16352437_Week4_DataPreprocessing.ipynb

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        "What is AI bias?\n",
        "\n",
        "AI bias is an anomaly in the output of machine learning algorithms. These could be due to the prejudiced assumptions made during the algorithm development process or prejudices in the training data.\n",
        "\n",
        "What are the types of AI bias?\n",
        "\n",
        "AI systems contain biases due to two reasons:\n",
        "\n",
        "Cognitive biases: These are effective feelings towards a person or a group based on their perceived group membership. More than 180 human biases have been defined and classified by psychologists, and each can affect individuals we make decisions. These biases could seep into machine learning algorithms via either:\n",
        "\n",
        "\n",
        "1.  designers unknowingly introducing them to the model\n",
        "2. a training data set which includes those biases\n",
        "\n",
        "\n",
        "Lack of complete data: If data is not complete, it may not be representative and therefore it may include bias. For example, most psychology research studies include results from undergraduate students which are a specific group and do not represent the whole population.\n",
        "\n",
        "\n",
        "What are examples of AI bias?\n",
        "\n",
        "Racial bias in healthcare risk algorithm\n",
        "\n",
        "A health care risk-prediction algorithm that is used on more than 200 million U.S. citizens, demonstrated racial bias because it relied on a faulty metric for determining the need.\n",
        "\n",
        "The algorithm was designed to predict which patients would likely need extra medical care, however, then it is revealed that the algorithm was producing faulty results that favor white patients over black patients.\n",
        "\n",
        "The algorithm’s designers used previous patients’ healthcare spending as a proxy for medical needs. This was a bad interpretation of historical data because income and race are highly correlated metrics and making assumptions based on only one variable of correlated metrics led the algorithm to provide inaccurate results.\n",
        "\n",
        "Bias in Facebook ads\n",
        "\n",
        "There are numerous examples of human bias and we see that happening in tech platforms. Since data on tech platforms is later used to train machine learning models, these biases lead to biased machine learning models.\n",
        "\n",
        "In 2019, Facebook was allowing its advertisers to intentionally target adverts according to gender, race, and religion. For instance,  women were prioritized in job adverts for roles in nursing or secretarial work, whereas job ads for janitors and taxi drivers had been mostly shown to men, in particular men from minority backgrounds.\n",
        "\n",
        "As a result, Facebook will no longer allow employers to specify age, gender or race targeting in its ads.\n"
      ]
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    {
      "cell_type": "code",
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        "id": "T67DyOtbmPsv"
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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "execution_count": 134,
      "outputs": []
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    {
      "cell_type": "markdown",
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      "source": [
        " imported the dataset with Pandas"
      ]
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      "source": [
        "df = pd.read_csv('https://raw.githubusercontent.com/shrikant-temburwar/Loan-Prediction-Dataset/master/train.csv')"
      ],
      "execution_count": 135,
      "outputs": []
    },
    {
      "cell_type": "markdown",
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      "source": [
        "lets take a quick look at data"
      ]
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    {
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      "source": [
        "df"
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      "execution_count": 127,
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        {
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          "data": {
            "text/plain": [
              "      Loan_ID  Gender Married Dependents     Education Self_Employed  \\\n",
              "0    LP001002    Male      No          0      Graduate            No   \n",
              "1    LP001003    Male     Yes          1      Graduate            No   \n",
              "2    LP001005    Male     Yes          0      Graduate           Yes   \n",
              "3    LP001006    Male     Yes          0  Not Graduate            No   \n",
              "4    LP001008    Male      No          0      Graduate            No   \n",
              "..        ...     ...     ...        ...           ...           ...   \n",
              "609  LP002978  Female      No          0      Graduate            No   \n",
              "610  LP002979    Male     Yes         3+      Graduate            No   \n",
              "611  LP002983    Male     Yes          1      Graduate            No   \n",
              "612  LP002984    Male     Yes          2      Graduate            No   \n",
              "613  LP002990  Female      No          0      Graduate           Yes   \n",
              "\n",
              "     ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
              "0               5849                0.0         NaN             360.0   \n",
              "1               4583             1508.0       128.0             360.0   \n",
              "2               3000                0.0        66.0             360.0   \n",
              "3               2583             2358.0       120.0             360.0   \n",
              "4               6000                0.0       141.0             360.0   \n",
              "..               ...                ...         ...               ...   \n",
              "609             2900                0.0        71.0             360.0   \n",
              "610             4106                0.0        40.0             180.0   \n",
              "611             8072              240.0       253.0             360.0   \n",
              "612             7583                0.0       187.0             360.0   \n",
              "613             4583                0.0       133.0             360.0   \n",
              "\n",
              "     Credit_History Property_Area Loan_Status  \n",
              "0               1.0         Urban           Y  \n",
              "1               1.0         Rural           N  \n",
              "2               1.0         Urban           Y  \n",
              "3               1.0         Urban           Y  \n",
              "4               1.0         Urban           Y  \n",
              "..              ...           ...         ...  \n",
              "609             1.0         Rural           Y  \n",
              "610             1.0         Rural           Y  \n",
              "611             1.0         Urban           Y  \n",
              "612             1.0         Urban           Y  \n",
              "613             0.0     Semiurban           N  \n",
              "\n",
              "[614 rows x 13 columns]"
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              "      <th>Loan_ID</th>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 614,\n  \"fields\": [\n    {\n      \"column\": \"Loan_ID\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 614,\n        \"samples\": [\n          \"LP002139\",\n          \"LP002223\",\n          \"LP001570\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Female\",\n          \"Male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Married\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Yes\",\n          \"No\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Dependents\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"1\",\n          \"3+\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Education\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Not Graduate\",\n          \"Graduate\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Self_Employed\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Yes\",\n          \"No\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ApplicantIncome\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6109,\n        \"min\": 150,\n        \"max\": 81000,\n        \"num_unique_values\": 505,\n        \"samples\": [\n          8333,\n          4342\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CoapplicantIncome\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2926.2483692241885,\n        \"min\": 0.0,\n        \"max\": 41667.0,\n        \"num_unique_values\": 287,\n        \"samples\": [\n          1840.0,\n          2042.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LoanAmount\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 85.58732523570545,\n        \"min\": 9.0,\n        \"max\": 700.0,\n        \"num_unique_values\": 203,\n        \"samples\": [\n          100.0,\n          70.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Loan_Amount_Term\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 65.12040985461256,\n        \"min\": 12.0,\n        \"max\": 480.0,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          84.0,\n          120.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Credit_History\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3648783192364048,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.0,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Property_Area\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Urban\",\n          \"Rural\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Loan_Status\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"N\",\n          \"Y\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 127
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ORvuHNdOXhLF"
      },
      "source": [
        "Data Exploration:\n",
        "\n",
        "Next, lets take a closer look at the output variable, Loan_Status. In particular, we want to see the breakdown of the frequencies between its values:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 178
        },
        "id": "GJfu1OiAXU0y",
        "outputId": "cfc6f200-e1b8-447e-8eca-7888b2781e9b"
      },
      "source": [
        "class_counts= df['Loan_Status'].value_counts()\n",
        "class_counts"
      ],
      "execution_count": 128,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Loan_Status\n",
              "Y    422\n",
              "N    192\n",
              "Name: count, dtype: int64"
            ],
            "text/html": [
              "<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>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Loan_Status</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Y</th>\n",
              "      <td>422</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>N</th>\n",
              "      <td>192</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 128
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YTpHLWsgX5z4"
      },
      "source": [
        "Clearly, there is a significant imbalance between classes. Our model could easily end up highly biased towards the majority class (and in fact, if the model simply always predicts loan approval, it would already have around 72% accuracy with this data).\n",
        "\n",
        "One possible approach to mitigate this would have been to use the SMOTE algorithm (Synthetic Minority Oversampling Technique) to upsample the minority with synthetic samples."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 550
        },
        "id": "4o2sfAAeXfCE",
        "outputId": "452a12c1-dec8-4ef1-d1e8-7a0dcb209dea"
      },
      "source": [
        "classDist = df['Loan_Status'].value_counts(ascending=True)\n",
        "print ('Frequency Table for Class distribution')\n",
        "print(classDist)\n",
        "classDist.plot(kind='bar')"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Frequency Table for Class distribution\n",
            "Loan_Status\n",
            "N    192\n",
            "Y    422\n",
            "Name: count, dtype: int64\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Loan_Status'>"
            ]
          },
          "metadata": {},
          "execution_count": 5
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6OtigzosYQK-"
      },
      "source": [
        "Lets do some more data exploration\n",
        "\n",
        "Lets look at the Gender\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 178
        },
        "id": "SM2m4TitmdNW",
        "outputId": "c205b2e3-47e4-4151-c0d7-63392025bdce"
      },
      "source": [
        "\n",
        "df['Gender'].value_counts()"
      ],
      "execution_count": 136,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Gender\n",
              "Male      489\n",
              "Female    112\n",
              "Name: count, dtype: int64"
            ],
            "text/html": [
              "<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>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Gender</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Male</th>\n",
              "      <td>489</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Female</th>\n",
              "      <td>112</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 136
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kchEa6lMbnk4"
      },
      "source": [
        "Clearly the data is bias becuase there are more male than female."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 590
        },
        "id": "dCYzb5nOYwg6",
        "outputId": "6e28d0a3-2047-4a59-aa09-6a6f42c406bd"
      },
      "source": [
        "genderDist = df['Gender'].value_counts(ascending=True)\n",
        "print ('Frequency Table for Gender')\n",
        "print(genderDist)\n",
        "genderDist.plot(kind='bar')"
      ],
      "execution_count": 137,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Frequency Table for Gender\n",
            "Gender\n",
            "Female    112\n",
            "Male      489\n",
            "Name: count, dtype: int64\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Gender'>"
            ]
          },
          "metadata": {},
          "execution_count": 137
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zkcwb-UCbwn2"
      },
      "source": [
        "**males are more in number than female**\n",
        "\n",
        "Lets look at the portion of male and female from the the loan status is either Yes or No"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 842
        },
        "id": "lja8xnL8YygJ",
        "outputId": "a556bd62-adfc-4ad0-b364-fa92107e6914"
      },
      "source": [
        "res = df.groupby(['Gender', 'Loan_Status']).size().unstack()\n",
        "res.plot(kind='bar', figsize=(15,10))\n",
        "plt.show()"
      ],
      "execution_count": 138,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1000 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EEusuucyb7Ef"
      },
      "source": [
        "**Again we see that the distribution is unfair becuase there are more males with Loan_status as Yes than females**\n",
        "\n",
        "**Next lets see the relation of ApplicantIncome with Gender**\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "id": "-yOn4c2YcU0H",
        "outputId": "c0c905ce-71ec-4d02-b579-c7c357fd4a88"
      },
      "source": [
        "sns.relplot(x=\"ApplicantIncome\", y=\"LoanAmount\", data=df, col=\"Gender\",color=\"Red\",alpha=0.4)\n",
        "plt.show()"
      ],
      "execution_count": 139,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "faWOqgF2crYn"
      },
      "source": [
        "\n",
        "Lets look at the eduction distribution of the applicants"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "sqpznT1UcyNQ",
        "outputId": "20f3df64-18de-4f75-ac15-2c16c45b7d3f"
      },
      "source": [
        "fig,ax=plt.subplots(figsize=(4,5))\n",
        "sns.countplot(x = \"Education\", data=df, order = df[\"Education\"].value_counts().index, hue=\"Education\")\n",
        "plt.show()"
      ],
      "execution_count": 140,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 400x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 665
        },
        "id": "HscdNThjho5c",
        "outputId": "2e380cde-147f-481f-8479-192f5bc20c8c"
      },
      "source": [
        "ggs=sns.relplot(x=\"Loan_Amount_Term\", y=\"LoanAmount\", data=df,kind=\"line\",hue=\"Education\",ci=None)\n",
        "ggs.fig.set_size_inches(15,7)\n",
        "plt.show()"
      ],
      "execution_count": 141,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/seaborn/axisgrid.py:854: FutureWarning: \n",
            "\n",
            "The `ci` parameter is deprecated. Use `errorbar=None` for the same effect.\n",
            "\n",
            "  func(*plot_args, **plot_kwargs)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x700 with 1 Axes>"
            ],
            "image/png": 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Ak8nEzNjuuDtb2JiUyedbjxodSURERESkTjSa78itVit//OMfGTRoEN2723dfTUtLw8XFBT8/vxrHtmjRgrS0tOpjTi9Lq56veu5sZs2aha+vb/UtPDy8jt+NiIiIyMVZd+gU+1JzcXe2cHe/NkbHuSThAR48em0kADOX7CM9r9jgRCIiIiIil6/RFKZTp05l9+7dfPrpp/V+renTp5OTk1N9O3pUIyJERETEGLNXJwJwR5/W+HlceCmhxub+Qe2ICvMlt7ic5xbvNTqOiIiIiMhlaxSF6UMPPcQ333zDzz//TOvWrasfb9myJaWlpWRnZ9c4/sSJE7Rs2bL6mBMnTpzxfNVzZ+Pq6oqPj0+Nm4iIiEhDO3AijxUJJzGZ4P5BEUbHqRUni5lZY6KwmE0s2ZnKsn0nLvwiEREREZFGzNDC1Gaz8dBDD7Fw4UKWL19ORETNDwq9e/fG2dmZZcuWVT+WkJDAkSNHGDBgAAADBgxg165dpKenVx/z448/4uPjQ9euXRvmjYiIiIjUwntr7GuXXte1Be2CPA1OU3vdw3yZNNj+fdzTi3aTX1JucCIRERERkdoztDCdOnUqH330EZ988gne3t6kpaWRlpZGUVERAL6+vkycOJFHH32Un3/+ma1bt3L//fczYMAA+vfvD8B1111H165duffee9mxYwdLly7l6aefZurUqbi6uhr59kRERETO6WReCQu2Hwdg8pD2Bqe5fH+8JpI2AR6k5hTz4tIEo+OIiIiIiNSaoYXp22+/TU5ODsOHD6dVq1bVt88++6z6mFdeeYWRI0cyduxYhg4dSsuWLVmwYEH18xaLhW+++QaLxcKAAQO45557uO+++5gxY4YRb0lERETkony44TCl5Vaiw/3o3dbf6DiXzd3FwszR9o0731+fzLYjWQYnEhERERGpHZPNZrMZHcJoubm5+Pr6kpOTo/VMRUREpN4Vl1Uw8IXlZBaU8uZdvbi5RyujI9WZR+fHsWDbcTq18Gbxw4NxcWoUS+aLiIiIiFw0fQcrIiIi0sC+3HaMzIJSwvzcub5bC6Pj1Kmnb+5KgKcLCSfyeGfVIaPjiIiIiIhcMhWmIiIiIg3IarVVb/Y0YXAETpam9e1YgKcLz460b7z57+UHSTyZb3AiEREREZFL07S+QxcRERFp5H5OSCfxZAHebk7c2Tfc6Dj14tboUIZGBlNabuXJhbvQClAiIiIi4khUmIqIiIg0oNmrEwG468o2eLk6GZymfphMJmbGdsfd2cKGxEzmbzlqdCQRERERkYumwlRERESkgew+nsOGxEyczCbGD2pndJx6FR7gwaPXRgIwc8k+0vOKDU4kIiIiInJxVJiKiIiINJB3K0eX3tyjFa183Q1OU//uH9SOqDBfcovLeW7xXqPjiIiIiIhcFBWmIiIiIg0gNaeIb3amAjB5SHuD0zQMJ4uZWWOisJhNLNmZyrJ9J4yOJCIiIiJyQSpMRURERBrAvLXJlFtt9G8fQPcwX6PjNJjuYb5MGhwBwNOLdpNfUm5wIhERERGR81NhKiIiIlLP8kvK+WTTEaD5jC493R+viaRNgAepOcW8uDTB6DgiIiIiIuelwlRERESknn22+Sh5xeW0D/ZkRKcQo+M0OHcXCzNHdwfg/fXJbDuSZXAiEREREZFzU2EqIiIiUo/KK6zMXZsEwMTBEZjNJoMTGWNIx2DG9ArDZoPpX+6irMJqdCQRERERkbNSYSoiIiJSj5buOcGxrCICPF0Y26u10XEM9fTNXQnwdCHhRB7vrEo0Oo6IiIiIyFmpMBURERGpJzabjdmr7cXgPf3b4uZsMTiRsQI8XXhmZBcAXlt2gMST+QYnEhERERE5kwpTERERkXqy9XAWcUezcXEyc9+AtkbHaRRio8MYGhlMabmVJxfuwmazGR1JRERERKQGFaYiIiIi9aRqdOmYmDCCvFwNTtM4mEwmZsZ2x93ZwobETOZvOWp0JBERERGRGlSYioiIiNSDw6cK+GHvCcC+2ZP8IjzAg0evjQRg5pJ9pOcVG5xIREREROQXKkxFRERE6sGcNUnYbDC8UzAdW3gbHafRuX9QO6LCfMktLue5xXuNjiMiIiIiUk2FqYiIiEgdyy4sZf6WYwBMHtLe4DSNk5PFzKwxUVjMJpbsTGXZvhNGRxIRERERAVSYioiIiNS5jzceoaisgi6tfBh4RaDRcRqt7mG+TKpcruDpRbvJLyk3OJGIiIiIiApTERERkTpVWm7l/XXJAEwaHIHJZDI2UCP3x2siCQ9wJzWnmBeXJhgdR0REREREhamIiIhIXVq8I4X0vBJa+Lgyqmeo0XEaPXcXC8+PjgLg/fXJbD+SZXAiEREREWnuVJiKiIiI1BGbzcbs1YkAjBvYDhcnfat1MYZ0DGZMTBg2G0xfsIuyCqvRkURERESkGdN38SIiIiJ1ZO3BU8Sn5eHhYuHuK9saHcehPD2yKwGeLsSn5fHOqkSj44iIiIhIM6bCVERERKSOVI0uvaNPOL4ezgancSwBni48M7ILAK8tO0DiyXyDE4mIiIhIc6XCVERERKQO7D+Rx8r9JzGZ4P5B7YyO45Bio8MYGhlMabmVJxfuwmazGR1JRERERJohFaYiIiIideC91UkAXN+1JW0DPQ1O45hMJhMzY7vj7mxhQ2Im87ccNTqSiIiIiDRDKkxFRERELtPJvBIWbj8OwOShEQancWzhAR48em0kADOX7CM9r9jgRCIiIiLS3KgwFREREblMH65PprTCSkwbP3q3DTA6jsO7f1A7uof5kFtcznOL9xodR0RERESaGRWmIiIiIpehqLSCDzccBmDykPYGp2kanCxmXhjTA4vZxJKdqSzbd8LoSCIiIiLSjKgwFREREbkMX247RlZhGa393bmuawuj4zQZ3cN8mTjYvrzBM4t2k19SbnAiEREREWkuVJiKiIiI1JLVamPOGvtmTxMGReBk0bdWdWnaNZGEB7iTklPMi0sTjI4jIiIiIs2EvqsXERERqaXl8ekkZhTg7ebEHX3DjY7T5Li7WHh+dBQA769PZvuRLIMTiYiIiEhzoMJUREREpJZmr04E4K5+bfBydTI4TdM0pGMwY2LCsNlg+oJdlFVYjY4kIiIiIk2cClMRERGRWth1LIeNSZk4mU2MH9jO6DhN2tMjuxLg6UJ8Wh7vrEo0Oo6IiIiINHEqTEVERERqoWp06cgerWjl625wmqYtwNOFZ0Z2AeC1ZQdIPJlvcCIRERERacpUmIqIiIhcopTsIpbsSgVg0pD2BqdpHmKjwxgaGUxpuZUnF+7CZrMZHUlEREREmigVpiIiIiKXaN66ZCqsNga0D6R7mK/RcZoFk8nEzNjuuDmb2ZCYyfwtR42OJCIiIiJNlApTERERkUuQV1zG/zYeAWDy0AiD0zQv4QEePHptJAAzl+zjZF6JwYlEREREpClSYSoiIiJyCT7bfJS8knKuCPZkeGSI0XGanQmDIuge5kNucTnPLd5jdBwRERERaYJUmIqIiIhcpPIKK3PXJgMwcXB7zGaTsYGaISeLmRfG9MBiNvHNzlSWx58wOpKIiIiINDEqTEVEREQu0vd70jieXUSgpwtjeoUZHafZ6h7my8TB9uUQnl64m/yScoMTiYiIiEhTosJURERE5CLYbDZmr04C4J7+bXFzthicqHmbdk0k4QHupOQU8+LSBKPjiIiIiEgTosJURERE5CJsOZzFjqPZuDiZuXdAW6PjNHvuLhaeHx0FwPvrk9l+JMvgRCIiIiLSVKgwFREREbkIs1clAjC2VxhBXq4GpxGAIR2DGRMThs0G0xfsoqzCanQkEREREWkCVJiKiIiIXEByRgE/7rNvLlS1dqY0Dk+P7EqApwvxaXm8U1lqi4iIiIhcDhWmIiIiIhcwZ20SNhuM6BRMhxBvo+PIaQI8XXhmZBcAXlt2gMST+QYnEhERERFHp8JURERE5DyyC0v5fMsxACYPaW9wGjmb2OgwhnQMorTcypMLd2Gz2YyOJCIiIiIOTIWpiIiIyHl8vPEIRWUVdG3lw4ArAo2OI2dhMpmYGRuFm7OZDYmZ1QW3iIiIiEhtqDAVEREROYeS8grmrUsGYPLQCEwmk7GB5JzaBHrw6LWRAMz8dh8n80oMTiQiIiIijkqFqYiIiMg5LN6Rysm8Elr4uHJzVKjRceQCJgyKoHuYDzlFZTy3eI/RcURERETEQakwFRERETkLm83Gu6vtu66PHxiBi5O+bWrsnCxmXhjTA4vZxDc7U1kef8LoSCIiIiLigPSdv4iIiMhZrDmYQXxaHh4uFu66so3RceQidQ/zZeLgCACeXrib/JJygxOJiIiIiKNRYSoiIiJyFrNXJwFwR59wfD2cDU4jl2LaNZGEB7iTklPMi0sTjI4jIiIiIg5GhamIiIjIrySk5bFq/0nMJvu6mOJY3F0sPD86CoD31yez/UiWwYlERERExJGoMBURERH5lffW2Ncuvb5bS9oEehicRmpjSMdgxsSEYbPB9AW7KKuwGh1JRERERByEClMRERGR06TnFbNoewoAk4a0NziNXI6nbu6Cv4cz8Wl5vLMq0eg4IiIiIuIgVJiKiIiInObD9YcprbDSq40fvdv6Gx1HLkOglyvPjOwKwGvLDpCUUWBwIhERERFxBCpMRURERCoVlVbw0YbDAEzW6NImYXRMGEM6BlFabuXJBbuw2WxGRxIRERGRRs7QwnTVqlWMGjWK0NBQTCYTixYtqvG8yWQ66+1f//pX9THt2rU74/kXXnihgd+JiIiINAVfbDtGVmEZ4QHuXNetpdFxpA6YTCZmxkbh5mxmfeIpPt9yzOhIIiIiItLIGVqYFhQU0LNnT958882zPp+amlrjNmfOHEwmE2PHjq1x3IwZM2oc9/DDDzdEfBEREWlCrFYbc9YkATBhUAQWs8ngRFJX2gR68Oi1kQDM/HYfJ/NKDE4kIiIiIo2Zk5EXv/HGG7nxxhvP+XzLljVHdnz11VeMGDGC9u1rTpHz9vY+41gRERGRS7EsPp2kjAJ83Jy4o0+40XGkjk0YFMHXO1LYfTyX5xbv4Y27ehkdSUREREQaKYdZw/TEiRMsWbKEiRMnnvHcCy+8QGBgIDExMfzrX/+ivLz8vOcqKSkhNze3xk1ERESat9mr7buo39WvLZ6uhv5MWeqBk8XMC2N6YDGb+GZnKsvjTxgdSUREREQaKYcpTN9//328vb0ZM2ZMjccfeeQRPv30U37++Wd+//vf8/zzz/OnP/3pvOeaNWsWvr6+1bfwcI0iERERac52HstmU1ImTmYT4we2MzqO1JPuYb5MHBwBwNMLd5Nfcv4fsouIiIhI82SyNZKtQk0mEwsXLiQ2Nvasz3fu3Jlrr72W119//bznmTNnDr///e/Jz8/H1dX1rMeUlJRQUvLL2lW5ubmEh4eTk5ODj49Prd+DiIiIOKaH/7edxTtSGB0Txit3RhsdR+pRYWk5172yimNZRYwf2I6/3tLN6EgiIiIi0sg4xAjT1atXk5CQwKRJky54bL9+/SgvLyc5Ofmcx7i6uuLj41PjJiIiIs3T8ewivt2VCsCkIREGp5H65uHixPOjowB4f30ycUezjQ0kIiIiIo2OQxSm7733Hr1796Znz54XPDYuLg6z2UxISEgDJBMRERFHN29tEhVWGwOvCKRbqK/RcaQBDI0MZnRMGDYb/OXLnZRVWI2OJCIiIiKNiKE7GuTn53Pw4MHqXyclJREXF0dAQABt2rQB7NPlP//8c1566aUzXr9+/Xo2btzIiBEj8Pb2Zv369UybNo177rkHf3//BnsfIiIi4pjyisv4dNNRACYPaW9wGmlIT9/chRUJ6cSn5fHOqkSmjuhgdCQRERERaSQMHWG6ZcsWYmJiiImJAeDRRx8lJiaGZ599tvqYTz/9FJvNxm9/+9szXu/q6sqnn37KsGHD6NatGzNnzmTatGm88847DfYeRERExHF9tvkoeSXldAjxYlhksNFxpAEFernyzMiuALy27ABJGQUGJxIRERGRxqLRbPpkpNzcXHx9fbXpk4iISDNSXmFl2L9WcDy7iFljovjtlW2MjiQNzGazcd+cTaw+kMGA9oF8MrkfJpPJ6FgiIiIiYjCHWMNUREREpK59tzuN49lFBHq6MDomzOg4YgCTycTM2CjcnM2sTzzF51uOGR1JRERERBoBFaYiIiLS7NhsNt5dnQjAvQPa4uZsMTiRGKVNoAePXhsJwMxv93Eyr8TgRCIiIiJiNBWmIiIi0uxsTs5ix7EcXJ3M3Nu/rdFxxGATBkXQLdSHnKIynlu8x+g4IiIiImIwFaYiIiLS7MyuHF06pldrAr1cDU4jRnOymHlhTA/MJvhmZyrL408YHUlEREREDKTCVERERJqVpIwCftpnL8QmDo4wOI00FlGtfav/PDy9cDcFJeUGJxIRERERo6gwFRERkWZlzpokbDa4qnMIHUK8jI4jjci0ayNp7e9OSk4xL/6QYHQcERERETGIClMRERFpNrIKSvl861EAJg3R6FKpycPFiedHRwEwb10ycUezjQ0kIiIiIoZQYSoiIiLNxscbD1NcZqVbqA8D2gcaHUcaoaGRwYyOCcNmg798uZOyCqvRkURERESkgakwFRERkWahpLyC99cfBmDykPaYTCaDE0lj9fTNXfD3cCY+LY93ViUaHUdEREREGpgKUxEREWkWvo5L4WReCS193Li5Ryuj40gjFujlyjMjuwLw2rIDJGUUGJxIRERERBqSClMRERFp8mw2G++tSQJg/KB2OFv0LZCc3+iYMIZ0DKK03MqTC3Zhs9mMjiQiIiIiDUSfFkRERKTJW30gg/i0PDxdLPz2yjZGxxEHYDKZmBkbhZuzmfWJp/h8yzGjI4mIiIhIA1FhKiIiIk3e7NX2dSjv6BuOr7uzwWnEUbQJ9GDaNZEAzPx2HyfzSgxOJCIiIiINQYWpiIiINGnxabmsPpCB2QQTBkUYHUcczMTBEXQL9SGnqIwZ3+w1Oo6IiIiINAAVpiIiItKkvbfavnbpDd1bEh7gYXAacTROFjMvjOmB2QSLd6Twc3y60ZFEREREpJ6pMBUREZEmKz2vmK/iUgCYNKS9wWnEUUW19mXiYPvo5KcX7aagpNzgRCIiIiJSn1SYioiISJP1wbrDlFZY6d3Wn15t/I2OIw5s2rWRtPZ353h2ES/+kGB0HBERERGpRypMRUREpEkqLC3no42HAZg8RGuXyuXxcHHi+dFRAMxbl0zc0WxjA4mIiIhIvVFhKiIiIk3Sl1uPkV1YRpsAD67t2tLoONIEDI0MZnRMGDYb/OXLnZRVWI2OJCIiIiL1QIWpiIiINDlWq4331tg3e5owqB0Ws8ngRNJUPH1zF/w9nIlPy+OdVYlGxxERERGReqDCVERERJqcn/adIPlUIT5uTtzeJ9zoONKEBHq58szIrgC8tuwASRkFBicSERERkbqmwlRERESanHdX20eX3t2/LZ6uTgankaZmdEwYQzoGUVpu5ckFu7DZbEZHEhEREZE6pMJUREREmpQdR7PZlJyJs8XE+IHtjI4jTZDJZGJmbBRuzmbWJ57i863HjI4kIiIiInVIhamIiIg0KbNX29eVHNUzlBY+bgankaaqTaAH066JBGDmkn2czCsxOJGIiIiI1BUVpiIiItJkHMsq5LvdaQBMGtze4DTS1E0cHEG3UB9yisqY8c1eo+OIyHkUlJSz/0QePyeka+1hERG5IC3qJSIiIk3GvLXJVFhtDOoQSNdQH6PjSBPnZDHzwpge3PrmGhbvSGFMTBgjOocYHUuk2bHZbJwqKCUlu4jjWUUczy7iWFaR/deVt+zCsurj/3RDJ6YM72BgYhERaexUmIqIiEiTkFtcxqebjwIwaYhGl0rDiGrty8TBEcxencTTi3bzw7Sh2mhMpI6VV1hJyy2uLkOPZxWRkmMvRY9n24vR4jLrBc/j7eZEmJ873vp/VERELkD/UoiIiEiT8Nmmo+SXlNMxxIvhkcFGx5FmZNq1kXy3O41jWUW8+EMC/zeqm9GRRBxKYWk5Kdk1C9DTy9G03GKstgufJ8TblTB/d0L93Gnt506YvzthlV9D/dzxcXOu/zcjIiJNggpTERERcXhlFVbmrk0CYNKQCEwmk8GJpDnxcHFi5ugoxs3ZxLx1ydwaHUZ0uJ/RsUQaBZvNRlZhWWUBWsjx7OLT7heRkl1MZkHpBc/jbDER6udOqG/NIjTMz35r5eeGq5OlAd6RiIg0BypMRURExOF9tzuNlJxigrxcuDU6zOg40gwNiwwmNjqURXEp/OXLnSx+eDDOFu2vKk1feYWVE3kl9mny2b+sH3r6SNGisooLnsfL1almCVo5KjTMz53W/u4Ee7liNuuHYSIi0jBUmIqISIPZfTyH+LQ8RseEYdGHHqkjNpuNd1cnAnBv/3a4OWuEkRjjmZFdWbn/JPFpebyzKpGpI7SpjDi+otIKUnJqTpGv2kiparp8xUXMlw/ysk+Xr5oqH+rrRpi/R3U56uuu6fIiItJ4qDAVEZEGYbPZ+P2HWzmeXcT3u1N57Tcx2hhF6sSmpEx2HsvB1cnMPf3bGB1HmrFAL1eevrkrj32+g9eWHeCmqFZEBHkaHUvknGw2GzlFZdUjQo+fPjK08tenLmK6vJPZRCs/t8rp8R6E+blVjhT1IMzfnVa+bvphloiIOBR9UhURkQYRn5bH8ewiAH7al85t/1nPe+P6EOrnbnAycXSzV9vXLh3buzWBXq4Gp5HmbkyvMBbFHWf1gQyeWriLjyf105q6YpgKq430vF92lz+WVbMMTckuoqD0wtPlPV0s1VPlQ0+bNt+6ctp8iLebZo6IiEiTosJUREQaxMr9JwHo3NKbjPwS9qXmcuuba3n3vj701OYoUkuJJ/NZFn8CgAmDIgxOIwImk4mZsVFc9+pK1h06xedbj3FHn3CjY0kTVVxWUV2AVq0Xeuy0kaJpOcWUX9R0eZfq9UJPX0c0tLIU9XV3VvEvIiLNigpTERFpECsT7IXpb69sw9VdQpg4bwsJJ/K447/reeXOaG6KamVwQnFEc9YmYbPB1Z1D6BDiZXQcEQDaBHow7ZpIZn0Xz8wl+xjRKYRgb41+lktjs9nILSrnWHYhKdnFHM8qrLF26PHsYjLySy54HovZREsftxrrh/56pKimy4uIiNSkwlREROpdfkk5Ww5nAjC8UzCt/T344sEBPPy/7axIOMmUj7fxxPWdmDL8Co1gkYuWVVDKF1uPATBpSHuD04jUNHFwBF/vSGFPSi4zvtnL67+NMTqSNDJWq430vJJflaCFlVPlizmeXUR+SfkFz+PubKmxs/yvR4m28NF0eRERkUulwlREROrduoMZlFXYaBfoQdtA+wYo3m7OvHtfH/6+ZB/z1iXzr6UJHDqZz6wxUbg6aaSLXNhHGw5TXGale5gP/dsHGB1HpAYni5kXxvTg1jfXsHhHCmNiwhjROcToWNKASsorSK0sPqumyqecNl0+NaeIsooLT5cP8HSpLkF/vX5omJ87fh6aLi8iIlLXVJiKiEi9W1G5fumwyOAajztZzPz1lm5cEezJXxfvZcG24xzLLOI/9/YmwNPFiKjiIIrLKnh//WEAJg9pr7JAGqWo1r5MHBzB7NVJPL1oNz9MG4qnq779bipyi8vs5edpO8ufvn7oybxLmC7v507or3aWr3rMw0V/ZkRERBqa/vUVEZF6ZbPZqtcvHd7p7KOr7h3QjjaBnjz08TY2JWcy+q21vDeur9aklHP6ekcKGfkltPJ10/q30qhNuzaS73ancSyriBd/SOD/RnUzOpJcBKvVRkZ+SXUBmlJj2rz9a95FTJd3czbX2Dzpl6nyHoT6udHSxw0ni7kB3pGIiIhcChWmIiJSrw6dLOB4dhEuTmb6tw8853HDIoP5cspAJszbzOFThYx+ay1v392bwR2DGjCtOAKbzcZ7q5MAGD+wHc4qG6QR83BxYuboKMbN2cS8dcncGh1GdLif0bGavdJyK6k5pxWg2TVHiqZkF1NaYb3gefw9nH/ZXf60qfJVjwV4umgEvIiIiANSYSoiIvVqRUI6AP0iAnB3Of/apJEtvPlq6iB+9+FWth7OYtzcTcy4tRt392vbEFHFQaw6kEHCiTw8XSz85so2RscRuaBhkcHERoeyKC6Fv3y5k8UPD1bRX8/yisuqy8+q9UNPHymanleC7QLLh5pN0KJyuvzpmyqF+tl3mw/1c9cSCyIiIk2U/oUXEZF6tfIc65eeS6CXKx9P6sdfvtzJorgUnlq4m8STBTx5Uxft8isAvLs6EYA7+7bB193Z4DQiF+eZkV1Zuf8k8Wl5zF6dyJThHYyO5LBsNhsZ+aVn7Cx/vHqDpUJyiy88Xd7VyXzGzvKnb6rU0tdNxbaIiEgzpcJURETqTVFpBRuTMgEY3uniClMAN2cLr9wZTftgL17+cT/vrUkiOaOA134bg5dG8zRr+1JzWX0gA7MJ7h/Uzug4Ihct0MuVp2/uymOf7+C1nw5wU/dWtAvyNDpWo1RWYSUtp5hjp60XWr2GaOWttPzC0+V93Z3PKERPHykaqOnyIiIicg761CkiIvVmQ+IpSsuthPm5c0XwpW3gZDKZeOTqjkQEefLY5ztYFp/O7f9Zz3vj+hDq515PiaWxe2+Nfe3SG7u3IjzAw+A0IpdmTK8wFsUdZ/WBDJ5cuIuPJ/VrloVdQUl5dRF67LRp81WPncgrvuB0eZMJWni7VU+RrypBW582bV4/YBMREZHa0ncRIiJSb6qn43cKrnUpMKpnKGH+7vzugy3sS83l1jfX8u59feipTVOanfTcYr6KOw7ApCERBqcRuXQmk4mZsVFc9+pK1h06xedbj3FHn3CjY9Upm83GqYLSGqNCa4wUzSkiu7DsgudxcaraXb5yDVE/jxojRVv6uuHipOnyIiIiUj9UmIqISL2p2vBp+EWuX3ouvdr4s2jqICbO20LCiTzu+O96XrkzmpuiWtVFTHEQ769PpqzCRp+2/sS08Tc6jkittAn0YNo1kcz6Lp6ZS/YxolMIwd6uRse6aOUVVlJzin+ZIp9Vc6p8SnYRxWUXni7v4+Zk3zzpV5spVd0P8nTFrHWrRURExCAqTEVEpF4kZxSQfKoQJ7OJgR2CLvt8rf09+OLBATz8v+2sSDjJlI+38cT1nZgy/IpmOaW1uSksLeejDUcAmDSkvcFpRC7PxMERfBWXwt7UXGZ8s5fXfxtjdKRqhaXlpPxqVOjpu82n5RZjvcB0eYAQb9ca64X+eg1Rbzdt2CYiIiKNlwpTqRdlFVbun7uZuKPZRke5aGYTTBnRgQeGXWF0FJEmoWo6fp92/nW2jpy3mzPv3teHmd/uY+7aZP61NIFDJ/OZNSYKVydLnVxDGqcvth4jp6iMtoEeXNu1hdFxRC6Lk8XMP8b24NY317B4RwpjYsIY0Tmk3q9rs9nIKiyr3ln+2OllaGUhmnUx0+UtZlpVT5X/ZWf5qvVDW/q66e9kERERcWgqTKVerDmQwZqDGUbHuGT//D6ePm396dMuwOgoIg6vqjAd3qluSwAni5n/G9WN9sFe/PXrPSzYdpyjmYX8994+BHi61Om1pHGosNqYU7nZ04RBEVg0TVeagKjWvkwYFMG7a5J4etFufpg2FM/L/OFSeYWVE3kl1YVoSvbpO83bf11UVnHB83i7OlWPBA391c7yYX7uBHtpuryIiIg0bSpMpV4s3G7flOM3fcMdZsTmqz/tZ1FcCo/O38G3fxiinVVFLkNxWQXrD50CYNhlrl96Lvf2b0ubAA8e+ngbm5OziH1zLXPG96VDiFe9XE+M89O+EySfKsTX3Znb+7Q2Oo5InXn0uki+35PGsawiXvphP8+O6nre44tKK2qsFXr8V9Pm03KLqbiI+fLB3q5nTJE/vRz1ddd0eREREWne1AhJncsvKeeHvWkA3NWvDe2CPA1OdHFmxHZnc3IWRzIL+dvivfzjth5GRxJxWJuTMykqq6CFjyudW3rX23WGRQazYMpAJry/mSOZhYx+ay1v392bwR0vf81UaTzeXZ0IwN392uDhom9dpOnwcHFi5ugoxs3ZxLx1SVzdJQRfd+eamylV7ix/PKuIUwWlFzynk9l02nR5j+qp8lVlaCtfN9ycNV1eRERE5Hz0qUPq3NLdaRSXWWkf7ElUmK/RcS6aj5szL93Rk9/O3sBnW45ydZcQruvW0uhYIg5pZYJ9Ov6wyOB635CpYwtvFk0ZxO8+3MrWw1mMm7uJGbd24+5+bev1utIw4o5mszk5C2eLiXED2xkdR6TODYsMJjY6lEVxKdz97sYLHu/pYvnVFHkPQv3cKneb9yDY21XLVoiIiIhcJhWmUueqpuOPjg5zuJ2r+7cPZPKQ9ryzKpHpC3YR08afYG9Xo2OJOJwV+6sK0/rfxAQg0MuVjyf14y9f7mRRXApPLdxN4skCnrypi4oDBze7cnTpLT3DaOHjZnAakfrxzMiubErKJCWnmCAvlxpT5UNPmzrf2s8DH3cnh/v+SkRERMTRqDCVOnUit5i1h+ybPd0aHWZwmtp57LpIVu0/SXxaHtMX7GT2fX30wUTkEhzLKuRgej4Ws6lBp8a7OVt45c5o2gd78fKP+3lvTRLJGQW89tsYrUnsoI5mFvLdrlQAJg6OMDiNSP0J9HJl1Z9GUG61abq8iIiISCNgNjqANC1fx6Vgs0Gftv60CfQwOk6tuDrZSxcXi5mf9qXz2eajRkcScSgrK0eXxoT7NfjGISaTiUeu7sjrv43B1cnMsvh0bnt7HSnZRQ2aQ+rGvHXJWG0wuEMQXUN9jI4jUq+cLGaVpSIiIiKNhApTqVNV0/FjYxxzdGmVLq18ePz6SABmfLOX5IwCgxOJOI6q9UuHdwo2LMOonqF8+rv+BHm5EJ+Wx61vriXuaLZheeTS5RaXVf/AatIQjS4VEREREZGGo8JU6kxCWh57U3NxtpgY2aOV0XEu28TB7ekXEUBhaQWPzo+jvMJqdCSRRq+03Mq6Q6eAhlu/9Fxi2vizaOogOrf05mReCXf+dz3fVk7vlsbv001HyC8pJ7KFF8MijSvfRURERESk+VFhKnVmUZx9dOmITiH4ebgYnObyWcwmXrqjJ16uTmw7ks1/Vh4yOpJIo7f1cBb5JeUEebnQrRFMoW7t78HnDwxgRKdgSsqtTPl4G2/+fBCbzWZ0NDmPsgorc9cmAzBpcHutIy0iIiIiIg3K0MJ01apVjBo1itDQUEwmE4sWLarx/Pjx4zGZTDVuN9xwQ41jMjMzufvuu/Hx8cHPz4+JEyeSn5/fgO9CAKxWG19VTscf7eDT8U/X2t+D527pBsCrPx1g17EcgxOJNG5V65cO7RiMuZHsTu/t5szs+/pw/6B2APxraQKPzd9BSXmFscHknL7dlUpq5W7ht0SHGh1HRERERESaGUML04KCAnr27Mmbb755zmNuuOEGUlNTq2//+9//ajx/9913s2fPHn788Ue++eYbVq1axe9+97v6ji6/sjEpk5ScYrzdnBjR2dhpuHVtTK8wbopqSbnVxh8/205xmUoWkXNZkZAOwDAD1y89GyeLmf8b1Y2/xXbHYjaxYPtx7nl3I5kFpUZHk1+x2Wy8uzoJgPsGtNMmOCIiIiIi0uCcjLz4jTfeyI033njeY1xdXWnZsuVZn9u3bx/ff/89mzdvpk+fPgC8/vrr3HTTTbz44ouEhmpUSkNZVDm69OaoVk3uw63JZGJmbBRbkrM4dLKAF76L56+Vo05F5BcncouJT8vDZIIhHRtXYVrl3v5taRvgwdSPt7E5OYvYN9cyZ3wfOoR4Gx1NKm1MymTX8RzcnM3c07+t0XFERERERKQZavRrmK5YsYKQkBA6derEgw8+yKlTp6qfW79+PX5+ftVlKcA111yD2Wxm48aN5zxnSUkJubm5NW5Se8VlFdUbqcQ2oen4p/P3dOGft/UAYN66ZFZVTjsWkV9UTcfv0dqPAM/Gu47x0MhgFkwZSHiAO0cyCxn91jrWHMgwOpZUend1IgBje7Vu1H+ORERERESk6WrUhekNN9zABx98wLJly/jHP/7BypUrufHGG6mosE+JTktLIySk5vRvJycnAgICSEtLO+d5Z82aha+vb/UtPDy8Xt9HU7c8Pp28knLC/Ny5sl2A0XHqzfBOIdxbOdrpiS92kF2oqbwip1uZYC9MHWFH844tvFk0ZRB92vqTV1zOuLmb+HjjYaNjNXuJJ/P5aZ99WYeJgyMMTiMiIiIiIs1Voy5Mf/Ob33DLLbcQFRVFbGws33zzDZs3b2bFihWXdd7p06eTk5NTfTt69GjdBG6mFlZOx781OrTRbPJSX568qQvtgzw5kVvCU4t2a6dtkUrlFVZWH7AXpsMb2fql5xLo5cpHk/oxOiaMCquNpxbuZsbivVRY9f+1Ud5bY1+79JouIbQP9jI4jYiIiIiINFeNujD9tfbt2xMUFMTBgwcBaNmyJenp6TWOKS8vJzMz85zrnoJ9XVQfH58aN6mdrILS6k1eRjfR6finc3ex8Mqd0VjMJpbsTOWruBSjI4k0CnFHs8ktLsfPw5merf2MjnPR3JwtvHxHTx67NhKAOWuT+N0HW8gvKTc4WfOTWVDKF1uPATBpSHuD04iIiIiISHPmUIXpsWPHOHXqFK1atQJgwIABZGdns3Xr1upjli9fjtVqpV+/fkbFbFa+2ZVKWYWNbqE+dGzRPDZN6RnuxyNXdQTgma92czy7yOBEIsarWr90SMdgLA420txkMvHw1R15464YXJ3MLItP57a31+n/7Qb20YbDlJRbiQrzpV9E013eRUREREREGj9DC9P8/Hzi4uKIi4sDICkpibi4OI4cOUJ+fj5PPPEEGzZsIDk5mWXLlnHrrbfSoUMHrr/+egC6dOnCDTfcwOTJk9m0aRNr167loYce4je/+Q2hoaEGvrPmY1HldPzmMLr0dFNHXEF0uB95xeU8Pn8HVk3hlWauqjB1hPVLz2Vkj1A+/V1/grxciU/L49Y31hJ3NNvoWM1CcVkFH6xPBmDSkAhMJscq3UVEREREpGkxtDDdsmULMTExxMTEAPDoo48SExPDs88+i8ViYefOndxyyy1ERkYyceJEevfuzerVq3F1da0+x8cff0znzp25+uqruemmmxg8eDDvvPOOUW+pWTlyqpCth7Mwm2BUz+ZVUDtZzLxyZzTuzhbWJ55iztokoyOJGCYjv4Sdx3IAGBoZZHCayxPTxp9FUwfSuaU3Gfkl3Pnf9SzZmWp0rCbv67gUMvJLCfV146aoVkbHERERERGRZs7JyIsPHz78vJvmLF269ILnCAgI4JNPPqnLWHKRFsXZR5cO6hBECx83g9M0vIggT54e2YWnFu7mn0sTGNIxmE4tm8eyBCKnq9rsqVuoDyHejv93QWt/Dz5/YACP/G87PyecZOon20jKiGTqiA4a+VgPbDYb765JBGD8oHY4WxxqtSAREREREWmC9KlEasVms1VPx4+Nbl7T8U9315VtGNEpmNJyK3/8LI6S8gqjI4k0uBUJjj8d/9e83Zx5d1xfJgyKAODFH/bz2Pwd+n+8Hqzcf5L9J/LxcnXiN1e2MTqOiIiIiIiIClOpnZ3HckjMKMDd2cIN3VsaHccwJpOJf9zWA38PZ/al5vLKjweMjiTSoCqsNlZVrl86vFOIwWnqlsVs4tlRXfl7bHcsZhMLth/nnnc3kllQanS0JuXd1fYlTe7sG46Pm7PBaURERERERFSYSi0trBxdel23Fni6Grqyg+FCvN2YNaYHAP9ddYhNSZkGJxJpOLuP55BVWIa3qxMxbfyMjlMv7unflrnj++Lt6sTm5Cxi31zLwfQ8o2M1CXtTcllzMAOL2cT9g9oZHUdERERERARQYSq1UFZhZfGOFABiY5rvdPzT3dC9Jbf3bo3NBo/OjyOvuMzoSCINomo6/qAOQU167cmhkcEsmDKQ8AB3jmQWMvqtddVrt0rtvbfGPrr0xu4tae3vYXAaERERERERu6b76VbqzZoDGZwqKCXQ04UhHRx7R+y69OyorrT2d+dYVhEzFu81Oo5Ig1i5Px2A4Z2azvql59KxhTeLpgyiT1t/8orLGT93Mx9tOGx0LId1IreYr3fYZytMGtLe4DQiIiIiIiK/UGEql6xqOv6onqE4NeERZZfK282Zl++IxmSCz7ce4/vdqUZHEqlX2YWlxB3NBmBYMyhMAQK9XPl4cj9Gx4RRYbXx9KLdzFi8lwqrzehoDuf9dcmUVdjo286f6HA/o+OIiIiIiIhUU9sllyS/pJwf9qYBMFrT8c9wZUQAvx96BQDTF+wiPa/Y4EQi9Wf1gQysNujUwptWvu5Gx2kwrk4WXr6jJ49dGwnAnLVJ/O6DLeSXlBuczHEUlpbz8cYjgEaXioiIiIhI46PCVC7J0t1pFJdZaR/kSY/WvkbHaZQevTaSLq18yCos489f7MRm08gzaZpW7rev4dlcRpeezmQy8fDVHXnjrhhcncwsi0/ntrfXcTy7yOhoDuHzLcfIKSqjXaAH13RpYXQcERERERGRGlSYyiVZFGefjj86JgyTyWRwmsbJxcnMq3dG4+Jk5ueEk3yy6YjRkUTqnNVq+6UwjWx+hWmVkT1C+fR3/QnyciU+LY9b31hbvUyBnF2F1cactfbNniYMjsBi1r8lIiIiIiLSuKgwlYt2IreYtQczALg1WtPxz6dTS2/+dH0nAP7+zT6SMgoMTiRSt/al5XIyrwQPFwt92vkbHcdQMW38WTR1IJ1bepORX8Kd/13Pkp1aw/hcftx7gsOnCvF1d+a23q2NjiMiIiIiInIGFaZy0b6OS8Fqgz5t/WkT6GF0nEZvwqAIBl4RSFFZBdM+i6O8wmp0JJE6syLBPrp04BWBuDpZDE5jvNb+Hnzx4ECu6hxCSbmVqZ9s443lB7Qkx1m8uzoRgHv6t8HDxcngNCIiIiIiImdSYSoXbeF2+3T8WG32dFHMZhMv3t4Tbzcn4o5m8+bPh4yOJFJnflm/NMTgJI2Hl6sTs+/rw4RBEQC8+MN+Hpu/g5LyCoOTNR7bj2Sx5XAWLhYz4wa0MzqOiIiIiIjIWakwlYuSkJbH3tRcnC0mbo5qZXQchxHq587fY7sD8O/lB9ihtQ2lCcgtLmPb4SwAhnVsvuuXno3FbOLZUV35e2x3LGYTC7Yf5553N5JZUGp0tEbh3dX2tUtviQ4lxMfN4DQiIiIiIiJnp8JULkrVZk/DO4Xg7+licBrHckvPUEb2aEWF1ca0z+IoKtVoM3Fs6w5mUG610T7IU8tznMM9/dsy7/6+eLs5sTk5i9g313IwPc/oWIY6mlnId7vta7tOHBxhcBoREREREZFzU2EqF2S12viqcjr+aE3Hv2Qmk4m/x3anhY8riRkFPP/tPqMjiVyWX6bja3Tp+QzpGMyCBwcSHuDOkcxCRr+1jtUHThodyzBz1yZjtcGQjkF0aeVjdBwREREREZFzUmEqF7QpOZOUnGK83Zy4qrPWK6wNPw8XXry9JwAfbjjMzwnpBicSqR2bzVa94dOwSBWmF9KxhTeLpgyiT1t/8orLGT93Mx9tOGx0rAaXU1TGZ5uPADBpSHuD04iIiIiIiJyfClO5oIXb7KNLb45qhZuzdsOurSEdgxk/sB0Af/piJ1la01Ac0IH0fFJzinF1MtO/faDRcRxCoJcrH0/ux+iYMCqsNp5etJsZi/dSYbUZHa3BfLrpCAWlFXRq4c3QjkFGxxERERERETkvFaZyXsVlFXy7y77mXKym41+2v9zYmSuCPTmZV8KTC3dhszWfwkSahpWVo0v7tw/UD1AugauThZfv6Mnj10UCMGdtEpM/2EJ+SbnByepfWYWVeeuSAZg4JAKTyWRsIBERERERkQtQYSrntTw+nbySckJ93biyXYDRcRyem7OFV++Mwcls4rvdaSysXBtWxFGs2G9fTkLT8S+dyWTioas68sZdMbg6mVken85tb6/jeHaR0dHq1be7UknNKSbIy5Vbo0ONjiMiIiIiInJBKkzlvKoKvVtjwjCbNSqoLkS19uWP13QE4P++2sOxrEKDE4lcnIKScjYnZQEwXBs+1drIHqF89vsBBHm5Ep+Wx61vrCXuaLbRseqFzWZj9upEAMYNaIurk0Yli4iIiIhI46fCVM4pq6CUFZWbE43WdPw69cCwK+jd1p+8knIenb+jWa1lKI5r/aFTlFZYCQ9wJyLI0+g4Di063I+vHhpE55beZOSXcOd/1/PNzhSjY9W5DYmZ7D6ei5uzmXv6tzU6joiIiIiIyEVRYSrntGRXKmUVNrq28iGyhbfRcZoUJ4uZl+/oiYeLhU1Jmby3JtHoSCIXtHK/ff3S4ZEhWoeyDoT5ufPFgwO5qnMIJeVWHvpkO68vO9Ck1jZ+t3J06W29W+Pv6WJwGhERERERkYujwlTOaVHldPwxvTS6tD60DfTk2ZFdAXhx6X72peYanEjk3Gw2m9YvrQderk7Mvq8PEwdHAPDSj/t5dP4OSsorDE52+Q6m57MsPh2TCSYMijA6joiIiIiIyEVTYSpndeRUIVsOZ2E2waie2qSjvtzZN5xruoRQWmFl2mdxTaIkkaYpKaOAo5lFuFjMDLgi0Og4TYrFbOKZkV35e2x3LGYTC7cf5+7ZGzmVX2J0tMsyZ20SAFd3bkH7YC+D04iIiIiIiFw8FaZyVovi7KNLB3UIooWPm8Fpmi6TycSsMT0I9HQhPi2Pl37Yb3QkkbOqmo7fN8IfT1cng9M0Tff0b8u8+/vi7ebElsNZjH5rHQfT84yOVSun8kv4cusxACYP0ehSERERERFxLCpM5Qw2m616On5stKbj17dgb1deGNsDgNmrE1l/6JTBiUTOtCLBXphqOn79GtIxmIVTBhIe4M6RzEJGv7WO1QdOGh3rkn204Qgl5VZ6tPblyogAo+OIiIiIiIhcEhWmcoadx3JIzCjAzdnM9d1bGh2nWbi2awt+0zccmw0e/3wHucVlRkcSqVZcVsGGRHuRP7xTiMFpmr4OId4smjKIvu38ySsuZ/zczXy04bDRsS5acVkFH25IBmDSkPbaIExERERERByOClM5w8LK0aXXdW2Jl6beNpinR3alTYAHx7OL+OvXe4yOI1JtY1ImJeVWWvm60TFEa1E2hEAvVz6a1I8xMWFUWG08vWg3MxbvpcJqMzraBX0Vd5yM/FLC/Ny5ST90ExERERERB6TCVGooq7CyeEcKAKN7aTp+Q/JydeKVO3tiNsGCbcf5dleq0ZFEAFiRkA7Yp+NrtGDDcXWy8NIdPXn8ukjAvonS5A+2kF9SbnCyc7PZbLy72r7Z0/iB7XCy6NsMERERERFxPPokIzWsOZjBqYJSAj1dGNIhyOg4zU7vtgFMGd4BgCcX7uJEbrHBiUR+2fBpeCetX9rQTCYTD13VkTfv6oWrk5nl8enc9vY6jmcXGR3trFbsP8mB9Hy8XJ2488pwo+OIiIiIiIjUigpTqWHhNvt0/FE9QzUyyCCPXN2R7mE+ZBeW8cQXO7HZGv8UXGm6jmYWkniyACeziYH6IYphbu7Ris9+P4AgL1fi0/K49Y21xB3NNjrWGd5dnQjAb/qG4+PmbHAaERERERGR2lEjJtXyS8r5YW8aAKNjNB3fKC5OZl65IxpXJzOr9p90qM1epOlZUTm6tFdbfxVgBosO9+OrhwbRuaU3Gfkl3Pnf9XyzM8XoWNX2pOSw9uApLGYT9w+OMDqOiIiIiIhIrakwlWpLd6dRXGalfZAnPVr7Gh2nWevYwpu/3NgZgJnf7uPQyXyDE0lztTLBXpgOi9R0/MYgzM+dLx4cyFWdQygpt/LQJ9t5fdmBRjES/b019rVLb4pqRZifu8FpREREREREak+FqVRbFGefjh8bE6aNXRqBcQPaMaRjEMVlVqZ9FkdZhdXoSNLMlJRXsO5QBqDCtDHxcnVi9n19mFg5ivOlH/fz6PwdlJRXGJbpRG5x9YaBkzS6VEREREREHJwKUwHsH3bXHrQXI7HRmo7fGJjNJv51W0983Z3ZeSyH15cfNDqSNDNbk7MoLK0g2NuVbqE+RseR01jMJp4Z2ZW/x3bHYjaxcPtx7p69kVP5JYbkmbcumbIKG1e2C6BnuJ8hGUREREREROqKClMBYPGOFKw26NPWnzaBHkbHkUotfd34e2x3AN78+SDbjmQZnEiak6r1S4d2DNao80bqnv5tmXd/X7zdnNhyOIvYt9Zy4EReg2YoKCnn48q1licN0ehSERERERFxfCpMBYCF23+Zji+Ny6ieodwaHUqF1cajn8VRWFpudCRpJqrWLx3eSdPxG7MhHYNZOGUgbQI8OJpZxJi317H6wMkGu/7nW46SW1xORJAn13Rp0WDXFRERERERqS8qTIX9J/LYk5KLs8XEzVGtjI4jZzHjlu608nUj+VQhM5fsMzqONAOpOUUknMjDbILBHYKMjiMX0CHEm0VTB9G3nT95xeWMn7uZjypHfdanCquNOWuTAZgwOAKzWSORRURERETE8akwlerRpcM7heDv6WJwGjkbXw9nXrq9JwAfbzzC8vgTBieSpq5qdGnPcD/9veAgAjxd+GhSP8bEhFFhtfH0ot3MWLyXCqut3q754940jmQW4ufhzG29WtfbdURERERERBqSCtNmzmq18VVlYTpa0/EbtYEdgqp3xf7TF7sM29xFmoeVleuXDo8MMTiJXApXJwsv3dGTJ67vBMCctUlM/mAL+SX1s5TH7NVJANzTry3uLpZ6uYaIiIiIiEhDq1VhOmPGDAoLC894vKioiBkzZlx2KGk4m5IzSckpxtvVias6qxhp7J64vhMdQ7zIyC9h+oJd2Gz1N3JMmq+yCitrDmQAMEzrlzock8nE1BEdePOuXrg6mVken85tb6/jeHZRnV5n25Esth7OwsVi5r6Bbev03CIiIiIiIkaqVWH63HPPkZ+ff8bjhYWFPPfcc5cdShrOosrRpTdFtcLNWaODGjs3Zwuv/iYaZ4uJH/aeYHl8utGRpAnafiSbvJJyAjxd6BHma3QcqaWbe7Tis98PIMjLlfi0PG59Yy3bj2TV2fnfXZ0IwK3RoYR4u9XZeUVERERERIxWq8LUZrNhMp25scOOHTsICAi47FDSMIrLKliyKxWA0b00Hd9RdAv15c6+4QCsrhwFKFKXVu63F/FDOgZpEx8HFx3ux1cPDaJzS28y8kv4zTsb+GZnymWf92hmId/vTgNg0pD2l30+ERERERGRxuSSClN/f38CAgIwmUxERkYSEBBQffP19eXaa6/ljjvuqK+sUsd+jk8nr7icUF83rmynotuR9K387xV3NNvYINIkrajc8GlYpKbjNwVhfu588eBAru4cQkm5lYc+2c7ryw5c1pIec9YmYbXZS/VOLb3rMK2IiIiIiIjxnC7l4FdffRWbzcaECRN47rnn8PX9Zaqmi4sL7dq1Y8CAAXUeUurHgsrp+LfGhGkUmYOJDvcDYG9KLqXlVlyctH+b1I30vGL2pOQCMFSFaZPh5erEO/f14flv9/HemiRe+nE/iRkFvDA2ClenS1uOJaeojPmbjwIwWaNLRURERESkCbqkwnTcuHEAREREMHDgQJydnesllNS/rIJSViTYp92OjtF0fEfTJsADfw9nsgrL2JeaS8/KAlXkcq3ab1/mISrMlyAvV4PTSF2ymE08M7Ir7YM9efarPSzcfpyjmYX8997eBF7Cf+v/bTpCQWkFnVt6M6RjUD0mFhERERERMcYlFaZVhg0bhtVqZf/+/aSnp2O1Wms8P3To0DoJJ/Vnya5UyipsdG3lQ2QLTad0NCaTiZ7hfqxIOEnc0WwVplJnVu63T8cf3kmjS5uqu/u1pW2AJw9+vJUth7OIfWstc8b1peNF/FtQWm5l3tpkACYOjjjreuYiIiIiIiKOrlaF6YYNG7jrrrs4fPjwGWugmUwmKioq6iSc1J9FldPxNbrUcUVXFqY7tI6p1JEKq43VB7R+aXMwuGMQC6cMZMK8LRzJLGTMW+t4655eDOl4/v/u3+5KJS23mGBvV26JDm2gtCIiIiIiIg2rVgsfPvDAA/Tp04fdu3eTmZlJVlZW9S0zM7OuM0odO3KqkC2HszCb0AdeB1Y1qlQbP0ld2XEsm+zCMnzcnKrXyZWmq0OIN4umDqJvO3/ySsoZP3czH244fM7jbTYbs1cnAjBuQNtLXvtURERERETEUdRqhOmBAwf44osv6NChQ13nkQbwVZx9dOmgDkG08HEzOI3UVnRrPwASMwrIKSzD10NrCsvlWZlgH106pGMwThZtJNYcBHi68NGkfkxfsIsF247zzKLdJJ7M5+mbu2L51WaA6xNPsSclFzdnM3f3a2tQYhERERERkfpXq0/E/fr14+DBg3WdRRqAzWZjYeV0/NhoTcd3ZP6eLrQL9ADsIwNFLteK/ZqO3xy5Oll46faePHF9JwDmrk1m8gdbyC8pr3Hcu6uTALi9dzj+ni4NnlNERERERKSh1GqE6cMPP8xjjz1GWloaUVFRODvXHNnWo0ePOgkndW/nsRwSMwpwczZzffeWRseRy9Qz3I/kU4XEHc1mqEouuQyZBaXsrCzeh2nDp2bHZDIxdUQHIoI8mfZZHMvj07nt7XW8N74vYX7uHEzPY3l8OiYTTBgcYXRcERERERGRelWrwnTs2LEATJgwofoxk8mEzWbTpk+NXNXo0uu6tsTLtVb/+aURiQ7346u4FK1jKpdt9YGT2GzQuaW3lupoxm6KakWYnzuTPthCfFoet76xltn39Wb+lmMAXNOlBRFBnganFBERERERqV+1asySkpLqOoc0gLIKK4t3pAAwOkbT8ZuCqo15dhzNrv6BhUhtVK1fqtGl0jPcj0VTBzFx3mbi0/L4zTsbsFU+N3lIe0OziYiIiIiINIRaFaZt22qzB0e05mAGpwpKCfR0YXDHIKPjSB3o0soHZ4uJUwWlHMsqIjzAw+hI4oCsVhurDtgL0+GRIQankcYgzM+dLx4cyB/+t51l8ekA9GztS992/gYnExERERERqX+1Kkw/+OCD8z5/33331SqM1K9FldPxR/UMxVk7YDcJbs4WurbyYcexHLYfzVZhKrWyJyWXjPxSPF0s9G6rQkzsvFydeOe+PvxzaTwLtx3nzzd21ih2ERERERFpFmpVmP7hD3+o8euysjIKCwtxcXHBw8NDhWkjlF9SztI9aYCm4zc10eF+7DiWw46j2dzSM9ToOOKAVu63jyAc1CEIFyf9MEV+YTGbmH5jF6bf2MXoKCIiIiIiIg2mVp+Ms7Kyatzy8/NJSEhg8ODB/O9//6vrjFIHlu5Oo7jMSvsgT3q09jU6jtShnpXrmGrjJ6mtFVq/VERERERERKRanQ0l6tixIy+88MIZo0/PZ9WqVYwaNYrQ0FBMJhOLFi2qfq6srIw///nPREVF4enpSWhoKPfddx8pKSk1ztGuXTtMJlON2wsvvFBXb6vJWBRnn44fGxOmKZVNTNXGT7uP51BWYTU2jDicnMIyth3JAmBYpApTERERERERkTqde+nk5HRGoXk+BQUF9OzZkzfffPOM5woLC9m2bRvPPPMM27ZtY8GCBSQkJHDLLbecceyMGTNITU2tvj388MOX9T6amhO5xaw9mAFAbLSm4zc1EUGe+Lg5UVJuJSEtz+g44mDWHsrAaoMOIV609tcauCIiIiIiIiK1WsP066+/rvFrm81Gamoqb7zxBoMGDbro89x4443ceOONZ33O19eXH3/8scZjb7zxBldeeSVHjhyhTZs21Y97e3vTsmXLS3gHzcviHSlYbdC7rT9tAlWInFNRNrh6g9lidJJLYjKZ6Bnux+oDGWw/mk33MC25IBdvRYJ9/VKNLhURERERERGxq1VhGhsbW+PXJpOJ4OBgrrrqKl566aW6yHVWOTk5mEwm/Pz8ajz+wgsv8Le//Y02bdpw1113MW3aNJyczv3WSkpKKCkpqf51bm5ufUVuFBZu/2U6vpxD8hp4/xa4cjLc+A+j01yymMrCNO5INvf2b2t0HHEQNpuNlfvt65cO1/qlIiIiIiIiIkAtC1OrteHXSSwuLubPf/4zv/3tb/Hx8al+/JFHHqFXr14EBASwbt06pk+fTmpqKi+//PI5zzVr1iyee+65hohtuP0n8tiTkouzxcTIqFZGx2m8Nr8LtgrYMheG/Rk8AoxOdEmi2/gBsONYtqE5xLHEp+VxIrcEN2czfds51p95ERERERERkfpSq8L0dDabDaBeNxIqKyvjjjvuwGaz8fbbb9d47tFHH62+36NHD1xcXPj973/PrFmzcHV1Pev5pk+fXuN1ubm5hIeH1094gy2qHF06vFMI/p4uBqdppEryIOE7+/2KEtg5H/o/YGymS9SztR8Ah07mk1tcho+bs7GBxCFUjS4d0D4QN2fHWopCREREREREpL7UetOnDz74gKioKNzd3XF3d6dHjx58+OGHdZkN+KUsPXz4MD/++GON0aVn069fP8rLy0lOTj7nMa6urvj4+NS4NUVWq42v4uybcI3WdPxzi18C5cVAZem/7X2o/EGAowj0ciU8wB2bDXYezTE6jjiIlQlV0/FDDE4iIiIiIiIi0njUqjB9+eWXefDBB7npppuYP38+8+fP54YbbuCBBx7glVdeqbNwVWXpgQMH+OmnnwgMDLzga+Li4jCbzYSEqADYlJzJ8ewivF2duKqzfj/Oadfn9q/9p4CTO6TvhWObjc1UC9Hh/oCm5cvFyS8pZ8vhTEAbPomIiIiIiIicrlZT8l9//XXefvtt7rvvvurHbrnlFrp168Zf//pXpk2bdlHnyc/P5+DBg9W/TkpKIi4ujoCAAFq1asVtt93Gtm3b+Oabb6ioqCAtLQ2AgIAAXFxcWL9+PRs3bmTEiBF4e3uzfv16pk2bxj333IO/v39t3lqTUjUd/6aoVppuey75J+HQz/b7fSdCUSbs+B9sfR/CrzQ22yXq2dqXxTtS2H4k2+go4gDWHcygrMJGu0AP2gV5Gh1HREREREREpNGo1QjT1NRUBg4ceMbjAwcOJDU19aLPs2XLFmJiYoiJiQHs65HGxMTw7LPPcvz4cb7++muOHTtGdHQ0rVq1qr6tW7cOsE+t//TTTxk2bBjdunVj5syZTJs2jXfeeac2b6tJKS6rYMku+3+LWE3HP7e9i+ybPYX2gsAroPd4++N7FkCxY01tj6nc+CnuaHb12sIi57Kicv1SjS4VERERERERqalWI0w7dOjA/PnzefLJJ2s8/tlnn9GxY8eLPs/w4cPPW+xcqPTp1asXGzZsuOjrNSc/x6eTV1xOK183+kVo9+tzqpqOH3W7/Wt4PwjqBBkJ9uf6TjIu2yXqFuqLk9lERn4JKTnFhPm5Gx1JGimbzVa9fumwTipMRURERERERE5Xq8L0ueee484772TVqlUMGjQIgLVr17Js2TLmz59fpwGldhZWTse/NToMs9lkcJpGKisZjm4ETNB9jP0xkwl6j4OlT9qn5TtQYermbKFzK292H88l7ki2ClM5p0MnCzieXYSLk5n+7S+8NrSIiIiIiIhIc1KrKfljx45l48aNBAUFsWjRIhYtWkRQUBCbNm1i9OjRdZ1RLlF2YSk/J6QDMKaXpuOf064v7F8jhoJ3y18e7/lbsLhA2k5I2W5MtlqKDvcDIO5olrFBpFFbUfn3Q7+IADxcavVzMxEREREREZEmq9aflHv37s1HH31Ul1mkjnyzM5WyChtdW/kQ2cLb6DiNk8125nT8Kh4B0OUW2P0FbJ0HoTENHq+2osP9+WjDEXYcdaz1V6VhrdT6pSIijU/KdkjbBZjsM17O+tVceZ+LOOZ857nAcxc8D5dwrV8fc7bsl3KeunrvIiIiIud2WUOL0tPTSU9Px2q11ni8R48elxVKLs+iyun4o7XZ07md2AMn48HiCl1vOfP53uPshemuL+C6meDq1fAZayE63BeAXcdzKK+w4mSp1SByacKKSivYmJQJwHCtXyoi0jhsfg+WPGp0imamocvic12zvgtuDC6mGzLzJfw+t74Swvte3h8hEZF6YDKZWLhwIbGxsYbmGD9+PNnZ2SxatMjQHEaqVWG6detWxo0bx759+87YmMlkMlFRUVEn4eTSHTlVyJbDWZhMcEt0qNFxGq+q0aWR14Gb75nPtxsCAe0hMxF2f2kvUB1A+yAvvN2cyCsuJ+FEHt1Cz/LepFnbkHiK0nIrYX7uXBFswA8CfnwWDi6HKydB9N1gcW74DCIijcn6N+1rpwO0GQCuPoDNPhvGZv3l/hlfuYhjznUefvVr6/lfX33Mr193tmPO9RyXeK3K+/Xm9N/HeryMNE7Dp6swFZF6N378eN5///0zHr/++uv5/vvvDUh0puTkZCIiIti+fTvR0dHVj7/22msX3Ii9qatVYTphwgQiIyN57733aNGiBSZNa2k0voqzjy4ddEUQLXzcDE7TSFmt9hIUzpyOX8Vkgl7j4Kf/g23vO0xhajab6NnajzUHM9hxNEeFqZyhejp+p+CG/7s7KxnW/huwweI/wNrXYPiT0H0smDUaWkSaoVX/guV/t98fPA2u/r9fRtjJL2yXWs7+uiTmIovkcx1zjtdf9Hm4iGPqoWw+53u/lPNczu/dha5xofNwCde60A8FfnVMSNdL/EMoIlI7N9xwA3Pnzq3xmKurq0FpLp6vr6/REQxXq8I0MTGRL7/8kg4dOtR1HrkMNpuNhZWFaaym45/b0Y2Qc9Q+gqPjdec+Lvpu+4eY41vta4q1jGq4jJehZ7gvaw5mEHc0i7v6tTE6jjQyVRs+GbJ+6ZY5gA0CO0BRtn0E94JJsOZlGPEUdL5ZRYGINA82Gyz/G6x+yf7rEU/B0Cf0d+C5aN1RERFxUK6urrRs2fKszx04cICJEyeyadMm2rdvz2uvvVbj+RUrVjBixAiysrLw8/MDIC4ujpiYGJKSkmjXrh0Aa9eu5amnnmLTpk24urpy5ZVX8umnn+Lv78/333/P3//+d3bv3o3FYmHAgAG89tprXHHFFQBEREQAEBMTA8CwYcNYsWLFGVPyS0pKeOKJJ/j000/Jzc2lT58+vPLKK/Tt27dG1p9++ok///nP7N27l+joaObOnUunTp3q8re0wdRqSM/VV1/Njh076jqLXKZdx3NIPFmAm7OZG7qf/X9I4Zfp+F1GgbP7uY/zCobON9nvbz1zGH1jFR3uD0Dc0Wxjg0ijk5xRQPKpQpzMJgZ1CGrYi5cVw7YP7fevnQF/2AFXPQ2uvpC+Fz67G2aPgIM//TKqRESkKbLZYOlTv5Sl1/4Nhv1JhaCIiEgzYrVaGTNmDC4uLmzcuJH//Oc//PnPf77k88TFxXH11VfTtWtX1q9fz5o1axg1alT1UpkFBQU8+uijbNmyhWXLlmE2mxk9enT1XkSbNm0C4KeffiI1NZUFCxac9Tp/+tOf+PLLL3n//ffZtm0bHTp04PrrryczM7PGcU899RQvvfQSW7ZswcnJiQkTJlzye2osajXC9N1332XcuHHs3r2b7t274+xccw26W245yyY6Uu8WbLOPLr2ua0u8XC9rP6+mq6IM9iy034+67cLH9xoHe7+CnfPtJY+LR/3mqwM9Kzd+OpCeT35Juf4sSLWq6fh92vk3/J+LvYugKBN8WkPH68HiZB9N1XcSrHsdNvzHvkP0R2OhzUC4+hloO7BhM4qI1DerFb59rHLEPXDTi3DlZGMziYiISL355ptv8PKquXfEk08+SZ8+fYiPj2fp0qWEhtr3n3n++ee58cYbL+n8//znP+nTpw9vvfVW9WPdunWrvj927Ngax8+ZM4fg4GD27t1L9+7dCQ62zzwMDAw850jYgoIC3n77bebNm1edb/bs2fz444+89957PPHEE9XHzpw5k2HDhgHwl7/8hZtvvpni4mLc3BxvychafWJev349a9eu5bvvvjvjOW36ZIyyCiuLd6QAMFrT8c/t0M/20sYzBNoNvfDx7UeAXxvIPmIvTqN/W/8ZL1OItxthfu4czy5i57FsBl7RwCMJpdGqXr80MqThL775XfvXPuPtZWkVd3+4+lno96B9av7m9+DIOph7I1xxtb04DY1p+LwiInXNWgFfPQQ7PgFMcMvr0Oteo1OJiIhIPRoxYgRvv/12jccCAgL48MMPCQ8Pry5LAQYMGHDJ54+Li+P228+xNwv2af/PPvssGzduJCMjo3pk6ZEjR+jevftFXePQoUOUlZUxaNCg6secnZ258sor2bdvX41je/ToUX2/VatWAKSnp9OmjeMtF1irKfkPP/ww99xzD6mpqVit1ho3laXGWHMwg1MFpQR6ujC4owqyc9o13/61+5iapc25mM3Q6z77/a3z6i1WXYsO9wM0LV9+UVxWwfpDpwAY3qmB1y9NiYNjm8HsDDH3nf0Yr2C4YRY8sh16jwezExxaBu8Mh8/ugfR9Z3+diIgjqCiDLyfZy1KTBca+q7JURESkGfD09KRDhw41bgEBARf1WnPlxrin71ZfVlZW4xh39/MsMwiMGjWKzMxMZs+ezcaNG9m4cSMApaWll/I2LtrpM9CrNhmuKmkdTa0K01OnTjFt2jRatGhR13mklhZtt0/HH9UzFGeLdps+q9ICiF9ivx917p/AnCH6HvuHm6MbID2+frLVsarCdIcKU6m0OTmTorIKWvi40rmld8NefMt79q9dbwHvC/y74RsGo16DhzZDj98AJti3GN4aAAt+B6cO1XtcEZE6VV4C88fBngX2HxzdPu/ilgUSERGRJqtLly4cPXqU1NTU6sc2bNhQ45iq6fKnHxMXF1fjmB49erBs2bKzXuPUqVMkJCTw9NNPc/XVV9OlSxeysrJqHOPi4gJw3sGPV1xxBS4uLqxdu7b6sbKyMjZv3kzXrl3P8y4dW62atTFjxvDzzz/XdRappfyScpbuSQMgVtPxzy3hOygrBP8ICOt98a/zaQWRN9jvb/ugfrLVsZ4aYSq/sjKhajp+cPVP+hpEUTbsrNxore+ki39dQHsY81+Yst6+QRs22PkZvNEXFv8Bco7XR1oRkbpVWgj/+y0kLAGLK/zmE/sPj0RERKRZKCkpIS0trcYtIyODa665hsjISMaNG8eOHTtYvXo1Tz31VI3XdujQgfDwcP76179y4MABlixZwksvvVTjmOnTp7N582amTJnCzp07iY+P5+233yYjIwN/f38CAwN55513OHjwIMuXL+fRRx+t8fqQkBDc3d35/vvvOXHiBDk5OWe8B09PTx588EGeeOIJvv/+e/bu3cvkyZMpLCxk4sSJdf+b1kjUqjCNjIxk+vTpjB8/npdeeol///vfNW7SsH7Yk0ZxmZWIIE96tvY1Ok7jtauytIm6/dJ3ou09zv51xyf23b4buagwXyxmEydyS0jNKTI6jjQCK4xav3TH/6C8CEK6QptLX5OHkC5w50fwuxXQ4RqwVdiXx/h3DHw/HfJP1nViEZG6UZIPn9xhX17E2QPung+R1xmdSkRERBrQ999/T6tWrWrcBg8ejNlsZuHChRQVFXHllVcyadIkZs6cWeO1zs7O/O9//yM+Pp4ePXrwj3/8g7///e81jomMjOSHH35gx44dXHnllQwYMICvvvoKJycnzGYzn376KVu3bqV79+5MmzaNf/3rXzVe7+TkxL///W/++9//Ehoayq233nrW9/HCCy8wduxY7r33Xnr16sXBgwdZunQp/v7+dfsb1oiYbKcvhnCRIiIizn1Ck4nExMTLCtXQcnNz8fX1JScnBx8fH6PjXLJ739vI6gMZTLsmkj9c09HoOI1TYSa82BGs5TB1EwR3urTXWyvg1SjIPQ5j33OIqXQ3vbaavam5/OeeXtzQvZXRccRAx7IKGfyPn7GYTWx75lp83Z0v/KK6YLPBG33g1EG4+aVLG2F6LofXw/K/weHK6SDOntD/ARj4sH0DKRGRxqAoGz6+HY5tAhdvuPtzaFuLHxqJiIiIiCFqNcI0KSnpnDdHK0sdXXpuMWsPZgAwWtPxz23vIntZ2rLHpZelAGYLxFRuzuAgmz9VTcvfrmn5zd6q/fa/I2LC/RquLAVIWmkvS128oMeddXPOtgNg/BK4ZwGE9oKyAlj9ErzaE1b+C0ry6uY6IiK1VZgJH9xiL0vd/GDcVypLRURERBxMne4OtG/fPh5//PG6PKVcwNc7UrDaoHdbf9oEehgdp/Ha9YX966Vs9vRrMfcAJkhe7RAbz8RUrWN6JNvQHGK8FQnpgH390ga1+V37156/Adc63GjKZIIOV8Pk5XDnx/bp/iU58PPf4bVoWP+mQyydISJNUH46zLsZUneARxCM/+bS1k0XERERkUbhsgvTgoIC3nvvPQYOHEi3bt34/vvv6yKXXKSF2+0bn2izp/PIOVY5fdcE3cfW/jx+4dDxWvv9be/XSbT6FN3GD4Bdx3OosF7yyhvSRJSWW1l36BQAwzs14PqlOcch/lv7/T71tBC4yQRdRsIDa2DMu/aNogozYOmT9jVOt8yBirL6ubaIyK/lHIe5N0L6XvBqCfd/Cy2jjE4lIiIiIrVQ68J07dq1TJgwgRYtWvC73/2OgQMHsnfvXnbv3l2X+eQ89p/IY09KLk5mEyOjtEblOe3+0v617SDwvcxiuVfl5k/bP4by0ss7Vz27ItgLTxcLhaUVHEjXNOXmauvhLPJLygnycqFbaAOu0bztffsGTW0HQYuu9XstswV63G5fn3jUv8GnNeSlwDfT7Guo7vjUvg6xiEh9yTpsL0tPHQTfcHtZWpslgERERESkUbikwjQ9PZ1//vOfdO7cmdtuuw0/Pz9WrFiB2WxmwoQJdO7cub5yylksqhxdOrxTCP6eLganacR2fW7/WhcbNUVeD14t7KPYEr69/PPVI4vZRI/WfoCm5TdnK/fbd5Ef2jEYs9nUMBetKPtlrd++9TS69GwsztB7HDyyDW74B3iGQFYyLPw9vDUA9iwCq7Xh8ohI83DqkL0szT4M/hH2sjTwCqNTiYiIiMhluKTCtG3btuzatYvXXnuN48eP8/LLL9OnT5/6yibnYbXa+CouBdBmT+eVHg9pu8DsDF1vvfzzWZwr1zLFoabl7ziWbWgOMU5VYTqsUwOuXxr/DeSfsBeWnUc13HWrOLlC/wfgD3FwzV/tm65kJMDn42D2cDjwI9i0TIWI1IH0ffayNPc4BEXC/d+BXxujU4mIiIjIZbrkwnTNmjWsWrWK/fv311cmuQibkzM5nl2Et6sTV3dpwHUJHU3V6NIO14BHQN2cM+Ze+9dDy+2j1xqxnpUjTLdrhGmzdCK3mH2puZhMMKRjAxamm9+zf+09DpwMHP3u4gmDp8Efd8KwP4OLl30jlo9vgzk3QPIa47KJiONL3WHf4Cn/BLToDuO/BR8tkSQiIiLSFFxSYRofH89HH31Eamoqffv2pXfv3rzyyisAmEwNNNVTgF82e7opqhVuzhaD0zRSNlvdTsevEhAB7Yfb72/7sO7OWw9iKkeY7j+RR0FJubFhpMFVjS7t0dqPgIZatiM9HpJXg8kCve9vmGteiJsvjHgS/rADBjwETm5wdIO96PggFo5tNTqhiDiaY1vg/VFQeApCY2DcYvBqwB9MiYiIiEi9uuRNnwYNGsScOXNITU3lgQce4PPPP6eiooIpU6Ywe/ZsTp48WR855TTFZRUs2ZUKQKym45/bsS329cScPaHTjXV77t7j7V+3fwQVjbeIbOHjRitfN6w22H08x+g40sBWJlROx49syNGl79q/drrx8jdZq2ueQXD9THgkDvpMtC/VkfgzvHsV/O8uSNOmhSJyEZLXwge3QnEOhPeH+76qu1ksIiIiItIoXHJhWsXLy4vJkyezbt069uzZQ69evXj66acJDQ2ty3xyFj/Hp5NXXE4rXzf6Regb9HOqGl3aZaR9am5d6nQzeARBfhocWFq3565jVdPy445mG5pDGlZ5hZXVB+yF6fCGWr+0JM++Iz1A30kNc83a8GkFI1+Gh7dA9N1gMkPCEvjPYPhion0DFxGRszn0M3w0FkrzIWIo3POlfRS7iIiIiAMYP348sbGxRsdwCLUuTE/XpUsXXnrpJY4fP85nn31WF6eU86iajn9rdFjD7XrtaCrKYc8C+/2o2+v+/E4uEP1b+/2tjXvzp6qNn1SYNi87jmWTW1yOr7tzdWle73bOh9I8COwAEcMa5pqXw78dxL4FUzZC11jABru/gDf6wlcPQfZRgwOKSKOS8D18cieUF0GHa+Gu+eDqZXQqERERcWBpaWn84Q9/oEOHDri5udGiRQsGDRrE22+/TWFhodHxLsq8efPw8/MzOkadc6rtC61WKwcPHiQ9PR2r1Vr9eFBQUJ0Ek7PLLizl54R0AEZrOv65Ja2EgpPgEfjLeqN1rdd4WPc6HPwRco6Bb+v6uc5lig73A2CHCtNmZUXldPwhHYOwNMQPVmy2XzZ76jMRzHXy87iGERwJd7xv38Bl+Uz7qPHtH8LOz+zrsA55DLxbGJ1SRIy0ZyF8OQms5dB5JNw2B5xcjU4lIiIiDiwxMZFBgwbh5+fH888/T1RUFK6uruzatYt33nmHsLAwbrnlljNeV1ZWhrOzswGJm5dafaLdsGEDHTp0oEuXLgwdOpThw4dX30aMGFHXGeU0S3alUlZho0srHzq19DY6TuO16wv7126jwVJPf5EEdYC2g8Fmta9l2khFhfliNkFKTjHpucVGx5EGUrXh0/BOIQ1zwSMbIH0POLn/Mvra0bTqCXfPh4k/QrshUFEKm/4Lr/WEH/8PCjONTigiRtjxGXwxwV6WRt0Ot7+vslREREQu25QpU3BycmLLli3ccccddOnShfbt23PrrbeyZMkSRo0aBdg3WX/77be55ZZb8PT0ZObMmVRUVDBx4kQiIiJwd3enU6dOvPbaazXOX1FRwaOPPoqfnx+BgYH86U9/wmaz1TimXbt2vPrqqzUei46O5q9//Wv1r19++WWioqLw9PQkPDycKVOmkJ+fD8CKFSu4//77ycnJwWQyYTKZql9bUlLC448/TlhYGJ6envTr148VK1bU6e9hfapVYfrAAw/Qp08fdu/eTWZmJllZWdW3zEx9oKxPiyqn44/R6NJzKyuCfYvt9+tjOv7pqjZ/2vYhWCvq91q15OnqRGQLe7m+XaNMm4WM/BJ2HrNv8jU0soFG/Vdt9hR1G7j7N8w160v4lTD+G7jva2jd1z79du2r9uJ0xT+gONfohCLSULbOg4W/t/9wNOYeGP1fsNR6gpaIiIjUM5vNRmFpuSG3X5eR53Pq1Cl++OEHpk6diqfn2fdcMZl+mSn417/+ldGjR7Nr1y4mTJiA1WqldevWfP755+zdu5dnn32WJ598kvnz51e/5qWXXmLevHnMmTOHNWvWkJmZycKFCy/599RsNvPvf/+bPXv28P7777N8+XL+9Kc/ATBw4EBeffVVfHx8SE1NJTU1lccffxyAhx56iPXr1/Ppp5+yc+dObr/9dm644QYOHDhwyRmMUKvv+A4cOMAXX3xBhw4d6jqPnMfRzEI2J2dhMsEt0dpc65z2L7Wvo+jbBlpfWb/X6jLKXg7lHoODyyDyuvq9Xi1Fh/sRn5bHjqPZXN+tpdFxpJ5VbfbULdSHEG+3+r9gfjrs/cp+vzFv9nSp2g+zb+qyfyks/xuc2A0rnoeN/4HB0+DKyeDsbnRKEakvG/4D3//Zfr/vZLjxn4613IiIiEgzVFRWQddnjdmYee+M6/Fwubia7eDBg9hsNjp16lTj8aCgIIqL7TNDp06dyj/+8Q8A7rrrLu6///4axz733HPV9yMiIli/fj3z58/njjvuAODVV19l+vTpjBkzBoD//Oc/LF166b83f/zjH6vvt2vXjr///e888MADvPXWW7i4uODr64vJZKJly1+6hiNHjjB37lyOHDlSvTn8448/zvfff8/cuXN5/vnnLzlHQ6vVd339+vXj4MGDdZ1FLqBqdOmgK4Jo4dMAJYij2vW5/WvU2Pr/YOPsBj1+Y7+/rfFu/tSzch1TbfzUPKysXL90WGRww1xw2wdgLYOwPhAa3TDXbCgmE3S6AX6/2r5mYWAHKMqEH5+B16Jh02woLzU6pYjUtTWv/FKWDnwYbvqXylIRERGpd5s2bSIuLo5u3bpRUlJS/XifPn3OOPbNN9+kd+/eBAcH4+XlxTvvvMORI0cAyMnJITU1lX79+lUf7+TkdNbzXMhPP/3E1VdfTVhYGN7e3tx7772cOnXqvJtS7dq1i4qKCiIjI/Hy8qq+rVy5kkOHDl1yBiPUaoTpww8/zGOPPUZaWhpRUVFnLDbbo0ePOgknv7DZbCyMsxemsZqOf25FWXDgB/v9+p6OX6X3ONj4NiR8B3lp4N34RnBWbfy081gOFVZbw2wCJIawWm2sOpABNFBhaq2ALXPt95vS6NJfM5uh+1jocivs/NQ+NT/nCHz7OKz7Nwz7C/S4U1N1RRydzQYrZsFK+2gOhv0Zhk+3//BEREREGj13Zwt7Z1xv2LUvVocOHTCZTCQkJNR4vH379vZzudecyfbrafuffvopjz/+OC+99BIDBgzA29ubf/3rX2zcuPGSMpvN5jOWEigrK6u+n5yczMiRI3nwwQeZOXMmAQEBrFmzhokTJ1JaWoqHh8dZz5ufn4/FYmHr1q1YLDV/X7y8vC4po1Fq9clu7NixAEyYMKH6MZPJhM1mw2QyUVHRONdydGQ2G0y7JpLFO1K4vpt2az6nfYvtG7WEdIUW3RrmmiFdILwfHN1o3/xp6OMNc91LENnCGw8XC/kl5SSezKdjC20Y1lTtOp5DZkEp3q5O9GrbAGuJ7l9qX5LC3d++yVpTZ3Gyr2MYdYd9VPmqFyH7CHw1xT4ibcST0DVWI9FEHJHNBj8+a/8hCMDV/wdDHjU2k4iIiFwSk8l00dPijRQYGMi1117LG2+8wcMPP3zOdUzPZe3atQwcOJApU6ZUP3b6yE1fX19atWrFxo0bGTp0KADl5eVs3bqVXr16VR8XHBxMampq9a9zc3NJSkqq/vXWrVuxWq289NJLmCs/45y+TiqAi4vLGT1gTEwMFRUVpKenM2TIkEt6b41FrT7RJSUlnXFLTEys/ip1z2w2MapnKO/c1wdvt3ra9b0pqJ6Of1vDXrfXOPvXbR+A1dqw174IFrOJ7mG+gDZ+aupWVE7HH9QhCGdLA5R2VZs9xdxrX6KiuXBysa9h+sh2uPZv9sL41AH44n7471BI+N5evoiIY7Ba4dsnfilLb/iHylIRERGpV2+99Rbl5eX06dOHzz77jH379pGQkMBHH31EfHz8GSMzT9exY0e2bNnC0qVL2b9/P8888wybN2+uccwf/vAHXnjhBRYtWkR8fDxTpkwhOzu7xjFXXXUVH374IatXr2bXrl2MGzeuxnU7dOhAWVkZr7/+OomJiXz44Yf85z//qXGOdu3akZ+fz7Jly8jIyKCwsJDIyEjuvvtu7rvvPhYsWEBSUhKbNm1i1qxZLFmy5PJ/8xpArT5Nt23b9rw3EUPkpkLSavv97g1cmHYbDa6+kH0YklY27LUvUozWMW0WVu5PB2B4pwaYjn/qEBxaBpigz/0XPLxJcvGAQY/AH3bap+26eMOJXfC/O+G9ayGxcf59ICKnsVbA4kdg82zABCNfhf4PGJ1KREREmrgrrriC7du3c8011zB9+nR69uxJnz59eP3113n88cf529/+ds7X/v73v2fMmDHceeed9OvXj1OnTtUYbQrw2GOPce+99zJu3LjqafujR9ecFTh9+nSGDRvGyJEjufnmm4mNjeWKK66ofr5nz568/PLL/OMf/6B79+58/PHHzJo1q8Y5Bg4cyAMPPMCdd95JcHAw//znPwGYO3cu9913H4899hidOnUiNjaWzZs306ZNm8v9rWsQJtuvFyu4BHv37uXIkSOUltbc8OKWW2657GANKTc3F19fX3JycvDx8TE6jtTW+jdh6ZMQ3h8mGrAr3pLH7KPtusbCHY1vA6jvdqXy4Mfb6Bbqw5JHHHNIvJxfdmEpvf72I1YbrPvLVYT61fMO7kufgvVvQIdr4Z4v6vdajqIwE9a+ChvfgfIi+2MRQ+GqZyG8r6HRROQsKsph0QP2GSomM8S+DT1/Y3QqERERETFYrRZ2SExMZPTo0ezatat67VKwrxUBaA1TMYZR0/Gr9BpnL0zjl0D+SfBqoB3KL1LPyhGm8Wl5FJVW4O5y8QtSi2NYfSADqw0iW3jVf1laVmRfsxea9mZPl8ojAK6dAf2nwuqXYOtcSFoF710DkTfAiKeglTZGFGkUykvhywn29c/NTjD23eaxFrOIiIiIXFCtpuT/4Q9/ICIigvT0dDw8PNizZw+rVq2iT58+rFixoo4jilyEjIOQsh1MFuM+7LTqAaG9wFoGOz4xJsN5tPJ1I8TblQqrjd0pOUbHkXqwcr99/dLhnULq/2K7F0BxNvi1gY7X1v/1HI13C7jpn/DwVvv6riYL7P8e/jsEPh8PJ/cbnVCkeSsrgs/utpelFhe482OVpSIiIiJSrVaF6fr165kxYwZBQUGYzWbMZjODBw9m1qxZPPLII3WdUeTCdldOB77iKvAMMi5H79M2f2pkG76YTCaiK0eZ7tA6pk2O1WqrLkyHRTbA6OaqzZ76TACzRiufk18buPUNmLrpl7WV9yyEt/rBoimQddjYfCLNUWkBfHIHHPgBnNzhrs+g0w1GpxIRERGRRqRWhWlFRQXe3t4ABAUFkZKSAtg3g0pISKi7dCIXw2Y7bTr+7cZm6T4WXLzg1EE4vNbYLGdRNS1/uwrTJmdfWi4n80rwcLHQp51//V7s+FZI2WYflRVzb/1eq6kI6gC3vQcPrIVON4HNCnEfw+u97esf56YanVCkeSjOhQ/H2JfKcPGCe760/7BVREREROQ0tSpMu3fvzo4dOwDo168f//znP1m7di0zZsygffv2dRpQ5IJS4+wFpZM7dL7J2Cyu3vbSFGDrPEOjnE1MZWEadyTb0BxS91Yk2EeXDrwiEFeneh7xufk9+9duo40d0e2IWnaH3/4PJi2H9iPsS3hsfhf+HQ0/PA0Fp4xOKNJ0FWbCB7fC0Q3g5gv3fQXtBhmdSkREREQaoVoVpk8//TRWqxWAGTNmkJSUxJAhQ/j222/597//XacBRS5oZ+Xo0k432gtLo1VNy9/7tf3DWSMS1doXkwmOZxdxMq/E6DhShxpsOn5hJuz+0n5fmz3VXuvecN8iGL8EwvtDeTGsex1e6wk/Pw/FWmdYpE7ln4T3R9lHx7sHwLjF0LqP0alEREREpJGqVWF6/fXXM2bMGAA6dOhAfHw8GRkZpKenc9VVmtYkDcha8Ut5Y/R0/CqhvaBlFFSUwM7PjE5Tg7ebMx2CvQCtY9qU5BaXse1wFgDDIut5w6e4j+3lXssoaN23fq/VHLQbDBO+h7u/gJY9oDQPVv4DXu0Ba16xr7UoIpcnNxXm3QwndoNnCNz/LbTqaXQqEREREWnEalWYnu7YsWMcO3aMgIAATCZTXWQSuXjJayA/Ddz8oMM1RqexM5mgV+Uo063zGt3mT1UbP8WpMG0y1h3MoNxqo32QJ20CPervQlbrL9Px+06y/1mXy2cyQcdr4Xcr4fb3IagTFGfDT3+F16Jh43+hXCPCRWol+wjMvREyEsAnDO7/DkK6GJ1KRERERBq5WhWmVquVGTNm4OvrS9u2bWnbti1+fn787W9/q56qL9IgqjZ76hYLTi6GRqmhxx32NVVPxsPRTUanqSG6jR8AO45lG5pD6k71dPxO9TwdP3E5ZCWBq0/jGdHdlJjN9r/LpqyH0f8F/3ZQkA7f/cm+OdS2D6Ci3OiUIo4jMxHm3mT/e8uvrX1kaVAHo1OJiIiIiAOoVWH61FNP8cYbb/DCCy+wfft2tm/fzvPPP8/rr7/OM888U9cZRc6uvMS+Tij8f3v3HV9lfTZ+/HMyIWSxwx4iSyDMIi5w4izuPVDU2mrrfJ7W59dWW32qta21ferTPnUAWlv3Xq2LuFAhEJbIEmRvk0CAkHH//riTYMqQcU5OEj7v1+u8zn3u8b2vg7dRrnyv71X/kjdNsqBfuGxFfWv+lNsxGwhnmFZW1q/Zr9p3QRDUNHyK+fql1bNLB14MKc1ie6+DWUIi5F4IN0yF038PGe2haBm8/EN48Dsw69lwtq+k3Vs3Dx49Jfx3p2WPcGZp867xjkqSJOmg07VrVx544IF4h7HP9ithOnHiRB5++GG+//3vM2DAAAYMGMAPfvADHnroISZMmBDlEKXdWPAWlBaFyYTOR8Q7mp1Vl+XPeQG2FsY1lG/qnZNBk+QENm0r58v1ro/Y0C1Yu5lVRdtITUrg8O4tY3ejwqUw/81we+i42N1HOyQmw9Cr4EfTYPSvIK0lbFwEz42DvxwFX7xW75b8kOqF1bPCmaWbV0ObvmGyNKtDvKOSJEmqMXbsWCKRCPfee2+t/S+++OI+L3e5LwnJ6dOnc8EFF9CuXTtSU1Pp0qULp59+Oq+88gpBA/q7xdixYznzzDNjeo/9Sphu3LiR3r1777S/d+/ebNxYv7qCqxGrLsfvf05YylrfdPoOtO4D5Vt3xFoPJCUm0L9DFmDjp8Ygr2p26eHdW9IkOTF2N8qfAEEldDsGWveM3X20s+SmMOJ6uHEGHPtTSM2CtXPgyYvh4eNh0bsmTqVqK/JhwumwZX3Y2Gnsa5Ae42Z4kiRJ+6FJkyb8+te/5uuvv66T+7300kscfvjhbN68mYkTJzJ37lzefPNNzjrrLH76059SVFS0y+uCIKC8/OBbGmy/sky5ubn86U9/2mn/n/70JwYMGHDAQUnfalvxjtlu9a0cv1okAkOqmz9NrFcJjW+W5athmzR/LRDjcvzy0nD9TAibPSk+UjNg5H/ATTPg6FshuVmYHHr8rDBBtPSTeEcoxddXk2HimLBpWsfvwOUvQ1qLeEclSZK0SyeccAI5OTncc889ezzvueee47DDDiM1NZWuXbvyu9/9rubYqFGj+Oqrr7j55puJRCK7nZ1aUlLCuHHjOO2003jttdc46aST6N69O3369GHcuHHMmDGDrKxwYtWkSZOIRCK88cYbDBkyhNTUVD788EMWLVrEmDFjaNu2Lenp6QwbNoy333671n3Wrl3LGWecQdOmTenWrRtPPPFEreNLliwhEolQUFBQs6+wsJBIJMKkSZMAqKioYNy4cXTr1o2mTZvSq1cv/vCHP9Scf+eddzJx4kReeumlmu9cfe2yZcs4//zzyc7OpkWLFowZM4YlS5bs8c93d5L256L77ruP0047jbfffpsRI0YAMHnyZJYtW8brr7++X4FI++SL16B8G7TqCTn1OEk/4AJ46w5YMwtWToMOQ+IdEbCj8ZMJ04atpLScKYvD30aOimXDp7mvQMk6yGgHvU6N3X20d5o2h+N/DsO/Dx/eH64t+9WH8Oho6HEiHPdTaD8w3lFKdevLPPjHhVC2BboeDRc9Canp8Y5KkiTVtSAI/38gHpLTwolTeykxMZFf/epXXHzxxfzoRz+iY8eOO52Tn5/P+eefz5133skFF1zAxx9/zA9+8ANatmzJ2LFjef7558nNzeXaa6/lmmuu2e29/vWvf7Fhwwb+8z//c7fn/Huy9Sc/+Qm//e1v6d69O82bN2fZsmWceuqp/Pd//zepqak89thjnHHGGcybN4/OnTsDYan8ypUree+990hOTuZHP/oRa9eu3es/EwgbzXfs2JFnnnmGli1b8vHHH3PttdfSrl07zj//fG677Tbmzp1LcXEx48ePB6BFixaUlZUxevRoRowYwQcffEBSUhJ33303J598MjNnziQlZd8ahe9XwnTkyJHMnz+fBx98kC+++AKAs88+m2uvvZa7776bo48+en+GlfZeTTn+efv0A6nOpbWAvmNg1tPhLNP6kjDtlA3A3FXFbCuriG0pt2Jm8qINbK+opFOLpnRrFcMmTFMeDt+HjA3X1VT9kN4aTr4nLNd//zcw7XFY+Fb46vNdOPb/QZudl8+RGp35/4KnLoWKUjjkeLjgb5CSFu+oJElSPJRtgV+1j8+9/2vlPjfHPeussxg4cCB33HEHjzzyyE7H77//fo4//viaBus9e/bk888/5ze/+Q1jx46lRYsWJCYmkpGRQU5Ozm7vM3/+fAB69epVs2/KlCkce+yxNZ+ffPJJTj/99JrPv/zlLznxxBNrPrdo0YLc3Nyaz3fddRcvvPACL7/8MjfccAPz58/njTfe4LPPPmPYsGEAPPLII/Tp02ef/kySk5P5xS9+UfO5W7duTJ48maeffprzzz+f9PR0mjZtSmlpaa3v/Le//Y3KykoefvjhmuTv+PHjyc7OZtKkSZx00kn7FMd+L/zYvn17/vu//5vnnnuO5557jrvvvpuvv/56l/+ApajavBa+fC/c7ndOfGPZG9Vl+bOehdJN8Y2lSofsprRKT6G8MmDOyuJ4h6P9lDc/XL90ZM/W+7ww+F5bPRuWToZI4o5GZqpfsjrCGX+AG6ZA//OBCMx9Gf73cHj+e7BxcbwjlGJn7ivher4VpdDrNLjoHyZLJUlSg/LrX/+6Zk3Rfzd37lyOPPLIWvuOPPJIFixYQEVFxQHdd8CAARQUFFBQUEBJSclO65QOHTq01ufNmzdz22230adPH7Kzs0lPT2fu3LksXbq0JtakpCSGDNkxUax3795kZ2fvc2wPPvggQ4YMoXXr1qSnp/PXv/615j67M2PGDBYuXEhGRgbp6emkp6fTokULtm3bxqJFi/Y5hv2aYSrF1ZwXwuYzHYZAy0PiHc2363IktOwBGxbC7OfCWXpxFolEGNgpm7fnrqVgWSFDujSPd0jaR0EQ1KxfOqpnDBuaTK36JVif0yGzXezuowPX8hA45yE4+hZ477/DRNLMJ2H2szDoMjjmP+wUrsZl1rPw/LUQVMBhZ8HZDzkLXpKkg11yWjjTM1733g/HHHMMo0eP5vbbb2fs2LHRjanKoYceCsC8efM4/PDDAUhNTaVHjx67vaZZs9qzZW+77Tbeeustfvvb39KjRw+aNm3Kueeey/bt2/c6joSqht3BN3q8lJWV1TrnySef5LbbbuN3v/sdI0aMICMjg9/85jd8+umnexx78+bNDBkyZKd1UwFat973JexMmKrh+WY5fkMQiYQz8976WViWXw8SpkBNwnSG65g2SIvXl7Bs41ZSEhMYcUjL2NxkWzHMeCrcHrb79XBUz7TpE5Ykr5wO794NC9+G/PFQ8PewaddRN4fl/FJDNu1xePmHQAC5F8OYP0GCy8tIknTQi0T2uSy+Prj33nsZOHBgrZJ5gD59+vDRRx/V2vfRRx/Rs2dPEhPD//dJSUn51tmmJ510Ei1atODXv/41L7zwwn7F+NFHHzF27FjOOussIExQfrOhUu/evSkvLyc/P7+mJH/evHkUFhbWnFOduFy1ahWDBg0CqNUAqvo+RxxxBD/4wQ9q9v37DNFdfefBgwfz1FNP0aZNGzIzM/frO37TfpfkS3GxcTEsnwKRBDjs7HhHs/cGXgwJyWHjp1Uz4x0NALlV65ja+Klhqi7HH9atOc1SY/S7r5lPQVkJtOoFXY+KzT0UO+0HwaXPwZVvhDPdK0rhkwfhD7nwzl2wtTDeEUr757OH4OUbgACGXgVjHjRZKkmSGrT+/ftzySWX8Mc//rHW/ltvvZV33nmHu+66i/nz5zNx4kT+9Kc/cdttt9Wc07VrV95//31WrFjB+vXrdzl+eno6Dz/8MK+99hqnnXYa//znP/nyyy+ZOXMm9913H0BNAnZ3Dj30UJ5//nkKCgqYMWMGF198MZWVlTXHe/Xqxcknn8z3vvc9Pv30U/Lz87n66qtp2rRpzTlNmzbl8MMP595772Xu3Lnk5eXx05/+dKf7TJ06lX/+85/Mnz+fn/3sZ0yZMqXWOV27dmXmzJnMmzeP9evXU1ZWxiWXXEKrVq0YM2YMH3zwAYsXL2bSpEn86Ec/Yvny5Xv8bruyTwnTs88+e4+vm2++eZ8DkPbJ7GfD924jIaNtfGPZF81aQe/Twu1pE+MbS5UBHbMBWLpxCxs2l8Y3GO2zSfN2rF8aE0Gwo9nTsKvrd3M17VmXI2Dsa3Dp82EStawEPvgt/GEAvP9bKN0c7wilvffx/8DrVX9BOPwHcNr9kODv/yVJUsP3y1/+slYCEsJZk08//TRPPvkk/fr14+c//zm//OUva5Xu//KXv2TJkiUccsgheyw9P+uss/j4449JS0vj8ssvp1evXhx33HG8++67OzV82pX777+f5s2bc8QRR3DGGWcwevRoBg8eXOuc8ePH0759e0aOHFnTHL5Nm9pLyD366KOUl5czZMgQbrrpJu6+++5ax7/3ve9x9tlnc8EFFzB8+HA2bNhQa7YpwDXXXEOvXr0YOnQorVu35qOPPiItLY3333+fzp07c/bZZ9OnTx/GjRvHtm3b9mvGaST45sIB3+LKK6/cq/PGjx+/z4HEU3FxMVlZWRQVFUVl2q5iJAjgweGwfh6M+V8YdEm8I9o3i96Dx8+E1Ey49Yt6USZw/O8msWhdCePHDuPY3jFcB1NRta2sgtxf/IvS8kr+edMx9MrJiP5NlnwIE06D5GZw61xokhX9e6juBQF88Vq4xunaz8N9zVrDUbeEM/WSm8Q3Pml3ggDe/0347AIcfSsc9zN/mSNJkqSY2Kc6zoaWCFUjs2Z2mCxNTA0b0DQ03UZCdhco/ArmvFgvEr65nbJZtK6E6csKTZg2IJ8u3khpeSXtsprQs216bG7y2UPh+4DzTZY2JpFI+POz1ykw+3mY9CvY+CX883aY/KewMdSgS22co/olCOCdX8CHvw8/H/fT8FmVJEmSYiSuNUzvv/8+Z5xxBu3btycSifDiiy/WOh4EAT//+c9p164dTZs25YQTTmDBggW1ztm4cSOXXHIJmZmZZGdnM27cODZvtrywUapu9tRzdMNM4CQkwJArwu16UpY/yHVMG6RJ89YCYTl+JBazq4pXwRevhtvDxkV/fMVfQiIMOA+u/wzO+CNkdoTiFfDqTfCnYWGzr8o9Lxwv1YkggDd/siNZOvpXJkslSZIUc3FNmJaUlJCbm8uDDz64y+P33Xcff/zjH/nLX/7Cp59+SrNmzRg9ejTbtm2rOeeSSy5hzpw5vPXWW7z66qu8//77XHvttXX1FVRXKith1nPhdv/z4hvLgRh4CUQSYdmnsHZuvKNhYKfmAMxYVsg+rM6hOKtu+DSqV4zWL532GFSWQ6fDIad/bO6h+iExOfxFzg/z4eRfh+X5Xy+GF66FPx8Jn78cJqykeKishFduhE//En4+7X4YcX18Y5IkSdJBIa4J01NOOYW7776bs846a6djQRDwwAMP8NOf/pQxY8YwYMAAHnvsMVauXFkzE3Xu3Lm8+eabPPzwwwwfPpyjjjqK//mf/+HJJ59k5cqVdfxtFFPLPoHi5eH6n4eeFO9o9l9GTlgKC5Af/1mmvXIySElKoGhrGUs2bIl3ONoLyzZu4ct1JSQmRDiiR6vo36CiDPKrll8ZdnX0x1f9lNwEDr8ObpwBx98BTbJh3Vx4+jL46yhY8LaJU9WtinJ48fthRUYkAc78szPeJUmSVGfqbVvRxYsXs3r1ak444YSafVlZWQwfPpzJkycDMHnyZLKzsxk6dGjNOSeccAIJCQl8+umnux27tLSU4uLiWi/VczOfDt/7fLfhNyUZMjZ8n/EPKNu2x1NjLSUpgX7tw0ZnBcu+jmss2juTqmaXDuncnMwmMVhnct4bsGkVpLWCvt+N/viq31KawdG3hInTY/4TUtJhVQE8cQ785ahw9nHZ1nhHqcaufDs8dxXMfDKsyjjnYRh4cbyjkiRJ0kGk3iZMV69eDUDbtm1r7W/btm3NsdWrV9OmTe1GNUlJSbRo0aLmnF255557yMrKqnl16tQpytErqsq3w+cvhtv9z41rKFFxyHGQ1Qm2FcLcl+MdzTfK8oviHIn2Rt68MGE6Mlbl+FMeDt8HXw5JqbG5h+q/ptlw3P8LE6cjboDktLDx3ss/hPv7wNt3QuGyeEepxqhsGzx9OXz+EiSmwAWPQ79z4h2VJEmSDjL1NmEaS7fffjtFRUU1r2XL/EtfvbboXdj6NTRrA92OiXc0By4hMexCDfWiLD+3U9hAa7qNn+q90vIKPl60HggbPkXduvmwOA+IwNAroz++Gp5mrWD0f8Mtn8NJd0N25/Dn8Ye/hz/khomtJR9Zrq/o2L4F/nEhzH8DkprAhf+A3qfFOypJkiQdhOptwjQnJweANWvW1Nq/Zs2ammM5OTmsXbu21vHy8nI2btxYc86upKamkpmZWeulemzWM+F7v3PCZGNjMOjScE22rz6E9QvjG0rVDNO5K4spLbcrdn2Wv+RrtmyvoHVGKoe1j8HPramPhu89Tw4TY1K1ps3hiB/Cjwrgwr+Hv7wKKsJZgBNOhb8cDdMet1xf+690EzxxLnz5HiQ3g0uegUNP+PbrJEmSpBiotwnTbt26kZOTwzvvvFOzr7i4mE8//ZQRI0YAMGLECAoLC8nPz685591336WyspLhw4fXecyKgdLNMO/1cHvAefGNJZqyOkKPE8PtaRPiGkqnFk1p0SyF7RWVzF21Ka6xaM+q1y895tDWRCKR6A6+vQQK/h5u2+xJu5OQGM74u+IV+P5kGHIlJDWFNbPg5Rvg/r7w9i+gaHm8I1VDsrUQHjsTvvoobO542QuNo6JEkiRJDVZcE6abN2+moKCAgoICIGz0VFBQwNKlS4lEItx0003cfffdvPzyy8yaNYvLL7+c9u3bc+aZZwLQp08fTj75ZK655ho+++wzPvroI2644QYuvPBC2rdvH78vpuiZ9waUbYEW3aH94HhHE11DrgjfC/4O5aVxCyMSiZDbMSzLL1hq46f6LKbrl856FkqLoHnXcJ1d6du07QtnPBCW6594F2R1hq0b4cP74YEB8PQV8NVky/W1ZyUbYOIZsGJqOJP5ipehs7/0liRJUnzFNWE6depUBg0axKBBgwC45ZZbGDRoED//+c8B+M///E9++MMfcu211zJs2DA2b97Mm2++SZMmO7qkP/HEE/Tu3Zvjjz+eU089laOOOoq//vWvcfk+ioHqcvz+50G0Z9TF26GjIT0HtmyAL16LayjVjZ8KXMe03lpVtJV5azaREIGje7SK7uBBAFMeCreHjoOEelt8oPoorQUc+SO4sQAueAK6Hl1Vrv8ijD8Z/u8YmP5E2MxH+qZNa2DCabB6JjRrDVe8Cu0HxTsqSZIkiUgQOPWjuLiYrKwsioqKXM+0PinZAL/rCZXlcP0UaN0z3hFF3zt3wQe/he6j4PKX4hZG3vx1XPHoZ3Rr1Yz3bhsVtzi0e09+tpSfPD+LQZ2zeeEHR0Z38GVT4JETwiYrt8wNE2DSgVgzBz79P5j5NJRXrWua1jIs4R82DjKtAjnoFS2Hid+FjYsgox1c/nLj/O+8JEmSGiSnEan++vzFMFnaLrfx/iVq8GVABL6cBBsXxy2M6pL8xetLKNyyPW5xaPfyqtYvHdWzTfQHn/Jw+N7vHJOlio62h8F3/xiW65/wC8jqFM6m/+C38Pt+8MxYWPqJ5foHq42LYfwpYbI0uzNc+Ubj/e+8JEmSGiQTpqq/Zj0bvvdvRM2e/l3zrnDIseH2tMfiFkZ2WgrdWjUDLMuvj8oqKvlwwXogBuuXlmyAOc+H28PGRXdsKa0FHHUT/KgAzn8cuhwVluvPeQEeHQ1/HRmu42y5/sFj/QIYfyoULg3XJ7/yDWjRLd5RSZIkSbWYMFX9VLgMln4MROCws+MdTWwNrm7+9ARUlMUtjIGdsgGYsawobjFo16YvLWRTaTnN05Lp3yEryoM/DhXbw3UDOwyJ7thStcQk6PtduPI1uO4jGHx5uATEqhnw4vfh94fBu3dD8cp4R6pYWjMnnFm6aSW07h0mS7M6xjsqSZIkaScmTFU/za6aXdr1KMjqEN9YYq3XqWGzi81rYP4/4xZGdVl+wbKv4xaDdi1v/loAjunZmsSEKDY/q6yAqY+G28Oujt640p7k9IPv/k+4Xu4Jd0JmR9iyHt7/DTzQH569CpZ9Zrl+Y7OyIGzwVLIOcvrD2NcgIyfeUUmSJEm7ZMJU9VNNOf658Y2jLiSlwMCLw+38CXELY2Dn5kBYkm8vuPpl0rxw/dKRPaNcjr/wHSj8CppkN/6Z3Kp/0lrAUTfDjTPg/Megy5HhutWzn4NHToSHjoWCf0B5abwj1YFa9lnY4Gnr1+FM9itegWat4h2VJEmStFsmTFX/rPkc1syGhGTo8914R1M3qsvyF74dLkcQB33aZZCSmMDXW8pYtnFrXGLQztZu2saclcVAOMM0qqY8FL4PuhRS0qI7trS3EpOg7xi48nX43gfh85iYCiunw4vXVZXr/zcUr4p3pNofiz+Ax86E0iLofARc9iI0bR7vqCRJkqQ9MmGq+qe6HP/Qkw6ejt0tD4GuRwMBTP9bXEJITUqkT/tMAKZbll9vvD8/bPbUv0MWrdJTozfwxsWw4K1we+hV0RtXOhDtBsCYB8Ny/eN/DpkdwhLu9++DB/rBs+Ng2RTL9RuKhW/DE+dCWQl0HwWXPgtNMuMdlSRJkvStTJiqfgkCmPVMuH0wlON/05Cx4fv0x8O1JeNgUFXjp4JlhXG5v3aWNz9G5fj544EADjkuTNhL9UmzlnD0rWG5/nkToPOIqnL9Z+GRE+Ch42DGU5br12dfvA7/uAjKt8Gho+GipyClWbyjkiRJkvaKCVPVL8unQOFSSEmHnifHO5q61fv0sEyxeEU4KycOBlYlTGeYMK0XKioDPlgQJkxH9YpiwrRsG0x7PNy22ZPqs8RkOOwsuOpN+N77MLC6XH8avHAt/L4fvHcPbFod70j1TbOfh6cvg4rt4dI6F/wNkpvEOypJkiRpr5kwVf1SPbu09+kH35qKyU0gN77Nn3KrEqazVxazvbwyLjFohxnLCyncUkZmk6SaZHZUfP4ibN0Ydic/dHT0xpViqV0unPkg3PI5HPczyGgPJWsh794wcfrc1bB8aryjVME/4Llx4Yzg/ufDuePD5oaSJElSA2LCVPVHRXk4KwWg/3nxjSVehlQ1f5r/z7g0OOnaMo3stGS2l1fyxeriOr+/asubF84uPfrQ1iQlRvHH9ZSHw/ehY8OGO1JD0qwVHHMb3DQzTMZ1Ohwqy8JfuD18fFiuP/NpKN8e70gPPlMfDRt1BZUw+HI46y/+jJEkSVKDZMJU9cfiSbBlPaS1gu4j4x1NfLTuFa7VF1RAQd03f4pEIuR2zAYsy68PJsVi/dKVBeHSFwnJMOjy6I0r1bXEZOh3Noz7J1w7KZyhn5gCK/Lh+WvCJlGT7oVNa+Id6cFh8v/CqzeH28OvgzP+CAmJ8Y1JkiRJ2k8mTFV/zHo2fD/srPAvwgerwVWzTKc9BpV1XxZfXZY/3YRpXG0s2c7M5YUAHBPNhOnUR8L3vt+FjLbRG1eKp/aD4Kw/w82fw7E/hfQc2LwGJt0Dvz8Mnr82TKQqNt7/Lfzz9nD7yJvg5HshEolrSJIkSdKBMGGq+mH7Fpj7Srh9sJbjV+s7BlKzwuZXX75X57cfVJUwLTBhGlcfLFhHEEDvnAxysqLULGVrIcysWifYZk9qjNJbw8j/gJtnwzmPQKfhYbn+zKfCUv2HTwh/OWe5fnQEAbxzF7x7V/h51H/BCXeaLJUkSVKDZ8JU9cP8N2H7ZsjuDJ2+E+9o4islDXIvCLenTazz21fPMP1yXQlFW8vq/P4KVa9fOrJXFGeXzvgHlG+FNn3DpR+kxioxGfqfC+P+Bde8B7kXheX6y6eEDYke6A+Tfg2b18Y70oYrCOBfP4UPfht+PvGXMOrHJkslSZLUKJgwVf1QXY7f71z/sgU7yvK/eK3O/0LfolkKnVukAdSUhKtuVVYGvL8gTJiO6tkmOoMGwY5mT8Ou9t8zHTw6DA6bD908B479f1Xl+qth0q+qyvW/ByumxTvKhqWyEl67FSb/Kfx8ym/gyBvjG5MkSZIURSZMFX9bv4YF/wq3B5wf31jqi5x+0GEIVJZDwd/r/PYDq8vylxbW+b0Fc1YWs37zdpqlJDKkS/PoDLo4DzYshJQM/z3TwSm9DYz8T7hpVliu33EYVGyHmU/CQ8fCwyeGv7yrcGb9HlVWwMs3VK2HHIHv/g8MvzbeUUmSJElRZcJU8ff5y+Eac237QZs+8Y6m/hgyNnyfNjGcHViHqhOmM5xhGhd588NZxUf0aEVKUpR+TFfPLs29EFIzojOm1BAlpYTl+le/DVe/CwMugIRkWP7ZjnL9vN/A5nXxjrT+qSiD566GgicgkghnPwSDL493VJIkSVLUmTBV/M2qakLT/9z4xlHfHHY2pKTDxi9hyQd1euvcbzR+Cuo4WSvIm19Vjh+t9UuLVsAXr4fbw8ZFZ0ypMeg4BM7+a1iuP+q/IL0tbFoF790Nv+8LL3wfVk6Pd5T1Q3kpPDMW5jwfJpjPGw8DDvImjZIkSWq0TJgqvopXwpIPw+1+58Q3lvomNX1HEjm/bps/HdY+k+TECOs3b2f511vr9N4Hu6KtZUyrWgphZM8oJUynTYSgAroc5SxuaVcy2oYNi26aDWc/DB2GhuX6M/4Ofx0Fj5wEs587eMv1y7bCkxfDF69CYipc+AT0HRPvqCRJkqSYMWGq+Jr9PBCEHbuzO8c7mvqnuix/7stQsqHObtskOZE+7TIBy/Lr2kcL11NRGdCjTTodm6cd+IAVZZA/Idx2dqm0Z0kp4azJa96Bq9+B/ueHsymXfQrPXhWW67//GyhZH+9I607pZnjiPFj4NiSnwSVPQ8/R8Y5KkiRJiikTpoovy/H3rP0gyBmwozFJHcrtmA3Y+KmuTZoXrl8atdmlX7wKm9eEpca9T4/OmNLBoONQOOchuHk2jPwJNGsTluu/ezfc3xde/AGsmhHvKGNrWxH87exwWZiUDLj0eeg+Kt5RSZIkSTFnwlTxs34BrCqAhCToe1a8o6m/qmeZ5tdt86eB31jHVHUjCIKa9UujljD9rKrZ0+ArwtlzkvZNRg4ce3uYOD3rr9B+MFSUho2P/u8YePRkmPNC4yvX37IRJn43nF3bJBuueAm6jIh3VJIkSVKdMGGq+KmeXXrIcdCsZXxjqc/6nxeWQa6fB0s/qbPbDuycDcDslUWUVVTW2X0PZvPWbGJNcSlNkhP4TrcWBz7g2rnw1YdhN+vqxLuk/ZOUCrkXwLXvVZXrnxf+wm/p5LAZ0h9y4f3fNo5y/c1rYcLp4S8101rC2Fehw5B4RyVJkiTVGROmio8g+EY5vl1296hJJhx2drg9re6aP3Vr2YyMJklsK6tk3upNdXbfg9mkeeHs0hHdW9IkOfHAB5zySPje6xTI6nDg40kKdRwK5zwcNoka+WNo1hqKV8C7d1WV618Pq2bGO8r9U7wSxp8Ka+dAeg6MfR1y+sc7KkmSJKlOmTBVfKycBhu/hKSm0OvUeEdT/1XPDpzzAmz9uk5umZAQsSy/juVVJUxH9Wpz4IOVboIZVeveDrv6wMeTtLPMdnDsf8HNc+Cs/4N2A6vK9f8G/3c0PHoKzHkRKsrjHene+forGH8KbFgAWZ3gytehTe94RyVJkiTVOROmio9Zz4bvvU+F1PT4xtIQdBwKbfpC+TaY+Uyd3bY6YTrDhGnMbS4tZ+pXG4EorV8682nYvgla9oBuIw98PEm7l5QKuRfCtZNg3FvQ75yqcv2P4ZkrwnL9D+6Hkg3xjnT3NiwKZ5Z+vQSadw2TpS0PiXdUkiRJUlyYMFXdq6yA2c+F2/3Pj28sDUUkEjbtgbAsv46aP+V2zAacYVoXPl64nrKKgC4t0+jaqtmBDRYEO8rxh46DBH/US3UiEoFO34FzH4WbZsEx/wFpraB4ObzzC/h9X3jpBlg9K96R1rb2i3BmafFyaNUTrnwDsjvHOypJkiQpbvxbtOrekg9g8xpo2jxs+KS9M+B8SGoCa2bDiml1csvqxk8L121m07ZG1gG6npk0v6ocPxqzS5d+Eq4/mNQUBl504ONJ2neZ7eG4n4bl+mf+BdrlhlUC0x+HvxwVzub8/KX4l+uvmgkTTg3/u9y2X7hmaWb7+MYkSZIkxZkJU9W96mZPfc+EpJS4htKgpLWAvmPC7fzxdXLLVumpdGzeNOzRtbyoTu55MAqCoGb90pG9opAwnfJw+N7/3PAXE5LiJ7lJ+IuLa/Pgqn+FTfwiifDVR/D05WG5/oe/hy0b6z625fkw8XTYsgHaD4IrXoH0KPwMkiRJkho4E6aqW2Xb4PNXwu3+58U3loaouix/9vOwrbhObplbtY7pdMvyY2bRuhJWFG4lJSmBw7u3PLDBNq8NZ62BzZ6k+iQSgc7D4bzxYbn+0bdBWsuwDP7tO+H+PvDyD2H17LqJ56uP4bExsK0IOg2Hy18KfzEnSZIkyYSp6tjCt6C0CDI7QOcR8Y6m4elyBLQ8FMpKYPazdXLLQVUJU9cxjZ1J89YCMLxbC9JSkg5ssGmPQWUZdBwG7QceeHCSoi+rAxz/M7j5cxjzv5AzICzXn/YY/OVImHA6zH0lduX6i96Dx88OG8N1OwYufR6aZMXmXpIkSVIDZMJUdau6HL/fOTai2R+RCAypmmWaP7FObjnwGwnToI6aTR1s8qrWLx15oOuXVlbA1KrlGpxdKtV/yU1g0CXwvffhyjfDpWoiieFa309dCn8cBB/9Ibrl+vPehL9fAOVboceJcPHTkJoevfElSZKkRsCMlerOtqLwL2pgOf6ByL0YElNgVQGsLIj57Q5rn0ViQoR1m0pZVbQt5vc72GzdXsGni8NkyKgDXb90/j/D8t6mLcLEi6SGIRKBLiPg/Ilw00w46pbw3+OipfDWz+H+vvDKjbDm8wO7z5wX4alLoKIUep8OFz4ByU2j8hUkSZKkxsSEqerO3FfDv6S16gU5/eMdTcPVrGX4F12AabGfZdo0JZHeORmAZfmx8MmXG9heXkmH7KYc0voAZ3lVN3safFk4c01Sw5PVEU64A275HMY8CG37h7NB8yfAn0dUleu/Gs4o3xcznoJnr4TKcuh3Lpw3AZJSY/ENJEmSpAbPhKnqTnU5fv/zwtk02n/VZfkzn4HtJTG/XXVZ/gwTplFXU47fqzWRA/n3YsMiWPQOEIEhV0YnOEnxk9wUBl0K130AV74BfcdAJKGqXP8S+ONA+OiPsPXrbx8rfyK88D0IKmHgpXD2XyExOeZfQZIkSWqoTJiqbmxaA4vzwu3+58Q3lsag6zHQvFvYsGPOCzG/XW5VwnS6CdOoq274dMDrl059NHw/9ERo0e0Ao5JUb0QiYcO/8x+DG2fCUTdD0+ZQuBTe+llVuf5NsHburq//9P/glR8BQbi28Xf/BxIS6/IbSJIkSQ2OCVPVjTkvhDNbOg6DFt3jHU3Dl5AAgy8Pt/MnxPx2g6oSprOWF1FeURnz+x0slqwvYcmGLSQlRDjikJb7P1DZVpj+t3DbZk9S45XdCU64E26ZGyY+2/aDsi2QPx7+93CY+F344vUd5fofPgBv/Ge4PeIGOPW3NlyUJEmS9oL/16y68c1yfEXHwEsgIQmWTznwRiDf4pDW6aSnJrG1rIL5azbH9F4Hk+py/KFdm5PR5ADKY2c/D9sKIbsz9DghOsFJqr+Sm4a/NLvuQxj7GvQ5IyzXX5wHT14EfxwEz46Dt+8Izz/mP+Gku10OR5IkSdpLJkwVexu/hBVTw7/MHXZWvKNpPDLaQq9Twu0YN39KSIgwoGMWADOWF8b0XgeTmvVLe7Y5sIGmPBS+D73KUlvpYBKJQNej4IK/wY0z4MgboUk2FH4Fs58Nzzn+53Dc/zNZKkmSJO0DE6aKvVnPhe/dR0H6ASaGVNvgseH7jH+EZdkxVN34qWBpYUzvc7DYVlbB5EUbABjV6wDWL12RDyunQ2IKDLosStFJanCyO8OJvwzL9c/4I3QbCac/AEffGu/IJEmSpAYnKd4BqJELApj1dLhtOX70HXIsZHWComXw+cuQe0HMblWTMLXxU1RMWbKRrWUVtMlIpXdOxgEM9Ej4fthZ0KxVdIKT1HClpMGQK8KXJEmSpP3iDFPF1upZsH4+JKZC79PjHU3jk5BYZ82fqhOm89duYnNpeUzvdTDIm1ddjt+ayP6Wym7ZCLOrZnDb7EmSJEmSpKgwYarYqp5d2utkaJIZ31gaq4GXhOvDLv0Y1s2P2W3aZDahfVaTcNLw8qKY3edgMalq/dJRvQ5gmYqCJ6B8G+T0h47DohSZJEmSJEkHNxOmip3Kyh3rl1qOHztZHeDQ0eF2jJs/DeycDViWf6CWf72FhWs3kxCBo3rsZxl9ZeWOcvxhV9vQRZIkSZKkKDFhqthZ+jFsWgmpWdDjxHhH07hVr1VX8HcoL43ZbXI7ZgMww4TpAXl//noABnduTlZa8v4N8uW78PXi8N8vfyEhSZIkSVLUmDBV7Mx6JnzvewYkN4lvLI1djxMhox1s3QhfvBqz29j4KTomzVsLhOuX7rfq2aUDL4aUZlGISpIkSZIkgQlTxUr5dpjzYrjd//y4hnJQSEyCQZeF2/mxK8vv3zGLhAisLt7G6qJtMbtPY7a9vJKPF20AYGSv/UyYFi6F+W+G28PGRSkySZIkSZIEJkwVK+vmQmU5pOdA16PiHc3BYfBlQAQW58HGL2Nyi7SUJHq2zQCcZbq/8r/6ms2l5bRslkK/9ln7OcgECCqh20hodWhU45MkSZIk6WBnwlSx0S4XblsAlzwDCYnxjubgkN0ZDjku3J72WMxuM8jGTwckb/46AI7p2ZqEhP1o1FReuuOf77CroxiZJEmSJEkCE6aKpZQ0aDcg3lEcXIaMDd+nPwEVZTG5xY51TL+OyfiNXXXCdNT+luPPfQVK1oVr1vY6NYqRSZIkSZIkMGEqNS69ToFmbaBkLcx7Iya3yK1KmM5aXkRFZRCTezRWa4q3MXdVMZEIHNWj1f4NMuXh8H3IleHatZIkSZIkKapMmEqNSWJy2DUdYFpsmj8d2iaDZimJlGyvYOHazTG5R2NVPbt0QIcsWqan7vsAq2fD0smQkASDL49ydJIkSZIkCUyYSo1PdSJt4Tvw9VdRHz4xIUL/jmGzIsvy903evDBhOrJXm/0bYOoj4Xvv0yGzXZSikiRJkiRJ32TCVGpsWh4C3Y4BApj+t5jcIrdmHdOimIzfGJVXVPLBgqqEac/9WL90WzHMeCrcttmTJEmSJEkxY8JUaoxqmj/9DSrKoz78oJqEaWHUx26sZiwvpHhbOVlNk2saZ+2TmU9BWQm06gVdj4p6fJIkSZIkKWTCVGqMep8OTVvAppWw8K2oDz+wU3MA5q0uZsv26CdkG6NJVeX4Rx/aisSEyL5dHATw2UPh9rCrIbKP10uSJEmSpL1mwlRqjJJSdzR/yo9+86ecrCa0zUylMoDZK4qjPn5jVN3wab/K8Zd8COvnQXIzyL0gypFJkiRJkqRvqvcJ065duxKJRHZ6XX/99QCMGjVqp2PXXXddnKOW6oHBV4TvC/4JxSujPvzAmrJ8Gz99m/WbS5m5PFzvdb8SplMeDt8HnA9NsqIYmSRJkiRJ+nf1PmE6ZcoUVq1aVfN6662wvPi8886rOeeaa66pdc59990Xr3Cl+qN1T+h8BASVMWn+VF2W7zqm36662VPfdpm0yWyybxcXr4IvXg23h42LcmSSJEmSJOnfJcU7gG/TunXt2Vj33nsvhxxyCCNHjqzZl5aWRk5OTl2HJtV/Q66ApR/DtMfh6NsgIXq/I8ntFM50nLGsKGpjNlZ5VeuXjuq1H7NLpz0GleXQ6XDI6R/lyCRJkiRJ0r+r9zNMv2n79u387W9/46qrriLyjaYnTzzxBK1ataJfv37cfvvtbNmyZY/jlJaWUlxcXOslNUp9x4Ql3EVL4ct3ozr0gI7ZRCKwonArazdti+rYjUllZcD7C9YD+1GOX1EG+ePD7e9cE+XIJEmSJEnSrjSohOmLL75IYWEhY8eOrdl38cUX87e//Y333nuP22+/nccff5xLL710j+Pcc889ZGVl1bw6deoU48ilOEluCgMuDLfzJ0R16PTUJHq2yQCgYGlhVMduTGatKGJjyXYyUpMY3KX5vl087w3YtAqatYY+Z8QmQEmSJEmSVEuDSpg+8sgjnHLKKbRv375m37XXXsvo0aPp378/l1xyCY899hgvvPACixYt2u04t99+O0VFRTWvZcuW1UX4UnwMqWr+NO8N2LQmqkPXlOUvL4zquI3JpKpy/CN7tCI5cR9/5FY3exp8OSSlRjkySZIkSZK0Kw0mYfrVV1/x9ttvc/XVV+/xvOHDhwOwcOHC3Z6TmppKZmZmrZfUaLU9DDoOC9fBLHgiqkPb+Onb5c1fC8DIfV2/dN18WJwHkQQYMjb6gUmSJEmSpF1qMAnT8ePH06ZNG0477bQ9nldQUABAu3bt6iAqqYEYXDXLdNpjUFkZtWEHdsoGYOayIiorg6iN21gUbtlek0ze5/VLpz4avvc8GbI7RzcwSZIkSZK0Ww0iYVpZWcn48eO54oorSEpKqtm/aNEi7rrrLvLz81myZAkvv/wyl19+OccccwwDBgyIY8RSPdPvbEjJgK8Xw5IPojZsz7bpNE1OZFNpOV+u3xy1cRuLDxaspzII/5zaZzfd+wu3l0DB38PtYeNiE5wkSZIkSdqlBpEwffvtt1m6dClXXXVVrf0pKSm8/fbbnHTSSfTu3Ztbb72Vc845h1deeSVOkUr1VEozGHBeuB3F5k9JiQn07xCuYzrdxk87yZsfrl+6z7NLZz0LpUXQvBt0Py4GkUmSJEmSpN1J+vZT4u+kk04iCHYu9+3UqRN5eXlxiEhqgAZfEZZ5f/EqlGyAZi2jMuzAztl8tmQjBcsKOW9op6iM2RhUVgY1CdNRvdrs/YVBAFMeCreHjYOEBvF7LUmSJEmSGg3/Ji4dLNoPhHYDoWI7zPhH1IbN7ZgNwIzlhVEbszGYu7qYdZtKSUtJZGjX5nt/4fKpsHoWJDWBgZfELkBJkiRJkrRLJkylg8mQquZP+RPCmYxRMLBzNgBfrNrEtrKKqIzZGFTPLj3ikJakJiXu/YVTHg7f+50DaS1iEJkkSZIkSdoTE6bSwaTfuZCcBhsWwNLJURmyfVYTWmekUl4ZMHtFUVTGbAwmzduP9UtLNsCc58Ntmz1JkiRJkhQXJkylg0mTzHDmIkD+xKgMGYlEasryC5YVRmXMhq54WxnTvvoagJE992H90umPh0smtB8EHYbEKDpJkiRJkrQnJkylg82QseH75y/C1q+jMuSgqrJ8E6ahjxeup7wyoHurZnRumbZ3F1VWwNRHwu1hV8cuOEmSJEmStEcmTKWDTYch0LYflG+DmU9HZciBnbIBE6bVqtcvPWZfyvEXvg2FS6FJNhx2dmwCkyRJkiRJ38qEqXSwiURgcHSbP/XvmEUkAsu/3sr6zaUHPF5DFgQBeVXrl47qtQ8J0+pmT4MuhZS9nJUqSZIkSZKizoSpdDAacB4kNYG1n8PyqQc8XGaTZA5pnQ7AjIN8lumCtZtZWbSN1KQEDu/ecu8u2rgYFrwVbg+9KnbBSZIkSZKkb2XCVDoYNW0Oh50Vbk+bEJUhLcsPVc8uHd69JU2SE/fuovzxQACHHA8tD4ldcJIkSZIk6VuZMJUOVtVl+bOfh23FBzxcrglTACbNXwvAqL1dv7RsG0x7PNy22ZMkSZIkSXFnwlQ6WHU+HFr1grItMOuZAx5uUFXCdMayQiorD3xd1IaopLScKYu/BmDk3q5f+vmLsHUjZHWCnqNjF5wkSZIkSdorJkylg1UkAkOqZplOm3jAw/XKySA1KYHibeUs3lBywOM1RJ98uYHtFZV0atGU7q2a7d1F1c2ehoyFhL0s4ZckSZIkSTFjwlQ6mA24EBJTYNUMWDn9gIZKTkygX4cs4OBt/DSpav3SkT1bE4lEvv2ClQWwfAokJMPgy2MbnCRJkiRJ2ismTKWDWbOW0OeMcDv/wGeZHsyNn4IgqFm/dGTPNnt30dRHwve+YyB9L6+RJEmSJEkxZcJUOtgNGRu+z3oWSjcf0FAHc8J08foSlm3cSnJihCMOafntF2wthJlVa8fa7EmSJEmSpHrDhKl0sOt6NLToDts3wZznD2io6oTp3FXFbCuriEJwDUfe/LAcf1jXFjRLTfr2C2b8A8q3QpvDwgZckiRJkiSpXjBhKh3sIhEYXNX86QDL8js2b0rLZimUVQR8vqo4CsE1HNUJ01G9Wn/7yUGwo9nTsHHhPwNJkiRJklQvmDCVBAMvhoQkWDEVVs/e72EikciOsvylhdGJrQHYVlbB5EUbgL1cv3RxHmxYCCkZMOD8GEcnSZIkSZL2hQlTSWHDoV6nhtvTDmyWaW5VwnTG8sIDi6kB+XTxRkrLK8nJbELPtunffkH17NLcCyE1I7bBSZIkSZKkfWLCVFKouvnTzKdg+5b9HuZgbPw0ad5aICzHj3xbeX3RCvji9XB72LgYRyZJkiRJkvaVCVNJoe7HQnZn2FYEn7+038PkdswG4KsNW9hYsj1KwdVv1euXjuy5F+uXTpsIQQV0OQra9IlxZJIkSZIkaV+ZMJUUSkiAQZeH2wdQlp+Vlkz3Vs2Ag6Msf9nGLXy5roTEhAhHHtpqzyeXb4f8CeG2s0slSZIkSaqXTJhK2mHQJRBJhKWTYd28/R7mYGr8NKlqdumQzs3JbJK855O/eBU2r4H0ttD79DqITpIkSZIk7SsTppJ2yGwPPUeH2/n7P8t0YOds4OBYxzRvXlU5fq+9KMef8kj4PmQsJKXELihJkiRJkrTfTJhKqm3wFeH7jH9Aeel+DVG9jumM5YUEQRClwOqf0vIKPl60HtiL9UvXzoWvPgxn8Fb/GUuSJEmSpHrHhKmk2nqcAJkdYOtGmPvKfg3Rp10mKUkJFG4p46sNW6IcYP2Rv+RrtmyvoFV6Kn3bZe755OrZpb1PhawOsQ9OkiRJkiTtFxOmkmpLTIJBl4bb1Q2K9lFKUgKHtQ8TiI25LL96/dKRPVuTkBDZ/Ymlm2DGk+H2sKvrIDJJkiRJkrS/TJhK2tmgy4AILPkANizaryGqy/Ibc8J0r9cvnfk0bN8ELQ+FbiPrIDJJkiRJkrS/TJhK2ll2p7A0H2Da/jV/GtTIGz+tKtrKvDWbSIjA0T1a7f7EINhRjj9sHET2MBNVkiRJkiTFnQlTSbs2pKoxUcHfoXz7Pl8+sFM2AJ+vLKa0vCKKgdUP1bNLcztl07zZHjreL/0E1s6BpKaQe1EdRSdJkiRJkvaXCVNJu9bzZEhvCyXrYP4b+3x55xZpNE9LZntFJV+s2hSDAOMr7xvrl+7RlIfD9wHnQdPs2AYlSZIkSZIOmAlTSbuWmAwDLwm396P5UyQSIbdqlmljK8svq6jkwwXrARjVq83uT9y8Fj5/KdweOq4OIpMkSZIkSQfKhKmk3Rt8Wfi+6D34+qt9vnxgI02YTl9ayKbScpqnJdO/Q9buT5z2GFSWQcdh0H5gncUnSZIkSZL2nwlTSbvXojt0HwUEMP3xfb68eobpjEaWMM2bvxaAow9tTWLCbpo4VVbA1PHh9rCr6ygySZIkSZJ0oEyYStqzwVXNn6b/DSrK9+nSgR2zAfhyfQlFW8qiHFj8TKpq+DSq1x7WL53/TyheDk1bQN8z6yYwSZIkSZJ0wEyYStqz3qdBWkvYtAoW/GufLm3eLIWuLdMAKFheGIPg6t7aTduYs7IYCGeY7lZ1s6fBl0FykzqITJIkSZIkRYMJU0l7lpQKAy8Ot6dN3OfLG1tZ/vvzw2ZP/Ttk0TojddcnbVgEi94BIjDkyroLTpIkSZIkHTATppK+XXVZ/oJ/QdGKfbq0sTV+ypsfluOP7LmH2aVTHw3fDz0RWnSrg6gkSZIkSVK0mDCV9O1aHQpdjoKgMlzLdB98M2EaBEEMgqs7FZUBHyyoSpjubv3S7Vt2/BkNu6aOIpMkSZIkSdFiwlTS3hlSNct02mNhB/i91KddJsmJETaWbGf511tjFFzdmLG8kMItZWQ0SWJQVSJ4J3Oeh22FkN0Fehxfl+FJkiRJkqQoMGEqae/0+S40yQ47vy96d68va5KcSN92mQBMb+Bl+XnzwtmlRx/aiqTE3fz4rG72NPQqSEiso8gkSZIkSVK0mDCVtHeSm0DuReF2/oR9urSmLH9pYVRDqmuTqtYvHdWzza5PWJEPK6dDYioMuqwOI5MkSZIkSdFiwlTS3qsuy5/3BmxavdeX5VYlTGcsL4x+THVkY8l2ZlbFf8zuGj5NeSR8P+wsaNaybgKTJEmSJElRZcJU0t5r0wc6fgeCCih4Yq8vq55hOntFEWUVlTEKLrY+WLCOIIDeORnkZDXZ+YQtG2H2c+H2sKvrNjhJkiRJkhQ1Jkwl7ZshY8P3aY9B5d4lP7u1akZmkyRKyyv5YtWm2MUWQ9Xrl47stZvZpQVPQPk2yBkAHYfWYWSSJEmSJCmaTJhK2jeHnQmpmfD1Elict1eXRCKRmrL8ggZYll9ZGfD+gqqE6a7K8Ssrd5TjD7saIpE6jE6SJEmSJEWTCVNJ+yalGfQ/L9yeNnGvLxvUgBs/zVlZzPrN22mWksjQLi12PuHLd+HrxZCaBf3PrfsAJUmSJElS1JgwlbTvqsvy574KJev36pKBnbMBKFj2dWxiiqG8+WsBOKJHK1KSdvFjs3p26cCLw4SyJEmSJElqsEyYStp37QZA+0FQWQYFf9+rS3I7ZgOwaF0JxdvKYhhc9OXN30M5fuFSmP9muD1sXB1GJUmSJEmSYsGEqaT9U9P8aSIEwbee3jI9lU4tmgIwc1lRDAOLrqKtZUyrWkZglwnT/AkQVEK3kdDq0DqNTZIkSZIkRZ8JU0n7p985kNwMNiyErz7aq0sGdmoONKyy/I8WrqeiMuCQ1s3o1CKt9sHyUpj2WLg97Oq6D06SJEmSJEWdCVNJ+yc1A/qfE27n713zp9yOWQAUNKAZppPmheuXjurVZueDc1+BknWQ0Q56nVrHkUmSJEmSpFgwYSpp/1WX5X/+EmzZ+K2nD6pp/FRIsBdl/PEWBMGe1y+d8nD4PuRKSEyqw8gkSZIkSVKsmDCVtP/aD4a2/aGiFGY+9a2nH9Y+i6SECOs3l7KicGsdBHhg5q3ZxJriUpokJ/Cdbi1qH1w9G5ZOhoQkGHJFfAKUJEmSJElRZ8JU0v6LRHYkC/O/vflTk+REerfLAGBGAyjLnzQvnF06ontLmiQn1j449ZHwvc8ZkJFTx5FJkiRJkqRYMWEq6cD0Pw+SmsK6ubB8yreePrBTNtAwGj/lzdtNOf62IphRNaPWZk+SJEmSJDUqJkwlHZim2XDYWeF2/oRvPX1gp+ZAuI5pfba5tJypX4Xrsu7U8GnGU1BWAq17Q5cj4xCdJEmSJEmKFROmkg5cdVn+7OfD2Zd7MLBTFgCzVhRRXlEZ68j228cL11NWEdClZRpdWzXbcSAIdjR7GnZ1uCyBJEmSJElqNOp1wvTOO+8kEonUevXu3bvm+LZt27j++utp2bIl6enpnHPOOaxZsyaOEUsHqU7Dw9mW5Vth1jN7PLV7q3QymiSxraySeWs21VGA+y5v/m7K8Zd8COvnQXIzGHBBHCKTJEmSJEmxVK8TpgCHHXYYq1atqnl9+OGHNcduvvlmXnnlFZ555hny8vJYuXIlZ599dhyjlQ5SkQgMrm7+NGGPzZ8SEiLkdswG6m9ZfhAENQ2fRvX6t4Rp9ezS3AugSWYdRyZJkiRJkmKt3idMk5KSyMnJqXm1atUKgKKiIh555BHuv/9+jjvuOIYMGcL48eP5+OOP+eSTT+IctXQQyr0QElNh9SxYOX3Pp1aV5c+opwnTRetKWFG4lZTEBA7v3nLHgeJV8MWr4fbQcfEJTpIkSZIkxVS9T5guWLCA9u3b0717dy655BKWLl0KQH5+PmVlZZxwwgk15/bu3ZvOnTszefLkPY5ZWlpKcXFxrZekA5TWAvp+N9z+luZP9b3x06R5awEY3r0FaSlJOw5Mewwqy6HzCMjpF6foJEmSJElSLNXrhOnw4cOZMGECb775Jn/+859ZvHgxRx99NJs2bWL16tWkpKSQnZ1d65q2bduyevXqPY57zz33kJWVVfPq1KlTDL+FdBCpLsuf/RyUbt7tadUzTBes3cymbWV1Edk+2eX6pRVlkD8+3B52dRyikiRJkiRJdaFeJ0xPOeUUzjvvPAYMGMDo0aN5/fXXKSws5Omnnz6gcW+//XaKiopqXsuWLYtSxNJBrutR0OIQ2L45TJruRpuMJnTIbkoQwKwVRXUY4Lfbur2CTxdvBP4tYTrvDdi0Cpq1hj5nxCk6SZIkSZIUa/U6YfrvsrOz6dmzJwsXLiQnJ4ft27dTWFhY65w1a9aQk5Ozx3FSU1PJzMys9ZIUBZEIDPlG86c9GNgpG6h/ZfmffLmB7eWVdMhuSo826TsOVDd7Gnw5JKXGJzhJkiRJkhRzDSphunnzZhYtWkS7du0YMmQIycnJvPPOOzXH582bx9KlSxkxYkQco5QOcrkXQ0IyrJwWNoDajZqE6dLCuolrL1WX4x/TszWRSCTcuW4+LM6DSAIMGRu/4CRJkiRJUszV64TpbbfdRl5eHkuWLOHjjz/mrLPOIjExkYsuuoisrCzGjRvHLbfcwnvvvUd+fj5XXnklI0aM4PDDD4936NLBK7019D4t3M6fuNvTcqsSpjOWF8Y+pn1Q3fBpVK9vlONPfTR873kyZHeOQ1SSJEmSJKmu1OuE6fLly7nooovo1asX559/Pi1btuSTTz6hdeswkfH73/+e008/nXPOOYdjjjmGnJwcnn/++ThHLammLH/m07B9yy5P6d8hi8SECGuKS1lVtLUOg9u9JetLWLJhC0kJEY44pGW4c3sJFPw93B42Ln7BSZIkSZKkOpEU7wD25Mknn9zj8SZNmvDggw/y4IMP1lFEkvZKt1GQ3QUKv4LPX4SBF+90StOURHq1zeDzVcUULC2kXf+mdR3lTt5fEJbjD+nSnIwmyeHOWc9CaRG06A7dj4tjdJIkSZIkqS7U6xmmkhqohISwORLsVVl+QT0py580L0yYjurVJtwRBDDloXB76Ljwe0mSJEmSpEbNv/1Lio1Bl0IkEZZ9Amvn7vqUetT4aVtZBZMXbQBgZM+q9UuXTw0bVyU12eUsWUmSJEmS1PiYMJUUGxk50OuUcHvaY7s8ZWDnbABmrSiiojKoo8B2bcqSjWwtq6BNRip92mVU7Xw4fO93LqS1iF9wkiRJkiSpzpgwlRQ7g6uaP834B5Rt2+nwIa3TaZaSyJbtFSxYu6mOg6str6ocf2TP1kQiEShZD3OqmsjZ7EmSJEmSpIOGCVNJsdPjeMjsCFu/hrmv7HQ4MSHCgI7ZQPzL8vPmVyVMe1WV409/HCq2Q/vB0GFwHCOTJEmSJEl1yYSppNhJSITBl4Xb03bd/Km6LL9gWWHdxLQLKwq3smDtZhIicHSP1lBZAVMfDQ8OuzpucUmSJEmSpLpnwlRSbA26FCIJsOQDWL9wp8O51TNM45gwrS7HH9S5OVlpybDwbShcCk2yod/ZcYtLkiRJkiTVPROmkmIrqyP0OCHc3sUs00FVM0znr9lESWl5HQa2w6R5awEY1bOqHL+62dOgSyG5aVxikiRJkiRJ8WHCVFLsDRkbvhf8Hcq31zrUNrMJ7bKaUBnArBVFdR7a9vJKPl60Aahav3TjYljwVnhw6FV1Ho8kSZIkSYovE6aSYu/Q0ZCeA1vWw7zXdjpcXZY/Iw5l+dOWfs3m0nJaNkuhX/ssyB8PBHDI8dDykDqPR5IkSZIkxZcJU0mxl5gUlrcD5O9clh/Pxk+TqtYvPaZnaxIqSmHa4+EBmz1JkiRJknRQMmEqqW4Mvix8//I9+HpJrUMDO2UD8UmY5s0PE6Yje7aGz1+ErRshqxP0HF3nsUiSJEmSpPgzYSqpbjTvCt2PDbenPVbrUP8OWSREYFXRNtYUb6uzkNYUb2PuqmIiETj60FY7mj0NGQsJiXUWhyRJkiRJqj9MmEqqO9XNn6Y/ARVlNbubpSbRs20GULezTKtnlw7okEXL4rmwfAokJMPgy+ssBkmSJEmSVL+YMJVUd3qdCmmtYPNqmP/PWofiUZafV7V+6chebWDqI+HOvmMgvU2dxSBJkiRJkuoXE6aS6k5SCgy8ONyeVrv5U25VwnRGHSVMyysq+WBBmDA9rksKzHwmPGCzJ0mSJEmSDmomTCXVrcFXhO8L34bCZTW7q2eYzlxeREVlEPMwZiwvpHhbOVlNkxmw4XUo3wpt+0Hnw2N+b0mSJEmSVH+ZMJVUt1r1gK5HQ1AJ0/9Ws7tn2wzSUhLZXFrOonWbYx7GpKpy/KN7tCShuhx/2DiIRGJ+b0mSJEmSVH+ZMJVU96pnmU7/G1RWAJCYEKFfhyygbtYxrW74dF7LL2HDQkjJgP7nx/y+kiRJkiSpfjNhKqnu9TkDmjaH4uWw8J2a3YPqqPHT+s2lzFxeBMDw9S+EOwdeBKnpMb2vJEmSJEmq/0yYSqp7yU0g96JwO39Cze7qdUwLlhbG9PbVzZ6ObrudJoveDHcOHRfTe0qSJEmSpIbBhKmk+Kguy5//JmxaDUBuVcJ03ppNbN1eEbNb51WtX3pd+vsQVIRrqrbpHbP7SZIkSZKkhsOEqaT4aNMbOh0eJiyrmj+1y2pCm4xUKioDZq8sisltKysD3l+wnmTKGbbxlXDnMGeXSpIkSZKkkAlTSfEzpGqW6bTHoLKSSCQS87L8WSuK2FiynTGp00jZug7S20Lv02NyL0mSJEmS1PCYMJUUP33PhNQsKPwKFk8CdpTlFywvjMktJ1WV41/b9L1wx5CxkJgck3tJkiRJkqSGx4SppPhJSYMB54fbVc2fBsV4hmne/LUcGllOz20zIJK4Yy1VSZIkSZIkTJhKirfqsvwvXofN6+jfMYtIBFYUbmXdptKo3qpwy3YKlhVyaeJb4Y7ep0JWh6jeQ5IkSZIkNWwmTCXFV05/6DAEKstgxt/JaJJMj9bpAMxYVhjVW32wYD1Ng62cm/RhuGPY1VEdX5IkSZIkNXwmTCXFX3VZfP5ECIIdjZ+inDDNm7+OMxM/ohlboeWh0G1kVMeXJEmSJEkNnwlTSfHX7xxISYeNi2DJhwzsnA1EN2FaWRmQN2/tjnL8YeMgEona+JIkSZIkqXEwYSop/lLTof+54fa0ieR2zAZgxvJCKiuDqNxi7upiupTMpE/CMoLkNMi9KCrjSpIkSZKkxsWEqaT6obos//OX6J1ZRpPkBDZtK+fL9SVRGT5v/jouS3obgEj/86BpdlTGlSRJkiRJjYsJU0n1Q/tBkDMAKraTNPtp+nfIAqJXlj/98/mckvBp+MFmT5IkSZIkaTdMmEqqHyIRGFI1y3TaRHKrEqYzopAwLd5WRu+VL5ISqaA0Zyi0G3DAY0qSJEmSpMbJhKmk+qP/eZCcBuu+4Nj0xUB0Zph+vGANFyaG5fipI6494PEkSZIkSVLjZcJUUv3RJAsOOxuAgWtfAmDuqmK2lVUc0LBrp75Mh8gGSpKyoe+YA41SkiRJkiQ1YiZMJdUvVWX5aQteoWuzMsorA+asLN7v4YIgoOfSpwBYf+j5kNwkKmFKkiRJkqTGyYSppPql4zBo3YdI+VauyZ4KHFhZ/pL5Mzk8KKAyiJBz3PejFKQkSZIkSWqsTJhKql8iERgyFoDR294EggNKmG768K8AzGz6HVJbdz/w+CRJkiRJUqNmwlRS/TPgfEhMpVXJAnIji5ixvwnT7VvovvxFANb2vjRq4UmSJEmSpMbLhKmk+ietRU1zpgsT32Ppxi1s2Fy6z8OUzniG9GAzSytbc8gImz1JkiRJkqRvZ8JUUv1UVZZ/ZtJkmrGVGcsL93mI0o/DcvxXU06he5vMKAYnSZIkSZIaKxOmkuqnLkdAy0Npyja+m/gxBcuK9u36Fflkfj2b0iCZwt7nE4lEYhOnJEmSJElqVEyYSqqfIhEYcgUQluXva+OnYMrDALxaOZxhfXtGOzpJkiRJktRImTCVVH/lXkRlQjK5CV9SunQ6QRDs3XVbNhLMeh6AJ4OTOOKQljEMUpIkSZIkNSYmTCXVX81aEfQ+HYDTy//Fkg1b9u66gidIqNjG7MquJHf+Ds1Sk2IYpCRJkiRJakxMmEqq1xKHjgVgTOJHzFq84tsvqKyEKY8A8FjFiYzs1SaG0UmSJEmSpMbGhKmk+q3rMWxM6UBmZCsVs1749vO/fBe+XkxxkMbLFUcwyoSpJEmSJEnaByZMJdVvCQms7nEeAH1W7kXCtGp26TMVI8nOzKJn2/RYRidJkiRJkhoZE6aS6r3Mw6+kLEikd/lctq+ctfsTC5fC/DcBeKLieEb1ak0kEqmjKCVJkiRJUmNgwlRSvdehUxc+iAwBoPDDh3d/Yv4ECCqZmpjLl0F7RvZsXTcBSpIkSZKkRsOEqaR6LxKJUNBmDABZ85+Hsm07n1ReCtMeA+ChrceRmBDhiB6t6jJMSZIkSZLUCJgwldQgJB16AsuDVqSWF8Pcl3c+Ye4rULKOktQ2vF05mCGdm5PVNLnuA5UkSZIkSQ2aCVNJDUJul5Y8XT4q/JA/YecTpoSl+m81PZUKEhnZy3J8SZIkSZK070yYSmoQcjtm8XTFSCqCCHz1EaxfsOPg6tmwdDJBQhL3bzwcwPVLJUmSJEnSfjFhKqlByE5LoWmrzrxXOTDcMW3ijoNVs0s3dDqJpdszaZWeSt92mXUfpCRJkiRJavBMmEpqMAZ2yubJiuPCDwV/Dxs9bSuCmU8D8GbT0wE4pmcrEhIi8QpTkiRJkiQ1YCZMJTUYuR2zeK9yIF8ntoQtG+CL12DGU1BWAq178/jKjgCM6tUmzpFKkiRJkqSGql4nTO+55x6GDRtGRkYGbdq04cwzz2TevHm1zhk1ahSRSKTW67rrrotTxJJiaWDn5lSQyLMVo8Id+eNryvGL+l3OvLWbSYjA0T1axS9ISZIkSZLUoNXrhGleXh7XX389n3zyCW+99RZlZWWcdNJJlJSU1DrvmmuuYdWqVTWv++67L04RS4qlPu0ySElMYGLpMQREYPH7sH4eJDfjraRjAcjtlE3zZilxjlSSJEmSJDVUSfEOYE/efPPNWp8nTJhAmzZtyM/P55hjjqnZn5aWRk5OTl2HJ6mOpSYl0qd9JjOWVbK2zZG0XftheCD3At5ZvBWAkT1bxzFCSZIkSZLU0NXrGab/rqioCIAWLVrU2v/EE0/QqlUr+vXrx+23386WLVv2OE5paSnFxcW1XpIahkGdsgHISz+lZl/Z4Kv4cMF6wISpJEmSJEk6MPV6huk3VVZWctNNN3HkkUfSr1+/mv0XX3wxXbp0oX379sycOZMf//jHzJs3j+eff363Y91zzz384he/qIuwJUXZwKqE6dOb+nP+gAshI4fppR3YVLqU5mnJDOiYHdf4JEmSJElSwxYJgiCIdxB74/vf/z5vvPEGH374IR07dtztee+++y7HH388Cxcu5JBDDtnlOaWlpZSWltZ8Li4uplOnThQVFZGZmRn12CVFz+L1JRz720mkJCUw+87RpCQl8Jt/fsGD7y3iu7nt+eNFg+IdoiRJkiRJasAaREn+DTfcwKuvvsp77723x2QpwPDhwwFYuHDhbs9JTU0lMzOz1ktSw9C1ZRrZaclsL6/ki9XhchqT5q0DYFQvy/ElSZIkSdKBqdcJ0yAIuOGGG3jhhRd499136dat27deU1BQAEC7du1iHJ2keIhEIuRWld0XLCtk7aZtzFkZJk6PPtSEqSRJkiRJOjD1eg3T66+/nr///e+89NJLZGRksHr1agCysrJo2rQpixYt4u9//zunnnoqLVu2ZObMmdx8880cc8wxDBgwIM7RS4qV3E7Z5M1fR8GyQpqlhD/G+nXIpHVGapwjkyRJkiRJDV29Tpj++c9/BmDUqFG19o8fP56xY8eSkpLC22+/zQMPPEBJSQmdOnXinHPO4ac//WkcopVUVwZVNX4qWFZIWUW4DPOonm3iGJEkSZIkSWos6nXC9Nv6UXXq1Im8vLw6ikZSfZFblTD9cl0J64rDBm4jXb9UkiRJkiRFQb1ew1SSdqVFsxQ6t0gDYFNpORlNkmpmnUqSJEmSJB0IE6aSGqSB30iQHn1oK5IS/XEmSZIkSZIOnBkGSQ3SNxOmI3taji9JkiRJkqLDhKmkBim3VsLUhk+SJEmSJCk66nXTJ0nandyOWXw3tz1tM1PJyWoS73AkSZIkSVIjEQm+rRX9QaC4uJisrCyKiorIzMyMdziSJEmSJEmS4sSSfEmSJEmSJEmqYsJUkiRJkiRJkqqYMJUkSZIkSZKkKiZMJUmSJEmSJKmKCVNJkiRJkiRJqmLCVJIkSZIkSZKqmDCVJEmSJEmSpComTCVJkiRJkiSpiglTSZIkSZIkSapiwlSSJEmSJEmSqpgwlSRJkiRJkqQqJkwlSZIkSZIkqYoJU0mSJEmSJEmqYsJUkiRJkiRJkqqYMJUkSZIkSZKkKiZMJUmSJEmSJKmKCVNJkiRJkiRJqmLCVJIkSZIkSZKqmDCVJEmSJEmSpComTCVJkiRJkiSpiglTSZIkSZIkSapiwlSSJEmSJEmSqpgwlSRJkiRJkqQqJkwlSZIkSZIkqUpSvAOoD4IgAKC4uDjOkUiSJEmSFMrIyCASicQ7DEk66JgwBTZt2gRAp06d4hyJJEmSJEmhoqIiMjMz4x2GJB10IkH19MqDWGVlJStXrozab++Ki4vp1KkTy5Yt8z9ualR8ttUY+VyrMfK5VmPkc63G6Nuea2eYSlJ8OMMUSEhIoGPHjlEfNzMz0/+ZU6Pks63GyOdajZHPtRojn2s1Rj7XklS/2PRJkiRJkiRJkqqYMJUkSZIkSZKkKiZMYyA1NZU77riD1NTUeIciRZXPthojn2s1Rj7Xaox8rtUY+VxLUv1k0ydJkiRJkiRJquIMU0mSJEmSJEmqYsJUkiRJkiRJkqqYMJUkSZIkSZKkKiZMJUmSJEmSJKmKCdMYePDBB+natStNmjRh+PDhfPbZZ/EOSdqt999/nzPOOIP27dsTiUR48cUXax0PgoCf//zntGvXjqZNm3LCCSewYMGCWuds3LiRSy65hMzMTLKzsxk3bhybN2+uw28h7XDPPfcwbNgwMjIyaNOmDWeeeSbz5s2rdc62bdu4/vrradmyJenp6ZxzzjmsWbOm1jlLly7ltNNOIy0tjTZt2vAf//EflJeX1+VXkWr585//zIABA8jMzCQzM5MRI0bwxhtv1Bz3uVZDd++99xKJRLjppptq9vlcqyG68847iUQitV69e/euOe5zLUn1nwnTKHvqqae45ZZbuOOOO5g2bRq5ubmMHj2atWvXxjs0aZdKSkrIzc3lwQcf3OXx++67jz/+8Y/85S9/4dNPP6VZs2aMHj2abdu21ZxzySWXMGfOHN566y1effVV3n//fa699tq6+gpSLXl5eVx//fV88sknvPXWW5SVlXHSSSdRUlJSc87NN9/MK6+8wjPPPENeXh4rV67k7LPPrjleUVHBaaedxvbt2/n444+ZOHEiEyZM4Oc//3k8vpIEQMeOHbn33nvJz89n6tSpHHfccYwZM4Y5c+YAPtdq2KZMmcL//d//MWDAgFr7fa7VUB122GGsWrWq5vXhhx/WHPO5lqQGIFBUfec73wmuv/76ms8VFRVB+/btg3vuuSeOUUl7BwheeOGFms+VlZVBTk5O8Jvf/KZmX2FhYZCamhr84x//CIIgCD7//PMACKZMmVJzzhtvvBFEIpFgxYoVdRa7tDtr164NgCAvLy8IgvAZTk5ODp555pmac+bOnRsAweTJk4MgCILXX389SEhICFavXl1zzp///OcgMzMzKC0trdsvIO1B8+bNg4cfftjnWg3apk2bgkMPPTR46623gpEjRwY33nhjEAT+vFbDdccddwS5ubm7POZzLUkNgzNMo2j79u3k5+dzwgkn1OxLSEjghBNOYPLkyXGMTNo/ixcvZvXq1bWe6aysLIYPH17zTE+ePJns7GyGDh1ac84JJ5xAQkICn376aZ3HLP27oqIiAFq0aAFAfn4+ZWVltZ7r3r1707lz51rPdf/+/Wnbtm3NOaNHj6a4uLhmNp8UTxUVFTz55JOUlJQwYsQIn2s1aNdffz2nnXZarecX/Hmthm3BggW0b9+e7t27c8kll7B06VLA51qSGoqkeAfQmKxfv56Kiopa/2EDaNu2LV988UWcopL23+rVqwF2+UxXH1u9ejVt2rSpdTwpKYkWLVrUnCPFS2VlJTfddBNHHnkk/fr1A8JnNiUlhezs7Frn/vtzvavnvvqYFC+zZs1ixIgRbNu2jfT0dF544QX69u1LQUGBz7UapCeffJJp06YxZcqUnY7581oN1fDhw5kwYQK9evVi1apV/OIXv+Doo49m9uzZPteS1ECYMJUkNVrXX389s2fPrrVumNSQ9erVi4KCAoqKinj22We54ooryMvLi3dY0n5ZtmwZN954I2+99RZNmjSJdzhS1Jxyyik12wMGDGD48OF06dKFp59+mqZNm8YxMknS3rIkP4patWpFYmLiTh0O16xZQ05OTpyikvZf9XO7p2c6Jydnp6Zm5eXlbNy40edecXXDDTfw6quv8t5779GxY8ea/Tk5OWzfvp3CwsJa5//7c72r5776mBQvKSkp9OjRgyFDhnDPPfeQm5vLH/7wB59rNUj5+fmsXbuWwYMHk5SURFJSEnl5efzxj38kKSmJtm3b+lyrUcjOzqZnz54sXLjQn9eS1ECYMI2ilJQUhgwZwjvvvFOzr7KyknfeeYcRI0bEMTJp/3Tr1o2cnJxaz3RxcTGffvppzTM9YsQICgsLyc/Prznn3XffpbKykuHDh9d5zFIQBNxwww288MILvPvuu3Tr1q3W8SFDhpCcnFzruZ43bx5Lly6t9VzPmjWr1i8D3nrrLTIzM+nbt2/dfBFpL1RWVlJaWupzrQbp+OOPZ9asWRQUFNS8hg4dyiWXXFKz7XOtxmDz5s0sWrSIdu3a+fNakhqKeHedamyefPLJIDU1NZgwYULw+eefB9dee22QnZ1dq8OhVJ9s2rQpmD59ejB9+vQACO6///5g+vTpwVdffRUEQRDce++9QXZ2dvDSSy8FM2fODMaMGRN069Yt2Lp1a80YJ598cjBo0KDg008/DT788MPg0EMPDS666KJ4fSUd5L7//e8HWVlZwaRJk4JVq1bVvLZs2VJzznXXXRd07tw5ePfdd4OpU6cGI0aMCEaMGFFzvLy8POjXr19w0kknBQUFBcGbb74ZtG7dOrj99tvj8ZWkIAiC4Cc/+UmQl5cXLF68OJg5c2bwk5/8JIhEIsG//vWvIAh8rtU4jBw5MrjxxhtrPvtcqyG69dZbg0mTJgWLFy8OPvroo+CEE04IWrVqFaxduzYIAp9rSWoITJjGwP/8z/8EnTt3DlJSUoLvfOc7wSeffBLvkKTdeu+99wJgp9cVV1wRBEEQVFZWBj/72c+Ctm3bBqmpqcHxxx8fzJs3r9YYGzZsCC666KIgPT09yMzMDK688spg06ZNcfg2UrDL5xkIxo8fX3PO1q1bgx/84AdB8+bNg7S0tOCss84KVq1aVWucJUuWBKecckrQtGnToFWrVsGtt94alJWV1fG3kXa46qqrgi5dugQpKSlB69atg+OPP74mWRoEPtdqHP49YepzrYboggsuCNq1axekpKQEHTp0CC644IJg4cKFNcd9riWp/osEQRDEZ26rJEmSJEmSJNUvrmEqSZIkSZIkSVVMmEqSJEmSJElSFROmkiRJkiRJklTFhKkkSZIkSZIkVTFhKkmSJEmSJElVTJhKkiRJkiRJUhUTppIkSZIkSZJUxYSpJEmSJEmSJFUxYSpJkiRJkiRJVUyYSpIUBWPHjuXMM8+Mdxjfqnfv3qSmprJ69ep4h7JPJkyYQHZ29l6du2TJEiKRyB5fEyZMiGm8kiRJkhouE6aSJB0kPvzwQ7Zu3cq5557LxIkT4x1OzHTq1IlVq1bVvG699VYOO+ywWvsuuOCCvR4vCALKy8tjGLEkSZKk+sSEqSRJMZaXl8d3vvMdUlNTadeuHT/5yU9qJeDefPNNjjrqKLKzs2nZsiWnn346ixYtqjlePWPy+eef59hjjyUtLY3c3FwmT568T3E88sgjXHzxxVx22WU8+uijOx3v2rUrd999N5dffjnp6el06dKFl19+mXXr1jFmzBjS09MZMGAAU6dOrXXdc889x2GHHUZqaipdu3bld7/7Xa3jkUiEF198sda+7Ozsmlme3/b9Jk2axJVXXklRUVHNDNE777xzt98zMTGRnJycmld6ejpJSUk1n9u0acMDDzxAt27daNq0Kbm5uTz77LM110+aNIlIJMIbb7zBkCFDSE1N5cMPP2TUqFH88Ic/5KabbqJ58+a0bduWhx56iJKSEq688koyMjLo0aMHb7zxxj78U5EkSZJU35gwlSQphlasWMGpp57KsGHDmDFjBn/+85955JFHuPvuu2vOKSkp4ZZbbmHq1Km88847JCQkcNZZZ1FZWVlrrP/3//4ft912GwUFBfTs2ZOLLrpor2c+btq0iWeeeYZLL72UE088kaKiIj744IOdzvv973/PkUceyfTp0znttNO47LLLuPzyy7n00kuZNm0ahxxyCJdffjlBEACQn5/P+eefz4UXXsisWbO48847+dnPfrZfJe+7+35HHHEEDzzwAJmZmTUzRG+77bZ9Hr/aPffcw2OPPcZf/vIX5syZw80338yll15KXl5erfN+8pOfcO+99zJ37lwGDBgAwMSJE2nVqhWfffYZP/zhD/n+97/PeeedxxFHHMG0adM46aSTuOyyy9iyZct+xydJkiQpzgJJknTArrjiimDMmDE77f+v//qvoFevXkFlZWXNvgcffDBIT08PKioqdjnWunXrAiCYNWtWEARBsHjx4gAIHn744Zpz5syZEwDB3Llz9yq+v/71r8HAgQNrPt94443BFVdcUeucLl26BJdeemnN51WrVgVA8LOf/axm3+TJkwMgWLVqVRAEQXDxxRcHJ554Yq1x/uM//iPo27dvzWcgeOGFF2qdk5WVFYwfP36vv9/48eODrKysvfqu/+6OO+4IcnNzgyAIgm3btgVpaWnBxx9/XOuccePGBRdddFEQBEHw3nvvBUDw4osv1jpn5MiRwVFHHVXzuby8PGjWrFlw2WWX1eyr/jObPHnyfsUqSZIkKf6cYSpJUgzNnTuXESNGEIlEavYdeeSRbN68meXLlwOwYMECLrroIrp3705mZiZdu3YFYOnSpbXGqp7lCNCuXTsA1q5du1dxPProo1x66aU1ny+99FKeeeYZNm3atNt7tG3bFoD+/fvvtK/6vnPnzuXII4+sNcaRRx7JggULqKio2KvYdnXvff1+e2vhwoVs2bKFE088kfT09JrXY489VmsZBIChQ4fuMcbExERatmy5xz8fSZIkSQ1PUrwDkCTpYHfGGWfQpUsXHnroIdq3b09lZSX9+vVj+/bttc5LTk6u2a5OwP572f6ufP7553zyySd89tln/PjHP67ZX1FRwZNPPsk111yzx3vs732/eU1QVcJfraysbKfzDvQ+e2Pz5s0AvPbaa3To0KHWsdTU1FqfmzVrtscYIYyzLuKWJEmSVHdMmEqSFEN9+vThueeeIwiCmmTaRx99REZGBh07dmTDhg3MmzePhx56iKOPPhoIu9lH0yOPPMIxxxzDgw8+WGv/+PHjeeSRR2olTPdVnz59+Oijj2rt++ijj+jZsyeJiYkAtG7dmlWrVtUcX7BgwT6v8ZmSkrLPM1Z3pW/fvqSmprJ06VJGjhx5wONJkiRJanxMmEqSFCVFRUUUFBTU2nfttdfywAMP8MMf/pAbbriBefPmcccdd3DLLbeQkJBA8+bNadmyJX/9619p164dS5cu5Sc/+UnUYiorK+Pxxx/nl7/8Jf369at17Oqrr+b+++9nzpw5HHbYYfs1/q233sqwYcO46667uOCCC5g8eTJ/+tOf+N///d+ac4477jj+9Kc/MWLECCoqKvjxj3+800zNb9O1a1c2b97MO++8Q25uLmlpaaSlpe1zvBkZGdx2223cfPPNVFZWctRRR1FUVMRHH31EZmYmV1xxxT6PKUmSJKlxcQ1TSZKiZNKkSQwaNKjW66677uL111/ns88+Izc3l+uuu45x48bx05/+FICEhASefPJJ8vPz6devHzfffDO/+c1vohbTyy+/zIYNGzjrrLN2OtanTx/69OnDI488st/jDx48mKeffponn3ySfv368fOf/5xf/vKXjB07tuac3/3ud3Tq1Imjjz6aiy++mNtuu22fk51HHHEE1113HRdccAGtW7fmvvvu2++Y77rrLn72s59xzz330KdPH04++WRee+01unXrtt9jSpIkSWo8IsG/LyomSZIkSZIkSQcpZ5hKkiRJkiRJUhUTppIkNXCnnHIK6enpu3z96le/ind4MfHBBx/s9junp6fHOzxJkiRJDZgl+ZIkNXArVqxg69atuzzWokULWrRoUccRxd7WrVtZsWLFbo/36NGjDqORJEmS1JiYMJUkSZIkSZKkKpbkS5IkSZIkSVIVE6aSJEmSJEmSVMWEqSRJkiRJkiRVMWEqSZIkSZIkSVVMmEqSJEmSJElSFROmkiRJkiRJklTFhKkkSZIkSZIkVfn/Ul4o4dVSg9YAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6r-PGlxBc43o"
      },
      "source": [
        "**Again we see the bias in data, there are more graduate people in data**\n",
        "\n",
        "lets look at the dependent cloumn"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        },
        "id": "2WjYaQW5dYqu",
        "outputId": "b42dbb06-1a8d-4c17-e52d-6a6ebd04ab38"
      },
      "source": [
        "fig,ax=plt.subplots(figsize=(4,5))\n",
        "sns.countplot(x = \"Dependents\", data=df, order = df[\"Dependents\"].value_counts().index,hue=\"Dependents\")\n",
        "plt.show()"
      ],
      "execution_count": 142,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 400x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rBq6vNzRdlEW"
      },
      "source": [
        "**Again the distribution is not fair. Clearly this data is not complete**\n",
        "\n",
        "Lets look at the Loan_status distribution with dependents"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 523
        },
        "id": "L_L-dLZvduKM",
        "outputId": "5b6d45ce-a1a1-49a4-93b2-1fea7d00fd93"
      },
      "source": [
        "sns.catplot(x='Dependents',kind='count',data=df,hue='Loan_Status')"
      ],
      "execution_count": 143,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7d43f19fbf70>"
            ]
          },
          "metadata": {},
          "execution_count": 143
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 595.736x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3ZAQyszKd-Sw"
      },
      "source": [
        "**The distribution is unfair**\n",
        "\n",
        "Now lets take a look at the Married column"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        },
        "id": "yPZeWHHWeLEn",
        "outputId": "efce065b-6785-4374-9496-9be95adbf92d"
      },
      "source": [
        "fig,ax=plt.subplots(figsize=(4,5))\n",
        "sns.countplot(x = \"Married\", data=df, order = df[\"Married\"].value_counts().index, hue='Married')\n",
        "plt.show()"
      ],
      "execution_count": 144,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 400x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8olbqfwCeWnJ"
      },
      "source": [
        "**We can clearly see the imbalance in the data above**\n",
        "\n",
        "Lets count the vlaues for married column and then check the loan_status distribution for this column  "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 178
        },
        "id": "02QpGwuGe4g8",
        "outputId": "406a80c9-0e04-4108-a721-befccd31b238"
      },
      "source": [
        "df.Married.value_counts()"
      ],
      "execution_count": 145,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Married\n",
              "Yes    398\n",
              "No     213\n",
              "Name: count, dtype: int64"
            ],
            "text/html": [
              "<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>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Married</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Yes</th>\n",
              "      <td>398</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>No</th>\n",
              "      <td>213</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 145
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BwsbepGgfpDj"
      },
      "source": [
        "**This column has bias issue**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 523
        },
        "id": "u7ubzGyQenaa",
        "outputId": "62d3facc-8b87-4d91-d2bb-63f5c87d6dea"
      },
      "source": [
        "sns.catplot(x='Married',kind='count',data=df,hue='Loan_Status')"
      ],
      "execution_count": 146,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7d43ee86f760>"
            ]
          },
          "metadata": {},
          "execution_count": 146
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 595.736x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "29IosiyCfxIU"
      },
      "source": [
        "lets create another chart to understand the numbers better"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "KSIYwcFGfeIq",
        "outputId": "10f51479-d579-4dfc-9141-423b04e8bf5f"
      },
      "source": [
        "print(pd.crosstab(df[\"Married\"],df[\"Loan_Status\"]))\n",
        "Married=pd.crosstab(df[\"Married\"],df[\"Loan_Status\"])\n",
        "Married.div(Married.sum(1).astype(float),axis=0).plot(kind=\"bar\",stacked=True,figsize=(5,5))\n",
        "plt.xlabel(\"Married\")\n",
        "plt.ylabel(\"Percentage\")\n",
        "plt.show()"
      ],
      "execution_count": 147,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loan_Status    N    Y\n",
            "Married              \n",
            "No            79  134\n",
            "Yes          113  285\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rFGUZ7qvgMzt"
      },
      "source": [
        "Lets look into income column"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "mW7bkcj3mkyH",
        "outputId": "6d0e3e97-c161-46ba-f6b2-90061b734f42"
      },
      "source": [
        "df['ApplicantIncome'].hist(bins=40)\n",
        "plt.show()\n"
      ],
      "execution_count": 148,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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KSkr09ttvq2vXrqqurpYkJSYmKj4+XomJiZo4caIKCwuVnJwsj8ej+++/X16vVyNGjJAk5eTkqF+/fho/frzmz5+v6upqzZw5UwUFBSc9SwIAAH54QiooCxYskCRdc801QcsXLlyou+66S5L07LPPKjo6Wvn5+fL5fMrNzdWLL74YGBsTE6MVK1Zo8uTJ8nq96ty5syZMmKDHH3/83JIAAADHCKmgtOWCn06dOqm4uFjFxcWnHNOrVy+tXLkylF0DAIAfEH6LBwAAWIeCAgAArENBAQAA1qGgAAAA61BQAACAdSgoAADAOhQUAABgHQoKAACwDgUFAABYh4ICAACsQ0EBAADWoaAAAADrUFAAAIB1KCgAAMA6FBQAAGAdCgoAALAOBQUAAFiHggIAAKxDQQEAANahoAAAAOtQUAAAgHUoKAAAwDoUFAAAYB0KCgAAsA4FBQAAWIeCAgAArENBAQAA1qGgAAAA61BQAACAdSgoAADAOhQUAABgHQoKAACwDgUFAABYh4ICAACsQ0EBAADWoaAAAADrUFAAAIB1KCgAAMA6FBQAAGAdCgoAALAOBQUAAFiHggIAAKxDQQEAANahoAAAAOtQUAAAgHUoKAAAwDoUFAAAYB0KCgAAsA4FBQAAWCfkgrJhwwaNGTNG6enpioqK0rJly4LW33XXXYqKigq6XX/99UFjDh48qHHjxsnj8SgpKUkTJ07U4cOHzykIAABwjpALypEjRzRw4EAVFxefcsz111+vAwcOBG5vvPFG0Ppx48Zp586dKi0t1YoVK7RhwwZNmjQp9NkDAABHig31AaNHj9bo0aNPO8btdistLe2k6z755BOtWrVKH374oYYOHSpJeuGFF3TDDTfo6aefVnp6eqhTAgAADhNyQWmL9evXKyUlReedd56uu+46/fKXv1S3bt0kSeXl5UpKSgqUE0nKzs5WdHS0Nm/erFtuuaXV9nw+n3w+X+B+Q0ODJMnv98vv94dt3i3bCmWb7hgT8vY7ytnkixROziaRL9I5OZ+Ts0nka6/9tUWUMabt/8Ke+OCoKC1dulQ333xzYNnixYuVkJCg3r17a8+ePXr00UfVpUsXlZeXKyYmRk899ZReffVVVVVVBW0rJSVFc+bM0eTJk1vtp6ioSHPmzGm1vKSkRAkJCWc7fQAA8D1qbGzUHXfcofr6enk8ntOODfsZlLFjxwb+u3///howYIAuvvhirV+/XiNHjjyrbc6YMUOFhYWB+w0NDcrIyFBOTs4ZA4bC7/ertLRUo0aNksvlatNjriha3ebtVxblnu3UwuJs8kUKJ2eTyBfpnJzPydkk8oVbyzsgbdEub/F810UXXaTu3btr9+7dGjlypNLS0lRbWxs05vjx4zp48OApP7fidrvldrtbLXe5XO3yhIayXV9TVEjbtUF7PW82cHI2iXyRzsn5nJxNIl8499NW7f49KH/961/19ddfq0ePHpIkr9eruro6VVRUBMasW7dOzc3NGj58eHtPBwAARICQz6AcPnxYu3fvDtzfu3evtm3bpuTkZCUnJ2vOnDnKz89XWlqa9uzZo0ceeUR9+vRRbu63b2/07dtX119/ve6991699NJL8vv9mjJlisaOHcsVPAAAQNJZnEHZunWrBg8erMGDB0uSCgsLNXjwYM2aNUsxMTHavn27brzxRl166aWaOHGihgwZoj/+8Y9Bb9G8/vrrysrK0siRI3XDDTfoqquu0ssvvxy+VAAAIKKFfAblmmuu0eku/Fm9+swfGk1OTlZJSUmouwYAAD8Q/BYPAACwDgUFAABYh4ICAACsQ0EBAADWoaAAAADrUFAAAIB1KCgAAMA6FBQAAGAdCgoAALAOBQUAAFiHggIAAKxDQQEAANahoAAAAOuE/GvGOHsXTn+nzWP/Mi+vHWcCAIDdOIMCAACsQ0EBAADWoaAAAADrUFAAAIB1KCgAAMA6FBQAAGAdCgoAALAOBQUAAFiHggIAAKxDQQEAANahoAAAAOtQUAAAgHUoKAAAwDoUFAAAYB0KCgAAsA4FBQAAWIeCAgAArENBAQAA1qGgAAAA61BQAACAdSgoAADAOhQUAABgHQoKAACwDgUFAABYh4ICAACsQ0EBAADWoaAAAADrUFAAAIB1KCgAAMA6FBQAAGAdCgoAALAOBQUAAFiHggIAAKxDQQEAANYJuaBs2LBBY8aMUXp6uqKiorRs2bKg9cYYzZo1Sz169FB8fLyys7P12WefBY05ePCgxo0bJ4/Ho6SkJE2cOFGHDx8+pyAAAMA5Qi4oR44c0cCBA1VcXHzS9fPnz9fzzz+vl156SZs3b1bnzp2Vm5uro0ePBsaMGzdOO3fuVGlpqVasWKENGzZo0qRJZ58CAAA4SmyoDxg9erRGjx590nXGGP3mN7/RzJkzddNNN0mS/ud//kepqalatmyZxo4dq08++USrVq3Shx9+qKFDh0qSXnjhBd1www16+umnlZ6efg5xAACAE4RcUE5n7969qq6uVnZ2dmBZYmKihg8frvLyco0dO1bl5eVKSkoKlBNJys7OVnR0tDZv3qxbbrml1XZ9Pp98Pl/gfkNDgyTJ7/fL7/eHbf4t2wplm+4YE7b9n2wu7bHN9th2R3NyNol8kc7J+ZycTSJfe+2vLcJaUKqrqyVJqampQctTU1MD66qrq5WSkhI8idhYJScnB8acaO7cuZozZ06r5WvWrFFCQkI4ph6ktLS0zWPnDwv77iVJK1eubJ8NK7R8kcbJ2STyRTon53NyNol84dLY2NjmsWEtKO1lxowZKiwsDNxvaGhQRkaGcnJy5PF4wrYfv9+v0tJSjRo1Si6Xq02PuaJoddj2/12VRblh3+bZ5IsUTs4mkS/SOTmfk7NJ5Au3lndA2iKsBSUtLU2SVFNTox49egSW19TUaNCgQYExtbW1QY87fvy4Dh48GHj8idxut9xud6vlLperXZ7QULbra4oK+/5b5tBe2ut5s4GTs0nki3ROzufkbBL5wrmftgrr96D07t1baWlpWrt2bWBZQ0ODNm/eLK/XK0nyer2qq6tTRUVFYMy6devU3Nys4cOHh3M6AAAgQoV8BuXw4cPavXt34P7evXu1bds2JScnKzMzUw8++KB++ctf6pJLLlHv3r312GOPKT09XTfffLMkqW/fvrr++ut177336qWXXpLf79eUKVM0duxYruABAACSzqKgbN26Vddee23gfstnQyZMmKBFixbpkUce0ZEjRzRp0iTV1dXpqquu0qpVq9SpU6fAY15//XVNmTJFI0eOVHR0tPLz8/X888+HIQ4AAHCCkAvKNddcI2NOfWltVFSUHn/8cT3++OOnHJOcnKySkpJQdw0AAH4g+C0eAABgHQoKAACwDgUFAABYh4ICAACsQ0EBAADWoaAAAADrUFAAAIB1KCgAAMA6FBQAAGAdCgoAALAOBQUAAFiHggIAAKxDQQEAANahoAAAAOtQUAAAgHUoKAAAwDoUFAAAYB0KCgAAsA4FBQAAWIeCAgAArENBAQAA1qGgAAAA61BQAACAdSgoAADAOhQUAABgHQoKAACwDgUFAABYh4ICAACsQ0EBAADWoaAAAADrUFAAAIB1KCgAAMA6FBQAAGAdCgoAALAOBQUAAFiHggIAAKxDQQEAANahoAAAAOtQUAAAgHUoKAAAwDoUFAAAYB0KCgAAsA4FBQAAWIeCAgAArBPb0ROw0RVFq+VriuroaQAA8IPFGRQAAGAdCgoAALAOBQUAAFiHggIAAKwT9oJSVFSkqKiooFtWVlZg/dGjR1VQUKBu3bqpS5cuys/PV01NTbinAQAAIli7nEG5/PLLdeDAgcBt48aNgXUPPfSQli9friVLlqisrEz79+/Xrbfe2h7TAAAAEapdLjOOjY1VWlpaq+X19fV65ZVXVFJSouuuu06StHDhQvXt21ebNm3SiBEj2mM6AAAgwrRLQfnss8+Unp6uTp06yev1au7cucrMzFRFRYX8fr+ys7MDY7OyspSZmany8vJTFhSfzyefzxe439DQIEny+/3y+/1hm3fLttzRJmzbPFvhzHXiNttj2x3Nydkk8kU6J+dzcjaJfO21v7aIMsaE9V/jd999V4cPH9Zll12mAwcOaM6cOfrqq69UWVmp5cuX6+677w4qG5I0bNgwXXvttfrVr3510m0WFRVpzpw5rZaXlJQoISEhnNMHAADtpLGxUXfccYfq6+vl8XhOOzbsBeVEdXV16tWrl5555hnFx8efVUE52RmUjIwM/f3vfz9jwFD4/X6Vlpbqsa3R8jV37DfJVhblhn2bLflGjRoll8sV9u13JCdnk8gX6Zycz8nZJPKFW0NDg7p3796mgtLuX3WflJSkSy+9VLt379aoUaN07Ngx1dXVKSkpKTCmpqbmpJ9ZaeF2u+V2u1std7lc7fKE+pqjOvyr7tvzD6W9njcbODmbRL5I5+R8Ts4mkS+c+2mrdi8ohw8f1p49ezR+/HgNGTJELpdLa9euVX5+viSpqqpK+/btk9frbe+pRJQLp78T0vi/zMtrp5kAAPD9C3tB+fnPf64xY8aoV69e2r9/v2bPnq2YmBjdfvvtSkxM1MSJE1VYWKjk5GR5PB7df//98nq9XMEDAAACwl5Q/vrXv+r222/X119/rfPPP19XXXWVNm3apPPPP1+S9Oyzzyo6Olr5+fny+XzKzc3Viy++GO5pAACACBb2grJ48eLTru/UqZOKi4tVXFwc7l0DAACH4Ld4AACAdSgoAADAOhQUAABgHQoKAACwDgUFAABYh4ICAACsQ0EBAADWoaAAAADrUFAAAIB1KCgAAMA6FBQAAGAdCgoAALAOBQUAAFiHggIAAKxDQQEAANaJ7egJIDwunP7OGce4Y4zmD/seJgMAwDniDAoAALAOBQUAAFiHggIAAKxDQQEAANahoAAAAOtQUAAAgHUoKAAAwDoUFAAAYB0KCgAAsA4FBQAAWIeCAgAArENBAQAA1qGgAAAA61BQAACAdSgoAADAOhQUAABgHQoKAACwDgUFAABYh4ICAACsQ0EBAADWoaAAAADrxHb0BPD9u6JotXxNUW0a+5d5ee08GwAAWuMMCgAAsA4FBQAAWIe3eBBWF05/p81jefsIAHAqnEEBAADWoaAAAADr8BYPOgxvBwEAToUzKAAAwDoUFAAAYB3e4sFphfI2DAAA4cIZFAAAYJ0OPYNSXFysX//616qurtbAgQP1wgsvaNiwYR05JVjqTGdy3DFG84d9+zX+VU/+v+9pVgCA9tJhZ1B+97vfqbCwULNnz9af//xnDRw4ULm5uaqtre2oKQEAAEt02BmUZ555Rvfee6/uvvtuSdJLL72kd955R7/97W81ffr0jpoWHCDUz81wCfP343TH5btnwHxNURwTAB1TUI4dO6aKigrNmDEjsCw6OlrZ2dkqLy9vNd7n88nn8wXu19fXS5IOHjwov98ftnn5/X41NjYq1h+tpua2/dpvJIltNmpsbHZkvnPJ1ufnb7Z57OYZI0OdWli0/G1+/fXXcrlcIT12+Ny1bR4bar5Qtn26F5sTj9/XX38d0jxsd+LxC+V5kzru764tzuVvs0V7/o2eq3Dks8XJnmd3tNHMwc0a9Iu35DvhtbM9nutDhw5JkowxZx5sOsBXX31lJJkPPvggaPnUqVPNsGHDWo2fPXu2kcSNGzdu3Lhxc8Dtyy+/PGNXiIjLjGfMmKHCwsLA/ebmZh08eFDdunVTVFT4zgQ0NDQoIyNDX375pTweT9i2awsn53NyNol8kc7J+ZycTSJfuBljdOjQIaWnp59xbIcUlO7duysmJkY1NTVBy2tqapSWltZqvNvtltvtDlqWlJTUbvPzeDyO/ENs4eR8Ts4mkS/SOTmfk7NJ5AunxMTENo3rkKt44uLiNGTIEK1d+3/vhzU3N2vt2rXyer0dMSUAAGCRDnuLp7CwUBMmTNDQoUM1bNgw/eY3v9GRI0cCV/UAAIAfrg4rKLfddpv+9re/adasWaqurtagQYO0atUqpaamdtSU5Ha7NXv27FZvJzmFk/M5OZtEvkjn5HxOziaRryNFGdOWa30AAAC+P/wWDwAAsA4FBQAAWIeCAgAArENBAQAA1qGg/ENxcbEuvPBCderUScOHD9eWLVs6ekrasGGDxowZo/T0dEVFRWnZsmVB640xmjVrlnr06KH4+HhlZ2frs88+Cxpz8OBBjRs3Th6PR0lJSZo4caIOHz4cNGb79u366U9/qk6dOikjI0Pz589vNZclS5YoKytLnTp1Uv/+/bVy5cpzyjZ37lxdeeWV6tq1q1JSUnTzzTerqqoqaMzRo0dVUFCgbt26qUuXLsrPz2/15X779u1TXl6eEhISlJKSoqlTp+r48eNBY9avX68f/ehHcrvd6tOnjxYtWtRqPuE+/gsWLNCAAQMCX37k9Xr17rvvOiLbycybN09RUVF68MEHHZGxqKhIUVFRQbesrCxHZJOkr776Sj/72c/UrVs3xcfHq3///tq6dWtgfSS/tlx44YWtjl1UVJQKCgokRf6xa2pq0mOPPabevXsrPj5eF198sZ544omg37aJ5OMX5Nx/WSfyLV682MTFxZnf/va3ZufOnebee+81SUlJpqampkPntXLlSvOLX/zCvPXWW0aSWbp0adD6efPmmcTERLNs2TLz8ccfmxtvvNH07t3bfPPNN4Ex119/vRk4cKDZtGmT+eMf/2j69Oljbr/99sD6+vp6k5qaasaNG2cqKyvNG2+8YeLj481//ud/Bsb86U9/MjExMWb+/Plm165dZubMmcblcpkdO3acdbbc3FyzcOFCU1lZabZt22ZuuOEGk5mZaQ4fPhwYc99995mMjAyzdu1as3XrVjNixAjz4x//OLD++PHj5oorrjDZ2dnmo48+MitXrjTdu3c3M2bMCIz5/PPPTUJCgiksLDS7du0yL7zwgomJiTGrVq0KjGmP4/+HP/zBvPPOO+bTTz81VVVV5tFHHzUul8tUVlZGfLYTbdmyxVx44YVmwIAB5oEHHggsj+SMs2fPNpdffrk5cOBA4Pa3v/3NEdkOHjxoevXqZe666y6zefNm8/nnn5vVq1eb3bt3B8ZE8mtLbW1t0HErLS01ksz7779vjInsY2eMMU8++aTp1q2bWbFihdm7d69ZsmSJ6dKli3nuuecCYyL5+H0XBcUYM2zYMFNQUBC439TUZNLT083cuXM7cFbBTiwozc3NJi0tzfz6178OLKurqzNut9u88cYbxhhjdu3aZSSZDz/8MDDm3XffNVFRUearr74yxhjz4osvmvPOO8/4fL7AmGnTppnLLrsscP9f/uVfTF5eXtB8hg8fbv7t3/4tbPlqa2uNJFNWVhbI4nK5zJIlSwJjPvnkEyPJlJeXG2O+LXDR0dGmuro6MGbBggXG4/EE8jzyyCPm8ssvD9rXbbfdZnJzcwP3v6/jf95555n//u//dlS2Q4cOmUsuucSUlpaaf/qnfwoUlEjPOHv2bDNw4MCTrov0bNOmTTNXXXXVKdc77bXlgQceMBdffLFpbm6O+GNnjDF5eXnmnnvuCVp26623mnHjxhljnHX8fvBv8Rw7dkwVFRXKzs4OLIuOjlZ2drbKy8s7cGant3fvXlVXVwfNOzExUcOHDw/Mu7y8XElJSRo6dGhgTHZ2tqKjo7V58+bAmKuvvlpxcXGBMbm5uaqqqtL//u//BsZ8dz8tY8L5/NTX10uSkpOTJUkVFRXy+/1B+83KylJmZmZQvv79+wd9uV9ubq4aGhq0c+fONs39+zj+TU1NWrx4sY4cOSKv1+uobAUFBcrLy2s1Dydk/Oyzz5Senq6LLrpI48aN0759+xyR7Q9/+IOGDh2qf/7nf1ZKSooGDx6s//qv/wqsd9Jry7Fjx/Taa6/pnnvuUVRUVMQfO0n68Y9/rLVr1+rTTz+VJH388cfauHGjRo8eLclZx+8HX1D+/ve/q6mpqdU32Kampqq6urqDZnVmLXM73byrq6uVkpIStD42NlbJyclBY062je/u41RjwvX8NDc368EHH9RPfvITXXHFFYF9xsXFtfpRyBPzne3cGxoa9M0337Tr8d+xY4e6dOkit9ut++67T0uXLlW/fv0ckU2SFi9erD//+c+aO3duq3WRnnH48OFatGiRVq1apQULFmjv3r366U9/qkOHDkV8ts8//1wLFizQJZdcotWrV2vy5Mn6j//4D7366qtB83PCa8uyZctUV1enu+66K7C/SD52kjR9+nSNHTtWWVlZcrlcGjx4sB588EGNGzcuaI5OOH4d9lX3QIuCggJVVlZq48aNHT2VsLrsssu0bds21dfX6/e//70mTJigsrKyjp5WWHz55Zd64IEHVFpaqk6dOnX0dMKu5f+NStKAAQM0fPhw9erVS2+++abi4+M7cGbnrrm5WUOHDtVTTz0lSRo8eLAqKyv10ksvacKECR08u/B65ZVXNHr0aKWnp3f0VMLmzTff1Ouvv66SkhJdfvnl2rZtmx588EGlp6c77vj94M+gdO/eXTExMa0+xV1TU6O0tLQOmtWZtcztdPNOS0tTbW1t0Prjx4/r4MGDQWNOto3v7uNUY8Lx/EyZMkUrVqzQ+++/r549ewaWp6Wl6dixY6qrqzttvrOdu8fjUXx8fLse/7i4OPXp00dDhgzR3LlzNXDgQD333HOOyFZRUaHa2lr96Ec/UmxsrGJjY1VWVqbnn39esbGxSk1NjfiM35WUlKRLL71Uu3fvjvjj16NHD/Xr1y9oWd++fQNvYTnlteWLL77Qe++9p3/9138NLIv0YydJU6dODZxF6d+/v8aPH6+HHnoocCbTKcdPoqAoLi5OQ4YM0dq1awPLmpubtXbtWnm93g6c2en17t1baWlpQfNuaGjQ5s2bA/P2er2qq6tTRUVFYMy6devU3Nys4cOHB8Zs2LBBfr8/MKa0tFSXXXaZzjvvvMCY7+6nZcy5PD/GGE2ZMkVLly7VunXr1Lt376D1Q4YMkcvlCtpvVVWV9u3bF5Rvx44dQf9DKy0tlcfjCbwAn2nu3+fxb25uls/nc0S2kSNHaseOHdq2bVvgNnToUI0bNy7w35Ge8bsOHz6sPXv2qEePHhF//H7yk5+0uqT/008/Va9evSRF/mtLi4ULFyolJUV5eXmBZZF+7CSpsbFR0dHB/3THxMSoublZknOOnyQuMzbm28vB3G63WbRokdm1a5eZNGmSSUpKCvoUd0c4dOiQ+eijj8xHH31kJJlnnnnGfPTRR+aLL74wxnx7KVlSUpJ5++23zfbt281NN9100kvJBg8ebDZv3mw2btxoLrnkkqBLyerq6kxqaqoZP368qaysNIsXLzYJCQmtLiWLjY01Tz/9tPnkk0/M7Nmzz/lSssmTJ5vExESzfv36oEsCGxsbA2Puu+8+k5mZadatW2e2bt1qvF6v8Xq9gfUtlwPm5OSYbdu2mVWrVpnzzz//pJcDTp061XzyySemuLj4pJcDhvv4T58+3ZSVlZm9e/ea7du3m+nTp5uoqCizZs2aiM92Kt+9iifSMz788MNm/fr1Zu/eveZPf/qTyc7ONt27dze1tbURn23Lli0mNjbWPPnkk+azzz4zr7/+uklISDCvvfZaYEwkv7YY8+0VM5mZmWbatGmt1kXysTPGmAkTJpgLLrggcJnxW2+9Zbp3724eeeSRwJhIP34tKCj/8MILL5jMzEwTFxdnhg0bZjZt2tTRUzLvv/++kdTqNmHCBGPMt5eTPfbYYyY1NdW43W4zcuRIU1VVFbSNr7/+2tx+++2mS5cuxuPxmLvvvtscOnQoaMzHH39srrrqKuN2u80FF1xg5s2b12oub775prn00ktNXFycufzyy80777xzTtlOlkuSWbhwYWDMN998Y/793//dnHfeeSYhIcHccsst5sCBA0Hb+ctf/mJGjx5t4uPjTffu3c3DDz9s/H5/0Jj333/fDBo0yMTFxZmLLrooaB8twn3877nnHtOrVy8TFxdnzj//fDNy5MhAOYn0bKdyYkGJ5Iy33Xab6dGjh4mLizMXXHCBue2224K+JySSsxljzPLly80VV1xh3G63ycrKMi+//HLQ+kh+bTHGmNWrVxtJreZsTOQfu4aGBvPAAw+YzMxM06lTJ3PRRReZX/ziF0GXA0f68WsRZcx3vn4OAADAAj/4z6AAAAD7UFAAAIB1KCgAAMA6FBQAAGAdCgoAALAOBQUAAFiHggIAAKxDQQEAANahoAAAAOtQUAAAgHUoKAAAwDoUFAAAYJ3/D82hkikpU2cjAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "bI-iYFwAmgYS",
        "outputId": "dae74461-fb2c-4586-99a9-eff6440bb311"
      },
      "source": [
        "df.boxplot(column='ApplicantIncome')\n",
        "plt.show()\n"
      ],
      "execution_count": 149,
      "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",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 461
        },
        "id": "0HlweN0kOJ4a",
        "outputId": "abd54d43-2122-4c20-dad3-66f03bb9928b"
      },
      "source": [
        "df"
      ],
      "execution_count": 150,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Loan_ID  Gender Married Dependents     Education Self_Employed  \\\n",
              "0    LP001002    Male      No          0      Graduate            No   \n",
              "1    LP001003    Male     Yes          1      Graduate            No   \n",
              "2    LP001005    Male     Yes          0      Graduate           Yes   \n",
              "3    LP001006    Male     Yes          0  Not Graduate            No   \n",
              "4    LP001008    Male      No          0      Graduate            No   \n",
              "..        ...     ...     ...        ...           ...           ...   \n",
              "609  LP002978  Female      No          0      Graduate            No   \n",
              "610  LP002979    Male     Yes         3+      Graduate            No   \n",
              "611  LP002983    Male     Yes          1      Graduate            No   \n",
              "612  LP002984    Male     Yes          2      Graduate            No   \n",
              "613  LP002990  Female      No          0      Graduate           Yes   \n",
              "\n",
              "     ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
              "0               5849                0.0         NaN             360.0   \n",
              "1               4583             1508.0       128.0             360.0   \n",
              "2               3000                0.0        66.0             360.0   \n",
              "3               2583             2358.0       120.0             360.0   \n",
              "4               6000                0.0       141.0             360.0   \n",
              "..               ...                ...         ...               ...   \n",
              "609             2900                0.0        71.0             360.0   \n",
              "610             4106                0.0        40.0             180.0   \n",
              "611             8072              240.0       253.0             360.0   \n",
              "612             7583                0.0       187.0             360.0   \n",
              "613             4583                0.0       133.0             360.0   \n",
              "\n",
              "     Credit_History Property_Area Loan_Status  \n",
              "0               1.0         Urban           Y  \n",
              "1               1.0         Rural           N  \n",
              "2               1.0         Urban           Y  \n",
              "3               1.0         Urban           Y  \n",
              "4               1.0         Urban           Y  \n",
              "..              ...           ...         ...  \n",
              "609             1.0         Rural           Y  \n",
              "610             1.0         Rural           Y  \n",
              "611             1.0         Urban           Y  \n",
              "612             1.0         Urban           Y  \n",
              "613             0.0     Semiurban           N  \n",
              "\n",
              "[614 rows x 13 columns]"
            ],
            "text/html": [
              "\n",
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              "      <th>Loan_ID</th>\n",
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              "      <td>LP001002</td>\n",
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              "    <tr>\n",
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              "      <td>LP001003</td>\n",
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              "      <td>Yes</td>\n",
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              "      <td>LP001006</td>\n",
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              "      <td>No</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>141.0</td>\n",
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              "      <td>1.0</td>\n",
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              "      <td>Y</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>609</th>\n",
              "      <td>LP002978</td>\n",
              "      <td>Female</td>\n",
              "      <td>No</td>\n",
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              "      <td>71.0</td>\n",
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              "      <td>Y</td>\n",
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              "    <tr>\n",
              "      <th>610</th>\n",
              "      <td>LP002979</td>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>3+</td>\n",
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              "      <td>No</td>\n",
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              "    <tr>\n",
              "      <th>611</th>\n",
              "      <td>LP002983</td>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
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              "      <td>Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>8072</td>\n",
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              "      <td>Y</td>\n",
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              "      <td>LP002984</td>\n",
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              "    <tr>\n",
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              "      <td>0.0</td>\n",
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              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>614 rows × 13 columns</p>\n",
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              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 614,\n  \"fields\": [\n    {\n      \"column\": \"Loan_ID\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 614,\n        \"samples\": [\n          \"LP002139\",\n          \"LP002223\",\n          \"LP001570\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Female\",\n          \"Male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Married\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Yes\",\n          \"No\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Dependents\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"1\",\n          \"3+\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Education\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Not Graduate\",\n          \"Graduate\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Self_Employed\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Yes\",\n          \"No\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ApplicantIncome\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6109,\n        \"min\": 150,\n        \"max\": 81000,\n        \"num_unique_values\": 505,\n        \"samples\": [\n          8333,\n          4342\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CoapplicantIncome\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2926.2483692241885,\n        \"min\": 0.0,\n        \"max\": 41667.0,\n        \"num_unique_values\": 287,\n        \"samples\": [\n          1840.0,\n          2042.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LoanAmount\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 85.58732523570545,\n        \"min\": 9.0,\n        \"max\": 700.0,\n        \"num_unique_values\": 203,\n        \"samples\": [\n          100.0,\n          70.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Loan_Amount_Term\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 65.12040985461256,\n        \"min\": 12.0,\n        \"max\": 480.0,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          84.0,\n          120.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Credit_History\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3648783192364048,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.0,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Property_Area\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Urban\",\n          \"Rural\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Loan_Status\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"N\",\n          \"Y\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 150
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MVbgMOsBkf8C"
      },
      "source": [
        "Cleaning the Data\n",
        "\n",
        "First, lets encode categorical features into binary representations through one hot encoding.\n",
        "I manually encoded the Gender and Loan Status columns, since I wanted to keep them as one column each, respectively, and I used the pd.get_dummies function to convert the other categorical variables into indicator variables:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sqayhL4-OAM5"
      },
      "source": [
        "# Encode Male as 1, Female as 0\n",
        "df.loc[df.Gender == 'Male', 'Gender'] = 1\n",
        "df.loc[df.Gender == 'Female', 'Gender'] = 0\n"
      ],
      "execution_count": 151,
      "outputs": []
    },
    {
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          "height": 458
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      "source": [
        "df.Gender"
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      "execution_count": 152,
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      "source": [
        "# Encode Y Loan_Status as 1, N Loan_Status as 0\n",
        "df[\"Loan_Status\"] = df[\"Loan_Status\"].map({'Y':1, 'N':0})\n"
      ],
      "execution_count": 153,
      "outputs": []
    },
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        "id": "LhIE8IuEkwpA"
      },
      "source": [
        "# Replace the categorical values with the numeric equivalents that we have above\n",
        "categoricalFeatures = ['Property_Area', 'Married', 'Dependents', 'Education', 'Self_Employed']\n",
        "# Iterate through the list of categorical features and one hot encode them.\n",
        "for feature in categoricalFeatures:\n",
        "    onehot = pd.get_dummies(df[feature], prefix=feature)\n",
        "    df = df.drop(feature, axis=1)\n",
        "    df = df.join(onehot)"
      ],
      "execution_count": 154,
      "outputs": []
    },
    {
      "cell_type": "code",
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      "source": [
        "df"
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      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Loan_ID Gender  ApplicantIncome  CoapplicantIncome  LoanAmount  \\\n",
              "0    LP001002      1             5849                0.0         NaN   \n",
              "1    LP001003      1             4583             1508.0       128.0   \n",
              "2    LP001005      1             3000                0.0        66.0   \n",
              "3    LP001006      1             2583             2358.0       120.0   \n",
              "4    LP001008      1             6000                0.0       141.0   \n",
              "..        ...    ...              ...                ...         ...   \n",
              "609  LP002978      0             2900                0.0        71.0   \n",
              "610  LP002979      1             4106                0.0        40.0   \n",
              "611  LP002983      1             8072              240.0       253.0   \n",
              "612  LP002984      1             7583                0.0       187.0   \n",
              "613  LP002990      0             4583                0.0       133.0   \n",
              "\n",
              "     Loan_Amount_Term  Credit_History  Loan_Status  Property_Area_Rural  \\\n",
              "0               360.0             1.0            1                False   \n",
              "1               360.0             1.0            0                 True   \n",
              "2               360.0             1.0            1                False   \n",
              "3               360.0             1.0            1                False   \n",
              "4               360.0             1.0            1                False   \n",
              "..                ...             ...          ...                  ...   \n",
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              "610             180.0             1.0            1                 True   \n",
              "611             360.0             1.0            1                False   \n",
              "612             360.0             1.0            1                False   \n",
              "613             360.0             0.0            0                False   \n",
              "\n",
              "     Property_Area_Semiurban  ...  Married_No  Married_Yes  Dependents_0  \\\n",
              "0                      False  ...        True        False          True   \n",
              "1                      False  ...       False         True         False   \n",
              "2                      False  ...       False         True          True   \n",
              "3                      False  ...       False         True          True   \n",
              "4                      False  ...        True        False          True   \n",
              "..                       ...  ...         ...          ...           ...   \n",
              "609                    False  ...        True        False          True   \n",
              "610                    False  ...       False         True         False   \n",
              "611                    False  ...       False         True         False   \n",
              "612                    False  ...       False         True         False   \n",
              "613                     True  ...        True        False          True   \n",
              "\n",
              "     Dependents_1  Dependents_2  Dependents_3+  Education_Graduate  \\\n",
              "0           False         False          False                True   \n",
              "1            True         False          False                True   \n",
              "2           False         False          False                True   \n",
              "3           False         False          False               False   \n",
              "4           False         False          False                True   \n",
              "..            ...           ...            ...                 ...   \n",
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              "611          True         False          False                True   \n",
              "612         False          True          False                True   \n",
              "613         False         False          False                True   \n",
              "\n",
              "     Education_Not Graduate  Self_Employed_No  Self_Employed_Yes  \n",
              "0                     False              True              False  \n",
              "1                     False              True              False  \n",
              "2                     False             False               True  \n",
              "3                      True              True              False  \n",
              "4                     False              True              False  \n",
              "..                      ...               ...                ...  \n",
              "609                   False              True              False  \n",
              "610                   False              True              False  \n",
              "611                   False              True              False  \n",
              "612                   False              True              False  \n",
              "613                   False             False               True  \n",
              "\n",
              "[614 rows x 21 columns]"
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              "      <th>3</th>\n",
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              "      <td>True</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
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              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
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              "      <td>2900</td>\n",
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              "      <td>True</td>\n",
              "      <td>False</td>\n",
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              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>610</th>\n",
              "      <td>LP002979</td>\n",
              "      <td>1</td>\n",
              "      <td>4106</td>\n",
              "      <td>0.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>180.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>611</th>\n",
              "      <td>LP002983</td>\n",
              "      <td>1</td>\n",
              "      <td>8072</td>\n",
              "      <td>240.0</td>\n",
              "      <td>253.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
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              "    <tr>\n",
              "      <th>612</th>\n",
              "      <td>LP002984</td>\n",
              "      <td>1</td>\n",
              "      <td>7583</td>\n",
              "      <td>0.0</td>\n",
              "      <td>187.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>613</th>\n",
              "      <td>LP002990</td>\n",
              "      <td>0</td>\n",
              "      <td>4583</td>\n",
              "      <td>0.0</td>\n",
              "      <td>133.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>...</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>614 rows × 21 columns</p>\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FScfbIKOutHZ"
      },
      "source": [
        "Lets find correlation between the columns"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 803
        },
        "id": "J5kK7T1-kLP3",
        "outputId": "7c641e02-c828-482f-dfcf-6fd0d20960cf"
      },
      "source": [
        "numeric_df = df.select_dtypes(include=[np.number])\n",
        "\n",
        "# Calculate the correlation matrix\n",
        "corr_matrix = numeric_df.corr().round(2)\n",
        "\n",
        "# Create the heatmap\n",
        "plt.figure(figsize=(10, 8))  # Optional: Adjust the figure size if needed\n",
        "sns.heatmap(data=corr_matrix, annot=True, linewidths=0.5, cmap=\"Blues\")\n",
        "plt.show()\n",
        "\n",
        "#fig,ax=plt.subplots(figsize=(15,8))\n",
        "#sns.heatmap(data=df.corr().round(2),annot=True,linewidths=0.5,cmap=\"Blues\")\n",
        "#plt.show()"
      ],
      "execution_count": 155,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mzdpxsEnoDyw"
      },
      "source": [
        "Lets check the missing values"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 742
        },
        "id": "_G0zerd0nWGS",
        "outputId": "84bd44a4-89a2-4d35-dd62-de3339304027"
      },
      "source": [
        "df.isnull().sum()"
      ],
      "execution_count": 156,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Loan_ID                     0\n",
              "Gender                     13\n",
              "ApplicantIncome             0\n",
              "CoapplicantIncome           0\n",
              "LoanAmount                 22\n",
              "Loan_Amount_Term           14\n",
              "Credit_History             50\n",
              "Loan_Status                 0\n",
              "Property_Area_Rural         0\n",
              "Property_Area_Semiurban     0\n",
              "Property_Area_Urban         0\n",
              "Married_No                  0\n",
              "Married_Yes                 0\n",
              "Dependents_0                0\n",
              "Dependents_1                0\n",
              "Dependents_2                0\n",
              "Dependents_3+               0\n",
              "Education_Graduate          0\n",
              "Education_Not Graduate      0\n",
              "Self_Employed_No            0\n",
              "Self_Employed_Yes           0\n",
              "dtype: int64"
            ],
            "text/html": [
              "<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>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Loan_ID</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Gender</th>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ApplicantIncome</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CoapplicantIncome</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LoanAmount</th>\n",
              "      <td>22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Loan_Amount_Term</th>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Credit_History</th>\n",
              "      <td>50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Loan_Status</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Property_Area_Rural</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Property_Area_Semiurban</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Property_Area_Urban</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Married_No</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Married_Yes</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Dependents_0</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Dependents_1</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Dependents_2</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Dependents_3+</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Education_Graduate</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Education_Not Graduate</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Self_Employed_No</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Self_Employed_Yes</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 156
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rKza6y6AoHrI"
      },
      "source": [
        "Lets replace the missing values of loanAmount and LoanAmountTerm with the mean values of the cloumns"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aid-9uDcnecG"
      },
      "source": [
        "df.LoanAmount.fillna(df.LoanAmount.mean(),inplace=True)\n",
        "df.Loan_Amount_Term.fillna(df.Loan_Amount_Term.mean(),inplace=True)"
      ],
      "execution_count": 157,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PxPSw3B1oZcv"
      },
      "source": [
        "lets check the missing values again"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 742
        },
        "id": "iCTAQEnpoVUo",
        "outputId": "adf503eb-9cf2-4f08-fb46-4a7fb001ddfc"
      },
      "source": [
        "df.isnull().sum()"
      ],
      "execution_count": 158,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Loan_ID                     0\n",
              "Gender                     13\n",
              "ApplicantIncome             0\n",
              "CoapplicantIncome           0\n",
              "LoanAmount                  0\n",
              "Loan_Amount_Term            0\n",
              "Credit_History             50\n",
              "Loan_Status                 0\n",
              "Property_Area_Rural         0\n",
              "Property_Area_Semiurban     0\n",
              "Property_Area_Urban         0\n",
              "Married_No                  0\n",
              "Married_Yes                 0\n",
              "Dependents_0                0\n",
              "Dependents_1                0\n",
              "Dependents_2                0\n",
              "Dependents_3+               0\n",
              "Education_Graduate          0\n",
              "Education_Not Graduate      0\n",
              "Self_Employed_No            0\n",
              "Self_Employed_Yes           0\n",
              "dtype: int64"
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              "      <td>50</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <th>Dependents_2</th>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>Dependents_3+</th>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>Education_Graduate</th>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>Education_Not Graduate</th>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>Self_Employed_No</th>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>Self_Employed_Yes</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 158
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7-OYXSDjogU1"
      },
      "source": [
        "lets drop the other values with missing records"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2SeJv4sBokZh"
      },
      "source": [
        "df.dropna(how=\"any\",inplace=True)"
      ],
      "execution_count": 159,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hy6FcA6aopDy"
      },
      "source": [
        "lets check the data again"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 742
        },
        "id": "BFDRtwkqoz_e",
        "outputId": "b1a60d0d-9098-4146-f8eb-d110c30c351f"
      },
      "source": [
        "df.isnull().sum()"
      ],
      "execution_count": 160,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Loan_ID                    0\n",
              "Gender                     0\n",
              "ApplicantIncome            0\n",
              "CoapplicantIncome          0\n",
              "LoanAmount                 0\n",
              "Loan_Amount_Term           0\n",
              "Credit_History             0\n",
              "Loan_Status                0\n",
              "Property_Area_Rural        0\n",
              "Property_Area_Semiurban    0\n",
              "Property_Area_Urban        0\n",
              "Married_No                 0\n",
              "Married_Yes                0\n",
              "Dependents_0               0\n",
              "Dependents_1               0\n",
              "Dependents_2               0\n",
              "Dependents_3+              0\n",
              "Education_Graduate         0\n",
              "Education_Not Graduate     0\n",
              "Self_Employed_No           0\n",
              "Self_Employed_Yes          0\n",
              "dtype: int64"
            ],
            "text/html": [
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
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              "  <thead>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 160
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gqbxrBSdJefc"
      },
      "source": [
        "How to fix biases in machine learning algorithms?\n",
        "\n",
        "Firstly, if your data set is complete, you should acknowledge that AI biases can only happen due to the prejudices of humankind and you should focus on removing those prejudices from the data set. However, it is not as easy as it sounds.\n",
        "\n",
        "A naive approach is removing protected classes (such as sex or race) from data is to delete the labels that make the algorithm bias, yet, this approach may not work because removed labels may affect the understanding of the model and your results’ accuracy may get worse.\n",
        "\n",
        "So there are no quick fixes to removing all biases but there are high level recommendations that one can apply to overcome the issue.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## To Solve the data imbalance issue I have applied \"SMOTE\"(Synthetic Minority Oversampling Technique) approach on the attributes"
      ],
      "metadata": {
        "id": "ZllJ0twgGuMw"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## First Pair of attributes I have chosen is ApplicantIncome,Loan_status"
      ],
      "metadata": {
        "id": "xiPA8HxJG5m4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from imblearn.over_sampling import SMOTE\n",
        "from sklearn.model_selection import train_test_split"
      ],
      "metadata": {
        "id": "CrWAc3bIJTsm"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X = df[['ApplicantIncome']]  # Feature\n",
        "y = df['Loan_Status'] # Target"
      ],
      "metadata": {
        "id": "hDTtJIOfHwX2"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Encode the Loan_Status because it is categorial**"
      ],
      "metadata": {
        "id": "Qv2gf9BDPjj-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Encode 'Loan_Status'  as 1 for 'Y' (Yes) and 0 for 'N' (No)\n",
        "df['Loan_Status'] = df['Loan_Status'].map({'Y': 1, 'N': 0})"
      ],
      "metadata": {
        "id": "rAgEV45mPuCn"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Apply SMOTE for the First Pair**"
      ],
      "metadata": {
        "id": "zFVEdraoJAJU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Applying SMOTE for ApplicantIncome and Loan Status\n",
        "smote = SMOTE(random_state=42)\n",
        "X_resampled, y_resampled = smote.fit_resample(X, y)\n",
        "\n",
        "# Check the class distribution after resampling\n",
        "print(\"Original Loan Status distribution:\\n\", y.value_counts())\n",
        "print(\"Resampled Loan Status distribution:\\n\", pd.Series(y_resampled).value_counts())\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vzfFgK98JJER",
        "outputId": "189d6216-406a-4fc3-f7bc-ff6560dc1e98"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Original Loan Status distribution:\n",
            " Loan_Status\n",
            "1    378\n",
            "0    174\n",
            "Name: count, dtype: int64\n",
            "Resampled Loan Status distribution:\n",
            " Loan_Status\n",
            "1    378\n",
            "0    378\n",
            "Name: count, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Class Distribution Before and After SMOTE**"
      ],
      "metadata": {
        "id": "Y9gP2L7hJ5zl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Before SMOTE: Credit History Distribution\n",
        "print(\"Original Credit History distribution:\")\n",
        "print(y1.value_counts())\n",
        "# After SMOTE: Credit History Distribution\n",
        "print(\"Resampled Credit History distribution:\")\n",
        "print(pd.Series(y1_resampled).value_counts())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8ZrzoXxkJ-tW",
        "outputId": "d0b2c124-8a3d-4b31-b6c9-0cb71662c1cf"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Original Credit History distribution:\n",
            "Credit_History\n",
            "1.0    466\n",
            "0.0     86\n",
            "Name: count, dtype: int64\n",
            "Resampled Credit History distribution:\n",
            "Credit_History\n",
            "1.0    466\n",
            "0.0    466\n",
            "Name: count, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Before SMOTE: The original dataset likely has an imbalance in the `Loan_Status` variable, leading to biased predictions from models trained on the imbalanced data.\n",
        "\n",
        "After SMOTE: The resampled dataset balances the minority class (e.g., `Loan_Status = 0`), leading to fairer and more accurate predictions.\n"
      ],
      "metadata": {
        "id": "5BvBSZRSYk-e"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Visualizing the Results**"
      ],
      "metadata": {
        "id": "_Y4_D-cjK6Hp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Calculate means and standard deviations for original and resampled data\n",
        "original_means = [\n",
        "    X['ApplicantIncome'][y == 0].mean(),  # No Loan\n",
        "    X['ApplicantIncome'][y == 1].mean()   # Loan Approved\n",
        "]\n",
        "\n",
        "resampled_means = [\n",
        "    X_resampled['ApplicantIncome'][y_resampled == 0].mean(),  # No Loan\n",
        "    X_resampled['ApplicantIncome'][y_resampled == 1].mean()   # Loan Approved\n",
        "]\n",
        "\n",
        "# Calculate standard deviations for error bars\n",
        "original_stds = [\n",
        "    X['ApplicantIncome'][y == 0].std(),\n",
        "    X['ApplicantIncome'][y == 1].std()\n",
        "]\n",
        "\n",
        "resampled_stds = [\n",
        "    X_resampled['ApplicantIncome'][y_resampled == 0].std(),\n",
        "    X_resampled['ApplicantIncome'][y_resampled == 1].std()\n",
        "]\n",
        "# Create bar plot\n",
        "labels = ['No Loan', 'Loan Approved']\n",
        "x = np.arange(len(labels))  # the label locations\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(10, 6))\n",
        "bar_width = 0.35\n",
        "\n",
        "# Original data bars\n",
        "ax.bar(x - bar_width/2, original_means, bar_width, yerr=original_stds, label='Original', alpha=0.7, color='saddlebrown')\n",
        "\n",
        "# Resampled data bars\n",
        "ax.bar(x + bar_width/2, resampled_means, bar_width, yerr=resampled_stds, label='Resampled', alpha=0.7, color='tan')\n",
        "\n",
        "# Add labels, title, and custom x-axis tick labels\n",
        "ax.set_ylabel('Applicant Income')\n",
        "ax.set_title('Comparison of Applicant Income vs Loan Status')\n",
        "ax.set_xticks(x)\n",
        "ax.set_xticklabels(labels)\n",
        "ax.legend()\n",
        "\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        },
        "id": "wdpOJsJjK-ma",
        "outputId": "92207c5d-d0f7-408b-c215-c4683d97e00f"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Model Training on Resampled Data**: After resampling, we will train a decision tree classifier using \"ApplicantIncome\" as the feature and \"LoanStatus\" as the target. This will help us evaluate the model's performance on the balanced dataset compared to the original one."
      ],
      "metadata": {
        "id": "MZlaozLYVtIM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.metrics import classification_report\n",
        "\n",
        "# Train-Test split for resampled data (Applicant Income and Loan Status)\n",
        "X_train, X_test, y_train, y_test = train_test_split(X_resampled, y_resampled, test_size=0.3, random_state=42)\n",
        "\n",
        "# Train a decision tree classifier\n",
        "clf = DecisionTreeClassifier()\n",
        "clf.fit(X_train, y_train)\n",
        "\n",
        "# Predict and evaluate the model\n",
        "y_pred = clf.predict(X_test)\n",
        "print(\"Classification Report for Loan Status (Resampled Applicant Income Data):\")\n",
        "print(classification_report(y_test, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WYjHe748V2Ry",
        "outputId": "5bdbbbe7-788f-4c93-cbd9-18a570450fd0"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Classification Report for Loan Status (Resampled Applicant Income Data):\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.57      0.70      0.63       104\n",
            "           1       0.69      0.55      0.61       123\n",
            "\n",
            "    accuracy                           0.62       227\n",
            "   macro avg       0.63      0.63      0.62       227\n",
            "weighted avg       0.63      0.62      0.62       227\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Second Pair of Attributes I have chosen are Credit_History,Loan_Status"
      ],
      "metadata": {
        "id": "IXlHBHr6Xzsx"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Load and Encode the data and Apply the SMOTE Approach**"
      ],
      "metadata": {
        "id": "KkohgkZYZSGV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = df.drop(columns=['Loan_Status'])\n",
        "y = df['Loan_Status']\n",
        "\n",
        "# Store the original 'Credit_History'\n",
        "Credit_History= df['Credit_History']\n",
        "\n",
        "# Before applying SMOTE\n",
        "print(\"Original dataset class distribution with respect to 'Credit_History', 'Loan_Status':\")\n",
        "print(df.groupby(['Credit_History','Loan_Status']).size())\n",
        "\n",
        "# Apply SMOTE to handle data imbalance\n",
        "smote = SMOTE(random_state=42)\n",
        "\n",
        "# Ensure we drop any unnecessary string columns\n",
        "X_resampled, y_resampled = smote.fit_resample(X.select_dtypes(exclude='object'), y)\n",
        "\n",
        "# Create a DataFrame from the resampled data\n",
        "df_resampled = pd.DataFrame(X_resampled, columns=X.select_dtypes(exclude='object').columns)\n",
        "df_resampled['Loan_Status'] = y_resampled\n",
        "\n",
        "# Add Loan_Status back\n",
        "\n",
        "# Add back 'Credit_History'\n",
        "df_resampled['Credit_History'] = Credit_History.sample(n=df_resampled.shape[0], replace=True).values\n",
        "\n",
        "# Check the new balance after applying SMOTE\n",
        "print(\"\\nClass distribution after applying SMOTE with respect to 'Credit_History' and 'Loan_Status':\")\n",
        "print(df_resampled.groupby(['Credit_History', 'Loan_Status']).size())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Te3m6jgcvKDD",
        "outputId": "73aaf1fb-276e-4e09-c345-b064e73d82d1"
      },
      "execution_count": 164,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Original dataset class distribution with respect to 'Credit_History', 'Loan_Status':\n",
            "Credit_History  Loan_Status\n",
            "0.0             0               79\n",
            "                1                7\n",
            "1.0             0               95\n",
            "                1              371\n",
            "dtype: int64\n",
            "\n",
            "Class distribution after applying SMOTE with respect to 'Credit_History' and 'Loan_Status':\n",
            "Credit_History  Loan_Status\n",
            "0.0             0               63\n",
            "                1               51\n",
            "1.0             0              315\n",
            "                1              327\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Visualization results**"
      ],
      "metadata": {
        "id": "DwZT6Y0hxKtf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.set(style=\"whitegrid\")\n",
        "\n",
        "# Create a figure to hold the subplots\n",
        "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(14, 6))\n",
        "\n",
        "# Original class distribution\n",
        "original_distribution.unstack().plot(kind='bar', ax=axes[0], color=['lightblue', 'salmon'])\n",
        "axes[0].set_title('Original Class Distribution')\n",
        "axes[0].set_ylabel('Count')\n",
        "axes[0].set_xlabel('Credit History')\n",
        "axes[0].legend(title='Loan Status', loc='upper right')\n",
        "\n",
        "# Resampled class distribution\n",
        "resampled_distribution.unstack().plot(kind='bar', ax=axes[1], color=['lightblue', 'salmon'])\n",
        "axes[1].set_title('Class Distribution After SMOTE')\n",
        "axes[1].set_ylabel('Count')\n",
        "axes[1].set_xlabel('Credit History')\n",
        "axes[1].legend(title='Loan Status', loc='upper right')\n",
        "\n",
        "# Adjust layout\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 505
        },
        "id": "pLa8U94RxF6Q",
        "outputId": "d7b7825f-079c-42b1-ebea-8a555cda1bb6"
      },
      "execution_count": 165,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}