EDA.ipynb

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        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt"
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        "df = pd.read_csv('/content/sample_data/c_final - Sheet1 (1).csv')"
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        "df.head()\n"
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              "   Label                                            Comment\n",
              "0    0.0  I had an accident with an Uber driver in Mexic...\n",
              "1    1.0  I have had my account completely hacked to whe...\n",
              "2    1.0  I requested an 8 mile ride in Boston on a Satu...\n",
              "3    1.0  Uber is overcharging for Toll fees. When In Fl...\n",
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              "             Label\n",
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              "Index(['Label', 'Comment'], dtype='object')"
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              "<bound method DataFrame.info of       Label                                            Comment\n",
              "0       0.0  I had an accident with an Uber driver in Mexic...\n",
              "1       1.0  I have had my account completely hacked to whe...\n",
              "2       1.0  I requested an 8 mile ride in Boston on a Satu...\n",
              "3       1.0  Uber is overcharging for Toll fees. When In Fl...\n",
              "4       1.0  I had an airport flight today. Uber would not ...\n",
              "...     ...                                                ...\n",
              "1698    1.0  I tried to log in to Uber. I have only one ema...\n",
              "1699    1.0  When I saw an ad online for the soon to be Ube...\n",
              "1700    1.0  As a driver I was assaulted by a couple guys. ...\n",
              "1701    1.0  I attempted to sign up as a driver about a mon...\n",
              "1702    1.0  They don't communicate and you can't contact t...\n",
              "\n",
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              "      Label                                            Comment\n",
              "0       0.0  I had an accident with an Uber driver in Mexic...\n",
              "1       1.0  I have had my account completely hacked to whe...\n",
              "2       1.0  I requested an 8 mile ride in Boston on a Satu...\n",
              "3       1.0  Uber is overcharging for Toll fees. When In Fl...\n",
              "4       1.0  I had an airport flight today. Uber would not ...\n",
              "...     ...                                                ...\n",
              "1698    1.0  I tried to log in to Uber. I have only one ema...\n",
              "1699    1.0  When I saw an ad online for the soon to be Ube...\n",
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              "1701    1.0  I attempted to sign up as a driver about a mon...\n",
              "1702    1.0  They don't communicate and you can't contact t...\n",
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              "      <td>I attempted to sign up as a driver about a mon...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1702</th>\n",
              "      <td>1.0</td>\n",
              "      <td>They don't communicate and you can't contact t...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1689 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b5161a03-e4b7-4f45-a322-7de8860ea6d2')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-b5161a03-e4b7-4f45-a322-7de8860ea6d2 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-b5161a03-e4b7-4f45-a322-7de8860ea6d2');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vCXhgauQD4_M",
        "outputId": "7723ae0e-6ebc-47f5-bc09-68c718518bd4"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1703, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#count of the columns\n",
        "label_counts = df[\"Label\"].value_counts()\n",
        "\n",
        "\n",
        "labels = [\"fair\", \"unfair\"]\n",
        "sizes = [label_counts[1], label_counts[0]]\n",
        "colors = [\"green\", \"yellow\"]\n",
        "#plotting pie chart\n",
        "plt.pie(sizes, labels=labels, colors=colors, autopct=\"%1.1f%%\", startangle=90)\n",
        "\n",
        "\n",
        "plt.title(\"Distribution of fair and unfair reviews\")\n",
        "\n",
        "\n",
        "plt.show()\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "id": "aFZpyvEVEl2j",
        "outputId": "df7f4556-4cb0-42b6-ffe9-63912fae4094"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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kpKQSZcrPl19+yYULF5g7d26OUVD2TUaulvl7HxAQQNeuXQt9fP/+/XnxxReZNWsW1apV4/Tp0wwYMKDA52RuEq5atWqh66hVqxa1atVixYoV7Nq1K2sTaIcOHXj00Uf57LPPSEtLo0OHDjmeV9j7jS/w+E1kS5cu5aWXXqJ27drcfvvt+T7u+PHjl92XORvowoULAFnHReT1hl8SH3/8cY79QjNnziQpKYkbb7wx6746deqwZs0aUlNTs+6bN28ev/32W45lFSfbTTfdRFpaGm+99VaO+8eOHYvD4cix/tK46aabOHjwYI7NE5cuXWL8+PGEhYXRsWNHp6wH7E/kDocjxyf2PXv28MUXXzhtHdnXBTk/7X///fd89913JVpedHQ0zZo1Y8qUKTk2MS1evJiffvqpSMuoU6cOy5cvz3Hf+++/n+8IpqTy+tlPnTrlkiLPT9WqVenUqRMTJkzIs0CPHDmS4+sGDRrQpEkTpk+fzvTp04mOjr7szT63bt26ER4ezqhRo7h48WKh62jfvj1Lly5l7dq1WQXTrFkzKlSowD//+U9CQkJo2bJl1uOL8n7jCzxqBLNgwQJ+/vlnLl26xKFDh1i6dCmLFy8mLi6OuXPnEhwcnO9zR44cyfLly+nRowdxcXEcPnyYd955h9jY2KwhcJ06dahYsSLvvfceFSpUIDQ0lNatWxd7m3KmSpUqkZiYyF133cWhQ4d44403qFu3bo6p1Pfccw8zZ86ke/fu9OvXj507d/LJJ5/k2Ole3Gw333wz1113Hc888wx79uzhqquuYtGiRcyZM4dHHnnksmWX1NChQ5kwYQKDBw9mw4YNxMfHM3PmTFatWsUbb7xR4D6x4urRowdjxoyhe/fu3HbbbRw+fJi3336bunXrsmXLFqetB6Bnz57Mnj2bPn360KNHD3bv3s17771Hw4YNSUlJKdEyX3nlFXr06EFiYiJDhgzh+PHjjB8/nkaNGhVpmffccw9//etf6du3L9dffz2bN29m4cKFVKlSpUR58nPDDTcQGBjIzTffzH333UdKSgoffPABVatWdfpoqSBvv/02iYmJNGnShHvvvZeEhAQOHTrEd999x++//87mzZtzPL5///4899xzBAcHc/fddxd6YGd4eDjvvvsud9xxBy1atGDAgAFERUWxb98+vvrqK9q1a5fjA1r79u3597//jcPhyHq/8Pf3p23btixcuJBOnTrl2D9blPcbn2BuAlvRZU4xzLwFBgZa1atXt66//npr3LhxOabDZso9TXnJkiXWn/70J6tGjRpWYGCgVaNGDWvgwIHWr7/+muN5c+bMsRo2bGiVK1cuzwMt81LQAWcjRoywqlataoWEhFg9evSw9u7de9nzX3/9dSsmJsYKCgqy2rVrZ61fv/6yZRaULa+D9ZKTk63hw4dbNWrUsAICAqx69eoVeKBlbvlNn87t0KFD1l133WVVqVLFCgwMtJo0aZLnVGpnTFP+8MMPrXr16llBQUFW/fr1rcmTJ+d5QG1+U3cLmuaeXXp6ujVq1CgrLi7OCgoKspo3b27Nmzfvstc5+4GCuZHHtOJZs2ZZDRo0sIKCgqyGDRsW60DLtLQ068knn8w6cLJbt27Wjh07ivyzZv5OZh60aln5T1OeO3eu1bRpUys4ONiKj4+3Xn31VWvSpEmXHaiY3+99UU+RVNDrZ1mWtXPnTuvOO++0qlevbgUEBFgxMTFWz549rZkzZ1722O3bt2e9P6xcufKy7+d3oOWyZcusbt26WREREVZwcLBVp04da/Dgwdb69etzPG7r1q0WYDVo0CDH/f/4xz8swHr22Wdz3F/U9xtv57AsN9qTKyIiXsPj98GIiIh7UsGIiIhLqGBERMQlVDAiIuISKhgREXEJFYyIiLiECkZERFxCBSMiIi6hghEREZdQwYiIiEuoYERExCVUMCIi4hIqGBERcQkVjIiIuIQKRkREXEIFIyIiLqGCERERl1DBiIiIS6hgRETEJVQwIiLiEioYERFxCRWMiIi4hApGRERcQgUjIiIuoYIRERGXUMGIiIhLqGBERMQlVDAiIuISKhgREXEJFYyIiLiECkZERFxCBSMiIi6hghEREZdQwYiIiEuoYERExCVUMCIi4hIqGBERcQkVjIiIuIQKRkREXEIFIyIiLqGCERERl1DBiIiIS6hgRETEJcqZDiDiKifPn+RgykGSkpM4mHKQgykHOXL2CBcuXeBS+qWcN+sSj7SuTvPoJMAf+7NXIBAGVADCM/6skO3rKCAGCDXy84m4OxWMeJzUtFR+OvIT+07tyyqOpOQkDp45mPX1wZSDnL90vljLfeDqDsDyEiQKB2pk3GIy/qwFXAFcCdQswTJFPJ8KRtza6Qun2XRwExuTNrLxoH3bdmQbF9Mvmo6WzemM28/5fD+MP8qmfsatGVAPcJRBPhEzVDDiNg4kH7isTHaf2I2FZTpaKaUAP2TcsqsItAKuzrhdgz0CEvEOKhgxZtPBTczfPp/le5ez8eBGDp85bDpSGTsJfJNxy1QDuBboDHTBHvWIeCYVjJSZ5AvJLN61mPnb57NgxwIOJB8wHckNHQBmZdzAHtF0yXbTCEc8hwpGXOqnIz8xf/t85m+fz8p9K91s34kn2A98nHEDaAD0Avpgb1LTPhxxXyoYcaqzF8+ydPfSrFHKnpN7TEfyMtsybq9ij2Z6Y5dNR/TfWdyNfiOl1NLS0/h6x9d8uPFDFuxYUOzpwVJS+4G3M26VgJuB27E3pekYajFPBSMltvfkXj7c+CGTN03m99O/m47j444DUzJuNYE7gMHYU6FFzFDBSLFcTLvInF/mMPGHiSzetZh0K910JLnMb8CojFtb4C6gH/YBoSJlRwUjRfLrsV+Z+MNEpmye4oPTiT3Z6ozb34ABwEPYB3mKuJ4KRvJ1/tJ5Zv40kw9++IDle0tyChVxH2eBSRm3ROyi6Yt93jUR11DByGWOnzvO66tf593173Li/AnTccTpVmbc4oCHgXvQ5jNxBRWMZDl5/iRjvhvDuO/HcfrCadNxxOX2Ao8BLwL3A48DlY0mEu+ighFOnT/F2DVjeWPNG5y6cMp0HClzp4F/Am8BD2AXTRWjicQ7qGB82OkLpxm3Zhxj14zVpjDBPinnq9jH1WSOaKKMJhLPpoLxQSmpKbz5/Zu8/t3rHD933HQccTspwGvYRfMA8BQQaTSReCYVjA85k3qG8WvH86/V/+LYuWOm44jbO4NdNB8Cz2GPavSWIUWn80n4gHQrnfHfj6f2uNqMWDJC5SLFdAz7OJrGwJeGs4gn0ccRL7cxaSND5w1l/YH1pqOIx/sF+0zOXYAxQFOzccTtaQTjpc6knuGxhY9x9QdXq1zEyZYAzYFhgGYdSv5UMF7oq1+/otE7jRizZgxpVprpOOKV0oH3sK9P85nhLOKuVDBe5GDKQfp91o+en/Zk76m9puOIT0jCPpFmL0Bn1JacVDBewLIs3lv/HvXfqs9nP+nTpJjwJdAQeBN7dCOigvF4Ww9vJXFyIsO+Gqaj8MWwZOzZZm2BXw1nEXeggvFQFy5d4Jklz9B8QnNW/7badByRbL7HngQwwXQQMUzTlD3QnpN7uGXGLWxI2mA6ikg+zgJ/BeZjH6ipc5v5Io1gPMy8X+fRYkILlYt4iLlAE+Br00HEABWMh0hLT+PpJU/T69NeOjGleJiDwE3Y1565YDiLlCVtIvMAh1IOMXDWQJbtWWY6ikgJWcB4YA0wC6hpNo6UCY1g3NzKfStp8X4LlYt4iXVAS0C/z75ABePGXl/9OtdNuY4DyQdMRxFxoiPA9cC/TAcRF9MmMjd0+sJpBn8xmM9//tx0FBEXSQOewB7RfAiEmY0jLqGCcTNbDm2h74y+7Di+w3QUkTIwA9gKzAPizUYRp9MmMjcy86eZtJnYRuUiPmYr0AbQWb+9jQrGTby99m36z+zPuUvnTEcRMeAQ0Ald0My7qGDcwDNLnuHBBQ+SbukkgeLLzgB9gHdNBxEn0T4Yg9LS0xj65VAmbZpkOoqIm0gD7gd2A68CDrNxpFRUMIacu3iOfjP7Me/XeaajiLih0djXl/kYvU15Lv3LGZB8IZke/+nBin0rTEcRcWOfAueA6UCg4SxSEtoHU8ZOnDtB16ldVS4iRfIF9n6Z84ZzSEmoYMrQkTNH6PxxZ9buX2s6iogHmQ/cjH0JAPEkKpgykpScRKcpndh0cJPpKCIe6BvgRuyrZoqnUMGUgd9O/UaHjzrw05GfTEcR8WDLgRuA06aDSBGpYFzs+Lnj3PDJDTo6X8Qp1gA90OYyz6CCcaHzl87T69Ne/Hz0Z9NRRLzISuwd/7p4mbtTwbhIupXObbNuY9Vvq0xHEfFCi4CB2AdmirtSwbjIwwse1un2RVzqc+A+0yGkACoYF3h15au8ve5t0zFEfMCHwAjTISQfKhgn+2TLJ4xYol94kbLzT+At0yEkDyoYJ/pm1zcMmTMEC8t0FBEf8wiw0HQIyUUF4ySbDm7iz9P/zMX0i6ajiPigNKA/sM10EMlGBeMEe0/u5aZ/30Ryqo4yFjHnFNATOGo6iGRQwZTS8XPH6f7v7iSlJJmOIiLsAv4MpJoOIqhgSiXdSueWGbfoQEoRt7ICTV92DyqYUvjH8n+wbM8y0zFE5DIfAeNNh/B5KpgSWrF3BSP/O9J0DBHJ1+PAOtMhfJoKpgSOnzvO7bNvJ83SaSpE3Fcq0A84aTiH71LBlMCQOUP47fRvpmOISKH2AIMNZ/BdKphiGv/9eOb8Msd0DBEpsjnAGNMhfJIKphg2HdzEE4ufMB1DRIrtKexryUhZUsEUUUpqCv1n9udCmq5BIeJ5LgID0CWXy5YKpogemP8Avx771XQMESmxvcCjpkP4FBVMEXyy5RM+3vyx6RgiUmoTgQWmQ/gMFUwhth/bzrCvhpmOISJOcw9wwnQIn6CCKUC6lc7ts28nJTXFdBQRcZoDwEOmQ/gEFUwBJqyfwLoDOhJYxPv8G/uSy+JKKph8HD17lGeWPmM6hoi4zF+B46ZDeDUVTD6e+uYpTpzXdloR73UYeNp0CK+mgsnD979/z6SNk0zHEBGX+wCdENN1VDC5pFvpPDD/ASws01FExOXSgWEZf4qzqWBy+WDDB2xI2mA6hoiUmQ3ABNMhvJIKJptjZ4/x9FJtkxXxPc8AR0yH8DoqmGxGLBnB8XOaVSLie04AfzcdwuuoYDKs27+ODzd+aDqGiBgzBdhoOoRXUcHwx479dEs7+kR8l4V9Wn9xFhUMMPGHiTpiX0SARcBS0yG8hs8XzNmLZ/m/pf9nOoaIuI0nQYcpOIXPF8yE9RM4clazR0Qk03pgpukQXsGnCyY1LZXXv3vddAwRcTvPAJdMh/B4Pl0wH236iP3J+03HEBG3sx3QrNLS8tmCSUtP47VVr5mOISJu659oFFM6Plsw036cxs4TO03HEBG3tQeYZjqER/PJgrEsi1dWvmI6hoi4vX+iGWUl55MFM+eXOWw9stV0DBFxe1uBL02H8Fg+WTCjVowyHUFEPIa2dpSUzxXM4p2LddS+iBTDGuBb0yE8ks8VzMsrXjYdQUQ8zqumA3gknyqY1b+t5r97/2s6hoh4nIWAZp0Wl08VjGaOiUjJWMB7pkN4HJ8pmAPJB5i/fb7pGCLisSYD502H8Cg+UzCfbPlE13sRkVI4BnxmOoRH8ZmC+Xjzx6YjiIjHe8d0AI/iEwXzQ9IPOrBSRJxgDbDJdAiP4RMFM2XTFNMRRMRraGd/UXl9wVxKv8SnP35qOoaIeI3pwAXTITyC1xfMgu0LdMVKEXGik8BXpkN4BK8vmCmbtXlMRJxtqukAHsGrC+bEuRPM+3We6Rgi4nXmY49kpCBeXTDTfpzGhTRtKxURZ0sFZpsO4fa8umA+3qJjX0TEVXS1y8J4bcH8euxX1vy+xnQMEfFaS4GjpkO4Na8tmH9v+bfpCCLi1dLQbLKCeW3BLNixwHQEEfF6mkRUEK8smJPnT7IhaYPpGCLi9RYBF02HcFteWTBLdy/VmZNFpAycBlaYDuG2vLJgvtn1jekIIuIztJksPyoYEZFSUcHkx+sKZt+pfWw/vt10DBHxGdszbpKb1xWMRi8iUvb0vpMXFYyISKlpR39evKpgLMtiye4lpmOIiM9RweTFqwrmf4f/x+Ezh03HEBGf8zuwy3QIt+NVBbN452LTEUTEZ2kUk5tXFcw3u7X/RURMWW46gNvxmoK5mHaRFXv1CUJETNH7T25eUzBbj2zlzMUzpmOIiM/aDhwxHcKteE/BHN5qOoKI+LxNpgO4Fe8pmCMqGBExbZPpAG5FBSMi4jSbTAdwK95TMNpEJiLGbTIdwK14RcGcu3iO3Sd3m44hIj7vF+C86RBuwysKZtvRbbrAmIi4gTTgf6ZDuA2vKBhtHhMR97HZdAC34R0Fox38IuI2fjUdwG2oYEREnGqn6QBuwzsKRpvIRMRt6KzKmTy+YM6knmHPyT2mY4iIZFDBZPL4gtl2dBsWlukYIiIZTgNHTYdwC8UqGMuyGDp0KJUqVcLhcLBp06YCH79nz54iPa40th3Z5rJli4iUjEYxAOWK8+Cvv/6ajz76iG+//ZaEhASqVKlS4ONr1qxJUlJSoY8rjf3J+122bBGRktkFXGM6hHHFKpidO3cSHR1N27Zti/R4f39/qlevnu/3LcsiLS2NcuWKFSOHQymHSvxcERHX2Gs6gFso8iaywYMH89BDD7Fv3z4cDgfx8fF8/fXXJCYmUrFiRSpXrkzPnj3ZufOPKXq5N5F9++23OBwOFixYQMuWLQkKCmLlypWl+gEOnz1cqueLiDifrgsDxSiYcePGMXLkSGJjY0lKSmLdunWcOXOGRx99lPXr17NkyRL8/Pzo06cP6ekFn7blqaee4p///Cfbtm2jadOmpfoBDp9RwYiIu9H7EhRjE1lERAQVKlTIsdmrb9++OR4zadIkoqKi+Omnn2jcuHG+yxo5ciTXX399CSPnpE1kIuJ+NIKBUk5T3r59OwMHDiQhIYHw8HDi4+MB2LdvX4HPa9WqVWlWm4NGMCLiflQwUMyd/LndfPPNxMXF8cEHH1CjRg3S09Np3LgxqampBT4vNDS0NKvNYlkWR896yHzzFcASoDVwI3ACGJfPY28FGuXzvRfyuf96oB1wCZgL/AyEAT2AOtketwo4BdxU9OgiUlwqGChFwRw7doxffvmFDz74gPbt2wOUeod9cSWnJpNmpZXpOktkP7ABqJbtvgjgsVyP2wCsBuoWsKzcz9kBzAEaZFvGAeAeYDswC3gCcGCX2gZgaLF/AhEpFhUMlKJgIiMjqVy5Mu+//z7R0dHs27ePp556ypnZCnX6wukyXV+JXMB+k78ZWJ7tfj+gQq7H/ow9cgkqYHl5Pac2UCnj6yPAlUBVIBJYDJwFQoF52COd4OL+ECJSPOeAM9j/8XxXiffB+Pn5MW3aNDZs2EDjxo0ZPnw4o0ePdma2Qp06f6pM11ci84EryLmZKi8HgINA82IsOwV7lJL9OdWBfcBF7NFNGFAe2IL9caIBIlImkk0HMM5hWZbHnsjru9++o+2koh30acT/sPe93AsEAJOxC+DGPB47D9gDPFiM5a/MuD2WsXywL6j3NXbxlAe6AVHAB8BgYD3wI/aI509AeDHW5+W+v6cD18QsL/yBIkWyG4g3HcKoUu3kN82tN5Gdwn6jv4M/3vzzcxG7jDoWcx0bgaa5lu+PvWM/uy+wJxckYW9SG4a9s38B0L+Y6xSRIjpvOoBxKhhXOYC9CXZCtvss7DNIrAWe5Y8NlD9hl8xVxVj+XuAY9oyzguzGPuarF7AIqAcEYu/rWVuM9YlIMalgPLpgklPdeBtnAvZIIbs5QBXs6cTZ9379gL1jvjj7A38AorE3ueXnIvAV0DdjfRaQeZKFtGx/FxEXUMF49PVg/BxuHD8Ie1py9lsAEELO6crHsEcjLfJZzngg9xUJzmOPevJ7Tqbl2COW6Iyva2Ys6yD26KVWEX4OESkhFYwbv0MXLtA/0HSE0tuIvaM9v1lmx7j89/RH7NFIkwKWewjYClyX7b6G2IUzOeP73UuQV0SKSAXj0ZvIAvwK23vuZu7K476uGbf8vJDHfa0ybgWpBjyc6z4/oGfGTURcrOAzmvgCjWBEMoQGeOyMfXFLHvYB2AVUMCIZqoedMR3BrbzwAjgcOW/169vfO34cHnoIrrwSQkKgVi14+GE4Vcixzy+8YC8jNBQiI6FrV/j++z++f+EC3HEHhIfDFVfAN9/kfP7o0fZ6PYMKxqM3kalgxJkignXph9waNcr5Jp958dkDB+zbv/4FDRvC3r3w17/a982cmf/yrrgC3noLEhLg3DkYOxZuuAF27ICoKHj/fdiwAb77DhYsgNtug0OH7HLbvRs++ADWr3ftz+w8KhgVjAhQITAQf0eS6Rhup1w5yOuq540bw6xZf3xdpw68/DL85S9w6dIfRZTbbbfl/HrMGPjwQ9iyBbp0gW3boFcvu9gSEuCJJ+DoUbt8hg2DV1+1RzeeQe9P2kQmAnSKr4nDoQODctu+HWrUsN/sb78dCrrU06lT9pt/fuWSW2qqPWKJiICrMg4yvuoqWLnSHt0sXAjR0VClCvz73xAcDH36lP5nKjsawXj0CCbAX/+A4hxtYisDO03HcCutW8NHH9n7WZKS4MUXoX17+PFHqJDrrN5Hj8JLL8HQIlwKYt48GDAAzp61C2TxYrtEAIYMsUczDRva982YASdOwHPPwbffwv/9H0ybZo+YJk2CmBhn/9TOpPcnjWBEgKZVQ0xHcDs33gi33gpNm0K3bjB/Ppw8ab/pZ3f6NPToYZfCCy8UvtzrroNNm2D1aujeHfr1g8MZF6YNCIC337b3t6xbB4mJ8Nhj9gSCjRvhiy9g82Zo08a+z72ZL5hVq1bRpEkTAgIC6N27d5GeM3jw4CI/tjAePYJRwYizJFTS5rHCVKxo76TfseOP+5KT7ZKoUAE+/9wuiMKEhkLduvatTRuoV8/eDzNixOWPXbYMtm6FiRPt/TE33WQ/v18/e7KAezN/LZhHH32UZs2asWDBAsLCwor0nHHjxuGsk+xrBCMCVA9LMR3B7aWkwM6d9mYtsEcuN9wAgYEwd669j6Qk0tPt6cm5nT8PDzwAEyaAvz+kpcHFi/b3Ll60v3ZvFU0HYOfOnXTu3JnY2FgqVixanoiIiAIfm5pa9ANIVTAiQHiQpijn9vjj8N//wp499uasPn3sN/qBA/8olzNn7NHH6dNw8KB9y/7GX7++PbIB+7FPPw1r1tjTmjdssPe57N9vb4rL7aWX7BFL84wL6rVrB7Nn2/to3nrL/tp9+VHaiy3Fx8fzxhtv5LivWbNmvJCxHdLhcDBx4kT69OlD+fLlqVevHnPnzgVgz549OBwOjh07xpAhQ3A4HHz00UekpaVx9913U7t2bUJCQrjyyisZN25cjnXk3kTWqVMnHnzwQR555BGqVKlCt27divwzePQmspBy2m4upRcZHKwpynn4/Xe7TI4ds6cJJyba5RAVZe9wzzxAsm7dnM/bvRvi4+2///LLHwdf+vvDzz/DlCn2pIDKleHqq2HFCntacnY//mjv69m06Y/7brnFXm/79vbEg//8x/k/s/OEAw6Xr+XFF1/ktddeY/To0YwfP57bb7+dvXv3UrNmTZKSkrjyyisZOXIk/fv3JyIigvT0dGJjY/nss8+oXLkyq1evZujQoURHR9OvX7981zNlyhSGDRvGqlWripXPowsmMiSSkHIhnLt0znQU8WAd42NxOHYU/kAfM21a/t/r1AmKspk++2OCg+0RSFE0bmxPkc7Ozw/eece+ub8qZbKWwYMHM3DgQABGjRrFm2++ydq1a+nevTvVq1fH4XAQERFB9WwHM7344otZf69duzbfffcdM2bMKLBg6tWrx2uvvVbsfB69iQygZkRN0xHEw7WJqWw6gnidsimYpk2bZv09NDSU8PBwDmdOycvH22+/TcuWLYmKiiIsLIz333+ffQUd4AS0bNmyRPk8vmBqReiiJlI6TaqWcO+0SL6iSr0EPz+/y2ZzXcyc5ZAhINe0PYfDQXp6/jMip02bxuOPP87dd9/NokWL2LRpE3fddVehO+5DQ0s2I86jN5EBxEXEmY4gHk5TlMX5Sl8wUVFRJCX9sW/w9OnT7N69u1TLXLVqFW3btuX+++/Pum/nTtcdYKwRjPi86mGnTUcQr1P696XOnTszdepUVqxYwf/+9z8GDRqEv79/qZZZr1491q9fz8KFC/n111959tlnWbduXamz5kcFIz6vQqCmKIuzxZd6CSNGjKBjx4707NmTHj160Lt3b+rUye/St0Vz33338ec//5n+/fvTunVrjh07lmM042wOy1mHbBqydPdSunzcxXQM8VBR5ctz+ImzpmOI1/kW6Gg6hHEawYhP6xQfazqCeCXtGwYvKJia4TVxlMEBTeKdromJNB1BvI4/oA8u4AUFE1QuiKqhVU3HEA/VOEpTlMXZYvGCCbpO4fEFA9pMJiWXUMntz5goHkebxzKpYMSnVQvVFGVxtitMB3AbKhjxaWGBB01HEK/TxHQAt+EVBXNFZX1ikOKLDgvD36/g8zaJFJ8KJpNXFMzVNa42HUE8UMc4t76gu3gsFUwmryiYptWaElxOs4GkeK6JrWQ6gnid6pTVmZQ9gVcUTIB/AM2rNzcdQzxM4yhdEVWcTaOX7LyiYABax7Q2HUE8TO3IS6YjiNdRwWTnNQVzTcw1piOIh6mqKcridE0Lf4gP8ZqCaR2rEYwUT1hgUuEPEimWa00HcCteUzAJkQlUKa+da1I0NcPD8XMcNR1DvEoUOsgyJ68pGNBmMim6DnE1TEcQr6PRS25eVTDa0S9FpbMoi/O1Mx3A7XhVwWgEI0XVSFOUxelUMLmpYMQnxVfUFGVxpiCglekQbserCqZSSCXqVqprOoZ4gKqhp0xHEK/SErtkJDuvKhiANrFtTEcQDxAaeMB0BPEqnU0HcEteVzA31r3RdARxc/EREfg5jpuOIV5F7zt58bqC6VGvBwF+AaZjiBvrVFtnURZnigQ0gzUvXlcwEcERdIrvZDqGuLGra1Q0HUG8yvWAv+kQbsnrCgagd/3epiOIG2sYpRGuOJM2j+XHKwvmT1f+CQcO0zHETcVXvGg6gngNB9DddAi35ZUFExMeQ6sampMueatS/qTpCOI1rsK+yJjkxSsLBqBP/T6mI4ibKh+gKcriLDebDuDWvLZgtB9G8lI3MhI/x0nTMcRr9DcdwK15bcE0iGrAlZWvNB1D3EzHeJ1FWZylUcZN8uO1BQP2zn6R7FppirI4jUYvhfHqgtFmMsmtYVQ50xHEa6hgCuPVBdMmtg3VwzTDQ/4QF5FqOoJ4hWbo6pWF8+qCcTgc2kwmOWiKsjiHRi9F4dUFAzC42WDTEcSNlA/YbzqCeDwHMMB0CI/g9QXTJrYNLaNbmo4hbqBhVBUcjtOmY4jH6wrEmw7hEby+YAAeuPoB0xHEDXSoFW06gniFoaYDeAyfKJgBjQdQKaSS6RhiWMsa4aYjiMerBmi/blH5RMGEBIQwpNkQ0zHEsAZVNEVZSmswoLNxF5VPFAzAsKuH4efwmR9X8lBTU5SlVBzAvaZDeBSfecdNiEyge12dVtuXVSl/wnQE8WhdgDqmQ3gUnykY0M5+XxdS7nfTEcSj/dV0AI/jUwXTvW53EiITTMcQA5pUjcLhSDEdQzxWHUCXACkunyoYP4cfw1oNMx1DDOgQpynKUhqP4WNvl07hc6/YkOZDCCkXYjqGlLGW0ZqiLCUVhT17TIrL5wqmUkglBjTWaR58Tf0q/qYjiMd6ENCH0pLwuYIBeOiah0xHkDJWM+KC6QjikcoDmhxUUj5ZMM2jm9OnvnbY+ZLKIcdNRxCPNASobDqEx/LJggEY1WUU/g5tNvEFDiBYU5Sl2AKwd+5LSflswdSvUp9BVw0yHUPKQIvo6jgcZ03HEI8zBJ01uXR8tmAAXrzuRYLLBZuOIS6WWKua6QjicYKA/zMdwuP5dMHEhsfq6H4f0KJ6BdMRxOPcB8SaDuHxfLpgAJ5u/zQRQRGmY4gLXakpylIsYcAzpkN4BZ8vmEohlXii7ROmY4gL1Qw/bzqCeJRHgaqmQ3gFny8YgOHXDqd6WHXTMcRFKpU/ZjqCeIwqwOOmQ3gNFQxQPqA8z3Z41nQMcQEHEOS/33QM8RgvAtpn5ywqmAz3triXOpG61oO3ubpGNA7HOdMxxCNchb1zX5xFBZMhwD+Al657yXQMcbLEOE1RlqJ6C9CEEGdSwWQzoPEAmldvbjqGOJGmKEvR3A4kmg7hdVQw2TgcDt6/+X2dQsaLXFlZv+JSmDDgNdMhvJL+9+XSqkYrHm+rWSTeIiZc+1+kMM8CNUyH8EoqmDy82OlF6lepbzqGOEGlkKOmI4hbuxJ4xHQIr6WCyUNQuSAm9ZqEn0MvjyfzdzgI1BRlyZcfMAkINB3Ea+kdNB/X1ryWR1o/YjqGlEKb2Bo4HLrQmOTnYaCt6RBeTQVTgH90/gf1KtUzHUNKqG1Nne5D8lMXGGU6hNdTwRQgJCCED3t9iAOH6ShSAs2rh5mOIG7Jgb1pLMR0EK+ngilE+7j2PHjNg6ZjSAlcoSnKkqcHgfamQ/gE/Q8sgle6vEJCZILpGFJMseG6iqXklgC8YjqEz1DBFEFoYCgTb56oTWUepmKwpihLduWAT4BQ00F8hgqmiK6rfR33tdSJ8DxFgJ8fgf6/m44hbuUl4FrTIXyKCqYYXrv+NepWqms6hhRB25oxOBwXTccQt9ENeNJ0CJ+jgimGCkEVmNVvFuUDypuOIoVopynKkiUa+Bi0ibvMqWCKqWm1pkzoOcF0DClEs+razi5gv8V9gi6BbIYKpgT+0vQv3N/qftMxpAD1KptOIO7haaCz6RA+SwVTQmO7j6VNbBvTMSQfNSpoirLcALxgOoRPU8GUUKB/IDNvnUnVUA293VHF4COmI4hR9YDp6AqVZqlgSiEmPIaZt84k0F9nY3UnQf7+BPjpLMq+KwL4EqhoOIeoYEqpfVx73uvxnukYkk27WjE4HJdMxxAj/IBp2Nd5EdNUME5wV/O7eOzax0zHkAyaouzLXgW6mw4hGVQwTvLa9a/Ro14P0zEEuKqajlPyTXcCuty5O1HBOImfw49P+35Ko6hGpqP4PE1R9kVdgQ9Mh5BcVDBOVCGoAvNum0dseKzpKD6tRtgZ0xGkTLUEPkeXPnY/Khgni68Yz5I7l1A9rLrpKD4rQlOUfUg9YAGgi8u5IxWMC1xR+QqW3rlUx8gYEFKuHOU0RdlHRAOLgCjTQSQfKhgXaRDVgG/u+IbKIdohUJY6xtXE4UgzHUNcriKwEIg3G0MKpIJxoSbVmrD4jsVEBkeajuIzrq1ZxXQEcblQYC7QxHQQKYQKxsWaRzdn4V8WEh4UbjqKT2iqKcpeLhSYD7Q3HUSKQAVTBq6OuZoFty8gLFA7Il2tbqV00xHEZcKAr4EOpoNIEalgykjbmm356ravdLEyF4vWFGUvVQG7XBJNB5FiUMGUoQ5xHZg7YC7B5YJNR/Fa4UGHTUcQpwvH3qHfznQQKSYVTBnrktCFz/t/TpB/kOkoXqdCYCDl/A6YjiFOFYFdLteaDiIloIIxoHvd7swZMIcKgRVMR/EqHeJicTi0D8Z71ACWA7qwn6dSwRjSrW43Vg1ZRa2IWqajeI1rYzVF2XvUB1YDTU0HkVJQwRjUpFoT1t6zlmtirjEdxSs0rRZiOoI4RVtgFRBnOoiUkgrGsGph1fh20Lfc2vBW01E8Xp1IbR7zfH8CvgEqmQ4iTqCCcQMhASFMv2U6IxJHmI7i0apXSDEdQUrlPmAWoJGot1DBuAmHw8GoLqOY/KfJBPrrtOMlER50yHQEKRF/4HXgvYy/i7dQwbiZwc0Gs+gvi6gUok0ExREZHIy/I8l0DCm2isBXwKOGc4grqGDcUMf4jqy5ew31KtUzHcVjdIyPxeGwTMeQYmkIfA90Mx1EXEQF46bqVa7HmnvW0DGuo+koHqF1jEZ8nqUvdrlcYTqIuJAKxo1VCqnEojsWMbzNcBw4TMdxa02qasewZ/AHXgFmoqtQej8VjJsL9A9kTLcxLL5jMbHhsabjuK0ETVH2AHHYR+Y/ZTqIlBEVjIfoktCFLX/dQv9G/U1HcUvVw5JNR5AC9Qc2Yx9EKb5CBeNBIkMimXbLND7p8wkRQRGm47iV8KCDpiNInkKBScA07BNXii9RwXig25vezpZhW+gU38l0FLdQOSQEP4eOgXE/zYEfgLtMBxFDVDAeqlZELZbcuYTXur7m8wdmdtIUZTcTAPwfsAbNEvNtKhgP5ufw44l2T7D2nrU0rtrYdBxjWsdUNh1BsrQC1gMvAb79wUdUMF7hqupXsf7e9T47nblJVV28zbwQ4DXsUYtOsS82FYyXCCoXxJhuY1h+13JaRrc0HadMJVRKMx3Bx3UCtgBPoHOJSXYqGC+TWCuRdfeuY/KfJhMdFm06TpmoFnradAQfVRWYCCwF6hrOIu5IBeOFHA4Hg5sNZvtD23mm/TMElws2HcmlwgI1RblsBQKPA9uBu8EHN8tK0Tgsy9L0Gy+379Q+/r7470zfOt10FKerFhrKwcfPmI7hQ3oCYwCdiFUKpxGMD6gVUYtpt0xj1ZBVXF3jatNxnKpjfIzpCD6iAfA18CUqFykqFYwPaVuzLd/f8z0f9/6YmAre8cassyi7Wi3gA+yd+DqtvhSPCsbHOBwO7rjqDn596Fee6/Ac5QPKm45UKo2jNEXZNWoAb2PvZ7kHKGc2jngk7YPxcUfOHOGttW/xzvp3OHr2qOk4xfbrg4nUq7zSdAwvUg37bMd/Bbx7coi4ngpGADh38RxTNk9hzHdj2H58u+k4RXbqySaEB//PdAwvEAM8AtwPePaoVtyHCkZySLfSmfvLXP61+l+s+m2V6TiFSnuuCn4Ozxt5uY+rgMeAAdjnEBNxHhWM5GvN72v41+p/8fnPn5Nuud8FvWIqVOD3R3UdmOJzADdiF0tnw1nEm6lgpFC7TuxizHdjmLxpMmcvnjUdJ8vtTerzyZ9/Nh3Dg4QBA4Hh2NOORVxLBSNFdvzccd5d9y6TN01m54mdpuMwrvu1PNz6O9MxPEBb7CPu+2GXjEjZUMFIiazbv45Pf/yUGVtnsD95v5EM39zRkS4J/zWybvcXBdyJXSwarYgZKhgpFcuyWLFvBdN+nMbMn2Zy5OyRMlv3jofaUaeS+09EKDuhQA+gP3Az2mkvpqlgxGnS0tNYsnsJ036cxuc/f87J8yddur7TTzWiQtBWl67D/YVhl8kt2DvuQ8zGEclGBSMukZqWytc7vmbaj9OY+8tczlx0/gkp056rjJ/jmNOX6/4qYZfJLUB3dECkuCsVjLjc2YtnWb53edZt3YF1pKallmqZtSLC2fuIr1wHxh9oiV0m3YFr0IW9xBOoYKTMnb90njW/r8kqnO9+/67Y05/vbNqAKX22uSihaQ7sHfOdgK7AdUBFg3lESkZnsJMyF1wumE7xnegU3wmAi2kX2ZC0IatwVv22qtD9N9fERLo+aJmpBLQG2mTcWgMRRhOJOINGMOJ20q10thzawurfVrP18Fa2Hd3GT0d+4tCZQ1mPWXpnR66r7WlTlB1AHNAo49YEe3PXFSZDibiMCkY8xolzJ9h2dBvbjmyjc+1D1I5cD+wEdgEphtNlcmBfq75mxq0e0BC7UBpgTyUW8Q0qGPESx4GDwKE8/jwEnAbOZtzOZPv7+TyW5Zdx88+4hWLvA8m8RWT7e2Uglj8KJQbQNWpEQAUjPi8dSCVnoYiIM6hgRETEJXTJZBERcQkVjIiIuIQKRkREXEIFIyIiLqGCERERl1DBiIiIS6hgRETEJVQwIiLiEioYERFxCRWMiIi4hApGRERcQgUjIiIuoYIRERGXUMGIiIhLqGBERMQlVDAiIuISKhgREXEJFYyIiLiECkZERFxCBSMiIi6hghEREZdQwYiIiEuoYERExCVUMCIi4hIqGBERcQkVjIiIuIQKRkREXEIFIyIiLqGCERERl1DBiIiIS6hgRETEJVQwIiLiEioYERFxCRWMiIi4hApGRERcQgUjIiIuoYIRERGXUMGIiIhLqGBERMQlVDAiIuISKhgREXEJFYyIiLiECkZERFxCBSMiIi6hghEREZf4f9fEGEwgN4qfAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}