titanic-standard.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Imports\n",
    "from sklearn.cluster import KMeans\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn import svm\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.preprocessing import MinMaxScaler, StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = pd.read_csv('train.csv')\n",
    "test_data = pd.read_csv('test.csv')\n",
    "datasets = [train_data, test_data]\n",
    "all_data = pd.concat(datasets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def kmeans_chart(prop, size = 0):\n",
    "    krange = range(1,size if size > 0 else train_data[prop].value_counts().size)\n",
    "    inertias = []\n",
    "    for i in krange:\n",
    "        kmeans = KMeans(n_clusters=i, random_state=0).fit(train_data[prop].values.reshape(train_data[prop].size,1))\n",
    "        inertias.append(kmeans.inertia_)\n",
    "\n",
    "    plt.plot(krange, inertias)\n",
    "    plt.title(prop)\n",
    "    plt.xlabel('cluster')\n",
    "    plt.ylabel('inertia')\n",
    "def bar_chart(feature):\n",
    "    survived = train_data[train_data['Survived']==1][feature].value_counts()\n",
    "    dead = train_data[train_data['Survived']==0][feature].value_counts()\n",
    "    df = pd.DataFrame([survived,dead])\n",
    "    df.index = ['Survived','Dead']\n",
    "    df.plot(kind='bar',stacked=True, figsize=(10,5))\n",
    "    \n",
    "def scatter_chart(feature):\n",
    "    survived = train_data[train_data['Survived']==1][feature]\n",
    "    dead = train_data[train_data['Survived']==0][feature]\n",
    "    y_survived = np.ones(survived.size)\n",
    "    y_dead = np.zeros(dead.size)\n",
    "    count_survived = survived.value_counts().to_dict()\n",
    "    size_survived = survived.map(lambda x: count_survived[x]/survived.size*1000)\n",
    "    count_dead = dead.value_counts().to_dict()\n",
    "    size_dead = dead.map(lambda x: count_dead[x]/dead.size*1000)\n",
    "    ax = plt.subplot(111)\n",
    "#     ax.figure.figsize=[20, 20]\n",
    "    ax.figure.set_figheight(20)\n",
    "    ax.figure.set_figwidth(20)\n",
    "#     ax.figure.colorbar()\n",
    "    ax.scatter(y_survived, survived, marker='o', s=size_survived, alpha=0.3, cmap='viridis')\n",
    "    ax.scatter(y_dead, dead, marker='o', c='r', s=size_dead, alpha=0.3)\n",
    "    \n",
    "#     plt.colorbar(ax.figure, ax=ax)\n",
    "    ax.figure.show()\n",
    "\n",
    "#     plt.scatter(y, survived, marker='o');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId       0\n",
       "Survived        418\n",
       "Pclass            0\n",
       "Name              0\n",
       "Sex               0\n",
       "Age             263\n",
       "SibSp             0\n",
       "Parch             0\n",
       "Ticket            0\n",
       "Fare              1\n",
       "Cabin          1014\n",
       "Embarked          2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# see the missing values\n",
    "all_data.isnull().sum() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mr          757\n",
       "Miss        260\n",
       "Mrs         197\n",
       "Master       61\n",
       "Dr            8\n",
       "Rev           8\n",
       "Col           4\n",
       "Mlle          2\n",
       "Major         2\n",
       "Ms            2\n",
       "Jonkheer      1\n",
       "Capt          1\n",
       "Sir           1\n",
       "Lady          1\n",
       "Countess      1\n",
       "Mme           1\n",
       "Don           1\n",
       "Dona          1\n",
       "Name: Name, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get information from the name\n",
    "all_names = all_data['Name'].str.extract('([A-Za-z]+)\\.', expand=False)\n",
    "all_names.value_counts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "names = train_data['Name'].str.extract('([A-Za-z]+)\\.', expand=False)\n",
    "train_data['Title'] = names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_data.groupby('Survived')['Title'].value_counts()\n",
    "scatter_chart('Title')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Title\n",
    "# title_mapping = { \"Mr\": 1, \"Miss\": 0.2, \"Mrs\": 0.6, \"Master\": 0.8, \"Dr\": 0.4, \"Rev\": 0.4, \n",
    "#                  \"Mlle\": 0.4, \"Major\": 0.4, \"Col\": 0.4, \"Mme\": 0.4, \"Jonkheer\": 0.4, \"Countess\": 0.4, \n",
    "#                  \"Don\": 0.4, \"Lady\": 0.4, \"Capt\": 0.4, \"Ms\": 0.4, \"Sir\": 0.4, \"Dona\": 0.6}\n",
    "\n",
    "# title_mapping = { \"Mr\": 0.1,                 \n",
    "#                  \"Major\": 0.4, \"Col\": 0.4, \"Jonkheer\": 0.4,  \n",
    "#                  \"Don\": 0.4,  \"Capt\": 0.4, \"Ms\": 0.4, \"Sir\": 0.4,  \"Rev\": 0.4, \n",
    "#                  \"Dr\": 0.6, \"Master\": 0.6,\n",
    "#                  \"Mrs\": 0.8,\n",
    "#                  \"Miss\": 1, \"Mlle\": 1, \"Mme\": 1, \"Dona\": 1, \"Lady\": 1, \"Countess\": 1          \n",
    "#                   }\n",
    "title_mapping = { \"Mr\": 1,                 \n",
    "                 \"Major\": 2, \"Col\": 3, \"Jonkheer\": 4,  \n",
    "                 \"Don\": 5,  \"Capt\": 6, \"Ms\": 7, \"Sir\": 8,  \"Rev\": 9, \n",
    "                 \"Dr\": 10, \"Master\": 11,\n",
    "                 \"Mrs\": 12,\n",
    "                 \"Miss\": 13, \"Mlle\": 14, \"Mme\": 15, \"Dona\": 16, \"Lady\": 17, \"Countess\": 18          \n",
    "                  }\n",
    "\n",
    "train_data['Title'] = names.map(title_mapping)\n",
    "\n",
    "title_scaller = MinMaxScaler()\n",
    "train_data['Title'] = title_scaller.fit_transform(train_data['Title'].values.reshape(-1,1), train_data['Survived'])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Survived  Title   \n",
       "0         0.000000    436\n",
       "          0.705882     55\n",
       "          0.647059     26\n",
       "          0.588235     17\n",
       "          0.470588      6\n",
       "          0.529412      4\n",
       "          0.058824      1\n",
       "          0.117647      1\n",
       "          0.176471      1\n",
       "          0.235294      1\n",
       "          0.294118      1\n",
       "1         0.705882    127\n",
       "          0.647059     99\n",
       "          0.000000     81\n",
       "          0.588235     23\n",
       "          0.529412      3\n",
       "          0.764706      2\n",
       "          0.058824      1\n",
       "          0.117647      1\n",
       "          0.352941      1\n",
       "          0.411765      1\n",
       "          0.823529      1\n",
       "          0.941176      1\n",
       "          1.000000      1\n",
       "Name: Title, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.groupby('Survived')['Title'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data.drop(columns=['Name'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Sex\n",
    "sex_mapping = { \"male\": 0, \"female\": 1}\n",
    "train_data['Sex'] = train_data.Sex.map(sex_mapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "      <td>0.647059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0.705882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "      <td>0.647059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  Sex   Age  SibSp  Parch            Ticket  \\\n",
       "0            1         0       3    0  22.0      1      0         A/5 21171   \n",
       "1            2         1       1    1  38.0      1      0          PC 17599   \n",
       "2            3         1       3    1  26.0      0      0  STON/O2. 3101282   \n",
       "3            4         1       1    1  35.0      1      0            113803   \n",
       "4            5         0       3    0  35.0      0      0            373450   \n",
       "\n",
       "      Fare Cabin Embarked     Title  \n",
       "0   7.2500   NaN        S  0.000000  \n",
       "1  71.2833   C85        C  0.647059  \n",
       "2   7.9250   NaN        S  0.705882  \n",
       "3  53.1000  C123        S  0.647059  \n",
       "4   8.0500   NaN        S  0.000000  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Survived  Embarked\n",
       "0         S           42\n",
       "          C           17\n",
       "          Q            1\n",
       "1         S           74\n",
       "          C           48\n",
       "          Q            1\n",
       "Name: Embarked, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.dropna().groupby('Survived')['Embarked'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>62</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113572</td>\n",
       "      <td>80.0</td>\n",
       "      <td>B28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.705882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>829</th>\n",
       "      <td>830</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113572</td>\n",
       "      <td>80.0</td>\n",
       "      <td>B28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.647059</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass  Sex   Age  SibSp  Parch  Ticket  Fare  \\\n",
       "61            62         1       1    1  38.0      0      0  113572  80.0   \n",
       "829          830         1       1    1  62.0      0      0  113572  80.0   \n",
       "\n",
       "    Cabin Embarked     Title  \n",
       "61    B28      NaN  0.705882  \n",
       "829   B28      NaN  0.647059  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Lets see the missing data\n",
    "train_data[pd.isnull(train_data['Embarked'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data['Embarked'].fillna('C', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "scatter_chart('Embarked')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Embarked\n",
    "# embarked_mapping = { \"S\": 0.7, \"C\": 1, \"Q\": 0.3 }\n",
    "embarked_mapping = { \"S\": 1, \"C\": 2, \"Q\": 3 }\n",
    "\n",
    "train_data['Embarked'] = train_data.Embarked.map(embarked_mapping)\n",
    "\n",
    "embarked_scaller = MinMaxScaler()\n",
    "train_data['Embarked'] = embarked_scaller.fit_transform(train_data['Embarked'].values.reshape(-1,1), train_data['Survived'])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.647059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.705882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.647059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  Sex   Age  SibSp  Parch            Ticket  \\\n",
       "0            1         0       3    0  22.0      1      0         A/5 21171   \n",
       "1            2         1       1    1  38.0      1      0          PC 17599   \n",
       "2            3         1       3    1  26.0      0      0  STON/O2. 3101282   \n",
       "3            4         1       1    1  35.0      1      0            113803   \n",
       "4            5         0       3    0  35.0      0      0            373450   \n",
       "\n",
       "      Fare Cabin  Embarked     Title  \n",
       "0   7.2500   NaN       0.0  0.000000  \n",
       "1  71.2833   C85       0.5  0.647059  \n",
       "2   7.9250   NaN       0.0  0.705882  \n",
       "3  53.1000  C123       0.0  0.647059  \n",
       "4   8.0500   NaN       0.0  0.000000  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Survived         0\n",
       "Pclass           0\n",
       "Sex              0\n",
       "Age            177\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             0\n",
       "Cabin          687\n",
       "Embarked         0\n",
       "Title            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>244373</td>\n",
       "      <td>13.0000</td>\n",
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       "      <th>19</th>\n",
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       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>28</th>\n",
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       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349216</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17569</td>\n",
       "      <td>146.5208</td>\n",
       "      <td>B78</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.647059</td>\n",
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       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>335677</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.705882</td>\n",
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       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2677</td>\n",
       "      <td>7.2292</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>43</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>48</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14311</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.705882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2662</td>\n",
       "      <td>21.6792</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>19947</td>\n",
       "      <td>35.5000</td>\n",
       "      <td>C52</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>65</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17605</td>\n",
       "      <td>27.7208</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>66</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2661</td>\n",
       "      <td>15.2458</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.588235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>77</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349208</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>78</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>374746</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    PassengerId  Survived  Pclass  Sex  Age  SibSp  Parch           Ticket  \\\n",
       "5             6         0       3    0  NaN      0      0           330877   \n",
       "17           18         1       2    0  NaN      0      0           244373   \n",
       "19           20         1       3    1  NaN      0      0             2649   \n",
       "26           27         0       3    0  NaN      0      0             2631   \n",
       "28           29         1       3    1  NaN      0      0           330959   \n",
       "29           30         0       3    0  NaN      0      0           349216   \n",
       "31           32         1       1    1  NaN      1      0         PC 17569   \n",
       "32           33         1       3    1  NaN      0      0           335677   \n",
       "36           37         1       3    0  NaN      0      0             2677   \n",
       "42           43         0       3    0  NaN      0      0           349253   \n",
       "45           46         0       3    0  NaN      0      0  S.C./A.4. 23567   \n",
       "46           47         0       3    0  NaN      1      0           370371   \n",
       "47           48         1       3    1  NaN      0      0            14311   \n",
       "48           49         0       3    0  NaN      2      0             2662   \n",
       "55           56         1       1    0  NaN      0      0            19947   \n",
       "64           65         0       1    0  NaN      0      0         PC 17605   \n",
       "65           66         1       3    0  NaN      1      1             2661   \n",
       "76           77         0       3    0  NaN      0      0           349208   \n",
       "77           78         0       3    0  NaN      0      0           374746   \n",
       "\n",
       "        Fare Cabin  Embarked     Title  \n",
       "5     8.4583   NaN       1.0  0.000000  \n",
       "17   13.0000   NaN       0.0  0.000000  \n",
       "19    7.2250   NaN       0.5  0.647059  \n",
       "26    7.2250   NaN       0.5  0.000000  \n",
       "28    7.8792   NaN       1.0  0.705882  \n",
       "29    7.8958   NaN       0.0  0.000000  \n",
       "31  146.5208   B78       0.5  0.647059  \n",
       "32    7.7500   NaN       1.0  0.705882  \n",
       "36    7.2292   NaN       0.5  0.000000  \n",
       "42    7.8958   NaN       0.5  0.000000  \n",
       "45    8.0500   NaN       0.0  0.000000  \n",
       "46   15.5000   NaN       1.0  0.000000  \n",
       "47    7.7500   NaN       1.0  0.705882  \n",
       "48   21.6792   NaN       0.5  0.000000  \n",
       "55   35.5000   C52       0.0  0.000000  \n",
       "64   27.7208   NaN       0.5  0.000000  \n",
       "65   15.2458   NaN       0.5  0.588235  \n",
       "76    7.8958   NaN       0.0  0.000000  \n",
       "77    8.0500   NaN       0.0  0.000000  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get all the missing ages\n",
    "age_test = train_data[pd.isnull(train_data['Age'])]\n",
    "age_test.head(19)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.647059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.705882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.647059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  Sex   Age  SibSp  Parch            Ticket  \\\n",
       "0            1         0       3    0  22.0      1      0         A/5 21171   \n",
       "1            2         1       1    1  38.0      1      0          PC 17599   \n",
       "2            3         1       3    1  26.0      0      0  STON/O2. 3101282   \n",
       "3            4         1       1    1  35.0      1      0            113803   \n",
       "4            5         0       3    0  35.0      0      0            373450   \n",
       "\n",
       "      Fare Cabin  Embarked     Title  \n",
       "0   7.2500   NaN       0.0  0.000000  \n",
       "1  71.2833   C85       0.5  0.647059  \n",
       "2   7.9250   NaN       0.0  0.705882  \n",
       "3  53.1000  C123       0.0  0.647059  \n",
       "4   8.0500   NaN       0.0  0.000000  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "means = train_data.groupby('Title')['Age'].mean().to_dict()\n",
    "median = train_data['Age'].mean()\n",
    "def median_age(row):\n",
    "    if(np.isnan(row['Age'])):\n",
    "#         row['Age'] = means[row['Title']]\n",
    "        row['Age'] = median\n",
    "    return row\n",
    "\n",
    "\n",
    "# asd = age_test.fillna(train_data.groupby('Title')['Age'].mean().to_dict(), axis='Title')\n",
    "\n",
    "train_data = train_data.apply(median_age, axis=1)\n",
    "\n",
    "train_data.head()\n",
    "# asd.head()\n",
    "# train_data.groupby('Title')['Age'].mean().to_dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "scatter_chart('Age')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "scatter_chart('Pclass')\n",
    "train_data.groupby('Survived')['Pclass'].value_counts()\n",
    "\n",
    "# pclass_mapping = { 1: 1, 2: 0.7, 3: 0.4 }\n",
    "# train_data['Pclass'] = train_data['Pclass'].map(pclass_mapping)\n",
    "\n",
    "pclass_scaller = StandardScaler()\n",
    "train_data['Pclass'] = pclass_scaller.fit_transform(train_data['Pclass'].values.reshape(-1,1), train_data['Survived'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "scatter_chart('Pclass')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "scatter_chart('Parch')\n",
    "train_data.groupby('Survived')['Parch'].value_counts()\n",
    "\n",
    "# pclass_mapping = { 1: 1, 2: 0.7, 3: 0.4 }\n",
    "# train_data['Pclass'] = train_data['Pclass'].map(pclass_mapping)\n",
    "\n",
    "parch_scaller = StandardScaler()\n",
    "train_data['Parch'] = parch_scaller.fit_transform(train_data['Parch'].values.reshape(-1,1), train_data['Survived'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "scatter_chart('Parch')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AACgm7gAAAAAUE3cAAAAAiok7AAAAAMXEHQAAAIBi4g4AAABAMXEHAAAAoJi4AwAAAFBM3AEAAAAoJu4AAAAAFBN3AAAAAIqJOwAAAADFxB0AAACAYuIOAAAAQDFxBwAAAKCYuAMAAABQTNwBAAAAKCbuAAAAABQ7sMhJY4y/SfJgklNJHp1zri9zFAAAAACLWSjunPFv5pz3L23JXnPPPcmttybf/u3Jc5+76jUAAAAAW9pJ3Dl/fPzjyYtelHzuc8nBg8kNNyTXXrvqVQAAAAD/yKL33JlJbh1j3D7GuG6Zg/aEm29OTp5MLr44efTR03EHAAAAYA9a9Mqd75pz3j3GeFqSd48x/mLO+b5/eMKZ6HNdklx66aXneOYue85zTv/8zGdOx50rrljtHgAAAIDHsVDcmXPefebnfWOMW5I8P8n7zjrneJLjSbK+vj7P8c7d9YM/mNx5Z3LLLcnVVye/8AurXgQAAACwpW3jzhjjgiT75pwPnvnzv0/yy0tftmqve93pFwAAAMAetsiVO09PcssY4yvn/6855x8sdRUAAAAAC9k27sw570xy5S5sAQAAAGCHFn1aFgAAAAB7kLgDAAAAUEzcAQAAACgm7gAAAAAUE3cAAAAAiok7AAAAAMXEHQAAAIBi4g4AAABAMXEHAAAAoJi4AwAAAFBM3AEAAAAoJu4AAAAAFBN3AAAAAIqJOwAAAADFxB0AAACAYuIOAAAAQDFxBwAAAKCYuAMAAABQTNwBAAAAKCbuAAAAABQTdwAAAACKiTsAAAAAxcQdAAAAgGLiDgAAAEAxcQcAAACgmLgDAAAAUEzcAQAAACgm7gAAAAAUE3cAAAAAiok7AAAAAMXEHQAAAIBi4g4AAABAMXEHAAAAoJi4AwAAAFBM3AEAAAAoJu4AAAAAFBN3AAAAAIqJOwAAAADFxB0AAACAYuIOAAAAQDFxBwAAAKCYuAMAAABQTNwBAAAAKCbuAAAAABQTdwAAAACKiTsAAAAAxcQdAAAAgGLiDgAAAEAxcQcAAACgmLgDAAAAUEzcAQAAACgm7gAAAAAUE3cAAAAAiok7AAAAAMXEHQAAAIBi4g4AAABAMXEHAAAAoJi4AwAAAFBM3AEAAAAoJu4AAAAAFBN3AAAAAIqJOwAAAADFxB0AAACAYuIOAAAAQDFxBwAAAKCYuAMAAABQTNwBAAAAKCbuAAAAABQTdwAAAACKiTsAAAAAxcQdAAAAgGLiDgAAAEAxcQcAAACgmLgDAAAAUEzcAQAAACgm7gAAAAAUE3cAAAAAiok7AAAAAMXEHQAAAIBi4g4AAABAMXEHAAAAoJi4AwAAAFBM3AEAAAAoJu4AAAAAFBN3AAAAAIqJOwAAAADFxB0AAACAYuIOAAAAQDFxBwAAAKCYuAMAAABQTNwBAAAAKCbuAAAAABQTdwAAAACKiTsAAAAAxcQdAAAAgGLiDgAAAEAxcQcAAACgmLgDAAAAUEzcAQAAACgm7gAAAAAUE3cAAAAAiok7AAAAAMXEHQAAAIBi4g4AAABAMXEHAAAAoJi4AwAAAFBM3AEAAAAoJu4AAAAAFBN3AAAAAIqJOwAAAADFxB0AAACAYuIOAAAAQDFxBwAAAKCYuAMAAABQbOG4M8bYP8b4szHGu5Y5aE84fjw5eDAZI9m3L7n++lUvAgAAANjSgR2c+5okH0tybElb9oY/+qPkJ37ia+/nTN70puRJT0p+9VdXtwsAAADOc3d84oH83w/fnW96ytH8yL+8NIcO7V/1pD1hoSt3xhiXJHlxkjcvd84e8MpXbn381399d3cAAAAAX3Xv5x7Of/4/H80dJx7I//7AJ/P2D5xY9aQ9Y9GvZb0+yc8leezxThhjXDfG2BhjbGxubp6TcSvxwANbH3/00d3dAQAAAHzV5oOP5JFHT+Ubn3Q0hw/ty4kHvrjqSXvGtnFnjPGSJPfNOW9/ovPmnMfnnOtzzvW1tbVzNnDXvfCFWx9v/jcBAABAuSuedlEuf9qFOfGZh5Ik1/6Li1e8aO9Y5J4735Xk+8cY35fkcJJjY4y3zTl/dLnTVuSWW5LDh5NHHvn64xsbq9kDAAAA5NCh/fmvL70yf3nfg1m76Bty8ZOOrHrSnrHtlTtzzp+fc14y57wsycuT/OE/2bDzFV/6UvKqVyWXX5685CWnb6p86aWrXgUAAADntUOH9ud5lzxZ2DnLTp6WdX554xtXvQAAAABgWzuKO3PO9yZ571KWAAAAALBjiz4tCwAAAIA9SNwBAAAAKCbuAAAAABQTdwAAAACKiTsAAAAAxcQdAAAAgGLiDgAAAEAxcQcAAACgmLgDAAAAUEzcAQAAACgm7gAAAAAUE3cAAAAAiok7AAAAAMXEHQAAAIBi4g4AAABAMXEHAAAAoJi4AwAAAFBM3AEAAAAoJu4AAAAAFBN3AAAAAIqJOwAAAADFxB0AAACAYuIOAAAAQDFxBwAAAKCYuAMAAABQTNwBAAAAKCbuAAAAABQTdwAAAACKiTsAAAAAxcQdAAAAgGLiDgAAAEAxcQcAAACgmLgDAAAAUEzcAQAAACgm7gAAAAAUE3cAAAAAiok7AAAAAMXEHQAAAIBi4g4AAABAMXEHAAAAoJi4AwAAAFBM3AEAAAAoJu4AAAAAFBN3AAAAAIqJOwAAAADFxB0AAACAYuIOAAAAQDFxBwAAAKCYuAMAAABQTNwBAAAAKCbuAAAAABQTdwAAAACKiTsAAAAAxcQdAAAAgGLiDgAAAEAxcQcAAACgmLgDAAAAUEzcAQAAACgm7gAAAAAUE3cAAAAAiok7AAAAAMXEHQAAAIBi4g4AAABAMXEHAAAAoJi4AwAAAFBM3AEAAAAoJu4AAAAAFBN3AAAAAIqJOwAAAADFxB0AAACAYuIOAAAAQDFxBwAAAKCYuAMAAABQTNwBAAAAKCbuAAAAABQTdwAAAACKiTsAAAAAxcQdAAAAgGLiDgAAAEAxcQcAAACgmLgDAAAAUEzcAQAAACgm7gAAAAAUE3cAAAAAiok7AAAAAMXEHQAAAIBi4g4AAABAMXEHAAAAoJi4AwAAAFBM3AEAAAAoJu4AAAAAFBN3AAAAAIqJOwAAAADFxB0AAACAYuIOAAAAQDFxBwAAAKCYuAMAAABQTNwBAAAAKCbuAAAAABQTdwAAAACKiTsAAAAAxcQdAAAAgGLiDgAAAEAxcQcAAACgmLgDAAAAUEzcAQAAACgm7gAAAAAUE3cAAAAAiok7AAAAAMXEHQAAAIBi4g4AAABAMXEHAAAAoJi4AwAAAFBs27gzxjg8xvjTMcb/G2N8ZIzxS7sxDAAAAIDtLXLlziNJ/u2c88okVyX53jHGC5Y7a8Xe+tbk4MFkjGTfvuTVr171IgAAADjv3f/Ql3LrR+/Nn3/q5Kqn7CkHtjthzjmTPHTm7cEzr7nMUSt1773Jj//4197Pmfzmbybr68mP/djqdgEAAMB57J6TD+c/vmUjn/3iI0lGrv9Xz8p/+M5nr3rWnrDQPXfGGPvHGB9Mcl+Sd885b1vurBV6/etP/9y372uvJHnDG1a3CQAAAM5z7/rQ3bn/oUfyjCcfzbHDB3LTn55Y9aQ9Y6G4M+c8Nee8KsklSZ4/xnju2eeMMa4bY2yMMTY2NzfP9c7dc9FFp38+9tjXHz9yZPe3AAAAAEmSIwf3J0lOnTqVL586lcP796940d6xo6dlzTlPJnlvku/d4rPjc871Oef62traOZq3Aq95zen77SSnA89XIs9XrugBAAAAdt3Lrr4kz3vmsXz6wUeyLyM/d+23rHrSnrHtPXfGGGtJ/m7OeXKMcSTJi5L8l6UvW5ULL0xOnEiuvDL57GeTo0eTG29Mrrpq1csAAADgvHX48IH8j1c8P/c/9KVceOBADh/eNmmcNxb5n/jGJG8ZY+zP6St93jHnfNdyZ63YxRcnn/70qlcAAAAAZ3nqhYdXPWHPWeRpWR9KcvUubAEAAABgh3Z0zx0AAAAA9hZxBwAAAKCYuAMAAABQTNwBAAAAKCbuAAAAABQTdwAAAACKiTsAAAAAxcQdAAAAgGLiDgAAAEAxcQcAAACgmLgDAAAAUEzcAQAAACgm7gAAAAAUE3cAAAAAiok7AAAAAMXEHQAAAIBi4g4AAABAMXEHAAAAoJi4AwAAAFBM3AEAAAAoJu4AAAAAFBN3AAAAAIqJOwAAAADFxB0AAACAYuIOAAAAQDFxBwAAAKCYuAMAAABQTNwBAAAAKCbuAAAAABQTdwAAAACKiTsAAAAAxcQdAAAAgGLiDgAAAEAxcQcAAACgmLgDAAAAUEzcAQAAACgm7gAAAAAUE3cAAAAAiok7AAAAAMXEHQAAAIBi4g4AAABAMXEHAAAAoJi4AwAAAFBM3AEAAAAoJu4AAAAAFBN3AAAAAIqJOwAAAADFxB0AAACAYuIOAAAAQDFxBwAAAKCYuAMAAABQTNwBAAAAKCbuAAAAABQTdwAAAACKiTsAAAAAxcQdAAAAgGLiDgAAAEAxcQcAAACgmLgDAAAAUEzcAQAAACgm7gAAAAAUE3cAAAAAiok7AAAAAMXEHQAAAIBi4g4AAABAMXEHAAAAoJi4AwAAAFBM3AEAAAAoJu4AAAAAFBN3AAAAAIqJOwAAAADFxB0AAACAYuIOAAAAQDFxBwAAAKCYuAMAAABQTNwBAAAAKCbuAAAAABQTdwAAAACKiTsAAAAAxcQdAAAAgGLiDgAAAEAxcQcAAACgmLgDAAAAUEzcAQAAACgm7gAAAAAUE3cAAAAAiok7AAAAAMXEHQAAAIBi4g4AAABAMXEHAAAAoJi4AwAAAFBM3AEAAAAoJu4AAAAAFBN3AAAAAIqJOwAAAADFxB0AAACAYuIOAAAAQDFxBwAAAKCYuAMAAABQTNwBAAAAKCbuAAAAABQTdwAAAACKiTsAAAAAxcQdAAAAgGLiDgAAAEAxcQcAAACgmLgDAAAAUEzcAQAAACgm7gAAAAAUE3cAAAAAiok7AAAAAMXEHQAAAIBi4g4AAABAMXEHAAAAqPGej96duz/74Kpn7CkHtjthjPFNSd6a5OIkjyU5Puf8jWUPW6kTJ5Jrrknuuiu56KLkzW9OfuiHVr0KAAAAzls//KY/zm1/c/Kr74/sT37vp74nVzzjohWu2hsWuXLn0SQ/O+f850lekOSnxhjfutxZK3TiRPLN35z89V8np04lJ08mL3tZcuONq14GAAAA56WfvPEDXxd2kuThU8n3/ff3rWjR3rJt3Jlz3jPnvOPMnx9M8rEkz1z2sJV56Uu3Pn799bu7AwAAAEiS/P5H7tvy+KkkN7z/zt0dswft6J47Y4zLklyd5LYtPrtujLExxtjY3Nw8N+tW4a67tj7+hS/s7g4AAAAgSTKf4LP3/lVxgzhHFo47Y4wLk/xuktfOOT9/9udzzuNzzvU55/ra2tq53Li7Lr986+MXXLC7OwAAAIAkTxwv/vVzihvEObJQ3BljHMzpsHPTnPPm5U5asVtv3fr4W96yuzsAAACAJMmLn/f0LY8fGMkrXvjsXV6z92wbd8YYI8n/TPKxOefft3c/oZbedx3HP18ypliMiSaDShKd0MSaCInoWIVG2+jCpAGDoNBYTEkLQbRFN9LBhS66GReKqK0llMmfTbPQopGJKS5aU0mDnUJMGktlmj9NqJBJKxYqNKT9ubh36GQyc++ZmXPOc773eb3gcjnPec7hu7g/7j3v+/z5i9WPNLFLL01eeCG5/vpk375k//7k0UfdLQsAAAAm8tfvOZibr/3h1217877k6Ad/eaKJNkuNsdOZa0lV3Zzks0meztat0JPkj8cYj5ztNQcPHhzHjh1b2pAAAAAASfLki1/PWy67OJdcMr9boFfVF8YYB0/fvm+3F44x/i1JrWQqAAAAgHPwM1dfPvUIG+ec7pYFAAAAwGYRdwAAAAAaE3cAAAAAGhN3AAAAABoTdwAAAAAaE3cAAAAAGhN3AAAAABoTdwAAAAAaE3cAAAAAGhN3AAAAABoTdwAAAAAaE3cAAAAAGhN3AAAAABoTdwAAAAAaE3cAAAAAGhN3AAAAABoTdwAAAAAaE3cAAAAAGhN3AAAAABoTdwAAAAAaE3cAAAAAGj3wSAIAAA8mSURBVBN3AAAAABoTdwAAAAAaE3cAAAAAGhN3AAAAABoTdwAAAAAaE3cAAAAAGhN3AAAAABoTdwAAAAAaE3cAAAAAGhN3AAAAABoTdwAAAAAaE3cAAAAAGhN3AAAAABoTdwAAAAAaE3cAAAAAGhN3AAAAABoTdwAAAAAaE3cAAAAAGhN3AAAAABoTdwAAAAAaE3cAAAAAGhN3AAAAABoTdwAAAAAaE3cAAAAAGhN3AAAAABoTdwAAAAAaE3cAAAAAGhN3AAAAABoTdwAAAAAaE3cAAAAAGhN3AAAAABoTdwAAAAAaE3cAAAAAGhN3AAAAABoTdwAAAAAaE3cAAAAAGhN3AAAAABoTdwAAAAAaE3cAAAAAGhN3AAAAABoTdwAAAAAaE3cAAAAAGhN3AAAAABoTdwAAAAAaE3cAAAAAGhN3AAAAABoTdwAAAAAaE3cAAAAAGhN3AAAAABoTdwAAAAAaE3cAAAAAGhN3AAAAABoTdwAAAAAaE3cAAAAAGhN3AAAAABoTdwAAAAAaE3cAAAAAGhN3AAAAABoTdwAAAAAaE3cAAAAAGhN3AAAAABoTdwAAAAAaE3cAAAAAGhN3AAAAABoTdwAAAAAaE3cAAAAAGhN3AAAAABoTdwAAAAAaE3cAAAAAGhN3AAAAABoTdwAAAAAaE3cAAAAAGhN3AAAAABoTdwAAAAAaE3cAAAAAGhN3AAAAABoTdwAAAAAaE3cAAAAAGhN3AAAAABoTdwAAAAAaE3cAAAAAGhN3AAAAABoTdwAAAAAaE3cAAAAAGhN3AAAAABoTdwAAAAAa2zf1AAAAAAC7OXDo6Bu2XZPk04dvX/8wG0bcAQAAADbSLYeO5rkdnn8u34s+z8848jgt62zuuy+59tqt7wAAAMBaHdgl7Jxp/7kSd051331J1dbX+96XfOUrW99Pbnvve6eeEAAAAPa88w01cw084s5Jb3rTVsjZyYMPbkUeAAAAYCUuNNDMMfCIO8nW6Vevvrr4/gIPAAAAbKy5BR5xJ9k6/QoAAACY1NyizLLsGneq6khVvVxVX1zHQGv39ref3+scvQMAAABsgEWO3Lk/ya0rnmM6jz8+9QQAAADAks3pKKBd484Y47Ek31jDLAAAAACco6Vdc6eq7qmqY1V17MSJE8t6WwAAAAB2sLS4M8a4d4xxcIxxcP/+/ct6WwAAAAB24G5ZF1889QQAAADAkr3j8qknWB9x59vfPr/XjbHcOQAAAICleeCPbp96hLVZ5Fbon0jyuSRvraqXqur9qx8LAAAAmJvnD88nyCzTvt12GGPcuY5BJjVGUnVu+wMAAAAbaW6RyGlZJ42RXL7ACXnCDgAAAKzMhYaZOV1r5yRx51SvvLIVb44cef32u+7a2i7sAAAAwMqdb+C5JvO61s5J4s6Z3H3392LOGMkDD0w9EQAAAMzKuQaeP/zR5NMzOx3rpF2vuQMAAAAwhZOB55ZDR/PcLvvMmbgDAAAAbLS5HpGzKKdlAQAAADQm7gAAAAA0Ju4AAAAANCbuAAAAADQm7gAAAAA0Ju4AAAAANCbuAAAAADQm7gAAAAA0Ju4AAAAANCbuAAAAADQm7gAAAAA0Ju4AAAAANCbuAAAAADQm7gAAAAA0Ju4AAAAANCbuAAAAADQm7gAAAAA0Ju4AAAAANCbuAAAAADQm7gAAAAA0Ju4AAAAANCbuAAAAADQm7gAAAAA0Ju4AAAAANCbuAAAAADQm7gAAAAA0Ju4AAAAANCbuAAAAADQm7gAAAAA0Ju4AAAAANCbuAAAAADQm7gAAAAA0Ju4AAAAANCbuAAAAADQm7gAAAAA0Ju4AAAAANCbuAAAAADQm7gAAAAA0Ju4AAAAANCbuAAAAADQm7gAAAAA0Ju4AAAAANCbuAAAAADQm7gAAAAA0Ju4AAAAANCbuAAAAADQm7gAAAAA0Ju4AAAAANCbuAAAAADQm7gAAAAA0Ju4AAAAANCbuAAAAADQm7gAAAAA0Ju4AAAAANCbuAAAAADQm7gAAAAA0Ju4AAAAANCbuAAAAADQm7gAAAAA0Ju4AAAAANCbuAAAAADQm7gAAAAA0Ju4AAAAANCbuAAAAADQm7gAAAAA0Ju4AAAAANCbuAAAAADQm7gAAAAA0Ju4AAAAANCbuAAAAADQm7gAAAAA0Ju4AAAAANCbuAAAAADQm7gAAAAA0Ju4AAAAANCbuAAAAADQm7gAAAAA0Ju4AAAAANCbuAAAAADQm7gAAAAA0Ju4AAAAANCbuAAAAADQm7gAAAAA0Ju4AAAAANCbuAAAAADQm7gAAAAA0Ju4AAAAANCbuAAAAADQm7gAAAAA0Ju4AAAAANCbuAAAAADQm7gAAAAA0Ju4AAAAANCbuAAAAADQm7gAAAAA0Ju4AAAAANCbuAAAAADS2b+oBNlLVG7eNsf45AAAAAHYh7iRnjjm77SP2AAAAwNocOHT0DdueP3z7BJNsHnFnkbCz0+tEHgAAAFiJMwWdsz0/59Az37hzvlHnTO8j8AAAAMDS7BZ1dnrNHCPPPC+ovKyws6r3AwAAgJk6n7Bz+usv9D26mV/cWVWIEXgAAADggiwzyswp8Mwv7qySwAMAAADnZU4xZtnmFXfEFwAAANg4qwo7cwlG84k76wo7AhIAAABsjDkEnvnEHQAAAGDjzCG+rJq4swqO3gEAAICNceMeD0gLxZ2qurWqvlxVx6vq0KqHWjqxBQAAAGbrm1MPsGK7xp2quijJR5LcluSGJHdW1Q2rHgwAAADY2/b6ETXrssiRO29LcnyM8ewY49UkDyW5Y7VjAQAAAHvdXj+iZl0WiTtXJnnxlMcvbW8DAAAAYGKLxJ0zXbBmvGGnqnuq6lhVHTtx4sSFTwYAAADArhaJOy8lufqUx1cl+drpO40x7h1jHBxjHNy/f/+y5gMAAABgB4vEnc8nua6qrqmqi5O8O8nDqx0LAAAA2OueP3z71CPsCft222GM8VpVfSDJp5JclOTIGOOZlU+2TGO4HToAAACwJ+0ad5JkjPFIkkdWPMveMd5wSSIAAABgInv9CKFFTssCAAAAWIm9Hl7WYT5xZ11H0zhqBwAAADbGHOLRfOIOAAAAsJHmEGBWaV5xZ9VH1ThqBwAAAM7LD67gPecSjeYVd5LVBRhhBwAAAM7bU0sOMXMJO8kc406y/BAj7AAAAMAFe/7w7UuJMnMKO8lc406yvCAj7AAAAMBSnW+cWVYc6mbf1ANM6mSYqbqw1wMAAABLdWqkOXDo6ML7ztG8485J5xJ5BB0AAABYq7nHm92IO6cSbgAAAIBm5nvNHQAAAIA9QNwBAAAAaEzcAQAAAGhM3AEAAABoTNwBAAAAaEzcAQAAAGhM3AEAAABoTNwBAAAAaEzcAQAAAGhM3AEAAABoTNwBAAAAaEzcAQAAAGhM3AEAAABoTNwBAAAAaEzcAQAAAGhM3AEAAABoTNwBAAAAaEzcAQAAAGhM3AEAAABoTNwBAAAAaEzcAQAAAGhM3AEAAABoTNwBAAAAaEzcAQAAAGhM3AEAAABoTNwBAAAAaEzcAQAAAGhM3AEAAABoTNwBAAAAaEzcAQAAAGhM3AEAAABoTNwBAAAAaEzcAQAAAGhM3AEAAABoTNwBAAAAaEzcAQAAAGhM3AEAAABoTNwBAAAAaEzcAQAAAGhM3AEAAABoTNwBAAAAaKzGGMt/06oTSV5Y+htP44okr0w9BGwo6wN2Zo3A2VkfsDNrBHY21zXyE2OM/advXEnc2Uuq6tgY4+DUc8Amsj5gZ9YInJ31ATuzRmBn1sjrOS0LAAAAoDFxBwAAAKAxcWd39049AGww6wN2Zo3A2VkfsDNrBHZmjZzCNXcAAAAAGnPkDgAAAEBj4k6Sqrq1qr5cVcer6tAZnq+q+qvt55+qqp+dYk6YygJr5D3ba+Opqnq8qm6aYk6Ywm7r45T9fr6qvlNVv7nO+WBqi6yRqnpnVT1ZVc9U1b+ue0aY0gJ/Z11aVf9UVf+xvUbunmJOmEJVHamql6vqi2d53mf1bbOPO1V1UZKPJLktyQ1J7qyqG07b7bYk121/3ZPkb9c6JExowTXyXJJ3jDFuTPLhOP+VmVhwfZzc78+SfGq9E8K0FlkjVXVZko8m+fUxxk8n+a21DwoTWfD3yO8n+c8xxk1J3pnkz6vq4rUOCtO5P8mtOzzvs/q22cedJG9LcnyM8ewY49UkDyW547R97kjy4NjyRJLLqurH1j0oTGTXNTLGeHyM8T/bD59IctWaZ4SpLPI7JEk+mOTvk7y8zuFgAyyyRn47ySfHGF9NkjGGdcKcLLJGRpJLqqqS/ECSbyR5bb1jwjTGGI9l62f+bHxW3ybuJFcmefGUxy9tbzvXfWCvOtef//cn+eeVTgSbY9f1UVVXJvmNJB9b41ywKRb5HfKTSX6oqj5TVV+oqrvWNh1Mb5E18jdJrk/ytSRPJ/mDMcZ31zMebDyf1bftm3qADVBn2Hb6LcQW2Qf2qoV//qvqlmzFnZtXOhFsjkXWx18m+dAY4ztb/3SFWVlkjexL8nNJfjXJ9yf5XFU9Mcb4r1UPBxtgkTXya0meTPIrSd6S5F+q6rNjjG+uejhowGf1beLOVtm7+pTHV2Wrip/rPrBXLfTzX1U3Jvl4ktvGGF9f02wwtUXWx8EkD22HnSuSvKuqXhtj/MN6RoRJLfp31itjjG8l+VZVPZbkpiTiDnOwyBq5O8nhMcZIcryqnkvyU0n+fT0jwkbzWX2b07KSzye5rqqu2b4w2buTPHzaPg8nuWv7Sty/mOR/xxj/ve5BYSK7rpGq+vEkn0zyO/7Tyszsuj7GGNeMMQ6MMQ4k+bskvyfsMCOL/J31j0l+qar2VdWbk/xCki+teU6YyiJr5KvZOrItVfUjSd6a5Nm1Tgmby2f1bbM/cmeM8VpVfSBbdzC5KMmRMcYzVfW7289/LMkjSd6V5HiS/8tWPYdZWHCN/EmSy5N8dPvohNfGGAenmhnWZcH1AbO1yBoZY3ypqh5N8lSS7yb5+BjjjLe8hb1mwd8jH05yf1U9na1TUD40xnhlsqFhjarqE9m6S9wVVfVSkj9N8n2Jz+qnq62j+wAAAADoyGlZAAAAAI2JOwAAAACNiTsAAAAAjYk7AAAAAI2JOwAAAACNiTsAAAAAjYk7AAAAAI2JOwAAAACN/T+smWwr0lKuZwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "scatter_chart('SibSp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Survived  SibSp\n",
       "0         0        398\n",
       "          1         97\n",
       "          2         15\n",
       "          4         15\n",
       "          3         12\n",
       "          8          7\n",
       "          5          5\n",
       "1         0        210\n",
       "          1        112\n",
       "          2         13\n",
       "          3          4\n",
       "          4          3\n",
       "Name: SibSp, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.groupby('Survived')['SibSp'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# sibsp_mapping = {\n",
    "#     5: 0.2, 6: 0.2, 7:0.2, 8: 0.2,\n",
    "#     4: 0.6, 3: 0.6,\n",
    "#     0: 0.5,\n",
    "#     2: 0.7,\n",
    "#     1: 0.9\n",
    "# }\n",
    "# sibsp_mapping = {\n",
    "#     5: 0.1, 6: 0.1, 7:0.1, 8: 0.1,\n",
    "#     4: 0.2, 3: 0.2,\n",
    "#     0: 0.4,\n",
    "#     1: 0.6,\n",
    "#     2: 0.7,\n",
    "# }\n",
    "# train_data['SibSp'] = train_data['SibSp'].map(sibsp_mapping)\n",
    "\n",
    "sibsp_scaller = StandardScaler()\n",
    "# fare_scaller.fit_transform(train_data['Fare'], train_data['Survived'])\n",
    "# fares = train_data['Fare'].values.reshape(-1,1);\n",
    "train_data['SibSp'] = sibsp_scaller.fit_transform(train_data['SibSp'].values.reshape(-1,1), train_data['Survived'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "scatter_chart('SibSp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# median = train_data['Age'].median()\n",
    "# std = train_data['Age'].std()\n",
    "outliers = train_data[train_data['Age'] > 65]\n",
    "def age_outliers(row):\n",
    "    if(row['Age'] > 65):\n",
    "        row['Age'] = 65\n",
    "    return row\n",
    "train_data = train_data.apply(age_outliers, axis=1)\n",
    "# outliers.head(20)\n",
    "# train_data.loc[outliers, 'Age'] = np.nan\n",
    "# train_data['Age'].fillna(train_data['Age'].nlargest(8).values[-1], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "scatter_chart('Age')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "age_scaller = StandardScaler()\n",
    "train_data['Age'] = age_scaller.fit_transform(train_data['Age'].values.reshape(-1,1), train_data['Survived'])\n",
    "scatter_chart('Age')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data['isAlone'] = train_data['Parch'] + train_data['SibSp'] > 0\n",
    "train_data['isAlone'] = train_data['isAlone'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data['Parents'] = train_data['Parch'] + train_data['SibSp']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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/70lu+erDQ0yaGk7ujHLqqcniYrK0lCwsJCeeOPQiAAAAOKxtPnb92L+dsOHIH+CS6SPujPLBD64Enj17khNOSP7sz4ZeBAAAAIe1d/zCDydZOanTDz733X/f8i+eOcSkqSHujHLOOcl99yXz88lDDyUXXzz0IgAAADisnXbipvzWpef84+Pvhp03/MS2POuMzcOMmhKt9776qx6n2dnZvn379if8fQEAAAD++DN3J0lee/E5q7zy/y+ttdt677OHPu8DlQEAAIBSDreosxrXsgAAAAAKE3cAAAAAChN3AAAAAAoTdwAAAAAKE3cAAAAAChN3AAAAAAoTdwAAAAAKE3cAAAAAChN3AAAAAAoTdwAAAAAKE3cAAAAAChN3AAAAAAoTdwAAAAAKE3cAAAAAChN3AAAAAAoTdwAAAAAKE3cAAAAAChN3AAAAAAoTdwAAAAAKE3cAAAAAChN3AAAAAAoTdwAAAAAKE3cAAAAAChN3AAAAAAoTdwAAAAAKmxl6AAAAAMCkdu+Zz4fu+IckycsvOCObN60feNHwxB0AAACghAd27c4rrvkf+dbu+STJf/7UV/NX/+bHc+bmTQMvG5ZrWeM88EDygQ8kX/va0EsAAACAJFf/zT351p75bN64Pps3rs/O/Yt528d2DD1rcOLOOA8+mHz968k3vzn0EgAAACDJw7v3pX3P4yNa8vCexcH2TAvXssb5oR9KTjwxOfXUoZcAAAAASS55xtbc9NVH8uiBhbS09N7y4vNPG3rW4MSdcWZmkrPOGnoFAAAAcNC//NGz8sieA/nT/35vepJXXnR6Xvu8s4eeNThxBwAAACjjDS94at7wgqcOPWOq+MwdAAAAgMLEHQAAAIDCxB0AAACAwsQdAAAAgMJWjTuttSe11j7VWtvRWruztfbGH8QwAAAAAFY3ybdlLSb5rd7751trxya5rbX2id77XWu8DQAAAIBVrHpyp/d+f+/98wd/fzTJjiRnrPUwAAAAAFb3uD5zp7W2LclFST434m+Xt9a2t9a2z83NPTHrAAAAAHhME8ed1tqmJH+V5N/13r9z6N9779f23md777Nbtmx5IjcCAAAAMMZEcae1dmRWws77e+8fXo2w0GMAABd8SURBVNtJAAAAAExqkm/Lakn+S5Idvfd3rP0kAAAAACY1ycmd5yX5V0le2Fq74+DPS9d4FwAAAAATWPWr0Hvvn0nSfgBbAAAAAHicHte3ZQEAAAAwXcQdAAAAgMLEHQAAAIDCxB0AAACAwsQdAAAAgMLEHQAAAIDCxB0AAACAwsQdAAAAgMLEHQAAAIDCxB0AAACAwsQdAAAAgMLEHQAAAOB/t3f/wZbX9X3HX5/du2QRRBS27gZIl41KpRFiWNNG4rhRSdB1goxtMVgplDbTJlrHyR9uGkeMNu0WhrEGtQxaoOaPkozJqGFJ4g9KxSDRBQGLDIT9ISI/sqhZkGSB5X76x9ktl8vde88u95zv+dzv4zFz5977PZ9z5r139jN3z3O/33NomLgDAAAA0DBxBwAAAKBh4g4AAABAw6a6HgAAAABgWA/+7d/nqpt2ZHo6+Venr80JL35B1yN1zpk7AAAAQDOuv/vhPLR7T37w4yfyxTsf6nqcieDMHQAAAKAZq49amSf3TqeUZM1RK7seZyKIOwAAAEAzfumkf5Bjj/yJTNfk1ONf1PU4E0HcAQAAAJqxbNmynHrCi7seY6J4zR0AAACAhok7AAAAAA0TdwAAAAAaJu4AAAAANEzcAQAAAGiYuAMAAADQMHEHAAAAoGHiDgAAAEDDxB0AAACAhok7AAAAAA0TdwAAAAAaJu4AAAAANEzcAQAAAGiYuAMAAADQMHEHAAAAoGHiDgAAAEDDproeAAAAAGAYu3btyluv+FYefuypJMmqI1bkf1746pz8k6s6nqxbztwBAAAAJt4nvnxXXnPpN/5/2EmSXY8/lbf8/jdyyZZvdzhZ98SdA7nwwuSkk5Kzz0727Ol6GgAAAOi1S768/YC3feLG+8Y4yeQRd+byy7+cXHVVsm1b8vnPJ+vXdz0RAAAA9Na/verrC65522VfHcMkk8lr7szla19LpqaSlSuTp55K7r6764kAAACgt/5qx98uuObuhx8bwySTyZk7czn88GR6evD13r2D0AMAAAB0YsXUwvlixbL+Jo7+/snnc+mlyfLlyeOPD77/0Ic6HQcAAAD67PfO/pkF17zvjJ8ewySTySkpczn//OTMM5Prr09e+9pk7dquJwIAAIDeOvNVxyW5bd41F7zuFeMZZgI5c+dAVq9Ozj1X2AEAAIAJsHPzxqyY4/iyfbf1mTN3AAAAgCb89eaN2bVrV/7jtfcmSf7zW1+WVatWdTxV98QdAAAAoBmrVq3Kpy4QdGZyWRYAAABAw8QdAAAAgIaJOwAAAAANE3cAAAAAGibuAAAAADRM3AEAAABomLgDAAAA0DBxBwAAAKBh4g4AAABAw8QdAAAAgIaJOwAAAAANE3cAAAAAGibuAAAAADRM3AEAAABomLgDAAAA0DBxBwAAAKBh4g4AAABAw8QdAAAAgIaJOwAAAAANE3cAAAAAGibuAAAAADRsqusBAAAAAIb15bu359Jrd+app6ez6ax1edNJ67oeqXPiDgAAADDxfu9zd+RTN3/vWcf+zVV3Jbkr73j16mw+57RuBpsALss6kHXrkpUrkxNOSB5+uOtpAAAAoLc2/eEtzwk7M13zrYdywaduGuNEk0Xcme2ii5JSkh07kieeSO6/P1m9OjnnnK4nAwAAgF665lsPLbjmf2/70RgmmUzizmwf/vDcx//oj8Y7BwAAAJBzPnnj0GvfdPGXRzjJ5BJ3Zrroovlv37BhLGMAAAAAA39136NDr733h0+McJLJJe7MdPPN89++Y8d45gAAAAAYkrgz0yWXzH/7+eePZQwAAABgQLhYmJ/RTKecMv/tv/u745kDAAAASJJcccErh1578VuHX7uUiDuz1Zosm+PH8tDCr8wNAAAALK43nbRu6LX/4heHX7uUiDtzefrpQeS5/fbB51qTl76066kAAACgl3Zu3rgoa5YqcWc+C12mBQAAAIzFzs0b88pVhz/n+Mte8hO9DjtJMtX1AAAAAADD+LPfekPXI0wkZ+4AAAAANEzcAQAAAGiYuAMAAADQMHEHAAAAoGHiDgAAAEDDxB0AAACAhok7AAAAAA0TdwAAAAAaJu4AAAAANEzcAQAAAGiYuAMAAADQMHEHAAAAoGHiDgAAAEDDxB0AAACAhok7AAAAAA0TdwAAAAAaJu4AAAAANEzcAQAAAGiYuAMAAADQMHEHAAAAoGHiDgAAAEDDxB0AAACAhok7AAAAAA0TdwAAAAAaJu7M5YMfTEp55uPCC7ueCAAAAEiyZ8/eXHvH9/Ont38/e/bs7XqciTDV9QAT5y//MvnIR5597Mork9e/PjnvvG5mAgAAALJ37978hz+8NXd8f3eS5LO33J//cd5pmZrqd95w5s5sV145+Lxs2TMfM48DAAAAndj+yN/l2w88mjVHrcyao1bmnod+nDsfeKzrsTrX77Q1l1WrBp+np58JO0ly9NHdzAMAAAAkSV64ckWWl5I9T09neUqWL6t50QtWdD1W55y5M9sHPpAcdtjg6+npwUcpyeWXdzsXAAAA9Nyaow/Pv3/9ifn7J5/Oo0/szfmnn5i1xx7Z9Vidc+bObEcemXz3u8nppycPPpi85CXJtdcmq1d3PRkAAAD03jt/YV3Oec1PJUnvX2tnPz+FuaxenWzb1vUUAAAAwBxEnWdzWRYAAABAw8QdAAAAgIaJOwAAAAANE3cAAAAAGibuAAAAADRM3AEAAABomLgDAAAA0DBxBwAAAKBh4g4AAABAw8QdAAAAgIaJOwAAAAANE3cAAAAAGibuAAAAADRM3AEAAABomLgDAAAA0DBxBwAAAKBh4g4AAABAw8QdAAAAgIaJOwAAAAANE3cAAAAAGibuAAAAADRM3AEAAABomLgDAAAA0DBxBwAAAKBh4g4AAABAw8QdAAAAgIaJOwAAAAANE3cAAAAAGibuAAAAADRM3AEAAABomLgDAAAA0DBxBwAAAKBh4g4AAABAw8QdAAAAgIaJOwAAAAANE3cAAAAAGibuAAAAADRM3AEAAABomLgDAAAA0DBxBwAAAKBh4g4AAABAw8QdAAAAgIaJOwAAAAANE3cAAAAAGibuAAAAADRM3AEAAABomLgDAAAA0DBxBwAAAKBhU10PAAAAADCs3/iDb+Qrd+1Kkpz+spfkyn/9Cx1P1D1n7gAAAAAT7+qv/nXWbtqS6+7clSemkyemk+vv+WHWbtqSy770na7H65S4M5fHHktOOy05+ujk5JOTBx7oeiIAAADorQcffDAfuu6eA95+6Vd25Nb7HhzjRJNF3JnLMcckt96a7N6d3HVXcvzxAg8AAAB05Fcu/9aCa8694tYxTDKZxJ3Z3vnO5KmnBl+XMvhca/K2t3U3EwAAAPTYo0/UBdfs2TuGQSaUuDPb9u2Dz/vDzv7PP/hBN/MAAAAAzEPcme2MMwafa33251e9qpt5AAAAAOYh7sz24Q8n69YNvt4fdo45Jvnc57qbCQAAAHrsFasOX3DNT77wsDFMMpnEnbls25Zcdlly1lnJJZckjzzS9UQAAADQW1/8rTcsuOam3zljDJNMplLrwi9KdLDWr19ft27duuiPCwAAAPTX2k1b5jz+9ff+XNasWTPmacavlHJLrXX97ONTXQwDAAAAcLB2bt6YJPnAHw/eGv03X7u6F1FnIeIOAAAA0JT/9PZXdz3CRPGaOwAAAAANE3cAAAAAGibuAAAAADRM3AEAAABomLgDAAAA0DBxBwAAAKBh4g4AAABAw8QdAAAAgIaJOwAAAAANE3cAAAAAGibuAAAAADRM3AEAAABomLgDAAAA0DBxBwAAAKBh4g4AAABAw8QdAAAAgIaJOwAAAAANE3cAAAAAGibuAAAAADRsaphFpZQzk3wsyfIkn661bh7pVAAAAACzfPRLN+RjX3n8Wcfe+8Yj8r4zNnQz0IQotdb5F5SyPMk9Sc5Icn+Sbyb5tVrrdw50n/Xr19etW7cu5pwAAABAT/3qR7fkjofnX7PuRcn1v71xPAN1pJRyS611/ezjw1yW9fNJ7q21bq+1PpnkmiRnLfaAE+Pii5NSnvvxnvd0PRkAAAD0zqt/Z+GwkyTbdyev2LRl9ANNoGHiznFJvjfj+/v3HVt6li9P3v/+uW/7+McHkQcAAAAYi395xRfzo6eHX/9kBmf59M0wcWeuovGca7lKKb9eStlaStm6a9eu5z/ZuB13XDI9vfC65ctHPwsAAACQr21/6qDvM8xZPkvNMHHn/iQnzPj++CQPzF5Ua72i1rq+1rp+1apVizXf+DzwnD/S3IYJQAAAAEBnbrjhhq5HGKth4s43k7y8lHJiKeWwJO9I8oXRjjVmF198cOt/9mdHMwcAAACQJFn7PF4/5/w/f3zhRUvIgm+FXmvdW0p5d5K/yOCt0K+std458snG6WMfO7j1t98+mjkAAAAADtKCcSdJaq3XJbluxLN0Z+XKricAAAAAOCTDXJa19H3mMwe3/t3vHs0cAAAAQBLB4mD4WSXJ6acf3PrLLhvNHAAAAECSZPvmjYd8353P474tEnf2G/ZsnFNPHe0cAAAAAAdB3NnvssuSt799/jXr1iW33TaeeQAAAKDnrj7ziLHcp3Xizkyf/WxSa3LUUc8+vmLF4Pi2bd3MBQAAAD20YcOGvPeNw8eas085Ihs2bBjdQBNK3JnL7t2DmLP/48knu54IAAAAeul9Z2wY6jV0rj7ziHz03A2jH2gCDfVW6AAAAABd2h943vBftmT77sGxNYcnX7+oXy+ePBdxBwAAAGjG9b8t5szmsiwAAACAhok7AAAAAA0TdwAAAAAaJu4AAAAANEzcAQAAAGiYuAMAAADQMHEHAAAAoGHiDgAAAEDDxB0AAACAhok7AAAAAA0TdwAAAAAaJu4AAAAANEzcAQAAAGiYuAMAAADQMHEHAAAAoGHiDgAAAEDDxB0AAACAhok7AAAAAA0TdwAAAAAaJu4AAAAANEzcAQAAAGiYuAMAAADQMHEHAAAAoGHiDgAAAEDDxB0AAACAhok7AAAAAA0TdwAAAAAaJu4AAAAANEzcAQAAAGiYuAMAAADQMHEHAAAAoGHiDgAAAEDDxB0AAACAhok7AAAAAA0TdwAAAAAaJu4AAAAANEzcAQAAAGiYuAMAAADQMHEHAAAAoGHiDgAAAEDDxB0AAACAhok7AAAAAA0TdwAAAAAaJu4AAAAANEzcAQAAAGiYuAMAAADQMHEHAAAAoGHiDgAAAEDDxB0AAACAhok7AAAAAA0TdwAAAAAaJu4AAAAANEzcAQAAAGiYuAMAAADQMHEHAAAAoGHiDgAAAEDDxB0AAACAhok7AAAAAA0TdwAAAAAaJu4AAAAANEzcAQAAAGiYuAMAAADQMHEHAAAAoGHiDgAAAEDDxB0AAACAhok7AAAAAA0TdwAAAAAaJu4AAAAANGyq6wEmSikLr6l19HMAAAAADEncSYaLOrPXijwAAADABOh33DmYqHOg+4o8AAAAMHLv2rQlNx7gttcl+YPNG8c5zkTpb9x5PmFn9uMIPAAAADASazdtWXDNjTPW7exh5OnnCyovVtgZ1eMBAAAAQ4Wdue7zrkO4X8v6F3dGFWIEHgAAAFgUP7NpyyGFnf1u3PcYfdG/uDNKAg8AAAA8bz9epMfoyxk8/Yo74gsAAABMtOdzxs5sB3oB5qWmX3FnHAQkAAAAOCSLGXZG+ZiTpj9xR3QBAAAAlqD+xB0AAACAJUjcAQAAADo3ysunlvqlWeLOKLgEDAAAABgTcQcAAACgYeIOAAAAQMPEHQAAAICGiTujUGvXEwAAAAA9Ie4AAAAAndu5eWOTjz0JxB0AAACAhvUn7rhUCgAAAFiC+hN3xkVEAgAAgEMyisunlvolWUnf4o7wAgAAABNtMWNMH8JO0re4M2riEQAAADxvixFlXrcIc7RiqusBxq7WpJTRPC4AAACwKHZu3pi1m7Yc8n37pJ9n7ix2iBF2AAAAYNEdSqTpW9hJ+njmzn6LdQaPsAMAAAAjMzPWHOhMnj4GnZn6G3eSZ8LMoUQeUQcAAADGqu8R50D6HXf2mxlq5gs9gg4AAAAwYcSd2QQcAAAAoCH9fEFlAAAAgCVC3AEAAABomLgDAAAA0DBxBwAAAKBh4g4AAABAw8QdAAAAgIaJOwAAAAANE3cAAAAAGibuAAAAADRM3AEAAABomLgDAAAA0DBxBwAAAKBh4g4AAABAw8QdAAAAgIaJOwAAAAANE3cAAAAAGibuAAAAADRM3AEAAABomLgDAAAA0DBxBwAAAKBh4g4AAABAw8QdAAAAgIaJOwAAAAANE3cAAAAAGibuAAAAADRM3AEAAABomLgDAAAA0DBxBwAAAKBh4g4AAABAw8QdAAAAgIaJOwAAAAANE3cAAAAAGibuAAAAADRM3AEAAABomLgDAAAA0DBxBwAAAKBh4g4AAABAw8QdAAAAgIaJOwAAAAANE3cAAAAAGibuAAAAADRM3AEAAABomLgDAAAA0DBxBwAAAKBhpda6+A9ayq4k3130B+7GsUke6XoImFD2B8zPHoH52SMwP3sE5tfHPfIPa62rZh8cSdxZSkopW2ut67ueAyaR/QHzs0dgfvYIzM8egfnZI89wWRYAAABAw8QdAAAAgIaJOwu7ousBYILZHzA/ewTmZ4/A/OwRmJ89so/X3AEAAABomDN3AAAAABom7iQppZxZSrm7lHJvKWXTHLeXUsrv77v9jlLKz3UxJ3RliD3yzn17445Syk2llFO7mBO6stAembHuNaWUp0sp/2yc80HXhtkjpZQNpZTbSil3llL+z7hnhC4N8W+tF5VS/rSUcvu+PXJBF3NCF0opV5ZS/qaU8n8PcLvn6xF3UkpZnuQTSd6c5OQkv1ZKOXnWsjcnefm+j19P8t/HOiR0aMg9siPJ62utpyT5SFz7So8MuUf2r/uvSf5ivBNCt4bZI6WUo5N8Msmv1lr/cZJ/PvZBoSND/h75zSTfqbWemmRDkktLKYeNdVDoztVJzpznds/XI+4kyc8nubfWur3W+mSSa5KcNWvNWUk+UwduTnJ0KWXNuAeFjiy4R2qtN9Vaf7Tv25uTHD/mGaFLw/weSZL3JPnjJH8zzuFgAgyzR85N8ie11vuSpNZqn9Anw+yRmuSFpZSS5MgkP0yyd7xjQjdqrV/N4O/8gXi+HnEnSY5L8r0Z39+/79jBroGl6mD//l+Y5M9GOhFMlgX3SCnluCRnJ7l8jHPBpBjm98grkry4lHJDKeWWUsp5Y5sOujfMHvl4klcmeSDJt5O8t9Y6PZ7xYOJ5vp5kqusBJkCZ49jstxAbZg0sVUP//S+l/FIGcecXRzoRTJZh9sh/S/L+WuvTg/90hV4ZZo9MJTktyRuTHJ7k66WUm2ut94x6OJgAw+yRX0lyW5I3JPnpJF8qpdxYa3101MNBAzxfj7iTDKreCTO+Pz6DIn6wa2CpGurvfynllCSfTvLmWusPxjQbTIJh9sj6JNfsCzvHJnlLKWVvrfVz4xkROjXsv7UeqbU+nuTxUspXk5yaRNyhD4bZIxck2VxrrUnuLaXsSPKPknxjPCPCRPN8PS7LSpJvJnl5KeXEfS9K9o4kX5i15gtJztv3Ktz/NMnuWuuD4x4UOrLgHiml/FSSP0nyLv/LSg8tuEdqrSfWWtfWWtcm+WyS3xB26JFh/q31+SSvK6VMlVJekOSfJLlrzHNCV4bZI/dlcGZbSikvTXJSku1jnRIml+frceZOaq17SynvzuDdS5YnubLWemcp5d/tu/3yJNcleUuSe5P8XQblHHphyD3ywSTHJPnkvjMT9tZa13c1M4zTkHsEemuYPVJrvauU8udJ7kgyneTTtdY53/IWlpohf498JMnVpZRvZ3AJyvtrrY90NjSMUSnlf2XwLnHHllLuT3JRkhWJ5+szlcGZfQAAAAC0yGVZAAAAAA0TdwAAAAAaJu4AAAAANEzcAQAAAGiYuAMAAADQMHEHAAAAoGHiDgAAAEDDxB0AAACAhv0/cs+dcoPJhr8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "scatter_chart('Parents')\n",
    "parent_scaller = StandardScaler()\n",
    "train_data['Parents'] = parent_scaller.fit_transform(train_data['Parents'].values.reshape(-1,1), train_data['Survived'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fare_outliers(row):\n",
    "    distance = 1.5 * (np.percentile(train_data['Fare'], 85) - np.percentile(train_data['Fare'], 15))\n",
    "    if(row['Fare'] > distance + np.percentile(train_data['Fare'], 85)):\n",
    "        row['Fare'] = distance + np.percentile(train_data['Fare'], 85)\n",
    "    elif(row['Fare'] < np.percentile(train_data['Fare'], 15) - distance):\n",
    "        row['Fare'] = np.percentile(train_data['Fare'], 15) - distance\n",
    "    return row\n",
    "\n",
    "train_data = train_data.apply(fare_outliers, axis=1)\n",
    "\n",
    "fare_scaller = StandardScaler()\n",
    "# fare_scaller = MinMaxScaler()\n",
    "# fare_scaller.fit_transform(train_data['Fare'], train_data['Survived'])\n",
    "# fares = train_data['Fare'].values.reshape(-1,1);\n",
    "train_data['Fare'] = fare_scaller.fit_transform(train_data['Fare'].values.reshape(-1,1), train_data['Survived'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "scatter_chart('Fare')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     -0.672644\n",
       "1      1.353500\n",
       "2     -0.651286\n",
       "3      0.778143\n",
       "4     -0.647331\n",
       "         ...   \n",
       "886   -0.490703\n",
       "887    0.047212\n",
       "888   -0.160043\n",
       "889    0.047212\n",
       "890   -0.656823\n",
       "Name: Fare, Length: 891, dtype: float64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data['Fare']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = train_data['Survived']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for now drop the remainings rows\n",
    "train_data.drop(columns=['Cabin', 'Ticket', 'PassengerId', 'Survived', 'Parch', 'SibSp', 'Title', 'isAlone'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pclass      0\n",
       "Sex         0\n",
       "Age         0\n",
       "Fare        0\n",
       "Embarked    0\n",
       "Parents     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "# scaler = MinMaxScaler()\n",
    "X = scaler.fit_transform(train_data, y)\n",
    "# X = train_data\n",
    "k_fold = KFold(n_splits=8, shuffle=True, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "models = {}\n",
    "def create_model(name, model):\n",
    "    scoring='accuracy'\n",
    "    scores = cross_val_score(model, X, y, cv=k_fold, n_jobs=1, scoring=scoring)\n",
    "    model.fit(X,y)\n",
    "    print(\"[%s] Accuracy: %0.2f (+/- %0.2f)\" % (name, scores.mean(), scores.std() * 2))\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[RandomForest] Accuracy: 0.81 (+/- 0.03)\n"
     ]
    }
   ],
   "source": [
    "models['RandomForest'] = create_model('RandomForest', RandomForestClassifier())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[SVM] Accuracy: 0.81 (+/- 0.05)\n"
     ]
    }
   ],
   "source": [
    "models['SVM'] = create_model('SVM', svm.SVC())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Gaussian] Accuracy: 0.79 (+/- 0.07)\n"
     ]
    }
   ],
   "source": [
    "models['Gaussian'] = create_model('Gaussian', GaussianNB())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LogisticRegression] Accuracy: 0.80 (+/- 0.02)\n"
     ]
    }
   ],
   "source": [
    "models['LogisticRegression'] = create_model('LogisticRegression', LogisticRegression(max_iter=300))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[KNN] Accuracy: 0.79 (+/- 0.06)\n"
     ]
    }
   ],
   "source": [
    "models['KNN'] = create_model('KNN', KNeighborsClassifier(n_neighbors=7))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age             86\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             1\n",
       "Cabin          327\n",
       "Embarked         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_test_data = test_data.copy()\n",
    "test_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data['isAlone'] = test_data['Parch'] + test_data['SibSp'] > 0\n",
    "test_data['isAlone'] = test_data['isAlone'].astype(int)\n",
    "\n",
    "# Fare nulls\n",
    "test_data['Fare'].fillna(test_data['Fare'].dropna().median(), inplace=True)\n",
    "test_data = test_data.apply(fare_outliers, axis=1)\n",
    "test_data['Fare'] = fare_scaller.transform(test_data['Fare'].values.reshape(-1,1))\n",
    "# Title\n",
    "test_names = test_data['Name'].str.extract('([A-Za-z]+)\\.', expand=False)\n",
    "test_data['Title'] = test_names.map(title_mapping)\n",
    "test_data['Title'] = title_scaller.transform(test_data['Title'].values.reshape(-1,1))\n",
    "\n",
    "# Embarked\n",
    "test_data['Embarked'] = test_data.Embarked.map(embarked_mapping)\n",
    "test_data['Embarked'] = embarked_scaller.transform(test_data['Embarked'].values.reshape(-1,1))\n",
    "\n",
    "# Age\n",
    "test_data = test_data.apply(median_age, axis=1)\n",
    "test_data = test_data.apply(age_outliers, axis=1)\n",
    "test_data['Age'] = age_scaller.transform(test_data['Age'].values.reshape(-1,1))\n",
    "\n",
    "# Sex\n",
    "test_data['Sex'] = test_data.Sex.map(sex_mapping)\n",
    "\n",
    "# SibSp\n",
    "# test_data['SibSp'] = test_data['SibSp'].map(sibsp_mapping)\n",
    "test_data['SibSp'] = sibsp_scaller.transform(test_data['SibSp'].values.reshape(-1,1))\n",
    "\n",
    "# Pclass\n",
    "# test_data['Pclass'] = test_data['Pclass'].map(pclass_mapping)\n",
    "test_data['Pclass'] = pclass_scaller.transform(test_data['Pclass'].values.reshape(-1,1))\n",
    "\n",
    "# Parch\n",
    "test_data['Parch'] = parch_scaller.transform(test_data['Parch'].values.reshape(-1,1))\n",
    "\n",
    "test_data.drop(columns=['Name', 'Cabin', 'Ticket', 'PassengerId', 'SibSp', 'Parch', 'Title'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pclass      0\n",
       "Sex         0\n",
       "Age         0\n",
       "Fare        0\n",
       "Embarked    0\n",
       "isAlone     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mr        240\n",
       "Miss       78\n",
       "Mrs        72\n",
       "Master     21\n",
       "Rev         2\n",
       "Col         2\n",
       "Ms          1\n",
       "Dr          1\n",
       "Dona        1\n",
       "Name: Name, dtype: int64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_names.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'Title'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0mTraceback (most recent call last)",
      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2645\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2646\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2647\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'Title'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mKeyError\u001b[0mTraceback (most recent call last)",
      "\u001b[0;32m<ipython-input-54-03951fc63faf>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtest_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Title'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2798\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2799\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2800\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2801\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2802\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2646\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2647\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2648\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2649\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2650\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'Title'"
     ]
    }
   ],
   "source": [
    "test_data['Title']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "inputs = scaler.transform(test_data)\n",
    "# inputs = test_data\n",
    "prediction = models['LogisticRegression'].predict(inputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission = pd.DataFrame({\n",
    "        \"PassengerId\": raw_test_data[\"PassengerId\"],\n",
    "        \"Survived\": prediction\n",
    "    })\n",
    "\n",
    "filename = 'std-log.csv'\n",
    "submission.to_csv('predictions/'+filename, index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_file = 'predictions/'+filename\n",
    "best_file = 'predictions/all-std-svm.csv'\n",
    "\n",
    "df1 = pd.read_csv(new_file)\n",
    "df2 = pd.read_csv(best_file)\n",
    "\n",
    "\n",
    "\n",
    "result = pd.DataFrame(columns=['PassengerId'])\n",
    "df1['isEqual'] = np.where(df1['Survived'] == df2['Survived'],1,0)\n",
    "\n",
    "df1[df1['isEqual']== 0]['PassengerId'].values\n",
    "# raw_test_data.where([df1['isEqual' == 0]['PassengerId']]\n",
    "# df1[df1['isEqual']== 0]['PassengerId'].values\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "diference = test_data.iloc[df1[df1['isEqual']== 0]['PassengerId'].index, :]\n",
    "# diference['Age'].max()\n",
    "diference.head(20)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# a = [ 910,  911,  913,  920,  925,  928,  929,  933,  964,  967,  972,\n",
    "#         982, 1023, 1030, 1036, 1040, 1049, 1050, 1053, 1061, 1073, 1083,\n",
    "#        1084, 1091, 1093, 1126, 1136, 1160, 1172, 1173, 1185, 1198, 1199,\n",
    "#        1215, 1225, 1227, 1231, 1236, 1251, 1259, 1274, 1275, 1284, 1295,\n",
    "#        1304, 1309]\n",
    "\n",
    "#     [ 910,  911,  913,  915,  920,  925,  926,  928,  929,  931,  967,\n",
    "#         972,  979,  980,  982,  990, 1023, 1053, 1061, 1084, 1089, 1093,\n",
    "#        1136, 1160, 1173, 1185, 1198, 1199, 1215, 1225, 1231, 1236, 1237,\n",
    "#        1259, 1275, 1284, 1295, 1296, 1309]\n",
    "test_data['Pclass'].value_counts()"
   ]
  }
 ],
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