AdvML_4(Retail).ipynb

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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from mlxtend.frequent_patterns import apriori\n",
        "from mlxtend.frequent_patterns import association_rules"
      ]
    },
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      "source": [
        "#Read\n",
        "df=pd.read_csv(\"/content/Groceries_dataset.csv\")\n",
        "df"
      ],
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            "/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"
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              "       Member_number        Date        itemDescription\n",
              "0               1808  21-07-2015         tropical fruit\n",
              "1               2552  05-01-2015             whole milk\n",
              "2               2300  19-09-2015              pip fruit\n",
              "3               1187  12-12-2015       other vegetables\n",
              "4               3037  01-02-2015             whole milk\n",
              "...              ...         ...                    ...\n",
              "38760           4471  08-10-2014          sliced cheese\n",
              "38761           2022  23-02-2014                  candy\n",
              "38762           1097  16-04-2014               cake bar\n",
              "38763           1510  03-12-2014  fruit/vegetable juice\n",
              "38764           1521  26-12-2014               cat food\n",
              "\n",
              "[38765 rows x 3 columns]"
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              "      <th>38762</th>\n",
              "      <td>1097</td>\n",
              "      <td>16-04-2014</td>\n",
              "      <td>cake bar</td>\n",
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              "    <tr>\n",
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              "      <td>1510</td>\n",
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      "source": [
        "#Check Null values\n",
        "df.isnull().sum()"
      ],
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          "height": 234
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          "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"
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      "source": [
        "#Check Duplicates\n",
        "df.duplicated().sum()"
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        },
        "id": "httt9rR328Mc",
        "outputId": "55c1f390-78a5-48a3-8b19-e3bbc1c8e793"
      },
      "execution_count": 4,
      "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": "execute_result",
          "data": {
            "text/plain": [
              "759"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Drop Duplicates\n",
        "df.drop_duplicates(inplace=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VU-Hq4Ki3WcJ",
        "outputId": "e86578f8-0581-473a-9d15-a7130b67a047"
      },
      "execution_count": 5,
      "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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.duplicated().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2PzUkUR73kTk",
        "outputId": "25ad8375-2604-4d54-d0c8-c1fcd9120532"
      },
      "execution_count": 6,
      "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": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Plot for itemDescription\n",
        "top_items = df['itemDescription'].value_counts().head(10)\n",
        "import matplotlib.pyplot as plt\n",
        "plt.figure(figsize=(10,6))\n",
        "top_items.plot(kind='bar',color='skyblue')\n",
        "plt.title('Top 10 Items based on count')\n",
        "plt.xlabel('Items')\n",
        "plt.ylabel('Count')\n",
        "plt.xticks(rotation=45, ha='right')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 900
        },
        "id": "69h7nz5r3m2a",
        "outputId": "2e80cb8d-25e0-4d67-c2dd-ec336d501ca1"
      },
      "execution_count": 7,
      "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": "execute_result",
          "data": {
            "text/plain": [
              "(array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]),\n",
              " [Text(0, 0, 'whole milk'),\n",
              "  Text(1, 0, 'other vegetables'),\n",
              "  Text(2, 0, 'rolls/buns'),\n",
              "  Text(3, 0, 'soda'),\n",
              "  Text(4, 0, 'yogurt'),\n",
              "  Text(5, 0, 'root vegetables'),\n",
              "  Text(6, 0, 'tropical fruit'),\n",
              "  Text(7, 0, 'bottled water'),\n",
              "  Text(8, 0, 'sausage'),\n",
              "  Text(9, 0, 'citrus fruit')])"
            ]
          },
          "metadata": {},
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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+vf7000+aNWtWdXFx0QIFCuimTZte6r2bN29uDGP+/N/s2bNtlr179662atVKU6RIoYkTJ9ayZcvqnj17Xup9Yhv6/cSJE1qmTBlNlCiRiojNMPA3btzQ9u3bq4+Pj8aPH1+9vLy0YsWKOn36dGMZ69Dvv/76q83rzp49W0UkRm3WYcCtQ71v2LBBa9Wqpd7e3pogQQL19vbWRo0a6alTp/7187xsu9+7d09btGihKVOm1KRJk2rlypX1xIkT6uvra/N5hw0bpkWLFlUPDw9NlCiRZs+eXb/++mubIe1VVc+cOaPNmjVTLy8vjR8/vqZNm1arV6+uixcvtlnu0KFDWrZsWU2YMKGmTZtWhw4dqj/88MNLD2UeFBSkJUuW1ESJEqmbm5vWqFFDjx079o/taWVt/5d5nz///FM//PBDdXV11SRJkmjevHlthl9/2VpUVdevX6+5c+fWBAkSqJ+fn/70008vHPq9ffv2MZ7//P8TVdWhQ4dq2rRpNV68eAwDD8AUFlWuFgWAuMpisUj79u1jdDkEAABxH9dsAQAAAIAJCFsAAAAAYALCFgAAAACYgNEIASAO47JaAADeXZzZAgAAAAATcGbrJURGRsrVq1fF1dWVO84DAAAADkxV5f79++Lt7S3x4v3zuSvC1ku4evWq+Pj42LsMAAAAAHHEpUuXJF26dP+4DGHrJbi6uopIVIO6ubnZuRoAAAAA9hIaGio+Pj5GRvgnhK2XYO066ObmRtgCAAAA8FKXFzFABgAAAACYgLAFAAAAACYgbAEAAACACQhbAAAAAGACwhYAAAAAmICwBQAAAAAmIGwBAAAAgAkIWwAAAABgAsIWAAAAAJiAsAUAAAAAJiBsAQAAAIAJCFsAAAAAYALCFgAAAACYgLAFAAAAACYgbAEAAACACQhbAAAAAGACwhYAAAAAmICwBQAAAAAmIGwBAAAAgAmc7V2Aoxr51217l/BCvQqktHcJAAAAwDuPM1sAAAAAYALCFgAAAACYgLAFAAAAACYgbAEAAACACQhbAAAAAGACwhYAAAAAmICwBQAAAAAmIGwBAAAAgAkIWwAAAABgAsIWAAAAAJiAsAUAAAAAJiBsAQAAAIAJCFsAAAAAYALCFgAAAACYgLAFAAAAACYgbAEAAACACQhbAAAAAGACwhYAAAAAmICwBQAAAAAmIGwBAAAAgAkIWwAAAABgAsIWAAAAAJiAsAUAAAAAJiBsAQAAAIAJCFsAAAAAYALCFgAAAACYgLAFAAAAACYgbAEAAACACQhbAAAAAGACwhYAAAAAmICwBQAAAAAmIGwBAAAAgAkIWwAAAABgAsIWAAAAAJiAsAUAAAAAJiBsAQAAAIAJCFsAAAAAYALCFgAAAACYgLAFAAAAACYgbAEAAACACQhbAAAAAGACwhYAAAAAmICwBQAAAAAmIGwBAAAAgAkIWwAAAABgAsIWAAAAAJiAsAUAAAAAJiBsAQAAAIAJCFsAAAAAYALCFgAAAACYgLAFAAAAACYgbAEAAACACQhbAAAAAGACwhYAAAAAmICwBQAAAAAmIGwBAAAAgAkIWwAAAABgAsIWAAAAAJiAsAUAAAAAJiBsAQAAAIAJCFsAAAAAYALCFgAAAACYgLAFAAAAACawa9gaMWKEFClSRFxdXcXT01Nq164tJ0+etFnmyZMn0r59e0mRIoUkTZpU6tatKzdu3LBZ5uLFi1KtWjVJnDixeHp6Svfu3SU8PNxmmc2bN0vBggXFxcVFsmTJInPmzDH74wEAAABwYHYNW1u2bJH27dvLzp07JTAwUMLCwqRSpUry8OFDY5muXbvKb7/9Jr/++qts2bJFrl69Kh999JExPyIiQqpVqybPnj2T7du3y9y5c2XOnDkyYMAAY5lz585JtWrVpHz58nLgwAHp0qWLtG7dWtatW/dWPy8AAAAAx2FRVbV3EVa3bt0ST09P2bJli5QpU0ZCQkIkVapUsmDBAqlXr56IiJw4cUJy5MghO3bskOLFi8vvv/8u1atXl6tXr0rq1KlFRGTatGnSs2dPuXXrliRIkEB69uwpq1evliNHjhjv1bBhQwkODpa1a9f+a12hoaHi7u4uISEh4ubm9kY+68i/br+R1zFDrwIp7V0CAAAAECe9SjaIU9dshYSEiIhI8uTJRURk3759EhYWJv7+/sYy2bNnl/Tp08uOHTtERGTHjh2SJ08eI2iJiFSuXFlCQ0Pl6NGjxjLRX8O6jPU1nvf06VMJDQ21+QMAAACAVxFnwlZkZKR06dJFSpYsKblz5xYRkevXr0uCBAnEw8PDZtnUqVPL9evXjWWiBy3rfOu8f1omNDRUHj9+HKOWESNGiLu7u/Hn4+PzRj4jAAAAAMcRZ8JW+/bt5ciRI7Jo0SJ7lyK9e/eWkJAQ4+/SpUv2LgkAAADAO8bZ3gWIiHTo0EFWrVolW7dulXTp0hnTvby85NmzZxIcHGxzduvGjRvi5eVlLLN7926b17OOVhh9medHMLxx44a4ublJokSJYtTj4uIiLi4ub+SzAQAAAHBMdj2zparSoUMHWbZsmWzcuFEyZsxoM79QoUISP3582bBhgzHt5MmTcvHiRSlRooSIiJQoUUIOHz4sN2/eNJYJDAwUNzc3yZkzp7FM9NewLmN9DQAAAAB40+x6Zqt9+/ayYMECWbFihbi6uhrXWLm7u0uiRInE3d1dWrVqJd26dZPkyZOLm5ubdOzYUUqUKCHFixcXEZFKlSpJzpw5pWnTpjJq1Ci5fv269OvXT9q3b2+cnWrXrp1MmjRJevToIS1btpSNGzfKL7/8IqtXr7bbZwcAAADwfrPrma2pU6dKSEiIlCtXTtKkSWP8/fzzz8Yy48aNk+rVq0vdunWlTJky4uXlJUuXLjXmOzk5yapVq8TJyUlKlCghTZo0kWbNmsmQIUOMZTJmzCirV6+WwMBAyZcvn4wZM0ZmzpwplStXfqufFwAAAIDjiFP32YqruM8WAAAAAJF3+D5bAAAAAPC+IGwBAAAAgAkIWwAAAABgAsIWAAAAAJiAsAUAAAAAJiBsAQAAAIAJCFsAAAAAYALCFgAAAACYgLAFAAAAACYgbAEAAACACQhbAAAAAGACwhYAAAAAmICwBQAAAAAmIGwBAAAAgAkIWwAAAABgAsIWAAAAAJiAsAUAAAAAJiBsAQAAAIAJCFsAAAAAYALCFgAAAACYgLAFAAAAACYgbAEAAACACQhbAAAAAGACwhYAAAAAmICwBQAAAAAmcLZ3AcDLGvnXbXuX8EK9CqS0dwkAAACIYzizBQAAAAAmIGwBAAAAgAkIWwAAAABgAsIWAAAAAJiAsAUAAAAAJiBsAQAAAIAJCFsAAAAAYALCFgAAAACYgLAFAAAAACYgbAEAAACACQhbAAAAAGACwhYAAAAAmICwBQAAAAAmIGwBAAAAgAkIWwAAAABgAsIWAAAAAJiAsAUAAAAAJiBsAQAAAIAJCFsAAAAAYALCFgAAAACYgLAFAAAAACYgbAEAAACACQhbAAAAAGACwhYAAAAAmICwBQAAAAAmIGwBAAAAgAkIWwAAAABgAsIWAAAAAJiAsAUAAAAAJiBsAQAAAIAJCFsAAAAAYALCFgAAAACYgLAFAAAAACZwtncBAMw18q/b9i7hhXoVSGnvEgAAAEzDmS0AAAAAMAFhCwAAAABMQNgCAAAAABMQtgAAAADABIQtAAAAADABYQsAAAAATEDYAgAAAAATELYAAAAAwASELQAAAAAwAWELAAAAAExA2AIAAAAAExC2AAAAAMAEhC0AAAAAMAFhCwAAAABMQNgCAAAAABMQtgAAAADABIQtAAAAADABYQsAAAAATEDYAgAAAAATELYAAAAAwASELQAAAAAwAWELAAAAAExA2AIAAAAAE9g1bG3dulVq1Kgh3t7eYrFYZPny5TbzP/30U7FYLDZ/VapUsVnm7t278sknn4ibm5t4eHhIq1at5MGDBzbLHDp0SEqXLi0JEyYUHx8fGTVqlNkfDQAAAICDs2vYevjwoeTLl08mT578wmWqVKki165dM/4WLlxoM/+TTz6Ro0ePSmBgoKxatUq2bt0qbdu2NeaHhoZKpUqVxNfXV/bt2yfffvutDBo0SKZPn27a5wIAAAAAZ3u+eUBAgAQEBPzjMi4uLuLl5RXrvOPHj8vatWtlz549UrhwYRERmThxolStWlVGjx4t3t7eMn/+fHn27JnMmjVLEiRIILly5ZIDBw7I2LFjbUJZdE+fPpWnT58aj0NDQ1/zEwIAAABwVHH+mq3NmzeLp6en+Pn5yeeffy537twx5u3YsUM8PDyMoCUi4u/vL/HixZNdu3YZy5QpU0YSJEhgLFO5cmU5efKk3Lt3L9b3HDFihLi7uxt/Pj4+Jn06AAAAAO+rOB22qlSpIj/++KNs2LBBvvnmG9myZYsEBARIRESEiIhcv35dPD09bZ7j7OwsyZMnl+vXrxvLpE6d2mYZ62PrMs/r3bu3hISEGH+XLl160x8NAAAAwHvOrt0I/03Dhg2Nf+fJk0fy5s0rmTNnls2bN0vFihVNe18XFxdxcXEx7fUBAAAAvP/i9Jmt52XKlElSpkwpp0+fFhERLy8vuXnzps0y4eHhcvfuXeM6Ly8vL7lx44bNMtbHL7oWDAAAAAD+q3cqbF2+fFnu3LkjadKkERGREiVKSHBwsOzbt89YZuPGjRIZGSnFihUzltm6dauEhYUZywQGBoqfn58kS5bs7X4AAAAAAA7DrmHrwYMHcuDAATlw4ICIiJw7d04OHDggFy9elAcPHkj37t1l586dcv78edmwYYPUqlVLsmTJIpUrVxYRkRw5ckiVKlWkTZs2snv3btm2bZt06NBBGjZsKN7e3iIi0rhxY0mQIIG0atVKjh49Kj///LN899130q1bN3t9bAAAAAAOwK5ha+/evVKgQAEpUKCAiIh069ZNChQoIAMGDBAnJyc5dOiQ1KxZU7JlyyatWrWSQoUKyR9//GFzPdX8+fMle/bsUrFiRalataqUKlXK5h5a7u7usn79ejl37pwUKlRIvvzySxkwYMALh30HAAAAgDfBrgNklCtXTlT1hfPXrVv3r6+RPHlyWbBgwT8ukzdvXvnjjz9euT4AAAAAeF3v1DVbAAAAAPCuiNNDvwOAPYz867a9S3ihXgVS2rsEAADwkjizBQAAAAAmIGwBAAAAgAkIWwAAAABgAsIWAAAAAJiAsAUAAAAAJiBsAQAAAIAJCFsAAAAAYALCFgAAAACYgLAFAAAAACYgbAEAAACACQhbAAAAAGACwhYAAAAAmICwBQAAAAAmIGwBAAAAgAkIWwAAAABgAsIWAAAAAJiAsAUAAAAAJiBsAQAAAIAJCFsAAAAAYALCFgAAAACYgLAFAAAAACZ4rbCVKVMmuXPnTozpwcHBkilTpv9cFAAAAAC8614rbJ0/f14iIiJiTH/69KlcuXLlPxcFAAAAAO8651dZeOXKlca/161bJ+7u7sbjiIgI2bBhg2TIkOGNFQcAAAAA76pXClu1a9cWERGLxSLNmze3mRc/fnzJkCGDjBkz5o0VBwAAAADvqlcKW5GRkSIikjFjRtmzZ4+kTJnSlKIAAAAA4F33SmHL6ty5c2+6DgAAAAB4r7xW2BIR2bBhg2zYsEFu3rxpnPGymjVr1n8uDAAAAADeZa8VtgYPHixDhgyRwoULS5o0acRisbzpugAAAADgnfZaYWvatGkyZ84cadq06ZuuBwAAAADeC68Vtp49eyYffPDBm64FAPAOG/nXbXuX8EK9CjCgEwDg7Xutmxq3bt1aFixY8KZrAQAAAID3xmud2Xry5IlMnz5dgoKCJG/evBI/fnyb+WPHjn0jxQEAAADAu+q1wtahQ4ckf/78IiJy5MgRm3kMlgEAAAAArxm2Nm3a9KbrAAAAAID3ymtdswUAAAAA+GevdWarfPny/9hdcOPGja9dEAAAAAC8D14rbFmv17IKCwuTAwcOyJEjR6R58+Zvoi4AAAAAeKe9VtgaN25crNMHDRokDx48+E8FAQAAAMD74I1es9WkSROZNWvWm3xJAAAAAHgnvdGwtWPHDkmYMOGbfEkAAAAAeCe9VjfCjz76yOaxqsq1a9dk79690r9//zdSGAAAAAC8y14rbLm7u9s8jhcvnvj5+cmQIUOkUqVKb6QwAAAAAHiXvVbYmj179puuAwAAAADeK68Vtqz27dsnx48fFxGRXLlySYECBd5IUQAAAADwrnutsHXz5k1p2LChbN68WTw8PEREJDg4WMqXLy+LFi2SVKlSvckaAQAAAOCd81qjEXbs2FHu378vR48elbt378rdu3flyJEjEhoaKp06dXrTNQIAAADAO+e1zmytXbtWgoKCJEeOHMa0nDlzyuTJkxkgAwAAAADkNc9sRUZGSvz48WNMjx8/vkRGRv7nogAAAADgXfdaYatChQrSuXNnuXr1qjHtypUr0rVrV6lYseIbKw4AAAAA3lWvFbYmTZokoaGhkiFDBsmcObNkzpxZMmbMKKGhoTJx4sQ3XSMAAAAAvHNe65otHx8f2b9/vwQFBcmJEydERCRHjhzi7+//RosDAAAAgHfVK53Z2rhxo+TMmVNCQ0PFYrHIhx9+KB07dpSOHTtKkSJFJFeuXPLHH3+YVSsAAAAAvDNeKWyNHz9e2rRpI25ubjHmubu7y2effSZjx459Y8UBAAAAwLvqlcLWwYMHpUqVKi+cX6lSJdm3b99/LgoAAAAA3nWvFLZu3LgR65DvVs7OznLr1q3/XBQAAAAAvOteKWylTZtWjhw58sL5hw4dkjRp0vznogAAAADgXfdKYatq1arSv39/efLkSYx5jx8/loEDB0r16tXfWHEAAAAA8K56paHf+/XrJ0uXLpVs2bJJhw4dxM/PT0RETpw4IZMnT5aIiAjp27evKYUCAAAAwLvklcJW6tSpZfv27fL5559L7969RVVFRMRisUjlypVl8uTJkjp1alMKBQAAAIB3ySvf1NjX11fWrFkj9+7dk9OnT4uqStasWSVZsmRm1AcAAAAA76RXDltWyZIlkyJFirzJWgAAAADgvfFKA2QAAAAAAF4OYQsAAAAATEDYAgAAAAATELYAAAAAwASELQAAAAAwAWELAAAAAExA2AIAAAAAExC2AAAAAMAEhC0AAAAAMAFhCwAAAABMQNgCAAAAABMQtgAAAADABIQtAAAAADABYQsAAAAATOBs7wIAAHBUI/+6be8SXqhXgZT2LgEA3nmc2QIAAAAAExC2AAAAAMAEhC0AAAAAMIFdw9bWrVulRo0a4u3tLRaLRZYvX24zX1VlwIABkiZNGkmUKJH4+/vL33//bbPM3bt35ZNPPhE3Nzfx8PCQVq1ayYMHD2yWOXTokJQuXVoSJkwoPj4+MmrUKLM/GgAAAAAHZ9cBMh4+fCj58uWTli1bykcffRRj/qhRo2TChAkyd+5cyZgxo/Tv318qV64sx44dk4QJE4qIyCeffCLXrl2TwMBACQsLkxYtWkjbtm1lwYIFIiISGhoqlSpVEn9/f5k2bZocPnxYWrZsKR4eHtK2bdu3+nkBAMB/x8AiAN4Vdg1bAQEBEhAQEOs8VZXx48dLv379pFatWiIi8uOPP0rq1Kll+fLl0rBhQzl+/LisXbtW9uzZI4ULFxYRkYkTJ0rVqlVl9OjR4u3tLfPnz5dnz57JrFmzJEGCBJIrVy45cOCAjB079oVh6+nTp/L06VPjcWho6Bv+5AAAAADed3H2mq1z587J9evXxd/f35jm7u4uxYoVkx07doiIyI4dO8TDw8MIWiIi/v7+Ei9ePNm1a5exTJkyZSRBggTGMpUrV5aTJ0/KvXv3Yn3vESNGiLu7u/Hn4+NjxkcEAAAA8B6Ls2Hr+vXrIiKSOnVqm+mpU6c25l2/fl08PT1t5js7O0vy5MltlontNaK/x/N69+4tISEhxt+lS5f++wcCAAAA4FC4qXEsXFxcxMXFxd5lAAAAAHiHxdkzW15eXiIicuPGDZvpN27cMOZ5eXnJzZs3beaHh4fL3bt3bZaJ7TWivwcAAAAAvGlxNmxlzJhRvLy8ZMOGDca00NBQ2bVrl5QoUUJEREqUKCHBwcGyb98+Y5mNGzdKZGSkFCtWzFhm69atEhYWZiwTGBgofn5+kixZsrf0aQAAAAA4GruGrQcPHsiBAwfkwIEDIhI1KMaBAwfk4sWLYrFYpEuXLjJs2DBZuXKlHD58WJo1aybe3t5Su3ZtERHJkSOHVKlSRdq0aSO7d++Wbdu2SYcOHaRhw4bi7e0tIiKNGzeWBAkSSKtWreTo0aPy888/y3fffSfdunWz06cGAAAA4Ajses3W3r17pXz58sZjawBq3ry5zJkzR3r06CEPHz6Utm3bSnBwsJQqVUrWrl1r3GNLRGT+/PnSoUMHqVixosSLF0/q1q0rEyZMMOa7u7vL+vXrpX379lKoUCFJmTKlDBgwgHtsAQAAADCVXcNWuXLlRFVfON9isciQIUNkyJAhL1wmefLkxg2MXyRv3rzyxx9/vHadAAAAAPCq4uw1WwAAAADwLiNsAQAAAIAJCFsAAAAAYALCFgAAAACYgLAFAAAAACYgbAEAAACACQhbAAAAAGACwhYAAAAAmICwBQAAAAAmcLZ3AQAAADDXyL9u27uEF+pVIKW9SwBMQ9gCAAAAYkFIxX9FN0IAAAAAMAFhCwAAAABMQNgCAAAAABMQtgAAAADABIQtAAAAADABYQsAAAAATEDYAgAAAAATELYAAAAAwASELQAAAAAwAWELAAAAAExA2AIAAAAAExC2AAAAAMAEhC0AAAAAMAFhCwAAAABMQNgCAAAAABMQtgAAAADABIQtAAAAADABYQsAAAAATEDYAgAAAAATONu7AAAAAADvh5F/3bZ3CS/Uq0DKt/6enNkCAAAAABMQtgAAAADABIQtAAAAADABYQsAAAAATEDYAgAAAAATELYAAAAAwASELQAAAAAwAWELAAAAAExA2AIAAAAAExC2AAAAAMAEhC0AAAAAMAFhCwAAAABMQNgCAAAAABMQtgAAAADABIQtAAAAADABYQsAAAAATEDYAgAAAAATELYAAAAAwASELQAAAAAwAWELAAAAAExA2AIAAAAAExC2AAAAAMAEhC0AAAAAMAFhCwAAAABMQNgCAAAAABMQtgAAAADABIQtAAAAADABYQsAAAAATEDYAgAAAAATELYAAAAAwASELQAAAAAwAWELAAAAAExA2AIAAAAAExC2AAAAAMAEhC0AAAAAMAFhCwAAAABMQNgCAAAAABMQtgAAAADABIQtAAAAADABYQsAAAAATEDYAgAAAAATELYAAAAAwASELQAAAAAwAWELAAAAAExA2AIAAAAAExC2AAAAAMAEhC0AAAAAMAFhCwAAAABMQNgCAAAAABMQtgAAAADABIQtAAAAADBBnA5bgwYNEovFYvOXPXt2Y/6TJ0+kffv2kiJFCkmaNKnUrVtXbty4YfMaFy9elGrVqknixInF09NTunfvLuHh4W/7owAAAABwMM72LuDf5MqVS4KCgozHzs7/K7lr166yevVq+fXXX8Xd3V06dOggH330kWzbtk1ERCIiIqRatWri5eUl27dvl2vXrkmzZs0kfvz4Mnz48Lf+WQAAAAA4jjgftpydncXLyyvG9JCQEPnhhx9kwYIFUqFCBRERmT17tuTIkUN27twpxYsXl/Xr18uxY8ckKChIUqdOLfnz55ehQ4dKz549ZdCgQZIgQYJY3/Pp06fy9OlT43FoaKg5Hw4AAADAeytOdyMUEfn777/F29tbMmXKJJ988olcvHhRRET27dsnYWFh4u/vbyybPXt2SZ8+vezYsUNERHbs2CF58uSR1KlTG8tUrlxZQkND5ejRoy98zxEjRoi7u7vx5+PjY9KnAwAAAPC+itNhq1ixYjJnzhxZu3atTJ06Vc6dOyelS5eW+/fvy/Xr1yVBggTi4eFh85zUqVPL9evXRUTk+vXrNkHLOt8670V69+4tISEhxt+lS5fe7AcDAAAA8N6L090IAwICjH/nzZtXihUrJr6+vvLLL79IokSJTHtfFxcXcXFxMe31AQAAALz/4vSZred5eHhItmzZ5PTp0+Ll5SXPnj2T4OBgm2Vu3LhhXOPl5eUVY3RC6+PYrgMDAAAAgDflnQpbDx48kDNnzkiaNGmkUKFCEj9+fNmwYYMx/+TJk3Lx4kUpUaKEiIiUKFFCDh8+LDdv3jSWCQwMFDc3N8mZM+dbrx8AAACA44jT3Qi/+uorqVGjhvj6+srVq1dl4MCB4uTkJI0aNRJ3d3dp1aqVdOvWTZInTy5ubm7SsWNHKVGihBQvXlxERCpVqiQ5c+aUpk2byqhRo+T69evSr18/ad++Pd0EAQAAAJgqToety5cvS6NGjeTOnTuSKlUqKVWqlOzcuVNSpUolIiLjxo2TePHiSd26deXp06dSuXJlmTJlivF8JycnWbVqlXz++edSokQJSZIkiTRv3lyGDBlir48EAAAAwEHE6bC1aNGif5yfMGFCmTx5skyePPmFy/j6+sqaNWvedGkAAAAA8I/eqWu2AAAAAOBdQdgCAAAAABMQtgAAAADABIQtAAAAADABYQsAAAAATEDYAgAAAAATELYAAAAAwASELQAAAAAwAWELAAAAAExA2AIAAAAAExC2AAAAAMAEhC0AAAAAMAFhCwAAAABMQNgCAAAAABMQtgAAAADABIQtAAAAADABYQsAAAAATEDYAgAAAAATELYAAAAAwASELQAAAAAwAWELAAAAAExA2AIAAAAAExC2AAAAAMAEhC0AAAAAMAFhCwAAAABMQNgCAAAAABMQtgAAAADABIQtAAAAADABYQsAAAAATEDYAgAAAAATELYAAAAAwASELQAAAAAwAWELAAAAAExA2AIAAAAAExC2AAAAAMAEhC0AAAAAMAFhCwAAAABMQNgCAAAAABMQtgAAAADABIQtAAAAADABYQsAAAAATEDYAgAAAAATELYAAAAAwASELQAAAAAwAWELAAAAAExA2AIAAAAAExC2AAAAAMAEhC0AAAAAMAFhCwAAAABMQNgCAAAAABMQtgAAAADABIQtAAAAADABYQsAAAAATEDYAgAAAAATELYAAAAAwASELQAAAAAwAWELAAAAAExA2AIAAAAAExC2AAAAAMAEhC0AAAAAMAFhCwAAAABMQNgCAAAAABMQtgAAAADABIQtAAAAADABYQsAAAAATEDYAgAAAAATELYAAAAAwASELQAAAAAwAWELAAAAAExA2AIAAAAAExC2AAAAAMAEhC0AAAAAMAFhCwAAAABMQNgCAAAAABMQtgAAAADABIQtAAAAADABYQsAAAAATEDYAgAAAAATELYAAAAAwASELQAAAAAwAWELAAAAAExA2AIAAAAAEzhU2Jo8ebJkyJBBEiZMKMWKFZPdu3fbuyQAAAAA7ymHCVs///yzdOvWTQYOHCj79++XfPnySeXKleXmzZv2Lg0AAADAe8jZ3gW8LWPHjpU2bdpIixYtRERk2rRpsnr1apk1a5b06tXLZtmnT5/K06dPjcchISEiIhIaGvrG6nny4P4be603LTQ0gb1LiBVt9npot1dHm70e2u3V0Wavh3Z7dbTZ66HdXp0jtJk1E6jqvy5r0ZdZ6h337NkzSZw4sSxevFhq165tTG/evLkEBwfLihUrbJYfNGiQDB48+C1XCQAAAOBdcenSJUmXLt0/LuMQZ7Zu374tERERkjp1apvpqVOnlhMnTsRYvnfv3tKtWzfjcWRkpNy9e1dSpEghFovF9HpfRWhoqPj4+MilS5fEzc3N3uW8M2i3V0ebvR7a7dXRZq+Hdnt1tNnrod1eHW32euJqu6mq3L9/X7y9vf91WYcIW6/KxcVFXFxcbKZ5eHjYp5iX5ObmFqe+hO8K2u3V0Wavh3Z7dbTZ66HdXh1t9npot1dHm72euNhu7u7uL7WcQwyQkTJlSnFycpIbN27YTL9x44Z4eXnZqSoAAAAA7zOHCFsJEiSQQoUKyYYNG4xpkZGRsmHDBilRooQdKwMAAADwvnKYboTdunWT5s2bS+HChaVo0aIyfvx4efjwoTE64bvKxcVFBg4cGKPbI/4Z7fbqaLPXQ7u9Otrs9dBur442ez2026ujzV7P+9BuDjEaodWkSZPk22+/levXr0v+/PllwoQJUqxYMXuXBQAAAOA95FBhCwAAAADeFoe4ZgsAAAAA3jbCFgAAAACYgLAFAAAAACYgbAEAAACACQhbeG8w1gsAAADiEsJWHEVweDWRkZFisVhERCQsLExEaEMgrmLdfHmRkZH2LgEOZvXq1bJy5Up7l4H3UPTtmXVf7dGjR/Yq560hbMVRFotF1q1bJ7///ru9S4nzIiMjJV68qK/ymDFjpHPnzvLw4UMjfOGfRd/xZSf41dFm/+7AgQMSHh4uIiLjxo2Tffv22bmid0P0bdsvv/wihw4dsnNF74YDBw4YO3Xjx4+X/fv327mid8euXbukcePGcvfuXYI+3rh48eLJmTNn5NixYxI/fnxZsmSJDBw4UB48eGDv0kxF2Iojjhw5Yvw7MjJSQkJCpHfv3mzsXoJ1Z6RHjx4ybtw4yZkzp9y8edOYz87wi0VERBih9NGjR2KxWGivf2Ftn4cPH0pYWJhERETYuaK47fDhw9KiRQvp37+/dOrUSb788ktxdXW1d1lxnqoa27bevXtLz5495bfffpMHDx6wjv6DgwcPSosWLWTAgAHSuXNn6datmyRNmtTeZb0Tzp07J6tWrZKuXbvKp59+anz/8OrYd4vd48ePZcCAAVK0aFEZN26cfPzxx5I/f/73fh11tncBENm8ebNUqFBBZs+eLc2bN5d48eKJu7u7PH36VJydnUVVOUvzLxYuXCjz5s2TlStXSpEiRUREJDw8XO7fvy/JkiWzc3VxU0REhDg5OYmISIcOHeTJkycybtw4doT/gXVdXLNmjfzwww9y+vRp8ff3lxo1aki5cuXsXV6clDVrVqlbt65MmjRJHjx4ILt27RI/Pz8JDw8XZ2d+gl7Eus3/+uuvZcaMGbJ27VrJkyePuLi42LmyuOnIkSOSO3duyZYtm9SpU0emTJkiDx8+lD179ki2bNn4vv2Ls2fPSsOGDeXKlSvy+eefi4iw7/GSZs+eLQcOHJCIiAjJnz+/tG7dmqD6nFWrVkn16tUlUaJEMnz4cDl+/Lj06NFDvv76a/nkk0/e+/WTb0McUK5cOenRo4e0a9dO5s2bJyJidLnx8PBgY/cSTp06JR988IEUKVJEDh8+LN99953ky5dPcuXKJZMnT7Z3eXGSNWjVrl1b/vzzTwkICJDHjx8b8zl6HpPFYpGVK1dKvXr1JHfu3NK8eXM5e/asdOzYkS6/z7Ee2U2YMKGkS5dOnJycJEOGDLJs2TJ5/PixODs7c1bwX9y9e1e2bt0qkyZNksKFC8uNGzckKChIGjZsKGPGjJErV67Yu8Q4oW/fvjJ06FAJDw+XRIkSSbZs2SQiIkJ8fX1l+fLl8uTJE75v/yJTpkxSu3ZtERFZv369XLhwgX2Pl9CjRw/p06ePODs7S2RkpPTq1Uvat29v77LilD179kizZs2M7ZWrq6vEixdPsmXLJlOmTJEjR468/+unwm4WLFigR48eNR737t1bnZ2ddebMmXrnzh3NkSOHnjhxIsbzIiMj32aZcY7180dvhzlz5qjFYtEvvvhCc+TIofXr19fx48drnz59NGHChHrlyhV7lRunjRs3TrNnz65Xr141pl27dk0vXbqk4eHhdqwsbnjw4IHN42PHjmmePHl0+vTpqqp679499fT01OzZs2v27Nl1zZo19igzzom+bn7xxRdaokQJ/fvvv3XYsGFarFgx/fLLL/Xx48cvfA6iPH78WHPkyKHNmzfXLVu2aJ06dbRYsWJarVo1dXJy0kGDBtm7xDhh3759GhYWpqqq169f10ePHunJkyd10KBBWrRoUe3evbs+efLE5jmO/n2L/jsavS3Gjx+vOXLk0K5du+rFixftVd47YcOGDZopUybdsWOHqqr+/PPPmjhxYp05c6bNco7+XQsLC9M7d+6oqhr7tLdv39YTJ05ojRo1NG3atHro0CFVVWO/4969e3ap1SyELTuIiIjQ4OBgtVgsWr58eT158qQxr0+fPpogQQKdPHmy5smTR0uXLq19+vTRnj17avv27bVz5846ZcoUh115IyIijH9fvnxZb968aayUY8aM0VKlSunUqVP177//VlXVkydPaokSJfTcuXN2qDbu69y5szZo0EBVVYOCgrR3796aOnVqdXd31zFjxti5OvuaMmWK5s6d2yaonzlzRlu3bq3BwcF68eJFzZIli7Zr1063b9+uOXPmVD8/P122bJn9io5jzp07pwUKFNBNmzapalR4HTBggBYrVkx79Oihz549U1XVL7/8Ug8cOGDHSu0v+rZN9X87aAsWLNBMmTJpkiRJtGfPnrphwwZVjWqzBg0axHieI/vll180ffr0+scff6iqanBwsPbp00eLFSumvXr1MnbkvvrqK2PnzhFZv1tBQUHaqlUr/eijj7Rr16768OFDVVUdNWqUFihQQLt160bg+gdz587VEiVKqKrqkiVL1NXVVadNm6aqqvfv39egoCB7lhfnXL58WS0Wi3bu3NmYtn//fq1Zs6amS5dOjxw5oqpR37+ePXvq06dP7VTpm0fYsgPr0fKzZ8+ql5eXfvjhhzZnsHr37q0Wi0UzZ86szZo107Zt22rTpk31o48+0qZNm+rBgwftVbpdRQ+YQ4cO1eLFi2uuXLk0d+7cunnzZlVVY+WMjIzUp0+fakBAgH744YcOG06ji22nbM6cOers7KzVqlXTTJkyaatWrfS3337TIUOGqKurq964ccMOlcYNf//9t/r6+mrZsmVtAtfNmzdVVbVNmzbaqFEjYwelfv366uXlpR988IGGhoY6/Hdu9OjRWqdOHW3RooU+e/bM2NF9+PChDhw4UIsWLaoffvihVqpUST09PY0zE44o+ro5e/Zs7dixo7Zu3VoXL16sqlGh4cyZM8YykZGRWr58ee3evftbrzUusbabdV37/ffftVatWlqoUCHdunWrqqqGhoZq3759tUiRIlq5cmWtXLmyw3/fVFWXLVumrq6u+vnnn+vkyZPVw8NDy5Ytq7dv31ZV1ZEjR2qRIkX0s88+08uXL9u52rjpt99+00aNGunChQs1adKkRtBSVV27dq127NjR4dsu+u9gRESEfv/995o4cWLt0aOHMX3//v1ap04ddXZ21jp16mi8ePH0r7/+skO15iFsvWVDhgzR77//3ujScO7cOU2RIkWMwDV8+HCNFy+eLly40F6lxlkDBgzQlClT6sqVK/Xo0aNasmRJTZEihXEE7tGjRzp//nwtW7asFihQwDh67shHgKN3CTx27JjeunVLHz9+rOHh4Tp79mytX7++rlu3Tq9du6aqqn/88YeWLVvWeOxorN+VCxcuaLZs2bRMmTJ66dIlY/6TJ0+0WLFi2q9fP2P5tm3b6oQJE/TWrVt2qdneoq9fDx480N69e6u7u7uWKlXKmG49GPLo0SOdMWOGtmzZUps3b26so47edbV79+6aLl06bdu2rfbq1UstFosOGzbMmP/gwQPdvHmzBgQEaN68eR0+MFitWrXK+PfGjRu1bt26mj9/fiNw3b9/X6dNm6bNmzfXZs2aOfz37dq1a5o/f34dO3asqkaF+TRp0miHDh1sdo779eunZcqUceiDbs9bsGCBccBt//796uHhoRaLRSdNmmQs8+jRI61SpYp++umnDn/QTVV106ZN+ueff6pqVPiaPXu2xo8f3yZwXbp0SceMGaMdOnTQY8eO2atU0xC23rKuXbsa12k9evRIVaPOcMUWuHr16qWJEye26Tbo6CvunTt3tGzZsrpy5UpVVV2+fLkmS5ZMp0yZoqpR7XPv3j2dMGGCduzY0dgZceSdkujfmcaNG2vmzJk1U6ZM2rVrVz1//ryq/q99nj17pleuXNHcuXPrp59+apd67S36NQynTp3SxYsXq8Vi0Zo1axpHKcPCwrRFixZapkwZnTdvnn711Vfq4+NjE8gclfXM/eXLl/Xrr79Wi8Wi3377rTHfuqP7PEdcR6MH1KCgIE2fPr1u375dVaOOjFssFp01a5axzPr167Vhw4YaEBDg8IHB6tixY2qxWLRZs2bGtA0bNsQIXM+3k6N936L/Dly8eFHz5s2rjx8/1osXL6q3t7e2bdvWmL9u3Trj39YzXVD966+/NGfOnFqjRg3jGqRly5apxWLRrl276vLlyzUwMFD9/f1tDoY4+n6bv7+/urm5Gdu2iIiIWAOX6vu7PSNsvSXPr2ybNm3SiRMnGkeMXhS4OnfurKlSpdKQkJC3Wm9cULduXR04cKDNtDNnzqi7u7teu3ZN169fr0mTJtWpU6eqalT3pOHDh+vdu3dtfkjf15X3ZUTfmRs3bpzmypVLN2/erL169dJy5cpp1apVjevbrl+/rpMmTdJcuXJp7dq1jec56g/FkiVLNFmyZNq1a1ctX768Jk+eXEuXLm10KVy3bp3R1zx37ty6b98+O1dsfz/88IN6e3sbA65cv35dBw4cqEmSJNHx48cbyz2/Tjrad+ybb74x/m1dR+fNm6dVqlRRVdXFixdr0qRJ9fvvv1fVqDMPhw8fVlXVQ4cOGc9xtMCgGvO78vjxY12wYIG6u7trixYtjOkbNmzQevXqaaFChYzr3F70Gu8j63ck+kA01muX79y5o9mzZ9fp06drpkyZ9LPPPjPC++nTp7VcuXK6cePGt15zXDZq1Cht3LixZs2aVZ2dnbV69erGGa6ffvpJc+TIoalTp9bixYtrrVq1OBjynKpVq2ratGl127Ztqvq/wJU4cWLt0KGDnaszH2HLTtq1a6epUqXSKVOmGCts9MAVfdAM63xHEhYWpsOGDVNnZ2cdPXq0zbx69eppy5YtNUmSJDpjxgxj+pkzZ9Tf319/++23t11unDd37lxt166dTdssXLhQK1SooFWrVtWzZ8/qw4cPddq0aTpy5EhjGUftennlyhX18fHRUaNGqWpU97d9+/ZppkyZtFSpUnr9+nVVVb17965eunTJYbsOPu/MmTOaN29ezZcvnxG4rl27poMHD1Y3NzedMGGCnSu0v8OHD6vFYtE6derYTF++fLmWL19ef/jhB3V1dTUOIqmqrlixQhs0aGDTnctR183YPHnyRBcuXKhJkiSxCVwbN27U8uXLa/Pmze1XnB2dP39eP//8c7148aIuWbJELRaLHj9+XJ89e6aff/65urm5abVq1Wye07t3by1cuDAj+EbzzTffaNKkSfX333/X/fv36+DBg7VgwYIaEBBgbPuvXbum58+f12vXrhlh3pEPhlh7bkU/sFG5cmX19va26VI4bdo09fT0fO+7qhK27Kh9+/aaKVMmnTRpkk3g8vLy0qJFixpnHBzVkydP9LvvvtN48eIZ3ZAiIyO1e/fumiRJEptubvfv39eAgAD19/fnSNJz1qxZo9mzZ9fkyZPbdA9RjQpc/v7+Wq1aNT19+rTNPEfemTt//rz6+voaPwpWu3fvVnd3d61Tp47Dj9L1otHzzp8/rwULFtRcuXLZBK4hQ4aoxWLRX3755a3XGpdERkbqpk2b1MvLy+YM8r59+7RYsWLq4uKiw4cPN6Y/evRIq1Wrpi1atHCIMzIvEv2zjxs3Trt27Woz3xq4EiRIoO3btzem792712G3ZUuXLtUcOXJo+fLlNWHChDpv3jxj3vbt27VUqVJarlw5nTZtmi5dulTbt2+vbm5uDj8yqFVkZKQ+ePBAq1SpYnObBeu1zlmzZtVatWrp3bt3YzzXUb9zqqp79uxRPz8/PXXqlKrarrv+/v6aIUMG4wxXZGSkBgcH26XOt4mw9RZYv2gXL17UK1eu2Hyx2rVrFyNwnT59WjNnzqwXLlywS71xyZMnT3T8+PEaL148o+tNWFiY1qtXT3PlyqVVq1bVL774QkuWLKl58+ZlMAyNvYvMhAkTNHPmzBoQEBBj0ItFixZpnjx5YpxBdGRPnjzRNGnSxOjGev/+fS1atKhaLBYNCAgg2Kvq/PnzY4wKd/78eS1QoIDmyZPHCFyXL1/WWbNmOeTR3ths3LhRPT09bQLXd999p2nSpNH27dvr6tWr9ffff9dKlSo5/PUf0bfnBw4c0G+++UZdXV118ODBNss9efJEW7ZsqRaLRRs2bPjC13Ak/fv3V4vFoiVLljR2fq02btyobdq00VSpUmmBAgW0UqVKDj0k/otUqVJFGzVqFGN6s2bN1GKx2AQuR1w/Vf/3uffv36+XLl3S3Llza8GCBY2DuNb178CBA+rk5KReXl66c+dOu9X7thG23pLFixdr1qxZ1dPTU5s1a6bLly835lkD15QpU4zuSY66QxLbD+LDhw913LhxarFYdMSIEaoa1T4TJ07Upk2bapMmTXTw4MEMhvGcrl272nTbGj9+vJYoUUJbtmwZ45S9I230nveiH8dhw4Zpnjx59IcffrCZ/vnnn2tgYKDNUNyO6sqVK5okSRItU6ZMjEF8Dh8+rClSpNAKFSrEGDjEEdfR579n4eHhunHjRk2VKpXWqFHDmP7NN99opUqVNH78+FqqVCmHvv4jMjLS5jehR48e2qRJEz18+LCOHz9ePTw8YhwQGTFihFavXl0DAgIcNmCp/m8d+/bbb7VHjx5arFgxbdq0aYxrSyMiIvTOnTv64MED4zYWiGL9/vXr10+LFi2q27dvt1kHx48frzVr1tRKlSppjx49HG79fN6aNWvU3d1dt2zZojdu3DAOuEXvNfPXX39p8+bNtXbt2jaXy7zvCFtvwdmzZzVLliw6depUnTZtmgYEBGjZsmX1xx9/NJbp0KGDenh46IwZMzQiIsIhj45E/2E8deqUcZ8Fa1uMGTPGJnDFxtE3dlY3b97Uli1batq0aW3CwpgxY/SDDz7QVq1axdpH2tG+d9bPGxgYqN26ddMaNWronDlz9NSpU3r37l1t3bq15syZU3v06KErV67Ujh07qqenp3G2xtFE/35YA8COHTs0Y8aMWqFCBZv59+7d0w8++EAtFos2btz4rdcal0TfLoWHh9uEzY0bN2rKlCm1evXqxrTQ0FA9deqU3rt3z2Gv/7CO2mu1b98+zZ8/v+7YsUNVo66XHDt2rCZLlswIXKGhodqgQQOdOXOm8TxHDlzRzZ8/XwsXLqxNmzbV/fv3G9MZ2Cemixcv6q1bt4zrsW7fvq05c+bUMmXKaGBgoBFM69Spo+PHj9cuXbponjx5HGogs99++03v379v03Orbdu2OnHiRGOZGzduGF3Kt2/frlevXtXBgwdrkyZNHG57RtgySfSdjgsXLmizZs2Mjf7+/fu1YcOGWqpUKZs+1N26dXPI67T69u1rc+S7R48emjZtWnVzc9Ps2bPrsGHDjGAwZswYdXJyshlKGrGHzNOnT2u3bt00bdq0NjsfY8eO1ZIlS2qdOnUc6sfhRZYuXaoJEybUZs2a6Ycffqh58uTR0qVL6759+/TevXs6evRoTZ8+vWbPnl1z5cr13t1s8WVF32n9+uuvddiwYcb3Z+fOnerj46MVKlQwvotPnz7VFi1a6MGDBx12h/f48eM2j0eOHKn16tXTYsWK6YwZM4z51sBVs2bNWF/H0dqvcOHCWq5cOeNzjxgxQlu0aKGffvqpzbbuzp07OnHiRE2YMKFmzpxZs2XLpnny5HHYLpfWz7t7926dPn26Tpw40WZ7NX/+fC1SpIg2bdpU169fr4MHD9YECRIYw5hDdejQoVqsWDHNlCmTVq9eXdevX6+qUcGhcOHCmi9fPs2YMaPmzZtXs2bNqqpRwSNr1qzv/SAPVl999ZU6OTkZlyTs3r1ba9SooQULFjSGd7eup8HBwVq6dGl1d3fXzJkza8qUKW3CvqMgbJnAusFbv369tm3bVr/44osYI0/t379fGzRooOXKlbMZUc/R3LhxQ5MmTaqlS5fWGzdu6M8//6xp06bV5cuX66FDh7RLly5avHhx/eKLL4z7fUyYMEEtFovOnz/fztXHPc/3tz99+rR27dpVvb29dfbs2cb0wYMH65AhQ95ydXHPtWvXtECBAvrdd98Z04KCgrRRo0ZatmxZPXv2rKpGnVW4efOmQ1zIG5voO/uXL1/W2rVra+LEiXXChAnGfbV27typGTNm1Ny5c2vv3r21dOnSWrx4ceO5jnbWedSoUWqxWIwLwQcPHqzJkiXTr776Shs2bKiZM2fWunXrGl14N27cqGnSpNEyZcrYs2y7Gz9+vGbJksV4HBYWpt9++61aLBbNnj17jFHywsPD9ciRIzp48GCdMGGCEbQc7ftm3e+w3rKiUqVKmjFjRvX397fpTr5o0SItU6aMZsmSRX19fXX37t32KjnO6devn6ZKlUqXLFmia9as0SpVqmiKFCmMUXyDg4N1+fLlOnLkSJ06darxXWvTpo1WrFjR2Ba+z44eParZsmXTLVu2qGpUV/ILFy5okSJFNH78+Dbftejr4OLFi3XZsmXGb6qjIWyZZN26derk5KRVqlTRTJkyqZOTk3HPFKu//vpLq1atqgEBARoSEuJwR+Gszp49q9myZdOKFSvq+PHjY5y1GjNmjObOnVsXLFigqlE3Tf3ll18c7jT0v5k+fbqmSpVK16xZYzP91KlT2rhxY02ZMqUuWrQoxvMc9XunGnXW2dvbWxcvXmwzPTAwULNnz65LliyxU2VxU7du3TR//vz66aefav78+Y2zzNadjCtXrmitWrW0SpUq+vHHHzv0gDXPnj3TevXqqaenp/7555/atm1bDQoKMuYvX75cK1WqpI0bN9YbN25oRESE/v7771qtWjWHbC+rH3/8UXPmzKlnzpzR/v37a6dOnVT1fwfZvv76a5sz8rG1laP+NmzdulW9vLx0+vTpqhp1xiFJkiSaJ08em1t6HDt2TPft22fcpB1R92UrWLCgcXDk999/V1dXVy1evLi6u7vr77//HuM5e/bs0c6dO2vy5Mn14MGDb7tku7h3756mS5dOBwwYoGvWrFE3NzcNDQ3Vw4cPa/ny5bVkyZK6YsUKY/mnT5/asdq4g7BlggsXLuhPP/2kU6ZMUdWoIwFffPGF5siRw6Y7l6rqwYMH2eBpVODKnj27WiwW/eyzz2LMr1q1qlaoUCHGdEf9UY3N1q1btWnTpponTx5dvXq1zbwff/xRnZyc1GKx2Nys0tGClvXz/vXXX3rx4kW9c+eOFihQQCdNmqSqtjtuxYoVs7lnj6NbtmyZurm56b59+4wQNXjwYLVYLPrtt9/qvXv3jGWfPHli/NuR19Fnz55pnTp11NXVVdOmTaubN2+2mf/rr79q8uTJjbML0ddHRw1cO3fu1MqVK2uuXLk0QYIENkfCR4wYoRaLRceNG6f379+3Y5Vxi3Ugh+HDh+vnn3+uqlG/qZkyZdKGDRtq48aN1dfX1+YMPmwdPXpUe/Xqpaqqa9euVU9PT502bZqeOHFC/fz8NFmyZDYDm6mqzpo1S4sWLeowQSsiIkKfPn2q8+bNU09PT40fP77NAdz9+/dr+fLlNSAgwOaeno66LYuOsPWG/f333+rq6qpp0qSx+RIeP35cO3TooH5+fjpr1iw7Vhj3WHcwzp07pwULFtRs2bLFGKVmxIgRWrFiRZudOEcVGRn5wh3Yffv2aePGjTVXrly6atUqY/ratWu1TZs2Dn3DZ+v3bNmyZert7a39+vVT1f/dYNza19y6bLVq1XTYsGF2qTUumjt3rubNm1dDQ0Ntfjz79OmjLi4uOnHixBjXfjhamI/N06dPtW3btmqxWHTGjBkaGRlp0y5ZsmT5x0F/HFGVKlXUxcVFP/zwwxgDZXz99dfq5OSk3333Hdec/j/rbWOuXbumBw4c0IcPH+oHH3xgHCz6+++/NXny5Jo+fXodM2aMPUuNc86dO2ccPLJ2E69du7YRvFRVa9WqpZkzZ9bKlSurqu12zRG7lv/yyy9qsVg0efLk+vXXX9vM27dvn5YvX15r1KhBz5BoCFtv2JUrV7Rv377q5uYW4x4gJ06c0C5dumiqVKlsBsZwNP90lMM6cmPx4sV13759GhwcrA8ePNAPPvhAP/7447dYZdwTW9AcOnSodu3aVTt27GjslBw9elQ/+eQTzZgxo06YMEE3b96sBQoUsLlGy1GPNK1atUoTJUqkM2bMsBmU5eOPP1ZPT08dOXKk/vDDD9qtWzd1c3OLMcCBI/vpp5/UxcXFuD2F9ft46NAhTZgwoSZJksS4/tRRv18v+tyPHz/WBg0aaLJkyTQoKMhY7s6dO5o1a1aHvm43OutIjQ0bNtTRo0drhQoVbK5rsxo5cqRaLJZYu0U7msOHD2u1atX0yJEjxrTt27drrly5jN+Ew4cPa6VKlfTLL7/k/p3RDB48WBs0aKDr1683AtTt27c1Q4YMxrVHwcHB+vHHH+vq1attQpYjH0iaO3euzps3T8ePH68+Pj46YMAAm/n79+/XAgUK6Mcff8wZ6P9H2DLB1atXtW/fvpogQQKjK6HV0aNHtUePHjb3HXAk0XdGZs6cqV999ZU2adJE//rrL2Oe9RquZMmSadGiRbV+/fpaqFAh4+iTI27kIiIitGzZslqxYkVjWs2aNdXHx0fr1q2rWbJk0SxZsuh3332nz54905MnT2r37t01QYIEmj17dv3oo4+M5zli+6lG7fB+/PHH2qdPH1WNun/bqVOndPTo0bp27VqtVauWli9fXrNkyaJly5Zl1MHnhIeHa8mSJbVUqVLGkMiqUdcE9ujRQ3v16qWJEyeOceNURxG93bZv366bN2/WvXv3GtPCwsL0o48+UldXV+3QoYOOHTtWq1evrrlz53borpbR2+35bdPixYu1TJkyWrduXd21a5fNvHnz5jl0u1lt3LhRs2XLZtNjZtu2bZo+fXrj9jIDBgzQRo0a2XT1dXQ9e/bUlClT6rJly2KMIti0aVPNmjWrjho1SsuWLavFixc3Bntw1ANJsbl27ZqOGDEi1sB14MABPX/+vJ0qi3sIWya5du2a9u/fX11dXWMELmtocGQ9e/ZUb29v/eSTT7Ru3brq5uam8+bN09DQUFWNClwlSpRQi8Wi+/btMzZwjvzjumrVKk2bNq1+/PHHevnyZa1atarN/Z6++OILzZcvn/7000/GtLNnz9p0w3HkH4pHjx5p4cKFtWPHjnrnzh3t0KGDlilTRtOkSaO+vr46ZswYvXv3rt68edNhuydF/378+OOP2qtXLx0wYIBxrcLWrVu1RIkSmi9fPt20aZMGBQVplSpVtHbt2nr37l319vbWyZMn26t8u4keEvr06WPcKsDZ2VkHDx5sjKAXFhamTZs2VYvFonXr1tWJEyc67Oh5w4cPN/79fMiK/j1csmSJlilTRuvVqxcjcKk63m9C9DMwVkOHDtUkSZIY17ddvHhR69Spo5kzZ1Y/Pz9Nnjy5Qw63/SLr1q1TX19f44Das2fP9Nq1axoUFKSPHz/WM2fOaNOmTTV//vxap04dhx7o599cv37dCFzP9+bC/xC2TGQNXMmTJ6efdDQzZ85UHx8fY+P/559/qsViUVdXV502bZoRuP7++2+tV6+esYFz5A3d6tWrdc+ePbp+/Xr18PDQ3Llza7ly5TQ4ONhmR6V+/fqaL1++WF/DUc9oRTd37lxNlCiRurm5aZ06dXTu3LmqqtqxY0etUKGCw+24vchXX32lKVKk0Pr162vu3LnVz89P27Vrp6pRffKrV6+uSZIk0UyZMukHH3ygYWFh+ujRI82ZM6dD99P/+uuvNU2aNLp161ZVVe3Vq5daLBbt3LmzcWDk6dOnGhAQYHNPLUcLWkePHlWLxWLTBs9vn6I/Xrp0qZYvX14rVKgQ4xouRxQYGKi5cuWyuaa0Xr16WrNmTeO6ydOnT+vChQt1woQJDnn/zn+ydu1azZ07t166dEmPHTumffr0UV9fX02XLp3mzp3bGF01+u+rI/42vOxnv379uo4aNUqTJEnC9acvQNh6DbHttL5oR/batWvarVs39fHx0Xv37jnkDm/0HYnIyEgdM2aMMTTt8uXL1c3NTefPn6/dunVTV1dXnTVrVoyLTh1tZyS6kJAQzZ8/v/bv319Vo+7f5ufnp97e3kb3B+vwqgcOHNCkSZM6bBe4l3H06FHjRpXWAN++fXtt1qwZA7Bo1I5c2rRp9c8//1RV1dDQUJ05c6Zmy5ZNu3btaix37NgxvXz5srFN6927t2bNmtWhrgmJvj0/c+aM1q5dW5cuXaqqUQEhWbJk2rZtW40XL5527tzZuE4wIiLCoQ8eRUZG6pYtW9Tb21tr1KhhTH++TaK37/z58/WLL75w6HazWrBggVosFk2RIoUWL15ct2/frtOmTdM6deror7/+Shu9gPVs4NatWzVv3rxapkwZTZkypbZs2VJnzJihf/zxh6ZLly7GQFKO2J7Wde/PP//UmTNnGtfqvsjVq1d13LhxDtuN/N8Qtl6R9Qu4c+dOnTJlig4aNEj37dv3j8+5fv26MVqQo3n8+LHx7xUrVuj9+/f12LFjeunSJT1z5ozmzp1bx48fr6pRw3HHjx9fLRZLjCFWHd3mzZs1Xbp0xo0EAwMD1dPTU2vXrm0TRDdt2qTp06fXEydO2KvUd8rx48e1T58+6u7urocPH7Z3OXbx/AGgX3/9VTNkyGBzYXNISIiOHj1aCxUqpGfOnFHV/+2A7NmzRz///HNNliyZw3ZVunbtmoaHh+v8+fP1wYMHun37dvXx8TEusu/atas6OztrixYtbLp/OeJOXHRbtmxRLy+vlw5cL1rmffd8G1y+fFnbtm2rU6ZM0Xbt2mmdOnW0Xbt26uvrq40aNbJTlXHb999/rxkyZDBGOl61apWOGjVKly1bpnfv3lVV1Rs3bmj+/Pltbo/iiKzft8WLF6u7u7sOHTrUOKP8TycMHPmg+L8hbL2GxYsXq6enp/r7+2vt2rXVYrHo+PHj9eHDh/YuLU5Zt26dZsmSRVWjboaaJUsWm6MjGzZs0Pz58+uxY8dUVXXv3r3ar18/nTRpkkOesn+RyMhIffLkiXbu3Fn79etnDP2+fv16TZkypX744Yc6Y8YMXbFihebKlctmMAy82N69e7VRo0aaI0cOPXDggL3LsbuZM2fqhAkTdMOGDZopUyabofBVo84IOjk5GWcFo0//7rvvYtyu4X22ePFi48DHV199pU2bNlVVNbofde/eXevXr6+PHj1SVdWBAwdq5cqVtVSpUg4XFP5JZGSkbt68+bUCl6PZunWr1qxZ0zhw+/PPP2uOHDn02rVrumvXLh02bJi6u7urxWKhK1cs7ty5o9myZdOCBQvGOPsSFhamN2/e1OrVq+sHH3xAaNCo71uyZMli3BvWup/LOvlqCFuv6PDhw+rt7W0M1RsSEqIWi0UHDhxo38LioOPHj2vevHk1bdq06u7uHqPf+K+//qoJEiTQ33//XY8fP67Vq1c3dlpUHbOPtFVsG7JFixZphgwZjB+KZ8+e6fr16zVTpkxqsVh06NChNt9DNob/7NGjR7p161a9ePGivUuxuydPnmjVqlX1o48+0rt372r27Nn1008/tbmh7MWLFzVfvnz6xx9/xHi+I+2cPH78WJs0aaIWi0UbNGigSZIkMcK69eaytWrV0nr16umzZ880IiJCa9asqZs2bTJewxED14s+87Nnz3Tz5s2aOnVqm8DF9ut/IiMjdcOGDZo1a1bNlSuXMerswIEDtWzZskaoX716tZYuXdo4gIko1u/e3bt3NWfOnFqgQAGjjZ49e6ZjxozRypUra5EiRYzBMBxpmxabgQMHarVq1VQ16iBSYGCgNm3aVKtXr66rV6+2c3XvHsLWP1i/fn2Mazg2bdqklSpVUtWoARzSpUunbdu2NeZbjzrxQxHliy++UIvFolmzZjU2eNE3Yo0bN1aLxaK+vr5aoEABRmpU2/Z5vp908+bNtXLlykb3zPDwcA0KClInJyebUS8dcWcOr8e6rdq7d68mTZpUd+/erTt37tRkyZJpgwYNdNq0abplyxatVKmSFipUyOF3QlSj1q+MGTOqs7OzMeR29HaZP3++WiwWrVixoubKlUtz5cplHDxyxN+G6Nuj+fPn69ChQ7VHjx42BzqsgeufBs2Aao8ePbRChQpasGBBnT17tjZq1EjnzJljtDHXnf7PokWLYuyT3bt3T3PlyqWFChUyAtdvv/2mo0aNMtZRRzzQa20f63+//fZbLVSokH7//fdau3ZtrVatmlaoUEGbNWumKVKk0HPnztmx2ncPYesFTp06pRaLRTt06GAMPqCqunDhQs2ePbseP35cM2TIoG3btjU2cr///rvWr1/f6P/riJ5fYf/44w/97bfftFChQporVy7jPh/Rr+Xavn27bty40dhZccQNnVX0Hbb27dtrixYtbDZqu3fv1tq1a9t05QoPD7e5oSXwT160AxsSEqIff/yxdujQQVWj7t9TtWpVTZs2rebJk0f9/f056vv/QkJCNCAgQAMCAjRRokTGWauwsDDj9+Dnn3/Wdu3aaY8ePRx2ePfn9ezZU318fDQgIED9/f01WbJkNmf8rINmfPDBB/Yr0o5++eWXF3bHjf7d2bFjh3bq1MkYxbdUqVJGF1YCapRbt26pi4uL+vv7G9dJWtvm0qVLmipVKq1SpUqM305HXkcDAwN18eLFqhp1DX29evU0Y8aM+umnn2pQUJCqRh0UKVasmF67ds2epb5zCFv/YOnSpZo4cWLt3LmzcbTo2rVr6u/vr4kTJza6vFl/XHv06KGVK1c2hl51NNGPXt6+fVsvX75sbLhOnjypefPm1Vy5chlDu6uqzp4922bkQUfe0EVvv1q1amnOnDk1MDDQ5oaLjx8/1gYNGmj9+vX/9TWAfzJ27FgdPXq0MUKequr06dM1ceLExg5fSEiI3rhxQ8+ePevQQyDHtl6Fh4drRESEfvLJJzaByyr6QBiqjtlu0U2dOlXTpk1rDCi1cuVKY0S96N2S1q9fr9WrV3eobVlkZKTeuXNHLRaLBgQEGIPQPO/5Nlm9erUWLlxY3d3dbe65iCiHDx/W9OnTa5UqVWxuxB4aGqqlSpVSi8WizZo1s2OFcUubNm3UYrEYt+948OCBcY9Aqz59+mjhwoUddj/3dRG2/sXy5cs1QYIE2rlzZw0LC9PIyEgdOXKkZs2aVTt16qTXr1/XI0eOaK9evTRZsmSMaKaqgwYN0vLly6uHh4c2b95cp02bpqqqJ06c0EKFCmnWrFl1zZo1WrFiRS1RooRD/ag+79mzZzG6Ck6cOFFz5sxpE7KuXLlijPR27do1TZs2rTF8PvCqHj16pD179lR3d3etUKGCtmzZUu/cuaOPHz/WTz75RNu1a2dzRt/KEdfV6J951qxZ2r17d23Xrp0uXrxYIyIi9OnTp9q8eXNNkiSJrlu3Th88eKD16tUzzhAiqutWv379jPvarVixQl1dXXXKlCnapEkTTZkypQYGBsZ4nqN836y/n8eOHdMUKVJo9erVX/reWOfOnfvXYbkdTfT9kSNHjmiaNGlsAld4eLi2a9dOT5065dAHeGPzxRdfaKJEiYztm9Wff/6pXbp0UQ8PDwaUeg2ErZdgDVzWH8+IiAjt27evFi5cWJ2dnTV//vyaO3du7m2kqgMGDNAUKVLoypUrddu2berv76/e3t7GD8e5c+e0QoUKmiNHDq1YsaLRLckRuz6EhYVppUqVtFOnTjZnF/r06aMVK1ZUVdWgoCDt0aOHenl5aaJEibR79+6qGnVWokWLFg57SwG8GZcuXdLp06drwYIFNXv27NqsWTOtVq2aVqtWzRj63RHXzdh0795dPT099auvvtIGDRpopkyZtFOnTqoa1WWpbdu2arFYNF++fJotWzaHvv40tpC0Y8cOvXDhgh4/flyzZctmDIu/Zs0atVgsarFYYoyA6SgiIyONgxtHjx7VpEmTarNmzRxqhM83YcqUKdq+fXutXr26btmyxbhswTqwWb58+bRjx45arlw5LViwYKzXkTuakJCQGNM+++wzTZIkiS5ZskTDwsL0ypUr2rZtW/X399dDhw7Zocp3H2HrOS/asViyZIkmSJBA27dvbyx369YtYyS96GchHNXFixe1RIkSunbtWlWNGto9ceLE+sMPP6iq7Qbt1KlTDt0tyWrw4MHq6+ur/fr10/Pnz6tq1LUe6dKl0xIlSmjGjBn1888/18WLF+usWbPU2dlZr169qtu2bdPcuXPrrl277PwJ8L6YPn26du7c2djxHTZsmL1LijPWrVunGTNmNNa3xYsXa8KECfXHH3+0WW7lypX6448/OvT1p9GD1uTJk3XcuHE285ctW6bFixfXy5cvq2rUdVpdunTR0aNHO2R7qf5vv2PJkiU6bNgwzZcvn1osFv3oo4/09OnTdq7u3dCrVy9NnTq1fvHFF9qwYUNNlSqVjh492uheeefOHa1Tp47WqFFD69evbxwMcZSzp7E5cuSIJkuWTLdt2xZjXsuWLdXDw0NXrFihqlE9ap7vGo2XR9iKxrrB27Jli44YMULbt2+v69evN1ZWa+Dq0KGDQx+1tHo+mF67dk2zZ8+uV69e1eXLl2vSpEl16tSpqhp1rdHs2bNjXIzqiBu6x48f65o1a4zHY8aM0bRp02rfvn31+vXrev/+fV2yZIm2bt1aN27caHR92Lx5s5YsWdLYSQkKCorRnxp4Vc+vx7t379bmzZtr1apVYz3q6YjmzJmjpUqVUtWoW1a4uroa27b79+/r5s2bYzzHkY+Wq0bdf8zHx0f79+9vbLNUo+7n5uzsrEePHtXLly9rjRo1tE2bNsZ8Rw1cQUFB6uLiolOnTtXffvtNf/rpJ3V1ddUaNWoQuP7FnDlz1NfX1+hqv3v3brVYLJomTRodNmyY8TsZERFhs8/hiN816/Z+27ZtevHiRa1WrZqmS5dOd+7caTP/7NmzmjRpUnVycjICF14fYes5S5Ys0USJEmmdOnXUz89Pc+bMqbVq1TI2dkuWLNEkSZJoq1atYr2mwVHEdgbw/Pnzmjt3bv3yyy81WbJkOnnyZGPegQMHtEaNGg5/Z3bVqFEG8+fPrwsXLjSmjR49Wr29vbVPnz5G/3trGz958kQvXLigefPm5WJevBU7d+5UFxcX48a9jiS2bdu8efO0YcOGunr1apuDSKpR3cy7d+/u8NfNRG+3+fPnq6enp+7ZsyfGcmFhYVq5cmW1WCyaKVMmzZMnDwcvNSqcWm8rY/XXX3+pq6ur1q1bly6F/2DGjBk6ceJEVY06c+ru7q7z5s3Tvn37qouLi44cOVIvXLhg8xxH7h4dGBioSZIk0VWrVunjx4+1Ro0a6uXlZQQu1aiw9dlnn2n79u31+PHjdqz2/UDYiub8+fOaLVs2m/sVzZs3T6tUqaIfffSR0VXw559/1tSpUzvsj2v0jdSiRYu0U6dOxtGi4cOHq8ViMbpbqkaNaFOtWjWtVKmSwx/tVY26CLpatWrq7++v8+fPN6ZbA1ffvn2N+8/cvHlTp0yZonny5NE6deoYyzryDwXMZf1uFS9e3BjQwFFEX6+WLVtmjAp38uRJTZgwoVosFp09e7axzKNHj7RKlSraokULh10nY7vBab9+/bRx48aq+r8zfM/3Yvj55591xYoVDt3lUvV/37k2bdpohQoVjOnWEZC///57tVgsWqNGjReOUuioli5dqpcvX9aLFy/q1atX9cKFC5ovXz4dO3asqqpevXpV3d3dNUmSJDG6/DqqixcvaqdOnYw2UlV9+vSp1qpVS1OlSqXLly/X48eP6+DBg7VSpUocCHlDCFvRHDx4UL28vHTHjh3GtMjISJ0zZ47myJHDZrr14nFHE/0Hc+fOnVqzZk1Nly6dDh061JjXvn17tVgs2rZtW23RooWWL19ec+fOTR9p/d9nP3XqlFapUkUrVqwYI3ClTZtW+/Xrp1euXNGQkBCdOXOmzXUPjtx+eDusO3iO1H0p+nq1fft2LVCggDZq1Mg4Ir5kyRJNnDixdunSRYOCgjQwMFA//PBDzZs3r8PesHjy5MnGiLLR269169ZaokQJ47G1XR4/fmxzj0ArDsJFjdCYIEECY9htq4ULF2qpUqXUx8fH5ibQjq53797q7e2tEyZMMHoZ7dixQ3PmzGmcUT106JB26NBBJ02axHdMo86UVqxYUXPmzGlcyhC9XZo2bapJkiTRDBkyaOrUqY1umfjvCFvRnDhxQv38/IyNXfQfDx8fH+3Xr5+9SotzunXrpuXLl9datWppxowZNV26dNq/f3/jR3Xq1Klav359bdSokQ4aNMih78z+POv36uTJky8MXD4+Ptq3b98YNw4kaOFtOH36tB49etTeZbw10UPS6NGjtVWrVpoxY0ZNmDChNmnSxAhcixYtUh8fH02XLp0WKlRIa9as6dA3er5x44bxuQ8ePGhMnzBhgmbJkkXXr19v093+1q1bWrJkSYe+BiT6MO9r1qzR33//3bim6LPPPtOsWbMaN5aNiIjQXr166ZgxY4wzXVAdMmSIpkyZUnfv3m1z385NmzZp6tSpde7cubpnzx6tXr26NmrUyJjvKOto9P0E679v376td+/e1apVq2qCBAm0Z8+exjLR22XTpk26ZcuWGN0u8d84VNiybuTCw8ONfz9/JLJcuXKaP39+m9P1z5490woVKuj333//9oqNw3799VdNnjy57tmzR8PCwvTRo0f6xRdfaMGCBXXgwIHGyv348WOb5znKhi42z3/26D+4VapU0QoVKtgErjFjxmi8ePH0119/fat1Ao5s5MiR6ubmpqtWrdL9+/drr169tGDBgtq4cWPjrMKNGzf077//1kuXLjGi6v/bsGGDWiwWnTlzpqpGtUehQoWMa1MvXbqkJ0+e1GrVqmmJEiUc9rfA+n1ZvHixZsiQQfPkyaOlSpXSNGnS6OHDh/X06dPaoUMHjR8/vhYtWlSLFi2qrq6u3Ncomjt37qi/v7/+9NNPqqp6+fJl3bJli7Zq1UqXLFmi5cuXV09PT02fPr0WKVLEYbvBnThxwthn/fnnn7VkyZL66NEjvX79utatW1cLFixo0yXaUdvpbXGosHXs2DFV/V/SX79+vbZo0UK7dOlinM26f/++ZsuWTfPmzatz5szR9evXa8+ePdXDw0NPnTplt9rjkvHjx2vOnDn10aNHxrRbt25po0aNNEWKFDp48GCjjR2tW01sou9YTJ8+XYcOHarjxo0zumhFP8O1YMECY9noIxYCME9kZKQ+ePBAy5cvrwMHDrSZN2HCBM2YMaM2bdo01qO9nG2Out7ZOjDSjBkzVDVq561y5cqaO3dujR8/vhYoUECLFSvmUGcCYzuou2PHDnV3dzd2hLds2aIWi0WHDBmiqlH3PQoKCtKePXvq119/zeAEz7l7965xbfOWLVu0QYMGWrRoUS1UqJCmS5dOZ8+erbt379YdO3Y47PWAkZGR+t1336nFYtE2bdrEuNb0ypUrWrt2bS1TpozOmTPHmM62zDwOE7YCAwPVYrEYZw9Wr16tLi4uWqtWLS1durR6eHjomDFjVDVqQIcqVaporly5NH369FqwYEH6rur/fhznzZunOXLkMMKndQU9cuSIJk+eXIsUKaLDhw+3W51xVc2aNdXPz0+LFCmi+fPn14QJE+rKlStVVfX48eNatWpV9ff3N44OW7EBBN6OgIAAbdu2bYzpjRo10iRJkmizZs1sbkDuiF60Pbpy5Yp+9dVXmjRpUp0+fbqqRv1mHDp0SJcuXap//PGHw+38Rr8BrLXdZsyYoU2bNlVV1QsXLqiPj4/NgFJ37959u0W+g2bOnKnJkiVTNzc37dGjhwYGBqpq1HraokULm2UdIdRbjR071uY2Cw0aNFCLxWKMYhwZGWmse5cvX9batWtrhQoVdNq0aXap15E4TNg6d+6cduzYUZMlS6YLFy7UOXPmGMP3Xr16VUeMGKEWi0VHjx5tPOfChQt65swZvXPnjr3Ktqvnf1StG61Lly5pqlSptFmzZhocHGzM37t3r9atW1fbtGmjxYoVc/g+v9Hb77vvvtP06dPr1atX9dmzZ3rr1i3t2LGjJkyYUP/8809VjTrtX6RIER05cqS9SgYcwosCQ+fOnTVbtmwxziaMGDFCP/zwQ61QoYKOGDHCYc/YR//cs2bN0mHDhmnv3r319OnTGh4ervfu3TMC1/MHjawcZed348aNarFYdNasWTbThw4dqnXq1NGzZ8+qj4+Ptm3b1mjX3377Tfv166cPHz60R8nvlAsXLtj0NoqIiNCKFStq37597ViV/QQHB2uhQoX0xIkTxrR27dppzZo11WKx6HfffaeqUeuw9ezy5cuXtWLFilqlShWbfTm8eQ4TtlSjujp07txZ3dzc1M/PT5ctW2bMCw4ONgLX83e8d0TRd0YmT56sn332mZYvX17nzp2rkZGRunv3bk2cOLE2bNhQlyxZovv27dMqVapox44d9erVqzZnER1R9PZbv3699u/fXxs2bKiq/9thefbsmTZq1EgLFiyo9+7dU1V12NsJAG9L9HVzw4YN+ttvvxkDEoSHh2vBggW1YMGCunfvXg0ODtanT59qnTp19Mcff9R27dpptmzZYlyP6giiB62vvvpK3dzc1N/fX9OnT68ZM2bUcePG6YMHD/TOnTvavXt39fDwsLnXoqO5ePGi0bUyeheupUuX6gcffKBeXl7aqlUrVY1q28jISO3QoYO2atVKHzx4YKeq3z3379/XP/74Q6tXr6558uRxmLOmsbF+9j///NPmDNfIkSNtApfVvXv3NCQkxOHP1r8NDhG2ol8/FBwcrN27d9f48eMbN8Gz/oiEhIToqFGj1GKx2Ny00pH16NFDvb29tUePHjpkyBC1WCzapUsXVVXds2eP5s+fX319fdXHx0eLFSumjx490vv372uePHl0w4YNdq7ePqLvlDRo0EArVaqkrVu31syZMxsbQ+t3cs6cOZopUybjHm6xvQaAN69nz57q6+urJUqU0BQpUmiFChX0wIEDeu/ePS1UqJBmzpxZs2fPrnny5NHMmTOratQNjHPkyOHQXb1u3bqllStX1j179hjbKeuN2q3Xa124cEHbtWun/v7+Dr0tu3DhgnGmzxq4wsLCtHr16kY38ocPH+qdO3e0V69e6unpaVxbjn8XGRmpmzZt0urVq2vlypUd6nrA2ERGRuqTJ080S5YsmiNHDiNwPX78WL/55huNFy+ejh8/XsPDw3XIkCFarlw5gv1b4hBhS1V19+7d2qFDB338+LFeunRJO3bsqC4uLrpo0SKb5e7du6fjxo1zqGGPX2Tz5s2aMWNG454V+/fvV4vFYowCpBrVXmfOnNFDhw4ZP6q9e/fWDBkyOPzRknXr1mnNmjX13LlzunXrVi1YsKAOGDDA5nT9unXrNG/evHr+/Hk7Vgo4lqlTp9rcR2bGjBlqsVg0KCjIWGbevHn6zTff6Lhx44yDJK1atdLy5cs7bDevcePGabZs2bRs2bJ69epVm3mffvqp+vn5Gb8DN2/efOGov+8j6wG06GdWHj16pBEREfrVV1+pq6urzeAhxYsX15w5c2qqVKm0fPnymj59eq4Nfw1PnjzR/fv3x9r+jsK6flm3SxcuXNDcuXNrkSJFjP2wx48f69ixY9VisWjhwoU1adKkunfvXrvV7GgcJmyNHz9ec+XKZdwL5OzZs9qlSxd1c3OLEbgc4YchNs9fx7B69WrjjvaLFi3SpEmT6pQpU1Q16izg8yvqwYMH9eOPP1ZPT0/966+/3krNcdXXX3+tJUuW1EqVKqlq1A9Ajx499IMPPtAWLVrorl27dNOmTZorVy7j4lUAb0enTp20T58+qhp101h3d3dj2xb9vj1WR48e1Q4dOmjy5MltBj1wNOvXr1c/Pz/18PDQs2fPqqoa99G6evWqJk6cOMZNix3p9/TMmTNGV62FCxdq8uTJ9eHDh3rx4kXt0aOHurq6GoOHhIWF6dq1a3XChAm6Zs0ablj8BjjiYFLW9SswMFC7detm7JddunRJs2fPbhO4VKNGw5w+fbqx/uLteG/DlvULGH148jJlymjp0qWNx9bAlTx5cpvhLx2d9cjQ4sWL1c/PT+fPn2+zM6KqumzZMq1bt67N0c2rV6/qsGHD6Aahqrt27dLMmTOrl5eXsfELCwvTkSNHapkyZTRevHiaJ08ebdCggfEcR9opAd6W59erp0+favny5XX8+PG6d+9eTZo0qdFtPDw8XIcNG2ZzC4YHDx7ovHnztFixYjY37n3fxbbj+vTpU928ebN6e3trlSpVbM4iHD16VNOnT687dux4m2XGKcOGDVN3d3dt2rSpJkyY0GaQkMuXLxuBy3qGC3gTlixZookSJdKhQ4caPZFUowJXjhw5YgQuvH3vbdhSVV27dq02adJE161bp6pRp1azZMmigwcPNpa5cOGCtm7dWn18fDQ0NNThd3hnzJihhQsXVtWoI7xVqlRRi8WiX3/9tbHM48ePtUaNGtq4ceMY7eXo7Rfd4cOHNUeOHNqsWTPjppTW9jl48KDNkSVHPCIHvE2jRo3S3bt3q2rU/e7SpUunTk5O+uOPPxrLhISE6IcffqiDBg2yeW54eLhDjdYVfXv0119/6ebNm/X8+fPGwCCbNm3SVKlSaYUKFfTXX3/VDRs2aNWqVbVgwYIOeb1M9N+9Tz75RC0Wi9avXz/GQCrWwJU8eXKuC8cbcfLkSc2YMaPNwXDV/63Dly5d0ty5c2u2bNlsBs3A2xVP3lOqKkuWLJH58+dLo0aNZNCgQRIRESEtWrSQo0ePyt69e0VEJH369DJw4EDZs2ePuLq6isVisXPl9uXl5SUiIjt37hRXV1dp3LixFC1aVDZs2CCBgYGyYMECqV27tpw7d07mzp0rFotFIiMjjec7evtFlzt3blmwYIEcOXJEJk2aJEeOHDHaJ2/evJIxY0YRifquxov33q6KgN09fvxYAgMDZcKECRIWFiYlS5aUYsWKiZ+fn2TIkEFERM6fPy8NGzaU4OBg6du3r4hErZsiIk5OTuLu7m6v8t+q6NujXr16Sc2aNaVx48aSO3du6dChg+zZs0fKlSsnv/zyi5w9e1bq168vK1eulMyZM8u2bdvEyclJIiIi7PwpzBfb797ff/8tjx49krp168ru3btlwoQJcvPmTWO5tGnTSseOHaVBgwby9ddfS0hIiPEdA17HxYsXJX78+FK1alVjWvR1OF26dLJq1Spxd3eXsLAwe5Xp8N6rPbzoGy2LxSKtW7eWhg0bypdffimrV6+WUaNGyblz5+Tw4cPy559/GsumS5dOUqdObY+S7Sq2jXyJEiXk2bNnMmfOHBERadq0qXTq1Enc3NykTp06MnnyZPHw8JD9+/eLs7OzREREEBT+Qf78+WXmzJly4MABmTBhghw8eDDGMgRUwFyJEiWS6tWry8GDB+X27duSM2dOadWqlWTPnl0CAgIkc+bMUqtWLQkODpZt27YZ2zZHXDetn3ny5Mkya9YsmT17tuzfv1+mTJkiZ86ckREjRsihQ4ekXLlyMnv2bMmUKZOcP39eJkyYIAkTJpSnT5+Kk5OTnT+F+eLFiyfnzp2TevXqiYjI8uXL5dNPP5Vhw4bJr7/+Ko0bN5bJkyfL7Nmz5datW8bzXF1dZcqUKbJ3715xd3d3yO8Y3pwHDx7I48ePjceRkZHGd2rz5s2yb98+8fX1lR07dhgHlmAH9jupZo4NGzYY/aEjIiK0Q4cO2rJlSw0NDdUpU6Zo69at1WKxqMVi0Z07d9q52rjBOlyq1Zo1azRlypQ2I3OpqtGNxNplwhFH/Xld+/fv13Tp0ukPP/xg71KA99o/dcnNmTOnfvrpp8bjq1ev6ubNm3XWrFkaGBhodIFzxG1b9FukqKo2atRIP//8c5tlfvvtN82XL58OHTpUVaN+OzZt2qSenp760Ucfvd2C44C1a9dqunTptFChQrHeW7Jfv37q6+urI0aM0LNnz+qAAQPU09NTnzx5YqeK8b45e/asJkqUyBjwJ7ouXbrogAEDYuzj4e17r8JWeHi4Dh8+XC0WizZt2lT//PNPjYyM1IIFC+qQIUNUNapPfocOHTRt2rT6999/27li+xs+fLjWqVPH5qaLN2/e1MqVKxvXtj1/byhVrs16HQzvDrw9c+bM0S1btmhISIgxbe7cuVqsWDFjiO3YgpmjX3O0fft2ffTokTZu3FibNm2qqrZt0rt3b/Xx8TGuR4qMjNTNmzers7OzfvLJJ2+38Digf//+arFYNG/evMa06NdqDRo0SDNmzKi5cuXSNGnScJAXb9wPP/yg8ePH1+7du+vhw4f12LFj2qNHD/Xw8NDjx4/buzyoqkX1/eswfOjQIenevbs8ePBAihQpIlWqVJFp06ZJjx495IMPPhARkeDgYPHw8LBvoXHAxo0bZeLEifL3339L4sSJpW/fvuLv7y8rV66Utm3byqlTpyRNmjT2LvO9EhkZSddLwESPHj2SggULiqurq7i4uMg333wj+fLlkydPnkjx4sWlZcuW0qdPH1FVh+/GFX179OWXX8rixYtl37598v3338vIkSNl165dkjNnTmP52bNny+zZs2XdunWSKFEiEYnqkr5t2zbx9PSUbNmy2eVzvG3WdpszZ44cO3ZMVq9eLd7e3hIYGCgiUdcJWttn69atcvv2bSlQoIBxrS7wpkRGRsqSJUvks88+kyRJkkjChAnFyclJFi5cKAUKFLB3eRCR9zJsiYjcuHFD1q9fL2PHjpW///5bPD09pXHjxjJs2DB7lxZnWHc0Hj58KFeuXJG+ffvK+fPnJSwsTHr37i2DBw+WKlWqyDfffCPx48e3d7kA8NLCw8Nl48aNMm/ePFm9erVUqFBBWrZsKbdu3ZLevXvL5s2bHSYYvIy7d+9Kr169pEGDBlKxYkUREalWrZocOXJEfvnlF/H19RVXV1epVauWuLu7y+LFix0yqMYW0FVV1qxZI127dhVfX18jcImI/PXXX5I9e3YjeAFmuXr1qly4cEEsFotkzJjRIcciiKve27BlFRYWJj179pRJkyZJsmTJ5PTp0+Lq6mrvst6658+mhIeHi7Ozc4zltm/fLitXrpQZM2bIvXv3pHHjxvLTTz+9zVIB4KXFtvP7/PZu1apVsmXLFpk8ebJkyJBBTpw4IXPnzpWmTZu+7XLjjOht9MMPP0jHjh3Fz89PFi1aJH5+fiIicvPmTWnXrp1s3LhRkidPLkmTJhWLxSJ79+6V+PHjO9yZQevn3bx5s6xfv14uXbok1apVk/Lly0vq1Knl999/ly5duki6dOlk4cKFMnHiRFm5cqUEBQVJqlSp7F0+ADt5r8NW9B+CoKAgyZo1q/j6+tq5KvuaOHGitG/fPkY3tud3Tg4ePCg7d+6UVq1aibOzs8P9qAJ4tzx48ECSJk1qM+357dbZs2eNIbcXLVoU6wEnRxC9XcLCwuTUqVPSpUsX+eOPP2T79u1SsGBBm9+ElStXSkhIiIiING7cWJycnF54wO59t3TpUmnSpIn4+/vLs2fPZNu2bVK7dm3p0qWLFCpUSAIDA6Vjx45y//59iRcvnixdulSKFCli77IB2NF7HbZEYj/q6Uii/2BOnjxZOnbsKLt27Xqljb+j/qgCiLvWrFkjRYsWlZQpU0rfvn0lPDxchg8f/sJhx63bwidPnkjChAlFxDG3bZs2bZKrV6/KJ598Ip999pm4uLjI+PHj5cSJE9KiRQsJCQmR7du3S/LkySUsLCzWLuQREREOMbz78y5duiSVKlWSzp07S7t27UREZN26ddK/f3/JkSOHjBs3Tjw8PCQ4OFj27NkjuXLlknTp0tm5agD29t6HLUTZvHmzbN26VQoUKCA1atSwdzkA8NpCQ0OlfPnycuvWLQkICJCffvpJdu7cKXny5Hnp13C0A3GqKg8ePJC6devKs2fPxM3NTbZs2SJ//vmn0W7Hjx+XJk2ayJMnT+TPP/+UZMmSOWywis2lS5ekTJky8v3330ulSpWM6evWrZM6derIzz//zO8rgBgYEs0BbN26VZo2bSrjx483rleLiIiwc1UA8Hrc3NwkMDBQHj58KD/++KOsWrVK8uTJI+Hh4S/9Go4UtESiPq+rq6ssWrRIrl+/LqtWrZI+ffrYBNQcOXLITz/9JIkSJZKyZcvKnTt3HD5oWY9Hq6o8evRIHj58KPfv3xcRkSdPnoiISOXKlSV//vyyadMmu9UJIO4ibDmAdOnSSdOmTSU8PFxWrFghIiJOTk4SGRlp58oA4PXcv39fUqVKJZkyZZJ27drJtWvXxNnZme3av4gXL55kzpxZSpcuLRs2bJD58+cb81TVCFz37t2Tzp0727FS+wkPDzdClvX7ZLFYxM/PT+rWrSstW7aUkydPGt1RVVWcnZ3F29vbbjUDiLvoRvieeX6gC2sXkJs3b8p3330n8+bNk3bt2kmfPn1iXR4A4qLYtlUPHz6U0NBQqVWrlty7d0/++OMP8fLyMuZzP8UXu379urRq1UoeP34srVq1kk8++cSY9/TpU7l586Z4e3s71JmtY8eO2dxTLCgoSJYsWSJOTk6SPXt2ad++vTx69Ejq168vW7dulUmTJknixIll//79Mm3aNNm1axe3EwAQA2HrPfL8YBjHjx+Xw4cPS6dOnaR06dLi4uIi3377rSxZskSaNWsmvXv3FhHHu3YBwLsl+rZt48aNcvfuXXF3d5eCBQtKihQp5MKFC1K/fn0JDg6WwMBASZs2rbRo0UKyZs0q/fv3t3P1cde5c+ekY8eO8uzZM2ncuLE0bdpUKlasKCVLlpSvv/5aRBxnMIxffvlFRo4cKd26dZMmTZrIxo0bxd/fXxo0aCCXLl2S27dvS7JkyeSPP/6Q8PBw6dmzpyxbtkySJEkirq6u8v3333MDWQCxImy9h3r27Clz586Vtm3byoMHD2T27NnSqFEjmTRpkly5ckWmT58uS5culVq1asnw4cPtXS4AvJTu3bvLvHnzJGXKlHLy5Enx9/eXli1byscffyznz5+XJk2ayIEDByRnzpxy9+5dOX78ODdk/xfnzp2Tr776So4fPy5Pnz6VxIkTy759+yRBggT2Lu2tOn36tHTo0EHCwsKkefPmsmPHDvHz85MuXbpIWFiY7NmzRz777DNxc3OTbdu2iYjIxYsXJWnSpBIvXjzOoAJ4IcLWe2bTpk3SqlUrWbx4sRQsWFD27NkjxYoVk59++kkaN24sIlHdR4YPHy63b9+W+fPnc1YLQJw3d+5c6dGjh6xYsULy588vJ06ckCFDhkhISIh069ZNqlWrJk+fPpXp06dLZGSktG/fXpydnR3mzMx/ce3aNdm3b5/cuHFDmjdvLs7Ozg45LP758+elffv2YrFY5PLlyzJixAgJCAgQkagzfNu3b5e2bdtKjx49pEWLFnTDB/BS2Eq8Z548eSI+Pj5SsGBBWbhwoVSsWFEmT54sjRs3lvv378uuXbvEy8tL+vXrZwQt8vb/tXf/MVXXexzHX4cDUiiEix8ehw6znDIghK38VSFuLJo4s9V0zIARW/xS1jAz1OWPTMWmoM5aKsN+YRhKkkJmiBD0wxkg2dpkkJ1EkomoIBPO4f7hODeu7V7vvR45R56Pje3w/Z7v2efL2Pec13l/Pu8vAEfX0NCgJ554QtOmTZO7u7vCwsK0Zs0a9fb2qqioSJLk7u6ujIwMLV26lKD1XzCZTJo7d67tJvYWi2XYBS1JCgwM1Pbt22U0GtXQ0KCTJ0/a9hmNRkVERGjEiBE6d+6cJBG0ANwRrhT3mStXrujy5cs6cuSIUlJStHHjRqWkpEi6tdj3vffe04ULF+Tn52cLWlS2ADiqgW5wRqNR3d3dtt+tVqtCQkKUlpamTz/9VGaz+bYvjgha/5vh/Hd75JFHtHPnTsXGxqq8vFwFBQW2fR4eHhozZoysVqv6+/v5ohLAHSFsOane3l7b47+2Ol6wYIFGjhypuXPnav369UpNTZV0q7vU3r171dvbK5PJZHs+QQuAI/nX1u0D1YPp06eroqJCRUVFMhgMtu1eXl4KDg6Wu7s71zPcFQEBAcrNzdXYsWP17rvv6rXXXlNhYaGWL1+uyspKxcfHy2Aw8P8G4I6wZsvJnDlzZtBNKN9//31bu9no6GiFh4ertLRUq1atkpeXl9atWyez2awPP/xQZrNZP/30k1xdXaloAXA4f10Dc/z4cXV0dMjd3V3R0dFyd3fXG2+8oa1bt2rnzp2aOXOmvL29bWtnysvLuabhrmppaVFmZqZKS0sVGhqq2bNnKykpaVB7eAD4TwhbTiQ7O1vV1dV66623NHv2bK1fv145OTmKiYnRiRMnFBYWpldffVXz58/XV199pXfeeUeNjY2aOHGiJkyYoH379snNzY11DAAc2rJly/TZZ59J+mdl68svv1RQUJDWrl2rnJwceXl5ydPTU56enqqpqZGbmxsNC3DXmc1mxcXFKSAgQDt27NDo0aOHekgAnAxhy4kcO3ZMa9eulY+Pj+Li4nT48GElJydr1qxZamxs1MqVK9XZ2aklS5bo+eeflyQ1NTVpzJgx8vDwkMFgGJYdpgA4j/z8fGVlZamsrEwBAQHq6OhQVlaW6uvrVVtbq/Hjx+v06dPq7OxUb2+v5syZI6PRyLUNdtPS0iKj0ahx48YN9VAAOCHClpMYqEZVVlYqOztb3t7e6ujo0KFDh+Tr6yvpVreu1atX69q1a0pKSrK1eh/A1EEAjuZfq1HLly+X2WzWxx9/bNt29epVxcbGymq1qqKi4rZQRbUeAOComG/hJAY+SMyaNUvbt29Xe3u7zpw5o5qaGttzQkNDtW7dOnl7e2vTpk06fvz4oNcgaAFwJP39/bag9dFHH6mrq0s3btxQXV2d7TkWi0VeXl5KSkrSpUuX1N7eftvrELQAAI6KsOXgiouLVVtbK0nKyspSUlKSpk6dqq1btyo4OFi7d+8eFKpCQkKUnZ2t5557TpGRkUM0agD49/5aac/JyVFWVpaam5s1b948GQwGbdu2TX19fbYg5efnJxcXl0GdWAEAcHRMcHdgN27cUFFRkfbv36+FCxfqiy++UHV1taRbbZA3btyoN998Uzt27JDBYFBUVJQkKTw8XOHh4ZKYXgPAMQ0ErVOnTqmxsVH5+fkKDg7W+PHjNWPGDJWUlKizs1OZmZm6fPmy8vLyFBgYqICAgCEeOQAAd441Ww7OYrFo4sSJunDhgnbv3q2XX35Zvb29cnNzkyRVVlZq5cqV8vf3V1JSkmJiYoZ4xABwZ/bv36/Nmzfr+vXrOnjwoK2l9qVLl7RhwwaVl5erqalJkyZN0gMPPEDXQQCA0+HdysFdu3ZNkyZNUlRUlFJTU1VdXS03Nzf19fXJarXqmWee0dtvv62GhgZVVlYO9XAB4I5FRETIZDLp/PnzKi0ttW339fXVhg0bVFVVpcLCQu3atUvfffed7dpH0AIAOAsqWw7m776x7evrU19fnxITE3X48GGVl5dr5syZtv09PT36448/FBgYyJRBAE7FbDYrNTVV7e3tSk9Pt3VR/btrIdOiAQDOhrDlQP764aKkpEQXL16UJEVHR2vChAnq7u7WK6+8otLSUpWUlOjJJ59UfHy8TCaT8vLyJPFhBIDzaW5uVkZGhrq7u5WcnKxFixZJ4nYVAADnR9hyQFlZWSooKNDkyZNVV1enKVOmKC4uTkuXLlVXV5fS09NVUFCgxx9/XN3d3WpsbLSt4QIAZ9Tc3KwlS5aop6dHCxcuVFJS0lAPCQCA/xsT3x3MgQMH9Mknn6isrEwnT57U77//rrCwMB04cEB79uzRyJEjlZ+fr4MHDyozM1Nnz561rWMAAGc1YcIE5eXl6fr166qvrx/q4QAAcFdQ2XIwmzdvVnFxsaqqqmQ0GuXi4qK2tjalpqaqs7NTX3/99W3HMHUQwP2itbVV/v7+NMEAANwXeDdzEAOZ19XVVT09Pbp586ZcXFzU19cnf39/rVixQt98843q6upuO5agBeB+YTKZ5OLiIqvVOtRDAQDg/0bYchADi8CfffZZNTY2asuWLZJuhS/pVvUqODhYXl5eQzZGALhXqGwBAO4HrkM9AAwWFBSkPXv2KDk5WVevXtULL7yg0aNHa82aNfL29lZgYOBQDxEAAADAHWDNloMqLi5Wenq6DAaDPDw85OfnpxMnTsjNze1v7z8DAAAAwLEQthzYxYsX1dbWpps3byoiIsK2hmtgaiEAAAAAx0XYciJUtAAAAADnQdgCAAAAADugTAIAAAAAdkDYAgAAAAA7IGwBAAAAgB0QtgAAAADADghbAAAAAGAHhC0AAAAAsAPCFgAAAADYAWELAAAAAOyAsAUAGBYSEhI0f/58SVJkZKQyMzOHdDwAgPsfYQsAAAAA7ICwBQAYVhISElRZWanc3FwZDAYZDAa1tLRIkhobGxUTE6NRo0bJ399fixcvVnt7u+3YyMhIZWRkKDMzU6NHj5a/v78++OADdXV1KTExUZ6ennr00Ud19OhR2zEdHR2Ki4uTr6+vHnzwQT322GPKz8+/16cNABgChC0AwLCSm5ur6dOnKzk5Wa2trWptbdW4ceN05coVRUVFaerUqTp16pTKysrU1taml156adDxBQUF8vHx0Q8//KCMjAylpKToxRdf1IwZM3T69GlFR0dr8eLF6u7uliStWrVKZ8+e1dGjR/XLL79o165d8vHxGYpTBwDcY4b+/v7+oR4EAAD2lpCQoCtXrujQoUOKjIxUWFiYtm3bZtu/fv16VVVVqby83LbNbDZr3Lhx+vXXXzVp0iRFRkbKYrGoqqpKkmSxWPTQQw9pwYIF2rdvnyTp4sWLMplMqq2t1bRp0zRv3jz5+Pho79699/R8AQBDj8oWAACS6uvrVVFRoVGjRtl+Jk+eLElqamqyPS80NNT22Gg06uGHH1ZISIhtm7+/vyTpzz//lCSlpKSosLBQYWFhev3111VTU3MvTgcA4ABch3oAAAA4guvXrys2NlabNm26bZ/JZLI9dnNzG7TPYDAM2mYwGCRJVqtVkhQTE6PffvtNR44c0bFjxzRnzhylpaVpy5Yt9jgNAIADIWwBAIadESNGyGKxDNoWHh6uzz//XIGBgXJ1vbtvj76+voqPj1d8fLyeeuopLVu2jLAFAMMA0wgBAMNOYGCgvv/+e7W0tKi9vV1Wq1VpaWm6fPmyFi1apB9//FFNTU0qLy9XYmLibcHsv7F69WqVlJTo3Llz+vnnn1VaWqopU6bcxbMBADgqwhYAYNjJysqS0WhUUFCQfH19df78eY0dO1bffvutLBaLoqOjFRISoszMTHl7e8vF5X9/uxwxYoRWrFih0NBQPf300zIajSosLLyLZwMAcFR0IwQAAAAAO6CyBQAAAAB2QNgCAAAAADsgbAEAAACAHRC2AAAAAMAOCFsAAAAAYAeELQAAAACwA8IWAAAAANgBYQsAAAAA7ICwBQAAAAB2QNgCAAAAADsgbAEAAACAHfwD22s7cs9fatUAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Unstack\n",
        "data_unstack= df.groupby(['Member_number','itemDescription'])['itemDescription'].count().unstack().fillna(0)\n",
        "data_unstack"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 546
        },
        "id": "G6L1u8W54wlV",
        "outputId": "d04e17f7-945d-4e60-dba8-d72e33be7fb6"
      },
      "execution_count": 8,
      "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": "execute_result",
          "data": {
            "text/plain": [
              "itemDescription  Instant food products  UHT-milk  abrasive cleaner  \\\n",
              "Member_number                                                        \n",
              "1000                               0.0       0.0               0.0   \n",
              "1001                               0.0       0.0               0.0   \n",
              "1002                               0.0       0.0               0.0   \n",
              "1003                               0.0       0.0               0.0   \n",
              "1004                               0.0       0.0               0.0   \n",
              "...                                ...       ...               ...   \n",
              "4996                               0.0       0.0               0.0   \n",
              "4997                               0.0       0.0               0.0   \n",
              "4998                               0.0       0.0               0.0   \n",
              "4999                               0.0       0.0               0.0   \n",
              "5000                               0.0       0.0               0.0   \n",
              "\n",
              "itemDescription  artif. sweetener  baby cosmetics  bags  baking powder  \\\n",
              "Member_number                                                            \n",
              "1000                          0.0             0.0   0.0            0.0   \n",
              "1001                          0.0             0.0   0.0            0.0   \n",
              "1002                          0.0             0.0   0.0            0.0   \n",
              "1003                          0.0             0.0   0.0            0.0   \n",
              "1004                          0.0             0.0   0.0            0.0   \n",
              "...                           ...             ...   ...            ...   \n",
              "4996                          0.0             0.0   0.0            0.0   \n",
              "4997                          0.0             0.0   0.0            0.0   \n",
              "4998                          0.0             0.0   0.0            0.0   \n",
              "4999                          0.0             0.0   0.0            0.0   \n",
              "5000                          0.0             0.0   0.0            0.0   \n",
              "\n",
              "itemDescription  bathroom cleaner  beef  berries  ...  turkey  vinegar  \\\n",
              "Member_number                                     ...                    \n",
              "1000                          0.0   0.0      0.0  ...     0.0      0.0   \n",
              "1001                          0.0   1.0      0.0  ...     0.0      0.0   \n",
              "1002                          0.0   0.0      0.0  ...     0.0      0.0   \n",
              "1003                          0.0   0.0      0.0  ...     0.0      0.0   \n",
              "1004                          0.0   0.0      0.0  ...     0.0      0.0   \n",
              "...                           ...   ...      ...  ...     ...      ...   \n",
              "4996                          0.0   0.0      0.0  ...     0.0      0.0   \n",
              "4997                          0.0   0.0      0.0  ...     0.0      0.0   \n",
              "4998                          0.0   0.0      0.0  ...     0.0      0.0   \n",
              "4999                          0.0   0.0      2.0  ...     0.0      0.0   \n",
              "5000                          0.0   0.0      0.0  ...     0.0      0.0   \n",
              "\n",
              "itemDescription  waffles  whipped/sour cream  whisky  white bread  white wine  \\\n",
              "Member_number                                                                   \n",
              "1000                 0.0                 0.0     0.0          0.0         0.0   \n",
              "1001                 0.0                 1.0     0.0          1.0         0.0   \n",
              "1002                 0.0                 0.0     0.0          0.0         0.0   \n",
              "1003                 0.0                 0.0     0.0          0.0         0.0   \n",
              "1004                 0.0                 0.0     0.0          0.0         0.0   \n",
              "...                  ...                 ...     ...          ...         ...   \n",
              "4996                 0.0                 0.0     0.0          0.0         0.0   \n",
              "4997                 0.0                 0.0     0.0          0.0         1.0   \n",
              "4998                 0.0                 0.0     0.0          0.0         0.0   \n",
              "4999                 0.0                 1.0     0.0          0.0         0.0   \n",
              "5000                 0.0                 0.0     0.0          0.0         0.0   \n",
              "\n",
              "itemDescription  whole milk  yogurt  zwieback  \n",
              "Member_number                                  \n",
              "1000                    2.0     1.0       0.0  \n",
              "1001                    2.0     0.0       0.0  \n",
              "1002                    1.0     0.0       0.0  \n",
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              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-d675943c-5d02-455d-8afb-5dbcbe78d9e9 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_8d333174-7ab5-497a-8c1a-5400640ea570\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
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              "        fill: #D2E3FC;\n",
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              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
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              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('data_unstack')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "        document.querySelector('#id_8d333174-7ab5-497a-8c1a-5400640ea570 button.colab-df-generate');\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('data_unstack');\n",
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              "\n",
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              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data_unstack"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Encoding\n",
        "def encode_units(x):\n",
        "  if x<=0:\n",
        "    return 0\n",
        "  if x>=0:\n",
        "    return 1\n",
        "\n",
        "data_set=data_unstack.applymap(encode_units)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "imvHBIXV6aLX",
        "outputId": "f51e631b-e941-4827-de4d-fc530638c7eb"
      },
      "execution_count": 9,
      "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-9-0c68d0818cdd>:8: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n",
            "  data_set=data_unstack.applymap(encode_units)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Transforming the Data:\n",
        "df_unstack=df.groupby(['Member_number','itemDescription'])['itemDescription'].count().unstack().reset_index().fillna(0).set_index('Member_number')\n",
        "df_unstack"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 546
        },
        "id": "UTugfnr-6eL8",
        "outputId": "e60da143-eaf1-47db-a460-4c68ceaabaea"
      },
      "execution_count": 10,
      "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": "execute_result",
          "data": {
            "text/plain": [
              "itemDescription  Instant food products  UHT-milk  abrasive cleaner  \\\n",
              "Member_number                                                        \n",
              "1000                               0.0       0.0               0.0   \n",
              "1001                               0.0       0.0               0.0   \n",
              "1002                               0.0       0.0               0.0   \n",
              "1003                               0.0       0.0               0.0   \n",
              "1004                               0.0       0.0               0.0   \n",
              "...                                ...       ...               ...   \n",
              "4996                               0.0       0.0               0.0   \n",
              "4997                               0.0       0.0               0.0   \n",
              "4998                               0.0       0.0               0.0   \n",
              "4999                               0.0       0.0               0.0   \n",
              "5000                               0.0       0.0               0.0   \n",
              "\n",
              "itemDescription  artif. sweetener  baby cosmetics  bags  baking powder  \\\n",
              "Member_number                                                            \n",
              "1000                          0.0             0.0   0.0            0.0   \n",
              "1001                          0.0             0.0   0.0            0.0   \n",
              "1002                          0.0             0.0   0.0            0.0   \n",
              "1003                          0.0             0.0   0.0            0.0   \n",
              "1004                          0.0             0.0   0.0            0.0   \n",
              "...                           ...             ...   ...            ...   \n",
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              "4999                          0.0             0.0   0.0            0.0   \n",
              "5000                          0.0             0.0   0.0            0.0   \n",
              "\n",
              "itemDescription  bathroom cleaner  beef  berries  ...  turkey  vinegar  \\\n",
              "Member_number                                     ...                    \n",
              "1000                          0.0   0.0      0.0  ...     0.0      0.0   \n",
              "1001                          0.0   1.0      0.0  ...     0.0      0.0   \n",
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              "5000                          0.0   0.0      0.0  ...     0.0      0.0   \n",
              "\n",
              "itemDescription  waffles  whipped/sour cream  whisky  white bread  white wine  \\\n",
              "Member_number                                                                   \n",
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              "...                  ...                 ...     ...          ...         ...   \n",
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              "4999                 0.0                 1.0     0.0          0.0         0.0   \n",
              "5000                 0.0                 0.0     0.0          0.0         0.0   \n",
              "\n",
              "itemDescription  whole milk  yogurt  zwieback  \n",
              "Member_number                                  \n",
              "1000                    2.0     1.0       0.0  \n",
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              "\n",
              "[3898 rows x 167 columns]"
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              "      <th>itemDescription</th>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>4999</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5000</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "  @keyframes spin {\n",
              "    0% {\n",
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              "    80% {\n",
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              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Generating Frequent Itemsets\n",
        "df_set_freq = apriori(data_set, min_support=0.07, use_colnames=True)\n",
        "df_set_freq"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 516
        },
        "id": "Cm1e8n_j8efh",
        "outputId": "cd9f2608-d5d8-4251-a435-beecedc94ec6"
      },
      "execution_count": 11,
      "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/mlxtend/frequent_patterns/fpcommon.py:161: DeprecationWarning: DataFrames with non-bool types result in worse computationalperformance and their support might be discontinued in the future.Please use a DataFrame with bool type\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     support                                    itemsets\n",
              "0   0.078502                                  (UHT-milk)\n",
              "1   0.119548                                      (beef)\n",
              "2   0.079785                                   (berries)\n",
              "3   0.158799                              (bottled beer)\n",
              "4   0.213699                             (bottled water)\n",
              "..       ...                                         ...\n",
              "78  0.075680                    (tropical fruit, yogurt)\n",
              "79  0.079785            (whipped/sour cream, whole milk)\n",
              "80  0.150590                        (yogurt, whole milk)\n",
              "81  0.082093  (rolls/buns, other vegetables, whole milk)\n",
              "82  0.071832      (yogurt, other vegetables, whole milk)\n",
              "\n",
              "[83 rows x 2 columns]"
            ],
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              "\n",
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              "      <th></th>\n",
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              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>0.078502</td>\n",
              "      <td>(UHT-milk)</td>\n",
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              "      <th>1</th>\n",
              "      <td>0.119548</td>\n",
              "      <td>(beef)</td>\n",
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              "      <td>0.079785</td>\n",
              "      <td>(berries)</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.158799</td>\n",
              "      <td>(bottled beer)</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.213699</td>\n",
              "      <td>(bottled water)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>78</th>\n",
              "      <td>0.075680</td>\n",
              "      <td>(tropical fruit, yogurt)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>79</th>\n",
              "      <td>0.079785</td>\n",
              "      <td>(whipped/sour cream, whole milk)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>80</th>\n",
              "      <td>0.150590</td>\n",
              "      <td>(yogurt, whole milk)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>81</th>\n",
              "      <td>0.082093</td>\n",
              "      <td>(rolls/buns, other vegetables, whole milk)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>82</th>\n",
              "      <td>0.071832</td>\n",
              "      <td>(yogurt, other vegetables, whole milk)</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "<p>83 rows × 2 columns</p>\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_set_freq",
              "summary": "{\n  \"name\": \"df_set_freq\",\n  \"rows\": 83,\n  \"fields\": [\n    {\n      \"column\": \"support\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.07202874216008459,\n        \"min\": 0.07029245767060031,\n        \"max\": 0.4581836839404823,\n        \"num_unique_values\": 75,\n        \"samples\": [\n          0.21369933299127758,\n          0.11133914828116984,\n          0.18547973319651104\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"itemsets\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 83,\n        \"samples\": [\n          \"frozenset({'sausage'})\",\n          \"frozenset({'UHT-milk'})\",\n          \"frozenset({'newspapers'})\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Creating Association Rules\n",
        "df_rules = association_rules(df_set_freq, metric=\"lift\", min_threshold=1,num_itemsets=len(df_set_freq))\n",
        "df_rules"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 549
        },
        "id": "EKCGhFceBaNs",
        "outputId": "e8e9d133-1a97-4092-ce7b-335fe0a67a7e"
      },
      "execution_count": 12,
      "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": "execute_result",
          "data": {
            "text/plain": [
              "                       antecedents                     consequents  \\\n",
              "0                   (bottled beer)                    (whole milk)   \n",
              "1                     (whole milk)                  (bottled beer)   \n",
              "2               (other vegetables)                 (bottled water)   \n",
              "3                  (bottled water)              (other vegetables)   \n",
              "4                     (rolls/buns)                 (bottled water)   \n",
              "..                             ...                             ...   \n",
              "93            (whole milk, yogurt)              (other vegetables)   \n",
              "94  (other vegetables, whole milk)                        (yogurt)   \n",
              "95                        (yogurt)  (other vegetables, whole milk)   \n",
              "96              (other vegetables)            (whole milk, yogurt)   \n",
              "97                    (whole milk)      (other vegetables, yogurt)   \n",
              "\n",
              "    antecedent support  consequent support   support  confidence      lift  \\\n",
              "0             0.158799            0.458184  0.085428    0.537964  1.174124   \n",
              "1             0.458184            0.158799  0.085428    0.186450  1.174124   \n",
              "2             0.376603            0.213699  0.093894    0.249319  1.166680   \n",
              "3             0.213699            0.376603  0.093894    0.439376  1.166680   \n",
              "4             0.349666            0.213699  0.079271    0.226706  1.060863   \n",
              "..                 ...                 ...       ...         ...       ...   \n",
              "93            0.150590            0.376603  0.071832    0.477002  1.266589   \n",
              "94            0.191380            0.282966  0.071832    0.375335  1.326434   \n",
              "95            0.282966            0.191380  0.071832    0.253853  1.326434   \n",
              "96            0.376603            0.150590  0.071832    0.190736  1.266589   \n",
              "97            0.458184            0.120318  0.071832    0.156775  1.303003   \n",
              "\n",
              "    representativity  leverage  conviction  zhangs_metric   jaccard  \\\n",
              "0                1.0  0.012669    1.172672       0.176297  0.160714   \n",
              "1                1.0  0.012669    1.033988       0.273711  0.160714   \n",
              "2                1.0  0.013414    1.047450       0.229175  0.189147   \n",
              "3                1.0  0.013414    1.111969       0.181695  0.189147   \n",
              "4                1.0  0.004548    1.016820       0.088219  0.163752   \n",
              "..               ...       ...         ...            ...       ...   \n",
              "93               1.0  0.015119    1.191967       0.247793  0.157746   \n",
              "94               1.0  0.017678    1.147870       0.304344  0.178458   \n",
              "95               1.0  0.017678    1.083727       0.343217  0.178458   \n",
              "96               1.0  0.015119    1.049608       0.337631  0.157746   \n",
              "97               1.0  0.016704    1.043235       0.429190  0.141772   \n",
              "\n",
              "    certainty  kulczynski  \n",
              "0    0.147247    0.362207  \n",
              "1    0.032871    0.362207  \n",
              "2    0.045300    0.344347  \n",
              "3    0.100694    0.344347  \n",
              "4    0.016541    0.298827  \n",
              "..        ...         ...  \n",
              "93   0.161050    0.333869  \n",
              "94   0.128822    0.314594  \n",
              "95   0.077259    0.314594  \n",
              "96   0.047263    0.333869  \n",
              "97   0.041443    0.376895  \n",
              "\n",
              "[98 rows x 14 columns]"
            ],
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              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>antecedents</th>\n",
              "      <th>consequents</th>\n",
              "      <th>antecedent support</th>\n",
              "      <th>consequent support</th>\n",
              "      <th>support</th>\n",
              "      <th>confidence</th>\n",
              "      <th>lift</th>\n",
              "      <th>representativity</th>\n",
              "      <th>leverage</th>\n",
              "      <th>conviction</th>\n",
              "      <th>zhangs_metric</th>\n",
              "      <th>jaccard</th>\n",
              "      <th>certainty</th>\n",
              "      <th>kulczynski</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>(bottled beer)</td>\n",
              "      <td>(whole milk)</td>\n",
              "      <td>0.158799</td>\n",
              "      <td>0.458184</td>\n",
              "      <td>0.085428</td>\n",
              "      <td>0.537964</td>\n",
              "      <td>1.174124</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.012669</td>\n",
              "      <td>1.172672</td>\n",
              "      <td>0.176297</td>\n",
              "      <td>0.160714</td>\n",
              "      <td>0.147247</td>\n",
              "      <td>0.362207</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>(whole milk)</td>\n",
              "      <td>(bottled beer)</td>\n",
              "      <td>0.458184</td>\n",
              "      <td>0.158799</td>\n",
              "      <td>0.085428</td>\n",
              "      <td>0.186450</td>\n",
              "      <td>1.174124</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.012669</td>\n",
              "      <td>1.033988</td>\n",
              "      <td>0.273711</td>\n",
              "      <td>0.160714</td>\n",
              "      <td>0.032871</td>\n",
              "      <td>0.362207</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>(other vegetables)</td>\n",
              "      <td>(bottled water)</td>\n",
              "      <td>0.376603</td>\n",
              "      <td>0.213699</td>\n",
              "      <td>0.093894</td>\n",
              "      <td>0.249319</td>\n",
              "      <td>1.166680</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.013414</td>\n",
              "      <td>1.047450</td>\n",
              "      <td>0.229175</td>\n",
              "      <td>0.189147</td>\n",
              "      <td>0.045300</td>\n",
              "      <td>0.344347</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>(bottled water)</td>\n",
              "      <td>(other vegetables)</td>\n",
              "      <td>0.213699</td>\n",
              "      <td>0.376603</td>\n",
              "      <td>0.093894</td>\n",
              "      <td>0.439376</td>\n",
              "      <td>1.166680</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.013414</td>\n",
              "      <td>1.111969</td>\n",
              "      <td>0.181695</td>\n",
              "      <td>0.189147</td>\n",
              "      <td>0.100694</td>\n",
              "      <td>0.344347</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>(rolls/buns)</td>\n",
              "      <td>(bottled water)</td>\n",
              "      <td>0.349666</td>\n",
              "      <td>0.213699</td>\n",
              "      <td>0.079271</td>\n",
              "      <td>0.226706</td>\n",
              "      <td>1.060863</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.004548</td>\n",
              "      <td>1.016820</td>\n",
              "      <td>0.088219</td>\n",
              "      <td>0.163752</td>\n",
              "      <td>0.016541</td>\n",
              "      <td>0.298827</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>93</th>\n",
              "      <td>(whole milk, yogurt)</td>\n",
              "      <td>(other vegetables)</td>\n",
              "      <td>0.150590</td>\n",
              "      <td>0.376603</td>\n",
              "      <td>0.071832</td>\n",
              "      <td>0.477002</td>\n",
              "      <td>1.266589</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.015119</td>\n",
              "      <td>1.191967</td>\n",
              "      <td>0.247793</td>\n",
              "      <td>0.157746</td>\n",
              "      <td>0.161050</td>\n",
              "      <td>0.333869</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>94</th>\n",
              "      <td>(other vegetables, whole milk)</td>\n",
              "      <td>(yogurt)</td>\n",
              "      <td>0.191380</td>\n",
              "      <td>0.282966</td>\n",
              "      <td>0.071832</td>\n",
              "      <td>0.375335</td>\n",
              "      <td>1.326434</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.017678</td>\n",
              "      <td>1.147870</td>\n",
              "      <td>0.304344</td>\n",
              "      <td>0.178458</td>\n",
              "      <td>0.128822</td>\n",
              "      <td>0.314594</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>95</th>\n",
              "      <td>(yogurt)</td>\n",
              "      <td>(other vegetables, whole milk)</td>\n",
              "      <td>0.282966</td>\n",
              "      <td>0.191380</td>\n",
              "      <td>0.071832</td>\n",
              "      <td>0.253853</td>\n",
              "      <td>1.326434</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.017678</td>\n",
              "      <td>1.083727</td>\n",
              "      <td>0.343217</td>\n",
              "      <td>0.178458</td>\n",
              "      <td>0.077259</td>\n",
              "      <td>0.314594</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>96</th>\n",
              "      <td>(other vegetables)</td>\n",
              "      <td>(whole milk, yogurt)</td>\n",
              "      <td>0.376603</td>\n",
              "      <td>0.150590</td>\n",
              "      <td>0.071832</td>\n",
              "      <td>0.190736</td>\n",
              "      <td>1.266589</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.015119</td>\n",
              "      <td>1.049608</td>\n",
              "      <td>0.337631</td>\n",
              "      <td>0.157746</td>\n",
              "      <td>0.047263</td>\n",
              "      <td>0.333869</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>97</th>\n",
              "      <td>(whole milk)</td>\n",
              "      <td>(other vegetables, yogurt)</td>\n",
              "      <td>0.458184</td>\n",
              "      <td>0.120318</td>\n",
              "      <td>0.071832</td>\n",
              "      <td>0.156775</td>\n",
              "      <td>1.303003</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.016704</td>\n",
              "      <td>1.043235</td>\n",
              "      <td>0.429190</td>\n",
              "      <td>0.141772</td>\n",
              "      <td>0.041443</td>\n",
              "      <td>0.376895</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>98 rows × 14 columns</p>\n",
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              "\n",
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              "  .colab-df-quickchart-complete:disabled,\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
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              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df_rules')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_rules",
              "summary": "{\n  \"name\": \"df_rules\",\n  \"rows\": 98,\n  \"fields\": [\n    {\n      \"column\": \"antecedents\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 23,\n        \"samples\": [\n          \"frozenset({'tropical fruit'})\",\n          \"frozenset({'newspapers'})\",\n          \"frozenset({'bottled beer'})\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"consequents\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 23,\n        \"samples\": [\n          \"frozenset({'tropical fruit'})\",\n          \"frozenset({'newspapers'})\",\n          \"frozenset({'whole milk'})\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"antecedent support\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 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\"max\": 0.1913801949717804,\n        \"num_unique_values\": 41,\n        \"samples\": [\n          0.11980502821959979,\n          0.09415084658799384,\n          0.09235505387378143\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"confidence\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.1193564754890551,\n        \"min\": 0.15341545352743563,\n        \"max\": 0.5970149253731343,\n        \"num_unique_values\": 98,\n        \"samples\": [\n          0.3103448275862069,\n          0.3194822888283379,\n          0.3753351206434316\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"lift\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.06424867426054116,\n        \"min\": 1.037641660958853,\n        \"max\": 1.3264336357824993,\n        \"num_unique_values\": 63,\n        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\"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.05363064911189632,\n        \"min\": 1.0116135926558907,\n        \"max\": 1.3445071546662106,\n        \"num_unique_values\": 98,\n        \"samples\": [\n          1.0396998460749103\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"zhangs_metric\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.07080260485860158,\n        \"min\": 0.04733997133646566,\n        \"max\": 0.4291903409090908,\n        \"num_unique_values\": 98,\n        \"samples\": [\n          0.11466785224704538\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"jaccard\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.03512419848923704,\n        \"min\": 0.13490891186607584,\n        \"max\": 0.2974481658692185,\n        \"num_unique_values\": 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            }
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "source": [
        "#Building a Recommendation System:\n",
        "def get_recommendations(product, rules,num_recommendations=5):\n",
        "  recommendations=[] # Define recommendations outside the loop\n",
        "  for idx, row in rules.iterrows():\n",
        "    if product in row['antecedents']:\n",
        "      recommended_item=next((iter(row['consequents'])))\n",
        "      recommendations.append((recommended_item,row['lift']))\n",
        "  recommendations.sort(key=lambda x:x[1],reverse=True) # Now recommendations is in scope\n",
        "  return recommendations[0:num_recommendations]\n"
      ],
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Zz_Y2AYTABUt",
        "outputId": "3f32869c-7e9d-4915-9733-863376dd3c07"
      },
      "execution_count": 13,
      "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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Testing Recommendations\n",
        "product_to_recommend='tropical fruit'\n",
        "recommendations=get_recommendations(product_to_recommend, df_rules)\n",
        "print(f\"Recommendations for'{product_to_recommend}':\")\n",
        "for item, lift in recommendations:\n",
        " print(f'{item}(lift:{lift:.2f})')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QZvAOTG8AGfN",
        "outputId": "b231303e-a613-4ce8-a736-4bc3c5a32e1e"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Recommendations for'tropical fruit':\n",
            "yogurt(lift:1.14)\n",
            "soda(lift:1.12)\n",
            "whole milk(lift:1.09)\n",
            "rolls/buns(lift:1.08)\n",
            "other vegetables(lift:1.04)\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"
          ]
        }
      ]
    }
  ]
}