Titanic Survival Prediction.ipynb

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    "tags": []
   },
   "source": [
    "## **Notebook Imports**"
   ]
  },
  {
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   "id": "909b377c",
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   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set()\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import KFold, cross_val_score\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from lightgbm import LGBMClassifier\n",
    "from xgboost import XGBClassifier\n",
    "from catboost import CatBoostClassifier\n",
    "\n",
    "import tensorflow as tf\n",
    "\n",
    "import optuna"
   ]
  },
  {
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     "status": "completed"
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    "tags": []
   },
   "source": [
    "## **Importing Data**"
   ]
  },
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
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       "      <th>SibSp</th>\n",
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       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
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       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
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       "      <td>38.0</td>\n",
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       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
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       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
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       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
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     "execution_count": 4,
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   ],
   "source": [
    "train = pd.read_csv('Data/train.csv')\n",
    "test = pd.read_csv('Data/test.csv')\n",
    "train.head()"
   ]
  },
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       "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
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       "      <td>Wirz, Mr. Albert</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
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       "   PassengerId  Pclass                                          Name     Sex  \\\n",
       "0          892       3                              Kelly, Mr. James    male   \n",
       "1          893       3              Wilkes, Mrs. James (Ellen Needs)  female   \n",
       "2          894       2                     Myles, Mr. Thomas Francis    male   \n",
       "3          895       3                              Wirz, Mr. Albert    male   \n",
       "4          896       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)  female   \n",
       "\n",
       "    Age  SibSp  Parch   Ticket     Fare Cabin Embarked  \n",
       "0  34.5      0      0   330911   7.8292   NaN        Q  \n",
       "1  47.0      1      0   363272   7.0000   NaN        S  \n",
       "2  62.0      0      0   240276   9.6875   NaN        Q  \n",
       "3  27.0      0      0   315154   8.6625   NaN        S  \n",
       "4  22.0      1      1  3101298  12.2875   NaN        S  "
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   ],
   "source": [
    "test.head()"
   ]
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   "outputs": [],
   "source": [
    "target = train['Survived'].astype(int)\n",
    "test_ids = test['PassengerId']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8f9285b9",
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   "outputs": [],
   "source": [
    "train1 = train.drop(['PassengerId', 'Survived'], axis= 1)\n",
    "test1 = test.drop('PassengerId', axis=1)"
   ]
  },
  {
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       "      <th>4</th>\n",
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       "   Pclass                                               Name     Sex   Age  \\\n",
       "0       3                            Braund, Mr. Owen Harris    male  22.0   \n",
       "1       1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0   \n",
       "2       3                             Heikkinen, Miss. Laina  female  26.0   \n",
       "3       1       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0   \n",
       "4       3                           Allen, Mr. William Henry    male  35.0   \n",
       "\n",
       "   SibSp  Parch            Ticket     Fare Cabin Embarked  \n",
       "0      1      0         A/5 21171   7.2500   NaN        S  \n",
       "1      1      0          PC 17599  71.2833   C85        C  \n",
       "2      0      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      1      0            113803  53.1000  C123        S  \n",
       "4      0      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1 = pd.concat([train1, test1], axis= 0).reset_index(drop= True)\n",
    "data1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "273d36e2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-03T05:08:23.121793Z",
     "iopub.status.busy": "2022-09-03T05:08:23.121190Z",
     "iopub.status.idle": "2022-09-03T05:08:23.130025Z",
     "shell.execute_reply": "2022-09-03T05:08:23.129086Z"
    },
    "papermill": {
     "duration": 0.027509,
     "end_time": "2022-09-03T05:08:23.132109",
     "exception": false,
     "start_time": "2022-09-03T05:08:23.104600",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pclass         0\n",
       "Name           0\n",
       "Sex            0\n",
       "Age          263\n",
       "SibSp          0\n",
       "Parch          0\n",
       "Ticket         0\n",
       "Fare           1\n",
       "Cabin       1014\n",
       "Embarked       2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "336cba2a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-03T05:08:23.165103Z",
     "iopub.status.busy": "2022-09-03T05:08:23.164687Z",
     "iopub.status.idle": "2022-09-03T05:08:23.170690Z",
     "shell.execute_reply": "2022-09-03T05:08:23.169610Z"
    },
    "papermill": {
     "duration": 0.025635,
     "end_time": "2022-09-03T05:08:23.173183",
     "exception": false,
     "start_time": "2022-09-03T05:08:23.147548",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "data1.drop(['Name', 'Ticket', 'Cabin'], axis= 1, inplace= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ce6e8a62",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-03T05:08:23.206920Z",
     "iopub.status.busy": "2022-09-03T05:08:23.206112Z",
     "iopub.status.idle": "2022-09-03T05:08:23.220473Z",
     "shell.execute_reply": "2022-09-03T05:08:23.219326Z"
    },
    "papermill": {
     "duration": 0.033897,
     "end_time": "2022-09-03T05:08:23.222844",
     "exception": false,
     "start_time": "2022-09-03T05:08:23.188947",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>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>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass     Sex   Age  SibSp  Parch     Fare Embarked\n",
       "0       3    male  22.0      1      0   7.2500        S\n",
       "1       1  female  38.0      1      0  71.2833        C\n",
       "2       3  female  26.0      0      0   7.9250        S\n",
       "3       1  female  35.0      1      0  53.1000        S\n",
       "4       3    male  35.0      0      0   8.0500        S"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30530f66",
   "metadata": {
    "papermill": {
     "duration": 0.01586,
     "end_time": "2022-09-03T05:08:23.254298",
     "exception": false,
     "start_time": "2022-09-03T05:08:23.238438",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## **EDA**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e2ca083e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-03T05:08:23.288371Z",
     "iopub.status.busy": "2022-09-03T05:08:23.287563Z",
     "iopub.status.idle": "2022-09-03T05:08:23.445149Z",
     "shell.execute_reply": "2022-09-03T05:08:23.444012Z"
    },
    "papermill": {
     "duration": 0.177448,
     "end_time": "2022-09-03T05:08:23.447621",
     "exception": false,
     "start_time": "2022-09-03T05:08:23.270173",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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0e/ZsLVq0SDt37pTL5VJ6ero3RwMANMCrcUhPT9fixYtlt9slScePH1eXLl3UuXNnWa1WxcXFKTs7WwUFBSovL1dYWJgkadSoUcrOzvbmaACABli9eePLly+vc9rpdMpms7lP2+12ORwOY7nNZpPD4fDmaACABng1Dv/N5XIZyywWS73LmyIoqN1XngtoiM3W3tcjALfkzcdms8YhODhYRUVF7tNOp1N2u91YXlhY6N4U5ani4hLV1pqR8RRPAKhPYeF1n66fxybqczuPTT8/S4Mvqpv1o6yhoaE6e/as8vPzVVNTo8zMTEVGRiokJEQBAQHKy8uTJGVkZCgyMrI5RwMA3KRZ3zkEBARoxYoVmjFjhioqKhQVFaUhQ4ZIklatWqWFCxeqtLRUPXv2VFJSUnOOBgC4SbPEYe/eve5/R0REaMeOHcZlevTooS1btjTHOACARvANaQCAgTgAAAzEAQBgIA4AAANxAAAYiAMAwEAcAAAG4gAAMBAHAICBOAAADMQBAGAgDgAAA3EAABiIAwDAQBwAAAbiAAAwEAcAgIE4AAAMxAEAYCAOAAADcQAAGIgDAMBAHAAABuIAADAQBwCAgTgAAAzEAQBgIA4AAANxAAAYWlQcPvzwQw0bNkyDBg3Se++95+txAOC+ZfX1AF9yOBxavXq1tm7dqtatW2vs2LEKDw9X165dfT0aANx3WkwccnJy9MQTT+jhhx+WJMXExCg7O1svvPCCR9f387Pc9gydvt7mtm8D95478di6XZ3bt/f1CGiBbuex2dh1W0wcnE6nbDab+7Tdbtfx48c9vn6HDg/c9gyHXv3Jbd8G7j1BQe18PYLy/ud/fD0CWiBvPjZbzD4Hl8tlLLNYfP+KDQDuRy0mDsHBwSoqKnKfdjqdstvtPpwIAO5fLSYOP/zhD3Xw4EFdunRJZWVl2rVrlyIjI309FgDcl1rMPofg4GClpKQoKSlJVVVVSkhI0GOPPebrsQDgvmRx3WpjPwDgvtZiNisBAFoO4gAAMBAHAICBOAAADMQBbhz4EC1ZSUmJhg8frvPnz/t6lPsCcYCk/z/w4fvvv6/t27frgw8+0GeffebrsQBJ0rFjxzRu3DidO3fO16PcN4gDJNU98GHbtm3dBz4EWoL09HQtXryYoyY0oxbzJTj41u0e+BDwpuXLl/t6hPsO7xwgiQMfAqiLOEASBz4EUBdxgCQOfAigLvY5QBIHPgRQFwfeAwAY2KwEADAQBwCAgTgAAAzEAQBgIA4AAAMfZUWLsGzZMh05ckSSdPr0aYWEhKhNmzaSpGeeeUYlJSWaMmWKNm/erMrKSiUmJn6l9Zw4cULr16/XmjVrPL7Oxx9/rN/85je6cuWKXC6XvvGNb2ju3Lnq1q3bV5rhv23atEnXr1/XlClTmnzdf/zjH3rppZeUmZlpnPfd735X3bt3l5+fnywWi8rKytSuXTv96le/Uq9evRq83du9n3EPcAEtTHR0tOv48eO3PG/u3Lmut956q9lmqaiocPXr18918uRJ97KMjAxXVFSUq7q6utnmqM+hQ4dcsbGxtzyve/furuLi4jrL3nrrLdeYMWMavd3mvp/R8vDOAS3ea6+9psuXLysiIkJ79+7VgQMH1KZNG8XExOjFF19UcXGxCgsLFRISot/+9rcKCgpS//79NXLkSB08eFAXL17U0KFDNWfOnDqvtEtLS7Vs2TIdPXpU/v7+GjhwoFJSUuocU6qsrEzXr1/XjRs33MueeuoptWvXTjU1NcrNza3zyv3m23/ttdf08ccfy+l0qnv37srNzdXrr7/uftWekpKiH/zgByouLtbly5fVv39/vfzyy/rwww8lSdeuXdOAAQO0e/duHT16VG+++aYqKyt16dIljRgxQrNmzWrS/VhdXa2LFy/qoYcekiQVFRXd8v47evRonfs5MTFR69at065du1RbW6uQkBAtXrxYwcHBt/OfFS0cccBdY9CgQdqzZ4+6deumxMREvfvuuwoLC9OUKVPkcrk0ZcoUbd++Xc8++6wk6caNG3r//fflcDg0aNAgjRs3rs7trVmzRhUVFcrKylJNTY2effZZHT58WOHh4e7LPPTQQ5o9e7YmT56sr3/963r88ccVHh6u2NhYtW7dutGZCwoKlJmZKavVqjVr1mjbtm3q1auXrl69qpycHC1dulTvvPOOJOlHP/qRSktLdeLECfXq1UuZmZmKiorSgw8+qN///vdasWKFvvWtb8nhcCg6OlpJSUmNrn/ChAmyWCy6dOmSAgICFB0drbS0NEnSn//853rvv5vv54yMDH3yySfavHmzrFarPvjgAy1cuFDr16/39D8d7kLEAXetCRMmKDc3Vxs2bNC5c+f06aefKjQ01H3+gAEDJH1xaJCgoCBdvXq1zvVzcnI0f/58+fv7y9/fX3/84x9vuZ5JkyZp9OjROnLkiI4cOaL169dr/fr12rJlS6MzhoWFyWr94n+z+Ph4JSQkaN68ecrMzFR0dLTat2/vvqzFYlFCQoI7IFu3btXs2bNlsVj0u9/9Tvv27VNmZqZOnz4tl8ulsrKyRtf/7rvvKjAwUP/+97+VnJys3r17KygoyKP770t/+9vfdOLECcXHx0uSamtrPVo37m7EAXetV155RcePH1d8fLzCw8NVXV1d59DjAQEB7n9bLBbjsORWq7XOJqSLFy+qTZs26tChg3tZXl6e/vnPf2ry5MmKjo5WdHS0fvGLXyguLk4HDhxQYGBgndutqqqqs462bdu6/x0SEqKePXtq37592rp1q1JTU42/KT4+XiNGjNDo0aN1/fp1hYeH68aNGxo5cqQGDhyovn37Kj4+Xrt3777lYdbr07NnT82fP18LFy5UaGioOnXq1Oj996Xa2lpNnjxZ48ePlyRVVlYaocW9h4+y4q7i7++v6upqSdJHH32kCRMmaMSIEQoKClJOTo5qamo8vq2IiAht27ZNtbW1qqys1MyZM92fmPpSYGCg1q1bp9zcXPeywsJClZWVqXv37goMDNSFCxdUXFwsl8ul3bt3N7jOMWPGaP369SovL1efPn2M84ODgxUaGqoXX3xRCQkJkqT8/HyVlJRo1qxZ6t+/vw4fPqzKykrV1tZ6/LdK0vDhwxUWFqZf//rXkhq+/26+n3/84x9ry5YtKikpkSS9+uqrmjNnTpPWjbsP7xxwV4mMjNRLL70kSXr++ee1cuVKrV27Vv7+/nr88cf1+eefe3xbL7zwgpYvX66nn35aNTU1GjZsmAYPHlznMt/+9rf1xhtvaPXq1frPf/6jgIAAtW/fXkuXLtV3vvMdSdLYsWMVHx8vm82mJ598ssF19u/fX0uWLFFycnK9lxk9erR+/vOfa926dZK++Ejqk08+qaFDh+rBBx/UN7/5TXXt2lX5+fke7fe42aJFi/TUU0/p73//e4P33833c3JyshwOh8aMGSOLxaKOHTtqxYoVTVov7j4clRUAYGCzEgDAQBwAAAbiAAAwEAcAgIE4AAAMxAEAYCAOAAADcQAAGP4X24StNAF8E0AAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x = target, palette= 'winter')\n",
    "plt.xlabel('Titanic Survival Rate');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fafb5fe5",
   "metadata": {
    "papermill": {
     "duration": 0.016536,
     "end_time": "2022-09-03T05:08:23.480641",
     "exception": false,
     "start_time": "2022-09-03T05:08:23.464105",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "So, it's clear from the above plot that majority of the people onboarding the titanic did not survived."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8127d30c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-03T05:08:23.515606Z",
     "iopub.status.busy": "2022-09-03T05:08:23.514895Z",
     "iopub.status.idle": "2022-09-03T05:08:23.817179Z",
     "shell.execute_reply": "2022-09-03T05:08:23.815827Z"
    },
    "papermill": {
     "duration": 0.323938,
     "end_time": "2022-09-03T05:08:23.821002",
     "exception": false,
     "start_time": "2022-09-03T05:08:23.497064",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize= (16, 8))\n",
    "sns.heatmap(data1.corr(), annot = True, cmap= 'YlGnBu', fmt= '.2f');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aab8c784",
   "metadata": {
    "papermill": {
     "duration": 0.015998,
     "end_time": "2022-09-03T05:08:23.853645",
     "exception": false,
     "start_time": "2022-09-03T05:08:23.837647",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "It seems like most of our independent varaibles are not correlated except `SibSp` and `Parch`. We will deal with that while doing feature engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "663dccfd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-03T05:08:23.889027Z",
     "iopub.status.busy": "2022-09-03T05:08:23.888222Z",
     "iopub.status.idle": "2022-09-03T05:08:25.035048Z",
     "shell.execute_reply": "2022-09-03T05:08:25.034016Z"
    },
    "papermill": {
     "duration": 1.168171,
     "end_time": "2022-09-03T05:08:25.038121",
     "exception": false,
     "start_time": "2022-09-03T05:08:23.869950",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x936 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_context('notebook', font_scale= 1.2)\n",
    "fig, ax = plt.subplots(2, figsize = (20, 13))\n",
    "\n",
    "plt.suptitle('Distribution of Age and Fair based on target variable', fontsize = 20)\n",
    "\n",
    "# I am using the training dataset only to plot these as we don't have target variable in our test dataset\n",
    "ax1 = sns.histplot(x ='Age', data= train, hue= 'Survived', kde= True, ax= ax[0], palette= 'winter')\n",
    "ax1.set(xlabel = 'Age', title= 'Distribution of Age based on target variable')\n",
    "\n",
    "ax2 = sns.histplot(x ='Fare', data= train, hue= 'Survived', kde= True, ax= ax[1], palette= 'viridis')\n",
    "ax2.set(xlabel = 'Fare', title= 'Distribution of Fare based on target variable')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20d323aa",
   "metadata": {
    "papermill": {
     "duration": 0.017738,
     "end_time": "2022-09-03T05:08:25.074598",
     "exception": false,
     "start_time": "2022-09-03T05:08:25.056860",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "It is evident from the plot that children did tend to have more chances of survival as compared to older individuals\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "74bb4b17",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-03T05:08:25.113006Z",
     "iopub.status.busy": "2022-09-03T05:08:25.112231Z",
     "iopub.status.idle": "2022-09-03T05:08:25.320615Z",
     "shell.execute_reply": "2022-09-03T05:08:25.319771Z"
    },
    "papermill": {
     "duration": 0.230463,
     "end_time": "2022-09-03T05:08:25.323126",
     "exception": false,
     "start_time": "2022-09-03T05:08:25.092663",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x = 'Sex', data= train, hue= 'Survived', palette= 'pastel')\n",
    "plt.title('Survival chance based on Gender', fontsize = 15);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8db8fa5d",
   "metadata": {
    "papermill": {
     "duration": 0.018175,
     "end_time": "2022-09-03T05:08:25.359824",
     "exception": false,
     "start_time": "2022-09-03T05:08:25.341649",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Now that's a clear pattern here. It seems like females were 3 times more likely to survive as compared to males."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "50e055c2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-03T05:08:25.398519Z",
     "iopub.status.busy": "2022-09-03T05:08:25.397825Z",
     "iopub.status.idle": "2022-09-03T05:08:25.626672Z",
     "shell.execute_reply": "2022-09-03T05:08:25.625904Z"
    },
    "papermill": {
     "duration": 0.250638,
     "end_time": "2022-09-03T05:08:25.628767",
     "exception": false,
     "start_time": "2022-09-03T05:08:25.378129",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x = 'Pclass', data= train, hue= 'Survived', palette= 'pastel')\n",
    "plt.title('Survival chance based on Ticket Class', fontsize = 15);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e07fe21c",
   "metadata": {
    "papermill": {
     "duration": 0.018325,
     "end_time": "2022-09-03T05:08:25.665730",
     "exception": false,
     "start_time": "2022-09-03T05:08:25.647405",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "We can also conclude that people travelling in 3rd class were less likely to survive as compared to people travelling in first class\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "071db7b0",
   "metadata": {
    "papermill": {
     "duration": 0.01824,
     "end_time": "2022-09-03T05:08:25.702651",
     "exception": false,
     "start_time": "2022-09-03T05:08:25.684411",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## **Filling Missing Values**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "badf668e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-03T05:08:25.741901Z",
     "iopub.status.busy": "2022-09-03T05:08:25.741451Z",
     "iopub.status.idle": "2022-09-03T05:08:25.749250Z",
     "shell.execute_reply": "2022-09-03T05:08:25.748084Z"
    },
    "papermill": {
     "duration": 0.0306,
     "end_time": "2022-09-03T05:08:25.751660",
     "exception": false,
     "start_time": "2022-09-03T05:08:25.721060",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def knn_impute(df, na_target):\n",
    "    df = df.copy()\n",
    "    \n",
    "    numeric_df = df.select_dtypes(np.number)\n",
    "    non_na_columns = numeric_df.loc[: ,numeric_df.isna().sum() == 0].columns\n",
    "    \n",
    "    y_train = numeric_df.loc[numeric_df[na_target].isna() == False, na_target]\n",
    "    X_train = numeric_df.loc[numeric_df[na_target].isna() == False, non_na_columns]\n",
    "    X_test = numeric_df.loc[numeric_df[na_target].isna() == True, non_na_columns]\n",
    "    \n",
    "    knn = KNeighborsRegressor()\n",
    "    knn.fit(X_train, y_train)\n",
    "    \n",
    "    y_pred = knn.predict(X_test)\n",
    "    \n",
    "    df.loc[df[na_target].isna() == True, na_target] = y_pred\n",
    "    \n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "272a1dbd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-03T05:08:25.790761Z",
     "iopub.status.busy": "2022-09-03T05:08:25.790320Z",
     "iopub.status.idle": "2022-09-03T05:08:26.288884Z",
     "shell.execute_reply": "2022-09-03T05:08:26.287811Z"
    },
    "papermill": {
     "duration": 0.520684,
     "end_time": "2022-09-03T05:08:26.291199",
     "exception": false,
     "start_time": "2022-09-03T05:08:25.770515",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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2ltjY2Cq/v7C98/l57L1wjpc6d6vx9y4tL7fpCdBeAUF8En+Il7Zu5e0e/Wy2HSHqq1sGx1dffcUXX3wBXBnEbvz48dd908/MzMTPz892FQq7tPHklW6qIWERNf7eDmo1z2/YYtU6cwcPqPJrPfV6Ovk0YVlcHI+Et6KTb1NrSxSiQbtlcAwaNIgLF670BR85coQuXbpcdzWTs7MzAwZU/ZdW1E1uHgZ0jv+/u2w+nUKk0Ui3iKDrXlsXLpeMbuLH6fxcXv1pOxvvfwS1nd6DIoQ9umVwODk58eSTTwLg5+fH4MGD0drRWESi9ugcHSqOAgpKzOxITaVbU//rjgycnXUVl8VacxRQ27QaDW/268dja9bwvxPH7HK4FCHsVZXPcYwYMYLjx49z+PBhSkpKrjtRPmrUqBovTtinpMvZADTz9LrNK+3b6LZtmf/Lbmbu/pHBYeG4OMqXIiGqosrBsWjRIhYsWIC7u/t13VUqlUqCowFJuXwZN62ORnrD7V9sx9QqFa/36MXgVV/w7v59TIvurnRJQtQJVQ6O5cuX89xzz/GXv/zFlvUIO1daXk5qXg6tvY12OzaVNe7ybcr9zVqw6OCvjGrZmkA3d6VLEsLuVfk+DpPJxMCBA21Zi6gD0vJyKS0vJ6wO3btxO3/v2hONSsU/d+1UuhQh6oQqB8eAAQP45ptvbFmLqAOSL2fjoFYT4OqmdCk1pqmLK092uIt1yYn8cjZN6XKEsHtV7qrS6/UsXryYzZs3ExgYiKOjY6Xn58+fX+PFCftisVhIyblMkKs7Dmq7GK2mxkxu34kVx47w6s/b+e6BUWjq2ecToiZZ1VU1ZMgQ2rRpg7u7O05OTpV+RP2XZSoi11xMqIeH0qXUOCdHR2Z0vZsjmRl8fjxe6XKEsGtVPuKYNWuWLesQdUDK5csAhLp7KFqHrQwLj2DpkYP8a89PDA2LwE1XvblEhKjvqhwcO3bsuOXzMTExd1yMsG8pOdkYDU64auvnH1SVSsXM7r2I/WoF//5tN//oJvu0EDdS5eB44oknbrhcp9Ph6+srwVHPZRcVcTY/j871fFyndo19eKRFJB/GHeDRVm3r1Mi/QtSWKgfH8ePHKz3+Y0Kl1157jWHDhtV4YcK+fJucjAUaxB/SaV16sDY5kX/8spNPB8m+LcS1qn3piEajISQkhBdffJEFCxbUZE3CDm1JTkav0dDEuXpzt9clPk7O/DWqM5tPJbM9LVXpcoSwO3d8zWFhYSHZ2dk1UYuwUxaLhS3JyQS6uTeYUWSfaNeRIDd3pv+8nVKZZlaISqrcVTVnzpzrlhUUFPD999/Ts2fPGi1K2JcT2Vmcy8sjNihU6VJqjU7jwD+7xTB281r+Gx/H+DbtlS5JCLtR5eA4fPhwpccqlQpHR0ceeOABxo0bV+OFCfvxR3dNUAMbx+mekDB6+gUwZ+8v3NesOZ51fFBHIWpKlYNj2bJltqxD2LHtaak09/bGvYHd16D6ffTcPiuXM2ffLmb17KN0SULYhSoHB8CJEydYsmQJSUlJlJWVER4ezujRo+nYsaOt6hMKM5WWsuvcGR7v2BEaYFd/K28jj0W25eMjh3ikRSRtjT5KlySE4qp8cnzHjh3cd999XL58mQEDBjBw4EDy8vIYM2YMP/30ky1rFArae+EcRaWlxIaFKV2KYl6O7k4jgxPPbd9KmZwoF6LqRxzvvPMOkyZNqphK9g/vvfce8+fPp0ePHjVenFDe9rRTOKrV9AoOZkfiSaXLUYS7Ts8bPXrxl2838FnyUZ7t1tXq9yguKSX3cpENqhOi9lU5OJKTk3nnnXeuWz5kyBA+/PDDOypi7dq1zJgxo9Iyk8lE165dWbRoEVFRUZXmOu/QoQMfffTRHW1TVM32tFTu8m2KSwOfa35oWARfBMYzY8d2EtIzrB52xZ7nXxfCWlUOjqZNm3L06FGCgoIqLY+Pj8fb2/uOihg6dChDhw6teHzs2DHGjh3LCy+8wPHjx/H29mbnTplkp7ZdLCzgSGYGL8uUqqhUKt68uy93f/Fftp0+xbDw5kqXJIRiqhwco0eP5h//+Afp6em0a9cOgAMHDvD+++8zfvz4GiuopKSE559/nieffJIWLVqwYsUKWrZsWWPvL6pu55nTAPQKCLrNKxuGIDd3ZsTE8NL335N0+RLhHl5KlySEIqwKjoKCAt5//32ys7NRqVQ0btyYp556itGjR9dYQZ999hkajYZRo0YBV45oMjIyGDJkCFlZWXTq1IlXXnkFHx+5usXWtqel4qXX06ZRY6VLsRvPdu3KW7/8wvenTxHo6o5Wo1G6JCFq3W2vqjKbzaxcuZLc3FyeeOIJdu3axc8//8yUKVOYOHEiDz30UI0VYzabWbp0KU8++STq32dgc3JyIioqiv/+979s2rQJnU7HpEmTamyb4sYsFgvb01K52z9IZsO7iqNGQ2xQKHlmMz+ePa10OUIo4pZHHPn5+YwfP56jR4/SrFkzOnToAIC3tzf5+fl8+OGHbNiwgffffx9nZ+c7LubHH38EoG/fvhXLXn311UqvmTZtGl27duXMmTP4+/tX+b29vas3OJ/R6Fqt9eq6w+npXCwsYEir5hVt4OxctRPCV7+uquvc6j1suU51/n2b+TQiOs+PPWfP0raJL8FVnBHxZttqqPtYdUl7WccW7XXL4Fi0aBGFhYV8++23NGnSpNJzL730Eo888giPP/44H3zwAc8888wdF/P9999zzz33VBxtWCwW3nnnHYYNG0Zo6JVxksxmM3BlHhBrZGXlU15usWodo9GVjIw8q9apL1bFHQWgo7sPGRl5GI2uFBQU33Y9Z2ddpddVZZ0bqc561VnH2n/fP9qhS+OmnMjMYvWx44yNbFulLqsbbash72PVIe1lnTtpL7VaddMv3Lfsg9iyZQsvvvjidaHxh6CgIJ5//nk2b95crcKudejQIdq3b1/xWKVSER8fz5w5c8jLyyMnJ4eZM2cSExOD0WiskW2KG9uelkqEpxd+rvLt7kYcNRruCQ4j11xccRGBEA3FLYMjMzOTkJCQW75Bq1atSE9Pr5Fizp49e10gzJ49G4PBQL9+/ejbty86nY65c+fWyPbEjRWVlrD73Bli/OVqqlvxc3UlyqcJBzPSSc3NUbocIWrNLbuqfH19SU1Nxc/P76avOX36NI0aNaqRYg4ePHjdMm9vb+bNm1cj7y+qZs/5c5jKyugdKMFxOz38AjiZk82mk0k8FtkWg4Oj0iUJYXO3POIYOHAgCxYsqDivcC2z2cy7775Lr169bFGbUMgfw4x0bRqgdCl2z1GtZnBIMwpLS/n2VAoWi3Xn0YSoi24ZHBMmTCAvL4/77ruPL7/8kqNHj5KWlsaRI0f47LPPGD58OJcuXWLy5Mm1Va+oBdvTUolu4oezo3x7rgofZ2d6+AWQeDmbw5kZSpcjhM3dsqvK2dmZL7/8krlz5zJnzhwKCwuBK1c7ubu7M2TIEKZMmYKnp2etFCtsL70gn6NZmbzaRQattMZdPk04lXOZbWmn8Hd1xUsmfRL12G3vHHdxceGf//wnr7zyCmlpaeTm5uLp6UlgYGDFZbOi/tghw4xUi0ql4p6QcP4bH8e65ERGtmyNo/x+iHqqynu2VqslLCyMDh06EBwcLKFRT21PS8Vbb6C1DDNiNVetlntCwsgoKmTb6VNKlyOEzVg1A6Co3ywWCzvSUokJCEStUildjs2VlpfX+F21YR6eRPs2Zc+Fc/i7uBLZSO43EvWPBIeocDQrk4yiQnoFBCtdSq1wUKt5fsMWq9apyrwa3f0COJufx3enT9LY2Rmjwam6JQphl6S/SVTYnpYKQIx/oMKV1G1qlYp7Q5uhVWv4JikBU2mp0iUJUaMkOESF7WmptPDypomLDDNyp1y0WoaFRZBrLmZ9SiKlMle5qEckOATw+zAj588QI1dT1Rg/V1f6BQZzKjeHaVu3Kl2OEDVGgkMAsPvcWYrLyugtwVGj2hp9aG/04a1du/jy+FGlyxGiRsjJcQFc6abSqjV0aVL1OU5E1fQOCMLT2cCz27/Fz9WFHn5yDknUbXLEIYDfhxlp6oeTDDNS4zRqNV8/+CAh7h6M3bSO45cylS5JiDsiwSFIL8jn2KVMuVvchjwNBj6/9z70Dg6MXL+a9IJ8pUsSotokOIQMM1JLAlzdWDFoOJdMJh5ev5rLJpPSJQlRLRIcgm2nT9HI4ESkt9zlbGvtGvvw8cAhJGRnMXLDagpuMmWBEPZMgqOBKysv54fTp+gbGNwghhmxB70Dg1ncfxD7L15gxJdfUlwmNwiKukWuqmrgfk0/T3axif5BoUqX0qAMCYvgnd4lPL1tC3/ZsoEPBwxGp6n86+jmYUDnaN2vaHFJKbmXi2qyVCGuI8HRwG1NPYlGpSImQC4RrW0Pt4hEo9cwZeNGxm1ex9IBQ9A7/P+vpM7RwSZjaQlxp6SrqoH7LjWF6CZ+uOv0SpfSIE2+6y7eiunHd6kneXTTNxSVlihdkhC3JcHRgJ3Ny+NoVib9gkKULqVBezSyLe/0jmVHWioj168mt7hY6ZKEuCUJjgZs6+mTAHJ+ww6MbNmahf3uYc+Fcwxd8yUX5D4PYcckOBqwrakpBLq6EeHppXQpAnggoiXLBw3nVE4Og1d9wYlMucNc2Ce7CY6VK1cSGRlJhw4dKn5Wr16N2WxmxowZREdHEx0dzaxZsygrK1O63DrPVFrKj2dO0y8oBJVchmtzf8w2eO0PUOnxQ1Ft2PHnsZjKSum6dCmpuTkKVy7E9ezmqqojR47w+OOP88wzz1Ra/vbbb5OUlMSWLVswmUxMnDiRpUuXMmHCBIUqrR9+OZdGYWmpdFPVkpvNNujsrKOg4PpzGsPCIth14SxfJRyjd0AQHRr7SsALu2E3Rxzx8fG0bNnyuuWrV69mwoQJeHh44Ovry8SJE/n6668VqLB++S71JAYHB7r5yWi49shDp2fX+PGEeniyLS2VLakplMhkUMJO2EVwlJSUkJCQwKpVq+jRowf9+/fngw8+ICcnh4yMDMLCwipeGxoaSmpqKmYZqqHayi0WNqYkERMQhMFBRsO1V646HcPDIujSxI8jmRmsOHaYzKJCpcsSwj66qi5dukTbtm257777eO+990hKSmLy5MmUlFy5pt1gMFS81mAwYLFYMJlMaLXaKm/D29ulWrX90Q9dn+w+c4bzBfnMad/fqs/n7Kyz+nVVXae626or69xqvVu9n4uLngER4YQ18mL18eMsP3aEQeHhtPe9eddVfdxnr1bfP19Ns0V72UVw+Pj4sGLFiorHrVq1YsyYMXzzzTcAmK4aRbSo6MpwCk5OTlZtIysrn/Jyi1XrGI2uZGTkWbVOXfDprwdxVKvp6tW0yp/PaHS9YV/8ta7ts6/KOjdSnfXseZ2brXezcxzXrtNE58yjLduw8WQSaxMSSMzMol9QyHXDlAD1cp/9Q339nbSVO2kvtVp10y/cdtFVdeLECd57771Ky4qLizEajRiNRlJSUiqWp6SkEBwcjIODXWRenWOxWNiQkkiMfxBuuup9cxbKcNFqeSCiJd2b+nP8UhafxMfJVVdCEXYRHM7OznzwwQd8/fXXlJeXExcXx/Lly3nggQcYOnQoCxcuJCsri/T0dBYvXszw4cOVLrnOisu4yOm8XIaENVO6FFENapWKrk39eaRFJA4qNf9LOMZ3qScxyyXqohbZxdd2f39/Fi1axL///W9mzpyJp6cnU6ZM4Z577qFPnz7Mnj2boUOHUlpayrBhw+RS3DuwPiURjUrFqE7t8Layu0/Yj6Yurjwa2ZafzqbxW/p5TuVcZkCwXFotaoddBAdAjx496NGjx3XLdTod06dPZ/r06QpUVb9YLBbWJSfQJySEN3/40ap1ZdRV++OoVtM7IIhmHp5sPpXCyoRj+GzaxLPtonGWueOFDdlFV5WoHccuZZKSc5n7b3C/jKi7/F3deLRVGzo29uXdvXvps3IZe8+fU7osUY9JcDQg65ITUatUDG/RQulSRA3TajT0CQzmh8ceo6y8nKFrvuS1XTsxlcrsgqLmSXA0EBaLhTVJJ+jaxA8fl+rd0yLsX6/gYLY/9CijWrbmvQO/EvvVCuIy0pUuS9QzEhwNxP6LF0i+nM2fmrdSuhRhYy5aLW/36s/ng0eQbTIx8OvPeWvfLkrkyitRQyQ4Goj/nTiKXqORy3AbkL5BIex8+FGGhUUwZ98uhqz+ktNy34eoARIcDYC5rIw1SScYGBKOq1Zu+mtIPPUG/tN/EEti7yXpcjZ9/7ecTSeTlC5L1HF2czmuvXLzMKBztK6ZiktKyb1cZKOKrPddagqXTCYebC5XUzVUQ8MjaGtszIRvN/DYprU80bYjf+/aE61Go3Rpog6S4LgNnaPDDedRuBV7u+fh0/g4mjq70CsgWOlShIKC3T1Yd99DvPbLj7wft5+9F86ydMAQ/F3dlC5N1DHSVVXPpebmsD0tlVGt2uCgln/uhk6nceCNnr35aMAQki5nE/vVCnafO6N0WaKOkb8k9dyKo4dRqVSMbNFa6VKEHbk3rBmb7x+Ju07P/Wu/YtnROKVLEnWIdFXVY6bSUpYfO0y/wBD8XGUOA1FZM08vNt//CJN/2Mxz27eSUpDDvAEDcKzieQ97O5cnao8ERz22KvE4mUVFTGjXUelShJ1y1+nZNGoUXT5YwsJ9+1h7/ARDwyIwVGHaAns7lydqjwRHPWWxWHj/0H5aeTeip1+A0uWIWlJaXl6tGd96BQRhNDjxbWoKnx07wn3NmuOpN9x+RdEgSXDUUz+kpXLsUiYL+gy46RSjov5xUKurfRVgZCMj7jod3yQnsOLYEYaGRRDo5m6LMkUdJyfH6yGLxcLbv+7Gz8WVEc2aK12OqEP8Xd0Y1bI1To5avko8zuHMi0qXJOyQBEc9tOPMafZdOMfTHTvfcE5qIW7FQ6dnZItIAlzd2HIqhR1pqVgsFqXLEnZEgqOesVgszNn7C02dXRjZMlLpckQdpXdw4P5mLWhn9GFf+nm+SU6Q6WlFBQmOeuabpAR+TT/P3+7qKkcb4o6oVSr6BQbTJyCY5MvZfHEinjxzsdJlCTsgwVGPFJWW8NqunUR6G3mkhRxtiDunUqno6OPLiPDmXDYVs+LYES4U5CtdllCYBEc98s5vezmTn8frPWLQyPAiogaFenjySItI1CoVX5w4SkL2JaVLEgqSvy71RFxGOgv27+Wh5q3o4ReodDmiHjI6OTGqZWuMBifWJifw+o4dlMtJ8wZJgqMeKCwp4anvN9PI4MTr3XspXY6ox5wdtTzYvBUtvRoxfft2Hlm/isyiQqXLErVMgqOOs1gs/G3HVo5fymJ+nwF46PVKlyTqOUe1mkEhYSwePJhfzp2h78rl7D5/VumyRC2ym+CIi4tj5MiRdOrUiV69evHuu+9isVgwmUxERkbSoUOHip9x48YpXa7deO/gr3yVcIwXOnejT2Cw0uWIBkKlUvFEp05svO8R9A4OjFizkgX790rXVQNhF9drFhQU8MQTTzBlyhSWLVtGWloa48ePx8vLi8jISLy9vdm5c6fSZdqd/8bH8fquHxkWFsEzUdFKlyMaoDbGxnz3wCie2f4tM3f/xPenTzK/9wCC3T2ULk3YkF0ccZw/f56OHTsyevRoNBoNwcHB9O/fn/379xMfH0/LljLl6dUsFgtz9+3i+R1b6R8UwsJ+96CW8aiEQtx0OpbE3ss7vWM5kplBry+XsfTwQTn6qMfs4ogjPDychQsXVjw2m83s3LmTESNGEB8fT0ZGBkOGDCErK4tOnTrxyiuv4OPjo2DF18sqKuJcfh6ZRYU8+L//kVNgwkGtxtfZmaYurvi7utHO6EOou8cdDTp4Lj+Pv23fytbTJ3moeSve7tVf5o0WilOpVIxs2ZoY/yCe2f4t037cxlcJR3mzZ1/aNbav31Vx5+wiOK5mNpt59tln0Wq1jBo1in//+99ERUUxadIkNBoNM2fOZNKkSaxatcqq9/X2dql2Tc7OuhsuLy4tZf/58+y/cIHMwitXljiq1eSVmnHWajGXlbHr/BmyTaaKdTz1eu7y8yP695+7/Pxo7OwMXBkS+2bTu57OyWHh3r0s3LePcouFBQMH8mTnztUOoZt9pppY5+rXVWc71V3Pnte51Xq3ej97/0zXDuFuNLqyLeQxlsXF8cJ33xH71QomREXxeu/eGH/fz+9UdYaNb8hs0V52FRwZGRk89dRTAHz88cc4OTnx6quvVnrNtGnT6Nq1K2fOnMHf37/K752VlU95uXWHzn80eEFB5WEWyi0WDl5M55dzaZjKyvBzcaVvYDBBbu546vS8de/ASkNbm8vKyCku5kJBPucL8jlw7jzfJSfzRzVuWi1GgzODmjfD20GPt94JlQpyik2czLnMb+nnOXAxHbVKxfDwCF7s3J0Qdw8yM6t3B6/R6HrdZ6qKqqzj7Kyr9LrqbKe669nzOjdb79r2qolt1eZnysjIu+Hye5qG0v3hsczZ9wtL9u9neVwck9t3YlK7KFy02mptC67suzfbprjenbSXWq266RduuwmOhIQEHn/8cbp168Zrr72GVqvFYrHwzjvvMGzYMEJDQ4ErRyQAOl31viHdqUumIjakJJFeWECQqzs9/ANo4nzroxmtRoPRyQmjkxNtjI2BK2FysbCA87+HySWTiU8OHiTv98/3BycHR9oYG/NKdA+GhUfISUdhN243aZQRV973H8pfe3Tl7z/8wNx9u/j4yCEmtO3A2Mh2cul4HWYXwZGdnc24ceMYOnQoL7zwQsVylUpFfHw8J06cYO7cuZSXlzNz5kxiYmIwGo21XmdC9iU2nUxCo1Jxb2gzmnt6VburSKvR4O/qhr+rW8WyOYNiSTqTSXaxCYvFgrtOj6deLye+hV2yZtKoEGd3RrVsTVaJiX/t+Zl3ftvLmFZtmNCuIwFX/Q6IusEugmPNmjVkZGTw2Wef8fnnn1cs79mzJ7Nnz2bmzJn069ePsrIyYmJieOONN2q9xr3nz7Hz7GmaOLswNCwC1zs43L4ZlUqFh14v38REvdTE2YXlg+9n+7EUFh38laVHDrLk8AGGhkXwaGRbujX1l9kq6wi7CI4///nP/PnPf77p8/PmzavFaiqzWCz8fDaNXefP0sLLm4HBYTc9ga0ENw8DOke7+GcUokoiGxlZ2O8eXo7uwftx+/ns2BFWJ50gxN2DUS1b83CLSBo71cyJdGEb8hfnFiwWC69s28au82dp7W0kNjjUpt1Gt+szvpnqzjEthJL8XF15rXsML3XuxrrkRFYcO8zM3T/x5t5fiA0KZUyrNvQKCJKRnu2QBMctvLt3L7N++ol2xsb0Cwyx+WG0NX3Gf5AQEHWdk6MjD7VoxUMtWpGYfYnlRw+z8sRRNp5Mws/FlUdaRDKyZetK5wOFsiQ4bqGpqytz+vUjPTtP+l6FqAXNPL34Z/cYXunSg80nk1l+7DBv/7qbt3/dTe/AYKZ06UwXzyY4yk2vipLguIUHWrUCrO8KEkLcmDXdseN9oxjfNYrErCyWxcXx0YED3L9yJY2dnRnbrh0ToqII8/K64brFJaXkXi6qydLFVSQ4hBC1prrdsUWFZh6KaMWF4gL2njnL3F9+Yc4vv9DM04u7fJrQ1MX1unWE7UhwCCHqBLVKRTNvb5rqXcg3mzlw8QIHM9JJzL6En4srd/k2IczdU7qVa4EEhxCiznHRaunpH0h0Ez8OZ17k1/TzrElKoLGTEz38ArDIyLw2Jde5CSHqLK1GQ5RPE/7SpgP3BIdRXFrGqsQT9Pz4Y3adO6N0efWWHHEIIeo8tUpFZCMjLby8OZx5kfhLmQxbs5LeAUFM73o3kY1qf4ii+kyCQwhRb2jUato39mXVyId5/7ff+NePP9Jn5TIea9+e13v3xt/t5veCyJVYVSfBIYSod1x1OtKz8xjVojW7z59l2aFDLI+LI6qxL52bNEWnuf5Pn1yJVXUSHEKIekvv4ECvgCDaN/bhp7Np7LlwjrjMi3Rr6k/bRo1lOJNqklYTQtR7Hjo994Y2Y3TL1njrDXx/+hSfxMeRmH1JrsCqBjniEEI0GL7OLjzUvBUpOZfZcSaVb5IT8HNxpZd/oNKl1SkSHEKIBkWlUhHm4UmIuweHMy/y89kzrDgeT8lX8Fz7aEJkls3bkuAQQjRIapWKdkYfWno1Yt+Fc6xPSGD1sWP8uXU7nonqgrfBoHSJdkvOcQghGjStRkN3vwASn3qKh5q3Ysnhg3Rc9iEv7vyelMvZSpdnl+SIQwghuDKNwr97xzKxXRSLDv7KiqNH+OTIIfoGhjCyZWtig0PRynDugASHEEJUEuHlzTt9BvBylx58dOQgnx07wrgt6/DWGxjerDmDQ8Pp0sTfrqaQrm0SHEIIcQONnZx5qXN3/tapKzvSUvns+BFWHD3M0sMH8dLr6RMYQg+/ALo19SfIzb1BjcorwSGEELfgoFbTNyiEvkEhFJSUsO30STamJPHD6VN8lXAMgEB3d2KCgujs50cHX1/a+friotXe9D3r+vAmEhxCCFFFzo6ODAmLYEhYBBaLhYTsS8TlZjB/126+PnqMZXFxFa/11Olp7OSEp96At96Ap16Pl96AVqOp88Ob1IngOHHiBDNmzOD48eP4+voybdo0YmJilC5LCNGAqVQqmnt506N5MCcvXrkDPb/EzMXCQtILC7hYWMCFwgISsi9x9b3pLo6O/Jp5AV+9MwGubgS4uuHv6kqAqztNnV3qxHzqdh8cZrOZSZMmMWbMGJYtW8bOnTv561//yvr16/Hz81O6PCGEAK4EiatWh6tWR5iHZ8Xy0vJyLhebuGQycclUxCVTEcWlpWxPO0V6QUGlUFGrVPg6OdPUxZXGTs4YnZyu/Ndw5b+NnZwwOjnjpdfj4qhV7LyK3QfHnj17MJlMjB07FpVKRd++fencuTPr1q1j4sSJSpcnhBC35KBW08jgRCODU8WyWff0x0Gtpri0lLTcXFIvXyY1J6fiv2fz8jidn8u+9HNkFhZyo9G01CoV7lodbjodHjr97//9/f+1Oty0OtoHNqGXMaDGA8bugyM5OZmwsLBKHzw0NJTExESr3ketrn7DeRr0tbJObW7LVus46XVoy/+/rRtqO1R1vWvbqya2pfRnsuU6t2uvO9lOaXk5RqOr1bVVZ1sOajX/2rbjuuU61ES4eRLh9v9HLGXlFkxlpTzYrjXZRUVkFhVx2WQix2Qit7iYnN9/couLyTGZOH85i1yTieKyMtadSqTbfY9guMEw8rdzq7+ZKoudDw25aNEiDhw4wIcfflixbP78+Zw4cYJFixYpWJkQQjRMdn8Hi5OTEyaTqdIyk8mEk5PTTdYQQghhS3YfHGFhYZw8ebLSspSUFMLDwxWqSAghGja7D47o6Gg0Gg0ffPABZrOZbdu2sWfPHgYPHqx0aUII0SDZ/TkOgISEBP7xj39w/PhxfHx8eOGFF+jdu7fSZQkhRINUJ4JDCCGE/bD7riohhBD2RYJDCCGEVSQ4hBBCWEWCQwghhFUkOG7gxIkTPPzww7Rv356BAweyY8f1QwM0dHFxcYwcOZJOnTrRq1cv3n33XSwWC2azmRkzZhAdHU10dDSzZs2irKxM6XLtRmFhIQMHDmTp0qUA5OXlMXXqVDp16kT37t354IMPFK7QPly8eJHJkycTFRVF9+7dmT9/PoDsX7cQFxfHgw8+SFRUFH369GHZsmWAjdrMIiopLi629O7d2/LRRx9ZzGazZevWrZb27dtbzpw5o3RpdiM/P9/SpUsXy7JlyyylpaWWkydPWvr06WNZvny55a233rKMHDnSkp2dbTl//rxl2LBhlvfff1/pku3GtGnTLC1atLAsWbLEYrFYLM8884zl6aefthQWFlqSkpIsvXr1smzYsEHhKpV3//33W2bMmGEpLi62nD592hITE2NZt26d7F83UVZWZunWrZtl9erVFovFYjl69Kilffv2ln379tmkzeSI4xpXj8br6OhYaTReccX58+fp2LEjo0ePRqPREBwcTP/+/dm/fz+rV69mwoQJeHh44Ovry8SJE/n666+VLtkubNq0iVOnTtGxY0fgytHHli1beOqppzAYDISFhTF69Gi++uorhStV1qFDh0hNTeXll19Gq9USEBDAsmXL6Ny5s+xfN5GTk0NmZibl5eWUl5ejUqlQq9U4OjrapM0kOK5RU6Px1mfh4eEsXLiw4rHZbGbnzp20aNGCjIwMwsLCKp4LDQ0lNTUVs9msRKl249y5c8ydO5c5c+agVl/5tUtNTaW8vJyQkJCK18m+BkeOHCEiIoIFCxbQo0cP+vbty3fffYder5f96yY8PT0ZNWoUL7/8Mq1bt2bYsGGMGzeOkJAQm7SZBMc1CgsL0esrD5Gs1+spKqq78wPbktls5tlnn0Wr1TJo0CAADAZDxfMGgwGLxXLdQJUNSVlZGc8//zxTp07F39+/YnlBQQFarRbNVTO+yb525dvzgQMH0Gq1bNu2jYULF7J06VK2bdsGyP51I+Xl5RgMBubOncvBgwf59NNP+eSTT2zWZhIc15DReKsuIyODRx99lMzMTD7++GNcXFwAKrXfH38EG3L7/ec//8HHx4dhw4ZVWu7k5ERJSQnl5eUVy2RfA61Wi8Fg4KmnnkKr1dKiRQvuu+8+Vq9eDcj+dSPfffcde/bsYciQIWi1WqKjo23aZnY/kVNtCwsLY8mSJZWWpaSk0KFDB4Uqsk8JCQk8/vjjdOvWjddeew2tVguA0WgkJSWlYlrflJQUgoODcXBouLva+vXruXjxIp06dQKuHNUeOnSIpKQkVCoVp06dIjQ0FJCRn+FKV0p5eTmlpaU4OjoCUFpairu7u+xfN3Hu3Lnrup4cHBzw8vKySZvJEcc1ZDTe28vOzmbcuHHce++9vPnmmxWhATB06FAWLlxIVlYW6enpLF68mOHDhytXrB3YvHkz+/fv59dff+XXX38lKiqKqVOnMmvWLPr168fbb79Nfn4+ycnJLF++vMG3V/fu3XFzc2PevHmYzWaOHz/OqlWrGDx4sOxfN9G9e3dSUlL4/PPPsVgsHD58mK+++spmbSaDHN6AjMZ7ax9//DFvvvkmBoOh0kUEPXv2ZO7cucyePZstW7ZQWlrKsGHDePHFFyv14zd0Y8aMoVevXowfP56cnBxee+01fvrpJxwdHRkzZgxPPPGE0iUqLi0tjddff51Dhw6h1WoZO3Ys48ePp7i4WPavm9ixYwfz588nNTWVRo0a8fjjj/OnP/3JJm0mwSGEEMIq0lUlhBDCKhIcQgghrCLBIYQQwioSHEIIIawiwSGEEMIqEhxCCCGsIsEhhI3s27eP5s2bM336dKVLEaJGSXAIYSNr164lODiYjRs3NvhB+ET9IsEhhA2YzWa2bNnCxIkTKS4uZsuWLUqXJESNkeAQwga2b99OXl4eMTExdOnS5bqJczZu3MiAAQNo27YtTzzxBDNnzuSll16qeH7Hjh0MGzaMtm3bMnjwYJmsSNgVCQ4hbGDt2rV07NgRLy8vYmNj2bt3L2lpaQDs37+f559/npEjR7JmzRqaN2/O8uXLK9ZNTEzk6aefZuTIkaxfv54pU6Ywe/ZsNmzYoNTHEaISCQ4halhubi47duygf//+APTt2xeNRlMxN8KKFSvo3bs3jz32GKGhoTz77LO0bdu2Yv0lS5YwdOhQHnroIQIDAxk0aBDjxo3jo48+UuTzCHGthj2IvRA2sGnTJsxmM7GxsQB4eXlx1113sWbNGp588klOnDjBkCFDKq3Tvn17cnNzgStHHAkJCaxfv77i+dLS0gY/54SwH7InClHD1q5dC0C/fv0qlpWXl2OxWNi1axcODg6VZv27VllZGWPGjOHhhx+2ea1CVIcEhxA16OzZs/z222889dRTFUcccOWIYfTo0Xz99dc0a9aM+Pj4SusdPnyYoKAg4MoslKmpqRWPAb744guSk5N55ZVXaueDCHELco5DiBq0bt06dDodjz76KBERERU/rVq1YsSIEWzdupUxY8bwww8/8Omnn3Lq1Cnee+899u/fXzEp1rhx49i+fTuLFy8mNTWVTZs2MXv2bHx8fBT+dEJcIRM5CVGDBg8eTNu2bZk1a9Z1z6WkpDBo0CD+/ve/4+Liwvz588nIyKBnz54ANGrUiNdeew2ArVu3smDBAlJSUjAajTz88MNMmDCh0oyLQihFgkOIWhYXF4ezszNhYWEVyyZMmEDbtm158sknFaxMiKqRriohatnBgwcZP348+/bt4+zZs3z55Zfs3r270jkRIeyZHHEIUctKS0t56623WL9+Pbm5uYSFhTF16lR69eqldGlCVIkEhxBCCKtIV5UQQgirSHAIIYSwigSHEEIIq0hwCCGEsIoEhxBCCKtIcAghhLDK/wGTKMZz76SDOQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(data1['Age'], color= 'teal', kde= True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "fd100efe",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-03T05:08:26.331739Z",
     "iopub.status.busy": "2022-09-03T05:08:26.331287Z",
     "iopub.status.idle": "2022-09-03T05:08:26.349266Z",
     "shell.execute_reply": "2022-09-03T05:08:26.348042Z"
    },
    "papermill": {
     "duration": 0.041347,
     "end_time": "2022-09-03T05:08:26.352028",
     "exception": false,
     "start_time": "2022-09-03T05:08:26.310681",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "data2 = knn_impute(data1, 'Age')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "154c6ada",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-03T05:08:26.391830Z",
     "iopub.status.busy": "2022-09-03T05:08:26.391418Z",
     "iopub.status.idle": "2022-09-03T05:08:26.574364Z",
     "shell.execute_reply": "2022-09-03T05:08:26.573192Z"
    },
    "papermill": {
     "duration": 0.205988,
     "end_time": "2022-09-03T05:08:26.577032",
     "exception": false,
     "start_time": "2022-09-03T05:08:26.371044",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x = data2['Embarked'], palette= 'Set2');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "696ab83d",
   "metadata": {
    "papermill": {
     "duration": 0.018947,
     "end_time": "2022-09-03T05:08:26.615629",
     "exception": false,
     "start_time": "2022-09-03T05:08:26.596682",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Majority of the people embarked from Southampton, so we will just fill the missing values in Embarked column with `S`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "5520d70e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-03T05:08:26.657375Z",
     "iopub.status.busy": "2022-09-03T05:08:26.656956Z",
     "iopub.status.idle": "2022-09-03T05:08:26.663112Z",
     "shell.execute_reply": "2022-09-03T05:08:26.661970Z"
    },
    "papermill": {
     "duration": 0.029091,
     "end_time": "2022-09-03T05:08:26.665244",
     "exception": false,
     "start_time": "2022-09-03T05:08:26.636153",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "data2['Embarked'].fillna('S', inplace= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "40ad7d10",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-03T05:08:26.705823Z",
     "iopub.status.busy": "2022-09-03T05:08:26.705300Z",
     "iopub.status.idle": "2022-09-03T05:08:27.173159Z",
     "shell.execute_reply": "2022-09-03T05:08:27.172241Z"
    },
    "papermill": {
     "duration": 0.491509,
     "end_time": "2022-09-03T05:08:27.176031",
     "exception": false,
     "start_time": "2022-09-03T05:08:26.684522",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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HvQgf9CJ8mK0XQQ9uZWVlmjFjhiTpxRdfVFRUlH75y182WWb27Nm66qqr9NVXX8nlcqmurq7JfLfbrejoloWg8vJq+f3GhRV/FsnJsSorq5Ik2e1Wud31qqn5pm63PVIVFTVqaPC3WQ1odHIvEFr0InzQi/BBL8JHOPbCarWc9WBTUK/u37lzp2677TalpaXppZdeUkJCggzD0Pz581VcXBxYzuv1SpKcTqcyMzObzPP5fCopKVFGRkYwSwcAAAi5oAW3iooKTZo0SaNGjdKTTz4ph8MhSbJYLCooKNC8efNUVVWlyspK5eXlafjw4UpOTtaoUaO0ceNGbd68WV6vVwsWLFBSUpL69u0brNIBAADCQtBOlf75z39WWVmZ/vjHP+qVV14JjF9zzTWaO3eu8vLydOONN8rn82n48OGaM2eOJKlbt26B+QcPHlRWVpYWL14sm80WrNIBAADCgsUwjLa78CuMBPsatxUFn2lfZWVg/uVxcZqY1Ztr3IIgHK9ZuFTRi/BBL8IHvQgf4diLsLrGDQAAAOev2cFt//79Ot3BOZ/Pp88//7xViwIAAMCpmh3cbrjhBlVUVJwyvn//fv3kJz9p1aIAAABwqrN+OOH111/Xq6++KkkyDEOTJ08+5UMBhw8fVufOnduuQgAAAEg6R3C7+eabdfDgQUnS559/ru9973un3Pg2OjpaN910U9tVCAAAAEnnCG5RUVG69957JUmdO3fWLbfcErj/GgAAAIKr2fdx+8EPfqDt27frs88+U319/SkfVOA6NwAAgLbV7OD2/PPPa8GCBYqLizvldKnFYiG4AQAAtLFmB7eVK1fqv//7v3XXXXe1ZT0AAAA4g2bfDsTj8WjkyJFtWQsAAADOotnB7aabbtIbb7zRlrUAAADgLJp9qjQyMlJLlizR+vXrlZqaqoiIiCbzn3322VYvDgAAAN9odnDzeDwaPXp0W9YCAACAs2h2cHviiSfasg4AAACcQ7OD27vvvnvW+cOHD7/gYgAAAHBmzQ5ud99992nHnU6nOnbsSHADAABoY80Obtu3b28y7fP5VFJSoscee0xjx45t9cIAAADQVLNvB/JtNptNXbt21YMPPqgFCxa0Zk0AAAA4jfMObifU1taqoqKiNWoBAADAWTT7VOm8efNOGaupqdE//vEPXXPNNa1aFAAAAE7V7OD22WefNZm2WCyKiIjQ+PHjNWnSpFYvDAAAAE01O7itWLGiLesAAADAOTQ7uEnSjh079MILL6iwsFA+n0+ZmZnKzc1V//7926o+AAAAHNfsDye8++67uvXWW3X06FHddNNNGjlypKqqqjRx4kS9//77bVkjAAAA1IIjbr/97W81bdo03XvvvU3Gn3vuOT377LMaOnRoqxcHAACAbzT7iFtRUdFpHzI/evRo7dy5s1WLAgAAwKmaHdw6deqkL7744pTxgoICJSYmtmpRAAAAOFWzT5Xm5ubq0UcfVWlpqfr27StJ2rp1q373u99p8uTJzdpGfn6+nnzySe3cuVMxMTG67bbbdO+996q+vl5z5szR+vXrJUnjxo3TAw88IJvNJklav369nnnmGR06dEi9evVSXl6e0tLSWvhSAQAAzK1Fwa2mpka/+93vVFFRIYvFog4dOmjGjBnKzc095/o1NTW6++67NX36dK1YsUL79u3T5MmT1b59ex08eFCFhYXasGGDPB6Ppk6dquXLl2vKlCnatWuXZs+erWXLlqlPnz5atGiRpk+frnXr1slqveAHPwAAAJjGOZOP1+vVa6+9pmPHjunuu+/WBx98oM2bN2v69OmaOnWqfvjDHzZrRwcOHFD//v2Vm5srm82mtLQ0jRgxQp988onWrFmjKVOmKD4+Xh07dtTUqVO1atUqSdLatWs1bNgwDRw4UA6HQzNnzlRpaam2bdt2QS8cAADAbM4a3KqrqzVx4kT9z//8j4qKigLjiYmJqq6u1pNPPqn/+q//Uk1NzTl3lJmZqUWLFgWmvV6vNm3apO7du6usrEwZGRmBeenp6dq7d6+8Xq+KioqUmZkZmGez2ZSamqrCwsIWvVAAAACzO2twe/7551VbW6u3335b/fr1azLvoYce0tq1a1VaWqqlS5e2aKder1f333+/HA6Hbr75ZkmSy+UKzHe5XDIMQx6PR7W1tYqMjGyyvsvlUm1tbYv2CQAAYHZnvcZtw4YN+vWvf63LLrvstPOvuOIK/fznP9fTTz+t++67r1k7LCsr04wZMyRJL774YuA6NY/HE1jG7XZLkqKiouRyuVRXV9dkG263W9HR0c3a3wmJiTEtWv58JCfHBr53uSIU3eBsMp2Q0LKacf5O7gVCi16ED3oRPuhF+DBbL84a3A4fPqyuXbuedQM9e/ZUaWlps3a2c+dO3XnnnRoyZIgee+wxORwOSVJycrKKi4vVuXNnSVJxcbHS0tJkt9uVmZmp4uLiwDZ8Pp9KSkqanFptjvLyavn9RovWaYnk5FiVlVVJkux2q9zuetXUfBM43fZIVVTUqKHB32Y1oNHJvUBo0YvwQS/CB70IH+HYC6vVctaDTWc9VdqxY0ft3bv3rDsoKSlRUlLSOQupqKjQpEmTNGrUKD355JOB0CZJY8aM0aJFi1ReXq7S0lItWbJE48aNkySNGjVKGzdu1ObNm+X1erVgwQIlJSUFbkkCAABwqTjrEbeRI0dqwYIFgU90fpvX69XChQt17bXXnnNHf/7zn1VWVqY//vGPeuWVVwLj11xzjZ566inNnTtXY8aMUUNDg8aOHaspU6ZIkrp166a5c+cqLy9PBw8eVFZWlhYvXhy4xxsAAMClwmIYxhnPH9bU1Oj222+XxWLRxIkT1bt3b8XGxqqyslL5+flauXKlfD6fXnnlFbVv3z6YdbdYsE+Vrij4TPsqKwPzL4+L08Ss3pwqDYJwPPR9qaIX4YNehA96ET7CsRfnOlV61iNu0dHR+tOf/qSnnnpK8+bNC3yS0zAMxcXFafTo0Zo+fboSEhJat2oAAACc4pxPToiJidGvf/1r/eIXv9C+fft07NgxJSQkKDU1lScXAAAABFGzH3nlcDha/ElOAAAAtB4OmQEAAJgEwQ0AAMAkCG4AAAAmQXADAAAwCYIbAACASRDcAAAATILgBgAAYBIENwAAAJMguAEAAJgEwQ0AAMAkCG4AAAAmQXADAAAwCYIbAACASdhDXcClzG5vmpsbGvwhqgQAAJgBwS1E7Har3i4pVmlVjSQpJTZaOanphDcAAHBGBLcQKq2q0b7KylCXAQAATIJr3AAAAEyC4AYAAGASBDcAAACTILgBAACYBMENAADAJAhuAAAAJkFwAwAAMAmCGwAAgEkQ3AAAAEwiJMEtPz9fgwYNCkx7PB5lZWWpX79+ga9JkyYF5q9fv145OTnKzs5Wbm6u9uzZE4KqAQAAQivoj7x666239PDDD8vn8wXGtm/frsTERG3atOmU5Xft2qXZs2dr2bJl6tOnjxYtWqTp06dr3bp1slo5YAgAAC4dQU0+8+fP1wsvvKB77rmnyXhBQYF69Ohx2nXWrl2rYcOGaeDAgXI4HJo5c6ZKS0u1bdu2IFQMAAAQPoIa3CZMmKDVq1crKyuryXhBQYHKyso0evRoDRkyJBDOJKmoqEiZmZmBZW02m1JTU1VYWBjM0gEAAEIuqKdKU1JSTjseFRWlAQMGaNq0abLZbMrLy9O0adO0evVq1dbWKjIyssnyLpdLtbW1Ldp3YmLMedfdXMnJsYHvXa4IRTc4m0wnJEQ3Wf7kZU43H+fv5F4gtOhF+KAX4YNehA+z9SLo17idzi9/+csm07Nnz9ZVV12lr776Si6XS3V1dU3mu91uRUe3LOSUl1fL7zcuuNYzSU6OVVlZlSTJbrfK7a5XTc03dbvtkaqoqFFDg/+0y3x7Ps7fyb1AaNGL8EEvwge9CB/h2Aur1XLWg00hv7rfMAzNnz9fxcXFgTGv1ytJcjqdyszMbDLP5/OppKREGRkZQa8VAAAglEIe3CwWiwoKCjRv3jxVVVWpsrJSeXl5Gj58uJKTkzVq1Cht3LhRmzdvltfr1YIFC5SUlKS+ffuGunQAAICgCnlwk6S5c+fK5XLpxhtv1A033CCn06mnnnpKktStWzfNnTtXeXl5Gjx4sD7++GMtXrxYNpstxFUDAAAEV0iucRs8eLC2bt0amE5MTNT8+fPPuHxOTo5ycnKCURoAAEDYCosjbgAAADg3ghsAAIBJENwAAABMguAGAABgEgQ3AAAAkyC4AQAAmATBDQAAwCQIbgAAACYRFg+ZR/PZ7U2zNg+lBwDg0kFwMxG73aq3S4pVWlUjSUqJjVZOajrhDQCASwTBLUisFotstm+Olp38fUuUVtVoX2Vla5UFAABMhOAWJMnRUdqwp0gHjlVLknp0SJKFKwwBAEALENyC6GB1deBoWUpMdIirAQAAZsMxHwAAAJMguAEAAJgEwQ0AAMAkCG4AAAAmQXADAAAwCT5VGgJH6zz63+1fyG61qnNUrKwWS6hLAgAAJkBwC6Ijbrde3/ml9hz75ga6ya4o3XhFV10eFxfCygAAgBkQ3ILEbxj6/z7L1/7qag3p1EW5vXrro4P79b9ffqlXthco1ukMdYkAACDMcY1bkKzbtVO7K4/qxiu6akinLmrvcik7paP+q1dfJblcevXLAh31eEJdJgAACGMEtyA4VFuj17Z/oeyUFPVon9hknsNm0/fTMlVd79VD774TogoBAIAZENzamGEYWr+7SLEOh37Yvacsp/kgQkp0tHLS0vXaji/0VnFhCKoEAABmQHBrY/uqjumQu1Y/6pGlaIfjjMuNSEtXr6Rk/fL9jar3+YJYIQAAMAuCWxvbVlaqSJtdQ7pcftbl7Far/t/vDdVX1VVaW7QzSNUBAAAzIbi1oWqvV7sqjqhXUrIcNts5l8/pmq4rE9rrua0fyTCMIFQIAADMhODWhvIPH5IhKTs5pVnLWy0WTc8eqILyMv1z3962LQ4AAJhOSIJbfn6+Bg0aFJj2er165JFHNHjwYA0ePFhPPPGEfCdd57V+/Xrl5OQoOztbubm52rNnTwiqbhmf36/8slKltYtTfGRks9e79cru6hgdree2ftiG1QEAADMKenB76623NGnSJNXX1wfGFi5cqMLCQm3YsEFvvPGGtmzZouXLl0uSdu3apdmzZ+vxxx/Xv//9bw0YMEDTp0+X3+8Pdukt8vnhMlXX16tfh44tWs9ps+vuPgP03tf79Omh0jaqDgAAmFFQg9v8+fP1wgsv6J577mkyvmbNGk2ZMkXx8fHq2LGjpk6dqlWrVkmS1q5dq2HDhmngwIFyOByaOXOmSktLtW3btmCW3mJbSw8qyh6hrnHxLV53Ys/ectntWvHFZ61fGAAAMK2gBrcJEyZo9erVysrKCowdO3ZMZWVlysjICIylp6dr79698nq9KioqUmZmZmCezWZTamqqCgvD935ndQ0N+qK8TJnxCef1APl2TqdGpX9Hawq3q/akI5MAAODSFtRnlaaknHqRfm1trSTJ5XIFxlwulwzDkMfjUW1trSK/dY2Yy+UKrNdciYkx51FxyyQnx0qS/rprl+p8PvXq2EHR0Y3PIHU67Yr0RZxx2uWKUEJCdGBb0773Xf3vzi/1/uGv9JM+fQLjLleEohtOvw6+caIXCD16ET7oRfigF+HDbL0I+UPmTwQ2z0nP6XS73ZKkqKgouVwu1dXVNVnH7XYrOrplgaW8vFp+f9vdYiM5OVZlZVWSpD/lfy6nzaYOjijV1DTWXlfXIE9d/Rmn3fZIVVTUqKGh8dq9ntGJSm0Xp999+LFyLusqSbLbrXK7z7wOGp3cC4QWvQgf9CJ80IvwEY69sFotZz3YFPLbgcTFxSk5OVnFxcWBseLiYqWlpclutyszM7PJPJ/Pp5KSkianVsOJ3zC0vrhIPRKTZLee/3+v1WLRj7r11HtflajkWGUrVggAAMwq5MFNksaMGaNFixapvLxcpaWlWrJkicaNGydJGjVqlDZu3KjNmzfL6/VqwYIFSkpKUt++fUNb9Bl8UnpApbU16p3c4YK39cPuWbJI+tOOLy68MAAAYHphEdxmzZqlnj17asyYMRozZowGDRqkKVOmSJK6deumuXPnKi8vT4MHD9bHH3+sxYsXy9aMJxGEwl93F8lutapnYvIFb+vy2Ha6pkuq/rS9gCcpAACA0FzjNnjwYG3dujUw7XQ69fDDD+vhhx8+7fI5OTnKyckJVnkX5K+7CzW08+WKiohQeStsb/yVPTTznQ36qPSArurSpRW2CAAAzCosjrhdLPZUHlXh0QqNTG+96+9uSc+U02bTml3bW22bAADAnAhurejdr0okSdelprXaNmMdTo24Il1/LtyphjB/WgQAAGhbBLdW9O6+veoUHaPM+IRW3e4PvtNNh921eu94MAQAAJcmglsr8fn9ev/rEg2//ApZzuNpCWdz4xVdFRPh0KqdnC4FAOBSRnBrJZ8cOKCjdXUa1iW11bftskfo5vRMvVm4i9OlAABcwghureTvx28SPKzLFW2y/Vu/003HvHX6orysTbYPAADCH8GtlfytuFhZiclKjopqk+1f0zlVSS6XPjl4sE22DwAAwh/BrRXU1tdr8759bXKa9IQIm01jM7vp88OH5PX52mw/AAAgfBHcWsG/Dnwtr8+n4Ze3zWnSE27r1l31fr8Kjx5p0/0AAIDwRHBrBZ+VHVKk3a7vXda5Tfcz6LLOindG6ssjrfFMBgAAYDYheeTVxebHPbI0tnd3RUVEtOl+rBaL+qd01D/37ZW7ob5N9wUAAMIPR9xaQYeoaH23c9sebTthQMfL5DcM7azgdCkAAJcagpvJdI6JVfvISH1ZfjjUpQAAgCAjuJmMxWJR9/ZJ+qq6Skc9nlCXAwAAgojgZkI92idKkj4p5Z5uAABcSghuJpQQ6VJKVLQ+KT0Q6lIAAEAQEdxMqnv7RO2rOqaioxWhLgUAAAQJwc2kurdPlEXSqh1fhroUAAAQJAQ3k4p1OJURn6BVO7fLMIxQlwMAAIKA4GZi/VMu066KI/r8cFmoSwEAAEFAcDOx7JQU2a1Wvb6T06UAAFwKCG4mFh3h0E1p6frfnV+q3ucLdTkAAKCNEdxMbkLPXjrsrtU/SvaEuhQAANDGCG4md+MVXZXsitIft38e6lIAAEAbI7iZXITNptu79dTf9+7WodqaUJcDAADaEMHtIvDj7llq8Pv5kAIAABc5gttF4Mr2iRqQcple+bKAe7oBAHARC5vg9tprrykrK0v9+vULfK1Zs0Zer1ePPPKIBg8erMGDB+uJJ56Qj09QnmJCjyztqCjXloP7Q10KAABoI2ET3D7//HPdeeed2rp1a+DrBz/4gRYuXKjCwkJt2LBBb7zxhrZs2aLly5eHutxWZ7VYZLNZZbd/89USt36nh+KcTv3+s21tUyAAAAi5sAluBQUF6tGjxynja9as0ZQpUxQfH6+OHTtq6tSpWrVqVQgqbFvJ0VHasKdIKwo+04qCz/R2SXGLwlt0RIR+3L2X3izepYM11W1YKQAACJWwCG719fXauXOnVq9eraFDh2rEiBFaunSpKisrVVZWpoyMjMCy6enp2rt3r7xebwgrbhsHq6u1r7JS+yorVVrV8k+ITurVVz6/X38o+LQNqgMAAKFmD3UBknTkyBH16dNHt956q5577jkVFhbqnnvuUX19vSTJ5XIFlnW5XDIMQx6PRw6HI1Qlh6W0uHiNuCJdLxV8pvsGDJbTFhbtBQAArSQsfrOnpKTo5ZdfDkz37NlTEydO1BtvvCFJ8ng8gXlut1uSFBUV1aJ9JCbGtEKlZ5ecHBv43uWKUHSDMzDtdNoV6YtQdLSzWdMuV4QSEqJP2cfJ242OcpyyzP1Dr9LIl1/Wu4f26Sd9+rTuCzSRk3uB0KIX4YNehA96ET7M1ouwCG47duzQ3/72N917772Bsbq6OiUnJ+vIkSMqLi5W586dJUnFxcVKS0uT3d6y0svLq+X3t92tMpKTY1VWViVJstutcrvrVVNTF5hfV9cgT903Y+eadtsjVVFRo4YGf2Ab395uTHyE/jf/cx041nhNW0pstG68vKu+E99eT773vkZ0TJPFYmmz1xyuTu4FQotehA96ET7oRfgIx15YrZazHmwKi2vcoqOjtXTpUq1atUp+v1/5+flauXKlxo8frzFjxmjRokUqLy9XaWmplixZonHjxoW65LDx7evirBaLZvT/rj4/XKa39xaHujwAANCKwiK4denSRc8//7xefvllDRgwQD/72c80ffp0ff/739esWbPUs2dPjRkzRmPGjNGgQYM0ZcqUUJcc1m77TneltovTMx/9ixvyAgBwEQmLU6WSNHToUA0dOvSUcafTqYcfflgPP/xwCKoKnRP3dTvZt6fPJMJm06z+39V///Pv2rhvr65PTWuDCgEAQLCFTXBDUyfu63bi+jVJ6tEhSZZmHiP9YbcsPfPRFj3z0b903eVXXJLXugEAcLEJi1OlOL2Tr1/bV1mpI7XuZq/rsNl0b7/v6t8H9+udkj1tVyQAAAgagttFbGLP3kqPi9evNv9TXp7vCgCA6RHcLmIOm03/c/W1Kjxaod9/vi3U5QAAgAtEcLvI3XhFV12fmqbffPgvldXWhrocAABwAQhuFzmLxaLHhgxXbUO98v71XqjLAQAAF4Dgdgm4sn2ipvUdoFe2F2j97qJQlwMAAM4Twe0S8cCgq9QrKVn3bXxbpbU1oS4HAACcB4LbJcJps2vxjTerpt6r+955mycqAABgQgS3S0i39ol6ZMgw/b1kt37z0b/OuJzdbm3yBQAAwgNPTrjIfTt4TeqVrW2HSvXUhx+oS0ysftyj1ynLv11SrNKqxtOpKbHRyklNV0ODP2g1AwCA0yO4XcTOFMKeuXaEDtbU6P5//k0p0dG6PrVrk/VKq2q0r7IyFCUDAICz4DzYRe5ECNtXWRkIcBE2m14cOVo9EpP007+u1bqinSGuEgAANAfB7RIV43Do9THj1Sc5RXdueFO/+/STUJcEAADOgeB2CWsf6dLrY27TzemZ+tXmf2rq395Shaf5D7IHAADBxTVulziXPUIv5IzS/I+36JmPt2jz1/t0S8Z3lOBwymKxyGqxyGZrmu/5oAIAAKFBcINsVqv+n+9epZu6ZmjmOxv0+8+26bLoGA3p1EUDOl2mDXuKdOBYtaTW+5Tp6W4zQiAEAODsCG6t6EQY+fYRKrPondRB7/wwV7P+8bb+UrxLq3Zt15aD+zWw42W6LCpa0RGOVtnPtz/tKnHbEQAAmoPg1grsdqv+vH27Sg4flST16JAkSwiy27dPa55PgIyw2XRV5y66LCpaX5QfVvGxo/rzrh2SpMuiY9QvpaMy4uPVMyFZMY7zD3LccgQAgJYjuLWSsppvgkhKTHRIakiOjmpyWvNCAqTNalXv5A76r+xs5R8q1T/37tXuyqP6a3Gh3ioulEVSZnx79e2QouzkFPVMStKVCYlKdkXJYrGc9YkLZj0iCQBAqBHcLjIHq6tbPUB2jGm83m1Ipy5KcLmUmZCgTw4e1KeHSvX+1yV6feeXgWXjnU5d2T5R0Y4ItYuIVEp0tK5OvVxOmy1wavR0gZIPQQAAcG4EN7RIjMOhEWnpuq5LWmDsYE21th8p166Kcu04ckSFR4/ow/0HVF3vlSQt/fQTRVitSnBGqr3Lpd4dOijW4VCErIp1OCWderSQa94AADgVwQ0XrGN0jDpGx+jay6+Q1HjN34qCz7Sz/LDK3W7FRjq1q+KI9hw9qv3VjSHvhHYOp7rExupYg1cdoqLkrquXxWIJ1UsBLsi3LxHgDw8ArY3ghjbjskeoS2yEBnbupN4dOqikovEUbu+UDvqs7JA+OXBA+6qrtLvyqL4oPyypMchlxCfoe506y+vzyapvQpxZfimapU60rjM9G5j+A2hNBDe0yOmuRZNaFk6cdrtS4+Ikv9Q/5TIZhqFOce20Zf/X+vjAAX1WVqqthw5q5Ref6frL05STlq6RGRn6cP+BsP+lyC/vSxuflgbQ1ghul5Azha6TnWv+t69Fky48nFgsFnWOjdXQyy9Xakw71ft8qvU1yONr0IbdxXqjaKds71iUFhevztGxSo+PV7bR8bz2FQyt8cv7Ujpqx82YAaD5CG6XkNOFrh4dknTU42nRLURO/uSq1Dr3jztZhM2mXu3ba2JWb3nrfdp26KD+VrJbr35ZoE1fl2jT1yVqX7RT+WWluv7yrhp8WSfFOSObtW0zBKJL6agdN2MGgJYhuF1ivh26UmKidaj2wu5B15r3j/s2q8Wi/imXaVDnzkptF6cvyg5pd+VRHaip0Z+2f6Hff/apLJK6tU/Udzt20nc7dlKOJVNx/gjZrE2LMFMgupROuV1KrxUALpQpgtuOHTv0yCOPaPv27erYsaNmz56t4cOHh7osnKS17x93pqN4sQ6n+iSn6JbMON3erYf+76uv9OHB/frw4H6tLdqpFV98Jr2zQRFWq9LaxSs9Pl5d4xKUHhevzu1i9eHBA6ry1Mllt8swjGbVYoajdJJ56jwf53ptF/NrBxA64fizJeyDm9fr1bRp0zRx4kStWLFCmzZt0s9+9jO9+eab6ty5c6jLQxtpzlE8p92ua7qk6pouqZIkv2FoV8UR7aqt0NZ9B1R0tEK7jx3Vu/tK5PE1nLIPm8WiRzdvkstuV+SJL5vt+L92Oe122a1WHaqtUV2DTxaLFGm3q2N0jGRIluP7NCQZMmQYkizS3mOVctfXy5Dkstv1l6Jd8vuNxmVOrGMoMG23WBQVESFXRIQibXZFOSJUeOSI3PX1irDaVFJ1TDEREYqxOxTvjFRcpFPxzki1czhlP35U0UxHE5vDMAw1+P1q8Pslq/T3ot0qq66V3WrVZe1iNPKKjMBra63XHo4/oAGETrj+XA374LZlyxZ5PB7dcccdslgsuuGGGzRo0CCtW7dOU6dODXV5aEMtPYpntVjUrX2ihnZL06gumYE33YFj1aqs8ygpOkqflx3S3opK1TbUKy4yUnX+Bh1116ne75PNalFshFPuhnodrfPIU+OTLFKFx6N6n0+GpAirVeVu9/HwZchqscgiiyyWxv1bLBYdrfOowdf4xq7z+WSzNt7UxKLGD2JYjt/i5MS0z/CrqLJCtfX1qvf75TcMeRoaVO//5ofDazu+OO1rjolwKN7pVHxkpDwNjeE00mZXUnSU8g+WKjbCoThnpOKdzuP/Rqqd06l4p1NOW+u//f2GodqGetXU16uqrk67vce0t7RCVd46HaurU6W3Tse8daqq8+qYt07V9V7trDiiY3V1qvM1qM7nk9fn06x/bDjrfuxWqxxWqyJsNkVYrar3+2U5Pu6yR+i3sbFyWG1y2e1y2uxyNgnkNjltdkVYrfIZhnx+vwyLVHj0iKq8Xvn9hiIj7OoYFSN3fb3qfD7V+RrkafAdr/Hk70/6t8F3PIo3/lFgt9r0xL/eV5Q9Qu0cTsU6nYqNcKid06l2DodiHU61czT2op3TqbjjPYpzNAbzGIdDVu5pCIRUOF7KEfbBraioSBkZGU1uypqenq5du3a1aDtWa9v9ALRaLboiPl52f+Nf7J3axSrG6VBMhDOwzLfHWjrdGtsI17rOZ52kaJfsdtsZ++pw2GS1WhUVEaG4yEjFRUYqMzFBafHxOlRdK0nKTEzQsbq6wHRStEvXXp4m/0mByWq16p/79uhwjfuMy5zs28tnJibIYbfrqNsTWCbO5VRWQnJgG6db51hdnUqratTg9yvKaVdWYgdVejyBAHTM2xh8jtV5VeWtU5XXq5KqSlXVeeXxNWh/dZVe/vLz09Z4gtNmV+TxEOOw2eS02eS02+W02uS022SzWuQ3pBNnlA3DCBxZNGTI42uQu77x07+13nq5fQ2B8Hg2FknRDodiIhyKdTh0ebt2slosijweshJckcqMT5BVFtmtVtlsVu2pPKrqunr5DL+cdps6x8bK2+BTvd+vBsOvkmPHVOP1qsHvl9VqUXSEQ3X1DfL6faqqr9PhOp+8DT7V+RsDVt1JR2BPBL7GUN0Ypu11VlV4PN/8v9jsiou0y2mPOj5ml8NmleN4KIywNv7flVRVqrrOK8Mw5LBblRwVrRpvvWrqvar2elXl9erwMbeqvV7VHH+yyNn+n2KdjeEu1uEIhE6H1SbH8SB6oj671SqbxSqLRbLIcvyPiON/TBx/TVGRDnnrGgJ/YFzMwv31uUoi5PbUX9A2znipx/Fx49Sh4+PGSd+ffnvGaZY9076bbOPkbZ9m1aZ1nH57TZY/vtQZ62/muM/wq8Ev+fx++Qz/8enGP9qsdqs8dfXyyS/f8WUaDH/jsjJUWVcnb4Ovscavpbf3FmnR9d9XjMNx2ppbw7nyStgHt9raWkVGNv3EYGRkpNxud4u2k5DQtg9+z4nLkDLadBdogbi4KEnS6J7dL3hbLd3G+eyzNeoEAFz8Wumzf20nKipKHo+nyZjH41FUVFSIKgIAAAiNsA9uGRkZ2r17d5Ox4uJiZWZmhqgiAACA0Aj74DZ48GDZbDYtXbpUXq9X77zzjrZs2aJbbrkl1KUBAAAElcVo7s2sQmjnzp169NFHtX37dqWkpOiBBx7QddddF+qyAAAAgsoUwQ0AAAAmOFUKAACARgQ3AAAAkyC4AQAAmATBDQAAwCQIbhdox44d+tGPfqTs7GyNHDlS7777bqhLuiTk5+dr0KBBgWmv16tHHnlEgwcP1uDBg/XEE0/I5/MF5q9fv145OTnKzs5Wbm6u9uzZE4KqLy75+fmaMGGCBg4cqGuvvVYLFy6UYRj0IgQ2btyo0aNHq1+/fhoxYoReffVVSbwvQqW2tlYjR47U8uXLJUlVVVWaNWuWBg4cqKuvvlpLly5tsvzLL7+s4cOHq1+/fpo6daoOHz4cirIvOq+99pqysrLUr1+/wNeaNWvM/74wcN7q6uqM6667zvj9739veL1e4+9//7uRnZ1tfPXVV6Eu7aL2l7/8xRgwYICRnZ0dGPvNb35jTJgwwaioqDAOHDhgjB071vjd735nGIZh7Ny508jOzjY+/PBDo66uznjmmWeMm2++2fD5fKF6CaZXXV1tfO973zNWrFhhNDQ0GLt37zauv/56Y+XKlfQiyL7++mujV69exnvvvWcYhmF8+eWXRu/evY1PP/2UXoTI7Nmzje7duxsvvPCCYRiGcd999xkzZ840amtrjcLCQuPaa681/vKXvxiGYRibNm0yrrrqKmPHjh1GbW2t8eCDDxqTJ08OZfkXjV/96lfGM888c8q42d8XHHG7AFu2bJHH49Edd9yhiIgI3XDDDRo0aJDWrVsX6tIuWvPnz9cLL7yge+65p8n4mjVrNGXKFMXHx6tjx46aOnWqVq1aJUlau3athg0bpoEDB8rhcGjmzJkqLS3Vtm3bQvAKLg4HDhxQ//79lZubK5vNprS0NI0YMUKffPIJvQiyTp066YMPPtDQoUPl9/tVUVEhm82mmJgYehECf/3rX7Vnzx71799fUuPRtw0bNmjGjBlyuVzKyMhQbm6uXn/9dUmNP7vGjRunK6+8Ui6XSw888IDef/99lZaWhvJlXBQKCgrUo0ePU8bN/r4guF2AoqIiZWRkyGKxBMbS09O1a9euEFZ1cZswYYJWr16trKyswNixY8dUVlamjIyMwFh6err27t0rr9eroqKiJo9Is9lsSk1NVWFhYVBrv5hkZmZq0aJFgWmv16tNmzape/fu9CIEYmJiVF1drV69eumOO+7QxIkTlZSURC+CbP/+/Xrqqac0b948Wa2Nv1737t0rv9+vrl27BpY7+ffEt/vQvn17xcXF0YcLVF9fr507d2r16tUaOnSoRowYoaVLl6qystL07wt7qAsws9raWkVGRjYZi4yMlNvtDlFFF7+UlJRTxmprayVJLpcrMOZyuWQYhjwez2n75HK5Auvhwni9Xt1///1yOBy6+eab9Zvf/IZehIDL5dK2bdu0fft23XXXXYH/Z3oRHD6fTz//+c81a9YsdenSJTBeU1Mjh8Mhm80WGDv598SZ+sDvkQtz5MgR9enTR7feequee+45FRYW6p577lF9fb0kc78vCG4XICoqSh6Pp8mYx+NRVFRUiCq6NJ14A57cixM/9KKiouRyuVRXV9dkHbfbrejo6OAVeZEqKyvTjBkzJEkvvvhi4CgDvQg+m80mm82mPn36aPz48crPz5dEL4Jl8eLFSklJ0dixY5uMR0VFqb6+Xn6/v8n748TviTP1gd8jFyYlJUUvv/xyYLpnz56aOHGi3njjDUnmfl9wqvQCZGRkaPfu3U3GiouLmxxmRduLi4tTcnKyiouLA2PFxcVKS0uT3W5XZmZmk3k+n08lJSVNDpWj5Xbu3KnbbrtNaWlpeumll5SQkEAvQuCDDz7Q7bff3mSsvr5e7dq1oxdB9Oabb+qf//ynBg4cqIEDB+rjjz/Ws88+qxUrVshisTT5ZOLJvye+3YcjR47o6NGj9OEC7dixQ88991yTsbq6OiUnJ5v+fUFwuwCDBw+WzWbT0qVL5fV69c4772jLli265ZZbQl3aJWfMmDFatGiRysvLVVpaqiVLlmjcuHGSpFGjRmnjxo3avHmzvF6vFixYoKSkJPXt2ze0RZtYRUWFJk2apFGjRunJJ5+Uw+EIzKMXwdWjRw+VlJTopZdeks/n00cffaQ1a9Zo/Pjx9CKI1q9fr08++UQfffSRPvroIw0YMECzZs3SE088oRtvvFFPP/20qqurVVRUpJUrVwb6MHbsWL3++usqKCiQ2+3WvHnzNGTIkNNeFoLmi46O1tKlS7Vq1Sr5/X7l5+dr5cqVF8f7IrQfajW/HTt2GD/+8Y+Nfv36GSNHjjTeeeedUJd0SfjXv/7V5HYgHo/H+PWvf20MGTLEGDRokDFnzhyjoaEhMH/Dhg3GyJEjjezsbOMnP/mJUVxcHIqyLxq///3vjSuvvNLo27evkZ2dHfiaMWMGvQiB/Px844c//KHRv39/Y9SoUcaGDRsMw+B9EUq5ubmB24EcPXrUuP/++41BgwYZV199tbFkyZImy7788svG9ddfb/Tr18+YMmWKUVZWFoqSLzrvvfee8YMf/MDIzs42rrvuOmPlypWGYZj/fWExDMMIdXgEAADAuXGqFAAAwCQIbgAAACZBcAMAADAJghsAAIBJENwAAABMguAGAABgEjzyCsAl7/rrr9fXX3992nk7duwIcjUAcGYENwCQdP/99+vWW28NdRkAcFYENwBQ4yNykpOTQ10GAJwV17gBwDn84Q9/0E033aRevXpp0KBBeuCBB1RbWytJWrhwoe68805NnjxZAwYM0Jo1ayRJy5cv13XXXad+/frpxz/+sbZt2xbCVwDgYkFwA4CzWLdunRYuXKiHHnpIGzZs0BNPPKG///3veu211wLLvPfeexo0aJBee+01DR8+XK+++qpWrlypRx99VGvWrNHw4cP105/+VF999VUIXwmAiwHPKgVwybv++ut16NAhRURENBlftmyZGhoaVFVVpREjRgTGp06dqvbt2+vxxx/XwoULtXz5cm3dulUWi0WSdN111+n+++/X6NGjA+tMmjRJ3bp104MPPhicFwXgosQ1bgAg6e6779aYMWOajKWkpCgyMlIFBQX67W9/q+LiYhUWFqq4uFjjxo0LLNe5c+dAaKupqdH+/fv1y1/+Ug8//HBgGa/XK4fDEZTXAuDiRXADAEkJCQm64oorThlfvXq1Hn30Ud1666265pprNG3aNC1cuLDJMk6nM/C9z+eTJD355JPq2bNnk+UiIyPboHIAlxKCGwCcxYsvvqjJkydr1qxZkiTDMLR371716tXrtMu3a9dOycnJKi0t1fe///3A+GOPPaaePXtq/PjxQakbwMWJ4AYAZ9GhQwdt2bJFhYWFMgxDf/jDH1RYWKgrr7zyjOvceeedev7559WhQwf16tVLa9eu1Z/+9CetWLEiiJUDuBgR3ADgLH7xi1/oV7/6lW677TbFxsbqqquu0t13360NGzaccZ3//M//lMfj0VNPPaXDhw+ra9euWrBggfr37x/EygFcjPhUKQAAgElwHzcAAACTILgBAACYBMENAADAJAhuAAAAJkFwAwAAMAmCGwAAgEkQ3AAAAEyC4AYAAGASBDcAAACT+P8BpsKbASb7+44AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize= (10, 6))\n",
    "sns.histplot(data2['Fare'], color= 'Teal', kde= True);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e03a0134",
   "metadata": {
    "papermill": {
     "duration": 0.025231,
     "end_time": "2022-09-03T05:08:27.222684",
     "exception": false,
     "start_time": "2022-09-03T05:08:27.197453",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Distribution of `Fare` is clearly skewed, therefore we will just fill the only missing value we have in this column with median"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "1ac97bb2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-03T05:08:27.275554Z",
     "iopub.status.busy": "2022-09-03T05:08:27.274947Z",
     "iopub.status.idle": "2022-09-03T05:08:27.282672Z",
     "shell.execute_reply": "2022-09-03T05:08:27.281820Z"
    },
    "papermill": {
     "duration": 0.038197,
     "end_time": "2022-09-03T05:08:27.284841",
     "exception": false,
     "start_time": "2022-09-03T05:08:27.246644",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "data2['Fare'].fillna(data1['Fare'].median(), inplace= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "fa558910",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-03T05:08:27.331910Z",
     "iopub.status.busy": "2022-09-03T05:08:27.330551Z",
     "iopub.status.idle": "2022-09-03T05:08:27.339829Z",
     "shell.execute_reply": "2022-09-03T05:08:27.339023Z"
    },
    "papermill": {
     "duration": 0.034605,
     "end_time": "2022-09-03T05:08:27.341967",
     "exception": false,
     "start_time": "2022-09-03T05:08:27.307362",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pclass      0\n",
       "Sex         0\n",
       "Age         0\n",
       "SibSp       0\n",
       "Parch       0\n",
       "Fare        0\n",
       "Embarked    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2.isna().sum()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e666f89b",
   "metadata": {
    "papermill": {
     "duration": 0.019412,
     "end_time": "2022-09-03T05:08:27.381612",
     "exception": false,
     "start_time": "2022-09-03T05:08:27.362200",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "We don't have any more missing values in the dataset. Let's now move on to Encoding our categorical variables"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a36e26d7",
   "metadata": {},
   "source": [
    "### Hacktoberfest - good first issue\n",
    "\n",
    "Adding a new feature, age bucket, in which the if the age is between 0 - 30 then it will be given a score of 2 since the people with age between 0-30 have more chances of surviving. Similarly with age between 30-60 it will be given a score of 1 and rest will be given a score of 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "9c404041",
   "metadata": {},
   "outputs": [],
   "source": [
    "data3 = data2.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "1daab782",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass     Sex   Age  SibSp  Parch     Fare Embarked\n",
       "0       3    male  22.0      1      0   7.2500        S\n",
       "1       1  female  38.0      1      0  71.2833        C\n",
       "2       3  female  26.0      0      0   7.9250        S\n",
       "3       1  female  35.0      1      0  53.1000        S\n",
       "4       3    male  35.0      0      0   8.0500        S"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "3f32b03d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#  generally the people with age group between 0 - 40 are more likely to survive\n",
    "def get_bucket(age):\n",
    "    if age > 60:\n",
    "        return 0\n",
    "    if age > 30:\n",
    "        return 1\n",
    "    return 2\n",
    "data3['age bucket'] = data3['Age'].apply(get_bucket)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "ee750f6e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "      <th>age bucket</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass     Sex   Age  SibSp  Parch     Fare Embarked  age bucket\n",
       "0       3    male  22.0      1      0   7.2500        S           2\n",
       "1       1  female  38.0      1      0  71.2833        C           1\n",
       "2       3  female  26.0      0      0   7.9250        S           2\n",
       "3       1  female  35.0      1      0  53.1000        S           1\n",
       "4       3    male  35.0      0      0   8.0500        S           1"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data3.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b01cf5f",
   "metadata": {
    "papermill": {
     "duration": 0.019447,
     "end_time": "2022-09-03T05:08:27.420891",
     "exception": false,
     "start_time": "2022-09-03T05:08:27.401444",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## **Encoding**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "e4fc7dd8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-03T05:08:27.462510Z",
     "iopub.status.busy": "2022-09-03T05:08:27.461355Z",
     "iopub.status.idle": "2022-09-03T05:08:27.485823Z",
     "shell.execute_reply": "2022-09-03T05:08:27.484569Z"
    },
    "papermill": {
     "duration": 0.04817,
     "end_time": "2022-09-03T05:08:27.488645",
     "exception": false,
     "start_time": "2022-09-03T05:08:27.440475",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1309 entries, 0 to 1308\n",
      "Data columns (total 7 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   Pclass    1309 non-null   int64  \n",
      " 1   Sex       1309 non-null   object \n",
      " 2   Age       1309 non-null   float64\n",
      " 3   SibSp     1309 non-null   int64  \n",
      " 4   Parch     1309 non-null   int64  \n",
      " 5   Fare      1309 non-null   float64\n",
      " 6   Embarked  1309 non-null   object \n",
      "dtypes: float64(2), int64(3), object(2)\n",
      "memory usage: 71.7+ KB\n"
     ]
    }
   ],
   "source": [
    "data3 = data2.copy()\n",
    "data3.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "296dfb5c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-03T05:08:27.532054Z",
     "iopub.status.busy": "2022-09-03T05:08:27.531315Z",
     "iopub.status.idle": "2022-09-03T05:08:27.541611Z",
     "shell.execute_reply": "2022-09-03T05:08:27.540738Z"
    },
    "papermill": {
     "duration": 0.034078,
     "end_time": "2022-09-03T05:08:27.543691",
     "exception": false,
     "start_time": "2022-09-03T05:08:27.509613",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "data3 = pd.get_dummies(data3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "70273f13",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-03T05:08:27.586554Z",
     "iopub.status.busy": "2022-09-03T05:08:27.585856Z",
     "iopub.status.idle": "2022-09-03T05:08:27.599141Z",
     "shell.execute_reply": "2022-09-03T05:08:27.598324Z"
    },
    "papermill": {
     "duration": 0.037671,
     "end_time": "2022-09-03T05:08:27.601197",
     "exception": false,
     "start_time": "2022-09-03T05:08:27.563526",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Sex_female</th>\n",
       "      <th>Sex_male</th>\n",
       "      <th>Embarked_C</th>\n",
       "      <th>Embarked_Q</th>\n",
       "      <th>Embarked_S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass   Age  SibSp  Parch     Fare  Sex_female  Sex_male  Embarked_C  \\\n",
       "0       3  22.0      1      0   7.2500           0         1           0   \n",
       "1       1  38.0      1      0  71.2833           1         0           1   \n",
       "2       3  26.0      0      0   7.9250           1         0           0   \n",
       "3       1  35.0      1      0  53.1000           1         0           0   \n",
       "4       3  35.0      0      0   8.0500           0         1           0   \n",
       "\n",
       "   Embarked_Q  Embarked_S  \n",
       "0           0           1  \n",
       "1           0           0  \n",
       "2           0           1  \n",
       "3           0           1  \n",
       "4           0           1  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data3.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eeae0b44",
   "metadata": {
    "papermill": {
     "duration": 0.019961,
     "end_time": "2022-09-03T05:08:27.641376",
     "exception": false,
     "start_time": "2022-09-03T05:08:27.621415",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## **Scaling**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "4838f4d9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-03T05:08:27.685058Z",
     "iopub.status.busy": "2022-09-03T05:08:27.684297Z",
     "iopub.status.idle": "2022-09-03T05:08:27.694306Z",
     "shell.execute_reply": "2022-09-03T05:08:27.693107Z"
    },
    "papermill": {
     "duration": 0.034368,
     "end_time": "2022-09-03T05:08:27.696953",
     "exception": false,
     "start_time": "2022-09-03T05:08:27.662585",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "sc = StandardScaler()\n",
    "data3[['Age', 'Fare']] = sc.fit_transform(data3[['Age', 'Fare']])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "54b0e574",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-03T05:08:27.739512Z",
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     "shell.execute_reply": "2022-09-03T05:08:27.752670Z"
    },
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     "duration": 0.038756,
     "end_time": "2022-09-03T05:08:27.756172",
     "exception": false,
     "start_time": "2022-09-03T05:08:27.717416",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "      <th>Pclass</th>\n",
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       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
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       "   Pclass       Age  SibSp  Parch      Fare  Sex_female  Sex_male  Embarked_C  \\\n",
       "0       3 -0.510002      1      0 -0.503291           0         1           0   \n",
       "1       1  0.678017      1      0  0.734744           1         0           1   \n",
       "2       3 -0.212997      0      0 -0.490240           1         0           0   \n",
       "3       1  0.455263      1      0  0.383183           1         0           0   \n",
       "4       3  0.455263      0      0 -0.487824           0         1           0   \n",
       "\n",
       "   Embarked_Q  Embarked_S  \n",
       "0           0           1  \n",
       "1           0           0  \n",
       "2           0           1  \n",
       "3           0           1  \n",
       "4           0           1  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data3.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb59493f",
   "metadata": {
    "papermill": {
     "duration": 0.01997,
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     "start_time": "2022-09-03T05:08:27.776541",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## **Feature Enginerring**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5667bf05",
   "metadata": {
    "papermill": {
     "duration": 0.034677,
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   },
   "source": [
    "Both the `SibSp` and `Parch` column suggests whether the person was person was travelling with his family or not. So we will convert these features into a single feature called family"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "8ac05b56",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-03T05:08:27.896311Z",
     "iopub.status.busy": "2022-09-03T05:08:27.895581Z",
     "iopub.status.idle": "2022-09-03T05:08:27.903326Z",
     "shell.execute_reply": "2022-09-03T05:08:27.902424Z"
    },
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "data3['Family'] = np.where(data3['SibSp'] + data3['Parch'] > 0, 1, 0)\n",
    "data3.drop(['SibSp', 'Parch'], axis= 1, inplace= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "5e993c89",
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    "execution": {
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     "status": "completed"
    },
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   },
   "outputs": [
    {
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      "text/plain": [
       "   Pclass       Age      Fare  Sex_female  Sex_male  Embarked_C  Embarked_Q  \\\n",
       "0       3 -0.510002 -0.503291           0         1           0           0   \n",
       "1       1  0.678017  0.734744           1         0           1           0   \n",
       "2       3 -0.212997 -0.490240           1         0           0           0   \n",
       "3       1  0.455263  0.383183           1         0           0           0   \n",
       "4       3  0.455263 -0.487824           0         1           0           0   \n",
       "\n",
       "   Embarked_S  Family  \n",
       "0           1       1  \n",
       "1           0       1  \n",
       "2           1       0  \n",
       "3           1       1  \n",
       "4           1       0  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "8b6159a9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-03T05:08:28.006479Z",
     "iopub.status.busy": "2022-09-03T05:08:28.005736Z",
     "iopub.status.idle": "2022-09-03T05:08:28.011535Z",
     "shell.execute_reply": "2022-09-03T05:08:28.010693Z"
    },
    "papermill": {
     "duration": 0.03,
     "end_time": "2022-09-03T05:08:28.013599",
     "exception": false,
     "start_time": "2022-09-03T05:08:27.983599",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "train_final = data3.loc[:train.index.max(), :].copy()\n",
    "test_final = data3.loc[train.index.max() + 1:, :].reset_index(drop=True).copy()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4566557b",
   "metadata": {
    "papermill": {
     "duration": 0.020048,
     "end_time": "2022-09-03T05:08:28.054167",
     "exception": false,
     "start_time": "2022-09-03T05:08:28.034119",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## **Modelling**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "451a0d3d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-03T05:08:28.096743Z",
     "iopub.status.busy": "2022-09-03T05:08:28.096130Z",
     "iopub.status.idle": "2022-09-03T05:08:28.105248Z",
     "shell.execute_reply": "2022-09-03T05:08:28.104315Z"
    },
    "papermill": {
     "duration": 0.033211,
     "end_time": "2022-09-03T05:08:28.107656",
     "exception": false,
     "start_time": "2022-09-03T05:08:28.074445",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "models = {\n",
    "    'xgboost' : XGBClassifier(),\n",
    "    'catboost' : CatBoostClassifier(verbose=0),\n",
    "    'lightgbm' : LGBMClassifier(),\n",
    "    'gradient boosing' : GradientBoostingClassifier(),\n",
    "    'random forest' : RandomForestClassifier(),\n",
    "    'logistic regression': LogisticRegression(),\n",
    "    'naive bayes': GaussianNB(),\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "32882892",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-03T05:08:28.151411Z",
     "iopub.status.busy": "2022-09-03T05:08:28.150829Z",
     "iopub.status.idle": "2022-09-03T05:08:28.155177Z",
     "shell.execute_reply": "2022-09-03T05:08:28.154198Z"
    },
    "papermill": {
     "duration": 0.028739,
     "end_time": "2022-09-03T05:08:28.157294",
     "exception": false,
     "start_time": "2022-09-03T05:08:28.128555",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "target = target.astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "c3763be0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-03T05:08:28.199976Z",
     "iopub.status.busy": "2022-09-03T05:08:28.199583Z",
     "iopub.status.idle": "2022-09-03T05:08:30.177338Z",
     "shell.execute_reply": "2022-09-03T05:08:30.175524Z"
    },
    "papermill": {
     "duration": 2.003416,
     "end_time": "2022-09-03T05:08:30.181115",
     "exception": false,
     "start_time": "2022-09-03T05:08:28.177699",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "xgboost trained\n",
      "catboost trained\n",
      "lightgbm trained\n",
      "gradient boosing trained\n",
      "random forest trained\n",
      "logistic regression trained\n",
      "naive bayes trained\n"
     ]
    }
   ],
   "source": [
    "for name, model in models.items():\n",
    "    model.fit(train_final, target)\n",
    "    print(f'{name} trained')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e5cc3f1",
   "metadata": {
    "papermill": {
     "duration": 0.02058,
     "end_time": "2022-09-03T05:08:30.223114",
     "exception": false,
     "start_time": "2022-09-03T05:08:30.202534",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## **Evaluation**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "6b470bc2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-03T05:08:30.266327Z",
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     "shell.execute_reply": "2022-09-03T05:08:48.573266Z"
    },
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     "duration": 18.334273,
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     "exception": false,
     "start_time": "2022-09-03T05:08:30.243632",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "results = {}\n",
    "\n",
    "kf = KFold(n_splits= 10)\n",
    "\n",
    "for name, model in models.items():\n",
    "    result = cross_val_score(model, train_final, target, scoring = 'roc_auc', cv= kf)\n",
    "    results[name] = np.mean(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "b3780757",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------\n",
      "xgboost\n",
      "ROC score: 0.862\n",
      "-------\n",
      "catboost\n",
      "ROC score: 0.873\n",
      "-------\n",
      "lightgbm\n",
      "ROC score: 0.867\n",
      "-------\n",
      "gradient boosing\n",
      "ROC score: 0.865\n",
      "-------\n",
      "random forest\n",
      "ROC score: 0.858\n",
      "-------\n",
      "logistic regression\n",
      "ROC score: 0.843\n",
      "-------\n",
      "naive bayes\n",
      "ROC score: 0.815\n"
     ]
    }
   ],
   "source": [
    "for name, result in results.items():\n",
    "    print(\"-------\\n\" + name)\n",
    "    print(f'ROC score: {round(result, 3)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "66ed15da",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-03T05:08:48.674690Z",
     "iopub.status.busy": "2022-09-03T05:08:48.673920Z",
     "iopub.status.idle": "2022-09-03T05:08:48.687909Z",
     "shell.execute_reply": "2022-09-03T05:08:48.686633Z"
    },
    "papermill": {
     "duration": 0.038781,
     "end_time": "2022-09-03T05:08:48.690256",
     "exception": false,
     "start_time": "2022-09-03T05:08:48.651475",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>ROC Score</th>\n",
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       "    <tr>\n",
       "      <th>catboost</th>\n",
       "      <td>0.872516</td>\n",
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       "      <td>0.867494</td>\n",
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       "      <th>gradient boosing</th>\n",
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       "      <td>0.842527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>naive bayes</th>\n",
       "      <td>0.814845</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     ROC Score\n",
       "catboost              0.872516\n",
       "lightgbm              0.867494\n",
       "gradient boosing      0.864926\n",
       "xgboost               0.862251\n",
       "random forest         0.858137\n",
       "logistic regression   0.842527\n",
       "naive bayes           0.814845"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_df = pd.DataFrame(results, index=range(0,1)).T.rename(columns={0: 'ROC Score'}).sort_values('ROC Score', ascending=False)\n",
    "results_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "d20c8072",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-03T05:08:48.735544Z",
     "iopub.status.busy": "2022-09-03T05:08:48.734407Z",
     "iopub.status.idle": "2022-09-03T05:08:48.994450Z",
     "shell.execute_reply": "2022-09-03T05:08:48.993227Z"
    },
    "papermill": {
     "duration": 0.285539,
     "end_time": "2022-09-03T05:08:48.997262",
     "exception": false,
     "start_time": "2022-09-03T05:08:48.711723",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (15, 6))\n",
    "sns.barplot(x= results_df.index, y = results_df['ROC Score'], palette = 'summer')\n",
    "plt.xlabel('Model')\n",
    "plt.ylabel('ROC Score')\n",
    "plt.title('ROC Score of different models');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd23f345",
   "metadata": {
    "papermill": {
     "duration": 0.021586,
     "end_time": "2022-09-03T05:08:49.040818",
     "exception": false,
     "start_time": "2022-09-03T05:08:49.019232",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### Clearly, catboost had the best ROC Score, lets do some hyperparameter optimization and make the final predictions based on it"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd5e8a60",
   "metadata": {
    "papermill": {
     "duration": 0.021518,
     "end_time": "2022-09-03T05:08:49.084156",
     "exception": false,
     "start_time": "2022-09-03T05:08:49.062638",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## **Hyperparamer Optimization**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "e4edaed9",
   "metadata": {
    "_kg_hide-output": true,
    "execution": {
     "iopub.execute_input": "2022-09-03T05:08:49.130471Z",
     "iopub.status.busy": "2022-09-03T05:08:49.129502Z",
     "iopub.status.idle": "2022-09-03T05:08:49.135361Z",
     "shell.execute_reply": "2022-09-03T05:08:49.134181Z"
    },
    "papermill": {
     "duration": 0.031885,
     "end_time": "2022-09-03T05:08:49.137837",
     "exception": false,
     "start_time": "2022-09-03T05:08:49.105952",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# def catboost_objective(trial):\n",
    "#     learning_rate = trial.suggest_float('learning_rate', 0, 0.5)\n",
    "#     depth = trial.suggest_int('depth', 3, 10)\n",
    "#     n_estimators = trial.suggest_int('n_estimators', 50, 600)\n",
    "    \n",
    "#     model = CatBoostClassifier(\n",
    "#         learning_rate= learning_rate,\n",
    "#         depth= depth,\n",
    "#         n_estimators= n_estimators,\n",
    "#         verbose= 0\n",
    "#     )\n",
    "\n",
    "#     model.fit(train_final, target)\n",
    "#     cv_score = cross_val_score(model, train_final, target, scoring= 'roc_auc', cv= kf)\n",
    "\n",
    "#     return np.mean(cv_score)\n",
    "\n",
    "# study = optuna.create_study(direction= 'maximize')\n",
    "# study.optimize(catboost_objective, n_trials= 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "8b031c0f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-03T05:08:49.183515Z",
     "iopub.status.busy": "2022-09-03T05:08:49.182366Z",
     "iopub.status.idle": "2022-09-03T05:08:49.188209Z",
     "shell.execute_reply": "2022-09-03T05:08:49.187199Z"
    },
    "papermill": {
     "duration": 0.03098,
     "end_time": "2022-09-03T05:08:49.190537",
     "exception": false,
     "start_time": "2022-09-03T05:08:49.159557",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "catboost_params = {\n",
    "    'learning_rate': 0.1682046368673911, \n",
    "     'depth': 6, \n",
    "     'n_estimators': 540,\n",
    "     'verbose':0\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "7807c85a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-03T05:08:49.245253Z",
     "iopub.status.busy": "2022-09-03T05:08:49.244663Z",
     "iopub.status.idle": "2022-09-03T05:08:49.844330Z",
     "shell.execute_reply": "2022-09-03T05:08:49.843316Z"
    },
    "papermill": {
     "duration": 0.629654,
     "end_time": "2022-09-03T05:08:49.847002",
     "exception": false,
     "start_time": "2022-09-03T05:08:49.217348",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "cb = CatBoostClassifier(**catboost_params)\n",
    "cb.fit(train_final, target)\n",
    "pred_cb = cb.predict(test_final)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4970845e",
   "metadata": {
    "papermill": {
     "duration": 0.021322,
     "end_time": "2022-09-03T05:08:49.890262",
     "exception": false,
     "start_time": "2022-09-03T05:08:49.868940",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## **ANN**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "df62d4f1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-03T05:08:49.935738Z",
     "iopub.status.busy": "2022-09-03T05:08:49.934934Z",
     "iopub.status.idle": "2022-09-03T05:08:49.943996Z",
     "shell.execute_reply": "2022-09-03T05:08:49.943004Z"
    },
    "papermill": {
     "duration": 0.034442,
     "end_time": "2022-09-03T05:08:49.946172",
     "exception": false,
     "start_time": "2022-09-03T05:08:49.911730",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891, 9)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_final.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "9d6e5a6e",
   "metadata": {
    "execution": {
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   "source": [
    "train_final = np.asarray(train_final).astype(np.float32)\n",
    "test_final = np.asarray(test_final).astype(np.float32)\n",
    "target = np.asarray(target).astype(np.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "10d088d7",
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "23/23 [==============================] - 1s 12ms/step - loss: 0.6658 - auc: 0.6015 - val_loss: 0.6464 - val_auc: 0.6662\n",
      "Epoch 2/100\n",
      "23/23 [==============================] - 0s 3ms/step - loss: 0.6433 - auc: 0.7585 - val_loss: 0.6204 - val_auc: 0.7879\n",
      "Epoch 3/100\n",
      "23/23 [==============================] - 0s 3ms/step - loss: 0.6244 - auc: 0.8163 - val_loss: 0.5988 - val_auc: 0.8318\n",
      "Epoch 4/100\n",
      "23/23 [==============================] - 0s 3ms/step - loss: 0.6084 - auc: 0.8292 - val_loss: 0.5796 - val_auc: 0.8478\n",
      "Epoch 5/100\n",
      "23/23 [==============================] - 0s 2ms/step - loss: 0.5942 - auc: 0.8320 - val_loss: 0.5631 - val_auc: 0.8618\n",
      "Epoch 6/100\n",
      "23/23 [==============================] - 0s 3ms/step - loss: 0.5805 - auc: 0.8380 - val_loss: 0.5460 - val_auc: 0.8685\n",
      "Epoch 7/100\n",
      "23/23 [==============================] - 0s 3ms/step - loss: 0.5679 - auc: 0.8355 - val_loss: 0.5312 - val_auc: 0.8695\n",
      "Epoch 8/100\n",
      "23/23 [==============================] - 0s 3ms/step - loss: 0.5557 - auc: 0.8391 - val_loss: 0.5191 - val_auc: 0.8728\n",
      "Epoch 9/100\n",
      "23/23 [==============================] - 0s 3ms/step - loss: 0.5446 - auc: 0.8376 - val_loss: 0.5056 - val_auc: 0.8760\n",
      "Epoch 10/100\n",
      "23/23 [==============================] - 0s 3ms/step - loss: 0.5342 - auc: 0.8397 - val_loss: 0.4933 - val_auc: 0.8749\n",
      "Epoch 11/100\n",
      "23/23 [==============================] - 0s 3ms/step - loss: 0.5260 - auc: 0.8328 - val_loss: 0.4826 - val_auc: 0.8785\n",
      "Epoch 12/100\n",
      "23/23 [==============================] - 0s 3ms/step - loss: 0.5190 - auc: 0.8328 - val_loss: 0.4756 - val_auc: 0.8815\n",
      "Epoch 13/100\n",
      "23/23 [==============================] - 0s 3ms/step - loss: 0.5118 - auc: 0.8374 - val_loss: 0.4666 - val_auc: 0.8789\n",
      "Epoch 14/100\n",
      "23/23 [==============================] - 0s 2ms/step - loss: 0.5064 - auc: 0.8379 - val_loss: 0.4606 - val_auc: 0.8813\n",
      "Epoch 15/100\n",
      "23/23 [==============================] - 0s 3ms/step - loss: 0.5016 - auc: 0.8398 - val_loss: 0.4548 - val_auc: 0.8828\n",
      "Epoch 16/100\n",
      "23/23 [==============================] - 0s 2ms/step - loss: 0.4975 - auc: 0.8373 - val_loss: 0.4489 - val_auc: 0.8829\n",
      "Epoch 17/100\n",
      "23/23 [==============================] - 0s 2ms/step - loss: 0.4936 - auc: 0.8389 - val_loss: 0.4445 - val_auc: 0.8817\n",
      "Epoch 18/100\n",
      "23/23 [==============================] - 0s 3ms/step - loss: 0.4904 - auc: 0.8401 - val_loss: 0.4405 - val_auc: 0.8814\n",
      "Epoch 19/100\n",
      "23/23 [==============================] - 0s 2ms/step - loss: 0.4876 - auc: 0.8404 - val_loss: 0.4363 - val_auc: 0.8829\n",
      "Epoch 20/100\n",
      "23/23 [==============================] - 0s 2ms/step - loss: 0.4850 - auc: 0.8433 - val_loss: 0.4327 - val_auc: 0.8825\n",
      "Epoch 21/100\n",
      "23/23 [==============================] - 0s 2ms/step - loss: 0.4831 - auc: 0.8425 - val_loss: 0.4300 - val_auc: 0.8837\n",
      "Epoch 22/100\n",
      "23/23 [==============================] - 0s 2ms/step - loss: 0.4802 - auc: 0.8437 - val_loss: 0.4266 - val_auc: 0.8829\n",
      "Epoch 23/100\n",
      "23/23 [==============================] - 0s 2ms/step - loss: 0.4777 - auc: 0.8461 - val_loss: 0.4246 - val_auc: 0.8835\n",
      "Epoch 24/100\n",
      "23/23 [==============================] - 0s 3ms/step - loss: 0.4760 - auc: 0.8466 - val_loss: 0.4228 - val_auc: 0.8838\n",
      "Epoch 25/100\n",
      "23/23 [==============================] - 0s 2ms/step - loss: 0.4747 - auc: 0.8474 - val_loss: 0.4207 - val_auc: 0.8837\n",
      "Epoch 26/100\n",
      "23/23 [==============================] - 0s 3ms/step - loss: 0.4725 - auc: 0.8486 - val_loss: 0.4190 - val_auc: 0.8839\n",
      "Epoch 27/100\n",
      "23/23 [==============================] - 0s 3ms/step - loss: 0.4710 - auc: 0.8489 - val_loss: 0.4177 - val_auc: 0.8855\n",
      "Epoch 28/100\n",
      "23/23 [==============================] - 0s 3ms/step - loss: 0.4695 - auc: 0.8509 - val_loss: 0.4162 - val_auc: 0.8838\n",
      "Epoch 29/100\n",
      "23/23 [==============================] - 0s 3ms/step - loss: 0.4682 - auc: 0.8491 - val_loss: 0.4140 - val_auc: 0.8873\n",
      "Epoch 30/100\n",
      "23/23 [==============================] - 0s 2ms/step - loss: 0.4669 - auc: 0.8501 - val_loss: 0.4127 - val_auc: 0.8871\n",
      "Epoch 31/100\n",
      "23/23 [==============================] - 0s 3ms/step - loss: 0.4653 - auc: 0.8501 - val_loss: 0.4113 - val_auc: 0.8841\n",
      "Epoch 32/100\n",
      "23/23 [==============================] - 0s 2ms/step - loss: 0.4635 - auc: 0.8512 - val_loss: 0.4090 - val_auc: 0.8876\n",
      "Epoch 33/100\n",
      "23/23 [==============================] - 0s 2ms/step - loss: 0.4623 - auc: 0.8521 - val_loss: 0.4075 - val_auc: 0.8863\n",
      "Epoch 34/100\n",
      "23/23 [==============================] - 0s 3ms/step - loss: 0.4607 - auc: 0.8517 - val_loss: 0.4058 - val_auc: 0.8849\n",
      "Epoch 35/100\n",
      "23/23 [==============================] - 0s 2ms/step - loss: 0.4595 - auc: 0.8516 - val_loss: 0.4045 - val_auc: 0.8839\n",
      "Epoch 36/100\n",
      "23/23 [==============================] - 0s 2ms/step - loss: 0.4583 - auc: 0.8520 - val_loss: 0.4032 - val_auc: 0.8845\n",
      "Epoch 37/100\n",
      "23/23 [==============================] - 0s 3ms/step - loss: 0.4573 - auc: 0.8529 - val_loss: 0.4022 - val_auc: 0.8836\n",
      "Epoch 38/100\n",
      "23/23 [==============================] - 0s 2ms/step - loss: 0.4562 - auc: 0.8549 - val_loss: 0.4017 - val_auc: 0.8851\n",
      "Epoch 39/100\n",
      "23/23 [==============================] - 0s 3ms/step - loss: 0.4553 - auc: 0.8532 - val_loss: 0.4002 - val_auc: 0.8848\n",
      "Epoch 40/100\n",
      "23/23 [==============================] - 0s 2ms/step - loss: 0.4540 - auc: 0.8556 - val_loss: 0.3992 - val_auc: 0.8865\n",
      "Epoch 41/100\n",
      "23/23 [==============================] - 0s 2ms/step - loss: 0.4535 - auc: 0.8551 - val_loss: 0.3984 - val_auc: 0.8878\n",
      "Epoch 42/100\n",
      "23/23 [==============================] - 0s 2ms/step - loss: 0.4530 - auc: 0.8535 - val_loss: 0.3980 - val_auc: 0.8880\n",
      "Epoch 43/100\n",
      "23/23 [==============================] - 0s 2ms/step - loss: 0.4515 - auc: 0.8549 - val_loss: 0.3965 - val_auc: 0.8872\n",
      "Epoch 44/100\n",
      "23/23 [==============================] - 0s 3ms/step - loss: 0.4505 - auc: 0.8561 - val_loss: 0.3961 - val_auc: 0.8883\n",
      "Epoch 45/100\n",
      "23/23 [==============================] - 0s 2ms/step - loss: 0.4495 - auc: 0.8563 - val_loss: 0.3949 - val_auc: 0.8881\n",
      "Epoch 46/100\n",
      "23/23 [==============================] - 0s 2ms/step - loss: 0.4487 - auc: 0.8569 - val_loss: 0.3937 - val_auc: 0.8894\n",
      "Epoch 47/100\n",
      "23/23 [==============================] - 0s 2ms/step - loss: 0.4480 - auc: 0.8565 - val_loss: 0.3932 - val_auc: 0.8880\n",
      "Epoch 48/100\n",
      "23/23 [==============================] - 0s 2ms/step - loss: 0.4474 - auc: 0.8575 - val_loss: 0.3931 - val_auc: 0.8898\n",
      "Epoch 49/100\n",
      "23/23 [==============================] - 0s 2ms/step - loss: 0.4465 - auc: 0.8578 - val_loss: 0.3918 - val_auc: 0.8878\n",
      "Epoch 50/100\n",
      "23/23 [==============================] - 0s 2ms/step - loss: 0.4456 - auc: 0.8574 - val_loss: 0.3906 - val_auc: 0.8909\n",
      "Epoch 51/100\n",
      "23/23 [==============================] - 0s 3ms/step - loss: 0.4450 - auc: 0.8573 - val_loss: 0.3898 - val_auc: 0.8889\n",
      "Epoch 52/100\n",
      "23/23 [==============================] - 0s 3ms/step - loss: 0.4462 - auc: 0.8560 - val_loss: 0.3918 - val_auc: 0.8892\n",
      "Epoch 53/100\n",
      "23/23 [==============================] - 0s 3ms/step - loss: 0.4433 - auc: 0.8598 - val_loss: 0.3891 - val_auc: 0.8907\n",
      "Epoch 54/100\n",
      "23/23 [==============================] - 0s 3ms/step - loss: 0.4431 - auc: 0.8593 - val_loss: 0.3884 - val_auc: 0.8916\n",
      "Epoch 55/100\n",
      "23/23 [==============================] - 0s 3ms/step - loss: 0.4421 - auc: 0.8589 - val_loss: 0.3873 - val_auc: 0.8909\n",
      "Epoch 56/100\n",
      "23/23 [==============================] - 0s 3ms/step - loss: 0.4419 - auc: 0.8583 - val_loss: 0.3870 - val_auc: 0.8910\n",
      "Epoch 57/100\n",
      "23/23 [==============================] - 0s 2ms/step - loss: 0.4408 - auc: 0.8599 - val_loss: 0.3858 - val_auc: 0.8914\n",
      "Epoch 58/100\n",
      "23/23 [==============================] - 0s 2ms/step - loss: 0.4410 - auc: 0.8596 - val_loss: 0.3852 - val_auc: 0.8896\n",
      "Epoch 59/100\n",
      "23/23 [==============================] - 0s 2ms/step - loss: 0.4399 - auc: 0.8602 - val_loss: 0.3849 - val_auc: 0.8910\n",
      "Epoch 60/100\n",
      "23/23 [==============================] - 0s 2ms/step - loss: 0.4387 - auc: 0.8608 - val_loss: 0.3841 - val_auc: 0.8892\n",
      "Epoch 61/100\n",
      "23/23 [==============================] - 0s 2ms/step - loss: 0.4381 - auc: 0.8606 - val_loss: 0.3838 - val_auc: 0.8901\n",
      "Epoch 62/100\n",
      "23/23 [==============================] - 0s 2ms/step - loss: 0.4377 - auc: 0.8604 - val_loss: 0.3834 - val_auc: 0.8895\n",
      "Epoch 63/100\n",
      "23/23 [==============================] - 0s 3ms/step - loss: 0.4372 - auc: 0.8608 - val_loss: 0.3829 - val_auc: 0.8904\n",
      "Epoch 64/100\n",
      "23/23 [==============================] - 0s 3ms/step - loss: 0.4363 - auc: 0.8611 - val_loss: 0.3826 - val_auc: 0.8887\n",
      "Epoch 65/100\n",
      "23/23 [==============================] - 0s 3ms/step - loss: 0.4357 - auc: 0.8614 - val_loss: 0.3834 - val_auc: 0.8910\n",
      "Epoch 66/100\n",
      "23/23 [==============================] - 0s 2ms/step - loss: 0.4361 - auc: 0.8604 - val_loss: 0.3826 - val_auc: 0.8925\n",
      "Epoch 67/100\n",
      "23/23 [==============================] - 0s 3ms/step - loss: 0.4347 - auc: 0.8609 - val_loss: 0.3817 - val_auc: 0.8912\n",
      "Epoch 68/100\n",
      "23/23 [==============================] - 0s 3ms/step - loss: 0.4340 - auc: 0.8606 - val_loss: 0.3802 - val_auc: 0.8916\n",
      "Epoch 69/100\n",
      "23/23 [==============================] - 0s 3ms/step - loss: 0.4336 - auc: 0.8609 - val_loss: 0.3803 - val_auc: 0.8908\n",
      "Epoch 70/100\n",
      "23/23 [==============================] - 0s 2ms/step - loss: 0.4335 - auc: 0.8609 - val_loss: 0.3800 - val_auc: 0.8901\n",
      "Epoch 71/100\n",
      "23/23 [==============================] - 0s 2ms/step - loss: 0.4331 - auc: 0.8608 - val_loss: 0.3778 - val_auc: 0.8931\n",
      "Epoch 72/100\n",
      "23/23 [==============================] - 0s 2ms/step - loss: 0.4320 - auc: 0.8619 - val_loss: 0.3788 - val_auc: 0.8908\n",
      "Epoch 73/100\n",
      "23/23 [==============================] - 0s 2ms/step - loss: 0.4315 - auc: 0.8617 - val_loss: 0.3784 - val_auc: 0.8925\n",
      "Epoch 74/100\n",
      "23/23 [==============================] - 0s 2ms/step - loss: 0.4322 - auc: 0.8618 - val_loss: 0.3772 - val_auc: 0.8929\n",
      "Epoch 75/100\n",
      "23/23 [==============================] - 0s 3ms/step - loss: 0.4310 - auc: 0.8620 - val_loss: 0.3772 - val_auc: 0.8941\n",
      "Epoch 76/100\n",
      "23/23 [==============================] - 0s 3ms/step - loss: 0.4303 - auc: 0.8628 - val_loss: 0.3767 - val_auc: 0.8942\n",
      "Epoch 77/100\n",
      "23/23 [==============================] - 0s 3ms/step - loss: 0.4299 - auc: 0.8624 - val_loss: 0.3762 - val_auc: 0.8942\n",
      "Epoch 78/100\n",
      "23/23 [==============================] - 0s 3ms/step - loss: 0.4295 - auc: 0.8632 - val_loss: 0.3767 - val_auc: 0.8937\n",
      "Epoch 79/100\n",
      "23/23 [==============================] - 0s 2ms/step - loss: 0.4290 - auc: 0.8625 - val_loss: 0.3765 - val_auc: 0.8935\n",
      "Epoch 80/100\n",
      "23/23 [==============================] - 0s 3ms/step - loss: 0.4286 - auc: 0.8629 - val_loss: 0.3763 - val_auc: 0.8932\n",
      "Epoch 81/100\n",
      "23/23 [==============================] - 0s 2ms/step - loss: 0.4283 - auc: 0.8632 - val_loss: 0.3752 - val_auc: 0.8950\n",
      "Epoch 82/100\n",
      "23/23 [==============================] - 0s 2ms/step - loss: 0.4279 - auc: 0.8632 - val_loss: 0.3766 - val_auc: 0.8944\n",
      "Epoch 83/100\n",
      "23/23 [==============================] - 0s 3ms/step - loss: 0.4279 - auc: 0.8634 - val_loss: 0.3758 - val_auc: 0.8929\n",
      "Epoch 84/100\n",
      "23/23 [==============================] - 0s 3ms/step - loss: 0.4276 - auc: 0.8632 - val_loss: 0.3739 - val_auc: 0.8929\n",
      "Epoch 85/100\n",
      "23/23 [==============================] - 0s 3ms/step - loss: 0.4265 - auc: 0.8636 - val_loss: 0.3743 - val_auc: 0.8946\n",
      "Epoch 86/100\n",
      "23/23 [==============================] - 0s 2ms/step - loss: 0.4266 - auc: 0.8634 - val_loss: 0.3746 - val_auc: 0.8941\n",
      "Epoch 87/100\n",
      "23/23 [==============================] - 0s 2ms/step - loss: 0.4265 - auc: 0.8640 - val_loss: 0.3734 - val_auc: 0.8927\n",
      "Epoch 88/100\n",
      "23/23 [==============================] - 0s 2ms/step - loss: 0.4262 - auc: 0.8632 - val_loss: 0.3752 - val_auc: 0.8908\n",
      "Epoch 89/100\n",
      "23/23 [==============================] - 0s 2ms/step - loss: 0.4253 - auc: 0.8648 - val_loss: 0.3744 - val_auc: 0.8931\n",
      "Epoch 90/100\n",
      "23/23 [==============================] - 0s 2ms/step - loss: 0.4246 - auc: 0.8650 - val_loss: 0.3735 - val_auc: 0.8943\n",
      "Epoch 91/100\n",
      "23/23 [==============================] - 0s 2ms/step - loss: 0.4250 - auc: 0.8639 - val_loss: 0.3752 - val_auc: 0.8936\n",
      "Epoch 92/100\n",
      "23/23 [==============================] - 0s 2ms/step - loss: 0.4243 - auc: 0.8649 - val_loss: 0.3735 - val_auc: 0.8935\n",
      "Epoch 93/100\n",
      "23/23 [==============================] - 0s 3ms/step - loss: 0.4237 - auc: 0.8661 - val_loss: 0.3732 - val_auc: 0.8951\n",
      "Epoch 94/100\n",
      "23/23 [==============================] - 0s 3ms/step - loss: 0.4234 - auc: 0.8653 - val_loss: 0.3729 - val_auc: 0.8947\n",
      "Epoch 95/100\n",
      "23/23 [==============================] - 0s 3ms/step - loss: 0.4231 - auc: 0.8653 - val_loss: 0.3736 - val_auc: 0.8922\n",
      "Epoch 96/100\n",
      "23/23 [==============================] - 0s 3ms/step - loss: 0.4231 - auc: 0.8652 - val_loss: 0.3724 - val_auc: 0.8955\n",
      "Epoch 97/100\n",
      "23/23 [==============================] - 0s 2ms/step - loss: 0.4226 - auc: 0.8654 - val_loss: 0.3733 - val_auc: 0.8918\n",
      "Epoch 98/100\n",
      "23/23 [==============================] - 0s 2ms/step - loss: 0.4231 - auc: 0.8654 - val_loss: 0.3737 - val_auc: 0.8939\n",
      "Epoch 99/100\n",
      "23/23 [==============================] - 0s 2ms/step - loss: 0.4219 - auc: 0.8657 - val_loss: 0.3721 - val_auc: 0.8941\n",
      "Epoch 100/100\n",
      "23/23 [==============================] - 0s 2ms/step - loss: 0.4218 - auc: 0.8664 - val_loss: 0.3715 - val_auc: 0.8908\n"
     ]
    }
   ],
   "source": [
    "model = tf.keras.Sequential([\n",
    "    tf.keras.Input(9),\n",
    "    tf.keras.layers.Dense(100, activation = 'relu'),\n",
    "    tf.keras.layers.Dense(100, activation = 'relu'),\n",
    "    tf.keras.layers.Dense(1, activation = 'sigmoid')\n",
    "])\n",
    "\n",
    "model.compile(\n",
    "    optimizer = tf.keras.optimizers.Adam(learning_rate = 0.0001),\n",
    "    loss = 'binary_crossentropy',\n",
    "    metrics = [tf.keras.metrics.AUC(name='auc')]\n",
    ")\n",
    "\n",
    "EPOCHS = 100\n",
    "\n",
    "history = model.fit(train_final, target, validation_split = 0.20, epochs = EPOCHS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "421f82d9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-03T05:09:03.192646Z",
     "iopub.status.busy": "2022-09-03T05:09:03.191285Z",
     "iopub.status.idle": "2022-09-03T05:09:03.474583Z",
     "shell.execute_reply": "2022-09-03T05:09:03.473448Z"
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "epochs = range(1, EPOCHS + 1)\n",
    "train_loss = history.history['loss']\n",
    "val_loss = history.history['val_loss']\n",
    "\n",
    "plt.plot(epochs, train_loss, label=\"Training Loss\")\n",
    "plt.plot(epochs, val_loss, label=\"Validation Loss\")\n",
    "\n",
    "plt.title(\"Training and Validation Loss\")\n",
    "plt.xlabel(\"Epoch\")\n",
    "plt.ylabel(\"Loss\")\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37a34388",
   "metadata": {
    "papermill": {
     "duration": 0.039305,
     "end_time": "2022-09-03T05:09:03.558545",
     "exception": false,
     "start_time": "2022-09-03T05:09:03.519240",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Clearly our ANN is doing a better job in classifying our target column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "ae03c724",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-03T05:09:03.640310Z",
     "iopub.status.busy": "2022-09-03T05:09:03.639200Z",
     "iopub.status.idle": "2022-09-03T05:09:03.789361Z",
     "shell.execute_reply": "2022-09-03T05:09:03.788162Z"
    },
    "papermill": {
     "duration": 0.194452,
     "end_time": "2022-09-03T05:09:03.792350",
     "exception": false,
     "start_time": "2022-09-03T05:09:03.597898",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "pred_ann = model.predict(test_final)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "28730fad",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-03T05:09:03.874247Z",
     "iopub.status.busy": "2022-09-03T05:09:03.873653Z",
     "iopub.status.idle": "2022-09-03T05:09:03.879063Z",
     "shell.execute_reply": "2022-09-03T05:09:03.878172Z"
    },
    "papermill": {
     "duration": 0.048583,
     "end_time": "2022-09-03T05:09:03.881236",
     "exception": false,
     "start_time": "2022-09-03T05:09:03.832653",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "submission = pd.DataFrame(test_ids, index= None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "32abefe2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-03T05:09:03.962918Z",
     "iopub.status.busy": "2022-09-03T05:09:03.962267Z",
     "iopub.status.idle": "2022-09-03T05:09:03.967172Z",
     "shell.execute_reply": "2022-09-03T05:09:03.966286Z"
    },
    "papermill": {
     "duration": 0.048092,
     "end_time": "2022-09-03T05:09:03.969284",
     "exception": false,
     "start_time": "2022-09-03T05:09:03.921192",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "submission['Survived'] = pred_ann"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "661d42b2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-03T05:09:04.050539Z",
     "iopub.status.busy": "2022-09-03T05:09:04.049949Z",
     "iopub.status.idle": "2022-09-03T05:09:04.055558Z",
     "shell.execute_reply": "2022-09-03T05:09:04.054742Z"
    },
    "papermill": {
     "duration": 0.048599,
     "end_time": "2022-09-03T05:09:04.057601",
     "exception": false,
     "start_time": "2022-09-03T05:09:04.009002",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "submission['Survived'] = submission['Survived'].apply(lambda x: 1 if x>0.5 else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "ea6a6bda",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-03T05:09:04.139030Z",
     "iopub.status.busy": "2022-09-03T05:09:04.138384Z",
     "iopub.status.idle": "2022-09-03T05:09:04.147534Z",
     "shell.execute_reply": "2022-09-03T05:09:04.146471Z"
    },
    "papermill": {
     "duration": 0.05274,
     "end_time": "2022-09-03T05:09:04.149858",
     "exception": false,
     "start_time": "2022-09-03T05:09:04.097118",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived\n",
       "0          892         0\n",
       "1          893         0\n",
       "2          894         0\n",
       "3          895         0\n",
       "4          896         0"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submission.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "10a2c11c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-03T05:09:04.235971Z",
     "iopub.status.busy": "2022-09-03T05:09:04.235301Z",
     "iopub.status.idle": "2022-09-03T05:09:04.242613Z",
     "shell.execute_reply": "2022-09-03T05:09:04.241827Z"
    },
    "papermill": {
     "duration": 0.054488,
     "end_time": "2022-09-03T05:09:04.244883",
     "exception": false,
     "start_time": "2022-09-03T05:09:04.190395",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "submission.to_csv('submission.csv', index= None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c5dd9518",
   "metadata": {
    "papermill": {
     "duration": 0.039117,
     "end_time": "2022-09-03T05:09:04.324236",
     "exception": false,
     "start_time": "2022-09-03T05:09:04.285119",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.10.1 64-bit",
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   "name": "python3"
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   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
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  "papermill": {
   "default_parameters": {},
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