Titanic Survival Final Project_ Jonathan_Garcia_CISA4313-Copy5.ipynb

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    "# Programming for Data Analytics - TAMUSA\n",
    "# Final Project (Titanic Survival) - Progress Report 2 \n",
    "# Author - Jonathan Garcia\n",
    "# In this project I will look through the data provided on Kaggle for the passengers of the Titanic and use it to determine \n",
    "# who suvived and who died\n",
    "# This is a regression problem (binary - 0 or 1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "bd142372",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data wrangling\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# Data visualisation\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "# Machine learning models\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report\n",
    "\n",
    "\n",
    "# Remove warnings\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c2e2195f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# it is important to understand the problem\n",
    "# we will look at the relationship between variables in terms of survivability on the titanic\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "70716745",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a training and test set \n",
    "train = pd.read_csv('train.csv')\n",
    "test = pd.read_csv('test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "77712b49",
   "metadata": {
    "scrolled": true
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   "outputs": [
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     "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     891 non-null    int64  \n",
      " 2   Pclass       891 non-null    int64  \n",
      " 3   Name         891 non-null    object \n",
      " 4   Sex          891 non-null    object \n",
      " 5   Age          714 non-null    float64\n",
      " 6   SibSp        891 non-null    int64  \n",
      " 7   Parch        891 non-null    int64  \n",
      " 8   Ticket       891 non-null    object \n",
      " 9   Fare         891 non-null    float64\n",
      " 10  Cabin        204 non-null    object \n",
      " 11  Embarked     889 non-null    object \n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.7+ KB\n"
     ]
    }
   ],
   "source": [
    "# this helps us understand the data by giving the column names and their data types\n",
    "train.info()"
   ]
  },
  {
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   "execution_count": 54,
   "id": "55782028",
   "metadata": {},
   "outputs": [
    {
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 418 entries, 0 to 417\n",
      "Data columns (total 8 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  418 non-null    int64  \n",
      " 1   Pclass       418 non-null    int64  \n",
      " 2   Sex          418 non-null    object \n",
      " 3   Age          332 non-null    float64\n",
      " 4   SibSp        418 non-null    int64  \n",
      " 5   Parch        418 non-null    int64  \n",
      " 6   Fare         417 non-null    float64\n",
      " 7   Embarked     418 non-null    object \n",
      "dtypes: float64(2), int64(4), object(2)\n",
      "memory usage: 26.3+ KB\n"
     ]
    }
   ],
   "source": [
    "test.info()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ba87d90f",
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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",
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       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
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       "      <td>Moran, Mr. James</td>\n",
       "      <td>male</td>\n",
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       "      <td>0</td>\n",
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       "      <td>330877</td>\n",
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       "      <td>7</td>\n",
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       "      <td>McCarthy, Mr. Timothy J</td>\n",
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       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Palsson, Master. Gosta Leonard</td>\n",
       "      <td>male</td>\n",
       "      <td>2.0</td>\n",
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       "      <td>9</td>\n",
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       "      <td>Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</td>\n",
       "      <td>female</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>347742</td>\n",
       "      <td>11.1333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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       "      <td>Nasser, Mrs. Nicholas (Adele Achem)</td>\n",
       "      <td>female</td>\n",
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       "      <td>C</td>\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",
       "5            6         0       3   \n",
       "6            7         0       1   \n",
       "7            8         0       3   \n",
       "8            9         1       3   \n",
       "9           10         1       2   \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",
       "5                                   Moran, Mr. James    male   NaN      0   \n",
       "6                            McCarthy, Mr. Timothy J    male  54.0      0   \n",
       "7                     Palsson, Master. Gosta Leonard    male   2.0      3   \n",
       "8  Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)  female  27.0      0   \n",
       "9                Nasser, Mrs. Nicholas (Adele Achem)  female  14.0      1   \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",
       "5      0            330877   8.4583   NaN        Q  \n",
       "6      0             17463  51.8625   E46        S  \n",
       "7      1            349909  21.0750   NaN        S  \n",
       "8      2            347742  11.1333   NaN        S  \n",
       "9      0            237736  30.0708   NaN        C  "
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     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# display first ten rows of the training set \n",
    "train.head(10)"
   ]
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  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "710da511",
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       "   PassengerId  Pclass                                          Name     Sex  \\\n",
       "0          892       3                              Kelly, Mr. James    male   \n",
       "1          893       3              Wilkes, Mrs. James (Ellen Needs)  female   \n",
       "2          894       2                     Myles, Mr. Thomas Francis    male   \n",
       "3          895       3                              Wirz, Mr. Albert    male   \n",
       "4          896       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)  female   \n",
       "5          897       3                    Svensson, Mr. Johan Cervin    male   \n",
       "6          898       3                          Connolly, Miss. Kate  female   \n",
       "7          899       2                  Caldwell, Mr. Albert Francis    male   \n",
       "8          900       3     Abrahim, Mrs. Joseph (Sophie Halaut Easu)  female   \n",
       "9          901       3                       Davies, Mr. John Samuel    male   \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  \n",
       "5  14.0      0      0       7538   9.2250   NaN        S  \n",
       "6  30.0      0      0     330972   7.6292   NaN        Q  \n",
       "7  26.0      1      1     248738  29.0000   NaN        S  \n",
       "8  18.0      0      0       2657   7.2292   NaN        C  \n",
       "9  21.0      2      0  A/4 48871  24.1500   NaN        S  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# display the first 10 columns of the test set \n",
    "test.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a92c65d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data Description \n",
    "# Sirvided: 0 = Died, 1 = Survived \n",
    "# Pclass: 1= First Class, 2= 2nd Class, 3= 3rd Class (Proxy for socio-economic status)\n",
    "# Sex: Male or female\n",
    "# Age: Age in years (if less than one, fractional)\n",
    "# SibSp: # of siblings or spouses aboard titanic\n",
    "# Parch: # og parents or children aboard titanic\n",
    "# Ticket: Passenger Ticket #\n",
    "# Fare: Passenger Cost \n",
    "# Cabin: Cabin #\n",
    "# Embarked: Passenger Embark Location; C: Cherbourg, Q: Queenstown, S= Southampton"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d1d98fdc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training shape:  (891, 12)\n",
      "Test shape  (418, 11)\n"
     ]
    }
   ],
   "source": [
    "# display the shape of our test and training data sets \n",
    "print(\"Training shape: \", train.shape)\n",
    "print(\"Test shape \", test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d0322641",
   "metadata": {},
   "outputs": [],
   "source": [
    "# The training set has 891 rows and 12 columnss which are our independent variables\n",
    "# the test set contains 418 rowss and 11 columns. The 12th is ommited because that is our target variable (Survived)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e2972fa3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now look at all variables that ar emissing values and fill missing values for better results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "476d1b03",
   "metadata": {},
   "outputs": [],
   "source": [
    "# display missing values in our dataset train \n",
    "missing_values = train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d36df653",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Age         177\n",
      "Cabin       687\n",
      "Embarked      2\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# display variables with missing values\n",
    "print(missing_values[missing_values > 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "fdd90b56",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop variables, Name, Cabin and Ticket becuase these are similar to Sex, Pclass and Fare price. \n",
    "train = train.drop([ 'Embarked'], axis = 1)\n",
    "test = test.drop(['Embarked'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a8bb40a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# creates a new column 'FamilySize' by adding 'SibSp' and 'Parch' together\n",
    "train['FamilySize'] = train['SibSp'] + train['Parch']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "87d206de",
   "metadata": {},
   "outputs": [],
   "source": [
    "# drops the original 'SibSp' and 'Parch' columns if needed\n",
    "train = train.drop(['SibSp', 'Parch'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "e44e80e5",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   PassengerId  Survived  Pclass        Age     Fare Embarked  FamilySize  \\\n",
      "0            1         0       3  22.000000   7.2500        S           1   \n",
      "1            2         1       1  38.000000  71.2833        C           1   \n",
      "2            3         1       3  26.000000   7.9250        S           0   \n",
      "3            4         1       1  35.000000  53.1000        S           1   \n",
      "4            5         0       3  35.000000   8.0500        S           0   \n",
      "5            6         0       3  29.699118   8.4583        Q           0   \n",
      "6            7         0       1  54.000000  51.8625        S           0   \n",
      "7            8         0       3   2.000000  21.0750        S           4   \n",
      "8            9         1       3  27.000000  11.1333        S           2   \n",
      "9           10         1       2  14.000000  30.0708        C           1   \n",
      "\n",
      "   male  \n",
      "0     1  \n",
      "1     0  \n",
      "2     0  \n",
      "3     0  \n",
      "4     1  \n",
      "5     1  \n",
      "6     1  \n",
      "7     1  \n",
      "8     0  \n",
      "9     0  \n"
     ]
    }
   ],
   "source": [
    "# display the updated DataFrame\n",
    "print(train.head(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "36802a9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate the mean age\n",
    "mean_age = train['Age'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c08ded24",
   "metadata": {},
   "outputs": [],
   "source": [
    "# fill missing ages with the median age\n",
    "train['Age'].fillna(mean_age, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "dec57533",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    PassengerId  Survived  Pclass     Sex        Age     Fare Embarked  \\\n",
      "0             1         0       3    male  22.000000   7.2500        S   \n",
      "1             2         1       1  female  38.000000  71.2833        C   \n",
      "2             3         1       3  female  26.000000   7.9250        S   \n",
      "3             4         1       1  female  35.000000  53.1000        S   \n",
      "4             5         0       3    male  35.000000   8.0500        S   \n",
      "5             6         0       3    male  29.699118   8.4583        Q   \n",
      "6             7         0       1    male  54.000000  51.8625        S   \n",
      "7             8         0       3    male   2.000000  21.0750        S   \n",
      "8             9         1       3  female  27.000000  11.1333        S   \n",
      "9            10         1       2  female  14.000000  30.0708        C   \n",
      "10           11         1       3  female   4.000000  16.7000        S   \n",
      "11           12         1       1  female  58.000000  26.5500        S   \n",
      "12           13         0       3    male  20.000000   8.0500        S   \n",
      "13           14         0       3    male  39.000000  31.2750        S   \n",
      "14           15         0       3  female  14.000000   7.8542        S   \n",
      "15           16         1       2  female  55.000000  16.0000        S   \n",
      "16           17         0       3    male   2.000000  29.1250        Q   \n",
      "17           18         1       2    male  29.699118  13.0000        S   \n",
      "18           19         0       3  female  31.000000  18.0000        S   \n",
      "19           20         1       3  female  29.699118   7.2250        C   \n",
      "\n",
      "    FamilySize  \n",
      "0            1  \n",
      "1            1  \n",
      "2            0  \n",
      "3            1  \n",
      "4            0  \n",
      "5            0  \n",
      "6            0  \n",
      "7            4  \n",
      "8            2  \n",
      "9            1  \n",
      "10           2  \n",
      "11           0  \n",
      "12           0  \n",
      "13           6  \n",
      "14           0  \n",
      "15           0  \n",
      "16           5  \n",
      "17           0  \n",
      "18           1  \n",
      "19           0  \n"
     ]
    }
   ],
   "source": [
    "# Display the updated DataFrame\n",
    "print(train.head(20))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f0a5719f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Age         177\n",
      "Cabin       687\n",
      "Embarked      2\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# display variables with missing values\n",
    "print(missing_values[missing_values > 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "03946231",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create dummy variables for the 'Sex' column\n",
    "train = pd.get_dummies(train, columns=['Sex'], prefix='', prefix_sep='')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "c4599236",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   PassengerId  Survived  Pclass   Age     Fare  FamilySize  male\n",
      "0            1         0       3  22.0   7.2500           1     1\n",
      "1            2         1       1  38.0  71.2833           1     0\n",
      "2            3         1       3  26.0   7.9250           0     0\n",
      "3            4         1       1  35.0  53.1000           1     0\n",
      "4            5         0       3  35.0   8.0500           0     1\n"
     ]
    }
   ],
   "source": [
    "# display the updated DataFrame\n",
    "print(train.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "2f7ee399",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "male      577\n",
       "female    314\n",
       "dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Amount of female and male passengers\n",
    "train[['male', 'female']].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "53e5c178",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "\"['female'] not found in axis\"",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[114], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m# drop the 'female' column\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m train \u001b[38;5;241m=\u001b[39m \u001b[43mtrain\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdrop\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mfemale\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\util\\_decorators.py:331\u001b[0m, in \u001b[0;36mdeprecate_nonkeyword_arguments.<locals>.decorate.<locals>.wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    325\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(args) \u001b[38;5;241m>\u001b[39m num_allow_args:\n\u001b[0;32m    326\u001b[0m     warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[0;32m    327\u001b[0m         msg\u001b[38;5;241m.\u001b[39mformat(arguments\u001b[38;5;241m=\u001b[39m_format_argument_list(allow_args)),\n\u001b[0;32m    328\u001b[0m         \u001b[38;5;167;01mFutureWarning\u001b[39;00m,\n\u001b[0;32m    329\u001b[0m         stacklevel\u001b[38;5;241m=\u001b[39mfind_stack_level(),\n\u001b[0;32m    330\u001b[0m     )\n\u001b[1;32m--> 331\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\frame.py:5399\u001b[0m, in \u001b[0;36mDataFrame.drop\u001b[1;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[0;32m   5251\u001b[0m \u001b[38;5;129m@deprecate_nonkeyword_arguments\u001b[39m(version\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, allowed_args\u001b[38;5;241m=\u001b[39m[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mself\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlabels\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n\u001b[0;32m   5252\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdrop\u001b[39m(  \u001b[38;5;66;03m# type: ignore[override]\u001b[39;00m\n\u001b[0;32m   5253\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   5260\u001b[0m     errors: IgnoreRaise \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mraise\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m   5261\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m DataFrame \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m   5262\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m   5263\u001b[0m \u001b[38;5;124;03m    Drop specified labels from rows or columns.\u001b[39;00m\n\u001b[0;32m   5264\u001b[0m \n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   5397\u001b[0m \u001b[38;5;124;03m            weight  1.0     0.8\u001b[39;00m\n\u001b[0;32m   5398\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m-> 5399\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdrop\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   5400\u001b[0m \u001b[43m        \u001b[49m\u001b[43mlabels\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlabels\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   5401\u001b[0m \u001b[43m        \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   5402\u001b[0m \u001b[43m        \u001b[49m\u001b[43mindex\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mindex\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   5403\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   5404\u001b[0m \u001b[43m        \u001b[49m\u001b[43mlevel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlevel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   5405\u001b[0m \u001b[43m        \u001b[49m\u001b[43minplace\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minplace\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   5406\u001b[0m \u001b[43m        \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   5407\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\util\\_decorators.py:331\u001b[0m, in \u001b[0;36mdeprecate_nonkeyword_arguments.<locals>.decorate.<locals>.wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    325\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(args) \u001b[38;5;241m>\u001b[39m num_allow_args:\n\u001b[0;32m    326\u001b[0m     warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[0;32m    327\u001b[0m         msg\u001b[38;5;241m.\u001b[39mformat(arguments\u001b[38;5;241m=\u001b[39m_format_argument_list(allow_args)),\n\u001b[0;32m    328\u001b[0m         \u001b[38;5;167;01mFutureWarning\u001b[39;00m,\n\u001b[0;32m    329\u001b[0m         stacklevel\u001b[38;5;241m=\u001b[39mfind_stack_level(),\n\u001b[0;32m    330\u001b[0m     )\n\u001b[1;32m--> 331\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\generic.py:4505\u001b[0m, in \u001b[0;36mNDFrame.drop\u001b[1;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[0;32m   4503\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m axis, labels \u001b[38;5;129;01min\u001b[39;00m axes\u001b[38;5;241m.\u001b[39mitems():\n\u001b[0;32m   4504\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m labels \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m-> 4505\u001b[0m         obj \u001b[38;5;241m=\u001b[39m \u001b[43mobj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_drop_axis\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlabels\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlevel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlevel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   4507\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m inplace:\n\u001b[0;32m   4508\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_update_inplace(obj)\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\generic.py:4546\u001b[0m, in \u001b[0;36mNDFrame._drop_axis\u001b[1;34m(self, labels, axis, level, errors, only_slice)\u001b[0m\n\u001b[0;32m   4544\u001b[0m         new_axis \u001b[38;5;241m=\u001b[39m axis\u001b[38;5;241m.\u001b[39mdrop(labels, level\u001b[38;5;241m=\u001b[39mlevel, errors\u001b[38;5;241m=\u001b[39merrors)\n\u001b[0;32m   4545\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 4546\u001b[0m         new_axis \u001b[38;5;241m=\u001b[39m \u001b[43maxis\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdrop\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlabels\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   4547\u001b[0m     indexer \u001b[38;5;241m=\u001b[39m axis\u001b[38;5;241m.\u001b[39mget_indexer(new_axis)\n\u001b[0;32m   4549\u001b[0m \u001b[38;5;66;03m# Case for non-unique axis\u001b[39;00m\n\u001b[0;32m   4550\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\indexes\\base.py:6934\u001b[0m, in \u001b[0;36mIndex.drop\u001b[1;34m(self, labels, errors)\u001b[0m\n\u001b[0;32m   6932\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m mask\u001b[38;5;241m.\u001b[39many():\n\u001b[0;32m   6933\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m errors \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mignore\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m-> 6934\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlist\u001b[39m(labels[mask])\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m not found in axis\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m   6935\u001b[0m     indexer \u001b[38;5;241m=\u001b[39m indexer[\u001b[38;5;241m~\u001b[39mmask]\n\u001b[0;32m   6936\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdelete(indexer)\n",
      "\u001b[1;31mKeyError\u001b[0m: \"['female'] not found in axis\""
     ]
    }
   ],
   "source": [
    "# drop the 'female' column\n",
    "train = train.drop(['female'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "f2f8eab9",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    PassengerId  Survived  Pclass        Age     Fare  FamilySize  male\n",
      "0             1         0       3  22.000000   7.2500           1     1\n",
      "1             2         1       1  38.000000  71.2833           1     0\n",
      "2             3         1       3  26.000000   7.9250           0     0\n",
      "3             4         1       1  35.000000  53.1000           1     0\n",
      "4             5         0       3  35.000000   8.0500           0     1\n",
      "5             6         0       3  29.699118   8.4583           0     1\n",
      "6             7         0       1  54.000000  51.8625           0     1\n",
      "7             8         0       3   2.000000  21.0750           4     1\n",
      "8             9         1       3  27.000000  11.1333           2     0\n",
      "9            10         1       2  14.000000  30.0708           1     0\n",
      "10           11         1       3   4.000000  16.7000           2     0\n",
      "11           12         1       1  58.000000  26.5500           0     0\n",
      "12           13         0       3  20.000000   8.0500           0     1\n",
      "13           14         0       3  39.000000  31.2750           6     1\n",
      "14           15         0       3  14.000000   7.8542           0     0\n",
      "15           16         1       2  55.000000  16.0000           0     0\n",
      "16           17         0       3   2.000000  29.1250           5     1\n",
      "17           18         1       2  29.699118  13.0000           0     1\n",
      "18           19         0       3  31.000000  18.0000           1     0\n",
      "19           20         1       3  29.699118   7.2250           0     0\n",
      "20           21         0       2  35.000000  26.0000           0     1\n",
      "21           22         1       2  34.000000  13.0000           0     1\n",
      "22           23         1       3  15.000000   8.0292           0     0\n",
      "23           24         1       1  28.000000  35.5000           0     1\n",
      "24           25         0       3   8.000000  21.0750           4     0\n"
     ]
    }
   ],
   "source": [
    "# display the updated DataFrame\n",
    "print(train.head(25))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "61a97d51",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Age         177\n",
      "Cabin       687\n",
      "Embarked      2\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# display variables with missing values\n",
    "print(missing_values[missing_values > 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "220a1455",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    491\n",
       "1    216\n",
       "2    184\n",
       "Name: Pclass, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# observing Pclass \n",
    "train['Pclass'].value_counts(dropna = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "cd198446",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.629630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.472826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.242363</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass  Survived\n",
       "0       1  0.629630\n",
       "1       2  0.472826\n",
       "2       3  0.242363"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Mean of survival by passenger class\n",
    "\n",
    "train[['Pclass', 'Survived']].groupby(['Pclass'], as_index = False).mean().sort_values(by = 'Survived', ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "f10c1958",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split data into features (X) and target variable (y)\n",
    "X = train[['Age', 'FamilySize', 'Pclass', 'male']]\n",
    "y = train['Survived']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "b04bb9f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the data into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "b429247e",
   "metadata": {},
   "outputs": [],
   "source": [
    "logreg = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "a35f416a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fit the model on the training data\n",
    "logreg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "582b9b31",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make predictions on the testing data\n",
    "y_pred = logreg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "bb86cb0f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Evaluate the model\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "conf_matrix = confusion_matrix(y_test, y_pred)\n",
    "class_report = classification_report(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "59d611d2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.8212290502793296\n",
      "Confusion Matrix:\n",
      " [[93 12]\n",
      " [20 54]]\n",
      "Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.82      0.89      0.85       105\n",
      "           1       0.82      0.73      0.77        74\n",
      "\n",
      "    accuracy                           0.82       179\n",
      "   macro avg       0.82      0.81      0.81       179\n",
      "weighted avg       0.82      0.82      0.82       179\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Print the results\n",
    "print(\"Accuracy:\", accuracy)\n",
    "print(\"Confusion Matrix:\\n\", conf_matrix)\n",
    "print(\"Classification Report:\\n\", class_report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "dbdfad5d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "80.06"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# Logistic Regression\n",
    "logreg = LogisticRegression()\n",
    "logreg.fit(X_train, y_train)\n",
    "Y_pred = logreg.predict(X_test)\n",
    "acc_log = round(logreg.score(X_train, y_train) * 100, 2)\n",
    "acc_log\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "5c225973",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "876fa789",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Logistic Regression\n",
    "logreg = LogisticRegression()\n",
    "logreg.fit(X_train, y_train)\n",
    "y_pred_logreg = logreg.predict(X_test)\n",
    "\n",
    "# Decision Tree\n",
    "decision_tree = DecisionTreeClassifier()\n",
    "decision_tree.fit(X_train, y_train)\n",
    "y_pred_decision_tree = decision_tree.predict(X_test)\n",
    "\n",
    "# Random Forest\n",
    "random_forest = RandomForestClassifier(n_estimators=100)\n",
    "random_forest.fit(X_train, y_train)\n",
    "y_pred_random_forest = random_forest.predict(X_test)\n",
    "\n",
    "# Support Vector Classifier (SVC)\n",
    "svc = SVC()\n",
    "svc.fit(X_train, y_train)\n",
    "y_pred_svc = svc.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "94714010",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Logistic Regression:\n",
      "\n",
      "\n",
      "Accuracy: 0.8212290502793296\n",
      "Confusion Matrix:\n",
      " [[93 12]\n",
      " [20 54]]\n",
      "Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.82      0.89      0.85       105\n",
      "           1       0.82      0.73      0.77        74\n",
      "\n",
      "    accuracy                           0.82       179\n",
      "   macro avg       0.82      0.81      0.81       179\n",
      "weighted avg       0.82      0.82      0.82       179\n",
      "\n",
      "\n",
      "Decision Tree:\n",
      "\n",
      "\n",
      "Accuracy: 0.7877094972067039\n",
      "Confusion Matrix:\n",
      " [[88 17]\n",
      " [21 53]]\n",
      "Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.81      0.84      0.82       105\n",
      "           1       0.76      0.72      0.74        74\n",
      "\n",
      "    accuracy                           0.79       179\n",
      "   macro avg       0.78      0.78      0.78       179\n",
      "weighted avg       0.79      0.79      0.79       179\n",
      "\n",
      "\n",
      "Random Forest:\n",
      "\n",
      "\n",
      "Accuracy: 0.8268156424581006\n",
      "Confusion Matrix:\n",
      " [[92 13]\n",
      " [18 56]]\n",
      "Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.84      0.88      0.86       105\n",
      "           1       0.81      0.76      0.78        74\n",
      "\n",
      "    accuracy                           0.83       179\n",
      "   macro avg       0.82      0.82      0.82       179\n",
      "weighted avg       0.83      0.83      0.83       179\n",
      "\n",
      "\n",
      "Support Vector Classifier:\n",
      "\n",
      "\n",
      "Accuracy: 0.6089385474860335\n",
      "Confusion Matrix:\n",
      " [[104   1]\n",
      " [ 69   5]]\n",
      "Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.60      0.99      0.75       105\n",
      "           1       0.83      0.07      0.13        74\n",
      "\n",
      "    accuracy                           0.61       179\n",
      "   macro avg       0.72      0.53      0.44       179\n",
      "weighted avg       0.70      0.61      0.49       179\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the models\n",
    "def evaluate_model(y_true, y_pred, model_name):\n",
    "    accuracy = accuracy_score(y_true, y_pred)\n",
    "    conf_matrix = confusion_matrix(y_true, y_pred)\n",
    "    class_report = classification_report(y_true, y_pred)\n",
    "    print(f\"\\n{model_name}:\")\n",
    "    print(\"\\n\")\n",
    "    print(\"Accuracy:\", accuracy)\n",
    "    print(\"Confusion Matrix:\\n\", conf_matrix)\n",
    "    print(\"Classification Report:\\n\", class_report)\n",
    "\n",
    "# Evaluate Logistic Regression\n",
    "evaluate_model(y_test, y_pred_logreg, \"Logistic Regression\")\n",
    "\n",
    "# Evaluate Decision Tree\n",
    "evaluate_model(y_test, y_pred_decision_tree, \"Decision Tree\")\n",
    "\n",
    "# Evaluate Random Forest\n",
    "evaluate_model(y_test, y_pred_random_forest, \"Random Forest\")\n",
    "\n",
    "# Evaluate SVC\n",
    "evaluate_model(y_test, y_pred_svc, \"Support Vector Classifier\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "011555f9",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Logistic Regression:\n",
      "\n",
      "\n",
      "Accuracy: 0.8212290502793296\n",
      "Confusion Matrix:\n",
      " [[93 12]\n",
      " [20 54]]\n",
      "Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.82      0.89      0.85       105\n",
      "           1       0.82      0.73      0.77        74\n",
      "\n",
      "    accuracy                           0.82       179\n",
      "   macro avg       0.82      0.81      0.81       179\n",
      "weighted avg       0.82      0.82      0.82       179\n",
      "\n",
      "\n",
      "Random Forest:\n",
      "\n",
      "\n",
      "Accuracy: 0.8268156424581006\n",
      "Confusion Matrix:\n",
      " [[92 13]\n",
      " [18 56]]\n",
      "Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.84      0.88      0.86       105\n",
      "           1       0.81      0.76      0.78        74\n",
      "\n",
      "    accuracy                           0.83       179\n",
      "   macro avg       0.82      0.82      0.82       179\n",
      "weighted avg       0.83      0.83      0.83       179\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Evaluate Logistic Regression\n",
    "evaluate_model(y_test, y_pred_logreg, \"Logistic Regression\")\n",
    "\n",
    "# Evaluate Random Forest\n",
    "evaluate_model(y_test, y_pred_random_forest, \"Random Forest\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "289d6dd5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy - Logistic Regression: 80.06\n",
      "Testing Accuracy - Logistic Regression: 82.12\n",
      "\n",
      "\n",
      "Training Accuracy - Decision Tree: 91.43\n",
      "Testing Accuracy - Decision Tree: 78.77\n",
      "\n",
      "\n",
      "Training Accuracy - Random Forest: 91.43\n",
      "Testing Accuracy - Random Forest: 81.56\n",
      "\n",
      "\n",
      "Training Accuracy - SVC: 64.89\n",
      "Testing Accuracy - SVC: 60.89\n"
     ]
    }
   ],
   "source": [
    "# Logistic Regression\n",
    "logreg = LogisticRegression()\n",
    "logreg.fit(X_train, y_train)\n",
    "\n",
    "# Training accuracy\n",
    "acc_train_logreg = round(logreg.score(X_train, y_train) * 100, 2)\n",
    "print(\"Training Accuracy - Logistic Regression:\", acc_train_logreg)\n",
    "\n",
    "# Testing accuracy\n",
    "acc_test_logreg = round(logreg.score(X_test, y_test) * 100, 2)\n",
    "print(\"Testing Accuracy - Logistic Regression:\", acc_test_logreg)\n",
    "print(\"\\n\")\n",
    "\n",
    "# Decision Tree\n",
    "decision_tree = DecisionTreeClassifier()\n",
    "decision_tree.fit(X_train, y_train)\n",
    "\n",
    "# Training accuracy\n",
    "acc_train_decision_tree = round(decision_tree.score(X_train, y_train) * 100, 2)\n",
    "print(\"Training Accuracy - Decision Tree:\", acc_train_decision_tree)\n",
    "\n",
    "# Testing accuracy\n",
    "acc_test_decision_tree = round(decision_tree.score(X_test, y_test) * 100, 2)\n",
    "print(\"Testing Accuracy - Decision Tree:\", acc_test_decision_tree)\n",
    "print(\"\\n\")\n",
    "\n",
    "# Random Forest\n",
    "random_forest = RandomForestClassifier()\n",
    "random_forest.fit(X_train, y_train)\n",
    "\n",
    "# Training accuracy\n",
    "acc_train_random_forest = round(random_forest.score(X_train, y_train) * 100, 2)\n",
    "print(\"Training Accuracy - Random Forest:\", acc_train_random_forest)\n",
    "\n",
    "# Testing accuracy\n",
    "acc_test_random_forest = round(random_forest.score(X_test, y_test) * 100, 2)\n",
    "print(\"Testing Accuracy - Random Forest:\", acc_test_random_forest)\n",
    "print(\"\\n\")\n",
    "\n",
    "# Support Vector Classifier\n",
    "svc = SVC()\n",
    "svc.fit(X_train, y_train)\n",
    "\n",
    "# Training accuracy\n",
    "acc_train_svc = round(svc.score(X_train, y_train) * 100, 2)\n",
    "print(\"Training Accuracy - SVC:\", acc_train_svc)\n",
    "\n",
    "# Testing accuracy\n",
    "acc_test_svc = round(svc.score(X_test, y_test) * 100, 2)\n",
    "print(\"Testing Accuracy - SVC:\", acc_test_svc)"
   ]
  }
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