Quantium_Data_Analysis_Task_1.ipynb

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      "source": [
        "# Task - 1 Data preparation and customer analytics"
      ],
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      "source": [
        "### Author : Siddharth Patondikar"
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      "source": [
        "### Objective\n",
        "\n",
        "We need to present a strategic recommendation to Julia that is supported by data which she can then use for the upcoming category review however to do so we need to analyse the data to understand the current purchasing trends and behaviours. The client is particularly interested in customer segments and their chip purchasing behaviour. Consider what metrics would help describe the customers’ purchasing behaviour.  \n",
        "\n",
        "To get started, download the resource csv data files below and begin performing high level data checks such as:\n",
        "\n",
        "- Creating and interpreting high level summaries of the data\n",
        "- Finding outliers and removing these (if applicable)\n",
        "- Checking data formats and correcting (if applicable)\n",
        "\n",
        "You will also want to derive extra features such as pack size and brand name from the data and define metrics of interest to enable you to draw insights on who spends on chips and what drives spends for each customer segment. Remember our end goal is to form a strategy based on the findings to provide a clear recommendation to Julia the Category Manager so make sure your insights can have a commercial application."
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      "source": [
        "#Importing Libraries\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "%matplotlib inline\n",
        "sns.set()\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Importing Data\n",
        "\n",
        "transact_df = pd.read_excel('QVI_transaction_data.xlsx', sheet_name='in')\n",
        "\n",
        "purchase_df = pd.read_csv('QVI_purchase_behaviour.csv')"
      ],
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        "id": "ADdYmt63p_N2"
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      "execution_count": 2,
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      "source": [
        "#First look for transaction data\n",
        "transact_df.head()"
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              "    DATE  STORE_NBR  LYLTY_CARD_NBR  TXN_ID  PROD_NBR  \\\n",
              "0  43390          1            1000       1         5   \n",
              "1  43599          1            1307     348        66   \n",
              "2  43605          1            1343     383        61   \n",
              "3  43329          2            2373     974        69   \n",
              "4  43330          2            2426    1038       108   \n",
              "\n",
              "                                  PROD_NAME  PROD_QTY  TOT_SALES  \n",
              "0    Natural Chip        Compny SeaSalt175g         2        6.0  \n",
              "1                  CCs Nacho Cheese    175g         3        6.3  \n",
              "2    Smiths Crinkle Cut  Chips Chicken 170g         2        2.9  \n",
              "3    Smiths Chip Thinly  S/Cream&Onion 175g         5       15.0  \n",
              "4  Kettle Tortilla ChpsHny&Jlpno Chili 150g         3       13.8  "
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      "cell_type": "code",
      "source": [
        "#Total rows and cols\n",
        "transact_df.shape"
      ],
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        "id": "aBlxIhAlsBmg",
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(264836, 8)"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
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      "source": [
        "Data has 264836 rows and 8 columns"
      ],
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      }
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      "source": [
        "transact_df.info()"
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        "outputId": "a243a506-eecc-4ae2-e678-ab64565a6b0f"
      },
      "execution_count": 5,
      "outputs": [
        {
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          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 264836 entries, 0 to 264835\n",
            "Data columns (total 8 columns):\n",
            " #   Column          Non-Null Count   Dtype  \n",
            "---  ------          --------------   -----  \n",
            " 0   DATE            264836 non-null  int64  \n",
            " 1   STORE_NBR       264836 non-null  int64  \n",
            " 2   LYLTY_CARD_NBR  264836 non-null  int64  \n",
            " 3   TXN_ID          264836 non-null  int64  \n",
            " 4   PROD_NBR        264836 non-null  int64  \n",
            " 5   PROD_NAME       264836 non-null  object \n",
            " 6   PROD_QTY        264836 non-null  int64  \n",
            " 7   TOT_SALES       264836 non-null  float64\n",
            "dtypes: float64(1), int64(6), object(1)\n",
            "memory usage: 16.2+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Checking duplicate values\n",
        "transact_df.duplicated().value_counts()"
      ],
      "metadata": {
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        },
        "id": "klmsjDrvrjIN",
        "outputId": "0e434762-a39b-4693-89c2-f5092299d921"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False    264835\n",
              "True          1\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
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      "cell_type": "markdown",
      "source": [
        "There is one duplicate row in the data, hence dropping that row"
      ],
      "metadata": {
        "id": "6nRvynAIr81b"
      }
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    {
      "cell_type": "code",
      "source": [
        "#Dropping duplicates\n",
        "transact_df.drop_duplicates(inplace=True)\n",
        "transact_df.shape"
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      "execution_count": 7,
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        {
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          "data": {
            "text/plain": [
              "(264835, 8)"
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          },
          "metadata": {},
          "execution_count": 7
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    {
      "cell_type": "code",
      "source": [
        "#Checking Null values\n",
        "transact_df.isnull().sum()"
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      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DATE              0\n",
              "STORE_NBR         0\n",
              "LYLTY_CARD_NBR    0\n",
              "TXN_ID            0\n",
              "PROD_NBR          0\n",
              "PROD_NAME         0\n",
              "PROD_QTY          0\n",
              "TOT_SALES         0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
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      "cell_type": "markdown",
      "source": [
        "There are no null values in the data."
      ],
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        "sns.heatmap(transact_df.isnull())"
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vwpVLgHX8+HEAFi5cqHPt0KFDJCQkcOvWLQA++OADreutW7dm8+bNync/Pz8WLFjArFmzyM3NxdXVlRkzZijZ2QvExMTwzz//KIlFC+Pt7Y1Go2HdunXKUTnBwcFPlcVdCCGEeO7I238Vrlwyub9qJJO7EEKIJ/VMMrlHbiuzvqq4DHl8JaHjmSUaFUIIIYR4VZR7HiwhhBBCPGOyRFjhyiXACg8PJygoiJiYGFJTU6lbty6dO3dm7NixSrqGtWvXEhISwo0bN8jNzeWNN95g0KBBvP/++1pH42zZsoWjR48SHR3N/fv3WbZsGd26ddMZMzExkfnz53Ps2DH09fXp0qUL06dP1zqaZ9q0afz00086bYOCguRNQiGEEC8PCbAqXLkEWA8ePMDBwYHhw4djYmLC5cuX8ff35/Lly6xbtw6AlJQUPDw8ePPNNzE0NCQyMpL58+eTmprKxx9/rPS1Z88eADp06MDu3bsLHS8nJ4ePPvoIgCVLlpCZmcmiRYv49NNPWbNmjVbdN954g8WLF2uVWVlZldWjCyGEEEKUT4DVu3dvre9t2rTBwMCAmTNnkpiYSN26dZk0aZJWnbfffpubN2/y008/aQVY33//PXp6ety4caPIAGv//v1cvnyZ0NBQJaGoWq3Gy8uLs2fP4uDgoNStUqUKTk5OZfOgQgghxHNIo3k+EoTGxsYyf/58zpw5g5GREb1792bixIkYGBgU206j0RAUFMTWrVuVt/6nT5+u9e93UatSAJ9++imjRo0qtl55r149sz1YJiYmQP5sU1Fee+01neuFHa/zqKNHj2Jtba2V3b1t27aYmJgQHh6uFWAJIYQQL73nYIkwKSmJESNGYGFhgb+/P4mJiSxcuJDMzExmzZpVbNugoCCWL1/OlClTsLa2ZsuWLYwcOZI9e/YoqZVGjx7N4MGDtdqFhoayceNGncCpIlavyjXAevjwIbm5ucTExLBy5Urc3NwwMzPTqpObm0tmZianTp1i9+7djB07tsTjxMXF6Rydo1KpaNSoEXFxcVrlV69epUWLFmRlZdGkSRNGjx5N586dS/5wQgghhCjS999/T1paGitWrFAmWR4+fMjcuXPx8fEp9JQXgKysLNasWcPIkSOVXJktWrSgW7duBAcHM2fOHADMzc0xNzfXartkyRIaN27MW2+9pVVeEatX5RpgvfPOOyQmJgLQrl07lixZonX96tWrWhnZP/nkE53Eo08iOTm50LMOa9SoQVJSkvLdxsYGe3t7GjduTEpKCtu2bWPMmDFFbpwXQgghXkjPQSb3o0eP4uLiogRXAO7u7syePZvjx4/Tr1+/QtudPn2a1NRU3N3dlTIDAwO6dOnCwYMHixwvMTGRU6dOFXq2cUUo1wArMDCQjIwMYmJiCAgI4OOPP2b9+vVUqlQJgPr167Nz507S09M5deoUQUFB6OnpMX78+HK5nxEjRmh9d3NzY/DgwSxfvlwCLCGEEC+P52CJMC4ujv79+2uVqdVqateurbO69Gg7QGdlysrKio0bN5KZmUmVKlV02oWEhJCXl0f37t11rlXE6lW5BlgFU3TNmjXD3t6e3r17c/DgQSWYMTAwwN7eHsjfCG9sbMyiRYsYMmQItWvXfuJx1Go1qampOuVJSUnUr1+/yHZ6enq8++67fPPNN0X+hQkhhBCvsk6dOhV7/dChQ4WWJycno1ardcofXV0qrJ2BgQGGhoZa5Wq1Go1GQ1JSUpEBVrNmzXSOv6uo1atntsnd2toafX19rl27VmSdpk2b8vDhQxISEkoUYFlaWnLp0iWtMo1GQ3x8PG3bti31PQshhBAvpOdgifBZio2N5cKFC8ycOVPnWkWtXj2zACs6OpqcnBydTe7/dvr0aVQqVbF1CtO+fXt+/vlnrly5goWFBQCRkZE8ePCADh06FNkuLy+PsLAw3nzzTZm9EkII8fIowyXComaoHketVpOSkqJTnpSURI0aNYptl52dTVZWltYsVnJyMiqVqtC2e/fupXLlynh4eDz2vp7V6lW5BFhjx47Fzs4Oa2trqlSpwn//+1+Cg4Oxtramc+fOpKSk4O3tTa9evWjYsCG5ubmcOHGCTZs2MWjQIGrVqqX09ffff5OQkMC9e/eA/EANoGbNmrRu3RqArl27smbNGsaNG8fkyZPJyMjg66+/pmPHjkqKhoSEBKZNm0b37t1p2LAhSUlJbNu2jXPnzuHv718eP4MQQghRMZ6DGSxLS0udvVYpKSncuXNHZ3/Vo+0A4uPjtd4GjIuLw9TUtNCA6JdffsHFxYWaNWuW0d0/vXIJsBwcHAgNDSUwMBCNRkODBg0YOHAgXl5eSnKxRo0asWHDBhITE6lSpQrm5ubMnTuXPn36aPW1ZcsWrQRhBZngW7duzebNmwHQ19dn7dq1zJ8/n8mTJ1O5cmW6dOmCr6+v0s7IyAhjY2MCAgK4e/cu+vr62NnZERQURLt27crjZxBCCCFeWe3bt2f16tVae7HCwsLQ09MrdvtO8+bNMTY2Zt++fUqAlZOTw4EDBwpNDBodHc21a9cYM2bME93Xs1q9Umk0Gk259f6KOGXWp6JvQQghxAui5Y3d5T5Gxr7lZdZXVffSvdmflJRE9+7dadSoET4+Pkqi0Z49e2olGh0xYgQ3b97USsEQGBiIv78/U6ZMoUmTJmzbto1jx45pJRotMH/+fH744QciIiIwMjLSulbU6tWJEyfw9/enS5cupXq2J/HM9mAJIYQQ4hl5DtI01KhRg40bN/LFF18wZswYjIyMGDBggM5ReXl5eTx8qH20j7e3NxqNhnXr1ilH5QQHB+sEVw8fPiQsLIx33nlHJ7iCil29khmsMiAzWEIIIZ7UM5nB+sWvzPqq2n1imfX1KpEZLCGEEOJl8xxscn/VPf4k5TKQlpZG+/btsba25u+//y60zq+//oq1tTU9evTQKs/Ozubrr7/m/fffx8nJCWtra+WNwqLcunWLZs2aFVpXo9EQGBiovGE4aNAgoqKinur5hBBCiOdKXl7ZfUSpPJMAa9WqVTrrq/+WmZnJV199pZWe4d/XfvjhBwwNDWnRosUTjbdw4UKqVatW6LWCE7o/+OAD1qxZQ+3atRk5ciTXr19/socRQgghhHiMcg+wYmNj2bp1K+PGjSuyzpo1azA1NS10w5larebkyZOsW7eu0POFHhUZGUlkZCReXl461x49odvFxYVvv/0WExMTgoODS/ZgQgghxPNKk1d2H1Eq5R5gzZ8/n8GDB9OoUaNCr1+7do3169czY8aMIvtQqVRPNFZOTg5ffPEF48aN0zq9u0BxJ3QfPXr0icYQQgghnnuyRFjhyjXACgsL49KlS8Um//ryyy/p3bu3VrbW0tq0aROVKlViyJAhhV4v7oTumzdvkpmZ+dT3IIQQQghRbm8RZmRksHDhQiZNmoSxsXGhdQ4fPsyZM2cICwt76vESExNZuXIlK1eupFKlSoXWKe0J3UIIIcQLRZb2Kly5BVgBAQG8/vrr9O/fv9DrWVlZfPXVV4wbN65Mzg76+uuvadu2LS4uLk/dlxBCCPFCk6W9ClcuAVZCQgLr1q1j5cqVykna6enpyn/T0tLYsmULenp6dO/eneTkZCB/D1VeXh7JyclUqVJFObfwcc6cOcP+/fvZsWOH0ldGRgaQnyKiatWqVK1atVQndAshhBAvHAmwKly5BFg3btwgJyeHUaNG6Vzz9PTE0dERS0tLrl69WuiMU6tWrZgzZ06Re6keFR8fT05ODn379tW51rlzZzw8PFi6dGmpTugWQgghhCipcgmwbGxs2LRpk1bZxYsXWbBgAXPnzsXe3p4qVaroBESBgYHEx8ezYMECLCwsnni8du3a6Yz3+++/ExQUxMqVK5W+SnpCtxBCCPFCklPwKly5BFhqtZo2bdoUeq1p06Y0bdoUyH97799++uknEhMTddqGh4eTkZHBuXPnADhy5AhGRkY0btyYxo0bU7t2bWrXrq3VJiEhAcgPqgr2eBkaGuLj44O/vz81a9ZUTuh+8OBBoXmzhBBCiBeSLBFWuBfiLMK5c+cqAROAr68vAGPHji02gWlhnvSEbiGEEEKI0lJpNDKP+LROmfWp6FsQQgjxgmh5Y3e5j5GxZWaZ9VX1/S/KrK9XyQsxgyWEEEKIEpA8WBXumRz2LIQQQgjxKimXGawff/yR6dOn65R7e3szZcoUAIYPH87Jkyd16oSGhiqb37Ozs/Hz8yM6Oprz58+TkZFBZGSkTmJSNzc3rT1a/7Z9+3acnJyKrXf27Fmd7O5CCCHEC0s2uVe4cl0iXLt2LdWrV1e+161bV+t68+bNmTp1qlaZmZmZ8ufMzEx++OEH7O3tadGiBceOHSt0nBUrVpCdna1VtnjxYmJjY7Gzs9Mq79q1KyNHjtQqe9KEpkIIIcQLQbZXV7hyDbCaNm1a7DE4arVamV0q6vrJkydRqVT8+OOPRQZYtra2Wt/T09M5f/48ffr0oXJl7UesVatWsWMKIYQQQjyt536Tu0qlKnGbQ4cOkZ6eTs+ePcvhjoQQQojnnCwRVrhy3eTeo0cPbGxs6NSpE2vWrOHhw4da10+ePImTkxP29vYMGzaMP//8s0zGDQkJoUGDBjRv3lzn2t69e7Gzs6NZs2Z4e3vzzz//lMmYQgghxHMjL6/sPqJUymUGq3bt2owbNw5HR0dUKhWHDx/Gz8+PxMREZs2aBeSfN9i7d28sLCy4ffs2wcHBfPjhh2zevJlmzZqVeuz79+9z/PhxnX1WkL/J3cHBAVNTU65fv87q1asZOnQou3fvlkSjQgghXh6SpqHClUuA1a5dO9q1a6d8d3V1xdDQkI0bN/Lxxx9Tp04dxo8fr9WmY8eO9OjRg1WrVhEUFFTqsfft20dOTg49evTQuTZjxgzlzy1btqRt27a4u7sTHBzMnDlzSj2mEEIIIcS/PbM8WO7u7jx8+JCLFy8Wer1atWp06NCB8+fPP9U4ISEhWFtb06RJk8fWrVOnDi1atHjqMYUQQojniSZPU2YfUTovVaLRmzdvcvr06UJnr4QQQohXhuzBqnDPLMAKDQ2lUqVKOikVCqSnp/Pbb79hb29f6jFCQkIAnjjASkxM5K+//nqqMYUQQgghHlUue7C8vLxo06YN1tbWQH7ahB07duDp6Unt2rU5deoUa9eupUuXLjRo0IDbt2+zfv167ty5w7Jly7T6Cg8PJyMjg3PnzgFw5MgRjIyMaNy4MY0bN9aqGxISQvPmzTE1NdW5p5CQEI4cOUKHDh2oU6cO169fJzAwkEqVKvHhhx+Wx88ghBBCVAzZ5F7hyiXAatSoEbt27eLWrVvk5eVhYWGBr68vw4cPB/LfMszJyWHp0qU8ePCAqlWr0qxZM+bOnYuDg4NWX3PnztU63sbX1xeAsWPHMm7cOKU8JiaGf/75h9mzZxd6T2ZmZty+fZuvvvqKlJQUqlevjrOzM+PHj5c3CIUQQrxcZO9UhVNpNJJP/2mdMutT0bcghBDiBdHyxu5yHyN95dgy66vamBVl1ter5LnP5C6EEEKIEpLN6RVOAiwhhBDiZSMBVoUr1wDrp59+YuPGjcTGxlKtWjXs7e1ZsWIFVapUASArK4vVq1ezZ88ebt++Ta1atXB3d2fq1KkA3L59mw0bNnD8+HGuXbtG9erVadWqFZMnT6ZBgwbKOPfu3WPVqlVER0dz8eJF9PX1OXPmTKH3VJBVPj4+HlNTU0aNGkX//v3L82cQQgghxCum3AKsgIAAgoKC+Pjjj3FycuL+/ftERkYq5xHm5eUxevRorl+/ztixYzEzM+PmzZvEx8crfZw/f56DBw/Sv39/HB0duX//PgEBAQwcOJCQkBBq1qwJ5KdbCA0NxcHBATs7uyLPFzx16hRjx45lwIAB+Pr68scff/D5559jZGREt27dyuunEEIIIZ4t2V5d4colwIqLi2PFihWsWrWKDh06KOVdu3ZV/rxr1y6io6MJDQ2lTp06hfbTokUL9u3bR+XK/3ebzZs3p2PHjuzevVs5b9Da2pqIiAgA/P39iwywAgICcHBwYN68eQA4Oztz/fp1li9fLgGWEEKIl8dzskQYGxvL/PnzOXPmDEZGRvTu3ZuJEydiYGBQbDuNRkNQUBBbt27l3r172NjYMH36dJycnJQ6J06cwNPTU6eth4cHS5cu1SqriNWrcgmwfvzxR8zMzLSCq0f98MMPdOvWrcjgCkCtVuuU1atXj5o1a3L79m2lTE/v8flSs7OzOXHiBFOmTNEq9/DwICQkhBs3bmBmZvbYfoQQQojn3nOQpiEpKYkRI0ZgYWGBv78/iYmJLFy4kMzMTGbNmlVs26CgIJYvX86UKVOwtrZmy5YtjBw5kj179uikVlqwYAGWlpbK99dee03rekWtXpVLgBUdHU2TJk1YtWoVmzdvJiUlBTs7O6ZPn46joyM5OTlcuHCBjh078p///IcDBw6gUqlo3749M2bMoHbt2kX2HR8fz927d7GysirRPV27do2cnBytvwRA6ScuLk4CLCGEEKKMfP/996SlpbFixQpMTEwAePjwIXPnzsXHx4e6desW2i4rK4s1a9YwcuRIPvjgAyB/Ratbt24EBwczZ84crfpvvvlmsSeyVNTqVbkclXPnzh2OHTvGnj17mD17NitXrkSlUjFy5Eju3r3LgwcPyMnJISgoiAcPHrBixQrmzp3L6dOntZKHPkqj0TB//nzq1KlD9+7dS3RPSUlJgO6sWMH3gutCCCHEC0+TV3afUjp69CguLi5KcAXg7u5OXl4ex48fL7Ld6dOnSU1Nxd3dXSkzMDCgS5cuHD16tET3ULB69Wgg5eHhQWxsLDdu3ChRfyVRLjNYGo2G9PR0li1bxltvvQWAo6Mjbm5ufPfddwwePBgAIyMjVqxYoazF1qpViw8//JDIyEhcXFx0+vX39+ePP/5g7dq1VKtWrTxuXQghhHjxPQdLhHFxcTr7nNRqNbVr1yYuLq7YdkChK04bN24kMzNTyUYAMGrUKB48eEDt2rXp3r07EyZMUK5X5OpVuQRYarUaExMTJbgCMDExwdbWlpiYGNRqNSqViubNm2ttdGvdujWVKlUiJiZGJ8DasWMHK1eu5Msvvyw0+HqcGjVqAJCSkqJVnpycrHVdCCGEEP+nU6dOxV4/dOhQoeXJycmF7qWuUaNGsatGycnJGBgYYGhoqFWuVqvRaDQkJSVRpUoVqlevzkcffUSrVq0wNDTkjz/+YN26dcTFxbFmzRqgYlevyiXAaty4MdeuXSv0WlZWFlWrVtXKY1VYnX87ePAgc+bMYfz48QwYMKBU92Rubo6+vj5xcXG0a9dOKS8qUhZCCCFeVJrn5C3C8mRra4utra3y3cXFhTp16jBv3jzOnj2rc7bxs1YuAdY777zDjz/+yMWLF7GxsQHg/v37nD9/Xtmw9s477xAWFkZWVpYSpf7xxx88fPiQpk2bKn2dOHGCyZMnM3DgQMaMGVPqezIwMKBNmzbs37+fESNGKOWhoaFYWVnJBnchhBAvjzJcIixqhupx1Gq1zqoR5M8aFbdqpFaryc7O1ooPIH9mS6VSFdvW3d2defPmce7cORwcHCp09apcAqzOnTtjb2/P+PHjmTRpEoaGhgQGBmJgYMDQoUMB8PLyYs+ePYwePRpPT0/u3bvHkiVLaNGiBc7OzkB+/owxY8ZgYWFB7969iYqKUsaoWbMm5ubmyvewsDAAYmJiePjwofLd3t5emS375JNP8PT0ZM6cObi7u3PixAlCQkJ08mUIIYQQ4ulYWlrq7LVKSUnhzp07xa4aFVyLj4/X2moUFxeHqamp1v6rx6nI1atyCbD09PQIDAxkwYIFzJo1i5ycHFq2bMmWLVuUFAz169dn06ZNfPXVV4wbN46qVavSqVMnpk2bhkqlAvLTPaSkpJCSksKQIUO0xujbty8LFy5Uvk+YMEHresH3BQsW0K9fPwBatmyJv78/fn5+7Ny5E1NTU+bPn6/1poIQQgjxwnuKt//KSvv27Vm9erXWXqywsDD09PRo27Ztke2aN2+OsbEx+/btUwKsnJwcDhw4QPv27Ysd85dffgFQ0jZU5OqVSqORfPpP65RZn4q+BSGEEC+Iljd2l/sYafPeL7O+jGZtKVW7pKQkunfvTqNGjfDx8VESjfbs2VMr0eiIESO4efMmBw8eVMoCAwPx9/dnypQpNGnShG3btinpnwoSjU6ZMoWGDRtia2urbHLfsGEDHTp0YOXKlUpfp06dwtPTk/fee09ZvVq1ahVLly4t1wmWcj3sWQghhBCvpho1arBx40a++OILxowZg5GREQMGDGDSpEla9fLy8pRzigt4e3uj0WhYt26dclROcHCwVhb3N998k71797Ju3TpycnJo0KABH3/8MaNGjdLqq6JWr2QGqwzIDJYQQogn9UxmsOYMeXylJ2Q0Z1uZ9fUqkRksIYQQ4mXzHCQafdWVS4A1fPhwTp48Wei1b7/9FkdHxyITlxkYGPD3338DcPbsWbZt28apU6e4ffs2devWpWvXrnzyySdFZnK/desW7u7upKenExkZSc2aNZVrbm5uJCQk6LQ5e/asTkIzIYQQ4oX1HGxyf9WVS4A1e/ZsUlNTtco2btzIgQMHcHFxwdjYmO3bt2td12g0fPTRR0qKBoB9+/Zx9epVPvroIywsLIiJiWH58uVER0ezadOmQsdeuHAh1apVIz09vdDrXbt2ZeTIkVpl/84mL4QQQgjxtMotk/ujPv30U9q2bavMKDk5OWldP3HiBKmpqfTo0UMp8/b21pqBatOmDWq1milTpnDu3Dns7Oy0+oiMjCQyMhIfHx8WLVpU6L3VqlVLZ2whhBDipSJLhBXumezBOn36NDdu3GDixIlF1gkJCcHY2Bg3Nzel7N/BVYGCtPi3b9/WKs/JyeGLL75g3LhxchC0EEKIV9qrcFTO807vWQwSEhJCtWrVitx3VZBArEuXLo/dC/XXX38ButlXN23aRKVKlXQSkj5q79692NnZ0axZM7y9vfnnn39K8CRCCCGEEI9X7jNYubm57Nu3Dzc3tyJnlo4ePcqDBw+0lgcLc+/ePfz9/enUqRMWFhZKeWJiIitXrmTlypVUqlSpyPZubm44ODhgamrK9evXWb16NUOHDmX37t1auTWEEEKIF5osEVa4cg+wjh8/zr1794oNnvbu3UutWrVwcXEpsk5OTg6TJ08GYM6cOVrXvv76a9q2bVtse4AZM2Yof27ZsiVt27bF3d2d4OBgnT6FEEKIF5YEWBWu3AOskJAQTExMcHV1LfR6WloaR44cYeDAgUXOPmk0Gnx9fTl79ixbt26lTp06yrUzZ86wf/9+duzYoZyOnZGRofRdtWpVqlatWmi/derUoUWLFpw/f/5pHlEIIYQQQku5BliZmZn8+uuv9OrVC319/ULrHDx4kMzMTHr27FlkP4sWLWLfvn0EBQVpnawN+adt5+Tk0LdvX512nTt3xsPDg6VLlz7dgwghhBAvEsmDVeHKNcA6fPgw6enpxQZPISEhmJub4+joWOj1wMBANmzYwOLFiwtdAmzXrp1OTqzff/+doKAgVq5cqbVX61GJiYn89ddf9O7d+8keSAghhHgRyBJhhSvXAGvv3r2YmprSokWLQq/fu3ePyMhIvL29i2y/ZMkSevXqhZmZGVFRUco1c3NzatasSe3ataldu7ZWu4Js7c2bN1dSPYSEhHDkyBE6dOhAnTp1uH79OoGBgVSqVIkPP/ywDJ5WCCGEeD5oJMCqcOUWYCUlJfH7778zYsQIVCpVoXX27dtHbm5ukTNcx48fB+Dnn3/m559/1rq2YMEC+vXr98T3Y2Zmxu3bt/nqq69ISUmhevXqODs7M378eHmDUAghhBBlSqXRaCTMfUqnzPpU9C0IIYR4QbS8sbvcx0gZX3zao5KovjykzPp6lTyTTO5CCCGEeIYkk3uFeyaZ3IUQQgghXiXlFmAdOnSIgQMH0qxZM1xdXZkwYQLXr1/XqpORkcGSJUvo1KkTjo6OdO3aldWrV5Obm6vUOXv2LNOnT6dLly44Ojry7rvvsmTJEtLT03XG/OGHH+jZsydOTk506NCBGTNmcPfuXa06Go2GwMBAOnbsiIODA4MGDdLaPC+EEEK88PI0ZfcRpVIuS4QnTpxg7Nix9OnTh0mTJvHgwQOWLVvGyJEj2bt3L1WqVAFg3rx5HDhwgMmTJ2NlZUVUVBTLly8nIyODSZMmAfkb4a9evcpHH32EhYUFMTExLF++nOjoaK30DLt372bGjBl4eXnRrl07bt68ydKlS4mJieH7779X6gUFBbF8+XKmTJmCtbU1W7ZsYeTIkezZs0c2uwshhHg5SGBU4colwPrll18wNTXlq6++Ut4grFmzJiNGjODcuXO0bNmSvLw89u3bh5eXF++//z4Azs7OxMfH88svvygBlre3t5JqAaBNmzao1WqmTJnCuXPnsLOzA/JTOrRu3Zr//Oc/Wvfi6+vL//t//4/69euTlZXFmjVrGDlyJB988AEALVq0oFu3bnJcjhBCCCHKTLkEWLm5uRgZGWmlZ6hevTqQv0RX8N/c3Fyl/N/1/v1i47+DqwK2trYA3L59W2tMY2Njnb7+Pebp06dJTU3F3d1dqWNgYECXLl04ePBgyR9UCCGEeA5JgoCKVy57sPr160dsbCxbtmwhJSWF69ev8+2332Jra0vz5s0BqFSpEv369eO7777j7NmzpKWlERERwZ49exg2bFix/f/1118AWFpaKmUDBgzg999/JywsjNTUVC5fvszq1at55513MDU1BSAuLk6nHYCVlRU3b94kMzOzzH4DIYQQosLIHqwKVy4zWC1btmTFihV8+umnzJs3DwAbGxvWrl2rdaDz7NmzmT17NgMHDlTKfHx8is2sfu/ePfz9/enUqZPWMTg9e/YkIyODKVOmkJOTA8Dbb7+tdQ5hcnIyBgYGGBoaavWpVqvRaDQkJSUp+8OEEEIIIUqrXAKs06dP85///If33nuPjh078uDBA1atWsWoUaPYunWrEsQsXryY3377jfnz52NhYUFUVBQrV65ErVbz0Ucf6fSbk5PD5MmTAXT2Sx04cICFCxfyySef0KpVK27evMny5cuZOHEiq1evLjKbvBBCCPHSkZmnClcuAdb8+fNxdnZm2rRpSpmTkxMdO3Zkz549DBo0iEuXLrFu3ToCAgJwc3MDoFWrVuTm5rJs2TIGDx6stadKo9Hg6+vL2bNn2bp1K3Xq1NG6Nnv2bN577z3GjBmjlL/xxhsMHTqU48eP4+rqilqtJjs7m6ysLK1ZrOTkZFQqFTVq1CiPn0MIIYR4puQswopXLnuwYmNjeeutt7TK6tWrx2uvvca1a9cAiImJAfKXDv/N1taW7OxsEhMTtcoXLVrEvn37WLlypU7f9+7d4969ezrlBZvhC8Ys2HsVHx+vVS8uLg5TU1NZHhRCCPFykD1YFa5cAixTU1MuXLigVZaQkMD9+/dp0KABgPLf8+fPa9U7d+4cKpVK2ZgOEBgYyIYNG1i4cCEuLi4649WsWZOqVavqjFnQd8FYzZs3x9jYmH379il1cnJyOHDgAO3bty/t4wohhBBCaCmXJcLBgwfz1VdfMX/+fNzc3Hjw4AEBAQG8/vrrSooEOzs77OzsmD17Nnfv3sXc3JyzZ88SGBhI//79qVq1KpCf32rJkiX06tULMzMzrazr5ubm1KxZE5VKxXvvvcfWrVsxNjZW9mCtWLGCN998UwnKDA0N8fHxwd/fn5o1a9KkSRO2bdvGgwcP8PLyKo+fQgghhHj25CjCCqfSlEOyDI1Gw/fff8+2bdu4fv06RkZGODk5MWnSJKysrJR6d+7cYdmyZURERHD37l3q1atHjx498Pb2Vpbrpk2bxk8//VToOAsWLKBfv34AZGdns27dOvbs2cPNmzd57bXXaNOmDZMmTaJevXpa9xYYGMjWrVu5d+8eNjY2TJ8+nWbNmpX6eU+Z9Sl1WyGEEK+Wljd2l/sYD953K7O+TLYcLrO+XiXlEmC9aiTAEkII8aQkwHo1lMsSoRBCCCEqkGxOr3ASYAkhhBAvG9mDVeHK5S1CIYQQQohXWbkFWEeOHKFv377Y2dnRoUMHli9fzsOHD7XqaDQagoKCcHNzw87Ojh49ehAaGqrT15QpU3j33XdxcnKiVatWvP/++xw7dkyrTnZ2Nl9//TXvv/8+Tk5OWFtbc+/ePZ2+/P39sba21vls27atbH8AIYQQooJo8jRl9hGlUy5LhFFRUYwePZru3bszefJkYmJi8PPzIyMjg6lTpyr11q5di5+fH5988glOTk4cPnyYyZMnU6VKFSW7O+Tnqvrggw+wsLAgKyuLnTt3MmrUKDZt2kTLli0ByMzM5IcffsDe3p4WLVroBGD/VqVKFTZu3KhV9sYbb5TxryCEEEJUkOdkiTA2Npb58+dz5swZjIyM6N27NxMnTsTAwKDYdgUTMI++8e/k5KTUiYiI4IcffiA6Opq7d+/SoEED+vXrx4gRI9DX11fqFZWNICgoqFxzYJZLgOXv74+NjQ2LFy8GoF27dmg0Gr799lu8vLyoVasW2dnZBAQEMHz4cMaOHQuAq6srN2/exM/PTyvAWrZsmVb/7du3p1OnTuzZs0cJsNRqNSdPnkSlUvHjjz8WG2Dp6elp/SUJIYQQomwlJSUxYsQILCws8Pf3JzExkYULF5KZmcmsWbOKbRsUFMTy5cuZMmUK1tbWbNmyhZEjR7Jnzx5lQuT7778nMzOT8ePHU79+faKjo/H39yc2NpYFCxZo9ffGG28oMUmBf6eNKg/lEmBdvHiR/v37a5W5urqyaNEijh07Rp8+fbh+/TppaWm0bdtWp94XX3zBzZs3tbK5/1ulSpWoXr06OTk5WuVyoLMQQgjxfJxF+P3335OWlsaKFSswMTEB4OHDh8ydOxcfHx/q1q1baLusrCzWrFnDyJEj+eCDDwBo0aIF3bp1Izg4mDlz5gAwZ84catasqbRr06YNeXl5+Pn58dlnn2ldq1KlyjOfWCmXPVhZWVk6038F32NjY5U6/y4vql4BjUZDbm4u9+/fJzg4mKtXrzJo0KBS3V9mZibOzs7Y2tri4eHBjh07StWPEEII8VzKK8NPKR09ehQXFxcluAJwd3cnLy+P48ePF9nu9OnTpKamKie/QH5s0KVLF44ePaqU/TuAKmBjY4NGo+HOnTulv/EyUi4zWA0bNuTs2bNaZQVH3CQlJQH5x9yoVCrOnj1LmzZtiqxXYOfOncyYMQOAatWqsXTp0lJlXzc3N2fKlCnY2tqSlZXF3r17mTlzJikpKXJcjhBCiJeC5jnYgxUXF6ezmqVWq6lduzZxcXHFtgOwtLTUKreysmLjxo1kZmYqp7086vTp0xgYGGBmZqZVfvXqVVq0aEFWVhZNmjRh9OjRdO7cuTSP9cTKJcAaOnQon3/+ORs3bqR3797KJvdKlSopdYyNjenVqxdr166lSZMmODk5ceTIEX755RdAd7mvU6dOvPXWW9y/f5+wsDAmTpzIihUr6NChQ4nurXfv3lrfO3bsSE5ODgEBAXh6emptjBNCCCFedZ06dSr2+qFDhwotT05ORq1W65TXqFFDZxLl0XYGBgYYGhpqlavVajQaDUlJSYUGWFeuXGHTpk0MHjwYIyMjpdzGxgZ7e3saN25MSkoK27ZtY8yYMSxbtoxu3boV+2xPo1wCrH79+nHp0iW+/vprvvrqK/T19Rk7diwbN26kTp06Sr3p06fzv//9j1GjRgHw2muvMWHCBBYtWkTt2rW1+qxZs6YyHdi+fXuSkpL45ptvShxgFcbd3Z39+/dz7dq1ct/0JoQQQpS752AG61lKTU1l3LhxmJmZMWnSJK1rI0aM0Pru5ubG4MGDWb58+YsXYOnp6eHr68u4ceNISEjA1NSU3Nxcli5diqOjo1LvtddeY926dSQmJpKUlISFhQWHDh1CX18fW1vbYsdo2rSp1lqsEEIIIfKV5RJhUTNUj6NWq0lJSdEpT0pKokaNGsW2y87OJisrS2sWKzk5GZVKpdM2OzubMWPGkJSUxPbt26lWrVqx96Wnp8e7777LN998U+xy49Mq10zu1atX56233kKtVrN582bMzMx4++23derVrVuXJk2aUKlSJbZt24aHhwfGxsbF9v3XX3+VWe6q0NBQ1Go15ubmZdKfEEII8aqztLTU2WuVkpLCnTt3dPZXPdoOID4+Xqs8Li4OU1NTrYAoLy+PKVOmcP78eYKCgqhfv34ZPsHTKZcZrLNnz3Ly5ElsbGzIzMzk8OHD7Nmzh6CgIK19WD///DNZWVmYm5tz+/Zttm/fzo0bN7RyVfz222/s3r2bjh07Ur9+fZKSkggJCeHYsWN8++23WuOGh4eTkZHBuXPngPxs8kZGRjRu3JjGjRsD+cuXffr0wdLSkszMTPbu3cuBAwfw9fWV/VdCCCFeDs/BEmH79u1ZvXq11l6ssLAw9PT0dFI0/Vvz5s0xNjZm3759vPXWW0B+wvEDBw7oJAadO3cuR44cITg4GGtr6ye6r7y8PMLCwnjzzTfLbfYKyinA0tfX58CBA6xcuRIAR0dHNm/erPPWn0ajYd26ddy4cYNq1arRoUMHFi9erLVP64033iA7O5slS5Zw//59XnvtNaytrdm8eTOtW7fW6m/u3LkkJCQo3319fQEYO3Ys48aNA/LfItywYQP/+9//UKlUNGnShG+++YZevXqVx08hhBBCPHPPw1uEgwcPZvPmzYwZMwYfHx8SExP5+uuvGTx4sFYOrBEjRnDz5k0OHjwIgKGhIT4+Pvj7+1OzZk2aNGnCtm3bePDggdbb/qtXr+b777/Hy8sLAwMDJQsBQOPGjTE2NiYhIYFp06bRvXt3GjZsSFJSEtu2bePcuXP4+/uX6/OrNBpNxWcje8GdMutT0bcghBDiBdHyxu5yH+NOl6d/AaxA7YPhpW4bGxvLF198oXVUzqRJk7RyYA4fPpyEhAQOHz6slGk0GgIDA3WOyvn3RM3w4cM5efJkoeNu2rSJNm3a8ODBA6ZPn86FCxe4e/cu+vr62NnZMWrUKNq1a1fq53oSEmCVAQmwhBBCPKlnEWDd7lR2AVadQ6UPsF5l5bJEKIQQQoiK8zwsEb7qSvwW4dWrV5k1axa9e/fG1taWHj166NQJDQ1l3LhxtG/fHmtra4KDgwvtKyUlBV9fX1q3bk2zZs0YP348t2/f1qrz008/8d5779G6dWvs7e3p2rUrK1euJDs7W6ve8OHDsba21vk8euTOk4wphBBCCPE0SjyDdfnyZcLDw3F0dCQvL4/CVhjDwsK4fv06HTt2ZPv27UX2NXHiRGJiYpgzZw6Ghob4+fnh7e3Nrl27qFw5/9aSkpJo164do0aNwtjYmLNnz7JixQpu3brFF198odVf8+bNmTp1qlbZo+nyn2RMIYQQ4oWmUT2+jihXJY4o3NzclPN7pk2bpqRE+Dc/Pz/09PInx4oKsM6cOcOxY8cIDg7G1dUVgEaNGuHh4cGBAwfw8PAAUE7SLuDs7ExaWhobNmxgzpw5Wmkf1Gp1sadlP+mYQgghxItMlggrXomXCAsCp6etc/ToUdRqtVYuDEtLS2xsbB6bod3ExITc3Fzy8kr2/6CnGVMIIYR4UWjyVGX2EaVTrpncixMXF0ejRo10DnUuLPMrQG5uLhkZGZw6dYqNGzcyZMgQncSgJ0+exMnJCXt7e4YNG8aff/75VGMKIYQQQpRGhW06Sk5Opnr16jrlNWrU0Fl2zM3NpWnTpsr3vn37KklEC7Rq1YrevXtjYWHB7du3CQ4O5sMPP9RKcFqSMYUQQogXlSwRVrwXYld35cqV2blzJ1lZWZw7d46AgACmT5/OokWLlDrjx4/XatOxY0d69OjBqlWrCAoKeta3LIQQQlQYjWxyr3AVFmCp1Wpu3bqlU17UKdv29vYAtGzZEjMzM8aMGcOwYcOU8kcVHL2zf//+Uo8phBBCCFEaFbYHy9LSkvj4eJ00D/Hx8cWesg1gZ2cHwLVr157ZmEIIIcSLQpNXdh9ROhUWYLVv356kpCQiIyOVsvj4eC5cuKBzWvaj/vrrLyD/IOiipKen89tvv2nNcD3NmEIIIcSLQt4irHglXiLMyMggPDz/XKKEhARSU1MJCwsDoHXr1tSsWZOYmBhiYmKUNpcuXSIsLIyqVavSoUP++UjNmjXD1dUVX19fpk6diqGhIUuXLsXa2pp3331Xafv+++/TpUsXLC0t0dPTIzo6mnXr1tGuXTscHBwAOHXqFGvXrqVLly40aNCA27dvs379eu7cucOyZcuUvp50TCGEEEKIp1Hiw55v3LhBp06dCr1WcHq1v78/K1as0LneoEEDrdOyU1JSWLBgAQcPHiQ3NxdXV1dmzJhB3bp1lToLFizg999/5+bNm1SuXBkzMzP69OnD0KFDldO4r169yrx58/jnn3948OABVatWpVmzZowdO1YJwkoyZknJYc9CCCGe1LM47Play8L/nS4N81OHyqyvV0mJAyyhSwIsIYQQT+pZBFhXm3cus74anv61zPp6lVTYHiwhhBBCiJfVC5EHSwghhBBPTjanV7wSB1hXr14lODiY6OhoLl++jKWlJSEhIcr11NRU1q9fT3h4OFeuXMHAwAAHBwcmTZqEtbW1Ui87Oxs/Pz+io6M5f/48GRkZREZGUrNmTa3xfvrpJ7Zt28aVK1fIyMjA1NSUXr164e3trezBgvzN96tWrSI0NJT//e9/1KtXj759+/LRRx9RuXL+Yxa1f8zR0ZEdO3aU9KcQQgghnkuy+afilTjAunz5MuHh4Tg6OpKXl6eTU+rmzZts376d/v37M3HiRLKysli3bh2DBg1i165dWFlZAZCZmckPP/yAvb09LVq04NixY4WOl5SURLt27Rg1ahTGxsacPXuWFStWcOvWLb744gul3rx58zhw4ACTJ0/GysqKqKgoli9fTkZGBpMmTdLqc/LkybRp00b5bmRkVNKfQQghhHhuyQxWxStxgOXm5kbnzvmb56ZNm6Zzhp+ZmRkHDx6katWqSpmzszNubm5s3bqVmTNnAvlZ1U+ePIlKpeLHH38sMsD64IMPtL47OzuTlpbGhg0bmDNnDpUqVSIvL499+/bh5eXF+++/r9SLj4/nl19+0QmwGjZsiJOTU0kfXQghhBDiiZQ4wNLTK35ffLVq1XTKjIyMMDc35/bt21rlKlXpImwTExNyc3PJy8ujUqVKaDQacnNzdQ5yrl69us4MmxBCCPGyk7MIK94zeYswOTlZ2a9VWrm5uWRkZHDq1Ck2btzIkCFD0NfXB6BSpUr069eP7777jrNnz5KWlkZERAR79uxh2LBhOn3NmTMHGxsbXFxcmDFjBg8ePCj1fQkhhBDPGzkqp+I9k7cIv/nmG1QqFUOGDClV+9zcXJo2bap879u3L76+vlp1Zs+ezezZsxk4cKBS5uPjw4cffqh8NzAwYMiQIbi6uqJWq4mOjmb16tWcO3eOH374QQnYhBBCCCGeRrkHWLt27WLHjh0sXLiQevXqlaqPypUrs3PnTrKysjh37hwBAQFMnz6dRYsWKXUWL17Mb7/9xvz587GwsCAqKoqVK1eiVqv56KOPAKhTpw5z5sxR2rRu3Zo333wTHx8fDh48iIeHx1M9qxBCCPE8yJMlwgpXrgFWeHg4s2bNYvTo0fTt2/ep+io4tLlly5aYmZkxZswYhg0bhr29PZcuXWLdunUEBATg5uYGQKtWrcjNzWXZsmUMHjwYY2PjQvvt0KED1apV4/z58xJgCSGEeCnIHqyKV257sKKiopgwYQJ9+vRhwoQJZdq3nZ0dANeuXQNQDpa2sbHRqmdra0t2djaJiYllOr4QQgghRHHKJcCKiYnBx8cHZ2dn5s6dW+b9//XXXwC88cYbQP4h0gDnz5/Xqnfu3DlUKhWmpqZF9nXkyBHS09OVGTIhhBDiRafJU5XZR5ROiZcIMzIyCA8PByAhIYHU1FTCwsKA/D1NGo0GLy8vDA0NGTFihFaeLGNjYxo3bqx8Dw8PJyMjQ6lz5MgRjIyMaNy4sVLv/fffp0uXLlhaWqKnp0d0dDTr1q2jXbt2ODg4APkzWnZ2dsyePZu7d+9ibm7O2bNnCQwMpH///kpOroULF6JSqXByckKtVnP27FnWrFmDnZ2dkttLCCGEeNFJhqKKp9KUMFFUUcfNAGzatAkAT0/PQq+3bt2azZs3K9/d3NxISEjQqTd27FjGjRsHwIIFC/j999+5efMmlStXxszMjD59+jB06FCto3Lu3LnDsmXLiIiI4O7du9SrV48ePXrg7e1NlSpVAPjhhx/Ytm0bV69eJTMzk7p169K5c2fGjx9f5B6tJ3HKrE+p2wohhHi1tLyxu9zHuPhm2e0ptrkcWmZ9vUpKHGAJXRJgCSGEeFLPIsC6YNW9zPqyjf2lzPp6lTyTPFhCCCGEeHYkTUPFkwBLCCGEeMlImoaK90yOyhFCCCGEeJWUeAbr6tWrBAcHEx0drZwvGBISolVn0aJFHD16lJs3b6JSqWjUqBEjR46ke/f/WxOOi4vju+++448//iAhIYHXX3+ddu3aMWHCBGrWrFno2Glpabi7u5OYmMjOnTuV1AqpqamsX7+e8PBwrly5goGBAQ4ODkyaNAlra2ulfXZ2Nn5+fkRHR3P+/HkyMjKIjIwscjwhhBDiRSS7qyteiWewLl++THh4OA0bNsTKyqrQOmlpaQwcOJBly5axbNkybGxsmDx5Mnv37lXqREREcOrUKQYNGkRgYCDjxo3j6NGjvP/++2RnZxfa76pVq3j48KFO+c2bN9m+fTtt27bFz8+PL774gpSUFAYNGkRsbKxSLzMzkx9++AFDQ0NatGhR0kcXQgghXgh5GlWZfZ5GbGwsH374IU5OTrRt25avv/66yH/j/02j0RAYGEjHjh1xcHBg0KBBREVF6dRLTExk3LhxNGvWjNatW/P555+TmpqqU+/w4cP06tULe3t7unbtyq5du57quZ5Eid8izMvLQ08vPy6bNm0a586d05nBKszgwYOpVq0a69atA+D+/fuYmJigUv3fX97p06cZMmQIy5cvp2vXrlrtY2NjGTBgAFOnTmX27NlaM1jp6emoVCol3xXkB3lubm706NGDmTNnKuUajQaVSsWPP/7I9OnTy2QGS94iFEII8aSexVuEUQ17lVlfTld/LlW7pKQkunfvjoWFBT4+PiQmJrJw4UJ69erFrFmzim0bGBjI8uXLmTJlCtbW1mzZsoWIiAj27NmjJBnPycmhX79+AEyaNInMzEwWLVrEW2+9xZo1a5S+Tp06haenJwMGDMDDw4M//viD1atX4+fnR7du3Ur1bE+ixEuEBcFVSZmYmJCWlqZ8f+2113Tq2NraAnD79m2da/Pnz2fw4ME0atRI51q1atV0yoyMjDA3N9fp698BnRBCCPEyeh42uX///fekpaWxYsUKTExMAHj48CFz587Fx8eHunXrFtouKyuLNWvWMHLkSD744AMAWrRoQbdu3QgODmbOnDkA7N+/n8uXLxMaGoqlpSUAarUaLy8vzp49qyQjDwgIwMHBgXnz5gHg7OzM9evXWb58ebkGWOW2yV2j0ZCbm0tycjK7d+/m+PHjvP/++8W2KTgC59Glx7CwMC5dusSYMWOeePzk5GRlj5gQQgjxKtFoyu5TWkePHsXFxUUJrgDc3d3Jy8vj+PHjRbY7ffo0qampuLu7K2UGBgZ06dKFo0ePavVvbW2t9e9827ZtMTExUU6cyc7O5sSJEzqBlIeHB7Gxsdy4caP0D/gY5RZgRUZG0rRpU1q1asXnn3/O559/XmykmJWVxaJFi7C1tcXFxUUpz8jIYOHChUyaNKlE2da/+eYbVCoVQ4YMearnEEIIIUTJxcXF6UxyqNVqateuTVxcXLHtAJ22VlZW3Lx5k8zMzCL7L3ixrqCPa9eukZOTU2hf/x6rPJRbHiwHBwd27txJamoqR48eZf78+VSqVImBAwcWWn/27NncuHGD77//XmsZLyAggNdff53+/fs/8di7du1ix44dLFy4kHr16j31swghhBAvkrJMNFrU8XgFDh06VGh5cnIyarVap7xGjRokJSUV2V9ycjIGBgYYGhpqlavVajQaDUlJSVSpUoXk5GSqV69ebP8F/330Pgq+F3cfT6vcAixjY2NlE7qLiwsPHz5k4cKF9OvXj0qVKmnVXbp0KXv37mX16tU0adJEKU9ISGDdunWsXLmSlJQUIH9De8F/09LSMDIy0uorPDycWbNmMXr0aPr27VtejyeEEEI8t56HPVivumeWyb1p06Zs3LiRe/fuUbt2baV88+bNrFmzhoULF9KuXTutNjdu3CAnJ4dRo0bp9Ofp6YmjoyM7duxQyqKiopgwYQJ9+vRhwoQJ5fcwQgghxCuiqBmqx1Gr1crkyL8lJSVRo0aNYttlZ2eTlZWlNYuVnJyMSqVS2qrV6kJTMiQlJVG/fn0Ape6j95GcnKx1vTw8swDrr7/+wtjYWOvtwZCQEL788ksmT55Mnz59dNrY2NiwadMmrbKLFy+yYMEC5s6dq8yQAcTExODj44OzszNz584tt+cQQgghnnfPw1mElpaWOnucUlJSuHPnTrEvoBVci4+P56233lLK4+LiMDU1pUqVKkq9S5cuabXVaDTEx8fTtm1bAMzNzdHX1ycuLk5rEqeofV5lqcQBVkZGhrI7PyEhgdTUVMLCwgBo3bo1t2/fZvHixXTr1o0GDRqQnp7Ob7/9xg8//MDkyZOpXDl/yJMnTzJt2jScnZ1p3bq1VgKxevXqUa9ePdRqNW3atCn0Ppo2bUrTpk0BuHv3Ll5eXhgaGjJixAjOnTun1DM2NqZx48bK9/DwcDIyMpQ6R44cwcjIiMaNG2vVE0IIIV5Uz0Mi9/bt27N69WqtvVhhYWHo6ekpAVBhmjdvjrGxMfv27VMCrJycHA4cOED79u21+v/555+5cuUKFhYWQP4Ldg8ePKBDhw5A/tuHbdq0Yf/+/YwYMUJpGxoaipWVFWZmZmX92IoSB1h3797VWX4r+L5p0yasrKxQq9WsWrWKO3fuUL16dSwtLVmxYgWdO3dW2pw4cYKcnBwiIyOJjIzU6m/s2LGMGzfuie8pJiaGW7duASg5Mwq0bt2azZs3K9/nzp1LQkKC8t3X17dUYwohhBDPq+dhBmvw4MFs3ryZMWPGKIlGv/76awYPHqyVA2vEiBHcvHmTgwcPAmBoaIiPjw/+/v7UrFmTJk2asG3bNh48eICXl5fSrmvXrqxZs4Zx48YxefJkMjIy+Prrr5Xs7wU++eQTPD09mTNnDu7u7pw4cYKQkBCWLl1ars9f4kzuQpdkchdCCPGknkUm94j6T/7m/eO8/f9Kf6xMbGwsX3zxBWfOnMHIyIjevXszadIkDAwMlDrDhw8nISGBw4cPK2UFR+Vs3bqVe/fuYWNjw/Tp02nWrJlW/4mJicyfP59jx45RuXJlunTpgq+vr05ap0OHDuHn50d8fDympqaMGjWKAQMGlPq5noQEWGVAAiwhhBBP6lkEWMfrlV3w0PbWzjLr61XyzDa5CyGEEOLZyKvoGxAlz+R+9epVZs2aRe/evbG1taVHjx7F1v/111+xtrbWqXf9+nV8fHxo37499vb2uLq6Mn78eOLj44vsKy0tjfbt22Ntbc3ff/+tdW348OFYW1vrfGJjY3X6iYqK4oMPPqBZs2Y0b96c9957j4sXL5bgVxBCCCGEKFqJZ7AuX75MeHg4jo6O5OXlUdwKY2ZmJl999RW1atXSuZaWlkatWrWYPHky9evX586dO6xZswZPT0/27NlDzZo1ddqsWrWKhw8fFjle8+bNmTp1qlbZo28IREZGMmrUKPr374+3tze5ubmcPXuWjIyMxz26EEII8ULQUPGb3F91JQ6w3NzclLcBp02bppUS4VFr1qzB1NQUMzMznXpvvfUWX375pVaZnZ0dXbt25fjx4/Ts2VPrWmxsLFu3bmXq1KnMnj270PHUajVOTk5F3k9ubi6ff/45np6efPbZZ0p5weucQgghxMsgT3ZXV7gSLxHq6T1Zk2vXrrF+/XpmzJjxxH0XnLidk5Ojc23+/PkMHjyYRo0aPXF/j4qIiCAhIQFPT89S9yGEEEII8TglDrCe1Jdffknv3r21srAWJi8vj5ycHG7cuMEXX3xB/fr16dKli1adsLAwLl26xJgxY4rt6+TJkzg5OWFvb8+wYcP4888/ta5HR0djYmLC33//TdeuXbG1taVr167s3r27VM8ohBBCPI/yUJXZR5ROubxFePjwYc6cOaNkeC/Of/7zH/bu3Qvkp7Rfv3691unYGRkZLFy4kEmTJunktfi3Vq1a0bt3bywsLLh9+zbBwcF8+OGHbN68WcmbcefOHTIyMvD19WX8+PFYWVkREhLC1KlTef3113XOQhRCCCFeRLIHq+KVeYCVlZXFV199xbhx4wrdqP6oCRMm4Onpyf/7f/+PjRs38uGHH7J161ZMTU0BCAgI4PXXX6d//+KTpo0fP17re8eOHenRowerVq0iKCgIyE9clpWVxZQpUxg2bBgALi4uxMXFsXr1agmwhBBCCFEmynyJcOPGjejp6dG9e3eSk5NJTk4mJyeHvLw8kpOTyc7O1qr/xhtv4ODgQNeuXQkODubhw4esXbsWyD/rcN26dYwfP56UlBSSk5NJT08HID09nbS0tCLvo1q1anTo0IHz588rZQVnITk7O2vVdXFxISYmpkyeXwghhKhoeWX4EaVT5jNYcXFxXL16FRcXF51rrVq1Ys6cOQwZMqTQtlWrVsXKyoqrV68CcOPGDXJychg1apROXU9PTxwdHdmxY8cT39ubb75Z5LWsrKwn7kcIIYR4nskSYcUr8wDL29ubvn37apUFBgYSHx/PggULlBOvC5Oamso///xD165dAbCxsWHTpk1adS5evMiCBQuYO3cu9vb2RfaVnp7Ob7/9plXH1dUVfX19IiIiaNKkiVIeERFB06ZNS/KYQgghxHNLZp4qXokDrIyMDMLDw4H8JbzU1FRlM3vr1q2xsrLCyspKq81PP/1EYmIibdq0Ucr8/f1JSUmhefPm1KxZk4SEBDZv3kx2djYjRowA8pf0/t3m35o2baoERadOnWLt2rV06dKFBg0acPv2bdavX8+dO3dYtmyZ0qZWrVoMHz6cZcuWoVKpsLKy4pdffiEqKkpZlhRCCCGEeFolDrDu3r3LhAkTtMoKvm/atKnIgOhRtra2bNiwgT179pCenk7dunVp1aoVy5Yt44033ijRPdWuXZucnByWLl3KgwcPqFq1Ks2aNWPu3Lk4ODho1f3000+pVq0awcHB3Lt3DysrK1auXImrq2uJxhRCCCGeVzKDVfFUmuLOuhFP5JRZn4q+BSGEEC+Iljd2l/sYv9QtfK9zaXRP3FZmfb1Kyi3RqBBCCCHEq6pcEo0KIYQQouLkyUuEFU4CLCGEEOIlI0fcVLwSLxFevXqVWbNm0bt3b2xtbenRo4dOneHDh2Ntba3ziY2NLbLf0aNHY21tTXBwcJF1bt26RbNmzbC2tubevXta11JSUpg5cyZt2rTB0dGR4cOHc/HiRa062dnZfP3117z//vs4OTkV2o8QQgghxNMq8QzW5cuXCQ8Px9HRkby8PIraI9+8eXOmTp2qVWZmZlZo3fDwcKKjox879sKFC6lWrZqSzf3fJk+ezLlz5/jss8+oVasWGzZsYMSIEezZs4f69esDkJmZyQ8//IC9vT0tWrTg2LFjjx1TCCGEeNHI22sVr8QzWG5uboSHh7N8+fJik3Oq1WqcnJy0PoaGhjr1srOz+fLLL5k8eXKx40ZGRhIZGYmXl5fOtaioKI4ePcqXX37JgAED6NixIwEBAVSuXFlrRkytVnPy5EnWrVtH9+7dS/DUQgghxItDjsqpeCUOsPT0yvbFw+DgYNRqNf369SuyTk5ODl988QXjxo3DxMRE5/qFCxdQqVS0bdtWKatatSotW7bkyJEjWnVVKlmXFkIIIUT5Krc0DSdPnsTJyQl7e3uGDRvGn3/+qVPn5s2bBAYGMmPGjGIDn02bNlGpUqUizzDMzs5GT0+PSpUqaZXr6+uTkJBAZmbm0z2MEEII8QLJU6nK7CNKp1wCrFatWvH555+zdu1aFi1aREZGBh9++CFnzpzRqrdgwQK6dOmCk5NTkX0lJiaycuVKfH19dQKoAg0bNuThw4dcuHBBKcvLy+PcuXNoNBqSk5PL5LmEEEKIF4GmDD+idMolTcP48eO1vnfs2JEePXqwatUqgoKCADh27BjHjh1TzjEsytdff03btm1xcXEpsk7btm0xNzdn9uzZLFq0iNdff53AwECuX78OyLKgEEKIV4vsnap4zySTe7Vq1ejQoQPnz59XyubPn4+npydVq1YlOTlZmWXKyspS/nzmzBn279/PJ598otTJyMgAIC0tTfmzgYEBS5cuJT09nZ49e/L2228TERHBiBEj0NfXL3TflhBCCCFEeamwRKPx8fGsXr2a1atXa5UvW7aMZcuWcfbsWeLj48nJyaFv37467Tt37oyHhwdLly4FwM7OjrCwMK5evYpGo8HCwoJ58+bRtGlT9PX1n8kzCSGEEM8DyeRe8Z5JgJWens5vv/2Gvb29UrZp0yadep6engwePBgPDw/09fVp166dTr3ff/+doKAgVq5ciYWFhdY1lUqllN27d4/Q0FA+++yzMn8eIYQQ4nkmmdwrXokDrIyMDMLDwwFISEggNTVV2UfVunVr4uLiWLt2LV26dKFBgwbcvn2b9evXc+fOHZYtW6b006ZNm0L7Nzc3V67Vrl2b2rVra11PSEgA8hOZ1qxZUykPCAigYcOGvP7668THx7NmzRrs7Ox00j+Eh4eTkZHBuXPnADhy5AhGRkY0btyYxo0bl/TnEEIIIYTQUeIA6+7du0yYMEGrrOD7pk2bqFevHjk5OSxdupQHDx5QtWpVmjVrxty5c3FwcCibuy5EcnIyixYt4u7du9SpU4devXoxevRonbxdc+fOVYI0AF9fXwDGjh3LuHHjyu3+hBBCiGdF3v6reCpNUWfdiCd2yqxPRd+CEEKIF0TLG7vLfYxNDYaVWV+eCd+VWV+vkmfyFqEQQgghxKukwt4iFEIIIUT5kDxYFa/EAdbVq1cJDg4mOjqay5cvY2lpSUhIiE695ORkli9fTlhYGElJSdStW5ehQ4cycuRIpY61tbVOu1q1anH8+HHl+48//sj06dN16nl7ezNlyhQAUlNTWb9+PeHh4Vy5cgUDAwMcHByYNGlSoWNERUXh5+dHdHQ0KpWKxo0bM3fuXGxsbEr6cwghhBDPHdn7U/FKHGBdvnyZ8PBwHB0dycvLo7AtXOnp6QwfPpxKlSrh6+vL66+/zpUrV0hNTdWpO3z4cHr06KF8Lypn1dq1a6levbryvW7dusqfb968yfbt2+nfvz8TJ04kKyuLdevWMWjQIHbt2oWVlZVSNzIyklGjRtG/f3+8vb3Jzc3l7NmzStJSIYQQQlSMw4cP4+fnR3x8PKampsq/14+TkpLCggUL+PXXX8nJyaFdu3bMmDGDOnXqKHW+//57Dhw4wD///ENGRgaNGzdm1KhRdO7cWasvNzc3rZfhCpw9exZDQ8MnfpYSB1hubm7KzUybNk1Jd/BvgYGBpKWl8fPPP1OtWjWg6LQM9evXL/YswgJNmzbVSsvwb2ZmZhw8eJCqVasqZc7Ozri5ubF161ZmzpwJQG5uLp9//jmenp5a+bE6dOjw2PGFEEKIF8WLmGj01KlTjB07lgEDBuDr68sff/zB559/jpGREd26dSu27cSJE4mJiWHOnDkYGhri5+eHt7c3u3btonLl/FBn9erVuLq6MmTIEKpVq0ZYWBhjxoxh4cKFOgnNu3btqrXiBvmnxpREiQOsR9MeFGbnzp28//77SnBV3gobx8jICHNzc27fvq2URUREkJCQgKen5zO5LyGEEKIivIh7sAICAnBwcGDevHlA/kTJ9evXWb58ebEB1pkzZzh27BjBwcG4uroC0KhRIzw8PDhw4AAeHh5A/pajf0/UtG3bloSEBNatW6cTYNWqVeuJJn+KU+ZvEd64cYM7d+7w2muv8fHHH2NnZ0fr1q2ZMWMGaWlpOvUDAwNp2rQpLVu2ZOLEidy8ebPQfnv06IGNjQ2dOnVizZo1PHz4sNj7SE5OVvaIFYiOjsbExIS///6brl27YmtrS9euXdm9e/dTPbMQQgjxPMkrw8+zkJ2dzYkTJ3QCKQ8PD2JjY7lx40aRbY8ePYparaZt27ZKmaWlJTY2Nhw9elQpK2wVzMbGRmsipiyV+VuE//vf/wBYtGgR7777LkFBQVy5coUlS5aQnp7Ot99+q9Tt06cPHTt2pFatWly6dImAgACGDh3Knj17qFGjBpCfzX3cuHE4OjqiUqmU9dnExERmzZpV5H188803qFQqhgwZopTduXOHjIwMfH19GT9+PFZWVoSEhDB16lRef/112rVrV9Y/hxBCCCEe49q1a+Tk5GhNigDKHuq4uDjMzMwKbRsXF0ejRo1QqbTXRS0tLYmLiyt23L/++ktnTIC9e/eyY8cO9PX1admyJVOmTCn0pbnilHmAlZeXH+82atSIRYsWAeDi4kLlypWZMWMGkyZN4o033gBQrgO0atWKFi1a0K9fP3bs2IG3tzcA7dq10wp8XF1dMTQ0ZOPGjXz88cdaG9gK7Nq1ix07drBw4ULq1aunlGs0GrKyspgyZQrDhg1T7i0uLo7Vq1dLgCWEEOKloCnDPVidOnUq9vqhQ4eeeoykpCQA1Gq1VnnB94LrhUlOTtZ6Ca5AjRo1Ct0nXmDv3r2cOXOGlStXapW7ubnh4OCAqakp169fZ/Xq1QwdOpTdu3cr8cuTKPMAq2Dm6dFN7c7OzkD+W4hF3eBbb71Fo0aNOH/+fLFjuLu7s27dOi5evKgTYIWHhzNr1ixGjx6ts6Za8BdVcC8FXFxc2LJly2OeTAghhHgxPA97sFJSUp5o+a0kQUtZ+e9//8vs2bPp16+fzluEM2bMUP7csmVL2rZti7u7O8HBwcyZM+eJxyjzAOuNN94odqd9VlZWWQ+piIqKYsKECfTp00fnvESAN998s0LuSwghhHhRlXaGKiwsTCtYKUpoaKgyOZOSkqJ1LTk5Gfi/yZvCqNVqbt26pVOelJRUaLuEhAS8vb21NtQXp06dOrRo0eKxkz+PKvMAy8DAgLZt2xIZGalVHhERAeSnWyjKxYsXiY+Pp1+/fsWOERoaSqVKlbC1tVXKYmJi8PHxwdnZmblz5xbaztXVFX19fSIiImjSpInWvRV3X0IIIcSL5HmYwRo4cCADBw58orrZ2dno6+sTFxentV2nYA9VYfukClhaWhIZGYlGo9HahxUfH6/1bz3AvXv38PLy4vXXX2fFihVF5t4sCyUOsDIyMggPDwfyo8DU1FTCwsIAaN26NTVr1mTs2LEMHjyYTz/9lL59+3L16lWWLFlCz549MTc3ByA4OJhr167Rpk0batasyeXLl1m9ejX16tXT+gvx8vKiTZs2yuayQ4cOsWPHDjw9PalduzYAd+/excvLC0NDQ0aMGKG15mpsbEzjxo2B/Ncuhw8fzrJly1CpVFhZWfHLL78QFRXF2rVrS/P7CSGEEM+dFy2Tu4GBAW3atGH//v2MGDFCKQ8NDcXKyqrIDe4A7du3Z9WqVURGRvL2228D+cHVhQsX+Oijj5R6aWlpeHt7k5OTw6ZNmzA2Nn6ie0tMTOSvv/6id+/eJXomlaawVOzFuHHjRpEb3jZt2qTsvYqMjGTx4sVcunSJGjVq0LNnTyZNmqQsHx4+fJg1a9YQHx9PWloar732Gu3bt2fixIla+6rmz5/P77//zq1bt8jLy8PCwoKBAwcyfPhwJVI9ceJEkbmtWrduzebNm5Xvubm5BAQE8MMPP3Dv3j2srKwYP378YzfxFeeUWZ9StxVCCPFqaXljd7mP4f/GsDLra9z178qsr+KcOnUKT09P3nvvPdzd3Tlx4gSrVq1i6dKluLu7K/VsbW3p06cPX331lVLm5eVFbGwsU6dOxdDQkKVLl6Knp6eVaHTkyJGcOHGCL7/8EgsLC62xC3JehYSEcOTIETp06ECdOnW4fv06gYGBJCUlsWvXrhLtFytxgCV0SYAlhBDiST2LAGuZedkFWBOuPZsAC/JXqR49KmfAgAFadaytrenbty8LFy5UygqOyjl48CC5ubm4uroyY8YMrWP1ikuz8M8//wD5e7mXLFnC5cuXSUlJoXr16jg7OzN+/PhilykLIwFWGZAASwghxJN6FgHW0jIMsCY9wwDrZVLmmdyFEEIIIV51Jd7kfvXqVYKDg4mOjlaOogkJCVGuF7dHy8DAgL///lurLCoqCj8/P6Kjo1GpVDRu3Ji5c+diY2MD5J8dNH36dJ2+vL29mTJlilZZcnIyy5cvJywsjKSkJOrWrcvQoUO1DmxcuXIlp06d4u+//yYlJYWdO3dib29f0p9BCCGEeG49D28RvupKHGBdvnyZ8PBwHB0dycvL49EVxjp16rB9+3atMo1Gw0cffaST4DMyMpJRo0bRv39/vL29yc3N5ezZs2RkZOiMu3btWq1Mrf9eVwVIT09n+PDhVKpUCV9fX15//XWuXLlCamqqVr3t27djbm7O22+/zf79+0v6+EIIIcRzT/b+VLwSB1hubm5K1tNp06bppKE3MDDQOYH6xIkTpKam0qNHD6UsNzeXzz//HE9PTz777DOlvEOHDoWO27Rp00IPaiwQGBhIWloaP//8M9WqVQN0s8kD/Pbbb+jp6XHixAkJsIQQQryU8srwqBxROiXeg6WnV/JtWyEhIRgbG+Pm5qaURUREkJCQUGR6hZLauXMn/fv3V4KropTm/oUQQgghSqLco42cnBwOHDhAly5dMDQ0VMqjo6MxMTHh77//pmvXrtja2tK1a1d2795daD89evTAxsaGTp06sWbNGh4+fKhcu3HjBnfu3OG1117j448/xs7OjtatWzNjxgzS0tLK+xGFEEKI50peGX5E6ZT5UTmPOnr0KA8ePNBaHgS4c+cOGRkZ+Pr6Mn78eKysrAgJCWHq1Km8/vrrSqr82rVrM27cOBwdHVGpVBw+fBg/Pz8SExOZNWsWAP/73/8AWLRoEe+++y5BQUFcuXKFJUuWkJ6ezrffflvejymEEEI8N2QPVsUr9wBr79691KpVCxcXF61yjUZDVlYWU6ZMYdiw/HwdLi4uxMXFsXr1aiXAateunda5RK6urhgaGrJx40Y+/vhj6tSpQ15efozdqFEjFi1apPRVuXJlZsyYwaRJkyrktG4hhBBCvJrKdYkwLS2NI0eO4O7uTqVKlbSuqdVqAJ03C11cXIiJiSm2X3d3dx4+fMjFixeB/ztl+9FN7QV9X758ufQPIYQQQrxg8tCU2UeUTrnOYB08eJDMzEx69uypc+3NN98ssl1WVlaJxnnjjTeUMw7Loj8hhBDiRSZ7pypeuc5ghYSEYG5ujqOjo841V1dX9PX1iYiI0CqPiIigadOmxfYbGhpKpUqVsLW1BfJTQ7Rt25bIyEidvoDH9ieEEEIIUZZKPIOVkZFBeHg4AAkJCaSmphIWFgZA69atlVxV9+7dIzIyEm9v70L7qVWrFsOHD2fZsmWoVCqsrKz45ZdfiIqKYu3atUo9Ly8v2rRpoxzSeOjQIXbs2IGnpye1a9dW6o0dO5bBgwfz6aef0rdvX65evcqSJUvo2bMn5ubmSr2TJ09y7949ZRnyjz/+ICEhgQYNGkhGdyGEEC8FWdireCUOsO7evcuECRO0ygq+b9q0SdkHtW/fPnJzcwtdHizw6aefUq1aNYKDg7l37x5WVlasXLkSV1dXpU6jRo3YtWsXt27dIi8vDwsLC3x9fRk+fLhWX3Z2dgQFBbF48WI++eQTatSowaBBg5g0aZJWPX9/f06ePKl8X7x4MYDOydxCCCHEi0qWCCueSvPoWTeixE6Z9anoWxBCCPGCaHljd7mPMafh+2XX19UtZdbXq6Tc0zQIIYQQ4tmSo3IqngRYQgghxEtG0itUPAmwhBBCiJeMhFcVr8RpGq5evcqsWbPo3bs3tra2OkfgQP6bhkuWLKFTp044OjrStWtXVq9eTW5urlLH398fa2vrQj8FR+AUV2/btm1aY6akpDBz5kzatGmDo6Mjw4cPVxKRPioqKooPPviAZs2a0bx5c957770i6wohhBBClFSJZ7AuX75MeHg4jo6O5OXlUdge+Xnz5nHgwAEmT56MlZUVUVFRLF++nIyMDOWtvoEDB2odgQPw559/snjxYtq3b69VXqVKFTZu3KhV9ujRN5MnT+bcuXN89tln1KpViw0bNjBixAj27NlD/fr1lXqRkZGMGjWK/v374+3tTW5uLmfPniUjI6OkP4UQQgjxXJK3CCteiQMsNzc3OnfuDMC0adM4d+6c1vW8vDz27duHl5cX77+f/xaDs7Mz8fHx/PLLL0qAVa9ePerVq6fV9vvvv6dGjRo6AZaenh5OTk5F3lNUVBRHjx4lICAANzc3IP/YnE6dOhEcHMyMGTMAyM3N5fPPP8fT05PPPvtMad+hQ4eS/gxCCCHEc0v2YFW8Ei8R6ukV30Sj0ZCbm0v16tW1yqtXr17obFeBrKwsDh48SNeuXYs99qYwFy5cQKVS0bZtW6WsatWqtGzZkiNHjihlERERJCQk4OnpWaL+hRBCCCFKosyPyqlUqRL9+vXju+++4+zZs6SlpREREcGePXsYNmxYke2OHDlCampqoXu6MjMzcXZ2xtbWFg8PD3bs2KF1PTs7Gz09PZ0DpfX19UlISCAzMxOA6OhoTExM+Pvvv+natSu2trZ07dqV3bt3P/2DCyGEEM8JTRl+ROmUy1uEs2fPZvbs2QwcOFAp8/Hx4cMPPyyyTUhICHXr1qVVq1Za5ebm5kyZMgVbW1uysrLYu3cvM2fOJCUlBS8vLwAaNmzIw4cPuXDhAg4ODkD+UuW5c+fQaDQkJydTpUoV7ty5Q0ZGBr6+vowfPx4rKytCQkKYOnUqr7/+us6eMCGEEOJFJHuwKl65BFiLFy/mt99+Y/78+VhYWBAVFcXKlStRq9V89NFHOvWTk5MJDw9n2LBhOkuQvXv31vresWNHcnJyCAgIwNPTE319fdq2bYu5uTmzZ89m0aJFvP766wQGBnL9+nUAVKr8jGsajYasrCymTJmizKa5uLgQFxfH6tWrJcASQgghRJko8yXCS5cusW7dOubNm8fAgQNp1aoV3t7e+Pj4sGzZMlJTU3Xa7N+/n+zs7GLPLfw3d3d3UlJSuHbtGgAGBgYsXbqU9PR0evbsydtvv01ERAQjRoxAX18fExMTANRqNZC/6f7fXFxclMOfhRBCiBddHpoy+4jSKfMZrIJAxcbGRqvc1taW7OxsEhMTMTY21roWEhKCpaUltra2pR7Xzs6OsLAwrl69ikajwcLCgnnz5tG0aVP09fUBePPNN4tsn5WVVeqxhRBCiOeJhEUVr8xnsBo0aADA+fPntcrPnTuHSqXC1NRUq/z27ducPHmy0M3tRQkNDUWtVmNubq5VrlKpsLCwoFGjRty/f5/Q0FCtfWCurq7o6+sTERGh1S4iIoKmTZs+8fhCCCGEEMUp8QxWRkYG4eHhACQkJJCamkpYWBgArVu3xs7ODjs7O2bPns3du3cxNzfn7NmzBAYG0r9/f6pWrarVX2hoKHl5eUUuD/br148+ffpgaWlJZmYme/fu5cCBA/j6+iozUwABAQE0bNiQ119/nfj4eNasWYOdnR39+vVT6tSqVYvhw4ezbNkyVCoVVlZW/PLLL0RFRbF27dqS/hRCCCHEc0k2uVe8EgdYd+/eZcKECVplBd83bdpEmzZtWL16NcuWLWPNmjXcvXuXevXq8dFHH+Ht7a3T3969e3FwcNCZjSpgbm7Ohg0b+N///odKpaJJkyZ888039OrVS6tecnIyixYt4u7du9SpU4devXoxevRonU3zn376KdWqVSM4OJh79+5hZWXFypUrcXV1LelPIYQQQjyXNLJIWOFUmuKyf4oncsqsT0XfghBCiBdEyxu7y32MsRaDyqyvFVe2l1lfr5Iy34MlhBBCCPGqK5c8WEIIIYSoOJJeoeKVeAbr6tWrzJo1i969e2Nra1vo23/Z2dl88803uLq64uDgwIABA4iMjNSpl5CQwOTJk3F1daVZs2b079+f/fv3FzruTz/9RJ8+fbC3t6dNmzZ89NFHyhE4AGvXrqVPnz60bNkSJycnevbsyXfffadz/uGWLVvw8fHB2dkZa2trZYO+EEII8bKQo3IqXolnsC5fvkx4eDiOjo7k5eUVeoDzV199xZ49e5g4cSKNGjXixx9/xNvbm+3btyvpELKzs5Ws7r6+vtSoUYM9e/YwYcIEgoKCtLKqBwQEEBQUxMcff4yTkxP3798nMjKShw8fKnVSUlLw8PDgzTffxNDQkMjISObPn09qaioff/yxUm/Pnj0AdOjQQc4gFEIIIUS5KPEm97y8POXNvGnTpnHu3DlCQkKU64mJibzzzjtMnz6d4cOHA/lH1PTq1QszMzMCAgIAiIqKYtCgQcqbhwV9d+7cGWdnZ7766isA4uLi6NmzJ6tWraJDhw4lerhPP/2Uc+fOac2KFdz/jRs36NSpE8uWLaNbt24l6vdRssldCCHEk3oWm9x9LAY+vtITWnPlhzLr63EOHz6Mn58f8fHxmJqaMmrUKPr37//YdikpKSxYsIBff/2VnJwc2rVrx4wZM6hTp45Sx9/fnxUrVui0nTNnDkOGDFG+azQagoKC2Lp1K/fu3cPGxobp06fj5ORUomcp8QzWo2kPHvXf//6Xhw8f0rZtW6VMpVLh6urKd999R3Z2NgYGBuTm5gJQvXp1rb6NjIy0ZsV+/PFHzMzMShxcAbz22mvk5OSU6P6FEEKIF92LmAfr1KlTjB07lgEDBuDr68sff/zB559/jpGR0WMnQiZOnEhMTAxz5szB0NAQPz8/vL292bVrF5Ur/1+oU6VKFTZu3KjV9o033tD6HhQUxPLly5kyZQrW1tZs2bKFkSNHsmfPHp26xSnzTe7Z2dlA/vmA/2ZgYEB2djY3btzA0tISJycn3nzzTZYuXcqsWbOUJcIrV64wb948pV10dDRNmjRh1apVbN68mZSUFOzs7Jg+fTqOjo464+fm5pKZmcmpU6fYvXs3Y8eOLetHFEIIIUQZCwgIwMHBQYkBnJ2duX79OsuXLy82wDpz5gzHjh0jODhYyWnZqFEjPDw8OHDgAB4eHkpdPT29YmeisrKyWLNmDSNHjuSDDz4AoEWLFnTr1o3g4GDmzJnzxM9T5tM5DRs2BODs2bNa5VFRUQAkJSUBULlyZTZu3EhSUhKdO3emVatWLF68mKVLl9KsWTOl3Z07dzh27Bh79uxh9uzZrFy5EpVKxciRI7l7967WGFevXqVp06a0aNECHx8fhg0bpvxAQgghxKtCU4b/exays7M5ceKETiDl4eFBbGwsN27cKLLt0aNHUavVWitnlpaW2NjYcPTo0RLdx+nTp0lNTcXd3V0pMzAwoEuXLiXuq8xnsJo0aULLli1ZvHgx9evXx8LCgh9//JE///wTyF8uBMjMzGT8+PFoNBpWrlyJkZERYWFhfPrppwQFBdG6dWsgfy00PT2dZcuW8dZbbwHg6OiIm5sb3333nVZW+fr167Nz507S09M5deoUQUFB6OnpMX78+LJ+TCGEEOK59aItEV67do2cnBwsLS21yq2srID8/dhmZmaFto2Li6NRo0ZKfFHA0tKSuLg4rbLMzEycnZ1JTk7GwsKCDz74gPfee0+rr4K2j97Hxo0byczMpEqVKk/0TOWSB2vhwoVMnDiRwYMHA/kHQI8ePRp/f39q164NwM6dOzl79izh4eHUrFkTABcXF65du8a3337L999/D4BarcbExEQJrgBMTEywtbUlJiZGa1wDAwPs7e0BaNOmDcbGxixatIghQ4Yo4wohhBDiyXXq1KnY64cOHXrqMQpWt9RqtVZ5wfeC64VJTk7W2s9doEaNGpw7d075bm5uzpQpU7C1tSUrK4u9e/cyc+ZMUlJS8PLyUvoyMDDA0NBQ5z40Gg1JSUkVG2C98cYb7Nq1ixs3bpCZmUmjRo1Yv349tWvXpkGDBgDExMRQt25dJbgqYGNjo5U+oXHjxly7dq3QcbKysoq9j6ZNm/Lw4UMSEhIkwBJCCPHKeB7OIkxJSeH27duPrVeSjeNPo3fv3lrfO3bsSE5ODgEBAXh6eqKvr1+m45VrJveC6bzMzEx27tzJwIH/99qoqakpt27d4t69e1pB1vnz55UgDOCdd97hxx9/5OLFi9jY2ABw//59zp8//9j9VadPn0alUhU5rSiEEEK8jMpyibC0M1RhYWHMmDHjsfVCQ0OpUaMGkB+U/VtycjKAcr0warWaW7du6ZQnJSUV2w7A3d2d/fv3c+3aNaysrFCr1WRnZ5OVlaU1i5WcnIxKpXpsf/9W4gArIyOD8PBwID8Te2pqqpINvXXr1tSsWZPvvvsOY2Nj6tevT0JCAuvXr8fQ0BBvb2+ln549e7JmzRq8vb0ZNWqUsgfrjz/+4Ouvv1bqde7cGXt7e8aPH8+kSZMwNDQkMDAQAwMDhg4dCuT/hXh7e9OrVy8aNmxIbm4uJ06cYNOmTQwaNIhatWop/f39998kJCRw7949IP8tRYCaNWsq+76EEEKIF1leyVJclouBAwdqTawUJzs7G319feLi4rQSjRe1J+rfLC0tiYyMRKPRaO3Dio+Pp0mTJiW654Jx4uPjtbYmxcXFYWpq+sTLg1CKAOvu3btaG8sB5XtB0tDs7GxWrFjBrVu3MDEx4d1332XChAlUq1ZNaVO/fn02bdqEn58fc+fOJTMzEwsLC77++mutaTw9PT0CAwNZsGABs2bNIicnh5YtW7JlyxZl2c/Q0JBGjRqxYcMGEhMTqVKlCubm5sydO5c+ffpo3euWLVv46aeflO/r1q0D8oPDzZs3l/TnEEIIIcRTMjAwoE2bNuzfv58RI0Yo5aGhoVhZWRW7EtW+fXtWrVpFZGQkb7/9NpAfIF24cEE5MaYooaGhqNVqzM3NAWjevDnGxsbs27dPCbBycnI4cOAA7du3L9EzlTiTu9AlmdyFEEI8qWeRyX1Yw35l1td3V38ss76Kc+rUKTw9PXnvvfdwd3fnxIkTrFq1iqVLl2qlTbC1taVPnz7KiS8AXl5exMbGMnXqVAwNDVm6dCl6enpaiUb79etHnz59sLS0JDMzk7179xIWFoavr69WUBcYGIi/vz9TpkyhSZMmbNu2TUkXVaGJRoUQQghRsfKeg03uJdWyZUv8/f3x8/Nj586dmJqaMn/+fK3gCuDhw4fk5WnvMvPz81NWunJzc3F1dWXGjBlaWdzNzc3ZsGED//vf/1CpVDRp0oRvvvmGXr16afXl7e2NRqNh3bp1ylE5wcHBJd6MLzNYZUBmsIQQQjypZzGDNbRh3zLra+vVnx5fSeiQGSwhhBDiJfM8pGl41ZXoqJx9+/bxySef0L59e5ycnOjduzc7d+7k0UmwH374ga5du2Jvb0+vXr04cuSITl+xsbF4e3vj5OREq1at+Oyzz5Q3+x71008/0adPH+zt7WnTpg0fffQRmZmZWnWysrJYtmwZbm5u2NnZ0bFjRxYtWqTTV2JiIlOnTsXZ2RkHBwfc3d35+eefS/IzCCGEEM+1vDL8iNIp0QzWhg0baNCgAdOmTeO1114jIiKCmTNncuvWLeVQ5V9++YWZM2fy8ccf4+zsTGhoKGPHjmXLli3KAYupqamMGDGCunXrsnjxYjIzM/n222/x8fFh+/bt6On9X9wXEBBAUFAQH3/8MU5OTty/f5/IyEgePnyo1MnLy2P06NFcv36dsWPHYmZmxs2bN4mPj9e6/9u3bzNo0CAaNWrEF198gbGxMZcvX1YOqBZCCCGEKAslCrACAgK0koK6uLjw4MED1q9fz+jRo9HT02P58uV0796diRMnAvmnYV+6dImVK1cSFBQEwNatW0lJSWH37t1KjqqGDRsyYMAADh06RJcuXYD8vBMrVqxg1apVdOjQQRm3a9euWve1a9cuoqOjCQ0NpU6dOkXe/zfffEO9evVYu3YtlSpVUp5BCCGEeJm8iJvcXzYlWiJ89FgbyD/aJjU1lfT0dK5fv86VK1d0dvx7eHgQGRmpzBRduHCBt956SysBqL29PSYmJhw+fFgp+/HHHzEzM9MKrgrzww8/0K1bt2KDq9TUVPbt28fQoUOV4EoIIYR4GWnK8H+idEoUYBXmr7/+om7duhgbGysZVxs1aqRVx8rKipycHK5fvw7k75cyMDDQ6cvAwEDr5Ovo6GiaNGnCqlWrcHFxwc7OjsGDByvZ1yE/AdiFCxcwNTXlP//5D05OTjRr1owJEyZw584dpd758+fJycmhcuXKDBs2jKZNm9K2bVu++eYbcnJynvZnEEIIIYRQPFWAderUKUJDQxk5ciTw5KdhW1hYcOnSJa2N6jdv3uTOnTs8ePBAKbtz546S3Gv27NmsXLkSlUrFyJEjuXv3LgAPHjwgJyeHoKAgHjx4wIoVK5g7dy6nT59m3LhxSl//+9//AJgxYwZ2dnYEBwczYsQINm7cyPLly5/mZxBCCCGeK7LJveKVOk3DrVu3mDRpEm3atMHT07NEbQcOHMimTZuYNWsWn376KZmZmcycORM9PT2tc4Q0Gg3p6eksW7ZMSVnv6OiIm5sb3333HRMmTFCSjRkZGbFixQplZqxWrVp8+OGHREZG4uLiotR7++23mTZtGpC/PywtLY1169YxZsyYEp0xJIQQQjyvJMVlxSvVDFZycjLe3t6YmJjg7++vvPX3pKdhW1pa8uWXX3LkyBHat2/Pu+++S40aNWjfvr1yviDkz3yZmJhoHbhoYmKCra0tMTExSh2VSkXz5s21lh1bt25NpUqVtOpBflD1by4uLmRnZ3P16tXS/BRCCCHEcycPTZl9ROmUeAYrMzMTHx8fUlJS2L59O9WrV1euFZxCHRcXp3XydVxcHPr6+lpp5vv06YOHhwdXrlyhRo0a1K1bl+7du+Pm5qbUady4MdeuXSv0PrKysgCoWrUqDRo0KPJ+C+o1bty42OcqqCeEEEII8bRKNIOVm5vLxIkTiYuLY+3atdStW1fr+htvvIGFhQVhYWFa5aGhobi4uOhsbDcwMKBJkybUrVuXyMhIrly5Qt++/5fe/5133uHBgwdcvHhRKbt//z7nz5+nadOmWvVOnz6tFST98ccfPHz4UKnXoEEDmjRpQkREhNY9REREUKVKlccGYEIIIcSLQvZgVbwSzWDNnTuXI0eOMG3aNFJTU4mKilKu2draYmBgwLhx45gyZQrm5ua0adOG0NBQzp49y3fffafUTU9Px9/fn1atWmFoaEhUVBSBgYGMHTtWa+arc+fO2NvbM378eCZNmoShoSGBgYEYGBgwdOhQpZ6Xlxd79uxh9OjReHp6cu/ePZYsWUKLFi20lgQnTZrE6NGj+fLLL+nYsSN///0369atw8vLi2rVqpXm9xNCCCGeO5JeoeKV6LBnNzc3EhISCr126NAhzMzMgPy8VEFBQdy8eZNGjRoxefJk3nnnHaVuZmYmY8aM4dy5c6Snp2NpacmIESPo16+fTr/37t1jwYIFHDlyhJycHFq2bMn06dN1ZpwuXrzIV199RXR0NFWrVqVTp05MmzZN543G0NBQVq1axZUrV6hTpw6DBg1i1KhRWpvrS0oOexZCCPGknsVhzz3Mu5dZXyHXfimzvl4lJQqwROEkwBJCCPGknkWA5WHuUWZ9hV4LLbO+XiWlTtMghBBCiOeTzJ1UvKfO5C6EEEIIIbSVeAZr3759/Pzzz5w/f57k5GQaNmzI8OHD6d+/PyqVitTUVNavX094eDhXrlzBwMAABwcHJk2ahLW1tVZfKSkpLFiwgF9//ZWcnBzatWvHjBkztM4ULG7f1/bt23FycgLyo/WgoCC2bt3KvXv3sLGxYfr06cp1yN/PtWrVKqKjo7l48SL6+vqcOXOmpD+BEEII8VyTt/8qXon3YA0aNIgGDRrQuXNnXnvtNSIiIli7di1jxoxh7NixXLp0iZEjR9K/f39atWpFVlYW69at4/z58+zatQsrKyulLy8vL2JiYpg6dSqGhob4+fmhp6fHrl27qFw5P/a7cOGCckh0gcWLFxMbG8vvv/+u1AsMDGT58uVMmTIFa2trtmzZQkREBHv27FHyb128eBEvLy8cHBx48OAB//zzT5kEWLIHSwghxJN6Fnuw3n2jW5n1deB62OMrCR0lnsEKCAigZs2ayncXFxcePHjA+vXrGT16NGZmZhw8eJCqVasqdZydnXFzc2Pr1q3MnDkTgDNnznDs2DGCg4NxdXUF8g+J9vDw4MCBA3h45G/Qs7W11Ro/PT2d8+fP06dPHyW4ysrKYs2aNYwcOZIPPvgAgBYtWtCtWzeCg4OZM2cOANbW1koeLH9/f/7555+SPr4QQgghxGOVeA/Wv4OrAjY2NqSmppKenk61atW0givIPyfQ3Nyc27dvK2VHjx5FrVbTtm1bpczS0hIbGxuOHj1a5PiHDh0iPT2dnj17KmWnT58mNTUVd3d3pczAwIAuXbpo9VVwpI8QQgjxMpOjcipemUQcf/31F3Xr1sXY2LjQ68nJyVy+fFnn+JxGjRrp5J+ytLQkLi6uyLFCQkJo0KABzZs31+qroO2/WVlZcfPmTTIzM0v8TEIIIcSLSqPRlNlHlM5TB1inTp0iNDSUkSNHFlnnm2++QaVSMWTIEKUsOTlZ6xzDAjVq1CApKanQfu7fv8/x48fp0aOHVnlycjIGBgYYGhpqlavVajQaTZH9CSGEEC8jmcGqeE+VB+vWrVtMmjSJNm3a4OnpWWidXbt2sWPHDhYuXEi9evWeZjj27dtHTk6OToAlhBBCCPE8KfUMVnJyMt7e3piYmODv71/o/qbw8HBmzZrF6NGjtQ5xhvzZpdTUVJ02SUlJ1KhRo9AxQ0JCsLa2pkmTJjp9ZWdnax32XHCPKpWqyP6EEEKIl5GmDP8nSqdUAVZmZiY+Pj6kpKSwdu3aQpf6oqKimDBhAn369GHChAk61y0tLYmPj9dZ342Pj9fZSwVw8+ZNTp8+XejsVUH9+Ph4rfK4uDhMTU2pUqVKiZ5PCCGEeJHlaTRl9hGlU+IAKzc3l4kTJxIXF8fatWupW7euTp2YmBh8fHxwdnZm7ty5hfbTvn17kpKSiIyMVMri4+O5cOEC7du316kfEhICUGiA1bx5c4yNjdm3b59SlpOTw4EDBwrtSwghhBCiPJV4D9bcuXM5cuQI06ZNIzU1laioKOWara0tKSkpeHl5YWhoyIgRIzh37pxy3djYmMaNGwPQrFkzXF1d8fX1VRKNLl26FGtra959912dcUNCQmjevDmmpqY61wwNDfHx8cHf35+aNWvSpEkTtm3bxoMHD/Dy8tKqGxaWnzAtJiaGhw8fKt/t7e1p0KBBSX8OIYQQ4rkj804Vr8SZ3Is7uubQoUMkJCQUueG9devWbN68WflecFTOwYMHyc3NxdXVlRkzZujMisXExNC9e3dmz57N0KFDC+1bo9EQGBioc1ROs2bNtOo9elxPgQULFtCvX78in7s4ksldCCHEk3oWmdzbNnArs76OJxwus75eJSUOsIQuCbCEEEI8KQmwXg1PlaZBCCGEEM8fyV9V8STAEkIIIV4ysjhV8UoUYO3bt4+ff/6Z8+fPk5ycTMOGDRk+fDj9+/dXjrxZtGgRR48e5ebNm6hUKho1asTIkSPp3r27Vl8rV67k1KlT/P3336SkpLBz507s7e216gwfPpyTJ08Wei/ffvut0ueTjgkQGxvLt99+y8mTJ8nJycHCwoLPPvtM60xEIYQQQoinUaIAa8OGDTRo0IBp06bx2muvERERwcyZM7l16xZjx44FIC0tjYEDB2JpaYlKpWL//v1MnjyZvLw8rQOat2/fjrm5OW+//Tb79+8vdLzZs2frJCPduHEjBw4cwMXFRSl70jEvX77MkCFDcHV15ZtvvkFfX5/z58+TkZFRkp9BCCGEeK7JEmHFK9Em93v37lGzZk2tspkzZxIaGsqff/5ZaDZ3gMGDB1OtWjXWrVunlOXl5aGnp8eJEyfw9PQsdAarMJ06dcLKyorAwMBi6xU25tChQ6lTpw5+fn6PHackZJO7EEKIJ/UsNrm3Mi27HJB/3jxaZn09zuHDh/Hz8yM+Ph5TU1NGjRpF//79H9uuICvBr7/+Sk5ODu3atWPGjBnUqVNHqVNcFoTt27fj5ORUbL2zZ8/qnHlcnBLNYD0aXAHY2NiwY8cO0tPTMTY2LrSdiYkJaWlpWmVFBWPFOX36NDdu3GDixImPrfvomLGxsfz1119s3bq1xOMKIYQQL5IXcQ/WqVOnGDt2LAMGDMDX15c//viDzz//HCMjI7p161Zs24kTJxITE8OcOXMwNDTEz88Pb29vdu3aReXK+aHOihUryM7O1mq3ePFiYmNjsbOz0yrv2rUrI0eO1CozMDAo0fM89Sb3v/76i7p162oFVxqNhocPH5Kens7hw4c5fvw433zzzdMORUhICNWqVaNTp0461x43ZnR0NADp6en07duXf/75hzp16jB8+HCdZKRCCCGEeLYCAgJwcHBg3rx5ADg7O3P9+nWWL19ebIB15swZjh07RnDw/2/vzuNi3r8/gL8mt0iRkFS2hEp0USS7rCE3S9e+hLjckGyF7rXcr4RsyVKixZatNMka11Za3GvfbuVeabO0l/b5/eHR/IyZaqbGfD4zzvM+PK75fN7NnCnTnHl/3u9z/NCvXz8AgL6+PkaNGoUrV65g1KhRAD4XQ/9SYWEhnj59CltbW34SVql58+b8Ga3aqnWzZ+BzthkRESGU5UVHR8PExAQ9e/bE2rVrsXbt2hqzz5qUlZXh4sWLsLKyQsOGDYXO1/SYHz58AACsWLECw4cPx+HDhzFmzBhs27YNJ0+erFNshBBCCJtUgCe1P7JQUlKCmJgYoVxh1KhRSExMxNu3b6v82lu3bqFx48YCm9Xat28PY2Nj3LpV9eXNyMhIFBYWCqzVlqZaz2Clp6dj2bJlsLCwEKrcbmpqijNnziA/Px+3bt3CH3/8gXr16sHOzq7Wgd69exeZmZkiexGK85gVFRUAAFtbWyxcuBDA5+w4PT0dBw4cwOTJk2sdGyGEEMIm8naJ8M2bNygtLUX79u0FjhsYGAAAkpKS0KpVK5Ffm5SUBH19fX41g0rt27dHUlJSlY8ZHh4OPT099OjRQ+gcl8vFqVOnoKysDHNzc6xYsaLKTjBVqVWClZubCwcHBzRp0gReXl5C66nU1dX5C9YtLS1RXl6OLVu2YPz48ahXr15tHhLh4eFo0qQJf/rvazU9ZuPGjQF8Tqq+ZGlpCS6Xi/z8/CrXkBFCCCHfK1HLcr4UGRlZ58fIyckBAP57daXK25XnRcnNzUWjRo2EjmtoaAj0Q/5SVlYW7t69K3QFDvi8yN3U1BS6urpITk7GgQMHMHXqVISGhqJ169ZiPyeJE6yioiIsWLAAeXl5CA4OFvmkvmZiYoKAgABkZmZCS0tL0odEUVERrl27hrFjx0JZWVmsr/n6MTt27Fjt+K8XvhFCCCHyig1lGvLy8vDu3bsax0mStEjLxYsXUVpaKvKq2Lp16/h/Nzc3R9++fWFtbQ0/Pz+sX79e7MeQKMEqKyuDk5MTkpKScOzYMaGmzFW5f/8+1NXVoampKcnD8V2/fl3i66RfP2a3bt3QpEkTREVFYfDgwfxxUVFR0NXVFblDkhBCCJFHPCkmWLWdobp06ZJAslKViIgIaGhoAPiclH0pNzcXAPjnRWncuDHS09OFjufk5FT5deHh4TA0NESnTp1qjK9FixYwMzPD06dPaxz7JYkSrA0bNuDGjRtwcXFBfn4+Hjx4wD/XuXNnJCUlYfv27Rg5ciT09PRQWFiIP//8E6dPn4azs7PAKv3Y2FhkZmYiISEBAHDv3j2kpKRAT09PqB4Wl8uFrq4uzMzMhGJ68eKFWI+prKyMxYsXw93dHRoaGujRowdu376NCxcuYNOmTZJ8GwghhBBSAzs7O7HXXpeUlEBZWRlJSUno378//3jlGqqv12Z9qX379oiOjgaPxxNYh/X69WuRCVRqair++usvODs7i/tUakWiBOvu3bsAgC1btgidi4yMRPPmzdG4cWPs27cP79+/R6NGjdC+fXvs3bsXQ4cOFRjv5eUl0AZn+/btAIBx48YJ3H9OTg5u376NWbNmCS1gAyDRY06fPh08Hg8BAQE4cOAA9PT0sGnTpjotvieEEELYpkLOFrmrqKjAwsICly9fxqxZs/jHIyIiYGBgUOUCdwAYMGAA9u3bh+joaPTp0wfA5+Tq2bNnmDdvntD48PBwAKhy09zXMjIycP/+ffz000+SPCXJKrkT0aiSOyGEEHHJopK7ibaF1O7raUaM1O6rOvHx8Zg5cyZ+/vlnWFtbIyYmBvv27cPOnTthbW3NH9e5c2fY2tpi8+bN/GNz585FYmIiVq9ejfr162Pnzp1QUlISKDRaaezYsVBXVxdZeDw8PBw3btzAwIED0aJFCyQnJ8PHxwc5OTk4e/bst13kTgghhBAibebm5vDy8sKuXbtw5swZ6Orq4o8//hBIrgCgvLycX3qp0q5du+Du7o7ffvsNZWVl6NevH9atWyeUXCUkJODly5f4/fffRcbQqlUrvHv3Dps3b0ZeXh4aNWqE3r17Y8mSJRIvxqcZLCmgGSxCCCHiksUMlnGLXlK7r+fvYmseRITQDBYhhBCiYKS5i5DUjsQJ1sWLFxEWFoanT58iNzcXbdu2xYwZMzBhwgSRi9CvXbuGX3/9FR07duQvLAM+d6U+ceIE4uPj8e7dO2hra2PEiBFYuHChQCsccbpfx8TECFWTr6Svr49Lly4BADIzM7Fv3z48fPgQz58/h7KyMv7++29JvwWEEEIIq8nbIndFJHGC5e/vDz09Pbi4uEBTUxNRUVFwc3NDeno6HB0dBcYWFRVh8+bNaN68udD9XLx4Ef/99x/mzZuHdu3aISEhAXv27MHDhw8RGBjIHydO92sTExMEBwcLjMnPz4eDgwMGDBjAP5aRkYGIiAiYmpqiS5cuePnypaRPnxBCCCGkRhInWPv37xcoymlpaYns7GwcOXIEixYtEmibc/DgQejq6qJVq1ZC5eodHBwE7sfCwgKNGzfGihUr8OTJE37yJE73a3V1daGu1+fOnUNFRYXANkxDQ0NERUUB+FwmghIsQgghioguETJPqeYhgkRVPDc2NkZ+fj4KCwv5x968eYMjR45UWcVV1P1UJlPVldYXt/t1eHg42rVrB1NTU/6xr3smEkIIIYqogseT2h9SO1LJOO7fvw9tbW2BZsn/+9//8NNPP8HIyEii+wGqr9haXffrSh8+fMC9e/fELiJGCCGEECJNdd5FGB8fj4iICKxevZp/7Pr16/j777/5i8vFkZmZCS8vLwwZMgTt2rUTOaa67tdfioiIQHl5OSVYhBBCvkt0iZB5dUqw0tPTsWzZMlhYWPB38RUXF2Pz5s1YvHix2A2US0tL+T2BqutUXV336y9xuVyYmJhAX19fvCdCCCGEKBAer6LmQeSbqvUlwtzcXDg4OKBJkybw8vLir28KCAiAkpISRo8ejdzcXOTm5qK0tBQVFRXIzc0V2hHI4/GwZs0aPHr0CL6+vmjRokWVjylO9+s3b97g0aNHGDt2bG2fGiGEEEJIndRqBquoqAgLFixAXl4egoOD0ahRI/65pKQk/Pfff7C0tBT6up49e2L9+vWYMmUK/5iHhwcuXrwIX1/fatdridv9msvlQklJCaNGjarFMyOEEELkXwVdImScxAlWWVkZnJyckJSUhGPHjkFbW1vgvIODA8aNGydwzMfHB69fv4a7u7vA+iofHx/4+/tj+/btIhOyL4nb/frChQvo1atXtTNhhBBCiCKjLnjMkzjB2rBhA27cuAEXFxfk5+fjwYMH/HOdO3eGgYEBDAwMBL4mJCQEGRkZsLD4/+7eXC4Xnp6eGDt2LFq1aiVwP23atBFavxUeHo4ePXpAV1e3ytiePXuGxMRE2NvbVzmmcuF9QkICysvL+be7du0KPT29Gp8/IYQQwnY0g8U8iROsu3fvAgC2bNkidC4yMhKtWrWS6H7CwsIQFhYmcM7d3R3jx4/n366p+3UlLpcLFRUVjBgxosoxS5cuFXn768ckhBBCCKktDo/mEessvpUt0yEQQgiRE+ZvQ7/5Y+hpmkjtvlKynkrtvr4nda6DRQghhBB2oQrszKPeMYQQQgghUibRDNbFixcRFhaGp0+fIjc3F23btsWMGTMwYcIEcDgcAMCMGTMQGxsr9LURERH8xe/Jycn4448/8Pz5c2RlZUFDQwM9evTAsmXLhIqDRkZG4sCBA0hISICamhrMzMywYsUKtG7dWmDc6dOncejQIaSmpkJfXx/Lli3D4MGDheJ48OABdu3ahYcPH4LD4aBDhw7YsGEDjI2NJflWEEIIIaxFldyZJ1GC5e/vDz09Pbi4uEBTUxNRUVFwc3NDeno6HB0d+eN69Ogh0DoHgMDi94KCAjRv3hzOzs7Q0dHB+/fvcfDgQcycORPnz5/n7yCMiYmBo6MjbG1tsWzZMmRnZ2P37t2YM2cOuFwuGjRoAOBzaQY3Nzf88ssv6N27NyIiIuDo6Ihjx46hW7du/MeNjo7G/PnzMWHCBDg4OKCsrAyPHj3Cp0+fJP7GEUIIIWxFy6uZJ9Ei98zMTKHyCW5uboiIiEBcXByUlJQwY8YMNGzYEAcPHpQokH///RcjRozA9u3bYWNjAwD47bffcPfuXVy7do0/Q3bv3j3MmjULx44dg7m5OQBgxIgR6NKlCzw9Pfn3N3nyZDRq1Ai+vr4APtfvGj58OKytrbFy5UqJYqsJLXInhBAiLlksctfWqLpwt6Qycl5I7b6+JxKtwRLVW9DY2Bj5+fkoLCysUyBNmjQB8LkvYaWysjKoqanxkysA/KrxlXlhcnIy/v33X1hbWwvc36hRoxAdHc1vzRMVFYWUlBR+z0RCCCFEUVWAJ7U/pHbqvMj9/v370NbWhrq6Ov9YbGwsunXrhq5du2L69OmIi4sT+bUVFRUoLS3F27dvsWnTJujo6GDYsGH88+PHj0diYiKOHTuGvLw8JCcnY8eOHejcuTN69OgB4HNrHgBCa7cMDAxQWlqK5ORkAMDDhw/RpEkTPH78GCNGjEDnzp0xYsQIhIaG1vVbQAghhLAKj8eT2h9SO3Uq0xAfH4+IiAiB9VY9e/bETz/9hHbt2uHdu3fw8/ODvb09goKC0L17d4GvX7VqFbhcLoDP1duPHDki0NfQ3Nwce/fuxfLly7Fx40YAn2fMDh06hHr16gEAcnJyAACNGzcWuO/K25Xn379/j0+fPmHNmjVYsmQJDAwMEB4ejtWrV6NZs2bo379/Xb4VhBBCCCF8tU6w0tPTsWzZMlhYWAhcdluyZInAuEGDBmHMmDHYt28ffz1UpaVLl2LmzJlIS0tDQEAA7O3tcfz4cX47nL/++gurVq3Czz//jEGDBiE7Oxv79u3D/Pnzcfz4cf4id3HweDwUFxdjxYoVmD59OgDA0tISSUlJOHDgACVYhBBCFAbVwWJerS4R5ubmwsHBAU2aNIGXlxeUlKq+m4YNG2LgwIF4+lS4Emzr1q1hamqKESNGwM/PD+Xl5Th06BD//B9//IHevXvDxcUFvXv3xsiRI+Hj44Nnz57h/PnzAAANDQ0AQF5enlCMX56vnNHq3bu3wDhLS0skJCRI+i0ghBBCWIsuETJP4gSrqKgICxYsQF5eHg4dOiRwSa8uVFVVYWBggP/++49/LDExEUZGgjshWrZsCU1NTbx58wYA0L59ewD/vxarUlJSEpSVlfn1sjp27FjlYxcXF0vlORBCCCFsQIvcmSdRglVWVgYnJyckJSXh0KFD0NbWrvFrCgsL8eeff6Jr167VjsvPz8fLly8FCojq6uri2bNnAuNSUlKQlZUFPT09AJ9nwdq1a4dLly4JjIuIiIClpSVUVFQAAP369YOysjKioqIExkVFRcHERHo9mwghhBBCJFqDtWHDBty4cQMuLi7Iz8/HgwcP+Oc6d+6MR48e4dChQxg2bBj09PTw7t07HDlyBO/fv8fu3bv5Y728vJCXl4cePXqgadOmSElJQVBQEEpKSjBr1iz+uMmTJ2Pz5s34448/YGVlhezsbOzfvx/NmjUTKMuwePFirFixAm3atIGFhQUiIiLw6NEjHD16lD+mefPmmDFjBnbv3g0OhwMDAwNcuHABDx48ELgsSQghhMg7urTHPIkKjVpZWSElJUXkucjISJSXl2Pjxo14+fIlsrOzoaqqiu7du8PR0RGmpqYCY/39/fHq1SsUFhZCW1sbPXv2xKJFiwRmsHg8Hk6ePIkTJ04gOTkZampq6NatG5YtW8Zvu1Pp9OnT8PX15bfKcXZ2FmqVU1ZWhv379+P06dPIzMyEgYEBlixZgiFDhoj7LRCJCo0SQggRlywKjao31K95kJjyC19L7b6+JxIlWEQ0SrAIIYSIixKs70Od6mARQgghhH2o2TPzKMEihBBCFAzVwWJenVvlEEIIIYQQQTSDRQghhCgYWl7NPJrBIoQQQhQMT4r/ydL169cxduxYdO3aFSNGjMDZs2dr/JqSkhJs3boV06ZNQ7du3WBoaIjMzEyRY//66y9MmjQJpqamGDx4MHx8fISSUR6PBx8fHwwaNAimpqaYNGmSQFkqcVGCRQghhBDGxcfHw9HREd26dYOvry+sra2xdu1aoULiXysqKsLp06dRv359mJmZVTnuv//+w9y5c6GlpYWDBw9i1qxZ2LNnDw4fPiwwztfXF3v27MHs2bNx8OBBaGlpYc6cOUhOTpbo+VCZBimgMg2EEELEJYsyDSr1W0ntvkqK30rtvqozd+5cFBQU4OTJk/xjy5cvx/PnzxEREVHt1/J4PHA4HJw7dw6urq6Ijo5G06ZNBcb89ttvuHPnDi5dusTv8rJjxw6cOHECd+/ehYqKCoqLi9GnTx9MmzYNzs7OAD7PkI0cORIDBgzA+vXrxX4+NINFCCGEKBh5a/ZcUlKCmJgYjBw5UuD4qFGjkJiYiLdvq0/yOBxOjY9x69YtDBkyhJ9cVd5/bm4u/v77bwCfLyHm5+cLdItRUVHBsGHDcOvWLUmeEiVYhBBCiKLhSfGPLLx58walpaVo3769wPHKri1JSUl1uv/CwkKkpaUJ3X/79u3B4XD491/5f1FxpKamoqioSOzHpF2EhBBCCKlSTe3kIiMj6/wYOTk5AIDGjRsLHK+8XXm+tvLy8kTev4qKClRVVfn3n5ubCxUVFdSvX18oDh6Ph5ycHDRo0ECsx6QESwpkcT2dEEIIEVdZiei+wbVR2369eXl5ePfuXY3jvuxBrEgowSKEEEJIlWo7Q3Xp0iWsW7euxnERERHQ0NAA8P8zTZVyc3MBgH++tho1aiTy/ktKSvDp0yf+/Tdu3BglJSUoLi4WmMXKzc0Fh8ORKA5KsAghhBAidXZ2drCzsxNrbElJCZSVlZGUlIT+/fvzj1e1JkpSDRs2hI6OjtBartevX4PH4/Hvv/L/r1+/hpGRkUAcurq6Yl8eBGiROyGEEEIYpqKiAgsLC1y+fFngeEREBAwMDNCqVd3LTgwYMACRkZEoLS0VuP/GjRuje/fuAIAePXpAXV0dFy9e5I8pLS3FlStXMGDAAIkej2awCCGEEMK4hQsXYubMmVi/fj2sra0RExOD8PBw7Ny5U2Bc586dYWtri82bN/OP3bx5E58+fcKTJ08AADdu3ICamho6dOiADh06APhcZ4vL5WL58uWYMmUKXr16BT8/PyxbtoxfuqF+/fpYsGABvLy80LRpU3Tq1AknTpxAdnY25s6dK9HzoUKjhBBCCGGFyMhI7Nq1C69fv4auri7mz5+PiRMnCowxNDTEuHHjsGXLFv4xKysrpKQIL+x3dHTE4sWL+bf/+usvbNmyBc+fP0fTpk0xbdo0ODg4CNTRqmyVc/z4cWRmZsLY2Biurq78WS5xUYJFCCGEECJltAaLEEIIIUTKKMEihBBCCJEySrAIIYQQQqSMEixCCCGEECmjBIsQQgghRMoowSKEEEIIkTJKsAghhBBCpIwquRPyHUhLS8P79+/B4XCgpaWFli1bMh0SIYQoNEqwCGN4PJ5A9VwiXRUVFfD19cWxY8fw/v17gXPa2tqYPn065s6dSz8D8l0pLS1FcXEx1NXVBY6/f/8ehw8fRmJiIrS0tDB58mR07dqVoSiJIqBK7kTmSkpKEBISgsOHDws19pRHcXFx6NmzJ9NhCPn1118RGRmJrl27om/fvtDR0QGPx0N6ejpu376Np0+fYvjw4dizZw/ToUpNcnIyWrduzXQYCsvd3R1OTk5QVVVlOpRa27RpE+7cuSPwuycrKws2Njb48OEDNDQ0kJ+fj3r16iE4OBjGxsYMRkvkGo+wxu+//857+/atwLGwsDBeTk6OwLGEhASevb29LEOTSHJyMu/gwYO89evX8/z8/HiZmZk8Ho/HKyoq4vn6+vL69u3LMzQ05E2dOpXhSOvm6tWrvEmTJvGMjIyYDkXIxYsXeYaGhrxTp05VOebkyZM8Q0ND3pUrV2QY2bfx/PlznrOzM8/ExITpUKqkCK9vMzMz3qBBg3iRkZFMh1JrY8eO5Xl7ewsc8/T0FHi9fPjwgTdmzBje0qVLGYiQKApa5M4iwcHB+PjxI/92eXk5Vq1aheTkZIFx+fn5iI6OlnV4Ynnw4AFsbGywY8cOnDhxAlu3boWdnR2eP3+OsWPHYvv27WjTpg2OHDmCY8eOMR1ulSovr1lbW6Nbt24YM2YMLl26BAC4c+cObGxssHjxYnz48AEbN25kOFphXC4XI0aMgJ2dXZVjJk2ahBEjRiA0NFR2gdVSREQE5s2bh9GjR+OXX37BX3/9BQB49eoV5s+fj3HjxuHmzZuYN28ew5FWTRFe3xcvXkS3bt2waNEiLF68GO/evWM6JImlpaUJzUpFRkZCX1+f/3pp1qwZ5syZg0ePHjERIlEQlGCxCE/E1VpRx9jMy8sLWlpaOHHiBB4+fIgLFy5AV1cXU6dORWZmJry8vHD8+HFYWloyHWq1jh49Ck9PT3A4HAwePBhqampwdnbGzp074eDggJycHGzcuBGXLl2qNolhyrNnzzB06NAaxw0dOhTPnj2TQUS1d/78eTg7O+Px48dQU1PDs2fPYG9vj+DgYEyYMAFxcXFYsGABIiMj4eTkxHS4VVKE17eWlhZ27twJPz8/vHr1CtbW1ggKCpKr51FaWooGDRrwb+fm5iIpKQm9e/cWGNe6dWuBhJgQSdEidyJVL168gKurK7p37w4AMDAwwIYNG2BtbY1NmzZh2LBhDEcontOnT2PMmDHYtm0bfxG4n58ftm3bBnNzc/j4+KBhw4YMR1m1zMxM6Ojo1DhOR0cHmZmZMoio9gICAtC3b1/s3bsXqqqq4PF4cHd3x/r169GhQwf4+vrSrkgZ69u3L7hcLnbs2IHNmzdjx44dQpslOBwO7t+/z1CEVWvTpg0ePHjA/5B3584dABD60JednY3GjRvLPD6iOCjBIlL18eNHoUXGlbcNDQ2ZCKlW3r59CxcXF4E3jQkTJmDbtm2YP38+q5MrACguLoaKikqN45SVlVFSUiKDiGrvzZs3WLp0KX9hNYfDgYODAwIDA7F06VJKrhhy7do1XLhwAU2aNMHPP/8MZWVlpkMSy8SJE+Hp6QkAaN68Ofbv349mzZphwIABAuNiYmLQvn17JkIkCoISLJbJz89HdnY2gM9rNL4+BgB5eXkMRCY+UZ9kAeCHH+Tnn9unT5/QqFEjgWOVt5s1a8ZESBKLiYlBenp6tWP+/fdf2QRTB/n5+dDU1BQ41rRpUwCQu+RKEV7fycnJ2LBhA+7cuYPx48dj1apVaNKkCdNhiW3q1KlITEyEt7c3ysrKoKurix07dghdNjx//jzmz5/PYKRE3snPO953Yu7cuULHZs+eLXCbx/L6UStWrED9+vWFji9btkxgVoXD4SAsLEyWoUkkKSkJ9erV49+ufENMSkoSGmtiYiKzuMRV+Sm9Jmz+t1Tp6ySk8mdRUFAgcBwAq9/s5f31vW/fPvj4+EBHRwdBQUGsLE9Sk3r16mH9+vVwcXFBYWEhP1n/UsOGDXH58mWhWlmESILqYLFISEiIROPHjRv3jSKpPVdXV4nGu7u7f6NI6sbIyEjkm1zly6XyXOWb4fPnz2UaX01SUlIkGq+np/eNIqm76n4Woo6z7WdRSRFe36amppg/fz7mz58v1iVoQr5nlGCxSGpqKrS0tORmLYMii42NlWh8r169vlEkRBESE0WxfPlyODk5yXUxVwcHB6xZswb6+vr8YwcOHMDEiRPRvHlz/rEXL15g6dKlClEMmTCDLhGyyJAhQxAcHAxTU1OmQ/nuUcLEHpQwsUdERARmzZol1wnW7du3Bda5lZeXY/fu3ejfv79AglVcXIw3b94wESJREJRgsYiiTCbyeDwkJSWhrKwMnTp1AofDwbt37+Dv74+EhARoamrCzs4O5ubmTIdaa5mZmVBXV2ftZRIbGxuxx7J9LZyikOTyOYfDwebNm79hNLWjKL+jvqaoz4swixIsIlWpqalYsGABEhISAHyug7V7927MmzcPGRkZ0NTURFZWFi5cuICAgACYmZkxHHHV/v77b0RGRqKsrAzDhw9Hjx49EBoaiq1btyIrKws//PADxo8fDzc3N9btkDQxMWHtQunaSE5Oxp07d1BWVoZBgwahdevWiI6OhqenJz9pnzJlCqt3fYWEhEBNTQ1t2rSp8Q1dkX52hHyv2PWuQIR2rlWHrTvXCgsLsXfvXjRs2BDe3t5wcHCAtrY2zp49i6ZNm+LDhw9YuHAhvL29cfjwYaZDFikyMhKLFy+GhoYGGjRogKCgILi4uMDd3R0DBw6EgYEBXr16hVOnTkFfX19oJxjTtmzZwnQIUhMfHw8HBwcUFRWhXr168PT0hIeHB1auXAl9fX0MHjwYCQkJ2LlzJ7S0tFh7SbFbt254+PAhysvLMWbMGIwePZrVmwuq4uHhIVTCRBQOh4P9+/fLICJC2IkSLJYR5zICW3euAUBcXBxWrFiBIUOGAABatGiB0aNHY+3atfzt0M2bN8fcuXNZ2cOvko+PD4YPHw5PT0/Uq1cP+/fvh4eHB+zt7bFy5Ur+uM2bN+Ps2bOsS7Bqq7y8HF26dMGZM2dYk8B7eXnB1NQUXl5eaNCgAdzd3eHi4oLRo0cL7EJduXIlgoKCWJtgnTx5Eqmpqbhw4QLCw8Oxc+dOfp9La2trkeUC2OjTp09QUpLvLmtffpCtqvyKqHIshEiCEiyWcXNzQ4cOHZgOo9bev3+PNm3a8G9X/l1bW1tgXMuWLZGVlSXT2CSRmJiIxYsX838JT548Gbt378bAgQMFxg0aNAinT59mIsRvhm3rUV68eIEtW7bw25YsXLgQJ06cEFpnZmNjA2dnZyZCFJuuri4cHBzg4OCAhIQEhIeHIzAwEO7u7ujduzdmzJgh9G+MbdavXy/3G3FEfZBduXKlwKVZNtcjI/KBEiyW6dKli1z/8uLxeAKfbiv/XlV1d7bKz88X6ENW+Xc1NTWBcWpqaigqKpJpbN+bnJwcger5lX/X0NAQGKehoYGCggKZxlYXHTp0gJOTExYuXIjdu3fD398fDRo0YH2CJe8CAwOZDoF8JyjBIlL3ZYuWiooKcDgcxMTECBS/lIcWLaKwPTEk8qG8vBx37txBREQEIiMjUa9ePUycOBETJ05kOjSFJ24JlpKSElbPshP2owRLzvB4PERFRYHL5bJ2IbOoFi1bt24VOsb2ZEXUYt7NmzcLtM9ge984RXH48GF+jaLKS5h+fn4C65Y+fPjASGySiIuLQ3h4OC5duoSSkhIMGTIE27dvR79+/Vi3E1WUnj17Cs3iKqobN27AycmJlWtdiXxg/yv6OxIYGAgDAwOR5x49egQul4uLFy/iw4cPrG04HBkZyXQIUlHZY+3LS06ijikpKcl1PS95oKuri0ePHgkde/DggdBYHR0dGUUluYEDByIrKwsDBgzA+vXrYWVlJbJnJ5sFBQXx/15aWopnz54hPT0dHA4H2traMDY2Zm1tOEJkjRIsFvl66vrff/8Fl8tFeHg4v6Jw3759MXXqVAwYMICJEGskj9vORfnyjYQw6/r160yHIBUZGRn44YcfcPfuXURFRVU7lsPh4P79+zKKTDIlJSXw9vbG8ePHkZ+fL9CfU11dHT/99BNWrFiBBg0aAPi861BVVZXJkAlhBCVYLPP+/XtcuHABXC4Xz549A4fDQc+ePTFlyhR4eHhgwYIFctnBnsgHDocDXV1dmoX4BhwdHZkOoc5KSkowZ84cxMfHo0+fPrCysuLPGqalpeHGjRs4evQoYmNjERwcjLy8PPzyyy84d+4cw5ETInuUYLGIvb09YmNjUVFRgc6dO2P16tUYNWoUWrRogby8PNauufqSlZWV2GurOBwOrl279o0jqp24uDiJxitK0qukpMS6GaPU1FSJxuvq6n6jSOpGERKsw4cP49mzZwgICICFhYXQ+enTpyM2NhYLFy7EqlWr8PTpU0rWyXeLEiwWiY6OBvD5MqCjoyO6d+/OcESSGzJkSI0J1suXLxETE8PqRe4zZswAh8OptiZUZfwcDgfPnj2TVWhimTlzpthjORwOAgICvmE0dSNp0s62n4UkcnNzcenSJXC5XFZepuZyuViwYIHI5KpSr1694ODggF27dmHAgAEiN70w6ZdffhFrnDxsmiDsRgkWi3h5eYHL5eLmzZuYOnUq9PT0MHr0aNjY2AgV6mSrtWvXVnnu+fPn8Pb2RmxsLNq0acPqvnGhoaHVnn///j38/Pxw7949Vu6qUldXrzEp+fjxIx48eMDqRBcA9u7dW+35kpISnDp1Cvfu3RO7zRSblJSUIDIyElwuF7dv30ZpaSk6d+7MdFgiJScno0ePHjWOMzMzA4fDgY+Pjwyikoy4tdJUVVVpAwupE0qwWGTYsGEYNmwY8vPzceXKFXC5XBw6dAg+Pj7Q19cHh8ORy7IAjx8/hre3N27evIl27dphy5YtsLGxYXW7DSMjI5HH3717h0OHDuH06dNQVlaGo6OjRLNFsrJv374qz71//x6HDh3C3bt30ahRI0yfPl2GkUlu6NChIo8XFxfj5MmT8PPzQ2ZmJsaNG4cFCxbIOLra+bLcytWrV1FQUAAOhwNbW1vMnj0bhoaGTIcokqqqqli1obKysgQK9bIJG2cGiWLi8NjWF4MIeP/+PSIiIsDlcvHkyRMoKSnBzMwM48aNw/jx45kOr1oPHjzA3r17cffuXXTo0AELFy6EtbU162dMRElPT4ePjw/Onj0LVVVV2NvbY9q0aQI1sdguIyMDvr6+OHPmDBo0aICZM2di5syZcvUcgM+70o4fP44jR44gJycH48aNw/z589GqVSumQ6vRl+VWPn78CHV1dQwbNgyDBg3CkiVLEBQUxOr1fIsWLUJ+fj4CAgKqfB3zeDzMnj0bampq1Sb6bFe5i3vx4sVMh0LkFCVYcuTNmzcICwtDeHg4/vvvP9YWwIuLi8O+ffsQHR2Nzp07Y+HChRg2bBjTYdVKSkoKDh48iJCQEGhoaMDe3h5TpkxBw4YNmQ5NbGlpaThw4ABCQkKgrq6O2bNnY9q0aay8tFmdgoICHD16FP7+/igoKMDEiRPh4ODA6tpXXxoxYgTevHmD+vXrY8CAAbCxscHAgQOhoqKCvLw89OzZk/UJ1sOHDzFt2jT06tULS5cuxY8//ihw/tGjR9i9ezdiYmJw/PhxuWv79eUu7qdPn0JZWRmPHz9mOiwip+gSIQu9ffsWp0+fxoMHD/DhwwdwOBw0b94cPXr0wMSJE+Ho6IinT58yHaZIM2bMQHx8PExNTXHw4EG57av25s0b7N+/H1wuF02bNsXKlSsxadIkuSoMmZycjIMHDyI0NBRNmjSBs7MzJk+ezK9PJC/y8/Ph7++PoKAgFBcXY9KkSZg7dy5atGjBdGgS+e+//wB87jc6fPhw9O/fX+522P3444/YunUr1q1bh8mTJ0NDQ4O/azM1NRU5OTlQVVXF1q1b5Sa5+nJJRuUubkNDQ6xduxZjxoxhOjwix2gGi2W4XC7Wrl2LkpISaGtrQ0dHBzweD+np6cjIyED9+vXh7u6OUaNGMR2qSJVrl1RVVWu8FMjmYoomJiaoqKiAhYUFJk6cWOMb4fDhw2UUmXhcXFwQHh4OLS0tzJs3D3Z2dnL3Zl6pZ8+eyM/Ph4WFBebMmVNjFwMTExMZRSaZ58+fIywsDBcvXkR6ejpUVVUxZMgQ2NjYoGvXrujTpw/rZ7AqvXv3DqdOnUJ8fDwyMjIAAC1btkTPnj0xceJE1ie/paWluHnzJsLCwnDz5k0UFxejdevWGDJkCAICAhAYGCgXPwfCbpRgsUhiYiJsbW1hZmYGNzc3obY5//zzDzZt2oQHDx7g/Pnz0NfXZyjSqtW04+trbK0NVNUid1E4HA7rLtdWxt+2bdsaEysOh4OwsDBZhFUrX/4sqkvaeTweK38WX+PxeIiNjeUvcM/NzUWjRo2Ql5eHjRs3ws7OjukQv4nQ0FAMHjwYGhoajMaxbt06/ve9WbNmGDlyJGxsbPDjjz/KzaVaIh8owWKRTZs2ITo6GqGhoVW+KZaUlMDW1hZ9+vTBunXrZBzht5WamooWLVqwoultSkqKROPZ1iLIxcVFos0E7u7u3zCauomNjZVo/Nctp9iscialsjxLcXExdHV1YWtrq1CLq8vLy9GlSxecOXOG8RlGIyMjcDgc9OnTBxs3bhR47VKCRaSJEiwWsbGxwYQJEzB79uxqx/n7++PcuXOsnnWQFJt+AdcFj8eDt7c3Jk2aBC0tLabD+e6xZdZEHAUFBbh69Sq4XC5iYmLw5MkTpkOSmvLycpiYmODs2bOMv779/f35i9grm7Xb2NhgxIgR/NZklGARaWBvIaLvUFpamlj1bwwNDSWeYZEHipDrV1RUwNvbG+/evWM6FLF9/PiR6RC+ifLycri6uuLt27dMhyIWNTU12Nraws/PD7du3WI6HIU1e/ZsnD17FpcuXcLChQuRkZEBNzc39O3bF05OTuBwOKioqGA6TKIAKMFikYKCArG2zjds2BCFhYUyiIjUBhsSxZ9//hkvXryocdyxY8dgbW0tg4iYwYafRaXU1FSx/xQVFTEdrsJr164dFi9ejMuXL+PUqVOYMmUKXr58CR6Ph4ULF8LV1RUxMTFMh0nkGPOLXQgfm94MiHwrKyvDxIkTMWPGDCxZsgSqqqoC5588eYLff/8dL168wJQpUxiK8vvyPfVUlDempqYwNTWFq6sr7t27h7CwMFy7dg2hoaGs3zRB2IsSLJaZNWtWjb+EKREjNTlz5gwCAwOxZ88eXLp0CW5ubrCyskJ+fj48PT1x6tQpmJiY4PTp06zte6doFL2noiLgcDiwtLSEpaUlNmzYgD///JPpkIgcowSLRdhasoDIHyUlJcyePRsjR47Exo0b8euvv6JPnz54+fIlSkpK4ObmhkmTJsll2yJ5pYg9FRVNbm4u/v33X2hpaUFHR4d19e2IfKEEi0UowSLS1rJlS6xatQovX77E3bt3oaSkhD179lT5Zk9kR557KkpKSUkJjo6OrChAeuvWLcTFxWH58uUCx728vHDw4EGUl5cDAIYNG4bt27fLbYFewjxKsAgrVG6Plrf+eGxWWlqKgwcPwtfXF7q6uvDw8EBwcDCWLFmCCRMmYMWKFXJRvkDRyHtPReDzc4iIiMDff/8t1M5r1KhRQmv+OBwOaz5AHj9+XKhd1PXr1+Ht7Y3OnTtj/PjxeP36NU6cOIETJ05g1qxZDEVK5B3VwSJS9ejRI3Ts2FHoF+zXsrKycOfOHdjY2MgoMtkJCQmBlZUVo8nLvXv3sGHDBqSkpGD+/PmYP38+/5P46dOn4enpCSUlJaxatQq2traMxfktsa0mmaL0VIyOjsayZcuQnZ2NH374AU2aNAEAZGdno6ysDM2aNcOuXbtYW0dq4MCBcHJywrhx4/jHfv31V0RFReH69evQ1NQE8Ln4bmxsLEJCQpgKlcg5SrCIVBkbGyM4OJjf6LWiogKmpqY4deqUwGLqhw8fYvLkyazfoVNT4+3KRrdsY2RkhL59++L3339HmzZthM5nZWXBw8MDoaGh/MKKbCfprAnbKEJPxfT0dIwZMwY6OjpYsWIFLC0t+Yl7SUkJoqKisH37dmRkZCA8PBza2toMRyzM1NQUhw4d4lf85/F4MDc3R8+ePXHgwAH+uNu3b2P58uUSdxIgpBJdIiRS9XW+zuPxUFZWJpc7H6tqvP369Wvcu3cPfn5+rG287enpidGjR1d5XlNTE1u2bMH48eOxYcMGGUZWO9XNmpw9exY7duxg9awJ8LkNC/B5drG6+kps7ql49OhRqKur49ixY2jcuLHAORUVFQwaNAjdu3fHTz/9hGPHjsHZ2ZmhSKumqakpUFz35cuXKCgogJmZmcC4+vXr89djEVIblGARIkJiYiLWrFlTY+NtFxcXGBsbs67xdmlpKbKysviXO6rSq1cvhIaGyiaoWkpPT8fixYuho6MDDw+PKmdNFi1axNpZEwAIDAxkOoQ6i46OxuTJk4WSqy9paGhg8uTJuHLlCisTrB9//BFBQUEYMmQIVFRUcOLECXA4HFhZWQmM++eff1j7b4nIB0qwCBHh+PHjaN26NXx8fETuIurYsSMOHToEW1tbHDt2jHWNt11dXREcHFxjggUAysrKMoio9hRh1gSoWxNqtvRUTE5ORpcuXWoc16VLFxw+fFgGEUlu6dKlsLOzg6WlJdTV1ZGRkQEbGxuhD1EXLlwQmtUiRBLUKofIhLzVW4qNjcXPP/9c7RZtFRUV/Pzzz6xcoyGPl2SrIsmsyZ07d2QYmWywqadifn4+GjVqVOO4Ro0aIT8/XwYRSc7AwADnz5/H9OnTMWTIEPzvf/+Dh4eHwJiPHz/C2NgYdnZ2DEVJFAHNYBGp8/DwEPolvHnzZqirq/NvV65HYavvvfE2myjCrEldsSVhrqioEPvDEltiFqV169ZYtmxZleebNWsGNzc3gWNs25VK2I8SLCJVlYuMCwoKqj2mpKQEc3Nz2QYnAUVovB0eHo779+/XOI7D4WD27NnfPqBaUoRZE0WyYsUK1K9fv9oxxcXFMopGdioqKuDt7Y3BgwdTgkXEQgkWkSp52O4vDjZ/+haXuIuq2Z5gKcqsiSL4snZUTRRx/RL9+yKSoASLMCY5ORmtW7dmOowqyXvj7VOnTvHrkcm773XWhG3c3d2ZDoEQuUEJFpG5Fy9ewNfXF5cvX8aTJ0+YDkcktrT1IDRrQgiRT5RgEamLiIjAuXPnkJaWhtatW2P+/Pno0aMHXr16he3bt+P27dtQU1PDvHnzmA61SpRgsQfNmrDPixcvcPToUcTFxSEjIwMAoK2tjV69emHatGkwMjJiOEJCmEdlGohUnT9/Hs7Oznj8+DHU1NTw7Nkz2NvbIzg4GBMmTEBcXBwWLFiAyMhIODk5MR1unZWXl7O+UCeRb0pKSnB0dGRNz0J/f39MnDgRoaGhUFNTQ79+/dCvXz+oqakhJCQEEydOhL+/P9NhEsI46kVIpGr8+PHQ1NTE3r17oaqqCh6PB3d3dwQFBaFDhw7w9fVFy5YtmQ5TIpmZmdDU1BRYj1VUVITg4GAEBAQgLS2NlW1NFI2izJrIc0/FmzdvYsGCBbC1tcWSJUuEenGmpaVhz549CA0NhY+PD/r3789QpNJXXl4OExMTnD17lpV9Ign7UIJFpMrc3Byenp4YOHAg/9j79+/Rv39/7N27F0OHDmUwOvEVFxfD3d0d58+fR1FREdTU1LBo0SLMmTMHp06dwq5du5CZmQkzMzM4OjrC0tKS6ZAVmr+/P7Zv3w4A6NSpE/+NPTU1Fa9evQLweSE8m3dDAtX3VCwrK0OzZs1Y3VNx9uzZaNy4Mfbs2VPtuCVLliA3N5eVM1kzZ87E77//LlS5XRwhISGwsrJivKI+kQ+0BotIVX5+vlB7lqZNmwKAXM1c+fj44OTJk+jbty+MjY2RkpKCHTt24OnTp7hw4QI6d+4MT09PSqxk4ObNm9iyZUuNsyYeHh4wMDBg7ayJIvRUfPLkCT/Rrc6ECROwYsUKGUQkudjYWIGafJKQZMMFIZRgEanLz89HdnY2/3ZlR/qCggKB4wD4n+DZJiIiAjNnzsSaNWv4x0JDQ+Hi4oKhQ4diz549UFKiJYyycOTIEQwfPhxbtmwReV5HRwfu7u4oKCiAn58faxMsReipWFZWhgYNGtQ4rn79+igrK5NBRISwFyVYROrmzp0r8rioyzdsXbuUkpICKysrgWOVlzenTZtGyZUMKcKsCSBZT8UrV66wMsFq3749bt++jd69e1c77tatW9DX15dRVISwEyVYRKoUZUt9SUkJGjZsKHCscvFxdW+QRPoUZdZEEXoqjh8/Hh4eHujYsSNsbW1FjgkNDUVQUBBcXFxkG5wExClcC3zuchAWFiaDiIgiogSLSJUirVGIiYlBeno6/3Zly5aYmBihBs/Dhw+XdXjfDUWZNVGEnopTp05FVFQUXFxc4OPjg4EDBwpsOLh9+zYSExNhZWWFqVOnMhxt1fT19flrQwn5VijBIt/Uu3fv+ElKy5YtWVPLRxyenp4ij2/dulXgNofDYe2lTkWgKLMmitBTUUlJCd7e3ggKCkJgYCCOHDkicL5Vq1ZYs2YNpk+fLvZzZcKvv/6qMG2kCHtRmQbyTRw7dgwBAQFITk4WON66dWvMnj2b1Z9uAQjNUNVET0/vG0VCKioq4OjoiOvXr6N9+/bVzprs3buXtW/sRkZGaNOmjVg9FZOTk+UiaU9PTxeoSSYPO4WNjIwUqk8nYS9KsIhUlZeXY+nSpbh27RratWuHwYMHQ0dHB8Dn7fQ3b95EUlIShg0bht27dyvEYnG2N61WBDwejz9r8vbtW4FzrVq1wqxZs1g/a+Lq6irReHlfzxgXF8fKel6UYBFZoQSLSFVQUBA8PDywceNGjBs3TuQbXmhoKNatWwdXV1dMmzaNgSjrLjMzExcvXgSXy8XDhw/lYrZBUcjjrMn35Nq1azh06BBrXxeurq5YtGgRfSgi3xwlWESqxo0bhz59+mDlypXVjtu2bRuioqIQEhIio8jq7tOnT7h69SrCw8MRFRWFsrIydO7cGba2tpg5cybT4RGwd9ZEUVRUVMDPz4/fzL1Vq1ZwdHTEyJEjcefOHXh4eCAhIQF6enpYsGAB7OzsmA65Wmlpabh//77AOlFzc3NK2olU0CJ3IlWvX78Wa6Fx//79cezYMRlEVDfl5eW4ffs2uFwurl+/jqKiIjRv3hzl5eXYsWMHRo0axXSIBOyfNfmSPPdUPHr0KDw9PdG+fXsMHjwYqampcHZ2xvPnz+Hj4wMtLS3+7PUPP7D37eXjx4/YsGEDrl27Bh6PJ7CpQElJCcOHD4ebmxuaNWvGYJRE3rH3FUDkkrKyMj59+lTjuKKiIigrK8sgotq5f/8+wsPDcenSJWRlZaFJkyYYO3YsbGxs0LFjR1hYWEBLS4vpML8LksyabNy4kelwq/V1T8V+/foB+LxYPyQkBCEhIazuqXj69GmMGTMG27Zt41/+9/Pzw7Zt22Bubg4fHx+h+nFsk5OTg6lTpyItLQ0TJkyAlZWVwDrRGzduIDQ0FC9evMCpU6eo7h2pNUqwiFR16dIF586dw6BBg6odd+7cOXTt2lU2QdXCtGnTwOFwYGFhAXt7e/Tt25f/iTwvL4/h6L4vijJrogg9Fd++fQsXFxeBtZUTJkzAtm3bMH/+fNYnVwBw4MAB5OXlITQ0FO3btxc4Z2RkhMGDB8Pe3h4zZszAgQMHsGrVKoYiJfJO/rdwEVaxt7fHlStXsGbNGqSlpQmdz8jIwNq1a3H16lXY29szEKF4OnXqBB6Ph7i4OAQGBiI8PJy1xR8VXeWsyYULF7Bz504EBwdj+fLlOHjwIMzMzHDp0iXY2dmxOrkCBHsqfp1cAf/fU3HYsGHw8/NjIMKaffr0SahYauVtebmcdvXqVSxatEgoufqSvr4+FixYgCtXrsgwMqJo2P0bicidAQMGYNWqVfD09ERYWBgMDQ0Faha9evUKFRUVWLVqFSs/oVcKCwtDQkICwsLCcOHCBbi4uKBBgwYYOHAgBg8ezOpyAIpGEWZNAMXpqZiUlIR69erxb1c2c09KShIaa2JiIrO4xJWRkSHWOjdjY2O8e/dOBhERRUUJFpG6OXPmoHfv3ggKCkJ8fDz++ecfAJ8X8trY2GDGjBkwNjZmOMqadejQAc7OznB2duavybp8+TIuX74MDoeDwMBAAKBda9+YIsyaAIrTU9HV1VVgUXhl4rty5Ur+33k8Hms7HGhoaAi0wKpKWloarb8idUIJFpGq1NRUaGlpoXPnznJfKPFLZmZmMDMzw7p163Dnzh2Eh4cjMjISV69ehZ6eHiIjI5kOUaHJ+6wJoBg9FSs/VMgzCwsL+Pn5YdiwYVVW1S8qKsKRI0dq/FkRUh2qg0WkytjYGMHBwd9FleSioiJcu3YN4eHhOHDgANPhKCwjIyNwOByhWZPK2/IwawJ8Xqzv4eGBTZs2VdtT0c3NDS4uLqwtwpuQkICTJ0/i7du3aNGiBUaOHIk+ffowHZbYkpKSMGHCBLRq1QoLFy7EoEGD+JeZCwsL8eeff2L//v1ISUnB2bNnWZvsEvajBItIlSK1oXj58iU0NDSqLDqYkZGB7OxsGBoayjiy70tsbKxE43v16vWNIqkbReipGB8fD3t7e5SVlaFp06bIzs5GRUUFfvvtN0yZMoXp8MQWFxcHZ2dnfPjwAQD4lwJzc3PB4/HQokUL7NixA+bm5kyGSeQcJVhEqhQlwbp8+TJWrlyJM2fOoFOnTiLHvHr1CnZ2dtixYweGDBki4wi/L/I+a1JJ3nsqzpo1C9nZ2Thw4AB0dHSQn58PV1dXxMbGIiYmhunwJFJcXIyLFy8KFXzt2bMnrK2ta2zKTUhNKMEiUmVkZIQtW7agY8eOYo1n63qZefPmoXXr1vj999+rHbdp0ya8efMGvr6+Mors+6MosyZfk8eeipaWltiwYQOGDx/OP/b27VsMHToUN27c4BfsZLPQ0FAMHDgQmpqaTIdCFBwtcidS5+rqWuMYtq+Xefz4MaZPn17juP79+2P16tUyiOj75eXlhfbt2wvNmuzatUuuE6yWLVtWmVSxtadiVlaWUMyVSVVWVpZcJFiurq4IDg6mBIt8c5RgEalzc3NDhw4dmA6jTgoLC6Gurl7jOHV1dRQWFsogou/Xq1evsGHDBv6bt7q6OlavXo2hQ4ciLS1NLt7UxSVPPRXlFV20IbJCCRaRui5dusj9GiwtLS0kJibWuMg1ISGBehJ+Y4owawIoTk/FWbNmiVwjVtleqhKHw8H9+/dlGRohrEIJFiEi9O3bF4cPH4aNjU2VlcILCgrg7+/Pb9hLSHUUoaeio6Mj0yFIRXh4uFjJH4fDYW3jbcJ+tMidSJWi7CJMSUmBra0tdHR0sHz5clhaWkJFRQUAUFJSgujoaOzcuROpqakICQmBnp4ewxErLiMjI6iqqgrNmhQWFgodZ/OsiY2NDQwNDbFt2zZ+zH5+fti2bRvMzc3h4+MjN21/5Jk4bXIqsXmdKGE/SrCIVMXGxsLExARqampMh1JnDx48gJOTEzIyMlCvXj1oamqCw+EgMzMT5eXlaNmyJXbt2oUff/yR6VAV2t69eyUaz9ZZlu7du2Pv3r3o27cv/1h2djZ69+4NHx8fDBgwgMHovh+K8iGQsB8756GJ3OrQoQOSk5OFPiW+ePEC+/btQ2JiIpo3b45Zs2bBysqKoSjF061bN1y5cgURERGIj48X2FLfq1cvjBw5kj+rRb4dtiZMklKUnoqEEPFQgkWkaseOHXj69ClCQkL4x1JSUjBt2jQUFRXB0NAQ//zzDxwdHREQEMDKrehfUlFRga2tbZWtTb7G4/Hg7e2NSZMm0eJ3IkQReioSQsRDCRaRqr/++gsTJ04UOObv74/CwkL4+vqiX79+KCoqgr29PXx9fVmfYEmqoqIC3t7eGDx4MCVYRIirq6tQT0UAWLlypdz0VCSEiIcSLCJVGRkZQlXcb9y4AWNjY/5uuwYNGmD69OnYunUrEyF+c7SskYgSGBjIdAgEn5crECILlGARqeJwOAK7uj58+IC3b99i1qxZAuO0tbWRlZUl6/AIYUyvXr0UpqciIaRmSkwHQBSLvr4+oqKi+Ldv3LgBDocjsHMKAN6/f4+mTZvKOjxCGBMfH49x48bh2LFjePz4Mc6ePYu5c+fixIkTTIdGCPkGaAaLSNWMGTOwevVq5Obmonnz5jhx4gTatGkj9Cn9zp076NSpE0NREiJ7itpTkRAiGs1gEakaO3YsnJ2dcfv2bQQEBKBjx47Yu3evQGXqjx8/4saNGxg8eDCDkRIiW69evcKvv/4q1FMxJycHaWlpDEdHCJE2msEiUufg4AAHB4cqzzdr1kzgMiIh3wNF6alICBEPzWARIkJgYGCtdgPWq1cP7u7uaNWq1TeIihBCiLygVjmEiNClSxcYGRlh06ZNMDY2ZjocogAUpaciIUQ8dImQEBHOnDmD9evXw87ODtOnT8fSpUuhqqrKdFhEjilKyx9CiHhoBouQapw6dQqenp5o2LAh3NzcWN8/kRBCCDtQgkVIDbKysuDu7g4ul4u2bdtCWVlZ4DyHw0FYWBhD0RFCCGEjukRISA3evHmDly9fQllZGSYmJlBRUWE6JEIIISxHCRYhVcjLy8O2bdtw5swZWFhYYM+ePWjbti3TYRFCCJEDdImQEBHOnz+PrVu3oqKiAqtXr4atrS3TIRFCCJEjlGARIoKRkRHGjRuHVatWQVNTk+lwCCGEyBkqNEqICD179oSTkxMlV4QQQmqFEixCRIiPj0dGRgbTYRBCCJFTlGARIgJdOSeEEFIXlGARQgghhEgZlWkgpAqHDx9G8+bNxRq7bt26bxwNIYQQeUK7CAkRwcjICM2bNxerqCiHw0FkZKQMoiKEECIvaAaLkCrs27cPpqamTIdBCCFEDtEaLELqoKSkhHYbEkIIEUIJFiF1cOPGDQwaNIjpMAghhLAMJViEiKCrq0tNnQkhhNQarcEiRITr168zHQIhhBA5RjNYhBBCCCFSRgkWIYQQQoiU0SVCQkT45ZdfxBr34cOHbxwJIYQQeUQJFiEiFBQUiDVOVVUV5ubm3zgaQggh8oYquRNCCCGESBmtwSJEhMDAQNBnD0IIIbVFCRYhImzduhV2dnZ4/vw506EQQgiRQ5RgESLCmTNn8MMPP8DOzg5btmzBp0+fmA6JEEKIHKE1WIRU49SpU/D09ETDhg3h5uYGKysrpkMihBAiByjBIqQGWVlZcHd3B5fLRdu2baGsrCw0hsvlMhAZIYQQtqIyDYTU4M2bN3j58iWUlZVhYmJCPQoJIYTUiBIsQqqQl5eHbdu24cyZM7CwsMCePXvQtm1bgTFpaWmIj49nKEJCCCFsRZcICRHh/Pnz2Lp1KyoqKrB69WrY2tqKHHf58mU4OTnRbkNCCCECaBchISKsXr0aAwYMQERERJXJFSGEEFIVukRIiAiBgYHo1asX02EQQgiRUzSDRYgIlFwRQgipC0qwCCGEEEKkjC4REiJC9+7dweFwahxXXl4ug2gIIYTIG0qwCBFhzpw5YiVYhBBCiChUpoEQQgghRMpoDRYhhBBCiJRRgkUIIYQQImWUYBFCCCGESBklWIQQQgghUkYJFiGEEEKIlFGCRQghhBAiZZRgEUIIIYRIGSVYhBBCCCFS9n+zxa14hU+Z9gAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "transact_df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "btm00mGEtMJv",
        "outputId": "bf0ad252-bf88-4572-c9cb-d2c97f2e6a62"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                DATE      STORE_NBR  LYLTY_CARD_NBR        TXN_ID  \\\n",
              "count  264835.000000  264835.000000    2.648350e+05  2.648350e+05   \n",
              "mean    43464.036600     135.080216    1.355496e+05  1.351584e+05   \n",
              "std       105.389336      76.784306    8.058011e+04  7.813316e+04   \n",
              "min     43282.000000       1.000000    1.000000e+03  1.000000e+00   \n",
              "25%     43373.000000      70.000000    7.002100e+04  6.760100e+04   \n",
              "50%     43464.000000     130.000000    1.303580e+05  1.351380e+05   \n",
              "75%     43555.000000     203.000000    2.030945e+05  2.027015e+05   \n",
              "max     43646.000000     272.000000    2.373711e+06  2.415841e+06   \n",
              "\n",
              "            PROD_NBR       PROD_QTY      TOT_SALES  \n",
              "count  264835.000000  264835.000000  264835.000000  \n",
              "mean       56.583201       1.907308       7.304205  \n",
              "std        32.826692       0.643655       3.083231  \n",
              "min         1.000000       1.000000       1.500000  \n",
              "25%        28.000000       2.000000       5.400000  \n",
              "50%        56.000000       2.000000       7.400000  \n",
              "75%        85.000000       2.000000       9.200000  \n",
              "max       114.000000     200.000000     650.000000  "
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-bd4a2c4b-74b9-4518-b6b8-ae66df2847ee\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>DATE</th>\n",
              "      <th>STORE_NBR</th>\n",
              "      <th>LYLTY_CARD_NBR</th>\n",
              "      <th>TXN_ID</th>\n",
              "      <th>PROD_NBR</th>\n",
              "      <th>PROD_QTY</th>\n",
              "      <th>TOT_SALES</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>264835.000000</td>\n",
              "      <td>264835.000000</td>\n",
              "      <td>2.648350e+05</td>\n",
              "      <td>2.648350e+05</td>\n",
              "      <td>264835.000000</td>\n",
              "      <td>264835.000000</td>\n",
              "      <td>264835.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>43464.036600</td>\n",
              "      <td>135.080216</td>\n",
              "      <td>1.355496e+05</td>\n",
              "      <td>1.351584e+05</td>\n",
              "      <td>56.583201</td>\n",
              "      <td>1.907308</td>\n",
              "      <td>7.304205</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>105.389336</td>\n",
              "      <td>76.784306</td>\n",
              "      <td>8.058011e+04</td>\n",
              "      <td>7.813316e+04</td>\n",
              "      <td>32.826692</td>\n",
              "      <td>0.643655</td>\n",
              "      <td>3.083231</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>43282.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000e+03</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>43373.000000</td>\n",
              "      <td>70.000000</td>\n",
              "      <td>7.002100e+04</td>\n",
              "      <td>6.760100e+04</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>5.400000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>43464.000000</td>\n",
              "      <td>130.000000</td>\n",
              "      <td>1.303580e+05</td>\n",
              "      <td>1.351380e+05</td>\n",
              "      <td>56.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>7.400000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>43555.000000</td>\n",
              "      <td>203.000000</td>\n",
              "      <td>2.030945e+05</td>\n",
              "      <td>2.027015e+05</td>\n",
              "      <td>85.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>9.200000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>43646.000000</td>\n",
              "      <td>272.000000</td>\n",
              "      <td>2.373711e+06</td>\n",
              "      <td>2.415841e+06</td>\n",
              "      <td>114.000000</td>\n",
              "      <td>200.000000</td>\n",
              "      <td>650.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bd4a2c4b-74b9-4518-b6b8-ae66df2847ee')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-3ac05e2c-5a2e-4894-8199-f8a9ad9552a4\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-3ac05e2c-5a2e-4894-8199-f8a9ad9552a4')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\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-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-3ac05e2c-5a2e-4894-8199-f8a9ad9552a4 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-bd4a2c4b-74b9-4518-b6b8-ae66df2847ee button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-bd4a2c4b-74b9-4518-b6b8-ae66df2847ee');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Checking unique values in all the columns\n",
        "for col in transact_df.columns.tolist():\n",
        "  print(f'Unique values in {col} is : {transact_df[col].nunique()}')"
      ],
      "metadata": {
        "id": "pRHSGXZlrRvZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7054041d-b059-4954-a675-2f23ae77a2da"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Unique values in DATE is : 364\n",
            "Unique values in STORE_NBR is : 272\n",
            "Unique values in LYLTY_CARD_NBR is : 72637\n",
            "Unique values in TXN_ID is : 263127\n",
            "Unique values in PROD_NBR is : 114\n",
            "Unique values in PROD_NAME is : 114\n",
            "Unique values in PROD_QTY is : 6\n",
            "Unique values in TOT_SALES is : 112\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Converting DATE column to date format\n",
        "transact_df['DATE'] = pd.to_datetime(transact_df['DATE'], origin='1899-12-30', unit='D').dt.strftime('%Y-%m-%d')"
      ],
      "metadata": {
        "id": "qXkbvujny7VA"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "transact_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "7EBzVDorzGT_",
        "outputId": "17e590ac-0889-443b-d948-dfda4c53547d"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         DATE  STORE_NBR  LYLTY_CARD_NBR  TXN_ID  PROD_NBR  \\\n",
              "0  2018-10-17          1            1000       1         5   \n",
              "1  2019-05-14          1            1307     348        66   \n",
              "2  2019-05-20          1            1343     383        61   \n",
              "3  2018-08-17          2            2373     974        69   \n",
              "4  2018-08-18          2            2426    1038       108   \n",
              "\n",
              "                                  PROD_NAME  PROD_QTY  TOT_SALES  \n",
              "0    Natural Chip        Compny SeaSalt175g         2        6.0  \n",
              "1                  CCs Nacho Cheese    175g         3        6.3  \n",
              "2    Smiths Crinkle Cut  Chips Chicken 170g         2        2.9  \n",
              "3    Smiths Chip Thinly  S/Cream&Onion 175g         5       15.0  \n",
              "4  Kettle Tortilla ChpsHny&Jlpno Chili 150g         3       13.8  "
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-1c0edc85-b189-4029-b971-15a00d2b22c7\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>DATE</th>\n",
              "      <th>STORE_NBR</th>\n",
              "      <th>LYLTY_CARD_NBR</th>\n",
              "      <th>TXN_ID</th>\n",
              "      <th>PROD_NBR</th>\n",
              "      <th>PROD_NAME</th>\n",
              "      <th>PROD_QTY</th>\n",
              "      <th>TOT_SALES</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2018-10-17</td>\n",
              "      <td>1</td>\n",
              "      <td>1000</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>Natural Chip        Compny SeaSalt175g</td>\n",
              "      <td>2</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2019-05-14</td>\n",
              "      <td>1</td>\n",
              "      <td>1307</td>\n",
              "      <td>348</td>\n",
              "      <td>66</td>\n",
              "      <td>CCs Nacho Cheese    175g</td>\n",
              "      <td>3</td>\n",
              "      <td>6.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2019-05-20</td>\n",
              "      <td>1</td>\n",
              "      <td>1343</td>\n",
              "      <td>383</td>\n",
              "      <td>61</td>\n",
              "      <td>Smiths Crinkle Cut  Chips Chicken 170g</td>\n",
              "      <td>2</td>\n",
              "      <td>2.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2018-08-17</td>\n",
              "      <td>2</td>\n",
              "      <td>2373</td>\n",
              "      <td>974</td>\n",
              "      <td>69</td>\n",
              "      <td>Smiths Chip Thinly  S/Cream&amp;Onion 175g</td>\n",
              "      <td>5</td>\n",
              "      <td>15.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2018-08-18</td>\n",
              "      <td>2</td>\n",
              "      <td>2426</td>\n",
              "      <td>1038</td>\n",
              "      <td>108</td>\n",
              "      <td>Kettle Tortilla ChpsHny&amp;Jlpno Chili 150g</td>\n",
              "      <td>3</td>\n",
              "      <td>13.8</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1c0edc85-b189-4029-b971-15a00d2b22c7')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-0891488b-5d33-48af-922c-56579f96544c\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-0891488b-5d33-48af-922c-56579f96544c')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\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-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-0891488b-5d33-48af-922c-56579f96544c button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-1c0edc85-b189-4029-b971-15a00d2b22c7 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-1c0edc85-b189-4029-b971-15a00d2b22c7');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Checking PROD_NAME columns\n",
        "transact_df['PROD_NAME'].describe()"
      ],
      "metadata": {
        "id": "rwWzBvjfziAC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "99a7b9f6-28ba-4b6b-cbee-980f20969117"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count                                     264835\n",
              "unique                                       114\n",
              "top       Kettle Mozzarella   Basil & Pesto 175g\n",
              "freq                                        3304\n",
              "Name: PROD_NAME, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "split_prods = transact_df[\"PROD_NAME\"].str.replace(r'([0-9]+[gG])','').str.replace(r'[^\\w]',' ').str.split()\n",
        "split_prods"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eoDCBXi7QLjo",
        "outputId": "e2584fba-39a6-477c-cd6b-b817fc765dcc"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0                  [Natural, Chip, Compny, SeaSalt]\n",
              "1                              [CCs, Nacho, Cheese]\n",
              "2            [Smiths, Crinkle, Cut, Chips, Chicken]\n",
              "3           [Smiths, Chip, Thinly, S, Cream, Onion]\n",
              "4         [Kettle, Tortilla, ChpsHny, Jlpno, Chili]\n",
              "                            ...                    \n",
              "264831    [Kettle, Sweet, Chilli, And, Sour, Cream]\n",
              "264832                 [Tostitos, Splash, Of, Lime]\n",
              "264833                          [Doritos, Mexicana]\n",
              "264834     [Doritos, Corn, Chip, Mexican, Jalapeno]\n",
              "264835                 [Tostitos, Splash, Of, Lime]\n",
              "Name: PROD_NAME, Length: 264835, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "word_counts = {}\n",
        "def count_words(line):\n",
        "  for word in line:\n",
        "    if word not in word_counts:\n",
        "      word_counts[word] = 1\n",
        "    else:\n",
        "      word_counts[word] += 1\n",
        "split_prods.apply(lambda line: count_words(line))\n",
        "print(pd.Series(word_counts).sort_values(ascending = False))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FHyKWDyuQnHs",
        "outputId": "332690d7-9c25-490b-b04c-3eaf69f31450"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Chips       49770\n",
            "Kettle      41288\n",
            "Smiths      28859\n",
            "Salt        27976\n",
            "Cheese      27890\n",
            "            ...  \n",
            "Sunbites     1432\n",
            "Pc           1431\n",
            "Garden       1419\n",
            "NCC          1419\n",
            "Fries        1418\n",
            "Length: 198, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Getting Brand Names for products\n",
        "def get_brand_name(x):\n",
        "  brand = x.split(' ')[0]\n",
        "  if brand == \"Dorito\":\n",
        "        return \"Doritos\"\n",
        "  elif brand == \"GrnWves\" or brand == \"Grain\":\n",
        "        return \"Grain Waves\"\n",
        "  elif brand == \"Infzns\":\n",
        "        return \"Infuzions\"\n",
        "  elif brand == \"Natural\" or brand == \"NCC\":\n",
        "        return \"Natural Chip Co\"\n",
        "  elif brand == \"Red\":\n",
        "        return \"RRD\"\n",
        "  elif brand == \"Smith\":\n",
        "        return \"Smiths\"\n",
        "  elif brand == \"Snbts\":\n",
        "        return \"Sunbites\"\n",
        "  elif brand == \"WW\":\n",
        "        return \"Woolworths\"\n",
        "  else:\n",
        "        return brand\n",
        "\n",
        "transact_df['Brand_name'] = transact_df['PROD_NAME'].apply(get_brand_name)"
      ],
      "metadata": {
        "id": "hHqAxJ6jYS7S"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Getting Packs size for products\n",
        "def get_pack_size(x):\n",
        "  lst = x.split(' ')\n",
        "  size = lst[-1]\n",
        "  size = size[-4:-1]\n",
        "  return size\n",
        "\n",
        "transact_df['Pack_size'] = transact_df[\"PROD_NAME\"].apply(get_pack_size)"
      ],
      "metadata": {
        "id": "GxPCtmQBWoSi"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "transact_df['Pack_size'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xJsrWNpCd6MH",
        "outputId": "a779e218-e13b-47ab-b0c1-cbee92ab3408"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "175    66389\n",
              "150    43131\n",
              "134    25102\n",
              "110    22387\n",
              "170    19983\n",
              "165    15297\n",
              "300    15166\n",
              "330    12540\n",
              "380     6418\n",
              "270     6285\n",
              "210     6272\n",
              "200     4473\n",
              "Sal     3257\n",
              "250     3169\n",
              "90      3008\n",
              "190     2995\n",
              "160     2970\n",
              "220     1564\n",
              "70      1507\n",
              "180     1468\n",
              "125     1454\n",
              "Name: Pack_size, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "transact_df[transact_df['Pack_size']=='Sal'].head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "JG80Fz5CeBaT",
        "outputId": "66e152a8-e0bd-443e-c4c7-3c1ef9d02f5c"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           DATE  STORE_NBR  LYLTY_CARD_NBR  TXN_ID  PROD_NBR  \\\n",
              "65   2019-05-20         83           83008   82099        63   \n",
              "153  2019-05-17        208          208139  206906        63   \n",
              "174  2018-08-20        237          237227  241132        63   \n",
              "177  2019-05-17        243          243070  246706        63   \n",
              "348  2018-10-26          7            7077    6604        63   \n",
              "\n",
              "                        PROD_NAME  PROD_QTY  TOT_SALES Brand_name Pack_size  \n",
              "65   Kettle 135g Swt Pot Sea Salt         2        8.4     Kettle       Sal  \n",
              "153  Kettle 135g Swt Pot Sea Salt         1        4.2     Kettle       Sal  \n",
              "174  Kettle 135g Swt Pot Sea Salt         2        8.4     Kettle       Sal  \n",
              "177  Kettle 135g Swt Pot Sea Salt         1        4.2     Kettle       Sal  \n",
              "348  Kettle 135g Swt Pot Sea Salt         2        8.4     Kettle       Sal  "
            ],
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              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
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              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "    .dataframe thead th {\n",
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              "      <th>65</th>\n",
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              "    <tr>\n",
              "      <th>348</th>\n",
              "      <td>2018-10-26</td>\n",
              "      <td>7</td>\n",
              "      <td>7077</td>\n",
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              "      <td>63</td>\n",
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              "      <td>Sal</td>\n",
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              "  </tbody>\n",
              "</table>\n",
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              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
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              "\n",
              "\n",
              "\n",
              "    <div id=\"df-21a9608a-1156-4f7c-82f1-9b646a4ea2f0\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-21a9608a-1156-4f7c-82f1-9b646a4ea2f0')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
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              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-21a9608a-1156-4f7c-82f1-9b646a4ea2f0 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-b5a9c75a-8a0f-48fb-86eb-e821b81fd679 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-b5a9c75a-8a0f-48fb-86eb-e821b81fd679');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Some pack size are named to 'Sal' this is because in the product name, the pack size occur in the beginning rather than end. Hence, changing the pack size"
      ],
      "metadata": {
        "id": "uOOmVgvWd8ME"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "transact_df['Pack_size'] = transact_df['Pack_size'].apply(lambda x: 135 if x=='Sal' else int(x))"
      ],
      "metadata": {
        "id": "67R8TyezeSSa"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "transact_df['Pack_size'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vne-OnhfeeUa",
        "outputId": "ef610b1b-1a03-494f-ca29-6843fa1bbd38"
      },
      "execution_count": 22,
      "outputs": [
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              "175    66389\n",
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              "220     1564\n",
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              "125     1454\n",
              "Name: Pack_size, dtype: int64"
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        "transact_df.head()"
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      "execution_count": 23,
      "outputs": [
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              "         DATE  STORE_NBR  LYLTY_CARD_NBR  TXN_ID  PROD_NBR  \\\n",
              "0  2018-10-17          1            1000       1         5   \n",
              "1  2019-05-14          1            1307     348        66   \n",
              "2  2019-05-20          1            1343     383        61   \n",
              "3  2018-08-17          2            2373     974        69   \n",
              "4  2018-08-18          2            2426    1038       108   \n",
              "\n",
              "                                  PROD_NAME  PROD_QTY  TOT_SALES  \\\n",
              "0    Natural Chip        Compny SeaSalt175g         2        6.0   \n",
              "1                  CCs Nacho Cheese    175g         3        6.3   \n",
              "2    Smiths Crinkle Cut  Chips Chicken 170g         2        2.9   \n",
              "3    Smiths Chip Thinly  S/Cream&Onion 175g         5       15.0   \n",
              "4  Kettle Tortilla ChpsHny&Jlpno Chili 150g         3       13.8   \n",
              "\n",
              "        Brand_name  Pack_size  \n",
              "0  Natural Chip Co        175  \n",
              "1              CCs        175  \n",
              "2           Smiths        170  \n",
              "3           Smiths        175  \n",
              "4           Kettle        150  "
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          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "num = ['PROD_QTY','TOT_SALES']\n",
        "\n",
        "\n",
        "#Checking outliers\n",
        "for col in num:\n",
        "  sns.boxplot(transact_df[col], color='teal', orient='h')\n",
        "  plt.title(f'{col} Boxplot')\n",
        "  plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 897
        },
        "id": "46t3E1Vhtr7d",
        "outputId": "8ebff1be-d9a0-44a9-8091-34139888b563"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "From the above boxplots we can see, the following columns have outliers:\n",
        "- Product Qty = 200\n",
        "- Total Sales = 650"
      ],
      "metadata": {
        "id": "UT-p01yfmaPl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "transact_df[transact_df['PROD_QTY'] == 200]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "fw_QH-zLzzmk",
        "outputId": "f604bab3-773e-4f6e-a307-7fc8b2564fbe"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             DATE  STORE_NBR  LYLTY_CARD_NBR  TXN_ID  PROD_NBR  \\\n",
              "69762  2018-08-19        226          226000  226201         4   \n",
              "69763  2019-05-20        226          226000  226210         4   \n",
              "\n",
              "                              PROD_NAME  PROD_QTY  TOT_SALES Brand_name  \\\n",
              "69762  Dorito Corn Chp     Supreme 380g       200      650.0    Doritos   \n",
              "69763  Dorito Corn Chp     Supreme 380g       200      650.0    Doritos   \n",
              "\n",
              "       Pack_size  \n",
              "69762        380  \n",
              "69763        380  "
            ],
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              "      <th>DATE</th>\n",
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              "\n",
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            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "There are two outliers in product quantity, both are same product and  bought by the same customer."
      ],
      "metadata": {
        "id": "1z1qXocuoM-c"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "transact_df[transact_df['LYLTY_CARD_NBR']==226000]"
      ],
      "metadata": {
        "id": "ceqBdumO0Jij",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "outputId": "71142e20-486c-4fba-a40b-728159efd039"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             DATE  STORE_NBR  LYLTY_CARD_NBR  TXN_ID  PROD_NBR  \\\n",
              "69762  2018-08-19        226          226000  226201         4   \n",
              "69763  2019-05-20        226          226000  226210         4   \n",
              "\n",
              "                              PROD_NAME  PROD_QTY  TOT_SALES Brand_name  \\\n",
              "69762  Dorito Corn Chp     Supreme 380g       200      650.0    Doritos   \n",
              "69763  Dorito Corn Chp     Supreme 380g       200      650.0    Doritos   \n",
              "\n",
              "       Pack_size  \n",
              "69762        380  \n",
              "69763        380  "
            ],
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              "\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "After checking all the transactions, this customer has made only two transactions, hence we will remove these outliers"
      ],
      "metadata": {
        "id": "F1F0NLDuqn5x"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Filter out the above customer\n",
        "transact_df = transact_df[transact_df['LYLTY_CARD_NBR']!=226000]"
      ],
      "metadata": {
        "id": "O00jLAjwpJlM"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Checking transactions by date\n",
        "print(transact_df['DATE'].nunique())"
      ],
      "metadata": {
        "id": "3iEYRig8pmQU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "35fa1d8c-b017-4e88-ac90-b9d223edc504"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "364\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "start_date = '2018-07-01'\n",
        "end_date = '2019-06-30'\n",
        "date_range = pd.date_range(start=start_date, end=end_date, freq='D')\n",
        "\n",
        "# Find the missing date\n",
        "missing_dates = pd.Series(date_range[~date_range.isin(transact_df['DATE'])])\n",
        "\n",
        "print(\"Missing date:\", missing_dates)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S72wBvX1Q1sQ",
        "outputId": "6b48b3ca-a9f8-4222-ab48-87114c552eec"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Missing date: 0   2018-12-25\n",
            "dtype: datetime64[ns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "start_date = '2018-12-01'\n",
        "end_date = '2018-12-30'\n",
        "date_range_november = pd.date_range(start=start_date, end=end_date, freq='D')\n",
        "dec_data = transact_df[(transact_df['DATE'] >= start_date) & (transact_df['DATE'] <= end_date)]\n",
        "\n",
        "# Count the occurrences of each date in December 2018\n",
        "transactions_per_day_dec = pd.DataFrame(dec_data['DATE'].value_counts()).reset_index().rename(columns={'index':'Date','DATE':'count'})\n",
        "new_row = {'Date': '2018-12-25', 'count': 0}\n",
        "transactions_per_day_dec = transactions_per_day_dec.append(new_row, ignore_index=True)\n",
        "transactions_per_day_dec.sort_values('Date',inplace=True)\n",
        "\n",
        "# Sort the dates in ascending order\n",
        "#transactions_per_day_dec = transactions_per_day_dec.sort_index()\n",
        "\n",
        "# Plot the line plot\n",
        "plt.figure(figsize=(12, 6))\n",
        "plt.plot(transactions_per_day_dec['Date'], transactions_per_day_dec['count'], marker='o',color='teal')\n",
        "plt.xlabel('Date')\n",
        "plt.ylabel('Number of Transactions')\n",
        "plt.title('Number of Transactions per Day in December 2018')\n",
        "plt.xticks(rotation=45)\n",
        "plt.tight_layout()\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 566
        },
        "id": "oHbNaqg2WDXL",
        "outputId": "9788af83-4c87-4fee-9433-ecde91db5754"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Sales are increasing in the lead up to christmas, but there are no sales on christmas day itself as the shops are closed."
      ],
      "metadata": {
        "id": "kaBsIAJeb3qm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Checking distribution of pack size\n",
        "plt.figure(figsize=(10,10))\n",
        "sns.distplot(transact_df['Pack_size'],color='teal')\n",
        "plt.title('Distribution of pack size in grams')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 898
        },
        "id": "2hK9kWfcZUNM",
        "outputId": "69c312e6-52e9-40ea-b9bc-319886b696c4"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Distribution of pack size in grams')"
            ]
          },
          "metadata": {},
          "execution_count": 31
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Most of the pack size are between 150g and 200g"
      ],
      "metadata": {
        "id": "ZjK_kfqXfHr4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Checking Brand Names\n",
        "brand = pd.DataFrame(transact_df['Brand_name'].value_counts()).reset_index().rename(columns = {'index':'Brands','Brand_name':'count'})\n",
        "\n",
        "sns.barplot(data=brand,y='Brands',x='count',palette='viridis')\n",
        "plt.title('Brands with most products sold')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 497
        },
        "id": "poNUgp3YfHXB",
        "outputId": "be60bb07-5c92-44e0-be6c-9b803d4b373b"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Brands with most products sold')"
            ]
          },
          "metadata": {},
          "execution_count": 32
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Kettle brand has had most number of transactions, hence most number of chips are sold by Kettle."
      ],
      "metadata": {
        "id": "0-8rFNRWgURT"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Checking purchase behaviour data"
      ],
      "metadata": {
        "id": "rgYSFGbuhVnj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Checking purchase behaviour dataframe\n",
        "purchase_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "AQ7o6kzZcTky",
        "outputId": "81d33fd0-ff0a-477b-c9bb-8bbf0fa5e67b"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   LYLTY_CARD_NBR               LIFESTAGE PREMIUM_CUSTOMER\n",
              "0            1000   YOUNG SINGLES/COUPLES          Premium\n",
              "1            1002   YOUNG SINGLES/COUPLES       Mainstream\n",
              "2            1003          YOUNG FAMILIES           Budget\n",
              "3            1004   OLDER SINGLES/COUPLES       Mainstream\n",
              "4            1005  MIDAGE SINGLES/COUPLES       Mainstream"
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-ce3367dc-33e1-4028-8dd0-96ca3e80e504\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>LYLTY_CARD_NBR</th>\n",
              "      <th>LIFESTAGE</th>\n",
              "      <th>PREMIUM_CUSTOMER</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1000</td>\n",
              "      <td>YOUNG SINGLES/COUPLES</td>\n",
              "      <td>Premium</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1002</td>\n",
              "      <td>YOUNG SINGLES/COUPLES</td>\n",
              "      <td>Mainstream</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1003</td>\n",
              "      <td>YOUNG FAMILIES</td>\n",
              "      <td>Budget</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1004</td>\n",
              "      <td>OLDER SINGLES/COUPLES</td>\n",
              "      <td>Mainstream</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1005</td>\n",
              "      <td>MIDAGE SINGLES/COUPLES</td>\n",
              "      <td>Mainstream</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ce3367dc-33e1-4028-8dd0-96ca3e80e504')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
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              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-26874a1b-3784-401a-b2f8-5ff64281d17f\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-26874a1b-3784-401a-b2f8-5ff64281d17f')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
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              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
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              "\n",
              "  .colab-df-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
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              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-ce3367dc-33e1-4028-8dd0-96ca3e80e504 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-ce3367dc-33e1-4028-8dd0-96ca3e80e504');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "purchase_df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IDfdt3jxhfaD",
        "outputId": "266048a4-9729-4ab8-a8b3-9356a09c8bdd"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(72637, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "purchase_df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TIYk35l2hjIT",
        "outputId": "9f97358f-e137-4425-c740-64f35f164cfe"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 72637 entries, 0 to 72636\n",
            "Data columns (total 3 columns):\n",
            " #   Column            Non-Null Count  Dtype \n",
            "---  ------            --------------  ----- \n",
            " 0   LYLTY_CARD_NBR    72637 non-null  int64 \n",
            " 1   LIFESTAGE         72637 non-null  object\n",
            " 2   PREMIUM_CUSTOMER  72637 non-null  object\n",
            "dtypes: int64(1), object(2)\n",
            "memory usage: 1.7+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "purchase_df.duplicated().value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rOs-8f5_hldb",
        "outputId": "6edcbaa8-f40d-4633-cd96-1f99ee91e212"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False    72637\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "purchase_df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1LnLpRYJhpRt",
        "outputId": "4e775e1f-4074-49bc-f3a2-10812b0843ad"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LYLTY_CARD_NBR      0\n",
              "LIFESTAGE           0\n",
              "PREMIUM_CUSTOMER    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "There are no duplicates or null values in purchase behaviour data."
      ],
      "metadata": {
        "id": "xS3JXeVPhwWt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(transact_df['LYLTY_CARD_NBR'].nunique())\n",
        "print(purchase_df['LYLTY_CARD_NBR'].nunique())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-O2M7XEZht3F",
        "outputId": "47caa82e-91e7-4700-881a-62ff6fd2583c"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "72636\n",
            "72637\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Merging both dataset"
      ],
      "metadata": {
        "id": "Ev7B_x0fiLws"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cust_df = pd.merge(transact_df,purchase_df, on='LYLTY_CARD_NBR',how='left')"
      ],
      "metadata": {
        "id": "rxoXiU5CiHg6"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cust_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "id": "MahbQ_GaioNW",
        "outputId": "5008caaf-4f2d-4bf1-e835-b5495a40da20"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         DATE  STORE_NBR  LYLTY_CARD_NBR  TXN_ID  PROD_NBR  \\\n",
              "0  2018-10-17          1            1000       1         5   \n",
              "1  2019-05-14          1            1307     348        66   \n",
              "2  2019-05-20          1            1343     383        61   \n",
              "3  2018-08-17          2            2373     974        69   \n",
              "4  2018-08-18          2            2426    1038       108   \n",
              "\n",
              "                                  PROD_NAME  PROD_QTY  TOT_SALES  \\\n",
              "0    Natural Chip        Compny SeaSalt175g         2        6.0   \n",
              "1                  CCs Nacho Cheese    175g         3        6.3   \n",
              "2    Smiths Crinkle Cut  Chips Chicken 170g         2        2.9   \n",
              "3    Smiths Chip Thinly  S/Cream&Onion 175g         5       15.0   \n",
              "4  Kettle Tortilla ChpsHny&Jlpno Chili 150g         3       13.8   \n",
              "\n",
              "        Brand_name  Pack_size               LIFESTAGE PREMIUM_CUSTOMER  \n",
              "0  Natural Chip Co        175   YOUNG SINGLES/COUPLES          Premium  \n",
              "1              CCs        175  MIDAGE SINGLES/COUPLES           Budget  \n",
              "2           Smiths        170  MIDAGE SINGLES/COUPLES           Budget  \n",
              "3           Smiths        175  MIDAGE SINGLES/COUPLES           Budget  \n",
              "4           Kettle        150  MIDAGE SINGLES/COUPLES           Budget  "
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-45f848aa-f39f-4d46-9e0c-5ba4999811a4\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>DATE</th>\n",
              "      <th>STORE_NBR</th>\n",
              "      <th>LYLTY_CARD_NBR</th>\n",
              "      <th>TXN_ID</th>\n",
              "      <th>PROD_NBR</th>\n",
              "      <th>PROD_NAME</th>\n",
              "      <th>PROD_QTY</th>\n",
              "      <th>TOT_SALES</th>\n",
              "      <th>Brand_name</th>\n",
              "      <th>Pack_size</th>\n",
              "      <th>LIFESTAGE</th>\n",
              "      <th>PREMIUM_CUSTOMER</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2018-10-17</td>\n",
              "      <td>1</td>\n",
              "      <td>1000</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>Natural Chip        Compny SeaSalt175g</td>\n",
              "      <td>2</td>\n",
              "      <td>6.0</td>\n",
              "      <td>Natural Chip Co</td>\n",
              "      <td>175</td>\n",
              "      <td>YOUNG SINGLES/COUPLES</td>\n",
              "      <td>Premium</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2019-05-14</td>\n",
              "      <td>1</td>\n",
              "      <td>1307</td>\n",
              "      <td>348</td>\n",
              "      <td>66</td>\n",
              "      <td>CCs Nacho Cheese    175g</td>\n",
              "      <td>3</td>\n",
              "      <td>6.3</td>\n",
              "      <td>CCs</td>\n",
              "      <td>175</td>\n",
              "      <td>MIDAGE SINGLES/COUPLES</td>\n",
              "      <td>Budget</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2019-05-20</td>\n",
              "      <td>1</td>\n",
              "      <td>1343</td>\n",
              "      <td>383</td>\n",
              "      <td>61</td>\n",
              "      <td>Smiths Crinkle Cut  Chips Chicken 170g</td>\n",
              "      <td>2</td>\n",
              "      <td>2.9</td>\n",
              "      <td>Smiths</td>\n",
              "      <td>170</td>\n",
              "      <td>MIDAGE SINGLES/COUPLES</td>\n",
              "      <td>Budget</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2018-08-17</td>\n",
              "      <td>2</td>\n",
              "      <td>2373</td>\n",
              "      <td>974</td>\n",
              "      <td>69</td>\n",
              "      <td>Smiths Chip Thinly  S/Cream&amp;Onion 175g</td>\n",
              "      <td>5</td>\n",
              "      <td>15.0</td>\n",
              "      <td>Smiths</td>\n",
              "      <td>175</td>\n",
              "      <td>MIDAGE SINGLES/COUPLES</td>\n",
              "      <td>Budget</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2018-08-18</td>\n",
              "      <td>2</td>\n",
              "      <td>2426</td>\n",
              "      <td>1038</td>\n",
              "      <td>108</td>\n",
              "      <td>Kettle Tortilla ChpsHny&amp;Jlpno Chili 150g</td>\n",
              "      <td>3</td>\n",
              "      <td>13.8</td>\n",
              "      <td>Kettle</td>\n",
              "      <td>150</td>\n",
              "      <td>MIDAGE SINGLES/COUPLES</td>\n",
              "      <td>Budget</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-45f848aa-f39f-4d46-9e0c-5ba4999811a4')\"\n",
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              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
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              "  }\n",
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              "  .colab-df-quickchart:hover {\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-f3f80c02-922e-450e-9451-7b9fcd395ff6 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-45f848aa-f39f-4d46-9e0c-5ba4999811a4 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-45f848aa-f39f-4d46-9e0c-5ba4999811a4');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Checking total sales per lifestage\n",
        "life_df = cust_df.groupby(['LIFESTAGE','PREMIUM_CUSTOMER'])['TOT_SALES'].sum().reset_index()\n",
        "\n",
        "life_df.sort_values('TOT_SALES',ascending=False,inplace=True)\n",
        "\n",
        "life_df['LIFESTAGE_CUST'] = life_df['LIFESTAGE'] +' '+ life_df['PREMIUM_CUSTOMER']\n",
        "\n",
        "#Plotting total sales per lifestage\n",
        "sns.barplot(data=life_df,x='TOT_SALES',y='LIFESTAGE_CUST',palette='viridis')\n",
        "plt.title('Total Sales for Lifestage')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 497
        },
        "id": "_nDO5NEkj1-O",
        "outputId": "6c86f5e8-fe71-42da-a18b-de70ae77f8c7"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Total Sales for Lifestage')"
            ]
          },
          "metadata": {},
          "execution_count": 41
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Top 3 Lifestages + customer segment with the most sales are:\n",
        "1. Old Family / Budget Customer\n",
        "2. Young singles/couples / Mainstream Customer\n",
        "3. Retirees / Mainstream Customer"
      ],
      "metadata": {
        "id": "Kl8duWKuEZuz"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Focusing on Top 3 Lifestages and customer category with most sales"
      ],
      "metadata": {
        "id": "s_C_fXOIiFDv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Checking the unique number of customers in each of the top 3 lifestages by sale\n",
        "life_unique_df = cust_df.groupby(['LIFESTAGE','PREMIUM_CUSTOMER'])['LYLTY_CARD_NBR'].nunique().reset_index()\n",
        "\n",
        "life_unique_df = life_unique_df[life_unique_df['LIFESTAGE'].isin(['OLDER FAMILIES','YOUNG SINGLES/COUPLES','RETIREES'])]\n",
        "\n",
        "life_unique_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 331
        },
        "id": "bs-fW8ezdT5z",
        "outputId": "acc7728a-6469-4914-cf5e-3af3ccf5fb14"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                LIFESTAGE PREMIUM_CUSTOMER  LYLTY_CARD_NBR\n",
              "6          OLDER FAMILIES           Budget            4675\n",
              "7          OLDER FAMILIES       Mainstream            2831\n",
              "8          OLDER FAMILIES          Premium            2273\n",
              "12               RETIREES           Budget            4454\n",
              "13               RETIREES       Mainstream            6479\n",
              "14               RETIREES          Premium            3872\n",
              "18  YOUNG SINGLES/COUPLES           Budget            3779\n",
              "19  YOUNG SINGLES/COUPLES       Mainstream            8088\n",
              "20  YOUNG SINGLES/COUPLES          Premium            2574"
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-552ef5e6-04df-4587-a6b7-79bddad5f7ab\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>LIFESTAGE</th>\n",
              "      <th>PREMIUM_CUSTOMER</th>\n",
              "      <th>LYLTY_CARD_NBR</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>OLDER FAMILIES</td>\n",
              "      <td>Budget</td>\n",
              "      <td>4675</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>OLDER FAMILIES</td>\n",
              "      <td>Mainstream</td>\n",
              "      <td>2831</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>OLDER FAMILIES</td>\n",
              "      <td>Premium</td>\n",
              "      <td>2273</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>RETIREES</td>\n",
              "      <td>Budget</td>\n",
              "      <td>4454</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>RETIREES</td>\n",
              "      <td>Mainstream</td>\n",
              "      <td>6479</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>RETIREES</td>\n",
              "      <td>Premium</td>\n",
              "      <td>3872</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>YOUNG SINGLES/COUPLES</td>\n",
              "      <td>Budget</td>\n",
              "      <td>3779</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>YOUNG SINGLES/COUPLES</td>\n",
              "      <td>Mainstream</td>\n",
              "      <td>8088</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>YOUNG SINGLES/COUPLES</td>\n",
              "      <td>Premium</td>\n",
              "      <td>2574</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-552ef5e6-04df-4587-a6b7-79bddad5f7ab')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-20d907d2-555f-4408-a62b-9478e89698c1\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-20d907d2-555f-4408-a62b-9478e89698c1')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\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-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-20d907d2-555f-4408-a62b-9478e89698c1 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-552ef5e6-04df-4587-a6b7-79bddad5f7ab button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-552ef5e6-04df-4587-a6b7-79bddad5f7ab');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Plotting Unique buyers for top 3 lifestages\n",
        "sns.barplot(data=life_unique_df,x='LYLTY_CARD_NBR',y='LIFESTAGE',palette='viridis',hue='PREMIUM_CUSTOMER')\n",
        "plt.title('Unique buyers for lifestages')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 497
        },
        "id": "dD-yARrAeqxn",
        "outputId": "e7768138-ab77-4d43-8144-1f2afe964f6a"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Unique buyers for lifestages')"
            ]
          },
          "metadata": {},
          "execution_count": 57
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The number of unique buyers in Older Families/ Budget are less compared to unique Mainstream buyers in Young singles/couples and Retirees.\n",
        "\n",
        "Further, let's check sales per customer for these 3."
      ],
      "metadata": {
        "id": "zs2s3XtdiiDo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Merging both df to get sales per customer for top 3 lifestages\n",
        "life_df_final = life_unique_df.merge(life_df, on=['LIFESTAGE','PREMIUM_CUSTOMER'],how='left')\n",
        "\n",
        "life_df_final['Sales_per_customer'] = life_df_final['TOT_SALES']/life_df_final['LYLTY_CARD_NBR']\n",
        "\n",
        "life_df_final.drop('LIFESTAGE_CUST', axis=1, inplace=True)\n",
        "\n",
        "#Plotting sales per customer for top 3 lifestages\n",
        "sns.barplot(data=life_df_final, x='Sales_per_customer',y='LIFESTAGE',hue='PREMIUM_CUSTOMER',palette='viridis')\n",
        "\n",
        "plt.title('Sales per customer')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 497
        },
        "id": "hIG3hNsjfbcq",
        "outputId": "fbee4755-5337-48d7-a07f-7aa5e6398351"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Sales per customer')"
            ]
          },
          "metadata": {},
          "execution_count": 56
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "From above we can see that sales per customer is very high for older familes/ Budget customers.\n",
        "\n",
        "From this we can observe:\n",
        "1. Unique buyers in Older Familes/Budget are less but sales per customer is high, this leads to highest total sales\n",
        "2. Although sales per customer in young singles/couples /Mainstream and Retirees/Mainstream is less but they have very high unqiue buyers, therefore total sales are high."
      ],
      "metadata": {
        "id": "mXSLeUNti64K"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Things to do\n",
        "\n",
        "1. Plot the frequency of visit to these stores but taking unique dates\n",
        "2. Plot Top 3 brands for these slots\n",
        "3. Plot Top pack size for these slots.\n",
        "\n",
        "Finally\n",
        "1. Summarize these points\n",
        "2. Give recommendation to increase sales in these 3 segments"
      ],
      "metadata": {
        "id": "bfMnQ_bhkdHA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "life_date_df = cust_df.groupby(['LIFESTAGE','PREMIUM_CUSTOMER'])['DATE'].count().reset_index()\n",
        "\n",
        "life_date_df = life_date_df[life_date_df['LIFESTAGE'].isin(['OLDER FAMILIES','YOUNG SINGLES/COUPLES','RETIREES'])]\n",
        "\n",
        "#Plotting frequency of purchase\n",
        "sns.barplot(data=life_date_df, x='DATE',y='LIFESTAGE',hue='PREMIUM_CUSTOMER',palette='viridis')\n",
        "\n",
        "plt.title('Frequency of purchase')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 497
        },
        "id": "yKWcViavVG3P",
        "outputId": "25c55cce-a932-44ae-dbcc-309ef33f0d61"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Frequency of purchase')"
            ]
          },
          "metadata": {},
          "execution_count": 48
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Frequency of purchase is high for all top 3 segments"
      ],
      "metadata": {
        "id": "O-TKjgBnbMG0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Creating new dataframe for getting top brand name and pack size for top 3 segments\n",
        "\n",
        "life_brand_df = cust_df.groupby(['LIFESTAGE','PREMIUM_CUSTOMER'])['Brand_name'].agg(lambda x: x.value_counts().idxmax()).reset_index()\n",
        "\n",
        "life_brand_df = life_brand_df[life_brand_df['LIFESTAGE'].isin(['OLDER FAMILIES','YOUNG SINGLES/COUPLES','RETIREES'])]\n",
        "\n",
        "life_pack_df = cust_df.groupby(['LIFESTAGE','PREMIUM_CUSTOMER'])['Pack_size'].agg(lambda x: x.value_counts().idxmax()).reset_index()\n",
        "\n",
        "#Merging both dataframes\n",
        "life_pack_df = life_pack_df[life_pack_df['LIFESTAGE'].isin(['OLDER FAMILIES','YOUNG SINGLES/COUPLES','RETIREES'])]\n",
        "\n",
        "life_brand_pack_df = life_brand_df.merge(life_pack_df, on=['LIFESTAGE','PREMIUM_CUSTOMER'],how='left')\n",
        "\n",
        "life_brand_pack_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 331
        },
        "id": "cNbryWL_WXLR",
        "outputId": "3a41f608-9dc9-446e-edc3-bb03da304b85"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               LIFESTAGE PREMIUM_CUSTOMER Brand_name  Pack_size\n",
              "0         OLDER FAMILIES           Budget     Kettle        175\n",
              "1         OLDER FAMILIES       Mainstream     Kettle        175\n",
              "2         OLDER FAMILIES          Premium     Smiths        175\n",
              "3               RETIREES           Budget     Kettle        175\n",
              "4               RETIREES       Mainstream     Kettle        175\n",
              "5               RETIREES          Premium     Kettle        175\n",
              "6  YOUNG SINGLES/COUPLES           Budget     Smiths        175\n",
              "7  YOUNG SINGLES/COUPLES       Mainstream     Kettle        175\n",
              "8  YOUNG SINGLES/COUPLES          Premium     Kettle        175"
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-e104f2d6-9d03-40e3-8803-2130bf2c7000\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>LIFESTAGE</th>\n",
              "      <th>PREMIUM_CUSTOMER</th>\n",
              "      <th>Brand_name</th>\n",
              "      <th>Pack_size</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>OLDER FAMILIES</td>\n",
              "      <td>Budget</td>\n",
              "      <td>Kettle</td>\n",
              "      <td>175</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>OLDER FAMILIES</td>\n",
              "      <td>Mainstream</td>\n",
              "      <td>Kettle</td>\n",
              "      <td>175</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>OLDER FAMILIES</td>\n",
              "      <td>Premium</td>\n",
              "      <td>Smiths</td>\n",
              "      <td>175</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>RETIREES</td>\n",
              "      <td>Budget</td>\n",
              "      <td>Kettle</td>\n",
              "      <td>175</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>RETIREES</td>\n",
              "      <td>Mainstream</td>\n",
              "      <td>Kettle</td>\n",
              "      <td>175</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>RETIREES</td>\n",
              "      <td>Premium</td>\n",
              "      <td>Kettle</td>\n",
              "      <td>175</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>YOUNG SINGLES/COUPLES</td>\n",
              "      <td>Budget</td>\n",
              "      <td>Smiths</td>\n",
              "      <td>175</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>YOUNG SINGLES/COUPLES</td>\n",
              "      <td>Mainstream</td>\n",
              "      <td>Kettle</td>\n",
              "      <td>175</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>YOUNG SINGLES/COUPLES</td>\n",
              "      <td>Premium</td>\n",
              "      <td>Kettle</td>\n",
              "      <td>175</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e104f2d6-9d03-40e3-8803-2130bf2c7000')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\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-quickchart: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",
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              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
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              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "  }\n",
              "</style>\n",
              "\n",
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              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e104f2d6-9d03-40e3-8803-2130bf2c7000 button.colab-df-convert');\n",
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              "\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "From above we can see,\n",
        "\n",
        "1. Kettle is the most bought brand in all top 3 lifestage+premium customer segments\n",
        "2. Pack size 175 gm is the most bought in top 3 segments."
      ],
      "metadata": {
        "id": "fe_oCXWvWW04"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## EDA Summary\n",
        "\n",
        "### Transaction data\n",
        "\n",
        "1. Sales are increasing in the lead up to christmas, but there are no sales on christmas day itself as the shops are closed.\n",
        "\n",
        "2. Kettle is the brand with the most chips sold\n",
        "\n",
        "3. 175g is the pack size with most chips sold\n",
        "\n",
        "### Purchase Behaviour\n",
        "\n",
        "1. Top 3 segments (Lifestages + customer segment) with the most sales are:\n",
        "\n",
        "    - Old Family / Budget Customer\n",
        "\n",
        "    - Young singles/couples / Mainstream Customer\n",
        "\n",
        "    - Retirees / Mainstream Customer\n",
        "\n",
        "2. Frequency of purchase is high for all top 3 segments\n",
        "\n",
        "3. Older Family/Budget segment has low unique cutomers but the total sale per customer is high leading to overall highest total sale\n",
        "\n",
        "4. In Young singles/couple Mainstream and Retirees/Mainstream segment, the number of unique customer is high but the total sales per customer is low, leading to overall high total sales.\n",
        "\n",
        "5. The top brand sold in all 3 segments is Kettle.\n",
        "\n",
        "6. Top pack size sold in all 3 segments is 175g."
      ],
      "metadata": {
        "id": "rtSNZMsVZ-g0"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Recommendations\n",
        "\n",
        "1. Older Families\n",
        "\n",
        "  (Focus : Budget Customers)\n",
        "\n",
        "  - As the frequency of purchase and the sales per customer is high in this segment, to increase sales we can promotion that encourages the customers to buy even more chips per visit.\n",
        "\n",
        "  - For Mainstream and Premium customers, as the sales per customer is almost same as budget segment but the frequency of customers is less. To increase more frequent visits, discounts can be offered on the top brand and pack size in these segments to increase sales.\n",
        "\n",
        "  - As the overall number of unique buyers are less in Older families, to retain customers that keep buying, a loyalty program can be started that benefits the buyers.\n",
        "\n",
        "2. Young singles/couples\n",
        "\n",
        "  (Focus : Mainstream Customers)\n",
        "\n",
        "  - The frequency of purchase and the unique number of buyers are high, but the sales per customer is low. To increase this, discounts and be promoted on top brands and pack size to increase sales.\n",
        "\n",
        "3. Retirees\n",
        "\n",
        "  (Focus : Mainstream Customers)\n",
        "\n",
        "  - The frequency of purchase and the unique number of buyers are high, but the sales per customer is low. Again to increase this, discounts and be promoted on top brands and pack size to increase sales.\n",
        "\n",
        "4. Others\n",
        "\n",
        "  - Kettle is the brand and 175g is the pack size with most sales, so promotion and discounts can be offered on these to increase sales.\n",
        "\n"
      ],
      "metadata": {
        "id": "nf0C0PohcNWj"
      }
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "IfYKKIJTaAyy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Exporting this new df for future use\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "\n",
        "file_path = '/content/drive/My Drive/Colab Notebooks/cust_data.csv'\n",
        "\n",
        "cust_df.to_csv(file_path, index=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3ZyczBskixBn",
        "outputId": "90344aba-df62-44f5-bede-f838be3895f6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    }
  ]
}