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    "# Facebook Comments Volume\n",
    "\n",
    "Trata-se de um problema de regressão descrito no\n",
    "artigo [Facebook Comments\n",
    "Volume](http://uksim.info/uksim2015/data/8713a015.pdf).\n",
    "\n",
    "O objetivo que se\n",
    "deseja alcançar nesta solução é de acordo com as informações de entrada sobre um\n",
    "determinado post, predizer qual o número de comentários ele irá receber em uma\n",
    "determinada quantidade de horas.\n",
    "\n",
    "Os dados foram obtidos através de um\n",
    "***crawler*** nas páginas do facebook, logo após foram pré-processadas e\n",
    "divididas temporalmente. O [conjunto de dados](./Dataset.zip) fornecidos pelo\n",
    "problema estão separados em dados para treino e para teste.\n",
    "\n",
    "De acordo com o\n",
    "autor, foram obtidos 5 diferentes tipos de dados para o treino, separados\n",
    "temporalmente em Variante 1, 2, 3, 4 e 5.\n",
    "\n",
    "## Dados\n",
    "\n",
    "Há um total de 54\n",
    "***features***, essas features são representações para as colunas abaixo.\n",
    "\n",
    "|Número da coluna|Nome|Origem da feature|Descrição|\n",
    "|----------------|----|-----------------|---------|\n",
    "|1|Popularidade/*Likes* da páginaPage|*Feature* da página|Define a popularidade da página ou o suporte para a fonte dos documentos.|\n",
    "|2|Página Checking|*Feature* da página|Descreve quantos individuos diferentes visitaram a página|\n",
    "|3|Falano sobre a página|*Feature* da página|Define o interese diário de indivídos em relação ao conteúdo da página. Ou seja, pessoas que voltam ou tem alguma ação sobre a página.|\n",
    "|4|Categoria da página|*Feature* da página|Define a categoria da página. Ex.: lugar, instituição...|\n",
    "|5 - 29|Derivado|*Feature* derivada |São features que foram associadas a página calculando através de outras features básicas.|\n",
    "|30|CC1|*Feature* essencial|Número total de comentários antes do tempo base selecionado.|\n",
    "|31|CC2|*Feature* essencial|O número de comentários nas 24 horas atrás|\n",
    "|32|CC3|*Feature* essencial|O número de comentários nas 48 horas atras e 24 horas relativas ao tempo base.|\n",
    "|33|CC4|*Feature* essencial|O número de comentários nas primeiras 24 horas após a publicação, mas antes do tempo base.|\n",
    "|34|CC5|*Feature* essencial|Diferença entre CC2 e CC3.|\n",
    "|35|Tempo base|Outra *feature*|Tempo selecionado para simular o cenário.|\n",
    "|36|Tamanho do *post*|Outra *feature*|Contagem de caracteres no *post*.|\n",
    "|37|*Post* compartilhados|Outra *feature*|É a contagem de pessoas que compartilharam o *post* em sua linha do tempo.|\n",
    "|38|Promoção do estado do *Post*|Outra *feature*|Pessoas que promoveram (1) ou não (0) a página que apareceu na lista de novas notícias.|\n",
    "|39|H Local|Outra *feature*|Descreve o número de horas que passaram para o alvo de comentários recebidos.|\n",
    "|40-46|Dia da semana da publicação|Dia da semana|Representa o dia da semana que foi publicado o post (Domingo até Sábado).|\n",
    "|47-53|Dia do tempo base|Dia da semana|Representa o dia (Domingo até Sábado) que foi selecionado o tempo base.|\n",
    "|54|Variável alvo|Alvo|O número de comentários nas próximas H horas.|\n",
    "\n",
    "## Obtenção dos dados\n",
    "\n",
    "Os dados\n",
    "estão organizados na pasta\n",
    "`./Dataset/Training/Features_Variant_X.csv`, em que X\n",
    "é um dos valores de\n",
    "variação (1, 2, 3, 4, 5).\n",
    "\n",
    "|Número de registros|Nome do arquivo|Tamanho|\n",
    "|-------------------|---------------|-------|\n",
    "|40949|Features_Variant_1.csv|13M|\n",
    "|81312|Features_Variant_2.csv|26M|\n",
    "|121098|Features_Variant_3.csv|39M|\n",
    "|160424|Features_Variant_4.csv|51M|\n",
    "|199030|Features_Variant_5.csv|64M|\n",
    "\n",
    "\n",
    "A primeira amostra que trabalharemos\n",
    "consistem em 40948 inputs de treinamento e 10043 inputs de teste, ambos com 53\n",
    "colunas cada.\n",
    "Lembrando que para rodar o código parser.py é necessário\n",
    "descompactar o arquivo do dataset.\n",
    "Primeiro precisamos obter as pastas onde se\n",
    "encontram os arquivos. Para isso usaremos o import os (ou Operating system) e\n",
    "como a extenção dos arquivos é .csv, será utilizada a biblioteca\n",
    "[pandas](http://pandas.pydata.org/)."
   ]
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   "execution_count": 1,
   "metadata": {
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    "deletable": true,
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/marcelof/UnB/ml/CommentVolumeML/Dataset/Training\n",
      "/home/marcelof/UnB/ml/CommentVolumeML/Dataset/Testing/TestSet\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "# Load dirs name\n",
    "cur_dir = os.path.realpath('.')\n",
    "data_dir = os.path.join(cur_dir,'Dataset')\n",
    "# Obtaining directories\n",
    "train_dir = os.path.join(data_dir,'Training')\n",
    "print(train_dir)\n",
    "test_dir = os.path.join(data_dir,'Testing', 'TestSet')\n",
    "print(test_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "#### Obtendo local dos arquivos\n",
    "\n",
    "Agora com os diretórios em mãos podemos obter\n",
    "os arquivos do tipo csv para teste e treinamento. Ao final iremos ordenar a\n",
    "lista para podemos acessar o arquivos que queremos pelo indice, ja que não\n",
    "necessáriamente ele vai ler em ordem alfabetica."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train List:  ['/home/marcelof/UnB/ml/CommentVolumeML/Dataset/Training/Features_Variant_1.csv', '/home/marcelof/UnB/ml/CommentVolumeML/Dataset/Training/Features_Variant_2.csv', '/home/marcelof/UnB/ml/CommentVolumeML/Dataset/Training/Features_Variant_3.csv', '/home/marcelof/UnB/ml/CommentVolumeML/Dataset/Training/Features_Variant_4.csv', '/home/marcelof/UnB/ml/CommentVolumeML/Dataset/Training/Features_Variant_5.csv'] \n",
      "\n",
      "Test List:  ['/home/marcelof/UnB/ml/CommentVolumeML/Dataset/Testing/TestSet/Test_Case_1.csv', '/home/marcelof/UnB/ml/CommentVolumeML/Dataset/Testing/TestSet/Test_Case_10.csv', '/home/marcelof/UnB/ml/CommentVolumeML/Dataset/Testing/TestSet/Test_Case_2.csv', '/home/marcelof/UnB/ml/CommentVolumeML/Dataset/Testing/TestSet/Test_Case_3.csv', '/home/marcelof/UnB/ml/CommentVolumeML/Dataset/Testing/TestSet/Test_Case_4.csv', '/home/marcelof/UnB/ml/CommentVolumeML/Dataset/Testing/TestSet/Test_Case_5.csv', '/home/marcelof/UnB/ml/CommentVolumeML/Dataset/Testing/TestSet/Test_Case_6.csv', '/home/marcelof/UnB/ml/CommentVolumeML/Dataset/Testing/TestSet/Test_Case_7.csv', '/home/marcelof/UnB/ml/CommentVolumeML/Dataset/Testing/TestSet/Test_Case_8.csv', '/home/marcelof/UnB/ml/CommentVolumeML/Dataset/Testing/TestSet/Test_Case_9.csv']\n"
     ]
    }
   ],
   "source": [
    "list_train = []\n",
    "list_test = []\n",
    "# Obtain train files\n",
    "for x in os.listdir(train_dir):\n",
    "    if(x.endswith(\".csv\")):\n",
    "        list_train.append(os.path.join(train_dir, x))\n",
    "\n",
    "for x in os.listdir(test_dir):\n",
    "    if(x.endswith(\".csv\")):\n",
    "        list_test.append(os.path.join(test_dir, x))\n",
    "\n",
    "# Sorting array to access the required ones\n",
    "list_train = sorted(list_train)\n",
    "list_test = sorted(list_test)\n",
    "\n",
    "print(\"Train List: \", list_train, \"\\n\")\n",
    "print(\"Test List: \", list_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "Tendo os paths dos arquivos em mãos podemos obter os dados. Para fazer isso\n",
    "de\n",
    "forma eficiente podemos utilizar a biblioteca Pandas.\n",
    "\n",
    "#### Definição das colunas\n",
    "\n",
    "Para a correa montagem do data frame, definimos as\n",
    "colunas e o\n",
    "caminho para o arquivo onde está presente o primeiro dataset do\n",
    "problema.\n",
    "Portanto, é esperado que, caso o pandas leia corretamente, tenham\n",
    "40949\n",
    "registros em 54 colunas."
   ]
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   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
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   "outputs": [
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "54 ['Page Popularity/likes', 'Page Checkinsâ€™s', 'Page talking about', 'Page Category', 'Derived', 'Derived', 'Derived', 'Derived', 'Derived', 'Derived', 'Derived', 'Derived', 'Derived', 'Derived', 'Derived', 'Derived', 'Derived', 'Derived', 'Derived', 'Derived', 'Derived', 'Derived', 'Derived', 'Derived', 'Derived', 'Derived', 'Derived', 'Derived', 'Derived', 'CC1', 'CC2', 'CC3', 'CC4', 'CC5', 'Base time', 'Post length', 'Post Share Count', 'Post Promotion Status', 'H Local', 'Post Sunday', 'Post Monday', 'Post Tuesday', 'Post Wednesday', 'Post Thursday', 'Post Friday', 'Post Saturday', 'Base Sunday', 'Base Monday', 'Base Tuesday', 'Base Wednesday', 'Base Thursday', 'Base Friday', 'Base Saturday', 'Target Variable']\n"
     ]
    }
   ],
   "source": [
    "columns = [\"Page Popularity/likes\", \"Page Checkinsâ€™s\", \"Page talking about\",\n",
    "           \"Page Category\", \"Derived\", \"Derived\", \"Derived\", \"Derived\",\n",
    "           \"Derived\", \"Derived\", \"Derived\", \"Derived\", \"Derived\",\n",
    "           \"Derived\", \"Derived\", \"Derived\", \"Derived\", \"Derived\",\n",
    "           \"Derived\", \"Derived\", \"Derived\", \"Derived\", \"Derived\",\n",
    "           \"Derived\", \"Derived\", \"Derived\", \"Derived\", \"Derived\",\n",
    "           \"Derived\", \"CC1\", \"CC2\", \"CC3\", \"CC4\", \"CC5\", \"Base time\",\n",
    "           \"Post length\", \"Post Share Count\", \"Post Promotion Status\", \"H Local\",\n",
    "           \"Post Sunday\", \"Post Monday\", \"Post Tuesday\", \"Post Wednesday\", \"Post Thursday\", \"Post Friday\", \"Post Saturday\",\n",
    "           \"Base Sunday\", \"Base Monday\", \"Base Tuesday\", \"Base Wednesday\", \"Base Thursday\", \"Base Friday\", \"Base Saturday\",\n",
    "           \"Target Variable\"]\n",
    "print(len(columns), columns)"
   ]
  },
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   "cell_type": "markdown",
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    "deletable": true,
    "editable": true
   },
   "source": [
    "#### Leitura dos dados\n",
    "\n",
    "Agora, faremos a leitura dos dados com o suporte da\n",
    "biblioteca pandas, em que passamos o local do arquivo e o nome das colunas."
   ]
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   "cell_type": "code",
   "execution_count": 4,
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train file:  /home/marcelof/UnB/ml/CommentVolumeML/Dataset/Training/Features_Variant_5.csv\n",
      "Test file:  /home/marcelof/UnB/ml/CommentVolumeML/Dataset/Testing/TestSet/Test_Case_1.csv\n",
      "Quantidade de dados de treinamento\n",
      "199030\n",
      "Quantidade de dados de teste\n",
      "100\n"
     ]
    },
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       "      <th>Page Popularity/likes</th>\n",
       "      <th>Page Checkinsâ€™s</th>\n",
       "      <th>Page talking about</th>\n",
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       "      <th>Base Sunday</th>\n",
       "      <th>Base Monday</th>\n",
       "      <th>Base Tuesday</th>\n",
       "      <th>Base Wednesday</th>\n",
       "      <th>Base Thursday</th>\n",
       "      <th>Base Friday</th>\n",
       "      <th>Base Saturday</th>\n",
       "      <th>Target Variable</th>\n",
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       "  </thead>\n",
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       "   Page Popularity/likes  Page Checkinsâ€™s  Page talking about  \\\n",
       "0                 634995                  0                 463   \n",
       "1                 634995                  0                 463   \n",
       "2                 634995                  0                 463   \n",
       "3                 634995                  0                 463   \n",
       "4                 634995                  0                 463   \n",
       "\n",
       "   Page Category  Derived  Derived.1  Derived.2  Derived.3  Derived.4  \\\n",
       "0              1      0.0     1280.0  13.158779        1.0   94.99364   \n",
       "1              1      0.0     1280.0  13.158779        1.0   94.99364   \n",
       "2              1      0.0     1280.0  13.158779        1.0   94.99364   \n",
       "3              1      0.0     1280.0  13.158779        1.0   94.99364   \n",
       "4              1      0.0     1280.0  13.158779        1.0   94.99364   \n",
       "\n",
       "   Derived.5       ...         Post Friday  Post Saturday  Base Sunday  \\\n",
       "0        0.0       ...                   0              0            0   \n",
       "1        0.0       ...                   0              0            1   \n",
       "2        0.0       ...                   1              0            0   \n",
       "3        0.0       ...                   1              0            0   \n",
       "4        0.0       ...                   0              0            0   \n",
       "\n",
       "   Base Monday  Base Tuesday  Base Wednesday  Base Thursday  Base Friday  \\\n",
       "0            0             0               0              1            0   \n",
       "1            0             0               0              0            0   \n",
       "2            0             0               0              0            0   \n",
       "3            1             0               0              0            0   \n",
       "4            0             0               0              1            0   \n",
       "\n",
       "   Base Saturday  Target Variable  \n",
       "0              0                0  \n",
       "1              0                0  \n",
       "2              1                0  \n",
       "3              0                0  \n",
       "4              0                0  \n",
       "\n",
       "[5 rows x 54 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas\n",
    "trainData = pandas.read_csv(list_train[4], names=columns)\n",
    "testData = pandas.read_csv(list_test[1], names=columns)\n",
    "print('Train file: ', list_train[4])\n",
    "print('Test file: ', list_test[0])\n",
    "print(\"Quantidade de dados de treinamento\")\n",
    "print(len(trainData))\n",
    "print(\"Quantidade de dados de teste\")\n",
    "print(len(testData))\n",
    "trainData.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "#### Plotando os dados\n",
    "\n",
    "Plotaremos os dados das colunas geradas pelo autor dos dados para verificar se\n",
    "há relação entre as colunas. Utilizou-se apenas uma amostragem de 5000 dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
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       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        this.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 48.3 s, sys: 6.3 s, total: 54.6 s\n",
      "Wall time: 48.3 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "%matplotlib notebook\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from pandas.plotting import scatter_matrix\n",
    "scatter_matrix(testData.loc[:5000, 'Derived':'CC5'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Tratamento\n",
    "\n",
    "Será realizada as etapas de *feature selection* e *feature\n",
    "engineering*.\n",
    "\n",
    "#### Correlação entre features\n",
    "\n",
    "Será realizada uma análise da\n",
    "correlação entre as *features*. Visto que há um total de 24 colunas que foram\n",
    "fruto de engenharia de caracerísticas, e que, o autor não especificou quais\n",
    "foram as operações realizadas entre elas e portanto, esta análize ajudará a\n",
    "identificar as relações entre as *features*.\n",
    "\n",
    "##### O que é\n",
    "\n",
    "A correlação entre duas variáveis é quando existe algum laço matemático que\n",
    "envolve o valor de duas variáveis de alguma forma ([ESTATÍSTICA II - CORRELAÇÃO\n",
    "E REGRESSÃO](http://www.ctec.ufal.br/professor/mgn/05CorrelacaoERegressao.pdf)).\n",
    "\n",
    "Uma das maneiras mais simples de se identificar a correlação entre duas\n",
    "variáveis é plotando-as em um gráfico, para tentar identificar alguma relação\n",
    "entre elas.\n",
    "\n",
    "Suponha os seguintes dados: X representa o número de visitas totais em uma\n",
    "página do facebook e Y o número de curtidas que esta página possui.\n",
    "\n",
    "|Visitas|Curtidas|\n",
    "|-------|--------|\n",
    "|25000|5000|\n",
    "|1000|95|\n",
    "|10000|1500|\n",
    "|12000|1900|\n",
    "|20005|3700|\n",
    "|5000|1200|\n",
    "|3000|600|\n",
    "|15000|3000|"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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8M1/LzCeBucBuxW1uZs7LzGXA5cWyklQ/XnwcLnw/3HUOjDkaJv7WdE5JklQTenpN3w+A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\nVOCWzNwBuKW4D3A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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f72b6b794e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "X, Y = 0, 1\n",
    "\n",
    "import numpy as np\n",
    "from scipy.stats import linregress\n",
    "\n",
    "fig, (ax, ax2) = plt.subplots(1, 2, figsize=[15,5])\n",
    "data = np.array([[25000,1000,10000,12000,20005, 5000, 3000, 15000, 250000, 100000], [5000,95,1500,1900,3700, 1200, 600, 3000, 50000, 20000]])\n",
    "\n",
    "ax.plot(data[X], data[Y], '.')\n",
    "\n",
    "# Calcula regressão linear para a*m + b\n",
    "m, b, R, p, SEm = linregress(data[X], data[Y])\n",
    "x2 = np.array([0, data[X].max()])\n",
    "ax.plot(x2, m * x2 + b)\n",
    "ax.set_title('linear')\n",
    "\n",
    "# Dato não linear, relação de raiz quadrada\n",
    "ax2.set_title('não-linear')\n",
    "data[Y] = data[X]**(1/6)\n",
    "chart = ax2.plot(data[X], data[Y], 'o')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Com o gráfico, fica bastante claro a correlação das variáveis X e Y do exemplo.\n",
    "E inclusive o tipo de correlação não-linear.\n",
    "\n",
    "#### Tipos de correlação\n",
    "\n",
    "Existem vários métodos para calculo do coeficiente de correlação entre duas\n",
    "variáveis, pearson, kendall e spearman.\n",
    "* [Pearson](https://pt.wikipedia.org/wiki/Coeficiente_de_correla%C3%A7%C3%A3o_de\n",
    "_Pearson): mede o grau da correlação (e a direcção dessa correlação - se\n",
    "positiva ou negativa) entre duas variáveis de escala métrica (intervalar ou de\n",
    "rácio/razão).\n",
    "* [Kendall](https://pt.wikipedia.org/wiki/Coeficiente_de_correla%C3%A7%C3%A3o_ta\n",
    "u_de_Kendall): medir a correlação de postos entre duas quantidades medidas.\n",
    "* [Spearman](): A correlação de Spearman entre duas variáveis é igual à\n",
    "correlação de Pearson entre os valores de postos daquelas duas variáveis.\n",
    "Enquanto a correlação de Pearson avalia relações lineares, a correlação de\n",
    "Spearman avalia relações monótonas, sejam elas lineares ou não.\n",
    "\n",
    "Para nosso problema utilizaremos o método de pearson, pois queremos medir apenas\n",
    "o grau de correlação entre as variáveis do problema."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
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       "      <th>Base Monday</th>\n",
       "      <th>Base Tuesday</th>\n",
       "      <th>Base Wednesday</th>\n",
       "      <th>Base Thursday</th>\n",
       "      <th>Base Friday</th>\n",
       "      <th>Base Saturday</th>\n",
       "      <th>Target Variable</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Page Popularity/likes</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.045090</td>\n",
       "      <td>0.622700</td>\n",
       "      <td>-0.042003</td>\n",
       "      <td>0.046600</td>\n",
       "      <td>0.157902</td>\n",
       "      <td>0.165026</td>\n",
       "      <td>0.147022</td>\n",
       "      <td>0.173462</td>\n",
       "      <td>0.046528</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.009949</td>\n",
       "      <td>0.002727</td>\n",
       "      <td>-0.003590</td>\n",
       "      <td>-0.001345</td>\n",
       "      <td>0.002757</td>\n",
       "      <td>0.007740</td>\n",
       "      <td>0.001449</td>\n",
       "      <td>-0.001514</td>\n",
       "      <td>-0.005663</td>\n",
       "      <td>0.064537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Page Checkinsâ€™s</th>\n",
       "      <td>0.045090</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.167326</td>\n",
       "      <td>-0.060345</td>\n",
       "      <td>-0.007382</td>\n",
       "      <td>0.158831</td>\n",
       "      <td>0.151055</td>\n",
       "      <td>0.129424</td>\n",
       "      <td>0.169037</td>\n",
       "      <td>-0.006570</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.006156</td>\n",
       "      <td>0.008358</td>\n",
       "      <td>0.000764</td>\n",
       "      <td>0.000087</td>\n",
       "      <td>0.000197</td>\n",
       "      <td>-0.001587</td>\n",
       "      <td>0.002071</td>\n",
       "      <td>-0.001521</td>\n",
       "      <td>-0.000045</td>\n",
       "      <td>0.035152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Page talking about</th>\n",
       "      <td>0.622700</td>\n",
       "      <td>0.167326</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.148164</td>\n",
       "      <td>0.118685</td>\n",
       "      <td>0.484869</td>\n",
       "      <td>0.515621</td>\n",
       "      <td>0.457754</td>\n",
       "      <td>0.539581</td>\n",
       "      <td>0.119260</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.027714</td>\n",
       "      <td>-0.007980</td>\n",
       "      <td>-0.014358</td>\n",
       "      <td>-0.006598</td>\n",
       "      <td>0.004262</td>\n",
       "      <td>0.023153</td>\n",
       "      <td>0.022747</td>\n",
       "      <td>-0.009419</td>\n",
       "      <td>-0.021087</td>\n",
       "      <td>0.194768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Page Category</th>\n",
       "      <td>-0.042003</td>\n",
       "      <td>-0.060345</td>\n",
       "      <td>-0.148164</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.026591</td>\n",
       "      <td>-0.319812</td>\n",
       "      <td>-0.233917</td>\n",
       "      <td>-0.193385</td>\n",
       "      <td>-0.291309</td>\n",
       "      <td>-0.023868</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002795</td>\n",
       "      <td>-0.002126</td>\n",
       "      <td>0.002386</td>\n",
       "      <td>-0.004504</td>\n",
       "      <td>-0.003690</td>\n",
       "      <td>-0.001920</td>\n",
       "      <td>-0.001325</td>\n",
       "      <td>0.003916</td>\n",
       "      <td>0.005049</td>\n",
       "      <td>-0.069001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Derived</th>\n",
       "      <td>0.046600</td>\n",
       "      <td>-0.007382</td>\n",
       "      <td>0.118685</td>\n",
       "      <td>-0.026591</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.072641</td>\n",
       "      <td>0.335366</td>\n",
       "      <td>0.409813</td>\n",
       "      <td>0.130532</td>\n",
       "      <td>0.930690</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.003884</td>\n",
       "      <td>-0.005278</td>\n",
       "      <td>-0.000426</td>\n",
       "      <td>-0.006805</td>\n",
       "      <td>0.001074</td>\n",
       "      <td>0.002660</td>\n",
       "      <td>-0.000129</td>\n",
       "      <td>0.006651</td>\n",
       "      <td>-0.003232</td>\n",
       "      <td>0.138996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Derived.1</th>\n",
       "      <td>0.157902</td>\n",
       "      <td>0.158831</td>\n",
       "      <td>0.484869</td>\n",
       "      <td>-0.319812</td>\n",
       "      <td>0.072641</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.715212</td>\n",
       "      <td>0.582076</td>\n",
       "      <td>0.896815</td>\n",
       "      <td>0.079879</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.010944</td>\n",
       "      <td>-0.009929</td>\n",
       "      <td>-0.005159</td>\n",
       "      <td>-0.005468</td>\n",
       "      <td>0.001918</td>\n",
       "      <td>0.011149</td>\n",
       "      <td>0.007315</td>\n",
       "      <td>-0.001381</td>\n",
       "      <td>-0.008924</td>\n",
       "      <td>0.238743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Derived.2</th>\n",
       "      <td>0.165026</td>\n",
       "      <td>0.151055</td>\n",
       "      <td>0.515621</td>\n",
       "      <td>-0.233917</td>\n",
       "      <td>0.335366</td>\n",
       "      <td>0.715212</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.963216</td>\n",
       "      <td>0.903036</td>\n",
       "      <td>0.340154</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.017454</td>\n",
       "      <td>-0.004056</td>\n",
       "      <td>-0.008361</td>\n",
       "      <td>-0.005993</td>\n",
       "      <td>0.003581</td>\n",
       "      <td>0.012472</td>\n",
       "      <td>0.009093</td>\n",
       "      <td>-0.003123</td>\n",
       "      <td>-0.008304</td>\n",
       "      <td>0.359536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Derived.3</th>\n",
       "      <td>0.147022</td>\n",
       "      <td>0.129424</td>\n",
       "      <td>0.457754</td>\n",
       "      <td>-0.193385</td>\n",
       "      <td>0.409813</td>\n",
       "      <td>0.582076</td>\n",
       "      <td>0.963216</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.773769</td>\n",
       "      <td>0.412988</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.015895</td>\n",
       "      <td>-0.001976</td>\n",
       "      <td>-0.007473</td>\n",
       "      <td>-0.005297</td>\n",
       "      <td>0.003135</td>\n",
       "      <td>0.010544</td>\n",
       "      <td>0.008291</td>\n",
       "      <td>-0.002518</td>\n",
       "      <td>-0.007247</td>\n",
       "      <td>0.349452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Derived.4</th>\n",
       "      <td>0.173462</td>\n",
       "      <td>0.169037</td>\n",
       "      <td>0.539581</td>\n",
       "      <td>-0.291309</td>\n",
       "      <td>0.130532</td>\n",
       "      <td>0.896815</td>\n",
       "      <td>0.903036</td>\n",
       "      <td>0.773769</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.146383</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.016705</td>\n",
       "      <td>-0.009671</td>\n",
       "      <td>-0.009642</td>\n",
       "      <td>-0.006710</td>\n",
       "      <td>0.003410</td>\n",
       "      <td>0.014783</td>\n",
       "      <td>0.011331</td>\n",
       "      <td>-0.003686</td>\n",
       "      <td>-0.010263</td>\n",
       "      <td>0.316593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Derived.5</th>\n",
       "      <td>0.046528</td>\n",
       "      <td>-0.006570</td>\n",
       "      <td>0.119260</td>\n",
       "      <td>-0.023868</td>\n",
       "      <td>0.930690</td>\n",
       "      <td>0.079879</td>\n",
       "      <td>0.340154</td>\n",
       "      <td>0.412988</td>\n",
       "      <td>0.146383</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.004534</td>\n",
       "      <td>-0.003742</td>\n",
       "      <td>-0.002532</td>\n",
       "      <td>-0.005779</td>\n",
       "      <td>0.001494</td>\n",
       "      <td>0.003371</td>\n",
       "      <td>0.003135</td>\n",
       "      <td>0.003903</td>\n",
       "      <td>-0.003902</td>\n",
       "      <td>0.153853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Derived.6</th>\n",
       "      <td>0.157932</td>\n",
       "      <td>0.152888</td>\n",
       "      <td>0.484452</td>\n",
       "      <td>-0.318472</td>\n",
       "      <td>0.057220</td>\n",
       "      <td>0.968435</td>\n",
       "      <td>0.705025</td>\n",
       "      <td>0.576554</td>\n",
       "      <td>0.870439</td>\n",
       "      <td>0.059425</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.012854</td>\n",
       "      <td>-0.008538</td>\n",
       "      <td>-0.005896</td>\n",
       "      <td>-0.004755</td>\n",
       "      <td>0.002065</td>\n",
       "      <td>0.011020</td>\n",
       "      <td>0.008132</td>\n",
       "      <td>-0.001753</td>\n",
       "      <td>-0.009376</td>\n",
       "      <td>0.229139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Derived.7</th>\n",
       "      <td>0.164715</td>\n",
       "      <td>0.131894</td>\n",
       "      <td>0.537943</td>\n",
       "      <td>-0.217248</td>\n",
       "      <td>0.348978</td>\n",
       "      <td>0.683586</td>\n",
       "      <td>0.983718</td>\n",
       "      <td>0.947674</td>\n",
       "      <td>0.879570</td>\n",
       "      <td>0.362163</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.020600</td>\n",
       "      <td>-0.007184</td>\n",
       "      <td>-0.011552</td>\n",
       "      <td>-0.007818</td>\n",
       "      <td>0.002882</td>\n",
       "      <td>0.015089</td>\n",
       "      <td>0.015785</td>\n",
       "      <td>-0.003765</td>\n",
       "      <td>-0.011614</td>\n",
       "      <td>0.368003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Derived.8</th>\n",
       "      <td>0.132272</td>\n",
       "      <td>0.066060</td>\n",
       "      <td>0.447466</td>\n",
       "      <td>-0.132765</td>\n",
       "      <td>0.538629</td>\n",
       "      <td>0.455341</td>\n",
       "      <td>0.835920</td>\n",
       "      <td>0.876121</td>\n",
       "      <td>0.630849</td>\n",
       "      <td>0.571435</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.018013</td>\n",
       "      <td>-0.009413</td>\n",
       "      <td>-0.010666</td>\n",
       "      <td>-0.010237</td>\n",
       "      <td>-0.000021</td>\n",
       "      <td>0.013393</td>\n",
       "      <td>0.019318</td>\n",
       "      <td>-0.001115</td>\n",
       "      <td>-0.011880</td>\n",
       "      <td>0.339229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Derived.9</th>\n",
       "      <td>0.177494</td>\n",
       "      <td>0.162535</td>\n",
       "      <td>0.557773</td>\n",
       "      <td>-0.275676</td>\n",
       "      <td>0.179533</td>\n",
       "      <td>0.847315</td>\n",
       "      <td>0.932083</td>\n",
       "      <td>0.823059</td>\n",
       "      <td>0.973750</td>\n",
       "      <td>0.177010</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.019952</td>\n",
       "      <td>-0.008896</td>\n",
       "      <td>-0.011017</td>\n",
       "      <td>-0.006608</td>\n",
       "      <td>0.003306</td>\n",
       "      <td>0.015697</td>\n",
       "      <td>0.014239</td>\n",
       "      <td>-0.004683</td>\n",
       "      <td>-0.011834</td>\n",
       "      <td>0.329067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Derived.10</th>\n",
       "      <td>0.007668</td>\n",
       "      <td>-0.001514</td>\n",
       "      <td>-0.001126</td>\n",
       "      <td>-0.003424</td>\n",
       "      <td>0.229902</td>\n",
       "      <td>0.000314</td>\n",
       "      <td>0.033223</td>\n",
       "      <td>0.044852</td>\n",
       "      <td>-0.003737</td>\n",
       "      <td>0.134967</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.002758</td>\n",
       "      <td>-0.002655</td>\n",
       "      <td>0.004702</td>\n",
       "      <td>-0.002631</td>\n",
       "      <td>-0.002653</td>\n",
       "      <td>-0.002767</td>\n",
       "      <td>-0.002857</td>\n",
       "      <td>0.002821</td>\n",
       "      <td>0.003450</td>\n",
       "      <td>0.004411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Derived.11</th>\n",
       "      <td>0.169412</td>\n",
       "      <td>0.152377</td>\n",
       "      <td>0.484815</td>\n",
       "      <td>-0.316907</td>\n",
       "      <td>0.061871</td>\n",
       "      <td>0.967804</td>\n",
       "      <td>0.720203</td>\n",
       "      <td>0.587950</td>\n",
       "      <td>0.891848</td>\n",
       "      <td>0.068723</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.014625</td>\n",
       "      <td>-0.007281</td>\n",
       "      <td>-0.004922</td>\n",
       "      <td>-0.003350</td>\n",
       "      <td>0.004349</td>\n",
       "      <td>0.011342</td>\n",
       "      <td>0.006487</td>\n",
       "      <td>-0.003889</td>\n",
       "      <td>-0.010433</td>\n",
       "      <td>0.234408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Derived.12</th>\n",
       "      <td>0.165150</td>\n",
       "      <td>0.151482</td>\n",
       "      <td>0.507033</td>\n",
       "      <td>-0.230690</td>\n",
       "      <td>0.312735</td>\n",
       "      <td>0.711221</td>\n",
       "      <td>0.991463</td>\n",
       "      <td>0.955776</td>\n",
       "      <td>0.899324</td>\n",
       "      <td>0.326257</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.016003</td>\n",
       "      <td>-0.004045</td>\n",
       "      <td>-0.008096</td>\n",
       "      <td>-0.005614</td>\n",
       "      <td>0.003836</td>\n",
       "      <td>0.012956</td>\n",
       "      <td>0.007867</td>\n",
       "      <td>-0.003796</td>\n",
       "      <td>-0.007731</td>\n",
       "      <td>0.356985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Derived.13</th>\n",
       "      <td>0.136281</td>\n",
       "      <td>0.085762</td>\n",
       "      <td>0.337302</td>\n",
       "      <td>-0.146569</td>\n",
       "      <td>0.536163</td>\n",
       "      <td>0.462642</td>\n",
       "      <td>0.817802</td>\n",
       "      <td>0.869841</td>\n",
       "      <td>0.614855</td>\n",
       "      <td>0.551978</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.007122</td>\n",
       "      <td>-0.001000</td>\n",
       "      <td>-0.001537</td>\n",
       "      <td>-0.006043</td>\n",
       "      <td>0.002129</td>\n",
       "      <td>0.004088</td>\n",
       "      <td>0.001061</td>\n",
       "      <td>0.001298</td>\n",
       "      <td>-0.001212</td>\n",
       "      <td>0.303185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Derived.14</th>\n",
       "      <td>0.176066</td>\n",
       "      <td>0.173045</td>\n",
       "      <td>0.543663</td>\n",
       "      <td>-0.277379</td>\n",
       "      <td>0.196230</td>\n",
       "      <td>0.850476</td>\n",
       "      <td>0.945294</td>\n",
       "      <td>0.845321</td>\n",
       "      <td>0.981744</td>\n",
       "      <td>0.211251</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.017409</td>\n",
       "      <td>-0.007758</td>\n",
       "      <td>-0.009647</td>\n",
       "      <td>-0.005579</td>\n",
       "      <td>0.004158</td>\n",
       "      <td>0.015204</td>\n",
       "      <td>0.010507</td>\n",
       "      <td>-0.004689</td>\n",
       "      <td>-0.010663</td>\n",
       "      <td>0.333375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Derived.15</th>\n",
       "      <td>0.046702</td>\n",
       "      <td>-0.007384</td>\n",
       "      <td>0.119181</td>\n",
       "      <td>-0.026634</td>\n",
       "      <td>0.999831</td>\n",
       "      <td>0.072921</td>\n",
       "      <td>0.336087</td>\n",
       "      <td>0.410633</td>\n",
       "      <td>0.131102</td>\n",
       "      <td>0.932214</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.003876</td>\n",
       "      <td>-0.005250</td>\n",
       "      <td>-0.000565</td>\n",
       "      <td>-0.006795</td>\n",
       "      <td>0.001128</td>\n",
       "      <td>0.002722</td>\n",
       "      <td>-0.000076</td>\n",
       "      <td>0.006672</td>\n",
       "      <td>-0.003296</td>\n",
       "      <td>0.139476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Derived.16</th>\n",
       "      <td>0.161415</td>\n",
       "      <td>0.168068</td>\n",
       "      <td>0.484676</td>\n",
       "      <td>-0.315275</td>\n",
       "      <td>0.064036</td>\n",
       "      <td>0.993784</td>\n",
       "      <td>0.710613</td>\n",
       "      <td>0.578172</td>\n",
       "      <td>0.889590</td>\n",
       "      <td>0.069781</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.011426</td>\n",
       "      <td>-0.009229</td>\n",
       "      <td>-0.004581</td>\n",
       "      <td>-0.005567</td>\n",
       "      <td>0.001924</td>\n",
       "      <td>0.010617</td>\n",
       "      <td>0.007372</td>\n",
       "      <td>-0.001547</td>\n",
       "      <td>-0.008757</td>\n",
       "      <td>0.232497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Derived.17</th>\n",
       "      <td>0.163614</td>\n",
       "      <td>0.155669</td>\n",
       "      <td>0.517404</td>\n",
       "      <td>-0.237812</td>\n",
       "      <td>0.325613</td>\n",
       "      <td>0.719683</td>\n",
       "      <td>0.999367</td>\n",
       "      <td>0.960293</td>\n",
       "      <td>0.905480</td>\n",
       "      <td>0.329415</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.017671</td>\n",
       "      <td>-0.003731</td>\n",
       "      <td>-0.008366</td>\n",
       "      <td>-0.005721</td>\n",
       "      <td>0.003644</td>\n",
       "      <td>0.012558</td>\n",
       "      <td>0.009093</td>\n",
       "      <td>-0.003419</td>\n",
       "      <td>-0.008415</td>\n",
       "      <td>0.356769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Derived.18</th>\n",
       "      <td>0.143504</td>\n",
       "      <td>0.132150</td>\n",
       "      <td>0.458361</td>\n",
       "      <td>-0.195856</td>\n",
       "      <td>0.405650</td>\n",
       "      <td>0.587740</td>\n",
       "      <td>0.964908</td>\n",
       "      <td>0.997833</td>\n",
       "      <td>0.778630</td>\n",
       "      <td>0.409973</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.016034</td>\n",
       "      <td>-0.001593</td>\n",
       "      <td>-0.007461</td>\n",
       "      <td>-0.004895</td>\n",
       "      <td>0.003345</td>\n",
       "      <td>0.010515</td>\n",
       "      <td>0.007990</td>\n",
       "      <td>-0.002849</td>\n",
       "      <td>-0.007186</td>\n",
       "      <td>0.348033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Derived.19</th>\n",
       "      <td>0.172852</td>\n",
       "      <td>0.175695</td>\n",
       "      <td>0.539670</td>\n",
       "      <td>-0.293863</td>\n",
       "      <td>0.112013</td>\n",
       "      <td>0.897922</td>\n",
       "      <td>0.896825</td>\n",
       "      <td>0.765073</td>\n",
       "      <td>0.998057</td>\n",
       "      <td>0.125494</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.017000</td>\n",
       "      <td>-0.009228</td>\n",
       "      <td>-0.009464</td>\n",
       "      <td>-0.006440</td>\n",
       "      <td>0.003460</td>\n",
       "      <td>0.014666</td>\n",
       "      <td>0.011322</td>\n",
       "      <td>-0.004030</td>\n",
       "      <td>-0.010278</td>\n",
       "      <td>0.309988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Derived.20</th>\n",
       "      <td>-0.172505</td>\n",
       "      <td>-0.165716</td>\n",
       "      <td>-0.469272</td>\n",
       "      <td>0.315585</td>\n",
       "      <td>-0.051319</td>\n",
       "      <td>-0.936367</td>\n",
       "      <td>-0.699224</td>\n",
       "      <td>-0.571628</td>\n",
       "      <td>-0.862083</td>\n",
       "      <td>-0.057282</td>\n",
       "      <td>...</td>\n",
       "      <td>0.015054</td>\n",
       "      <td>0.004075</td>\n",
       "      <td>0.003488</td>\n",
       "      <td>0.001795</td>\n",
       "      <td>-0.004669</td>\n",
       "      <td>-0.009675</td>\n",
       "      <td>-0.004094</td>\n",
       "      <td>0.004140</td>\n",
       "      <td>0.009269</td>\n",
       "      <td>-0.221228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Derived.21</th>\n",
       "      <td>0.156867</td>\n",
       "      <td>0.155924</td>\n",
       "      <td>0.485113</td>\n",
       "      <td>-0.315748</td>\n",
       "      <td>0.057084</td>\n",
       "      <td>0.959630</td>\n",
       "      <td>0.706574</td>\n",
       "      <td>0.579248</td>\n",
       "      <td>0.867535</td>\n",
       "      <td>0.059620</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.013894</td>\n",
       "      <td>-0.008706</td>\n",
       "      <td>-0.006585</td>\n",
       "      <td>-0.005062</td>\n",
       "      <td>0.001843</td>\n",
       "      <td>0.011419</td>\n",
       "      <td>0.008715</td>\n",
       "      <td>-0.001495</td>\n",
       "      <td>-0.009442</td>\n",
       "      <td>0.230101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Derived.22</th>\n",
       "      <td>0.068704</td>\n",
       "      <td>-0.007664</td>\n",
       "      <td>0.329704</td>\n",
       "      <td>-0.048690</td>\n",
       "      <td>0.266636</td>\n",
       "      <td>0.200981</td>\n",
       "      <td>0.393547</td>\n",
       "      <td>0.377039</td>\n",
       "      <td>0.310085</td>\n",
       "      <td>0.271152</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.023745</td>\n",
       "      <td>-0.013284</td>\n",
       "      <td>-0.016175</td>\n",
       "      <td>-0.010505</td>\n",
       "      <td>-0.001881</td>\n",
       "      <td>0.013374</td>\n",
       "      <td>0.032513</td>\n",
       "      <td>-0.001504</td>\n",
       "      <td>-0.017594</td>\n",
       "      <td>0.192558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Derived.23</th>\n",
       "      <td>-0.049798</td>\n",
       "      <td>-0.062373</td>\n",
       "      <td>-0.022884</td>\n",
       "      <td>0.082024</td>\n",
       "      <td>0.017251</td>\n",
       "      <td>-0.226443</td>\n",
       "      <td>-0.288756</td>\n",
       "      <td>-0.279650</td>\n",
       "      <td>-0.272449</td>\n",
       "      <td>0.018043</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.006034</td>\n",
       "      <td>-0.009531</td>\n",
       "      <td>-0.008434</td>\n",
       "      <td>-0.006209</td>\n",
       "      <td>-0.007180</td>\n",
       "      <td>0.007437</td>\n",
       "      <td>0.024605</td>\n",
       "      <td>-0.002001</td>\n",
       "      <td>-0.009541</td>\n",
       "      <td>-0.065052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Derived.24</th>\n",
       "      <td>0.177010</td>\n",
       "      <td>0.175052</td>\n",
       "      <td>0.554103</td>\n",
       "      <td>-0.271007</td>\n",
       "      <td>0.221919</td>\n",
       "      <td>0.825208</td>\n",
       "      <td>0.962248</td>\n",
       "      <td>0.873120</td>\n",
       "      <td>0.967301</td>\n",
       "      <td>0.228965</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.019810</td>\n",
       "      <td>-0.006678</td>\n",
       "      <td>-0.010354</td>\n",
       "      <td>-0.005752</td>\n",
       "      <td>0.003938</td>\n",
       "      <td>0.015281</td>\n",
       "      <td>0.012387</td>\n",
       "      <td>-0.005082</td>\n",
       "      <td>-0.011207</td>\n",
       "      <td>0.339319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC1</th>\n",
       "      <td>0.103817</td>\n",
       "      <td>0.095028</td>\n",
       "      <td>0.324374</td>\n",
       "      <td>-0.147156</td>\n",
       "      <td>0.210977</td>\n",
       "      <td>0.449935</td>\n",
       "      <td>0.629093</td>\n",
       "      <td>0.605953</td>\n",
       "      <td>0.568094</td>\n",
       "      <td>0.213988</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.015047</td>\n",
       "      <td>-0.011126</td>\n",
       "      <td>-0.007069</td>\n",
       "      <td>-0.005198</td>\n",
       "      <td>0.004127</td>\n",
       "      <td>0.007898</td>\n",
       "      <td>0.006734</td>\n",
       "      <td>-0.001384</td>\n",
       "      <td>-0.005566</td>\n",
       "      <td>0.327086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC2</th>\n",
       "      <td>0.078747</td>\n",
       "      <td>0.063056</td>\n",
       "      <td>0.257182</td>\n",
       "      <td>-0.103863</td>\n",
       "      <td>0.166841</td>\n",
       "      <td>0.326812</td>\n",
       "      <td>0.470300</td>\n",
       "      <td>0.453068</td>\n",
       "      <td>0.420508</td>\n",
       "      <td>0.173144</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.016794</td>\n",
       "      <td>-0.009926</td>\n",
       "      <td>-0.012140</td>\n",
       "      <td>-0.003959</td>\n",
       "      <td>0.005939</td>\n",
       "      <td>0.012159</td>\n",
       "      <td>0.015002</td>\n",
       "      <td>-0.005331</td>\n",
       "      <td>-0.012452</td>\n",
       "      <td>0.545260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC3</th>\n",
       "      <td>0.070811</td>\n",
       "      <td>0.064950</td>\n",
       "      <td>0.217399</td>\n",
       "      <td>-0.098913</td>\n",
       "      <td>0.134090</td>\n",
       "      <td>0.304948</td>\n",
       "      <td>0.425107</td>\n",
       "      <td>0.409806</td>\n",
       "      <td>0.385601</td>\n",
       "      <td>0.139888</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.008482</td>\n",
       "      <td>-0.010484</td>\n",
       "      <td>-0.009179</td>\n",
       "      <td>-0.006930</td>\n",
       "      <td>0.001743</td>\n",
       "      <td>0.001868</td>\n",
       "      <td>0.006307</td>\n",
       "      <td>0.006012</td>\n",
       "      <td>-0.000318</td>\n",
       "      <td>0.073260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC4</th>\n",
       "      <td>0.103212</td>\n",
       "      <td>0.098200</td>\n",
       "      <td>0.326392</td>\n",
       "      <td>-0.150018</td>\n",
       "      <td>0.205405</td>\n",
       "      <td>0.453995</td>\n",
       "      <td>0.630427</td>\n",
       "      <td>0.605778</td>\n",
       "      <td>0.571201</td>\n",
       "      <td>0.207804</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.015473</td>\n",
       "      <td>-0.011370</td>\n",
       "      <td>-0.007632</td>\n",
       "      <td>-0.005388</td>\n",
       "      <td>0.004591</td>\n",
       "      <td>0.008509</td>\n",
       "      <td>0.006902</td>\n",
       "      <td>-0.001715</td>\n",
       "      <td>-0.005739</td>\n",
       "      <td>0.342653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC5</th>\n",
       "      <td>0.007075</td>\n",
       "      <td>-0.000789</td>\n",
       "      <td>0.033954</td>\n",
       "      <td>-0.005014</td>\n",
       "      <td>0.027459</td>\n",
       "      <td>0.020698</td>\n",
       "      <td>0.040529</td>\n",
       "      <td>0.038829</td>\n",
       "      <td>0.031934</td>\n",
       "      <td>0.027924</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.006688</td>\n",
       "      <td>0.000327</td>\n",
       "      <td>-0.002450</td>\n",
       "      <td>0.002281</td>\n",
       "      <td>0.003348</td>\n",
       "      <td>0.008186</td>\n",
       "      <td>0.006968</td>\n",
       "      <td>-0.008934</td>\n",
       "      <td>-0.009631</td>\n",
       "      <td>0.375318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Base time</th>\n",
       "      <td>-0.005673</td>\n",
       "      <td>-0.002663</td>\n",
       "      <td>-0.024554</td>\n",
       "      <td>0.004179</td>\n",
       "      <td>-0.000878</td>\n",
       "      <td>-0.012827</td>\n",
       "      <td>-0.015482</td>\n",
       "      <td>-0.012201</td>\n",
       "      <td>-0.018437</td>\n",
       "      <td>-0.003805</td>\n",
       "      <td>...</td>\n",
       "      <td>0.009981</td>\n",
       "      <td>0.008838</td>\n",
       "      <td>0.032117</td>\n",
       "      <td>0.025795</td>\n",
       "      <td>-0.015311</td>\n",
       "      <td>-0.032247</td>\n",
       "      <td>-0.011179</td>\n",
       "      <td>-0.000480</td>\n",
       "      <td>0.002767</td>\n",
       "      <td>-0.224366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Post length</th>\n",
       "      <td>-0.003579</td>\n",
       "      <td>-0.005250</td>\n",
       "      <td>-0.011192</td>\n",
       "      <td>0.037062</td>\n",
       "      <td>-0.006734</td>\n",
       "      <td>0.003698</td>\n",
       "      <td>-0.003077</td>\n",
       "      <td>-0.010728</td>\n",
       "      <td>0.005128</td>\n",
       "      <td>-0.006768</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000619</td>\n",
       "      <td>0.005787</td>\n",
       "      <td>-0.000739</td>\n",
       "      <td>-0.003471</td>\n",
       "      <td>-0.003817</td>\n",
       "      <td>-0.000731</td>\n",
       "      <td>0.003901</td>\n",
       "      <td>0.003115</td>\n",
       "      <td>0.001444</td>\n",
       "      <td>-0.001237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Post Share Count</th>\n",
       "      <td>0.335598</td>\n",
       "      <td>0.051490</td>\n",
       "      <td>0.447861</td>\n",
       "      <td>-0.028027</td>\n",
       "      <td>0.041384</td>\n",
       "      <td>0.126574</td>\n",
       "      <td>0.148193</td>\n",
       "      <td>0.133862</td>\n",
       "      <td>0.147677</td>\n",
       "      <td>0.041778</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.005783</td>\n",
       "      <td>-0.007140</td>\n",
       "      <td>-0.002607</td>\n",
       "      <td>-0.002415</td>\n",
       "      <td>0.003515</td>\n",
       "      <td>0.003362</td>\n",
       "      <td>-0.002618</td>\n",
       "      <td>0.000031</td>\n",
       "      <td>0.000762</td>\n",
       "      <td>0.124638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Post Promotion Status</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H Local</th>\n",
       "      <td>-0.022333</td>\n",
       "      <td>-0.011026</td>\n",
       "      <td>-0.112727</td>\n",
       "      <td>0.015562</td>\n",
       "      <td>-0.019740</td>\n",
       "      <td>-0.061907</td>\n",
       "      <td>-0.067624</td>\n",
       "      <td>-0.056196</td>\n",
       "      <td>-0.079931</td>\n",
       "      <td>-0.022060</td>\n",
       "      <td>...</td>\n",
       "      <td>0.049085</td>\n",
       "      <td>0.047251</td>\n",
       "      <td>0.047737</td>\n",
       "      <td>0.046825</td>\n",
       "      <td>0.047217</td>\n",
       "      <td>0.049258</td>\n",
       "      <td>-0.098329</td>\n",
       "      <td>-0.135113</td>\n",
       "      <td>0.048326</td>\n",
       "      <td>0.012255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Post Sunday</th>\n",
       "      <td>-0.004079</td>\n",
       "      <td>-0.004993</td>\n",
       "      <td>0.008062</td>\n",
       "      <td>-0.007374</td>\n",
       "      <td>-0.003077</td>\n",
       "      <td>0.010550</td>\n",
       "      <td>0.005563</td>\n",
       "      <td>0.003367</td>\n",
       "      <td>0.008933</td>\n",
       "      <td>-0.005149</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.154605</td>\n",
       "      <td>-0.148827</td>\n",
       "      <td>0.001014</td>\n",
       "      <td>0.213461</td>\n",
       "      <td>0.209278</td>\n",
       "      <td>0.056261</td>\n",
       "      <td>-0.160193</td>\n",
       "      <td>-0.153387</td>\n",
       "      <td>-0.152214</td>\n",
       "      <td>0.000587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Post Monday</th>\n",
       "      <td>0.001362</td>\n",
       "      <td>-0.006242</td>\n",
       "      <td>-0.003002</td>\n",
       "      <td>-0.004756</td>\n",
       "      <td>-0.002862</td>\n",
       "      <td>-0.004329</td>\n",
       "      <td>-0.001326</td>\n",
       "      <td>-0.000376</td>\n",
       "      <td>-0.002191</td>\n",
       "      <td>-0.001896</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.169302</td>\n",
       "      <td>-0.162975</td>\n",
       "      <td>-0.164651</td>\n",
       "      <td>0.002997</td>\n",
       "      <td>0.233284</td>\n",
       "      <td>0.213008</td>\n",
       "      <td>0.048737</td>\n",
       "      <td>-0.167968</td>\n",
       "      <td>-0.166684</td>\n",
       "      <td>-0.000575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Post Tuesday</th>\n",
       "      <td>0.010492</td>\n",
       "      <td>0.002177</td>\n",
       "      <td>0.013568</td>\n",
       "      <td>0.004873</td>\n",
       "      <td>0.008969</td>\n",
       "      <td>0.003781</td>\n",
       "      <td>0.006768</td>\n",
       "      <td>0.007008</td>\n",
       "      <td>0.005915</td>\n",
       "      <td>0.012143</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.173474</td>\n",
       "      <td>-0.166991</td>\n",
       "      <td>-0.168709</td>\n",
       "      <td>-0.165487</td>\n",
       "      <td>0.010409</td>\n",
       "      <td>0.224407</td>\n",
       "      <td>0.206916</td>\n",
       "      <td>0.047329</td>\n",
       "      <td>-0.170791</td>\n",
       "      <td>0.004769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Post Wednesday</th>\n",
       "      <td>0.001912</td>\n",
       "      <td>0.003247</td>\n",
       "      <td>0.032922</td>\n",
       "      <td>-0.003504</td>\n",
       "      <td>-0.001709</td>\n",
       "      <td>0.012160</td>\n",
       "      <td>0.013144</td>\n",
       "      <td>0.010010</td>\n",
       "      <td>0.018626</td>\n",
       "      <td>0.000880</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.178804</td>\n",
       "      <td>-0.172122</td>\n",
       "      <td>-0.173892</td>\n",
       "      <td>-0.170571</td>\n",
       "      <td>-0.172001</td>\n",
       "      <td>0.011975</td>\n",
       "      <td>0.246820</td>\n",
       "      <td>0.203664</td>\n",
       "      <td>0.035480</td>\n",
       "      <td>0.022337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Post Thursday</th>\n",
       "      <td>-0.002845</td>\n",
       "      <td>0.003324</td>\n",
       "      <td>-0.016740</td>\n",
       "      <td>0.009583</td>\n",
       "      <td>0.007466</td>\n",
       "      <td>-0.001245</td>\n",
       "      <td>-0.002837</td>\n",
       "      <td>-0.002334</td>\n",
       "      <td>-0.005194</td>\n",
       "      <td>0.001684</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.169856</td>\n",
       "      <td>-0.163508</td>\n",
       "      <td>0.076766</td>\n",
       "      <td>-0.162035</td>\n",
       "      <td>-0.163394</td>\n",
       "      <td>-0.170454</td>\n",
       "      <td>-0.019334</td>\n",
       "      <td>0.215791</td>\n",
       "      <td>0.218821</td>\n",
       "      <td>-0.009496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Post Friday</th>\n",
       "      <td>-0.009949</td>\n",
       "      <td>-0.006156</td>\n",
       "      <td>-0.027714</td>\n",
       "      <td>0.002795</td>\n",
       "      <td>-0.003884</td>\n",
       "      <td>-0.010944</td>\n",
       "      <td>-0.017454</td>\n",
       "      <td>-0.015895</td>\n",
       "      <td>-0.016705</td>\n",
       "      <td>-0.004534</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.164847</td>\n",
       "      <td>0.226752</td>\n",
       "      <td>0.079712</td>\n",
       "      <td>-0.164732</td>\n",
       "      <td>-0.171850</td>\n",
       "      <td>-0.177436</td>\n",
       "      <td>-0.004109</td>\n",
       "      <td>0.221572</td>\n",
       "      <td>-0.010470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Post Saturday</th>\n",
       "      <td>0.002727</td>\n",
       "      <td>0.008358</td>\n",
       "      <td>-0.007980</td>\n",
       "      <td>-0.002126</td>\n",
       "      <td>-0.005278</td>\n",
       "      <td>-0.009929</td>\n",
       "      <td>-0.004056</td>\n",
       "      <td>-0.001976</td>\n",
       "      <td>-0.009671</td>\n",
       "      <td>-0.003742</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.164847</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.214537</td>\n",
       "      <td>0.229300</td>\n",
       "      <td>0.070235</td>\n",
       "      <td>-0.165428</td>\n",
       "      <td>-0.170806</td>\n",
       "      <td>-0.163548</td>\n",
       "      <td>0.003327</td>\n",
       "      <td>-0.008114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Base Sunday</th>\n",
       "      <td>-0.003590</td>\n",
       "      <td>0.000764</td>\n",
       "      <td>-0.014358</td>\n",
       "      <td>0.002386</td>\n",
       "      <td>-0.000426</td>\n",
       "      <td>-0.005159</td>\n",
       "      <td>-0.008361</td>\n",
       "      <td>-0.007473</td>\n",
       "      <td>-0.009642</td>\n",
       "      <td>-0.002532</td>\n",
       "      <td>...</td>\n",
       "      <td>0.226752</td>\n",
       "      <td>0.214537</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.158875</td>\n",
       "      <td>-0.160207</td>\n",
       "      <td>-0.167129</td>\n",
       "      <td>-0.172562</td>\n",
       "      <td>-0.165230</td>\n",
       "      <td>-0.163967</td>\n",
       "      <td>-0.008700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Base Monday</th>\n",
       "      <td>-0.001345</td>\n",
       "      <td>0.000087</td>\n",
       "      <td>-0.006598</td>\n",
       "      <td>-0.004504</td>\n",
       "      <td>-0.006805</td>\n",
       "      <td>-0.005468</td>\n",
       "      <td>-0.005993</td>\n",
       "      <td>-0.005297</td>\n",
       "      <td>-0.006710</td>\n",
       "      <td>-0.005779</td>\n",
       "      <td>...</td>\n",
       "      <td>0.079712</td>\n",
       "      <td>0.229300</td>\n",
       "      <td>-0.158875</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.157147</td>\n",
       "      <td>-0.163937</td>\n",
       "      <td>-0.169267</td>\n",
       "      <td>-0.162075</td>\n",
       "      <td>-0.160836</td>\n",
       "      <td>-0.003049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Base Tuesday</th>\n",
       "      <td>0.002757</td>\n",
       "      <td>0.000197</td>\n",
       "      <td>0.004262</td>\n",
       "      <td>-0.003690</td>\n",
       "      <td>0.001074</td>\n",
       "      <td>0.001918</td>\n",
       "      <td>0.003581</td>\n",
       "      <td>0.003135</td>\n",
       "      <td>0.003410</td>\n",
       "      <td>0.001494</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.164732</td>\n",
       "      <td>0.070235</td>\n",
       "      <td>-0.160207</td>\n",
       "      <td>-0.157147</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.165312</td>\n",
       "      <td>-0.170686</td>\n",
       "      <td>-0.163433</td>\n",
       "      <td>-0.162184</td>\n",
       "      <td>0.004611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Base Wednesday</th>\n",
       "      <td>0.007740</td>\n",
       "      <td>-0.001587</td>\n",
       "      <td>0.023153</td>\n",
       "      <td>-0.001920</td>\n",
       "      <td>0.002660</td>\n",
       "      <td>0.011149</td>\n",
       "      <td>0.012472</td>\n",
       "      <td>0.010544</td>\n",
       "      <td>0.014783</td>\n",
       "      <td>0.003371</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.171850</td>\n",
       "      <td>-0.165428</td>\n",
       "      <td>-0.167129</td>\n",
       "      <td>-0.163937</td>\n",
       "      <td>-0.165312</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.178061</td>\n",
       "      <td>-0.170495</td>\n",
       "      <td>-0.169192</td>\n",
       "      <td>0.023267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Base Thursday</th>\n",
       "      <td>0.001449</td>\n",
       "      <td>0.002071</td>\n",
       "      <td>0.022747</td>\n",
       "      <td>-0.001325</td>\n",
       "      <td>-0.000129</td>\n",
       "      <td>0.007315</td>\n",
       "      <td>0.009093</td>\n",
       "      <td>0.008291</td>\n",
       "      <td>0.011331</td>\n",
       "      <td>0.003135</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.177436</td>\n",
       "      <td>-0.170806</td>\n",
       "      <td>-0.172562</td>\n",
       "      <td>-0.169267</td>\n",
       "      <td>-0.170686</td>\n",
       "      <td>-0.178061</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.176038</td>\n",
       "      <td>-0.174692</td>\n",
       "      <td>-0.000927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Base Friday</th>\n",
       "      <td>-0.001514</td>\n",
       "      <td>-0.001521</td>\n",
       "      <td>-0.009419</td>\n",
       "      <td>0.003916</td>\n",
       "      <td>0.006651</td>\n",
       "      <td>-0.001381</td>\n",
       "      <td>-0.003123</td>\n",
       "      <td>-0.002518</td>\n",
       "      <td>-0.003686</td>\n",
       "      <td>0.003903</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.004109</td>\n",
       "      <td>-0.163548</td>\n",
       "      <td>-0.165230</td>\n",
       "      <td>-0.162075</td>\n",
       "      <td>-0.163433</td>\n",
       "      <td>-0.170495</td>\n",
       "      <td>-0.176038</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.167270</td>\n",
       "      <td>-0.007210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Base Saturday</th>\n",
       "      <td>-0.005663</td>\n",
       "      <td>-0.000045</td>\n",
       "      <td>-0.021087</td>\n",
       "      <td>0.005049</td>\n",
       "      <td>-0.003232</td>\n",
       "      <td>-0.008924</td>\n",
       "      <td>-0.008304</td>\n",
       "      <td>-0.007247</td>\n",
       "      <td>-0.010263</td>\n",
       "      <td>-0.003902</td>\n",
       "      <td>...</td>\n",
       "      <td>0.221572</td>\n",
       "      <td>0.003327</td>\n",
       "      <td>-0.163967</td>\n",
       "      <td>-0.160836</td>\n",
       "      <td>-0.162184</td>\n",
       "      <td>-0.169192</td>\n",
       "      <td>-0.174692</td>\n",
       "      <td>-0.167270</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.008304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Target Variable</th>\n",
       "      <td>0.064537</td>\n",
       "      <td>0.035152</td>\n",
       "      <td>0.194768</td>\n",
       "      <td>-0.069001</td>\n",
       "      <td>0.138996</td>\n",
       "      <td>0.238743</td>\n",
       "      <td>0.359536</td>\n",
       "      <td>0.349452</td>\n",
       "      <td>0.316593</td>\n",
       "      <td>0.153853</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.010470</td>\n",
       "      <td>-0.008114</td>\n",
       "      <td>-0.008700</td>\n",
       "      <td>-0.003049</td>\n",
       "      <td>0.004611</td>\n",
       "      <td>0.023267</td>\n",
       "      <td>-0.000927</td>\n",
       "      <td>-0.007210</td>\n",
       "      <td>-0.008304</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>54 rows × 54 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Page Popularity/likes  Page Checkinsâ€™s  \\\n",
       "Page Popularity/likes               1.000000           0.045090   \n",
       "Page Checkinsâ€™s                   0.045090           1.000000   \n",
       "Page talking about                  0.622700           0.167326   \n",
       "Page Category                      -0.042003          -0.060345   \n",
       "Derived                             0.046600          -0.007382   \n",
       "Derived.1                           0.157902           0.158831   \n",
       "Derived.2                           0.165026           0.151055   \n",
       "Derived.3                           0.147022           0.129424   \n",
       "Derived.4                           0.173462           0.169037   \n",
       "Derived.5                           0.046528          -0.006570   \n",
       "Derived.6                           0.157932           0.152888   \n",
       "Derived.7                           0.164715           0.131894   \n",
       "Derived.8                           0.132272           0.066060   \n",
       "Derived.9                           0.177494           0.162535   \n",
       "Derived.10                          0.007668          -0.001514   \n",
       "Derived.11                          0.169412           0.152377   \n",
       "Derived.12                          0.165150           0.151482   \n",
       "Derived.13                          0.136281           0.085762   \n",
       "Derived.14                          0.176066           0.173045   \n",
       "Derived.15                          0.046702          -0.007384   \n",
       "Derived.16                          0.161415           0.168068   \n",
       "Derived.17                          0.163614           0.155669   \n",
       "Derived.18                          0.143504           0.132150   \n",
       "Derived.19                          0.172852           0.175695   \n",
       "Derived.20                         -0.172505          -0.165716   \n",
       "Derived.21                          0.156867           0.155924   \n",
       "Derived.22                          0.068704          -0.007664   \n",
       "Derived.23                         -0.049798          -0.062373   \n",
       "Derived.24                          0.177010           0.175052   \n",
       "CC1                                 0.103817           0.095028   \n",
       "CC2                                 0.078747           0.063056   \n",
       "CC3                                 0.070811           0.064950   \n",
       "CC4                                 0.103212           0.098200   \n",
       "CC5                                 0.007075          -0.000789   \n",
       "Base time                          -0.005673          -0.002663   \n",
       "Post length                        -0.003579          -0.005250   \n",
       "Post Share Count                    0.335598           0.051490   \n",
       "Post Promotion Status                    NaN                NaN   \n",
       "H Local                            -0.022333          -0.011026   \n",
       "Post Sunday                        -0.004079          -0.004993   \n",
       "Post Monday                         0.001362          -0.006242   \n",
       "Post Tuesday                        0.010492           0.002177   \n",
       "Post Wednesday                      0.001912           0.003247   \n",
       "Post Thursday                      -0.002845           0.003324   \n",
       "Post Friday                        -0.009949          -0.006156   \n",
       "Post Saturday                       0.002727           0.008358   \n",
       "Base Sunday                        -0.003590           0.000764   \n",
       "Base Monday                        -0.001345           0.000087   \n",
       "Base Tuesday                        0.002757           0.000197   \n",
       "Base Wednesday                      0.007740          -0.001587   \n",
       "Base Thursday                       0.001449           0.002071   \n",
       "Base Friday                        -0.001514          -0.001521   \n",
       "Base Saturday                      -0.005663          -0.000045   \n",
       "Target Variable                     0.064537           0.035152   \n",
       "\n",
       "                       Page talking about  Page Category   Derived  Derived.1  \\\n",
       "Page Popularity/likes            0.622700      -0.042003  0.046600   0.157902   \n",
       "Page Checkinsâ€™s                0.167326      -0.060345 -0.007382   0.158831   \n",
       "Page talking about               1.000000      -0.148164  0.118685   0.484869   \n",
       "Page Category                   -0.148164       1.000000 -0.026591  -0.319812   \n",
       "Derived                          0.118685      -0.026591  1.000000   0.072641   \n",
       "Derived.1                        0.484869      -0.319812  0.072641   1.000000   \n",
       "Derived.2                        0.515621      -0.233917  0.335366   0.715212   \n",
       "Derived.3                        0.457754      -0.193385  0.409813   0.582076   \n",
       "Derived.4                        0.539581      -0.291309  0.130532   0.896815   \n",
       "Derived.5                        0.119260      -0.023868  0.930690   0.079879   \n",
       "Derived.6                        0.484452      -0.318472  0.057220   0.968435   \n",
       "Derived.7                        0.537943      -0.217248  0.348978   0.683586   \n",
       "Derived.8                        0.447466      -0.132765  0.538629   0.455341   \n",
       "Derived.9                        0.557773      -0.275676  0.179533   0.847315   \n",
       "Derived.10                      -0.001126      -0.003424  0.229902   0.000314   \n",
       "Derived.11                       0.484815      -0.316907  0.061871   0.967804   \n",
       "Derived.12                       0.507033      -0.230690  0.312735   0.711221   \n",
       "Derived.13                       0.337302      -0.146569  0.536163   0.462642   \n",
       "Derived.14                       0.543663      -0.277379  0.196230   0.850476   \n",
       "Derived.15                       0.119181      -0.026634  0.999831   0.072921   \n",
       "Derived.16                       0.484676      -0.315275  0.064036   0.993784   \n",
       "Derived.17                       0.517404      -0.237812  0.325613   0.719683   \n",
       "Derived.18                       0.458361      -0.195856  0.405650   0.587740   \n",
       "Derived.19                       0.539670      -0.293863  0.112013   0.897922   \n",
       "Derived.20                      -0.469272       0.315585 -0.051319  -0.936367   \n",
       "Derived.21                       0.485113      -0.315748  0.057084   0.959630   \n",
       "Derived.22                       0.329704      -0.048690  0.266636   0.200981   \n",
       "Derived.23                      -0.022884       0.082024  0.017251  -0.226443   \n",
       "Derived.24                       0.554103      -0.271007  0.221919   0.825208   \n",
       "CC1                              0.324374      -0.147156  0.210977   0.449935   \n",
       "CC2                              0.257182      -0.103863  0.166841   0.326812   \n",
       "CC3                              0.217399      -0.098913  0.134090   0.304948   \n",
       "CC4                              0.326392      -0.150018  0.205405   0.453995   \n",
       "CC5                              0.033954      -0.005014  0.027459   0.020698   \n",
       "Base time                       -0.024554       0.004179 -0.000878  -0.012827   \n",
       "Post length                     -0.011192       0.037062 -0.006734   0.003698   \n",
       "Post Share Count                 0.447861      -0.028027  0.041384   0.126574   \n",
       "Post Promotion Status                 NaN            NaN       NaN        NaN   \n",
       "H Local                         -0.112727       0.015562 -0.019740  -0.061907   \n",
       "Post Sunday                      0.008062      -0.007374 -0.003077   0.010550   \n",
       "Post Monday                     -0.003002      -0.004756 -0.002862  -0.004329   \n",
       "Post Tuesday                     0.013568       0.004873  0.008969   0.003781   \n",
       "Post Wednesday                   0.032922      -0.003504 -0.001709   0.012160   \n",
       "Post Thursday                   -0.016740       0.009583  0.007466  -0.001245   \n",
       "Post Friday                     -0.027714       0.002795 -0.003884  -0.010944   \n",
       "Post Saturday                   -0.007980      -0.002126 -0.005278  -0.009929   \n",
       "Base Sunday                     -0.014358       0.002386 -0.000426  -0.005159   \n",
       "Base Monday                     -0.006598      -0.004504 -0.006805  -0.005468   \n",
       "Base Tuesday                     0.004262      -0.003690  0.001074   0.001918   \n",
       "Base Wednesday                   0.023153      -0.001920  0.002660   0.011149   \n",
       "Base Thursday                    0.022747      -0.001325 -0.000129   0.007315   \n",
       "Base Friday                     -0.009419       0.003916  0.006651  -0.001381   \n",
       "Base Saturday                   -0.021087       0.005049 -0.003232  -0.008924   \n",
       "Target Variable                  0.194768      -0.069001  0.138996   0.238743   \n",
       "\n",
       "                       Derived.2  Derived.3  Derived.4  Derived.5  \\\n",
       "Page Popularity/likes   0.165026   0.147022   0.173462   0.046528   \n",
       "Page Checkinsâ€™s       0.151055   0.129424   0.169037  -0.006570   \n",
       "Page talking about      0.515621   0.457754   0.539581   0.119260   \n",
       "Page Category          -0.233917  -0.193385  -0.291309  -0.023868   \n",
       "Derived                 0.335366   0.409813   0.130532   0.930690   \n",
       "Derived.1               0.715212   0.582076   0.896815   0.079879   \n",
       "Derived.2               1.000000   0.963216   0.903036   0.340154   \n",
       "Derived.3               0.963216   1.000000   0.773769   0.412988   \n",
       "Derived.4               0.903036   0.773769   1.000000   0.146383   \n",
       "Derived.5               0.340154   0.412988   0.146383   1.000000   \n",
       "Derived.6               0.705025   0.576554   0.870439   0.059425   \n",
       "Derived.7               0.983718   0.947674   0.879570   0.362163   \n",
       "Derived.8               0.835920   0.876121   0.630849   0.571435   \n",
       "Derived.9               0.932083   0.823059   0.973750   0.177010   \n",
       "Derived.10              0.033223   0.044852  -0.003737   0.134967   \n",
       "Derived.11              0.720203   0.587950   0.891848   0.068723   \n",
       "Derived.12              0.991463   0.955776   0.899324   0.326257   \n",
       "Derived.13              0.817802   0.869841   0.614855   0.551978   \n",
       "Derived.14              0.945294   0.845321   0.981744   0.211251   \n",
       "Derived.15              0.336087   0.410633   0.131102   0.932214   \n",
       "Derived.16              0.710613   0.578172   0.889590   0.069781   \n",
       "Derived.17              0.999367   0.960293   0.905480   0.329415   \n",
       "Derived.18              0.964908   0.997833   0.778630   0.409973   \n",
       "Derived.19              0.896825   0.765073   0.998057   0.125494   \n",
       "Derived.20             -0.699224  -0.571628  -0.862083  -0.057282   \n",
       "Derived.21              0.706574   0.579248   0.867535   0.059620   \n",
       "Derived.22              0.393547   0.377039   0.310085   0.271152   \n",
       "Derived.23             -0.288756  -0.279650  -0.272449   0.018043   \n",
       "Derived.24              0.962248   0.873120   0.967301   0.228965   \n",
       "CC1                     0.629093   0.605953   0.568094   0.213988   \n",
       "CC2                     0.470300   0.453068   0.420508   0.173144   \n",
       "CC3                     0.425107   0.409806   0.385601   0.139888   \n",
       "CC4                     0.630427   0.605778   0.571201   0.207804   \n",
       "CC5                     0.040529   0.038829   0.031934   0.027924   \n",
       "Base time              -0.015482  -0.012201  -0.018437  -0.003805   \n",
       "Post length            -0.003077  -0.010728   0.005128  -0.006768   \n",
       "Post Share Count        0.148193   0.133862   0.147677   0.041778   \n",
       "Post Promotion Status        NaN        NaN        NaN        NaN   \n",
       "H Local                -0.067624  -0.056196  -0.079931  -0.022060   \n",
       "Post Sunday             0.005563   0.003367   0.008933  -0.005149   \n",
       "Post Monday            -0.001326  -0.000376  -0.002191  -0.001896   \n",
       "Post Tuesday            0.006768   0.007008   0.005915   0.012143   \n",
       "Post Wednesday          0.013144   0.010010   0.018626   0.000880   \n",
       "Post Thursday          -0.002837  -0.002334  -0.005194   0.001684   \n",
       "Post Friday            -0.017454  -0.015895  -0.016705  -0.004534   \n",
       "Post Saturday          -0.004056  -0.001976  -0.009671  -0.003742   \n",
       "Base Sunday            -0.008361  -0.007473  -0.009642  -0.002532   \n",
       "Base Monday            -0.005993  -0.005297  -0.006710  -0.005779   \n",
       "Base Tuesday            0.003581   0.003135   0.003410   0.001494   \n",
       "Base Wednesday          0.012472   0.010544   0.014783   0.003371   \n",
       "Base Thursday           0.009093   0.008291   0.011331   0.003135   \n",
       "Base Friday            -0.003123  -0.002518  -0.003686   0.003903   \n",
       "Base Saturday          -0.008304  -0.007247  -0.010263  -0.003902   \n",
       "Target Variable         0.359536   0.349452   0.316593   0.153853   \n",
       "\n",
       "                            ...         Post Friday  Post Saturday  \\\n",
       "Page Popularity/likes       ...           -0.009949       0.002727   \n",
       "Page Checkinsâ€™s           ...           -0.006156       0.008358   \n",
       "Page talking about          ...           -0.027714      -0.007980   \n",
       "Page Category               ...            0.002795      -0.002126   \n",
       "Derived                     ...           -0.003884      -0.005278   \n",
       "Derived.1                   ...           -0.010944      -0.009929   \n",
       "Derived.2                   ...           -0.017454      -0.004056   \n",
       "Derived.3                   ...           -0.015895      -0.001976   \n",
       "Derived.4                   ...           -0.016705      -0.009671   \n",
       "Derived.5                   ...           -0.004534      -0.003742   \n",
       "Derived.6                   ...           -0.012854      -0.008538   \n",
       "Derived.7                   ...           -0.020600      -0.007184   \n",
       "Derived.8                   ...           -0.018013      -0.009413   \n",
       "Derived.9                   ...           -0.019952      -0.008896   \n",
       "Derived.10                  ...           -0.002758      -0.002655   \n",
       "Derived.11                  ...           -0.014625      -0.007281   \n",
       "Derived.12                  ...           -0.016003      -0.004045   \n",
       "Derived.13                  ...           -0.007122      -0.001000   \n",
       "Derived.14                  ...           -0.017409      -0.007758   \n",
       "Derived.15                  ...           -0.003876      -0.005250   \n",
       "Derived.16                  ...           -0.011426      -0.009229   \n",
       "Derived.17                  ...           -0.017671      -0.003731   \n",
       "Derived.18                  ...           -0.016034      -0.001593   \n",
       "Derived.19                  ...           -0.017000      -0.009228   \n",
       "Derived.20                  ...            0.015054       0.004075   \n",
       "Derived.21                  ...           -0.013894      -0.008706   \n",
       "Derived.22                  ...           -0.023745      -0.013284   \n",
       "Derived.23                  ...           -0.006034      -0.009531   \n",
       "Derived.24                  ...           -0.019810      -0.006678   \n",
       "CC1                         ...           -0.015047      -0.011126   \n",
       "CC2                         ...           -0.016794      -0.009926   \n",
       "CC3                         ...           -0.008482      -0.010484   \n",
       "CC4                         ...           -0.015473      -0.011370   \n",
       "CC5                         ...           -0.006688       0.000327   \n",
       "Base time                   ...            0.009981       0.008838   \n",
       "Post length                 ...            0.000619       0.005787   \n",
       "Post Share Count            ...           -0.005783      -0.007140   \n",
       "Post Promotion Status       ...                 NaN            NaN   \n",
       "H Local                     ...            0.049085       0.047251   \n",
       "Post Sunday                 ...           -0.154605      -0.148827   \n",
       "Post Monday                 ...           -0.169302      -0.162975   \n",
       "Post Tuesday                ...           -0.173474      -0.166991   \n",
       "Post Wednesday              ...           -0.178804      -0.172122   \n",
       "Post Thursday               ...           -0.169856      -0.163508   \n",
       "Post Friday                 ...            1.000000      -0.164847   \n",
       "Post Saturday               ...           -0.164847       1.000000   \n",
       "Base Sunday                 ...            0.226752       0.214537   \n",
       "Base Monday                 ...            0.079712       0.229300   \n",
       "Base Tuesday                ...           -0.164732       0.070235   \n",
       "Base Wednesday              ...           -0.171850      -0.165428   \n",
       "Base Thursday               ...           -0.177436      -0.170806   \n",
       "Base Friday                 ...           -0.004109      -0.163548   \n",
       "Base Saturday               ...            0.221572       0.003327   \n",
       "Target Variable             ...           -0.010470      -0.008114   \n",
       "\n",
       "                       Base Sunday  Base Monday  Base Tuesday  Base Wednesday  \\\n",
       "Page Popularity/likes    -0.003590    -0.001345      0.002757        0.007740   \n",
       "Page Checkinsâ€™s         0.000764     0.000087      0.000197       -0.001587   \n",
       "Page talking about       -0.014358    -0.006598      0.004262        0.023153   \n",
       "Page Category             0.002386    -0.004504     -0.003690       -0.001920   \n",
       "Derived                  -0.000426    -0.006805      0.001074        0.002660   \n",
       "Derived.1                -0.005159    -0.005468      0.001918        0.011149   \n",
       "Derived.2                -0.008361    -0.005993      0.003581        0.012472   \n",
       "Derived.3                -0.007473    -0.005297      0.003135        0.010544   \n",
       "Derived.4                -0.009642    -0.006710      0.003410        0.014783   \n",
       "Derived.5                -0.002532    -0.005779      0.001494        0.003371   \n",
       "Derived.6                -0.005896    -0.004755      0.002065        0.011020   \n",
       "Derived.7                -0.011552    -0.007818      0.002882        0.015089   \n",
       "Derived.8                -0.010666    -0.010237     -0.000021        0.013393   \n",
       "Derived.9                -0.011017    -0.006608      0.003306        0.015697   \n",
       "Derived.10                0.004702    -0.002631     -0.002653       -0.002767   \n",
       "Derived.11               -0.004922    -0.003350      0.004349        0.011342   \n",
       "Derived.12               -0.008096    -0.005614      0.003836        0.012956   \n",
       "Derived.13               -0.001537    -0.006043      0.002129        0.004088   \n",
       "Derived.14               -0.009647    -0.005579      0.004158        0.015204   \n",
       "Derived.15               -0.000565    -0.006795      0.001128        0.002722   \n",
       "Derived.16               -0.004581    -0.005567      0.001924        0.010617   \n",
       "Derived.17               -0.008366    -0.005721      0.003644        0.012558   \n",
       "Derived.18               -0.007461    -0.004895      0.003345        0.010515   \n",
       "Derived.19               -0.009464    -0.006440      0.003460        0.014666   \n",
       "Derived.20                0.003488     0.001795     -0.004669       -0.009675   \n",
       "Derived.21               -0.006585    -0.005062      0.001843        0.011419   \n",
       "Derived.22               -0.016175    -0.010505     -0.001881        0.013374   \n",
       "Derived.23               -0.008434    -0.006209     -0.007180        0.007437   \n",
       "Derived.24               -0.010354    -0.005752      0.003938        0.015281   \n",
       "CC1                      -0.007069    -0.005198      0.004127        0.007898   \n",
       "CC2                      -0.012140    -0.003959      0.005939        0.012159   \n",
       "CC3                      -0.009179    -0.006930      0.001743        0.001868   \n",
       "CC4                      -0.007632    -0.005388      0.004591        0.008509   \n",
       "CC5                      -0.002450     0.002281      0.003348        0.008186   \n",
       "Base time                 0.032117     0.025795     -0.015311       -0.032247   \n",
       "Post length              -0.000739    -0.003471     -0.003817       -0.000731   \n",
       "Post Share Count         -0.002607    -0.002415      0.003515        0.003362   \n",
       "Post Promotion Status          NaN          NaN           NaN             NaN   \n",
       "H Local                   0.047737     0.046825      0.047217        0.049258   \n",
       "Post Sunday               0.001014     0.213461      0.209278        0.056261   \n",
       "Post Monday              -0.164651     0.002997      0.233284        0.213008   \n",
       "Post Tuesday             -0.168709    -0.165487      0.010409        0.224407   \n",
       "Post Wednesday           -0.173892    -0.170571     -0.172001        0.011975   \n",
       "Post Thursday             0.076766    -0.162035     -0.163394       -0.170454   \n",
       "Post Friday               0.226752     0.079712     -0.164732       -0.171850   \n",
       "Post Saturday             0.214537     0.229300      0.070235       -0.165428   \n",
       "Base Sunday               1.000000    -0.158875     -0.160207       -0.167129   \n",
       "Base Monday              -0.158875     1.000000     -0.157147       -0.163937   \n",
       "Base Tuesday             -0.160207    -0.157147      1.000000       -0.165312   \n",
       "Base Wednesday           -0.167129    -0.163937     -0.165312        1.000000   \n",
       "Base Thursday            -0.172562    -0.169267     -0.170686       -0.178061   \n",
       "Base Friday              -0.165230    -0.162075     -0.163433       -0.170495   \n",
       "Base Saturday            -0.163967    -0.160836     -0.162184       -0.169192   \n",
       "Target Variable          -0.008700    -0.003049      0.004611        0.023267   \n",
       "\n",
       "                       Base Thursday  Base Friday  Base Saturday  \\\n",
       "Page Popularity/likes       0.001449    -0.001514      -0.005663   \n",
       "Page Checkinsâ€™s           0.002071    -0.001521      -0.000045   \n",
       "Page talking about          0.022747    -0.009419      -0.021087   \n",
       "Page Category              -0.001325     0.003916       0.005049   \n",
       "Derived                    -0.000129     0.006651      -0.003232   \n",
       "Derived.1                   0.007315    -0.001381      -0.008924   \n",
       "Derived.2                   0.009093    -0.003123      -0.008304   \n",
       "Derived.3                   0.008291    -0.002518      -0.007247   \n",
       "Derived.4                   0.011331    -0.003686      -0.010263   \n",
       "Derived.5                   0.003135     0.003903      -0.003902   \n",
       "Derived.6                   0.008132    -0.001753      -0.009376   \n",
       "Derived.7                   0.015785    -0.003765      -0.011614   \n",
       "Derived.8                   0.019318    -0.001115      -0.011880   \n",
       "Derived.9                   0.014239    -0.004683      -0.011834   \n",
       "Derived.10                 -0.002857     0.002821       0.003450   \n",
       "Derived.11                  0.006487    -0.003889      -0.010433   \n",
       "Derived.12                  0.007867    -0.003796      -0.007731   \n",
       "Derived.13                  0.001061     0.001298      -0.001212   \n",
       "Derived.14                  0.010507    -0.004689      -0.010663   \n",
       "Derived.15                 -0.000076     0.006672      -0.003296   \n",
       "Derived.16                  0.007372    -0.001547      -0.008757   \n",
       "Derived.17                  0.009093    -0.003419      -0.008415   \n",
       "Derived.18                  0.007990    -0.002849      -0.007186   \n",
       "Derived.19                  0.011322    -0.004030      -0.010278   \n",
       "Derived.20                 -0.004094     0.004140       0.009269   \n",
       "Derived.21                  0.008715    -0.001495      -0.009442   \n",
       "Derived.22                  0.032513    -0.001504      -0.017594   \n",
       "Derived.23                  0.024605    -0.002001      -0.009541   \n",
       "Derived.24                  0.012387    -0.005082      -0.011207   \n",
       "CC1                         0.006734    -0.001384      -0.005566   \n",
       "CC2                         0.015002    -0.005331      -0.012452   \n",
       "CC3                         0.006307     0.006012      -0.000318   \n",
       "CC4                         0.006902    -0.001715      -0.005739   \n",
       "CC5                         0.006968    -0.008934      -0.009631   \n",
       "Base time                  -0.011179    -0.000480       0.002767   \n",
       "Post length                 0.003901     0.003115       0.001444   \n",
       "Post Share Count           -0.002618     0.000031       0.000762   \n",
       "Post Promotion Status            NaN          NaN            NaN   \n",
       "H Local                    -0.098329    -0.135113       0.048326   \n",
       "Post Sunday                -0.160193    -0.153387      -0.152214   \n",
       "Post Monday                 0.048737    -0.167968      -0.166684   \n",
       "Post Tuesday                0.206916     0.047329      -0.170791   \n",
       "Post Wednesday              0.246820     0.203664       0.035480   \n",
       "Post Thursday              -0.019334     0.215791       0.218821   \n",
       "Post Friday                -0.177436    -0.004109       0.221572   \n",
       "Post Saturday              -0.170806    -0.163548       0.003327   \n",
       "Base Sunday                -0.172562    -0.165230      -0.163967   \n",
       "Base Monday                -0.169267    -0.162075      -0.160836   \n",
       "Base Tuesday               -0.170686    -0.163433      -0.162184   \n",
       "Base Wednesday             -0.178061    -0.170495      -0.169192   \n",
       "Base Thursday               1.000000    -0.176038      -0.174692   \n",
       "Base Friday                -0.176038     1.000000      -0.167270   \n",
       "Base Saturday              -0.174692    -0.167270       1.000000   \n",
       "Target Variable            -0.000927    -0.007210      -0.008304   \n",
       "\n",
       "                       Target Variable  \n",
       "Page Popularity/likes         0.064537  \n",
       "Page Checkinsâ€™s             0.035152  \n",
       "Page talking about            0.194768  \n",
       "Page Category                -0.069001  \n",
       "Derived                       0.138996  \n",
       "Derived.1                     0.238743  \n",
       "Derived.2                     0.359536  \n",
       "Derived.3                     0.349452  \n",
       "Derived.4                     0.316593  \n",
       "Derived.5                     0.153853  \n",
       "Derived.6                     0.229139  \n",
       "Derived.7                     0.368003  \n",
       "Derived.8                     0.339229  \n",
       "Derived.9                     0.329067  \n",
       "Derived.10                    0.004411  \n",
       "Derived.11                    0.234408  \n",
       "Derived.12                    0.356985  \n",
       "Derived.13                    0.303185  \n",
       "Derived.14                    0.333375  \n",
       "Derived.15                    0.139476  \n",
       "Derived.16                    0.232497  \n",
       "Derived.17                    0.356769  \n",
       "Derived.18                    0.348033  \n",
       "Derived.19                    0.309988  \n",
       "Derived.20                   -0.221228  \n",
       "Derived.21                    0.230101  \n",
       "Derived.22                    0.192558  \n",
       "Derived.23                   -0.065052  \n",
       "Derived.24                    0.339319  \n",
       "CC1                           0.327086  \n",
       "CC2                           0.545260  \n",
       "CC3                           0.073260  \n",
       "CC4                           0.342653  \n",
       "CC5                           0.375318  \n",
       "Base time                    -0.224366  \n",
       "Post length                  -0.001237  \n",
       "Post Share Count              0.124638  \n",
       "Post Promotion Status              NaN  \n",
       "H Local                       0.012255  \n",
       "Post Sunday                   0.000587  \n",
       "Post Monday                  -0.000575  \n",
       "Post Tuesday                  0.004769  \n",
       "Post Wednesday                0.022337  \n",
       "Post Thursday                -0.009496  \n",
       "Post Friday                  -0.010470  \n",
       "Post Saturday                -0.008114  \n",
       "Base Sunday                  -0.008700  \n",
       "Base Monday                  -0.003049  \n",
       "Base Tuesday                  0.004611  \n",
       "Base Wednesday                0.023267  \n",
       "Base Thursday                -0.000927  \n",
       "Base Friday                  -0.007210  \n",
       "Base Saturday                -0.008304  \n",
       "Target Variable               1.000000  \n",
       "\n",
       "[54 rows x 54 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "corrData=trainData.corr('pearson')\n",
    "corrData"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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LvLb0obbs7GzDGmuUbu53/zasCe10iyRp74lThjUtG9SVZD8MQSo9PXVlQIO7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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f728f41e6a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import cm as cm\n",
    "import seaborn as sns\n",
    "\n",
    "corr = testData.corr()\n",
    "sns.set(style=\"white\")\n",
    "mask = np.zeros_like(corr, dtype=np.bool)\n",
    "mask[np.triu_indices_from(mask)] = True\n",
    "f, ax = plt.subplots(figsize=(11, 9))\n",
    "cmap = sns.diverging_palette(220, 10, as_cmap=True)\n",
    "sns.heatmap(corr, mask=mask, cmap=cmap, vmax=.3, center=0,\n",
    "            square=True, linewidths=.9, cbar_kws={\"shrink\": .5},\n",
    "            yticklabels=columns,xticklabels=columns)\n",
    "plt.yticks(rotation=0)\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "##### Triangulo superior\n",
    "\n",
    "Como a matriz de correlação gerada pelo dataframe é\n",
    "uma matriz espelho. Então será removido a parte inferior da matriz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Page Popularity/likes</th>\n",
       "      <th>Page Checkinsâ€™s</th>\n",
       "      <th>Page talking about</th>\n",
       "      <th>Page Category</th>\n",
       "      <th>Derived</th>\n",
       "      <th>Derived.1</th>\n",
       "      <th>Derived.2</th>\n",
       "      <th>Derived.3</th>\n",
       "      <th>Derived.4</th>\n",
       "      <th>Derived.5</th>\n",
       "      <th>...</th>\n",
       "      <th>Post Friday</th>\n",
       "      <th>Post Saturday</th>\n",
       "      <th>Base Sunday</th>\n",
       "      <th>Base Monday</th>\n",
       "      <th>Base Tuesday</th>\n",
       "      <th>Base Wednesday</th>\n",
       "      <th>Base Thursday</th>\n",
       "      <th>Base Friday</th>\n",
       "      <th>Base Saturday</th>\n",
       "      <th>Target Variable</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Page Popularity/likes</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.04509</td>\n",
       "      <td>0.622700</td>\n",
       "      <td>0.042003</td>\n",
       "      <td>0.046600</td>\n",
       "      <td>0.157902</td>\n",
       "      <td>0.165026</td>\n",
       "      <td>0.147022</td>\n",
       "      <td>0.173462</td>\n",
       "      <td>0.046528</td>\n",
       "      <td>...</td>\n",
       "      <td>0.009949</td>\n",
       "      <td>0.002727</td>\n",
       "      <td>0.003590</td>\n",
       "      <td>0.001345</td>\n",
       "      <td>0.002757</td>\n",
       "      <td>0.007740</td>\n",
       "      <td>0.001449</td>\n",
       "      <td>0.001514</td>\n",
       "      <td>0.005663</td>\n",
       "      <td>0.064537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Page Checkinsâ€™s</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.167326</td>\n",
       "      <td>0.060345</td>\n",
       "      <td>0.007382</td>\n",
       "      <td>0.158831</td>\n",
       "      <td>0.151055</td>\n",
       "      <td>0.129424</td>\n",
       "      <td>0.169037</td>\n",
       "      <td>0.006570</td>\n",
       "      <td>...</td>\n",
       "      <td>0.006156</td>\n",
       "      <td>0.008358</td>\n",
       "      <td>0.000764</td>\n",
       "      <td>0.000087</td>\n",
       "      <td>0.000197</td>\n",
       "      <td>0.001587</td>\n",
       "      <td>0.002071</td>\n",
       "      <td>0.001521</td>\n",
       "      <td>0.000045</td>\n",
       "      <td>0.035152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Page talking about</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.148164</td>\n",
       "      <td>0.118685</td>\n",
       "      <td>0.484869</td>\n",
       "      <td>0.515621</td>\n",
       "      <td>0.457754</td>\n",
       "      <td>0.539581</td>\n",
       "      <td>0.119260</td>\n",
       "      <td>...</td>\n",
       "      <td>0.027714</td>\n",
       "      <td>0.007980</td>\n",
       "      <td>0.014358</td>\n",
       "      <td>0.006598</td>\n",
       "      <td>0.004262</td>\n",
       "      <td>0.023153</td>\n",
       "      <td>0.022747</td>\n",
       "      <td>0.009419</td>\n",
       "      <td>0.021087</td>\n",
       "      <td>0.194768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Page Category</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.026591</td>\n",
       "      <td>0.319812</td>\n",
       "      <td>0.233917</td>\n",
       "      <td>0.193385</td>\n",
       "      <td>0.291309</td>\n",
       "      <td>0.023868</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002795</td>\n",
       "      <td>0.002126</td>\n",
       "      <td>0.002386</td>\n",
       "      <td>0.004504</td>\n",
       "      <td>0.003690</td>\n",
       "      <td>0.001920</td>\n",
       "      <td>0.001325</td>\n",
       "      <td>0.003916</td>\n",
       "      <td>0.005049</td>\n",
       "      <td>0.069001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Derived</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.072641</td>\n",
       "      <td>0.335366</td>\n",
       "      <td>0.409813</td>\n",
       "      <td>0.130532</td>\n",
       "      <td>0.930690</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003884</td>\n",
       "      <td>0.005278</td>\n",
       "      <td>0.000426</td>\n",
       "      <td>0.006805</td>\n",
       "      <td>0.001074</td>\n",
       "      <td>0.002660</td>\n",
       "      <td>0.000129</td>\n",
       "      <td>0.006651</td>\n",
       "      <td>0.003232</td>\n",
       "      <td>0.138996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Derived.1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.715212</td>\n",
       "      <td>0.582076</td>\n",
       "      <td>0.896815</td>\n",
       "      <td>0.079879</td>\n",
       "      <td>...</td>\n",
       "      <td>0.010944</td>\n",
       "      <td>0.009929</td>\n",
       "      <td>0.005159</td>\n",
       "      <td>0.005468</td>\n",
       "      <td>0.001918</td>\n",
       "      <td>0.011149</td>\n",
       "      <td>0.007315</td>\n",
       "      <td>0.001381</td>\n",
       "      <td>0.008924</td>\n",
       "      <td>0.238743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Derived.2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.963216</td>\n",
       "      <td>0.903036</td>\n",
       "      <td>0.340154</td>\n",
       "      <td>...</td>\n",
       "      <td>0.017454</td>\n",
       "      <td>0.004056</td>\n",
       "      <td>0.008361</td>\n",
       "      <td>0.005993</td>\n",
       "      <td>0.003581</td>\n",
       "      <td>0.012472</td>\n",
       "      <td>0.009093</td>\n",
       "      <td>0.003123</td>\n",
       "      <td>0.008304</td>\n",
       "      <td>0.359536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Derived.3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.773769</td>\n",
       "      <td>0.412988</td>\n",
       "      <td>...</td>\n",
       "      <td>0.015895</td>\n",
       "      <td>0.001976</td>\n",
       "      <td>0.007473</td>\n",
       "      <td>0.005297</td>\n",
       "      <td>0.003135</td>\n",
       "      <td>0.010544</td>\n",
       "      <td>0.008291</td>\n",
       "      <td>0.002518</td>\n",
       "      <td>0.007247</td>\n",
       "      <td>0.349452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Derived.4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.146383</td>\n",
       "      <td>...</td>\n",
       "      <td>0.016705</td>\n",
       "      <td>0.009671</td>\n",
       "      <td>0.009642</td>\n",
       "      <td>0.006710</td>\n",
       "      <td>0.003410</td>\n",
       "      <td>0.014783</td>\n",
       "      <td>0.011331</td>\n",
       "      <td>0.003686</td>\n",
       "      <td>0.010263</td>\n",
       "      <td>0.316593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Derived.5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.004534</td>\n",
       "      <td>0.003742</td>\n",
       "      <td>0.002532</td>\n",
       "      <td>0.005779</td>\n",
       "      <td>0.001494</td>\n",
       "      <td>0.003371</td>\n",
       "      <td>0.003135</td>\n",
       "      <td>0.003903</td>\n",
       "      <td>0.003902</td>\n",
       "      <td>0.153853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Derived.6</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.012854</td>\n",
       "      <td>0.008538</td>\n",
       "      <td>0.005896</td>\n",
       "      <td>0.004755</td>\n",
       "      <td>0.002065</td>\n",
       "      <td>0.011020</td>\n",
       "      <td>0.008132</td>\n",
       "      <td>0.001753</td>\n",
       "      <td>0.009376</td>\n",
       "      <td>0.229139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Derived.7</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.020600</td>\n",
       "      <td>0.007184</td>\n",
       "      <td>0.011552</td>\n",
       "      <td>0.007818</td>\n",
       "      <td>0.002882</td>\n",
       "      <td>0.015089</td>\n",
       "      <td>0.015785</td>\n",
       "      <td>0.003765</td>\n",
       "      <td>0.011614</td>\n",
       "      <td>0.368003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Derived.8</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.018013</td>\n",
       "      <td>0.009413</td>\n",
       "      <td>0.010666</td>\n",
       "      <td>0.010237</td>\n",
       "      <td>0.000021</td>\n",
       "      <td>0.013393</td>\n",
       "      <td>0.019318</td>\n",
       "      <td>0.001115</td>\n",
       "      <td>0.011880</td>\n",
       "      <td>0.339229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Derived.9</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.019952</td>\n",
       "      <td>0.008896</td>\n",
       "      <td>0.011017</td>\n",
       "      <td>0.006608</td>\n",
       "      <td>0.003306</td>\n",
       "      <td>0.015697</td>\n",
       "      <td>0.014239</td>\n",
       "      <td>0.004683</td>\n",
       "      <td>0.011834</td>\n",
       "      <td>0.329067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Derived.10</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002758</td>\n",
       "      <td>0.002655</td>\n",
       "      <td>0.004702</td>\n",
       "      <td>0.002631</td>\n",
       "      <td>0.002653</td>\n",
       "      <td>0.002767</td>\n",
       "      <td>0.002857</td>\n",
       "      <td>0.002821</td>\n",
       "      <td>0.003450</td>\n",
       "      <td>0.004411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Derived.11</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.014625</td>\n",
       "      <td>0.007281</td>\n",
       "      <td>0.004922</td>\n",
       "      <td>0.003350</td>\n",
       "      <td>0.004349</td>\n",
       "      <td>0.011342</td>\n",
       "      <td>0.006487</td>\n",
       "      <td>0.003889</td>\n",
       "      <td>0.010433</td>\n",
       "      <td>0.234408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Derived.12</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.016003</td>\n",
       "      <td>0.004045</td>\n",
       "      <td>0.008096</td>\n",
       "      <td>0.005614</td>\n",
       "      <td>0.003836</td>\n",
       "      <td>0.012956</td>\n",
       "      <td>0.007867</td>\n",
       "      <td>0.003796</td>\n",
       "      <td>0.007731</td>\n",
       "      <td>0.356985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Derived.13</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.007122</td>\n",
       "      <td>0.001000</td>\n",
       "      <td>0.001537</td>\n",
       "      <td>0.006043</td>\n",
       "      <td>0.002129</td>\n",
       "      <td>0.004088</td>\n",
       "      <td>0.001061</td>\n",
       "      <td>0.001298</td>\n",
       "      <td>0.001212</td>\n",
       "      <td>0.303185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Derived.14</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.017409</td>\n",
       "      <td>0.007758</td>\n",
       "      <td>0.009647</td>\n",
       "      <td>0.005579</td>\n",
       "      <td>0.004158</td>\n",
       "      <td>0.015204</td>\n",
       "      <td>0.010507</td>\n",
       "      <td>0.004689</td>\n",
       "      <td>0.010663</td>\n",
       "      <td>0.333375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Derived.15</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003876</td>\n",
       "      <td>0.005250</td>\n",
       "      <td>0.000565</td>\n",
       "      <td>0.006795</td>\n",
       "      <td>0.001128</td>\n",
       "      <td>0.002722</td>\n",
       "      <td>0.000076</td>\n",
       "      <td>0.006672</td>\n",
       "      <td>0.003296</td>\n",
       "      <td>0.139476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Derived.16</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.011426</td>\n",
       "      <td>0.009229</td>\n",
       "      <td>0.004581</td>\n",
       "      <td>0.005567</td>\n",
       "      <td>0.001924</td>\n",
       "      <td>0.010617</td>\n",
       "      <td>0.007372</td>\n",
       "      <td>0.001547</td>\n",
       "      <td>0.008757</td>\n",
       "      <td>0.232497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Derived.17</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.017671</td>\n",
       "      <td>0.003731</td>\n",
       "      <td>0.008366</td>\n",
       "      <td>0.005721</td>\n",
       "      <td>0.003644</td>\n",
       "      <td>0.012558</td>\n",
       "      <td>0.009093</td>\n",
       "      <td>0.003419</td>\n",
       "      <td>0.008415</td>\n",
       "      <td>0.356769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Derived.18</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.016034</td>\n",
       "      <td>0.001593</td>\n",
       "      <td>0.007461</td>\n",
       "      <td>0.004895</td>\n",
       "      <td>0.003345</td>\n",
       "      <td>0.010515</td>\n",
       "      <td>0.007990</td>\n",
       "      <td>0.002849</td>\n",
       "      <td>0.007186</td>\n",
       "      <td>0.348033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Derived.19</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.017000</td>\n",
       "      <td>0.009228</td>\n",
       "      <td>0.009464</td>\n",
       "      <td>0.006440</td>\n",
       "      <td>0.003460</td>\n",
       "      <td>0.014666</td>\n",
       "      <td>0.011322</td>\n",
       "      <td>0.004030</td>\n",
       "      <td>0.010278</td>\n",
       "      <td>0.309988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Derived.20</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.015054</td>\n",
       "      <td>0.004075</td>\n",
       "      <td>0.003488</td>\n",
       "      <td>0.001795</td>\n",
       "      <td>0.004669</td>\n",
       "      <td>0.009675</td>\n",
       "      <td>0.004094</td>\n",
       "      <td>0.004140</td>\n",
       "      <td>0.009269</td>\n",
       "      <td>0.221228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Derived.21</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.013894</td>\n",
       "      <td>0.008706</td>\n",
       "      <td>0.006585</td>\n",
       "      <td>0.005062</td>\n",
       "      <td>0.001843</td>\n",
       "      <td>0.011419</td>\n",
       "      <td>0.008715</td>\n",
       "      <td>0.001495</td>\n",
       "      <td>0.009442</td>\n",
       "      <td>0.230101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Derived.22</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.023745</td>\n",
       "      <td>0.013284</td>\n",
       "      <td>0.016175</td>\n",
       "      <td>0.010505</td>\n",
       "      <td>0.001881</td>\n",
       "      <td>0.013374</td>\n",
       "      <td>0.032513</td>\n",
       "      <td>0.001504</td>\n",
       "      <td>0.017594</td>\n",
       "      <td>0.192558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Derived.23</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.006034</td>\n",
       "      <td>0.009531</td>\n",
       "      <td>0.008434</td>\n",
       "      <td>0.006209</td>\n",
       "      <td>0.007180</td>\n",
       "      <td>0.007437</td>\n",
       "      <td>0.024605</td>\n",
       "      <td>0.002001</td>\n",
       "      <td>0.009541</td>\n",
       "      <td>0.065052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Derived.24</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.019810</td>\n",
       "      <td>0.006678</td>\n",
       "      <td>0.010354</td>\n",
       "      <td>0.005752</td>\n",
       "      <td>0.003938</td>\n",
       "      <td>0.015281</td>\n",
       "      <td>0.012387</td>\n",
       "      <td>0.005082</td>\n",
       "      <td>0.011207</td>\n",
       "      <td>0.339319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.015047</td>\n",
       "      <td>0.011126</td>\n",
       "      <td>0.007069</td>\n",
       "      <td>0.005198</td>\n",
       "      <td>0.004127</td>\n",
       "      <td>0.007898</td>\n",
       "      <td>0.006734</td>\n",
       "      <td>0.001384</td>\n",
       "      <td>0.005566</td>\n",
       "      <td>0.327086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.016794</td>\n",
       "      <td>0.009926</td>\n",
       "      <td>0.012140</td>\n",
       "      <td>0.003959</td>\n",
       "      <td>0.005939</td>\n",
       "      <td>0.012159</td>\n",
       "      <td>0.015002</td>\n",
       "      <td>0.005331</td>\n",
       "      <td>0.012452</td>\n",
       "      <td>0.545260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.008482</td>\n",
       "      <td>0.010484</td>\n",
       "      <td>0.009179</td>\n",
       "      <td>0.006930</td>\n",
       "      <td>0.001743</td>\n",
       "      <td>0.001868</td>\n",
       "      <td>0.006307</td>\n",
       "      <td>0.006012</td>\n",
       "      <td>0.000318</td>\n",
       "      <td>0.073260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.015473</td>\n",
       "      <td>0.011370</td>\n",
       "      <td>0.007632</td>\n",
       "      <td>0.005388</td>\n",
       "      <td>0.004591</td>\n",
       "      <td>0.008509</td>\n",
       "      <td>0.006902</td>\n",
       "      <td>0.001715</td>\n",
       "      <td>0.005739</td>\n",
       "      <td>0.342653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.006688</td>\n",
       "      <td>0.000327</td>\n",
       "      <td>0.002450</td>\n",
       "      <td>0.002281</td>\n",
       "      <td>0.003348</td>\n",
       "      <td>0.008186</td>\n",
       "      <td>0.006968</td>\n",
       "      <td>0.008934</td>\n",
       "      <td>0.009631</td>\n",
       "      <td>0.375318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Base time</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.009981</td>\n",
       "      <td>0.008838</td>\n",
       "      <td>0.032117</td>\n",
       "      <td>0.025795</td>\n",
       "      <td>0.015311</td>\n",
       "      <td>0.032247</td>\n",
       "      <td>0.011179</td>\n",
       "      <td>0.000480</td>\n",
       "      <td>0.002767</td>\n",
       "      <td>0.224366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Post length</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000619</td>\n",
       "      <td>0.005787</td>\n",
       "      <td>0.000739</td>\n",
       "      <td>0.003471</td>\n",
       "      <td>0.003817</td>\n",
       "      <td>0.000731</td>\n",
       "      <td>0.003901</td>\n",
       "      <td>0.003115</td>\n",
       "      <td>0.001444</td>\n",
       "      <td>0.001237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Post Share Count</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005783</td>\n",
       "      <td>0.007140</td>\n",
       "      <td>0.002607</td>\n",
       "      <td>0.002415</td>\n",
       "      <td>0.003515</td>\n",
       "      <td>0.003362</td>\n",
       "      <td>0.002618</td>\n",
       "      <td>0.000031</td>\n",
       "      <td>0.000762</td>\n",
       "      <td>0.124638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Post Promotion Status</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H Local</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.049085</td>\n",
       "      <td>0.047251</td>\n",
       "      <td>0.047737</td>\n",
       "      <td>0.046825</td>\n",
       "      <td>0.047217</td>\n",
       "      <td>0.049258</td>\n",
       "      <td>0.098329</td>\n",
       "      <td>0.135113</td>\n",
       "      <td>0.048326</td>\n",
       "      <td>0.012255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Post Sunday</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.154605</td>\n",
       "      <td>0.148827</td>\n",
       "      <td>0.001014</td>\n",
       "      <td>0.213461</td>\n",
       "      <td>0.209278</td>\n",
       "      <td>0.056261</td>\n",
       "      <td>0.160193</td>\n",
       "      <td>0.153387</td>\n",
       "      <td>0.152214</td>\n",
       "      <td>0.000587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Post Monday</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.169302</td>\n",
       "      <td>0.162975</td>\n",
       "      <td>0.164651</td>\n",
       "      <td>0.002997</td>\n",
       "      <td>0.233284</td>\n",
       "      <td>0.213008</td>\n",
       "      <td>0.048737</td>\n",
       "      <td>0.167968</td>\n",
       "      <td>0.166684</td>\n",
       "      <td>0.000575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Post Tuesday</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.173474</td>\n",
       "      <td>0.166991</td>\n",
       "      <td>0.168709</td>\n",
       "      <td>0.165487</td>\n",
       "      <td>0.010409</td>\n",
       "      <td>0.224407</td>\n",
       "      <td>0.206916</td>\n",
       "      <td>0.047329</td>\n",
       "      <td>0.170791</td>\n",
       "      <td>0.004769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Post Wednesday</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.178804</td>\n",
       "      <td>0.172122</td>\n",
       "      <td>0.173892</td>\n",
       "      <td>0.170571</td>\n",
       "      <td>0.172001</td>\n",
       "      <td>0.011975</td>\n",
       "      <td>0.246820</td>\n",
       "      <td>0.203664</td>\n",
       "      <td>0.035480</td>\n",
       "      <td>0.022337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Post Thursday</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.169856</td>\n",
       "      <td>0.163508</td>\n",
       "      <td>0.076766</td>\n",
       "      <td>0.162035</td>\n",
       "      <td>0.163394</td>\n",
       "      <td>0.170454</td>\n",
       "      <td>0.019334</td>\n",
       "      <td>0.215791</td>\n",
       "      <td>0.218821</td>\n",
       "      <td>0.009496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Post Friday</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.164847</td>\n",
       "      <td>0.226752</td>\n",
       "      <td>0.079712</td>\n",
       "      <td>0.164732</td>\n",
       "      <td>0.171850</td>\n",
       "      <td>0.177436</td>\n",
       "      <td>0.004109</td>\n",
       "      <td>0.221572</td>\n",
       "      <td>0.010470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Post Saturday</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.214537</td>\n",
       "      <td>0.229300</td>\n",
       "      <td>0.070235</td>\n",
       "      <td>0.165428</td>\n",
       "      <td>0.170806</td>\n",
       "      <td>0.163548</td>\n",
       "      <td>0.003327</td>\n",
       "      <td>0.008114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Base Sunday</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.158875</td>\n",
       "      <td>0.160207</td>\n",
       "      <td>0.167129</td>\n",
       "      <td>0.172562</td>\n",
       "      <td>0.165230</td>\n",
       "      <td>0.163967</td>\n",
       "      <td>0.008700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Base Monday</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.157147</td>\n",
       "      <td>0.163937</td>\n",
       "      <td>0.169267</td>\n",
       "      <td>0.162075</td>\n",
       "      <td>0.160836</td>\n",
       "      <td>0.003049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Base Tuesday</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.165312</td>\n",
       "      <td>0.170686</td>\n",
       "      <td>0.163433</td>\n",
       "      <td>0.162184</td>\n",
       "      <td>0.004611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Base Wednesday</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.178061</td>\n",
       "      <td>0.170495</td>\n",
       "      <td>0.169192</td>\n",
       "      <td>0.023267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Base Thursday</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.176038</td>\n",
       "      <td>0.174692</td>\n",
       "      <td>0.000927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Base Friday</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.167270</td>\n",
       "      <td>0.007210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Base Saturday</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.008304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Target Variable</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>54 rows × 54 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Page Popularity/likes  Page Checkinsâ€™s  \\\n",
       "Page Popularity/likes                    NaN            0.04509   \n",
       "Page Checkinsâ€™s                        NaN                NaN   \n",
       "Page talking about                       NaN                NaN   \n",
       "Page Category                            NaN                NaN   \n",
       "Derived                                  NaN                NaN   \n",
       "Derived.1                                NaN                NaN   \n",
       "Derived.2                                NaN                NaN   \n",
       "Derived.3                                NaN                NaN   \n",
       "Derived.4                                NaN                NaN   \n",
       "Derived.5                                NaN                NaN   \n",
       "Derived.6                                NaN                NaN   \n",
       "Derived.7                                NaN                NaN   \n",
       "Derived.8                                NaN                NaN   \n",
       "Derived.9                                NaN                NaN   \n",
       "Derived.10                               NaN                NaN   \n",
       "Derived.11                               NaN                NaN   \n",
       "Derived.12                               NaN                NaN   \n",
       "Derived.13                               NaN                NaN   \n",
       "Derived.14                               NaN                NaN   \n",
       "Derived.15                               NaN                NaN   \n",
       "Derived.16                               NaN                NaN   \n",
       "Derived.17                               NaN                NaN   \n",
       "Derived.18                               NaN                NaN   \n",
       "Derived.19                               NaN                NaN   \n",
       "Derived.20                               NaN                NaN   \n",
       "Derived.21                               NaN                NaN   \n",
       "Derived.22                               NaN                NaN   \n",
       "Derived.23                               NaN                NaN   \n",
       "Derived.24                               NaN                NaN   \n",
       "CC1                                      NaN                NaN   \n",
       "CC2                                      NaN                NaN   \n",
       "CC3                                      NaN                NaN   \n",
       "CC4                                      NaN                NaN   \n",
       "CC5                                      NaN                NaN   \n",
       "Base time                                NaN                NaN   \n",
       "Post length                              NaN                NaN   \n",
       "Post Share Count                         NaN                NaN   \n",
       "Post Promotion Status                    NaN                NaN   \n",
       "H Local                                  NaN                NaN   \n",
       "Post Sunday                              NaN                NaN   \n",
       "Post Monday                              NaN                NaN   \n",
       "Post Tuesday                             NaN                NaN   \n",
       "Post Wednesday                           NaN                NaN   \n",
       "Post Thursday                            NaN                NaN   \n",
       "Post Friday                              NaN                NaN   \n",
       "Post Saturday                            NaN                NaN   \n",
       "Base Sunday                              NaN                NaN   \n",
       "Base Monday                              NaN                NaN   \n",
       "Base Tuesday                             NaN                NaN   \n",
       "Base Wednesday                           NaN                NaN   \n",
       "Base Thursday                            NaN                NaN   \n",
       "Base Friday                              NaN                NaN   \n",
       "Base Saturday                            NaN                NaN   \n",
       "Target Variable                          NaN                NaN   \n",
       "\n",
       "                       Page talking about  Page Category   Derived  Derived.1  \\\n",
       "Page Popularity/likes            0.622700       0.042003  0.046600   0.157902   \n",
       "Page Checkinsâ€™s                0.167326       0.060345  0.007382   0.158831   \n",
       "Page talking about                    NaN       0.148164  0.118685   0.484869   \n",
       "Page Category                         NaN            NaN  0.026591   0.319812   \n",
       "Derived                               NaN            NaN       NaN   0.072641   \n",
       "Derived.1                             NaN            NaN       NaN        NaN   \n",
       "Derived.2                             NaN            NaN       NaN        NaN   \n",
       "Derived.3                             NaN            NaN       NaN        NaN   \n",
       "Derived.4                             NaN            NaN       NaN        NaN   \n",
       "Derived.5                             NaN            NaN       NaN        NaN   \n",
       "Derived.6                             NaN            NaN       NaN        NaN   \n",
       "Derived.7                             NaN            NaN       NaN        NaN   \n",
       "Derived.8                             NaN            NaN       NaN        NaN   \n",
       "Derived.9                             NaN            NaN       NaN        NaN   \n",
       "Derived.10                            NaN            NaN       NaN        NaN   \n",
       "Derived.11                            NaN            NaN       NaN        NaN   \n",
       "Derived.12                            NaN            NaN       NaN        NaN   \n",
       "Derived.13                            NaN            NaN       NaN        NaN   \n",
       "Derived.14                            NaN            NaN       NaN        NaN   \n",
       "Derived.15                            NaN            NaN       NaN        NaN   \n",
       "Derived.16                            NaN            NaN       NaN        NaN   \n",
       "Derived.17                            NaN            NaN       NaN        NaN   \n",
       "Derived.18                            NaN            NaN       NaN        NaN   \n",
       "Derived.19                            NaN            NaN       NaN        NaN   \n",
       "Derived.20                            NaN            NaN       NaN        NaN   \n",
       "Derived.21                            NaN            NaN       NaN        NaN   \n",
       "Derived.22                            NaN            NaN       NaN        NaN   \n",
       "Derived.23                            NaN            NaN       NaN        NaN   \n",
       "Derived.24                            NaN            NaN       NaN        NaN   \n",
       "CC1                                   NaN            NaN       NaN        NaN   \n",
       "CC2                                   NaN            NaN       NaN        NaN   \n",
       "CC3                                   NaN            NaN       NaN        NaN   \n",
       "CC4                                   NaN            NaN       NaN        NaN   \n",
       "CC5                                   NaN            NaN       NaN        NaN   \n",
       "Base time                             NaN            NaN       NaN        NaN   \n",
       "Post length                           NaN            NaN       NaN        NaN   \n",
       "Post Share Count                      NaN            NaN       NaN        NaN   \n",
       "Post Promotion Status                 NaN            NaN       NaN        NaN   \n",
       "H Local                               NaN            NaN       NaN        NaN   \n",
       "Post Sunday                           NaN            NaN       NaN        NaN   \n",
       "Post Monday                           NaN            NaN       NaN        NaN   \n",
       "Post Tuesday                          NaN            NaN       NaN        NaN   \n",
       "Post Wednesday                        NaN            NaN       NaN        NaN   \n",
       "Post Thursday                         NaN            NaN       NaN        NaN   \n",
       "Post Friday                           NaN            NaN       NaN        NaN   \n",
       "Post Saturday                         NaN            NaN       NaN        NaN   \n",
       "Base Sunday                           NaN            NaN       NaN        NaN   \n",
       "Base Monday                           NaN            NaN       NaN        NaN   \n",
       "Base Tuesday                          NaN            NaN       NaN        NaN   \n",
       "Base Wednesday                        NaN            NaN       NaN        NaN   \n",
       "Base Thursday                         NaN            NaN       NaN        NaN   \n",
       "Base Friday                           NaN            NaN       NaN        NaN   \n",
       "Base Saturday                         NaN            NaN       NaN        NaN   \n",
       "Target Variable                       NaN            NaN       NaN        NaN   \n",
       "\n",
       "                       Derived.2  Derived.3  Derived.4  Derived.5  \\\n",
       "Page Popularity/likes   0.165026   0.147022   0.173462   0.046528   \n",
       "Page Checkinsâ€™s       0.151055   0.129424   0.169037   0.006570   \n",
       "Page talking about      0.515621   0.457754   0.539581   0.119260   \n",
       "Page Category           0.233917   0.193385   0.291309   0.023868   \n",
       "Derived                 0.335366   0.409813   0.130532   0.930690   \n",
       "Derived.1               0.715212   0.582076   0.896815   0.079879   \n",
       "Derived.2                    NaN   0.963216   0.903036   0.340154   \n",
       "Derived.3                    NaN        NaN   0.773769   0.412988   \n",
       "Derived.4                    NaN        NaN        NaN   0.146383   \n",
       "Derived.5                    NaN        NaN        NaN        NaN   \n",
       "Derived.6                    NaN        NaN        NaN        NaN   \n",
       "Derived.7                    NaN        NaN        NaN        NaN   \n",
       "Derived.8                    NaN        NaN        NaN        NaN   \n",
       "Derived.9                    NaN        NaN        NaN        NaN   \n",
       "Derived.10                   NaN        NaN        NaN        NaN   \n",
       "Derived.11                   NaN        NaN        NaN        NaN   \n",
       "Derived.12                   NaN        NaN        NaN        NaN   \n",
       "Derived.13                   NaN        NaN        NaN        NaN   \n",
       "Derived.14                   NaN        NaN        NaN        NaN   \n",
       "Derived.15                   NaN        NaN        NaN        NaN   \n",
       "Derived.16                   NaN        NaN        NaN        NaN   \n",
       "Derived.17                   NaN        NaN        NaN        NaN   \n",
       "Derived.18                   NaN        NaN        NaN        NaN   \n",
       "Derived.19                   NaN        NaN        NaN        NaN   \n",
       "Derived.20                   NaN        NaN        NaN        NaN   \n",
       "Derived.21                   NaN        NaN        NaN        NaN   \n",
       "Derived.22                   NaN        NaN        NaN        NaN   \n",
       "Derived.23                   NaN        NaN        NaN        NaN   \n",
       "Derived.24                   NaN        NaN        NaN        NaN   \n",
       "CC1                          NaN        NaN        NaN        NaN   \n",
       "CC2                          NaN        NaN        NaN        NaN   \n",
       "CC3                          NaN        NaN        NaN        NaN   \n",
       "CC4                          NaN        NaN        NaN        NaN   \n",
       "CC5                          NaN        NaN        NaN        NaN   \n",
       "Base time                    NaN        NaN        NaN        NaN   \n",
       "Post length                  NaN        NaN        NaN        NaN   \n",
       "Post Share Count             NaN        NaN        NaN        NaN   \n",
       "Post Promotion Status        NaN        NaN        NaN        NaN   \n",
       "H Local                      NaN        NaN        NaN        NaN   \n",
       "Post Sunday                  NaN        NaN        NaN        NaN   \n",
       "Post Monday                  NaN        NaN        NaN        NaN   \n",
       "Post Tuesday                 NaN        NaN        NaN        NaN   \n",
       "Post Wednesday               NaN        NaN        NaN        NaN   \n",
       "Post Thursday                NaN        NaN        NaN        NaN   \n",
       "Post Friday                  NaN        NaN        NaN        NaN   \n",
       "Post Saturday                NaN        NaN        NaN        NaN   \n",
       "Base Sunday                  NaN        NaN        NaN        NaN   \n",
       "Base Monday                  NaN        NaN        NaN        NaN   \n",
       "Base Tuesday                 NaN        NaN        NaN        NaN   \n",
       "Base Wednesday               NaN        NaN        NaN        NaN   \n",
       "Base Thursday                NaN        NaN        NaN        NaN   \n",
       "Base Friday                  NaN        NaN        NaN        NaN   \n",
       "Base Saturday                NaN        NaN        NaN        NaN   \n",
       "Target Variable              NaN        NaN        NaN        NaN   \n",
       "\n",
       "                            ...         Post Friday  Post Saturday  \\\n",
       "Page Popularity/likes       ...            0.009949       0.002727   \n",
       "Page Checkinsâ€™s           ...            0.006156       0.008358   \n",
       "Page talking about          ...            0.027714       0.007980   \n",
       "Page Category               ...            0.002795       0.002126   \n",
       "Derived                     ...            0.003884       0.005278   \n",
       "Derived.1                   ...            0.010944       0.009929   \n",
       "Derived.2                   ...            0.017454       0.004056   \n",
       "Derived.3                   ...            0.015895       0.001976   \n",
       "Derived.4                   ...            0.016705       0.009671   \n",
       "Derived.5                   ...            0.004534       0.003742   \n",
       "Derived.6                   ...            0.012854       0.008538   \n",
       "Derived.7                   ...            0.020600       0.007184   \n",
       "Derived.8                   ...            0.018013       0.009413   \n",
       "Derived.9                   ...            0.019952       0.008896   \n",
       "Derived.10                  ...            0.002758       0.002655   \n",
       "Derived.11                  ...            0.014625       0.007281   \n",
       "Derived.12                  ...            0.016003       0.004045   \n",
       "Derived.13                  ...            0.007122       0.001000   \n",
       "Derived.14                  ...            0.017409       0.007758   \n",
       "Derived.15                  ...            0.003876       0.005250   \n",
       "Derived.16                  ...            0.011426       0.009229   \n",
       "Derived.17                  ...            0.017671       0.003731   \n",
       "Derived.18                  ...            0.016034       0.001593   \n",
       "Derived.19                  ...            0.017000       0.009228   \n",
       "Derived.20                  ...            0.015054       0.004075   \n",
       "Derived.21                  ...            0.013894       0.008706   \n",
       "Derived.22                  ...            0.023745       0.013284   \n",
       "Derived.23                  ...            0.006034       0.009531   \n",
       "Derived.24                  ...            0.019810       0.006678   \n",
       "CC1                         ...            0.015047       0.011126   \n",
       "CC2                         ...            0.016794       0.009926   \n",
       "CC3                         ...            0.008482       0.010484   \n",
       "CC4                         ...            0.015473       0.011370   \n",
       "CC5                         ...            0.006688       0.000327   \n",
       "Base time                   ...            0.009981       0.008838   \n",
       "Post length                 ...            0.000619       0.005787   \n",
       "Post Share Count            ...            0.005783       0.007140   \n",
       "Post Promotion Status       ...                 NaN            NaN   \n",
       "H Local                     ...            0.049085       0.047251   \n",
       "Post Sunday                 ...            0.154605       0.148827   \n",
       "Post Monday                 ...            0.169302       0.162975   \n",
       "Post Tuesday                ...            0.173474       0.166991   \n",
       "Post Wednesday              ...            0.178804       0.172122   \n",
       "Post Thursday               ...            0.169856       0.163508   \n",
       "Post Friday                 ...                 NaN       0.164847   \n",
       "Post Saturday               ...                 NaN            NaN   \n",
       "Base Sunday                 ...                 NaN            NaN   \n",
       "Base Monday                 ...                 NaN            NaN   \n",
       "Base Tuesday                ...                 NaN            NaN   \n",
       "Base Wednesday              ...                 NaN            NaN   \n",
       "Base Thursday               ...                 NaN            NaN   \n",
       "Base Friday                 ...                 NaN            NaN   \n",
       "Base Saturday               ...                 NaN            NaN   \n",
       "Target Variable             ...                 NaN            NaN   \n",
       "\n",
       "                       Base Sunday  Base Monday  Base Tuesday  Base Wednesday  \\\n",
       "Page Popularity/likes     0.003590     0.001345      0.002757        0.007740   \n",
       "Page Checkinsâ€™s         0.000764     0.000087      0.000197        0.001587   \n",
       "Page talking about        0.014358     0.006598      0.004262        0.023153   \n",
       "Page Category             0.002386     0.004504      0.003690        0.001920   \n",
       "Derived                   0.000426     0.006805      0.001074        0.002660   \n",
       "Derived.1                 0.005159     0.005468      0.001918        0.011149   \n",
       "Derived.2                 0.008361     0.005993      0.003581        0.012472   \n",
       "Derived.3                 0.007473     0.005297      0.003135        0.010544   \n",
       "Derived.4                 0.009642     0.006710      0.003410        0.014783   \n",
       "Derived.5                 0.002532     0.005779      0.001494        0.003371   \n",
       "Derived.6                 0.005896     0.004755      0.002065        0.011020   \n",
       "Derived.7                 0.011552     0.007818      0.002882        0.015089   \n",
       "Derived.8                 0.010666     0.010237      0.000021        0.013393   \n",
       "Derived.9                 0.011017     0.006608      0.003306        0.015697   \n",
       "Derived.10                0.004702     0.002631      0.002653        0.002767   \n",
       "Derived.11                0.004922     0.003350      0.004349        0.011342   \n",
       "Derived.12                0.008096     0.005614      0.003836        0.012956   \n",
       "Derived.13                0.001537     0.006043      0.002129        0.004088   \n",
       "Derived.14                0.009647     0.005579      0.004158        0.015204   \n",
       "Derived.15                0.000565     0.006795      0.001128        0.002722   \n",
       "Derived.16                0.004581     0.005567      0.001924        0.010617   \n",
       "Derived.17                0.008366     0.005721      0.003644        0.012558   \n",
       "Derived.18                0.007461     0.004895      0.003345        0.010515   \n",
       "Derived.19                0.009464     0.006440      0.003460        0.014666   \n",
       "Derived.20                0.003488     0.001795      0.004669        0.009675   \n",
       "Derived.21                0.006585     0.005062      0.001843        0.011419   \n",
       "Derived.22                0.016175     0.010505      0.001881        0.013374   \n",
       "Derived.23                0.008434     0.006209      0.007180        0.007437   \n",
       "Derived.24                0.010354     0.005752      0.003938        0.015281   \n",
       "CC1                       0.007069     0.005198      0.004127        0.007898   \n",
       "CC2                       0.012140     0.003959      0.005939        0.012159   \n",
       "CC3                       0.009179     0.006930      0.001743        0.001868   \n",
       "CC4                       0.007632     0.005388      0.004591        0.008509   \n",
       "CC5                       0.002450     0.002281      0.003348        0.008186   \n",
       "Base time                 0.032117     0.025795      0.015311        0.032247   \n",
       "Post length               0.000739     0.003471      0.003817        0.000731   \n",
       "Post Share Count          0.002607     0.002415      0.003515        0.003362   \n",
       "Post Promotion Status          NaN          NaN           NaN             NaN   \n",
       "H Local                   0.047737     0.046825      0.047217        0.049258   \n",
       "Post Sunday               0.001014     0.213461      0.209278        0.056261   \n",
       "Post Monday               0.164651     0.002997      0.233284        0.213008   \n",
       "Post Tuesday              0.168709     0.165487      0.010409        0.224407   \n",
       "Post Wednesday            0.173892     0.170571      0.172001        0.011975   \n",
       "Post Thursday             0.076766     0.162035      0.163394        0.170454   \n",
       "Post Friday               0.226752     0.079712      0.164732        0.171850   \n",
       "Post Saturday             0.214537     0.229300      0.070235        0.165428   \n",
       "Base Sunday                    NaN     0.158875      0.160207        0.167129   \n",
       "Base Monday                    NaN          NaN      0.157147        0.163937   \n",
       "Base Tuesday                   NaN          NaN           NaN        0.165312   \n",
       "Base Wednesday                 NaN          NaN           NaN             NaN   \n",
       "Base Thursday                  NaN          NaN           NaN             NaN   \n",
       "Base Friday                    NaN          NaN           NaN             NaN   \n",
       "Base Saturday                  NaN          NaN           NaN             NaN   \n",
       "Target Variable                NaN          NaN           NaN             NaN   \n",
       "\n",
       "                       Base Thursday  Base Friday  Base Saturday  \\\n",
       "Page Popularity/likes       0.001449     0.001514       0.005663   \n",
       "Page Checkinsâ€™s           0.002071     0.001521       0.000045   \n",
       "Page talking about          0.022747     0.009419       0.021087   \n",
       "Page Category               0.001325     0.003916       0.005049   \n",
       "Derived                     0.000129     0.006651       0.003232   \n",
       "Derived.1                   0.007315     0.001381       0.008924   \n",
       "Derived.2                   0.009093     0.003123       0.008304   \n",
       "Derived.3                   0.008291     0.002518       0.007247   \n",
       "Derived.4                   0.011331     0.003686       0.010263   \n",
       "Derived.5                   0.003135     0.003903       0.003902   \n",
       "Derived.6                   0.008132     0.001753       0.009376   \n",
       "Derived.7                   0.015785     0.003765       0.011614   \n",
       "Derived.8                   0.019318     0.001115       0.011880   \n",
       "Derived.9                   0.014239     0.004683       0.011834   \n",
       "Derived.10                  0.002857     0.002821       0.003450   \n",
       "Derived.11                  0.006487     0.003889       0.010433   \n",
       "Derived.12                  0.007867     0.003796       0.007731   \n",
       "Derived.13                  0.001061     0.001298       0.001212   \n",
       "Derived.14                  0.010507     0.004689       0.010663   \n",
       "Derived.15                  0.000076     0.006672       0.003296   \n",
       "Derived.16                  0.007372     0.001547       0.008757   \n",
       "Derived.17                  0.009093     0.003419       0.008415   \n",
       "Derived.18                  0.007990     0.002849       0.007186   \n",
       "Derived.19                  0.011322     0.004030       0.010278   \n",
       "Derived.20                  0.004094     0.004140       0.009269   \n",
       "Derived.21                  0.008715     0.001495       0.009442   \n",
       "Derived.22                  0.032513     0.001504       0.017594   \n",
       "Derived.23                  0.024605     0.002001       0.009541   \n",
       "Derived.24                  0.012387     0.005082       0.011207   \n",
       "CC1                         0.006734     0.001384       0.005566   \n",
       "CC2                         0.015002     0.005331       0.012452   \n",
       "CC3                         0.006307     0.006012       0.000318   \n",
       "CC4                         0.006902     0.001715       0.005739   \n",
       "CC5                         0.006968     0.008934       0.009631   \n",
       "Base time                   0.011179     0.000480       0.002767   \n",
       "Post length                 0.003901     0.003115       0.001444   \n",
       "Post Share Count            0.002618     0.000031       0.000762   \n",
       "Post Promotion Status            NaN          NaN            NaN   \n",
       "H Local                     0.098329     0.135113       0.048326   \n",
       "Post Sunday                 0.160193     0.153387       0.152214   \n",
       "Post Monday                 0.048737     0.167968       0.166684   \n",
       "Post Tuesday                0.206916     0.047329       0.170791   \n",
       "Post Wednesday              0.246820     0.203664       0.035480   \n",
       "Post Thursday               0.019334     0.215791       0.218821   \n",
       "Post Friday                 0.177436     0.004109       0.221572   \n",
       "Post Saturday               0.170806     0.163548       0.003327   \n",
       "Base Sunday                 0.172562     0.165230       0.163967   \n",
       "Base Monday                 0.169267     0.162075       0.160836   \n",
       "Base Tuesday                0.170686     0.163433       0.162184   \n",
       "Base Wednesday              0.178061     0.170495       0.169192   \n",
       "Base Thursday                    NaN     0.176038       0.174692   \n",
       "Base Friday                      NaN          NaN       0.167270   \n",
       "Base Saturday                    NaN          NaN            NaN   \n",
       "Target Variable                  NaN          NaN            NaN   \n",
       "\n",
       "                       Target Variable  \n",
       "Page Popularity/likes         0.064537  \n",
       "Page Checkinsâ€™s             0.035152  \n",
       "Page talking about            0.194768  \n",
       "Page Category                 0.069001  \n",
       "Derived                       0.138996  \n",
       "Derived.1                     0.238743  \n",
       "Derived.2                     0.359536  \n",
       "Derived.3                     0.349452  \n",
       "Derived.4                     0.316593  \n",
       "Derived.5                     0.153853  \n",
       "Derived.6                     0.229139  \n",
       "Derived.7                     0.368003  \n",
       "Derived.8                     0.339229  \n",
       "Derived.9                     0.329067  \n",
       "Derived.10                    0.004411  \n",
       "Derived.11                    0.234408  \n",
       "Derived.12                    0.356985  \n",
       "Derived.13                    0.303185  \n",
       "Derived.14                    0.333375  \n",
       "Derived.15                    0.139476  \n",
       "Derived.16                    0.232497  \n",
       "Derived.17                    0.356769  \n",
       "Derived.18                    0.348033  \n",
       "Derived.19                    0.309988  \n",
       "Derived.20                    0.221228  \n",
       "Derived.21                    0.230101  \n",
       "Derived.22                    0.192558  \n",
       "Derived.23                    0.065052  \n",
       "Derived.24                    0.339319  \n",
       "CC1                           0.327086  \n",
       "CC2                           0.545260  \n",
       "CC3                           0.073260  \n",
       "CC4                           0.342653  \n",
       "CC5                           0.375318  \n",
       "Base time                     0.224366  \n",
       "Post length                   0.001237  \n",
       "Post Share Count              0.124638  \n",
       "Post Promotion Status              NaN  \n",
       "H Local                       0.012255  \n",
       "Post Sunday                   0.000587  \n",
       "Post Monday                   0.000575  \n",
       "Post Tuesday                  0.004769  \n",
       "Post Wednesday                0.022337  \n",
       "Post Thursday                 0.009496  \n",
       "Post Friday                   0.010470  \n",
       "Post Saturday                 0.008114  \n",
       "Base Sunday                   0.008700  \n",
       "Base Monday                   0.003049  \n",
       "Base Tuesday                  0.004611  \n",
       "Base Wednesday                0.023267  \n",
       "Base Thursday                 0.000927  \n",
       "Base Friday                   0.007210  \n",
       "Base Saturday                 0.008304  \n",
       "Target Variable                    NaN  \n",
       "\n",
       "[54 rows x 54 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corrDataAbs = corrData.abs()\n",
    "np.fill_diagonal(corrDataAbs.values, np.NaN)\n",
    "upper_matrix = np.triu(np.ones(corrDataAbs.shape)).astype(np.bool)\n",
    "\n",
    "upperTriangleCorr = corrDataAbs.where(upper_matrix)\n",
    "upperTriangleCorr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "##### Apenas valores válidos\n",
    "\n",
    "A matriz de correlação acima nos trás todos os\n",
    "valores de relacionamento entre cada uma das colunas, logo, desejamos saber\n",
    "apenas quais são as colunas que possuem uma forte ligação. Usaremos a correlação\n",
    "forte como sendo > X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Derived', 'Derived.15') 0.999830712749\n",
      "('Derived.1', 'Derived.6') 0.968434639652\n",
      "('Derived.1', 'Derived.11') 0.96780381851\n",
      "('Derived.1', 'Derived.16') 0.993783500124\n",
      "('Derived.1', 'Derived.21') 0.959629922118\n",
      "('Derived.2', 'Derived.3') 0.963215780774\n",
      "('Derived.2', 'Derived.7') 0.983718262825\n",
      "('Derived.2', 'Derived.12') 0.991463268195\n",
      "('Derived.2', 'Derived.17') 0.999367190265\n",
      "('Derived.2', 'Derived.18') 0.964908039337\n",
      "('Derived.2', 'Derived.24') 0.96224784044\n",
      "('Derived.3', 'Derived.12') 0.955776303102\n",
      "('Derived.3', 'Derived.17') 0.960293395747\n",
      "('Derived.3', 'Derived.18') 0.997833442999\n",
      "('Derived.4', 'Derived.9') 0.973749708348\n",
      "('Derived.4', 'Derived.14') 0.981743632745\n",
      "('Derived.4', 'Derived.19') 0.998057004017\n",
      "('Derived.4', 'Derived.24') 0.967300818605\n",
      "('Derived.6', 'Derived.16') 0.978002904034\n",
      "('Derived.6', 'Derived.21') 0.998145498564\n",
      "('Derived.7', 'Derived.12') 0.965884786594\n",
      "('Derived.7', 'Derived.17') 0.983235892906\n",
      "('Derived.7', 'Derived.18') 0.950895995354\n",
      "('Derived.7', 'Derived.24') 0.951319473634\n",
      "('Derived.9', 'Derived.14') 0.970076435549\n",
      "('Derived.9', 'Derived.19') 0.975522710155\n",
      "('Derived.9', 'Derived.24') 0.989702623522\n",
      "('Derived.11', 'Derived.16') 0.97319241145\n",
      "('Derived.11', 'Derived.20') -0.987733682788\n",
      "('Derived.12', 'Derived.17') 0.989819289892\n",
      "('Derived.12', 'Derived.18') 0.956016772509\n",
      "('Derived.12', 'Derived.24') 0.952819566824\n",
      "('Derived.14', 'Derived.19') 0.979903749084\n",
      "('Derived.14', 'Derived.24') 0.985477296883\n",
      "('Derived.16', 'Derived.20') -0.951986094702\n",
      "('Derived.16', 'Derived.21') 0.970046335779\n",
      "('Derived.17', 'Derived.18') 0.963343844056\n",
      "('Derived.17', 'Derived.24') 0.966359306043\n",
      "('Derived.19', 'Derived.24') 0.968615431541\n",
      "('CC1', 'CC4') 0.996582427983\n"
     ]
    }
   ],
   "source": [
    "upperTriangleCorr = upperTriangleCorr.where(upperTriangleCorr>0.95)\n",
    "strongCorrData = upperTriangleCorr.dropna(how='all', axis=(0,1))\n",
    "b = strongCorrData[strongCorrData.notnull()].stack().index\n",
    "for c in b:\n",
    "    print(c, corrData[c[1]][c[0]])\n",
    "columnsRemove = list(map(lambda x: x[1], b))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Validação\n",
    "\n",
    "Após identificar quais features possuem uma forte correlação com outras,\n",
    "precisamos validar se removendo as colunas com forte correlação irão influênciar\n",
    "na precisão do modelo proposto.\n",
    "\n",
    "#### Cross-Validation\n",
    "\n",
    "A validação cruzada é uma dos modelos para validar se um modelo está tendo uma\n",
    "alta acertividade, separando um subconjunto dos dados disponíveis para realizar\n",
    "a validação, e o seu complemento para o treino ([TÉCNICAS DE APRENDIZAGEM DE\n",
    "MÁQUINA PARA PREVISÃO DE SUCESSO EM\n",
    "IMPLANTESDENTÁRIOS](http://tcc.ecomp.poli.br/CarolinaBaldisserotto.pdf)). Este\n",
    "tipo de validação é bom para detectar [*overffiting* e\n",
    "*underffiting*](http://docs.aws.amazon.com/machine-learning/latest/dg/model-fit-\n",
    "underfitting-vs-overfitting.html) e possibilita generalizar a solução\n",
    "adequadamente para o domínio.\n",
    "\n",
    "Um dos métodos mais comuns para aplicar a validação cruzada é o k-fold, neste\n",
    "método, separa-se o conjunto de dados em **k** subconjuntos, utiliza-se\n",
    "**(k-1)** subconjuntos para treinar o modelo e 1 para validálo. Este processo é\n",
    "repetido **k** vezes sempre excluindo 1 subconjunto diferente para cada iteração\n",
    "([Cross-Validation](http://docs.aws.amazon.com/machine-learning/latest/dg/cross-\n",
    "validation.html)).\n",
    "\n",
    "#### R²\n",
    "\n",
    "O [R²](http://leg.ufpr.br/~silvia/CE003/node76.html) é chamado de coeficiente de\n",
    "determinação. Ele é uma variável que é explicada pela variabilidade de outras\n",
    "variáveis, conhecido como quadrado do coeficiente de correlação de Pearson, ou\n",
    "seja, ele indica o quanto da variação total está relacionada aos valores\n",
    "analisados em pares.\n",
    "\n",
    "Os valores de R² variam entre -infinito e 1, pois são determinados pela fórmula:\n",
    "\n",
    "\\begin{equation*}\n",
    "R^2 = \\left( 1 - \\frac{(Variação explicada de Y)}{(Variação total de Y)}\\right)\n",
    "\\end{equation*}\n",
    "\n",
    "É possível que a variação total seja próxima e 0 e a variação explicada de Y\n",
    "seja grande, fazendo com que gere valores negativos.\n",
    "\\begin{equation*}\n",
    "Variação total de Y  =  \\sum_{k=0}^n (Y_i - \\bar{Y})^2\n",
    "\\end{equation*}\n",
    "\\begin{equation*}\n",
    "Variação total de Y  =  \\sum_{k=0}^n (Y_i - F_i)^2\n",
    "\\end{equation*}\n",
    "\n",
    "#### Média quadrática do erro\n",
    "\n",
    "\n",
    "A [média quadrática do erro](http://www.statisticshowto.com/mean-squared-error/)\n",
    "é uma métrica para modelos de regressão que indica o quanto os pontos estão se\n",
    "distânciando da reta traçada. A distância é a medida de **erro** da linha e o\n",
    "quadrado é útil para obter os valores positivos e amplifica (adiciona maior\n",
    "peso) para quando o ponto se distância bastante.\n",
    "\n",
    "Nesta seção, ele será utilizado para determinar o quanto o modelo de regressão\n",
    "melhorou ou piorou após aplicar a remoção das colunas que possuem forte\n",
    "correlação.\n",
    "\n",
    "#### Dados originais"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.999999600651 0.665933668109\n",
      "CPU times: user 1min, sys: 180 ms, total: 1min 1s\n",
      "Wall time: 1min 1s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "\n",
    "y_test, x_test = testData.loc[:, 'Target Variable'], testData.drop('Target Variable', 1)\n",
    "Y, X = trainData.loc[:, 'Target Variable'], trainData.drop('Target Variable', 1)\n",
    "\n",
    "regressor = DecisionTreeRegressor()\n",
    "\n",
    "regressor.fit(X, Y)\n",
    "y = regressor.predict(x_test)\n",
    "raw = ('Raw decision tree', regressor.score(x_test, y_test))\n",
    "score = cross_val_score(regressor, X, Y, scoring='neg_mean_squared_error', cv=10)\n",
    "print(regressor.score(X, Y), regressor.score(x_test, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "#### Pearson correlation\n",
    "\n",
    "Agora, após rodar o modelo de [Árvore de regressão](#Decision-Tree-Regression),\n",
    "iremos retirar as features que possuem uma forte correlação linear e verificar o\n",
    "score novamente para ver se a performance é melhorada ou não."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.999999600651 0.794247700044\n",
      "CPU times: user 10.8 s, sys: 50 ms, total: 10.8 s\n",
      "Wall time: 10.8 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "filteredData = trainData.drop(columnsRemove, 1)\n",
    "filteredTest = testData.drop(columnsRemove, 1)\n",
    "YY, XX = filteredData.loc[:, 'Target Variable'], filteredData.drop('Target Variable', 1)\n",
    "y_test, x_test = filteredTest.loc[:, 'Target Variable'], filteredTest.drop('Target Variable', 1)\n",
    "\n",
    "regressor = DecisionTreeRegressor()\n",
    "\n",
    "filteredScore = cross_val_score(regressor, XX, YY, scoring='neg_mean_squared_error')\n",
    "regressor.fit(XX,YY)\n",
    "yy = regressor.predict(x_test)\n",
    "pearson = ('Pearson decision tree', regressor.score(x_test, y_test))\n",
    "print(regressor.score(XX, YY), regressor.score(x_test, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "#### Normalização\n",
    "\n",
    "Normalize samples individually to unit norm.\n",
    "Each sample (i.e. each row of the data matrix) with at least one non zero\n",
    "component is rescaled independently of other samples so that its norm (l1 or l2)\n",
    "equals one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.99999865887 -0.454240727809\n",
      "CPU times: user 25 s, sys: 60 ms, total: 25.1 s\n",
      "Wall time: 25.1 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "from sklearn.preprocessing import Normalizer\n",
    "\n",
    "XXX, YYY = Normalizer().fit_transform(trainData.drop('Target Variable', 1)), trainData.loc[:, 'Target Variable']\n",
    "y_test, x_test = testData.loc[:, 'Target Variable'], testData.drop('Target Variable', 1)\n",
    "regressor = DecisionTreeRegressor()\n",
    "\n",
    "normalizedScore = cross_val_score(regressor, XXX, YYY, scoring='neg_mean_squared_error')\n",
    "regressor.fit(XXX, YYY)\n",
    "yyy = regressor.predict(x_test)\n",
    "normalized = ('Normalized decision tree', regressor.score(x_test, y_test))\n",
    "print(regressor.score(XXX, YYY), regressor.score(x_test, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "#### Resultados\n",
    "\n",
    "Obervou-se que com a remoção das colunas dependentes, não houve grandes\n",
    "melhorias na performance do modelo Regression tree. A seguir é apresentada uma\n",
    "métrica diferente para esta análise o mean squared error."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('Score: -967.4605323318092 +- 452.31511261102816',\n",
       " 'Score: -858.2428926393235 +- 121.96090333886659',\n",
       " 'Score: -1200.3571733552355 +- 245.46714673092202')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_score = \"Score: {} +- {}\".format(score.mean(), score.std())\n",
    "main_filtered = \"Score: {} +- {}\".format(filteredScore.mean(), filteredScore.std())\n",
    "main_normalized = \"Score: {} +- {}\".format(normalizedScore.mean(), normalizedScore.std())\n",
    "\n",
    "main_score, main_filtered, main_normalized"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Na plotagem do gráfico a baixo, é visível que não se concretizou uma linha\n",
    "quando se faz a plotagem em relação ao dado predito e o dado real."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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zUmlpcP31UKpU2MmkYsNCJUmSVNzs2gVPPx2ckfruO/j8c3jgAShXLuxkUrHj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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f72875cacc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "max_value = max(y_test.max(), y.max(), yy.max(), yyy.max())\n",
    "min_value = min(y_test.min(), y.min(), yy.min(), yyy.min())\n",
    "\n",
    "plt.figure(figsize=[12, 7])\n",
    "plt.scatter(y_test, y, c='green', marker='o', s=70, alpha=0.7, label='Raw data')\n",
    "plt.scatter(y_test, yy, c='blue', marker='v', s=70, alpha=0.7, label='Filtered data')\n",
    "plt.scatter(y_test, yyy, c='red', marker='*', s=70, alpha=0.7, label='Normalized data')\n",
    "plt.legend(loc='upper left')\n",
    "plt.plot([min_value, max_value], [min_value, max_value], color='red', linestyle='-', linewidth=1)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Machine Learning\n",
    "\n",
    "## Facebook Comment Volume\n",
    "\n",
    "Para o dado problema, foi feito um levantamento dos modelos a serem utilizados\n",
    "para a proposta de solução. Os modelos selecionados forma baseados na literatura\n",
    "de artigos, nos quais foram feitos estudos sobre a mesma massa de dados, ou\n",
    "semelhantes, que está sendo trabalhada neste problema. Com base nos artigos [Kam\n",
    "aljot](https://archive.ics.uci.edu/ml/datasets/Facebook+Comment+Volume+Dataset)\n",
    "e [Kamaljot, Ranjeet](http://uksim.info/uksim2015/data/8713a015.pdf), ambos\n",
    "tratam o problema utilizando modelos de regressão supervisionado. Em ambos os\n",
    "artigos, o modelo no qual foi retratado como melhor desempenho para a solução,\n",
    "foi o *Decision Tree*.\n",
    "\n",
    "Os modelos selecionados para testes neste problema foram:\n",
    "* Decision tree\n",
    "* Random Forest\n",
    "* KNM\n",
    "\n",
    "Para avaliação dos modelo, foi coletado o valor da pontuação do mesmo utilizando\n",
    "duas métricas comrparativas, para validar a seleção do modelos com relação a sua\n",
    "performance sobre a base de dados:\n",
    "* [R2](http://scikit-\n",
    "learn.org/stable/modules/generated/sklearn.metrics.r2_score.html), métrica na\n",
    "qual é mais uma é utilizada para determinar a pontuação de um modelo de\n",
    "regressão, seu melhor resultado é dado pela nota \"1\" e podendo ser negativ, pois\n",
    "o modelo pode ser arbitrariamente pior.\n",
    "* [MSE](http://scikit-learn.org/stable/modules/model_evaluation.html#median-\n",
    "absolute-error), métrica utilizada para para avaliar o modelo sendo o valor\n",
    "esperado da perda de erro ao quadrado ou perda quadrática. A diferença ocorre\n",
    "devido à aleatoriedade ou porque o estimador não contabiliza informações que\n",
    "possam produzir uma estimativa mais precisa, sendo necessário no caso para\n",
    "validar o resultado da métrica anterior.\n",
    "\n",
    "## Tratamento da Base de dados\n",
    "\n",
    "Tratamento para a base de testes e treino."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X values and Y values ready for training and testing!!!\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "# Set features and independent variables vector\n",
    "X_train = trainData.iloc[:, :-1].values\n",
    "y_train = trainData.iloc[:, -1].values\n",
    "\n",
    "X_test = testData.iloc[:, :-1].values\n",
    "y_test = testData.iloc[:, -1].values\n",
    "\n",
    "print(\"X values and Y values ready for training and testing!!!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 0 ns, sys: 0 ns, total: 0 ns\n",
      "Wall time: 5.01 µs\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "def plot_graphs(y_train, y_train_pred, y_test, y_test_pred):\n",
    "    xy_min = 0\n",
    "    xy_max = 1500\n",
    "\n",
    "    f, (ax1, ax2) = plt.subplots(1, 2, sharey=True, figsize=(15, 5))\n",
    "\n",
    "    ax1.scatter(y_train, y_train_pred, c='blue', marker='o', s=50, alpha=0.7, label='Test with Trained Data')\n",
    "    ax1.set_title('Prediction on Train set')\n",
    "    ax1.set_xlim([xy_min, xy_max])\n",
    "    ax1.set_ylim([xy_min, xy_max])\n",
    "    ax1.plot([xy_min, xy_max], [xy_min, xy_max], color='red', linestyle='-', linewidth=2)\n",
    "\n",
    "    ax2.scatter(y_test, y_test_pred, c='darkorange', marker='o', s=50, alpha=0.8, label='Test with Test Data')\n",
    "    ax2.set_title('Prediction on Test set')\n",
    "    ax2.set_xlim([xy_min, xy_max])\n",
    "    ax2.set_ylim([xy_min, xy_max])\n",
    "    ax2.plot([xy_min, xy_max], [xy_min, xy_max], color='red', linestyle='-', linewidth=2)\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "    plt.figure(figsize=(15, 5))\n",
    "    plt.scatter(y_train_pred, y_train - y_train_pred, c='black', marker='o', s=75, alpha=0.7, label='Training data')\n",
    "    plt.scatter(y_test_pred, y_test - y_test_pred, c='lightgreen', marker='s', s=75, alpha=0.7, label='Test data')\n",
    "    plt.xlabel('Predicted values', fontsize=18)\n",
    "    plt.ylabel('Residuals', fontsize=18)\n",
    "    plt.legend(loc='upper right')\n",
    "    plt.plot([0, 1250],[0, 0], color='red', linestyle='-', linewidth=2)\n",
    "    plt.xlim([-20, 1250])\n",
    "    plt.ylim([-1000, 1500])\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Representação gráfica\n",
    "\n",
    "Para reO gráfico demonstra os testes do modelo utilizando as bases distintas do\n",
    "nosso dataset, sendo os testes na base de treino, gŕafico à esquerda e os testes\n",
    "na base de teste, gráfico a direita. Abaixo temos a representação dos testes no\n",
    "modelo, onde é representado os dados residuais, nos quais alguns dos valores\n",
    "ficaram distantes a função do modelo.\n",
    "\n",
    "## Decision Tree Regression\n",
    "\n",
    "Uma árvore de regressão é idêntica a uma árvore de decisão porque também é\n",
    "formada por um conjunto de nós de decisão, perguntas, mas o resultado, em vez de\n",
    "uma categoria, é um escalar. As mesmas são boas candidatas, pois elas capturam\n",
    "iterações complexas nos dados.\n",
    "\n",
    "Para o estudo foi feito os testes do modelo utilizando oa parêmetros *default*\n",
    "da árvore. sendo esse encontrardo neste [link](http://scikit-learn.org/stable/mo\n",
    "dules/generated/sklearn.tree.DecisionTreeRegressor.html#sklearn.tree.DecisionTre\n",
    "eRegressor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Runnning Regression Decision Tree...\n"
     ]
    },
    {
     "data": {
      "image/png": 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/fvbu3UuVKlW4//772bt3L/v37ycqKorQ0FAaa0iuiIhkhAsX4L33LEExaxYU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\nwEIppZRSSimllFIh5/8BgBfiaX8zbuIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f72821c9978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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uH6GREBERUSwYsCAiIqIxzWg0Rj1uMBhGaCREREQUCwYsiIiIaEwzmUyQJCnsMUmSYDKZ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\niIiIiIiIyO0wYUFEREREREREbucf8F0Q3DUuqaMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f728217f358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R² Score, on Training set: 0.708, on Testing set: 0.873\n",
      "Mean Squared Error Score on Testing set: 1808.69\n",
      "CPU times: user 7.34 s, sys: 1.24 s, total: 8.58 s\n",
      "Wall time: 7.18 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "def decision_tree_regressor(X_train, y_train, X_test, y_test):\n",
    "    print(\"Runnning Regression Decision Tree...\")\n",
    "\n",
    "    # Runing with best params\n",
    "    regressor = DecisionTreeRegressor(min_samples_leaf=30, max_depth= 35, max_features= 46)\n",
    "    regressor.fit(X_train, y_train)\n",
    "\n",
    "    y_train_pred = regressor.predict(X_train)\n",
    "    y_test_pred = regressor.predict(X_test)\n",
    "\n",
    "    plot_graphs(y_train, y_train_pred, y_test, y_test_pred)\n",
    "\n",
    "    print(\"R² Score, on Training set: %.3f, on Testing set: %.3f\" % (r2_score(y_train, y_train_pred), r2_score(y_test, y_test_pred)))\n",
    "    print(\"Mean Squared Error Score on Testing set: %.2f\" % (mean_squared_error(y_test, y_test_pred)))\n",
    "\n",
    "decision_tree_regressor(X_train, y_train, X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Random Forest Regression\n",
    "\n",
    "É um meta-estimador que se adapta a uma série de árvores de decisão de\n",
    "classificação em várias sub-amostras do conjunto de dados e utiliza a média para\n",
    "melhorar a precisão preditiva e controlar a sobreposição.\n",
    "\n",
    "O modelo tem como parâmetro livre a seleção da quantidade de árvores de decisão."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Runnning Random Forest with 50 Trees...\n"
     ]
    },
    {
     "data": {
      "image/png": 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AgQPExMQQFhZGHbXkiohISrhwAT75xAKKGTMgVy744gvYuFFhhYiIiCc6dw4+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bAAAg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\naJl8hYfLRD0sDFJSI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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7281d857f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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PPICjR49CkiQAQFtbG2pra9HY2Ignn3ySSQsiIiIiIqIRcEkIUZhVV1e7JSsESZJw5MgR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\nK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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7281a89a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R² Score, on Training set: 0.719, on Testing set: 0.761\n",
      "Mean Squared Error Score on Testing set: 3391.78\n",
      "CPU times: user 4min 5s, sys: 1.31 s, total: 4min 7s\n",
      "Wall time: 1min 6s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "def random_forest_regressor(X_train, y_train, X_test, y_test):\n",
    "    n_trees = 50\n",
    "    print(\"Runnning Random Forest with\",n_trees,\"Trees...\")\n",
    "\n",
    "    regressor = RandomForestRegressor(n_estimators=n_trees, min_samples_leaf=30, max_depth= 35, max_features= 46, n_jobs=-1)\n",
    "    regressor.fit(X_train, y_train)\n",
    "\n",
    "    y_train_pred = regressor.predict(X_train)\n",
    "    y_test_pred = regressor.predict(X_test)   \n",
    "\n",
    "    plot_graphs(y_train, y_train_pred, y_test, y_test_pred)\n",
    "\n",
    "    print(\"R² Score, on Training set: %.3f, on Testing set: %.3f\" % (r2_score(y_train, y_train_pred), r2_score(y_test, y_test_pred)))\n",
    "    print(\"Mean Squared Error Score on Testing set: %.2f\" % (mean_squared_error(y_test, y_test_pred)))\n",
    "    return ('Random forest best params', regressor.score(X_test, y_test))\n",
    "random_forest = random_forest_regressor(X_train, y_train, X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# K Ideal\n",
    "Na maioria dos algoritmos de machine learning, o K no KNN é um hiperparametro\n",
    "que deve ser escolhido para que o melhor resultado possível possa ser encontrado\n",
    "em um conjunto de dados. O K pode ser interpretado como o controlador do formato\n",
    "do limite de decisão do algoritmo.\n",
    "\n",
    "Exemplificando, quando o K é pequeno, a região de uma suposta predição é\n",
    "restringida e força o classificador a focar menos na distribuição geral. Um\n",
    "valor menor de 5 fornece a combinação mais flexivel, o que vai gerar um\n",
    "desequilibrio menor mas uma variancia maior."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ideal K:  19\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f728190fe10>]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f72819baf28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "\n",
    "def idealK(x,target,y,target2):\n",
    "    K = 1\n",
    "    ab = 0\n",
    "    vetScore = []\n",
    "    for i in range(200):\n",
    "        knn = KNeighborsRegressor(n_neighbors=K)\n",
    "        knn.fit(x,target)\n",
    "        score = knn.score(y,target2)\n",
    "        vetScore.append(score)\n",
    "        if score > ab:\n",
    "            ab = score\n",
    "            d = K\n",
    "        K += 2\n",
    "    return d,vetScore\n",
    "D = numpy.arange(1, 400, 2)\n",
    "K,score = idealK(X_train, y_train, X_test, y_test)\n",
    "print(\"ideal K: \",K)\n",
    "plt.plot(D,score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Algoritmo K-nearest neighbors\n",
    "\n",
    "No reconhecimento de padrões o algoritmo KNN é um método não para-métrico usado\n",
    "para classificação e regressão.\n",
    "Nos dois casos, o input consiste nos k exemplos de treinamento mais proximos no\n",
    "espaço de amostragem. O output depende se o Knn é usado para classificação ou\n",
    "regressão.\n",
    "\n",
    "KNN é um tipo de aprendizado baseado em instâncias, onde a função é aproximada\n",
    "apenas localmente e toda a computação é deferida até a classificação."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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IBi2wNm7cmC9/+cs5cOBAlixZkhtvvHGwvlURVZWeRecj8WzLcLsc5y5JABrZ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f728204fa20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def regressionKnn(x,target,y,target2):\n",
    "    knn = KNeighborsRegressor(n_neighbors=K)\n",
    "    knn.fit(x,target)\n",
    "    vetPredict = knn.predict(y)\n",
    "    plt.plot(target2, vetPredict, 'o')\n",
    "    return ('Knn', knn.score(y, target2))\n",
    "\n",
    "knn = regressionKnn(X_train, y_train, X_test, y_test)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Otimização\n",
    "\n",
    "Tentaremos fazer a otimização do modelo de árvore de decisão por ter sido o\n",
    "modelo com o melhor score, com esta otimização, espera-se aumentar o valor do\n",
    "score R².\n",
    "\n",
    "## Escolha\n",
    "\n",
    "Será feita a escolha com base no modelo que apresentar melhor performance com a\n",
    "otimização dos parametros livres, entre random forest e decision tree."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "def search_params(regressor_class, tree_parameters):\n",
    "    regressor = regressor_class()\n",
    "    grid_search = GridSearchCV(estimator=regressor,\n",
    "                              param_grid=tree_parameters,\n",
    "#                           scoring='neg_mean_squared_error',\n",
    "                              cv = 10,\n",
    "                              n_jobs=-1)\n",
    "    grid_search = grid_search.fit(X_train, y_train)\n",
    "    print(grid_search.best_score_ * -1, grid_search.best_params_)\n",
    "    return grid_search.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.60578357972 {'max_depth': 50, 'max_features': 46, 'min_samples_leaf': 30}\n",
      "CPU times: user 1min 3s, sys: 407 ms, total: 1min 3s\n",
      "Wall time: 11min 29s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "tree_parameters = [{'max_depth': [30, 35, 40, 50],\n",
    "                    'max_features': [40, 46, 53],\n",
    "                    'min_samples_leaf': [25, 30, 35]}]\n",
    "\n",
    "# BEST SCORE: 469.696918012 {'min_samples_leaf': 30, 'max_depth': 35, 'max_features': 46}\n",
    "\n",
    "regressor_score = search_params(DecisionTreeRegressor, tree_parameters)\n",
    "\n",
    "\n",
    "# Get best parameters for Decision Tree Regressor\n",
    "\n",
    "\n",
    "decision_tree = ('Decision tree best parameters', regressor_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "%%time\n",
    "\n",
    "random_parameters = [{'max_depth': [30, 35, 40, 50],\n",
    "                    'max_features': [40, 46, 53],\n",
    "                    'min_samples_leaf': [25, 30, 35]}]\n",
    "\n",
    "regressor_score = search_params(RandomForestRegressor, random_parameters)\n",
    "\n",
    "random_forest_best = ('Random Forest best parameters', regressor_score) # grid_search.best_score_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Resultados\n",
    "\n",
    "Os resultados finais dos modelos estão armazenados nas variáveis:\n",
    "\n",
    "* knn - Knn model\n",
    "* random_forest - Random forest default\n",
    "* pearson - Remove columns from decision tree data\n",
    "* normalized - Apply normalization to data run in decision tree\n",
    "* raw - Raw data in decision tree (base score)\n",
    "* decision_tree - Decision tree model with best params\n",
    "* random_forest_best - Random forest with best params\n",
    "\n",
    "E serão exibidos os resultados abaixo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Knn', 0.36086668170729996) ('Random forest best params', 0.76099820536606699) ('Pearson decision tree', 0.79424770004376788) ('Normalized decision tree', -0.45424072780923036) ('Raw decision tree', 0.66593366810854349) ('Decision tree best parameters', 0.60578357972017116)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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DePRNU85zJkpLS2FhYYGzZ88abZdD4XNIR0E+//xz2NjYwN3dHSqVCvHx8Sgo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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f72819ebc18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(knn, random_forest, pearson, normalized, raw, decision_tree)\n",
    "plt.bar([1,2,3,4,5,6], [knn[1], random_forest[1], pearson[1], normalized[1], raw[1], decision_tree[1]])\n",
    "plt.xticks([1,2,3,4,5,6], [knn[0], random_forest[0], pearson[0], normalized[0], raw[0], decision_tree[0]], rotation=90)\n",
    "\n",
    "plt.rcParams['figure.figsize'] = 10, 10\n",
    "plt.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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    "editable": true
   },
   "source": [
    "# Referencias\n",
    "Artigo relacionado(Kamaljot Singh*, Ranjeet Kaur\n",
    "Department of Computer Science\n",
    "DAV University): http://uksim.info/uksim2015/data/8713a015.pdf\n",
    "KNN: https://goo.gl/XSLTFX\n",
    "K ideal: https://kevinzakka.github.io/2016/07/13/k-nearest-neighbor/#more-on-k\n",
    "Decision Tree Regressor: http://scikit-\n",
    "learn.org/stable/auto_examples/tree/plot_tree_regression.html#sphx-glr-auto-\n",
    "examples-tree-plot-tree-regression-py\n",
    "Random Forest Regressor: http://scikit-\n",
    "learn.org/stable/modules/generated/sklearn.ensemble.RandomForestRegressor.html\n",
    "GridSearchCV: http://scikit-\n",
    "learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html\n",
    "Mean Squared Error metric: http://scikit-learn.org/stable/modules/generated/skle\n",
    "arn.metrics.mean_squared_error.html#sklearn.metrics.mean_squared_error\n",
    "Overfitting intuition: https://machinelearningmastery.com/a-simple-intuition-\n",
    "for-overfitting/"
   ]
  }
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