Matatu_Income_Data_Analysis.ipynb

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      "mount_file_id": "1MinFBD02Bm1iuELCi7Y3HQaQLlnhZ9sj",
      "authorship_tag": "ABX9TyPSCZMl3sEXeRTUEH2Opoay",
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      "name": "python"
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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/LexMainye/Transport-Data/blob/main/Matatu_Income_Data_Analysis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# import libraries\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from google.colab import drive\n",
        "import os\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import matplotlib.dates as mdates\n",
        "import warnings"
      ],
      "metadata": {
        "id": "H-rAo8kXZoGX"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "warnings.filterwarnings(\"ignore\", category=DeprecationWarning, module=\"ipykernel\")"
      ],
      "metadata": {
        "id": "CaxfEzCydFDi"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Mount Google Drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VYhV2R1banxl",
        "outputId": "7f4d0827-55ea-4635-c59e-4dc270e68128"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
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        "id": "Xf5ppWMnYxYp",
        "outputId": "2c4be710-91a9-4cd6-efbd-265efa616be8"
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Date    Time Period Payment Channel  Amount (KES)  Gender\n",
              "0 2024-11-01  Early Morning    Airtel Money           100    Male\n",
              "1 2024-11-01  Early Morning          T-Kash           100    Male\n",
              "2 2024-11-01  Early Morning          M-Pesa            80    Male\n",
              "3 2024-11-01  Early Morning          T-Kash            80  Female\n",
              "4 2024-11-01  Early Morning          M-Pesa            70  Female"
            ],
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              "      <td>Early Morning</td>\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "week_data",
              "summary": "{\n  \"name\": \"week_data\",\n  \"rows\": 2104,\n  \"fields\": [\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2024-11-01 00:00:00\",\n        \"max\": \"2024-11-07 00:00:00\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"2024-11-01 00:00:00\",\n          \"2024-11-02 00:00:00\",\n          \"2024-11-06 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Time Period\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"Early Morning\",\n          \"Morning\",\n          \"Evening\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Payment Channel\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"T-Kash\",\n          \"Cash\",\n          \"Airtel Money\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Amount (KES)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 26,\n        \"min\": 70,\n        \"max\": 150,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          80,\n          150,\n          70\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Female\",\n          \"Male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "# Define parameters\n",
        "dates = pd.date_range(start=\"2024-11-01\", end=\"2024-11-07\")\n",
        "time_periods = ['Early Morning', 'Morning', 'Late Morning', 'Early Afternoon', 'Late Afternoon', 'Evening', 'Night']\n",
        "payment_channels = ['Cash', 'M-Pesa', 'Airtel Money','T-Kash']\n",
        "genders = ['Male', 'Female']\n",
        "\n",
        "# Maximum transactions per day\n",
        "max_transactions_per_day = 300\n",
        "\n",
        "# Maximum transactions per time period and day\n",
        "max_transactions_per_time_period = 70\n",
        "max_transactions_per_day = 300\n",
        "\n",
        "# Generate data\n",
        "np.random.seed(42)\n",
        "data = []\n",
        "\n",
        "for date in dates:\n",
        "    # Determine the total number of transactions for the current day (200 to 300)\n",
        "    total_transactions = np.random.randint(200, max_transactions_per_day + 1)\n",
        "\n",
        "    # Calculate transactions for each time period, ensuring the max for each period is 70\n",
        "    transactions_per_time_period = {time_period: min(np.random.randint(20, max_transactions_per_time_period + 1), total_transactions) for time_period in time_periods}\n",
        "\n",
        "    # Adjust to ensure total transactions for the day is correct\n",
        "    total_assigned_transactions = sum(transactions_per_time_period.values())\n",
        "    if total_assigned_transactions < total_transactions:\n",
        "        remaining_transactions = total_transactions - total_assigned_transactions\n",
        "        for time_period in time_periods:\n",
        "            if remaining_transactions > 0 and transactions_per_time_period[time_period] < max_transactions_per_time_period:\n",
        "                transactions_per_time_period[time_period] += 1\n",
        "                remaining_transactions -= 1\n",
        "\n",
        "    # Generate transactions for each time period\n",
        "    for time_period, num_transactions in transactions_per_time_period.items():\n",
        "        for _ in range(num_transactions):\n",
        "            payment_channel = np.random.choice(payment_channels)\n",
        "            gender = np.random.choice(genders)\n",
        "\n",
        "            # Assign standard fares based on time periods\n",
        "            if time_period == 'Night':\n",
        "                fare = 120  # Fixed fare for Nighttime\n",
        "            elif time_period in ['Morning', 'Evening']:\n",
        "                fare = np.random.choice([120, 150])  # Peak periods\n",
        "            else:\n",
        "                fare = np.random.choice([70, 80, 100])  # Off-peak periods\n",
        "\n",
        "            # Append transaction\n",
        "            data.append([date, time_period, payment_channel, fare, gender])\n",
        "\n",
        "# view the head of the data\n",
        "columns = ['Date', 'Time Period', 'Payment Channel', 'Amount (KES)', 'Gender']\n",
        "week_data = pd.DataFrame(data, columns=columns)\n",
        "week_data.head()\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Specify the folder path\n",
        "folder_path = '/content/drive/MyDrive/Nganya Project/Matatu Data'\n",
        "if not os.path.exists(folder_path):\n",
        "    os.makedirs(folder_path)\n",
        "\n",
        "# File path in the folder (change the extension to .xlsx)\n",
        "file_path = os.path.join(folder_path, 'Matatu.xlsx')\n",
        "\n",
        "# Save DataFrame to Excel\n",
        "week_data.to_excel(file_path, index=False)\n",
        "\n",
        "print(f\"Dataset saved to: {file_path}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WtZTHn9Qab5E",
        "outputId": "bf0d238d-55f7-4701-d54e-f87a0ec4f5c8"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset saved to: /content/drive/MyDrive/Nganya Project/Matatu Data/Matatu.xlsx\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate total revenue per day\n",
        "week_data['Date'] = pd.to_datetime(week_data['Date'])  # Ensure 'Date' is in datetime format\n",
        "total_revenue_per_day = week_data.groupby('Date')['Amount (KES)'].sum()\n",
        "# Get unique dates from the dataset\n",
        "unique_dates = total_revenue_per_day.index\n",
        "\n",
        "#Plot the bar graph\n",
        "ax = total_revenue_per_day.plot(kind='bar', title=\"Total Revenue Per Day\")\n",
        "\n",
        "# Add numbers on top of bars (abbreviated)\n",
        "for p in ax.patches:\n",
        "    height = p.get_height()\n",
        "    # Abbreviate the number if it's greater than 1000\n",
        "    if height >= 1000:\n",
        "        label_text = f'{int(height/1000)}K'\n",
        "    else:\n",
        "        label_text = int(height)\n",
        "    ax.text(p.get_x() + p.get_width() / 2, height + 5, label_text, ha='center', va='bottom')\n",
        "\n",
        "# Set x-axis ticks and labels using unique dates\n",
        "ax.set_xticks(range(len(unique_dates)))  # Set tick positions\n",
        "ax.set_xticklabels(unique_dates.strftime('%Y-%m-%d'), rotation=45, ha='right')  # Set labels\n",
        "\n",
        "\n",
        "## Define the path to the folder in your Google Drive\n",
        "google_drive_folder = '/content/drive/MyDrive/Nganya Project/Plots'\n",
        "\n",
        "# File name with extension\n",
        "file_name = 'Total Revenue Per Day.png'\n",
        "\n",
        "# Create the full file path\n",
        "full_file_path = os.path.join(google_drive_folder, file_name)\n",
        "\n",
        "# Check if the folder exists and create it if necessary\n",
        "if not os.path.exists(google_drive_folder):\n",
        "    os.makedirs(google_drive_folder)\n",
        "\n",
        "# Add the tag horizontally on the right side of the graph\n",
        "plt.gcf().text(0.92, 0.4, 'GitHub:@LexMainye', fontsize=12, color='red',\n",
        "               fontweight='bold', ha='left', va='center', rotation=90)\n",
        "\n",
        "# Save the plot to the Google Drive folder\n",
        "plt.savefig(full_file_path, dpi=300, bbox_inches='tight')\n",
        "plt.show()\n",
        "plt.close()\n",
        "\n",
        "print(f\"Plot saved to: {full_file_path}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 541
        },
        "id": "--cNv3ACnkst",
        "outputId": "573d0ce8-f14f-49d9-a35d-23d6c0ef2b9e"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Plot saved to: /content/drive/MyDrive/Nganya Project/Plots/Total Revenue Per Day.png\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate number of transactions per time period\n",
        "transactions_per_time_period = week_data.groupby('Time Period').size()\n",
        "\n",
        "# Define the desired order of time periods\n",
        "time_period_order = ['Early Morning', 'Morning', 'Late Morning', 'Early Afternoon', 'Late Afternoon', 'Evening', 'Night']\n",
        "\n",
        "# Reindex the data to match the desired order\n",
        "transactions_per_time_period = transactions_per_time_period.reindex(time_period_order)\n",
        "\n",
        "# Create the bar chart\n",
        "ax = transactions_per_time_period.plot(kind='bar', title=\" Number of Transactions per Time Period\")\n",
        "\n",
        "# Add numbers on top of bars with reduced distance\n",
        "for p in ax.patches:\n",
        "    height = p.get_height()\n",
        "    ax.text(p.get_x() + p.get_width() / 2, height + 1, int(height), ha='center', va='bottom')\n",
        "\n",
        "# Define the path to the folder in your Google Drive\n",
        "google_drive_folder = '/content/drive/MyDrive/Nganya Project/Plots'\n",
        "\n",
        "# File name with extension\n",
        "file_name = 'Number of Transactions per Time Period.png'\n",
        "\n",
        "# Create the full file path\n",
        "full_file_path = os.path.join(google_drive_folder, file_name)\n",
        "\n",
        "# Check if the folder exists and create it if necessary\n",
        "if not os.path.exists(google_drive_folder):\n",
        "    os.makedirs(google_drive_folder)\n",
        "\n",
        "# Add the tag horizontally on the right side of the graph\n",
        "plt.gcf().text(0.92, 0.4, 'GitHub:@LexMainye', fontsize=12, color='red',\n",
        "               fontweight='bold', ha='left', va='center', rotation=90)\n",
        "\n",
        "# Save the plot to the Google Drive folder\n",
        "plt.savefig(full_file_path, dpi=300, bbox_inches='tight')\n",
        "plt.show()\n",
        "plt.close()\n",
        "\n",
        "print(f\"Plot saved to: {full_file_path}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 582
        },
        "id": "sNqxgalFn_ZM",
        "outputId": "22b5b583-ab4b-4470-957c-f7bdba3aca8b"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Plot saved to: /content/drive/MyDrive/Nganya Project/Plots/Number of Transactions per Time Period.png\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Gender distribution\n",
        "gender_distribution = week_data['Gender'].value_counts()\n",
        "\n",
        "# Create pie chart with percentage values and custom colors\n",
        "gender_distribution.plot(kind='pie', autopct='%1.1f%%', title=\"Gender Distribution\",\n",
        "                         colors=['lightblue', 'lightpink'])  # Specify colors for Male and Female\n",
        "\n",
        "# Remove the y-label\n",
        "plt.ylabel('')\n",
        "\n",
        "# Define the path to the folder in your Google Drive\n",
        "google_drive_folder = '/content/drive/MyDrive/Nganya Project/Plots'\n",
        "\n",
        "# File name with extension\n",
        "file_name = 'Gender Distribution.png'\n",
        "\n",
        "# Create the full file path\n",
        "full_file_path = os.path.join(google_drive_folder, file_name)\n",
        "\n",
        "# Check if the folder exists and create it if necessary\n",
        "if not os.path.exists(google_drive_folder):\n",
        "    os.makedirs(google_drive_folder)\n",
        "\n",
        "# Add the tag horizontally on the right side of the graph\n",
        "plt.gcf().text(0.92, 0.4, 'GitHub:@LexMainye', fontsize=12, color='red',\n",
        "               fontweight='bold', ha='left', va='center', rotation=90)\n",
        "\n",
        "# Save the plot to the Google Drive folder\n",
        "plt.savefig(full_file_path, dpi=300, bbox_inches='tight')\n",
        "plt.show()\n",
        "plt.close()\n",
        "\n",
        "print(f\"Plot saved to: {full_file_path}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 445
        },
        "id": "ONnRsczGosmu",
        "outputId": "d2e544d8-8499-4648-8a38-f463c2920fad"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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66aesXr2a8847j2nTprFr166j1vKTn/yEgoIC3n//fVauXMmoUaM4++yzKS4u7rC/v4iITxkixzBr1izD4XAYsbGxjY+LL77YOPvss40HH3ywyb6vvPKKkZ6e3vgaMO66667G1998840BGC+++GLjtn/+859GVFTUMWsYOnSo8dRTTzW+zszMNJ588knDMAzjyy+/NBISEoza2tomX9OvXz9j4cKFbf77iohYQdeM5bjOOussnnvuucbXsbGxnHDCCSxbtqxJS9jtdlNbW0t1dTUxMTEAnHDCCY3vd+vWDYDhw4c32VZbW0t5eTkJCQlUVlYyb948/vOf/5CXl4fL5aKmpuaoLeO1a9dSWVlJly5dmmyvqalp0v0tIuLPFMZyXLGxsWRnZzfZVllZyX333cePf/zjZvtHRUU1/jk8PLzxzzab7ajbPB4PALfeeisff/wxjz32GNnZ2URHR3PxxRfjdDpbrK2yspL09HSWLl3a7L2kpKTW/QVFRCymMBavjBo1ii1btjQL6fZatmwZs2fPZvr06YAZtjt37jxmHfn5+YSFhZGVleXTWkREOovCWLxyzz33MHXqVHr37s3FF1+M3W5n7dq1bNiwgQceeMDr4/bv358lS5Ywbdo0bDYbd999d2OruSUTJ07k1FNP5cILL+SRRx5hwIAB7Nu3j//85z9Mnz6d0aNHe12LiEhn0Whq8cqkSZN49913+eijjxgzZgynnHIKTz75JJmZme067hNPPEFycjJjx45l2rRpTJo0iVGjRh11f5vNxnvvvccZZ5zBlVdeyYABA7j00kvJzc1tvEYtIuLvbIZhGFYXISIiEsrUMhYREbGYwlhERMRiCmMRERGLKYxFREQspjAWERGxmMJYRETEYgpjERERiymMRURELKblMCXouDweal0enG4P9R7j4LMHl8dofHgOrnVjGGBgYBz8M4ANcNhtOGw2HHYbYQefHXYbYXYbkQ47kQ47UWEOIhz6PCsi7acwloBT63JT4XRRXe+mxuWmpt5DtctNzcHX9Z7OW1TObqMxmCMddmLCHcSGO4iLCCMuIozYcAf2g3emEhE5Gi2HKX6rxuWmvM5FhdN18LmeijoXzk4M2/aywcGADiM+wkFiVDhJkeEkRIYppEWkkcJY/ILL46G4pp7i2npKap0U19RT5z763ZoCnd0GCZFmMCdFhZEcFU5iZLgCWiREKYzFEjX1bgqq6yiqMYO33OmyuiTLOWw2ukSH0zUmgtToSJKjFc4ioUJhLJ3C5fFQWO1kf3UdBVVOKhS+x+Ww2UiJDic1JoK0mEiSo8KxKZxFgpLCWDpMhdPFvopa9lfVUVzrJIAu9fqlSIed7nGRpMdGkRYbSZhdwSwSLBTG4lNldfXsrahlX0Wtup47kMMGqTGRpMdFkR4XSVSYw+qSRKQdFMbSbqW1BwO4soYKp9vqckJSakwEveKj6REfRbjmPosEHIWxeKXO7WF3eQ25ZdWU1akF7C/sNugeG0VmYjTdYiM1AEwkQCiMpdUMw6Cg2snOsmryKmt1DdjPRTrs9EqIpk9SDPERWt9HxJ8pjOW4alxuckqryS2rpsYVvHN/g1laTAT9kmPpHhupEdkifkhhLEdVWlvPtpIq9pTXoB+S4BAT7qBvYgyZSTFE6tqyiN9QGEsThmGQV1XH9uIqCmucVpcjHcRug14J0QxIiVMXtogfUBgLAG6Pwc6yanaUVFFZrxHRoaRHfBSDUuJIjAq3uhSRkKUwDnFuj0FOWTVbiyqpDeK1oOX4usdGMqhLHCnREVaXIhJyFMYhqqElvEUhLEdIi4lgUJd4usYolEU6i8I4xHgMg9yyGjYXVWhktBxT99hIhqXGkxCp7muRjqYwDiF5lbWsLyjXNWFpNRuQmRjN4K7xRGvJTZEOozAOAeV19awrKKegWqOjxTsOm43slFgGpMQSbteUKBFfUxgHsTq3h02FFeSUVmuesPhEpMPO0NR4MhOitXiIiA8pjIOQxzD4obSaTYUV1GvNSukAXaLDObFbIom6niziEwrjIFNSW8+q/FLdvEE6nA3ITo5lcNc4wtR1LdIuCuMg4fYYbCysYHtJlbqkpVNFhzkYkZZARnyU1aWIBCyFcRA4UF3HqvwyqjRKWiyUERfJid0SidKoa5E2UxgHMKfbw/oD5eSW1VhdighgDvAa2S1RrWSRNlIYB6gD1XWsyCvVwh3il3onRDMiLYFw3RlKpFUUxgHGYxhsKqxgS3GV1aWIHFN0mIOTuieSFhtpdSkifk9hHEAqnS6W55VSUltvdSkirZadHMuw1HjsmpcsclQK4wCRW1bN2oJyXJo3LAEoJSqckzOSiQnX4C6RliiM/ZzbY7B6fxm7yjVISwJbhMPG6PQkusdqcJfIkRTGfqyq3sX/9pZoAQ8JKgNT4hjSNU7LaYocRmHspwqq6vgurwSnW/88EnxSYyIYk56kOckiBymM/dDW4kq+P1ChlbQkqEWH2Tm1RwpJUVrfWkRh7EdcHoNV+aXsqai1uhSRThFmszEmI4n0OF1HltCmMPYTtS43X+8toVTTliQEDU+Np39KnNVliFhGYewHKp0ulu0p1trSEtL6JMYwoluC5iNLSFIYW6y4xsnXe0twurWspUhaTAQ/ykjWMpoSchTGFtpXUcvyvBI0YFrkkMTIME7rmaKR1hJSFMYW2VFSxdqCcqvLEPFLseEOTu+ZQmxEmNWliHQKhbEFNhdVsLGw0uoyRPxalMPO6b1SSIjU1CcJfgrjTraxsILNRQpikdaIcNg4rWcXkjUXWYKcwrgTfX+ggi3FCmKRtgi32xjbM4Uu0RFWlyLSYRTGnWR9QTnbSnQPYhFvOGw2Tu+ZQpcYBbIEJ80f6ATrFMQi7eI2DJbtLda9vCVoKYw72LqCcrYriEXazeUxWLa7iLI6BbIEH4VxB9pcVKEgFvEhp8fgq93FVDh1W1EJLgrjDvJDaZWmL4l0gDq3hy93F1GlQJYgojDuAHsqalizXwt6iHSUWpeHL/cUU+PSeu4SHBTGPra/qo4VeaVWlyES9Krr3Xy9pxiXR+u6S+BTGPtQcY2T/+0twaPJYiKdoqzOxXf7StEMTQl0CmMfqa538c3eEtz6pSDSqfKr6lijdd4lwCmMfaDe7eHrPSXU6TaIIpbIKa1mm1a3kwCmMG4nwzD4Lq+Uco3sFLHU+gMV7K2osboMEa8ojNtpw4EK9lfVWV2GiAAr8kq1SpcEJIVxO+wqq9Yylx3stace46JBGU0eN5w/rvH9j177O/dccRE/O2kAFw3KoKq87LjHrKms5KUH7+GaCWO4bERf7rx0GtvXr2myz79ffI4rxw7nyrHDeeelPzd5b+vaVdz240m4XeoN8TduA77dV4JTl4wkwOjO3V4qrnGyav/xf/FL+/XqP5B7X3qt8bUjzNH4Z2dtDSeOG8+J48bzjyceatXxnr37FnZt28Kv5z9FSlo3vnjnLe678hIW/GcpXbqls3PLRhY/9Sh3/vlvGIbBQ9fOYsRpZ5I5cDBul4vn5/2Wa3//KI4w/ffxR9X1bpbnlTK2RzI2m83qckRaRb9NvOB0e/gur1RTmDqJw+EgOTWtxfemzroKgA3fft2qY9XV1vC/j97jjmcWMXTMKQBccsOtrPj8Yz7859+4/MbfsveH7WQOHMLwU04HIHPgYPbmbCdz4GD+/eJzDB59CtnDT2z/X0w6zP6qOjYVVTKka7zVpYi0isLYCyvzS6mu18o/nSUvN4dfjBtJeGQkA088iZ/ePJfUjJ5eHcvjcuNxuwmPjGyyPSIqis0rvwMgc8Bg8nb+wIF9ezAM2LfzB3r3H0T+rp18tuQ1Hn3rg3b/naTjbS6qJDkqnPS4KKtLETkuhXEbbS+uIq9SA7Y6S/8Ro7j+oQVk9OlHSUEBbzzzOHf9bDoL3vmc6Li4Nh8vOi6OgSeexJvPLqBn3/4kdk3lq//8i61rVtK9dxYAPfv15/Kb7uD3cy4F4Kc3z6Vnv/7Mu3IGV9z2O9Z8tZTXnnmcsLAwrrzz/sYWtvifFXmlTMjsSmyEftWJf9NPaBsU1zhZf0CLC3SmUWdMaPxz1sAhDBgxkmsnnMyyD95h4sWXe3XMXz/yFM/ceTNXnTkKu8NB3yHDOX3Khez4fl3jPpMuncmkS2c2vv787deJjo1j4ImjueH8ccx/4z2K8vN48uZf8tyn/yM8IrKlU4nF6j0G3+4rYXxmV+y6fix+TGHcSg3XiXWZ2FqxCYmkZ/UlP3en18fo3juL+/++hNrqamoqK0hO68bjN11Dt16ZLe5fXlLE6888wQN/X8K2davIyOrb+HC76tmX8wOZAwd7XY90rNI6FxsLKxiWmmB1KSJHpalNrbQqv0zXif1ATVUV+3fnHnVAV1tExcSQnNaNyrJS1nz1X8ZMmNTifosemse0WVfRpXsGHrcHt+vQPFa3243Ho58Lf7e1uIrCaqfVZYgclVrGrZBbVs2+ylqrywhJf51/H6PPOpfUjJ4UF+Tz2tOPYbfbOX3qdABKDhRQWlhA/q4cAHK3biY6Npau6T2IT0oGYN7sGZw88Twm/2wOAKu/XAoYZPTpR35uDn979H569M1mwo8vaXb+tcv+S97OH7jh4T8CkD18BHt/2MGqLz6jMG8fdrudjD79Ovz7IO23Ir+UszO7Eu5QG0T8j8L4OGpcbtZpEXrLFO3P48lbfkVFaQkJKV0YfNIYHnrtXRJTugDw0eK/8fozTzTuf/fPzJC+7sEnG8M1f9dOKkqKG/epriznH088RFF+HnFJSZxyzmQuv+kOwsLDm5y7rraGv9z/O25+8s/Y7eYv8C7dM/j5XffzzJ03ERYRwQ0P/5HIqOgO/R6Ib1TXu1lbUM7o9CSrSxFpxmbo3mPH9PWeYvK13KVI0Dg5I4me8foAJf5F/TXHsLOsWkEsEmTW5JdR69J1fvEvCuOjqK53s17d0yJBx+kxdOlJ/I7C+ChW7y+jXutdigSlPRW15FdpUKb4D4VxC3aX1+i2iCJBbs3+clz6wC1+QmF8hHqPR93TIiGgut7NpsIKq8sQARTGzWwqrKRW90IVCQnbS6oora0//o4iHUxhfJjyunp2lFRZXYaIdBIDc3yIZniK1RTGh1mzv1xrT4uEmJLaenaV11hdhoQ4hfFBu8trKKzR2rUioej7wgoN5hJLKYwBl8fQrRFFQlity8O24kqry5AQpjDGHMRR69KgLZFQtrW4ihqtzCUWCfkwdrr1iVhEwG0YbNRUJ7FIyIfxlqJKrbQlIgDkltVQpqlOYoGQDuPqejc7SjWVSUQO2aDWsVggpMN4U1EFahSLyOH2V9VRrJkV0slCNowrnC52lWluoYg0t6lI40ikc4VsGG8urNACHyLSov1VdZTUqnUsnSckw7jK6WJPhW6fJiJHt6lQrWPpPCEZxttKqtQqFpFjyq+q000kpNOEXBjXutzkllVbXYaIBIBNRRpZLZ0j5MJ4R0kVbjWLRaQV8irrKK9T61g6XkiFcb3Hww+lahWLSOttL9HvDOl4IRXGOaXVWm1LRNpkd3k1dW6tXS8dK2TC2DAMftAnXBFpI7cBOVqpTzpYyIRxflUd1boji4h44YfSajyGetWk44RMGOfoWrGIeKnW5WFfpdYmkI4TEmFcVe8iv6rO6jJEJIDpMpd0pJAIY7WKRaS9CmucVDhdVpchQSrow9hjGOTqhhAi4gNaMEg6StCH8b6KWk1LEBGf2F1eg6GBXNIBgj6Mc8vVKhYR36hxeSio1t2cxPeCOozrXB4KNHBLRHxol7qqpQMEdRjvrazR3ZlExKf2VdZRr0tf4mNBHca7yzUvUER8y20Y7NX90MXHgjaMq+vdFNXo2o6I+N7uCo1FEd8K2jDeo/8sItJBCqudONVVLT4UtGG8W6OoRaSDGECelscUHwrKMK5yuiir00o5ItJxtFZ1CCsuhq+/ho8/9tkhgzKM8zSdSUQ6WEFVHS7dHz205ObClCmQlgbjxsH550NtLQwdCv36wcqVXh86OMNYn1hFpIO5DdivD/6hY+9eGDsWPvgAPB4wDPMRFQUnnAA5ObB4sdeHD7owrnd7KNQKOSLSCfTBP4TMmwd5eWYAZ2U1fe/0083nzz7z+vBBF8YF1XVa6ENEOkV+Za3Wqg4V778PNhv89rfwyitN32sI5z17vD580IWxuo1EpLM4PQalGiwaGg4cMJ8nTmz+nsNhPpeVeX34oAvjfIWxiHSiA9X6nRMSunQxn1esaP5ew6jqbt28PnxQhXFFnYtalybii0jnKajSGJWQcOaZ5vXie+6Bhx46tH3OHFiwwOzCPussrw9vM4LogkdOaTWr93vfTSAi0lYOm41p/btht9msLkU60vr1cPLJ4Gzhw1fDqOoVK2DIEK8OH1Qt40KtRS0incxtGBTX1FtdhnS04cNhyRLo2vXQtKaGR2oqvPWW10EMQdYy/mBHAdUut9VliEiIGdQljiFd460uQzpDTQ189BFs3Wq+HjAAzjkHYmLaddgwH5TmF6rr3QpiEbGE1jYIAatXw8iREB0NF1zg88MHTTe1uqhFxColtfWabxzsTjrJ7Ib+/e9h2zafHz5ouqlX55eRU1ZtdRkiEqLOzupKYmS41WVIR7HbzRHTDUaOhMsvh0sugR492n/4dh/BTxSpZSwiFiqp1SCuoHbBBeZ14YZBW6tXw223QWYmjB8Pzz9v3s3JS0HRMnZ7DN7Zlq9lMEXEMn0SYxjZPdHqMqQj1dXBp5/C//0fvPuuefMIONRiDgszB3PNmgUXXWS2plspKMK4uMbJ0l1FVpchIiEsKTKcCVldrS5DOtPy5XDzzbBs2aFtDcE8ZIgZ2kfeVOIogqKbukxrw4qIxcrq6nHr/sahYc0as4t6+nT4+uum15LDw81u7I0b4YYbWn3IIAljXasREWsZ6HdRUNu5Ex58EIYONUdWP/EE7NtnBm94OFx8sXlnp6oq8z3DgC++aPXhg6Kb+r+7CinSCjgiYrFR3RLJSmrf4g/ipw4fTd0Qm0OHws9/DldccehGEmDevSk52dzf3br1LwJ+0Q/DMNRNLSJ+ocKp30VBzTAgPh4uvdQM4ZNPbnm/mBi49942HTrgW8ZVThcf5hywugwREbrHRjK2Z4rVZUhHOOMMM4B/8pN2L33ZkoBvGZfrk6iI+IlK/T4KXm24/uuNgA/j6nqtRy0i/qGq3o3HMHQ7xWBVVgavvgrbt0Np6aFrxw1sNnjxRa8OHfBhXKUwFhE/YWC2jhO0LGbw+fxzcypTRcWx9/MyjAN+alNVvbqFRMR/VDrVQAhKN90E5eXN72V8+KMdAr9lrB98EfEjlWogBKfNm81u6BNOgLlzzalMYb6L0MAPY3VTi4gfqdHvpODUsyfk5MADD8CUKT4/fEB3U9e63LgDe2aWiASZWpfH6hKkI1x/vdkV/fXXHXL4gG4ZayS1iPibGpd+LwWlpCTo2xcefhg2bYIzzzRX2TrSzJleHT6gF/3Iq6zlm70lVpchItIoJszBef3SrC5DfK1hOUzDaHpjiMPZbODybsxAQLeM69zqDhIR/1LbyrWIJQA1tF07oA0b2GGsazMi4mc8hvm7KTIsoIfkyJHauNZ0WwV0GDvVMhYRP1TrciuMg00Hh3FA/7Som1pE/JHTo99N0jYB3TJWGIuIP3J5AnZcrDSYMMEckPWnP5n3LZ4w4fhfY7PBp596dbqADmN1U4uIP1IYB4GlS81wLStr+vpojjXKuhUCOozr1RUkIn5Iv5uCVAfOBA7oMNbPu4j4I7WMg0BOjvmcnt70dQcJ6DDWUpgi4o8UxkEgM/PYr30soMPYozAWET+kMA5iLpd5B6fS0pa7Z884w6vDBnQYq2UsIv7IpWtowemBB+DRR6GysuX3Q3U5TH34FBF/pHZCEHrpJbjnng47fMAu+uFWEouIn9JvpyD0l7+YLd/+/c3XNhuccw4MGWK+Hj3a6zs2QQCHsaEfdxER6SwbN5rP999/aNu998L69XD55fD99zBnjteHD9huahveT66W0BVmeEjBIMnjJt7jItblItpVT4SzHpsWkREf8RjJkJ5kdRniSzU15nP37uBwmIO3amrMFvLMmfDqq3D77fDNN14dPmDD2K4slhZEGAYpeBrDNsblIrq+nog6J466OnDW62OcdLzEOKsrEF9LToYDB6C+3vxzURH8859w6qnw4YfmPuvWeX34gA1jWzuWHZPAFYWHLoaHBI+bBLcZtlH19YQ7nThq67DVezeSUcSn1FoIPr16mWFcWgojR8LHH8OiReYDzBZyr15eHz5gwxjMn3eN4wousYaHZMNNouEm3u0mxuUi0ukkwunEXluHzaUbt0sAUGMh+IweDStXmnOMf/1rM4yPHDZ/661eH95mGIE7CP/fW/M11zjAxBsekvGQ6HER53IT46onst5JeJ0Te60Tm1thK0EgMwOGZFtdhfhSdTVUVEBMDMTHw5Il8MwzsHevuTrX1VfDRRd5ffiADuP/25ZPvZrG/sMwSMRDsuEh0eMm1u0ipr6eyPp6wuoOtmy1GIKEgqweMLif1VVIAAnwbmobmtHXeWyGQRIGKYabhINhG+00W7ZhdU5stXXYAveznYjvqJta2iigw9hht4F6NX3G3jAS2XCT4D4YtvX1RDgPhm2dU2Er0hoOh9UViC9kZLRtf5vN7Lb2QkCHcYTDTnW90ri1jjrHtq4eR12dGbZWFykSDCLDra5AfCE/3wzYIxshLfV8GEa7ekQCO4w1faAJzbEV8ROREVZXIL50ZCB3QA9hYIexI2BX8/SK5tiKBAiFcfBJSIDLLoPZs81VuHwsoEdTr9lfxg+l1VaX4TOaYysSJMafDNFRVlch7fXWW+YNIj7+2Fz+0maDsDCYMgWuugrOO89ng/UCOow3Flawuego95X0Q5pjKxIizj0dQqznLqjt2mWG8qJFhwZo2WzQowdceSVcc03bB3sdIaDDeHtxFesOlFtdhklzbEUEIDwMJo61ugrpCB4PvPeeudhHw3rUNpt596Z23us4wK8Zd95wJM2xFZFW0fXi4LV1KyxdCqtWHRrUZRgQ1f5LEgEdxtHhvpvLpzm2IuITCuPgUlMDr71mdlM33B7RMMxlMS++GH7xCzj99HafJrDDOKz1YWzOsfWQ5PEcmmNbb97HVnNsRcRnIjTHOGhcey0sXmyuSd3QGBs50gzgn/7UHGHtIwF9zdhjGPxraz6gObYi4if69IRBfa2uQnzBbj/UHZ2QAJdeCqNGHftrrr7aq1MFdBgD1H+7jrCKSs2xFRH/MLAP9PX+vrbiRxrCuC28nBUT0N3UAOGGBxTEIuIvdM04uLSlvRqqy2ECEBMNJX4yvUlERGEcPGbN6rRTBUEYa5UbEfEj+p0UPBYt6rRTBf4SMTHRVlcgImIKc2gZzGD1ww/H3+fzz70+fOCHcazCWET8REKcz9YqFj8zYgQ891zL71VXw3XXwTnneH34wA9jtYxFxF8kxFldgXSUqiq4/nozcHfvPrT9v/+F4cPhz39u160VA/+acXiYOWCizml1JSIS6hTGwat7d8jPh88+M8N3/nzYsMFsLTcsizlmjNeHD/yWMUBSvNUViIgojIPZ5s3mbRMBysvhV7+CZ581bx4RFWWGc8NymV4IkjD23ZJkIiJesdshNsbqKqSjJCTAwoXwxz8e2mYY5v2N338fbrvN/BnwksJYRMQX4mPArsFbQaumBm6/HW655dASmWCuuHXBBfDCC+06fHCEcaJGMIqIxdRFHdyGDIHHH4f6erNb+rHHYM4cM5TLysybSpx5pteHD/i1qRstWwXllVZXISKhamg29M6wugrpKA1d0GPHmouB9O9vvv7gA/PmEHv2mI1CL9emDo6WMUCyuqpFxEJqGQe3qCizZfzll4eCGOC888xR1e1cOjPwpzY1SNSIahGxiA2Ij7W6CulIa9bAgAEtv5eQYLaWZ8zw+vDB001dXQP/XW51FSISiuJiYNxoq6uQzlJcbE51qqpq16pbhwuebuqYaIgIt7oKEQlF6qIODbm5MGUKpKXBuHFw/vlQWwtDh0K/frBypdeHDp4wBk1xEhFrpCRZXYF0tL17zcFbH3xgLvTRsOpWVBSccALk5MDixV4fPsjCWNeNRcQCqclWVyAdbd48yMszAzgrq+l7p59uPn/2mdeHD7IwVstYRDpZQhxERVpdhXS09983py799rfwyitN32sI5z17vD58cIVxYrwW/xCRzpWaYnUF0hkOHDCfJ05s/p7DYT6XlXl9+OAK4zCH5huLSOdSGIeGLl3M5xUrmr/38cfmc7duXh8+uMIYoFtXqysQkVARHq6xKqHizDPN68X33AMPPXRo+5w5sGCB2St71lleHz545hk3qK2Dz7+1ugoRCQUZaTBikNVVSGdYvx5OPhmczubvNYyqXrHCXMPaC8HXMo6K1GpcItI51EUdOoYPhyVLoGvXQ9OaGh6pqfDWW14HMQRjyxjgh92wJcfqKkQkmNmAs081u6oldNTUwEcfwdat5usBA8xVuGLady/r4Azjqhr4QktjikgHSk6AU060ugrxBzt3wq5d5p/POMOrQwRfNzVAbLQWbReRjqUuammwcKE5eGvCBK8PEZxhDNBdo6pFpAOldbG6AvEnDdePvRS8YawpTiLSUaIi1fsmPhW8YRwfa3ZXi4j4mnrexMeCN4xBrWMR6Rg9u1tdgQSZMKsL6FDdu5rTnEREfCUpQV3UoaK1A7J++KHdpwruME6Mh+hIqKmzuhIRCRa91CoOGUuXdtrNh4K7mxrM5epERHwhzAHpqVZXIZ3pyNW2jvZop+BuGQP0zoAf9vjkmyUiIS4j7dDt8iT4LVrUaacKzhW4jrR6E+QfsLoKEQl0p42ChDirq5AgFPzd1ABZGVZXICKBLjFeQSwdJjTCODlR/4lEpH00cCu05efDXXfBqFEQHw+xseZdmubOhZKSdh8+NLqpAfbkw/qtVlchIoHI4YAJp5gDuCT0vPEG/PznUFkJp5xiBnJYGHz6KXz/PWRlwbJlkJ7u9SlCo2UMkJ4GEbrVmYh4ISNVQRyq/vEPuPRS8Hjg3Xfh66/h6adhwQJYvx7mzDHv2vSb35j7v/UWOJ1tPk3otIwBtu6EHbusrkJEAs3YkeY1Ywkte/fC0KFQUWGGbEoLd+rKy4PLLjPva71+vdl1ffHFsHhxm04V/FObDtc73VyRK4Q+f4hIOyXEKYhD1fPPQ3k5nHkmXHgh2O1HXwTE5YKaGpg0yezWvvxy+H//r9WnCp1uajDvtKIF3kWkLbJ6WF2BWOXf/zbDd8YM8/VDD0G/fuafJ0wwAzo62mwx3347pKbClClmg++559p0qtAKY4BM/ccSkVaKjdYqfqEsN9d87t/ffF6/HnbsgIcfho8/NruuP/4Yiovh7beha9dD+373XZtOFXphnJygLicRaZ3szE5bm1j8kMdjPjdc2nz7bfN50KBD+wwcaD5v22aOrG74eamvb9OpQi+MATK1CIiIHEdcjNahDnUNXdLr15vPw4aZz7feCq+9Zo6uvvxyc1tUlLn/1oNTaLOy2nSq0Azj9DSz+0lE5Gj6q1Uc8qZNM1vFDWtUv/ACZGSYreDLL4cLLoCPPjKvGy9cCAkJ8Prr5s/NlCltOlVoTW06XH4hrN5odRUi4o/iY811qBXGoa2w0LwGXF5uBvLMmeYc4sWLzday0wl9+5rzkLt1gyVLzGlNcXGwZUubFgEJ3TAG+GYNlJZbXYWI+JtRQ6FbF6urEH+weDH89KcQGQmvvAIXXdTyfu+9Z4ZyVdWh4G6D0A7jkjL431qrqxARf5IYB2NHWV2F+JOXX4brroPaWjj/fJg1yxzEFRYGGzfC3/8O77xjLvzxpz/B1Ve3+RShHcYAq76H/UVWVyEi/uKkYZDWwkpLEtp27IDf/x7efNNc3KPhEoZhmIO3LrwQ7r4bBg/26vAK46pq+HKlVuUSEUiKh1NHWl2F+DOnE1asgH37zNfp6TB6tNmN3Q4KY4Dvt8GuPKurEBGrjRkOXZOtrkJCUGhObTpSdqZ5izQRCV3JCQpisUxo3SjiaCIjoE9P2J5rdSUiYpUBfayuQPzZnDnms80GL77Y+vdaSd3UDVxu+GI51LX9PpQiEuB6dofhA6yuQvzZ4Xdscrtb/15rD9+O0oJLmAOye1tdhYh0tohwGKRWsbTCsdqu7WzXqpv6cL3SYedeqKqxuhIR6SxD+pnzQ0WO5fPPvXuvldRNfaSiEvhuvdVViEhnSE2B0cOsrkJE3dTNdEmG3q1fT1REApTDDkOzra5CApHbDbt3mw8vrxEfSWHckoF9ISbK6ipEpCP1z4Jo/T+XNti+HX7yE/PuTFlZ5iMhwdzWcOtEL6mb+miKy+BbrVstEpQS48yVtnRXJmmt1athwgTzDk5HxqbNZt6paelSGOndCm5qGR9NSiJk9bC6ChHxNZsNhg1QEEvb3HgjlJUdCuLkZEg5uIa5YUBFBdx0k9eHVxgfy4AsiI22ugoR8aWsHpAQZ3UVEmiWLzc/wI0ZY3ZJFxWZ9zveuhVOPtnc57vvvD68wvhYHA4YPhD0AVokOERHQf9Mq6uQQNS1q/l8112QfdjAv+xs+N3vzD+neH+3L4Xx8SQnQJ9eVlchXnj49X9gm3wmNy58qnFbfnERVzz6AN1/Op3Y6ZMYdcMveOur/x7zOF+sX8u0eXeQ8bMfY5t8Jv/6+stm+zz21mLSLruAtMsu4PElrzV579vNGznp11fhcrt88xcT7w3rr3XoxTs//7nZHb1rV/P3GrbNnOn14bXoR2tkZ0JBEVRWW12JtNLyrZtY+P47nNCnX5PtMx9/kNKqSt6550G6JiTy6tJPmPHwPFb8cSEj+7W8HGJVbQ0j+mQz59zJ/PiBu5u9vy5nB/f8/SXevfdhDAymzruDc0eOZniffrjcLq59+nGe//WthDn0381SvdJ1IwhpvS++aPp63Dg44QS44w4oKGjaNf3kkzBggDnAy0v67dAaDjucMBC+WaP7HgeAyppqfvrIA7zw69t4YPErTd77etP3PHfdTZw80LwB+F2XzeTJf73Bym1bjxrG5485hfPHnHLU823encsJWf2YcOIoAE7I6sfmPbsY3qcfj765mDOGjWDMAO9uOC4+khgPg/sdfz+RBuPHtzzIzzDggQeab9+6Fc47D1ze9YCpm7q1EuOhr7qrA8F1zy5gysmnMnHk6GbvjR08lNe++JziinI8Hg+L//sptU4n40840evzDc/qy9a9u9lVsJ/c/fls3bebYZl92JG3l0Ufv88DM3/Rjr+NtFt4GIwcbH6oFmkLw2j+ON52L6ll3BbZveFAMZRXWl2JHMXi/37Kqu1bWf7HhS2+//rceVzy8H10uWQaYQ4HMZFRvH33A2Rn9PT6nIN7Z/HgrKs453e3APDQrKsZ3DuLiXfezCNzruXDVd8x7x8vE+5w8Mdrfs0Zw0d4fS7xwohBWtxD2m7WrE49ncK4Lex2GDUEvl4Nznqrq5Ej7D5QwG8WPsXHf3icqIjIFve5+5UXKa2s5JMHn6BrQiL/+uYrZjw0jy8f+RPD+3jfjXntlAu4dsoFja//+skHxEdHc+rgoQy8+gqWL1jInsICLp1/HzmLFhMZHuH1uaQNsjPN9adF2mrRok49ncK4raKjzED+dp2uH/uZldu2UFBawqgbrmrc5va4+WLDWp7+v7fZ8sIrPP1/b7PhuZcZmmneMm9E32y+/H4dz7z7L/58wy0+qaOwrJT7Xn2ZLx75E99u2cSAHj3pf/BR73Kxdc/udgW/tFJqim6LKgFDYeyN5ERzisT69q1FKr519oknsf7Zpp9mr3zyYQb17M1vf3I51bW1ANiPGJThsNvxGB6f1XHT809z04U/oWfXNJZv3Uz9YQM6XB43bo/vziVHER0JIwZqlS3xnTlzjr+PzQYvvujV4RXG3urZHcqrIHev1ZXIQfExMQzL6ttkW2xUNF0SEhmW1Zd6l4vsjB5c89TjPPaLX9ElIYF/ffMVH69ewbvzHm78mrPn3sT0seO4ftqPAXN09vZ9h/6dc/bnsWbHNlLiE+id1q3J+T5etZyte/fw11vuBGDMgEFs3rOL95f/j92FBTjsDgb2VGutQ9ntMHKI7lEsvvXyy8f+cGcYCmPLDO4LVdVQWGJ1JdIK4WFhvHffI9yxaCHT7ptLZU0N2Rk9+OvNc5l82NSlHXn7KCwra3y9YtsWzrrjxsbXN7/wDACzJp7HyzfPbdxeU1fH9c/9kdfuuBe73Ry527NrGk9d+xuuXDCfyLBw/nrzXKIjW76eLT4ypJ85+0HE1452adIHPTC6a1N71bvMAV3VNVZXIiI9u5lL2Ir42n9bWKmvsBDef98c7DV4MPzpT14v/KGWcXuFh8FJQ+Gb1eDyzU2mRcQLCXEwpL/VVUiwOvPMlrdfdBFER8Ozz8I333gdxpoF7wtxMXCiVlgSsUxkhHmdWAt7iBX69ze7sJ9/3utDqJval37YDVtyrK5CJLSEOeBHI3RbROlYf/tb821uN+TnwxNPmLdUjIyEGu8uWSqMfW3dZthbYHUVIqHBbocxwyAlyepKJNjZ7ccfqHXqqfDVV94d3quvkqMbOgBSEq2uQiT42YATBymIpfO0tCZ1w6NXL3jmGa8PrZZxR3C7YcUGKC47/r4i4p1h/c3bIop0hvvua77NZoPERMjOhkmTIMz7MdEK447iOhjIJQpkEZ8bkAX9tHiKBA91U3eUMAeMHgbJCVZXIhJc+vVWEEvQUcu4o7ncsGI9lJRbXYlI4OvTEwb1Pf5+Ir7Q1jnDNht8+qlXp1IYdwYFskj7ZfYwl7oU6SytGUHdoGFtard3iz+pm7ozNHRZJ6nLWsQrvdIVxGKNY42gPvzRTloOs7OEhZmBvGI9lFZYXY1I4OjVHYZmW12FhKJFTW/JimGYt1K02WDuXHPlLR9RN3Vnq3fB8vVQpkAWOa7sTOifaXUVIoc0dF1/+SWMHeu7w/rsSNI64WEwZrhu8SZyLDYbDB+gIJaQoTC2QkMga6UukeYaxlj07G51JSKdRmFslYZA7pFmdSUi/iMywrzpQ9dkqysRObbWjrJu7eF0zdgPbM+FbblWVyFirbgYGD0coiOtrkTE5HA039YQmS2Fsc0GLpdXp1IY+4t9BbB+C3j0zyEhqEuSeT/icE3wED/SMFjr8JhsCOGWorMd84wVxv6kuAxWbYT6eqsrEek8GWnmYC27rpqJn2nrz6TCOIhU18Kq76GiyupKRDpev14woI/VVYi0LNeLy4eZ3s0AUBj7I7cb1m+FvANWVyLSMWw2GJINvXULRBHQaGr/5HDAiYPVYpDgFBMFp5yoIJbAYrebKyl+/XXz9zZsMG8qcfbZXh9eoyX8Wb9ekBALazZ7PUJPxK9kpJlLW7bjJuwiljlaR3JZGSxd2q7pTmoZ+7vUFBg7EpK0YpcEMIcDThgIIwYpiCWwtRS4K1ce/b3WHlbXjAOEYcAPu835yPonk0CSGG+GcGy01ZWItM1998Hvf2/++Vjzixve79EDdu/26lT6iBoobDbo19tsKa/botHWEhj69jLXl9a0JQlURzZ+jtUYmjrV69MojANNQpzZbb0912wpq5Es/igywuyW1rKWEsiSkg5NVcrNNRtF3bpB5GGrxNntkJwMZ50F997r9anUTR3ISsvNVnJVjdWViBySmmIGcUS41ZWI+E4H3TqxgcI40LndsCUHcvdZXYmEOrsNBvaFrB5WVyLiey+/bIbxlCnQtavPD68wDhZFJbBuK9TWWV2JhKLUFBjcT4O0RLykMA4m9S7YvAP27Le6EgkVMdEwuC+kdbG6EhHfstvNxxdfmN3SLd3B6UjtuGuTBnAFk/AwGD4Q0tNg8w8acS0dx2E3R/f36amR0hK8Dm+rdnC7VWEcjLomw2mjYO9+2LoT6pxWVyTBJD0VBvWFKN13WIJY795mSzcqqunrDqJu6mDndkPOXnMalJe39hIBID7WvLlDSqLVlYgEHYVxqKhzmnOTd+drBS9pm/Aw6J9l3tihA1sGIn6putp8jow0rxvv3g2LFjXfLyIC7rjD69MojENNZTVszYH9RVZXIoGgVzoMyNKcYQlNH30E559vhvC6dTBoECxbBuPGtfzB9P334dxzvTqVRl6EmrgYGDUUfjTCXDNY5Eg2G/TsBuNGw7D+CmIJXW+/bfYknnuuGcRHMoxDD4B//cvrU2kAV6hKSTSX1cw7YC4aUlNrdUViNYcDenWHrJ4QrcFZInz77aGFPlry29+azytWwKefwv/+5/WpFMahLj0VuneF/ELI3Qsl5VZXJJ0tPBwyM8yHWsEih+w7uLLhgAEtv//QQ+bz+++bYZyT4/WpFMZifvJLTzUfZRWwc6/ZYtZwguAWFWnOE+7VvXULGoiEmpIS89njObStXz946qmm+zVMf6ryfm0HhbE01XDv2UF9Ydc+2JUHznqrqxJfiosxb22YnmauJy0iLUtKgsJC+O47OOccc1v37nDddU33W73afE70ftqfRlPLsbk9kFdg3oiivNLqaqQ9khLMEE5L0RQlkdY491z45BPzxhArVpgLfxwpPx9Gj4a8PHOU9dKlXp1KLWM5NocdenY3H8WlsHMfFBTqPsqBIjoSMtIgo5vZIhaR1vvxj80wLioyA/e228z7FiclwZ498PXX8PjjZne2zWbu7yW1jKXtamphd5456Ev3UvY/4WHmoLyMbpCcoFawiLfq6uDEE2HrVnMMTUv/lxoitF8/cy5ytHd3LlMYS/tUVEH+ATOYK6utriZ02W2Q2gV6pJm3M9TNG0R8Y+tWmDQJcnObbrfZDgVx797wwQctz0VuJYWx+E5l9aFg1h2jOkdKotkN3T3VbBGLiO+VlcGjj8LixfDDD4e29+0Ll1wCt94KycntOoXCWDpGVY0ZyvsPQJkGfvmMzQZJ8WYrOCMVoqOsrkgktFRVmeGcmAixsT47rMJYOl5N7cFgLjTnMXv0I9dqNiAhHrokmY/kBM0JFglCCmPpXB6P2VIuLTdX+yot1/2Wj5QQZ3Y/d0mC5ER1P4uEAIWxWK+6tmk4V1SG1tSpuBgzeFOSzBDWkpQiIUdhLP7H5Yayw8O5CmoDvPVsw7y+Gxtjhm9sDMRFm38OV/iKhDqFsQQGjwdq6qC6xrwGXVNntqhras3nej9ZstNhN4M2Nvqw0I2BmGjzPRGRFiiMJTi4XE3DuabW3Ob2mEF+zGd3y4PKHHZzsJTDAWEHn8PDzEdEuPkID4eIMPM5Jsq8+YIW2RCRNlIYi4A5ed9zMKBtdjOIFaoi0kkUxiIiIhbTRSwRERGLKYxFREQspjAWERGxmMJYRETEYgpjERERiymMRURELKYwFrHYzp07sdlsrFmzxupSRMQiCmMRL8yePRubzca1117b7L3rrrsOm83G7NmzO78wEQlICmMRL/Xq1YvFixdTU1PTuK22tpZXX32V3r17W1iZiAQahbGIl0aNGkWvXr1YsmRJ47YlS5bQu3dvRo4c2bjtgw8+4PTTTycpKYkuXbowdepUduzYccxjb9iwgfPPP5+4uDi6devGFVdcQWFhYYf9XUTEWgpjkXaYM2cOixYtanz90ksvceWVVzbZp6qqiptvvpkVK1bw6aefYrfbmT59Oh6Pp8VjlpaWMmHCBEaOHMmKFSv44IMP2L9/PzNmzOjQv4uIWCfM6gJEAtnPfvYz5s6dS25uLgDLli1j8eLFLF26tHGfiy66qMnXvPTSS6SmprJx40aGDRvW7JhPP/00I0eO5MEHH2zyNb169WLr1q0MGDCgY/4yImIZhbFIO6SmpjJlyhRefvllDMNgypQpdO3atck+27Zt45577uHbb7+lsLCwsUW8a9euFsN47dq1fP7558TFxTV7b8eOHQpjkSCkMBZppzlz5nD99dcD8MwzzzR7f9q0aWRmZvLCCy+QkZGBx+Nh2LBhOJ3OFo9XWVnJtGnTmD9/frP30tPTfVu8iPgFhbFIO5133nk4nU5sNhuTJk1q8l5RURFbtmzhhRdeYNy4cQB89dVXxzzeqFGjeOutt8jKyiIsTP9FRUKBBnCJtJPD4WDTpk1s3LgRh8PR5L3k5GS6dOnC888/z/bt2/nss8+4+eabj3m86667juLiYi677DKWL1/Ojh07+PDDD7nyyitxu90d+VcREYsojEV8ICEhgYSEhGbb7XY7ixcvZuXKlQwbNoybbrqJRx999JjHysjIYNmyZbjdbs4991yGDx/OjTfeSFJSEna7/suKBCObYRiG1UWIiIiEMn3MFhERsZjCWERExGIKYxEREYspjEVERCymMBYREbGYwlhERMRiCmMRERGLKYxFREQspjAWERGxmMJYRETEYgpjERERiymMRURELKYwFhERsZjCWERExGIKYxEREYspjEVERCymMBYREbGYwlhERMRiCmMRERGLKYxFREQspjAWERGxmMJYRETEYgpjERERiymMRURELKYwFhERsZjCWERExGIKYxEREYv9f0Cd+5vzpMgZAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Plot saved to: /content/drive/MyDrive/Nganya Project/Plots/Gender Distribution.png\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Payment channel analysis by time period\n",
        "payment_channel_by_time_period = week_data.groupby(['Time Period', 'Payment Channel']).size().unstack()\n",
        "# Define the desired order of time periods\n",
        "time_period_order = ['Early Morning', 'Morning', 'Late Morning', 'Early Afternoon', 'Late Afternoon', 'Evening', 'Night']\n",
        "\n",
        "# Reindex the data to match the desired order\n",
        "transactions_per_time_period = transactions_per_time_period.reindex(time_period_order)\n",
        "\n",
        "# Create the bar chart with custom colors and legend outside\n",
        "ax = payment_channel_by_time_period.plot(kind='bar', stacked=True, title=\"Payment Channel Distribution by Time Period\", color={'Airtel Money': 'red', 'Cash': 'orange', 'M-Pesa': 'green','T-Kash':'blue'})\n",
        "# Set the x-axis tick labels to the desired order\n",
        "ax.set_xticklabels(time_period_order, rotation=45, ha='right')\n",
        "\n",
        "# Move the legend outside the plot\n",
        "plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left')\n",
        "\n",
        "# Add numbers inside the bars\n",
        "for p in ax.patches:\n",
        "    width, height = p.get_width(), p.get_height()\n",
        "    x, y = p.get_xy()\n",
        "    ax.text(x + width/2, y + height/2, int(height), ha='center', va='center', color='white')\n",
        "\n",
        "# Define the path to the folder in your Google Drive\n",
        "google_drive_folder = '/content/drive/MyDrive/Nganya Project/Plots'\n",
        "\n",
        "# File name with extension\n",
        "file_name = 'Payment Channel Distribution by Time Period'\n",
        "\n",
        "# Create the full file path\n",
        "full_file_path = os.path.join(google_drive_folder, file_name)\n",
        "\n",
        "# Check if the folder exists and create it if necessary\n",
        "if not os.path.exists(google_drive_folder):\n",
        "    os.makedirs(google_drive_folder)\n",
        "\n",
        "# Add the tag horizontally on the right side of the graph\n",
        "plt.gcf().text(0.92, 0.4, 'GitHub:@LexMainye', fontsize=12, color='red',\n",
        "               fontweight='bold', ha='left', va='center', rotation=90)\n",
        "\n",
        "# Save the plot to the Google Drive folder\n",
        "plt.savefig(full_file_path, dpi=300, bbox_inches='tight')\n",
        "plt.show()\n",
        "plt.close()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 544
        },
        "id": "yqbdQ2m7o_yX",
        "outputId": "6d13931a-a2ed-438d-84bf-ebf63a8766e5"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate average fare per time period\n",
        "avg_fare_per_time_period = week_data.groupby('Time Period')['Amount (KES)'].mean()\n",
        "\n",
        "ax = avg_fare_per_time_period.plot(kind='bar', title=\"Average Fare per Time Period\")\n",
        "\n",
        "# Set the x-axis tick labels to the desired order\n",
        "ax.set_xticklabels(time_period_order, rotation=45, ha='right')\n",
        "\n",
        "# Add numbers on top of bars with reduced distance\n",
        "for p in ax.patches:\n",
        "    height = p.get_height()\n",
        "    ax.text(p.get_x() + p.get_width() / 2, height + 1, int(height), ha='center', va='bottom')\n",
        "\n",
        "# Define the path to the folder in your Google Drive\n",
        "google_drive_folder = '/content/drive/MyDrive/Nganya Project/Plots'\n",
        "\n",
        "# File name with extension\n",
        "file_name = 'Average Fare per Time Period'\n",
        "\n",
        "# Create the full file path\n",
        "full_file_path = os.path.join(google_drive_folder, file_name)\n",
        "\n",
        "# Check if the folder exists and create it if necessary\n",
        "if not os.path.exists(google_drive_folder):\n",
        "    os.makedirs(google_drive_folder)\n",
        "\n",
        "\n",
        "# Add the tag horizontally on the right side of the graph\n",
        "plt.gcf().text(0.92, 0.4, 'GitHub:@LexMainye', fontsize=12, color='red',\n",
        "               fontweight='bold', ha='left', va='center', rotation=90)\n",
        "\n",
        "# Save the plot to the Google Drive folder\n",
        "plt.savefig(full_file_path, dpi=300, bbox_inches='tight')\n",
        "plt.show()\n",
        "plt.close()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 544
        },
        "id": "yOdeI9CYwBg9",
        "outputId": "47f90a0e-0514-44a8-ed8f-d9650fa51e94"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from statsmodels.tsa.holtwinters import ExponentialSmoothing\n",
        "\n",
        "# Aggregate data for forecasting\n",
        "daily_transactions = week_data.groupby('Date').size()\n",
        "\n",
        "# Check if you have enough data for two full seasonal cycles\n",
        "if len(daily_transactions) < 2 * 7:  # 2 cycles * 7 days/cycle\n",
        "    print(\"Insufficient data for reliable seasonal forecasting. Consider collecting more data.\")\n",
        "    # You can either stop here or proceed with caution, but results may be unreliable\n",
        "else:\n",
        "    # Fit a Holt-Winters model\n",
        "    model = ExponentialSmoothing(daily_transactions, trend='add', seasonal='add', seasonal_periods=7).fit()\n",
        "\n",
        "    # Forecast the next 7 days\n",
        "    forecast = model.forecast(steps=7)\n",
        "\n",
        "    forecast.plot(title=\"7-Day Transaction Forecast\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8l5y85duwsjC",
        "outputId": "51e871d3-4b0a-4dfc-cbd0-06669ed27838"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Insufficient data for reliable seasonal forecasting. Consider collecting more data.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate payment channel preferences by gender\n",
        "payment_channel_by_gender = week_data.groupby(['Gender', 'Payment Channel']).size().unstack()\n",
        "\n",
        "# Plot payment channel preferences by gender with customizations\n",
        "ax = payment_channel_by_gender.plot(kind='bar', stacked=True, title=\"Payment Channel by Gender(Weekly)\",\n",
        "                                   color={'Airtel Money': 'red', 'Cash': 'orange', 'M-Pesa': 'green', 'T-Kash': 'blue'})\n",
        "\n",
        "# Move the legend outside the plot\n",
        "plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left')\n",
        "\n",
        "# Add numbers inside the stacked bars\n",
        "for p in ax.patches:\n",
        "    width, height = p.get_width(), p.get_height()\n",
        "    x, y = p.get_xy()\n",
        "    ax.text(x + width/2, y + height/2, int(height), ha='center', va='center', color='white')\n",
        "\n",
        "# Define the path to the folder in your Google Drive\n",
        "google_drive_folder = '/content/drive/MyDrive/Nganya Project/Plots'\n",
        "\n",
        "# File name with extension\n",
        "file_name = 'Payment Channel by Gender(Weekly)'\n",
        "\n",
        "# Create the full file path\n",
        "full_file_path = os.path.join(google_drive_folder, file_name)\n",
        "\n",
        "# Check if the folder exists and create it if necessary\n",
        "if not os.path.exists(google_drive_folder):\n",
        "    os.makedirs(google_drive_folder)\n",
        "\n",
        "# Add the tag horizontally on the right side of the graph\n",
        "plt.gcf().text(0.92, 0.4, 'GitHub:@LexMainye', fontsize=12, color='red',\n",
        "               fontweight='bold', ha='left', va='center', rotation=90)\n",
        "\n",
        "# Save the plot to the Google Drive folder\n",
        "plt.savefig(full_file_path, dpi=300, bbox_inches='tight')\n",
        "plt.show()\n",
        "plt.close()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 508
        },
        "id": "-ZrAaBG9xV5A",
        "outputId": "6c174fe5-e6dd-485d-99b9-a5155cb1a63c"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Compare revenue between fare types for peak and off-peak periods\n",
        "# Define peak_periods\n",
        "peak_periods = ['Morning', 'Late Morning', 'Early Afternoon', 'Late Afternoon']  # Example peak periods\n",
        "off_peak_periods = ['Early Morning', 'Evening', 'Night']  # Example off-peak periods\n",
        "\n",
        "peak_fare_data = week_data[week_data['Time Period'].isin(peak_periods)]\n",
        "off_peak_fare_data = week_data[week_data['Time Period'].isin(off_peak_periods)]\n",
        "\n",
        "# Average fare for peak and off-peak periods\n",
        "peak_avg_fare = peak_fare_data['Amount (KES)'].mean()\n",
        "off_peak_avg_fare = off_peak_fare_data['Amount (KES)'].mean()\n",
        "\n",
        "# Create bar plot to compare average fare\n",
        "fare_comparison = {'Peak Periods': peak_avg_fare, 'Off-Peak Periods': off_peak_avg_fare}\n",
        "ax = pd.Series(fare_comparison).plot(kind='bar', title=\"Weekly Average Fare Comparison\", color=['green', 'orange'])\n",
        "plt.ylabel('Average Fare (KES)')\n",
        "\n",
        "# Add numbers on top of bars, rounded to the nearest integer and lowered\n",
        "for p in ax.patches:\n",
        "    height = p.get_height()\n",
        "    ax.text(p.get_x() + p.get_width() / 2, height + 0.5,  # Reduced vertical offset\n",
        "            int(round(height)),\n",
        "            ha='center', va='bottom')\n",
        "\n",
        "# Define the path to the folder in your Google Drive\n",
        "google_drive_folder = '/content/drive/MyDrive/Nganya Project/Plots'\n",
        "\n",
        "# File name with extension\n",
        "file_name = 'Weekly Average Fare Comparison'\n",
        "\n",
        "# Create the full file path\n",
        "full_file_path = os.path.join(google_drive_folder, file_name)\n",
        "\n",
        "# Check if the folder exists and create it if necessary\n",
        "if not os.path.exists(google_drive_folder):\n",
        "    os.makedirs(google_drive_folder)\n",
        "\n",
        "# Add the tag horizontally on the right side of the graph\n",
        "plt.gcf().text(0.92, 0.4, 'GitHub:@LexMainye', fontsize=12, color='red',\n",
        "               fontweight='bold', ha='left', va='center', rotation=90)\n",
        "\n",
        "# Save the plot to the Google Drive folder\n",
        "plt.savefig(full_file_path, dpi=300, bbox_inches='tight')\n",
        "plt.show()\n",
        "plt.close()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 551
        },
        "id": "b_tzZpuHyt66",
        "outputId": "87dd7429-8d9e-4d5e-9a3e-147722eb7ef7"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Assuming you have vehicle data, we can compare transaction load to vehicle availability\n",
        "# For simplicity, let's assume that each time period corresponds to 1 vehicle being available\n",
        "\n",
        "vehicle_data = {\n",
        "    'Early Morning': 1,\n",
        "    'Morning': 1,\n",
        "    'Late Morning': 1,\n",
        "    'Early Afternoon': 1,\n",
        "    'Late Afternoon': 1,\n",
        "    'Evening': 1,\n",
        "    'Night': 1\n",
        "}\n",
        "\n",
        "# Calculate transaction load by period\n",
        "transaction_load_by_period = week_data.groupby('Time Period').size()\n",
        "\n",
        "# Combine transaction load and vehicle availability into a DataFrame\n",
        "vehicle_data_df = pd.DataFrame({\n",
        "    'Transaction Load': transaction_load_by_period,\n",
        "    'Vehicles Available': pd.Series(vehicle_data)\n",
        "})\n",
        "\n",
        "# Plot the data with numbers on bars\n",
        "ax = vehicle_data_df.plot(kind='bar', title=\"Transaction Load vs. Vehicle Availability\", figsize=(10,6))\n",
        "plt.ylabel('Count')\n",
        "\n",
        "# Add numbers on top of bars\n",
        "for p in ax.patches:\n",
        "    height = p.get_height()\n",
        "    ax.text(p.get_x() + p.get_width() / 2, height + 1, int(height), ha='center', va='bottom')\n",
        "\n",
        "# Define the path to the folder in your Google Drive\n",
        "google_drive_folder = '/content/drive/MyDrive/Nganya Project/Plots'\n",
        "# File name with extension\n",
        "file_name = 'Transaction Load vs. Vehicle Availability.png'\n",
        "\n",
        "# Create the full file path\n",
        "full_file_path = os.path.join(google_drive_folder, file_name)\n",
        "\n",
        "# Check if the folder exists and create it if necessary\n",
        "if not os.path.exists(google_drive_folder):\n",
        "    os.makedirs(google_drive_folder)\n",
        "\n",
        "\n",
        "# Add the tag horizontally on the right side of the graph\n",
        "plt.gcf().text(0.92, 0.4, 'GitHub:@LexMainye', fontsize=12, color='red',\n",
        "               fontweight='bold', ha='left', va='center', rotation=90)\n",
        "\n",
        "# Save the plot to the Google Drive folder\n",
        "plt.savefig(full_file_path, dpi=300, bbox_inches='tight')\n",
        "plt.show()\n",
        "plt.close()\n",
        "\n",
        "print(f\"Plot saved to: {full_file_path}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 655
        },
        "id": "W9ahw6F70VBB",
        "outputId": "174159b9-3e9f-461d-e206-cab2d0f259b0"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Plot saved to: /content/drive/MyDrive/Nganya Project/Plots/Transaction Load vs. Vehicle Availability.png\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Aggregate data for trend analysis\n",
        "daily_transactions = week_data.groupby('Date').size()\n",
        "\n",
        "# Plot the trend of daily transactions\n",
        "ax = daily_transactions.plot(kind='line', title=\"Transaction Volume Trend\", color='blue', marker='o', markersize=10)  # Increased marker size\n",
        "plt.ylabel('Number of Transactions')\n",
        "\n",
        "# Define the path to the folder in your Google Drive\n",
        "google_drive_folder = '/content/drive/MyDrive/Nganya Project/Plots'\n",
        "\n",
        "# File name with extension\n",
        "file_name = 'Transaction Volume Trend.png'\n",
        "\n",
        "# Create the full file path\n",
        "full_file_path = os.path.join(google_drive_folder, file_name)\n",
        "\n",
        "# Check if the folder exists and create it if necessary\n",
        "if not os.path.exists(google_drive_folder):\n",
        "    os.makedirs(google_drive_folder)\n",
        "\n",
        "# Add the tag horizontally on the right side of the graph\n",
        "plt.gcf().text(0.92, 0.4, 'GitHub:@LexMainye', fontsize=12, color='red',\n",
        "               fontweight='bold', ha='left', va='center', rotation=90)\n",
        "\n",
        "# Save the plot to the Google Drive folder\n",
        "plt.savefig(full_file_path, dpi=300, bbox_inches='tight')\n",
        "plt.show()\n",
        "plt.close()\n",
        "\n",
        "print(f\"Plot saved to: {full_file_path}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 521
        },
        "id": "-HlGvnDe0uQs",
        "outputId": "27119a32-f93e-4c51-a1eb-61fc3b91d664"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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ztIiIiIgshIkWERERkYUw0SIiIiKyECZaRERERBbCRIuIiIjIQphoEREREVkI+2hB9va6fv06fHx8jNpWhIiIiPSjlMLdu3dRoEABuLjY5tgREy0A169fR+HChfUOg4iIiExw5cqVVNts2RImWkDS9iRXrlyBr6+vztEQERFRZsTGxqJw4cKpNoe3NUy0gKRyoa+vLxMtIiIiO2PL035ss6BJRERE5ACYaBERERFZCBMtIiIiIgthokVERERkIUy0iIiIiCyEiRYRERGRhTDRIiIiIrujFHD7tt5RPB8TLSIiIrIb0dHAxIlAqVJAiRJ6R/N8bFhKREREdmHtWqBdO+DBA70jyTwmWkRERGTz1q4FQkOlZKiU3tFkHkuHREREZNOio2UkSykgMVHvaIzDES0iIiKyaTNnSrnQnkayNBzRIiIiIpulFDB5st5RmI4jWkRERGSzbt8Gzp3TOwrTcUSLiIiIbNa9e3pHkDVMtIiIiMhmeXvrHUHWMNEiIiIim5UrF1CyJGAw6B2JaZhoERERkc0yGIB+/fSOwnRMtIiIiMimde0KZM8OuNhh1mKHIRMREZEz8fcHliyR0S17S7bsLFwiIiJyRs2aAStXAm5uekdiHCZaREREZBeaNQNatZLL/v66hpJpTLSIiIjILiQkAJs3y+W//gIuXNA1nExhZ3giIiKyCwcOSKd4X1/gpZeAhw/1juj5OKJFREREdmHtWjlv3Nh+5mox0SIiIiK7oCVazZrpG4cxmGgRERGRzYuJAXbtkstMtIiIiIjMaMMGmQxfpgxQrJje0WQeEy0iIiKyefZYNgSYaBEREZGNU4qJFhEREZFFnDkDXLoEuLsDDRroHY1xmGgRERGRTdNGs+rVA3Lk0DcWYzHRIiIiIpu2Zo2cN2+ubxymYKJFRERENuvRo+Rtd+xtfhbARIuIiIhs2PbtstVOgQJAhQp6R2M8JlpERERks7T5WSEhgMGgbyymYKJFRERENste2zpomGgRERGRTbp2DTh6VEaymjbVOxrTMNEiIiIim7RunZy/+CKQK5e+sZiKiRYRERHZJHsvGwJMtIiIiMgGJSQAYWFymYkWERERkRnt3w9ERQF+fkCtWnpHYzomWkRERGRztG7wTZoA2bLpG0tWMNEiIiIim+MI87MAJlpERERkY6Kjgd275TITLSIiIiIz2rBBJsO/8AJQpIje0WQNEy0iIiKyKY5SNgSYaBEREZENUSp5Inzz5vrGYg5MtIiIiMhmnDoFXLkCeHgA9evrHU3WMdEiIiIim6GVDevXB7Jn1zcWc2CiRURERDbDkeZnAUy0iIiIyEY8fAhs2SKXmWgRERERmdG2bZJsFSwIlC+vdzTmwUSLiIiIbELKsqHBoG8s5sJEi4iIiGyCo83PAnROtKZMmYJKlSrB19cXvr6+CA4OxurVq9PcTymFFi1awGAwYPny5aluu3z5MkJDQ5E9e3bkzZsXQ4cOxZMnT6z0HRAREZE5XL0KHD8OuLjIRtKOQtf9sAsVKoRvvvkGpUqVglIKM2fOROvWrXHw4EGUT1GcnTBhAgzpjCEmJCQgNDQUgYGB2LlzJ27cuIEuXbrAzc0NX3/9tTW/FSIiIsqCdevk/MUXgYAAfWMxJ11HtF555RW0bNkSpUqVQunSpfHVV1/B29sb//zzT9J9Dh06hO+//x7Tpk1L8/h169bhxIkTmDNnDqpUqYIWLVrgiy++wI8//ojHjx9b81shIiKiLHCkbvAp2cwcrYSEBCxYsAD3799HcHAwAODBgwfo2LEjfvzxRwQGBqZ5zK5du1CxYkXky5cv6bpmzZohNjYWx48fz/C14uLiEBsbm+pERERE+khIANavl8uOND8LsIFE6+jRo/D29oaHhwd69eqFZcuWoVy5cgCAgQMHonbt2mjdunW6j42IiEiVZAFI+joiIiLD1xwzZgz8/PySToULFzbTd0NERETG2rsXuHMH8PeX0qEj0XWOFgCUKVMGhw4dQkxMDBYvXoyuXbtiy5YtOHv2LDZu3IiDBw+a/TWHDx+OQYMGJX0dGxvLZItshlLA7dvAvXuAtzeQK5fjLHMmIkqPttqwSRMgm+6ZiXnp/u24u7sjKCgIAFC9enXs3bsXEydOhJeXF86dOwd/f/9U92/Xrh3q1auHzZs3IzAwEHv27El1e2RkJACkW2rUeHh4wMPDw7zfCFEWRUcDM2cCkycD584lX1+yJNCvH9C1q3zaIyJyNI7Y1kGje+nwaYmJiYiLi8OwYcNw5MgRHDp0KOkEAOPHj8f06dMBAMHBwTh69Chu3ryZ9PiwsDD4+vomlR+J7MHatUChQsDAgcD586lvO39eri9UKPmfERGRo7hzB9i9Wy47YqKl64jW8OHD0aJFCxQpUgR3797FvHnzsHnzZqxduxaBgYHpjkoVKVIExYsXBwCEhISgXLlyePvtt/Htt98iIiICn376Kfr06cMRK7Iba9cCoaFSMlQq7e3adQ8fyv1WrnTMf0ZE5JzWrwcSE4Fy5QBHnMWja6J18+ZNdOnSBTdu3ICfnx8qVaqEtWvXomnTppl6vKurK1asWIEPPvgAwcHByJEjB7p27YrPP//cwpETmUd0NNCunSRTiYnPvm9iojTya9dOGvuxjEhEjsCRy4aAzonW1KlTjbq/SufjftGiRbFq1SpzhURkVTNnAg8epD+SlZ7ERLn/rFlA//6WjY2IyNKUcvxEy+bmaBE5C6Vk4rspJk3KfHJGRGSrTp6UEXpPT6B+fb2jsQwmWkQ6uX1bVhcamzApJY+LirJMXERE1qJ1g2/QAPDy0jcWS2GiRaSTe/ey9vi7d80TBxGRXhy9bAgw0SLSjbd31h7v42OeOIiI9PDwIbB1q1xmokVEZpcrlzQjNbbru8Egj3Ok3e2JyPls3Qo8eiQ9AsuW1Tsay2GiRaQTg0E6vpuif39uy0NE9i1l2dCR/58x0SLSUdeuQPbs0h8rM1xc5P5dulg2LiIiS9Mmwjdvrm8clsZEi0hH/v7AkiXyae55yZaLi9xv6VI2KyUi+3blirR2cHEBGjfWOxrLYqJFpLNmzWRbHTe3jO9jMMjS51WrgJAQ68VGRGQJWtmwVi0gZ059Y7E0JlpENqBZM+kjA6Q/WvV//wdcu8Yki4gcgzO0ddAw0SKyAZGRwIYNcnnXLuDWLeDCheROyblyAX5++sVHRGQuT57IRtIAEy0ispL584GEBKBmTeCFFySxKlYMaNNGbl+xQs/oiIjMZ88eIDpaSoYvvqh3NJbHRIvIBsyeLedPryZs1UrOt25lJ3gicgxa2bBpU8DVVd9YrIGJFpHOjh8HDhwAsmUD3nwz9W2lSskpPh4IC9MnPiIic3Km+VkAEy0i3WmjWaGhQO7caW8PDZVzlg+JyN5FRQF798plZ1ncw0SLSEcJCcCcOXL57bfTv4+WaK1aBSQmWicuIiJLWL9e/o+VLy9b7zgDJlpEOtq0Sdo2+Psnz8d6Wv36sgF1ZKSUGImI7JWzdINPiYkWkY60smGHDoCHR/r3cXdPHmJfudI6cRERmZtSzjc/C2CiRaSb+/dl+x0g47KhhvO0iMjeHT8OXL8uu1zUq6d3NNbDRItIJ8uWSbJVsiQQHPzs+7ZsKef79gEREZaPjYjI3LTRrAYNAE9PfWOxJiZaRDqZNUvO335b9jJ8lsBAoEYNubx6tWXjIiKyBGcsGwJMtIh0ce1a8pY7zysbalg+JCJ79eCBNF4GnGsiPMBEi0gX8+bJEue6dYESJTL3GC3RCgsDHj+2XGxEROa2ZQsQFwcUKQKUKaN3NNbFRIvIypRKXTbMrOrVgXz5ZCuebdssExsRkSWkLBs+b6qEo2GiRWRlhw8Dx45JO4c33sj841xckifFs3xIRPbEWednAUy0iKxOG8165RXZvd4YWvmQ/bSIyF5cugScOiUbSDdurHc01sdEi8iKnjyR+VkA0KWL8Y9v2hRwcwPCw+VERGTrtNGsl16SXTCcDRMtIisKC5OtdHLnNm3lja+vbMkDcFSLrEcp4NYt4OJFOVdK74jInjhz2RBgokVkVdqWO2+9JSNTpmCbB7KW6Ghg4kSgVCkgTx6geHE5L1VKro+O1jtCsnVPniS3snHWRMugFD+bxMbGws/PDzExMfD19dU7HHJQsbGyavDRI2Dv3uQGpMYKDwdKl5ZE7fZtwMfHvHESATIK0a6d9D8CUo9iaavGsmeXbaSc9QBKz7djh7SxCQgAbt6UeVrmZA/Hb45oEVnJ4sWSZL3wgrRqMFWpUnKKj5dSJJG5rV0rI6cPH0qC9fTHce26hw/lflppiOhp2u9G06bmT7LsBRMtIivRyoZdumS9jwzLh2Qp0dEykqWUNNV9lsREuV+7diwjUvrWrJFzZ+sGnxITLSIruHQJ2LxZLnfqlPXn0xKtVauefzAkMsbMmVIuzOzvVWKi3F9rW0KkuXUL2LdPLoeE6BuLnphoEVnB3Lly/vLLsgVFVtWvD3h7ywrGAwey/nxEgIxOTZ5s2mMnTeJqREpt/Xr5nahYEShQQO9o9MNEi8jCTN1y51nc3ZM/IbLNA5nL7dvAuXPGJ0xKyeOioiwTF9knZ2/roDFLohXN4jxRhvbtA06fBry8ZC6LuXCeFpnbvXtZe/zdu+aJg+yfUky0NEYnWmPHjsXChQuTvm7fvj1y5cqFggUL4vDhw2YNjsgRaKNZr70mDUfNRdv3cN8+ICLCfM9LzsvbO2uPZ6sR0hw9Cty4IS1A6tbVOxp9GZ1o/fzzzyhcuDAAICwsDGFhYVi9ejVatGiBoUOHmj1AInv2+DEwf75cNlfZUBMYmNyLa/Vq8z43OadcuYCSJY1fFWswyOMCAiwTF9kfbTSrYUPA01PXUHRndKIVERGRlGitWLEC7du3R0hICD766CPs3bvX7AES2bM1a2TeS2Ag0KSJ+Z+f5UMyJ4MB6NfPtMf275/1tiXkOFg2TGZ0opUzZ05cuXIFALBmzRo0+e/ooZRCQkKCeaMjsnNa2bBjRyBbNvM/v5ZohYXJ6BlRVnXtKuUel0weHVxc5P6mbJJOjun+fWDbNrnMRMuERKtt27bo2LEjmjZtitu3b6NFixYAgIMHDyIoKMjsARLZqzt3gL//lsuWOghVry7b+ty9m/yPjSgr/P1lWx2D4fnJlouL3G/pUnkcEQBs2SIf/IoWle3CnJ3Ridb48ePRt29flCtXDmFhYfD+b/bkjRs30Lt3b7MHSGSvFi2SfzYVKwKVK1vmNVxckifFs3xI5tKsmbQN8fJ69v08PaVprjM3o6S0UnaDZzmZm0oDsI9NKcn+1KkD7NwJ/O9/wJAhlnudJUuA11+X/Q/PnLHc65Dz+fVX4P33ZQPz+Pjk6z09Zd/OTz4BvvpKv/jINpUpI/+Lli6V1daWZA/Hb5MSrfDwcGzatAk3b95E4lP7NIwcOdJswVmLPfygyL6cOwcEBcmI05Urlu2KHBsL5M4tB8IzZyThIjKHNm2AP/8E/u//gIEDpUTt4yOjWF26SGno/PnMz+cix3fxIlC8uGwgffs24Odn2dezh+O30dNzf/vtN3zwwQfInTs3AgMDYUgxLmgwGOwy0SIyN20D6SZNLL/1hK+vbMmzYYOUewYMsOzrkXOIjU0uAbVvL60fcuWSr19/XVYZXrokCzE44Zk02mrD4GDLJ1n2wujPIV9++SW++uorRERE4NChQzh48GDS6QA3XSOCUsmJlrVWYrHNA5nbihVAXJxMZq5YMfVtXl5A585y+fffrR8b2S62dUjL6ETrzp07eOONNywRC5FD2LlTyik5ckjpxRq0RGvrVm6DQubxxx9y/sYb6U9o7tlTzv/8E7h503pxke2Kj5eNpAGZCE/C6ETrjTfewLp16ywRC5FD0EazXn9dki1rKF1a5mbFx0sphygr7t5N3m0go8/VlSoBNWvK79zMmdaLjWzXP//I707u3EC1anpHYzuMnqMVFBSEESNG4J9//kHFihXh5uaW6vb+/fubLTgie/PoEaBtBWrtBo6hocCECVLyadvWuq9NjiVl2bBSpYzv17MnsGePlA+HDOFSfmenlQ2bNuUCiZSMXnVYvHjxjJ/MYMD58+ezHJS12cOqBbIPixfLCEChQjJR2Jr/bNavl39w+fIB16/zHx2Zrm1bYNkyWW345ZcZ3+/uXSB/fukEvmWLLMog5/Xii7LJ/YwZssOANdjD8dvoEa0LFy5YIg4ih6CVDTt3tn6iU78+4O0NREYCBw4kbzhNZIx7955fNtT4+ABvvSUjWr/9xkTLmf37L7B/v1xmA9vUsnQoUEqB/U6JxL//Sn8hAHj7beu/vrt78j+4lSut//rkGFaskBJ4qVLPLhtqtEnxixfLtlPknMLCZMV15coyyknJTEq0Zs2ahYoVK8LLywteXl6oVKkSZmsf5Ymc1MKFwJMnsv9guXL6xMA2D5RVixbJeUarDZ/24ouSkD16BMyZY9nYyHaxrUPGjE60xo0bhw8++AAtW7bEokWLsGjRIjRv3hy9evXC+PHjjXquKVOmoFKlSvD19YWvry+Cg4Ox+r8x66ioKPTr1w9lypSBl5cXihQpgv79+yMmJibVc1y+fBmhoaHInj078ubNi6FDh+LJkyfGfltEWTZrlpzrMZql0fY93LcPiIjQLw6yT8aUDTUGA/Duu3L5t99kVIOci1KA1oyAiVY6lJGKFSumZs6cmeb6GTNmqGLFihn1XH/99ZdauXKlOnPmjDp9+rT65JNPlJubmzp27Jg6evSoatu2rfrrr7/U2bNn1YYNG1SpUqVUu3btkh7/5MkTVaFCBdWkSRN18OBBtWrVKpU7d241fPhwo+KIiYlRAFRMTIxRjyPSnDypFKCUq6tSkZH6xlKjhsQybZq+cZD9mT9ffneCgpRKTMz846KilPL0lMfu3m25+Mg2HTokP/vs2ZV69Mi6r20Px2+jEy0PDw8VHh6e5vozZ84oDw+PLAeUM2dO9fvvv6d726JFi5S7u7uKj49XSim1atUq5eLioiIiIpLuM2XKFOXr66vi4uIy/Zr28IMi2/bJJ/KPplUrvSNRatQoiaVtW70jIXvTtq387hj5WVUppVTnzvLYd981f1xk28aOlZ99aKj1X9sejt9Glw6DgoKwSCvip7Bw4UKUysJutgkJCViwYAHu37+P4ODgdO+jLd/Mlk0WS+7atQsVK1ZEvnz5ku7TrFkzxMbG4vjx4xm+VlxcHGJjY1OdiEyVmJg8N0XPsqFGm6cVFgY8fqxvLGQ/7t1LXsxhyuYf2qT4+fO5O4Gz0fbEZDf49Bnd3mH06NF48803sXXrVtSpUwcAsGPHDmzYsCHdBOx5jh49iuDgYDx69Aje3t5YtmwZyqUzk/jWrVv44osv8N577yVdFxERkSrJApD0dcQzJqiMGTMGo0ePNjpWovRs3QpcviwbqL7yit7RyGT8fPmkzcO2bUDjxnpHRPZg5UqZ0F6yJFClivGPr1dPGpyeOQMsWJCceJFju3cP2L5dLnN+VvqMHtFq164ddu/ejdy5c2P58uVYvnw5cufOjT179uC1114zOoAyZcrg0KFD2L17Nz744AN07doVJ06cSHWf2NhYhIaGoly5cvjss8+Mfo2nDR8+HDExMUmnK1euZPk5yXlpk+DfeEM229Wbi0vypHiuPqTMet7ehs+TclI8N5p2Hps3yzZMxYsDQUF6R2ObjB7RAoDq1atjjpnW8bq7uyPov59O9erVsXfvXkycOBG//PILAODu3bto3rw5fHx8sGzZslRb/gQGBmLPnj2pni8yMjLptox4eHjAw8PDLPGTc3vwQPoHAdbfcudZQkOB6dNllMLIxcDkhO7fz1rZUNO1q3ST37MHOHIkc324yL6lbOvALZjSl6kRrZRzmJ6e22TuuU6JiYmIi4tLeq2QkBC4u7vjr7/+gqenZ6r7BgcH4+jRo7iZYuv4sLAw+Pr6plt+JDK3P/+U+SjFigH/VdJtQtOmgJsbEB4uJ6JnWbkSePgQKFECqFrV9OfJmxdo3Vou//abeWIj28b+Wc+XqUQrZ86cScmMv78/cubMmeakXW+M4cOHY+vWrbh48SKOHj2K4cOHY/PmzejUqVNSknX//n1MnToVsbGxiIiIQEREBBISEgAAISEhKFeuHN5++20cPnwYa9euxaeffoo+ffpwxIqsQuvT+/bbtrW3oK9v8nYo7BJPz5PVsmFK2tysOXMkeSPHdf68fJDLlg1o1EjvaGxXpkqHGzduREBAAABg06ZNZnvxmzdvokuXLrhx4wb8/PxQqVIlrF27Fk2bNsXmzZuxe/duAEgqLWouXLiAYsWKwdXVFStWrMAHH3yA4OBg5MiRA127dsXnn39uthiJMhIRkfxpzhZWGz4tNBTYsEHmaQ0YoHc0ZKvu309Oxtu3z/rzNWkCFC0qm6ovXmybfxtkHtr/v9q15cMdpS9TiVaDBg2SLhcvXhyFCxeG4amPPUopoyeVT506NcPbGjZsmKl9FIsWLYpV2uQCIiuaN09aO7z0kuwLZ2tCQ4FBg2RV5N27sgEw0dNWrTJP2VDj4gL06AGMHCmT4ploOS6WDTPH6GJH8eLF8e+//6a5PioqCsWLFzdLUET2QCsb2tIk+JRKl5YEMD5eemoRpcecZUNN9+6ScG3dCpw+bZ7nJNsSHw9s3CiXmWg9m9GJllIqzWgWANy7dy/NZHUiR3X0KHDokEw4N0e5xVK4yTQ9S8qyYVZWGz6tUKHkFiNs9eCYdu2SkfI8ecwzEurIMt3eYdCgQQAAg8GAESNGIHv27Em3JSQkYPfu3ahiSpc7IjukjWa1agXkyqVvLM8SGgpMmCDlocRE25qwT/pbtUpalBQvDlSrZt7nfvddSfBnzgS++gpwdzfv85O+tG7wISH8v/I8mU60Dh48CEBGtI4ePQr3FH817u7uqFy5MoYMGWL+CIlsTEJC8pY7tlo21NSvD3h7S5f4AweAGjX0johsiSXKhprQUCB/fuDGDeCvv4DXXzfv85O+OD8r8zKdaGmrDbt3746JEyfCl0sMyElt2CAHj4CA5PKIrXJ3l0+cS5dKiYiJFmkePLBM2VCTLZvM1fr6a+mpxUTLcdy8KR/cAPn/Qs9m9IDfhAkT8OTJkzTXR0VFcXNmcgpa2bBDB/soh3CeFqVHKxsWKyb7Y1pCjx5yHhYGXLxomdcg69MW11SpIvuq0rMZnWh16NABCxYsSHP9okWL0KFDB7MERWSr7t2T0SHAfpata6Nu+/ZJ7y8iwLJlQ02JEtJXSyngGd18yM44dNkwKgrYudOsS7WNTrR2796Nl19+Oc31DRs2TGowSuSoli6VUYBSpYBatfSOJnMCA5NLhqtX6xsL2YYHD5JHOC1RNkxJ22h62jQgnWII2ZnExOREq3lzfWMxq0uXZPg/b16gXj2gRQvg0SOgfHmgZElg/36Tn9roRCsuLi7d0mF8fDwecr8FcnCzZsn522/b1waqLB9SSqtXJ5cNLT1vr00bWZl7/XrySjWyX4cPyxwtb2/pCO8Qrl2Tb2bNGskklZKTp6fsjH7hApBOJS+zjE60atasiV9//TXN9T///DOqW6rQT2QDrl5NbtDXubO+sRhLS7TCwoDHj/WNhfSnlQ1ff93yHxg8PICuXeUyN5q2f9po1ssv28cc1Uz57DNZ4aSUfPpIqW5dOdf++Zsg06sONV9++SWaNGmCw4cPo3HjxgCADRs2YO/evVi3bp3JgRDZurlz5e+wfn3pO2RPqleXSauRkcC2bcB/f7rkhB4+tF7ZUPPuu8C4cbLK8fp1oEAB67wumZ9Dzs9avVo+cXz0kTRHrFcv+TYt8bp61eSnN3pEq06dOti1axcKFy6MRYsW4e+//0ZQUBCOHDmCeimDI3IgSqUuG9obF5fkSfEsHzq31aulI3zRosCLL1rnNcuWBerUkR5006db5zXJ/O7eBXbskMsOlWhp2wo2aZL2NldXOY+JMfnpTernWqVKFcydOxfHjx/Hvn37MG3aNJSyxV11iczk4EHgxAkpg1hrFMDctPKh1juJnJM1Vhump2dPOZ86VabBkP3ZtEn2OCxZEggK0jsaM9K299i3L+1t2urDLPSxyFLj/EePHiE2NjbVicgRaaNZrVsDfn76xmKqpk1lb8bwcDmR83n4EPj7b7ls7Q8Mb7whfzsXLmRpugvpyCHLhgDQoIGULUaOBMaMSb7+nXdkDzODQSalmcjoROvBgwfo27cv8ubNixw5ciBnzpypTkSOJj4emD9fLtv6ljvP4usr88sAjmo5qzVrrF821GTPDnTqJJc5Kd4+OWyi9cknUq548iR5vhYgG3UqJbd99JHJT290ojV06FBs3LgRU6ZMgYeHB37//XeMHj0aBQoUwCztYz+RA1m3TpYz58lj/9tNsM2Dc7PmasP0aOXDZcuSp8WQfTh3Tk7ZsmVpcMc2VawoTRJz505u7aCd8uQBliwBypUz+emNXnX4999/Y9asWWjYsCG6d++OevXqISgoCEWLFsXcuXPRSfvIQuQgtC13OnaU0ps9Cw0FBg0Ctm6Via0+PnpHRNby8KFs7gzoN8+wShVZAbt/v5TjBw/WJw4ynjaaVaeOg/7faNFC9olatw44c0auK11a5lxkz56lpzZ6RCsqKgolSpQAAPj6+iIqKgoAULduXWzdujVLwRDZmuhoYPlyuWzPZUNN6dLS1T4+3qw7TJAd0MqGRYoANWvqF4c2qvX77zJgQPZBazbrUN3gNQcPyrmXl0zEHTpUTq1bZznJAkxItEqUKIELFy4AAF544QUsWrQIgIx0+fv7ZzkgIluyeDEQFyejxlWr6h2NebB86Jz0Lhtq3npLjl2nTiW3CiDb9vixrDgEHHB+FiDDrOXKAZ9/bpGVQkYnWt27d8fhw4cBAMOGDcOPP/4IT09PDBw4EEOHDjV7gER60sqGXbrY15Y7z6IlWqtWcZm9s9BzteHTfH2BDh3kMifF24edO4F792QbwMqV9Y7GQk6fBkaPBl54QfalGjdOtuYxA4NSWRu8vXTpEvbv34+goCBUqlTJLEFZW2xsLPz8/BATEwNfX1+9wyEbcfGidIA3GIDLl4FChfSOyDweP5a2MffuAXv3Wn6vO9Lf8uXAa68BhQvL3rl6f2jYtUu2lvPykk7xLIbYtuHDgW++ka3HtA+ftsIsx+/XXgPWr5faOpD8B2IwyBY8HTvKUHBAgElPn6U+WgBQtGhRNGrUyG6TLKKMzJkj540aOU6SBcj+ZNrqSbZ5cA62UjbUvPQSUL68jLTNm6d3NPQ8DtvWQbNsGXDrlsyneP992SNKKRny37YN+OADIH9+2Z7njz+MLgUYnWiNHTsWCxcuTPq6ffv2yJUrFwoWLJhUUiSyd/a+5c7zcJ6W83j0yHbKhhqDIXlS/G+/cVK8LYuMTJ4rbu/tbZ7Jw0P2KZsyBbhyBdi9W5ZYam0e4uOlx1aHDlI/vXgx009tdKL1888/o3DhwgCAsLAwhIWFYfXq1WjRogXnaJHD2LNH5kRmzw60bat3NOan7Xu4bx8QEaFvLGRZa9dKK49ChYBatfSOJtnbb8ux7dAhafdAtmndOjmvVk3maDm8Q4dkxeFrr8nktJRDwG5uknSdOAH065fppzQ60YqIiEhKtFasWIH27dsjJCQEH330Efbu3Wvs0xHZJG00q21bx+wZExiYPDdr9Wp9YyHLSlk2dMnyZBHzCQgA2rWTy5wUb7scvmwIyOjU119LPbt6dZkIf/26JFVubvLHo+3GPm6cXG9EOyuj/+xy5syJK1euAADWrFmDJv/tdq2UQkJCgrFPR2RzHj8GFiyQy45YNtSwfOj4Hj3Sv0nps7z7rpzPmyeLM8i2JCYmj2g5dKJVogQwYoT0HNFKheXLJ688XLRI3gBXV6B7d3mMEb+wRneGb9u2LTp27IhSpUrh9u3baNGiBQDg4MGDCHKo7bzJWa1aBURFydzHxo31jsZyQkNlNXNYmCSX7u56R0Tmtm5dctnwpZf0jiathg2BoCDg7Fk5lr3zjt4RUUqHDslWSd7eQHCw3tFYmFJSvujQAejRI+OuvtmzA6NGGfXURo9ojR8/Hn379kW5cuUQFhYGb29vAMCNGzfQu3dvY5/Opty+zUmZlFw27NRJPsA4qurVgXz55EC8bZve0ZAl2GrZUGMwJI9qsXxoe7Ru8I0bO/gHsbp1genTgRs3gF9+efbWCW5ukmgZkWxluY+WI9D6cAAxKFnSF/36AV27sreLM4qKkvlL8fHAkSOy16gje+cd+f8yYAAwfrze0ZA5xcXJ5OXYWOnAXru23hGlLyJC+ns9eQIcPQpUqKB3RKRp0ECmIv30k3Q4sEX20AfTpEQrPDwcmzZtws2bN5H4VD+JkSNHmi04a0mZaBkM8oPKnl027HboujSlMWUK0Lu3bH6rLWl2ZEuWyGhHqVLJ+6iSY/j7b+DVV4GCBaXhri2OaGnatpVWRv37AxMn6h0NAZKg58olCfC5czKNyRaZLdGKiZHJgmfPyia3T6dGBgMwdapJT230HK3ffvsNH3zwAXLnzo3AwEAYUix9NBgMdplopaS9tw8fyhyWlSuZbDkTR+6dlZ6mTWUkPDxcTqVK6R0RmYutlw1T6tlTEq3Zs4GxYwFPT70jok2bJMkKCrLdJMtsNm2Sdg537z77fiYmWkb/+X355Zf46quvEBERgUOHDuHgwYNJpwMHDpgUhC1KTJSkq107SW7J8YWHA//8Iweljh31jsY6fH2B+vXlMrvEO464OODPP+WyLa42fFpICFCkCHDnDrB0qd7REOAkbR00AwfKEJ624jC9UxYYnWjduXMHb9jDX64ZJCYCDx4kj3KQY9P28AoJkXlazoJtHhzPunVy3ChY0D5Wi7m6Jq845KR4/SmVPBG+eXN9Y7GKU6ekNFi5svT2CQuTUa6Up40bTX56oxOtN954A+u0xhpOYtIkrkZ0dImJyYlWly76xmJtWqK1devzR87JPmhlw3btbL9sqOneXY51mzfL6DLp5+xZ4MIFmVbQsKHe0ViBtpntl18C7dvLMssGDdKeTGT0HK2goCCMGDEC//zzDypWrAg3N7dUt/fv39/kYGyRUjIRMCpKJgaSY9qxQ5oD+/gArVvrHY11lS4tc7PCw+WDnCNuOeRM7K1sqClSREZPVq+WqTDffKN3RM5LKxvWrSs9tBxe377AoEGy5Y72ydOMjE60fv31V3h7e2PLli3YsmVLqtsMBoPDJVqau3eZaDkybTTr9ddlxamzCQ0FJkyQ8iETLfsWFiZlwwIFbLelQ0Z69pREa8YM4IsvZESFrM+p5mcB0supRAnJ7k+elNGrnDnT3s/Ecgf7aCF1ewcg/eWht24x0XJUDx9KF/iYGCnFO8VQ+VPWr5cViPnyyRZf9lJuorS6dpV5pf36ybQHexIfLz21IiOl9QiTfuuLi5Nj3f370uKmShW9I3o2s7R3cHGRurVSqTeRTslgkGWYpjy9aVE5D4MBKFlSNkAlx/T335JkFSmSvALP2dSvLyWCyEjAgRYPOx17LRtq3NySt5LjpHh97NghSVZgoMwNdxramJMFVh4aXToEgKtXr+Kvv/7C5cuX8fjx41S3jRs3zuRgbFX//hknuWT/tLJh587OO5Lj7i6rLZculTYPNWroHRGZYv16+dCQPz9Qp47e0ZimRw+p4KxdC1y6BBQtqndEzkUrG4aEONFxz8i9C41ldKK1YcMGvPrqqyhRogROnTqFChUq4OLFi1BKoVq1apaIUTcuLoCXl/OtQnMmN2/KnBDAeZqUZiQ0VBKtFSss/n+HLMQeVxs+LSgIePllKeNPnw589pneETkXp5ufBVj8H57Rf4rDhw/HkCFDcPToUXh6emLJkiW4cuUKGjRo4FD9tQwGOS1dyj0PHdmCBUBCAvDii8ALL+gdjb5atpTzfftk/zmyL48fJ5cN27fXN5as6tlTzqdNk79Pso6ICODwYTn2NW2qdzSOw+gRrZMnT2L+/Pny4GzZ8PDhQ3h7e+Pzzz9H69at8YGt7jxpJINB5u6EhOgdCVmSs2258yyBgVIy3LdPRvm0uTJkH9avl10s7LlsqHntNZkXe+WKjLBoHwLIsrQWmdWqAXny6BuLRTVqJAf5SZOA8uXl6+cxGIANG0x6OaMTrRw5ciTNy8qfPz/OnTuH8uXLAwBu3bplUhC2pFgxmRD88CFw4gTQooXeEZGlnDgB7N8PZMsGdOigdzS2ITRUEq0VK5ho2RtHKBtqPD3lw8/EiTIpnomWdThNN/jNmyVxiolJ/XVGnrUaMROM/nN86aWXsH37dgBAy5YtMXjwYHz11Vd455138NJLL5kciC24cAE4fx744Qf5esQI+ZockzYJvmVLB//0ZgStV19YmJSiyD48fgwsXy6XHWUGh1Y+/PtvlrKtITFR/u4BJ5ufpbHQPoeACX20zp8/j3v37qFSpUq4f/8+Bg8ejJ07d6JUqVIYN24citrhEpGn+3AoJR34N20CmjSR4VSnWX3hJBITZTXT1asyEvD663pHZBsSE6XRZWSklKIaN9Y7IsqM1avlA0NgoPxOu7rqHZF51K4N7NoFjBkDDBumdzSObd8+mavq4wPcvm0/zWJN6qN16ZKc588vS661r5/HxPzGqNJhQkICrl69ikqVKgGQMuLPP/9s0gvbMoMB+OUXoFIlOdjMmiVNAMlxbN4sByR/f6BVK72jsR0uLnLAnj5dyodMtOzDokVy3q6d4yRZgIxq7doF/P478NFH9l8StWXaasPGje0nyTLZ0wmThQeIjPq1dXV1RUhICO7cuWOpeGxGqVLJy4oHDZI2AOQ4tEnw7dvLfBBKppUPV67UNw7KHEcsG2rat5cRlnPn5MMRWY5TtnV42pMnwLFjwPbtwNataU8mMvrzQYUKFXDeSSYuDRok2w9ERQEDBugdDZnL/fuyvQfAHmnpadpUPtGGh8uJbNuGDbLaMF8+2QTYkeTIAXTsKJfZKd5yYmJkP2XAiROtL7+UvYcqV5a9Dl9+OfUpMysTM2B0ovXll19iyJAhWLFiBW7cuIHY2NhUJ0fi5iZD1i4uwPz5/ITvKJYvB+7dkz1E7W3TXWvw9U3eioi/87Yv5WpDRyobarRJ8UuXytwhMr+NG6VfWenSQPHiekejg2nTgJEjgbt3LTIpPtOJ1ueff4779++jZcuWOHz4MF599VUUKlQIOXPmRM6cOeHv74+c6e12beeqVwcGDpTLH3wgPweyb9pqw7ff5iKHjGjlwxUr9I2Dni0+3nHLhprq1YGqVaVEqv3tknk5fdnw99/lYFCqlHytdWwtV06+rlEjS+WPTK86dHV1xY0bN3Dy5Mln3q9BgwYmB6OX561auH8fqFhR2j/07y+9Xcg+Xb8OFC4sq+vOnpUNwymtM2eAMmVkVPf2bZknQ7ZnzRrp9ZcvH3DtmmOOaAHAlClA795y3Dt2jB+QzEkpGd2/eFE+WGkfsuyFSasOn+bvL6Mo8+dLU0WDAdi2DQgOlk/ky5bJH1u9eiY9faZXHWr5mD0mUlmVI4esQgwJASZPBt56C7DzlmFOa948SbJq12aS9SylS8uHu/Bw6a3Ttq3eEVF6tLJh27aOm2QBMk9r8GBpMrxrF0v+5hQeLkmWuzvQsKHe0ejk4UM5DwyUP6TERLnOYJCRrHnzZNnrrl0mPb1Rc7QMTvwxomlTeb+VAt59l80c7ZVWeuAk+Odj+dC2OUPZUOPnl7x/4++/6xuLo9G6wderJ4MKTkmb9hQfn3x5/nzgwYPkuuqRIyY/vVGJVunSpREQEPDMkyMbN046iB8/Dowdq3c0ZKzDh+Vvxd3d/jfdtQYt0Vq1Sj7gkW3ZuFFWROfNm7x4wZFpk+IXLgQcbN2Vrpx+fhYg80kAWb5btaqMqEyfLnMmJkyQkS3tPiYwqmHp6NGj4efnZ/KLPW3KlCmYMmUKLl68CAAoX748Ro4ciRb/bTD46NEjDB48GAsWLEBcXByaNWuGn376Cfny5Ut6jsuXL+ODDz7Apk2b4O3tja5du2LMmDHIls3obRyfK1cumZ/VsaOsBH3jDeCFF8z+MmQh2mjWK68kf2ihjNWvD3h7S5f4AwdkPijZDmcpG2pq1wbKlgVOnpRKTq9eekdk/+LikvuTOXWiVaOGbHx76pRMxA4LS7vKcMgQ059fZZLBYFCRkZGZvXum/PXXX2rlypXqzJkz6vTp0+qTTz5Rbm5u6tixY0oppXr16qUKFy6sNmzYoPbt26deeuklVbt27aTHP3nyRFWoUEE1adJEHTx4UK1atUrlzp1bDR8+3Kg4YmJiFAAVExPz3PsmJirVsqWs9axbV6mEBOO+Z9JHfLxSgYHyc/vzT72jsR9t28p79tlnekdCKT1+rFRAgPxsNm7UOxrrGTdOvudq1fSOxDGsXy/vZ/78cmyzR8YcvzN0/75SERFKxcbK10uWKNWokVJlyigVEqLU4sVZijHTiZaLi4vZE6305MyZU/3+++8qOjpaubm5qT/++CPptpMnTyoAateuXUoppVatWqVcXFxURERE0n2mTJmifH19VVxcXKZf09gf1KVLSuXIIb+gU6Zk+mVIR6tXy88rVy6ljPjVcHpTp8r7VqOG3pFQSmvXys8lb16lnjzROxrr+fdfpdzd5Xvfv1/vaOzf0KHyXnbtqnckpjNLomVhmZ6jpcywg/WzJCQkYMGCBbh//z6Cg4Oxf/9+xMfHo0mTJkn3eeGFF1CkSBHs+m/m/65du1CxYsVUpcRmzZohNjYWx48fz/C14uListRotUgR4Ouv5fJHH8myarJtWtnwrbdkjhZlTsuWcr5vHxARoW8slMzZyoaa3LmB116Ty5wUn3XaRPjmzfWNw9FlOtFKTExE3rx5zR7A0aNH4e3tDQ8PD/Tq1QvLli1DuXLlEBERAXd3d/j7+6e6f758+RDx33/8iIiIVEmWdrt2W0bGjBkDPz+/pFNhEya59ekD1KolrTf69MlS01iysLt3pQ0KIC1RKPMCA5PnZq1erW8sJOLjk3+fHX21YXq0SfFz50qPQzLN9evA0aPJvTmdToECxp0KFjT5pXTfC71MmTI4dOgQdu/ejQ8++ABdu3bFiRMnLPqaw4cPR0xMTNLpypUrRj+Hq6t8osqWDfjzT9kegmzTkiXSEqVMGeDFF/WOxv6wzYNt2bRJmsjmyeMcqw2f9vLL0mAzNjZ5ZI+Mt26dnNeoIQu9nE5EhKz0iYhIfYqMTHvSbjOR7omWu7s7goKCUL16dYwZMwaVK1fGxIkTERgYiMePHyM6OjrV/SMjIxEYGAgACAwMRGRkZJrbtdsy4uHhAV9f31QnU1SoAAwfLpf79gXu3DHpacjCZs2Sc265Yxot0QoLY/84W5CybGiBxdU2z8UF6NFDLnOjadOxrcN/nj4omHGPQ43uidbTEhMTERcXh+rVq8PNzQ0bNmxIuu306dO4fPkygoODAQDBwcE4evQobt68mXSfsLAw+Pr6opy2R5GFffKJjJRERMh8LbItly8nL1/u3FnXUOxW9eqyxcvdu7IrBenH2cuGmu7dpaqwc6d0iyfjJCTIByeAiRYAwNcXeP996fx+4UL6p/PnTX/+zMyYr1q1qoqKilJKKTV69Gh1//59s8zEHzZsmNqyZYu6cOGCOnLkiBo2bJgyGAxq3bp1Silp71CkSBG1ceNGtW/fPhUcHKyCg4OTHq+1dwgJCVGHDh1Sa9asUXny5LFoe4f0bN2anPpu2mTSU5CFfP21/FwaNtQ7EvvWvbu8jwMG6B2Jc1u3Tn4OuXNLyxJn1rq1vBcDB+odif3ZvVveOz8/+/89Mvn4vXixUs2bK+XqqpTBoJSLiyxpfe01pVatMmu/i0wlWp6enurKlStKKfO2eXjnnXdU0aJFlbu7u8qTJ49q3LhxUpKllFIPHz5UvXv3Vjlz5lTZs2dXr732mrpx40aq57h48aJq0aKF8vLyUrlz51aDBw9W8Ub+5phjeWivXvKLW6qUUg8emPw0ZEaJiUq98IL8XKZO1Tsa+7Z4cfLvN+mnZ0/5Obz3nt6R6G/FiuSWLY8e6R2Nffn8c3nv2rbVO5Ksy/Lx+9IlpUaMUKpQIUm4tKSrcGGlRo5U6tq1LMdoUOr5Bcjg4GB4e3ujbt26GD16NIYMGQJvb+907zty5EjTh9d0Yo7dv2NiZGf569dl3pbW/oH0s2+fTH739JT5jKZu7E4y8Th3bildnTkjG06TdT15AuTPD9y6JWWfFJ1vnNKTJ0CxYtJeZ/58oEMHvSOyH3XrAjt2AL/8Arz3nt7RZI05jt8AZJ+xVauAH39MnsBmMACjRgFZzGsyNUdrxowZyJUrF1asWAGDwYDVq1dj2bJlaU7LtR1OnZCfn/x8AODbb2VfPdKXNgm+TRsmWVnl65u8wm3lSn1jcVabN0uSlTs30LCh3tHoL1s24J135DJ7amVeTAzwzz9ymfOzUjhzRv7IDhxIniCvlHxSz6JMjWil5OLigoiICIv01NKL2TJiAK+/Lu0EatSQX2ZnaiZoS+LjpfXJrVvyIeW/7TMpC8aPBwYNAho3Btav1zsa5/P++8Cvv0ofqV9/1Tsa23DpElC8uBwPz54FSpbUOyLbt3Qp0K6dLOI6dUrvaLIuS8fvhw9ll/Lff5eJ8ID8MmXPLgfzd9+V4b8sMnrVoaUalzqKyZNldGvfPmDSJL2jcV5r1kiSlS+fkzbjswCtzcPWrbICkaznyZPkXn3OvNrwaUWLAiEhcnnqVH1jsRfsBv+fXr2kFt+jhyxfVQqoUkVKU9evAzNmmCXJAkwY0QKAc+fOYcKECTh58iQAoFy5cvjwww9R0k4/TphzRAuQ5LhnT0mKjx2TT1xkXe3bS7+hgQOBceP0jsZxlColIwdLlkgfJ7KODRtkTlauXNJKxhn7Z2VkyRIZfAgMlHYubm56R2S7lJJ5bZcvO85Iv8nHbxcXKREqJXMjOnQAqlV79mNMnNBmdKK1du1avPrqq6hSpQrq1KkDANixYwcOHz6Mv//+G03tcPjA3ImWUtK9eMsW+bS1Zg0bZVrTnTvyQSUuDjh4UD6kkHkMHAhMmCB9jKZN0zsa59Grl0xcZtkwrcePgUKFgH//lR5jbdroHZHtOnUKKFsW8PAAoqJkMMDeZTnRMkZCgnH3117K2AcMGzYMAwcOxO7duzFu3DiMGzcOu3fvxoABA/Dxxx+bFISjMRjkn6GHh2xzMGeO3hE5lz/+kCSrQgWgcmW9o3EsWvlw1SpZpEOWx7Lhs7m7A926yWVOin82bTFdvXqOkWRlWXpd4DM6ZYHRA9AnT57EokWL0lz/zjvvYMKECVkKxpGULi2rQj/5REYBmjeXvcnI8mbPlvMuXTiSaG716wPe3tIu48CB5A2nyXK2bpXRmly5ZKSc0nr3XeB//5ONz69elREuSovb7qTQtavVXsroRCtPnjw4dOgQSj3VSOfQoUOcJP+UIUOABQuAI0ck2eLIluWdPw9s3y4JVseOekfjeNzdpRy+dKm0eWCiZXna3oavvca5WRkpXRpo0ECma0ybluW2Rw7p0aPk7cicfiI8AEyfbrWXMrp02LNnT7z33nsYO3Ystm3bhm3btuGbb77B+++/j549e1oiRrvl5iZD2S4uwNy58mmLLEtLZps0AQoW1DcWR6WVD1es0DcOZ8CyYea9+66cT51q8lQah7Ztm3QzKFgQKF9e72hsTGb2Mdy0yfTnN7aVfGJioho3bpwqWLCgMhgMymAwqIIFC6oJEyaoRDPuDWRN5tiC51kGDpQib5EiSt29a5GXICVb7pQsKe/17Nl6R+O4btxInrjw1I5YZGYbNsj7HBCg1OPHekdj2x48UMrfX96vNWv0jsb2DB4s70337npHYl5mOX57eyv100/p33b/vlK9e8ueiCYyekTLYDBg4MCBuHr1KmJiYhATE4OrV6/iww8/hIETYtL1xRfJS2pHjNA7Gsf1zz/AuXNAjhxSZiHLCAwEqleXyxyltayUZUO2LXg2Ly/g7bfl8m+/6RuLLeL8rGe4fx/o21eaLl65knz9li1AxYrAzz9naUK80YlWSj4+PvDx8cnKUziFHDnk5wQAEycCu3frG4+j0rbcaddO3nOynFat5JzlQ8tJSGDZ0Fja7JU//5QFGySuXZOejgYD98hMV2CgJFIbN0pi9csvQL9+sg3GxYtyWxYmpGYp0aLMa9YM6NxZfl49e0rvFzKfuDjZSQFI/lRLlqPN0woL4++ypWzdCty8CQQEAI0a6R2NfahYEahVS+a2zZypdzS2QxvNqllTVq/SU06dSs7SY2OB3r2Bn36SHjaensDYsclb9JiAiZYVjR8vG8IePSpLkcl8Vq6URqUFC3IJvDVUry7bG929K5Nsyfy0smGbNiwbGkObFP/771luf+QwWDZ8Dl9fGcWaODH5OqVkme/q1cDQobKqzURMtKwod27pqg3IvK3Tp3UNx6FoZcNOnbiRtzW4uAAtW8pllg/Nj2VD03XoIL3ewsNlVNDZJSTIyDPARCtDDx8CH30EDB6cvC0PIG9e69ZZnvRnVKIVHx+Pxo0bIzw8PEsv6sw6dpQeJnFxsm0Su2tn3a1b0qkcYNnQmrTy4cqV+sbhiLZtkzlGOXPKNBHKPG9v4K235DInxQP79slov5+flA4pHeXKAd9/D8THS6nwu++Ad96RhCsmRvbAatDA5Kc3KtFyc3PDkSNHTH4xkmR5yhTZ/mDrVm4ZYQ4LF8rfR7Vqsu0OWUfTplLSCg+XE5kPy4ZZo023WbxY9vRzZlrZsEkTNrzN0KVLklTVrg0cOgQMGiQH51WrZJsBpaQTtomMLh127twZU6dONfkFSVo9fPWVXB46FLh+Xddw7J5WNuRolnX5+sqWPABHtcwpIQFYskQus2xomho1gEqVpHLg7DtyrFkj5+wG/wyenjKitW0bkHLXm+bNZblmFrfrMShl3HTBfv36YdasWShVqhSqV6+OHE+tox83blyWAtKDybt/Z0FCAhAcDOzdC7Rtm/yPlYxz+jTwwgsyL+vaNZmgTdYzfrx8+GvcGFi/Xu9oHMOWLUDDhlI2jIiQbY/IeD/8ICv0K1SQbdCcsc3jnTsyNzgxUfo4Fi6sd0TmZ5bj95kzso/Ts6xeDbRoYdLTGz2idezYMVSrVg0+Pj44c+YMDh48mHQ6dOiQSUE4I1dXGZnMlk0mvWoTX8k42gbSzZoxydKDNk9r61ZZgUhZl7JsyCTLdJ06yUDFsWPAnj16R6OPDRskySpb1jGTLLNJmWRFRQE7dyavINCYmGQBJmwqvSkr+/1QKpUqyUKHr7+WprSNGgH+/npHZT8SE5PLAl266BuLsypdGggKAs6elf9LbdvqHZF9Y9nQfHLmlPdw9myZFF+rlt4RWR/bOhjh0iXpn7V2rczJMhiAe/ekl82jR8CiRclbYhjJ5PYOZ8+exdq1a/Hw4UMAgJEVSPrPiBFysLpxA/j4Y72jsS/btsnfhq8v8OqrekfjvNgl3nx27JByob8/VxuagzYpfsEC5xtxVYqJVqZduyYT4deskU/w2naunp4yInLhgvwSmcjoROv27dto3LgxSpcujZYtW+LGjRsAgB49emDw4MEmB+KsPD2BX3+Vy7/+yr4vxtDKhm+8IfuckT608uGqVWxXklUsG5pX3bpAmTKyld38+XpHY10nT8q2fZ6eWepM4Bw++0xGO5SS1Wop1a0r5xs3mvz0RidaAwcOhJubGy5fvozs2bMnXf/mm29ijba8gYzSoIH01ALkE9ijR/rGYw8ePpSRXIBlQ73Vry+9iyIjgQMH9I7GfiUkSDsCgGVDczEYUneKdybaaFb9+vwg+lyrV8svy8cfJ3+C12iJ19WrJj+90YnWunXrMHbsWBQqVCjV9aVKlcKlS5dMDsTZjR0L5M8vix++/FLvaGzfn39KKaBo0eQPHKQPd3cgJEQus82D6VKWDbnxr/l07Sq9yPbuBQ4f1jsa62HZ0Aj//ivn6f3haVuNxMSY/PRGJ1r3799PNZKliYqKgoeHh8mBODt/f1mODEjSxb6wz6Z96Hj77SxtQUVmopUPOU/LdFrZsHVrlg3NKU8eKcUCztMp/uFDaRMCMNHKFG2n7X370t6mrT7MwrJ2ow9R9erVwyytQyQAg8GAxMREfPvtt3iZu/lmSdu2wGuvyc7zPXtKKYHSioxM/rTGJqW2Qdv3cN8+GZUh4yQmcrWhJWnlwzlzgAcP9I3FGrZtkykoBQvK7jL0HA0ayPyskSOBMWOSr3/nHdmg2GAAspDfGJ1offvtt/j111/RokULPH78GB999BEqVKiArVu3YuzYsSYHQuKHH2QV3Z49ySNclNr8+ZKE1qr1/B5zZB2Bgckrn1ev1jcWe7Rjh8zF9fOTrY3IvJo0kak2MTHO0Rw6ZTd4Z2zUarRPPgE8PGSUQ5uvBQAzZ0oC5uEhvZhMZHSiVaFCBZw5cwZ169ZF69atcf/+fbRt2xYHDx5EyZIlTQ6ERIECwLffyuX/+z/g4kVdw7FJ2oAqJ8HbFrZ5MB3Lhpbl4gL06CGXnaF8yPlZRqpYUbqG586d3NpBO+XJI9l5FoYGjd6CxxHpsQXPsyQmyhYc27bJJ5JVq/ipRHPsmPxNuLnJCIBWWif97d0L1KwJ+PgAt24xYcisxETp2n39OvD338kJK5nXtWtAkSLyfp88KVt3OaIrV+T7dHGRv8OcOfWOyLLMevx++BBYt05WpQFSMmnaFEhnXroxTNrL+86dO5g6dSpOnjwJAChXrhy6d++OgICALAVDwsVFPnVVqiRDwPPnAx076h2VbdAmwYeGMsmyNdWry3zRyEj5kMCGm5mzc6ckWb6+LBtaUsGC8n/j77+l1cN33+kdkWWsWyfnNWs6fpJldl5eMqyc0sWLslEkIL0yTGB06XDr1q0oVqwYJk2ahDt37uDOnTuYNGkSihcvjq3stmk2ZcrIvDwA+PBD+WTi7BISkrfc4SR42+PikjwpnuXDzEtZNuTCbcvSJsXPnAk8fqxvLJbCsqGZ/fKLTIRv1MjkpzA60erTpw/efPNNXLhwAUuXLsXSpUtx/vx5dOjQAX369DE5EEpr6FApk926BQwapHc0+tu0ST7558yZ3E6AbIv2c2E/rcxJTExuUtq+vb6xOIOWLWUe7K1b0ovP0Tx5ktyNoHlzfWNxKNp8LRMZnWidPXsWgwcPhqvWxAuAq6srBg0ahLNnz5ocCKXl7i4lRINBSmbaJxVnpU2Cf/NNfvK3VU2byvy58HA50bPt2sWyoTVlywZ07y6XHXFS/N69QHS0fBh98UW9oyGN0YlWtWrVkuZmpXTy5ElUrlzZLEFRslq1gP795XKvXrJnlzO6d08WhQBcbWjLfH2BevXkMke1no9lQ+vTVh+GhclewY5E+zDepElyQ3PSX6YSrSNHjiSd+vfvjw8//BDfffcdtm/fju3bt+O7777DwIEDMXDgQEvH65S+/FJWkVy8mDxvy9ksWyZJZlAQ8NJLekdDz8I2D5mTsmzIJqXWU7x48k4rU6fqG4u5cX6WbcpUewcXFxcYDAY8764GgwEJdtjO3NbaO6Rn9WqZX+DiAvzzj/MNC4eEyCfQ0aOdN9m0F2fOyGIONzfg9m1p90Bp7dwJ1Kkjo4A3b3JEy5oWLZIpCAUKAJcuSUnR3kVFScunxERp8fDUdsQOy+Tjd2Ynt58/L6sODQaTt2vJ1K/XBUcbX7VDLVpIi4d582TlzL59ciBzBteuAevXy+XOnfWNhZ6vdGkZeTx7VpLjtm31jsg2LVok56++yiTL2lq3lt6U16/Lh9hXXtE7oqxbv16SrPLlnSfJypLNm63WoDJTpcOiRYtm+kSWM2GC9I46cgT4/nu9o7GeuXNlwUfdukCJEnpHQ5nB8uGzsWyoLw8PoGtXuewok+JZNjTB013gMzplkUmd4a9fv47t27fj5s2bSExMTHVbf23mth2xh9KhZvZsmQzu4QEcPQqUKqV3RJallLS4OH4c+PVX2WybbN/69bKKLl8+GTVwMXrZjWPTyoY+PlI29PTUOyLnc/Kk7Kri4iKVoYIF9Y7IdErJ7gLXrknCFRKid0TWY/Lxe+ZM419My86NZHSiNWPGDLz//vtwd3dHrly5YEgx9GYwGHD+/HmTAtGTPSVaSkl/lHXrZMPxjRsd+yB28CBQrZoklhERgL+/3hFRZjx+LKOv9+7JkvMaNfSOyLYMHCgj1J06JTfhJeurVw/Yvl0WHP3f/+kdjemOHwcqVJCEPSpKGpw7C3s4fht9iB4xYgRGjhyJmJgYXLx4ERcuXEg62WOSZW8MBuDnn2XrpS1bgGnT9I7IsrQtd159lUmWPXF3T+4LxTYPqbFsaDu0EfKpU+XnYq+0smGDBs6VZNkLoxOtBw8eoEOHDnBx5GEUG1e8OPDFF3J56FDZXNkRPXki87MA9s6yR5ynlb7du4GrV6VsyPk0+nr9dcDPT/ppbdigdzSmW7NGztkN3kQREcCnn0r5xMcHyJFD6srDhwN37mT56Y3Olnr06IE/tC57pJv+/aUcEx2d3NDU0axbJ/NX8uThAckeafse7tsn/8dIaP8+X3mFc7P0lj178kpme50U/+ABoG0zzP+TJvjjD1kq/fXX8gfZtasMdbq6AmPHAtWrZ3k0w+g5WgkJCWjVqhUePnyIihUrwu2pHgPjxo3LUkB6sIcab3oOH5bfgYQEaejZpo3eEZnXW28BCxZIIjlxot7RkClq1AD275cSt7b1iTNLTASKFZM+R474N2uPDh0CqlaVdjnXrskHO3uyZo20/ylcWHqCWaljgc3I0vF77lwpl3h5Sb8V7dOh5t135Z/X66/L7UuWyCckd3ejXsboEa0xY8Zg7dq1iIyMxNGjR3Hw4MGk06FDh4x9OsqCypWldAgAffoAMTH6xmNOMTHA8uVy+e23dQ2FsoDlw9T27JEky9ubow+2okoV+UAQH5+8n6o9SdnWwdmSrCy5dk0OnICsSPH2lqHBlCdtoumff0on5jffNG0eizKSv7+/mj59urEPs2kxMTEKgIqJidE7FKM9eKBUUJA0+/jgA72jMZ+pU+V7KltWqcREvaMhU+3ZIz9HHx+l4uL0jkZ/gwbJ+9Gxo96RUEq//CI/lzJl7O//TdmyEvsff+gdiT5MPn6PHKmUwaBUw4bytcGglItL2pN2/aFDSrVsKZf//NOolzJ6RMvDwwN16tQxPqMji/Dykv5SADBliixVdgTaJ8u33+anNHtWvbr00rp7F9i2Te9o9KUUVxvaqrfekvnPp0/b1//Qy5elH5ira/L+jZRJf/4pB5f27eXrMWOAkiXlcqNGUtf38gICAoCPPpKacmio/CFPmWLUSxmdaH344YeYPHmysQ8jC3r5ZSklAzKH79EjfePJqkuXpHWFwSB9hsh+ubgkT3tw9vLhnj1yYGTZ0Pb4+EhVCLCvSfFa2bBWLba/MdqlS3Kudf0+ehQ4dw745hvZO2zJEjmPipIJlblzJ993zx6jXsroRGvPnj2YOXMmSpQogVdeeQVt27ZNdSJ9fPutjBycOiWLJ+yZ1sDx5ZeBIkX0jYWyLjRUzp29n1bK1YbsdWR7tJ5af/whq7ntAbfdyQKtcZq2HnDZMjl/4YXk+5QpI+fh4dIVViuvxMcb9VJGJ1r+/v5o27YtGjRogNy5c8PPzy/VifSRMyfwww9yecwY4NgxfeMxlVKpy4Zk/5o2lRVd4eFyckZKJSdaLBvaplq1pLv6o0fJ/fts2ZMnstUVwETLJFqZ8OhROa9QQc6HDAEWLpQh+I4d5TpPT7n/mTPydbFixr2WcbPHHJM9T4ZPKTFRqdatZWJkrVpKPXmid0TG271b4vfyUio2Vu9oyFwaNZKf6/jxekeij3/+ke/f21sWsJBtmjhRfk6VK9v+pPgdOyTWgAD7/F9vLlmeDF+hgnx9+LBShQqlnhRvMCiVPbtSs2fLfRo0kOuHDTPqpdje3YEYDMCPPwK+vtJ9+qef9I7IeNpo1muvybwJcgzO3uZBG81q1YplQ1vWubPsq3r4sDTatWVaN/imTWUyPBmpXz/ZFuDECTnwVKokc7RmzAAGDQL69gXGjwfOn5dfjKVLpeVDjhxGdwnPZmxsxYsXT7WR9NO436G+ChaUuXy9e8vuAa1b2888p8ePpUEpwC13HE1oqPzv2rpVViA6UxLN1Yb2IyAAaNcOmDdPJsW/+KLeEWWM87OyKHduWT3YqRPQq5ckUO3apX/wWbUK6NZNRjN++AHIn9+olzJ6RGvAgAH48MMPk069e/dGcHAwYmJi8N577xn1XGPGjMGLL74IHx8f5M2bF23atMHp06dT3SciIgJvv/02AgMDkSNHDlSrVg1LlixJdZ+oqCh06tQJvr6+8Pf3R48ePXDv3j1jvzWH8f77QJ06wP37wAcfJM/1s3WrVwO3b8vvcOPGekdD5lS6NBAUJHNIw8L0jsa69u6VBU45ckgHb7Jt2qT4+fMBWz2M3L4tv1cAEBKibyx2rUMH2VFca/PQqpUMPx89Kn0zliyR8sorr8hIwJQp1mlYmpEffvhBdevWzajHNGvWTE2fPl0dO3ZMHTp0SLVs2VIVKVJE3bt3L+k+TZs2VS+++KLavXu3OnfunPriiy+Ui4uLOnDgQNJ9mjdvripXrqz++ecftW3bNhUUFKTeeuutTMfhKHO0UjpxQil3d6nhz5+vdzSZ066dxDt4sN6RkCUMGCA/3+7d9Y7EuoYMke/7zTf1joQyIzFRqVKl5Gf2++96R5O+BQskPm16kTMzy/H77FmlunSR+VhPz9Hy8lLqrbfkoGoisyVa586dUz4+Pll6jps3byoAasuWLUnX5ciRQ82aNSvV/QICAtRvv/2mlFLqxIkTCoDau3dv0u2rV69WBoNBXbt2LVOv64iJllJKff65/DHmyaPUrVt6R/Nst28nJ4aHD+sdDVlCWJj8fPPlUyohQe9orCMxUamiReX7XrxY72gos8aOTV5UZIu6d+eHUo1Zj99xcbLK4I8/5LR9u1KPHmX5ac02GX7x4sUICAjI0nPE/LdZX8rnqV27NhYuXIioqCgkJiZiwYIFePToERo2bAgA2LVrF/z9/VGjRo2kxzRp0gQuLi7YvXt3uq8TFxeH2NjYVCdH9PHHQPnywL//AoMH6x3Nsy1aJCOzlSrJiRxP/frSrDMyEjhwQO9orGPfPikbZs/OsqE96doVyJZNFhVpq/9thVLJ87OaN9c3Fofj7g7Uri2bSL/+uszB8fDI8tManWhVrVoV1apVSzpVrVoV+fPnxyeffIJPPvnE5EASExMxYMAA1KlTBxW0fhYAFi1ahPj4eOTKlQseHh54//33sWzZMgQFBQGQOVx58+ZN9VzZsmVDQEAAIiIi0n2tMWPGpOr9VbhwYZPjtmXu7jKh02AAZs607bkxs2fLOSfBOy539+Q9Wp2leWnK1YbZs+sbC2VevnzAq6/K5d9/1zeWpx07Bly/LqtX69bVOxrKDKNXHbZp0ybV1y4uLsiTJw8aNmyIF1J2VDVSnz59cOzYMWx/aqOpESNGIDo6GuvXr0fu3LmxfPlytG/fHtu2bUPFihVNeq3hw4dj0KBBSV/HxsY6bLIVHCyrVCdPlknyx47Z3j/8s2eBnTtluxatPxw5platpAHzihXAqFF6R2NZbFJq33r2lBX9s2cDY8dKz0pboI1mNWxoOzHZvXfekXODQSbHZ/a2zMp6UTPr+vTpowoVKqTOnz+f6vqzZ88qAOrYsWOprm/cuLF6//33lVJKTZ06Vfn7+6e6PT4+Xrm6uqqlS5dm6vUddY6WJjZWqcKFpaY/ZIje0aQ1apTE1qyZ3pGQpd24IT9rQC47sr175fvMnl2p+/f1joaM9eSJUkWKyM9wzhy9o0nWpInENGGC3pHYBrMcv1NOgDfmtkzStWGpUgp9+/bFsmXLsHHjRhQvXjzV7Q8ePAAgo2Ypubq6IvG/fYqCg4MRHR2N/fv3J92+ceNGJCYmolatWhb+DuyDj0/yZuPjxgEp3irdKcWyoTMJDASqV5fLq1frG4ulsWxo31xdgR495LKtbDR9/770ogPYP8vsntUHKYs9kjKdaLm4uMDV1fWZp2zZjKtE9unTB3PmzMG8efPg4+ODiIgIRERE4OHDhwCAF154AUFBQXj//fexZ88enDt3Dt9//z3CwsKSSphly5ZF8+bN0bNnT+zZswc7duxA37590aFDBxQoUMCoeBxZaKi0DElMBN591+g9MS1m505pvOvtDTxVlSYH5Qxd4lk2dAzdu8uUhi1bkre509OWLbJoqGjR5P2OyQw2bZLTxo3G3ZZJBqUyl6r9+eefGd62a9cuTJo0CYmJiXj06FHmXzyDDvPTp09Ht27dAADh4eEYNmwYtm/fjnv37iEoKAhDhgzB2yl2HI6KikLfvn3x999/w8XFBe3atcOkSZPg7e2dqThiY2Ph5+eHmJgY+Pr6Zjp+e3PzJlC2LBAVJXMOPvpI74hk3tivv0rT3enT9Y6GrGHvXqBmTRlpvXVLJsk7mv37gRo1ZCTr3385omXPQkOlMfhHH8n/TT19+CEwaRLw3nvAL7/oG4utsIvjt8lFR6XUqVOnVJs2bZSrq6vq0qWLunjxYlaeTjeOPkcrpRkzpL7v6alUeLi+sTx8qJSfn8SzYYO+sZD1JCRILy1AqfXr9Y7GMj7+WL6/N97QOxLKqmXL5GeZN6+0WdJTmTISy5Il+sZhS8x+/H7yRKnLl+Vkpt26TZqjdf36dfTs2RMVK1bEkydPcOjQIcycORNFixY1bxZIZtelC9CkCfDokXwq0nN7nhUrgJgYoHBhWUFDzsHFJbmnlCOWD1k2dCyhoTK38OZN4O+/9Yvj0iXg9GmZO8Ytyizg7Fn5g/X1BYoVk5Ovr1yXxbqxUYlWTEwMPv74YwQFBeH48ePYsGED/v7771R9r8i2GQwy5OzlJWVnPct1s2bJeadOcvAl56HN03LEfloHDsi8Qy8voGVLvaOhrHJzk6kNgL6T4rW2Di+9BPj56ReHQzp4UHYQX7oUePhQWxgtl5culdsOHjT56TN9ePv2229RokQJrFixAvPnz8fOnTtRr149k1+Y9FOiBPD553J5yBAgg76uFvXvv8mrzlJMtyMn0bSpHMDCw+XkSLTRrNBQ2Uia7N+778r5unUysqSHNWvknN3gLWDAACmvaCWenDkBbYcapYC7d4GBA01++kxPhndxcYGXlxeaNGkCV1fXDO+3dOlSk4PRi11MpjOzJ0+AWrXk03f79sDChdZ9/cmTgf79ZcKwtgs9OZfGjWUhz/jx8n/OESgFBAXJiNbChfK3RY5B+30dORIYPdq6rx0fD+TODcTGAnv2yAALCbMcv7NnB+Li5IA0d678EQNSTuzcWd50T0/gv5ZTxsr0iFaXLl3Qvn17BAQEpNq+5ukT2Yds2WRrCVdX2Wfwr7+s+/pa7yyOZjkvR2zzcPBgctkwNFTvaMicevaU82nTgIQE67727t2SZOXKBVSrZt3Xdgq5c8v5p58mJ1mAXP6//5PLWdjLOdONr2bMmGHyi5BtqlpVNpv+9lugd2+ZkG6NAb2TJ2UUK1s26e1Fzik0FBg0SBow3r0r7R7snVY2bNmSZUNH06aNHGuvXpUynjUTaW1+VtOm8uGYzKxHDxmmvHw57W3adVnoqG30XofkWEaNApYsAc6dAz75BPjhB8u/pjaa1bw58NR+4ORESpeWD4xnz8qG523b6h1R1nC1oWPz9JRj7YQJUg3QI9FiN3gz0drra+rVAypVAoYNk+WlNWvK9Xv2yNyG0qWBRo1MfrlMz9FyZM44RyuljRtl/oHBAGzbBtSpY7nXSkyUVbNXrkjJkgck5zZwoBy4uneXkow9O3hQyjqenrLYI5P9ksmOnDgBlC8vo0pXrgD581v+NW/dkg+kSgHXr1vnNe2JScdvFxc54D1NqYyvd3GRyc0m4KJ6QqNGskG5UjIPIS7Ocq+1ZYv8g/LzA155xXKvQ/ZBGxVYtUqScHuWsmzIJMsxlSsH1K4tc7SsNZsmLEz+N1eqxCTLrJL3t08+Pe96E7F0SACA//1PJiWfPAmMGQN89pllXkcrG7ZvL5/8ybnVry9JSWSkrICtUUPviEzDsqHz6NlT9mj9/Xfg448t3wOQZUML6NrVqi/H0iFYOtQsWgS8+ab0Nzp0SD69mdODB0C+fMC9e1KirFvXvM9P9qltW2DZMknuR43SOxrTHDoki0tYNnR89+8DBQrIKsD16y3bpV0poGBB4MYNy7+WvbKH4zdLh5TkjTeknBcfLw36zF3KWb5ckqzixS07D4zsiyO0eWDZ0HnkyAF07CiXf//dsq919KgkWdmz84OpPWPpkJIYDMBPPwGbNwO7dgFTpgB9+pjv+VP2zkpvviE5J22bmn37ZJeCwEB94zEWy4bOp2dP4OefZXeWW7eS2zCZm9YN/uWXAQ8Py7wGQSYpP4/BAEydatLTs3QI+xh6tKYffwT69pVP5idOyKbPWXXjBlCokIySnTkDlCqV9eckx1GjBrB/v6w87N5d72iMc/gwUKUKy4bOpnp1mVc4blyWdmd5Jq0b/aRJQL9+lnkNe2eW43dGqxA12mpEEzvVsnRIaXzwARAcLGW+3r2ztNgiyfz5kmQFBzPJorTsuXyojWa1aMEky5lo+x/+9pt5/kc+7f59YPt2ucyJ8FaQ3mpDM/1gmWhRGi4uMvfAzU0OfNqBJCtmzZLzLDTXJQemtXkICwMeP9Y3FmMoJYtIAJYNnU3HjjJ36uRJmWphbps3y99CsWL8cGpxmzalPf3xR3JJsVw5+edkIiZalK5y5ZK3eOrXD4iKMv25jhyR8oq7OzfZpfRVry4rUu/elRWp9uLIESA8XObPaKNy5Bz8/JL/n/32m/mfP2VbB85ptbAGDdKe2rWTEYfevbOcTTPRogwNGwaULSs7EgwZYvrzaJPgW7XK0r6c5MBcXKT0BthX+TBl2dAR9mok42gbTS9cCMTEmPe5tYnwzZub93nJSKVKydD1r7+a/BScDA9Ohn+WnTtlWbFSpvVxSUiQyfQ3bkivpDZtLBImOYAlS4DXX5f/a2fO6B3N8ykFvPCCxDp3bvKSf3IeSgEVKsiioZ9+kvmt5nDhAlCiBJAtG3D7NsDDUsbMcvzW5raklJAgy6DHjZMfgocH8PChSU/P9g70TLVry8jpjz8C778vpZLs2TP/+A0bJMkKCEhexk+UnqZNZV5geLicbH1eytGjkmR5eHA7KWdlMMik+EGDpHxorkRLKxsGBzPJsopu3Z5dnzUYZH6DiVg6pOf6+mtpzXDuHDB6tHGP1T4odOggc7SIMuLrC9SrJ5dXrtQ3lszQyobNm7Ns6Mzeflv+tx08KO0ezIHb7uggo1WHSklZ5scfTX5qjmjRc/n6yrD4q68C338vSVPVqs9/3N27Ui4EuNqQMqdVK+kbtGIFMGCA3tFkjE1KSZM7t2wjtWCBjGpNmZK154uPl0oAwETLatLb+8tgkBUPQUHyg8hmerrEOVrgHK3MevNNWcperRqwe/fzf+9mzpQR2dKlgVOnuHKGnu/MGaBMGSkh3r5tuyNFR48ClSpJ2fDmTZZ3nN3GjTJ/1cdHpkrkyGH6c23dKovecueWzdYtvWm1vbOH4zd/hJRpkyYBOXPK8PiECc+/P7fcIWOVLi0fIOPjs9S2xuK00axmzZhkEdCwoUxev3s3ua+aqbSyYUgIkyxHwdIhZVq+fFI6fOcdYORI4LXXgJIl07/vlSvyKQ8AOne2Xoxk/0JDgYkTpXzYtq3e0aSVsmzIvnAESEL07rvAJ59I66WsbCPF+VlW0qiRcfc3GJJrukZi6RD2MfRoK5QCmjRJHioPC0t/tOqbb4Dhw4H69YEtW6wfJ9mv9etlBWK+fMD167b3qf7YMaBiRZYNKbUbN2TOdEKC/I6UL2/8c/z7r/zeKyXPZ28brOvB5OP38/Y3TIl7HZI1GQzAL7/IBrobNqTffkSp5LIhJ8GTserXlz0DIyPNt4rLnFg2pPTkz5/c5uP33017jrAw+f9ZuTKTLKt41kpDM+53yNIhGS0oSNo8fPyx7FrfvDmQN69MXr53T9pAnDghydjrr+sdLdkbd3cZ0Vq2TNo81Kihd0SpcbUhZaRnT2D5cvkAOmaM/A80BrvBW9H06am/VkrmxRgMUo4xYyM/lg7B0qEpnjwBataU3jFVqwKxsZJgpVS1qpQY/f11CZHs2LRpQI8ekmTt3at3NMmOH5dO4O7uUjb089M7IrIlCQmyCfTVq8C8ecBbb2X+sYmJQIECMpK7cSPw8ssWC9OhmPX4rZUTt22Tbt1mwtIhmSRbtuSNzQ8eBM6fT3ufQ4ek0ak2uZMos7RdBPbtk10wbIW2oqxZMyZZlJara/L/RWPLh0eOSJKVIwdQp475YyP9MNEik6xdm7qhZHrjokrJ1lChoUy2yDiBgck7XqxerW8sKbFsSM+jVZ82bkw7yv8s2v/Il1/mLhqOhokWGS06GmjXLnNzBBMT5X7t2snjiDIrNFTOV6zQNw7N8ePAyZNyEHz1Vb2jIVtVtGhyawZjRrXY1sGGmLnxIxMtMtrMmcCDB5JEZUZiotw/vRWKRBlp1UrOw8KAx4/1jQVIHs0KCWHZkJ7t3XflfPp0ab77PPfuAdu3y2VOhLcSV9e0J0BGBurWTXtbFrbgYaJFRlEKmDzZtMdOmmSWlbLkJKpXl55Cd+/K3FS9sWxImfXKK7ISOzIycxukb9okCVmJErKqm6xAOxilbONgMMjJzG0emGiRUW7flnkHxv7OKSWPi4qyTFzkeFxcgBYt5LLe5cMTJ+Tk5sayIT2fu7vs8wrIRtPPw7KhTp4+kJmpb9bT2EeLjHLvXtYef/cukCuXeWIhx9eqFTBjhowKjB+vXxwpy4ZsV0KZ8e67wLffSm+sK1eka3xGmGjp4MIFq70UEy0yird31h7v42OeOMg5NG0qo0jh4XIyYw9Bo7BsSMYqVQpo0EC2IJs2DRg1Kv37nTsHnD0rU4DYO8uKiha12kuxdEhGyZVLNpI2dlGGwSCPCwiwTFzkmHx9gXr15HJm5rpYwsmTsuLQzQ1o3VqfGMg+9ewp51OnZrxNnjaaVacOt3TSjYuLZLo7d6a97dgx2YC6cWPTnz4LoZETMhiAfv1Me2z//mZfNUtOQFt9qNc8LZYNyVTt2gE5c0rpMCws/fuwbGgjMpqbFRMDbN4sJxMx0SKjde0KZM8uHwIyw8VF7s8NpskUWj+trVtljp+1sWxIpvL0BN5+Wy6nNyn+8WNpbAow0bIJ6Y0E7N+f8W2ZxESLjObvDyxZIr93z0u2tK2jli7laACZpnRpWfIeH5/xqIClnDollQOWDclUWk+tv/6Sdg8p7dolC4zy5AGqVLF6aM5t9OjM9c8aOFBuz5/f5JdiokUmadZM5sx4eSW3HklJu87LC1i1SsouRKbSq0u8NprVtCk/KJBpKlYEatUCnjyRFbQpaWXDkJDMVwjIjJ5u5/Cs/lnaHAYT8EdLJmvWTHapnzBBGu2lVKKEXH/tGpMsyjrtf9yqVZnfkcAcWDYkc9Amxf/+e+rj+po1cs5u8Drw95eVh9rqQ4NBNlnVritaFCheHKhWDRg8GPjf/0x+KYNS7NUdGxsLPz8/xMTEwJfLPkyilDQjvXtXWjgEBHDiO5nP48ey4vXePWDvXqBGDcu/5qlTQNmyUjaMjJRJzUSmuHdPKk/37smcrIoVpY1TzZpye2SkdJIn45nl+K3Ncdm2Dahd27wBgiNaZCYGgxwIixWTcyZZZE7u7lK+A6zX5kEbzWrShEkWZY23t6xABGRngTx5kpMsd3dg/nwgOlq38GjaNDmVLm2Rp2eiRUR2wdptHlg2JHNZuxZYtEguP727xuPHMt+6UKHkOVtkZd26yXL63Lkt8vRMtIjILrRsKef79gEREZZ9rdOngaNHpYchVxtSVqxdK4s54uIyvo9SwMOHcj8mW1bwdIPSp1capnfKZvpGOky0iMguBAYC1avL5dWrLftaKcuG3M2ATBUdLSVDpZ6/iCMxUe7Xrh3LiFaRmdWG6a0+NAH3OiQiuxEaKv0DV6wAune33OuwbEjmMHMm8OBB5o/RiYly/1mzZCcNspAiRWQisadn6q8thKsOwVWHRPZi716ZROzjA9y6JROJze3MGaBMGakUREZyRItMo5RsLH3+vHGDIQaDtMcJD+eiosywh+M3R7SIyG5Urw7kyycJ0LZtWdrnNUPaaFbjxkyyyHS3bwPnzhn/OKXkcVFRsoKbLOjBAzn38JB5WFeuANOnp72fuzswbJjJL8NEi4jshosL0KKFdNhescKyiRbLhpQVT68uNNbdu0y0LGrdOvln4uoKHDkCvPACcPky8Nln6Q8lVqtmcvdtToYnIruitXmwRD+t8HDg8GEpG7ZpY/7nJ+fh7Z21x/v4mCcOysCyZTJ8GBIiSdbTnp4Ev3y5yS+la6I1ZswYvPjii/Dx8UHevHnRpk0bnD59Os39du3ahUaNGiFHjhzw9fVF/fr18fDhw6Tbo6Ki0KlTJ/j6+sLf3x89evTAvax+nCAim9S0qXRrDw+XkzmlLBtyNIGyIlcuoGRJ4+dZGQzyOJatLWz3bnmztY1Un/bxx3Jq3FiSrX/+MfmldE20tmzZgj59+uCff/5BWFgY4uPjERISgvv37yfdZ9euXWjevDlCQkKwZ88e7N27F3379oVLih04O3XqhOPHjyMsLAwrVqzA1q1b8d577+nxLRGRhfn6AvXqyWVzj2qxbEjmYjAA/fqZ9tj+/TkR3uKuX5fzjLrBjxkjp0GD5OsLF0x+KZtadfjvv/8ib9682LJlC+rXrw8AeOmll9C0aVN88cUX6T7m5MmTKFeuHPbu3Ysa/22AtmbNGrRs2RJXr15FgQIFnvu69rBqgYiSjRsn+7w2bgysX2+e5zx7VlaJubrKZHuOaFFWRUdLx/eHDzO3GbqLC+DlBVy9Knse0/OZfPz28ACePJGdvbX9vSIigCVL5HKfPnK+aZP8o8mWTdr4m8Cm5mjFxMQAAAL+GzO9efMmdu/ejbx586J27drIly8fGjRogO3btyc9ZteuXfD3909KsgCgSZMmcHFxwe7du9N9nbi4OMTGxqY6EZH90OZpbd0qk4bNgWVDMjd/fzluGwySRD2Ltq/x0qVMsqxCe5P37Em+LjBQEiwtyQKAgwfl3M/P5JeymUQrMTERAwYMQJ06dVChQgUAwPnz5wEAn332GXr27Ik1a9agWrVqaNy4McL/m5wRERGBvE9te54tWzYEBAQgIoN9OsaMGQM/P7+kU+HChS34nRGRuZUuDQQFAfHxQFiYeZ6TZUOyhGbNpMTt5SWJ1NMlQe06Ly9g1SqTF7aRsSpXlrlXEyfKasP0RETI8LnBAJQvb/JL2Uyi1adPHxw7dgwLFixIui7xv7HW999/H927d0fVqlUxfvx4lClTBtOmTTP5tYYPH46YmJik05UrV7IcPxFZlzaH1RybTJ89Kx9cXV252pDMr1kzKQdOmCDNSFMqUUKuv3aNSZZVtW0r57dvAzVqAP/7n2ykevYssHkz8PXXklxpc7m0+5vAJvpo9e3bN2kSe6FChZKuz58/PwCgXLlyqe5ftmxZXP4vAw0MDMTNmzdT3f7kyRNERUUhMDAw3dfz8PCAh4eHOb8FIrKyVq3kw+iqVTL/5XmlmWfRRrMaNQJy5zZPfEQp+fvLJPd+/aQZ6d270sIhIIAT33XRvbv8AzlzRraZSK8hqTaFvUQJoGdPk19K1xEtpRT69u2LZcuWYePGjShevHiq24sVK4YCBQqkaflw5swZFC1aFAAQHByM6Oho7N+/P+n2jRs3IjExEbVq1bL8N0FEuqhfX3oVRUYCBw5k7blYNiRrMRhkDmCxYnLOJEsnHh7An3/KPodA2r5Z2nmRIsDff0tt10S6Jlp9+vTBnDlzMG/ePPj4+CAiIgIRERFJPbIMBgOGDh2KSZMmYfHixTh79ixGjBiBU6dOoUePHgBkdKt58+bo2bMn9uzZgx07dqBv377o0KFDplYcEpF9cndPXiyUlTYP584llw1fe808sRGRHShdGjh0CPjkk9Q1XaXk6+HD5Z9Deg1NjaBrewdDBqn89OnT0a1bt6Svv/nmG/z444+IiopC5cqV8e2336Ju3bpJt0dFRaFv3774+++/4eLignbt2mHSpEnwzmRrXrZ3ILJPU6cC774rUyz27jXtOb75Rv6fNmlivon1RGQdZj1+378PxMTICsMcOcwTIGysj5ZemGgR2acbNwBt4PrGDVmdbazq1aX0+MsvAPscE9kXezh+28yqQyIiY+XPL4kSAKxebfzjz5+XJItlQyKyFCZaRGTXstLmQZsE37AhkCeP2UIiIkrCRIuI7JrWJT4szPgdMrREq31788ZERKRhokVEdq16dSBfPulLtG1b5h93/jywfz/LhkRkWUy0iMiuubgALVrIZWPKh4sXyznLhkRkSUy0iMjuaeVDY/ppsUkpEVkDEy0isntNmwJubkB4uJye58IF2dbMxYVlQyKyLCZaRGT3fH2BevXkcmZGtVKWDfPmtVhYRERMtIjIMRjT5mHRIjln2ZCILI2JFhE5BG2e1tatsgIxIynLhm3bWic2InJeTLSIyCGULg0EBQHx8c/es1ArGzZowLIhEVkeEy0ichiZKR9ytSERWRMTLSJyGFr5cNUqIDEx7e0XLwJ797JsSETWw0SLiBxG/fqAtzcQGSmbRT9NKxvWry/d5ImILI2JFhE5DHd36akFpN/mgWVDIrI2JlpE5FAymqd16RKwZw9gMLBsSETWw0SLiBxKy5Zyvm8fEBGRfH3KsmFgoPXjIiLnxESLiBxK/vxA9epyefXq5Ou1smH79taPiYicFxMtInI4Wvnw77+BW7eAHTuA3bvlOpYNiciamGgRkcNp0EDOly8H8uQB6taVrz09gYULgehovSIjImfDRIuIHMratcCrr8plpVLf9ugRMHAgUKiQ3I+IyNKYaBGRw1i7VsqGDx9mfB+l5PbQUCZbRGR5TLSIyCFERwPt2kkilV5X+JQSE+V+7dqxjEhElsVEi4gcwsyZwIMHz0+yNImJcv9ZsywbFxE5NyZaRGT3lAImTzbtsZMmpZ3LRURkLky0iMju3b4NnDtnfMKklDwuKsoycRERMdEiIrt3717WHn/3rnniICJ6GhMtIrJ73t5Ze7yPj3niICJ6GhMtIrJ7uXIBJUvKhtHGMBjkcQEBlomLiIiJFhHZPYMB6NfPtMf27298gkZElFlMtIjIIXTtCmTPDrhk8r+ai4vcv0sXy8ZFRM6NiRYROQR/f2DJEhmdel6y5eIi91u6VB5HRGQpTLSIyGE0awasXAl4eUki9XRJULvOywtYtQoICdEnTiJyHky0iMihNGsGXL0KTJgAlCiR+rYSJeT6a9eYZBGRdRiUYk/k2NhY+Pn5ISYmBr6+vnqHQ0RmopQ0I717V1o4BARw4juRI7GH43c2vQMgIrIUg0FaP+TKpXckROSsWDokIiIishAmWkREREQWwkSLiIiIyEKYaBERERFZCBMtIiIiIgvhqkMAWoeL2NhYnSMhIiKizNKO27bcqYqJFoDbt28DAAoXLqxzJERERGSs27dvw8/PT+8w0sVEC0BAQAAA4PLlyzb7g7IXL774Ivbu3at3GA6B76X58L00D76P5sP30jxiYmJQpEiRpOO4LWKiBcDlvx1o/fz8bLazrL1wdXXle2gmfC/Nh++lefB9NB++l+bl8ryd5HVku5GRXerTp4/eITgMvpfmw/fSPPg+mg/fS+fBvQ5hH3slERERUWr2cPzmiBYADw8PjBo1Ch4eHnqHQkRERJlkD8dvjmgRERERWQhHtIiIiIgshIkWERERkYUw0SKT/PjjjyhWrBg8PT1Rq1Yt7NmzBwAQFRWFfv36oUyZMvDy8kKRIkXQv39/xMTE6Byx7crovQSA999/HyVLloSXlxfy5MmD1q1b49SpUzpGa9ue9V5qlFJo0aIFDAYDli9fbv0g7cCz3seGDRvCYDCkOvXq1UvHaG3b834nd+3ahUaNGiFHjhzw9fVF/fr18fDhQ52iJUtgokVGW7hwIQYNGoRRo0bhwIEDqFy5Mpo1a4abN2/i+vXruH79Or777jscO3YMM2bMwJo1a9CjRw+9w7ZJz3ovAaB69eqYPn06Tp48ibVr10IphZCQECQkJOgcue153nupmTBhAgwGg05R2r7MvI89e/bEjRs3kk7ffvutjhHbrue9l7t27ULz5s0REhKCPXv2YO/evejbt69N94QiEygiI9WsWVP16dMn6euEhARVoEABNWbMmHTvv2jRIuXu7q7i4+OtFaLdMPa9PHz4sAKgzp49a60Q7UZm3suDBw+qggULqhs3bigAatmyZTpEatue9z42aNBAffjhhzpFZ1+e917WqlVLffrpp3qFR1bidGnzs4Zxf/31VzRs2BC+vr4wGAyIjo7WL1Ab9fjxY+zfvx9NmjRJus7FxQVNmjTBrl270n2M1t8kWzZuRJCSse/l/fv3MX36dBQvXpz7cj4lM+/lgwcP0LFjR/z4448IDAzUK1Sbltnfyblz5yJ37tyoUKEChg8fjgcPHugRrk173nt58+ZN7N69G3nz5kXt2rWRL18+NGjQANu3b9cxatuW0fH74sWLacrZ2umPP/7QOWonKx0+bxj3wYMHaN68OT755BOdI7Vdt27dQkJCAvLly5fq+nz58iEiIiLd+3/xxRd47733rBWi3cjse/nTTz/B29sb3t7eWL16NcLCwuDu7m7tcG1aZt7LgQMHonbt2mjdurUeIdqFzLyPHTt2xJw5c7Bp0yYMHz4cs2fPRufOnfUI16Y97708f/48AOCzzz5Dz549sWbNGlSrVg2NGzdGeHi4HiHbtGcdvwsXLpyqlH3jxg2MHj0a3t7eaNGihd6hO9deh+PGjUPPnj3RvXt3AMDPP/+MlStXYtq0aRg2bBgGDBgAANi8ebN+QTqQ2NhYhIaGoly5cvjss8/0DsduderUCU2bNsWNGzfw3XffoX379tixYwc8PT31Ds1u/PXXX9i4cSMOHjyodyh2L+WHpooVKyJ//vxo3Lgxzp07h5IlS+oYmX1JTEwEIAtetGNS1apVsWHDBkybNg1jxozRMzyb87zj99Oj1MuWLUP79u3h7e2tR7ipOM2IliklL0ord+7ccHV1RWRkZKrrIyMjU/2i3717F82bN4ePjw+WLVsGNzc3a4dq8zL7Xvr5+aFUqVKoX78+Fi9ejFOnTmHZsmXWDtemPe+93LhxI86dOwd/f39ky5YtqYzdrl07NGzYUIeIbVNmfydTqlWrFgDg7NmzFo/PnjzvvcyfPz8AoFy5cqluL1u2LC5fvmy1OO2Bscfv/fv349ChQzazCMtpEi1jS16UPnd3d1SvXh0bNmxIui4xMREbNmxAcHAwABnJCgkJgbu7O/766y+OvGQgM+/l05RSUEohLi7OWmHahee9l8OGDcORI0dw6NChpBMAjB8/HtOnT9cpattjyu+k9l5qiQOJ572XxYoVQ4ECBXD69OlUjztz5gyKFi1q7XBtmrHH76lTp6Js2bKoXbu2tUJ8JqcqHZJ5DBo0CF27dkWNGjVQs2ZNTJgwAffv30f37t2TkqwHDx5gzpw5iI2NRWxsLAAgT548cHV11Tl62/Ks9/L8+fNYuHAhQkJCkCdPHly9ehXffPMNvLy80LJlS71DtznPei/z5cuX7ohMkSJFULx4cR2itV3Peh/PnTuHefPmoWXLlsiVKxeOHDmCgQMHon79+qhUqZLeoducZ72XBoMBQ4cOxahRo1C5cmVUqVIFM2fOxKlTp7B48WK9Q7dbDx8+xLx58zBixAi9Q0mm97JHa4mLi1Ourq5plnN36dJFvfrqq6mu27RpkwKg7ty5Y70A7czkyZNVkSJFlLu7u6pZs6b6559/lFLJ7116pwsXLugbtI3K6L28du2aatGihcqbN69yc3NThQoVUh07dlSnTp3SOWLbldF7mR6wvUOGMnofL1++rOrXr68CAgKUh4eHCgoKUkOHDlUxMTE6R2y7nvc7OWbMGFWoUCGVPXt2FRwcrLZt26ZTpLbLmOP3rFmzlJubm7p586YVI3w2p9pUulatWqhZsyYmT54MQIZxixQpgr59+2LYsGFJ99u8eTNefvll3LlzB/7+/jpFS0REREDmj98NGzZE7ty5bWpU0KlKh88axgWAiIgIREREJE3qPHr0KHx8fFCkSBEEBAToGToREZHTet7xG5AFGVu3bsWqVat0jDQtp0q03nzzTfz7778YOXIkIiIiUKVKFaxZsyZpgt3PP/+M0aNHJ92/fv36AIDp06ejW7dueoRMRETk9J53/AaAadOmoVChQggJCdEx0rScqnRIREREZE1O096BiIiIyNqYaBERERFZCBMtIiIiIgthokVERERkIUy0iIiIiCzEIROtbt26wWAw4Jtvvkl1/fLly2EwGHSKioiIiJyNQyZaAODp6YmxY8fizp07eodCRERETsphE60mTZogMDAQY8aMyfA+S5YsQfny5eHh4YFixYrh+++/T7rtk08+Qa1atdI8pnLlyvj8888tEjMRERE5FodNtFxdXfH1119j8uTJuHr1aprb9+/fj/bt26NDhw44evQoPvvsM4wYMQIzZswAAHTq1Al79uzBuXPnkh5z/PhxHDlyBB07drTWt0FERER2zGETLQB47bXXUKVKFYwaNSrNbePGjUPjxo0xYsQIlC5dGt26dUPfvn3xv//9DwBQvnx5VK5cGfPmzUt6zNy5c1GrVi0EBQVZ7XsgIiIi++XQiRYAjB07FjNnzsTJkydTXX/y5EnUqVMn1XV16tRBeHg4EhISAMiolpZoKaUwf/58dOrUyTqBExERkd1z+ESrfv36aNasGYYPH270Y9966y2cPn0aBw4cwM6dO3HlyhW8+eabFoiSiIiIHFE2vQOwhm+++QZVqlRBmTJlkq4rW7YsduzYkep+O3bsQOnSpeHq6goAKFSoEBo0aIC5c+fi4cOHaNq0KfLmzWvV2ImIiMh+OUWiVbFiRXTq1AmTJk1Kum7w4MF48cUX8cUXX+DNN9/Erl278MMPP+Cnn35K9dhOnTph1KhRePz4McaPH2/t0ImIiMiOGZRSSu8gzK1bt26Ijo7G8uXLk667ePEiypQpg8ePH0P7lpcsWYKRI0ciPDwc+fPnR79+/TBkyJBUzxUdHY3AwEC4uroiMjIS3t7e1vxWiIiIyI45ZKJFREREZAscfjI8ERERkV6YaBERERFZCBMtIiIiIgthokVERERkIUy0iIiIiCzEIRKtMWPG4MUXX4SPjw/y5s2LNm3a4PTp06nu8+jRI/Tp0we5cuWCt7c32rVrh8jIyKTbDx8+jLfeeguFCxeGl5cXypYti4kTJ2b4mjt27EC2bNlQpUoVS31bREREZOccItHasmUL+vTpg3/++QdhYWGIj49HSEgI7t+/n3SfgQMH4u+//8Yff/yBLVu24Pr162jbtm3S7fv370fevHkxZ84cHD9+HP/3f/+H4cOH44cffkjzetHR0ejSpQsaN25sle+PiIiI7JND9tH6999/kTdvXmzZsgX169dHTEwM8uTJg3nz5uH1118HAJw6dQply5bFrl278NJLL6X7PH369MHJkyexcePGVNd36NABpUqVgqurK5YvX45Dhw5Z+lsiIiIiO+QQI1pPi4mJAQAEBAQAkNGq+Ph4NGnSJOk+L7zwAooUKYJdu3Y983m059BMnz4d58+fx6hRoywQORERETkSh9vrMDExEQMGDECdOnVQoUIFAEBERATc3d3h7++f6r758uVDREREus+zc+dOLFy4ECtXrky6Ljw8HMOGDcO2bduQLZvDvXVERERkZg6XLfTp0wfHjh3D9u3bTX6OY8eOoXXr1hg1ahRCQkIAAAkJCejYsSNGjx6N0qVLmytcIiIicmAOlWj17dsXK1aswNatW1GoUKGk6wMDA/H48WNER0enGtWKjIxEYGBgquc4ceIEGjdujPfeew+ffvpp0vV3797Fvn37cPDgQfTt2xeAjJ4ppZAtWzasW7cOjRo1suw3SERERHbFIRItpRT69euHZcuWYfPmzShevHiq26tXrw43Nzds2LAB7dq1AwCcPn0aly9fRnBwcNL9jh8/jkaNGqFr16746quvUj2Hr68vjh49muq6n376CRs3bsTixYvTvCYRERGRQyRaffr0wbx58/Dnn3/Cx8cnad6Vn58fvLy84Ofnhx49emDQoEEICAiAr68v+vXrh+Dg4KQVh8eOHUOjRo3QrFkzDBo0KOk5XF1dkSdPHri4uCTN+dLkzZsXnp6eaa4nIiIiAhwk0ZoyZQoAoGHDhqmunz59Orp16wYAGD9+PFxcXNCuXTvExcWhWbNm+Omnn5Luu3jxYvz777+YM2cO5syZk3R90aJFcfHiRUt/C0REROSAHLKPFhEREZEtcMg+WkRERES2gIkWERERkYUw0SIiIiKyECZaRERERBbCRIuIiIjIQphoEREREVkIEy0iIiIiC2GiRURERGQhTLSIiIiILISJFhHZvG7dusFgMMBgMMDNzQ358uVD06ZNMW3aNCQmJmb6eWbMmAF/f3/LBUpE9BQmWkRkF5o3b44bN27g4sWLWL16NV5++WV8+OGHaNWqFZ48eaJ3eERE6WKiRUR2wcPDA4GBgShYsCCqVauGTz75BH/++SdWr16NGTNmAADGjRuHihUrIkeOHChcuDB69+6Ne/fuAQA2b96M7t27IyYmJml07LPPPgMAxMXFYciQIShYsCBy5MiBWrVqYfPmzfp8o0TkUJhoEZHdatSoESpXroylS5cCAFxcXDBp0iQcP34cM2fOxMaNG/HRRx8BAGrXro0JEybA19cXN27cwI0bNzBkyBAAQN++fbFr1y4sWLAAR44cwRtvvIHmzZsjPDxct++NiByDQSml9A6CiOhZunXrhujoaCxfvjzNbR06dMCRI0dw4sSJNLctXrwYvXr1wq1btwDIHK0BAwYgOjo66T6XL19GiRIlcPnyZRQoUCDp+iZNmqBmzZr4+uuvzf79EJHzyKZ3AEREWaGUgsFgAACsX78eY8aMwalTpxAbG4snT57g0aNHePDgAbJnz57u448ePYqEhASULl061fVxcXHIlSuXxeMnIsfGRIuI7NrJkydRvHhxXLx4Ea1atcIHH3yAr776CgEBAdi+fTt69OiBx48fZ5ho3bt3D66urti/fz9cXV1T3ebt7W2Nb4GIHBgTLSKyWxs3bsTRo0cxcOBA7N+/H4mJifj+++/h4iLTTxctWpTq/u7u7khISEh1XdWqVZGQkICbN2+iXr16VoudiJwDEy0isgtxcXGIiIhAQkICIiMjsWbNGowZMwatWrVCly5dcOzYMcTHx2Py5Ml45ZVXsGPHDvz888+pnqNYsWK4d+8eNmzYgMqVKyN79uwoXbo0OnXqhC5duuD7779H1apV8e+//2LDhg2oVKkSQkNDdfqOicgRcNUhEdmFNWvWIH/+/ChWrBiaN2+OTZs2YdKkSfjzzz/h6uqKypUrY9y4cRg7diwqVKiAuXPnYsyYMameo3bt2ujVqxfefPNN5MmTB99++y0AYPr06ejSpQsGDx6MMmXKoE2bNti7dy+KFCmix7dKRA6Eqw6JiIiILIQjWkREREQWwkSLiIiIyEKYaBERERFZCBMtIiIiIgthokVERERkIUy0iIiIiCyEiRYRERGRhTDRIiIiIrIQJlpEREREFsJEi4iIiMhCmGgRERERWcj/A3Wv9tQvl/gVAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Plot saved to: /content/drive/MyDrive/Nganya Project/Plots/Transaction Volume Trend.png\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Profitability (Revenue - Costs) per payment channel\n",
        "# Assuming you have cost data for mobile payments (e.g., 5% transaction fee for mobile payments)\n",
        "cost_per_transaction = {'Cash': 0, 'M-Pesa': 0.0, 'Airtel Money': 0.00, 'T-Kash': 0.00}\n",
        "\n",
        "# Apply the cost per transaction\n",
        "week_data['Cost'] = week_data['Payment Channel'].map(cost_per_transaction) * week_data['Amount (KES)']\n",
        "week_data['Profit'] = week_data['Amount (KES)'] - week_data['Cost']\n",
        "\n",
        "# Profit by payment channel\n",
        "profit_by_payment_channel = week_data.groupby('Payment Channel')['Profit'].sum()\n",
        "\n",
        "# Define colors for payment channels\n",
        "colors = {'Airtel Money': 'red', 'M-Pesa': 'green', 'Cash': 'orange'}\n",
        "\n",
        "# Plot profitability by payment channel with custom colors\n",
        "ax = profit_by_payment_channel.plot(kind='bar', title=\"Profitability by Payment Channel\", color=[colors.get(x, 'blue') for x in profit_by_payment_channel.index])\n",
        "plt.ylabel('Profit (KES)')\n",
        "\n",
        "# Function to format numbers with abbreviations\n",
        "def format_number(num):\n",
        "    if num >= 1e6:\n",
        "        return f'{num/1e6:.1f}M'\n",
        "    elif num >= 1e3:\n",
        "        return f'{num/1e3:.1f}k'\n",
        "    else:\n",
        "        return num\n",
        "\n",
        "# Add abbreviated numbers on top of bars\n",
        "for p in ax.patches:\n",
        "    height = p.get_height()\n",
        "    ax.text(p.get_x() + p.get_width() / 2, height + 0.5,\n",
        "            format_number(height),\n",
        "            ha='center', va='bottom')\n",
        "\n",
        "# Define the path to the folder in your Google Drive\n",
        "google_drive_folder = '/content/drive/MyDrive/Nganya Project/Plots'\n",
        "\n",
        "# File name with extension\n",
        "file_name = 'Profitability by Payment Channel.png'\n",
        "\n",
        "# Create the full file path\n",
        "full_file_path = os.path.join(google_drive_folder, file_name)\n",
        "\n",
        "# Check if the folder exists and create it if necessary\n",
        "if not os.path.exists(google_drive_folder):\n",
        "    os.makedirs(google_drive_folder)\n",
        "\n",
        "# Add the tag horizontally on the right side of the graph\n",
        "plt.gcf().text(0.92, 0.4, 'GitHub:@LexMainye', fontsize=12, color='red',\n",
        "               fontweight='bold', ha='left', va='center', rotation=90)\n",
        "# Save the plot to the Google Drive folder\n",
        "plt.savefig(full_file_path, dpi=300, bbox_inches='tight')\n",
        "plt.show()\n",
        "plt.close()\n",
        "\n",
        "print(f\"Plot saved to: {full_file_path}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 562
        },
        "id": "oEbos5Kc2CHL",
        "outputId": "8a38bfec-2870-494d-a406-1f3e377d16eb"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Plot saved to: /content/drive/MyDrive/Nganya Project/Plots/Profitability by Payment Channel.png\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# Create a churn label: 1 for return customers, 0 for non-returning\n",
        "week_data['Churn'] = week_data.groupby('Gender')['Date'].transform(lambda x: x.diff().gt(pd.Timedelta(days=1)).astype(int))\n",
        "\n",
        "# Select features and target variable\n",
        "X = week_data[['Amount (KES)', 'Time Period', 'Payment Channel']]\n",
        "X = pd.get_dummies(X, drop_first=True)  # Encode categorical variables\n",
        "y = week_data['Churn']\n",
        "\n",
        "# Train-test split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "# Train Random Forest model\n",
        "model = RandomForestClassifier(random_state=42)\n",
        "model.fit(X_train, y_train)\n",
        "\n",
        "# Predict churn on test data\n",
        "y_pred = model.predict(X_test)\n",
        "\n",
        "# Evaluate the model\n",
        "from sklearn.metrics import classification_report\n",
        "print(classification_report(y_test, y_pred))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MNoxj3qL2IXJ",
        "outputId": "5f3be430-bacf-4d47-ffdc-864097232dd4"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00       421\n",
            "\n",
            "    accuracy                           1.00       421\n",
            "   macro avg       1.00      1.00      1.00       421\n",
            "weighted avg       1.00      1.00      1.00       421\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from mlxtend.frequent_patterns import apriori, association_rules\n",
        "\n",
        "# Prepare data for association rule mining\n",
        "# Convert categorical variables to one-hot encoding\n",
        "# Exclude 'Amount (KES)' from one-hot encoding\n",
        "basket_data = pd.get_dummies(week_data[['Payment Channel', 'Time Period']], drop_first=True)  # Added drop_first=True for better model performance in some cases\n",
        "\n",
        "# Optional: You can discretize 'Amount (KES)' into bins and then include it\n",
        "week_data['Amount_Bin'] = pd.qcut(week_data['Amount (KES)'], q=4, labels=['Low', 'Medium', 'High', 'Very High'])\n",
        "basket_data = pd.get_dummies(week_data[['Payment Channel', 'Time Period', 'Amount_Bin']], drop_first=True)\n",
        "\n",
        "# Apply the Apriori algorithm to find frequent itemsets\n",
        "frequent_itemsets = apriori(basket_data, min_support=0.01, use_colnames=True)\n",
        "\n",
        "# Generate association rules from frequent itemsets\n",
        "# Provide the 'num_itemsets' argument\n",
        "rules = association_rules(frequent_itemsets, metric='lift', min_threshold=1, num_itemsets=frequent_itemsets['itemsets'].apply(len))\n",
        "# num_itemsets is dynamically calculated using the length of 'itemsets'\n",
        "\n",
        "# View the generated rules\n",
        "print(rules)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Jq0IlyXb4aZM",
        "outputId": "d1f7d2a9-3920-44e0-9dfb-6313a32d8a4f"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                                     antecedents  \\\n",
            "0                    (Time Period_Early Morning)   \n",
            "1                         (Payment Channel_Cash)   \n",
            "2                     (Time Period_Late Morning)   \n",
            "3                         (Payment Channel_Cash)   \n",
            "4                          (Time Period_Morning)   \n",
            "..                                           ...   \n",
            "139  (Payment Channel_T-Kash, Time Period_Night)   \n",
            "140         (Time Period_Night, Amount_Bin_High)   \n",
            "141                     (Payment Channel_T-Kash)   \n",
            "142                            (Amount_Bin_High)   \n",
            "143                          (Time Period_Night)   \n",
            "\n",
            "                                     consequents  antecedent support  \\\n",
            "0                         (Payment Channel_Cash)            0.108840   \n",
            "1                    (Time Period_Early Morning)            0.267110   \n",
            "2                         (Payment Channel_Cash)            0.176331   \n",
            "3                     (Time Period_Late Morning)            0.267110   \n",
            "4                         (Payment Channel_Cash)            0.164924   \n",
            "..                                           ...                 ...   \n",
            "139                            (Amount_Bin_High)            0.036597   \n",
            "140                     (Payment Channel_T-Kash)            0.145437   \n",
            "141         (Time Period_Night, Amount_Bin_High)            0.245247   \n",
            "142  (Payment Channel_T-Kash, Time Period_Night)            0.306084   \n",
            "143    (Payment Channel_T-Kash, Amount_Bin_High)            0.145437   \n",
            "\n",
            "     consequent support   support  confidence      lift  representativity  \\\n",
            "0              0.267110  0.031369    0.288210  1.078991               1.0   \n",
            "1              0.108840  0.031369    0.117438  1.078991               1.0   \n",
            "2              0.267110  0.049430    0.280323  1.049467               1.0   \n",
            "3              0.176331  0.049430    0.185053  1.049467               1.0   \n",
            "4              0.267110  0.049430    0.299712  1.122053               1.0   \n",
            "..                  ...       ...         ...       ...               ...   \n",
            "139            0.306084  0.036597    1.000000  3.267081               1.0   \n",
            "140            0.245247  0.036597    0.251634  1.026042               1.0   \n",
            "141            0.145437  0.036597    0.149225  1.026042               1.0   \n",
            "142            0.036597  0.036597    0.119565  3.267081               1.0   \n",
            "143            0.072719  0.036597    0.251634  3.460378               1.0   \n",
            "\n",
            "     leverage  conviction  zhangs_metric   jaccard  certainty  kulczynski  \n",
            "0    0.002296    1.029643       0.082149  0.091034   0.028789    0.202824  \n",
            "1    0.002296    1.009741       0.099890  0.091034   0.009647    0.202824  \n",
            "2    0.002330    1.018360       0.057226  0.125452   0.018029    0.232688  \n",
            "3    0.002330    1.010703       0.064315  0.125452   0.010590    0.232688  \n",
            "4    0.005377    1.046554       0.130259  0.129193   0.044484    0.242383  \n",
            "..        ...         ...            ...       ...        ...         ...  \n",
            "139  0.025395         inf       0.720276  0.119565   1.000000    0.559783  \n",
            "140  0.000929    1.008534       0.029701  0.103356   0.008462    0.200429  \n",
            "141  0.000929    1.004452       0.033629  0.103356   0.004432    0.200429  \n",
            "142  0.025395    1.094236       1.000000  0.119565   0.086120    0.559783  \n",
            "143  0.026021    1.239075       0.832021  0.201571   0.192946    0.377451  \n",
            "\n",
            "[144 rows x 14 columns]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the folder path in Google Drive\n",
        "folder_path = '/content/drive/MyDrive/Nganya Project'  # Replace with your desired path\n",
        "\n",
        "# Create the folder if it doesn't exist\n",
        "if not os.path.exists(folder_path):\n",
        "    os.makedirs(folder_path)\n",
        "\n",
        "# Define the file path for the Excel file\n",
        "file_path = os.path.join(folder_path, 'association_rules.xlsx')\n",
        "\n",
        "# Export the rules DataFrame to Excel\n",
        "rules.to_excel(file_path, index=False)\n",
        "\n",
        "print(f\"Association rules exported to: {file_path}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XP_ha5cj-Qvz",
        "outputId": "99053a7d-bac3-4f7c-8c00-cfd8aa80a08c"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Association rules exported to: /content/drive/MyDrive/Nganya Project/association_rules.xlsx\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from statsmodels.tsa.arima.model import ARIMA\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Aggregate daily transactions (or fare revenue)\n",
        "daily_transactions = week_data.groupby('Date').size()\n",
        "\n",
        "# Fit ARIMA model\n",
        "model = ARIMA(daily_transactions, order=(1, 1, 1))  # ARIMA(p,d,q)\n",
        "model_fit = model.fit()\n",
        "\n",
        "# Forecast the next 7 days\n",
        "forecast = model_fit.forecast(steps=7)\n",
        "\n",
        "# Get the dates for the forecast\n",
        "forecast_dates = pd.date_range(start=daily_transactions.index[-1] + pd.DateOffset(days=1), periods=7)\n",
        "\n",
        "# Plot forecasted values using dates on the x-axis\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.plot(daily_transactions.index, daily_transactions.values, label='Observed')\n",
        "plt.plot(forecast_dates, forecast, label='Forecasted', color='red')\n",
        "plt.legend()\n",
        "plt.title(\"Transaction Forecast using ARIMA\")\n",
        "plt.xlabel(\"Date\")\n",
        "plt.ylabel(\"Number of Transactions\")\n",
        "\n",
        "\n",
        "# Define the path to the folder in your Google Drive\n",
        "google_drive_folder = '/content/drive/MyDrive/Nganya Project/Plots'\n",
        "\n",
        "# File name with extension\n",
        "file_name = 'Transaction Forecast using ARIMA.png'\n",
        "\n",
        "# Create the full file path\n",
        "full_file_path = os.path.join(google_drive_folder, file_name)\n",
        "\n",
        "# Check if the folder exists and create it if necessary\n",
        "if not os.path.exists(google_drive_folder):\n",
        "    os.makedirs(google_drive_folder)\n",
        "\n",
        "# Add the tag horizontally on the right side of the graph\n",
        "plt.gcf().text(0.92, 0.4, 'GitHub:@LexMainye', fontsize=12, color='red',\n",
        "               fontweight='bold', ha='left', va='center', rotation=90)\n",
        "\n",
        "# Save the plot to the Google Drive folder\n",
        "plt.savefig(full_file_path, dpi=300, bbox_inches='tight')\n",
        "plt.show()\n",
        "plt.close()\n",
        "\n",
        "print(f\"Plot saved to: {full_file_path}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 737
        },
        "id": "7pl7_lxA5vKU",
        "outputId": "113be78a-1fd0-458c-f949-e66c0717aa84"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
            "  self._init_dates(dates, freq)\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
            "  self._init_dates(dates, freq)\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
            "  self._init_dates(dates, freq)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Plot saved to: /content/drive/MyDrive/Nganya Project/Plots/Transaction Forecast using ARIMA.png\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LinearRegression\n",
        "\n",
        "# Select features and target variable\n",
        "X = week_data[['Time Period', 'Gender', 'Payment Channel']]\n",
        "X = pd.get_dummies(X, drop_first=True)  # Encode categorical variables\n",
        "y = week_data['Amount (KES)']\n",
        "\n",
        "# Split data into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "# Apply linear regression model\n",
        "model = LinearRegression()\n",
        "model.fit(X_train, y_train)\n",
        "\n",
        "# Evaluate the model\n",
        "y_pred = model.predict(X_test)\n",
        "print(f\"R-squared: {model.score(X_test, y_test)}\")\n",
        "\n",
        "# Create a scatter plot of actual vs. predicted values\n",
        "plt.figure(figsize=(8, 6))  # Adjust figure size if needed\n",
        "plt.scatter(y_test, y_pred, alpha=0.5)  # Plot actual vs. predicted\n",
        "plt.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'k--', lw=2)  # Add a diagonal line\n",
        "plt.xlabel(\"Actual Amount (KES)\")\n",
        "plt.ylabel(\"Predicted Amount (KES)\")\n",
        "plt.title(\"Linear Regression: Actual vs. Predicted Fare Amounts\")\n",
        "\n",
        "# Define the path to the folder in your Google Drive\n",
        "google_drive_folder = '/content/drive/MyDrive/Nganya Project/Plots'\n",
        "\n",
        "# File name with extension\n",
        "file_name = 'Linear Regression: Actual vs. Predicted Fare Amounts.png'\n",
        "\n",
        "# Create the full file path\n",
        "full_file_path = os.path.join(google_drive_folder, file_name)\n",
        "\n",
        "# Check if the folder exists and create it if necessary\n",
        "if not os.path.exists(google_drive_folder):\n",
        "    os.makedirs(google_drive_folder)\n",
        "\n",
        "# Add the tag horizontally on the right side of the graph\n",
        "plt.gcf().text(0.92, 0.4, 'GitHub:@LexMainye', fontsize=12, color='red',\n",
        "               fontweight='bold', ha='left', va='center', rotation=90)\n",
        "\n",
        "# Save the plot to the Google Drive folder\n",
        "plt.savefig(full_file_path, dpi=300, bbox_inches='tight')\n",
        "plt.show()\n",
        "plt.close()\n",
        "\n",
        "print(f\"Plot saved to: {full_file_path}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 652
        },
        "id": "geF8eeBb6Who",
        "outputId": "6f18c581-7858-48ef-f8bf-f0105982fbb8"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "R-squared: 0.7838583223604558\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Plot saved to: /content/drive/MyDrive/Nganya Project/Plots/Linear Regression: Actual vs. Predicted Fare Amounts.png\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.cluster import KMeans\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "# Select relevant features for clustering (e.g., amount, time period, payment channel)\n",
        "data_for_clustering = week_data[['Amount (KES)', 'Time Period', 'Payment Channel']]\n",
        "\n",
        "# 1. Create a LabelEncoder instance\n",
        "encoder = LabelEncoder()\n",
        "\n",
        "# 2. Fit and transform the 'Time Period' column to numerical labels\n",
        "data_for_clustering['Time Period'] = encoder.fit_transform(data_for_clustering['Time Period'])\n",
        "\n",
        "# Convert categorical variables (payment channel) into numeric values\n",
        "data_for_clustering = pd.get_dummies(data_for_clustering, columns=['Payment Channel'])\n",
        "\n",
        "# Standardize the data\n",
        "scaler = StandardScaler()\n",
        "scaled_data = scaler.fit_transform(data_for_clustering)\n",
        "\n",
        "# Apply K-means clustering\n",
        "kmeans = KMeans(n_clusters=3, random_state=42)\n",
        "week_data['Cluster'] = kmeans.fit_predict(scaled_data)\n",
        "\n",
        "# Analyze the clusters\n",
        "# Include only numeric features in cluster analysis\n",
        "# Filter out non-numeric columns before calculating the mean\n",
        "cluster_analysis = week_data.groupby('Cluster')[[\n",
        "    'Amount (KES)'\n",
        "] + [\n",
        "    col for col in week_data.columns\n",
        "    if 'Payment Channel' in col and week_data[col].dtype != 'object'  # Filter out non-numeric columns\n",
        "]].mean()\n",
        "\n",
        "print(cluster_analysis)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UCOnMrEi7H2P",
        "outputId": "23269b32-92d7-4141-8a7e-211d4ba160a0"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n",
            "<ipython-input-38-de84cff6f89b>:12: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  data_for_clustering['Time Period'] = encoder.fit_transform(data_for_clustering['Time Period'])\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "         Amount (KES)\n",
            "Cluster              \n",
            "0          103.377358\n",
            "1          104.415323\n",
            "2          103.432282\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Example Linear Programming for pricing optimization (simplified)\n",
        "from scipy.optimize import linprog\n",
        "\n",
        "# Objective function: Maximize revenue (assuming fare rates for peak and off-peak periods)\n",
        "c = [-120, -100]  # Negative because linprog minimizes, we want to maximize\n",
        "A = [[1, 1]]  # Total transactions constraint (e.g., no more than 70 transactions per period)\n",
        "b = [140]  # Max total fare for peak and off-peak periods\n",
        "\n",
        "# Bounds for each fare rate (in KES)\n",
        "x0_bounds = (100, 150)\n",
        "x1_bounds = (80, 100)\n",
        "\n",
        "# Run Linear Programming to optimize fares\n",
        "res = linprog(c, A_ub=A, b_ub=b, bounds=[x0_bounds, x1_bounds], method='highs')\n",
        "\n",
        "# Print optimal fares\n",
        "print(\"Optimal Fare Rates:\", res.x)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_REC1bBZ8MzX",
        "outputId": "ea5336c0-28ad-46b8-f972-6e6d69cad9d6"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Optimal Fare Rates: None\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        }
      ]
    }
  ]
}