titanic_project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Titanic Machine Learning Classification Project"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this project, I will try to predict a classification between survival or deceased.\n",
    "\n",
    "*Data Dictionary*\n",
    "\n",
    "|Variable  |Definition                                   |Key                                           |\n",
    "|--------- |:-------------------------------------------:| --------------------------------------------:|\n",
    "|survival  |  Survival                                   |0 = No, 1 = Yes                               |\n",
    "|pclass    |  Ticket class                               |1 = 1st, 2 = 2nd, 3 = 3rd                     |\n",
    "|sex       |  Sex                                        |                                              |\n",
    "|Age       |  Age in years                               |                                              |\n",
    "|sibsp     |  # of siblings / spouses aboard the Titanic |                                              |\n",
    "|parch     |  # of parents / children aboard the Titanic |                                              |\n",
    "|ticket    |  Ticket number                              |                                              |\n",
    "|fare      |  Passenger fare                             |                                              |\n",
    "|cabin     |  Cabin number                               |                                              |\n",
    "|embarked  |  Port of Embarkation                        |C = Cherbourg, Q = Queenstown, S = Southampton|\n",
    "\n",
    "\n",
    "\n",
    "So,I will follow these steps described below:\n",
    "\n",
    "1. EDA -  To get an overview from the data set;\n",
    "2. Cleaning - Deal with missing values; drop some column that is not necessary for the project; and other intrinsic  data manipulation;\n",
    "3. Create some featuring engineering."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For titanic project, I will use a \"semi-cleaned\" train data set and make more cleaning steps to prepare for the machine learning model evaluation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv('titanic_train.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Some information about the data set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "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": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
       "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
       "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
       "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
       "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
       "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
       "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
       "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
       "\n",
       "            Parch        Fare  \n",
       "count  891.000000  891.000000  \n",
       "mean     0.381594   32.204208  \n",
       "std      0.806057   49.693429  \n",
       "min      0.000000    0.000000  \n",
       "25%      0.000000    7.910400  \n",
       "50%      0.000000   14.454200  \n",
       "75%      0.000000   31.000000  \n",
       "max      6.000000  512.329200  "
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "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>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": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### *Missing Values*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "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.heatmap(train.isnull(),yticklabels=False,cbar=False,cmap='viridis')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The yellow marks demonstrate the missing values in the table.\n",
    "\n",
    "Roughly 20 percent of the Age data is missing. The proportion of Age missing is likely small enough for reasonable replacement with some form of imputation. Looking at the Cabin column, it looks like are just missing too much of that data to do something useful with at a basic level. Probably I will drop this later, or change it to another feature like \"Cabin Known: 1 or 0\"."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now I will focus on *Survived* column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "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=train,palette='RdBu_r')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "About the above results, 0 = Did not survive and 1 = Did Survive, so almost 550 pessengers did not survive versus approximately 300 who did survive. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now looking about the gender of the survived column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "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=train,palette='RdBu_r')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The result demonstrate a higher number of men then women who did not survive. And almost 100% more women survived than men."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now I will analyze the pessenger's class distribution on survived feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "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='Pclass',data=train,palette='rainbow')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Age distribution about the pessengers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "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.distplot(train['Age'].dropna(),kde=False,color='darkred',bins=30)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The distribution about passegers' fare."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,4))\n",
    "sns.distplot(train['Fare'],kde=False,bins=40,color='green')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### *Data Cleaning*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To fill the missing age data instead of just dropping the missing age data rows. One way to do this is by filling in the mean age of all the passengers (imputation). So I will check the average age by passenger class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAs0AAAGpCAYAAAB2wgtQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAfo0lEQVR4nO3dfZBd510f8O9PljSxnRAn2nVisgk2sA5vg02rMYHMUGITOSDAniahMJQ6Oy4mY7VF0E5JYagxLyFMGRAFidQlLG4mQIKd2J6IYnmMaQgvTpRgJXGcsiGCsPhFu/JL7ESDZevpH7pOHSP5SNa9e6Szn8+M5u45e++erzS69nd+ep5zq7UWAADg6Nb0HQAAAE52SjMAAHRQmgEAoIPSDAAAHZRmAADosLbvAMdiamqqnXvuuX3HAABgwD7ykY8st9amj/S9U6I0n3vuudm9e3ffMQAAGLCq+rujfc/yDAAA6KA0AwBAB6UZAAA6KM0AANBBaQYAgA5KMwAAdFCaAQCgg9IMAAAdlGYAAOigNAMAQAelGQAAOijNAADQYaKluap+rKrurqpPVNXvVdXzquq8qrqzqhaq6t1VtX6SGQAA4ERNrDRX1cuS/IckG1tr35DktCTfn+SXkvxqa202yUNJrpxUBgAAGIdJL89Ym+T0qlqb5Iwk9yW5OMkNo+9fn+TyCWcAAIATsnZSP7i19g9V9ctJPpvkQJJdST6S5OHW2hOjpy0medmkMgzNtm3bsrCw0HeMY7a4uJgkmZmZ6TnJ8Zmdnc3WrVv7jgEAnEQmuTzjRUkuS3Jeki9PcmaS7zzCU9tRXn9VVe2uqt1LS0uTiskEHThwIAcOHOg7BgDACZvYpDnJdyTZ21pbSpKqem+Sb01yVlWtHU2bZ5Lce6QXt9auS3JdkmzcuPGIxXq1OdWmn1u2bEmSbN++veckAAAnZpJrmj+b5FVVdUZVVZJLknwyyR1J3jB6zhVJbp5gBgAAOGETK82ttTtzeMPfR5N8fHSt65L8RJIfr6pPJ9mQ5B2TygAAAOMwyeUZaa1dk+SaZ5z+TJKLJnldAAAYJ58ICAAAHZRmAADooDQDAEAHpRkAADoozQAA0EFpBgCADkozAAB0UJoBAKCD0gwAAB2UZgAA6KA0AwBAB6UZAAA6KM0AANBBaQYAgA5KMwAAdFCaAQCgg9IMAAAdlGYAgDFZXl7O1Vdfnf379/cdhTFTmgEAxmR+fj579uzJ/Px831EYM6UZAGAMlpeXs3PnzrTWsnPnTtPmgVGaAQDGYH5+Pq21JMmhQ4dMmwdGaQYAGINdu3bl4MGDSZKDBw/m1ltv7TkR46Q0AwCMwaZNm7Ju3bokybp163LppZf2nIhxUpoBAMZgbm4uVZUkWbNmTebm5npOxDgpzQAAYzA1NZXNmzenqrJ58+Zs2LCh70iM0dq+AwAADMXc3Fz27t1ryjxASjMAwJhMTU1lx44dfcdgAizPAACADkozAAB0UJoBAKCD0gwAAB2UZgAA6KA0AwCMyfLycq6++urs37+/7yiMmdIMADAm8/Pz2bNnT+bn5/uOwpgpzQADZNoFK295eTk7d+5May07d+70/huYiZXmqnplVd31tF+fq6qtVfXiqrqtqhZGjy+aVAaA1cq0C1be/Px8WmtJkkOHDnn/DczESnNr7f+21i5srV2Y5J8n+UKS9yV5S5LbW2uzSW4fHQMwJqZd0I9du3bl4MGDSZKDBw/m1ltv7TkR47RSyzMuSfI3rbW/S3JZkutH569PcvkKZQBYFUy7oB+bNm3KunXrkiTr1q3LpZde2nMixmmlSvP3J/m90dcvaa3dlySjx7OP9IKquqqqdlfV7qWlpRWKCXDqM+2CfszNzaWqkiRr1qzJ3Nxcz4kYp4mX5qpan+R7k/zB8byutXZda21ja23j9PT0ZMIBDJBpF/RjamoqmzdvTlVl8+bN2bBhQ9+RGKOVmDR/Z5KPttYeGB0/UFXnJMnocd8KZABYNUy7oD9zc3O54IILvO8GaCVK8w/k/y/NSJJbklwx+vqKJDevQAaAVcO0C/ozNTWVHTt2eN8N0NpJ/vCqOiPJa5P8yNNOvy3Je6rqyiSfTfLGSWYAWI3m5uayd+9e0y6AMZloaW6tfSHJhmec25/Dd9MAYEKemnYBMB4+ERAAADoozQAA0EFpBgCADkozAAB0UJoBAKCD0gwAAB2UZgAA6KA0AwzQ8vJyrr766uzfv7/vKACDoDQDDND8/Hz27NmT+fn5vqMADILSDDAwy8vL2blzZ1pr2blzp2kzwBgozQADMz8/n9ZakuTQoUOmzQBjoDQDDMyuXbty8ODBJMnBgwdz66239pwI4NSnNAMMzKZNm7Ju3bokybp163LppZf2nAhWD5twh0tpBhiYubm5VFWSZM2aNZmbm+s5EaweNuEOl9IMMDBTU1PZvHlzqiqbN2/Ohg0b+o4Eq4JNuMOmNAMM0NzcXC644AJTZlhBNuEOm9IMMEBTU1PZsWOHKTOsIJtwh01pBgAYA5twh01pBgAYA5twh01pBgAYA5twh01pBgAYk8suuyxnnHFGLr/88r6jMGZKMwDAmNx88835whe+kJtuuqnvKIyZ0gwAMAbu0zxsSjMAwBi4T/OwKc0AAGPgPs3DpjQDDNDy8nKuvvpq/zwMK8h9modNaQYYoPn5+ezZs8c/D8MKcp/mYVOaAQbGZiToh/s0D5vSDDAwNiNBf+bm5nLBBReYMg+Q0gwwMDYjAYyf0gwwMDYjQX/sJxgupRlgYGxGgn7YTzBsSjPAwNiMBP2wn2DYlGaAAbIZCVae/QTDNtHSXFVnVdUNVfWpqrqnqr6lql5cVbdV1cLo8UWTzAAAsBLsJxi2SU+afy3JH7XWvibJBUnuSfKWJLe31maT3D46BmCMbEaClWc/wbBNrDRX1Zcl+bYk70iS1trjrbWHk1yW5PrR065PcvmkMgCsRjYjQT/sJxi2SU6avzLJUpL5qvqrqvqtqjozyUtaa/clyejx7AlmAFh1bEaC/thPMFyTLM1rk/yzJL/ZWvumJJ/PcSzFqKqrqmp3Ve1eWlqaVEaAwbEZCWD8JlmaF5MsttbuHB3fkMMl+oGqOidJRo/7jvTi1tp1rbWNrbWN09PTE4wJMCybNm3KmjWH//O+Zs0am5FgBdlPMFwTK82ttfuT/H1VvXJ06pIkn0xyS5IrRueuSHLzpDIArEaXXXZZDh06lOTw8ozLL7d1BFaC/QTDNum7Z/z7JO+qqo8luTDJW5O8Lclrq2ohyWtHxwCMyc033/zFHfxVlZtuuqnnRLA62E8wbBMtza21u0ZLLL6xtXZ5a+2h1tr+1tolrbXZ0eODk8wAsNrs2rXri//jbq1Z0wwrxH6CYfOJgAAD4wMWoB/ee8OmNAMMjA9YgH547w2b0gwwMD5gAfrhvTdsa/sOAMD4zc3NZe/evSZdsMK894bLpBlggB588MEsLCzkoYce6jsKrCpTU1PZsWOHKfMAKc0AA3Tttdfm85//fK655pq+owAMgtIMMDB//dd/nb179yZJ9u7dm09/+tM9JwI49SnNAANz7bXXfsmxaTPAiVOaAQbmqSnz0Y4BOH5KM8DAnHfeec96DMDxU5oBBuaZyzGeuVwDmJzl5eVcffXV2b9/f99RGDOlGWBgzj///C9Ol88777x89Vd/dc+JYPWYn5/Pnj17Mj8/33cUxkxpBhiga665JmeeeaYpM6yg5eXl7Ny5M6217Ny507R5YJRmgAE6//zzc9ttt5kywwqan59Pay1JcujQIdPmgVGaAQDGYNeuXTl48GCS5ODBg7n11lt7TsQ4re07AMDJbtu2bVlYWOg7xnFZXFxMkszMzPSc5NjNzs5m69atfceA52zTpk15//vfn4MHD2bdunW59NJL+47EGJk0AwzQgQMHcuDAgb5jwKoyNzeXqkqSrFmzJnNzcz0nYpxMmgE6nIrTzy1btiRJtm/f3nMSWD2mpqayefPm3HTTTdm8eXM2bNjQdyTGSGkGABiTubm57N2715R5gJRmAIAxmZqayo4dO/qOwQRY0wwAAB2UZgAA6KA0AwBAB6UZAAA6KM0AANBBaQYAgA5KMwAAdFCaAQCgg9IMAAAdlGYAAOigNAMAQAelGQAAOijNAADQYW3fAQAAjmTbtm1ZWFjoO8ZxWVxcTJLMzMz0nOTYzc7OZuvWrX3HOOkpzQAAY3LgwIG+IzAhEy3NVfW3SR5N8mSSJ1prG6vqxUneneTcJH+b5Ptaaw9NMgcAcOo5FaefW7ZsSZJs37695ySM20qsaX5Na+3C1trG0fFbktzeWptNcvvoGAAATlp9bAS8LMn1o6+vT3J5DxkAAOCYTbo0tyS7quojVXXV6NxLWmv3Jcno8ewjvbCqrqqq3VW1e2lpacIxAQDg6Ca9EfDVrbV7q+rsJLdV1aeO9YWtteuSXJckGzdubJMKCAAAXSY6aW6t3Tt63JfkfUkuSvJAVZ2TJKPHfZPMAAAAJ2pik+aqOjPJmtbao6OvNyX52SS3JLkiydtGjzdPKsOzORXv/XiqeerP96mdxEyOe2wCwGRNcnnGS5K8r6qeus7vttb+qKo+nOQ9VXVlks8meeMEMxzVwsJCPnLXPXn8tC/v4/KrwtonD/9Dxl98/JGekwzb+ifv7TsCAAzexEpza+0zSS44wvn9SS6Z1HWPx+OnfXmWXvjmvmPACZl+5O19RwCAwevjlnMAAHBKUZoBAKCD0gwAAB2UZgAA6KA0AwBAB6UZAAA6KM0AANBBaQYAgA5KMwAAdFCaAQCgg9IMAAAdlGYAAOigNAMAQAelGQAAOijNAADQQWkGAIAOSjMAAHRQmgEAoIPSDAAAHZRmAADooDQDAEAHpRkAADoozQAA0EFpBgCADkozAAB0UJoBAKCD0gwAAB2UZgAA6KA0AwBAB6UZAAA6dJbmqnpJVb2jqv736PjrqurKyUcDAICTw7FMmn8nya1Jvnx0/NdJtk4qEAAAnGyOpTRPtdbek+RQkrTWnkjy5ERTAQDASeRYSvPnq2pDkpYkVfWqJI9MNBUAAJxE1h7Dc348yS1Jvqqq/izJdJI3HOsFquq0JLuT/ENr7bur6rwkv5/kxUk+muSHWmuPH3dyAABYIZ2T5tbaR5P8iyTfmuRHknx9a+1jx3GNH01yz9OOfynJr7bWZpM8lMSmQgAATmrHcveMf5nke5O8Msn5Sb6nqi6pqrOP4bUzSTYn+a3RcSW5OMkNo6dcn+Ty5xYdAABWxrEsz7gyybckuWN0/O1J/jLJ+VX1s621dz7La7cl+c9JXjA63pDk4dFmwiRZTPKyI72wqq5KclWSvOIVrziGmAAAMBnHshHwUJKvba29vrX2+iRfl+Qfk3xzkp842ouq6ruT7GutfeTpp4/w1Hak17fWrmutbWytbZyenj6GmAAAMBnHMmk+t7X2wNOO9yU5v7X2YFUdfJbXvTrJ91bVdyV5XpIvy+HJ81lVtXY0bZ5Jcu9zzA4AACviWCbNf1pV76+qK6rqiiQ3J/lAVZ2Z5OGjvai19l9aazOttXOTfH+SP26t/WAOL/N46u4bT/08AAA4aR1Lad6SZD7JhaNfH0rSWmufb6295jlc8yeS/HhVfTqH1zi/4zn8DAAAWDGdyzNaa62q/iaH1zB/X5K9SW48nou01v4kyZ+Mvv5MkouONygAAPTlqKW5qs7P4WUVP5Bkf5J3J6nnOF0GAIBT1rNNmj+V5E+TfE9r7dNJUlU/tiKpAADgJPJsa5pfn+T+JHdU1f+sqkty5FvGAQDAoB21NLfW3tda+1dJviaH1yP/WJKXVNVvVtWmFcoHAAC967x7xuguGe9qrX13Dt9X+a4kb5l4MgAAOEkcyy3nvqi19mBr7X+01i6eVCAAADjZHMsnAg7S4uJi1j/xaKYfeXvfUeCErH/i3iwuPtp3DAAYtOOaNAMAwGq0aifNMzMz+fuHHsnSC9/cdxQ4IdOPvD0zMy/sOwYADJpJMwAAdFCaAQCgw6pdngH0Y9u2bVlYWOg7xuA99We8ZcuWnpMM2+zsbLZu3dp3DGAFKM3AilpYWMjuj92dx58/3XeUQVv3eEuS/Pln9vWcZLjWP7bUdwRgBSnNwIp7/PnTuf/C1/cdA07IS++6se8IwAqyphkAADoozQAA0EFpBgCADkozAAB0UJoBAKCD0gwAAB2UZgAA6KA0AwBAB6UZAAA6KM0AANBBaQYAgA5KMwAAdFCaAQCgg9IMAAAd1vYdAABYGdu2bcvCwkLfMQbtqT/fLVu29Jxk2GZnZ7N169YVvabSDACrxMLCQnbf/Yk8Pn1W31EGa117Mkny5/sWe04yXOuXHu7lukozAKwij0+flfvf8Jq+Y8Bz9tIb7ujlutY0AwBAB6UZAAA6KM0AANBBaQYAgA4TK81V9byq+lBV7amqu6vq2tH586rqzqpaqKp3V9X6SWUAAIBxmOSk+R+TXNxauyDJhUleV1WvSvJLSX61tTab5KEkV04wAwAAnLCJleZ22GOjw3WjXy3JxUluGJ2/Psnlk8oAAADjMNE1zVV1WlXdlWRfktuS/E2Sh1trT4yespjkZUd57VVVtbuqdi8tLU0yJgAAPKuJlubW2pOttQuTzCS5KMnXHulpR3ntda21ja21jdPT05OMCQAAz2pF7p7RWns4yZ8keVWSs6rqqU8inEly70pkAACA52piH6NdVdNJDrbWHq6q05N8Rw5vArwjyRuS/H6SK5LcPKkMwMlncXEx6x97JC+968a+o8AJWf/YUhYXH+87BrBCJlaak5yT5PqqOi2HJ9rvaa29v6o+meT3q+rnk/xVkndMMAMAAJywiZXm1trHknzTEc5/JofXNwOr0MzMTD77+Prcf+Hr+44CJ+Sld92YmZmz+44BrBCfCAgAAB2UZgAA6KA0AwBAB6UZAAA6KM0AANBBaQYAgA6TvE/zSW/9k/dm+pG39x1jsNY+uZwkeeK0qZ6TDNv6J+9N8sK+YwDAoK3a0jw7O9t3hMFbWNiXJJmdVegm64X+PgPAhK3a0rx169a+Iwzeli1bkiTbt2/vOQkAwImxphkAADoozQAA0EFpBgCADkozAAB0UJoBAKCD0gwAAB2UZgAA6KA0AwBAB6UZAAA6KM0AANBh1X6MNgCsNouLi1n/yMN56Q139B0FnrP1Sw9n8fGVv65JMwAAdDBpBoBVYmZmJp9dn9z/htf0HQWes5fecEdmzp5Z8euaNAMAQAeTZmDFrX9sKS+968a+YwzaugMPJ0kOnn5Wz0mGa/1jS0nO7jsGsEKUZmBFzc7O9h1hVVhYeCRJMvuVSt3knO3vM6wiSjOworZu3dp3hFVhy5YtSZLt27f3nARgGKxpBgCADkozAAB0UJoBAKCD0gwAAB2UZgAA6KA0AwBAB6UZAAA6KM0AANBhYqW5ql5eVXdU1T1VdXdV/ejo/Iur6raqWhg9vmhSGQAAYBwmOWl+Isl/bK19bZJXJdlSVV+X5C1Jbm+tzSa5fXQMAAAnrYmV5tbafa21j46+fjTJPUleluSyJNePnnZ9kssnlQEAAMZhRdY0V9W5Sb4pyZ1JXtJauy85XKyTnH2U11xVVburavfS0tJKxAQAgCOaeGmuqucnuTHJ1tba5471da2161prG1trG6enpycXEAAAOky0NFfVuhwuzO9qrb13dPqBqjpn9P1zkuybZAYAADhRk7x7RiV5R5J7Wmu/8rRv3ZLkitHXVyS5eVIZAABgHNZO8Ge/OskPJfl4Vd01OveTSd6W5D1VdWWSzyZ54wQzAADACZtYaW6tfTBJHeXbl0zqugAAMG4+ERAAADoozQAA0GGSa5oBgJPM+qWH89Ib7ug7xmCte/ixJMnBs57fc5LhWr/0cHL2zIpfV2kGgFVidna27wiDt/DIQpJktodSt2qcPdPL32WlGQBWia1bt/YdYfC2bNmSJNm+fXvPSRg3a5oBAKCD0gwAAB2UZgAA6KA0AwBAB6UZAAA6KM0AANBBaQYAgA5KMwAAdFCaAQCgg9IMAAAdlGYAAOigNAMAQAelGQAAOijNAADQQWkGAIAOSjMAAHRQmgEAoIPSDAAAHZRmAADooDQDAEAHpRkAADoozQAA0EFpBgCADkozAAB0UJoBAKCD0gwAAB2UZgAA6KA0AwBAB6UZAAA6KM0AANBhYqW5qn67qvZV1Seedu7FVXVbVS2MHl80qesDAMC4THLS/DtJXveMc29JcntrbTbJ7aNjAAA4qU2sNLfWPpDkwWecvizJ9aOvr09y+aSuDwAA47LSa5pf0lq7L0lGj2cf7YlVdVVV7a6q3UtLSysWEAAAnumk3QjYWruutbaxtbZxenq67zgAAKxiK12aH6iqc5Jk9Lhvha8PAADHbaVL8y1Jrhh9fUWSm1f4+gAAcNwmecu530vyF0leWVWLVXVlkrcleW1VLSR57egYAABOamsn9YNbaz9wlG9dMqlrAgDAJJy0GwEBAOBkoTQDAEAHpRkAADoozQAA0EFpBgCADkozAAB0UJoBAKCD0gwAAB2UZgAA6KA0AwBAB6UZAAA6KM0AANBBaQYAgA5KMwAAdFCaAQCgg9IMAAAdlGYAAOigNAMAQAelGQAAOqztOwDAyW7btm1ZWFjoO8ZxeSrvli1bek5y7GZnZ7N169a+YwAckUkzwACtWbMmjz32WD73uc/1HQVgEEyaATqcitPPSy+9NEmyb9++vPOd7+w5DcCpz6QZYGA+9KEP5dFHH02SPProo9m9e3fPiQBOfUozwMD89E//9Jcc/9RP/VRPSQCGQ2kGGJinpsxHOwbg+CnNAAPzghe84FmPATh+SjPAwPzcz/3clxz/wi/8Qk9JAIZDaQYYmLPOOutZjwE4fkozwMBce+21X3J8zTXX9JQEYDiUZoCB2bt377MeA3D8lGaAgTnvvPOe9RiA46c0AwzMm970pi85vvLKK/sJAjAgPkb7FLJt27YsLCz0HeOYPZV1y5YtPSc5PrOzs6fkxybDU3bs2PElx7/xG7+Riy++uKc0AMOgNDMxp59+et8RYFV64IEHvuT4/vvv7ykJwHD0Upqr6nVJfi3JaUl+q7X2tj5ynGpMPwEA+rHipbmqTkuyPclrkywm+XBV3dJa++RKZwEYojPOOCNf+MIXvnh85pln9pgGnrtTbVlicmouTbQs8dj0sRHwoiSfbq19prX2eJLfT3JZDzkABumtb33rlxz/4i/+Yk9JYPU5/fTTLU8cqD6WZ7wsyd8/7XgxyTc/80lVdVWSq5LkFa94xcokAxiAiy666IvT5jPPPDMbN27sOxI8J6afnEz6mDTXEc61f3Kitetaaxtbaxunp6dXIBbAcLz1rW/NmjVrTJkBxqSPSfNikpc/7Xgmyb095AAYrIsuuigf/OAH+44BMBh9TJo/nGS2qs6rqvVJvj/JLT3kAACAY7Lik+bW2hNV9e+S3JrDt5z77dba3SudAwAAjlUv92lurf1hkj/s49oAAHC8+lieAQAApxSlGQAAOijNAADQQWkGAIAOSjMAAHRQmgEAoIPSDAAAHZRmAADooDQDAEAHpRkAADpUa63vDJ2qainJ3/Wdg+dkKsly3yFglfL+g3547526vqK1Nn2kb5wSpZlTV1Xtbq1t7DsHrEbef9AP771hsjwDAAA6KM0AANBBaWbSrus7AKxi3n/QD++9AbKmGQAAOpg0AwBAB6UZAAA6KM1MRFX9dlXtq6pP9J0FVpOqenlV3VFV91TV3VX1o31ngtWgqp5XVR+qqj2j9961fWdivKxpZiKq6tuSPJbkf7XWvqHvPLBaVNU5Sc5prX20ql6Q5CNJLm+tfbLnaDBoVVVJzmytPVZV65J8MMmPttb+sudojIlJMxPRWvtAkgf7zgGrTWvtvtbaR0dfP5rkniQv6zcVDF877LHR4brRL5PJAVGaAQaqqs5N8k1J7uw3CawOVXVaVd2VZF+S21pr3nsDojQDDFBVPT/JjUm2ttY+13ceWA1aa0+21i5MMpPkoqqyPHFAlGaAgRmtp7wxybtaa+/tOw+sNq21h5P8SZLX9RyFMVKaAQZktBnpHUnuaa39St95YLWoqumqOmv09elJviPJp/pNxTgpzUxEVf1ekr9I8sqqWqyqK/vOBKvEq5P8UJKLq+qu0a/v6jsUrALnJLmjqj6W5MM5vKb5/T1nYozccg4AADqYNAMAQAelGQAAOijNAADQQWkGAIAOSjMAAHRQmgFOYlX15Oi2cZ+oqj+oqjOe5bk/U1X/aSXzAawWSjPAye1Aa+3C1to3JHk8yZv7DgSwGinNAKeOP03y1UlSVf+mqj5WVXuq6p3PfGJV/XBVfXj0/RufmlBX1RtHU+s9VfWB0bmvr6oPjSbaH6uq2RX9XQGcAny4CcBJrKoea609v6rWJrkxyR8l+UCS9yZ5dWttuape3Fp7sKp+JsljrbVfrqoNrbX9o5/x80keaK39elV9PMnrWmv/UFVntdYerqpfT/KXrbV3VdX6JKe11g708hsGOEmZNAOc3E6vqruS7E7y2STvSHJxkhtaa8tJ0lp78Aiv+4aq+tNRSf7BJF8/Ov9nSX6nqn44yWmjc3+R5Cer6ieSfIXCDPBPre07AADP6kBr7cKnn6iqStL1z4S/k+Ty1tqeqnpTkm9Pktbam6vqm5NsTnJXVV3YWvvdqrpzdO7Wqvq3rbU/HvPvA+CUZtIMcOq5Pcn3VdWGJKmqFx/hOS9Icl9VrcvhSXNGz/2q1tqdrbX/mmQ5ycur6iuTfKa19t+T3JLkGyf+OwA4xZg0A5xiWmt3V9UvJPk/VfVkkr9K8qZnPO2nk9yZ5O+SfDyHS3SS/LfRRr/K4fK9J8lbkvzrqjqY5P4kPzvx3wTAKcZGQAAA6GB5BgAAdFCaAQCgg9IMAAAdlGYAAOigNAMAQAelGQAAOijNAADQ4f8B22R9/kL/mhsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 7))\n",
    "sns.boxplot(x='Pclass',y='Age',data=train,palette='winter')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now I will calculate de mean age for each class and after fill the missing data rows. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The age average for the 1º class is:  38.233440860215055\n",
      "The age average for the 2º class is:  29.87763005780347\n",
      "The age average for the 3º class is:  25.14061971830986\n"
     ]
    }
   ],
   "source": [
    "#Mean for first class\n",
    "m_1c = train[train['Pclass']==1]['Age'].mean()\n",
    "\n",
    "#Mean for second class\n",
    "m_2c = train[train['Pclass']==2]['Age'].mean()\n",
    "\n",
    "#Mean for third class\n",
    "m_3c = train[train['Pclass']==3]['Age'].mean()\n",
    "\n",
    "\n",
    "print('The age average for the 1º class is: ',m_1c)\n",
    "print('The age average for the 2º class is: ',m_2c)\n",
    "print('The age average for the 3º class is: ',m_3c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "def impute_age(cols):\n",
    "    Age = cols[0]\n",
    "    Pclass = cols[1]\n",
    "    \n",
    "    if pd.isnull(Age):\n",
    "        if Pclass == 1:\n",
    "            return m_1c\n",
    "        elif Pclass == 2:\n",
    "            return m_2c\n",
    "        else:\n",
    "            return m_3c\n",
    "    else:\n",
    "        return Age"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Applying the *impute_age* function for the missing and refilling the age column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['Age'] = train[['Age','Pclass']].apply(impute_age,axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The cabin column has many missing value, so I will drop this column, because is difficult to reach a good solution for this feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.drop('Cabin',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now I will plot a new heatmap to get a new overview about the missing values from the data set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "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.heatmap(train.isnull(),yticklabels=False,cbar=False,cmap='viridis')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Converting Categorical Features \n",
    "\n",
    "Now I will convert categorical features to dummy variables, otherwise the machine learning algorithm won't be able to directly take in those features as inputs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 889 entries, 0 to 890\n",
      "Data columns (total 11 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  889 non-null    int64  \n",
      " 1   Survived     889 non-null    int64  \n",
      " 2   Pclass       889 non-null    int64  \n",
      " 3   Name         889 non-null    object \n",
      " 4   Sex          889 non-null    object \n",
      " 5   Age          889 non-null    float64\n",
      " 6   SibSp        889 non-null    int64  \n",
      " 7   Parch        889 non-null    int64  \n",
      " 8   Ticket       889 non-null    object \n",
      " 9   Fare         889 non-null    float64\n",
      " 10  Embarked     889 non-null    object \n",
      "dtypes: float64(2), int64(5), object(4)\n",
      "memory usage: 83.3+ KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "sex = pd.get_dummies(train['Sex'],drop_first=True)\n",
    "embark = pd.get_dummies(train['Embarked'],drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.drop(['Sex','Embarked','Name','Ticket'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.concat([train,sex,embark],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "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>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>1</td>\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>2</td>\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>3</td>\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>4</td>\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>5</td>\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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass   Age  SibSp  Parch     Fare  male  Q  S\n",
       "0            1         0       3  22.0      1      0   7.2500     1  0  1\n",
       "1            2         1       1  38.0      1      0  71.2833     0  0  0\n",
       "2            3         1       3  26.0      0      0   7.9250     0  0  1\n",
       "3            4         1       1  35.0      1      0  53.1000     0  0  1\n",
       "4            5         0       3  35.0      0      0   8.0500     1  0  1"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Building a Logistic Regression model\n",
    "\n",
    "I will start by splitting the data into a training set and test set.\n",
    "\n",
    "## Train Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = train.drop('Survived',axis=1)\n",
    "y = train['Survived']\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training and Predicting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "                   intercept_scaling=1, l1_ratio=None, max_iter=1000,\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": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logmodel = LogisticRegression(max_iter=1000)\n",
    "logmodel.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = logmodel.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now to check precision,recall,f1-score I will use classification report!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.82      0.91      0.86       163\n",
      "           1       0.84      0.68      0.75       104\n",
      "\n",
      "    accuracy                           0.82       267\n",
      "   macro avg       0.83      0.80      0.81       267\n",
      "weighted avg       0.83      0.82      0.82       267\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,predictions))"
   ]
  }
 ],
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