logistic-regression-tutorial.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Regresión logística: "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conocemos un poco nuestro dataset a través de gráficas: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# cargamos el fichero de entrenamiento\n",
    "entrenamiento = pd.read_csv('./datasets/titanic/test-original.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "entrenamiento.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa6833f37d0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Vamos a conocer los que han sobrevivido frente a los que no han sobrevivido\n",
    "sns.countplot(x='Survived', data=entrenamiento)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa682bb0210>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Vamos a conocer los que han sobrevivido frente a los que no han sobrevivido POR SEXOS ('hue' es el parámetro extra)\n",
    "sns.countplot(x='Survived', data=entrenamiento, hue='Sex')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa682b0fe10>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Vamos a conocer los que han sobrevivido frente a los que no han sobrevivido por categoría (clase 1 la mejor y clase 3 la peor) de embarco\n",
    "sns.countplot(x='Survived', data=entrenamiento, hue='Pclass')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa682aac4d0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ZxNnZ/lv8/XTdUJRFTLiL33+w39+hGOMXlgDMiBxp6uBIcwdzJ3ACiI8MY8WCLF7eVk1LR7e/wzFmzFkCMCNSWuluNpnICQDgtiW5dPcqHzvjGYwJJpYAzIhsrWokzCUUZsT7O5TTkp8Wy4VnpLFxXz09fbZusAkulgDMiJQebGRWRjyRYS5/h3Labl+aR2tnD9urbJZQE1wsAZhh6+1Tth9qYm7WxG7+OeH8glRSYyP4cG8dqjbfoAkelgDMsFXUtNLa2TOhewB5CgkRlkxL4VDjcSobjvs7HGPGzPiewN2MS1tG6QbwUFMyj6Z5OYmsKzvCh3tryUm2qZ9NcLArADNsWysbiYsMJT81xt+h+ExEqIuiqUnsONRE03HrEmqCgyUAM2xbqxqZm5VISIBNonbutFRUYeO+On+HYsyYsARghqWju5fdh1uYm53g71B8LjkmnJmT4/h4Xz3dvdYl1AQ+SwBmWMqqm+jp04DpAdTfkoJU2rt6bX4gExQsAZhhKa1095UPlB5A/eWnxjAp3rqEmuBgCcAMS2llI1MSIkmPn3gzgHpDRFiSn8rhpg7217X7OxxjRpUlADMsWysbJ/z8P0OZm51IVJiLD/fW+jsUY0aVV+MARGQZ8BvABTyuqj/rtz8CWI17mcc64EZV3S8ilwE/A8KBLuAHqrreOWYB8HsgCvdyk99Ru+Ye1x57t4KD9e0UZsT7tc/+aAsPDeGc3GTe+6yGxvYuEqPD/R2SMaNiyCsAEXEBjwJXAIXATSJS2K/YHUCDqhYADwMPOdtrgatV9UzgNuBpj2N+C9wFTHd+lp3GeZgxUNXgbhLJSo7ycySjb7GzytmJVc+MCUTeNAEtBMpVtUJVu4BngeX9yiwHnnIerwEuERFR1S2qWu1sLwMiRSRCRDKAeFXd4HzrXw1ce9pnY0ZVZcNxBMhMDPwEkBgdTuGUeDbtb6Crx7qEmsDkTRNQJlDp8bwKWDRYGVXtEZEmIAX3FcAJXwK2qGqniGQ69XjWmTnQi4vIXbivFMjJsSH6/lTV0M6k+EgiQif+DKDeWDItlbLqZkorG4ds8rp5kf1tmonHmyuAgYZ79m+rP2UZEZmNu1noH4dRp3uj6ipVLVLVorS0NC/CNaNBValqOE5WUuB/+z8hNyWajIRIPtxba11CTUDyJgFUAdkez7OA6sHKiEgokADUO8+zgBeBW1V1r0f5rCHqNOPIwfp22rt6yUqK9ncoY0ZEWDItlWMtnVTUtvk7HGN8zpsEsAmYLiJ5IhIOrASK+5Upxn2TF2AFsF5VVUQSgVeBH6nqBycKq+phoEVEFouIALcCL53muZhRdGIJyOwguAHs6aysBGLCXXxYbl1CTeAZMgGoag9wD7AO2AU8r6plIvKgiFzjFHsCSBGRcuB7wL3O9nuAAuA+ESl1ftKdfd8AHgfKgb3Aa746KeN7pZXuJSDT4wJzANhgwlwhLMxLZveRFurbuvwdjjE+5dU4AFVdi7uvvue2+z0edwA3DHDcT4GfDlJnCTBnOMEa/9la2ciUxChcATYDqDcW5qXwzqc1bNhby5VnTfF3OMb4jI0ENkPq7u1jR3Uz2UHU/u8pISqMOZkJlBxooLOn19/hGOMzlgDMkHYfbqGrpy+oegD1t2RaKp09fXxy0GYJNYHDEoAZUqkzNXKwXgEAZCdFkZUUxYfltfRZl1ATICwBmCFtrWwkNTacxOgwf4fiNyLCeQWp1LV1sbO62d/hGOMTlgDMkEor3UtAunvsBq85mQkkx4Tz7mc1NjDMBARLAOaUmju62VvTGvBTQHsjRIQLpqdR1XDcBoaZgGAJwJzSjqomVAN3BbDhmpeTSFxEKO/sqfF3KMacNksA5pS2OCOAz8oKvEXgRyLMFcLSglTKa1pPTo9tzETl1UAwE7y2VjaSlxoz4RdF8eUCNgvzknnn0xrW7z7Grefm+qxeY8aaXQGYU9pa1WjNP/1Ehrk4b3oqu4+02FWAmdAsAZhBHW46ztHmTuZa88/nnJufQlSYi/W7j/k7FGNGzBKAGdRWp/3fegB9XmSYi6UF7quAQw3H/R2OMSNiCcAMqrSyiTCXUDgl3t+hjEtLprmvAt7cddTfoRgzIpYAzKBKKxsozIgPmiUghysyzMUFM9LYc7TFFo83E5IlADOg3j5le1WTNf8MYcm0FOIjQ/nZa7ttdLCZcCwBmAGVH2ulravXegANIcwVwqWzJlFa2ci6siP+DseYYbEEYAZkN4C9Ny8niYL0WH6+bg/dvX3+DscYr9lAsDE21ICkmxfljFEkp1Za1UhcZCh5KTH+DmXcc4UI9y6byZ2rS3h6wwH+/rw8f4dkjFe8ugIQkWUiskdEykXk3gH2R4jIc87+jSKS62xPEZG3RKRVRB7pd8zbTp391wo240DpQfcAsJAgXAJyJC6Zlc4FM9J4+M1PqW3t9Hc4xnhlyAQgIi7gUeAKoBC4SUQK+xW7A2hQ1QLgYeAhZ3sHcB/w/UGqv0VVz3Z+bETNOHG8q5c9R1uYm2XNP94SEe6/qpDjXb38Yt0ef4djjFe8aQJaCJSragWAiDwLLAd2epRZDjzgPF4DPCIioqptwPsiUuC7kM1oOdE8ta+2jd4+pel4t0/n0Al0Bemx3L40l8ff38fNi3I4yxKoGee8aQLKBCo9nlc52wYso6o9QBOQ4kXdv3Oaf+6TQVYbEZG7RKREREpqamwK3rFwsN49v01OcvAuATlS375kOqmxEdz7wna7IWzGPW+uAAb6YO7f4dmbMv3doqqHRCQOeAH4KrD6c5WorgJWARQVFVlH6zFwsL6dlJhwYiKsj8BwxUWG8ZPlc/j6f23msfcq+OaF7ovfiXLz3wQXb64AqoBsj+dZQPVgZUQkFEgA6k9Vqaoecn63AM/gbmoyfqaqHKxrY2qKffsfqWVzJvPFMyfz6zc/Y29Nq7/DMWZQ3iSATcB0EckTkXBgJVDcr0wxcJvzeAWwXk8xLFJEQkUk1XkcBlwF7Bhu8Mb36tu6aOvqJSfZun+ejgeumU1UmIt/WrON3j67cDXj05AJwGnTvwdYB+wCnlfVMhF5UESucYo9AaSISDnwPeBkV1ER2Q/8CviaiFQ5PYgigHUisg0oBQ4Bj/nutMxIHTjR/m9XAKclPS6SH18zm5IDDfz7W+X+DseYAXnVyKuqa4G1/bbd7/G4A7hhkGNzB6l2gXchmrF0sL6diNAQ0uMi/B3KhHftvEze2nOMX//tM/7hvDxybFCdGWdsKgjzPxysaycnOZqQgTtlmWH6ybVzyEiI5LmSSjq6e/0djjH/gyUAc1JHdy9Hmzus+6cPxUeG8ZuVZ9N0vJs/lVTSZzOGmnHEEoA5qbKhHcX6//vagqnJfPHMDHYdaeHtPTbg3YwflgDMSQfr2hEg2xKAz52bn8LZ2Yn8bdcxdh9u9nc4xgCWAIyHito2JidEEhlmK4D5mohw7dmZZCRG8uymSg412jrCxv8sARjA3f5fWd9Ofqr1VBkt4aEh3Lo4l+hwF6s/3E9De5e/QzJBzhKAAWDLwUZ6+pT8tFh/hxLQ4qPCuG1JLt19fTz14X7aOnv8HZIJYpYADAAbKuoQIM+uAEbdpPhIvrJ4KvVtXTz5wT7auywJGP+wBGAA+GhvHVMSo6z9f4zkp8bylcVTOdbSye8+2E9zR7e/QzJByBKA4XhXL1sqG8hPs2//Y2nGpDhuWZjDkaYObnvyY1qtOciMMUsAhk8ONtDdq3YD2A9mZsSzcmE226qa+PvfbbLmIDOmLAEYNuytwxUi5NpcNX4xe0oCv77xbEoO1HPH70s43mVTRpixYQnAsKGijjMzE4iw9n+/uXruFH755bl8tK+OO57aZEnAjAlLAEGu6Xg3WysbWTLNmxU8zWi6bl4Wv7xhLhsqLAmYsWEJIMi982kNPX3KJbPS/R2KAa6fb0nAjB1b9DXI/W3XUZJjwjk7O4k9R2z5wpEaas3f4R6/Yn4WazZXceW/vceti3P52tLc06rfmIHYFUAQ6+nt4+09NVx0RjquEJv/fzyZl5PEigVZ7KtpY/VH++1KwIwKrxKAiCwTkT0iUi4i9w6wP0JEnnP2bxSRXGd7ioi8JSKtIvJIv2MWiMh255h/FbEVSMba5gMNNB3v5lJr/hmXPJOANQeZ0TBkAhARF/AocAVQCNzkrOvr6Q6gQVULgIeBh5ztHcB9wPcHqPq3wF3AdOdn2UhOwIzc33YfI8wlnD8jzd+hmEGcSAIbKuq4c/UmW1XM+JQ3VwALgXJVrVDVLuBZYHm/MsuBp5zHa4BLRERUtU1V38edCE4SkQwgXlU3qKoCq4FrT+dEzPC9uesoi/NTiI2wW0Hj2bycJH6xYi4flNfxv54rpbfPVhUzvuFNAsgEKj2eVznbBiyjqj1AE3CqfoWZTj2nqtOMon21bVTUtHHJTGv+mQi+tCCL/3PlLF7bcYQHistQW1rS+IA3X/0Gapvv/9fnTZkRlReRu3A3FZGTk3OKKs1wvLqtGoBLCyf5ORLjrTvPz+dYSyer3q1gUnwE91w83d8hmQnOmyuAKiDb43kWUD1YGREJBRKA+iHqzBqiTgBUdZWqFqlqUVqatVX7gqqyZnMVi/OTyUqy5R8nknuXzeS6eZn84o1PeW7T6XU9NcabBLAJmC4ieSISDqwEivuVKQZucx6vANbrKa5RVfUw0CIii53eP7cCLw07ejMiJQca2F/XzooF2UMXNuNKSIjw8xVnccGMNH705+28ufOov0MyE9iQCcBp078HWAfsAp5X1TIReVBErnGKPQGkiEg58D3gZFdREdkP/Ar4mohUefQg+gbwOFAO7AVe880pmaGsKakiJtzFF8+c7O9QzAiEuUL47S3zOTMzgXv++Anbq5r8HZKZoLzq/qGqa4G1/bbd7/G4A7hhkGNzB9leAszxNlDjG+1dPby6/TBfPDOD6HDr/TNRxUSE8vht53Dtox9wx1ObeOmepWQkRPk7LDPB2EjgIPP6jiO0dvZwQ5E1/0x0aXERPPG1Itq7ernj9yW2vrAZNksAQURV+cPGg0xNieac3CR/h2N8YObkeB65eR67jzTznWdtjIAZHksAQeS9z2rZfKCBO8/Px2beCBwXnpHOP189mzd3HeVnr+3ydzhmArFG4CChqvzqr5+SmRjFl4uyhj7AjCtDzTZ625JcKmpaeey9feSlxnLzIhszY4ZmVwBB4u09NZRWNvKtiwuICLWVvwLRfVcVcuEZadz30g7e/6zW3+GYCcASQBA48e0/OzmKLy2wb/+BKtQVwr/dNI+CtFi+8YfNlB9r8XdIZpyzJqAgsOrdCrYfauKXN8wlzGU5P5DFRYbxxNeKuPbRD/n735fw4jeXkBIbccomJGsuCl72aTBOdXT30nS8+7Tr+XhfPT9ft4cr5kzm+vk2314wyEqK5rFbF3C0uYN/fHoznT02hbQZmF0BjBPdvX3sONTEnz+pYtuhJrp6+gCYFB/BmZmJXDAjlSvPzCAlNsLrOmtbO/nWHz8hOymKh1acZT1/gsi8nCR++eW53PPMFu59YTtFU5Ps/TefYwlgHCg/1spzJZW0dfaQGhvOwtxkosNdhIhwpLmDLQcbeHPXUX788k4umJ7KtfMyuaxw0udG8npe5lc1tPPHjw/S0tHD1/9uGq9sPWyX+gFssCaeywon8eKWQzS0d3F5oU39Yf4nSwB+pKq8+1ktb5QdIS0ugpXnZJOfGjPgN7UjTR109fZRXHqI7zxbSnS4i8sLJ7F8XiZLp6USHhqCqnKspZMdh5p4e08NcZGh3HFeHlMSbYqAYHXhjDQa2rp4e08NMeGhLC1I9XdIZhyxBOAnqsor2w6zoaKOMzMTuH5+5im7Z05OiOTmRTn88AtnsGl/PX8prebVbdX8pdQ9i3ZCVBjdvX20O+vGFmbEc/38TJvvJ8iJCNfOy+R4dy+vbj9MVLiL+Tk2Cty42aeDn3y4t44NFXUsnZbCF8/M8Lp9NiREWJSfwqL8FB64ppB39tSw83Az9W1dlFU3k5McTUF6LEnR4aN8BmaiCBHhxqJsntqwnxc2VyG47xEYYwnAD3ZWN7N2+2EKM+K5Yhgf/v1FhLq4fPZkLp/tbj/jZ2sAABAcSURBVNsdarSoCV6hrhC+ujiX1Rv2s2azezVWSwLGuoGOsYa2Lp7fXElmUhRfLsomxHpmmDESHhrCrefmkpcaw5rNVWzcV+fvkIyfWQIYQ319ygufuL993XRODuGh9s9vxtaJJDBjUhwvlVbz5q6jtsB8ELNPoDG0esN+KmrbuPLMDJJirI3e+Ed4aAhfWTyVBTlJrN99jO//aRsd3TZYLBjZPYAxcqCujZ+9vpsZk2Ipmmptr8a/XCHC9fMzSYwO44VPqig/1sJ/fHWBrSoWZLy6AhCRZSKyR0TKReTeAfZHiMhzzv6NIpLrse9HzvY9IvIFj+37RWS7iJSKSIkvTmY8+79rdxEiwnXzsmxEphkXRIRLZk3iP7+6gPJjrVz1r+/z1u5j/g7LjKEhE4CIuIBHgSuAQuAmj4XdT7gDaFDVAuBh4CHn2EJgJTAbWAb8u1PfCRep6tmqWnTaZzKObdhbx7qyo3zj76aREBXm73CM+R++MHsyL92zlLS4CG7//SYeKC6zJqEg4U0T0EKgXFUrAETkWWA5sNOjzHLgAefxGuARcX/NXQ48q6qdwD4RKXfq2+Cb8Me/3j7lp6/uZEpCJP9wQT5//uTQiOuybp5mtBSkx/GXu5fys9d28/sP9/P2nmP8y3Vn2sjhAOdNE1AmUOnxvMrZNmAZVe0BmoCUIY5V4A0R2Swidw0/9InhhU+qKKtu5p+umElkmC3EYsavyDAXD1wzmz/cuQiAWx7fyPeeL6W+rcvPkZnR4k0CGKjBun+/scHKnOrYpao6H3fT0t0icsGALy5yl4iUiEhJTU2NF+GOH22dPfy/dXuYl5PINXOn+DscY7yytCCV1797AXdfNI3i0mou+eXbrNlcZd1FA5A3TUBVQLbH8yygepAyVSISCiQA9ac6VlVP/D4mIi/ibhp6t/+Lq+oqYBVAUVHRhPoL/I939lLT0sl/fnWB3fg1E0pkmIsffGEm18zN5B9Wl/D9P23lkfWfcfXcKZ/rKWSzzE5c3lwBbAKmi0ieiITjvqlb3K9MMXCb83gFsF7dXxeKgZVOL6E8YDrwsYjEiEgcgIjEAJcDO07/dMaPQ43HWfVuBdfMnWKTb5kJ64zJcdx1QT7XnZ3JsZZOHn2rnFe2VdtN4gAx5BWAqvaIyD3AOsAFPKmqZSLyIFCiqsXAE8DTzk3eetxJAqfc87hvGPcAd6tqr4hMAl50vhWHAs+o6uujcH5+89BruwH4pytm+jkSY05PiAjn5CUze0o8b+w6yoa9dWyramLZnMnMy070d3jmNHg1EExV1wJr+2273+NxB3DDIMf+C/Av/bZVAHOHG+xE8cnBBoq3VnPPRQVk2lz8JkBER4Ry7dmZFE1N4uWt1azZXMWmffUcae445QAyayIav2wqCB9TVX7yyk7S4iL4xoXT/B2OMT6XlRTNP/7dNK6fl0lNayePrC9n7fbDJ5cxNROHTQXhY8Vbq9lysJGff+ksYiLsn9cEphARinKTKZwSz7qyI7xfXsvOw81cPy+T/LRYf4dnvGSfUD7U0d3LQ6/tpjAjni8tyPJ3OMZ45XQGGEaHh3LdvCzOykrkxS2HePz9fZyTm8wVcybbuJcJwJqAfOixdyuoburgvqsKcYVYt08TPKalxfLti6dzfkEqJfvr+fWbn7L7SLO/wzJDsATgI5X17Tz6djlfPHMy505L8Xc4xoy58NAQrjgzg29cOI2ocBerNxzgTyWVNLbbSOLxyhKAj/zklZ0Iwv+5sv88ecYEl6ykaO6+qICLZ6aztaqRS3/1Lq/vOOzvsMwALAH4wNt7jvHGzqN865ICpli3T2MIDQnh0lmTuPuiAibFR/D1//qEu5/5hNrWTn+HZjxYAjhN7V09/HNxGfmpMdx5Xr6/wzFmXMlIiOIvdy/lB184g7+WHeWyX73DS6WHbF6hccJ6AZ2mh17bzYG6dp69a7Gt8WvMAMJcIdx9UQGXFU7iB2u28Z1nS1mzuYp/vno2BelDdxkdqpeSDTQbOfvEOg0flNfy1IYD3L40l8X5duPXmFOZMSmOP39jCf98dSGllY0s+/W7PPjyTuqsWchv7ApghJo7uvnhmm3kp8bwwy/YfD/GDKb/N/iIUBffung6b5Qd4Xcf7OO5TQe58/x8bl+aS2J0uJ+iDE6WAEagt0/57rOlHG3u4Pmvn0tUuA14MWY4YiNCuX5+FucVpLLnaAu/+dtnPPZeBSvPyeH2pblkJ0f7O8SgYAlgBH711z2s332MnyyfbVM9G3Ma0uMj+e5lM9h1uJnH3q1g9Yb9/O7DfZxXkMrKc3K4eGa6v0MMaJYAhuml0kM8+tZeblqYzVcWT/V3OMNiawqb8WpWRjy/uvFsfrDsDJ7bVMlzmyq5+5lPiApzMS09ljlT4jljchwRoXa17UuWAIbh5a3VfO/5rSzMTebH18yxVb6M8bGMhCi+e+kMvnXxdDZW1LF2x2H+sqWaHYeaCHMJ09JimTEpjunpsaTERvg73AnPEoCX/rLlEN97vpSiqck8efs51uXTmFHkChGWFKSypCCVmZPj2V/Xxo5Dzew50szuIy0AJMeEM2NSLOlxEZw7LcVm3x0B+xcbQndvH794Yw//+U4F5+an8MTXiogOt382Y8ZKiAj5qbHkp8aiZ2VQ19bFp0db+OxoK5sPNPBRRQlhLmF+ThKL8lNYnJfMvJwk65zhBfskO4XyYy18/0/bKK1s5OZFOdx/VaFNcWuMH4kIqbERpMZGsGRaKj29fRSkx/LOpzV8uLeOR9Z/xr8qhLmEuVmJLMpPZlFeCgumJtkVwgC8+hcRkWXAb3CvCfy4qv6s3/4IYDWwAKgDblTV/c6+HwF3AL3At1V1nTd1+lNVQzv/+rfPWLO5ipiIUB69eT5XnpXh77CMMf2EukJONhWBe3xOyf56NlbU89G+ev7jnQoefWsvIQJ5qTHMyoincEo8szLimTEpjsnxkUE9dfuQCUBEXMCjwGVAFbBJRIpVdadHsTuABlUtEJGVwEPAjSJSiHuB+NnAFOBNEZnhHDNUnWOquvE4739Wy19KD7Ghoo6wkBBuX5rHNy+cZjebjBnHBurdNjUlhqkpMVw7dwrT0mMpOdDArsPNlFY28sq2/56ZNMwlxEWGkRQdRlJ0OHGRYcREuIgODyUmwkVMeCg3L8ohOSY8IK/+vbkCWAiUOwu5IyLPAssBzw/r5cADzuM1wCPi7iKzHHhWVTuBfSJS7tSHF3X6THNHN03t3TQd/++fhvYuKuuPs6+2lR2HmjnUeByAqSnRfPvi6Xz5nGxb0N2YCS4izMUFM9K4YEbayW1Nx7vZfbiZ8ppWDjUc5/3yWhrauth9pIW2zh76T1P3yFvl7rpCQ4iLDCUuMozYiFD3T2Soe9vJx+59cZGhRIeHEh4aQkRoyMnfEaEhhLtcRISFEO5ybz/VFYhnR8NwV4jPex56kwAygUqP51XAosHKqGqPiDQBKc72j/odm+k8HqpOn7n2kQ+oqG373PYwlzA1JYazsxO58/w8zslNZvaUeOveaUwAS4gKY1F+Couc+buyPK4g+lQ53tVLW1cP7Z29tHf1MCczgfr2Lprau2np7KG1o4eWjm5aO3uorG+npaOH1k73T2/f6M1yuvsny3x+FeJNAhjo07D/WQ5WZrDtA/WhHPBfTkTuAu5ynraKyJ5B4hyRcuBvvqxwcKlA7di81LgQTOcbTOcKPj7fW3xV0ejVPy7e36iHTuvwAUetepMAqoBsj+dZQPUgZapEJBRIAOqHOHaoOgFQ1VXAKi/iHNdEpERVi/wdx1gJpvMNpnMFO99A4s1opk3AdBHJE5Fw3Dd1i/uVKQZucx6vANare8WHYmCliESISB4wHfjYyzqNMcaMoiGvAJw2/XuAdbi7bD6pqmUi8iBQoqrFwBPA085N3nrcH+g45Z7HfXO3B7hbVXsBBqrT96dnjDFmMGJLs40NEbnLac4KCsF0vsF0rmDnG0gsARhjTJCyGc2MMSZIWQIYAyKyTET2iEi5iNzr73h8SUSyReQtEdklImUi8h1ne7KI/FVEPnN+B9TKOSLiEpEtIvKK8zxPRDY65/uc07lhwhORRBFZIyK7nff43EB+b0Xkfzl/xztE5I8iEhmo7y1YAhh1HlNpXAEUAjc5U2QEih7gf6vqLGAxcLdzfvcCf1PV6biHWgRU4gO+A+zyeP4Q8LBzvg24p0cJBL8BXlfVmcBc3OcckO+tiGQC3waKVHUO7g4qJ6a2CcT31hLAGDg5lYaqdgEnpr0ICKp6WFU/cR634P6AyMR9jk85xZ4CrvVPhL4nIlnAlcDjznMBLsY9DQoEyPmKSDxwAe5efqhql6o2EsDvLe6ekVHOeKZo4DAB+N6eYAlg9A00lUbmIGUnNBHJBeYBG4FJqnoY3EkCCKTFXX8N/BDoc56nAI2q2uM8D5T3OB+oAX7nNHc9LiIxBOh7q6qHgF8AB3F/8DcBmwnM9xawBDAWvJlKY8ITkVjgBeC7qtrs73hGi4hcBRxT1c2emwcoGgjvcSgwH/itqs4D2giQ5p6BOPcylgN5uGcvjsHddNtfILy3gCWAseDNVBoTmoiE4f7w/4Oq/tnZfFREMpz9GcAxf8XnY0uBa0RkP+7mvItxXxEkOs0GEDjvcRVQpaobnedrcCeEQH1vLwX2qWqNqnYDfwaWEJjvLWAJYCwE9LQXTvv3E8AuVf2Vxy7P6UFuA14a69hGg6r+SFWzVDUX93u5XlVvAd7CPQ0KBMj5quoRoFJEznA2XYJ7VH9Avre4m34Wi0i083d94nwD7r09wQaCjQER+SLub4knpr34Fz+H5DMich7wHrCd/24T//9w3wd4HsjB/R/rBlWt90uQo0RELgS+r6pXiUg+7iuCZGAL8BVnHYwJTUTOxn2zOxyoAG7H/cUxIN9bEfkxcCPu3m1bgDtxt/kH3HsLlgCMMSZoWROQMcYEKUsAxhgTpCwBGGNMkLIEYIwxQcoSgDHGBClLAMZ4QUSuExEVkZn+jsUYX7EEYIx3bgLex1nu1JhAYAnAmCE48xwtxT0N8EpnW4iI/Lszd/wrIrJWRFY4+xaIyDsisllE1p2YNsGY8cYSgDFDuxb3nPifAvUiMh+4HsgFzsQ9WvRcODkv0r8BK1R1AfAkEDAjv01gCR26iDFB7ybcU3mAe0qAm4Aw4E+q2gccEZG3nP1nAHOAv7qnk8GFe2phY8YdSwDGnIKIpOCe8XOOiCjuD3QFXhzsEKBMVc8doxCNGTFrAjLm1FYAq1V1qqrmqmo2sA+oBb7k3AuYBFzolN8DpInIySYhEZntj8CNGYolAGNO7SY+/23/BdwLhlQBO4D/xD37aZOz7OcK4CER2QqU4p5T3phxx2YDNWaERCRWVVudZqKPgaXOHPrGTAh2D8CYkXtFRBJxz5X/E/vwNxONXQEYY0yQsnsAxhgTpCwBGGNMkLIEYIwxQcoSgDHGBClLAMYYE6QsARhjTJD6/wHk5D69FgWxSgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# vamos a visualizar la distribución por edades.\n",
    "# con '.dropna()' pedimos que borre los nulos\n",
    "# si ponemos kde=False, no se representaría la parte curva\n",
    "sns.distplot(entrenamiento['Age'].dropna(), bins=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa6829584d0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Podemos hacer lo mismo con la librería plot.hist (histograma) a través de un dataframe (bins=numero de columnas)\n",
    "entrenamiento['Age'].plot.hist(bins=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "linkText": "Export to plot.ly",
        "plotlyServerURL": "https://plot.ly",
        "showLink": true
       },
       "data": [
        {
         "histfunc": "count",
         "histnorm": "",
         "marker": {
          "color": "rgba(255, 153, 51, 1.0)",
          "line": {
           "color": "#4D5663",
           "width": 1.3
          }
         },
         "name": "Age",
         "nbinsx": 30,
         "opacity": 0.8,
         "orientation": "v",
         "type": "histogram",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Si queremos crear un gráfico interactivo con cufflinks\n",
    "import cufflinks as cf\n",
    "# lo hacemos offline\n",
    "cf.go_offline()\n",
    "entrenamiento['Age'].iplot(kind='hist', bins=30)"
   ]
  },
  {
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   "metadata": {},
   "source": [
    "## Vamos a hacer una limpieza de nuestro dataset:\n",
    "\n",
    "Los pasos a seguir van a ser:\n",
    "1. Eliminar los valores nulos, bien calculando la media e insertandola en donde no haya valores (columna 'Age')\n",
    "2. Eliminando directamente la columna, si hay exceso de valores nulos (columna 'Cabin')\n",
    "3. Las columnas que tienen labels en forma de string, transformarlos a valores lógicos (0 o 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Eliminación de los valores nulos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# mostramos los 5 primeros valores\n",
    "entrenamiento.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa67f8fb3d0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# creamos un gráfico que nos permite observar que columnas tienen más valores nulos, en este caso Age y Cabin\n",
    "sns.heatmap(entrenamiento.isnull())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa67b7e23d0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"\n",
    " Vamos a cambiar este valor nulo por el valor medio de cada columna\n",
    " Vamos a obtener el valor medio con un diagrama de cajas\n",
    " La caja representa el 50% de los valores y la linea representa el valor medio\n",
    " Según el diagrama las medias aproximadas que nos encontramos serían:\n",
    "     Pclass 1: ~ 38\n",
    "     Pclass 2: ~ 30\n",
    "     Pclass 3: ~ 25\n",
    "\"\"\"    \n",
    "sns.boxplot(x='Pclass', y='Age', data=entrenamiento, showmeans=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pclass\n",
       "1    37.0\n",
       "2    29.0\n",
       "3    24.0\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Obtenemos los valores medios exactos\n",
    "medians = entrenamiento.groupby(['Pclass'])['Age'].median()\n",
    "medians"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creamos una función devuelve los valores medios de edad según la clase\n",
    "def getMedianAge(columns):\n",
    "    age = columns[0]    # primera columna\n",
    "    pclass = columns[1] # segunda columna\n",
    "    if pd.isnull(age): # comprueba si el elemento de age es nulo\n",
    "        if pclass is 1: \n",
    "            return medians[0]\n",
    "        elif pclass is 2:\n",
    "            return medians[1]\n",
    "        else:\n",
    "            return medians[2]\n",
    "    else:\n",
    "        return age\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n",
       "       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# visualizamos las columnas\n",
    "entrenamiento.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Pclass</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>27.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>19.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>26.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>32.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Age  Pclass\n",
       "0    22.0       3\n",
       "1    38.0       1\n",
       "2    26.0       3\n",
       "3    35.0       1\n",
       "4    35.0       3\n",
       "..    ...     ...\n",
       "886  27.0       2\n",
       "887  19.0       1\n",
       "888   NaN       3\n",
       "889  26.0       1\n",
       "890  32.0       3\n",
       "\n",
       "[891 rows x 2 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Seleccionamos nuestras 2 columnas\n",
    "columns = entrenamiento[['Age','Pclass']]\n",
    "columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# aplica sobre las 2 columnas la función 'getMedianAge'.\n",
    "# el parámetro axis=1 sirve para indicarle que lo aplique sobre las columnas\n",
    "entrenamiento['Age'] = columns.apply(getMedianAge, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa67b755350>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# comprobamos que se han rellenado los valores de Age\n",
    "sns.heatmap(entrenamiento.isnull())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Eliminamos la columna Cabin\n",
    "# con axis=1 le estamos diciendo que se trata de una columna\n",
    "# con inplace=True decimos que aplique el cambio definitivamente sobre el dataframe\n",
    "entrenamiento.drop('Cabin', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa67b68a610>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Comprobamos que se ha hecho una limpieza de los valores nulos\n",
    "sns.heatmap(entrenamiento.isnull())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dejamos solo variables numéricas y eliminamos las alfanuméricas:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Embarked  \n",
       "0      0         A/5 21171   7.2500        S  \n",
       "1      0          PC 17599  71.2833        C  \n",
       "2      0  STON/O2. 3101282   7.9250        S  \n",
       "3      0            113803  53.1000        S  \n",
       "4      0            373450   8.0500        S  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "entrenamiento.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Eliminamos algunas columnas que no son necesarias: \n",
    "entrenamiento.drop(['Name','PassengerId','Ticket'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass     Sex   Age  SibSp  Parch     Fare Embarked\n",
       "0         0       3    male  22.0      1      0   7.2500        S\n",
       "1         1       1  female  38.0      1      0  71.2833        C\n",
       "2         1       3  female  26.0      0      0   7.9250        S\n",
       "3         1       1  female  35.0      1      0  53.1000        S\n",
       "4         0       3    male  35.0      0      0   8.0500        S"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "entrenamiento.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "</table>\n",
       "<p>891 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     female  male\n",
       "0         0     1\n",
       "1         1     0\n",
       "2         1     0\n",
       "3         1     0\n",
       "4         0     1\n",
       "..      ...   ...\n",
       "886       0     1\n",
       "887       1     0\n",
       "888       1     0\n",
       "889       0     1\n",
       "890       0     1\n",
       "\n",
       "[891 rows x 2 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# necesitamos convertir a binario las columnas 'Sex' y 'Embarked'\n",
    "# Creamos 2 columnas (0 si no viene, 1 si viene)\n",
    "pd.get_dummies(entrenamiento['Sex'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "     male\n",
       "0       1\n",
       "1       0\n",
       "2       0\n",
       "3       0\n",
       "4       1\n",
       "..    ...\n",
       "886     1\n",
       "887     0\n",
       "888     0\n",
       "889     1\n",
       "890     1\n",
       "\n",
       "[891 rows x 1 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# vamos a decirle que borre la primera columna\n",
    "# si es 1 es un hombre, si es un 0 es una mujer\n",
    "sex = pd.get_dummies(entrenamiento['Sex'], drop_first=True)\n",
    "sex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# añadimos la nueva columna concatenando dataframes con la función concat\n",
    "entrenamiento = pd.concat([entrenamiento, sex], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# borramos la antigua columna\n",
    "entrenamiento.drop('Sex', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>3</th>\n",
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       "      <td>1</td>\n",
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       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass   Age  SibSp  Parch     Fare Embarked  male\n",
       "0         0       3  22.0      1      0   7.2500        S     1\n",
       "1         1       1  38.0      1      0  71.2833        C     0\n",
       "2         1       3  26.0      0      0   7.9250        S     0\n",
       "3         1       1  35.0      1      0  53.1000        S     0\n",
       "4         0       3  35.0      0      0   8.0500        S     1"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "entrenamiento.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>887</th>\n",
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       "    <tr>\n",
       "      <th>889</th>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     C  Q  S\n",
       "0    0  0  1\n",
       "1    1  0  0\n",
       "2    0  0  1\n",
       "3    0  0  1\n",
       "4    0  0  1\n",
       "..  .. .. ..\n",
       "886  0  0  1\n",
       "887  0  0  1\n",
       "888  0  0  1\n",
       "889  1  0  0\n",
       "890  0  1  0\n",
       "\n",
       "[891 rows x 3 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Falta hacer lo mismo con la columna 'Embarked' (puerto de embarque)\n",
    "puerto = pd.get_dummies(entrenamiento['Embarked'])\n",
    "puerto"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<p>891 rows × 2 columns</p>\n",
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      ],
      "text/plain": [
       "     Q  S\n",
       "0    0  1\n",
       "1    0  0\n",
       "2    0  1\n",
       "3    0  1\n",
       "4    0  1\n",
       "..  .. ..\n",
       "886  0  1\n",
       "887  0  1\n",
       "888  0  1\n",
       "889  0  0\n",
       "890  1  0\n",
       "\n",
       "[891 rows x 2 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Vamos a convertir las 3 columnas numéricas obtenidas en 2.\n",
    "# C Q S :son las iniciales de las ciudades de las que desenbarcó\n",
    "# si eliminamos la primera columna (C) podemos deducir que cuando Q y S sean 0, habrá desembarcado de C\n",
    "puerto = pd.get_dummies(entrenamiento['Embarked'], drop_first=True)\n",
    "puerto"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>male</th>\n",
       "      <th>Q</th>\n",
       "      <th>S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>29.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Survived  Pclass   Age  SibSp  Parch     Fare  male  Q  S\n",
       "0           0       3  22.0      1      0   7.2500     1  0  1\n",
       "1           1       1  38.0      1      0  71.2833     0  0  0\n",
       "2           1       3  26.0      0      0   7.9250     0  0  1\n",
       "3           1       1  35.0      1      0  53.1000     0  0  1\n",
       "4           0       3  35.0      0      0   8.0500     1  0  1\n",
       "..        ...     ...   ...    ...    ...      ...   ... .. ..\n",
       "886         0       2  27.0      0      0  13.0000     1  0  1\n",
       "887         1       1  19.0      0      0  30.0000     0  0  1\n",
       "888         0       3  29.0      1      2  23.4500     0  0  1\n",
       "889         1       1  26.0      0      0  30.0000     1  0  0\n",
       "890         0       3  32.0      0      0   7.7500     1  1  0\n",
       "\n",
       "[891 rows x 9 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# añadimos la nueva columna concatenando dataframes con la función concat\n",
    "entrenamiento = pd.concat([entrenamiento, puerto], axis=1)\n",
    "# eliminamos la columna alfanumérica antigua:\n",
    "entrenamiento.drop('Embarked', axis=1, inplace=True)\n",
    "# visualizamos el resultado\n",
    "entrenamiento"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Aplicamos machine learning (regresion logística). Realización de predicciones:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Obtenemos las características y el valor objetivo:\n",
    "# A partir del resto de características vamos a predecir si sobrevive o no\n",
    "\n",
    "X = entrenamiento.drop('Survived', axis=1) # características (borramos la columna Survived)\n",
    "y = entrenamiento['Survived'] # valores objetivo\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    1\n",
       "2    1\n",
       "3    1\n",
       "4    0\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
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       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>22.0</td>\n",
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       "      <td>7.2500</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass   Age  SibSp  Parch     Fare  male  Q  S\n",
       "0       3  22.0      1      0   7.2500     1  0  1\n",
       "1       1  38.0      1      0  71.2833     0  0  0\n",
       "2       3  26.0      0      0   7.9250     0  0  1\n",
       "3       1  35.0      1      0  53.1000     0  0  1\n",
       "4       3  35.0      0      0   8.0500     1  0  1"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# dividimos según test_size el porcentaje de datos que aplicaremos para testear nuestro entrenamiento\n",
    "# random_state: semilla para que coja de forma aleatoria los valores de la tabla\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y, test_size=0.2, random_state=45)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>male</th>\n",
       "      <th>Q</th>\n",
       "      <th>S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>332</th>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>153.4625</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>281</th>\n",
       "      <td>3</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.8542</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>3</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>34.3750</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1</td>\n",
       "      <td>58.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>26.5500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>405</th>\n",
       "      <td>2</td>\n",
       "      <td>34.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>21.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>725</th>\n",
       "      <td>3</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>607</th>\n",
       "      <td>1</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.5000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>544</th>\n",
       "      <td>1</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>106.4250</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>643</th>\n",
       "      <td>3</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>56.4958</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>3</td>\n",
       "      <td>44.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>712 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pclass   Age  SibSp  Parch      Fare  male  Q  S\n",
       "332       1  38.0      0      1  153.4625     1  0  1\n",
       "281       3  28.0      0      0    7.8542     1  0  1\n",
       "147       3   9.0      2      2   34.3750     0  0  1\n",
       "11        1  58.0      0      0   26.5500     0  0  1\n",
       "405       2  34.0      1      0   21.0000     1  0  1\n",
       "..      ...   ...    ...    ...       ...   ... .. ..\n",
       "725       3  20.0      0      0    8.6625     1  0  1\n",
       "607       1  27.0      0      0   30.5000     1  0  1\n",
       "544       1  50.0      1      0  106.4250     1  0  0\n",
       "643       3  29.0      0      0   56.4958     1  0  1\n",
       "414       3  44.0      0      0    7.9250     1  0  1\n",
       "\n",
       "[712 rows x 8 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 80% de valores (características) cogidos aleatoriamente para entrenar\n",
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# creamos el modelo\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "modelo = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ikari/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
      "\n",
      "lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
       "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
       "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# entrenamos el modelo con los datos de entrenamiento\n",
    "modelo.fit(X_train,y_train) # características de entrenamiento y su resultado"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0,\n",
       "       0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0,\n",
       "       1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0,\n",
       "       0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1,\n",
       "       1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0,\n",
       "       0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0,\n",
       "       0, 1, 0])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predicciones = modelo.predict(X_test)\n",
    "y_predicciones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived_test</th>\n",
       "      <th>Survived_predict</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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      "text/plain": [
       "     Survived_test  Survived_predict\n",
       "248              1                 0\n",
       "197              0                 0\n",
       "133              1                 1\n",
       "169              0                 0\n",
       "736              0                 0\n",
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      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# comparamos los valores de test con los obtenidos en las predicciones\n",
    "dicc = {'Survived_test': y_test, 'Survived_predict': y_predicciones}\n",
    "df_comparative = pd.DataFrame(dicc)\n",
    "df_comparative.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.87      0.87      0.87       117\n",
      "           1       0.76      0.76      0.76        62\n",
      "\n",
      "    accuracy                           0.83       179\n",
      "   macro avg       0.81      0.81      0.81       179\n",
      "weighted avg       0.83      0.83      0.83       179\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Obtenemos los datos resultantes de la predicción para ver su precisión a través de las métricas\n",
    "from sklearn.metrics import classification_report\n",
    "\n",
    "# con esto podemos saber que tenemos un 87% de precisión para los no supervivientes y un 76% para los supervivientes\n",
    "print(classification_report(y_test, y_predicciones))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[102,  15],\n",
       "       [ 15,  47]])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# matriz de confusión para ver los falsos positivos o falsos negativos\n",
    "from sklearn.metrics import confusion_matrix\n",
    "# nos indica los falsos positivos y negativos (15 y 15)\n",
    "\n",
    "\"\"\" ---- Matriz de confusión ----\n",
    "positivos_correctos, negativos_incorrectos\n",
    "positivos_incorrectos, negativos_correctos\n",
    "\"\"\"\n",
    "confusion_matrix(y_test, y_predicciones)"
   ]
  }
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