decission tree on titanic dataset.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <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",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
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       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\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>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
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       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
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       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
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       "      <td>Allen, Mr. William Henry</td>\n",
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       "      <th>...</th>\n",
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       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Montvila, Rev. Juozas</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>211536</td>\n",
       "      <td>13.0000</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>B42</td>\n",
       "      <td>S</td>\n",
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       "      <th>888</th>\n",
       "      <td>889</td>\n",
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       "      <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>23.4500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
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       "      <th>889</th>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
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       "      <td>26.0</td>\n",
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       "      <td>Dooley, Mr. Patrick</td>\n",
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       "      <td>32.0</td>\n",
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       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
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       "<p>891 rows × 12 columns</p>\n",
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      "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",
       "886          887         0       2   \n",
       "887          888         1       1   \n",
       "888          889         0       3   \n",
       "889          890         1       1   \n",
       "890          891         0       3   \n",
       "\n",
       "                                                  Name     Sex   Age  SibSp  \\\n",
       "0                              Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                               Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                             Allen, Mr. William Henry    male  35.0      0   \n",
       "..                                                 ...     ...   ...    ...   \n",
       "886                              Montvila, Rev. Juozas    male  27.0      0   \n",
       "887                       Graham, Miss. Margaret Edith  female  19.0      0   \n",
       "888           Johnston, Miss. Catherine Helen \"Carrie\"  female   NaN      1   \n",
       "889                              Behr, Mr. Karl Howell    male  26.0      0   \n",
       "890                                Dooley, Mr. Patrick    male  32.0      0   \n",
       "\n",
       "     Parch            Ticket     Fare Cabin Embarked  \n",
       "0        0         A/5 21171   7.2500   NaN        S  \n",
       "1        0          PC 17599  71.2833   C85        C  \n",
       "2        0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3        0            113803  53.1000  C123        S  \n",
       "4        0            373450   8.0500   NaN        S  \n",
       "..     ...               ...      ...   ...      ...  \n",
       "886      0            211536  13.0000   NaN        S  \n",
       "887      0            112053  30.0000   B42        S  \n",
       "888      2        W./C. 6607  23.4500   NaN        S  \n",
       "889      0            111369  30.0000  C148        C  \n",
       "890      0            370376   7.7500   NaN        Q  \n",
       "\n",
       "[891 rows x 12 columns]"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv('train.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df.drop(['PassengerId', 'Name', 'Ticket'], axis =1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
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       "     Survived  Pclass     Sex   Age  SibSp  Parch     Fare Cabin Embarked\n",
       "0           0       3    male  22.0      1      0   7.2500   NaN        S\n",
       "1           1       1  female  38.0      1      0  71.2833   C85        C\n",
       "2           1       3  female  26.0      0      0   7.9250   NaN        S\n",
       "3           1       1  female  35.0      1      0  53.1000  C123        S\n",
       "4           0       3    male  35.0      0      0   8.0500   NaN        S\n",
       "..        ...     ...     ...   ...    ...    ...      ...   ...      ...\n",
       "886         0       2    male  27.0      0      0  13.0000   NaN        S\n",
       "887         1       1  female  19.0      0      0  30.0000   B42        S\n",
       "888         0       3  female   NaN      1      2  23.4500   NaN        S\n",
       "889         1       1    male  26.0      0      0  30.0000  C148        C\n",
       "890         0       3    male  32.0      0      0   7.7500   NaN        Q\n",
       "\n",
       "[891 rows x 9 columns]"
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     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Survived      0\n",
       "Pclass        0\n",
       "Sex           0\n",
       "Age         177\n",
       "SibSp         0\n",
       "Parch         0\n",
       "Fare          0\n",
       "Cabin       687\n",
       "Embarked      2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
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       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>885</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>29.1250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
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       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>B42</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C148</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>784 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Survived  Pclass     Sex   Age  SibSp  Parch     Fare Cabin Embarked\n",
       "0           0       3    male  22.0      1      0   7.2500   NaN        S\n",
       "1           1       1  female  38.0      1      0  71.2833   C85        C\n",
       "2           1       3  female  26.0      0      0   7.9250   NaN        S\n",
       "3           1       1  female  35.0      1      0  53.1000  C123        S\n",
       "4           0       3    male  35.0      0      0   8.0500   NaN        S\n",
       "..        ...     ...     ...   ...    ...    ...      ...   ...      ...\n",
       "885         0       3  female  39.0      0      5  29.1250   NaN        Q\n",
       "887         1       1  female  19.0      0      0  30.0000   B42        S\n",
       "888         0       3  female   NaN      1      2  23.4500   NaN        S\n",
       "889         1       1    male  26.0      0      0  30.0000  C148        C\n",
       "890         0       3    male  32.0      0      0   7.7500   NaN        Q\n",
       "\n",
       "[784 rows x 9 columns]"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df.drop(['Cabin'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
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       "      <td>3</td>\n",
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       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
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       "      <th>1</th>\n",
       "      <td>1</td>\n",
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       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
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       "      <th>3</th>\n",
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       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
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       "      <th>...</th>\n",
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       "      <th>885</th>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>29.1250</td>\n",
       "      <td>Q</td>\n",
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       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C</td>\n",
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       "      <th>890</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
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       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>784 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Survived  Pclass     Sex   Age  SibSp  Parch     Fare Embarked\n",
       "0           0       3    male  22.0      1      0   7.2500        S\n",
       "1           1       1  female  38.0      1      0  71.2833        C\n",
       "2           1       3  female  26.0      0      0   7.9250        S\n",
       "3           1       1  female  35.0      1      0  53.1000        S\n",
       "4           0       3    male  35.0      0      0   8.0500        S\n",
       "..        ...     ...     ...   ...    ...    ...      ...      ...\n",
       "885         0       3  female  39.0      0      5  29.1250        Q\n",
       "887         1       1  female  19.0      0      0  30.0000        S\n",
       "888         0       3  female   NaN      1      2  23.4500        S\n",
       "889         1       1    male  26.0      0      0  30.0000        C\n",
       "890         0       3    male  32.0      0      0   7.7500        Q\n",
       "\n",
       "[784 rows x 8 columns]"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "d=pd.get_dummies(df['Sex'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "d1=pd.get_dummies(df['Embarked'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=pd.concat([df,d,d1],axis='columns')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>888</th>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "</table>\n",
       "<p>784 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Survived  Pclass     Sex   Age  SibSp  Parch     Fare Embarked  female  \\\n",
       "0           0       3    male  22.0      1      0   7.2500        S       0   \n",
       "1           1       1  female  38.0      1      0  71.2833        C       1   \n",
       "2           1       3  female  26.0      0      0   7.9250        S       1   \n",
       "3           1       1  female  35.0      1      0  53.1000        S       1   \n",
       "4           0       3    male  35.0      0      0   8.0500        S       0   \n",
       "..        ...     ...     ...   ...    ...    ...      ...      ...     ...   \n",
       "885         0       3  female  39.0      0      5  29.1250        Q       1   \n",
       "887         1       1  female  19.0      0      0  30.0000        S       1   \n",
       "888         0       3  female   NaN      1      2  23.4500        S       1   \n",
       "889         1       1    male  26.0      0      0  30.0000        C       0   \n",
       "890         0       3    male  32.0      0      0   7.7500        Q       0   \n",
       "\n",
       "     male  C  Q  S  \n",
       "0       1  0  0  1  \n",
       "1       0  1  0  0  \n",
       "2       0  0  0  1  \n",
       "3       0  0  0  1  \n",
       "4       1  0  0  1  \n",
       "..    ... .. .. ..  \n",
       "885     0  0  1  0  \n",
       "887     0  0  0  1  \n",
       "888     0  0  0  1  \n",
       "889     1  1  0  0  \n",
       "890     1  0  1  0  \n",
       "\n",
       "[784 rows x 13 columns]"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
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  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=data.drop(['Embarked' ,'Sex', 'Fare'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
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       "     Survived  Pclass   Age  SibSp  Parch  female  male  C  Q  S\n",
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       "\n",
       "[784 rows x 10 columns]"
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     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df"
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   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Survived      0\n",
       "Pclass        0\n",
       "Age         106\n",
       "SibSp         0\n",
       "Parch         0\n",
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       "male          0\n",
       "C             0\n",
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       "dtype: int64"
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     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "784"
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     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "29.869351032448375"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "m=df['Age'].mean()\n",
    "m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "m=30"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Age']=df['Age'].fillna(value=m)"
   ]
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   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "4.0"
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     "execution_count": 163,
     "metadata": {},
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   "metadata": {},
   "outputs": [],
   "source": []
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   "cell_type": "code",
   "execution_count": 164,
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      "text/plain": [
       "    Survived  Pclass   Age  SibSp  Parch  female  male  C  Q  S\n",
       "0          0       3  22.0      1      0       0     1  0  0  1\n",
       "1          1       1  38.0      1      0       1     0  1  0  0\n",
       "2          1       3  26.0      0      0       1     0  0  0  1\n",
       "3          1       1  35.0      1      0       1     0  0  0  1\n",
       "4          0       3  35.0      0      0       0     1  0  0  1\n",
       "5          0       3  30.0      0      0       0     1  0  1  0\n",
       "6          0       1  54.0      0      0       0     1  0  0  1\n",
       "7          0       3   2.0      3      1       0     1  0  0  1\n",
       "8          1       3  27.0      0      2       1     0  0  0  1\n",
       "9          1       2  14.0      1      0       1     0  1  0  0\n",
       "10         1       3   4.0      1      1       1     0  0  0  1\n",
       "11         1       1  58.0      0      0       1     0  0  0  1\n",
       "12         0       3  20.0      0      0       0     1  0  0  1\n",
       "13         0       3  39.0      1      5       0     1  0  0  1\n",
       "14         0       3  14.0      0      0       1     0  0  0  1"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "58.0"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Age'][11]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "df['Age'][1]= (df['Age'][1])//10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [],
   "source": [
    "df =df.drop(['Age', 'male', 'S'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
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       "\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",
       "      <th>Pclass</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>female</th>\n",
       "      <th>C</th>\n",
       "      <th>Q</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>885</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>784 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Survived  Pclass  SibSp  Parch  female  C  Q\n",
       "0           0       3      1      0       0  0  0\n",
       "1           1       1      1      0       1  1  0\n",
       "2           1       3      0      0       1  0  0\n",
       "3           1       1      1      0       1  0  0\n",
       "4           0       3      0      0       0  0  0\n",
       "..        ...     ...    ...    ...     ... .. ..\n",
       "885         0       3      0      5       1  0  1\n",
       "887         1       1      0      0       1  0  0\n",
       "888         0       3      1      2       1  0  0\n",
       "889         1       1      0      0       0  1  0\n",
       "890         0       3      0      0       0  0  1\n",
       "\n",
       "[784 rows x 7 columns]"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split as tts\n",
    "inputs=df.drop('Survived',axis='columns')\n",
    "output=df['Survived']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,y_train,y_test=tts(inputs,output,test_size=0.2,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DecisionTreeClassifier(max_depth=4, random_state=0)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1,\n",
       "       0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0,\n",
       "       0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0,\n",
       "       0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0,\n",
       "       1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0,\n",
       "       1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1,\n",
       "       0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0,\n",
       "       0, 1, 0], dtype=int64)"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model=DecisionTreeClassifier(max_depth=4, random_state=0)\n",
    "model.fit(X_train,y_train)\n",
    "print(model)\n",
    "y_pred = model.predict(X_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7898089171974523"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "a=metrics.accuracy_score(y_pred,y_test)\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "from sklearn import tree\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(167.4, 190.26, 'X[3] <= 0.5\\ngini = 0.485\\nsamples = 627\\nvalue = [367, 260]'),\n",
       " Text(83.7, 135.9, 'X[0] <= 1.5\\ngini = 0.347\\nsamples = 399\\nvalue = [310, 89]'),\n",
       " Text(41.85, 81.53999999999999, 'X[1] <= 0.5\\ngini = 0.476\\nsamples = 100\\nvalue = [61, 39]'),\n",
       " Text(20.925, 27.180000000000007, 'gini = 0.448\\nsamples = 68\\nvalue = [45, 23]'),\n",
       " Text(62.775000000000006, 27.180000000000007, 'gini = 0.5\\nsamples = 32\\nvalue = [16, 16]'),\n",
       " Text(125.55000000000001, 81.53999999999999, 'X[2] <= 0.5\\ngini = 0.279\\nsamples = 299\\nvalue = [249, 50]'),\n",
       " Text(104.625, 27.180000000000007, 'gini = 0.247\\nsamples = 249\\nvalue = [213, 36]'),\n",
       " Text(146.475, 27.180000000000007, 'gini = 0.403\\nsamples = 50\\nvalue = [36, 14]'),\n",
       " Text(251.10000000000002, 135.9, 'X[0] <= 2.5\\ngini = 0.375\\nsamples = 228\\nvalue = [57, 171]'),\n",
       " Text(209.25, 81.53999999999999, 'X[0] <= 1.5\\ngini = 0.125\\nsamples = 134\\nvalue = [9, 125]'),\n",
       " Text(188.32500000000002, 27.180000000000007, 'gini = 0.074\\nsamples = 78\\nvalue = [3, 75]'),\n",
       " Text(230.175, 27.180000000000007, 'gini = 0.191\\nsamples = 56\\nvalue = [6, 50]'),\n",
       " Text(292.95, 81.53999999999999, 'X[2] <= 1.5\\ngini = 0.5\\nsamples = 94\\nvalue = [48, 46]'),\n",
       " Text(272.02500000000003, 27.180000000000007, 'gini = 0.493\\nsamples = 68\\nvalue = [30, 38]'),\n",
       " Text(313.875, 27.180000000000007, 'gini = 0.426\\nsamples = 26\\nvalue = [18, 8]')]"
      ]
     },
     "execution_count": 76,
     "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": [
    "tree.plot_tree(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}