Titanic project.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "49d7776e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5c1cf64f",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"titanic-train.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "b484c5d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891, 12)"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(data)\n",
    "df1 = df.copy()\n",
    "df1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "31a853ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1.head()\n",
    "###EMBARKED-\n",
    "#C = Cherbourg, Q = Queenstown, S = Southampton\n",
    "df1.drop('PassengerId',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "716f054d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></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>Embarked</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>S</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>C</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>S</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>S</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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>S</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                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 Embarked     Fare Cabin  Pclass  Survived  \n",
       "0      0         A/5 21171        S   7.2500   NaN       3         0  \n",
       "1      0          PC 17599        C  71.2833   C85       1         1  \n",
       "2      0  STON/O2. 3101282        S   7.9250   NaN       3         1  \n",
       "3      0            113803        S  53.1000  C123       1         1  \n",
       "4      0            373450        S   8.0500   NaN       3         0  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Swapping the survived column with the last column\n",
    "col_list = list(df1.columns)\n",
    "survived = col_list.pop(10)\n",
    "col_list.insert(9,survived)\n",
    "df1 = df1[col_list]\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "49c40b6c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name          0\n",
       "Sex           0\n",
       "Age         177\n",
       "SibSp         0\n",
       "Parch         0\n",
       "Ticket        0\n",
       "Embarked      2\n",
       "Fare          0\n",
       "Cabin       687\n",
       "Pclass        0\n",
       "Survived      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Looking for the missing values\n",
    "missing = df1.isna().sum()\n",
    "missing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "a6220871",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891, 11)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# df1.describe()\n",
    "df1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "9946b2d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></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>Embarked</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>S</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>C</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>S</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>S</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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>S</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                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 Embarked     Fare  Pclass  Survived  \n",
       "0      0         A/5 21171        S   7.2500       3         0  \n",
       "1      0          PC 17599        C  71.2833       1         1  \n",
       "2      0  STON/O2. 3101282        S   7.9250       3         1  \n",
       "3      0            113803        S  53.1000       1         1  \n",
       "4      0            373450        S   8.0500       3         0  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Dropping the cabin columns because of massive amounts of null values\n",
    "df1.drop(\"Cabin\",axis=1,inplace=True)\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "1615b4e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>female</th>\n",
       "      <td>27.915709</td>\n",
       "      <td>0.694268</td>\n",
       "      <td>0.649682</td>\n",
       "      <td>44.479818</td>\n",
       "      <td>2.159236</td>\n",
       "      <td>0.742038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>30.726645</td>\n",
       "      <td>0.429809</td>\n",
       "      <td>0.235702</td>\n",
       "      <td>25.523893</td>\n",
       "      <td>2.389948</td>\n",
       "      <td>0.188908</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Age     SibSp     Parch       Fare    Pclass  Survived\n",
       "Sex                                                                 \n",
       "female  27.915709  0.694268  0.649682  44.479818  2.159236  0.742038\n",
       "male    30.726645  0.429809  0.235702  25.523893  2.389948  0.188908"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Out of the peaple who survived,how many of them were male and female??\n",
    "gender = df1.groupby(\"Sex\").mean()\n",
    "gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "4cc2d4b2",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "### PLotting the results of survival on the basis of gender\n",
    "sex = [\"female\",\"male\"]\n",
    "survived = df1.groupby(\"Sex\").sum()[\"Survived\"]\n",
    "plt.bar(sex,survived,color=\"green\")\n",
    "plt.xlabel(\"Gender\")\n",
    "plt.ylabel(\"Number of peaple survived\")\n",
    "plt.title(\"Survival on basis of age\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "fe336edd",
   "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>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>S</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>C</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>S</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>S</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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>S</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                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 Embarked     Fare  Pclass  Survived  \n",
       "0      0         A/5 21171        S   7.2500       3         0  \n",
       "1      0          PC 17599        C  71.2833       1         1  \n",
       "2      0  STON/O2. 3101282        S   7.9250       3         1  \n",
       "3      0            113803        S  53.1000       1         1  \n",
       "4      0            373450        S   8.0500       3         0  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "834c2fe2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></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>Embarked</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Survived</th>\n",
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       "  </thead>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Moran, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330877</td>\n",
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       "      <td>8.4583</td>\n",
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       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Williams, Mr. Charles Eugene</td>\n",
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       "      <td>0</td>\n",
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       "      <td>13.0000</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Masselmani, Mrs. Fatima</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2649</td>\n",
       "      <td>C</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Emir, Mr. Farred Chehab</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2631</td>\n",
       "      <td>C</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>O'Dwyer, Miss. Ellen \"Nellie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330959</td>\n",
       "      <td>Q</td>\n",
       "      <td>7.8792</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>859</th>\n",
       "      <td>Razi, Mr. Raihed</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2629</td>\n",
       "      <td>C</td>\n",
       "      <td>7.2292</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>863</th>\n",
       "      <td>Sage, Miss. Dorothy Edith \"Dolly\"</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>CA. 2343</td>\n",
       "      <td>S</td>\n",
       "      <td>69.5500</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868</th>\n",
       "      <td>van Melkebeke, Mr. Philemon</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>345777</td>\n",
       "      <td>S</td>\n",
       "      <td>9.5000</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>878</th>\n",
       "      <td>Laleff, Mr. Kristo</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349217</td>\n",
       "      <td>S</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>S</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
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       "</table>\n",
       "<p>177 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         Name     Sex  Age  SibSp  Parch  \\\n",
       "5                            Moran, Mr. James    male  NaN      0      0   \n",
       "17               Williams, Mr. Charles Eugene    male  NaN      0      0   \n",
       "19                    Masselmani, Mrs. Fatima  female  NaN      0      0   \n",
       "26                    Emir, Mr. Farred Chehab    male  NaN      0      0   \n",
       "28              O'Dwyer, Miss. Ellen \"Nellie\"  female  NaN      0      0   \n",
       "..                                        ...     ...  ...    ...    ...   \n",
       "859                          Razi, Mr. Raihed    male  NaN      0      0   \n",
       "863         Sage, Miss. Dorothy Edith \"Dolly\"  female  NaN      8      2   \n",
       "868               van Melkebeke, Mr. Philemon    male  NaN      0      0   \n",
       "878                        Laleff, Mr. Kristo    male  NaN      0      0   \n",
       "888  Johnston, Miss. Catherine Helen \"Carrie\"  female  NaN      1      2   \n",
       "\n",
       "         Ticket Embarked     Fare  Pclass  Survived  \n",
       "5        330877        Q   8.4583       3         0  \n",
       "17       244373        S  13.0000       2         1  \n",
       "19         2649        C   7.2250       3         1  \n",
       "26         2631        C   7.2250       3         0  \n",
       "28       330959        Q   7.8792       3         1  \n",
       "..          ...      ...      ...     ...       ...  \n",
       "859        2629        C   7.2292       3         0  \n",
       "863    CA. 2343        S  69.5500       3         0  \n",
       "868      345777        S   9.5000       3         0  \n",
       "878      349217        S   7.8958       3         0  \n",
       "888  W./C. 6607        S  23.4500       3         0  \n",
       "\n",
       "[177 rows x 10 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Dealing with the null values in Age\n",
    "age = df1[df1.Age.isna() == True]\n",
    "age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "87fef139",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Replacing the null values in age with the mean values\n",
    "df1[\"Age\"] = df1.Age.fillna(df1.Age.mean())\n",
    "age_null = df1.Age.isna().sum()\n",
    "age_null"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "0bfe4d3e",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Dealing with age zero\n",
    "df1.loc[df1[\"Age\"] == 0,\"Age\"] = df1.Age.mean().astype(\"int32\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "95d0bd35",
   "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>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13</td>\n",
       "      <td>11</td>\n",
       "      <td>210.0417</td>\n",
       "      <td>19</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21</td>\n",
       "      <td>13</td>\n",
       "      <td>375.3625</td>\n",
       "      <td>26</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>154.6917</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16</td>\n",
       "      <td>14</td>\n",
       "      <td>295.4333</td>\n",
       "      <td>26</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>90.8708</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10.5000</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>89.2500</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>84.1584</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.7750</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>70 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     SibSp  Parch      Fare  Pclass  Survived\n",
       "Age                                          \n",
       "1       13     11  210.0417      19         5\n",
       "2       21     13  375.3625      26         3\n",
       "3       11      8  154.6917      15         5\n",
       "4       16     14  295.4333      26         7\n",
       "5        7      5   90.8708      11         4\n",
       "..     ...    ...       ...     ...       ...\n",
       "66       0      0   10.5000       2         0\n",
       "70       1      1   89.2500       6         0\n",
       "71       0      0   84.1584       2         0\n",
       "74       0      0    7.7750       3         0\n",
       "80       0      0   30.0000       1         1\n",
       "\n",
       "[70 rows x 5 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Visualising the chances of survial on the basis of age\n",
    "df1[\"Age\"] = df1[\"Age\"].astype(\"int32\")\n",
    "# df1.head()\n",
    "Age = df1.groupby(\"Age\").sum()\n",
    "Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "a8492b0d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Visualisation \n",
    "ages = np.array(df1[\"Age\"].unique())\n",
    "ages = np.sort(ages)\n",
    "survival = df1.groupby(\"Age\").sum()[\"Survived\"]\n",
    "plt.figure(figsize=(8,6))\n",
    "plt.xticks(np.arange(0,90,5))\n",
    "plt.bar(ages,survival)\n",
    "plt.xlabel(\"Age\")\n",
    "plt.ylabel(\"Number of peaple survived\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "0d643947",
   "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>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>S</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>C</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>S</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>S</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>S</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                Name     Sex  Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male   22      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female   38      1   \n",
       "2                             Heikkinen, Miss. Laina  female   26      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female   35      1   \n",
       "4                           Allen, Mr. William Henry    male   35      0   \n",
       "\n",
       "   Parch            Ticket Embarked     Fare  Pclass  Survived  \n",
       "0      0         A/5 21171        S   7.2500       3         0  \n",
       "1      0          PC 17599        C  71.2833       1         1  \n",
       "2      0  STON/O2. 3101282        S   7.9250       3         1  \n",
       "3      0            113803        S  53.1000       1         1  \n",
       "4      0            373450        S   8.0500       3         0  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "46d688b1",
   "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>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>37.078704</td>\n",
       "      <td>0.416667</td>\n",
       "      <td>0.356481</td>\n",
       "      <td>84.154687</td>\n",
       "      <td>0.629630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30.277174</td>\n",
       "      <td>0.402174</td>\n",
       "      <td>0.380435</td>\n",
       "      <td>20.662183</td>\n",
       "      <td>0.472826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>26.368635</td>\n",
       "      <td>0.615071</td>\n",
       "      <td>0.393075</td>\n",
       "      <td>13.675550</td>\n",
       "      <td>0.242363</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Age     SibSp     Parch       Fare  Survived\n",
       "Pclass                                                    \n",
       "1       37.078704  0.416667  0.356481  84.154687  0.629630\n",
       "2       30.277174  0.402174  0.380435  20.662183  0.472826\n",
       "3       26.368635  0.615071  0.393075  13.675550  0.242363"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Which ticket class should I choose so that my chances of survival can increase\n",
    "# df1.Pclass.unique()\n",
    "tclass = df1.groupby(\"Pclass\").mean()\n",
    "tclass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "76598d9a",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "### Visualising the results of class vs survival chances\n",
    "# cls = np.array(df1.Pclass.unique())\n",
    "# cls = np.sort(cls)\n",
    "cls = [\"1\",\"2\",\"3\"]\n",
    "survived = df1.groupby(\"Pclass\").sum()[\"Survived\"]\n",
    "plt.bar(cls,survived)\n",
    "plt.xlabel(\"Ticket class\")\n",
    "plt.ylabel(\"Number of people survived\")\n",
    "plt.title(\"Ticket class v/s survival\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "1b5bed9b",
   "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>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>S</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>C</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>S</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>S</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>S</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                Name     Sex  Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male   22      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female   38      1   \n",
       "2                             Heikkinen, Miss. Laina  female   26      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female   35      1   \n",
       "4                           Allen, Mr. William Henry    male   35      0   \n",
       "\n",
       "   Parch            Ticket Embarked     Fare  Pclass  Survived  \n",
       "0      0         A/5 21171        S   7.2500       3         0  \n",
       "1      0          PC 17599        C  71.2833       1         1  \n",
       "2      0  STON/O2. 3101282        S   7.9250       3         1  \n",
       "3      0            113803        S  53.1000       1         1  \n",
       "4      0            373450        S   8.0500       3         0  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "0bb06960",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "### Checking the corelation of fair v/s ticket class\n",
    "cls = np.array(df1.Pclass.unique())\n",
    "cls = np.sort(cls)\n",
    "fare = df1.groupby(\"Pclass\").mean()[\"Fare\"]\n",
    "plt.plot(cls,fare,\"r.-\")\n",
    "plt.xlabel(\"Ticket class\")\n",
    "plt.ylabel(\"fare\")\n",
    "plt.title(\"Ticket class v/s survival\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "067fa45c",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "### Does the port embarked from matter in chances of survival??\n",
    "port = df1.groupby(\"Embarked\").sum()[\"Survived\"]\n",
    "cls = [\"Cherbourg\",\"Queenstown\",\"Southampton\"]\n",
    "plt.bar(cls,port)\n",
    "plt.xticks(cls,rotation=\"vertical\")\n",
    "plt.xlabel(\"Port Embarked\")\n",
    "plt.ylabel(\"Peaple Survived\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "5b81032c",
   "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>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>S</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>C</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>S</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>S</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>S</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                Name     Sex  Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male   22      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female   38      1   \n",
       "2                             Heikkinen, Miss. Laina  female   26      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female   35      1   \n",
       "4                           Allen, Mr. William Henry    male   35      0   \n",
       "\n",
       "   Parch            Ticket Embarked     Fare  Pclass  Survived  \n",
       "0      0         A/5 21171        S   7.2500       3         0  \n",
       "1      0          PC 17599        C  71.2833       1         1  \n",
       "2      0  STON/O2. 3101282        S   7.9250       3         1  \n",
       "3      0            113803        S  53.1000       1         1  \n",
       "4      0            373450        S   8.0500       3         0  "
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "83803998",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 3, 4, 2, 5, 8], dtype=int64)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.SibSp.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "7abfdd2a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEGCAYAAABo25JHAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8rg+JYAAAACXBIWXMAAAsTAAALEwEAmpwYAAAXMUlEQVR4nO3debRlZX3m8e9DIaIMmkipyGCVNJoujXGo4BznNDZaGMEOtBO2ghpLQaNt2bpoxXQvlGhoE9oWkVbjgIjJsiKFxlYG41piFYiMoiVWpIhDiSJqDFLy6z/2vnC83GFXcfc5t2p/P2uddffwnr1/d1Oc5+7hvG+qCknScO0y6QIkSZNlEEjSwBkEkjRwBoEkDZxBIEkDt+ukC9hW++yzTy1btmzSZUjSDuWSSy75cVUtnWndDhcEy5YtY8OGDZMuQ5J2KEn+ebZ1XhqSpIEzCCRp4AwCSRo4g0CSBs4gkKSBMwgkaeAMAkkaOINAkgbOIJCkgdvhvll8Vyxbc+5Y97fp5MPGuj9J2h6eEUjSwBkEkjRwBoEkDZxBIEkDZxBI0sAZBJI0cAaBJA2cQSBJA2cQSNLA9RoESQ5Ncm2SjUnWzLD+mCRbklzWvl7eZz2SpDvrrYuJJEuA04BnApuB9UnWVtXV05p+sqpW91WHJGlufZ4RHAJsrKrrqurXwFnA4T3uT5K0HfoMgv2A60fmN7fLpjsiyeVJzklyQI/1SJJmMOmbxf8ALKuqhwNfAD48U6MkxyXZkGTDli1bxlqgJO3s+gyCG4DRv/D3b5fdrqpurKpb2tkzgEfPtKGqOr2qVlbVyqVLl/ZSrCQNVZ9BsB44OMnyJLsBRwFrRxsk2XdkdhVwTY/1SJJm0NtTQ1W1Nclq4PPAEuDMqroqyUnAhqpaC7w2ySpgK/AT4Ji+6pEkzazXEcqqah2wbtqyE0em3wy8uc8aJElzm/TNYknShBkEkjRwBoEkDZxBIEkDZxBI0sAZBJI0cAaBJA2cQSBJA9frF8qkbbFszblj3d+mkw8b6/6kxcozAkkaOINAkgbOIJCkgTMIJGngDAJJGjiDQJIGziCQpIEzCCRp4AwCSRo4g0CSBs4gkKSBMwgkaeAMAkkaOINAkgbOIJCkgTMIJGngDAJJGjiDQJIGziCQpIEzCCRp4AwCSRq4XoMgyaFJrk2yMcmaOdodkaSSrOyzHknSnfUWBEmWAKcBzwJWAEcnWTFDu72A44GL+6pFkjS7Ps8IDgE2VtV1VfVr4Czg8BnavQN4J/BvPdYiSZrFrEGQ5OdJbp7h9fMkN3fY9n7A9SPzm9tlo/t4FHBAVZ0714aSHJdkQ5INW7Zs6bBrSVJXu862oqr26nPHSXYB3gMcM1/bqjodOB1g5cqV1WddkjQ0swbBdEnuC+w+NV9V35vnLTcAB4zM798um7IX8DDggiQA9wfWJllVVRu61iVJumvmvUeQZFWSbwPfBS4ENgHnddj2euDgJMuT7AYcBaydWllVP6uqfapqWVUtA74KGAKSNGZdbha/A3gs8K2qWg48neZDe05VtRVYDXweuAY4u6quSnJSklV3oWZJ0gLqcmno1qq6MckuSXapqvOTnNpl41W1Dlg3bdmJs7R9SpdtSpIWVpcguCnJnsBFwMeS/Aj4Zb9lSZLGpculocOBfwVeB3wO+A7wnD6LkiSNT5czglcAn6yqG4AP91yPJGnMupwR7AX8Y5IvJ1md5H59FyVJGp95g6Cq3l5VDwVeDewLXJjk//VemSRpLLalr6EfAT8AbgTu2085kqRx6/KFsj9LcgHwReA+wLFV9fC+C5MkjUeXm8UHACdU1WU91yJJmoBZgyDJ3lV1M3BKO/+7o+ur6ic91yZJGoO5zgg+DjwbuAQoICPrCnhQj3VJksZkrm6on93+XD6+ciRJ49blZvHaJEcnuec4CpIkjVeXx0ffDTwJuCbJOUmOTLL7fG+SJO0Y5n1qqKoupPkS2RLgacCxwJnA3j3XJkkag04jlCW5B01Hc38KPAr7HJKknca8QZDkbOAQmp5H/wa4sKpu67uwnd2yNeeOdX+bTj5srPuTtOOYMwjaAeYvA46uqt+MpSJJ0ljNebO4/cv/+YaAJO28ujw19MUkRyTJ/E0lSTuaLkHwCuBTwC1Jbk7y8yQ391yXJGlMujw+utc4CpEkTUaXp4b+aKblVXXRwpcjSRq3Lt8jeOPI9O40j5JeQvPlMknSDq7LpaHnjM4nOQA4ta+CJEnjtS1DVU7ZDPz7hS5EkjQZXe4R/DXN+APQBMcjgEt7rEmSNEZd7hFsGJneCnyiqr7SUz2SpDHrco/g9g7mkvwOzRjGkqSdRJeBaS5Isnc7ZvGlwAeS/FX/pUmSxqHLzeJ7tYPYPw/4SFU9Bnh6v2VJksalSxDsmmRf4D8Bn92WjSc5NMm1STYmWTPD+lcmuSLJZUn+KcmKbdm+JOmu6xIEJwGfBzZW1fokDwK+Pd+b2hHNTgOeBawAjp7hg/7jVfX7VfUI4F3Ae7aleEnSXdflZvGnaDqdm5q/Djiiw7YPoQmP6wCSnAUcDlw9sq3Rzuv24I7HVCVJY9JpqMrttB9w/cj8ZuAx0xsleTXwemA3Zum2IslxwHEABx544IIXKklDtj3fLF5QVXVaVR0EvAl46yxtTq+qlVW1cunSpeMtUJJ2crMGQZLj259P2M5t38Bvf+dg/3bZbM4Cnrud+5Ikbae5zghe2v786+3c9nrg4CTLk+wGHAWsHW2Q5OCR2cPocBNakrSw5rpHcE2SbwMPSHL5yPIAVVUPn2vDVbU1yWqaJ46WAGdW1VVJTgI2VNVaYHWSZwC3Aj8FXnJXfhlJ0rabNQiq6ugk96f5IF+1PRuvqnXAumnLThyZPn57titJWjhzPjVUVT8A/qC9tPPgdvG1VXVr75VJksaiSzfUTwY+AmyiuSx0QJKXOFSlJO0cunyP4D3AH1fVtQBJHgx8Anh0n4VJksajy/cI7jYVAgBV9S3gbv2VJEkap04D0yQ5A/hoO/8CfnuwGknSDqxLELwKeDXw2nb+y8D/7q0iSdJYdel07haa+wT2DCpJO6GJ9zUkSZosg0CSBm6bgiDJLkn27qsYSdL4dRm8/uPt4PV7AFcCVyd5Y/+lSZLGocsZwYp2JLHnAucBy4EX9VmUJGl8On2hLMndaIJgbdvPkENKStJOosv3CN5P08/QN4CLkjwQuHnOd0g7uGVrzh3bvjadfNjY9iXNpMv3CN4LvHdk0T8neWp/JUmSxqnLzeL7JflgkvPa+RU4gIwk7TS63CP4EM3gNA9o578FnNBTPZKkMesSBPtU1dnAbdAMQQn8pteqJElj0yUIfpnkPrRPCiV5LPCzXquSJI1Nl6eGXg+sBQ5K8hVgKXBkr1VJksamy1NDl7bDVT6EZqhKxyyWpJ1Il6eGXg3sWVVXVdWVwJ5J/qz/0iRJ49DlHsGxVXXT1ExV/RQ4treKJElj1SUIliTJ1EySJcBu/ZUkSRqnLjeLPwd8Msn72/lXtMskSTuBLkHwJpoP/1e1818AzuitIknSWHV5aug24H3tS5K0k5k3CJI8AXgb8MC2fYCqqgf1W5okaRy6XBr6IPA64BLsWkKSdjpdnhr6WVWdV1U/qqobp15dNp7k0CTXJtmYZM0M61+f5Ooklyf5YjvWgSRpjLoEwflJTknyuCSPmnrN96b2MdPTgGcBK4Cj2y6sR30dWFlVDwfOAd61jfVLku6iLpeGHtP+XDmyrICnzfO+Q4CNVXUdQJKzgMOBq2/fSNX5I+2/CrywQz2SpAXU5amh7R2NbD/g+pH5zdwRKjN5GXDedu5LkrSdupwRkOQw4KHA7lPLquqkhSoiyQtpzjiePMv644DjAA488MCF2q0kiW6dzv0f4E+B19A8Ovp8mkdJ53MDcMDI/P7tsunbfwbwFmBVVd0y04aq6vSqWllVK5cuXdph15KkrrrcLH58Vb0Y+GlVvR14HPDgDu9bDxycZHmS3YCjaMY1uF2SRwLvpwmBH21b6ZKkhdAlCH7V/vzXJA8AbgX2ne9N7ZCWq2nGO74GOLuqrkpyUpJVbbNTgD2BTyW5LMnaWTYnSepJl3sEn01yb5oP7Utpnhjq1NdQVa0D1k1bduLI9DM6VypJ6kWXp4be0U5+Oslngd2ryjGLJWkn0fWpoccDy6baJ6GqPtJjXZKkMenS6dzfAgcBl3FHX0MFGASStBPockawElhRVdV3MZKk8evy1NCVwP37LkSSNBmznhEk+QeaS0B7AVcn+Rpw+xe+qmrVbO+VJO045ro09Jdjq0KSNDFzBcENwP2q6iujC5M8Efh+r1VpbJatOXes+9t08mFj3Z+k+c11j+BU4OYZlv+sXSdJ2gnMFQT3q6orpi9sly3rrSJJ0ljNFQT3nmPdPRa4DknShMwVBBuSHDt9YZKX0wxkL0naCcx1s/gE4O+TvIA7PvhXArsBf9JzXZKkMZk1CKrqh8DjkzwVeFi7+Nyq+tJYKpMkjUWX3kfPB86fr50kacfUpYsJSdJOzCCQpIEzCCRp4AwCSRo4g0CSBs4gkKSBMwgkaeAMAkkaOINAkgbOIJCkgTMIJGngDAJJGjiDQJIGziCQpIEzCCRp4HoNgiSHJrk2ycYka2ZY/0dJLk2yNcmRfdYiSZpZb0GQZAlwGvAsYAVwdJIV05p9DzgG+HhfdUiS5jbvCGV3wSHAxqq6DiDJWcDhwNVTDapqU7vuth7rkCTNoc9LQ/sB14/Mb26XbbMkxyXZkGTDli1bFqQ4SVJjh7hZXFWnV9XKqlq5dOnSSZcjSTuVPoPgBuCAkfn922WSpEWkzyBYDxycZHmS3YCjgLU97k+StB16C4Kq2gqsBj4PXAOcXVVXJTkpySqAJH+YZDPwfOD9Sa7qqx5J0sz6fGqIqloHrJu27MSR6fU0l4wkSROyQ9wsliT1xyCQpIEzCCRp4AwCSRo4g0CSBs4gkKSBMwgkaeAMAkkaOINAkgbOIJCkgTMIJGngDAJJGjiDQJIGziCQpIEzCCRp4AwCSRo4g0CSBs4gkKSBMwgkaeAMAkkaOINAkgbOIJCkgTMIJGngDAJJGjiDQJIGziCQpIEzCCRp4AwCSRo4g0CSBs4gkKSB6zUIkhya5NokG5OsmWH93ZN8sl1/cZJlfdYjSbqz3oIgyRLgNOBZwArg6CQrpjV7GfDTqvp3wF8B7+yrHknSzPo8IzgE2FhV11XVr4GzgMOntTkc+HA7fQ7w9CTpsSZJ0jSpqn42nBwJHFpVL2/nXwQ8pqpWj7S5sm2zuZ3/Ttvmx9O2dRxwXDv7EODaXoqe3T7Aj+dtNSwekzvzmMzM43JnkzgmD6yqpTOt2HXMhWyXqjodOH1S+0+yoapWTmr/i5HH5M48JjPzuNzZYjsmfV4augE4YGR+/3bZjG2S7ArcC7ixx5okSdP0GQTrgYOTLE+yG3AUsHZam7XAS9rpI4EvVV/XqiRJM+rt0lBVbU2yGvg8sAQ4s6quSnISsKGq1gIfBP42yUbgJzRhsRhN7LLUIuYxuTOPycw8Lne2qI5JbzeLJUk7Br9ZLEkDZxBI0sAZBPOYr5uMoUlyQJLzk1yd5Kokx0+6psUiyZIkX0/y2UnXshgkuXeSc5J8M8k1SR436ZomLcnr2v9vrkzyiSS7T7omMAjm1LGbjKHZCvx5Va0AHgu82mNyu+OBayZdxCLyv4DPVdXvAX/AwI9Nkv2A1wIrq+phNA/RLIoHZAyCuXXpJmNQqur7VXVpO/1zmv+595tsVZOXZH/gMOCMSdeyGCS5F/BHNE8GUlW/rqqbJlrU4rArcI/2e1P3BP5lwvUABsF89gOuH5nfjB96t2t7i30kcPGES1kMTgX+K3DbhOtYLJYDW4D/214uOyPJHpMuapKq6gbgL4HvAd8HflZV/zjZqhoGgbZLkj2BTwMnVNXNk65nkpI8G/hRVV0y6VoWkV2BRwHvq6pHAr8EBn2PLcnv0FxRWA48ANgjyQsnW1XDIJhbl24yBifJ3WhC4GNV9XeTrmcReAKwKskmmsuHT0vy0cmWNHGbgc1VNXW2eA5NMAzZM4DvVtWWqroV+Dvg8ROuCTAI5tOlm4xBabsJ/yBwTVW9Z9L1LAZV9eaq2r+qltH8G/lSVS2Kv/Qmpap+AFyf5CHtoqcDV0+wpMXge8Bjk9yz/f/o6SySG+g7RO+jkzJbNxkTLmvSngC8CLgiyWXtsv9WVesmV5IWqdcAH2v/iLoOeOmE65moqro4yTnApTRP332dRdLVhF1MSNLAeWlIkgbOIJCkgTMIJGngDAJJGjiDQJIGziDQgkpSSd49Mv+GJG9boG1/KMmRC7Gtefbz/La3zPM7tl/X9rS5LMmVs7S5IMmCD1aeZE2SFyz0djUsBoEW2i3A85LsM+lCRrWdfHX1MuDYqnpql8ZV9R8n2KHafwAWRX812nEZBFpoW2m+JPO66Sum/0Wf5Bftz6ckuTDJZ5Jcl+TkJC9I8rUkVyQ5aGQzz0iyIcm32j5+psYBOCXJ+iSXJ3nFyHa/nGQtM3yrNcnR7favTPLOdtmJwBOBDyY5ZVr7fZNclOSy9j1PapdvGgm+XZN8rD2jOCfJPWfY7y+S/I8k30jy1ST3a5cf1M5fkeQvRo7PbPvdG9itqra0ZzFXttu8qF1/THtML0jy7ST/faSG17ftr0xyQrvst85oRs/mkrw2zRgUlyc5q122R5Iz2/9OX08y6J55d2hV5cvXgr2AXwB7A5uAewFvAN7WrvsQcORo2/bnU4CbgH2Bu9P05/T2dt3xwKkj7/8czR8wB9P0Z7M7cBzw1rbN3YENNB17PYWms7PlM9T5AJqv/C+l+Yb9l4DntusuoOkzfvp7/hx4Szu9BNirnd4E7AMsAwp4Qrv8TOAN07fZtnlOO/2ukdo/CxzdTr9y5PjMtt/nASe101cA+7XT925/HkPTy+V9gHsAVwIrgUe37fcA9gSuoulFdhlw5cjvO/rf7l+Au0/b/v8EXji1DPgWsMek/w362vaXZwRacNX0RvoRmkE4ulpfzVgHtwDf4Y7LHVfQfEBNObuqbquqb9N0W/B7wB8DL267vLiY5oPv4Lb916rquzPs7w+BC6rpAGwr8DGa/vPnrBF4aftX8u9XMx7DdNdX1Vfa6Y/SnF1M92uaD32AS0Z+v8cBn2qnP95hv4cC57XTXwE+lORYmrCY8oWqurGqfkXTydkT29ffV9Uvq+oX7fInzfWLA5fTdBfxQpqzPmiO+5r2uF9AE8oHzrMdLUIGgfpyKs219tE+6LfS/ptLsguw28i6W0ambxuZv43f7hNrep8oBQR4TVU9on0trzv6ef/lXfklfmtHVRfRhMUNNB+6L56p2TzzALdW1dTy3zBPn19z7PcQ4Gttm1cCb6XpLfeSJPfZhnqm3P7fpzU6jOJhNKP1PQpY395zCXDEyHE/sKoWRSdq2jYGgXpRVT8BzqYJgymbaC5LAKwC7rYdm35+kl3a+wYPAq6l6RTwVWm6xybJgzP/IChfA56cZJ80Q5IeDVw41xuSPBD4YVV9gGYkspm6VT4wd4zN+5+Bf+r6iwFfBY5op28fwnCm/SZ5KPDNqvpN2+agqrq4qk6kGRBmqvv0Zyb53ST3AJ5Lc+bwZeC5aXrB3AP4k3bZD4H7JrlPkrsDU/dgdgEOqKrzgTfRXPLbk+a4vyZJ2naP3IbfVYuIvY+qT+8GVo/MfwD4TJJv0Fzr356/1r9H8yG+N/DKqvq3JGfQXF65tP1Q2kLzoTerqvp+kjXA+TR/2Z5bVZ+ZZ99PAd6Y5FaaeyEznRFcSzOO85k0N6jf1/H3AjgB+GiSt9Acn5/Nsd8j2jZTTklycPu7fBH4BvAImmP1aZqxND5aVRuguXHfrgM4o6q+3i4/qV1+A/DNdv2Stq57tdt/b1XdlOQdNGd+l7dh8V3a8NCOxd5HpUWifcLoV1VVSY6iuXE845M4Sb4AvLiqvj/H9o6huUG9erY2EnhGIC0mjwb+pj2ruQn4L7M1rKpnjqso7fw8I5CkgfNmsSQNnEEgSQNnEEjSwBkEkjRwBoEkDdz/B2yKq6nOEYVeAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### In siblings/spouses travelling together what is the average number of siblings/spouse actually survived out of them??\n",
    "sib = df1.groupby(\"SibSp\").mean()[\"Survived\"]\n",
    "number = df.SibSp.unique()\n",
    "plt.bar(number,sib)\n",
    "plt.xlabel(\"Number of siblngs/spouse\")\n",
    "plt.ylabel(\"Chances of survival\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "f91abcfc",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "### Same for parent and children\n",
    "par = df1.groupby(\"Parch\").mean()[\"Survived\"]\n",
    "number = df.Parch.unique()\n",
    "plt.bar(number,sib)\n",
    "plt.xlabel(\"Number of children\")\n",
    "plt.ylabel(\"Chances of survival\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04e92d4f",
   "metadata": {},
   "source": [
    "### Feature selection on the basis of analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "00fa9b35",
   "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>Age</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22.0</td>\n",
       "      <td>male</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38.0</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26.0</td>\n",
       "      <td>female</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35.0</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35.0</td>\n",
       "      <td>male</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age     Sex  Pclass  Survived\n",
       "0  22.0    male       3         0\n",
       "1  38.0  female       1         1\n",
       "2  26.0  female       3         1\n",
       "3  35.0  female       1         1\n",
       "4  35.0    male       3         0"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fdata = pd.DataFrame(df1[\"Age\"])\n",
    "cols = [\"Sex\",\"Pclass\"]\n",
    "for column in cols:\n",
    "    fdata[column] = df1[column]\n",
    "fdata[\"Survived\"] = df1[\"Survived\"]\n",
    "fdata.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2e35948",
   "metadata": {},
   "source": [
    "### Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "a6882013",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age         177\n",
       "Sex           0\n",
       "Pclass        0\n",
       "Survived      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fdata.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "f52dcb9b",
   "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>Age</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22</td>\n",
       "      <td>male</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26</td>\n",
       "      <td>female</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35</td>\n",
       "      <td>male</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age     Sex  Pclass  Survived\n",
       "0   22    male       3         0\n",
       "1   38  female       1         1\n",
       "2   26  female       3         1\n",
       "3   35  female       1         1\n",
       "4   35    male       3         0"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fdata.Age.fillna(fdata.Age.mean(),inplace=True)\n",
    "fdata[\"Age\"] = fdata[\"Age\"].astype(\"int32\")\n",
    "fdata.loc[df1[\"Age\"] == 0,\"Age\"] = fdata.Age.mean().astype(\"int32\")\n",
    "fdata.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "9d5cb4ad",
   "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>Age</th>\n",
       "      <th>gender</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>27</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>29</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>26</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>32</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Age  gender  Pclass  Survived\n",
       "0     22       2       3         0\n",
       "1     38       4       1         1\n",
       "2     26       4       3         1\n",
       "3     35       4       1         1\n",
       "4     35       2       3         0\n",
       "..   ...     ...     ...       ...\n",
       "886   27       2       2         0\n",
       "887   19       4       1         1\n",
       "888   29       4       3         0\n",
       "889   26       2       1         1\n",
       "890   32       2       3         0\n",
       "\n",
       "[891 rows x 4 columns]"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## replacing male and female by 2 & 4\n",
    "def replace(gender):\n",
    "    if gender == \"male\":\n",
    "        return 2\n",
    "    elif gender == \"female\":\n",
    "        return 4\n",
    "# fdata[\"gender\"] = fdata[\"Sex\"].apply(replace)\n",
    "# fdata.drop(\"Sex\",inplace=True,axis=1)\n",
    "# col = list(fdata.columns)\n",
    "# gen = col.pop(3)\n",
    "# col.insert(1,gen)\n",
    "# fdata = fdata[col]\n",
    "fdata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "d8b57a85",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = fdata.iloc[:,:-1].values\n",
    "y = fdata.iloc[:,-1:].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "32c76c46",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Encoding the categorical data\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "ct = ColumnTransformer(transformers=[(\"encode\",OneHotEncoder(),[2])],remainder = \"passthrough\")\n",
    "X = np.array(ct.fit_transform(X))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "a11fc8dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.,  0.,  1., 22.,  2.],\n",
       "       [ 1.,  0.,  0., 38.,  4.],\n",
       "       [ 0.,  0.,  1., 26.,  4.],\n",
       "       ...,\n",
       "       [ 0.,  0.,  1., 29.,  4.],\n",
       "       [ 1.,  0.,  0., 26.,  2.],\n",
       "       [ 0.,  0.,  1., 32.,  2.]])"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X\n",
    "# 0,0,1 - 3rd class , 1,0,0 - 1st class , 0,1,0 - 2nd class "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "548fbe1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Splitting the data int test set and train test\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,y_train,y_test = train_test_split(X,y,random_state=1,test_size=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9df2b68a",
   "metadata": {},
   "source": [
    "  ### Training the Logistic regression model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "37a6ad8b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\python3.7\\lib\\site-packages\\sklearn\\utils\\validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "classifier = LogisticRegression()\n",
    "classifier.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "a82366ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 1],\n",
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       "       [1, 1],\n",
       "       [1, 0],\n",
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       "       [0, 0],\n",
       "       [1, 1],\n",
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       "       [0, 0],\n",
       "       [0, 0],\n",
       "       [1, 1],\n",
       "       [1, 0],\n",
       "       [0, 0],\n",
       "       [1, 1],\n",
       "       [1, 1],\n",
       "       [1, 0],\n",
       "       [0, 0],\n",
       "       [0, 0],\n",
       "       [1, 1],\n",
       "       [0, 1],\n",
       "       [1, 1],\n",
       "       [0, 0],\n",
       "       [1, 1],\n",
       "       [0, 0],\n",
       "       [0, 0],\n",
       "       [0, 0],\n",
       "       [1, 1]], dtype=int64)"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Predicting the test results by Logistic regression classifier\n",
    "y_pred = classifier.predict(X_test)\n",
    "np.concatenate((y_test.reshape(y_test.size,1),y_pred.reshape(y_pred.size,1)),1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "0f323bbe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[92, 14],\n",
       "       [23, 50]], dtype=int64)"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Confusion matrix\n",
    "from sklearn.metrics import confusion_matrix,accuracy_score\n",
    "matrix = confusion_matrix(y_test,y_pred)\n",
    "matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "f3952ef0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy of logistic regression model is: 79%\n"
     ]
    }
   ],
   "source": [
    "accuracy = accuracy_score(y_test,y_pred)\n",
    "print(f\"accuracy of logistic regression model is: {int(accuracy*100)}%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b55b507",
   "metadata": {},
   "source": [
    "### Training the SVM KERNEL model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "b638bd4f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\python3.7\\lib\\site-packages\\sklearn\\utils\\validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "SVC(random_state=0)"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "classifier1 = SVC(kernel=\"rbf\",random_state=0)\n",
    "classifier1.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "12570388",
   "metadata": {},
   "outputs": [
    {
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       "       [1, 0],\n",
       "       [0, 0],\n",
       "       [0, 0],\n",
       "       [1, 1],\n",
       "       [0, 0],\n",
       "       [1, 0],\n",
       "       [0, 0],\n",
       "       [0, 0],\n",
       "       [0, 0],\n",
       "       [0, 0],\n",
       "       [0, 0],\n",
       "       [1, 1],\n",
       "       [0, 0],\n",
       "       [0, 1],\n",
       "       [1, 0],\n",
       "       [1, 0],\n",
       "       [0, 0],\n",
       "       [0, 0],\n",
       "       [0, 0],\n",
       "       [1, 0],\n",
       "       [0, 0],\n",
       "       [0, 0],\n",
       "       [0, 0],\n",
       "       [1, 0],\n",
       "       [1, 0],\n",
       "       [0, 0],\n",
       "       [1, 0],\n",
       "       [1, 0],\n",
       "       [1, 0],\n",
       "       [0, 0],\n",
       "       [0, 0],\n",
       "       [1, 1],\n",
       "       [0, 1],\n",
       "       [1, 1],\n",
       "       [0, 0],\n",
       "       [1, 0],\n",
       "       [0, 0],\n",
       "       [0, 0],\n",
       "       [0, 0],\n",
       "       [1, 1]], dtype=int64)"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "###Predicting the test results\n",
    "y_pred1 = classifier1.predict(X_test)\n",
    "np.concatenate((y_test.reshape(y_test.size,1),y_pred1.reshape(y_pred1.size,1)),1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "496ed9ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[99,  7],\n",
       "       [58, 15]], dtype=int64)"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Confusion matrix for SVM Kernel\n",
    "from sklearn.metrics import confusion_matrix,accuracy_score\n",
    "matrix1 = confusion_matrix(y_test,y_pred1)\n",
    "matrix1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "0c503bd2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy of SVM model is: 63%\n"
     ]
    }
   ],
   "source": [
    "accuracy1 = accuracy_score(y_test,y_pred1)\n",
    "print(f\"accuracy of SVM model is: {int(accuracy1*100)}%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "086295e4",
   "metadata": {},
   "source": [
    "### Predicting the test results on kaggle test dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "id": "2776ae89",
   "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>PassengerId</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>892</td>\n",
       "      <td>3</td>\n",
       "      <td>Kelly, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>34.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330911</td>\n",
       "      <td>7.8292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>3</td>\n",
       "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
       "      <td>female</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>363272</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>2</td>\n",
       "      <td>Myles, Mr. Thomas Francis</td>\n",
       "      <td>male</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>240276</td>\n",
       "      <td>9.6875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>3</td>\n",
       "      <td>Wirz, Mr. Albert</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>315154</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>3</td>\n",
       "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
       "      <td>female</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3101298</td>\n",
       "      <td>12.2875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   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  "
      ]
     },
     "execution_count": 234,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test = pd.read_csv(\"Titanic-test.csv\")\n",
    "fdata1 = pd.DataFrame(test[\"Age\"])\n",
    "cols = [\"Sex\",\"Pclass\"]\n",
    "for column in cols:\n",
    "    fdata1[column] = test[column]\n",
    "# fdata1.head()\n",
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "id": "56ea73e1",
   "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>Age</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>34</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>47</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>62</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>27</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Pclass  gender\n",
       "0   34       3       2\n",
       "1   47       3       4\n",
       "2   62       2       2\n",
       "3   27       3       2\n",
       "4   22       3       4"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fdata1.Age.fillna(fdata1.Age.mean(),inplace=True)\n",
    "fdata1[\"Age\"] = fdata1[\"Age\"].astype(\"int32\")\n",
    "fdata1.loc[fdata1[\"Age\"] == 0,\"Age\"] = fdata1.Age.mean().astype(\"int32\")\n",
    "# fdata1.Pclass.unique()\n",
    "fdata1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "id": "2edb138a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(418, 3)"
      ]
     },
     "execution_count": 231,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fdata1.head()\n",
    "# cols = list(fdata1.columns)\n",
    "# class1 = cols.pop(1)\n",
    "# cols.insert(0,class1)\n",
    "# fdata1 = fdata1[cols]\n",
    "fdata1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "id": "87cc3149",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_t = fdata1.iloc[:,:].values\n",
    "## Encoding the categorical data\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "c = ColumnTransformer(transformers=[(\"encode\",OneHotEncoder(),[0])],remainder = \"passthrough\")\n",
    "x_t = np.array(c.fit_transform(x_t))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "id": "8697482f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.,  0.,  1., 34.,  2.],\n",
       "       [ 0.,  0.,  1., 47.,  4.],\n",
       "       [ 0.,  1.,  0., 62.,  2.],\n",
       "       ...,\n",
       "       [ 0.,  0.,  1., 38.,  2.],\n",
       "       [ 0.,  0.,  1., 30.,  2.],\n",
       "       [ 0.,  0.,  1., 30.,  2.]])"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "id": "dfb0e69a",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_kaggle = classifier.predict(x_t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "id": "d7a186cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0,\n",
       "       1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1,\n",
       "       1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1,\n",
       "       1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1,\n",
       "       1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0,\n",
       "       0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0,\n",
       "       1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,\n",
       "       0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1,\n",
       "       1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1,\n",
       "       0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0,\n",
       "       1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1,\n",
       "       0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1,\n",
       "       0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0,\n",
       "       0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0,\n",
       "       1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0,\n",
       "       0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0,\n",
       "       1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1,\n",
       "       0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0],\n",
       "      dtype=int64)"
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Kaggle prediction\n",
    "y_pred_kaggle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "id": "fee0196a",
   "metadata": {},
   "outputs": [],
   "source": [
    "### saving the submission files of prediction\n",
    "submission = pd.DataFrame(test[\"PassengerId\"])\n",
    "submission[\"Survived\"] = y_pred_kaggle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "id": "7de67bf5",
   "metadata": {},
   "outputs": [],
   "source": [
    "submission.to_csv(\"Submission.csv\",index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b97a9f6b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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