machine_learning_algorithm.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 276,
   "id": "supreme-pipeline",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
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       "      <td>22.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <th>886</th>\n",
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       "      <td>0</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>890</th>\n",
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       "      <td>32.000000</td>\n",
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       "      <td>7.7500</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Survived  Pclass  Sex        Age  SibSp  Parch     Fare  Embarked\n",
       "0           0       3    0  22.000000      1      0   7.2500         0\n",
       "1           1       1    1  38.000000      1      0  71.2833         1\n",
       "2           1       3    1  26.000000      0      0   7.9250         0\n",
       "3           1       1    1  35.000000      1      0  53.1000         0\n",
       "4           0       3    0  35.000000      0      0   8.0500         0\n",
       "..        ...     ...  ...        ...    ...    ...      ...       ...\n",
       "886         0       2    0  27.000000      0      0  13.0000         0\n",
       "887         1       1    1  19.000000      0      0  30.0000         0\n",
       "888         0       3    1  29.699118      1      2  23.4500         0\n",
       "889         1       1    0  26.000000      0      0  30.0000         1\n",
       "890         0       3    0  32.000000      0      0   7.7500         2\n",
       "\n",
       "[891 rows x 8 columns]"
      ]
     },
     "execution_count": 276,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load data and imports\n",
    "import pandas as pd\n",
    "import seaborn as sbn\n",
    "from sklearn.model_selection import train_test_split\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "clean_titanic_df = pd.read_csv('preprocessed_titanic.csv')\n",
    "\n",
    "clean_titanic_df = clean_titanic_df.drop(columns=['Name', 'PassengerId', 'Unnamed: 0'])\n",
    "\n",
    "train, test= train_test_split(clean_titanic_df,random_state=23, test_size = 0.2)\n",
    "\n",
    "X_train, y_train = train.drop(columns=['Survived']), train['Survived']\n",
    "X_test, y_test = test.drop(columns=['Survived']), test['Survived']\n",
    "\n",
    "clean_titanic_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "id": "answering-consensus",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Pclass  Survived\n",
      "0       1  0.626437\n",
      "1       2  0.490066\n",
      "2       3  0.245478\n",
      "   Sex  Survived\n",
      "0    0  0.190789\n",
      "1    1  0.746094\n",
      "   Embarked  Survived\n",
      "0         0  0.338521\n",
      "1         1  0.570423\n",
      "2         2  0.410714\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'\"\"\\nInsights and further adjustemnts to the data:\\n\\n    Survival is correlated to age, fare, class, and sex. \\n    \\n    based on the the histograms below:\\n    \\n        - people who did not survive were mainly around 30 years old and older.\\n        \\n        - people who did survive were 30 years old and younger. There is special density in the group of age<5.\\n        \\n        - Most people who belonged to the first class survived\\n        \\n        - most people who belonged to the third class did not survive\\n        \\n        - most people who did not survive did not pay a fare over 50 dollars\\n    \\n    possible data tunning:\\n        \\n        - consider turning age and fare into categorical data by using bins\\n        \\n        - combine SibSp and Parch into one column. Possibily turn it into a binary category of family and no family\\n    \\n'"
      ]
     },
     "execution_count": 257,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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HZfLm4A3dfXNV3Zfk+qr65SSfSHL1sP7VSd5VVbuTfCHJhRtRNAAwXQIhAIAR6e67kzz7EOOfSfLcQ4z/RZKfWYfSAIB15JIxAAAAgJERCAEAAACMjEAIAAAAYGQEQgAAAAAjIxACAAAAGBmBEAAAAMDICIQAAAAARkYgBAAAADAyAiEAAACAkREIAQAAAIyMQAgAAABgZARCAAAAACOzYiBUVadV1e1VdV9V3VtVrxnGn1pVt1bVp4fvJw7jVVVvq6rdVXV3VT1n1j8EAAAAAKu3mjOEDiS5rLvPSHJ2kkur6owklye5rbtPT3LbMJ8kL0py+vC1M8lVU68aAAAAgKO2YiDU3Q9198eH6a8kuT/JKUnOS3LtsNq1Sc4fps9L8s6e+HCSE6rq5GkXDgAAAMDR2baWlatqe5JnJ/lIkoXufmhY9NkkC8P0KUkeXPawvcPYQ8vGUlU7MzmDKAsLC1laWlpj6StbOD657MwDU9/urMyiB9O0f//+TV/jPNHP6dLP6dLP6dJPAAA2m1UHQlX15CTvT/Lz3f3lqvrGsu7uquq1PHF370qyK0l27NjRi4uLa3n4qrz9uhtz5T1ryrw21J6LFje6hCNaWlrKLP6dxko/p0s/p0s/p0s/AQDYbFb1KWNV9fhMwqDruvu3huHPPXYp2PD94WF8X5LTlj381GEMAAAAgE1gNZ8yVkmuTnJ/d79l2aKbklw8TF+c5MZl468YPm3s7CSPLLu0DAAAAIANtprrqZ6X5OVJ7qmqu4ax1yW5IskNVXVJkgeSXDAsuyXJuUl2J3k0yaumWTAAAAAAx2bFQKi7fz9JHWbxCw6xfie59BjrAgAAAGBGVnUPIQAAAAC2DoEQAAAAwMgIhAAAAABGRiAEAAAAMDICIQAAAICREQgBAAAAjIxACAAAAGBkBEIAAAAAIyMQAgAAABgZgRAAAADAyAiEAAAAAEZGIAQAAAAwMgIhAAAAgJERCAEAAACMjEAIAAAAYGQEQgAAAAAjIxACAAAAGBmBEAAAAMDICIQAAAAARkYgBAAAADAyAiEAAACAkREIAQAAAIyMQAgAAABgZARCAAAAACMjEAIAAAAYGYEQAAAAwMgIhAAAAABGRiAEAAAAMDICIQAAAICREQgBAAAAjMyKgVBVXVNVD1fVJ5eNvaGq9lXVXcPXucuWvbaqdlfVp6rqhbMqHACAtauq06rq9qq6r6rurarXDONPrapbq+rTw/cTh/GqqrcNx3d3V9VzNvYnAACmYTVnCL0jyTmHGH9rd581fN2SJFV1RpILk3z/8Jhfr6rjplUsAADH7ECSy7r7jCRnJ7l0OIa7PMlt3X16ktuG+SR5UZLTh6+dSa5a/5IBgGlbMRDq7g8l+cIqt3dekuu7+2vd/SdJdid57jHUBwDAFHX3Q9398WH6K0nuT3JKJsdx1w6rXZvk/GH6vCTv7IkPJzmhqk5e36oBgGk7lnsIvXo4bfiax04pzuRg4sFl6+wdxgAA2GSqanuSZyf5SJKF7n5oWPTZJAvDtOM7ANiCth3l465K8sYkPXy/MsnPrmUDVbUzk9OOs7CwkKWlpaMs5fAWjk8uO/PA1Lc7K7PowTTt379/09c4T/RzuvRzuvRzuvSTzaiqnpzk/Ul+vru/XFXfWNbdXVW9xu3N/Nhu3vjdny39nT2vp2bLPjxb+ruyowqEuvtzj01X1W8kuXmY3ZfktGWrnjqMHWobu5LsSpIdO3b04uLi0ZRyRG+/7sZcec/RZl7rb89FixtdwhEtLS1lFv9OY6Wf06Wf06Wf06WfbDZV9fhMwqDruvu3huHPVdXJ3f3QcEnYw8P4qo7v1uPYbt743Z8t/Z09r6dmyz48W/q7sqO6ZOyg68Z/Ksljn0B2U5ILq+qJVfWsTG4++NFjKxEAgGmpyalAVye5v7vfsmzRTUkuHqYvTnLjsvFXDJ82dnaSR5ZdWgYAzKkV496qek+SxSQnVdXeJK9PslhVZ2VyydieJD+XJN19b1XdkOS+TD7B4tLu/vpMKgcA4Gg8L8nLk9xTVXcNY69LckWSG6rqkiQPJLlgWHZLknMz+bCQR5O8al2rBQBmYsVAqLtfeojhq4+w/puSvOlYigIAYDa6+/eT1GEWv+AQ63eSS2daFACw7o7lU8YAAAAAmEMCIQAAAICREQgBAAAAjIxACAAAAGBkBEIAAAAAIyMQAgAAABgZgRAAAADAyAiEAAAAAEZGIAQAAAAwMgIhAAAAgJERCAEAAACMjEAIAAAAYGQEQgAAAAAjIxACAAAAGBmBEAAAAMDICIQAAAAARkYgBAAAADAyAiEAAACAkREIAQAAAIyMQAgAAABgZARCAAAAACMjEAIAAAAYGYEQAAAAwMgIhAAAAABGRiAEAAAAMDICIQAAAICREQgBAAAAjIxACAAAAGBkBEIAAAAAIyMQAgAAABgZgRAAAADAyKwYCFXVNVX1cFV9ctnYU6vq1qr69PD9xGG8quptVbW7qu6uqufMsngAAAAA1m41Zwi9I8k5B41dnuS27j49yW3DfJK8KMnpw9fOJFdNp0wAAAAApmXFQKi7P5TkCwcNn5fk2mH62iTnLxt/Z098OMkJVXXylGoFAAAAYAqO9h5CC9390DD92SQLw/QpSR5ctt7eYQwAAACATWLbsW6gu7uqeq2Pq6qdmVxWloWFhSwtLR1rKd9i4fjksjMPTH27szKLHkzT/v37N32N80Q/p0s/p0s/p0s/AQDYbI42EPpcVZ3c3Q8Nl4Q9PIzvS3LasvVOHca+RXfvSrIrSXbs2NGLi4tHWcrhvf26G3PlPcecea2bPRctbnQJR7S0tJRZ/DuNlX5Ol35Ol35Ol34CALDZHO0lYzcluXiYvjjJjcvGXzF82tjZSR5ZdmkZAAAAAJvAiqfPVNV7kiwmOamq9iZ5fZIrktxQVZckeSDJBcPqtyQ5N8nuJI8medUMagYAAADgGKwYCHX3Sw+z6AWHWLeTXHqsRQEAAAAwO0d7yRgAAAAAc0ogBAAAADAyAiEAAACAkREIAQAAAIyMQAgAAABgZARCAAAAACMjEAIAAAAYGYEQAAAAwMgIhAAAAABGRiAEAAAAMDICIQCAEamqa6rq4ar65LKxp1bVrVX16eH7icN4VdXbqmp3Vd1dVc/ZuMoBgGkSCAEAjMs7kpxz0NjlSW7r7tOT3DbMJ8mLkpw+fO1MctU61QgAzJhACABgRLr7Q0m+cNDweUmuHaavTXL+svF39sSHk5xQVSevS6EAwEwJhAAAWOjuh4bpzyZZGKZPSfLgsvX2DmMAwJzbttEFAACweXR3V1Wv9XFVtTOTy8qysLCQpaWlaZc2d/bv368PM6S/s7dwfHLZmQc2uoxVm7f9wT48W/q7MoEQAACfq6qTu/uh4ZKwh4fxfUlOW7beqcPYt+juXUl2JcmOHTt6cXFxhuXOh6WlpejD7Ojv7L39uhtz5T3z85Jxz0WLG13CmtiHZ0t/V+aSMQAAbkpy8TB9cZIbl42/Yvi0sbOTPLLs0jIAYI7NT9wLAMAxq6r3JFlMclJV7U3y+iRXJLmhqi5J8kCSC4bVb0lybpLdSR5N8qp1LxgAmAmBEADAiHT3Sw+z6AWHWLeTXDrbigCAjSAQ2kS2X/7BjS7hiC4780BeuazGPVe8eAOrAQAAAI6WewgBAAAAjIxACAAAAGBkBEIAAAAAIyMQAgAAABgZgRAAAADAyAiEAAAAAEZGIAQAAAAwMgIhAAAAgJERCAEAAACMjEAIAAAAYGS2HcuDq2pPkq8k+XqSA929o6qemuS9SbYn2ZPkgu7+4rGVCQAAAMC0TOMMob/d3Wd1945h/vIkt3X36UluG+YBAAAA2CRmccnYeUmuHaavTXL+DJ4DAAAAgKN0rIFQJ/mdqrqzqnYOYwvd/dAw/dkkC8f4HAAAAABM0THdQyjJ87t7X1V9Z5Jbq+qPli/s7q6qPtQDhwBpZ5IsLCxkaWnpGEv5VgvHJ5edeWDq2x2rg/s5i3+zMdm/f78eTpF+Tpd+Tpd+AgCw2RxTINTd+4bvD1fVB5I8N8nnqurk7n6oqk5O8vBhHrsrya4k2bFjRy8uLh5LKYf09utuzJX3HGvmxWMuO/PAX+nnnosWN66YLWBpaSmz2O/HSj+nSz+nSz8BANhsjvqSsap6UlU95bHpJD+R5JNJbkpy8bDaxUluPNYiAQAAAJieYzl9ZiHJB6rqse28u7t/u6o+luSGqrokyQNJLjj2MgEAAACYlqMOhLr7M0l+4BDjf5bkBcdSFAAAAACzM4uPnQcAAABgExMIAQAAAIyMQAgAAABgZARCAAAAACMjEAIAAAAYGYEQAAAAwMgIhAAAAABGRiAEAAAAMDICIQAAAICREQgBAAAAjIxACAAAAGBkBEIAAAAAIyMQAgAAABgZgRAAAADAyAiEAAAAAEZGIAQAAAAwMgIhAAAAgJERCAEAAACMjEAIAAAAYGS2bXQBzK/tl39wo0tYsz1XvHijSwAAAIAN5wwhAAAAgJERCAEAAACMjEAIAAAAYGQEQgAAAAAjIxACAAAAGBmBEAAAAMDICIQAAAAARkYgBAAAADAyAiEAAACAkREIAQAAAIyMQAgAAABgZLbNasNVdU6Sf5HkuCT/uruvmNVzwWptv/yDG13CN1x25oG8coV69lzx4nWqBgAOz3EdAGw9MwmEquq4JL+W5MeT7E3ysaq6qbvvm8XzwVa1mQKszW41AdvBBG6zZf/9pqPZP1di/2W9OK4DgK1pVpeMPTfJ7u7+THf/tyTXJzlvRs8FAMDsOK4DgC1oVoHQKUkeXDa/dxgDAGC+OK4DgC1oZvcQWklV7Uyyc5jdX1WfmsHTnJTk8zPY7ij9H/o5Vfo5XUfTz3rzjIrZGuyfUzSL3/cZ7r/PnNmW2dLW6dhu3vhbOlv6O3tz1eM5PLabq/7OIf2dOOyx3awCoX1JTls2f+ow9g3dvSvJrhk9f5Kkqu7o7h2zfI4x0c/p0s/p0s/p0s/p0k/m3IrHdcn6HNvNG7/7s6W/s6fHs6W/s6W/K5vVJWMfS3J6VT2rqp6Q5MIkN83ouQAAmB3HdQCwBc3kDKHuPlBVr07yHzL5eNJruvveWTwXAACz47gOALammd1DqLtvSXLLrLa/Sk5bni79nC79nC79nC79nC79ZK5tkuO6eeR3f7b0d/b0eLb0d7b0dwXV3RtdAwAAAADraFb3EAIAAABgk9qygVBVnVNVn6qq3VV1+UbXM4+qak9V3VNVd1XVHcPYU6vq1qr69PD9xI2uc7Oqqmuq6uGq+uSysUP2rybeNuyvd1fVczau8s3nML18Q1XtG/bPu6rq3GXLXjv08lNV9cKNqXrzqqrTqur2qrqvqu6tqtcM4/bPo3CEftpHYWTWcpxUVd9eVXur6lfXs8Z5tpr+VtVZVfUHw9/ju6vq725ErfNkpddNVfXEqnrvsPwjVbV9A8qca6vo8S8MxxF3V9VtVXXYjwnnW632tX9V/a9V1VXlk8cGWzIQqqrjkvxakhclOSPJS6vqjI2tam797e4+a9nH9V2e5LbuPj3JbcM8h/aOJOccNHa4/r0oyenD184kV61TjfPiHfnWXibJW4f986zh/hYZftcvTPL9w2N+ffibwDcdSHJZd5+R5Owklw59s38encP1M7GPwtis5TjpjUk+tC5VbR2r6e+jSV7R3Y/9jf2Vqjph/UqcL6t83XRJki929/ckeWuSN69vlfNtlT3+RJId3f0/JXlfkn+2vlXOr9W+9q+qpyR5TZKPrG+Fm9uWDISSPDfJ7u7+THf/tyTXJzlvg2vaKs5Lcu0wfW2S8zeulM2tuz+U5AsHDR+uf+cleWdPfDjJCVV18roUOgcO08vDOS/J9d39te7+kyS7M/mbwKC7H+rujw/TX0lyf5JTYv88Kkfo5+HYR2HrWtVxUlX9z0kWkvzO+pS1ZazY3+7+L9396WH6vyZ5OMnT16vAObSa103L+/6+JC+oqlrHGufdij3u7tu7+9Fh9sNJTl3nGufZal/7vzGTMPMv1rO4zW6rBkKnJHlw2fzeHPngnEPrJL9TVXdW1c5hbKG7HxqmP5vJwQyrd7j+2WePzquHU2uvWXbauF6uwXDa97MzebfE/nmMDupnYh+FsVnxOKmqHpfkyiS/uJ6FbRFrOg6tqucmeUKSP551YXNsNf8nfWOd7j6Q5JEkT1uX6raGtf6/f0mS/3emFW0tK/Z3uN3Bad39wfUsbB7M7GPn2RKe3937quo7k9xaVX+0fGF3d1X5mLqjpH/H7KpMkv4evl+Z5Gc3tKI5U1VPTvL+JD/f3V9e/maf/XPtDtFP+yhsQVX1u0m+6xCL/snymSP8Hf2HSW7p7r1OsvhWU+jvY9s5Ocm7klzc3X853SphNqrq7yXZkeRHNrqWrWII4d+S5JUbXMqmtFUDoX1JTls2f+owxhp0977h+8NV9YFMTsf7XFWd3N0PDf/RPryhRc6fw/XPPrtG3f25x6ar6jeS3DzM6uUqVNXjMwkvruvu3xqG7Z9H6VD9tI/C1tTdP3a4ZVW1muOkH07yN6vqHyZ5cpInVNX+7nZfxkylv6mqb0/ywST/ZLjUmcNbzf9Jj62zt6q2JfmOJH+2PuVtCav6f7+qfiyT4PNHuvtr61TbVrBSf5+S5G8kWRpC+O9KclNVvaS771i3KjeprXrJ2MeSnF5Vz6qqJ2Ry886bNrimuVJVTxpuvJWqelKSn0jyyUz6ePGw2sVJbtyYCufW4fp3U5JX1MTZSR5Zdko0h3DQPWx+KpP9M5n08sLhEzGelcmNkD+63vVtZsN1/1cnub+737Jskf3zKByun/ZRGKUVj5O6+6LufkZ3b8/ksrF3CoNWbcX+Dsf+H8ikr+9bx9rm1WpeNy3v+08n+b3udhbx6q3Y46p6dpJ/leQl3e0N97U5Yn+7+5HuPqm7tw9/dz+cSZ9HHwYlW/QMoe4+UFWvTvIfkhyX5JruvneDy5o3C0k+MKSo25K8u7t/u6o+luSGqrokyQNJLtjAGje1qnpPksUkJ1XV3iSvT3JFDt2/W5Kcm8nNZR9N8qp1L3gTO0wvF6vqrEwux9mT5OeSpLvvraobktyXyac/XdrdX9+Asjez5yV5eZJ7ququYex1sX8ercP186X2URidQ/4drclHHP+D7v77G1ncFrCa/l6Q5G8leVpVvXJ43Cu7+671L3fzO9zrpqr6v5Pc0d03ZfKmx7uqancmH/Jx4cZVPH9W2eN/nskZg785vP76/7r7JRtW9BxZZX85jBLuAgAAAIzLVr1kDAAAAIDDEAgBAAAAjIxACAAAAGBkBEIAAAAAIyMQAgAAABgZgRAAAADAyAiEAAAAAEZGIAQAAAAwMv8/PxlVoptEZaYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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M7+wtnJjsP/vodpexZvP28+BneLaM7+pWDYSSXJPkF5O8ctmym5Nc0d1Hq+qlSa5I8oJh/vkFSZ6Q5KuT/F5VPb67PzPdsgEA2KiqengmYdB13f36YfEHq+qU7r53mBJ237D8niSnLdt8z7Ds83T3gSQHkmTv3r29uLg4q/LnxtLSUozD7Bjf2bvquhty5W1r+ZNxZzh00eJ2l7AufoZny/iubtUpY9391iT3H7PsTd39YFR8SyYnBslkjvlruvtT3f2+JAeTnDPFegEA2ISaXAr0iiR3dvfPLlt1Y5KLh8cXJ7lh2fLvG+429pQkDyybWgYAzKlpxL0/kOS1w+NTMwmIHmSOOQDAzvLUJM9NcltVvWtY9sIkL0lyfVVdmuT9SZ4zrLspyXmZvNH3ySTfv6XVAgAzsalAqKr+TSa3Lr1uA9vOfJ75bp7zutvnQ+7m/unbfNK3+aRv8IW6+w+S1HFWP22F9p3kspkWBQBsuQ0HQlV1SSYfNv204UQhWeMc82Rr5pnv5jmvu30+5G7un77NJ32bT/oGAAAr29Bt56vq3CQ/meSZ3f3JZatuTHJBVT2yqs5IcmaSt22+TAAAAACmZS23nX91ksUkJ1fV4SQvyuSuYo9McvNwi9Jbuvtfd/ftVXV9kjsymUp2mTuMAQAAAOwsqwZC3X3hCotf8RDtX5zkxZspCgAAAIDZ2dCUMQAAAADml0AIAAAAYGQEQgAAAAAjIxACAAAAGBmBEAAAAMDICIQAAAAARkYgBAAAADAyAiEAAACAkREIAQAAAIyMQAgAAABgZARCAAAAACMjEAIAAAAYGYEQAAAAwMgIhAAAAABGRiAEAAAAMDICIQAAAICREQgBAAAAjIxACAAAAGBkBEIAAAAAIyMQAgAAABgZgRAAAADAyAiEAAAAAEZm1UCoqq6uqvuq6j3Llj22qm6uqr8c/n3MsLyq6heq6mBVvbuqnjzL4gEAAABYv7VcIXRNknOPWXZ5kjd395lJ3jw8T5JnJDlz+NqX5OXTKRMAAACAaVk1EOrutya5/5jF5ye5dnh8bZJnLVv+yp64JclJVXXKlGoFAAAAYApO2OB2C9197/D4A0kWhsenJrl7WbvDw7J7c4yq2pfJVURZWFjI0tLSBkt5iCJPTPaffXTq+52V9YzBkSNHZjJmO8Vu7p++zSd9m0/6BgAAK9toIPRZ3d1V1RvY7kCSA0myd+/eXlxc3GwpX+Cq627Ilbdtuotb5tBFi2tuu7S0lFmM2U6xm/unb/NJ3+aTvgEAwMo2epexDz44FWz4975h+T1JTlvWbs+wDAAAAIAdYqOB0I1JLh4eX5zkhmXLv2+429hTkjywbGoZAAAAADvAqvOpqurVSRaTnFxVh5O8KMlLklxfVZcmeX+S5wzNb0pyXpKDST6Z5PtnUDMAAAAAm7BqINTdFx5n1dNWaNtJLttsUQAAAADMzkanjAEAAAAwpwRCAAAAACMjEAIAGJGqurqq7quq9yxb9lNVdU9VvWv4Om/Zuiuq6mBVvbeqnr49VQMA0yYQAgAYl2uSnLvC8pd195OGr5uSpKrOSnJBkicM2/xyVT1syyoFAGZGIAQAMCLd/dYk96+x+flJXtPdn+ru92VyJ9lzZlYcALBlBEIAACTJD1fVu4cpZY8Zlp2a5O5lbQ4PywCAObfqbecBANj1Xp7kp5P08O+VSX5gPTuoqn1J9iXJwsJClpaWplzi/Dly5IhxmCHjO3sLJyb7zz663WWs2bz9PPgZni3juzqBEBt2+uVvnNm+9599NJfMYP+HXvIdU98nAMy77v7gg4+r6leTvGF4ek+S05Y13TMsW2kfB5IcSJK9e/f24uLiTGqdJ0tLSzEOs2N8Z++q627IlbfNz5+Mhy5a3O4S1sXP8GwZ39WZMgYAMHJVdcqyp9+d5ME7kN2Y5IKqemRVnZHkzCRv2+r6AIDpm5+4FwCATauqVydZTHJyVR1O8qIki1X1pEymjB1K8q+SpLtvr6rrk9yR5GiSy7r7M9tQNgAwZQIhAIAR6e4LV1j8iodo/+IkL55dRQDAdjBlDAAAAGBkBEIAAAAAIyMQAgAAABgZgRAAAADAyAiEAAAAAEZGIAQAAAAwMgIhAAAAgJERCAEAAACMjEAIAAAAYGQEQgAAAAAjs6lAqKp+rKpur6r3VNWrq+pRVXVGVd1aVQer6rVV9YhpFQsAAADA5m04EKqqU5P8aJK93f0NSR6W5IIkL03ysu7+2iQfSXLpNAoFAAAAYDo2O2XshCQnVtUJSb44yb1Jvi3J64b11yZ51ia/BwAAAABTtOFAqLvvSfKfk/xVJkHQA0nekeSj3X10aHY4yambLRIAAACA6TlhoxtW1WOSnJ/kjCQfTfKbSc5dx/b7kuxLkoWFhSwtLW20lONaODHZf/bR1RvuEOsZgyNHjsxkzNZjlmM7q2O33WOW7IxjNyv6Np/0bT7t5r4BADB7Gw6EkvyzJO/r7g8lSVW9PslTk5xUVScMVwntSXLPSht394EkB5Jk7969vbi4uIlSVnbVdTfkyts208WtdeiixTW3XVpayizGbD0uufyNM9v3/rOPzuTYrWeMZ2UnHLtZ0bf5pG/zaTf3DQCA2dvMZwj9VZKnVNUXV1UleVqSO5K8JcmzhzYXJ7lhcyUCAAAAME2b+QyhWzP58Oh3Jrlt2NeBJC9I8uNVdTDJVyR5xRTqBAAAAGBKNjUnp7tflORFxyy+K8k5m9kvAAAAALOz2dvOAwAAADBnBEIAAAAAIyMQAgAAABgZgRAAAADAyAiEAAAAAEZGIAQAAAAwMgIhAAAAgJERCAEAAACMjEAIAAAAYGQEQgAAAAAjIxACAAAAGBmBEAAAAMDICIQAAAAARkYgBAAAADAyAiEAAACAkREIAQAAAIyMQAgAAABgZARCAAAAACMjEAIAAAAYGYEQAMCIVNXVVXVfVb1n2bLHVtXNVfWXw7+PGZZXVf1CVR2sqndX1ZO3r3IAYJoEQgAA43JNknOPWXZ5kjd395lJ3jw8T5JnJDlz+NqX5OVbVCMAMGObCoSq6qSqel1V/XlV3VlV/+R47zABALD9uvutSe4/ZvH5Sa4dHl+b5FnLlr+yJ25JclJVnbIlhQIAM7XZK4R+Psl/7+6vT/LEJHfm+O8wAQCwMy10973D4w8kWRgen5rk7mXtDg/LAIA5d8JGN6yqL0/yLUkuSZLu/nSST1fV+UkWh2bXJllK8oLNFAkAwNbo7q6qXu92VbUvk2llWVhYyNLS0rRLmztHjhwxDjNkfGdv4cRk/9lHt7uMNZu3nwc/w7NlfFe34UAoyRlJPpTkv1TVE5O8I8nzcvx3mAAA2Jk+WFWndPe9w5Sw+4bl9yQ5bVm7PcOyL9DdB5IcSJK9e/f24uLiDMudD0tLSzEOs2N8Z++q627Ilbdt5k/GrXXoosXtLmFd/AzPlvFd3Wb+d5+Q5MlJfqS7b62qn88x08Me6h2mrXgXaTcn2jsh7Zzl2M7q2G33mCU749jNir7NJ32bT7u5b2yLG5NcnOQlw783LFv+w1X1miT/OMkDy974AwDm2GYCocNJDnf3rcPz12USCB3vHabPsxXvIu3mRHsnpJ2XXP7Gme17/9lHZ3LsdsK7Bjvh2M2Kvs0nfZtPu7lvzFZVvTqT6f0nV9XhJC/KJAi6vqouTfL+JM8Zmt+U5LwkB5N8Msn3b3nBAMBMbPgv7u7+QFXdXVVf193vTfK0JHcMXyu9wwQAwDbr7guPs+ppK7TtJJfNtiIAYDts9hKMH0lyXVU9Isldmbxr9EVZ+R0mAAAAAHaATQVC3f2uJHtXWPUF7zABAAAAsDN80XYXAAAAAMDWEggBAAAAjIxACAAAAGBkBEIAAAAAIyMQAgAAABgZgRAAAADAyAiEAAAAAEZGIAQAAAAwMgIhAAAAgJERCAEAAACMjEAIAAAAYGQEQgAAAAAjIxACAAAAGBmBEAAAAMDICIQAAAAARkYgBAAAADAyAiEAAACAkREIAQAAAIyMQAgAAABgZARCAAAAACMjEAIAAAAYmU0HQlX1sKr606p6w/D8jKq6taoOVtVrq+oRmy8TAAAAgGmZxhVCz0ty57LnL03ysu7+2iQfSXLpFL4HAAAAAFOyqUCoqvYk+Y4kvzY8ryTfluR1Q5NrkzxrM98DAAAAgOna7BVCP5fkJ5P83fD8K5J8tLuPDs8PJzl1k98DAAAAgCk6YaMbVtV3Jrmvu99RVYsb2H5fkn1JsrCwkKWlpY2WclwLJyb7zz66esMdYj1jcOTIkZmM2XrMcmxndey2e8ySnXHsZkXf5pO+zafd3DcAAGZvw4FQkqcmeWZVnZfkUUm+LMnPJzmpqk4YrhLak+SelTbu7gNJDiTJ3r17e3FxcROlrOyq627Ilbdtpotb69BFi2tuu7S0lFmM2XpccvkbZ7bv/WcfncmxW88Yz8pOOHazom/zSd/m027uGwAAs7fhKWPdfUV37+nu05NckOT3u/uiJG9J8uyh2cVJbth0lQAAAABMzTTuMnasFyT58ao6mMlnCr1iBt8DAAAAgA2aypyc7l5KsjQ8vivJOdPYLwAAAADTN4srhAAAAADYwQRCAAAAACMjEAIAAAAYGYEQAAAAwMgIhAAAAABGZip3GQMAYP5V1aEkH0/ymSRHu3tvVT02yWuTnJ7kUJLndPdHtqtGAGA6XCEEAMBy39rdT+ruvcPzy5O8ubvPTPLm4TkAMOcEQgAAPJTzk1w7PL42ybO2rxQAYFoEQgAAPKiTvKmq3lFV+4ZlC9197/D4A0kWtqc0AGCafIYQAAAP+ubuvqeqvirJzVX158tXdndXVa+04RAg7UuShYWFLC0tzbzYne7IkSPGYYaM7+wtnJjsP/vodpexZvP28+BneLaM7+oEQgAAJEm6+57h3/uq6reTnJPkg1V1SnffW1WnJLnvONseSHIgSfbu3duLi4tbVPXOtbS0FOMwO8Z39q667oZcedv8/Ml46KLF7S5hXfwMz5bxXZ0pYwAApKoeXVVf+uDjJN+e5D1Jbkxy8dDs4iQ3bE+FAMA0zU/cCwDALC0k+e2qSibniK/q7v9eVX+S5PqqujTJ+5M8ZxtrBACmRCAEAEC6+64kT1xh+d8kedrWVwQAzJIpYwAAAAAjIxACAAAAGBmBEAAAAMDICIQAAAAARkYgBAAAADAyAiEAAACAkREIAQAAAIyMQAgAAABgZDYcCFXVaVX1lqq6o6pur6rnDcsfW1U3V9VfDv8+ZnrlAgAAALBZm7lC6GiS/d19VpKnJLmsqs5KcnmSN3f3mUnePDwHAAAAYIfYcCDU3fd29zuHxx9PcmeSU5Ocn+Taodm1SZ61yRoBAAAAmKKpfIZQVZ2e5BuT3JpkobvvHVZ9IMnCNL4HAAAAANNxwmZ3UFVfkuS3kjy/uz9WVZ9d191dVX2c7fYl2ZckCwsLWVpa2mwpX2DhxGT/2Uenvt9ZWc8YHDlyZCZjth6zHNtZHbvtHrNkZxy7WdG3+aRv82k39w0AgNnbVCBUVQ/PJAy6rrtfPyz+YFWd0t33VtUpSe5badvuPpDkQJLs3bu3FxcXN1PKiq667oZcedumM68tc+iixTW3XVpayizGbD0uufyNM9v3/rOPzuTYrWeMZ2UnHLtZ0bf5pG/zaTf3DQCA2dvMXcYqySuS3NndP7ts1Y1JLh4eX5zkho2XBwAAAMC0beYSjKcmeW6S26rqXcOyFyZ5SZLrq+rSJO9P8pxNVQgAAADAVG04EOruP0hSx1n9tI3uFwAAAIDZmspdxgAAAACYHwIhAAAAgJERCAEAAACMjEAIAAAAYGQEQgAAAAAjIxACAAAAGBmBEAAAAMDICIQAAAAARkYgBAAAADAyAiEAAACAkREIAQAAAIyMQAgAAABgZARCAAAAACNzwnYXAOwet93zQC65/I3bXcaaHXrJd2x3CQAAANvCFUIAAAAAIyMQAgAAABgZgRAAAADAyAiEAAAAAEZGIAQAAAAwMu4yBjAnTp/RHdz2n310JneHcxc3AADYuQRCAJDZBW6zcs25j97uEgAAmGMzmzJWVedW1Xur6mBVXT6r7wMAwGw5rwOA3WcmgVBVPSzJLyV5RpKzklxYVWfN4nsBADA7zusAYHea1RVC5yQ52N13dfenk7wmyfkz+l4AAMyO8zoA2IVmFQidmuTuZc8PD8sAAJgvzusAYBfatg+Vrqp9SfYNT49U1Xtn8G1OTvLhGex3Juql62o+V31brx+dUf/WOcazspuP3Vz1zf+5Cf/f5tO3vnRmffuaGeyTEdiic7t5s2tfg3YI4zt7czXGO+TcYz3manznkPGdOO653awCoXuSnLbs+Z5h2Wd194EkB2b0/ZMkVfX27t47y++xXXZz35Ld3T99m0/6Np/0DaZi1fO6ZGvO7eaN/6ezZXxnzxjPlvGdLeO7ullNGfuTJGdW1RlV9YgkFyS5cUbfCwCA2XFeBwC70EyuEOruo1X1w0l+N8nDklzd3bfP4nsBADA7zusAYHea2WcIdfdNSW6a1f7XaDdftryb+5bs7v7p23zSt/mkbzAFO+S8bh75fzpbxnf2jPFsGd/ZMr6rqO7e7hoAAAAA2EKz+gwhAAAAAHaoXREIVdW5VfXeqjpYVZevsP6RVfXaYf2tVXX6NpS5IWvo2yVV9aGqetfw9S+3o86NqKqrq+q+qnrPcdZXVf3C0Pd3V9WTt7rGjVpD3xar6oFlx+3fb3WNG1VVp1XVW6rqjqq6vaqet0KbuTx2a+zbXB67qnpUVb2tqv5s6Nt/WKHNXL5WrrFvc/tamSRV9bCq+tOqesMK6+byuMFustr52tDmOct+v7xqq2ucZ2s4H37c8Pv7T4fzjvO2o855tZvPyXeCNYzvRcO43lZVf1RVT9zqGufZauO7rN03VdXRqnr2VtU2D+Y+EKqqhyX5pSTPSHJWkgur6qxjml2a5CPd/bVJXpbkpVtb5cassW9J8truftLw9WtbWuTmXJPk3IdY/4wkZw5f+5K8fAtqmpZr8tB9S5L/b9lx+49bUNO0HE2yv7vPSvKUJJet8HM5r8duLX1L5vPYfSrJt3X3E5M8Kcm5VfWUY9rM5Wtl1ta3ZH5fK5PkeUnuPM66eT1usCus5Xytqs5MckWSp3b3E5I8f6vrnFdrPB/+t0mu7+5vzOQueL+8tVXOvWuye8/Jd4Jr8tDj+74k/0d3n53kp+Nzb9brmqzyd9fwOvLSJG/aioLmydwHQknOSXKwu+/q7k8neU2S849pc36Sa4fHr0vytKqqLaxxo9bSt7nV3W9Ncv9DNDk/ySt74pYkJ1XVKVtT3easoW9zq7vv7e53Do8/nskfqace02wuj90a+zaXhmNxZHj68OHr2A+Rm8vXyjX2bW5V1Z4k35HkeCHWXB432EXWcr72fyX5pe7+SJJ0931bXOM8W8v4dpIvGx5/eZK/3sL65t5uPiffCVYb3+7+owdfG5LckmTPlhS2S6zx764fSfJbSbz2HmM3BEKnJrl72fPD+cI/4D7bpruPJnkgyVdsSXWbs5a+Jck/Hy4zfF1VnbY1pW2JtfZ/Xv2TYYrLf6uqJ2x3MRsxTE35xiS3HrNq7o/dQ/QtmdNjN0w7elcmvwxv7u7jHrc5e61cS9+S+X2t/LkkP5nk746zfm6PG+wSa/md9/gkj6+qP6yqW6pqtauI+Zy1jO9PJfneqjqcyd3wfmRrShuNuT+vmyOXJvlv213EblJVpyb57riybUW7IRAau/+a5PTu/kdJbs7n3iVmZ3tnkq8ZprhcleR3trec9auqL8kkaX9+d39su+uZplX6NrfHrrs/091PyuSdp3Oq6hu2uaSpWUPf5vK1sqq+M8l93f2O7a4F2JQTMplus5jkwiS/WlUnbWdBu8yFSa7p7j1Jzkvy61Xl7xzmSlV9ayaB0Au2u5Zd5ueSvKC7j/fG2qjthhfKe5Isf6d3z7BsxTZVdUIml5L+zZZUtzmr9q27/6a7PzU8/bUk/9sW1bYV1nJs51J3f+zBKS7dfVOSh1fVydtc1ppV1cMzCUyu6+7Xr9Bkbo/dan2b92OXJN390SRvyRfOt57X18rPOl7f5vi18qlJnllVhzKZJvFtVfUbx7SZ++MGc24tv/MOJ7mxu/9Xd78vyV9kEhCxurWM76VJrk+S7v7jJI9KMle/m3e4uT2vmxdV9Y8yOT85v7v9Dp+uvUleM5xLPTvJL1fVs7a1oh1kNwRCf5LkzKo6o6oekckHyd14TJsbk1w8PH52kt/v7nn4fIlV+3bM/N1n5vgfOjqPbkzyfcOdDZ6S5IHuvne7i5qGqvp7D37GR1Wdk8n/xbl48R/qfkWSO7v7Z4/TbC6P3Vr6Nq/Hrqq+8sF3o6vqxCT/Z5I/P6bZXL5WrqVv8/pa2d1XdPee7j49k98Bv9/d33tMs7k8brCLrOVc9HcyuToow5sIj09y1xbWOM/WMr5/leRpSVJV/zCTQOhDW1rl7jaX53Xzoqoel+T1SZ7b3X+x3fXsNt19RnefPpxLvS7JD3X372xvVTvHCdtdwGZ199Gq+uEkv5vkYUmu7u7bq+o/Jnl7d9+YyR94v15VBzP5wKkLtq/itVtj3360qp6Zyd2R7k9yybYVvE5V9epMTo5OHuZ8vyiTD4NNd/9KJnPAz0tyMMknk3z/9lS6fmvo27OT/GBVHU3yt0kumKM/4J6a5LlJbhs+syVJXpjkccncH7u19G1ej90pSa4d7rLwRZncjeUNu+G1Mmvr29y+Vq5klxw32BXWeL72u0m+varuSPKZJD/hKoC1WeP47s9kGt6PZfIB05fMye/mHWE3n5PvBGsY33+fyWf//fLwnuPR7t67PdXOnzWMLw+hvFYCAAAAjMtumDIGAAAAwDoIhAAAAABGRiAEAAAAMDICIQAAAICREQgBAAAAjIxACAAAAGBkBEIAAAAAIyMQAgAAABiZ/x9trBnTWwPORQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"\"\"\n",
    "First visualize the data to look for correlation or other patterns\n",
    "\"\"\"\"\"\n",
    "\n",
    "# check correlation between pairs of features. We are gonna see the average of people who survived by category for each categoric feature\n",
    "\n",
    "survived_class = train[['Pclass', 'Survived']].groupby(['Pclass'], as_index=False).mean().sort_values(by='Survived', ascending=False)\n",
    "print(survived_class)\n",
    "\n",
    "survived_sex = train[['Sex', 'Survived']].groupby(['Sex'], as_index = False).mean().sort_values(by='Sex')\n",
    "print(survived_sex)\n",
    "\n",
    "survived_embarked = train[['Embarked', 'Survived']].groupby(['Embarked'], as_index=False).mean().sort_values(by='Embarked')\n",
    "print(survived_embarked)\n",
    "\n",
    "# using histograms to visualize correlation between numerical data and survival\n",
    "# graph = sbn.FacetGrid(train, col = 'Survived')\n",
    "# graph.map(plt.hist, 'Age', bins = 20)\n",
    "\n",
    "# correlating age with survival\n",
    "train[['Age', 'Survived']].groupby('Survived').hist(bins=20, figsize = (20, 5))\n",
    "\n",
    "# correlating age and class with survival\n",
    "train[['Age', 'Survived', 'Pclass']].groupby(['Survived', 'Pclass']).hist(bins=20, figsize = (20, 5))\n",
    "\n",
    "# correlating fare with survival\n",
    "train[['Fare', 'Survived']].groupby(['Survived']).hist(bins=10, figsize = (20, 5))\n",
    "\n",
    "# correlating fare with survival\n",
    "train[['Parch', 'Survived']].groupby(['Survived']).hist(bins=10, figsize = (20, 5))\n",
    "\n",
    "train[['SibSp', 'Survived']].groupby(['Survived']).hist(bins=10, figsize = (20, 5))\n",
    "\n",
    "\"\"\"\"\"\n",
    "Insights and further adjustemnts to the data:\n",
    "\n",
    "    Survival is correlated to age, fare, class, and sex. \n",
    "    \n",
    "    based on the the histograms below:\n",
    "    \n",
    "        - people who did not survive were mainly around 30 years old and older.\n",
    "        \n",
    "        - people who did survive were 30 years old and younger. There is special density in the group of age<5.\n",
    "        \n",
    "        - Most people who belonged to the first class survived\n",
    "        \n",
    "        - most people who belonged to the third class did not survive\n",
    "        \n",
    "        - most people who did not survive did not pay a fare over 50 dollars\n",
    "    \n",
    "    possible data tunning:\n",
    "        \n",
    "        - consider turning age and fare into categorical data by using bins\n",
    "        \n",
    "        - combine SibSp and Parch into one column. Possibily turn it into a binary category of family and no family\n",
    "    \n",
    "\"\"\"\"\"\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "id": "available-galaxy",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.81      0.82      0.81       115\n",
      "           1       0.67      0.66      0.66        64\n",
      "\n",
      "    accuracy                           0.76       179\n",
      "   macro avg       0.74      0.74      0.74       179\n",
      "weighted avg       0.76      0.76      0.76       179\n",
      "\n",
      "0.7597765363128491\n"
     ]
    }
   ],
   "source": [
    "# logistic regression\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import classification_report\n",
    "\n",
    "lor = LogisticRegression(penalty= 'none', max_iter= 1000, solver='newton-cg').fit(X_train, y_train)\n",
    "print(classification_report(y_test, lor.predict(X_test)))\n",
    "print(lor.score(X_test, y_test))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "id": "complex-password",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.81      0.89      0.85       115\n",
      "           1       0.75      0.62      0.68        64\n",
      "\n",
      "    accuracy                           0.79       179\n",
      "   macro avg       0.78      0.76      0.77       179\n",
      "weighted avg       0.79      0.79      0.79       179\n",
      "\n",
      "0.7932960893854749\n"
     ]
    }
   ],
   "source": [
    "# categorical bayes classifier\n",
    "\n",
    "from sklearn.naive_bayes import CategoricalNB\n",
    "\n",
    "categorical_X = clean_titanic_df[['Pclass', 'Sex', 'Embarked']]\n",
    "categorical_y = clean_titanic_df['Survived']\n",
    "\n",
    "categorical_NB = CategoricalNB()\n",
    "categorical_NB.fit(X_train, y_train)\n",
    "print(classification_report(y_test, categorical_NB.predict(X_test)))\n",
    "print(categorical_NB.score(X_test, y_test))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "id": "optical-healthcare",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.85      0.89      0.87       115\n",
      "           1       0.78      0.72      0.75        64\n",
      "\n",
      "    accuracy                           0.83       179\n",
      "   macro avg       0.81      0.80      0.81       179\n",
      "weighted avg       0.82      0.83      0.83       179\n",
      "\n",
      "0.8268156424581006\n"
     ]
    }
   ],
   "source": [
    "# gaussian bayes classifier \n",
    "\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "\n",
    "NB = GaussianNB()\n",
    "numericaldata = X_train[]\n",
    "NB.fit(X_train, y_train)\n",
    "print(classification_report(y_test, NB.predict(X_test)))\n",
    "print(NB.score(X_test, y_test))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "id": "exempt-record",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "          -1       0.82      0.86      0.84       115\n",
      "           1       0.72      0.66      0.69        64\n",
      "\n",
      "    accuracy                           0.79       179\n",
      "   macro avg       0.77      0.76      0.76       179\n",
      "weighted avg       0.78      0.79      0.79       179\n",
      "\n",
      "0.7877094972067039\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
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       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.798546</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.465072</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>742</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.648048</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4.592978</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>712 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pclass  Sex       Age  SibSp  Parch      Fare  Embarked\n",
       "151       1    1 -0.571912      1      0  0.677439         0\n",
       "753       3    0 -0.495775      0      0 -0.496656         0\n",
       "746       3    0 -1.028731      1      1 -0.249570         0\n",
       "684       2    0  2.321278      1      1  0.125434         0\n",
       "887       1    1 -0.800322      0      0 -0.054568         0\n",
       "..      ...  ...       ...    ...    ...       ...       ...\n",
       "31        1    1  0.014272      1      0  2.275871         1\n",
       "488       3    0  0.037181      0      0 -0.493572         0\n",
       "40        3    1  0.798546      1      0 -0.465072         0\n",
       "742       1    1 -0.648048      2      2  4.592978         1\n",
       "595       3    0  0.494000      1      1 -0.171569         0\n",
       "\n",
       "[712 rows x 7 columns]"
      ]
     },
     "execution_count": 263,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# support vector machine \n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "clf = SVC(kernel = 'linear')\n",
    "\n",
    "# scale numerical attributes\n",
    "scaler = StandardScaler()\n",
    "scaled_numerical_columns = scaler.fit_transform(X_train[['Age', 'Fare']])\n",
    "X_train[['Age', 'Fare']] = scaled_numerical_columns\n",
    "svm_y_train = y_train.replace({0: -1})\n",
    "\n",
    "scaled_numerical_columns = scaler.fit_transform(X_test[['Age', 'Fare']])\n",
    "X_test[['Age', 'Fare']] = scaled_numerical_columns\n",
    "svm_y_test = y_test.replace({0: -1})\n",
    "\n",
    "clf.fit(X_train, svm_y_train.to_numpy() )\n",
    "\n",
    "\n",
    "\n",
    "print(classification_report(svm_y_test, clf.predict(X_test)))\n",
    "print(clf.score(X_test, svm_y_test))\n",
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "id": "painted-appliance",
   "metadata": {},
   "outputs": [],
   "source": [
    "# results upon data readjustment\n",
    "\n",
    "# create age categories (DO NO RUN THIS CELL TWICE! IF RAN A SECOND TIME, MOST VALUES WILL BE TURNED INTO 1):\n",
    "age_bins = pd.cut( clean_titanic_df['Age'], 10, retbins = True)\n",
    "\n",
    "i = 0\n",
    "j = 0\n",
    "while i < 72:\n",
    "    clean_titanic_df.loc[(clean_titanic_df['Age'] > i) &  (clean_titanic_df['Age'] <= i +8), 'Age'] = j\n",
    "    j += 1\n",
    "    i += 8\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "id": "closing-glasgow",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0        (-0.001, 7.854]\n",
      "1      (39.688, 512.329]\n",
      "2          (7.854, 10.5]\n",
      "3      (39.688, 512.329]\n",
      "4          (7.854, 10.5]\n",
      "             ...        \n",
      "886       (10.5, 21.679]\n",
      "887     (21.679, 39.688]\n",
      "888     (21.679, 39.688]\n",
      "889     (21.679, 39.688]\n",
      "890      (-0.001, 7.854]\n",
      "Name: Fare, Length: 891, dtype: category\n",
      "Categories (5, interval[float64]): [(-0.001, 7.854] < (7.854, 10.5] < (10.5, 21.679] < (21.679, 39.688] < (39.688, 512.329]], array([  0.    ,   7.8542,  10.5   ,  21.6792,  39.6875, 512.3292]))\n"
     ]
    }
   ],
   "source": [
    "# create Fare category\n",
    "\n",
    "fare_bins = pd.qcut(clean_titanic_df['Fare'], 5, retbins = True)\n",
    "print(fare_bins)\n",
    "\n",
    "clean_titanic_df.loc[clean_titanic_df['Fare'] <= 7, 'Fare'] = 0\n",
    "clean_titanic_df.loc[(clean_titanic_df['Fare'] > 7) & (clean_titanic_df['Fare'] <= 10 ), 'Fare'] = 1\n",
    "clean_titanic_df.loc[(clean_titanic_df['Fare'] > 10) & (clean_titanic_df['Fare'] <= 21 ), 'Fare'] = 2\n",
    "clean_titanic_df.loc[(clean_titanic_df['Fare'] > 21) & (clean_titanic_df['Fare'] <= 40 ), 'Fare'] = 3\n",
    "clean_titanic_df.loc[clean_titanic_df['Fare'] > 40 , 'Fare'] = 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "id": "finite-sponsorship",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0</td>\n",
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       "  </tbody>\n",
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       "<p>712 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pclass  Sex  Age  SibSp  Parch  Fare  Embarked\n",
       "151       1    1  2.0      1      0   4.0         0\n",
       "753       3    0  2.0      0      0   1.0         0\n",
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       "684       2    0  7.0      1      1   3.0         0\n",
       "887       1    1  2.0      0      0   3.0         0\n",
       "..      ...  ...  ...    ...    ...   ...       ...\n",
       "31        1    1  3.0      1      0   4.0         1\n",
       "488       3    0  3.0      0      0   1.0         0\n",
       "40        3    1  4.0      1      0   1.0         0\n",
       "742       1    1  2.0      2      2   4.0         1\n",
       "595       3    0  4.0      1      1   3.0         0\n",
       "\n",
       "[712 rows x 7 columns]"
      ]
     },
     "execution_count": 266,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train, test= train_test_split(clean_titanic_df,random_state=23, test_size = 0.2)\n",
    "\n",
    "X_train, y_train = train.drop(columns=['Survived']), train['Survived']\n",
    "X_test, y_test = test.drop(columns=['Survived']), test['Survived']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "id": "noble-verse",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "          -1       0.82      0.86      0.84       115\n",
      "           1       0.72      0.66      0.69        64\n",
      "\n",
      "    accuracy                           0.79       179\n",
      "   macro avg       0.77      0.76      0.76       179\n",
      "weighted avg       0.78      0.79      0.79       179\n",
      "\n",
      "0.7877094972067039\n"
     ]
    }
   ],
   "source": [
    "# rerun the support vector machine\n",
    "\n",
    "clf2 = SVC(kernel = 'linear')\n",
    "clf2.fit(X_train, svm_y_train.to_numpy())\n",
    "print(classification_report(svm_y_test, clf2.predict(X_test)))\n",
    "print(clf2.score(X_test, svm_y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "id": "private-switzerland",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.81      0.89      0.85       115\n",
      "           1       0.75      0.62      0.68        64\n",
      "\n",
      "    accuracy                           0.79       179\n",
      "   macro avg       0.78      0.76      0.77       179\n",
      "weighted avg       0.79      0.79      0.79       179\n",
      "\n",
      "0.7932960893854749\n"
     ]
    }
   ],
   "source": [
    "# rerun the categorical bayes classifier\n",
    "\n",
    "categorical_NB2 = CategoricalNB()\n",
    "categorical_NB2.fit(X_train, y_train)\n",
    "print(classification_report(y_test, categorical_NB2.predict(X_test)))\n",
    "print(categorical_NB2.score(X_test, y_test))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "id": "thousand-capability",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.81      0.82      0.81       115\n",
      "           1       0.67      0.66      0.66        64\n",
      "\n",
      "    accuracy                           0.76       179\n",
      "   macro avg       0.74      0.74      0.74       179\n",
      "weighted avg       0.76      0.76      0.76       179\n",
      "\n",
      "0.7597765363128491\n"
     ]
    }
   ],
   "source": [
    "# rerun the logistic regression model\n",
    "\n",
    "lor2 = LogisticRegression(penalty= 'none', max_iter= 1000, solver='newton-cg').fit(X_train, y_train)\n",
    "print(classification_report(y_test, lor2.predict(X_test)))\n",
    "print(lor2.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "sticky-disposition",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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