Jan_04_2023_tcomb_iterative_impute_normal_dist_cdf.ipynb

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        "<a href=\"https://colab.research.google.com/github/panimesh14/user-relevance-scoring-model-improved/blob/main/Jan_04_2023_tcomb_iterative_impute_normal_dist_cdf.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    },
    {
      "cell_type": "markdown",
      "id": "5ce31796",
      "metadata": {
        "id": "5ce31796"
      },
      "source": [
        "# Standard Libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "86356ba1",
      "metadata": {
        "id": "86356ba1"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "12f26825",
      "metadata": {
        "id": "12f26825"
      },
      "outputs": [],
      "source": [
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "6a638e5c",
      "metadata": {
        "id": "6a638e5c",
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.3989422804014327"
            ]
          },
          "metadata": {},
          "execution_count": 543
        }
      ],
      "source": [
        "import scipy as sp\n",
        "sp.stats.norm.pdf(0.5,loc=0.5,scale=1)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "037d8bbc",
      "metadata": {
        "id": "037d8bbc"
      },
      "source": [
        "# Data Loading"
      ]
    },
    {
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              "            username  booking_count  gross_earning  core_earning  \\\n",
              "0            anshul0           3107         138633        138293   \n",
              "1  PratikShrivastava           1743          31437         31437   \n",
              "2      lokesh_bagora           1624          18275         18275   \n",
              "3  deepika_rajagopal           1585           1782          1782   \n",
              "4       shreya_gupta            862           9050          9050   \n",
              "\n",
              "   profile_views firstbooking lastbooking  active_days  core_earning_per_day  \\\n",
              "0           5813   26-12-2021  22-12-2022          360            384.147222   \n",
              "1            323   05-01-2022  24-12-2022          353             89.056657   \n",
              "2            652   10-01-2022  09-10-2022          271             67.435424   \n",
              "3           2800   31-01-2022  31-03-2022           59             30.203390   \n",
              "4           3000   01-02-2022  26-03-2022           52            174.038462   \n",
              "\n",
              "  date_joined  age_in_days                                              title  \\\n",
              "0  20-12-2021          367  Founder @Tech and Target | Helping Students in...   \n",
              "1  23-12-2021          353  10+ Yrs Exp | R&D Specialist at Nokia Networks...   \n",
              "2  10-01-2022          271  Mock Interview , Career Guidance , Placement P...   \n",
              "3  31-01-2022           59  IT proffesional | Help aspiring fresher's in c...   \n",
              "4  23-02-2022           52  Mock Interview(All MNCs), Career Guidance, Pla...   \n",
              "\n",
              "                                         description currenc  followers  \\\n",
              "0                                                NaN     INR     6705.0   \n",
              "1  Hi, This is Pratik Shrivastava here. I have 10...     INR     8619.0   \n",
              "2  Hello, everyone, I am Lokesh Bagora from Indor...     INR        NaN   \n",
              "3  Deepika Here !\\nI am the content creator of On...     INR        NaN   \n",
              "4  Hello, I am Shreya Gupta pursuing my final yea...     INR     1363.0   \n",
              "\n",
              "                               company name  company tier numerical  \\\n",
              "0                                topmate.io                     5.0   \n",
              "1                             OnlineStudy4U                     5.0   \n",
              "2                                      Self                     5.0   \n",
              "3                             OnlineStudy4U                     5.0   \n",
              "4  Ernst & Young Global Consulting Services                     8.0   \n",
              "\n",
              "                                        college name  college tier numerical  \\\n",
              "0                                     GLA University                     7.0   \n",
              "1                    ramaiah-institute-of-technology                     6.0   \n",
              "2   Shivajirao Kadam Institute of Technology and ...                     6.0   \n",
              "3                                                NaN                     5.0   \n",
              "4        Noida Institute of Engineering & Technology                     7.0   \n",
              "\n",
              "                                            industry  Industry_numerical  \\\n",
              "0                                 Software Developer                 9.0   \n",
              "1                                 Software Developer                 9.0   \n",
              "2                                 Software Developer                 9.0   \n",
              "3                                 Software Developer                 9.0   \n",
              "4  User Summary:\\nI am a recent college graduate ...                 6.0   \n",
              "\n",
              "   years of experience  # of posts/week  avg engagement per post last 30 days  \\\n",
              "0                  5.0         1.166667                                  13.0   \n",
              "1                 12.0              NaN                                   NaN   \n",
              "2                  4.0              NaN                                   NaN   \n",
              "3                  3.0              NaN                                   NaN   \n",
              "4                  2.0         0.000000                                   0.0   \n",
              "\n",
              "                                             title.1  \\\n",
              "0  Founder@Tech and Target | Youtuber | 140k+ Sub...   \n",
              "1                                                NaN   \n",
              "2                                                NaN   \n",
              "3                                                NaN   \n",
              "4                         Analyst in Ernst and Young   \n",
              "\n",
              "                                        user summary  # of Posts last 30 days  \\\n",
              "0  I am a final year undergraduate pursuing Compu...                      1.0   \n",
              "1                                                NaN                      NaN   \n",
              "2                                                NaN                      NaN   \n",
              "3                                                NaN                      NaN   \n",
              "4                                                  0                      0.0   \n",
              "\n",
              "   # of Comments last 30 days  # of Reactions last 30 days  Gender  \\\n",
              "0                         0.0                          0.0    male   \n",
              "1                         NaN                          NaN     NaN   \n",
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              "4                         0.0                          0.0  female   \n",
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              "0             1                     1.0  \n",
              "1             1                     NaN  \n",
              "2             1                     NaN  \n",
              "3             0                     NaN  \n",
              "4             0                     0.0  "
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              "      <td>23-12-2021</td>\n",
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              "      <td>Hi, This is Pratik Shrivastava here. I have 10...</td>\n",
              "      <td>INR</td>\n",
              "      <td>8619.0</td>\n",
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              "      <td>INR</td>\n",
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          },
          "metadata": {},
          "execution_count": 544
        }
      ],
      "source": [
        "details_user =pd.read_csv('user_details_college_company - Sheet2.csv');\n",
        "details_user.head()"
      ]
    },
    {
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          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 2478 entries, 0 to 2477\n",
            "Data columns (total 32 columns):\n",
            " #   Column                                Non-Null Count  Dtype  \n",
            "---  ------                                --------------  -----  \n",
            " 0   username                              2478 non-null   object \n",
            " 1   booking_count                         2478 non-null   int64  \n",
            " 2   gross_earning                         2478 non-null   int64  \n",
            " 3   core_earning                          2478 non-null   int64  \n",
            " 4   profile_views                         2478 non-null   int64  \n",
            " 5   firstbooking                          2478 non-null   object \n",
            " 6   lastbooking                           2478 non-null   object \n",
            " 7   active_days                           2478 non-null   int64  \n",
            " 8   core_earning_per_day                  2478 non-null   float64\n",
            " 9   date_joined                           2478 non-null   object \n",
            " 10  age_in_days                           2478 non-null   int64  \n",
            " 11  title                                 1617 non-null   object \n",
            " 12  description                           1414 non-null   object \n",
            " 13  currenc                               2478 non-null   object \n",
            " 14  followers                             1818 non-null   float64\n",
            " 15  company name                          1851 non-null   object \n",
            " 16  company tier numerical                1851 non-null   float64\n",
            " 17  college name                          1915 non-null   object \n",
            " 18  college tier numerical                1916 non-null   float64\n",
            " 19  industry                              1830 non-null   object \n",
            " 20  Industry_numerical                    1875 non-null   float64\n",
            " 21  years of experience                   1786 non-null   float64\n",
            " 22  # of posts/week                       1779 non-null   float64\n",
            " 23  avg engagement per post last 30 days  1779 non-null   float64\n",
            " 24  title.1                               1778 non-null   object \n",
            " 25  user summary                          1752 non-null   object \n",
            " 26  # of Posts last 30 days               1779 non-null   float64\n",
            " 27  # of Comments last 30 days            1779 non-null   float64\n",
            " 28  # of Reactions last 30 days           1779 non-null   float64\n",
            " 29  Gender                                1779 non-null   object \n",
            " 30  gender_score                          2478 non-null   int64  \n",
            " 31  # of promotional posts                1779 non-null   float64\n",
            "dtypes: float64(12), int64(7), object(13)\n",
            "memory usage: 619.6+ KB\n"
          ]
        }
      ],
      "source": [
        "details_user.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "9426067f",
      "metadata": {
        "id": "9426067f"
      },
      "outputs": [],
      "source": [
        "pd.set_option('display.max_columns',None)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "44f69340",
      "metadata": {
        "scrolled": true,
        "id": "44f69340",
        "outputId": "519037fd-3937-42db-f088-3c505a1e7843",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            username  booking_count  gross_earning  core_earning  \\\n",
              "0            anshul0           3107         138633        138293   \n",
              "1  PratikShrivastava           1743          31437         31437   \n",
              "\n",
              "   profile_views firstbooking lastbooking  active_days  core_earning_per_day  \\\n",
              "0           5813   26-12-2021  22-12-2022          360            384.147222   \n",
              "1            323   05-01-2022  24-12-2022          353             89.056657   \n",
              "\n",
              "  date_joined  age_in_days                                              title  \\\n",
              "0  20-12-2021          367  Founder @Tech and Target | Helping Students in...   \n",
              "1  23-12-2021          353  10+ Yrs Exp | R&D Specialist at Nokia Networks...   \n",
              "\n",
              "                                         description currenc  followers  \\\n",
              "0                                                NaN     INR     6705.0   \n",
              "1  Hi, This is Pratik Shrivastava here. I have 10...     INR     8619.0   \n",
              "\n",
              "    company name  company tier numerical                     college name  \\\n",
              "0     topmate.io                     5.0                   GLA University   \n",
              "1  OnlineStudy4U                     5.0  ramaiah-institute-of-technology   \n",
              "\n",
              "   college tier numerical            industry  Industry_numerical  \\\n",
              "0                     7.0  Software Developer                 9.0   \n",
              "1                     6.0  Software Developer                 9.0   \n",
              "\n",
              "   years of experience  # of posts/week  avg engagement per post last 30 days  \\\n",
              "0                  5.0         1.166667                                  13.0   \n",
              "1                 12.0              NaN                                   NaN   \n",
              "\n",
              "                                             title.1  \\\n",
              "0  Founder@Tech and Target | Youtuber | 140k+ Sub...   \n",
              "1                                                NaN   \n",
              "\n",
              "                                        user summary  # of Posts last 30 days  \\\n",
              "0  I am a final year undergraduate pursuing Compu...                      1.0   \n",
              "1                                                NaN                      NaN   \n",
              "\n",
              "   # of Comments last 30 days  # of Reactions last 30 days Gender  \\\n",
              "0                         0.0                          0.0   male   \n",
              "1                         NaN                          NaN    NaN   \n",
              "\n",
              "   gender_score  # of promotional posts  \n",
              "0             1                     1.0  \n",
              "1             1                     NaN  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4056d3b3-07f2-491a-a781-4b8a89088f9f\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "      <th>username</th>\n",
              "      <th>booking_count</th>\n",
              "      <th>gross_earning</th>\n",
              "      <th>core_earning</th>\n",
              "      <th>profile_views</th>\n",
              "      <th>firstbooking</th>\n",
              "      <th>lastbooking</th>\n",
              "      <th>active_days</th>\n",
              "      <th>core_earning_per_day</th>\n",
              "      <th>date_joined</th>\n",
              "      <th>age_in_days</th>\n",
              "      <th>title</th>\n",
              "      <th>description</th>\n",
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              "      <th>college tier numerical</th>\n",
              "      <th>industry</th>\n",
              "      <th>Industry_numerical</th>\n",
              "      <th>years of experience</th>\n",
              "      <th># of posts/week</th>\n",
              "      <th>avg engagement per post last 30 days</th>\n",
              "      <th>title.1</th>\n",
              "      <th>user summary</th>\n",
              "      <th># of Posts last 30 days</th>\n",
              "      <th># of Comments last 30 days</th>\n",
              "      <th># of Reactions last 30 days</th>\n",
              "      <th>Gender</th>\n",
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              "    </tr>\n",
              "  </thead>\n",
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              "      <th>0</th>\n",
              "      <td>anshul0</td>\n",
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              "      <td>138633</td>\n",
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              "      <td>5813</td>\n",
              "      <td>26-12-2021</td>\n",
              "      <td>22-12-2022</td>\n",
              "      <td>360</td>\n",
              "      <td>384.147222</td>\n",
              "      <td>20-12-2021</td>\n",
              "      <td>367</td>\n",
              "      <td>Founder @Tech and Target | Helping Students in...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>INR</td>\n",
              "      <td>6705.0</td>\n",
              "      <td>topmate.io</td>\n",
              "      <td>5.0</td>\n",
              "      <td>GLA University</td>\n",
              "      <td>7.0</td>\n",
              "      <td>Software Developer</td>\n",
              "      <td>9.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.166667</td>\n",
              "      <td>13.0</td>\n",
              "      <td>Founder@Tech and Target | Youtuber | 140k+ Sub...</td>\n",
              "      <td>I am a final year undergraduate pursuing Compu...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>male</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>PratikShrivastava</td>\n",
              "      <td>1743</td>\n",
              "      <td>31437</td>\n",
              "      <td>31437</td>\n",
              "      <td>323</td>\n",
              "      <td>05-01-2022</td>\n",
              "      <td>24-12-2022</td>\n",
              "      <td>353</td>\n",
              "      <td>89.056657</td>\n",
              "      <td>23-12-2021</td>\n",
              "      <td>353</td>\n",
              "      <td>10+ Yrs Exp | R&amp;D Specialist at Nokia Networks...</td>\n",
              "      <td>Hi, This is Pratik Shrivastava here. I have 10...</td>\n",
              "      <td>INR</td>\n",
              "      <td>8619.0</td>\n",
              "      <td>OnlineStudy4U</td>\n",
              "      <td>5.0</td>\n",
              "      <td>ramaiah-institute-of-technology</td>\n",
              "      <td>6.0</td>\n",
              "      <td>Software Developer</td>\n",
              "      <td>9.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4056d3b3-07f2-491a-a781-4b8a89088f9f')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http:https://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </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-4056d3b3-07f2-491a-a781-4b8a89088f9f 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-4056d3b3-07f2-491a-a781-4b8a89088f9f');\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": 547
        }
      ],
      "source": [
        "details_user.head(2)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "590fae11",
      "metadata": {
        "id": "590fae11"
      },
      "source": [
        "### Data Filtering "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "50d5116a",
      "metadata": {
        "scrolled": true,
        "id": "50d5116a"
      },
      "outputs": [],
      "source": [
        "cols_det=['booking_count', 'age_in_days','core_earning','active_days', 'followers', 'company tier numerical', 'college tier numerical', 'years of experience', '# of posts/week', 'avg engagement per post last 30 days', '# of Posts last 30 days', '# of Comments last 30 days', '# of Reactions last 30 days', 'gender_score','# of promotional posts']\n",
        "details_profile_int=details_user[cols_det]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "0c320c8f",
      "metadata": {
        "id": "0c320c8f",
        "outputId": "a6781295-9457-4969-f05a-10f96f2592bf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 2478 entries, 0 to 2477\n",
            "Data columns (total 15 columns):\n",
            " #   Column                                Non-Null Count  Dtype  \n",
            "---  ------                                --------------  -----  \n",
            " 0   booking_count                         2478 non-null   int64  \n",
            " 1   age_in_days                           2478 non-null   int64  \n",
            " 2   core_earning                          2478 non-null   int64  \n",
            " 3   active_days                           2478 non-null   int64  \n",
            " 4   followers                             1818 non-null   float64\n",
            " 5   company tier numerical                1851 non-null   float64\n",
            " 6   college tier numerical                1916 non-null   float64\n",
            " 7   years of experience                   1786 non-null   float64\n",
            " 8   # of posts/week                       1779 non-null   float64\n",
            " 9   avg engagement per post last 30 days  1779 non-null   float64\n",
            " 10  # of Posts last 30 days               1779 non-null   float64\n",
            " 11  # of Comments last 30 days            1779 non-null   float64\n",
            " 12  # of Reactions last 30 days           1779 non-null   float64\n",
            " 13  gender_score                          2478 non-null   int64  \n",
            " 14  # of promotional posts                1779 non-null   float64\n",
            "dtypes: float64(10), int64(5)\n",
            "memory usage: 290.5 KB\n"
          ]
        }
      ],
      "source": [
        "details_profile_int.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ae222a29",
      "metadata": {
        "id": "ae222a29"
      },
      "source": [
        "details_profile_int.drop(['Industry_numerical'],axis=1,inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "cebd7f4b",
      "metadata": {
        "id": "cebd7f4b",
        "outputId": "dc249fcf-8a26-4b4f-b077-719be1b1a2b2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['booking_count', 'age_in_days', 'core_earning', 'active_days',\n",
              "       'followers', 'company tier numerical', 'college tier numerical',\n",
              "       'years of experience', '# of posts/week',\n",
              "       'avg engagement per post last 30 days', '# of Posts last 30 days',\n",
              "       '# of Comments last 30 days', '# of Reactions last 30 days',\n",
              "       'gender_score', '# of promotional posts'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 550
        }
      ],
      "source": [
        "details_profile_int.columns"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9f595aad",
      "metadata": {
        "id": "9f595aad"
      },
      "source": [
        "# Exploratory Data Analysis"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "85e5c586",
      "metadata": {
        "id": "85e5c586",
        "outputId": "e21f9db0-bfd9-4ea3-e010-273d0f273bf9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    1\n",
              "1    1\n",
              "2    1\n",
              "3    0\n",
              "4    0\n",
              "5    0\n",
              "6    1\n",
              "Name: gender_score, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 551
        }
      ],
      "source": [
        "details_profile_int['gender_score'].head(7)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "36a99ac7",
      "metadata": {
        "scrolled": true,
        "id": "36a99ac7",
        "outputId": "840fe78b-f470-46a7-b44d-4a16aaf62011",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "booking_count                           1\n",
              "age_in_days                             1\n",
              "core_earning                            1\n",
              "active_days                             1\n",
              "followers                               2\n",
              "company tier numerical                  2\n",
              "college tier numerical                  2\n",
              "years of experience                     2\n",
              "# of posts/week                         2\n",
              "avg engagement per post last 30 days    2\n",
              "# of Posts last 30 days                 2\n",
              "# of Comments last 30 days              2\n",
              "# of Reactions last 30 days             2\n",
              "gender_score                            1\n",
              "# of promotional posts                  2\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 552
        }
      ],
      "source": [
        "details_profile_int.isna().nunique()\n",
        "#followers, company tier numerical, college tier numerical, years of experience, # of posts/week, avg engagement per post last 30 days, # of posts last 30 days, # of Comments last 30 days, # of Reactions last 30 days, Gender, # of promotional posts"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "2730e608",
      "metadata": {
        "id": "2730e608",
        "outputId": "3460bc15-b7f2-43b2-f73a-914a53c171b0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "booking_count                             0\n",
              "age_in_days                               0\n",
              "core_earning                              0\n",
              "active_days                               0\n",
              "followers                               660\n",
              "company tier numerical                  627\n",
              "college tier numerical                  562\n",
              "years of experience                     692\n",
              "# of posts/week                         699\n",
              "avg engagement per post last 30 days    699\n",
              "# of Posts last 30 days                 699\n",
              "# of Comments last 30 days              699\n",
              "# of Reactions last 30 days             699\n",
              "gender_score                              0\n",
              "# of promotional posts                  699\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 553
        }
      ],
      "source": [
        "details_profile_int.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b245f46c",
      "metadata": {
        "id": "b245f46c",
        "outputId": "71c1d36d-4299-4c7b-c6f3-2a271a77d322",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2478"
            ]
          },
          "metadata": {},
          "execution_count": 554
        }
      ],
      "source": [
        "len(details_profile_int)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e4830c12",
      "metadata": {
        "id": "e4830c12",
        "outputId": "bf327988-45da-461a-c159-34b5810eb2ed",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "booking_count                            0.000000\n",
              "age_in_days                              0.000000\n",
              "core_earning                             0.000000\n",
              "active_days                              0.000000\n",
              "followers                               26.634383\n",
              "company tier numerical                  25.302663\n",
              "college tier numerical                  22.679580\n",
              "years of experience                     27.925747\n",
              "# of posts/week                         28.208232\n",
              "avg engagement per post last 30 days    28.208232\n",
              "# of Posts last 30 days                 28.208232\n",
              "# of Comments last 30 days              28.208232\n",
              "# of Reactions last 30 days             28.208232\n",
              "gender_score                             0.000000\n",
              "# of promotional posts                  28.208232\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 555
        }
      ],
      "source": [
        "100*details_profile_int.isnull().sum()/len(details_profile_int)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Dropping Nans"
      ],
      "metadata": {
        "id": "-xZ8wEuI3PpJ"
      },
      "id": "-xZ8wEuI3PpJ"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "0c1b1077",
      "metadata": {
        "id": "0c1b1077",
        "outputId": "5e091ec7-c4b0-49b9-aff7-acffd959682f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1714"
            ]
          },
          "metadata": {},
          "execution_count": 556
        }
      ],
      "source": [
        "\n",
        "details_profile_int_dr = details_profile_int.dropna()\n",
        "len(details_profile_int_dr)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Iterative Imputer"
      ],
      "metadata": {
        "id": "x7XmHQ1_3RoJ"
      },
      "id": "x7XmHQ1_3RoJ"
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "xmvbEods3XR_"
      },
      "id": "xmvbEods3XR_",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5e22f827",
        "outputId": "890d53f1-311b-45cd-bdda-283b1605c468",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0"
            ]
          },
          "metadata": {},
          "execution_count": 557
        }
      ],
      "source": [
        "from sklearn.experimental import enable_iterative_imputer\n",
        "from sklearn.impute import IterativeImputer\n",
        "iterpute = IterativeImputer()\n",
        "details_profile_int_sim = iterpute.fit_transform(details_profile_int)\n",
        "data_ret = len(details_profile_int_sim)/len(details_profile_int)\n",
        "data_loss = 1-data_ret\n",
        "data_loss"
      ],
      "id": "5e22f827"
    },
    {
      "cell_type": "code",
      "source": [
        "details_profile_int_dr = pd.DataFrame(details_profile_int_sim,columns=details_profile_int.columns)\n",
        "details_profile_int_dr.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ajYMM1Qj3bIw",
        "outputId": "7530fcb2-d7f6-43ff-cca0-36afe59b81fb"
      },
      "id": "ajYMM1Qj3bIw",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "booking_count                           0\n",
              "age_in_days                             0\n",
              "core_earning                            0\n",
              "active_days                             0\n",
              "followers                               0\n",
              "company tier numerical                  0\n",
              "college tier numerical                  0\n",
              "years of experience                     0\n",
              "# of posts/week                         0\n",
              "avg engagement per post last 30 days    0\n",
              "# of Posts last 30 days                 0\n",
              "# of Comments last 30 days              0\n",
              "# of Reactions last 30 days             0\n",
              "gender_score                            0\n",
              "# of promotional posts                  0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 558
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "deca84d4",
      "metadata": {
        "id": "deca84d4",
        "outputId": "f354d37c-e7ac-4d3d-f002-ed128298aed0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0"
            ]
          },
          "metadata": {},
          "execution_count": 559
        }
      ],
      "source": [
        "data_ret = len(details_profile_int_dr)/len(details_profile_int)\n",
        "data_loss = 1-data_ret\n",
        "data_loss"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "286c64d4",
      "metadata": {
        "id": "286c64d4"
      },
      "outputs": [],
      "source": [
        "# def gen_to_bin(x):\n",
        "#     if(x=='male'):\n",
        "#         return 0\n",
        "#     else:\n",
        "#         return 1\n",
        "\n",
        "# details_profile_int_dr['Gender'] = details_profile_int_dr['Gender'].apply(gen_to_bin)\n",
        "# details_profile_int_dr['Gender']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "01de0e05",
      "metadata": {
        "id": "01de0e05"
      },
      "outputs": [],
      "source": [
        "\n",
        "# details_profile_int_dr['t1']=(details_profile_int_dr['core_earning']/(details_profile_int_dr['active_days']+1))\n",
        "# details_profile_int_dr['t2']=(details_profile_int_dr['core_earning']/(details_profile_int_dr['booking_count']+1))\n",
        "# details_profile_int_dr.info() "
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# details_profile_int_dr['t1'].describe(percentiles=[0.6,0.7,0.8,0.9,0.99])"
      ],
      "metadata": {
        "id": "5udwPMmoULmJ"
      },
      "id": "5udwPMmoULmJ",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# details_profile_int_dr['t2'].describe(percentiles=[0.6,0.7,0.8,0.9,0.99])"
      ],
      "metadata": {
        "id": "JeORv7jSWLLs"
      },
      "id": "JeORv7jSWLLs",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "NtyKEXmz8i9A"
      },
      "id": "NtyKEXmz8i9A"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "59359c44",
      "metadata": {
        "scrolled": true,
        "id": "59359c44",
        "outputId": "b36db7f2-cb18-445a-a8a4-bd19abfb3cb0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "details_profile_int_dr['target_score']=(details_profile_int_dr['core_earning']/(details_profile_int_dr['active_days']+1))+(details_profile_int_dr['core_earning']/(details_profile_int_dr['booking_count']+1))\n",
        "#details_profile_int_dr['target_score2']=\n",
        "\n",
        "details_profile_int_dr['target_score'] = sp.stats.norm.cdf(details_profile_int_dr['target_score'],loc=0,scale=1)\n",
        "details_profile_int_dr['target_score'].hist(bins=100)\n",
        "#details_profile_int_dr[details_profile_int_dr['target_score']<0.9]['core_earning'].plot()#hist(bins=100)\n",
        "plt.grid()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "1607616f",
      "metadata": {
        "id": "1607616f",
        "outputId": "59aaa33d-3324-4ea1-a885-87cb5793cc3d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f4ef91b4340>"
            ]
          },
          "metadata": {},
          "execution_count": 565
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "details_profile_int_dr[details_profile_int_dr['core_earning']<5000]['core_earning'].hist(bins=100)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "359c6882",
      "metadata": {
        "scrolled": true,
        "id": "359c6882",
        "outputId": "7f35a25b-d35e-4e52-c215-25a3913a924c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f4ef7f7dca0>"
            ]
          },
          "metadata": {},
          "execution_count": 566
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAD4CAYAAAAAczaOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAASV0lEQVR4nO3db4xc1XnH8e9TO0CCW8yfaIVsq+soViI3JClZgSOqasFtYkgU84JEICs4qatVJZKSghRMIxW1VSSilhCQUtRVbOFICENIIiyCSl3DKPILHHBC+ecQNmCCLYKbYJyOUZLaffpijrejzRp77+zOsnu+H2m095x75t5zDvJv7py5M0RmIkmqw+/NdgckSf1j6EtSRQx9SaqIoS9JFTH0JakiC2e7A2/mnHPOycHBwUbPPXz4MKeffvr0dmiOqX0OHH/d44d652D37t2/yMx3TrbvLR36g4ODPP74442e22q1GB4ent4OzTG1z4Hjr3v8UO8cRMRLx9vn8o4kVcTQl6SKGPqSVBFDX5IqYuhLUkUMfUmqiKEvSRUx9CWpIoa+JFXkLf2N3F4Nbvze+Pbemz82iz2RpLcGr/QlqSKGviRVxNCXpIoY+pJUEUNfkipi6EtSRQx9SaqIoS9JFTH0Jakihr4kVcTQl6SKGPqSVJEThn5EbI6IAxHxdFfdP0XEjyPiyYj4bkQs7tp3Y0SMRcRzEfHRrvo1pW4sIjZO/1AkSSdyMlf6dwJrJtRtB96Xme8HfgLcCBARK4ErgT8qz/mXiFgQEQuArwOXAiuBq0pbSVIfnTD0M/P7wGsT6v49M4+U4qPA0rK9Ftiamb/JzBeBMeCC8hjLzBcy87fA1tJWktRH0/F7+n8B3FO2l9B5EThmX6kDeHlC/YWTHSwiRoARgIGBAVqtVqNOtdttrj/v6Hi56XHmsna7XeW4j3H8dY8fnIPJ9BT6EfEl4Ahw1/R0BzJzFBgFGBoayuHh4UbHabVa3LLz8Hh577pmx5nLWq0WTedvPnD8dY8fnIPJNA79iPgM8HFgdWZmqd4PLOtqtrTU8Sb1kqQ+aXTLZkSsAb4IfCIz3+jatQ24MiJOjYjlwArgB8BjwIqIWB4Rp9D5sHdbb12XJE3VCa/0I+JuYBg4JyL2ATfRuVvnVGB7RAA8mpl/lZnPRMS9wLN0ln2uycyj5TifAx4CFgCbM/OZGRiPJOlNnDD0M/OqSao3vUn7LwNfnqT+QeDBKfVOkjSt/EauJFXE0Jekihj6klQRQ1+SKmLoS1JFDH1JqoihL0kVMfQlqSKGviRVxNCXpIoY+pJUEUNfkipi6EtSRQx9SaqIoS9JFTH0Jakihr4kVcTQl6SKGPqSVBFDX5IqYuhLUkUMfUmqiKEvSRU5YehHxOaIOBART3fVnRUR2yPi+fL3zFIfEXF7RIxFxJMRcX7Xc9aX9s9HxPqZGY4k6c2czJX+ncCaCXUbgR2ZuQLYUcoAlwIrymMEuAM6LxLATcCFwAXATcdeKCRJ/XPC0M/M7wOvTaheC2wp21uAy7vqv5kdjwKLI+Jc4KPA9sx8LTMPAtv53RcSSdIMW9jweQOZ+UrZ/jkwULaXAC93tdtX6o5X/zsiYoTOuwQGBgZotVqNOthut7n+vKPj5abHmcva7XaV4z7G8dc9fnAOJtM09MdlZkZETkdnyvFGgVGAoaGhHB4ebnScVqvFLTsPj5f3rmt2nLms1WrRdP7mA8df9/jBOZhM07t3Xi3LNpS/B0r9fmBZV7ulpe549ZKkPmoa+tuAY3fgrAfu76q/utzFswo4VJaBHgI+EhFnlg9wP1LqJEl9dMLlnYi4GxgGzomIfXTuwrkZuDciNgAvAZ8qzR8ELgPGgDeAzwJk5msR8Y/AY6XdP2TmxA+HJUkz7IShn5lXHWfX6knaJnDNcY6zGdg8pd5JkqaV38iVpIoY+pJUEUNfkipi6EtSRQx9SaqIoS9JFTH0Jakihr4kVcTQl6SKGPqSVBFDX5IqYuhLUkUMfUmqiKEvSRUx9CWpIoa+JFXE0Jekihj6klQRQ1+SKmLoS1JFDH1JqoihL0kV6Sn0I+JvIuKZiHg6Iu6OiNMiYnlE7IqIsYi4JyJOKW1PLeWxsn9wOgYgSTp5jUM/IpYAfw0MZeb7gAXAlcBXgFsz893AQWBDecoG4GCpv7W0kyT1Ua/LOwuBt0fEQuAdwCvAJcB9Zf8W4PKyvbaUKftXR0T0eH5J0hQ0Dv3M3A/8M/AzOmF/CNgNvJ6ZR0qzfcCSsr0EeLk890hpf3bT80uSpm5h0ydGxJl0rt6XA68D3wLW9NqhiBgBRgAGBgZotVqNjtNut7n+vKPj5abHmcva7XaV4z7G8dc9fnAOJtM49IE/A17MzP8CiIjvABcBiyNiYbmaXwrsL+33A8uAfWU56AzglxMPmpmjwCjA0NBQDg8PN+pcq9Xilp2Hx8t71zU7zlzWarVoOn/zgeOve/zgHEymlzX9nwGrIuIdZW1+NfAs8AhwRWmzHri/bG8rZcr+hzMzezi/JGmKelnT30XnA9kfAk+VY40CNwDXRcQYnTX7TeUpm4CzS/11wMYe+i1JaqCX5R0y8ybgpgnVLwAXTNL218AnezmfJKk3fiNXkipi6EtSRQx9SaqIoS9JFTH0Jakihr4kVcTQl6SKGPqSVBFDX5IqYuhLUkUMfUmqiKEvSRUx9CWpIoa+JFXE0Jekihj6klQRQ1+SKmLoS1JFDH1JqoihL0kVMfQlqSKGviRVxNCXpIr0FPoRsTgi7ouIH0fEnoj4cEScFRHbI+L58vfM0jYi4vaIGIuIJyPi/OkZgiTpZPV6pX8b8G+Z+V7gA8AeYCOwIzNXADtKGeBSYEV5jAB39HhuSdIUNQ79iDgD+FNgE0Bm/jYzXwfWAltKsy3A5WV7LfDN7HgUWBwR5zbuuSRpyiIzmz0x4oPAKPAsnav83cC1wP7MXFzaBHAwMxdHxAPAzZm5s+zbAdyQmY9POO4InXcCDAwMfGjr1q2N+tdut3nx0NHx8nlLzmh0nLms3W6zaNGi2e7GrHH8dY8f6p2Diy++eHdmDk22b2EPx10InA98PjN3RcRt/P9SDgCZmRExpVeVzByl82LC0NBQDg8PN+pcq9Xilp2Hx8t71zU7zlzWarVoOn/zgeOve/zgHEymlzX9fcC+zNxVyvfReRF49diyTfl7oOzfDyzrev7SUidJ6pPGoZ+ZPwdejoj3lKrVdJZ6tgHrS9164P6yvQ24utzFswo4lJmvND2/JGnqelneAfg8cFdEnAK8AHyWzgvJvRGxAXgJ+FRp+yBwGTAGvFHaSpL6qKfQz8wngMk+LFg9SdsErunlfJKk3viNXEmqiKEvSRUx9CWpIoa+JFXE0Jekihj6klQRQ1+SKmLoS1JFDH1JqoihL0kVMfQlqSKGviRVxNCXpIoY+pJUEUNfkipi6EtSRQx9SaqIoS9JFTH0Jakihr4kVcTQl6SKGPqSVBFDX5Iq0nPoR8SCiPhRRDxQyssjYldEjEXEPRFxSqk/tZTHyv7BXs8tSZqa6bjSvxbY01X+CnBrZr4bOAhsKPUbgIOl/tbSTpLURz2FfkQsBT4GfKOUA7gEuK802QJcXrbXljJl/+rSXpLUJwt7fP7XgC8Cv1/KZwOvZ+aRUt4HLCnbS4CXATLzSEQcKu1/0X3AiBgBRgAGBgZotVqNOtZut7n+vKPj5abHmcva7XaV4z7G8dc9fnAOJtM49CPi48CBzNwdEcPT1aHMHAVGAYaGhnJ4uNmhW60Wt+w8PF7eu67ZceayVqtF0/mbDxx/3eMH52AyvVzpXwR8IiIuA04D/gC4DVgcEQvL1f5SYH9pvx9YBuyLiIXAGcAvezi/JGmKGq/pZ+aNmbk0MweBK4GHM3Md8AhwRWm2Hri/bG8rZcr+hzMzm55fkjR1M3Gf/g3AdRExRmfNflOp3wScXeqvAzbOwLklSW+i1w9yAcjMFtAq2y8AF0zS5tfAJ6fjfJKkZvxGriRVxNCXpIoY+pJUEUNfkipi6EtSRQx9SaqIoS9JFTH0Jakihr4kVcTQl6SKGPqSVBFDX5IqYuhLUkUMfUmqiKEvSRUx9CWpIoa+JFXE0Jekihj6klQRQ1+SKmLoS1JFDH1JqoihL0kVaRz6EbEsIh6JiGcj4pmIuLbUnxUR2yPi+fL3zFIfEXF7RIxFxJMRcf50DUKSdHJ6udI/AlyfmSuBVcA1EbES2AjsyMwVwI5SBrgUWFEeI8AdPZxbktRA49DPzFcy84dl+7+BPcASYC2wpTTbAlxettcC38yOR4HFEXFu455LkqYsMrP3g0QMAt8H3gf8LDMXl/oADmbm4oh4ALg5M3eWfTuAGzLz8QnHGqHzToCBgYEPbd26tVGf2u02Lx46Ol4+b8kZjY4zl7XbbRYtWjTb3Zg1jr/u8UO9c3DxxRfvzsyhyfYt7PXgEbEI+Dbwhcz8VSfnOzIzI2JKryqZOQqMAgwNDeXw8HCjfrVaLW7ZeXi8vHdds+PMZa1Wi6bzNx84/rrHD87BZHq6eyci3kYn8O/KzO+U6lePLduUvwdK/X5gWdfTl5Y6SVKf9HL3TgCbgD2Z+dWuXduA9WV7PXB/V/3V5S6eVcChzHyl6fklSVPXy/LORcCngaci4olS97fAzcC9EbEBeAn4VNn3IHAZMAa8AXy2h3NLkhpoHPrlA9k4zu7Vk7RP4Jqm55Mk9c5v5EpSRQx9SaqIoS9JFTH0Jakihr4kVcTQl6SKGPqSVBFDX5Iq0vMPrs0Vgxu/N7699+aPzWJPJGn2eKUvSRUx9CWpIoa+JFXE0Jekihj6klQRQ1+SKmLoS1JFqrlPv5v37EuqVZWhfzJ8YZA0H1Uf+t3hLknzXfWhfzKOd9XvuwFJc42hP0XHe2dwMi8AvkhImm2G/iw5mWUlXxgkTTdDfwZM1+cEU333MNGda06fln5Imj/6HvoRsQa4DVgAfCMzb+53H+aiJi8kT+0/xGcmed7JvIM43vn8TEOa2/oa+hGxAPg68OfAPuCxiNiWmc/2sx+1O5kPpmf6XDNhYv9n+p2OL3qai/p9pX8BMJaZLwBExFZgLWDoz5KpBv3JfJA9Xefq1fHe6RxPLy+Ab8Vbf68/70jfxj9dTuadpHfT9SYys38ni7gCWJOZf1nKnwYuzMzPdbUZAUZK8T3Acw1Pdw7wix66Ox/UPgeOv+7xQ71z8IeZ+c7JdrzlPsjNzFFgtNfjRMTjmTk0DV2as2qfA8df9/jBOZhMv39wbT+wrKu8tNRJkvqg36H/GLAiIpZHxCnAlcC2PvdBkqrV1+WdzDwSEZ8DHqJzy+bmzHxmhk7X8xLRPFD7HDh+OQcT9PWDXEnS7PJ/oiJJFTH0Jaki8zL0I2JNRDwXEWMRsXG2+zMTImJzRByIiKe76s6KiO0R8Xz5e2apj4i4vczHkxFx/uz1fHpExLKIeCQino2IZyLi2lJf0xycFhE/iIj/LHPw96V+eUTsKmO9p9w0QUScWspjZf/gbPZ/ukTEgoj4UUQ8UMpVjX+q5l3od/3Uw6XASuCqiFg5u72aEXcCaybUbQR2ZOYKYEcpQ2cuVpTHCHBHn/o4k44A12fmSmAVcE3571zTHPwGuCQzPwB8EFgTEauArwC3Zua7gYPAhtJ+A3Cw1N9a2s0H1wJ7usq1jX9qMnNePYAPAw91lW8Ebpztfs3QWAeBp7vKzwHnlu1zgefK9r8CV03Wbr48gPvp/KZTlXMAvAP4IXAhnW+gLiz14/8e6Nw19+GyvbC0i9nue4/jXkrnxf0S4AEgahp/k8e8u9IHlgAvd5X3lboaDGTmK2X758BA2Z7Xc1Lepv8xsIvK5qAsbTwBHAC2Az8FXs/MI6VJ9zjH56DsPwSc3d8eT7uvAV8E/reUz6au8U/ZfAx9Adm5nJn39+NGxCLg28AXMvNX3ftqmIPMPJqZH6RzxXsB8N5Z7lLfRMTHgQOZuXu2+zKXzMfQr/mnHl6NiHMByt8DpX5ezklEvI1O4N+Vmd8p1VXNwTGZ+TrwCJ3ljMURceyLl93jHJ+Dsv8M4Jd97up0ugj4RETsBbbSWeK5jXrG38h8DP2af+phG7C+bK+ns859rP7qcgfLKuBQ1xLInBQRAWwC9mTmV7t21TQH74yIxWX77XQ+09hDJ/yvKM0mzsGxubkCeLi8G5qTMvPGzFyamYN0/p0/nJnrqGT8jc32hwoz8QAuA35CZ33zS7Pdnxka493AK8D/0Fm33EBnfXIH8DzwH8BZpW3QuaPpp8BTwNBs938axv8ndJZungSeKI/LKpuD9wM/KnPwNPB3pf5dwA+AMeBbwKml/rRSHiv73zXbY5jGuRgGHqh1/FN5+DMMklSR+bi8I0k6DkNfkipi6EtSRQx9SaqIoS9JFTH0Jakihr4kVeT/AJJvYwGvMa+UAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "details_profile_int_dr['active_days'].hist(bins=100)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "1a288b70",
      "metadata": {
        "id": "1a288b70",
        "outputId": "09278f3c-9c45-4e77-ccbf-89c300d38dcc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f4ef7e61130>"
            ]
          },
          "metadata": {},
          "execution_count": 567
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "details_profile_int_dr['age_in_days'].hist(bins=100)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "811b994a",
      "metadata": {
        "id": "811b994a",
        "outputId": "764efe2c-6091-460c-8bbc-5a8fa312aa09",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f4ef7e13dc0>"
            ]
          },
          "metadata": {},
          "execution_count": 568
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "details_profile_int_dr['booking_count'].hist(bins=100)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "2ca0b3f2",
      "metadata": {
        "id": "2ca0b3f2",
        "outputId": "81c333e0-5c07-4007-bf77-496dd32b723e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f4ef7b99be0>"
            ]
          },
          "metadata": {},
          "execution_count": 569
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "details_profile_int_dr[details_profile_int_dr['booking_count']<400]['booking_count'].hist(bins=100)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "7b9fb1b4",
      "metadata": {
        "id": "7b9fb1b4",
        "outputId": "013e38ea-45a5-4c38-eb04-c0672d418de8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f4ef7a690a0>"
            ]
          },
          "metadata": {},
          "execution_count": 570
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "details_user['profile_views'].hist(bins=100)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "38a4be86",
      "metadata": {
        "scrolled": true,
        "id": "38a4be86",
        "outputId": "a8a21618-9ca0-404a-fd51-bbf57ff5fd6b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f4ef78bc760>"
            ]
          },
          "metadata": {},
          "execution_count": 571
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "details_user[details_user['profile_views']<2500]['profile_views'].hist(bins=100)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "9995d6cb",
      "metadata": {
        "id": "9995d6cb",
        "outputId": "901badc3-f3b8-47ce-d8c5-cc86c2b51dde",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   booking_count  age_in_days  core_earning  active_days     followers  \\\n",
              "0         3107.0        367.0      138293.0        360.0   6705.000000   \n",
              "1         1743.0        353.0       31437.0        353.0   8619.000000   \n",
              "2         1624.0        271.0       18275.0        271.0  20619.957204   \n",
              "3         1585.0         59.0        1782.0         59.0   8701.845218   \n",
              "4          862.0         52.0        9050.0         52.0   1363.000000   \n",
              "\n",
              "   company tier numerical  college tier numerical  years of experience  \\\n",
              "0                     5.0                     7.0                  5.0   \n",
              "1                     5.0                     6.0                 12.0   \n",
              "2                     5.0                     6.0                  4.0   \n",
              "3                     5.0                     5.0                  3.0   \n",
              "4                     8.0                     7.0                  2.0   \n",
              "\n",
              "   # of posts/week  avg engagement per post last 30 days  \\\n",
              "0         1.166667                             13.000000   \n",
              "1         2.836936                            155.766060   \n",
              "2         2.777625                            185.883403   \n",
              "3         2.401853                             80.361750   \n",
              "4         0.000000                              0.000000   \n",
              "\n",
              "   # of Posts last 30 days  # of Comments last 30 days  \\\n",
              "0                 1.000000                    0.000000   \n",
              "1                 4.914843                    1.190502   \n",
              "2                 4.293077                    1.250093   \n",
              "3                 3.076529                    0.475918   \n",
              "4                 0.000000                    0.000000   \n",
              "\n",
              "   # of Reactions last 30 days  gender_score  # of promotional posts  \\\n",
              "0                     0.000000           1.0                1.000000   \n",
              "1                     2.629054           1.0                3.612194   \n",
              "2                     2.667245           1.0                3.415630   \n",
              "3                     1.471971           0.0                2.910151   \n",
              "4                     0.000000           0.0                0.000000   \n",
              "\n",
              "   target_score  \n",
              "0           1.0  \n",
              "1           1.0  \n",
              "2           1.0  \n",
              "3           1.0  \n",
              "4           1.0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d20e9dbb-f8ef-48e3-babb-969a2c069a73\">\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>booking_count</th>\n",
              "      <th>age_in_days</th>\n",
              "      <th>core_earning</th>\n",
              "      <th>active_days</th>\n",
              "      <th>followers</th>\n",
              "      <th>company tier numerical</th>\n",
              "      <th>college tier numerical</th>\n",
              "      <th>years of experience</th>\n",
              "      <th># of posts/week</th>\n",
              "      <th>avg engagement per post last 30 days</th>\n",
              "      <th># of Posts last 30 days</th>\n",
              "      <th># of Comments last 30 days</th>\n",
              "      <th># of Reactions last 30 days</th>\n",
              "      <th>gender_score</th>\n",
              "      <th># of promotional posts</th>\n",
              "      <th>target_score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3107.0</td>\n",
              "      <td>367.0</td>\n",
              "      <td>138293.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>6705.000000</td>\n",
              "      <td>5.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.166667</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1743.0</td>\n",
              "      <td>353.0</td>\n",
              "      <td>31437.0</td>\n",
              "      <td>353.0</td>\n",
              "      <td>8619.000000</td>\n",
              "      <td>5.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>2.836936</td>\n",
              "      <td>155.766060</td>\n",
              "      <td>4.914843</td>\n",
              "      <td>1.190502</td>\n",
              "      <td>2.629054</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.612194</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1624.0</td>\n",
              "      <td>271.0</td>\n",
              "      <td>18275.0</td>\n",
              "      <td>271.0</td>\n",
              "      <td>20619.957204</td>\n",
              "      <td>5.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.777625</td>\n",
              "      <td>185.883403</td>\n",
              "      <td>4.293077</td>\n",
              "      <td>1.250093</td>\n",
              "      <td>2.667245</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.415630</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1585.0</td>\n",
              "      <td>59.0</td>\n",
              "      <td>1782.0</td>\n",
              "      <td>59.0</td>\n",
              "      <td>8701.845218</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.401853</td>\n",
              "      <td>80.361750</td>\n",
              "      <td>3.076529</td>\n",
              "      <td>0.475918</td>\n",
              "      <td>1.471971</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.910151</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>862.0</td>\n",
              "      <td>52.0</td>\n",
              "      <td>9050.0</td>\n",
              "      <td>52.0</td>\n",
              "      <td>1363.000000</td>\n",
              "      <td>8.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d20e9dbb-f8ef-48e3-babb-969a2c069a73')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http:https://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-d20e9dbb-f8ef-48e3-babb-969a2c069a73 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-d20e9dbb-f8ef-48e3-babb-969a2c069a73');\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": 572
        }
      ],
      "source": [
        "details = details_profile_int_dr\n",
        "details.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a0d1d84e",
      "metadata": {
        "scrolled": true,
        "id": "a0d1d84e",
        "outputId": "1ae7ba48-4f7f-4cb9-fdfd-e9c4fc27a402",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 362
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       booking_count  age_in_days   core_earning  active_days      followers  \\\n",
              "count    2478.000000  2478.000000    2478.000000  2478.000000    2478.000000   \n",
              "mean       14.823245    50.011299    2627.464891    48.302260    9617.237715   \n",
              "std        91.945523    85.964183   16697.330980    84.562023   21608.838633   \n",
              "min         1.000000     0.000000       0.000000     1.000000       0.000000   \n",
              "25%         1.000000     0.000000       0.000000     1.000000    2202.000000   \n",
              "50%         2.000000     5.000000       0.000000     3.000000    6951.258954   \n",
              "75%         9.000000    64.000000     499.750000    59.000000    9045.637449   \n",
              "max      3107.000000   475.000000  503395.000000   473.000000  504239.000000   \n",
              "\n",
              "       company tier numerical  college tier numerical  years of experience  \\\n",
              "count             2478.000000             2478.000000          2478.000000   \n",
              "mean                 8.166190                7.908506             5.051163   \n",
              "std                  1.129338                0.973191             3.486106   \n",
              "min                  1.000000                1.000000             0.000000   \n",
              "25%                  8.000000                7.000000             3.000000   \n",
              "50%                  8.000000                7.959158             5.017584   \n",
              "75%                  8.315324                8.000000             5.212004   \n",
              "max                 10.000000               10.000000            35.000000   \n",
              "\n",
              "       # of posts/week  avg engagement per post last 30 days  \\\n",
              "count      2478.000000                           2478.000000   \n",
              "mean          1.202675                             87.780304   \n",
              "std           1.985341                            318.326234   \n",
              "min           0.000000                              0.000000   \n",
              "25%           0.000000                              0.000000   \n",
              "50%           0.875000                             38.250000   \n",
              "75%           1.193657                             68.869590   \n",
              "max          28.000000                           9876.000000   \n",
              "\n",
              "       # of Posts last 30 days  # of Comments last 30 days  \\\n",
              "count              2478.000000                 2478.000000   \n",
              "mean                  3.015248                    1.169857   \n",
              "std                   5.466908                    2.345759   \n",
              "min                   0.000000                    0.000000   \n",
              "25%                   0.000000                    0.000000   \n",
              "50%                   2.000000                    0.894590   \n",
              "75%                   3.030426                    1.014109   \n",
              "max                  65.000000                   36.000000   \n",
              "\n",
              "       # of Reactions last 30 days  gender_score  # of promotional posts  \\\n",
              "count                  2478.000000   2478.000000             2478.000000   \n",
              "mean                      1.037351      0.804681                1.019096   \n",
              "std                       2.476011      0.396526                2.247521   \n",
              "min                       0.000000      0.000000                0.000000   \n",
              "25%                       0.000000      1.000000                0.000000   \n",
              "50%                       0.000000      1.000000                0.679582   \n",
              "75%                       1.000000      1.000000                1.000000   \n",
              "max                      35.000000      1.000000               35.000000   \n",
              "\n",
              "       target_score  \n",
              "count   2478.000000  \n",
              "mean       0.718867  \n",
              "std        0.246194  \n",
              "min        0.500000  \n",
              "25%        0.500000  \n",
              "50%        0.500000  \n",
              "75%        1.000000  \n",
              "max        1.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4bb0a7f4-9676-4d7a-b837-57fde0998b72\">\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>booking_count</th>\n",
              "      <th>age_in_days</th>\n",
              "      <th>core_earning</th>\n",
              "      <th>active_days</th>\n",
              "      <th>followers</th>\n",
              "      <th>company tier numerical</th>\n",
              "      <th>college tier numerical</th>\n",
              "      <th>years of experience</th>\n",
              "      <th># of posts/week</th>\n",
              "      <th>avg engagement per post last 30 days</th>\n",
              "      <th># of Posts last 30 days</th>\n",
              "      <th># of Comments last 30 days</th>\n",
              "      <th># of Reactions last 30 days</th>\n",
              "      <th>gender_score</th>\n",
              "      <th># of promotional posts</th>\n",
              "      <th>target_score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>2478.000000</td>\n",
              "      <td>2478.000000</td>\n",
              "      <td>2478.000000</td>\n",
              "      <td>2478.000000</td>\n",
              "      <td>2478.000000</td>\n",
              "      <td>2478.000000</td>\n",
              "      <td>2478.000000</td>\n",
              "      <td>2478.000000</td>\n",
              "      <td>2478.000000</td>\n",
              "      <td>2478.000000</td>\n",
              "      <td>2478.000000</td>\n",
              "      <td>2478.000000</td>\n",
              "      <td>2478.000000</td>\n",
              "      <td>2478.000000</td>\n",
              "      <td>2478.000000</td>\n",
              "      <td>2478.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>14.823245</td>\n",
              "      <td>50.011299</td>\n",
              "      <td>2627.464891</td>\n",
              "      <td>48.302260</td>\n",
              "      <td>9617.237715</td>\n",
              "      <td>8.166190</td>\n",
              "      <td>7.908506</td>\n",
              "      <td>5.051163</td>\n",
              "      <td>1.202675</td>\n",
              "      <td>87.780304</td>\n",
              "      <td>3.015248</td>\n",
              "      <td>1.169857</td>\n",
              "      <td>1.037351</td>\n",
              "      <td>0.804681</td>\n",
              "      <td>1.019096</td>\n",
              "      <td>0.718867</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>91.945523</td>\n",
              "      <td>85.964183</td>\n",
              "      <td>16697.330980</td>\n",
              "      <td>84.562023</td>\n",
              "      <td>21608.838633</td>\n",
              "      <td>1.129338</td>\n",
              "      <td>0.973191</td>\n",
              "      <td>3.486106</td>\n",
              "      <td>1.985341</td>\n",
              "      <td>318.326234</td>\n",
              "      <td>5.466908</td>\n",
              "      <td>2.345759</td>\n",
              "      <td>2.476011</td>\n",
              "      <td>0.396526</td>\n",
              "      <td>2.247521</td>\n",
              "      <td>0.246194</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2202.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>6951.258954</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>7.959158</td>\n",
              "      <td>5.017584</td>\n",
              "      <td>0.875000</td>\n",
              "      <td>38.250000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.894590</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.679582</td>\n",
              "      <td>0.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>9.000000</td>\n",
              "      <td>64.000000</td>\n",
              "      <td>499.750000</td>\n",
              "      <td>59.000000</td>\n",
              "      <td>9045.637449</td>\n",
              "      <td>8.315324</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>5.212004</td>\n",
              "      <td>1.193657</td>\n",
              "      <td>68.869590</td>\n",
              "      <td>3.030426</td>\n",
              "      <td>1.014109</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>3107.000000</td>\n",
              "      <td>475.000000</td>\n",
              "      <td>503395.000000</td>\n",
              "      <td>473.000000</td>\n",
              "      <td>504239.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>35.000000</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>9876.000000</td>\n",
              "      <td>65.000000</td>\n",
              "      <td>36.000000</td>\n",
              "      <td>35.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>35.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4bb0a7f4-9676-4d7a-b837-57fde0998b72')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http:https://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",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-4bb0a7f4-9676-4d7a-b837-57fde0998b72 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-4bb0a7f4-9676-4d7a-b837-57fde0998b72');\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": 573
        }
      ],
      "source": [
        "details_profile_int_dr.describe()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(15,15))\n",
        "sns.heatmap(details_profile_int_dr.corr(),annot=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "K_vxrjyVD8BB",
        "outputId": "f243768d-903c-46ed-a2b7-933b160ccf90"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f4ef78904f0>"
            ]
          },
          "metadata": {},
          "execution_count": 574
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x1080 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "id": "K_vxrjyVD8BB"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "8cb35dc9",
      "metadata": {
        "id": "8cb35dc9",
        "outputId": "29d8c216-8742-448d-db1c-4dc5bfd55564",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "sns.countplot(x='gender_score',data=details_profile_int_dr)\n",
        "plt.grid()\n",
        "#Male:Female~4:1\n",
        "#Highlights Gender Imbalance in the dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "5a944b24",
      "metadata": {
        "scrolled": true,
        "id": "5a944b24",
        "outputId": "57bd33da-8b43-4d2c-9aa7-094227def7ad",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "#sns.displot(x='booking_count',data=details_profile_int_dr)\n",
        "plt.hist(details_profile_int_dr['booking_count'],bins=50)\n",
        "plt.grid()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e57addae",
      "metadata": {
        "id": "e57addae",
        "outputId": "4f37ffb5-02ae-49a8-dee3-6c09992fd2d3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "#sns.displot(x='booking_count',data=details_profile_int_dr)\n",
        "plt.hist(details_profile_int_dr['booking_count'],bins=50)\n",
        "plt.grid()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c430bbd3",
      "metadata": {
        "id": "c430bbd3",
        "outputId": "6c0c58b2-8e70-4131-aa15-94b9e5d49c1b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      6705.000000\n",
              "1      8619.000000\n",
              "2     20619.957204\n",
              "3      8701.845218\n",
              "4      1363.000000\n",
              "5    106015.000000\n",
              "6     18745.000000\n",
              "7      2552.000000\n",
              "8      3369.000000\n",
              "9        52.000000\n",
              "Name: followers, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 578
        }
      ],
      "source": [
        "details_profile_int_dr['followers'].head(10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "87e2c647",
      "metadata": {
        "id": "87e2c647",
        "outputId": "b0439955-873d-48db-b7af-5206aea1f83b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 2478 entries, 0 to 2477\n",
            "Data columns (total 16 columns):\n",
            " #   Column                                Non-Null Count  Dtype  \n",
            "---  ------                                --------------  -----  \n",
            " 0   booking_count                         2478 non-null   float64\n",
            " 1   age_in_days                           2478 non-null   float64\n",
            " 2   core_earning                          2478 non-null   float64\n",
            " 3   active_days                           2478 non-null   float64\n",
            " 4   followers                             2478 non-null   float64\n",
            " 5   company tier numerical                2478 non-null   float64\n",
            " 6   college tier numerical                2478 non-null   float64\n",
            " 7   years of experience                   2478 non-null   float64\n",
            " 8   # of posts/week                       2478 non-null   float64\n",
            " 9   avg engagement per post last 30 days  2478 non-null   float64\n",
            " 10  # of Posts last 30 days               2478 non-null   float64\n",
            " 11  # of Comments last 30 days            2478 non-null   float64\n",
            " 12  # of Reactions last 30 days           2478 non-null   float64\n",
            " 13  gender_score                          2478 non-null   float64\n",
            " 14  # of promotional posts                2478 non-null   float64\n",
            " 15  target_score                          2478 non-null   float64\n",
            "dtypes: float64(16)\n",
            "memory usage: 309.9 KB\n"
          ]
        }
      ],
      "source": [
        "details_profile_int_dr.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "bd6cb1ec",
      "metadata": {
        "scrolled": true,
        "id": "bd6cb1ec",
        "outputId": "19282123-8cad-46f2-f872-ee6e3e2dd713",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dtype('float64')"
            ]
          },
          "metadata": {},
          "execution_count": 580
        }
      ],
      "source": [
        "#details_profile_int_dr['followers'] = details_profile_int_dr['followers'].astype('int64')\n",
        "#details_profile_int_dr['company tier numerical'] = details_profile_int_dr['company tier numerical'].astype('int64')\n",
        "#details_profile_int_dr['college tier numerical'] = details_profile_int_dr['college tier numerical'].astype('int64')\n",
        "#details_profile_int_dr['company tier numerical']=details_profile_int_dr['company_tier_numerical'].astype('int64')\n",
        "details_profile_int_dr['company tier numerical'].dtypes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "6227564b",
      "metadata": {
        "id": "6227564b",
        "outputId": "a0e18a36-f702-40d9-c521-690dfe377c07",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "#plt.hist(details_profile_int_dr['followers'],bins=10)\n",
        "sns.displot(details_profile_int_dr,x='followers',binwidth=56000,bins=10)\n",
        "plt.grid()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d3014eff",
      "metadata": {
        "id": "d3014eff",
        "outputId": "52f6b44d-bb2d-4173-ffd3-8f9a3ffd350d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['booking_count', 'age_in_days', 'core_earning', 'active_days',\n",
              "       'followers', 'company tier numerical', 'college tier numerical',\n",
              "       'years of experience', '# of posts/week',\n",
              "       'avg engagement per post last 30 days', '# of Posts last 30 days',\n",
              "       '# of Comments last 30 days', '# of Reactions last 30 days',\n",
              "       'gender_score', '# of promotional posts', 'target_score'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 582
        }
      ],
      "source": [
        "details_profile_int_dr.columns"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "0bddab47",
      "metadata": {
        "id": "0bddab47"
      },
      "source": [
        "sns.countplot(x='company tier numerical',data=details_profile_int_dr,binwidth=56000,bins=10)\n",
        "plt.grid()"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "5Ugm72H9eACA"
      },
      "id": "5Ugm72H9eACA",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "dedaf07a",
      "metadata": {
        "id": "dedaf07a",
        "outputId": "eda10b7a-d750-4032-97b6-e92fa934a652",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f4ef9097b20>"
            ]
          },
          "metadata": {},
          "execution_count": 583
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "details_profile_int_dr['target_score'].hist()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###here we have passed the target through a function to range bound the target\n",
        "ideally if i combine this with username i should atleast be able to see if categorisation of 0/1 is correct for target"
      ],
      "metadata": {
        "id": "z7Y6YuN_edwz"
      },
      "id": "z7Y6YuN_edwz"
    },
    {
      "cell_type": "code",
      "source": [
        "details_profile_int_dr.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nWlBvFf6eBIg",
        "outputId": "c9dce779-0188-44b0-bca6-bde7d26521a3"
      },
      "id": "nWlBvFf6eBIg",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['booking_count', 'age_in_days', 'core_earning', 'active_days',\n",
              "       'followers', 'company tier numerical', 'college tier numerical',\n",
              "       'years of experience', '# of posts/week',\n",
              "       'avg engagement per post last 30 days', '# of Posts last 30 days',\n",
              "       '# of Comments last 30 days', '# of Reactions last 30 days',\n",
              "       'gender_score', '# of promotional posts', 'target_score'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 584
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "details_profile_int_dr.to_csv('ranged_target.csv')"
      ],
      "metadata": {
        "id": "E1kHj5mbgfnE"
      },
      "id": "E1kHj5mbgfnE",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WHtp3nNcgHrk"
      },
      "outputs": [],
      "source": [
        "# details_user = details_user.dropna()\n",
        "# #len(details_profile_int_dr)"
      ],
      "id": "WHtp3nNcgHrk"
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# t = pd.DataFrame(details_profile_int_dr['target_score'],columns=['ranged_target'])\n",
        "# rt = pd.concat([details_user,t],axis=1,join='inner')\n",
        "# rt.to_csv('ranged_target.csv')"
      ],
      "metadata": {
        "id": "7zJrfZ1Ae-bm"
      },
      "id": "7zJrfZ1Ae-bm",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "470b51ad",
      "metadata": {
        "id": "470b51ad",
        "outputId": "b7249e1d-6095-4455-9cbb-9dd1dfcd7a8d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f4ef7730f10>"
            ]
          },
          "metadata": {},
          "execution_count": 587
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "def targ_to_bin(x):\n",
        "    t = 0.85;\n",
        "    if(x<t):\n",
        "        return 0;\n",
        "    else:\n",
        "        return 1;\n",
        "\n",
        "# details_profile_int_dr[details_profile_int_dr['target_score']>=0.4]['target_score']=1\n",
        "# details_profile_int_dr[details_profile_int_dr['target_score']<0.4]['target_score']=0\n",
        "details_profile_int_dr['target_score'] = details_profile_int_dr['target_score'].apply(targ_to_bin)\n",
        "details_profile_int_dr['target_score'].hist()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "ab3dd110",
      "metadata": {
        "scrolled": true,
        "id": "ab3dd110"
      },
      "outputs": [],
      "source": [
        "#sns.pairplot(details_profile_int_dr, hue='target_score')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "eedc09ba",
      "metadata": {
        "id": "eedc09ba",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "962bf7cb-46d8-4c41-810e-2f473daf9029"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['booking_count', 'age_in_days', 'core_earning', 'active_days',\n",
              "       'followers', 'company tier numerical', 'college tier numerical',\n",
              "       'years of experience', '# of posts/week',\n",
              "       'avg engagement per post last 30 days', '# of Posts last 30 days',\n",
              "       '# of Comments last 30 days', '# of Reactions last 30 days',\n",
              "       'gender_score', '# of promotional posts', 'target_score'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ],
      "source": [
        "#details_profile_int_dr.to_csv('Actual.csv',index=True)\n",
        "details_profile_int_dr.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "7c2dbd48",
      "metadata": {
        "id": "7c2dbd48",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 468
        },
        "outputId": "634fda15-0ffa-4f0a-8f89-005e4857367b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         followers  company tier numerical  college tier numerical  \\\n",
              "0      6705.000000                5.000000                7.000000   \n",
              "1      8619.000000                5.000000                6.000000   \n",
              "2     20619.957204                5.000000                6.000000   \n",
              "3      8701.845218                5.000000                5.000000   \n",
              "4      1363.000000                8.000000                7.000000   \n",
              "...            ...                     ...                     ...   \n",
              "2473   6969.093359                8.118411                7.959558   \n",
              "2474   6969.093359                8.118411                7.959558   \n",
              "2475   6969.093359                8.118411                7.959558   \n",
              "2476   6969.093359                8.118411                7.959558   \n",
              "2477   3264.000000                9.000000               10.000000   \n",
              "\n",
              "      years of experience  # of posts/week  \\\n",
              "0                5.000000         1.166667   \n",
              "1               12.000000         2.836936   \n",
              "2                4.000000         2.777625   \n",
              "3                3.000000         2.401853   \n",
              "4                2.000000         0.000000   \n",
              "...                   ...              ...   \n",
              "2473             5.063251         1.137857   \n",
              "2474             5.063251         1.137857   \n",
              "2475             5.063251         1.137857   \n",
              "2476             5.063251         1.137857   \n",
              "2477             5.000000         2.456140   \n",
              "\n",
              "      avg engagement per post last 30 days  # of Posts last 30 days  \\\n",
              "0                                13.000000                 1.000000   \n",
              "1                               155.766060                 4.914843   \n",
              "2                               185.883403                 4.293077   \n",
              "3                                80.361750                 3.076529   \n",
              "4                                 0.000000                 0.000000   \n",
              "...                                    ...                      ...   \n",
              "2473                             67.251734                 3.020917   \n",
              "2474                             67.251734                 3.020917   \n",
              "2475                             67.251734                 3.020917   \n",
              "2476                             67.251734                 3.020917   \n",
              "2477                             43.550000                17.000000   \n",
              "\n",
              "      # of Comments last 30 days  # of Reactions last 30 days  gender_score  \\\n",
              "0                       0.000000                     0.000000           1.0   \n",
              "1                       1.190502                     2.629054           1.0   \n",
              "2                       1.250093                     2.667245           1.0   \n",
              "3                       0.475918                     1.471971           0.0   \n",
              "4                       0.000000                     0.000000           0.0   \n",
              "...                          ...                          ...           ...   \n",
              "2473                    1.000935                     0.776265           1.0   \n",
              "2474                    1.000935                     0.776265           1.0   \n",
              "2475                    1.000935                     0.776265           1.0   \n",
              "2476                    1.000935                     0.776265           1.0   \n",
              "2477                    9.000000                     8.000000           1.0   \n",
              "\n",
              "      # of promotional posts  target_score  \n",
              "0                   1.000000             1  \n",
              "1                   3.612194             1  \n",
              "2                   3.415630             1  \n",
              "3                   2.910151             1  \n",
              "4                   0.000000             1  \n",
              "...                      ...           ...  \n",
              "2473                0.958230             1  \n",
              "2474                0.958230             1  \n",
              "2475                0.958230             1  \n",
              "2476                0.958230             1  \n",
              "2477               15.000000             1  \n",
              "\n",
              "[2478 rows x 12 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-869dca25-b336-4ec0-a6df-ec7ab85cddfc\">\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>followers</th>\n",
              "      <th>company tier numerical</th>\n",
              "      <th>college tier numerical</th>\n",
              "      <th>years of experience</th>\n",
              "      <th># of posts/week</th>\n",
              "      <th>avg engagement per post last 30 days</th>\n",
              "      <th># of Posts last 30 days</th>\n",
              "      <th># of Comments last 30 days</th>\n",
              "      <th># of Reactions last 30 days</th>\n",
              "      <th>gender_score</th>\n",
              "      <th># of promotional posts</th>\n",
              "      <th>target_score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>6705.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>1.166667</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>8619.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>2.836936</td>\n",
              "      <td>155.766060</td>\n",
              "      <td>4.914843</td>\n",
              "      <td>1.190502</td>\n",
              "      <td>2.629054</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.612194</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>20619.957204</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>2.777625</td>\n",
              "      <td>185.883403</td>\n",
              "      <td>4.293077</td>\n",
              "      <td>1.250093</td>\n",
              "      <td>2.667245</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.415630</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>8701.845218</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.401853</td>\n",
              "      <td>80.361750</td>\n",
              "      <td>3.076529</td>\n",
              "      <td>0.475918</td>\n",
              "      <td>1.471971</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.910151</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1363.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1</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",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2473</th>\n",
              "      <td>6969.093359</td>\n",
              "      <td>8.118411</td>\n",
              "      <td>7.959558</td>\n",
              "      <td>5.063251</td>\n",
              "      <td>1.137857</td>\n",
              "      <td>67.251734</td>\n",
              "      <td>3.020917</td>\n",
              "      <td>1.000935</td>\n",
              "      <td>0.776265</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.958230</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2474</th>\n",
              "      <td>6969.093359</td>\n",
              "      <td>8.118411</td>\n",
              "      <td>7.959558</td>\n",
              "      <td>5.063251</td>\n",
              "      <td>1.137857</td>\n",
              "      <td>67.251734</td>\n",
              "      <td>3.020917</td>\n",
              "      <td>1.000935</td>\n",
              "      <td>0.776265</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.958230</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2475</th>\n",
              "      <td>6969.093359</td>\n",
              "      <td>8.118411</td>\n",
              "      <td>7.959558</td>\n",
              "      <td>5.063251</td>\n",
              "      <td>1.137857</td>\n",
              "      <td>67.251734</td>\n",
              "      <td>3.020917</td>\n",
              "      <td>1.000935</td>\n",
              "      <td>0.776265</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.958230</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2476</th>\n",
              "      <td>6969.093359</td>\n",
              "      <td>8.118411</td>\n",
              "      <td>7.959558</td>\n",
              "      <td>5.063251</td>\n",
              "      <td>1.137857</td>\n",
              "      <td>67.251734</td>\n",
              "      <td>3.020917</td>\n",
              "      <td>1.000935</td>\n",
              "      <td>0.776265</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.958230</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2477</th>\n",
              "      <td>3264.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>2.456140</td>\n",
              "      <td>43.550000</td>\n",
              "      <td>17.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2478 rows × 12 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-869dca25-b336-4ec0-a6df-ec7ab85cddfc')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http:https://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\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",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\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",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-869dca25-b336-4ec0-a6df-ec7ab85cddfc 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-869dca25-b336-4ec0-a6df-ec7ab85cddfc');\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",
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              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ],
      "source": [
        "details_profile_int_dr=details_profile_int_dr[['followers', 'company tier numerical', 'college tier numerical','years of experience', '# of posts/week','avg engagement per post last 30 days', '# of Posts last 30 days','# of Comments last 30 days', '# of Reactions last 30 days',\n",
        "       'gender_score', '# of promotional posts','target_score', ]]\n",
        "details_profile_int_dr   "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "7eec0f70",
      "metadata": {
        "scrolled": true,
        "id": "7eec0f70"
      },
      "outputs": [],
      "source": [
        "# #sns.displot(x='booking_count',data=details_profile_int_dr)\n",
        "# plt.hist(details_profile_int_dr['booking_count'],bins=50)\n",
        "# plt.grid()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "63db395f",
      "metadata": {
        "id": "63db395f",
        "outputId": "57bf4a31-748d-437e-8afe-930c35344bdc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['followers', 'company tier numerical', 'college tier numerical',\n",
              "       'years of experience', '# of posts/week',\n",
              "       'avg engagement per post last 30 days', '# of Posts last 30 days',\n",
              "       '# of Comments last 30 days', '# of Reactions last 30 days',\n",
              "       'gender_score', '# of promotional posts', 'target_score'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 79
        }
      ],
      "source": [
        "details_profile_int_dr.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "ccde9172",
      "metadata": {
        "scrolled": true,
        "id": "ccde9172",
        "outputId": "60308f24-4d16-4608-ea80-645221abb3aa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-80-2857bd4240f7>: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",
            "  details_profile_int_dr['followers'] = details_profile_int_dr['followers'].astype('int64')\n"
          ]
        }
      ],
      "source": [
        "details_profile_int_dr['followers'] = details_profile_int_dr['followers'].astype('int64')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c31a7c8f",
      "metadata": {
        "id": "c31a7c8f"
      },
      "outputs": [],
      "source": [
        "#plt.figure(figsize=(12,12))\n",
        "#i = 1;\n",
        "#for ind in details_profile_int_dr.columns:\n",
        "#    plt.subplot(4,4,i)\n",
        "#    sns.scatterplot(x=ind,y='target_score',data=details_profile_int_dr)\n",
        "#    plt.grid()\n",
        "#    i=i+1;"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "7be5de36",
      "metadata": {
        "id": "7be5de36"
      },
      "source": [
        "# Feature Elimination"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "ca43f875",
      "metadata": {
        "id": "ca43f875"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "1b9533b3",
      "metadata": {
        "id": "1b9533b3"
      },
      "outputs": [],
      "source": [
        "X = details_profile_int_dr.drop(['target_score'],axis=1)\n",
        "y = details_profile_int_dr['target_score']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "fda97874",
      "metadata": {
        "id": "fda97874"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "id": "b9654474",
      "metadata": {
        "id": "b9654474"
      },
      "source": [
        "# Train_Test_Split"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "54b92cc0",
      "metadata": {
        "id": "54b92cc0",
        "outputId": "e0b4c359-dcc0-4853-8079-9cc5031d4e29",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.8/dist-packages (1.0.2)\n",
            "Requirement already satisfied: numpy>=1.14.6 in /usr/local/lib/python3.8/dist-packages (from scikit-learn) (1.21.6)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.8/dist-packages (from scikit-learn) (1.2.0)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.8/dist-packages (from scikit-learn) (3.1.0)\n",
            "Requirement already satisfied: scipy>=1.1.0 in /usr/local/lib/python3.8/dist-packages (from scikit-learn) (1.7.3)\n"
          ]
        }
      ],
      "source": [
        "#!python.exe -m pip install --upgrade pip\n",
        "#!pip install sklearn\n",
        "!pip install scikit-learn"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "833238a5",
      "metadata": {
        "id": "833238a5"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "Xtr,Xte,ytrain,ytest=train_test_split(X,y,test_size=0.3)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Data Scaling and Transformation"
      ],
      "metadata": {
        "id": "y58EWshJ2sAY"
      },
      "id": "y58EWshJ2sAY"
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "scaler = MinMaxScaler()\n",
        "Xtrain= scaler.fit_transform(Xtr)\n",
        "Xtest = scaler.transform(Xte)"
      ],
      "metadata": {
        "id": "Uyepui5525iP"
      },
      "id": "Uyepui5525iP",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "id": "de633688",
      "metadata": {
        "id": "de633688"
      },
      "source": [
        "# Model Building "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "8c4c7bf8",
      "metadata": {
        "id": "8c4c7bf8"
      },
      "outputs": [],
      "source": [
        "#Importing Models for Majority Vote\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.naive_bayes import MultinomialNB, GaussianNB, BernoulliNB\n",
        "from sklearn.svm import LinearSVC\n",
        "from sklearn.ensemble import RandomForestClassifier"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e1f76249",
      "metadata": {
        "id": "e1f76249"
      },
      "outputs": [],
      "source": [
        "logreg=LogisticRegression(penalty=\"l2\")\n",
        "MultiNB=MultinomialNB()\n",
        "GaussNB=GaussianNB()\n",
        "BernNB=BernoulliNB()\n",
        "svc_clf=LinearSVC()\n",
        "rfc=RandomForestClassifier(n_estimators=100,max_depth=1000)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "69dd13fb",
      "metadata": {
        "scrolled": true,
        "id": "69dd13fb",
        "outputId": "def37121-cc35-4536-fdfd-734b10bb5aff",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 1734 entries, 2184 to 1110\n",
            "Data columns (total 11 columns):\n",
            " #   Column                                Non-Null Count  Dtype  \n",
            "---  ------                                --------------  -----  \n",
            " 0   followers                             1734 non-null   int64  \n",
            " 1   company tier numerical                1734 non-null   float64\n",
            " 2   college tier numerical                1734 non-null   float64\n",
            " 3   years of experience                   1734 non-null   float64\n",
            " 4   # of posts/week                       1734 non-null   float64\n",
            " 5   avg engagement per post last 30 days  1734 non-null   float64\n",
            " 6   # of Posts last 30 days               1734 non-null   float64\n",
            " 7   # of Comments last 30 days            1734 non-null   float64\n",
            " 8   # of Reactions last 30 days           1734 non-null   float64\n",
            " 9   gender_score                          1734 non-null   float64\n",
            " 10  # of promotional posts                1734 non-null   float64\n",
            "dtypes: float64(10), int64(1)\n",
            "memory usage: 162.6 KB\n"
          ]
        }
      ],
      "source": [
        "Xtr.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f721810a",
      "metadata": {
        "id": "f721810a"
      },
      "source": [
        "# Model Fitting"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "fc4a59e9",
      "metadata": {
        "id": "fc4a59e9",
        "outputId": "42a36cfa-65ba-444f-e85f-78ab1763c68e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "followers                               0\n",
              "company tier numerical                  0\n",
              "college tier numerical                  0\n",
              "years of experience                     0\n",
              "# of posts/week                         0\n",
              "avg engagement per post last 30 days    0\n",
              "# of Posts last 30 days                 0\n",
              "# of Comments last 30 days              0\n",
              "# of Reactions last 30 days             0\n",
              "gender_score                            0\n",
              "# of promotional posts                  0\n",
              "target_score                            0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 89
        }
      ],
      "source": [
        "details_profile_int_dr.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "details_profile_int_dr.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m2xEepw88HgB",
        "outputId": "8900823e-d055-4f3f-bd85-b704757f0c27"
      },
      "id": "m2xEepw88HgB",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['followers', 'company tier numerical', 'college tier numerical',\n",
              "       'years of experience', '# of posts/week',\n",
              "       'avg engagement per post last 30 days', '# of Posts last 30 days',\n",
              "       '# of Comments last 30 days', '# of Reactions last 30 days',\n",
              "       'gender_score', '# of promotional posts', 'target_score'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "89e487f4",
      "metadata": {
        "scrolled": true,
        "id": "89e487f4",
        "outputId": "6c27dd32-f9cd-4836-9033-be9a7489be32",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 304
        }
      },
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-91-9199865d6947>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mlogreg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mXtrain\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mytrain\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0myhat_logreg\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlogreg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mXtest\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m   1552\u001b[0m         \u001b[0mclasses_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclasses_\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1553\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mn_classes\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1554\u001b[0;31m             raise ValueError(\n\u001b[0m\u001b[1;32m   1555\u001b[0m                 \u001b[0;34m\"This solver needs samples of at least 2 classes\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1556\u001b[0m                 \u001b[0;34m\" in the data, but the data contains only one\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 1"
          ]
        }
      ],
      "source": [
        "logreg.fit(Xtrain,ytrain)\n",
        "yhat_logreg=logreg.predict(Xtest)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "1150d243",
      "metadata": {
        "id": "1150d243"
      },
      "outputs": [],
      "source": [
        "MultiNB.fit(Xtrain,ytrain)\n",
        "yhat_MultiNB=MultiNB.predict(Xtest)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "497d7867",
      "metadata": {
        "id": "497d7867"
      },
      "outputs": [],
      "source": [
        "GaussNB.fit(Xtrain,ytrain)\n",
        "yhat_GaussNB=GaussNB.predict(Xtest)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "9b3edb85",
      "metadata": {
        "id": "9b3edb85"
      },
      "outputs": [],
      "source": [
        "BernNB.fit(Xtrain,ytrain)\n",
        "yhat_BernNB=BernNB.predict(Xtest)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f59c16e8",
      "metadata": {
        "id": "f59c16e8"
      },
      "outputs": [],
      "source": [
        "svc_clf.fit(Xtrain,ytrain)\n",
        "yhat_svc=svc_clf.predict(Xtest)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "2ccda3a8",
      "metadata": {
        "id": "2ccda3a8"
      },
      "outputs": [],
      "source": [
        "rfc.fit(Xtrain,ytrain)\n",
        "yhat_rfc=rfc.predict(Xtest)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4e64ca20",
      "metadata": {
        "id": "4e64ca20"
      },
      "source": [
        "# Model Evaluation and Testing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "cfa65c8b",
      "metadata": {
        "id": "cfa65c8b"
      },
      "outputs": [],
      "source": [
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "#Logistic Regression \n",
        "print(\"Logistic Regression\")\n",
        "print(classification_report(ytest,yhat_logreg))\n",
        "\n",
        "##Multinomial Naive Bayes\n",
        "print(\"Multinomial Naive Bayes\")\n",
        "print(classification_report(ytest,yhat_MultiNB))\n",
        "\n",
        "#Gaussian Naive Bayes\n",
        "print(\"Gaussian Naive Bayes\")\n",
        "print(classification_report(ytest,yhat_GaussNB))\n",
        "#Gausaian Naive Bayes Model displays poor recall on the data and results in only ~50-55% accuracy overall.\n",
        "#f1 score suggests overall performance\n",
        "\n",
        "#Bernoulli Naive Bayes\n",
        "print(\"Bernoulli Naive Bayes\")\n",
        "print(classification_report(ytest,yhat_BernNB))\n",
        "#Despite simplicity of Bernoulli trials, it shows a good performance overall\n",
        "\n",
        "#Support Vector Machine\n",
        "print(\"Support Vector Machine\")\n",
        "print(classification_report(ytest,yhat_svc))\n",
        "\n",
        "#Random Forest Classifier\n",
        "print(\"Random Forest Classifier\")\n",
        "print(classification_report(ytest,yhat_rfc))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "736827a3",
      "metadata": {
        "id": "736827a3"
      },
      "source": [
        "# Ensemble Model- Majority Voting Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f265c067",
      "metadata": {
        "id": "f265c067"
      },
      "outputs": [],
      "source": [
        "#Majority Vote Class\n",
        "from scipy.stats import mode\n",
        "class MajorityVote:\n",
        "    def __init__(self,*classifiers):\n",
        "        self.classifier=classifiers;\n",
        "\n",
        "    def predict(self,sample,confidence=False):\n",
        "        vote=[];\n",
        "        for clf in self.classifier:\n",
        "            plabel=clf.predict(sample)\n",
        "            vote.append(plabel)\n",
        "        if(confidence):\n",
        "           maj_vote_count=mode(vote)[1];            \n",
        "           conf=(maj_vote_count/len(vote))*100;\n",
        "           return mode(vote)[0], conf        \n",
        "        else:\n",
        "            return mode(vote)[0]\n",
        "vote_combined_clf=MajorityVote(logreg,MultiNB,BernNB,svc_clf,rfc);"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "2a9c0493",
      "metadata": {
        "scrolled": true,
        "id": "2a9c0493"
      },
      "outputs": [],
      "source": [
        "yhat_mvote= (vote_combined_clf.predict(Xtest)).T\n",
        "print(yhat_mvote[5:10])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "8a1c622f",
      "metadata": {
        "scrolled": true,
        "id": "8a1c622f"
      },
      "outputs": [],
      "source": [
        "print(classification_report(ytest,yhat_mvote))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "7c7e5a41",
      "metadata": {
        "id": "7c7e5a41"
      },
      "outputs": [],
      "source": [
        "new_file='test_v2.csv';\n",
        "details_user_t=pd.read_csv(new_file);\n",
        "#det_cols=['booking_count', 'age_in_days','core_earning','active_days', 'followers', 'company tier numerical', 'college tier numerical', 'years of experience', '# of posts/week', 'avg engagement per post last 30 days', '# of Posts last 30 days', '# of Comments last 30 days', '# of Reactions last 30 days', 'gender_score','# of promotional posts']\n",
        "#cols_det=['followers', 'years of experience', '# of posts/week', 'avg engagement per post last 30 days', '# of Posts last 30 days', '# of Comments last 30 days', '# of Reactions last 30 days']\n",
        "cols_det=['followers', 'company tier numerical', 'college tier numerical','years of experience', '# of posts/week',\n",
        "       'avg engagement per post last 30 days', '# of Posts last 30 days',\n",
        "       '# of Comments last 30 days', '# of Reactions last 30 days',\n",
        "       'gender_score', '# of promotional posts']\n",
        "details_profile_int_dr_t=details_user_t[cols_det]\n",
        "details_profile_int_dr_t.dropna(inplace=True)\n",
        "#pd=details_user[det_cols]#profile details\n",
        "X = details_profile_int_dr_t\n",
        "X_scaled = scaler.transform(X)\n",
        "#y = details_profile_int_dr_t['target_score']\n",
        "y_pred = vote_combined_clf.predict(X_scaled)\n",
        "y = pd.DataFrame(y_pred.T,columns=['Estimator'])\n",
        "df = pd.concat([details_user_t,y],axis=1,join='inner')\n",
        "df.to_csv('tcomb_iterative_impute_normal_dist_cdf.csv')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pwd"
      ],
      "metadata": {
        "id": "g6zTBRMW8865"
      },
      "id": "g6zTBRMW8865",
      "execution_count": null,
      "outputs": []
    }
  ],
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    "kernelspec": {
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      "file_extension": ".py",
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      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
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    },
    "colab": {
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