Atharv_Patawar_A4_Classification.ipynb

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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ]
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      "source": [
        "df=pd.read_csv('/content/Ad Click Data.csv')"
      ],
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              "     Daily Time Spent on Site  Age  Area Income  Daily Internet Usage  \\\n",
              "0                       68.95   35     61833.90                256.09   \n",
              "1                       80.23   31     68441.85                193.77   \n",
              "2                       69.47   26     59785.94                236.50   \n",
              "3                       74.15   29     54806.18                245.89   \n",
              "4                       68.37   35     73889.99                225.58   \n",
              "..                        ...  ...          ...                   ...   \n",
              "995                     72.97   30     71384.57                208.58   \n",
              "996                     51.30   45     67782.17                134.42   \n",
              "997                       NaN   51     42415.72                120.37   \n",
              "998                     55.55   19     41920.79                187.95   \n",
              "999                     45.01   26     29875.80                178.35   \n",
              "\n",
              "                             Ad Topic Line            City  Male  \\\n",
              "0       Cloned 5thgeneration orchestration     Wrightburgh   0.0   \n",
              "1       Monitored national standardization       West Jodi   1.0   \n",
              "2         Organic bottom-line service-desk        Davidton   0.0   \n",
              "3    Triple-buffered reciprocal time-frame  West Terrifurt   1.0   \n",
              "4            Robust logistical utilization    South Manuel   0.0   \n",
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              "995          Fundamental modular algorithm       Duffystad   1.0   \n",
              "996        Grass-roots cohesive monitoring     New Darlene   1.0   \n",
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              "998   Proactive bandwidth-monitored policy     West Steven   0.0   \n",
              "999        Virtual 5thgeneration emulation     Ronniemouth   0.0   \n",
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              "                    Country        Timestamp  Clicked on Ad  \n",
              "0                   Tunisia   3/27/2016 0:53              0  \n",
              "1                     Nauru    4/4/2016 1:39              0  \n",
              "2                San Marino  3/13/2016 20:35              0  \n",
              "3                     Italy   1/10/2016 2:31              0  \n",
              "4                   Iceland    6/3/2016 3:36              0  \n",
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              "996  Bosnia and Herzegovina   4/22/2016 2:07              1  \n",
              "997                Mongolia   2/1/2016 17:24              1  \n",
              "998               Guatemala   3/24/2016 2:35              0  \n",
              "999                  Brazil   6/3/2016 21:43              1  \n",
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        "df['Timestamp']=pd.to_datetime(df['Timestamp'])      #converting to standard format date time"
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              "     Daily Time Spent on Site  Age  Area Income  Daily Internet Usage  \\\n",
              "0                       68.95   35     61833.90                256.09   \n",
              "1                       80.23   31     68441.85                193.77   \n",
              "2                       69.47   26     59785.94                236.50   \n",
              "3                       74.15   29     54806.18                245.89   \n",
              "4                       68.37   35     73889.99                225.58   \n",
              "..                        ...  ...          ...                   ...   \n",
              "995                     72.97   30     71384.57                208.58   \n",
              "996                     51.30   45     67782.17                134.42   \n",
              "997                       NaN   51     42415.72                120.37   \n",
              "998                     55.55   19     41920.79                187.95   \n",
              "999                     45.01   26     29875.80                178.35   \n",
              "\n",
              "                             Ad Topic Line            City  Male  \\\n",
              "0       Cloned 5thgeneration orchestration     Wrightburgh   0.0   \n",
              "1       Monitored national standardization       West Jodi   1.0   \n",
              "2         Organic bottom-line service-desk        Davidton   0.0   \n",
              "3    Triple-buffered reciprocal time-frame  West Terrifurt   1.0   \n",
              "4            Robust logistical utilization    South Manuel   0.0   \n",
              "..                                     ...             ...   ...   \n",
              "995          Fundamental modular algorithm       Duffystad   1.0   \n",
              "996        Grass-roots cohesive monitoring     New Darlene   1.0   \n",
              "997           Expanded intangible solution   South Jessica   1.0   \n",
              "998   Proactive bandwidth-monitored policy     West Steven   0.0   \n",
              "999        Virtual 5thgeneration emulation     Ronniemouth   0.0   \n",
              "\n",
              "                    Country           Timestamp  Clicked on Ad  \n",
              "0                   Tunisia 2016-03-27 00:53:00              0  \n",
              "1                     Nauru 2016-04-04 01:39:00              0  \n",
              "2                San Marino 2016-03-13 20:35:00              0  \n",
              "3                     Italy 2016-01-10 02:31:00              0  \n",
              "4                   Iceland 2016-06-03 03:36:00              0  \n",
              "..                      ...                 ...            ...  \n",
              "995                 Lebanon 2016-02-11 21:49:00              1  \n",
              "996  Bosnia and Herzegovina 2016-04-22 02:07:00              1  \n",
              "997                Mongolia 2016-02-01 17:24:00              1  \n",
              "998               Guatemala 2016-03-24 02:35:00              0  \n",
              "999                  Brazil 2016-06-03 21:43:00              1  \n",
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              "      <td>236.50</td>\n",
              "      <td>Organic bottom-line service-desk</td>\n",
              "      <td>Davidton</td>\n",
              "      <td>0.0</td>\n",
              "      <td>San Marino</td>\n",
              "      <td>2016-03-13 20:35:00</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>74.15</td>\n",
              "      <td>29</td>\n",
              "      <td>54806.18</td>\n",
              "      <td>245.89</td>\n",
              "      <td>Triple-buffered reciprocal time-frame</td>\n",
              "      <td>West Terrifurt</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Italy</td>\n",
              "      <td>2016-01-10 02:31:00</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>68.37</td>\n",
              "      <td>35</td>\n",
              "      <td>73889.99</td>\n",
              "      <td>225.58</td>\n",
              "      <td>Robust logistical utilization</td>\n",
              "      <td>South Manuel</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Iceland</td>\n",
              "      <td>2016-06-03 03:36:00</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>995</th>\n",
              "      <td>72.97</td>\n",
              "      <td>30</td>\n",
              "      <td>71384.57</td>\n",
              "      <td>208.58</td>\n",
              "      <td>Fundamental modular algorithm</td>\n",
              "      <td>Duffystad</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Lebanon</td>\n",
              "      <td>2016-02-11 21:49:00</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>996</th>\n",
              "      <td>51.30</td>\n",
              "      <td>45</td>\n",
              "      <td>67782.17</td>\n",
              "      <td>134.42</td>\n",
              "      <td>Grass-roots cohesive monitoring</td>\n",
              "      <td>New Darlene</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Bosnia and Herzegovina</td>\n",
              "      <td>2016-04-22 02:07:00</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>997</th>\n",
              "      <td>NaN</td>\n",
              "      <td>51</td>\n",
              "      <td>42415.72</td>\n",
              "      <td>120.37</td>\n",
              "      <td>Expanded intangible solution</td>\n",
              "      <td>South Jessica</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Mongolia</td>\n",
              "      <td>2016-02-01 17:24:00</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>998</th>\n",
              "      <td>55.55</td>\n",
              "      <td>19</td>\n",
              "      <td>41920.79</td>\n",
              "      <td>187.95</td>\n",
              "      <td>Proactive bandwidth-monitored policy</td>\n",
              "      <td>West Steven</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Guatemala</td>\n",
              "      <td>2016-03-24 02:35:00</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>999</th>\n",
              "      <td>45.01</td>\n",
              "      <td>26</td>\n",
              "      <td>29875.80</td>\n",
              "      <td>178.35</td>\n",
              "      <td>Virtual 5thgeneration emulation</td>\n",
              "      <td>Ronniemouth</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>2016-06-03 21:43:00</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1000 rows × 10 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-acde5871-3194-4960-ba24-745862c1a241')\"\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",
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              "  </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",
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              "\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",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-acde5871-3194-4960-ba24-745862c1a241 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-acde5871-3194-4960-ba24-745862c1a241');\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",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "poe8qbN1gQMM",
        "outputId": "922351fa-5d1b-4087-c246-3b03cf68b332"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1000 entries, 0 to 999\n",
            "Data columns (total 10 columns):\n",
            " #   Column                    Non-Null Count  Dtype         \n",
            "---  ------                    --------------  -----         \n",
            " 0   Daily Time Spent on Site  987 non-null    float64       \n",
            " 1   Age                       1000 non-null   int64         \n",
            " 2   Area Income               987 non-null    float64       \n",
            " 3   Daily Internet Usage      989 non-null    float64       \n",
            " 4   Ad Topic Line             1000 non-null   object        \n",
            " 5   City                      999 non-null    object        \n",
            " 6   Male                      997 non-null    float64       \n",
            " 7   Country                   991 non-null    object        \n",
            " 8   Timestamp                 1000 non-null   datetime64[ns]\n",
            " 9   Clicked on Ad             1000 non-null   int64         \n",
            "dtypes: datetime64[ns](1), float64(4), int64(2), object(3)\n",
            "memory usage: 78.2+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import datetime        #datetime to integer\n",
        "for i in range(len(df)):  \n",
        "  df['Timestamp'][i]=int(df['Timestamp'][i].strftime(\"%Y%m%d%H%M%S\"))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bW8BcA37hiVk",
        "outputId": "88b12412-ecc6-4918-bb59-768dfa6d1ec8"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-34-dd96fa92c443>:3: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['Timestamp'][i]=int(df['Timestamp'][i].strftime(\"%Y%m%d%H%M%S\"))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "7sx-mVLEilyd",
        "outputId": "7fb2447e-c719-4529-9707-3daef0a0ed8d"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Daily Time Spent on Site  Age  Area Income  Daily Internet Usage  \\\n",
              "0                       68.95   35     61833.90                256.09   \n",
              "1                       80.23   31     68441.85                193.77   \n",
              "2                       69.47   26     59785.94                236.50   \n",
              "3                       74.15   29     54806.18                245.89   \n",
              "4                       68.37   35     73889.99                225.58   \n",
              "..                        ...  ...          ...                   ...   \n",
              "995                     72.97   30     71384.57                208.58   \n",
              "996                     51.30   45     67782.17                134.42   \n",
              "997                       NaN   51     42415.72                120.37   \n",
              "998                     55.55   19     41920.79                187.95   \n",
              "999                     45.01   26     29875.80                178.35   \n",
              "\n",
              "                             Ad Topic Line            City  Male  \\\n",
              "0       Cloned 5thgeneration orchestration     Wrightburgh   0.0   \n",
              "1       Monitored national standardization       West Jodi   1.0   \n",
              "2         Organic bottom-line service-desk        Davidton   0.0   \n",
              "3    Triple-buffered reciprocal time-frame  West Terrifurt   1.0   \n",
              "4            Robust logistical utilization    South Manuel   0.0   \n",
              "..                                     ...             ...   ...   \n",
              "995          Fundamental modular algorithm       Duffystad   1.0   \n",
              "996        Grass-roots cohesive monitoring     New Darlene   1.0   \n",
              "997           Expanded intangible solution   South Jessica   1.0   \n",
              "998   Proactive bandwidth-monitored policy     West Steven   0.0   \n",
              "999        Virtual 5thgeneration emulation     Ronniemouth   0.0   \n",
              "\n",
              "                    Country       Timestamp  Clicked on Ad  \n",
              "0                   Tunisia  20160327005300              0  \n",
              "1                     Nauru  20160404013900              0  \n",
              "2                San Marino  20160313203500              0  \n",
              "3                     Italy  20160110023100              0  \n",
              "4                   Iceland  20160603033600              0  \n",
              "..                      ...             ...            ...  \n",
              "995                 Lebanon  20160211214900              1  \n",
              "996  Bosnia and Herzegovina  20160422020700              1  \n",
              "997                Mongolia  20160201172400              1  \n",
              "998               Guatemala  20160324023500              0  \n",
              "999                  Brazil  20160603214300              1  \n",
              "\n",
              "[1000 rows x 10 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d769673d-10be-4012-bbcb-d789227a0200\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Daily Time Spent on Site</th>\n",
              "      <th>Age</th>\n",
              "      <th>Area Income</th>\n",
              "      <th>Daily Internet Usage</th>\n",
              "      <th>Ad Topic Line</th>\n",
              "      <th>City</th>\n",
              "      <th>Male</th>\n",
              "      <th>Country</th>\n",
              "      <th>Timestamp</th>\n",
              "      <th>Clicked on Ad</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>68.95</td>\n",
              "      <td>35</td>\n",
              "      <td>61833.90</td>\n",
              "      <td>256.09</td>\n",
              "      <td>Cloned 5thgeneration orchestration</td>\n",
              "      <td>Wrightburgh</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Tunisia</td>\n",
              "      <td>20160327005300</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>80.23</td>\n",
              "      <td>31</td>\n",
              "      <td>68441.85</td>\n",
              "      <td>193.77</td>\n",
              "      <td>Monitored national standardization</td>\n",
              "      <td>West Jodi</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Nauru</td>\n",
              "      <td>20160404013900</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>69.47</td>\n",
              "      <td>26</td>\n",
              "      <td>59785.94</td>\n",
              "      <td>236.50</td>\n",
              "      <td>Organic bottom-line service-desk</td>\n",
              "      <td>Davidton</td>\n",
              "      <td>0.0</td>\n",
              "      <td>San Marino</td>\n",
              "      <td>20160313203500</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>74.15</td>\n",
              "      <td>29</td>\n",
              "      <td>54806.18</td>\n",
              "      <td>245.89</td>\n",
              "      <td>Triple-buffered reciprocal time-frame</td>\n",
              "      <td>West Terrifurt</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Italy</td>\n",
              "      <td>20160110023100</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>68.37</td>\n",
              "      <td>35</td>\n",
              "      <td>73889.99</td>\n",
              "      <td>225.58</td>\n",
              "      <td>Robust logistical utilization</td>\n",
              "      <td>South Manuel</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Iceland</td>\n",
              "      <td>20160603033600</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>995</th>\n",
              "      <td>72.97</td>\n",
              "      <td>30</td>\n",
              "      <td>71384.57</td>\n",
              "      <td>208.58</td>\n",
              "      <td>Fundamental modular algorithm</td>\n",
              "      <td>Duffystad</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Lebanon</td>\n",
              "      <td>20160211214900</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>996</th>\n",
              "      <td>51.30</td>\n",
              "      <td>45</td>\n",
              "      <td>67782.17</td>\n",
              "      <td>134.42</td>\n",
              "      <td>Grass-roots cohesive monitoring</td>\n",
              "      <td>New Darlene</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Bosnia and Herzegovina</td>\n",
              "      <td>20160422020700</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>997</th>\n",
              "      <td>NaN</td>\n",
              "      <td>51</td>\n",
              "      <td>42415.72</td>\n",
              "      <td>120.37</td>\n",
              "      <td>Expanded intangible solution</td>\n",
              "      <td>South Jessica</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Mongolia</td>\n",
              "      <td>20160201172400</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>998</th>\n",
              "      <td>55.55</td>\n",
              "      <td>19</td>\n",
              "      <td>41920.79</td>\n",
              "      <td>187.95</td>\n",
              "      <td>Proactive bandwidth-monitored policy</td>\n",
              "      <td>West Steven</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Guatemala</td>\n",
              "      <td>20160324023500</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>999</th>\n",
              "      <td>45.01</td>\n",
              "      <td>26</td>\n",
              "      <td>29875.80</td>\n",
              "      <td>178.35</td>\n",
              "      <td>Virtual 5thgeneration emulation</td>\n",
              "      <td>Ronniemouth</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>20160603214300</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1000 rows × 10 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d769673d-10be-4012-bbcb-d789227a0200')\"\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-d769673d-10be-4012-bbcb-d789227a0200 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-d769673d-10be-4012-bbcb-d789227a0200');\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": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TNIfXx1PiwgM",
        "outputId": "7fadf4ec-1ff4-4908-8a7e-5b4396330a3e"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1000 entries, 0 to 999\n",
            "Data columns (total 10 columns):\n",
            " #   Column                    Non-Null Count  Dtype  \n",
            "---  ------                    --------------  -----  \n",
            " 0   Daily Time Spent on Site  987 non-null    float64\n",
            " 1   Age                       1000 non-null   int64  \n",
            " 2   Area Income               987 non-null    float64\n",
            " 3   Daily Internet Usage      989 non-null    float64\n",
            " 4   Ad Topic Line             1000 non-null   object \n",
            " 5   City                      999 non-null    object \n",
            " 6   Male                      997 non-null    float64\n",
            " 7   Country                   991 non-null    object \n",
            " 8   Timestamp                 1000 non-null   object \n",
            " 9   Clicked on Ad             1000 non-null   int64  \n",
            "dtypes: float64(4), int64(2), object(4)\n",
            "memory usage: 78.2+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Timestamp']=df['Timestamp'].astype(str).astype(int)      #datetime object to integer"
      ],
      "metadata": {
        "id": "wRLF9Ge4jHzD"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "sns.heatmap(df.corr(),annot=True)            #plotting a correlation heatmap for all features"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 399
        },
        "id": "_OAdPFwdfpki",
        "outputId": "da81adb1-3f69-420c-9bfa-fe05a14e6ebc"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f33118e5c10>"
            ]
          },
          "metadata": {},
          "execution_count": 41
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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vAGp2vo603y//+5s75xNiOvQhpkMfvv9+BYPuvwOAdu1ak5qaRmJi8b1vd999G1999b1LmXN/bN9bu3Po0JHL+vYYf/E+VndaVH5G2vDS4zafANONuveAb0RkMI5HYjLc2N8SHPdbFUkDl4pSaoWINAM2GzEkHXgAR0v7El7ANKNvMgs4BzzmVJ8lIjsNu6FG2avADGCPiJiAY0BpjxLNNezjnPtZlVIJIvIcsAZHS/LHMj7q0waYJSJ5OG6S5imltolIfSebNRSkfv9VTv0VZtzLU9m2cw8XLqRyy4AHeOKRB7mzX88r7RaAvWviaBEbzZR1s8ixZrNg3Hv5dS8tfZNJfcbh4+/Dk/Oew8vbCzEJBzfvY93CFQDc+ewDhDeMRNkVSafP8UlFRwQD2G2kvzuD6lOmgclE1oql2P44jv/goeT9dpCcLT/jd9sdeEW3gbw87OnppE/7V/7mwR8tQgICEIsF75hOpE4Y6zKiuPy67OQs/RDfB58HMZG3cy3q3Cm8Yu/CHn8M26EdWG7oheWaNii7DazpZC92PD5lvi4G09XXYvGvhiXK0aORs3g29kQP6DI4s2oXtW+JovuWt8izZrs8ehK7agpruk0AYPdz84me+ZjjcZvVuznzk6MvteWUIZi8vbjxC8eo8OQdh9n97Pyijkrx323LW9gK+e+yagprDf97nptPayf/Zw3/v7+zhHZzn6LeoFisp86zbVhBv3lEn3acXbcXW2ZB4itl5xHif/iFm1dMQdlsXNx7nD8+We223uXL1tCzZyx7961zPG7z2Lj8us1blrqMBr7jzr7ccbvrG9Mef/xh+vTthi3PRnLKBYYPG+u27wrxF2+xivqT+0BEpC2O52A7/6mOy4iIrMXx/O32ytZS2VTVVPDjbcdXtoRimXpNecfjXVn8YiJLN6okVs6WypZQLFVTFQxK/bl0o0oiI/N4hQ9b1rZv3Lrm+La7s0qeoooOXioTRuvsG6DoQ4QajUaj0QDY8txb3EBEeonIIePxziKdxCJSz3iyYqfxaGaf4vZTFv7UKQ2VUlOBqX+mz/KilOpS2Ro0Go3m/yUeSgUbY1feBbrjeOZ/m4gsUUodcDJ7AfhSKfW+iDTHMZlO/Yr4/VNbrBqNRqPRlIrnBi+1Bw4rpY4az/UvAgpPiqNwzCYIjgG58VQQPQm/RqPRaKoWnhvxWwc46bR+CrihkM0rwAoRGYljYp5uFXWqW6wajUajqVIoZXNrcZ7IxliGlcPdQOBDpVRdoA/wifHERLnRLVaNRqPRVC3cbLE6T2RTAqdxvP3rEnWNMmcewTEDH0qpzSLiC9TE9dHMMqFbrBqNRqOpWnhuVPA2oImINDBeSnIfBdPhXuIEcAuAMfeBL445DcqNbrFqNBqNpmrhoVHBSqk8EXkSx4xzZmC+Umq/iEwCtiullgBjcMx5f+m93ENUBSd40IFVo9FoNFULD05XqJRaSqH3USulXnL6fADw6JsNdGDVaDQaTdXiLz6loQ6smlKpqlMHvr/9jcqWUCz/bvNS6UaVwJ6D7kzlXTncg19lSygWvyp6gT/zdJvKlnBlqcIT7LuDDqwajUajqVrowKrRaDQajQdxcx7gqooOrBqNRqOpWlTRFLy76MCq0Wg0mqqFTgVrNBqNRuNBdItVo9FoNBoPolusGo1Go9F4EJutshVUCB1YNRqNRlO10C1WjUaj0Wg8iA6sGo1Go9F4ED14SaPRaDQaD6JbrJrKRkQGAN8CzZRSBytTy8CXh9IitjU51hzmj53Fif3HitiM+mgi1cOCMZnN/L7tVxa+OA9lt9N/9H207t4Ou7KTdj6V+WNncfFsyhXV+8KU6azftJWQ4Bos/nT2FfVVHN1eeZBGsVHkWrP5cexczuw7XsTmpnF3c/0dnfCtHsD05v/ILw+KDKXv9OH4BvkjJhNrX/+Co2t2e0TX4FceISq2DTnWbGaPfYfj+44WsXn2oxepERaM2WLm4NZfWfDiXJTdzshZY4hoWAeAgKAAMlIzmNBndIX0tJg8mNq3RGGz5hD39Gwu7j1exKZ6ywZEzxyO2debMz/tYu8LHwNw3UuDCO8ejT03j4zjZ9g5ag65qZl4BVej/bynCY5qxIkv1rNnwodl0hQa24prJz+EmE2cWria4++4vuZTvC20mDWCoJYNyE1JZ/ewmWSdPEfITS1o+sJAxNuCysnjt0kLSd64H5OfN63+Mwr/+rVRNjvnVsbx++TPy3vIADA3jcLn1qFgMpG77Sdy131bvN11HfB7YByZs8ZjP30EzBZ8BgzHVLcRKEXO9/OxHdtfIS1lomJvbat09IvO/x4MBDYafyuNFl1aE9YgggldRvLxhNk88NqwYu1mj5jOP3uP5eUezxAYEkTbvjEALJ/7Ha/0HsOkPuPYs3oH/Z6++4prHtCnO7OnT77ifoqjYWwrghuEM+fmMSx7/gN6Th5SrN3hVXF81P/lIuUdR/bn4A+/sKDPC3w3chY9Xy1++7ISFRtNeINIRt/8BPOef5+hk4cXa/f2iGk833s047s/TVBoEB36dgTgnSf/zYQ+o5nQZzRbl21m27ItFdJT+5YoqjUMZ1XMaHaNnUer14cWr/v1oewaM49VMaOp1jCcsK6tADi7bi+ru4xnTdfnSD+aQJOnbgPAnp3Lr69/zb5/Liy7KJPQbOpQ4gZNZVPnMUTcfiMBTeu4mNQdFEvuhXQ2dhjFH3N+pOmLgwDITU5j54NvsrnLePY99R7XzxqRv83x939gU6cxbO72HDXaXUPNrlFl13YJMeFz26NYF7xG5lujsLTqhITVLWrn7Yv3jX2xnfgtv8irXTcArDNHk/XBP/Hu+xCIlF9LWcnLc2+poujA+hdHRKoBnYBHgPuMMpOIvCciB0VkpYgsFZG7jLo2IrJORHaIyHIRifCUlqge7dj837UAHN35O/6B/lSvVaOIXVa6FQCzxYzFy5J/d3qpHMDb3+dPuWttG9WC6kGBV9xPcTTp3oZ932wEIH7nEXyCAggIK3q84nceIePshSLlSoF3NcdbYXwC/UnzUOu+Tff2bPhmDQCHd/6Gf1AANcKCi9hZC53H4t4N3aHvjWxesqFCesJ7tuHEl459pMQdxivIH59Cx8knrAaWan6kxB0G4MSXG4jo1RaAc+v2omyO1GLKjsP4RYQCYMvMJnnrIezZuWXWVD26MZnHErH+cRaVayNx8c+EGf4uUatXW+K/XA/Ame9/IaTTdQCk7TtO9hnHuUo/eAqzrzfibcFuzSFl0wEAVK6N1L3H8IkMKbO2S5iuaow9KRGVcgZseeTt3oilWbsidt49BpKz7lvIy8kvk7C62I7uc2jJSEVZMzDVaVRuLWVG2d1bqig6sP716Q8sU0r9BiSJSBvgDqA+0Bx4EIgBEBEv4B3gLqVUG2A+8JqnhNSoHUpyfFL+ekpiMjXCQ4u1HfXxC0zf8QFZGVa2Ly1o0dw+diBv/DybDv07s3j6F56SViUJDA8mzel4pSUmE1i7aAAriY0z/st1t9/IE1ve5p4Px7HypY89ois43PU8JicmEVy7+Av8cx+/xOy4D7FmWPll6WaXumvbN+fi+QskHk+okB6/iGCs8cn561kJyfhFBBe1Sbi8DcDVA7twZvWuCukB8A0PIcvpGGXFJ+MT7nqMfCNCyDrtsFE2O3lpVrxCXG/iat96A6l7j6FyXFtfliB/avWIJnnDvnJrlKAQ1MXz+esqNRmp7vr/aIpsgKl6TWyH4lzK7Ql/YGnWFkwmJDgMc51GSPWa5dZSVpRdubW4g4j0EpFDInJYRJ4rweYeETkgIvtF5LOK6teB9a/PQGCR8XmRsd4J+EopZVdKJQJrjPprgOuBlSKyC3gBKCY3BCIyTES2i8j2g2lF+9cqyozBkxnT/lEs3l4063h9fvm30z5nfMfH2PLdBro+1Mvjfv9ONL8thn1fr+e9Dk/x5ZA36Tfj8T83XQdMHTyJJ9oNxcvbi+s6tnCp63hbZ36uYGvVkzR9uj/2PBunvtlU2VIACLimLk1eHMSBsfNcysVsouXspzgxbxnWP85eOQEi+PQdQvaPHxapytvxE/aLSfiNeAOfWx/GduLQn9tCtNvdW0pBRMzAu0BvHA2NgSLSvJBNE+B54Eal1HXAqIrK14OX/sKISAjQFWghIgowAwrHQKZiNwH2K6ViStu3UmouMBfgH/XvKvHWMPbBXnQeeAsAx3cfISSy4I44ODyEC4lJJW1KXnYuu1ZuI6p7Ow5s3ONS98viDTy9YAJL3vqyNKl/KaIHd6PVfbEAJOw5SqDT8QoMDyHtjPvp3Jb33syXgx0ve4+PO4zFxwv/kEAyk1LLrKv74N7E3tcdgKN7Drucx5DwUFLOJJe0KbnZuexYsZW2Pdqzb6Nj8JTJbKJdrw5MvHVsmbUANHi4O/XvdxynlF1H8XNKifpGhGBNcD1O1oQU/CJKtql3702Ed49m092eSdBkJSbj63SMfCNDyE50PUZZCcn41gklOyEZMZuwBPqRm5wGgE9ECFELxrDvyXex/nHGZbvm/36UjGMJnJj7vwppdLRQC1qZjhas0/+jtx+m2vXwGzbJUV+tBr6DnyPr46nYTx8hxyng+j32Gvbz8RXSUyY8F8TbA4eVUkcBRGQRjizfASebR4F3lVIpAEqpCt/N6BbrX5u7gE+UUlcrpeorpa4CjgHJwJ1GX2ttoIthfwioJSL5qWERua4iAtZ8soxJfcYxqc84dq7YSswdDlcNWzfBmpbJxXOufYM+/r75/a4ms4mWXaNJOHIagLD64fl2Ud3b5Zf/nYj7eBUL+kxkQZ+J/L5iB9ff2QmAyNaNyE7LLLYvtSRS45Oof6Pj9IU2jsTs41WuoAqw8uP/5Q842r7iFzrf6QhqjVs3xZqWyYVC/bc+/r75/a4ms4morm2IP3Iqv/76Tq2IP3Ka5MvcWF2OYwtWsqbbBNZ0m0DCsu3Uu6czAMHRjclLs5Jd6Dhln71AXrqV4OjGANS7pzOJy3cAEBbbksYjbmXLQ9OwWXPwBKk7j+DfMBy/erUQLzPhAzpy1vB3iXPLdxB5z00A1O53A8kbHaNqLUH+RC98lt8nf8aFbb+5bNP4uXuwBPpz6IWKp/Xtpw5jqhmBBIeB2YKlVSdsv24vMMjOJGPyw2S+8TiZbzyO/eRv+UEVL2/w8gHA3Lgl2O2os6dK8HQFsCu3FufMmrEUHjFZBzjptH7KKHOmKdBURDaJyBYRqXCqTLdY/9oMBF4vVPYN0AzHD+gAjh9VHHBRKZVjDGJ6W0Sq4zj/MwCPjKPfuyaOFrHRTFk3ixxrNgvGvZdf99LSN5nUZxw+/j48Oe85vLy9EJNwcPM+1i1cAcCdzz5AeMNIlF2RdPocn0yc6wlZl2Xcy1PZtnMPFy6kcsuAB3jikQe5s1/PK+4X4MjqXTSMbcXw9f8m15rD0rEF3/fhpa+xoM9EALo8fx/N+3fEy8+bJ7a8zZ5Fa9k447+snryQ3lP/QbtHeqEU/Dhmjkd07Vq9g6jYNry1/n2yrdnMGftOft2UpdOZ0Gc0Pv4+jJn3vHEeTRzYvJdVny7Pt4vp18ljaeAzq3ZR+5Youm95izxrNjtHFXzP2FVTWNNtAgC7n5tP9MzHHI/brN7NmZ8cfaktpwzB5O3FjV88D0DyjsPsfnY+AD22zcRSzQ+Tt4WIXm34+b6ppP1W+g2dstk5+PwCohdNQMwmTn++hoxDp2g0/m5Sdx/l3PIdnP5sDdfPGkGnLTPIvZDOnuFvA3DVIz3xb1CbhmPupOGYOwGIu3cK4mWh4TN3kP7baTqs+hcAJ+cv5/TCNSXquCx2O9lL5uE39EUQE7nbV2M/exLvbvdhO33YNcgWQgKqO7ZTCntqMllfvl0+DeXFzRG/zpm1CmABmuBogNQF1otIC6WU+3e5hZDiRvJp/vqISDWlVLqIhAJbcfQfJJZnX5dLBVcm729/o7IlFMu/27xU2RKKZQ8ZlS2hRO7J9qtsCcXiX0VHnnYcWvaRzH8W1f71TYU7+jNnDHfrmuM/as5lfRnZuVeUUj2N9ecBlFL/crKZDfyilFpgrP8EPKeU2lZO+ToV/DfmB2OA0gbg1fIGVY1Go/apztAAACAASURBVPnT8dDgJWAb0EREGoiIN45HEpcUslmM0V0mIjVxpIYrNGJTp4L/piilulS2Bo1GoykXbj5KUxpKqTwReRJYjmNw53yl1H4RmQRsV0otMep6iMgBwAaMU0qVb3CAgQ6sGo1Go6laeDAFr5RaCiwtVPaS02cFjDYWj6ADq0aj0WiqFCpPv+hco9FoNBrP4aFUcGWhA6tGo9FoqhZVdDS2u+jAqtFoNJqqhW6xajQajUbjQfSLzjUajUaj8SC6xarRaDQajQex6VHBmr85U685V9kSiqWqTh04ZsekypZQLGmPPFzZEkpk85bIypZQLCkmc2VLKJb+n5T8tqHK5qd/lW5TGkqngjUajUaj8SA6FazRaDQajQfRgVWj0Wg0Gg+in2PVaDQajcaD6BarRqPRaDSeQ+XpFqtGo9FoNJ5DjwrWaDQajcaD/MVTwabKFqDRaDQajQt25d7iBiLSS0QOichhEXnuMnZ3iogSkbYVla8Dq0aj0WiqFEopt5bSEBEz8C7QG2gODBSR5sXYBQJPA794Qr8OrBqNRqOpWuTZ3VtKpz1wWCl1VCmVAywC+hdj9yrwOpDlCfk6sGo0Go2mSqHsyq3FDeoAJ53WTxll+YhINHCVUupHT+nXg5eKQUQGAN8CzZRSB6+Qj+NAW6XU+Sux/8rAq217Ah4biZhNZP3vR6xffuZS79v3Nnz73Q52G8pqJX3mNGwn/kACgwh8cRJeTa8ha+UyMt6d6XFt3V55kEaxUeRas/lx7FzO7DtexOamcXdz/R2d8K0ewPTm/8gvD4oMpe/04fgG+SMmE2tf/4Kja3Z7XGNhXpgynfWbthISXIPFn86+4v4u4dW6Pf6PjgSTieyVP5L1jet59Ol1Gz69jfOYZSXjvWnYT/6RX2+qGUb1WR9hXfQhWYu/KJeGWrGtaD55MGI2cXLhGo68s8Sl3uRtodWsJ6jesgE5KensHDYT60nHv1Kjp/pz1aAuKJud/RM/4vzaPU4bCp1WTCErMZntD7wJQMu3hlG9VUMQIeNIArufeh9bZrZbOlu/OpiIW1phs+awddQcUvYeL2IT3LI+7Wc8htnXi4SfdrPzxY9d6q8Z3oeoV+7n2+uGk5Oc7vj+Mc1oPelBTF5mspPTWHPHZHcPXRFGTHqCG7q2I9uazRvPTOP3fYdLtH11/j+JqBfBP7oNA6BR84aMmvo03j7e2PJszJz4Dod2HSq3Frdxv/90GDDMqWiuUmquu25ExARMB4aURV5p6BZr8QwENhp/iyAi+oakMCYT1UaMIvWF8aQ8+hA+sbdgrne1i0n2mlVceOxhLjzxD6xffU7A8BEAqJwcMj/6gIz/vH9FpDWMbUVwg3Dm3DyGZc9/QM/JQ4q1O7wqjo/6v1ykvOPI/hz84RcW9HmB70bOouerxW/vaQb06c7s6eW/oJYLkwn/4aNI++d4Lj75EN6db8F0VaHzuG4VqU8/TOoz/yDr28/xHzrCpd7/kRHkxm2tgAbhuqkPs3XQ66zrPJbI2ztSralLI4OrBsWSeyGDtR2e4dicpVz74iAAqjWtQ+SAGNbfNI6tA6dy/etDwST52zV4tDfpv5922deBFz9hQ9fn2BD7LNbT56n/SE+3ZEZ0bUVgw3CWdhzD9nEf0GZq8S85aDN1KNvHzmNpxzEENgwnvGur/Dq/yBBqd2lBxqmC+2uvIH/aTH2YjUP+zbIuz/Lzo2+7pac42ndtR90GdRjc6WGmPzuDp//1VIm2nXrfiDXT6lI2bOKjfPLWpwzv+Tgf/vsjhk38Rwlbexi7e4tSaq5Sqq3TUjiongauclqva5RdIhC4HlhrNHY6AEsqOoBJB9ZCiEg1oBPwCHCfU3kXEdkgIkuAAyJiFpE3RWSbiOwRkeGXtheRn0QkTkT2ikhx+Xxnf/VF5FcR+Y+I7BeRFSLiZ9Q1FpFVIrLb2F8jcfCmiOwz9n+vk751IvKdiBwVkakicr+IbDXsGhl2tUTkG0P3NhG50RPHzXJNM2zxp7EnJkBeHtlrV+Md08nFRmVmFqz4+sGlm9LsLPL270Xl5HhCShGadG/Dvm82AhC/8wg+QQEEhNUoYhe/8wgZZy8UKVcKvKv5AeAT6E/a2ZQrorMwbaNaUD0o8E/xdQlLk2bYE09jP+M4jzkbVuPd3vU8Yi04j+LjdB4Brxs6YTuTgO3EsXJrqBHdmMxjiVj/OIvKtRG/eDO1e7le52r3asOpL9cDkPj9L9TsdL1R3pb4xZux5+RhPXGOzGOJ1IhuDIBvRAhh3VtzcuEal33lpRcEE7Ovt1uDYgDq9GrD8a82AJAUdxivIH98C/2ufMNq4BXoR1Kco5V4/KsN1O3VJr++9T8fZM+rnzt+ZAZX396RU0u3kXk6CYDspFS39BTHjT06suLrlQD8GneQakEBhISFFLHz9fflrkfvZOFM1+yEUgr/av4ABAQGkHQmqdxayoIHU8HbgCYi0kBEvHFc0/PTH0qpi0qpmkqp+kqp+sAW4Dal1PaK6Nctr6L0B5YppX4TkSQRaaOU2mHURQPXK6WOGSmIi0qpdiLiA2wSkRU48vm3K6VSRaQmsEVElqjL/7c2AQYqpR4VkS+BO4FPgYXAVKXUtyLii+NG6A4gCmgF1AS2ich6Yz+tgGZAMnAUmKeUai8iTwMjgVHATOAtpdRGEakHLDe2qRCm0JrYz53NX7efP4fl2qK79e03AL877gEvLy6OH1VRt24RGB5MWnzBBSEtMZnA2sHFBtHi2Djjv9z7ybO0GdIDb38fPh/kgfdiVVEktCa2807nMekclqZFz6NPnwH43uY4j2kvGOfR1w+/OwaR+vIY/AbcW24NvuHBWJ3OV1Z8Un5wzLeJCCHLCDzKZic3LROvkEB8w4O5sKMg1ZmVkIxveDAAzV8dzK+TPsNSzbeIz5YzhhPWrTXpv53iwCufuqXTLzyETCed1oRk/CKCyXL6XflFBJMZX/CKt8yEZPzCHYEtsmcbrInJXDhwwmW/gY3CEYuF2G8mYqnmx+/zlnH8q41uaSpMzfBQzsUXvPbxXMJ5aoaHknzW9bVzD48bwldzvyHL6poCf++V95m68F8Mf3EYJpMwsv+f8z/rqedYlVJ5IvIkjuucGZivlNovIpOA7UqpJZffQ/nQLdaiDMQxcgzjr3M6eKtS6tKteA9gsIjswjFEOxRHgBRgiojsAVbh6CivXYrPY0qpXcbnHUB9Y/h3HaXUtwBKqSylVCaO1vTnSimbUuoMsA5oZ2y7TSmVoJTKBo4AK4zyvUB943M3YJahewkQZLTSXRCRYSKyXUS2f3wqoRT57pP1/WJSHh5E5gdz8B802GP7vZI0vy2GfV+v570OT/HlkDfpN+NxECl9w78x2UsXc/GxQVg/moPfPY7z6HffELKWfAVZ1lK2/vMJ696anPOppO4pviW9Z9QcVrV8nPTf4onsH3PF9Zj9vGn+1G3se+PrInViNhPSsgHrH5jGuoFTaT7qdqo1DL9iWho1b0jk1RFsWrapSF2/wf14/5+zGdj+ft57ZTZjp42+YjqcUXnKrcWtfSm1VCnVVCnVSCn1mlH2UnFBVSnVpaKtVdAtVhdEJAToCrQQEYXjDkeJyDjDJMPZHBiplFpeaB9DgFpAG6VUrpG3L3qL7IrzbaIN8CvnV3Dej91p3U7BuTYBHZRSlx1WbvRVzAU43/PmUn/B9qTzmGqF5a+batbCfr7kcVnZa38iYOQzpe223EQP7kar+2IBSNhzlMDI0Py6wPAQ0s64n85tee/NfDn4DQDi4w5j8fHCPySQzAqk6KoqKuk85ppO5zG0Fvakks9jzoaf8H/McR4tTZvj3fFm/B4ajgRUA6VQOTlkL/22TBqyElPwczpfvpGhZCW6nq+shGR864SSlZCMmE14BfqTm5xGVmIKvnWcto0IISsxhdo92xDWM5rYW6Iw+XrhVc2PqHdHsGvEuwU7tSviF/9Mwyf7cWrRumK1NR7SnYb3O35XybuP4u+k0y8iBGuCq05rQgr+kQWpV/+IEKyJyVS7ujYB9WrR03gruF9ECD1WvMaq3i+RmZBMdko6Nms2Nms257YcpEbzeqQfTXTr+PV/qB99BvUB4NDuQ9SKrJVfVyuiJucTXdO5zds0p2nLpizc/DFmi5kaoTX491dvMubucfS4qzvvvvQeAOt+WM+YN6/c/6wLf+0ZDXWLtRB3AZ8opa42cu5XAceAzsXYLgceFxEvABFpKiIBQHXgrBFUY4Gri9m2VJRSacApY4QyIuIjIv7ABuBeo4+3FnATUJaRIitwpIUx9htVHn2FyTt0EHOduphqh4PFgk+XruRscb0DNkUWDEDxbh+D7fQpT7gulriPV7Ggz0QW9JnI7yt2cP2djn7CyNaNyE7LdDsNDJAan0T9G68DILRxJGYfr79lUAXI+/0gpoi6mMIc59G7c1dytxY6jxEF59GrbQz2BMd5TJswkovD7uPisPvI/v5rsr7+tMxBFeDiziMENAzHr14txMtM5IAYzizf4WJzZvkO6t5zEwDh/W7g/Mb9+eWRA2IweVvwq1eLgIbhXIg7zKHXFrG69ZOsafcUO4e/zflN+/ODqn/9goRS7Z5tyPg9vkRthz9cyYruE1jRfQKn/7ed+nc7Lg2h0Y3JTbO6pIEBss5eIDfNSqiRyq5/d2dOL9vBxYMn+a7FE/zQfhQ/tB+FNSGZFT0mknXuIqeX76BW+6aI2YTZz5vQ6EakXUZTYb776HuG93yc4T0fZ9Oyn+lxV3cAmkVfS0ZaRpE08Pef/MC9bQdyf8xgnr59NKeOnmbM3Y62RNKZJFrFtASg9Y1RnD7mvo6K4ME+1kpBt1hdGYjjIWFnvjHKCz83MA9HejVORAQ4BwzA0S/6vYjsBbYDFXlc50FgjtEfkAvcjeMxoBhgN45hI+OVUokicq2b+3wKeNdIVVuA9cBjFdDowG4j/d0ZVJ8yDUwmslYsxfbHcfwHDyXvt4PkbPkZv9vuwCu6DeTlYU9PJ31aQV9l8EeLkIAAxGLBO6YTqRPGYjvxx2Ucus+R1btoGNuK4ev/Ta41h6VjCwYOPrz0NRb0mQhAl+fvo3n/jnj5efPElrfZs2gtG2f8l9WTF9J76j9o90gvlIIfx8zxiK7SGPfyVLbt3MOFC6ncMuABnnjkQe7s596I1XJjt5E5dwaBrzjOY/ZPS7GdPI7foKHkHT5I7taf8e17B5ZWjvOoMtLJmOHZPmdls7Pv+Q9pv+h5xGzi1OdrST90iqbj7+LC7mOcXb6Dk5+tJWrWE3TZ8ha5F9KJG/4OAOmHTpGwZAs3bZiGyrOx77kFl++vE6HVO49jCfRDREjd/wf7xs93S2fCT7uIuCWKvpunk2fNYeszBb+LHiunsKL7BAB2PL+AG2YMx+zrTcLq3SSsvvyjWmm/x5OwZg89V08Fu52jn63l4qHy3YT+snorN3RtzycbPyQrK5s3R0/Lr5uz/H2G93z8sttPH/8WI/75BGaLiZzsXKY/O6NcOsrMX7zFKu6OgNP8/8WdVHBlMO/gVaUbVQJjdkyqbAnFkvZI8Y+DVAU2b4msbAnFkm6qmkm9ueZzpRtVEj+dWlHhAQhJ/dy75oR+v65KDnbQLVaNRqPRVClUXmUrqBg6sGo0Go2mavEXTwXrwKrRaDSaKoXSgVWj0Wg0Gs+hA6tGo9FoNB5EB1aNRqPRaDyJqpKDfd1GB1aNRqPRVCnseTqwajQajUbjMXQqWKPRaDQaD6J0Klij0Wg0Gs+hW6yavz1+MVVzurk9BzNKN6oEqurUgYEfLKhsCSXidd2EypZQLNXttsqWUCzrkvZXtoQrirL/tVusVXMiTI1Go9H8v0Up9xZ3EJFeInJIRA6LyHPF1I8WkQMiskdEfhKRcr2RzBkdWDUajUZTpbDnmdxaSkNEzMC7QG+gOTBQRJoXMtsJtFVKtQS+Bt6oqH4dWDUajUZTpfBgi7U9cFgpdVQplQMsAvq7+lJrlFKZxuoWoG5F9es+Vo1Go9FUKTzYx1oHOOm0fgq44TL2jwD/q6hTHVg1Go1GU6Vw93EbERkGDHMqmquUmlsenyLyANAWuLk82zujA6tGo9FoqhTuPm5jBNHLBdLTwFVO63WNMhdEpBswEbhZKZXtttAS0IFVo9FoNFUKm91jw3+2AU1EpAGOgHofMMjZQERaA3OAXkqps55wqgOrRqPRaKoUnupjVUrliciTwHLADMxXSu0XkUnAdqXUEuBNoBrwlYgAnFBK3VYRvzqwajQajaZK4e4zqu7tSy0FlhYqe8npczfPeXOgA6tGo9FoqhR/9ZmXdGDVaDQaTZXCrifhLxsiYgP2Al5AHvAx8JZSJY8DE5FI4G2l1F0i0gUYq5S61U1/Q3DMqvHkZWzqAx2VUp+5+TXKjaFnhVIqvpi6tTi+23YnXT8opa6/0ro8gblxK7x7DQaTiby4NeRuXOJSb4m6Ce/u92NPSwYgb+sK8uLWYAq/Gu++QxEff1B2ctZ/i23/Fo9qG/zKI0TFtiHHms3sse9wfN/RIjbPfvQiNcKCMVvMHNz6KwtenIuy2xk5awwRDesAEBAUQEZqBhP6jK6wJq/W7fF/dCSYTGSv/JGsb1x/fj69bsOn9+1gt6GyrGS8Nw37yT/y6001w6g+6yOsiz4ka/EXFdbjLi9Mmc76TVsJCa7B4k9nX3F/obGtuHbyQ4jZxKmFqzn+juvvSrwttJg1gqCWDchNSWf3sJlknTxHyE0taPrCQMTbgsrJ47dJC0ne6JhjN/z2jjR4egAoRXZiCntHvEtuclqZdNWMbUWzyQ+BoetYMbpaFtJlPXkOr+BqRH3wDNWjGnF60Tp+nVAwh3N4/xgajRoAJhPnVu7kt8kVvyS9NX0SvXt1JdNq5ZFHnmHnrn0u9dWqBbB2zbf563XrRLDws/8yZuzL1KtXh3lzp1OzVggpyRcYPOQpTp9OqLCm0tBvtyk7VqVUFICIhAGfAUHAyyVtYAShu66gpvo4Roq5/SsWEYtSKq8cvoYA+4AigfUvjQjefR4m65MpqNQkfB99jbxDO1DnXEe25+3fTM7SD13KVG422d++j0pORAKD8R32GtYjeyArE08QFRtNeINIRt/8BI1bN2Xo5OG8NODZInZvj5iGNd0KwKjZ4+nQtyObv9/IO0/+O9/m/heGkJnqAV0mE/7DR5H28hjsSecImjaHnK2bXAJn9rpVZC9zXKy92nfEf+gI0v85Pr/e/5ER5MZtrbiWMjKgT3cG3XkbE16dduWdmYRmU4ey457XyIpPosPyKZxbvoOM3wp+V3UHxZJ7IZ2NHUYRPiCGpi8OYs+wmeQmp7HzwTfJPpNCtWvrEr1oAuujnkDMJq6d/BCbOo8lNzmNJi8Oot7QnhyZ9nWZdDWfOpRthq6Y5VM4W4KuDU66dg+biT07l9+nfkngtVdR7dqCJ0G8gqtxzUv383OP58lNSqPF248T0vl6kjfsK06BW/Tu1ZUmjRtwbfNO3NA+mndn/YuOnfq52KSnZ9C2XY/89V+2/I/Fix1dkm+8/hKfLPyaTz75itguN/La5OcZ8vBT5dbjLra/eCq4Uqc0NIY2DwOeFAf1RWSDiMQZS0dwtNxExOXXJSImEfldRGo5rR++tF4cIvKhiLwtIj+LyFERuRSspwKdRWSXiDwjImYReVNEthkTMw83tu9i6FsCHDDW14rI1yJyUEQWijGsTETaiMg6EdkhIstFJMLw1xZYaPjyc/dYich1IrLV2G6PiDQxyhcbPvYbD0tfsn9ERH4ztvmPiMwyymuJyDfGd9smIje6q+FymOo0xp6ciEo5CzYbtn2bsVzT1q1tVVIiKjnR8TktBZWRivgHeUIWAG26t2fDN2sAOLzzN/yDAqgRFlzE7lJQNVvMWLwsqGJGUHToeyObl2yosCZLk2bYE09jP5MAeXnkbFiNd/tOhQQVBHDx8QMnOV43dMJ2JgHbiWMV1lJW2ka1oHpQ4J/iq3p0YzKPJWL94ywq10bi4p8J6+X6u6rVqy3xX64H4Mz3vxDS6ToA0vYdJ/tMCgDpB09h9vVGvC0gAghmfx8ALIF+ZBl27lKjGF21C+mqXUhXqKHLlpnNha2HsGfnutj7XR1G5rFEcpMcLeek9fsI79u+TLoK069fTz5Z6Lhh+GVrHNVrVCc8PKxE+yZNGhJWqyYbNv4CQLNmTVizZhMAa9Zu4rZ+PUrc1pMoJW4tVZVKnytYKXUUxzDoMOAs0F0pFQ3cC7x9me3swKfA/UZRN2C3UupcKS4jgE7ArTgCKsBzwAalVJRS6i0c01pdVEq1A9oBjxrPQQFEA08rpZoa662BUTgmeG4I3CgiXsA7wF1KqTbAfOA1pdTXwHbgfsOXtRStzjwGzDRa+21xTM0FMNTw0RZ4SkRCjdT5i0AH4EbgWqf9zMSRem8H3AnMK4OGEpGgYFRqUv66Sk1CgooGL3Oz9vg9/jo+94xCgkKK1JvqNELMFlTKGU/IAiA4PJTk+AJtyYlJBNcu6hvguY9fYnbch1gzrPyydLNL3bXtm3Px/AUSj1c8FSahNbGdL3hkzp50DlNozSJ2Pn0GUH32Z/gNeYzM/8x0FPr64XfHIKyLPqqwjqqOb3gIWU7nLis+GZ9w13PnGxFC1mmHjbLZyUuz4hXiGvhr33oDqXuPoXLyUHk2fn32AzqufYOb97xPtaZ1Ob1wdZl0+YSHYC1Fl09ECNZSdDmTeewMAY0i8LuqFmI2Eda7Lb51QsukqzB1IsM5dbIgOXb6VAJ1IsNLtL/3ntv46quClPaePQe4fUBvAAYM6E1QUCAhIUX/rz2NJ99uUxlUemAthBfwHxHZC3yFI1hdjvnAYOPzUMCdF04uVkrZlVIHgNol2PQABovILuAXIBRoYtRtVUo5NxO2KqVOGYF+F4608jXA9cBKYx8v4N7EzsX9VC6VbQYmiMizwNVOQfkpEdmNY/Loqwyd7YF1SqlkpVQujmN5iW7ALEPXEiBIRKoVdioiw0Rku4hsn7/jsBvSSyfvUBzWGU9hff9ZbEf24nP7E64+q9XA5/YnyP5udqX910wdPIkn2g3Fy9uL6zq2cKnreFtnfvZAa7UsZC9dzMXHBmH9aA5+9zh+6n73DSFryVeQVZb7sv+/BFxTlyYvDuLAWMc9pFjM1B3Snc23PM+6lo+TduCEo7+1ksm7mMH+Zz+g1dynuWHJK1hPnkPZ/tw3ft9zT38WfbE4f338s69y000d2LZ1OTd17sCpUwnYbFf+HbV2JW4tVZVKHxUsIg0BG47W6svAGaAVjqCfdbltlVInReSMiHTFEUzuv5y9gfN0VSWdGQFGKqWWF9LaBSj8dm3n/dlwHFMB9iulYtzQ40wS4Hw7GAKcB1BKfSYivwB9gaVGetqOI1DGKKUyjcFPvqX4MAEdlFKlHdv8qcIyXhlYapRTqSlIUMHdtQSFolILpdes6fkf8+JW493daQIUHz987h9PzuovsJ+qeCDvPrg3sfd1B+DonsOERBZoCwkPJeVMconb5mbnsmPFVtr2aM++jbsBMJlNtOvVgYm3jq2wNgCVdB5zzYKUnCm0Fvak8yXa52z4Cf/HngHA0rQ53h1vxu+h4UhANVAKlZND9tJvS9z+r0pWYjK+TufONzKE7ETXc5eVkIxvnVCyE5IRswlLoF/+QCSfiBCiFoxh35PvYv3DkQUJvN7xus1L62eWbKb+SJcXnpRKdmIyfqXoyk5Ixq8EXSVxbkUc51bEAVD3wVvKFVgff+whHnnEcSncvn0Xda+KzK+rUzeC0/GJxW7XsmVzLBYLcTv35pclJJzh7nseBSAgwJ87bu/LxYupZdZUVqpymtcdKrXFavSHzgZmKUeHVnUgwWj9PYgjRVwa83CkhL9SSpX3VioNcM7RLAceN1K6iEhTEQkow/4OAbVEJMbY3ktErivBlzNrgQcu9dMCDwFrjH00BI4qpd4GvgNa4jheKUZQvRZH6hcc03jdLCLBImLBkfK9xApg5KUVEYkqw/cqEXv8EUyh4UiNWmA2Y74+hrxDO1xspFqN/M/ma9pgP28M9DCb8b13NHm7N2A74JnBOCs//h8T+oxmQp/RbF/xC53vjAWgceumWNMyuXDWNej7+Pvm97uazCaiurYh/sip/PrrO7Ui/shpkhOT8AR5vx/EFFEXU1g4WCx4d+5K7tZNLjamiDr5n73axmBPcOhJmzCSi8Pu4+Kw+8j+/muyvv70bxlUAVJ3HsG/YTh+9WohXmbCB3Tk7HLX39W55TuIvOcmAGr3uyF/5K8lyJ/ohc/y++TPuLDtt3z77IQUqjWtg1eo498w5OaWZPxeZPrYy3LRDV1nC+lKMnRdDu+ajrEFluoB1BvSnVML15RJF8D7sz+ibbsetG3XgyVLlvPg/Y6hJDe0jyb1YiqJicXP2nffvf35wqm1ChAaGsyly9Fzz47kw48WlVlPedAt1rLjZ6QhLz1u8wkw3ah7D/hGRAYDyyjaOiyOJThSwO6kgUtiD2AzUqof4uiHrA/EGUHuHPwfe+cdX0XR/eHnpJGE0EJLQq8qNdIEBAQxFBFFERQsiL6Cyk/AhoD66quIWF5ExYING4rtFRsISBfpvShFaYGElgQI6cn5/bGb5CakXOCGvcF5+OyHu7Ozu987u7ln5syZGdz2Falqmh2o9LqIVMAq5ynANvv674hIMlZL09Wf9y5Wf+gmEVGs/thx9rGBwB0ikg7EAhOxyuc+EfkDy5ivtO9/UEQmAquBOOBP4IR9nZHAmyKy2da1FKv/9vzIyiJt9kcE3jEOxIeMDYvRo9H4d7uZrEN7yNyxDr8reuF3SWs0KxOSE0mdZQ3V8G3aAZ86l+IXHIJfpPVDlDbrHbJi9xV1R7fZuHAdkd1a8+rSt0lNTmXao2/kHJs4ezLjr32YMsFleOT9dpkGQAAAIABJREFUcfgH+CM+PmxfsYVfP8t1WHTo28mzbuCsTJLenUK5Z16xhtssmE3mgb0EDb6bjN1/kr76dwL73IRfy9aQkYGeTuT0lBc8d//z4LGnJ7Fmw2YSEk7Svd/tPHDPHfTv27NE7qWZWfw5bjqtZo5HfH04+MUiTu+IpsGYAZzc9DdH567j4OeLaDZ1BJ1WTiE9IZHNw63QjFr39CS4XnXqP9Kf+o9Ydcv1t0wk9XA8f73yLW1nPYNmZJASfYytI98+a13bx02nja0r+otFJO6IpuGYAZywdUV/vogWU0fQ2da1aXhuyMhVa97At1wQPgF+VO/dhjW3TOT0zoNcNmEI5ZpYLerdk78l6e/z68+fPWcBvXpdzY4/lpOUnMy//pU7TGztmnl5ooFv7t+Xvjfckef8q67qyPPPjUNRli1byYMjnzgvPe6S6cVG0x2koMjH0oSItMEKxunstBZvQkRCVDXRbrF+hzVH5jk1a9xxBTvBvdOL9GY7xtTIwt3MTlLug/Ope5Ysi5qOd1pCgVzYHk73uS7+wvb1nw0ZaQfP2youD7vZrd+cK2O/8UoL7Hgf6/kgImOB+3Gvb/WfxjNiLYUUiOX+nVVMfoPBYPAKvLVC4y6l2rCq6iRyh8wYXFBVz0TZGAwGwwVGC40rLR2UasNqMBgMhouPLK/sfHIfY1gNBoPB4FVket0UC2eHMawGg8Fg8CpKex9r6a4WGAwGg+GiQxG3NncQkV4issOeS35sAcfLiMiX9vFVYq0qdl4Yw2owGAwGryLLza04RMQXeBPojTVF7iARyT9V7j1YE+00BF4FXjxf/cawGgwGg8Gr8JRhxZrqdreq/q2qacBMIP/8lTcA2StafAN0d5n97pwwhtVgMBgMXoW7rmDXxULsbVi+S9UADrjsR9tpBeax19g+gbXwyjljgpcMBoPB4FVkuNlgdF0sxJswhtVQLPPf8c7B2gNxe534C8qKlRHFZ3IAfy+dNhCg27aJTksokHlNL8zcuGfLF5W7Oi2hRPHgMNaDWMtpZlPTTisoT7Q9BWwFrJXGzhnjCjYYDAaDV+HBPtY1QCMRqSciAcCtWAu3uPID1kpiADcDC/U8J9E3LVaDwWAweBVZ5xc7lIOqZojI/2EtBeqLtRjJNhF5Flirqj8AHwCfishurNXAbj3f+xrDajAYDAavwpMzGqrqbGB2vrR/u3xOAQZ48JbGsBoMBoPBuyjtMy8Zw2owGAwGr8LdqGBvxRhWg8FgMHgVpXxxG2NYDQaDweBdZJXuBqsxrAaDwWDwLkwfq8FgMBgMHsS4gg0Gg8Fg8CAZxhVsMBgMBoPnMK5gwwVDRBSYoaq32/t+QAywSlWvK+K8rsCjReXxFM0n3En17pFkJqexftQ7nNiy94w8FVrUo9Vrw/ENDODwgo1sefITAJr+ezBhUa3ISs/g9N7DbBg9jfSTSV6lxb9SCO3eH0WlyAbs/3Ipm8d/VKyOqt1a0mTCnYivDwdmLOKvN/LOqOYT4EfLqQ9QoUU90uIT2TDsNZIPHAOgwcgbqDW4K5qZxbYnPubY4s0uJwqd5k0kJTaOtbe/DECLV4dRoWV9EOH0XzFsGvk2mUmpxWqs3K0ll04Ygvj6ED1jIXvzaZQAP5pPHUH5FvVIj09k07DXSDlwlNAuzWn85CAkwA9Ny2DnszOI+20bAGE3dqTeqH6gSmpsPFtGvEl63KlitZwrT06czNLlqwmtVJFZn71TIvfw9LMMjAglcuoDBFSpAAr7P1vA3vd+AaBck9o0f/kefMsGknzgKBvvf5OMxGS3dEY+dyfh3VuSkZzGmtHTSCjg3a/Yoi7tptyHb6A/MQs2sfEp+90fczMRPVtDlpJy/CRrRr1DyuEEqna4jCs/epjT+48CED17DX+8+t25FmWRaClvsZq5gksXp4FmIpI9+3wUZ04o7RjVu0cSUj+MXzs8zMZH36fli3cXmC/yxbvZ+Mj7/NrhYULqh1Ht6pYAHFmyhYVdx7Do6rEk/h1Do5HXe52WrNR0/njxG7b+Z4Z7QnyEppOGsnrwiyzp/CgRN3YkpHHeVatqDe5GesJpFrd/iD3TZnPpU4MBCGlcg4h+HVja5TFWD5pEsxfvBp/cX5x69/YmcVfex7/9qU9ZdvVYlnV7nOSDx6h7T0+3NF426W7WD57E8s6PEH7jlZTNp7Hm4G6kJyTyW/vR7Jv2M41tjelxp9hwx8us6DqGrSPfotnUEQCIrw+XThjC2pueY0W3xzm1fT+173ZDy3nQ79oo3pk8oeRuUALPUjOy2P70Zyzt8hjLr32KOkN75FyzxeRh/DlhJsu6Pk7s7LXUH+FevTjs6paE1A9jTsdHWPfYB7SaNLTAfK0n3c3aR99nTsdHCKkfRpj97u9462fmdx/H/KjxxMzfQJOHb8o55+iqHcyPGs/8qPElZlTBo3MFO4IxrKWP2UAf+/Mg4IvsAyLSTkRWiMgGEfldRC7Jf7KIlBWRD0VktZ0v/6K/50xYz9bs/2oZAPHrd+NfPpgy1SrmyVOmWkX8QoKIX78bgP1fLSO8VxsAji7ZgmZafy7x63YTFH7uSyKWlJbMpFTiVu8gKzXdLR0VWzUkaU8syfuOoOmZHJq1gur2PbKp3qs10V8tBSD2x1VU6dTMTm/DoVkryErLIHn/UZL2xFKxVUMAAsNDqRZ1OQdmLMpzLdcWjW9gAO7MJV4hn8bYWb9TLZ/Gqr3acMjWePjHVYR2agrAqa17ST0cD0Din9H4BgYgAX4gAgi+wWUA8CsXRIqdr6RoE9mcCuXLldj1S+JZph5J4KTdmsw8nULiroMEhoUCULZBOHEr/gDg2JLNhPVp55bOiF6t2fe19e7Hrd9NQPlgAvO9+4HVKuJXLog4+93f9/UyInq1BvK+Q37BZeD85qM/J4xhNVxoZgK3ikgg0AJY5XLsT6Czql4O/BsoaC2uJ7BWb2gHdANeFpGynhAWFF6J5ENxOfspMXEEhVc6M09M0XkA6gzqyuGFG0u9lsCwSiQfyl2BKuXQcQLD8t4jMDyUlINWHs3MIv1UEv6h5QgMq5STnq0v+9wmz93JH89+jmad+fPSYspwrtn6DiGNItj7wVw3NIaSkkdjHGXsH/fCNGacSsY/NK8Rq37dFZzcsgdNy0AzMvnj8Q/ouPglrtr8NiGNa3JwxsJitXgzJfUsswmqVYUKzeqSYBu7xB3RVO9tGe7wvu0JquFeRTMoLJQkF51Jhb37Ln8fyTFxBLk882ZjB9Bn7evUvqkjW1/+Jie9cuuGRP06kU4zxlC+cf71wj2Hurl5K8awljJUdTNQF6u1Ojvf4QrA1yKyFXgVaFrAJXoAY0VkI7AYCARq588kIsNEZK2IrJ2XtNtzX8ANGo+6gayMTKK/XX5B7+vtWrKpFnU5acdOcnLzngKPbx49jV9b3E/izkNE3NDhgmgqe0lNGj01mO2Pvg+A+PlS864oVnQfx5IW93Nq+36rv9VQIL7BZWj9wUNsf+qTnBbjptHTqHNXFJ3mPY9fSBBZaRkXTM/WSV/zc5uR7P/f7zQc2gOA+C17+bntKOZfM57dH8yl4/SHS+z+GeLe5q2Y4KXSyQ/AK0BXwLUa+xywSFVvFJG6WIYzPwL0V9UdRd1AVd8F3gWYFTa40MphvaFR1L2tGwDxG/8mKCK31hsYHkpyTF73X3JMPEHhheepfUsXwqJasXzA80XJ83ot2aTExhMUkfuIAiMqkxKbV0dKTByBNSqTEhOH+PrgXy6Y9LhTpMTGE+jSSgkMDyUlNp7qPVtTrWcrunWPxCfQH/+QICLfHMHGEW/mXjRLOTTrd+r/X1+iZy4pRmMcgXk0hpIaG5c3j60x1dboVy4oJxCpTHgokdMfYev/vUnyvsMAlGtWByBn//APK6j7oMd6HRyhJJ4lWJWQ1h8+xMFvlxM7e01OntO7D7H6lhcAKFs/jGpRkYVqa3BXFPXtdz9u098ER1TOWak7uLB33+XvIyg8lOR8zxxg3/+W0/mzx9j+yrd5XMSxCzfhM8mXgNAQ0uISC9V1rnizm9cdTIu1dPIh8B9V3ZIvvQK5wUx3FXLuXOBBEWuWaxG5/HyE7Jk+n0XXjGfRNeOJ+WUttQd2BqBSq4ZknEom9UhCnvypRxLISEymkt1XWHtgZ2LnrgOgWrcWNBxxHSuHvEJmclqp1pLNiQ1/UbZ+GEG1qyL+vkT068Bh+x7ZHJ67jpoDuwAQ1vcKjtlRtYfnriOiXwd8AvwIql2VsvXDSFi/mx3Pz2Th5f/HorYj2TD8dY4t35ZjVIPrVs+5bvWerTm961CxGk9u+ItgF41h/TpyJJ/Go3PXEWFrrN73ipzIX7/ywbSa8Ti7JnxOwpqdOflTY+IJaVwD/8qWuzj0qhac3uU1cXbnREk8S7AiuRN3HWLPtLwOqIAq5a0PIjR86Eb2fbygUG1/fTQ/J6jo4Jy11BlgvfuhrRqSfiqZlHzvfsqRBDJOJRNqv/t1BnTm0C/Wdwmpl/sO1ejZmlO7YwAoU7VCTnqlyPqIj5SIUYXS7wo2LdZSiKpGA68XcOgl4GMReRL4uZDTnwOmAJtFxAfYA3hkGM7hXzdSvXskUStfJSM5lQ2jp+Uc6/brRBZdMx6ATWM/pNVr91lDXBZu4vACq/+yxcS78Anw58ovxwEQt243mx7/0Ou09FjzGn4hQfgE+BHeqzW/3zqJUzsLNhqamcXWcR/RbuY4ayjLF4tJ3BFN4zE3k7BpD0fmruPA54uJnPoAXVe+SnpCIuuHvwFYfWwxP6yky7JX0IxMto6dDllF/JyI0PKN+/ErF4SIcHLbPraOKb78NDOLP8dNp9XM8YivDwe/WMTpHdE0GDOAk5v+5ujcdRz8fBHNpo6g08oppCcksnm49frVuqcnwfWqU/+R/tR/pD8A62+ZSOrheP565VvaznoGzcggJfoYW0e+XayW8+GxpyexZsNmEhJO0r3f7Txwzx307+u5SOSSeJaV2l1CzYFdOLl9P50WWK3THRO/5OiCjUTc2JE6ths2dvZqor9Y7JbO2AUbCe8eSe8Vk8lMTmPNQ7nvftT8icyPst799eOm03aKNdQsduEmYhduAqD5E7dSrkE4mqUkRR9jnf3e17yuHQ2GXINmZJKZks7K+6Z6olgL5ELNFSwiocCXWN1re4GBqhqfL08k8DZQHsgEnlfVL4u8rjtRg4Z/NkW5gg1n4u+lf1P+XlzH77atoDg755nX9AmnJRRIko/3OhsHxMw4b7M4qc7tbr2sY/d9dl73EpGXgDhVnSQiY4FKqvp4vjyNAVXVXSISAawDLlPVhAIuCRhXsMFgMBi8jAvoCr4B+Nj+/DFwRoSdqu5U1V3250PAEaBqURc1rmCDwWAweBUZbppNERkGDHNJetcOvHSX6qoaY3+OBaoXlVlE2gEBwF9F5TOG1WAwGAxehbutUdfRC4UhIr8CYQUcyuPnV1W1p40t7DrhwKfAEFUtMnDZGFaDwWAweBWeHG6jqtcUdkxEDotIuKrG2IbzSCH5ymMFhD6hqiuLu6fpYzUYDAaDV5El7m0e4AdgiP15CPB9/gwiEgB8B3yiqt/kP14QxrAaDAaDwavIQt3aPMAkIEpEdgHX2PuISBsRed/OMxDoAtwlIhvtrfDZOjCuYIPBYDB4GZkX6D6qehzoXkD6WuBf9ufPgM/O5rrGsBoMBoPBq/BQa9QxjGE1GAwGg1dRus2qMawGg8Fg8DJK+yT8xrAaisVbV2cKKnoomWPE+/g6LaFAKmRdqJ6rs8dbpw7sse3cVzYqSRY0He+0hBLFuIINBoPBYPAgpdusGsNqMBgMBi8js5SbVmNYDQaDweBVeGcnj/sYw2owGAwGr8L0sRoMBoPB4EFKt1k1htVgMBgMXoZpsRoMBoPB4EFM8JLBYDAYDB7EBC8ZDAaDweBB1LRYDQaDwWDwHKbFajAYDAaDB8lS02L9RyAilYEF9m4Y1pKBR4GGWCvLP1BC9+0KpKnq7yVx/XOh+YQ7qdY9kszkNDaMeocTW/aekadCi3q0em04PoEBHFmwkS1PfgKAf8WytJk2kuBaVUk6cJS1w14n/cRpGj5wHTVv6giA+PlSrlEN5jQdTnrCaeoP602d27qBKif/OMCG0dPISk0vUmPlbi25dMIQxNeH6BkL2fvGD3mOS4AfzaeOoHyLeqTHJ7Jp2GukHDhKaJfmNH5yEBLgh6ZlsPPZGcT9tg2foABavjea4LrV0cwsjs5fz64JX5xT+V3+3J2Ed29JZnIaq0dPI76A8qvUoi7tptyHb6A/MQs2seGpT/Icv2T4tUQ+cxvfNR1OWlwiAFU7XMblz96Bj78vqXGnWHTTBLc1VenWkssmDAG7vPYUUF4t8pVX8oGj+FcKIfKDh6gQ2YCDM5fwx/jpOeeE3dCBBqP7gY8PR+dvYOeEz93SUrVbS5pMuBPx9eHAjEX8lU+LT4AfLac+QIUW9UiLT2TDsNdIPnAMgAYjb6DW4K5oZhbbnviYY4s3ExgRSuTUBwioUgEU9n+2gL3v/QJAuSa1af7yPfiWDST5wFE23v8mGYnJbpebOzw5cTJLl68mtFJFZn32jkevXRAl8SzF35cmL9xNaMcmaFYWu174ksM/ry6x73ChzKqIhAJfAnWBvcBAVY0vJG95YDswS1X/r6jr+nhW5sWLqh5X1UhVjQTeAV6190NKyqjadAU6luD1z4pq3SMpWz+MBR0eZtOj79PyxbsLzNfyxbvZ+Mj7LOjwMGXrh1Ht6pYANHrweo4t28qCjg9zbNlWGj3YF4Ddb/3E4mvGs/ia8Wx//kuOrfiD9ITTBIZVov6/erKk5xMs6vo44utDjX4dihbpI1w26W7WD57E8s6PEH7jlZRtXCNPlpqDu5GekMhv7Uezb9rPNH5qMADpcafYcMfLrOg6hq0j36LZ1BE55+x9+yeWd3qEFdeMpWLbS6hydeRZl1/41S0pVz+M2R0fYe1jH9B60tAC87WedDdrH32f2R0foVz9MMLs8gMIigiletfmnI4+lpPmXz6Y1pOG8ttd/+WXro/z+72vuy/KR2gy6W7WDp7Eb8WU17L2o9nrUl5ZqensmvQVO57Juw60f6UQLvn3bay+eQLLr3qMMtUqENq5mVtamk4ayurBL7Kk86NE3NiRkHxaag3uRnrCaRa3f4g902Zzqa0lpHENIvp1YGmXx1g9aBLNXrwbfATNyGL705+xtMtjLL/2KeoM7ZFzzRaTh/HnhJks6/o4sbPXUn/Ede6Xm5v0uzaKdya7X8k5L0rgWQI0GH0jacdOsKzjQ/zW+VHiVvxRol8jkyy3Ng8wFligqo2wGk5ji8j7HLDUnYsaw3qeiEhXEfnJ/vyMiHwsIstEZJ+I3CQiL4nIFhH5RUT87XytRWSJiKwTkbkiEm6njxSR7SKyWURmikhd4D7gIRHZKCKdRaSviKwSkQ0i8quIVD/Le+91SV8tIg3P5vuG92zNga+WARC/fjf+5YMpU61injxlqlXELySI+PW7ATjw1TLCe7XJOX+/ff5+l3RXat7YgYPf5TbQfXx98Q0MQHx98A0KICW2wAplDhVaNSRpTyzJ+46g6ZnEzvqdavnuU7VXGw59Zf2NHP5xFaGdmgJwauteUg9b10/8M9q6b4AfWclpxC/fDoCmZ3Jyyx7KRIS6UWJ5qdGrNXu/tr7/cbv8AvOVX2C1iviXC+K4XX57v15GzV6tc45f/p872PzcF+DiLqtzY0eiZ68h6eBxAFKPn3RbU8UCyqt6vvKqnq+8KtvllZmUSsLqHWd4EILqVCNpTyzpx09Z33XpVsL6tDtrLYdmrShAS2uibS2xP66iSqdmuRpnrSArLYPk/UdJ2hNLxVYNST2SwEnbK5B5OoXEXQcJDLOeXdkG4TlG4tiSzW5pPFvaRDanQvlyHr9uQZTEswSoMagbf7/+vbWjSnrcqRL9Hllubh7gBuBj+/PHQL+CMolIa6A6MM+dixrD6nkaAFcD1wOfAYtUtTmQDPSxDdwbwM2q2hr4EMhem2oscLmqtgDuU9W95G0dLwN+A9qr6uXATGCMu/d2yXfCTp8KTDmbLxcYXonkQ3E5+8kxcQSFV8qTJyi8EikxefME2nnKVK1A6pEEAFKPJFCmaoU85/oGBVCtW0sO2W6mlNh4dr/9Mz3WvUHPzW+RfjKZo0u2FK0xLJSUQ8dz9lMOxVEmLK8RDAwPJcU2QpqZRcapZPxD8/74Vb/uCk5u2YOmZeRJ9ysfTNUerYhbtrVIHQURFBZKkou2wsovyaWMk2LiCLL1R/RsTXJsHAnb9+c5p1yDMAIqlKXbt08QNXcCdQd0cltTmbBQkosprzLhoSQXU16uJO05TNkG4QTVqor4+lCtdxsCa1QuVktgWKV8Wo4TGJa3fPI/u/RTSfiHliMwrFJOOkBKTNwZ5wbVqkKFZnVJsCstiTuiqd7brvT1bU+QGxq9mZJ4ln7lgwFo9PhAOsx/gcj3RhOQ7+/W02Shbm0eoLqqxtifY7GMZx5ExAf4L/Couxc1htXzzFHVdGAL4Av8YqdvwfLjXwI0A+aLyEbgSaCmnWczMENEbgfy/prnUhOYKyJbgMeApmdx72y+cPm/QL+qiAwTkbUisnZu0u7ivvM5kz9GoXqPVsSt2Ul6wmkA/CuUJaxXa+a3G8XcliPwCy5Dzf5XlpiebMpeUpNGTw1m+6Pv50kXXx9avDOS/e//QvK+IyWuwxXfoACajLyerS99c8Yx8fUltEU9lt7+CksGTaLJ6BsJqR92QfW5knHiNNse/4CW747iih+eIfnAUTTT2VhP3+AytP7gIbY/9UlOP+qm0dOoc1cUneY9j19IEFlphf3Z/XMRP1+CalQmfs1OVkSNI2HtTi55+vYSvae6+c/1d8rehp2h3/LsbS1guyHPPVWVgrt3HwBmq2q0u/pN8JLnSQVQ1SwRSbcfFlieCz+sdcO3qWpBBq0P0AXoCzwhIs0LyPMGMFlVf7ADm545i3tno4V8zk1UfRd4F2DzuOla57ZuAMRv/JsgFxdoUHgoyTF5XbPJMfEEhufNk2LnST16gjLVKlqt1WoVSTt2Is+5NW/oQLSLG7hql2Yk7T9Cmu1SjJm9htC2jYn+dnlBsgFIiY0jMCK35REYEUpqbFzePDFxBNaoTGpMHOLrg1+5oBz3VpnwUCKnP8LW/3uT5H2H85zX5L/3cnpPDPvfnVPo/fPT8K4o6tvlF7fpb4JdtBVWfsEuZRwcHkpybBwhdapTtnZVei54IefcHvOe59fe/yYpJo7U+EQyk1PJTE7l6Mo/qdikNol/xxarLzU2jqBiyis1Jo6gQsqrMI7OW8/ReesBqHlHd7cMa0psfD4tlc9w/Wc/uxRbi3+5YNLjTpESG5+nVRwYHppzrvj50vrDhzj47XJiZ6/JyXN69yFW32KVZ9n6YVSLOvt+c2+iJJ5letwpMpJScoKVYn9cRY3B3UrmC9i4WwVz/Z0qIs81hR0TkcMiEq6qMXaXXEG15Q5AZxF5AAgBAkQkUVUL7Y81LdYLzw6gqoh0ABARfxFparsbaqnqIuBxoALWQzwFuPppKgAH7c9DzlHDLS7/rygu857p83MCi2J/WUutgZ0BqNSqIemnknNcu9mkHkkgIzGZSq2s7ttaAzsTM3cdADHz1lPbPr+2SzqAX7kgKne4jFiXtOToY1Rq3QjfoAAAqnRuyqldBymKkxv+Irh+GEG1qyL+voT168gRl2sCHJ27joiBXQCo3vcK4n7bZmkoH0yrGY+za8LnJKzZmeechmMH4lcumB1P5o3QLY7dH81nXtR45kWN5+CctdQdYH3/ynb5peQrv5QjCaSfSqayXX51B3Tm4C/rOPHnAb5v/gA/tRvNT+1GkxwTx7weT5By9AQH566jarvGOf3QlVs14NSuQ27pO+FGeR3JV17H7fIqioAq5QHwq1CW2ndFET1jkVtayrpoiejXgcP5tByeu46atpawvldwzNZyeO46Ivp1wCfAj6DaVSlbPyzH5dvi1WEk7jrEnmmzC9SICA0fupF9Hy+gNFNSz/LovPWEXtkEgMqdm3F6Z9F/g+eLqrq1eYAfyP0dHQJ8X4CW21S1tqrWxXIHf1KUUQXTYr3gqGqaiNwMvC4iFbCewRRgJ/CZnSbA66qaICI/At/YbosHsVqoX4tIPLAQqHcOMiqJyGasFu6gsznx8K8bqd49kmtWvkpmciobRk/LOdb114ksvmY8AJvHfsjlr92Hb2AAhxdu4siCjQDseuMH2r47ktqDu5EcfYw1w17LOT/82rYcWbKFzKTUnLT4DX9x6KdVXDVvIpqZyYkte9n36cIiNWpmFn+Om06rmeMRXx8OfrGI0zuiaTBmACc3/c3Rues4+Pkimk0dQaeVU0hPSGTzcCuKttY9PQmuV536j/Sn/iP9AVh/y0TE34/6D91E4s6DtP/VauEc+HAuB90wFq7ELNhIePdI+qyYTEZyGqsfyi2/HvMnMi/KKr9146ZzxZTh+AYGELNwEzELNxV53VO7DhGzaDM9F06CrCz+/nwxJ3a457nSzCy2j5tOG7u8or9YROKOaBqOGcAJu7yiP19Ei6kj6GyX16bhuVHHV615A99yQfgE+FG9dxvW3DKR0zsPctmEIZRrUgeA3ZO/JenvmMIk5NGyddxHtJs5ztaymMQd0TQeczMJm/ZwZO46Dny+mMipD9B15aukJySyfvgbgNVfGvPDSrosewXNyGTr2OmQpVRqdwk1B3bh5Pb9dLJb+zsmfsnRBRuJuLEjdYb2ACB29mqiv1jsVpmdDY89PYk1GzaTkHCS7v1u54F77qB/354evw+U3LPc8dzntJg6Av/n7iTt+Cm2jHq7RPRnk3HhZl6aBHwlIvcA+4CBACLSBivO5V/nclHxkNU3lBJEZC/QRlXKwCGDAAAgAElEQVSPFZc3m+/DBnvlSxKk3jk/S7yPr9MSCqRCVqbTEgolE3FaQoH02PZ88ZkcYEHT8U5LKJReh2ee98O8rnYft35zftr/s1e+OKbFajAYDAavwiwbZyhV2P0EBoPB4LWUdk+qMawGg8Fg8Cq8s5PHfYxhNRgMBoNX4aHpCh3DGFaDwWAweBXGFWwwGAwGgwcxwUsGg8FgMHgQNYbVYDAYDAbPYRY6NxgMBoPBg5Rus2oMq8FgMBi8jAwTFWy42Bl88vfiMznA4VGti8/kADd8Gld8JifwhSVHip9w3Qm+qNzVaQkF4q1TB3bfNtFpCSWKiQo2GAylAm81qgZDfkxUsMFgMBgMHsREBRsMBoPB4EGMK9hgMBgMBg9iXMEGg8FgMHiQTC9da9ldfJwWYDAYDAaDK+rmv/NFREJFZL6I7LL/r1RIvtoiMk9E/hCR7SJSt6jrGsNqMBgMBq8iS9WtzQOMBRaoaiNggb1fEJ8AL6vqZUA74EhRFzWG1WAwGAxexYVqsQI3AB/bnz8G+uXPICJNAD9VnQ+gqomqmlTURY1hNRgMBoNX4W6LVUSGichal23YWd6quqrG2J9jgeoF5GkMJIjI/0Rkg4i8LCK+RV3UBC8ZDAaDwatwN3hJVd8F3i0qj4j8CoQVcOiJfNdSESmoGewHdAYuB/YDXwJ3AR8Udk9jWA0Gg8HgVXhygghVvaawYyJyWETCVTVGRMIpuO80Gtioqn/b58wC2lOEYf1Hu4JFJExEZorIXyKyTkRmi0hjEakrIlvtPG1E5PVirpN4Hhr2ikiVcz2/mGvPEpGVRRzP+Z6e4uVXnmbzlsWsWjWHyMimZxwPCSnLipWzc7Z9+9fz0kv/BuD2229m7751OceG3HWLRzT5No4k+OHXCX50Kv5X3Vh4vqbtCXnhW3xqNLAT/CjTfwRBoyYTNPK/+NY78/ucLyOefYBPfpvOe/PfoVGzhkXmfe7D//D+r7mV8wZN6vPGD68xbe7bvPXzVC6JvMRjul6d/Cx/bv+N9evmc3lkszOOh4SUZe2aeTlb7KEt/PeV/wBQu3YN5v3yJevXzWfB/K+pUSP8vPVEPncnvX//L1ELXqBi87oF5qnYoi49Fk6i9+//JfK5O3PSm465magFLxA1fyKdZ44lsHpFAKp2uIx+O94jav5EouZP5LKHCn83CqJKt5Z0Xj6ZziunUO/B6884LgF+tHx3FJ1XTqH9nAkE1aoKgH+lENr+7ymu+fsjLps4NO85/r40feVeOv/+Kp1++y/V+7Q7K01ny5MTJ9Olz630u/2+Er3P2XIBg5d+AIbYn4cA3xeQZw1QUUSq2vtXA9uLuug/tsUqIgJ8B3ysqrfaaS2xfOwHsvOp6lpgrSMizwMRqQi0BhJFpH52bask6dmzKw0b1qNF8660bXs5U157nq5X5Y0FSEw8TYf21+bs/7b8R77//pec/W+//YlHHn7ac6LEhzLX30vyB8+iJ48TNOJFMv5Ygx6JzpsvIJCAK/uQuX9nTpJ/W6uim/zaw0jZ8gQOfZLkNx8HD80K0+7qttSsV4M7Ow3lslaXMuqFkfxf35EF5u3U+0qSk5LzpA174l4+ffUzVi9aQ7ur2zLsiX/xyIDHzltX715X06hhPS5t0okr2rXizakv0LFT3zx5EhNP06Ztj5z9VSvnMGvWbABeevHffDrjGz799Gu6db2S5yeM466hBX8vdwi7uiUh9cOY0/ERQls1pNWkoSzsc+Y70nrS3ax99H3i1u+m04wxhF3dktiFm9jx1s9se+kbABre05MmD9/E+sc/BODoqh0sv/OVsxflIzSZdDdrBj5PyqHjdJg7kSNz13F658GcLDUHdyM9IZFl7UcT1q8DjZ8azKZhr5GVms6uSV9R7tJahFxaK89lG4y+kbRjJ1jW8SEQwb9SyNlrOwv6XRvF4P7XM/65cyiDEuQCTmk4CfhKRO4B9gEDwWpQAfep6r9UNVNEHgUW2HZjHfBeURf9J7dYuwHpqvpOdoKqblLVZa6ZRKSriPxkfw4RkekiskVENotI/3x5q4jIChHpIyJVReRbEVljb1faeSrb46G2icj7gBQkTkQG2ffZKiIvuqQnisjzIrJJRFaKSEGd7QA3AT8CM4FbXc5vbZ+7CRhxFuVVLH2u68HnM/4HwJo1G6hQoRxhYVULzd+wYT2qVq3M8uWrPSkjDz61GpJ1PBaNPwyZGWRs+g2/y9qekS+gxyDSlnwHGWk5aVKtJpl/Ww16PX0STT6d25r1AFf26Mi8b+YD8Mf6PwkpX5bQaqFn5AsMDuTme/sz47XP86SrKsEhwQCULVeW44ePe0RX3749+XSGZYhWrV5PhYoVCAurVmj+Ro3qU61qFZb9tgqAyy5rxKJFywFYtHg51/ftUei57hDRqzX7vrb+LOPW7yagfDCB1SrmyRNYrSJ+5YKIW78bgH1fLyOil7X6UUZiboXEL7iMRypGFVs1JGlPLMn7jqDpmcTO+p3qvdrkyVO9VxsOfbUUgMM/rqJyJ8vjkZmUSsLqHWSlpp9x3RqDuvH363ajSZX0uFPnrbUo2kQ2p0L5ciV6j3NBNcut7fzvo8dVtbuqNlLVa1Q1zk5fq6r/csk3X1VbqGpzVb1LVdMKv+o/27A2w6p5nA1PASfswm0BLMw+YBu4n4F/q+rPwGvAq6raFugPvG9nfRr4TVWbYrWYa+e/iYhEAC9iuRwigbYikt30KwusVNWWwFLg3kK0DgK+sLdBLunTgQft8z1KRER1oqMP5ewfOhhLeERBMQMWNw/oy7ff/JQnrV+/3qxaNYfPZrzlEReilA9FTxzL2deTcUiFynny+ETUw6dCFTJ3rM+TnhWzD7/L2oCPD1KpGr41GiAVPOe1rxJWmaOHjubsH405RpWwymfkG/rYXXz97rekJKfmSX/rmbcZ9uS9fLF6Bvc9NYz3X/jQI7pqRIQRfSD3OR6MjqFGEc/xloHX8/XXP+Tsb968nRv79Qas51m+fDlCQwscd+8WQWGhJB3KrTQkxcQRFJ73ekHhlUg+lLtcX3JMHEFhuZWUZmMH0Gft69S+qSNbX/4mJ71y64ZE/TqRTjPGUL5xDbc1lQkLJdlFU8qhOMqE5a0UlQkPJfmglUczs8g4lYx/aOFGzK+8VUlq9PhAOsx/gcj3RhNQtYLbmi4mslC3Nm/ln2xYz4VrgDezd1Q13v7ojzW4eEz2WCc771QR2Yjlxy8vIiFAF+Az+/yfgexruNIWWKyqR1U1A5hhnweQBmRbo3VA3fwn20a+EZYB3wmki0gz2z1cUVWX2lk/Pcvv71FuvrkvX7n8IM+e/SuXXdqJK67ozcKFv/Hee/8teREilOlzF6k/f3TGoYx1C8g6cZygES9R5rqhZO7fARd4qrUGTeoTUSec5b8sP+NY3zv78vZ/3mFQu9t465l3ePSVhy+otmwGDryBmV/Oytkf8/hzdOnSnjWr59Klc3uio2PIzMx0RFs2Wyd9zc9tRrL/f7/TcKjVgo7fspef245i/jXj2f3BXDpOd6b8shE/X4JqVCZ+zU5WRI0jYe1OLnn6dkc1OUWmZrm1eSv/ZMO6DasP0hNkYBm5ni5pPkB7VY20txqqes5BTi6ka+7SD5kU3E8+EKgE7BGRvVjGd1AB+QrFdXxYRkbh7qhhw+/ICTaKjT1CzZoROcciaoQRcyi2wPOaN78MPz9fNm7IjZ2Ki0sgLc3ysHw0fSaRl58ZNHO2WC3U3Fam1YJ1cZkGBOFTvTZBw54leMzb+NRqTOCdYy2Xb1YWaT9/RPIbj5Ly6YtIYDBZxw4VcBf3uWFIX6bNfZtpc9/m+JE4qkbkusqrhlfhWGxed26T1k1o3KIxM1Z8wmvfTaZm/Rr89+uXAehxcxTLZv8GwJKflnLpeQQv3X/fkJxApJjYw9Sslfsca9QM52Ahz7FFiyb4+fmxfsOWnLSYmMMMGHgvbdv15Kl/W70YJ06cPCs9De6KygkqSjmSQHBEbks+ODyU5Ji89dHkmHiCInJbjEHhoSTHnrng/L7/LadmH6srICMxmcwkywsQu3ATPv6+BIS616eZGhtHkIumwIhQUvPdLzUmjqAaVh7x9cGvXFCRrt30uFNkJKVw+GerayT2x1WULyRQ62JHVd3avJV/smFdCJRxHVAsIi1EpHMR58zHpV/SZV5JBe4GLhWRx+20ecCDLnkj7Y9LgcF2Wm8sA5if1cBVdp+tL5ZRXHIW320Q0EtV66pqXawKxK2qmoA10LmTne+2wi6gqu+qahtVbePnV7j76t1pn9Kh/bV0aH8tP/44j8G33QRA27aXc/LkKWJjjxZ43oAB1/P11z/mSXPtj+1zXRQ7dvzlxlctmqzo3fhUCUcqVQNfP/xadiLzD5dYtNQkTk8YStJL95P00v1kHdhJyieTyDr4F/gHgH8ZAHwbtoCsrDODns6S7z/+keE972d4z/tZ/svv9Lg5CoDLWl3K6VOniTuS98f5x09/4pY2g7itw52MuvFhov8+mBOgdPzwcVp2aAHA5VdGcnDPuRv9t9/5mDZte9CmbQ9++GEud9x2MwBXtGvFyRMniY0teAa3W2+5gS9dWqsAlStXworxgLGPP8hHH888az1/fTSf+VHjmR81noNz1lJngPVnGdqqIemnkkk5kpAnf8qRBDJOJRPayoqsrjOgM4d+sXp6QurlhiHU6NmaU7ut+QDKuLhZK0XWR3yEtDj36r4nNvxFcP0wgmpXRfx9CevXkSNz8/YsHZm7joiBlqOpet8rOP5b8QvNH523ntArmwBQuXOzPMFQ/yQuYFRwifCPjQq2BwPfCEyxjWEKsBcYXcRpE4A37SEqmcB/gP/Z18sUkUHADyJyChhp592MVc5Lgfvsc74QkW3A71gDjvNrixGRscAirOCmn1W1oDDwM7Anh64D5AyzUdU9InJCRK4AhgIf2gOh57lzTXeZ+8sievbsxpatS0hOSmb4fbkRqitWzs4TDXxT/z7cdGPeoQb33z+Ua/tcQ2ZGJnHxCQwf9uj5i8rKIvWH9wm6+ykQH9LXLiTryAECrrmVzIO78xrZfEjZCtZ5qmSdjCPlqyJHXZ01qxau5oqr2/Hpbx+RkpLKyw/nRmZOm/s2w3veX+T5k8e8yoj/PICvnw9pqelMfnyKR3TNnrOAXr2uZscfy0lKTuZf/8p1ka5dMy9PNPDN/fvS94Y78px/1VUdef65cSjKsmUreXBknnH4Z03sgo2Ed4+k94rJZCanseahaTnHouZPZH7UeADWj5tO2ynD8Q0MIHbhJmIXbgKg+RO3Uq5BOJqlJEUfY50dEVzzunY0GHINmpFJZko6K++b6rYmzcxi+7jptJk5HvH1IfqLRSTuiKbhmAGc2PQ3R+euI/rzRbSYOoLOK6eQnpDIpuG5789Va97At1wQPgF+VO/dhjW3TOT0zoPseO5zWkwdgf9zd5J2/BRbRr19XmVXHI89PYk1GzaTkHCS7v1u54F77qB/357Fn1jClPaFzsWbm9MG76BscF2vfEkOj/KUJ9+z3PBpyUZynitLjhTfYnKKLyp3dVpCgZTLcrZvuDC6b5votIRC8a9Sv8CRDmdD9QqXuvWbc/jEn+d9r5LgH9tiNRgMBoN34s0Rv+5gDKvBYDAYvIrMLO+N+HUHY1gNBoPB4FWU9i5KY1gNBoPB4FUYV7DBYDAYDB7EtFgNBoPBYPAg3jxG1R2MYTUYDAaDV+HN0xW6gzGsBoPBYPAqjCvYYDAYDAYPUtpnXjKG1WAwGAxehWmxGgwGg8HgQUq7YTVzBRsuKCIyTFXfdVpHfrxVF3ivNqPr7PBWXeDd2koj/+Rl4wzOMKz4LI7grbrAe7UZXWeHt+oC79ZW6jCG1WAwGAwGD2IMq8FgMBgMHsQYVsOFxlv7cbxVF3ivNqPr7PBWXeDd2kodJnjJYDAYDAYPYlqsBoPBYDB4EGNYDQaDwWDwIMawGkocEQkSkUuc1lHaEJFgpzUUhLfqMhi8BWNYDSWKiPQFNgK/2PuRIvKDs6q8GxHpKCLbgT/t/ZYi8pbDsrxWVzbeVoETkfoi8qOIHBORIyLyvYjUd1jTFhHZXNjmpLaLCWNYDSXNM0A7IAFAVTcC9ZwUBCAi1UXkAxGZY+83EZF7nNZl8yrQEzgOoKqbgC6OKrLwVl3eWoH7HPgKCAMigK+BLxxVBNcBfbHK6RfgNnubbW8GD2AMq6GkSVfVE/nSvCEU/SNgLtYPHsBOYLRjavKhqgfyJWU6IiQf3qoL76zABavqp6qaYW+fAYFOClLVfaq6D4hS1TGqusXexgI9nNR2MWEMq6Gk2SYigwFfEWkkIm8AvzstCqiiql8BWQCqmoH3GIkDItIRUBHxF5FHgT+cFoX36gLvrMDNEZGxIlJXROqIyBhgtoiEikiow9pERK502emIsQcew6xuYyhpHgSeAFKxXGNzgeccVWRxWkQqY//4ikh7IP8Ps1PcB7wG1AAOAvOAEY4qsvBWXZCvAgeMxPkK3ED7/+H50m/Feu+c7G+9B/hQRCoAAsQDQx3Uc1FhJogwlCgiMkBVvy4u7UIjIq2AN4BmwFagKnCzqpoAjlKIHan8BJY7U7ArcKqa4qgwL8c2rKjqCRFpq6prnNZ0MWAMq6FEEZH1qtqquDQnEBE/4BKsH+IdqprusCQARKQeVku/Li5eJVW93ilNACLyegHJJ4C1qvr9hdbj7YiIL9CHM5/jZKc05UdEmgCDsFrRJ1S1jcOSLgqMK9hQIohIb+BaoEa+H+TyQIYzqnIRkZvyJTUWkRPAFlU94oQmF2YBHwA/YvcBewmBwKVY0a0A/YE9QEsR6aaqFzz4S0R+pIi+VIcrIz8CKcAWvOg5ikhdLGM6CEgH6gBtVHWvc6ouLoxhNZQUh4C1wPXAOpf0U8BDjijKyz1AB2CRvd8VS2c9EXlWVT91ShiQoqoFtQ6dpgVwpapmAojI28AyoBOW8XCCVxy6rzvUVNUWTotwRURWYFVuZwL9VXWXiOwxRtWzGMNqKBHsMY6bRGSGHXHrbfgBl6nqYbDGtQKfAFcASwEnDetrIvI0VnBQanaiqq53ThIAlYAQcoO8ygKhqpopIqmFn1ZyqOoSJ+7rJnNEpIeqznNaiAuHsYLPqmPFFezC+ejpiw5jWA0lgoh8paoDgQ0i4vqHK4B6QU2+VrZRtTlip8WJiNN9rc2BO4CryXUhqr3vJC8BG0VkMdZz7AJMFJGywK9OCrMjgV8AmuAyVlRVnYy8XQl8JyI+WC7X7He/vFOCVLWfHbB0E/CMXW4VRaSdqq52StfFhgleMpQIIhKuqjEiUqeg4/Ygdcewp+KrTd7+wmjgMeAnVe3moLbdQBNVTXNKQ2GISDjWRAwAa1T1kJN6shGR34CnsWaH6os1dMRHVf/toKY9wA1Y/fZe+UMrItWwhgUNAmqrai2HJV0UGMNquCDYY0a7APtVdV1x+S+AHsGqtXeyk+KB6qrq+LhMEZkFDPOCIKozEJFKQCPytgqXOqfIQkTWqWprEdmiqs1d0xzUtBToqqpeE7hUFCJSx+kK78WCcQUbSgQR+QkYq6pb7VbOeqxgpgYi8q6qTnFSn6qqiPwNtAcGYEW3fuukJhcqAn+KyBry9rE6PdzmX8AooCbWvLztgRU476IGSLVdrrtE5P+wJrAIcVjT38Biez5q1+foNcNtXDFG1XMYw2ooKeqp6lb781BgvqreKSLlgOWAI4ZVRBqTO9TgGPAllufGMddvATzttIBCGAW0BVaqajcRuRSY6LCmbEYBwVgzLj2HZeyHOKrIqqztAQLszfAPwRhWQ0nhGgDUHXgPQFVPiYiTrrE/sYaIXKequwFExBuG/+SgqkvsKOW2dtJqL3ELp6hqioggImVU9U9vWabNZcagRLxkaj5V/Y/TGgzOYAyroaQ4ICIPYgUEtSJ3Oa8gwN9BXTdhzTKzSER+wRrPJw7qOQMRGQi8DCzG0vaGiDymqt84KgyiRaQi1gQW80UkHnA6CK3IpeGcdJ+LSFVgDNCUvH3SjrvObW33cuasUHc7peliwgQvGUoEO9rwWSAceDN7LJ+IdANaq6qjA/vtISI3YLmEr8Yaw/qdN4w5FJFNWMt6HbH3qwK/qmpLZ5XlIiJXARWAX5yMXhaRo8ABrHVOV5GvkuTkOFcRmYfV1fAo1gIGQ4Cjqvq4U5qyEZHfsTw363BZ1UlVvSXOoFRjDKvhH48d6ToAuEVVu3uBnpzIVnvfB9jkmuYEItIAiFbVVBHpijUT0yeqmuCgJl8gCquC1AL4GfhCVbc5pSkbl0jlzdnjtkVkjaq2Le7cC6Bto6pGOq3jYsWsv2f4x6Oq8ar6rjcYVZtfRGSuiNwlIndhGYs5DmsCK2o6U0QaAu8CtbCWAnQMVc1U1V9UdQhWlPJurEjc/3NSl012nEGMiPQRkcsBp9dhzeYnEbnWaREXK6bFajB4IfYiAdljbJep6ndO6oHcVYlE5DGsQKY3RGSDql7usK4yWKvIDMLqM/wB+FBVDzqs6zosd2strCUKywPPqOqPTuoCEJFTWFNSppFbAXB0VqiLCRO8ZDB4GfaycbNV9X/2fpCI1PWCidLTRWQQVl9hXzvNyUA0ROQTrDV1ZwP/cRni5Q3Eq+oJrLmVuwGIyJXOSrJQ1XJOa7iYMS1WQ4liog/PHhFZC3TMDgoSkQBgudN9c/banfcBK1T1C7sCMFBVX3RQUxZw2t4taE5qx1pgBa07XFCaU4jI9VizoQEsVtWfnNRzMWFarIaS5nssd9ivuEQfGorEzzXSVlXTbOPqKKq6HWsChuz9PYBjRtXW4HVxIiLSAegIVBWRh10OlQd8nVGVFxGZhDVOeoadNEpErlTVcQ7KumgwhtVQ0gR7w/CCUsZREbleVX8AEJEbsGaJcgQR2ULe1qBi6VkEvKKqKY4I814CsKZT9ANcXa4ngZsdUXQm1wKR2fMYi8jHwAbAGFYPYFzBhhJFRCYAv6vqbKe1lBbsYS0zgAgsl+YB4M7smaIc0FPQCkWhWH2tZVX13gssqVTgOqm9PWQqRFVPOiwLABHZjLVAQJy9H4rlDnZ6OceLAmNYDSWKiT48d0QkBEBVE53WUhjeEBXsrYjI51h90pnAGixX8Guq+rKjwgA7CG0Sltche23dsar6paPCLhKMYTUYvAx7+Eh/zgz4etYpTYUhIpu8aUYobyJ7EgYRuQ1rWs+xwDpvaRXaq065zkcd66SeiwnTx2oocUz04VnzPdYQjXW4LDfmFCJSUBRrJeB2wPG1WL0YfxHxB/oBU1U1XUS8piWjqjFYY34NHsYYVkOJYqIPz4maqtrLaREu/DffvgLHsRYJePeCqyk9TAP2ApuApXZftVf0sRpKFuMKNpQodpCEa/ShL7DBW9xh3oiIvAu8oapbnNZi8Cwi4qeqGU7rMJQspsVquBBUBOLszxWcFFJK6ATcJSJ7sFzB2ZMdmMpIKcJeU3ciEKGqve0JNjoAHzioqci5irOjhA3nhzGshpLmBWCDiOSJPnRWktfT22kBBo/wETAdeMLe34m1jJxjhhWr316x/hZrA/H254rAfqCec9IuHowr2FDimOhD9/D21oSIlFHV1OLSDBbZS8S5DknyluXaROQ9rPWHZ9v7vYF+qjrcWWUXB6bFaihxTPSh27i2JvKjQP0LK+cMVmANGykuzWBxWkQqY89aJSLtsaK9vYH2rhN7qOocEXnJSUEXE8awGgxegqp6pRtORMKAGkCQvaZotuEvDwQ7Jsz7eRirQtlARJYDVfGeKQ0PiciTwGf2/m3AIQf1XFQYV7DBYCgSERkC3AW0Ada6HDoJfJy9vJ3hTETED7gEqzKyQ1XTiznlgmB3OzyNFfOgWOORn3W6u+FiwRhWQ4kiIp+q6h3FpRm8HxHpr6rfOq2jtGAPLevDmTNoTXZKU35EpKyqni4+p+Fs8LollwwXHU1dd+wfm9YOaTGcH8tF5AMRmQPW+qwico/ToryYH7Fa+pWxVrnJ3hxHRDqKyHbgD3u/pYi85bCsiwbTx2ooEURkHDAeq18ue7YZwZqM38zW4wYiUg0IzN5X1f0OygFr6Ii3DR/xZmp68djjV4Ge2EGFqrpJRLoUfYrBXUyL1VAiqOoLqloOeFlVy9tbOVWtbKYzLBoRuV5EdgF7gCVY0+LNcVSURRVV/QrIArBnEDKL1xfOHBHp4bSIwlDVA/mSzLP0EKbFaihRVHWciNQA6pC3n8lM3l44zwHtgV9V9XIR6YY14b3TePPwEW9kJfCdvRZrOrkzaHnDkokHRKQjoPZCAaOw3cKG88cYVkOJYk/CfyuwndwacXYUoqFg0lX1uIj4iIiPqi4SkSlOi8K7h494I5OxpjDcot4XJXof8BrWMKqDwDzgAUcVXUQYw2ooaW4ELjGz85wVCfYi58uAGSJyBHA8clNV14vIVXjh8BEv5QCw1QuNKkBbVb3NNUFE7gPecUjPRYUZbmMoUewI0gGqmui0ltKCiJQFkrFiIG7DWrhghqoed1QYVjQpZw4f+cQxQV6MiHyENVvWHFzW1fWG4TYi8jvwpKoutPcfA65WVTNPtQcwLVZDSZMEbBSRBeT9cRnpnCTvRlVP22t3NlLVj0UkGPB1WpeIfAo0ADaS161vDGvB7LG3AHvzJq4HfrINai/gUuAGZyVdPJgWq6FEsWftOQNV/fhCayktiMi9wDAgVFUbiEgj4B1V7e6wrj+AJl7q2jScJfZwrl+x5qi+2zxXz2EMq6HEEZEgoLaq7nBaS2lARDYC7YD/b+/eYuyq6jiOf382bSBCQ8GKqGhqtGKiSAkVSIkKWuIDIJeIlgBegPhAAeuLGiPijQSCBsIlgEpjMF5oFEIfUDCVqoBAhdIGSGlRmxALaSUCQqSh/fmw97TT6el0aM+atc/h90mamfgdzvYAAAlzSURBVLN3mvwyt/9Za6/1Xw+OOhVlte0PVc61BLi4PVQhdkHSdbYXSlpKu4J6NNunVIgFgKSX2H7Qg2lG0q+1n3dlxfLAy1RwFCXpZOAqml/gWZKOoOlJWu2PywB41fZmqel13/ab7cI74LcAT0h6iB2n9fO93NG5wEKan/tOafeWR2EprFHaZTSjr3sBbK+UVPv4s65bLmmka9V8mm0QSytnguZ7Gbv3NIDt5bWD7Iqk04Bltl9oXx8AfNz2HXWTDYdMBUdRkv5q+5gxhz2v6nCrt+rUDFXPB06kmbL7PfCTms/A2h7Pj9s+rFaGQSHpGZo9rD11ZFXwTgeuj/4djb2TEWuU9riks4Ap7SKci4H7K2fqrDEF7Me184ywvUXSGknv6kDP4q6bAuxH7wPru6JXO9vUgz7JFzJKu4imafurwC9pRl/fq5qowzpewGbQvFF6iFENK/KMdScbbH+3dojdWCHpR8D17esLaVYHRx9kKjgmhaTpNKsOX6qdpesk/QmYA3SqgLVdl3bS5WeJNQzClGrbhORbwCfbS/cA38/ZrP2RwhpFSZoL3ML2cyhfoNkzl3fHu9DlAjaqccUfRhpX5M3SjiQdaPv52jminhTWKErSKuBC239uXx8H3JDFSxPXfs0W2L6wco5ONq6IiZN0te2vdHGP7TDJM9YobctIUQWw/RdJr9UMNAgkzQHOAj5D0xbvN3UTAc1zuI8ADwLYXtt274nBcWv7sXN7bIdJCmuUtlzSTTQLlwx8FrhX0pHQnJhSM1yXSJoNLGj/bQJ+TTOrdHzVYNt1tXFFTNDII5guPFYYZpkKjqIk/XGc27Z9wqSF6ThJW2mOijvP9rr22t9td6KhhqQrgf/QdBa6iKZxxRO2v1k1WEyYpNX0fjM0cgh7HtH0QQprREdIOpXmUPh5wO+AX9E0hphVNVhL0puA8xjVuMJ2Z/baxu61i892yfb6ycoyzFJYo4i2R/CqkV9USZcCZwDrgUts/6Nmvi5rt0J8mmZK+ASaY9lut3135VyX2L5md9eiuyS9FzjY9n1jrs8DnrX9dJ1kw6VX942IfvgBsBFA0knA2cCXgDuBGyvm6jzbL9v+he2TgXcCjwJfqxwLoNcRgF+Y7BCxV64GXuxx/cX2XvRBRqxRhKTHbH+4/fwWYI3tK9rXj9g+smrAmDBJC2hWKB9H8wx4xP7A1my3GRySHrY9dxf3qh9NOCyyKjhKkaT9gFeATwA3jLq3T51IsYfuBzbQHBv3w1HXXwJWVUkUe+qAce7tO2kphlwKa5RyNbCSZorpSdsrYNv+zByUPUDa5+TrgWNrZ4m9tkLSBWMXnUk6n/QK7ptMBUcxkt4BvBV4zPbW9tohwNQONpiP3ZB0OnAFzfdUbN+iMb1qsJgwSQcDtwOb2V5IjwKmAafZfrZWtmGSwhoREyJpHXCy7SdrZ4m9I+l44IPty8dtL6uZZ9iksEbEhEi6z/a82jkiui6FNSImRNI1wNuAO2jO1wXA9m+rhYrooCxeiuLa01neZ3uxpJnAfmkQMZCm06zyPnHUNQMprBGjZMQaRUn6Ns3iiPfbni3p7cCSTClGxLDKiDVKOw2YAzwCYPtfkvYf/79El0i6lnFOsbF98STGiei8FNYobbNtSzJs64Mbg2VF7QARgySFNUq7rT2P9QBJF9D0C86JKAPE9s9qZ4gYJHnGGsVJms+OR43dUzlSREQxKawxKSRNZ9QMie3nK8aJiCgmU8FRlKQvA98B/gdspW2DB7ynZq54/SQdZPvftXNEdF1GrFGUpLXAsbY31c4Se6f9Xq4EFgN3OX88InrKQedR2tM0TQVi8M0GbgbOAdZKulzS7MqZIjonI9Yoqj0mbjHwIDu2wcvexwHWNnH/OfBm4DHg67YfqJsqohvyjDVKuwlYBqymecYaA0rSQcDZNCPW54CLgDuBI4AlwKx66SK6I4U1Sptq+6u1Q0RfPADcCpxq+5lR11dIurFSpojOyVRwFCXpcuCfwFJ2nArOdpsBI0lZsBSxeymsUZSkXqfY2Ha22wwISUsZv1fwKZMYJ6LzUlgjYlySPjbefdvLJytLxCBIYY0iJJ1ge5mk03vdz+HYETGssngpSvkozWrgk3vcy+HYA0TSbbbPlLSaHlPCtg+vECuis1JYo5RpALa/WDtI7LVL2o8nVU0RMSAyFRxFSHrE9pG1c0RETLaMWKOUKZJm0DTd30m22wweSccA1wIfoJmRmAK8bHt61WARHZPCGqUcBvyN3oU1p9sMpuuAz9F0WToKOJemf3BEjJLCGqU8YXtO7RDRX7bXSZpiewuwWNKjwDdq54rokhTWiJioVyRNA1ZKuhLYQE7IithJfimilGtqB4i+O4fmb8ZC4GXgUOCMqokiOiirgiNiwiTNBLC9sXaWiK7KiDUixqXGZZI2AWuApyRtlHRp7WwRXZTCGkW1Z3jGYFsEzAPm2j7Q9gzgaGCepEV1o0V0T6aCoyhJa4GVwGLgrhw7Nnjalb/zbW8ac30mcHdWf0fsKCPWKG02cDPNwpe1ki6XlL2Pg2Xq2KIK256zTq2QJ6LTUlijKDfusb0AuAD4PPCQpOWSjq0cLyZm8x7ei3hDylRwFNU+Yz2bZsT6HPBT4E7gCGCJ7VkV48UESNpCs71mp1vAPrYzao0YJQ0iorQHgFuBU20/M+r6Ckk3VsoUr4PtKbUzRAySjFijKEnKgqWIeCNJYY0iJC2lx6HYI2yfMolxIiImTaaCo5SrageIiKghI9aIiIg+yog1ipB0m+0zJa2mx5Sw7cMrxIqIKC4j1ihC0iG2N0h6d6/7ttdPdqaIiMmQwhoREdFH6bwURUk6RtLDkv4rabOkLZJerJ0rIqKUFNYo7TpgAbAW2Bc4H7i+aqKIiIJSWKM42+uAKba32F4MfKp2poiIUrIqOEp7RdI0YKWkK4EN5A1dRAyx/IGL0s6h+TlbSNPI/VDgjKqJIiIKyqrgKK49EHvk/M6IiKGWEWsUocZlkjYBa4CnJG2UdGntbBERJaWwRimLgHnAXNsH2p4BHA3Mk7SobrSIiHIyFRxFSHoUmG9705jrM4G7bc+pkywioqyMWKOUqWOLKmx7zjq1Qp6IiEmRwhqlbN7DexERAy1TwVGEpC0022t2ugXsYzuj1ogYSimsERERfZSp4IiIiD5KYY2IiOijFNaIiIg+SmGNiIjooxTWiIiIPkphjYiI6KP/Ay5hc5+tgwiRAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_ml=df[['Daily Time Spent on Site','Age','Area Income','Daily Internet Usage','Clicked on Ad']]    #only selecting which are highly correlated to the target variable Clicked on Ad"
      ],
      "metadata": {
        "id": "lPmoqxeYkPAJ"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_ml"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "aRjrq-jKke7X",
        "outputId": "bea808ef-7c82-45b7-fa9e-f8ea8360870e"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Daily Time Spent on Site  Age  Area Income  Daily Internet Usage  \\\n",
              "0                       68.95   35     61833.90                256.09   \n",
              "1                       80.23   31     68441.85                193.77   \n",
              "2                       69.47   26     59785.94                236.50   \n",
              "3                       74.15   29     54806.18                245.89   \n",
              "4                       68.37   35     73889.99                225.58   \n",
              "..                        ...  ...          ...                   ...   \n",
              "995                     72.97   30     71384.57                208.58   \n",
              "996                     51.30   45     67782.17                134.42   \n",
              "997                       NaN   51     42415.72                120.37   \n",
              "998                     55.55   19     41920.79                187.95   \n",
              "999                     45.01   26     29875.80                178.35   \n",
              "\n",
              "     Clicked on Ad  \n",
              "0                0  \n",
              "1                0  \n",
              "2                0  \n",
              "3                0  \n",
              "4                0  \n",
              "..             ...  \n",
              "995              1  \n",
              "996              1  \n",
              "997              1  \n",
              "998              0  \n",
              "999              1  \n",
              "\n",
              "[1000 rows x 5 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5c5a9b50-7524-4a9f-966e-f5c1ee066c30\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Daily Time Spent on Site</th>\n",
              "      <th>Age</th>\n",
              "      <th>Area Income</th>\n",
              "      <th>Daily Internet Usage</th>\n",
              "      <th>Clicked on Ad</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>68.95</td>\n",
              "      <td>35</td>\n",
              "      <td>61833.90</td>\n",
              "      <td>256.09</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>80.23</td>\n",
              "      <td>31</td>\n",
              "      <td>68441.85</td>\n",
              "      <td>193.77</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>69.47</td>\n",
              "      <td>26</td>\n",
              "      <td>59785.94</td>\n",
              "      <td>236.50</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>74.15</td>\n",
              "      <td>29</td>\n",
              "      <td>54806.18</td>\n",
              "      <td>245.89</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>68.37</td>\n",
              "      <td>35</td>\n",
              "      <td>73889.99</td>\n",
              "      <td>225.58</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>995</th>\n",
              "      <td>72.97</td>\n",
              "      <td>30</td>\n",
              "      <td>71384.57</td>\n",
              "      <td>208.58</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>996</th>\n",
              "      <td>51.30</td>\n",
              "      <td>45</td>\n",
              "      <td>67782.17</td>\n",
              "      <td>134.42</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>997</th>\n",
              "      <td>NaN</td>\n",
              "      <td>51</td>\n",
              "      <td>42415.72</td>\n",
              "      <td>120.37</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>998</th>\n",
              "      <td>55.55</td>\n",
              "      <td>19</td>\n",
              "      <td>41920.79</td>\n",
              "      <td>187.95</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>999</th>\n",
              "      <td>45.01</td>\n",
              "      <td>26</td>\n",
              "      <td>29875.80</td>\n",
              "      <td>178.35</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1000 rows × 5 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5c5a9b50-7524-4a9f-966e-f5c1ee066c30')\"\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-5c5a9b50-7524-4a9f-966e-f5c1ee066c30 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-5c5a9b50-7524-4a9f-966e-f5c1ee066c30');\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": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_ml['Age'].min()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pxWOlbcynbl5",
        "outputId": "6afe4cd0-0574-4c2e-ec15-a564e1b21012"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "19"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Scatter Plot showing that younger users with a higher income are more likely to click on an ad as opposed to older users or younger users with a low income"
      ],
      "metadata": {
        "id": "CWzWjppxf1gN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(15,15))\n",
        "sns.scatterplot(x=df_ml['Age'],y=df_ml['Area Income'],hue=df_ml['Clicked on Ad'],size=df_ml['Daily Time Spent on Site'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 894
        },
        "id": "hZxnjD3aklhK",
        "outputId": "53faeeeb-2336-4587-d94c-f9d23b7b3902"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f330faa0910>"
            ]
          },
          "metadata": {},
          "execution_count": 60
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x1080 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_ml['Age Bins']=pd.cut(df['Age'],bins=[19,30,40,50,61],labels=['19-30','30-40','40-50','50-61'])    #creating age bins "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b33QEF09nR54",
        "outputId": "28bf2a22-7b74-4101-faf2-de4360ae66f5"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-55-0b08b7596cf5>:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df_ml['Age Bins']=pd.cut(df['Age'],bins=[19,30,40,50,61],labels=['19-30','30-40','40-50','50-61'])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_ml"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "1cWSEXNYoI7M",
        "outputId": "b3feba97-23e9-43b4-e5a7-2ab441bd348d"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Daily Time Spent on Site  Age  Area Income  Daily Internet Usage  \\\n",
              "0                       68.95   35     61833.90                256.09   \n",
              "1                       80.23   31     68441.85                193.77   \n",
              "2                       69.47   26     59785.94                236.50   \n",
              "3                       74.15   29     54806.18                245.89   \n",
              "4                       68.37   35     73889.99                225.58   \n",
              "..                        ...  ...          ...                   ...   \n",
              "995                     72.97   30     71384.57                208.58   \n",
              "996                     51.30   45     67782.17                134.42   \n",
              "997                       NaN   51     42415.72                120.37   \n",
              "998                     55.55   19     41920.79                187.95   \n",
              "999                     45.01   26     29875.80                178.35   \n",
              "\n",
              "     Clicked on Ad Age Bins  \n",
              "0                0    30-40  \n",
              "1                0    30-40  \n",
              "2                0    19-30  \n",
              "3                0    19-30  \n",
              "4                0    30-40  \n",
              "..             ...      ...  \n",
              "995              1    19-30  \n",
              "996              1    40-50  \n",
              "997              1    50-61  \n",
              "998              0      NaN  \n",
              "999              1    19-30  \n",
              "\n",
              "[1000 rows x 6 columns]"
            ],
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              "\n",
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              "      <th></th>\n",
              "      <th>Daily Time Spent on Site</th>\n",
              "      <th>Age</th>\n",
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              "      <th>Daily Internet Usage</th>\n",
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              "      <th>Age Bins</th>\n",
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              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>68.95</td>\n",
              "      <td>35</td>\n",
              "      <td>61833.90</td>\n",
              "      <td>256.09</td>\n",
              "      <td>0</td>\n",
              "      <td>30-40</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>80.23</td>\n",
              "      <td>31</td>\n",
              "      <td>68441.85</td>\n",
              "      <td>193.77</td>\n",
              "      <td>0</td>\n",
              "      <td>30-40</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>69.47</td>\n",
              "      <td>26</td>\n",
              "      <td>59785.94</td>\n",
              "      <td>236.50</td>\n",
              "      <td>0</td>\n",
              "      <td>19-30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>74.15</td>\n",
              "      <td>29</td>\n",
              "      <td>54806.18</td>\n",
              "      <td>245.89</td>\n",
              "      <td>0</td>\n",
              "      <td>19-30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>68.37</td>\n",
              "      <td>35</td>\n",
              "      <td>73889.99</td>\n",
              "      <td>225.58</td>\n",
              "      <td>0</td>\n",
              "      <td>30-40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>995</th>\n",
              "      <td>72.97</td>\n",
              "      <td>30</td>\n",
              "      <td>71384.57</td>\n",
              "      <td>208.58</td>\n",
              "      <td>1</td>\n",
              "      <td>19-30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>996</th>\n",
              "      <td>51.30</td>\n",
              "      <td>45</td>\n",
              "      <td>67782.17</td>\n",
              "      <td>134.42</td>\n",
              "      <td>1</td>\n",
              "      <td>40-50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>997</th>\n",
              "      <td>NaN</td>\n",
              "      <td>51</td>\n",
              "      <td>42415.72</td>\n",
              "      <td>120.37</td>\n",
              "      <td>1</td>\n",
              "      <td>50-61</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>998</th>\n",
              "      <td>55.55</td>\n",
              "      <td>19</td>\n",
              "      <td>41920.79</td>\n",
              "      <td>187.95</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>999</th>\n",
              "      <td>45.01</td>\n",
              "      <td>26</td>\n",
              "      <td>29875.80</td>\n",
              "      <td>178.35</td>\n",
              "      <td>1</td>\n",
              "      <td>19-30</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1000 rows × 6 columns</p>\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Histogram showing that most of the users are between 30-40 followed by 19-30"
      ],
      "metadata": {
        "id": "lEmzR4hfgTtq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.histplot(df_ml['Age Bins'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "fwK1xcqMoewV",
        "outputId": "07641ecf-cbbd-4a8a-a684-0b70ef257bcd"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f330f774490>"
            ]
          },
          "metadata": {},
          "execution_count": 65
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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0FduNC085tJflevhIktQxFCRJHUNBktQxFCRJHUNBktQxFCRJnYlckprkNuDnwP3AfVU1P8mewIXAXOA24NiqumsS9UnSzmqSewovqKp5VTW/jZ8GXFVVBwBXtXFJ0hhtS4ePFgDnteHzgKMnWIsk7ZQmFQoFfDbJkiQLW9s+VbWqDf8Y2Ge6jkkWJlmcZPGaNWvGUask7TQm9ZiL51XVyiSPA65McuvwxKqqJDVdx6paBCwCmD9//rTzSJK2zET2FKpqZfu5GrgEOBi4I8m+AO3n6knUJkk7s7GHQpJHJHnU2mHgRcBNwGXASW22k4BLx12bJO3sJnH4aB/gkvaI3BnAv1fVp5N8HbgoycnA7cCxE6hNknZqYw+Fqvo+8Ixp2u8Ejhh3PZKkdbalS1IlSRNmKEiSOoaCJKljKEiSOoaCJKljKEiSOoaCJKljKEiSOoaCJKljKEiSOoaCJKljKEiSOoaCJKljKEiSOoaCJKljKEiSOoaCJKmzzYVCkiOTfDvJsiSnTboeSdqZbFOhkGQX4H8BLwEOAk5IctBkq5Kkncc2FQrAwcCyqvp+Vf0auABYMOGaJGmnkaqadA2dJMcAR1bVq9v4K4Dfr6rXDM2zEFjYRn8X+PbYC+3X3sBPJl3EdsZttnncXptnR9xev1NVU9NNmDHuSh6qqloELJp0HX1Jsriq5k+6ju2J22zzuL02z862vba1w0crgTlD47NbmyRpDLa1UPg6cECS/ZPsBhwPXDbhmiRpp7FNHT6qqvuSvAb4DLALcE5V3TzhssZthz001iO32eZxe22enWp7bVMnmiVJk7WtHT6SJE2QoSBJ6hgKPUpyTpLVSW4aantGkq8muTHJJ5I8egN935bkhiRLk3w2yX6tPUne1x4DckOSZ41rffqWZI8k1yX5ZpKbk7ylte+f5Nq2zhe2ixA2tpzL1tvmeya5Msl328/H9r0u45RklyTfSHJ5Gx9peyX5fHukzNL2eVxr3731W9aWM3d8a9OvJLe1372lSRa3tpG+H+1374wk30lyS5K/bO0Htt/pe5P89TjXpw+GQr/OBY5cr+1DwGlV9TTgEuANG+j7T1X19KqaB1wOvKm1vwQ4oH0WAmdt7aIn6F7g8Kp6BjAPODLJIcA7gDOr6knAXcDJG1pAkj8FfrFe82nAVVV1AHBVG9+RvBa4ZWh85O0FnFhV89pndWs7Gbir9T+zLW9H8oK2vmvvPRj1+/HnDC6ZP7Cqfo/BExcAfgr8JfDO/koeH0OhR1X1RQZfmGFPBr7Yhq8E/mwDfX82NPoIYO0VAQuA82vga8DMJPtuvaonp63T2v/Qd22fAg4HLm7t5wFHT9c/ySOB1wP/uN6kBa3fRvtvj5LMBv6YwR8bJAkjbq+NGN5eFwNHtOXuqEb9fvw34K1V9QDA2hCtqtVV9XXgN30XOg6GwvjdzLrnOb2c375Z77e0XdXlwIms21OYBSwfmm1Fa9shtEMhS4HVDELze8DdVXVfm2Vj6/s24F3AL9dr36eqVrXhHwP7bN2qJ+o9wN8AD7TxvRh9ewH8azuU8j+H/uPvvmNtOfe05e4ICvhskiXtkTkw+vfjicBxSRYn+VSSA/oudhIMhfH7z8BfJFkCPAr49YZmrKo3VtUc4MPAazY0346kqu5vh8xmM3hA4oGj9EsyD3hiVV2yieUX6/a6tmtJXgqsrqolW7iIE9thzD9on1dsteK2Xc+rqmcxOAx7apI/HJ64ie/H7sCv2mGnDwLn9FrphBgKY1ZVt1bVi6rq2cBHGPwlTJK1f7FdMU23D7PuMNNO8SiQqrobuAZ4LoNDZGtvtJwNrFy7R9E+b23zzU9yG/Al4MlJPt/63LH2EFv7uZodw2HAn7R1voDBYaP3Mtr2oqpWtp8/B/6dQQjD0HesLecxwJ3jWaV+Da3zagbn9A5mA9+PJJ9p2+tDrfsK4ONt+BLg6eOsfVwMhTEbusLjYcDfA/8CUFWvaie/jmrTh3dNFwC3tuHLgFe2KyEOAe4Z2vXdriWZSjKzDT8ceCGDE6jXAMe02U4CLl27R9E+b6qqs6pqv6qaCzwP+E5VPb/1uaz16/qPZ436VVWnV9Xsts7HA1dX1YmMsL2SzEiyN0CSXYGXAmuv2BreXse05W73e1dJHpHkUWuHgRcxWOdpvx9V9eK2vV7dpv0f4AVt+I+A74yr9rGqKj89fRjsCaxicAJqBYOrOl7L4Mv0HeDttLvKp+n7MQZf2BuATwCzWnsYvIjoe8CNwPxJr+dW3F5PB77R1vkm4E2t/QnAdcAy4KPA7ptYzlzgpqHxvRhcVfJd4HPAnpNe1x623fOBy0fdXgwuXljStvXNDPYwdmnT9mj9lrXlPGHS67eVttETgG+2z83AGzfn+wHMBD7Zfu++Cjyjtf+H9vv9M+DuNvzoSa/vln58zIUkqePhI0lSx1CQJHUMBUlSx1CQJHUMBUlSx1CQmiRHJ6kkI91FPeIy35xkZbsJ6tYkZ7V7VEjy1iT/cWv9W9LWYChI65zA4G7oE7bycs+swaM7DgKexuDGJ2pw093ntvK/JT0khoJE94TV5zG4wfD4ofaHJXl/+yv/yiRXJDmmTXt2ki+0h6t9ZoSn1e7G4Mawu1r/c4eWdVuStyS5vj3v/8DW/kdDj6f4xto7cqW+GArSwALg01X1HeDOJM9u7X/K4A7pgxg8MO650D0a4p+BY2rwHKtzgDM2sOz/0Z78uorB4zeWbmC+n9TgYW1nAWtf1vLXwKltT+MPgP+35asobZqhIA2cwLqXplzAukNIzwM+WlUPVNWPGTxXCOB3gacCV7b/8P+ewcPnprP28NHjgEckOX4D86192NoSBkEE8GXg3e0tXzNr3SOxpV7M2PQs0o4tyZ4MnjD6tCQF7AJUkg29FQ8Gz6C6uaqeO+q/U1W/SfJp4A9ZF0DD7m0/76f9blbV25N8EjgK+HKSF1fVrdP0lbYK9xSkwZNA/62qfqeq5tbgHRY/YHC45svAn7VzC/swePAcwLeBqSTd4aQkT9nYP9JeYnMY7XHpo0jyxKq6sareAXydEd8vIW0pQ0EaHCpa/+U8H2vtH2Pw1MtvAf8buJ7B48p/zSBM3pHkm8BS4NANLH/tOYWbGOyFvH8zantdkpuS3MDgabuf2oy+0mbzKanSJiR5ZFX9IsleDB4lfVg7vyDtcDynIG3a5e3lP7sBbzMQtCNzT0GS1PGcgiSpYyhIkjqGgiSpYyhIkjqGgiSp8/8BeuSrsB0CcZ8AAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_clicked=df_ml[df_ml['Clicked on Ad']==1]"
      ],
      "metadata": {
        "id": "TbRkF9UPpEuI"
      },
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Histogram showing that people between 40-50 are more likely to click on an ad followed by people between 30-40"
      ],
      "metadata": {
        "id": "YpxnwoU-g1lP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.histplot(df_clicked['Age Bins'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "Y2uQwIHapRYo",
        "outputId": "1a3f7c32-4033-46df-808b-f90c422a647a"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f330f764550>"
            ]
          },
          "metadata": {},
          "execution_count": 67
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Bar plot showing that users between 19-30 spend most time on the web site followed by users between 30-40"
      ],
      "metadata": {
        "id": "RrZ_Q8athLz0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.barplot(x=df_ml['Age Bins'],y=df_ml['Daily Time Spent on Site'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "aQR5Hc3nm28a",
        "outputId": "dd65093c-ebca-4c61-8c68-699c44142a86"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f330f8e7970>"
            ]
          },
          "metadata": {},
          "execution_count": 62
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Bar Plot showing that out of the people who click on an ad, people between 19-30 spend most time on the site closely followed by users between 50-61"
      ],
      "metadata": {
        "id": "1aJ1na0Ihbr2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.barplot(x=df_clicked['Age Bins'],y=df_clicked['Daily Time Spent on Site'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "QSbaHbn5ooKs",
        "outputId": "2de3e17e-9716-4b67-ce6f-94656eacbc5e"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f330f71df70>"
            ]
          },
          "metadata": {},
          "execution_count": 68
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Dropping the Age Bins column as we no longer need it for the model training"
      ],
      "metadata": {
        "id": "8LXlx5_uh5QL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_ml.drop('Age Bins',axis=1,inplace=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4nKJlyUCpgLO",
        "outputId": "01bd375d-aecb-43b4-a076-c906b0e80a40"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/pandas/core/frame.py:4906: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  return super().drop(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_ml"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "o3l7XIDHq8XD",
        "outputId": "cd7bf60c-12d5-411f-bf2c-d4be2c8d0758"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Daily Time Spent on Site  Age  Area Income  Daily Internet Usage  \\\n",
              "0                       68.95   35     61833.90                256.09   \n",
              "1                       80.23   31     68441.85                193.77   \n",
              "2                       69.47   26     59785.94                236.50   \n",
              "3                       74.15   29     54806.18                245.89   \n",
              "4                       68.37   35     73889.99                225.58   \n",
              "..                        ...  ...          ...                   ...   \n",
              "995                     72.97   30     71384.57                208.58   \n",
              "996                     51.30   45     67782.17                134.42   \n",
              "997                       NaN   51     42415.72                120.37   \n",
              "998                     55.55   19     41920.79                187.95   \n",
              "999                     45.01   26     29875.80                178.35   \n",
              "\n",
              "     Clicked on Ad  \n",
              "0                0  \n",
              "1                0  \n",
              "2                0  \n",
              "3                0  \n",
              "4                0  \n",
              "..             ...  \n",
              "995              1  \n",
              "996              1  \n",
              "997              1  \n",
              "998              0  \n",
              "999              1  \n",
              "\n",
              "[1000 rows x 5 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e5971733-2f46-4b6e-8d63-7914cee72f3a\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Daily Time Spent on Site</th>\n",
              "      <th>Age</th>\n",
              "      <th>Area Income</th>\n",
              "      <th>Daily Internet Usage</th>\n",
              "      <th>Clicked on Ad</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>68.95</td>\n",
              "      <td>35</td>\n",
              "      <td>61833.90</td>\n",
              "      <td>256.09</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>80.23</td>\n",
              "      <td>31</td>\n",
              "      <td>68441.85</td>\n",
              "      <td>193.77</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>69.47</td>\n",
              "      <td>26</td>\n",
              "      <td>59785.94</td>\n",
              "      <td>236.50</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>74.15</td>\n",
              "      <td>29</td>\n",
              "      <td>54806.18</td>\n",
              "      <td>245.89</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>68.37</td>\n",
              "      <td>35</td>\n",
              "      <td>73889.99</td>\n",
              "      <td>225.58</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>995</th>\n",
              "      <td>72.97</td>\n",
              "      <td>30</td>\n",
              "      <td>71384.57</td>\n",
              "      <td>208.58</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>996</th>\n",
              "      <td>51.30</td>\n",
              "      <td>45</td>\n",
              "      <td>67782.17</td>\n",
              "      <td>134.42</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>997</th>\n",
              "      <td>NaN</td>\n",
              "      <td>51</td>\n",
              "      <td>42415.72</td>\n",
              "      <td>120.37</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>998</th>\n",
              "      <td>55.55</td>\n",
              "      <td>19</td>\n",
              "      <td>41920.79</td>\n",
              "      <td>187.95</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>999</th>\n",
              "      <td>45.01</td>\n",
              "      <td>26</td>\n",
              "      <td>29875.80</td>\n",
              "      <td>178.35</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1000 rows × 5 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e5971733-2f46-4b6e-8d63-7914cee72f3a')\"\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-e5971733-2f46-4b6e-8d63-7914cee72f3a 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-e5971733-2f46-4b6e-8d63-7914cee72f3a');\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": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_ml.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "01ECMtsPrbI6",
        "outputId": "bf8c627d-a332-4eed-c287-020180f678ac"
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Daily Time Spent on Site    13\n",
              "Age                          0\n",
              "Area Income                 13\n",
              "Daily Internet Usage        11\n",
              "Clicked on Ad                0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_ml.dropna(axis=0,inplace=True)       #dropping null values"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XeuTaNWvreiP",
        "outputId": "b38b9ee7-4800-414d-e8c3-6457551815b6"
      },
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/pandas/util/_decorators.py:311: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  return func(*args, **kwargs)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = df_ml.iloc[:, :-1].values\n",
        "y = df_ml.iloc[:, -1].values"
      ],
      "metadata": {
        "id": "J1Zrw66sq-2K"
      },
      "execution_count": 80,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#splitting\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 0)"
      ],
      "metadata": {
        "id": "mtcifaezrC0a"
      },
      "execution_count": 81,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#scaling for equal importance\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "sc = StandardScaler()\n",
        "X_train = sc.fit_transform(X_train)\n",
        "X_test = sc.transform(X_test)"
      ],
      "metadata": {
        "id": "QHu7Nw4urIsj"
      },
      "execution_count": 82,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#fitting the model\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "classifier = LogisticRegression(random_state = 0)\n",
        "classifier.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eYNVBHJIrUHv",
        "outputId": "156ebc75-145c-4bf6-f0f7-bbd5d549471b"
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(random_state=0)"
            ]
          },
          "metadata": {},
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = classifier.predict(X_test)     #predicting"
      ],
      "metadata": {
        "id": "QlP8M4d1rXyF"
      },
      "execution_count": 84,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix, accuracy_score\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "print(cm)\n",
        "accuracy_score(y_test, y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mGXnX-pfrsO_",
        "outputId": "8be1a406-0dfc-43e1-e316-a8b50503880a"
      },
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[143   2]\n",
            " [  3 142]]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9827586206896551"
            ]
          },
          "metadata": {},
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import metrics\n",
        "cm_display = metrics.ConfusionMatrixDisplay(confusion_matrix = cm, display_labels = [False, True])"
      ],
      "metadata": {
        "id": "LXk8LPXisO8T"
      },
      "execution_count": 93,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#confusion matrix plot\n",
        "cm_display.plot()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "sBbY0NjdsHez",
        "outputId": "6d88c4c5-0948-411c-b1cb-888fe56f8e1f"
      },
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}