "oShmBwuabKoX",
        "outputId": "2ef66897-0cf1-4ef4-fc6e-8cd15d35861b"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(7, 7))\n",
        "plt.scatter(y_test, y_preds)\n",
        "plt.plot([min(y_test), max(y_test)], [min(y_test), max(y_test)], linestyle='--', color='red', linewidth=2)\n",
        "plt.title('Actual vs Predicted Values')\n",
        "plt.xlabel('Actual Values')\n",
        "plt.ylabel('Predicted Values')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 641
        },
        "id": "1867-LFHbLT6",
        "outputId": "77d95da1-1999-4fc9-90bd-a865f15a5404"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 700x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Support Vector Machine"
      ],
      "metadata": {
        "id": "U88tnG7pbROn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.svm import SVR\n",
        "\n",
        "def get_score(kernel):\n",
        "    model = SVR(kernel=kernel)\n",
        "    model = LinearRegression()\n",
        "    model.fit(X_train,y_train)\n",
        "    y_preds = model.predict(X_test)\n",
        "    return mean_absolute_error(y_test,y_preds)\n",
        ""
      ],
      "metadata": {
        "id": "1umqlehXbLXe"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "kernels = [\"linear\",'poly',\"rbf\",\"sigmoid\"]\n",
        "scores = [get_score(kernel) for kernel in kernels]\n",
        "scores"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7Wj_G45ybXjO",
        "outputId": "bea7b02b-199e-4cf6-d786-b1d403306f71"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.4467375570670194,\n",
              " 0.4467375570670194,\n",
              " 0.4467375570670194,\n",
              " 0.4467375570670194]"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.bar(np.array(kernels), np.array(scores), color='blue')\n",
        "\n",
        "# Adding labels and title\n",
        "plt.xlabel('Kernels')\n",
        "plt.ylabel('Scores')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "KRZR0AKNbXnf",
        "outputId": "9d871805-073d-4bd9-92d6-ffef625c2092"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = SVR(kernel='linear')\n",
        "model = LinearRegression()\n",
        "model.fit(X_train,y_train)\n",
        "y_preds = model.predict(X_test)\n",
        "mean_absolute_error(y_test,y_preds)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VcgPFpqNbcpD",
        "outputId": "69085730-f492-4a3f-b844-33672b9e7462"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.4467375570670194"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(7, 7))\n",
        "plt.scatter(y_test, y_preds)\n",
        "plt.plot([min(y_test), max(y_test)], [min(y_test), max(y_test)], linestyle='--', color='red', linewidth=2)\n",
        "plt.title('Actual vs Predicted Values')\n",
        "plt.xlabel('Actual Values')\n",
        "plt.ylabel('Predicted Values')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 641
        },
        "id": "gOyBBJoObeeF",
        "outputId": "2ef7b198-fc81-4d08-eb10-054569f7d6d0"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 700x700 with 1 Axes>"
            ],
            "image/png": 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QvXp1Ydq0aUJGRobR59VMsbBhwwajx2mmxLh//77e72/atElo0aKFULhwYaFw4cJC9erVhbFjxwqXL1/WOe7HH38UQkJCBA8PD6FRo0bCoUOH8vwM9U3vIAiCcO7cOaF3796Cv7+/4OnpKVSrVk347LPPdI6ZOnWqULp0acHFxSXP9BhS1pifzMxMITg4WAAg7Nq1K8/3//jjD6Fp06aCl5eXUKpUKeHDDz8U9uzZk2e6C32/N/fv3xf69u0reHt7C0WLFhXefPNN4dy5c3p/ZnFxccKQIUOEoKAgwc3NTShdurTQrVs3YePGjdpjLP3dIVIalSCY8KcNEREREVkVx5QRERERKQBDGREREZECMJQRERERKQBDGREREZECMJQRERERKQBDGREREZECON3ksWq1Gnfu3IGPj4/RJWmIiIiIpCAIAp4+fYpSpUrBxcVwe5jThbI7d+6gbNmycpdBRERETubmzZsoU6aMwe87XSjz8fEBIP5gfH19Za6GiIiIHF1ycjLKli2rzSCGOF0o03RZ+vr6MpQRERGRzeQ3bIoD/YmIiIgUgKGMiIiISAEYyoiIiIgUgKGMiIiISAEYyoiIiIgUgKGMiIiISAEYyoiIiIgUgKGMiIiISAEYyoiIiIgUgKGMiIiISAEYyoiIiIgUgKGMiIiISAEYyoiIiIgUgKGMiIiISAEYyoiIiIgUgKGMiIiISAEYyoiIiIgUQNZQNnnyZKhUKp2v6tWrG33Mhg0bUL16dXh6eqJ27drYtWuXjaolIiIish7ZW8pq1qyJhIQE7deRI0cMHnv06FFERERgxIgROHPmDHr16oVevXrh3LlzNqyYiIiISHqyh7JChQohKChI+1W8eHGDx86ZMwedO3fGBx98gBo1amDq1Klo0KAB5s6da8OKiYiIiKQneyi7cuUKSpUqhYoVK2LQoEG4ceOGwWNjYmLQoUMHnX2dOnVCTEyMwcekp6cjOTlZ54uIiIgIp04BS5fKXYWWrKGsSZMmWLFiBXbv3o358+cjPj4eLVu2xNOnT/Uen5iYiJIlS+rsK1myJBITEw2+xvTp0+Hn56f9Klu2rKTnQERERHbo5EmgQwdg5Ehg0SK5qwEgcyjr0qULXn31VdSpUwedOnXCrl278OTJE/zyyy+SvUZkZCSSkpK0Xzdv3pTsuYmIiMgOPX8O9OgBPHkibq9ZA2RlyVoSABSSu4Cc/P39UbVqVVy9elXv94OCgnD37l2dfXfv3kVQUJDB5/Tw8ICHh4ekdRIREZEd8/ICfvoJ6N4daNoU2LEDcHWVuyr5x5TllJKSgri4OAQHB+v9flhYGPbt26ezLyoqCmFhYbYoj4iIiBxFx45AdDSwcydQpIjc1QCQOZS9//77OHjwIK5du4ajR4+id+/ecHV1RUREBABgyJAhiIyM1B4/fvx47N69G99++y0uXbqEyZMn4+TJkxg3bpxcp0BERET24PbtvPuaNQMKF7Z9LQbIGspu3bqFiIgIVKtWDf369UOxYsVw7NgxlChRAgBw48YNJCQkaI9v1qwZ1qxZg0WLFqFu3brYuHEjtm7dilq1asl1CkRERKR0R48CNWoAX34pdyVGqQRBEOQuwpaSk5Ph5+eHpKQk+Pr6yl0OERERWdMffwCdOwMpKeL26tXAwIE2LcHU7KGoMWVEREREkjlyRDeQdewI9O4tb01GMJQRERGR4zl8WDeQvfwysG2beOelQjGUERERkWM5dAjo0gVITRW3O3UCtm5VdCADGMqIiIjIkRw8qBvIOne2i0AGMJQRERGRozh0COjaFXj2TNzu2hXYsgXw9JS3LhMxlBEREZFjKFkS0NzdGB4ObN5sN4EMYCgjIiIiR1GtGrB/PzB8OLBpE2Bnyywqau1LIiIiogKpXh1YulTuKizCljIiIiKyT7//DgwbBmRmyl2JJNhSRkRERPZn716gZ08gLQ1ITwd+/hkoZN+xhi1lREREZF/27AF69BADGSD+V62WtyYJMJQRERGR/fjtN7GFLD1d3O7bF1i/HnB3l7cuCTCUERERkX3YtQvo1Ss7kL3yCrB2LeDmJmtZUmEoIyIiIuXbuVNcTDwjQ9x+9VVgzRqHCWQAQxkREREp3fbtuoGsf3+HC2QAQxkREREpmVoNfPUV8OKFuD1gALBqld3faamP450REREROQ4XF7Hrsn17oEYNh5j6whDHPCsiIiJyHAEB4vJJRYo4bCAD2H1JREREShMVBTx5orvP39+hAxnAUEZERERKsmkT0KUL0KkTkJQkdzU2xVBGREREyrBhg3hnZVYWcPw4MG+e3BXZFEMZERERye+XX4CICDGQAeJC4x99JG9NNsZQRkRERPJatw4YODA7kA0fDixZAri6yluXjTGUERERkXzWrgUGDcoOZCNHAosXi1NhOBnnO2MiIiJShjVrgMGDxQliAWDUKGDhQqcMZABDGREREcnhwAHgtdeyA9kbbwALFjhtIAMYyoiIiEgOzZsDPXuK/x49Gpg/36kDGcAZ/YmIiEgObm7iAP+ffgJGjHD6QAawpYyIiIhs5dkz3W13d3EcGQMZAIYyIiIisoVly8QFxePi5K5EsRjKiIiIyLqWLhWnurhxA2jbFrh/X+6KFImhjIiIiKxnyRIxkAmCuN23L1C8uLw1KRRDGREREVnHokXimDGN994DvvsOUKnkq0nBGMqIiIhIegsXAm++mb39v/8B33zDQGYEQxkRERFJa8ECce4xjQ8+AGbNYiDLB0MZERERSefHH4ExY7K3P/wQmDmTgcwEDGVEREQknYSE7H9PnAjMmMFAZiLO6E9ERETS+eKL7PUsv/ySgcwMDGVEREQkHZVKDGOaf5PJ2H1JRERElps7FzhwQHefSsVAZgGGMiIiIrLM7NnA228D4eHAwYNyV2P3GMqIiIjIfN9+K04GC4gLjf/xh7z1OACGMiIiIjLPrFnA++9nb0+ZAnz8sXz1OAgO9CciIiLTff018NFH2dtTpwKffipfPQ6ELWVERERkmhkzdAPZl18ykEmIoYyIiIjy99VXQGSk7vYnn8hXjwNi9yUREREZd+EC8Nln2du5W8xIEmwpIyIiIuNCQ4GVKwEXF3EdSwYyq2BLGREREeVv4ECgfn2gRg25K3FYbCkjIiKivE6fzruPgcyqGMqIiIgomyAAkyYBDRsCS5fKXY1TUUwomzFjBlQqFSZMmGDwmBUrVkClUul8eXp62q5IIiIiRyYIwOefA198IW6PGiUO8iebUMSYshMnTmDhwoWoU6dOvsf6+vri8uXL2m0VFzwlIiIqOEEQ77CcNi173/ffi4P8ySZkD2UpKSkYNGgQFi9ejC+//DLf41UqFYKCgkx+/vT0dKSnp2u3k5OTLaqTiIjIYQmCOOfY9OnZ+374ARg3Tr6anJDs3Zdjx45FeHg4OnToYNLxKSkpKF++PMqWLYuePXvi/PnzRo+fPn06/Pz8tF9ly5aVomwiIiLHIAjiupU5A9ncuQxkMpA1lK1btw6nT5/G9Jy/CEZUq1YNy5Ytw7Zt27Bq1Sqo1Wo0a9YMt27dMviYyMhIJCUlab9u3rwpVflERET2TRCAiRPFyWA15s0Dxo6VryYnJlv35c2bNzF+/HhERUWZPFg/LCwMYWFh2u1mzZqhRo0aWLhwIaZOnar3MR4eHvDw8JCkZiIiIocyZYq4wLjG/PnA6NHy1ePkZGspO3XqFO7du4cGDRqgUKFCKFSoEA4ePIj/+7//Q6FChZCVlZXvc7i5uaF+/fq4evWqDSomIiJyMF26AL6+4r8XLmQgk5lsLWXt27fH2bNndfYNGzYM1atXx0cffQRXV9d8nyMrKwtnz55F165drVUmERGR42rSBNizR5z2YvhwuatxerKFMh8fH9SqVUtnX+HChVGsWDHt/iFDhqB06dLaMWdffPEFmjZtisqVK+PJkyeYNWsWrl+/jpEjR9q8fiIiIrsjCOJ/c04n1bSp+EWyk31KDGNu3LgBF5fsHtbHjx9j1KhRSExMRNGiRdGwYUMcPXoUoZxDhYiIyDhBAMaPFxcVnz1bN5g5qSy1gOPxj3DvaRoCfTzROCQAri7y/VxUgqCJzc4hOTkZfn5+SEpKgq+mH52IiMiRCQLw9tvinZUA8N57wLffyluTzHafS8CU7ReQkJSm3Rfs54lJ3UPRuVawpK9lavaQfZ4yIiIiJclSC4iJe4htsbcRE/cQWWo7b7sQBHHOMU0gU6kAE1bQcWS7zyVgzKrTOoEMABKT0jBm1WnsPpcgS12K7r4kIiKyJVu2ntiEWi0GsvnzxW2VClixAhgyRNay5JSlFjBl+wXoi9oCABWAKdsvoGNokM27MtlSRkREBOW2nlhMrQbeeis7kLm4AD//7NSBDACOxz/Kc41zEgAkJKXhePwj2xX1H4YyIiJyevm1ngBi64nddGWq1cCYMeLcY0B2IBs8WN66FODeU8OBzJLjpMRQRkRETk/JrSdmU6uBN98EFi0St11cgFWrgEGD5K1LIQJ9TFtFyNTjpMRQRkRETk/JrSdme/QIiI4W/+3iAqxeDUREyFuTgjQOCUCwn/HAFewnTo9hawxlRGQ3HO6uOFIMJbeemK14cWD/fqBqVWDNGmDAALkrUhRXFxV61DV+00aPusGyzFfGuy+JyC7IfVec0iaZJGk1DgmAv7cbnjx7YfAYf283WVpPLFKuHHD2LODuLnclipOlFvDrX8Zv2vj1rwR82LmGzf8fZyiTGN+4iaSnuSsud7uY5q64+YMbWDWY7T6XgMm/XkBicnYgDPL1xOQeypomge8/BfM8I6tA35dNVhbwww/iYuKeOVryGMj0ym/8IJA9fjCsUjEbVSViKJOQ3H/JEzkiuecU2n0uAaNXnc6zPzE5DaNXncYCKwdCU/H9p2COXn2A9Ey10WPSM9U4evUBWlYtYaOqTJCVBQwbBqxcCezdC2zerBvMKI/EpOeSHicljimTiMPNb0OkEHLeFZelFjBx81mjx0RuPiv72Da+/xTc5tO3JD3OJrKygNdfFwMZAERFASdOyFqSPbibbNrNGqYeJyWGMgk43Pw2RAoi511xx/59aHSMEQA8fvYCx/59KPlrm4rvP9JINbFr0tTjrC4zU5wEdtUqcbtQIWDDBqBlS3nrsgP7L92V9DgpMZRJwKHmtyFSGDnvijsa90DS46yB7z/SaFjeX9LjrEoTyNasEbfd3ICNG4FevWQty16cT3gq6XFSYiiTgEPNb0OkMJo5hQyNFlPBenMK3X5s2pgSU4+zBr7/SCM02E/S46wmM1OclX/tWnHbzQ3YtAno2VPeuuyIYGKjsanHSYmhTAIONb8NkcK4uqgwqXsoAOQJZprtSd1DrTLIv5S/af/PmnqcNfD9RxqPnmVIepxVvHgBDBwIrF8vbru7iwP7u3cHoLx5/JRWj0b5Yl6SHicl3n0pAc1f8olJaXrHdagABMk0OzCRI+hcKxjzBzfIc3dhkJXvLmxeqQR+PPCvScfJhe8/0rCLcDtzpjhuDMgOZOHhAJR3963S6snpgw7VMWzlSZOOszW2lElAzr/kiZxF51rBOPJRO6wd1RRzBtTD2lFNceSjdlZ9g29aqRj8vd2MHuPv7YamNp7LKCe+/0hDzm5yk737LtCqlRjItmzRCWS2uPvW1JYvpd8N3KpGIArl8/9DIRcVWtUItFFF2RjKJKL5Sz4o13paQX6eVp/YkshZuLqoEFapGHrWK42wSsWsHjRcXVSY0ae20WNm9Kkte+Dh+0/B2UW4LVwY2LkT2LcP6NoVgO3uvt19LgEtZkYjYvExjF8Xi4jFx9BiZnSegGUPdwO7uqgwd2B9o8fMHVhflmutEgQ5hrLJJzk5GX5+fkhKSoKvr6/kz88ZtYkcjzij/3kkJqdr9wX5emByj5qKCjx8/yk4RXW7ZWQAycniWpYGxMQ9RMTiY/k+1dpRTS2end7Qihqa36ycwd8W9Uhl97kEfLblb9xPzdTuK1G4EKb2riP5tTY1e3BMmcQ0f8kTkePoXCsYHUODFB94+P5TcIq51hkZQL9+wJUrQHQ0ULKk3sOsffetuStq2NPdwIq51jkwlBERmYCBx3nIfq0zMoBXXwV+/VXc7tYN+PNPwCXviCNr36Bgzjx4YZWK2ccNEznIfq1z4ZgyIiIipUhPB155JTuQeXkBM2boDWSA9W9QMLflyy5umFAwhjIiIiIlSE8H+vYFtm8Xt728gB07gPbtDT7E2jcomNvyZRc3TCgYQxkREZHc0tKAPn3EuysBMZDt3Am0a5fvQ615960lLV+8G9hyvPuSiIhITppA9ttv4ra3txjI2rQx62msdfet5u5LADoD/vXdfWmLeuyRqdmDoYyIiEgu6eniQuK7d4vb3t7Arl1A69aylpWboqYKsUOcEoOIiEjp3NyA0qXFfxcuLAayVq3krUkPJU4f4YgYyoiISHGcpuvLxQVYtEgcQ9avH9CypdwVGaS06SMcEUMZEREpitN1lbm4AD/8IHcVpAC8+5KIiBRD6YtZF9izZ0D//sBff8ldCSkQQxkRESlCfkv6CJB/MesCefYM6N4d+OUXce6xv/+WuyJSGHZfEhGRIuS3pA+gu6SPUukdD/f8v0C2f7940IsXYkgj5xk/aAKGMiIiUoTEZNOW9DH1ODnoGw8X4iVg046vEHAyRtzh6wvs3Qs0aSJTlcrhdOMH88HuSyIiUoRHKemSHmdr+sbDeWc8x4ylkdmBzM8PiIqyeSDLUguIiXuIbbG3ERP3UBFdwA4/ftACbCkjIiJFCCjsLulxtqRvPJx3xnMs3zAZTW6dBwAkexZB4b1RcG38ksnPKUW3nhJbo/IbP6iCOH6wY2iQU3VlMpQREZEiBPl5SXqcLeUeD1c4/RmWb5yMxrcuAACSPApjUP8v8Umxiggz4fmkClKa1qjc4UfTGiXXWpT5jR8UYB/jB6XG7ksiIlIEzeLXxuRe/Fopco9za/vvSW0ge+JZBAMHTMO5oMomjYeTqlsvv9YoQL67We89NW1coKnHOQqGMiIiUgRXFxUmdQ+Foc4qFYBJ3UMV2Z2Ve5zbjhqtMLXdSDz29MGgAdNwPqiy3uNykzJImdMaZWuBPsbDt7nHOQqGMiIiUozOtYIxf3CDPC1mwX6eNutqM3dQfJZawMPUjDz7l77UC+1GLcD5kpW0+/IbDydlkFJya5SmVdRYAFdqq6g1cUwZERHpkHveKDkXvzZ3LJfm+JR7DxGWeBUx5evqfP+xt5/Odn7j4aQMUkpujdK0io5edVrv9wUot1XUmhjKiIhISyl36smx+LW5g+I1x/ukpWDlL58j9O6/eKtXJH6von+6C1NafqQMUprWqMSkNL3doSoAQU7YGqVk7L4kIiIAjjFvlKXzcZk7lktzvE9aCn7+5TPUS/gH7upMTN37Izxe5B03Zup4OCm79TStUZrH5X4emFiTNWh+foZopsRQwnxqtsRQRkREir5Tz1S7zyWgxcxoRCw+hvHrYhGx+BhazIw2KUyaO5brePwjpN59gJXrP0O9hCsAgAfefhjabwrS3Tx0HmvOeDipg5RmjF5QrjF6QTYco6ePkm9CkBO7L4mIyG7mjTI03q2g83GZO5br8a0ErF73CWrfjQMgBrKBA6bhnxIVdI4f17YS3u1YzazWKE2Qyt2NHGRhN7KcY/QMUfJNCHJiKCMiIrv4kDQ03u2z8BqYuvOiybPDZ2SqsTLmGq4/eobyAd54LayCeWO5Hj1Cq7cGosh/gey+tz8GDpiGKyXK5zm+eeUSFoUfqYOUHGP0jFHyTQhyYigjIiLFf0gaawl7a80Zo4/N2cp34PJdLD4cj5y9sNN2XcSIFhVMGxTvKwDtO6DIhbMAgPuF/REx4CtcLV5O//EFGESvtCAlJd6EoB/HlBERkaLnjTJlvJsplhyOw8JDuoEMANQCsPjwNdQq7QvAyFiu8Opw7d4NiI0FAKQXK4GIAdMRpyeQAcqZ0kGJi5Er+SYEOakEQZD/6thQcnIy/Pz8kJSUBF9fX7nLISKSTe7xWY9TMzB2jThvVM4PBs3HolwDw2PiHiJi8bECP48KxkOciwqYM6A+vtp10fCUILt2Ab17AwEBwP792J3pZ7MpRCyZP04pU5wYovT6pGJq9mAoIyJyQoY+DHvUDcavfyUo6kNyW+xtjF8Xa/HjVQB8PAshOS0z32M/C6+B15uHGA8/u3cDISFAtWoAbDPZriXhxVCXr9whOze5Jyu2BVOzB8eUERE5GWPjsxYdise8gfVRtLCHYj4kzRnHlrs1TFN1/XJFcfCf+/k+/vqjZ7pjuZ4/F5vQcurcWWfT2mO/LLmzNL8u39w3P8jJkcfOmUsxY8pmzJgBlUqFCRMmGD1uw4YNqF69Ojw9PVG7dm3s2rXLNgUSETkAU8ZnTd15EY1DAtCzXmmEVSom+4e2qePdfhxoeD6uVlWKm/Ra5QO8szfu3QMaNwa+/tqywiVg6fxxnAfMPimipezEiRNYuHAh6tSpY/S4o0ePIiIiAtOnT0e3bt2wZs0a9OrVC6dPn0atWrVsVC0Rkf2yl/nIctIMCh+z6rTBljBNN16nWvqnkcjIVGParot5Bvnn5KICXgurIG7cvQu0awdcuAB89BFQpAjw1lvWOcEccnflqdWCRddLiilOnKFbUWlkD2UpKSkYNGgQFi9ejC+//NLosXPmzEHnzp3xwQcfAACmTp2KqKgozJ07FwsWLLBFuUREds0e5iPTx9QJVQ11hbkXcsGoliFYeCje4GuMahkC90IuuoEMAMqUAV5+WdoT0kPfuDF/LzeTHpv7ehV0ihNnGYCvNLKHsrFjxyI8PBwdOnTIN5TFxMTgvffe09nXqVMnbN261eBj0tPTkZ6evQ5ZcnJygeolIrJnSp+PzJiCTqga2VWcgiH3PGUqAN3qBOHDzjWAxEQxkF28KH6zbFlg/36gUiWJz0aXoXFjT56/MOnxua9XQeYBK+jqCGQ5WceUrVu3DqdPn8b06dNNOj4xMRElS5bU2VeyZEkkJiYafMz06dPh5+en/SpbtmyBaiYismdKno/MFJqWMEvHu0V2DcWlqV3wSoPS8HZ3BSB2AW7/OxE9P9mAlGYtswNZuXLAgQNWD2TGxo2ZQt/1snQeMEdYA9WeyRbKbt68ifHjx2P16tXw9LTeX2SRkZFISkrSft28edNqr0VEpHSctBOIvnQXm07fxrOMLO2+EimPMGfR/1Ak/qq4o3x5MZBVrGj1evIb55efHnWD9V4vSxYj5w0C8pKt+/LUqVO4d+8eGjRooN2XlZWFQ4cOYe7cuUhPT4erq6vOY4KCgnD37l2dfXfv3kVQUJDB1/Hw8ICHh4e0xRMR2TGpF7y2J/paggKfPsTadR+j0qPbAIAE/5K4vWIzbie5IzDuodUHuBd0/N6vfyXgw841DAYzc7p87XXMoaOQLZS1b98eZ8+e1dk3bNgwVK9eHR999FGeQAYAYWFh2Ldvn860GVFRUQgLC7N2uUREDkXqBa9tqSB3BeprCfJJfwa/tBQAwE2/khgwYDpu704AkADA+gPcCzp+L7+7Zc2ZB8yexxw6AtlCmY+PT55pLAoXLoxixYpp9w8ZMgSlS5fWjjkbP348WrdujW+//Rbh4eFYt24dTp48iUWLFtm8fiIie2ePk3YW9K5AfS08ccXLImLAV5i5+/8wrudHuOMbqPN9aw9wz29QvimkarniQuHyUszksfrcuHEDCQkJ2u1mzZphzZo1WLRoEerWrYuNGzdi69atnKOMiMgJaO4KzN3SpQlNu89lf14YWoTbUAvPlRLl0WfwN3kCGWD9Ae7GxvmZSqqWK445lBfXviQiIpuxtOsxSy2gxcxog4PQNS04Rz5qh6gLiQZb0zqGBqFP5Dp0PrgZs1q9BrVL3qEyxqwd1dRqrYv6WgGDfD2QlqlG0rMXRluujnzUTtKgxHnKpMW1L4mISFEK8kFv6l2Bc6Ov4PvfrxicY2t5+0CsXRMJ71vXEZj6CB90GW9WMLPmAHdD4/yiLiTmu5qB1C1X9jzm0J4xlBERkdUVdEJSU8PQ8j+uGZxjq0zSPVTpPxLej8W5LV9KuAz/tBQ88vYz7SRguJtQqiWJ9I3zk+tuWXscc2jvGMqIiKjAjIWS/CYkVUEcr9UxNMhgkDF1zJShGfDLJN3F2rUfo3TSf9MqVa2K0r/vw7wML9x7moYAL3cMXXE837UxG5Yvmme/Lbr62HLlHBjKiIioQPILJVIsgm7KHYq5u/c0yiTdxbo1kSiTfA8A8KBUBVxbvgn1S5dG2H+hJibuodFABgBqATh1/bFOjbZckogtV45P0XdfEhGRsplyR+TvFwwvhZeTseNMuUNRbyB7koh1ayZqA1lcQBl06f0FXvn1OlrMjNbesWnJpKnWWJLI0F2j5BzYUkZERBYxpVty4uazePLMtEW1t8TexsfhhgetGxpb5aKC3lausk8SsW5NJEo/vQ8AuBpQBhER03G/iNgFmbM1y5JJU6VoAcyJdzwSW8qIiMgipoQSUwMZADxKfZHvmoqdawXjyEftsHZUU8wZUA+fhdcw2O04+feF2kB2pVhZDBiYHcg09QFia1bD8kXNXqhdyiWJzJmDjRwXQxkREVnEGtNDmPKcmrFVPeuVRnEfw2sbf9B1Ai4VL49/ipVDRMRXeFA47yB9TWvWqeuPzZ40VaoliazRDUr2id2XRERkEWusf2jKc+a80/PB03SDxz3y9sOgAdMAAA8L+xt9zntP09CzXmmzpp6QakkiqbtByX4xlBERkUU0ocRYoDBV7gBjaIoNfeOuNHddlnucgEfefkjx8NZ+L78wpqEJg+ZMPaG5+cCSiV1znt+Vu09NqtGaE9eSMjCUERGRRTShZPSq0wV+LgHZAcbQgPcedYOx6FB8nlYpAUDFh7ewdt3HuOlXEkNfnYLUHMHMGH2tWeZMPWHJxK76zs8U1miZJGVhKCMiIot1rhWMEc0rYOkf1wr0PEW93dAxNMjgvF8JSWlYeChe72M1gaxkyiOUTHmET/Yvxced3zb5tQu6TJE5rWuGzs8YU7tByf4xlBERUYF0CA0qcCh7/OwFjv370OCAd0MqPbyJtWs/RmDqYwDAhcAQzGo1xKTH+nu5YUbf2pJMN2FK65qxAf2GWHN9S1Ie3n1JREQFohlbVtDIEBP30KwuvUoPbmLd2khtIDsfWBEDB0zDYxPXspw3SLrZ9k2R34B+fYL8PCVdFYCUjS1lRERkMkMD8I0NeDe9Zcj0Iys/uIG16z5GidQnAIBzJSthUP8vMbx7A6w9fgOJyYbvytR0BzataNs7GROTTQtk49pWRpWSRbi+pRNiKCMiIpPkN+O8oQHvn4XXwNSdF/OdOiKsYnHM3R+Xbx1V7l/HmnWfoMSzJwCAsyUr4bX+X8I7qATGtauCce2qYG70Vcz+/R+9rwUY7w40tri6pXafS8DUHedNOrZ55eKc+sJJMZQREVG+TF14O/eA94bli+LU9cfoWkv/uLOcIalppWL5Ljpe7nEC1q77GMWfJQEA/g6qjNf6f4lkzyKYkSNoje9QBdWCiph1V6TmPKVe6sjUwf0c0E8qQRCcaorg5ORk+Pn5ISkpCb6+vnKXQ0SkeFlqAS1mRhscD6UJE0c+aqfToqQv4ORepzJ34NEEGED/vF+jw0qj2cS30PJSDP4KqoLX+k9F4ZLFDYYmc1q9DIUnzdGWjO3K72cnxWuQ8pmaPdhSRkRERlky47yhgKNpBhjevAI6hgblCUmmzPuV9XI0bk/4CLcGv4mFpUoaDVrG7orMGdiKF/bA5F+NL64+ZfsFdAwNMqsr09TB/QGF3TGtdy0GMifHUEZEZEXWGJ9ka+YuvJ3fWo4qAL+dS8Qn4frHdeXpBi3igcYVi2mPdfXyROmFc1DawvMBzJ/A1dKljkz92X0aXoOBjBjKiIisxRrjk+Rg7sLbx/41PrWFKQFH28J15gzQfySwaRNQoYK5petlyQSuGuYudWTqzy7Iz8uCasjRcJ4yIiIr0Hzw5w4nmoHxu88lyFSZ+fKbh0wFMWw2DgnA7nMJGLvatGWX8g04p08D7duL/23TBrhxw5yy9bJkAtecDIWsLLWAmLiH2BZ7GzFxD5H138A5U+Zwc1EBj1MNT+FBzoMtZUREEjOl+86S8UnWouliTUx6jkepGQgo4oEg3+yuVlMX3o66kGhWC5TRVqTTp4EOHYDH4sSwKF0a8Pc3+9xys2QCV8D4nZH5tYhqfnaGqAVg7JozmO+isqsWVJIeQxkRkcQsGRgvF2Njq3IGi/wG4HcMDUKLmdEmBbJ8p344dUoMZE+eiNvNmwO//Qb4+Jh9frmZ2/0IGJ/bzNSpQuYNbIBxa0/r3Hmam5KCOsmDoYyISGLmDow3hbUmNDXWspWQK1gYW3jb3CWSDE7eevIk0LFjdiBr0QLYtUuSQAaYPsYrJ0Nzm5nTIlq0sLvRQKakoE7yYSgjIpKYuQPj82ONGwbMGVuVswXH0BQTpgZMf283zOhjYBHwEyfEQJYkTgyLli3FQFakiEnPbQrNGC9jqwuU9PXAt/3q4UFKutEAbE6LqKk/n8Sk56adCDkkDvQnIpKYOQPj82PpDQOGBp5rmDq2KmewMMbUgDkvwsDkqMePi12WmkDWurXkgQyAdnwcgDzXR7M9uUdNNK9cHD3rlUZYpWIGWyTNaRE19eczdedFu7oJhKTFUEZEJDFTPviNrb2okV/3GCC2YuUOXLvPJaDFzGhELD6G8etiEbH4GFrMjNb5sDd3bFV+x5tyl2Gxwu54yVAQ3bULSE4W/92mDbBzp+SBTEMzPi7ITzcoBfl5mjWjvjktoqb8fADgcWqG3d2dS9JhKCMis+TXAkMiKT74zeke0zC1Zc3csVX5HW8siGo8TM1A61n79QeOSZOA998H2rYFduwAChc2qz5zda4VjCMftcPaUU0xZ0A9rB3VFEc+amdWd7A5LaI5fz7GGAvb5Pi49iURmcxRJkO1pYIM0N8Wexvj18Xme9ycAfXQs15ps9aoBGDymoz61rU0JL+Z8o2u8SgIQEYG4OFhNysh5LdWZ+7z3H0uAR9vOYtHqS/yfe61o5py0L+DMDV7sKWMiEziSJOh2pJmYHx+45P0MfeGAXNa1jQtN6ZUY0pXq0bnWsE4+EFbBBR2M1gDAGyZ+wuyDh3W/aZKBXh4mNT9qhTmtoh2rhWMz7rVNOm5LZm+g+wb774konwpdTJUe2lNASyr1ZQ7BXPO92XuVByG5h7TyNkKaqh+fftPXX9stCWo4a3z+HbDZGCVC7BnjzgP2X9MnfdLSYxNFaJPkK+0d+eS42AoI6J8KXEyVHvqSrW0VlNn0td8+FsyFUfOQGFoRn9D9feoG4xf/0rIs79rrSCDr/3SzXNYsWEyCr9IAzIAzJ6tDWVKDf+mMDRViD7mhG17+sODCo5jyogoX+aObbI2Q60pRscryUSKWk0NdZoxZfl92Js6PsxY/YbkDo85Nb55Dss1gQzAk5bt4L93J+AphsSYuIeIWHws39dwhLFWpoxFA2A3f3iQcaZmD7aUEVnImf6ClXoy1IKwp9YUqWo1pXtM8/vYpVYQlv1xzaSWNVPqn/yreYt3a451UYnj9jXbTW6cxfKNk+H9Qlx4O6bqS2i8e4c2kAHWWQnB2ix9H8hv2SoAdteNSwXHUEZkAXvqOpOCuWObrMncrlQ5w7OUtRrrHtP3+6j6LxRpGFoqyJi50VeQmGxZANLM5qAC0PT631i6aYo2kEVXbITMNevh6u2l8xglhX9TFPR9wFDYBmBwHVGl/eFh75T2xzVDmcSUdoFJevY4ELmgzB3bZE3mtKbIHZ5tUauh30dNKBrRvAI6hAaZ/V60+1wCZv9+xeTj9RnevALubfsNszZOgVemGMj+qNYEL9asQ6cGFfIcb274l/P9Vqr3AX1hO791RK0xhtMZP7vkfn/Qh6FMQkq8wCQte+o6k1p+3S22+h03tZXk2oNn+P73f6weno19mFm71vzWr1QB2HUuER+HmxeYs9QCJm4+a/LxhnQpWQiN1k+B6r9A9qjty3hp+1acSnyGbbG39bYGmhr+rfl+m5GpxsqYa7j+6BnKB3jjtbAKcC+UPYOUtd8HbN2N64yfXUr945qhTCJKvcAkLSXehWhL5t76bw2mLii99viNfJcnKmh4zu/DTIpajX3AS/37qAmYP8fE48mz/Cc3NUTTotWgUVWo5s4Fhg0DunXDicn/h8n/d9Toh78p4d+a77fTd13A4sPxyDmZ/rRdFzGqZQgiu4pjvaz9PmDLblxn/OxS8h/XDGUSUPIFJmnZ40BkqZlz67+1Xj+/1pSIxuXy7XoraHg29mE2etVpvNuhCioUL4wBL5XF7N+vWFyrsQ94KX8f85uJ3xwCcnRnDx0KlCqFPcWrYvT6cyZ9+BsL/9Z8v52+6wIWHorPs18tQLs/smuo1d8HbDWG01k/u5T8xzVn9JeAJevTkX0qXsRD0uPIMvnNol6umGnrJu46e8ei9TtNWSh89u9XMH5dLGb/fgX+3m7w89ad4V5Ta4XiptWq7wPenO5RYwyt1mCJEimP8+zLat8Bk3dfNWthdUMrIVjr/TYjU43Fh/MGspwWH45HRqba6i1ZUi1onx9n/exS8h/XbCmTgJIvMEnM1M9up5r9Tx7GWlOWHv7XpOdYeewGVh67Yfb4mfw+zHJL+q8r8N0OVVGhuLdOrTFxD016Dn0f8A3LF4WLCsgvU87+/R9UCypi0bg0c7T+9xQWbPkKn3Z6C1O2e2pbWKRsmbDW++3KmGv5/hzVgnjc681DrN6SZYsxnM762aXku3wZyiSg5AtM0nqQmi7pcVQwhrpSAwq7m/U85o6fMfdDStMVtO7EjTwTtxakq+rU9cf5BgkNS8elmapN3Eks3DINHlkvMGvn9xjkUwLH4+shrFIxST/8rfV+e/2R8dbEnMfZ6m5ka4/hdNbPLiVN8ZMbuy8loLnAhv43UUEczCrHBSZpOeubmL0J8vPK/6AcDHWhGWLJ9TXUFVSQripzwqGhbigpWkHaxJ3Awi1fwiNLbBH8rVoznCiTPfZKyv9vrPV+Wz7A26zjzF2I3FIFWdA+P8762WWr7mFLMJRJQMkXmKTlrG9i9kZzncxhzviZ/H4PjLn3NA1ZagExcQ+xLfY2YuIeomNokEUf8OaGw4KMSzOk3dXj/7WQZQIAdlRrgfHdP0CmayHtczcOCYB/rjF1uRX1djPp/xtrvd++FlYB+T3ERSUep9G5VjCOfNQOa0c1xZwB9bB2VFMc+aid3dyt6MyfXbYK1eZi96VElDKHE1mXkiZRJcNyXidzx0qZ0nJk7PcgP9cePEOLmdF6p4U48lE7s7qqNOHQ1O5HfQEsv64cY9pf/RPzt0yHu1oMZNurt8SE7u8jy8XV7D9OzHlta7zfuhdywaiWIXrvvtQY1TJEZ74yQP67kQvKmT+7lDDFT25ckFxizjgrsjNyxskW7ZEl0zyYs9i1Oc+vAuDn7YakZy8kXUjdlAXD9S1EnvO96tqDVL3Tdmh4u7viWUaWzr4OV/7Ej1uzA9mvNVrh3W7/Q5aLKwDgx4H10bVOKQDWW2jcGu+3+uYpc1EBI1qE4JPw0AI9t5Lxs8u6uCC5TOz9ryYyjRL/wqK8cl6nxOQ0TN1xHo9S9U+Kasng3ty/B5qZ+YG8raiabannhNK0dEzcfFbvhK/6WnD1hUlN92LO59D8oQEAo1ed1u7PHci2hrbG/8Lf0wYyAChaOHtaGGvd5WeN99vIrqGoXcoPkVvP4mmaGETVArDj7wQ0LF/UYf/o4meXMjCUEVmIb2L2Ied18nJzwZj/woVUXc+5fw+qBRXR2xWkmUTWkIJMWKkJh3Ojr2D5H9fw5Hl2sMrdDWWoZc3YtB0A8OPABhi39jTUAvDYywcZhdzgnpGJLaFt8H74uzqBDNANWPZ0g8zucwl4e12sU81wT8rB7ksiciq26HrW1xW04+87GL8uNt/HjmheAZ91rynpa+fsssw9ni0nfd2cOe36+w7eWnMGANDo1nn0Pr8fn3UcA3WuQAbodkVqXje/KQgMva6tFPTnQ2SIXXRfzp8/H/Pnz8e1a9cAADVr1sTnn3+OLl266D1+xYoVGDZsmM4+Dw8PpKU51sR2RGQ9tuh61teKamor0JbY22YvIJ7fa2sUdBLXrnVK4UcA49aewckyNXGyTN7wqK8b2NVFhc/Ca2gDXe7jAWXcIKPk5XfIOcgaysqUKYMZM2agSpUqEAQBP/30E3r27IkzZ86gZk39fyn6+vri8uXL2m2Vin+tEJF55Oh6bhwSgIDC7niUmmH0uEepL6z2oW/x2K6NG4FTp4CvvkLXOqUwFyq8teZ0nscZCli7zyVg6s6Lel9LSXf5OesM96Qcsoay7t2762xPmzYN8+fPx7FjxwyGMpVKhaCgIFuUR0QkGVcXFXrVK4Vlf1zL91hrfehbNLZrwwYgIgLIygLUamDGDHStE4wFLqZNo5Df3aGfhSsjkAH2NfaNHJNiBvpnZWVhw4YNSE1NRVhYmMHjUlJSUL58eajVajRo0ABfffWVwQAHAOnp6UhPz17yJjk5WdK6iYhM1TE0yKRQZq0PfbOXl1m/Hhg0SAxkAPDgASAIgEplUjdwfutqqgBM3XkBnWqZd8eptSh5+R1yDrLP6H/27FkUKVIEHh4eGD16NLZs2YLQUP1zwVSrVg3Lli3Dtm3bsGrVKqjVajRr1gy3bt0y+PzTp0+Hn5+f9qts2bLWOhUiIqPyW2nA2itCmDWD+9q1wMCB2YFs5Ehg8WLAxUXn+YwtAWTOGC0lcOYZ7kkZZA9l1apVQ2xsLP7880+MGTMGQ4cOxYULF/QeGxYWhiFDhqBevXpo3bo1Nm/ejBIlSmDhwoUGnz8yMhJJSUnar5s3b1rrVMiJ5V42x5T1E8n5aD70Vcj7oQ+IIeWzAgzyN4VJy8usWQMMHix2VwLAqFHAwoU6gcwU9jhGS6nL75BzkL370t3dHZUrVwYANGzYECdOnMCcOXOMBi0NNzc31K9fH1evXjV4jIeHBzw8PAx+n6igOLs/mcPQsjYaU3degIsLrPq7Y7TrcfVqYMiQ7ED25pvAjz+aHcgA+x2jxcmhSS6yh7Lc1Gq1zhgwY7KysnD27Fl07drVylUR6WdoEDMnmiRjOtcKhlot6J0iwtzfHUuXx9F7B+rKlcDrr2cHsjFjgLlzLQpkANCwfFG4qABjDccuKvE4peHk0CQHWUNZZGQkunTpgnLlyuHp06dYs2YNDhw4gD179gAAhgwZgtKlS2P69OkAgC+++AJNmzZF5cqV8eTJE8yaNQvXr1/HyJEj5TwNclLGBjEXZNkccnxZasHgFBHm/O6Y20prNMClpQGTJ2cHsrfeEgNZAaYdOnX9sdFABoiB7dT1xwxARJAglCUnJyM6OhrVqlVDjRo1zHrsvXv3MGTIECQkJMDPzw916tTBnj170LFjRwDAjRs34JLjL7THjx9j1KhRSExMRNGiRdGwYUMcPXrU4I0BRNbEiSbJUlL87pjbSqt3vUsvNwxrHoJx7SrD1dMT2LcPaN0a6NED+L//K1AgA+xzTBmRnMwOZf369UOrVq0wbtw4PH/+HI0aNcK1a9cgCALWrVuHvn37mvxcS5cuNfr9AwcO6GzPnj0bs2fPNrdkIqvgB47jsrRL0FQF/d0xt5XWUIB78vwFZv/+D5YfjceMPrXRuVYF4ORJoHjxAgcywH7HlBHJxeyBAocOHULLli0BAFu2bIEgCHjy5An+7//+D19++aXkBRIpFT9wHNPucwloMTMaEYuPYfy6WEQsPoYWM6Ox+1yCZK9R0N8dc1rajAW4Vv+egnvmCzx59gKjV50Wz7FECUkCGZA9BYihZ7P2FCBE9sbsUJaUlISAAPF/oN27d6Nv377w9vZGeHg4rly5InmBRErFDxzHo2lRyh14NF2CUgWzgv7umNPSZijA9f9rD37eMAnztk2HW9YLAGLrmpTTuXDeLyLzmB3KypYti5iYGKSmpmL37t14+eWXAYjjvTw92SJAzoMfOI4lvy5BQLrQkvN3Rx8BQI+6wQZ/d8xpadMX4CJid2Pm7h8AAB2vHkev8wcAWGciV877RWQ6s8eUTZgwAYMGDUKRIkVQrlw5tGnTBoDYrVm7dm2p6yNSNENzTilpkWUyja1v3OhcKxhvtArBwkPxer+/6FA86pcrqvd3KL/lgIDslrbcIWtg7G/4as+87Nd5qTc21O6g3bbGGEjO+0VkGrND2VtvvYXGjRvj5s2b6Nixo/buyIoVK3JMGTklfuA4BlvfuJGlFvDrX8a7Qw1Ni6FpaRuz6jRUgN5g9vxFFqIuJKJjaBCC/TyRkJSGQWd2YdreH7XHLGjcBzPaDNMZQ2atMZCc94sofxbNCNioUSOEh4fj9u3byMzMBACEh4ejefPmkhZHZC/yWwOQlM/WN24UdF1ITSutn7eb3u9rBu/Pjb6Cz8Jr4LXTO3UDWZO+OoGMYyCJ5Gd2KHv27BlGjBgBb29v1KxZEzdu3AAAvP3225gxY4bkBRIR2YKtb9yQomWuY2gQPAsZfxuf/fsVnP90BqZGzdfu+7HpK5jR+nWdQAZwDCSR3MwOZZGRkfjrr79w4MABnYH9HTp0wPr16yUtjojIVmx944YULXPH4x8hMdn4snRdLx3BB9t/0G6fGPAmFnUaqdNlyUH35stSC4iJe4htsbcRE/dQ0rtWyXmZPaZs69atWL9+PZo2bQpVjv+pa9asibi4OEmLIyKyJVveuJHfYH3Vf69rrGXOlNa2gyENcLJ0DTS6fRHL2wzEkFU/4pRKxTGQRmRkqrEy5hquP3qG8gHeeC2sAtxztEiau7wVkanMDmX3799HYGBgnv2pqak6IY2IyB7Z6sYNY4P1TW2ZM6W1LdXDG6+/OgXdLh7CurqdUP2auM4kB93rN33XBSw+HK+zZue0XRcxqmUIIruGmr28FZE5zA5ljRo1ws6dO/H2228DgDaILVmyBGFhYdJWR0RWZ+0lheyRte4UzP2z7hgaVKCWOUOtbR6ZGUgv5K7dTvHwxrp6nQFw2S9jpu+6oHeKErUALDwkBrUdfyeYvLwVkbnMDmVfffUVunTpggsXLiAzMxNz5szBhQsXcPToURw8eNAaNRKRlbAbxnaM/ayPfNTOomCcs7VNY9Sfm9HnfDQGDZiGR95+eR7DZb/0y8hUY/Fh/XPGaSw9otuClpvUc9mR8zF7oH+LFi0QGxuLzMxM1K5dG3v37kVgYCBiYmLQsGFDa9RIRFZgqyWFnFXOgeBzfv8Ho438rKMuJFo8pYp2xnxfD7zx5yZ8cmAZaty/htXrPoHni+zX45QXxq2MuWY0cAHI9/sabI0kS5ndUgYAlSpVwuLFi6WuhYhsJL8lhdgNUzD6WsX0kepn3blWMF7e8RNcDizX7ttVrTnS3MRWMaVNeaHELvPrj55J9lxsjSRLmR3KNPOSGVKuXDmLiyEi27D1kkLOxNBAcENy/qw1yyKZHVZmzIBLZKR2c0HHYfihQV/ttpKW/VJql3n5AG+TjvPxdEVKWpbFd8wSGWN2KKtQoYLRuyyzsrIKVBARWZ+tlxRyFsZaIPOz4GAcxq45jUepGdp9JoWVr74CPvkke3vaNIyaGIm6CmuJAgwHViXcufhaWAVM23XRaBeliwqY3qs23l4Xa/Eds0TGmB3Kzpw5o7P94sULnDlzBt999x2mTZsmWWFEZD22XlLIWeTXAmnMwX/u59mXb1iZNg349NPs7enTgYkT4QooroVT6V3m7oVcMKql4QXiAWBUyxB0q1cahQq52GQuO3I+ZoeyunXr5tnXqFEjlCpVCrNmzUKfPn0kKYyIDCvomBwpJi51Nrl/5g3LF8Wp6491roHULYtGw8rUqcDnn2dvz5wJfPihpK8PSDf+yx66zCO7iis65J6nzEUF7TxlgO3msiPnY9FAf32qVauGEydOSPV0RGSAFGNypJi41Jno+5m7qHTvxgv288SAl6QfU6s3rAgCcPVq9kGzZgHvvy/5a0s5/steuswju4bify9XNzqjP2C9uezIuZkdypKTk3W2BUFAQkICJk+ejCpVqkhWGBHlJeWYHFsuKWTPDP3Mc489SkhKw/e//wN/bzckPXth0bgyY3KGlSwBOP7JLJR5mIqsOnVR9r3/wVXi15N6/Jc9dZm7F3LBiJYV5S6DnJDZoczf3z/PQH9BEFC2bFmsW7dOssKISJc1xuSwG8Y4cwfuCwBeZKm110NfC+SEDlXxMDUdP8dcN6sWTVjRab2q+TqgViF4ZrSkQdoav2vmdpkrcdoMImszO5Tt379fZ9vFxQUlSpRA5cqVUaiQZL2hRJSLtcbksBvGMEsG7qemZ6F7nSCcvP7EYAtkTNxDk0OZCkCQrweabFiMI9WbYsyx1OxQ898fyFLfvWiN3zVzusyVOm0GkbWZnaJat25tjTqIKB/2MibHkVj6szx85SGOf9Ihz40Ampae/FqNNFQAIAhYdW0HXBbPQWhhf1Qa8BWuFtcduyb13YvW+l0zpctcydNmEFmbSaHs119/NfkJe/ToYXExRGSYPY3JcRSW/iyfPH+BU9cfG2xFMtZqlFOQrwdWxf+KSkt+AAAEpD5Bo1sX8oQyQNq7F635u2asy1zp02YQWZtJoaxXr14mPZlKpeLksURWwmksbM/UFi198mtFMtRqFFDYDb3rlUaHGiXRZPEsuPwXyADgs46jsa5e5wK9rims/btmqMvcHqbNILImk0KZWq22dh1ElA9OY5HNVoPATW3R0seUViSDrUYqABMnAl9/rT323ylfY+WzUEleNz9y/a6xi56cHUfmE9kRTmNh+7UTNT/zyb9eQGKyaWHA39vN5FakPK1GgiBOAvvNN9n75s9H+TfeRPDMaJu1lJr6uyZlQGYXPTk7i0JZamoqDh48iBs3biAjI0Pne++8844khRGRfs48jYW8g8BNbycb1izEsushCOIksN99l71v4ULgjTfgCti89Sq/3zWpAzK76MnZqQRBMGuoxJkzZ9C1a1c8e/YMqampCAgIwIMHD+Dt7Y3AwED8+++/1qpVEsnJyfDz80NSUhJ8fX3lLoeITJSlFtBiZrTBMUeaD+wjH7WTNJgYCoKG+Hu74dSnHS2rYf9+oF277O1Fi4BRo/LUo4TpIgz9XDRnbWlA1jwvoD948u5LskemZg8Xg98x4N1330X37t3x+PFjeHl54dixY7h+/ToaNmyIb3I2txMRScicQeBSMXfyWACY0ae25aGwbVtgxgxx/rElS/IEMkBsvTryUTusHdUUcwbUw9pRTXHko3Y2DSr53SUJiHdJZuVe9sAEmm7TID/dLsogP08GMnJ4ZndfxsbGYuHChXBxcYGrqyvS09NRsWJFfP311xg6dCgXJCciq5BjELg5k8dK1lr10UdAly5AnToGD5F7wl9r3yXpzF30cuEKCspgdihzc3ODi4vYwBYYGIgbN26gRo0a8PPzw82bNyUvkIgIkGcQuKkBb1zbyni3Y1XzP8QEATh/HqhVS3e/kUCmBLYIyHIHT2eilC5xsqD7sn79+jhx4gQAcXb/zz//HKtXr8aECRNQK/cbCxGRRDSDwA3FHhXEDxIpB4GbGvCaVy5uWSAbNw5o2BD47TcLqpMP75KUX5ZaQEzcQ2yLvY2YuIcWdRUD2WP4crd8am6e2X0uQYpyyUQmhzLNpLBfffUVgoPF5Dxt2jQULVoUY8aMwf3797Fo0SLrVElETk8zdxaAPMHMWncfWi0IqtXA2LHAjz8CGRlA375AYmJBy7UZOQIyZdt9LgEtZkYjYvExjF8Xi4jFx9BiZrTZAcqaYwPJMiaHstKlS2PixInw9fVF27ZtAYjdl7t370ZycjJOnTqFunXrWq1QIiJbDwK3ShBUq4G33gLmzxe3XVzEuyyDggpcr63IEZBJJGXLlhw3z5BxJoeysWPHYuPGjahRowZatmyJFStW4NmzZ9asjYgoD1vffShpEFSrgTFjxLnHADGQrVwJDB4sYcW2wbskbU/qli1rjw2UqovVmZg9T9mBAwewfPlybNq0Ca6urujXrx9GjhyJJk2aWKtGSXGeMiKyhCV3p+k8prA7msyMhMvSpeI3XVyAVauAiAgbVG89vGvPdmLiHiJi8bF8j1s7qqlJN0lI/Xw58eYBXaZmD7PvvmzTpg3atGmDefPmYd26dVixYgXCwsJQo0YNjBgxAu+9916BCiciUiJz7wbM+aGkEtSYvnsuwv7eK37TxQVYvRoYMMBK1doO75K0Halbtqy1goK8K2/YN7PvvtQoUqQIRo4ciSNHjmD79u1ITEzEBx98IGVtRER2Kfe4n6l752PAf4EsU+WC2JnzHCKQkW1JfderNcYG8uaBgrE4lD179gwrVqxA69at0aNHDxQrVgzTpk2TsjYiIruj70Npf6VGyHAphEyVC8b3+BBjXlThhxKZzRp3vUo9NpA3DxSM2d2XR48exbJly7BhwwZkZmbilVdewdSpU9GqVStr1EdEZFf0fSjtq9wEY3tNhFtWJnZVbwEUYLZ7cl6ali2pF6WXcgUFOVbecCQmh7Kvv/4ay5cvxz///INGjRph1qxZiIiIgI+PjzXrIyJSBFMHtN97miZODKvS/V5UlaZ5jyMyk6ZlK/cg+qACDqKXamwgJxYuGJND2axZszB48GBs2LCBM/cTkVMx506yQG83fLfzO5wvWQlLX+pl8Dn5oUSWUvLaoNa6ecBZmBzK7ty5Azc3N2vWQkSkOGbdSZaZiSaT34XL+f3oc34/slQuWNGoh87j+KFEUlDqXa/W6mJ1FiYP9GcgIyJ7ZekklmbdSZaZCQwZApe1awAAGS6FcMcvUOcx/FAiZ8CJhS1n9kB/IiJ7UpBJLE2+k+zKPYRNngCsWyd+w80NZ79dhLMpZQEJx/0Q2Qsld7EqGUMZETmsgk5iacpgfFd1Fsq9MwrYu13c4e4ObNqEht264QhnuycnptQuViVjKCMih5Rf16MKYtdjx9Agg0Epv8H4hbIy8f32b1D68hFxh7s7sHkzEB4OgB9KJB0uZ+UcTAplycnJJj8h15MkIiUwZxJLQ8HJ2J1khbIy8cOvX6PLP0fFHR4ewJYtQJcu0pwA0X+4jqTzMCmU+fv7Q6UyLZFnZWUVqCAiIilIMYmlsTvJSiffR5Ob58QNDw9g61agc2eL6yXSh+tIOheT7r7cv38/oqOjER0djWXLliEwMBAffvghtmzZgi1btuDDDz9EyZIlsWzZMrNefP78+ahTpw58fX3h6+uLsLAw/Pbbb0Yfs2HDBlSvXh2enp6oXbs2du3aZdZrEpFzkGoSS0N3kmVUCMHFlZuA0qWBbdsYyEhyXEfS+agEQTDrarZv3x4jR45ERESEzv41a9Zg0aJFOHDggMnPtX37dri6uqJKlSoQBAE//fQTZs2ahTNnzqBmzZp5jj969ChatWqF6dOno1u3blizZg1mzpyJ06dPmzyhbXJyMvz8/JCUlMSuViIHlqUW0GJmdL6TWB75qJ1JY3MMjulJSwM8lT0RLMcj2aeYuIeIWHws3+PWjmrKsYsKZ2r2MDuUeXt746+//kKVKlV09v/zzz+oV68enj17ZlnF/wkICMCsWbMwYsSIPN/r378/UlNTsWPHDu2+pk2bol69eliwYIFJz89QRuQ8NF0/gP5JLM3q+snIAJYsAUaPBlxMnuJRdhyPZL+2xd7G+HWx+R43Z0A99KxX2voFkcVMzR5mv7OULVsWixcvzrN/yZIlKFu2rLlPp5WVlYV169YhNTUVYWFheo+JiYlBhw4ddPZ16tQJMTExBp83PT0dycnJOl9E5Bwkm8QyPR145RVg7FjgjTcAtdoK1UpPE0pz3/CgGY+0+1yCTJWRKbiOpPMxe0qM2bNno2/fvvjtt9/QpEkTAMDx48dx5coVbNq0yewCzp49i7CwMKSlpaFIkSLYsmULQkND9R6bmJiIkiVL6uwrWbIkEhMTDT7/9OnTMWXKFLPrIiLHUOBJLDWBTNNCv2YN8N57gIH3KaWQYkoQkhfXkXQ+ZreUde3aFf/88w+6d++OR48e4dGjR+jevTv++ecfdO3a1ewCqlWrhtjYWPz5558YM2YMhg4digsXLpj9PIZERkYiKSlJ+3Xz5k3JnpuIrM/SJZJy0swX1rNeaYRVKmZ6CElLA/r0yQ5kXl7Azp2KD2SAeVOCkDJp7v4FsrvcNbhkl2OyaPLYsmXL4quvvpKkAHd3d1SuXBkA0LBhQ5w4cQJz5szBwoUL8xwbFBSEu3fv6uy7e/cugoKCDD6/h4cHPDw8JKmViGxL1vFQmkCmuSPc21sMZG3aWPd1JSLFlCAkP00XfO7/D7hkl2OyKJQdPnwYCxcuxL///osNGzagdOnSWLlyJUJCQtCiRYsCFaRWq5Genq73e2FhYdi3bx8mTJig3RcVFWVwDBoR2S9Z52dKSwN69QL27BG3vb2BXbuA1q2t83pWwPFIjoPrSDoPs7svN23ahE6dOsHLywunT5/WBqikpCSzW88iIyNx6NAhXLt2DWfPnkVkZCQOHDiAQYMGAQCGDBmCyMhI7fHjx4/H7t278e233+LSpUuYPHkyTp48iXHjxpl7GkSkYLLOz/T8OdCzZ3YgK1xYbC2zo0AGZI9HMvSxrYLY6sjxSPbB4i54sitmh7Ivv/wSCxYswOLFi+Hm5qbd37x5c5w+fdqs57p37x6GDBmCatWqoX379jhx4gT27NmDjh07AgBu3LiBhITsu4OaNWumnQ+tbt262LhxI7Zu3WryHGVEZB9kHQ/1/vvA3r3ivzWBrFUr6V/Hyjgeicj+mN19efnyZbTS8wbl5+eHJ0+emPVcS5cuNfp9fRPRvvrqq3j11VfNeh0isi+yjof6/HMI+/dDfeMGjv6wEoVKhaKxWrDL8MLxSET2xexQFhQUhKtXr6JChQo6+48cOYKKFStKVRcROTE5x0Ptvq/GD72mwO3WTcRedgcuH7PryVY5HonIfpgdykaNGoXx48dj2bJlUKlUuHPnDmJiYvD+++/js88+s0aNRORkbDo/U2oqIAhAkSI5bi7wBkpV0x6SmJSG0atO490OVVCheGG7Czaa8UhEpGxmh7KJEydCrVajffv2ePbsGVq1agUPDw+8//77ePvtt61Ro13hGnNEBacZDzVm1WmooH+JJEnGQ6WmAt26AWo1srbvyPfmgtm/X9HuC/L1xOQe9tl6RkTKZPbalxoZGRm4evUqUlJSEBoaiiJFikhdm1VYc+1LrjFHJC2r/j+VmgqEhwMHDwIAHrXvjAaNzL+Te8HgBuwedDD849p52OpaW21B8uHDh2POnDnw8fHR2Z+amoq3334by5Yts6xiG7FWKDM0p5JFCx8TOZiCvPFZ5U0zJUUMZIcOidt+fjg4dxWGnjP/eQt7uKKIeyHcfZo9vyJb0ewX/7h2Hra81lYLZa6urkhISEBgYKDO/gcPHiAoKAiZmZmWVWwj1ghlWWoBLWZGG7yFXzP+5chH7fjXFjkdxX3IpaQAXbsChw+L235+QFQUYgIqImLxMUlfagH/GLMr/OPaedj6WpuaPUyepyw5ORlJSUkQBAFPnz5FcnKy9uvx48fYtWtXnqDmLLjGHJF+mje+3P9/aGbl330uwcAjreTpU6BLl+xA5u8P/P478NJL+U62aonIzWetM8EtSU7WCYvJppR8rU0OZf7+/ggICIBKpULVqlVRtGhR7Vfx4sUxfPhwjB071pq1KhbXmCPKS3FvfJpAduSIuF20qBjIGjUCYHyyVUs9fvYCx/59KNGzkTXxj2vnoeRrbfLdl/v374cgCGjXrh02bdqEgIDsW9Hd3d1Rvnx5lCpVyipFKh3XmCPKy5w3PqtP1/D0KdC5M3D0qLitCWQNGugcZmiy1YKIiXuI5pWLS/JcZD3849p5KPlamxzKWv+37lt8fDzKlSsHlYpjozRsOqcSkZ1Q1BufmxugGccRECAGsvr19R6ac7LVaTvP49ydpwV8cXZ32QP+ce08lHytzV77Mjo6Ghs3bsyzf8OGDfjpp58kKcrecI05orwU9cbn6Qls2QIMHAjs22cwkGloJlt9vVlIgV86rCJbyewBF3B3Hkq+1maHsunTp6N48bxvMoGBgfjqq68kKcoeabo9gvx0P2CC/Dx5xw45JcW98Xl6AqtXA/XqmfyQ0kW9C/SS/t5uaCpB12yWWkBM3ENsi72NmLiHHGxuBfzj2nko+VqbPSWGp6cnLl26lGfty2vXrqFGjRp4/vy5lPVJzpqTxwKcdJAoJ83dl4D+Wfmt9gfLkyfAG28A33wDlCtn8dPkN91NfqSYEkNxU4o4OP68nYdDzFNWrlw5zJ07Fz169NDZv23bNowdOxa3bt2yrGIbsXYoIyJdNv+Qe/wYePll4ORJoGJF4MABoGxZi58uv2D5RqsQbIu9g8TknJPHemByj5qSBDLOm2V7/OPaeShtRn+z176MiIjAO++8Ax8fH7Rq1QoAcPDgQYwfPx4DBgywvGIickg5B85b/UPu8WOgY0fg1Clx++lTIDm5QE9p6I7MoBzB8sPONSQ/v/ymFFFBnFKkY2gQA4PEuIC781DatTa7pSwjIwOvvfYaNmzYgEKFxEynVqsxZMgQLFiwAO7u7lYpVCpsKSNyUI8eiYHstNiqhcBAIDoaqFlTkqe3detJTNxDk1YYWDuqqaI+VIgoL6u1lLm7u2P9+vWYOnUq/vrrL3h5eaF27dooX758gQomIrLYw4dAhw5AbKy4XbKkGMhCQyV7CVv/Ra2oKUWIyCbMDmUaVatWRdWqVaWshYjIfLkDWVAQsH8/UL26rGUVlKKmFCEimzAplL333nuYOnUqChcujPfee8/osd99950khRER5evBAzGQ/fWXuO0ggQzgpNREzsikUHbmzBm8ePFC+29DOMs/EdnUTz9lB7LgYDGQVasmb00S0cylNGbVaaig/85PzptF5FjMHuhv7zjQn8iBCALw7rvAhg1iIHPAIRWcN4vI/lltnjJ7x1BG5GAEAbh3Txzc76A4bxaRfZP07ss+ffqY/MKbN282+VgiIrPcuwfcuAE0apS9T6Vy6EAGKG8uJSKyDpPWvvTz89N++fr6Yt++fTh58qT2+6dOncK+ffvg5+dntUKJyMndvQu0bQu0bw/8+afc1RARSc6klrLly5dr//3RRx+hX79+WLBgAVxdXQEAWVlZeOutt9gdSETWkZgItGsHXLwobo8aJU6B4WLS35VERHbB7DFlJUqUwJEjR1At1x1Oly9fRrNmzfDw4UNJC5Qax5QR2ZmEBDGQXbokbpcrJw7qr1hR3rqIiExkavYw+8/MzMxMXNK8OeZw6dIlqNVqc5+OiMiwhASxy1LznlO+vLjAOAMZETkgs2f0HzZsGEaMGIG4uDg0btwYAPDnn39ixowZGDZsmOQFEpGTunNHDGT//CNuawJZhQpyVkVEZDVmh7JvvvkGQUFB+Pbbb5GQkAAACA4OxgcffID//e9/khdIRE7o9m0xkF25Im5XqCAGMq6xW2CcXoNIuQo0T1lycjIA2NXYLI4pI1K49HSgbl3g8mVxOyREHEPGQFZgnIiWSB5WG1MGiOPKfv/9d6xdu1a7tNKdO3eQkpJiWbVERBoeHsAHH4j/rliRLWQS2X0uAWNWndYJZACQmJSGMatOY/e5BJkqIyINs7svr1+/js6dO+PGjRtIT09Hx44d4ePjg5kzZyI9PR0LFiywRp1E5ExGjAC8vICWLYGyZeWuxu5lqQVM2X5B78LmAsS1NKdsv4COoUHsyiSSkdktZePHj0ejRo3w+PFjeHl5aff37t0b+/btk7Q4InIS6el59w0cyEAmkePxj/K0kOUkAEhISsPx+Ee2K4qI8jA7lB0+fBiffvop3N3ddfZXqFABt2/flqwwInIS168DtWoBa9bIXYnDuvfUcCCz5Dgisg6zQ5larUZWVlae/bdu3YKPj48kRRGRk7h2DWjTBrh6FXjtNWDrVpkLckyBPp6SHkdE1mF2KHv55Zfx/fffa7dVKhVSUlIwadIkdO3aVcraiMiRaQLZtWviduXKwH9zH5K0GocEINjPE4ZGi6kg3oXZOCTAlmURUS5mh7JvvvkGf/zxB0JDQ5GWloaBAwdquy5nzpxpjRqJyNHExwOtW4tdlwBQrZp4l2WpUrKW5ahcXVSY1D0UAPIEM832pO6hHORPJDOL5inLzMzE+vXr8ddffyElJQUNGjTAoEGDdAb+KxXnKSOS2b//ii1kN2+K29WrA9HRQDDnybI2zlNGJA9Ts4dZoezFixeoXr06duzYgRo1akhSqK0xlBHZVs4Z5Ms+SkD91/tApQlkNWqIgSwoSN4inQhn9CeyPVOzh1nzlLm5uSEtjXfnEJFpcrbMlH98B2vXfgzV0wfiN0NDxUBWsqS8RToZVxcVwioVk7sMItLD7DFlY8eOxcyZM5GZmWmNeojIQeSeQT4w5RH8054CAC4XL4foH9cxkBER5WD2jP4nTpzAvn37sHfvXtSuXRuFCxfW+f7mzZslK46I7JO+GeRPlK2F4a9MwkcHfsLIVz6H+9F7ONJScPquM3YnEpGG2aHM398fffv2tUYtROQgDM0gf6xcHfR+7RtApQL+m0HembvSOPCeiHIyO5QtX77cGnUQkQO59zQNFR/ewstXjmFBk75iCNPI8W9nnkFe072b+04rzQLh8wc3YDAjcjImjylTq9WYOXMmmjdvjpdeegkTJ07E8+fPrVkbEdmpcvduYt3aSEw8uALvHV4FGLjJ21lnkM9vgXBAXCA8S232jEVEZMdMDmXTpk3Dxx9/jCJFiqB06dKYM2cOxo4da83aiMgeXbyIekN6ITD1MQCgfdwJeGbqLjju7DPIc4FwItLH5FD2888/48cff8SePXuwdetWbN++HatXr4ZarbZmfXYnSy0gJu4htsXeRkzcQ/6lS87lwgWgbVuo7t4FAJwrWQmDBkxDmlt2ixhnkOcC4USkn8ljym7cuKGztmWHDh2gUqlw584dlClTxirF2RsO2iWndv480K4dcO+euN2gAe7+30p4HUrAkxz/TwTx/wkuEE5EepkcyjIzM+HpqfsG4ebmhhcvXkhelD3ioF1yaufOiYHs/n1xu2FDICoK7YsWRZuwGpzyIRfNAuGJSWl6x5WpIIZXZ+3eJXJWJocyQRDw+uuvw8PDQ7svLS0No0eP1pmrzBnnKctv0K4K4qDdjqFBTv9hRA7o7FkxkD34b6b+Ro2AqCjA3x8AZ5DXR7NA+JhVp6ECdN472L1L5LxMDmVDhw7Ns2/w4MGSFmOvzBm0yw8nciiCAAwdmh3IXnoJ2LtXG8jIsM61gjF/cIM8Qx7YvUvkvEwOZdaYn2z69OnYvHkzLl26BC8vLzRr1gwzZ85EtWrVDD5mxYoVGDZsmM4+Dw8PWdfk5KBdcloqFbBhA9CmDVC6NLBnD+DnJ3dVdqNzrWB0DA1i9y4RAbBg8lgpHTx4EGPHjsVLL72EzMxMfPzxx3j55Zdx4cKFPMs35eTr64vLly9rt1Uqed/AOGiXnFqlSsChQ0BAAAOZBdi9S0Qasoay3bt362yvWLECgYGBOHXqFFq1amXwcSqVCkFBQSa9Rnp6OtLTs+dISk5OtqxYIzhol5zKP/8AISGAm1v2vpAQ+eohInIQJs9TZgtJSUkAgIAA4+ElJSUF5cuXR9myZdGzZ0+cP3/e4LHTp0+Hn5+f9qts2bKS1gxkD9oFsgfpanDQLjmU06eBpk2BAQMA3nlNRCQplSAYWP/ExtRqNXr06IEnT57gyJEjBo+LiYnBlStXUKdOHSQlJeGbb77BoUOHcP78eb3zpelrKStbtiySkpLg6+sr6TlwnjJyaKdOAR06AE+eiNuTJwOTJslZERGRXUhOToafn1++2UMxoWzMmDH47bffcOTIEbMmo33x4gVq1KiBiIgITJ06Nd/jTf3BWCpLLXDQLjmekyeBjh2zA1nLlsDOnYCPj6xlERHZA1Ozh6xjyjTGjRuHHTt24NChQ2avDuDm5ob69evj6tWrVqrOPBy0Sw7nxAkxkP03vACtWomBrEgReesiInIwso4pEwQB48aNw5YtWxAdHY0QCwYLZ2Vl4ezZswgOZvcgkeSOHxe7LDWBrHVrBjIiIiuRtaVs7NixWLNmDbZt2wYfHx8kJiYCAPz8/ODl5QUAGDJkCEqXLo3p06cDAL744gs0bdoUlStXxpMnTzBr1ixcv34dI0eOlO08iBzSn38CL78MaO5YbtMG2LEDMDJdDRERWU7WUDZ//nwAQJs2bXT2L1++HK+//joAcSF0F5fsBr3Hjx9j1KhRSExMRNGiRdGwYUMcPXoUoaGhtiqbyPGdOSN2WT59Km63bQts385ARkRkRYoZ6G8r1h7oT+QQkpLEVrLjx8V1LbdvB7y95a6KiMgumZo9FDVPGREphJ+fuGTShAkMZERENqKIuy+JSAEEQVzLUsPfH5g9W7ZyiIicDVvKiAg4fFi8s/LRI7krISJyWgxlRM7u0CGgSxcxmHXoADx+LHdFREROiaGMyJkdPCgGstRUcbtkSeC/6WiIiMi2GMqInNWBA0DXrsCzZ+J2ly7Ali2Ap6esZREROSuGMiJnFB2tG8jCwxnIiIhkxlBG5Gz27QO6dQOePxe3u3UDNm0CPDzkrYuIyMkxlBE5k99/1w1kPXoAGzcykBERKQBDGZEzWbsWSEsT/92zJ7BhAwMZEZFCcPJYImeycKE4jiwtDVi/HnB3l7siIiL6D0MZkTMpVAhYuRJQqxnIiIgUht2XRI5s717g0iXdfYUKMZARESkQQxmRo9q1C+jeHWjbFrh8We5qiIgoHwxlRI5o506gd28gIwNITAR++EHuioiIKB8MZUSOZvv27EAGAP37A99/L2tJRESUP4YyIkfy669A377Aixfi9oABwKpV4jgyIiJSNIYyIkexbRvwyivZgWzgQPFOSwYyIiK7wFBG5Ai2bNENZIMGAT/9xEBGRGRHGMqI7N0//wD9+gGZmeL24MEMZEREdoihjMjeVa0KTJ4s/vu114AVKwBXVzkrIiIiC/BPaSJH8MknQK1a4mLjDGRERHaJLWVE9ujBg7z7evZkICMismMMZUT2Zv16ICQEiIqSuxIiIpIQQxmRPVm7VpzqIiUF6NEDOHdO7oqIiEgiDGVE9mLNGvHOSrVa3B48GAgNlbcmIiKSDEMZkT1YvVq8s1ITyN54A1i4EHDh/8JERI6C7+hESrdyJTBkSHYgGz0amD+fgYyIyMHwXZ1IyX7+GRg6NDuQjRkDzJvHQEZE5IA4TxmRUv30EzBsGCAI4vbYscAPPwAqlbx1OakstYDj8Y9w72kaAn080TgkAK4uvBZEJB2GMiKlKlJEbBHLygLefhuYM4eBTCa7zyVgyvYLSEhK0+4L9vPEpO6h6FwrWMbKiMiRqARB82e4c0hOToafnx+SkpLg6+srdzlkxzIy1VgZcw3XHz1D+QBvvBZWAe6FJO5W3LQJOHoU+OYbBjKZ7D6XgDGrTiP3G6Xmaswf3IDBjIiMMjV7MJQRWWD6rgtYfDge6hz/97iogFEtQxDZldNUOIostYAWM6N1WshyUgEI8vPEkY/asSuTiAwyNXtwtDCRmabvuoCFh3QDGQCoBWDhoXhM33XBsidetAiYO7fgBZJkjsc/MhjIAEAAkJCUhuPxj2xXFBE5LIYyIjNkZKqx+HC80WMWH45HRqbavCdeuBB4801x7Ni8eQWokKR076nhQGbJcURExjCUEZlhZcy1PC1kuakF8TiTzZ8vzj2mcf26RbWR9AJ9PCU9jojIGIYyIjNcf/RM0uPw44/AW29lb3/4ITBzpgWVkTU0DglAsJ8nDI0WU0G8C7NxSIAtyyIiB8VQRmSG8gHe0h03d64495jGxInAjBm8y1JBXF1UmNRdvHEj91XRbE/qHspB/kQkCYYyIjO8FlYB+X3+uqjE44z64Qdx/JjGxx8DX33FQKZAnWsFY/7gBgjy0+2iDPLz5HQYRCQpTh5LZAb3Qi4Y1TIECw8ZHuw/qmWI8fnK5swBJkzI3v70U+CLLxjIFKxzrWB0DA3ijP5EZFUMZURm0sxDZtE8ZQ8eiAFM47PPgClTGMjsgKuLCmGVisldBhE5ME4eS2Qhi2f0P30aaN8eGD8emDzZ6nUSEZG8TM0ebCkjspB7IReMaFnR/Ac2aABcuAAEcywSERFl40B/ImvbuxdQ55pMloGMiIhyYSgjsqavvwY6dRKnvnCukQJERGQmhjIia5kxA/joI/HfCxYAe/bIWw8RESkaQxmRNXz1FRAZqbvdubN89RARkeJxoD+R1KZNE+ce08jZYkZERGQAW8qIpDR1qm4gmzmTgYyIiEzCUEYklSlTgM8/z96eNUtcYJyIiMgEsoay6dOn46WXXoKPjw8CAwPRq1cvXL58Od/HbdiwAdWrV4enpydq166NXbt22aBaIiPmzdOdCPabb4D335etHCIisj+yhrKDBw9i7NixOHbsGKKiovDixQu8/PLLSE1NNfiYo0ePIiIiAiNGjMCZM2fQq1cv9OrVC+fOnbNh5US59OkDVK0q/vu774D//U/eeoiIyO4oapml+/fvIzAwEAcPHkSrVq30HtO/f3+kpqZix44d2n1NmzZFvXr1sGDBgnxfg8sskdXcuSNOezFsmNyVEBGRgpiaPRQ1piwpKQkAEBAQYPCYmJgYdOjQQWdfp06dEBMTo/f49PR0JCcn63wRFZggABkZuvtKlWIgIyIiiykmlKnVakyYMAHNmzdHrVq1DB6XmJiIkiVL6uwrWbIkEhMT9R4/ffp0+Pn5ab/Kli0rad3khAQB+OQToEsX4NkzuashIiIHoZhQNnbsWJw7dw7r1q2T9HkjIyORlJSk/bp586akz09ORhCAjz8Gpk8HoqOBnj2BrCy5qyIiIgegiMljx40bhx07duDQoUMoU6aM0WODgoJw9+5dnX13795FUFCQ3uM9PDzg4eEhWa3kxAQBmDhRXM9So3dvwNVVvpqIiMhhyNpSJggCxo0bhy1btiA6OhohISH5PiYsLAz79u3T2RcVFYWwsDBrlUkkBrIPP9QNZPPnA2+9JV9NRETkUGRtKRs7dizWrFmDbdu2wcfHRzsuzM/PD15eXgCAIUOGoHTp0pg+fToAYPz48WjdujW+/fZbhIeHY926dTh58iQWLVok23mQgxME4IMPgG+/zd63YAHw5pvy1URERA5H1pay+fPnIykpCW3atEFwcLD2a/369dpjbty4gYSEBO12s2bNsGbNGixatAh169bFxo0bsXXrVqM3BxBZTBDEOcdyBrJFixjIiIhIcoqap8wWOE8ZmUwQgHffBebMEbdVKmDxYmDECHnrIiIiu2Jq9lDEQH8iRcrIAM6fF/+tUgFLlgDDh8tbExEROSzFTIlBpDgeHsC2bcDLLwPLljGQERGRVbGljMgYb2/gt98AF/79QkRE1sVPGiINtRqYOhW4fVt3PwMZERHZAD9tiAAxkI0dC3z+OdC2rbi4OBERkQ0xlBGp1cCYMeLcYwAQFwf8+ae8NRERkdPhmDJybmo1MHq0ONUFIHZVrlwpLp9ERERkQwxl5LzUauCNN4ClS8VtFxdg9WpgwAB56yIiIqfEUEbOSa0GRo4Eli8Xt11dxUDWv7+8dRERkdNiKCPnk5UlBrIVK8RtV1dg7Vrg1VdlLYuIiJwbQxk5n7VrdQPZunXAK6/IWhIRERHvviTnM2gQ8NZbQKFCwPr1DGRERKQIDGXkfFQqYO5c4PhxoG9fuashIiICwFBGziAzE7hyRXefSgXUry9PPURERHowlJFjy8wEhgwBGjcGTp2SuxoiIiKDGMrIcWVmAq+9Jg7sf/IECA8HUlPlroqIiEgv3n1JjikzUxzQ/8sv4ra7O7BkCVC4sLx1ERERGcBQRo7nxQsxkG3YIG67uwNbtgBdu8pbFxERkREMZeRYXrwAIiKATZvEbQ8PMZB16SJvXURERPlgKCPH8eKFuG7l5s3itocHsHUr0LmzrGURERGZgqGMHIMg5A1k27YBnTrJWxcREZGJePclOQaVCujYUfy3pyfw668MZEREZFfYUkaOY/Ro8b+VKmUHNCIiIjvBUEb2SxDEFrKcNMGMiIjIzrD7kuxTejrQp0/2tBdERER2ji1lZH/S0sSFxHftArZvB1xcuLA4ERHZPYYysi9paWIL2W+/idseHkCxYvLWREREJAGGMrIfaWlA797A7t3itre32FrWurW8dREREUmAoYzsw/PnQK9ewN694nbhwmIga9VK1rKIiIikwlBGyvf8OdCzJxAVJW4XKSJ2X7ZoIW9dREREEmIoI2V79kwMZL//Lm4XKSJ2XzZvLm9dREREEuOUGKRs584BR46I//bxAfbsYSAjIiKHxFBGyta4sTjtRcmSYiBr1kzuioiIiKyC3ZekfB06AP/+K95tSURE5KDYUkbKkpoK/PRT3v0MZERE5ODYUkbKkZIChIcDhw4B168Dn38ud0VEREQ2w5YyUoaUFKBrVzGQAcDs2UBCgrw1ERER2RBDGcnv6VOgSxfg8GFx299fnAIjOFjWsoiIiGyJ3ZckL00g++MPcbtoUTGQNWggb11EREQ2xpYykk9yMtC5MwMZERER2FJGcklKEgPZsWPidkCAGMjq15e3LiIiIpkwlJE8hg/XDWT79gH16slaEhERkZzYfUnymD5dHMhfrBgQHc1ARkRETo8tZSSPqlWB/fuB9HSgTh25qyEiIpIdQxnZRlISULgwUCjHr1y1avLVQ0REpDDsviTre/QIaNcOGDQIyMyUuxoiIiJFYksZWdejR+KC4mfOAKdPA8WLA/PmyV0VERGR4rCljKzn4UOgfXsxkAFAyZLA2LHy1kRERKRQbCkj63jwQGwh++svcbtkSXFgf40a8tZFRESkUAxlJL0HD8QWsr//FreDgsRAVr26vHUREREpmKzdl4cOHUL37t1RqlQpqFQqbN261ejxBw4cgEqlyvOVmJhom4JNkKUWEBP3ENtibyMm7iGy1ILcJdnW/fvioH5NIAsOBg4cYCAjIiLKh6wtZampqahbty6GDx+OPn36mPy4y5cvw9fXV7sdGBhojfLMtvtcAqZsv4CEpDTtvmA/T0zqHorOtYJlrMxG7t8XW8jOnhW3S5USW8iqVpW3LiIiIjsgayjr0qULunTpYvbjAgMD4e/vL31BBbD7XALGrDqN3O1iiUlpGLPqNOYPbuD4waxQIcDNTfx36dJiIKtSRd6aiIiI7IRd3n1Zr149BAcHo2PHjvjjjz+MHpueno7k5GSdL6llqQVM2X4hTyADoN03ZfsFx+/KLFoUiIoCuncXuywZyIiIiExmV6EsODgYCxYswKZNm7Bp0yaULVsWbdq0wenTpw0+Zvr06fDz89N+lS1bVvK6jsc/0umyzE0AkJCUhuPxjyR/bcUJCAB+/RWoXFnuSoiIiOyKXYWyatWq4c0330TDhg3RrFkzLFu2DM2aNcPs2bMNPiYyMhJJSUnar5s3b0pe172nhgOZJcfZjcREYPBg4MkTuSshIiKye3Y/JUbjxo1x5MgRg9/38PCAh4eHVWsI9PGU9Di7kJAg3mV56RLwzz/A3r2Awsb5ERER2RO7ainTJzY2FsHB8g6gbxwSgGA/T6gMfF8F8S7MxiEBtizLehISgLZtxUAGAHfvsrWMiIiogGRtKUtJScHVq1e12/Hx8YiNjUVAQADKlSuHyMhI3L59Gz///DMA4Pvvv0dISAhq1qyJtLQ0LFmyBNHR0di7d69cpwAAcHVRYVL3UIxepX9smwBgUvdQuLoYim125M4dMZD984+4Xb68OKi/QgU5q5JFllrA8fhHuPc0DYE+Yuh2iGtMRESykDWUnTx5Em3bttVuv/feewCAoUOHYsWKFUhISMCNGze038/IyMD//vc/3L59G97e3qhTpw5+//13necgK7p9WwxkV66I2xUqiIGsfHk5q5KF089JR0REklMJguDg8zToSk5Ohp+fH5KSknQmoC2ILLWA2pP34FlGlsFjCru74u/Jney3JeXWLTGQaVo2Q0LEecicNJDpm5NOc2WdYk46IiIymanZw+7HlCnB0asPjAYyAEjNyMLRqw9sVJHEbt4E2rTJDmQVKzptCxnnpCMiImthKJPA+pM38j/IjOMUZ/ZsIC5O/HelSmIgK1dO1pLkwjnpiIjIWux+SgwliL2RJOlxijNzJnDjBhAbKwayMmXkrkg2TjsnHRERWR1DmQQKuZo2TszU4xTHzQ1YuxZ49AgoWVLuamTllHPSERGRTbD7UgLNKhWT9DjZXbuWfYelhpub0wcywAnnpCMiIpthKJPAZ91qSnqcrK5dEwf157zTkrQ0c9IByBPMNNsOMycdERHZFEOZBLzcXdExNNDoMR1DA+Hl7mqjiiwUHw+0bg1cvy7OSTZmjNwVKVLnWsGYP7gBgvx0uyiD/Dw5HQYREVmM85RJaNTPJxB14V6e/R1DA7F4yEuSvpbk/v1XbCHTLNhevbo4D1lQkKxlKRln9CciIlOYmj0YyiT2PCMLX+26gGsPn6FCMW983DVU+S1kcXFid6UmkNWoIQYyjiEjIiIqMFOzB+++lJiXuyum9qotdxmmu3pVDGS3bonboaFAdDQDGRERkY1xTJkzu3JF7LLUBLKaNdlCRkREJBOGMmf18KEYyG7fFrdr1RJbyAKN37BARERE1sFQ5qyKFQNGjhT/Xbs2AxkREZHMOKbMmU2eLAaxfv2AEiXkroaIiMipMZQ5kxcvxJn5NVQqYOxY+eohIiIiLXZfOouLF7PnHiMiIiLFYShzBhcuiNNe/PsvEB4OxMTIXRERERHlwlDm6M6fFwPZ3bvido0aQLVq8tZEREREeTCUObJz58RAdu+/pZ8aNgR+/x0ICJC3LiIiIsqDocxRnT0LtGsH3L8vbjdqBERFAUWLylsXERER6cVQ5oj+/ls3kL30EgMZERGRwjGUOZq//hID2YMH4naTJmIg8/eXtSwiIiIyjqHM0Vy5Ajx+LP67aVNgzx7Az0/emoiIiChfDGWO5pVXgNWrgZYtGciIiIjsCEOZIxowADhwAPD1lbsSIiIiMhFDmb07dQpYtizvfhdeWiIiInvCtS/t2cmTQMeOwJMnQFYWMGqU3BURERGRhdicYq9OnAA6dBADGSCOI8vKkrUkIiIishxDmT36808xkCUlidutWgE7dgCurvLWRURERBZjKLM3x44BL78MJCeL223aALt2AUWKyFoWERERFQxDmT2JidENZG3bii1khQvLWxcREREVGEOZvTh6FOjUCXj6VNxu146BjIiIyIEwlNmD9HRx7jFNIGvfHti+HfD2lrcuIiIikgxDmT3w8AA2bhQng+3YkYGMiIjIAXGeMnvRuDFw5AhQuTLg5SV3NURERCQxtpQp1T//AIKgu692bQYyIiIiB8VQpkQHDwL16wPjx+cNZkREROSQGMqU5sABoGtX4Nkz4IcfgCVL5K6IiIiIbIChTEmio7MDGQCEhwNDhshbExEREdkEQ5lS7NsHdOsGPH8ubnfrBmzaJN55SURERA6PoUwJfv9dN5B17y5OgcFARkRE5DQYyuQWFSWGsLQ0cbtnTwYyIiIiJ8RQJqf9+3UDWa9ewC+/AO7uspZFREREtsdQJqcqVYAyZcR/9+4NrF/PQEZEROSkGMrkVKaM2Fo2fjwDGRERkZPjMku2JgiASpW9XbYs8P33spVDREREysCWMlvasUMcyK8ZQ0ZERET0H4YyW9m+HejTR/xvr14MZkRERKSDocwWfv0V6NsXePFC3C5aFCjEnmMiIiLKxlBmbdu2Aa+8kh3IIiKAlSsZyoiIiEiHrKHs0KFD6N69O0qVKgWVSoWtW7fm+5gDBw6gQYMG8PDwQOXKlbFixQqr12mxLVt0A9mgQcDPPzOQERERUR6yhrLU1FTUrVsX8+bNM+n4+Ph4hIeHo23btoiNjcWECRMwcuRI7Nmzx8qVWmDzZqBfPyAzU9x+7TXgp58YyIiIiEgvWRNCly5d0KVLF5OPX7BgAUJCQvDtt98CAGrUqIEjR45g9uzZ6NSpk7XKNN+mTcCAAdmBbMgQYNkywNVV3rqIiIhIsexqTFlMTAw6dOigs69Tp06IiYkx+Jj09HQkJyfrfFmVIAALF2YHstdfZyAjIiKifNlVKEtMTETJkiV19pUsWRLJycl4/vy53sdMnz4dfn5+2q+yZctat0iVShxL1ro1MGwYsGQJAxkRERHly65CmSUiIyORlJSk/bp586b1X7RwYWDXLgYyIiIiMpldjToPCgrC3bt3dfbdvXsXvr6+8PLy0vsYDw8PeHh42KI8Xd7etn9NIiIislt21VIWFhaGffv26eyLiopCWFiYTBURERERSUPWUJaSkoLY2FjExsYCEKe8iI2NxY0bNwCIXY9DhgzRHj969Gj8+++/+PDDD3Hp0iX8+OOP+OWXX/Duu+/KUT4RERGRZGQNZSdPnkT9+vVRv359AMB7772H+vXr4/PPPwcAJCQkaAMaAISEhGDnzp2IiopC3bp18e2332LJkiXKmg6DiIiIyAIqQRAEuYuwpeTkZPj5+SEpKQm+vr5yl0NEREQOztTsYVdjyoiIiIgcFUMZERERkQIwlBEREREpAEMZERERkQIwlBEREREpAEMZERERkQIwlBEREREpAEMZERERkQIwlBEREREpAEMZERERkQIwlBEREREpAEMZERERkQIwlBEREREpAEMZERERkQIwlBEREREpAEMZERERkQIwlBEREREpQCG5C7A1QRAAAMnJyTJXQkRERM5Akzk0GcQQpwtlT58+BQCULVtW5kqIiIjImTx9+hR+fn4Gv68S8ottDkatVuPOnTvw8fGBSqWyymskJyejbNmyuHnzJnx9fa3yGkrkjOftjOcM8Lyd6byd8ZwB5zxvZzxnwDbnLQgCnj59ilKlSsHFxfDIMadrKXNxcUGZMmVs8lq+vr5O9Yut4Yzn7YznDPC8nYkznjPgnOftjOcMWP+8jbWQaXCgPxEREZECMJQRERERKQBDmRV4eHhg0qRJ8PDwkLsUm3LG83bGcwZ43s503s54zoBznrcznjOgrPN2uoH+RERERErEljIiIiIiBWAoIyIiIlIAhjIiIiIiBWAoIyIiIlIAhjIzHTp0CN27d0epUqWgUqmwdevWfB9z4MABNGjQAB4eHqhcuTJWrFhh9TqlZu55HzhwACqVKs9XYmKibQqWwPTp0/HSSy/Bx8cHgYGB6NWrFy5fvpzv4zZs2IDq1avD09MTtWvXxq5du2xQrXQsOe8VK1bkudaenp42qlga8+fPR506dbQTSIaFheG3334z+hh7v9bmnrMjXGd9ZsyYAZVKhQkTJhg9zt6vd06mnLMjXO/JkyfnOYfq1asbfYyc15mhzEypqamoW7cu5s2bZ9Lx8fHxCA8PR9u2bREbG4sJEyZg5MiR2LNnj5UrlZa5561x+fJlJCQkaL8CAwOtVKH0Dh48iLFjx+LYsWOIiorCixcv8PLLLyM1NdXgY44ePYqIiAiMGDECZ86cQa9evdCrVy+cO3fOhpUXjCXnDYizYee81tevX7dRxdIoU6YMZsyYgVOnTuHkyZNo164devbsifPnz+s93hGutbnnDNj/dc7txIkTWLhwIerUqWP0OEe43hqmnjPgGNe7Zs2aOudw5MgRg8fKfp0FshgAYcuWLUaP+fDDD4WaNWvq7Ovfv7/QqVMnK1ZmXaac9/79+wUAwuPHj21Sky3cu3dPACAcPHjQ4DH9+vUTwsPDdfY1adJEePPNN61dntWYct7Lly8X/Pz8bFeUjRQtWlRYsmSJ3u854rUWBOPn7GjX+enTp0KVKlWEqKgooXXr1sL48eMNHuso19ucc3aE6z1p0iShbt26Jh8v93VmS5mVxcTEoEOHDjr7OnXqhJiYGJkqsq169eohODgYHTt2xB9//CF3OQWSlJQEAAgICDB4jCNeb1POGwBSUlJQvnx5lC1bNt/WFqXLysrCunXrkJqairCwML3HONq1NuWcAce6zmPHjkV4eHie66iPo1xvc84ZcIzrfeXKFZQqVQoVK1bEoEGDcOPGDYPHyn2dnW5BcltLTExEyZIldfaVLFkSycnJeP78Oby8vGSqzLqCg4OxYMECNGrUCOnp6ViyZAnatGmDP//8Ew0aNJC7PLOp1WpMmDABzZs3R61atQweZ+h629NYupxMPe9q1aph2bJlqFOnDpKSkvDNN9+gWbNmOH/+PMqUKWPDigvm7NmzCAsLQ1paGooUKYItW7YgNDRU77GOcq3NOWdHuc4AsG7dOpw+fRonTpww6XhHuN7mnrMjXO8mTZpgxYoVqFatGhISEjBlyhS0bNkS586dg4+PT57j5b7ODGVkFdWqVUO1atW0282aNUNcXBxmz56NlStXyliZZcaOHYtz584ZHYvgiEw977CwMJ3WlWbNmqFGjRpYuHAhpk6dau0yJVOtWjXExsYiKSkJGzduxNChQ3Hw4EGDIcURmHPOjnKdb968ifHjxyMqKsruBq5bypJzdoTr3aVLF+2/69SpgyZNmqB8+fL45ZdfMGLECBkr04+hzMqCgoJw9+5dnX13796Fr6+vw7aSGdK4cWO7DDXjxo3Djh07cOjQoXz/OjR0vYOCgqxZolWYc965ubm5oX79+rh69aqVqrMOd3d3VK5cGQDQsGFDnDhxAnPmzMHChQvzHOso19qcc87NXq/zqVOncO/ePZ1W+6ysLBw6dAhz585Feno6XF1ddR5j79fbknPOzV6vd07+/v6oWrWqwXOQ+zpzTJmVhYWFYd++fTr7oqKijI7ZcFSxsbEIDg6WuwyTCYKAcePGYcuWLYiOjkZISEi+j3GE623JeeeWlZWFs2fP2tX11ketViM9PV3v9xzhWutj7Jxzs9fr3L59e5w9exaxsbHar0aNGmHQoEGIjY3VG07s/Xpbcs652ev1ziklJQVxcXEGz0H262yT2wkcyNOnT4UzZ84IZ86cEQAI3333nXDmzBnh+vXrgiAIwsSJE4XXXntNe/y///4reHt7Cx988IFw8eJFYd68eYKrq6uwe/duuU7BIuae9+zZs4WtW7cKV65cEc6ePSuMHz9ecHFxEX7//Xe5TsFsY8aMEfz8/IQDBw4ICQkJ2q9nz55pj3nttdeEiRMnarf/+OMPoVChQsI333wjXLx4UZg0aZLg5uYmnD17Vo5TsIgl5z1lyhRhz549QlxcnHDq1ClhwIABgqenp3D+/Hk5TsEiEydOFA4ePCjEx8cLf//9tzBx4kRBpVIJe/fuFQTBMa+1uefsCNfZkNx3Ijri9c4tv3N2hOv9v//9Tzhw4IAQHx8v/PHHH0KHDh2E4sWLC/fu3RMEQXnXmaHMTJqpHnJ/DR06VBAEQRg6dKjQunXrPI+pV6+e4O7uLlSsWFFYvny5zesuKHPPe+bMmUKlSpUET09PISAgQGjTpo0QHR0tT/EW0ne+AHSuX+vWrbU/A41ffvlFqFq1quDu7i7UrFlT2Llzp20LLyBLznvChAlCuXLlBHd3d6FkyZJC165dhdOnT9u++AIYPny4UL58ecHd3V0oUaKE0L59e204EQTHvNbmnrMjXGdDcgcUR7zeueV3zo5wvfv37y8EBwcL7u7uQunSpYX+/fsLV69e1X5faddZJQiCYJs2OSIiIiIyhGPKiIiIiBSAoYyIiIhIARjKiIiIiBSAoYyIiIhIARjKiIiIiBSAoYyIiIhIARjKiIiIiBSAoYyIiIhIARjKiIj0UKlU2Lp1q1Vfo02bNpgwYYJVX4OI7AdDGRHJKiYmBq6urggPDzf7sRUqVMD3338vfVH56N69Ozp37qz3e4cPH4ZKpcLff/9t46qIyN4xlBGRrJYuXYq3334bhw4dwp07d+QuxyQjRoxAVFQUbt26led7y5cvR6NGjVCnTh0ZKiMie8ZQRkSySUlJwfr16zFmzBiEh4djxYoVeY7Zvn07XnrpJXh6eqJ48eLo3bs3ALHr7/r163j33XehUqmgUqkAAJMnT0a9evV0nuP7779HhQoVtNsnTpxAx44dUbx4cfj5+aF169Y4ffq0yXV369YNJUqUyFNvSkoKNmzYgBEjRuDhw4eIiIhA6dKl4e3tjdq1a2Pt2rVGn1dfl6m/v7/O69y8eRP9+vWDv78/AgIC0LNnT1y7dk37/QMHDqBx48YoXLgw/P390bx5c1y/ft3kcyMi+TCUEZFsfvnlF1SvXh3VqlXD4MGDsWzZMgiCoP3+zp070bt3b3Tt2hVnzpzBvn370LhxYwDA5s2bUaZMGXzxxRdISEhAQkKCya/79OlTDB06FEeOHMGxY8dQpUoVdO3aFU+fPjXp8YUKFcKQIUOwYsUKnXo3bNiArKwsREREIC0tDQ0bNsTOnTtx7tw5vPHGG3jttddw/Phxk+vM7cWLF+jUqRN8fHxw+PBh/PHHHyhSpAg6d+6MjIwMZGZmolevXmjdujX+/vtvxMTE4I033tAGViJStkJyF0BEzmvp0qUYPHgwAKBz585ISkrCwYMH0aZNGwDAtGnTMGDAAEyZMkX7mLp16wIAAgIC4OrqCh8fHwQFBZn1uu3atdPZXrRoEfz9/XHw4EF069bNpOcYPnw4Zs2apVPv8uXL0bdvX/j5+cHPzw/vv/++9vi3334be/bswS+//KINluZav3491Go1lixZog1ay5cvh7+/Pw4cOIBGjRohKSkJ3bp1Q6VKlQAANWrUsOi1iMj22FJGRLK4fPkyjh8/joiICABi61P//v2xdOlS7TGxsbFo37695K999+5djBo1ClWqVIGfnx98fX2RkpKCGzdumPwc1atXR7NmzbBs2TIAwNWrV3H48GGMGDECAJCVlYWpU6eidu3aCAgIQJEiRbBnzx6zXiO3v/76C1evXoWPjw+KFCmCIkWKICAgAGlpaYiLi0NAQABef/11dOrUCd27d8ecOXPMakEkInmxpYyIZLF06VJkZmaiVKlS2n2CIMDDwwNz586Fn58fvLy8zH5eFxcXnS5FQOz2y2no0KF4+PAh5syZg/Lly8PDwwNhYWHIyMgw67VGjBiBt99+G/PmzcPy5ctRqVIltG7dGgAwa9YszJkzB99//z1q166NwoULY8KECUZfQ6VSGa09JSUFDRs2xOrVq/M8tkSJEgDElrN33nkHu3fvxvr16/Hpp58iKioKTZs2NevciMj22FJGRDaXmZmJn3/+Gd9++y1iY2O1X3/99RdKlSqlHRBfp04d7Nu3z+DzuLu7IysrS2dfiRIlkJiYqBNuYmNjdY75448/8M4776Br166oWbMmPDw88ODBA7PPo1+/fnBxccGaNWvw888/Y/jw4dpuxT/++AM9e/bE4MGDUbduXVSsWBH//POP0ecrUaKETsvWlStX8OzZM+12gwYNcOXKFQQGBqJy5co6X35+ftrj6tevj8jISBw9ehS1atXCmjVrzD43IrI9hjIisrkdO3bg8ePHGDFiBGrVqqXz1bdvX20X5qRJk7B27VpMmjQJFy9exNmzZzFz5kzt81SoUAGHDh3C7du3taGqTZs2uH//Pr7++mvExcVh3rx5+O2333Rev0qVKli5ciUuXryIP//8E4MGDbKoVa5IkSLo378/IiMjkZCQgNdff13nNaKionD06FFcvHgRb775Ju7evWv0+dq1a4e5c+fizJkzOHnyJEaPHg03Nzft9wcNGoTixYujZ8+eOHz4MOLj43HgwAG88847uHXrFuLj4xEZGYmYmBhcv34de/fuxZUrVziujMhOMJQRkc0tXboUHTp00Gnd0ejbty9OnjyJv//+G23atMGGDRvw66+/ol69emjXrp3O3YtffPEFrl27hkqVKmm772rUqIEff/wR8+bNQ926dXH8+HGdAfea13/8+DEaNGiA1157De+88w4CAwMtOpcRI0bg8ePH6NSpk05X7KeffooGDRqgU6dOaNOmDYKCgtCrVy+jz/Xtt9+ibNmyaNmyJQYOHIj3338f3t7e2u97e3vj0KFDKFeuHPr06YMaNWpgxIgRSEtLg6+vL7y9vXHp0iX07dsXVatWxRtvvIGxY8fizTfftOjciMi2VELuAQxEREREZHNsKSMiIiJSAIYyIiIiIgVgKCMiIiJSAIYyIiIiIgVgKCMiIiJSAIYyIiIiIgVgKCMiIiJSAIYyIiIiIgVgKCMiIiJSAIYyIiIiIgVgKCMiIiJSgP8Hn6fgAJSsy7oAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(8, 6))\n",
        "sns.kdeplot(y_test, label='Actual Values', fill=True)\n",
        "sns.kdeplot(y_preds, label='Predicted Values', fill=True)\n",
        "plt.title('Distribution of Actual vs Predicted Values')\n",
        "plt.xlabel('Values')\n",
        "plt.ylabel('Density')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 527
        },
        "id": "3GLagxcHbjB6",
        "outputId": "41134c38-4335-4d49-e653-d191a1874c4e"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}