numeric_classification_titanic_survive.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "id": "2c69b6e8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-20T13:35:34.865581Z",
     "start_time": "2024-12-20T13:35:34.849890Z"
    }
   },
   "source": [
    "# !IMPORTANT - to run this notebook download first train_and_test2.csv dataset from https://www.kaggle.com/datasets/heptapod/titanic and put it in data/ folder\n",
    "\n",
    "# import libraries\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import model_selection, preprocessing, linear_model, naive_bayes, metrics, svm, ensemble\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# import .csv data into DataFrame\n",
    "passengers = pd.read_csv('data/train_and_test2.csv')\n",
    "passengers.head()\n"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   Passengerid   Age     Fare  Sex  sibsp  zero  zero.1  zero.2  zero.3  \\\n",
       "0            1  22.0   7.2500    0      1     0       0       0       0   \n",
       "1            2  38.0  71.2833    1      1     0       0       0       0   \n",
       "2            3  26.0   7.9250    1      0     0       0       0       0   \n",
       "3            4  35.0  53.1000    1      1     0       0       0       0   \n",
       "4            5  35.0   8.0500    0      0     0       0       0       0   \n",
       "\n",
       "   zero.4  ...  zero.12  zero.13  zero.14  Pclass  zero.15  zero.16  Embarked  \\\n",
       "0       0  ...        0        0        0       3        0        0       2.0   \n",
       "1       0  ...        0        0        0       1        0        0       0.0   \n",
       "2       0  ...        0        0        0       3        0        0       2.0   \n",
       "3       0  ...        0        0        0       1        0        0       2.0   \n",
       "4       0  ...        0        0        0       3        0        0       2.0   \n",
       "\n",
       "   zero.17  zero.18  2urvived  \n",
       "0        0        0         0  \n",
       "1        0        0         1  \n",
       "2        0        0         1  \n",
       "3        0        0         1  \n",
       "4        0        0         0  \n",
       "\n",
       "[5 rows x 28 columns]"
      ],
      "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>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Sex</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>zero</th>\n",
       "      <th>zero.1</th>\n",
       "      <th>zero.2</th>\n",
       "      <th>zero.3</th>\n",
       "      <th>zero.4</th>\n",
       "      <th>...</th>\n",
       "      <th>zero.12</th>\n",
       "      <th>zero.13</th>\n",
       "      <th>zero.14</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>zero.15</th>\n",
       "      <th>zero.16</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>zero.17</th>\n",
       "      <th>zero.18</th>\n",
       "      <th>2urvived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>22.0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>38.0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>26.0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>35.0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>35.0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 4
  },
  {
   "cell_type": "code",
   "id": "dfaf5b84",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-20T13:35:35.031245Z",
     "start_time": "2024-12-20T13:35:34.897080Z"
    }
   },
   "source": [
    "# Gender distribution\n",
    "passengers['Sex'].hist(bins=2) "
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 5
  },
  {
   "cell_type": "code",
   "id": "4a522db9",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-20T13:35:35.223091Z",
     "start_time": "2024-12-20T13:35:35.087828Z"
    }
   },
   "source": [
    "# distribution of \"survived\"\n",
    "passengers['2urvived'].hist(bins=2) "
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 6
  },
  {
   "cell_type": "code",
   "id": "b71db296",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-20T13:35:35.429874Z",
     "start_time": "2024-12-20T13:35:35.340711Z"
    }
   },
   "source": [
    "# distribution of Age\n",
    "passengers['Age'].plot.box() "
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 7
  },
  {
   "cell_type": "code",
   "id": "99feebc0",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-20T13:35:35.467534Z",
     "start_time": "2024-12-20T13:35:35.456636Z"
    }
   },
   "source": [
    "# removed empty columns and not needed \n",
    "passengers.drop(['Passengerid', 'zero', 'zero.1', 'zero.2', 'zero.3', 'zero.4', 'zero.5', 'zero.6', 'zero.7',\n",
    "                 'zero.8', 'zero.9', 'zero.10', 'zero.11', 'zero.12', 'zero.13', 'zero.14', 'zero.15', 'zero.16',\n",
    "                 'zero.17', 'zero.18'], axis=1, inplace = True)\n",
    "passengers"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "       Age      Fare  Sex  sibsp  Parch  Pclass  Embarked  2urvived\n",
       "0     22.0    7.2500    0      1      0       3       2.0         0\n",
       "1     38.0   71.2833    1      1      0       1       0.0         1\n",
       "2     26.0    7.9250    1      0      0       3       2.0         1\n",
       "3     35.0   53.1000    1      1      0       1       2.0         1\n",
       "4     35.0    8.0500    0      0      0       3       2.0         0\n",
       "...    ...       ...  ...    ...    ...     ...       ...       ...\n",
       "1304  28.0    8.0500    0      0      0       3       2.0         0\n",
       "1305  39.0  108.9000    1      0      0       1       0.0         0\n",
       "1306  38.5    7.2500    0      0      0       3       2.0         0\n",
       "1307  28.0    8.0500    0      0      0       3       2.0         0\n",
       "1308  28.0   22.3583    0      1      1       3       0.0         0\n",
       "\n",
       "[1309 rows x 8 columns]"
      ],
      "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",
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       "    .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>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Sex</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>2urvived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22.0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38.0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26.0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35.0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35.0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.0</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>1304</th>\n",
       "      <td>28.0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1305</th>\n",
       "      <td>39.0</td>\n",
       "      <td>108.9000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1306</th>\n",
       "      <td>38.5</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1307</th>\n",
       "      <td>28.0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1308</th>\n",
       "      <td>28.0</td>\n",
       "      <td>22.3583</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1309 rows × 8 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 8
  },
  {
   "cell_type": "code",
   "id": "cecb2e2e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-20T13:35:35.817436Z",
     "start_time": "2024-12-20T13:35:35.536232Z"
    }
   },
   "source": [
    "# generate correlation matrix\n",
    "\n",
    "correlations = passengers.corr()\n",
    "fig, ax = plt.subplots(figsize=(10, 10))\n",
    "\n",
    "colormap = sns.color_palette(\"BrBG\", 10)\n",
    "\n",
    "sns.heatmap(correlations, \n",
    "    cmap=colormap, \n",
    "    annot=True, \n",
    "    fmt=\".2f\")\n",
    "ax.set_yticklabels(passengers.columns);\n",
    "\n",
    "plt.show()\n",
    "\n",
    "# columns meanings:\n",
    "# Variable\tDefinition\tKey\n",
    "# survival \tSurvival \t0 = No, 1 = Yes\n",
    "# pclass \tTicket class \t1 = 1st, 2 = 2nd, 3 = 3rd\n",
    "# sex \tSex \t\n",
    "# Age \tAge in years \t\n",
    "# sibsp \t# of siblings / spouses aboard the Titanic \t\n",
    "# parch \t# of parents / children aboard the Titanic \t\n",
    "# ticket \tTicket number \t\n",
    "# fare \tPassenger fare \t\n",
    "# cabin \tCabin number \t\n",
    "# embarked \tPort of Embarkation \tC = Cherbourg, Q = Queenstown, S = Southampton"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x1000 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 9
  },
  {
   "cell_type": "code",
   "id": "aaa87baf",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-20T13:35:35.873384Z",
     "start_time": "2024-12-20T13:35:35.867071Z"
    }
   },
   "source": [
    "# fill missing values\n",
    "passengers.fillna(passengers.mean(), inplace=True)\n",
    "passengersCopy = passengers.copy()"
   ],
   "outputs": [],
   "execution_count": 10
  },
  {
   "cell_type": "code",
   "id": "ed5f9735",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-20T13:35:35.948633Z",
     "start_time": "2024-12-20T13:35:35.933732Z"
    }
   },
   "source": [
    "#what if some of columns were categorical instead numeric? \n",
    "maps = {\n",
    "    0: 'm',\n",
    "    1: 'w'\n",
    "}\n",
    "passengers['Sex'] = passengers['Sex'].apply(lambda x: maps[x])\n",
    "print(passengers)\n",
    "\n",
    "###################################################################\n",
    "\n",
    "maps = {\n",
    "    'm': 0,\n",
    "    'w': 1\n",
    "}\n",
    "passengers['Sex'] = passengers['Sex'].apply(lambda x: maps[x])\n",
    "passengers"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       Age      Fare Sex  sibsp  Parch  Pclass  Embarked  2urvived\n",
      "0     22.0    7.2500   m      1      0       3       2.0         0\n",
      "1     38.0   71.2833   w      1      0       1       0.0         1\n",
      "2     26.0    7.9250   w      0      0       3       2.0         1\n",
      "3     35.0   53.1000   w      1      0       1       2.0         1\n",
      "4     35.0    8.0500   m      0      0       3       2.0         0\n",
      "...    ...       ...  ..    ...    ...     ...       ...       ...\n",
      "1304  28.0    8.0500   m      0      0       3       2.0         0\n",
      "1305  39.0  108.9000   w      0      0       1       0.0         0\n",
      "1306  38.5    7.2500   m      0      0       3       2.0         0\n",
      "1307  28.0    8.0500   m      0      0       3       2.0         0\n",
      "1308  28.0   22.3583   m      1      1       3       0.0         0\n",
      "\n",
      "[1309 rows x 8 columns]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "       Age      Fare  Sex  sibsp  Parch  Pclass  Embarked  2urvived\n",
       "0     22.0    7.2500    0      1      0       3       2.0         0\n",
       "1     38.0   71.2833    1      1      0       1       0.0         1\n",
       "2     26.0    7.9250    1      0      0       3       2.0         1\n",
       "3     35.0   53.1000    1      1      0       1       2.0         1\n",
       "4     35.0    8.0500    0      0      0       3       2.0         0\n",
       "...    ...       ...  ...    ...    ...     ...       ...       ...\n",
       "1304  28.0    8.0500    0      0      0       3       2.0         0\n",
       "1305  39.0  108.9000    1      0      0       1       0.0         0\n",
       "1306  38.5    7.2500    0      0      0       3       2.0         0\n",
       "1307  28.0    8.0500    0      0      0       3       2.0         0\n",
       "1308  28.0   22.3583    0      1      1       3       0.0         0\n",
       "\n",
       "[1309 rows x 8 columns]"
      ],
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       "    .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>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Sex</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>2urvived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22.0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38.0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26.0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35.0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35.0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.0</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1304</th>\n",
       "      <td>28.0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1305</th>\n",
       "      <td>39.0</td>\n",
       "      <td>108.9000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1306</th>\n",
       "      <td>38.5</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1307</th>\n",
       "      <td>28.0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1308</th>\n",
       "      <td>28.0</td>\n",
       "      <td>22.3583</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1309 rows × 8 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 11
  },
  {
   "cell_type": "code",
   "id": "e1673cf7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-20T13:35:36.039048Z",
     "start_time": "2024-12-20T13:35:36.034735Z"
    }
   },
   "source": [
    "# create training set\n",
    "X = passengers.drop('2urvived', axis=1).to_numpy()\n",
    "X"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[22.    ,  7.25  ,  0.    , ...,  0.    ,  3.    ,  2.    ],\n",
       "       [38.    , 71.2833,  1.    , ...,  0.    ,  1.    ,  0.    ],\n",
       "       [26.    ,  7.925 ,  1.    , ...,  0.    ,  3.    ,  2.    ],\n",
       "       ...,\n",
       "       [38.5   ,  7.25  ,  0.    , ...,  0.    ,  3.    ,  2.    ],\n",
       "       [28.    ,  8.05  ,  0.    , ...,  0.    ,  3.    ,  2.    ],\n",
       "       [28.    , 22.3583,  0.    , ...,  1.    ,  3.    ,  0.    ]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 12
  },
  {
   "cell_type": "code",
   "id": "ec312504",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-20T13:35:36.095192Z",
     "start_time": "2024-12-20T13:35:36.091667Z"
    }
   },
   "source": [
    "# create labels for training set\n",
    "y = passengers.loc[:, '2urvived'].to_numpy()\n",
    "y"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 1, ..., 0, 0, 0])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 13
  },
  {
   "cell_type": "code",
   "id": "62b7d54d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-20T13:35:36.242192Z",
     "start_time": "2024-12-20T13:35:36.239132Z"
    }
   },
   "source": [
    "# split data for training model and validation\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=12345) "
   ],
   "outputs": [],
   "execution_count": 14
  },
  {
   "cell_type": "code",
   "id": "78556f08",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-20T13:35:36.374983Z",
     "start_time": "2024-12-20T13:35:36.372420Z"
    }
   },
   "source": [
    "# universal method for training and evaluation of models\n",
    "\n",
    "def train_model(classifier, feature_vector_train, label, feature_vector_valid):\n",
    "    # train model\n",
    "    classifier.fit(feature_vector_train, label)\n",
    "    \n",
    "    # generate labels for validation set\n",
    "    predictions = classifier.predict(feature_vector_valid)\n",
    "    \n",
    "    # evealuate model based on test set labels\n",
    "    scores = list(metrics.precision_recall_fscore_support(predictions, y_test))\n",
    "    score_vals = [\n",
    "        scores[0][0],\n",
    "        scores[1][0],\n",
    "        scores[2][0]\n",
    "    ]\n",
    "    score_vals.append(metrics.accuracy_score(predictions, y_test))\n",
    "    return score_vals"
   ],
   "outputs": [],
   "execution_count": 15
  },
  {
   "cell_type": "code",
   "id": "dcd4da67",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-20T13:35:36.480584Z",
     "start_time": "2024-12-20T13:35:36.460846Z"
    }
   },
   "source": [
    "# MODEL 1 - logistic regression\n",
    "accuracy = train_model(linear_model.LogisticRegression(), X_train, y_train, X_test)\n",
    "accuracy_compare = {'LR': accuracy}\n",
    "print (\"LR: \", accuracy)"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR:  [0.9329896907216495, 0.8190045248868778, 0.8722891566265061, 0.7977099236641222]\n"
     ]
    }
   ],
   "execution_count": 16
  },
  {
   "cell_type": "code",
   "id": "30c497cc",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-20T13:35:36.547144Z",
     "start_time": "2024-12-20T13:35:36.515319Z"
    }
   },
   "source": [
    "# MODEL 2 - Support Vector Machine\n",
    "accuracy = train_model(svm.SVC(), X_train, y_train, X_test)\n",
    "accuracy_compare['SVM'] = accuracy\n",
    "print (\"SVM\" , accuracy)"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVM [0.979381443298969, 0.7509881422924901, 0.8501118568232662, 0.7442748091603053]\n"
     ]
    }
   ],
   "execution_count": 17
  },
  {
   "cell_type": "code",
   "id": "49654c3d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-20T13:35:36.737529Z",
     "start_time": "2024-12-20T13:35:36.580683Z"
    }
   },
   "source": [
    "# MODEL 3 - Random Forest Tree \n",
    "accuracy = train_model(ensemble.RandomForestClassifier(), X_train, y_train, X_test)\n",
    "accuracy_compare['RF'] = accuracy\n",
    "print (\"RF: \", accuracy)"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RF:  [0.8865979381443299, 0.8349514563106796, 0.86, 0.7862595419847328]\n"
     ]
    }
   ],
   "execution_count": 18
  },
  {
   "cell_type": "code",
   "id": "23bed1fe",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-20T13:35:36.863950Z",
     "start_time": "2024-12-20T13:35:36.751822Z"
    }
   },
   "source": [
    "# compare models\n",
    "\n",
    "df_compare = pd.DataFrame(accuracy_compare, index = ['precision', 'recall', 'f1 score', 'accuracy'])\n",
    "df_compare.plot(kind='bar')"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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XF3lk/xbLhaX/bbYz8qWpKQEBgVU6I/IrwgjwG1arRYEBVj26PFNZx0+7ZZ9tGtfTwuGx3KkCeDmLxaKgoGAP7VuqXbu2goKKfCqMuAthBChH1vHT2pv9i+kyAMAvVOrcSlpamuLj49WhQwcNGTJE27dvv2T/1atXa9CgQbr++uvVrVs3TZ06VTabrVIFAwAA3+JyGMnIyFBKSoomTJig9PR0derUSWPHjlV2dna5/bdv364pU6Zo6NChWrt2rV5++WXt3r1b06dPr3LxAACg5nM5jCxdulQJCQkaNmyYIiMjlZycrCZNmmjZsmXl9t+1a5eaNWumUaNGqUWLFrrhhht09913a8+ePVUuHgAA1HwuzRkpLCzU3r17NW7cuFLtXbt2VWZmZrnbxMbG6qWXXtJ//vMf3XbbbcrPz9eGDRvUvXt3l4u1uPduS1QQn7v7XOqz/PU105+36eOjejHe1cNb/n1Xt4q+X5fCiM1mk91uV3h4eKn2iIgI5ebmlrtNXFyc5s+fr0mTJqmwsFDFxcWKj4/X008/7cqhJUnh4fVd3gZVExZW13QJPqOin6XJ73PG278w3tWP32Plq9TdNJbfRR2n01mm7VdZWVmaPXu2Jk6cqG7duik3N1cvvPCCZsyYoblz57p03Pz8U9V+S1RAgNWv/8HabGdkt/veIj0X48nxvtxnabFc+EF1ue9zkzX6Gv59+9d4W60WNWjomXWEfjl56XWEKvrv29f8+r4vx6UwEhYWpoCAAOXl5ZVqz8/PV0RERLnbLF68WHFxcUpKSpIktWvXTiEhIUpMTNSkSZPUuHHjCh/f6ZRfDaK34DN3n4p8lqa/zxlv/+JP422xeG4dIYvFUqEHxpn+9+2tXAojwcHBio6O1ubNm9W7d++S9i1btqhnz57lbnP+/HkFBASUavv1a56QCACobqwj5H1cvkwzevRoTZ48WTExMYqNjdUHH3ygnJwcDR8+XJK0YMECHTt2TC+88IIk6fbbb9fTTz+t999/X7feequOHz+uuXPn6rrrrtOVV17p3ncDAEAN54/Px3I5jPTr1082m02LFi3S8ePHFRUVpdTUVDVr1kySlJubq5ycnJL+Q4YM0ZkzZ5SWlqbnn39e9evX180336y//OUv7nsXAAD4AKvVotCwEAVYAy7f2QV2h10FtnNeG0gqNYE1MTFRiYmJ5b42b968Mm0jR47UyJEjK3MoAAD8htVqUYA1QE99/pR+PPmjW/Z5dcOrNe+2eV79fCyeTQMAgJf58eSP2ndin+kyqo17728CAABwEWEEAAAYRRgBAABGEUYAAIBRhBEAAGAUYQQAABhFGAEAAEYRRgAAgFEsegZUk4AKPrb8cv0quh8AqCkII4CHNapXS3LY1aBBSIX6h4XV9XBFAOBdCCOAhzUICZSsAdLKJCnv+6rvsE0vqeczVd8PAHgJwghQXfK+l3J2VX0/EVFV3wcAt+IybNUQRgAAqCQuw7oHYQQAgEriMqx7EEYAAKgqLsNWiX9enAIAAF6DMAIAAIwijAAAAKMIIwAAwCjCCAAAMIowAgAAjCKMAAAAowgjAADAKMIIAAAwijACAACMIowAAACjCCMAAMAowggAADCKMAIAAIwijAAAAKMIIwAAwCjCCAAAMIowAgAAjCKMAAAAowgjAADAKMIIAAAwijACAACMIowAAACjCCMAAMAowggAADCKMAIAAIwijAAAAKMIIwAAwCjCCAAAMIowAgAAjCKMAAAAowgjAADAKMIIAAAwijACAACMIowAAACjCCMAAMAowggAADCKMAIAAIwijAAAAKMIIwAAwCjCCAAAMIowAgAAjCKMAAAAowgjAADAqEqFkbS0NMXHx6tDhw4aMmSItm/ffsn+hYWFeumll3T77bcrJiZGvXr10ooVKypVMAAA8C2Brm6QkZGhlJQUzZgxQ3FxcVq+fLnGjh2rdevW6aqrrip3m0cffVT5+fmaM2eOWrZsqRMnTqi4uLjKxQMAgJrP5TCydOlSJSQkaNiwYZKk5ORkbdq0ScuWLdMTTzxRpv/nn3+ubdu2aePGjQoNDZUkNW/evGpVAwAAn+HSZZrCwkLt3btX3bp1K9XetWtXZWZmlrvNp59+qpiYGL355pu69dZb1adPHz3//PM6f/585asGAAA+w6UzIzabTXa7XeHh4aXaIyIilJubW+42R44c0Y4dO1SrVi29+uqrstlsmjVrlgoKCpSSkuJSsRaLS93hJnzu/oXx9i+Mt3+p7vGu6PFcvkxzYeel9+50Osu0/f61+fPnq379+pKkp556So888ohmzJih2rVrV/i44eH1K1MuqiAsrK7pElCNGG//wnj7F28eb5fCSFhYmAICApSXl1eqPT8/XxEREeVu06hRI1155ZUlQUSSIiMj5XQ69fPPP6t169YVPn5+/ik5na5UXHUBAVavHkBPs9nOyG53mC6j2jDejLc/Ybz9i4nxtlgqdiLBpTASHBys6Ohobd68Wb179y5p37Jli3r27FnuNnFxcfr444915swZ1a174Zvg4MGDslqtatKkiSuHl9Opag8j4DP3N4y3f2G8/Yu3jrfL64yMHj1aK1as0IoVK3TgwAHNnTtXOTk5Gj58uCRpwYIFmjx5ckn/AQMGKDQ0VFOnTlVWVpa2bdumF198UQkJCS5dogEAAL7J5Tkj/fr1k81m06JFi3T8+HFFRUUpNTVVzZo1kyTl5uYqJyenpH/dunW1ZMkSzZ49WwkJCQoNDVXfvn01adIkt70JAABQc1VqAmtiYqISExPLfW3evHll2iIjI7V06dLKHAoAAPg4nk0DAACMIowAAACjCCMAAMAowggAADCKMAIAAIwijAAAAKMIIwAAwCjCCAAAMIowAgAAjCKMAAAAowgjAADAKMIIAAAwijACAACMIowAAACjCCMAAMAowggAADCKMAIAAIwijAAAAKMIIwAAwCjCCAAAMIowAgAAjCKMAAAAowgjAADAKMIIAAAwijACAACMIowAAACjCCMAAMAowggAADCKMAIAAIwijAAAAKMIIwAAwCjCCAAAMIowAgAAjCKMAAAAowgjAADAKMIIAAAwijACAACMIowAAACjCCMAAMAowggAADCKMAIAAIwijAAAAKMIIwAAwCjCCAAAMIowAgAAjCKMAAAAowgjAADAKMIIAAAwijACAACMIowAAACjCCMAAMAowggAADCKMAIAAIwijAAAAKMIIwAAwCjCCAAAMIowAgAAjCKMAAAAowgjAADAKMIIAAAwijACAACMqlQYSUtLU3x8vDp06KAhQ4Zo+/btFdpux44dat++ve68887KHBYAAPggl8NIRkaGUlJSNGHCBKWnp6tTp04aO3assrOzL7ndqVOnNGXKFHXp0qXSxQIAAN/jchhZunSpEhISNGzYMEVGRio5OVlNmjTRsmXLLrndM888owEDBqhjx46VrRUAAPggl8JIYWGh9u7dq27dupVq79q1qzIzMy+63cqVK3X48GE99NBDlasSAAD4rEBXOttsNtntdoWHh5dqj4iIUG5ubrnbHDp0SAsWLFBaWpoCA106XBkWS5U2RyXxufsXxtu/MN7+pbrHu6LHq1Q6sPxu706ns0ybJNntdj3xxBN6+OGH9Yc//KEyhyolPLx+lfcB14SF1TVdAqoR4+1fGG//4s3j7VIYCQsLU0BAgPLy8kq15+fnKyIiokz/M2fOaM+ePdq3b5+ee+45SZLD4ZDT6VT79u311ltvuTShNT//lJxOVyquuoAAq1cPoKfZbGdktztMl1FtGG/G258w3v7FxHhbLBU7keBSGAkODlZ0dLQ2b96s3r17l7Rv2bJFPXv2LNO/Xr16WrNmTam2999/X1999ZX+9re/qXnz5q4cXk6nqj2MgM/c3zDe/oXx9i/eOt4uX6YZPXq0Jk+erJiYGMXGxuqDDz5QTk6Ohg8fLklasGCBjh07phdeeEFWq1VRUVGltg8PD1etWrXKtAMAAP/kchjp16+fbDabFi1apOPHjysqKkqpqalq1qyZJCk3N1c5OTluLxQAAPimSk1gTUxMVGJiYrmvzZs375LbPvzww3r44Ycrc1gAAOCDeDYNAAAwijACAACMIowAAACjCCMAAMAowggAADCKMAIAAIwijAAAAKMIIwAAwCjCCAAAMIowAgAAjCKMAAAAowgjAADAKMIIAAAwijACAACMIowAAACjCCMAAMAowggAADCKMAIAAIwijAAAAKMIIwAAwCjCCAAAMIowAgAAjCKMAAAAowgjAADAKMIIAAAwijACAACMIowAAACjCCMAAMAowggAADCKMAIAAIwijAAAAKMIIwAAwCjCCAAAMIowAgAAjCKMAAAAowgjAADAKMIIAAAwijACAACMIowAAACjCCMAAMAowggAADCKMAIAAIwijAAAAKMIIwAAwCjCCAAAMIowAgAAjCKMAAAAowgjAADAKMIIAAAwijACAACMIowAAACjCCMAAMAowggAADCKMAIAAIwijAAAAKMIIwAAwCjCCAAAMIowAgAAjCKMAAAAowgjAADAKMIIAAAwqlJhJC0tTfHx8erQoYOGDBmi7du3X7TvP//5T40ePVo333yz4uLidPfdd+uLL76odMEAAMC3uBxGMjIylJKSogkTJig9PV2dOnXS2LFjlZ2dXW7/bdu26ZZbblFqaqr+8Y9/qHPnzpowYYL++9//Vrl4AABQ87kcRpYuXaqEhAQNGzZMkZGRSk5OVpMmTbRs2bJy+ycnJ2vs2LG67rrr1Lp1az3++ONq1aqVPv300yoXDwAAaj6XwkhhYaH27t2rbt26lWrv2rWrMjMzK7QPh8OhM2fOKDQ01JVDAwAAHxXoSmebzSa73a7w8PBS7REREcrNza3QPpYsWaJz586pb9++rhxakmSxuLwJ3IDP3b8w3v6F8fYv1T3eFT2eS2Hk/+289N6dTmeZtvKsXbtWr7zyihYtWlQm0FREeHh9l7dB1YSF1TVdAqoR4+1fGG//4s3j7VIYCQsLU0BAgPLy8kq15+fnKyIi4pLbZmRkKDk5WQsXLtQtt9zieqWS8vNPyems1KaVFhBg9eoB9DSb7YzsdofpMqoN4814+xPG27+YGG+LpWInElwKI8HBwYqOjtbmzZvVu3fvkvYtW7aoZ8+eF91u7dq1mjZtmv7617+qR48erhyyFKdT1R5GwGfubxhv/8J4+xdvHW+XL9OMHj1akydPVkxMjGJjY/XBBx8oJydHw4cPlyQtWLBAx44d0wsvvCDpQhCZMmWKpk2bpuuvv75kbknt2rVVvz6XXQAA8Hcuh5F+/frJZrNp0aJFOn78uKKiopSamqpmzZpJknJzc5WTk1PS/4MPPlBxcbGeffZZPfvssyXtd911l+bNm+eGtwAAAGqySk1gTUxMVGJiYrmv/T5gvPfee5U5BAAA8BM8mwYAABhFGAEAAEYRRgAAgFGEEQAAYBRhBAAAGEUYAQAARhFGAACAUYQRAABgFGEEAAAYRRgBAABGEUYAAIBRhBEAAGAUYQQAABhFGAEAAEYRRgAAgFGEEQAAYBRhBAAAGEUYAQAARhFGAACAUYQRAABgFGEEAAAYRRgBAABGEUYAAIBRhBEAAGAUYQQAABhFGAEAAEYRRgAAgFGEEQAAYBRhBAAAGEUYAQAARhFGAACAUYQRAABgFGEEAAAYRRgBAABGEUYAAIBRhBEAAGAUYQQAABhFGAEAAEYRRgAAgFGEEQAAYBRhBAAAGEUYAQAARhFGAACAUYQRAABgFGEEAAAYRRgBAABGEUYAAIBRhBEAAGAUYQQAABhFGAEAAEYRRgAAgFGEEQAAYBRhBAAAGEUYAQAARhFGAACAUYQRAABgFGEEAAAYRRgBAABGEUYAAIBRhBEAAGAUYQQAABhVqTCSlpam+Ph4dejQQUOGDNH27dsv2X/r1q0aMmSIOnTooJ49e2rZsmWVKhYAAPgel8NIRkaGUlJSNGHCBKWnp6tTp04aO3assrOzy+1/5MgRjRs3Tp06dVJ6errGjx+vOXPmaMOGDVUuHgAA1Hwuh5GlS5cqISFBw4YNU2RkpJKTk9WkSZOLnu1Yvny5mjZtquTkZEVGRmrYsGEaMmSIlixZUuXiAQBAzRfoSufCwkLt3btX48aNK9XetWtXZWZmlrvNzp071bVr11Jtt956q1auXKmioiIFBQVV+PhWq+R0ulKx+0Rf1UAhwQFu2Vdko3oX/qPJdVJQnarvMLyNJOnaK65VSGBI1fcnqXWD1iX/bfXDmUWMt39hvP0L4119LJYK9nM6K/7r/dixY7rtttu0bNkyxcXFlbS//vrrWrVqVbmXXvr06aO77rpL48ePL2n75ptvNGLECH3xxRdq3LhxRQ8PAAB8UKUykuV3UcfpdJZpu1z/8toBAID/cSmMhIWFKSAgQHl5eaXa8/PzFRERUe42ERERys3NLdV24sQJBQYGKjQ01LVqAQCAz3EpjAQHBys6OlqbN28u1b5lyxbFxsaWu03Hjh21ZcuWUm2bNm1STEyMS/NFAACAb3L5Ms3o0aO1YsUKrVixQgcOHNDcuXOVk5Oj4cOHS5IWLFigyZMnl/QfPny4srOzlZKSogMHDmjFihVauXKlxowZ4753AQAAaiyX7qaRpH79+slms2nRokU6fvy4oqKilJqaqmbNmkmScnNzlZOTU9K/RYsWSk1NVUpKitLS0tS4cWMlJyerT58+7nsXAACgxnLpbhoAAAB388M7zAEAgDchjAAAAKMIIwAAwCjCCAAAMIowAgAAjCKMAEA12b59u5588kndfffdOnbsmCQpPT1d27dvN1wZPOHrr782XUKNQRgBgGqwYcMG3X///apdu7b++9//qrCwUJJ05swZLV682HB18ISkpCT16tVLixYtKrX+FspinREvdPDgQW3dulX5+flyOBylXnvooYcMVQV3cWUMX3nlFQ9Wguo0ePBg3XfffRo8eLBiY2O1evVqtWjRQvv27VNSUlKZx2yg5isoKNDq1au1atUqfffdd+rSpYsSEhLUq1cvBQcHmy7Pq7i8Ais868MPP9TMmTMVFhamiIiIUk82tlgshBEfUL9+fdMlwICDBw/qhhtuKNNer149/fLLLwYqgqeFhoZq1KhRGjVqlPbt26eVK1fq2Wef1axZszRw4EANHTpU7dq1M12mVyCMeJnXXntNkyZN0rhx40yXAg9JSUkxXQIMaNSokQ4fPqzmzZuXat+xY4datGhhqCpUl2uvvVZjx45Vw4YNlZqaqpUrV+r9999Xx44dNWvWLF1zzTWmSzSKOSNe5uTJk+rbt6/pMgC42d133605c+Zo165dslgsOnbsmFavXq3nn39e99xzj+ny4CFFRUX6+OOPNXbsWMXHx2vTpk165plntHnzZn3yySdq2rSpHn30UdNlGsecES8zbdo0dejQQSNGjDBdCjxk8ODBpS6/XcqqVas8XA2q00svvaS3335b//vf/yRJwcHBGjNmjCZNmmS2MHjEc889p7Vr10qSBg0apGHDhikqKqpUn+zsbMXHx2v//v0mSvQaXKbxMq1atdLChQu1a9cuRUVFKTCw9BCNGjXKUGVwl169epkuAdXMbrdrx44dGj16tMaPH6+srCw5nU5FRkaqbt26psuDh2RlZenpp5/WHXfccdEJq40bN9a7775bzZV5H86MeJn4+PiLvmaxWPTJJ59UYzUA3KVDhw7KyMhgfghQDs6MeJlPP/3UdAkAPCAqKkpHjx4ljPiRxYsXKzw8XEOHDi3VvmLFCp04cYIbFX6DMyNe7Nehqej8AtQ8drtdb7/9ttavX6+cnBwVFRWVen3r1q2GKoO7bdq0SfPnz9ejjz6q6Oho1alTp9Tr9erVM1QZPCU+Pl7z589XXFxcqfZdu3bpscce44/P3+DMiBdKT0/XW2+9pUOHDkmSWrdurfvvv1+DBw82Whfc75VXXtFHH32k0aNHa+HChRo/frx++uknbdy4URMnTjRdHtwoKSlJkjRhwoRSf2A4nU5ZLBbt27fPVGnwkNzcXDVq1KhM+xVXXKHc3FwDFXkvwoiXWbp0qRYuXKjExERNmjRJTqdT33zzjWbOnKmCggLdd999pkuEG61Zs0azZ89Wjx499Morr2jAgAFq2bKl2rZtq127dpkuD27EJEX/07RpU33zzTdlLs3t2LFDjRs3NlSVdyKMeJn33ntPM2fOLHUWpFevXrrmmmv097//nTDiY/Ly8kpu9atbt65OnTolSbr99tu1cOFCk6XBzW666SbTJaCaDR06VHPnzlVxcbFuvvlmSdKXX36pF198UWPGjDFcnXchjHiZ3NxcxcbGlmmPjY3ltJ4PuvLKK5Wbm6urrrpKLVu21ObNmxUdHa3du3fz7Aof9Msvv2jFihU6cOCALBaL2rRpo4SEBB4R4KPGjh2rkydPatasWSXzwWrVqqWkpCQ98MADhqvzLkxg9TIDBgzQgAEDNH78+FLtixYt0vr167VmzRpDlcET5s+fr3r16mn8+PH6+OOP9cQTT6hZs2bKzs7WfffdpyeffNJ0iXCT3bt3KykpSbVq1dJ1110np9OpPXv26Pz581qyZImio6NNlwgPOXPmjA4cOKDatWurdevW/KFRDsKIl9mwYYMee+wxdenSRXFxcbJYLNqxY4e++uorvfzyy+rdu7fpEuFBu3bt0jfffKOWLVuqZ8+epsuBG91zzz1q1aqVnnvuuZLFDIuLizV9+nQdOXJEaWlphisEzCGMeKE9e/bo7bff1o8//liySuOYMWPUvn1706UBqKTrrrtOq1atUmRkZKn2rKwsJSQkMGHZR3377bf6+OOPy711/5VXXjFUlfdhzogXiomJ0fz5802XgWrAokj+o169esrJySkTRnJyclgS3ketW7dOU6ZMUdeuXbV582Z169ZNhw4dUl5eHme5f4en9nqB06dPl/rvS/0PvuWDDz7Q1VdfXab9mmuu0fLlyw1UBE/p16+fkpOTlZGRoZycHP38889at26dpk+frv79+5suDx7w+uuva+rUqVq8eLGCgoKUnJys9evXq2/fvmratKnp8rwKZ0a8wI033qhNmzYpPDxcN9xwQ7krrrIwkm9iUST/MXny5JL/t9vtkqTAwECNGDGCico+6siRI+revbukC09oPnv2rCwWi+677z7de++9euSRRwxX6D0II17gnXfeUcOGDSWxMJK/YVEk/xEcHKzp06friSee0OHDh+V0OtWqVSuFhISYLg0e0rBhQ505c0bShdv4f/jhB7Vt21a//PKLzp07Z7g670IY8QK/XQyJhZH8C4si+Y9Tp07JbrcrNDRUbdu2LWkvKChQYGAgz6bxQTfccIO2bNmitm3bqm/fvpozZ46++uorbdmyRV26dDFdnlfhbhov8/nnn6tOnTq64YYbJElpaWn68MMP1aZNGz3zzDMlZ1DgG5xOp+bPn6/33nuvzKJIDz30kOHq4E5JSUm6/fbblZiYWKp92bJl+vTTT/XGG28YqgyeUlBQoP/973+68sor5XA49NZbb2nHjh1q1aqVHnzwQX6e/wZhxMsMHDhQTz75pLp3767vvvtOCQkJGjNmjL766itFRkYqJSXFdInwABZF8n033XSTli1bVuZumgMHDuiee+7R119/bagyeEJxcbHWrFmjbt26lTsvDKVxN42XOXr0aMkPq3/+85+Kj4/X448/rhkzZujzzz83XB08JS8vTydPnlTLli0VHBws/kbwPYWFhSouLi7TXlxcrPPnzxuoCJ4UGBiomTNnqrCw0HQpNQJhxMsEBQWV/GDasmWLunbtKunCRChu7fU9NptN9957r/r06aNx48aV3EGTnJysefPmGa4O7tShQwd9+OGHZdqXL1/OUvA+6rrrruMOyApiAquXiYuLU0pKiuLi4rR79269/PLLkqRDhw6pSZMmZouD26WkpCgwMFCfffaZ+vbtW9Ler18/paSk6KmnnjJYHdxp0qRJGj16tPbv318yefHLL7/U7t27tWTJEsPVwRPuuecezZs3Tz///LOio6PL3DnVrl07Q5V5H+aMeJns7GzNmjVLOTk5GjlypIYNGyZJmjt3rhwOh6ZPn264QrhT165d9dZbb6ldu3aKjY3V6tWr1aJFCx05ckSDBg1SZmam6RLhRvv27dObb76p/fv3q1atWmrbtq0eeOABtW7d2nRp8IDywobFYmHdqHJwZsTLXHXVVVq8eHGZ9mnTphmoBp529uxZ1a5du0y7zWZjEqsPuvbaa7VgwQLTZaCafPLJJ6ZLqDEII17g9OnTJWsMXG5eCGsR+JYbb7xR6enpmjRpUknbr7cAdu7c2VxhcLu9e/cqMDCwZI2RjRs36h//+IfatGmjhx56iPDpg5o1a2a6hBqDyzRe4Nprry1ZDr5du3YsB+9HDhw4oD//+c+Kjo7WV199pfj4eGVlZenkyZNatmyZWrZsabpEuElCQoLGjRunPn366MiRI+rXr5/uuOMO7d69W927d1dycrLpEuFm6enpl3x98ODB1VJHTUAY8QJbt25VXFycAgMDtXXr1kv2ZYVW31FUVKQxY8boiSee0Oeff669e/fK4XCoffv2SkxMZDl4H9OpUyetWrVKLVu2VGpqqr7++uuSRbAef/xx/ec//zFdItzsxhtvLPV1cXGxzp07p6CgIIWEhFz2570/4TKNF2A5eP8UFBSkH374QaGhoTwwyw84nU45HA5JF+6i6dGjh6QLzyey2WwGK4OnbNu2rUzboUOHNHPmTN1///0GKvJerDPiZVauXKn169eXaV+/fr1WrVploCJ40uDBg7VixQrTZaAaxMTE6LXXXlN6erq2bdtWEkaOHj2qiIgIs8Wh2rRu3VpPPPGE5syZY7oUr8KZES+TmpqqWbNmlWkPDw/X008/rbvuustAVfCUoqIiffTRR9qyZYtiYmLKrEMwdepUQ5XB3aZNm6a//OUv2rhxo8aPH69WrVpJkjZs2KDY2FjD1aE6BQQE6Pjx46bL8CqEES+TnZ2t5s2bl2m/6qqrlJOTY6AieNL333+v9u3bS5IOHjxY6rXyJjKj5mrXrp3WrFlTpn3y5MmyWjlJ7Yt+f2uv0+lUbm6u0tLSFBcXZ6gq70QY8TLh4eH67rvvygSS/fv3KzQ01ExR8Jj33nvPdAkwrFatWqZLgIdMnDix1NcWi0VXXHGFbr75Zk2ZMsVQVd6JMOJl+vXrpzlz5qhu3bolM7G3bt2quXPnqn///oarAwBU1P79+02XUGNwa6+XKSws1OTJk/Xxxx8rMPBCVnQ4HLrzzjs1a9YsFkYCAPgcwoiXOnjwoPbv36/atWsrKiqKlfwAoIZ55JFHFBMTo3HjxpVqf/PNN/Xtt9/qb3/7m6HKvA+XabxUs2bN5HQ61bJly5IzJACAmmPr1q1l5o1I0q233sqTmn+HKdxe5ty5c5o2bZo6duyoAQMGlNxBM3v2bKWmphquDoC75eTkcAu3jzp79qyCgoLKtAcGBl72OWT+hjDiZRYsWKD9+/fr3XffLTXLvkuXLsrIyDBYGQBPOHny5GWfYYKa6Zprrin353ZGRobatGljoCLvxfl/L/PJJ5/opZdeUseOHUu1t2nTRocPHzZTFIBKu9xj5I8cOVJNlaC6Pfjgg3rkkUd05MgR3XzzzZIuPApg3bp1WrhwoeHqvAthxMucOHFC4eHhZdrPnTvHIlhADTRx4kRZLBZd6l4B/m37pp49e+rVV1/V66+/rg0bNqhWrVpq27atli5dynPIfocw4mU6dOigzz77TCNHjizV/uGHH5Y5WwLA+zVq1EgzZsxQr169yn193759GjJkSDVXherSo0ePkucQ4eIII17m8ccfV1JSkrKysmS32/Xuu+8qKytLO3fuZLVOoAaKjo7W3r17LxpGLnfWBDXXt99+K6fTqeuvv75U+65du2S1WtWhQwdDlXkfJrB6mbi4OC1fvlznz59Xy5YttXnzZoWHh2v58uWKiYkxXR4AFyUlJV3yQXgtW7bUu+++W40Vobo8++yz5T5T7NixY3r22WcNVOS9WPTMixQVFemZZ57Rgw8+qBYtWpguB0AV7d+/X1FRUTwIz0/FxsZq9erVZX6eHzlyRIMGDVJmZqahyrwP/0K8SFBQkP71r3+ZLgOAm9x1112y2WySLkxm/PW/4R+Cg4OVl5dXpj03N5fFLH+HMOJlevfurY0bN5ouA4AbNGjQQEePHpUk/fTTT8wN8TO33HKL/vrXv+rUqVMlbb/88oteeukl3XLLLQYr8z5cpvEyr732mpYsWaIuXbooOjpaISEhpV4fNWqUocoAuOrpp59Wenq6GjVqpJycHDVp0uSil2wutx4Jap5jx44pMTFRBQUFuvbaayVduHQXHh6upUuXqmnTpoYr9B6EES8THx9/0dcsFgs/sIAa5vPPP9fhw4c1e/ZsPfLII6pbt265/e69995qrgzV4ezZs1qzZk3Jg0/btm2r/v37l7tMvD8jjHixX4eGBZGAmm/q1KlKTk5WvXr1TJeCapaVlaXs7GwVFRWVau/Zs6ehirwPYcQLffTRR3rnnXd06NAhSVLr1q117733atiwYWYLAwBU2JEjRzRx4kR9//33JevJ/PaPy3379hmszrswndfLvPzyy3rnnXf05z//uWTF1Z07d2ru3Lk6evSoHnvsMbMFAgAqZM6cOWrevLnefvtt9ezZUx999JEKCgr0/PPPa8qUKabL8yqcGfEynTt31tNPP60BAwaUal+7dq2ee+45ff3114YqAwC4onPnznrnnXfUrl07derUSR999JGuvvpqffnll3r++ed5WvNvcGuvl3E4HOWutBodHS273W6gIgBAZTgcjpIJy2FhYTp+/LgkqVmzZjp48KDJ0rwOYcTLDBo0SMuWLSvT/uGHH2rgwIEGKgIAVMY111yj7777TpJ0/fXX680339SOHTv06quvssr273CZxss899xzSk9PV9OmTUserrRr1y7l5ORo8ODBpVbtmzp1qqkyAQCX8cUXX+jcuXO64447dOTIET3wwAP68ccfFRoaqpdeekldunQxXaLXIIx4mZEjR1aon8Vi4eFaAFDDFBQUqGHDhizZ8DuEEQAAYBRzRgAAgFGEEQAAYBRhBAAAGEUYAQAARhFGAACAUYQRAABgFGEEAAAYRRgBAABG/V+dngPGsOVbJAAAAABJRU5ErkJggg=="
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 19
  },
  {
   "cell_type": "code",
   "id": "c346b09d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-20T13:35:37.549025Z",
     "start_time": "2024-12-20T13:35:36.877856Z"
    }
   },
   "source": [
    "# corrections and adjustments - use artificial neural network\n",
    "\n",
    "# MODEL 4 - neural network\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "mlp = MLPClassifier(hidden_layer_sizes=(10, 10, 10), max_iter=1000)\n",
    "accuracy = train_model(mlp, X_train, y_train, X_test)\n",
    "accuracy_compare['neural network'] = accuracy\n",
    "print (\"neural network\" , accuracy)"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "neural network [0.9175257731958762, 0.839622641509434, 0.8768472906403941, 0.8091603053435115]\n"
     ]
    }
   ],
   "execution_count": 20
  },
  {
   "cell_type": "code",
   "id": "c0e7636a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-20T13:35:37.568256Z",
     "start_time": "2024-12-20T13:35:37.565777Z"
    }
   },
   "source": [
    "#0.979381443298969, 0.7509881422924901, 0.8501118568232662, 0.7442748091603053]"
   ],
   "outputs": [],
   "execution_count": 21
  },
  {
   "cell_type": "code",
   "id": "0ddcdd18",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-20T13:35:37.728035Z",
     "start_time": "2024-12-20T13:35:37.623012Z"
    }
   },
   "source": [
    "# corrections and adjustments - use different hyperparameters\n",
    "\n",
    "# MODEL 5 - Support Vector Machine\n",
    "accuracy = train_model(svm.SVC(gamma='auto'), X_train, y_train, X_test)\n",
    "accuracy_compare['SVM'] = accuracy\n",
    "print (\"SVM gamma='auto'\" , accuracy)\n",
    "\n",
    "# MODEL 6 - Support Vector Machine\n",
    "accuracy = train_model(svm.SVC(kernel='sigmoid'), X_train, y_train, X_test)\n",
    "accuracy_compare['SVM'] = accuracy\n",
    "print (\"SVM kernel='sigmoid'\" , accuracy)\n",
    "\n",
    "# MODEL 7 - Support Vector Machine\n",
    "accuracy = train_model(svm.SVC(degree=4), X_train, y_train, X_test)\n",
    "accuracy_compare['SVM'] = accuracy\n",
    "print (\"SVM degree=4\" , accuracy)"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVM gamma='auto' [0.9639175257731959, 0.751004016064257, 0.8442437923250564, 0.7366412213740458]\n",
      "SVM kernel='sigmoid' [0.8608247422680413, 0.7660550458715596, 0.8106796116504854, 0.7022900763358778]\n",
      "SVM degree=4 [0.979381443298969, 0.7509881422924901, 0.8501118568232662, 0.7442748091603053]\n"
     ]
    }
   ],
   "execution_count": 22
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}