Apziva_Projetc_1.4.ipynb

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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_curve, roc_auc_score, precision_recall_curve, confusion_matrix, classification_report\n",
        "from sklearn.model_selection import StratifiedKFold, cross_val_score\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Load data\n",
        "data = pd.read_csv('/content/drive/My Drive/Apziva/ACME-HappinessSurvey2020.csv')"
      ],
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        "id": "EFLZO0GVawfn"
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      "execution_count": 3,
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        "data.head()"
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              "   Y  X1  X2  X3  X4  X5  X6\n",
              "0  0   3   3   3   4   2   4\n",
              "1  0   3   2   3   5   4   3\n",
              "2  1   5   3   3   3   3   5\n",
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              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 126,\n  \"fields\": [\n    {\n      \"column\": \"Y\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"X1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 5,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          5,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"X2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 5,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"X3\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 5,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"X4\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 5,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          5,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"X5\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 5,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          4,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"X6\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 5,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          3,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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        "data.info()"
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      "execution_count": null,
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          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 126 entries, 0 to 125\n",
            "Data columns (total 7 columns):\n",
            " #   Column  Non-Null Count  Dtype\n",
            "---  ------  --------------  -----\n",
            " 0   Y       126 non-null    int64\n",
            " 1   X1      126 non-null    int64\n",
            " 2   X2      126 non-null    int64\n",
            " 3   X3      126 non-null    int64\n",
            " 4   X4      126 non-null    int64\n",
            " 5   X5      126 non-null    int64\n",
            " 6   X6      126 non-null    int64\n",
            "dtypes: int64(7)\n",
            "memory usage: 7.0 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check for null values\n",
        "null_values = data.isnull().sum()\n",
        "print(\"Null values in each column:\\n\", null_values)"
      ],
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        "colab": {
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        },
        "id": "HBo-MFWBl2qH",
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      },
      "execution_count": 5,
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          "text": [
            "Null values in each column:\n",
            " Y     0\n",
            "X1    0\n",
            "X2    0\n",
            "X3    0\n",
            "X4    0\n",
            "X5    0\n",
            "X6    0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
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      "source": [
        "# Bar Plot for single variable X1, how does the deliery time impact customer's satisfication\n",
        "df1 = data[['Y', 'X1']].value_counts().reset_index()\n",
        "df1['Y'] = df1['Y'].map({0: 'Unhappy', 1: 'Happy'})\n",
        "df1.columns = ['Y', 'X1', 'Count']\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.barplot(x='X1', y='Count', hue='Y', data=df1)\n",
        "plt.xlabel('Delivered on Time Leve')\n",
        "plt.ylabel('Number of Each on Time Level')\n",
        "plt.title('Customer Satisfication on Delivery Time')\n",
        "plt.legend(title='Satisfaction')\n",
        "plt.show()\n",
        "\n"
      ],
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      "execution_count": 6,
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        {
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              "<Figure size 1000x600 with 1 Axes>"
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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Bar Plot for single variable for X2, how do the contents of order expectation impact the happiness\n",
        "df2 = data[['Y', 'X2']].value_counts().reset_index()\n",
        "df2['Y'] = df2['Y'].map({0: 'Unhappy', 1: 'Happy'})\n",
        "df2.columns = ['Y', 'X2', 'Count']\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.barplot(x='X2', y='Count', hue='Y', data=df2)\n",
        "plt.xlabel('Contens Expectation')\n",
        "plt.ylabel('Number of Each Level')\n",
        "plt.title('Customer Satisfication on Order Contens Expectation')\n",
        "plt.legend(title='Satisfaction')\n",
        "plt.show()\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "outputId": "cd13b6c5-adfa-4fdd-e6e8-bf39c4cfd750",
        "id": "zYHQ2eERfJuu"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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aaP369WrZsqW2bdumMWPGqGnTpipatKi++OILh77WEYP0vzbXr19f+/fvV1JSUqaWZ5W+LZYtW6bz5887/XqrtLQ0LV68WC1atLjq+RTp56B4eXnZLxpx+fJlnTx5Uv7+/ipbtqx+/PHHm1r2119/LXd3d/Xp08eh/YUXXpAxRt98841De+PGjR3O84mJiVFgYKD2799/w+U4s7+1a9dOd9xxh/15+ojYjZZz9uzZG37W0qenfzbTdevWzWG0ZsuWLTpx4oR69OghT09Pe3tsbKzDZ0GSFixYoPLly6tcuXIO+/m9994rSRn28/r166tChQrXrdNaY2a/P77++mvVqFFDdevWtbf5+/ure/fuOnjwoH755ReH/p06dXJYt8xuZ8n5dc7MvnMr32mSNHXqVH377bcOjyv34UqVKmnYsGF655131LRpU/3111+aM2fOVc/r6tChg8O2b926tYoUKaKvv/5akpSQkKA9e/bo8ccf18mTJ+3b4Ny5c2rUqJHWrFmjtLS0TH/Gr8f6HXb27Fn99ddfqlevns6fP69ff/0109so3dGjR5WQkKDY2Fjlz5/f3h4TE6MmTZrY19GqR48eDs/r1aunkydPZvgsAbcTDrcDXCQwMFDSvz/UstqYMWPUsWNHhYeHq2rVqmrevLk6dOigqKioG772999/V+nSpTNcqS390I7ff//dob148eIOz9N/SbQecmRt//vvvyVJe/bskST7L05XSt8+6fLly6dixYrdsP501atX12effabU1FRt27ZNixYt0oQJE9S6dWslJCTYfxFdu3athgwZovXr12cINUlJSRl+6b2REiVKqF+/fho/frw+/PBD1atXTy1bttSTTz7p9Lz+/PNPnTlz5oaHDKalpWnSpEmaNm2aDhw4oMuXL9unXe9Qt+v5/fffFRYWluGX8MzuB5J0xx132N/v6y3nVva39MB0o+UEBATc8LOWPv3Kdb7yqmjpNZUuXdqh3cPDI8NnbM+ePdq1a5dCQkKuuswrLyRytSuwXY2z3x+///67/Q8YVtbtbN3PbnY7S86vc2b2nVv5TpOkGjVqZOrCDf3799e8efO0adMmvfHGG9cMrFe+9zabTaVKlbJfOS79+61jx47XXFZSUpJSU1Mz9Rm/np07d+qVV17RqlWrMoSSm/lDT/r+XbZs2QzTypcvr2XLlmW4gMn19pcrv8uB2wUhCXCRwMBAhYWFaceOHZnqf62/KFp/IU7Xtm1b1atXT4sWLdLy5cs1duxYjR49Wp999pmaNWt2S3Vf6VrnQ1yr3fz/uw6kpaVJ+ve8pNDQ0Az9rvzrrXW0xBmenp6qXr26qlevrjJlyqhTp05asGCBhgwZon379qlRo0YqV66cxo8fr/DwcHl6eurrr7/WhAkT7DU6a9y4cYqNjdXnn3+u5cuXq0+fPho5cqQ2bNjgVNDLrDfeeEOvvvqqOnfurNdee0358+eXm5ub+vbte9Pr4Kwbvd+uXk758uX1008/KSUl5ZqXOf/555/l4eGR4Rdg61/qnZWWlqbo6GiNHz/+qtOv/GNCZpdVrlw5Sf/eDyz9RsRZ6VbeT2fXOTPLyqnvtP3799sDzvbt2296Pumfu7Fjx6pKlSpX7ePv769Tp07d9DIk6fTp06pfv74CAwM1fPhwlSxZUt7e3vrxxx81cODAPPf5B3ISIQlwoQcffFBvv/221q9fr1q1al23b/pf5k6fPu3QfuVf2tMVKVJEPXv2VM+ePXXixAndddddev311+2/UFwrdEVEROjnn39WWlqaQyhJP2wjIiIiU+t2I+mH1xQqVEiNGzfOknneSPpfko8ePSrp35P5U1JS9MUXXzj8JfRqh/pl5rAXq+joaEVHR+uVV17RunXrVKdOHc2YMUMjRozI9DxCQkIUGBh4wyC9cOFCNWzYUO+++65D++nTpx3uAePMOkRERGjFihUZDlPL6v0gp/a3Bx98UOvXr9eCBQv05JNPZph+8OBBff/992rcuPENg0p6TXv27HEYCb148aIOHDjgcJJ/yZIltW3bNjVq1Mjpfeh66tatqzvuuEMff/yx/ve//93w4g0RERHavXt3hvZb2c7XWp/sWucbfafdqrS0NMXGxiowMFB9+/bVG2+8odatW+uRRx7J0Dc9SKUzxmjv3r2KiYmR9H/fb4GBgdf9fsvsZ/xa2zE+Pl4nT57UZ599pnvuucfefuDAgUzP40rp+8K19peCBQvm2GXwAVfinCTAhQYMGCA/Pz917dpVx48fzzA9/QaX0r8/bAsWLKg1a9Y49Jk2bZrD88uXL2c4xKJQoUIKCwtTSkqKvc3Pz++qh2I0b95cx44d0yeffGJvu3TpkqZMmSJ/f3/Vr1/f+RW9iqZNmyowMFBvvPGGLl68mGH6n3/+edPzXr169VX/gpl+LH36YSTpv1ha+yYlJWn27NkZXuvn55choF7NmTNndOnSJYe26Ohoubm5OWz/zHBzc1OrVq20ZMkSbdmyJcP09Lrd3d0zrO+CBQvs539Z10HKGLSvpnnz5rp8+bLeeusth/YJEybIZrNl2S+mObW/Pf300ypUqJD69++f4byaCxcuqFOnTjLGaPDgwTecV7Vq1RQSEqIZM2YoNTXV3h4XF5dh27Zt21aHDx/WrFmzMsznn3/+sV8tz1m+vr4aOHCgdu3apYEDB151f//ggw+0adMmSf9u502bNmn9+vX26efOndPbb7+tyMjITJ0HdaVrfYdk9Tpn9jvtVo0fP17r1q3T22+/rddee021a9fWM888k+GqeJL0/vvvOxzquHDhQh09etT+uahatapKliypN998U8nJyRlen/79ltnP+LU+u1f7DktNTc3wcyF9Hpk5/K5IkSKqUqWK5syZ47C8HTt2aPny5fYbMgN5HSNJgAuVLFlSH330kdq1a6fy5curQ4cOqlSpklJTU7Vu3Tr7pZDTde3aVaNGjVLXrl1VrVo1rVmzRr/99pvDPM+ePatixYqpdevWqly5svz9/bVixQpt3rxZ48aNs/erWrWqPvnkE/Xr10/Vq1eXv7+/WrRooe7du2vmzJmKjY3V1q1bFRkZqYULF2rt2rWaOHFill1oIjAwUNOnT9dTTz2lu+66S4899phCQkKUmJior776SnXq1MnwC3pmPfvsszp//rwefvhhlStXzr49P/nkE0VGRqpTp06SpPvuu0+enp5q0aKFnn76aSUnJ2vWrFkqVKiQfbQpXdWqVTV9+nSNGDFCpUqVUqFCha56PtWqVavUu3dvtWnTRmXKlNGlS5c0d+5cubu769FHH3V6Xd544w0tX75c9evXt19O+ejRo1qwYIF++OEHBQcH68EHH9Tw4cPVqVMn1a5dW9u3b9eHH36Y4XyNkiVLKjg4WDNmzFBAQID8/PxUs2bNq54H06JFCzVs2FAvv/yyDh48qMqVK2v58uX6/PPP1bdvX4cT7W9FTu1vBQoU0MKFC/XAAw/orrvuUteuXVWhQgUdO3ZMcXFx2rt3ryZNmpSpy/F7eHhoxIgRevrpp3XvvfeqXbt2OnDggGbPnp1hmz/11FOaP3++evToodWrV6tOnTq6fPmyfv31V82fP1/Lli276Zuc9u/fXzt37tS4ceO0evVqtW7dWqGhoTp27JgWL16sTZs2ad26dZKkl156SR9//LGaNWumPn36KH/+/JozZ44OHDigTz/99KYOZb3Wd0hWr3Nmv9Ou55tvvrnqRQxq166tqKgo7dq1S6+++qpiY2PVokULSf+G3ipVqqhnz56aP3++w+vy58+vunXrqlOnTjp+/LgmTpyoUqVKqVu3bpL+DT/vvPOOmjVrpooVK6pTp04qWrSoDh8+rNWrVyswMFBLliyRlLnPeJUqVeTu7q7Ro0crKSlJXl5euvfee1W7dm3dcccd6tixo/r06SObzaa5c+deNTRf6/26mrFjx6pZs2aqVauWunTpYr8EeFBQkIYOHZqpbQ7c9nL8enoAMvjtt99Mt27dTGRkpPH09DQBAQGmTp06ZsqUKQ6XcD1//rzp0qWLCQoKMgEBAaZt27bmxIkTDpeNTklJMf379zeVK1c2AQEBxs/Pz1SuXDnDDVKTk5PN448/boKDgzPcCPL48eOmU6dOpmDBgsbT09NER0dnuGy09eaeVumXyL3yEtjXuqnj6tWrTdOmTU1QUJDx9vY2JUuWNLGxsWbLli32Puk3k82sb775xnTu3NmUK1fO+Pv7G09PT1OqVCnz7LPPmuPHjzv0/eKLL0xMTIzx9vY2kZGRZvTo0ea9994zksyBAwfs/Y4dO2YeeOABExAQ4HCZ5ysvAb5//37TuXNnU7JkSePt7W3y589vGjZsmOEmlZm9BLgxxvz++++mQ4cOJiQkxHh5eZmoqCjTq1cv+yWaL1y4YF544QVTpEgR4+PjY+rUqWPWr19/1Rubfv755/YbleoGN5M9e/asef75501YWJjx8PAwpUuXvu7NZK8UERFhOnbsmKH9Sreyv11rm13LgQMHTLdu3Uzx4sWNh4eHKViwoGnZsmWGy1Ubc+MbkU6bNs2UKFHCeHl5mWrVql3zZrKpqalm9OjRpmLFisbLy8vccccdpmrVqmbYsGEmKSnJYT1u5jLXCxcuNPfdd5/Jnz+/yZcvnylSpIhp166diY+Pd+iXfjPZ4OBg4+3tbWrUqHHNm8le+fm92mWir/cdcqvrbN13MvuddjXXuwR4+vpcunTJVK9e3RQrVszhdgbG/N+lzz/55BOH7fPxxx+bQYMGmUKFChkfHx/zwAMPmN9//z3D8n/66SfzyCOPmAIFChgvLy8TERFh2rZta1auXOnQ70afcWOMmTVrlomKijLu7u4O3zlr1641d999t/Hx8TFhYWH2Wx5Y+1zv/brW7SVWrFhh6tSpY3x8fExgYKBp0aLFNW8m++eff151u1u/Q4Hbjc0YzqoDAAC4kfj4eDVs2FALFixQ69atXV0OgGzEOUkAAAAAYEFIAgAAAAALQhIAAAAAWHBOEgAAAABYMJIEAAAAABaEJAAAAACwyPM3k01LS9ORI0cUEBAgm83m6nIAAAAAuIgxRmfPnlVYWNh1b6Sd50PSkSNHFB4e7uoyAAAAAOQSf/zxh4oVK3bN6Xk+JAUEBEj6d0MEBga6uBoAAAAArnLmzBmFh4fbM8K15PmQlH6IXWBgICEJAAAAwA1Pw+HCDQAAAABgQUgCAAAAAAtCEgAAAABY5PlzkgAAAICbcfnyZV28eNHVZcAJ7u7uypcv3y3f+oeQBAAAAFwhOTlZhw4dkjHG1aXASb6+vipSpIg8PT1veh6EJAAAAMDi8uXLOnTokHx9fRUSEnLLoxLIGcYYpaam6s8//9SBAwdUunTp694w9noISQAAAIDFxYsXZYxRSEiIfHx8XF0OnODj4yMPDw/9/vvvSk1Nlbe3903Nhws3AAAAAFfBCNLt6WZHjxzmkQV1AAAAAECeQUgCAAAAAAtCEgAAAHAbiYyM1MSJEzPV9/z583r00UcVGBgom82m06dPZ0tNBw8elM1mU0JCQrbMP6cRkgAAAIBs8Oeff+qZZ55R8eLF5eXlpdDQUDVt2lRr167N1Ovj4uIUHBycoX3z5s3q3r17puYxZ84cff/991q3bp2OHj2qoKAgZ1bhqmJjY9WqVSuHtvDwcB09elSVKlW65fnnBlzdDgAAAMgGjz76qFJTUzVnzhxFRUXp+PHjWrlypU6ePHlL8w0JCcl033379ql8+fLZHl7c3d0VGhqarcvISYwkAQAAAFns9OnT+v777zV69Gg1bNhQERERqlGjhgYNGqSWLVtKksaPH6/o6Gj5+fkpPDxcPXv2VHJysiQpPj5enTp1UlJSkmw2m2w2m4YOHSrJ8XA7Y4yGDh1qH60KCwtTnz59JEkNGjTQuHHjtGbNGtlsNjVo0ECSNHfuXFWrVk0BAQEKDQ3V448/rhMnTjjUv3PnTj344IMKDAxUQECA6tWrp3379mno0KGaM2eOPv/8c3td8fHxVz3c7rvvvlONGjXk5eWlIkWK6KWXXtKlS5fs0xs0aKA+ffpowIAByp8/v0JDQ+3r6GqEJAAAACCL+fv7y9/fX4sXL1ZKSspV+7i5uWny5MnauXOn5syZo1WrVmnAgAGSpNq1a2vixIkKDAzU0aNHdfToUb344osZ5vHpp59qwoQJmjlzpvbs2aPFixcrOjpakvTZZ5+pW7duqlWrlo4eParPPvtM0r/3gXrttde0bds2LV68WAcPHlRsbKx9nocPH9Y999wjLy8vrVq1Slu3blXnzp116dIlvfjii2rbtq3uv/9+e121a9fOUNfhw4fVvHlzVa9eXdu2bdP06dP17rvvasSIEQ795syZIz8/P23cuFFjxozR8OHD9e23397UNs9KHG4HAAAAZLF8+fIpLi5O3bp104wZM3TXXXepfv36euyxxxQTEyNJ6tu3r71/ZGSkRowYoR49emjatGny9PRUUFCQbDbbdQ9jS0xMVGhoqBo3biwPDw8VL15cNWrUkCTlz59fvr6+8vT0dJhH586d7f+PiorS5MmTVb16dSUnJ8vf319Tp05VUFCQ5s2bJw8PD0lSmTJl7K/x8fFRSkrKdeuaNm2awsPD9dZbb8lms6lcuXI6cuSIBg4cqMGDB9vvZRQTE6MhQ4ZIkkqXLq233npLK1euVJMmTTK7qbMFI0kAAABANnj00Ud15MgRffHFF7r//vsVHx+vu+66S3FxcZKkFStWqFGjRipatKgCAgL01FNP6eTJkzp//nyml9GmTRv9888/ioqKUrdu3bRo0SKHQ9quZuvWrWrRooWKFy+ugIAA1a9fX9K/gUuSEhISVK9ePXtAuhm7du1SrVq1HG7IW6dOHSUnJ+vQoUP2tvTAmK5IkSIZDv1zBUISAAAAkE28vb3VpEkTvfrqq1q3bp1iY2M1ZMgQHTx4UA8++KBiYmL06aefauvWrZo6daokKTU1NdPzDw8P1+7duzVt2jT5+PioZ8+euueee3Tx4sWr9j937pyaNm2qwMBAffjhh9q8ebMWLVrksFwfH59bXOvMuzKI2Ww2paWl5djyr4WQBAAAAOSQChUq6Ny5c9q6davS0tI0btw43X333SpTpoyOHDni0NfT01OXL1++4Tx9fHzUokULTZ48WfHx8Vq/fr22b99+1b6//vqrTp48qVGjRqlevXoqV65chpGbmJgYff/999cMWpmpq3z58lq/fr2MMfa2tWvXKiAgQMWKFbvhOrka5yQBwH9M1f7vu7oEl9g6toOrSwDwH3Ly5Em1adNGnTt3VkxMjAICArRlyxaNGTNGDz30kEqVKqWLFy9qypQpatGihdauXasZM2Y4zCMyMlLJyclauXKlKleuLF9fX/n6+jr0iYuL0+XLl1WzZk35+vrqgw8+kI+PjyIiIq5aV/HixeXp6akpU6aoR48e2rFjh1577TWHPr1799aUKVP02GOPadCgQQoKCtKGDRtUo0YNlS1bVpGRkVq2bJl2796tAgUKXPXeSz179tTEiRP17LPPqnfv3tq9e7eGDBmifv362c9Hys1yf4UAAADAbcbf3181a9bUhAkTdM8996hSpUp69dVX1a1bN7311luqXLmyxo8fr9GjR6tSpUr68MMPNXLkSId51K5dWz169FC7du0UEhKiMWPGZFhOcHCwZs2apTp16igmJkYrVqzQkiVLVKBAgavWFRISori4OC1YsEAVKlTQqFGj9Oabbzr0KVCggFatWqXk5GTVr19fVatW1axZs+yHxnXr1k1ly5ZVtWrVFBISctWb4xYtWlRff/21Nm3apMqVK6tHjx7q0qWLXnnllZvdpDnKZqxjYHnQmTNnFBQUpKSkJAUGBrq6HABwOUaSAOD6Lly4oAMHDqhEiRLy9vZ2dTlw0vXev8xmA0aSAAAAAMCCkAQAAAAAFoQkAAAAALAgJAEAAACABSEJAAAAACwISQAAAABgQUgCAAAAAAtCEgAAAABYEJIAAAAAwCKfqwsAAAAAbgdV+7+fo8vbOrZDji4P/4eRJAAAACCPiI2NVatWrTK0x8fHy2az6fTp0zle0+2IkAQAAAAAFoQkAAAA4D/k5MmTat++vYoWLSpfX19FR0fr448/dujToEED9e7dW71791ZQUJAKFiyoV199VcYYe5/IyEi99tprat++vfz8/FS0aFFNnTrVPr1z58568MEHHeZ78eJFFSpUSO+++272ruQtIiQBAAAA/yEXLlxQ1apV9dVXX2nHjh3q3r27nnrqKW3atMmh35w5c5QvXz5t2rRJkyZN0vjx4/XOO+849Bk7dqwqV66sn376SS+99JKee+45ffvtt5Kkrl27aunSpTp69Ki9/5dffqnz58+rXbt22b+it4ALNwAAAAB5yJdffil/f3+HtsuXL9v/X7RoUb344ov2588++6yWLVum+fPnq0aNGvb28PBwTZgwQTabTWXLltX27ds1YcIEdevWzd6nTp06eumllyRJZcqU0dq1azVhwgQ1adJEtWvXVtmyZTV37lwNGDBAkjR79my1adMmQ325DSNJAAAAQB7SsGFDJSQkODysI0CXL1/Wa6+9pujoaOXPn1/+/v5atmyZEhMTHeZz9913y2az2Z/XqlVLe/bscQhctWrVcnhNrVq1tGvXLvvzrl27avbs2ZKk48eP65tvvlHnzp2zdH2zAyNJAAAAQB7i5+enUqVKObQdOnTI/v+xY8dq0qRJmjhxoqKjo+Xn56e+ffsqNTU1y2vp0KGDXnrpJa1fv17r1q1TiRIlVK9evSxfTlYjJAEAAAD/IWvXrtVDDz2kJ598UpKUlpam3377TRUqVHDot3HjRofnGzZsUOnSpeXu7u7QdmWf8uXL258XKFBArVq10uzZs7V+/Xp16tQpq1cnWxCSAAAAgP+Q0qVLa+HChVq3bp3uuOMOjR8/XsePH88QkhITE9WvXz89/fTT+vHHHzVlyhSNGzfOoc/atWs1ZswYtWrVSt9++60WLFigr776yqFP165d9eCDD+ry5cvq2LFjtq9fViAkAQAAAJmwdWwHV5eQJV555RXt379fTZs2la+vr7p3765WrVopKSnJoV+HDh30zz//qEaNGnJ3d9dzzz2n7t27O/R54YUXtGXLFg0bNkyBgYEaP368mjZt6tCncePGKlKkiCpWrKiwsLBsX7+sQEgCAAAA8oi4uLirtjdo0MDhHkeLFy++4bw8PDw0ceJETZ8+/Zp9AgMDNX/+/OvO59y5c/r777/VpUuXGy4ztyAkAQAAAMhyaWlp+uuvvzRu3DgFBwerZcuWri4p0whJAAAAALJcYmKiSpQooWLFiikuLk758t0+0eP2qRQAAABAjoiPj79hn4MHD153emRkpMMhfrcTbiYLAAAAABaEJAAAAACwICQBAAAAgAUhCQAAAAAsCEkAAAAAYEFIAgAAAAALLgEOAAAAZELi8OgcXV7xwdtzdHnpIiMj1bdvX/Xt29cly88NGEkCAAAA8oAGDRpcNdjExcUpODg4x+u5nRGSAAAAAMCCkAQAAAD8R8TGxqpVq1Z68803VaRIERUoUEC9evXSxYsXHfqdP39enTt3VkBAgIoXL663337bYfrAgQNVpkwZ+fr6KioqSq+++qrDPIYOHaoqVapo5syZCg8Pl6+vr9q2baukpKQMtQwbNkwhISEKDAxUjx49lJqaKkl6//33VaBAAaWkpDgsu1WrVnrqqaeyetM4ICQBAAAA/yGrV6/Wvn37tHr1as2ZM0dxcXGKi4tz6DNu3DhVq1ZNP/30k3r27KlnnnlGu3fvtk8PCAhQXFycfvnlF02aNEmzZs3ShAkTHOaxd+9ezZ8/X0uWLNHSpUvt87JauXKldu3apfj4eH388cf67LPPNGzYMElSmzZtdPnyZX3xxRf2/idOnNBXX32lzp07Z/FWccSFG4Bcomr/911dgktsHdvB1SUAAPCfcscdd+itt96Su7u7ypUrpwceeEArV65Ut27d7H2aN29uDzQDBw7UhAkTtHr1apUtW1aS9Morr9j7RkZG6sUXX9S8efM0YMAAe/uFCxf0/vvvq2jRopKkKVOm6IEHHtC4ceMUGhoqSfL09NR7770nX19fVaxYUcOHD1f//v312muvycfHR48//rhmz56tNm3aSJI++OADFS9eXA0aNMjWbcRIEgAAAPAfUrFiRbm7u9ufFylSRCdOnHDoExMTY/+/zWZTaGioQ59PPvlEderUUWhoqPz9/fXKK68oMTHRYR7Fixe3ByRJqlWrltLS0hxGpCpXrixfX1+HPsnJyfrjjz8kSd26ddPy5ct1+PBhSf9ehCI2NlY2m+1WNsENEZIAAACAPCAwMNDhnJ90p0+fVlBQkP25h4eHw3Sbzaa0tDSHtuv1Wb9+vZ544gk1b95cX375pX766Se9/PLL9nOJstKdd96pypUr6/3339fWrVu1c+dOxcbGZvlyrsThdgAAAEAeULZsWS1fvjxD+48//qgyZcpk2XLWrVuniIgIvfzyy/a233//PUO/xMREHTlyRGFhYZKkDRs2yM3NzX7IniRt27ZN//zzj3x8fOx9/P39FR4ebu/TtWtXTZw4UYcPH1bjxo0dpmUXRpIAAACAPOCZZ57Rb7/9pj59+ujnn3/W7t27NX78eH388cd64YUXsmw5pUuXVmJioubNm6d9+/Zp8uTJWrRoUYZ+3t7e6tixo7Zt26bvv/9effr0Udu2be3nI0lSamqqunTpol9++UVff/21hgwZot69e8vN7f9iyuOPP65Dhw5p1qxZ2X7BhnSMJAEAAACZUHzwdleXcF1RUVFas2aNXn75ZTVu3FipqakqV66cFixYoPvvvz/LltOyZUs9//zz6t27t1JSUvTAAw/o1Vdf1dChQx36lSpVSo888oiaN2+uU6dO6cEHH9S0adMc+jRq1EilS5fWPffco5SUFLVv3z7DfIKCgvToo4/qq6++UqtWrbJsPa7HZowxObIkFzlz5oyCgoKUlJSkwMBAV5cDXBNXt0NOYV8DgOu7cOGCDhw4oBIlSsjb29vV5dyWhg4dqsWLFyshIeGafWJjY3X69GktXrz4hvNr1KiRKlasqMmTJ9+w7/Xev8xmA0aSAAAAAORKf//9t+Lj4xUfH59hFCo7EZIAAAAA5Ep33nmn/v77b40ePdrhgg/ZjZAEAAAAIEsNHTo0w7lFV4qLi7vhfA4ePJgl9TiLq9sBAAAAgAUhCQAAALiKPH59szwrK943QhIAAABg4e7uLunfe/jg9nP+/HlJkoeHx03Pg3OSAAAAAIt8+fLJ19dXf/75pzw8PBxubIrcyxij8+fP68SJEwoODraH3ZtBSAIAAAAsbDabihQpogMHDuj33393dTlwUnBwsEJDQ29pHi4NSSNHjtRnn32mX3/9VT4+Pqpdu3aGy/tduHBBL7zwgubNm6eUlBQ1bdpU06ZNU+HChV1YOQAAAPIyT09PlS5dmkPubjMeHh63NIKUzqUh6bvvvlOvXr1UvXp1Xbp0Sf/73/9033336ZdffpGfn58k6fnnn9dXX32lBQsWKCgoSL1799YjjzyitWvXurJ0AAAA5HFubm7y9vZ2dRlwAZeGpKVLlzo8j4uLU6FChbR161bdc889SkpK0rvvvquPPvpI9957ryRp9uzZKl++vDZs2KC7777bFWUDAAAAyMNy1VloSUlJkqT8+fNLkrZu3aqLFy+qcePG9j7lypVT8eLFtX79+qvOIyUlRWfOnHF4AAAAAEBm5ZqQlJaWpr59+6pOnTqqVKmSJOnYsWPy9PRUcHCwQ9/ChQvr2LFjV53PyJEjFRQUZH+Eh4dnd+kAAAAA8pBcE5J69eqlHTt2aN68ebc0n0GDBikpKcn++OOPP7KoQgAAAAD/BbniEuC9e/fWl19+qTVr1qhYsWL29tDQUKWmpur06dMOo0nHjx+/5mX9vLy85OXlld0lAwAAAMijXDqSZIxR7969tWjRIq1atUolSpRwmF61alV5eHho5cqV9rbdu3crMTFRtWrVyulyAQAAAPwHuHQkqVevXvroo4/0+eefKyAgwH6eUVBQkHx8fBQUFKQuXbqoX79+yp8/vwIDA/Xss8+qVq1aXNkOAAAAQLZwaUiaPn26JKlBgwYO7bNnz1ZsbKwkacKECXJzc9Ojjz7qcDNZAAAAAMgOLg1Jxpgb9vH29tbUqVM1derUHKgIAAAAwH9drrm6HQAAAADkBoQkAAAAALAgJAEAAACABSEJAAAAACwISQAAAABgQUgCAAAAAAtCEgAAAABYEJIAAAAAwIKQBAAAAAAWhCQAAAAAsCAkAQAAAIAFIQkAAAAALPK5ugAAAHJC4vBoV5fgEsUHb3d1CQBw22EkCQAAAAAsCEkAAAAAYEFIAgAAAAALQhIAAAAAWBCSAAAAAMCCkAQAAAAAFoQkAAAAALAgJAEAAACABSEJAAAAACwISQAAAABgQUgCAAAAAAtCEgAAAABYEJIAAAAAwIKQBAAAAAAWhCQAAAAAsCAkAQAAAIAFIQkAAAAALAhJAAAAAGBBSAIAAAAAC0ISAAAAAFgQkgAAAADAgpAEAAAAABaEJAAAAACwICQBAAAAgAUhCQAAAAAsCEkAAAAAYEFIAgAAAAALQhIAAAAAWBCSAAAAAMCCkAQAAAAAFoQkAAAAALAgJAEAAACABSEJAAAAACwISQAAAABgQUgCAAAAAAtCEgAAAABYEJIAAAAAwIKQBAAAAAAWhCQAAAAAsCAkAQAAAIAFIQkAAAAALAhJAAAAAGBBSAIAAAAAC0ISAAAAAFgQkgAAAADAgpAEAAAAABaEJAAAAACwICQBAAAAgAUhCQAAAAAsCEkAAAAAYEFIAgAAAAALQhIAAAAAWBCSAAAAAMCCkAQAAAAAFoQkAAAAALAgJAEAAACABSEJAAAAACwISQAAAABgkS8znSZPnpzpGfbp0+emiwEAAAAAV8tUSJowYUKmZmaz2QhJAAAAAG5rmQpJBw4cyO46AAAAACBXuOlzklJTU7V7925dunQpK+sBAAAAAJdyOiSdP39eXbp0ka+vrypWrKjExERJ0rPPPqtRo0ZleYEAAAAAkJOcDkmDBg3Stm3bFB8fL29vb3t748aN9cknn2RpcQAAAACQ0zJ1TpLV4sWL9cknn+juu++WzWazt1esWFH79u3L0uIAAAAAIKc5PZL0559/qlChQhnaz5075xCaAAAAAOB25HRIqlatmr766iv78/Rg9M4776hWrVpZVxkAAAAAuIDTh9u98cYbatasmX755RddunRJkyZN0i+//KJ169bpu+++y44aAQAAACDHOD2SVLduXSUkJOjSpUuKjo7W8uXLVahQIa1fv15Vq1bNjhoBAAAAIMc4PZIkSSVLltSsWbOyuhYAAAAAcDmnR5IaN26suLg4nTlzJjvqAQAAAACXcjokVaxYUYMGDVJoaKjatGmjzz//XBcvXsyO2gAAAAAgxzkdkiZNmqTDhw9r8eLF8vPzU4cOHVS4cGF1797d6Qs3rFmzRi1atFBYWJhsNpsWL17sMD02NlY2m83hcf/99ztbMgAAAABkmtMhSZLc3Nx03333KS4uTsePH9fMmTO1adMm3XvvvU7N59y5c6pcubKmTp16zT7333+/jh49an98/PHHN1MyAAAAAGTKTV24Id2xY8c0b948ffDBB/r5559Vo0YNp17frFkzNWvW7Lp9vLy8FBoaeitlAgAAAECmOT2SdObMGc2ePVtNmjRReHi4pk+frpYtW2rPnj3asGFDlhcYHx+vQoUKqWzZsnrmmWd08uTJ6/ZPSUnRmTNnHB4AAAAAkFlOjyQVLlxYd9xxh9q1a6eRI0eqWrVq2VGXpH8PtXvkkUdUokQJ7du3T//73//UrFkzrV+/Xu7u7ld9zciRIzVs2LBsqwkAAABA3uZ0SPriiy/UqFEjubnd1OlMTnnsscfs/4+OjlZMTIxKliyp+Ph4NWrU6KqvGTRokPr162d/fubMGYWHh2d7rQAAAADyBqeTTpMmTZSWlqYVK1Zo5syZOnv2rCTpyJEjSk5OzvICraKiolSwYEHt3bv3mn28vLwUGBjo8AAAAACAzHJ6JOn333/X/fffr8TERKWkpKhJkyYKCAjQ6NGjlZKSohkzZmRHnZKkQ4cO6eTJkypSpEi2LQMAAADAf5vTI0nPPfecqlWrpr///ls+Pj729ocfflgrV650al7JyclKSEhQQkKCJOnAgQNKSEhQYmKikpOT1b9/f23YsEEHDx7UypUr9dBDD6lUqVJq2rSps2UDAAAAQKY4PZL0/fffa926dfL09HRoj4yM1OHDh52a15YtW9SwYUP78/RziTp27Kjp06fr559/1pw5c3T69GmFhYXpvvvu02uvvSYvLy9nywYAAACATHE6JKWlpeny5csZ2g8dOqSAgACn5tWgQQMZY645fdmyZc6WBwAAAAC3xOnD7e677z5NnDjR/txmsyk5OVlDhgxR8+bNs7I2AAAAAMhxTo8kjRs3Tk2bNlWFChV04cIFPf7449qzZ48KFiyojz/+ODtqBAAAAIAc43RIKlasmLZt26ZPPvlE27ZtU3Jysrp06aInnnjC4UIOAAAAAHA7cjokSVK+fPn0xBNP6IknnrC37d+/Xz169NDy5cuzrDgAAAAAyGlOn5N0LWfPnnX6EuAAAAAAkNtkWUgCAAAAgLyAkAQAAAAAFoQkAAAAALDI9IUb7rzzTtlstmtOP3/+fJYUBAAAAACulOmQ1KpVq2wsAwAAAAByh0yHpCFDhmRnHQAAAACQK3BOEgAAAABY3NTNZP9LqvZ/39UluMTWsR1cXQIAAADgEowkAQAAAIAFIQkAAAAALAhJAAAAAGBxU+ckrVy5UitXrtSJEyeUlpbmMO29997LksIAAAAAwBWcDknDhg3T8OHDVa1aNRUpUuS6N5gFAAAAgNuN0yFpxowZiouL01NPPZUd9QAAAACASzl9TlJqaqpq166dHbUAAAAAgMs5HZK6du2qjz76KDtqAQAAAACXy9Thdv369bP/Py0tTW+//bZWrFihmJgYeXh4OPQdP3581lYIAAAAADkoUyHpp59+cnhepUoVSdKOHTsc2rmIAwAAAIDbXaZC0urVq7O7DgAAAADIFZw+JykpKUmnTp3K0H7q1CmdOXMmS4oCAAAAAFdxOiQ99thjmjdvXob2+fPn67HHHsuSogAAAADAVZwOSRs3blTDhg0ztDdo0EAbN27MkqIAAAAAwFWcDkkpKSm6dOlShvaLFy/qn3/+yZKiAAAAAMBVnA5JNWrU0Ntvv52hfcaMGapatWqWFAUAAAAArpKpq9tZjRgxQo0bN9a2bdvUqFEjSdLKlSu1efNmLV++PMsLBAAAAICc5PRIUp06dbR+/XqFh4dr/vz5WrJkiUqVKqWff/5Z9erVy44aAQAAACDHOD2SJP17M9kPP/wwq2tBLpI4PNrVJbhE8cHbXV3Cfw77GgAAyG1uKiSlu3DhglJTUx3aAgMDb6kgAAAAAHAlpw+3O3/+vHr37q1ChQrJz89Pd9xxh8MDAAAAAG5nToek/v37a9WqVZo+fbq8vLz0zjvvaNiwYQoLC9P777+fHTUCAAAAQI5x+nC7JUuW6P3331eDBg3UqVMn1atXT6VKlVJERIQ+/PBDPfHEE9lRJwAAAADkCKdHkk6dOqWoqChJ/55/dOrUKUlS3bp1tWbNmqytDgAAAABymNMhKSoqSgcOHJAklStXTvPnz5f07whTcHBwlhYHAAAAADnN6ZDUqVMnbdu2TZL00ksvaerUqfL29tbzzz+v/v37Z3mBAAAAAJCTnD4n6fnnn7f/v3Hjxvr111+1detWlSpVSjExMVlaHAAAAADktFu6T5IkRUREKCIiIitqAQAAAACXy/Thds2bN1dSUpL9+ahRo3T69Gn785MnT6pChQpZWhwAAAAA5LRMh6Rly5YpJSXF/vyNN96wX9lOki5duqTdu3dnbXUAAAAAkMMyHZKMMdd9DgAAAAB5gdNXtwMAAACAvCzTIclms8lms2VoAwAAAIC8JNNXtzPGKDY2Vl5eXpKkCxcuqEePHvLz85Mkh/OVAAAAAOB2lemQ1LFjR4fnTz75ZIY+HTp0uPWKAAAAAMCFMh2SZs+enZ11AAAAAECuwIUbAAAAAMCCkAQAAAAAFoQkAAAAALAgJAEAAACARaZC0l133aW///5bkjR8+HCdP38+W4sCAAAAAFfJVEjatWuXzp07J0kaNmyYkpOTs7UoAAAAAHCVTF0CvEqVKurUqZPq1q0rY4zefPNN+fv7X7Xv4MGDs7RAAAAAAMhJmQpJcXFxGjJkiL788kvZbDZ98803ypcv40ttNhshCQAAAMBtLVMhqWzZspo3b54kyc3NTStXrlShQoWytTAAAAAAcIVMhSSrtLS07KgDAAAAAHIFp0OSJO3bt08TJ07Url27JEkVKlTQc889p5IlS2ZpcQAAAACQ05y+T9KyZctUoUIFbdq0STExMYqJidHGjRtVsWJFffvtt9lRIwAAAADkGKdHkl566SU9//zzGjVqVIb2gQMHqkmTJllWHAAAAADkNKdHknbt2qUuXbpkaO/cubN++eWXLCkKAAAAAFzF6ZAUEhKihISEDO0JCQlc8Q4AAADAbc/pw+26deum7t27a//+/apdu7Ykae3atRo9erT69euX5QUCAAAAQE5yOiS9+uqrCggI0Lhx4zRo0CBJUlhYmIYOHao+ffpkeYEAAAAAkJOcDkk2m03PP/+8nn/+eZ09e1aSFBAQkOWFAQAAAIAr3NR9ktIRjgAAAADkNU5fuAEAAAAA8jJCEgAAAABYEJIAAAAAwMKpkHTx4kU1atRIe/bsya56AAAAAMClnApJHh4e+vnnn7OrFgAAAABwOacPt3vyySf17rvvZkctAAAAAOByTl8C/NKlS3rvvfe0YsUKVa1aVX5+fg7Tx48fn2XFAQAAAEBOczok7dixQ3fddZck6bfffnOYZrPZsqYqAAAAAHARp0PS6tWrs6MOAAAAAMgVbvoS4Hv37tWyZcv0zz//SJKMMVlWFAAAAAC4itMh6eTJk2rUqJHKlCmj5s2b6+jRo5KkLl266IUXXsjyAgEAAAAgJzkdkp5//nl5eHgoMTFRvr6+9vZ27dpp6dKlWVocAAAAAOQ0p89JWr58uZYtW6ZixYo5tJcuXVq///57lhUGAAAAAK7g9EjSuXPnHEaQ0p06dUpeXl5ZUhQAAAAAuIrTIalevXp6//337c9tNpvS0tI0ZswYNWzYMEuLAwAAAICc5vThdmPGjFGjRo20ZcsWpaamasCAAdq5c6dOnTqltWvXZkeNAAAAAJBjnB5JqlSpkn777TfVrVtXDz30kM6dO6dHHnlEP/30k0qWLJkdNQIAAABAjrmp+yQFBQXp5Zdf1vz58/X1119rxIgRKlKkiNPzWbNmjVq0aKGwsDDZbDYtXrzYYboxRoMHD1aRIkXk4+Ojxo0ba8+ePTdTMgAAAABkyk2FpL///ltvvvmmunTpoi5dumjcuHE6deqU0/M5d+6cKleurKlTp151+pgxYzR58mTNmDFDGzdulJ+fn5o2baoLFy7cTNkAAAAAcENOh6Q1a9YoMjJSkydP1t9//62///5bkydPVokSJbRmzRqn5tWsWTONGDFCDz/8cIZpxhhNnDhRr7zyih566CHFxMTo/fff15EjRzKMOFmlpKTozJkzDg8AAAAAyCynL9zQq1cvtWvXTtOnT5e7u7sk6fLly+rZs6d69eql7du3Z0lhBw4c0LFjx9S4cWN7W1BQkGrWrKn169frscceu+rrRo4cqWHDhmVJDQAAAM5KHB7t6hJcovjgrPkdEMgNnB5J2rt3r1544QV7QJIkd3d39evXT3v37s2ywo4dOyZJKly4sEN74cKF7dOuZtCgQUpKSrI//vjjjyyrCQAAAEDe5/RI0l133aVdu3apbNmyDu27du1S5cqVs6ywm+Xl5cVNbQEAAADctEyFpJ9//tn+/z59+ui5557T3r17dffdd0uSNmzYoKlTp2rUqFFZVlhoaKgk6fjx4w5Xzjt+/LiqVKmSZcsBAAAAAKtMhaQqVarIZrPJGGNvGzBgQIZ+jz/+uNq1a5clhZUoUUKhoaFauXKlPRSdOXNGGzdu1DPPPJMlywAAAACAK2UqJB04cCBbFp6cnOxwHtOBAweUkJCg/Pnzq3jx4urbt69GjBih0qVLq0SJEnr11VcVFhamVq1aZUs9AAAAAJCpkBQREZEtC9+yZYsaNmxof96vXz9JUseOHRUXF6cBAwbo3Llz6t69u06fPq26detq6dKl8vb2zpZ6AAAAAMDpCzdI0pEjR/TDDz/oxIkTSktLc5jWp0+fTM+nQYMGDofwXclms2n48OEaPnz4zZQJAAAAAE5zOiTFxcXp6aeflqenpwoUKCCbzWafZrPZnApJAAAAAJDbOB2SXn31VQ0ePFiDBg2Sm5vTt1kCAAAAgFzN6ZRz/vx5PfbYYwQkAAAAAHmS00mnS5cuWrBgQXbUAgAAAAAu5/ThdiNHjtSDDz6opUuXKjo6Wh4eHg7Tx48fn2XFAQAAAEBOu6mQtGzZMpUtW1aSMly4AQAAAABuZ06HpHHjxum9995TbGxsNpQDAAAAAK7l9DlJXl5eqlOnTnbUAgAAAAAu53RIeu655zRlypTsqAUAAAAAXM7pw+02bdqkVatW6csvv1TFihUzXLjhs88+y7LiAAAAACCnOR2SgoOD9cgjj2RHLQAAAADgck6HpNmzZ2dHHQAAAACQKzh9ThIAAAAA5GVOjySVKFHiuvdD2r9//y0VBAAAAACu5HRI6tu3r8Pzixcv6qefftLSpUvVv3//rKoLAAAAAFzC6ZD03HPPXbV96tSp2rJlyy0XBAAAAACulGXnJDVr1kyffvppVs0OAAAAAFwiy0LSwoULlT9//qyaHQAAAAC4hNOH2915550OF24wxujYsWP6888/NW3atCwtDgAAAABymtMhqVWrVg7P3dzcFBISogYNGqhcuXJZVRcAAAAAuITTIWnIkCHZUQcAAAAA5ArcTBYAAAAALDI9kuTm5nbdm8hKks1m06VLl265KAAAAABwlUyHpEWLFl1z2vr16zV58mSlpaVlSVEAAAAA4CqZDkkPPfRQhrbdu3frpZde0pIlS/TEE09o+PDhWVocAAAAAOS0mzon6ciRI+rWrZuio6N16dIlJSQkaM6cOYqIiMjq+gAAAAAgRzkVkpKSkjRw4ECVKlVKO3fu1MqVK7VkyRJVqlQpu+oDAAAAgByV6cPtxowZo9GjRys0NFQff/zxVQ+/AwAAAIDbXaZD0ksvvSQfHx+VKlVKc+bM0Zw5c67a77PPPsuy4gAAAAAgp2U6JHXo0OGGlwAHAAAAgNtdpkNSXFxcNpYBAAAAALnDTV3dDgAAAADyKkISAAAAAFgQkgAAAADAgpAEAAAAABaEJAAAAACwICQBAAAAgAUhCQAAAAAsCEkAAAAAYEFIAgAAAAALQhIAAAAAWBCSAAAAAMCCkAQAAAAAFoQkAAAAALAgJAEAAACABSEJAAAAACwISQAAAABgQUgCAAAAAAtCEgAAAABYEJIAAAAAwCKfqwsAAAB5U9X+77u6BJdYFODqCgDcKkaSAAAAAMCCkAQAAAAAFoQkAAAAALAgJAEAAACABSEJAAAAACwISQAAAABgQUgCAAAAAAtCEgAAAABYEJIAAAAAwIKQBAAAAAAWhCQAAAAAsCAkAQAAAIAFIQkAAAAALAhJAAAAAGBBSAIAAAAAC0ISAAAAAFgQkgAAAADAgpAEAAAAABaEJAAAAACwICQBAAAAgAUhCQAAAAAsCEkAAAAAYEFIAgAAAAALQhIAAAAAWBCSAAAAAMCCkAQAAAAAFoQkAAAAALAgJAEAAACABSEJAAAAACwISQAAAABgQUgCAAAAAAtCEgAAAABYEJIAAAAAwCJXh6ShQ4fKZrM5PMqVK+fqsgAAAADkYflcXcCNVKxYUStWrLA/z5cv15cMAAAA4DaW6xNHvnz5FBoa6uoyAAAAAPxH5OrD7SRpz549CgsLU1RUlJ544gklJiZet39KSorOnDnj8AAAAACAzMrVIalmzZqKi4vT0qVLNX36dB04cED16tXT2bNnr/makSNHKigoyP4IDw/PwYoBAAAA3O5ydUhq1qyZ2rRpo5iYGDVt2lRff/21Tp8+rfnz51/zNYMGDVJSUpL98ccff+RgxQAAAABud7n+nCSr4OBglSlTRnv37r1mHy8vL3l5eeVgVQAAAADyklw9knSl5ORk7du3T0WKFHF1KQAAAADyqFwdkl588UV99913OnjwoNatW6eHH35Y7u7uat++vatLAwAAAJBH5erD7Q4dOqT27dvr5MmTCgkJUd26dbVhwwaFhIS4ujQAAAAAeVSuDknz5s1zdQkAAAAA/mNy9eF2AAAAAJDTCEkAAAAAYEFIAgAAAAALQhIAAAAAWBCSAAAAAMCCkAQAAAAAFoQkAAAAALAgJAEAAACABSEJAAAAACwISQAAAABgQUgCAAAAAAtCEgAAAABY5HN1AQAAAMCtqNr/fVeX4BJbx3ZwdQl5FiNJAAAAAGBBSAIAAAAAC0ISAAAAAFgQkgAAAADAgpAEAAAAABaEJAAAAACwICQBAAAAgAUhCQAAAAAsCEkAAAAAYEFIAgAAAAALQhIAAAAAWBCSAAAAAMCCkAQAAAAAFoQkAAAAALAgJAEAAACABSEJAAAAACwISQAAAABgQUgCAAAAAAtCEgAAAABYEJIAAAAAwIKQBAAAAAAWhCQAAAAAsCAkAQAAAIAFIQkAAAAALAhJAAAAAGBBSAIAAAAAC0ISAAAAAFgQkgAAAADAgpAEAAAAABaEJAAAAACwICQBAAAAgAUhCQAAAAAsCEkAAAAAYEFIAgAAAAALQhIAAAAAWBCSAAAAAMCCkAQAAAAAFvlcXQAAAAAA5yUOj3Z1CS5RfPD2bF8GI0kAAAAAYEFIAgAAAAALQhIAAAAAWBCSAAAAAMCCkAQAAAAAFoQkAAAAALAgJAEAAACABSEJAAAAACwISQAAAABgQUgCAAAAAAtCEgAAAABYEJIAAAAAwIKQBAAAAAAWhCQAAAAAsCAkAQAAAIAFIQkAAAAALAhJAAAAAGBBSAIAAAAAC0ISAAAAAFgQkgAAAADAgpAEAAAAABaEJAAAAACwICQBAAAAgAUhCQAAAAAsCEkAAAAAYEFIAgAAAAALQhIAAAAAWBCSAAAAAMCCkAQAAAAAFoQkAAAAALAgJAEAAACABSEJAAAAACwISQAAAABgQUgCAAAAAIvbIiRNnTpVkZGR8vb2Vs2aNbVp0yZXlwQAAAAgj8r1IemTTz5Rv379NGTIEP3444+qXLmymjZtqhMnTri6NAAAAAB5UK4PSePHj1e3bt3UqVMnVahQQTNmzJCvr6/ee+89V5cGAAAAIA/K5+oCric1NVVbt27VoEGD7G1ubm5q3Lix1q9ff9XXpKSkKCUlxf48KSlJknTmzJmbquFyyj839brb3VmPy64uwSVudj/JCuxr/y3sazmPfS3nsa/9t7Cv5Tz2tZt/rTHmuv1ydUj666+/dPnyZRUuXNihvXDhwvr111+v+pqRI0dq2LBhGdrDw8Ozpca8qpKrC3CVkUGuruA/h30NOYV9DTmFfQ05hX3t5p09e1ZBQdeeT64OSTdj0KBB6tevn/15WlqaTp06pQIFCshms7mwstvHmTNnFB4erj/++EOBgYGuLgd5GPsacgr7GnIK+xpyCvvazTHG6OzZswoLC7tuv1wdkgoWLCh3d3cdP37cof348eMKDQ296mu8vLzk5eXl0BYcHJxdJeZpgYGBfOiQI9jXkFPY15BT2NeQU9jXnHe9EaR0ufrCDZ6enqpatapWrlxpb0tLS9PKlStVq1YtF1YGAAAAIK/K1SNJktSvXz917NhR1apVU40aNTRx4kSdO3dOnTp1cnVpAAAAAPKgXB+S2rVrpz///FODBw/WsWPHVKVKFS1dujTDxRyQdby8vDRkyJAMhy0CWY19DTmFfQ05hX0NOYV9LXvZzI2ufwcAAAAA/yG5+pwkAAAAAMhphCQAAAAAsCAkAQAAAIAFIQkAAAAALAhJcLBmzRq1aNFCYWFhstlsWrx4satLQh40cuRIVa9eXQEBASpUqJBatWql3bt3u7os5EHTp09XTEyM/WaLtWrV0jfffOPqsvAfMGrUKNlsNvXt29fVpSCPGTp0qGw2m8OjXLlyri4rzyEkwcG5c+dUuXJlTZ061dWlIA/77rvv1KtXL23YsEHffvutLl68qPvuu0/nzp1zdWnIY4oVK6ZRo0Zp69at2rJli+6991499NBD2rlzp6tLQx62efNmzZw5UzExMa4uBXlUxYoVdfToUfvjhx9+cHVJeU6uv08SclazZs3UrFkzV5eBPG7p0qUOz+Pi4lSoUCFt3bpV99xzj4uqQl7UokULh+evv/66pk+frg0bNqhixYouqgp5WXJysp544gnNmjVLI0aMcHU5yKPy5cun0NBQV5eRpzGSBMDlkpKSJEn58+d3cSXIyy5fvqx58+bp3LlzqlWrlqvLQR7Vq1cvPfDAA2rcuLGrS0EetmfPHoWFhSkqKkpPPPGEEhMTXV1SnsNIEgCXSktLU9++fVWnTh1VqlTJ1eUgD9q+fbtq1aqlCxcuyN/fX4sWLVKFChVcXRbyoHnz5unHH3/U5s2bXV0K8rCaNWsqLi5OZcuW1dGjRzVs2DDVq1dPO3bsUEBAgKvLyzMISQBcqlevXtqxYwfHUyPblC1bVgkJCUpKStLChQvVsWNHfffddwQlZKk//vhDzz33nL799lt5e3u7uhzkYdbTImJiYlSzZk1FRERo/vz56tKliwsry1sISQBcpnfv3vryyy+1Zs0aFStWzNXlII/y9PRUqVKlJElVq1bV5s2bNWnSJM2cOdPFlSEv2bp1q06cOKG77rrL3nb58mWtWbNGb731llJSUuTu7u7CCpFXBQcHq0yZMtq7d6+rS8lTCEkAcpwxRs8++6wWLVqk+Ph4lShRwtUl4T8kLS1NKSkpri4DeUyjRo20fft2h7ZOnTqpXLlyGjhwIAEJ2SY5OVn79u3TU0895epS8hRCEhwkJyc7/CXiwIEDSkhIUP78+VW8eHEXVoa8pFevXvroo4/0+eefKyAgQMeOHZMkBQUFycfHx8XVIS8ZNGiQmjVrpuLFi+vs2bP66KOPFB8fr2XLlrm6NOQxAQEBGc6r9PPzU4ECBTjfElnqxRdfVIsWLRQREaEjR45oyJAhcnd3V/v27V1dWp5CSIKDLVu2qGHDhvbn/fr1kyR17NhRcXFxLqoKec306dMlSQ0aNHBonz17tmJjY3O+IORZJ06cUIcOHXT06FEFBQUpJiZGy5YtU5MmTVxdGgDclEOHDql9+/Y6efKkQkJCVLduXW3YsEEhISGuLi1PsRljjKuLAAAAAIDcgvskAQAAAIAFIQkAAAAALAhJAAAAAGBBSAIAAAAAC0ISAAAAAFgQkgAAAADAgpAEAAAAABaEJAAAAACwICQBAHAbioyM1MSJE11dBgDkSYQkAMhjjh07pmeffVZRUVHy8vJSeHi4WrRooZUrV2bpcho0aKC+fftm6TxvRlxcnGw2W4aHt7e3q0tzMHToUFWpUsXp18XFxSk4ODhD++bNm9W9e/dbLwwAkEE+VxcAAMg6Bw8eVJ06dRQcHKyxY8cqOjpaFy9e1LJly9SrVy/9+uuvri4xWwQGBmr37t0ObTabzUXV5IyQkBBXlwAAeRYjSQCQh/Ts2VM2m02bNm3So48+qjJlyqhixYrq16+fNmzYYO+XmJiohx56SP7+/goMDFTbtm11/Phx+/T0UY+5c+cqMjJSQUFBeuyxx3T27FlJUmxsrL777jtNmjTJPnJz8OBBSdKOHTvUrFkz+fv7q3Dhwnrqqaf0119/2efdoEED9enTRwMGDFD+/PkVGhqqoUOH2qcbYzR06FAVL15cXl5eCgsLU58+fa673jabTaGhoQ6PwoULS5L+/PNPhYaG6o033rD3X7dunTw9Pe2ja+nrO3PmTIWHh8vX11dt27ZVUlKSw3LeeecdlS9fXt7e3ipXrpymTZvmMP3QoUNq37698ufPLz8/P1WrVk0bN25UXFychg0bpm3bttm3V1xcnCRp/Pjxio6Olp+fn8LDw9WzZ08lJydLkuLj49WpUyclJSXZX5e+ra483O5W31MAwP8hJAFAHnHq1CktXbpUvXr1kp+fX4bp6YdspaWl6aGHHtKpU6f03Xff6dtvv9X+/fvVrl07h/779u3T4sWL9eWXX+rLL7/Ud999p1GjRkmSJk2apFq1aqlbt246evSojh49qvDwcJ0+fVr33nuv7rzzTm3ZskVLly7V8ePH1bZtW4d5z5kzR35+ftq4caPGjBmj4cOH69tvv5Ukffrpp5owYYJmzpypPXv2aPHixYqOjr7p7RISEqL33ntPQ4cO1ZYtW3T27Fk99dRT6t27txo1amTvt3fvXs2fP19LlizR0qVL9dNPP6lnz5726R9++KEGDx6s119/Xbt27dIbb7yhV199VXPmzJEkJScnq379+jp8+LC++OILbdu2TQMGDFBaWpratWunF154QRUrVrRvr/Tt7ebmpsmTJ2vnzp2aM2eOVq1apQEDBkiSateurYkTJyowMND+uhdffDHDOmbFewoAsDAAgDxh48aNRpL57LPPrttv+fLlxt3d3SQmJtrbdu7caSSZTZs2GWOMGTJkiPH19TVnzpyx9+nfv7+pWbOm/Xn9+vXNc8895zDv1157zdx3330ObX/88YeRZHbv3m1/Xd26dR36VK9e3QwcONAYY8y4ceNMmTJlTGpqaqbWe/bs2UaS8fPzc3jcf//9Dv169uxpypQpYx5//HETHR1tLly4YJ82ZMgQ4+7ubg4dOmRv++abb4ybm5s5evSoMcaYkiVLmo8++ijD+taqVcsYY8zMmTNNQECAOXny5FXrHDJkiKlcufIN12fBggWmQIECDusXFBSUoV9ERISZMGGCMSbr3lMAwL84JwkA8ghjTKb67dq1S+Hh4QoPD7e3VahQQcHBwdq1a5eqV68u6d/DuQICAux9ihQpohMnTlx33tu2bdPq1avl7++fYdq+fftUpkwZSVJMTIzDNOu827Rpo4kTJyoqKkr333+/mjdvrhYtWihfvmv/yAoICNCPP/7o0Obj4+Pw/M0331SlSpW0YMECbd26VV5eXg7TixcvrqJFi9qf16pVS2lpadq9e7cCAgK0b98+denSRd26dbP3uXTpkoKCgiRJCQkJuvPOO5U/f/5r1nk1K1as0MiRI/Xrr7/qzJkzunTpki5cuKDz58/L19c3U/PIzvcUAP6LCEkAkEeULl1aNpstyy7O4OHh4fDcZrMpLS3tuq9JTk5WixYtNHr06AzTihQpkql5h4eHa/fu3VqxYoW+/fZb9ezZU2PHjtV3332X4XXp3NzcVKpUqevWtm/fPh05ckRpaWk6ePCgU4fwpZ8jNGvWLNWsWdNhmru7u6SMoSwzDh48qAcffFDPPPOMXn/9deXPn18//PCDunTpotTU1EyHpMy6mfcUAP6LOCcJAPKI/Pnzq2nTppo6darOnTuXYfrp06clSeXLl9cff/yhP/74wz7tl19+0enTp1WhQoVML8/T01OXL192aLvrrru0c+dORUZGqlSpUg6Pq50ndS0+Pj5q0aKFJk+erPj4eK1fv17bt2/P9OuvlJqaqieffFLt2rXTa6+9pq5du2YYQUlMTNSRI0fszzds2CA3NzeVLVtWhQsXVlhYmPbv359hvUqUKCHp39GxhIQEnTp16qo1XG17bd26VWlpaRo3bpzuvvtulSlTxqGGa73uSln1ngIA/kVIAoA8ZOrUqbp8+bJq1KihTz/9VHv27NGuXbs0efJk1apVS5LUuHFjRUdH64knntCPP/6oTZs2qUOHDqpfv76qVauW6WVFRkZq48aNOnjwoP766y+lpaWpV69eOnXqlNq3b6/Nmzdr3759WrZsmTp16nTDX/TTxcXF6d1339WOHTu0f/9+ffDBB/Lx8VFERMQ1X2OM0bFjxzI80kdJXn75ZSUlJWny5MkaOHCgypQpo86dOzvMw9vbWx07dtS2bdv0/fffq0+fPmrbtq1CQ0MlScOGDdPIkSM1efJk/fbbb9q+fbtmz56t8ePHS5Lat2+v0NBQtWrVSmvXrtX+/fv16aefav369fbtdeDAASUkJOivv/5SSkqKSpUqpYsXL2rKlCnav3+/5s6dqxkzZmTYzsnJyVq5cqX++usvnT9/PsP6Z9V7CgD4FyEJAPKQqKgo/fjjj2rYsKFeeOEFVapUSU2aNNHKlSs1ffp0Sf8eYvX555/rjjvu0D333KPGjRsrKipKn3zyiVPLevHFF+Xu7q4KFSooJCREiYmJCgsL09q1a3X58mXdd999io6OVt++fRUcHCw3t8z9yAkODtasWbNUp04dxcTEaMWKFVqyZIkKFChwzdecOXNGRYoUyfA4ceKE4uPjNXHiRM2dO1eBgYFyc3PT3Llz9f3339u3iSSVKlVKjzzyiJo3b6777rtPMTExDpf47tq1q9555x3Nnj1b0dHRql+/vuLi4uwjSZ6enlq+fLkKFSqk5s2bKzo6WqNGjbIfjvfoo4/q/vvvV8OGDRUSEqKPP/5YlStX1vjx4zV69GhVqlRJH374oUaOHOmwbrVr11aPHj3Url07hYSEaMyYMRnWP6veUwDAv2wms2f6AgCQRw0dOlSLFy9WQkKCq0sBAOQCjCQBAAAAgAUhCQAAAAAsONwOAAAAACwYSQIAAAAAC0ISAAAAAFgQkgAAAADAgpAEAAAAABaEJAAAAACwICQBAAAAgAUhCQAAAAAsCEkAAAAAYPH/ABnK22jX1hccAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Bar Plot for single variable for X3, how does right order impact the happiness\n",
        "df3 = data[['Y', 'X3']].value_counts().reset_index()\n",
        "df3['Y'] = df3['Y'].map({0: 'Unhappy', 1: 'Happy'})\n",
        "df3.columns = ['Y', 'X3', 'Count']\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.barplot(x='X3', y='Count', hue='Y', data=df3)\n",
        "plt.xlabel('Right Order')\n",
        "plt.ylabel('Number of Each Level')\n",
        "plt.title('Customer Satisfication on Right Order')\n",
        "plt.legend(title='Satisfaction')\n",
        "plt.show()\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "outputId": "041e7987-708e-40de-d782-d64092578231",
        "id": "Efki4dHAoitZ"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Bar Plot for single variable for X4, how does order price impact the happiness\n",
        "df4 = data[['Y', 'X4']].value_counts().reset_index()\n",
        "df4['Y'] = df4['Y'].map({0: 'Unhappy', 1: 'Happy'})\n",
        "df4.columns = ['Y', 'X4', 'Count']\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.barplot(x='X4', y='Count', hue='Y', data=df4)\n",
        "plt.xlabel('Good Price')\n",
        "plt.ylabel('Number of Each Level')\n",
        "plt.title('Customer Satisfication on Order Price')\n",
        "plt.legend(title='Satisfaction')\n",
        "plt.show()\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "outputId": "0ae9427a-7f3e-4608-dd0c-979adbcf1cd0",
        "id": "tzjjdzR5pSKT"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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RRx8pODg4W7+/L0lsni2xh7u7u+rUqaM6deqoQoUK6tGjh5YuXaoxY8bo0KFDat68uSpVqqSpU6cqNDRU7u7u+uabbzRt2rS7vgn97bffVnR0tL788kutWbNGQ4YM0cSJE7Vt2za7gl5OvfHGG3r11VfVs2dPvfbaaypSpIhcXFw0bNiwPLuR/k5/384+TuXKlfXLL78oPT39lsuc7969W25ubtnCj3m20V5ZWVmqVq2apk6detP9f/9lgr3H8vb2VlRU1B37/ZPP8HdZWVlq0aKFRo0addP9FSpUcNixADgHIQmAU7Vp00bvvfeetm7dqsjIyNv2vfEb86SkJJv2v/+m/YYSJUpowIABGjBggM6ePauaNWvq9ddft4akW4WuUqVKaffu3crKyrIJJb///rt1vyPcuDQrKCgoRz/kOcKNS4hOnTol6a+b+dPT0/XVV1/ZzFDc7FK/W31ft1KtWjVVq1ZNr7zyirZs2aIGDRpo7ty5mjBhQo7HCAwMlJ+f3x2D9GeffaZmzZrpgw8+sGlPSkpSsWLF7uozlCpVSmvXrs12mZqjz4O8Ot/atGmjrVu3aunSpTZLZt9w5MgRff/994qKirpjoLhR04EDB2xmQq9du6bExETVqFHD2lauXDnt2rVLzZs3t/scyms5Pd/KlSun1NTUPPt3CyDvcU8SAKcaNWqUvL291bt3b505cybb/hsPuJT+mnkqVqxYtuv958yZY7OdmZmZ7V6aoKAghYSE2Cw57O3tfdN7blq3bq3Tp09r8eLF1rbr169r1qxZ8vHxUZMmTez/oDfRsmVL+fn56Y033tC1a9ey7b+xfPHd2LBhw01nFr755htJUsWKFSX9/1kJc9/k5GTNmzcv23u9vb2zBdSbSUlJ0fXr123aqlWrJhcXl2xLPt+Ji4uL2rdvrxUrVmjHjh3Z9t+o29XVNdvnXbp0qfX+L/NnkLIH7Ztp3bq1MjMz9c4779i0T5s2TRaLxWEzknl1vvXr109BQUEaOXJktvuXrl69qh49esgwDI0ePfqOY9WuXVuBgYGaO3euzbLYcXFx2b7bjh076sSJE3r//fezjXPlyhXrann5QU7Pt44dO2rr1q1avXp1tj5JSUnZzn8A9x5mkgA4Vbly5bRw4UJ16tRJlStXVrdu3VS1alVlZGRoy5Yt1qWQb+jdu7cmTZqk3r17q3bt2tq0aZP++OMPmzEvXbqkkiVL6sknn1SNGjXk4+OjtWvXavv27Xr77bet/WrVqqXFixdrxIgRqlOnjnx8fNS2bVv17dtX7777rqKjo7Vz506VLl1an332mTZv3qzp06c7bKEJPz8/xcbG6tlnn1XNmjXVuXNnBQYG6ujRo/r666/VoEGDbD+g59TgwYN1+fJl/ec//1GlSpWs3+fixYtVunRp9ejRQ5L0yCOPyN3dXW3btlW/fv2Umpqq999/X0FBQdbZphtq1aql2NhYTZgwQeHh4QoKCrrp/VTr16/XoEGD9NRTT6lChQq6fv26PvroI7m6uqpDhw52f5Y33nhDa9asUZMmTaxLSJ86dUpLly7VDz/8oICAALVp00bjx49Xjx49VL9+fe3Zs0effPJJtvtjypUrp4CAAM2dO1e+vr7y9vZWvXr1bnq/Stu2bdWsWTO9/PLLOnLkiGrUqKE1a9boyy+/1LBhw7I9a+hu5dX5VrRoUX322Wd67LHHVLNmTfXu3VtVqlTR6dOnFRcXp4MHD2rGjBk5Wo7fzc1NEyZMUL9+/fTwww+rU6dOSkxM1Lx587J9588++6yWLFmi/v37a8OGDWrQoIEyMzP1+++/a8mSJVq9evVNF0lwlpycbyNHjtRXX32lNm3aKDo6WrVq1VJaWpr27Nmjzz77TEeOHLGZwQRwD3LWsnoAYPbHH38Yffr0MUqXLm24u7sbvr6+RoMGDYxZs2ZZl8w2jL+Wq+7Vq5fh7+9v+Pr6Gh07djTOnj1rs4xvenq6MXLkSKNGjRqGr6+v4e3tbdSoUcOYM2eOzTFTU1ONp59+2ggICDAk2Sz/fObMGaNHjx5GsWLFDHd3d6NatWrZlo2+sSTzm2++adN+Y0nsvy+BfWM55b8vLbxhwwajZcuWhr+/v+Hp6WmUK1fOiI6ONnbs2GHt0717d8Pb2zvH3+e3335r9OzZ06hUqZLh4+NjuLu7G+Hh4cbgwYONM2fO2PT96quvjOrVqxuenp5G6dKljcmTJxsffvihIclITEy09jt9+rTx2GOPGb6+vjbLPP99CfDDhw8bPXv2NMqVK2d4enoaRYoUMZo1a2asXbvW5rg5XQLcMAzjzz//NLp162YEBgYaHh4eRtmyZY2BAwdal5++evWq8fzzzxslSpQwvLy8jAYNGhhbt241mjRpkm3Z8i+//NKoUqWKUahQIZvlwP++BLhh/LVM+/Dhw42QkBDDzc3NKF++vPHmm29al4I213yzJedLlSpldO/ePVv73/2T8+1W39mtJCYmGn369DHCwsIMNzc3o1ixYka7du2yLdFtGLc+Z2+YM2eOUaZMGcPDw8OoXbu2sWnTppt+5xkZGcbkyZONiIgIw8PDw7jvvvuMWrVqGePGjTOSk5NtPsedlu43u9U59He3G/duzjfD+OvciImJMcLDww13d3ejWLFiRv369Y233nrrpkvtA7i3WAzDwXeUAgAAAMA9jHuSAAAAAMCEkAQAAAAAJoQkAAAAADAhJAEAAACACSEJAAAAAEwISQAAAABgUuAfJpuVlaWTJ0/K19dXFovF2eUAAAAAcBLDMHTp0iWFhITIxeXW80UFPiSdPHlSoaGhzi4DAAAAQD5x7NgxlSxZ8pb7C3xI8vX1lfTXF+Hn5+fkagAAAAA4S0pKikJDQ60Z4VYKfEi6cYmdn58fIQkAAADAHW/DYeEGAAAAADAhJAEAAACACSEJAAAAAEwK/D1JAAAAwN3IzMzUtWvXnF0G7ODq6qpChQr940f/EJIAAACAv0lNTdXx48dlGIazS4GdChcurBIlSsjd3f2uxyAkAQAAACaZmZk6fvy4ChcurMDAwH88K4G8YRiGMjIydO7cOSUmJqp8+fK3fWDs7RCSAAAAAJNr167JMAwFBgbKy8vL2eXADl5eXnJzc9Off/6pjIwMeXp63tU4LNwAAAAA3AQzSPemu509shnDAXUAAAAAQIFBSAIAAAAAE0ISAAAAcA8pXbq0pk+fnqO+ly9fVocOHeTn5yeLxaKkpKRcqenIkSOyWCxKSEjIlfHzGiEJAAAAyAXnzp3Tc889p7CwMHl4eCg4OFgtW7bU5s2bc/T+uLg4BQQEZGvfvn27+vbtm6Mx5s+fr++//15btmzRqVOn5O/vb89HuKno6Gi1b9/epi00NFSnTp1S1apV//H4+QGr2wEAAAC5oEOHDsrIyND8+fNVtmxZnTlzRuvWrdP58+f/0biBgYE57nvo0CFVrlw518OLq6urgoODc/UYeYmZJAAAAMDBkpKS9P3332vy5Mlq1qyZSpUqpbp16yomJkbt2rWTJE2dOlXVqlWTt7e3QkNDNWDAAKWmpkqS4uPj1aNHDyUnJ8tischisWjs2LGSbC+3MwxDY8eOtc5WhYSEaMiQIZKkpk2b6u2339amTZtksVjUtGlTSdJHH32k2rVry9fXV8HBwXr66ad19uxZm/r37t2rNm3ayM/PT76+vmrUqJEOHTqksWPHav78+fryyy+tdcXHx9/0cruNGzeqbt268vDwUIkSJfTSSy/p+vXr1v1NmzbVkCFDNGrUKBUpUkTBwcHWz+hshCQAAADAwXx8fOTj46Ply5crPT39pn1cXFw0c+ZM7d27V/Pnz9f69es1atQoSVL9+vU1ffp0+fn56dSpUzp16pReeOGFbGN8/vnnmjZtmt59910dOHBAy5cvV7Vq1SRJX3zxhfr06aPIyEidOnVKX3zxhaS/ngP12muvadeuXVq+fLmOHDmi6Oho65gnTpxQ48aN5eHhofXr12vnzp3q2bOnrl+/rhdeeEEdO3bUo48+aq2rfv362eo6ceKEWrdurTp16mjXrl2KjY3VBx98oAkTJtj0mz9/vry9vfXjjz9qypQpGj9+vL777ru7+s4dyamX28XGxio2NlZHjhyRJEVERGj06NFq1aqVJOnq1at6/vnntWjRIqWnp6tly5aaM2eOihcv7sSqAQAAgNsrVKiQ4uLi1KdPH82dO1c1a9ZUkyZN1LlzZ1WvXl2SNGzYMGv/0qVLa8KECerfv7/mzJkjd3d3+fv7y2Kx3PYytqNHjyo4OFhRUVFyc3NTWFiY6tatK0kqUqSIChcuLHd3d5sxevbsaf1z2bJlNXPmTNWpU0epqany8fHR7Nmz5e/vr0WLFsnNzU2SVKFCBet7vLy8lJ6eftu65syZo9DQUL3zzjuyWCyqVKmSTp48qRdffFGjR4+2PsuoevXqGjNmjCSpfPnyeuedd7Ru3Tq1aNEip191rnDqTFLJkiU1adIk7dy5Uzt27NDDDz+sxx9/XHv37pUkDR8+XCtWrNDSpUu1ceNGnTx5Uk888YQzSwYAAABypEOHDjp58qS++uorPfroo4qPj1fNmjUVFxcnSVq7dq2aN2+u+++/X76+vnr22Wd1/vx5Xb58OcfHeOqpp3TlyhWVLVtWffr00bJly2wuabuZnTt3qm3btgoLC5Ovr6+aNGki6a/AJUkJCQlq1KiRNSDdjX379ikyMtLmgbwNGjRQamqqjh8/bm27ERhvKFGiRLZL/5zBqSGpbdu2at26tcqXL68KFSro9ddfl4+Pj7Zt26bk5GR98MEHmjp1qh5++GHVqlVL8+bN05YtW7Rt2zZnlg0AAADkiKenp1q0aKFXX31VW7ZsUXR0tMaMGaMjR46oTZs2ql69uj7//HPt3LlTs2fPliRlZGTkePzQ0FDt379fc+bMkZeXlwYMGKDGjRvr2rVrN+2flpamli1bys/PT5988om2b9+uZcuW2RzXy8vrH37qnPt7ELNYLMrKysqz499KvrknKTMzU4sWLVJaWpoiIyO1c+dOXbt2TVFRUdY+lSpVUlhYmLZu3XrLcdLT05WSkmLzAgAAAPKDKlWqKC0tTTt37lRWVpbefvttPfTQQ6pQoYJOnjxp09fd3V2ZmZl3HNPLy0tt27bVzJkzFR8fr61bt2rPnj037fv777/r/PnzmjRpkho1aqRKlSplm7mpXr26vv/++1sGrZzUVblyZW3dulWGYVjbNm/eLF9fX5UsWfKOn8nZnL4E+J49exQZGamrV6/Kx8dHy5YtU5UqVZSQkCB3d/dsa8MXL15cp0+fvuV4EydO1Lhx43K5agAAcCe1Ri5wdglOsfPNbs4uAfnA+fPn9dRTT6lnz56qXr26fH19tWPHDk2ZMkWPP/64wsPDde3aNc2aNUtt27bV5s2bNXfuXJsxSpcurdTUVK1bt041atRQ4cKFVbhwYZs+cXFxyszMVL169VS4cGF9/PHH8vLyUqlSpW5aV1hYmNzd3TVr1iz1799fv/76q1577TWbPoMGDdKsWbPUuXNnxcTEyN/fX9u2bVPdunVVsWJFlS5dWqtXr9b+/ftVtGjRmz57acCAAZo+fboGDx6sQYMGaf/+/RozZoxGjBhhvR8pP3N6hRUrVlRCQoJ+/PFHPffcc+revbt+++23ux4vJiZGycnJ1texY8ccWC0AAABwZz4+PqpXr56mTZumxo0bq2rVqnr11VfVp08fvfPOO6pRo4amTp2qyZMnq2rVqvrkk080ceJEmzHq16+v/v37q1OnTgoMDNSUKVOyHScgIEDvv/++GjRooOrVq2vt2rVasWKFihYtetO6AgMDFRcXp6VLl6pKlSqaNGmS3nrrLZs+RYsW1fr165WamqomTZqoVq1aev/9962XxvXp00cVK1ZU7dq1FRgYeNOH495///365ptv9NNPP6lGjRrq37+/evXqpVdeeeVuv9I8ZTHMc2D5QFRUlMqVK6dOnTqpefPmunjxos1sUqlSpTRs2DANHz48R+OlpKTI399fycnJ8vPzy6WqAQDA3zGThHvV1atXlZiYqDJlysjT09PZ5cBOt/v7y2k2cPpM0t9lZWUpPT1dtWrVkpubm9atW2fdt3//fh09elSRkZFOrBAAAABAQebUe5JiYmLUqlUrhYWF6dKlS1q4cKHi4+O1evVq+fv7q1evXhoxYoSKFCkiPz8/DR48WJGRkXrooYecWTYAAACAAsypIens2bPq1q2bTp06JX9/f1WvXl2rV6+2Pjxq2rRpcnFxUYcOHWweJgsAAAAAucWpIemDDz647X5PT0/Nnj3bumY8AAAAAOS2fHdPEgAAAAA4EyEJAAAAAEwISQAAAABgQkgCAAAAABNCEgAAAACYOHV1OwAAAOBeUWvkgjw93s43u+Xp8fD/MZMEAAAAFBDR0dFq3759tvb4+HhZLBYlJSXleU33IkISAAAAAJgQkgAAAIB/kfPnz6tLly66//77VbhwYVWrVk2ffvqpTZ+mTZtq0KBBGjRokPz9/VWsWDG9+uqrMgzD2qd06dJ67bXX1KVLF3l7e+v+++/X7Nmzrft79uypNm3a2Ix77do1BQUF6YMPPsjdD/kPEZIAAACAf5GrV6+qVq1a+vrrr/Xrr7+qb9++evbZZ/XTTz/Z9Js/f74KFSqkn376STNmzNDUqVP13//+16bPm2++qRo1auiXX37RSy+9pKFDh+q7776TJPXu3VurVq3SqVOnrP1Xrlypy5cvq1OnTrn/Qf8BFm4AAAAACpCVK1fKx8fHpi0zM9P65/vvv18vvPCCdXvw4MFavXq1lixZorp161rbQ0NDNW3aNFksFlWsWFF79uzRtGnT1KdPH2ufBg0a6KWXXpIkVahQQZs3b9a0adPUokUL1a9fXxUrVtRHH32kUaNGSZLmzZunp556Klt9+Q0zSQAAAEAB0qxZMyUkJNi8zDNAmZmZeu2111StWjUVKVJEPj4+Wr16tY4ePWozzkMPPSSLxWLdjoyM1IEDB2wCV2RkpM17IiMjtW/fPut27969NW/ePEnSmTNn9O2336pnz54O/by5gZkkAAAAoADx9vZWeHi4Tdvx48etf37zzTc1Y8YMTZ8+XdWqVZO3t7eGDRumjIwMh9fSrVs3vfTSS9q6dau2bNmiMmXKqFGjRg4/jqMRkgAAAIB/kc2bN+vxxx/XM888I0nKysrSH3/8oSpVqtj0+/HHH222t23bpvLly8vV1dWm7e99KleubN0uWrSo2rdvr3nz5mnr1q3q0aOHoz9OriAkAQAAAP8i5cuX12effaYtW7bovvvu09SpU3XmzJlsIeno0aMaMWKE+vXrp59//lmzZs3S22+/bdNn8+bNmjJlitq3b6/vvvtOS5cu1ddff23Tp3fv3mrTpo0yMzPVvXv3XP98jkBIAgAAAHJg55vdnF2CQ7zyyis6fPiwWrZsqcKFC6tv375q3769kpOTbfp169ZNV65cUd26deXq6qqhQ4eqb9++Nn2ef/557dixQ+PGjZOfn5+mTp2qli1b2vSJiopSiRIlFBERoZCQkFz/fI5ASAIAAAAKiLi4uJu2N23a1OYZR8uXL7/jWG5ubpo+fbpiY2Nv2cfPz09Lliy57ThpaWm6ePGievXqdcdj5heEJAAAAAAOl5WVpf/97396++23FRAQoHbt2jm7pBwjJAEAAABwuKNHj6pMmTIqWbKk4uLiVKjQvRM97p1KAQAAAOSJ+Pj4O/Y5cuTIbfeXLl3a5hK/ewkPkwUAAAAAE0ISAAAAAJgQkgAAAADAhJAEAAAAACaEJAAAAAAwISQBAAAAgAlLgAMAAAA5cHR8tTw9XtjoPXl6vBtKly6tYcOGadiwYU45fn7ATBIAAABQADRt2vSmwSYuLk4BAQF5Xs+9jJAEAAAAACaEJAAAAOBfIjo6Wu3bt9dbb72lEiVKqGjRoho4cKCuXbtm0+/y5cvq2bOnfH19FRYWpvfee89m/4svvqgKFSqocOHCKlu2rF599VWbMcaOHasHHnhA7777rkJDQ1W4cGF17NhRycnJ2WoZN26cAgMD5efnp/79+ysjI0OStGDBAhUtWlTp6ek2x27fvr2effZZR381NghJAAAAwL/Ihg0bdOjQIW3YsEHz589XXFyc4uLibPq8/fbbql27tn755RcNGDBAzz33nPbv32/d7+vrq7i4OP3222+aMWOG3n//fU2bNs1mjIMHD2rJkiVasWKFVq1aZR3LbN26ddq3b5/i4+P16aef6osvvtC4ceMkSU899ZQyMzP11VdfWfufPXtWX3/9tXr27Ongb8UWIQkAAAD4F7nvvvv0zjvvqFKlSmrTpo0ee+wxrVu3zqZP69atNWDAAIWHh+vFF19UsWLFtGHDBuv+V155RfXr11fp0qXVtm1bvfDCC1qyZInNGFevXtWCBQv0wAMPqHHjxpo1a5YWLVqk06dPW/u4u7vrww8/VEREhB577DGNHz9eM2fOVFZWlry8vPT0009r3rx51v4ff/yxwsLC1LRp09z5cv4fQhIAAADwLxIRESFXV1frdokSJXT27FmbPtWrV7f+2WKxKDg42KbP4sWL1aBBAwUHB8vHx0evvPKKjh49ajNGWFiY7r//fut2ZGSksrKybGakatSoocKFC9v0SU1N1bFjxyRJffr00Zo1a3TixAlJfy1CER0dLYvF8k++gjsiJAEAAAAFgJ+fn809PzckJSXJ39/fuu3m5maz32KxKCsry6btdn22bt2qrl27qnXr1lq5cqV++eUXvfzyy9Z7iRzpwQcfVI0aNbRgwQLt3LlTe/fuVXR0tMOP83c8JwkAAAAoACpWrKg1a9Zka//5559VoUIFhx1ny5YtKlWqlF5++WVr259//pmt39GjR3Xy5EmFhIRIkrZt2yYXFxdVrFjR2mfXrl26cuWKvLy8rH18fHwUGhpq7dO7d29Nnz5dJ06cUFRUlM2+3MJMEgAAAFAAPPfcc/rjjz80ZMgQ7d69W/v379fUqVP16aef6vnnn3fYccqXL6+jR49q0aJFOnTokGbOnKlly5Zl6+fp6anu3btr165d+v777zVkyBB17NhRwcHB1j4ZGRnq1auXfvvtN33zzTcaM2aMBg0aJBeX/x9Tnn76aR0/flzvv/9+ri/YcAMzSQAAAEAOhI3e4+wSbqts2bLatGmTXn75ZUVFRSkjI0OVKlXS0qVL9eijjzrsOO3atdPw4cM1aNAgpaen67HHHtOrr76qsWPH2vQLDw/XE088odatW+vChQtq06aN5syZY9OnefPmKl++vBo3bqz09HR16dIl2zj+/v7q0KGDvv76a7Vv395hn+N2LIZhGHlyJCdJSUmRv7+/kpOT5efn5+xyAAD416g1coGzS3CKnW92c3YJ+IeuXr2qxMRElSlTRp6ens4u5540duxYLV++XAkJCbfsEx0draSkJC1fvvyO4zVv3lwRERGaOXPmHfve7u8vp9mAmSQAAAAA+dLFixcVHx+v+Pj4bLNQuYmQBAAAACBfevDBB3Xx4kVNnjzZZsGH3EZIAgAAAOBQY8eOzXZv0d/FxcXdcZwjR444pB57sbodAAAAAJgQkgAAAICbKODrmxVYjvh7IyQBAAAAJq6urpL+eoYP7j2XL1+WJLm5ud31GNyTBAAAAJgUKlRIhQsX1rlz5+Tm5mbzYFPkX4Zh6PLlyzp79qwCAgKsYfduEJIAAAAAE4vFohIlSigxMVF//vmns8uBnQICAhQcHPyPxiAkAQAAAH/j7u6u8uXLc8ndPcbNze0fzSDdQEgCAAAAbsLFxUWenp7OLgNOwAWWAAAAAGBCSAIAAAAAE0ISAAAAAJgQkgAAAADAhJAEAAAAACaEJAAAAAAwISQBAAAAgAkhCQAAAABMCEkAAAAAYEJIAgAAAAATQhIAAAAAmBCSAAAAAMCEkAQAAAAAJoQkAAAAADBxakiaOHGi6tSpI19fXwUFBal9+/bav3+/TZ+mTZvKYrHYvPr37++kigEAAAAUdE4NSRs3btTAgQO1bds2fffdd7p27ZoeeeQRpaWl2fTr06ePTp06ZX1NmTLFSRUDAAAAKOgKOfPgq1atstmOi4tTUFCQdu7cqcaNG1vbCxcurODg4LwuDwAAAMC/UL66Jyk5OVmSVKRIEZv2Tz75RMWKFVPVqlUVExOjy5cv33KM9PR0paSk2LwAAAAAIKecOpNklpWVpWHDhqlBgwaqWrWqtf3pp59WqVKlFBISot27d+vFF1/U/v379cUXX9x0nIkTJ2rcuHF5VTYAAACAAsZiGIbh7CIk6bnnntO3336rH374QSVLlrxlv/Xr16t58+Y6ePCgypUrl21/enq60tPTrdspKSkKDQ1VcnKy/Pz8cqV2AACQXa2RC5xdglPsfLObs0sAcAspKSny9/e/YzbIFzNJgwYN0sqVK7Vp06bbBiRJqlevniTdMiR5eHjIw8MjV+oEAAAAUPA5NSQZhqHBgwdr2bJlio+PV5kyZe74noSEBElSiRIlcrk6AAAAAP9GTg1JAwcO1MKFC/Xll1/K19dXp0+fliT5+/vLy8tLhw4d0sKFC9W6dWsVLVpUu3fv1vDhw9W4cWNVr17dmaUDAAAAKKCcGpJiY2Ml/fXAWLN58+YpOjpa7u7uWrt2raZPn660tDSFhoaqQ4cOeuWVV5xQLQAAAIB/A6dfbnc7oaGh2rhxYx5VAwAAAAD57DlJAAAAAOBshCQAAAAAMCEkAQAAAIAJIQkAAAAATAhJAAAAAGBCSAIAAAAAE0ISAAAAAJgQkgAAAADAhJAEAAAAACaEJAAAAAAwISQBAAAAgAkhCQAAAABMCEkAAAAAYEJIAgAAAAATQhIAAAAAmBCSAAAAAMCEkAQAAAAAJoQkAAAAADAhJAEAAACACSEJAAAAAEwISQAAAABgQkgCAAAAAJNCzi4AAACgIDk6vpqzS3CKsNF7nF0C4DDMJAEAAACACSEJAAAAAEwISQAAAABgQkgCAAAAABNCEgAAAACYEJIAAAAAwISQBAAAAAAmhCQAAAAAMCEkAQAAAIAJIQkAAAAATAhJAAAAAGBCSAIAAAAAE0ISAAAAAJgQkgAAAADAhJAEAAAAACaEJAAAAAAwISQBAAAAgAkhCQAAAABMCEkAAAAAYEJIAgAAAAATQhIAAAAAmBCSAAAAAMCEkAQAAAAAJoQkAAAAADAhJAEAAACACSEJAAAAAEwISQAAAABgQkgCAAAAABNCEgAAAACYFHJ2AQCAvFVr5AJnl+AUO9/s5uwSAAD3CGaSAAAAAMCEkAQAAAAAJjm63G7mzJk5HnDIkCF3XQwAAAAAOFuOQtK0adNyNJjFYiEkAQAAALin5SgkJSYm5nYdAAAAAJAv3PU9SRkZGdq/f7+uX7/uyHoAAAAAwKnsDkmXL19Wr169VLhwYUVEROjo0aOSpMGDB2vSpEkOLxAAAAAA8pLdISkmJka7du1SfHy8PD09re1RUVFavHixQ4sDAAAAgLxm98Nkly9frsWLF+uhhx6SxWKxtkdEROjQoUMOLQ4AAAAA8prdM0nnzp1TUFBQtva0tDSb0JQTEydOVJ06deTr66ugoCC1b99e+/fvt+lz9epVDRw4UEWLFpWPj486dOigM2fO2Fs2AAAAAOSI3SGpdu3a+vrrr63bN4LRf//7X0VGRto11saNGzVw4EBt27ZN3333na5du6ZHHnlEaWlp1j7Dhw/XihUrtHTpUm3cuFEnT57UE088YW/ZAAAAAJAjdl9u98Ybb6hVq1b67bffdP36dc2YMUO//fabtmzZoo0bN9o11qpVq2y24+LiFBQUpJ07d6px48ZKTk7WBx98oIULF+rhhx+WJM2bN0+VK1fWtm3b9NBDD9lbPgAAAADclt0zSQ0bNlRCQoKuX7+uatWqac2aNQoKCtLWrVtVq1atf1RMcnKyJKlIkSKSpJ07d+ratWuKioqy9qlUqZLCwsK0devWm46Rnp6ulJQUmxcAAAAA5JTdM0mSVK5cOb3//vsOLSQrK0vDhg1TgwYNVLVqVUnS6dOn5e7uroCAAJu+xYsX1+nTp286zsSJEzVu3DiH1gYAuPcdHV/N2SU4RdjoPc4uAQDuOXbPJEVFRSkuLs7hMzQDBw7Ur7/+qkWLFv2jcWJiYpScnGx9HTt2zEEVAgAAAPg3sDskRUREKCYmRsHBwXrqqaf05Zdf6tq1a/+oiEGDBmnlypXasGGDSpYsaW0PDg5WRkaGkpKSbPqfOXNGwcHBNx3Lw8NDfn5+Ni8AAAAAyCm7Q9KMGTN04sQJLV++XN7e3urWrZuKFy+uvn372r1wg2EYGjRokJYtW6b169erTJkyNvtr1aolNzc3rVu3ztq2f/9+HT161O6V9AAAAAAgJ+wOSZLk4uKiRx55RHFxcTpz5ozeffdd/fTTT9YV6HJq4MCB+vjjj7Vw4UL5+vrq9OnTOn36tK5cuSJJ8vf3V69evTRixAht2LBBO3fuVI8ePRQZGcnKdgAAAAByxV0t3HDD6dOntWjRIn388cfavXu36tata9f7Y2NjJUlNmza1aZ83b56io6MlSdOmTZOLi4s6dOig9PR0tWzZUnPmzPknZQMAAADALdkdklJSUvT5559r4cKFio+PV9myZdW1a1ctXrxY5cqVs2sswzDu2MfT01OzZ8/W7Nmz7S0VAAAAAOxmd0gqXry47rvvPnXq1EkTJ05U7dq1c6MuAAAAAHAKu0PSV199pebNm8vF5a5uZwIAAACAfM3upNOiRQtlZWVp7dq1evfdd3Xp0iVJ0smTJ5WamurwAgEAAAAgL9k9k/Tnn3/q0Ucf1dGjR5Wenq4WLVrI19dXkydPVnp6uubOnZsbdQIAAABAnrB7Jmno0KGqXbu2Ll68KC8vL2v7f/7zH5vnGQEAAADAvcjumaTvv/9eW7Zskbu7u0176dKldeLECYcVBgAAAADOYPdMUlZWljIzM7O1Hz9+XL6+vg4pCgAAAACcxe6Q9Mgjj2j69OnWbYvFotTUVI0ZM0atW7d2ZG0AAAAAkOfsvtzu7bffVsuWLVWlShVdvXpVTz/9tA4cOKBixYrp008/zY0aAQAAACDP2B2SSpYsqV27dmnx4sXatWuXUlNT1atXL3Xt2tVmIQcAAAAAuBfZHZIkqVChQuratau6du1qbTt8+LD69++vNWvWOKw4AAAAAMhrdt+TdCuXLl1iCXAAAAAA9zyHhSQAAAAAKAgISQAAAABgQkgCAAAAAJMcL9zw4IMPymKx3HL/5cuXHVIQAAAAADhTjkNS+/btc7EMAAAAAMgfchySxowZk5t1AAAAAEC+wD1JAAAAAGBCSAIAAAAAE0ISAAAAAJgQkgAAAADAhJAEAAAAACY5Xt3ObN26dVq3bp3Onj2rrKwsm30ffvihQwoDAAAAAGewOySNGzdO48ePV+3atVWiRInbPmAWAAAAAO41doekuXPnKi4uTs8++2xu1AMAAAAATmX3PUkZGRmqX79+btQCAAAAAE5nd0jq3bu3Fi5cmBu1AAAAAIDT5ehyuxEjRlj/nJWVpffee09r165V9erV5ebmZtN36tSpjq0QAAAAAPJQjkLSL7/8YrP9wAMPSJJ+/fVXm3YWcQAAAABwr8tRSNqwYUNu1wEAAAAA+YLd9yQlJyfrwoUL2dovXLiglJQUhxQFAAAAAM5id0jq3LmzFi1alK19yZIl6ty5s0OKAgAAAABnsTsk/fjjj2rWrFm29qZNm+rHH390SFEAAAAA4Cx2h6T09HRdv349W/u1a9d05coVhxQFAAAAAM5id0iqW7eu3nvvvWztc+fOVa1atRxSFAAAAAA4S45WtzObMGGCoqKitGvXLjVv3lyStG7dOm3fvl1r1qxxeIEAAAAAkJfsnklq0KCBtm7dqtDQUC1ZskQrVqxQeHi4du/erUaNGuVGjQAAAACQZ+yeSZL+epjsJ5984uhaAAAAAMDp7iok3XD16lVlZGTYtPn5+f2jggAAAADAmey+3O7y5csaNGiQgoKC5O3trfvuu8/mBQAAAAD3MrtD0siRI7V+/XrFxsbKw8ND//3vfzVu3DiFhIRowYIFuVEjAAAAAOQZuy+3W7FihRYsWKCmTZuqR48eatSokcLDw1WqVCl98skn6tq1a27UCQAAAAB5wu6ZpAsXLqhs2bKS/rr/6MKFC5Kkhg0batOmTY6tDgAAAADymN0hqWzZskpMTJQkVapUSUuWLJH01wxTQECAQ4sDAAAAgLxmd0jq0aOHdu3aJUl66aWXNHv2bHl6emr48OEaOXKkwwsEAAAAgLxk9z1Jw4cPt/45KipKv//+u3bu3Knw8HBVr17docUBAAAAQF77R89JkqRSpUqpVKlSjqgFAAAAAJwux5fbtW7dWsnJydbtSZMmKSkpybp9/vx5ValSxaHFAQAAAEBey3FIWr16tdLT063bb7zxhnVlO0m6fv269u/f79jqAAAAACCP5TgkGYZx220AAAAAKAjsXt0OAAAAAAqyHIcki8Uii8WSrQ0AAAAACpIcr25nGIaio6Pl4eEhSbp69ar69+8vb29vSbK5XwkAAAAA7lU5Dkndu3e32X7mmWey9enWrds/rwgAAAAAnCjHIWnevHm5WQcAAAAA5Ass3AAAAAAAJoQkAAAAADAhJAEAAACACSEJAAAAAExyFJJq1qypixcvSpLGjx+vy5cv52pRAAAAAOAsOQpJ+/btU1pamiRp3LhxSk1NzdWiAAAAAMBZcrQE+AMPPKAePXqoYcOGMgxDb731lnx8fG7ad/To0Q4tEAAAAADyUo5mkuLi4lS0aFGtXLlSFotF3377rZYtW5bttXz5crsOvmnTJrVt21YhISGyWCzZ3h8dHS2LxWLzevTRR+06BgAAAADYI0czSRUrVtSiRYskSS4uLlq3bp2CgoL+8cHT0tJUo0YN9ezZU0888cRN+zz66KM2D7L18PD4x8cFAAAAgFvJUUgyy8rKctjBW7VqpVatWt22j4eHh4KDgx12TAAAAAC4HbtDkiQdOnRI06dP1759+yRJVapU0dChQ1WuXDmHFidJ8fHxCgoK0n333aeHH35YEyZMUNGiRW/ZPz09Xenp6dbtlJQUh9cEAAAAoOCyOyStXr1a7dq10wMPPKAGDRpIkjZv3qyIiAitWLFCLVq0cFhxjz76qJ544gmVKVNGhw4d0v/5P/9HrVq10tatW+Xq6nrT90ycOFHjxo1zWA0AAADI32qNXODsEpxi55vdnF1CgWV3SHrppZc0fPhwTZo0KVv7iy++6NCQ1LlzZ+ufq1WrpurVq6tcuXKKj49X8+bNb/qemJgYjRgxwrqdkpKi0NBQh9UEAAAAoGDL0ep2Zvv27VOvXr2ytffs2VO//fabQ4q6lbJly6pYsWI6ePDgLft4eHjIz8/P5gUAAAAAOWV3SAoMDFRCQkK29oSEBIeseHc7x48f1/nz51WiRIlcPQ4AAACAfy+7L7fr06eP+vbtq8OHD6t+/fqS/ronafLkyTaXueVEamqqzaxQYmKiEhISVKRIERUpUkTjxo1Thw4dFBwcrEOHDmnUqFEKDw9Xy5Yt7S0bAAAAAHLE7pD06quvytfXV2+//bZiYmIkSSEhIRo7dqyGDBli11g7duxQs2bNrNs3Qlb37t0VGxur3bt3a/78+UpKSlJISIgeeeQRvfbaazwrCQAAAECusTskWSwWDR8+XMOHD9elS5ckSb6+vnd18KZNm8owjFvuX7169V2NCwAAAAB3666ek3TD3YYjAAAAAMiv7F64AQAAAAAKMkISAAAAAJgQkgAAAADAxK6QdO3aNTVv3lwHDhzIrXoAAAAAwKnsCklubm7avXt3btUCAAAAAE5n9+V2zzzzjD744IPcqAUAAAAAnM7uJcCvX7+uDz/8UGvXrlWtWrXk7e1ts3/q1KkOKw4AAAAA8prdIenXX39VzZo1JUl//PGHzT6LxeKYqgAAAADASewOSRs2bMiNOgAAAAAgX7jrJcAPHjyo1atX68qVK5IkwzAcVhQAAAAAOIvdIen8+fNq3ry5KlSooNatW+vUqVOSpF69eun55593eIEAAAAAkJfsDknDhw+Xm5ubjh49qsKFC1vbO3XqpFWrVjm0OAAAAADIa3bfk7RmzRqtXr1aJUuWtGkvX768/vzzT4cVBgAAAADOYPdMUlpams0M0g0XLlyQh4eHQ4oCAAAAAGexOyQ1atRICxYssG5bLBZlZWVpypQpatasmUOLAwAAAIC8ZvfldlOmTFHz5s21Y8cOZWRkaNSoUdq7d68uXLigzZs350aNAAAAAJBn7J5Jqlq1qv744w81bNhQjz/+uNLS0vTEE0/ol19+Ubly5XKjRgAAAADIM3bPJEmSv7+/Xn75ZUfXAgAAAABOd1ch6eLFi/rggw+0b98+SVKVKlXUo0cPFSlSxKHFAQAAAEBes/tyu02bNql06dKaOXOmLl68qIsXL2rmzJkqU6aMNm3alBs1AgAAAECesXsmaeDAgerUqZNiY2Pl6uoqScrMzNSAAQM0cOBA7dmzx+FFAgAAAEBesXsm6eDBg3r++eetAUmSXF1dNWLECB08eNChxQEAAABAXrM7JNWsWdN6L5LZvn37VKNGDYcUBQAAAADOkqPL7Xbv3m3985AhQzR06FAdPHhQDz30kCRp27Ztmj17tiZNmpQ7VQIAAABAHslRSHrggQdksVhkGIa1bdSoUdn6Pf300+rUqZPjqgMAAACAPJajkJSYmJjbdQAAAABAvpCjkFSqVKncrgMAAAAA8oW7epjsyZMn9cMPP+js2bPKysqy2TdkyBCHFAYAAAAAzmB3SIqLi1O/fv3k7u6uokWLymKxWPdZLBZCEgAAAIB7mt0h6dVXX9Xo0aMVExMjFxe7VxAHAAAAgHzN7pRz+fJlde7cmYAEAAAAoECyO+n06tVLS5cuzY1aAAAAAMDp7L7cbuLEiWrTpo1WrVqlatWqyc3NzWb/1KlTHVYcAAAAAOS1uwpJq1evVsWKFSUp28INAAAAAHAvszskvf322/rwww8VHR2dC+UAAAAAgHPZfU+Sh4eHGjRokBu1AAAAAIDT2R2Shg4dqlmzZuVGLQAAAADgdHZfbvfTTz9p/fr1WrlypSIiIrIt3PDFF184rDgAAAAAyGt2h6SAgAA98cQTuVELAAAAADid3SFp3rx5uVEHAAAAAOQLdt+TBAAAAAAFmd0zSWXKlLnt85AOHz78jwoCAAAAAGeyOyQNGzbMZvvatWv65ZdftGrVKo0cOdJRdQEAAACAU9gdkoYOHXrT9tmzZ2vHjh3/uCAAAAAAcCaH3ZPUqlUrff75544aDgAAAACcwmEh6bPPPlORIkUcNRwAAAAAOIXdl9s9+OCDNgs3GIah06dP69y5c5ozZ45DiwMAAACAvGZ3SGrfvr3NtouLiwIDA9W0aVNVqlTJUXUBAAAAgFPYHZLGjBmTG3UAAAAAQL7Aw2QBAAAAwCTHM0kuLi63fYisJFksFl2/fv0fFwUAAAAAzpLjkLRs2bJb7tu6datmzpyprKwshxQFAAAAAM6S45D0+OOPZ2vbv3+/XnrpJa1YsUJdu3bV+PHjHVocAAAAAOS1u7on6eTJk+rTp4+qVaum69evKyEhQfPnz1epUqUcXR8AAAAA5Cm7QlJycrJefPFFhYeHa+/evVq3bp1WrFihqlWr5lZ9AAAAAJCncny53ZQpUzR58mQFBwfr008/venldwAAAABwr8txSHrppZfk5eWl8PBwzZ8/X/Pnz79pvy+++MJhxQEAAABAXstxSOrWrdsdlwAHAAAAgHtdjkNSXFxcLpYBAAAAAPnDXa1uBwAAAAAFFSEJAAAAAEycGpI2bdqktm3bKiQkRBaLRcuXL7fZbxiGRo8erRIlSsjLy0tRUVE6cOCAc4oFAAAA8K/g1JCUlpamGjVqaPbs2TfdP2XKFM2cOVNz587Vjz/+KG9vb7Vs2VJXr17N40oBAAAA/FvkeOGG3NCqVSu1atXqpvsMw9D06dP1yiuvWJ/JtGDBAhUvXlzLly9X586d87JUAAAAAP8S+faepMTERJ0+fVpRUVHWNn9/f9WrV09bt2695fvS09OVkpJi8wIAAACAnHLqTNLtnD59WpJUvHhxm/bixYtb993MxIkTNW7cuFytDQAAAHC2o+OrObsEpwgbvSfXj5FvZ5LuVkxMjJKTk62vY8eOObskAAAAAPeQfBuSgoODJUlnzpyxaT9z5ox13814eHjIz8/P5gUAAAAAOZVvQ1KZMmUUHBysdevWWdtSUlL0448/KjIy0omVAQAAACjInHpPUmpqqg4ePGjdTkxMVEJCgooUKaKwsDANGzZMEyZMUPny5VWmTBm9+uqrCgkJUfv27Z1XNAAAAIACzakhaceOHWrWrJl1e8SIEZKk7t27Ky4uTqNGjVJaWpr69u2rpKQkNWzYUKtWrZKnp6ezSgYAAABQwDk1JDVt2lSGYdxyv8Vi0fjx4zV+/Pg8rAoAAADAv1m+vScJAAAAAJyBkAQAAAAAJoQkAAAAADAhJAEAAACACSEJAAAAAEwISQAAAABgQkgCAAAAABNCEgAAAACYEJIAAAAAwISQBAAAAAAmhCQAAAAAMCEkAQAAAIAJIQkAAAAATAhJAAAAAGBCSAIAAAAAE0ISAAAAAJgQkgAAAADAhJAEAAAAACaEJAAAAAAwISQBAAAAgAkhCQAAAABMCEkAAAAAYEJIAgAAAAATQhIAAAAAmBCSAAAAAMCEkAQAAAAAJoQkAAAAADAhJAEAAACACSEJAAAAAEwISQAAAABgQkgCAAAAABNCEgAAAACYEJIAAAAAwISQBAAAAAAmhCQAAAAAMCEkAQAAAIAJIQkAAAAATAhJAAAAAGBCSAIAAAAAE0ISAAAAAJgQkgAAAADAhJAEAAAAACaEJAAAAAAwISQBAAAAgAkhCQAAAABMCEkAAAAAYEJIAgAAAAATQhIAAAAAmBCSAAAAAMCEkAQAAAAAJoQkAAAAADAhJAEAAACACSEJAAAAAEwISQAAAABgQkgCAAAAABNCEgAAAACYEJIAAAAAwISQBAAAAAAmhCQAAAAAMCEkAQAAAIAJIQkAAAAATAhJAAAAAGCSr0PS2LFjZbFYbF6VKlVydlkAAAAACrBCzi7gTiIiIrR27VrrdqFC+b5kAAAAAPewfJ84ChUqpODgYGeXAQAAAOBfIl9fbidJBw4cUEhIiMqWLauuXbvq6NGjt+2fnp6ulJQUmxcAAAAA5FS+Dkn16tVTXFycVq1apdjYWCUmJqpRo0a6dOnSLd8zceJE+fv7W1+hoaF5WDEAAACAe12+DkmtWrXSU089perVq6tly5b65ptvlJSUpCVLltzyPTExMUpOTra+jh07locVAwAAALjX5ft7kswCAgJUoUIFHTx48JZ9PDw85OHhkYdVAQAAAChI8vVM0t+lpqbq0KFDKlGihLNLAQAAAFBA5euQ9MILL2jjxo06cuSItmzZov/85z9ydXVVly5dnF0aAAAAgAIqX19ud/z4cXXp0kXnz59XYGCgGjZsqG3btikwMNDZpQEAAAAooPJ1SFq0aJGzSwAAAADwL5OvL7cDAAAAgLxGSAIAAAAAE0ISAAAAAJgQkgAAAADAhJAEAAAAACaEJAAAAAAwISQBAAAAgAkhCQAAAABM8vXDZAEUfEfHV3N2CU4RNnqPs0sAAAC3wEwSAAAAAJgQkgAAAADAhJAEAAAAACaEJAAAAAAwISQBAAAAgAkhCQAAAABMCEkAAAAAYEJIAgAAAAATQhIAAAAAmBCSAAAAAMCEkAQAAAAAJoQkAAAAADAhJAEAAACACSEJAAAAAEwISQAAAABgQkgCAAAAABNCEgAAAACYEJIAAAAAwISQBAAAAAAmhCQAAAAAMCEkAQAAAIAJIQkAAAAATAhJAAAAAGBCSAIAAAAAE0ISAAAAAJgQkgAAAADAhJAEAAAAACaEJAAAAAAwKeTsAvK7WiMXOLsEp9j5ZjdnlwAAAAA4BTNJAAAAAGBCSAIAAAAAE0ISAAAAAJgQkgAAAADAhJAEAAAAACaEJAAAAAAwISQBAAAAgAkhCQAAAABMCEkAAAAAYEJIAgAAAAATQhIAAAAAmBCSAAAAAMCEkAQAAAAAJoQkAAAAADAp5OwCAPyl1sgFzi7BKZb5OrsCAAAAW8wkAQAAAIAJIQkAAAAATAhJAAAAAGBCSAIAAAAAE0ISAAAAAJgQkgAAAADAhJAEAAAAACaEJAAAAAAwISQBAAAAgAkhCQAAAABM7omQNHv2bJUuXVqenp6qV6+efvrpJ2eXBAAAAKCAyvchafHixRoxYoTGjBmjn3/+WTVq1FDLli119uxZZ5cGAAAAoADK9yFp6tSp6tOnj3r06KEqVapo7ty5Kly4sD788ENnlwYAAACgACrk7AJuJyMjQzt37lRMTIy1zcXFRVFRUdq6detN35Oenq709HTrdnJysiQpJSXlrmrITL9yV++7193t94W792891y65ZTq7BKdw5r8xzrV/F861vMe5lvc41/5d/sm5duO9hmHctp/FuFMPJzp58qTuv/9+bdmyRZGRkdb2UaNGaePGjfrxxx+zvWfs2LEaN25cXpYJAAAA4B5y7NgxlSxZ8pb78/VM0t2IiYnRiBEjrNtZWVm6cOGCihYtKovF4sTK7h0pKSkKDQ3VsWPH5Ofn5+xyUIBxriGvcK4hr3CuIa9wrt0dwzB06dIlhYSE3LZfvg5JxYoVk6urq86cOWPTfubMGQUHB9/0PR4eHvLw8LBpCwgIyK0SCzQ/Pz/+0SFPcK4hr3CuIa9wriGvcK7Zz9/f/4598vXCDe7u7qpVq5bWrVtnbcvKytK6detsLr8DAAAAAEfJ1zNJkjRixAh1795dtWvXVt26dTV9+nSlpaWpR48ezi4NAAAAQAGU70NSp06ddO7cOY0ePVqnT5/WAw88oFWrVql48eLOLq3A8vDw0JgxY7Jdtgg4Guca8grnGvIK5xryCuda7srXq9sBAAAAQF7L1/ckAQAAAEBeIyQBAAAAgAkhCQAAAABMCEkAAAAAYEJIgo1Nmzapbdu2CgkJkcVi0fLly51dEgqgiRMnqk6dOvL19VVQUJDat2+v/fv3O7ssFECxsbGqXr269WGLkZGR+vbbb51dFv4FJk2aJIvFomHDhjm7FBQwY8eOlcVisXlVqlTJ2WUVOIQk2EhLS1ONGjU0e/ZsZ5eCAmzjxo0aOHCgtm3bpu+++07Xrl3TI488orS0NGeXhgKmZMmSmjRpknbu3KkdO3bo4Ycf1uOPP669e/c6uzQUYNu3b9e7776r6tWrO7sUFFARERE6deqU9fXDDz84u6QCJ98/Jwl5q1WrVmrVqpWzy0ABt2rVKpvtuLg4BQUFaefOnWrcuLGTqkJB1LZtW5vt119/XbGxsdq2bZsiIiKcVBUKstTUVHXt2lXvv/++JkyY4OxyUEAVKlRIwcHBzi6jQGMmCYDTJScnS5KKFCni5EpQkGVmZmrRokVKS0tTZGSks8tBATVw4EA99thjioqKcnYpKMAOHDigkJAQlS1bVl27dtXRo0edXVKBw0wSAKfKysrSsGHD1KBBA1WtWtXZ5aAA2rNnjyIjI3X16lX5+Pho2bJlqlKlirPLQgG0aNEi/fzzz9q+fbuzS0EBVq9ePcXFxalixYo6deqUxo0bp0aNGunXX3+Vr6+vs8srMAhJAJxq4MCB+vXXX7meGrmmYsWKSkhIUHJysj777DN1795dGzduJCjBoY4dO6ahQ4fqu+++k6enp7PLQQFmvi2ievXqqlevnkqVKqUlS5aoV69eTqysYCEkAXCaQYMGaeXKldq0aZNKlizp7HJQQLm7uys8PFySVKtWLW3fvl0zZszQu+++6+TKUJDs3LlTZ8+eVc2aNa1tmZmZ2rRpk9555x2lp6fL1dXViRWioAoICFCFChV08OBBZ5dSoBCSAOQ5wzA0ePBgLVu2TPHx8SpTpoyzS8K/SFZWltLT051dBgqY5s2ba8+ePTZtPXr0UKVKlfTiiy8SkJBrUlNTdejQIT377LPOLqVAISTBRmpqqs1vIhITE5WQkKAiRYooLCzMiZWhIBk4cKAWLlyoL7/8Ur6+vjp9+rQkyd/fX15eXk6uDgVJTEyMWrVqpbCwMF26dEkLFy5UfHy8Vq9e7ezSUMD4+vpmu6/S29tbRYsW5X5LONQLL7ygtm3bqlSpUjp58qTGjBkjV1dXdenSxdmlFSiEJNjYsWOHmjVrZt0eMWKEJKl79+6Ki4tzUlUoaGJjYyVJTZs2tWmfN2+eoqOj874gFFhnz55Vt27ddOrUKfn7+6t69epavXq1WrRo4ezSAOCuHD9+XF26dNH58+cVGBiohg0batu2bQoMDHR2aQWKxTAMw9lFAAAAAEB+wXOSAAAAAMCEkAQAAAAAJoQkAAAAADAhJAEAAACACSEJAAAAAEwISQAAAABgQkgCAAAAABNCEgAAAACYEJIAAAWWxWLR8uXLHT5u6dKlNX36dIePCwDIHwhJAIBcc/r0aQ0dOlTh4eHy9PRU8eLF1aBBA8XGxury5cvOLk/R0dGyWCyyWCxyd3dXeHi4xo8fr+vXr9/2fdu3b1ffvn3zqEoAQF4r5OwCAAAF0+HDh9WgQQMFBATojTfeULVq1eTh4aE9e/bovffe0/3336927do5u0w9+uijmjdvntLT0/XNN99o4MCBcnNzU0xMTLa+GRkZcnd3V2BgoBMqBQDkFWaSAAC5YsCAASpUqJB27Nihjh07qnLlyipbtqwef/xxff3112rbtq2179GjR/X444/Lx8dHfn5+6tixo86cOWMzXmxsrMqVKyd3d3dVrFhRH330kc3+AwcOqHHjxvL09FSVKlX03Xff5ahODw8PBQcHq1SpUnruuecUFRWlr776StJfM03t27fX66+/rpCQEFWsWFFS9svtkpKS1K9fPxUvXlyenp6qWrWqVq5cad3/ww8/qFGjRvLy8lJoaKiGDBmitLQ0u75PAEDeYSYJAOBw58+f15o1a/TGG2/I29v7pn0sFoskKSsryxqQNm7cqOvXr2vgwIHq1KmT4uPjJUnLli3T0KFDNX36dEVFRWnlypXq0aOHSpYsqWbNmikrK0tPPPGEihcvrh9//FHJyckaNmzYXdXu5eWl8+fPW7fXrVsnPz+/W4aurKwstWrVSpcuXdLHH3+scuXK6bfffpOrq6sk6dChQ3r00Uc1YcIEffjhhzp37pwGDRqkQYMGad68eXdVIwAgdxGSAAAOd/DgQRmGYZ15uaFYsWK6evWqJGngwIGaPHmy1q1bpz179igxMVGhoaGSpAULFigiIkLbt29XnTp19NZbbyk6OloDBgyQJI0YMULbtm3TW2+9pWbNmmnt2rX6/ffftXr1aoWEhEiS3njjDbVq1SrHNRuGoXXr1mn16tUaPHiwtd3b21v//e9/5e7uftP3rV27Vj/99JP27dunChUqSJLKli1r3T9x4kR17drVGtrKly+vmTNnqkmTJoqNjZWnp2eOawQA5A0utwMA5JmffvpJCQkJioiIUHp6uiRp3759Cg0NtQYkSapSpYoCAgK0b98+a58GDRrYjNWgQQOb/aGhodaAJEmRkZE5qmnlypXy8fGRp6enWrVqpU6dOmns2LHW/dWqVbtlQJKkhIQElSxZ0hqQ/m7Xrl2Ki4uTj4+P9dWyZUtlZWUpMTExRzUCAPIWM0kAAIcLDw+XxWLR/v37bdpvzLB4eXk5o6ybatasmWJjY+Xu7q6QkBAVKmT7v8ZbXS54w50+S2pqqvr166chQ4Zk2xcWFmZ/wQCAXMdMEgDA4YoWLaoWLVronXfeueMCBZUrV9axY8d07Ngxa9tvv/2mpKQkValSxdpn8+bNNu/bvHmzzf5jx47p1KlT1v3btm3LUa3e3t4KDw9XWFhYtoCUE9WrV9fx48f1xx9/3HR/zZo19dtvvyk8PDzb63YzVAAA5yEkAQByxZw5c3T9+nXVrl1bixcv1r59+7R//359/PHH+v33360LG0RFRalatWrq2rWrfv75Z/3000/q1q2bmjRpotq1a0uSRo4cqbi4OMXGxurAgQOaOnWqvvjiC73wwgvWMSpUqKDu3btr165d+v777/Xyyy/nyeds0qSJGjdurA4dOui7775TYmKivv32W61atUqS9OKLL2rLli0aNGiQEhISdODAAX355ZcaNGhQntQHALAfIQkAkCvKlSunX375RVFRUYqJiVGNGjVUu3ZtzZo1Sy+88IJee+01SX+tcvfll1/qvvvuU+PGjRUVFaWyZctq8eLF1rHat2+vGTNm6K233lJERITeffddzZs3T02bNpUkubi4aNmyZbpy5Yrq1q2r3r176/XXX8+zz/r555+rTp066tKli6pUqaJRo0YpMzNT0l8zTRs3btQff/yhRo0a6cEHH9To0aNt7p8CAOQvFsMwDGcXAQAAAAD5BTNJAAAAAGBCSAIAAAAAE0ISAAAAAJgQkgAAAADAhJAEAAAAACaEJAAAAAAwISQBAAAAgAkhCQAAAABMCEkAAAAAYEJIAgAAAAATQhIAAAAAmPxf1WuUQiBZZ28AAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Bar Plot for single variable for X5, how does satisfication of courier impact the happiness\n",
        "df5 = data[['Y', 'X5']].value_counts().reset_index()\n",
        "df5['Y'] = df5['Y'].map({0: 'Unhappy', 1: 'Happy'})\n",
        "df5.columns = ['Y', 'X5', 'Count']\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.barplot(x='X5', y='Count', hue='Y', data=df5)\n",
        "plt.xlabel('Satisfication of Couriers')\n",
        "plt.ylabel('Number of Each Level')\n",
        "plt.title('Customer Satisfication on Order Couriers')\n",
        "plt.legend(title='Satisfaction')\n",
        "plt.show()\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "outputId": "f4fa4b47-bd9f-417e-a0a2-cbb455bd1d65",
        "id": "OwVA8IRHqZew"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Bar Plot for single variable for X6, how does the app easiness impact the happiness\n",
        "df6 = data[['Y', 'X6']].value_counts().reset_index()\n",
        "df6['Y'] = df6['Y'].map({0: 'Unhappy', 1: 'Happy'})\n",
        "df6.columns = ['Y', 'X6', 'Count']\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.barplot(x='X6', y='Count', hue='Y', data=df6)\n",
        "plt.xlabel('App Easiness of Using')\n",
        "plt.ylabel('Number of Each Level')\n",
        "plt.title('Customer Satisfication on App Easiness')\n",
        "plt.legend(title='Satisfaction')\n",
        "plt.show()\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "outputId": "fe181a34-46f2-43cd-9f62-5019c96a2dc6",
        "id": "XhBQMPb8q2pY"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visulization of the dataset, the visualization not really meaningful,\n",
        "# The visualization did not show significant linear relationship.\n",
        "sns.pairplot(data)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "oi_0xhEycWLR",
        "outputId": "793ab06e-f099-490c-b16e-cd88ae205c1f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1750x1750 with 56 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Correlation matrice, no specific correlation between feature variables and y\n",
        "plt.figure(figsize=(10, 8))\n",
        "correlation_matrix = data.corr()\n",
        "sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt='.2f')\n",
        "plt.title('Correlation Matrix')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 699
        },
        "id": "hL8cIO-Cp3LV",
        "outputId": "9253f3d9-8b60-4f98-c99d-c012e084ae96"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.pairplot(data, hue='Y', diag_kind='kde')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 993
        },
        "id": "nkqKbuI9p4Yo",
        "outputId": "2ccf89a1-b1df-4cfd-b7ef-b6d193014455"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1558.88x1500 with 42 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Split the data into features (X1 to X6) and target (Y)\n",
        "x = data[['X1', 'X2', 'X3', 'X4', 'X5', 'X6']]\n",
        "y = data['Y']\n"
      ],
      "metadata": {
        "id": "cKmndwPBrsZE"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Split the data into training and test sets\n",
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42, stratify=y)"
      ],
      "metadata": {
        "id": "NIRzBZx0sS3r"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Scale the features\n",
        "scaler = StandardScaler()\n",
        "x_train = scaler.fit_transform(x_train)\n",
        "x_test = scaler.transform(x_test)\n"
      ],
      "metadata": {
        "id": "4cykiGdqsd_9"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Using LogisticRegression as the classification algorithm and train\n",
        "model = LogisticRegression()\n",
        "model.fit(x_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "zVbfrjk-sjMi",
        "outputId": "7202b577-737a-417b-c62e-a19dcc355eaf"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Evaluate the model, the accuracy not that good, there may be bias because smaller testing dataset\n",
        "y_pred = model.predict(x_test)\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "precision = precision_score(y_test, y_pred)\n",
        "recall = recall_score(y_test, y_pred)\n",
        "f1 = f1_score(y_test, y_pred)\n",
        "\n",
        "print(f'Accuracy: {accuracy:.2f}')\n",
        "print(f'Precision: {precision:.2f}')\n",
        "print(f'Recall: {recall:.2f}')\n",
        "print(f'F1-score: {f1:.2f}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bGT6KseNs8Zv",
        "outputId": "2f592a60-563f-4db5-fbdf-a71df4aea27c"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.62\n",
            "Precision: 0.60\n",
            "Recall: 0.86\n",
            "F1-score: 0.71\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Precision-Recall Curve\n",
        "y_pred_prob = model.predict_proba(x_test)[:, 1]\n",
        "precision, recall, _ = precision_recall_curve(y_test, y_pred_prob)\n",
        "plt.figure(figsize=(8, 6))\n",
        "plt.plot(recall, precision, marker='.')\n",
        "plt.xlabel('Recall')\n",
        "plt.ylabel('Precision')\n",
        "plt.title('Precision-Recall Curve')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "hM__hx_9tPpX",
        "outputId": "ece1a5b5-83b3-4223-97cd-bfd830f77d71"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#ROC Curve\n",
        "fpr, tpr, thresholds = roc_curve(y_test, y_pred_prob)\n",
        "roc_auc = roc_auc_score(y_test, y_pred_prob)\n",
        "\n",
        "plt.figure(figsize=(8, 6))\n",
        "plt.plot(fpr, tpr, label=f'AUC = {roc_auc:.2f}')\n",
        "plt.plot([0, 1], [0, 1], 'k--')\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.title('ROC Curve')\n",
        "plt.legend(loc='lower right')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "3sD0F_NJthuB",
        "outputId": "d8039b2d-7058-436f-dcf2-668ba1019b43"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Using K-fold method to split and evaluate the model\n",
        "# Create the StratifiedKFold cross-validator\n",
        "x_scaled = scaler.fit_transform(x)\n",
        "cv = StratifiedKFold(n_splits=5, shuffle=True)\n",
        "\n",
        "# Perform cross-validation\n",
        "cv_scores = cross_val_score(model, x_scaled, y, cv=cv, scoring='accuracy')\n",
        "print(f'Cross-Validation Accuracy Scores: {cv_scores}')\n",
        "print(f'Mean Cross-Validation Accuracy: {np.mean(cv_scores):.2f}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FSJgsQhotsnF",
        "outputId": "4581caa9-f378-4dbe-a03d-86ee63071934"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cross-Validation Accuracy Scores: [0.57692308 0.56       0.56       0.64       0.56      ]\n",
            "Mean Cross-Validation Accuracy: 0.58\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install catboost"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "02aACzerfXaz",
        "outputId": "aabdc53b-c283-41eb-b5bd-3dc6a6579ff7"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting catboost\n",
            "  Downloading catboost-1.2.5-cp310-cp310-manylinux2014_x86_64.whl.metadata (1.2 kB)\n",
            "Requirement already satisfied: graphviz in /usr/local/lib/python3.10/dist-packages (from catboost) (0.20.3)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from catboost) (3.7.1)\n",
            "Requirement already satisfied: numpy>=1.16.0 in /usr/local/lib/python3.10/dist-packages (from catboost) (1.26.4)\n",
            "Requirement already satisfied: pandas>=0.24 in /usr/local/lib/python3.10/dist-packages (from catboost) (2.1.4)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from catboost) (1.13.1)\n",
            "Requirement already satisfied: plotly in /usr/local/lib/python3.10/dist-packages (from catboost) (5.15.0)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from catboost) (1.16.0)\n",
            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.24->catboost) (2.8.2)\n",
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            "Downloading catboost-1.2.5-cp310-cp310-manylinux2014_x86_64.whl (98.2 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m98.2/98.2 MB\u001b[0m \u001b[31m7.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: catboost\n",
            "Successfully installed catboost-1.2.5\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# K-fold with pipeline as feature selection\n",
        "from sklearn.feature_selection import SelectKBest, f_classif\n",
        "from sklearn.pipeline import Pipeline\n",
        "\n",
        "selector = SelectKBest(score_func=f_classif, k=2)  # Select top 2 features\n",
        "\n",
        "# Create a pipeline with feature selection and a model\n",
        "pipeline = Pipeline([\n",
        "    ('scaler', StandardScaler()),  # Feature scaling\n",
        "    ('selector', selector),  # Feature selection\n",
        "    ('classifier', LogisticRegression(random_state=42))  # Model\n",
        "])\n",
        "\n",
        "# Define the cross-validation strategy\n",
        "cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)\n",
        "\n",
        "# Evaluate the pipeline using cross-validation\n",
        "cv_scores = cross_val_score(pipeline, x, y, cv=cv, scoring='accuracy')\n",
        "\n",
        "print(f'Cross-Validation Accuracy Scores: {cv_scores}')\n",
        "print(f'Mean Cross-Validation Accuracy: {np.mean(cv_scores):.2f}')"
      ],
      "metadata": {
        "id": "9dchCd_odI9T",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "83854dff-2680-4a5f-db83-0f25c98f4b39"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cross-Validation Accuracy Scores: [0.61538462 0.48       0.56       0.6        0.52      ]\n",
            "Mean Cross-Validation Accuracy: 0.56\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# catboost feature selection model\n",
        "from catboost import CatBoostClassifier, Pool\n",
        "\n",
        "model_cat = CatBoostClassifier(iterations=100, learning_rate=0.1, depth=6, loss_function='Logloss')\n",
        "model_cat.fit(x, y)\n",
        "importances = model_cat.feature_importances_\n",
        "\n",
        "# visualization\n",
        "features = pd.DataFrame({'feature': x.columns, 'importance': importances})\n",
        "features = features.sort_values(by='importance', ascending=False)\n",
        "print(features)\n",
        "\n"
      ],
      "metadata": {
        "id": "_0LlcF3ndLg-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "667a1adf-a1d0-4d66-8b6b-db7a591b8a20"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
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            "  feature  importance\n",
            "0      X1   27.789212\n",
            "2      X3   21.666867\n",
            "4      X5   13.570066\n",
            "5      X6   12.771368\n",
            "3      X4   12.385500\n",
            "1      X2   11.816986\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# xgboost feature selection\n",
        "from xgboost import XGBClassifier\n",
        "\n",
        "model_xgb = XGBClassifier()\n",
        "model_xgb.fit(x, y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "id": "iQH1jV7iY0eY",
        "outputId": "a628736b-1fb6-42c5-f2ae-be49b17c5650"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
              "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "              gamma=None, grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
              "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "              multi_strategy=None, n_estimators=None, n_jobs=None,\n",
              "              num_parallel_tree=None, random_state=None, ...)"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
              "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "              gamma=None, grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
              "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "              multi_strategy=None, n_estimators=None, n_jobs=None,\n",
              "              num_parallel_tree=None, random_state=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
              "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "              gamma=None, grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
              "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "              multi_strategy=None, n_estimators=None, n_jobs=None,\n",
              "              num_parallel_tree=None, random_state=None, ...)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Sort the DataFrame by importance\n",
        "importances = model_xgb.feature_importances_\n",
        "features = pd.DataFrame({'Feature': x.columns, 'Importance': importances})\n",
        "features = features.sort_values(by='Importance', ascending=False)\n",
        "\n",
        "# Plot the feature importances\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.barh(features['Feature'], features['Importance'], color='skyblue')\n",
        "plt.xlabel('Importance')\n",
        "plt.ylabel('Feature')\n",
        "plt.title('Feature Importance')\n",
        "plt.gca().invert_yaxis()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "oH49eb_bZk1J",
        "outputId": "83dbfe90-ada7-451e-e277-6522cabec9ca"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# lightgbm feature selection\n",
        "from lightgbm import LGBMClassifier\n",
        "\n",
        "model_lgb = LGBMClassifier()\n",
        "model_lgb.fit(x, y)\n",
        "importances = model_lgb.feature_importances_\n",
        "\n",
        "# visualization\n",
        "features = pd.DataFrame({'feature': x.columns, 'importance': importances})\n",
        "features = features.sort_values(by='importance', ascending=False)\n",
        "print(features)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yAMThMyZY3tV",
        "outputId": "c52283ee-d4c0-492e-a423-88981a8510cb"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/dask/dataframe/__init__.py:42: FutureWarning: \n",
            "Dask dataframe query planning is disabled because dask-expr is not installed.\n",
            "\n",
            "You can install it with `pip install dask[dataframe]` or `conda install dask`.\n",
            "This will raise in a future version.\n",
            "\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[LightGBM] [Info] Number of positive: 69, number of negative: 57\n",
            "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.003893 seconds.\n",
            "You can set `force_col_wise=true` to remove the overhead.\n",
            "[LightGBM] [Info] Total Bins 31\n",
            "[LightGBM] [Info] Number of data points in the train set: 126, number of used features: 6\n",
            "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.547619 -> initscore=0.191055\n",
            "[LightGBM] [Info] Start training from score 0.191055\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
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            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
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            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
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            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
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            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
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            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
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            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
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            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
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            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "  feature  importance\n",
            "1      X2          70\n",
            "5      X6          69\n",
            "4      X5          61\n",
            "2      X3          60\n",
            "3      X4          49\n",
            "0      X1          48\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# decision tree feature selection model\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "model_dt = DecisionTreeClassifier()\n",
        "model_dt.fit(x, y)\n",
        "importances = model_dt.feature_importances_\n",
        "\n",
        "# visualization\n",
        "features = pd.DataFrame({'feature': x.columns, 'importance': importances})\n",
        "features = features.sort_values(by='importance', ascending=False)\n",
        "print(features)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XQ_5AozfY6ei",
        "outputId": "64ef5bb9-8607-4183-f644-f7cf117ba70f"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  feature  importance\n",
            "4      X5    0.216981\n",
            "0      X1    0.198075\n",
            "2      X3    0.178844\n",
            "1      X2    0.174610\n",
            "5      X6    0.130269\n",
            "3      X4    0.101220\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# After compare feature selection models, feature X2 and X4 have high probability of useless feature\n",
        "# The new model training will be discard these two features.\n",
        "\n",
        "x_new = x.drop(columns=['X2', 'X4'])\n"
      ],
      "metadata": {
        "id": "2jE5mDoatbAY"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Split the new data into training and test sets\n",
        "x_train, x_test, y_train, y_test = train_test_split(x_new, y, test_size=0.2, random_state=42)\n",
        "\n",
        "# Train a CatBoost model after drop X2, X4\n",
        "model = CatBoostClassifier(iterations=100, depth=6, learning_rate=0.1, random_seed=42, verbose=0)\n",
        "model.fit(x_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kEIHf12euj-m",
        "outputId": "a29fb315-603b-4b9f-f2a9-a0434957383d"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<catboost.core.CatBoostClassifier at 0x7c5fe7811480>"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Make predictions\n",
        "y_pred = model.predict(x_test)\n",
        "\n",
        "# Evaluate the model\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "precision = precision_score(y_test, y_pred)\n",
        "recall = recall_score(y_test, y_pred)\n",
        "f1 = f1_score(y_test, y_pred)\n",
        "\n",
        "print(f'Accuracy: {accuracy:.2f}')\n",
        "print(f'Precision: {precision:.2f}')\n",
        "print(f'Recall: {recall:.2f}')\n",
        "print(f'F1-score: {f1:.2f}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H6wrp6aDvha_",
        "outputId": "21760d4e-7014-42fa-ba4c-f3a0d4001401"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.65\n",
            "Precision: 0.57\n",
            "Recall: 0.73\n",
            "F1-score: 0.64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Train the XGBoost model after drop feature X2, X4\n",
        "model = XGBClassifier(n_estimators=100, learning_rate=0.1, max_depth=6, random_state=42)\n",
        "model.fit(x_train, y_train)\n",
        "\n",
        "# Make predictions\n",
        "y_pred = model.predict(x_test)\n",
        "\n",
        "# Evaluate the model\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "precision = precision_score(y_test, y_pred)\n",
        "recall = recall_score(y_test, y_pred)\n",
        "f1 = f1_score(y_test, y_pred)\n",
        "\n",
        "print(f'Accuracy: {accuracy:.2f}')\n",
        "print(f'Precision: {precision:.2f}')\n",
        "print(f'Recall: {recall:.2f}')\n",
        "print(f'F1-score: {f1:.2f}')\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OWJpCuMrwq4g",
        "outputId": "d1edf225-5715-4653-8141-5c4afb264aba"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.65\n",
            "Precision: 0.57\n",
            "Recall: 0.73\n",
            "F1-score: 0.64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Train lightgbm model after drop X2,X4\n",
        "import lightgbm as lgb\n",
        "\n",
        "# Convert the classic dataset into lightgbm dataset\n",
        "train_data = lgb.Dataset(x_train, label=y_train)\n",
        "test_data = lgb.Dataset(x_test, label=y_test, reference=train_data)\n",
        "\n",
        "params = {\n",
        "    'objective': 'multiclass',\n",
        "    'num_class': 2,\n",
        "    'metric': 'multi_logloss',\n",
        "    'boosting_type': 'gbdt',            # Gradient Boosting Decision Tree\n",
        "    'num_leaves': 8,\n",
        "    'learning_rate': 0.1,\n",
        "    'feature_fraction': 0.9\n",
        "}\n",
        "\n",
        "model = lgb.train(params, train_data, num_boost_round=100, valid_sets=[test_data])\n",
        "\n",
        "# Make predictions\n",
        "y_pred = model.predict(x_test)\n",
        "y_pred = [np.argmax(pred) for pred in y_pred]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pGpxRwcZxrgd",
        "outputId": "a7661a1b-0b66-4933-b629-3c7eb3a67f47"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000033 seconds.\n",
            "You can set `force_col_wise=true` to remove the overhead.\n",
            "[LightGBM] [Info] Total Bins 20\n",
            "[LightGBM] [Info] Number of data points in the train set: 100, number of used features: 4\n",
            "[LightGBM] [Info] Start training from score -0.867501\n",
            "[LightGBM] [Info] Start training from score -0.544727\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
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            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate the model\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "precision = precision_score(y_test, y_pred)\n",
        "recall = recall_score(y_test, y_pred)\n",
        "f1 = f1_score(y_test, y_pred)\n",
        "\n",
        "print(f'Accuracy: {accuracy:.2f}')\n",
        "print(f'Precision: {precision:.2f}')\n",
        "print(f'Recall: {recall:.2f}')\n",
        "print(f'F1-score: {f1:.2f}')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5-tlSpcm13kK",
        "outputId": "07ab0d6d-8418-4eac-df36-d8e8178d1dbe"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.62\n",
            "Precision: 0.54\n",
            "Recall: 0.64\n",
            "F1-score: 0.58\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Conclusion after discard the two features X2 and X4, the model had improved the accuracy but not that much.\n",
        "# The accuracy between 0.62-0.65 indicates moderate overall performance.\n",
        "# A precision between 0.54-0.57 suggests that the model has a relatively high rate of false positives,\n",
        "# meaning that nearly half of the positive predictions are incorrect.\n",
        "# A recall between 0.64-0.73 indicates that the model is better at identifying actual positives, with a lower rate of false negatives.\n",
        "# The F1-score between 0.58-0.64 indicates a balance between precision and recall,\n",
        "# but it is somewhat closer to recall, given the higher recall value."
      ],
      "metadata": {
        "id": "ZkGlWvtd3PWh"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}