Task-2: Titanic_Classifiaction.ipynb

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        "# Importing Libraries\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ]
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      "source": [
        "# Loading the Dataset\n",
        "titanic = pd.read_csv('/content/drive/MyDrive/titanic dataset.csv')\n",
        "titanic"
      ],
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              "     PassengerId  Survived  Pclass  \\\n",
              "0              1         0       3   \n",
              "1              2         1       1   \n",
              "2              3         1       3   \n",
              "3              4         1       1   \n",
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              "..           ...       ...     ...   \n",
              "886          887         0       2   \n",
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              "888          889         0       3   \n",
              "889          890         1       1   \n",
              "890          891         0       3   \n",
              "\n",
              "                                                  Name     Sex   Age  SibSp  \\\n",
              "0                              Braund, Mr. Owen Harris    male  22.0      1   \n",
              "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
              "2                               Heikkinen, Miss. Laina  female  26.0      0   \n",
              "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
              "4                             Allen, Mr. William Henry    male  35.0      0   \n",
              "..                                                 ...     ...   ...    ...   \n",
              "886                              Montvila, Rev. Juozas    male  27.0      0   \n",
              "887                       Graham, Miss. Margaret Edith  female  19.0      0   \n",
              "888           Johnston, Miss. Catherine Helen \"Carrie\"  female   NaN      1   \n",
              "889                              Behr, Mr. Karl Howell    male  26.0      0   \n",
              "890                                Dooley, Mr. Patrick    male  32.0      0   \n",
              "\n",
              "     Parch            Ticket     Fare Cabin Embarked  \n",
              "0        0         A/5 21171   7.2500   NaN        S  \n",
              "1        0          PC 17599  71.2833   C85        C  \n",
              "2        0  STON/O2. 3101282   7.9250   NaN        S  \n",
              "3        0            113803  53.1000  C123        S  \n",
              "4        0            373450   8.0500   NaN        S  \n",
              "..     ...               ...      ...   ...      ...  \n",
              "886      0            211536  13.0000   NaN        S  \n",
              "887      0            112053  30.0000   B42        S  \n",
              "888      2        W./C. 6607  23.4500   NaN        S  \n",
              "889      0            111369  30.0000  C148        C  \n",
              "890      0            370376   7.7500   NaN        Q  \n",
              "\n",
              "[891 rows x 12 columns]"
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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "# Reading first 5 rows\n",
        "titanic.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 310
        },
        "id": "pIjAV0t_YZ3G",
        "outputId": "d83ddedb-69b7-440e-c14e-f76cc3124da3"
      },
      "execution_count": null,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "   PassengerId  Survived  Pclass  \\\n",
              "0            1         0       3   \n",
              "1            2         1       1   \n",
              "2            3         1       3   \n",
              "3            4         1       1   \n",
              "4            5         0       3   \n",
              "\n",
              "                                                Name     Sex   Age  SibSp  \\\n",
              "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
              "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
              "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
              "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
              "4                           Allen, Mr. William Henry    male  35.0      0   \n",
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              "   Parch            Ticket     Fare Cabin Embarked  \n",
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              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Reading last 5 rows\n",
        "titanic.tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        },
        "id": "0s-f8Z8nYjJe",
        "outputId": "256e1c7b-a644-49ac-df5e-ee10d9dce8bc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     PassengerId  Survived  Pclass                                      Name  \\\n",
              "886          887         0       2                     Montvila, Rev. Juozas   \n",
              "887          888         1       1              Graham, Miss. Margaret Edith   \n",
              "888          889         0       3  Johnston, Miss. Catherine Helen \"Carrie\"   \n",
              "889          890         1       1                     Behr, Mr. Karl Howell   \n",
              "890          891         0       3                       Dooley, Mr. Patrick   \n",
              "\n",
              "        Sex   Age  SibSp  Parch      Ticket   Fare Cabin Embarked  \n",
              "886    male  27.0      0      0      211536  13.00   NaN        S  \n",
              "887  female  19.0      0      0      112053  30.00   B42        S  \n",
              "888  female   NaN      1      2  W./C. 6607  23.45   NaN        S  \n",
              "889    male  26.0      0      0      111369  30.00  C148        C  \n",
              "890    male  32.0      0      0      370376   7.75   NaN        Q  "
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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-daa6a0d2-cadd-423f-8abd-7061092064fc 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-daa6a0d2-cadd-423f-8abd-7061092064fc');\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-6e44cf5d-a679-41e6-87be-7d5c04df1d56\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-6e44cf5d-a679-41e6-87be-7d5c04df1d56')\"\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-6e44cf5d-a679-41e6-87be-7d5c04df1d56 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Showing no. of rows and columns of dataset\n",
        "titanic.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "78wco340YneW",
        "outputId": "65cb3069-44d6-4a21-a1e0-ae81ead1c5e8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(891, 12)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# checking for columns\n",
        "titanic.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CbRPzby3YsiZ",
        "outputId": "6c087b94-a91f-4668-af46-118763cf2cb0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n",
              "       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Data Preprocessing and Data Cleaning"
      ],
      "metadata": {
        "id": "21SahUZkY02q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Checking for data types\n",
        "titanic.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V--Br0FZY2uq",
        "outputId": "2d831a39-0dfe-4ce5-e853-2708d261d891"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PassengerId      int64\n",
              "Survived         int64\n",
              "Pclass           int64\n",
              "Name            object\n",
              "Sex             object\n",
              "Age            float64\n",
              "SibSp            int64\n",
              "Parch            int64\n",
              "Ticket          object\n",
              "Fare           float64\n",
              "Cabin           object\n",
              "Embarked        object\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# checking for duplicated values\n",
        "titanic.duplicated().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7DHhNh87Y-nr",
        "outputId": "ba51f0b6-3b67-4b55-c7a5-7125d45fbe12"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# checking for null values\n",
        "nv = titanic.isna().sum().sort_values(ascending=False)\n",
        "nv = nv[nv>0]\n",
        "nv\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I2AYHavxZIIV",
        "outputId": "d24a02f3-6036-4698-bdc3-3533bfaa354e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Cabin       687\n",
              "Age         177\n",
              "Embarked      2\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Cheecking what percentage column contain missing values\n",
        "titanic.isnull().sum().sort_values(ascending=False)*100/len(titanic)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xvKebp_vZM2v",
        "outputId": "78c72b5b-a8dc-4f0c-c19d-74b1c1439402"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Cabin          77.104377\n",
              "Age            19.865320\n",
              "Embarked        0.224467\n",
              "PassengerId     0.000000\n",
              "Survived        0.000000\n",
              "Pclass          0.000000\n",
              "Name            0.000000\n",
              "Sex             0.000000\n",
              "SibSp           0.000000\n",
              "Parch           0.000000\n",
              "Ticket          0.000000\n",
              "Fare            0.000000\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Since Cabin Column has more than 75 % null values .So , we will drop this column\n",
        "titanic.drop(columns = 'Cabin', axis = 1, inplace = True)\n",
        "titanic.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FdoO0jxoZS7k",
        "outputId": "72f0b08f-794a-40c0-e866-7a0cf21eb741"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n",
              "       'Parch', 'Ticket', 'Fare', 'Embarked'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Filling Null Values in Age column with mean values of age column\n",
        "titanic['Age'].fillna(titanic['Age'].mean(),inplace=True)\n",
        "\n",
        "# filling null values in Embarked Column with mode values of embarked column\n",
        "titanic['Embarked'].fillna(titanic['Embarked'].mode()[0],inplace=True)"
      ],
      "metadata": {
        "id": "9ogaV9sVZZoV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# checking for null values\n",
        "titanic.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "51qoJDyMZbrl",
        "outputId": "c33e37df-793c-41c0-e25b-b8513bb86b2f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PassengerId    0\n",
              "Survived       0\n",
              "Pclass         0\n",
              "Name           0\n",
              "Sex            0\n",
              "Age            0\n",
              "SibSp          0\n",
              "Parch          0\n",
              "Ticket         0\n",
              "Fare           0\n",
              "Embarked       0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Finding no. of unique values in each column of dataset\n",
        "titanic[['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n",
        "       'Parch', 'Ticket', 'Fare', 'Embarked']].nunique().sort_values()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SyqKDtehZi3n",
        "outputId": "0b8a7070-0e24-44c2-9302-3c1f79e7cd04"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Survived         2\n",
              "Sex              2\n",
              "Pclass           3\n",
              "Embarked         3\n",
              "SibSp            7\n",
              "Parch            7\n",
              "Age             89\n",
              "Fare           248\n",
              "Ticket         681\n",
              "PassengerId    891\n",
              "Name           891\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "titanic['Survived'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gfTCFdrfZzqR",
        "outputId": "c5e09187-fcdd-4f75-d40c-5dae2125a911"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "titanic['Sex'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vBny7CfeZ5BE",
        "outputId": "a83f60a8-bd37-497d-a7b6-a34f9e3c70db"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['male', 'female'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "titanic['Pclass'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6BFE15PcZ9oK",
        "outputId": "77136083-36aa-43ef-dcfb-78de22299e20"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([3, 1, 2])"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "titanic['SibSp'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jEdi0nH1aC96",
        "outputId": "12048aa8-0f57-488b-96aa-b18322fc4aaf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 0, 3, 4, 2, 5, 8])"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "titanic['Parch'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-VrkXNySaI7x",
        "outputId": "272e7e6c-5ded-44cf-9db1-a26642e2844d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 2, 5, 3, 4, 6])"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "titanic['Embarked'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a9FneuRkaLi9",
        "outputId": "14ea1197-d309-44a1-86ae-cb8bb35258fe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['S', 'C', 'Q'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Dropping Some Unnecessary Columns"
      ],
      "metadata": {
        "id": "GMrg-8AjabJG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "titanic.drop(columns=['PassengerId','Name','Ticket'],axis=1,inplace=True)\n",
        "titanic.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kXfkrdtTaacB",
        "outputId": "b3f8d92d-cbca-443e-bcd6-9fbbbca16209"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Survived', 'Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare',\n",
              "       'Embarked'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Showing inforamation about the dataset\n",
        "titanic.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "txgMp4aaahjU",
        "outputId": "e2dce266-8a23-4aaa-ff86-c40a1a894d00"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 891 entries, 0 to 890\n",
            "Data columns (total 8 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   Survived  891 non-null    int64  \n",
            " 1   Pclass    891 non-null    int64  \n",
            " 2   Sex       891 non-null    object \n",
            " 3   Age       891 non-null    float64\n",
            " 4   SibSp     891 non-null    int64  \n",
            " 5   Parch     891 non-null    int64  \n",
            " 6   Fare      891 non-null    float64\n",
            " 7   Embarked  891 non-null    object \n",
            "dtypes: float64(2), int64(4), object(2)\n",
            "memory usage: 55.8+ KB\n"
          ]
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      ]
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    {
      "cell_type": "code",
      "source": [
        "# showing info. about numerical columns\n",
        "titanic.describe()\n"
      ],
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          "height": 300
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        "outputId": "1c4e578d-0edc-4d29-86a3-7e7906f397cb"
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      "execution_count": null,
      "outputs": [
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              "         Survived      Pclass         Age       SibSp       Parch        Fare\n",
              "count  891.000000  891.000000  891.000000  891.000000  891.000000  891.000000\n",
              "mean     0.383838    2.308642   29.699118    0.523008    0.381594   32.204208\n",
              "std      0.486592    0.836071   13.002015    1.102743    0.806057   49.693429\n",
              "min      0.000000    1.000000    0.420000    0.000000    0.000000    0.000000\n",
              "25%      0.000000    2.000000   22.000000    0.000000    0.000000    7.910400\n",
              "50%      0.000000    3.000000   29.699118    0.000000    0.000000   14.454200\n",
              "75%      1.000000    3.000000   35.000000    1.000000    0.000000   31.000000\n",
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              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-76847c75-3268-4de9-a7a6-66a80e400f3b');\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
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              "        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",
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          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# showing info. about numerical columns\n",
        "titanic.describe()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
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        "id": "eKzqOFtoa3Lt",
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      "execution_count": null,
      "outputs": [
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          "data": {
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              "         Survived      Pclass         Age       SibSp       Parch        Fare\n",
              "count  891.000000  891.000000  891.000000  891.000000  891.000000  891.000000\n",
              "mean     0.383838    2.308642   29.699118    0.523008    0.381594   32.204208\n",
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              "    [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-2d8da366-8a68-401d-bf02-cacb265e5ac8 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-2d8da366-8a68-401d-bf02-cacb265e5ac8');\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-0751b530-3154-4648-aa56-0da50c6f85fa\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-0751b530-3154-4648-aa56-0da50c6f85fa')\"\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",
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              "    <g>\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",
              "      --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",
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              "\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-0751b530-3154-4648-aa56-0da50c6f85fa button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# showing info. about categorical columns\n",
        "titanic.describe(include='O')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "m3DA1de-a7ed",
        "outputId": "f29b6a90-f3a8-4aa7-c749-afbd5d799683"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Sex Embarked\n",
              "count    891      891\n",
              "unique     2        3\n",
              "top     male        S\n",
              "freq     577      646"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a1b83707-78c3-4a89-b503-6268a5806636\" 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",
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              "    .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>Sex</th>\n",
              "      <th>Embarked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>891</td>\n",
              "      <td>891</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unique</th>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>top</th>\n",
              "      <td>male</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freq</th>\n",
              "      <td>577</td>\n",
              "      <td>646</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "  </svg>\n",
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              "\n",
              "  <style>\n",
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              "      display:flex;\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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              "      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",
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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-a1b83707-78c3-4a89-b503-6268a5806636 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-a1b83707-78c3-4a89-b503-6268a5806636');\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-30936a21-b069-46f6-8577-c2fd40f85551\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-30936a21-b069-46f6-8577-c2fd40f85551')\"\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",
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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",
              "      --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-30936a21-b069-46f6-8577-c2fd40f85551 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Data Visualization"
      ],
      "metadata": {
        "id": "KnycQduPbAhM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "d1 = titanic['Sex'].value_counts()\n",
        "d1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vmjVHRYWbCI2",
        "outputId": "fac00ca7-2b97-4b3a-c604-6411dc61bb7f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "male      577\n",
              "female    314\n",
              "Name: Sex, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(x=titanic['Sex'])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 451
        },
        "id": "NzcoZ0I2bGWP",
        "outputId": "ae15687c-88e8-4283-f4d1-e6a5a811e69a"
      },
      "execution_count": null,
      "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": [
        "# Plotting Percantage Distribution of Sex Column\n",
        "plt.figure(figsize=(5,5))\n",
        "plt.pie(d1.values,labels=d1.index,autopct='%.2f%%')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 422
        },
        "id": "DAKEFAkYbSJS",
        "outputId": "3fe015fb-0a81-4e89-ea99-d83d228b49b5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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UC6qu9fD3j7Yz6/Md2sNdAo7TAdcN7sS95/UgJlyHE7QSFYrFfLW7mLve2MD2gjLTUUSOq118BE+M7seQLsmmo4ifqFAsoqbWy/SPt/Hckh3ap0SChtMBNw7L5O5RWZqtWIAKxQI27Snh7jc38E1+qekoIk3Su30c0/+nP110cq+gpkIJYj6fj6c/zWbaou1aKpGgFxnq4v9d2JPrBncyHUWaSIUSpIrKa7jj9fUs2XbAdBQRvzq3Zyp/vaoPSdFhpqNII6lQgtD6XcXc9tpa9hRXmo4i0iJSYsN54uq+DOueYjqKNIIKJci8vDyXP837mhqP13QUkRbldMBdo7K47RddTUeRE6RCCRIVNbXc9/ZG3t+gQ6eIvVzYJ53Hr+rboue1F/9QoQSB7IJSbnl1Ldnat0Rsqmd6HDPHDtSJvAKcCiXALdycz52vr9dxuMT2kqLDePraAdoRMoCpUALYC0u/5ZF5W3QWRZHvhDgdPHhhT8adkWE6ijRAhRKAvF4fD8/bwovLck1HEQlI15zakYcv601YiPauDyQqlABT5fZwx9z1LNicbzqKSEAbkpnMrBtO1QEmA4gKJYAcLKtm4uw1rMsrNh1FJCic0j6el8YPIjkm3HQUQYUSML4tLGfci6vYebDCdBSRoJKZEs0rE06nfUKk6Si2p0IJAF/uLGLiy6spqnCbjiISlNLjI3hlwml0bRtrOoqtqVAMW7njIL9+aTXl2ixYpFkSo0J5cfxp9OuYYDqKbalQDPriuzLRPiYi/hEd5uLZsQM5q5uOAWaCCsWQ5TmFTHhpDZVulYmIP4W5nMwY04/zeqebjmI72ojbgOXZKhORllLj8TLpX+v4+Ov9pqPYjgqllS3LLuTXL69WmYi0ILfHx62vrWVZdqHpKLaiQmlFS7cXMuHl1VS5deh5kZZWXevlxtlrWJN7yHQU21ChtBKViUjrq6jxMP7F1WzcXWI6ii2oUFrBpj0l3PzKGqprVSYira20upbrX1jJ1vxS01EsT4XSwnYdqmC89jMRMaqows2vnl/JjgM6p1BLUqG0oKLyGm54cRUHSqtNRxGxvcKyan71/Er2FFeajmJZKpQWUuX2MHH2GnYcKDcdRUS+s6+kigkvraa8utZ0FEtSobQAr9fH7f9ax5c7i0xHEZGf+Ca/lNv/tQ6vzlzndyqUFvD79zfz4RbtVCUSqD7+poA/f/C16RiWo0Lxs38szuaVL3aajiEiP+P5pd8yd1We6RiWokLxo4+27OexhVtNxxCRE/R/721ieY72pvcXFYqf5BaW87s31qNDbYoED7fHx29eXcu3hdp4xh9UKH5QWePhlle/pLRKW46IBJuSSjcTXlpNiU5w12wqFD+4/99f8Y32whUJWjsKy7l97jp0No/mUaE008vLc3l3/V7TMUSkmZZsO8DMz3aYjhHUVCjN8OXOIh6Zt8V0DBHxk8c/3Mq6PO0/1lQqlCYqLKvmttfW4vZoEVnEKtweH5P+tY6SSs1TmkKF0gRer49Jc9aRf7jKdBQR8bPdRZXc9/ZXpmMEJRVKEzyzJIcVOw6ajiEiLWT+pnztoNwEKpRG2rSnhGmLtpmOISIt7JH/bmHL3sOmYwQVh0/byZ2wKreHi55cSnaBvc6pUFtaSPHil6jc8SW+2mpCEtJJvuAOwtO7HXXbgwufomz9AhLPvpG4QZce8zF3P/NrPIcLjro+pv+FJI/6Tf3X1Xu+puizV6jZtxUcTsLaZtJ29B9xhobjq3VzcMEMKrZ/gSs6kaRRtxLZuV/9fUtWvo3n8AGSRt7SvBdAbCszJZr/TjqTqLAQ01GCgl6lRnh0/je2KxNPVRn5r95LxEl9aHv1Qzij4qkt2oszIuao21ZsW0713q24YpJ+9nHTb/g7eH84g2VN4U4KXn+Q6B5n1F9Xvedr9r/xe+KHXE3SuTfjcLqoKfgWh6Nuwbp0wwJq8rNJu+5xKnd8SeF/HqPDb1/F4XDgLs6nbMNC0m+Y1vwXQWxrx4FyHp3/DX+8tLfpKEFBq7xO0Bc7DvLyilzTMVrd4S/eIiSuDW0uvIPwdlmEJqQRmTGA0MT0I25XW1rIoY+eo81Fd4Pz5/9OcUXF44pJrL9UZq8iJCGd8I6n1N/m0MfPEzfwYuIHX01YSidCkzsQ3fMsHCGhALgP7iKy6+mEpXQidsCFeCtK8FbWraI49OE/SBwxDmd4lB9fDbGjV77YyZrcQ6ZjBAUVygmoqKnl3re+suVxuiqzVxKW1o0D705l15O/Yu+Lt1O6fsERt/H5vBT+92/EnX4FYSmdGv0cPo+b8i2LiekzEofDAYCnvJiafVtxRieQ/8rd7HryOvLn3EfV7s319wtrm0H17i143dVUfbsWV0wSzsg4yjZ/iiMkjKjuQ5v3w4sAPh9Mfvsrqmt1Gu+fo0I5AX+Z/w15hypMxzDCXZxP6boPCElsR+roPxLb/wKKPp5J2caP629z+Iu3cDhdxA68pEnPUbHtC7xVZUT3Pqf+utrifABKls4hpu8vSR39B8JSu7B/7v/DfWgPADGnjCS0bQZ7/3krJSveoM2lk/FWlVGy9DWSzr2Zos9eYc9zN7L/9f+jtlRHlJWmyzlQzlOfZJuOEfA0Q/kZX+w4yGw7bz7o8xGe1pXE4TcAEJbaBXfhTkrXf0DMKedQnZ/N4S/fJ/2G6fVLF41V9tWHRGYOJCQ2+UdPW7c4GNPvPGL6jAQgKbULVTs3ULbxIxKHj8PhCjligA9QOG8asQMvpmb/Diq3ryB9/JMcXvk2RYtmknL5A03KJwLw7JIcLuyTTo+0ONNRApaWUI7D7fHy4LubbLmq63uumERC25x0xHWhyR3xHD4AQPWuzXjLS9jzzHh2/vUSdv71EjyHCyj69J/sfubXP/v4tSUFVO3cQEzfXx71vECDz1373XP/VNXOr3Af3EnsgIuoyvuKyMxTcYZFENXjTKryNp7wzyzSELfHx+S3N+rUwcehJZTjeGlZru226vqp8Pa9cB/afcR17kN7CIlrC0B0718Q0bnvEd8veGMK0SefTcwp5/7s45dt/AhXVDyRXQYdcX1IfCqumCRqDx793JGZA496HF9tDYc+eoY2F9+Nw+kCnxff9xuReT34fN6j7iPSWBt2FfPCsm+ZeFam6SgBSUsox1BQWsX0j7ebjmFc3KBLqd67lZIVb+Au2kv5lsWUbVhAzIALAXBFxhGW0vmIC84QXNGJhCZ3qH+c/XMf4PCX/znisX0+L2UbFxHd+5y6EvgRh8NB3GlXcvjL/1D+zVLcRXsp/uwVag/tJqbPqKNyFi+fS2TmqYSldgHqirBi23JqCr6ldO1/iWjf08+vjNjVEx9uY5dNZ6o/R0soxzD1g28oq9YJs8LTu5Ny+f+jeMnLFC/7FyHxqSSefSMxJ/+iUY/jLsonvPLIvY6rctfjOXygfkbyU3GDLsXnqaHok+fxVpUSlpJB22sePmqT5ZoDuVR88znp456svy6qxxlU7dpI/muTCU1uT5uL72lUXpFjqXR7+L/3NvHS+NNMRwk42lO+AWtyD3HVsytMxxCRAPbi+EH8Iqut6RgBRau8fsLr9THlvc0/f0MRsbU/zfuaWo9mcz+mQvmJ11buZMs+HRBORI4vu6BMRyT+CRXKjxSV1/DERzqSsIicmGmLtlNSoZNxfU+F8iPTP95Osd4cInKCSirdPL1Ye9B/T4Xynb3FlcxZlWc6hogEmZeW57KnuNJ0jICgQvnOk59kU1OrAZuINE5NrZcnPtxqOkZAUKEAuw5V8NaXu0zHEJEg9e66PTq7IyoUAJ78ZDtuj3bHEZGm8frqPkfszvaFkltYzr/X7jEdQ0SC3ILN+WQXlJqOYZTtC2XGx9up1dFDRaSZfD74x+Ic0zGMsnWhZBeU8d6GvaZjiIhFvL9+L7uL7HvgSFsXyvSPt+PR0omI+Emt18dzS3aYjmGMbQsl72AF877S0omI+Ncba3ZxoLTadAwjbFsoL6/IRQsnIuJv1bVenl9qz6UUWxZKeXUtb6zRfici0jJe+yKPkkr7HcbJloXy9trdlFbp5Fki0jLKqmuZvTzXdIxWZ7tC8fl8vGTDX7SItK45q/Jst9GP7QplybYD7DhQbjqGiFjcvpIqPv2mwHSMVmW7QtHSiYi0FrsdwdxWhbLjQBlLth0wHUNEbGLJtgPstdGh7W1VKC8vz8Vnr1WaImKQx+tj7mr7bFFqm0KprvXw73U6CKSItK43Vu+yzXDeNoXyydcF2lRYRFpd/uEqPrHJcN42hfKOlk5ExJA5K3eajtAqbFEoJRVuFm/VMF5EzLDLcN4WhfLfjXup8eh88SJihtcH/7XBwWhtUSjvanWXiBg2b2O+6QgtzvKFsutQBWt2FpmOISI2t2FXMXssvtrL8oXy/oa92vdERALC/I37TEdoUZYvFG3dJSKBYv4ma6/2snShbNtfSnZBmekYIiIArM0rIr+kynSMFmPpQrHbkT5FJLD5fDB/k3VXe1m7ULaqUEQksMy38NZeli2U0io3X2rrLhEJMGt2HqKg1JqrvSxbKEu3F+L2aPMuEQksXh8s/saaR+6wbKFodZeIBKplOYWmI7QIyxaKjt0lIoFqec5B0xFahCULZfPeEgpKq03HEBFp0IHSarbvLzUdw+8sWShaOhGRQLcs23qrvSxZKDpvvIgEOiuu9rJcobg9XjbsKjYdQ0TkuL7YcRCvxU4NbLlC2bz3MNW1OveJiAS2w1W1bNpbYjqGX1muUNblaWdGEQkOy7KttdrLcoWyNq/YdAQRkRPyxQ4VSkDTEoqIBItNe7TKK2AVlFaxu8jaZ0QTEes4WF7DvhLrfGZZqlDWaXWXiASZTXsOm47gN5YqlLVa3SUiQWazhbb0slShaAlFRILN5r1aQglIWyz0ixERe9hsocG8ZQpl/+EqyqprTccQEWmUvSVVFJXXmI7hF5YplJyCMtMRRESaxCp7zFunUA6oUEQkOFlljmKhQik3HUFEpEm277fGH8QWKhRr/EJExH52FVWYjuAXlimUHVpCEZEgtfuQCiVgVLk97LXQ4QtExF7yD1dRY4HTbliiUHIOlOGz1nlqRMRGvD7YWxz8fxRbolC0uktEgp0VDmxriUKxQrOLiL1ZYTBviULZf7jadAQRkWbZZYHBvDUKpbTKdAQRkWbZpVVegaHgsApFRIKbllACREGpVnmJSHA7ZIEDRFqiUA6WBf8vQkTs7XCV23SEZgv6Qqmp9eqw9SIS9EqravEF+Q51QV8oVlhMFBHxeH1B/8exCkVEJECUVAb3aq+gL5TiShWKiFjD4UotoRhV7Q7+A6qJiICWUIyrtsAROkVEIPi39Ar6QqnxqFBExBoOawnFLCucQ0BEBOBwlWYoRrm1hCIiFlEb5J9nQV8oWkIREavwBvd+jSoUEZFA4dWe8mZpKC8iVuEJ8kWUENMBmktLKNIaft/5a3qE5puOIRYXF+kAupmO0WRBXyjB3ugS+CZ22MW4/X/C4dMfL9LCuqYCF5hO0WRBv8orPCTofwQJYFnRFdxf8bjKRFqHM7g/z4I7PRAZ5jIdQSzK5fDyeso/cVUcMB1F7MIR3J9nQV8oEaHB/QuQwPVq1yUk5K8wHUPsxBncn2cqFJEGTOywi8G7/2k6htiNllDMilShiJ9pbiLGhEWbTtAswV8oYUH/I0gA0dxEjIpMNJ2gWYL+0zgiREso4j+am4hRKhSzIrSVl/iJ5iZinArFLC2hiD9obiIBITLBdIJmCfpCiYsM+p39xTDNTSRgaAnFrLaxEaYjSJDT3EQCgitcW3mZFhbiJDEq1HQMCVKam0jACPLVXWCBQgFIjdNSijSe5iYSUIJ8dRdYpFDaqlCkkTQ3kYCjQgkMqbHhpiNIkNHcRAJOVLLpBM1mjULREoo0guYmEpASOplO0GwWKRQtociJ0dxEAlZShukEzWaJQtEMRU6E5iYS0FQogSFNhSInQHMTCWhJmaYTNJslCqVTcpTpCBLgNDeRgOYM1QwlUCREhZGiLb3kGDQ3kYCX0DHoz9YIFikUgG5tY0xHkACkuYkEBQus7gIVilic5iYSFBKDfyAPViqU1FjTESTAaG4iQUNLKIFFSyjyY5qbSFBRoQSW7lpCke9obiJBp21P0wn8wjKFkhgdRpuYMNMxJABobiJBJaoNJAb/JsNgoUIB6NZWSyl2p7mJBJ32A0wn8BtLFUpWmgrFzjQ3kaDUfqDpBH5jqULpf1KC6QhiiOYmErRUKIFpUOck0xHEEM1NJGipUAJTu4RI2sXrQJF2o7mJBK3EzhBlnT+ELVUoAKdqKcVWNDeRoGahpROwZKEE/3mZ5cRobiJBT4US2AZ2UqHYheYmEvRUKIGtZ1ocseEhpmNIC9PcRIKeKwzS+5pO4VeWKxSn00E/bT5saZqbiCV0GgqhkaZT+JXlCgW0+bCVaW4iltH1XNMJ/M6ShXJG12TTEaSFaG4iltF1pOkEfmfJQunfMZHkaB0o0mo0NxHLiO8IbXuYTuF3liwUp9PB8KwU0zHEjzQ3EUux4OousGihAJzbM9V0BPETzU3EclQowWVY9xTCXJb98WxFcxOxFGcoZA43naJFWPYTNyY8hNMztbVXsNPcRCznpMEQbs1TbVi2UADO7tHWdARpBs1NxJIsuroLLF4omqMEL81NxLK6jTKdoMVYulA6JkXRPTXGdAxpAs1NxJJSekJqL9MpWoylCwVgVK800xGkkTQ3EcvqM9p0ghZl+UK5fEB70xGkETQ3EetyqFCCXZeUGPp1TDAdQ06A5iZiaZ3OgPgOplO0KMsXCsCVA639S7QKzU3E0iy+dAI2KZRL+rQjLMQWP2rQ0txELC0kAk6+zHSKFmeLT9n4qFDO7al9UgKV5iZied1/CRHxplO0OFsUCsCVA7TaKxBpbiK20Oca0wlahW0KZXj3FNrE6JD2gUZzE7G8yCRL78z4Y7YplBCXk0v7aRPiQKK5idhC7yvAFWo6RauwTaEAXKWtvQKG5iZiG4NuNJ2g1diqUHqmx3GazjdvnOYmYhtdzrHkmRmPxVaFAjD+jM6mI9ie5iZiG4NvNZ2gVdmuUEadnEb7hEjTMWxLcxOxjTZZ0PUc0ylale0KxeV0MHZIJ9MxbElzE7GVwb8Bh8N0ilZlu0IBGDPoJKLDXKZj2IrmJmIrkUnQ939Mp2h1tiyU+KhQRg/qaDqGrWhuIrZy6q8h1H6r1m1ZKAATz8okxGmvxVFTNDcRW3GFwWn22VT4x2xbKO0TIrmwT7rpGJanuYnYzsmXQ6w9T+xn20IBuHlYF7vNzFqV5iZiPw4Y8lvTIYyxdaH0ahfHeSfb8y+J1qC5idhOr0shvY/pFMbYulAA7hrVHZdmKX6nuYnYjsMFZz9oOoVRti+Urm1juUwHjfQrzU3ElvqNgTbdTKcwyvaFAnDHud0Ic+ml8AfNTcSWXOEw4n7TKYzTpyjQMSmKMadpvxR/0NxEbGnQBIjX0cxVKN/57dndiAzV3vPNobmJ2FJYDJx1l+kUAUGF8p2U2HDG6UjETaa5idjW4Fshuo3pFAFBhfIjtwzrQlxEiOkYQUdzE7GtyEQYOsl0ioChQvmR+KhQbvtFV9Mxgo7mJmJbZ94JEXGmUwQM/Tn+E78+M4O3vtzN9oIy01GCguYmJ+6Z1TU8s6aG3OK61YInt3UxZVgY53erO9/4iJfKWbLTc8R9bh4YyrMXNXyQQbfHx4OfVPNBdi07irzEhzs4NzOER88Np13s0X8rVtf6OP35cjbs97Lu5mj6pdXNDHOLvVz/TiVf7vMwMN3F7Msj6Zzww/0vmlPB+H6hXNnLHudFP2FJmXD6LaZTBBQtofxEqMvJw5f1Nh0jKGhu0jgd4hw8em44X94UzZqbojm7s4tL51ayueCHErlxQCj77oqpv/x1ZMQxH6/CDWvzPfzfsHDW3hTNv6+JZOtBD5f8q6LB29/7UXWDRXPXh1W0j3Ow/uZo0mMd3P1hVf33Xt/kxulAZdKQCx6DkHDTKQKKCqUBgzOTuby/dnY8Hs1NGu/irFAu6BZKt2QX3ZNd/OmcCGLC4IvdPxRKVKiDtBhn/SUu/NhHcYiPcPDR2GhGnxxKVhsXgzuE8NT5kXy5z0teyZElP3+7mw931PL4qKM/AL8+4OWGvmF0S3Yxrm8oXxfW3be4yseDn1bz9AXHLjXb6nERdD3XdIqAo0I5hgcu6EmsBvTHpLlJ83i8PuZuclPuhiEdf9hc/bWNbtr8tZTe/yjj/kVVVLh9jXrckmofDiAh4oci2l/m5cb/VPHK5ZFEhR5dUH3TnCzaUYvX5+PDHA99Uus+Fu75sIrbBoXSMV4fE0cIjYLzHjWdIiDpE/MYUmLDuXtUFr9/f7PpKAFHc5Om27jfw5B/llNVCzFh8M41kfRKqSuUa08JpVO8k3axDr7a72Xyoiq2HvTy72uiTuixq2p9TF5UxZhTQuqXbHw+H+Peq+SWU8M4tZ2rfn7zY4+PjODm/1bReVoZfVJdPHdRBJ/trGX9fg9/GRnB6DcrWLPXw6guIcw4P4Iwl82PfTfsHkjQjtANcfh8vsb9CWQjXq+PS55eyqY9h01HCRhZ0RV8EH6/VnU1UY3HR16Jj5IqH29tcfP8OjdLxkXVl8qPffJtLefMriB7Ugxdko6/lOD2+LjyjUp2H/ayeFx0faHMWFnNG5trWTIuCpfTQW6xl4zpZUcM5X+qutbHwJnlvHxZJK9tdFNS5ePZiyI477UKLssKZdLpYc1/IYJV25Ph5s/Apb/FG6Jl2eNwOh08ctkp6GDEdTQ3ab4wl4OuSU4GtnMx9dwI+qY6mf5FTYO3Pb193Qd+9qHjb/Tg9vgY/VYlO0u8fDQ26oi5yyffelix20P4I6WE/PEwXWfUbb146sxybni3ssHH+/Pn1YzqEsLAdi4W59ZyZa8QQl0OrugRyuKdtU35sa3B4YRLZqhMjkOvzM/o1zGBMaedxGsr80xHMe7VrktI2KW5iT95fVDtafh76/PrvpEee+y/aL4vk+0HvXx6QxTJUUf+jTjj/AgeOfuHlRB7S3388tUKXr8qktM7HL2E8vUBD3M21bL+5mgAPD5wf5fP7fXhsfMGfYMmQodTTacIaCqUE/DABT1Zml3IzoMNb45pB5qbNN/9i6o4v1sIJ8U7Ka32MWejm8W5HhZeF07OIS9zNrq5oFsIyVEOvtrv4c6FVQzr5KJP6g8f/D2eKmPqOeFc3jMUt8fHVW9Wsnafh/+OicLjg/yyuk/8pEgHYS4HJ/1koB4TVvf9LklOOsQd+T2fz8dN/63i778MJzqsrsTO6Ohi1lo33ZOdzN7gZkxvm24+HNcezpliOkXAU6GcgOjwEP42uh+jn1uBx2u/kZP2N/GPgnIf179Tyb4yH/HhDvqkOll4XRQju4Swq8TLom9rmbayhvIaHx3jnVzZM5QHhx25me/Wg15Kquveg3tKfby/tW4VVL/nyo+43ac3RDGic+P+9575pZvUaAcXdf+hNB4aEc61b1dy+vPlnNc1hNtOs+P8xAGXPg3hsaaDBDwN5Rvh8YVbeerTbNMxWpXL4eXLTk9rE2Gxr6GTYNQjplMEBQ3lG+F/z+3GKe3jTcdoVdrfRGwtvR+c83vTKYKGCqURQl1O/n5NPyJC7fGyaW4ithYaDVf+E1w2nRs1gT0+Gf2oa9sYHrigp+kYLU5zE7G98x+FNjr6eGOoUJrg+iGdGd49xXSMFqP9TcT2el0GA643nSLoqFCa6LGr+pAcbc0tXjQ3EVuL7wgXTzedIiipUJqobVwET17bH5fFdqPX3ERszeGEK2ZCZILpJEFJhdIMQ7u04f7ze5iO4Team4jtDbsHOg01nSJoqVCaaeJZmVzar53pGM2muYnYXtYFMOJ+0ymCmgrFDx69og+90oP7vNKam4ittT0ZrpgFDmutwm5tKhQ/iAxz8dzYgSRGBef26pqbiK1FtYFr50J4jOkkQU+F4icdk6J4csyAoBvSa24ituYKg2tehYSTTCexBBWKH53ZrQ33/DLLdIwTprmJ2N6Ff4NOQ0ynsAwVip/dMrwLVwxobzrGCdHcRGxtyG9hwFjTKSxFhdIC/nplH0ZkBfae9JqbiK11HQkjHzadwnJUKC0gxOXkH78aQL+OCaajNEhzE7G1lB5w1Qvg1Mefv+kVbSFRYSG8OG4QXVKiTUc5guYmYmsJJ8F1/4aI4N7MP1CpUFpQYnQYsyecTlpchOko9TQ3EduKTYfr34f44JhxBiMVSgtrnxDJ7AmnER9pfh8VzU3EtqLawPXvQVKG6SSWpkJpBd1TY3n+hlONnphLcxOxrYh4GPtvSAmeTfqDlQqllQzqnMRTYwYQ5mr9l1xzE7GtsBj41VuQ3td0EltQobSic3ul8uzYAYSFtO7LrrmJ2FJIBIz5F3Q8zXQS21ChtLKze6Tyz1Zc/aW5idiSMxRGvwIZw0wnsRWHz+fzmQ5hRytyDjLh5dVU1Hha7Dmyoiv4IPx+reoSe3GGwlX/hF6Xmk5iO1pCMWRIl2Rm//o0YsNDWuTxNTcRWwqNhmtfV5kYokIx6NTOSbwy8XTiIvxfKpqbiO1EJcMN/4Gu55hOYlsqFMP6dUxgzo2D/XouFc1NxHbiO8L4BdBhoOkktqYZSoDYml/K+BdXsbekqlmPo7mJ2E5Kj7rDqWgPeOO0hBIgstJiefe2MzilfXyTH0NzE7GdDoNg/HyVSYBQoQSQtnERvHHzEEb1Sm3S/TU3EVvpem7dsbmikkwnke+oUAJMZJiLZ68byI1nNe6YQ5qbiK30uQbGzIWwKNNJ5Ec0Qwlgr63cye/f20yt9/i/Is1NxDYcLhj5Bxg6yXQSaYAKJcB9tu0At722ltLq2ga/73J4+bLT01rVJdYXlQxXvQiZw00nkWPQKq8AN6x7Cm/fOpQOiZENfl9zE7GF9L5w0xKVSYBToQSB7qmxzJt0Fuf0aHvE9ZqbiC30vRZ+/SEkdDSdRH6GVnkFEZ/Pxz8W5/C3j7bRNbJccxOxNmco/PLPcPpNppPICVKhBKHlOYV0//x/aZP7X9NRRFpGdFsYPRs6DTGdRBpBhRKsyg7AOzdDzsemk4j4V8ZwuPxZiGtnOok0kgolmPl8sHwGfPwweN2m04g0T0gknPsQnH4zOBym00gTqFCsYPeX8PavoSjXdBKRpmk3AK6YCW26mU4izaBCsYqacvjkEVj5LPi8ptOInBhnCAy7F866C1wtc24gaT0qFKvZtRrenwQHvjadROT42mTBFc9Bu/6mk4ifqFCsqLYGPn8Clv4NPDWm04j8hAMG/wbO+T2ERpgOI36kQrGygq/hvd/CnjWmk4jUSekBFz4Bnc80nURagArF6rzeurnKJ4+Au9x0GrGr8HgYcR+cdpNmJRamQrGLolyYdxdkLzKdRGzFAf2urdscOKbtz95agpsKxW6yP4aPpsD+TaaTiNW16w/nPwYdB5lOIq1EhWJHXi9smAOf/AlK95pOI1YT1QbOmQL9x4JTx5+1ExWKndVUwBdPw9LpUFNqOo0EO2cInDoBfvEARCaYTiMGqFCk7rhgi6fC2pfB2/CJvESOyeGCPqNh+GRIatypq8VaVCjygwPbYNFDsHWe6SQSFBxw8uUw4n5I6W46jAQAFYocLX8TLJsOm/+tJRY5msMJPS+BYfdAWm/TaSSAqFDk2Ip2woqnYN2r4K4wnUZMc4ZA76vqjrulJRJpgApFfl75QVj1HKyaCZVFptNIawuNrpuRnPG/mpHIcalQ5MTVlMOXL8OKp+HwbtNppKUld4NBE6HfGIiIN51GgoAKRRrP44bN78K62fDt54DeQpbhcEHW+XDajZA5wnQaCTIqFGmeolxY9xqsn6OllmAWkwoDroeB4yG+vek0EqRUKOIfXi/s+KRugP/NPB02Pxg4nNDpDBg4DnpdCq5Q04kkyKlQxP8qDsFXb9SVy/6NptPIjzmccNKQuv1Hel4Csakt+nQ+n4+bb76Zt956i6KiItatW0e/fv1a9DkbkpubS0ZGhrHntwsdR1r8LyoJBt9Sd9m/Gb75oG5nyb3r0bzFBAd0PL2uRHpdCnHprfbMCxYs4KWXXmLx4sVkZmbSpk2bVntuaX0qFGlZqSfXXYbfA4f3wtb5dZdvPwNPtel0FuaADqd+VyKXGZuL5OTkkJ6eztChQ408v7QuHQpUWk9cOxg0Aa57C+7dAaNnQ5//gchE08msIa499B0Dlz0Lv9sCExfBkNuMlcm4ceOYNGkSeXl5OBwOOnfujNfrZerUqWRkZBAZGUnfvn1566236u+zePFiHA4HCxcupH///kRGRnL22WdTUFDA/Pnz6dmzJ3FxcVx77bVUVPyws+2CBQs488wzSUhIIDk5mYsuuoicnJzj5tu0aRPnn38+MTExpKamMnbsWAoLC1vs9bADzVDEPK8Hdq2CvOWQ9wXsWglVJaZTBb7IROh8FmQOh4wR0Kar6URHKCkpYcaMGcycOZPVq1fjcrmYOXMmr776KtOmTaNbt2589tln3HLLLSxcuJDhw4ezePFifvGLXzB48GAef/xxoqKiGD16NO3btyc8PJxHH32UsrIyLr/8cu655x4mT54MwNtvv43D4aBPnz6UlZUxZcoUcnNzWb9+PU6n86gZSnFxMd27d2fixIlcf/31VFZWMnnyZGpra/nkk08Mv3LBS4Uigcfng4KvIW9FXbnkrYDiPNOpzItMgvYDIGMYZAyHtD4Bf76RadOmMW3aNHJzc6muriYpKYlFixYxZMiQ+ttMnDiRiooK5syZU18oixYt4pxzzgHg0Ucf5f777ycnJ4fMzEwAbrnlFnJzc1mwYEGDz1tYWEhKSgobN26kd+/eRxXKI488wueff87ChQvr77N79246duzI1q1b6d5dh5ZpCs1QJPA4HJDaq+4yaELddYf3/VAw+ZugcCuUHzCbsyUldIK0UyC9b92/aX2Cfv+Q7OxsKioqGDly5BHX19TU0L9//yOu69OnT/1/p6amEhUVVV8m31+3atWq+q+3b9/OlClTWLlyJYWFhXi9XgDy8vLo3fvoA1hu2LCBTz/9lJiYmKO+l5OTo0JpIhWKBIe4dOh9Rd3lexWH4MDWunI5sO2Hf0t2ERxbkzkgOgXiO0BKj+8KpE/dvxY81ElZWRkA8+bNo337I8sxPDz8iK9DQ3/YJ8bhcBzx9ffXfV8aABdffDGdOnVi1qxZtGvXDq/XS+/evampaXh/qLKyMi6++GL+8pe/HPW99PTW2wrOalQoEryikqDTkLrLj9WUQ+F2OJhdt2VZaT6U7qv7tywfyguh+nDL53OF1y1VxHf87tIBEr77N75j3RA9NKLlcwSIXr16ER4eTl5eHsOHD/fb4x48eJCtW7cya9YszjrrLACWLl163PsMGDCAt99+m86dOxMSoo9Bf9ErKdYTFg3t+tVdjsXjhoqDP1yqy8DrrrveW1u3p3/9f7vrvv7+v0MjICzmu0s0hMf85OvYun9DI1vrJw4KsbGx3H333dx55514vV7OPPNMSkpKWLZsGXFxcdxwww1NetzExESSk5OZOXMm6enp5OXlcd999x33PrfddhuzZs1izJgx3HvvvSQlJZGdnc3cuXN5/vnncblcTcpidyoUsSdXKMSm1V2k1Tz88MOkpKQwdepUduzYQUJCAgMGDOCBBx5o8mM6nU7mzp3L7bffTu/evcnKymLGjBmMGDHimPdp164dy5YtY/LkyYwaNYrq6mo6derEeeedhzPAN3QIZNrKS0RE/EJVLCIifqFCERERv1ChiIiIX6hQRETEL1QoIiLiFyoUERHxCxWKiIj4hQpFRET8QoUiIiJ+oUIRERG/UKGIiIhfqFBERMQvVCgiIuIXKhQREfELFYqIiPiFCkVERPxChSIiIn6hQhEREb9QoYiIiF+oUERExC9UKCIi4hcqFBER8QsVioiI+IUKRURE/EKFIiIifqFCERERv1ChiIiIX6hQRETEL1QoIiLiFyoUERHxCxWKiIj4hQpFRET8QoUiIiJ+oUIRERG/UKGIiIhfqFBERMQvVCgiIuIXKhQREfELFYqIiPiFCkVERPxChSIiIn6hQhEREb9QoYiIiF+oUERExC9UKCIi4hcqFBER8Yv/D/EKw5f3T2pQAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Showing Distribution of Sex Column Survived Wise\n",
        "sns.countplot(x=titanic['Sex'],hue=titanic['Survived']) # In Sex (0 represents female and 1 represents male)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "gfxNw5nsdkHE",
        "outputId": "ee74796e-29ee-40a7-bc8e-5f09ab5f3a3c"
      },
      "execution_count": null,
      "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": [
        "# Showing Distribution of Embarked Sex wise\n",
        "sns.countplot(x=titanic['Embarked'],hue=titanic['Sex'])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "DOyF-eHGdpzh",
        "outputId": "b05f2e91-6338-46f8-b5fc-9d1a6a9564c0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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5RElJSVfcJzExUWfOnNEDDzyg3bt3KycnR5s2bdLYsWNVXV1d51l+Dld2AAAw3JNPPqmwsDClpqbqu+++U0hIiHr37q2ZM2fW+ZheXl5au3atpkyZom7duqlz585atGiRBg4ceNl9IiMjtX37ds2YMUNDhgxRRUWF2rdvr6FDh8rLq+Guv1jsdru9wY5+lSgtLVVwcLBKSkpktVrrdIw+01e7eKqrU/YLD7t7BABwifLycuXm5io6Olr+/v7uHqfJutJ/h1/67zcvYwEAAKMROwAAwGjEDgAAMBqxAwAAjEbsAABwBXyOx71c8fsndgAAqIW3t7ckXfbbgNE4Lv6V9f/7bc6/Bt+zAwBALXx8fNS8eXOdOnVKzZo1a9DvgcGl7Ha7ysrKVFRUpJCQEEd81gWxAwBALSwWi9q0aaPc3Fz913/9l7vHabJCQkIUERFRr2MQOwAAXIavr686derES1lu0qxZs3pd0bmI2AEA4Aq8vLz4BuWrHC9AAgAAoxE7AADAaMQOAAAwGrEDAACMRuwAAACjETsAAMBoxA4AADAasQMAAIxG7AAAAKMROwAAwGjEDgAAMBqxAwAAjEbsAAAAoxE7AADAaMQOAAAwGrEDAACMRuwAAACjETsAAMBoxA4AADAasQMAAIxG7AAAAKMROwAAwGjEDgAAMBqxAwAAjEbsAAAAoxE7AADAaMQOAAAwGrEDAACMRuwAAACjETsAAMBoxA4AADAasQMAAIxG7AAAAKMROwAAwGjEDgAAMBqxAwAAjEbsAAAAoxE7AADAaMQOAAAwGrEDAACMRuwAAACjETsAAMBoxA4AADAasQMAAIxG7AAAAKMROwAAwGjEDgAAMBqxAwAAjEbsAAAAoxE7AADAaG6NnaVLl6pHjx6yWq2yWq2y2WzauHGjY315ebkSExPVsmVLBQUFKT4+XoWFhU7HyMvL04gRI9S8eXO1bt1a06dP14ULFxr7VAAAgIdya+y0bdtWzz77rLKzs7Vnzx7Fxsbqrrvu0sGDByVJ06ZN0/r167Vu3TplZmYqPz9f99xzj2P/6upqjRgxQpWVldqxY4fS0tK0atUqpaSkuOuUAACAh7HY7Xa7u4f4V6GhoXrhhRd07733KiwsTGvWrNG9994rSTp8+LC6du2qrKws9evXTxs3btQdd9yh/Px8hYeHS5KWLVumGTNm6NSpU/L19a31MSoqKlRRUeG4X1paqqioKJWUlMhqtdZp7j7TV9dpP9Nkv/Cwu0cAADQRpaWlCg4O/tl/vz3mPTvV1dVau3atzp8/L5vNpuzsbFVVVWnQoEGObbp06aJ27dopKytLkpSVlaXu3bs7QkeS4uLiVFpa6rg6VJvU1FQFBwc7blFRUQ13YgAAwK3cHjsHDhxQUFCQ/Pz89Mc//lHvvfeeYmJiVFBQIF9fX4WEhDhtHx4eroKCAklSQUGBU+hcXH9x3eUkJyerpKTEcTtx4oRrTwoAAHgMH3cP0LlzZ+3bt08lJSV65513lJCQoMzMzAZ9TD8/P/n5+TXoYwAAAM/g9tjx9fVVx44dJUl9+vTR7t279fLLL+v+++9XZWWliouLna7uFBYWKiIiQpIUERGhXbt2OR3v4qe1Lm4DAACaNre/jPV/1dTUqKKiQn369FGzZs20detWx7ojR44oLy9PNptNkmSz2XTgwAEVFRU5ttmyZYusVqtiYmIafXYAAOB53HplJzk5WcOGDVO7du109uxZrVmzRhkZGdq0aZOCg4M1btw4JSUlKTQ0VFarVZMnT5bNZlO/fv0kSUOGDFFMTIxGjx6t559/XgUFBZo9e7YSExN5mQoAAEhyc+wUFRXp4Ycf1smTJxUcHKwePXpo06ZNGjx4sCRp4cKF8vLyUnx8vCoqKhQXF6clS5Y49vf29taGDRs0YcIE2Ww2BQYGKiEhQfPnz3fXKQEAAA/jcd+z4w6/9HP6V8L37PyE79kBADSWq+57dgAAABoCsQMAAIxG7AAAAKMROwAAwGjEDgAAMBqxAwAAjEbsAAAAoxE7AADAaMQOAAAwGrEDAACMRuwAAACjETsAAMBoxA4AADAasQMAAIxG7AAAAKMROwAAwGjEDgAAMBqxAwAAjEbsAAAAoxE7AADAaMQOAAAwGrEDAACMRuwAAACjETsAAMBoxA4AADAasQMAAIxG7AAAAKMROwAAwGjEDgAAMBqxAwAAjFan2ImNjVVxcfEly0tLSxUbG1vfmQAAAFymTrGTkZGhysrKS5aXl5frs88+q/dQAAAAruLzazbev3+/4+dvvvlGBQUFjvvV1dVKT0/Xtdde67rpAAAA6ulXxc4NN9wgi8Uii8VS68tVAQEBeuWVV1w2HAAAQH39qtjJzc2V3W5Xhw4dtGvXLoWFhTnW+fr6qnXr1vL29nb5kAAAAHX1q2Knffv2kqSampoGGQYAAMDVflXs/KujR49q27ZtKioquiR+UlJS6j0YAACAK9QpdlasWKEJEyaoVatWioiIkMVicayzWCzEDgAA8Bh1ip2nnnpKTz/9tGbMmOHqeQAAAFyqTt+z88MPP+i+++5z9SwAAAAuV6fYue+++7R582ZXzwIAAOBydXoZq2PHjpozZ4527typ7t27q1mzZk7rp0yZ4pLhAAAA6qtOsfP3v/9dQUFByszMVGZmptM6i8VC7AAAAI9Rp9jJzc119RwAAAANok7v2QEAALha1OnKziOPPHLF9StXrqzTMAAAAK5Wp9j54YcfnO5XVVXp66+/VnFxca1/IBQAAMBd6hQ777333iXLampqNGHCBP3mN7+p91AAAACu4rL37Hh5eSkpKUkLFy501SEBAADqzaVvUM7JydGFCxdceUgAAIB6qdPLWElJSU737Xa7Tp48qQ8//FAJCQkuGQwAAMAV6hQ7e/fudbrv5eWlsLAw/fWvf/3ZT2oBAAA0pjrFzrZt21w9BwAAQIOoU+xcdOrUKR05ckSS1LlzZ4WFhblkKAAAAFep0xuUz58/r0ceeURt2rTRrbfeqltvvVWRkZEaN26cysrKXD0jAABAndUpdpKSkpSZman169eruLhYxcXF+uc//6nMzEz9+c9/dvWMAAAAdVanl7HeffddvfPOOxo4cKBj2fDhwxUQEKCRI0dq6dKlrpoPAACgXup0ZaesrEzh4eGXLG/dujUvYwEAAI9Sp9ix2WyaO3euysvLHct+/PFHPfHEE7LZbC4bDgAAoL7q9DLWSy+9pKFDh6pt27bq2bOnJOmrr76Sn5+fNm/e7NIBAQAA6qNOsdO9e3cdPXpUb775pg4fPixJeuCBBzRq1CgFBAS4dEAAAID6qFPspKamKjw8XOPHj3davnLlSp06dUozZsxwyXAAAAD1Vaf37CxfvlxdunS5ZPm//du/admyZfUeCgAAwFXqFDsFBQVq06bNJcvDwsJ08uTJeg8FAADgKnWKnaioKG3fvv2S5du3b1dkZGS9hwIAAHCVOr1nZ/z48Zo6daqqqqoUGxsrSdq6dav+8pe/8A3KAADAo9QpdqZPn67Tp09r4sSJqqyslCT5+/trxowZSk5OdumAAAAA9VGn2LFYLHruuec0Z84cHTp0SAEBAerUqZP8/PxcPR8AAEC91Cl2LgoKCtK///u/13n/1NRU/ed//qcOHz6sgIAA9e/fX88995w6d+7s2Ka8vFx//vOftXbtWlVUVCguLk5Llixx+nMVeXl5mjBhgrZt26agoCAlJCQoNTVVPj71Oj3UQd787u4ewSO0Szng7hEAAP+rTm9QdpXMzEwlJiZq586d2rJli6qqqjRkyBCdP3/esc20adO0fv16rVu3TpmZmcrPz9c999zjWF9dXa0RI0aosrJSO3bsUFpamlatWqWUlBR3nBIAAPAwFrvdbnf3EBedOnVKrVu3VmZmpm699VaVlJQoLCxMa9as0b333itJOnz4sLp27aqsrCz169dPGzdu1B133KH8/HzH1Z5ly5ZpxowZOnXqlHx9fX/2cUtLSxUcHKySkhJZrdY6zd5n+uo67Wea91q84O4RPAJXdgCg4f3Sf7/demXn/yopKZEkhYaGSpKys7NVVVWlQYMGObbp0qWL2rVrp6ysLElSVlaWunfv7vSyVlxcnEpLS3Xw4MFaH6eiokKlpaVONwAAYCaPiZ2amhpNnTpVN998s7p16ybppy8v9PX1VUhIiNO24eHhKigocGzzr6Fzcf3FdbVJTU1VcHCw4xYVFeXiswEAAJ7CY2InMTFRX3/9tdauXdvgj5WcnKySkhLH7cSJEw3+mAAAwD084uNKkyZN0oYNG/Tpp5+qbdu2juURERGqrKxUcXGx09WdwsJCRUREOLbZtWuX0/EKCwsd62rj5+fHx+QBAGgi3Hplx263a9KkSXrvvff0ySefKDo62ml9nz591KxZM23dutWx7MiRI8rLy5PNZpMk2Ww2HThwQEVFRY5ttmzZIqvVqpiYmMY5EQAA4LHcemUnMTFRa9as0T//+U+1aNHC8R6b4OBgBQQEKDg4WOPGjVNSUpJCQ0NltVo1efJk2Ww29evXT5I0ZMgQxcTEaPTo0Xr++edVUFCg2bNnKzExkas3AADAvbGzdOlSSdLAgQOdlr/22msaM2aMJGnhwoXy8vJSfHy805cKXuTt7a0NGzZowoQJstlsCgwMVEJCgubPn99YpwEAADyYW2Pnl3zFj7+/vxYvXqzFixdfdpv27dvro48+cuVoAADAEB7zaSwAAICGQOwAAACjETsAAMBoxA4AADAasQMAAIxG7AAAAKMROwAAwGjEDgAAMBqxAwAAjEbsAAAAoxE7AADAaMQOAAAwGrEDAACMRuwAAACjETsAAMBoxA4AADAasQMAAIxG7AAAAKMROwAAwGjEDgAAMBqxAwAAjEbsAAAAoxE7AADAaMQOAAAwGrEDAACMRuwAAACjETsAAMBoxA4AADAasQMAAIxG7AAAAKMROwAAwGjEDgAAMBqxAwAAjEbsAAAAoxE7AADAaMQOAAAwGrEDAACMRuwAAACjETsAAMBoxA4AADAasQMAAIxG7AAAAKMROwAAwGjEDgAAMBqxAwAAjEbsAAAAoxE7AADAaMQOAAAwGrEDAACMRuwAAACjETsAAMBoxA4AADAasQMAAIxG7AAAAKMROwAAwGjEDgAAMBqxAwAAjEbsAAAAoxE7AADAaMQOAAAwGrEDAACMRuwAAACjETsAAMBoxA4AADAasQMAAIxG7AAAAKMROwAAwGjEDgAAMBqxAwAAjObW2Pn000915513KjIyUhaLRe+//77TervdrpSUFLVp00YBAQEaNGiQjh496rTNmTNnNGrUKFmtVoWEhGjcuHE6d+5cI54FAADwZG6NnfPnz6tnz55avHhxreuff/55LVq0SMuWLdMXX3yhwMBAxcXFqby83LHNqFGjdPDgQW3ZskUbNmzQp59+qscee6yxTgEAAHg4H3c++LBhwzRs2LBa19ntdr300kuaPXu27rrrLknS6tWrFR4ervfff1+///3vdejQIaWnp2v37t3q27evJOmVV17R8OHDtWDBAkVGRjbauQAAAM/kse/Zyc3NVUFBgQYNGuRYFhwcrJtuuklZWVmSpKysLIWEhDhCR5IGDRokLy8vffHFF5c9dkVFhUpLS51uAADATB4bOwUFBZKk8PBwp+Xh4eGOdQUFBWrdurXTeh8fH4WGhjq2qU1qaqqCg4Mdt6ioKBdPDwAAPIXHxk5DSk5OVklJieN24sQJd48EAAAaiMfGTkREhCSpsLDQaXlhYaFjXUREhIqKipzWX7hwQWfOnHFsUxs/Pz9ZrVanGwAAMJPHxk50dLQiIiK0detWx7LS0lJ98cUXstlskiSbzabi4mJlZ2c7tvnkk09UU1Ojm266qdFnBgAAnsetn8Y6d+6cjh075rifm5urffv2KTQ0VO3atdPUqVP11FNPqVOnToqOjtacOXMUGRmpu+++W5LUtWtXDR06VOPHj9eyZctUVVWlSZMm6fe//z2fxAIk9Zm+2t0jeITsFx529wgA3MitsbNnzx7ddtttjvtJSUmSpISEBK1atUp/+ctfdP78eT322GMqLi7WLbfcovT0dPn7+zv2efPNNzVp0iTdfvvt8vLyUnx8vBYtWtTo5wIAADyTW2Nn4MCBstvtl11vsVg0f/58zZ8//7LbhIaGas2aNQ0xHgAAMIDHvmcHAADAFYgdAABgNGIHAAAYjdgBAABGI3YAAIDRiB0AAGA0YgcAABiN2AEAAEYjdgAAgNGIHQAAYDRiBwAAGI3YAQAARiN2AACA0YgdAABgNB93DwAADS1vfnd3j+AR2qUccPcIgFtwZQcAABiN2AEAAEYjdgAAgNGIHQAAYDRiBwAAGI3YAQAARiN2AACA0YgdAABgNGIHAAAYjdgBAABGI3YAAIDRiB0AAGA0YgcAABiN2AEAAEYjdgAAgNGIHQAAYDRiBwAAGI3YAQAARiN2AACA0YgdAABgNGIHAAAYjdgBAABGI3YAAIDRiB0AAGA0YgcAABjNx90DAACajj7TV7t7BI+Q/cLD7h6hSeHKDgAAMBqxAwAAjEbsAAAAo/GeHQAAGlne/O7uHsEjtEs50CiPw5UdAABgNGIHAAAYjdgBAABGI3YAAIDRiB0AAGA0YgcAABiN2AEAAEYjdgAAgNGIHQAAYDRiBwAAGI3YAQAARiN2AACA0YgdAABgNGIHAAAYjdgBAABGI3YAAIDRiB0AAGA0YgcAABiN2AEAAEYjdgAAgNGIHQAAYDRiBwAAGI3YAQAARiN2AACA0YyJncWLF+u6666Tv7+/brrpJu3atcvdIwEAAA9gROy89dZbSkpK0ty5c/Xll1+qZ8+eiouLU1FRkbtHAwAAbmZE7Lz44osaP368xo4dq5iYGC1btkzNmzfXypUr3T0aAABwMx93D1BflZWVys7OVnJysmOZl5eXBg0apKysrFr3qaioUEVFheN+SUmJJKm0tLTOc1RX/FjnfU1ytlm1u0fwCPV5LrkSz8uf8Lz8iSc8L3lO/oTn5E/q+5y8uL/dbr/idld97Hz//feqrq5WeHi40/Lw8HAdPny41n1SU1P1xBNPXLI8KiqqQWZsSrq5ewBPkRrs7gnwL3he/i+elx6D5+T/ctFz8uzZswoOvvyxrvrYqYvk5GQlJSU57tfU1OjMmTNq2bKlLBaLGye7upWWlioqKkonTpyQ1Wp19ziAJJ6X8Dw8J13Hbrfr7NmzioyMvOJ2V33stGrVSt7e3iosLHRaXlhYqIiIiFr38fPzk5+fn9OykJCQhhqxybFarfwPGB6H5yU8Dc9J17jSFZ2Lrvo3KPv6+qpPnz7aunWrY1lNTY22bt0qm83mxskAAIAnuOqv7EhSUlKSEhIS1LdvX91444166aWXdP78eY0dO9bdowEAADczInbuv/9+nTp1SikpKSooKNANN9yg9PT0S960jIbl5+enuXPnXvISIeBOPC/haXhONj6L/ec+rwUAAHAVu+rfswMAAHAlxA4AADAasQMAAIxG7AAAAKMRO3CJU6dOacKECWrXrp38/PwUERGhuLg4bd++3d2joQkrKCjQ5MmT1aFDB/n5+SkqKkp33nmn0/dyATAfsQOXiI+P1969e5WWlqZvv/1WH3zwgQYOHKjTp0+7ezQ0UcePH1efPn30ySef6IUXXtCBAweUnp6u2267TYmJie4eD03QiRMn9MgjjygyMlK+vr5q3769/vSnP/H/k42Aj56j3oqLi3XNNdcoIyNDAwYMcPc4gCRp+PDh2r9/v44cOaLAwECndcXFxfyJGDSq7777TjabTddff72eeuopRUdH6+DBg5o+fboqKyu1c+dOhYaGuntMY3FlB/UWFBSkoKAgvf/++6qoqHD3OIDOnDmj9PR0JSYmXhI6En8LD40vMTFRvr6+2rx5swYMGKB27dpp2LBh+vjjj/U///M/mjVrlrtHNBqxg3rz8fHRqlWrlJaWppCQEN18882aOXOm9u/f7+7R0EQdO3ZMdrtdXbp0cfcogM6cOaNNmzZp4sSJCggIcFoXERGhUaNG6a233hIvtDQcYgcuER8fr/z8fH3wwQcaOnSoMjIy1Lt3b61atcrdo6EJ4h8NeJKjR4/Kbrera9euta7v2rWrfvjhB506daqRJ2s6iB24jL+/vwYPHqw5c+Zox44dGjNmjObOnevusdAEderUSRaLRYcPH3b3KIDDz0W4r69vI03S9BA7aDAxMTE6f/68u8dAExQaGqq4uDgtXry41udgcXFx4w+FJqtjx46yWCw6dOhQresPHTqksLAw3kvWgIgd1Nvp06cVGxurN954Q/v371dubq7WrVun559/XnfddZe7x0MTtXjxYlVXV+vGG2/Uu+++q6NHj+rQoUNatGiRbDabu8dDE9KyZUsNHjxYS5Ys0Y8//ui0rqCgQG+++abGjBnjnuGaCD56jnqrqKjQvHnztHnzZuXk5KiqqkpRUVG67777NHPmzEvekAc0lpMnT+rpp5/Whg0bdPLkSYWFhalPnz6aNm2aBg4c6O7x0IQcPXpU/fv3V9euXS/56LmPj48+++wzBQUFuXtMYxE7AAA0guPHj2vevHlKT09XUVGR7Ha77rnnHr3++utq3ry5u8czGrEDAIAbzJ07Vy+++KK2bNmifv36uXscoxE7AAC4yWuvvaaSkhJNmTJFXl68jbahEDsAAMBoZCQAADAasQMAAIxG7AAAAKMROwAAwGjEDgAAMBqxA+CqMm/ePN1www0NcuyMjAxZLBaX/u2s48ePy2KxaN++fS47JoBfh9gB0GDGjBkji8VyyW3o0KHuHg1AE+Lj7gEAmG3o0KF67bXXnJb5+fm5aZrLq6qqcvcIABoIV3YANCg/Pz9FREQ43a655hpJksVi0fLly3XHHXeoefPm6tq1q7KysnTs2DENHDhQgYGB6t+/v3Jyci457vLlyxUVFaXmzZtr5MiRKikpcazbvXu3Bg8erFatWik4OFgDBgzQl19+6bS/xWLR0qVL9dvf/laBgYF6+umnL3mMsrIyDRs2TDfffLPjpa1//OMf6tq1q/z9/dWlSxctWbLEaZ9du3apV69e8vf3V9++fbV37976/goB1BOxA8CtnnzyST388MPat2+funTpogcffFB/+MMflJycrD179shut2vSpElO+xw7dkxvv/221q9fr/T0dO3du1cTJ050rD979qwSEhL0+eefa+fOnerUqZOGDx+us2fPOh1n3rx5+t3vfqcDBw7okUcecVpXXFyswYMHq6amRlu2bFFISIjefPNNpaSk6Omnn9ahQ4f0zDPPaM6cOUpLS5MknTt3TnfccYdiYmKUnZ2tefPm6fHHH2+g3xyAX8wOAA0kISHB7u3tbQ8MDHS6Pf3003a73W6XZJ89e7Zj+6ysLLsk+6uvvupY9h//8R92f39/x/25c+favb297f/93//tWLZx40a7l5eX/eTJk7XOUV1dbW/RooV9/fr1jmWS7FOnTnXabtu2bXZJ9kOHDtl79Ohhj4+Pt1dUVDjW/+Y3v7GvWbPGaZ8nn3zSbrPZ7Ha73b58+XJ7y5Yt7T/++KNj/dKlS+2S7Hv37v3Z3xeAhsF7dgA0qNtuu01Lly51WhYaGur4uUePHo6fw8PDJUndu3d3WlZeXq7S0lJZrVZJUrt27XTttdc6trHZbKqpqdGRI0cUERGhwsJCzZ49WxkZGSoqKlJ1dbXKysqUl5fnNEffvn1rnXnw4MG68cYb9dZbb8nb21uSdP78eeXk5GjcuHEaP368Y9sLFy4oODhYknTo0CH16NFD/v7+TrMBcC9iB0CDCgwMVMeOHS+7vlmzZo6fLRbLZZfV1NT84sdMSEjQ6dOn9fLLL6t9+/by8/OTzWZTZWXlJbPVZsSIEXr33Xf1zTffOMLr3LlzkqQVK1bopptuctr+YhAB8EzEDoCrTl5envLz8xUZGSlJ2rlzp7y8vNS5c2dJ0vbt27VkyRINHz5cknTixAl9//33v/j4zz77rIKCgnT77bcrIyNDMTExCg8PV2RkpL777juNGjWq1v26du2q119/XeXl5Y6rOzt37qzPqQJwAWIHQIOqqKhQQUGB0zIfHx+1atWqzsf09/dXQkKCFixYoNLSUk2ZMkUjR45URESEJKlTp056/fXX1bdvX5WWlmr69OkKCAj4VY+xYMECVVdXKzY2VhkZGerSpYueeOIJTZkyRcHBwRo6dKgqKiq0Z88e/fDDD0pKStKDDz6oWbNmafz48UpOTtbx48e1YMGCOp8nANfg01gAGlR6erratGnjdLvlllvqdcyOHTvqnnvu0fDhwzVkyBD16NHD6SPgr776qn744Qf17t1bo0eP1pQpU9S6detf/TgLFy7UyJEjFRsbq2+//VaPPvqo/vGPf+i1115T9+7dNWDAAK1atUrR0dGSpKCgIK1fv14HDhxQr169NGvWLD333HP1OlcA9Wex2+12dw8BAADQULiyAwAAjEbsAAAAoxE7AADAaMQOAAAwGrEDAACMRuwAAACjETsAAMBoxA4AADAasQMAAIxG7AAAAKMROwAAwGj/H4P7/hujp68qAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plotting CountPlot for Pclass Column\n",
        "sns.countplot(x=titanic['Pclass'])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "89BylXHBdsP1",
        "outputId": "84e07a09-c16b-4fab-e72c-f9572facfb64"
      },
      "execution_count": null,
      "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": [
        "# Showing Distribution of Pclass Sex wise\n",
        "sns.countplot(x=titanic['Pclass'],hue=titanic['Sex'])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "tgrd7gtId0O3",
        "outputId": "b533f121-2a77-4cb6-dbe7-36f48a76cacd"
      },
      "execution_count": null,
      "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": [
        "# Age Distribution\n",
        "sns.kdeplot(x=titanic['Age'])\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "ae3olP6Od6mc",
        "outputId": "4920566c-e722-4f66-bf8b-0c049e174257"
      },
      "execution_count": null,
      "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": [
        "# Plotting CountPlot for Survived Column\n",
        "print(titanic['Survived'].value_counts())\n",
        "sns.countplot(x=titanic['Survived'])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 501
        },
        "id": "F-FWiMFHeEf6",
        "outputId": "7d79479b-2bd2-4da8-ff53-a4aa23bc96aa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0    549\n",
            "1    342\n",
            "Name: Survived, dtype: int64\n"
          ]
        },
        {
          "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": [
        "# Showing Distribution of Parch Survived Wise\n",
        "sns.countplot(x=titanic['Parch'],hue=titanic['Survived'])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "EV5j7BSfeJh6",
        "outputId": "16d7e344-7c2c-4ce8-f93c-0862633956e8"
      },
      "execution_count": null,
      "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": [
        "# Showing Distribution of SibSp Survived Wise\n",
        "sns.countplot(x=titanic['SibSp'],hue=titanic['Survived'])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "f-CwvxXGeL_j",
        "outputId": "7be53ce0-2ffc-4811-d51a-cea0f6b9859f"
      },
      "execution_count": null,
      "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": [
        "# Showing Distribution of Embarked Survived wise\n",
        "sns.countplot(x=titanic['Embarked'],hue=titanic['Survived'])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "VufqYaxieTK6",
        "outputId": "38a95815-e6d2-40fb-fbe0-3d90b73f0b82"
      },
      "execution_count": null,
      "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": [
        "# Showinf Distribution of Age Survived Wise\n",
        "sns.kdeplot(x=titanic['Age'],hue=titanic['Survived'])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "nfjobI0SeYLL",
        "outputId": "95fadf0e-f0e9-4c15-c8db-4fd05d6df535"
      },
      "execution_count": null,
      "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": [
        "# Plotting Histplot for Dataset\n",
        "titanic.hist(figsize=(10,10))\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 853
        },
        "id": "pN-rd4gqeeNs",
        "outputId": "ca592f9f-5ca5-49c4-ce28-ec73a7933685"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 6 Axes>"
            ],
            "image/png": 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mTZum66+/Xrt375bT6VRwcLAiIiK81omJiZHT6ZQkOZ1Or+BTvbx62Zm4XC65XC7PdGlpqaQfr3l3u92mxlDd3ux6VkbNzKNm5lAv8+pSMzPrEH4AAJY1ePBgz7979Oihvn37Kj4+Xq+99ppatmzZYPudPXt2jQctSFJOTo7CwsJ82mZubm5du2U51Mw8amYO9TLPl5qVlZXVui3hBwCA/4iIiNAVV1yhAwcOaNCgQaqoqFBJSYnX2Z+ioiLPPUKxsbHatm2b1zaKioo8y85k4sSJysrK8kxXP6Y1OTnZp6e95ebmatCgQTxVqpaomXnUzBzqZV5dalZ99rw2CD8AAPzH8ePH9emnn2rEiBHq1auXbDab8vLylJaWJknat2+fCgsL5XA4JEkOh0MzZ85UcXGxoqOjJf34V0u73a6EhIQz7ickJEQhISE15ttsNp+/KNVlXauiZuZRM3Ool3m+1MxMe8uGn25T18lV2fjvuPh8Tmqj7xMAcHq/+c1vNGTIEMXHx+vQoUOaMmWKgoKCdO+99yo8PFyjRo1SVlaWIiMjZbfbNW7cODkcDiUmJkqSkpOTlZCQoBEjRmju3LlyOp2aNGmSMjIyThtuAOB8d/Hjb/tlvyFBhub2afj9WDb8AADw1Vdf6d5779V3332ntm3bqn///tqyZYvatm0rSZo3b54CAwOVlpbm9ZLTakFBQVq9erXGjBkjh8OhVq1aKT09XdOnT/fXkAAAZ0H4AQBY1vLly8+6PDQ0VNnZ2crOzj5jm/j4eK1Zs6a+uwYAaAC85wcAAACAJRB+AAAAAFgC4QcAAACAJRB+AAAAAFgC4QcAAACAJRB+AAAAAFgC4QcAAACAJRB+AAAAAFgC4QcAAACAJRB+AAAAAFgC4QcAAACAJZgKP1OnTlVAQIDXT5cuXTzLy8vLlZGRoaioKLVu3VppaWkqKiry2kZhYaFSU1MVFham6OhoPfLIIzp58mT9jAYAAAAAzqCF2RWuvPJKrV+//r8baPHfTUyYMEFvv/22VqxYofDwcGVmZmro0KH64IMPJEmVlZVKTU1VbGysNm/erMOHD2vkyJGy2WyaNWtWPQwHAAAAAE7PdPhp0aKFYmNja8w/evSoFi1apGXLlmnAgAGSpMWLF6tr167asmWLEhMTlZOTo71792r9+vWKiYlRz549NWPGDD322GOaOnWqgoOD6z4iAAAAADgN0/f87N+/X+3bt9cll1yi4cOHq7CwUJJUUFAgt9utpKQkT9suXbqoY8eOys/PlyTl5+ere/fuiomJ8bRJSUlRaWmp9uzZU9exAAAAAMAZmTrz07dvXy1ZskSdO3fW4cOHNW3aNF1//fXavXu3nE6ngoODFRER4bVOTEyMnE6nJMnpdHoFn+rl1cvOxOVyyeVyeaZLS0slSW63W26328wQPO1DAg1T69UXs/2t7/36a//+wJitwYpjluo2bqvVCgCAaqbCz+DBgz3/7tGjh/r27av4+Hi99tpratmyZb13rtrs2bM1bdq0GvNzcnIUFhbm0zZn9K6qa7d8smbNGr/st1pubq5f9+8PjNkarDhmybdxl5WVNUBPAAA4/5m+5+enIiIidMUVV+jAgQMaNGiQKioqVFJS4nX2p6ioyHOPUGxsrLZt2+a1jeqnwZ3uPqJqEydOVFZWlme6tLRUcXFxSk5Olt1uN9Vnt9ut3NxcPbEjUK6qAFPr1ofdU1MafZ/Sf8c9aNAg2Ww2v/ShsTFmxtyc1WXc1WfPAQCwmjqFn+PHj+vTTz/ViBEj1KtXL9lsNuXl5SktLU2StG/fPhUWFsrhcEiSHA6HZs6cqeLiYkVHR0v68a+WdrtdCQkJZ9xPSEiIQkJCasy32Ww+f9lxVQXIVdn44cffX87qUrOmijFbgxXHLPk2bivWCQAAyWT4+c1vfqMhQ4YoPj5ehw4d0pQpUxQUFKR7771X4eHhGjVqlLKyshQZGSm73a5x48bJ4XAoMTFRkpScnKyEhASNGDFCc+fOldPp1KRJk5SRkXHacAMAAAAA9cVU+Pnqq69077336rvvvlPbtm3Vv39/bdmyRW3btpUkzZs3T4GBgUpLS5PL5VJKSormz5/vWT8oKEirV6/WmDFj5HA41KpVK6Wnp2v69On1OyoAAAAAOIWp8LN8+fKzLg8NDVV2drays7PP2CY+Pt7vN/0DAAAAsB7T7/kBAAAAgKaI8AMAAADAEgg/AAAAACyB8AMAAADAEgg/AAAAACyB8AMAAADAEgg/AAAAACyB8AMAAADAEgg/AAAAACyB8AMAwH/MmTNHAQEBGj9+vGdeeXm5MjIyFBUVpdatWystLU1FRUVe6xUWFio1NVVhYWGKjo7WI488opMnTzZy7wEA50L4AQBA0vbt2/XHP/5RPXr08Jo/YcIEvfXWW1qxYoU2btyoQ4cOaejQoZ7llZWVSk1NVUVFhTZv3qyXX35ZS5Ys0eTJkxt7CACAcyD8AAAs7/jx4xo+fLheeuklXXDBBZ75R48e1aJFi/TMM89owIAB6tWrlxYvXqzNmzdry5YtkqScnBzt3btXf/3rX9WzZ08NHjxYM2bMUHZ2tioqKvw1JADAabTwdwcAAPC3jIwMpaamKikpSU8++aRnfkFBgdxut5KSkjzzunTpoo4dOyo/P1+JiYnKz89X9+7dFRMT42mTkpKiMWPGaM+ePbr66qtr7M/lcsnlcnmmS0tLJUlut1tut9tU36vbm13PyqiZedTMnKZcr5Agwz/7Dfxxv77UzMw6hB8AgKUtX75cH374obZv315jmdPpVHBwsCIiIrzmx8TEyOl0etr8NPhUL69edjqzZ8/WtGnTaszPyclRWFiYL8NQbm6uT+tZGTUzj5qZ0xTrNbePf/fvS83Kyspq3ZbwAwCwrC+//FIPP/ywcnNzFRoa2mj7nThxorKysjzTpaWliouLU3Jysux2u6ltud1u5ebmatCgQbLZbPXd1WaJmplHzcxpyvXqNnWdX/YbEmhoRu8qn2pWffa8Ngg/AADLKigoUHFxsa655hrPvMrKSm3atEl/+MMftG7dOlVUVKikpMTr7E9RUZFiY2MlSbGxsdq2bZvXdqufBlfd5lQhISEKCQmpMd9ms/n8Raku61oVNTOPmpnTFOvlqgzw6/59qZmZ9jzwAABgWQMHDtSuXbu0c+dOz0/v3r01fPhwz79tNpvy8vI86+zbt0+FhYVyOBySJIfDoV27dqm4uNjTJjc3V3a7XQkJCY0+JgDAmXHmBwBgWW3atFG3bt285rVq1UpRUVGe+aNGjVJWVpYiIyNlt9s1btw4ORwOJSYmSpKSk5OVkJCgESNGaO7cuXI6nZo0aZIyMjJOe3YHAOA/hB8AAM5i3rx5CgwMVFpamlwul1JSUjR//nzP8qCgIK1evVpjxoyRw+FQq1atlJ6erunTp/ux1wCA0yH8AADwE++9957XdGhoqLKzs5WdnX3GdeLj47VmzZoG7hkAoK645wcAAACAJRB+AAAAAFgC4QcAAACAJRB+AAAAAFgC4QcAAACAJRB+AAAAAFhCncLPnDlzFBAQoPHjx3vmlZeXKyMjQ1FRUWrdurXS0tJUVFTktV5hYaFSU1MVFham6OhoPfLIIzp58mRdugIAAAAAZ+Vz+Nm+fbv++Mc/qkePHl7zJ0yYoLfeeksrVqzQxo0bdejQIQ0dOtSzvLKyUqmpqaqoqNDmzZv18ssva8mSJZo8ebLvowAAAACAc/Ap/Bw/flzDhw/XSy+9pAsuuMAz/+jRo1q0aJGeeeYZDRgwQL169dLixYu1efNmbdmyRZKUk5OjvXv36q9//at69uypwYMHa8aMGcrOzlZFRUX9jAoAAAAATuFT+MnIyFBqaqqSkpK85hcUFMjtdnvN79Klizp27Kj8/HxJUn5+vrp3766YmBhPm5SUFJWWlmrPnj2+dAcAAAAAzqmF2RWWL1+uDz/8UNu3b6+xzOl0Kjg4WBEREV7zY2Ji5HQ6PW1+Gnyql1cvOx2XyyWXy+WZLi0tlSS53W653W5T/a9uHxJomFqvvpjtb33v11/79wfGbA1WHLNUt3FbrVYAAFQzFX6+/PJLPfzww8rNzVVoaGhD9amG2bNna9q0aTXm5+TkKCwszKdtzuhdVddu+WTNmjV+2W+13Nxcv+7fHxizNVhxzJJv4y4rK2uAngAAcP4zFX4KCgpUXFysa665xjOvsrJSmzZt0h/+8AetW7dOFRUVKikp8Tr7U1RUpNjYWElSbGystm3b5rXd6qfBVbc51cSJE5WVleWZLi0tVVxcnJKTk2W3280MQW63W7m5uXpiR6BcVQGm1q0Pu6emNPo+pf+Oe9CgQbLZbH7pQ2NjzIy5OavLuKvPngMAYDWmws/AgQO1a9cur3kPPPCAunTposcee0xxcXGy2WzKy8tTWlqaJGnfvn0qLCyUw+GQJDkcDs2cOVPFxcWKjo6W9ONfLu12uxISEk6735CQEIWEhNSYb7PZfP6y46oKkKuy8cOPv7+c1aVmTRVjtgYrjlnybdxWrBMAAJLJ8NOmTRt169bNa16rVq0UFRXlmT9q1ChlZWUpMjJSdrtd48aNk8PhUGJioiQpOTlZCQkJGjFihObOnSun06lJkyYpIyPjtAEHAAAAAOqD6QcenMu8efMUGBiotLQ0uVwupaSkaP78+Z7lQUFBWr16tcaMGSOHw6FWrVopPT1d06dPr++uAAAAAIBHncPPe++95zUdGhqq7OxsZWdnn3Gd+Ph4v9/4DwAAAMBafHrPDwAAAAA0NYQfAAAAAJZA+AEAAABgCYQfAAAAAJZA+AEAAABgCYQfAAAAAJZA+AEAAABgCYQfAAAAAJZA+AEAAABgCYQfAAAAAJZA+AEAAABgCYQfAAAAAJZA+AEAAABgCYQfAIBlLViwQD169JDdbpfdbpfD4dA777zjWV5eXq6MjAxFRUWpdevWSktLU1FRkdc2CgsLlZqaqrCwMEVHR+uRRx7RyZMnG3soAIBaIPwAACyrQ4cOmjNnjgoKCrRjxw4NGDBAt912m/bs2SNJmjBhgt566y2tWLFCGzdu1KFDhzR06FDP+pWVlUpNTVVFRYU2b96sl19+WUuWLNHkyZP9NSQAwFm08HcHAKApu/jxt/2y35AgQ3P7+GXXzcqQIUO8pmfOnKkFCxZoy5Yt6tChgxYtWqRly5ZpwIABkqTFixera9eu2rJlixITE5WTk6O9e/dq/fr1iomJUc+ePTVjxgw99thjmjp1qoKDg/0xLADAGXDmBwAA/XgWZ/ny5Tpx4oQcDocKCgrkdruVlJTkadOlSxd17NhR+fn5kqT8/Hx1795dMTExnjYpKSkqLS31nD0CAJw/OPMDALC0Xbt2yeFwqLy8XK1bt9bKlSuVkJCgnTt3Kjg4WBEREV7tY2Ji5HQ6JUlOp9Mr+FQvr152Ji6XSy6XyzNdWloqSXK73XK73ab6X93e7HpWRs3Mo2bmNOV6hQQZ/tlv4I/79aVmZtYh/AAALK1z587auXOnjh49qtdff13p6enauHFjg+5z9uzZmjZtWo35OTk5CgsL82mbubm5de2W5VAz86iZOU2xXv6+pNqXmpWVldW6LeEHAGBpwcHBuuyyyyRJvXr10vbt2/Xcc8/pnnvuUUVFhUpKSrzO/hQVFSk2NlaSFBsbq23btnltr/ppcNVtTmfixInKysryTJeWliouLk7Jycmy2+2m+u92u5Wbm6tBgwbJZrOZWteqqJl51MycplyvblPX+WW/IYGGZvSu8qlm1WfPa4PwAwDAT1RVVcnlcqlXr16y2WzKy8tTWlqaJGnfvn0qLCyUw+GQJDkcDs2cOVPFxcWKjo6W9ONfLe12uxISEs64j5CQEIWEhNSYb7PZfP6iVJd1rYqamUfNzGmK9XJVBvh1/77UzEx7wg8AwLImTpyowYMHq2PHjjp27JiWLVum9957T+vWrVN4eLhGjRqlrKwsRUZGym63a9y4cXI4HEpMTJQkJScnKyEhQSNGjNDcuXPldDo1adIkZWRknDbcAAD8i/ADALCs4uJijRw5UocPH1Z4eLh69OihdevWadCgQZKkefPmKTAwUGlpaXK5XEpJSdH8+fM96wcFBWn16tUaM2aMHA6HWrVqpfT0dE2fPt1fQwIAnAXhBwBgWYsWLTrr8tDQUGVnZys7O/uMbeLj47VmzZr67hoAoAHwnh8AAAAAlkD4AQAAAGAJpsLPggUL1KNHD9ntdtntdjkcDr3zzjue5eXl5crIyFBUVJRat26ttLQ0zyM/qxUWFio1NVVhYWGKjo7WI488opMnT9bPaAAAAADgDEyFnw4dOmjOnDkqKCjQjh07NGDAAN12223as2ePJGnChAl66623tGLFCm3cuFGHDh3S0KFDPetXVlYqNTVVFRUV2rx5s15++WUtWbJEkydPrt9RAQAAAMApTD3wYMiQIV7TM2fO1IIFC7RlyxZ16NBBixYt0rJlyzRgwABJ0uLFi9W1a1dt2bJFiYmJysnJ0d69e7V+/XrFxMSoZ8+emjFjhh577DFNnTpVwcHB9TcyAAAAAPgJn+/5qays1PLly3XixAk5HA4VFBTI7XYrKSnJ06ZLly7q2LGj8vPzJUn5+fnq3r27YmJiPG1SUlJUWlrqOXsEAAAAAA3B9KOud+3aJYfDofLycrVu3VorV65UQkKCdu7cqeDgYEVERHi1j4mJkdPplCQ5nU6v4FO9vHrZmbhcLrlcLs90aWmpJMntdsvtdpvqf3X7kEDD1Hr1xWx/63u//tq/PzBma/D3mEOC/PO7pPp3mC/jttLnAwCAnzIdfjp37qydO3fq6NGjev3115Wenq6NGzc2RN88Zs+erWnTptWYn5OTo7CwMJ+2OaN3VV275RN/vwsiNzfXr/v3B8ZsDf4a89w+ftmthy/jLisra4CeAABw/jMdfoKDg3XZZZdJknr16qXt27frueee0z333KOKigqVlJR4nf0pKipSbGysJCk2Nlbbtm3z2l710+Cq25zOxIkTlZWV5ZkuLS1VXFyckpOTZbfbTfXf7XYrNzdXT+wIlKsqwNS69WH31JRG36f033EPGjRINpvNL31obIyZMTeGblPXNfo+pR/P/MzoXeXTuKvPngMAYDWmw8+pqqqq5HK51KtXL9lsNuXl5SktLU2StG/fPhUWFsrhcEiSHA6HZs6cqeLiYkVHR0v68a+WdrtdCQkJZ9xHSEiIQkJCasy32Ww+f9lxVQXIVdn44cffX0jrUrOmijFbg7/G7I/fIz/ly7it9tkAAKCaqfAzceJEDR48WB07dtSxY8e0bNkyvffee1q3bp3Cw8M1atQoZWVlKTIyUna7XePGjZPD4VBiYqIkKTk5WQkJCRoxYoTmzp0rp9OpSZMmKSMj47ThBgAAAADqi6nwU1xcrJEjR+rw4cMKDw9Xjx49tG7dOg0aNEiSNG/ePAUGBiotLU0ul0spKSmaP3++Z/2goCCtXr1aY8aMkcPhUKtWrZSenq7p06fX76gAAAAA4BSmws+iRYvOujw0NFTZ2dnKzs4+Y5v4+Hi/3/QPAAAAwHp8fs8PAAAAADQlhB8AAAAAlkD4AQAAAGAJhB8AAAAAlkD4AQAAAGAJhB8AAAAAlkD4AQAAAGAJhB8AAAAAlkD4AQAAAGAJhB8AAAAAlkD4AQAAAGAJhB8AAAAAlkD4AQAAAGAJhB8AAAAAlkD4AQAAAGAJhB8AAAAAltDC3x0AAAB1123qOrkqAxp9v5/PSW30fQKArzjzAwAAAMASCD8AAMuaPXu2rr32WrVp00bR0dG6/fbbtW/fPq825eXlysjIUFRUlFq3bq20tDQVFRV5tSksLFRqaqrCwsIUHR2tRx55RCdPnmzMoQAAaoHwAwCwrI0bNyojI0NbtmxRbm6u3G63kpOTdeLECU+bCRMm6K233tKKFSu0ceNGHTp0SEOHDvUsr6ysVGpqqioqKrR582a9/PLLWrJkiSZPnuyPIQEAzoJ7fgAAlrV27Vqv6SVLlig6OloFBQW64YYbdPToUS1atEjLli3TgAEDJEmLFy9W165dtWXLFiUmJionJ0d79+7V+vXrFRMTo549e2rGjBl67LHHNHXqVAUHB/tjaACA0yD8AADwH0ePHpUkRUZGSpIKCgrkdruVlJTkadOlSxd17NhR+fn5SkxMVH5+vrp3766YmBhPm5SUFI0ZM0Z79uzR1VdfXWM/LpdLLpfLM11aWipJcrvdcrvdpvpc3T4k0DC1Xn0x29/zQXWfm2Lf/YWamdOU6xUS5J/fJdW/w3ypmZl1CD8AAEiqqqrS+PHj1a9fP3Xr1k2S5HQ6FRwcrIiICK+2MTExcjqdnjY/DT7Vy6uXnc7s2bM1bdq0GvNzcnIUFhbmU/9n9K7yab26WrNmjV/2Wx9yc3P93YUmh5qZ0xTrNbePf/fvS83Kyspq3ZbwAwCApIyMDO3evVvvv/9+g+9r4sSJysrK8kyXlpYqLi5OycnJstvtprbldruVm5urJ3YEylXV+I+63j01pdH3WVfVNRs0aJBsNpu/u9MkUDNzmnK9uk1d55f9hgQamtG7yqeaVZ89rw3CDwDA8jIzM7V69Wpt2rRJHTp08MyPjY1VRUWFSkpKvM7+FBUVKTY21tNm27ZtXturfhpcdZtThYSEKCQkpMZ8m83m8xclV1WAX97z09S+2P1UXeptVdTMnKZYL3/8HvkpX2pmpj1PewMAWJZhGMrMzNTKlSu1YcMGderUyWt5r169ZLPZlJeX55m3b98+FRYWyuFwSJIcDod27dql4uJiT5vc3FzZ7XYlJCQ0zkAAALXCmR8AgGVlZGRo2bJl+sc//qE2bdp47tEJDw9Xy5YtFR4erlGjRikrK0uRkZGy2+0aN26cHA6HEhMTJUnJyclKSEjQiBEjNHfuXDmdTk2aNEkZGRmnPbsDAPAfU2d+eBkcAKA5WbBggY4ePaobb7xR7dq18/y8+uqrnjbz5s3TLbfcorS0NN1www2KjY3VG2+84VkeFBSk1atXKygoSA6HQ7/4xS80cuRITZ8+3R9DAgCchakzP9Uvg7v22mt18uRJ/fa3v1VycrL27t2rVq1aSfrxZXBvv/22VqxYofDwcGVmZmro0KH64IMPJP33ZXCxsbHavHmzDh8+rJEjR8pms2nWrFn1P0IAAM7AMM79SNfQ0FBlZ2crOzv7jG3i4+Ob9FPPAMAqTIUfXgYHAABgXd2mrmv0G+I/n5PaqPtD81ane354GZx5/nrZVVN+2ZavGLM1+HvMzf1lcAAANCc+hx9eBucbf18W0RRftlVXjNka/DXm5v4yOAAAmhOfww8vg/ONv14G15RftuUrxsyYG0NzfxkcAADNiU/hh5fB+c7fX0ib4su26ooxW4O/xtzcXwYHAEBzYupR17wMDgAAAEBTZerMDy+DAwAAANBUmQo/CxYskCTdeOONXvMXL16s+++/X9KPL4MLDAxUWlqaXC6XUlJSNH/+fE/b6pfBjRkzRg6HQ61atVJ6ejovgwMAAADQoEyFH14GBwAAAKCpMnXPDwAAAAA0VYQfAAAAAJZA+AEAAABgCYQfAAAAAJZA+AEAAABgCYQfAAAAAJZA+AEAAABgCYQfAAAAAJZA+AEAAABgCYQfAAAAAJZA+AEAAABgCYQfAAAAAJZA+AEAAABgCYQfAAAAAJZA+AEAAABgCYQfAAAAAJZA+AEAAABgCYQfAAAAAJZA+AEAAABgCYQfAAAAAJZA+AEAAABgCYQfAIClbdq0SUOGDFH79u0VEBCgVatWeS03DEOTJ09Wu3bt1LJlSyUlJWn//v1ebY4cOaLhw4fLbrcrIiJCo0aN0vHjxxtxFACA2iD8AAAs7cSJE7rqqquUnZ192uVz587V888/r4ULF2rr1q1q1aqVUlJSVF5e7mkzfPhw7dmzR7m5uVq9erU2bdqk0aNHN9YQAAC11MLfHQAAwJ8GDx6swYMHn3aZYRh69tlnNWnSJN12222SpFdeeUUxMTFatWqVhg0bpo8//lhr167V9u3b1bt3b0nSCy+8oJtvvllPPfWU2rdv32hjAQCcHWd+AAA4g4MHD8rpdCopKckzLzw8XH379lV+fr4kKT8/XxEREZ7gI0lJSUkKDAzU1q1bG73PAIAz48wPAABn4HQ6JUkxMTFe82NiYjzLnE6noqOjvZa3aNFCkZGRnjancrlccrlcnunS0lJJktvtltvtNtXH6vYhgYap9eqL2f6eD6r73BT77i/+/Jw1xf9OTfkzFhLkn98l1Z8tX2pmZh3T4WfTpk36/e9/r4KCAh0+fFgrV67U7bff7lluGIamTJmil156SSUlJerXr58WLFigyy+/3NPmyJEjGjdunN566y0FBgYqLS1Nzz33nFq3bm22OwAANDmzZ8/WtGnTaszPyclRWFiYT9uc0buqrt3yyZo1a/yy3/qQm5vr7y40Of74nPEZa1xz+/h3/77UrKysrNZtTYef6htDf/nLX2ro0KE1llffGPryyy+rU6dOeuKJJ5SSkqK9e/cqNDRU0o83hh4+fFi5ublyu9164IEHNHr0aC1btsxsdwAAaDCxsbGSpKKiIrVr184zv6ioSD179vS0KS4u9lrv5MmTOnLkiGf9U02cOFFZWVme6dLSUsXFxSk5OVl2u91UH91ut3Jzc/XEjkC5qgJMrVsfdk9NafR91lV1zQYNGiSbzebv7jQJ/vyc8RlrXN2mrvPLfkMCDc3oXeVTzarPnteG6fDDjaEAAKvo1KmTYmNjlZeX5wk7paWl2rp1q8aMGSNJcjgcKikpUUFBgXr16iVJ2rBhg6qqqtS3b9/TbjckJEQhISE15ttsNp+/KLmqAuSqbPzw09S+2P1UXeptVf74nDXl/0ZN8TPmj98jP+VLzcy0r9d7fs51Y+iwYcPOeWPoHXfcUWO7XBtdf/ttitee+ooxW4O/x9zcr422guPHj+vAgQOe6YMHD2rnzp2KjIxUx44dNX78eD355JO6/PLLPVc0tG/f3nPJd9euXfXzn/9cDz30kBYuXCi3263MzEwNGzaMP+gBwHmmXsNPQ90YyrXR9acpXntaV4zZGvw15uZ+bbQV7NixQzfddJNnuvpytPT0dC1ZskSPPvqoTpw4odGjR6ukpET9+/fX2rVrPZdyS9LSpUuVmZmpgQMHeu5lff755xt9LACAs2sST3vj2ui6a8rXnvqKMTPmxtDcr422ghtvvFGGceYzeAEBAZo+fbqmT59+xjaRkZHctwoATUC9hp+GujGUa6Prd//+7kNjY8zW4K8xN/drowEAaE7q9SWnP70xtFr1jaEOh0OS942h1c51YygAAAAA1JXpMz/cGAoAAACgKTIdfrgxFAAAAEBTZDr8cGMoAAAAgKaoXu/5AQAAAIDzFeEHAAAAgCUQfgAAAABYAuEHAAAAgCUQfgAAAABYAuEHAAAAgCUQfgAAAABYAuEHAAAAgCUQfgAAAABYAuEHAAAAgCUQfgAAAABYAuEHAAAAgCUQfgAAAABYAuEHAAAAgCUQfgAAAABYAuEHAAAAgCUQfgAAAABYAuEHAAAAgCUQfgAAAABYAuEHAAAAgCUQfgAAAABYAuEHAAAAgCUQfgAAAABYAuEHAAAAgCUQfgAAAABYAuEHAAAAgCX4NfxkZ2fr4osvVmhoqPr27att27b5szsAANQJxzUAOL/5Lfy8+uqrysrK0pQpU/Thhx/qqquuUkpKioqLi/3VJQAAfMZxDQDOf34LP88884weeughPfDAA0pISNDChQsVFhamP//5z/7qEgAAPuO4BgDnvxb+2GlFRYUKCgo0ceJEz7zAwEAlJSUpPz+/RnuXyyWXy+WZPnr0qCTpyJEjcrvdpvbtdrtVVlamFu5AVVYF+DgC33333XeNvk/pv+P+7rvvZLPZ/NKHxsaYGXNjaHHyRKPvU5JaVBkqK6vyadzHjh2TJBmG0RBdsySOa02Lv39vNEX+/JzxGWtczf245pfw8+2336qyslIxMTFe82NiYvTJJ5/UaD979mxNmzatxvxOnTo1WB8byoVP+7sHAJqL++q4/rFjxxQeHl4vfbE6jmtAw+EzZh2NcVzzS/gxa+LEicrKyvJMV1VV6ciRI4qKilJAgLm/PpSWliouLk5ffvml7HZ7fXf1vGXFcTNmxtyc1WXchmHo2LFjat++fQP1DufCcc2/qJl51Mwc6mVeYx3X/BJ+LrzwQgUFBamoqMhrflFRkWJjY2u0DwkJUUhIiNe8iIiIOvXBbrdb8sNoxXEzZmuw4pgl38fNGZ/6xXGtaaJm5lEzc6iXeQ19XPPLAw+Cg4PVq1cv5eXleeZVVVUpLy9PDofDH10CAMBnHNcAoGnw22VvWVlZSk9PV+/evdWnTx89++yzOnHihB544AF/dQkAAJ9xXAOA85/fws8999yjb775RpMnT5bT6VTPnj21du3aGjeL1reQkBBNmTKlxuUGzZ0Vx82YrcGKY5asO+7zGce1poOamUfNzKFe5jVWzQIMnnUKAAAAwAL89pJTAAAAAGhMhB8AAAAAlkD4AQAAAGAJhB8AAAAAltAsw092drYuvvhihYaGqm/fvtq2bdtZ269YsUJdunRRaGiounfvrjVr1jRST+uXmXG/9NJLuv7663XBBRfoggsuUFJS0jnrdD4y+9+62vLlyxUQEKDbb7+9YTvYAMyOuaSkRBkZGWrXrp1CQkJ0xRVXNLnPuNkxP/vss+rcubNatmypuLg4TZgwQeXl5Y3U27rbtGmThgwZovbt2ysgIECrVq065zrvvfeerrnmGoWEhOiyyy7TkiVLGryfaBx8HswzW7P33ntPAQEBNX6cTmfjdNjPZs+erWuvvVZt2rRRdHS0br/9du3bt++c6zWX709m+VKvJUuW1Ph8hYaGNlKP/W/BggXq0aOH5wWmDodD77zzzlnXaajPV7MLP6+++qqysrI0ZcoUffjhh7rqqquUkpKi4uLi07bfvHmz7r33Xo0aNUr//Oc/dfvtt+v222/X7t27G7nndWN23O+9957uvfdevfvuu8rPz1dcXJySk5P19ddfN3LPfWd2zNU+//xz/eY3v9H111/fSD2tP2bHXFFRoUGDBunzzz/X66+/rn379umll17SRRdd1Mg9953ZMS9btkyPP/64pkyZoo8//liLFi3Sq6++qt/+9reN3HPfnThxQldddZWys7Nr1f7gwYNKTU3VTTfdpJ07d2r8+PF68MEHtW7dugbuKRoDnwfzzNas2r59+3T48GHPT3R0dAP18PyyceNGZWRkaMuWLcrNzZXb7VZycrJOnDhxxnWay/cnX/hSL0my2+1en68vvviikXrsfx06dNCcOXNUUFCgHTt2aMCAAbrtttu0Z8+e07Zv0M+X0cz06dPHyMjI8ExXVlYa7du3N2bPnn3a9nfffbeRmprqNa9v377G//zP/zRoP+ub2XGf6uTJk0abNm2Ml19+uaG6WO98GfPJkyeN6667zvjTn/5kpKenG7fddlsj9LT+mB3zggULjEsuucSoqKhorC7WO7NjzsjIMAYMGOA1Lysry+jXr1+D9rOhSDJWrlx51jaPPvqoceWVV3rNu+eee4yUlJQG7Bn8gc+DebWp2bvvvmtIMr7//vtG6dP5rri42JBkbNy48Yxtmsv3p/pQm3otXrzYCA8Pb7xONQEXXHCB8ac//em0yxry89WszvxUVFSooKBASUlJnnmBgYFKSkpSfn7+adfJz8/3ai9JKSkpZ2x/PvJl3KcqKyuT2+1WZGRkQ3WzXvk65unTpys6OlqjRo1qjG7WK1/G/Oabb8rhcCgjI0MxMTHq1q2bZs2apcrKysbqdp34MubrrrtOBQUFnkvjPvvsM61Zs0Y333xzo/TZH5rD7zHUHz4PvuvZs6fatWunQYMG6YMPPvB3d/zm6NGjknTW7wR8zv6rNvWSpOPHjys+Pl5xcXFnPevR3FVWVmr58uU6ceKEHA7Hads05OerRZ23cB759ttvVVlZWeNt2jExMfrkk09Ou47T6Txt+6Z0na8v4z7VY489pvbt29f4oJ2vfBnz+++/r0WLFmnnzp2N0MP658uYP/vsM23YsEHDhw/XmjVrdODAAY0dO1Zut1tTpkxpjG7XiS9jvu+++/Ttt9+qf//+MgxDJ0+e1K9+9asmddmbWWf6PVZaWqoffvhBLVu29FPP4A98Hsxr166dFi5cqN69e8vlculPf/qTbrzxRm3dulXXXHONv7vXqKqqqjR+/Hj169dP3bp1O2O75vD9qT7Utl6dO3fWn//8Z/Xo0UNHjx7VU089peuuu0579uxRhw4dGrHH/rNr1y45HA6Vl5erdevWWrlypRISEk7btiE/X80q/MA3c+bM0fLly/Xee+8125vvjh07phEjRuill17ShRde6O/uNJqqqipFR0frxRdfVFBQkHr16qWvv/5av//975tE+PHFe++9p1mzZmn+/Pnq27evDhw4oIcfflgzZszQE0884e/uATgPde7cWZ07d/ZMX3fddfr00081b948/eUvf/FjzxpfRkaGdu/erffff9/fXWkSalsvh8PhdZbjuuuuU9euXfXHP/5RM2bMaOhunhc6d+6snTt36ujRo3r99deVnp6ujRs3njEANZRmFX4uvPBCBQUFqaioyGt+UVGRYmNjT7tObGysqfbnI1/GXe2pp57SnDlztH79evXo0aMhu1mvzI75008/1eeff64hQ4Z45lVVVUmSWrRooX379unSSy9t2E7XkS//ndu1ayebzaagoCDPvK5du8rpdKqiokLBwcEN2ue68mXMTzzxhEaMGKEHH3xQktS9e3edOHFCo0eP1v/+7/8qMLBZXe0r6cy/x+x2O3/ltyA+D/WjT58+lgsAmZmZWr16tTZt2nTOsxHN4ftTXZmp16lsNpuuvvpqHThwoIF6d/4JDg7WZZddJknq1auXtm/frueee05//OMfa7RtyM9Xs/oWEBwcrF69eikvL88zr6qqSnl5eWe8ptDhcHi1l6Tc3Nwztj8f+TJuSZo7d65mzJihtWvXqnfv3o3R1XpjdsxdunTRrl27tHPnTs/Prbfe6nkaUlxcXGN23ye+/Hfu16+fDhw44Al6kvTvf/9b7dq1O++Dj+TbmMvKymoEnOrwZxhGw3XWj5rD7zHUHz4P9WPnzp1q166dv7vRKAzDUGZmplauXKkNGzaoU6dO51zHyp8zX+p1qsrKSu3atcsyn7HTqaqqksvlOu2yBv181fmRCeeZ5cuXGyEhIcaSJUuMvXv3GqNHjzYiIiIMp9NpGIZhjBgxwnj88cc97T/44AOjRYsWxlNPPWV8/PHHxpQpUwybzWbs2rXLX0Pwidlxz5kzxwgODjZef/114/Dhw56fY8eO+WsIppkd86ma4tPezI65sLDQaNOmjZGZmWns27fPWL16tREdHW08+eST/hqCaWbHPGXKFKNNmzbG3/72N+Ozzz4zcnJyjEsvvdS4++67/TUE044dO2b885//NP75z38akoxnnnnG+Oc//2l88cUXhmEYxuOPP26MGDHC0/6zzz4zwsLCjEceecT4+OOPjezsbCMoKMhYu3atv4aAesTnwTyzNZs3b56xatUqY//+/cauXbuMhx9+2AgMDDTWr1/vryE0qjFjxhjh4eHGe++95/WdoKyszNOmuX5/8oUv9Zo2bZqxbt0649NPPzUKCgqMYcOGGaGhocaePXv8MYRG9/jjjxsbN240Dh48aPzrX/8yHn/8cSMgIMDIyckxDKNxP1/NLvwYhmG88MILRseOHY3g4GCjT58+xpYtWzzLfvaznxnp6ele7V977TXjiiuuMIKDg40rr7zSePvttxu5x/XDzLjj4+MNSTV+pkyZ0vgdrwOz/61/qimGH8MwP+bNmzcbffv2NUJCQoxLLrnEmDlzpnHy5MlG7nXdmBmz2+02pk6dalx66aVGaGioERcXZ4wdO7ZJPcK2+rG7p/5UjzM9Pd342c9+VmOdnj17GsHBwcYll1xiLF68uNH7jYbB58E8szX73e9+5/mdERkZadx4443Ghg0b/NN5PzhdrSR5fW6a8/cns3yp1/jx4z3HsZiYGOPmm282Pvzww8bvvJ/88pe/NOLj443g4GCjbdu2xsCBAz3BxzAa9/MVYBjN9DoQAAAAAPiJZnXPDwAAAACcCeEHAAAAgCUQfgAAAABYAuEHAAAAgCUQfgAAAABYAuEHAAAAgCUQfgAAAABYAuEHAAAAgCUQfgAAAABYAuEHAAAAgCUQfgAAAABYAuEHAAAAgCUQfgAAAABYAuEHAAAAgCUQfgAAAABYAuEHAAAAgCUQfgAAAABYAuEHAAAAgCUQfgAAAABYAuEHAAAAgCUQfgAAACzm4osv1v333++ZXrJkiQICArRjxw7/dQpoBIQfoJbmz5+vgIAA9e3b199dAQDgjHbt2qU777xT8fHxCg0N1UUXXaRBgwbphRdeqJftV1VV6ZVXXlHfvn0VGRmpNm3a6IorrtDIkSO1ZcuWetkH0FBa+LsDQFOxdOlSXXzxxdq2bZsOHDigyy67zN9dAgDAy+bNm3XTTTepY8eOeuihhxQbG6svv/xSW7Zs0XPPPadx48ZJkvbt26fAQN/+Bv7rX/9a2dnZuu222zR8+HC1aNFC+/bt0zvvvKNLLrlEiYmJ9TkkoF4RfoBaOHjwoDZv3qw33nhD//M//6OlS5dqypQp/u4WAABeZs6cqfDwcG3fvl0RERFey4qLiz3/DgkJ8Wn7RUVFmj9/vh566CG9+OKLXsueffZZffPNNz5tF2gsXPYG1MLSpUt1wQUXKDU1VXfeeaeWLl1ao813332nESNGyG63KyIiQunp6froo48UEBCgJUuWeLX95JNPdOeddyoyMlKhoaHq3bu33nzzzUYaDQCgufr000915ZVX1gg+khQdHe3596n3/FQrKyvT//zP/ygqKkp2u10jR47U999/71l+8OBBGYahfv361Vg3ICDAax/V9xFt2rTprNsEGhPhB6iFpUuXaujQoQoODta9996r/fv3a/v27Z7lVVVVGjJkiP72t78pPT1dM2fO1OHDh5Wenl5jW3v27FFiYqI+/vhjPf7443r66afVqlUr3X777Vq5cmVjDgsA0MzEx8eroKBAu3fv9mn9zMxMffzxx5o6dapGjhyppUuX6vbbb5dhGJ7tS9KKFStUVlZWL9sEGhOXvQHnUFBQoE8++cRzo2j//v3VoUMHLV26VNdee60kadWqVcrPz9ezzz6rhx9+WJI0ZswYDRo0qMb2Hn74YXXs2FHbt2/3XHYwduxY9e/fX4899pjuuOOORhoZAKC5+c1vfqPBgwerZ8+e6tOnj66//noNHDhQN910k2w22znXDw4OVl5enqdtfHy8Hn30Ub311lu69dZb1a5dO40cOVKvvPKKOnTooBtvvFH9+vVTamqqunTp4tM2gcbEmR/gHJYuXaqYmBjddNNNkn48rX/PPfdo+fLlqqyslCStXbtWNptNDz30kGe9wMBAZWRkeG3ryJEj2rBhg+6++24dO3ZM3377rb799lt99913SklJ0f79+/X111833uAAAM3KoEGDlJ+fr1tvvVUfffSR5s6dq5SUFF100UW1urx69OjRXiFpzJgxatGihdasWeOZt3jxYv3hD39Qp06dtHLlSv3mN79R165dNXDgwNMew2qzTaCxEH6As6isrNTy5ct100036eDBgzpw4IAOHDigvn37qqioSHl5eZKkL774Qu3atVNYWJjX+qc+Ee7AgQMyDENPPPGE2rZt6/VT/QCFn96QCgCAWddee63eeOMNff/999q2bZsmTpyoY8eO6c4779TevXvPuu7ll1/uNd26dWu1a9dOn3/+uWde9R/3CgoK9O233+of//iHBg8erA0bNmjYsGE+bRNoLFz2BpzFhg0bdPjwYS1fvlzLly+vsXzp0qVKTk6u9faqqqok/XhZQkpKymnb8AhtAEB9CA4O1rXXXqtrr71WV1xxhR544AGtWLGiXp9WGhUVpVtvvVW33nqrbrzxRm3cuFFffPGF594g4HxD+AHOYunSpYqOjlZ2dnaNZW+88YZWrlyphQsXKj4+Xu+++67Kysq8zv4cOHDAa51LLrlEkmSz2ZSUlNSwnQcA4D969+4tSTp8+PBZ2+3fv99zmbckHT9+XIcPH9bNN99cq31s3LhRhw8f9go/ddkmUN+47A04gx9++EFvvPGGbrnlFt155501fjIzM3Xs2DG9+eabSklJkdvt1ksvveRZv6qqqkZoio6O1o033qg//vGPpz0A8X4EAEBdvPvuu6d9ilr1/TWdO3c+6/ovvvii3G63Z3rBggU6efKkBg8eLElyOp2nvXSuoqJCeXl5CgwMrHEFw7m2CTQmzvwAZ/Dmm2/q2LFjZ3wSTWJiotq2baulS5dq5cqV6tOnj/7f//t/OnDggLp06aI333xTR44ckfTjQxKqZWdnq3///urevbseeughXXLJJSoqKlJ+fr6++uorffTRR40yPgBA8zNu3DiVlZXpjjvuUJcuXVRRUaHNmzfr1Vdf1cUXX6wHHnjgrOtXVFRo4MCBuvvuu7Vv3z7Nnz9f/fv39xwLv/rqK/Xp00cDBgzQwIEDFRsbq+LiYv3tb3/TRx99pPHjx+vCCy80tU2gURkATmvIkCFGaGioceLEiTO2uf/++w2bzWZ8++23xjfffGPcd999Rps2bYzw8HDj/vvvNz744ANDkrF8+XKv9T799FNj5MiRRmxsrGGz2YyLLrrIuOWWW4zXX3+9oYcFAGjG3nnnHeOXv/yl0aVLF6N169ZGcHCwcdlllxnjxo0zioqKPO3i4+ON9PR0z/TixYsNScbGjRuN0aNHGxdccIHRunVrY/jw4cZ3333naVdaWmo899xzRkpKitGhQwfDZrMZbdq0MRwOh/HSSy8ZVVVVprcJNKYAw+ANU0BDWbVqle644w69//77p30bNgAAzdWSJUv0wAMPaPv27Z57jgB/454foJ788MMPXtOVlZV64YUXZLfbdc011/ipVwAAAKjGPT9APRk3bpx++OEHORwOuVwuvfHGG9q8ebNmzZqlli1b+rt7AAAAlkf4AerJgAED9PTTT2v16tUqLy/XZZddphdeeEGZmZn+7hoAAAAkcc8PAAAAAEvgnh8AAAAAlkD4AQAAAGAJTfKen6qqKh06dEht2rTxenkkAODcDMPQsWPH1L59ewUG8jew8wHHNQDwnZnjWpMMP4cOHVJcXJy/uwEATdqXX36pDh06+LsbEMc1AKgPtTmuNcnw06ZNG0k/DtBut9d6PbfbrZycHCUnJ8tmszVU95oVamYeNTOPmplXl5qVlpYqLi7O87sU/ufrcU3i/x9fUDPzqJk51Mu8xjquNcnwU31JgN1uNx1+wsLCZLfb+SDWEjUzj5qZR83Mq4+acXnV+cPX45rE/z++oGbmUTNzqJd5jXVc42JvAAAAAJZA+AEAAABgCYQfAAAAAJZA+AEAAABgCYQfAAAAAJZA+AEAAABgCYQfAIClff311/rFL36hqKgotWzZUt27d9eOHTs8yw3D0OTJk9WuXTu1bNlSSUlJ2r9/v9c2jhw5ouHDh8tutysiIkKjRo3S8ePHG3soAIBzIPwAACzr+++/V79+/WSz2fTOO+9o7969evrpp3XBBRd42sydO1fPP/+8Fi5cqK1bt6pVq1ZKSUlReXm5p83w4cO1Z88e5ebmavXq1dq0aZNGjx7tjyEBAM6iSb7kFE3LxY+/7bd9fz4n1W/7BnD++93vfqe4uDgtXrzYM69Tp06efxuGoWeffVaTJk3SbbfdJkl65ZVXFBMTo1WrVmnYsGH6+OOPtXbtWm3fvl29e/eWJL3wwgu6+eab9dRTT6l9+/aNMpZuU9fJVdn4L67l9yyApoTwAwCwrDfffFMpKSm66667tHHjRl100UUaO3asHnroIUnSwYMH5XQ6lZSU5FknPDxcffv2VX5+voYNG6b8/HxFRER4go8kJSUlKTAwUFu3btUdd9xRY78ul0sul8szXVpaKunHN5y73W5TY6huHxJomFqvvpjt7/mgus9Nse/+Qs3MoV7m1aVmZtYh/AAALOuzzz7TggULlJWVpd/+9rfavn27fv3rXys4OFjp6elyOp2SpJiYGK/1YmJiPMucTqeio6O9lrdo0UKRkZGeNqeaPXu2pk2bVmN+Tk6OwsLCfBrLjN5VPq1XV2vWrPHLfutDbm6uv7vQ5FAzc6iXeb7UrKysrNZtCT8AAMuqqqpS7969NWvWLEnS1Vdfrd27d2vhwoVKT09vsP1OnDhRWVlZnunS0lLFxcUpOTlZdrvd1Lbcbrdyc3P1xI5Auaoa/7K33VNTGn2fdVVds0GDBslms/m7O00CNTOHeplXl5pVnz2vDcIPAMCy2rVrp4SEBK95Xbt21d///ndJUmxsrCSpqKhI7dq187QpKipSz549PW2Ki4u9tnHy5EkdOXLEs/6pQkJCFBISUmO+zWbz+YuSqyrAL/f8NOUvdnWpt1VRM3Ool3m+1MxMe572BgCwrH79+mnfvn1e8/79738rPj5e0o8PP4iNjVVeXp5neWlpqbZu3SqHwyFJcjgcKikpUUFBgafNhg0bVFVVpb59+zbCKAAAtcWZHwCAZU2YMEHXXXedZs2apbvvvlvbtm3Tiy++qBdffFGSFBAQoPHjx+vJJ5/U5Zdfrk6dOumJJ55Q+/btdfvtt0v68UzRz3/+cz300ENauHCh3G63MjMzNWzYsEZ70hsAoHYIPwAAy7r22mu1cuVKTZw4UdOnT1enTp307LPPavjw4Z42jz76qE6cOKHRo0erpKRE/fv319q1axUaGupps3TpUmVmZmrgwIEKDAxUWlqann/+eX8MCQBwFoQfAICl3XLLLbrlllvOuDwgIEDTp0/X9OnTz9gmMjJSy5Yta4juAQDqEff8AAAAALAEwg8AAAAASyD8AAAAALAEwg8AAAAASyD8AAAAALAEwg8AAAAASyD8AAAAALAEwg8AAAAASyD8AAAAALAEwg8AAAAASyD8AAAAALAEwg8AAAAASyD8AAAAALAEU+FnwYIF6tGjh+x2u+x2uxwOh9555x3P8vLycmVkZCgqKkqtW7dWWlqaioqKvLZRWFio1NRUhYWFKTo6Wo888ohOnjxZP6MBAAAAgDMwFX46dOigOXPmqKCgQDt27NCAAQN02223ac+ePZKkCRMm6K233tKKFSu0ceNGHTp0SEOHDvWsX1lZqdTUVFVUVGjz5s16+eWXtWTJEk2ePLl+RwUAAAAAp2hhpvGQIUO8pmfOnKkFCxZoy5Yt6tChgxYtWqRly5ZpwIABkqTFixera9eu2rJlixITE5WTk6O9e/dq/fr1iomJUc+ePTVjxgw99thjmjp1qoKDg+tvZAAAAADwEz7f81NZWanly5frxIkTcjgcKigokNvtVlJSkqdNly5d1LFjR+Xn50uS8vPz1b17d8XExHjapKSkqLS01HP2CAAAAAAagqkzP5K0a9cuORwOlZeXq3Xr1lq5cqUSEhK0c+dOBQcHKyIiwqt9TEyMnE6nJMnpdHoFn+rl1cvOxOVyyeVyeaZLS0slSW63W263u9Z9r25rZh2rq4+ahQQZ9dUd0/zx35rPmXnUzLy61Iw6AwCsynT46dy5s3bu3KmjR4/q9ddfV3p6ujZu3NgQffOYPXu2pk2bVmN+Tk6OwsLCTG8vNze3PrplKXWp2dw+9dgRk9asWeO3ffM5M4+amedLzcrKyhqgJwAAnP9Mh5/g4GBddtllkqRevXpp+/bteu6553TPPfeooqJCJSUlXmd/ioqKFBsbK0mKjY3Vtm3bvLZX/TS46janM3HiRGVlZXmmS0tLFRcXp+TkZNnt9lr33e12Kzc3V4MGDZLNZqv1elZWHzXrNnVdPfeq9nZPTWn0ffI5M4+amVeXmlWfPQcAwGpMh59TVVVVyeVyqVevXrLZbMrLy1NaWpokad++fSosLJTD4ZAkORwOzZw5U8XFxYqOjpb0418t7Xa7EhISzriPkJAQhYSE1Jhvs9l8+qLk63pWVpeauSoD6rk3tefP/858zsyjZub5UjNqDACwKlPhZ+LEiRo8eLA6duyoY8eOadmyZXrvvfe0bt06hYeHa9SoUcrKylJkZKTsdrvGjRsnh8OhxMRESVJycrISEhI0YsQIzZ07V06nU5MmTVJGRsZpww0AAAAA1BdTT3srLi7WyJEj1blzZw0cOFDbt2/XunXrNGjQIEnSvHnzdMsttygtLU033HCDYmNj9cYbb3jWDwoK0urVqxUUFCSHw6Ff/OIXGjlypKZPn16/owIAoBamTp2qgIAAr58uXbp4lvPybgBoXkyd+Vm0aNFZl4eGhio7O1vZ2dlnbBMfH+/Xm9ABAPipK6+8UuvXr/dMt2jx30PjhAkT9Pbbb2vFihUKDw9XZmamhg4dqg8++EDSf1/eHRsbq82bN+vw4cMaOXKkbDabZs2a1ehjAQCcXZ3v+QEAoClr0aLFaR+6c/ToUV7eDQDNjM8vOQUAoDnYv3+/2rdvr0suuUTDhw9XYWGhJPHybgBohjjzAwCwrL59+2rJkiXq3LmzDh8+rGnTpun666/X7t275XQ6z/uXd1evI0khgf55oXRTfGkuL1Y2j5qZQ73Ma6yXdxN+AACWNXjwYM+/e/Toob59+yo+Pl6vvfaaWrZs2WD7re+Xd0vSjN5Vde2WT5ryfby8WNk8amYO9TKvoV/eTfgBAOA/IiIidMUVV+jAgQMaNGjQef/ybum/L7x9YkegXFWN/141f7xMuq54sbJ51Mwc6mVeY728m/ADAMB/HD9+XJ9++qlGjBjRpF7eLUmuqgC/vFS6KX+x48XK5lEzc6iXeQ398m7CDwDAsn7zm99oyJAhio+P16FDhzRlyhQFBQXp3nvv5eXdANAMEX4AAJb11Vdf6d5779V3332ntm3bqn///tqyZYvatm0r6ceXdwcGBiotLU0ul0spKSmaP3++Z/3ql3ePGTNGDodDrVq1Unp6Oi/vBoDzFOEHAGBZy5cvP+tyXt4NAM0L7/kBAAAAYAmEHwAAAACWQPgBAAAAYAmEHwAAAACWQPgBAAAAYAmEHwAAAACWQPgBAAAAYAmEHwAAAACWQPgBAAAAYAmEHwAAAACWQPgBAAAAYAmEHwAAAACWQPgBAAAAYAmEHwAAAACWQPgBAAAAYAmEHwAAAACWQPgBAAAAYAmEHwAAAACWQPgBAAAAYAmEHwAAAACWQPgBAAAAYAmEHwAAAACWQPgBAAAAYAmEHwAA/mPOnDkKCAjQ+PHjPfPKy8uVkZGhqKgotW7dWmlpaSoqKvJar7CwUKmpqQoLC1N0dLQeeeQRnTx5spF7DwA4F8IPAACStm/frj/+8Y/q0aOH1/wJEyborbfe0ooVK7Rx40YdOnRIQ4cO9SyvrKxUamqqKioqtHnzZr388stasmSJJk+e3NhDAACcA+EHAGB5x48f1/Dhw/XSSy/pggsu8Mw/evSoFi1apGeeeUYDBgxQr169tHjxYm3evFlbtmyRJOXk5Gjv3r3661//qp49e2rw4MGaMWOGsrOzVVFR4a8hAQBOo4W/OwAAgL9lZGQoNTVVSUlJevLJJz3zCwoK5Ha7lZSU5JnXpUsXdezYUfn5+UpMTFR+fr66d++umJgYT5uUlBSNGTNGe/bs0dVXX11jfy6XSy6XyzNdWloqSXK73XK73ab6Xt0+JNAwtV59Mdvf80F1n5ti3/2FmplDvcyrS83MrEP4AQBY2vLly/Xhhx9q+/btNZY5nU4FBwcrIiLCa35MTIycTqenzU+DT/Xy6mWnM3v2bE2bNq3G/JycHIWFhfkyDM3oXeXTenW1Zs0av+y3PuTm5vq7C00ONTOHepnnS83Kyspq3ZbwAwCwrC+//FIPP/ywcnNzFRoa2mj7nThxorKysjzTpaWliouLU3Jysux2u6ltud1u5ebm6okdgXJVBdR3V89p99SURt9nXVXXbNCgQbLZbP7uTpNAzcyhXubVpWbVZ89rg/ADALCsgoICFRcX65prrvHMq6ys1KZNm/SHP/xB69atU0VFhUpKSrzO/hQVFSk2NlaSFBsbq23btnltt/ppcNVtThUSEqKQkJAa8202m89flFxVAXJVNn74acpf7OpSb6uiZuZQL/N8qZmZ9jzwAABgWQMHDtSuXbu0c+dOz0/v3r01fPhwz79tNpvy8vI86+zbt0+FhYVyOBySJIfDoV27dqm4uNjTJjc3V3a7XQkJCY0+JgDAmXHmBwBgWW3atFG3bt285rVq1UpRUVGe+aNGjVJWVpYiIyNlt9s1btw4ORwOJSYmSpKSk5OVkJCgESNGaO7cuXI6nZo0aZIyMjJOe3YHAOA/hB8AAM5i3rx5CgwMVFpamlwul1JSUjR//nzP8qCgIK1evVpjxoyRw+FQq1atlJ6erunTp/ux1wCA0yH8AADwE++9957XdGhoqLKzs5WdnX3GdeLj45v0U88AwCq45wcAAACAJRB+AAAAAFgC4QcAAACAJRB+AAAAAFgC4QcAAACAJZgKP7Nnz9a1116rNm3aKDo6Wrfffrv27dvn1aa8vFwZGRmKiopS69atlZaW5nnTdbXCwkKlpqYqLCxM0dHReuSRR3Ty5Mm6jwYAAAAAzsBU+Nm4caMyMjK0ZcsW5ebmyu12Kzk5WSdOnPC0mTBhgt566y2tWLFCGzdu1KFDhzR06FDP8srKSqWmpqqiokKbN2/Wyy+/rCVLlmjy5Mn1NyoAAAAAOIWp9/ysXbvWa3rJkiWKjo5WQUGBbrjhBh09elSLFi3SsmXLNGDAAEnS4sWL1bVrV23ZskWJiYnKycnR3r17tX79esXExKhnz56aMWOGHnvsMU2dOlXBwcH1NzoAAAAA+I86veT06NGjkqTIyEhJUkFBgdxut5KSkjxtunTpoo4dOyo/P1+JiYnKz89X9+7dFRMT42mTkpKiMWPGaM+ePbr66qtr7MflcsnlcnmmS0tLJUlut1tut7vW/a1ua2Ydq6uPmoUEGfXVHdP88d+az5l51My8utSMOgMArMrn8FNVVaXx48erX79+6tatmyTJ6XQqODhYERERXm1jYmLkdDo9bX4afKqXVy87ndmzZ2vatGk15ufk5CgsLMx033Nzc02vY3V1qdncPvXYEZP8+cZ1PmfmUTPzfKlZWVlZA/QEAIDzn8/hJyMjQ7t379b7779fn/05rYkTJyorK8szXVpaqri4OCUnJ8tut9d6O263W7m5uRo0aJBsNltDdLXZqY+adZu6rp57VXu7p6Y0+j75nJlHzcyrS82qz54DAGA1PoWfzMxMrV69Wps2bVKHDh0882NjY1VRUaGSkhKvsz9FRUWKjY31tNm2bZvX9qqfBlfd5lQhISEKCQmpMd9ms/n0RcnX9aysLjVzVQbUc29qz5//nfmcmUfNzPOlZtQYAGBVpp72ZhiGMjMztXLlSm3YsEGdOnXyWt6rVy/ZbDbl5eV55u3bt0+FhYVyOBySJIfDoV27dqm4uNjTJjc3V3a7XQkJCXUZCwAAAACckakzPxkZGVq2bJn+8Y9/qE2bNp57dMLDw9WyZUuFh4dr1KhRysrKUmRkpOx2u8aNGyeHw6HExERJUnJyshISEjRixAjNnTtXTqdTkyZNUkZGxmnP7gAAAABAfTAVfhYsWCBJuvHGG73mL168WPfff78kad68eQoMDFRaWppcLpdSUlI0f/58T9ugoCCtXr1aY8aMkcPhUKtWrZSenq7p06fXbSQAAAAAcBamwo9hnPuRxaGhocrOzlZ2dvYZ28THx/v1KVwAAAAArMfUPT8AAAAA0FQRfgAAAABYAuEHAAAAgCUQfgAAAABYAuEHAAAAgCUQfgAAAABYAuEHAGBZCxYsUI8ePWS322W32+VwOPTOO+94lpeXlysjI0NRUVFq3bq10tLSVFRU5LWNwsJCpaamKiwsTNHR0XrkkUd08uTJxh4KAKAWTL3nB03bxY+/bXqdkCBDc/tI3aauk6syoAF6BQD+06FDB82ZM0eXX365DMPQyy+/rNtuu03//Oc/deWVV2rChAl6++23tWLFCoWHhyszM1NDhw7VBx98IEmqrKxUamqqYmNjtXnzZh0+fFgjR46UzWbTrFmz/Dw6AMCpCD8AAMsaMmSI1/TMmTO1YMECbdmyRR06dNCiRYu0bNkyDRgwQJK0ePFide3aVVu2bFFiYqJycnK0d+9erV+/XjExMerZs6dmzJihxx57TFOnTlVwcLA/hgUAOAMuewMAQD+exVm+fLlOnDghh8OhgoICud1uJSUledp06dJFHTt2VH5+viQpPz9f3bt3V0xMjKdNSkqKSktLtWfPnkYfAwDg7DjzAwCwtF27dsnhcKi8vFytW7fWypUrlZCQoJ07dyo4OFgRERFe7WNiYuR0OiVJTqfTK/hUL69ediYul0sul8szXVpaKklyu91yu92m+l/dPiTQMLVefTHb3/NBdZ+bYt/9hZqZQ73Mq0vNzKxD+AEAWFrnzp21c+dOHT16VK+//rrS09O1cePGBt3n7NmzNW3atBrzc3JyFBYW5tM2Z/Suqmu3fLJmzRq/7Lc+5Obm+rsLTQ41M4d6medLzcrKymrdlvADALC04OBgXXbZZZKkXr16afv27Xruued0zz33qKKiQiUlJV5nf4qKihQbGytJio2N1bZt27y2V/00uOo2pzNx4kRlZWV5pktLSxUXF6fk5GTZ7XZT/Xe73crNzdUTOwLlqmr8B9PsnprS6Pusq+qaDRo0SDabzd/daRKomTnUy7y61Kz67HltEH4AAPiJqqoquVwu9erVSzabTXl5eUpLS5Mk7du3T4WFhXI4HJIkh8OhmTNnqri4WNHR0ZJ+/Kul3W5XQkLCGfcREhKikJCQGvNtNpvPX5RcVQF+eSpnU/5iV5d6WxU1M4d6medLzcy0J/wAACxr4sSJGjx4sDp27Khjx45p2bJleu+997Ru3TqFh4dr1KhRysrKUmRkpOx2u8aNGyeHw6HExERJUnJyshISEjRixAjNnTtXTqdTkyZNUkZGxmnDDQDAvwg/AADLKi4u1siRI3X48GGFh4erR48eWrdunQYNGiRJmjdvngIDA5WWliaXy6WUlBTNnz/fs35QUJBWr16tMWPGyOFwqFWrVkpPT9f06dP9NSQAwFkQfgAAlrVo0aKzLg8NDVV2drays7PP2CY+Pr5J3/QPAFbCe34AAAAAWALhBwAAAIAlEH4AAAAAWALhBwAAAIAlEH4AAAAAWALhBwAAAIAlEH4AAAAAWALhBwAAAIAlEH4AAAAAWALhBwAAAIAlEH4AAAAAWALhBwAAAIAlEH4AAAAAWALhBwAAAIAlEH4AAAAAWALhBwAAAIAlEH4AAAAAWALhBwAAAIAlEH4AAAAAWALhBwAAAIAlEH4AAAAAWALhBwAAAIAlEH4AAAAAWALhBwBgWbNnz9a1116rNm3aKDo6Wrfffrv27dvn1aa8vFwZGRmKiopS69atlZaWpqKiIq82hYWFSk1NVVhYmKKjo/XII4/o5MmTjTkUAEAttPB3BwAA8JeNGzcqIyND1157rU6ePKnf/va3Sk5O1t69e9WqVStJ0oQJE/T2229rxYoVCg8PV2ZmpoYOHaoPPvhAklRZWanU1FTFxsZq8+bNOnz4sEaOHCmbzaZZs2b5c3iN4uLH3/bbvj+fk+q3fQNomgg/aNb8cVAOCTI0t0+j7xaAD9auXes1vWTJEkVHR6ugoEA33HCDjh49qkWLFmnZsmUaMGCAJGnx4sXq2rWrtmzZosTEROXk5Gjv3r1av369YmJi1LNnT82YMUOPPfaYpk6dquDgYH8MDQBwGoQfAAD+4+jRo5KkyMhISVJBQYHcbreSkpI8bbp06aKOHTsqPz9fiYmJys/PV/fu3RUTE+Npk5KSojFjxmjPnj26+uqra+zH5XLJ5XJ5pktLSyVJbrdbbrfbVJ+r24cEGqbWaw7M1urU9Xxd34qomTnUy7y61MzMOoQfAAAkVVVVafz48erXr5+6desmSXI6nQoODlZERIRX25iYGDmdTk+bnwaf6uXVy05n9uzZmjZtWo35OTk5CgsL86n/M3pX+bReU7ZmzZo6rZ+bm1tPPbEOamYO9TLPl5qVlZXVui3hBwAASRkZGdq9e7fef//9Bt/XxIkTlZWV5ZkuLS1VXFyckpOTZbfbTW3L7XYrNzdXT+wIlKsqoL67el7bPTXFp/WqazZo0CDZbLZ67lXzRM3MoV7m1aVm1WfPa4PwAwCwvMzMTK1evVqbNm1Shw4dPPNjY2NVUVGhkpISr7M/RUVFio2N9bTZtm2b1/aqnwZX3eZUISEhCgkJqTHfZrP5/EXJVRUgV6W1wk9dv1TWpd5WRc3MoV7m+VIzM+151DUAwLIMw1BmZqZWrlypDRs2qFOnTl7Le/XqJZvNpry8PM+8ffv2qbCwUA6HQ5LkcDi0a9cuFRcXe9rk5ubKbrcrISGhcQYCAKgV0+Fn06ZNGjJkiNq3b6+AgACtWrXKa7lhGJo8ebLatWunli1bKikpSfv37/dqc+TIEQ0fPlx2u10REREaNWqUjh8/XqeBAABgVkZGhv76179q2bJlatOmjZxOp5xOp3744QdJUnh4uEaNGqWsrCy9++67Kigo0AMPPCCHw6HExERJUnJyshISEjRixAh99NFHWrdunSZNmqSMjIzTnt0BAPiP6fBz4sQJXXXVVcrOzj7t8rlz5+r555/XwoULtXXrVrVq1UopKSkqLy/3tBk+fLj27Nmj3Nxcz2UGo0eP9n0UAAD4YMGCBTp69KhuvPFGtWvXzvPz6quvetrMmzdPt9xyi9LS0nTDDTcoNjZWb7zxhmd5UFCQVq9eraCgIDkcDv3iF7/QyJEjNX36dH8MCQBwFqbv+Rk8eLAGDx582mWGYejZZ5/VpEmTdNttt0mSXnnlFcXExGjVqlUaNmyYPv74Y61du1bbt29X7969JUkvvPCCbr75Zj311FNq3759HYYDAEDtGca5Hw8dGhqq7OzsM/7RT5Li4+Pr/OQxAEDDq9cHHhw8eFBOp9PrfQjh4eHq27ev8vPzNWzYMOXn5ysiIsITfCQpKSlJgYGB2rp1q+64444a262v9yFY/ZnrIUHm3wFR/d4IK74/wlfVtbLq58wXVv9/0xeN9T4EAACak3oNP9XvMzjd+w5++j6E6Oho7060aKHIyMhGex+CVZ+5PreP7+ta8f0RdWXVz1ldUDPzGvp9CAAANCdN4lHX9fU+BKs/c73b1HWm1wkJNDSjd5Ul3x/hq+qaWfVz5gur/7/pi8Z6HwIAAM1JvYaf6vcZFBUVqV27dp75RUVF6tmzp6fNTx8HKkknT57UkSNHGu19CFZ95npd3v9gxfdH1JVVP2d1Qc3Ma+j3IQAA0JzU63t+OnXqpNjYWK/3IZSWlmrr1q1e70MoKSlRQUGBp82GDRtUVVWlvn371md3AAAAAMDD9Jmf48eP68CBA57pgwcPaufOnYqMjFTHjh01fvx4Pfnkk7r88svVqVMnPfHEE2rfvr1uv/12SVLXrl3185//XA899JAWLlwot9utzMxMDRs2jCe9AQAAAGgwpsPPjh07dNNNN3mmq+/FSU9P15IlS/Too4/qxIkTGj16tEpKStS/f3+tXbtWoaGhnnWWLl2qzMxMDRw4UIGBgUpLS9Pzzz9fD8MBAAAAgNMzHX5uvPHGs74XISAgQNOnTz/ry90iIyO1bNkys7sGAAAAAJ/V6z0/AAAAAHC+IvwAAAAAsATCDwAAAABLIPwAAAAAsATCDwAAAABLIPwAAAAAsATCDwAAAABLIPwAAAAAsATCDwAAAABLIPwAAAAAsATCDwAAAABLaOHvDgDNVbep6+SqDGj0/X4+J7XR9wkAANAUcOYHAAAAgCUQfgAAAABYAuEHAGBpmzZt0pAhQ9S+fXsFBARo1apVXssNw9DkyZPVrl07tWzZUklJSdq/f79XmyNHjmj48OGy2+2KiIjQqFGjdPz48UYcBQCgNgg/AABLO3HihK666iplZ2efdvncuXP1/PPPa+HChdq6datatWqllJQUlZeXe9oMHz5ce/bsUW5urlavXq1NmzZp9OjRjTUEAEAt8cADAIClDR48WIMHDz7tMsMw9Oyzz2rSpEm67bbbJEmvvPKKYmJitGrVKg0bNkwff/yx1q5dq+3bt6t3796SpBdeeEE333yznnrqKbVv377RxgIAODvO/AAAcAYHDx6U0+lUUlKSZ154eLj69u2r/Px8SVJ+fr4iIiI8wUeSkpKSFBgYqK1btzZ6nwEAZ8aZHwAAzsDpdEqSYmJivObHxMR4ljmdTkVHR3stb9GihSIjIz1tTuVyueRyuTzTpaWlkiS32y23222qj9XtQwINU+s1B2Zrdep6vq5vRdTMHOplXl1qZmYdwg8AAI1s9uzZmjZtWo35OTk5CgsL82mbM3pX1bVbTc6aNWvqtH5ubm499cQ6qJk51Ms8X2pWVlZW67aWDD/+evmkxAsoAaApiY2NlSQVFRWpXbt2nvlFRUXq2bOnp01xcbHXeidPntSRI0c8659q4sSJysrK8kyXlpYqLi5OycnJstvtpvrodruVm5urJ3YEylXln2Obv+yemuLTetU1GzRokGw2Wz33qnmiZuZQL/PqUrPqs+e1YcnwAwBAbXTq1EmxsbHKy8vzhJ3S0lJt3bpVY8aMkSQ5HA6VlJSooKBAvXr1kiRt2LBBVVVV6tu372m3GxISopCQkBrzbTabz1+UXFUBfvvDnr/U9UtlXeptVdTMHOplni81M9Oe8AMAsLTjx4/rwIEDnumDBw9q586dioyMVMeOHTV+/Hg9+eSTuvzyy9WpUyc98cQTat++vW6//XZJUteuXfXzn/9cDz30kBYuXCi3263MzEwNGzaMJ70BwHmG8AMAsLQdO3bopptu8kxXX46Wnp6uJUuW6NFHH9WJEyc0evRolZSUqH///lq7dq1CQ0M96yxdulSZmZkaOHCgAgMDlZaWpueff77RxwIAODvCDwDA0m688UYZxpmflBYQEKDp06dr+vTpZ2wTGRmpZcuWNUT3AAD1iPDTyC5+/G1/dwEAAACwJMIP0Mz4M2DzNEMAAHA+C/R3BwAAAACgMRB+AAAAAFgC4QcAAACAJRB+AAAAAFgC4QcAAACAJRB+AAAAAFgC4QcAAACAJRB+AAAAAFgC4QcAAACAJRB+AAAAAFgC4QcAAACAJbTwdwcANB8XP/62T+uFBBma20fqNnWdXJUBptf/fE6qT/sFAADWwpkfAAAAAJZA+AEAAABgCYQfAAAAAJZA+AEAAABgCTzwAAAANEk8ZAWAWZz5AQAAAGAJhB8AAAAAlsBlbwAAAKgVXy8VrAsuM0R9IvwAaPJ8ve6/PnBQBqyH3zlA0+XXy96ys7N18cUXKzQ0VH379tW2bdv82R0AAOqE4xoAnN/8Fn5effVVZWVlacqUKfrwww911VVXKSUlRcXFxf7qEgAAPuO4BgDnP79d9vbMM8/ooYce0gMPPCBJWrhwod5++239+c9/1uOPP+6vbgEA4BOOawCaA39d1ln9CPqG5pfwU1FRoYKCAk2cONEzLzAwUElJScrPz6/R3uVyyeVyeaaPHj0qSTpy5Ijcbnet9+t2u1VWVqYW7kBVVjXuzXpNVYsqQ2VlVdTMBGpmXlOu2WW/ec0v+w0JNDTp6ip99913stlsptY9duyYJMkwjIbomiX567gmcWzzRVP+nfPdd9/5Zb/+/Jz56/esJG2dONCn9arr5cvvaH9rcfKEf/b7n/8vG/q45pfw8+2336qyslIxMTFe82NiYvTJJ5/UaD979mxNmzatxvxOnTo1WB/xX/f5uwNNEDUzj5qZV9eaHTt2TOHh4fXSF6vjuNb0NNXfORc+7e8eWAv1blyNcVxrEk97mzhxorKysjzTVVVVOnLkiKKiohQQUPu/PpSWliouLk5ffvml7HZ7Q3S12aFm5lEz86iZeXWpmWEYOnbsmNq3b99AvcO51NdxTeL/H19QM/OomTnUy7zGOq75JfxceOGFCgoKUlFRkdf8oqIixcbG1mgfEhKikJAQr3kRERE+799ut/NBNImamUfNzKNm5vlaM8741C9/H9ck/v/xBTUzj5qZQ73Ma+jjml+e9hYcHKxevXopLy/PM6+qqkp5eXlyOBz+6BIAAD7juAYATYPfLnvLyspSenq6evfurT59+ujZZ5/ViRMnPE/JAQCgKeG4BgDnP7+Fn3vuuUfffPONJk+eLKfTqZ49e2rt2rU1bhatTyEhIZoyZUqNSw1wZtTMPGpmHjUzj5qdf/xxXJP4LPiCmplHzcyhXuY1Vs0CDJ51CgAAAMAC/HLPDwAAAAA0NsIPAAAAAEsg/AAAAACwBMIPAAAAAEuwTPjJzs7WxRdfrNDQUPXt21fbtm3zd5fOG7Nnz9a1116rNm3aKDo6Wrfffrv27dvn1aa8vFwZGRmKiopS69atlZaWVuNlflY2Z84cBQQEaPz48Z551Kymr7/+Wr/4xS8UFRWlli1bqnv37tqxY4dnuWEYmjx5stq1a6eWLVsqKSlJ+/fv92OP/auyslJPPPGEOnXqpJYtW+rSSy/VjBkz9NPn1FAzcHyrvdoc73BmpzvWoaZzHevwX7U5ztU7wwKWL19uBAcHG3/+85+NPXv2GA899JARERFhFBUV+btr54WUlBRj8eLFxu7du42dO3caN998s9GxY0fj+PHjnja/+tWvjLi4OCMvL8/YsWOHkZiYaFx33XV+7PX5Y9u2bcbFF19s9OjRw3j44Yc986mZtyNHjhjx8fHG/fffb2zdutX47LPPjHXr1hkHDhzwtJkzZ44RHh5urFq1yvjoo4+MW2+91ejUqZPxww8/+LHn/jNz5kwjKirKWL16tXHw4EFjxYoVRuvWrY3nnnvO04aaWRvHN3Nqc7zD6Z3pWAdvtTnW4b9qc5yrb5YIP3369DEyMjI805WVlUb79u2N2bNn+7FX56/i4mJDkrFx40bDMAyjpKTEsNlsxooVKzxtPv74Y0OSkZ+f769unheOHTtmXH755UZubq7xs5/9zHNAoGY1PfbYY0b//v3PuLyqqsqIjY01fv/733vmlZSUGCEhIcbf/va3xujieSc1NdX45S9/6TVv6NChxvDhww3DoGbg+FZXpx7vcHpnOtahpnMd6+DtXMe5htDsL3urqKhQQUGBkpKSPPMCAwOVlJSk/Px8P/bs/HX06FFJUmRkpCSpoKBAbrfbq4ZdunRRx44dLV/DjIwMpaametVGoman8+abb6p379666667FB0drauvvlovvfSSZ/nBgwfldDq9ahYeHq6+fftatmbXXXed8vLy9O9//1uS9NFHH+n999/X4MGDJVEzq+P4VnenHu9wemc61qGmcx3r4O1cx7mG0KLBtnye+Pbbb1VZWVnjDdsxMTH65JNP/NSr81dVVZXGjx+vfv36qVu3bpIkp9Op4OBgRUREeLWNiYmR0+n0Qy/PD8uXL9eHH36o7du311hGzWr67LPPtGDBAmVlZem3v/2ttm/frl//+tcKDg5Wenq6py6n+3/VqjV7/PHHVVpaqi5duigoKEiVlZWaOXOmhg8fLknUzOI4vtXN6Y53qOlsxzrUdK5jHbyd6zjXEJp9+IE5GRkZ2r17t95//31/d+W89uWXX+rhhx9Wbm6uQkND/d2dJqGqqkq9e/fWrFmzJElXX321du/erYULF3JAOIPXXntNS5cu1bJly3TllVdq586dGj9+vNq3b0/NgDrieHduHOvM41hnjj+Oc83+srcLL7xQQUFBNZ6yVVRUpNjYWD/16vyUmZmp1atX691331WHDh0882NjY1VRUaGSkhKv9lauYUFBgYqLi3XNNdeoRYsWatGihTZu3Kjnn39eLVq0UExMDDU7Rbt27ZSQkOA1r2vXriosLJQkT134f/W/HnnkET3++OMaNmyYunfvrhEjRmjChAmaPXu2JGpmdRzffHem4x28netYV1lZ6e8unnfOdayDt3Md5xpCsw8/wcHB6tWrl/Ly8jzzqqqqlJeXJ4fD4ceenT8Mw1BmZqZWrlypDRs2qFOnTl7Le/XqJZvN5lXDffv2qbCw0LI1HDhwoHbt2qWdO3d6fnr37q3hw4d7/k3NvPXr16/GI2X//e9/Kz4+XpLUqVMnxcbGetWstLRUW7dutWzNysrKFBjo/Ws6KChIVVVVkqiZ1XF8M+9cxzt4O9exLigoyN9dPO+c61gHb+c6zjWIBnuUwnlk+fLlRkhIiLFkyRJj7969xujRo42IiAjD6XT6u2vnhTFjxhjh4eHGe++9Zxw+fNjzU1ZW5mnzq1/9yujYsaOxYcMGY8eOHYbD4TAcDocfe33+OfUJONTM27Zt24wWLVoYM2fONPbv328sXbrUCAsLM/7617962syZM8eIiIgw/vGPfxj/+te/jNtuu83Sj21OT083LrroIs8jQN944w3jwgsvNB599FFPG2pmbRzfzKnN8Q5nx9Pezq42xzr8V22Oc/XNEuHHMAzjhRdeMDp27GgEBwcbffr0MbZs2eLvLp03JJ32Z/HixZ42P/zwgzF27FjjggsuMMLCwow77rjDOHz4sP86fR469YBAzWp66623jG7duhkhISFGly5djBdffNFreVVVlfHEE08YMTExRkhIiDFw4EBj3759fuqt/5WWlhoPP/yw0bFjRyM0NNS45JJLjP/93/81XC6Xpw01A8e32qvN8Q5nR/g5t3Md6/BftTnO1bcAw2jIV6gCAAAAwPmh2d/zAwAAAAAS4QcAAACARRB+AAAAAFgC4QcAAACAJRB+AAAAAFgC4QcAAACAJRB+AAAAAFgC4QcAAACAJRB+AAAAAFgC4QcAAACAJRB+AAAAAFgC4QcAAACAJRB+AAAAAFgC4QcAAACAJRB+AAAAAFgC4QcAAACAJRB+AAAAAFgC4QcAAACAJRB+AAAAAFgC4QcAAACAJRB+gPPEkiVLFBAQoB07dvi7KwAAAM0S4QeWVx06qn9CQ0N1xRVXKDMzU0VFRf7uHgAA9eLU491Pfx5//HF/dw9oFC383QHgfDF9+nR16tRJ5eXlev/997VgwQKtWbNGu3fvVlhYmL+7BwBAvag+3v1Ut27d/NQboHERfoD/GDx4sHr37i1JevDBBxUVFaVnnnlG//jHP3Tvvff6tM2qqipVVFQoNDS0PrsKAIDPfnq8qw8nTpxQq1at6m17QEPisjfgDAYMGCBJOnjwoJ566ildd911ioqKUsuWLdWrVy+9/vrrNdYJCAhQZmamli5dqiuvvFIhISFau3atJOnrr7/WqFGj1L59e4WEhKhTp04aM2aMKioqvLbhcrmUlZWltm3bqlWrVrrjjjv0zTffNPyAAQCW9cUXX2js2LHq3LmzWrZsqaioKN111136/PPPvdpVXzq3ceNGjR07VtHR0erQoYNn+TvvvKPrr79erVq1Ups2bZSamqo9e/Y08miAM+PMD3AGn376qSQpKipKTz75pG699VYNHz5cFRUVWr58ue666y6tXr1aqampXutt2LBBr732mjIzM3XhhRfq4osv1qFDh9SnTx+VlJRo9OjR6tKli77++mu9/vrrKisrU3BwsGf9cePG6YILLtCUKVP0+eef69lnn1VmZqZeffXVRh0/AKB5Onr0qL799luvedu3b9fmzZs1bNgwdejQQZ9//rkWLFigG2+8UXv37q1x+ffYsWPVtm1bTZ48WSdOnJAk/eUvf1F6erpSUlL0u9/9TmVlZVqwYIH69++vf/7zn7r44osba4jAGRF+gP+oPhiUl5frgw8+0PTp09WyZUvdcsstGjlypFq2bOlpm5mZqWuuuUbPPPNMjfCzb98+7dq1SwkJCZ556enpcjqd2rp1q9elBtOnT5dhGF7rR0VFKScnRwEBAZJ+vHTu+eef19GjRxUeHt4QQwcAWEhSUlKNeWVlZbrzzju95g0ZMkQOh0N///vfNWLECK9lkZGRysvLU1BQkCTp+PHj+vWvf60HH3xQL774oqddenq6OnfurFmzZnnNB/yF8AP8x6kHg/j4eC1dulQXXXSR1/zvv/9elZWVuv766/W3v/2txnZ+9rOfeQWfqqoqrVq1SkOGDDntNdbVIafa6NGjveZdf/31mjdvnr744gv16NHDp7EBAFAtOztbV1xxhde8n/6Bz+12q7S0VJdddpkiIiL04Ycf1gg/Dz30kCf4SFJubq5KSkp07733ep1VCgoKUt++ffXuu+820GgAcwg/wH9UHwxatGihmJgYde7cWYGBP94Wt3r1aj355JPauXOnXC6XZ51Tg4ukGk/Q+eabb1RaWlrrJ+l07NjRa/qCCy6Q9GPoAgCgrvr06VPjj3E//PCDZs+ercWLF+vrr7/2uirh6NGjNbZx6rFu//79kv57v+yp7HZ7XbsN1AvCD/AfpzsYSNL//d//6dZbb9UNN9yg+fPnq127drLZbFq8eLGWLVtWo/1P/3rmi5/+Je2nTr08DgCA+jJu3DgtXrxY48ePl8PhUHh4uAICAjRs2DBVVVXVaH/qsa66zV/+8hfFxsbWaN+iBV85cX7gkwicw9///neFhoZq3bp1CgkJ8cxfvHhxrdZv27at7Ha7du/e3VBdBACgTl5//XWlp6fr6aef9swrLy9XSUlJrda/9NJLJUnR0dGnvacIOF/wqGvgHIKCghQQEKDKykrPvM8//1yrVq2q1fqBgYG6/fbb9dZbb2nHjh01lnNGBwDgb0FBQTWORy+88ILXse9sUlJSZLfbNWvWLLnd7hrLeWUDzhec+QHOITU1Vc8884x+/vOf67777lNxcbGys7N12WWX6V//+lettjFr1izl5OToZz/7mUaPHq2uXbvq8OHDWrFihd5//31FREQ07CAAADiLW265RX/5y18UHh6uhIQE5efna/369YqKiqrV+na7XQsWLNCIESN0zTXXaNiwYWrbtq0KCwv19ttvq1+/fvrDH/7QwKMAzo3wA5zDgAEDtGjRIs2ZM0fjx49Xp06d9Lvf/U6ff/55rcPPRRddpK1bt+qJJ57Q0qVLVVpaqosuukiDBw+u8e4EAAAa23PPPaegoCAtXbpU5eXl6tevn9avX6+UlJRab+O+++5T+/btNWfOHP3+97+Xy+XSRRddpOuvv14PPPBAA/YeqL0Ag2tuAAAAAFgA9/wAAAAAsATCDwAAAABLIPwAAAAAsATCDwAAAABLIPwAAAAAsATCDwAAAABLaJLv+amqqtKhQ4fUpk0bBQQE+Ls7ANCkGIahY8eOqX379goM5G9g5wOOawDgOzPHtSYZfg4dOqS4uDh/dwMAmrQvv/xSHTp08Hc3II5rAFAfanNca5Lhp02bNpJ+HKDdbje1rtvtVk5OjpKTk2Wz2Rqie42mOY1Fal7jYSznJ8byo9LSUsXFxXl+l8L/OK7VHuNt3hhv89WQYzVzXGuS4af6kgC73e7TQSIsLEx2u73Jf8ia01ik5jUexnJ+YizeuLzq/MFxrfYYb/PGeJuvxhhrbY5rXOwNAAAAwBIIPwAAAAAsgfADAAAAwBIIPwAAAAAsgfADAAAAwBIIPwAAAAAsgfADAAAAwBIIPwAAAAAsoUm+5LQ+dJu6Tq7Kxn/B3+dzUht9nwCA5o/jGgCcG2d+AAAAAFgC4QcAAACAJRB+AAAAAFgC4QcAAACAJRB+AAAAAFgC4QcAAACAJRB+AAAAAFgC4QcAAACAJRB+AAAAAFgC4QcAAACAJRB+AAAAAFgC4QcAAACAJRB+AAAAAFgC4QcAAACAJRB+AAAAAFgC4QcAAACAJRB+AAAAAFgC4QcAAACAJZgOP19//bV+8YtfKCoqSi1btlT37t21Y8cOz3LDMDR58mS1a9dOLVu2VFJSkvbv3++1jSNHjmj48OGy2+2KiIjQqFGjdPz48bqPBgAAAADOwFT4+f7779WvXz/ZbDa988472rt3r55++mldcMEFnjZz587V888/r4ULF2rr1q1q1aqVUlJSVF5e7mkzfPhw7dmzR7m5uVq9erU2bdqk0aNH19+oAAAAAOAULcw0/t3vfqe4uDgtXrzYM69Tp06efxuGoWeffVaTJk3SbbfdJkl65ZVXFBMTo1WrVmnYsGH6+OOPtXbtWm3fvl29e/eWJL3wwgu6+eab9dRTT6l9+/b1MS4AAAAA8GIq/Lz55ptKSUnRXXfdpY0bN+qiiy7S2LFj9dBDD0mSDh48KKfTqaSkJM864eHh6tu3r/Lz8zVs2DDl5+crIiLCE3wkKSkpSYGBgdq6davuuOOOGvt1uVxyuVye6dLSUkmS2+2W2+02NeDq9iGBhqn16ovZ/tZmW/W5TX9qTuNhLOcnxuK9LgAAVmMq/Hz22WdasGCBsrKy9Nvf/lbbt2/Xr3/9awUHBys9PV1Op1OSFBMT47VeTEyMZ5nT6VR0dLR3J1q0UGRkpKfNqWbPnq1p06bVmJ+Tk6OwsDAzQ/CY0bvKp/Xqas2aNfW+zdzc3Hrfpj81p/EwlvOT1cdSVlbWAD0BAOD8Zyr8VFVVqXfv3po1a5Yk6eqrr9bu3bu1cOFCpaenN0gHJWnixInKysryTJeWliouLk7Jycmy2+2mtuV2u5Wbm6sn/n979x4cVX3HffyTkM1CgCRcTDYoYDqlAuUqgbBibUdCog0dkYyKEzW1DEwxUSE+qPEREFSCtCrFIoi1YEcplioqCJQUJIwSAkQZuWjEioVBkrRiCBfZbLLn+cMnW5drdnM5YX/v18zOuOf89pzvN2B++XByfmdXpDy+iOYu9ZL2PpHRbMdq6GXMmDFyOBzNdly7hFM/9NI20cv3Gq6eAwBgmqDCT1JSkvr37x+wrV+/fnrzzTclSS6XS5JUWVmppKQk/5jKykoNGTLEP6aqqirgGHV1dTp27Jj/82dzOp1yOp3nbHc4HCH/AOPxRchT3/rhpyV+4GrK16EtCqd+6KVtMr2XcOkdAIBgBbXa26hRo1ReXh6w7fPPP1fv3r0lfb/4gcvl0qZNm/z7a2pqVFpaKrfbLUlyu92qrq5WWVmZf8zmzZvl8/mUmpoaciMAAAAAcDFBXfmZNm2arrvuOs2dO1e33367duzYoaVLl2rp0qWSpIiICE2dOlVPPfWU+vTpo+TkZM2YMUM9evTQuHHjJH1/peimm27SpEmTtGTJEnm9XuXl5WnChAms9AYAAACgxQQVfoYPH67Vq1eroKBAc+bMUXJyshYsWKDs7Gz/mIcfflinTp3S5MmTVV1dreuvv14bNmxQ+/bt/WNef/115eXlafTo0YqMjFRWVpYWLlzYfF0BAAAAwFmCCj+SNHbsWI0dO/aC+yMiIjRnzhzNmTPngmO6du2qFStWBHtqAAAAAAhZUPf8AAAAAMDlivADAAAAwAiEHwCA0Y4cOaK77rpL3bp1U4cOHTRw4EDt2rXLv9+yLM2cOVNJSUnq0KGD0tLSdODAgYBjHDt2TNnZ2YqNjVV8fLwmTpyokydPtnYrAIBLIPwAAIz17bffatSoUXI4HFq/fr3279+vZ599Vl26dPGPmT9/vhYuXKglS5aotLRUHTt2VEZGhs6cOeMfk52drX379qmoqEhr167V1q1bNXnyZDtaAgBcRNALHgAAEC6eeeYZ9ezZU8uWLfNvS05O9v+3ZVlasGCBHn/8cd1yyy2SpL/85S9KTEzU22+/rQkTJujTTz/Vhg0btHPnTqWkpEiSXnjhBf3yl7/U73//ex7jAABtCOEHAGCsd999VxkZGbrttttUXFysK6+8Uvfdd58mTZokSTp48KAqKiqUlpbm/0xcXJxSU1NVUlKiCRMmqKSkRPHx8f7gI0lpaWmKjIxUaWmpbr311nPO6/F45PF4/O9ramokSV6vV16vN6geGsY7I62gPtdcgq23uc7X2ue1C/2GN5P6bclegzkm4QcAYKwvv/xSixcvVn5+vh577DHt3LlTDzzwgKKjo5WTk6OKigpJUmJiYsDnEhMT/fsqKiqUkJAQsD8qKkpdu3b1jzlbYWGhZs+efc72jRs3KiYmJqRenkzxhfS5plq3bp0t5y0qKrLlvHah3/BmUr8t0evp06cbPZbwAwAwls/nU0pKiubOnStJGjp0qPbu3aslS5YoJyenxc5bUFCg/Px8//uamhr17NlT6enpio2NDepYXq9XRUVFmrErUh5fRHOXekl7n8ho1fM19DtmzBg5HI5WPbcd6De8mdRvS/bacPW8MQg/AABjJSUlqX///gHb+vXrpzfffFOS5HK5JEmVlZVKSkryj6msrNSQIUP8Y6qqqgKOUVdXp2PHjvk/fzan0ymn03nOdofDEfIPBR5fhDz1rR9+7PqBrSlfq8sR/YY3k/ptiV6DOR6rvQEAjDVq1CiVl5cHbPv888/Vu3dvSd8vfuByubRp0yb//pqaGpWWlsrtdkuS3G63qqurVVZW5h+zefNm+Xw+paamtkIXAIDG4soPAMBY06ZN03XXXae5c+fq9ttv144dO7R06VItXbpUkhQREaGpU6fqqaeeUp8+fZScnKwZM2aoR48eGjdunKTvrxTddNNNmjRpkpYsWSKv16u8vDxNmDCBld4AoI0h/AAAjDV8+HCtXr1aBQUFmjNnjpKTk7VgwQJlZ2f7xzz88MM6deqUJk+erOrqal1//fXasGGD2rdv7x/z+uuvKy8vT6NHj1ZkZKSysrK0cOFCO1oCAFwE4QcAYLSxY8dq7NixF9wfERGhOXPmaM6cORcc07VrV61YsaIlygMANCPu+QEAAABgBMIPAAAAACMQfgAAAAAYgfADAAAAwAiEHwAAAABGIPwAAAAAMALhBwAAAIARCD8AAAAAjED4AQAAAGAEwg8AAAAAIxB+AAAAABiB8AMAAADACIQfAAAAAEYg/AAAAAAwAuEHAAAAgBEIPwAAAACMQPgBAAAAYATCDwAAAAAjEH4AAAAAGIHwAwAAAMAIhB8AAAAARiD8AAAAADAC4QcAAACAEQg/AAAAAIxA+AEAAABgBMIPAAAAACMQfgAAAAAYgfADAAAAwAiEHwAAAABGIPwAAAAAMALhBwAAAIARCD8AAAAAjED4AQAAAGAEwg8AAAAAIxB+AAAAABiB8AMAAADACE0KP/PmzVNERISmTp3q33bmzBnl5uaqW7du6tSpk7KyslRZWRnwuUOHDikzM1MxMTFKSEjQ9OnTVVdX15RSAAAAAOCiQg4/O3fu1EsvvaRBgwYFbJ82bZrWrFmjVatWqbi4WF9//bXGjx/v319fX6/MzEzV1tZq27ZtevXVV7V8+XLNnDkz9C4AAAAA4BJCCj8nT55Udna2Xn75ZXXp0sW//fjx43rllVf03HPP6cYbb9SwYcO0bNkybdu2Tdu3b5ckbdy4Ufv379drr72mIUOG6Oabb9aTTz6pRYsWqba2tnm6AgAAAICzRIXyodzcXGVmZiotLU1PPfWUf3tZWZm8Xq/S0tL82/r27atevXqppKREI0eOVElJiQYOHKjExET/mIyMDE2ZMkX79u3T0KFDzzmfx+ORx+Pxv6+pqZEkeb1eeb3eoGpvGO+MtIL6XHMJtt7GHKs5j2mncOqHXtomegn8LAAApgk6/KxcuVIfffSRdu7cec6+iooKRUdHKz4+PmB7YmKiKioq/GN+GHwa9jfsO5/CwkLNnj37nO0bN25UTExMsC1Ikp5M8YX0uaZat25dsx+zqKio2Y9pp3Dqh17aJtN7OX36dAtUAgBA2xdU+Dl8+LAefPBBFRUVqX379i1V0zkKCgqUn5/vf19TU6OePXsqPT1dsbGxQR3L6/WqqKhIM3ZFyuOLaO5SL2nvExnNdqyGXsaMGSOHw9Fsx7VLOPVDL20TvXyv4eo5AACmCSr8lJWVqaqqStdee61/W319vbZu3ao//vGP+sc//qHa2lpVV1cHXP2prKyUy+WSJLlcLu3YsSPguA2rwTWMOZvT6ZTT6Txnu8PhCPkHGI8vQp761g8/LfEDV1O+Dm1ROPVDL22T6b2ES+8AAAQrqAUPRo8erT179mj37t3+V0pKirKzs/3/7XA4tGnTJv9nysvLdejQIbndbkmS2+3Wnj17VFVV5R9TVFSk2NhY9e/fv5naAgAAAIBAQV356dy5swYMGBCwrWPHjurWrZt/+8SJE5Wfn6+uXbsqNjZW999/v9xut0aOHClJSk9PV//+/XX33Xdr/vz5qqio0OOPP67c3NzzXt0BAAAAgObQpIecns/zzz+vsWPHKisrSzfccINcLpfeeust//527dpp7dq1ateundxut+666y7dc889mjNnTnOXAgBAUHh4NwCEt5CWuv6hLVu2BLxv3769Fi1apEWLFl3wM717926RVc8AAAjVxR7e/d5772nVqlWKi4tTXl6exo8frw8//FDS/x7e7XK5tG3bNh09elT33HOPHA6H5s6da0crAIALaPYrPwAAXG54eDcAmKHJV34AALjc8fDu0LX2Q3PD6WHFjUG/4c2kfluy12COSfgBABiNh3c3jV2/xh5ODytuDPoNbyb12xK9BvPwbsIPAMBYPLy76Zrz4d2NEU4PK24M+g1vJvXbkr0G8/Buwg8AwFg8vLvp7PqBLZweVtwY9BveTOq3JXoN5ngseAAAMBYP7wYAs3DlBwBgLB7eDQBmIfwAAHARzz//vCIjI5WVlSWPx6OMjAy9+OKL/v0ND++eMmWK3G63OnbsqJycHB7eDQBtEOEHAIAf4OHdABC+uOcHAAAAgBEIPwAAAACMQPgBAAAAYATCDwAAAAAjEH4AAAAAGIHwAwAAAMAIhB8AAAAARiD8AAAAADAC4QcAAACAEQg/AAAAAIxA+AEAAABgBMIPAAAAACMQfgAAAAAYgfADAAAAwAiEHwAAAABGIPwAAAAAMALhBwAAAIARCD8AAAAAjED4AQAAAGAEwg8AAAAAIxB+AAAAABiB8AMAAADACIQfAAAAAEYg/AAAAAAwAuEHAAAAgBEIPwAAAACMQPgBAAAAYATCDwAAAAAjEH4AAAAAGIHwAwAAAMAIhB8AAAAARiD8AAAAADAC4QcAAACAEQg/AAAAAIxA+AEAAABgBMIPAAAAACMQfgAAAAAYgfADAAAAwAiEHwAAAABGIPwAAAAAMEJQ4aewsFDDhw9X586dlZCQoHHjxqm8vDxgzJkzZ5Sbm6tu3bqpU6dOysrKUmVlZcCYQ4cOKTMzUzExMUpISND06dNVV1fX9G4AAAAA4AKCCj/FxcXKzc3V9u3bVVRUJK/Xq/T0dJ06dco/Ztq0aVqzZo1WrVql4uJiff311xo/frx/f319vTIzM1VbW6tt27bp1Vdf1fLlyzVz5szm6woAAAAAzhIVzOANGzYEvF++fLkSEhJUVlamG264QcePH9crr7yiFStW6MYbb5QkLVu2TP369dP27ds1cuRIbdy4Ufv379c///lPJSYmasiQIXryySf1yCOP6IknnlB0dHTzdQcAAAAA/19Q4edsx48flyR17dpVklRWViav16u0tDT/mL59+6pXr14qKSnRyJEjVVJSooEDByoxMdE/JiMjQ1OmTNG+ffs0dOjQc87j8Xjk8Xj872tqaiRJXq9XXq83qJobxjsjraA+11yCrbcxx2rOY9opnPqhl7aJXgI/CwCAaUIOPz6fT1OnTtWoUaM0YMAASVJFRYWio6MVHx8fMDYxMVEVFRX+MT8MPg37G/adT2FhoWbPnn3O9o0bNyomJiak+p9M8YX0uaZat25dsx+zqKio2Y9pp3Dqh17aJtN7OX36dAtUcnkqLCzUW2+9pc8++0wdOnTQddddp2eeeUbXXHONf8yZM2f00EMPaeXKlfJ4PMrIyNCLL74YMJcdOnRIU6ZM0fvvv69OnTopJydHhYWFiopq0r8xAgCaWcjflXNzc7V371598MEHzVnPeRUUFCg/P9//vqamRj179lR6erpiY2ODOpbX61VRUZFm7IqUxxfR3KVe0t4nMprtWA29jBkzRg6Ho9mOa5dw6ode2iZ6+V7D1XP8717W4cOHq66uTo899pjS09O1f/9+dezYUdL397K+9957WrVqleLi4pSXl6fx48frww8/lPS/e1ldLpe2bdumo0eP6p577pHD4dDcuXPtbA8AcJaQwk9eXp7Wrl2rrVu36qqrrvJvd7lcqq2tVXV1dcDVn8rKSrlcLv+YHTt2BByvYTW4hjFnczqdcjqd52x3OBwh/wDj8UXIU9/64aclfuBqytehLQqnfuilbTK9l3DpvTlwLysAmCWo1d4sy1JeXp5Wr16tzZs3Kzk5OWD/sGHD5HA4tGnTJv+28vJyHTp0SG63W5Lkdru1Z88eVVVV+ccUFRUpNjZW/fv3b0ovAAA0SbD3skq64L2sNTU12rdvXytWDwC4lKCu/OTm5mrFihV655131LlzZ/89OnFxcerQoYPi4uI0ceJE5efnq2vXroqNjdX9998vt9utkSNHSpLS09PVv39/3X333Zo/f74qKir0+OOPKzc397xXdwAAaA2teS8rC/k0/XymLNxBv+HNpH5bstdgjhlU+Fm8eLEk6Re/+EXA9mXLlunXv/61JOn5559XZGSksrKyAm4MbdCuXTutXbtWU6ZMkdvtVseOHZWTk6M5c+YEUwoAAM2qNe9lZSGfpgunhUsag37Dm0n9tkSvwSzkE1T4saxL/6tS+/bttWjRIi1atOiCY3r37m3bN0sAAM7W2veyspBP6MJp4ZLGoN/wZlK/LdlrMAv5sAYnAMBYlmXp/vvv1+rVq7Vly5aL3sualZUl6fz3sj799NOqqqpSQkKCpEvfy8pCPs1z3nD/YfGH6De8mdRvS/QazPEIPwAAY3EvKwCYhfADADAW97ICgFkIPwAAY3EvKwCYJajn/AAAAADA5YrwAwAAAMAIhB8AAAAARiD8AAAAADAC4QcAAACAEQg/AAAAAIxA+AEAAABgBMIPAAAAACMQfgAAAAAYgfADAAAAwAiEHwAAAABGIPwAAAAAMALhBwAAAIARCD8AAAAAjBBldwGmufrR95rtWM52luaPkAY88Q956iMuOf6reZnNdm4AAADgcsOVHwAAAABGIPwAAAAAMALhBwAAAIARCD8AAAAAjED4AQAAAGAEVnsDAAAha85VTBvjhyudlj89tlXPDeDyx5UfAAAAAEYg/AAAAAAwAuEHAAAAgBEIPwAAAACMwIIHaHGNvRn2hzexeuojmuXcX83LbJbjAAAA4PLHlR8AAAAARiD8AAAAADAC4QcAAACAEQg/AAAAAIxA+AEAAABgBMIPAAAAACMQfgAAAAAYgfADAAAAwAiEHwAAAABGIPwAAAAAMALhBwAAAIARCD8AAAAAjED4AQAAAGAEwg8AAAAAIxB+AAAAABghyu4CAAAAQnH1o+/Zct6v5mXacl4ATceVHwAAAABGIPwAAAAAMALhBwAAAIARbA0/ixYt0tVXX6327dsrNTVVO3bssLMcAACahHkNANo22xY8eOONN5Sfn68lS5YoNTVVCxYsUEZGhsrLy5WQkGBXWQAAhIR5Da3BrkUeJBZ6QHiw7crPc889p0mTJunee+9V//79tWTJEsXExOjPf/6zXSUBABAy5jUAaPtsufJTW1ursrIyFRQU+LdFRkYqLS1NJSUl54z3eDzyeDz+98ePH5ckHTt2TF6vN6hze71enT59WlHeSNX7IkLsoG2I8lk6fdrX6F6++eabVqjqXFF1pxo3Lsh+GsOunhv+ng35v2/Jc5n/PXNGWnp8qK9RvZQWjG6lqgKlFm5q1Lhgemksu3pu+Dv2zTffyOFwBPXZEydOSJIsy2qJ0ozEvNZ6WmKuCFZrzi1n/7/e2Dm1Jfz4//ytxc9xvu/Tdn2fbQ1N+V7eUho7pwarMXNwqH/WQc1rlg2OHDliSbK2bdsWsH369OnWiBEjzhk/a9YsSxIvXrx48WrG1+HDh1vr237YY17jxYsXL/tfjZnXLouHnBYUFCg/P9//3ufz6dixY+rWrZsiIoL7V5+amhr17NlThw8fVmxsbHOX2qrCqRcpvPqhl7aJXr5nWZZOnDihHj16tFB1uBTmtdDRb3ij3/DVkr0GM6/ZEn66d++udu3aqbKyMmB7ZWWlXC7XOeOdTqecTmfAtvj4+CbVEBsbGzZ/ycKpFym8+qGXtolepLi4uBaoxlzMa62PfsMb/Yavluq1sfOaLQseREdHa9iwYdq06X+/U+jz+bRp0ya53W47SgIAIGTMawBwebDt197y8/OVk5OjlJQUjRgxQgsWLNCpU6d077332lUSAAAhY14DgLbPtvBzxx136D//+Y9mzpypiooKDRkyRBs2bFBiYmKLntfpdGrWrFnn/LrB5SicepHCqx96aZvoBS2Jea110G94o9/w1VZ6jbAs1joFAAAAEP5se8gpAAAAALQmwg8AAAAAIxB+AAAAABiB8AMAAADACMaFn0WLFunqq69W+/btlZqaqh07dthdUki2bt2qX/3qV+rRo4ciIiL09ttv211SSAoLCzV8+HB17txZCQkJGjdunMrLy+0uKySLFy/WoEGD/A/vcrvdWr9+vd1lNYt58+YpIiJCU6dOtbuUoD3xxBOKiIgIePXt29fuskJ25MgR3XXXXerWrZs6dOiggQMHateuXXaXBRuZMq9ZlqWZM2cqKSlJHTp0UFpamg4cOBAw5tixY8rOzlZsbKzi4+M1ceJEnTx5shW7aJzGzH1nzpxRbm6uunXrpk6dOikrK+uch+geOnRImZmZiomJUUJCgqZPn666urrWbKVRLjU/hlOvZzvf/BlO/V5qjm2LvRoVft544w3l5+dr1qxZ+uijjzR48GBlZGSoqqrK7tKCdurUKQ0ePFiLFi2yu5QmKS4uVm5urrZv366ioiJ5vV6lp6fr1KlTdpcWtKuuukrz5s1TWVmZdu3apRtvvFG33HKL9u3bZ3dpTbJz50699NJLGjRokN2lhOynP/2pjh496n998MEHdpcUkm+//VajRo2Sw+HQ+vXrtX//fj377LPq0qWL3aXBJibNa/Pnz9fChQu1ZMkSlZaWqmPHjsrIyNCZM2f8Y7Kzs7Vv3z4VFRVp7dq12rp1qyZPntxaLTRaY+a+adOmac2aNVq1apWKi4v19ddfa/z48f799fX1yszMVG1trbZt26ZXX31Vy5cv18yZM+1o6aIuNT+GU68/dKH5M9z6vdgc2yZ7tQwyYsQIKzc31/++vr7e6tGjh1VYWGhjVU0nyVq9erXdZTSLqqoqS5JVXFxsdynNokuXLtaf/vQnu8sI2YkTJ6w+ffpYRUVF1s9//nPrwQcftLukoM2aNcsaPHiw3WU0i0ceecS6/vrr7S4DbYgp85rP57NcLpf1u9/9zr+turracjqd1l//+lfLsixr//79liRr586d/jHr16+3IiIirCNHjrRa7aE4e+6rrq62HA6HtWrVKv+YTz/91JJklZSUWJZlWevWrbMiIyOtiooK/5jFixdbsbGxlsfjad0GQtAwP4ZrrxeaP8Ot34vNsW21V2Ou/NTW1qqsrExpaWn+bZGRkUpLS1NJSYmNleGHjh8/Lknq2rWrzZU0TX19vVauXKlTp07J7XbbXU7IcnNzlZmZGfD/zeXowIED6tGjh370ox8pOztbhw4dsrukkLz77rtKSUnRbbfdpoSEBA0dOlQvv/yy3WXBJibNawcPHlRFRUVAr3FxcUpNTfX3WlJSovj4eKWkpPjHpKWlKTIyUqWlpa1eczDOnvvKysrk9XoD+u3bt6969eoV0O/AgQMDHqKbkZGhmpqaNv0bB2fPj+Ha64Xmz3Ds90JzbFvtNapFjtoG/fe//1V9ff05T9pOTEzUZ599ZlNV+CGfz6epU6dq1KhRGjBggN3lhGTPnj1yu906c+aMOnXqpNWrV6t///52lxWSlStX6qOPPtLOnTvtLqVJUlNTtXz5cl1zzTU6evSoZs+erZ/97Gfau3evOnfubHd5Qfnyyy+1ePFi5efn67HHHtPOnTv1wAMPKDo6Wjk5OXaXh1Zm0rxWUVEhSefttWFfRUWFEhISAvZHRUWpa9eu/jFt0fnmvoqKCkVHRys+Pj5g7Nn9nu/r0bCvrbnQ/Lh79+6w6/Vi82e4/dlebI5tq70aE37Q9uXm5mrv3r2X7f0YknTNNddo9+7dOn78uP7+978rJydHxcXFl10AOnz4sB588EEVFRWpffv2dpfTJDfffLP/vwcNGqTU1FT17t1bf/vb3zRx4kQbKwuez+dTSkqK5s6dK0kaOnSo9u7dqyVLlhB+gMtUOMx9jXGh+THchNP82RgXm2M7dOhgY2UXZsyvvXXv3l3t2rU7Z4WJyspKuVwum6pCg7y8PK1du1bvv/++rrrqKrvLCVl0dLR+/OMfa9iwYSosLNTgwYP1hz/8we6yglZWVqaqqipde+21ioqKUlRUlIqLi7Vw4UJFRUWpvr7e7hJDFh8fr5/85Cf64osv7C4laElJSecE6X79+l22v8aHpjFpXmvo52K9ulyucxZ6qKur07Fjx9rs1+NCc5/L5VJtba2qq6sDxp/d7/m+Hg372poLzY/h1uul5s/ExMSw6vdsP5xj2+qfrTHhJzo6WsOGDdOmTZv823w+nzZt2nRZ35NxubMsS3l5eVq9erU2b96s5ORku0tqVj6fTx6Px+4ygjZ69Gjt2bNHu3fv9r9SUlKUnZ2t3bt3q127dnaXGLKTJ0/qX//6l5KSkuwuJWijRo06Zznczz//XL1797apItjJpHktOTlZLpcroNeamhqVlpb6e3W73aqurlZZWZl/zObNm+Xz+ZSamtrqNV/Mpea+YcOGyeFwBPRbXl6uQ4cOBfS7Z8+egMBXVFSk2NjYy+K3DRrmx3Dr9VLzZ0pKSlj1e7YfzrFt9s+2RZZRaKNWrlxpOZ1Oa/ny5db+/futyZMnW/Hx8QErTFwuTpw4YX388cfWxx9/bEmynnvuOevjjz+2/v3vf9tdWlCmTJlixcXFWVu2bLGOHj3qf50+fdru0oL26KOPWsXFxdbBgwetTz75xHr00UetiIgIa+PGjXaX1iwu19XeHnroIWvLli3WwYMHrQ8//NBKS0uzunfvblVVVdldWtB27NhhRUVFWU8//bR14MAB6/XXX7diYmKs1157ze7SYBOT5rV58+ZZ8fHx1jvvvGN98skn1i233GIlJydb3333nf8YN910kzV06FCrtLTU+uCDD6w+ffpYd955p10tXVBj5r7f/va3Vq9evazNmzdbu3btstxut+V2u/376+rqrAEDBljp6enW7t27rQ0bNlhXXHGFVVBQYEdLF3Wp+TGcej2fs+fPcOr3UnNsW+zVqPBjWZb1wgsvWL169bKio6OtESNGWNu3b7e7pJC8//77lqRzXjk5OXaXFpTz9SDJWrZsmd2lBe03v/mN1bt3bys6Otq64oorrNGjR4dN8LGsyzf83HHHHVZSUpIVHR1tXXnlldYdd9xhffHFF3aXFbI1a9ZYAwYMsJxOp9W3b19r6dKldpcEm5kyr/l8PmvGjBlWYmKi5XQ6rdGjR1vl5eUBx/jmm2+sO++80+rUqZMVGxtr3XvvvdaJEyds6ObiGjP3fffdd9Z9991ndenSxYqJibFuvfVW6+jRowHH+eqrr6ybb77Z6tChg9W9e3froYcesrxebyt3c2mXmh/DqdfzOXv+DKd+LzXHtsVeIyzLslrmmhIAAAAAtB3G3PMDAAAAwGyEHwAAAABGIPwAAAAAMALhBwAAAIARCD8AAAAAjED4AQAAAGAEwg8AAAAAIxB+AAAAABiB8AMAAADACIQfAAAAAEYg/AAAAAAwAuEHAAAAgBH+HyG3bqOiQhugAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plotting Boxplot for dataset\n",
        "# Checking for outliers\n",
        "sns.boxplot(titanic)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "3rxfeIxDelMo",
        "outputId": "08a3a100-766f-4f09-8036-018500c1b9fb"
      },
      "execution_count": null,
      "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": [
        "# showing Correlation\n",
        "titanic.corr()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "feUAoEm3eq6D",
        "outputId": "4cc7d671-5413-4d7a-89c2-1ea0cbe5976f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          Survived    Pclass       Age     SibSp     Parch      Fare\n",
              "Survived  1.000000 -0.338481 -0.069809 -0.035322  0.081629  0.257307\n",
              "Pclass   -0.338481  1.000000 -0.331339  0.083081  0.018443 -0.549500\n",
              "Age      -0.069809 -0.331339  1.000000 -0.232625 -0.179191  0.091566\n",
              "SibSp    -0.035322  0.083081 -0.232625  1.000000  0.414838  0.159651\n",
              "Parch     0.081629  0.018443 -0.179191  0.414838  1.000000  0.216225\n",
              "Fare      0.257307 -0.549500  0.091566  0.159651  0.216225  1.000000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f13fcd06-3226-4150-85ee-e5db21106dd5\" 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>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Fare</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Survived</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.338481</td>\n",
              "      <td>-0.069809</td>\n",
              "      <td>-0.035322</td>\n",
              "      <td>0.081629</td>\n",
              "      <td>0.257307</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pclass</th>\n",
              "      <td>-0.338481</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.331339</td>\n",
              "      <td>0.083081</td>\n",
              "      <td>0.018443</td>\n",
              "      <td>-0.549500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Age</th>\n",
              "      <td>-0.069809</td>\n",
              "      <td>-0.331339</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.232625</td>\n",
              "      <td>-0.179191</td>\n",
              "      <td>0.091566</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SibSp</th>\n",
              "      <td>-0.035322</td>\n",
              "      <td>0.083081</td>\n",
              "      <td>-0.232625</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.414838</td>\n",
              "      <td>0.159651</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Parch</th>\n",
              "      <td>0.081629</td>\n",
              "      <td>0.018443</td>\n",
              "      <td>-0.179191</td>\n",
              "      <td>0.414838</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.216225</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fare</th>\n",
              "      <td>0.257307</td>\n",
              "      <td>-0.549500</td>\n",
              "      <td>0.091566</td>\n",
              "      <td>0.159651</td>\n",
              "      <td>0.216225</td>\n",
              "      <td>1.000000</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-f13fcd06-3226-4150-85ee-e5db21106dd5')\"\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-f13fcd06-3226-4150-85ee-e5db21106dd5 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-f13fcd06-3226-4150-85ee-e5db21106dd5');\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-0ff286d2-a6e2-4c42-bbb1-ad545a07fc7b\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-0ff286d2-a6e2-4c42-bbb1-ad545a07fc7b')\"\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-0ff286d2-a6e2-4c42-bbb1-ad545a07fc7b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Showing Correlation Plot\n",
        "sns.heatmap(titanic.corr(),annot=True,cmap='coolwarm')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 435
        },
        "id": "9xCTJ8RXevyk",
        "outputId": "802ec343-b0b7-4562-ffb3-0c7926789835"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plotting pairplot\n",
        "sns.pairplot(titanic)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 960
        },
        "id": "zvDBsHPWe1f4",
        "outputId": "aaad0444-a6e7-451b-80c6-69035679afc1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1500 with 42 Axes>"
            ],
            "image/png": 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UFzghTkRERERERERERER5gRPiAGKxGNxuN1hflEjdGKtEuYPxSpQ7GK9EuYGxSpQ7GK9E6sYJcQAdHR2w2+3o6OjIdlOISAJjlSh3MF6JcgfjlSg3MFaJcgfjlUjdOCFORERERERERERERHmBE+JERERERERERERElBc4IU5EREREREREREREeYET4kRERERERERERESUFzghTkRERERERERERER5QdUT4pFIBMuXL0dpaSksFgvGjBmDVatWIRaLdd0nFovh/vvvx/Dhw2GxWHDDDTfg8OHDWWw1Uf5q9wn4+LQHDcda8fFnHrT7hLxsAxER0UDD/pUoNzBWiQYuxjdR+uiz3QApa9euxU9/+lNs2bIF5eXl2Lt3L+bOnQu73Y5FixYBAB555BE8/vjj2LJlC0pLS7F8+XLceOONeO+992A2m7P8Dojyx4k2PxZv24/dh5u7bptS5sSaWRMwotiSN20gIiIaaNi/EuUGxirRwMX4JkovVa8Q/+tf/4pbb70VN998My688EJ89atfxb/927/h7bffBtC5Ovyxxx7DsmXLcOutt2LChAn45S9/iRMnTmDHjh3ZbTxRHmn3CUmdMwC8cbgZS7btV+SXazW0gYiIaKBh/0qUGxirRAMX45so/VQ9IX7ttdfilVdewUcffQQA+Mc//oH6+nrcdNNNAIAjR47g1KlTuOGGG7oeY7fbcdVVV+HNN99M+bzBYBButzvhPyI6f80eIalzjnvjcDOaPefXQfclVjPVBiLqHfatRLmD/StRbmCsEuWOTI6FGd9E6afqCfElS5bgtttuw9ixY2EwGFBZWYnvfOc7uOOOOwAAp06dAgAMHTo04XFDhw7tOibm4Ycfht1u7/pv5MiRmXsTRHnAHQhJHu+QOZ5KX2I1U20got5h30qUO9i/EuUGxipR7sjkWJjxTZR+qp4Q/3//7//hmWeewbPPPot9+/Zhy5YtePTRR7Fly5Z+Pe+9996L9vb2rv+OHz+ephYT5acis0HyeKHM8VT6EquZagMR9Q77VqLcwf6VKDcwVolyRybHwoxvovRTdVHN733ve12rxAGgoqICR48excMPP4zZs2dj2LBhAICmpiYMHz6863FNTU24/PLLUz6vyWSCyWTKaNuJ8onTZsSUMifeENnGNaXMCafNeF7P25dYzVQbiKh32LcS5Q72r0S5gbFKlDsyORZmfBOln6pXiPt8Pmi1iU3U6XSIRqMAgNLSUgwbNgyvvPJK13G324233noL11xzjaJtJcpndqsRa2ZNwJQyZ8LtU8qcWDtrAuzWzHfQamgDERHRQMP+lSg3MFaJBi7GN1H6qXqF+C233IIHH3wQo0aNQnl5ORoaGvCjH/0IdXV1AACNRoPvfOc7WL16NcrKylBaWorly5djxIgRmDFjRnYbT5RnRhRbsK62Es0eAR2BEArNBjhtRkU7ZzW0gYiIaKBh/0qUGxirRAMX45sovVQ9Ib5u3TosX74c8+fPx+nTpzFixAh861vfwv333991n+9///vwer2466670NbWhurqarz00kswm81ZbDlRfrJbs98hq6ENREREAw37V6LcwFglGrgY30Tpo4nFYrFsNyLb3G437HY72tvbUVRUlO3mEFEKjFWi3MF4JcodjFei3MBYJcodjFcidVN1DnEiIiIiIiIiIiIionThhDgRERERERERERER5QVOiBMRERERERERERFRXuCEOBERERERERERERHlBX22G5Ar2n0Cmj0C3IEQiiwGOAtY3ZeIiHIT+zQi6iueN4hyA2OVKDcxdomUxQnxXjjR5sfibfux+3Bz121TypxYM2sCRhRbstgyIiKivmGfRkR9xfMGUW5grBLlJsYukfKYMkVGu09IOjEBwBuHm7Fk2360+4QstYyIiKhv2KcRUV/xvEGUGxirRLmJsUuUHZwQl9HsEZJOTHFvHG5Gs4cnJyIiyg3s04ior3jeIMoNjFWi3MTYJcoOTojLcAdCksc7ZI4TERGpBfs0IuornjeIcgNjlSg3MXaJsoMT4jKKzAbJ44Uyx4mIiNSCfRoR9RXPG0S5gbFKlJsYu0TZwQlxGU6bEVPKnKLHppQ54bSx6i8REeUG9mlE1Fc8bxDlBsYqUW5i7BJlByfEZditRqyZNSHpBDWlzIm1sybAbuXJiYiIcgP7NCLqK543iHIDY5UoNzF2ibJDE4vFYtluRLa53W7Y7Xa0t7ejqKhI9D4n2vxo94fQEQih0GyA3WLAiGKLwi0lym+9idUmdwCtXgHuQBhFFj1KrEYMLTIr3FIidWv3CWj2CF19mtNmTPtguzfxSkTqwLEwUW5grBLljr6OhfsyPo/f1x0IochigLOg8349b+NkOlFq+mw3IBcca/Hi3u0HsKexpeu2apcDD82swChHQRZbRkTdMVaJesdu5QCZiHqP/StRbmCsEuWu3o7PT7T5sXjbfuw+3AwAsBp12DRnMjbsasTuxuau+00pc2LNrAn8QYwoBaZMkdHkDmBpj0EFANQ3tmDp9gNocgey1DIi6q7JHUi6AAAYq0Ri2n0CPj7tQcOxVnz8mQftPiHbTSIileJYmCg3MFaJcltvxuftPiFhMhwA6qpLsW7X4YTJcAB443Azlmzbz3E+UQpcIS6j1Sfgg1Md2Dh7EoYUmeAJRFBo1qPJHcDibfvR6hOYjoFIBVq9AhqOtWHh9S5UjixGMByF2aDDvmOt2FR/BK1exmq2iG3p4+rk7Om5qgTgChIiSq3VJ2CfVP+axrEw+wui86dkrPYG45mo9060+XH/8wcxdngRKkcW42R7AKetBowaZMUFJdau+zV7hIQxPABUjizG+l2Nos/7xuFmNHuErthjXBKdwwlxGX4hjGfuvBoP7DyUtPXsmTuvhk8IZbF1RBTnCYbxeG0lNu85kjAgqHI58HhtJTzBcBZbl784+aouYqtKgHMrSNbVVnJQTEQJvDL9qzdN/Sv7C6L+USpWe4PxTNR77T4B9z9/ELddOSopfqtdDqz5ygR8blDnpLg7kDz/FAxHJZ+/4+xjGJdEiZgyRcagAlPSZDjQufVs1c5DGFRgylLLiKg7R4ERm/ccSYrVPY0t2LznCBwFnORTmtzkK7fvKU9sVUlcfAUJEVF3JVbp/rUkDT+isb8g6j8lYrU3GM9EfdPsETB2eJFo/NY3tuDe7Qe64qbIbEh6vEkvPa1XaDYwLolEcEJchk+IJJ2U4uobW+ATIgq3iIjEhCKxlLG6p7EFoUhM4RYRJ1/VR2xVSXcdMseJKP8I4ahk/yrIrEzrDfYXRP2nRKz2BuOZqG/cgRAqRxanjN/d3eLGaTNiSpkz4XjD8TZUuRyij51S5oTTZmRcEonghLgMuckBTh4QqYNXkN4G6pM5TunHyVf1EVtV0l2hzHEiyj8dMmkW0pGSjP0FUf8pEau9wXgm6psis6HXaU/sViPWzJqQMCm+qf4I7r6+DDU9JsqnlDmxdtYE2K1GxiWRCOYQlyE3eSB3nIiUwYk+9eFnoj7xVSVviKwQia8gISLqrtgifa62yxzvDfYXRP2nRKz2BuOZqG+cNiOa3L2PmxHFFqyrrUSzR0BHIIRCswFOmxHrRW6L1wZiXBIl4wpxGSUFRlSn2H5S7XKghHmJiVRBbPtYHCf6soOfifqIrSoBEleQEBF1N6TQlLTqLK6mzIkhhf2vp8P+gqj/lIjV3mA8E/WN3WrEaIc15byTWNzYrUaMGWLD5aNKMGaIDXarUfS2OMYlUTJOiMsYWmTGQzMrkk5O1S4HHppZgaFF5iy1jIi640Sf+vAzUaf4qpJX7vk8dsy/Fq/c83msq63EcFaXJyIRdqsRa1Ocyx9J07mc/QVR/ykRq71tB+OZqG8uKLFizVcmSKY96Q/GJVEyTSwWy/tKc263G3a7He3t7SgqKhK9T5M7gFavAHcgjCKzHiUFRk6GEymsN7Ha7hNSbhWj7OBnkp96E69EpA5q6V/ZXxBJU0us9oZa2kGULeczFs503DAuic5hDvFeGlpk5gQ4UQ6Ibxcj9eBnQkSU+5Q4l7O/IOo/tcSRWtpBlEsyHTeMS6JzOCHeS/Ff0tyBEIosBjgLeCIhIuoNnj+JiHIfz+VEuYGxSpS7GL9EyuGEeC+caPNj8bb92H24ueu2KWVOrJk1ASOYc5WIKCWeP4mIch/P5US5gbFKlLsYv0TKYlFNGe0+IemkBABvHG7Gkm370e4TstQyIiJ14/mTiCj38VxOlBsYq0S5i/FLpDyuEJfR7BHwz2YvXlhYBb1Oiw5/59aVUCSKBc/sQ7NH4BYWIhVJKIBr0aPEygK42dLsEZIGdXFvHG7m+TOLuB2TiHqLY2Gi3NDsEfD+STc2zp6EIUUmeAIRFJr1aHIHsHjbfsVjlWMNot5r8Qq4bGQx5lx7IYLhKMwGHfYda8Wm+iPYe7QVbb7QecUT45AoNU6IywiEQvjlvCuxbMdB7Gls6bq92uXAL+ddCX8wlMXWEVF3x1q8uHf7gaRYfWhmBUY5CrLYsvzkDkifHztkjlNmcDsmEfUFx8JEucEnhPDMnVfjgZ2HkmL1mTuvhl9QLlY51iDqmxiAhmOtWL+rseu2KpcDG26fiBhiWLbjAHZ3i+vexBPjkEiaJhaLxbLdiGxzu92w2+1ob29HUVFRwrHjLV6sfPEQLh1hR+XI4oRf694/0Y6Vt5RjJCfaiBQhFatN7gCW/n5/ylh96CsTuFJcYR+f9uALP3o95fFX7vk8xgyxKdgiavcJWLi1QXTl/pQyJ9bVVqZt1YhUvBKRusiNhVe8eAjjFBgLcyUbkTS1xKoUJcca1Dc8xyqrt2Phdp+Ahc/uS5jwjnto5ni88n6T6DXuhyfdePRrl4l+hozD9GDMDGxcIS7DG4qg9qrR2LznSNKvdXOrSuENRbLYOiKKa/MLkrHa5hc4Ia4wp82IKWVOvJFiIOa0cTChNKaxIaK+8oYiuF2BsTBXshH1j08mVn0KXbdyrKFOPMeqV7NHEJ0MB4ARdovkNW6LVzyeGIf9x5gZ+FhUU04M2LznSMK2MwDY09iCzXuOdO5tIaKsi8nEKvfCKM9uNWLNrAmYUuZMuH1KmRNrZ03gICwLmMaGiPpMgbEwi4kR9Z9axsIca6gPz7HqJhUzdqtBMq4jUfHAZhz2D2MmP3CFuIwYkHTyidvT2ML5cKJuslnQMhaTiVUGa1aMKLZgXW0lmj0COgIhFJoNcNq41SxbiswGWI061FWXJm273FR/BIVmQ7abSEQqo8RYmCvZiPpPLdetRTJjCY41lMdzrLrFY0ZsjG7S6yTjOtWEuFwcFlkMTAcigTGTHzghLsMTCEsfD0ofJ8oX2S5oyVhVL7uVgyu1cNqM2DRnMtbtOpy07XLTnMlMY0NESXwy/adP6H//ypVsRP0nF4vpiNXeYMo89eE5Vt2cNiO+eOkQ/PuVo5JSo2ycPUnysT5BPBWSVBx+8dIhMOq0STnGmQ7kHMZMfuCEuIxCi15mNR3/hERN7gAe/tP7qKsqxZKbxsITiKDQrEeTO4A1f3ofK6aPz/hK8UKzHk6bEWtnTcCQIlNCGxZv249CE2M1W7j6QF027GoU3Xap1WiwvrYyS60iIrUqshgwssSCDXdMhF6nRYe/81weikSx4Jl9sqvQesMm00cXsA8nklVoko7VQpMyK7PjKfOWbNufMBnHlHnZw1X76ma3GrFyejmWP38QlaNKUFdV2jXv5LQZ4bQZcduVo0Tno+wW8c9OKg5XTi/HihcO4bKRxZhz7YUJz7ni+YMpC3XmE8ZMfuDoUsYgqxEbZ0/C+lcbk1bTbZw9CYPy/ERBBADtfgHfueESPLDzUNIK8eXTytGuQEHLQqMOz911NVa8kNyG5+66GhYdSyZkA4uRqEtn0R7x7X+7uf2PiERYdFr8at6VuG/HwaT+9VfzroQemn6/hlGnRZXLIbotvMrlgJF9OJEss04jGataBdMHMmWeunDVvvoFw1HRorg1Z69lH/rj+33e3ZkqDlu8Am4TWY0uV6gznzBm8gNHlzJiAJ54VXw13ROvfcwc4kQADFpt0mQ4ANQ3tmDVzkMwaDN/qgnFYkmT4fE2rHzhEEJMIq44FiNRH27/I6K+imqQNMEGdPavy3YcRLT/8+Fo8wuYW1WKKpcj4fb4xXm7n/0FkSytRjJWoU1DsPaB3WrEmCE2XD6qBGOG2PJ+gi2bWOhe/cLRmGjxzN1nr2XHjbAn3L6nsQUbdjUiEI5KPq9YHKZ6LblCnfmEMZMfuEJcRqtXwLvH2rDwepfoFpVWb+ZXvhKpXSAcTVnso76xRbajTgd/SLoN/lDm20CJWIxEfbj9j4j6yitEJPtXb4r8pX1hMxlQ+4u3UFdd2rVV3KTXouF4GxZtbcCLC6v7/RpEA50SsUq5i6v21S0ajUnG79yqUgDJhTfPeDt/MO7LnJTUa0kV6sw3jJmBjxPiMjzBMB6vrRTdTvJ4bSUL9RFBflWpEqtO1dAGSsTVyOrjtBlRU+YU/aGihtv/iEiEEv2r02bEpNElCWPtOG5NJuodjoVJDgvdq9OJNj9avNI7oYLhKKxGnejcVLXLgYdmVmCUo6BXrydfgJc/nsUxZgY21adM+de//oX/+I//gMPhgMViQUVFBfbu3dt1PBaL4f7778fw4cNhsVhwww034PDhw2l7fUeBUXI7iaOAwUEkt+o0HQW3ZNuQoqBIb49T+nE1sjotmOoSTUuwYKorSy0iIjVTon/l1mSi/uNYmCj3xFNMBmV2VJv0WtRVl4rOTdU3tmDp9gNocgd69Zp2i3SfmqpQJ9FAo+oV4q2traiqqsLUqVPxpz/9CYMHD8bhw4dRUlLSdZ9HHnkEjz/+OLZs2YLS0lIsX74cN954I9577z2Yzf1PZRKMRPHhqQ5snD0JQ4pM8AQiKDTr0eQOdJ64IkzDQFRSYES1y4F6ka1X1S4HShT44cgkU5DLxIJcinPajPjipUNwyfCipJRTH550c8VfFjR7BNQ9/Y5oWoK6p9/BiwurOfFERAmU6l9HFFvw8Fcq4A6E4faHYLcYUGjWYzgLMBP1itrGwk3uAFq9AtyBMIosepRYjUw1StRDPMXkZSOLU8bvF8YOBgDcWD4U44YXYV71Rdj/aRs0GmD8CHvXNZavl9kLWDCSqJOqJ8TXrl2LkSNHYvPmzV23lZaWdv1/LBbDY489hmXLluHWW28FAPzyl7/E0KFDsWPHDtx22239boMvGMIzd16dVDCw2uXAM3deDW+QRX6IhhaZ8eDMCty3/UDCpHi1y4EHZ1YoMvht8wm4f1o5Vu08lNSG+28pRxsLOCrObjVi+bRxuHf7AdFtfZx4VZ47EIJPiIimJQC4nZqIkrX6gpL9a6sviFLY+v06R1u8WLr9QNJ4+8GZFRjdy23gRPlMqVjtjWMtXtwrEs99SetAlA/iKSY31R/B47WVAJAQNzdcOgTLp41Lus6ucTkxf+oYzNuytyvFSc3ZXVUjZH5I9gsR3H9LOVa+cDDpXLF6xnheo1He0MRiMdVmzB83bhxuvPFGfPrpp3j99ddxwQUXYP78+fjP//xPAMAnn3yCMWPGoKGhAZdffnnX4z7/+c/j8ssvx09+8pNevY7b7Ybdbkd7ezuKiooSjokNzuM4SCfq1OQOYOnv9+PSEfaulcDxVafvn2jHQ1+ZkJZJcalYPfKZB6v+8B7GibThvRPtWH7zOJQOVuYigDq1+wQs3Nogmq96SpkT62orOeBS2MenPfjCj15PefyVez6PMUPSEydS8UpE6iIVr5985sFqif512c3jcFE/+9cTbX5873f/SDnefuSrl8le4BPlg1wYCze5A7jn//09ZTz/8OuXc6U45YXejIW7j817Fsw06bUoG2LDfdsPYndj8vVUlcuBylGJ9TfkrrHafQL+ePAU/vLeKdFzxYcn3Xj0a5fxGo3ygqpXiH/yySf46U9/invuuQdLly7FO++8g0WLFsFoNGL27Nk4deoUAGDo0KEJjxs6dGjXMTHBYBDBYLDr3263O+V9fTLVullwgAho9Qp45YPP8MoHn6U8fj4D377EajASxa4PPsOuFG34/peY3khp8S2AYt443Ixmj8DBlsIyWVSzL/FKRNnVl3gNyfSvi9PQv7b7Q5Lj7XZ/iBPilJdycSzc6hUk4/l8rwuI1O58xsLd05f03MVZ5XLguzdcLDoZDnSuJK+rKk24Te4aq9kjYEihSfJcwWs0yheqTqobjUYxceJEPPTQQ6isrMRdd92F//zP/8TPfvazfj3vww8/DLvd3vXfyJEjU96X1bqJ5LkD0vnK5I6n0pdYdfulY1HuOKVfu186TU07P5OsyFRRzb7EKxFlV1/i1SOTk1TueG/I9dEcb1O+ysWxcKauC4jU7nzGwnarEQ/NrEC1yNh8blUpPvMEUzyyk1gxTqk+0x0IyRbwZJ9L+ULVE+LDhw/HuHHjEm679NJLcezYMQDAsGHDAABNTU0J92lqauo6Jubee+9Fe3t713/Hjx9Ped9Cs3SFXbnjRPmgyCy92UTueCqM1dxmNUp/7lajTqGWUFy8qGblqBJsnD0JT9wxERtnT0LlqBLUPf0Omj3nn2u/L/FKRNnVl3iVP5f3f8NpkYV9OJGYXBwLZ+q64Hy1+wR8fNqDhmOt+PgzD9pZV4gy5HzHwkIkistHleDFu6sSxuaLtjbAKFMM16RPPi4V60Vmg+hjevt44Pxiqt0n4JPPPPioqQMfnHJj39EzjEfKOlWnTKmqqsKHH36YcNtHH32E0aNHA+gssDls2DC88sorXTnE3W433nrrLXz7299O+bwmkwkmk6lXbTDrtah2ORKKDcRVuxwwy5xMiPJBSYFRMk5KCs5vy1VfYtVuMaDG5RTdUlbjcsIuc7FN6afValJWS69yOaDTarLQqvyWyaKafYlXIsquvsSrXgvJVEvpGArbLQbJcQT7cMpXfb1ulRoLK3XdmqnrgvNxos2Pxdv2J5y/ppQ5saYXhQeJ+up8x8Lt/hDW72rEuOFFmP/MvoRjDcfbJK+nGo63Jdw2RSYFotNmxNv/PJPyOeUefz4xdaLNj/ufP4jbrhyFzXuOJLwu45GySdUT4t/97ndx7bXX4qGHHsLXv/51vP3223jyySfx5JNPAgA0Gg2+853vYPXq1SgrK0NpaSmWL1+OESNGYMaMGWlpg0GrwQ9uLcfbn5zBkCIzguEozAYdmtr9uPIiBwyc0CFCLBrDyunlWPlCclX7ldPHIxZVoHZvLIYFU8cgilhCJ9uVCkK99YMHLL1Wg7tqLsK0iuFJ588RxRZOiGdBkUpWjxFR7rDodVg4dQxuGj8MQ7udy0+1+zFmsA0Wff93+4wotuDBmRW4b/uBpHHEgzMreKFM1AsaDbDoCy58uWJY0rjLNcQGjULDLrNei5XTx2PlCwdFrwuUmphv9wlJE3dAZ47lJdv2s7g7qUZ8fG416PCdG8pQ7XIiHImhwKSDRqPBv40biv996QPs7hZPNS4n5k91Yd6Wd7pum1LmxNpZEyS/13arEdddPBilzgIASLhurpF5/PnEVPwxl40sTpoMl3ssUaapekJ88uTJ2L59O+6991488MADKC0txWOPPYY77rij6z7f//734fV6cdddd6GtrQ3V1dV46aWXYDanp1CHNxTByfYA/njgZNIJaHixBcPtLAhC1OoP4Rsb38LaWROw+Kax8AQisJl1OO0O4rYn38Sv5l2FYRm+mG0PhFG3ZS/qqktRV1WaUC27bss72PbtazEioy2gnhwFRkRjwFO7P+lx/nRgxfTxcCi4Qog6dS/c05PcihAiyk+eUAReIYI/HjiZMLlV43JgbnUpPKH0FJgf7SjAI1+9DO3+EDoCIRSaDbBbDJwMJ+olIRqF3WoUuW51YPkt5RCiyhTVbPYIuO3JN1NeF/zmrmsUmfhicXfKFU6bEV+8dAhKbEa8+88zeOzlw13HqlwOLLq+DKtnjMfHzT4EQhGY9Foc+Fc73v5nC9bVViIYjuIiZwGG2829+k4PL7bAatThoRkV8Aph+IQI7BYDhhSaJB9/PjEVf8ycay9MuUOV8UjZouoJcQCYNm0apk2blvK4RqPBAw88gAceeCAjrx8D8IvdnyT9krW7sRlRxLB82jjxBxLlEbc/hGaPgHlb9ooeV6Iwh9ufuVQQdH4C4ShWvHBQ5PzZgpUvHMQPv3457FlqW76yW41YM2sClmzbnzAp3psVJUSUp2LAxvrkVV27G1sQBbD85vSNhUcUWzgBTnSeDBotlqUYdz3w4iGsunW8Iu1wB7J/XRBvhxReG5Ba2K1GrJxejiXb9if8mAWcW8E9rWIE/tXuT3mtu2P+tX0ax9utxj6P+88npuKPYSFPUiPVT4hnWywG0dxKQOftzMJA1FkMy2kzYu2sCRhSZIInEEGhWY8mdwCLt+1XJA1DkcUAq1GHuupSVI4s7tomuu9YKzbVH2EqiCxo9Qopz5/1jS1o9QoYWsRdNkobUWzButpKNHuErlWYTlvfB8VElB9iABqOtWHh9S7R/pVDYSJ1CISjkuOugMyEVLoUmdUxJmeaOMolgVA0aTI8bk9jC+qqSjGkqDM/uVh8lfRjHN/uE9DsEeAOhDqv6wvErwvOJ6bij+lvIU+iTOCEuAxPICx9PCh9nCgflFgMeO6uq7HihUMJA/FqlwPP3XU1bMbMn2rsZj02zZ6Mda8eTvjlvMrlwKbZk2FXuKI9AW6Z86fcccq8GAAwlTsRSfAFw3i8thKb9xxJ6l8fr62ET+C5nEgN5FZYKrUC02kzYtOcyVi3S2RMPmeyYunZmCaOconc6ut4OlCrUSfaJ59vccq+FMk8n5iKP0aqOCjjkbJFmYoWOaxQZhKt0MRJNiKNVpM0GQ50rkZZ+cIhaBQonhgDsOHVw0lt2NPYgg2vNnIFWxYUyZw/5Y5TZpxo82Ph1gZ84UevY+YTf8UXfvg67t7agBNt/mw3jYhUaFCBUbQQ1p7GFmzecwSDuLuESBWKLNIrLOWOp9OGXY0px+RKiaeJm1LmTLidaeJIjaxG6QLVJr0WQ4vM2DRnErZIFKds9wm9fk25Ipk9n+t8Yir+mA9PujG3qhRVLkevH0uUaZyNkGE16lDtciQUEYqrdjlkT1xE+UANqTHcgXDKbWa7G5vhDoRxQUZbQD2VFBglz58lLKqpuPOpDk9E+S0YSZ2GYU9jC4IRZdIwEJE0k06bcgVmlcsBk06ZtXDNHgG7G8UL7+1WuHge08RRLmj3Cdh3rA01Lofo9WyVy4EmdwBDCk2IRJHymrevxSnPp0jm+cTUiGILHv3aZWjxClh5Szki0VhXIU/GI2UTJ8RlBMIRrJxejpUvHEqY1Kl2ObBy+ngEwpEsto5IHeS2eCmRGqPDr45tonSOWa/FyunjsfKFg6LnT7NMLjlKv/MZ+BJRfnPL9K9yx4lIGWe8AuZWlQJIrIFV5XJgblUpzvgElCrQDrUVszyf4oFESmr2CFi18z2sv70SgCbhB6UqlwMLp5bBZtLhxy9/hK9eMVLyufoSX+cbq+cTU4xDUiNOiMuIRIF5v3wHG+6YiKU6LTr8IRRaDAhHopi7+W08+c1J2W4iUdbJFcGQSz2UljbIbANloQ7lNXsELP39P/Do1y6HV4jA7e8s1FJg1OF/fvt3PPyVyzgwUpjaLlKJSP3k+/j09a//avXBHQjD7Q/BbjGg0KzHBSXWtD0/0UBmNenxjU1vo666FHVVpV05hxuOt2HR1gZs+/a1irSDxSyT9bZoIeUndyAEnxDBwmcbsGDqGHz/pkvgF6IoMOpgNepw9IwPxVYD7qoZA7vVgIXXu7Cp/gh8QvLizAKTHh+f9sATDKHYaoQQjsITDCd979p9Aiwy2Q7yMVYpv3BCXIZfCGPF9HKsfemDpF/aV0wvh5+FhIhgNWpR43KKbo+scTlhNWZ+JbDFoJVMz2ExcDWy0oRwCI989XLcu/1AUrHVR756OfxC73PcUXrwIpWI+sqsl+5f07Xb52iLF0tF+osHZ1ZgtKMgLa9BNJDptcAVo0sSCu3F1ZQ5odTGPKfNiJoyp+iOtJo8LJ7Xl6KFlJ+6j88vGVaENX/qnHuyGnXYetfV2Lj7k4Q0KdVni1ov2tqQMCleU+bE3qOtWLXzPTxeW4lH/vxhQp8a/95pAfzzjA+ffOZhoUvKa5whklFilS4kVMJfdomghwYLpo5JKpJR5XJgwVQX9Mh8UU29RoOV08tR3aMN8fQcek3m20CJbCYj7ttxQLTY6rIdB2Az8fyptHildzEc+BKRGC0g2b+m42LiX62+pMlwoLO/uG/7Afyr1ZeGVyEa2Cx6HeZfJz4en3+dCxa9crWvFkx1pbwuyCd9LVpI+Sk+Pq+rLk2Ye6qrLsX/vvRBUs7w+sYWPL3nCOqqzyVBqilzYsFUF1btfC/peeLi37t/nvFh3a7DWP2H91nokvIaV4jLYCEhInluIYK6LXtFt2jWbXkH2759bcYLWnYIEdz1y71Mb6QiHiEiWWzVI7LNjzIrXul9ybb9eKPHSiUOfIlIjD8SxX889RbWzpqAxTeNhScQgc2sw2l3ELc9+SZ+fedV/X4NdyAs2V+kszA2UxfQQNUhRDAvxXh83tnxuBKaPQLqnn5H/Lrg6Xfw4sLqvIk51m6h3oiPz//Z7E3Y4VE5slh0xwfQ2Tcuvmksql1ORKMxjCyxYOZP/4q66lLcWD4U44YXYV71Rdh3rDUhvcobh5vxvS9d0tXnLtrakBSrrsE2DOfuBcoDnBCX4faxkBCRnI6zec9SddhK5CX2BEKS6Y28Qcaq0uTOj8xXnR3nUx2eiPKX2xdCs0fAvC17xY+nYSysVH/B1AU0kHX4sz8eB87lQ852O9SAtVuot0YUW3Cq3Z9wWzAsvfjy+Bk/THotxg0vQrMngDWzJmDzniMJsVclkl7FEzi3KEksVnfMvxajwVRlNPBxQlxGoaVzomDtrAkYUmSCJxBBoVmPJncAi7ftZ75VIgBFFgOsRh3qqktRObIYwXAUZoOu6xfpIpmCl+ngsJmwac8R1FWVYsnZFWzxWP3du8fxvRvHZrwNlEjuc+f5M3tY6Z2IekuJsbDcOCIdryGXumBdbSXPi5TTiqwGjCyxYMMdE6E/u1uyyGJAKBLFgmf2KTIeBzrzIfP6uRNrt1Bf2C3GhNjRyqQdNem1+FyJBcOLLRDCUax56UPRVL9AZ/qV+MS3zaxL2ec+9/YxlFiN+Pi0hzupaMDjhLgMu1mP5+66GiteOJRU5Oe5u66G1aBcLjYitbLotdg0ezLWvXo46RfpTbMnw6JAFZ9ILIrv3HAJHtiZHKvLp5UjEmN6I6XZzXrJokp2M7sgIiK1Kzbr8dxd12DFCwdFxsLXoCANRavtZr3kOCId/QVTF9BAZ9Vp8at5V+K+Hcmx+qt5V0IbU6YdTptR8pyRT/VK4rmh3xA597B2C/XUM3YWXu9KWfSyyuVAkzuA0x1BFJoNEGRS/dZVdeYbn1LmRDAUEe1zrx87GM/ddQ2W7TiI3Y3cSUUDH4tqytFokibDgc6cTStfOASwUB8RYgA2vHpY9BfpDa82Qonxt16jTZoMBzpjddXOQ9BreLpTml6nlSzupNfxMyEiUruYRpM0sQXEx8IHEUvHWFijkRxHpGO8zdQFNOBpNUmT4UC8mPlBQKvMdWsgHJU8ZwRk0kAMJPHc0D0LmrN2C4npGTub6o+IFr2scjmwcGoZhtstWLXzPSzZtl+2DwuGo13fu+F2i2ifO26EHSteSJwMB1gElgYuLs+T0e4PSRb5afeH+EsZ5T1/KIp3j7Vh4fUu0a3O/lDmB76BcBQNEm3Ip8G3WpzxCpLFnX7/7WsxpMic7WbmpRNtfrT7Q3D7Q7BbDCiyGNiXEZGodn9Isn9Nx1i43R/C7hTj7d2NzWl5DaYuoIHOK0Tw4akObJw9STRViVehYuatXkHy+rnVK2BoHo3/WLuFeutMj9jxCREs2tqAb33+Iiz+0ljotBp4g51xfardj+/9bj98QgRvHG7G0i9fKvncpc4CrJoxHlajDifbA6LX7kVmfcrc/9xJRQMRJ8RlsCgckTxvMITHaytTFvFQoqClGtpAidyBsGRRJXcgrHCLCACOtnixdPuBpG3MD86swGgHC+gQUSJPIPP9q9x4Oh3jbaYuoIHOFwzhmTuvFk0f+MydV8MbVGZ1p9z4Lh/Hf6zdQnKOtnjR7hPv68pH2LH2pQ8S4rrK5cCaWRO6imXqtJqUfVyVy4E/HDiJ9bsaMaXMiUVfcIn26xtnT5JsI+e+aKDhhLgMJYr8EOU6R4EJP35ZfKszAKy+dXxetIESFZn10sVWmUNccSfa/Livx2Q40Lli677tB/DIVy/jSnEiSuCwmfDYK6n711Vp6F/liv2loxig3dpZqOy1jz7DkEJTV5/U5A5g6sWDOVlFOc9ZaE76wRs4lz7wwZkVirRDbnzH8R9Rovj4fPFNY5OunUaWWLH2pfdli2XqtBqsmTUBS7btT5gUr3E5sPyWcvzp4ElYjTq8cbgZK24pF71ulmM26NBwrFW20Ga7T0CzR8ipopy52GbqP/ZGMmxGHTbOnoT1rzYmrYrZOHsSbEYW1SQKyhTxCEYyn65EDW2gRDx/qk+7P4R6pgEjoj4IyfSvoTT0ryadVrJwmClNNSdiAP64/2RSsbDPXzw4Lc9PlE0+ISKZqsSnUMqUAqMONS5nUh5iAKhxOVHA8R9Rgvj4/M4OIenaaePsSSnH7vFimVPKnHCcncBdV1uJU+4APm31AwAajrdhxoY9qBxVjMdrKztXlIfCoueKhuNtKfviapcDO8+uMgdSF9o80ebH4m37E4pYq70oZy62mdKDFc1kBMIRPPFqo+gvck+89jECYWUGFkRqJpdaSO74QGkDJZM6f5LyPDLblOWOE1H+USL9QZtfSFk4bG5VKdr8/U/10O4TOi94WSyMBiglUg/1RjQWw4Kp4kXVF0x1IRqLKdIOolwRH38fOtmedO0U7EUdrIdmViSsZl79h/cxb8tezNuyF+t3NXb9WLZ5zxHUVZfiX60B0eeJF/Gs6VEEttrlwJyqUmyqP9J1m1jf2dXPHs6dfjYX20zpwxXiMsJRpC7yc7gZrNNHJF+ISonUQmpoAyXqECKS588OhVYq0Tk2mW3KcseJKP/YTDLnDZnjvWE26LFo69uiRZgXbW3Atm9f2+/XaPYISRe8cSwWRgOBWsbCvlAUC7c2YO2sCVhy01h4AhHYzHqcdgewcOs+/GreVYq0gygXtPuErvH32GFF+N8/f5Rw3KSXXsM63G6B0G2nVrNHwLtHW1MWwq6rKk35XPEinjsXVkOIRuELRmA16rDzwEks2toAAEnP2+YLdfWdudjP5mKbKX145StDdjVdkKvpiMx6reTWSLNMR65IGwzcEKM0FiVWH50WknGSpqwERDSAaDXS5w2tpv+vodMAV4wuES3CXFPmhC4Nr+FWyepZokxRw3gc6Cx0v2bWBGzac0S0CKCX189EAM6l6rhsZDGqXA7R1eAH/tUuGdehSBRCt6wFnqB0IexwNIYzXgE1LofowqXKUcX4/d//1a0AZxnW72qE1agTfd6aMifWnk0tkov9bC62mdKHl74yiiwsCkIkR6OB5NZITRouZOXotRrJNuiVaAQlkCuCxlX7yjPrdJifIk7mT3XBrGNeTyJKZNJpJc8b6cjvbTHoMP+6FK9xnQsWQ//PTUUqWT1LlCkayIzHFWqHo8CEzT0mwwF0pWxwFHC1JVH3VB3xVCV2kWsnjQaSfbBOm9h/FVuMkvE3ssSCMYMLMLc6dZqyeGqUNw43Qzg7SV9XXSr6vLu7pRbJxX42F9tM6cPZXBkGnRY3jR+Kr14xEkOKTPAEIig069HkDuB37x6HgcvpiBAIJ2+NjMfJwq378Os7M7810iNEJNvA7ZnKKzLr8YWxg3HpCHvSlr33T7TzB8UsCEai+P7v9mPDHROh12nR4e+spB6KRLHgmX3YNGdytptIRCoTiERxd4r+9e409fEeIYIVzx/Chv+YiHAkhg5/CIUWA/Q6DRb8eh9+UlvZ79dw2oyYUubEGyJbo6eUOeG0cZKOclsgkv3xOACEIjGZQrzK5hBv9wlo9ghwBzrHPM6zxQfzrQ2kLt1TdfiECJZs249Ncybj2TuvQps/1HXNVHFBsWQf/Kt5V0GnBf5xvBWFZgM0AOqqSnHHVaO7nuO5t4/htitHoXJkMbzBCPxCBFpocO+XLkUUMQRDUWg0QH1jc2fRzW5pLf/6SQtqypyoHFksuosLOJdaJBf72VxsM6UPZyNkuP0Cvv+lsVi242BCx17tcmDVjPFMsk8EwBOQ3hrpUWCrkfz2TG53UtoFJVbcf0s5lm4/kDCAqnY58ODMClxQYs1i6/JTIBTGU7Mn44Gdh5L6tKdmT0YwxG3MRJTIEwhL969pKKoZDIXw829eITre/vk3r0C7L9jv17BbjVgzawKWbNufcOE75ex2b05OUa6TG497Fdr67xWkzwk+mePpFE9JsbtHzK85m+IhX9pA6tM9VYfVqMOaWROw9qUPkmJ3isspGdfHzvjwyzf/iXnVpTjjDWFT/ScJqVCuHzsYz9x5NR7YeSjpemxOVWnXBHh8dXhPm+qP4MW7q/HPZq/k++kIhDBmiC3n+lmODfIbJ8RlDCowYeWLh1A5qqSryE/8l7bVO9/DilvKs91EoqxzFJjwxGsfi8bJs28dxfKbx+VFGyhRkzuA+7YfSFolVN/Ygvu2H8APv345hhaZs9S6/FRsMWJFij7tkZfex0r2aUTUg6PAiCdea8xo/+ooMKc8N63e+V7azk0jii1YV1uJZo+AjkAIhWYDnDau1KSBQS1j4SKzAVajDnXVpaJF/ZRKQdA9JUV3b5xN8bCutjLjsa+GNpA6dU/VUVddimffOioau4UWPX6y67BoChQAWPylsdjT2AItgJsqhuPdY20JhS+HFpnx7tEzaDjWlvD4+sYWxM6+9vpdjV3PF/93nE+IQANg1CDphUzxuM7FfjYX20zpwQlxGb5QBLdfNVq0KMHcqlL4QhGJRxPlByEalYwTIZpcIGQgtoEStfoE1KfYMlvf2IJWn8AJcYWxTyOivlKif/XKnJu8aTw32a28yKWBSS1jYafNiE1zJmPdrsNJ7dg0Z7JiKQi6p6ToKZ7iIdPnAjW0gdSpe6qOSaNKcPnIYtHYnXn5BUmT2XF7Glu6cnzvbmzBvOqLJAtq9kyHsqexBXXdVoX3/DfQuVI6nve/t6lFcrGfzcU2U/8xAbacGCSLEkDZFGhEqhSLSsdJTIHxtxraQIk6ZLbRyx2n9IvJ9Gkx9mlE1IMi/SvH20T9pqax8IZuK067t2PDq+I5iDPBLZMipkOBFDJqaAOpUzxVx5QyJ+xWQ8rYXfnCQdRVJ6cyifvMcy6lmNTzbN5zRPR5guFoyn93TxvSvb3dMbUI5TKuEJcRA5JOKHF7zm4zIcp3aogTNbSBEtlM0l2M3HFKP8YJEfWVEucNnpuI+k8tcdTsEbC7UXxV9G4FV0UXyaRmUSJ1ixraQOoVT9Vxoj2QMnZ3N7Zgjkhu7zij7twaV71OI3kO6L76O57WaOQgC564Y2JXipYxgwuwY/61omlDmFqEBhrORsjwBqVXMModJ8oHaogTNbSBEuk0nVv0xAZmVS4HdBpNFlqV3xgnRNRXSpw3eG4i6j+1xJFaVkV3T0nRU88UDwO5DaRudqsRH532nNdjq1wONBxvAwDUuBxw+6VjPL7622rUiaZWqXY5cNukkbhkWJFkezkBTgMFU6bIsJllVjjKHCfKByUynaLc8XRgrKqPVqvBvOpS1LgcCbfXuByYV10KLXsgxXHVPhH1lRL9K/twov5TSxypZVW0GlI8qKEN3bX7BHx82oOGY634+DMP2n2Coq9P4gplYnO43YKqHtdT8doAm+qPdP5/dSnsFunYMuk7L77qqktFU6vUN7bg3u0H0OQOnMe7OH/8XlK2cHQpw6TTSq5wNOk4o0PktBlRU+YULRpTo9DqB5NeixqXU3SLZo3L2TUAIOUYdVrYjHrcVDEcc85WTDfptWhyB2Az6hO2+JEyCs16VLscosVOq10O2QE5EeUfJcbCNoNOchxhM+j6/RpEA51arlvVcF0Qp4YUD2poAwCcaPNj8bb9CZ/LlDIn1syagBHFFkXbQomKzIaU4/MqlwMH/9WGxV8ai2aPgGA4gsE2E4x6Lc54BPzmW1cjFgPOeAQYdRrJ6+HBhSY8ccdEjBxkTVgZ3t3uw834+LQHkWhMke8Fv5eUTbzylXHGJ2Du2VxL3QcX8V/kWn0CUmd0IsoPnmAY868bg2gslhQn869zwRMMZ3zQF4pEMX/qGEQh0oapLoQirKqptGgshnW7DmO3yOCuxuXEqhnlWWhVnovGsHJ6OVa+cChh0F3tcmDl9PFAlJl6iShRqwJj4RggOY7gmYlInhKx2hs+ISIZzz4hArtVgYacpYYUD9luQ7tPSJp0BIA3Djdjybb9WFdbmfW/UT6LRqJYOb0cP3jhUMJ1Uzx2F21twMRRxbh8VEnXRHaVy4GFU8uw64PTePvIGcytKsVjL3+EB24tx7LnDybEXo3LibnVF+K2J/8GnxDBE3dMlGxPmz+kyPeC30vKNk6Iy7Aa9fjWr97F2lkTsOSmsfAEIrCZ9TjtDmDxtv341byrst1EoqxzB8KYt2Uv6qpLUddtJXDD8TbM2/IOtn37WlyQ4TZEopBtAynLH4ri3WNtWHi9C5UjixEMR7sKtmyqPwJ/iD9SKM0tRLD4d//A47WV8Iei6PCHUGgxwGLQYtHWBqz96mUZj1Uiyi0Wox6LNr4t2r8u2tqQlv7VI0QU68PbfQKaPQLcgRCKLAY4C7I/WUaUDhajHktUcN3a5g/JxvNwrvxUVLNHEF2xD3ROPipV6JTEdZwdn//ktkp80uxN6mcB4PJRJfhyxXBUjXGi0KxHkzuAu7fuw6/mXYUpZYPR7gvhoiGFiCKGh2ZUwCuE4QlGUGDSIRSJot0XRl11Z4oVuZ3TnyuxYE5VKdp8oYx+L/i9pGzjhLgMvRZYf/tErNt1OOkX7vW3TwSzMBABbn8IPiGScuuVEsVzPIGwZBs8ARbkUpo3GBIt2FLlcuDx2koWScuCgBDCT2orcd+OxJUj1S4HflJbiXZfMIutIyI10gCoHFUs2r9WuRxIR3nkjoAy4whuzaaBTKsB1tVWYv2rjUnXretqK6FVqJa5UvFMvaeWQqckLnh2fP7JZ17M27I34ViqAphVLgfWzJoATyCEr/38b1233zB2CC4fVYJPz/jw4B/fTzoXPF5biTNeIWWx1yqXA//3XhPW72pEzdlc95nqH/m9pGzL6IS4IAg4cuQIxowZA70+N+fezXodNux6LykX257GFmihwUMzx2epZUTqUSRTwEOJ4jlqKSRE5zgKTPjxy4dFz58AsPpWnj+V5rSZsfLFQ6gcVdK1aiu+an/1zvew8hamsSGiRFotcFfNRZhWMRxDisxd542mdj9GFFvSUiBZbhwhd7w3uDWbBjqjXosnekyGA+euWx9QKFWdEvFMfaOWQqdqoqbdQk6bGbsbmzH+c3Y8ccfEhB21qQpgxv/9g+njYTXqUFddisqRxQhFYzjc1IG9R1vRcKwt6TEaAI/MmoAqlzOpT+yeogXozCeeyf4x3d9LNX2mlBsyMkPk8/lw9913Y8uWLQCAjz76CBdddBHuvvtuXHDBBViyZEkmXjYjPEJENP8tAOxubIZHiCjcIiL1KTDqJAt4FBgzXwxLDW2gREIkKlrYCegckAnM6644byiC2qtGi64ymVtVCm+IfRoRJTJqtbigxIKndn+SMCaucTmwYno59Jr+LztVohggt2bTQBcMRyWvW4NhZcZdLHSvPk6bMeWK4CkKFzpVA7XtFgpFY9h54CTu3X6w67b4am6tRpNyt8WexhYEQhHJHbmLtjbA123Oqr6xBf5QFGOG2PDo1y7Dx6c90Go18AbDXSlaut8/k/1jOr+XavtMKTdkpDe699578Y9//AOvvfYazGZz1+033HADfvOb32TiJTNGLs2Ch1v+iRAIR7Bg6hhUuRwJt1e5HFgw1YVAOPOTbGpoAyVyy5w/O5jGRnEaIOUqk817jqQl9QERDSwxACt6FPoCgN2NLVj5wqG0FLyMFwMU68PjxQD7i1uzaaDr8Ktj3CWcLXQvFs8sdJ8ddqsRa2ZNwJQyZ8LtU86mxMinHwPldgu1p6G/6Wt77u9RBBM4NzbXy+Q6avMJkmP7uurkUrrx/m5okRkXOgvgDXbWA1u/qzFhMrzn/dMtXd9LtX2mlDsyskJ8x44d+M1vfoOrr74amm6rRsrLy/Hxxx9n4iUzRjYNg4lpGIgiUWDh1oaEIj7xYh8Lzxb7UKINdSkK+NSxqGZW2Ez6hC18PYtqFvD8qbhoDGiQKHQaTcfMFhENKIFwFB+e6sDG2ZMwpMiU0Mcv3rYfgTSsOlWicCdTBtBAZzNLj7uUum6NstC9Ko0otmBdbSWaPQI6AiEUmg1w2vIvpYTadgs1ewTsbmzpit1Jo0pgtxqg12nR6hUwuNCEhde7sKn+iOhktcNmktyRu+SmSwEg4fHd+7sRxRbZuk6Z7B/T8b3szWcavx/TqVB3GekVP/vsMwwZMiTpdq/XmzBBngu0Gkhu+VKqOAmRmgWEMNbMmoBNPX6djhf7CIQyvyLFF5QuqukTuBpZaQYdsGn2ZKx79XDSFr5NsyfDoOMJVGm+YFhyWyXjhIh68gshPHPn1Xhg56GkYrzP3Hk1vMH+r7xSonAnUwbQQKfTSo+70pB5qFdkC91zh3XW2K2cBFTbbiF3INRVOPPZt47i8pHFePT/Pkzqb8XSn9SUyc9HHT/jQ8Ox1q7HTxpdktTfDSk0ZbV/7O/3Uu4zbfMLWPniIaZToSQZ6RYnTZqEP/zhD13/jk+CP/XUU7jmmmsy8ZIZY9JpJbd8pSOnIVGuK7YaJbdqFVsyP/Dibg71Mep02PCqeFHNDa82wsjzp+IGFUjH6qA8v0giomQOmzlpMhzozEO6auchOGzmFI/sPa0WkilT0lG4kykDaKAz66XHXWa9MvV0CmXG5IUck1MWqW23UJHZ0FU4c9wIu+g4vb6xBU/3SH9S5XJg/nUu2espk17bNc5fPm2caH+X6/2j3GcaDEWZToVEZaQ3euihh3DTTTfhvffeQzgcxk9+8hO89957+Otf/4rXX389Ey+ZMYFIFHenSAVx99Z9+PWdmU8FQaR2QZniiUEFcgUaZApyGTj5qjivTFFiL4sSKy4YiUqmTFEiVokot/iESMo+vr6xRXQLd1/ptVo8+9ZRVI4qSUqx8OxbR7Hs5nH9fg2gc2v2/37tMrR6BbgDYRRZ9CixGjG0qP+T+kTZppZxV4FRh2qXA/Uibal2ORQvdN/uE5gqQWWy+ZmobbeQ02bEtRc5sH5XI+qqSlPurKhvbMHim8Zi3PCihBREv59/LWrKnKIpQ6pcDjQcbwPQeU2+8pZyDE+xIjqdKXWU/nylPtOaMif++on4eZEFtSkjE+LV1dX4+9//jjVr1qCiogL/93//h4kTJ+LNN99ERUVFJl4yYzyBkGQqCA8L8BDB7ZOOA7c/83HS4gliblXnr+Y9Y3VuVSnOeIO4aLAt4+2gc+Q+dxYwU54nEJJMmcI+jYh6UuJc3uIJ4varRouem9LZh59o8ycV3uK2aRoo5GJRqXGXOyBg1YzxWL7jYMKkeLXLgVUzKtAREAAUKNKWE21+LP7d/oT0p4z57Mr2eTi+GnrJtv0JE6jZWg1ttxph0Hcu3BJkFqZ82urH/Gf2Jdx2os2P2ddeiGgsJnoNvGhrQ9dtcrnC05FSJxufr9RnumJ6OW5ZV5/ysbwezW8Z2680ZswY/OIXv0jrc65Zswb33nsv/uu//guPPfYYACAQCOC///u/8dxzzyEYDOLGG2/EE088gaFDh6blNR0FJvz4ZfGtZwCw+tbxaXkdolxWaMn+1jOb2YDZm9/JaEEu6psiFXwvKBH7NCLqKyXO5TazAd9+Zl/CjkybWY/TZwt3pqM4d7tPSLpIB85tm15XW8lVYpTT5GJV7ni62ExGbHjtMFZML0c4EkOHP4RCiwF6nQY/fe0wFlxXpkg72n1C0mQ40Bnzi7ftx/o8jPlsr5ZXy3lYTQVG231CV5qhwTaT5H3Fjg8uNOFEWwBLvnQpguEIPvMEUWQ2IBCK4O4eOcczfe3Vm88XyExxy1SfaYtXkNzJxuvR/JaRCXG32y16u0ajgclkgtHY9y/8O++8g5///OeYMGFCwu3f/e538Yc//AG//e1vYbfbsXDhQnzlK1/Bnj17zqvtPakhFQSR2hXKbI0sVGBrpM2ow8QUBbmqXQ7YFN6eSZ2fiVRRYn4mymOfRkR9pUT6A7tZj3W1lVj/amPSCrd1tZWwy+Qk7o1mjyC6pRzgtmkaGEwy6QOVqn0VjkUxr3oMVr6QXIh3+bRyhGPKjDVOdwRFx6AAsPtwM053BPMq5rO9MhtQ13lYDQVG45/JD6aXo8rlgFEvHcNGfWIMV7sc+POhpq7r3/iq8P/85V6sq61MmAhWIh2M1Oe792grWn0hLH/+YMa+g6k+UzWlyCF1yUivWFxcjJKSkqT/iouLYbFYMHr0aKxYsQLRaO86Q4/HgzvuuAO/+MUvUFJS0nV7e3s7Nm7ciB/96Ee4/vrrccUVV2Dz5s3461//ir/97W9peS9y20SVSAVBlAtWTi9HdY9iWNUuB1ZOV2bFaTASkWxDMMJ81UqLxmJYkKIo8YKpLkRjsSy1LH+xTyOivmrxBrF8mnj/ev8t5WjxBvv9GjEAT/SYDAc6f6h74rWPkY7ewq2SdBJEmdLiDUoWp01HrPaGFhrJQrxaaBRpR5vMmKY9j8Y8cit3lSosyPPwOd0/k1af0JkezCNIxvDJ9kDXbdUuB+ZUlWJT/ZGu2+LFM+uqO3dLx9UolA6m3Z/6e1RXXYrlOw4o/h3M9YKhlFkZWSH+9NNP47777sOcOXNw5ZVXAgDefvttbNmyBcuWLcNnn32GRx99FCaTCUuXLpV9vgULFuDmm2/GDTfcgNWrV3fd/u677yIUCuGGG27oum3s2LEYNWoU3nzzTVx99dWizxcMBhEMnhsQpFrRDnRuobAadairLhUtQMYtFkRAhxDBXb/ciw13TMRSnRYd/s4tUKFIFHM3v40nvznpvJ63L7EaigD/tXUfNvzHRIQjMbjPtkGv02D+r9/FT85u0SLl+EJR3P/8oa7PpPuW2QW/3sfPJAsy2af1JV6JKLv6Eq9mgx6rdx7EwzMnwCNEuvpXm1GHpdv3Y+nN5f1ujzsQxrsSBX/dgTAu6OdrFMmc3zimJzXqS6wWmAz45qbspw8MhKULeAfCyqwQl9u9Ys2jnYpqWZldNMDnVvoSr80eAe8ebcXC612wGvVo9ggYUWLBV3/2V9EYXrJtP35z1zX403/VwBMMw2LQ4fWPTic9757GFtRVlWJEsQW//dY1KDDpUGDSi37f230CWrwCwtEYorEYfMEw7FajZBoTqbQ7VmPq6cXKkeI7uYHMfwfVlCKH1CUjE+JbtmzBD3/4Q3z961/vuu2WW25BRUUFfv7zn+OVV17BqFGj8OCDD8pOiD/33HPYt28f3nnnnaRjp06dgtFoRHFxccLtQ4cOxalTp1I+58MPP4wf/OAHvXovBUYtNs2ZjHW7DicV+dk0ZzIKjMpsPSNSM08ghBXTy7H2pQ+StjqvmF4Ob/D8fu3vS6wGQmGsu32i6PbMdbdPhE/InxUHahEMhfDzb16BZTsOJn0mP//mFWj3KbNSic6x6LXYNHsy1r0q0qfNngyL4fz7tL7EKxFlV1/i1aQHVs2YgCXbDySdy1fPmIBorP87sLxB6YK/5zuO6M5pM3LbNOWcvsSqxajFFaNLRCedasqcsCp03apEPPdGgVEvmX6iQGLybqBRy8psp80oObeS6+fhvsSrRyROvnNDGa4YlRzDVqMOG2dPwooXEgvV1ricWH97JRY+25CUJ/vvx9tw7+8PdP27Z2qSE21+3P/8Qdx25Shs3nMkIU5SpTGRS7uj1WpSxpycTH8H1ZAih9QnI73iX//6V1RWJq/8q6ysxJtvvgkAqK6uxrFjxySf5/jx4/iv//ovPPPMMzCbzWlr37333ov29vau/44fP57yvlposGGXeAGyDbsaFdvyRaRmDpspqSMFzm3bGlQgXSAklb7EqqPAhFUS2zMd59kGOn+OAnPSZDjQ+Zks33EQjoL0ndepd7RaDTa8mqJPe7URWs3592l9iVciyq6+xKtBq8OyHQdEz+XLdhyAQdv/VZaOAulxRDr6cG6bplzUl1jVQ4OFU12o6ZFuocblwMKpLugUum5VIp57o9hqwN3Xl4mmn7j7+jIUW3N7NXJfqGWHjFeISM6teCWKH+aCvsRrscWIZ986ispRJdg4exKeuGMiPn/xYNz75bFJ39llN1+KJ15tTKrlsbuxGZvrO1OkdDesyIxVO99LuC2emqTJHcDRZi8W/+4fGDu8SDRW44Vnm9znUrT0Ju2OXqtJmfJlSJF03CtR8PPj0x40HGvFx595FEsTROqWkZ9FR44ciY0bN2LNmjUJt2/cuBEjR44EALS0tCTkAxfz7rvv4vTp05g4cWLXbZFIBG+88QbWr1+PP//5zxAEAW1tbQmrxJuamjBs2LCUz2symWAy9a4j7hAi2J3iF67djc3oyPGTNlE6hGQK9YXOs1BfX2LVK0REC34BnRftuT7AykUeIZLye1Hf2AIPPxPFeWX6tP7ESV/ilYiyqy/x6pHpX9NxLleq4C+3TVOu6VOshiLwCRHcVDEcc7qlW2hyB+ATIvCElBl3qaWAt91qxOhBVkybMCIh/cTpjiAuHGTNq7hXyw6Zdn9Ichza7g8pVuAzE/oSr6FoFLdfNTppJ8X1Ywdj8ZfGotkjwGrUwRsMw24xYOn2g6LPs7uxBXOqzk2I15Q58Zf3m5JWjAOdk9cfn/YgEI52PS5VGpPdZ+8bicYwotjSq7Q7TpsRD//xfVSOKklK+XK4qQM1ZU7R56jJ8HdQDQVlSZ0yMiH+6KOP4mtf+xr+9Kc/YfLkyQCAvXv34v3338e2bdsAAO+88w7+/d//XfJ5vvCFL+DAgQMJt82dOxdjx47F4sWLMXLkSBgMBrzyyiuYNWsWAODDDz/EsWPHcM0116TlvXgC4X4dJ8oHbr90HLgViBPZWA0yVpUmV6AxnwrnqAU/EyLqKyXGwm6fcgV/uW2aBqwY8FT9JylThCy/eZwizVBTAe/hxRZ8efywhB/BJo0uybtzQHyHzJJt+xMmxZXeISM3zsyncWg0BtHV2bs++AzBcBSVo0owbngR5j+zD0/cMTHFs3SKF9CcUubEiunluGVdfcr7tvlDsJn0CY+Tuu+SbfuxrrayV2l3xgyx4Qe3jseSbfsTJtqnlDlx++SRGFFsRTQWS0qxumCqS/K5+0NuZfu62sq8Ox/QORmZEJ8+fTo+/PBD/OxnP8NHH30EALjpppuwY8cOeDweAMC3v/1t2ecpLCzE+PHjE24rKCiAw+Houn3evHm45557MGjQIBQVFeHuu+/GNddck7KgZl/ZzNJ/IrnjRPlANk5MmY8TNbSBEhVZ1LE9k87hZ0JEfaXEWLhQwXOTVEEwolwWAyRXZscUaodcvCo91uCPYJ3UsENGbd+NbIpGY5LxWtdt1bdJL53p+HMlFvzpv2owwm5Gi1cQXR3e/blKCgy9el6TXtu1+ru3aXdSfc+aPQLqnhYv+lv39Dt4cWF1Rr6LaikoS+qUsRmiCy+8sCtlitvtxtatW/Hv//7v2Lt3LyKR9G3X+vGPfwytVotZs2YhGAzixhtvxBNPPJG259dqIFmMQ8sU4kSqiBMNpNvQj9TIdJ4KjDpUuxyiW+2rXQ4UiFQ7p8yyyXwmNn4mRNSDEn28UucmbpumgcwrsxtS7ni6qG38xx/Bzsn2jwNmvVbyu2GWmaAdSHyCdDwGw1G8d9KNKpcDDcfbUvbDNS4HDv3LjUkXdu58cPtDKf/G8ef6wtghss8bPw50rv4udRb0Ou2O2Pfsk2YvfEIkZYqWTO0OUEtBWVKnjJ5x3njjDcyePRsjRozAD3/4Q0ydOhV/+9vf+vWcr732Gh577LGuf5vNZmzYsAFnzpyB1+vF73//e8n84X2l0SBlYYC5VaWcZCNCZ5zMqy4VLeIzr1qhOJGJVVKeNgasnjEe1T0+k2qXA6tnVECr1FIl6hKLxrB6RoXoZ/LgzArEovxQiKgHuf41DX28FsAPpo9HjSux4GWNy4kf3Do+LRcsvSkIRpTL1LKzORSNYtUM8XhePaMCoagyOcQB4HSbHy1eAUIkCiHc+V+LV8DpNr9ibaBEy6eVi45D77+lPEstyg67RfqHCZNei031RzC3qhTvn2gX7YdrXE4svL4MV15Ugo5ACB81deDNT1owr/qipPtWuxxYeUs5DDoNjHoNVtwyDqNLrFgxrTzpGj7ev2+qPwKgc/V3z8LUVqMOC6934dk7r8LdXyjDKXegqwinWAHLbBV2VUtBWeqfTBVFTXuveOrUKTz99NPYuHEj3G43vv71ryMYDGLHjh0YN06ZvGXpZNRr8dzbx0QLAzz39jEsm3ZptptIlHVWvRY2k160iI/NpIdVgV/7Cw06yVj9wbTcO//kOl84inlb3sGGOyZiqU6LDn8IhRYDwpEovrHxLWycPTnbTcw7IURhQOcAdvFNY+EJRGAzdxbs0cQ6jxMRdWfV67D1raOi/evWt45iZRomMfzRCAxaHW6qGIY5VReeK37nDkCv0cAf7f/uUm6bpoHOatBJFq2zGpRZma2HBuEYROM5EotBn45f0Xqh3SfAF45i+fMHE1bAxhdmtPsY80obVGDEfdsPYG5VacI49LQ7iMdf/ggPzqzIdhMV47QZU8ery4nRDis2zZkMu9WA737xEsRiMSy/eRxi6NztUWDSIxKNQacFblm3pytNStXZBWlXlg5K6LMP/Ksdeq0Gbx85g5+//gnW316J5//xL6x48RDqqku7CnMOKTLhlfdPY9HWBviESMLq73g6lBavgFgMWPnCwYQV3/EFNo+89AH+cOBU1+1Typx4+CsVWSnsqpaCsnT+Mrm7L60T4rfccgveeOMN3HzzzXjsscfwpS99CTqdDj/72c/S+TKK0kODedWlWP9qY0KwV7kcWDi1TLEOnUjdNFj3ymHRquE1Licenjle5DHpJxWrpDx3IITjrX5MX78n5XFSllGrw5LtB0S3Rla7HHg4jy5EiKh3tADu/fKlWPnCoaQL35XT07N626TAuYnbpmmg0wBYcN0Y8aJ117kUu2qNAri/xyR0XLXLgVW3KnNd4PaHcN+O5PNKfWMLlu04gIdnVnBCXGF2qxFLbx6X9eKearFgqks0XmdXXYjp6zsnuatcDlSOKsH6XY2i/z9ueFFCzvD4c1WOKsG8LXu7bl94vasrLhde78LG+nMFPXteO1eOKumaDO/5uditRgTCUdzz//4uGlvxHzy6T4i/cbgZK184hIdmVmDp9gOKfvZqKShL5yfTRVHTOiH+pz/9CYsWLcK3v/1tlJUNjAmoDiGC7/9uPzbcMRH6sysciywGhCJRLHhmH5785qRsN5Eo6zqEiOhkOADsbmxGh0Rhj3RxCxHM27JXtFDHvC3vYNu3r8UFGW8FdVdg0sNq1KGuuhSVI4sRDEdhNuiw71grNtUfQQELnSrOI0TQcKwNC693iX4mHgVilYhyi1uIYMXzB/Do1y6HV4jAfXYsXGDU4X9++3f84NaKfvevHiGSsrhYfWNLWs5N3DZNA51HiOB7KrhuDYSjkvEcCCuzG02J8wr1nRqKe6pB9yKT9908DsfP+DDCbgE0MXiDEfzmrqsBaLDrwyZM/FwJcHbsXmg24Evlw9DkDsCo1UIQSUG0p7EFi780FhNHlXSN8StHFndNfHf/f7HHLv3ypbhlwnAUW40YWmROuk+rV5CMrcU3jU26/S/vn8a9X740K589v3O5q9kj4N2jrSmvXfu7uy+tsxH19fXYuHEjrrjiClx66aX4xje+gdtuuy2dL6E4XzCEjXMmY9WLhxIm/GpcDmycMxmeAPMNEsmtqlJi1ZXbH8pKoQ5KTasBNs6eJLpqf+PsSSxKnAXeQAjrb6/EpvojCZ9JjcuB9bdXwhtknBBRIiEUwiNfvRz3bT+QNBZ+5KuXw+0P9Ps13P7MjyO4bZoGOr+gjutWNVwXAMqcV+j8ZLu4pxq4A+euXa+72IlhRWY88tL7CbHbudPZheF2Mza/2Z60S2vVjPH408GTos//aasf85/ZhyqXA4/XVibsEAnK/Ch1tMWH+c/sS5mWwh2QLgjqCYj/2OT2h3DRYFtWPnt+53KTJxjC47WV2LznSNJ8wuO1/b92TWti36uvvhq/+MUvcPLkSXzrW9/Cc889hxEjRiAajeIvf/kLOjo60vlyihhcaE4aVADA7sYWrHrxPThtyb+YEeUbu0V6VZXc8XQoknkNrvxSnlGnxROvNiatINjT2IInXv0YRl3+VJJXC0ehCZvrjyRVnt/d2ILN9f+Ew2bKUsuISK0cBWYs33FAdCy8fMdBDLL2fyysRB/esyBYHLdN00DhtKnjulVuN4bc8bS1g9cGpGLxOLAadRhSaMYjf/4gKXb3NLZg/auNePPjFowbYU84Vn+2Dx47rEj0+U1na3jtaWzB03uOYESJJelYKvHjqYpOF8kW8BWvV8DdwdRXdosBm/ccEZ1P2LzniOx5Xk5GZiMKCgpQV1eH+vp6HDhwAP/93/+NNWvWYMiQIZg+fXomXjJjfCHpVBC+ELdaERl02qRK1nFVLgcMCkx8Wo26pIrlcdUuB6xGZQoJ0TlCJCp5/hQiLOCoNCEs85kotI2ZiHKHR2Ys7EnDWNgm04fb0tSHx7dNv3LP57Fj/rV45Z7PY11tJYb3sygTkRqo5bq1QCaeCxQakxeZ9ZLtkJvUI8qk+K6luupSfNrqT5mCZE9jC4YUmTFpdEnSsfrGFgwuTF7MUuVyoOF4W8L9wpEYas7+INxwvE3y2r37Y+NFp7srKTBKxtZpd1D0ebkYivpKKgXXnjSk4Mr4N/KSSy7BI488gk8//RRbt27N9MulndsvvR2kQ+Y4UT5o8QQxt6o0qWOtcjkwt6oUZ7zJnWK6NXcEsHxaeVLnXO1y4P5bytHi6f+WbuobFjBTH7k+Te44EeUfJcbCnmAAq2dUiPbhD86sgDeYvj7cbjVizBAbLh9VgjFDsrN1mygT1HLd6gkKKeN59cwKeILKpG65oMSKB2emPq9cUGJVpB1EYuK7lqrGONAmk94nGI6iwKQXTe3lDSb+0BW//t5UfyTh9n+1+bFyejmmlDmxqf6I5LV7z8f2vGYbWmTGQylia9WMCvzu3eOiz9vuZ7ph6ptMX7sq9rOoTqfDjBkzMGPGDKVeMi0KTDo4bUasnTUBQ4pM8AQiKDTr0eQOYPG2/bCauOqUyGY24J7/94+sFvGxmgz43m//jsdrK+EPRbuKflkMWiza2oC1X70s422gRCxgpj5Wk06y0Cn7NCLqSYmxsNlgwmMvf4jvf2kslvYYR/z4Lx/iv75wcRreiXLafQKaPQLcgc734Sxg7lLKPLVctxaYjPjBi4dw+agSzO1R6H7Vi4ew4pZyRdoBAKMdBVgzawI6AmF0+EMotBhQaNbjc5wMJxUYUWzByXY/rAYdvvvFMky9ZAgAwCdEYNBpsfvwZ3jyjU9g0mshhKNYO2sC5m3Zm/AcdosBG2dP6vr/PR834+C/2rGutjJhnG/SaaEBuopLeoMhrL51PFq8AmKxGEwGHYRwFJ95glh/+8SuawOfEBG9ZhvlKMAPv345Wr0C3IEwisx6FBh1uP2pt3Br5QXYOHtkQuwv2tqAFxdWZ/xvSgOL3E6e/u704T4hGTaDDs/ddTVWvHAoYal+tcuB5+66GgYNq8IRFRl12Dx3smicbJ47GRYFtkfZjTo8XluJ+3YcTGrD47WVMDJWFRdPpSO2zUmpVDqUyGrQSRY6tRo4IU5EiZQYCwvRKKZddgHWvvRBwmvEV5UJ0dxJ53SizY/F2/Zjd7finakKkxGlk1quW71CBK988Ble+eAz0eP/c6NyKUePtXhx7/YDSX+Ph2ZWYJSjQLF2EKViM+lh0Glx5YWDkvrAGpcTm+dMxtEWL461+nFj+dCEx8ZTEN29tQE+IYLn/vMqlI+wY/OeI3js5cMJ9/vy+GFwnP1xNv4DbbtPwEN/fB8Lry8T7X8fr63Ec28fS1l0emiRGUOLztUmaPcJKHUWJFxjxLF4NZ2PeHqenvWvgM7vdUlB/75TnI2QodFqkgYVQGceppUvHIJGy0k2olAsJhknoVgs422IAEmT4fE2LNtxEMz2rzw1pNKhHjSQLHQKdmlE1IMSY+FYFJJFkxQYRqRFu09ImgwHUhcmI0onrUysahW6bvUEpLewyx1PlyZ3IGkyHOj8eyzdfgBNbqZTpOwz6rU49K92rBcZn+9ubMb6Vxtx6XA7NtUfgSdw7oq22uXAyunjsfefZ1BXXQqgc/JQrC+tb2zBw3/6IOm17VYjVk4vx6N//kC0/316zxH8979d0uv3wuLVlG5S6XkemlmR8IPM+eAKcRleIYKGY21YeL1LdHu5V+A0G5E/FJWME38o8yu7PEIkZcGF+sYWeBirirOZDZi9+R3UVZeirseW2UVbG7Dt29dmu4l5JxCK4l2JWA0oEKtElFuUGAvHAMmiSemcEM9kOpNmj5A0GR4XL0zGCQHKFI9MrCo1FraZ9ZLp2WwKFbNs9QqS1watXqHfkylE/RUMRzGkyCwZu595gvAJERRbDfjt/3cNCkx6aAGcbPPDNbQQgwo6C2vGYp19Zqr4a/F29kHtPgGnO4Jo84dgM+lTFuOtb2zByfYAjDptr/uuePHqZo+AjkAIhWYDnDamDaPzJ5aep6TAmJbzNyfEZXiDITxeW4nNe44kbS9/vLYS3iCLwhGpIU7cMsVIWMBReTajDhNHFYtum6t2OWAzMj2H0nxCWDJWfQKLahJRIiX6eNkVpcH0nJsync6ExaQpmzyB7I/HAUCnhWR6NqUy5rllzityx4mU4AuGEY7GJGM3FImixuVEgVGHR//8QUI6ohqXA8umjcMNlw6BTwjDejaNqNhzzbz8Apxs8+P7v9uP3Y2d/eATd0yUbF8gFOlz39U9LQtROvRMz5MunBCXMajAhB+/fFh0CwkArLp1fDaaRaQqaoiTIgsLOKqNBsADt47H6Y4ACkz6ruJOnmAIQwrNzM6RBYOsRvzoLx+ljNXV7NOIqAcl+vhCi/SK0sI0rCiVS2eyrray3xfwLCZN2eSwmfDYK9m/bjXpdfj1m/9EXVUpltw0NqG456/fPIrv3zRWkXZkuhgbUTrYLUboddqkHN7Audhd9uVLsXrmeJxoC+Bbn3fhu1+8BM0dQRw62Y6xw4rwaasf37vxElj0Oqy/vRImvQ53XDUa86ov6upH9zS2YOWLh/Dl8cO7JsMBwKSX/oXKpNcm9V3p3mnFQtSULewFZIQiUcktnKEIt5cTqSFObEYdalzOhA4+rsbl5GrkLPBHItBrtVi3KzEnXrXLgVUzxsMfYRobpQVlYjXIPo2IelCij7cYtNg0ZzLW7TqctKJt05zJsBj6v6RUiXQmTpsRU8qceEPkdVhQjDJNDeNxAAhHo1h0wyV4YGdycc/l08oRVqhIbqaLsRGlg9NmREdLWDJ2tVoNlm0/0JXaxGrUYePsSfjbxy343z9/1HXbpjmTsbn+SEIKlPgq80VbG7D7cDPmVpUmPH/D8TZUuRyir1/lcuB0RxCTRpd03ZbunVYsRE3ZxKKaMjpktlLJHSfKB2qIEw2ABVPHiBZwXHC9i6uRs8Ck02FZikKny3cchEnHHymUJpdaSO44EeUfJdIOaKHBhl3iK1s37GqENg29uBLpTFhQjLJJLSlCtNAkTYYDneO/VTsPpSWee8Os12Ll9PGixdhWTh8Ps8zKWCIl2K1GhGV+rGrxCAmT3HXVpVj/amPSbet2HU7KBx4vTh0vvKnXJcbfpvojmFtVKnoNfff1ZZh68eCuvivdhaNZiJqyjSvEZRSYpP9EcseJ8kGBSXqrsxJx0iFEULdlr2gBx7qn32EBxyzwyhQ6ZVFi5clt1+d2fiLqySbTh8sd7w2PEElZ1Gt3Y3NaigEqlc5kRLEF//u1y84Vf7LoUWJNT/EnIilKxGpvBMKpV6rXN7YgEFZmhXizR8BtT76JtbMmYPHZ1C02sw6n3UHc9uSb+M1d1/BHKlIFuWvleGrQ+PX2jeVDMW54UUJKlMqR4nWbgM5J8bqzK8NLrIl9nU+IYNHWBtRVl2LZzePgCYRhM+th1mvR7hfgDUW6JqZPtgdQe+UozK0q7Xpd39n++Xx2WrEQNWUbZ3NlaADJLSRcdUoEaDXAlrmT0Xjak3D7BXYztsydDK0CgdIRCMEnRFIOBFjISnksdKo+BUad5PbhAqYWIqIetBrpsXA6+ni3PwSnzYi1syZgSJEpIefw4m3709JfKJXO5ESbH4u7FSwDgJqzK8S5/Xvg+7TVh45AGG5/CHaLATazHp8rsSry2krEam90qGT85w6EcPnninDxEBs8QgRaTWcqv4uH2HD554o4DiVV+LTVB5NeK5H60wGbqTMditWoQzQWw58PNXVNRte4HFh/eyWCMj80hSIxPHfXVdBrtfjNXVejyGJAKBLFgmf24XirHx+ccGPm5RegwKTHiTY/NBoN9h1rxXNvH8P62ydiw67GxH7N5cCOBVX408GT+Pnrn8AnnCu+KZYTHEDSbZneuRVvhycYQrHVCCEchScYZp5y6sIJcRl6PfDgjAos23EgYQKh2uXA6pkVCEe5wpHIpNciEo1h54GTCYPw+FYruWId6WCXKaopd5zSj4VO1UcbjWB1ij7twZkV0LJPI6IedDpg9YzxWL7jYNJ5Y9WMCkRi/T9vFBcY8MydV4vmHH7mzqsBTazfr2G3GrFqxnjctz35/Ldqxvi0XBi3+wQs/t0/kla77z7cjMXb9mN9Ggp3knodbfFi6fYDSd/hB2dWYLSjIOOvr0Ss9kahSsZ/QywGLJs2HktEPpPVMypgiPX/vJJrWLxQXeLnDC1iWD1jAu4TGZ+vmlGB1TsP4eUPPuu6PSEveGMLAA2+88Uyyde60GHFqj+8lxQLv5x3JX76WiPmVY/BihcSzx1VLgeemj0Zj7/yUdJk/e7GFvzgxUO4uWJ4V1uKLAbRnOA1ZU4smOpC3dPvdK0on1LmxH03XyrZ5r6eK7p/vwuMerx7rBU//L8PsWbWBDzy5w8T3jvzlBPACXFZJq0OK148hMtHlWBujzQMq148hJW3lGe7iURZF4sB615tFM39CQCrFahqbzFIr3y1GLjyVWlcjaw+Ma0OD6To0x5gn0ZEIqTGwqt3pue8YdXrkiatgHM5hx+eWdHv1zjR5scPJM5/q2ZU9PvC+JQ7kDr1y+FmnHIHOPk0QH3a6kuaDAc6v8P3bT+ANbMmZHyluBKx2htqGf/F9Frct22/6GeybEfnZ5JPWLxQXbqfM9743nWi43MAeGDnIezqNhkOnLvGrqsuxfqzK7eXTbs05Q6RmjIn9h1rTVnXafWM8bhPpO7TnsYWaPEhLhtVnNSG+PG6qlJs2nMEy6eNQ4FJj//57T+S0qDsPtyMaCzW1V6gMyXKTcfa0rZzS+z73X1Cv+d7i+cpX8cfqvMaJ8RleIQIdn3wmegJAAD+50aupiPyhVLnit7T2AJfKPNx4hXCmFNVihiQtEp9TlUpvAIL4Cqt2RPE8mnlWLXzUNJqh/tvKUeLJ4hRCqyYonPYpxFRXylx3vDI1JxIRw7xdn9I+n34Q/2eFHL7ZYoayhyn3NURCEt+h5UoMK+WPr5FJeM/NXwmaiFXvJCTgsrr/v30ChG8/MFnCavAAWDj7Ekp47l7XnCgs49bONUFrUaTNCn8g+nlmLauXvR56htb4A+lzvu/u7EZc6ouTPk+gmdrBtw/rTP/eKqc4D3bCwCrdr6HPy6qwf3PH0yYFO9rIepU32+5CX3mKSdOiMtgDlwied5gRLKopi+Y+QF4R+BcQZCeRTUXbW3AlrlXZrwNlMhk0OF7v/07Hq+thD8UhdvfuT3TYtBi0dYGPJxnK3PUgH0aEfWV2x+S7OPTcd5Q4tykxPuwyqx8tZq4M2qgUkP/qsR3vDeMeh3u3PIONtwxEUt1WnScHf+FIlHMe/od/OwbVyjSDjV8JmrB4oXq0/37Gf//nvErV2zTatRj05zJ2HesFYVmPf7vvSb8979djDnXXphwLfxZR7ArVYkYubz/UvnJ4yvZ/UIEMUhf8/d8Hp8QgdsvYF1tJZo9AjoCIRSaDXDa+pbKR+r7LTehn0/nAUrGCXEZzIFLJM9u1ePx2kps3nMkoahlPL9ZkTXzp5oii16yqGahhac7pRUadHi8tjJpC1712e+FNv9SN2Yd+zQi6iu71SDdx6ehRocS5yYl3ofFqJMsasj0bQOXGvpXJb7jvWE16vDIVydg7UsfJO3afOSrE2BVKA7U8JmoRaaLF1Lfdf9+FlkMsBp1SfG7cfYkyefwhyKoe/odVLsc+OrEz+Hnr3+C8SPsmLdlb8L9biwfKvk8cnn/U9XiqnI50HC8rfM5ehFPYnXFCkwG2K39y2Uv9/2WmtDPp/MAJct8pbscV3g2B5qYapcDhcyBS4QCvQ6b9xwRzTv29J4jKNBnPk5sBh1qXE7RYzUuJ2y8CFWcVqsRzUfXmbvxILRaTZZalr/YpxFRX9kM0n18OvpXJc5NhSa95PsolFmJ1xtmvRYLp7pQ1eO9VLkcWDi1DGYFioxTdhSa9dLfYXPmF2YoEau9YdBq8ESK2kJPvPoxDAqN/2wy5xVbHo15imQm/TgpqLzu54wCow7Lb740KX4bjrcl9Sdx1S4HYmcLw9Y3tmD58wdRV12KhuNtSd/70+6gZCzodZqUr1PlcmBQgRE1Zc6k2+dWlWJT/ZGufN9OmxFTysSvx7tPnsf1NU94KnLf71QT+ul6fcpdXDIpwxuOoK66FBogoUhOjcuBudWl8IaZb5XIK5FDvL6xBV4FcojrAayeMR7LdhxIitXVM8fzZJcFHUIEDcfasPB6l/jW3TTkhKW+MUTDeHBGhWgV+wdnVsAQzZ98mkTUO56Q9Lnck4Y+3heOSNYB8aVhvB2QyJFa39iCQCj1CrLeGlFiRTgaw7SK4Qnp2067A7ig2IwRGS6qSNnzuRIrHpxZgfu2i/evmS6oCXTmEJeMVYXGXZ5QJHVx2cbmtJwzeqPIYsDqGRVYlmLMo9SKeTWIT1Smo3ghpUf3c8b//PbveGBGBe7dfjAhbUo4GsP0y0Zg1YuHEmIq3jfqNOd+XNp9uBlzrr0Qz751FKtnVGD5joPY3dj5eS/eth/P3XUNVr5wMCkWVs+swMlWH75341gAibs6asqcWHXreJRYDVhfW4lT7gA+bfUDQFda0kmjSxLyfa+ZNQFLtu1P+K7VlDmxYKoLdU+/03VbX/OES5H6fled/UGy5+6tdL4+5S7OEclw+0LQQIObKoZjTreBbZM7AA00svmWiPKBXI4+uePp0OwPoMRsxg9uHY9gOIoOfwiFFgNMei30Z48PAws4KskTCElu3fUGef5U2il/BCOsejw8swIeIdIVJzajDuYYcMIfgfSmSiLKNx1+6XO5Jw1b7dt8Ick6IE/Pndzv11AqZcAoRwF0Wg3cgXBXPtSxwwpxASfDB7zRjgKsmTUBHd0++0KzXpHJcKDzOywZqwqNu9RSXNZuNcLvD4mOeQxnj+cLu9UoOlHJScHsOnfOCMETCIumTbEadVh286VYevM4/LPFC6PuXN/46NcuS3i+IrMBK24px9o/vY/LRhVjTlVnLvFiiwEmHXDvly+FRqOB52wshCJRfOOpt3C81Y8bxg7BQzMq0OIV0NQRwOhBVlxQbEn4btitRgwrMqPZI8BRYMTMyy9Iyvc9otgimhMcAF5cWH3eecKl2K1GrJ4xHkt7/CBZ43LggRnj4Q2E8NCMCgiRKLzBcNpfn3IXJ8RlOGwmPPZK8pZ/oHNwserW8VloFZG6xPOepSrio8QKDLvFjP/9y4eoq74Iep0W8fTUnmAY6+o/wT1fvCTjbaBEnefPw6JbZgHw/JkFgwpMWPziIYwbYe+KVY8Qwb5jrXj/RDtW3lKe7SYSkco4bCb89PWPUTmqpGuyOt7HP/vWUSy7eVy/X8NuMUjWAUm13bkvlEwZcEGJFRek7dkolyg1+S1GiVjtjSKZuj1yx9OlyR3A//SYIIurdjnww69fjqFFZkXaogapJio5KZhdnyux4miLF22+EOqqS5PSpviECJZuP4galxOTSksQisRQObIYj37tMowaZMXC613YVH8EPiECjQa4b3t8t3RTwuvUuJxY/KWx0OuBdbsOJ+3iePmD0wiGI7jiwkF47OXDeOWez4t+N3qT7zvVfTL1XWv3CXhg53u4fFQJ5vb4Uf2hP7yPR792Gb/nJIoT4jJCkdTbK/c0tiAU6f/2SqJcZ9RpsXH2JKx/tTFpRcrG2ZNg1GU+Z6Y3FMG0yy4QLeAzt6pUkbQtlIjnT/XxhiK4/arRoqvHGCdEJCYcjUqeN8LR/p/LDTqtZDFKQxrGEU5bZw7U3SJbqmuYMoAGACVitTfMeul4ViqXfqtPEJ0MBzrTJLX6hLyaEAd6N5lJyguEIigw6XHNRY6UPwy/e6wVy6eNww92HhLdAfLcW8dg1Gsl0xUt0QBajUbiPi34/k1jcehf7TnVJzZ7BLz8/mm8/P7plMf5vScxrOwioyMgvaVL7jhRPgiGo5LFcwQlJj5jSFlIaPOeI+haMk6KaZdJlSN3nDKAcUJEfRSNSp83omk4b5zxBDG3qlS0GOXcqlKc8Qb7/yIAFqQoeLlgqistz0+UTVGZPj4dsdobbT5BMp7bfMqM/3gdT7kiFgMee/mjhJzgPdVVl2LVzkMpi+YumzYOZzyC5Ouccgdk04Oddgexcnp5Tk0gK5USjQYerhCXUSBTcV7uOFE+iAGSv0YrMQDvWYiruz2NLZznywK57eesaK+8GCBZcItxQkQ9yfavaThxFJgN+Obmd1LmEN/27Wv7/RrNHgF1T4u/Rt3T7+DFhdU5NQFA1FMslvlY7Q2zQY8l297F2lkTsOSmsfAEIig069HkDmDxtv349byrFGmHzaSH02bE2lkTMKTIlNQOG6/jSQXafQJiAP72yRnJH2crRxanXD1e39gCrxDGMLv8jgerUfp7b7cY0BEIo+FYK4osBjgL0ruroN0noNkjwB0Ipe35lUyJRgMLewEZGnTmW4pX6O2uxuWENvWPeER5wxuUXmEhd3ygtIES2Yw6yfOnzajLQqvym18ISxbc8guMEyJK5JHpP+WO94bZoMUVo0tEL/ZrypwwG/q/qdUdCEnmKecKMsp1HpkVz+mI1d7Q6zRYXzsR6149nJTGcH3tROh1ylxAG3TAc3ddjRUvJK6qrXY58NxdVyvSBiIpJ9r8WLxtP77zhTI8XluJD091pEw3JOdoiw/vnXSj2uUQTRVU5XKg4XgbRg2ypk4f5nLio6YOLN1+sOu2KWVOrJk1ASOKLX1uU0/x97u7R2HX/j6/02bElDJnQsHY7s+fS+lfSFmcEJdh0msxf+oYRBFL6tDnT3UpkhuZSO0KzdKnErnjA6UNlEgLYEGK8+eCqS7m7MqCEqsRP/zLRykLna5moVNVu232nTjZ3Cp6bLizBM9teUrhFlE+KJLpP+WO94Zeo8HCqS4gFkvYcVbjcmDhVBf0EtvIe6vIbMDFQ2zY8B8TEY7E4PZ3rk7T6zRY8Ot9aV1BlokVcERyCmWKVSo1FjbptNjwqnhRdS00eGimMmMNk06He7cfSGpHfWMLVr5wCA/PrFCkHUBngc9WrwB3IIwiix4lVmPe5S+nRO0+oWty+IHp5fjRyx+h4VgbHq+tBJC426PG5cCQIpPk85kNOhh0Gtw/rRyrdr6XsCCpyuXA4i+NhVYDRGJRPDC9HPe/cChhYrqmzIkF17lQt+WdhOd943Azlmzbj3W1lf3qx7q/33Q/v91qxJpZE7Bk2/6ESfEpZU6snTWhV8+bqt9mfz6wcYZIhhCJYt6WvaLbK+dteQfb5/d/CydRrjPKFMNS4ocjo14rWSzLqFABHzrHLURQl+L8WbflHWz79rW4INuNzDOCTKFTRfL903k72dyK4bf+j/ix5x9VuDWUL5QoeOkNReATIripYjjmdOsvmtwB+IRIWgr+Om1GPPnNSbhvx4Gk1aJPfnMSSqzpmRDP1Ao4IjkmmVg1KbSQyytEJFMpegVlCnh7hYhkUU2l2nGsxZs0MV/tcuChmRUY5ShQpA2kPs0eoaufCHYbny/a2pB07TS40IRX3j8tGd9OmxFvHzmDn7/+CeqqSzGn6kIAwJAiE7TQwBMM4edvfILbrxqNZ986istGFmPOtRciGI7CbjFghN2Mr/38TfhE4uKNw839LkzZ/f1m4vlHFFuwrrYSzR4BHYEQCs0GOG29m7wW67e/eOkQLJ82DvftOMj+fADjhLgMtz8sub3SzWIcRGg9WzwHQNJK4LlVpWj1CSjNcBs0MWD+dWMQjYns5rjOBQ2TIyvOE5A+f8pt7aX0c/ul/+bs04iopzPeoGQff8YbxEWDbf17kRjwVP0nKS/0l988rn/Pj850ET0nw4HOibFlOw70ehWZlEyugCOSc8YrPR4/o8B4HOhFMUuFUreooR1N7kDKVepLtx/AD79+OVeK56nuhSDd/nP/L3bt9MQdE/HkG5+kWD3uxP/ceAkee/ncDtDuj69xObDgehfW7WpE5aiSrsK7uz74LOE1alwO3HblqIylFVOi8KXd2vfV26n67UuGF4nGLvvzgYUT4jJsMlvLWIyDCLAY9Vi08e2MFsOS4w9L7+ZQog2USPb8yTQ2imOfRkR9VWAy4JubMlvwUonC2O5AOOVr1De2wB0I93vXUqZXwBFJsZr0+Mam7I7HAfWMNdTQjlavIHneafUKnBDPU90LQcql7DLptfAJEdHV46MdVnx6xp80wR23u7EF379pLPY0tqCuqjTlhPfuxhbMqUr9k1n3Np5PGhG1Fr5M1W9LFTFlfz5w8MpXhlYDya0pLKpJ1Fl8tnKUeKdR5XJAiTDpYLEs1eH5U334mRBRX5n1WkxM0cdXuxwwpyElmRKFsbuvwBOTjnGCEivgiFLRaqTH40r18RqZsUYaSgL0rh2QaYcCbZDbecedefmreyHIIqNOotClA03uAIDk1ePVZ3d/BMPSKQ89gc40KHL3S6V7YcrzTQum1sKXqfptub8V+/OBgUl1ZWg0wNyqUlS5HAm3x7eeKdWhE6maTJwoMeKU+1U5W7865zOeP9WHnwkR9ZUQjWL5tHJU9zhvVLscuP+WcgjR/tceUGJHUZEl8+MEuRWnBdyFQxmklj5eLe1Qw/WJEkWJKTfFC0FOKXMC6Ez9WeNyJtynyuXA3OpSDLdbkr7HNS4nVs2owO/ePQ6TzA/TNrMOAGTvN6TIlPQ63QtTyqUFa/cJvX6/Ys+fDalWrsv9rdifDwz8FGXotVpse/c46qpKseSmsfAEIrCZ9TjtDuB37x7H924cm+0mEmWd1aDD1reOonJUSdIWza1vHcWKW8oz3oYCow7VLodo8ZxqlwMFRl3G20CJ9Fotnk3xvXj2raNYloacsNQ37NOIqK/Meh0e+8uH+P6XxmKpTosOf+cW6VAkip++ehjf+eIl/X4Nq0G6D7ca+t+H22TGCbY0jBPUUGSc8pda+nijzPgvHTUBeqNA5vpkpQLXJyUFRsnzTkkBUy7ks3ghyBPtAczbshcbbq/Ef//bJQiEI7AaddBAg10fNuFXbx7FPV+8GN+94WKc7gii2GKAzazHt365Fw/MGI+hhabUK8zLnPAGI6hyOdBwvE2yj3rl/dNd8QIAowZZMaTQ1DVZ3du0YKlSqowotuB/v3YZWr0C3IEwiix6lFiNWU0blGrlesPxtpSxy/584OCEuIxWbxDfueESPLDzUFJl6OXTytHmCwLoZyEhohzX6g1g2bRxWL7jYNI2rlUzKtDqDWS8inq7L4DVMyqwbMeBhI6r2uXA6pkVaPcFAFZyV1SLJ4jbrxqNzXuOJHwv0lqIjfqEfRoR9dUZTwCLbrgYy3YcTDpvrJoxHq3eAEb3s3/tCAhYNWM8lu84mNSHr5pRAU9AANC/13DLvEZHGl6jzS9d1LDd3//XIEpFLX18i1eQGf8JKB2c8WbAFxKwfFo5lu04kHR98uDMCvhCmY/HoUVmPDSzAku3J1+fPDSzgvnDCXarER+c6gDQWU/jh//3AXb36D/W1VYiEIrim5veRuWoYsytKsWi5xqwrrYSG3YdxrvH2vB4bSWisZhI7I/DZ+4A7p5ahid3fyzaR1W7HJhTVYpFWxvgEyJdq7aH90iB0pu0YKlSqqydNQEx4LzSrWRSfOX6km37EybFPzjpxvJp5UnnU/bnA4smFoulo05NTnO73bDb7Whvb0dRUVHCsU8+82D58wdT/oq26tbxnNChvHfkMw9+8spHqKu+CPoeq8c21X+C//rCxShNQ5xIxeqxFi/W/Ol9zLpiJIYUmeAJRFBo1qPJHcC2d49jyU2XZnxSnhK9f9KNb2x8C2tnTej6TOIrlRZv249fzbsKlw4vkn8iShsl+zSpeKXz8/mbZ2H4rf8jeuzk84/i9T9sU7hFNFBkeyysxDji49MefPvX72LDf0xEOBKD++xr6HUaLPj1Pvz0P67AmCH9f41b1tejrroUlSOLE1akbqo/ghcXVvf7NSi/ZTtWe+PDU27MfOKvKeNg+/xrccmwzI8Ljrd48dCf3sdXU1wb3HvTpRip0LVBkztwblWsWY+SguyuiiVl9HYs/P5JN/586BT2/vOM6IrkmjInltw0FlqNBkadFi3eIBwFJqx84WDX5LnVqOuKOQCwWwx47aPP8N6Jdlw2shh6rQb/Nm4YwtEoNNAgBsATCMNm1kODzlRHGmig12ngSFEk8+PTHnzhR6+nfB9/+e4UPLDzPdFV5A9/pQJ/3H8SuxvFc4ivq63MapHK+Kr2jkAIhWYDDFoNXjxwAleVOhCOxFBg0iEG4NUPT+Pnr3/C/nyA4ApxGaFIVLLqfSjS/7yJRLlOiEYx7bILsPalD0R/QU1HflE5vlAEX7liJDbtOSLaBl8okvE2UKJCow7raiux/tXGpM9kXW0lCpnGRnHs04ior5Q4bygxjnDajHhgxnisfCF5tdcDM8anpaCX02bEpNElokUNs1k0jPKDWvp4o04rWdxTqVQDvlAEs1RybTC0yMwJcEqp0KjDFy8disdePix6fPfhZsy/LoQLii34+DMvnqr/BHVVpQkryXsW3Nw4e1LXv++4ajTmbdmL//3zR9g4exLu3tqAx2srsblHbNTI5POWK4xp1GlTplQZUmgSnQwHEtOtZIvdmvgjwKdnfPjrxy343z9/1HVb/NxRNcbB/nyAUHXim4cffhiTJ09GYWEhhgwZghkzZuDDDz9MuE8gEMCCBQvgcDhgs9kwa9YsNDU1pa0NHTKVnz2sDE2EWBRJHSrQOfjevOcIFNmHEpNuA/J+L4zy/OEInugxGQ50fiZPvPYx/GH+SKE0uT5N7jgR5R+3AucNJcYRXiGCDbsOi77Ghl2N8Ar975PUWjSM8oNa+vgWb1CymGWLN3XhvXSKyVwbcJ88qUUMQJtfOh2JQafFY3/5ECfb/djT2IJgWPoHru7He/5/XXWpaGzslimOKdfHtflTx7Zceztk0rEoqd0n4N7tB0TPHU/vOYKV08vZnw8Qql4h/vrrr2PBggWYPHkywuEwli5din/7t3/De++9h4KCzu1N3/3ud/GHP/wBv/3tb2G327Fw4UJ85StfwZ49e9LSBrnqsVZWlyVCDEDDsTYsvN7VtTXSbNBh37FWbKpXZsAZAyRXxXDMq7xwFAkrF7rbfbgZMuMiygC5Po0V04moJ5sC5w3ZPjwNnXi7P5S6T2psRrs/lJYcpvEiad23Xjtt4tvPidJJLX18gcmA/+/X+7B21oSu4p7xVCXxlHlK4LUB5QqPEEGRxSB5H7NBh9///SRuvuwCAIBJL722tfvxnv9fOVJ8Bwcgv1pbqo/zSfywLNfeQrP0+1eSVPHQ+sYWBEK8iB0oVH3l+9JLLyX8++mnn8aQIUPw7rvvYsqUKWhvb8fGjRvx7LPP4vrrrwcAbN68GZdeein+9re/4eqrr+53G7QaoMblFN3eUeNyQqvp90sQ5TxfMNy17apn8ZzHayvhEzK/IkVut4YnyJWvSvPK/M3ljlP66TSQrEKvY59GRD3otDLnjTTsN5Xro9PRh7tlVt+lc3Vaz63XJw7u/AABAABJREFUREpQy3WrxaCVTJlnNSizSZ3jUMoV3mAINpNBMn4j0c6fcISzqY8ajrehyuVIWTOg4Xhbyv8fJ1PDSa4/TNXHSaVUOd0RlEy3oqYUJL0pHkoDg6onxHtqb28HAAwaNAgA8O677yIUCuGGG27ous/YsWMxatQovPnmm2mZEDfptFh4vQtALGFVSY3LgYXXu2BSKAcakZoNKjBiw2uNqBxVgrqq0oQV4s++dRTLbx6X8TbYzHo4bcaEAo7dV6PIrXCj9FPLSiU6x2LQYeFUFxAT6dOmumAxMK87ESWy6HVYdL0LXx4/DEOKzF19fFO7H64hNlj0/T9vFJml+wO54716DZnVd+lcnRYvzuUOdBbudKYoUEaUTiadFou+4MKXK8RjVanrVq1GkzJlnlajwYMzxivSDpvMeUPuOJESmtwBOApMCEYiuPfLY4E/fpAwKV7lcmD+VBcsRi2sRh0uGVKIF++uRptPwJfKh2H/p21Y/Yf3u1ZnXz92ML5zw8Vo9gjYOHsShhaZ8fL7TfjC2MGovWo0Fm1twLraSsk2mQ06NBxr7XP/FU+psmTb/oSJ7yllTky9eDA+f/Fg0WNqSylWJDMeUNNqduqfnOkFotEovvOd76Cqqgrjx3d2oqdOnYLRaERxcXHCfYcOHYpTp06lfK5gMIhgMNj1b7fbnfq+0Sh8QgQ3VQzHnLMTfSa9Fk3uAHxCBEEFigUSqZ0QjeL2q0aLrhDvTzGsvsRqicWA5+66Git6FMuqdjnw3F1Xw2bMmdPdgFFs1kuuKizmhYjivKGIZJ/m7UeBqb7EKxFlV1/i1ReOwG414o8HTib9kLb8lnL40lAPwqjTSq6MS0cRPrvFgGqXA/Uiq+mqXQ7YZSbMe+tEmx+Lt+1P6PumlDmxZtaEtKRkofzSl1gVYlHJWBViyly3BsNRyZR5crmE06XEapSM+RIVTcDRwNDXsfCJNj/+2eyFo9AIg1aHB196D5eNKsacqgsRDEdhtxhQaNaj7ul38Ot5V2Hj7ElY8cLBpPjeMb8KJ9v9GF5shkmnw7IdBxLvU+bEgzMrIIQjWFdbicGFJtS4HKJxWu1yYOeBk13X9H3tv+TShuVCSjGple7VLgfMCu1yoczLmU9ywYIFOHjwIJ577rl+P9fDDz8Mu93e9d/IkSNT3jcWBZ6q/wRLtx/EvC17Mf+ZfZi3ZS+Wbj+Ip+o/gULjCiJVky2GdZ5x0pdYjcRiSZPhQGeer5UvHEKElXMUFwUw/7oxokWV5l/nAk+fWRCT7tP6k1CzL/FKRNnVl3g1arX4wYuHki6cdze24IEXD8Go7f/lhBCJYv7UFP3FVFfXFvH+GFFswYMzK1Dd4zWqXQ48OLMiLZPV7T4haTIc6MzJKlWojCiVvsSqQSMdqwaNMpf+cukElCruqQWwcnq5aMyvnD4+dyZCKGf0JV7j/UWbP4RYFFj5wkHs+uAzrN/V2DVGv+Opt7D2pQ9w25WjAABPvNooHt87D6GkwIh/HG/Hsh0Hk+9zuBnLth/AYJsJZUNsePzlw5gtUvi22uXAnKpSbKo/0nXb+fRfdqsRY4bYcPmoEowZYkuY8JY6phZ2qxEPiYwXqs7+fVa+cIj9+QCRE8vzFi5ciJ07d+KNN97A5z73ua7bhw0bBkEQ0NbWlrBKvKmpCcOGDUv5fPfeey/uueeern+73e6UJysW4yCSl6k46UusugPhlG2ob2yBOxDGBefZDjo/7kAY3//dfmy4YyL0Oi06/J1bx0ORKBY8sw9PfnMSPxOFZbJP60u8ElF29SVeA+GoZP8aSMNqz2gMmLdlL+qqS7tSr5n0WjQcb8O8Le9g27ev7fdrAMBoRwHWzJqAjkAYHf4QCs+uvvtciTUtzy9ViEuuUBmRGLXFam9YjXqMLLGkHP9ZjcqkZ2vxCvj+7/6Bx2sr4Q9Fu2LeYtBi0dYGPPLVyzC4yKxIWyg/9CVe4/3FnGsvRAxIuatiT2ML6qpKJe9T39iCOo+AQQVG0Z1WwLk+aMwQGx6YMR4dwRBW3zoegXC0a7V2fIdTz+KYbxxuRotX6Gp3LqYD62sqs1AkirlVpVh8tjCwzazH6bOpWJs9AvvzAULVE+KxWAx33303tm/fjtdeew2lpaUJx6+44goYDAa88sormDVrFgDgww8/xLFjx3DNNdekfF6TyQSTydSrNrAYB5E8uTjwnWec9CVWlSyWRb3jC4bw1OzJeGBnchqbp2ZPhjfIX9aVlsk+rS/xSkTZ1Zd4lV/t2f/+1RsMwydEEtKu9TyeDsdavLh3+4GkPumhmRUY5Sjo9/OzEBelm9pitTdMeg1+Ne8q3LcjOdZ+Ne8qKLXFOhAK4ye1lbhvx8GkdvyktpIrPCnt+nTtejYeD/yrvVcpu1q90t/XQC/SHnYEQjjR5sf9zx/EbVeOStrhXeVyYM2sCVi0tSFhUtxq1CEGYOHWhpxMB3Y+qcyiADZJ/H3Ynw8Mqp4QX7BgAZ599lk8//zzKCws7MoLbrfbYbFYYLfbMW/ePNxzzz0YNGgQioqKcPfdd+Oaa65JS0FNACiUyXErd5woH8jFgRJFa5QslkW94yw0Y2mPiQegcxXDqp2H8ODMiiy1LH+xTyOivpLrP9PRv9rMeliNOtRVl6JyZHFCce5N9UfSMo5ocgeSJsOBzj5p6fYD+OHXL8fQfq4WVboQF4t3UndKxGpvGLRaLE4Ra8t2HMAahcZ/jgJTyphfvuMgHuY4lLIo3l9oNPLj72FFZtldnCb9uSRAqfpTm1mPxdv247KRxSnTnQJAXXVpwg/UddWlWPn8Qbx7rA0Lr3clPO/rH32GL48fptq+Ry6V2brayqS2t/sErHz+oOTfh3MLA4OqU2f99Kc/RXt7O6677joMHz6867/f/OY3Xff58Y9/jGnTpmHWrFmYMmUKhg0bht///vdpa0O8yI+YdBX5Icp1Bp02KQdZXJXLAYMCcVJg1EnGaoFC2zPpHJ8Qkdy623M7HmWeGmKViHKL1ahLyqMZV+1ypCX9gVmvxabZk9FwrLUrd2rd0++g4VgrNs2eDLO+/+emVq8g2SfJrb7rjXghLjFTypxw2tI3YXCizY+FWxvwhR+9jplP/BVf+OHruHtrA060+dP2GpRbCmRiVamxcIfM+K9DofGfR4iIFtSMt8PDcShlUby/GD/C3jnnlKLvqHE58Jf3m2A2SI/hG4634XRHEF+8dAger61M6k//fqwVBq0W7x5tReXIYskUipUjixOff4wD7x5rE33enftPoNWn3tXSvUllJvoYib/PtRc50tqfU/ao+so3FouJ/jdnzpyu+5jNZmzYsAFnzpyB1+vF73//e8n84X2lRJEfolx3xhPEXJHCHFUuB+ZWleKMN5jikekTCEWwIEWsLpjqQiDMQa/SmMZGfVq9gmSspmNCiIgGljOeIJZPEy9Md/8t5Tjj6X8fH4sBG149LLoaa8OrjWmp2eOWKeQnd7w37FYj1syakDQpPqXMibWzJqRtBR2Ld5KYQDgiWURSqbGwWsZ/HpmYljtOlEnx/gLonHNafvM40fH57KpSPPnGJ2jxdI7ha1KM4T886cbUiwdj5fRy0dXf9Y0tWPb8QdRVd9bpkNL9+JQyJww6LeqqS1OuKl/+/EHV9jvnk8pM7jEmg1a1K+Kpb7g3WoZSRX6IclmB2YBvbn5HNE4WbW1QJE4isc68ZmtnTcCSHsUvFm7d15mzkBRVZDHAaTNi7awJGFJkgicQQaFZj6azBUm41Ux5VpMe39j0dlZjlYhyi9moxx1P/Q1rZ03oVlxKh9PuIG7/xd/S0r8GwtGUq7F2NzYjEOr/ApQimbQsRWlKGTWi2IJ1tZVo9ghdhcqctvSmMmn2CHj3aGvS1vX4e0lnsS+mZckdkShw+0bxWL3tyTcVGwurJY2hXKolJVI6xjGOSMyIYgu8wTDC0Ria2v2oHFUiOj73CRHYLQbYzHrcd/M4hKMxtPoEDC40wajTotUr4LtfvBj+UARCJHVx3XgRTzmlzgL85btToNNq4Cgw4mR7AJUji1PW+dit4qLR55PKTO4xxRb1vU86P5wQl6FUkR+iXGY2aHHFqBLROKlxOWE2ZH4zil8IY82sCSmLX/gFxqrSCo06PHfX1VjxQnJRzefuuhpmpudQnFYDVI4SH9BWuRzQarLQKCJSNZ0GuHRYEeZt2Zt0rMblhC4N5w0ligEOKjBi4+xJWP9qY8I5sMrlwMbZkzCoIH0XuHZrZie7PMEQHq+txOY9R5Ley+O1lfAG07MC93wKkVH2eINhNHsE0ViNH1eCzahDTZlTNE1BTZkTNoVSt2g1nTEhNjmo5JiHcURShhSacPi0B3uPtaLhWKv49XSZE/s/bcP3tx0A0JnKbOPsSXhw53sJPyZXuRxYMa0cVqNOMjVlw/E2ydj4w4GT2H+8DWvO7mxq6pDfCabWnb/x1DRviJyPUqUyO5/HUG7ibIQMFiAjkqcBJNOVKDHeLLEaU27j2rznCEpU+Iv1QBcFkibDgc4teytfOAQmnFKeRgPJlCkaTogTUQ9GnVYyfWA66unIrSiVO94bJr0WT7zaKDpOeOK1jxMKkqldsUV6zGNPw+o1pmXJPWq6bp1/XYpzxnUuxdqghjEP44jk2K1GFJr12FR/JOX3dcUt43DSHei6ra66FOtfbUzaWbWnsQWrdr6HuurSlK83rMiMD0+6JWNjU/2RhO+oXqvBkCKT5PtQ687f80llplT6M8o+zubKiBcgS/XrGQuQEXXmGeuZriSeGmPh1n145s7Mb9GU2h62p7GF+f6zgEU11Uev1WLbu8dRV1WaFKu/e/c4vnfj2Gw3kYhUJhCJ4u4UffzdW/fh12no4006La4fOxjjRtiTUoC8d6IdpjSMt6WKZKl5u7cYJcY8vSlElit/r3xhkIkjpa5bPUJEFSlHjTotnn3rqGgaimffOor7p43LeBsYR9QbRp0W117kwMF/teO7N1yMu6fGUGDSIQbgg5Nu/OHASVSNceLnr38CnxCRTl/S2IxvXzcm5W7Qv7zfhGU3j0MkFsPKW8oRjsZwpNmblKIFOPcdddqMePdoK2pcDtF+VC2rplOlJjqfVGZKpD+j7OOEuIyWs8UCASSlYYgXC7xosC1bzSNShY5ASDJdiRJbqNplCvjIHaf0U6KAGfVNuy+I739pLJbtOJiUxmbVjPFo9wUBsE8jonM8fuk+3pOGPr7NJ2Dxly7FAzsPJVzEV7scWD6tHG1pWEV5PoW11Mojk/oiHakxBtLfK1+0eYMycaRMH+/2hyRTjir13Wn3h3D7VaNFUwvNrSqVLf6ZDowj6o1WXxD3TbsUy3YcxGMvH+66PR67jzz1N4wfYcfjtZVYtLVBtiimQZ+8qDP+vV+0tQHXXTwYl48qAQA0HGvF/Gf2pXyujkAIY4bYUO1yYuQgK6JInBerUcmqabnUROeTyizT6c8o+zghLsNmNmB2losFEqmdo8CEH798WHTrLgCsvnV8xtugxHZr6hu5AmXpKmBGvTfIZsZ92w+IprFZvuMgHpxZkaWWEZFaDbKZ8ONXUvfxq9LQxxdbjVj2/EHRc9MDOw+lZRxxPoW11KrIbJAsEJqO96LEa1B6lRSYMh5HvaGaopomAxY+24ANd0yEXqdFh79z1WgoEsWCZ/bh6blXZrwNA+m8Q5lTbDXh/h2pY/e2K0cBADbvOYK66lLZFF82kx51VaWYV30RAqFI0urv7t+73n5HhxdbYDXq8NCMCniFzjp7RWYDTHotTncE4AtFslYsVi410braSk5skyjORsiwGXWYmKIAWbXLoVhRECI1C8ps3Q0qkK7EJJPeKB3bralvSgqMqHY5UC/ymVS7HChJYwEz6h2fEBH9PACmsSEicSGZPj6Uhj5eiXHEQCqS5bQZsWnOZKzbdThp5eumOZPT8l6UeA1KLzWMxwGgwKiTHP8VKHT97LQZsXnulVjxQvKuuM1zr1TkOzyQzjuUOaFINGVKrz2NLZh/nQtvftKCPY0tqKsqlSyKWe1yIBSJ4tm3jqL2qtH4n9/+I2F83/N715fvaPcV02oqFsvURHS+OEMkJxrD6hkVqO5RcKDa5ehcSReNZalhROoht+VQiS2J7X5BsjgIU6Yoz6zXYuX08aLnz5XTx8OcQwXMBgq5rbncuktEPSmRkqzDL53ioyMNKbYGWpGsDbvEC4RueFU8TYVaX4PSRw3jcQBo8QaxfFq56Pjv/lvK0eINKtKOQDiaNBkOxIu7H0RAJu1EOgy08w5lhlw/qtNqsKn+CIDO2l2b6o/g7qku1LgSv1dVLgfmVJXisZc/wqUj7Hj67IryOLHv3fl8R9VWLJapieh8cYW4DE84iv/a2oAN/zER4UgMHf4QCi0G6HUa/OeWvfhJbWW2m0iUdXLb/ZTYDmg26LFo69sp0xv9numNFNfsETB389vYcMdELD27VbXQYkA4EsXczW/j6blX8kJAYWqIVSLKLUqcN2wyKbRspvRcsgyUIlmdBULFV8Olq0CoEq9B6aWWPt5s0OOOp/6GtbMmYPHZQrw2sw6n3UHc/ou/4Vfz+l+ItzdavYJkcfdWr4ChReaMt2OgnHcoc+Ri0ydEulZ5j3ZY8et5V8Fu1eOKC0swp+rCpOtenxDBHVeNxvpdjVh28zjcMHaI5Peur99Rta3IZmoiOl+cEJfREQjho9MefPFHb6Q8TpTv1LA1UqvVoDJFeqMqlwM6rSbjbaBEnmAIK6aXY+1LHyQVdVkxvRzeIM+fSjPrtZKxylX7RNSTzahDjcspOjla43KmJX2gRgPJtGfp7MIHQpEsJVbDccVd7rHKjMetCqUq0WmAS4YVYt6WvUnHqlwO6DTKjMnVVNx9IJx3KHMKJPrZKpcD+461dv3//uPtGFxkQlNHIKEAZ0/xwpuBUKSrgKaUvnxH1dY/MDURnS9e+cpQyy/tRGqmBbByuvjWyJXTxytyotFrNZIpUzghrrxiixGb9xwR3W69ec8R2C0cnChNA+lYZZQQUU8aAAumjhHtXxdMdaXlvKHRQLIP58kpkRKr4bjiLveoYTwOdC5SkYpnrUINYXF3yhVS/ezcqlJsqj/S9f+r/vAeNu85gsE2k+RzxgtvZuJcrbb+gamJ6HyxF5Bhtxgkf2m3y1TRJsoHHUIE39j4lujWyNuefFORrZGOAiMe/uP7qBxVkpQy5TdvH8OjX7ss422gRIJMcSdBoeJOdE4wEsUdT6WO1WfuVGYbMxHlDo8QQd2WvaIpyeq2vJOWlGRFJgOee/tD0T78ubePYdWt49PwTgYOJVbDccVd7vGFIqro4w1aLZ5966hoPD/71lGsmFauSDtY3J1yRSAcTehnhUgUg20mGPVanGwP4Dffuhp/PtTUlQ5lT2MLFn9JK7mzquF4W8bO1WrsH5iaiM4HJ8RljCi24MGZFbhv+4GEzjReVFPpCrpEatQRCKHZI4hujew8nvktiXarET+4dTyWbNufkDaFvwxnjyco/bl7ZY5T+nmDYclY9QYjorcTUf5yB8LwCRHRlGTx4/1lNepwZ3Up1r3amPA6VS4H7r6+TLFUD7kivhpuybb9CRMS6RzzKPEalF5q6ePb/AJuv2o0Nu85khTPnYXuBQAFGW/H0CIzHppZgaUi1/EPzaxQJH84UW94gtL97BN3TEw6drI9gLlVpdAACd/veJz95u1jGTtXq7V/YGoi6itOiPfCaEcB1syagI5AGG5/CEUWAwrNenyuxJrtphGpQqHZAKtRh7rqUlSOLEYwHIXZoMO+Y63YVH8EhQptSRxRbMH/fu0ytHoFuANhFFn0KLEaOeDNErVtpyPAapSORU46EVFPSqQdaPYIWLi1AWtnTcCSsytbC816NLkDWPjsPvzmrmt4kduDEmMejqtyi1r6eJvJgNpfvJWy0P2LC6sVaQcAjOJ1POUAuWumz5VYsGnOZOz/tA0aDTB+hB1FZgOKrHo88tXL4BciaPMLsBr10ADQaIHVGf7Rh/0DDQScEO+Foy1eLN1+IGE7SnyF+GhH5n/dJlI7i0GLjbMnYb3Iyq6NsyfBalAmWeCJNj8Wb9ufUPV6SpkTa2ZN4G6OLFDjdrp8p9dpUFPmFK0MX1PmhEHHRL1ElKikwChZVDMdaQc8wRDWzJqATT3qTlS5HFgzawKLMItQYszDcVVu0Wk1krGqVD0dp82ISaNLRFe7Kj3+43U85QKnzZhyfF7lcuD/3mvC+l2NqHE5MX/qGMzbshc+oXPHx5QyJ1bPGI+fvvYxXn7/dNfjMn2uZv9AAwGLaso40ebHD148hMpRJdg4exKeuGMiNs2ZjMtHleCBFw/hRJs/200kyjqtRoMnXm0ULZ74xGsfQ6NANfl2n5DUKQPAG4ebsWTbfrT7hIy3gRKxwIn6mHRaLJw6Bg/NHJ/Qpz00czwWTnXBqOOwgIgSmfVaLJjqEi+qeb0LZn3/zxsDrQhzu0/Ax6c9aDjWio8/86R9DKLEmIfjqtxj0mkxP0VhvvlTXTAp1MerZfzH63jKFXarEatuHS9ZVNNq1OGyUcWIxoCf/ccV2DRnMhZe78Leo61Yuv0Axg4vSnhsJs/V7B9ooOAKcRnuQEgyB5o7EMII8Bcwym/BcBS7UxRP3H24GcFw5osnNnsE0V/Vgc7OudkjcAI2C1jgRF28oQi8QgR/PHAyId9gjcuBudWl8IaYQ5yIEjV7BNRteUe8qObT7+DFhdX9PqcPpCLMSqyaU2LMw3FV7glGopiXogDuvC3v4PkFVYq1RQ3jP17HUy4psRowbcII1FWVosCkhzcY7kozBACP11aKfpcfr63Eoq0NmFtVmvScmTpXs3+ggYIT4nJiSLliBQCW3zwuG60iUhU1FE90B6S3U3fIHKfMYYETFYkBG+uT+7TdjS2Ign0aESVr9wuSxb7a/f3vX9UwjkgHuVVz62or09IfKjHm4bgq98gV5lM6jrI+/uN1POUQu9WIz188GEu27cdtV47C/Gf2dR1beL1L8rtcV12acgFaJs7V7B9ooOCEuIwYgIZjbVh4vUu0WGAs2w0kUgE1FE9UQxuI1I59GhH1lRKF+opki3Onrw9v9wlo9ghwBzoL7DkL0jdpp9SqOSXGPEp+JpQeahsLN7kDWS24FwMkd55wzENqE99ZcaojkDBWHzWoswhsw7G2rtzhcXsaW1BXVYpiqwEbZ09S5FyttnMN0fnihLgMvxCW3J7iF3JjxQpRJkkVAqlRqHgOCzgSyWOfRkR9pdVqUOVyiE4sVbkcaSnU57QZsWnOZKzbdTjp3LRpzuS09eGZTmei1Ko5JcZdSn0mlD5qGgsfa/HiXpFilg/NrMAohYpZyq2Iz5WdJ5Rf7FYj2vwhNBxrTZkepeekuEYDfPL/s3fv4XHWdf7/X5nzTJKZhAytlB6MJID2oOUgrkkrFTwj0q3uiriWgrq7UFH5rUJRoYqlrevXZTmIxyJ+RVG3VpH19F1h7WFFsBRbzo12aUuhNSGZSTLnw++PdKaZZI7JZOaemefjunpd7X183/f9ed+fz7w7c9/HRvSprfsylp+pe7WR7jXAdPD2rALaXflf8tPOYwAAhWMJXXV+jpf4nN9VkWeIG+UFPoCR0acBKJWpSVrT05nzZV/leG92KJbQnQ/uz3pvuvPBPoXKMI6oxEvAWuz5v2vUXGB+sSo17rrzwewvTL/zoeyP5EB1GWUsfNQfmlQMl6SdfQO6Yds+HfWHKhJHqyN/vhWaD1TDUX9In8mSP6mx+hW9k58VforHqSMT8mom79VGudcA00UvUEC4wEt+wjX0kh9gprw8GtHHfrBHm1ct0fXvOFMjobhaHBYd84f0sR88pu9deZ5mVeAnkkZ4gQ8mq/ZPZnECfRqAUllNJn3/D89r6fz2SS/q+/4fntdNFy2c9j4GRyO5X87d16/B0ci0+41KPM7EZjbl/Ta9zVye7yJlG3e1Oiw6WsZxV/9IRDv6sp+vHbw0zbCMMBYeHI3kHGvs7BsoSz4Xw1ogH61lykegnAZHIxkvvh8v9XiU8VJ9y4ozZunrv/tLxrfHZ/JebYR7DTBdFMQL8Bd4UVCh+UAjGAnHtGnVEm2Z8M3Tnq4ObVq1pODLssqp6i/wQQYj/GQWJ9CnASiVPxTRdW9/tW5+4MmMn2/3dnXoxncv1HAoIml693N/KP84odD84vYx848zGQpGtOZ4sWLieGhNT6d8wemfK6ky4y5emla7qj0WLpSvw2XI52IMjITz5uPLo2G96uSWisQCFKtQ/oz/BVCqHz4wMKp7dh3I+kiVmbxXV/teA0wXBfEC3M78LwQoNB9oBB3NNv3bfz2X883XX3zPomqEhSo76g/phjw/mf0/f/c6vileYfRpAErldti06VdPa01Pp66b8G3kf/t/z+r6t796+vtw5v9IUmh+UfuowEvAXDaLrvnBH3RFb+ekb9Nf84M9+tnVPdPeh1SZcRcvTcNUeSqQz8VocVi1+u5Hc+bj1n9+Y0XiAErhLvAon3knOfXVy85Kt+VNv3xan7jwdO3oG1BC0hW9nRn/ec29GsiNgngB9gI/tbLzUyuAxzAgq8FA7p/87ewb0GCgMj+ZxQn0aQBKFU0ktOrseVm/jbymp1PRxPT7eIcl/73JYZn+vakSLwGzmU1aOr8toxiRUs5HpkTjybzjrmg8Oe198NI0TJVRHlXSYjPrrBz52NvVoRabuSJxAKVob7apt6sj62eonq4O/frJo5Pa9FXnd0ma/EgV7tVAfnzyLWAwEMn7IqHBMryAB6h1hX76WKmfRsJYaBfGU6hPG6JPAzBBIqm8L+NNTr/2Kl8wmvfeVI7HOVXiJWCpR6bkOo6xR6ZM33C4wONMCswvBi9Nw1S9PBrOmwcvj4YrEkckkdD6ixeqd0IcvV0dWn/xIkXK8J95QLnNdjt0y8rFk9ptKn+27DwwaZ2/jpzIqdQjVbhXA4XxDfECnDaLrv+/u7O+LPC6rXv1f688r9ohAlXXYrfI22LT5lVLNMttz/g59XVb96rFzq2mEbXYLXLZzLqit1NL57UpHEvIYTXrsYOD2rLzAO2iCujTAJQqkUjq2ZeG9e3V52Tt4+OJ6VfEW+xWXfrN3I8a+fna3jIcycy/BKxSx+Gy5e9fXbby9K+8NA1T0Wy36kNbqv+okmgsqY9894+687KzdIPZpOFgVG6nVdF4QmvufkTf/NA5FYkDKJXHadXN71mkQCSuQCQul92sXz7xkq7fujfrfX92q10um1mBSFxz25369upz1HVyi05pc1b7UABDoxpRgKlJuv3Spbrjob5JPxO9/dKlMjVVMTjAIKxm6b6PvkE33f/kpJcn3vfRN1QxMlSTxSR9e/U5uuOhvoyf9vV0dejbq8+RhRtoxZlN0h2XnqXbH9o/qU+749KzxBNTAEwUicV074ffoC88MLmPv/fDb1A4Ov1f+3hbbDpnQXvWRxuU+yffM/kSsEodh6kpf/9azu6Vl6ahVA6rSWfnyINl3V45rJUZbISiMd108UJt/tUzk8Y8N128UKFoPM/aQHUcGQrquq17tWPc46puWblIzxzxa9OqJbp714FJL7h+56JX6M4PnKX/+/D/6jdPjT1S5adXvVELyvASZ6CeURAvwGY26asTiuHS2M9ETWrSF96zsEqRAcZhN5t108+f1NL57elvgqT+x3rjL57W+neTJ43IZjHnvX9+cSUvW600h8WsOx96Kuc12cA1ATBBu8ues4//0q/K08enHs9x/da9Gc+snomffPsCEfWPROQPjX1b1NtcvoJvpY6DzyfIZybbeDEsTU266vzTlEgmJxWirzq/S5amynwhot1l17/+JvfLZ2+5ZHFF4gCK5QtEdOPPntBr57Xp8je+Mt3f7nthSP/ytjN0839OHsPv7BvQhl88rYsWn6JPXHi63v+NhyXxMk2gGBTEC4jGk9qR46U1O/r6y/LSGqDWBaJxfeC8BZP+xzr1rLMA38BoSJFYIu/9MxLj2Y2VFojE816TQIRcBZCpUn18k6R3LD5Fq48XAewWk44Nl/dZw9m+ebe826tNq5ZoTpl+Wl6Jx4zw+QS5VKKNFzIaievKe/6Y9ZEpV97zqH5SoUemROKJvC+fjcQZh8JYBkYjev/r52ftby84c3be9nxFT6f6RyIKROK8TBMoEgXxAkYj+X8GGigwH2gEyTwv3JKkz73rNdUIC1U2HCrw0i9eqllxBV90GuaaAMhUiT7eF4jo0xOKeCnLu726/dKl0y4o+wKRSYVCSdq+v1/Xb91bln2kzPRjRkYKvDRzhHt5Q6pkG8/HH4oqEIlnfWTK2PzKtE9/gXFooflApcUSyZz97VF/KO+6vEwTKB0F8QLcDmvel9bwUxRASkrac3BIa9/clTVPKvk9pWr/TBQnFH7pl7naITacFgcvwK2k96/+sF7sH8w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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Checking the target variable\n"
      ],
      "metadata": {
        "id": "nU1DN5oXe_i9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "titanic['Survived'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4zOJm3_kfBG9",
        "outputId": "af804099-ff2f-4953-880c-020101f03685"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    549\n",
              "1    342\n",
              "Name: Survived, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(x=titanic['Survived'])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "hYJuY8W-fK7N",
        "outputId": "373d4b41-21ed-42e5-bb4f-7d7c99c7e188"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Label Encoding"
      ],
      "metadata": {
        "id": "XzDcO4NvfQhM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "# Create an instance of LabelEncoder\n",
        "le = LabelEncoder()\n",
        "\n",
        "# Apply label encoding to each categorical column\n",
        "for column in ['Sex','Embarked']:\n",
        "    titanic[column] = le.fit_transform(titanic[column])\n",
        "\n",
        "titanic.head()\n",
        "\n",
        "# Sex Column\n",
        "\n",
        "# 0 represents female\n",
        "# 1 represents Male\n",
        "\n",
        "# Embarked Column\n",
        "\n",
        "# 0 represents C\n",
        "# 1 represents Q\n",
        "# 2 represents S"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "R2lJ3byXfXcR",
        "outputId": "2417f017-e713-471f-c675-3004f244a2d8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Survived  Pclass  Sex   Age  SibSp  Parch     Fare  Embarked\n",
              "0         0       3    1  22.0      1      0   7.2500         2\n",
              "1         1       1    0  38.0      1      0  71.2833         0\n",
              "2         1       3    0  26.0      0      0   7.9250         2\n",
              "3         1       1    0  35.0      1      0  53.1000         2\n",
              "4         0       3    1  35.0      0      0   8.0500         2"
            ],
            "text/html": [
              "\n",
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              "    <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",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Embarked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>2</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-347b4195-893f-4a2f-9b4d-022236c9bea9')\"\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",
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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",
              "      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-347b4195-893f-4a2f-9b4d-022236c9bea9 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-347b4195-893f-4a2f-9b4d-022236c9bea9');\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-8bfcdc83-bb4e-4562-88b5-b910fd13b304\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8bfcdc83-bb4e-4562-88b5-b910fd13b304')\"\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-8bfcdc83-bb4e-4562-88b5-b910fd13b304 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Data Modelling"
      ],
      "metadata": {
        "id": "sPhksW7kflGL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# importing libraries\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.ensemble import AdaBoostClassifier\n",
        "from sklearn.metrics import confusion_matrix,classification_report,accuracy_score"
      ],
      "metadata": {
        "id": "SfC_jMY6fodI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Selecting the independent and dependent Features"
      ],
      "metadata": {
        "id": "iN8sPUBUfxJK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cols = ['Pclass','Sex','Age','SibSp','Parch','Fare','Embarked']\n",
        "x = titanic[cols]\n",
        "y = titanic['Survived']\n",
        "print(x.shape)\n",
        "print(y.shape)\n",
        "print(type(x))  # DataFrame\n",
        "print(type(y))  # Series"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2oBWkcKGfyfo",
        "outputId": "fe7dc481-f838-461e-f169-b87adfc0f994"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(891, 7)\n",
            "(891,)\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "<class 'pandas.core.series.Series'>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "J5I64igQf4vO",
        "outputId": "e5132ac6-b473-4b08-9848-0462c5150cad"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Pclass  Sex   Age  SibSp  Parch     Fare  Embarked\n",
              "0       3    1  22.0      1      0   7.2500         2\n",
              "1       1    0  38.0      1      0  71.2833         0\n",
              "2       3    0  26.0      0      0   7.9250         2\n",
              "3       1    0  35.0      1      0  53.1000         2\n",
              "4       3    1  35.0      0      0   8.0500         2"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-703eb250-6f89-4a11-b2fb-e0ecbcbbbd60\" 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",
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              "\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>Pclass</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Embarked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>2</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-703eb250-6f89-4a11-b2fb-e0ecbcbbbd60')\"\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-703eb250-6f89-4a11-b2fb-e0ecbcbbbd60 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-703eb250-6f89-4a11-b2fb-e0ecbcbbbd60');\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-47604b1f-bed4-463d-b549-d4aa18381367\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-47604b1f-bed4-463d-b549-d4aa18381367')\"\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-47604b1f-bed4-463d-b549-d4aa18381367 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_CO0Upvef76i",
        "outputId": "643bff0f-231f-49aa-fe66-4883d1de5ca2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0\n",
              "1    1\n",
              "2    1\n",
              "3    1\n",
              "4    0\n",
              "Name: Survived, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Train_Test_Split"
      ],
      "metadata": {
        "id": "CMR_s9n0gFtk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(891*0.10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8DKGxsL7gJ8Z",
        "outputId": "77bcd28c-3a8e-4a8a-cb20-c0ff8ab31036"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "89.10000000000001\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.10,random_state=1)\n",
        "print(x_train.shape)\n",
        "print(x_test.shape)\n",
        "print(y_train.shape)\n",
        "print(y_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-tlhcS3KgPZS",
        "outputId": "64ba0599-7bc6-4424-f351-97211bf1c6b4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(801, 7)\n",
            "(90, 7)\n",
            "(801,)\n",
            "(90,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Creating Functions to compute Confusion Matrix, Classification Report and to generate Training and the Testing Score(Accuracy)\n"
      ],
      "metadata": {
        "id": "CKhUDYoUgYc1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def cls_eval(ytest,ypred):\n",
        "    cm = confusion_matrix(ytest,ypred)\n",
        "    print('Confusion Matrix\\n',cm)\n",
        "    print('Classification Report\\n',classification_report(ytest,ypred))\n",
        "\n",
        "def mscore(model):\n",
        "    print('Training Score',model.score(x_train,y_train))  # Training Accuracy\n",
        "    print('Testing Score',model.score(x_test,y_test))     # Testing Accuracy"
      ],
      "metadata": {
        "id": "GVyYMj5Lgc7d"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Building the logistic Regression Model\n",
        "lr = LogisticRegression(max_iter=1000,solver='liblinear')\n",
        "lr.fit(x_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "WIo67EGZglnR",
        "outputId": "62198aa0-6482-42e7-c55b-04af7273bf2c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(max_iter=1000, solver='liblinear')"
            ],
            "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>LogisticRegression(max_iter=1000, solver=&#x27;liblinear&#x27;)</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\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(max_iter=1000, solver=&#x27;liblinear&#x27;)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Computing Training and Testing score\n",
        "mscore(lr)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YDyLpVAQgnub",
        "outputId": "4ede1e58-923d-4682-9443-54fb7653a143"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Score 0.8052434456928839\n",
            "Testing Score 0.7666666666666667\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Generating Prediction\n",
        "ypred_lr = lr.predict(x_test)\n",
        "print(ypred_lr)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JlZwJxNxgsCe",
        "outputId": "4b05e25d-0a73-4fc2-834f-c384e65b94c2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1 0 1 1 1 0 0 1 0 1 0 1 0 0 1 0 0 0 0 1 0 0 1 0 1 0 1 1 0 1 1 0 0 1 0 0 0\n",
            " 0 0 0 1 1 1 0 0 0 0 0 1 0 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0 1 0 0 1 0 0 0 0 0\n",
            " 1 0 1 0 0 1 0 0 0 0 1 0 0 0 0 1]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate the model - confusion matrix, classification Report, Accuracy score\n",
        "cls_eval(y_test,ypred_lr)\n",
        "acc_lr = accuracy_score(y_test,ypred_lr)\n",
        "print('Accuracy Score',acc_lr)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ULJA6OQkgxBB",
        "outputId": "f265fcfd-7613-4f7b-81d6-700d14e26305"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion Matrix\n",
            " [[46  7]\n",
            " [14 23]]\n",
            "Classification Report\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.77      0.87      0.81        53\n",
            "           1       0.77      0.62      0.69        37\n",
            "\n",
            "    accuracy                           0.77        90\n",
            "   macro avg       0.77      0.74      0.75        90\n",
            "weighted avg       0.77      0.77      0.76        90\n",
            "\n",
            "Accuracy Score 0.7666666666666667\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Building the knnClassifier Model\n",
        "knn=KNeighborsClassifier(n_neighbors=8)\n",
        "knn.fit(x_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "Lq3kZoreg4Fg",
        "outputId": "6d1b951a-8139-4c79-ec7a-b512d2823a77"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier(n_neighbors=8)"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 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-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 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-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 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-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 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-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier(n_neighbors=8)</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-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier(n_neighbors=8)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Computing Training and Testing score\n",
        "mscore(knn)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nLXn_GcHg8ce",
        "outputId": "dfc2d319-bc33-45d3-8ad4-c994fc18c7d4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Score 0.7752808988764045\n",
            "Testing Score 0.6777777777777778\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Generating Prediction\n",
        "ypred_knn = knn.predict(x_test)\n",
        "print(ypred_knn)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ifteAi51g-oW",
        "outputId": "25ae4a7b-0bc6-499d-b71f-39ef41fb746d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1 0 0 1 1 0 0 1 1 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 1\n",
            " 0 1 0 0 0 1 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 1 0\n",
            " 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate the model - confusion matrix, classification Report, Accuracy score\n",
        "cls_eval(y_test,ypred_knn)\n",
        "acc_knn = accuracy_score(y_test,ypred_knn)\n",
        "print('Accuracy Score',acc_knn)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "68BNIK4ihCK_",
        "outputId": "a33e9eab-bbf2-45b6-97ea-e9e44b6999ce"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion Matrix\n",
            " [[47  6]\n",
            " [23 14]]\n",
            "Classification Report\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.67      0.89      0.76        53\n",
            "           1       0.70      0.38      0.49        37\n",
            "\n",
            "    accuracy                           0.68        90\n",
            "   macro avg       0.69      0.63      0.63        90\n",
            "weighted avg       0.68      0.68      0.65        90\n",
            "\n",
            "Accuracy Score 0.6777777777777778\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Building Support Vector Classifier Model\n",
        "svc = SVC(C=1.0)\n",
        "svc.fit(x_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "iec9NX_8hKBR",
        "outputId": "e3cbecdc-ba51-46f0-da15-c8775fdd4f73"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC()"
            ],
            "text/html": [
              "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Computing Training and Testing score\n",
        "mscore(svc)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i4gBrO9MhL_5",
        "outputId": "80c43e23-2c03-45ed-a223-875b5b30818e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Score 0.6891385767790262\n",
            "Testing Score 0.6333333333333333\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Generating Prediction\n",
        "ypred_svc = svc.predict(x_test)\n",
        "print(ypred_svc)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5RHlgqKRhRJ1",
        "outputId": "7b8097c4-1eeb-4d8f-b5ad-c6a5a267493b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 1 0 0 1 0 0 0 0 0 0 0\n",
            " 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 1 0\n",
            " 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate the model - confusion matrix, classification Report, Accuracy score\n",
        "cls_eval(y_test,ypred_svc)\n",
        "acc_svc = accuracy_score(y_test,ypred_svc)\n",
        "print('Accuracy Score',acc_svc)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gD9IXrAOhXxR",
        "outputId": "b6d9adc0-15b4-4fca-9e5e-4def55724f8a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion Matrix\n",
            " [[48  5]\n",
            " [28  9]]\n",
            "Classification Report\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.63      0.91      0.74        53\n",
            "           1       0.64      0.24      0.35        37\n",
            "\n",
            "    accuracy                           0.63        90\n",
            "   macro avg       0.64      0.57      0.55        90\n",
            "weighted avg       0.64      0.63      0.58        90\n",
            "\n",
            "Accuracy Score 0.6333333333333333\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Building the RandomForest Classifier Model\n",
        "rfc=RandomForestClassifier(n_estimators=80,criterion='entropy',min_samples_split=5,max_depth=10)\n",
        "rfc.fit(x_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 92
        },
        "id": "dNEIKugghdmg",
        "outputId": "6e88e045-ad83-4c0b-d218-160048aaf4db"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(criterion='entropy', max_depth=10, min_samples_split=5,\n",
              "                       n_estimators=80)"
            ],
            "text/html": [
              "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 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-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 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-4 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-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 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-4 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-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 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-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 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-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 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-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(criterion=&#x27;entropy&#x27;, max_depth=10, min_samples_split=5,\n",
              "                       n_estimators=80)</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-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(criterion=&#x27;entropy&#x27;, max_depth=10, min_samples_split=5,\n",
              "                       n_estimators=80)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Computing Training and Testing score\n",
        "mscore(rfc)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9VhMFybUhf0L",
        "outputId": "647dbb48-8974-4984-9617-8287223571cc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Score 0.9250936329588015\n",
            "Testing Score 0.7666666666666667\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Generating Prediction\n",
        "ypred_rfc = rfc.predict(x_test)\n",
        "print(ypred_rfc)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2gZvmpQVhkdt",
        "outputId": "30f5aa57-e683-418c-be44-ae9fef557f92"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1 0 1 1 1 0 0 1 1 1 0 0 0 0 1 1 0 0 0 1 0 0 1 0 1 0 1 1 0 1 1 0 0 0 0 0 1\n",
            " 0 1 0 0 1 1 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0\n",
            " 1 0 1 0 0 1 0 0 0 0 1 0 0 0 0 1]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate the model - confusion matrix, classification Report, Accuracy score\n",
        "cls_eval(y_test,ypred_rfc)\n",
        "acc_rfc = accuracy_score(y_test,ypred_rfc)\n",
        "print('Accuracy Score',acc_rfc)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CjiMqUFFhrPq",
        "outputId": "110cb62e-9fe5-4751-e645-3f1e3d9fd2f3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion Matrix\n",
            " [[47  6]\n",
            " [15 22]]\n",
            "Classification Report\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.76      0.89      0.82        53\n",
            "           1       0.79      0.59      0.68        37\n",
            "\n",
            "    accuracy                           0.77        90\n",
            "   macro avg       0.77      0.74      0.75        90\n",
            "weighted avg       0.77      0.77      0.76        90\n",
            "\n",
            "Accuracy Score 0.7666666666666667\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Building the DecisionTree Classifier Model\n",
        "dt = DecisionTreeClassifier(max_depth=5,criterion='entropy',min_samples_split=10)\n",
        "dt.fit(x_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "bHHTWbTkhwu4",
        "outputId": "c2db6ca7-a900-40a8-f974-d569f2e411ab"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(criterion='entropy', max_depth=5, min_samples_split=10)"
            ],
            "text/html": [
              "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 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-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 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-5 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-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 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-5 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-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 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-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 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-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 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-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(criterion=&#x27;entropy&#x27;, max_depth=5, min_samples_split=10)</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-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(criterion=&#x27;entropy&#x27;, max_depth=5, min_samples_split=10)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Computing Training and Testing score\n",
        "mscore(dt)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PBpAZ8p5hy8H",
        "outputId": "80edd40b-3dab-4572-a685-e51947bc96d4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Score 0.8526841448189763\n",
            "Testing Score 0.7777777777777778\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Generating Prediction\n",
        "ypred_dt = dt.predict(x_test)\n",
        "print(ypred_dt)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q2q39wsVh3Qe",
        "outputId": "4867a3af-0be1-42c6-f5e1-f3a74d040dd5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1 0 1 1 1 0 0 1 0 1 0 1 0 0 1 0 0 0 0 1 0 0 1 0 1 0 1 1 0 1 1 0 0 1 0 0 1\n",
            " 0 0 0 1 1 1 0 1 0 0 0 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0 1 0 0 1 0 0 0 0 0\n",
            " 1 0 1 0 0 1 0 0 0 0 1 0 0 0 0 1]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate the model - confusion matrix, classification Report, Accuracy score\n",
        "cls_eval(y_test,ypred_dt)\n",
        "acc_dt = accuracy_score(y_test,ypred_dt)\n",
        "print('Accuracy Score',acc_dt)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PNmBUkTSh7K4",
        "outputId": "e1dbfbcf-f33a-4ce9-a09f-d2a2078a27d4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion Matrix\n",
            " [[46  7]\n",
            " [13 24]]\n",
            "Classification Report\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.78      0.87      0.82        53\n",
            "           1       0.77      0.65      0.71        37\n",
            "\n",
            "    accuracy                           0.78        90\n",
            "   macro avg       0.78      0.76      0.76        90\n",
            "weighted avg       0.78      0.78      0.77        90\n",
            "\n",
            "Accuracy Score 0.7777777777777778\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Builing the Adaboost model\n",
        "ada_boost  = AdaBoostClassifier(n_estimators=80)\n",
        "ada_boost.fit(x_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "gjvjluaAiHGN",
        "outputId": "9264c63c-9f7e-4d39-c850-49925a26503b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "AdaBoostClassifier(n_estimators=80)"
            ],
            "text/html": [
              "<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 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-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 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-6 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-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 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-6 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-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 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-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 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-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 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-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>AdaBoostClassifier(n_estimators=80)</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-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">AdaBoostClassifier</label><div class=\"sk-toggleable__content\"><pre>AdaBoostClassifier(n_estimators=80)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Computing the Training and Testing Score\n",
        "mscore(ada_boost)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UpcmfKN8iMBI",
        "outputId": "453217ae-311e-4886-d3b4-c45f21b10244"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Score 0.8564294631710362\n",
            "Testing Score 0.7666666666666667\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Generating the predictions\n",
        "ypred_ada_boost = ada_boost.predict(x_test)"
      ],
      "metadata": {
        "id": "tjuldkaliRC6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate the model - confusion matrix, classification Report, Accuracy Score\n",
        "cls_eval(y_test,ypred_ada_boost)\n",
        "acc_adab = accuracy_score(y_test,ypred_ada_boost)\n",
        "print('Accuracy Score',acc_adab)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JCsoUTIUiXOo",
        "outputId": "25f91bb1-d6f1-45bb-8135-18fb04b3aec5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion Matrix\n",
            " [[45  8]\n",
            " [13 24]]\n",
            "Classification Report\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.78      0.85      0.81        53\n",
            "           1       0.75      0.65      0.70        37\n",
            "\n",
            "    accuracy                           0.77        90\n",
            "   macro avg       0.76      0.75      0.75        90\n",
            "weighted avg       0.77      0.77      0.76        90\n",
            "\n",
            "Accuracy Score 0.7666666666666667\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "models = pd.DataFrame({\n",
        "    'Model': ['Logistic Regression','knn','SVC','Random Forest Classifier','Decision Tree Classifier','Ada Boost Classifier'],\n",
        "    'Score': [acc_lr,acc_knn,acc_svc,acc_rfc,acc_dt,acc_adab]})\n",
        "\n",
        "models.sort_values(by = 'Score', ascending = False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "o8-ZK2GcimKF",
        "outputId": "04b03321-d4ba-495b-f495-74986693f942"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                      Model     Score\n",
              "4  Decision Tree Classifier  0.777778\n",
              "0       Logistic Regression  0.766667\n",
              "3  Random Forest Classifier  0.766667\n",
              "5      Ada Boost Classifier  0.766667\n",
              "1                       knn  0.677778\n",
              "2                       SVC  0.633333"
            ],
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              "\n",
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              "        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>Model</th>\n",
              "      <th>Score</th>\n",
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              "      <td>Decision Tree Classifier</td>\n",
              "      <td>0.777778</td>\n",
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              "      <td>0.766667</td>\n",
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              "      <td>Ada Boost Classifier</td>\n",
              "      <td>0.766667</td>\n",
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              "\n",
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              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "colors = [\"blue\", \"green\", \"red\", \"yellow\",\"orange\",\"purple\"]\n",
        "\n",
        "sns.set_style(\"whitegrid\")\n",
        "plt.figure(figsize=(15,5))\n",
        "plt.ylabel(\"Accuracy %\")\n",
        "plt.xlabel(\"Algorithms\")\n",
        "sns.barplot(x=models['Model'],y=models['Score'], palette=colors )\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 360
        },
        "id": "8gVa1Amtioyl",
        "outputId": "c20c7607-bcb3-41df-b69a-c2ed062ef5fc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x500 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    }
  ]
}