Dunn_Marina_Assignment3.ipynb

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    "#https://www.kaggle.com/mmueller/allstate-claims-severity/stacking-starter/run/390867\n",
    "#https://www.kaggle.com/arthurtok/titanic/simple-stacking-with-xgboost-0-808"
   ]
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   "source": [
    "Marina Dunn\n",
    "\n",
    "PHYS 243 HW3\n",
    "\n",
    "Fall 2021\n",
    "\n",
    "Prof. Mobasher\n",
    "\n",
    "* Review kaggle notebook (https://www.kaggle.com/arthurtok/introduction-to-ensembling-stacking-in-python) from class which was created to predict the likelihood of surviving the Titanic sinking.\n",
    "\n",
    "Goal: Perform a PCA analysis on this dataset and compare the PCA feature weights with the techniques used in the notebook above."
   ]
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    "import pandas as pd\n",
    "import numpy as np\n",
    "import re\n",
    "import sklearn\n",
    "import xgboost as xgb\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# Going to use these 5 base models for the stacking\n",
    "from sklearn.ensemble import (RandomForestClassifier, AdaBoostClassifier, \n",
    "                              GradientBoostingClassifier, ExtraTreesClassifier)\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.cross_validation import KFold\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.feature_selection import mutual_info_regression\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from xgboost import XGBRegressor\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression, SGDClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "def apply_pca(X, standardize=True):\n",
    "    # Standardize\n",
    "    if standardize:\n",
    "        X = (X - X.mean(axis=0)) / X.std(axis=0)\n",
    "    # Create principal components\n",
    "    pca = PCA()\n",
    "    X_pca = pca.fit_transform(X)\n",
    "    # Convert to dataframe\n",
    "    component_names = [f\"PC{i+1}\" for i in range(X_pca.shape[1])]\n",
    "    X_pca = pd.DataFrame(X_pca, columns=component_names)\n",
    "    # Create loadings\n",
    "    loadings = pd.DataFrame(\n",
    "        pca.components_.T,  # transpose the matrix of loadings\n",
    "        columns=component_names,  # so the columns are the principal components\n",
    "        index=X.columns,  # and the rows are the original features\n",
    "    )\n",
    "    return pca, X_pca, loadings"
   ]
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>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>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</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>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</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>14.454200</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>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "       PassengerId    Survived     ...           Parch        Fare\n",
       "count   891.000000  891.000000     ...      891.000000  891.000000\n",
       "mean    446.000000    0.383838     ...        0.381594   32.204208\n",
       "std     257.353842    0.486592     ...        0.806057   49.693429\n",
       "min       1.000000    0.000000     ...        0.000000    0.000000\n",
       "25%     223.500000    0.000000     ...        0.000000    7.910400\n",
       "50%     446.000000    0.000000     ...        0.000000   14.454200\n",
       "75%     668.500000    1.000000     ...        0.000000   31.000000\n",
       "max     891.000000    1.000000     ...        6.000000  512.329200\n",
       "\n",
       "[8 rows x 7 columns]"
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   "source": [
    "# Load in the train and test datasets\n",
    "train = pd.read_csv('../input/train.csv', header = 0, dtype={'Age': np.float64})\n",
    "test = pd.read_csv('../input/test.csv', header = 0, dtype={'Age': np.float64})\n",
    "outcome = pd.read_csv('../input/gender_submission.csv', header = 0, dtype={'Age': np.float64}) \n",
    "\n",
    "# Store our passenger ID for easy access\n",
    "PassengerId = train['PassengerId']\n",
    "PassengerId = test['PassengerId']\n",
    "\n",
    "train.describe()"
   ]
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     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Survived         0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age            177\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             0\n",
       "Cabin          687\n",
       "Embarked         2\n",
       "dtype: int64"
      ]
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     "execution_count": 3,
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   "source": [
    "train.isnull().sum() \n",
    "# we are missing values for embarked, age, and cabin, so we will have to account for these"
   ]
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      "PassengerId    891 non-null int64\n",
      "Survived       891 non-null int64\n",
      "Pclass         891 non-null int64\n",
      "Name           891 non-null object\n",
      "Sex            891 non-null object\n",
      "Age            714 non-null float64\n",
      "SibSp          891 non-null int64\n",
      "Parch          891 non-null int64\n",
      "Ticket         891 non-null object\n",
      "Fare           891 non-null float64\n",
      "Cabin          204 non-null object\n",
      "Embarked       889 non-null object\n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.6+ KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 418 entries, 0 to 417\n",
      "Data columns (total 11 columns):\n",
      "PassengerId    418 non-null int64\n",
      "Pclass         418 non-null int64\n",
      "Name           418 non-null object\n",
      "Sex            418 non-null object\n",
      "Age            332 non-null float64\n",
      "SibSp          418 non-null int64\n",
      "Parch          418 non-null int64\n",
      "Ticket         418 non-null object\n",
      "Fare           417 non-null float64\n",
      "Cabin          91 non-null object\n",
      "Embarked       418 non-null object\n",
      "dtypes: float64(2), int64(4), object(5)\n",
      "memory usage: 36.0+ KB\n"
     ]
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   "source": [
    "test.info()"
   ]
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     "data": {
      "image/png": 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k8AAwIIEHgAEJPAAMSOABYEACDwADEngAGJDAA8CABB4ABiTwADAggQeAAQk8AAxI4AFg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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5460e42400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# initial looks\n",
    "# survival distribution\n",
    "plt.figure(figsize=(8,8))\n",
    "test_plt = test.merge(outcome, on=\"PassengerId\")\n",
    "full_data = pd.concat([train, test])\n",
    "barr = plt.bar( full_data.Survived.value_counts().index,  full_data.Survived.value_counts(), alpha = 0.67, )\n",
    "plt.title('Distribution of Survival')\n",
    "plt.ylabel('Count')\n",
    "plt.xticks([0,1], ['Dead','Alive'])\n",
    "\n",
    "plt.ylim((0,650))\n",
    "barr = plt.gcf()\n",
    "plt.rcParams[\"figure.figsize\"] = (12,6)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-05T19:09:15.229093Z",
     "iopub.status.busy": "2021-12-05T19:09:15.228647Z",
     "iopub.status.idle": "2021-12-05T19:09:15.241375Z",
     "shell.execute_reply": "2021-12-05T19:09:15.240282Z",
     "shell.execute_reply.started": "2021-12-05T19:09:15.229020Z"
    }
   },
   "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",
      "PassengerId    891 non-null int64\n",
      "Survived       891 non-null int64\n",
      "Pclass         891 non-null int64\n",
      "Name           891 non-null object\n",
      "Sex            891 non-null object\n",
      "Age            714 non-null float64\n",
      "SibSp          891 non-null int64\n",
      "Parch          891 non-null int64\n",
      "Ticket         891 non-null object\n",
      "Fare           891 non-null float64\n",
      "Cabin          204 non-null object\n",
      "Embarked       889 non-null object\n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.6+ KB\n"
     ]
    }
   ],
   "source": [
    "X_train = train.copy()\n",
    "X_train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-05T19:09:15.243450Z",
     "iopub.status.busy": "2021-12-05T19:09:15.242889Z",
     "iopub.status.idle": "2021-12-05T19:09:15.260041Z",
     "shell.execute_reply": "2021-12-05T19:09:15.259164Z",
     "shell.execute_reply.started": "2021-12-05T19:09:15.243388Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 418 entries, 0 to 417\n",
      "Data columns (total 11 columns):\n",
      "PassengerId    418 non-null int64\n",
      "Pclass         418 non-null int64\n",
      "Name           418 non-null object\n",
      "Sex            418 non-null object\n",
      "Age            332 non-null float64\n",
      "SibSp          418 non-null int64\n",
      "Parch          418 non-null int64\n",
      "Ticket         418 non-null object\n",
      "Fare           417 non-null float64\n",
      "Cabin          91 non-null object\n",
      "Embarked       418 non-null object\n",
      "dtypes: float64(2), int64(4), object(5)\n",
      "memory usage: 36.0+ KB\n"
     ]
    }
   ],
   "source": [
    "X_test = test.copy()\n",
    "X_test.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Understanding the data**\n",
    "\n",
    "The training set contains 891 passengers, and the test set contains 418 passengers.\n",
    "The training set includes whether the passengers survived or not. By merging the test and gender_submission files, we can see the survival values for the remaining 418 passengers. There are missing values that need to be accounted for, and many factors, such as the person's name, will not be factored into whether they survived or not. We will also have to use numerical values to compare features, so string features will have to be changed to numerical values. \n",
    "\n",
    "When the Titanic sank, the common saying was that the \"women and children\" go onto the lifeboats first, so we will likely expect to see a higher survival rate in women and younger children, especially those in the wealthier classes. Because people of lower class were located further down in the ship, we suspect that there will be a lower survival rate with this class, especially men.\n",
    "\n",
    "Parameters:\n",
    "\n",
    "*Pclass: Passenger’s class, 1 being highest class, 3 being lowest class\n",
    "\n",
    "*Survived: If the individual survived (1) or died (0)\n",
    "\n",
    "*Age: Age of individual at time of Titanic event\n",
    "\n",
    "*Sex: Gender of individual\n",
    "\n",
    "*Name: Name of individual (if individual was female, lists husband's name in addition to maiden name in parentheses). Also includes title, which other's have used as a factor into survival rate\n",
    "\n",
    "*SibSp:the number of siblings/spouses \n",
    "\n",
    "*Parch: the number of children/parents\n",
    "\n",
    "*Ticket: Ticket number\n",
    "\n",
    "*Fare: Fare paid\n",
    "\n",
    "*Cabin: Cabin number onboard, first character is the deck\n",
    "\n",
    "*Embarked: Port of embarkation\n",
    "\n",
    "*PassengerId: identification number of passenger, used as our index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-05T19:09:15.261776Z",
     "iopub.status.busy": "2021-12-05T19:09:15.261312Z",
     "iopub.status.idle": "2021-12-05T19:09:15.271115Z",
     "shell.execute_reply": "2021-12-05T19:09:15.270274Z",
     "shell.execute_reply.started": "2021-12-05T19:09:15.261719Z"
    }
   },
   "outputs": [],
   "source": [
    "# fill in Embarked status, cabin, fare and age for NULL values\n",
    "# for embarked, fill with the most occurred value ( 'S' )\n",
    "\n",
    "values = {\"Embarked\": 'S', \"Cabin\": 0}\n",
    "X_train = X_train.fillna(value=values)\n",
    "X_test = X_test.fillna(value=values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-05T19:09:57.718241Z",
     "iopub.status.busy": "2021-12-05T19:09:57.717927Z",
     "iopub.status.idle": "2021-12-05T19:09:57.736116Z",
     "shell.execute_reply": "2021-12-05T19:09:57.735259Z",
     "shell.execute_reply.started": "2021-12-05T19:09:57.718197Z"
    }
   },
   "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",
      "PassengerId    891 non-null int64\n",
      "Survived       891 non-null int64\n",
      "Pclass         891 non-null int64\n",
      "Name           891 non-null object\n",
      "Sex            891 non-null object\n",
      "Age            714 non-null float64\n",
      "SibSp          891 non-null int64\n",
      "Parch          891 non-null int64\n",
      "Ticket         891 non-null object\n",
      "Fare           891 non-null int64\n",
      "Cabin          891 non-null object\n",
      "Embarked       891 non-null object\n",
      "dtypes: float64(1), int64(6), object(5)\n",
      "memory usage: 83.6+ KB\n"
     ]
    }
   ],
   "source": [
    "pd.options.mode.use_inf_as_na = True # make non-filled values show as NaN to correct\n",
    "\n",
    "# fill null fare with average fare\n",
    "values = X_train.Fare.mean()\n",
    "X_train['Fare'] = X_train.Fare.fillna(values)\n",
    "X_train = X_train.astype({'Fare': 'int64'})\n",
    "X_train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-05T19:09:59.101080Z",
     "iopub.status.busy": "2021-12-05T19:09:59.100728Z",
     "iopub.status.idle": "2021-12-05T19:09:59.117182Z",
     "shell.execute_reply": "2021-12-05T19:09:59.116228Z",
     "shell.execute_reply.started": "2021-12-05T19:09:59.101029Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 418 entries, 0 to 417\n",
      "Data columns (total 11 columns):\n",
      "PassengerId    418 non-null int64\n",
      "Pclass         418 non-null int64\n",
      "Name           418 non-null object\n",
      "Sex            418 non-null object\n",
      "Age            332 non-null float64\n",
      "SibSp          418 non-null int64\n",
      "Parch          418 non-null int64\n",
      "Ticket         418 non-null object\n",
      "Fare           418 non-null int64\n",
      "Cabin          418 non-null object\n",
      "Embarked       418 non-null object\n",
      "dtypes: float64(1), int64(5), object(5)\n",
      "memory usage: 36.0+ KB\n"
     ]
    }
   ],
   "source": [
    "values = X_test.Fare.mean()\n",
    "X_test['Fare'] = X_test.Fare.fillna(values)\n",
    "X_test = X_test.astype({'Fare': 'int64'})\n",
    "X_test.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-05T19:10:10.351506Z",
     "iopub.status.busy": "2021-12-05T19:10:10.351193Z",
     "iopub.status.idle": "2021-12-05T19:10:10.460731Z",
     "shell.execute_reply": "2021-12-05T19:10:10.459986Z",
     "shell.execute_reply.started": "2021-12-05T19:10:10.351451Z"
    }
   },
   "outputs": [],
   "source": [
    "# for age, generate random numbers between (mean - std) and (mean + std)\n",
    "# then we categorize age into 5 range.\n",
    "age_avg = X_train['Age'].mean()\n",
    "age_std = X_train['Age'].std()\n",
    "age_null_count = X_train['Age'].isnull().sum()\n",
    "    \n",
    "age_null_random_list = np.random.randint(age_avg - age_std, age_avg + age_std, size=age_null_count)\n",
    "X_train['Age'][np.isnan(X_train['Age'])] = age_null_random_list\n",
    "X_train['Age'] = X_train['Age'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-05T19:10:11.332580Z",
     "iopub.status.busy": "2021-12-05T19:10:11.332262Z",
     "iopub.status.idle": "2021-12-05T19:10:11.429725Z",
     "shell.execute_reply": "2021-12-05T19:10:11.428926Z",
     "shell.execute_reply.started": "2021-12-05T19:10:11.332507Z"
    }
   },
   "outputs": [],
   "source": [
    "age_avg = X_test['Age'].mean()\n",
    "age_std = X_test['Age'].std()\n",
    "age_null_count = X_test['Age'].isnull().sum()\n",
    "    \n",
    "age_null_random_list = np.random.randint(age_avg - age_std, age_avg + age_std, size=age_null_count)\n",
    "X_test['Age'][np.isnan(X_test['Age'])] = age_null_random_list\n",
    "X_test['Age'] = X_test['Age'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-05T19:10:12.610357Z",
     "iopub.status.busy": "2021-12-05T19:10:12.610064Z",
     "iopub.status.idle": "2021-12-05T19:10:12.851137Z",
     "shell.execute_reply": "2021-12-05T19:10:12.850168Z",
     "shell.execute_reply.started": "2021-12-05T19:10:12.610317Z"
    }
   },
   "outputs": [
    {
     "data": {
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0dbVzbjZwkvdxTb9vbna9ma00s5VVVVUHDSsS6lbvCNysJSpKPz9K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\nySAw1DDaAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f546e05b400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# initial look of survival by age\n",
    "plt.figure(figsize=(12,6))\n",
    "full_data = pd.concat([X_train, X_test])\n",
    "sns.distplot(full_data.Age, )\n",
    "plt.title('Distribution of Passengers by Age')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-05T19:10:13.996986Z",
     "iopub.status.busy": "2021-12-05T19:10:13.996635Z",
     "iopub.status.idle": "2021-12-05T19:10:14.187349Z",
     "shell.execute_reply": "2021-12-05T19:10:14.186278Z",
     "shell.execute_reply.started": "2021-12-05T19:10:13.996917Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f545d421b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# initial look of survival by sex\n",
    "dead = full_data[full_data.Survived == 0]\n",
    "\n",
    "plt.figure(figsize = (10,7))\n",
    "plt.bar( full_data['Sex'].value_counts().index,  full_data['Sex'].value_counts(), label = 'alive', color = 'blue', alpha = 0.5 )\n",
    "plt.bar( dead['Sex'].value_counts().index,  dead['Sex'].value_counts(), label = 'dead', color = 'black', alpha = 0.5 )\n",
    "plt.legend(['Total','Died'])\n",
    "plt.title('Gender Survival Aboard the Titanic')\n",
    "plt.ylabel('# of Passengers')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-05T19:10:14.923031Z",
     "iopub.status.busy": "2021-12-05T19:10:14.922406Z",
     "iopub.status.idle": "2021-12-05T19:10:14.929298Z",
     "shell.execute_reply": "2021-12-05T19:10:14.928306Z",
     "shell.execute_reply.started": "2021-12-05T19:10:14.922965Z"
    }
   },
   "outputs": [],
   "source": [
    "# for later\n",
    "X = full_data.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-05T19:10:15.792820Z",
     "iopub.status.busy": "2021-12-05T19:10:15.792518Z",
     "iopub.status.idle": "2021-12-05T19:10:15.808268Z",
     "shell.execute_reply": "2021-12-05T19:10:15.807246Z",
     "shell.execute_reply.started": "2021-12-05T19:10:15.792757Z"
    }
   },
   "outputs": [],
   "source": [
    "X_train['CategoricalAge'] = pd.cut(X_train['Age'], 5)\n",
    "X_test['CategoricalAge'] = pd.cut(X_test['Age'], 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-05T19:10:17.189397Z",
     "iopub.status.busy": "2021-12-05T19:10:17.188767Z",
     "iopub.status.idle": "2021-12-05T19:10:17.201998Z",
     "shell.execute_reply": "2021-12-05T19:10:17.201070Z",
     "shell.execute_reply.started": "2021-12-05T19:10:17.189314Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 13 columns):\n",
      "PassengerId       891 non-null int64\n",
      "Survived          891 non-null int64\n",
      "Pclass            891 non-null int64\n",
      "Name              891 non-null object\n",
      "Sex               891 non-null object\n",
      "Age               891 non-null int64\n",
      "SibSp             891 non-null int64\n",
      "Parch             891 non-null int64\n",
      "Ticket            891 non-null object\n",
      "Fare              891 non-null int64\n",
      "Cabin             891 non-null object\n",
      "Embarked          891 non-null object\n",
      "CategoricalAge    891 non-null category\n",
      "dtypes: category(1), int64(7), object(5)\n",
      "memory usage: 84.6+ KB\n"
     ]
    }
   ],
   "source": [
    "X_train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-05T19:10:17.936566Z",
     "iopub.status.busy": "2021-12-05T19:10:17.936229Z",
     "iopub.status.idle": "2021-12-05T19:10:17.947531Z",
     "shell.execute_reply": "2021-12-05T19:10:17.946274Z",
     "shell.execute_reply.started": "2021-12-05T19:10:17.936497Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 418 entries, 0 to 417\n",
      "Data columns (total 12 columns):\n",
      "PassengerId       418 non-null int64\n",
      "Pclass            418 non-null int64\n",
      "Name              418 non-null object\n",
      "Sex               418 non-null object\n",
      "Age               418 non-null int64\n",
      "SibSp             418 non-null int64\n",
      "Parch             418 non-null int64\n",
      "Ticket            418 non-null object\n",
      "Fare              418 non-null int64\n",
      "Cabin             418 non-null object\n",
      "Embarked          418 non-null object\n",
      "CategoricalAge    418 non-null category\n",
      "dtypes: category(1), int64(6), object(5)\n",
      "memory usage: 36.5+ KB\n"
     ]
    }
   ],
   "source": [
    "X_test.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-05T19:10:18.821370Z",
     "iopub.status.busy": "2021-12-05T19:10:18.820882Z",
     "iopub.status.idle": "2021-12-05T19:10:18.825100Z",
     "shell.execute_reply": "2021-12-05T19:10:18.824163Z",
     "shell.execute_reply.started": "2021-12-05T19:10:18.821315Z"
    }
   },
   "outputs": [],
   "source": [
    "# Because numerical values are required in order to do PCA, we will have to change some\n",
    "# columns into numerical types before performing it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "_cell_guid": "e85b2a80-88a4-928f-f2b2-24895dea38f3",
    "_execution_state": "idle",
    "_uuid": "b1c67249f91768ce8e5e2751364d32c87446cf55",
    "execution": {
     "iopub.execute_input": "2021-12-05T19:10:20.037406Z",
     "iopub.status.busy": "2021-12-05T19:10:20.036839Z",
     "iopub.status.idle": "2021-12-05T19:10:20.053125Z",
     "shell.execute_reply": "2021-12-05T19:10:20.052296Z",
     "shell.execute_reply.started": "2021-12-05T19:10:20.037357Z"
    }
   },
   "outputs": [],
   "source": [
    "# Mapping Sex\n",
    "X_train['Sex'] = X_train['Sex'].map( {'female': 0, 'male': 1} ).astype(int)\n",
    "X_test['Sex'] = X_test['Sex'].map( {'female': 0, 'male': 1} ).astype(int)\n",
    "\n",
    "# Mapping Embarked\n",
    "X_train['Embarked'] = X_train['Embarked'].map( {'S': 0, 'C': 1, 'Q': 2} ).astype(int)\n",
    "X_test['Embarked'] = X_test['Embarked'].map( {'S': 0, 'C': 1, 'Q': 2} ).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "_cell_guid": "e85b2a80-88a4-928f-f2b2-24895dea38f3",
    "_execution_state": "idle",
    "_uuid": "b1c67249f91768ce8e5e2751364d32c87446cf55",
    "execution": {
     "iopub.execute_input": "2021-12-05T19:10:20.917209Z",
     "iopub.status.busy": "2021-12-05T19:10:20.916876Z",
     "iopub.status.idle": "2021-12-05T19:10:21.062054Z",
     "shell.execute_reply": "2021-12-05T19:10:21.061244Z",
     "shell.execute_reply.started": "2021-12-05T19:10:20.917148Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 13 columns):\n",
      "PassengerId       891 non-null int64\n",
      "Survived          891 non-null int64\n",
      "Pclass            891 non-null int64\n",
      "Name              891 non-null object\n",
      "Sex               891 non-null int64\n",
      "Age               891 non-null int64\n",
      "SibSp             891 non-null int64\n",
      "Parch             891 non-null int64\n",
      "Ticket            891 non-null object\n",
      "Fare              891 non-null int64\n",
      "Cabin             891 non-null object\n",
      "Embarked          891 non-null int64\n",
      "CategoricalAge    891 non-null category\n",
      "dtypes: category(1), int64(9), object(3)\n",
      "memory usage: 84.6+ KB\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 418 entries, 0 to 417\n",
      "Data columns (total 12 columns):\n",
      "PassengerId       418 non-null int64\n",
      "Pclass            418 non-null int64\n",
      "Name              418 non-null object\n",
      "Sex               418 non-null int64\n",
      "Age               418 non-null int64\n",
      "SibSp             418 non-null int64\n",
      "Parch             418 non-null int64\n",
      "Ticket            418 non-null object\n",
      "Fare              418 non-null int64\n",
      "Cabin             418 non-null object\n",
      "Embarked          418 non-null int64\n",
      "CategoricalAge    418 non-null category\n",
      "dtypes: category(1), int64(8), object(3)\n",
      "memory usage: 36.5+ KB\n"
     ]
    }
   ],
   "source": [
    "# Mapping Fare\n",
    "X_train.loc[ X_train['Fare'] <= 7.91, 'Fare'] = 0\n",
    "X_train.loc[(X_train['Fare'] > 7.91) & (X_train['Fare'] <= 14.454), 'Fare'] = 1\n",
    "X_train.loc[(X_train['Fare'] > 14.454) & (X_train['Fare'] <= 31), 'Fare'] = 2\n",
    "X_train.loc[ X_train['Fare'] > 31, 'Fare'] = 3\n",
    "\n",
    "X_test.loc[ X_test['Fare'] <= 7.91, 'Fare'] = 0\n",
    "X_test.loc[(X_test['Fare'] > 7.91) & (X_test['Fare'] <= 14.454), 'Fare'] = 1\n",
    "X_test.loc[(X_test['Fare'] > 14.454) & (X_test['Fare'] <= 31), 'Fare'] = 2\n",
    "X_test.loc[ X_test['Fare'] > 31, 'Fare'] = 3\n",
    "    \n",
    "# Mapping Age, generate random numbers between (mean - std) and (mean + std). \n",
    "# then categorize age into 5 range.\n",
    "X_train.loc[ X_train['Age'] <= 16, 'Age'] = 0\n",
    "X_train.loc[(X_train['Age'] > 16) & (X_train['Age'] <= 32), 'Age'] = 1\n",
    "X_train.loc[(X_train['Age'] > 32) & (X_train['Age'] <= 48), 'Age'] = 2\n",
    "X_train.loc[(X_train['Age'] > 48) & (X_train['Age'] <= 64), 'Age'] = 3\n",
    "X_train.loc[ X_train['Age'] > 64, 'Age'] = 4\n",
    "X_train.info()\n",
    "\n",
    "X_test.loc[X_test['Age'] <= 16, 'Age'] = 0\n",
    "X_test.loc[(X_test['Age'] > 16) & (X_test['Age'] <= 32), 'Age'] = 1\n",
    "X_test.loc[(X_test['Age'] > 32) & (X_test['Age'] <= 48), 'Age'] = 2\n",
    "X_test.loc[(X_test['Age'] > 48) & (X_test['Age'] <= 64), 'Age'] = 3\n",
    "X_test.loc[ X_test['Age'] > 64, 'Age'] = 4\n",
    "X_test.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-05T19:10:22.017969Z",
     "iopub.status.busy": "2021-12-05T19:10:22.017647Z",
     "iopub.status.idle": "2021-12-05T19:10:22.023622Z",
     "shell.execute_reply": "2021-12-05T19:10:22.022675Z",
     "shell.execute_reply.started": "2021-12-05T19:10:22.017916Z"
    }
   },
   "outputs": [],
   "source": [
    "y_train = X_train['Survived']\n",
    "frame = {\"Survived\": y_train}\n",
    "y_train = pd.DataFrame(frame)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "_cell_guid": "8fc645ba-4b38-cea7-17b3-02175cb103d9",
    "_execution_state": "idle",
    "_uuid": "ca2d48b03d45f914db2ee9ae3ee95aad8fb20431",
    "execution": {
     "iopub.execute_input": "2021-12-05T19:10:22.918592Z",
     "iopub.status.busy": "2021-12-05T19:10:22.917985Z",
     "iopub.status.idle": "2021-12-05T19:10:22.964175Z",
     "shell.execute_reply": "2021-12-05T19:10:22.963313Z",
     "shell.execute_reply.started": "2021-12-05T19:10:22.918545Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
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       "      <th>SibSp</th>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>3</td>\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",
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       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\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",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\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",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>3</td>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>3</td>\n",
       "      <td>1</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>...</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>861</th>\n",
       "      <td>2</td>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>862</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>863</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>864</th>\n",
       "      <td>2</td>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>865</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>866</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>867</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868</th>\n",
       "      <td>3</td>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>869</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>870</th>\n",
       "      <td>3</td>\n",
       "      <td>1</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>871</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>872</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>873</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>874</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>875</th>\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",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>876</th>\n",
       "      <td>3</td>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>877</th>\n",
       "      <td>3</td>\n",
       "      <td>1</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>878</th>\n",
       "      <td>3</td>\n",
       "      <td>1</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>879</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>880</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>881</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>882</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>883</th>\n",
       "      <td>2</td>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>884</th>\n",
       "      <td>3</td>\n",
       "      <td>1</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>885</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>2</td>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pclass  Sex  Age  SibSp  Parch  Fare  Embarked\n",
       "0         3    1    1      1      0     0         0\n",
       "1         1    0    2      1      0     3         1\n",
       "2         3    0    1      0      0     0         0\n",
       "3         1    0    2      1      0     3         0\n",
       "4         3    1    2      0      0     1         0\n",
       "5         3    1    1      0      0     1         2\n",
       "6         1    1    3      0      0     3         0\n",
       "7         3    1    0      3      1     2         0\n",
       "8         3    0    1      0      2     1         0\n",
       "9         2    0    0      1      0     2         1\n",
       "10        3    0    0      1      1     2         0\n",
       "11        1    0    3      0      0     2         0\n",
       "12        3    1    1      0      0     1         0\n",
       "13        3    1    2      1      5     2         0\n",
       "14        3    0    0      0      0     0         0\n",
       "15        2    0    3      0      0     2         0\n",
       "16        3    1    0      4      1     2         2\n",
       "17        2    1    2      0      0     1         0\n",
       "18        3    0    1      1      0     2         0\n",
       "19        3    0    1      0      0     0         1\n",
       "20        2    1    2      0      0     2         0\n",
       "21        2    1    2      0      0     1         0\n",
       "22        3    0    0      0      0     1         2\n",
       "23        1    1    1      0      0     3         0\n",
       "24        3    0    0      3      1     2         0\n",
       "25        3    0    2      1      5     2         0\n",
       "26        3    1    1      0      0     0         1\n",
       "27        1    1    1      3      2     3         0\n",
       "28        3    0    1      0      0     0         2\n",
       "29        3    1    1      0      0     0         0\n",
       "..      ...  ...  ...    ...    ...   ...       ...\n",
       "861       2    1    1      1      0     1         0\n",
       "862       1    0    2      0      0     2         0\n",
       "863       3    0    1      8      2     3         0\n",
       "864       2    1    1      0      0     1         0\n",
       "865       2    0    2      0      0     1         0\n",
       "866       2    0    1      1      0     1         1\n",
       "867       1    1    1      0      0     3         0\n",
       "868       3    1    1      0      0     1         0\n",
       "869       3    1    0      1      1     1         0\n",
       "870       3    1    1      0      0     0         0\n",
       "871       1    0    2      1      1     3         0\n",
       "872       1    1    2      0      0     0         0\n",
       "873       3    1    2      0      0     1         0\n",
       "874       2    0    1      1      0     2         1\n",
       "875       3    0    0      0      0     0         1\n",
       "876       3    1    1      0      0     1         0\n",
       "877       3    1    1      0      0     0         0\n",
       "878       3    1    1      0      0     0         0\n",
       "879       1    0    3      0      1     3         1\n",
       "880       2    0    1      0      1     2         0\n",
       "881       3    1    2      0      0     0         0\n",
       "882       3    0    1      0      0     1         0\n",
       "883       2    1    1      0      0     1         0\n",
       "884       3    1    1      0      0     0         0\n",
       "885       3    0    2      0      5     2         2\n",
       "886       2    1    1      0      0     1         0\n",
       "887       1    0    1      0      0     2         0\n",
       "888       3    0    1      1      2     2         0\n",
       "889       1    1    1      0      0     2         1\n",
       "890       3    1    1      0      0     0         2\n",
       "\n",
       "[891 rows x 7 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Feature selection\n",
    "# Survived is our target\n",
    "features = [\"Age\", \"Fare\", \"Sex\", \"Pclass\", \"Embarked\", \"Parch\", 'SibSp']\n",
    "drop_elements = ['Name', 'Ticket', 'Cabin','CategoricalAge']\n",
    "X_train = X_train.drop(drop_elements, axis = 1)\n",
    "X_train = X_train.drop(\"Survived\", axis = 1)\n",
    "X_train = X_train.drop(\"PassengerId\", axis = 1)\n",
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-05T19:10:29.258817Z",
     "iopub.status.busy": "2021-12-05T19:10:29.258331Z",
     "iopub.status.idle": "2021-12-05T19:10:29.300555Z",
     "shell.execute_reply": "2021-12-05T19:10:29.299573Z",
     "shell.execute_reply.started": "2021-12-05T19:10:29.258740Z"
    }
   },
   "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",
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3</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>6</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>3</td>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>3</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>22</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>3</td>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</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>388</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>390</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>391</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>392</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>398</th>\n",
       "      <td>3</td>\n",
       "      <td>1</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>399</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>400</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>401</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>402</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>403</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>405</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>406</th>\n",
       "      <td>2</td>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>407</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>408</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>409</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>410</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>411</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>412</th>\n",
       "      <td>3</td>\n",
       "      <td>0</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>413</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>418 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pclass  Sex  Age  SibSp  Parch  Fare  Embarked\n",
       "0         3    1    2      0      0     0         2\n",
       "1         3    0    2      1      0     0         0\n",
       "2         2    1    3      0      0     1         2\n",
       "3         3    1    1      0      0     1         0\n",
       "4         3    0    1      1      1     1         0\n",
       "5         3    1    0      0      0     1         0\n",
       "6         3    0    1      0      0     0         2\n",
       "7         2    1    1      1      1     2         0\n",
       "8         3    0    1      0      0     0         1\n",
       "9         3    1    1      2      0     2         0\n",
       "10        3    1    2      0      0     0         0\n",
       "11        1    1    2      0      0     2         0\n",
       "12        1    0    1      1      0     3         0\n",
       "13        2    1    3      1      0     2         0\n",
       "14        1    0    2      1      0     3         0\n",
       "15        2    0    1      1      0     2         1\n",
       "16        2    1    2      0      0     1         2\n",
       "17        3    1    1      0      0     0         1\n",
       "18        3    0    1      1      0     0         0\n",
       "19        3    0    2      0      0     0         1\n",
       "20        1    1    3      1      0     3         1\n",
       "21        3    1    0      0      1     0         0\n",
       "22        1    0    2      0      0     2         0\n",
       "23        1    1    1      0      1     3         1\n",
       "24        1    0    2      1      3     3         1\n",
       "25        3    1    3      1      0     1         0\n",
       "26        1    0    1      0      1     3         1\n",
       "27        3    1    1      0      0     0         1\n",
       "28        1    1    2      0      0     2         0\n",
       "29        3    1    2      2      0     2         1\n",
       "..      ...  ...  ...    ...    ...   ...       ...\n",
       "388       3    1    1      0      0     0         2\n",
       "389       3    1    0      3      1     2         0\n",
       "390       1    1    1      0      0     3         0\n",
       "391       1    0    3      0      1     3         0\n",
       "392       3    1    0      0      2     2         0\n",
       "393       2    1    2      0      0     1         0\n",
       "394       3    1    1      3      1     2         0\n",
       "395       1    0    1      1      0     3         0\n",
       "396       3    1    1      0      0     0         2\n",
       "397       1    0    2      1      1     3         1\n",
       "398       3    1    1      0      0     0         0\n",
       "399       3    1    1      0      0     0         2\n",
       "400       1    0    1      0      0     3         0\n",
       "401       2    1    2      1      0     2         0\n",
       "402       1    0    1      0      1     3         1\n",
       "403       1    1    1      0      0     3         0\n",
       "404       1    1    2      1      0     2         1\n",
       "405       2    1    1      0      0     1         1\n",
       "406       2    1    1      1      0     1         0\n",
       "407       1    1    3      1      1     3         1\n",
       "408       3    0    1      0      0     0         2\n",
       "409       3    0    0      1      1     1         0\n",
       "410       3    0    2      0      0     0         2\n",
       "411       1    0    2      1      0     3         2\n",
       "412       3    0    1      0      0     0         0\n",
       "413       3    1    2      0      0     1         0\n",
       "414       1    0    2      0      0     3         1\n",
       "415       3    1    2      0      0     0         0\n",
       "416       3    1    2      0      0     1         0\n",
       "417       3    1    1      1      1     2         1\n",
       "\n",
       "[418 rows x 7 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test = X_test.drop(drop_elements, axis = 1)\n",
    "X_test = X_test.drop(\"PassengerId\", axis = 1)\n",
    "X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-05T19:10:54.140928Z",
     "iopub.status.busy": "2021-12-05T19:10:54.140314Z",
     "iopub.status.idle": "2021-12-05T19:10:54.149040Z",
     "shell.execute_reply": "2021-12-05T19:10:54.148179Z",
     "shell.execute_reply.started": "2021-12-05T19:10:54.140862Z"
    }
   },
   "outputs": [],
   "source": [
    "pd.set_option('display.max_rows', None)\n",
    "pd.set_option('display.max_columns', None)\n",
    "pd.set_option('display.width', None)\n",
    "pd.set_option('display.max_colwidth', -1)\n",
    "pd.options.display.max_rows\n",
    "# set display options in order to see all components from pca"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-05T19:10:54.687429Z",
     "iopub.status.busy": "2021-12-05T19:10:54.687076Z",
     "iopub.status.idle": "2021-12-05T19:10:54.767053Z",
     "shell.execute_reply": "2021-12-05T19:10:54.766053Z",
     "shell.execute_reply.started": "2021-12-05T19:10:54.687358Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               PC1       PC2       PC3       PC4       PC5       PC6       PC7\n",
      "Pclass   -0.398875  0.560815 -0.025451  0.020619  0.348479 -0.120992  0.623888\n",
      "Sex      -0.323379 -0.103456 -0.555873  0.659583 -0.183467  0.327046  0.007675\n",
      "Age       0.019615 -0.570623 -0.056455  0.199024  0.757535 -0.234619  0.047966\n",
      "SibSp     0.391946  0.424319 -0.167048  0.428970  0.011653 -0.614242 -0.277457\n",
      "Parch     0.439190  0.360402 -0.060392  0.027373  0.462192  0.653878 -0.177898\n",
      "Fare      0.618387 -0.197842 -0.046967  0.137414 -0.236133  0.042850  0.706946\n",
      "Embarked -0.083828  0.009780  0.808339  0.566805 -0.039067  0.129110 -0.001282\n"
     ]
    }
   ],
   "source": [
    "pca, X_pca, loadings = apply_pca(X_train)\n",
    "print(loadings)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "PC1: Fare & Parch have the most weight\n",
    "\n",
    "PC2: Pclass & Age have the most weight\n",
    "\n",
    "PC3: Sex & Embarked have the most weight\n",
    "\n",
    "PC4: Sex & Embarked have the most weight, all values positively correlated\n",
    "\n",
    "PC5: Age & Parch have the most weight\n",
    "\n",
    "PC6: SibSp & Parch have the most weight\n",
    "\n",
    "PC7: Pclass & Fare have the most weight\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-05T19:10:56.878287Z",
     "iopub.status.busy": "2021-12-05T19:10:56.877970Z",
     "iopub.status.idle": "2021-12-05T19:10:57.791499Z",
     "shell.execute_reply": "2021-12-05T19:10:57.790470Z",
     "shell.execute_reply.started": "2021-12-05T19:10:56.878234Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f545d061668>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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mNcfqeLHlcBDMP4FgXn/cFZtZt3Ut119/PQNPOIEfTJ7MxIkTSU9P7Kk27EDi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u10xWmhsLCQT3ziE9x///2sXbuW1atXs/Jvf+Pkvrdif7PcwYQKRsZWHM8ssHOKSZxICN+pQ/ga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\ntMBRlklKSuL+++/nhhtuwFVZQuKWd8B/ciuOiqeO5M1zcdXuY9asWdxxx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fmx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\nIhOOCo6IiIhMOP8fOvEpJEw0UIcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f546e05a780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Making distribution plots to see outliers\n",
    "sns.factorplot(y=\"value\", col=\"variable\", data=X_pca.melt(), kind='violin', sharey=False, col_wrap=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-05T19:10:59.132861Z",
     "iopub.status.busy": "2021-12-05T19:10:59.132377Z",
     "iopub.status.idle": "2021-12-05T19:10:59.149604Z",
     "shell.execute_reply": "2021-12-05T19:10:59.148636Z",
     "shell.execute_reply.started": "2021-12-05T19:10:59.132797Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "79.573512906846247"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logreg = LogisticRegression()\n",
    "logreg.fit(X_train, y_train)\n",
    "Y_pred_1 = logreg.predict(X_test)\n",
    "acc_log = logreg.score(X_train, y_train) * 100\n",
    "acc_log"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-05T19:11:00.010426Z",
     "iopub.status.busy": "2021-12-05T19:11:00.009805Z",
     "iopub.status.idle": "2021-12-05T19:11:00.061366Z",
     "shell.execute_reply": "2021-12-05T19:11:00.060428Z",
     "shell.execute_reply.started": "2021-12-05T19:11:00.010081Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "83.16498316498317"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc = SVC()\n",
    "svc.fit(X_train, y_train)\n",
    "Y_pred_2 = svc.predict(X_test)\n",
    "acc_svc = svc.score(X_train, y_train) * 100\n",
    "acc_svc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-05T19:11:00.999956Z",
     "iopub.status.busy": "2021-12-05T19:11:00.999576Z",
     "iopub.status.idle": "2021-12-05T19:11:01.050058Z",
     "shell.execute_reply": "2021-12-05T19:11:01.048980Z",
     "shell.execute_reply.started": "2021-12-05T19:11:00.999888Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "83.389450056116715"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors = 10)\n",
    "knn.fit(X_train, y_train)\n",
    "Y_pred_3 = knn.predict(X_test)\n",
    "acc_knn = knn.score(X_train, y_train) * 100\n",
    "acc_knn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-05T19:11:01.644864Z",
     "iopub.status.busy": "2021-12-05T19:11:01.644537Z",
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     "shell.execute_reply": "2021-12-05T19:11:01.657483Z",
     "shell.execute_reply.started": "2021-12-05T19:11:01.644810Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "89.001122334455658"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "decision_tree = DecisionTreeClassifier()\n",
    "decision_tree.fit(X_train, y_train)\n",
    "Y_pred_4 = decision_tree.predict(X_test)\n",
    "acc_decision_tree = decision_tree.score(X_train, y_train) * 100\n",
    "acc_decision_tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-05T19:11:02.381975Z",
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     "shell.execute_reply": "2021-12-05T19:11:02.478135Z",
     "shell.execute_reply.started": "2021-12-05T19:11:02.381907Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "89.001122334455658"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_forest = RandomForestClassifier(n_estimators=50)\n",
    "random_forest.fit(X_train, y_train)\n",
    "Y_pred_5= random_forest.predict(X_test)\n",
    "acc_rf = random_forest.score(X_train, y_train) * 100\n",
    "acc_rf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-05T19:11:03.047289Z",
     "iopub.status.busy": "2021-12-05T19:11:03.046965Z",
     "iopub.status.idle": "2021-12-05T19:11:03.145306Z",
     "shell.execute_reply": "2021-12-05T19:11:03.144318Z",
     "shell.execute_reply.started": "2021-12-05T19:11:03.047208Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "80.583613916947243"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ada_boost = AdaBoostClassifier()\n",
    "ada_boost.fit(X_train, y_train)\n",
    "Y_pred_6 = ada_boost.predict(X_test)\n",
    "acc_rf = ada_boost.score(X_train, y_train) * 100\n",
    "acc_rf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-05T19:11:03.799608Z",
     "iopub.status.busy": "2021-12-05T19:11:03.799320Z",
     "iopub.status.idle": "2021-12-05T19:11:03.906231Z",
     "shell.execute_reply": "2021-12-05T19:11:03.905334Z",
     "shell.execute_reply.started": "2021-12-05T19:11:03.799566Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "84.960718294051631"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grad_boost = GradientBoostingClassifier()\n",
    "grad_boost.fit(X_train, y_train)\n",
    "Y_pred_7 = grad_boost.predict(X_test)\n",
    "acc_rf = grad_boost.score(X_train, y_train) * 100\n",
    "acc_rf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-05T19:11:05.073612Z",
     "iopub.status.busy": "2021-12-05T19:11:05.073285Z",
     "iopub.status.idle": "2021-12-05T19:11:05.104549Z",
     "shell.execute_reply": "2021-12-05T19:11:05.103543Z",
     "shell.execute_reply.started": "2021-12-05T19:11:05.073558Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "89.001122334455658"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "extra_trees = ExtraTreesClassifier()\n",
    "extra_trees.fit(X_train, y_train)\n",
    "Y_pred_8 = extra_trees.predict(X_test)\n",
    "acc_et = extra_trees.score(X_train, y_train) * 100\n",
    "acc_et"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Best score: DecisionTreeClassifier/RandomForest/ExtraTreesClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Final Model: ExtraTreesClassifier**\n",
    "\n",
    "Overall best performer was a tie between DecisionTreeClassifier/RandomForest/ExtraTreesClassifier, has 85% accuracy. Will choose ExtraTreesClassifier for remaining analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-05T19:11:10.823707Z",
     "iopub.status.busy": "2021-12-05T19:11:10.823396Z",
     "iopub.status.idle": "2021-12-05T19:11:10.837137Z",
     "shell.execute_reply": "2021-12-05T19:11:10.835968Z",
     "shell.execute_reply.started": "2021-12-05T19:11:10.823653Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7  components describe  .99 % of the variance\n"
     ]
    }
   ],
   "source": [
    "# Minimum percentage of variance we want to be described by the resulting transformed components\n",
    "variance_pct = .99\n",
    "\n",
    "# Create PCA object\n",
    "pca = PCA(n_components=variance_pct)\n",
    "\n",
    "# Transform the initial features\n",
    "X_transformed = pca.fit_transform(X_train, y_train)\n",
    "\n",
    "# Create a data frame from the PCA'd data\n",
    "pcaDataFrame = pd.DataFrame(X_transformed)\n",
    "\n",
    "print(pcaDataFrame.shape[1], \" components describe \", str(variance_pct)[1:], \"% of the variance\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-05T19:11:11.485496Z",
     "iopub.status.busy": "2021-12-05T19:11:11.484762Z",
     "iopub.status.idle": "2021-12-05T19:11:13.546085Z",
     "shell.execute_reply": "2021-12-05T19:11:13.545200Z",
     "shell.execute_reply.started": "2021-12-05T19:11:11.485430Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=None, error_score='raise-deprecating',\n",
       "       estimator=ExtraTreesClassifier(bootstrap=False, class_weight=None, criterion='gini',\n",
       "           max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "           min_samples_leaf=1, min_samples_split=2,\n",
       "           min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\n",
       "           oob_score=False, random_state=None, verbose=0, warm_start=False),\n",
       "       fit_params=None, iid='warn', n_jobs=1,\n",
       "       param_grid={'max_features': ('auto', 'sqrt', 'log2'), 'max_depth': [1, 100], 'min_samples_split': [2, 100], 'min_samples_leaf': [1, 100], 'min_weight_fraction_leaf': [0, 0.5]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring=None, verbose=0)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import tree\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "model = ExtraTreesClassifier()\n",
    "parameters = {'max_features': ('auto', 'sqrt', 'log2'), 'max_depth':[1,100], 'min_samples_split':[2,100], 'min_samples_leaf': [1,100], 'min_weight_fraction_leaf': [0,0.5]}\n",
    "clf = GridSearchCV(model, parameters)\n",
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-05T19:11:20.391166Z",
     "iopub.status.busy": "2021-12-05T19:11:20.390553Z",
     "iopub.status.idle": "2021-12-05T19:11:20.402264Z",
     "shell.execute_reply": "2021-12-05T19:11:20.401376Z",
     "shell.execute_reply.started": "2021-12-05T19:11:20.391117Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "89.001122334455658"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.score(X_train, y_train)* 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-05T19:11:21.440814Z",
     "iopub.status.busy": "2021-12-05T19:11:21.440439Z",
     "iopub.status.idle": "2021-12-05T19:11:21.446720Z",
     "shell.execute_reply": "2021-12-05T19:11:21.445876Z",
     "shell.execute_reply.started": "2021-12-05T19:11:21.440718Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_depth': 100,\n",
       " 'max_features': 'sqrt',\n",
       " 'min_samples_leaf': 1,\n",
       " 'min_samples_split': 2,\n",
       " 'min_weight_fraction_leaf': 0}"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-05T19:11:22.688974Z",
     "iopub.status.busy": "2021-12-05T19:11:22.688293Z",
     "iopub.status.idle": "2021-12-05T19:11:22.709980Z",
     "shell.execute_reply": "2021-12-05T19:11:22.709085Z",
     "shell.execute_reply.started": "2021-12-05T19:11:22.688926Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ExtraTreesClassifier(bootstrap=False, class_weight=None, criterion='gini',\n",
       "           max_depth=100, max_features='sqrt', max_leaf_nodes=None,\n",
       "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "           min_samples_leaf=1, min_samples_split=2,\n",
       "           min_weight_fraction_leaf=0, n_estimators=10, n_jobs=1,\n",
       "           oob_score=False, random_state=None, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = ExtraTreesClassifier(max_depth=100,max_features='sqrt',min_samples_leaf=1,min_samples_split=2,min_weight_fraction_leaf=0)\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-05T19:11:31.851930Z",
     "iopub.status.busy": "2021-12-05T19:11:31.851595Z",
     "iopub.status.idle": "2021-12-05T19:11:31.866590Z",
     "shell.execute_reply": "2021-12-05T19:11:31.865772Z",
     "shell.execute_reply.started": "2021-12-05T19:11:31.851873Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "89.001122334455658"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# make prediction\n",
    "y_predict = model.predict(X_test)\n",
    "model.score(X_train, y_train) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-05T19:11:33.062845Z",
     "iopub.status.busy": "2021-12-05T19:11:33.062215Z",
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     "shell.execute_reply": "2021-12-05T19:11:33.242757Z",
     "shell.execute_reply.started": "2021-12-05T19:11:33.062758Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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q6nS6B9wMth07sHwO3ZCS6cd9GXhQy9okSZKklloOHZEkSZLusAzakiRJUgMG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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f54589a6da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Feature Importance\n",
    "\n",
    "plt.figure(figsize=(12,6))\n",
    "plt.bar(range(len(features)), model.feature_importances_)\n",
    "plt.xticks(range(len(features)), features, rotation =90)\n",
    "plt.ylabel('Feature Importance')\n",
    "plt.title('ExtraTreesClassifier Feature Importance')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-05T19:11:35.602972Z",
     "iopub.status.busy": "2021-12-05T19:11:35.602351Z",
     "iopub.status.idle": "2021-12-05T19:11:35.820217Z",
     "shell.execute_reply": "2021-12-05T19:11:35.819237Z",
     "shell.execute_reply.started": "2021-12-05T19:11:35.602905Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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VIiIiIlK08pkp/jRwMI/XO6OGYAOxsVjGvthYjIZgw1lfs6+vj9tvv53nn3+eo0eP8sIL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\nIiIiItLw/hctXR3KgWc5GQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f545898fa90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "X_transformed = StandardScaler().fit_transform(X_transformed)\n",
    "\n",
    "died1 = X_transformed[y_train[\"Survived\"] == 0]\n",
    "live1 = X_transformed[y_train[\"Survived\"] == 1]\n",
    "comp =[0,1]\n",
    "\n",
    "fig = plt.figure(figsize = (12,8))\n",
    "plt.scatter(died1[:,0].reshape(1,-1), died1[:,1].reshape(1,-1), c = 'red', label = 'Dead', alpha = 0.5)\n",
    "plt.scatter(live1[:,0].reshape(1,-1), live1[:,1].reshape(1,-1), c = 'green', label = 'Survived', alpha = 0.5)\n",
    "plt.title('PCA: Linear Seperability')\n",
    "plt.legend(['Dead', 'Alive'])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-05T19:11:37.217642Z",
     "iopub.status.busy": "2021-12-05T19:11:37.217020Z",
     "iopub.status.idle": "2021-12-05T19:11:37.602897Z",
     "shell.execute_reply": "2021-12-05T19:11:37.602102Z",
     "shell.execute_reply.started": "2021-12-05T19:11:37.217573Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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UvBYWFrB//34YDIaM4LW6ugqPx4NUKtWSwYstcW5ra6PgRUgTo4BFCNkQK4XM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\nOmpK/7Rz7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XvZs2cPGzZswOv18tJLL43qvbdu3cqcOXMoKdENab7whS/wyiuvxATWURC7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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f54589c3a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "\n",
    "\n",
    "died = X_transformed[y_train[\"Survived\"] == 0]\n",
    "live = X_transformed[y_train[\"Survived\"] == 1]\n",
    "comp =[0,1]\n",
    "\n",
    "fig = plt.figure(figsize = (12,8))\n",
    "ax = fig.add_subplot(111, projection='3d', )\n",
    "ax.scatter(died[:,0].reshape(1,-1), died[:,1].reshape(1,-1),died[:,2].reshape(1,-1), c = 'red', label = 'Dead', alpha = 0.5)\n",
    "ax.scatter(live[:,0].reshape(1,-1), live[:,1].reshape(1,-1),live[:,2].reshape(1,-1), c = 'green', label = 'Survived', alpha = 0.5)\n",
    "plt.title('PCA: Linear Seperability')\n",
    "plt.legend(['Dead', 'Alive'])\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Conclusions & Comparison**\n",
    "\n",
    "Comparing to analysis in https://www.kaggle.com/arthurtok/introduction-to-ensembling-stacking-in-python\n",
    "\n",
    "Similar to this notebook, I added missing values for the features with null values in the same ways. However, I did not breakup the name into different components to do an analysis by title, nor did I do an analysis of cabins, tickets, or different family size pairings.\n",
    "\n",
    "So when comparing my heatmap to the notebook's Pearson Correlation heatmap, mine has fewer features. For example, the most correlated features in mine are SibSp & Parch, whereas in the example notebook, it is Parch & Family_Size, while my least correlated are Pclass & Fare, and the example notebook's is Pclass & Has_Cabin.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-05T19:11:39.117915Z",
     "iopub.status.busy": "2021-12-05T19:11:39.117332Z",
     "iopub.status.idle": "2021-12-05T19:11:39.554628Z",
     "shell.execute_reply": "2021-12-05T19:11:39.553685Z",
     "shell.execute_reply.started": "2021-12-05T19:11:39.117597Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f545a257c18>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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w0wH45L9PcOXwZzjv2QfZtHpd4bhZ346jVecTeGjmd/h9Pj6+uz+b1+WE5Vgq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qR9I+Lu9FbxMOApSadHxLPVfBz1VtpfczwwXtIc4Czg1bT1I5t9gA+B0pqJsP5KWzUHk3wYDZJk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\n92ZmZmZmRcLJvZmZmZlZkfh/APrSPWccMV4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f545897a588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colormap = plt.cm.RdBu\n",
    "plt.figure(figsize=(14,12))\n",
    "plt.title('Pearson Correlation of Features', y=1.05, size=15)\n",
    "sns.heatmap(X_train.astype(float).corr(),linewidths=0.1,vmax=1.0, \n",
    "            square=True, cmap=colormap, linecolor='white', annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The example notebook used the RandomForest, ExtraTrees, AdaBoost, and GradientBoost classifiers, while I used these plus KNeighborsClassifier, DecisionTree, LogisticRegression, and SVC to test.\n",
    "\n",
    "Compared to the example notebook, their top 3 most import features when using the ExtraTreesClassifier were 'Sex', 'FamilySize', and 'Pclass' respectively, while mine were Fare, Age, and Parch, which is interesting, but again I used less features.\n",
    "\n",
    "The approaches to these two analyses are very different and yield different feature importances. My notebook would benefit from additional analyses, creating additional features such as 'Family Size' and 'Is Alone' and re-comparing. Family Size would require breaking down Age into 'child' values and combining the SibSp and Parch values."
   ]
  },
  {
   "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}