sessions_high_eng.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "sessions_high_eng.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "FR3_iD_-xkUd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#importando bibliotecas\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt \n",
        "import seaborn as sns"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oA2tab3ejiJ7",
        "colab_type": "text"
      },
      "source": [
        "## Análise Correlação de nível de engajamento por evento de ida à loja\n",
        "### 1. Hipótese: usuários com mais eventos de ir à loja retornam ao site com mais frequência?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nAnqk1LvyBrT",
        "colab_type": "code",
        "outputId": "2e5428ca-0e16-4365-d5b4-da9c661354ab",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 74
        }
      },
      "source": [
        "#update de arquivos\n",
        "from google.colab import files\n",
        "uploaded = files.upload()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-2bf632bd-ae4f-466c-b187-b9214bee353e\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-2bf632bd-ae4f-466c-b187-b9214bee353e\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving base_csv.csv to base_csv.csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jcmRcQMNbKrW",
        "colab_type": "code",
        "outputId": "ff0fe7fa-2322-4502-9367-cf14c8f093c7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        }
      },
      "source": [
        "#criando o data set e exibindo as primeiras linhas\n",
        "outs_full = pd.read_csv('outs.csv')\n",
        "outs_full.head()\n",
        "#outs_full.info()"
      ],
      "execution_count": 0,
      "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>user_id</th>\n",
              "      <th>sessions</th>\n",
              "      <th>month</th>\n",
              "      <th>outs</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>27</td>\n",
              "      <td>41</td>\n",
              "      <td>march</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>50</td>\n",
              "      <td>100</td>\n",
              "      <td>march</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>120</td>\n",
              "      <td>5</td>\n",
              "      <td>march</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>128</td>\n",
              "      <td>31</td>\n",
              "      <td>march</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>138</td>\n",
              "      <td>76</td>\n",
              "      <td>march</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   user_id  sessions  month  outs\n",
              "0       27        41  march     5\n",
              "1       50       100  march     6\n",
              "2      120         5  march     1\n",
              "3      128        31  march     1\n",
              "4      138        76  march    11"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BaqFrjCdb3XN",
        "colab_type": "code",
        "outputId": "e006913b-013e-4ba8-8a7f-2bed0e528669",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 137
        }
      },
      "source": [
        "#estatísticas descritivas do dataset\n",
        "outs_full.describe().T\n",
        "#1º quartil (Q1)\t25% dos dados são menores que ou iguais a este valor.\n",
        "#2º quartil (Q2)\tA mediana. 50% dos dados são menores que ou iguais a este valor.\n",
        "#3º quartil (Q3)\t75% dos dados são menores que ou iguais a este valor.\n",
        "#Amplitude interquartílica\tA distância entre o 1o. e o 3o. quartis (Q3-Q1); assim, ele ultrapassa o meio de 50% dos dados."
      ],
      "execution_count": 0,
      "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>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>user_id</th>\n",
              "      <td>231292.0</td>\n",
              "      <td>453901.845719</td>\n",
              "      <td>218836.571653</td>\n",
              "      <td>17.0</td>\n",
              "      <td>261655.0</td>\n",
              "      <td>502466.5</td>\n",
              "      <td>654606.0</td>\n",
              "      <td>739693.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sessions</th>\n",
              "      <td>231292.0</td>\n",
              "      <td>45.696431</td>\n",
              "      <td>105.674874</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>39.0</td>\n",
              "      <td>4314.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>outs</th>\n",
              "      <td>231292.0</td>\n",
              "      <td>6.305129</td>\n",
              "      <td>18.352594</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2274.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             count           mean            std  ...       50%       75%       max\n",
              "user_id   231292.0  453901.845719  218836.571653  ...  502466.5  654606.0  739693.0\n",
              "sessions  231292.0      45.696431     105.674874  ...      10.0      39.0    4314.0\n",
              "outs      231292.0       6.305129      18.352594  ...       1.0       5.0    2274.0\n",
              "\n",
              "[3 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3NzLiLt1cB5T",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#agrupando as sessões e idas à loja por usuário, independente do mês de ocorrência do evento, por média\n",
        "outs_full.groupby('user_id')['sessions', 'outs'].mean()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dcbL0AuYdqJc",
        "colab_type": "code",
        "outputId": "db5d4a94-dbd7-4bdb-c137-a91c141f6b03",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 227
        }
      },
      "source": [
        "#agrupando as sessões e idas à loja por usuário, independente do mês de ocorrência do evento, por somatória pois a média não era a melhor abordagem\n",
        "#criando um novo data set com essas informações\n",
        "outs_corr = outs_full.groupby('user_id')['sessions', 'outs'].sum()\n",
        "outs_corr.head()"
      ],
      "execution_count": 0,
      "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>sessions</th>\n",
              "      <th>outs</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>user_id</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>8</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>471</td>\n",
              "      <td>49</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>248</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>91</th>\n",
              "      <td>30</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>120</th>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         sessions  outs\n",
              "user_id                \n",
              "17              8    10\n",
              "27            471    49\n",
              "50            248    18\n",
              "91             30     4\n",
              "120             5     1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Uc1EoVEDebRD",
        "colab_type": "code",
        "outputId": "e8ab87a2-d3af-440e-f873-aa24a97fa543",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 107
        }
      },
      "source": [
        "#descrevendo o novo data set\n",
        "outs_corr.describe().T"
      ],
      "execution_count": 0,
      "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>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>sessions</th>\n",
              "      <td>102699.0</td>\n",
              "      <td>102.914527</td>\n",
              "      <td>302.036192</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>9555.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>outs</th>\n",
              "      <td>102699.0</td>\n",
              "      <td>14.200002</td>\n",
              "      <td>50.853214</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>8235.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             count        mean         std  min  25%   50%   75%     max\n",
              "sessions  102699.0  102.914527  302.036192  1.0  4.0  13.0  60.0  9555.0\n",
              "outs      102699.0   14.200002   50.853214  0.0  0.0   1.0   8.0  8235.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4QHCw4piemI1",
        "colab_type": "code",
        "outputId": "cf056206-1d44-45cd-c8c4-0462085ec5ff",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        }
      },
      "source": [
        "#plotando um histograma de sessões e ida à loja desse data set\n",
        "outs_corr.hist(bins = 10)\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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pVt4ojQEQESEp+4VQRCwHlgO0trZGW1tb1fluX72Wm7b17TTffXn1voaLSqVCd/urWeUM\n3X6Xy1w6sCa0F9gbEVvS8/spQuf5dLySfh5Mr+8DJpWWn5jaemqfWKXdrGEN5E6moUoH/SkbwNCW\nDur9Nr7ZxxcRByTtkXROROwAZgJPpsdCYFn6uTYtsg64WtIairv3IxGxX9IG4H+X3uyfBVwbEYck\nHZU0g+LufQFwe7YNMhsCAwmZhiod9KdsAENbOqj323iPD4BPAKtTeXgXcCVFReA+SYuA54BL0rzr\nKd6DbKd4H/JKgBQmXwQeSfN9ISIOpemP88b7kA/i8rA1uIGETLXSwVJS6SBdsfW2dNDWpb2CSwdW\nhyLicYr3D7uaWWXeAK7qpp8VwIoq7Y9SfJDGbFjo93syEXEA2CPpnNTUWTpYR1EygONLBwvSF9Rm\nkEoHwAZglqQxqXwwC9iQXjsqaUb6ZNqCUl9mZtYABvrpMpcOzMysWwMKGZcOzMysJ/7Gv5mZZeOQ\nMTOzbBwyZmaWjUPGzMyycciYmVk2DhkzM8vGIWNmZtk4ZMzMLBuHjJmZZeOQMTOzbBwyZmaWjUPG\nzMyycciYmVk2DhkzM8vGIWNmZtk4ZMzMLBuHjJmZZeOQMTOzbBwyZmaWjUPGzMyycciYmVk2Dhkz\nM8vGIWNmZtk4ZMzMLBuHjJmZZeOQMTOzbBwyZmaWjUPGzMyycciYmVk2DhmzPpI0QtKPJH07PZ8i\naYukdkn3Sjo5tZ+Snren1yeX+rg2te+QdFGpfXZqa5e0dKi3zWywOWTM+u4a4KnS8xuBWyLibOAw\nsCi1LwIOp/Zb0nxImgbMB34PmA18OQXXCOAOYA4wDbgszWvWsAYcMr6qs2YiaSLwQeBr6bmA9wP3\np1lWAhen6XnpOen1mWn+ecCaiHglIp4F2oEL0qM9InZFxKvAmjSvWcMaOQh9dF7VnZ6ed17VrZH0\nFYqruTspXdVJmp/mu7TLVd1ZwHckvTP1dQfwAWAv8IikdRHx5CCM2ay//gb4DPDW9PxM4MWIOJae\n7wUmpOkJwB6AiDgm6UiafwKwudRneZk9XdovrDYISYuBxQAtLS1UKpWqg20ZBUvOO1b1te5019dw\n0dHRMey3sa86Ojqy9T2gkCld1d0AfKp0VffRNMtK4HqKkJmXpqG4qvvbrld1wLOSOq/qIF3VpXV1\nXtU5ZKwmJH0IOBgRWyW11XIsEbEcWA7Q2toabW3Vh3P76rXctK1vp/nuy6v3NVxUKhW621/NKmfo\nDvROpmGu6vpzRQdDe1VX71dYHh/vAT4saS7wZoq791uB0ZJGpuN+IrAvzb8PmATslTQSOAN4odTe\nqbxMd+1mDanfIdNoV3X9uaKDob2qq/crrGYfX0RcC1wLkI75T0fE5ZK+CXyE4j2UhcDatMi69PwH\n6fXvRkRIWgd8Q9LNFCXiqcAPAQFTJU2hCJf5vFEVMGtIA7mT8VWdWeGzwBpJXwJ+BNyV2u8C7k4l\n4EMUoUFEbJd0H0Xp9xhwVUS8BiDpamADMAJYERHbh3RLzAZZv0PGV3XWzCKiAlTS9C7eeB+xPM+v\ngD/uZvkbKN7L7Nq+Hlg/iEM1q6nB+HRZV76qMzMzYJBCxld1ZmZWjb/xb2Zm2ThkzMwsG4eMmZll\n45AxM7NsHDJmZpaNQ8bMzLJxyJiZWTYOGTMzy8YhY2Zm2ThkzMwsG4eMmZll45AxM7NsHDJmZpaN\nQ8bMzLJxyJiZWTYOGTMzy8YhY2Zm2ThkzMwsG4eMmZll45AxM7NsHDJmZpaNQ8bMzLJxyJiZWTYO\nGTMzy8YhY2Zm2ThkzMwsG4eMmZll45AxM7NsHDJmZpaNQ8bMzLJxyJj1kqRJkjZJelLSdknXpPax\nkjZK2pl+jkntknSbpHZJT0g6v9TXwjT/TkkLS+3TJW1Ly9wmSUO/pWaDp98h4xPOmtAxYElETANm\nAFdJmgYsBR6OiKnAw+k5wBxganosBu6E4hwBrgMuBC4Arus8T9I8HystN3sItsssm4HcyfiEs6YS\nEfsj4rE0/RLwFDABmAesTLOtBC5O0/OAVVHYDIyWNB64CNgYEYci4jCwEZidXjs9IjZHRACrSn2Z\nNaR+h4xPOGtmkiYDfwBsAVoiYn966QDQkqYnAHtKi+1NbT21763SbtawRg5GJ7U+4SQtprg7oqWl\nhUqlctw8LaNgyXnHer9RSbW+cuno6BjS9fWVx1eQ9BbgH4BPRsTRchU3IkJSDMEYTnjMQ/+O+3r+\nNx4M9X4c10JHR0e2vgccMvVwwkXEcmA5QGtra7S1tR03z+2r13LTtr5v7u7Lj+8rl0qlQrWx1wuP\nDySdRHG8r46Ib6Xm5yWNj4j96Q78YGrfB0wqLT4xte0D2rq0V1L7xCrzH6c3xzz077gfymO+Fur9\nOK6FnKE7oE+X9XTCpdd7e8J1196rE85sKKQPntwFPBURN5deWgd0fmBlIbC21L4gfehlBnAk3eVv\nAGZJGpPef5wFbEivHZU0I61rQakvs4Y0kE+X+YSzZvMe4E+A90t6PD3mAsuAD0jaCfxheg6wHtgF\ntANfBT4OEBGHgC8Cj6THF1IbaZ6vpWWeAR4cig0zy2Ug5bLOE26bpMdT2+coTrD7JC0CngMuSa+t\nB+ZSnDy/AK6E4oST1HnCwfEn3NeBURQnm084q5mI+D7Q3cfoZ1aZP4CruulrBbCiSvujwLkDGKZZ\nXel3yPiEMzOzE/E3/s3MLBuHjJmZZeOQMTOzbBwyZmaWjUPGzMyycciYmVk2DhkzM8vGIWNmZtk4\nZMzMLBuHjJmZZeOQMTOzbBwyZmaWjUPGzMyycciYmVk2DhkzM8vGIWNmZtk4ZMzMLBuHjJmZZeOQ\nMTOzbBwyZmaWjUPGzMyycciYmVk2DhkzM8vGIWNmZtk4ZMzMLBuHjJmZZeOQMTOzbBwyZmaWjUPG\nzMyycciYmVk2DhkzM8tmZK0HYGb1ZfLSB/q8zO5lH8wwEhsO6v5ORtJsSTsktUtaWuvxmOXmY96G\nk7oOGUkjgDuAOcA04DJJ02o7KrN8fMzbcFPv5bILgPaI2AUgaQ0wD3iypqMyy6chj3mX2Kw79R4y\nE4A9ped7gQu7ziRpMbA4Pe2QtKNKX+OAn/d1ALqxr0sMSL/GOISaZXxvH4Q++mswj3mo43+zIT63\nyup2n9TQODId9/UeMr0SEcuB5T3NI+nRiGgdoiH1S72P0eOrH7055qG59klveZ8cL+2TyTn6ruv3\nZIB9wKTS84mpzWy48jFvw0q9h8wjwFRJUySdDMwH1tV4TGY5+Zi3YaWuy2URcUzS1cAGYASwIiK2\n97O7E5YW6kC9j9Hjy2yQj3kYBvskA++T42XbJ4qIXH2bmVmTq/dymZmZNTCHjJmZZdMUIVOrX9Mh\naZKkTZKelLRd0jWp/XpJ+yQ9nh5zS8tcm8a5Q9JFubdB0m5J29I4Hk1tYyVtlLQz/RyT2iXptjSG\nJySdX+pnYZp/p6SFgzS2c0r76HFJRyV9sp72X71qwu2t2+N4KElaIemgpJ+U2gZtP0ianvZze1pW\nJxxURAzrB8Wbp88A7wBOBn4MTBuidY8Hzk/TbwV+SvGrQq4HPl1l/mlpfKcAU9K4R+TcBmA3MK5L\n218BS9P0UuDGND0XeBAQMAPYktrHArvSzzFpekyGf8cDFF8Yq5v9V4+PZtvetM0NcRwPwX54H3A+\n8JMc+wH4YZpXadk5JxpTM9zJ/ObXdETEq0Dnr+nILiL2R8Rjafol4CmKb3R3Zx6wJiJeiYhngXaK\n8Q/1NswDVqbplcDFpfZVUdgMjJY0HrgI2BgRhyLiMLARmD3IY5oJPBMRz51g3PWw/2qt2ba3O/V4\nHGcVEd8DDnVpHpT9kF47PSI2R5E4q0p9dasZQqbar+no6T/6LCRNBv4A2JKark63qCs6b1/pfqw5\ntyGAhyRtVfGrSgBaImJ/mj4AtNRwfJ3mA/eUntfL/qtHzba90DjHcS0M1n6YkKa7tveoGUKm5iS9\nBfgH4JMRcRS4E/gPwO8D+4Gbaji890bE+RS/9fcqSe8rv5iuWGr6OXcVX0r8MPDN1FRP+8/qQ90f\nx/WgFvuhGUKmpr+mQ9JJFAGzOiK+BRARz0fEaxHxOvBVivJGT2PNtg0RsS/9PAj8YxrL8+nWmPTz\nYK3Gl8wBHouI59NY62b/1alm295GOY5rZbD2w7403bW9R80QMjX7NR3pkxd3AU9FxM2l9vGl2f4I\n6PwkyDpgvqRTJE0BplK80ZZlGySdJumtndPArDSWdUDnJ0oWAmtL41uQPpUyAziSbsM3ALMkjUml\nq1mpbbBcRqlUVi/7r4411fY20HFcK4OyH9JrRyXNSP+3LSj11b1afxpiKB4Un6L4KcUnbj4/hOt9\nL8Wt6RPA4+kxF7gb2Jba1wHjS8t8Po1zB6VPbuTYBopPH/04PbZ39gucCTwM7AS+A4xN7aL4g1rP\npPG3lvr6U4o32tuBKwdxH54GvACcUWqri/1Xz49m2t5GOI6HcF/cQ1FC/jXFeyaLBnM/AK0UAf4M\n8Lek3xrT08O/VsbMzLJphnKZmZnViEPGzMyycciYmVk2DhkzM8vGIWNmZtk4ZMzMLBuHjJmZZfP/\nAWXSUA/4GSuDAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t47yNgr3fFcs",
        "colab_type": "code",
        "outputId": "f2f508fc-a96a-4f8c-b0d3-4543a088886b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        }
      },
      "source": [
        "#plotando visualmente a correlação entre sessões e idas à loja\n",
        "sns.pairplot(outs_corr)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.PairGrid at 0x7ff25b3f0048>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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V/RvQ1dfKpE6OV+C6Lq51H3Bf99XOmL4T8Ak+n4/PDs/l7pllXNumr/nuGePI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\n",
            "text/plain": [
              "<Figure size 360x360 with 6 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "skr5NXnKfw2U",
        "colab_type": "code",
        "outputId": "360af5af-bc5c-4d9f-bcf0-3b112988c195",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 107
        }
      },
      "source": [
        "#correlação entre sessões e ida à loja\n",
        "outs_corr.corr()"
      ],
      "execution_count": 0,
      "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>sessions</th>\n",
              "      <th>outs</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>sessions</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.645435</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>outs</th>\n",
              "      <td>0.645435</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          sessions      outs\n",
              "sessions  1.000000  0.645435\n",
              "outs      0.645435  1.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U4iuTvswfTvv",
        "colab_type": "code",
        "outputId": "845acb0d-aab2-4f86-9ff7-dbba7cfab491",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        }
      },
      "source": [
        "#plotando a correalação entre sessões e idas à loja\n",
        "#coeficiente encontrado não indica forte correlação \n",
        "sns.heatmap(outs_corr.corr(), cmap='bwr', vmax=1, vmin=-1, center=0, square=True, annot=True)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7ff25dc9de80>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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rsLXt+vXw+OOw//41KdusHM0UZndJ+i0wFlgoaTjwSvXKqqPVq7NgGj4864Edcww88si2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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HbX6Hr60-4B-",
        "colab_type": "code",
        "outputId": "db45b793-922c-43bd-b34c-98d439ccc6fd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 227
        }
      },
      "source": [
        "#criando novos datasets para dividir usuários com e sem registro de evento de ir á loja\n",
        "#dataset com registro de ir á loja == 0\n",
        "outs_without = outs_corr.query('outs == 0')\n",
        "outs_without.head()"
      ],
      "execution_count": 0,
      "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>sessions</th>\n",
              "      <th>outs</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>user_id</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>189</th>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>299</th>\n",
              "      <td>50</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>310</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>453</th>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>640</th>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         sessions  outs\n",
              "user_id                \n",
              "189            10     0\n",
              "299            50     0\n",
              "310             1     0\n",
              "453             3     0\n",
              "640             4     0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lGKGnHvGA5kJ",
        "colab_type": "code",
        "outputId": "b8f5151f-136e-469d-d101-5c5c00c1b98e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 107
        }
      },
      "source": [
        "outs_without.describe().T"
      ],
      "execution_count": 0,
      "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>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>sessions</th>\n",
              "      <td>48414.0</td>\n",
              "      <td>8.026129</td>\n",
              "      <td>20.593356</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>1625.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>outs</th>\n",
              "      <td>48414.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            count      mean        std  min  25%  50%  75%     max\n",
              "sessions  48414.0  8.026129  20.593356  1.0  2.0  4.0  8.0  1625.0\n",
              "outs      48414.0  0.000000   0.000000  0.0  0.0  0.0  0.0     0.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gcj4p6QVAUy-",
        "colab_type": "code",
        "outputId": "9c106b2f-dc16-4c07-cdb7-c06213746790",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 227
        }
      },
      "source": [
        "#dataset com registro de ir à loja >= 1\n",
        "outs_withy = outs_corr.query('outs >= 1')\n",
        "outs_withy.head()"
      ],
      "execution_count": 0,
      "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>sessions</th>\n",
              "      <th>outs</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>user_id</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>8</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>471</td>\n",
              "      <td>49</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>248</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>91</th>\n",
              "      <td>30</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>120</th>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         sessions  outs\n",
              "user_id                \n",
              "17              8    10\n",
              "27            471    49\n",
              "50            248    18\n",
              "91             30     4\n",
              "120             5     1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wnGmrBhaBMEM",
        "colab_type": "code",
        "outputId": "a649ee00-8b98-45a7-fbd8-851cd78b97ae",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 107
        }
      },
      "source": [
        "outs_withy.describe().T"
      ],
      "execution_count": 0,
      "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>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>sessions</th>\n",
              "      <td>54285.0</td>\n",
              "      <td>187.540610</td>\n",
              "      <td>396.253590</td>\n",
              "      <td>1.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>51.0</td>\n",
              "      <td>176.0</td>\n",
              "      <td>9555.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>outs</th>\n",
              "      <td>54285.0</td>\n",
              "      <td>26.864253</td>\n",
              "      <td>67.470248</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>8235.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            count        mean         std  min   25%   50%    75%     max\n",
              "sessions  54285.0  187.540610  396.253590  1.0  17.0  51.0  176.0  9555.0\n",
              "outs      54285.0   26.864253   67.470248  1.0   2.0   7.0   25.0  8235.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XqdFBtQxtb77",
        "colab_type": "code",
        "outputId": "1e02f2e6-2d01-457b-cdcc-019042fbbd26",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 107
        }
      },
      "source": [
        "outs_without.describe().T"
      ],
      "execution_count": 0,
      "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>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>sessions</th>\n",
              "      <td>48414.0</td>\n",
              "      <td>8.026129</td>\n",
              "      <td>20.593356</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>1625.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>outs</th>\n",
              "      <td>48414.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            count      mean        std  min  25%  50%  75%     max\n",
              "sessions  48414.0  8.026129  20.593356  1.0  2.0  4.0  8.0  1625.0\n",
              "outs      48414.0  0.000000   0.000000  0.0  0.0  0.0  0.0     0.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SQKie5W3C0RW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#plotando o comportamento em sessões de usuários com e sem registro de ir à loja\n",
        "outs_without.plot('outs', color='gray')\n",
        "plt.title('usuários SEM registro de evento de ir à loja')\n",
        "plt.xlabel('SESSÕES')\n",
        "plt.rcParams['figure.figsize'] = (11 ,7)\n",
        "plt.savefig('usuários SEM registro de evento de ir à loja.png')\n",
        "\n",
        "outs_withy.plot('outs', color='purple')\n",
        "plt.title('usuários COM registro de evento de ir à loja')\n",
        "plt.xlabel('SESSÕES')\n",
        "plt.rcParams['figure.figsize'] = (11 ,7)\n",
        "plt.savefig('usuários COM registro de evento de ir à loja.png')\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h7HIt7CSE6zC",
        "colab_type": "code",
        "outputId": "60d94c39-c4b6-49bb-dd90-5383b7e26909",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 158
        }
      },
      "source": [
        "sns.pairplot(outs_full, hue = 'outs')"
      ],
      "execution_count": 0,
      "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-1-b6a7270ac006>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpairplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mouts_full\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'outs'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'sns' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1aVDu1x3Fyi-",
        "colab_type": "code",
        "outputId": "d4a99ead-357c-4b58-961e-d1ad472a38fd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        }
      },
      "source": [
        "sns.pairplot(outs_withy)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.PairGrid at 0x7ff259d26048>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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sa2VcB9srcHMnx3oYeLjremdM7wn4BJ/Px5eGDuCBaWXclJRrfmDqaLL9TiLP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\n",
            "text/plain": [
              "<Figure size 360x360 with 6 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3FxEU1PTvqWw",
        "colab_type": "code",
        "outputId": "3325a6f6-fb4c-4620-e339-2288dda397b4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        }
      },
      "source": [
        "sns.pairplot(outs_without)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.PairGrid at 0x7fec5c383a20>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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SiLgnio+ov653/oj4TURsqH3zzczSk1Zwz4yIJ5P3fwBmlqkzC9hc8rkzKZuV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\n8/8BjRxKYVRP3QoAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 360x360 with 6 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-sVVj016PGBg",
        "colab_type": "code",
        "outputId": "bbdfb1b9-3e67-4dfa-9c8d-eb3bf3fdd24c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 107
        }
      },
      "source": [
        "outs_withy.corr()\n"
      ],
      "execution_count": 0,
      "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>sessions</th>\n",
              "      <th>outs</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>sessions</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.616475</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>outs</th>\n",
              "      <td>0.616475</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          sessions      outs\n",
              "sessions  1.000000  0.616475\n",
              "outs      0.616475  1.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dvirxS88PzSa",
        "colab_type": "code",
        "outputId": "62f3ab7e-76ea-4e26-f704-023ab9d39265",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        }
      },
      "source": [
        "sns.heatmap(outs_withy.corr(), cmap='bwr', vmax=1, vmin=-1, center=0, square=True, annot=True)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7ff259acccc0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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q4Opx+97fc/03wJ8O8jldEpckqQA2bElS8aa6YY+CgS1JKl4Nge2SuCRJBbBhS5KKV0PD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\nXbB+/bcQNQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 792x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JijdNvKigY4q",
        "colab_type": "text"
      },
      "source": [
        "### OUTS X SESSIONS = CORR 0.62\n",
        "###Correlação fraca entre eventos de sessões e ir à loja "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EHhiA2j22JDp",
        "colab_type": "text"
      },
      "source": [
        "### 2. Hipótese: usuários que mais postam ofertas retornam ao site com mais frequência?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ApJL-15L2AUC",
        "colab_type": "code",
        "outputId": "1996a0b4-6b15-4aed-999a-2b32036392fe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        }
      },
      "source": [
        "#criando o data set e exibindo as primeiras linhas\n",
        "posted_offers_full = pd.read_csv('posted_offers.csv')\n",
        "posted_offers_full.head()\n",
        "#outs_full.info()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "\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>user_id</th>\n",
              "      <th>sessions</th>\n",
              "      <th>month</th>\n",
              "      <th>posted_offers</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>27</td>\n",
              "      <td>41</td>\n",
              "      <td>march</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>50</td>\n",
              "      <td>100</td>\n",
              "      <td>march</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>120</td>\n",
              "      <td>5</td>\n",
              "      <td>march</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>128</td>\n",
              "      <td>31</td>\n",
              "      <td>march</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>138</td>\n",
              "      <td>76</td>\n",
              "      <td>march</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   user_id  sessions  month  posted_offers\n",
              "0       27        41  march              0\n",
              "1       50       100  march              0\n",
              "2      120         5  march              0\n",
              "3      128        31  march              0\n",
              "4      138        76  march              0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s_-j8ZZB8LeX",
        "colab_type": "code",
        "outputId": "86be292d-11b6-4d5a-8c16-17731cd52f29",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 137
        }
      },
      "source": [
        "posted_offers_full.describe().T"
      ],
      "execution_count": 0,
      "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>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>user_id</th>\n",
              "      <td>231292.0</td>\n",
              "      <td>453901.845719</td>\n",
              "      <td>218836.571653</td>\n",
              "      <td>17.0</td>\n",
              "      <td>261655.0</td>\n",
              "      <td>502466.5</td>\n",
              "      <td>654606.0</td>\n",
              "      <td>739693.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sessions</th>\n",
              "      <td>231292.0</td>\n",
              "      <td>45.696431</td>\n",
              "      <td>105.674874</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>39.0</td>\n",
              "      <td>4314.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>posted_offers</th>\n",
              "      <td>231292.0</td>\n",
              "      <td>0.053538</td>\n",
              "      <td>5.012765</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1775.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  count           mean  ...       75%       max\n",
              "user_id        231292.0  453901.845719  ...  654606.0  739693.0\n",
              "sessions       231292.0      45.696431  ...      39.0    4314.0\n",
              "posted_offers  231292.0       0.053538  ...       0.0    1775.0\n",
              "\n",
              "[3 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "36n756rwUIuR",
        "colab_type": "code",
        "outputId": "78a8411a-d108-45cd-dfc6-5cd831dfb3f4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        }
      },
      "source": [
        "posted_offers_full.hist(bins = 10)\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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lDxieXMzDPLqS5OCq2tZm/zMw+XDplcCHk/wh8FRgFfA5IMCqJIfRG0NPBn69qirJtcBL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\nh+u/4KfScCoAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 792x504 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vQhtI_TdwIPJ",
        "colab_type": "code",
        "outputId": "b0d4ac6c-f1dd-4816-db0b-0dac09f1aae1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 457
        }
      },
      "source": [
        "#plotando o comportamento em sessões de usuários com e sem registro de ir à loja\n",
        "posted_offers_full.plot('sessions', color='gray')\n",
        "plt.title('usuários SEM registro de evento de ir à loja')\n",
        "plt.xlabel('SESSÕES')\n",
        "plt.rcParams['figure.figsize'] = (11 ,7)\n",
        "plt.savefig('usuários SEM registro de evento de ir à loja.png')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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spaSjJJ0pSc65ryR9ZWYTJB3tVbtX0lJJl0uaIOk+55yT9KqZlZlZlVf3Gefc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\n",
            "text/plain": [
              "<Figure size 792x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r7LABd5VUVBW",
        "colab_type": "code",
        "outputId": "962a00f6-2c63-4226-b78e-59da5a78e4ac",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 137
        }
      },
      "source": [
        "posted_offers_full.corr()"
      ],
      "execution_count": 0,
      "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>user_id</th>\n",
              "      <th>sessions</th>\n",
              "      <th>posted_offers</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>user_id</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.188025</td>\n",
              "      <td>-0.007755</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sessions</th>\n",
              "      <td>-0.188025</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.063014</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>posted_offers</th>\n",
              "      <td>-0.007755</td>\n",
              "      <td>0.063014</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                user_id  sessions  posted_offers\n",
              "user_id        1.000000 -0.188025      -0.007755\n",
              "sessions      -0.188025  1.000000       0.063014\n",
              "posted_offers -0.007755  0.063014       1.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fVzug9uTUeho",
        "colab_type": "code",
        "outputId": "2f0658ed-1302-4376-f2b1-d196722d1e5c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 451
        }
      },
      "source": [
        "sns.heatmap(posted_offers_full.corr(), cmap='bwr', vmax=1, vmin=-1, center=0, square=True, annot=True)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7ff2599d5470>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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IO21JUjGUoNM2tCVJxVCC0C7+CCVJKgg7bUlSMZSg0za0JUnFUILQLv4IJUkqCDttSVIx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\nvCM0LgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 792x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4kxepgRtYyYE",
        "colab_type": "code",
        "outputId": "e7004f7d-b2e3-4c35-e102-b495fdc6fa6f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 227
        }
      },
      "source": [
        "posted_offers_corr = posted_offers_full.groupby('user_id')['sessions', 'posted_offers'].sum()\n",
        "posted_offers_corr.head()"
      ],
      "execution_count": 0,
      "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>sessions</th>\n",
              "      <th>posted_offers</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>user_id</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>471</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>248</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>91</th>\n",
              "      <td>30</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>120</th>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         sessions  posted_offers\n",
              "user_id                         \n",
              "17              8              0\n",
              "27            471              0\n",
              "50            248              0\n",
              "91             30              0\n",
              "120             5              0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OqLl-3a2Uq5q",
        "colab_type": "code",
        "outputId": "370f0ce0-c857-4a3b-d885-e9f6c869ed8c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 107
        }
      },
      "source": [
        "#dataset com registro de ofertas postadas\n",
        "offers_withy = posted_offers_corr.query('posted_offers >= 1')\n",
        "offers_withy.head()\n",
        "offers_withy.describe().T"
      ],
      "execution_count": 0,
      "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>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>sessions</th>\n",
              "      <td>1319.0</td>\n",
              "      <td>662.886277</td>\n",
              "      <td>896.505536</td>\n",
              "      <td>1.0</td>\n",
              "      <td>125.5</td>\n",
              "      <td>352.0</td>\n",
              "      <td>830.5</td>\n",
              "      <td>9240.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>posted_offers</th>\n",
              "      <td>1319.0</td>\n",
              "      <td>9.388173</td>\n",
              "      <td>96.409137</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2854.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                count        mean         std  min    25%    50%    75%     max\n",
              "sessions       1319.0  662.886277  896.505536  1.0  125.5  352.0  830.5  9240.0\n",
              "posted_offers  1319.0    9.388173   96.409137  1.0    1.0    1.0    3.0  2854.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SvhLhFntL5mn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mLTbD2O3U_K3",
        "colab_type": "code",
        "outputId": "11085c80-0de9-45d1-bdf7-5aec468f9f90",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 107
        }
      },
      "source": [
        "offers_without = posted_offers_corr.query('posted_offers == 0')\n",
        "offers_without.describe().T"
      ],
      "execution_count": 0,
      "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>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>sessions</th>\n",
              "      <td>101380.0</td>\n",
              "      <td>95.629039</td>\n",
              "      <td>278.982095</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>57.0</td>\n",
              "      <td>9555.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>posted_offers</th>\n",
              "      <td>101380.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  count       mean         std  min  25%   50%   75%     max\n",
              "sessions       101380.0  95.629039  278.982095  1.0  3.0  12.0  57.0  9555.0\n",
              "posted_offers  101380.0   0.000000    0.000000  0.0  0.0   0.0   0.0     0.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZLU87nr4Vh37",
        "colab_type": "code",
        "outputId": "81ca023b-48a0-4947-84f2-a3bc3cbb3078",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 107
        }
      },
      "source": [
        "offers_withy.corr()"
      ],
      "execution_count": 0,
      "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>sessions</th>\n",
              "      <th>posted_offers</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>sessions</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.133199</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>posted_offers</th>\n",
              "      <td>0.133199</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               sessions  posted_offers\n",
              "sessions       1.000000       0.133199\n",
              "posted_offers  0.133199       1.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G4MWTymM5_KE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "offers_without.plot('posted_offers', color='grey')\n",
        "plt.title('sessões de usuários SEM registro ofertas enviadas')\n",
        "plt.xlabel('SESSÕES')\n",
        "plt.rcParams['figure.figsize'] = (11 ,7)\n",
        "\n",
        "offers_withy.plot('posted_offers', color='purple')\n",
        "plt.title('sessões de usuários COM registro ofertas enviadas')\n",
        "plt.xlabel('SESSÕES')\n",
        "plt.rcParams['figure.figsize'] = (11 ,7)\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SlF6y4UG6pCC",
        "colab_type": "code",
        "outputId": "9a553d01-856c-4218-aced-d6daaa7a5d08",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "sns.pairplot(posted_offers_full, hue = 'posted_offers')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/numpy/core/_methods.py:140: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
            "  keepdims=keepdims)\n",
            "/usr/local/lib/python3.6/dist-packages/numpy/core/_methods.py:132: RuntimeWarning: invalid value encountered in double_scalars\n",
            "  ret = ret.dtype.type(ret / rcount)\n",
            "/usr/local/lib/python3.6/dist-packages/statsmodels/nonparametric/kde.py:487: RuntimeWarning: invalid value encountered in true_divide\n",
            "  binned = fast_linbin(X, a, b, gridsize) / (delta * nobs)\n",
            "/usr/local/lib/python3.6/dist-packages/statsmodels/nonparametric/kdetools.py:34: RuntimeWarning: invalid value encountered in double_scalars\n",
            "  FAC1 = 2*(np.pi*bw/RANGE)**2\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.PairGrid at 0x7ff2592e2860>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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dSq9+fSFQqBMRERGJAV1+FREREYkBhToRERGRGFCoExEREYkBhToRERGRGFCoExEREYkB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\nT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kD3C1W9dZfjHnABcPRQyh8q66kzxhhjzLjiiptMVNb3OZaorMcVNznYMstz5m4AVqvq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\nSZJawFAnSZLUAv8/b4kVD6HOdZsAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 615.625x540 with 12 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I8Z8Wj5CVrBa",
        "colab_type": "code",
        "outputId": "29fc5075-a26f-422c-97c5-7b5a1d0cdc6c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 451
        }
      },
      "source": [
        "sns.heatmap(offers_withy.corr(), cmap='bwr', vmax=1, vmin=-1, center=0, square=True, annot=True)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7ff2598e7ef0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Xr1EfA/wIGA+cnZlLI+JkYHFmzgfOAs6PiGXAn2kF+4jECEO/Pbfc0qNVCVIPuRBNTZU5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\nSX3i/wFHDIc7ObkseAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 792x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GxvSQ_Ws4D1R",
        "colab_type": "text"
      },
      "source": [
        "### POSTED OFFERS X SESSIONS = CORR 0.13\n",
        "###Correlação fraca entre sessões e ofertas postadas"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "loZvJJhK4vNd",
        "colab_type": "text"
      },
      "source": [
        "###3. Hipótese: usuários que curtem mais ofertas retornam ao site com mais frequência?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lpqQ_Yfc44c5",
        "colab_type": "code",
        "outputId": "30ceccf3-c092-406f-d9f6-cb2aab5a54e0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        }
      },
      "source": [
        "#criando o data set\n",
        "offer_likes_full = pd.read_csv('offer_likes.csv')\n",
        "offer_likes_full.head()"
      ],
      "execution_count": 0,
      "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>user_id</th>\n",
              "      <th>sessions</th>\n",
              "      <th>month</th>\n",
              "      <th>offer_likes</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>27</td>\n",
              "      <td>41</td>\n",
              "      <td>march</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>50</td>\n",
              "      <td>100</td>\n",
              "      <td>march</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>120</td>\n",
              "      <td>5</td>\n",
              "      <td>march</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>128</td>\n",
              "      <td>31</td>\n",
              "      <td>march</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>138</td>\n",
              "      <td>76</td>\n",
              "      <td>march</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   user_id  sessions  month  offer_likes\n",
              "0       27        41  march            0\n",
              "1       50       100  march            0\n",
              "2      120         5  march            0\n",
              "3      128        31  march            0\n",
              "4      138        76  march            0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3o3M_sVP5TfU",
        "colab_type": "code",
        "outputId": "03d8a6d2-0e30-4797-b8cd-60fe93b303d2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 176
        }
      },
      "source": [
        "offer_likes_full.info()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 231292 entries, 0 to 231291\n",
            "Data columns (total 4 columns):\n",
            "user_id        231292 non-null int64\n",
            "sessions       231292 non-null int64\n",
            "month          231292 non-null object\n",
            "offer_likes    231292 non-null int64\n",
            "dtypes: int64(3), object(1)\n",
            "memory usage: 7.1+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P9YfD65YIlQ_",
        "colab_type": "code",
        "outputId": "acac964d-2fa1-47e8-ec54-988721438e35",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 137
        }
      },
      "source": [
        "offer_likes_full.describe().T"
      ],
      "execution_count": 0,
      "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>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>user_id</th>\n",
              "      <td>231292.0</td>\n",
              "      <td>453901.845719</td>\n",
              "      <td>218836.571653</td>\n",
              "      <td>17.0</td>\n",
              "      <td>261655.0</td>\n",
              "      <td>502466.5</td>\n",
              "      <td>654606.0</td>\n",
              "      <td>739693.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sessions</th>\n",
              "      <td>231292.0</td>\n",
              "      <td>45.696431</td>\n",
              "      <td>105.674874</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>39.0</td>\n",
              "      <td>4314.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>offer_likes</th>\n",
              "      <td>231292.0</td>\n",
              "      <td>6.695614</td>\n",
              "      <td>148.810760</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>9480.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                count           mean  ...       75%       max\n",
              "user_id      231292.0  453901.845719  ...  654606.0  739693.0\n",
              "sessions     231292.0      45.696431  ...      39.0    4314.0\n",
              "offer_likes  231292.0       6.695614  ...       0.0    9480.0\n",
              "\n",
              "[3 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nlQxzR5DJGwK",
        "colab_type": "code",
        "outputId": "e54df5a7-ddcf-4df9-9c73-b5252891a6c9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        }
      },
      "source": [
        "offer_likes_full.hist(bins = 10)\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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WEq+QaklK4n/GJEkaECakGjhJKsmL+pYvSvL7bf7gJFcleSLJ1iR/n+RH2rrn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\n",
            "text/plain": [
              "<Figure size 792x504 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cMVkWtC6LVue",
        "colab_type": "code",
        "outputId": "46a1af57-3645-4eb2-d533-8fc27a3deac3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 137
        }
      },
      "source": [
        "offer_likes_full.corr()"
      ],
      "execution_count": 0,
      "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>user_id</th>\n",
              "      <th>sessions</th>\n",
              "      <th>offer_likes</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>user_id</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.188025</td>\n",
              "      <td>-0.032285</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sessions</th>\n",
              "      <td>-0.188025</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.102052</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>offer_likes</th>\n",
              "      <td>-0.032285</td>\n",
              "      <td>0.102052</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              user_id  sessions  offer_likes\n",
              "user_id      1.000000 -0.188025    -0.032285\n",
              "sessions    -0.188025  1.000000     0.102052\n",
              "offer_likes -0.032285  0.102052     1.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "et-AIL0YLfxN",
        "colab_type": "code",
        "outputId": "4500716f-b4e6-4de8-d41d-2233783da6e9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 451
        }
      },
      "source": [
        "sns.heatmap(offer_likes_full.corr(), cmap='bwr', vmax=1, vmin=-1, center=0, square=True, annot=True)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7ff25345d7b8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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47rn3l64XLnyOQYNa240fNKi13fJ2R2MADjxwJCeeOIoTTjgZgD/84THOPffb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\nkqRewtCWJKmXMLQlSeol/j/yN86HZ2ejWAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 792x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H7x5d3Z0L7wX",
        "colab_type": "code",
        "outputId": "9c7b4cf3-68af-4fea-fd18-851c8b30b933",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        }
      },
      "source": [
        "#separando datasets\n",
        "likes_withy = offer_likes_full.query('offer_likes >= 1')\n",
        "likes_withy.head()\n",
        "#likes_withy.describe().T"
      ],
      "execution_count": 0,
      "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>user_id</th>\n",
              "      <th>sessions</th>\n",
              "      <th>month</th>\n",
              "      <th>offer_likes</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>142</td>\n",
              "      <td>9</td>\n",
              "      <td>march</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>167</td>\n",
              "      <td>49</td>\n",
              "      <td>march</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>189</td>\n",
              "      <td>10</td>\n",
              "      <td>march</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>207</td>\n",
              "      <td>2</td>\n",
              "      <td>march</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>227</td>\n",
              "      <td>2</td>\n",
              "      <td>march</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    user_id  sessions  month  offer_likes\n",
              "5       142         9  march            2\n",
              "6       167        49  march            2\n",
              "11      189        10  march            1\n",
              "13      207         2  march            1\n",
              "16      227         2  march            2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZdewDkzVNhHm",
        "colab_type": "code",
        "outputId": "a365444f-859a-43ca-8a8a-924e9619bb11",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 137
        }
      },
      "source": [
        "likes_withy.describe().T"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>user_id</th>\n",
              "      <td>42536.0</td>\n",
              "      <td>402831.820623</td>\n",
              "      <td>225501.232008</td>\n",
              "      <td>27.0</td>\n",
              "      <td>212529.0</td>\n",
              "      <td>424792.0</td>\n",
              "      <td>615057.0</td>\n",
              "      <td>739625.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sessions</th>\n",
              "      <td>42536.0</td>\n",
              "      <td>128.092957</td>\n",
              "      <td>182.791953</td>\n",
              "      <td>1.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>65.0</td>\n",
              "      <td>163.0</td>\n",
              "      <td>3058.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>offer_likes</th>\n",
              "      <td>42536.0</td>\n",
              "      <td>36.407796</td>\n",
              "      <td>345.446557</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>9480.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               count           mean  ...       75%       max\n",
              "user_id      42536.0  402831.820623  ...  615057.0  739625.0\n",
              "sessions     42536.0     128.092957  ...     163.0    3058.0\n",
              "offer_likes  42536.0      36.407796  ...       4.0    9480.0\n",
              "\n",
              "[3 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GaXDSKJINy0X",
        "colab_type": "code",
        "outputId": "e0acdb56-647a-4a4c-b67e-11be63431360",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        }
      },
      "source": [
        "#separando datasets\n",
        "likes_without = offer_likes_full.query('offer_likes == 0')\n",
        "likes_without.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>user_id</th>\n",
              "      <th>sessions</th>\n",
              "      <th>month</th>\n",
              "      <th>offer_likes</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>27</td>\n",
              "      <td>41</td>\n",
              "      <td>march</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>50</td>\n",
              "      <td>100</td>\n",
              "      <td>march</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>120</td>\n",
              "      <td>5</td>\n",
              "      <td>march</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>128</td>\n",
              "      <td>31</td>\n",
              "      <td>march</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>138</td>\n",
              "      <td>76</td>\n",
              "      <td>march</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   user_id  sessions  month  offer_likes\n",
              "0       27        41  march            0\n",
              "1       50       100  march            0\n",
              "2      120         5  march            0\n",
              "3      128        31  march            0\n",
              "4      138        76  march            0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A30i7fFqN6Zf",
        "colab_type": "code",
        "outputId": "ae16774b-1d7d-4a48-952c-b5dc1cc785a1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 137
        }
      },
      "source": [
        "likes_without.describe().T"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th></th>\n",
              "      <th>count</th>\n",
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              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>user_id</th>\n",
              "      <td>188756.0</td>\n",
              "      <td>465410.431340</td>\n",
              "      <td>215643.631548</td>\n",
              "      <td>17.0</td>\n",
              "      <td>281661.0</td>\n",
              "      <td>519242.0</td>\n",
              "      <td>660249.0</td>\n",
              "      <td>739693.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sessions</th>\n",
              "      <td>188756.0</td>\n",
              "      <td>27.128446</td>\n",
              "      <td>65.418336</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>23.0</td>\n",
              "      <td>4314.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>offer_likes</th>\n",
              "      <td>188756.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                count           mean  ...       75%       max\n",
              "user_id      188756.0  465410.431340  ...  660249.0  739693.0\n",
              "sessions     188756.0      27.128446  ...      23.0    4314.0\n",
              "offer_likes  188756.0       0.000000  ...       0.0       0.0\n",
              "\n",
              "[3 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z73zoiAyOXg8",
        "colab_type": "code",
        "outputId": "b4960853-a32b-4bc1-b57a-e5bb390cec2c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 227
        }
      },
      "source": [
        "offer_likes_corr = offer_likes_full.groupby('user_id')['sessions', 'offer_likes'].sum()\n",
        "offer_likes_corr.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .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>sessions</th>\n",
              "      <th>offer_likes</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>user_id</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>471</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>248</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>91</th>\n",
              "      <td>30</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>120</th>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         sessions  offer_likes\n",
              "user_id                       \n",
              "17              8            0\n",
              "27            471            3\n",
              "50            248            0\n",
              "91             30            0\n",
              "120             5            0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wRFK5zIgOon9",
        "colab_type": "code",
        "outputId": "13271443-8119-4e34-feca-29634d483c98",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 141
        }
      },
      "source": [
        "offer_likes_corr.info()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 102699 entries, 17 to 739693\n",
            "Data columns (total 2 columns):\n",
            "sessions       102699 non-null int64\n",
            "offer_likes    102699 non-null int64\n",
            "dtypes: int64(2)\n",
            "memory usage: 2.4 MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "314JM44cPLLr",
        "colab_type": "code",
        "outputId": "46d5bce7-e0e1-4c0c-c469-b1ceb7ab7dff",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 227
        }
      },
      "source": [
        "#separando datasets\n",
        "likes_withy = offer_likes_corr.query('offer_likes >= 1')\n",
        "likes_withy.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th>offer_likes</th>\n",
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              "    <tr>\n",
              "      <th>user_id</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "      <th>27</th>\n",
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              "    <tr>\n",
              "      <th>138</th>\n",
              "      <td>202</td>\n",
              "      <td>3</td>\n",
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              "    <tr>\n",
              "      <th>142</th>\n",
              "      <td>158</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>167</th>\n",
              "      <td>133</td>\n",
              "      <td>2</td>\n",
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              "    <tr>\n",
              "      <th>182</th>\n",
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              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         sessions  offer_likes\n",
              "user_id                       \n",
              "27            471            3\n",
              "138           202            3\n",
              "142           158          102\n",
              "167           133            2\n",
              "182           465            1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_dAksAnAPRPJ",
        "colab_type": "code",
        "outputId": "f2163879-fee7-420b-c791-129a56a71a4e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 107
        }
      },
      "source": [
        "likes_withy.describe().T"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
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              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>sessions</th>\n",
              "      <td>25336.0</td>\n",
              "      <td>311.021116</td>\n",
              "      <td>528.980642</td>\n",
              "      <td>1.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>118.0</td>\n",
              "      <td>366.0</td>\n",
              "      <td>9555.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>offer_likes</th>\n",
              "      <td>25336.0</td>\n",
              "      <td>61.124171</td>\n",
              "      <td>821.111948</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>35628.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               count        mean         std  min   25%    50%    75%      max\n",
              "sessions     25336.0  311.021116  528.980642  1.0  32.0  118.0  366.0   9555.0\n",
              "offer_likes  25336.0   61.124171  821.111948  1.0   1.0    2.0    5.0  35628.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "05bPfiwDPYoA",
        "colab_type": "code",
        "outputId": "3b8240df-74d0-4077-ca7e-9bb3aa8b24a5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 227
        }
      },
      "source": [
        "#separando datasets\n",
        "likes_without = offer_likes_corr.query('offer_likes == 0')\n",
        "likes_without.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "      <th></th>\n",
              "      <th>sessions</th>\n",
              "      <th>offer_likes</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>user_id</th>\n",
              "      <th></th>\n",
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              "      <th>17</th>\n",
              "      <td>8</td>\n",
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              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>248</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>91</th>\n",
              "      <td>30</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>120</th>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>128</th>\n",
              "      <td>40</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         sessions  offer_likes\n",
              "user_id                       \n",
              "17              8            0\n",
              "50            248            0\n",
              "91             30            0\n",
              "120             5            0\n",
              "128            40            0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b6lJg-5yPfIM",
        "colab_type": "code",
        "outputId": "22f889af-61d8-4068-c4e2-b8b799b3cba2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 107
        }
      },
      "source": [
        "likes_without.describe().T"
      ],
      "execution_count": 0,
      "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>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>sessions</th>\n",
              "      <td>77363.0</td>\n",
              "      <td>34.760648</td>\n",
              "      <td>103.132904</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>6418.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>offer_likes</th>\n",
              "      <td>77363.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               count       mean         std  min  25%  50%   75%     max\n",
              "sessions     77363.0  34.760648  103.132904  1.0  3.0  7.0  24.0  6418.0\n",
              "offer_likes  77363.0   0.000000    0.000000  0.0  0.0  0.0   0.0     0.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yy4Y7sQuPzcZ",
        "colab_type": "code",
        "outputId": "f796d7af-930f-4f36-a7ed-7ba750cfe73c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        }
      },
      "source": [
        "likes_withy.hist(bins = 10)\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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TSdqIU4AkO0ecfngMtUnSslJVjwD/9wztTwGvnaG9gAv2sK8NwIaFrlGSxmG+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\n",
            "text/plain": [
              "<Figure size 792x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tUK49tW2P7Pq",
        "colab_type": "code",
        "outputId": "e3679846-2875-4010-c84d-2ed4d5398ffb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 107
        }
      },
      "source": [
        "offer_likes_corr.corr()"
      ],
      "execution_count": 0,
      "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>sessions</th>\n",
              "      <th>offer_likes</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>sessions</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.108555</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>offer_likes</th>\n",
              "      <td>0.108555</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             sessions  offer_likes\n",
              "sessions     1.000000     0.108555\n",
              "offer_likes  0.108555     1.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zm4B5bVsQAGb",
        "colab_type": "code",
        "outputId": "704ee6a5-4386-4ead-a7e9-71fcea8967cf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 107
        }
      },
      "source": [
        "likes_withy.corr()"
      ],
      "execution_count": 0,
      "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>sessions</th>\n",
              "      <th>offer_likes</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>sessions</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.095766</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>offer_likes</th>\n",
              "      <td>0.095766</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             sessions  offer_likes\n",
              "sessions     1.000000     0.095766\n",
              "offer_likes  0.095766     1.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sbPg-ldKQEZQ",
        "colab_type": "code",
        "outputId": "8862ef15-db41-4f66-c664-2e546cb92598",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        }
      },
      "source": [
        "sns.pairplot(offer_likes_corr)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.PairGrid at 0x7ff24f3f3940>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 96
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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X88Pfvs3yK06lyO9wy4UncuuLm1m2dlti/8Lv/GYd2/a0EHWVa3/7lw457d/O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\nQUEJBx0EQVBQwkEHQRAUlP8DVgaY9NeiJTYAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 360x360 with 6 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-0XA3awVTyhg",
        "colab_type": "code",
        "outputId": "71f590b3-22b1-4943-daac-a1515079cd65",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        }
      },
      "source": [
        "sns.pairplot(likes_withy)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.PairGrid at 0x7ff254fc97b8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 97
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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u/ErvpjksQBuTI1RB0Piu3c2hCIJSWuDDIbqr96ad9fzgd2+x/KrTKfB5uOPi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\nKOGggyAICko46CAIgoLyf3BZvWpTreAnAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 360x360 with 6 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qA07plBRS48X",
        "colab_type": "code",
        "outputId": "4bbe73d2-ffc3-4ea5-ccdd-e8926c381845",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 451
        }
      },
      "source": [
        "sns.heatmap(offer_likes_full.corr(), cmap='bwr', vmax=1, vmin=-1, center=0, square=True, annot=True)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7ff24da6d6d8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 66
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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47rn3l64XLnyOQYNa240fNKi13fJ2R2MADjxwJCeeOIoTTjgZgD/84THOPffb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\nkqRewtCWJKmXMLQlSeol/j/yN86HZ2ejWAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 792x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ero26km3S-lR",
        "colab_type": "code",
        "outputId": "84452f53-45a3-49e2-bcdd-b70b7fa6d7b8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 451
        }
      },
      "source": [
        "sns.heatmap(likes_withy.corr(), cmap='bwr', vmax=1, vmin=-1, center=0, square=True, annot=True)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7ff24e1de390>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 67
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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IVWjahrYkqQiGtiRJHaIKoe1r2pIkdQibtiSpCFVo2oa2JKkIVQhtl8clSeoQ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\nFz1t6QAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 792x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T4GzLD9TTnt5",
        "colab_type": "text"
      },
      "source": [
        "### OFFER LIKES X SESSIONS = CORR 0.09\n",
        "###Correlação fraca entre eventos de curtir ofertas"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NK18Qk-cUL1k",
        "colab_type": "text"
      },
      "source": [
        "###4. Hipótese: usuários que comentam nas ofertas retornam ao site com mais frequência?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VJDpzQ6jULQi",
        "colab_type": "code",
        "outputId": "7c6b9f9f-7506-47fa-ee6d-af6f549bb9a7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        }
      },
      "source": [
        "#criando o data set\n",
        "comments_full = pd.read_csv('comments.csv')\n",
        "comments_full.head()"
      ],
      "execution_count": 0,
      "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>user_id</th>\n",
              "      <th>sessions</th>\n",
              "      <th>month</th>\n",
              "      <th>comments</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>27</td>\n",
              "      <td>41</td>\n",
              "      <td>march</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>50</td>\n",
              "      <td>100</td>\n",
              "      <td>march</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>120</td>\n",
              "      <td>5</td>\n",
              "      <td>march</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>128</td>\n",
              "      <td>31</td>\n",
              "      <td>march</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>138</td>\n",
              "      <td>76</td>\n",
              "      <td>march</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   user_id  sessions  month  comments\n",
              "0       27        41  march         0\n",
              "1       50       100  march         0\n",
              "2      120         5  march         0\n",
              "3      128        31  march         0\n",
              "4      138        76  march         1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GvVk9-jaU8R6",
        "colab_type": "code",
        "outputId": "46077063-77b8-4ec3-aaca-9dd5ba15f27b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 176
        }
      },
      "source": [
        "comments_full.info()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 231292 entries, 0 to 231291\n",
            "Data columns (total 4 columns):\n",
            "user_id     231292 non-null int64\n",
            "sessions    231292 non-null int64\n",
            "month       231292 non-null object\n",
            "comments    231292 non-null int64\n",
            "dtypes: int64(3), object(1)\n",
            "memory usage: 7.1+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bzaL6XoDU-xM",
        "colab_type": "code",
        "outputId": "81016a80-3afd-4db2-c67d-1cafb0744fa6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 137
        }
      },
      "source": [
        "comments_full.describe().T"
      ],
      "execution_count": 0,
      "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>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>user_id</th>\n",
              "      <td>231292.0</td>\n",
              "      <td>453901.845719</td>\n",
              "      <td>218836.571653</td>\n",
              "      <td>17.0</td>\n",
              "      <td>261655.0</td>\n",
              "      <td>502466.5</td>\n",
              "      <td>654606.0</td>\n",
              "      <td>739693.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sessions</th>\n",
              "      <td>231292.0</td>\n",
              "      <td>45.696431</td>\n",
              "      <td>105.674874</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>39.0</td>\n",
              "      <td>4314.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>comments</th>\n",
              "      <td>231292.0</td>\n",
              "      <td>0.423625</td>\n",
              "      <td>5.413541</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1120.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             count           mean            std  ...       50%       75%       max\n",
              "user_id   231292.0  453901.845719  218836.571653  ...  502466.5  654606.0  739693.0\n",
              "sessions  231292.0      45.696431     105.674874  ...      10.0      39.0    4314.0\n",
              "comments  231292.0       0.423625       5.413541  ...       0.0       0.0    1120.0\n",
              "\n",
              "[3 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VbTMK0J5VR3J",
        "colab_type": "code",
        "outputId": "22df223a-e272-4166-edae-cf8ed43f9a79",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 141
        }
      },
      "source": [
        "comments_corr = comments_full.groupby('user_id')['sessions', 'comments'].sum()\n",
        "comments_corr.info()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 102699 entries, 17 to 739693\n",
            "Data columns (total 2 columns):\n",
            "sessions    102699 non-null int64\n",
            "comments    102699 non-null int64\n",
            "dtypes: int64(2)\n",
            "memory usage: 2.4 MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
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      "source": [
        "comments_corr.describe().T"
      ],
      "execution_count": 0,
      "outputs": [
        {
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              "      <th>comments</th>\n",
              "      <td>102699.0</td>\n",
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            "text/plain": [
              "             count        mean         std  min  25%   50%   75%     max\n",
              "sessions  102699.0  102.914527  302.036192  1.0  4.0  13.0  60.0  9555.0\n",
              "comments  102699.0    0.954060   15.173537  0.0  0.0   0.0   0.0  3842.0"
            ]
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      },
      "source": [
        "comments_corr.corr()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    .dataframe tbody tr th:only-of-type {\n",
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            "text/plain": [
              "          sessions  comments\n",
              "sessions  1.000000  0.181959\n",
              "comments  0.181959  1.000000"
            ]
          },
          "metadata": {
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          },
          "execution_count": 81
        }
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        "colab": {
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      "source": [
        "comments_withy = comments_corr.query('comments >= 1')\n",
        "comments_withy.head()"
      ],
      "execution_count": 0,
      "outputs": [
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          "data": {
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              "      <th></th>\n",
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              "      <th>138</th>\n",
              "      <td>202</td>\n",
              "      <td>10</td>\n",
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              "      <th>142</th>\n",
              "      <td>158</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>167</th>\n",
              "      <td>133</td>\n",
              "      <td>4</td>\n",
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              "    <tr>\n",
              "      <th>168</th>\n",
              "      <td>7</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>190</th>\n",
              "      <td>459</td>\n",
              "      <td>15</td>\n",
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            "text/plain": [
              "         sessions  comments\n",
              "user_id                    \n",
              "138           202        10\n",
              "142           158         3\n",
              "167           133         4\n",
              "168             7         2\n",
              "190           459        15"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 82
        }
      ]
    },
    {
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        "colab": {
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      "source": [
        "comments_withy.describe().T"
      ],
      "execution_count": 0,
      "outputs": [
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              "      <th>sessions</th>\n",
              "      <td>10843.0</td>\n",
              "      <td>433.881583</td>\n",
              "      <td>635.817264</td>\n",
              "      <td>1.0</td>\n",
              "      <td>71.0</td>\n",
              "      <td>211.0</td>\n",
              "      <td>543.5</td>\n",
              "      <td>9240.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>comments</th>\n",
              "      <td>10843.0</td>\n",
              "      <td>9.036337</td>\n",
              "      <td>45.910908</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>3842.0</td>\n",
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              "</table>\n",
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            ],
            "text/plain": [
              "            count        mean         std  min   25%    50%    75%     max\n",
              "sessions  10843.0  433.881583  635.817264  1.0  71.0  211.0  543.5  9240.0\n",
              "comments  10843.0    9.036337   45.910908  1.0   1.0    2.0    7.0  3842.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
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        "colab_type": "code",
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        "colab": {
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        }
      },
      "source": [
        "comments_withy.corr()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "          sessions  comments\n",
              "sessions  1.000000  0.168134\n",
              "comments  0.168134  1.000000"
            ]
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          "metadata": {
            "tags": []
          },
          "execution_count": 84
        }
      ]
    },
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      "source": [
        "comments_without = comments_corr.query('comments == 0')\n",
        "comments_without.head()"
      ],
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          "data": {
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            "text/plain": [
              "         sessions  comments\n",
              "user_id                    \n",
              "17              8         0\n",
              "27            471         0\n",
              "50            248         0\n",
              "91             30         0\n",
              "120             5         0"
            ]
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            "tags": []
          },
          "execution_count": 85
        }
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        "colab": {
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      },
      "source": [
        "comments_without.describe().T"
      ],
      "execution_count": 0,
      "outputs": [
        {
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          "data": {
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              "      <th>sessions</th>\n",
              "      <td>91856.0</td>\n",
              "      <td>63.846031</td>\n",
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              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
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              "      <td>38.0</td>\n",
              "      <td>9555.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>comments</th>\n",
              "      <td>91856.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
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            ],
            "text/plain": [
              "            count       mean         std  min  25%   50%   75%     max\n",
              "sessions  91856.0  63.846031  199.551781  1.0  3.0  10.0  38.0  9555.0\n",
              "comments  91856.0   0.000000    0.000000  0.0  0.0   0.0   0.0     0.0"
            ]
          },
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            "tags": []
          },
          "execution_count": 89
        }
      ]
    },
    {
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        "colab": {
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          "height": 444
        }
      },
      "source": [
        "comments_withy.hist(bins = 10)\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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kAYOnJI2xqvp7ILtZfPwu+hdw5m62dTFw8eKNTpLmxkvtkiRJ6mLewdNv05Ak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\n3AAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 792x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u1VHlnjoXv7V",
        "colab_type": "code",
        "outputId": "27d6761e-297c-4b93-8a75-ff5b1f484c50",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        }
      },
      "source": [
        "comments_without.hist(bins = 10)\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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r6uAk90zy65kc3Z9TVWdlcnT/gnz10f2jMzlyf/TU0f22JC3J1VV1aR9EF4/ur0pyWSZH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\naJaZbPPzAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 792x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "juwXWfeyXj33",
        "colab_type": "code",
        "outputId": "e9314ad2-1146-4b8b-a0b0-9c85f9b8d1cd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        }
      },
      "source": [
        "comments_corr.hist(bins = 10)\n",
        "plt.show()\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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s/V4vf3eSN+49AzbJXX3APbnX29FauzNJqmp+Buyty2wzwFBaaxcmuXCv4jvz7bFyuu4/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\nG7pRocA2eusEAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 792x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e40-fFeFYOEE",
        "colab_type": "code",
        "outputId": "add30bf1-f22f-4abe-8fe6-04d6384f8b88",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        }
      },
      "source": [
        "sns.pairplot(comments_withy)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.PairGrid at 0x7ff255b59d30>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 95
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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JRGaJyHIRWV5bW9ujlTMmIZ37sLOJj6JxpSDPR8APz63azICgv90+NuIvd/SV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\nMcZkIQvOxhiThf4/HzEDq9nKaXYAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 360x360 with 6 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B5WrbSQ4cz9M",
        "colab_type": "code",
        "outputId": "dae7d45f-2530-4158-d0ff-1a6b2aa5b3f6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        }
      },
      "source": [
        "sns.heatmap(comments_corr.corr(), cmap='bwr', vmax=1, vmin=-1, center=0, square=True, annot=True)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7ff24e4af8d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 98
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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3AD/JHNrvC2zYkqTitRm0bRk5cvO3V7+T/hhwIzASmJmZD0XEecDczJwNzACujIjFwApa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\ngg==\n",
            "text/plain": [
              "<Figure size 792x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lMw_n3-Hc44a",
        "colab_type": "code",
        "outputId": "645411f3-ab42-48de-fe8a-382b83ed9545",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        }
      },
      "source": [
        "sns.heatmap(comments_withy.corr(), cmap='bwr', vmax=1, vmin=-1, center=0, square=True, annot=True)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7ff24cfcb588>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 99
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Vx5wA/GXze/QDYDxwQHPbXZn5WPO49wy5z1D+LmjgDOxRJDMfAF5K5y+af6TT\nuBdm5iHNZXJmntAcfhLwmeb4uRGxVWZ+FDgb2A74XkT88TCe/qkh11cCW2XmL+m0lO8C/x249NnP\nTnpWVv9crmLdn9FVrP3yomeaUF/nuCaAVx8TwDlDfpf2zcyb1nsOaH721x+EvwsaDQzsUSQidgee\nzMwvAh8DpgC7RMQRze1bR8SkiBgDTMzMW4C/pbOU/byI2K9pIBcBc+ksew81B3hz81gvAvYC7t/E\neHYGxmTmv9Np9y/t4XRVp+8Ap0XEeICI2InOyz5vbG5/M52f0167EXh7RGzdPO+LIuK5m7nPb4Dt\nm+P9XdDA+fWao8tk4GMRsQp4Bng7neXuT0bE8+n8//oXOq8BfrHZF8AnM/NXEfHh5kSy1UuGNwAT\nhjz+Z4HPRcT85nHPyMynImJj49kDuLz5BwLA3/VwrqpQZi6MiAuBWyNiJfAj4Bw6P2d/A/wcOLMP\nT30pnaXuu6PzA/9z4LWbuc8M4JsR8VPgvfi7oAHzbV2SJBXAJXFJkgpgYEuSVAADW5KkAhjYkiQV\nwMCWJKkABrYkSQUwsCVJKoCBLUlSAf4/Cjl2YsrrR7sAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 792x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p9DPbqU2dntG",
        "colab_type": "text"
      },
      "source": [
        "### COMMENTS X SESSIONS = CORR 0.17\n",
        "###Correlação fraca entre sessões e comentários"
      ]
    },
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              "             count        mean         std  min  25%   50%   75%     max\n",
              "sessions  102699.0  102.914527  302.036192  1.0  4.0  13.0  60.0  9555.0\n",
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              "            count        mean         std  min   25%   50%    75%     max\n",
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              "            count      mean        std  min  25%  50%  75%     max\n",
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              "                count        mean         std  min  25%   50%   75%      max\n",
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              "               count        mean         std  min   25%    50%    75%      max\n",
              "sessions     25336.0  311.021116  528.980642  1.0  32.0  118.0  366.0   9555.0\n",
              "offer_likes  25336.0   61.124171  821.111948  1.0   1.0    2.0    5.0  35628.0"
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              "               count       mean         std  min  25%  50%   75%     max\n",
              "sessions     77363.0  34.760648  103.132904  1.0  3.0  7.0  24.0  6418.0\n",
              "offer_likes  77363.0   0.000000    0.000000  0.0  0.0  0.0   0.0     0.0"
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              "                  count        mean         std  min  25%   50%   75%     max\n",
              "sessions       102699.0  102.914527  302.036192  1.0  4.0  13.0  60.0  9555.0\n",
              "posted_offers  102699.0    0.120576   10.972855  0.0  0.0   0.0   0.0  2854.0"
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              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>sessions</th>\n",
              "      <td>1319.0</td>\n",
              "      <td>662.886277</td>\n",
              "      <td>896.505536</td>\n",
              "      <td>1.0</td>\n",
              "      <td>125.5</td>\n",
              "      <td>352.0</td>\n",
              "      <td>830.5</td>\n",
              "      <td>9240.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>posted_offers</th>\n",
              "      <td>1319.0</td>\n",
              "      <td>9.388173</td>\n",
              "      <td>96.409137</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2854.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                count        mean         std  min    25%    50%    75%     max\n",
              "sessions       1319.0  662.886277  896.505536  1.0  125.5  352.0  830.5  9240.0\n",
              "posted_offers  1319.0    9.388173   96.409137  1.0    1.0    1.0    3.0  2854.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 108
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iOWcq-1HlKpJ",
        "colab_type": "code",
        "outputId": "af666349-0712-4ed3-fc19-50873868bd8a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 107
        }
      },
      "source": [
        "offers_without.describe().T\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>3.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>57.0</td>\n",
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              "      <th>posted_offers</th>\n",
              "      <td>101380.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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            "text/plain": [
              "                  count       mean         std  min  25%   50%   75%     max\n",
              "sessions       101380.0  95.629039  278.982095  1.0  3.0  12.0  57.0  9555.0\n",
              "posted_offers  101380.0   0.000000    0.000000  0.0  0.0   0.0   0.0     0.0"
            ]
          },
          "metadata": {
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          },
          "execution_count": 109
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c2lQDDfOmRNt",
        "colab_type": "code",
        "outputId": "10bba9d7-2e94-4ded-8076-98adffbc7c59",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 107
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      },
      "source": [
        "comments_corr.describe().T"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>sessions</th>\n",
              "      <td>102699.0</td>\n",
              "      <td>102.914527</td>\n",
              "      <td>302.036192</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>9555.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>comments</th>\n",
              "      <td>102699.0</td>\n",
              "      <td>0.954060</td>\n",
              "      <td>15.173537</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3842.0</td>\n",
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              "  </tbody>\n",
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            ],
            "text/plain": [
              "             count        mean         std  min  25%   50%   75%     max\n",
              "sessions  102699.0  102.914527  302.036192  1.0  4.0  13.0  60.0  9555.0\n",
              "comments  102699.0    0.954060   15.173537  0.0  0.0   0.0   0.0  3842.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 110
        }
      ]
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    {
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      "metadata": {
        "id": "-ynb5B2amRRj",
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        "colab": {
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          "height": 107
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      },
      "source": [
        "comments_withy.describe().T"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>sessions</th>\n",
              "      <td>10843.0</td>\n",
              "      <td>433.881583</td>\n",
              "      <td>635.817264</td>\n",
              "      <td>1.0</td>\n",
              "      <td>71.0</td>\n",
              "      <td>211.0</td>\n",
              "      <td>543.5</td>\n",
              "      <td>9240.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>comments</th>\n",
              "      <td>10843.0</td>\n",
              "      <td>9.036337</td>\n",
              "      <td>45.910908</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>3842.0</td>\n",
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            ],
            "text/plain": [
              "            count        mean         std  min   25%    50%    75%     max\n",
              "sessions  10843.0  433.881583  635.817264  1.0  71.0  211.0  543.5  9240.0\n",
              "comments  10843.0    9.036337   45.910908  1.0   1.0    2.0    7.0  3842.0"
            ]
          },
          "metadata": {
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          },
          "execution_count": 111
        }
      ]
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      "metadata": {
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        "colab": {
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          "height": 107
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      },
      "source": [
        "comments_without.describe().T"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    <tr>\n",
              "      <th>sessions</th>\n",
              "      <td>91856.0</td>\n",
              "      <td>63.846031</td>\n",
              "      <td>199.551781</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>38.0</td>\n",
              "      <td>9555.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>comments</th>\n",
              "      <td>91856.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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            ],
            "text/plain": [
              "            count       mean         std  min  25%   50%   75%     max\n",
              "sessions  91856.0  63.846031  199.551781  1.0  3.0  10.0  38.0  9555.0\n",
              "comments  91856.0   0.000000    0.000000  0.0  0.0   0.0   0.0     0.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 112
        }
      ]
    }
  ]
}