python_workshop.ipynb

{
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   "cell_type": "markdown",
   "id": "55b7fdc2-0057-4c9c-acfe-b966cea9a34a",
   "metadata": {},
   "source": [
    "## Python Workshop\n",
    "\n",
    "*Prepared by:*\n",
    "**Jude Michael Teves**  \n",
    "Faculty, Software Technology Department  \n",
    "College of Computer Studies - De La Salle University"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca6ba952-5b43-4177-9d67-5a97338d857e",
   "metadata": {},
   "source": [
    "## Import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "cf96b231-f848-4310-aed2-910a4925c145",
   "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",
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    },
    "tags": []
   },
   "outputs": [],
   "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",
   "id": "661fdb4b-a45a-4f59-8519-582b6e494b1f",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "19cbb834-eaaa-46f0-84d8-37c7278628f9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-29T03:20:38.042093Z",
     "iopub.status.busy": "2022-01-29T03:20:38.042093Z",
     "iopub.status.idle": "2022-01-29T03:20:38.986161Z",
     "shell.execute_reply": "2022-01-29T03:20:38.985163Z",
     "shell.execute_reply.started": "2022-01-29T03:20:38.042093Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "<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": 2,
     "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": "code",
   "execution_count": 3,
   "id": "1297aaaf-90d7-49c4-aa4b-0c3ad4bdb679",
   "metadata": {
    "execution": {
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     "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": [
    {
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sLVmyxDnt1KlTSk5OVkxMjMaPH6/8/HyXZQoKCmS32zV8+HAlJiaqrKysu8sGAHgRU4Ny8+bNOn78uPPnhoYGzZ07V9OmTVNpaakyMzOVlZWl06dPS5JKSkq0Zs0avfzyyyotLdWUKVOUkpKiy5cvm9UFAMA9zrSgPHr0qIqKijRhwgTntKKiIlmtVs2cOVN+fn6KjY3V5MmTlZeXJ0nKz89XQkKCRo4cKX9/f82ePVs2m0179+41qxsAgHucnxkrramp0bJly/T6668rNzfXOb2iokKRkZEu84aHh2vHjh2SpMrKSk2fPr1De3l5ucc1WCye142uxe/AHDe2O9vfezEGPOt7twdle3u70tPTNWfOHA0ePNilrampSYGBgS7TAgIC1Nzc7Fa7J/r2DfZ4GXQdmy3I7BK8Hu8BMAbc0+1B+ctf/lK9evXSrFmzOrQFBgbq0qVLLtNaWloUFBTkbG9paenQbrPZPK6jpuaSHA6PF/vCfH19CAlJdXVNunat3ewyvJLFcv0D0qz3AMzHGPhsG7ij24Py3Xff1fnz5zVq1ChJcgbf73//e7300ks6fPiwy/yVlZWKiIiQJEVERKiioqJDe1xcnMd1OBzy2gHSU7D9zcV7AIwB93T7xTz79+/XyZMndfz4cR0/flxPP/20nn76aR0/flx2u13V1dXKzc1Va2uriouLVVhY6DwvmZSUpMLCQhUXF6u1tVW5ubmqqamR3W7v7m4AALyEKRfz3IzNZlNOTo4yMzO1adMmhYSEaPny5Ro7dqwkKTY2VitXrlRGRobOnTun8PBwZWdny2q1mls4AOCeZXE4vHPHu7ranGPzfn7Xz1EmbDqoM1UXu78Akw0dcL/2pH1ddXVNamvjHKUZLBYpNDTYtPcAzMcY+GwbuINb2AEAYICgBADAAEEJAIABghIAAAMEJQAABghKAAAMEJQAABggKAEAMEBQAgBggKAEAMAAQQkAgAGCEgAAAwQlAAAGCEoAAAwQlAAAGCAoAQAwQFACAGCAoAQAwABBCQCAAYISAAADBCUAAAYISgAADBCUAAAYICgBADBAUAIAYICgBADAAEEJAIABghIAAAMEJQAABghKAAAMEJQAABggKAEAMEBQAgBggKAEAMCAKUF59OhRJScna8SIERo3bpzWrFmjlpYWSdKpU6eUnJysmJgYjR8/Xvn5+S7LFhQUyG63a/jw4UpMTFRZWZkZXQAAeIluD8ra2lrNmzdPzz77rI4fP66CggIdO3ZMb775phoaGjR37lxNmzZNpaWlyszMVFZWlk6fPi1JKikp0Zo1a/Tyyy+rtLRUU6ZMUUpKii5fvtzd3QAAeAm/7l5hSEiIjhw5oj59+sjhcKi+vl5XrlxRSEiIioqKZLVaNXPmTElSbGysJk+erLy8PEVHRys/P18JCQkaOXKkJGn27Nnavn279u7dq+nTp3tUh8XS5V2Dh/gdmOPGdmf7ey/GgGd97/aglKQ+ffpIkuLj43Xu3DmNGjVKiYmJ2rhxoyIjI13mDQ8P144dOyRJlZWVHQIxPDxc5eXlHtfQt2/wbVaPrmCzBZldgtfjPQDGgHtMCcobioqK1NDQoEWLFiktLU39+vVTYGCgyzwBAQFqbm6WJDU1NRm2e6Km5pIcjtuv/Xb5+voQEpLq6pp07Vq72WV4JYvl+gekWe8BmI8x8Nk2cEeXBWVjY6NzT9FdAQEBCggIUHp6upKTkzVr1ixdunTJZZ6WlhYFBV0PlsDAQOdFP//cbrPZPK7X4ZDXDpCegu1vLt4DYAy4x+OLeUaPHt3p9CeeeMKt5U+ePKmnnnpKV69edU67evWq/P39FR4eroqKCpf5KysrFRERIUmKiIgwbAcAoKu5tUf50UcfacWKFXI4HGpsbNR3v/tdl/bGxkbdf//9bq0wKipKLS0tWr9+vRYuXKgLFy7olVdeUVJSkiZOnKj169crNzdXM2fO1IkTJ1RYWKjXX39dkpSUlKTU1FR985vf1MiRI5WXl6eamhrZ7XYPuw0AgHvcCspBgwZpwoQJqqur08mTJzvsVfbq1Uvjx493a4VBQUHasmWL1q5dq3Hjxik4OFiTJ09WamqqevXqpZycHGVmZmrTpk0KCQnR8uXLNXbsWEnXr4JduXKlMjIydO7cOYWHhys7O1tWq9WzXgMA4CaLw+HZEep33nlH06ZNu0PldJ/qanNOYvv5Xb+YJ2HTQZ2putj9BZhs6ID7tSft66qra1JbGxfzmMFikUJDg017D8B8jIHPtoE7PL6YZ9q0aTp9+rT+/ve/6/MZey8EKAAA/8zjoNywYYOys7MVFhYmP7/PFrdYLAQlAOCe43FQvvvuu/rFL36h+Pj4O1EPAAA9isdfD2lublZcXNydqAUAgB7H46B84oknVFhYeCdqAQCgx/H40OuVK1e0ZMkS/eIXv1BoaKhL29tvv91lhQEA0BN4HJSRkZEdblwOAMC9yuOgXLBgwZ2oAwCAHsnjoFy6dOlN27Kysr5QMQAA9DQeX8zzeXV1ddq3b5969+7dFfUAANCjeLxH2dle45EjR/Tb3/62SwoCAKAn+cJ7lJL02GOPqbi4uCteCgCAHuULP7i5ra1Nu3fvVkhISFfUAwBAj+JxUA4ePFgWi8Vlmq+vr5YtW9ZlRQEA0FN4HJSfv6mAj4+PBg0apLCwsC4rCgCAnsLjc5SjR4/WqFGjFBAQoOrqaklS3759u7wwAAB6Ao/3KC9cuKD58+ervLxcVqtVdXV1euCBB5STk6P+/fvfiRoBADCNx3uUr7zyih544AEdO3ZMhw8fVklJiR5++GFuNgAAuCd5vEdZXFys/fv3KygoSJIUHBysjIwMPfnkk11eHAAAZvN4j7K9vb3DVa8Wi0X+/v5dVhQAAD2Fx0E5ZswYZWRkqLm5WZLU1NSkjIwMjR49usuLAwDAbB4fek1PT9ecOXM0evRoWa1W1dfX66GHHtKbb755J+oDAMBUHgWlw+FQW1ub9uzZo+PHj6umpkb/+Mc/9P3vf1++vr53qkYAAEzj9qHX5uZmPfvss1q3bp38/Pw0duxYjR07Vps3b9asWbOch2IBALiXuB2Ub7zxhvz9/bVq1SrntL59++rAgQNqa2vTL3/5yztSIAAAZnI7KN977z395Cc/6XAXnr59+2rVqlXav39/lxcHAIDZ3A7KmpoaDRo0qNO2hx9+WBcuXOiyogAA6CncDso+ffqorq6u07b6+noFBgZ2WVEAAPQUbgdlbGys8vLyOm377W9/q+HDh3dVTQAA9Bhufz1k3rx5SkxMVF1dnSZNmqSwsDCdP39e+/bt0+9+9ztt3br1TtYJ3FN8fCzy8bHcesY7yNfX4/uNdKn2dofa2x2m1gC4w+2g/NrXvqa33npLK1euVF5eniwWixwOhyIjI5Wdna1HHnnkTtYJ3DN8fCz6krW3/EwOKpstyNT1t11rV0N9M2GJHs+jGw6MGDFChYWFOnv2rGpraxUWFqYBAwbcqdqAe5KPj0V+vj56YVuZKs83ml2OKcK/3Ec/+3aMfHwsBCV6PI9vYSdJAwcO1MCBA7u6FsCrVJ5v1Jmqi2aXAeAWTDn2U15e7rxf7Lhx4/TSSy+ptrZWknTq1CklJycrJiZG48ePV35+vsuyBQUFstvtGj58uBITE1VWVmZGFwAAXqLbg7KlpUU/+MEPFBMTo0OHDmn37t2qr6/Xj3/8YzU0NGju3LmaNm2aSktLlZmZqaysLJ0+fVqSVFJSojVr1ujll19WaWmppkyZopSUFF2+fLm7uwEA8BLdHpRVVVUaPHiwUlNT1atXL9lsNs2YMUOlpaUqKiqS1WrVzJkz5efnp9jYWE2ePNn5tZT8/HwlJCRo5MiR8vf31+zZs2Wz2bR3797u7gYAwEvc1jnKL+LBBx/Uli1bXKa99957Gjp0qCoqKhQZGenSFh4erh07dkiSKisrNX369A7t5eXlHtdhMffKfIjfAa5jHHS/G9vcm7e9J33v9qD8Zw6HQxs3btSBAwe0detWvf322x3u8BMQEODykGijdk/07Rt8+4XjCzP7qwnoGRgH5uJz0D2mBWVjY6OWLl2qM2fOaOvWrYqKilJgYKAuXbrkMl9LS4uCgq6/mQIDA9XS0tKh3Wazebz+mppLcphwVbqvrw8fDpLq6pp07Vq72WWYgjHwGW8eB2ayWK6HpFmfgz3BjW3gDlOC8uOPP9YPf/hDDRgwQDt27FBISIgkKTIyUocPH3aZt7KyUhEREZKkiIgIVVRUdGiPi4vzuAaHQ147QHoKtj8kxoGZ+Bx0T7dfzNPQ0KDnnntOI0aM0FtvveUMSUmy2+2qrq5Wbm6uWltbVVxcrMLCQud5yaSkJBUWFqq4uFitra3Kzc1VTU2N7HZ7d3cDAOAlun2PcufOnaqqqtK+ffs6PMOyrKxMOTk5yszM1KZNmxQSEqLly5dr7Nixkq7fmH3lypXKyMjQuXPnFB4eruzsbFmt1u7uBgDAS3R7UM6ZM0dz5sy5afujjz6qbdu23bR96tSpmjp16p0oDQCADky96hUAvBVPkLl7niBDUAJAN+MJMtfdLU+QISgBoJvxBJm76wkyBCUAmIQnyNwdzN3vBwCghyMoAQAwQFACAGCAoAQAwABBCQCAAYISAAADBCUAAAYISgAADBCUAAAYICgBADBAUAIAYICgBADAAEEJAIABghIAAAMEJQAABghKAAAMEJQAABggKAEAMEBQAgBggKAEAMAAQQkAgAGCEgAAAwQlAAAGCEoAAAwQlAAAGCAoAQAwQFACAGCAoAQAwABBCQCAAYISAAADpgZlbW2t7Ha7SkpKnNNOnTql5ORkxcTEaPz48crPz3dZpqCgQHa7XcOHD1diYqLKysq6u2wAgBcxLShPnDihGTNm6OOPP3ZOa2ho0Ny5czVt2jSVlpYqMzNTWVlZOn36tCSppKREa9as0csvv6zS0lJNmTJFKSkpunz5slndAADc40wJyoKCAi1atEgvvviiy/SioiJZrVbNnDlTfn5+io2N1eTJk5WXlydJys/PV0JCgkaOHCl/f3/Nnj1bNptNe/fuNaMbAAAvYEpQPv7443r//fc1adIkl+kVFRWKjIx0mRYeHq7y8nJJUmVlpWG7JywWc/7hM2b9Dsz+B1dm/z4YA+br6b8HvzvX9ZsLCwvrdHpTU5MCAwNdpgUEBKi5udmtdk/07Rvs8TLoOjZbkNkloAdgHOBuGAOmBOXNBAYG6tKlSy7TWlpaFBQU5GxvaWnp0G6z2TxeV03NJTkct1/r7fL19bkrBsadVlfXpGvX2s0uwxSMgc946zhgDHzGrDFgsbi/w9SjgjIyMlKHDx92mVZZWamIiAhJUkREhCoqKjq0x8XFebwuh0OmBCU+w/aHxDhAzx8DPep7lHa7XdXV1crNzVVra6uKi4tVWFio6dOnS5KSkpJUWFio4uJitba2Kjc3VzU1NbLb7SZXDgC4V/WoPUqbzaacnBxlZmZq06ZNCgkJ0fLlyzV27FhJUmxsrFauXKmMjAydO3dO4eHhys7OltVqNbdwAMA9y/Sg/O///m+Xnx999FFt27btpvNPnTpVU6dOvdNlAQAgqYcdegUAoKchKAEAMEBQAgBggKAEAMAAQQkAgAGCEgAAAwQlAAAGCEoAAAwQlAAAGCAoAQAwQFACAGCAoAQAwABBCQCAAYISAAADBCUAAAYISgAADBCUAAAYICgBADBAUAIAYICgBADAAEEJAIABghIAAAMEJQAABghKAAAMEJQAABggKAEAMEBQAgBggKAEAMAAQQkAgAGCEgAAAwQlAAAGCEoAAAwQlAAAGCAoAQAwcFcGZU1NjZ5//nmNGjVKY8aMUWZmptra2swuCwBwD7org/I//uM/1Lt3bx08eFA7duzQ0aNHlZuba3ZZAIB70F0XlB999JGOHTum9PR0BQYGauDAgXr++eeVl5dndmkAgHuQn9kFeKqiokJWq1X9+vVzTnvooYdUVVWlixcv6v7773frdXx8JIfjTlV5a0MH3K/AXr7mFWCSB0ODnP/3uev+TOta3joGJMbBDYyB68wYAxaL+/PedUHZ1NSkwMBAl2k3fm5ubnY7KENCgru8Nk+sSxpm6vrNZrMF3Xqme5y3jwGJccAYuDvGwF33t1zv3r11+fJll2k3fg4K6vkbHABwd7nrgjIiIkL19fWqrq52Tvvwww/Vv39/BQebu5cIALj33HVB+cADD2jkyJFau3atGhsbdfbsWb3++utKSkoyuzQAwD3I4nCYeUnL7amurtbq1atVUlIiHx8fTZs2TYsWLZKvr3eeFAcA3Dl3ZVACANBd7rpDrwAAdCeCEgAAAwQlAAAGCEoAAAwQlAAAGCAovcjRo0eVnJysESNGaNy4cVqzZo1aWlrMLgsmqK2tld1uV0lJidmloJuVl5drzpw5Gj16tMaNG6eXXnpJtbW1ZpfVoxGUXqK2tlbz5s3Ts88+q+PHj6ugoEDHjh3Tm2++aXZp6GYnTpzQjBkz9PHHH5tdCrpZS0uLfvCDHygmJkaHDh3S7t27VV9frx//+Mdml9ajEZReIiQkREeOHFFiYqIsFovq6+t15coVhYSEmF0aulFBQYEWLVqkF1980exSYIKqqioNHjxYqamp6tWrl2w2m2bMmKHS0lKzS+vRCEov0qdPH0lSfHy8Jk+erLCwMCUmJppcFbrT448/rvfff1+TJk0yuxSY4MEHH9SWLVtc7mL23nvvaejQoSZW1fMRlF6oqKhIf/nLX+Tj46O0tDSzy0E3CgsLk5/fXfd0PdwBDodDr776qg4cOKBly5aZXU6PRlB6oYCAAPXr10/p6ek6ePCgGhoazC4JQDdqbGxUWlqaCgsLtXXrVkVFRZldUo9GUHqJkydP6qmnntLVq1ed065evSp/f/8OD8IGcO/6+OOPNX36dDU2NmrHjh2EpBsISi8RFRWllpYWrV+/XlevXtU//vEPvfLKK0pKSlKvXr3MLg9AN2hoaNBzzz2nESNG6K233uJiPjdxssJLBAUFacuWLVq7dq3GjRun4OBgTZ48WampqWaXBqCb7Ny5U1VVVdq3b5/279/v0lZWVmZSVT0fj9kCAMAAh14BADBAUAIAYICgBADAAEEJAIABghIAAAMEJQAABghKAAAMEJQAABggKAEAMEBQAgBggKAEAMDA/wP8uUIuXe1TsQAAAABJRU5ErkJggg==\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": "code",
   "execution_count": 4,
   "id": "c25d38d8-2f2a-4ae0-ab44-dbd791b0d165",
   "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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\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": "code",
   "execution_count": 5,
   "id": "76ea8367-35c7-4862-9401-075d4a4dc4cf",
   "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 500x300 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=(5,3), 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",
   "id": "5434ef32-a279-4306-9be4-c0bc8d2a3a6b",
   "metadata": {},
   "source": [
    "## Data Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c276e45c-882f-40f0-be26-88f4431c132d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-29T03:20:39.838498Z",
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     "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": 7,
   "id": "153395d2-d0ca-4469-b93c-4c2aa9b6fbee",
   "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",
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       "      <td>30.0000</td>\n",
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       "      <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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([X, y.to_frame()], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "909def5a-7180-4c28-9d1a-e0ff96be2f25",
   "metadata": {},
   "source": [
    "## Modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "949fdaf6-2c4f-40c3-bc23-addec880b880",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-29T03:20:39.901499Z",
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     "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": "code",
   "execution_count": 9,
   "id": "d60a336b-e38b-41b0-92c7-ab806243cafb",
   "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, display an error message\n",
    "    # your code here...\n",
    "    survived = model.predict([[pclass, fare, sex_male, sex_female]])[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": null,
   "id": "23bf3cc3-c18c-4a1a-addf-4801faf40ac4",
   "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",
    "# 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": "markdown",
   "id": "599efa2a-dff9-417c-ac5f-42c68a149561",
   "metadata": {
    "id": "tp9vhl21y7gK"
   },
   "source": [
    "## End\n",
    "<sup>made by **Jude Michael Teves**</sup> <br>\n",
    "<sup>for comments, corrections, suggestions, please email:</sup><sup> judemichaelteves@gmail.com or jude.teves@dlsu.edu.ph</sup><br>\n"
   ]
  }
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