titanic_Log_Regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "4ed4812c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import StandardScaler,OrdinalEncoder,OneHotEncoder\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "0bbf30e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "df= pd.read_csv('dataset/titanic.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "2fc877dd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  891 non-null    int64  \n",
      " 1   Survived     887 non-null    float64\n",
      " 2   Pclass       891 non-null    int64  \n",
      " 3   Name         891 non-null    object \n",
      " 4   Sex          891 non-null    object \n",
      " 5   Age          713 non-null    float64\n",
      " 6   SibSp        891 non-null    int64  \n",
      " 7   Parch        891 non-null    int64  \n",
      " 8   Ticket       891 non-null    object \n",
      " 9   Fare         888 non-null    float64\n",
      " 10  Cabin        204 non-null    object \n",
      " 11  Embarked     889 non-null    object \n",
      "dtypes: float64(3), int64(4), object(5)\n",
      "memory usage: 83.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "cf4ee1e5",
   "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>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>887.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>713.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>888.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.382187</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.704306</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.282530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486196</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>14.536033</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.758986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.917700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.456250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.068750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
       "count   891.000000  887.000000  891.000000  713.000000  891.000000   \n",
       "mean    446.000000    0.382187    2.308642   29.704306    0.523008   \n",
       "std     257.353842    0.486196    0.836071   14.536033    1.102743   \n",
       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
       "25%     223.500000    0.000000    2.000000   20.000000    0.000000   \n",
       "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
       "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
       "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
       "\n",
       "            Parch        Fare  \n",
       "count  891.000000  888.000000  \n",
       "mean     0.381594   32.282530  \n",
       "std      0.806057   49.758986  \n",
       "min      0.000000    0.000000  \n",
       "25%      0.000000    7.917700  \n",
       "50%      0.000000   14.456250  \n",
       "75%      0.000000   31.068750  \n",
       "max      6.000000  512.329200  "
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "f0080950",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Survived         4\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age            178\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             3\n",
       "Cabin          687\n",
       "Embarked         2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "1c76d8b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['Ticket','PassengerId','Cabin','Name','Fare'],axis=1, inplace =True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "9c66d350",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.fillna(method='ffill' , inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "33fac872",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
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       "      <th>0</th>\n",
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       "      <td>0</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>C</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
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       "      <td>S</td>\n",
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      ],
      "text/plain": [
       "   Survived  Pclass     Sex   Age  SibSp  Parch Embarked\n",
       "0       0.0       3    male  22.0      1      0        S\n",
       "1       0.0       1  female  38.0      1      0        C\n",
       "2       0.0       3  female  38.0      0      0        S\n",
       "3       0.0       1  female  35.0      1      0        S\n",
       "4       0.0       3    male  35.0      0      0        S"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "f450bd1f",
   "metadata": {},
   "outputs": [],
   "source": [
    "x= df.iloc[:,1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "ffe76e98",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      0.0\n",
       "1      0.0\n",
       "2      0.0\n",
       "3      0.0\n",
       "4      0.0\n",
       "      ... \n",
       "886    0.0\n",
       "887    1.0\n",
       "888    0.0\n",
       "889    1.0\n",
       "890    0.0\n",
       "Name: Survived, Length: 891, dtype: float64"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = df.iloc[:,0]\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "492f547e",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train , x_test , y_train , y_test = train_test_split(x,y, test_size=0.2,random_state=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "9b4f5406",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
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       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>C</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>873</th>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>47.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>876</th>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>534</th>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>584</th>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>493</th>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>71.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>527</th>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>712 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pclass     Sex   Age  SibSp  Parch Embarked\n",
       "30        1    male  40.0      0      0        C\n",
       "10        3  female   4.0      1      1        S\n",
       "873       3    male  47.0      0      0        S\n",
       "182       3    male   9.0      4      2        S\n",
       "876       3    male  20.0      0      0        S\n",
       "..      ...     ...   ...    ...    ...      ...\n",
       "534       3  female  30.0      0      0        S\n",
       "584       3    male  36.0      0      0        C\n",
       "493       1    male  71.0      0      0        C\n",
       "527       1    male  50.0      0      0        S\n",
       "168       1    male  45.0      0      0        S\n",
       "\n",
       "[712 rows x 6 columns]"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "90efa157",
   "metadata": {},
   "outputs": [
    {
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       "      <th>37</th>\n",
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       "      <th>615</th>\n",
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       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>17.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>S</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>846</th>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>42.0</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>870</th>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251</th>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>29.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>179 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pclass     Sex   Age  SibSp  Parch Embarked\n",
       "707       1    male  42.0      0      0        S\n",
       "37        3    male  21.0      0      0        S\n",
       "615       2  female  24.0      1      2        S\n",
       "169       3    male  28.0      0      0        S\n",
       "68        3  female  17.0      4      2        S\n",
       "..      ...     ...   ...    ...    ...      ...\n",
       "89        3    male  24.0      0      0        S\n",
       "80        3    male  22.0      0      0        S\n",
       "846       3    male  42.0      8      2        S\n",
       "870       3    male  26.0      0      0        S\n",
       "251       3  female  29.0      1      1        S\n",
       "\n",
       "[179 rows x 6 columns]"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "660cb579",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30     0.0\n",
       "10     1.0\n",
       "873    0.0\n",
       "182    0.0\n",
       "876    0.0\n",
       "      ... \n",
       "534    0.0\n",
       "584    0.0\n",
       "493    0.0\n",
       "527    0.0\n",
       "168    0.0\n",
       "Name: Survived, Length: 712, dtype: float64"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "bfe08358",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "707    1.0\n",
       "37     0.0\n",
       "615    1.0\n",
       "169    0.0\n",
       "68     1.0\n",
       "      ... \n",
       "89     0.0\n",
       "80     0.0\n",
       "846    0.0\n",
       "870    0.0\n",
       "251    0.0\n",
       "Name: Survived, Length: 179, dtype: float64"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "cfd341d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "tr = ColumnTransformer(transformers=[\n",
    "    ('t2',OneHotEncoder(drop='first',sparse=False),['Sex','Embarked'])\n",
    "] , remainder='passthrough')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "fe540755",
   "metadata": {},
   "outputs": [],
   "source": [
    "x1 = tr.fit_transform(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "f9f3d86d",
   "metadata": {},
   "outputs": [],
   "source": [
    "x2 = tr.fit_transform(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "80a70c1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "log = LogisticRegression(random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "4f2ee055",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(random_state=0)"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log.fit(x1,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "9151e413",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = log.predict(x2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "e2457399",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score,confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "e6ea0f0b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score 0.7821229050279329\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy Score\",accuracy_score(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "e6128c33",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>0</th>\n",
       "      <th>1</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>8</td>\n",
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       "      <td>31</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    0   1\n",
       "0  92   8\n",
       "1  31  48"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(confusion_matrix(y_test,y_pred),columns=list(range(2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "39036db2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>Actual Label</th>\n",
       "      <th>Decision Tree Prediction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>707</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>615</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>846</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>870</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>179 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Actual Label  Decision Tree Prediction\n",
       "707           1.0                       0.0\n",
       "37            0.0                       0.0\n",
       "615           1.0                       1.0\n",
       "169           0.0                       0.0\n",
       "68            1.0                       0.0\n",
       "..            ...                       ...\n",
       "89            0.0                       0.0\n",
       "80            0.0                       0.0\n",
       "846           0.0                       0.0\n",
       "870           0.0                       0.0\n",
       "251           0.0                       0.0\n",
       "\n",
       "[179 rows x 2 columns]"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = pd.DataFrame()\n",
    "result['Actual Label'] = y_test\n",
    "result['Decision Tree Prediction'] = y_pred\n",
    "result"
   ]
  }
 ],
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