Titanic Survival Predictions.ipynb

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     "text": [
      "/kaggle/input/titanic/train.csv\n",
      "/kaggle/input/titanic/test.csv\n",
      "/kaggle/input/titanic/gender_submission.csv\n"
     ]
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   "source": [
    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load\n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "\n",
    "# Input data files are available in the read-only \"../input/\" directory\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
    "\n",
    "import os\n",
    "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))\n",
    "\n",
    "# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
    "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
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    "**Survived: 0 = Did not survive, 1 = Survived**\n",
    "\n",
    "**Pclass: Ticket class where 1 = First class, 2 = Second class, 3 = Third class. This   can also be seen as a proxy for socio-economic status.**\n",
    "\n",
    "**Sex: Male or female**\n",
    "\n",
    "**Age: Age in years, fractional if less than 1**\n",
    "\n",
    "**SibSp: Number of siblings or spouses aboard the titanic**\n",
    "\n",
    "**Parch: Number of parents or children aboard the titanic**\n",
    "\n",
    "**Ticket: Passenger ticket number**\n",
    "\n",
    "**Fare: Passenger fare**\n",
    "\n",
    "**Cabin: Cabin number**\n",
    "\n",
    "**Embarked: Point of embarkation where C = Cherbourg, Q = Queenstown, S = Southampton**"
   ]
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   "source": [
    " **Importing main Libraries...**"
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   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "train = pd.read_csv(\"../input/titanic/train.csv\")"
   ]
  },
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       "   PassengerId  Survived  Pclass  \\\n",
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       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
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       "   Parch            Ticket     Fare Cabin Embarked  \n",
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       "1      0          PC 17599  71.2833   C85        C  \n",
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    "train.head()"
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   "source": [
    "test=pd.read_csv(\"../input/titanic/test.csv\")"
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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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    "test.head()"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No. of rows and columns in Training Set:  (891, 12)\n",
      "No. of rows and columns in Test Set:  (418, 11)\n"
     ]
    }
   ],
   "source": [
    "print(\"No. of rows and columns in Training Set: \", train.shape)\n",
    "print(\"No. of rows and columns in Test Set: \", test.shape)"
   ]
  },
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  891 non-null    int64  \n",
      " 1   Survived     891 non-null    int64  \n",
      " 2   Pclass       891 non-null    int64  \n",
      " 3   Name         891 non-null    object \n",
      " 4   Sex          891 non-null    object \n",
      " 5   Age          714 non-null    float64\n",
      " 6   SibSp        891 non-null    int64  \n",
      " 7   Parch        891 non-null    int64  \n",
      " 8   Ticket       891 non-null    object \n",
      " 9   Fare         891 non-null    float64\n",
      " 10  Cabin        204 non-null    object \n",
      " 11  Embarked     889 non-null    object \n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.7+ KB\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 418 entries, 0 to 417\n",
      "Data columns (total 11 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  418 non-null    int64  \n",
      " 1   Pclass       418 non-null    int64  \n",
      " 2   Name         418 non-null    object \n",
      " 3   Sex          418 non-null    object \n",
      " 4   Age          332 non-null    float64\n",
      " 5   SibSp        418 non-null    int64  \n",
      " 6   Parch        418 non-null    int64  \n",
      " 7   Ticket       418 non-null    object \n",
      " 8   Fare         417 non-null    float64\n",
      " 9   Cabin        91 non-null     object \n",
      " 10  Embarked     418 non-null    object \n",
      "dtypes: float64(2), int64(4), object(5)\n",
      "memory usage: 36.0+ KB\n"
     ]
    }
   ],
   "source": [
    "train.info()\n",
    "test.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "27b981cb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-29T15:16:33.673373Z",
     "iopub.status.busy": "2022-01-29T15:16:33.672423Z",
     "iopub.status.idle": "2022-01-29T15:16:33.681736Z",
     "shell.execute_reply": "2022-01-29T15:16:33.682338Z",
     "shell.execute_reply.started": "2022-01-29T15:12:03.564233Z"
    },
    "papermill": {
     "duration": 0.039084,
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     "exception": false,
     "start_time": "2022-01-29T15:16:33.643420",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId    891\n",
       "Survived         2\n",
       "Pclass           3\n",
       "Name           891\n",
       "Sex              2\n",
       "Age             88\n",
       "SibSp            7\n",
       "Parch            7\n",
       "Ticket         681\n",
       "Fare           248\n",
       "Cabin          147\n",
       "Embarked         3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.nunique()\n",
    "#Count number of distinct elements."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7bf96a46",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-29T15:16:33.737213Z",
     "iopub.status.busy": "2022-01-29T15:16:33.736159Z",
     "iopub.status.idle": "2022-01-29T15:16:33.744834Z",
     "shell.execute_reply": "2022-01-29T15:16:33.745345Z",
     "shell.execute_reply.started": "2022-01-29T15:12:07.008618Z"
    },
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     "duration": 0.037971,
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     "exception": false,
     "start_time": "2022-01-29T15:16:33.707866",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId    418\n",
       "Pclass           3\n",
       "Name           418\n",
       "Sex              2\n",
       "Age             79\n",
       "SibSp            7\n",
       "Parch            8\n",
       "Ticket         363\n",
       "Fare           169\n",
       "Cabin           76\n",
       "Embarked         3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ce9c6282",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-29T15:16:33.800778Z",
     "iopub.status.busy": "2022-01-29T15:16:33.799769Z",
     "iopub.status.idle": "2022-01-29T15:16:33.806144Z",
     "shell.execute_reply": "2022-01-29T15:16:33.806590Z",
     "shell.execute_reply.started": "2022-01-29T15:12:10.124234Z"
    },
    "papermill": {
     "duration": 0.035693,
     "end_time": "2022-01-29T15:16:33.806768",
     "exception": false,
     "start_time": "2022-01-29T15:16:33.771075",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "train['Sex'] = train['Sex'].astype(str)\n",
    "train['Embarked'] = train['Embarked'].astype(str)\n",
    "test['Sex'] = test['Sex'].astype(str)\n",
    "test['Embarked'] = test['Embarked'].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "bb0da950",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-29T15:16:33.862966Z",
     "iopub.status.busy": "2022-01-29T15:16:33.861899Z",
     "iopub.status.idle": "2022-01-29T15:16:34.006698Z",
     "shell.execute_reply": "2022-01-29T15:16:34.006004Z",
     "shell.execute_reply.started": "2022-01-29T15:12:13.066274Z"
    },
    "papermill": {
     "duration": 0.174235,
     "end_time": "2022-01-29T15:16:34.006838",
     "exception": false,
     "start_time": "2022-01-29T15:16:33.832603",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "#Label encoding converts the data in machine-readable form, but it assigns a unique number(starting from 0) to each class of data.\n",
    "#I used for coverting male and female to 1 and 0 respectively and embarking port from C, Q, S to 0,1,2 resp\n",
    "lblenc = LabelEncoder()\n",
    "\n",
    "# fit_transform(y) fits the label encoder and returns the encoded labels\n",
    "\n",
    "train['Embarked'] = lblenc.fit_transform(train['Embarked'])\n",
    "train['Sex'] = lblenc.fit_transform(train['Sex'])\n",
    "test['Embarked'] = lblenc.fit_transform(test['Embarked'])\n",
    "test['Sex'] = lblenc.fit_transform(test['Sex'])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "15784f81",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-29T15:16:34.063908Z",
     "iopub.status.busy": "2022-01-29T15:16:34.062930Z",
     "iopub.status.idle": "2022-01-29T15:16:34.077289Z",
     "shell.execute_reply": "2022-01-29T15:16:34.077807Z",
     "shell.execute_reply.started": "2022-01-29T15:12:19.826640Z"
    },
    "papermill": {
     "duration": 0.045263,
     "end_time": "2022-01-29T15:16:34.077976",
     "exception": false,
     "start_time": "2022-01-29T15:16:34.032713",
     "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",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>1</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>1</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>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name  Sex   Age  SibSp  Parch  \\\n",
       "0                            Braund, Mr. Owen Harris    1  22.0      1      0   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...    0  38.0      1      0   \n",
       "2                             Heikkinen, Miss. Laina    0  26.0      0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)    0  35.0      1      0   \n",
       "4                           Allen, Mr. William Henry    1  35.0      0      0   \n",
       "\n",
       "             Ticket     Fare Cabin  Embarked  \n",
       "0         A/5 21171   7.2500   NaN         2  \n",
       "1          PC 17599  71.2833   C85         0  \n",
       "2  STON/O2. 3101282   7.9250   NaN         2  \n",
       "3            113803  53.1000  C123         2  \n",
       "4            373450   8.0500   NaN         2  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e7e0a92c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-29T15:16:34.133768Z",
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     "shell.execute_reply.started": "2022-01-29T15:12:23.010879Z"
    },
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     "exception": false,
     "start_time": "2022-01-29T15:16:34.103704",
     "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",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>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>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>0.647587</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "      <td>1.538721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>0.477990</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "      <td>0.794231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Sex         Age  \\\n",
       "count   891.000000  891.000000  891.000000  891.000000  714.000000   \n",
       "mean    446.000000    0.383838    2.308642    0.647587   29.699118   \n",
       "std     257.353842    0.486592    0.836071    0.477990   14.526497   \n",
       "min       1.000000    0.000000    1.000000    0.000000    0.420000   \n",
       "25%     223.500000    0.000000    2.000000    0.000000   20.125000   \n",
       "50%     446.000000    0.000000    3.000000    1.000000   28.000000   \n",
       "75%     668.500000    1.000000    3.000000    1.000000   38.000000   \n",
       "max     891.000000    1.000000    3.000000    1.000000   80.000000   \n",
       "\n",
       "            SibSp       Parch        Fare    Embarked  \n",
       "count  891.000000  891.000000  891.000000  891.000000  \n",
       "mean     0.523008    0.381594   32.204208    1.538721  \n",
       "std      1.102743    0.806057   49.693429    0.794231  \n",
       "min      0.000000    0.000000    0.000000    0.000000  \n",
       "25%      0.000000    0.000000    7.910400    1.000000  \n",
       "50%      0.000000    0.000000   14.454200    2.000000  \n",
       "75%      1.000000    0.000000   31.000000    2.000000  \n",
       "max      8.000000    6.000000  512.329200    3.000000  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "24b89a79",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-29T15:16:34.229734Z",
     "iopub.status.busy": "2022-01-29T15:16:34.228718Z",
     "iopub.status.idle": "2022-01-29T15:16:34.243837Z",
     "shell.execute_reply": "2022-01-29T15:16:34.244554Z",
     "shell.execute_reply.started": "2022-01-29T15:12:26.752951Z"
    },
    "papermill": {
     "duration": 0.046524,
     "end_time": "2022-01-29T15:16:34.244781",
     "exception": false,
     "start_time": "2022-01-29T15:16:34.198257",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
      "  \n",
      "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:3: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    }
   ],
   "source": [
    "#I replaced the NaN values in train.csv and test.csv with the mean of values in the respective columns\n",
    "mean_of_train = train.mean()\n",
    "mean_of_test = test.mean()\n",
    "train = train.fillna(mean_of_train)\n",
    "test = test.fillna(mean_of_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "887258b6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-29T15:16:34.305165Z",
     "iopub.status.busy": "2022-01-29T15:16:34.304506Z",
     "iopub.status.idle": "2022-01-29T15:16:34.513729Z",
     "shell.execute_reply": "2022-01-29T15:16:34.513116Z",
     "shell.execute_reply.started": "2022-01-29T15:12:36.687709Z"
    },
    "papermill": {
     "duration": 0.241711,
     "end_time": "2022-01-29T15:16:34.513864",
     "exception": false,
     "start_time": "2022-01-29T15:16:34.272153",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train[\"Survived\"].value_counts().plot(kind=\"bar\")\n",
    "# Y-Axis shoes the number of people in each category and X-Axis denotes category(\"0\" denotes \"Died\" and \"1\" denotes \"Survived\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7a0a75c0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-29T15:16:34.577171Z",
     "iopub.status.busy": "2022-01-29T15:16:34.576133Z",
     "iopub.status.idle": "2022-01-29T15:16:35.008511Z",
     "shell.execute_reply": "2022-01-29T15:16:35.007617Z",
     "shell.execute_reply.started": "2022-01-29T15:12:39.813514Z"
    },
    "papermill": {
     "duration": 0.466962,
     "end_time": "2022-01-29T15:16:35.008667",
     "exception": false,
     "start_time": "2022-01-29T15:16:34.541705",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Age_bucket', ylabel='Survived'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bin_age = [-1,10,20,30,40,50,60,70,80]\n",
    "labels_age = [0,1,2,3,4,5,6,7]\n",
    "train['Age_bucket']=pd.cut(train[\"Age\"],bins = bin_age,labels=labels_age)\n",
    "test['Age_bucket']=pd.cut(test[\"Age\"],bins = bin_age,labels=labels_age)\n",
    "#Here, I separated all the various age data into 8 bins of (0,1],(1,2],(2,3] and so on...\n",
    "sns.barplot(x='Age_bucket', y='Survived', data=train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "683a2821",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-29T15:16:35.067763Z",
     "iopub.status.busy": "2022-01-29T15:16:35.067191Z",
     "iopub.status.idle": "2022-01-29T15:16:35.310652Z",
     "shell.execute_reply": "2022-01-29T15:16:35.310176Z",
     "shell.execute_reply.started": "2022-01-29T15:12:43.192394Z"
    },
    "papermill": {
     "duration": 0.27393,
     "end_time": "2022-01-29T15:16:35.310803",
     "exception": false,
     "start_time": "2022-01-29T15:16:35.036873",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Sex', ylabel='Survived'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=\"Sex\",y=\"Survived\",data=train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c033d120",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-29T15:16:35.376495Z",
     "iopub.status.busy": "2022-01-29T15:16:35.374511Z",
     "iopub.status.idle": "2022-01-29T15:16:35.650187Z",
     "shell.execute_reply": "2022-01-29T15:16:35.649696Z",
     "shell.execute_reply.started": "2022-01-29T15:12:48.686395Z"
    },
    "papermill": {
     "duration": 0.310506,
     "end_time": "2022-01-29T15:16:35.650323",
     "exception": false,
     "start_time": "2022-01-29T15:16:35.339817",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Pclass', ylabel='Survived'>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=\"Pclass\",y=\"Survived\",data=train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "75f660c1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-29T15:16:35.712098Z",
     "iopub.status.busy": "2022-01-29T15:16:35.711484Z",
     "iopub.status.idle": "2022-01-29T15:16:36.079779Z",
     "shell.execute_reply": "2022-01-29T15:16:36.079275Z",
     "shell.execute_reply.started": "2022-01-29T15:12:51.557792Z"
    },
    "papermill": {
     "duration": 0.40023,
     "end_time": "2022-01-29T15:16:36.079918",
     "exception": false,
     "start_time": "2022-01-29T15:16:35.679688",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Parch', ylabel='Survived'>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=\"Parch\",y=\"Survived\",data=train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f6dc1074",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-29T15:16:36.148472Z",
     "iopub.status.busy": "2022-01-29T15:16:36.147800Z",
     "iopub.status.idle": "2022-01-29T15:16:36.365102Z",
     "shell.execute_reply": "2022-01-29T15:16:36.364538Z",
     "shell.execute_reply.started": "2022-01-29T15:12:55.593953Z"
    },
    "papermill": {
     "duration": 0.254817,
     "end_time": "2022-01-29T15:16:36.365251",
     "exception": false,
     "start_time": "2022-01-29T15:16:36.110434",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Fare', ylabel='Survived'>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x=\"Fare\",y=\"Survived\",data=train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "5b1c1337",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-29T15:16:36.437139Z",
     "iopub.status.busy": "2022-01-29T15:16:36.435429Z",
     "iopub.status.idle": "2022-01-29T15:16:36.840323Z",
     "shell.execute_reply": "2022-01-29T15:16:36.839784Z",
     "shell.execute_reply.started": "2022-01-29T15:12:58.036968Z"
    },
    "papermill": {
     "duration": 0.443041,
     "end_time": "2022-01-29T15:16:36.840475",
     "exception": false,
     "start_time": "2022-01-29T15:16:36.397434",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='SibSp', ylabel='Survived'>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=\"SibSp\",y=\"Survived\",data=train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "82c02270",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-29T15:16:36.910330Z",
     "iopub.status.busy": "2022-01-29T15:16:36.909674Z",
     "iopub.status.idle": "2022-01-29T15:16:36.924356Z",
     "shell.execute_reply": "2022-01-29T15:16:36.924863Z",
     "shell.execute_reply.started": "2022-01-29T15:13:00.618784Z"
    },
    "papermill": {
     "duration": 0.050291,
     "end_time": "2022-01-29T15:16:36.925034",
     "exception": false,
     "start_time": "2022-01-29T15:16:36.874743",
     "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",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Age_bucket</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>1</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>1</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>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name  Sex   Age  SibSp  Parch  \\\n",
       "0                            Braund, Mr. Owen Harris    1  22.0      1      0   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...    0  38.0      1      0   \n",
       "2                             Heikkinen, Miss. Laina    0  26.0      0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)    0  35.0      1      0   \n",
       "4                           Allen, Mr. William Henry    1  35.0      0      0   \n",
       "\n",
       "             Ticket     Fare Cabin  Embarked Age_bucket  \n",
       "0         A/5 21171   7.2500   NaN         2          2  \n",
       "1          PC 17599  71.2833   C85         0          3  \n",
       "2  STON/O2. 3101282   7.9250   NaN         2          2  \n",
       "3            113803  53.1000  C123         2          3  \n",
       "4            373450   8.0500   NaN         2          3  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e3f87398",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-29T15:16:36.998609Z",
     "iopub.status.busy": "2022-01-29T15:16:36.997634Z",
     "iopub.status.idle": "2022-01-29T15:16:36.999977Z",
     "shell.execute_reply": "2022-01-29T15:16:37.000508Z",
     "shell.execute_reply.started": "2022-01-29T15:13:03.004965Z"
    },
    "papermill": {
     "duration": 0.041834,
     "end_time": "2022-01-29T15:16:37.000678",
     "exception": false,
     "start_time": "2022-01-29T15:16:36.958844",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "passenger_test = test[\"PassengerId\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "e024f8f5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-29T15:16:37.073841Z",
     "iopub.status.busy": "2022-01-29T15:16:37.072872Z",
     "iopub.status.idle": "2022-01-29T15:16:37.079882Z",
     "shell.execute_reply": "2022-01-29T15:16:37.080391Z",
     "shell.execute_reply.started": "2022-01-29T15:13:05.484888Z"
    },
    "papermill": {
     "duration": 0.045484,
     "end_time": "2022-01-29T15:16:37.080570",
     "exception": false,
     "start_time": "2022-01-29T15:16:37.035086",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "train.drop(['Name','PassengerId','Age','Ticket','Cabin'],axis=1,inplace=True)\n",
    "test.drop(['Name','PassengerId','Age','Ticket','Cabin'],axis=1,inplace=True)\n",
    "\n",
    "# axis = 0 or ‘index’, 1 or ‘columns’"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "04ac8265",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-29T15:16:37.150810Z",
     "iopub.status.busy": "2022-01-29T15:16:37.149869Z",
     "iopub.status.idle": "2022-01-29T15:16:37.155082Z",
     "shell.execute_reply": "2022-01-29T15:16:37.155541Z",
     "shell.execute_reply.started": "2022-01-29T15:13:09.882056Z"
    },
    "papermill": {
     "duration": 0.041334,
     "end_time": "2022-01-29T15:16:37.155718",
     "exception": false,
     "start_time": "2022-01-29T15:16:37.114384",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "X=train.drop([\"Survived\"],axis=1)\n",
    "y=train[\"Survived\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "bbfcbe68",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-29T15:16:37.229467Z",
     "iopub.status.busy": "2022-01-29T15:16:37.228756Z",
     "iopub.status.idle": "2022-01-29T15:16:37.289602Z",
     "shell.execute_reply": "2022-01-29T15:16:37.290099Z",
     "shell.execute_reply.started": "2022-01-29T15:13:12.602394Z"
    },
    "papermill": {
     "duration": 0.098896,
     "end_time": "2022-01-29T15:16:37.290290",
     "exception": false,
     "start_time": "2022-01-29T15:16:37.191394",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "c282bc6b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-29T15:16:37.360231Z",
     "iopub.status.busy": "2022-01-29T15:16:37.359280Z",
     "iopub.status.idle": "2022-01-29T15:16:37.365588Z",
     "shell.execute_reply": "2022-01-29T15:16:37.364889Z",
     "shell.execute_reply.started": "2022-01-29T15:13:14.562292Z"
    },
    "papermill": {
     "duration": 0.041866,
     "end_time": "2022-01-29T15:16:37.365726",
     "exception": false,
     "start_time": "2022-01-29T15:16:37.323860",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891, 7)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "f04d2f29",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-29T15:16:37.435760Z",
     "iopub.status.busy": "2022-01-29T15:16:37.435150Z",
     "iopub.status.idle": "2022-01-29T15:16:37.437486Z",
     "shell.execute_reply": "2022-01-29T15:16:37.437911Z",
     "shell.execute_reply.started": "2022-01-29T15:13:16.337860Z"
    },
    "papermill": {
     "duration": 0.039857,
     "end_time": "2022-01-29T15:16:37.438111",
     "exception": false,
     "start_time": "2022-01-29T15:16:37.398254",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891,)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "cb8c09b9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-29T15:16:37.508776Z",
     "iopub.status.busy": "2022-01-29T15:16:37.508104Z",
     "iopub.status.idle": "2022-01-29T15:16:37.514670Z",
     "shell.execute_reply": "2022-01-29T15:16:37.515116Z",
     "shell.execute_reply.started": "2022-01-29T15:13:18.183811Z"
    },
    "papermill": {
     "duration": 0.043933,
     "end_time": "2022-01-29T15:16:37.515289",
     "exception": false,
     "start_time": "2022-01-29T15:16:37.471356",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pclass        0\n",
       "Sex           0\n",
       "SibSp         0\n",
       "Parch         0\n",
       "Fare          0\n",
       "Embarked      0\n",
       "Age_bucket    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "d023c2df",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-29T15:16:37.588679Z",
     "iopub.status.busy": "2022-01-29T15:16:37.586026Z",
     "iopub.status.idle": "2022-01-29T15:16:37.601484Z",
     "shell.execute_reply": "2022-01-29T15:16:37.601933Z",
     "shell.execute_reply.started": "2022-01-29T15:13:20.226010Z"
    },
    "papermill": {
     "duration": 0.053453,
     "end_time": "2022-01-29T15:16:37.602135",
     "exception": false,
     "start_time": "2022-01-29T15:16:37.548682",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "78.77094972067039\n"
     ]
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.metrics import accuracy_score\n",
    "#Normal distribution, also known as the Gaussian distribution, is a probability distribution that is symmetric about the mean, showing that data near the mean are more frequent in occurrence than data far from the mean.\n",
    "#In graph form, normal distribution will appear as a bell curve.\n",
    "gnb = GaussianNB()\n",
    "gnb.fit(X_train,y_train)\n",
    "prediction = gnb.predict(X_test)\n",
    "print(accuracy_score(y_test,prediction)*100)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "b732f784",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-29T15:16:37.673757Z",
     "iopub.status.busy": "2022-01-29T15:16:37.671068Z",
     "iopub.status.idle": "2022-01-29T15:16:37.793386Z",
     "shell.execute_reply": "2022-01-29T15:16:37.792772Z",
     "shell.execute_reply.started": "2022-01-29T15:13:22.520506Z"
    },
    "papermill": {
     "duration": 0.158291,
     "end_time": "2022-01-29T15:16:37.793545",
     "exception": false,
     "start_time": "2022-01-29T15:16:37.635254",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "80.44692737430168\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score\n",
    "lr = LogisticRegression()\n",
    "lr.fit(X_train,y_train)\n",
    "prediction = lr.predict(X_test)\n",
    "print(accuracy_score(y_test,prediction)*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "6b314121",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-29T15:16:37.867851Z",
     "iopub.status.busy": "2022-01-29T15:16:37.867188Z",
     "iopub.status.idle": "2022-01-29T15:16:37.956101Z",
     "shell.execute_reply": "2022-01-29T15:16:37.955560Z",
     "shell.execute_reply.started": "2022-01-29T15:13:26.810350Z"
    },
    "papermill": {
     "duration": 0.127754,
     "end_time": "2022-01-29T15:16:37.956246",
     "exception": false,
     "start_time": "2022-01-29T15:16:37.828492",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "73.74301675977654\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "knn= KNeighborsClassifier()\n",
    "knn.fit(X_train,y_train)\n",
    "prediction =knn.predict(X_test)\n",
    "print(accuracy_score(y_test,prediction)*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "836d4e36",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-29T15:16:38.028868Z",
     "iopub.status.busy": "2022-01-29T15:16:38.028272Z",
     "iopub.status.idle": "2022-01-29T15:16:38.388708Z",
     "shell.execute_reply": "2022-01-29T15:16:38.389243Z",
     "shell.execute_reply.started": "2022-01-29T15:13:29.568585Z"
    },
    "papermill": {
     "duration": 0.398604,
     "end_time": "2022-01-29T15:16:38.389427",
     "exception": false,
     "start_time": "2022-01-29T15:16:37.990823",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "82.68156424581005\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "rf = RandomForestClassifier()\n",
    "rf.fit(X_train,y_train)\n",
    "prediction = rf.predict(X_test)\n",
    "print(accuracy_score(y_test,prediction)*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "2b15e6d1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-29T15:16:38.462637Z",
     "iopub.status.busy": "2022-01-29T15:16:38.461915Z",
     "iopub.status.idle": "2022-01-29T15:16:38.483463Z",
     "shell.execute_reply": "2022-01-29T15:16:38.482876Z",
     "shell.execute_reply.started": "2022-01-29T15:13:40.875194Z"
    },
    "papermill": {
     "duration": 0.059502,
     "end_time": "2022-01-29T15:16:38.483626",
     "exception": false,
     "start_time": "2022-01-29T15:16:38.424124",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "prediction_f = rf.predict(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "e13ffa1d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-29T15:16:38.563452Z",
     "iopub.status.busy": "2022-01-29T15:16:38.562668Z",
     "iopub.status.idle": "2022-01-29T15:16:38.566636Z",
     "shell.execute_reply": "2022-01-29T15:16:38.566067Z",
     "shell.execute_reply.started": "2022-01-29T15:13:44.705240Z"
    },
    "papermill": {
     "duration": 0.048859,
     "end_time": "2022-01-29T15:16:38.566765",
     "exception": false,
     "start_time": "2022-01-29T15:16:38.517906",
     "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>Survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>892</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>893</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>894</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>895</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>896</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1305</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1306</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1307</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1308</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1309</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>418 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Survived\n",
       "PassengerId          \n",
       "892                 0\n",
       "893                 0\n",
       "894                 0\n",
       "895                 0\n",
       "896                 0\n",
       "...               ...\n",
       "1305                0\n",
       "1306                1\n",
       "1307                0\n",
       "1308                0\n",
       "1309                0\n",
       "\n",
       "[418 rows x 1 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_df = pd.DataFrame({\"Survived\":prediction_f,\"PassengerId\":passenger_test})\n",
    "result_df.set_index('PassengerId')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "1de45799",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-29T15:16:38.667520Z",
     "iopub.status.busy": "2022-01-29T15:16:38.650514Z",
     "iopub.status.idle": "2022-01-29T15:16:38.808685Z",
     "shell.execute_reply": "2022-01-29T15:16:38.807804Z",
     "shell.execute_reply.started": "2022-01-29T15:13:47.608558Z"
    },
    "papermill": {
     "duration": 0.207538,
     "end_time": "2022-01-29T15:16:38.808829",
     "exception": false,
     "start_time": "2022-01-29T15:16:38.601291",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "result_df[\"Survived\"].value_counts().plot(kind ='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "7ddfd919",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-29T15:16:38.883068Z",
     "iopub.status.busy": "2022-01-29T15:16:38.882414Z",
     "iopub.status.idle": "2022-01-29T15:16:38.889820Z",
     "shell.execute_reply": "2022-01-29T15:16:38.890490Z",
     "shell.execute_reply.started": "2022-01-29T15:13:50.353906Z"
    },
    "papermill": {
     "duration": 0.046157,
     "end_time": "2022-01-29T15:16:38.890710",
     "exception": false,
     "start_time": "2022-01-29T15:16:38.844553",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Submission Successful!\n"
     ]
    }
   ],
   "source": [
    "result_df.to_csv(\"my_submissions\",index=False)\n",
    "print(\"Submission Successful!\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.12"
  },
  "papermill": {
   "default_parameters": {},
   "duration": 16.512805,
   "end_time": "2022-01-29T15:16:39.639006",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2022-01-29T15:16:23.126201",
   "version": "2.3.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}