ds_workshop_answered.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Science Workshop\n",
    "\n",
    "*Prepared by:*\n",
    "**Jude Michael Teves** and **Unisse Chua**  \n",
    "Faculty, Software Technology Department  \n",
    "College of Computer Studies - De La Salle University"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-29T03:20:35.142379Z",
     "iopub.status.busy": "2022-01-29T03:20:35.141378Z",
     "iopub.status.idle": "2022-01-29T03:20:38.040093Z",
     "shell.execute_reply": "2022-01-29T03:20:38.039094Z",
     "shell.execute_reply.started": "2022-01-29T03:20:35.142379Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda\\lib\\site-packages\\statsmodels\\tools\\_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
      "  import pandas.util.testing as tm\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn import datasets\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "sns.set_style('darkgrid')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading Data\n",
    "\n",
    "In the cell below, we will be loading the Titanic dataset, a popular toy dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('https://raw.githubusercontent.com/Cyntwikip/data-repository/main/titanic.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Dictionary\n",
    "\n",
    "|Variable|Definition|Key|\n",
    "|---|---|---|\n",
    "|Survived|Survival|0 = No, 1 = Yes|\n",
    "|Pclass|Ticket class. Can be used as proxy for socio-economic status|1 = Upper, 2 = Middle, 3 = Lower|\n",
    "|Sex|Sex||\t\n",
    "|Age|Age in years||\t\n",
    "|Sibsp|# of siblings / spouses aboard the Titanic||\n",
    "|Parch|\t# of parents / children aboard the Titanic||\t\n",
    "|Ticket|Ticket number||\n",
    "|Fare|Passenger fare||\t\n",
    "|Cabin|Cabin number||\t\n",
    "|Embarked|Port of Embarkation|C = Cherbourg, Q = Queenstown, S = Southampton|\n",
    "\n",
    "\n",
    "\n",
    "Notes:\n",
    "\n",
    "- Age: Age is fractional if less than 1. If the age is estimated, is it in the form of xx.5\n",
    "\n",
    "- Sibsp: The dataset defines family relations in this way...  \n",
    "Sibling = brother, sister, stepbrother, stepsister  \n",
    "Spouse = husband, wife (mistresses and fiancés were ignored)  \n",
    "\n",
    "- Parch: The dataset defines family relations in this way...  \n",
    "Parent = mother, father  \n",
    "Child = daughter, son, stepdaughter, stepson  \n",
    "Some children travelled only with a nanny, therefore parch=0 for them."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis\n",
    "\n",
    "Like most data science projects, we perform EDA so that we better understand the data we are dealing with."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Class Distribution\n",
    "\n",
    "We visualize the class distribution in the cell below. The dominant passenger class is the lower class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-29T03:20:38.990164Z",
     "iopub.status.busy": "2022-01-29T03:20:38.990164Z",
     "iopub.status.idle": "2022-01-29T03:20:39.364162Z",
     "shell.execute_reply": "2022-01-29T03:20:39.363164Z",
     "shell.execute_reply.started": "2022-01-29T03:20:38.990164Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,1, figsize=(5,3), dpi=100)\n",
    "df['Pclass'].value_counts().plot(kind='bar')\n",
    "plt.title('Class distribution')\n",
    "plt.xticks(rotation=0)\n",
    "plt.ylabel('Count')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sex Distribution\n",
    "\n",
    "There are more male passengers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-29T03:20:39.366163Z",
     "iopub.status.busy": "2022-01-29T03:20:39.366163Z",
     "iopub.status.idle": "2022-01-29T03:20:39.569495Z",
     "shell.execute_reply": "2022-01-29T03:20:39.568495Z",
     "shell.execute_reply.started": "2022-01-29T03:20:39.366163Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ifc+ePXh5eTF06FBcXV0JCgqiX79+xMTEABAbG0ufPn0IDAzEzc2NESNGYLVaiY+PL+9ZEBGRKqLc9yiLioo4deoUFouF9evXU1hYSNeuXZk6dSrJycn4+fkVG97Hx4dt27YBkJKSQlhYWIn+06dPO1yHyXT/8yByl9YjuR931xutP87jyLIv96DMyMigWbNm9OrVi+XLl5OZmcmMGTOYNm0adevWLXEI1d3dndzcXABycnIM+x1Ru3bN+58JEcBq9XB2CVLJ6Xuocij3oKxTp479UCqAxWJh2rRp/OIXvyA0NJS8vLxiw+fl5eHh4WEftrR+q9XqcB3p6Tew2e5jBpzMxcWsL+gKIjMzh8LCImeXIZWQyXQnJCvr99Dj4O5nUBblHpSnT5/mz3/+M7/+9a8x/f++b35+PmazmVatWvHRRx8VGz4lJQVfX18AfH19SU5OLtHfpUsXh+uw2dAKKg9M65A8CH0PVQ7lfjGPl5cXMTExrF+/ntu3b3Pp0iX+8z//k5dffplevXqRlpZGdHQ0BQUFJCQkEBcXZz8vGR4eTlxcHAkJCRQUFBAdHU16ejohISHlPRsiIlJFmGy28v975siRIyxdupQzZ85QvXp1+vTpw7Rp06hevTpff/01kZGRnDlzBm9vb8aPH09oaKh93J07d7J69WpSU1Px8fFh9uzZtG7d2uEa0tIq5yEPV9c7h177LD/AqUvXnV1OldT8qVp8PqkzmZk53L6tQ6/iOJMJ6tSpWWm/hx4Hdz+DMg3rjKCsCCrrCqqgdD4FpTwoBaXzORKUuoWdiIiIAQWliIiIAQWliIiIAQWliIiIAQWliIiIAQWliIiIAQWliIiIAQWliIiIAQWliIiIAQWliIiIAQWliIiIAQWliIiIAQWliIiIAQWliIiIAQWliIiIAQWliIiIAQWliIiIAQWliIiIAQWliIiIAQWliIiIAQWliIiIAQWliIiIAQWliIiIAQWliIiIAQWliIiIAQWliIiIAQWliIiIAQWliIiIAacGZWFhIcOGDeOtt96yt504cYJBgwYREBBAcHAwsbGxxcbZsWMHISEh+Pv7ExoayvHjx8u7bBERqUKcGpQrV67k6NGj9t+zsrIYM2YMAwcOJCkpicjISKKiojh58iQAiYmJzJ8/n4ULF5KUlET//v0ZN24cN2/edNYsiIjIY85pQXn48GH27NlDz5497W179uzBy8uLoUOH4urqSlBQEP369SMmJgaA2NhY+vTpQ2BgIG5ubowYMQKr1Up8fLyzZkNERB5zrs6YaHp6OrNmzWLVqlVER0fb25OTk/Hz8ys2rI+PD9u2bQMgJSWFsLCwEv2nT592uAaTyfG6RX5I65Hcj7vrjdYf53Fk2Zd7UBYVFTFt2jRGjhzJs88+W6wvJycHi8VSrM3d3Z3c3Nwy9Tuidu2aDo8j8n1Wq4ezS5BKTt9DlUO5B+XatWupVq0aw4YNK9FnsVi4ceNGsba8vDw8PDzs/Xl5eSX6rVarw3Wkp9/AZnN4NKdzcTHrC7qCyMzMobCwyNllSCVkMt0Jycr6PfQ4uPsZlEW5B+XOnTu5cuUKbdq0AbAH3xdffMH06dM5ePBgseFTUlLw9fUFwNfXl+Tk5BL9Xbp0cbgOmw2toPLAtA7Jg9D3UOVQ7hfz7Nq1i6+++oqjR49y9OhR+vbtS9++fTl69CghISGkpaURHR1NQUEBCQkJxMXF2c9LhoeHExcXR0JCAgUFBURHR5Oenk5ISEh5z4aIiFQRTrmY516sVisbN24kMjKS5cuX4+3tzezZs+nQoQMAQUFBzJ07l3nz5pGamoqPjw/r1q3Dy8vLyZWLiMjjymSzVc0d/7S0ynluwNX1zjnKPssPcOrSdWeXUyU1f6oWn0/qTGZmDrdv6xylOM5kgjp1alba76HHwd3PoCx0CzsREREDCkoREREDCkoREREDCkoREREDCkoREREDCkoREREDCkoREREDCkoREREDCkoREREDCkoREREDCkoREREDCkoREREDCkoREREDDy0os7OzH9ZbiYiIVBgOB2W7du1Kbe/WrduD1iIiIlLhlOnBzd999x1z5szBZrORnZ3NL3/5y2L92dnZ1KpV65EUKCIi4kxlCsqGDRvSs2dPMjMz+eqrr0rsVVarVo3g4OBHUqCIiIgzlSkoAYYOHQrA008/zcCBAx9ZQSIiIhVJmYPyroEDB3Ly5En+9a9/YbPZSvSJiIg8ThwOyqVLl7Ju3Trq1q2Lq+u/RzeZTApKERF57DgclDt37mTNmjV07dr1UdQjIiJSoTj87yG5ubl06dLlUdQiIiJS4TgclN26dSMuLu5R1CIiIlLhOHzo9datW7z11lusWbOGOnXqFOv7+OOPH1phIiIiFYHDQenn54efn9+jqEVERKTCcTgo33jjjUdRh4iIQ8xmE2azydllPBAXl8r9XIqiIhtFRbYfH7CSczgoZ86cec++qKioBypGRKQszGYTT3jVwLWSB43V6uHsEh7I7cIisq7lPvZh6XBQ/lBmZiYJCQmEhYWVeZzDhw+zdOlSzp49i8Vi4cUXX2TatGm4u7tz4sQJFixYQEpKClarlXHjxjFo0CD7uDt27GDVqlVcvXqVRo0a8Zvf/IaAgIAHnQ0RqUTMZhOuLmYithwn5YqeXOQMPk968t4rAZjNJgXlD5W213jo0CE2b95cpvEzMjIYO3Ys8+bNY+DAgaSlpTF69Gg++OADhg8fzpgxY5g0aRKDBw8mKSmJCRMm0KRJE1q1akViYiLz589n3bp1tGrVipiYGMaNG8e+ffuwWCyOzoqIVHIpV7I5dem6s8uQx9xDOW7RsWNHEhISyjSst7c3hw4dIjQ0FJPJxLVr17h16xbe3t7s2bMHLy8vhg4diqurK0FBQfTr14+YmBgAYmNj6dOnD4GBgbi5uTFixAisVivx8fEPYzZERERKeOCgvH37Nn/605/w9vYu8zienp4AdO3alX79+lG3bl1CQ0NJTk4ucUWtj48Pp0+fBiAlJcWw3xEmU+V8ScXi7PWhqr6kYnH2+vCo1yOHD70+++yzmH4wBRcXF2bNmuXoW7Fnzx6ysrKYOnUqkyZNol69eiUOobq7u5ObmwtATk6OYb8jateu6fA4It9X2S/EEHkYqsJ24HBQ/vCmAmazmYYNG1K3bl2HJ+7u7o67uzvTpk1j0KBBDBs2jBs3bhQbJi8vDw+POx+ExWIhLy+vRL/VanV42unpN7BVwvPPLi7mKrFiVgaZmTkUFhY5u4wqSdtBxVFZtwOTqew7TA4fem3Xrh1t2rTB3d2dtLQ0AGrXrl3m8b/66itefPFF8vPz7W35+fm4ubnh4+NDcnJyseFTUlLw9fUFwNfX17DfETZb5XxJxeLs9aGqvqRicfb68KjXI4eD8urVqwwaNIhXX32VyMhIfvnLX9K3b18uX75cpvGbNGlCXl4eS5YsIT8/n4sXL7Jo0SLCw8Pp1asXaWlpREdHU1BQQEJCAnFxcfZ/PQkPDycuLo6EhAQKCgqIjo4mPT2dkJAQR2dDRESkTBwOykWLFvHMM89w5MgRDh48SGJiIk2bNi3zzQY8PDxYv349ycnJPP/88wwbNoyOHTvy9ttvY7Va2bhxI7t27aJ9+/bMnj2b2bNn06FDBwCCgoKYO3cu8+bNo127dnz++eesW7cOLy8vR2dDRESkTBw+R5mQkMCuXbvs5w1r1qzJvHnzeOGFF8r8Hj4+PmzcuLHUvpYtW7Jly5Z7jjtgwAAGDBjgWNEiIiL3yeE9yqKiohJXvZpMJtzc3B5aUSIiIhWFw0HZvn175s2bV+xfNu4eChUREXncOHzoddq0aYwcOZJ27drh5eXFtWvXaNy4MR988MGjqE9ERMSpHApKm83G7du3+fzzzzl69Cjp6elcvHiR0aNH4+Li8qhqFBERcZoyH3rNzc3l1Vdf5fe//z2urq506NCBDh06sHLlSoYNG3Zfd8cRERGp6MoclKtXr8bNzY133nnH3la7dm327dvH7du3Wbt27SMpUERExJnKHJS7d+9mwYIFJe7CU7t2bd555x127dr10IsTERFxtjIHZXp6Og0bNiy1r2nTply9evWhFSUiIlJRlDkoPT09yczMLLXv2rVrenCyiIg8lsoclEFBQfYHKP/Q5s2b8ff3f2hFiYiIVBRl/veQsWPHEhoaSmZmJr1796Zu3bpcuXKFv/zlL3z66ads2rTpUdYpIiLiFGUOyp///Ods2LCBuXPnEhMTg8lkwmaz4efnx7p162jRosWjrFNERMQpHLrhwHPPPUdcXBznz58nIyODunXr8tRTTz2q2kRERJzO4VvYATRo0IAGDRo87FpEREQqHIdvii4iIlKVKChFREQMKChFREQMKChFREQMKChFREQMKChFREQMKChFREQMKChFREQMKChFREQMKChFREQMKChFREQMKChFREQMKChFREQMKChFREQMOCUoT58+zciRI2nXrh3PP/8806dPJyMjA4ATJ04waNAgAgICCA4OJjY2tti4O3bsICQkBH9/f0JDQzl+/LgzZkFERKqIcg/KvLw8XnvtNQICAvjb3/7Gn//8Z65du8bbb79NVlYWY8aMYeDAgSQlJREZGUlUVBQnT54EIDExkfnz57Nw4UKSkpLo378/48aN4+bNm+U9GyIiUkWUe1BeunSJZ599lgkTJlCtWjWsViuDBw8mKSmJPXv24OXlxdChQ3F1dSUoKIh+/foRExMDQGxsLH369CEwMBA3NzdGjBiB1WolPj6+vGdDRESqCNfynmCjRo1Yv359sbbdu3fTvHlzkpOT8fPzK9bn4+PDtm3bAEhJSSEsLKxE/+nTpx2uw2RyeBSRErQeiVTO7cCRmss9KL/PZrPx7rvvsm/fPjZt2sTHH3+MxWIpNoy7uzu5ubkA5OTkGPY7onbtmvdfuAhgtXo4uwQRp6sK24HTgjI7O5uZM2dy6tQpNm3aRJMmTbBYLNy4caPYcHl5eXh43PkgLBYLeXl5JfqtVqvD009Pv4HNdv/1O4uLi7lKrJiVQWZmDoWFRc4uo0rSdlBxVNbtwGQq+w6TU4Ly3LlzvP766zz11FNs27YNb29vAPz8/Dh48GCxYVNSUvD19QXA19eX5OTkEv1dunRxuAabjUoZlFKxaB0Sefy3g3K/mCcrK4vhw4fz3HPPsWHDBntIAoSEhJCWlkZ0dDQFBQUkJCQQFxdnPy8ZHh5OXFwcCQkJFBQUEB0dTXp6OiEhIeU9GyIiUkWU+x7l9u3buXTpEn/5y1/YtWtXsb7jx4+zceNGIiMjWb58Od7e3syePZsOHToAEBQUxNy5c5k3bx6pqan4+Piwbt06vLy8yns2RESkiij3oBw5ciQjR468Z3/Lli3ZsmXLPfsHDBjAgAEDHkVpIiIiJegWdiIiIgYUlCIiIgYUlCIiIgYUlCIiIgYUlCIiIgYUlCIiIgYUlCIiIgYUlCIiIgYUlCIiIgYUlCIiIgYUlCIiIgYUlCIiIgYUlCIiIgYUlCIiIgYUlCIiIgYUlCIiIgYUlCIiIgYUlCIiIgYUlCIiIgYUlCIiIgYUlCIiIgYUlCIiIgYUlCIiIgYUlCIiIgYUlCIiIgYUlCIiIgYUlCIiIgacGpQZGRmEhISQmJhobztx4gSDBg0iICCA4OBgYmNji42zY8cOQkJC8Pf3JzQ0lOPHj5d32SIiUoU4LSiPHTvG4MGDOXfunL0tKyuLMWPGMHDgQJKSkoiMjCQqKoqTJ08CkJiYyPz581m4cCFJSUn079+fcePGcfPmTWfNhoiIPOacEpQ7duxg6tSpTJkypVj7nj178PLyYujQobi6uhIUFES/fv2IiYkBIDY2lj59+hAYGIibmxsjRozAarUSHx/vjNkQEZEqwClB2alTJ/bu3Uvv3r2LtScnJ+Pn51eszcfHh9OnTwOQkpJi2O8Ik6lyvqRicfb6UFVfUrE4e3141OuR66NbdPdWt27dUttzcnKwWCzF2tzd3cnNzS1TvyNq167p8Dgi32e1eji7BBGnqwrbgVOC8l4sFgs3btwo1paXl4eHh4e9Py8vr0S/1Wp1eFrp6Tew2e6/VmdxcTFXiZ8bC9QAAAhgSURBVBWzMsjMzKGwsMjZZVRJ2g4qjsq6HZhMZd9hqlBB6efnx8GDB4u1paSk4OvrC4Cvry/Jyckl+rt06eLwtGw2KmVQSsWidUjk8d8OKtT/UYaEhJCWlkZ0dDQFBQUkJCQQFxdHWFgYAOHh4cTFxZGQkEBBQQHR0dGkp6cTEhLi5MpFRORxVaH2KK1WKxs3biQyMpLly5fj7e3N7Nmz6dChAwBBQUHMnTuXefPmkZqaio+PD+vWrcPLy8vJlYuIyOPK6UH5z3/+s9jvLVu2ZMuWLfccfsCAAQwYMOBRlyUiIgJUsEOvIiIiFY2CUkRExICCUkRExICCUkRExICCUkRExICCUkRExICCUkRExICCUkRExICCUkRExICCUkRExICCUkRExICCUkRExICCUkRExICCUkRExICCUkRExICCUkRExICCUkRExICCUkRExICCUkRExICCUkRExICCUkRExICCUkRExICCUkRExICCUkRExICCUkRExICCUkRExICCUkRExEClDMr09HTGjx9PmzZtaN++PZGRkdy+fdvZZYmIyGOoUgbl5MmTqVGjBgcOHGDbtm0cPnyY6OhoZ5clIiKPoUoXlN999x1Hjhxh2rRpWCwWGjRowPjx44mJiXF2aSIi8hhydXYBjkpOTsbLy4t69erZ2xo3bsylS5e4fv06tWrVKtP7mM1gsz2qKh+95k/VwlLNxdllVEmN6njYfzZXuj81Hy/aDpynsm8HJlPZh610QZmTk4PFYinWdvf33NzcMgelt3fNh15befp9eGtnl1DlWa0ePz6QPFLaDpyvKmwHle7vgBo1anDz5s1ibXd/9/B4/D8wEREpX5UuKH19fbl27RppaWn2trNnz1K/fn1q1qzce4kiIlLxVLqgfOaZZwgMDOR3v/sd2dnZnD9/nlWrVhEeHu7s0kRE5DFkstkq3yUtaWlp/Pa3vyUxMRGz2czAgQOZOnUqLi46qS8iIg9XpQxKERGR8lLpDr2KiIiUJwWliIiIAQWliIiIAQWliIiIAQWliIiIAQWlVAjBwcFs377d2WWI3FN8fDxBQUEEBgayb9++cpnmhQsXaNKkCRcuXCiX6UnpFJQiImUQGxtLnz59OHbsGN27d3d2OVKOFJRy3+7+tfunP/2J7t274+/vz8yZMzl69Cj9+/cnICCA4cOHk5GRQXZ2NrNnz6Znz574+/vTuXNn1qxZU+r75ufn89577/HCCy/Qrl07Xn/9db777rtynjuRfwsPDychIYEtW7bQo0cPzp07x69+9Svat29P9+7dWbZsGfn5+QBs376dIUOGsGjRItq1a0eHDh34wx/+wNatW+nevTuBgYHMmTPH/t5nz55l7NixdOvWjVatWtG7d+977rGmpaUxdepUnn/+eTp16sScOXPIzs4ul2VQlSko5YF9+eWXxMfHs3XrVnbu3Mn8+fNZt24d//Vf/8X//u//snnzZhYvXsyFCxfYtm0bx48fZ/bs2SxbtqzUAFy2bBn79+8nOjqaAwcO0Lp1a0aNGsWtW7ecMHcisG3bNtq0acPYsWP57LPPGDFiBL6+vvz1r39l8+bNHDp0iBUrVtiHP3bsGPXq1SMhIYFJkyYRFRVFYmIi8fHxREdHs23bNpKSkgCYOHEifn5+7N27l6NHj9KpUyfmzZtXooaioiLGjx+P2Wxm9+7dxMXFceXKlWKhK4+GglIe2KhRo7BYLPj5+VG3bl1efvll6tWrh7e3N/7+/ly8eJGJEyfy7rvv4unpyeXLl6levToAV65cKfZeNpuNLVu28Oabb9KgQQOqV6/OhAkTKCgoYP/+/U6YO5Hi9u/fT35+Pm+++SbVq1fnJz/5CREREcUeHl+jRg2GDx+O2WymU6dOFBYWMnr0aCwWCy1btuTJJ5/k4sWLAKxdu5aJEydis9m4ePEitWrVIjU1tcR0v/nmG06dOsXcuXPx9PTEarUyY8YMPv/8czIzM8tt/quiSvc8Sql4vLy87D+7uLgUeyao2WzGZrORnp5OZGQk//jHP3j66adp0aIFcOev5O/LyMggNzeXiIgIzN97GmxBQYH9i0XEmS5evEhGRgZt27a1t9lsNgoKCkhPTwfubBOm/38y8N31+Ifbxd11//Tp04wfP56rV6/SuHFjvL29Ke3OohcuXKCwsJCuXbsWa69WrRrnz5/HarU+3BkVOwWlPDBTGR4VHhERQXBwMBs2bMDV1ZXMzEy2bt1aYjir1Ur16tXZuHEj/v7+9vZvv/2WevXqPdS6Re5H/fr1+dnPfsauXbvsbdnZ2aSnp+Pt7Q2UbZsASE1NJSIigpUrVxIcHAzA7t272bNnT6nTdXd3JzEx0f4AiPz8fM6fP0/Dhg0fdLbEgA69Srm4ceMG7u7uuLi4kJGRwYIFC4A7e4rfZzabCQ8PZ8mSJVy+fJmioiJ27NhB3759dUGPVAjdu3cnJyeH9evXk5+fz/Xr15kxYwZTpkwpc0DelZOTQ2FhIRaLBYCUlBTef/99APvFQXe1atWKhg0bsnDhQnJycsjLy+N3v/sdI0aMoLCw8OHMnJRKQSnlIioqivj4eJ577jlCQ0OpV68ezZo148yZMyWGnTFjBq1bt2bIkCG0adOG6Oholi9fTrNmzZxQuUhxnp6eREdHk5iYSJcuXejRowdms5nVq1c7/F6NGjVi+vTpTJs2jcDAQCIiIggLC8PNza3EtuHq6sratWtJS0ujZ8+edOrUiXPnzvHhhx/az/nLo6HHbImIiBjQHqWIiIgBBaWIiIgBBaWIiIgBBaWIiIgBBaWIiIgBBaWIiIgBBaWIiIgBBaWIiIgBBaWIiIgBBaWIiIgBBaWIiIiB/wNX+2GhcrniSQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,1, figsize=(5,3), dpi=100)\n",
    "df['Sex'].value_counts().plot(kind='bar')\n",
    "plt.title('Sex distribution')\n",
    "plt.xticks(rotation=0)\n",
    "plt.ylabel('Count')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "but more female passengers survived..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,1, figsize=(6,3), dpi=100)\n",
    "\n",
    "sns.barplot(\n",
    "    data=df[['Survived','Sex','Pclass']].rename({'Pclass':'Passenger Count'}, axis=1).groupby(['Survived','Sex']).count().reset_index(), \n",
    "    x=\"Sex\", y=\"Passenger Count\", hue=\"Survived\", ax=ax\n",
    ")\n",
    "plt.title('Sex distribution')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fare Distribution\n",
    "\n",
    "The fare distribution is skewed to the right--most passenger have low fares."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-29T03:20:39.572660Z",
     "iopub.status.busy": "2022-01-29T03:20:39.572660Z",
     "iopub.status.idle": "2022-01-29T03:20:39.836497Z",
     "shell.execute_reply": "2022-01-29T03:20:39.835497Z",
     "shell.execute_reply.started": "2022-01-29T03:20:39.572660Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max Fare: 512.3292 | Min Fare: 0.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 700x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(f\"Max Fare: {df.Fare.max()} | Min Fare: {df.Fare.min()}\")\n",
    "fig, ax = plt.subplots(1,1, figsize=(7,3), dpi=100)\n",
    "df['Fare'].hist()\n",
    "plt.title('Fare distribution')\n",
    "plt.xlabel('Fare')\n",
    "plt.ylabel('Count')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 1. Title Distribution\n",
    "\n",
    "We will be extracting the titles of the passengers and visualize them. Time to practice your Python skills! Implement a function that extracts the title given a name."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_title(name:str) -> str:\n",
    "    '''\n",
    "    Extracts the title given a name.\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    name : str\n",
    "        The passenger's name.\n",
    "        \n",
    "    Returns\n",
    "    ----------\n",
    "    str\n",
    "        The title of the passenger extracted from the name.\n",
    "    '''\n",
    "    \n",
    "    title = name.split(\", \")[1].split()[0]\n",
    "    return title"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Mr.'"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_name = \"Braund, Mr. Owen Harris\" \n",
    "get_title(sample_name) # this should return Mr."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Mrs.'"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_name = \"Cumings, Mrs. John Bradley (Florence Briggs Thayer)\"\n",
    "get_title(sample_name) # this should return Mrs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Miss.'"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_name = \"Heikkinen, Miss. Laina\"\n",
    "get_title(sample_name) # this should return Miss."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mr.          517\n",
       "Miss.        182\n",
       "Mrs.         125\n",
       "Master.       40\n",
       "Dr.            7\n",
       "Rev.           6\n",
       "Mlle.          2\n",
       "Major.         2\n",
       "Col.           2\n",
       "Lady.          1\n",
       "the            1\n",
       "Jonkheer.      1\n",
       "Sir.           1\n",
       "Don.           1\n",
       "Capt.          1\n",
       "Mme.           1\n",
       "Ms.            1\n",
       "Name: Name, dtype: int64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# run the code below once you have implemented the `get_title` function\n",
    "title_counts = df['Name'].apply(get_title).value_counts()\n",
    "title_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,1, figsize=(5,6), dpi=100)\n",
    "title_counts.sort_values(ascending=True).plot(kind='barh')\n",
    "plt.title('Sex distribution')\n",
    "plt.xticks(rotation=0)\n",
    "plt.ylabel('Count')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Machine Learning\n",
    "\n",
    "Let's make this more fun! Let's use Machine Learning to predict if a passenger will survive. For now, we will only be using the `Pclass`, `Sex`, and `Fare` features to make things simple."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Preparation\n",
    "\n",
    "In Machine Learning, `X` and `y` are usually the variable names for the input and output data respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-29T03:20:39.838498Z",
     "iopub.status.busy": "2022-01-29T03:20:39.838498Z",
     "iopub.status.idle": "2022-01-29T03:20:39.867495Z",
     "shell.execute_reply": "2022-01-29T03:20:39.866496Z",
     "shell.execute_reply.started": "2022-01-29T03:20:39.838498Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "X = df[['Pclass', 'Sex', 'Fare']]\n",
    "X = pd.get_dummies(X)\n",
    "y = df['Survived']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-29T03:20:39.869496Z",
     "iopub.status.busy": "2022-01-29T03:20:39.869496Z",
     "iopub.status.idle": "2022-01-29T03:20:39.899496Z",
     "shell.execute_reply": "2022-01-29T03:20:39.898499Z",
     "shell.execute_reply.started": "2022-01-29T03:20:39.869496Z"
    },
    "tags": []
   },
   "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>Pclass</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Sex_female</th>\n",
       "      <th>Sex_male</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>7.9250</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>53.1000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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>2</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>1</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>3</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>1</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>3</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pclass     Fare  Sex_female  Sex_male  Survived\n",
       "0         3   7.2500           0         1         0\n",
       "1         1  71.2833           1         0         1\n",
       "2         3   7.9250           1         0         1\n",
       "3         1  53.1000           1         0         1\n",
       "4         3   8.0500           0         1         0\n",
       "..      ...      ...         ...       ...       ...\n",
       "886       2  13.0000           0         1         0\n",
       "887       1  30.0000           1         0         1\n",
       "888       3  23.4500           1         0         0\n",
       "889       1  30.0000           0         1         1\n",
       "890       3   7.7500           0         1         0\n",
       "\n",
       "[891 rows x 5 columns]"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([X, y.to_frame()], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modeling\n",
    "\n",
    "We will now be training our own Machine Learning model. I have already provided the function for training a Machine Learning model below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-29T03:20:39.901499Z",
     "iopub.status.busy": "2022-01-29T03:20:39.901499Z",
     "iopub.status.idle": "2022-01-29T03:20:39.915496Z",
     "shell.execute_reply": "2022-01-29T03:20:39.914498Z",
     "shell.execute_reply.started": "2022-01-29T03:20:39.901499Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def train_model(X:pd.DataFrame, y:pd.DataFrame):\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "    \n",
    "    model = KNeighborsClassifier(n_neighbors=3, p=2, n_jobs=-1) # instantiate model\n",
    "    model.fit(X_train, y_train) # train the model\n",
    "    print(f\"Train Set Score: {model.score(X_train, y_train)}\")\n",
    "    print(f\"Test Set Score: {model.score(X_test, y_test)}\")\n",
    "    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 2: Implement ML model's predict function "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-29T03:20:40.428190Z",
     "iopub.status.busy": "2022-01-29T03:20:40.427189Z",
     "iopub.status.idle": "2022-01-29T03:20:40.446188Z",
     "shell.execute_reply": "2022-01-29T03:20:40.445188Z",
     "shell.execute_reply.started": "2022-01-29T03:20:40.428190Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def predict(model, pclass, fare, sex) -> str:\n",
    "    # if sex is male, sex_male = 1 and sex_female = 0\n",
    "    # if sex is female, sex_male = 0 and sex_female = 1\n",
    "    # if not male or female, return an error message\n",
    "    # your code here...\n",
    "    survived = model.predict([[pclass, fare, sex_female, sex_male]])[0]\n",
    "    \n",
    "    # return \"person will survive\" if survived is 1, else return \"person will not survive\"\n",
    "    # your code here..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Set Score: 0.8623595505617978\n",
      "Test Set Score: 0.7821229050279329\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'person will survive'"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = train_model(X, y)\n",
    "predict(model, 3, 50, 'female') # this should return 'person will survive'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'person will not survive'"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict(model, 3, 50, 'male') # this should return 'person will not survive'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Invalid input'"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict(model, 3, 50, 'none') # this should return an error message"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Set Score: 0.8623595505617978\n",
      "Test Set Score: 0.7821229050279329\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'person will survive'"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def predict(model, pclass, fare, sex) -> str:\n",
    "    # if sex is male, sex_male = 1 and sex_female = 0\n",
    "    # if sex is female, sex_male = 0 and sex_female = 1\n",
    "    # if not male or female, return an error message\n",
    "    # your code here...\n",
    "    if sex == 'male':\n",
    "        sex_male = 1\n",
    "        sex_female = 0\n",
    "    elif sex == 'female':\n",
    "        sex_male = 0\n",
    "        sex_female = 1\n",
    "    else:\n",
    "        return \"Invalid input\"\n",
    "    \n",
    "    survived = model.predict([[pclass, fare, sex_female, sex_male]])[0]\n",
    "    \n",
    "    # return \"person will survive\" if survived is 1, else return \"person will not survive\"\n",
    "    # your code here...\n",
    "    if survived==1:\n",
    "        return \"person will survive\"\n",
    "    else:\n",
    "        return \"person will not survive\"\n",
    "    \n",
    "model = train_model(X, y)\n",
    "\n",
    "# predict(model, 3, 50, 'male')\n",
    "predict(model, 3, 50, 'female')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 3: Ask for user's input and integrate with the ML predict function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# your code here...\n",
    "# call train_model function and provide the necessary arguments and store the model in a variable named `model`\n",
    "\n",
    "iterations = int(input('Iterations'))\n",
    "# ask for the user's input `x` times where `x` is the number assigned in iterations variable.\n",
    "# Python's built-in `range` function might come in handy\n",
    "# each time you ask ask for user's input, get the pclass, fare, and sex\n",
    "# you might have to type-cast some of the values\n",
    "# call the predict function pass the necessary arguments\n",
    "# print the value return by predict function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Set Score: 0.8623595505617978\n",
      "Test Set Score: 0.7821229050279329\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Iterations 1\n",
      "Pclass:  1\n",
      "Fare:  500\n",
      "Sex:  female\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "person will survive\n"
     ]
    }
   ],
   "source": [
    "# your code here...\n",
    "model = train_model(X, y)\n",
    "\n",
    "iterations = int(input('Iterations'))\n",
    "# ask for the user's input `x` times where `x` is the number assigned in iterations variable\n",
    "# Hint: Python's built-in `range` function might come in handy\n",
    "# each time you ask ask for user's input, get the pclass, fare, and sex\n",
    "# you might have to type-cast some of the values. \n",
    "# Hint: Take note of the following data types. pclass - int, fare - float, sex- str\n",
    "# call the predict function pass the necessary arguments\n",
    "# print the value return by predict function\n",
    "for i in range(iterations):\n",
    "    pclass = int(input(\"Pclass: \"))\n",
    "    fare = float(input(\"Fare: \"))    \n",
    "    sex = input(\"Sex: \")    \n",
    "    output = predict(model, pclass, fare, sex)\n",
    "    print(output)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tp9vhl21y7gK"
   },
   "source": [
    "## End\n",
    "<sup>made by **Jude Michael Teves** and **Unisse Chua**</sup> <br>\n",
    "<sup>for comments, corrections, suggestions, please email:</sup> <sup>judemichaelteves@gmail.com or jude.teves@dlsu.edu.ph or unisse.chua@dlsu.edu.ph</sup><br>\n"
   ]
  }
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