LogisticRegression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "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",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Moran, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330877</td>\n",
       "      <td>8.4583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>McCarthy, Mr. Timothy J</td>\n",
       "      <td>male</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17463</td>\n",
       "      <td>51.8625</td>\n",
       "      <td>E46</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Palsson, Master. Gosta Leonard</td>\n",
       "      <td>male</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>349909</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</td>\n",
       "      <td>female</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>347742</td>\n",
       "      <td>11.1333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Nasser, Mrs. Nicholas (Adele Achem)</td>\n",
       "      <td>female</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>237736</td>\n",
       "      <td>30.0708</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</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",
       "5            6         0       3   \n",
       "6            7         0       1   \n",
       "7            8         0       3   \n",
       "8            9         1       3   \n",
       "9           10         1       2   \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",
       "5                                   Moran, Mr. James    male   NaN      0   \n",
       "6                            McCarthy, Mr. Timothy J    male  54.0      0   \n",
       "7                     Palsson, Master. Gosta Leonard    male   2.0      3   \n",
       "8  Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)  female  27.0      0   \n",
       "9                Nasser, Mrs. Nicholas (Adele Achem)  female  14.0      1   \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  \n",
       "5      0            330877   8.4583   NaN        Q  \n",
       "6      0             17463  51.8625   E46        S  \n",
       "7      1            349909  21.0750   NaN        S  \n",
       "8      2            347742  11.1333   NaN        S  \n",
       "9      0            237736  30.0708   NaN        C  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import math\n",
    "\n",
    "titanic_data = pd.read_csv('data/titanic.csv')\n",
    "titanic_data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# of passengers in original data: 891\n"
     ]
    }
   ],
   "source": [
    "print(\"# of passengers in original data: \" + str(len(titanic_data.index)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analyzing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Survived', ylabel='count'>"
      ]
     },
     "execution_count": 3,
     "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": [
    "sns.countplot(x=\"Survived\", data=titanic_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Survived', ylabel='count'>"
      ]
     },
     "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": [
    "sns.countplot(x=\"Survived\", hue=\"Sex\", data=titanic_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Survived', ylabel='count'>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEGCAYAAACKB4k+AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/d3fzzAAAACXBIWXMAAAsTAAALEwEAmpwYAAAXGklEQVR4nO3df5BdZZ3n8feHkCHMgD8wQWMCJlo48jMNtIlOEBkZFrQcUX5tEDWsKeMfiFjOuqujpYAVV2vEH+WOsiAMoCwYdVQWRxRhWMesCwaJmIAsUaJpiBCCrGGFmMTv/tEnhzbpJJ2kb99u+v2qunXPfe7zPPd7NdUfnnPOPSdVhSRJAHt1uwBJ0uhhKEiSWoaCJKllKEiSWoaCJKm1d7cL2BOTJ0+uGTNmdLsMSRpT7rzzzkeraspg743pUJgxYwZLly7tdhmSNKYk+dX23nP3kSSpZShIklqGgiSpNaaPKUhSt2zcuJG+vj6eeuqpbpeyXZMmTWL69OlMnDhxyGMMBUnaDX19fey///7MmDGDJN0uZxtVxbp16+jr62PmzJlDHufuI0naDU899RTPe97zRmUgACThec973i6vZAwFSdpNozUQttid+gwFSVLLUJCkYTRhwgR6eno44ogjOPPMM/n973+/3b4XXnghn/zkJ0ewup3zQLM6bu7n5na7hF225Pwl3S5BY9S+++7LsmXLADjnnHO49NJLee9739vdonaBKwVJ6pBXvepVrFy5EoBrrrmGo446ilmzZvHWt751m76XX345L3/5y5k1axann356u8L46le/yhFHHMGsWbM4/vjjAVixYgWzZ8+mp6eHo446ivvvv3/YanalIEkdsGnTJr7zne9wyimnsGLFChYtWsSSJUuYPHkyjz322Db9TzvtNN7xjncA8KEPfYgrrriC888/n4svvpjvfve7TJs2jccffxyASy+9lAsuuIBzzjmHP/zhD2zevHnY6nalIEnD6Mknn6Snp4fe3l4OPvhgFixYwK233soZZ5zB5MmTATjggAO2Gbd8+XJe9apXceSRR3LttdeyYsUKAObOncu5557L5Zdf3v7xf+UrX8nHPvYxPvGJT/CrX/2Kfffdd9jqd6UgScNo4DGFLapqp6eHnnvuuXzzm99k1qxZXHXVVdx2221A/6rg9ttv59vf/jY9PT0sW7aMN7/5zcyZM4dvf/vbnHzyyXzxi1/kNa95zbDU70pBkjrsxBNPZPHixaxbtw5g0N1H69evZ+rUqWzcuJFrr722bf/FL37BnDlzuPjii5k8eTKrV6/ml7/8JS9+8Yt597vfzRve8AbuvvvuYavVlYIkddjhhx/OBz/4QV796lczYcIEjj76aK666qo/6fPRj36UOXPm8KIXvYgjjzyS9evXA/C+972P+++/n6rixBNPZNasWXz84x/ny1/+MhMnTuQFL3gBH/7wh4et1lTVsE020np7e8ub7Ix+npKqZ6J7772XQw89tNtl7NRgdSa5s6p6B+vv7iNJUqtjoZBkUpI7kvw0yYokFzXtFyZ5MMmy5vG6AWM+kGRlkvuSnNyp2iRJg+vkMYUNwGuq6okkE4EfJvlO896nq+pPftud5DBgHnA48ELg+0leWlXDdwKuJGmHOrZSqH5PNC8nNo8dHcA4Fbi+qjZU1QPASmB2p+qTJG2ro8cUkkxIsgx4BLi5qm5v3npXkruTXJnkuU3bNGD1gOF9TdvWcy5MsjTJ0rVr13ayfEkadzoaClW1uap6gOnA7CRHAF8AXgL0AGuAS5rug/2yY5uVRVVdVlW9VdU7ZcqUjtQtSePViPxOoaoeT3IbcMrAYwlJLgdubF72AQcNGDYdeGgk6pOkPXXs+64Z1vnu/Ie37bTP29/+dm688UYOPPBAli9fPiyf28mzj6YkeU6zvS/wN8DPk0wd0O1NwJZvcgMwL8k+SWYChwB3dKo+SRrrzj33XG666aZhnbOTK4WpwNVJJtAfPour6sYkX0rSQ/+uoVXAOwGqakWSxcA9wCbgPM88kqTtO/7441m1atWwztmxUKiqu4GjB2nf9kLiT7+3CFjUqZokSTvmL5olSS1DQZLUMhQkSS0vnS1Jw2Aop5AOt7PPPpvbbruNRx99lOnTp3PRRRexYMGCPZrTUJCkMeq6664b9jndfSRJahkKkqSWoSBJahkKkqSWoSBJahkKkqSWp6RK0jD49cVHDut8B3/4Zzvts3r1at72trfxm9/8hr322ouFCxdywQUX7NHnGgqSNEbtvffeXHLJJRxzzDGsX7+eY489lpNOOonDDjtst+d095EkjVFTp07lmGOOAWD//ffn0EMP5cEHH9yjOQ0FSXoGWLVqFXfddRdz5szZo3kMBUka45544glOP/10PvOZz/CsZz1rj+YyFCRpDNu4cSOnn34655xzDqeddtoez2coSNIYVVUsWLCAQw89lPe+973DMqdnH0nSMBjKKaTDbcmSJXzpS1/iyCOPpKenB4CPfexjvO51r9vtOTsWCkkmAT8A9mk+52tV9ZEkBwBfAWYAq4Czquq3zZgPAAuAzcC7q+q7napPksa64447jqoa1jk7uftoA/CaqpoF9ACnJHkF8H7glqo6BLileU2Sw4B5wOHAKcDnk0zoYH2SpK10LBSq3xPNy4nNo4BTgaub9quBNzbbpwLXV9WGqnoAWAnM7lR9kqRtdfRAc5IJSZYBjwA3V9XtwPOrag1A83xg030asHrA8L6mbes5FyZZmmTp2rVrO1m+JI07HQ2FqtpcVT3AdGB2kiN20D2DTTHInJdVVW9V9U6ZMmWYKpUkwQidklpVjwO30X+s4OEkUwGa50eabn3AQQOGTQceGon6JEn9OhYKSaYkeU6zvS/wN8DPgRuA+U23+cC3mu0bgHlJ9kkyEzgEuKNT9UmSttXJ3ylMBa5uziDaC1hcVTcm+RGwOMkC4NfAmQBVtSLJYuAeYBNwXlVt7mB9kjRs5n5u7rDOt+T8JTt8/6mnnuL4449nw4YNbNq0iTPOOIOLLrpojz+3Y6FQVXcDRw/Svg44cTtjFgGLOlWTJD1T7LPPPtx6663st99+bNy4keOOO47Xvva1vOIVr9ijeb3MhSSNQUnYb7/9gP7rH23cuJFksPN1do2hIElj1ObNm+np6eHAAw/kpJNO2uPLZoOhIElj1oQJE1i2bBl9fX3ccccdLF++fI/nNBQkaYx7znOewwknnMBNN920x3MZCpI0Bq1du5bHH38cgCeffJLvf//7vOxlL9vjeb10tiQNg52dQjrc1qxZw/z589m8eTN//OMfOeuss3j961+/x/MaCpI0Bh111FHcddddwz6vu48kSS1DQZLUMhQkaTcN913Phtvu1GcoSNJumDRpEuvWrRu1wVBVrFu3jkmTJu3SOA80S9JumD59On19fYzmm31NmjSJ6dOn79IYQ0GSdsPEiROZOXNmt8sYdu4+kiS1DAVJUstQkCS1DAVJUstQkCS1OhYKSQ5K8q9J7k2yIskFTfuFSR5Msqx5vG7AmA8kWZnkviQnd6o2SdLgOnlK6ibg76rqJ0n2B+5McnPz3qer6pMDOyc5DJgHHA68EPh+kpdW1eYO1ihJGqBjK4WqWlNVP2m21wP3AtN2MORU4Pqq2lBVDwArgdmdqk+StK0ROaaQZAZwNHB70/SuJHcnuTLJc5u2acDqAcP62HGISJKGWcdDIcl+wNeB91TV74AvAC8BeoA1wCVbug4yfJuLiiRZmGRpkqWj+eflkjQWdTQUkkykPxCurap/Bqiqh6tqc1X9Ebicp3cR9QEHDRg+HXho6zmr6rKq6q2q3ilTpnSyfEkadzp59lGAK4B7q+pTA9qnDuj2JmB5s30DMC/JPklmAocAd3SqPknStjp59tFc4K3Az5Isa9r+Hjg7SQ/9u4ZWAe8EqKoVSRYD99B/5tJ5nnkkSSOrY6FQVT9k8OME/7KDMYuARZ2qSZK0Y/6iWZLUMhQkSS1DQZLUMhQkSS1DQZLUMhQkSS1DQZLUMhQkSS1DQZLUMhQkSS1DQZLUMhQkSS1DQZLUMhQkSa0hhUKSW4bSJkka23Z4P4Ukk4A/ByYneS5P3x/hWcALO1ybJGmE7ewmO+8E3kN/ANzJ06HwO+AfO1eWJKkbdhgKVfVZ4LNJzq+qz41QTZKkLhnS7Tir6nNJ/gqYMXBMVV3TobokSV0w1APNXwI+CRwHvLx59O5kzEFJ/jXJvUlWJLmgaT8gyc1J7m+enztgzAeSrExyX5KTd/tbSZJ2y5BWCvQHwGFVVbsw9ybg76rqJ0n2B+5McjNwLnBLVX08yfuB9wP/OclhwDzgcPqPYXw/yUuravMufKYkaQ8M9XcKy4EX7MrEVbWmqn7SbK8H7gWmAacCVzfdrgbe2GyfClxfVRuq6gFgJTB7Vz5TkrRnhrpSmAzck+QOYMOWxqp6w1AGJ5kBHA3cDjy/qtY049ckObDpNg343wOG9TVtW8+1EFgIcPDBBw+xfEnSUAw1FC7c3Q9Ish/wdeA9VfW7JNvtOkjbNrurquoy4DKA3t7eXdmdJUnaiaGeffQ/d2fyJBPpD4Rrq+qfm+aHk0xtVglTgUea9j7goAHDpwMP7c7nSpJ2z1DPPlqf5HfN46kkm5P8bidjAlwB3FtVnxrw1g3A/GZ7PvCtAe3zkuyTZCZwCHDHrnwZSdKeGepKYf+Br5O8kZ0fBJ4LvBX4WZJlTdvfAx8HFidZAPwaOLP5jBVJFgP30H/m0nmeeSRJI2uoxxT+RFV9szmddEd9fsjgxwkATtzOmEXAot2pSZK054YUCklOG/ByL/p/t+BBXkl6hhnqSuFvB2xvAlbR/7sCSdIzyFCPKfyHThciSeq+oZ59ND3JN5I8kuThJF9PMr3TxUmSRtZQL3PxT/SfMvpC+n9l/D+aNknSM8hQQ2FKVf1TVW1qHlcBUzpYlySpC4YaCo8meUuSCc3jLcC6ThYmSRp5Qw2FtwNnAb8B1gBnAB58lqRnmKGekvpRYH5V/Rb6b5RD/0133t6pwiRJI2+oK4WjtgQCQFU9Rv+lsCVJzyBDDYW9trpt5gHs5iUyJEmj11D/sF8C/K8kX6P/8hZn4TWKJOkZZ6i/aL4myVLgNfRf5O60qrqno5VJkkbckHcBNSFgEEjSM9hQjylIksYBQ0GS1DIUJEktQ0GS1DIUJEmtjoVCkiub+y8sH9B2YZIHkyxrHq8b8N4HkqxMcl+SkztVlyRp+zq5UrgKOGWQ9k9XVU/z+BeAJIcB84DDmzGfTzKhg7VJkgbRsVCoqh8Ajw2x+6nA9VW1oaoeAFYCsztVmyRpcN04pvCuJHc3u5e2XE9pGrB6QJ++pm0bSRYmWZpk6dq1aztdqySNKyMdCl8AXgL00H9fhkua9gzStwaboKouq6requqdMsWbv0nScBrRUKiqh6tqc1X9Ebicp3cR9QEHDeg6HXhoJGuTJI1wKCSZOuDlm4AtZybdAMxLsk+SmcAhwB0jWZskqYP3REhyHXACMDlJH/AR4IQkPfTvGloFvBOgqlYkWUz/Bfc2AedV1eZO1SZJGlzHQqGqzh6k+Yod9F+E92iQpK7yF82SpJahIElqeZ9laZQ69n3XdLuEXXbnP7yt2yVoD7lSkCS1DAVJUstQkCS1DAVJUstQkCS1DAVJUstQkCS1DAVJUstQkCS1/EWzpHFt7ufmdruEXbLk/CUdnd+VgiSpZShIklqGgiSpZShIklqGgiSpZShIklodC4UkVyZ5JMnyAW0HJLk5yf3N83MHvPeBJCuT3Jfk5E7VJUnavk6uFK4CTtmq7f3ALVV1CHBL85okhwHzgMObMZ9PMqGDtUmSBtGxUKiqHwCPbdV8KnB1s3018MYB7ddX1YaqegBYCczuVG2SpMGN9DGF51fVGoDm+cCmfRqwekC/vqZtG0kWJlmaZOnatWs7WqwkjTej5UBzBmmrwTpW1WVV1VtVvVOmTOlwWZI0voz0tY8eTjK1qtYkmQo80rT3AQcN6DcdeGiEaxszfn3xkd0uYdc891ndrkDSEI30SuEGYH6zPR/41oD2eUn2STITOAS4Y4Rrk6Rxr2MrhSTXAScAk5P0AR8BPg4sTrIA+DVwJkBVrUiyGLgH2AScV1WbO1WbJGlwHQuFqjp7O2+duJ3+i4BFnapHkrRzo+VAsyRpFPAmO5KGzZg7CQI8EWIrrhQkSS1DQZLUMhQkSa1xf0zh2Pdd0+0Sdtk39u92BZKeqVwpSJJahoIkqWUoSJJahoIkqWUoSJJahoIkqWUoSJJahoIkqWUoSJJahoIkqWUoSJJahoIkqWUoSJJaXblKapJVwHpgM7CpqnqTHAB8BZgBrALOqqrfdqM+SRqvurlS+Ouq6qmq3ub1+4FbquoQ4JbmtSRpBI2m3UenAlc321cDb+xeKZI0PnUrFAr4XpI7kyxs2p5fVWsAmucDBxuYZGGSpUmWrl27doTKlaTxoVt3XptbVQ8lORC4OcnPhzqwqi4DLgPo7e2tThUoSeNRV1YKVfVQ8/wI8A1gNvBwkqkAzfMj3ahNksazEQ+FJH+RZP8t28C/A5YDNwDzm27zgW+NdG2SNN51Y/fR84FvJNny+f+9qm5K8mNgcZIFwK+BM7tQmySNayMeClX1S2DWIO3rgBNHuh5J0tNG0ympkqQuMxQkSS1DQZLUMhQkSS1DQZLUMhQkSS1DQZLUMhQkSS1DQZLUMhQkSS1DQZLUMhQkSS1DQZLUMhQkSS1DQZLUMhQkSS1DQZLUMhQkSS1DQZLUGnWhkOSUJPclWZnk/d2uR5LGk1EVCkkmAP8IvBY4DDg7yWHdrUqSxo9RFQrAbGBlVf2yqv4AXA+c2uWaJGnc2LvbBWxlGrB6wOs+YM7ADkkWAgubl08kuW+Eahs1XtS5qScDj3Zu+rEj7063SxiT/LfZecP0b3O7/1eNtlAY7NvWn7yougy4bGTKGV+SLK2q3m7XIW3Nf5sjZ7TtPuoDDhrwejrwUJdqkaRxZ7SFwo+BQ5LMTPJnwDzghi7XJEnjxqjafVRVm5K8C/guMAG4sqpWdLms8cTdchqt/Lc5QlJVO+8lSRoXRtvuI0lSFxkKkqSWoSAvLaJRK8mVSR5JsrzbtYwXhsI456VFNMpdBZzS7SLGE0NBXlpEo1ZV/QB4rNt1jCeGgga7tMi0LtUiqcsMBe300iKSxg9DQV5aRFLLUJCXFpHUMhTGuaraBGy5tMi9wGIvLaLRIsl1wI+Av0zSl2RBt2t6pvMyF5KklisFSVLLUJAktQwFSVLLUJAktQwFSVLLUJCAJB9MsiLJ3UmWJZkzDHO+YbiuOpvkieGYR9oZT0nVuJfklcCngBOqakOSycCfVdVOf9mdZO/mtx6drvGJqtqv058juVKQYCrwaFVtAKiqR6vqoSSrmoAgSW+S25rtC5NcluR7wDVJbk9y+JbJktyW5Ngk5yb5r0me3cy1V/P+nydZnWRikpckuSnJnUn+LcnLmj4zk/woyY+TfHSE//fQOGYoSPA94KAk/yfJ55O8eghjjgVOrao303+58bMAkkwFXlhVd27pWFX/F/gpsGXevwW+W1Ub6b8h/flVdSzwH4HPN30+C3yhql4O/GaPv6E0RIaCxr2qeoL+P/ILgbXAV5Kcu5NhN1TVk832YuDMZvss4KuD9P8K8O+b7XnNZ+wH/BXw1STLgP9G/6oFYC5wXbP9pV35PtKe2LvbBUijQVVtBm4DbkvyM2A+sImn/8Np0lZD/t+AsQ8mWZfkKPr/8L9zkI+4AfgvSQ6gP4BuBf4CeLyqerZX1u59G2n3uVLQuJfkL5McMqCpB/gVsIr+P+AAp+9kmuuB/wQ8u6p+tvWbzWrkDvp3C91YVZur6nfAA0nObOpIklnNkCX0rygAztnlLyXtJkNBgv2Aq5Pck+Ru+u9VfSFwEfDZJP8GbN7JHF+j/4/44h30+QrwluZ5i3OABUl+Cqzg6VuhXgCcl+THwLN37etIu89TUiVJLVcKkqSWoSBJahkKkqSWoSBJahkKkqSWoSBJahkKkqTW/weEo2RiKl/rpAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=\"Survived\", hue=\"Pclass\", data=titanic_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Frequency'>"
      ]
     },
     "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": [
    "titanic_data[\"Age\"].plot.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Frequency'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "titanic_data[\"Fare\"].plot.hist(bins=20, figsize=(10,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  891 non-null    int64  \n",
      " 1   Survived     891 non-null    int64  \n",
      " 2   Pclass       891 non-null    int64  \n",
      " 3   Name         891 non-null    object \n",
      " 4   Sex          891 non-null    object \n",
      " 5   Age          714 non-null    float64\n",
      " 6   SibSp        891 non-null    int64  \n",
      " 7   Parch        891 non-null    int64  \n",
      " 8   Ticket       891 non-null    object \n",
      " 9   Fare         891 non-null    float64\n",
      " 10  Cabin        204 non-null    object \n",
      " 11  Embarked     889 non-null    object \n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.7+ KB\n"
     ]
    }
   ],
   "source": [
    "titanic_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='SibSp', ylabel='count'>"
      ]
     },
     "execution_count": 9,
     "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": [
    "sns.countplot(x=\"SibSp\", data=titanic_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Wrangling"
   ]
  },
  {
   "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>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass   Name    Sex    Age  SibSp  Parch  Ticket  \\\n",
       "0          False     False   False  False  False  False  False  False   False   \n",
       "1          False     False   False  False  False  False  False  False   False   \n",
       "2          False     False   False  False  False  False  False  False   False   \n",
       "3          False     False   False  False  False  False  False  False   False   \n",
       "4          False     False   False  False  False  False  False  False   False   \n",
       "..           ...       ...     ...    ...    ...    ...    ...    ...     ...   \n",
       "886        False     False   False  False  False  False  False  False   False   \n",
       "887        False     False   False  False  False  False  False  False   False   \n",
       "888        False     False   False  False  False   True  False  False   False   \n",
       "889        False     False   False  False  False  False  False  False   False   \n",
       "890        False     False   False  False  False  False  False  False   False   \n",
       "\n",
       "      Fare  Cabin  Embarked  \n",
       "0    False   True     False  \n",
       "1    False  False     False  \n",
       "2    False   True     False  \n",
       "3    False  False     False  \n",
       "4    False   True     False  \n",
       "..     ...    ...       ...  \n",
       "886  False   True     False  \n",
       "887  False  False     False  \n",
       "888  False   True     False  \n",
       "889  False  False     False  \n",
       "890  False   True     False  \n",
       "\n",
       "[891 rows x 12 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Survived         0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age            177\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             0\n",
       "Cabin          687\n",
       "Embarked         2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 12,
     "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": [
    "sns.heatmap(titanic_data.isnull(), yticklabels=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Pclass', ylabel='Age'>"
      ]
     },
     "execution_count": 13,
     "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": [
    "sns.boxplot(x=\"Pclass\", y=\"Age\", data=titanic_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data.drop('Cabin', axis=1, inplace=True)\n",
    "titanic_data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic_data.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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2oT2vVw37tqatdzrqzMPhzfgUWjPDo2kdXMdn5klj1h1qv522hOkxw67dgftm5pzTgzvULX+Oh7orTPCJiAtzxAkwMbVIekQcl5lVo4BKJ3j1Dt2ltODbnvaG2J22uPWxc/7iqte9mDao+RjgU5MwGLnmnNN8R2xXnb4PJ9FOcV9LG1r0fOAXmfn6sWvPt4jYEHgG8KyRP2SmO5YmI3OgTZD4bcHInHl5jiPig7RheIfSHv8rgNtn5gtGrDl9hjzqUfVK6i8CnpGZozYbjbHgzb1o7W8BfHEYRD6qmGUBloKapQugrOQ+nJOZD5w+KomIUzPzkWPXVo35eo6Hs4o3sfw04LeOeUAz40h3Xi4NFBGnZeaoq6n1Hr2wIW1c3cem9q2dI60EFRH7ZOa/AAdFxAqfHjniZYIqQvUmmPxdL4mIJ9I6Xe46j/dnwYmIe2Vbd2HWABi7SYV5eo6HcP37sevMsF20K74EbXLC5ECqatIPwBci4rW0s4vp0SLdxqD37nE+F9iUNkEhgNvRXiyXAi/JzHM615scRZ/d+XZvsog4Ctgrh2uiDR1576iYHAG8dehhfg1wMO2qGa8uqLsm2Zs2A+wdU/umP+BHbVul+DmOiEMy8+UR8Wlmmf6amaPNSqvo+L4JJu/b6Qk3CdytV4HebbqH0To2Thy2H0ubQngM8O7M3GGu31+Fug/IzK5j6W5G7RXanuajPUrjiIiHAD/MYV3oiHg+bdjY94EDeh4Bzai7Lq0N9+60BV+OyMzRh01FxBWZuWTomF5BZp469n1Y6HqH7gpDaCb7ZhuC0rHuKbQe9WOBozPzojHqrKT2+cDOmfnrYXtD2nq6Y/by7jfHjzOHqapadfM1Cy8iPk5rWjidNiLnB1kzy3GNP2AYxtvfh+VHi3y41+33bl74VUS8nvbChDaS4dfDVLrRpqpm5qOiXbHimcDhEbGENmRs9PV0aaed/xNtOnAO9+GgkWvO1pmxHm3O+EaAodvPvMzCoy1Uvg1ARBxBuxJxhZmXXl9Oxaic+TR0kO9MC93P0T7wvgx0C93eM9KeQ2vk/y/apaM3G/YtpoXRaDLzZ5n5Htop2XnAXEeDPet+mLYIy89ps5SeliMspj2j5jsmX7SFfm5Nm0lzNB3bngTM3yy86zufK5oVpiymTfC57Uq+Frrdac/zzzLzhcB2wK16Fuj6osnMy2inXrP5Ts9a0yLi3rSjkN1pc/OPpnU8jGaWNrfDKt8cQzPG3rQZO0fRZof9uqr+GuRjtOX+LqPNkjodYJiFN+YawpOefFi+N3/snvxLMvPAkW57dfD7zLwuIq4ZzpgvpfOBTO8hY1vTBnFvwfLX7Bq7h/dI2pvjsZn505FrTRzF8m1u9wZeVVE4Iv6VdnR9OLBNZv62ou6aKDMPiogvcsMsvEknyCJWfoDRo+589eTHPNW9pTg7Im5Hu/rwObT1Y7o27fTuSDsfOIx2Z69fkGSEoWLTNbsvvXYT61441ea2Fu3SLSWDuSPiOtqqYpMl967/EXXjGbUARcSGY43IWN1Eu8r4kux8de/ebVLXZOb7Ot/mnLKto7tRRKyTRWuODpZrc4uoO0DIzN5t8RLQdxLA6iraVaenl/DsGrq9j3QPoLWBHM/y67uOfUWB99PWeyhZc3Soee1UraB1Zv0Ojzal1VZEvJfWTzOZVfss4LvZ8eokvUP34ll2Z2aO2qO+snUQbiFTdSWtJiLiIuB+k7b7YRGcC7PjpYJ6j14oW990Rl3DVVIP36INdZ1cX3FTOjcv9B69cBvaMKbNMnPPiLgHcM/M/EzPOrPUPYXZ54mPPWpC0gIwtdbEBsA3IuLMYXsH4Ks9a/XuSDuSNnLhYcP2j2lTc0cNXZa/LPa6tLnxlQPKJa3e3l5VaJS1F2IeL9czdV9cV1bSn2WYGDE91+AWu7TjnyLi1gyn+hGxFVOjGMYyzM6aWAQ8CLjT2HUlLSwRsSdt7ZLf09aLmVyKvttggN6huz/twoGbRsRHgJ2AF3SuMZtzuKFN9xrasnsvLqgraWF5He3ad5eNVaD36IUvDEvh7Uj7hNhrzDsfEQ8GfjQZNTFjrdOvj1VX0oL1Xdp4+9H0btPdCTgvM6+KiOfSJiy8OzN/cCO/+ufWm5e1TiUtTBHxANqAgDNYfoJXt0t/9Q7dC2hLoW1LW3/yg7SlDkfp0JrupIuIQ2lXST1g2B5t0XRJC9MwVOzLtJUDr18DPDOP6lVjjLUXMiKeDLwnM48YTvnHsjgi1hqWVNyFdi2riTHXOpW0MF2TmStdxL2H3sF0ZUTsCzwXeMSwAtjanWtMm6+1TiUtTKcMIxg+zUjrx/RuXrgT7UoRZ2Xm6RGxGe36Yd0udTFLzR25Ya3Tq4Z9WwPr5/iXx5a0gFSsH9M7dNcD/jAst7g1cC/ghMy8+kZ+VZLWCL3XZT0NuFVE3AX4Iu26XR/qXEOSuoqIfaa+f8aMn72tZ63eoRuZ+TvapWQOzsynAt2WRJOkkewx9f2+M372+J6FuoduRDyUdrHEzw775utaT5J0U8VKvp9te5X0Dt29aJ8Sx2fmRRFxN+CUzjUkqbdcyfezba+Srh1pkrQ6mrr81vSltxi2183MbkNfe49e2BjYh9aOu+5kv4uJS1LTu3nhI8A3gS2BN9MWnjmrcw1JWm31PtI9JzMfGBEXZOa2wz4XE5ekQe9pwJNJEJdExBOBnwJ37VxDklZbvUP3rRGxAfAa4GBgCfDqzjUkabXVpXkhItYFXgrcnbYk2hHDyl+SpCm9QvfjtKaF04FdgR9k5l6rfMOStMD0Ct0LM3Ob4fu1gDMzc/tVvmFJWmB6DRm7fhUxmxUkaeV6HelOZnPA8jM6grYW5ZJVLiJJC4DTgCWpUO8ZaZKkORi6klTI0JWkQoauJBUydCWp0P8DH9W2FpitQNoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(titanic_data.isnull(), yticklabels=False, cbar=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId    0\n",
       "Survived       0\n",
       "Pclass         0\n",
       "Name           0\n",
       "Sex            0\n",
       "Age            0\n",
       "SibSp          0\n",
       "Parch          0\n",
       "Ticket         0\n",
       "Fare           0\n",
       "Embarked       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th {\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>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": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>male</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   male\n",
       "0     1\n",
       "1     0\n",
       "2     0\n",
       "3     0\n",
       "4     1"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sex = pd.get_dummies(titanic_data[\"Sex\"], drop_first=True)\n",
    "sex.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></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>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Q  S\n",
       "0  0  1\n",
       "1  0  0\n",
       "2  0  1\n",
       "3  0  1\n",
       "4  0  1"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embark = pd.get_dummies(titanic_data[\"Embarked\"], drop_first=True)\n",
    "embark.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   2  3\n",
       "0  0  1\n",
       "1  0  0\n",
       "2  0  1\n",
       "3  0  0\n",
       "4  0  1"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Pcl = pd.get_dummies(titanic_data[\"Pclass\"], drop_first=True)\n",
    "Pcl.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</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",
       "      <th>male</th>\n",
       "      <th>Q</th>\n",
       "      <th>S</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <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",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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  male  Q  S  2  3  \n",
       "0      0         A/5 21171   7.2500        S     1  0  1  0  1  \n",
       "1      0          PC 17599  71.2833        C     0  0  0  0  0  \n",
       "2      0  STON/O2. 3101282   7.9250        S     0  0  1  0  1  \n",
       "3      0            113803  53.1000        S     0  0  1  0  0  \n",
       "4      0            373450   8.0500        S     1  0  1  0  1  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data = pd.concat([titanic_data, sex, embark, Pcl], axis=1)\n",
    "titanic_data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>Survived</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",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</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",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</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",
       "      <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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</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",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived   Age  SibSp  Parch     Fare  male  Q  S  2  3\n",
       "0         0  22.0      1      0   7.2500     1  0  1  0  1\n",
       "1         1  38.0      1      0  71.2833     0  0  0  0  0\n",
       "2         1  26.0      0      0   7.9250     0  0  1  0  1\n",
       "3         1  35.0      1      0  53.1000     0  0  1  0  0\n",
       "4         0  35.0      0      0   8.0500     1  0  1  0  1"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data.drop([\"Sex\", \"Embarked\", \"PassengerId\", \"Name\", \"Ticket\", \"Pclass\"], axis=1, inplace=True)\n",
    "titanic_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = titanic_data.drop(\"Survived\", axis=1)\n",
    "y = titanic_data[\"Survived\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "logmodel = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/william/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:762: ConvergenceWarning: 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_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logmodel.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = logmodel.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'              precision    recall  f1-score   support\\n\\n           0       0.79      0.88      0.83       122\\n           1       0.81      0.70      0.75        92\\n\\n    accuracy                           0.80       214\\n   macro avg       0.80      0.79      0.79       214\\nweighted avg       0.80      0.80      0.80       214\\n'"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classification_report(y_test, predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[107,  15],\n",
       "       [ 28,  64]])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test, predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7990654205607477"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test, predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SUV PREDICTIONS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>User ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Purchased</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15624510</td>\n",
       "      <td>Male</td>\n",
       "      <td>19</td>\n",
       "      <td>19000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15810944</td>\n",
       "      <td>Male</td>\n",
       "      <td>35</td>\n",
       "      <td>20000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15668575</td>\n",
       "      <td>Female</td>\n",
       "      <td>26</td>\n",
       "      <td>43000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15603246</td>\n",
       "      <td>Female</td>\n",
       "      <td>27</td>\n",
       "      <td>57000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15804002</td>\n",
       "      <td>Male</td>\n",
       "      <td>19</td>\n",
       "      <td>76000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>15728773</td>\n",
       "      <td>Male</td>\n",
       "      <td>27</td>\n",
       "      <td>58000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>15598044</td>\n",
       "      <td>Female</td>\n",
       "      <td>27</td>\n",
       "      <td>84000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>15694829</td>\n",
       "      <td>Female</td>\n",
       "      <td>32</td>\n",
       "      <td>150000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>15600575</td>\n",
       "      <td>Male</td>\n",
       "      <td>25</td>\n",
       "      <td>33000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>15727311</td>\n",
       "      <td>Female</td>\n",
       "      <td>35</td>\n",
       "      <td>65000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    User ID  Gender  Age  EstimatedSalary  Purchased\n",
       "0  15624510    Male   19            19000          0\n",
       "1  15810944    Male   35            20000          0\n",
       "2  15668575  Female   26            43000          0\n",
       "3  15603246  Female   27            57000          0\n",
       "4  15804002    Male   19            76000          0\n",
       "5  15728773    Male   27            58000          0\n",
       "6  15598044  Female   27            84000          0\n",
       "7  15694829  Female   32           150000          1\n",
       "8  15600575    Male   25            33000          0\n",
       "9  15727311  Female   35            65000          0"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = pd.read_csv(\"data/SUV_Predictions.csv\")\n",
    "dataset.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = dataset.iloc[:, [2,3]].values\n",
    "y = dataset.iloc[:, 4].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "sc = StandardScaler()\n",
    "X_train = sc.fit_transform(X_train)\n",
    "X_test = sc.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(random_state=0)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier = LogisticRegression(random_state=0)\n",
    "classifier.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "89.0"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test, y_pred)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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