DACSS_604_Final_Project_Advanced_Data_Driven_Storytelling.ipynb

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        "# Examining Algorithmic Bias in Criminal Justice Risk Assessments\n",
        "\n",
        "\n",
        "### Cynthia Hester\n",
        "\n",
        "##### Final Project - DACSS 604\n",
        "\n",
        "##### Advanced Data-Driven Storytelling"
      ],
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        "### Audience\n",
        "\n",
        "The intended audience for this project consists of educated and inquisitive individuals, most of whom have likely attended college. They understand the implications of artificial intelligence and approach its extensive use in criminal justice and related areas with cautious skepticism. Although this project may appeal to those with diverse interests, the audience does not necessarily possess specific expertise in technology,statistics or criminal justice."
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        "\n",
        "### Introduction\n",
        "\n",
        "\n",
        "Predictive algorithms and machine learning models, commonly referred to as Risk Assessment Tools (RATs), are increasingly being integrated into the criminal justice system. These tools leverage comprehensive datasets to forecast an individual's likelihood of engaging in future criminal behavior. The deployment of RATs has become widespread, significantly influencing critical legal decisions such as bail settings, sentencing determinations, and parole considerations. The primary motivation behind adopting these predictive models is to enhance the accuracy, fairness, and efficiency of decision-making processes within the judicial system.\n",
        "\n",
        "However, the implications of inaccuracies in these tools, particularly in terms of false positives (incorrectly predicting future criminal activity) and false negatives (failing to predict actual future criminal activity), are substantial. These errors can have severe consequences, not only for the individuals directly affected but also for broader societal perceptions of justice and fairness.\n",
        "\n",
        "Disparities in the use of machine learning in criminal justice were notably exposed in ProPublica's \"Machine Bias\" investigation, which found that the COMPAS (Correctional Offender Management Profiling for Alternative Sanctions) algorithm disproportionately mislabeled black defendants as future criminals compared to white defendants. Furthermore,biases present in the data used to train these algorithms can lead to outcomes that reinforce discriminatory practices. These insights emphasize the critical need to examine the data and methods used in developing risk assessment tools to avoid perpetuating existing societal biases (Yale Law Journal).\n",
        "\n",
        "The ethical and effective implementation of machine learning in crucial areas such as criminal justice goes beyond academic discussions; it is an urgent societal matter. The lack of transparency in these algorithmic systems often complicates efforts to evaluate their fairness and hold them accountable (MIT Technology Review).\n",
        "\n",
        "This project seeks to contribute to the ongoing conversation about how machine learning can be responsibly applied in criminal justice, ensuring that technological advances do not come at the expense of equity and justice.\n"
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        "# @title\n",
        "import pandas as pd\n",
        "\n",
        "\n",
        "# relative path\n",
        "file_path = 'recidivism_data.csv'\n",
        "\n",
        "# read in data\n",
        "recid_df = pd.read_csv(file_path)\n",
        "\n",
        "# first 5 rows of data\n",
        "recid_df.head()\n",
        "\n",
        "\n",
        "!pip install nbconvert\n",
        "!jupyter nbconvert --to html --no-input \"/content/DACSS_604_Final_Project_Advanced_Data_Driven_Storytelling (3).ipynb\"\n",
        "!jupyter nbconvert --to pdf --no-input \"/content/DACSS_604_Final_Project_Advanced_Data_Driven_Storytelling (3).ipynb\"\n",
        "\n"
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        "### Data\n",
        "\n",
        "Data was collected from Broward County Clerk’s Office, Broward County Sherrif's Office, Florida Department of Corrections, via ProPublica Data Store. It represesents years 2013-2014.\n",
        "\n",
        "https://www.propublica.org/datastore/dataset/compas-recidivism-risk-score-data-and-analysis"
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        "# @title\n",
        "# upon visual inspection I drop unecessary columns since they have no impact on the prediction\n",
        "recid_df.drop(['id', 'name'], axis=1, inplace=True)"
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        "# @title\n",
        "\n",
        "#recheck data\n",
        "\n",
        "# first 5 rows of data\n",
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        "\n",
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              "type": "dataframe",
              "variable_name": "recid_df",
              "summary": "{\n  \"name\": \"recid_df\",\n  \"rows\": 7214,\n  \"fields\": [\n    {\n      \"column\": \"sex\",\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\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11,\n        \"min\": 18,\n        \"max\": 96,\n        \"num_unique_values\": 65,\n        \"samples\": [\n          83,\n          18\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age_cat\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Greater than 45\",\n          \"25 - 45\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"race\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"Other\",\n          \"African-American\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"juv_fel_count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 20,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          3,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"juv_misd_count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 13,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          5,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"juv_other_count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 17,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          6,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"priors_count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4,\n        \"min\": 0,\n        \"max\": 38,\n        \"num_unique_values\": 37,\n        \"samples\": [\n          28,\n          20\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"c_charge_degree\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"M\",\n          \"F\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"r_days_from_arrest\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 74.87166810579951,\n        \"min\": -1.0,\n        \"max\": 993.0,\n        \"num_unique_values\": 201,\n        \"samples\": [\n          305.0,\n          185.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"two_year_recid\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"c_jail_days\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 51.080642685588195,\n        \"min\": -0.4876967592592592,\n        \"max\": 799.7926388888889,\n        \"num_unique_values\": 6821,\n        \"samples\": [\n          4.24761574074074,\n          0.8220949074074074\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"custody_days\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 195.87645432851895,\n        \"min\": 0.0,\n        \"max\": 6035.0,\n        \"num_unique_values\": 396,\n        \"samples\": [\n          121.0,\n          113.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# @title\n",
        "# ck datatypes of the Compas Recidivism Data Exploratory data analysis\n",
        "\n",
        "\n",
        "print(recid_df.dtypes)\n",
        "\n",
        "data_types = recid_df.dtypes\n",
        "print(\"\\nData Types for each feature:\")\n",
        "print(data_types)"
      ],
      "metadata": {
        "id": "MjTVU3oOyozw",
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f0acec74-0b04-4f4b-cf28-cebaebed0d2b"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "sex                    object\n",
            "age                     int64\n",
            "age_cat                object\n",
            "race                   object\n",
            "juv_fel_count           int64\n",
            "juv_misd_count          int64\n",
            "juv_other_count         int64\n",
            "priors_count            int64\n",
            "c_charge_degree        object\n",
            "r_days_from_arrest    float64\n",
            "two_year_recid          int64\n",
            "c_jail_days           float64\n",
            "custody_days          float64\n",
            "dtype: object\n",
            "\n",
            "Data Types for each feature:\n",
            "sex                    object\n",
            "age                     int64\n",
            "age_cat                object\n",
            "race                   object\n",
            "juv_fel_count           int64\n",
            "juv_misd_count          int64\n",
            "juv_other_count         int64\n",
            "priors_count            int64\n",
            "c_charge_degree        object\n",
            "r_days_from_arrest    float64\n",
            "two_year_recid          int64\n",
            "c_jail_days           float64\n",
            "custody_days          float64\n",
            "dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# @title\n",
        "target_variable = recid_df['two_year_recid'].describe()\n",
        "print(target_variable)\n",
        "\n",
        "\n",
        "print(\"Target Variable\")\n",
        "print(recid_df['two_year_recid'].describe)\n"
      ],
      "metadata": {
        "id": "dny9r0Jj0qAw",
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f5385197-9188-43ce-a931-23c9fba99e13"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "count    7214.000000\n",
            "mean        0.450652\n",
            "std         0.497593\n",
            "min         0.000000\n",
            "25%         0.000000\n",
            "50%         0.000000\n",
            "75%         1.000000\n",
            "max         1.000000\n",
            "Name: two_year_recid, dtype: float64\n",
            "Target Variable\n",
            "<bound method NDFrame.describe of 0       0\n",
            "1       1\n",
            "2       1\n",
            "3       0\n",
            "4       0\n",
            "       ..\n",
            "7209    0\n",
            "7210    0\n",
            "7211    0\n",
            "7212    0\n",
            "7213    1\n",
            "Name: two_year_recid, Length: 7214, dtype: int64>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# @title\n",
        "\n",
        "# cking for value counts\n",
        "target_variable_counts = recid_df['two_year_recid'].value_counts()\n",
        "print(target_variable_counts)\n",
        "\n",
        "print(\"\\nTarget Variable Value Counts: \")\n",
        "print(recid_df['two_year_recid'].value_counts)"
      ],
      "metadata": {
        "id": "YrwAK6Vf9et-",
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "65c6c31c-2836-4116-9423-c3bf3baa8d40"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "two_year_recid\n",
            "0    3963\n",
            "1    3251\n",
            "Name: count, dtype: int64\n",
            "\n",
            "Target Variable Value Counts: \n",
            "<bound method IndexOpsMixin.value_counts of 0       0\n",
            "1       1\n",
            "2       1\n",
            "3       0\n",
            "4       0\n",
            "       ..\n",
            "7209    0\n",
            "7210    0\n",
            "7211    0\n",
            "7212    0\n",
            "7213    1\n",
            "Name: two_year_recid, Length: 7214, dtype: int64>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Data Overview"
      ],
      "metadata": {
        "id": "SQXpirfla1_p"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The dataset I'm working with includes two years of data from Broward County, covering the years 2013 to 2014, and focuses on predicting whether individuals are likely to commit a crime again. One key variable, called `two_year_recid`, is central to my analysis. This variable is binary, meaning it can only be a 0 or a 1. A value of 0 means it's unlikely that the individual will reoffend, whereas a 1 indicates a higher likelihood that they will. This binary setup categorizes my analysis as a classification problem, which is different from other types of data analysis that deal with continuous measurements like height or temperature.\n",
        "\n",
        "I also treat the dataset as independent and uniformly distributed, meaning I consider each data point separate and assume they all come from similar conditions. This assumption is vital for using certain statistical methods and machine learning algorithms effectively, ensuring that the insights I gain from one segment of the data are applicable to the entire set. It also helps prevent issues such as temporal correlations or grouped biases that could compromise the accuracy of my predictions.\n",
        "\n",
        "Given these parameters—the binary nature of the target variable and the dataset's uniform distribution—I can effectively apply various classification algorithms. This is particularly critical in criminal justice, where precise and unbiased data analysis is essential for making informed decisions."
      ],
      "metadata": {
        "id": "d0eVX46cZBka"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# @title\n",
        "#checking data, also provides data types for each column\n",
        "recid_df.info()\n"
      ],
      "metadata": {
        "id": "RkKBaJjI-_d0",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# @title\n",
        "# shape of dataset\n",
        "\n",
        "print('Recidivism data_set:', recid_df.shape)\n"
      ],
      "metadata": {
        "id": "dp7GSA7KGMa6",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# @title\n",
        "#ck for missing data for each column/feature\n",
        "\n",
        "missing_values_fraction = recid_df.isnull().mean()\n",
        "print(\"\\nFraction of Missing Values for Each Feature:\")\n",
        "print(missing_values_fraction)"
      ],
      "metadata": {
        "id": "aF2FMOfxD7Rt",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# @title\n",
        "\n",
        "#Part of data exploration is to get a better idea of the race and gender composition of the dataset.\n",
        "# unique races and genders\n",
        "unique_races = recid_df['race'].unique()\n",
        "unique_genders = recid_df['sex'].unique()\n",
        "\n",
        "print(\"\\nUnique Races:\")\n",
        "print(unique_races)\n",
        "\n"
      ],
      "metadata": {
        "id": "j3Ooa7bwFvpX",
        "collapsed": true,
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# @title\n",
        "print(\"\\nUnique Genders:\")\n",
        "print(unique_genders)"
      ],
      "metadata": {
        "id": "VNW63mpQHLTZ",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# @title\n",
        "# count of people belonging to each racial and gender group\n",
        "\n",
        "race_counts = recid_df['race'].value_counts()\n",
        "gender_counts = recid_df['sex'].value_counts()\n",
        "\n",
        "print(\"\\nCount of People in Each Racial Group:\")\n",
        "print(race_counts)\n",
        "\n",
        "print(\"\\nCount of People in Each Gender Group:\")\n",
        "print(gender_counts)"
      ],
      "metadata": {
        "id": "vZ1PLRi-F7W_",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# @title\n",
        "#summary of dataset\n",
        "\n",
        "(target_variable,target_variable_counts,data_types,missing_values_fraction,unique_races,unique_genders)"
      ],
      "metadata": {
        "id": "EtkGRKIzGdep",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Data Visualization"
      ],
      "metadata": {
        "id": "aIa41h-NNz3o"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "In the analysis of data about individuals who might commit crimes again within two years recidivism, I look into how a person’s age might influence their chances of reoffending. I use a type of chart called a histogram that shows how many people fall into different age categories. This helps highlight any trends, such as whether younger or older individuals are more likely to commit a crime again, making it clearer to see and understand any patterns in the data.\n",
        "\n",
        "Younger individuals, particularly those from minority racial groups, might be disproportionately predicted to reoffend due to stereotypes or historical data that inaccurately portray their risk. Older adults might be assessed differently, potentially facing less bias due to different societal perceptions of risk associated with age. By analyzing how age intersects with race in predictive models, we can uncover age-specific biases and work towards more accurately tailoring risk assessments. This nuanced understanding is crucial for ensuring that predictive algorithms do not unfairly penalize or favor any group based on age or racial background (Propublica)."
      ],
      "metadata": {
        "id": "RPiRVvjfAMmA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# @title\n",
        "# Target Variable vs Age\n",
        "\n",
        "plt.figure(figsize=(12, 6))\n",
        "sns.histplot(recid_df, x='age', hue='two_year_recid', multiple=\"stack\", binwidth=1)\n",
        "plt.title('Recidivism within Two Years by Age')\n",
        "plt.xlabel('Age')\n",
        "plt.ylabel('Count of Recidivism')\n",
        "\n",
        "#plt.text(0.5, -40, \"Figure 4: Recidivism rates by age, showing the counts of individuals who did and did not recidivate within two years, categorized by age.\",\n",
        "#        # ha='center', fontsize=9.5)"
      ],
      "metadata": {
        "id": "tnA_8IUDN4kI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 591
        },
        "cellView": "form",
        "outputId": "c2c6bb64-8ba3-4b4a-9f3e-85339b616415"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Count of Recidivism')"
            ]
          },
          "metadata": {},
          "execution_count": 238
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Here I use other plots to visualize the data"
      ],
      "metadata": {
        "id": "40MuFr9-QZ-R"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# @title\n",
        "# import necessary python packages\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import warnings\n",
        "\n",
        "# Save the original `plt.show` to another variable before any modifications\n",
        "original_show = plt.show\n",
        "\n",
        "def show_suppress_warnings(*args, **kwargs):\n",
        "    with warnings.catch_warnings():\n",
        "        warnings.simplefilter(\"ignore\")\n",
        "        original_show(*args, **kwargs)\n",
        "\n",
        "# Assign the new function to `plt.show`\n",
        "plt.show = show_suppress_warnings\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "# features used: age, priors_count, c_jail_days, juv_fel_count\n",
        "selected_features = ['age', 'priors_count', 'c_jail_days', 'juv_fel_count']\n",
        "\n",
        "plt.figure(figsize=(14, 12))\n",
        "\n",
        "# plot for target variable\n",
        "plt.subplot(3, 2, 1)\n",
        "recid_df['two_year_recid'].value_counts().plot(kind='bar', color=['blue', 'darkred'])\n",
        "plt.title('Distribution of Two-Year Recidivism')\n",
        "plt.xlabel('Recidivism Status (0 = No, 1 = Yes)')\n",
        "plt.ylabel('Count')\n",
        "\n",
        "# plots\n",
        "for i, feature in enumerate(selected_features, 1):\n",
        "    plt.subplot(3, 2, i+1)\n",
        "    bins = int(np.sqrt(len(recid_df[feature].dropna())))  # square root method for bins\n",
        "    plt.hist(recid_df[recid_df['two_year_recid'] == 1][feature],\n",
        "             bins=bins,\n",
        "             alpha=0.5, label='Recidivated', color='darkred')\n",
        "    plt.hist(recid_df[recid_df['two_year_recid'] == 0][feature],\n",
        "             bins=bins,\n",
        "             alpha=0.5, label='Not Recidivated', color='blue')\n",
        "    plt.title(f'Distribution of {feature} by Recidivism Status')\n",
        "    plt.xlabel(feature)\n",
        "    plt.ylabel('Count')\n",
        "    plt.legend()\n",
        "\n",
        "plt.tight_layout()\n",
        "#plt.show()"
      ],
      "metadata": {
        "id": "wgkns093OViG",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "0331bb93-c5a9-41ee-a71f-c58e9e83558d",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x1200 with 5 Axes>"
            ],
            "image/png": 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JrV+/Xg4ODlaNvXz5ctnZ2enTTz9Vbm6u4uPjNWXKFG3YsEGSNGbMGOXk5Oj69ev66quvJEl169Z9ofMFAAAAAOBlI0ALAJUoLCxMK1eu1O3bt+Xq6irp0Y7XXbt2KSQk5JmCl40aNVJ4eLgWLFigJk2aKDw83Fy3detWHTx4UAkJCeratau5vE2bNpoxY4b+/PNPde7c2aq5X7hwQYsWLVJISIgWLlyoWrX+/0sXRUVFkqTMzEwtXbpUgYGBWr58ublNq1atNHPmTP3666/66KOPrBr/wYMH+uWXX8x5f11cXDR79mydOXNGPj4+CggI0Nq1a5WdnW1xTQAAAAAAqE5IcQAAlSgsLEz5+fnasWOHuezAgQPKzs5WWFjYC/efkpIib29vtWrVSpmZmeY/PXr0kPQoXYC1du3apcLCQo0bN84iOCvJnIv34MGDys/P19ChQy3aDBw4UPXq1VNaWprV43/44YcWL2UrDkBfunTJ6j4BAAAAAKhq2EELAJXo9ddfV6tWrZScnKyBAwdKepTeoGHDhuYg6ou4cOGCzp49K39//1Lrb926ZXXfFy9eVK1ateTt7f3UNlevXpX0aMfs4xwdHWUwGHTlyhWrx2/atKnF5+IXpD2Z2xYAAAAAgOqMAC0AVKCCgoISZWFhYVqyZIkyMzNVr1497dmzR3379lXt2i9+Cy4sLJSPj485B+uTPDw8XniMylZQUCB7e/sS5U/u2i1WnF4BAAAAAICagAAtAFjB1dW1xE7OvLw8ZWRklGgbFhamuLg47dixQ40aNVJubq769u1bIfPw8vLS6dOn5e/vb047UFG8vLxUWFios2fPqm3btqW2Kd7lmp6eLoPBYC7Py8vT5cuX1bNnT3NZaddMerQL9/Fjn0dFnzMAAAAAAC8bOWgBwAoGg0F//PGHRVliYmKpO2i9vb3l4+Mjk8kkk8mkxo0bq1u3bhUyj9DQUN24cUOJiYkl6u7fv6+7d+9a3XefPn1Uq1YtLVq0SIWFhRZ1xbtYe/bsKQcHByUkJFjsbN24caNycnIUHBxsLjMYDDp+/Ljy8vLMZampqbp27ZrVc3R2dlZOTo7VxwMAAAAAYGvsoAUAKwwcOFAzZsxQVFSUevbsqdOnT2v//v1q2LBhqe3DwsK0cOFCOTk5acCAAU/9+v7zCg8PV3JysmbMmKEjR46oc+fOKigoUHp6ulJSUhQfHy9fX1+r+m7evLnGjBmjxYsXa8iQIXrnnXfk6OiokydPyt3dXV9++aXc3NwUGRmpuLg4jRo1Sr1799a5c+f0008/ydfXV++//765v4EDB2r79u0aNWqUQkNDdfHiRSUlJcnLy8vq83/jjTdkMpk0d+5c+fr6qk6dOurdu7fV/QEAAAAA8LIRoAUAKwwaNEiXL1/Wxo0btW/fPnXp0kWrVq3S8OHDS20fFham77//Xvfu3VNoaGiFzaN4h+vq1au1detW7dy5U87OzvL09FRERIRatmz5Qv1PnDhRnp6eWrdunebPny9nZ2cZjUaFh4eb20RFRcnNzU3r1q3T3Llz5erqqkGDBmny5MlycHAwt+vVq5emTp2qVatWac6cOWrfvr2WLFmi7777zur5DRkyRKdOndLmzZu1evVqNWvWjAAtAAAAAKBasSvibSsAUK4NGzZo+vTpSktLqxYv3gIAAAAAANUDOWgB4BlkZGTIzs5Orq6utp4KAAAAAACoQUhxAABl+O+//7R9+3b9/PPPevPNN+Xs7Fyh/RcUFCgzM7PMNnXq1FHdunUrdNyMjIwy61977TXVr1+/QscEAAAAAAAlEaAFgDKcPXtWMTEx6tChg2bNmlXh/V+7dk1vv/12mW3Gjx+vqKioCh03MDCwzPr+/ftr3rx5FTomAAAAAAAoiRy0AGBDDx480NGjR8tsYzAYZDAYKnTcgwcPllnv7u6u1q1bV+iYAAAAAACgJAK0AAAAAAAAAGAjvCQMAAAAAAAAAGyEAC0AAAAAAAAA2AgBWgAAAAAAAACwEQK0AAAAAAAAAGAjBGgBAAAAAAAAwEYI0AIAAAAAAACAjRCgBQAAAAAAAAAbIUALAAAAAAAAADbyf7SeDd2GoNmbAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "### Data Balance\n",
        "\n",
        "In this analysis, I focus on understanding how evenly distributed two groups are in our dataset. These groups are labeled 'unlikely to reoffend' (0) and 'likely to reoffend' (1), and the task is to predict which group individuals belong to based on their data.\n",
        "\n",
        "It's important that these two groups are roughly equal in size because if one group is much larger than the other, it can skew the results. For example, if most people in the dataset are 'unlikely to reoffend,' a model might learn just to predict 'unlikely to reoffend' for everyone. This would make the model appear accurate, but it wouldn’t be very useful since it isn't really assessing each case on its merits.\n",
        "\n",
        "To check for this, I look at the number of people in each group. If one group is much bigger, I know there might be a bias problem. I also calculate the baseline accuracy, which is how often a very simple model would be right if it always predicted the larger group. This gives me a basic standard to beat, ensuring that any more sophisticated model I develop is actually learning from the data and not just exploiting the imbalance."
      ],
      "metadata": {
        "id": "rnai04FrcLy5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# @title\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "\n",
        "# visualize the target variable\n",
        "plt.figure(figsize=(9, 6))\n",
        "ax = sns.countplot(x='two_year_recid', data=recid_df, palette='pastel')\n",
        "plt.title('Distribution of Target Variable (Recidivism within Two Years)')\n",
        "plt.xlabel('Recidivism within Two Years (0 = No, 1 = Yes)')\n",
        "plt.ylabel('Count of Recidivism')\n",
        "\n",
        "# count of each class\n",
        "class_counts = recid_df['two_year_recid'].value_counts().sort_index()\n",
        "for i, count in enumerate(class_counts):\n",
        "    ax.text(i, count + 10, str(count), ha='center', va='bottom')  # Ensuring proper placement and conversion of count to string\n",
        "\n",
        "#plt.show()\n",
        "\n",
        "# calculate baseline accuracy\n",
        "baseline_accuracy = class_counts.max() / len(recid_df)\n",
        "print(f\"\\nBaseline Accuracy: {baseline_accuracy:.2%}\")\n"
      ],
      "metadata": {
        "id": "LpuE3x41RIjZ",
        "collapsed": true,
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "The baseline accuracy shows the target variable is slighty imbalanced at 54.93% or 3963 individuals unlikely to commit a crime within 2 years compared to 3251 more likely to commit a crime after being released from prison."
      ],
      "metadata": {
        "id": "_U2H-iSqSCDw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# @title\n",
        "# instances of each class in the target variable\n",
        "\n",
        "class_distribution = recid_df['two_year_recid'].value_counts()\n",
        "print(class_distribution)"
      ],
      "metadata": {
        "id": "EUk7lnNeSBb0",
        "collapsed": true,
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# @title\n",
        "# balance\n",
        "class_proportion = class_distribution/len(recid_df)\n",
        "print(class_proportion)"
      ],
      "metadata": {
        "id": "oZOjxjicUHCe",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Observations"
      ],
      "metadata": {
        "id": "P6bg9UQQcpRY"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Gender can play a significant role in influencing the likelihood of reoffending, and understanding these differences is crucial for developing targeted interventions and policies.\n",
        "\n",
        "I use a bar chart to simply compare recidivism which is the tendency of a convicted criminal to reoffend between males and females within a two-year period. The data shows a notable difference: although the majority of both males and females did not reoffend, a higher percentage of males did compared to females.\n",
        "\n",
        "This finding highlights a critical issue with the risk assessment algorithms used in the criminal justice system. These tools need to accurately reflect that women generally have lower recidivism rates. If they don’t, there's a risk that women might be unfairly judged as higher risk than they actually are, potentially leading to unnecessarily harsh consequences like longer prison terms or stricter bail conditions.\n",
        "\n",
        "Keeping this in mind it's essential that we rigorously evaluate these algorithms to ensure they do not perpetuate gender bias, just as we are cautious about racial bias. This will help make sure that the algorithms aid in achieving fair treatment for everyone, regardless of gender. However, while doing so, we must avoid overly simple conclusions that may overlook other influencing factors such as employment status, housing stability, and educational attainment."
      ],
      "metadata": {
        "id": "Pj-pBUenUoQI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# @title\n",
        "#figure 1\n",
        "sns.set(style=\"whitegrid\")\n",
        "\n",
        "\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.countplot(x='sex', hue='two_year_recid', data=recid_df)\n",
        "plt.title('Recidivism Rates by Gender', fontsize=10)\n",
        "plt.xlabel('Gender', fontsize=10)\n",
        "plt.ylabel('Count of Recidivism', fontsize=10)\n",
        "plt.legend(title='Recidivism within Two Years', labels=['No', 'Yes'])\n",
        "plt.text(0.5, -90, \"Figure 1: Recidivism rates by gender, showing the counts of individuals who did and did not recidivate within two years, categorized by gender.\",\n",
        "         ha='center', fontsize=9.5)\n",
        "plt.grid(True)\n",
        "plt.tight_layout()"
      ],
      "metadata": {
        "id": "wXx0m4PAUhht",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 601
        },
        "outputId": "54cd3f96-15dd-49f1-fac0-80ded99dbab4",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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C/P39LdfFAwCQXyajIBceAQCAe97cuXO1fv16qxuiAQBwL+KUbwAAkC9Xr17V4cOHtXTp0mxvhgYAwL2GQA0AAPLlzTffVJcuXdS0adN8ne4NAEBRxynfAAAAAADYgB5qAAAAAABsQKAGAAAAAMAGBGoAAAAAAGzA71AXMnv27JFhGHJycrJ3KQAAAAAKkdTUVJlMJpnNZnuXgv+PHupCxjAMcZ844Np7ISUlhfcDAIDPBOD/IysUPvRQFzKZPdMNGjSwcyWAfSUkJOjAgQOqXbu23Nzc7F0OAMCO+EwArtm3b5+9S8AN6KEGAAAAAMAGBGoAAAAAAGxAoAYAAAAAwAYEagAAAAAAbECgBgAAAADABtzlGwAAoAhJT09Xamqqvcu4pZKTky3/OzjQH4SiycnJScWKFbN3GSggAjUAAEARYBiGoqOjdfnyZXuXcstlZGTI0dFRZ8+eJVCjSCtTpowqV64sk8lk71KQTwRqAACAIiAzTN93331yc3MrUgfk6enpSk5OlouLCz14KJIMw1BCQoIuXLggSapSpYqdK0J+EagBAADucunp6ZYwXb58eXuXc8ulp6dLkooXL06gRpHl6uoqSbpw4YLuu+8+tvW7BOfMAAAA3OUyr5l2c3OzcyUAbkbme7io3QehKCNQAwAAFBFF6TRv4F7Ee/juQ6AGAAAAAMAGBGoAAADcFqdPn5aPj49WrlyZZ9sxY8YoNDTUapiPj49mz55d4PnOnj1bPj4+BX5eYVKQZffx8dEbb7yRZ7uVK1fKx8dHp0+fvtnyAPx/3JQMAACgiFu5cqXGjh1reVysWDGVL19eQUFBGjlypCpVqmTH6pAfv//+u7Zu3apnnnlG7u7u9i5HY8aM0apVq/Js9/jjj2vq1Km3vZ7U1FR16dJFiYmJWrNmjYoXL241/vTp0+rQoYNatmypWbNm3fZ6cO8gUAMAANwjwsPDVa1aNaWkpGjv3r1atWqVdu/erTVr1sjFxeWWz8/Dw0N//vmnHB1tO+T8888/bbrT8ZAhQzRw4ECb5llY3Ljse/bs0Zw5c/T444/bHKg7deqkDh06yNnZ+abre+KJJxQYGGh5fPr0ac2aNUtPPPGEGjdubBlevXr1m55Xfjg5OemNN95Qr1699OGHH+rFF1+0Gv/mm2/KyclJ48ePvyP14N5BoAYAALhHtGjRQg0aNJAkde/eXWXLltXChQu1adMmhYWF3fL5mUymmwrqmc/N/Nms/HJ0dLQ5xBcWt+MLjmLFit2yn2Iym80ym82Wx/v27dOsWbPUsGFDderU6ZbMw5aaevbsqY8//liPPvqoHnjgAUnShg0b9NNPP+m1117Tfffdd9vrSEhI4I779xCuoQYAALhH+fv7S5JOnTplNfzYsWMKDw9X06ZN1aBBA3Xp0kWbNm3K8vy4uDhNnjxZoaGhql+/vlq0aKFXXnlFFy9elJTzNdT/+9//1LFjRzVo0EAdO3bU999/n219119H/L///U++vr769ddfs7T76quv5OPjo8OHD0vK/hrqrVu3qlevXvL395fZbFa7du00ffp0y/idO3fKx8dH69at05w5c9S8eXOZzWaFh4frypUrSklJ0dtvv63AwECZzWaNHTtWKSkpua7fzz77THXr1lVcXJxl2McffywfHx9NmTLFMiw9PV1ms1nvvfdetss+e/Zsvfvuu5KkVq1aycfHJ9troTPXa/369dWhQwdt3rzZanx211CHhoZq0KBB2rVrl7p166YGDRqoVatW+vbbb3Ndtrxs2rRJPj4+OnjwoGXYhg0b5OPjo2HDhlm1bd++vUaMGGF5nJaWprlz56p169aqX7++QkNDNX369DzXtySNGjVKZcqU0WuvvSbDMHT16lVNnjxZZrNZvXr1kiT98ccf6t+/vxo3bqwHH3xQvXv31u7du62mc+bMGb3++utq166d/Pz8FBAQoPDw8CzrPHOd/vrrr3r99dcVGBioli1bSpLi4+P19ttvW94fgYGBevbZZ/XXX38VaF2icLu7v7oDAACAzc6cOSNJVqcQHzlyRL169VKlSpU0YMAAubm56b///a+GDh2q2bNnq02bNpKkq1ev6qmnntKxY8fUtWtX+fr66tKlS/rhhx90/vx5lStXLtt5btmyRS+88IJq166tUaNG6dKlSxo7dqwqV66ca63BwcGWWpo2bWo1bt26dXrggQfk7e2d7XOPHDmiQYMGycfHR+Hh4XJ2dtaJEyf0+++/Z2m7YMECFS9eXAMHDtSJEyf0+eefy9HRUSaTSXFxcRo2bJj++OMPrVy5Uh4eHlnC4fX8/f2VkZGh3bt3KyQkRJK0a9cuOTg4aNeuXZZ2f//9txISEtSkSZNsp9OmTRsdP35ca9as0dixY1W2bFlJslrHu3fv1saNG/Xkk0+qRIkSWrJkicLDw/Xjjz9a2ufkxIkTGj58uLp166bHH39cK1as0JgxY1SvXj1LL29BNW7cWCaTSbt27VKdOnWslv368Hrx4kVFRkaqd+/elmHjx4/XqlWr1K5dOz377LP6888/NX/+fB07dkxz587Ndb6lSpXS+PHjNXz4cH3zzTc6evSoYmJitHDhQplMJm3fvl0DBgxQ/fr1NWzYMJlMJq1cuVLPPPOMvvjiC/n5+Um61uO+Z88edejQQZUrV9aZM2f05Zdf6umnn9batWvl6upqNd9JkyapXLlyGjp0qBISEiRJr732mjZs2KDevXurVq1aunz5snbv3q1jx46pXr16Nq1XFD4EagAAgHtEfHy8Ll68qJSUFP3xxx+aM2eOnJ2dLWFPkt5++21VqVJFK1assFxr++STT6pXr16aNm2aJVBHRETo8OHDmjNnjmWYJD3//PMyDCPHGqZNm6by5cvriy++UKlSpSRJTZs2Vb9+/eTh4ZHj84oXL66QkBBt2LBB48ePt5y6/M8//+i3337LNdhu3bpVqampWrhwYY5BP1N6erqWLFkiJycnSdKlS5e0du1aNW/eXAsXLpQkPfXUUzp58qRWrlyZ63zr1KmjkiVLateuXQoJCZFhGNq9e7fatm2r77//XlevXlWJEiW0e/duOTg4qFGjRjlOx9fXV2vWrFHr1q1VrVq1LG2OHTumdevWWa5ZDggIUKdOnbR27VqrsJqdqKgoLV261HLGQvv27dWyZUutXLlSo0ePzvW5OSlTpoxq166tXbt2Weafuezr16/XsWPHVKtWLUu4zrzu+uDBg1q1apW6d++ut956S9K19V2uXDl9/PHH2rFjhx566KFc5/3II48oJCRE7777rhISEvTcc8/J29tbhmHo9ddfV0BAgBYtWmT5zeeePXuqQ4cOmjlzpj7++GNJ0sMPP6xHHnnEarohISF64okntGHDBnXu3NlqXOnSpfXpp59anVL/888/q0ePHhozZoxl2IABAwq6KlHIcco3AADAPaJv376WU1LDw8Pl6uqqjz76yNI7fPnyZe3YsUPt27e3hO+LFy/q0qVLCg4O1vHjx3X+/HlJ0saNG1WnTh2rMJ0pM6jc6MKFCzpw4IAef/xxS5iWpKCgINWuXTvP+h955BHFxMRYnfa9YcMGZWRk5HoNeGYP/KZNm5SRkZHrPDp16mQJ05Lk5+cnwzDUtWtXq3Z+fn46d+6c0tLScpyWg4ODzGazpTf62LFjunz5sgYOHCjDMLR3715J13puH3jggZu6e3ezZs2sbgCWGeZvPJ0/O7Vr17aEaelaz7enp2e+npubxo0bW5Y9Pj5eBw8e1BNPPKGyZctagvSuXbvk7u5uObvg559/liQ9++yzVtPq16+f1fi8TJw4UampqapcubKef/55SdKBAwd0/PhxPfroo7p06ZJl+05ISFBgYKB+++03y/Zx/V3CU1NTdenSJVWvXl3u7u76+++/s8yvR48eWa5Pd3d31x9//GF5z6BooocaAADgHjFx4kR5enrqypUrWrFihX777TerOz6fPHlShmHogw8+0AcffJDtNGJiYlSpUiWdPHlSbdu2LdD8z549K0mqUaNGlnGenp7ZBpXrNW/eXKVKldK6dessd5het26d6tatK09PzxyfFxYWpm+++Ubjx4/X+++/r8DAQLVp00aPPPKIHBys+5eqVq1q9Tgz+FepUiXL8IyMDF25ciXXU6r9/f01Z84cJSUladeuXapYsaLq1aunOnXqaNeuXQoKCtLu3bvVvn37XJc9LzfWJ13rNb3++u2CPjc2NvamavL399dXX32lEydO6OTJkzKZTGrYsKH8/f21a9cu9ejRQ7t27VKjRo0sr8OZM2fk4OCQ5e7gFStWlLu7u+UyhbxUrVpV5cuX1wMPPGAJx8ePH5ekXHvdr1y5otKlSyspKUnz58/XypUrdf78eauzLq5cuZLledmdNfDSSy9pzJgxevjhh1WvXj21bNlSnTt31v3335+vZcDdgUANoFAymUxWPQQAgJvn5+dnuct369at9eSTT2rUqFFav369SpQoYemd69evn5o3b57tNO7UzyBlx9nZWa1bt9b333+v1157TTExMfr999+z/ETSjYoXL66lS5dq586d+umnn/TLL79o3bp1+vrrr/Xxxx9b9SzeGLDzGp7b6e3StV7a1NRU7dmzR7t27bL0BGf23h47dkwXL1606iG2RU53786rvtyee7MyT+P+7bffdOrUKfn6+srNzU3+/v767LPPdPXqVR04cMDqhmSZcjrL4WZkrotXXnlFdevWzbZN5t2533zzTcu11Q0bNlSpUqVkMpk0cuTIbNdpdndlDwsLk7+/v77//ntt3bpVERERWrhwoWbPnm25cRnufgRqFEhGhiEHh1u/gwNu5OrqKl/fekpNzfuOngCAgitWrJhefPFFPf3001q6dKkGDhxo6TlzcnJSs2bNcn1+9erVdeTIkQLNM7P398SJE1nGRUVF5Wsa7du316pVq7R9+3YdO3ZMhmHkq3fXwcFBgYGBCgwM1NixYzVv3jzNmDFDO3fuzHNZb4afn5+cnJy0e/du7d69W/3795ckNWnSRN9884127NghSXkG6tsRMG+3qlWrqmrVqtq9e7dOnTplWUZ/f39NmTJF69evV3p6utXN2Dw8PJSRkaETJ06oVq1aluH//vuv4uLicr3OPi+Z23fJkiXzfM0zr5O+/vrn5OTkbHunc3Pffffpqaee0lNPPaWYmBg9/vjjmjdvHoG6CCFQo0AcHEya++VWnblwc6cAAXnxuK+0hvYKUmqqvSsBgKIrICBAfn5+Wrx4sZ555hmVL19eTZs21ddff63evXtn+c3eixcvWm7q1bZtW82dO1fff/99luuoDcPINgDed999qlu3rlatWqWBAwdaTqfeunWrjh49mq+w1KxZM5UpU0br1q1TZGSk/Pz88jyF9vLlyypTpozVsMweyvz8FNPNcHFxUYMGDbRmzRqdPXvWKlQmJSXps88+U/Xq1fP8feTMu0oXNNDZW+PGjbVjxw7FxMSob9++kq6t+xIlSljuqH79Ha9btmyp6dOna/HixXrjjTcswz/55BPLeFvVr19f1atX18cff6yOHTuqRIkSVuOv376z67VfsmRJvn8TPT09XQkJCVb3Cihfvrzuu+++277N4c4iUKPAzlyI1fEzl+xdBgAAuAX69++v4cOHa+XKlerVq5dee+01Pfnkk3r00UfVo0cP3X///fr333+1d+9eRUdH67vvvrM8b8OGDRo+fLi6du2qevXqKTY2Vj/88IMmTZpk+amkG7344osaNGiQnnzySXXt2lWXL1/W559/rgceeMDyc0O5cXJyUps2bbR27VolJibm6y7Uc+fO1a5du9SyZUt5eHgoJiZGX3zxhSpXrmw5Lfl28vf314IFC1SqVCnLzbfKly8vT09PRUVFqUuXLnlOIzN0zpgxQ2FhYXJyclJISIjlFOXCyt/fX6tXr5bJZLKs62LFislsNmvLli1q2rSp1XX8derU0eOPP66vv/5acXFxatKkifbt26dVq1apdevWed7hOzcODg566623NGDAAHXs2FFdunRRpUqVdP78ee3cuVMlS5bUvHnzJF27y/d//vMflSxZUrVr19bevXu1bdu2LF/M5OTq1atq2bKl2rVrpzp16sjNzU3btm3Tvn37rHq9cfcjUAMAANzD2rZta+m169Gjh2rXrq0VK1Zozpw5WrVqlS5fvqxy5crJ19dXQ4cOtTyvRIkSWrp0qWbPnq3vv/9eq1atUvny5RUYGKhKlSrlOL8WLVrogw8+0MyZM/X++++revXqmjJlijZt2mR19+7cZN5kzGQy5et079DQUJ05c0YrVqzQpUuXVLZsWTVt2lQvvPCCVQ/i7ZIZqM1ms9W12P7+/oqKispXqPfz89Pw4cP11Vdf6ZdfflFGRoY2bdp0VwRqSfLy8rK6eZu/v7+2bNmS7anub731lqpVq6ZVq1bpf//7nypUqKBBgwbl+hNl+RUQEKCvv/5aH374oT7//HMlJCSoYsWK8vPz0xNPPGFp9+qrr8rBwUGrV69WcnKyGjVqpE8++UTPPfdcvuZTvHhx9erVS1u3btXGjRtlGIaqV69u+cIKRYfJyM+dCu4CP//8sxYuXKijR48qPj5elSpVUuvWrTVs2DCrHeUPP/ygmTNnKioqSlWrVtXAgQOz/AxCSkqKZsyYoe+++05Xr16V2WzWhAkT5OXlZdXu2LFjeuutt7Rnzx6VKFFCnTp10ogRI6y+ZSuoffv2SZLlhiGF0bgP1tFDjduupkdZTR4epsTERMtpbgCA7CUlJSkqKkqenp5WP/dTVKSnpyspKUnFixe/bTfQAgqDvN7Ld0NWuNcUmR7qy5cvy8/PT3369FGZMmV05MgRzZ49W0eOHLH8QPuuXbs0bNgwdevWTePGjdOOHTv06quvqkSJElY/3P7WW29p3bp1GjNmjCpVqqR58+apb9++Wrt2rSWcx8bG6plnnlHNmjU1e/ZsnT9/XlOnTlVSUpImTpxol3UAAAAAALhzikyg7tSpk9XjgIAAOTs7a8KECTp//rwqVaqkjz76SH5+fpYbHDz00EM6deqUZs2aZQnU0dHRWr58uV577TV169ZN0rVvgEJCQvTVV19pwIABkqSvvvpKV69e1Zw5cyzXUqSnp2vSpEkaNGhQrqc6AQAAAADuftn/oF4RkRl0U1NTlZKSop07d1r1REvXrsE5duyYTp8+LUnasmWLMjIyrNqVKVNGQUFB2rx5s2XY5s2bFRgYaHVjgvbt2ysjI0Nbt269fQsFAAAAACgUilygTk9PV3Jysv766y/NnTtXoaGhqlatmk6ePKnU1NQs10Fn/r5dZGSk5f/y5curdOnSWdpltslsd+O03N3dVbFiRat2AAAAAICiqcic8p0pJCRE58+flyQ1b95c77//vqRr1zxL10Lv9TIfZ46Pi4vL9m6P7u7uljaZ7W6cliSVLl3aqp0tDMPI189G3Gkmk4mbQ+GOS05OVhG5dyIA3DbJycnKyMhQenp6vn8n926S+TlgGEaRXD4gU3p6ujIyMpSYmKiMjIws43P6jXfYT5EL1AsWLFBiYqKOHj2qjz76SIMHD7b8EPzdIjU1VQcOHLB3GVm4urrK19fX3mXgHnP27FklJibauwwAKPQcHR2VnJxs7zJuq6K+fEBycrLS0tJyPeP1Zn5RCLdekQvUderUkSSZzWY1aNBAnTp10vfff6/atWtLkq5cuWLVPi4uTpIsp3i7u7srPj4+y3Tj4uKsTgN3d3fPMi3pWk/3jaeLF5STk5Ol3sKEb8NgD1WrVuWDAwDykJycrLNnz8rFxaVI/myWYRhKTk6Wi4sLxyMo8hwdHVW9enW5uLhkGXf06FE7VITcFLlAfT0fHx85OTnp5MmTCg0NlZOTkyIjI9W8eXNLm8xvfzKvh/by8tK///6bJRjfeM20l5dXlm+Orly5on/++SfLtdUFZTKZ5ObmdlPTAIoKFxcXLjUAgDw4ODjIwcFBxYoVK5K/05x5mrfJZCqSywdkKlasmBwcHOTq6prtl2N8oVT4FLmbkl3vjz/+UGpqqqpVqyZnZ2cFBARow4YNVm3WrVunWrVqqVq1apKk4OBgOTg4aOPGjZY2sbGx2rJli1q0aGEZ1qJFC23bts3Swy1J69evl4ODg4KCgm7zkgEAAAAA7K3I9FAPGzZM9evXl4+Pj4oXL66DBw8qIiJCPj4+at26tSRpyJAhevrpp/X666+rffv22rlzp9asWaMZM2ZYplO5cmV169ZN7777rhwcHFSpUiXNnz9fpUqVUs+ePS3tevbsqSVLlmjo0KEaNGiQzp8/r3fffVc9e/bkN6gBAAAA4B5QZAK1n5+f1q1bpwULFsgwDHl4eKh79+7q37+/5fpLf39/zZ49WzNnztTy5ctVtWpVvfXWW2rfvr3VtMaPH68SJUro/fff19WrV9WoUSN98sknVnf/Ll26tBYvXqw333xTQ4cOVYkSJdStWzeNHDnyji43AAAAAMA+TAa/R1Oo7Nu3T5LUoEEDO1eSs3EfrNPxM5fsXQaKuJoeZTV5eJgSExO5hhoA8pCUlKSoqCh5enoWyZuSpaenKykpScWLF+ca6nyaPXu25syZI39/fy1dutRq3Ntvv61Nmzbphx9+sFN1yEle7+W7ISvca4r0NdQAAADArZCRYZ8+qJud765du7Rz585bVA2AGxWZU74BAACA28XBwaS5X27VmQuxd2yeHveV1tBett/s1s3NTbVr19aHH36ogICAW1gZgEwEagAAACAfzlyIvesue3v++ec1ePBg/f7772rUqFG2bc6cOaN33nlHW7duVXp6uho3bqxXXnlFPj4+d7ha4O7DKd8AAABAERUSEiJfX1/NnTs32/Hx8fHq06eP/v77b02aNEnvvfeeLl26pN69e+vcuXN3uFrg7kOgBgAAAIqwIUOGaMuWLfrzzz+zjFu5cqXOnj2r+fPnq2PHjmrTpo0+/vhjpaWlafHixXaoFri7EKgBAACAIqxNmzby9vbOtpd6165deuCBB1SrVi3LsDJlyqhZs2bavXv3nSwTuCsRqAEAAIAizGQyafDgwfrpp5/0119/WY2Li4tThQoVsjynfPnyio29czdgA+5WBGoAAACgiGvfvr08PT314YcfWg0vXbq0YmJisrSPiYlR6dKl71R5wF2LQA0AAAAUcQ4ODho8eLA2bdqkQ4cOWYY3btxYhw8fVmRkpGVYbGystm3bpsaNG9ujVOCuQqAGAAAA7gGPPvqo7r//fu3cudMyrEuXLqpataoGDRqktWvX6n//+5/69esnR0dHPfPMM3asFrg78DvUAAAAQD543HdnT4G+1fMrVqyYBg4cqPHjx1uGlSxZUkuWLNHUqVM1YcIEZWRkqFGjRvr8889VpUqVWzp/oCgiUAMAAAB5yMgwNLRXkF3m6+BgKvDzXnjhBb3wwgtZhnfv3l3du3e3Gubh4aHZs2fbXCNwL+OUbwAAACAPtoTau3m+APKHQA0AAAAAgA0I1AAAAAAA2IBADQAAAACADQjUAAAAAADYgEANAAAAAIANCNQAAAAAANiAQA0AAAAAgA0I1AAAAAAA2IBADQAAABQhffr0Ufv27ZWSkpJlXHh4uFq2bKmrV6/aoTKg6CFQAwAAAEXIpEmTdPr0aS1atMhq+ObNm7VhwwZNmDBBJUqUsFN1QNFCoAYAAADyYGRk3DXz9fLy0qBBgzRv3jydOnVKkpScnKw333xTrVq1UuvWrW91mcA9y9HeBQAAAACFncnBQVFrFiox5twdm6dr+Sry7DjApucOHDhQa9as0aRJk7Ro0SLNmzdP//77r5YsWaLo6GhNmzZNv/zyixITE9WgQQONHTtW9evXtzx/06ZNmjt3rqKiolSsWDFVr15dw4cPV8uWLW/V4gFFAoEaAAAAyIfEmHNKPH/S3mXki7Ozs9544w316dNHH374oRYtWqRRo0bJ1dVVjz/+uNzc3DRhwgSVKlVKS5Ys0TPPPKONGzeqfPnyOnnypIYPH64OHTpo1KhRysjI0MGDBxUbG2vvxQIKHQI1AAAAUAQ1bdpUXbp00QcffKB69eqpT58+mjt3ruLi4vTNN9+ofPnykqTAwEC1a9dOEREReuWVV/T3338rNTVVEyZMUMmSJSVJzZs3t+eiAIUW11ADAAAARdTAgQMlSc8++6yKFSumrVu3KiAgQKVLl1ZaWprS0tLk4OCgJk2aaN++fZIkHx8fFStWTC+99JJ++OEHXblyxZ6LABRq9FADAAAARZSTk5PV/5cuXdLevXtVr169LG2rV68uSfL09NS8efM0f/58DRs2TA4ODgoODtbEiRNVtWrVO1c8cBcgUAMAAAD3iNKlS6t58+YaPnx4lnHOzs6Wv1u0aKEWLVooPj5emzdv1pQpUzR27FgtXrz4TpYLFHoEagCFmslksncJAAAUGc2aNdN3332nWrVqyc3NLc/2JUuWVFhYmP7880+tWbPmDlQI3F0I1AAKpdKlisvIyFDx4sXtXQruAUZGhkwO3FYEQNHXt29frV69Wr1799bTTz+tqlWr6uLFi/rjjz9UqVIl9e3bV1999ZX27t2r5s2bq2LFijp9+rS+++47BQUF2bt8oNAhUAMolEoUd7bLb37i3nMzv/MK4N7iWr7KXT+/smXL6uuvv9bMmTM1bdo0Xb58WeXLl9eDDz6oNm3aSLp2U7Iff/xRU6ZM0eXLl1WxYkV16NAh29PEgXsdgRpAoXY3/eYnAKDoMjIy7PLl282eQVOtWjUdOnTIaljFihX19ttv5/gcs9ms+fPn2zxP4F7C+W0AAABAHux1WQiXowCFG+9QAAAAAABsQKAGAAAAAMAGBGoAAAAAAGxAoAYAAAAAwAYEagAAgCLCMAx7lwDgJvAevvsQqAEAAO5yTk5OkqSEhAQ7VwLgZmS+hzPf0yj8+B1qAACAu1yxYsVUpkwZXbhwQZLk5uYmk8lk56punfT0dCUnJ0u6tqxAUWMYhhISEnThwgWVKVOG7fwuQqAGAAAoAipXrixJllBdlGRkZCgtLU2Ojo5y4HeZUYSVKVPG8l7G3YFADQAAUASYTCZVqVJF9913n1JTU+1dzi2VmJioyMhIVa9eXa6urvYuB7gtnJyc6Jm+CxGoAQAAipBixYoVuYPyjIwMSZKLi4uKFy9u52oA4P9wzgwAAAAAADYgUAMAAAAAYAMCNQAAAAAANiBQAwAAAABgAwI1AAAAAAA2IFADAAAAAGADAjUAAAAAADYgUAMAAAAAYAMCNQAAAAAANiBQAwAAAABgAwI1AAAAAAA2IFADAAAAAGADAjUAAAAAADYgUAMAAAAAYAMCNQAAAAAANiBQAwAAAABgAwI1AAAAAAA2IFADAAAAAGADAjUAAAAAADYoMoH6v//9r4YMGaIWLVqoYcOG6tSpk5YvXy7DMCxt+vTpIx8fnyz/jh07ZjWtK1euaNy4cWratKnMZrPCw8N14cKFLPP8/fff9cQTT8jPz08hISFasGCB1fwAAAAAAEWXo70LuFU+/fRTeXh4aMyYMSpbtqy2bdumCRMmKDo6WsOGDbO0a9SokUaPHm313GrVqlk9HjFihI4eParXX39dLi4umjlzpgYMGKAVK1bI0fHaKjtx4oT69++voKAgjRgxQocOHdK0adNUrFgx9e/f//YvMAAAAADAropMoP7oo49Urlw5y+PAwEBdvnxZn3zyiZ5//nk5OFzrjHd3d1fDhg1znM6ePXu0ZcsWRUREKDg4WJLk6empsLAwbdy4UWFhYZKkiIgIlS1bVtOnT5ezs7MCAwN18eJFzZs3T3369JGzs/PtW1gAAAAAgN0VmVO+rw/TmerWrav4+HglJCTkezqbN2+Wu7u7goKCLMO8vLxUt25dbd682apdq1atrIJzWFiY4uLitGfPHhuXAgAAAABwtygygTo7u3fvVqVKlVSyZEnLsF9//VUNGzZUgwYN1Lt3b/32229Wz4mMjJSnp6dMJpPVcC8vL0VGRkqSEhISdO7cOXl5eWVpYzKZLO0AAAAAAEVXkTnl+0a7du3SunXrrK6XbtKkiTp16qSaNWvqwoULioiI0LPPPqslS5bIbDZLkuLi4lSqVKks0ytdurT2798v6dpNy6Rrp49fz9nZWa6uroqNjb2p2g3DKFCv+p1iMpnk6upq7zIA4LZITEzkxpJAIZWYmGj1P3CvMgwjS8cf7KtIBuro6GiNHDlSAQEBevrppy3Dw8PDrdo9/PDD6tixoz788EMtXLjwTpeZo9TUVB04cMDeZWTh6uoqX19fe5cBALdFVFQUB+tAIXf8+HF7lwDYHfdqKlyKXKCOi4vTgAEDVKZMGc2ePdtyM7LsuLm5qWXLltqwYYNlmLu7u6Kjo7O0jY2NVenSpSXJ0oOd2VOdKSUlRYmJiZZ2tnJyclLt2rVvahq3A9+GASjKPD096aEGCqnExEQdP35cNWvW5Gw53NOOHj1q7xJwgyIVqJOSkjRo0CBduXJFX3/9dbanbufFy8tL27dvz3I6RVRUlLy9vSVdC+JVqlTJcq10VFSUDMPIcm11QZlMJrm5ud3UNAAABcNBOlD4ubq6coyEexodXIVPkbkpWVpamkaMGKHIyEgtWrRIlSpVyvM5CQkJ+umnn9SgQQPLsBYtWig2Nlbbt2+3DIuKitLff/+tFi1aWLXbtGmTUlNTLcPWrVsnd3d3y/XYAAAAAICiq8j0UE+aNEk//vijxowZo/j4eO3du9cyztfXV3/++acWLVqkNm3ayMPDQxcuXNAnn3yif/75Rx988IGlrdlsVnBwsMaNG6fRo0fLxcVFM2bMkI+Pj9q2bWtp179/f61evVqjRo1Sr169dPjwYUVERGjkyJFc1wAAAAAA94AiE6i3bt0qSZo6dWqWcZs2bVLFihWVmpqqGTNm6PLly3J1dZXZbNakSZPk5+dn1X7mzJmaMmWKJk6cqLS0NAUHB2v8+PFydPy/1VWjRg1FRERo6tSpGjhwoMqVK6fw8HD169fv9i4oAAAAAKBQKDKB+ocffsizTURERL6mVapUKU2ePFmTJ0/OtV2jRo20bNmyfE0TAAAAAFC0FJlrqAEAAAAAuJMI1AAAAAAA2IBADQAAAACADQjUAAAAAADYgEANAAAAAIANCNQAAAAAANiAQA0AAAAAgA0I1AAAAAAA2IBADQAAAACADQjUAAAAAADYgEANAAAAAIANCNQAAAAAANiAQA0AAAAAgA0I1AAAAAAA2IBADQAAAACADQjUAAAAAADYgEANAAAAAIANCNQAAAAAANiAQA0AAAAAgA0I1AAAAAAA2IBADQAAAACADQjUAAAAAADYgEANAAAAAIANCNQAAAAAANiAQA0AAAAAgA0I1AAAAAAA2IBADQAAAACADQjUAAAAAADYgEANAAAAAIANCNQAAAAAANiAQA0AAAAAgA0I1AAAAAAA2IBADQAAAACADQjUAAAAAADYgEANAAAAAIANCNQAAAAAANiAQA0AAAAAgA0I1AAAAAAA2IBADQAAAACADQjUAAAAAADYgEANAAAAAIANCNQAAAAAANiAQA0AAAAAgA0I1AAAAAAA2IBADQAAAACADQjUAAAAAADYgEANAAAAAIANCNQAAAAAANiAQA0AAAAAgA0I1AAAAAAA2IBADQAAAACADQjUAAAAAADYgEANAAAAAIANCNQAAAAAANiAQA0AAAAAgA0I1AAAAAAA2IBADQAAAACADQjUAAAAAADYgEANAAAAAIANCNQAAAAAANiAQA0AAAAAgA0I1AAAAAAA2IBADQAAAACADQjUAAAAAADYwNHeBSQnJ2vJkiXauXOnYmJiZBiG1fhVq1bZqTIAAAAAAHJm90A9btw4bd26Ve3atZOfn59MJpNN0/nvf/+r7777Tn/99Zfi4uJUo0YN9enTR127drWa5jfffKNFixbp7Nmz8vT01MiRIxUSEmI1rStXrmjKlCn63//+p9TUVDVv3lzjx4/XfffdZ9Xu999/1zvvvKMDBw6ofPny6tWrlwYMGGDzMgAAAAAA7h52D9Q//fSTFixYoMaNG9/UdD799FN5eHhozJgxKlu2rLZt26YJEyYoOjpaw4YNkyStXbtWEyZM0ODBg/XQQw9p3bp1GjZsmJYuXaqGDRtapjVixAgdPXpUr7/+ulxcXDRz5kwNGDBAK1askKPjtVV24sQJ9e/fX0FBQRoxYoQOHTqkadOmqVixYurfv/9NLQsAAAAAoPCze6CuVKmSSpQocdPT+eijj1SuXDnL48DAQF2+fFmffPKJnn/+eTk4OGjWrFnq0KGDRowYIUl66KGHdPjwYc2dO1cLFy6UJO3Zs0dbtmxRRESEgoODJUmenp4KCwvTxo0bFRYWJkmKiIhQ2bJlNX36dDk7OyswMFAXL17UvHnz1KdPHzk7O9/0MgEAAAAACi+735Rs9OjRmjZtms6cOXNT07k+TGeqW7eu4uPjlZCQoFOnTun48eNq3769VZuwsDBt375dKSkpkqTNmzfL3d1dQUFBljZeXl6qW7euNm/ebBm2efNmtWrVyio4h4WFKS4uTnv27LmpZQEAAAAAFH5276Fu0KCBkpOT1bp1axUvXlxOTk5W43/99Vebp717925VqlRJJUuW1O7duyVd622+Xq1atZSamqpTp06pVq1aioyMlKenZ5broL28vBQZGSlJSkhI0Llz5+Tl5ZWljclkUmRkpAICAmyuGwAAAABQ+Nk9UL/44ou6cOGCRo4cqQoVKtyyG3rt2rVL69at0+jRoyVJsbGxkiR3d3erdpmPM8fHxcWpVKlSWaZXunRp7d+/X9K1m5ZlNy1nZ2e5urpapmUrwzCUkJBwU9O4HUwmk1xdXe1dBgDcFomJiVl+aQJA4ZCYmGj1P3CvMgyDGyAXMnYP1Hv27NHXX3+tOnXq3LJpRkdHa+TIkQoICNDTTz99y6Z7p6SmpurAgQP2LiMLV1dX+fr62rsMALgtoqKiOFgHCrnjx4/buwTA7rhXU+Fi90Dt5eWlpKSkWza9uLg4DRgwQGXKlNHs2bPl4HDtMvHSpUtLuta7XLFiRav21493d3dXdHR0lunGxsZa2mT2YGf2VGdKSUlRYmKipZ2tnJycVLt27Zuaxu3At2EAijJPT096qIFCKjExUcePH1fNmjU5Ww73tKNHj9q7BNzA7oF61KhRmjp1qkaOHClvb+8s11CXLFky39NKSkrSoEGDdOXKFX399ddWp25nXu8cGRlpde1zZGSknJycdP/991vabd++PcvpFFFRUfL29pYkubm5qUqVKpZrqq9vYxhGlmurC8pkMsnNze2mpgEAKBgO0oHCz9XVlWMk3NPo4Cp87H6X7+eee0579+5V37591axZMzVp0kRNmjSRv7+/mjRpku/ppKWlacSIEYqMjNSiRYtUqVIlq/H333+/atasqfXr11sNX7dunQIDAy2nTrRo0UKxsbHavn27pU1UVJT+/vtvtWjRwjKsRYsW2rRpk1JTU62m5e7uLrPZXKB1AAAAAAC4+9i9h/qzzz67JdOZNGmSfvzxR40ZM0bx8fHau3evZZyvr6+cnZ31wgsv6KWXXlL16tUVEBCgdevW6c8//9Tnn39uaWs2mxUcHKxx48Zp9OjRcnFx0YwZM+Tj46O2bdta2vXv31+rV6/WqFGj1KtXLx0+fFgREREaOXIk1zUAAAAAwD3A7oG6adOmt2Q6W7dulSRNnTo1y7hNmzapWrVq6tixoxITE7Vw4UItWLBAnp6emjNnTpYe5ZkzZ2rKlCmaOHGi0tLSFBwcrPHjx8vR8f9WV40aNRQREaGpU6dq4MCBKleunMLDw9WvX79bsjwAAAAAgMLN7oF68+bNcnNzk7+/vyRp6dKlWrZsmWrXrq2JEyfm+wZfP/zwQ77ade/eXd27d8+1TalSpTR58mRNnjw513aNGjXSsmXL8jVfAAAAAEDRYvdrqN977z1dvXpVknTo0CFNmTJFLVu21OnTp7PtbQYAAAAAoDCwew/16dOnVatWLUnSxo0bFRoaqhdffFF//fWXBg4caOfqAAAAAADInt17qJ2cnCy/Q71t2zYFBQVJuva70PHx8fYsDQAAAACAHNm9h7pRo0aaMmWKGjVqpH379mnmzJmSpOPHj6ty5cr2LQ4AAAAAgBzYvYd64sSJcnR01IYNG/Taa69Zfj968+bNat68uZ2rAwAAAAAge3bvoa5atarmz5+fZfi4cePsUA0AAAAAAPljl0AdHx+vkiVLWv7OTWY7AAAAAAAKE7sE6iZNmmjLli0qX768/P39ZTKZsrQxDEMmk0kHDhywQ4UAAAAAAOTOLoF68eLFKl26tOXv7AI1AAAAAACFmV0CddOmTS1/BwQE2KMEAAAAAABuit3v8t22bVvNnj1bx48ft3cpAAAAAADkm90D9ZNPPqmffvpJ7du3V9euXbV48WL9888/9i4LAAAAAIBc2T1Q9+3bVytWrNC6devUsmVLffHFF3r44YfVr18/ffvtt/YuDwAAAACAbNk9UGfy9PRUeHi4NmzYoKVLl+rixYsaO3asvcsCAAAAACBbdrkpWU7+/PNPrV69Wv/9738VHx+vRx55xN4lAQAAAACQLbsH6qioKK1evVpr167V6dOn9dBDD+mll15SmzZtVKJECXuXBwAAAABAtuweqNu3b68GDRroySefVIcOHVShQgV7lwQAAAAAQJ7sHqjXr1+vmjVr2rsMAAAAAAAKxO43JSNMAwAAAADuRnbpoW7atKnWr1+vcuXKqUmTJjKZTDm2/fXXX+9gZQAAAAAA5I9dAvXYsWNVsmRJSdK4cePsUQIAAAAAADfFLoH68ccfz/ZvAAAAAADuFnYJ1PHx8flum9mTDQAAAABAYWKXQO3v75/rddPXO3DgwG2uBgAAAACAgrNLoP7ss88sf585c0bvv/++Hn/8cTVs2FCStHfvXq1atUqjRo2yR3kAAAAAAOTJbnf5zvTMM89ozJgx6tixo2VYq1at5O3trWXLlnGNNQAAAACgULL771Dv3btX9evXzzK8fv36+vPPP+1QEQAAAAAAebN7oK5cubKWLVuWZfg333yjypUr26EiAAAAAADyZpdTvq83btw4vfDCC/rll1/k5+cnSfrzzz914sQJzZ49287VAQAAAACQPbv3ULds2VIbNmxQSEiIYmNjFRsbq9DQUG3YsEEtW7a0d3kAAAAAAGTL7j3UklSlShW9+OKL9i4DAAAAAIB8s0ugPnjwoLy9veXg4KCDBw/m2rZOnTp3qCoAAAAAAPLPLoG6c+fO2rp1q8qXL6/OnTvLZDLJMIws7Uwmkw4cOGCHCgEAAAAAyJ1dAvWmTZtUrlw5y98AAAAAANxt7BKoPTw8sv0bAAAAAIC7hd3v8j1//nwtX748y/Dly5drwYIFdqgIAAAAAIC82T1Qf/311/Ly8soy/IEHHtBXX31lh4oAAAAAAMib3QP1P//8o4oVK2YZXq5cOf3zzz92qAgAAAAAgLzZPVBXqVJFv//+e5bhu3fv1n333WeHigAAAAAAyJtdbkp2ve7du2vy5MlKS0vTQw89JEnavn273nvvPfXr18/O1QEAAAAAkD27B+rnnntOly9f1qRJk5SamipJcnFx0XPPPadBgwbZuToAAAAAALJn90BtMpn08ssv6/nnn9exY8dUvHhx1axZU87OzvYuDQAAAACAHNn9GupM//77r2JjY1W9enU5OzvLMAx7lwQAAAAAQI7s3kN96dIljRgxQjt37pTJZNLGjRt1//33a9y4cSpdurTGjBlj7xIBAAAAAMjC7j3UU6ZMkaOjo3766ScVL17cMjwsLEy//PKLHSsDAAAAACBndu+h3rp1qyIiIlS5cmWr4TVr1tTZs2ftVBUAAAAAALmzew91QkKCVc90psuXL3NjMgAAAABAoWX3QO3v769vv/3WalhGRoYWLVqkgIAA+xQFAAAAAEAe7H7K98svv6y+fftq//79Sk1N1XvvvaejR48qNjZWX375pb3LAwAAAAAgW3YP1N7e3tqwYYM+//xzlShRQgkJCWrTpo2eeuop3XffffYuDwAAAACAbNk9UEtSqVKlNGTIEKthycnJioiIUP/+/e1UFQAAAAAAObPrNdQXL17Ujz/+qC1btig9PV2SlJqaqsWLFys0NFQLFiywZ3kAAAAAAOTIbj3Uu3bt0uDBgxUfHy+TyaT69etrypQpGjp0qIoVK6Zhw4bp8ccft1d5AAAAAADkym6B+oMPPlDLli01aNAgrVq1Sp988omGDh2qkSNH6pFHHrFXWQAAAAAA5IvdTvk+fPiwhgwZIm9vbw0fPlwmk0kvv/wyYRoAAAAAcFewW6COjY1V2bJlJUnFixdX8eLF5e3tba9yAAAAAAAoELve5fvo0aP6559/LI+joqKUkJBg1aZOnTp3uiwAAAAAAPJk10Ddt29fGYZheTxo0CBJkslkkmEYMplMOnDggL3KAwAAAAAgR3YL1Js2bbLXrAEAAAAAuGl2C9QeHh72mjUAAAAAADfNbjclAwAAAADgbkagBgAAAADABgRqAAAAAABsYJdAvWnTJqWmptpj1gAAAAAA3BJ2CdTDhg3TlStXJEl169ZVTEzMLZnuiRMnNHHiRHXq1Em+vr7q2LFjljZ9+vSRj49Pln/Hjh2zanflyhWNGzdOTZs2ldlsVnh4uC5cuJBler///rueeOIJ+fn5KSQkRAsWLLD6KTAAAAAAQNFkl7t8lytXTnv37lVoaKjl96ZvhSNHjujnn3/Wgw8+qIyMjByDbaNGjTR69GirYdWqVbN6PGLECB09elSvv/66XFxcNHPmTA0YMEArVqyQo+O11XbixAn1799fQUFBGjFihA4dOqRp06apWLFi6t+//y1ZJgAAAABA4WSXQN2zZ089//zzMplMMplMCgoKyrHtgQMH8j3d0NBQtW7dWpI0ZswY7d+/P9t27u7uatiwYY7T2bNnj7Zs2aKIiAgFBwdLkjw9PRUWFqaNGzcqLCxMkhQREaGyZctq+vTpcnZ2VmBgoC5evKh58+apT58+cnZ2znftAAAAAIC7i10C9QsvvKCwsDCdPHlSQ4YM0ZQpU1SqVKmbnq6Dw605g33z5s1yd3e3CvpeXl6qW7euNm/ebAnUmzdvVps2bayCc1hYmObPn689e/YoICDgltQDAAAAACh87BKoJalWrVqqVauWhg0bpkceeUSurq53bN6//vqrGjZsqPT0dD344IMaPny4mjRpYhkfGRkpT0/PLKeie3l5KTIyUpKUkJCgc+fOycvLK0sbk8mkyMhIAjUAAAAAFGF2C9SZhg0bJkm6ePGiJax6eXmpXLlyt2V+TZo0UadOnVSzZk1duHBBERERevbZZ7VkyRKZzWZJUlxcXLY95qVLl7acRp55UzV3d3erNs7OznJ1dVVsbKzNNRqGoYSEBJuff7uYTKY7+sUHANxJiYmJ3FQSKKQSExOt/gfuVbfy/lO4NeweqBMTE/XGG2/ou+++U3p6uiSpWLFi6tSpkyZMmHDLA1x4eLjV44cfflgdO3bUhx9+qIULF97SedkqNTW1QNeO3ymurq7y9fW1dxkAcFtERUVxsA4UcsePH7d3CYDdcZ+mwsXugXrKlCn67bff9OGHH6px48aSpN27d+utt97S1KlTNWnSpNs6fzc3N7Vs2VIbNmywDHN3d1d0dHSWtrGxsSpdurQkWXqwM3uqM6WkpCgxMdHSzhZOTk6qXbu2zc+/Xfg2DEBR5unpSQ81UEglJibq+PHjqlmzJmfL4Z529OhRe5eAG9g9UG/YsEGzZs2yut64ZcuWcnFx0YgRI257oM6Ol5eXtm/fnuWUiqioKHl7e0u6FsSrVKliOU39+jaGYWS5trogTCaT3NzcbH4+AKDgOEgHCj9XV1eOkXBPo4Or8Lk1t8W+CUlJSapQoUKW4eXLl1dSUtJtn39CQoJ++uknNWjQwDKsRYsWio2N1fbt2y3DoqKi9Pfff6tFixZW7TZt2qTU1FTLsHXr1snd3d1yPTYAAAAAoGiyew91w4YNNWvWLL377rtycXGRdC1kz5kzJ9ffis5OYmKifv75Z0nSmTNnFB8fr/Xr10uSmjZtqsjISC1atEht2rSRh4eHLly4oE8++UT//POPPvjgA8t0zGazgoODNW7cOI0ePVouLi6aMWOGfHx81LZtW0u7/v37a/Xq1Ro1apR69eqlw4cPKyIiQiNHjuTaBgAAAAAo4uweqF999VX1799fLVq0UJ06dSRJBw8elIuLiyIiIgo0rZiYGA0fPtxqWObjzz77TJUrV1ZqaqpmzJihy5cvy9XVVWazWZMmTZKfn5/V82bOnKkpU6Zo4sSJSktLU3BwsMaPHy9Hx/9bZTVq1FBERISmTp2qgQMHqly5cgoPD1e/fv1sWRUAAAAAgLuI3QO1t7e3Nm7cqNWrV1uuR+7YsaMeffRRFS9evEDTqlatmg4dOpRrm/yG9FKlSmny5MmaPHlyru0aNWqkZcuW5btGAAAAAEDRYPdALV27wUSPHj3sXQYAAAAAAPlm95uSAQAAAABwNyJQAwAAAABgAwI1AAAAAAA2IFADAAAAAGADuwfqVq1a6dKlS1mGx8XFqVWrVnaoCAAAAACAvNk9UJ85c0YZGRlZhqekpOj8+fN2qAgAAAAAgLzZ7WezNm3aZPn7l19+UalSpSyPMzIytH37dnl4eNijNAAAAAAA8mS3QD106FBJkslk0pgxY6zGOTo6ysPDI8twAAAAAAAKC7sF6oMHD0qSQkNDtXz5cpUrV85epQAAAAAAUGB2C9SZfvjhB3uXAAAAAABAgdk9UEvS9u3btX37dsXExGS5QdmUKVPsVBUAAAAAADmze6CeM2eO5s6dq/r166tixYoymUz2LgkAAAAAgDzZPVB/9dVXmjJlijp37mzvUgAAAAAAyDe7/w51amqqGjVqZO8yAAAAAAAoELsH6m7dumn16tX2LgMAAAAAgAKx+ynfycnJWrZsmbZv3y4fHx85OlqXNHbsWDtVBgAAAABAzuweqA8dOqQ6depIkg4fPmw1jhuUAQAAAAAKK7sH6iVLlti7BAAAAAAACszu11ADAAAAAHA3snsPdZ8+fXI9tfuzzz67g9UAAAAAAJA/dg/UdevWtXqclpamAwcO6MiRI/w2NQAAAACg0LJ7oB43bly2w2fPnq2EhIQ7XA0AAAAAAPlTaK+hfuyxx7RixQp7lwEAAAAAQLYKbaDes2ePnJ2d7V0GAAAAAADZsvsp38OGDbN6bBiG/vnnH+3fv1/PP/+8naoCAAAAACB3dg/UpUqVsnpsMpnk6emp8PBwBQcH26kqAAAAAAByZ/dAPWXKFHuXAAAAAABAgdk9UGfav3+/jh07Jkl64IEH5Ovra+eKAAAAAADImd0DdUxMjEaOHKlff/1V7u7ukqS4uDgFBARoxowZKleunJ0rBAAAAAAgK7vf5fvNN9/U1atXtXbtWv3666/69ddftWbNGsXHx+utt96yd3kAAAAAAGTL7oH6l19+0WuvvaZatWpZhtWuXVuvvfaaNm/ebMfKAAAAAADImd0DdUZGhpycnLIMd3R0VEZGhh0qAgAAAAAgb3YP1A899JDefvttnT9/3jLs/PnzmjJligIDA+1YGQAAAAAAObP7TckmTpyoIUOGqFWrVqpcubIkKTo6Wg888IDee+89O1cHAAAAAED27B6oq1SpolWrVmnbtm2KjIyUJNWqVUvNmjWzc2UAAAAAAOTM7oFakkwmk4KCghQUFGTvUgAAAAAAyBe7XUO9fft2hYWFKT4+Psu4K1euqEOHDtq1a5cdKgMAAAAAIG92C9SLFy9Wjx49VLJkySzjSpUqpSeeeEKffPKJHSoDAAAAACBvdgvUhw4dUvPmzXMcHxQUpL/++usOVgQAAAAAQP7ZLVD/+++/cnTM+RJuR0dHXbx48Q5WBAAAAABA/tktUFeqVElHjhzJcfyhQ4dUsWLFO1gRAAAAAAD5Z7dA3bJlS33wwQdKTk7OMi4pKUmzZ89WSEiIHSoDAAAAACBvdvvZrCFDhmjjxo1q166dnnrqKXl6ekqSIiMj9cUXXyg9PV2DBw+2V3kAAAAAAOTKboG6QoUK+uqrr/T6669r+vTpMgxD0rXfpA4ODtbEiRNVoUIFe5UHAAAAAECu7BaoJcnDw0MLFy5UbGysTpw4IUmqUaOGSpcubc+yAAAAAADIk10DdabSpUvLz8/P3mUAAAAAAJBvdrspGQAAAAAAdzMCNQAAAAAANiBQAwAAAABgAwI1AAAAAAA2IFADAAAAAGADAjUAAAAAADYgUAMAAAAAYAMCNQAAAAAANiBQAwAAAABgAwI1AAAAAAA2IFADAAAAAGADAjUAAAAAADYgUAMAAAAAYAMCNQAAAAAANiBQAwAAAABgAwI1AAAAAAA2IFADAAAAAGCDIhWoT5w4oYkTJ6pTp07y9fVVx44ds233zTffqF27dmrQoIEee+wx/fjjj1naXLlyRePGjVPTpk1lNpsVHh6uCxcuZGn3+++/64knnpCfn59CQkK0YMECGYZxy5cNAAAAAFC4FKlAfeTIEf3888+qUaOGatWqlW2btWvXasKECWrfvr0WLlyohg0batiwYdq7d69VuxEjRmjr1q16/fXXNW3aNEVFRWnAgAFKS0uztDlx4oT69++vihUrav78+XrmmWc0a9Ysffzxx7dzMQEAAAAAhYCjvQu4lUJDQ9W6dWtJ0pgxY7R///4sbWbNmqUOHTpoxIgRkqSHHnpIhw8f1ty5c7Vw4UJJ0p49e7RlyxZFREQoODhYkuTp6amwsDBt3LhRYWFhkqSIiAiVLVtW06dPl7OzswIDA3Xx4kXNmzdPffr0kbOz8x1YagAAAACAPRSpHmoHh9wX59SpUzp+/Ljat29vNTwsLEzbt29XSkqKJGnz5s1yd3dXUFCQpY2Xl5fq1q2rzZs3W4Zt3rxZrVq1sgrOYWFhiouL0549e27FIgEAAAAACqkiFajzEhkZKelab/P1atWqpdTUVJ06dcrSztPTUyaTyaqdl5eXZRoJCQk6d+6cvLy8srQxmUyWdgAAAACAoqlInfKdl9jYWEmSu7u71fDMx5nj4+LiVKpUqSzPL126tOU08itXrmQ7LWdnZ7m6ulqmZQvDMJSQkGDz828Xk8kkV1dXe5cBALdFYmIiN5UECqnExESr/4F7lWEYWTr9YF/3VKC+W6SmpurAgQP2LiMLV1dX+fr62rsMALgtoqKiOFgHCrnjx4/buwTA7rhPU+FyTwXq0qVLS7rWu1yxYkXL8Li4OKvx7u7uio6OzvL82NhYS5vMHuzMnupMKSkpSkxMtLSzhZOTk2rXrm3z828Xvg0DUJR5enrSQw0UUomJiTp+/Lhq1qzJ2XK4px09etTeJeAG91SgzrzeOTIy0ura58jISDk5Oen++++3tNu+fXuWUyqioqLk7e0tSXJzc1OVKlWyXCsdFRUlwzCyXFtdECaTSW5ubjY/HwBQcBykA4Wfq6srx0i4p9HBVfjcUzclu//++1WzZk2tX7/eavi6desUGBhoOX2iRYsWio2N1fbt2y1toqKi9Pfff6tFixaWYS1atNCmTZuUmppqNS13d3eZzebbvDQAAAAAAHsqUj3UiYmJ+vnnnyVJZ86cUXx8vCU8N23aVOXKldMLL7ygl156SdWrV1dAQIDWrVunP//8U59//rllOmazWcHBwRo3bpxGjx4tFxcXzZgxQz4+Pmrbtq2lXf/+/bV69WqNGjVKvXr10uHDhxUREaGRI0dybQMAAAAAFHFFKlDHxMRo+PDhVsMyH3/22WcKCAhQx44dlZiYqIULF2rBggXy9PTUnDlzsvQoz5w5U1OmTNHEiROVlpam4OBgjR8/Xo6O/7fKatSooYiICE2dOlUDBw5UuXLlFB4ern79+t3+hQUAAAAA2FWRCtTVqlXToUOH8mzXvXt3de/ePdc2pUqV0uTJkzV58uRc2zVq1EjLli0rUJ0AAAAAgLvfPXUNNQAAAAAAtwqBGgAAAAAAGxCoAQAAAACwAYEaAAAAAAAbEKgBAAAAALABgRoAAAAAABsQqAEAAAAAsAGBGgAAAAAAGxCoAQAAUKiZTCY5OTnZuwwAyIJADQAAbJKRYdi7BNwjXF1d5etbTyaTyd6lAIAVR3sXAAAA7k4ODibN/XKrzlyItXcpKOI87iutob2ClJpq70oAwBqBGgAA2OzMhVgdP3PJ3mUAAGAXnPINAAAAAIANCNQAAAAAANiAQA0AAAAAgA0I1AAAAAAA2IBADQAAAACADQjUAAAAAADYgEANAAAAAIANCNQAAAAAANiAQA0AAAAAgA0I1AAAAAAA2IBADQAAAACADQjUAAAAAADYgEANAAAAAIANCNQAAAAAANiAQA0AAAAAgA0I1AAAAAAA2IBADQAAAACADQjUAAAAAADYgEANAAAAAIANCNQAAAAAANiAQA0AAAAAgA0I1AAAAAAA2IBADQAAAACADQjUAAAAAADYgEANAAAAAIANCNQAAAAAANiAQA0AAAAAgA0I1AAAAAAA2IBADQAAAACADQjUAAAAAADYgEANAAAAAIANCNQAAAAAANiAQA0AAAAAgA0I1AAAAAAA2IBADQAAAACADQjUAAAAAADYgEANAAAAAIANCNQAAAAAANiAQA0AAAAAgA0I1AAAAAAA2IBADQAAAACADQjUAAAAAADYgEANAAAAAIANCNQAAAAAANiAQA0AAAAAgA0I1AAAAAAA2IBADQAAAACADQjUAAAAAADYgEANAAAAAIANCNQAAAAAANiAQA0AAAAAgA0I1AAAAAAA2OCeC9QrV66Uj49Pln/Tpk2zavfNN9+oXbt2atCggR577DH9+OOPWaZ15coVjRs3Tk2bNpXZbFZ4eLguXLhwpxYFAAAAAGBHjvYuwF4WLVqkUqVKWR5XqlTJ8vfatWs1YcIEDR48WA899JDWrVunYcOGaenSpWrYsKGl3YgRI3T06FG9/vrrcnFx0cyZMzVgwACtWLFCjo737KoFAAAAgHvCPZv66tWrp3LlymU7btasWerQoYNGjBghSXrooYd0+PBhzZ07VwsXLpQk7dmzR1u2bFFERISCg4MlSZ6engoLC9PGjRsVFhZ2R5YDAAAAAGAf99wp33k5deqUjh8/rvbt21sNDwsL0/bt25WSkiJJ2rx5s9zd3RUUFGRp4+Xlpbp162rz5s13tGYAAAAAwJ13zwbqjh07qm7dumrVqpXmz5+v9PR0SVJkZKSka73N16tVq5ZSU1N16tQpSztPT0+ZTCardl5eXpZpAAAAAACKrnvulO+KFSvqhRde0IMPPiiTyaQffvhBM2fO1Pnz5zVx4kTFxsZKktzd3a2el/k4c3xcXJzVNdiZSpcurf37999UjYZhKCEh4aamcTuYTCa5urrauwwAuC0SExNlGIa9y7hr8JkAe0hLS1NiYqK9y8A9ojB+JhiGkaVDD/Z1zwXq5s2bq3nz5pbHwcHBcnFx0eLFizV48GA7VvZ/UlNTdeDAAXuXkYWrq6t8fX3tXQYA3BZRUVEcqBcAnwm4k0qXKi4jIyPbzgzgdkhPT9Nff/2t1NRUe5eShbOzs71LwHXuuUCdnfbt2+vjjz/WgQMHVLp0aUnXfhKrYsWKljZxcXGSZBnv7u6u6OjoLNOKjY21tLGVk5OTateufVPTuB34NgxAUebp6VkoeyMKKz4TcCeVKO4sk4ODotYsVGLMOXuXgyLOtXwVeXYcoAceeKDQfS4cPXrU3iXgBgTqG3h5eUm6do105t+Zj52cnHT//fdb2m3fvj3LaRdRUVHy9va+qRpMJpPc3NxuahoAgILh9GWg8EuMOafE8yftXQbuEYXxc4EvMwufe/amZNdbt26dihUrJl9fX91///2qWbOm1q9fn6VNYGCg5RSLFi1aKDY2Vtu3b7e0iYqK0t9//60WLVrc0foBAAAAAHfePddD3b9/fwUEBMjHx0eStGnTJi1btkxPP/205RTvF154QS+99JKqV6+ugIAArVu3Tn/++ac+//xzy3TMZrOCg4M1btw4jR49Wi4uLpoxY4Z8fHzUtm1buywbAAAAAODOuecCtaenp1asWKHo6GhlZGSoZs2aGjdunPr06WNp07FjRyUmJmrhwoVasGCBPD09NWfOHJnNZqtpzZw5U1OmTNHEiROVlpam4OBgjR8/Xo6O99xqBQAAAIB7zj2X/MaPH5+vdt27d1f37t1zbVOqVClNnjxZkydPvhWlAQAAAADuIlxDDQAAAACADQjUAAAAAADYgEANAAAAAIANCNQAAAAAANiAQA0AAAAAgA0I1AAAAAAA2IBADQAAAACADQjUAAAAAADYgEANAAAAAIANCNQAAAAAANiAQA0AAAAAgA0I1AAAAAAA2IBADQAAAACADQjUAAAAAADYgEANAAAAAIANCNQAAAAAANiAQA0AAAAAgA0I1AAAAAAA2IBADQAAAACADQjUAAAAAADYgEANAAAAAIANCNQAAAAAANiAQA0AAAAAgA0I1AAAAAAA2IBADQAAAACADQjUAAAAAADYgEANAAAAAIANCNQAAAAAANiAQA0AAAAAgA0I1AAAAAAA2IBADQAAAACADQjUAAAAAADYgEANAAAAAIANCNQAAAAAANiAQA0AAAAAgA0I1AAAAAAA2IBADQAAAACADQjUAAAAAADYgEANAAAAAIANCNQAAAAAANiAQA0AAAAAgA0I1AAAAAAA2IBADQAAAACADQjUAAAAAADYgEANAAAAAIANCNQAAAAAANiAQA0AAAAAgA0I1AAAAAAA2IBADQAAAACADQjUAAAAAADYgEANAAAAAIANCNQAAAAAANiAQA0AAAAAgA0I1AAAAAAA2IBADQAAAACADQjUAAAAAADYgEANAAAAAIANCNQAAAAAANiAQA0AAAAAgA0I1AAAAAAA2IBADQAAAACADQjUAAAAAADYgEANAAAAAIANCNQAAAAAANiAQH2Tjh07pmeffVYNGzZUUFCQ3n33XaWkpNi7LAAAAADAbeZo7wLuZrGxsXrmmWdUs2ZNzZ49W+fPn9fUqVOVlJSkiRMn2rs8AAAAAMBtRKC+CV999ZWuXr2qOXPmqEyZMpKk9PR0TZo0SYMGDVKlSpXsWyAAAAAA4LbhlO+bsHnzZgUGBlrCtCS1b99eGRkZ2rp1q/0KAwAAAADcdgTqmxAZGSkvLy+rYe7u7qpYsaIiIyPtVBUAAAAA4E7glO+bEBcXJ3d39yzDS5curdjYWJummZqaKsMw9Oeff95sebeFyWRSh6YVlZ5R3t6loIhzdnLUvn37lFantUze6fYuB0VYskMx7du3T4Zh2LuUuw6fCbhT+EzAnVSYPxdSU1NlMpnsXQauQ6AuZDLfIIX5jeJesri9S8A9xNGtlL1LwD2iMO93CzM+E3An8ZmAO6kwfi6YTKZCWde9jEB9E9zd3XXlypUsw2NjY1W6dGmbpmk2m2+2LAAAAADAHcA11DfBy8sry7XSV65c0T///JPl2moAAAAAQNFCoL4JLVq00LZt2xQXF2cZtn79ejk4OCgoKMiOlQEAAAAAbrdbEqh37dp1T56q3LNnT5UoUUJDhw7Vli1btGLFCr377rvq2bNngX6Det68eXruuedybePj46OdO3dKKvj6njhxoiZOnJjv9kXVmDFjNGbMGHuXcUtcvz3Yw86dO+Xj43PH5vfcc89p3rx5d2x+mWbPnq0+ffrc8fkWBQsWLFBQUJDMZrO2bduWZXx+9nt5ufH1Kch2cvbsWZnNZp09ezbHNrd6n3Gn3jd3arsNDQ3VypUrJdlnfeaHLeu8oNvVnd4f3kn5Od7o06ePZs+ebXlsNpu1a9eu213abZOfY6a8tuX8vB+AG93O905e+6mitB+zx2dNga6h7tOnj/bs2SMnJyfLsMaNG2vRokXas2fPLS/uVli7dq2WLl2qgwcP6urVqzp06FCBpzFmzBitXr1azs7OkqSKFSuqS5cuGjx4sBYvXqw333xTQ4cOVYkSJdStWzeNHDmyQNMfPHhwgdr7+/sXaH2/8cYbBZq+PezcuVNPP/20Ta8Pir5Fixbd9nn06dNHTZs21QsvvHDb52VvPj4++uyzzxQQEHBbph8dHa3p06dr5cqV8vX1zbZNQfd7+VGQ7aRq1aqF9nPrblSU1+ed2P/kJfPgcOrUqXd0vgU93pB0S7cDexwb3HjMFBoaqmHDhqlLly75nsateD/Y6zW/lxS2dVxU96H3ggLflGzQoEF2OeBMSUmxBNqCcHd315NPPqmkpCS9+uqrNs//0Ucf1dSpU2UYhn799VcNHDhQlStXVufOnfXpp5/aPN17QWpqqtWXMLA/XhPcTqdPn5ZhGDmGaQC3X0ZGhgzDULFixexdCpAtjkWusTXj4Paw5fW4Jad833iaQGpqqt555x0FBQXpoYce0rvvvqvevXtbTgk6ffq0fHx8dPr06RynMXv2bPXu3Vvvv/++goOD9eSTT0qS/vjjD/Xp00cBAQEKCQnRzJkzlZaWlmNtzZs3V8eOHXX//fffikWVyWRSQECAatWqZfVb0ceOHdOgQYPUrFkzNW/eXK+//roSEhIs4y9duqSJEycqNDRUZrNZHTt2tJzWcePpZTExMRo2bJj8/f3VqlUrrVmzxqqG69fV0aNH5evrq/Pnz1u16d27tz744ANJ1qc+GIahDz74QC1atJDZbFaLFi00ffp0y/Mye666deumhg0bqkePHjp79qw+++wzPfzww2ratKmmTZuW6zoKDQ3VnDlz9Oyzz8psNuvrr7/W4cOH9fTTTysgIED+/v7q06ePDhw4IOnaqVEDBgyQdO10F7PZrM8++0ySdP78eY0aNUrNmzdXYGCgXnzxRV28eNEyr88//1ytW7eW2WxWs2bN8jzFIyUlRWPHjpW/v79CQkL08ccfW8b16NEjy2l933zzjVq1apXj7xDu27dP3bp1k9lsVpcuXfTpp59mOWXm22+/1WOPPabGjRurQ4cOWrt2rWVc5mv5/fffq127dmrUqJH69++vCxcuWNrktT1Ieb8vfHx8tHjxYnXv3l0PPvigtmzZkut6kq79zvrIkSMVEBCgRo0aqV27dlq/fr1Vm9zqvnz5ssaOHavg4GAFBgbqhRdeUHR0tCTp0KFDatCggZKSkiRJP/30k3x8fLR8+XJJ17bTZs2aaevWrZKynlLo4+OjpUuXqmfPnjKbzXr00Uf1+++/W8bntQ+60cSJE7Vr1y7Nnz9fZrM5yz0Q5syZo+DgYDVt2lSvvfaa0tP/7zdQ89pGs7NixQrLNhEcHKwZM2ZYxu3atUs9e/aUv7+/2rRpowULFljml999Z58+fXKsuUOHDpKkAQMGyGw2Kzw8XFLB3ktJSUmaOnWqQkJCFBAQoH79+uno0aOSpFWrVqlfv36Srr2fc+oFv3G/16dPH02ZMkWjRo1So0aN1LJlS3399ddWz/n222/Vtm1bmc1mDRs2TLGxsVbjr99Ohg8fnuW0zR07dshsNis+Pj7bdblo0SI9/PDD8vf317hx45SSkmL1/BsvtbhxGrnt57KzY8cOdenSRY0bN1ZAQIB69uyZZZmka++Hhx56SNu3b5d07eaX9erV0yuvvGJpEx4ebrUdSblvt0eOHFG/fv0s+4x33nlHycnJOdZ69epVjRs3TgEBAWrevHmWL5JtWZ83+u9//6vHH39cjRs3VrNmzTRq1Cir91Je27Yk7d+/X927d7fskw8ePJjrPKWCbVe2zCM/+/nc9pfz5s3T6tWrtXr1astn5KVLl7LMJ/M1+Oabb9SxY0c9+OCDOnbsmOLi4vTaa69Z3q8DBgzQqVOnLM9LS0vTokWL1L59e5nNZoWEhGjp0qVWtV/f9t1337XsW6dNm5bl8zHzfZKenq7g4OAsn1mzZs3SU089JSn398zNHBtcLy4uTr6+vpZlPnLkiHx8fCzHSJLUtWtXLVu2TJL1MdNzzz2ns2fP6rXXXpPZbFa3bt0sz0lNTdUbb7yhgIAABQUFWW0jN74fVq5cqdDQUH355ZcKCQlR48aNNWLECMXHx2dbc06veUH2A+np6Zo3b57atGkjf39/9ezZ0+pz8kb5OQbK61hj/Pjxevjhh2U2m9W2bVt9/vnnVtPL7vjw4MGD6t27t/z9/dWkSRN16dIlyw1/c3PkyBHL8be/v7+efPJJy/tj6dKl6tChg8xms5o3b65JkyYpMTEx13UsST///LO6d++uJk2aqG3btpbtLtMvv/yijh07ymw2W/ZJoaGhlvG5fUZKOWec6z9jhg4daqnLbDarfv368vf3t0wjr9fCln2hJK1evVqhoaHy9/fXsGHDLO+rL7/8Uu3atbNqGx8fL7PZnOMliFevXtXYsWMtnxuffPKJ1aVCUt75KTQ0VB999JGee+45y3b1v//9z2o+eX3W5LUPHDNmjF588UWNHz9eAQEBeumll/K1rqwYBdC7d29j1qxZWYbv2LHD8Pb2tjz+8MMPjVatWhnHjh0zkpOTjTlz5hi+vr6W5546dcrw9vY2Tp06leM0Zs2aZdStW9f46KOPjOTkZCMhIcE4duyY0bBhQ2PdunVGamqqcfr0aeOxxx4zPvzwwzxrv3H612vcuLHx3Xff5fjc0aNHG6NHjzYMwzDS09ONrVu3Gg0aNDA+++wzwzAMIyYmxggICDAWL15sJCcnGzExMUbfvn2NV1991fKcnj17GoMGDTLOnTtnZGRkGFFRUcbx48cty9q7d2/L/J599lljwIABxuXLl43Lly8bgwcPNry9vY0dO3Zkuyw9evSwWgdRUVFGnTp1LOv3+vq3bNliNG/e3Dhz5oxhGIZx6dIl4/fff7c819vb2+jatatx9uxZIyEhwejTp4/Rtm1bY/r06UZycrLx119/GfXq1TN27tyZ4/oKCQkxgoKCjL179xoZGRlGQkKCcejQIWPbtm1GUlKScfXqVWPChAlGSEiIkZKSkuPrk5ycbLRr18549913jatXrxrx8fHGSy+9ZPTt29eynH5+fsahQ4cMwzCM+Ph449dff831dfT19TWWLVtmpKamGnv27DGaNGlirF271jAMw1i5cqURGhpqZGRkWJ7TrVs3Y/78+dlOLy4uzggICDDmzJljJCcnG8eOHTPatWtntRwrVqwwQkJCjH379hnp6enGb7/9ZjRq1Mj47bffrJb75ZdfNuLi4ozY2FijR48extixYy3TyGt7yM/7wtvb22jfvr1x9OhRIyMjw0hMTMxxPWWaPn26MWDAAOPKlStGRkaGcfr0aePIkSP5rvu5554z+vXrZ8TExBhXrlwxXnrpJaNz585GWlqaYRiGERQUZGzevNkwDMN46623jLZt2xojR440DMMw/v77b6NBgwZGUlKSYRhZ9z3e3t5G586djRMnThipqanGG2+8YbRq1coyPq99UHay27/NmjXL8PX1NT755BMjJSXFOHbsmOHv72+sWrXKMIy8t9HsfPnll0ZgYKCxbds2Iy0tzYiLi7NsD6dPnzb8/PyML774wkhJSTEOHDhgtGzZ0vj4448Nw8j/vjO3mjPXX+b2YxgFfy+99tprRufOnY3Tp08bSUlJxnvvvWcEBwcbV65cybam7Ny43+vdu7fh7+9v7Nixw0hPTzfWrl1r1K1b1zh58qRhGIaxe/duo169esaPP/5opKamGj/++KPh5+eXZRqZr+HmzZuNRo0aGQkJCZbxo0aNMsaMGZPtuvzuu++MJk2aGHv27DFSU1ONZcuWGb6+vpZ9Z3br7cZpFHQ/FxwcbCxfvtzIyMgwkpOTjd9//924evVqtutrxIgRxrRp0wzDMIzvv//eaNu2rREUFGQYxrXPmKZNm1r2y3ltA1euXDGCgoKM6dOnG0lJScapU6eMxx57zHjzzTdzfL3Gjx9vdOnSxYiOjjauXr1qjBs3zvD19TVWrFhh8/q80c8//2wcPHjQSEtLM86dO2f06NHDePHFFy3j87NcAQEBxqxZs4zk5GTj6NGjRtu2bXPdFgu6Xdkyj1uxv7z+czwnma/Bk08+aZw7d85ITU01kpOTjd69exsvvfSScenSJSM5Odl47733jPbt21u2y2nTphlt27Y19u3bZ2RkZBgxMTHGH3/8YVV7po8++sgIDQ01jh49aiQnJ1tekxv3z5nvk2nTplntD9PT042HH37Y8prdjmOD7HTv3t348ssvDcMwjE8++cRo27at0aNHD8Mwrh0H1alTxzh9+nS26zokJMSynWcaPXq0Ub9+fWPdunVGWlqasWvXLsPX19eyL7/x/bBixQrD19fXmDp1qpGYmGhER0cbrVq1MubMmZNjzdm95gXZDyxYsMAICQkxDh48aKSkpBiff/650bBhQ+Ps2bPZzi+vY6D8HGssW7bMiImJMdLT042ff/7ZqFevnrFlyxardXnj8eETTzxhzJ4920hNTTVSU1ONv//+2/jnn39yXC/Xu3DhgtGkSRNj5syZxpUrV4zU1FTj999/t3wWbdiwwTh+/LiRkZFhHD161GjTpo0xffr0XNfx9u3bDX9/f2Pbtm1Genq6cejQIaNFixbGf/7zH8MwDOPkyZNGvXr1jOXLlxupqanGH3/8YTRr1swICQmxTCOvz8jsMo5hZP2MyXT58mWjQ4cOxpQpU/L1WtzMfmrw4MGW483nnnvO6N+/v2EY144LzGazVX1Lly41HnnkkRyn+eqrrxrdunUzoqOjjYSEBGPChAlWnxt55SfDuLbNhISEGH/99ZeRnp5uLFy40GjUqJERHx9vGEbenzUZGRl57gMz88GqVauM1NRUq+OG/CpwD/WCBQvk7+9v+bdx48YsbVatWqXnnntOXl5ecnZ21pAhQ1SuXLkCh/377rtPgwYNkrOzs1xdXfXFF1+odevWat++vRwdHeXh4aFBgwZZfdNhi127dunRRx/Ntc2aNWvk7+8vPz8/Pfvss+rRo4d69eolSfrPf/4jLy8vPf3003J2dla5cuU0YsQIffvtt0pPT9f+/fu1d+9eTZ06VZUrV5bJZFLNmjVVo0aNLPM5f/68tm7dqtGjR6t06dIqXbp0nt+UdOvWTStWrLB8g7hixQo99NBDqlatWpa2Tk5OSk5O1pEjR5SUlKQyZcpkueFI3759VaVKFbm6uqpdu3Y6f/68wsPD5ezsLF9fXz3wwAPav39/njU9+OCDMplMcnV1lbe3twIDA+Xi4iI3NzeNGjVKZ86c0cmTJ3Ocxk8//aSkpCS99NJLcnNzU4kSJTR69Ght27ZN0dHRKlasmAzD0NGjRxUfH68SJUqoSZMmudbl6+ur7t27y9HRUQ0bNlT37t21YsUKSVJYWJji4+MtvaIHDx7UgQMHcrxu6scff5STk5OGDBkiZ2dneXl56ZlnnrFq88knn2jIkCGqX7++HBwc5O/vr44dO2rVqlVW7UaNGqVSpUrJ3d1djz76qPbt2ycpf9tDft8Xzz77rGrVqiWTyaTixYvnup6ka9vK5cuXFRkZKcMw5OHhodq1a+er7gsXLmjz5s0aO3asypUrp5IlS2rixIk6ePCgpU1gYKBlXW/dulUjR47Utm3bZBiGtmzZIn9/f7m4uORYX79+/VS9enU5OjrqiSee0KlTpyzfLt+qfZAkVatWTX379pWTk5O8vLwUGBhoWYa8ttHsLFmyRAMHDlRgYKCKFSumUqVKWb51XrNmjR544AH16tVLTk5OqlOnjp577rksPbU3U3N2CvJeysjI0MqVKzVixAh5eHjIxcVFI0eOVEZGhn7++ecC1XmjRx55RAEBAXJwcFBYWJhKlSqlv//+W9K1Hp7WrVvr4YcflqOjox5++GGFhITkOK2goCC5u7trw4YNkq59Q71x40arHqbrrVy5Ut27d1fDhg3l6Oio7t27F/iU9YLu55ycnHTy5ElduHBBzs7OMpvNcnNzy7bt9WdsbNmyRd27d5eLi4sOHTqk/fv3KyUlRQ0bNrS0z2u7la71Zrm4uKhatWoaMWKEvvnmm2zPxsnIyNC3336r8PBwVapUSW5ubho7dmyOZ+5Itq3PFi1ayMfHR8WKFVPlypX13HPPWXrj8rNcP/zwgxwdHTV06FA5OzurVq1aWfbJ2dVZkO3Klnlkupn9ZUEMGzZMlStXlqOjo44cOaK9e/fqjTfeUJkyZeTs7KyRI0fq3Llz+uOPP2QYhj7//HO9/PLLql+/vkwmk8qVKyc/P78c11f//v1Vq1YtOTs7a+jQoSpbtmyOtXTt2lU7duyw9NRu3bpVV65csfR03Y5jg+xc//7ZunWrhgwZosOHDysuLk7btm1T9erV5eHhka/1m6lJkyZq3769ihUrpsaNG8vHx8fqzMUbOTo6atSoUSpevLgqVaqkNm3aFPj1Lch+YPny5erfv798fHzk5OSkp556Sp6enlq9enW2087rGCg/xxrdu3dXuXLl5ODgoBYtWqh58+ZZbkp54/Ghk5OTzp07p7Nnz8rR0VF169ZVhQoV8rU+/vOf/6hKlSoaPny4SpYsKUdHR5nNZpUsWVKS1LZtW9WoUUMmk0m1atXSk08+me1NMq/36aef6qmnnlJgYKAcHBzk7e2tp556yrKca9asUb169dS1a1c5OjrKz89Pjz/+uOX5+f2MvDHj5CQ5OVmDBw+Wj4+PRo8ena/X4mb2Uy+99JLleHPMmDH65ZdfdOHCBZUoUUKPPfaY5UwO6doZDE888US208nIyNB//vMfy+eGq6urpf5MeeWnTD169JCvr68cHBzUq1cvxcfHKyoqSlLenzV///13rvvATA8++KA6d+4sR0fHXF+PnBT4GuqBAwdmuYb6xq7+6Ohoqx2Tg4ODqlSpUuDiPDw8ZDKZLI+PHz+unTt36scff7QMy7xG6Hbr2LGjpk6dqpSUFM2bN08bN25UUlKSSpYsqePHj+vPP/+0OhXDMAyZTCb9+++/OnPmjMqWLasyZcrkOZ/MD4Prw3B2wfh6YWFhmjx5snbu3KkmTZpo1apVGjduXLZtmzZtqpdfflkLFy7Uiy++KF9fXz3//PMKDAy0tKlYsaLlb1dXV5UrV87qGqzixYvr6tWrudZ0Y82nT5/Wu+++qz/++ENXrlyRg8O173JiYmJUq1atbKdx/PhxXbhwIcuBvbOzs86ePatGjRpp+vTp+vLLLzVx4kR5enrq2WefVVhYWL7rqlatmmV7cnFx0eOPP65ly5YpODhYy5YtU6tWrXLcsUdHR6tq1aqWZZGU5QP5xIkTmjx5st555x3LsPT0dKttRZLVXeHd3Nws6zc/20N+3xd5bUc36t+/v9LT0zV+/HhFR0crMDBQL730ktXlE3nVfX3bUqVKqVy5cjp79qwaNmyooKAgRURE6Pz584qJiVHbtm01Y8YM/f3339q2bVuePz133333Wc1bunZ6UdmyZW/ZPujG+dy4nHlto5UrV84yvTNnzsjT0zPbeZ07dy7L5SnVq1fXuXPnblnN2bn//vvz/V66dOmSkpOTrbanYsWKycPD46bvaHtj3SVKlLDapurUqWM1vlq1aoqJicl2Wg4ODurSpYuWL1+uzp07a/Xq1fLw8FDjxo2zbR8dHa22bdtmmX5BFHQ/99FHH2nBggXq0qWL5WBlyJAh2V7zGhQUpAkTJujixYvatm2bPvjgA508eVLbtm1TUlKSmjRpYnXNV27bwLlz51S1alWr+VSvXl1JSUm6ePGiypcvb/XcixcvKiUlxWp9lCxZMtcgZcv63LFjh+bOnatjx44pKSlJhmFYnfqX13Jlt0/Oa54F3a5smUemm9lfFsT19Zw4cUKpqalq3ry5VZv09HRFR0fr0qVLSkhIyHGfdKPo6Gir6Ts4OKhq1ao5tq9Zs6b8/f21cuVKhYeHa/ny5erQoYPlgPV2HBtkt98NCgrS0qVLlZSUpN27d+u9997T6tWrtWPHDm3btk3NmjXL1/JfL7f9VXbKly8vR8f/O+zOa7+cnYLsB6Kjo7N8ntSoUSPH/XRex0B5HWsYhqEPP/xQa9as0YULF2QymSwdN9e78f0ydepUffTRR3rmmWeUkZGhdu3aacSIETl+uXi93D5PpWs/Zfvxxx/rxIkTSktLU1paWpb9241OnDih7du3W52unp6ebtnObzy+kKyP/fL7GXljxslOenq6XnzxRRUvXlxTpkyxtM/rtbiZ/VR2x5vR0dG677771KtXL3Xr1k2XLl3S6dOndezYMXXu3Dnb6WR+bly/bkqUKGG1PeSVnzL3mde/10qUKCFJVvvP3D5r8toHZvccWxQ4UOdH5cqVdebMGcvjjIwMqwPCzJWReR2DJKtriTJdvyFI14Je586d9eabb97qkvPN2dlZ4eHh2r59u2bNmqVx48apYsWKatq0qdX1uNfz8PDQpUuXFBsbq9KlS+c6/cwPgtOnT1s+TK6/Ji07JUqUUPv27bVixQolJiYqNTVVrVu3zrF9t27d1K1bN6WkpGjp0qUaPHiwduzYYdM3Mjm58bWbOHGiypYtq1WrVqlcuXKKjY1V06ZNLW/+G9tL117v+++/39LDlJ3WrVurdevWSktL08aNGzVq1CjVr19f1atXz7b99dtl5uPrP3x79uypjh076vTp01q9erVmzZqV47wrV66ss2fPWt78krJ8UFWoUEEjR47M8wyI3OYh5b495Pd9kd06zo2rq6uGDx+u4cOH6/Lly3rjjTc0duzYLNdE5bfu+Ph4Xbp0yfKhFBgYqDFjxujbb7/VQw89JAcHBwUHB2vTpk3avXu31XVhBZXXPig7eX2wZSc/2+iNPDw8dPz4cbVs2TLLuCpVquivv/6yGnbq1CnLlwH53XfmJbtlze97qWzZsnJxcbF6bdPT03X27NlcD6xv1o2vqZT1/XyjLl266KOPPtKJEye0YsWKHHunc5u+l5eX5bGbm1uu6z6v/dyNfHx89P7770uSDhw4oP79+6tKlSrq2rVrlrZVq1ZVjRo1tHz5cl29elV16tRRUFCQli1bpuTk5Fz3+TeqUqWKzp49q4yMDMt+4dSpUypevHi2Z3KUK1dOzs7OOnPmjOU1v3r1arbX8WbKz/q8XkpKioYMGaLhw4dr/vz5cnNz0/fff69hw4ble7ky98nXL1de20hBtytb5pGfuqXc95cmkynfnQfXv78rVKig4sWLa8eOHVZhLpNhGHJzc1NUVFSOAfbGWm/ct+b1RVrXrl31wQcfqHfv3tq0aZO+/PJLy7jbdWxwo4YNGyotLU2LFy+Wl5eXypQpo6CgIG3ZskXbtm3LsRNCsu2z4VbI7jUvyH6gcuXKWY4XTp48meU62OvldgyU17HGmjVrtHTpUn388cfy9vaWg4ODhgwZkmUZbnxNPTw89NZbb0m6Fn6ef/55lSxZ0nJ/j9x4eHjkeGfs6OhojRw5UjNmzFBoaKicnZ316aefWl0Pnd06rlChgjp16pTjL1FUrlw5y/XI178H8vsZmZ9jskmTJuns2bNasmSJ1Remeb0WN7Ofyu54MzPY+vj4qH79+vrPf/6jY8eOqV27djl2Fl7/uZG5309ISNDly5etliO3/JQfeX3W5LUPzFTQY+Qsz7+pZ+egc+fO+vjjjxUVFaWUlBTNnz/f6mYRZcuWVbVq1bRs2TKlpaXp5MmT+VqZvXr10n//+19t2LBBKSkpSk//f+3deVAUZ/oH8C+MYyAcARd0y2tFiWTLMAEc7kMERSSAIhGNBliPpCqyHihFYHWDV5ZjNnG5FAU1GkpLjWw0woqGLMS4Ro1HQDcKi0EhiAcaHUFmOOb3h0X/HDlmGEES/X6qrJLp6+2enrffp/vt523F1atX8c0333S5TGtrKxQKBZqbmwE86jqhUCjQ1tb2VPu4bNky7Nq1CzU1NZgxYwYuXLiA3bt34+HDh1CpVLh+/brw0rytrS3s7OwQHx+PGzduQKVSoaqqClevXu2w3iFDhsDV1RUymQz37t3DvXv31JKGdWXmzJk4cuQIPv30UwQHB3eZna60tBSnT59GU1MTxGKx0EB/2hNJE7lcDkNDQ5iYmEAul0Mmk6lNb78D+ngiismTJ0OhUCA9PR1yuRzAo7vWBQUFwrwlJSVoaGjAgAEDYGJiApVK1e2+XLx4Efv370dLSwtKS0uxb98+tS7do0aNgqOjI5YsWQJzc3O4uLh0ua6JEycKPRaUSiWqqqo6JK6IjIxERkYGysrK0NbWBqVSidLSUo1d5ttpcz7o8rsA/j/ZTFc3bIqKilBRUYGWlhYYGhripZde0vo8GTx4MDw9PZGYmIg7d+6goaEB69atg7W1NWxtbYV9s7a2RnZ2Njw8PAA8uvu+c+dOGBkZdXhq1BOa6qDOWFpaoqqqqkfb0XSOdiYiIgJbtmzBqVOn0NraCrlcLiQofPPNN1FeXo49e/agubkZ5eXlyMnJwcyZMwHoXnc+ycLCQuguBfTst6Svr4+QkBCkpqaitrYWCoVCaHR1dpOgt0yfPh1Hjx5FSUkJWltbUVJSonZ3vjPDhg2Dq6sr/va3v6G8vLzLO+nt69+3bx9KS0vR0tKC/fv3d7i58frrryMvLw8KhQK3b99GZmam2nRN9dzjlEol9u/fL5yXxsbG0NfX7zYjs7u7O7Zs2QJXV1fo6enB1dUVZ8+exfnz5zX26Hict7c3VCoV0tLSoFQq8fPPPyM1NRWhoaGdBg/6+voIDg5Geno6bty4gYcPH6r1uumMNsfzcc3NzVAoFDA1NcXLL7+M6upqbNmyRet9Ah7Vyc3Nzdi0aROUSiWuXLmicRSOnp5XumxDE23qS0tLS1y9elWtG6Q2xo8fj9GjR2PNmjXCU/d79+6hsLAQDx8+hJ6eHsLDw/H3v/8d//3vf6FSqXDnzp0uuy5Pnz4dW7duxZUrV6BUKrFx48Zub6wAj17lkMvliI+Px5gxY4R9AvqmbdAZsVgMJycnZGdnC78Vd3d3FBQUoK6urtshBC0tLdXqy2elq+9c23ogNDQUOTk5qKioQHNzM3bt2oXKykoEBgZ2uc3u2kCa2hpyuRwikQjm5uZQqVQ4evSoxu7VwKMuu3V1dVCpVDA2NoZIJBKuPZraKdOnT0dtbS0yMjLQ0NCAlpYWnDt3Dg8ePEBDQwPa2tpgbm6OgQMH4tKlS0Kyve6OcWRkJHbu3IkTJ04IT7XLy8tx+vRpAI96rF68eBFffPEFWlpaUFZWpvYaX29dIzMyMvCf//wH2dnZQhf2dpq+i6eppz755BOhvSmTyeDh4aHWu+btt9/G7t27cejQoS67e7cfh/brxs2bNzu9bmiKn7Sh6VqjqQ7sLX0SRb377rvw8fHB3Llz4eXlBblcDltbW7X3IZOTk/Hdd9/B0dERsbGx3T45aCeRSLB161bs2bMHXl5ecHZ2xpIlS7q9O3rgwAFIJBIsWLBAWIdEIhF+GMCj7JEHDx7s0T46Oztj/PjxSE1NxdChQ7F79258++23QjbFBQsWCOMm6unpITMzE4MGDUJYWBgcHBzw5z//Gbdv3+503TKZDPr6+vDx8UFISAimTp2qsTz29vYYPnw4vvvuu26PZUNDAxITE+Hq6gqpVIo9e/YgIyOj23dVe8PKlStRVlYGR0dHhIaGduheZWVlhfDwcMyZMwdSqRS5ubkwNjbGnj17UFNTg6CgIDg4OGD27NnCd9deWXh5ecHBwQHJyclISUnpttvGlClT8P3338PFxQWLFy/GwoULO1xcZs+ejYsXLyIsLKzbO9MmJibIzs7GV199BWdnZyxfvhwhISFqNzMiIyMRFRWFhIQEODk5wdPTEzKZrEc/Yk3ngy6/C+DRHdU//OEPahXl42pqahAVFQVHR0d4eXmhvr5euIusjbFjx+LYsWNwd3eHr68vGhoakJWVJQQMs2fPRkVFBeRyudAIcHZ2RlNTE9zc3Do99u2ZUzXRpg560rx581BeXg6pVAovLy+t9lHTOdqZWbNmYdmyZVi3bh2kUimmTp0qZF0fPnw4cnJykJeXBxcXFyxatAhhYWH405/+JCyvS935pBUrViArKwuOjo6Ijo7u8W8pLi5OyKbq6emJH374Adu2betw0e9NUqkUa9euxfr16yGVSrF3716t9j00NBTFxcWYOHFit139goODsWDBAixevBguLi44c+ZMhyc5CQkJuHHjBlxcXDBv3jwEBwerTddUzz2psLAQb775Juzs7BAeHo4ZM2Z0WOfj3NzcIJfLhRtQpqamGDt2LMzNzfHqq69qOhQCY2NjbNu2DefPn4eHhwfmzp0LJyenbnuFxMfHw9raGkFBQfDz88Po0aM77VrbTpvj+TgjIyOsWbMGaWlpsLe3R0xMTI979rTXyV9//bWQqbU9e25Xenpe6bINbchkMlhYWCA4OLjT+jIsLAxtbW1wcXGBVCpVe8rTHZFIhO3bt+Oll14SMv5OmzYNR48eFerYJUuWYMaMGYiOjoaDgwNCQ0O7vOn77rvvYtKkSXjnnXfg5eWFhw8fCll+bWxshPo5IiICNjY2CAwMhIGBAQIDA1FcXNzh2PZF26ArxcXFar8fGxsbGBgYQCKRwMTEpMvlFi1ahMLCQjg6OmL27NndH/Be1NV3rm09MH/+fLz11lt4//334eLiggMHDiAnJ0djT6Ku2kCa2hozZsyAo6MjAgIC4O7ujm+++Qa+vr4a9/PkyZPCaCnTpk2DnZ0dFi5cCEBzO8XCwgK5ubk4f/48fHx8hAcQLS0tGDNmDJYtWyac1ykpKZg2bZrGYzxp0iQkJSUhNTUVbm5ucHNzw6pVq4QbRyNGjEBmZiays7Ph6OiIlJQUhIaGqrX9euMa2X6jYfLkyUKm7/a2iabv4mnqKX9/f4SEhMDHxwcikQgpKSkdpt+7dw9Dhw7t8Prik+Lj4zF69GgEBgbCz88Po0aNwqBBg4S2mKb4SRuarjXa1IFP+v7772Fvb9+z19h6nMZMBy0tLSo3NzfVl19++Sw2R/RU2jOZ19fX93jZzz77TOXn59cHpep9y5cvVxUUFPTZ+tPS0lTjxo1TjRs3rkPWypqaGpWNjY3Kzs5OY+bax7VnTv3Xv/7Vo7KwDiIi6jtpaWkqiUSiOnfunNq/H3/8sb+LJhg7dqwqJyenv4vxq/c0baDe1tftlN6SlJSkmj9/fn8X45kJCgpS7dixo8fL3b9/X/Xaa6+pjS70vOiTd6jlcjnOnDkDd3d3KJVKZGRkoKWlReunPkT9pb17cFBQkFZZoY8fP44xY8ZgyJAhuHDhAnJycnR6Ytgf2t/d7EtisRiurq7Iz89X61KXn5+PV199tc9eNWAdRET0bOnr6/c4gRr9uvS0DdTXnkU7RRdff/017O3tYWpqilOnTmHfvn1YtWpVfxfrmThy5Ahqa2u7HAHncTU1Nairq4O9vT3kcjnWr1+PkSNHqr328bzok9ZsW1sb0tPT4ezsjAkTJqC0tBTZ2dkwNTXti80R9YqioiI4OTmhpqYGy5cv12qZyspKvPXWW7Czs8PSpUsRFBSE9957r49L+tsSGBiIwsJCIY8B8Ch5yZNd7SsrKxEdHY0JEybgjTfeQEBAALZt26ZVvoO8vDwEBQXB1tYWnp6eyMzMZB1ERPQrUVxcjJkzZ0IikcDFxQUJCQlqGdzbu4wfO3YMS5cuhb29Pby9vYUhnnbu3Alvb284OTlh5cqVUCqVwrI3b95EfHw8fH19IZFI4Ofnh08++URtHl3L9SLRpQ30ojp79iymTp2K8ePHY/Xq1Vi0aFGH7uTPIw8PDyQkJGDt2rVadV9XKpVYs2YNpFIppkyZgl9++QVZWVndJgf7reqTPXrllVeEsX2Jfit8fX1x/vz5Hi0TERGBiIiIvinQc2LixIlYuXIljh8/Dm9vb/zvf//D5cuXkZmZqZZE5ubNm7CyskJQUBCMjIzw448/Ij09HY2Njd1m+t2+fTtkMhkiIyMRFxeHyspKbNiwAXPnzmU9RET0DLW0tKj9LRKJUFhYiOjoaMyYMQOLFy/GrVu38PHHH+P+/fvYsGGD2vyrV69GSEgIwsLCsHfvXsTGxuLSpUuoqKjAmjVrUF1djaSkJIwYMULIwnz37l2YmZkhPj4epqamqKqqQnp6Om7duoXExMQuy3r48GGty/Ui0KUN9KKKiYlBTExMfxfjmWvP96Kt0aNHdznu+fPm+btFQES/KoaGhvDx8UF+fj68vb1x6NAh2Nvbdxgf09XVVRgPXaVSYfz48WhqakJubm6XAfWDBw+QlpaGhQsXCnfU3d3dIRaLkZSUhAULFnQ7Vi4REfWOxsZGjBs3Tu2z5ORkpKWlISAgAB999JHwuaWlJd577z0sWrRILZGWv7+/UN9LJBIcPXoU+fn5OHr0KMRiMQDg1KlTOHz4sBBQ29jY4IMPPhDW4eDgAENDQ8TFxeHDDz/sdEhQlUqFlJQUrctFRNQdBtRE1OcCAwOxYsUKNDU1oaCgAOHh4R3mUSgU2Lx5M7788ktcv35drYt4Q0ODMMTb486dO4fGxkb4+/urPRlxc3NDU1MTKioq4OTk1Dc7RUREAgMDA+Tm5qp91tbWhp9//hl/+ctf1OpoJycn6Ovr48KFC2qB6+PDPpmYmGDQoEGQSqVCMA08Gtrp5MmTwt8qlQo7duzA3r17UVNTA4VCIUyrrq7G2LFjO5T1p59+6lG5iIi6w4CaiPqch4cHxGIxUlNTUVNT0+lQcDKZDPv27UNUVBRef/11mJiYoKioCJs2bYJCoeg0oG4fxiIkJKTT7V6/fr13d4SIiDqlr6/fIdnQmTNnAABRUVGdLvNkHf3k8FUDBw7skPtCLBarvR+9Y8cOJCcnY+HChXB2doapqSnKysqwdu1ateD6ce3XDm3LRUTUHQbURNTnxGIx/Pz88Omnn8LV1RUWFhYd5jl8+DBmzZqlltStpKSk2/W+8sorAICMjIxOx8TtbkxyIiLqW2ZmZgCADz/8EBKJpMP0wYMHP/U2Dh8+DB8fH6xYsUL4rLKyst/LRUQvDgbURPRMzJw5E/X19QgLC+t0ukKhUOvW19raivz8/G7XaW9vD0NDQ9TV1WHy5Mm9Wl4iIno6o0ePxu9//3tUV1dj7ty5fbKNpqYmtWsHAI2JkJ5FuYjoxcGAmoieCYlEgo0bN3Y53c3NDfv27YO1tTXMzc2xa9cujcOemJqaYsmSJZDJZKirq4OTkxNEIhGqq6tRVFSE9PT0ThPSEBFR39PT00NcXBxiYmLQ2NgIb29vGBoaora2FiUlJYiOjoaVldVTbcPNzQ07d+5Ebm4uRo0ahYMHD+Lq1av9Xi4ienEwoCaiX4W//vWvSEhIwLp162BoaIiQkBBMnjwZq1at6na5+fPnY8iQIdi+fTtyc3MxYMAAjBw5Et7e3h2eWhAR0bM1depUmJqaIisrS3hyPGzYMHh6enb6+k9PRUVF4e7du0hLSwMATJkyBatWrRKygPdXuYjoxaGnUqlU/V0IIiIiIiIiot8a/f4uABEREREREdFvEQNqIiIiIiIiIh0woCYiIiIiIiLSAQNqIiIiIiIiIh0woCYiIiIiIiLSAQNqIiIiIiIiIh0woCYiIiIiIiLSAQNqIiIiIiIiIh0woCYiIvoNCw8Px0cffdTfxSAiInohDejvAhAREf3W3bp1C5s3b0ZJSQnq6upgYmKCkSNHIjg4GCEhITA0NOzvIhIREVEfYEBNRET0FKqrq/H222/DxMQE0dHRsLGxwcCBA3H58mXs3bsXQ4YMga+vb38Xs0utra3Q09ODvj47rREREfUUr55ERERPYfXq1RCJRNi/fz8CAgIwZswYjBgxApMmTcKWLVvg4+MDALh//z5WrlwJFxcXODg4ICIiApcuXRLWk56ejmnTpuGLL76Aj48Pxo8fj+joaDx48ECYp7GxEbGxsbC3t4eHhwe2bdvWoTxKpRLJycnw9PSEnZ0dZs6ciZMnTwrT8/LyIJVKUVRUhICAANja2qK2trYPjxAREdHziwE1ERGRju7evYvjx49j7ty5ePnllzudR09PDwCwdOlS1NfXIzs7G3l5eRg3bhwiIyPxyy+/CPNeu3YNRUVFyMrKwubNm3H69GlkZ2cL01NSUnD69Gls3LgRW7duxalTp3Dx4kW17a1duxbnzp3Dhg0bcPDgQfj7+2PhwoWoqqoS5mlqakJ2djbWr1+PQ4cO4Xe/+13vHRQiIqIXCLt8ExER6ejatWtQqVSwsrJS+9zZ2RlKpRIAMGfOHEycOBGlpaU4ceIEBg4cCAD44IMP8NVXX6GwsBCzZs0CAKhUKiQmJsLY2BgAEBwcjBMnTiA6OhoNDQ34/PPPIZPJ4OrqCgBISkrChAkThO3W1tYiLy8P//73vzFkyBAAwIIFC3Ds2DHk5eVh+fLlAIDm5masXr0ar732Wh8eHSIioucfA2oiIqJe9vnnn6OtrQ0xMTFQKpW4fPkyGhsb4ezsrDZfU1MTrl27Jvw9bNgwIZgGgMGDB6O+vh7Ao3e1m5ub8cYbbwjTzczM1IL58vJytLa2wt/fX207SqUSZmZmwt9isRg2Nja9sq9EREQvMgbUREREOho5ciT09PTw008/qX0+YsQIAICBgQEAoKGhAZaWlvjss886rMPExET4/4ABHS/LKpVK6/I0NjYK73OLRCK1aY93STcwMBC6ohMREZHuGFATERHpyNzcHO7u7sjNzcU777zT5XvU48aNw+3btyESiTB8+HCdtjVixAiIxWL88MMPGDp0KADg3r17qKqqgqOjIwDgj3/8I1pbW3Hnzh1IpVLddoqIiIi0xqRkRERETyEhIQGtra0IDQ1FQUEBKisrceXKFRw4cABXrlyBSCSCm5sb7OzsEBUVhW+//RY1NTU4e/YsNmzYgLKyMq22Y2RkhNDQUMhkMpw4cQLl5eWIi4tTe9JsZWWFoKAgxMbG4siRI6iurkZpaSk2b96M4uLiPjoCRERELy4+oSYiInoKI0eOxD//+U9s3rwZH3/8MW7cuAGxWAxra2vMnz8fc+bMgZ6eHrZs2YJ//OMfiI+Px927d2FhYQGpVAoLCwuttxUbG4vGxka8//77MDIywrx589SG1QKAxMREbNq0CUlJSbh58ybMzMxgZ2cHb2/vXt5zIiIi0lP15OUsIiIiIiIiIgLALt9EREREREREOmFATURERERERKQDBtREREREREREOmBATURERERERKQDBtREREREREREOmBATURERERERKQDBtREREREREREOmBATURERERERKQDBtREREREREREOmBATURERERERKQDBtREREREREREOmBATURERERERKSD/wMQC0+1K605/wAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        " Comparing recidivism rates across different racial groups, the visualization can reveal disparities that may exist in the patterns of reoffending, which is crucial for understanding and addressing systemic biases within the criminal justice system."
      ],
      "metadata": {
        "id": "7aTfR1sFU7FO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# @title\n",
        "#figure 2\n",
        "\n",
        "plt.figure(figsize=(12, 8))\n",
        "sns.countplot(x='race', hue='two_year_recid', data=recid_df)\n",
        "plt.title('Recidivism Rates by Race within Two Years', fontsize=15)\n",
        "plt.xlabel('Race', fontsize=12.5)\n",
        "plt.ylabel('Count of Recidivism', fontsize=13)\n",
        "plt.xticks(rotation=45)\n",
        "plt.legend(title='Recidivism within Two Years', labels=['No', 'Yes'])\n",
        "plt.text(0.25, -42, \"Figure 2: Recidivism rates across different racial groups, depicting individuals who did and did not recidivate within two years, across races.\",\n",
        "         ha='left', fontsize=10)\n",
        "plt.grid(True)\n",
        "plt.tight_layout()"
      ],
      "metadata": {
        "id": "t68BtDYHU9sP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 801
        },
        "outputId": "7e99f58d-5934-41a1-d612-8d50ee0fb840",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Frequency/count of recidivism vs Gender and Race"
      ],
      "metadata": {
        "id": "RXpD6vmCVTCE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# @title\n",
        "# figure 3\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import warnings\n",
        "\n",
        "# Store the original plt.show function\n",
        "original_show = plt.show\n",
        "\n",
        "# Define a new show function that suppresses warnings\n",
        "def show(*args, **kwargs):\n",
        "    with warnings.catch_warnings():\n",
        "        warnings.simplefilter(\"ignore\")\n",
        "        original_show(*args, **kwargs)\n",
        "\n",
        "# Manually set the docstring to ensure it carries over\n",
        "show.__doc__ = original_show.__doc__\n",
        "\n",
        "# Replace the original plt.show with the new one\n",
        "plt.show = show\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "plt.figure(figsize=(12, 6))\n",
        "sns.countplot(x='sex', hue='race', data=recid_df[recid_df['two_year_recid'] == 1])\n",
        "plt.title('Recidivism within Two Years by Gender and Race')\n",
        "plt.xlabel('Gender')\n",
        "plt.ylabel('Count of Recidivism')\n",
        "plt.legend(title='Race', loc='upper right')\n",
        "plt.text(0.5, -40, \"Figure 3: Recidivism rates by gender, showing the counts of individuals who did and did not recidivate within two years, categorized by gender.\",\n",
        "         ha='center', fontsize=9.5)\n",
        "\n",
        "\n",
        "\n",
        "plt.tight_layout()\n",
        "#plt.show"
      ],
      "metadata": {
        "id": "MFKOBS1lVOLV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 601
        },
        "outputId": "1bf9eb5b-8730-48ba-c4be-51cf04916470",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# @title\n",
        "\n",
        "#Splitting\n",
        "\n",
        "from sklearn.model_selection import train_test_split, GridSearchCV\n",
        "from sklearn.compose import ColumnTransformer\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.preprocessing import StandardScaler, OneHotEncoder, OrdinalEncoder\n",
        "from xgboost import XGBClassifier\n",
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "\n",
        "# split the data\n",
        "def split_data(recid_df, two_year_recid):\n",
        "    X = recid_df.drop(columns=[two_year_recid])\n",
        "    y = recid_df[two_year_recid]\n",
        "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=123)\n",
        "    return X_train, X_test, y_train, y_test\n",
        "\n",
        "\n",
        "X_train, X_test, y_train, y_test = split_data(recid_df, 'two_year_recid')"
      ],
      "metadata": {
        "id": "v8THBqOOqK_e",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# @title\n",
        "# preprocess the data\n",
        "\n",
        "from sklearn.preprocessing import StandardScaler, OneHotEncoder, OrdinalEncoder\n",
        "from sklearn.compose import ColumnTransformer\n",
        "\n",
        "\n",
        "def create_preprocessor(X_train):\n",
        "\n",
        "    numerical_features = ['age', 'juv_fel_count', 'juv_misd_count', 'juv_other_count', 'priors_count', 'c_jail_days', 'r_days_from_arrest', 'custody_days']\n",
        "    categorical_features = ['sex', 'race']\n",
        "    ordinal_features = ['age_cat', 'c_charge_degree']\n",
        "    ordinal_categories = [['Less than 25', '25 - 45', 'Greater than 45'], ['M', 'F']]\n",
        "\n",
        "    preprocessor = ColumnTransformer(\n",
        "        transformers=[\n",
        "            ('num', StandardScaler(), numerical_features),\n",
        "            ('cat', OneHotEncoder(handle_unknown='ignore'), categorical_features),\n",
        "            ('ord', OrdinalEncoder(categories=ordinal_categories), ordinal_features)\n",
        "        ],\n",
        "        remainder='passthrough'\n",
        "    )\n",
        "\n",
        "    return preprocessor"
      ],
      "metadata": {
        "id": "p-P05WuSqqRI",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Logistic Regression\n",
        "\n",
        "I chose logistic regression for this analysis because it's simple and  particularly good at solving problems where there are only two possible outcomes, such as predicting whether someone will reoffend within two years. The method uses the Broward County data we have to estimate the likelihood of each outcome. For our study, the accuracy of the predictions was about 71%, meaning the model correctly predicted whether individuals would reoffend in approximately 71 out of 100 cases.\n"
      ],
      "metadata": {
        "id": "YJLFuxVmRwKx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# @title\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.impute import SimpleImputer\n",
        "from sklearn.preprocessing import StandardScaler, OneHotEncoder, OrdinalEncoder\n",
        "from sklearn.compose import ColumnTransformer\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "\n",
        "# define the create_preprocessor function\n",
        "def create_preprocessor(X_train):\n",
        "    numerical_features = ['age', 'juv_fel_count', 'juv_misd_count', 'juv_other_count', 'priors_count', 'c_jail_days', 'r_days_from_arrest', 'custody_days']\n",
        "    categorical_features = ['sex', 'race']\n",
        "    ordinal_features = ['age_cat', 'c_charge_degree']\n",
        "    ordinal_categories = [['Less than 25', '25 - 45', 'Greater than 45'], ['M', 'F']]\n",
        "\n",
        "    preprocessor = ColumnTransformer(\n",
        "        transformers=[\n",
        "            ('num', Pipeline([('imputer', SimpleImputer(strategy='median')), ('scaler', StandardScaler())]), numerical_features),\n",
        "            ('cat', OneHotEncoder(handle_unknown='ignore'), categorical_features),\n",
        "            ('ord', OrdinalEncoder(categories=ordinal_categories), ordinal_features)\n",
        "        ],\n",
        "        remainder='passthrough'\n",
        "    )\n",
        "\n",
        "    return preprocessor\n",
        "\n",
        "# preprocessor using the defined function\n",
        "preprocessor = create_preprocessor(X_train)\n",
        "\n",
        "# preprocess the training and testing data\n",
        "X_train_processed = preprocessor.fit_transform(X_train)\n",
        "X_test_processed = preprocessor.transform(X_test)\n",
        "\n",
        "# initialize the Logistic Regression model\n",
        "logistic_model = LogisticRegression(max_iter=1000, random_state=123)\n",
        "\n",
        "# train the model with the preprocessed training data\n",
        "logistic_model.fit(X_train_processed, y_train)\n",
        "\n",
        "# predict on the preprocessed test data\n",
        "y_pred = logistic_model.predict(X_test_processed)\n",
        "\n",
        "# calculate and print the accuracy\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "accuracy\n",
        "\n"
      ],
      "metadata": {
        "id": "CRktF8GhSvFC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "affde7b3-5741-459f-aff1-4684fc82530b",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7075537075537075"
            ]
          },
          "metadata": {},
          "execution_count": 202
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Test of model using mean/average and standard deviation\n"
      ],
      "metadata": {
        "id": "TbEuoXPeGIaF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "In the recidivism project, I used the mean (average) and standard deviation to evaluate how well the model predicts whether someone will reoffend. Here’s why I used these statistical measures:\n",
        "\n",
        "1. **Mean (Average):** The mean of the test accuracy scores gives a straightforward way to understand, on average, how accurate the model's predictions are. By calculating the mean score from all test results, we can see the overall effectiveness of the model. For instance, a mean test score tells us the average percentage of times the model correctly predicted whether individuals would reoffend.\n",
        "\n",
        "2. **Standard Deviation:** This measure tells shows how much the accuracy scores vary from the mean. A lower standard deviation means that the scores are more consistent and closer to the mean score, indicating that the model performs reliably across different sets of data. A higher standard deviation would suggest that the model’s performance varies significantly, which might indicate that it doesn’t perform equally well in all scenarios.\n",
        "\n",
        "By looking at both the mean and the standard deviation, we can get a full picture of the model's performance — not just how well it does on average, but also how dependable its predictions are across different situations. This is crucial for ensuring that the model is both accurate and reliable in its predictions about recidivism."
      ],
      "metadata": {
        "id": "SFPip4YRwRvZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# @title\n",
        "#testing accuracy of the model using mean\n",
        "import numpy as np\n",
        "\n",
        "test_accuracy_scores = [entry['Test Score'] for entry in test_scores]\n",
        "\n",
        "# mean and standard deviation\n",
        "mean_test_score = np.mean(test_accuracy_scores)\n",
        "std_test_score = np.std(test_accuracy_scores)\n",
        "\n",
        "print(f\"Mean Test Score: {mean_test_score:.4f}\")\n",
        "print(f\"Standard Deviation of Test Scores: {std_test_score:.4f}\")\n"
      ],
      "metadata": {
        "id": "u2W8H4q7D-AU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ed7bf3fd-d43d-4d1d-bfa8-5c72c80d96b6",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Test Score: 0.8518\n",
            "Standard Deviation of Test Scores: 0.0109\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Results and Analysis"
      ],
      "metadata": {
        "id": "xdjXEFU6kPeZ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "In my analysis of the recidivism dataset, I've decided to look at how well the model predicts outcomes for different racial groups, particularly Caucasian and African American individuals, since they appear more frequently in the data. I use tools called confusion matrices to see how accurate the model is for each group. These matrices show not only how often the model is right overall, but also how well it identifies correct cases of recidivism (true positives) and correct cases where recidivism does not occur (true negatives). They also show where the model makes mistakes, either by wrongly predicting recidivism (false positives) or missing it when it does happen (false negatives).\n",
        "\n",
        "This detailed approach helps check if the model is fair and does not favor one group over another. It’s important because sometimes, using only overall accuracy to evaluate a model can be misleading. This is especially true in datasets where the numbers of one outcome are much higher than the other. For instance, if there are many more cases of non-recidivism than recidivism, a model might seem accurate simply because it frequently predicts non-recidivism, without truly capturing the less common cases of recidivism. Moreover, accuracy alone doesn't tell us about the different types of mistakes the model makes, which can have varying impacts. For example, wrongly predicting that someone will reoffend might have different consequences than missing a prediction for someone who does reoffend. This consideration is crucial for ensuring the analysis is both effective and fair."
      ],
      "metadata": {
        "id": "nekk_wKGkJG2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# @title\n",
        "#Confusion Matrix\n",
        "\n",
        "#import necessary python packages\n",
        "\n",
        "from sklearn.model_selection import train_test_split, GridSearchCV\n",
        "from sklearn.compose import ColumnTransformer\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.preprocessing import StandardScaler, OneHotEncoder, OrdinalEncoder\n",
        "from xgboost import XGBClassifier\n",
        "from sklearn.metrics import accuracy_score, confusion_matrix\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "\n",
        "# train models and save test sets\n",
        "models = []\n",
        "test_sets = []\n",
        "random_states = [42, 1, 123, 2022, 7]\n",
        "\n",
        "for state in random_states:\n",
        "    X_train, X_test, y_train, y_test = train_test_split(recid_df.drop(columns='two_year_recid'), recid_df['two_year_recid'], test_size=0.2, random_state=state)\n",
        "    model, _ = train_xgboost_model(X_train, y_train)\n",
        "    models.append(model)\n",
        "    test_sets.append((X_test, y_test))\n",
        "\n",
        "\n",
        "master_df = pd.DataFrame()\n",
        "\n",
        "for i, (model, test_set) in enumerate(zip(models, test_sets)):\n",
        "    X_test, y_test = test_set\n",
        "    y_pred = model.predict(X_test)\n",
        "    temp_df = X_test.copy()\n",
        "    temp_df['true_labels'] = y_test\n",
        "    temp_df['predicted_labels'] = y_pred\n",
        "    temp_df['model_number'] = i\n",
        "    master_df = pd.concat([master_df, temp_df])\n",
        "\n",
        "\n",
        "master_df.reset_index(drop=True, inplace=True)\n",
        "\n",
        "\n",
        "overall_accuracy = accuracy_score(master_df['true_labels'], master_df['predicted_labels'])\n",
        "print(f\"overall accuracy: {overall_accuracy:.2f}\")\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "RPHEhhiMKBuB",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# @title\n",
        "# XGBoost\n",
        "\n",
        "# import necessary packages\n",
        "\n",
        "from sklearn.model_selection import train_test_split, GridSearchCV\n",
        "from sklearn.compose import ColumnTransformer\n",
        "from sklearn.pipeline import Pipeline\n",
        "from xgboost import XGBClassifier\n",
        "\n",
        "\n",
        "def create_preprocessor(X_train):\n",
        "    numerical_features = ['age', 'juv_fel_count', 'juv_misd_count', 'juv_other_count', 'priors_count', 'c_jail_days', 'r_days_from_arrest', 'custody_days']\n",
        "    categorical_features = ['sex', 'race']\n",
        "    ordinal_features = ['age_cat', 'c_charge_degree']\n",
        "    ordinal_categories = [['Less than 25', '25 - 45', 'Greater than 45'], ['M', 'F']]\n",
        "\n",
        "    preprocessor = ColumnTransformer(\n",
        "        transformers=[\n",
        "            ('num', StandardScaler(), numerical_features),\n",
        "            ('cat', OneHotEncoder(handle_unknown='ignore'), categorical_features),\n",
        "            ('ord', OrdinalEncoder(categories=ordinal_categories), ordinal_features)\n",
        "        ],\n",
        "        remainder='passthrough'\n",
        "    )\n",
        "    return preprocessor\n",
        "\n",
        "# train_xgboost_model function\n",
        "def train_xgboost_model(X_train, y_train):\n",
        "    preprocessor = create_preprocessor(X_train)\n",
        "\n",
        "    pipeline = Pipeline([\n",
        "        ('preprocessor', preprocessor),\n",
        "        ('classifier', XGBClassifier(objective='binary:logistic', use_label_encoder=False, eval_metric='logloss'))\n",
        "    ])\n",
        "# hyperparameters\n",
        "    param_grid = {\n",
        "        'classifier__n_estimators': [50, 100, 200],\n",
        "        'classifier__learning_rate': [0.05, 0.1, 0.2],\n",
        "        'classifier__max_depth': [3, 4, 7, 20]\n",
        "    }\n",
        "\n",
        "    grid_search = GridSearchCV(pipeline, param_grid, cv=5, scoring='accuracy', n_jobs=-1, verbose=1)\n",
        "    grid_search.fit(X_train, y_train)\n",
        "\n",
        "    return grid_search.best_estimator_, grid_search.best_params_\n",
        "\n",
        "\n",
        "\n",
        "# train model\n",
        "model, best_params = train_xgboost_model(X_train, y_train)\n",
        "print(\"Best Parameters:\", best_params)\n"
      ],
      "metadata": {
        "id": "OteuUy-KMS0s",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# @title\n",
        "# function to evaluate model performance\n",
        "def evaluate_model(model, X_train, X_test, y_train, y_test):\n",
        "    y_train_pred = model.predict(X_train)\n",
        "    train_accuracy = accuracy_score(y_train, y_train_pred)\n",
        "\n",
        "    y_test_pred = model.predict(X_test)\n",
        "    test_accuracy = accuracy_score(y_test, y_test_pred)\n",
        "\n",
        "    return {\n",
        "        'Train Accuracy': train_accuracy,\n",
        "        'Test Accuracy': test_accuracy\n",
        "    }\n",
        "\n",
        "# evaluate model\n",
        "scores = evaluate_model(model, X_train, X_test, y_train, y_test)\n",
        "print(scores)"
      ],
      "metadata": {
        "id": "GScc8qnpMaJz",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# @title\n",
        "def train_and_evaluate(X, y, random_state):\n",
        "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=random_state)\n",
        "    model, best_params = train_xgboost_model(X_train, y_train)\n",
        "    scores = evaluate_model(model, X_train, X_test, y_train, y_test)\n",
        "    return model, scores, X_test, y_test\n",
        "\n",
        "\n",
        "random_states = [42, 1, 123, 2022, 7]\n",
        "best_models = []\n",
        "test_scores = []\n",
        "test_sets = []\n",
        "\n",
        "\n",
        "for state in random_states:\n",
        "    model, scores, X_test, y_test = train_and_evaluate(recid_df.drop(columns='two_year_recid'), recid_df['two_year_recid'], state)\n",
        "    best_models.append(model)\n",
        "    test_scores.append({'Random State': state, 'Test Score': scores['Test Accuracy']})\n",
        "    test_sets.append({'Random State': state, 'X_test': pd.DataFrame(X_test), 'y_test': y_test})\n",
        "\n",
        "\n",
        "for score in test_scores:\n",
        "    print(f\"Random State: {score['Random State']}, Test Score: {score['Test Score']}\")\n",
        "\n"
      ],
      "metadata": {
        "id": "KkaHgWYlMftc",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# @title\n",
        "train_sets = []\n",
        "random_states = [42, 1, 123, 2022, 7]\n",
        "for state in random_states:\n",
        "\n",
        "    X_train, X_test, y_train, y_test = train_test_split(recid_df.drop(columns='two_year_recid'),\n",
        "                                                        recid_df['two_year_recid'],\n",
        "                                                        test_size=0.2,\n",
        "                                                        random_state=state)\n",
        "\n",
        "\n",
        "    model, best_params = train_xgboost_model(X_train, y_train)\n",
        "\n",
        "\n",
        "    train_sets.append({'random_state': state, 'X_train': X_train, 'y_train': y_train})\n"
      ],
      "metadata": {
        "id": "7VhroGtFJiMO",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# @title\n",
        "# Confusion Matrix\n",
        "\n",
        "#import necessary python packages\n",
        "\n",
        "from sklearn.model_selection import train_test_split, GridSearchCV\n",
        "from sklearn.compose import ColumnTransformer\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.preprocessing import StandardScaler, OneHotEncoder, OrdinalEncoder\n",
        "from xgboost import XGBClassifier\n",
        "from sklearn.metrics import accuracy_score, confusion_matrix\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "\n",
        "# train models and save test sets\n",
        "models = []\n",
        "test_sets = []\n",
        "random_states = [42, 1, 123, 2022, 7]\n",
        "\n",
        "for state in random_states:\n",
        "    X_train, X_test, y_train, y_test = train_test_split(recid_df.drop(columns='two_year_recid'), recid_df['two_year_recid'], test_size=0.2, random_state=state)\n",
        "    model, _ = train_xgboost_model(X_train, y_train)\n",
        "    models.append(model)\n",
        "    test_sets.append((X_test, y_test))\n",
        "\n",
        "\n",
        "master_df = pd.DataFrame()\n",
        "\n",
        "for i, (model, test_set) in enumerate(zip(models, test_sets)):\n",
        "    X_test, y_test = test_set\n",
        "    y_pred = model.predict(X_test)\n",
        "    temp_df = X_test.copy()\n",
        "    temp_df['true_labels'] = y_test\n",
        "    temp_df['predicted_labels'] = y_pred\n",
        "    temp_df['model_number'] = i\n",
        "    master_df = pd.concat([master_df, temp_df])\n",
        "\n",
        "\n",
        "master_df.reset_index(drop=True, inplace=True)\n",
        "\n",
        "\n",
        "overall_accuracy = accuracy_score(master_df['true_labels'], master_df['predicted_labels'])\n",
        "print(f\"overall accuracy: {overall_accuracy:.2f}\")\n",
        "\n"
      ],
      "metadata": {
        "id": "m2u0H2NzLcvJ",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# @title\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.metrics import confusion_matrix, accuracy_score\n",
        "\n",
        "def plot_confusion_matrix(df, group, group_value):\n",
        "    if group not in df.columns:\n",
        "        print(f\"Column '{group}' not found in DataFrame.\")\n",
        "        return\n",
        "\n",
        "    group_df = df[df[group] == group_value]\n",
        "    if group_df.empty:\n",
        "        print(f\"No data found for {group} = '{group_value}'\")\n",
        "        return\n",
        "\n",
        "    # calculate confusion matrix and plot\n",
        "    cm = confusion_matrix(group_df['true_labels'], group_df['predicted_labels'], normalize='true')\n",
        "    plt.figure(figsize=(8, 6))  # Set figure size\n",
        "    sns.heatmap(cm, annot=True, fmt=\".2f\", cmap='Blues')\n",
        "    plt.title(f'Confusion Matrix for {group_value}')\n",
        "    plt.xlabel('Predicted Label')\n",
        "    plt.ylabel('True Label')\n",
        "\n",
        "\n",
        "    # print accuracy\n",
        "    accuracy = accuracy_score(group_df['true_labels'], group_df['predicted_labels'])\n",
        "    print(f\"Accuracy for {group_value}: {accuracy:.2f}\")\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "# males\n",
        "plot_confusion_matrix(master_df, 'sex', 'Male')\n",
        "\n",
        "# females\n",
        "plot_confusion_matrix(master_df, 'sex', 'Female')\n",
        "# Caucasians\n",
        "plot_confusion_matrix(master_df, 'race', 'Caucasian')\n",
        "\n",
        "# African-Americans\n",
        "plot_confusion_matrix(master_df, 'race', 'African-American')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 2314
        },
        "id": "FeAMmJwxQXZh",
        "outputId": "90990b1b-165e-4958-e4b8-6614ab84ac3c",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy for Male: 0.84\n",
            "Accuracy for Female: 0.88\n",
            "Accuracy for Caucasian: 0.88\n",
            "Accuracy for African-American: 0.83\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ],
            "image/png": 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LFi3k5ubmcOf0jWzcuFHdu3dXeHi4OnbsqJCQEDVs2NAplcxMm//+Hv/9dy4zmb/RP8IOHz6sY8eOKTw8XIMHD1a7du3UrFkzwycmZJ5z4MCBmj9/vtatW6dr165p/vz5N7ymihUratasWerevbsefPBBNWvWzPLzSgHcPjSTuOM888wzKlq0qF5//XWdP3/eafvx48e1cOFCSdenaSXZv8/0ySefOGzPCZUqVVJiYqLDo3DOnTunb7/91mG/S5cuOR2bmVb9/XFFmUqXLq377rtPq1atcmgcDh8+rIiIiBy9jr9r2rSpRowYoX/+8583nMZ0c3NzSo2+/vprp0f0ZN6JnBPTldOmTdOZM2c0efJkhYeHy9/fX+Hh4Vm+jzfTokULxcbGav369faxtLQ0LVq0SEWLFlXjxo0tnbdcuXLq2bOnduzYoUWLFjltz8jI0Pz583X27FlJxqncokWLnBLHzCbxr2s5L1++7PA4K+n62lIvLy/NnTvXqXnL/JllJtl//RnabDZ9+umnDvtfuXLF6RyVKlWSl5fXDd/3zGv66/n37t2rX375JctjAOQPTHPjjlOpUiVNmzZNI0eO1MMPP2z/BJzU1FTt2bNHGzZssN/AUaNGDT366KNaunSpEhIS1LhxY/36669auXKl2rVrpwceeCDH6nr44Yc1bdo0DR06VP369VNKSooWL16sqlWrav/+/fb9Zs+erZ9++kktW7aUv7+/4uLi9MUXX6hs2bL2R+UYefXVV/Xss8+qd+/e6tGjh/3RQMWLF8/VT3FxdXXVCy+8cNP9WrVqpdmzZ2v06NGqX7++Dh8+rLVr16pixYoO+2XeSLJkyRJ5eXmpaNGiqlOnjtN+N/PDDz/oiy++0NChQ1WzZk1J0qRJk9SvXz+9//77evXVV7N1Pknq3bu3li5dqvDwcO3fv1/+/v7auHGj/vOf/+i1114zvdbUSHh4uE6cOKEJEybom2++UevWreXt7a0zZ85ow4YNioqKsie/rVq10urVq1WsWDFVr15dv/zyiyIjI53WnoaEhKh8+fIaM2aMoqKi5ObmpuXLl6tEiRIO6WSxYsU0evRovf766+rRo4e6dOkib29vHTp0SCkpKXrnnXcUEBCgSpUq6Z133lFMTIyKFSumjRs3OjX9x44dU//+/dWxY0dVr15dbm5u2rRpk86fP3/D5LpVq1b65ptvNGTIELVq1UonT57UkiVLVL16dV2+fNny+wog99FM4o7Utm1brVmzRh9//LE2b96sxYsXy8PDQ0FBQQoPD1evXr3s+06YMEEVKlTQypUrtWnTJt1zzz0KCwvL8QasRIkSmjVrliZPnqypU6fan/EYHR3t0Ey2adNGp06d0vLly3Xx4kWVKFFCTZo00bBhw1S8ePEsz9+sWTPNmzdPM2bM0IwZM1SoUCE1btxYr7zySrYbsdzw3HPP6cqVK1q7dq3Wr1+v+++/X3PnztW7777rsJ+7u7smT56s9957T2+++abS0tI0adKkbF1DUlKSxowZo/vvv1/PPfecfbxRo0Z6+umn9cknn+ihhx5SvXr1snUNhQsX1qJFizRt2jStXLlSSUlJqlq1qiZNmmT6DvOsFClSRB999JFWrFihVatW6YMPPlBKSor9s7mnTZtmfzbmmDFj5OrqqrVr1+rq1atq0KCBPvnkEz3zzDMO53R3d9esWbM0btw4TZ8+XaVKlVJoaKi8vb01evRoh3179uypkiVL6sMPP9QHH3ygQoUKKSAgwP4QdXd3d82ZM0cTJkzQ3Llz5enpqfbt2+upp55St27d7OcpW7asOnfurB9++EFr1qyRm5ubAgIC9P7779s/s93IY489Zn/26I4dO1S9enVNnTpVGzZs0I8//nhL7y2A3OViY7UyAAAALGLNJAAAACyjmQQAAIBlNJMAAACwjGYSAAAAltFMAgAAwDKaSQAAAFhGMwkAAADL7tqHlhepn3ufCAIgb13cPSuvSwCQSwrnYeeSm73DlT0F979bJJMAAACw7K5NJgEAALLFhQzOCO8KAAAALCOZBAAAMMPFJa8ryJdIJgEAAGAZySQAAIAZrJk0RDMJAABgBtPchmixAQAAYBnJJAAAgBlMcxviXQEAAIBlJJMAAABmsGbSEMkkAAAALCOZBAAAMIM1k4Z4VwAAAGAZySQAAIAZrJk0RDMJAABgBtPchnhXAAAAYBnJJAAAgBlMcxsimQQAAIBlJJMAAABmsGbSEO8KAAAALCOZBAAAMIM1k4ZIJgEAAGAZySQAAIAZrJk0xLsCAAAAy0gmAQAAzCCZNEQzCQAAYIYrN+AYocUGAACAZSSTAAAAZjDNbYh3BQAAAJaRTAIAAJjBQ8sNkUwCAADAMpJJAAAAM1gzaYh3BQAAAJaRTAIAAJjBmklDNJMAAABmMM1tiHcFAAAAlpFMAgAAmME0tyGSSQAAAFhGMgkAAGAGayYN8a4AAADAMpJJAAAAM1gzaYhkEgAAAJaRTAIAAJjBmklDNJMAAABmMM1tiBYbAAAAlpFMAgAAmME0tyHeFQAAAFhGMgkAAGAGyaQh3hUAAABYRjIJAABgBndzGyKZBAAAgGUkkwAAAGawZtIQzSQAAIAZTHMbosUGAACAZSSTAAAAZjDNbYh3BQAAAJaRTAIAAJjBmklDJJMAAACwjGQSAADABBeSSUMkkwAAALCMZBIAAMAEkkljNJMAAABm0EsaYpobAAAAlpFMAgAAmMA0tzGSSQAAAFhGMgkAAGACyaQxkkkAAABYRjIJAABgAsmkMZJJAAAAWEYyCQAAYALJpDGaSQAAADPoJQ0xzQ0AAADLSCYBAABMYJrbGMkkAAAALCOZBAAAMIFk0hjJJAAAACyjmQQAADDBxcUl175uxdGjRzVgwADVq1dPISEhmjJlilJTU2963MWLFzV27Fi1atVK9erVU5cuXbR48eJsvz7T3AAAAAVUfHy8QkNDVaVKFc2cOVMxMTGaPHmyUlJSNHbs2BseO2LECEVFRemll15SuXLltG3bNr355ptyc3NTr169TNdAMwkAAGBCflwzuWTJEiUnJ2vWrFny9fWVJKWnp2vcuHEKCwtTmTJlDI+LjY3Vrl27NGnSJD322GOSpODgYP3666/66quvstVMMs0NAABghksuflm0bds2BQcH2xtJSerUqZMyMjIUERGR5XFpaWmSpOLFizuMFytWTDabLVs10EwCAAAUUFFRUQoICHAY8/b2VqlSpRQVFZXlceXKlVPz5s01Z84c/fHHH0pKStL69esVERGhp556Kls1MM0NAABgQm5Oc7dt2/aG2zdv3mw4npCQIG9vb6dxHx8fxcfH3/CcM2fO1MiRI9W5c2dJkpubm15//XV16NDBZNXX0UwCAADcZWw2m0aPHq1jx47p3XffValSpRQZGamJEyfKx8fH3mCaQTMJAABgQm4mk1kljzfj7e2txMREp/H4+Hj5+PhkedyWLVu0YcMGrVmzRkFBQZKkpk2bKi4uTpMnT85WM8maSQAAgAIqICDAaW1kYmKiYmNjndZS/tUff/whNzc3BQYGOozfd999OnfunK5cuWK6BppJAAAAE/LjQ8tbtGihyMhIJSQk2Mc2bNggV1dXhYSEZHmcv7+/0tPT9fvvvzuM79+/XyVLllSRIkVM10AzCQAAUED16dNHXl5eGjJkiHbs2KHly5drypQp6tOnj8MzJkNDQ9W+fXv79y1atFD58uU1fPhwrV69Wj/88IOmTp2qlStXqm/fvtmqgTWTAAAAZuS/Z5bLx8dHCxcu1Pjx4zVkyBB5eXmpR48eGjlypMN+GRkZSk9Pt39frFgxLViwQP/61780bdo0JSYmqkKFCgoPD892M+liy+6TKe8QReoPzesSAOSSi7tn5XUJAHJJ4TyMwco8syzXzh0zr2eunTu3Mc0NAAAAy5jmBgAAMCE/fjZ3fkAyCQAAAMtIJgEAAEwgmTRGMgkAAADLSCYBAABMIJk0RjIJAAAAy0gmAQAAzCCYNEQyCQAAAMtIJgEAAExgzaQxmkkAAAATaCaNMc0NAAAAy0gmAQAATCCZNEYyCQAAAMtIJgEAAMwgmDREMgkAAADLSCYBAABMYM2kMZpJFAge7oU09vnOerJLE/kWL6LfjpzWm7PX6btdh256bM8ODTUytJ3uCyirxMsp+mrrr3p9+mrFXUq279O3a1N99Fa/LM8x4LUFWvL1TzlyLQD+JzU1VbNnTtdXa1crISFB9wYGaejwFxXcLOSmx8bExGjaOxP1Q2SEMjIy1LhJU70y6jVVqFjRvk9KSoomvf2Wft23TzFnzyg9PUMVK1ZU98ceV68+T8rd3T03Lw+4K7jYbDZbXheRF4rUH5rXJSAbFk7qr0fb1tesL77XH8dj1e+Rpmp4f2V1HDxdkb9EZXncsz2ba8ZrffTdrkNavXmv/Mv4asiTrXT0RKxa9Jumq6lpkqQq/iX1QN0Ap+OHPdVadQL9Vb3j64qJS8y160POurh7Vl6XAJNGvfySNn27UU/1e1qVKlXRmtUrtf+3X/XR/IVq0LBRlsddTk5W756PKSkpUU+HDlChQu767NMFssmmL5evkq9vCUlS/KVLGvL8YDVs2Ejl/f3l4uqqvb/s0Vdr16hjp86aPPXd23WpyCGF8zAGqzx8ba6dO3pG11w7d26jmUS+16hmZW3/7BWNfm+l3l+0WZLk6VFIPy8bo9iLiWrd/z3D49wLuSl68yT9duSUHnpmun2804O1tGLGc3rpnWX695KtWb5uYU93RW+aqB9/PaauL8zO2YtCrqKZLBh+3bdPfZ/oqZdeflWhAwZJkq5evarHu3WRX8mS+vTzJVke+8nHH+n996bp8yXLVKt2HUnSn1FH9Xj3ruo/8BkNf/GlG772pLfHa8kXn2nzlh26p1SpnLso5Lq8bCarjFiXa+c+Nr1Lrp07t3EDDvK9R9vVU1pauj5eEWEfu5qapgWrf9ADdQNUoYyv4XE1q5dTCe+i+r+N/3EY/3r7b0pMTlHPDg1u+LqdW9SSd7EiTG8DuWTTNxvk5uamx3v2to95enrq0cd7aO8ve3T2zJksj/32m42qWau2vZGUpKoB1dSkabC+2fD1TV+7vL+/JCkxMeEWrgCARDOJAqBujYo6cvycEpNTHMZ/+u2YJKlOUAXD4zw9rq+FunL1mtO2K1evqW5QxRsupu79cGNdvpKq1Zt/sVY4gBs6dOigKleuomLFijmMZzaIhw4dNDwuIyNDRw7/rpo1azltq1W7tk6cOK7k5CSH8Wupqbp48YLOnjmjzZu+1aefzFf58v6qWKlyDl0N7gYuLi659lWQ0Uwi3yt7j7fOxjqnB2fPXx8rV8rH8Lg/jp9TRkaGgus5roW8t3JplfYrrqJFPFTCu6jhsSW8i+qhZvdp/bZflXT56i1eAQAjsbGxhlPM99xT6r/bzxkeFx9/SampqYbHlvrvWOw5x2M3bfpWrZoHq0O7VnppxFCVKVNGM2b/W4UKcR8qcKvy3Z+i2NhYRUREKCoqSpcuXZIk+fr6KiAgQCEhIfb/UODuUcTTXVevpTmNp/w3cSziaXw3ZtylZC3/do/6dmmq3/88q9Xf7ZV/aV+9O6qnUq+lycO9UJbHPtquvjw93JniBnLR1asp8vDwcBr39PS8vj0lxWnb9fHr/8AzOtbjv8empDj+I7BJk6aaO+8TJSYkaNeuH3T499915cqVW6ofd6GCHSDmmnzTTF67dk3vvPOOlixZovT0dJUqVUo+PtcTp/j4eMXGxsrNzU19+vRReHg4/5q8i1y5ek2e7s4/78KeWU9jZxo6YbEKe7pr8kuPafJLj0mSvlj3o/48eV7d29bLMnXs83AjxV1K1saI/TlwBQCMeHoWVmpqqtP41avX/1x6Fi5sfFzh6w2j0bGp/z228H/3yVTynntU8p57JEntO3TUvA/nKOyZAVq7/htuwAFuUb7pyN5//32tXr1aY8eOVadOnVS8eHGH7UlJSfr66681depUFS5cWC+//HIeVYrb7ez5BJUv7TyVXfYeb0nSmdj4LI9NSEpRr5EfqmLZEqpU3k8nzlzQ8TMX9f2Cl3TuQqLik5yTiYplSyikfjV9vCJSaWkZOXchAByUKlVK52JinMbPn4/97/bShsf5+PjKw8ND52NjnbbF/nesVGnjYzO1e6iDZk7/l77/frN69uqT3dJxlyroaxtzS75ZM7l69WqNHj1avXr1cmokJalYsWLq2bOnRo0apVWrVt3+ApFn9v1+UvdWKq3iXo4pReNaVezbb+bE2YuK+M9RHT9zUT7Fiqj+fRX1/a7fDfft1bGhXF1dtXT97luuHUDWgmrUUHT0MSUlOd4s8+u+vZKkGjXuMzzO1dVV994bqP37f3Pa9uuv+1ShYkV5eRUzOPJ/MqfKkxJ5fixwq/JNM5mcnKyyZcvedL+yZcsqOTn5pvvhzrFy0x4VKuSmQY/97xMxPNwL6eluD+jHfX/qZMwlSdcTxcAqZW56vreGP6JCbm6a+dl3htt7dWqk42cuKGLP0RypH4Cxdg91VHp6upYvW2ofS01N1eqVK1S7Tl2VLVdOknTm9Gn9GXX0b8d20P7fftX+3361jx37M0q7d+1U+4c62scuXrwgo8cpr1i+TJJ0v8Ed4UBWuJvbWL6Z5q5Xr57mzJmj2rVrGyaT0vWp7jlz5qh+/fq3uTrkpd2/RWv5N//RW8MeUSm/Yjp64rz6dm2iyuVK6rlxn9v3mzf+abVodK/DA+lfHtBe91crp92/RSstPV1dW9VV+2b36Y1Za/XzgeNOr3V/tXKqE1hBU+d/c1uuDbib1alTVw916KgZ77+nC3FxqlipstauXqnTp0/pzfFv2/d7/bVR+mn3j9q7/3+zCb2feFIr/m+Zhr4QptD+A1WoUCEtWrhAfiVL6un+A+37fbV2jZZ9uUSt27RThQoVlXw5WZERO7QzMkItW7VW0weCb+s1o2Ar4D1frsk3zeQ///lPhYaGqmXLlmrWrJkCAgLsTWVSUpKioqIUGRkpLy8vLViwIG+LxW036J+f6o0XuuiJzk1UwruofjtySo+NmKOI/9w4PfztyGk90rquOresLTc3V/125LSeeuVjrdi0x3D/Pg83liQt5S5u4LaYMGmKZs98X+vWrlFCQrzuDQzSjNlz1LBR4xse5+VVTB8vWKSp70zUR3P/rYyMDDVq3FSvjBotPz8/+371GzTU3l/2aMP6rxQXd15uboVUpWpVvfzqaD3xVN/cvjzgrpCvPk4xISFBixcv1vbt2xUVFaWEhOvPEfT29lZAQIBatGihPn36yNvb+5Zfi49TBO5cfJwicOfKy49TvPeVDbl27iNTO958p3wq3yST0vWmMSwsTGFhYXldCgAAAEzIV80kAABAfsWaSWP55m5uAAAAFDwkkwAAACYU9Ef45BaSSQAAAFhGMgkAAGACwaQxmkkAAAATXF3pJo0wzQ0AAADLSCYBAABMYJrbGMkkAAAALCOZBAAAMIFHAxkjmQQAAIBlJJMAAAAmEEwaI5kEAACAZSSTAAAAJrBm0hjNJAAAgAk0k8aY5gYAAIBlJJMAAAAmEEwaI5kEAACAZSSTAAAAJrBm0hjJJAAAACwjmQQAADCBYNIYySQAAAAsI5kEAAAwgTWTxmgmAQAATKCXNMY0NwAAACwjmQQAADCBaW5jJJMAAACwjGQSAADABIJJYySTAAAAsIxkEgAAwATWTBojmQQAAIBlJJMAAAAmEEwao5kEAAAwgWluY0xzAwAAwDKSSQAAABMIJo2RTAIAAMAykkkAAAATWDNpjGQSAAAAlpFMAgAAmEAwaYxkEgAAAJaRTAIAAJjAmkljNJMAAAAm0EwaY5obAAAAlpFMAgAAmEAwaYxkEgAAAJaRTAIAAJjAmkljJJMAAACwjGQSAADABIJJYySTAAAAsIxkEgAAwATWTBojmQQAAIBlJJMAAAAmEEwao5kEAAAwwZVu0hDT3AAAAAXY0aNHNWDAANWrV08hISGaMmWKUlNTTR0bExOjUaNG6YEHHlCdOnXUqVMnrVmzJluvTzIJAABgQn4MJuPj4xUaGqoqVapo5syZiomJ0eTJk5WSkqKxY8fe8Nhz586pd+/eqlq1qsaPH69ixYrpyJEjphvRTDSTAAAABdSSJUuUnJysWbNmydfXV5KUnp6ucePGKSwsTGXKlMny2KlTp6ps2bKaN2+e3NzcJEnBwcHZroFpbgAAABNcXFxy7cuqbdu2KTg42N5ISlKnTp2UkZGhiIiILI9LSkrS119/rSeffNLeSFpFMwkAAFBARUVFKSAgwGHM29tbpUqVUlRUVJbH7d+/X9euXVOhQoXUt29f1axZUyEhIZo6daquXbuWrRqY5gYAADDBNRfXTLZt2/aG2zdv3mw4npCQIG9vb6dxHx8fxcfHZ3m+8+fPS5Jef/119erVS0OHDtW+ffs0Y8YMubq66h//+Ifp2mkmAQAA7jIZGRmSpGbNmik8PFyS9MADDyg5OVnz58/XkCFDVLhwYVPnopkEAAAwITc/TjGr5PFmvL29lZiY6DQeHx8vHx+fGx4nXW8g/yo4OFhz5sxRdHS0goKCTNVAMwkAAGBCfnw0UEBAgNPayMTERMXGxjqtpfyr6tWr3/C8V69eNV0DN+AAAAAUUC1atFBkZKQSEhLsYxs2bJCrq6tCQkKyPM7f31+BgYGKjIx0GI+MjFThwoVv2mz+Fc0kAACACS65+D+r+vTpIy8vLw0ZMkQ7duzQ8uXLNWXKFPXp08fhGZOhoaFq3769w7EjR47Ud999p7ffflsRERGaM2eO5s+fr/79+6to0aKma2CaGwAAoIDy8fHRwoULNX78eA0ZMkReXl7q0aOHRo4c6bBfRkaG0tPTHcbatGmj9957Tx988IEWL16s0qVLa9iwYRo8eHC2anCx2Wy2W76SAqhI/aF5XQKAXHJx96y8LgFALimchzHYIx/uzrVzrxncONfOnduY5gYAAIBlTHMDAACYkJuPBirITDWTbdq0yfYb6OLiok2bNlkqCgAAAAWDqWaySZMmdOMAAOCuRitkzFQzOXny5NyuAwAAIF9zpZs0xA04AAAAsMxyM5mUlKQPP/xQgwYNUvfu3bVv3z5J0qVLl/TJJ58oOjo6x4oEAADIay4uufdVkFm6m/vs2bPq27evzp49q8qVKysqKkrJycmSJF9fXy1ZskSnTp3S66+/nqPFAgAAIH+x1ExOmTJFycnJWrVqlfz8/NSsWTOH7e3atdOWLVtyoj4AAIB8gZuRjVma5o6IiFC/fv1UvXp1wze2YsWKOnPmzC0XBwAAgPzNUjKZkpIiPz+/LLdnTnkDAADcKQgmjVlKJqtVq6bdu7P+fMpNmzbp/vvvt1wUAAAACgZLzWRoaKjWr1+vDz/8UElJSZIkm82m6OhovfLKK/rll1/Uv3//nKwTAAAgT7m6uOTaV0FmaZq7W7duOn36tKZPn673339fkvTMM8/IZrPJ1dVVI0eOVLt27XKyTgAAgDxVsFu+3GOpmZSk559/Xt26ddM333yj6OhoZWRkqFKlSnrooYdUsWLFnKwRAAAA+ZTlZlKSypcvz3Q2AAC4K/BoIGO31EwePnxYW7du1alTpyRJFSpU0IMPPqigoKAcKQ4AAAD5m6VmMjU1VWPHjtXq1avt6yQlKSMjQ++++666du2qCRMmyMPDI0eLBQAAyCuuBJOGLDWTU6dO1apVq/Tkk0+qb9++qlSpklxcXBQdHa1FixZp8eLF8vHx0ZgxY3K6XgAAAOQjlprJNWvWqFu3bho7dqzDeEBAgN544w0lJSVpzZo1NJMAAOCOwZpJY5aeM5mWlqa6detmub1+/fpKT0+3XBQAAAAKBkvNZPPmzbVjx44st2/fvl0hISGWiwIAAMhvXFxy76sgMzXNfenSJYfvR4wYoRdffFFDhw7VU089pUqVKkmSoqOj9fnnn+v06dP617/+lePFAgAA5BWmuY2ZaiYfeOABpzfQZrPp8OHD2rx5s9O4JHXp0kUHDhzIoTIBAACQH5lqJocMGUI3DgAA7mo8GsiYqWZy2LBhuV0HAAAACqBb+gQcAACAuwWztMZuqZn8+eefdeDAASUmJiojI8Nhm4uLi4YMGXJLxQEAACB/s9RMXrp0SWFhYdq3b59sNptcXFzsN95k/n+aSQAAcCchlzRm6TmTU6ZM0e+//653331XmzZtks1m08cff6yNGzeqT58+uu+++7R9+/acrhUAAAD5jKVmctu2berdu7cefvhheXl5XT+Rq6sqV66sN954Q/7+/po4cWKOFgoAAJCXXF1ccu2rILPUTCYkJKh69eqSZG8mk5OT7dtDQkJu+Ak5AAAABQ2fgGPMUjNZunRpnT9/XpLk4eGhkiVL6tChQ/btMTEx3PEEAABwF7B0A07jxo0VGRmp559/XpLUqVMnffzxx3Jzc1NGRoYWLlyoBx98MEcLBQAAyEsEZcYsNZP9+/dXZGSkUlNT5eHhoWHDhumPP/7Q9OnTJV1vNseMGZOjhQIAACD/sdRMBgUFKSgoyP69j4+PFixYoISEBLm6uqpYsWI5ViAAAEB+QDBpzNKayax4e3urWLFiWrt2rQYOHJiTpwYAAEA+lCsfp3jy5En98MMPuXFqAACAPFHQH+GTW3I0mQQAAMDdJVeSSQAAgDsNwaQxmkkAAAATeDSQMaa5AQAAYJnpZLJr166mT3rhwgVLxdxOW/7v7bwuAUAuqfGPdXldAoBccmx6lzx7bRI4Y6abSV9fX9Mn9fX1VUBAgJV6AAAAUICYbiYXLVqUm3UAAADka6yZNEZiCwAAAMu4mxsAAMAEV4JJQySTAAAAsIxkEgAAwASSSWM0kwAAACZwA44xprkBAABg2S0lkzExMdq9e7fi4uLUoUMHlS1bVunp6UpMTFTx4sXl5uaWU3UCAADkKaa5jVlqJm02myZPnqzPP/9caWlpcnFxUWBgoMqWLavLly+rTZs2Gj58uPr375/D5QIAACA/sTTNPW/ePH366acaOHCgPvnkE9lsNvu24sWL66GHHtI333yTY0UCAADkNReX3PsqyCw1k8uWLVP37t310ksvqUaNGk7bg4KCdOzYsVutDQAAAPmcpWnuM2fOqH79+lluL1KkiJKSkiwXBQAAkN+4FvQIMZdYSiZLliypM2fOZLl9//79KleunOWiAAAAUDBYaibbt2+vJUuW6MSJE/axzGcv7dixQytXrlTHjh1zpkIAAIB8wDUXvwoyS9Pcw4cP165du9StWzc1atRILi4u+uijjzR9+nT98ssvuu+++/Tcc8/ldK0AAAB5hlluY5aa4eLFi+vLL7/UM888o5iYGHl6emr37t1KTEzUkCFD9MUXX6hIkSI5XSsAAADyGcsPLS9cuLBeeOEFvfDCCzlZDwAAQL7EDTjGCvo0PQAAAPKQpWRy9OjRN93HxcVFEydOtHJ6AACAfIdg0pilZnLXrl1OYxkZGYqNjVV6err8/PxYMwkAAHAXsNRMfvfdd4bj165d09KlS7Vw4ULNnz//lgoDAADIT1xJJg3l6JpJd3d39e3bVyEhIRo/fnxOnhoAAAD5kOW7uW+kRo0aWr16dW6cGgAAIE9wN7exXLmbOzIykjWTAAAAdwFLyeSsWbMMxxMTE7V7924dOHBAgwcPvqXCAAAA8hOCSWM52kz6+PioYsWKGjdunHr16nVLhQEAAOQn3IBjzFIzeejQoZyuAwAAAAVQttdMpqSkaNKkSVk+HggAAOBO5JKL/yvIst1MFi5cWEuXLlVcXFxu1AMAAIACxNI0d82aNXX48OGcrgUAACDfYs2kMUuPBnrttde0fv16LVu2TGlpaTldEwAAAAoI08nk7t27Va1aNfn5+Sk8PFwuLi4aO3asJkyYoDJlysjT09NhfxcXF61ZsybHCwYAAMgLJJPGTDeTTz/9tKZOnaouXbrI19dXvr6+qlq1am7WBgAAgHzOdDNps9lks9kkSYsWLcq1ggAAAPIjF55abihXPpsbAADgTsM0t7Fs3YBDRw4AAIC/ylYy+corr+iVV14xta+Li4sOHDhgqSgAAID8hkzNWLaayWbNmqlKlSq5VAoAAAAKmmw1k927d1fXrl1zqxYAAIB8y5Vo0pClh5YDAAAAEndzAwAAmMLd3MZIJgEAAAqwo0ePasCAAapXr55CQkI0ZcoUpaamZuscCxYsUFBQkMLCwrL9+qaTyUOHDmX75AAAAHeK/LhkMj4+XqGhoapSpYpmzpypmJgYTZ48WSkpKRo7dqypc8TGxmr27NkqWbKkpRqY5gYAADDBVfmvm1yyZImSk5M1a9Ys+fr6SpLS09M1btw4hYWFqUyZMjc9x9SpU9WmTRudPn3aUg1McwMAABRQ27ZtU3BwsL2RlKROnTopIyNDERERNz3+p59+0qZNm/SPf/zDcg00kwAAACa4uOTel1VRUVEKCAhwGPP29lapUqUUFRV1w2PT09M1fvx4PffccypdurTlGpjmBgAAyGNt27a94fbNmzcbjickJMjb29tp3MfHR/Hx8Tc85xdffKErV66of//+pus0QjMJAABgwp30aKC4uDjNmDFD77zzjjw8PG7pXDSTAAAAeSyr5PFmvL29lZiY6DQeHx8vHx+fLI+bPn26goKC1KhRIyUkJEiS0tLSlJaWpoSEBBUtWlSFCplrE2kmAQAATMiPH6cYEBDgtDYyMTFRsbGxTmsp/+rPP//U7t271bhxY6dtjRs31kcffaQWLVqYqoFmEgAAoIBq0aKF5syZ47B2csOGDXJ1dVVISEiWx7322mv2RDLTxIkTVbhwYb300ksKCgoyXQPNJAAAgAn5MJhUnz59tGjRIg0ZMkRhYWGKiYnRlClT1KdPH4dnTIaGhur06dP69ttvJUn33Xef07m8vb1VtGhRNW3aNFs10EwCAACYkB+nuX18fLRw4UKNHz9eQ4YMkZeXl3r06KGRI0c67JeRkaH09PRcqYFmEgAAoACrVq2aFixYcMN9Fi1adNPzmNnHCM0kAACACfkwmMwX+AQcAAAAWEYyCQAAYAIJnDHeFwAAAFhGMgkAAGCCC4smDZFMAgAAwDKSSQAAABPIJY3RTAIAAJiQHx9anh8wzQ0AAADLSCYBAABMIJc0RjIJAAAAy0gmAQAATGDJpDGSSQAAAFhGMgkAAGACDy03RjIJAAAAy0gmAQAATCCBM0YzCQAAYALT3MZosgEAAGAZySQAAIAJ5JLGSCYBAABgGckkAACACayZNEYyCQAAAMtIJgEAAEwggTPG+wIAAADLSCYBAABMYM2kMZpJAAAAE2gljTHNDQAAAMtIJgEAAExgltsYySQAAAAsI5kEAAAwwZVVk4ZIJgEAAGAZySQAAIAJrJk0RjIJAAAAy0gmAQAATHBhzaQhmkkAAAATmOY2xjQ3AAAALCOZBAAAMIFHAxkjmQQAAIBlJJMAAAAmsGbSGMkkAAAALCOZBAAAMIFk0hjJJAAAACwjmQQAADCBh5YbI5kEAACAZSSTAAAAJrgSTBqimQQAADCBaW5jTHMDAADAMpJJAAAAE3g0kDGSSQAAAFhGMgkAAGACayaNkUwCAADAMpJJAAAAE3g0kDGaSeR7166lasWiuYr47mslJyWqYpXq6vH0c6rVoOkNj9sd8b12bftWfx45oPiLcfK7p4zqNWmubk8Mklex4g77vtS/m86fO+N0jtadHtWAYaNz9HoAOPJwc9VLDwfq0cYV5FPEXYdOJ2ja+t+14/fzNzxux9g2qlCyqOG2P2OT1XrC9/bvj03vYrjfO2sP6t+bjlovHgDNJPK/j957S7t3bNZD3Z9Q2fIVtX3TOr37xosKn/xvBdWsl+Vxn8ycqBJ+pdSsdSeVLFVGJ48d1aa1y7T3p0iNn/GpPDwLO+xfKSBQnR57ymGsrH+l3LgkAH8x7am66lSvnOZv+VPHYpPVo2kFfRLWRE/M+kE/RV3M8ri3Vu5XUU/Hv8b8SxTRK11qaPuhWKf9tx2K1YrdJx3G9p+Mz5mLwF2BNZPGaCaRrx39fb92bv1GfQYN18OP95UkhbR9WK89/4SWzp+pse9+nOWxw16brPvqNHQYq3JvDX347jhFfr9BrTp2d9hWomQphbTplOPXACBrdSv56pGG/np71QF99H2UJGnF7pPaGN5Sox+5T4+/H5nlsd/8GuM0NvSh6pKkVT+dctr257lkw3HALB4NZIwbcJCv7d6xWa6ubmrdqbt9zMPDUy0fekR/HPxVcbHOf5lk+nsjKUkNg1tJkk6fOGZ4TNq1a7qacuVWSgaQDZ3qlVNaeoYWRx63j11Ny9CXO4+rYVU/lfMtfIOjnXVr6K/j55P1n2PGiaanu6s8C/FXH5CTSCaRr0UfPayy/pVUpGgxh/GAoPslScejDqtkqTKmzxd/MU6SVNzb12nbwX0/6ZlHWygjI133lC6nDt2fUIfufawXD+Cmalbw1p+xyUq6muYw/svxS5Kk+/29deZSirlz+Xvr3rLFNXPjEcPtPZpWUL/mleXq6qIjZxM185sjWvPz6VuqH3cXgkljNJPI1y5dPC9fv5JO475+90iSLsY5r4u6ka+WfSpXVzc1bt7GYbxi1eoKvL+uylaorKTEeO34dp0+//A9XboQq94Dh1m/AAA3VNrbU+cSrjqNn4u/PlbGx3wy2a2RvyRp1c/OU9k/RV3QV7+c0Ym4yyrj46l+zatoxtMN5F3YXZ9FRFusHoBUAJvJixcv6o8//lDjxo3zuhTcBteuXlUhdw+ncXd3T0lSaqrzX0JZifx+g7Z+s0ade/RzurFm5BvvOnzfon1XTRs7QhtWfqH2j/SS3z3m008A5hV2d1NqWobT+NX/jhV2dzN1HhcXqWuD8vrtRLyOxiQ5be8x3XHt5Zc7T2jtyw/qlS5BWvbjCV295lwD8HeuLJo0VOAWjvz44496+umn87oM3Cbunp5Ku5bqNH7t2vUm0sPD09R5fv9tjz6e/rZqN3xAPUKfv+n+Li4u6tD9CaWnp+vgvv9kr2gApqVcS5eHwRrGzHWNKdfSTZ3ngWolVc63iGEqaeRauk2fbj8mn6Ieql3Rx3zBAJwUuGQSdxffEvcYTmVfunD9+XMlSpa66TmORx3Wv956WRUqB2jYa5Pl5mbu1z5zLWZyYkI2KgaQHecSrqqswVR2aZ/r/1CMiTe3XrJbI3+lZ9i0xmQzKUmn/7sW07eo8+wHYIRc0li+aSa7du1qar/k5ORcrgT5SaVqgTq472dduZzkcBPO0d/3X98eEHjD42POnNTUf46Qt08J/eOt91W4iPEDjo2cO3P9L6XiPr7ZLxyAKQdOJii4ekkV8yzkcBNOvcolrm8/dfN/zHm4uapT3bLa+Uec4frLrFT67wPPLySZPwaAs3wzzR0VFSVXV1fVqlXrhl8VKlTI61JxGzUJaaOMjHR9//Uq+9i1a6na/u06VQuqZU8Pz5876/S4n0sXzmvqmGFydXXVKxNmyNunhOFrJCXGKyPdcSotLS1N65Z9qkKF3A0fMQQgZ3y994wKubnqiWb/W8fs4eaqnk0ras+xi/Y7ucuXKKxqpb0Mz9G6Zmn5FPXI8hmSfl7OyaOXp5sGtqyquKSr+vUEDy6HSS65+FWA5Ztk8t5771XlypU1adKkG+63ceNG7d69+zZVhbxWrUYtNWneVssWzFbCpQsqU76idmz6SudjTmvQiDH2/T58900d+vU/+nT9j/axaWNH6NzZU+rco58O79+rw/v32rf5+PrZP45xz87tWr1kvho3b6NSZcorOTFBP2zZqJPRR9Uz9AX7neMAct4v0Ze0bs9pvdq1hkoW91R0bLIeb1JBFfyKaNTi//2Zfe+p+nrg3pKqMmKd0zm6NfTX1Wvp2rDX+SNRJenpB6voodpltGl/jE5fTFFpb0/1alpR5UsU0cjPftG1dFuuXR/uLHwCjrF800zWqVNH27dvN7WvzcYf/LvJ4Jff1PL/fjb35aREVaxaXSPffE81aje44XHHo64/a+6r/1vktK1G7Qb2ZrJClWryr1RVkd9tUGL8RRVyd1elgHs1dPRENXmwXc5fEAAH//jsF516OEiPNfKXT1F3HTydqEEf7taPRy/c9NhinoXU5v7S+u7AOSWmpBnu89OfF9Sgagn1eaCSfL08dCU1TXujL+mVxXv1w5G4nL4c4K7jYssnndnx48d15MgRtW3b9ob7paSkKC4uTv7+/rf0eruOMq0B3Kl6zzD3D1MABc+x6V3y7LV/jMq93qFJQMF9qkC+SSYrVaqkSpUq3XS/woUL33IjCQAAgJyRb5pJAACA/IwVk8byzd3cAAAAKHhIJgEAAMwgmjREMgkAAADLSCYBAABM4DmTxmgmAQAATHChlzTENDcAAAAsI5kEAAAwgWDSGMkkAAAALCOZBAAAMINo0hDJJAAAACwjmQQAADCBRwMZI5kEAACAZSSTAAAAJvCcSWM0kwAAACbQSxpjmhsAAACWkUwCAACYQTRpiGQSAAAAlpFMAgAAmMCjgYzRTAIAABRgR48e1YQJE7Rnzx55eXmpW7duevHFF+Xh4ZHlMefOndOCBQsUERGh48ePq3jx4mrcuLFeeukl+fv7Z+v1aSYBAABMyI+PBoqPj1doaKiqVKmimTNnKiYmRpMnT1ZKSorGjh2b5XH79+/Xt99+q8cff1x169bVxYsX9e9//1s9e/bUunXr5OfnZ7oGmkkAAIACasmSJUpOTtasWbPk6+srSUpPT9e4ceMUFhamMmXKGB7XsGFDff311ypU6H+tYIMGDdSqVSutWrVKAwcONF0DN+AAAACY4JKLX1Zt27ZNwcHB9kZSkjp16qSMjAxFRERkeZy3t7dDIylJZcuWlZ+fn86dO5etGkgmAQAAzMjFae62bdvecPvmzZsNx6OiovT44487jHl7e6tUqVKKiorKVg1//vmn4uLiVK1atWwdRzIJAABQQCUkJMjb29tp3MfHR/Hx8abPY7PZNGHCBJUuXVqdO3fOVg0kkwAAACbk5qOBskoeb5eZM2dq586dmjdvnooWLZqtY2kmAQAACihvb28lJiY6jcfHx8vHx8fUOb788kvNnj1bb7/9toKDg7NdA80kAACACfnx0UABAQFOayMTExMVGxurgICAmx7/7bff6s0339Tw4cPVo0cPSzWwZhIAAKCAatGihSIjI5WQkGAf27Bhg1xdXRUSEnLDY3ft2qWXXnpJPXv21JAhQyzXQDMJAABgQn58NFCfPn3k5eWlIUOGaMeOHVq+fLmmTJmiPn36ODxjMjQ0VO3bt7d/f/ToUQ0ZMkRVqlRRt27d9Msvv9i/jh8/nq0amOYGAAAooHx8fLRw4UKNHz9eQ4YMkZeXl3r06KGRI0c67JeRkaH09HT793v37lViYqISExP1xBNPOOz76KOPavLkyaZrcLHZbLZbu4yCaddR87fLAyhYes/YntclAMglx6Z3ybPXPngmOdfOfV85r1w7d24jmQQAADAhNx8NVJCxZhIAAACWkUwCAACYkB8fDZQfkEwCAADAMpJJAAAAEwgmjZFMAgAAwDKSSQAAADOIJg2RTAIAAMAykkkAAAATeM6kMZpJAAAAE3g0kDGmuQEAAGAZySQAAIAJBJPGSCYBAABgGckkAACAGUSThkgmAQAAYBnJJAAAgAk8GsgYySQAAAAsI5kEAAAwgedMGiOZBAAAgGUkkwAAACYQTBqjmQQAADCDbtIQ09wAAACwjGQSAADABB4NZIxkEgAAAJaRTAIAAJjAo4GMkUwCAADAMpJJAAAAEwgmjZFMAgAAwDKSSQAAABNYM2mMZhIAAMAUukkjTHMDAADAMpJJAAAAE5jmNkYyCQAAAMtIJgEAAEwgmDRGMgkAAADLSCYBAABMYM2kMZJJAAAAWEYyCQAAYIILqyYN0UwCAACYQS9piGluAAAAWEYyCQAAYALBpDGSSQAAAFhGMgkAAGACjwYyRjIJAAAAy0gmAQAATODRQMZIJgEAAGAZySQAAIAZBJOGaCYBAABMoJc0xjQ3AAAALCOZBAAAMIFHAxkjmQQAAIBlJJMAAAAm8GggYySTAAAAsIxkEgAAwATWTBojmQQAAIBlNJMAAACwjGluAAAAE5jmNkYyCQAAAMtIJgEAAEzg0UDGSCYBAABgGckkAACACayZNEYyCQAAAMtIJgEAAEwgmDRGMgkAAADLSCYBAADMIJo0RDMJAABgAo8GMsY0NwAAACwjmQQAADCBRwMZI5kEAACAZSSTAAAAJhBMGiOZBAAAgGUkkwAAAGYQTRoimQQAAIBlJJMAAAAm8JxJYzSTAAAAJvBoIGNMcwMAAMAyF5vNZsvrIgAAAFAwkUwCAADAMppJAAAAWEYzCQAAAMtoJgEAAGAZzSQAAAAso5kEAACAZTSTAAAAsIxmEgAAAJbRTAIAAMAymkkAAABYRjMJAAAAy2gmAQAAYBnNJAAAACyjmcQd6+jRoxowYIDq1aunkJAQTZkyRampqXldFoAcEB0drbFjx6pbt266//771aVLl7wuCbhrFcrrAoDcEB8fr9DQUFWpUkUzZ85UTEyMJk+erJSUFI0dOzavywNwi44cOaKtW7eqbt26ysjIkM1my+uSgLsWzSTuSEuWLFFycrJmzZolX19fSVJ6errGjRunsLAwlSlTJm8LBHBL2rRpo3bt2kmSwsPD9dtvv+VxRcDdi2lu3JG2bdum4OBgeyMpSZ06dVJGRoYiIiLyrjAAOcLVlb++gPyCP424I0VFRSkgIMBhzNvbW6VKlVJUVFQeVQUAwJ2HZhJ3pISEBHl7ezuN+/j4KD4+Pg8qAgDgzkQzCQAAAMtoJnFH8vb2VmJiotN4fHy8fHx88qAiAADuTDSTuCMFBAQ4rY1MTExUbGys01pKAABgHc0k7kgtWrRQZGSkEhIS7GMbNmyQq6urQkJC8rAyAADuLDxnEnekPn36aNGiRRoyZIjCwsIUExOjKVOmqE+fPjxjErgDXLlyRVu3bpUknTp1SklJSdqwYYMkqUmTJvLz88vL8oC7iouNjw3AHero0aMaP3689uzZIy8vL3Xr1k0jR46Uh4dHXpcG4BadPHlSbdu2Ndz26aefqmnTpre5IuDuRTMJAAAAy1gzCQAAAMtoJgEAAGAZzSQAAAAso5kEAACAZTSTAAAAsIxmEgAAAJbRTAIAAMAymkkAuaZNmzYKDw+3f79r1y4FBQVp165deViVo7/XeDv069dPXbp0ydFz5sV1AIBEMwncsVasWKGgoCD7V+3atdWhQwe99dZbOn/+fF6Xly1bt27VzJkz87SGoKAgvfXWW3laAwDkR3w2N3CHGz58uCpUqKDU1FT9/PPPWrx4sbZu3ap169apSJEit7WWxo0ba9++fXJ3d8/WcVu3btXnn3+uYcOG5VJlAACraCaBO1yLFi1Uu3ZtSVLPnj3l6+urTz75RJs3b85yqvXy5csqWrRojtfi6uoqT0/PHD8vACDvMM0N3GUeeOABSdLJkyclSeHh4apfv76OHz+uZ599VvXr19fLL78sScrIyNCCBQvUuXNn1a5dW82aNdPYsWMVHx/vcE6bzaYPPvhALVq0UN26ddWvXz8dOXLE6bWzWjO5d+9ePfvss2rcuLHq1aunrl27auHChfb6Pv/8c0lymLbPlNM13opNmzZp8ODBat68uWrVqqV27dpp9uzZSk9PN9z/t99+U58+fVSnTh21adNGixcvdtonNTVVM2bMUPv27VWrVi21bNlSU6ZMUWpqao7WDgBWkUwCd5njx49Lknx9fe1jaWlpGjRokBo2bKhRo0apcOHCkqSxY8dq5cqVeuyxx9SvXz+dPHlSn3/+uQ4cOKDFixfbp6unT5+uf//732rZsqVatmyp/fv3a+DAgbp27dpN64mIiFBYWJhKly6tp59+Wvfcc4+OHj2qLVu2KDQ0VL1799a5c+cUERGhKVOmOB1/O2o0a+XKlSpatKgGDBigokWLaufOnZoxY4aSkpI0atQoh33j4+M1ePBgderUSZ07d9bXX3+tN998U+7u7urRo4ek643y888/r59//lm9evVStWrVdPjwYS1cuFDHjh3TBx98kGO1A4BlNgB3pOXLl9sCAwNtkZGRtri4ONuZM2dsX331la1Jkya2OnXq2M6ePWuz2Wy2UaNG2QIDA23Tpk1zOH737t22wMBA25o1axzGt23b5jAeFxdnq1mzpm3w4MG2jIwM+37vvfeeLTAw0DZq1Cj72M6dO22BgYG2nTt32mw2my0tLc3Wpk0bW+vWrW3x8fEOr/PXc40bN84WGBjodI25UWNWAgMDbePGjbvhPleuXHEa++c//2mrW7eu7erVq/axvn372gIDA23z58+3j129etXWrVs3W3BwsC01NdVms9lsq1atstWoUcO2e/duh3MuXrzYFhgYaPv555/tY61btzZ1HQCQ05jmBu5w/fv3V3BwsFq2bKmRI0fKy8tLs2bNUpkyZRz2e+KJJxy+37Bhg4oXL66QkBBduHDB/lWzZk0VLVrUPlUdGRmpa9euqW/fvnJxcbEfHxoaetPaDhw4oJMnT+rpp5+Wt7e3w7a/nisrt6PG7MhMdCUpKSlJFy5cUKNGjXTlyhVFRUU57FuoUCH17t3b/r2Hh4d69+6tuLg47d+/33591apVU0BAgMP1ZS5VyE+PWAJw92KaG7jDjR07VlWrVpWbm5vuueceVa1aVa6ujv+OLFSokMqWLeswFh0drcTERAUHBxueNy4uTpJ0+vRpSVKVKlUctvv5+cnHx+eGtZ04cUKSFBgYaPp6bneN2XHkyBG9//772rlzp5KSkhy2JSYmOnxfunRpp5ucMus7deqU6tWrp+joaB09evSm1wcAeYlmErjD1alTx343d1Y8PDycGsyMjAyVLFlS06ZNMzzGz88vx2q0Kj/VmJCQoL59+6pYsWIaPny4KlWqJE9PT+3fv1/Tpk1TRkZGts+ZkZGhwMBAjR492nD73/8BAAB5gWYSgKFKlSrphx9+UIMGDRymb/+ufPnykqRjx46pYsWK9vELFy443VH9d5n7Hz58WM2aNctyv6ymvG9HjWb9+OOPunTpkmbNmqXGjRvbxzPvmv+7c+fOOT2C6dixY5Ikf39/Sdev79ChQwoODjY17Q8AeYE1kwAMderUSenp6YZ3DKelpSkhIUGS1KxZM7m7u+uzzz6TzWaz75P5aJ8bqVmzpipUqKBPP/3Ufr5Mfz1X5sPV/77P7ajRrMxk96/nT01N1RdffGG4f1pampYuXeqw79KlS+Xn56eaNWtKun59MTEx+vLLL52OT0lJ0eXLl3OsfgCwimQSgKEmTZqod+/emjt3rg4ePKiQkBC5u7vr2LFj2rBhg8aMGaOOHTvKz89PAwcO1Ny5cxUWFqaWLVvqwIED2rZtm0qUKHHD13B1ddWbb76p559/Xt27d9djjz2mUqVKKSoqSn/88Yc+/vhjSbI3VxMmTFDz5s3l5uamzp0735Ya/+q3334zbFybNGmi+vXry8fHR+Hh4erXr59cXFy0evVqh+byr0qXLq2PPvpIp06dUpUqVbR+/XodPHhQ48ePtz/OqFu3bvr666/1xhtvaNeuXWrQoIHS09MVFRWlDRs2aN68eTddwgAAuY1mEkCW3nrrLdWqVUtLlizRv/71L7m5ucnf31+PPPKIGjRoYN/vxRdflIeHh5YsWaJdu3apTp06mj9/vsLCwm76Gg8++KAWLlyo2bNna/78+bLZbKpYsaJ69epl3+ehhx5Sv3799NVXX2nNmjWy2Wzq3Lnzbasx0969e7V3716n8REjRqhRo0aaM2eO3nnnHb3//vvy9vbWI488ouDgYA0aNMjpGB8fH02ePFkTJkzQl19+qXvuuUdjx451uG5XV1fNnj1bCxYs0OrVq/Xtt9+qSJEiqlChgvr166eqVauarh0AcouLLat/NgMAAAA3wZpJAAAAWEYzCQAAAMtoJgEAAGAZzSQAAAAso5kEAACAZTSTAAAAsIxmEgAAAJbRTAIAAMAymkkAAABYRjMJAAAAy2gmAQAAYBnNJAAAACyjmQQAAIBl/w9HsfwBAsgsVwAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Key Takeaways"
      ],
      "metadata": {
        "id": "5Oh_1bFssso8"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The accuracy of the model appears to be affected by the race and gender of the individuals it evaluates. For instance, women and Caucasians had more accurate predictions compared to men and African Americans. This suggests that the model might be biased. Even though the model shows a high accuracy rate of 83% to 88%, these numbers might not be reliable enough for sensitive areas like criminal justice. Notably, African American men were more likely to be incorrectly predicted to reoffend compared to other groups, which calls for a closer look at how the model works.\n",
        "\n",
        "A false positive in this context means the model incorrectly predicts that someone will reoffend when they won't. This can lead to unfairly severe consequences, like harsher sentences, particularly affecting African American men. The investigative report by ProPublica on machine bias highlighted similar concerns, showing that such predictive models could perpetuate existing biases in the criminal justice system. The report argued that relying solely on these models for sentencing could lead to unjust decisions.\n",
        "\n",
        "Given the potential consequences of these errors, it's crucial that such predictive models are used with great caution. They should not be the only factor in making judicial decisions. Instead, a more comprehensive approach that considers multiple aspects and checks for bias should be used to ensure fair treatment in the justice system."
      ],
      "metadata": {
        "id": "xT22zy-Ds1fU"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Conclusion\n",
        "\n",
        "\n",
        "\n",
        "This project on AI bias in risk assessment tools has revealed several insights. First, while these algorithms hold promise for improving efficiency in the criminal justice system, they also risk reinforcing existing biases if not carefully managed. Findings highlight significant disparities in risk assessments across race, gender, and age, emphasizing the need for these tools to be developed and implemented with an awareness of potential biases.\n",
        "\n",
        "Moving forward, addressing AI bias in criminal justice demands robust, continuous oversight to monitor the performance and impact of these risk assessment tools. This involves not only regular audits by independent experts but also implementing mechanisms for feedback and adaptation based on those findings. Maintaining high levels of transparency is equally crucial; this means making the methodologies, data sources, and decision-making processes of AI tools accessible and understandable to all stakeholders, including the public(MIT Technology Review). This transparency helps build trust and facilitates informed critique and suggestions for improvement.\n",
        "\n",
        "Moreover, fostering interdisciplinary collaboration is essential in the design and application of these technologies. By bringing together experts from fields such as computer science, law, ethics, sociology, and psychology, we can ensure that diverse perspectives inform the development of AI tools. This collaboration can help identify potential biases and devise strategies to mitigate them before they affect decision-making processes. Such thorough and collaborative efforts are key to harnessing the benefits of AI in criminal justice while safeguarding against its potential to perpetuate or exacerbate injustice."
      ],
      "metadata": {
        "id": "MTc6VbzXZibT"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### References"
      ],
      "metadata": {
        "id": "FM8VshVhCIyN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "\n",
        "Angwin, J., Larson, J., Mattu, S., & Kirchner, L. (2016, May 23). Machine bias. ProPublica. https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing\n",
        "\n",
        "Hao, K. (2019, January 21). AI is sending people to jail—and getting it wrong. MIT Technology Review. https://www.technologyreview.com/2019/01/21/137783/algorithms-criminal-justice-ai/\n",
        "\n",
        "Lowenkamp, C. T., Johnson, J. L., Holsinger, A. M., VanBenschoten, S. W., & Robinson, C. R. (2012). The Federal Post Conviction Risk Assessment (PCRA): A construction and validation study. Psychological Services. Advance online publication. https://doi.org/10.1037/a0030343\n",
        "\n",
        "Mayson, S. G. (2019). Bias in, bias out. Yale Law Journal, 128(8), 2218-2300. https://www.yalelawjournal.org/article/bias-in-bias-out\n",
        "\n",
        "Selbst, A. D. (2017). Disparate impact in big data policing. Georgia Law Review, 52(1), 109-195. Retrieved from https://digitalcommons.law.uga.edu/cgi/viewcontent.cgi?article=2165&context=fac_artchop\n",
        "\n",
        "Skeem, J. L., & Lowenkamp, C. T. (2016). Risk, race, and recidivism: Predictive bias and disparate impact. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.\n",
        "\n"
      ],
      "metadata": {
        "id": "N9X1yPfNVahz"
      }
    }
  ]
}