Copy_of_best_results.ipynb

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    "colab": {
      "name": "Copy of best_results.ipynb",
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      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/annyfan/550/blob/master/Copy_of_best_results.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UEBZVPWyWxPi"
      },
      "source": [
        "# Imports\n",
        "import os\n",
        "import numpy as np\n",
        "import random\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OManZEihWxPl"
      },
      "source": [
        "# Machine Learning\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.neighbors import KNeighborsRegressor\n",
        "from sklearn.svm import SVR\n",
        "from sklearn.linear_model import Lasso\n",
        "from sklearn.linear_model import Ridge\n",
        "from sklearn.neural_network import MLPRegressor\n",
        "from sklearn.tree import DecisionTreeRegressor\n",
        "from sklearn.tree import ExtraTreeRegressor\n",
        "from xgboost import XGBRegressor\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.ensemble import AdaBoostRegressor\n",
        "from sklearn.ensemble import GradientBoostingRegressor\n",
        "from sklearn.ensemble import BaggingRegressor\n",
        "\n",
        "# Metrics\n",
        "from sklearn import preprocessing\n",
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.model_selection import KFold\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "from sklearn.metrics import mean_squared_log_error\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.preprocessing import Normalizer\n",
        "from sklearn.preprocessing import RobustScaler\n",
        "from sklearn.preprocessing import PowerTransformer\n",
        "from sklearn.preprocessing import QuantileTransformer\n",
        "import sys"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LsPkQpTcDSc6",
        "outputId": "f7020f7c-e715-4571-afc2-2b936c0200b3"
      },
      "source": [
        "!wget \"https://www.dropbox.com/s/qvo45vf0kc7j41d/ift6758-a20.zip?dl=0\" -O data.zip\r\n",
        "!unzip data.zip"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617
        },
        "id": "myThf3-3W74J",
        "outputId": "b5a865e6-e50c-4fc0-cc92-351332c31703"
      },
      "source": [
        "train_df = pd.read_csv('train.csv')\n",
        "test_df = pd.read_csv('test.csv')\n",
        "train_df.head()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>Mf9vfld4Vfe</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Set</td>\n",
              "      <td>Verified</td>\n",
              "      <td>db1a2c</td>\n",
              "      <td>eaf0f2</td>\n",
              "      <td>e70409</td>\n",
              "      <td>False</td>\n",
              "      <td>39600.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Enabled</td>\n",
              "      <td>en</td>\n",
              "      <td>Thu Nov 27 05:24:59 +0000 2008</td>\n",
              "      <td>Sydney</td>\n",
              "      <td>95763</td>\n",
              "      <td>4289</td>\n",
              "      <td>30809</td>\n",
              "      <td>873</td>\n",
              "      <td>business</td>\n",
              "      <td>14.792</td>\n",
              "      <td>1.5761</td>\n",
              "      <td>AL85S14OMDPF01I9.png</td>\n",
              "      <td>2815</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>HI11QOPD7BLJTO7Q</td>\n",
              "      <td>xl9gaGN0hxM_</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Set</td>\n",
              "      <td>Verified</td>\n",
              "      <td>0099cc</td>\n",
              "      <td>f6ffd1</td>\n",
              "      <td>fff04d</td>\n",
              "      <td>False</td>\n",
              "      <td>NaN</td>\n",
              "      <td>mumbai</td>\n",
              "      <td>Enabled</td>\n",
              "      <td>en</td>\n",
              "      <td>Fri Jan 15 18:00:46 +0000 2010</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1018746</td>\n",
              "      <td>289</td>\n",
              "      <td>8150</td>\n",
              "      <td>290</td>\n",
              "      <td>unknown</td>\n",
              "      <td>8.183</td>\n",
              "      <td>11.2782</td>\n",
              "      <td>HI11QOPD7BLJTO7Q.png</td>\n",
              "      <td>1242</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>JS49LP5P72RI1OQB</td>\n",
              "      <td>d_uiMm</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Set</td>\n",
              "      <td>Not verified</td>\n",
              "      <td>1fc2de</td>\n",
              "      <td>efefef</td>\n",
              "      <td>1fc2de</td>\n",
              "      <td>False</td>\n",
              "      <td>-18000.0</td>\n",
              "      <td>NYC + 70 Countries Worldwide</td>\n",
              "      <td>Enabled</td>\n",
              "      <td>en</td>\n",
              "      <td>Fri Oct 02 20:15:06 +0000 2009</td>\n",
              "      <td>Central Time (US &amp; Canada)</td>\n",
              "      <td>13444</td>\n",
              "      <td>1876</td>\n",
              "      <td>4698</td>\n",
              "      <td>227</td>\n",
              "      <td>unknown</td>\n",
              "      <td>31.823</td>\n",
              "      <td>0.5725</td>\n",
              "      <td>JS49LP5P72RI1OQB.png</td>\n",
              "      <td>1559</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>S0GDSC09MACCLBJP</td>\n",
              "      <td>hfylaRr</td>\n",
              "      <td>https://blob/e/g9pex_vS.com</td>\n",
              "      <td>Not set</td>\n",
              "      <td>Verified</td>\n",
              "      <td>050000</td>\n",
              "      <td>616161</td>\n",
              "      <td>00090a</td>\n",
              "      <td>False</td>\n",
              "      <td>-14400.0</td>\n",
              "      <td>Indianapolis, In</td>\n",
              "      <td>Enabled</td>\n",
              "      <td>en</td>\n",
              "      <td>Thu Feb 19 14:37:22 +0000 2009</td>\n",
              "      <td>Eastern Time (US &amp; Canada)</td>\n",
              "      <td>339168</td>\n",
              "      <td>1148</td>\n",
              "      <td>53216</td>\n",
              "      <td>4035</td>\n",
              "      <td>business</td>\n",
              "      <td>23.052</td>\n",
              "      <td>4.0265</td>\n",
              "      <td>S0GDSC09MACCLBJP.png</td>\n",
              "      <td>6342</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>CRSEMK4QER6LDJSA</td>\n",
              "      <td>hRR1sDGlz5</td>\n",
              "      <td>https://blob/v/Szeo.h4/.com</td>\n",
              "      <td>Set</td>\n",
              "      <td>Not verified</td>\n",
              "      <td>58424d</td>\n",
              "      <td>f7f7f7</td>\n",
              "      <td>000000</td>\n",
              "      <td>False</td>\n",
              "      <td>-18000.0</td>\n",
              "      <td>777 Beach Blvd. Biloxi, MS</td>\n",
              "      <td>Enabled</td>\n",
              "      <td>en</td>\n",
              "      <td>Tue Mar 31 13:27:52 +0000 2009</td>\n",
              "      <td>Central Time (US &amp; Canada)</td>\n",
              "      <td>9215</td>\n",
              "      <td>93</td>\n",
              "      <td>3271</td>\n",
              "      <td>130</td>\n",
              "      <td>unknown</td>\n",
              "      <td>8.418</td>\n",
              "      <td>3.9229</td>\n",
              "      <td>CRSEMK4QER6LDJSA.png</td>\n",
              "      <td>1078</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 Id     User Name  ...         Profile Image Num of Profile Likes\n",
              "0  AL85S14OMDPF01I9   Mf9vfld4Vfe  ...  AL85S14OMDPF01I9.png                 2815\n",
              "1  HI11QOPD7BLJTO7Q  xl9gaGN0hxM_  ...  HI11QOPD7BLJTO7Q.png                 1242\n",
              "2  JS49LP5P72RI1OQB        d_uiMm  ...  JS49LP5P72RI1OQB.png                 1559\n",
              "3  S0GDSC09MACCLBJP       hfylaRr  ...  S0GDSC09MACCLBJP.png                 6342\n",
              "4  CRSEMK4QER6LDJSA    hRR1sDGlz5  ...  CRSEMK4QER6LDJSA.png                 1078\n",
              "\n",
              "[5 rows x 24 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZiLhbQMLWxPj"
      },
      "source": [
        "# Set random seeds (for reproducibility requirement)\n",
        "os.environ['PYTHONHASHSEED']=str(1)\n",
        "np.random.seed(67)\n",
        "random.seed(67)"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o2wPaDiVWxPk",
        "outputId": "e1bfabf6-56dc-4a77-eae9-b9838ebcb693"
      },
      "source": [
        "train_df.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Id                                                0\n",
              "User Name                                         0\n",
              "Personal URL                                   4244\n",
              "Profile Cover Image Status                       90\n",
              "Profile Verification Status                       0\n",
              "Profile Text Color                               66\n",
              "Profile Page Color                               78\n",
              "Profile Theme Color                              72\n",
              "Is Profile View Size Customized?                  0\n",
              "UTC Offset                                      486\n",
              "Location                                       1461\n",
              "Location Public Visibility                        0\n",
              "User Language                                     0\n",
              "Profile Creation Timestamp                        0\n",
              "User Time Zone                                  486\n",
              "Num of Followers                                  0\n",
              "Num of People Following                           0\n",
              "Num of Status Updates                             0\n",
              "Num of Direct Messages                            0\n",
              "Profile Category                                  0\n",
              "Avg Daily Profile Visit Duration in seconds      77\n",
              "Avg Daily Profile Clicks                         76\n",
              "Profile Image                                     0\n",
              "Num of Profile Likes                              0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GgH9VqRd-ziI",
        "outputId": "5b12541e-2fdf-4deb-e86f-2e2b7ec361c9"
      },
      "source": [
        "test_df.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Id                                                0\n",
              "User Name                                         0\n",
              "Personal URL                                   1462\n",
              "Profile Cover Image Status                       20\n",
              "Profile Verification Status                       0\n",
              "Profile Text Color                               36\n",
              "Profile Page Color                               27\n",
              "Profile Theme Color                              23\n",
              "Is Profile View Size Customized?                  0\n",
              "UTC Offset                                      148\n",
              "Location                                        476\n",
              "Location Public Visibility                        0\n",
              "User Language                                     0\n",
              "Profile Creation Timestamp                        0\n",
              "User Time Zone                                  148\n",
              "Num of Followers                                  0\n",
              "Num of People Following                           0\n",
              "Num of Status Updates                             0\n",
              "Num of Direct Messages                            0\n",
              "Profile Category                                  0\n",
              "Avg Daily Profile Visit Duration in seconds      26\n",
              "Avg Daily Profile Clicks                         27\n",
              "Profile Image                                     0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lCxgBe2T-ziI",
        "outputId": "a9381690-fae2-4114-f274-3fd0f515c167"
      },
      "source": [
        "train_df['Profile Creation Timestamp'].dtype"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dtype('O')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZllB_dPLuG-k",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "04e8d23a-2455-4b02-f913-95070d2d4271"
      },
      "source": [
        "# get the number of seconds to the unix epoch start.\n",
        "df_unix_sec = pd.to_datetime(train_df['Profile Creation Timestamp']).astype(int) / (10**9*60*60*24)\n",
        "pd.to_datetime(df_unix_sec)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      1970-01-01 00:00:00.000014210\n",
              "1      1970-01-01 00:00:00.000014624\n",
              "2      1970-01-01 00:00:00.000014519\n",
              "3      1970-01-01 00:00:00.000014294\n",
              "4      1970-01-01 00:00:00.000014334\n",
              "                    ...             \n",
              "7495   1970-01-01 00:00:00.000014994\n",
              "7496   1970-01-01 00:00:00.000015049\n",
              "7497   1970-01-01 00:00:00.000014317\n",
              "7498   1970-01-01 00:00:00.000014450\n",
              "7499   1970-01-01 00:00:00.000014964\n",
              "Name: Profile Creation Timestamp, Length: 7500, dtype: datetime64[ns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gnbjQ0dr-ziJ"
      },
      "source": [
        "days_since_creation = (pd.to_datetime('Tue Dec 01 00:00:00 +0000 2020') - pd.to_datetime(train_df['Profile Creation Timestamp'])).astype('timedelta64[D]')"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LdvsDPtK-ziK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ec39c888-780b-4deb-ea6f-a4ee0f188af7"
      },
      "source": [
        "days_since_creation"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       4386.0\n",
              "1       3972.0\n",
              "2       4077.0\n",
              "3       4302.0\n",
              "4       4262.0\n",
              "         ...  \n",
              "7495    3602.0\n",
              "7496    3547.0\n",
              "7497    4279.0\n",
              "7498    4146.0\n",
              "7499    3632.0\n",
              "Name: Profile Creation Timestamp, Length: 7500, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wTDe6sxl-ziK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b884e144-4920-4282-ab68-3b4f03216af8"
      },
      "source": [
        "df_unix_day = pd.to_datetime(train_df['Profile Creation Timestamp']).astype(int) / (10**9*60*60*24)\n",
        "df_unix_day - df_unix_day.min()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        765.078148\n",
              "1       1179.602998\n",
              "2       1074.696285\n",
              "3        849.461748\n",
              "4        889.413484\n",
              "           ...     \n",
              "7495    1549.701308\n",
              "7496    1604.012755\n",
              "7497     872.531701\n",
              "7498    1005.524977\n",
              "7499    1519.273032\n",
              "Name: Profile Creation Timestamp, Length: 7500, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0tx_EqtP-ziL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "27a4c149-bac6-4f77-c6fb-761fe9c06c0f"
      },
      "source": [
        "pd.DatetimeIndex(test_df['Profile Creation Timestamp']).year.min()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2007"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kzptrbP7D0h1",
        "outputId": "b69e33e8-63ac-4b9e-e7d2-c4548209dab7"
      },
      "source": [
        "!pip install emoji"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting emoji\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/ff/1c/1f1457fe52d0b30cbeebfd578483cedb3e3619108d2d5a21380dfecf8ffd/emoji-0.6.0.tar.gz (51kB)\n",
            "\r\u001b[K     |██████▍                         | 10kB 17.9MB/s eta 0:00:01\r\u001b[K     |████████████▉                   | 20kB 20.0MB/s eta 0:00:01\r\u001b[K     |███████████████████▎            | 30kB 10.6MB/s eta 0:00:01\r\u001b[K     |█████████████████████████▊      | 40kB 9.1MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 51kB 2.8MB/s \n",
            "\u001b[?25hBuilding wheels for collected packages: emoji\n",
            "  Building wheel for emoji (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for emoji: filename=emoji-0.6.0-cp36-none-any.whl size=49715 sha256=0623a731353c2fe6422e833cf69ae7cb895061610feb12a584db02d1b799401b\n",
            "  Stored in directory: /root/.cache/pip/wheels/46/2c/8b/9dcf5216ca68e14e0320e283692dce8ae321cdc01e73e17796\n",
            "Successfully built emoji\n",
            "Installing collected packages: emoji\n",
            "Successfully installed emoji-0.6.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jp2I0cZp-ziL"
      },
      "source": [
        "from emoji import UNICODE_EMOJI\n",
        "\n",
        "def count_emojis(s):\n",
        "    count = 0\n",
        "    for emoji in UNICODE_EMOJI:\n",
        "        count += s.count(emoji)\n",
        "    return count"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KV8Gy_U1-ziL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "1d224042-bee3-4e2c-986c-3ff8e37b65c5"
      },
      "source": [
        "train_df[train_df['Location'].apply(lambda x: count_emojis(str(x)))>0]"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Id</th>\n",
              "      <th>User Name</th>\n",
              "      <th>Personal URL</th>\n",
              "      <th>Profile Cover Image Status</th>\n",
              "      <th>Profile Verification Status</th>\n",
              "      <th>Profile Text Color</th>\n",
              "      <th>Profile Page Color</th>\n",
              "      <th>Profile Theme Color</th>\n",
              "      <th>Is Profile View Size Customized?</th>\n",
              "      <th>UTC Offset</th>\n",
              "      <th>Location</th>\n",
              "      <th>Location Public Visibility</th>\n",
              "      <th>User Language</th>\n",
              "      <th>Profile Creation Timestamp</th>\n",
              "      <th>User Time Zone</th>\n",
              "      <th>Num of Followers</th>\n",
              "      <th>Num of People Following</th>\n",
              "      <th>Num of Status Updates</th>\n",
              "      <th>Num of Direct Messages</th>\n",
              "      <th>Profile Category</th>\n",
              "      <th>Avg Daily Profile Visit Duration in seconds</th>\n",
              "      <th>Avg Daily Profile Clicks</th>\n",
              "      <th>Profile Image</th>\n",
              "      <th>Num of Profile Likes</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>87</th>\n",
              "      <td>2L9J60KAIP2049Q5</td>\n",
              "      <td>OPxR7gDhJefTK</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Set</td>\n",
              "      <td>Verified</td>\n",
              "      <td>2fc2ef</td>\n",
              "      <td>252429</td>\n",
              "      <td>1a1b1f</td>\n",
              "      <td>False</td>\n",
              "      <td>-18000.0</td>\n",
              "      <td>NYC &amp; the rest of the world ✈️</td>\n",
              "      <td>Enabled</td>\n",
              "      <td>en</td>\n",
              "      <td>Thu Jan 06 19:56:45 +0000 2011</td>\n",
              "      <td>Central Time (US &amp; Canada)</td>\n",
              "      <td>312455</td>\n",
              "      <td>333</td>\n",
              "      <td>10130</td>\n",
              "      <td>252</td>\n",
              "      <td>unknown</td>\n",
              "      <td>13.846</td>\n",
              "      <td>10.4373</td>\n",
              "      <td>2L9J60KAIP2049Q5.png</td>\n",
              "      <td>32845</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>178</th>\n",
              "      <td>JI0OGG3SM6PMEAGH</td>\n",
              "      <td>B5WPt</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Not set</td>\n",
              "      <td>Verified</td>\n",
              "      <td>211b1c</td>\n",
              "      <td>ddffcc</td>\n",
              "      <td>211b1c</td>\n",
              "      <td>False</td>\n",
              "      <td>-18000.0</td>\n",
              "      <td>Living, Loving, and Laughing❤️</td>\n",
              "      <td>Disabled</td>\n",
              "      <td>en</td>\n",
              "      <td>Sat Apr 04 23:53:24 +0000 2009</td>\n",
              "      <td>Quito</td>\n",
              "      <td>10868693</td>\n",
              "      <td>55</td>\n",
              "      <td>10157</td>\n",
              "      <td>17693</td>\n",
              "      <td>unknown</td>\n",
              "      <td>27.396</td>\n",
              "      <td>2.0480</td>\n",
              "      <td>JI0OGG3SM6PMEAGH.png</td>\n",
              "      <td>469</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>757</th>\n",
              "      <td>8C9SE1R2L139BSFQ</td>\n",
              "      <td>qqAEqO</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Not set</td>\n",
              "      <td>Verified</td>\n",
              "      <td>e8000d</td>\n",
              "      <td>bcc2c3</td>\n",
              "      <td>0051ba</td>\n",
              "      <td>False</td>\n",
              "      <td>-18000.0</td>\n",
              "      <td>Lawrence❤️Kansas</td>\n",
              "      <td>Enabled</td>\n",
              "      <td>en</td>\n",
              "      <td>Thu Apr 16 16:23:03 +0000 2009</td>\n",
              "      <td>Central Time (US &amp; Canada)</td>\n",
              "      <td>74331</td>\n",
              "      <td>502</td>\n",
              "      <td>29731</td>\n",
              "      <td>940</td>\n",
              "      <td>government</td>\n",
              "      <td>27.924</td>\n",
              "      <td>3.4291</td>\n",
              "      <td>8C9SE1R2L139BSFQ.png</td>\n",
              "      <td>41672</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>887</th>\n",
              "      <td>DNKR8C5EHN3KAB0T</td>\n",
              "      <td>iBaviV1tgW8KIt</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Set</td>\n",
              "      <td>Not verified</td>\n",
              "      <td>000000</td>\n",
              "      <td>000000</td>\n",
              "      <td>ff1199</td>\n",
              "      <td>False</td>\n",
              "      <td>-25200.0</td>\n",
              "      <td>LTW-Network™</td>\n",
              "      <td>Enabled</td>\n",
              "      <td>en</td>\n",
              "      <td>Mon Oct 25 09:21:42 +0000 2010</td>\n",
              "      <td>Pacific Time (US &amp; Canada)</td>\n",
              "      <td>3654074</td>\n",
              "      <td>100</td>\n",
              "      <td>35221</td>\n",
              "      <td>5235</td>\n",
              "      <td>business</td>\n",
              "      <td>27.082</td>\n",
              "      <td>1.8661</td>\n",
              "      <td>DNKR8C5EHN3KAB0T.png</td>\n",
              "      <td>34</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>928</th>\n",
              "      <td>OQOIDRL5CIT7DNKG</td>\n",
              "      <td>I5eg1ere_8r4z</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Set</td>\n",
              "      <td>Verified</td>\n",
              "      <td>009999</td>\n",
              "      <td>efefef</td>\n",
              "      <td>131516</td>\n",
              "      <td>False</td>\n",
              "      <td>-25200.0</td>\n",
              "      <td>LA ✈ UK</td>\n",
              "      <td>Enabled</td>\n",
              "      <td>en</td>\n",
              "      <td>Fri Nov 12 22:48:25 +0000 2010</td>\n",
              "      <td>Pacific Time (US &amp; Canada)</td>\n",
              "      <td>111699</td>\n",
              "      <td>4736</td>\n",
              "      <td>39834</td>\n",
              "      <td>103</td>\n",
              "      <td>business</td>\n",
              "      <td>36.261</td>\n",
              "      <td>3.2292</td>\n",
              "      <td>OQOIDRL5CIT7DNKG.png</td>\n",
              "      <td>53510</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>985</th>\n",
              "      <td>F5JRC675BJF6PTPK</td>\n",
              "      <td>C7U4t1MNB</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Set</td>\n",
              "      <td>Not verified</td>\n",
              "      <td>cc3366</td>\n",
              "      <td>e6f6f9</td>\n",
              "      <td>dbe9ed</td>\n",
              "      <td>False</td>\n",
              "      <td>-14400.0</td>\n",
              "      <td>877-KARS4KIDS 🎶</td>\n",
              "      <td>Enabled</td>\n",
              "      <td>en</td>\n",
              "      <td>Tue Jun 09 17:40:12 +0000 2009</td>\n",
              "      <td>Eastern Time (US &amp; Canada)</td>\n",
              "      <td>5152</td>\n",
              "      <td>384</td>\n",
              "      <td>6460</td>\n",
              "      <td>27</td>\n",
              "      <td></td>\n",
              "      <td>22.519</td>\n",
              "      <td>7.6970</td>\n",
              "      <td>F5JRC675BJF6PTPK.png</td>\n",
              "      <td>2749</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1056</th>\n",
              "      <td>8PN6P8JCGGAGLI0L</td>\n",
              "      <td>kfO0sTeA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Set</td>\n",
              "      <td>Verified</td>\n",
              "      <td>009999</td>\n",
              "      <td>efefef</td>\n",
              "      <td>131516</td>\n",
              "      <td>False</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Denmark ✈ Los Angeles</td>\n",
              "      <td>??</td>\n",
              "      <td>en</td>\n",
              "      <td>Sat Dec 22 19:49:11 +0000 2012</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1353798</td>\n",
              "      <td>774</td>\n",
              "      <td>18268</td>\n",
              "      <td>1711</td>\n",
              "      <td>unknown</td>\n",
              "      <td>11.334</td>\n",
              "      <td>4.7631</td>\n",
              "      <td>8PN6P8JCGGAGLI0L.png</td>\n",
              "      <td>11162</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1363</th>\n",
              "      <td>C57ON0ALPSNOR65E</td>\n",
              "      <td>77Ox8lg7qenl</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Set</td>\n",
              "      <td>Not verified</td>\n",
              "      <td>1da1f2</td>\n",
              "      <td>ddeef6</td>\n",
              "      <td>c0deed</td>\n",
              "      <td>True</td>\n",
              "      <td>-10800.0</td>\n",
              "      <td>♻️</td>\n",
              "      <td>Disabled</td>\n",
              "      <td>es</td>\n",
              "      <td>Wed Jun 20 15:08:35 +0000 2012</td>\n",
              "      <td>Buenos Aires</td>\n",
              "      <td>1221067</td>\n",
              "      <td>153</td>\n",
              "      <td>9184</td>\n",
              "      <td>560</td>\n",
              "      <td>unknown</td>\n",
              "      <td>16.608</td>\n",
              "      <td>1.4631</td>\n",
              "      <td>C57ON0ALPSNOR65E.png</td>\n",
              "      <td>1935</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1779</th>\n",
              "      <td>5D4NB617DQF5JOEL</td>\n",
              "      <td>EFi7QFRpt0eIlt</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Set</td>\n",
              "      <td>Verified</td>\n",
              "      <td>1da1f2</td>\n",
              "      <td>ddeef6</td>\n",
              "      <td>c0deed</td>\n",
              "      <td>True</td>\n",
              "      <td>NaN</td>\n",
              "      <td>STL✈️Dallas</td>\n",
              "      <td>Enabled</td>\n",
              "      <td>en</td>\n",
              "      <td>Tue Mar 13 00:13:39 +0000 2012</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2007961</td>\n",
              "      <td>1800</td>\n",
              "      <td>11575</td>\n",
              "      <td>1526</td>\n",
              "      <td>government</td>\n",
              "      <td>33.823</td>\n",
              "      <td>1.3171</td>\n",
              "      <td>5D4NB617DQF5JOEL.png</td>\n",
              "      <td>3070</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2125</th>\n",
              "      <td>MTBDQF69LKPDARP5</td>\n",
              "      <td>oIJQVhQDtLX</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Set</td>\n",
              "      <td>Verified</td>\n",
              "      <td>0084b4</td>\n",
              "      <td>ddeef6</td>\n",
              "      <td>022330</td>\n",
              "      <td>False</td>\n",
              "      <td>NaN</td>\n",
              "      <td>LA ✈️ NYC</td>\n",
              "      <td>Enabled</td>\n",
              "      <td>en</td>\n",
              "      <td>Mon Jan 21 16:24:58 +0000 2013</td>\n",
              "      <td>NaN</td>\n",
              "      <td>94402</td>\n",
              "      <td>737</td>\n",
              "      <td>3250</td>\n",
              "      <td>114</td>\n",
              "      <td>government</td>\n",
              "      <td>28.950</td>\n",
              "      <td>2.6214</td>\n",
              "      <td>MTBDQF69LKPDARP5.png</td>\n",
              "      <td>9535</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2144</th>\n",
              "      <td>FOI1IBEDAC7P3GPH</td>\n",
              "      <td>RoovXti8owQDU8o</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Set</td>\n",
              "      <td>Verified</td>\n",
              "      <td>1da1f2</td>\n",
              "      <td>ddeef6</td>\n",
              "      <td>c0deed</td>\n",
              "      <td>True</td>\n",
              "      <td>NaN</td>\n",
              "      <td>🇨🇦</td>\n",
              "      <td>disabled</td>\n",
              "      <td>en</td>\n",
              "      <td>Wed Nov 26 16:12:27 +0000 2014</td>\n",
              "      <td>NaN</td>\n",
              "      <td>10267934</td>\n",
              "      <td>310</td>\n",
              "      <td>922</td>\n",
              "      <td>8678</td>\n",
              "      <td></td>\n",
              "      <td>37.528</td>\n",
              "      <td>4.1281</td>\n",
              "      <td>FOI1IBEDAC7P3GPH.png</td>\n",
              "      <td>7783</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2593</th>\n",
              "      <td>T7RRJ8TR3T71NCR5</td>\n",
              "      <td>AGN3UiEPrclCD</td>\n",
              "      <td>https://blob/6chjpha9kd.com</td>\n",
              "      <td>Set</td>\n",
              "      <td>Verified</td>\n",
              "      <td>db0f20</td>\n",
              "      <td>901fdb</td>\n",
              "      <td>9ae4e8</td>\n",
              "      <td>False</td>\n",
              "      <td>-28800.0</td>\n",
              "      <td>The LAnd!! ❤</td>\n",
              "      <td>Disabled</td>\n",
              "      <td>en</td>\n",
              "      <td>Tue Feb 10 20:52:30 +0000 2009</td>\n",
              "      <td>Alaska</td>\n",
              "      <td>62187</td>\n",
              "      <td>626</td>\n",
              "      <td>38</td>\n",
              "      <td>683</td>\n",
              "      <td>unknown</td>\n",
              "      <td>10.180</td>\n",
              "      <td>12.0619</td>\n",
              "      <td>T7RRJ8TR3T71NCR5.png</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2919</th>\n",
              "      <td>PLN36ARL7JTB8PD7</td>\n",
              "      <td>zwGPQpvK7zV_goi</td>\n",
              "      <td>https://blob/li/t/kZhh.com</td>\n",
              "      <td>Set</td>\n",
              "      <td>Not verified</td>\n",
              "      <td>b40b43</td>\n",
              "      <td>e5507e</td>\n",
              "      <td>ff6699</td>\n",
              "      <td>False</td>\n",
              "      <td>25200.0</td>\n",
              "      <td>@ZaskiADORE 's heart ❤️</td>\n",
              "      <td>Disabled</td>\n",
              "      <td>en</td>\n",
              "      <td>Sat Sep 19 05:23:43 +0000 2009</td>\n",
              "      <td>Jakarta</td>\n",
              "      <td>2368282</td>\n",
              "      <td>352</td>\n",
              "      <td>7942</td>\n",
              "      <td>1220</td>\n",
              "      <td>unknown</td>\n",
              "      <td>17.944</td>\n",
              "      <td>7.8339</td>\n",
              "      <td>PLN36ARL7JTB8PD7.png</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2931</th>\n",
              "      <td>3F9HKDO7DQFGLAS7</td>\n",
              "      <td>DrmEm0BD80BG3fe</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Set</td>\n",
              "      <td>Verified</td>\n",
              "      <td>0084b4</td>\n",
              "      <td>ddeef6</td>\n",
              "      <td>c0deed</td>\n",
              "      <td>False</td>\n",
              "      <td>-25200.0</td>\n",
              "      <td>My Bikini Body EBook 👇🏼</td>\n",
              "      <td>Disabled</td>\n",
              "      <td>en</td>\n",
              "      <td>Wed Jul 06 18:42:46 +0000 2011</td>\n",
              "      <td>Pacific Time (US &amp; Canada)</td>\n",
              "      <td>919016</td>\n",
              "      <td>335</td>\n",
              "      <td>14968</td>\n",
              "      <td>1617</td>\n",
              "      <td>business</td>\n",
              "      <td>26.551</td>\n",
              "      <td>3.0953</td>\n",
              "      <td>3F9HKDO7DQFGLAS7.png</td>\n",
              "      <td>6850</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3556</th>\n",
              "      <td>1AACHASMRBDKE0DT</td>\n",
              "      <td>cI3xQaiiU1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Set</td>\n",
              "      <td>Verified</td>\n",
              "      <td>70284f</td>\n",
              "      <td>ffffff</td>\n",
              "      <td>ffffff</td>\n",
              "      <td>False</td>\n",
              "      <td>-18000.0</td>\n",
              "      <td>NYC/London ✨</td>\n",
              "      <td>Disabled</td>\n",
              "      <td>en</td>\n",
              "      <td>Tue Sep 08 20:46:12 +0000 2009</td>\n",
              "      <td>Quito</td>\n",
              "      <td>164201</td>\n",
              "      <td>553</td>\n",
              "      <td>15610</td>\n",
              "      <td>553</td>\n",
              "      <td>celebrity</td>\n",
              "      <td>28.194</td>\n",
              "      <td>2.1033</td>\n",
              "      <td>1AACHASMRBDKE0DT.png</td>\n",
              "      <td>5870</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4370</th>\n",
              "      <td>HJIGMEB6PDHTNQ3I</td>\n",
              "      <td>43HoMI</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Set</td>\n",
              "      <td>Verified</td>\n",
              "      <td>2fc2ef</td>\n",
              "      <td>252429</td>\n",
              "      <td>1a1b1f</td>\n",
              "      <td>False</td>\n",
              "      <td>-25200.0</td>\n",
              "      <td>New Vid!👉🏽</td>\n",
              "      <td>Enabled</td>\n",
              "      <td>en</td>\n",
              "      <td>Tue Mar 17 08:23:30 +0000 2009</td>\n",
              "      <td>Pacific Time (US &amp; Canada)</td>\n",
              "      <td>409439</td>\n",
              "      <td>6296</td>\n",
              "      <td>16722</td>\n",
              "      <td>1990</td>\n",
              "      <td>government</td>\n",
              "      <td>24.593</td>\n",
              "      <td>0.8878</td>\n",
              "      <td>HJIGMEB6PDHTNQ3I.png</td>\n",
              "      <td>3280</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4552</th>\n",
              "      <td>R7T2DRJ5TIG02GKR</td>\n",
              "      <td>eniw4YT6gG9</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Not set</td>\n",
              "      <td>Not verified</td>\n",
              "      <td>431f41</td>\n",
              "      <td>bdcfd7</td>\n",
              "      <td>ddd0c2</td>\n",
              "      <td>False</td>\n",
              "      <td>-14400.0</td>\n",
              "      <td>🚐</td>\n",
              "      <td>Enabled</td>\n",
              "      <td>en</td>\n",
              "      <td>Tue Feb 19 00:41:30 +0000 2008</td>\n",
              "      <td>Eastern Time (US &amp; Canada)</td>\n",
              "      <td>15280</td>\n",
              "      <td>230</td>\n",
              "      <td>641</td>\n",
              "      <td>228</td>\n",
              "      <td>unknown</td>\n",
              "      <td>16.671</td>\n",
              "      <td>2.9447</td>\n",
              "      <td>R7T2DRJ5TIG02GKR.png</td>\n",
              "      <td>36568</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5451</th>\n",
              "      <td>6N5QQQPFBPEDBFFM</td>\n",
              "      <td>Rp4Wqceoa8</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Not set</td>\n",
              "      <td>Verified</td>\n",
              "      <td>0084b4</td>\n",
              "      <td>ddffcc</td>\n",
              "      <td>abb8c2</td>\n",
              "      <td>False</td>\n",
              "      <td>-36000.0</td>\n",
              "      <td>The stars ✨</td>\n",
              "      <td>Disabled</td>\n",
              "      <td>en</td>\n",
              "      <td>Wed Mar 18 11:52:49 +0000 2009</td>\n",
              "      <td>Hawaii</td>\n",
              "      <td>1306838</td>\n",
              "      <td>698</td>\n",
              "      <td>7947</td>\n",
              "      <td>4501</td>\n",
              "      <td>unknown</td>\n",
              "      <td>35.774</td>\n",
              "      <td>3.3122</td>\n",
              "      <td>6N5QQQPFBPEDBFFM.png</td>\n",
              "      <td>5051</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6039</th>\n",
              "      <td>OCOCOR02NI69N3QF</td>\n",
              "      <td>keyrPYcbanm_mMl</td>\n",
              "      <td>https://blob/kO/u0ftctr.com</td>\n",
              "      <td>Set</td>\n",
              "      <td>Not verified</td>\n",
              "      <td>009999</td>\n",
              "      <td>efefef</td>\n",
              "      <td>131516</td>\n",
              "      <td>False</td>\n",
              "      <td>-10800.0</td>\n",
              "      <td>NYC ✈LA ✈PARIS</td>\n",
              "      <td>Enabled</td>\n",
              "      <td>en</td>\n",
              "      <td>Sat Jun 09 22:07:06 +0000 2012</td>\n",
              "      <td>Atlantic Time (Canada)</td>\n",
              "      <td>16237</td>\n",
              "      <td>11</td>\n",
              "      <td>1602</td>\n",
              "      <td>16</td>\n",
              "      <td>unknown</td>\n",
              "      <td>32.301</td>\n",
              "      <td>2.6030</td>\n",
              "      <td>OCOCOR02NI69N3QF.png</td>\n",
              "      <td>62</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6056</th>\n",
              "      <td>NT4F9HO1HPNQNB46</td>\n",
              "      <td>V7jFmpIz</td>\n",
              "      <td>https://blob/.w4ycdig0V.com</td>\n",
              "      <td>Set</td>\n",
              "      <td>Verified</td>\n",
              "      <td>3b94d9</td>\n",
              "      <td>ffffff</td>\n",
              "      <td>ffffff</td>\n",
              "      <td>False</td>\n",
              "      <td>-25200.0</td>\n",
              "      <td>🙏🏾A STATE OF GRATITUDE🙏🏾</td>\n",
              "      <td>Disabled</td>\n",
              "      <td>en</td>\n",
              "      <td>Tue Mar 03 02:58:58 +0000 2009</td>\n",
              "      <td>Pacific Time (US &amp; Canada)</td>\n",
              "      <td>1783400</td>\n",
              "      <td>507</td>\n",
              "      <td>8508</td>\n",
              "      <td>8183</td>\n",
              "      <td>unknown</td>\n",
              "      <td>4.716</td>\n",
              "      <td>0.7995</td>\n",
              "      <td>NT4F9HO1HPNQNB46.png</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6206</th>\n",
              "      <td>S9QJ2K5MB62EPJ9P</td>\n",
              "      <td>BaSUd3qSO4j_</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Not set</td>\n",
              "      <td>Not verified</td>\n",
              "      <td>7a1219</td>\n",
              "      <td>391310</td>\n",
              "      <td>13100b</td>\n",
              "      <td>False</td>\n",
              "      <td>-18000.0</td>\n",
              "      <td>In the ❤️ of San Diego</td>\n",
              "      <td>Enabled</td>\n",
              "      <td>en</td>\n",
              "      <td>Tue Jan 12 17:43:47 +0000 2010</td>\n",
              "      <td>Central Time (US &amp; Canada)</td>\n",
              "      <td>7028</td>\n",
              "      <td>421</td>\n",
              "      <td>4581</td>\n",
              "      <td>100</td>\n",
              "      <td>government</td>\n",
              "      <td>29.670</td>\n",
              "      <td>1.0082</td>\n",
              "      <td>S9QJ2K5MB62EPJ9P.png</td>\n",
              "      <td>2697</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6650</th>\n",
              "      <td>6H87LPT6DQKP172A</td>\n",
              "      <td>ctX3viFqvB2PU</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Set</td>\n",
              "      <td>Not verified</td>\n",
              "      <td>2fc2ef</td>\n",
              "      <td>252429</td>\n",
              "      <td>1a1b1f</td>\n",
              "      <td>False</td>\n",
              "      <td>-18000.0</td>\n",
              "      <td>Olympus w/ The Gods ✈️ Dallas</td>\n",
              "      <td>Disabled</td>\n",
              "      <td>en</td>\n",
              "      <td>Tue Mar 03 18:07:39 +0000 2009</td>\n",
              "      <td>Central Time (US &amp; Canada)</td>\n",
              "      <td>10345</td>\n",
              "      <td>5259</td>\n",
              "      <td>30835</td>\n",
              "      <td>77</td>\n",
              "      <td>celebrity</td>\n",
              "      <td>27.786</td>\n",
              "      <td>1.6888</td>\n",
              "      <td>6H87LPT6DQKP172A.png</td>\n",
              "      <td>392</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6652</th>\n",
              "      <td>TEEED4000AS54HJT</td>\n",
              "      <td>9J1rszYMH</td>\n",
              "      <td>https://blob/gbao6ainot.com</td>\n",
              "      <td>Set</td>\n",
              "      <td>Not verified</td>\n",
              "      <td>0084b4</td>\n",
              "      <td>ddeef6</td>\n",
              "      <td>c0deed</td>\n",
              "      <td>False</td>\n",
              "      <td>-14400.0</td>\n",
              "      <td>🇨🇴 - 🇲🇽 - 🇵🇦 - 🇻🇪</td>\n",
              "      <td>Disabled</td>\n",
              "      <td>es</td>\n",
              "      <td>Mon Feb 06 21:02:38 +0000 2012</td>\n",
              "      <td>Caracas</td>\n",
              "      <td>1617</td>\n",
              "      <td>926</td>\n",
              "      <td>2536</td>\n",
              "      <td>11</td>\n",
              "      <td>unknown</td>\n",
              "      <td>8.128</td>\n",
              "      <td>0.2490</td>\n",
              "      <td>TEEED4000AS54HJT.png</td>\n",
              "      <td>125</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7473</th>\n",
              "      <td>621FJP17P3MH21NQ</td>\n",
              "      <td>KdF2GmGGtoJJG</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Set</td>\n",
              "      <td>Verified</td>\n",
              "      <td>a1171e</td>\n",
              "      <td>0d0404</td>\n",
              "      <td>0d040d</td>\n",
              "      <td>False</td>\n",
              "      <td>-18000.0</td>\n",
              "      <td>🌍✈️</td>\n",
              "      <td>Disabled</td>\n",
              "      <td>en</td>\n",
              "      <td>Thu Mar 26 06:40:20 +0000 2009</td>\n",
              "      <td>Quito</td>\n",
              "      <td>637487</td>\n",
              "      <td>96</td>\n",
              "      <td>41473</td>\n",
              "      <td>1270</td>\n",
              "      <td>business</td>\n",
              "      <td>29.448</td>\n",
              "      <td>4.3610</td>\n",
              "      <td>621FJP17P3MH21NQ.png</td>\n",
              "      <td>273</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                    Id  ... Num of Profile Likes\n",
              "87    2L9J60KAIP2049Q5  ...                32845\n",
              "178   JI0OGG3SM6PMEAGH  ...                  469\n",
              "757   8C9SE1R2L139BSFQ  ...                41672\n",
              "887   DNKR8C5EHN3KAB0T  ...                   34\n",
              "928   OQOIDRL5CIT7DNKG  ...                53510\n",
              "985   F5JRC675BJF6PTPK  ...                 2749\n",
              "1056  8PN6P8JCGGAGLI0L  ...                11162\n",
              "1363  C57ON0ALPSNOR65E  ...                 1935\n",
              "1779  5D4NB617DQF5JOEL  ...                 3070\n",
              "2125  MTBDQF69LKPDARP5  ...                 9535\n",
              "2144  FOI1IBEDAC7P3GPH  ...                 7783\n",
              "2593  T7RRJ8TR3T71NCR5  ...                    1\n",
              "2919  PLN36ARL7JTB8PD7  ...                    2\n",
              "2931  3F9HKDO7DQFGLAS7  ...                 6850\n",
              "3556  1AACHASMRBDKE0DT  ...                 5870\n",
              "4370  HJIGMEB6PDHTNQ3I  ...                 3280\n",
              "4552  R7T2DRJ5TIG02GKR  ...                36568\n",
              "5451  6N5QQQPFBPEDBFFM  ...                 5051\n",
              "6039  OCOCOR02NI69N3QF  ...                   62\n",
              "6056  NT4F9HO1HPNQNB46  ...                    1\n",
              "6206  S9QJ2K5MB62EPJ9P  ...                 2697\n",
              "6650  6H87LPT6DQKP172A  ...                  392\n",
              "6652  TEEED4000AS54HJT  ...                  125\n",
              "7473  621FJP17P3MH21NQ  ...                  273\n",
              "\n",
              "[24 rows x 24 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dgKexyfF-ziM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4dbd963a-9a6e-4cc8-c354-9222a52b9827"
      },
      "source": [
        "train_df['Location'].fillna('').apply(lambda x: len(str(x)))"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        0\n",
              "1        6\n",
              "2       28\n",
              "3       16\n",
              "4       26\n",
              "        ..\n",
              "7495     0\n",
              "7496    14\n",
              "7497    17\n",
              "7498     6\n",
              "7499    10\n",
              "Name: Location, Length: 7500, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_MIaL2zy-ziM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "291e06dc-efe0-457c-f60a-dccf39e9f018"
      },
      "source": [
        "train_df[train_df['Location'].fillna('').str.contains(pat = '#', regex = True)]\n",
        "train_df[train_df['Location'].fillna('').str.contains(pat = '#', regex = True)]\n",
        "train_df[train_df['Location'].fillna('').str.contains(pat = '\\d', regex = True)==False]"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Id</th>\n",
              "      <th>User Name</th>\n",
              "      <th>Personal URL</th>\n",
              "      <th>Profile Cover Image Status</th>\n",
              "      <th>Profile Verification Status</th>\n",
              "      <th>Profile Text Color</th>\n",
              "      <th>Profile Page Color</th>\n",
              "      <th>Profile Theme Color</th>\n",
              "      <th>Is Profile View Size Customized?</th>\n",
              "      <th>UTC Offset</th>\n",
              "      <th>Location</th>\n",
              "      <th>Location Public Visibility</th>\n",
              "      <th>User Language</th>\n",
              "      <th>Profile Creation Timestamp</th>\n",
              "      <th>User Time Zone</th>\n",
              "      <th>Num of Followers</th>\n",
              "      <th>Num of People Following</th>\n",
              "      <th>Num of Status Updates</th>\n",
              "      <th>Num of Direct Messages</th>\n",
              "      <th>Profile Category</th>\n",
              "      <th>Avg Daily Profile Visit Duration in seconds</th>\n",
              "      <th>Avg Daily Profile Clicks</th>\n",
              "      <th>Profile Image</th>\n",
              "      <th>Num of Profile Likes</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
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              "      <td>AL85S14OMDPF01I9</td>\n",
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              "      <td>eaf0f2</td>\n",
              "      <td>e70409</td>\n",
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              "      <td>39600.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Enabled</td>\n",
              "      <td>en</td>\n",
              "      <td>Thu Nov 27 05:24:59 +0000 2008</td>\n",
              "      <td>Sydney</td>\n",
              "      <td>95763</td>\n",
              "      <td>4289</td>\n",
              "      <td>30809</td>\n",
              "      <td>873</td>\n",
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              "      <td>2815</td>\n",
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              "      <th>1</th>\n",
              "      <td>HI11QOPD7BLJTO7Q</td>\n",
              "      <td>xl9gaGN0hxM_</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Set</td>\n",
              "      <td>Verified</td>\n",
              "      <td>0099cc</td>\n",
              "      <td>f6ffd1</td>\n",
              "      <td>fff04d</td>\n",
              "      <td>False</td>\n",
              "      <td>NaN</td>\n",
              "      <td>mumbai</td>\n",
              "      <td>Enabled</td>\n",
              "      <td>en</td>\n",
              "      <td>Fri Jan 15 18:00:46 +0000 2010</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1018746</td>\n",
              "      <td>289</td>\n",
              "      <td>8150</td>\n",
              "      <td>290</td>\n",
              "      <td>unknown</td>\n",
              "      <td>8.183</td>\n",
              "      <td>11.2782</td>\n",
              "      <td>HI11QOPD7BLJTO7Q.png</td>\n",
              "      <td>1242</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>S0GDSC09MACCLBJP</td>\n",
              "      <td>hfylaRr</td>\n",
              "      <td>https://blob/e/g9pex_vS.com</td>\n",
              "      <td>Not set</td>\n",
              "      <td>Verified</td>\n",
              "      <td>050000</td>\n",
              "      <td>616161</td>\n",
              "      <td>00090a</td>\n",
              "      <td>False</td>\n",
              "      <td>-14400.0</td>\n",
              "      <td>Indianapolis, In</td>\n",
              "      <td>Enabled</td>\n",
              "      <td>en</td>\n",
              "      <td>Thu Feb 19 14:37:22 +0000 2009</td>\n",
              "      <td>Eastern Time (US &amp; Canada)</td>\n",
              "      <td>339168</td>\n",
              "      <td>1148</td>\n",
              "      <td>53216</td>\n",
              "      <td>4035</td>\n",
              "      <td>business</td>\n",
              "      <td>23.052</td>\n",
              "      <td>4.0265</td>\n",
              "      <td>S0GDSC09MACCLBJP.png</td>\n",
              "      <td>6342</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0FEKBQ59AF1LQKJP</td>\n",
              "      <td>dgYvLYV</td>\n",
              "      <td>https://blob/132hwfh8ee.com</td>\n",
              "      <td>Set</td>\n",
              "      <td>Verified</td>\n",
              "      <td>007cc2</td>\n",
              "      <td>ddeef6</td>\n",
              "      <td>3493d0</td>\n",
              "      <td>False</td>\n",
              "      <td>-14400.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Enabled</td>\n",
              "      <td>en</td>\n",
              "      <td>Wed Sep 30 17:40:18 +0000 2009</td>\n",
              "      <td>Eastern Time (US &amp; Canada)</td>\n",
              "      <td>51186</td>\n",
              "      <td>235</td>\n",
              "      <td>15881</td>\n",
              "      <td>468</td>\n",
              "      <td>celebrity</td>\n",
              "      <td>31.602</td>\n",
              "      <td>1.0216</td>\n",
              "      <td>0FEKBQ59AF1LQKJP.png</td>\n",
              "      <td>169</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>G47D4QN50M6JKLOO</td>\n",
              "      <td>KQni7ejFw_c</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>3b94d9</td>\n",
              "      <td>eed7ad</td>\n",
              "      <td>131516</td>\n",
              "      <td>False</td>\n",
              "      <td>-14400.0</td>\n",
              "      <td>Bronx, New York</td>\n",
              "      <td>Enabled</td>\n",
              "      <td>en</td>\n",
              "      <td>Wed Aug 12 22:38:20 +0000 2009</td>\n",
              "      <td>Eastern Time (US &amp; Canada)</td>\n",
              "      <td>7278163</td>\n",
              "      <td>330</td>\n",
              "      <td>14552</td>\n",
              "      <td>5702</td>\n",
              "      <td>celebrity</td>\n",
              "      <td>34.285</td>\n",
              "      <td>15.0537</td>\n",
              "      <td>G47D4QN50M6JKLOO.png</td>\n",
              "      <td>1035</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",
              "      <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>7495</th>\n",
              "      <td>KP5DR7CTN8CNLG8P</td>\n",
              "      <td>OkKZVeUwhipjmao</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Not set</td>\n",
              "      <td>Verified</td>\n",
              "      <td>490707</td>\n",
              "      <td>ddeef6</td>\n",
              "      <td>000000</td>\n",
              "      <td>False</td>\n",
              "      <td>-25200.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Disabled</td>\n",
              "      <td>en</td>\n",
              "      <td>Thu Jan 20 20:22:20 +0000 2011</td>\n",
              "      <td>Pacific Time (US &amp; Canada)</td>\n",
              "      <td>6086</td>\n",
              "      <td>76</td>\n",
              "      <td>985</td>\n",
              "      <td>56</td>\n",
              "      <td>unknown</td>\n",
              "      <td>24.792</td>\n",
              "      <td>8.1106</td>\n",
              "      <td>KP5DR7CTN8CNLG8P.png</td>\n",
              "      <td>1373</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7496</th>\n",
              "      <td>SGNF9P1P3922T34T</td>\n",
              "      <td>TPgyIiQieSq</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Not set</td>\n",
              "      <td>Not verified</td>\n",
              "      <td>000000</td>\n",
              "      <td>ffffff</td>\n",
              "      <td>ffffff</td>\n",
              "      <td>False</td>\n",
              "      <td>-10800.0</td>\n",
              "      <td>Belo Horizonte</td>\n",
              "      <td>Enabled</td>\n",
              "      <td>pt</td>\n",
              "      <td>Wed Mar 16 03:50:49 +0000 2011</td>\n",
              "      <td>America/Sao_Paulo</td>\n",
              "      <td>280190</td>\n",
              "      <td>38604</td>\n",
              "      <td>20652</td>\n",
              "      <td>172</td>\n",
              "      <td>unknown</td>\n",
              "      <td>26.006</td>\n",
              "      <td>0.8956</td>\n",
              "      <td>SGNF9P1P3922T34T.png</td>\n",
              "      <td>1407</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7497</th>\n",
              "      <td>O00FTLDT6O803LD0</td>\n",
              "      <td>piL8bSOPoaW4</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Set</td>\n",
              "      <td>Verified</td>\n",
              "      <td>0084b4</td>\n",
              "      <td>ddffcc</td>\n",
              "      <td>00133b</td>\n",
              "      <td>False</td>\n",
              "      <td>7200.0</td>\n",
              "      <td>Stockholm, Sweden</td>\n",
              "      <td>enabled</td>\n",
              "      <td>en</td>\n",
              "      <td>Sat Mar 14 16:18:06 +0000 2009</td>\n",
              "      <td>Stockholm</td>\n",
              "      <td>21647</td>\n",
              "      <td>49</td>\n",
              "      <td>2348</td>\n",
              "      <td>271</td>\n",
              "      <td></td>\n",
              "      <td>15.030</td>\n",
              "      <td>2.8867</td>\n",
              "      <td>O00FTLDT6O803LD0.png</td>\n",
              "      <td>1235</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7498</th>\n",
              "      <td>9F04JLR9JGT8R3EJ</td>\n",
              "      <td>buidBJStq59D</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Set</td>\n",
              "      <td>Not verified</td>\n",
              "      <td>91d2fa</td>\n",
              "      <td>ddffcc</td>\n",
              "      <td>fa743e</td>\n",
              "      <td>False</td>\n",
              "      <td>-10800.0</td>\n",
              "      <td>Brasil</td>\n",
              "      <td>enabled</td>\n",
              "      <td>pt</td>\n",
              "      <td>Sat Jul 25 16:08:25 +0000 2009</td>\n",
              "      <td>Brasilia</td>\n",
              "      <td>89005</td>\n",
              "      <td>256</td>\n",
              "      <td>73830</td>\n",
              "      <td>346</td>\n",
              "      <td>business</td>\n",
              "      <td>11.948</td>\n",
              "      <td>1.7756</td>\n",
              "      <td>9F04JLR9JGT8R3EJ.png</td>\n",
              "      <td>1105</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7499</th>\n",
              "      <td>LLKNQ56IFCSSCOT6</td>\n",
              "      <td>EBc5wEvI</td>\n",
              "      <td>https://blob/thh6/Q4T3.com</td>\n",
              "      <td>Set</td>\n",
              "      <td>Not verified</td>\n",
              "      <td>0084b4</td>\n",
              "      <td>ddeef6</td>\n",
              "      <td>c0deed</td>\n",
              "      <td>False</td>\n",
              "      <td>32400.0</td>\n",
              "      <td>DMは見ていません。</td>\n",
              "      <td>disabled</td>\n",
              "      <td>ja</td>\n",
              "      <td>Tue Dec 21 10:05:37 +0000 2010</td>\n",
              "      <td>Tokyo</td>\n",
              "      <td>313002</td>\n",
              "      <td>254541</td>\n",
              "      <td>28928</td>\n",
              "      <td>875</td>\n",
              "      <td>business</td>\n",
              "      <td>14.307</td>\n",
              "      <td>9.6908</td>\n",
              "      <td>LLKNQ56IFCSSCOT6.png</td>\n",
              "      <td>375</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>7296 rows × 24 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                    Id  ... Num of Profile Likes\n",
              "0     AL85S14OMDPF01I9  ...                 2815\n",
              "1     HI11QOPD7BLJTO7Q  ...                 1242\n",
              "3     S0GDSC09MACCLBJP  ...                 6342\n",
              "5     0FEKBQ59AF1LQKJP  ...                  169\n",
              "6     G47D4QN50M6JKLOO  ...                 1035\n",
              "...                ...  ...                  ...\n",
              "7495  KP5DR7CTN8CNLG8P  ...                 1373\n",
              "7496  SGNF9P1P3922T34T  ...                 1407\n",
              "7497  O00FTLDT6O803LD0  ...                 1235\n",
              "7498  9F04JLR9JGT8R3EJ  ...                 1105\n",
              "7499  LLKNQ56IFCSSCOT6  ...                  375\n",
              "\n",
              "[7296 rows x 24 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L_evCn-u-ziM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7c5cb54a-12f5-45b7-c31d-e947a28d1160"
      },
      "source": [
        "train_df.groupby(['Location'])['Id'].count().sort_values()"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Location\n",
              " - Paris -                          1\n",
              "Paris Expo Porte de Versailles      1\n",
              "Paris, Toulouse, Kourou             1\n",
              "Park City, UT                       1\n",
              "Parma, Italy                        1\n",
              "                                 ... \n",
              "London                             68\n",
              "Brasil                             68\n",
              "India                              74\n",
              "Los Angeles, CA                    82\n",
              "New York, NY                      119\n",
              "Name: Id, Length: 3035, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lopemsAY-ziN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "14ddcc08-c675-4a48-b991-4e538230c593"
      },
      "source": [
        "loc_test = train_df.replace(['Chicago, IL', 'Cairo, Egypt', 'Jakarta, Indonesia', 'Las Vegas', 'London, England',\n",
        "                              'London, UK','California, USA','Hollywood, CA', 'Los Angeles, CA','Los Angeles', 'Los Angeles, California','LA','San Francisco, CA','San Francisco','México','Ciudad de México','Sydney, Australia',\n",
        "                              'Caracas, Venezuela','Buenos Aires, Argentina','São Paulo', 'São Paulo, Brasil', 'Brazil','Rio de Janeiro, Brasil','São Paulo - Brasil',\n",
        "                             'Rio de Janeiro','Santiago, Chile','Mumbai', 'Mumbai, India','New Delhi, India','İstanbul','istanbul','İstanbul, Türkiye','Türkiye',\n",
        "                              'Istanbul','Paris', 'Paris, France','NYC','New York City', 'New York, New York','New York, NY','Barcelona','Madrid','Madrid, España', 'Madrid, Spain',\n",
        "                             'Washington, D.C.','Everywhere','Global','Earth','Manila, Philippines','UK','England', 'England, United Kingdom','USA','Bogotá, Colombia',\n",
        "                            'Dubai, UAE', 'Dubai, United Arab Emirates','Dubai','Italia','Milano','Amsterdam','Lima, Perú','Toronto','Kingdom of Saudi Arabia','Tokyo', 'Tokyo, Japan',], \n",
        "                             ['Chicago', 'Egypt', 'Jakarta', 'Las Vegas, NV', 'London', 'London','California','California','California','California','California','California','California','California','Mexico','Mexico',\n",
        "                             'Australia','Venezuela','Argentina','Brasil','Brasil','Brasil','Brasil','Brasil','Brasil','Chile','India','India','India','Turkey','Turkey',\n",
        "                              'Turkey','Turkey','Turkey','France','France','New York','New York','New York','New York','España','España','España','España','Washington, DC',\n",
        "                             'Worldwide','Worldwide','Worldwide','Philippines','United Kingdom','United Kingdom','United Kingdom','United States','Colombia','United Arab Emirates','United Arab Emirates','United Arab Emirates',\n",
        "                             'Italy','Italy','Nederland','Perú','Canada','Saudi Arabia', 'Japan','Japan',]).groupby(['Location'])['Id','Location'].count()\n",
        "loc_test[loc_test['Id']>5].index"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:12: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
            "  if sys.path[0] == '':\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Argentina', 'Atlanta, GA', 'Austin, TX', 'Australia', 'Boston, MA',\n",
              "       'Brasil', 'California', 'Canada', 'Charlotte, NC', 'Chicago', 'Chile',\n",
              "       'Colombia', 'Dallas, TX', 'Detroit, MI', 'Doha, Qatar',\n",
              "       'Dublin, Ireland', 'Ecuador', 'Egypt', 'España', 'France', 'Germany',\n",
              "       'Hong Kong', 'Houston, TX', 'India', 'Indianapolis, IN', 'Indonesia',\n",
              "       'Italy', 'Jakarta', 'Japan', 'Kuwait', 'Las Vegas, NV', 'London',\n",
              "       'Malaysia', 'Mexico', 'Miami, FL', 'Minneapolis, MN', 'Nashville, TN',\n",
              "       'Nederland', 'New Orleans, LA', 'New York', 'New Zealand', 'Nigeria',\n",
              "       'North America', 'Orlando, FL', 'Oxford, MS', 'Pakistan', 'Panamá',\n",
              "       'Perú', 'Philadelphia, PA', 'Philippines', 'Pittsburgh, PA',\n",
              "       'Portland, OR', 'Redmond, WA', 'Santa Monica, CA', 'Saudi Arabia',\n",
              "       'Seattle, WA', 'Seoul, Korea', 'Singapore', 'South Africa',\n",
              "       'Stanford, CA', 'Turkey', 'United Arab Emirates', 'United Kingdom',\n",
              "       'United States', 'Venezuela', 'Washington, DC', 'Worldwide', 'Москва',\n",
              "       'المملكة العربية السعودية'],\n",
              "      dtype='object', name='Location')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dMxzDLYM-ziN",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 132
        },
        "outputId": "3d071a12-97a0-4068-bc85-0f11f98c7248"
      },
      "source": [
        "values = pd.Series([\"a\",\"b\",\"a\",\"b\",\"c\",\"d\",\"e\",\"a\"])\n",
        "counts = pd.value_counts(values)\n",
        "mask = values.isin(counts[counts > 1].index)\n",
        "print( pd.get_dummies(values[mask])\n",
        "values['~mask'] = \"-\"\n",
        "print pd.get_dummies(values)"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "error",
          "ename": "SyntaxError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-24-dcc157750655>\"\u001b[0;36m, line \u001b[0;32m5\u001b[0m\n\u001b[0;31m    values['~mask'] = \"-\"\u001b[0m\n\u001b[0m         ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SHw0n136-ziO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fe180f95-c2cf-4500-9c82-e2eb780d146c"
      },
      "source": [
        "train_df['Location'].isin(['Argentina', 'Atlanta, GA', 'Austin, TX', 'Australia', 'Boston, MA',\n",
        "       'Brasil', 'California', 'Canada', 'Charlotte, NC', 'Chicago', 'Chile',\n",
        "       'Colombia', 'Dallas, TX', 'Detroit, MI', 'Doha, Qatar',\n",
        "       'Dublin, Ireland', 'Ecuador', 'Egypt', 'España', 'France', 'Germany',\n",
        "       'Hong Kong', 'Houston, TX', 'India', 'Indianapolis, IN', 'Indonesia',\n",
        "       'Italy', 'Jakarta', 'Japan', 'Kuwait', 'Las Vegas, NV', 'London',\n",
        "       'Malaysia', 'Mexico', 'Miami, FL', 'Minneapolis, MN', 'Nashville, TN',\n",
        "       'Nederland', 'New Orleans, LA', 'New York', 'New Zealand', 'Nigeria',\n",
        "       'North America', 'Orlando, FL', 'Oxford, MS', 'Pakistan', 'Panamá',\n",
        "       'Perú', 'Philadelphia, PA', 'Philippines', 'Pittsburgh, PA',\n",
        "       'Portland, OR', 'Redmond, WA', 'Santa Monica, CA', 'Saudi Arabia',\n",
        "       'Seattle, WA', 'Seoul, Korea', 'Singapore', 'South Africa',\n",
        "       'Stanford, CA', 'Turkey', 'United Arab Emirates', 'United Kingdom',\n",
        "       'United States', 'Venezuela', 'Washington, DC', 'Worldwide', 'Москва',\n",
        "       'المملكة العربية السعودية'])"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       False\n",
              "1       False\n",
              "2       False\n",
              "3       False\n",
              "4       False\n",
              "        ...  \n",
              "7495    False\n",
              "7496    False\n",
              "7497    False\n",
              "7498     True\n",
              "7499    False\n",
              "Name: Location, Length: 7500, dtype: bool"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-0iYnUFi-ziO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8cb71485-1a77-4352-9545-72a2a2cc5130"
      },
      "source": [
        "lang_test= train_df.groupby(['User Language'])['Id','User Language'].count()\n",
        "lang_test[lang_test['Id']>20].index"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['ar', 'de', 'en', 'en-gb', 'es', 'fr', 'it', 'ja', 'ko', 'nl', 'pt',\n",
              "       'ru', 'tr'],\n",
              "      dtype='object', name='User Language')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o3rY_KOe-ziP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8dceadcd-7235-41f7-d2cd-f10e57fe85f5"
      },
      "source": [
        "ptc_test = train_df.groupby(['Profile Text Color'])['Id', 'Profile Text Color'].count()\n",
        "ptc_test[ptc_test['Id']>50].index"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['000000', '0084b4', '009999', '0099b9', '1b95e0', '1da1f2', '2fc2ef',\n",
              "       '3b94d9', '4a913c', '990000', 'abb8c2', 'cc3366', 'dd2e44', 'e81c4f',\n",
              "       'ff0000', 'ff691f'],\n",
              "      dtype='object', name='Profile Text Color')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S7TrBu8R-ziP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3c2eef19-0082-4880-b8b3-7952bd82d51e"
      },
      "source": [
        "ptc_test = train_df.groupby(['Profile Page Color'])['Id', 'Profile Page Color'].count()\n",
        "ptc_test[ptc_test['Id']>50].index"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['000000', '252429', '7ac3ee', '95e8ec', 'c0dfec', 'ddeef6', 'ddffcc',\n",
              "       'e3e2de', 'e6f6f9', 'efefef', 'f3f3f3', 'f6f6f6', 'ffffff'],\n",
              "      dtype='object', name='Profile Page Color')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "emwHDJ-c-ziQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "156faf4a-ddb9-4ef3-a26a-68422ffe8253"
      },
      "source": [
        "ppc_test = train_df.groupby(['Profile Theme Color'])['Id', 'Profile Theme Color'].count()\n",
        "ppc_test[ppc_test['Id']>50].index"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['000000', '0099b9', '022330', '131516', '1a1b1f', '9ae4e8', 'c0deed',\n",
              "       'dbe9ed', 'ebebeb', 'ffffff'],\n",
              "      dtype='object', name='Profile Theme Color')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ON85AMw_-ziQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ad0aba96-ee16-4303-f4e5-7c1f177b1f1e"
      },
      "source": [
        "tz_test = train_df.groupby(['User Time Zone'])['Id', 'User Time Zone'].count()\n",
        "tz_test[tz_test['Id']>50].index"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Alaska', 'Amsterdam', 'Arizona', 'Athens', 'Atlantic Time (Canada)',\n",
              "       'Berlin', 'Bern', 'Bogota', 'Brasilia', 'Buenos Aires', 'Caracas',\n",
              "       'Central Time (US & Canada)', 'Eastern Time (US & Canada)', 'Greenland',\n",
              "       'Hawaii', 'Istanbul', 'Jakarta', 'London', 'Madrid', 'Mexico City',\n",
              "       'Mountain Time (US & Canada)', 'Mumbai', 'New Delhi',\n",
              "       'Pacific Time (US & Canada)', 'Paris', 'Quito', 'Riyadh', 'Rome',\n",
              "       'Santiago', 'Tokyo'],\n",
              "      dtype='object', name='User Time Zone')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rjpHvCMU-ziR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b4442c66-883f-41bf-f68d-adb35251b504"
      },
      "source": [
        "train_df['profile_colors'] = train_df[['Profile Text Color','Profile Page Color', 'Profile Theme Color']].fillna('unknown').values.tolist()\n",
        "train_df['profile_colors'].apply(lambda x: len(set(x)))\n"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       3\n",
              "1       3\n",
              "2       2\n",
              "3       3\n",
              "4       3\n",
              "       ..\n",
              "7495    3\n",
              "7496    2\n",
              "7497    3\n",
              "7498    3\n",
              "7499    3\n",
              "Name: profile_colors, Length: 7500, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6yGhRlsl-ziR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fac7b747-0a31-4550-9921-a1fdf2146ea7"
      },
      "source": [
        "train_df.groupby(['User Time Zone'])['Id'].count()"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "User Time Zone\n",
              "Abu Dhabi              33\n",
              "Adelaide                8\n",
              "Africa/Johannesburg     2\n",
              "Alaska                 60\n",
              "Almaty                  2\n",
              "                       ..\n",
              "Warsaw                 17\n",
              "Wellington              8\n",
              "West Central Africa    16\n",
              "Yerevan                 6\n",
              "Zagreb                  3\n",
              "Name: Id, Length: 145, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yst1wgTQ-ziR"
      },
      "source": [
        "column_mean_avg_clicks = train_df['Avg Daily Profile Clicks'].mean()   \n",
        "column_mean_avg_visits = train_df['Avg Daily Profile Visit Duration in seconds'].mean()"
      ],
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x-hHMK6R-ziS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "ef026a5c-f223-4001-e0be-e7eb4482a1d7"
      },
      "source": [
        "train_df['Num of Profile Likes'].hist(bins=100)"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f15aca45198>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1XSxMREd-ziS",
        "outputId": "32a1d1c8-bb77-4179-ce53-393a1e5bb12f"
      },
      "source": [
        "train_df['Num of Followers'].hist(bins=100)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<AxesSubplot:>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 131
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "afQ9sZzV-ziS",
        "outputId": "c78e56c3-0e5c-4878-8c31-893c60c9fcf9"
      },
      "source": [
        "train_df['Num of People Following'].hist(bins=100)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<AxesSubplot:>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 133
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W95oowXe-ziS",
        "outputId": "f803961b-d107-4654-8fd5-fa300ca2d816"
      },
      "source": [
        "train_df['Num of Status Updates'].hist(bins=100)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<AxesSubplot:>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 135
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PJYSBUa5-ziT",
        "outputId": "2aed4975-e393-461e-a1c3-e930a94ab78e"
      },
      "source": [
        "train_df['Num of Direct Messages'].hist(bins=100)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<AxesSubplot:>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 137
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5LC-PxrN-ziT",
        "outputId": "995a8079-243d-4e46-9771-f41a0295af58"
      },
      "source": [
        "log_num_likes = np.log10(1 + train_df['Num of Profile Likes'])\n",
        "log_num_likes.hist(bins=100)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<AxesSubplot:>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 130
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gGmXa6dV-ziT",
        "outputId": "eb201269-31bd-467a-de73-bdfa9e29ee6a"
      },
      "source": [
        "log_num_likes = np.log10(1 + train_df['Num of Followers'])\n",
        "log_num_likes.hist(bins=100)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<AxesSubplot:>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 132
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZHdnZZBz-ziT",
        "outputId": "947246c3-10aa-4328-8787-a41ab3b0a752"
      },
      "source": [
        "log_num_likes = np.log10(1 + train_df['Num of People Following'])\n",
        "log_num_likes.hist(bins=100)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<AxesSubplot:>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 134
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HiEyAK08-ziU",
        "outputId": "87a92550-51c8-49f7-9bf2-be7f9d6e718f"
      },
      "source": [
        "log_num_likes = np.log10(1 + train_df['Num of Status Updates'])\n",
        "log_num_likes.hist(bins=100)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<AxesSubplot:>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 139
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Seup8Y_5-ziU",
        "outputId": "4304cdbd-4660-4ef1-fae7-ffd274e7827f"
      },
      "source": [
        "log_num_likes = np.log10(1 + train_df['Num of Direct Messages'])\n",
        "log_num_likes.hist(bins=100)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<AxesSubplot:>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 138
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6jatEiiw-ziU"
      },
      "source": [
        "#Imports\n",
        "\n",
        "import os\n",
        "import sys\n",
        "\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "\n",
        "import cv2\n",
        "\n",
        "import ipywidgets as widgets\n",
        "from ipywidgets import interact, interact_manual"
      ],
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FdkD68yL-ziU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 642
        },
        "outputId": "43dce4e8-9825-4ad4-a31b-ba47903263a6"
      },
      "source": [
        "img_train_path = '/content/train_profile_images/profile_images_train/'\n",
        "TestImgRGB = cv2.imread(img_train_path+'001MIONMBAF4B471.png',cv2.IMREAD_UNCHANGED)\n",
        "\n",
        "print('DatatypeClass of Image:',type(TestImgRGB))\n",
        "print('Shape/Size of RGB Img:', TestImgRGB.shape)\n",
        "\n",
        "plt.figure(figsize=(10,10))\n",
        "#Note: matplotlib uses RGB format so had to convert BGR-to-RGB\n",
        "plt.imshow(cv2.cvtColor(TestImgRGB,cv2.COLOR_BGR2RGB))\n",
        "plt.title('RGB Image',color='c')\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "DatatypeClass of Image: <class 'numpy.ndarray'>\n",
            "Shape/Size of RGB Img: (32, 32, 3)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yWnUuhWi-ziV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 426
        },
        "outputId": "bd8decc7-4a2a-4f11-9258-24ed53505d87"
      },
      "source": [
        "img_name = []\n",
        "max_pixel = []\n",
        "min_pixel = []\n",
        "mean_pixel = []\n",
        "n_bright_pixel = []\n",
        "n_dark_pixel = []\n",
        "n_color_pixel = []\n",
        "r_mean_pixel = []\n",
        "g_mean_pixel = []\n",
        "b_mean_pixel = []\n",
        "r_central_pixel = []\n",
        "g_central_pixel = []\n",
        "b_central_pixel = []\n",
        "\n",
        "for filename in os.listdir(img_train_path):\n",
        "\n",
        "    img_name.append(filename)\n",
        "    img = np.array(cv2.imread(img_train_path+filename,cv2.IMREAD_UNCHANGED))\n",
        "    max_pixel.append(img.max())\n",
        "    min_pixel.append(img.min())\n",
        "    mean_pixel.append(img.mean())\n",
        "    n_bright_pixel.append(np.sum(img == img.max()))\n",
        "    n_dark_pixel.append(np.sum(img == img.min()))\n",
        "    n_color_pixel.append(np.sum(img > img.min()))\n",
        "    r_mean_pixel.append(img[:,:,0].mean())\n",
        "    g_mean_pixel.append(img[:,:,1].mean())\n",
        "    b_mean_pixel.append(img[:,:,2].mean())\n",
        "    r_central_pixel.append(img[16,16,0])\n",
        "    g_central_pixel.append(img[16,16,1])\n",
        "    b_central_pixel.append(img[16,16,2])\n",
        "\n",
        "# dictionary of lists  \n",
        "img_dict = {'Profile Image': img_name, 'max_pixel': max_pixel,\n",
        "                'min_pixel': min_pixel,'mean_pixel': mean_pixel,'n_bright_pixel': n_bright_pixel,'n_dark_pixel': n_dark_pixel,\n",
        "               'n_color_pixel': n_color_pixel,'r_mean_pixel': r_mean_pixel,'g_mean_pixel': g_mean_pixel,'b_mean_pixel': b_mean_pixel,'max_pixel': max_pixel,\n",
        "               'r_central_pixel': r_central_pixel,'g_central_pixel': g_central_pixel,'b_central_pixel': b_central_pixel}  \n",
        "    \n",
        "img_df = pd.DataFrame(img_dict) \n",
        "   \n",
        "img_df"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<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>Profile Image</th>\n",
              "      <th>max_pixel</th>\n",
              "      <th>min_pixel</th>\n",
              "      <th>mean_pixel</th>\n",
              "      <th>n_bright_pixel</th>\n",
              "      <th>n_dark_pixel</th>\n",
              "      <th>n_color_pixel</th>\n",
              "      <th>r_mean_pixel</th>\n",
              "      <th>g_mean_pixel</th>\n",
              "      <th>b_mean_pixel</th>\n",
              "      <th>r_central_pixel</th>\n",
              "      <th>g_central_pixel</th>\n",
              "      <th>b_central_pixel</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>29KKK5HDS9ES6M4N.png</td>\n",
              "      <td>233</td>\n",
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              "      <td>27.904297</td>\n",
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              "      <td>26.093750</td>\n",
              "      <td>26.625000</td>\n",
              "      <td>30.994141</td>\n",
              "      <td>35</td>\n",
              "      <td>52</td>\n",
              "      <td>176</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>OAM8KJSADJI6RGDP.png</td>\n",
              "      <td>242</td>\n",
              "      <td>1</td>\n",
              "      <td>35.864258</td>\n",
              "      <td>1</td>\n",
              "      <td>1659</td>\n",
              "      <td>1413</td>\n",
              "      <td>33.880859</td>\n",
              "      <td>49.113281</td>\n",
              "      <td>24.598633</td>\n",
              "      <td>123</td>\n",
              "      <td>106</td>\n",
              "      <td>45</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>9HBQNHS2CS9ROS7L.png</td>\n",
              "      <td>240</td>\n",
              "      <td>1</td>\n",
              "      <td>34.337240</td>\n",
              "      <td>1</td>\n",
              "      <td>1620</td>\n",
              "      <td>1452</td>\n",
              "      <td>36.479492</td>\n",
              "      <td>28.281250</td>\n",
              "      <td>38.250977</td>\n",
              "      <td>161</td>\n",
              "      <td>56</td>\n",
              "      <td>70</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>BL5SDBC40A93K6GQ.png</td>\n",
              "      <td>230</td>\n",
              "      <td>1</td>\n",
              "      <td>32.836589</td>\n",
              "      <td>1</td>\n",
              "      <td>1989</td>\n",
              "      <td>1083</td>\n",
              "      <td>35.476562</td>\n",
              "      <td>28.265625</td>\n",
              "      <td>34.767578</td>\n",
              "      <td>191</td>\n",
              "      <td>87</td>\n",
              "      <td>152</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>R7JF67EI1R9G2AT4.png</td>\n",
              "      <td>221</td>\n",
              "      <td>1</td>\n",
              "      <td>39.825846</td>\n",
              "      <td>3</td>\n",
              "      <td>1828</td>\n",
              "      <td>1244</td>\n",
              "      <td>45.420898</td>\n",
              "      <td>41.461914</td>\n",
              "      <td>32.594727</td>\n",
              "      <td>206</td>\n",
              "      <td>193</td>\n",
              "      <td>160</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",
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              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>7500 rows × 13 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "             Profile Image  max_pixel  ...  g_central_pixel  b_central_pixel\n",
              "0     29KKK5HDS9ES6M4N.png        233  ...               52              176\n",
              "1     OAM8KJSADJI6RGDP.png        242  ...              106               45\n",
              "2     9HBQNHS2CS9ROS7L.png        240  ...               56               70\n",
              "3     BL5SDBC40A93K6GQ.png        230  ...               87              152\n",
              "4     R7JF67EI1R9G2AT4.png        221  ...              193              160\n",
              "...                    ...        ...  ...              ...              ...\n",
              "7495  A6IJQ0G3D06H9N29.png        249  ...              155               59\n",
              "7496  9F04JLR9JGT8R3EJ.png        208  ...              167               69\n",
              "7497  NCB0GTMJB9EEPM3Q.png        243  ...              153              145\n",
              "7498  B98HB2JRFFBFBAMM.png        214  ...              125              211\n",
              "7499  TBOTOH7A4K9HCF8E.png        223  ...              194              160\n",
              "\n",
              "[7500 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1CN2_7yL-ziV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 167
        },
        "outputId": "88539a21-5c67-402f-a406-9e431f5f9890"
      },
      "source": [
        "max_pixel.shape"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-42-160f6377f19f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmax_pixel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m: 'list' object has no attribute 'shape'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uW1nLmJk-ziV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b2db7b9c-e858-45f4-88b5-2aa122f2069f"
      },
      "source": [
        "test_f = []\n",
        "for filename in os.listdir(img_train_path):\n",
        "    test_f.append(filename)\n",
        "test_f"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              " '0CM750R57C9P6OR8.png',\n",
              " '8BN9JHBNE6BISRO9.png',\n",
              " '89LBF4D3GNP4R2AG.png',\n",
              " 'N12GL0STJC03817L.png',\n",
              " 'DTAE2BNGA0EGRS8T.png',\n",
              " 'K3487935O9JBCERA.png',\n",
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              " 'L0GE7LI2TBSC1I0K.png',\n",
              " '6HB87LIIRNQIFI54.png',\n",
              " ...]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "knlhK-Ls-ziW"
      },
      "source": [
        "img = np.array(TestImgRGB)\n",
        "max_pixel = img.max()\n",
        "min_pixel = img.min()\n",
        "mean_pixel = img.mean()\n",
        "n_bright_pixel = np.sum(img == max_pixel)\n",
        "n_dark_pixel = np.sum(img == min_pixel)\n",
        "n_color_pixel = np.sum(img > min_pixel)\n",
        "r_mean_pixel = img[:,:,0].mean()\n",
        "g_mean_pixel = img[:,:,1].mean()\n",
        "b_mean_pixel = img[:,:,2].mean()\n",
        "r_central_pixel = img[16,16,0]\n",
        "g_central_pixel = img[16,16,1]\n",
        "b_central_pixel = img[16,16,2]"
      ],
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Kc6he85X-ziW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "433bc72c-1f1e-4115-b1e4-4418a3e9f15e"
      },
      "source": [
        "np.sum(img > img.min())"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1473"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0XhDc95A-ziW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d06be6c9-fa0d-4115-8536-53795d110c80"
      },
      "source": [
        "img[:,:,2].mean()"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "40.4736328125"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e-CKfNIt-ziW"
      },
      "source": [
        "img_train_path = '/content/train_profile_images/profile_images_train/'"
      ],
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vn4Q_bX2-ziX"
      },
      "source": [
        "# Pre-processing\n",
        "def pre_process(data):\n",
        "  \n",
        "  #data = data.merge(pd.get_dummies(data['Pclass'], prefix='Pclass'), left_index=True, right_index=True)\n",
        "\n",
        "  data['user_name_len'] = data['User Name'].fillna('').apply(lambda x: len(str(x)))\n",
        "  data['url_len'] = data['Personal URL'].fillna('').apply(lambda x: len(str(x)))\n",
        "  \n",
        "  data.loc[data['Personal URL'].notnull(), 'url_given']=1\n",
        "  data.loc[data['Personal URL'].isnull(), 'url_given']=0\n",
        "    \n",
        "  data.loc[data['Is Profile View Size Customized?']==True, 'profile_customized']=1\n",
        "  data.loc[data['Is Profile View Size Customized?']==False, 'profile_customized']=0\n",
        "    \n",
        "  data.loc[data['UTC Offset'].notnull(), 'offset_given']=1\n",
        "  data.loc[data['UTC Offset'].isnull(), 'offset_given']=0\n",
        "    \n",
        "  data.loc[data['Location'].notnull(), 'loc_given']=1\n",
        "  data.loc[data['Location'].isnull(), 'loc_given']=0\n",
        "    \n",
        "  data['Location Public Visibility'] = data['Location Public Visibility'].replace(['Enabled', 'Disabled', '??'], ['enabled', 'disabled', 'unknown'])\n",
        "\n",
        "  data['loc_len'] = data['Location'].fillna('').apply(lambda x: len(str(x)))\n",
        "  \n",
        "  '''\n",
        "    data.loc[data['Location'].notnull(), 'loc_given']=1\n",
        "  data.loc[data['Location'].isnull(), 'loc_given']=0\n",
        "    \n",
        "  data.loc[data['Location'].fillna('').str.contains(pat = '#', regex = True), 'loc_contains_hashtag']=1\n",
        "  data.loc[data['Location'].fillna('').str.contains(pat = '#', regex = True)==False, 'loc_contains_hashtag']=0\n",
        "  data.loc[data['Location'].fillna('').str.contains(pat = '@', regex = True), 'loc_contains_email']=1\n",
        "  data.loc[data['Location'].fillna('').str.contains(pat = '@', regex = True)==False, 'loc_contains_email']=0\n",
        "  data.loc[data['Location'].fillna('').str.contains(pat = '\\d', regex = True), 'loc_contains_digit']=1\n",
        "  data.loc[data['Location'].fillna('').str.contains(pat = '\\d', regex = True)==False, 'loc_contains_digit']=0\n",
        "    \n",
        "  data['Location']=data.fillna('unknown').replace(['Chicago, IL', 'Cairo, Egypt', 'Jakarta, Indonesia', 'Las Vegas', 'London, England',\n",
        "                              'London, UK','California, USA','Hollywood, CA', 'Los Angeles, CA','Los Angeles', 'Los Angeles, California','LA','San Francisco, CA','San Francisco','México','Ciudad de México','Sydney, Australia',\n",
        "                              'Caracas, Venezuela','Buenos Aires, Argentina','São Paulo', 'São Paulo, Brasil', 'Brazil','Rio de Janeiro, Brasil','São Paulo - Brasil',\n",
        "                             'Rio de Janeiro','Santiago, Chile','Mumbai', 'Mumbai, India','New Delhi, India','İstanbul','istanbul','İstanbul, Türkiye','Türkiye',\n",
        "                              'Istanbul','Paris', 'Paris, France','NYC','New York City', 'New York, New York','New York, NY','Barcelona','Madrid','Madrid, España', 'Madrid, Spain',\n",
        "                             'Washington, D.C.','Everywhere','Global','Earth','Manila, Philippines','UK','England', 'England, United Kingdom','USA','Bogotá, Colombia',\n",
        "                            'Dubai, UAE', 'Dubai, United Arab Emirates','Dubai','Italia','Milano','Amsterdam','Lima, Perú','Toronto','Kingdom of Saudi Arabia','Tokyo', 'Tokyo, Japan',], \n",
        "                             ['Chicago', 'Egypt', 'Jakarta', 'Las Vegas, NV', 'London', 'London','California','California','California','California','California','California','California','California','Mexico','Mexico',\n",
        "                             'Australia','Venezuela','Argentina','Brasil','Brasil','Brasil','Brasil','Brasil','Brasil','Chile','India','India','India','Turkey','Turkey',\n",
        "                              'Turkey','Turkey','Turkey','France','France','New York','New York','New York','New York','España','España','España','España','Washington, DC',\n",
        "                             'Worldwide','Worldwide','Worldwide','Philippines','United Kingdom','United Kingdom','United Kingdom','United States','Colombia','United Arab Emirates','United Arab Emirates','United Arab Emirates',\n",
        "                             'Italy','Italy','Nederland','Perú','Canada','Saudi Arabia', 'Japan','Japan',])\n",
        "\n",
        "  data.loc[data['Location'].isin(['Argentina', 'Atlanta, GA', 'Austin, TX', 'Australia', 'Boston, MA',\n",
        "       'Brasil', 'California', 'Canada', 'Charlotte, NC', 'Chicago', 'Chile',\n",
        "       'Colombia', 'Dallas, TX', 'Detroit, MI', 'Doha, Qatar',\n",
        "       'Dublin, Ireland', 'Ecuador', 'Egypt', 'España', 'France', 'Germany',\n",
        "       'Hong Kong', 'Houston, TX', 'India', 'Indianapolis, IN', 'Indonesia',\n",
        "       'Italy', 'Jakarta', 'Japan', 'Kuwait', 'Las Vegas, NV', 'London',\n",
        "       'Malaysia', 'Mexico', 'Miami, FL', 'Minneapolis, MN', 'Nashville, TN',\n",
        "       'Nederland', 'New Orleans, LA', 'New York', 'New Zealand', 'Nigeria',\n",
        "       'North America', 'Orlando, FL', 'Oxford, MS', 'Pakistan', 'Panamá',\n",
        "       'Perú', 'Philadelphia, PA', 'Philippines', 'Pittsburgh, PA',\n",
        "       'Portland, OR', 'Redmond, WA', 'Santa Monica, CA', 'Saudi Arabia',\n",
        "       'Seattle, WA', 'Seoul, Korea', 'Singapore', 'South Africa',\n",
        "       'Stanford, CA', 'Turkey', 'United Arab Emirates', 'United Kingdom',\n",
        "       'United States', 'Venezuela', 'Washington, DC', 'Worldwide', 'Москва',\n",
        "       'المملكة العربية السعودية','unknown'])==False, 'Location']='other'\n",
        "\n",
        "  '''\n",
        "  data.loc[data['User Language'].isin(['ar', 'de', 'en', 'en-gb', 'es', 'fr', 'it', 'ja', 'ko', 'nl', 'pt',\n",
        "       'ru', 'tr'])==False, 'User Language']='other'  \n",
        "\n",
        "  data['Profile Cover Image Status'] = data['Profile Cover Image Status'].fillna('unknown')\n",
        "\n",
        "  data['Profile Verification Status'] = data['Profile Verification Status'].fillna('unknown')\n",
        "\n",
        "  data['Profile Category'] = data['Profile Category'].replace([' '], ['unknown'])  \n",
        "\n",
        "\n",
        "  data['Avg Daily Profile Clicks'] = data['Avg Daily Profile Clicks'].fillna(column_mean_avg_clicks)\n",
        "    \n",
        "  data['Avg Daily Profile Visit Duration in seconds'] = data['Avg Daily Profile Visit Duration in seconds'].fillna(column_mean_avg_visits)\n",
        "\n",
        "  data['Profile Text Color'] = data['Profile Text Color'].fillna('unknown')\n",
        "  data.loc[data['Profile Text Color'].isin(['000000', '0084b4', '009999', '0099b9', '1b95e0', '1da1f2', '2fc2ef',\n",
        "       '3b94d9', '4a913c', '990000', 'abb8c2', 'cc3366', 'dd2e44', 'e81c4f',\n",
        "       'ff0000', 'ff691f','unknown'])==False, 'Profile Text Color']='other'\n",
        "    \n",
        "  data['Profile Page Color'] = data['Profile Page Color'].fillna('unknown')\n",
        "  data.loc[data['Profile Page Color'].isin(['000000', '252429', '7ac3ee', '95e8ec', 'c0dfec', 'ddeef6', 'ddffcc',\n",
        "       'e3e2de', 'e6f6f9', 'efefef', 'f3f3f3', 'f6f6f6', 'ffffff','unknown'])==False, 'Profile Page Color']='other'\n",
        "\n",
        "  data['Profile Theme Color'] = data['Profile Theme Color'].fillna('unknown')\n",
        "  data.loc[data['Profile Theme Color'].isin(['000000', '0099b9', '022330', '131516', '1a1b1f', '9ae4e8', 'c0deed',\n",
        "       'dbe9ed', 'ebebeb', 'ffffff','unknown'])==False, 'Profile Theme Color']='other'\n",
        "\n",
        "  data['profile_colors'] = data[['Profile Text Color','Profile Page Color', 'Profile Theme Color']].values.tolist()\n",
        "  data['profile_colors'] = data['profile_colors'].apply(lambda x: len(set(x)))\n",
        "    \n",
        "  data['User Time Zone'] = data['User Time Zone'].fillna('unknown')\n",
        "  data.loc[data['User Time Zone'].isin(['Alaska', 'Amsterdam', 'Arizona', 'Athens', 'Atlantic Time (Canada)',\n",
        "       'Berlin', 'Bern', 'Bogota', 'Brasilia', 'Buenos Aires', 'Caracas',\n",
        "       'Central Time (US & Canada)', 'Eastern Time (US & Canada)', 'Greenland',\n",
        "       'Hawaii', 'Istanbul', 'Jakarta', 'London', 'Madrid', 'Mexico City',\n",
        "       'Mountain Time (US & Canada)', 'Mumbai', 'New Delhi',\n",
        "       'Pacific Time (US & Canada)', 'Paris', 'Quito', 'Riyadh', 'Rome',\n",
        "       'Santiago', 'Tokyo','unknown'])==False, 'User Time Zone']='other'\n",
        "  \n",
        "  #Profile Creation Timestamp: convert to long numeric (the year matters)\n",
        "  data['days_since_profile_creation'] = (pd.to_datetime('Tue Dec 01 00:00:00 +0000 2020') - pd.to_datetime(train_df['Profile Creation Timestamp'])).astype('timedelta64[D]')\n",
        "  \n",
        "  data['profile_clicks_per_visitor'] = data['Avg Daily Profile Clicks']*data['days_since_profile_creation']\n",
        "  data['visits_duration_per_visitor'] = data['Avg Daily Profile Visit Duration in seconds']*data['days_since_profile_creation']\n",
        "\n",
        "  data['log_num_followers'] = np.log10(1 + data['Num of Followers'])\n",
        "  data['log_num_people_following'] = np.log10(1 + data['Num of People Following'])\n",
        "  data['log_num_status_updates'] = np.log10(1 + data['Num of Status Updates'])\n",
        "  data['log_num_direct_messages'] = np.log10(1 + data['Num of Direct Messages'])\n",
        "   \n",
        "  data['daily_profile_clicks'] = data['Avg Daily Profile Clicks']*data['Num of Followers']\n",
        "  data['daily_visits_duration'] = data['Avg Daily Profile Visit Duration in seconds']*data['Num of Followers']\n",
        "  data['profile_clicks'] = data['Avg Daily Profile Clicks']*data['days_since_profile_creation']*data['Num of Followers']\n",
        "  data['visits_duration'] = data['Avg Daily Profile Visit Duration in seconds']*data['days_since_profile_creation']*data['Num of Followers']\n",
        "\n",
        "  data['log_daily_profile_clicks'] = np.log10(1 + data['daily_profile_clicks'])\n",
        "  data['log_daily_visits_duration'] = np.log10(1 + data['daily_visits_duration'])\n",
        "  data['log_profile_clicks'] = np.log10(1 + data['profile_clicks'])\n",
        "  data['log_visits_duration'] = np.log10(1 + data['visits_duration'])\n",
        "    \n",
        "  if 'Num of Profile Likes' in data.columns:\n",
        "    data['log_num_likes'] = np.log10(1 + data['Num of Profile Likes'])\n",
        "\n",
        "  return data\n",
        "\n",
        "train_df_p = pre_process(train_df)\n",
        "test_df_p = pre_process(test_df) "
      ],
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SZAP3aSx-ziX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0d2388aa-601e-468e-a0a3-77886997c383"
      },
      "source": [
        "if 'Num of Profile Likes' in train_df.columns: print('yes')"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "yes\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NBqLSi_p-ziX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f0f3b393-f437-49b9-cd09-ee1a65d185a9"
      },
      "source": [
        "train_df_p.columns"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Id', 'User Name', 'Personal URL', 'Profile Cover Image Status',\n",
              "       'Profile Verification Status', 'Profile Text Color',\n",
              "       'Profile Page Color', 'Profile Theme Color',\n",
              "       'Is Profile View Size Customized?', 'UTC Offset', 'Location',\n",
              "       'Location Public Visibility', 'User Language',\n",
              "       'Profile Creation Timestamp', 'User Time Zone', 'Num of Followers',\n",
              "       'Num of People Following', 'Num of Status Updates',\n",
              "       'Num of Direct Messages', 'Profile Category',\n",
              "       'Avg Daily Profile Visit Duration in seconds',\n",
              "       'Avg Daily Profile Clicks', 'Profile Image', 'Num of Profile Likes',\n",
              "       'profile_colors', 'user_name_len', 'url_len', 'url_given',\n",
              "       'profile_customized', 'offset_given', 'loc_given', 'loc_len',\n",
              "       'days_since_profile_creation', 'profile_clicks_per_visitor',\n",
              "       'visits_duration_per_visitor', 'log_num_followers',\n",
              "       'log_num_people_following', 'log_num_status_updates',\n",
              "       'log_num_direct_messages', 'daily_profile_clicks',\n",
              "       'daily_visits_duration', 'profile_clicks', 'visits_duration',\n",
              "       'log_daily_profile_clicks', 'log_daily_visits_duration',\n",
              "       'log_profile_clicks', 'log_visits_duration', 'log_num_likes'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eMvdeAou-ziX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d14b3128-ce37-431f-a893-54edadbd8d90"
      },
      "source": [
        "train_df_p['Location']"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0                                NaN\n",
              "1                             mumbai\n",
              "2       NYC + 70 Countries Worldwide\n",
              "3                   Indianapolis, In\n",
              "4         777 Beach Blvd. Biloxi, MS\n",
              "                    ...             \n",
              "7495                             NaN\n",
              "7496                  Belo Horizonte\n",
              "7497               Stockholm, Sweden\n",
              "7498                          Brasil\n",
              "7499                      DMは見ていません。\n",
              "Name: Location, Length: 7500, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TDP6LuofWxPl"
      },
      "source": [
        "cols_x = ['Profile Cover Image Status','url_len',\n",
        "       'Profile Verification Status', 'Profile Text Color',\n",
        "       'Profile Page Color', 'Profile Theme Color',\n",
        "       'Location Public Visibility', 'User Language', 'Profile Category',\n",
        "       'User Time Zone', 'Num of Followers', 'offset_given',\n",
        "       'Num of People Following', 'Num of Status Updates','Num of Direct Messages',\n",
        "       'Avg Daily Profile Visit Duration in seconds','Avg Daily Profile Clicks',\n",
        "       'profile_colors', 'user_name_len', 'url_given', 'profile_customized',\n",
        "       'days_since_profile_creation', 'profile_clicks_per_visitor',\n",
        "       'visits_duration_per_visitor', 'daily_profile_clicks',\n",
        "       'daily_visits_duration', 'profile_clicks', 'visits_duration']\n",
        "\n",
        "cols_x_log = ['Profile Cover Image Status','url_len',\n",
        "       'Profile Verification Status', 'Profile Text Color',\n",
        "       'Profile Page Color', 'Profile Theme Color',\n",
        "       'Location Public Visibility', 'User Language', 'Profile Category',\n",
        "       'User Time Zone', 'offset_given', 'log_num_followers',\n",
        "       'log_num_people_following', 'log_num_status_updates',\n",
        "       'log_num_direct_messages',\n",
        "       'Avg Daily Profile Visit Duration in seconds','Avg Daily Profile Clicks',\n",
        "       'profile_colors', 'user_name_len', 'url_given', 'profile_customized',\n",
        "       'days_since_profile_creation', 'profile_clicks_per_visitor',\n",
        "       'visits_duration_per_visitor', 'log_daily_profile_clicks', 'log_daily_visits_duration',\n",
        "       'log_profile_clicks', 'log_visits_duration']\n",
        "\n",
        "cols_y = 'log_num_likes'"
      ],
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3LQa4SqRxU5W"
      },
      "source": [
        "x = train_df_p[cols_x]\n",
        "x_log = train_df_p[cols_x_log]\n",
        "y = train_df_p[cols_y]\n",
        "\n",
        "# split dataset to train and test\n",
        "X_train, X_test, y_train, y_test = train_test_split(x_log, y, test_size=0.1, shuffle=True, random_state=76) #  To guarantee that the split will always be the same \n",
        "\n",
        "#one hot encoding + log transformation\n",
        "X_train_dum = pd.get_dummies(X_train)\n",
        "X_test_dum = pd.get_dummies(X_test)\n",
        "y_test = np.around(np.maximum(10**y_test - 1, 0))"
      ],
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MGT5nQqO-ziY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5183d2b5-ad31-4f93-ca86-af58fcf729f6"
      },
      "source": [
        "i = 0\n",
        "for c in X_train_dum.columns:\n",
        "    print(i, c)\n",
        "    i+=1\n",
        "print(i)"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0 url_len\n",
            "1 offset_given\n",
            "2 log_num_followers\n",
            "3 log_num_people_following\n",
            "4 log_num_status_updates\n",
            "5 log_num_direct_messages\n",
            "6 Avg Daily Profile Visit Duration in seconds\n",
            "7 Avg Daily Profile Clicks\n",
            "8 profile_colors\n",
            "9 user_name_len\n",
            "10 url_given\n",
            "11 profile_customized\n",
            "12 days_since_profile_creation\n",
            "13 profile_clicks_per_visitor\n",
            "14 visits_duration_per_visitor\n",
            "15 log_daily_profile_clicks\n",
            "16 log_daily_visits_duration\n",
            "17 log_profile_clicks\n",
            "18 log_visits_duration\n",
            "19 Profile Cover Image Status_Not set\n",
            "20 Profile Cover Image Status_Set\n",
            "21 Profile Cover Image Status_unknown\n",
            "22 Profile Verification Status_Not verified\n",
            "23 Profile Verification Status_Pending\n",
            "24 Profile Verification Status_Verified\n",
            "25 Profile Text Color_000000\n",
            "26 Profile Text Color_0084b4\n",
            "27 Profile Text Color_009999\n",
            "28 Profile Text Color_0099b9\n",
            "29 Profile Text Color_1b95e0\n",
            "30 Profile Text Color_1da1f2\n",
            "31 Profile Text Color_2fc2ef\n",
            "32 Profile Text Color_3b94d9\n",
            "33 Profile Text Color_4a913c\n",
            "34 Profile Text Color_990000\n",
            "35 Profile Text Color_abb8c2\n",
            "36 Profile Text Color_cc3366\n",
            "37 Profile Text Color_dd2e44\n",
            "38 Profile Text Color_e81c4f\n",
            "39 Profile Text Color_ff0000\n",
            "40 Profile Text Color_ff691f\n",
            "41 Profile Text Color_other\n",
            "42 Profile Text Color_unknown\n",
            "43 Profile Page Color_000000\n",
            "44 Profile Page Color_252429\n",
            "45 Profile Page Color_7ac3ee\n",
            "46 Profile Page Color_95e8ec\n",
            "47 Profile Page Color_c0dfec\n",
            "48 Profile Page Color_ddeef6\n",
            "49 Profile Page Color_ddffcc\n",
            "50 Profile Page Color_e3e2de\n",
            "51 Profile Page Color_e6f6f9\n",
            "52 Profile Page Color_efefef\n",
            "53 Profile Page Color_f3f3f3\n",
            "54 Profile Page Color_f6f6f6\n",
            "55 Profile Page Color_ffffff\n",
            "56 Profile Page Color_other\n",
            "57 Profile Page Color_unknown\n",
            "58 Profile Theme Color_000000\n",
            "59 Profile Theme Color_0099b9\n",
            "60 Profile Theme Color_022330\n",
            "61 Profile Theme Color_131516\n",
            "62 Profile Theme Color_1a1b1f\n",
            "63 Profile Theme Color_9ae4e8\n",
            "64 Profile Theme Color_c0deed\n",
            "65 Profile Theme Color_dbe9ed\n",
            "66 Profile Theme Color_ebebeb\n",
            "67 Profile Theme Color_ffffff\n",
            "68 Profile Theme Color_other\n",
            "69 Profile Theme Color_unknown\n",
            "70 Location Public Visibility_disabled\n",
            "71 Location Public Visibility_enabled\n",
            "72 Location Public Visibility_unknown\n",
            "73 User Language_ar\n",
            "74 User Language_de\n",
            "75 User Language_en\n",
            "76 User Language_en-gb\n",
            "77 User Language_es\n",
            "78 User Language_fr\n",
            "79 User Language_it\n",
            "80 User Language_ja\n",
            "81 User Language_ko\n",
            "82 User Language_nl\n",
            "83 User Language_other\n",
            "84 User Language_pt\n",
            "85 User Language_ru\n",
            "86 User Language_tr\n",
            "87 Profile Category_business\n",
            "88 Profile Category_celebrity\n",
            "89 Profile Category_government\n",
            "90 Profile Category_unknown\n",
            "91 User Time Zone_Alaska\n",
            "92 User Time Zone_Amsterdam\n",
            "93 User Time Zone_Arizona\n",
            "94 User Time Zone_Athens\n",
            "95 User Time Zone_Atlantic Time (Canada)\n",
            "96 User Time Zone_Berlin\n",
            "97 User Time Zone_Bern\n",
            "98 User Time Zone_Bogota\n",
            "99 User Time Zone_Brasilia\n",
            "100 User Time Zone_Buenos Aires\n",
            "101 User Time Zone_Caracas\n",
            "102 User Time Zone_Central Time (US & Canada)\n",
            "103 User Time Zone_Eastern Time (US & Canada)\n",
            "104 User Time Zone_Greenland\n",
            "105 User Time Zone_Hawaii\n",
            "106 User Time Zone_Istanbul\n",
            "107 User Time Zone_Jakarta\n",
            "108 User Time Zone_London\n",
            "109 User Time Zone_Madrid\n",
            "110 User Time Zone_Mexico City\n",
            "111 User Time Zone_Mountain Time (US & Canada)\n",
            "112 User Time Zone_Mumbai\n",
            "113 User Time Zone_New Delhi\n",
            "114 User Time Zone_Pacific Time (US & Canada)\n",
            "115 User Time Zone_Paris\n",
            "116 User Time Zone_Quito\n",
            "117 User Time Zone_Riyadh\n",
            "118 User Time Zone_Rome\n",
            "119 User Time Zone_Santiago\n",
            "120 User Time Zone_Tokyo\n",
            "121 User Time Zone_other\n",
            "122 User Time Zone_unknown\n",
            "123\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2SIh46K6-ziY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "09fc13af-9b40-41eb-b16f-468b90b4ea41"
      },
      "source": [
        "set(X_train_dum) - set(X_test_dum)"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "set()"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RVnTAQDXMucA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "df33517f-ebe4-404d-efeb-ecb95727f60c"
      },
      "source": [
        "r2 = RandomForestRegressor(n_estimators=2000,random_state=67, max_depth=15, n_jobs=-1)\n",
        "r2 = r2.fit(X_train_dum,y_train)\n",
        "ypred = r2.predict(X_test_dum)\n",
        "\n",
        "ypred_original=10**ypred - 1\n",
        "ypred_original = np.around(np.maximum(ypred_original, 0))\n",
        "\n",
        "print(mean_squared_log_error(y_test,ypred_original))"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2.866029592808267\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R432e0gX-zia",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "646270de-780e-45b0-c0ec-f3291113d4a9"
      },
      "source": [
        "importances = r2.feature_importances_\n",
        "std = np.std([tree.feature_importances_ for tree in r2.estimators_],\n",
        "             axis=0)\n",
        "indices = np.argsort(importances)[::-1]\n",
        "\n",
        "# Print the feature ranking\n",
        "print(\"Feature ranking:\")\n",
        "\n",
        "for f in range(X_train_dum.shape[1]):\n",
        "    print(\"{}. {} ({})\".format(f + 1, X_train_dum.columns[indices[f]], importances[indices[f]]))\n",
        "\n",
        "# Plot the impurity-based feature importances of the forest\n",
        "plt.figure()\n",
        "plt.title(\"Feature importances\")\n",
        "plt.bar(range(X_train_dum.shape[1]), importances[indices],\n",
        "        color=\"r\", yerr=std[indices], align=\"center\")\n",
        "plt.xticks(range(X_train_dum.shape[1]), indices)\n",
        "plt.xlim([-1, X_train_dum.shape[1]])\n",
        "plt.show()"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Feature ranking:\n",
            "1. url_len (0.20364551510180223)\n",
            "2. log_num_status_updates (0.14500381934433007)\n",
            "3. log_num_people_following (0.13762463108010248)\n",
            "4. url_given (0.10193505152140514)\n",
            "5. log_num_direct_messages (0.03305564110887232)\n",
            "6. Avg Daily Profile Clicks (0.03302541762028086)\n",
            "7. days_since_profile_creation (0.031054923271867763)\n",
            "8. profile_clicks_per_visitor (0.023909876907646895)\n",
            "9. Avg Daily Profile Visit Duration in seconds (0.022980865963598072)\n",
            "10. visits_duration_per_visitor (0.020645437580151495)\n",
            "11. log_num_followers (0.01975524398018645)\n",
            "12. log_daily_visits_duration (0.016544173769607252)\n",
            "13. log_visits_duration (0.0152356434104546)\n",
            "14. log_profile_clicks (0.014311894105580201)\n",
            "15. Location Public Visibility_disabled (0.013955759603552926)\n",
            "16. log_daily_profile_clicks (0.013770831033432138)\n",
            "17. user_name_len (0.013345813662383648)\n",
            "18. Profile Verification Status_Verified (0.0067068246707929925)\n",
            "19. Location Public Visibility_enabled (0.006099844548622408)\n",
            "20. User Language_ja (0.005788806737599727)\n",
            "21. User Time Zone_Tokyo (0.0037811643509550142)\n",
            "22. profile_colors (0.003724197130313416)\n",
            "23. User Language_en (0.0033697630804823435)\n",
            "24. User Time Zone_Pacific Time (US & Canada) (0.003220122762182397)\n",
            "25. Profile Verification Status_Not verified (0.003205089448460048)\n",
            "26. User Time Zone_Istanbul (0.0029582851748897665)\n",
            "27. Profile Text Color_other (0.0028841821810261774)\n",
            "28. User Time Zone_Jakarta (0.002764117412655089)\n",
            "29. profile_customized (0.002604951215978029)\n",
            "30. Profile Theme Color_c0deed (0.0025815432982494477)\n",
            "31. Profile Page Color_ddeef6 (0.002445406886720282)\n",
            "32. Profile Text Color_1da1f2 (0.0023275557665348454)\n",
            "33. User Time Zone_Brasilia (0.0022497460465231668)\n",
            "34. User Time Zone_other (0.0022070157942657197)\n",
            "35. Profile Text Color_0084b4 (0.0021922372824963042)\n",
            "36. Profile Theme Color_other (0.002191869200728867)\n",
            "37. Profile Theme Color_000000 (0.0021462534382209056)\n",
            "38. Profile Verification Status_Pending (0.0019737684088370873)\n",
            "39. User Time Zone_Central Time (US & Canada) (0.0019420055341081235)\n",
            "40. Profile Page Color_other (0.0018989683276612744)\n",
            "41. Profile Category_celebrity (0.0018127154002164176)\n",
            "42. Profile Category_business (0.0017875116517821754)\n",
            "43. User Language_es (0.0017531457531686841)\n",
            "44. User Language_ko (0.0017482754108269886)\n",
            "45. User Language_tr (0.0016649167921750482)\n",
            "46. User Language_pt (0.0016600854506410667)\n",
            "47. Profile Cover Image Status_Set (0.0016599014305145009)\n",
            "48. User Language_ru (0.0015554545640682812)\n",
            "49. Profile Page Color_efefef (0.0015473503409275538)\n",
            "50. Profile Cover Image Status_Not set (0.001544321258006304)\n",
            "51. Profile Text Color_ff0000 (0.0015066021515908723)\n",
            "52. User Time Zone_Hawaii (0.001479631098042063)\n",
            "53. Profile Theme Color_131516 (0.0014770737010548064)\n",
            "54. Profile Text Color_009999 (0.0014571326875609688)\n",
            "55. Profile Category_unknown (0.0014387135094053114)\n",
            "56. User Time Zone_Eastern Time (US & Canada) (0.001407427733945337)\n",
            "57. Profile Category_government (0.0014062894106507432)\n",
            "58. User Time Zone_Quito (0.0013853407511014593)\n",
            "59. Profile Theme Color_ffffff (0.0012499185576202534)\n",
            "60. User Time Zone_London (0.0012238423151221883)\n",
            "61. User Time Zone_Santiago (0.0011935974672792693)\n",
            "62. Profile Page Color_000000 (0.0011523950533871116)\n",
            "63. Profile Page Color_252429 (0.0011418824567982432)\n",
            "64. Location Public Visibility_unknown (0.0011034277445736763)\n",
            "65. Profile Text Color_3b94d9 (0.001032519363619082)\n",
            "66. User Time Zone_Riyadh (0.000985734276393929)\n",
            "67. User Time Zone_unknown (0.0009751451312864059)\n",
            "68. User Time Zone_Caracas (0.0009700164575592822)\n",
            "69. Profile Page Color_ffffff (0.0009605816983556813)\n",
            "70. User Time Zone_Mexico City (0.0009464942924420751)\n",
            "71. offset_given (0.0009312370550972372)\n",
            "72. User Time Zone_Greenland (0.0008802328676450329)\n",
            "73. Profile Page Color_ddffcc (0.0008667414210173772)\n",
            "74. Profile Page Color_e3e2de (0.0008539473546322235)\n",
            "75. User Time Zone_Mumbai (0.0008451290541221113)\n",
            "76. Profile Page Color_f6f6f6 (0.0008241143151253342)\n",
            "77. Profile Page Color_f3f3f3 (0.0008115511213175593)\n",
            "78. User Time Zone_Amsterdam (0.0007812346961688876)\n",
            "79. Profile Theme Color_9ae4e8 (0.0007590189490256639)\n",
            "80. User Language_fr (0.0007459423300064426)\n",
            "81. User Language_ar (0.0007167238714655051)\n",
            "82. Profile Text Color_2fc2ef (0.000699125453001308)\n",
            "83. Profile Text Color_000000 (0.0006733722524928074)\n",
            "84. User Language_it (0.0006730354842688478)\n",
            "85. User Time Zone_Madrid (0.0006714126278677858)\n",
            "86. Profile Page Color_7ac3ee (0.0006626416123705329)\n",
            "87. User Language_en-gb (0.0006624252525269044)\n",
            "88. Profile Page Color_95e8ec (0.000646804975404929)\n",
            "89. User Time Zone_Rome (0.0006301342213144832)\n",
            "90. User Time Zone_Atlantic Time (Canada) (0.0006192449247435095)\n",
            "91. User Time Zone_Athens (0.0006014736457157869)\n",
            "92. Profile Text Color_unknown (0.0005673981722015508)\n",
            "93. Profile Text Color_1b95e0 (0.0005623885084240901)\n",
            "94. Profile Text Color_dd2e44 (0.0005580195897195923)\n",
            "95. Profile Theme Color_1a1b1f (0.000548736339446422)\n",
            "96. User Time Zone_Buenos Aires (0.0005240191771607607)\n",
            "97. Profile Theme Color_unknown (0.0005224304825740646)\n",
            "98. Profile Cover Image Status_unknown (0.0005171036089248076)\n",
            "99. User Time Zone_Alaska (0.0005108934421789853)\n",
            "100. Profile Page Color_c0dfec (0.0005024585878050132)\n",
            "101. User Time Zone_Paris (0.00048486479994727346)\n",
            "102. User Time Zone_New Delhi (0.0004625469116391247)\n",
            "103. Profile Theme Color_0099b9 (0.00045010761441799895)\n",
            "104. Profile Text Color_4a913c (0.0004242663750225474)\n",
            "105. Profile Theme Color_022330 (0.0004208155167201955)\n",
            "106. Profile Text Color_990000 (0.00040860805120145424)\n",
            "107. Profile Page Color_unknown (0.0004062678651096252)\n",
            "108. Profile Text Color_abb8c2 (0.0004013040958142878)\n",
            "109. Profile Text Color_0099b9 (0.00037995913168268795)\n",
            "110. User Time Zone_Arizona (0.0003698613827051714)\n",
            "111. Profile Theme Color_ebebeb (0.00035894988371808044)\n",
            "112. User Language_de (0.00035332756354804613)\n",
            "113. Profile Text Color_e81c4f (0.00034921794104419984)\n",
            "114. User Time Zone_Bern (0.00030058357542322523)\n",
            "115. Profile Theme Color_dbe9ed (0.0002248629729344275)\n",
            "116. User Language_nl (0.00022385478143553827)\n",
            "117. Profile Text Color_ff691f (0.00021487313877388073)\n",
            "118. User Time Zone_Bogota (0.00020605034079548045)\n",
            "119. Profile Page Color_e6f6f9 (0.0002048673947060819)\n",
            "120. User Time Zone_Berlin (0.00019440185920205287)\n",
            "121. Profile Text Color_cc3366 (0.00018628681197995343)\n",
            "122. User Time Zone_Mountain Time (US & Canada) (0.0001704448056768462)\n",
            "123. User Language_other (0.00014298411906240896)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JJnKmL2c-zia"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9MDWOOwM98BR",
        "outputId": "92070fd3-fd0c-4ef2-d4f7-9ce81a56d453"
      },
      "source": [
        "r2 = r2.fit(X_train_dum,y_train_log)\n",
        "ypred = r2.predict(X_test_dum)\n",
        "ypred_original=np.exp(ypred) - 1\n",
        "ypred_original = np.around(np.maximum(ypred_original, 0))\n",
        "\n",
        "print(mean_squared_log_error(y_test,ypred_original))\n",
        "\n",
        "r2 = r2.fit(X_train_freq,y_train_log)\n",
        "ypred = r2.predict(X_test_freq)\n",
        "ypred_original=np.exp(ypred) - 1\n",
        "ypred_original = np.around(np.maximum(ypred_original, 0))\n",
        "\n",
        "print(mean_squared_log_error(y_test,ypred_original))\n",
        "\n",
        "r2 = r2.fit(X_train1,y_train_log)\n",
        "ypred = r2.predict(X_test1)\n",
        "ypred_original=np.exp(ypred) - 1\n",
        "ypred_original = np.around(np.maximum(ypred_original, 0))\n",
        "\n",
        "print(mean_squared_log_error( y_test, ypred_original))\n",
        "\n",
        "\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2.8985061481720966\n",
            "2.93924191599428\n",
            "2.928552139266137\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2y40UR0C_UdN",
        "outputId": "dc4ee19f-6c94-4d60-c844-16e0d07454c9"
      },
      "source": [
        "r2 = r2.fit(X_train1,y_train1)\n",
        "ypred = r2.predict(X_test1)\n",
        "ypred_original=scale_y.inverse_transform(ypred.reshape(-1, 1)).ravel()\n",
        "ypred_original = np.around(np.maximum(ypred_original, 0))\n",
        "\n",
        "print(mean_squared_log_error( y_test, ypred_original))\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "7.543735710603255\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kipI11Rf_RHV"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hiVHJuf5uotR",
        "outputId": "3ef94512-1d9d-4111-c65c-0cc988852b8a"
      },
      "source": [
        "  models=[LinearRegression(),\n",
        "          KNeighborsRegressor(),\n",
        "          SVR(),\n",
        "          Ridge(),\n",
        "          Lasso(),\n",
        "          MLPRegressor(hidden_layer_sizes=(6, 4, 2,2),  activation='relu', solver='lbfgs', alpha=200, batch_size='auto',learning_rate='adaptive', learning_rate_init=0.0001, power_t=0.8, max_iter=5000, shuffle=True,random_state=67, tol=0.0001, verbose=False, warm_start=False, momentum=0.9, nesterovs_momentum=True,early_stopping=True,beta_1=0.9, beta_2=0.9, epsilon=1e-08),\n",
        "          DecisionTreeRegressor(),\n",
        "          ExtraTreeRegressor(),\n",
        "          RandomForestRegressor(n_estimators=100),\n",
        "          AdaBoostRegressor(),\n",
        "          GradientBoostingRegressor(),\n",
        "          BaggingRegressor(n_estimators=100) ,\n",
        "          XGBRegressor()\n",
        "          ]\n",
        "  models_str=['LinearRegression','KNNRegressor','SVR','Ridge','Lasso','MLPRegressor','DecisionTree','ExtraTree','RandomForest','AdaBoost','GradientBoost','Bagging',]\n",
        "  score_adapt=[]\n",
        "  for name,model in zip(models_str,models):\n",
        "\n",
        "    try:\n",
        "          print('---------------------------------')\n",
        "          #if name in ['LinearRegression','Ridge','ExtraTree']:   \n",
        "          print('start：'+name+' log dummy')\n",
        "          model=model\n",
        "          model.fit(X_train_dum,y_train_log)\n",
        "          y_pred_log=model.predict(X_test_dum)   \n",
        "          ypred_original=np.exp(y_pred_log) - 1 \n",
        "          ypred_original = np.around(np.maximum(ypred_original, 0))\n",
        "          score=model.score(X_test_dum,y_test_log)\n",
        "          score_adapt.append(str(score)[:5])\n",
        "          print(name +' msle:',mean_squared_log_error(y_test, ypred_original))\n",
        "    except:\n",
        "          print(print(\"Unexpected error:\", sys.exc_info()[0]))\n",
        "    try:\n",
        "          print('---------------------------------')  \n",
        "          #elif name in ['SVR','MLPRegressor','Bagging','AdaBoost','KNNRegressor']:\n",
        "          print('start：'+name+' standarnize')\n",
        "              \n",
        "          model.fit(X_train1,y_train_log)\n",
        "          y_pred1=model.predict(X_test1)\n",
        "          ypred_original=np.exp(y_pred1) - 1 \n",
        "          #ypred_original=scale_y.inverse_transform(y_pred1.reshape(-1, 1)).ravel()\n",
        "          ypred_original = np.around(np.maximum(ypred_original, 0))\n",
        "          score=model.score(X_test1,y_test1)\n",
        "          score_adapt.append(str(score)[:5])       \n",
        "          \n",
        "          print(name +' msle:',(mean_squared_log_error(y_test, ypred_original)))\n",
        "\n",
        "    except:\n",
        "          print(print(\"Unexpected error:\", sys.exc_info()[0]))\n",
        "    try:  \n",
        "          print('---------------------------------')      \n",
        "          print('start:'+name+' frequency')\n",
        "         \n",
        "          model.fit(X_train_freq,y_train_log)\n",
        "          y_pred_log=model.predict(X_test_freq)\n",
        "          ypred_original=np.exp(y_pred_log) - 1 \n",
        "          ypred_original = np.around(np.maximum(ypred_original, 0))\n",
        "          score_adapt.append(str(score)[:5])    \n",
        "          print(name +' msle:'+str(mean_squared_log_error(y_test, ypred_original)))\n",
        "\n",
        "         \n",
        "    except:\n",
        "          print(print(\"Unexpected error:\", sys.exc_info()[0]))\n",
        "    try:    \n",
        "          print('---------------------------------') \n",
        "          print('start: '+name+' normal')\n",
        "         \n",
        "          model.fit(X_train,y_train)\n",
        "          y_pred = model.predict(X_test)\n",
        "          y_pred = np.around(np.maximum(y_pred, 0))\n",
        "          score=model.score(X_test,y_test)\n",
        "          score_adapt.append(str(score)[:5])   \n",
        "          print(name +' msle:'+str(mean_squared_log_error(y_test, y_pred)))\n",
        "\n",
        "          print('---------------------------------')\n",
        "    except:\n",
        "          print(print(\"Unexpected error:\", sys.exc_info()[0]))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "---------------------------------\n",
            "开始训练模型：LinearRegression 平滑处理\n",
            "LinearRegression msle: 4.321707676131373\n",
            "---------------------------------\n",
            "开始训练模型：LinearRegression 标准化处理\n",
            "LinearRegression msle: 3.369062503187477\n",
            "---------------------------------\n",
            "开始训练模型：LinearRegression 频率\n",
            "LinearRegression msle:4.377363558351759\n",
            "---------------------------------\n",
            "开始训练模型：LinearRegression 普通\n",
            "LinearRegression msle:8.443293454575027\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "开始训练模型：KNNRegressor 平滑处理\n",
            "KNNRegressor msle: 7.669295586931648\n",
            "---------------------------------\n",
            "开始训练模型：KNNRegressor 标准化处理\n",
            "KNNRegressor msle: 3.5620427467374345\n",
            "---------------------------------\n",
            "开始训练模型：KNNRegressor 频率\n",
            "KNNRegressor msle:5.351600474683002\n",
            "---------------------------------\n",
            "开始训练模型：KNNRegressor 普通\n",
            "KNNRegressor msle:9.074183010634629\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "开始训练模型：SVR 平滑处理\n",
            "SVR msle: 6.457883579750597\n",
            "---------------------------------\n",
            "开始训练模型：SVR 标准化处理\n",
            "SVR msle: 3.0835065550998007\n",
            "---------------------------------\n",
            "开始训练模型：SVR 频率\n",
            "SVR msle:6.555814045463932\n",
            "---------------------------------\n",
            "开始训练模型：SVR 普通\n",
            "SVR msle:6.659914090110046\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "开始训练模型：Ridge 平滑处理\n",
            "Ridge msle: 4.321666750847191\n",
            "---------------------------------\n",
            "开始训练模型：Ridge 标准化处理\n",
            "Ridge msle: 3.3694683733860136\n",
            "---------------------------------\n",
            "开始训练模型：Ridge 频率\n",
            "Ridge msle:4.3775164904471024\n",
            "---------------------------------\n",
            "开始训练模型：Ridge 普通\n",
            "Ridge msle:8.442891041576598\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "开始训练模型：Lasso 平滑处理\n",
            "Lasso msle: 6.307148332349536\n",
            "---------------------------------\n",
            "开始训练模型：Lasso 标准化处理\n",
            "Lasso msle: 6.407430913996747\n",
            "---------------------------------\n",
            "开始训练模型：Lasso 频率\n",
            "Lasso msle:6.475251351454235\n",
            "---------------------------------\n",
            "开始训练模型：Lasso 普通\n",
            "Lasso msle:8.443057745173524\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "开始训练模型：MLPRegressor 平滑处理\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=4.60608e-20): result may not be accurate.\n",
            "  overwrite_a=True).T\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=1.12361e-17): result may not be accurate.\n",
            "  overwrite_a=True).T\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=2.91151e-19): result may not be accurate.\n",
            "  overwrite_a=True).T\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "MLPRegressor msle: 6.388021776284736\n",
            "---------------------------------\n",
            "开始训练模型：MLPRegressor 标准化处理\n",
            "MLPRegressor msle: 3.272626254310686\n",
            "---------------------------------\n",
            "开始训练模型：MLPRegressor 频率\n",
            "MLPRegressor msle:6.410629637453731\n",
            "---------------------------------\n",
            "开始训练模型：MLPRegressor 普通\n",
            "MLPRegressor msle:9.966373605038251\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "开始训练模型：DecisionTree 平滑处理\n",
            "DecisionTree msle: 5.176579374897448\n",
            "---------------------------------\n",
            "开始训练模型：DecisionTree 标准化处理\n",
            "DecisionTree msle: 5.25073562371081\n",
            "---------------------------------\n",
            "开始训练模型：DecisionTree 频率\n",
            "DecisionTree msle:5.208540459786922\n",
            "---------------------------------\n",
            "开始训练模型：DecisionTree 普通\n",
            "DecisionTree msle:6.243070538857801\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "开始训练模型：ExtraTree 平滑处理\n",
            "ExtraTree msle: 7.03765829352012\n",
            "---------------------------------\n",
            "开始训练模型：ExtraTree 标准化处理\n",
            "ExtraTree msle: 6.176779826667041\n",
            "---------------------------------\n",
            "开始训练模型：ExtraTree 频率\n",
            "ExtraTree msle:6.4768994315853075\n",
            "---------------------------------\n",
            "开始训练模型：ExtraTree 普通\n",
            "ExtraTree msle:6.164592469004295\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "开始训练模型：RandomForest 平滑处理\n",
            "RandomForest msle: 2.9376592871260896\n",
            "---------------------------------\n",
            "开始训练模型：RandomForest 标准化处理\n",
            "RandomForest msle: 2.9584309062437315\n",
            "---------------------------------\n",
            "开始训练模型：RandomForest 频率\n",
            "RandomForest msle:2.9773572590683046\n",
            "---------------------------------\n",
            "开始训练模型：RandomForest 普通\n",
            "RandomForest msle:5.471481243230078\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "开始训练模型：AdaBoost 平滑处理\n",
            "AdaBoost msle: 4.689457405198488\n",
            "---------------------------------\n",
            "开始训练模型：AdaBoost 标准化处理\n",
            "AdaBoost msle: 4.690258943106653\n",
            "---------------------------------\n",
            "开始训练模型：AdaBoost 频率\n",
            "AdaBoost msle:3.935144758323817\n",
            "---------------------------------\n",
            "开始训练模型：AdaBoost 普通\n",
            "AdaBoost msle:14.78505344905164\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "开始训练模型：GradientBoost 平滑处理\n",
            "GradientBoost msle: 2.8440579296144413\n",
            "---------------------------------\n",
            "开始训练模型：GradientBoost 标准化处理\n",
            "GradientBoost msle: 2.887542064813126\n",
            "---------------------------------\n",
            "开始训练模型：GradientBoost 频率\n",
            "GradientBoost msle:2.8910032805830705\n",
            "---------------------------------\n",
            "开始训练模型：GradientBoost 普通\n",
            "GradientBoost msle:7.074601729771068\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "开始训练模型：Bagging 平滑处理\n",
            "Bagging msle: 2.922186828883754\n",
            "---------------------------------\n",
            "开始训练模型：Bagging 标准化处理\n",
            "Bagging msle: 2.9385604020060176\n",
            "---------------------------------\n",
            "开始训练模型：Bagging 频率\n",
            "Bagging msle:2.992032732860954\n",
            "---------------------------------\n",
            "开始训练模型：Bagging 普通\n",
            "Bagging msle:5.468921934615315\n",
            "---------------------------------\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b8Ewxx_7us8h",
        "outputId": "36ee5eeb-a6fb-4ffc-c831-970ff5334489"
      },
      "source": [
        "gdboost = GradientBoostingRegressor(random_state=76, n_estimators=150) #loss : {'ls', 'lad', 'huber', 'quantile'}, optional (default='ls')\n",
        "\n",
        "gdboost = gdboost.fit(X_train_dum,y_train)\n",
        "ypred = gdboost.predict(X_test_dum)\n",
        "\n",
        "ypred_original=10**ypred - 1\n",
        "ypred_original = np.around(np.maximum(ypred_original, 0))\n",
        "\n",
        "print(mean_squared_log_error(y_test,ypred_original))"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2.7926107739664365\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FUQo11au-zib",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "outputId": "dac081dc-ea10-4042-f2ea-5fd81b8f248e"
      },
      "source": [
        "xgb_model = xgb.XGBRegressor(learning_rate=0.2, max_depth=2, min_child_weight=3, n_estimators=80)\n",
        "xgb_model.fit(X_train_dum,y_train)\n",
        "ypred = xgb_model.predict(X_test_dum)\n",
        "\n",
        "ypred_original=10**ypred - 1\n",
        "ypred_original = np.around(np.maximum(ypred_original, 0))\n",
        "\n",
        "print(mean_squared_log_error(y_test,ypred_original))"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-63-221b8ff8ec15>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mxgb_model\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mxgb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mXGBRegressor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlearning_rate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_depth\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmin_child_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_estimators\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m80\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[0mxgb_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train_dum\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mypred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mxgb_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test_dum\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mypred_original\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mypred\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'xgb' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 526
        },
        "id": "i5GHp0wdK7W6",
        "outputId": "f8aae541-1f01-465d-a7be-7db716d09b3d"
      },
      "source": [
        "x_dum"
      ],
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    <tr style=\"text-align: right;\">\n",
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              "      <th>Avg Daily Profile Clicks</th>\n",
              "      <th>profile_colors</th>\n",
              "      <th>user_name_len</th>\n",
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              "      <th>log_daily_profile_clicks</th>\n",
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              "      <th>Profile Cover Image Status_Not set</th>\n",
              "      <th>Profile Cover Image Status_Set</th>\n",
              "      <th>Profile Cover Image Status_unknown</th>\n",
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              "      <th>Profile Verification Status_Pending</th>\n",
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              "      <th>Profile Text Color_990000</th>\n",
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              "      <th>Profile Text Color_ff0000</th>\n",
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              "      <th>User Language_ru</th>\n",
              "      <th>User Language_tr</th>\n",
              "      <th>Profile Category_business</th>\n",
              "      <th>Profile Category_celebrity</th>\n",
              "      <th>Profile Category_government</th>\n",
              "      <th>Profile Category_unknown</th>\n",
              "      <th>User Time Zone_Alaska</th>\n",
              "      <th>User Time Zone_Amsterdam</th>\n",
              "      <th>User Time Zone_Arizona</th>\n",
              "      <th>User Time Zone_Athens</th>\n",
              "      <th>User Time Zone_Atlantic Time (Canada)</th>\n",
              "      <th>User Time Zone_Berlin</th>\n",
              "      <th>User Time Zone_Bern</th>\n",
              "      <th>User Time Zone_Bogota</th>\n",
              "      <th>User Time Zone_Brasilia</th>\n",
              "      <th>User Time Zone_Buenos Aires</th>\n",
              "      <th>User Time Zone_Caracas</th>\n",
              "      <th>User Time Zone_Central Time (US &amp; Canada)</th>\n",
              "      <th>User Time Zone_Eastern Time (US &amp; Canada)</th>\n",
              "      <th>User Time Zone_Greenland</th>\n",
              "      <th>User Time Zone_Hawaii</th>\n",
              "      <th>User Time Zone_Istanbul</th>\n",
              "      <th>User Time Zone_Jakarta</th>\n",
              "      <th>User Time Zone_London</th>\n",
              "      <th>User Time Zone_Madrid</th>\n",
              "      <th>User Time Zone_Mexico City</th>\n",
              "      <th>User Time Zone_Mountain Time (US &amp; Canada)</th>\n",
              "      <th>User Time Zone_Mumbai</th>\n",
              "      <th>User Time Zone_New Delhi</th>\n",
              "      <th>User Time Zone_Pacific Time (US &amp; Canada)</th>\n",
              "      <th>User Time Zone_Paris</th>\n",
              "      <th>User Time Zone_Quito</th>\n",
              "      <th>User Time Zone_Riyadh</th>\n",
              "      <th>User Time Zone_Rome</th>\n",
              "      <th>User Time Zone_Santiago</th>\n",
              "      <th>User Time Zone_Tokyo</th>\n",
              "      <th>User Time Zone_other</th>\n",
              "      <th>User Time Zone_unknown</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.981202</td>\n",
              "      <td>3.632457</td>\n",
              "      <td>4.488692</td>\n",
              "      <td>2.941511</td>\n",
              "      <td>14.792</td>\n",
              "      <td>1.5761</td>\n",
              "      <td>1</td>\n",
              "      <td>11</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4386.0</td>\n",
              "      <td>6912.7746</td>\n",
              "      <td>64877.712</td>\n",
              "      <td>5.178784</td>\n",
              "      <td>6.151225</td>\n",
              "      <td>8.820850</td>\n",
              "      <td>9.793293</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.008066</td>\n",
              "      <td>2.462398</td>\n",
              "      <td>3.911211</td>\n",
              "      <td>2.463893</td>\n",
              "      <td>8.183</td>\n",
              "      <td>11.2782</td>\n",
              "      <td>1</td>\n",
              "      <td>12</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3972.0</td>\n",
              "      <td>44797.0104</td>\n",
              "      <td>32502.876</td>\n",
              "      <td>7.060306</td>\n",
              "      <td>6.920979</td>\n",
              "      <td>10.659315</td>\n",
              "      <td>10.519988</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.128561</td>\n",
              "      <td>3.273464</td>\n",
              "      <td>3.672005</td>\n",
              "      <td>2.357935</td>\n",
              "      <td>31.823</td>\n",
              "      <td>0.5725</td>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4077.0</td>\n",
              "      <td>2334.0825</td>\n",
              "      <td>129742.371</td>\n",
              "      <td>3.886360</td>\n",
              "      <td>5.631271</td>\n",
              "      <td>7.496645</td>\n",
              "      <td>9.241610</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>27</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5.530416</td>\n",
              "      <td>3.060320</td>\n",
              "      <td>4.726050</td>\n",
              "      <td>3.605951</td>\n",
              "      <td>23.052</td>\n",
              "      <td>4.0265</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4302.0</td>\n",
              "      <td>17322.0030</td>\n",
              "      <td>99169.704</td>\n",
              "      <td>6.135343</td>\n",
              "      <td>6.893124</td>\n",
              "      <td>9.769013</td>\n",
              "      <td>10.526794</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>27</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.964542</td>\n",
              "      <td>1.973128</td>\n",
              "      <td>3.514813</td>\n",
              "      <td>2.117271</td>\n",
              "      <td>8.418</td>\n",
              "      <td>3.9229</td>\n",
              "      <td>2</td>\n",
              "      <td>10</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4262.0</td>\n",
              "      <td>16719.3998</td>\n",
              "      <td>35877.516</td>\n",
              "      <td>4.558115</td>\n",
              "      <td>4.889710</td>\n",
              "      <td>8.187716</td>\n",
              "      <td>8.519318</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <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",
              "      <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",
              "      <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",
              "      <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",
              "      <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",
              "      <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>7495</th>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.784403</td>\n",
              "      <td>1.886491</td>\n",
              "      <td>2.993877</td>\n",
              "      <td>1.755875</td>\n",
              "      <td>24.792</td>\n",
              "      <td>8.1106</td>\n",
              "      <td>3</td>\n",
              "      <td>15</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3602.0</td>\n",
              "      <td>29214.3812</td>\n",
              "      <td>89300.784</td>\n",
              "      <td>4.693394</td>\n",
              "      <td>5.178646</td>\n",
              "      <td>8.249929</td>\n",
              "      <td>8.735187</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>7496</th>\n",
              "      <td>0</td>\n",
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              "      <td>2.238046</td>\n",
              "      <td>26.006</td>\n",
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              "      <td>2</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>7499</th>\n",
              "      <td>26</td>\n",
              "      <td>1.0</td>\n",
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              "      <td>4.461333</td>\n",
              "      <td>2.942504</td>\n",
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              "      <td>3</td>\n",
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              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3632.0</td>\n",
              "      <td>35196.9856</td>\n",
              "      <td>51963.024</td>\n",
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              "      <td>0</td>\n",
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              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>7500 rows × 123 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      url_len  offset_given  ...  User Time Zone_other  User Time Zone_unknown\n",
              "0           0           1.0  ...                     1                       0\n",
              "1           0           0.0  ...                     0                       1\n",
              "2           0           1.0  ...                     0                       0\n",
              "3          27           1.0  ...                     0                       0\n",
              "4          27           1.0  ...                     0                       0\n",
              "...       ...           ...  ...                   ...                     ...\n",
              "7495        0           1.0  ...                     0                       0\n",
              "7496        0           1.0  ...                     1                       0\n",
              "7497        0           1.0  ...                     1                       0\n",
              "7498        0           1.0  ...                     0                       0\n",
              "7499       26           1.0  ...                     0                       0\n",
              "\n",
              "[7500 rows x 123 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IkRnjhZ6-zic",
        "outputId": "d1c8bc40-e932-411f-9f70-58ac36e9169b"
      },
      "source": [
        "np.sqrt(2.8137)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.6774087158471545"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 255
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U-DjjIqk-zic"
      },
      "source": [
        "x_dum = pd.get_dummies(x_log)"
      ],
      "execution_count": 76,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n6WbNGKfs6wn",
        "outputId": "f76780bc-747d-4278-8e33-a137d73027df"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.metrics import classification_report, make_scorer\n",
        "from sklearn.model_selection import KFold\n",
        "\n",
        "\n",
        "# Set the parameters by cross-validation\n",
        "tuned_parameters = [{\n",
        "  'loss': ['ls'],\n",
        "  'n_estimators':[100, 150, 200],\n",
        "  'max_features':['auto']}]\n",
        "def rmsle(y_test, y_pred):\n",
        "    y_pred = np.around(np.maximum(10**y_pred - 1, 0))\n",
        "    y_test = np.around(np.maximum(10**y_test - 1, 0))\n",
        "    return np.sqrt(mean_squared_log_error(y_test, y_pred))\n",
        "    \n",
        "metric = make_scorer(rmsle, greater_is_better=False)\n",
        "\n",
        "cv_strategy = KFold(n_splits=5, shuffle=True)\n",
        "gdboost = GradientBoostingRegressor(random_state=76)\n",
        "grid_search = GridSearchCV(\n",
        "    gdboost, tuned_parameters, scoring=metric, cv=cv_strategy\n",
        ")\n",
        "grid_search.fit(x_dum, y);\n",
        "print('Finished!')"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Finished!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8z7nfd0B-zic",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        },
        "outputId": "70462688-79ed-4204-adbd-20a2e4d2b1ce"
      },
      "source": [
        "dataplot =pd.DataFrame(x_dum)\r\n",
        "dataplot['log_num_likes'] = y\r\n",
        "\r\n",
        "sns.histplot(x='log_num_likes',data= dataplot, hue='Location Public Visibility' , multiple=\"dodge\")"
      ],
      "execution_count": 88,
      "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-88-38d1c14d1b67>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mdataplot\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'log_num_likes'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhistplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'log_num_likes'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0mdataplot\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhue\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Location Public Visibility'\u001b[0m \u001b[0;34m,\u001b[0m \u001b[0mmultiple\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"dodge\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/seaborn/distributions.py\u001b[0m in \u001b[0;36mhistplot\u001b[0;34m(data, x, y, hue, weights, stat, bins, binwidth, binrange, discrete, cumulative, common_bins, common_norm, multiple, element, fill, shrink, kde, kde_kws, line_kws, thresh, pthresh, pmax, cbar, cbar_ax, cbar_kws, palette, hue_order, hue_norm, color, log_scale, legend, ax, **kwargs)\u001b[0m\n\u001b[1;32m   1390\u001b[0m     p = _DistributionPlotter(\n\u001b[1;32m   1391\u001b[0m         \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1392\u001b[0;31m         \u001b[0mvariables\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0m_DistributionPlotter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_semantics\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlocals\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\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   1393\u001b[0m     )\n\u001b[1;32m   1394\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/seaborn/distributions.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, data, variables)\u001b[0m\n\u001b[1;32m    106\u001b[0m     ):\n\u001b[1;32m    107\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 108\u001b[0;31m         \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvariables\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvariables\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    109\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    110\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/seaborn/_core.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, data, variables)\u001b[0m\n\u001b[1;32m    602\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvariables\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    603\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 604\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0massign_variables\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvariables\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    605\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    606\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mvar\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcls\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_semantic_mappings\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\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/python3.6/dist-packages/seaborn/_core.py\u001b[0m in \u001b[0;36massign_variables\u001b[0;34m(self, data, variables)\u001b[0m\n\u001b[1;32m    666\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minput_format\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"long\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    667\u001b[0m             plot_data, variables = self._assign_variables_longform(\n\u001b[0;32m--> 668\u001b[0;31m                 \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mvariables\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    669\u001b[0m             )\n\u001b[1;32m    670\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/seaborn/_core.py\u001b[0m in \u001b[0;36m_assign_variables_longform\u001b[0;34m(self, data, **kwargs)\u001b[0m\n\u001b[1;32m    885\u001b[0m                     \u001b[0;31m# We don't know what this name means\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    886\u001b[0m                     \u001b[0merr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34mf\"Could not interpret value `{val}` for parameter `{key}`\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 887\u001b[0;31m                     \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merr\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    888\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    889\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Could not interpret value `Location Public Visibility` for parameter `hue`"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1TFwmTon2UiV",
        "outputId": "3762402b-5d1c-442f-b531-541188b03df7"
      },
      "source": [
        "print(\"Best parameters set found on development set:\")\n",
        "print()\n",
        "print(grid_search.best_params_)\n",
        "print(grid_search.best_score_)"
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Best parameters set found on development set:\n",
            "\n",
            "{'loss': 'ls', 'max_features': 'auto', 'n_estimators': 150}\n",
            "-1.7218176764105977\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C8KlRwtu-zid"
      },
      "source": [
        "import xgboost as xgb\n",
        "\n",
        "xgb_model = xgb.XGBRegressor()\n",
        "param_dist = {'max_depth': [3],\n",
        "              'min_child_weight':[3],\n",
        "              'learning_rate': [0.08,0.085,0.075],\n",
        "              \"n_estimators\": [165,170,185],\n",
        "              'booster': ['gbtree'],}\n",
        "    \n",
        "metric = make_scorer(rmsle, greater_is_better=False)\n",
        "\n",
        "cv_strategy = KFold(n_splits=5, shuffle=True)\n",
        "grid_search = GridSearchCV(\n",
        "    xgb_model, param_dist, scoring=metric, cv=cv_strategy\n",
        ")\n",
        "grid_search.fit(x_dum, y);\n",
        "print('Finished!')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "roryMrRqNogu",
        "outputId": "619eb5b6-1006-435f-8e94-6df2bacf665e"
      },
      "source": [
        "y"
      ],
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       3.449633\n",
              "1       3.094471\n",
              "2       3.193125\n",
              "3       3.802295\n",
              "4       3.033021\n",
              "          ...   \n",
              "7495    3.137987\n",
              "7496    3.148603\n",
              "7497    3.092018\n",
              "7498    3.043755\n",
              "7499    2.575188\n",
              "Name: log_num_likes, Length: 7500, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IsHmxFgw3s-G"
      },
      "source": [
        "gdboost_best = grid_search.best_estimator_\n",
        "test_dum =  pd.get_dummies(test_df_p[cols_x_log])\n",
        "\n",
        "predict_test = gdboost_best.predict(test_dum)\n",
        "ypred_original=10**predict_test - 1\n",
        "predict = np.around(np.maximum(ypred_original, 0))"
      ],
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ckRzmRaY-zie",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7efd17d7-8de3-45d4-8370-3c7959f561f0"
      },
      "source": [
        "xgb_best = xgb.XGBRegressor(learning_rate=0.2, max_depth=2, min_child_weight=3, n_estimators=80)\n",
        "xgb_best.fit(x_dum,y)\n",
        "predict_test = xgb_best.predict(test_dum)\n",
        "ypred_original=10**predict_test - 1\n",
        "predict = np.around(np.maximum(ypred_original, 0))"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[21:30:16] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k1RvKNOB9IGf",
        "outputId": "3ccafb60-75e8-4ab3-8eee-a58e78996a84"
      },
      "source": [
        "predict"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([4256., 1895.,  358., ..., 4221., 1141.,  194.], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 379
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HNqatCjvWxPl"
      },
      "source": [
        "# Get indexes\n",
        "out = pd.DataFrame(test_df['Id'])\n",
        "\n",
        "# Make predictions\n",
        "out['Predicted'] = predict # TODO: real predictions"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WciX9ywsWxPl"
      },
      "source": [
        "# Save output\n",
        "out.to_csv('C://Users//jfrpo//OneDrive//Escritorio//UdeM//IFT6758 - Data Science//IFT6758_Data_Science//IFT6758_Data_Science//kaggle//predictions_8.csv', index=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RIXbi0q7BcDd"
      },
      "source": [
        "----------------------------------------------------------------------"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5Y4JdyYtS1fF"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}