Titanic_Survival_Prediction_v1.ipynb

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   "source": [
    "# Titanic: Machine Learning from Disaster -> Predict if a passenger will survive or not\n",
    "\n",
    "On April 15, 1912, during her maiden voyage, the widely considered “unsinkable” RMS Titanic sank after colliding with an iceberg. Unfortunately, there weren’t enough lifeboats for everyone onboard, resulting in the death of 1502 out of 2224 passengers and crew.\n",
    "\n",
    "While there was some element of luck involved in surviving, it seems some groups of people were more likely to survive than others.\n",
    "\n",
    "In this notebook, we ask you to build a predictive model that answers the question: “what sorts of people were more likely to survive?” using passenger data (ie name, age, gender, socio-economic class, etc)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Install libraries if you are using Binder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install pandas numpy matplotlib seaborn sklearn xlrd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "MYHkRtC1PK1F"
   },
   "source": [
    "## Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "zRMz5_saPK1H"
   },
   "outputs": [],
   "source": [
    "#importing the libraries\n",
    "import pandas as pd\n",
    "import os\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import confusion_matrix\n",
    "import seaborn as sns\n",
    "import io"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "EDQa-qClPK1T"
   },
   "source": [
    "## Load Training Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "_0vVgNLaQdWs"
   },
   "outputs": [],
   "source": [
    "titanic_train =  pd.read_csv('./data/train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 464
    },
    "colab_type": "code",
    "id": "nXcnAazhPK1a",
    "outputId": "755b085a-6783-4e27-bb85-738bdabb7bfe"
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(891, 12)\n",
      "PassengerId      int64\n",
      "Survived         int64\n",
      "Pclass           int64\n",
      "Name            object\n",
      "Gender          object\n",
      "Age            float64\n",
      "SibSp            int64\n",
      "Parch            int64\n",
      "Ticket          object\n",
      "Fare           float64\n",
      "Cabin           object\n",
      "Embarked        object\n",
      "dtype: object\n"
     ]
    },
    {
     "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>Name</th>\n",
       "      <th>Gender</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",
       "    </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>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</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",
       "    </tr>\n",
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       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name  Gender   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#shape command will give number of rows/samples/examples and number of columns/features/predictors in dataset\n",
    "print(titanic_train.shape)\n",
    "print(titanic_train.dtypes)\n",
    "titanic_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "XmC7YLjwPK1j"
   },
   "source": [
    "#### Data Dictionary\n",
    "\n",
    "- Survival\n",
    "    - 0 = No\n",
    "    - 1 = Yes\n",
    "- pclass - Ticket class\n",
    "    - 1 = 1st\n",
    "    - 2 = 2nd\n",
    "    - 3 = 3rd\n",
    "- Gender Gender\t\n",
    "- Age\tAge in years\t\n",
    "- sibsp -\t# of siblings / spouses aboard the Titanic\t\n",
    "- parch - # of parents / children aboard the Titanic\t\n",
    "- ticket - Ticket number\t\n",
    "- fare - Passenger fare\t\n",
    "- cabin - Cabin number\t\n",
    "- embarked - Port of Embarkation\n",
    "    - C = Cherbourg\n",
    "    - Q = Queenstown\n",
    "    - S = Southampton\n",
    "\n",
    "\n",
    "#### Variable Notes\n",
    "- pclass: A proxy for socio-economic status (SES)\n",
    "    - 1st = Upper\n",
    "    - 2nd = Middle\n",
    "    - 3rd = Lower\n",
    "\n",
    "- age: Age is fractional if less than 1. If the age is estimated, is it in the form of xx.5\n",
    "\n",
    "- sibsp: The dataset defines family relations in this way...\n",
    "- Sibling = brother, sister, stepbrother, stepsister\n",
    "- Spouse = husband, wife (mistresses and fiancés were ignored)\n",
    "\n",
    "- parch: The dataset defines family relations in this way...\n",
    "- Parent = mother, father\n",
    "- child = daughter, son, stepdaughter, stepson\n",
    "    Some children travelled only with a nanny, therefore parch=0 for them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 302
    },
    "colab_type": "code",
    "id": "zhwRpksNPK1k",
    "outputId": "e667fac3-2e7f-4eb8-9028-8fd8a50ba201"
   },
   "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",
      "Gender         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": [
    "#info method provides information about dataset like \n",
    "#total values in each column, null/not null, datatype, memory occupied etc\n",
    "titanic_train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 284
    },
    "colab_type": "code",
    "id": "R8fUHht9PK1n",
    "outputId": "80df94f5-70af-4684-97bf-78f9c57bd223"
   },
   "outputs": [
    {
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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",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
       "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
       "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
       "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
       "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
       "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
       "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
       "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
       "\n",
       "            Parch        Fare  \n",
       "count  891.000000  891.000000  \n",
       "mean     0.381594   32.204208  \n",
       "std      0.806057   49.693429  \n",
       "min      0.000000    0.000000  \n",
       "25%      0.000000    7.910400  \n",
       "50%      0.000000   14.454200  \n",
       "75%      0.000000   31.000000  \n",
       "max      6.000000  512.329200  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Describe gives statistical information about numerical columns in the dataset\n",
    "titanic_train.describe()\n",
    "#you can check from count if there are missing vales in columns, here age has got missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 235
    },
    "colab_type": "code",
    "id": "xIhMhDn4PK1t",
    "outputId": "a97c1ef0-77be-4df6-fe29-049b6d3103c2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Survived         0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Gender           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"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check missing values\n",
    "titanic_train.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "GCcyC9JTPK1x"
   },
   "source": [
    "Describe only gives stats about numerical columns\n",
    "even Embarked and cabin has missing values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "-sRaygJ_PK1y"
   },
   "source": [
    "Age, Fare and cabin has missing values. we will see how to fill missing values next."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "TxkLgDvbPK1z"
   },
   "source": [
    "# Pandas Operations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "ArKFSruJPK10",
    "outputId": "0ff8c52a-ac40-437e-dc87-37dfd74c59c2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891, 12)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Create a sample dataframe\n",
    "titanic_sample = titanic_train.copy()\n",
    "titanic_sample.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 118
    },
    "colab_type": "code",
    "id": "vqau3XH-PK13",
    "outputId": "84fd5e6a-3076-400c-dc28-0742212e5c0a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    22.0\n",
       "1    38.0\n",
       "2    26.0\n",
       "3    35.0\n",
       "4    35.0\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# select the 'Age' Series using bracket notation\n",
    "titanic_sample['Age']\n",
    "\n",
    "# or equivalently, use dot notation\n",
    "titanic_sample.Age.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 195
    },
    "colab_type": "code",
    "id": "Wce40cQ0PK16",
    "outputId": "479713dd-9c16-47a6-aec2-a8cd2eec51ca"
   },
   "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>Age</th>\n",
       "      <th>Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22.0</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38.0</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26.0</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35.0</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35.0</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age                                               Name\n",
       "0  22.0                            Braund, Mr. Owen Harris\n",
       "1  38.0  Cumings, Mrs. John Bradley (Florence Briggs Th...\n",
       "2  26.0                             Heikkinen, Miss. Laina\n",
       "3  35.0       Futrelle, Mrs. Jacques Heath (Lily May Peel)\n",
       "4  35.0                           Allen, Mr. William Henry"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#selecting multiple features\n",
    "titanic_sample[['Age', 'Name']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 134
    },
    "colab_type": "code",
    "id": "klwxKgK-PK2A",
    "outputId": "0ab347bc-0b53-4231-80a7-edd150b92e9e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "before renaming Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Gender', 'Age', 'SibSp',\n",
      "       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],\n",
      "      dtype='object')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Gender', 'Age',\n",
       "       'SiblingSpouse', 'ParentChildren', 'Ticket', 'Fare', 'Cabin',\n",
       "       'Embarked'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#renaming a column in dataframe\n",
    "print('before renaming',titanic_sample.columns)\n",
    "titanic_sample.rename(columns={'Parch':'ParentChildren', 'SibSp':'SiblingSpouse'}, inplace=True)\n",
    "titanic_sample.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 67
    },
    "colab_type": "code",
    "id": "svNWFTOyPK2D",
    "outputId": "58ff7612-de56-4937-926d-b554fcf722a7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Survived', 'Pclass', 'Name', 'Gender', 'Age', 'SiblingSpouse',\n",
       "       'ParentChildren', 'Ticket', 'Fare', 'Cabin', 'Embarked'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#remove column from a dataframe\n",
    "titanic_sample.drop(['PassengerId'],axis = 1, inplace = True)\n",
    "titanic_sample.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 195
    },
    "colab_type": "code",
    "id": "M1azsJzsPK2G",
    "outputId": "23192406-d1d8-4117-b134-d228ce803d49"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>SiblingSpouse</th>\n",
       "      <th>ParentChildren</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Moran, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330877</td>\n",
       "      <td>8.4583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass                                               Name  \\\n",
       "0         0       3                            Braund, Mr. Owen Harris   \n",
       "1         1       1  Cumings, Mrs. John Bradley (Florence Briggs Th...   \n",
       "3         1       1       Futrelle, Mrs. Jacques Heath (Lily May Peel)   \n",
       "4         0       3                           Allen, Mr. William Henry   \n",
       "5         0       3                                   Moran, Mr. James   \n",
       "\n",
       "   Gender   Age  SiblingSpouse  ParentChildren     Ticket     Fare Cabin  \\\n",
       "0    male  22.0              1               0  A/5 21171   7.2500   NaN   \n",
       "1  female  38.0              1               0   PC 17599  71.2833   C85   \n",
       "3  female  35.0              1               0     113803  53.1000  C123   \n",
       "4    male  35.0              0               0     373450   8.0500   NaN   \n",
       "5    male   NaN              0               0     330877   8.4583   NaN   \n",
       "\n",
       "  Embarked  \n",
       "0        S  \n",
       "1        C  \n",
       "3        S  \n",
       "4        S  \n",
       "5        Q  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#drop a row (temporarily)\n",
    "titanic_sample.drop(2, axis=0).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 195
    },
    "colab_type": "code",
    "id": "7eK0SQbQPK2M",
    "outputId": "90255839-7883-40b1-c634-6c465ac6c6e4"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Pclass</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>McCarthy, Mr. Timothy J</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Bonnell, Miss. Elizabeth</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Andersson, Mr. Anders Johan</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Hewlett, Mrs. (Mary D Kingcome)</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 Name  Pclass\n",
       "1   Cumings, Mrs. John Bradley (Florence Briggs Th...       1\n",
       "6                             McCarthy, Mr. Timothy J       1\n",
       "11                           Bonnell, Miss. Elizabeth       1\n",
       "13                        Andersson, Mr. Anders Johan       3\n",
       "15                   Hewlett, Mrs. (Mary D Kingcome)        2"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_sample[titanic_sample.Age > 35][['Name','Pclass']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 195
    },
    "colab_type": "code",
    "id": "wOaZngHePK2Q",
    "outputId": "f833e006-2350-46da-fb24-1e6f74212ebf"
   },
   "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>Name</th>\n",
       "      <th>Pclass</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>McCarthy, Mr. Timothy J</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Bonnell, Miss. Elizabeth</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Andersson, Mr. Anders Johan</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Hewlett, Mrs. (Mary D Kingcome)</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 Name  Pclass\n",
       "1   Cumings, Mrs. John Bradley (Florence Briggs Th...       1\n",
       "6                             McCarthy, Mr. Timothy J       1\n",
       "11                           Bonnell, Miss. Elizabeth       1\n",
       "13                        Andersson, Mr. Anders Johan       3\n",
       "15                   Hewlett, Mrs. (Mary D Kingcome)        2"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_sample.loc[titanic_sample.Age > 35,['Name','Pclass']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 106
    },
    "colab_type": "code",
    "id": "vd0sC2YhPK2V",
    "outputId": "5a1cea64-c867-4646-dc13-5e4d802e3885"
   },
   "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>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass                                               Name  Gender\n",
       "0         0       3                            Braund, Mr. Owen Harris    male\n",
       "1         1       1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_sample.iloc[0:2, 0:4] # Exclusive of last row and column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 185
    },
    "colab_type": "code",
    "id": "GkZKScELPK2Y",
    "outputId": "b23ed093-0a5f-45f6-f40b-56fa40a0f05d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 22.0 3\n",
      "1 38.0 1\n",
      "2 26.0 3\n",
      "3 35.0 1\n",
      "4 35.0 3\n",
      "5 nan 3\n",
      "6 54.0 1\n",
      "7 2.0 3\n",
      "8 27.0 3\n",
      "9 14.0 2\n"
     ]
    }
   ],
   "source": [
    "#iterate throgh rows\n",
    "i = 0\n",
    "for index, row in titanic_sample.iterrows():\n",
    "    print (index,row.Age,row.Pclass)\n",
    "    i += 1\n",
    "    if i == 10:\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 101
    },
    "colab_type": "code",
    "id": "qqdbWPeQPK2a",
    "outputId": "1f3428f7-5f36-418c-d296-d67a16ff3eb9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pclass\n",
       "1    38.233441\n",
       "2    29.877630\n",
       "3    25.140620\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#calculate the mean age for each pclass category\n",
    "titanic_sample.groupby('Pclass').Age.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 166
    },
    "colab_type": "code",
    "id": "bJJrNTgAPK2d",
    "outputId": "5d9d50b4-7076-4154-b692-ba5e9adc47bc"
   },
   "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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       "\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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>186</td>\n",
       "      <td>38.233441</td>\n",
       "      <td>0.92</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>173</td>\n",
       "      <td>29.877630</td>\n",
       "      <td>0.67</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>355</td>\n",
       "      <td>25.140620</td>\n",
       "      <td>0.42</td>\n",
       "      <td>74.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        count       mean   min   max\n",
       "Pclass                              \n",
       "1         186  38.233441  0.92  80.0\n",
       "2         173  29.877630  0.67  70.0\n",
       "3         355  25.140620  0.42  74.0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_sample.groupby('Pclass').Age.agg(['count', 'mean', 'min', 'max'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 67
    },
    "colab_type": "code",
    "id": "kDg5Gj7CPK2g",
    "outputId": "9552a1db-8a46-4c76-9bf2-a386162bef6c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "male      577\n",
       "female    314\n",
       "Name: Gender, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# count how many times each value in the Series/Feature occurs\n",
    "titanic_sample.Gender.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 67
    },
    "colab_type": "code",
    "id": "as_3qRPkPK2i",
    "outputId": "3b9dc215-7a64-413e-e825-1bc5857117f6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "female    314\n",
       "male      577\n",
       "Name: Gender, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sorting series by count\n",
    "titanic_sample.Gender.value_counts().sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 67
    },
    "colab_type": "code",
    "id": "-0oYm3XnPK2l",
    "outputId": "b52fb574-5fb8-492f-d89e-2756f9ac0523"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "male      0.647587\n",
       "female    0.352413\n",
       "Name: Gender, dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# display percentages instead of raw counts\n",
    "titanic_sample.Gender.value_counts(normalize= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "yl3q5rxoPK2n",
    "outputId": "9dbf33b9-f5d7-4b58-9c6c-7bb7183e2f2c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 1, 2], dtype=int64)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#find unique values\n",
    "titanic_sample.Pclass.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 269
    },
    "colab_type": "code",
    "id": "lSbATZ3mPK2s",
    "outputId": "9ea2063c-0348-4f6c-ff86-954a5e55276c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 11 columns):\n",
      "Survived          891 non-null int64\n",
      "Pclass            891 non-null category\n",
      "Name              891 non-null object\n",
      "Gender            891 non-null object\n",
      "Age               714 non-null float64\n",
      "SiblingSpouse     891 non-null int64\n",
      "ParentChildren    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: category(1), float64(2), int64(3), object(5)\n",
      "memory usage: 70.7+ KB\n"
     ]
    }
   ],
   "source": [
    "# Convert a fetaure into categorical from continuous\n",
    "titanic_sample['Pclass'] = titanic_sample['Pclass'].astype('category')\n",
    "titanic_sample.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "I3vvgDSZPK2v"
   },
   "source": [
    "# EDA (Exploratory Data Analysis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "sWN6ow0dPK2w"
   },
   "outputs": [],
   "source": [
    "# this line allows ipython notebook to display the plots in the output\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2e011cf31d0>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='Gender', data=titanic_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Njm8KJbHPK27"
   },
   "source": [
    "- Gives the graphical representation of value_counts that gives the count of each unique values in a feature\n",
    "- Bar plot - Gives count of the different categories in the categorical feature"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "HZJ7JLSUPK28"
   },
   "source": [
    "# Pclass vs Fare"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 279
    },
    "colab_type": "code",
    "id": "5fs0OHxfPK29",
    "outputId": "7e11f16a-7758-4e4d-f6e5-41c496542c4f"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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VldqwYUPYpfQo00nll7v7k+3iesKyU+Sj8vLyjlFBWVmZMt3ccbCK2oqrk/fOylWZbl3xFTN7w8yazOyombWaWWib7LDsFPlq8eLFGjFiBKODiK246m7vrFyU6S2jR9W2bcV7kool3ZG6BmAAlZeX69e//jWjg4ituIrK3lkZ73bq7gkzi7l7q6T/Y2Z/ymJdANCjqK24isreWZmOEJrNbKikrWb2IzP775JGZLEuAOhR1xVWub7iKip7Z2UaCDenXrtIbctOx0v6u2wVBQDpRG3FVVT2zkobCO0Po7n7bnc/7O6fufsSd7/X3XN7FgfAoBW1FVdR2TurtzmEf5P0ZUkysxfcnVHBANrTFPyZynub294DjBse7Ga1e5pimhhoj0B6ixcvVnV1dc6PDtrdc889euSRR3J2dCD1Hggn3+g6P5uFoLPi4mKVZOFdz9HU8ryi84Lte6KU8+/SMLi89tprOnTokGprayPxb2/WrFmaNWtW2GWk1VsgeA8fh8rMZkqaGYV/BP1VUlKilStXBt5vdXW1JGWlb2AgtZ+a9vjjj2vOnDkhVzM49DapPNnMPjOzg5IuSX38mZkd5ME0AGHpemLa2rVrQ6pkcEkbCO4ec/fT3f00dy9IfdzeDvbmNgBkqOuZyo8//nhIlQwumS47BQAMchk/qRxlyWRSseZGFe/cGHYpGYk171cyeTzsMgDkGUYIACKn6zkId955Z0iVDC55MUIoKSnRX44UqOWLM8IuJSPFOzeqpGRc2GUAOWvevHmd5hFYZRQMRggAIqmiokKS9JWvfCXkSgaPvBghIH+sWrUq473xk8mkpLYRZCbKy8t1991397u2nmSr5mzVmytqa2slSa+++mrIlQwejBCQt1paWtTS0hJ2GX0SxZqzgecQsoMRAgaVvrwjzpWntqNYc9i6ew6BeYRTF8kRAmcqA0DwIhkIbF0BAMGLZCAAyG88h5AdBAKAyJk3b16nNvMHwSAQAETSySeQIRgEAoDI2bRpk9zbjmhxd23evDnkigYHAgFA5CxfvrxTe9myZSFVMrgQCAAi5/jx42nbuai2tlZXXnml6urqwi6lRwQCgMgpKChI285FNTU1OnHihB588MGwS+kRgQAgcqZMmdKp3b7RXa6qra1VU1OTJKmpqSlnRwkEAoDIad/Yrl2ub3BXU1PTqZ2rowQCAQCyrH100FM7VxAIAJBl8Xg8bTtXRDIQ2NwOQJR0vWW0ZMmScArpRSQDgc3tAERJRUVFx6ggHo9r6tSpIVfUvUgGAgBETU1NjYYMGZKzowMpjw7IiTUfUPHOjYH2OeTwZ5KkE8NOD7TfWPMBSeMC7RNAuCoqKrRp06awy0grLwKhvLw8K/0mEgfb+j8/6F/e47JWMwD0JC8CIVsHjXOcIXqyatUqJRKJwPtt77P9315QysvLs/b/CaIjLwJhsOvLL5++/kLJhV8UUfvlKknJZFLNf/1U58ZbA+136LG2ab8ju2t7eWXm9jTFAutroCxYsKDTucockBMMAiHPFBcXh11CnyUSCW3dtkOtw0cF2u+Qo23bJ9d9sDfQfmPNBxQfVqhz46164MufBdp3Nix/M9g5sFPR3/DfsmWLtmzZ0uPf58IbmyggEAaBfPiH3jp8lFq+OCPsMjJSvHOjdOJg2GUMegUFBTp+/LjOOuussEsZNAgEADmjL29ucmEOry8jmmQyKUkqKSnJ6PVhjGoIBAAYAC0tLWGX0CsCAQD6KWojmt7wpDIAQBKBAABIIRAAAJIIBABACpPKQBYcOXJEuw/Hcuqhr57sPhjTiNSSSOQ3RggAAEmMEICsKCoq0vjClshsXVGU4cNSGNwiOULgCE0ACF4kA4EjNAEgeJEMBABA8AgEAIAkAgEAkEIgAAAkEQgAgBQCAQAgiUAAAKQQCAAASWxdAQCd9OWc5L5o77P95LQgBXX+MoEAACdJJBJ6b/ufdW68NdB+hx5ruyFzZHdtoP3uaYoF1heBACCrovaOO5lM6tx4ayQ2JpQU6BbrBAKArEokEtq6bYdah48KtN8hR12SVPfB3sD6jDUfUHxYoVQYWJeRQiAAyLrW4aPU8sUZYZfRq+KdG6UTB8MuIzSsMgIASCIQAAApBAIAQBKBAABIIRAAAJJYZYQISCaTijU3tq0AiYBY834dMdeeI7FA14hL0t7mtvdw44afCKzPPU0xTQysN0QZgQBkQUFBgSZeOCnwfo+mHsYqOq88sD4nqm3rA4BAQM4rKSnRX44URGIdu9S2lv3C88dp5cqVgffd/lRuNvoGmEMAAEgiEAAAKdwyQiTEmg8EPqk85HDb5mUnhgU78RtrPiBpXKB9AgOBQEDOy9aEZyLRtmdN+flB//IexyQtIolAQM4L4uCP7jBBC3TGHAIAQBKBAABIyZlbRmZ2vqT/KWmku98Qdj3AQOnLiWJ9OSUsqHN2kT+yOkIws6fM7FMz29bl+rVm9q6ZJczsPkly9w/c/fZs1gNEXXFxsYqLi8MuA4NUtkcIqyU9Kunp9gtmFpP0mKSrJdVLesPM1rv7O1muBchJvItHrsjqCMHdX5F0oMvlSyUlUiOCo5LWSpqVaZ9mttDMas2sdt++fQFWCwD5LYxJ5RJJH53UrpdUYmajzexxSX9rZvf39Mnu/oS7V7h7xdixY7NdKwDkjTAmla2ba+7u+yXdOdDFAADahDFCqJc0/qR2qaSPQ6gDAHCSMALhDUkTzWyCmQ2VNEfS+hDqAACcJNvLTp+RtEXShWZWb2a3u/txSYsk/U7SDknPufv2PvY708yeaGxsDL5oAMhTWZ1DcPe5PVzfKKnfW1e6+wZJGyoqKhb0tw8A6M6RI0e0+3Dwx59my+6DMY1IJgPpi60rAACScmjrCgDIBUVFRRpf2KIHvvxZ2KVkZPmbp6uopCSQvggEAFmVTCYVa24M/ICjbIg179cRc6kw7ErCEclbRkwqA0DwIjlCYFIZiI6SkhLt+6/gb79k5whUU1HRUO1pOhr4pPLe5rb33+OGnwi03z1NMU0MqK9IBgKA6IjWEajjdOjQIY0YEXzNR1NblxedF2zfExXcf2MCAUBWcQRqmyjUG8k5BABA8AgEAIAkAgEAkBLJQGDZKQAEL5KB4O4b3H3hyJEjwy4FAAaNSAYCACB4BAIAQBKBAABIMXcPu4Z+M7N9knaHXUcWjZHUEHYR6Bd+dtE22H9+57n72K4XIx0Ig52Z1bp7Rdh1oO/42UVbvv78uGUEAJBEIAAAUgiE3PZE2AWg3/jZRVte/vyYQwAASGKEAABIIRAAAJIIhJxkZk+Z2admti3sWtA3ZjbezDab2Q4z225m1WHXhMyY2TAze93M3kr97JaEXdNAYw4hB5nZ1yQ1SXra3f8m7HqQOTM7W9LZ7v6mmZ0mqU7SN939nZBLQy/MzCSNcPcmMyuU9B+Sqt391ZBLGzCMEHKQu78i6UDYdaDv3P0Td38z9fFBSTsklYRbFTLhbZpSzcLUn7x6x0wgAFliZmWS/lbSa+FWgkyZWczMtkr6VNLv3T2vfnYEApAFZhaX9IKke9z9s7DrQWbcvdXdp0gqlXSpmeXVLVsCAQhY6v7zC5LWuPu/hl0P+s7d/yrpJUnXhlzKgCIQgAClJiZ/LmmHu68Iux5kzszGmtkZqY+LJV0laWe4VQ0sAiEHmdkzkrZIutDM6s3s9rBrQsYul3SzpCvNbGvqz4ywi0JGzpa02czelvSG2uYQXgy5pgHFslMAgCRGCACAFAIBACCJQAAApBAIAABJBAIAIIVAANIws9bU0tFtZva8mQ1P89oaM/veQNYHBIlAANJrcfcpqV1nj0q6M+yCgGwhEIDM/bukckkys/lm9nZq7/x/6fpCM1tgZm+k/v6F9pGFmd2YGm28ZWavpK5NSu3DvzXV58QB/a6AFB5MA9IwsyZ3j5tZgdr2J/qtpFck/auky929wcxGufsBM6uR1OTuPzaz0e6+P9XHUkl73X2Vmf2npGvdPWlmZ7j7X81slaRX3X2NmQ2VFHP3llC+YeQ1RghAesWp7ZBrJe1R2z5FV0r6v+7eIEnu3t3ZFX9jZv+eCoB5kialrv9R0mozWyAplrq2RdIDZvYPks4jDBCWgrALAHJcS2o75A6pDex6G1qvVttJaW+Z2a2SKiXJ3e80s8skXS9pq5lNcfdfmdlrqWu/M7M73H1TwN8H0CtGCEDf/UHSTWY2WpLMbFQ3rzlN0ieprbDntV80swvc/TV3/0dJDZLGm9n5kj5w959KWi/pkqx/B0A3GCEAfeTu281smaSXzaxV0p8l3drlZT9Q20lpuyX9p9oCQpIeSk0am9qC5S1J90n6ezM7Jukvkv4p698E0A0mlQEAkrhlBABIIRAAAJIIBABACoEAAJBEIAAAUggEAIAkAgEAkPL/ATvuvsEH8aPuAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.boxplot(x=\"Pclass\", y=\"Fare\", hue=\"Survived\", data=titanic_train)\n",
    "ax.set_yscale('log')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "rXkSZG3KPK3C"
   },
   "source": [
    "- Fares decrease as the Pclass increases"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "utpAHiI1PK3D"
   },
   "source": [
    "# Embarked vs Fare"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 279
    },
    "colab_type": "code",
    "id": "TODWQteuPK3F",
    "outputId": "c7ea1481-e57d-4acc-cc68-a882d5a1d66f"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.boxplot(x=\"Embarked\", y=\"Fare\", hue=\"Survived\", data=titanic_train)\n",
    "ax.set_yscale('log')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "VUjUMBG3PK3I"
   },
   "source": [
    "- Survival rates for passengers embarked at S and C who paid higher fare seems to be more"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "v-ChS87DPK3J"
   },
   "source": [
    "# Fare vs survival rates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "DLr23DjtPK3K"
   },
   "outputs": [],
   "source": [
    "#making fares into categories of ranges(<=7.91,[7.91,14.454],[14.454,31],[31,513])\n",
    "titanic_train['Fare_cat']=0\n",
    "titanic_train.loc[titanic_train['Fare']<=7.91,'Fare_cat']=0\n",
    "titanic_train.loc[(titanic_train['Fare']>7.91)&(titanic_train['Fare']<=14.454),'Fare_cat']=1\n",
    "titanic_train.loc[(titanic_train['Fare']>14.454)&(titanic_train['Fare']<=31),'Fare_cat']=2\n",
    "titanic_train.loc[(titanic_train['Fare']>31)&(titanic_train['Fare']<=513),'Fare_cat']=3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 422
    },
    "colab_type": "code",
    "id": "VPIXDYS6PK3N",
    "outputId": "f820a6c0-36ef-41c6-fc6e-4479d4411c06"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Softwares\\lib\\site-packages\\seaborn\\categorical.py:3666: UserWarning: The `factorplot` function has been renamed to `catplot`. The original name will be removed in a future release. Please update your code. Note that the default `kind` in `factorplot` (`'point'`) has changed `'strip'` in `catplot`.\n",
      "  warnings.warn(msg)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 970.5x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot('Fare_cat','Survived',data=titanic_train,hue='Gender',aspect=2.5)\n",
    "#aspect-> signifies the width of the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "FKMNKDd9PK3P"
   },
   "source": [
    "- Clearly, as the Fare_cat increases, the survival chances increases\n",
    "- Females who have paid higher fare are mostly survived but it's not the same case with Males"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "UQZe3Mu9PK3T"
   },
   "source": [
    "## Survival Rates "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 136
    },
    "colab_type": "code",
    "id": "RJiL1KQKPK3U",
    "outputId": "1b9c43c5-30c7-4830-f608-4e433ec025aa"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.629630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.472826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.242363</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass  Survived\n",
       "0       1  0.629630\n",
       "1       2  0.472826\n",
       "2       3  0.242363"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_train[['Pclass', 'Survived']].groupby(['Pclass'], as_index=False).mean().sort_values(by='Survived', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 422
    },
    "colab_type": "code",
    "id": "TmR2p370PK3a",
    "outputId": "cfa9eef9-bb9c-4732-84d2-fae996f8a7c4"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Softwares\\lib\\site-packages\\seaborn\\categorical.py:3666: UserWarning: The `factorplot` function has been renamed to `catplot`. The original name will be removed in a future release. Please update your code. Note that the default `kind` in `factorplot` (`'point'`) has changed `'strip'` in `catplot`.\n",
      "  warnings.warn(msg)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot(x='Pclass',y='Survived', data=titanic_train)\n",
    "#By default factorplot shows point plot\n",
    "#They are particularly good at showing interactions: \n",
    "#i.e.,how the relationship between levels of one categorical variable changes across levels of a second categorical variable\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "mt8dj4jLPK3d"
   },
   "source": [
    "- Survival rate is more for class 1 passengers\n",
    "- Survival rate decreases with Pclass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "5OJTw4oHPK3e"
   },
   "source": [
    "## Survival rate based on gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 118
    },
    "colab_type": "code",
    "id": "oucfDz38PK3g",
    "outputId": "3f2abb86-333e-4c6e-89c9-33992b648466"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Survived  Gender\n",
       "0         female     81\n",
       "          male      468\n",
       "1         female    233\n",
       "          male      109\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_train.groupby(['Survived','Gender'])['Survived'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 422
    },
    "colab_type": "code",
    "id": "EMohPc2_PK3j",
    "outputId": "35fbefc7-5ccc-4f1d-c3d2-225cdba0cce4"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Softwares\\lib\\site-packages\\seaborn\\categorical.py:3666: UserWarning: The `factorplot` function has been renamed to `catplot`. The original name will be removed in a future release. Please update your code. Note that the default `kind` in `factorplot` (`'point'`) has changed `'strip'` in `catplot`.\n",
      "  warnings.warn(msg)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot(x='Gender', col='Survived', kind='count', data=titanic_train);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 50
    },
    "colab_type": "code",
    "id": "O5s0CfwMPK3n",
    "outputId": "687b615c-0d33-4226-c0fd-67a00c4c9270"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "% of women survived:  0.7420382165605095\n",
      "% of men survived:    0.18890814558058924\n"
     ]
    }
   ],
   "source": [
    "print(\"% of women survived: \" , titanic_train[titanic_train.Gender == 'female'].Survived.sum()/titanic_train[titanic_train.Gender == 'female'].Survived.count())\n",
    "print(\"% of men survived:   \" , titanic_train[titanic_train.Gender == 'male'].Survived.sum()/titanic_train[titanic_train.Gender == 'male'].Survived.count())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "xLv0r55bPK3u"
   },
   "source": [
    " - females are most likely to survive than males\n",
    " - 74% of women survived while only 19% of men survived (233 out of 314 females survived while only 109 males out of 577 survived)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 439
    },
    "colab_type": "code",
    "id": "y3Gv8lDUPK3v",
    "outputId": "56753ebd-bb1f-4b69-abb6-ce2f2a9908d1"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Softwares\\lib\\site-packages\\seaborn\\categorical.py:3666: UserWarning: The `factorplot` function has been renamed to `catplot`. The original name will be removed in a future release. Please update your code. Note that the default `kind` in `factorplot` (`'point'`) has changed `'strip'` in `catplot`.\n",
      "  warnings.warn(msg)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2e0122774a8>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot(x='Gender',y='Survived',data=titanic_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "SRjUJxaRPK3x"
   },
   "source": [
    "- Most of the females survived (above 70%) & most of the males dies (below 20%)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 422
    },
    "colab_type": "code",
    "id": "hOJiR4yGPK3y",
    "outputId": "5763102c-4084-4eb0-bba9-518d900ebd46"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Softwares\\lib\\site-packages\\seaborn\\categorical.py:3666: UserWarning: The `factorplot` function has been renamed to `catplot`. The original name will be removed in a future release. Please update your code. Note that the default `kind` in `factorplot` (`'point'`) has changed `'strip'` in `catplot`.\n",
      "  warnings.warn(msg)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 430.5x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot(x='Pclass',y='Survived', hue='Gender',data=titanic_train)\n",
    "#adding the parameter 'hue' helps in comparing the plots based on the value of hue, here we gave 'Gender' as a value in hue and hence we got plot to compare the survival rates for each gender\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "omch21jWPK30"
   },
   "source": [
    "- Almost all women in Pclass 1 and 2 survived and nearly all men in Pclass 2 and 3 died"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "gnPsFcpHPK31"
   },
   "source": [
    "## Survival rate based on embarked(boarding) place"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 439
    },
    "colab_type": "code",
    "id": "oJxe3XxNPK31",
    "outputId": "4f10675e-a03a-40b7-db2a-17ea253a5d4a"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Softwares\\lib\\site-packages\\seaborn\\categorical.py:3666: UserWarning: The `factorplot` function has been renamed to `catplot`. The original name will be removed in a future release. Please update your code. Note that the default `kind` in `factorplot` (`'point'`) has changed `'strip'` in `catplot`.\n",
      "  warnings.warn(msg)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2e011fb2438>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot(x='Survived', col='Embarked', kind='count', data=titanic_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "027KFzxoPK34"
   },
   "source": [
    "- Most of the people died are Embarked on S"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 473
    },
    "colab_type": "code",
    "id": "2JjJAiY8PK35",
    "outputId": "693ec84b-3c41-44b4-e4f3-a222e7df1c1a"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Softwares\\lib\\site-packages\\seaborn\\categorical.py:3666: UserWarning: The `factorplot` function has been renamed to `catplot`. The original name will be removed in a future release. Please update your code. Note that the default `kind` in `factorplot` (`'point'`) has changed `'strip'` in `catplot`.\n",
      "  warnings.warn(msg)\n",
      "E:\\Softwares\\lib\\site-packages\\seaborn\\categorical.py:3672: UserWarning: The `size` paramter has been renamed to `height`; please update your code.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2e0125ac550>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g=sns.factorplot('Embarked','Survived', data=titanic_train,size=5)\n",
    "g.set_xticklabels([\"Southampton(S)\", \"Cherbourg(C)\", \"Queenstown(Q)\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "OaTD-svBPK38"
   },
   "source": [
    "- Most people embarked on C survived"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 439
    },
    "colab_type": "code",
    "id": "0VQU9W5IPK39",
    "outputId": "b1f711e0-971b-456b-efc4-dcefffd07805",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Softwares\\lib\\site-packages\\seaborn\\categorical.py:3666: UserWarning: The `factorplot` function has been renamed to `catplot`. The original name will be removed in a future release. Please update your code. Note that the default `kind` in `factorplot` (`'point'`) has changed `'strip'` in `catplot`.\n",
      "  warnings.warn(msg)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2e0139b7f28>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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3CiMREfGdwkhERHynMBIREd8pjERExHdaXM8PtRML/ioiEuEURn648SO/KxARKVc0TCciIr4LahiZ2RAzW2lma8xs9EmOucDMFpjZUjP7Npj1iIhI+RS0YToziwZeAC4G0oEUM5vsnFuW75jawL+AIc65jWbWMFj1iEgY0PXUiBXMa0a9gTXOuXUAZjYeGAYsy3fMdcAk59xGAOfctiDWIyLlna6nRqxgDtM1A9LyPU8PbMuvLVDHzL4xs7lmdmNRb2Rmt5hZqpmlbt++PUjlioiIX4IZRlbENlfoeQzQC7gUGAw8aGZtT3iRc+Occ0nOuaQGDRqUfaUiIuKrYA7TpQMJ+Z7HA5uLOGaHc+4AcMDMZgLdgFVBrEtERMqZYJ4ZpQBtzKyFmVUGrgEmFzrmY6CfmcWYWSxwNrA8iDWJiEg5FLQzI+dcjpndDnwBRAOvOeeWmtmtgf0vOeeWm9lUYBGQB7zinFsSrJpERKR8MucKX8Yp35KSklxqaqrfZYiIlKSo6+ZyEpqBQUREfKcwEhER3ymMRETEdwojERHxncJIRER8pzASERHfKYxERMR3CiMREfGdwkhERHynMBIREd8pjERExHcKIxER8Z3CSEREfKcwEhER3xW7npGZZXLiUuHHOOdqlXlFIiIScYoNI+dcTQAzewTYCryJt0bH9UDNoFcnIiIRobTDdIOdc/9yzmU65/Y5514Erg5mYSIiEjlKG0a5Zna9mUWbWZSZXQ/kBrMwERGJHKUNo+uAkUBG4DEisE1EROSMFXvN6Cjn3AZgWHBLERGRSFWqMyMza2tm081sSeB5VzN7ILiliYhIpCjtMN3/AfcCRwCcc4uAa4JVlIiIRJbShlGsc+6nQttyyroYERGJTKUNox1m1orADbBmNhzYErSqREQkopSqgQG4DRgHtDezTcB6vBtfRUREzlhpw+hn59xFZlYdiHLOZQazKBERiSylHaZbb2bjgHOA/UGsR0REIlBpw6gd8BXecN16M3vezPoGrywREYkkpQoj59wh59xE59xVQA+gFvBtUCsTEZFjzKyRmb1jZuvMbK6ZzTazK8vgfS8ws0/LosYzUer1jMzsfDP7FzAPqIo3PZCIiASZmRnwETDTOdfSOdcL717PeB9qKW2vwSkp1Zua2XpgATAR+KNz7kAwihERkSINBLKdcy8d3eCc+xn4p5lFA2OBC4AqwAvOuZfN7AJgDLAD6AzMBX7pnHNmNgR4JrBv3tH3DDSp/RPogpcPY5xzH5vZTcCleCci1QP1lKnSJlw359y+sv5wEREplU7kC41Cbgb2OueSzawKMMvMpgX29Qi8djMwCzjPzFLxZtUZCKwBJuR7r/uBGc65/zKz2sBPZvZVYF8foKtzbldZ/sGOKmml1z855/4GPG5mJ6z46pz7QzCKEhGRkzOzF4C+QDbwM9A1MBkBQBzQJrDvJ+dceuA1C4DmeB3R651zqwPb3wJuCbx2EHC5md0TeF4VSAz8/stgBRGUfGa0PPBrarAKEBGREi0l34KmzrnbzKw+3vfmjcAdzrkv8r8gMEx3ON+mXI5/zz/h5OLoy4CrnXMrC73X2UBQL88U28DgnPsk8NtFzrk3Cj+CWZiIiBwzA6hqZr/Lty028OsXwO/MrBIcW2WhejHvtQJoEZjiDeDafPu+AO4INExgZj3KpPpSKG033VNmtsLMHjWzTkGtSERECnDOOeAK4HwzW29mPwFvAH8GXgGWAfMCy/y8TDGjXs65LLxhuSlm9j3eMN9RjwKVgEWB93o0GH+eopj3ZyzFgWaN8dq5R+HdZzTBOfdYEGsrUlJSkktN1aihiJR75ncB4aTU9xk557Y6554DbsVr834oaFWJiEhEKe1Krx3MbEzgtO154Ad8uNlKREQqptLeZ/Rv4F1gkHNucxDrERGRCFRiGAXu7l3rnHs2BPWIiEgEKnGYzjmXC9Qzs8ohqEdERCJQqRfXw5tiYjL5bnxyzj0VlKpERCSilDaMNgceUUDN4JUjIhK5mo+eUhu4BO/77HwgZcPYS0t3/00QBGZxuMc5d1mwP6tUYeScezjYhYiIRLLmo6fcjXeTaWy+zbObj54ycsPYS9N9KitkStva/bWZzSj8CHZxIiKRoPnoKb8CnqRgEIE3U/a05qOnnPY1ezNrHphB5xUzW2Jmb5vZRWY2y8xWm1nvwOMHM5sf+LVdEe9T3cxeM7OUwHHDTremopR2mO6efL+vijdhX05ZFiIiEomaj54Shbd0w8l0AK4Cxp/Bx7QGRuBNA5QCXIc36/flwH3AjUB/51yOmV0E/IV8E7MGFLm8RFmtb1faYbq5hTbNMjMtOy4icuYSgVYlHDOQMwuj9c65xQBmthSYHlhkbzHeshJxwBtm1gZvRu9KRbzHyZaXWF7EsaestCu91s33NApIAhqXRQEiIhGuNA0KZ9rEkH8pibx8z/PwcuBR4Gvn3JVm1hz4poj3KHJ5ibJS2mG6uRz/y8gBNuCtLigiImdmI96yDu2LOeaLYvaVhThgU+D3NxVTwx1mdkfgrKqHc25+WRVQbAODmSWbWWPnXAvnXEvgYby/tBV4U5YXy8yGmNlKM1tjZqNL+JzcfCsViohEhEDrdnEdy/OByUEu42/AE2Y2C4g+yTFBXV6i2CUkzGwecJFzbpeZ9ccbs7wD6A50cM6dNDwC0witAi4G0vEuml3rnFtWxHFfAlnAa86594srWEtIiEiYOKUlJJqPnnIL8Fegdr7N04AbNoy9dFtZFlYelTRMF51vzfNRwDjn3AfAB4H11IvTG1jjnFsHYGbjgWGceEZ1B/ABkHxKlYuIVCAbxl46rvnoKW8BF+KtGTdvw9hLy6Q5IByUGEZmFuOcy8H7C7rlFF7bDEjL9zwdODv/AWbWDLgSr1NEYSQhd8Orc0jffYj4OtV48+azS36BSBBtGHvpQeATv+vwQ0mB8i7wrZntAA4B3wGYWWtgbwmvLeoUtfCY4DPAn51zuYEl14t+I7NbCARhYmJiCR8rUnrpuw+xfkeZ3CYhImeg2DByzj1uZtOBJsA0d/wCUxTe8Fpx0oGEfM/j8ea3yy8JGB8IovrAJWaW45z7qFAd44Bx4F0zKuFzRUQkzJTY2u2c+7GIbatK8d4pQBsza4HXMngN3l2/+d+nxdHfm9nrwKeFg0hERCq+0t5ndMoC00rcjtebHo3XKbfUzG4N7H8pWJ8tIiLhJWhhBOCc+wz4rNC2IkPIOXdTMGsRESn3xsSdsIQEY/ae8aUJM/sD8DtgnnPu+jN9vyLefwyw3zn3j9N9j1LN2i0iIkE2Ju5uvEsabwMvAXOAWYyJiy+Dd/89cEkwgqisKIxERPw2Jq7YJSQYE3cmS0i8BLQEJpvZ/UUtA2FmN5nZR2b2iZmtN7PbzezuwDE/Hp2f1Mx+E3jtQjP7wMwK14uZtTKzqWY218y+M7Pipjk6RmEkIuKnMXGlXULitDjnbsXrZB4AVMdbBiI58PzvZlY9cGhnvCaz3sDjwEHnXA9gNt4SEwCTnHPJzrlueLN1FzVH6TjgDudcL7zlh/5VmjqDes1IRERKFIolJI462TIQ4M3anQlkmtlejt98uxjoGvh9ZzN7DG/KohoUmsDVzGoA5wLv5bt3tEppClMYiYj4KxRLSBxV5DIQZnY2JS8zAfA6cIVzbqGZ3QRcUOj9o4A9zrnup1qYhulERPx1dAmJ4pTVEhJHl4EwADPrcYqvrwlsMbNKwAnNEM65fcB6MxsReH8zs26leWOFkYiIn7zW7VAtIXGmy0A8iNfl9yUnD9DrgZvNbCGwFG+C7BIVu4REeaQlJKQsDfjHN6zfcYAW9avz9T0X+F2OVCyntIQEY+JOuoQEY/ZG/BISIiISCmP2jmNMXIElJBizV0tIiIhIiI3ZG7FLSOiakYiI+E5hJCIivlMYiYiI7xRGIiLiO4WRiIj4TmEkIiK+UxiJiIjvFEYiIuI7hZFEpLw8x9QlW9i6NwuAjH1ZzFiRQbhNjyVSUSiMJOLk5TnunriAW9+ax6EjuQAczM7lv15P5X8nL1UgifhAYSQR5/156Xy0YHOR+/4z+2emLcsIcUUiojCSiPPOnI3F7n/3p+L3i0jZUxhJRDmck8vqbZnFHrNx58EQVSMiR2nWbokIK7dmMiEljQ/np3PgcG6xx8ZEn9oyNCJy5hRGUmFlZh3h00VbGJ+SxsK0PaV+3aqM/dw7aTEPXNqB6lX0X0QkFPQ/TSoU5xxzf97NhJQ0Pl205Vi33FG1YytxebemzP15N0s37zvp+7z700Z+WLuDp0Z2o9dZdYNdtkjEUxhJhbBj/2EmzUtnQkoaa7cfOGF/vzb1GZmUwKBOjagSE03WkVxem7Wep6atIifPUSnaGD20A41rVeWhj5ew80A2P+88yIiXZvPb81tx10VtqRyjS6wiwWLhdk9FUlKSS01N9bsMKQdy8xwzV21nfMpGpi/fRk5ewX/LTeKqMiIpgRG94kmoG1vkewz4xzes33GAFvWr8/U9FwBesN07aTFf5mvx7tCkFk+P6kb7xrWC9ueRCkcXH0+Bzowk7GzceZCJqWm8PzedrfuyCuyrFG1c3LERI5MS6NemAdFRp/79oH6NKoy7oRfvz03n4U+Wsf9wDsu37OPyf87ifwa15df9Wp7W+4rIySmMJCxkHcnli6VbmZCSxg9rd56wv03DGoxKTuDKHs2oV6PKGX+emTEiKYFzWtbjnvcWMmf9LrJz83ji8xVMX76NJ0d2O+nZloicOoWRlGvLNu9jQspGPlqwmb2HjhTYF1s5ml90bcrI5AR6JtbGrOzPVhLqxvLub87htVnr+dsXK8nOyeOnDbsY8sxMHrysI6OSE4LyuSKRRmEk5c6+rCNMXrCZCSlpLN6094T9PRNrMyo5gUu7NqVGCFqvo6KMX/drSf+2DbhrwgKWbt7HgexcRgeuKz1xdRca1qwa9DpEKjKFkZQLzjnmrN/FxJQ0PluyhawjeQX2161emat6NGNUcgJtGtX0pca2jWry4e/P47npq/nXN2vIczB9xTYGPz2TJ67qwpDOTXypS6QiUBiJr7bty+L9eem8l5rO+h0FW7LNoH+bBoxKTuCiDo3KRWt15Zgo7hncjoEdGnL3hAVs2HmQ3QePcOtb87iqRzPGDOtEraqV/C5TJOwojCTkcnLz+HrldiakpPH1ym3kFmrJbla7GiOTEhieFE+z2tV8qrJ4PRPr8Nl/9+OJz1bw5o8/AzBp/iZ+XLeTv4/oxnmt6/tcoUh4URhJyKzfcYCJqWl8MDedbZmHC+yrHB3FoE6NGJWcwHmt6hMVBq3TsZVjePSKzlzUsRF/en8hGfsOs3lvFte/ModfndecPw9pT9VK0X6XKRIWFEYSVIeyc/l8yRYmpKQxZ/2uE/a3b1yTkUleS3ad6pV9qPDMnd+2AV/c2Z8HP17KJwu9dZL+PWsDM1dt5+lR3ekaX9vnCkXKP4WRBMWSTXsZn7KRjxdsJjMrp8C+GlVi+EW3plyTnEDX+LgK0RpdO7Yy/7y2Bxd3bMSDHy1h76EjrN1+gKv+9QN3DGzD7we0olK0/9e8RMorhZGUmb0Hj/DRgk1MSElj2ZYTJyFNbl6HkUkJXNq1CbGVK+Y/vcu7NaV387r86YNFzFy1nZw8x9NfrWLGigyeGtWdVg1q+F2iSLlUMb8jSMjk5Tl+XLeTCalpfL5kK9k5BVuy69eozNU94xmRlEDrhpHxjbhxXFXe+FUyb83ZyF+mLOfQkVwWpu/lkme/496h7bmxT/OwuCYmEkoKIzktW/dm8f7cNCamprNxV8GVUaMMLmjXkJFJCVzYoWFEDk+ZGTeccxZ9W9fn7okLmL9xD4dz8hjzyTK+Wr6Nvw3vStNy2iko4geFkZTakdw8pi/fxsTUNL5ZuY1CHdkk1o1lZFI8w3sl0DhOMxIAtKhfnfd+24eXZ67j6S+95Sq+X7ODwc/M5NFhnRnWvWmFuGYmcqYURlKitdv3MzEljQ/mpbNjf3aBfZVjohjauTGjkhM4p0U9DT8VISY6itsGtOb8tg24e+ICVmXsJzMrhzsnLGDasq08dkUX6oZpJ6FIWVEYSZEOZucwZdEWJqamker5/+8AABIXSURBVLJh9wn7OzapxajkBK7o3oy4WM04UBqdm8Ux+fa+PDltJa98vx7n4LPFW0nZsJu/Xt2Fge0b+V2iiG8URnKMc46F6XuZkJLGJws3s/9wwZbsmlVjGNa9KdckJ9K5WZxPVYa3qpWiuf/SjlzYoRH/M3Ehm/YcYnvmYf7r9VSu7Z3IA5d2oHoIJn8VKW/0r17YfSCbD+d7LdkrMzJP2H92i7qMSk5gaOcmVKusGQXKwjkt6zH1zn48+ukyJqamA/DuTxuZtWYHT43sRlLzuj5XKBJaCqMIlZfnmLV2BxNS0pi2NIPs3IIt2Q1qVmF4r3hGJiXQon51n6qs2GpWrcTfhnfjog6NuHfSYnYeyGbjroOMfHk2vz2/FXde1IYqMQp/iQwKowizec8h3ktNZ2JqGpv2HCqwLzrKGNCuIdckJ3BBuwbERGBLth8GdWpMz7PqcN+kxUxblkGegxe/WcvXK7bx9KjudGhSy+8SRYJOYRQBsnPy+Gp5BuNT0vhu9XZcoZbs5vViGZWcyNU9m9Gwllqy/VC/RhVevqEX789N5+FPlrH/cA4rtmYy7PlZ3D2oLb/p15JodSpKBaYw8sENr84hffch4utU482bzw7a56zOyGRCShqT5m9i14GCLdlVK0VxSecmjEpOoHeLurrXpRwwM0YkJdCnVT3ueW8hP67bRXZuHmM/X8H05Rk8OaI7ifVi/S5TJCiCGkZmNgR4FogGXnHOjS20/3rgz4Gn+4HfOecWBrOm8iB996ETFpIrK/sP5zBl0WbGp6Qxf+OeE/Z3aRbHyOQELu/WlLhqaskuj+LrxPLOr8/htVnr+dsXK8nOySNlw26GPjuTBy/ryKjkBP3wIBVO0MLIzKKBF4CLgXQgxcwmO+eW5TtsPXC+c263mQ0FxgHBO1WooJxzzNu4hwkpG/l00RYOZucW2F+ragxX9mjGyOQEOjVVS3Y4iIoyft2vJf3bNuCuCQtYunkfB7JzGR24rjT26i40rKkhVak4gnlm1BtY45xbB2Bm44FhwLEwcs79kO/4H4H4INZT4ezcf5gP529ifEoaa7btP2H/ua3qMSo5gcGdGmuRt5OIr1OtwK/lTdtGNfnw9+fx/IzVvPDNWnLzHDNWbGPw0zP5y5VdGNqlid8lipSJYIZRMyAt3/N0ij/ruRn4PIj1VAi5eY6Zq7czMSWNr5ZncCS3YDdC41pVGZEUz4heCbq+UArBvGZXVirHRHH3oHYMaN+QuycuZP2OA+w+eITfvT2Pq3o0438v76QhVwl7wQyjoga1XRHbMLMBeGHU9yT7bwFuAUhMTCyr+sJK2q6DvJeaxntz09myN6vAvpgo48IODbkmOZH+bRuo66qC6pFYhyl/6MvYz1fwn9k/AzBp/iZ+XLeTv4/oxnmt6/tcocjpC2YYpQMJ+Z7HA5sLH2RmXYFXgKHOuZ1FvZFzbhze9SSSkpKKDLSKKOtILtOWZTAxJY1Za3ec0JLdskF1rklO4Moe8TSoWcWfIiWkYivH8MiwzlzUoRF/fH8hGfsOs3lvFte/Moebzm3O6KHtNSQrYSmYYZQCtDGzFsAm4BrguvwHmFkiMAm4wTm3Koi1lBs5uXnHFqBzhdMlYPmWfUxISeOjBZvYc/BIgX3VKkVzWVevJbvXWXXUVRWh+rdtwLQ7z+ehyUv4eIH3M97rP2zgu9XbeWpkd7ol1Pa5QpFTYyf7hlgmb252CfAMXmv3a865x83sVgDn3Etm9gpwNfBz4CU5zrmk4t4zKSnJpaamBq3mYHHO8cYPG3jx27Vk7DsMeMNrfxvelat6xpOZdYTJCzczMSWNhel7T3h9t4TaXJOcwGVdm1Czqq4PyHGfLtrM/R8uYe8h7weX6CjjjoGtuW1A64hc2LAc0U+KpyCoYRQM4RpGz3y1ime+Wl3kvh4JtVmxNZNDRwq2ZNeOrcRVPeIZlZxAu8Y1Q1GmhKmMfVn86f1FfLtq+7FtXePjeGpk94hZ7r0cUhidAoVRCOzYf5g+T0w/ofOtKGbQt3V9RiUncHHHRpooU0rNOcfbczby+JTlx36wqRITxeih7fl/fZpr4cPQ01/4KdB0QCEwY8W2EoOobvXK3HDOWYxIiie+jlqy5dSZGb885yz6tq7P3RMXMG/jHg7n5PHwJ8v4ankGfx/ejaa1y+f9VCIaUA6BQ4VmRCjKw7/oxF0Xt1UQyRlrXr86E3/bhz8ObkelaO+H81lrdjL4mZl8OD/9pI0zIn5SGIVAl/iSp+BR95OUpZjoKG4b0JqPbjuPto28a0aZWTncNWEhv3973gkT54r4TWEUAj0SatMz8eRhM6hjI82WIEHRqWkck2/vyy39W3L0LoDPl2xl0NMzmbEiw9/iRPJRGIWAmfGv63vRvoiOuKSz6vD34d18qEoiRdVK0dx3SQfG/+acY3Pw7dh/mP96PZV7Jy3iwOEcnysUUTddSOXk5vHV8m386f2F7MvKoVGtKswefaG6nCRkMrOO8Niny5mQenzayMS6sTw5shvJzev6WFmFpP/Yp0BnRiEUEx3FkM6NqVfDm7ontnKMgkhCqmbVSvx1eFf+78Yk6teoDMDGXQcZ+fJsnvh8OYdzSm62EQkGhZFIBLq4YyO+uLM/gzs1AsA5ePnbdQx7fhbLt+zzuTqJRAojkQhVr0YVXvplL54c0Y2aVbxbDldszeTy57/nxcDaSSKhojASiWBmxtW94vn8zn70aVkPgCO5jr9OXcGol2fz884DIa3nhlfnMOAf33DDq3NC+rniP4WRiBBfJ5a3f302D17Wkcox3reF1J93M/TZ73hnzsaQ3SibvvsQ63ccIH33oZB8npQfCiMRASAqyri5bwum3NGXzs1qAXAwO5f7PlzMzW+ksi0zq4R3EDl9CiMRKaBNo5p8+Pvz+MPA1sdWDZ6xYhuDn57JZ4u3+FydVFQKIx/E16lGi/rVj92AKFLeVIqO4u5B7Xj/1j60rF8dgN0Hj/D7t+dx14QFx9ZOEikrmrXbB2/efLbfJYiUSo/EOkz5Qz/Gfr6cN2Z7a2B+OH8TP67byd+Hd6Nvm/o+VygVhc6MRKRY1SpH8/Cwzrx5c28a16oKwJa9Wfzy1TmMmby0VLPSi5REYSQipdKvTQO+uLM/w7o3Pbbt9R82cOk/v2Nh2h4fK5OKQGEkIqUWF1uJZ6/pwfPX9aB2bCUA1m0/wFUv/sDTX67iSG6ezxVKuFIYicgpu6xrU764sz8XtGsAQG6e49npq7n6xR9Ys22/z9VJOFIYichpaVSrKv++KZnHr+xMtUrRACxK38ulz33Ha9+vJ0/TCckpUBiJyGkzM64/+yw+/+9+9DqrDgCHc/J45NNl3PDaHDbv0UwKUjoKIxE5Y83rV2fib/vwx8HtqBTt3Sg7a81OBj8zk0nz0kM2nZCEL4WRiJSJ6CjjtgGt+ei282jXyFvVODMrh7snLuR3b81j14FsnyuU8kxhJCJlqlPTOCbfcR6/7d8SC6wdOXXpVgY9PZPpyzP8LU7KLYWRiJS5KjHR3HtJBybc0ufYtFc79h/m5jdSGf3BIvYfzvG5QilvFEYiEjS9W9Rl6p39GZWUcGzb+JQ0hj47k5/W7/KxMilvFEYiElQ1qsTw1+FdeeXGJOrXqAxA2q5DjBo3myc+X87hHG86oZzcPLJzvJtm1fAQeSzcvuhJSUkuNTXV7zJE5DTs3H+Y+z9cwtSlW49ta9e4JgPbNWTS/HQy9h0GvGaIx6/ozDW9E/0qtSyY3wWEE4WRiISUc45J8zYxZvJSMku4dvTgZR25uW+LEFVW5hRGp0DDdCISUmbG1b3imXpXf3o3r1vssU9/uYoDanaICAojEfFFs9rVuKZ3QrHH7D+cw/drdoSoIvGTwkhEfJN1pORZvrVeUmRQGImIb7olxJV4TNf4ko+R8KcwEhHfdGoaR5+W9U66/8L2DWnZoEYIKxK/KIxExFfPXduDLs1OPPvpmVibJ0d286Ei8YPCSER81aBmFT667TxeuymJWlVjAGhUqwrv33outWMr+1ydhIrCSER8Fx1lDGzfiHo1qgAQWzmGqCjdphNJFEYiIuI7hZGIiPhOYSQiIr5TGImIiO8URiIi4juFkYiI+E5hJCIivlMYiYiI7xRGIiLiO4WRiIj4TmEkIiK+UxiJiIjvFEYiIuK7oIaRmQ0xs5VmtsbMRhex38zsucD+RWbWM5j1iIhI+RS0MDKzaOAFYCjQEbjWzDoWOmwo0CbwuAV4MVj1iIhI+RXMM6PewBrn3DrnXDYwHhhW6JhhwH+c50egtpk1CWJNIiJSDgUzjJoBafmepwe2neoxmNktZpZqZqnbt28v80JFRMRfwQyjopZpdKdxDM65cc65JOdcUoMGDcqkOBEpf+LrVKNF/erE16nmdykSYjFBfO90ICHf83hg82kcIyIR4s2bz/a7BPFJMM+MUoA2ZtbCzCoD1wCTCx0zGbgx0FV3DrDXObcliDWJiEg5FLQzI+dcjpndDnwBRAOvOeeWmtmtgf0vAZ8BlwBrgIPAr4JVj4iIlF/m3AmXaMq1pKQkl5qa6ncZIiIlKeqauJyEZmAQERHfKYxERMR3CiMREfGdwkhERHynMBIREd8pjERExHcKIxER8Z3CSEREfBd2N72a2XbgZ7/rKAP1gR1+FyGAvhblTUX5euxwzg3xu4hwEXZhVFGYWapzLsnvOkRfi/JGX4/IpGE6ERHxncJIRER8pzDyzzi/C5Bj9LUoX/T1iEC6ZiQiIr7TmZGIiPhOYSQiIr5TGIWYmd1vZkvNbJGZLTCzs/2uKZKZWWMzG29ma81smZl9ZmZt/a4rEplZvJl9bGarzWydmT1vZlX8rktCQ2EUQmbWB7gM6Omc6wpcBKT5W1XkMjMDPgS+cc61cs51BO4DGvlbWeQJfC0mAR8559oAbYBqwN98LUxCJsbvAiJME7y7sg8DOOcqwl3m4WwAcMQ599LRDc65BT7WE8kGAlnOuX8DOOdyzewu4Gczu985t9/f8iTYdGYUWtOABDNbZWb/MrPz/S4ownUG5vpdhADQiUJfC+fcPmAD0NqPgiS0FEYhFPjprhdwC7AdmGBmN/lalEj5YEBR95lYqAsRfyiMQsw5l+uc+8Y597/A7cDVftcUwZbi/XAg/lsKFJiPzsxq4V2/W+lLRRJSCqMQMrN2ZtYm36buVIwZyMPVDKCKmf3m6AYzS9bwqS+mA7FmdiOAmUUDTwLPO+cO+VqZhITCKLRqAG8EWogXAR2BMf6WFLmcN/3IlcDFgdbupXhfj82+FhaB8n0thpvZamAnkOece9zfyiRUNB2QiJQ7ZnYu8C5wlXNOTSYRQGEkIiK+0zCdiIj4TmEkIiK+UxiJiIjvFEYiIuI7hZGUe2aWG5jh/Ohj9Cm89gIz+/QMP/8bM0sq+cjgfL5IJNBEqRIODjnnuvvxwYGbL0UkyHRmJGHLzDaY2V/MbLaZpZpZTzP7InAD6635Dq1lZh8GbjZ+ycyiAq9/MfC6pWb2cKH3fcjMvgdG5NseZWZvmNljgeeDAp89z8zeM7Mage1DzGxF4PVXheQvQyTMKYwkHFQrNEw3Kt++NOdcH+A74HVgOHAO8Ei+Y3oD/wN0AVpxPCDud84lAV2B882sa77XZDnn+jrnxgeexwBvA6uccw+YWX3gAeAi51xPIBW428yqAv8H/ALoBzQuo78DkQpNw3QSDoobppsc+HUxUMM5lwlkmlmWmdUO7PvJObcOwMzeBfoC7wMjzewWvP8HTfCmZ1oUeM2EQp/zMjAx3/Q05wSOn+WtC0dlYDbQHljvnFsd+Ly38GZpF5FiKIwk3B0O/JqX7/dHnx/99114mhFnZi2Ae4Bk59xuM3sdqJrvmAOFXvMDMMDMnnTOZeEtbfClc+7a/AeZWfciPk9ESqBhOokEvc2sReBa0Sjge6AWXuDsNbNGwNAS3uNV4DPgPTOLAX4EzjOz1gBmFmtmbYEVQAszaxV43bVFvpuIFKAzIwkH1cws/3LgU51zpW7vxhs+G4t3zWgm8KFzLs/M5uOto7MOmFXSmzjnnjKzOOBN4HrgJuBdM6sSOOQB59yqwNDfFDPbgRd8nU+hVpGIpIlSRUTEdxqmExER3ymMRETEdwojERHxncJIRER8pzASERHfKYxERMR3CiMREfHd/weQyrElz5RM4QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 430.5x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot('Embarked','Survived', hue= 'Gender', data=titanic_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "X95NK1j7PK3_"
   },
   "source": [
    "- Approximately 85% women embarked at C survived.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Q1D5n8phPK4A"
   },
   "source": [
    "## Embarked, Pclass and Gender vs Survival"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 422
    },
    "colab_type": "code",
    "id": "D2-vAsJSPK4B",
    "outputId": "49c9a549-5090-4328-91be-3a6716be6953",
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Softwares\\lib\\site-packages\\seaborn\\categorical.py:3666: UserWarning: The `factorplot` function has been renamed to `catplot`. The original name will be removed in a future release. Please update your code. Note that the default `kind` in `factorplot` (`'point'`) has changed `'strip'` in `catplot`.\n",
      "  warnings.warn(msg)\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1150.5x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot('Embarked','Survived', col='Pclass', hue= 'Gender', data=titanic_train,ci=False)\n",
    "#separate plots for embarked\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "AXiyiGSrPK4D"
   },
   "source": [
    "- Practically all women of Pclass 2 that embarked in C and Q survived, also nearly all women of Pclass 1 survived.\n",
    "- All men of Pclass 1 and 2 embarked at Q died, survival rate for men in Pclass 2 and 3 is always below 0.2\n",
    "- For the remaining men in Pclass 1 that embarked at S and C, survival rate is approx. 0.4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 774
    },
    "colab_type": "code",
    "id": "Cy_GZUo2PK4E",
    "outputId": "f6bf6d1c-0e9f-4306-eb11-ac41ceadc9a1"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Softwares\\lib\\site-packages\\seaborn\\categorical.py:3666: UserWarning: The `factorplot` function has been renamed to `catplot`. The original name will be removed in a future release. Please update your code. Note that the default `kind` in `factorplot` (`'point'`) has changed `'strip'` in `catplot`.\n",
      "  warnings.warn(msg)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1150.5x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1080x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = sns.factorplot('Pclass', 'Survived', hue='Gender', col = 'Embarked', data=titanic_train)\n",
    "#separate plots for Pclass\n",
    "_ = sns.factorplot('Pclass', 'Survived', col = 'Embarked', data=titanic_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "MNwfOMatPK4G"
   },
   "source": [
    "- As noticed already before, the class 1 passengers had a higher survival rate.\n",
    "- Most of the women who died were from the 3rd class.\n",
    "- Embarked at Q as a 3rd class gave you slighly better survival chances than embarked in S for the same class."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Q4VTzjjEPK4H"
   },
   "source": [
    "# Embarked vs Pclass (categorical vs categorical)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 378
    },
    "colab_type": "code",
    "id": "QLE3Nd4XPK4H",
    "outputId": "9689b0ef-bb96-44f8-9655-eb71d5649221"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pclass      1    2    3\n",
      "Embarked               \n",
      "C          85   17   66\n",
      "Q           2    3   72\n",
      "S         127  164  353\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2e012437438>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tab = pd.crosstab(titanic_train['Embarked'],titanic_train['Pclass'])\n",
    "print(tab)\n",
    "tab_prop = tab.div(tab.sum(1).astype(float), axis=0)\n",
    "tab_prop.plot(kind=\"bar\", stacked=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "3CKiPuxUPK4K"
   },
   "source": [
    "## Age Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 404
    },
    "colab_type": "code",
    "id": "MI_Pc3lXPK4L",
    "outputId": "51db3d48-381f-4fe8-c9f1-75a37841c3bc",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "age_counts = titanic_train[['Age', 'PassengerId']].groupby('Age').count().reset_index()\n",
    "age_counts.columns = ['AGE', 'FREQUENCY']\n",
    "plt.figure(figsize=(12,6))\n",
    "plt.bar(age_counts.AGE, age_counts.FREQUENCY)\n",
    "plt.title('Bar Plot on Ages (AGE > 1 and AGE < 90)')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Count')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "y8LYX1poPK4Q"
   },
   "source": [
    "- Most of the passengers are aged between 18 and 36"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "nxwhkBnyPK4Q"
   },
   "source": [
    "## Survival based on Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 242
    },
    "colab_type": "code",
    "id": "opO_VezlPK4S",
    "outputId": "fb6aa2bf-a158-4ea1-e6c0-49a9d670e0c0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2e0124caeb8>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 915.875x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "facet = sns.FacetGrid(titanic_train, hue=\"Survived\",aspect=4)\n",
    "facet.map(sns.kdeplot,'Age',shade= True)\n",
    "facet.set(xlim=(0, titanic_train['Age'].max()))\n",
    "facet.add_legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 439
    },
    "colab_type": "code",
    "id": "0JgylXU2PK4V",
    "outputId": "3b6ff926-df62-4dea-d0cd-8476eebbdfc4"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Softwares\\lib\\site-packages\\seaborn\\axisgrid.py:230: UserWarning: The `size` paramter has been renamed to `height`; please update your code.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2e0124cae10>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(titanic_train, col='Survived',size=5)\n",
    "g.map(plt.hist, 'Age', bins=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "bPxFeaWmPK4X"
   },
   "source": [
    "- Infants (Age <=4) had high survival rate\n",
    "- Oldest passengers (Age = 80) survived\n",
    "- Large number of 15-25 year olds did not survive\n",
    "- Most passengers are in 15-35 age range"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 384
    },
    "colab_type": "code",
    "id": "J-phD6SxPK4X",
    "outputId": "56968c1f-dcf1-418a-fcd3-bf6cee248349"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n",
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows=1, ncols=2,figsize=(15,5))\n",
    "#create 1 row and 2 columns (2 subplots)\n",
    "females = titanic_train[titanic_train['Gender']=='female']\n",
    "males = titanic_train[titanic_train['Gender']=='male']\n",
    "\n",
    "ax = sns.distplot(females[females['Survived']==1].Age.dropna(), bins=30, ax = axes[0], kde =False)\n",
    "ax = sns.distplot(females[females['Survived']==0].Age.dropna(), bins=30, ax = axes[0], kde =False)\n",
    "#plot both on the same axes(axes[0])\n",
    "ax.legend()\n",
    "ax.set_title('Female')\n",
    "ax = sns.distplot(males[males['Survived']==1].Age.dropna(), bins=30, ax = axes[1], kde = False)\n",
    "ax = sns.distplot(males[males['Survived']==0].Age.dropna(), bins=30, ax = axes[1], kde = False)\n",
    "#plot both on axes[1]\n",
    "ax.legend()\n",
    "_ = ax.set_title('Male')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "woPv8mptPK4a"
   },
   "source": [
    "- Most of the Females with age 18-38 survived.\n",
    "- Most of the Males with age 18-35 died."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "nVVxcFjMPK4b"
   },
   "source": [
    "# Survival Rate vs Number of family members aboard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 774
    },
    "colab_type": "code",
    "id": "2mqU20olPK4d",
    "outputId": "3251dfa7-b878-457d-f14c-0d8019dc519d"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Softwares\\lib\\site-packages\\seaborn\\categorical.py:3666: UserWarning: The `factorplot` function has been renamed to `catplot`. The original name will be removed in a future release. Please update your code. Note that the default `kind` in `factorplot` (`'point'`) has changed `'strip'` in `catplot`.\n",
      "  warnings.warn(msg)\n"
     ]
    },
    {
     "data": {
      "image/png": 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fWx47bCIcPK7dS5LUCW13+5y6pC97a9rnlK+B9Wu2rX0OJO2z+u6+Zbi+oVd7wW4uzKz28fGvJX/en/p18umM2y+ACTOS78WSJEmSJDVgEN8VffzrsOcn4OWb4JVboLocCgbD539ryxlJmZWVtQPtc9bCyndh2mkQa5s/d+LDsPvRO16r1BZO/EHSmmn2n6GmHG45GyY+BIMOTLsySZIkSVIHYgPcrmrQgXDKryF/cDLO6WEIL6njysmF/F1hj+PgoPOaP2/okc42VscSAnzul3DgOcm4shimnw4r30u3LkmSJElSh2IQL0nqWE7+JYz63Jb7Bx8CZ0/3TUV1PFlZcNofYe+Tk/H6lcknO9YuTLcuSZIkSVKHYRAvSepYuveE82+Hix+DHn2TffmD4EuPJbPmpY4ouxuceSMMPy4ZlyyC6WNg3fJ065IkSZIkdQgG8ZKkjmm3w6BHv2S7W89k1rHUkXXLg/NuS/7sAqx6D6afkax/IEmSJEnaqZlqSJIktZXcfBh3NxTtm4w/eh1uPQeqytKtS1LXNG0M/N+hybOUAeNveIFP/Opxxt/wQtqlSJLU6RnES5IktaWe/WD8fdB3eDJe+DzccQHUVKZalqQuaO0CWP1+8ixlwKI15cxbWcaiNeVplyJJUqdnEC9JktTWCgbBhPuT9Q0A3n8M7vkS1NakW5ckSZIkKRUG8ZIkSZnQdziMn7FprYO3HoAHvw51damWJUmSJElqfwbxkiRJmTJgH7jgHuien4xfuRke/X8QY7p1SZIkSZLalUG8JElSJu12KJx/O+TkJePn/wRP/CLdmiRJkiRJ7cogXtL26zMM+o1IniVJzRt+LJx1E2TlJOPHfw7PX5NuTZIkSZKkdpOTdgGSOrEJM9KuQJI6j71PgtOvTRZtJcLM70JeARx8ftqVSZIkSZIyzBnxkiRJ7eVjZ8Ipv940vv9ymPNAevVIkiRJktqFQbwkSVJ7Ovwi+NTkZDvWwT0XwfuPpVmRJEmSJCnDDOIlSZLa27H/njwAaqvg9nGwcFa6NUmSJEmSMsYgXpIkKQ2f/CGMnpRsV6+HW86EZW+kW5MkSZIkKSMM4ru6PsOg34jkWZIkdRwhwMm/hgPOTMYVxTD9dFj1frp1SZIkSZLaXE7aBSjDJsxIuwJJktScrCw4/RqoLIW5j0DZcph2GkyaCYVD0q5OkiRJktRGnBEvSZKUpuxucPZNsPuxybh4IUwbA2Ur061LkiRJktRmDOIlSZLS1q0HnHcbDD4kGa+am7SpqShOty5JkiRJUpswiJckSeoI8gpg3D1QtE8yXvYa3HoOVK1Pty5JkiRJ0g4ziJckSeooeu0C4++DPrsn4wXPwZ3joaYq3bokSZIkSTvEIF6SJKkjKRgME+6H3gOT8Xt/h/sugbradOuSJEmSJG03g3hJkqSOpt8eycz4vD7J+M374KFvQIzp1iVJkiRJ2i4G8ZIkaae1LGsAH9QNZFnWgLRL2dKu+8EF90L33sn45Wnw6PcN4yVJkiSpE8pJuwBJkqS0fKfHj5hXVsYeBb34Z9rFNGXIYXDebXDzmVBbCc/9AXr0geO/nXZlkiRJkqRt4Ix4SZKkjmyP4+GsqRCyk/FjP4UXrku1JEmSJEnStjGIlyRJ6uj2ORlOvwYIyfiv34ZXb0+1JEmSJElS6xnES5IkdQYHng0n/3LTeMZX4O2H06tHkiRJktRqBvGSJEmdxREXw4k/SLZjLdw1ET54PM2KJEmSJEmtYBAvSZLUmRz3H3DM15Lt2iq47XxY9GK6NUmSJEmStsogXpIkqTMJAT79Yzj0i8m4ugxuHgsfvZluXZIkSZKkZhnES5IkdTYhwOf/F/Y/IxlXrIXpp8Oq99OtS5IkSZLUJIN4SVLH1WcY9BuRPEvaXFY2nH4tjPxMMl73EUwbAyVL0q1LkiRJkrQFg3hJUsc1YQZ87eXkWdKWcrrDWTfB7h9PxsULkjC+bFW6dUmSJEmSNmMQL0mS1Jl17wnn3Q6DDkrGK9+Bm8+AipJ065IkSZIkbWQQL0mS1NnlFcAF90L/Ucl46Stw27lQXZ5uXZIkSZIkwCBekiSpa+jVH8bPgML6NRU+fAbunAA1VenWJUmSJEkyiJckSeoyCndL1lToNSAZz30U7vsy1NWmW5ckqVP6RfkPeaz7N/lF+Q/TLkWSpE7PIF6SJKkr2WVEEsbn9UnGb94LD38TYky3LklSpzOwbjl7Zi1jYN3ytEuRJKnTM4iXJEnqanbdH8bdDd16JeOXpsLfJ6dZkSRJkiTt1AziJUmSuqKhh8N5t0J292T8zG/hqd+kW5MkSZIk7aQM4iVJkrqqPU+AM6dAyE7G//gRzP5zmhVJkiRJ0k4po0F8COGkEMI7IYT3QghXNnPOCSGEV0IIb4YQnshkPZIkSTudfb8Ap/1x0/jhb8Frd6ZXjyRJkiTthHIydeMQQjbwR+DTwCJgdgjhgRjjnAbn9AH+BJwUY1wQQhiQqXokSZJ2WgefB5Ul8NfvABHuuxRy82Hvz6VdmSRJkiTtFDI5I/4I4L0Y4wcxxirgduC0RuecD9wbY1wAEGN0KXZJkqRMOPLL8InvJ9uxFu78Isx7Mt2aJEmSJGknkckgfjdgYYPxovp9DY0C+oYQHg8hvBRCmNDUjUIIl4QQXgwhvLhixYoMlStJktTFHf8tOPqryXZtJdx2Hix+Kd2aJEmSJGknkMkgPjSxLzYa5wCHAacAnwV+EEIYtcVFMV4XYxwdYxxdVFTU9pVKkiTtDEKAz/wUDhmfjKvWwc1jYflb6dYlSZIkSV1cJoP4RcDQBuMhwJImzpkZYyyLMa4EngQOymBNkiRJO7cQ4Au/g/3GJOPyNTBtDKyel25dkiRJktSFZTKInw2MDCHsEULoDpwLPNDonPuB40IIOSGEnsCRgFOyJEmSMikrG864Hvb6VDJetwymnQYlS9OtS5IkSZK6qIwF8THGGuCrwCMk4fqdMcY3QwiXhhAurT/nLWAm8BowC/hzjPGNTNUkSZKkejnd4ezpMOzoZLz2Q5g+BtavTrcuSZIkSeqCMjkjnhjjX2KMo2KMI2KMP6vfd02M8ZoG5/wyxrhfjPGAGONvM1mPJEmSGujeE86/AwYemIxXvJ30jK8sTbcuSZIkSepiMhrES5IkqYPLK4QL7oVdRibjJS/DbedBdXm6dUmSJElSF2IQL0mStLPrXQQTZkDh0GQ8/ym460KorU63LkmSJEnqIgziJUmSBIVDYPwM6FWUjN/9K8y4DOrq0q1LkiRJkroAg3hJkiQl+u8F4++D3MJk/Ppd8JdvQYzp1iVJkiRJnZxBvCRJkjYZ+DEYdxd065mMX7wB/vHjdGuSJEmSpE5uq0F8CKE0hFDS3KO9ipQkSVI7GnYknHsLZHdPxk//Bp7+bbo1SZIkSVInttUgPsaYH2MsAH4LXAnsBgwBvgv8NPPlSZIkKRUjToSxN0Co/+/i338IL96Ybk2SJEmS1Em1tjXNZ2OMf4oxlsYYS2KMVwNjM1mYJEmSUrbfqXDqHzaNH/p3eP3u9OqRJEmSpE6qtUF8bQhhXAghO4SQFUIYB9RmsjBJkiR1AIeMg5Ouqh9EuO/L8O4jqZYkSZIkSZ1Na4P484GzgY/qH2fV75MkSVJXd9RlcML3ku26GrhzAsx/Ot2aJEmSJKkTaVUQH2OcH2M8LcbYP8ZYFGMcE2Ocn+HaJEmS1FH823fhqK8k2zUVcOu5sPjldGuSJEmSpE6iVUF8CGFUCOEfIYQ36scHhhC+n9nSJEmS1GGEAJ/5GRx8QTKuKoWbx8Lyt9OtS5IkSZI6gda2prke+B5QDRBjfA04N1NFSZIkqQPKyoIv/A72PTUZl6+G6WNgzfxUy5IkSZKkjq61QXzPGOOsRvtq2roYSZIaGn/DC3ziV48z/oYX0i5F0gbZOTD2zzDixGRcuhSmjYHSZenWJUmSJEkdWGuD+JUhhBFABAghnAkszVhVkiQBi9aUM29lGYvWlKddiqSGcnLhnJth6JHJeM08mH46rF+dbl2SJEmS1EG1Noi/HLgW2CeEsBj4BnBpxqqSJElSx9a9F5x/J+z6sWS8fA7cciZUlqZblyRJkiR1QK0N4j+MMX4KKAL2iTEeG2P8MIN1SZIkqaPr0QfG3wv9RiTjxS/B7edDdUW6dUmSJElSB9PaIH5eCOE64ChgXQbrkSRJahcfrFjHuopqAOrqYsrVdGK9B8CE+6FgSDKe9yTcPQlqXU5IkiRJkjZobRC/N/B3khY180IIfwghHJu5siRJkjKjeH01X7ppNif++glWrKsCYMHq9Vz7xPvEaCC/XfoMhQkzoGf/ZPzOw3D/5VBXl25dkiRJktRBtCqIjzGWxxjvjDGeARwCFABPZLQySZKkNhZj5OJpL/L3t5Zvvh/477++zc3P23lvu/UfmbSpyS1Mxq/dDjO/C765IUmSJEmtnhFPCOHfQgh/Al4G8oCzM1aVJElSBjz7/ipmzV/d7PE//PM9qmudxb3dBh0E4+6EnB7JeNZ18M+fpVuTJEmSpE4hhLBrCOHWEMIHIYSXQgjPhRBOb4P7nhBCeKgtatwROa05KYQwD3gFuBP4doyxLKNVSZIktZHq2jreX7GOt5aWMPWZ+Vs996OSSt79qJT9Bxe2T3Fd0bCj4Jyb4bZzoa4anvwl5BbAx7+WdmXpmDYG1i6APsOS9j2SJEmSthBCCMAM4KYY4/n1+3YHTk2hlpwYY5svetWqIB44KMZY0tYvLkmS1JZKKqp5e2kpc5YUM2dpCXOWlvDusnVUbcMsdzuptIGRn4Kx1yeLtsY6+NsPIK8ADpuYdmXtb+0CWP1+2lVIkiRJHd2JQFWM8ZoNO2KMHwK/DyFkA1cBJwC5wB9jjNeGEE4AJgMrgQOAl4ALYowxhHAS8Nv6Yy9vuGcIoRfwe+BjJNn45Bjj/SGEicApJJ1getXX06a2GsSHEL4TY/wF8LMQwhY/lsYYd9KpTZIkKU0xRpYUVzBnSQlzlpTwVn3ovmD1+h26b8/u2Ywc0LuNqtzJ7X86VK6DB76ajB/8RjIz/oAz0q1LkiRJUke0Pw0C80YuAopjjIeHEHKBZ0IIj9YfO6T+2iXAM8DHQwgvAteThOnvAXc0uNf/Ax6LMU4KIfQBZoUQ/l5/7GjgwBhj8/1Md0BLM+Lfqn9+MRMvLkmS1JKqmqS1zJwlSdi+4bm4vLrFa3vn5rDfoAL2G1zAfoMK2GdgPt+//w1eW1Tc5Pnrq2r5ycNz+NGpB5CdFdr6S9n5HDoeKkvgkf8EItx7CeTmw8hPp12ZJEmSpA4shPBH4FigCvgQODCEcGb94UJgZP2xWTHGRfXXvAIMB9YB82KMc+v33wxcUn/tZ4BTQwjfqh/nAcPqt/+WqRAeWgjiY4wP1m++FmP8V6aKkCRJAigur05mtzcI3d9b3rrWMrv16cG+G0P3fPYbVMiQvj3IahSo3zjxcL5xxys8NXflZvuzAtRFuPn5BawsreK35x5MXrfsNv36dkpHXw4VxfDE/yQ94+8YD+Pvhd2PSbsySZIkSR3Hm8DYDYMY4+UhhP4kE8QXAFfEGB9peEF9a5rKBrtq2ZR3N9d0NABjY4zvNLrXkUBG10VtbY/434QQBgF3AbfHGN/MYE2SJKmLizGyeG35FrPcF60pb/HanKzAXgN6b5zlvuG5T8/urXrtXXrnMv2iI3lraQnjb3iBleuqGNavJz87/QAunf4SZVW1zHxzGROmzOL6CaMp7NFtR79cnfA9KF8Ls66FmnK49Rz44oMw+OC0K5MkSZLUMTwG/DyEcFmM8er6fT3rnx8BLgshPBZjrA4hjAIWb+VebwN7hBBGxBjfB85rcOwR4IoQwhX1veQPaa8J6K0K4mOMnwghDATOBq4LIRQAd8QYf5rR6iRJUqdXVVPH3OWlm4Xuby0toaSi5UXo8/M2by2z3+AC9hrQm9ycHZ+pvu+gAvLzurFyXRXZWYHjRhZx+yVHM/HGWawqq2LWvNWcc+1zTL3wCAYW5u3w6+3UQoCTrkra1Lx6W/J88xlw4UwoGpV2dZIkSZJSVh+KjwH+N4TwHWAFyQz175JMDh8OvBxCCPXHxmzlXrf+qqoAACAASURBVBUhhEuAh0MIK4GnSRZzBfgJySKur9Xfaz7w+Yx8UY20dkY8McZlwP+FEP4JfAf4L8AgXpIkbbR2fRVzlpbw1tJNwft7y0uprm3uU4Gb7Nanxxaz3If07UHyf6P28bEhhdxz2TFMmDKLBavX8/ayUsZe/Sw3TTqCvVzEdcdkZcGpf4DKUnj7IVi/CqadBpNmQt/d065OkiRJUspijEuBc5s5/J/1j4Yer39suP6rDbZnAvs08RrlwJeb2D8VmLptFW+bVgXxIYR9gXOAM4FVwO3Af2SwLkmS1IHFGFm0ppw3G81yX7y25dYy3bIDIwfkbxa67zuwgMKeHaMFzPD+vbjnsmO4cOos3licfE1nXvMsUyYezqHD+qZdXueWnQNnToFbz4YPHofSJTB9TDIzPn/XtKuTJEmSpIxp7Yz4G4HbgM/EGJdksB5JktTBVNbUMvejdZtayywt4a0lJZRWttxaprBHN/YbVNBgEdWktUz3nKx2qHz7FeXncvslR3Pp9Jd4+r2VrF1fzfnXP8+fxh3KifsYGO+QnFw455YkgF80G1Z/kLSpmfgQ9PCNDkmSJEldU4tBfAghG3g/xvi7dqhHkiSlaE1ZFW8t3XwB1feWr6OmruXWMkP79UhmuA8qTEL3wQUMLsxr19Yybal3bg5TJh7Ot+56lQdeXUJFdR0XT3uJ/z7jY5w9emja5XVuub1h3F1w4ymw/E346A245WwYf19yTJIkSZK6mBaD+BhjbQhhlxBC9xhjVXsUJUmSMquuLrJwzfrNFlCds7SEpcUVLV7bPTuLUQN7b5rpPqiAfQYVUNijY7SWaUvdc7L47TkH0793LlOemUdtXeQ7d7/GitJKvnLCiE77JkOH0KNvErzfeFIyK37RLLhjHJx/ZzJrXpIkSZK6kNa2pvkQeCaE8ADJarUAxBh/k5GqJElSm6morm8ts7R4Y+D+1tJS1rWitUyfnt3qZ7kXbJzlPqKoN92yO3ZrmbaUlRX4wef3ZWBhLj//y9sA/PKRd1heUsF/fWF/srMM47db/q4wfgZMOSnpF//B43D3JDjrpqSfvCRJkiR1Ea39CWdJ/SMLyM9cOZIkaUesWlfJW0tLNwvd319RRm0rWsvsvkvPzUL3fQcVMKgTt5ZpSyEELjl+BP175/Kdu1+jpi5y03MfsnJdFb855yByc7LTLrHz6rs7TJgBN34O1q+Ctx+CB66A0/4IWTvPGz6SJEmSurZWBfExxh9luhBJktR6dXWRD1cnrWUa9nRfVtKK1jI5Wey9a/5ms9z3GZhPfl7Xay3T1s44dAj9enXnK7e8zPqqWh5+fSmryiq5bsJoCvz1235Fe8MF98JNX4DKEnj1VsgrgJOuAt8IkiRJkrqM4Vc+XAicDBQArwIvzL/qlJZnjmVICOEE4Fsxxs9n+rVaFcSHEP4JbPELEmM8sc0rkiRJm6moruWdZaWb9XJ/e2kJZVW1LV7bt2c39h9cv3hqfU/3PYt67VStZdraCXsP4LaLj+LCqbNZXVbF8x+s5pxrn+emCw9nQEFe2uV1XoMPhvPvgOmnQ00FvHAN5PWBT3wv7cokSZIktYHhVz78NeDnQK8Gu2cNv/Lhs+dfdcqHKZXVblrbmuZbDbbzgLFAy41lJUnSNlm5rnKLBVQ/WLGOVnSWYY/+vdh3UIOZ7oMK2bUg19YyGXDQ0D7cc9kxTJjyAgtXl/PW0hLOuPpZpk06gj2LeqddXue1+zFwzs1w27lQVwNPXJXMjD/68rQrkyRJkrQDhl/58AXA75o4dATw6PArHz5o/lWntPwR7yaEEIYDM4GngaNIZtrfCPwIGACMqz/1t0APoBy4MMb4TqP79AJ+D3yMJDefHGO8f3tqakprW9O81GjXMyGEJ9qqCEmSdja1dZH5q8qStjINgvflpZUtXpubk8U+A/M3znLfb3ABew8soHeui1u2pz369+Key45h4pTZzFlawqI15Zx5zXNMmXg4Bw/tk3Z5ndfIT8MZ1yeLthLhkf+E3AI4dHzalUmSJEnaDsOvfDgA39/KKaOAs4DpO/Aye9Xf4xJgNnA+cCxwKvCfwATg+BhjTQjhUyQz88c2usf/Ax6LMU4KIfQBZoUQ/h5jLNuBujZqbWuafg2GWcBoYGBbFCBJUldXXlXL28tKGrWWKaW8uuXWMrv06r5Z4L7foAL26N+LHFvLdAgD8vO448tH8eXpL/Hs+6tYXVbFedc9z58uOJRP7D0g7fI6rwPOSHrFP/j1ZPzg1yA3H/Yfk25dkiRJkrbHYGDvFs45kR0L4ufFGF8HCCG8CfwjxhhDCK8Dw4FC4KYQwkiSFuxNLfL1GeDUEMKG7jB5wDDgrR2oa6PWTp17iU094muA+cBFbVGAJEldyfLSCt5aWtpglnsx81aWtdhaJgTYY5de7NsgdN9/UAFF+baW6ejy87px44WH8807X+Xh15ZSXl3LxTe9yP+MPZCxhw1Ju7zO67CJUFECf/sBxDq450uQ2xv2+lTalUmSJEnaNnWtOGdHF2xt+PHyugbjOpIM/CfAP2OMp9e3snm8iXsEYGzjljVtZatBfAjhcGBhjHGP+vEXSabszwfmZKIgSZI6g9q6yLyVZZvNcp+zpISV61puLZPXLYt9BiZh+76DkuB9n4H59LK1TKeVm5PN7889hKLeuUx9dj41dZH/uOtVVqyr5MvH7+mbKdvr41+DimJ46ldQVw23XwATZsCwo9KuTJIkSVLrLQNeJ+m93pxHMlxDIbC4fnviVmq4IoRwRf1s+kNijP9qqwJa+on/WuBTACGE44H/Bq4ADgauA85sq0IkSeqoyipreHtZKXOWlmzs6f72shIqqlt+U79/79wmW8tkZxnMdjVZWYEffmE/BhTk8ouZyQSKq/76NstLKvn+KfuS5e/59jnx+0kYP/t6qCmHW86GiQ/BoAPTrkySJElSK8y/6pQ4/MqHfwTc3cwprwP3ZbiMX5C0pvkm8Fgz5/yEZEHX10Iym2o+8Pm2KqClID47xri6fvsc4LoY4z3APSGEV9qqCEmSOoIYIytKK3mzwSz3t5aUMG9VGbEVrWX27N+L/QYXbgzd9x2Uz4D8vPYpXh1CCIGvnLAXRb1zufLe16mti0x5Zh4r1lXyq7MOJDcnO+0SO58Q4HO/SHrGv3YHVBbD9NNh0iPQf6+0q5MkSZLUCvOvOuWe4Vc+fBHwS6DheqT/AMbPv+qUqu29d4xxPnBAg/HEZo6NanDZD+qPP059m5oYYznw5e2toyUtBvEhhJwYYw3wSZJVZ1t7rSRJHVZNbV2TrWVWlbX8vb9Ht2z2GZS/2Sz3vQfm07O73xqVOGv0UPr3zuUrt7xMeXUtD766hNVllVxzwWHk5zW1JpC2KisLTvsTVK6Ddx6G9Sth2mkwaSb0GZp2dZIkSZJaYf5Vp0wZfuXDt5HkzIXAK/OvOuXNlMtqNy0lBrcBT4QQVgLlwFMAIYS9gOIM1yZJUptYV1nD2xvaymxsLVNKZU3LrWUG5G/ZWmb3XWwto5Z9Yp8B3HrxkUyaOps166t55r1VnHvd89x44eF+UmJ7ZOfAmVPg1rNg3pNQsgimj4EL/wq9B6RdnSRJkqRWmH/VKeXAQ2nXkYatBvExxp+FEP4BDAIejXHjB/OzSHrFb1UI4STgd0A28OcY41XNnHc48DxwToyxuV5BkqSdxNr1Vdw6awFL1pYDsLqsiiVryxncp8dWr4sx8lFJJXOWFm82y33+qvUtvmZWgBFFvZPFU+sD930HFVCUn9smX5N2TocM68vdlx3DhBtmsXhtOW8uKWHs1c8ybdKR7NG/V9rldT7d8uDcW5PZ8ItfglXvwfQzkp7xPfqkXZ0kSZIkNavFz9DHGJ9vYt+7LV0XQsgG/gh8GlgEzA4hPBBjnNPEef9D5lfGlSR1Ah+uKuPc655naXHFxn3F5dV8+n+f4KYLj2D08KSVXHVtHR+sKNsYur+1NFlMdXUrWsv07J6dBO4NQvdRu+bTo7v9u9X2RhT15t6vHMMXp8zi7WWlLFxdzplXP8uNFx7OgUMMj7dZbj6MuxumngLL58BHr8OtZ8P4+6C7b25IkiRJ6pgy2cz2COC9GOMHACGE24HTgDmNzrsCuAc4PIO1SJI6iW/f9dpmIfwGZZW1XDh1Np/dfyDvLCvlnY9KqWpFa5mBBXkbF07db1Ah+w0uYPd+PcmytYza0a4Fedzx5aO5ZNqLvDBvNavKqjj3uue55oLDOH5UUdrldT49+yXB+5TPwpr5sPAFuOMCOO92yPFTLJIkSZI6nkwG8bsBCxuMFwFHNjwhhLAbcDpwIgbxkrTTm/tRKbPmr272eGlFDXe/tKjJY9lZgRFFvRrMci9k30H57NLbUE4dQ2GPbtw06Qj+/Y5X+Osby1hfVcukqbP51VkHMeaQ3dIur/PJHwgT7ocpJ0HpUnj/MbjnS3DmjUk/eUmSJEnqQDL5U0pTUw1jo/Fvge/GGGtDaH5mYgjhEuASgGHDhrVZgZKkjqGiupZZ81Yz/fkPW3V+79yc+hnuBfWz3ZPWMnndbC2jji2vWzZ/OP9QJj/wJtOf/5Causg37niFFaWVXHz8nmmX1/n0HQ7jZ8CNn4Py1fDWA/Dg1+HU30NWVtrVSZIkSWpscmEhcDJQALwKvMDk4saZ8TYLIXwNuAx4OcY4bkfv18T9JwPrYoy/2t57ZDKIXwQMbTAeAixpdM5o4Pb6EL4/cHIIoSbGOKPhSTHG64DrAEaPHr3DvzGSpHTFGJm7fB1PvruCJ95dwax5q6lsRZsZgO99bh8uPm5PW8uo08rOCvz4tP3ZtSCXXz2aLLvzs7+8xUclFfznyfv6Z3tbDdgHLrgHbjoVqkrhlZshrwA++3PYykQPSZIkSe1scuHXgJ8DDRd3msXkwrOZXNy6mXnN+wrwuRjjvB28T8ZkMoifDYwMIewBLAbOBc5veEKMcY8N2yGEqcBDjUN4SVLXsKasiqffW8mT767gqbkrWVayZR94SD5O1dw7rr26ZzPuqN0NKtXphRD46okjKcrP5Xv3vk5dhD8/PY+V6yr5xZkH0T3H2dzbZLdD4fzb4eaxUFMBz/8J8vrACd9NuzIpM8rXwLO/h7X1P6+WLIJX74ADz/YNKLWNFe/A079lcFwGQP+4Gha8AMOObOFCSZKaMbnwAuB3TRw5AniUyYUHMbmJBeNaIYRwDbAn8ED9OqUjgI+RZN+TY4z3hxAmAmOAbOAA4NdAd2A8UAmcHGNcHUK4mKQzS3fgPWB8jHF9o9cbAfwRKALWAxfHGN9uqc6MBfExxpoQwleBR0i+wCkxxjdDCJfWH78mU68tSUpfdW0d/1qwtj54X8Fri4uJzSTs+w8u4LiRRRw/qj8xwkU3zaaievMZ8gH46ekH0DvX3s/qOs45fBi79Mrl8ltfprKmjhmvLGFVWRVXX3CYf9a31fBj4expcPv5UFcDj/88mRl/1GVpVya1rbJVcONJsPLdTftqKuG+S2Dxi3DyL9OrTV3Dwtkw/TSoKmND078eVCRtwMZeDweMTbU8SVInNLkwAN/fyhmjgLOA6dtz+xjjpSGEk4BPAN8EHosxTgoh9AFmhRD+Xn/qAcAhQB5JyP7dGOMhIYT/BSaQtFG/N8Z4PUAI4afARcDvG73kdcClMca5IYQjgT+RrIG6VRn9CS/G+BfgL432NRnAxxgnZrIWSVLmfbiqjCfnJrPen3t/Fesqa5o8r3/vXI4f2Z/jRvXn2L2KKMrffEHV+77ycf70+Ps89NoSYoQe3bK5YeJojhnRvz2+DKldfWq/Xbn14iOZNPVFisureWruSs677nluvPBw+rvY8LYZ9Vk4/dpk0VYizLwS8grh4PNbvFTqNB7/+eYhfEOzroP9z4Ddj27fmtR1xAgPXAFVZU0cq03W4Rj5Wcjt3f61SZI6s8HA3i2ccyLbGcQ38hng1BDCt+rHecCGRUf/GWMsBUpDCMXAg/X7XwcOrN8+oD6A7wP0JplkvlEIoTdwDHBXgzVPW/WDm1OtJEnbrbSimufeX8VTc1fy5NwVfLhqfZPndc/OYvTwvhw/qojjRxaxz8D8rbaX2XdQAb8/7xDeWFzMvJVlDCzMM4RXl3bY7v2457KjmXDDLJYUV/D64mLGXv0s0yYdwe679Gr5BtrkY2dCZQk89O/J+P7LoXtv2O/UdOuS2kJdLbxy29bPmT4GcvPbpx51PXU1Seuj5lSWwtsPw0HntF9NkqSuoDWLwrXVuqABGBtjfGezncnM9cpGNVU22N6Qk08FxsQYX61vZ3NCo/tnAWtjjAdva2EG8ZKkVquri7yxpJgn313Bk++u5OUFa6ipa/p75YiiXhuD9yP37EfP7n7LkbZmrwH53POVY/jilFm8+9E6Ply1nrFXP8vUC4/ggN0K0y6vcxk9CSpK4O8/hFgH91wEuXfAiBY/LSp1LHW1sHwOLHwBFs6CBc9DdRMzlRuqqUgeUqaULk27AklS57OMZNb5x7ZyziNbObYtHgGuCCFcEWOMIYRDYoz/2obr84GlIYRuwDiStU83ijGWhBDmhRDOijHeFZJp8QfGGF9t6camIpKkrVpWXMGTc5MFVp+eu4I166ubPK8gL4djR/bn+JFFHDeqiN369GjnSqXOb1BhD+768jF8adpsZs9fw8p1VZxz7XNcO340x470UyHb5NhvQMVaePp/obYKbh8HE+6HoUekXZnUvPI1sOjFJHRf+AIsfgmq1m3bPXoVQeHQzNSnrq+mHJa/tfVz6ppuPShJUrMmF0cmF/4IuLuZM14H7mujV/sJSa/31+pD8vnA57fh+h8ALwAf1tfV1EcNxwFXhxC+D3QDbgcM4iVJ26aiupZZ81Yns97nruDdj5oOALICHDKsL8eN7M/xo4o4aEgfsrfSbkZS6xT27Mb0i47ka7f9i0fnfERZVS0XTp3Fr88+mFMPGpx2eZ3LJ38IFcXw4hSoXg+3nAkTH4aBW5uII7WTujpY9V4SuC+alYTvK97e+jW9iiC3AFa/3/w54+/zz7h2zJ8/nfyZbM4/f5a8QXTC9yDHtUwkSa00ufgeJhdeBPwS6NfgyD+A8UwurtqR28cYhzcYfrmJ41NJ2s5scX7DYzHGq4Grm7h+coPtecBJ21qjQbwk7eRijLz70TqemruCJ95dwax5q6msabp92259enD8qCL+bVR/jh7Rn8Ie3dq5WmnnkNctm6svOIwf3P8Gt76wgOrayNdu+xcrSiu56Ng90i6v8wgBTv510tP49buSUH766TDpEdhlRNrVaWdTuQ6WvLypzcyi2VvvxR2yYNf9YcgRMPTI5NMcfYcnbyrdPBYWPLflNZ/+sSG8dtyYP8HUz8O6ZZvvD1lJu69Yl3za6J2ZcPrVMPiQdOqUJHU+k4unMLnwNuCTQCHwCpOL30y5qnZjEC9JO6HVZVU8/d5Knnx3BU/NXcFHJZVNntezezZH77nLxlnve/TvRYNVwSVlUHZW4GdjDmBAfi6//ftcAH7y0ByWl1Zw5Un7+HextbKyYMzVSRj/7kwoWwHTToNJM6FwSNrVqauKEdZ+WN9ipr7NzEdvJAFmc/IK60P3+sduhzW96Gr3XkmbpVdvg79embQS6d4bxt0Fux+Tua9JO4/+I+HSp+HFG6h8/FfkUk0pvcj/6lPw4bMw83tQVQor3oLrPwnHfxuO/xZkO0FDktQKk4vLgYfSLiMNBvGStBOorq3j5Q/X8NTclTw5dwWvLy4mNrMe+f6DCzh+VBHHjezPYbv3JTcnu32LlbRRCIFvfGoURfm5/GDGG9RFuPaJD1hRWsn/jD2QbtlZaZfYOWR3g7Omwi1nwfynoHghTBuThPG97L2vNlBdAUtfrZ/t/kIy233dR1u/pv+o+tD9yCSA7z8qeeOoNXJy4bCJ8Mz/JW1qeu9qCK+21bsITriS5U/cxNC4hLWhkPxdRiSfJtrzBHjgq/DB4xBr4Ymr4J2/wOnXJJ/ikCRJTTKIl7Tdxt/wAovWlDOkbw+mX3Rk2uWokQ9XlfHkuyt44t2VPP/BKtZVNr2wVv/euRxfP+P943v1pyjfXp9SRzPuyN3p3zuXK277F1U1ddz78mJWraviT+MOpVeu/51rlW494Lzb4KZTk/Ygq+YmbWomPpTMRJa2RcnSTYH7wheSEL52K21Nu/WCIYdtajMzZDT07Nf8+VJH1mcojJ8BL94Aj/4XVJfBstfg2n+DT3wPjvk6ZPu9SZKkxvzuKGm7LVpTzryVZWmXoXqlFdU89/4qnpy7giffXcmC1eubPK97dhaH79GX40YWcfzIIvYdlG+LC6kT+Oz+A7nlS0dy0dTZlFTU8MS7Kzj/+ueZMvFwduntG2itkpsPF9wDN34uWRRz2Wtw6zlwwb3QvWfa1amjqq1O2spsaDGzcDYUL9j6NX1239TXfegRMGB/g0l1LSHA4V+CEZ+E+y+HD5+Bumr4x4/h7YdhzDVQNCrtKiVJ6lD836AkdVK1dZE3FhfX93lfycsL1lBT13S/mb0G9N7Y5/3IPfrRs7v//Eud0eHD+3HXpcfwxSmzWFZSwauLijnzmueYNukIhvYzSG6Vnv1g/H0w5aSkh/eC5+DO8XDubZDTPe3q1BGUrdo0033hrOQTFNVNv7kNQHZusljl0MM3tZnJ37X96pXS1G8P+OJD8MI18I8fQU0FLH4Jrj0OTvwBHHUZZNnmUNL/Z+++w6Oq0/ePv2cmvZNGSwgJhCZIC52gqKzYAMWuiNQFV3dX112/u/5cd92iu7pFXUGlY0PFAoq9JvQmvQRIISFAGullkpnz+2MCZCQgJZNJuV/X5RU4nzMzT7xCJrnPc56PiICCeBGRZuVYUWVtx3suaw7mcaK8ut7zgn09Gdk1nMT4cBK7RdAxxLeRKxURV+neLpD3HnCE8QdzSknLK+OWuWtZMmUwvToEubu85iGog2Ozy4VjofQYHPwK3p8Bty5UYNTa2O2OuyPqjpnJP3juxwS2d57t3v5yx8x2kdbKbIZhD0D8GPhgFhzZ7Ajkv3gc9n0ME+ZAaJy7qxQREXE7BfEiIk1YZbWNDWkFJKfkknQgl5TjpfWeZzGb6Bcdwqj4CBK7hdM3KgSLWeNmRFqqjiG+LJ81jKmLN7H1cCG5JVXc8co6XrlvIMO7aPPR8xIa6+iMX3w9VJyAPR/Cx0Fw0wuOkQvSMlUWO0LCk2NmsrZAVdHZzzdZoF0f5zEzwdH6GhGpT3g8TP0c1r4A3z3t2Dfh8DqYOwLGPAUJ085/Q2IREZEWSEG8iEgTYhgGKcdLSaoN3jekFWCtsdd7bscQX0Z1i+CKbuEM6xJOsK9nI1crIu4U4ufFG9OH8tBbW/lqbw4lVTXcv3AT/7mjHzdc3t7d5TUPbXvBPe/B0nFgLYWtS8E7CH72VwWtLYFhQEFqndnuGyFnD1D/GDcAfENrQ/faMTMd+oOXf6OVLNLsWTwg8RHoNhY++LljL47qcvjkUdj7EYz/H4R0cneVIiIibqEgXkTEzQrKrCQfcMx5Tz6Qy/HiqnrP8/OyMCwu7NSs99hwf22yKtLK+XpZePnegTz+wS7e3pyJ1Wbnwbe2kld6GZOHd3Z3ec1D1EC46y14/VawVcG6/4FvCIz6rbsrkwtlLYfsH5zHzJTnn+MBJojs6TxmJqyLLsKINIS2vWDGN5D8L0h6Fuw1kPY9zBkO1/4NBtynf2siItLqKIgXEWlk1ho7Pxw+UTvrPY9d2UUYZ2nO690xiMT4CEbFRzAgJgRvD80uFhFnHhYzz0zsQ2SQNy9+cxDDgCdX7ianpJJHf9ZdF+zOR+wouH0JLLsHDBt881fwDoYhM91dmZxLUdbpTvfMDXBspyPsOxvvIOg48PSYmagE8AluvHpFWhuLJ1z5f47u+A9nO+5IsZbAR790dMePexGCdAeXiIi0HgriRUQaQXpeGckHcvk+JY91h/Ios9rqPS8i0NvR8R4fwcj4cMIDtPmbiPw0k8nEb37WnchAb/64cjeGAS99e4ic4iqevqUPHhbN5P1J3a+Dm1+G92cCBnz6W/AOhH53ubsyAaixOkZc1B0zU5J97seEdnEeMxPRQ5vxirhDh34w8zv47hlY818w7HDwS5gzBK57Fi6/Xd3xIiLSKiiIFxFxgZLKatYeyicpxTFy5nBBeb3neVnMDIptw6j4CEZ1i6BHu0B1r4rIRZs0rDPhAd78atk2rDY7727JIr/Mykt3D8DXSwHkT7r8dqgscswyBljxC/AJgh43uLeu1qg0p86GqpscI2dqKs9+vocvdBxQZ8zMIPDXxsUiTYaHN1zzpOP76QezIP+A4/vtBzNh70q48T8QEOnuKkVERFxKQbyISAOw2Q12HikiuXaT1a2HC7HZ65830zUy4NSc96GxYQrHRKRBXdenPW38vZixZDMlVTV8sy+Hu+evZ+HkQbTx93J3eU3f4BlQVQxfP+UYU/Pu/XDPuxB3pZsLa8FsNY6RFVkbT4fvJ9LP/ZjgaEfYfnLMTLs+jjEYItK0RSXArGTHCLB1LwEG7PsYMtbCjf+Gy252d4UiIiIuoyBeROQiHS2qIDklj+8P5LLmYB6F5dX1nhfs68nIruGM6hbOyPgIOob4NnKlItLaDI0L451Zw5i8cCM5JVX8cLiQiS+vZenUwUS18XN3eU3fyEccnZprngebFd66GyavdARIcukqTkDW5tMjZo5sAWvp2c83e0L7vqfHzEQNhuCOjVeviDQsT1/Hhq09bnDMjj+RDhUFjgufe1bCDf8Cv1B3VykiItLgFMSLiJynymobG9IKSErJJSkllwM59YcGFrOJ/tEhjk1Wu4VzeVQIFrPGzYhI4+rZPoj3HxjOfQs3kppbRmpuGRPnrmXJ1MH0aBfk7vKaNpMJrvmzI4zfshiqy+D1iTDlE2h7mbura17sEhSosgAAIABJREFUdscIirqz3fP2n/sx/hGnO92jhzhCeE9dxBZpcWKGw6w18NWTsGm+49ju9yF9Ndz0PPS43r31iYiINDAF8SIiZ2EYBvuPl5ya874hrQBrjb3ec6Pa+DKqWwSj4iMY1iWMYF/dHi8i7hfVxo/ls4YzdfEmtmUWcry4itteXsf8+xIYEhfm7vKaNpMJbvg3VJXArvegshBeuxmmfAphXdxdXdNVVerocD81Zmaj4//d2ZjMjosb0UMcne7Rg6FNZ23cKNJaeAc4OuB73gQrHoSiTCjLgWV3Qd+7YOwz4Bvi7ipFREQahIJ4EZE68kurWH0wj6SUPJIP5JJTUlXveX5eFobFhTnC924RdA7z0yarItIkhfp78eaMIfzija18uz+XksoaJi3cyAt39mNs7/buLq9pM1vg5lccYfyBL6D0OCydAFM/02gUAMOAwgznbvfju8Co/6I1AD7BtYF7bcd7xwHgHdh4NYtI0xR3JcxeC188DluXOo5tfwtSv4fxL0LXa9xZnYiISINQEC8irZq1xs7WwydIPpBLUkoeu7KLMOrfY5XeHYMYFR9BYnwEA2Pa4OVhbtxiRUQukp+XB6/el8Dv39/J8i1ZWGvszH5jK0+N782koTHuLq9ps3jCbUvgjVshYw0UHT7dGe/fyu4qqK6Eo9ucg/eynHM/Jry7Y6579BDHf2HxYNb7p4jUwycIxr0IPcfByoeg5CiUZDtGgw2Y7Jgrrwt3IiLSjCmIF5FWxTAMMvLLSTrgmPO+7lA+ZVZbvedGBHqTGB/OFd0iGNE1nPAA70auVkSk4XhazDx76+VEBnoz57tDGAY88eEucosreXhMN93Vcy5efnDXMlhykyOIztsPr98Ckz9yBEctVfFRR+CetcnxMXsb2OvfmBwAT3+IGnh6zExUgjZcFJELFz8GHlgHnz4GO952HNu6BA59CxNegthR7q1PRETkIimIF5EWr7iymrUH8x1d7wdyySyoqPc8Lw8zgzuHkhgfzqhuEfRoF6hgSkRaFJPJxO/G9iAy0Js/f7wHw4AXvjlIbmkVfxnfGw+LOpXPyicI7n0fFl3nCOKPboO37oR732sZG4naqh1jZU51u29ydP+fS5vOp+e6Rw+ByF5g0a8XItIAfNvALa86uuM//jWU5Tq+Jy25CQb/HK55Erz83V2liIjIBdFPyiLS4tjsBjuPFJGU4uh6/yGzEJu9/nkzXSMDGBUfwahu4QyJDcPXy9LI1YqINL77R8QSHujNI29vx2qz89bGTHJLrPzv7v74eOr74Fn5h8F9H8LCa6HwsGNUzTv3wR1vgIeXu6u7MGX5zhuqHtkCNfVfqAbA4g0d+teG7oMdAXxg28arV0Rap543QqdhsOoR2POh49jGV+DglzBhLnQa6t76RERELoCCeBFpEY4WVZCcksf3B3JZczCPwvL6b50P9vVkZHw4o+LDSYyPoENIC+hiFBG5CDde3oFQPy9mvraF0qoavtp7nHvmb2DB5ARC/JpZqNyYgjrApA8dnfGlxx2buH7wc5g437G5a1Nkt0PuvtNz3bM2Qv7Bcz8msP3pTveowdD+cvDQiDYRcQP/MLh9Cex6D1b9BipOQEEqLBwLwx+E0f8PPH3cXaWIiMhPUhAvIs1ShdXGhrR8klLySD6Qy4Gc0nrPs5hN9I8OYVS3CBLjw7k8KgSLWeNmREQAhncN5+2fD+X+RZvILaliS8YJbnt5HUumDtaFynMJ6wKTPoBF10NlIex+3zG65sb/QlMYaVZZDEc2nx4zk7UFqorOfr7J4gja646ZCY5qGp+LiMhJvSdCzEjHqJr9nwAGrH0RUr6Am+dCx4HurlBEROScFMSLSLNgGAb7jpU45ryn5LExvQBrjb3ec6Pa+DKqWwSj4iMY1iWMYF/PRq5WRKT5uKxDMO/PHs59CzeSllfGgZxSJs5dy5Kpg+nWNtDd5TVdbS+De5bD0vFQXQZbFoNPMIx5qnHrMAxHZ+jJbvfMjZCzB6h/JBsAvqGOsP3kmJkO/TVrWUSah8C2cOebsH2ZYzPXqiLHvh3zx8DIh+GKx5rfqDAREWk1FMSLSJOVX1rF6oN5fJ+SS/KBPHJLquo9z8/LwvAuYbVd7xF0DvPTJqsiIhcgOtSP5bOGMXXxJrZnFXG0qJJb565l4f2DSOgc6u7ymq7oQXDXm/DGbWCzwprnwScEEh9x3WtayyH7B+cxM+X553iACSJ7Oo+ZCeuibncRab5MJuh3F8SOgpUPwaGvwbBB8nOQ8pljdnz7y91dpYiIyBkUxItIk2GtsbP18AnHJqsHctl1pPis5/buGFS7yWoEAzq1wcvD3IiVioi0PGEB3rw5Yyiz39hKUkouxZU13DN/Ay/e1Z+fXdbO3eU1XXFXwq2LHJu2Gjb4+s+OMTWDpl/6cxsGFGXVjpfZ5Ph4bCfYa87+GO8giEo4PWYmKsHRqS8i0tIEd4R734OtS+Dzx8FaCsd3wbzRjs74kQ+DRXfGiohI06EgXkTcxjAM0vPLSUrJJflALusO5VNmtdV7bkSgd23wHs7IruGEBWjDOBGRhubv7cGCyQk8tnwH7/9whKoaO7Ne38Lfbu7DXYM7ubu8pqvnjTBhjmPTVoBVj4LZ09G1ebJb3aj//c1JjRWO7ajtdt8AmZugJPvcjwnt4jxmJqJH0900VkSalR1ZhQQbjjFXhmFgtxuYm9peSyYTDLwf4kbDil9AerLjYuW3f4N9q+Dmlx13BYmIiDQBCuJFpFEVV1az9mA+SQdySUrJJetERb3neXmYGdw5lFHdwkmMj6BHu0CNmxERaQSeFjPP3daXiEBvXklKxW7A79/fSU5xFb+8uqu+F59N3zsdm6R++lvAgI9+6bx+Ih02LYBB004fK805vaFq5kbHyBlb/WPYAPDwhY4D6oyZGQT+4a74bESkFauw2vjVsh/4Ys9xvvEywAw1doOb56xh3uQEIgN93F3imdrEwH0rYdM8+PJJqKmAo9vglVEw+nEY/pAuUoqIiNspiBeRC2YYBpvST5BX6ggLTpRbOVpUQftg3zPOtdkNdmQVknwgj6SUXH7ILMRmr38DufjIABJru96HxIbh66Ufllu7qDa+Th9FpHGYzSZ+f31PIgK9+euqvQD856sUckoqeWp8byxNrSOyqRgy07Fx6oa59a+vegRy9kJVsSN8P5F+7ucLjq4dL1Pb7d6uj8YsiIjLPblyF1/sOX7G8e1ZRcx+fSvLZw1rmhdlzWYY8nPoeg18ONvxfdZmha+edHTHT5gL4V3dXaWIiLRiCuJF5ILY7Aa/Xb6d97ceOXWssLyaK579jhfu7MfY3u3JLqwg+UAuSSl5rD6YR1FFdb3PFezrycj4cK6Ij2BkfDgdQhS2irPXpg1xdwkirdr0xDgiAr159N3tVNsM3thwmLzSKp6/sz8+nrpYWi/7T4yg2TSv/uNmT2jf13nMTFCHhq9PROQcckuqnH7O/7EtGSfYeriQgTFtGrGqCxTWBaZ8Cutegm/+6rjTKGsjvDwSrvkTDJ7pCO1FREQamYJ4Ebkgi9ak1fvDubXGzgNvbCU61I+M/PJ6H2sxmxjQKaS26z2CPh2D1VUpItLEje/XkVB/L2a9toUyq43Pdx/nvgUbmTc5gWBfdWefIWvj+Z3nH3k6cI8eAu37gWcTHPcgIq3KjqxCas5y9+pJWzIKmnYQD44xNCN+CfE/c3THZ291jKv57DHY+xFMeAnadHZ3lSIi0sooiBeR82YYBgvXpJ113W5wRggfHerLqPgIEuMjGN41jCAfhTYiIs1NYnwEy2YOY8rijeSVWtmYXsDtL69jydTBtAtWeOzE4yf+f3gFwKzVjgCoKY52EJFWK7OgnHc2Z/3keat2HCUxPoKe7YMaoapLFNkDpn0Ja/4D3/0D7NWQsRrmDIdr/woDp+h7sYiINBoF8SJyBsMwyC2p4mBOKYdyS2s/lnEgp4TjxefYRA5H1/vo7o6O91HxEcSE+TXNGZIiInJB+kQF897s4dy3cCMZ+eXsP17CLXPWsHTaYLpGBrq7vKajx/WQuf7s65fdDKGxjVePiMhP2JZZyLzkVD7deZSfaIYHHLPir3s+mcT4cKYnxjEqPrxp/7xv8YBRv4VuY+GD2XB8J1SXwccPO7rjx70IwVHurlJERFoBBfEirVi1zc7hgnIO5ZRyMLeUQzllHMwtJTWnlJKqmot6zut7t+PFuwc0cKUiItIUxIT5s3zWcKYs3siuI8VkF1Vy68vrWDB5UNMfU9BYBkyGzQvr34jVJxhGPtzoJYmI/JjdbvDV3uPMT05jY3qB05qfl4Vya/37XQR4WyitcqwlH8gj+UAe3dsGMj0xlnH9OuDt0YT3D2nXB2Z8A0nPQvK/wLDBoW9gzjAY+wz0u1vd8SIi4lIK4kVagdKqGg45dbc7Otwz8suotp1H2wuOTveYUD/KrDaOF1ee9bzx/To2VNkiItIERQR6s2zmMGa9toXVB/MoLK/mnvnreenuAVzds627y3M/3xC4/xNHp+WBL4Da91kPb5j8sWMTQRERN6mw2nhvaxYLV6eRmlfmtBYX7s+0xFgmDohi+ZYsnv/6ANTeDGsCHrqqKw+O7spXe3N4NTmV7ZmFAOw/XsJvl+/gn5/v5/7hnblnSCdC/Lwa+TM7Tx5ecNXj0P06x+z43H1QVQwrHoC9K+Gm5yGwnburFBGRFspkGOcXwjUVCQkJxubNm91dhkiTYxgGOSVVdbrbT3e5HztHcP5j/l4WukQG0CUigC4R/nSt/XNMmD9eHmbS88q4ec4aTpRXn/HY0d0jWDB5EGZtwCoizcTo574jLa+M2HB/vn30SneX06xYa+w8+u52Vm7PBhwXbJ++uQ+3D4p2c2VNSFEWLBgDxdkQ2gV+udXdFUlL88IAKDikry/5SXmlVSxdl8Fr69LP+Dl+cGwoMxLjuLpHpNPP8dU2O0f/2otOxlEyTR2IfnLvqTXDMNiccYJ5Sal8ufc4dWMFX08LtydEMXVkLDFh/q7+1C5edSV893dY+yIYdscx3zZw/XPQe6K640WkNdM3QBdRR7xIM1Nts5ORX17b1X56fvuFjpOJDPSmS0RAbdDuT5dIx5/bBfmcc8Zj53B/3ps9nH9+tp/Pdh8DwGyCB67sykNXd1UILyLSSnh5mPnvHf2ICPRmweo0bHaD3723g5ySSn4xumvTnhfcWIKjwMPX3VWISCt2MKeUBatTeW/rEaw19lPHLWYT1/dpz4zEWC6PCqn3sZ4WM6azZDEmk4lBnUMZ1DmUtLwyFqxOZfmWLCqr7VRU21iyLoOl6zO4tlc7ZoyKa5rjyzx9YMxT0ONG+GCW46JWxQl4bxrsWQE3/gf8w91dpYiItCAK4kWaqJLKalJzy+qMknGE7hn55dSczy5KnB4nc7LD/WToHhcRQLCv50XXFhcRwMuTBnLFs9+SkV9Op1A/Hr22+0U/n4iINE9ms4knbuxF2yBv/v7JPgCe+yKFnJIqnrzpMiy6OCsi0ugMw2B9agHzklP5Zl+O05q/l4U7B3diyojORLXxa5DXiw33568T+vDImO68vj6DpevSySu1Yhjw2e5jfLb7GAM6hTBzVBxjerVreu8N0YNh1mr4+s+w4WXHsb0rIWOtI4zvNc699YmISIuhIF7EjU6Okzn44/ntFzlOpmtEQG3o7hgp0ynUMU7GVcy13Y7qehQRad1mjupCeIA3v1u+gxq7wdJ1GeSVVvHv2/vh49mEN+4TEWlBqm12Ptl5lHnJqew6Uuy01i7IhykjOnPn4E6X1JBzLqH+Xvzy6nhmjopjxbYjzEtO42BOKQBbDxcy6/WtxIT5MXVELLclROHn1YTiCC8/uO4f0PMm+PABKMyA8jx4ZxL0ud2x5hfq7ipFRKSZa0LvfCIt18lxMnW72w/VjpQpvcBxMl2dutsD6BLp/5PjZERERFztlgFRhPp78cAbWym32vhk5zHySzcyb3ICQT6uCX1ERASKK6t5e2Mmi9akkV3k3MzTq30QM0bFckOfDi5t0KnLx9PCHYM6cdvAaL5PyeXVpFTWpeYDkJFfzpMrd/PvL1O4d2gnJg/rTGSQT6PUdV46j4TZa+CLJ2DLIsexne9AWhKMewG6Xeve+kREpFlTEC/SgEoqqzmUW+a0Yeqh3IsYJxPm5xS2d40MIC7CX0GGiIg0aVd2j+StGUOZsngTBWVWNqQVcPvL61gydTBtm1LQIiLSAhwprGDR6jSWbco8o7nnyu4RzEiMY3iXMLc17JjNJkb3iGR0j0h2HSlifnIqH+04is1uUFRRzUvfHmJeUhrj+3VgemIc3dsFuqXOM3gHwk3/dXTHr3wIio9A6TF483body+M/Tv4BLu7ShERaYZcGsSbTKaxwPOABZhvGMYzP1q/B3is9q+lwGzDMLa7siaRS2UYBseLq5xGyZz8eLy46ryfx9/LUqer/WTg7u/ycTIiIiKu1Dc6hPdmD+e+hRvILKhg37ESbpmzlqXTBtMlIsDd5YmINHs7s4qYl5zKqp2OUPskL4uZCf0doXa3tk0k1K7Vu2Mw/72zP78b24PFa9N5a8NhSqpqsNrsvLsli3e3ZHFFN8fFgxFd3XfxwEnXq2H2Wvj8D7DtDcexba9D6ncw/n/QZbRbyxMRkebHZUG8yWSyAC8BY4AsYJPJZFppGMaeOqelAVcYhnHCZDJdB7wKDHFVTSIXwjFOpoyDOWV1Rslc+DiZtkHezqNkav/cNsi7afyAKSIi0sBiw/15b/Zw7l+4iT1HizlSWMGtc9ey8P5B9O/Uxt3liYg0O3a7wbf7c5iXnMr61AKntRA/TyYNjWHSsBgiA5v23UcdQnz5w/U9eeiqrry9KZNFa9I5UlgBwPcpuXyfkkvP9kHMSIzlxssbb5zOWfmGwIQ50HMcfPRLKD0OxVnw2gRImAZjngJvXWQWEZHz48qO+MHAQcMwUgFMJtMyYDxwKog3DGNtnfPXA1EurEekXsWV1aTmltXZKNUxVubwBYyT8agzTubHm6YGapyMiIi0QpGBPrz986H8/LUtrD2Uz4nyau6et4E59wxgdI9Id5cnItIsVFbb+OCHI8xPTuVQbpnTWkyYH9NHxjJxYBPb+PQ8BPp4Mj0xjsnDO/PJzqPMT05j55EiAPYeLeaRd7bzj8/2MWVELHe5cIPZ89Z9LESvh09+C7uWO45tXgCHvobxc6DzCPfWJyIizYIr3607Apl1/p7FubvdpwGfurAeacUMw+BYcSWHarvb626aeiHjZAK8PegS4X9qlMzJ7vZOoX7u79YQERFpYgJ9PFk0ZRCPvLOdVTuOUlFtY/rSzTxzSx9uS4h2d3kiIk1WQZmV19Zl8Nr6dPJKrU5rCTFtmJ4Yx5hebbGYm/cdtp4WM+P7dWRc3w5sSCtgXlIqX+/LAeB4cRXPfLqPF78+wO2Dopk6IpboUD/3FesXCrcugF7j4OOHoTwfTqTD4htg6Gy4+o/g6eu++kREpMlzZRBf308E9bYXm0ym0TiC+JFnWZ8JzATo1KlTQ9UnLZC1xs7hgpPd7c6bppZZbef9PG2DvJ02Sj0ZumucjIiIyIXx9rDw4p39iQjwZvHadGx2g98u30FuaRWzr+ii91URkTpSc0tZsDqN5VuyqKqxnzpuNsHY3u2YnhjHgBY44stkMjE0LoyhcWEczCllwepU3tt6BGuNnTKrjUVr0lmyNp3r+rRnRmIc/aJD3Fdsr/HQaTisehj2fgQYsH4OHPgCJrwM0YPcV5uIiDRprgzis4C6rU5RQPaPTzKZTJcD84HrDMPIr++JDMN4Fcf8eBISEs5vVogAMGnBBrJOVBDVxpfXprWc8fvFldW1M9vLnLrbL3acTN3QPU7jZEREWo2oNr5OH8U1zGYTT97Ui8ggb/752X4A/vnZfnKKq/jjjb0wN/OOThGRS2EYBpvST/BqUipf7zuOUefXGT8vC7cnOLrBO4W5sRu8EXWNDODpWy7nNz/rXntXQAYFZVbsBqzacZRVO44yuHMo0xNjuaZnW/e8hwREwO2vwc7l8MmjUFkI+Qdh4c9g+C9h9B/Aw7vx6xIRkSbNlUH8JiDeZDLFAkeAO4G7655gMpk6Ae8DkwzDSHFhLa1W1okK0vLKfvrEJqjuOJmDOSVOoXtOyQWOk6md1143dI8J88PTonEyIiKtWUu6SN3UmUwmHriyKxEB3vzf+zux2Q0Wr00nt7SKf9/eF28Pi7tLFBFpVDU2O5/tPsa8pFS2ZxU5rUUGejN5eGfuGdKJED8vN1XoXuEB3jw8phuzr+zCe1uzWJCcRmrt77Yb0wvYmF5AXLg/U0fGMnFAFL5ejfw+YjLB5bdB55Hw0a/gwOdg2GHNfyHlc7h5LnTo37g1iYhIk+ayIN4wjBqTyfQg8DlgARYahrHbZDLNql1/GfgjEAbMqb0tucYwjARX1SRNk7XGTkZ+WW1Xu/OmqRcyTqZdkA9dIv1PbZR68mNkoMbJiIiINBW3JUQTHuDNA29spaLaxqodRzlRZuWVSQN1R5qItAqlVTW8vSmThavTOFJY4bTWvW0gM0bFcVPf9rpAWcvH08I9Q2K4a1Anvt6Xw7zkVDamFQCQmlfG//twF//6Yj+ThnXmvmExhAc0cid6UHu4+23Y9gZ89nuoKobcvTDvahj1KCQ+Ch6t82KKiIg4c+nW6oZhfAJ88qNjL9f583RguitrkKbj5DiZg3VGyqTmlpJRUI7tAsfJ/Hh+u8bJiIiINB+je0Ty5owhTF28iRPl1aw9lM8dr6xn8dRBRAb6uLs8ERGXOFZUyaK1aby54TAllTVOa4nx4cxIjCMxPlxNRGdhNpsY06stY3q1ZXtmIfOSU/l01zFsdoMT5dW88PUBXv7+EBMHdGTayDi6RgY0XnEmE/S/F2KvgJUPQup3YNjg+3/A/k/g5leg7WWNV4+IiDRJLg3ipfU5OU7mYE5pnY1SyziYW0ruBYyTCfT2IO5UV/vpkTKdQjVORkREpCXo36kNy2cP574FGzlSWMGeo8VMnLuWpVOHEBvu7+7yREQazJ7sYuYnp7Jye7bTflaeFhPj+nZkemIsPdsHubHC5qdvdAj/u3sAmQXlLFqTztubDlNmtWGtsfPWxkze2pjJVT0imZEYx9C40Ma7uBESDZM+hM0L4Is/QnUZHNsJr1wBo38Pw38FFsUwIiKtld4B5KKcHCdzeqPU0x3uFzpOpmvt/PaT3e0aJyMiItI6dIkI4P0HhjN54Ub2HSshs6CCiXPXsuj+QfSNDnF3eSIiF80wDL5PyWV+chqrD+Y5rQX5eHDP0BjuH96ZtkG6C+hSRIf68cebevGra+JZtvEwi9akc6y4EoBv9uXwzb4cencMYkZiHNf3ad84TV0mEwyaDl2uhhW/gIw1YK+Gr5+CfatgwssQ0c31dYiISJOjIL6FKiy38uEPR8gvdXSh19jsF/U8RRXVp+a1n+xuv5hxMp3D/Z3C9q6RAcRFBBDgrS9BERGR1qxtkA9v/3wYM5duZkNaAQVlVu6at5659w7kim4R7i5PROSCVNXYWLEtm/nJqaQcL3Vaiw71ZeqIWG5PiMZfvwc1qGBfT35+RRemjIhl1c5sXk1KY+/RYgB2HSnmV8u28Y9P9zFlRCx3Do5unLGmobEw+WPY8DJ8/WeoqYQjW+CVRLjqCRg6G8zaB0BEpDXRu38L9NmuYzzyzjbK63SmZ56oYMHqNKaNjD3jfMMwOFpUyaHc0lMd7ifnuF/oOJkuTrPb/emicTIiIiLyE4J9PVkydTAPv72NT3cdo9xqY9riTTx72+Xc3D/K3eWJiPykwnIrb2w4zOK16Wf8DtUvOoQZiXFce1lbPPR7kUt5eZi5uX8UE/p1ZO2hfOYlp/Ld/lwAsosq+dsne3nh6wPcOTiaKSNi6RDi69qCzGYY9gDEj4EPZ0PWJkcg/8XjsO9jmDAHQuNcW4OIiDQZCuJbmAPHS3jwza1OswdP+svHe/C2mAkL8HIK2w/lljqF9j+lfbDPGWF714gAIjRORkRERC6Sj6eF/909gD+t3M1r6zOosRs8/PZ2ckuqmDmqi7vLExGpV0Z+GQtWp/Hu5iwqqk//TmUywZiebZk5Ko6BMW30e1IjM5lMjOgazoiu4aQcL2F+ciof/pCN1WanpKqGeclpLFyTzo2Xt2dGYhy9Owa7tqDweJj6Oax9Ab79O9iscHgdzB0BY56ChGmO0F5ERFo0BfEtzJJ16fWG8Cf9vxW7zut5PC0mOof5185sPz1SRuNkRERExFUsZhNPjb+MtkHePPdFCgB//2QfOcVV/OH6npjNCrJEpGnYklHAvKQ0Pt9zDKPOr18+nmZuGxjN1JGx2ni6iejWNpB/3tqXR6/tztK1Gby+IYPC8mpsdoMV27JZsS2boXGhzBwVx5XdIl33XmO2wMiHIf5a+HAWHN0O1eXwyaOwdyWMfwlCOrnmtUVEpElQotrCbMssvKDzT46TObVRau0c92iNkxERERE3MJlMPHhVPBGB3vz+/Z3YDZi/Oo3c0iqevbUvXh76+URE3MNmN/hi9zHmJaey9bDz713hAV5MHtaZe4bGEOrv5aYK5VwiA3149NruPDC6C8u3ZLFgdRoZ+eUArE8tYH1qAV0i/JmRGMeE/h3x8XTR/Pa2vWD615D8b0j6J9hrIC0J5gyHa/8GA+5z3FIhIiItjoL4FsbH49w/LPh4mHn8xl6OwF3jZERERKSJumNQJ8L8vfnFm1upqrGzYls2BWVW5t47UHfniUijKrfW8O5mR3B7uKDcaa1rZAAzEmMZ38+Fwa00KD8vD+4b1pl7hsTw5Z7jzEtOZUvGCQAO5Zbxf+/v5Lkv9jNpaGcmDXPRhRWLJ1z5GHQfCx/MhpzdYC2Bj34Jez+CcS9AUIeGf10REXErtRS1MGPaRYZGAAAgAElEQVR7tzvn+i0Do5g0NIbhXcKJDPJRCC8iIiJN1jW92vLmjCEE+3oCkHwgj7teXX9Bm8mLiFysnOJKnv18H8Oe/oYnV+52CuGHdwlj0f2D+OLXo7hjUCeF8M2QxWxibO92vDd7OO/NHs51vdtxcipNXqmV/3yVwvBnvubxD3aSmlvqmiLa94WZ30Lib8BUG88c/BLmDIXty3CaeyQiIs2egvgW5o5B0cRF1D+LsI2fJ7Ov0GZnIiIi0nwMjAnlvdnD6BDsA8DOI0Xc+vJaMvLL3FyZiLRU+4+V8Oi72xn5j2956dtDFFVUA+BhNjGhXwc+fmgkb84YyugeLpwnLo1qYEwb5t47kO8eHc39wzvjW3thpbLazhsbDnP1v79nxtLNbEwrwGjocNzDG67+I0z7CsK7OY5VFsEHP4dl90BpTsO+noiIuI2C+BYm0MeTZTOHckOf9ljq/FDo42nm3VnDiA71c2N10tJEtfElNtyfqDa+7i5FRERasK6Rgbz3wHC6tQ0AICO/nIlz17LrSJGbKxORlsIwDFYfyOO+hRu59r9JLN+ShdVmBxz7as0cFUfS70bz3zv707tjsJurFVfpFObHn8ZdxrrfX8Vvr+1OZKA34GhM/3LPcW5/ZR0T5qzl4x3Z1NR+fTSYqIHw8yQY9iBQ+7v8/lXw0hDY/UHDvpaIiLiFqcGv5rpYQkKCsXnzZneX0SzklVYx7sXVZBdVEhvuz7ePXunukkREREQuWlF5NdOXbmJTumOWr7+XhVcmJTAyPtzNlZ3DCwOg4BCEdoFfbnV3NdLS6Ovrkllr7Hy0PZt5yansO1bitNYxxJcpIzpzx6BoAn083VShe2X+uSfRRjaZpg5EP7nX3eU0uqoaGyu3ZTM/OY39x52/PqLa+DJ1RCy3D4pu+L1LMtbBh7PhRNrpY5fdAtc/B/5hDftaIiJn0u1eLqKO+BYsPMAbb80qFBERkRYi2M+T16YN4We92gJQZrUxZfFGVmw74ubKRKS5KaqoZu53h0j85zf85t3tTiF8n47BvHBXf77/7ZVMT4xrtSG8gLeHhdsSovns14ksmTqYxDoXfrNOVPDUx3sY9vTXPP3pXo4VVTbcC8cMg9lrYPDM08d2v++YHb9vVcO9joiINKoGvmwrIiIiIuI6Pp4W5t47kCdW7OLNDYepthn8atk2ckuqmJ4Y5+7yRKSJyywoZ+GaNN7elEm51ea0dk3PSKYnxjEkNhSTSc2AcprJZOKKbhFc0S2CPdnFzF+dykfbs6m2GZRU1vDK96ksSE5jXN8OTE+Mo1eHoEt/US9/uP5Z6HEDrHgQijKhLAeW3Q1974Kxz4BvyKW/joiINBoF8SIiIiLSrFjMJv42oTeRgd7896sDAPx11V5yS6p4bGwPbZ4oImfYllnIvORUPt15FHud6axeHmYmDohi2shYukYGuK9AaTZ6dQji37f343fX9mDJunTeWJ9BcWUNNXaD9384wvs/HGFk13CmJ8ZyRbeIS7+oE3clzF4LXzwOW5c6jm1/C1K/h3EvQvw1l/gZiYhIY1EQLyIiIiLNjslk4tfXdCMi0JsnPtyF3YBXklLJLaniH7dejqdFExhFWju73eCrvceZl5x6am+Jk0L9vZg0NIZJw2IID/B2U4XSnLUL9uGxsT14cHRX3tmcyYLVaWSdqABg9cE8Vh/Mo1vbAKYnxjG+Xwe8PS5hbKxPkCN07zkOVj4EJUehJBvemAgDJsO1fwPvwAb6zERExFUUxIuIiIhIs3XPEEeI9tBbP2CtsfP+D0fIK7My954B+Df05nkiTU1IJ+ePAkCF1cZ7W7NYsDqNtLwyp7W4cH+mJcYycUAUPtpP6ycdM0dSXW0n3zOSaHcX00T5e3swZUQsk4bG8Pnu47yanMr2zEIAUo6X8rvlO3j28/3cP7wz9wzpRIif18W/WPwYeGAdfPp/sGOZ49jWJXDoW5jwEsSOaoDPSEREXMVkGMZPn9WEJCQkGJs3b3Z3Gc3GpAUbyDpRQVQbX16bNsTd5YiIiIi4xKb0AqYt3kRxZQ0AfaOCWXj/IMLc3en6wgAoOAShXeCXW91bi0gLl1tSxWvr0nltfQYnyqud1gbHhjIzMY6rekRqfNUFGP3cd6TllREb7s+3j17p7nKaBcMw2JxxgnlJqXy59zh1IxdfTwu3JThGIcWE+V/aC+1bBR/9CspyTx8bPBOu+ZNjvryIyMXTG6WLKIgXERERkRZh/7ESJi/cyLHiSgBiw/1ZOnUw0aF+7itKQbyIyx3MKWF+chrv/3AEa4391HGL2cT1fdozIzGWy6O0qeXFUBB/adLyyliwOpXlW7KorD79tWkywbW92jFjVCwDY0Iv/gXK8uGT38DuD04fC42DCXOh09BLqFxEWjkF8S6iIF5EREREWowjhRVMXriRgzmlAEQEerN4yiAu6xDsnoIUxIu4hGEYrEvNZ35yGt/sy3Fa8/eycOfgTkwZ0ZmoNm68ENcCKIhvGAVlVl5fn8HSdenklVqd1gZ0CmFGYhw/u6wdlou9W2PX+7DqN1BRUHvABMN+AVc9AZ4+l1S7iLRKCuJdREG8iIiIiLQoheVWpi7exNbDjhm9Ad4evDppIMO7hjd+MQriRRpUtc3OJzuPMi85lV1Hip3W2gX5MGVEZ+4c3IlgX083VdiyKIhvWJXVNlZsO8K85LRTF4xP6hTqx7SRsdyWEIWf10XscVJyHD5+GPavOn0svBtMeBmiBl5i5SLSyiiIdxEF8SIiIiLS4lRYbTz01la+2uvolPWymPn3HX258fIOjVuIgniRBlFcWc3bGzNZtCaN7KJKp7Ve7YOYMSqWG/p0wMvD7KYKWyYF8a5htxt8n5LLq0mprEvNd1oL9vXk3qGdmDysM5FBF9jNbhiw42345HdQVeQ4ZrLAyF/DFY+Bh5v3TRGR5kJBvIsoiBcRERGRFqnGZufxD3bx9uZMwDGT98kbe3H/iNjGK0JBvMglOVJYwaLVaSzblElpVY3T2pXdI5iRGMfwLmGYTMoMXEFBvOvtOlLE/ORUPtpxFJv9dD7jZTEzrl8HZiTG0b1d4IU9aXE2rHwIDn51+ljb3o7Z8e0vb6DKRaQF05uqiyiIFxEREZEWyzAM/v1lCi9+c/DUsQeu7MJvr+3eOMGdgniRi7Izq4h5yams2nlmODmhfwemJ8bRre0FhpNywRTEN57swgoWr03nrQ2HKfnRRadR3SKYmRjHiK4XcNHJMGDrEvj8cbDWjsExezg640c+DBaNbxKRs1IQ7yIXMXhMRERERKR5MJlM/OZn3YkM9OaPK3djGDDnu0PklFTx9C198LRojIVIU2G3G3y7P4d5yamsTy1wWgvx82TS0BgmDYshMlCbT0rL0yHElz9c35OHrurK25syWbQmnSOFFQAkpeSSlJJLj3aBzEiM46a+5zGGyWSCgfdD3GhY8QtITwZ7DXz7N9i3Cm5+GSJ7uv4TExGRU9QRLyIiIiKtwqc7j/KrZduw2uwAjO4ewUv3DLi4TfHOlzriRX5SZbWND344wvzkVA7lljmtxYT5MX1kLBMHXuQGlnJJ1BHvPic3Jp6fnMbOI0VOa22DvLl/eCx3DznPjYntdti8AL78I1SXO45ZvGD04zD8ITBbXPAZiEgzpo54F1EQLyIiIiKtxvrUfGYs2Xzqtv9+0SEsvH8Qof5ernlBBfEiZ1VQZuW1dRm8tj6dvFKr01pCTBumJ8YxpldbLGblAe6iIN79DMNgQ1oB85JS+XpfjtOan5eFOwZFM3VELNGhfj/9ZPmH4MMHIHP96WNRgx2z48O7NnDlItKM6Y3XRRTEi4iIiEirsvdoMZMXbiSnpAqAuAh/lk4dTFSb8wgxLpSCeJEzpOaWsmB1Gsu3ZFFVYz913GyCsb3bMT0xjgGd2rixQjlJQXzTcjCnlAWrU3lv6xGsP/q3c12f9sxIjKNfdMi5n8Rug/Vz4Ou/gM3xPoiHL1zzJAz+OZg1sk1EFMS7ioJ4EREREWl1sk6Uc9/CjaTWjsGIDPRmydTB9Gwf1LAvpCBeBHB09W5KP8GrSal8ve84dX8N9fOycHuCo6u3U5gLLojJRVMQ3zTllVbV3k2SQUGZ890kgzq3YUZiHNf0bIv5XHeT5O6HD2ZBdp33ppiRMOElaNPZNYWLSHOhIN5FFMSLiIiISKtUUGZl6uJNbMssBCDQx4N59yUwNC6s4V5EQby0cjU2O5/tPsa8pFS2ZznPuY4M9Gby8M7cM6QTIX4uGg8ll0RBfNNWWW3jva1ZLEhOIzXPeX+F2HB/po2MZeKAKHy9zjID3lYDa/4L3z0D9mrHMU9/+NlfIGGqY8NXEWmN9I/fRRTEi4iIiEirVW6t4RdvbOXb/bkAeHmYef6OflzXp33DvICCeGmlSqtqeHtTJgtXp3GksMJprXvbQGaMiuOmvu3x9tAmkU2ZgvjmwW43+HpfDvOSU9mYVuC01sbPk0lDY5g0rDMRgd71P8GxXfDhLDi28/SxuNEw/n8QHOXCykWkiVIQ7yIK4kVERESkVau22fn9+ztZviULcDQAPjW+N5OGxlz6kyuIl1bmWFEli9am8eaGw5RU1jitJcaHMyMxjsT4cEzqtG0WFMQ3P9szC5mXnMqnu45hs5/Oe7w8zNzSvyPTE2PpGhl45gNrrJD8HCQ9B4bNccw7CMY+A/3uVne8SOuif/Au4uHuAkRERERE3MnTYubZWy8nMtCbOd8dwjDgiQ93kVNcySNjuikwFDkPe7KLmZ+cysrt2dTUCf88LSbG9XWEfw2+B4OInKFvdAj/u3sAmQXlLFqTztubDlNmtWGtsbNsUybLNmVyVY9IpifGMiwu7PR7nIcXjP4DdBsLH86G3H1QVQwrHoC9K+Gm5yGwnXs/ORGRZk4d8SIiIiIitRavSePPH+85tZHknYOi+euE3nhYzBf3hOqIlxbMMAy+T8llfnIaqw/mOa0F+Xhwz9AY7h/embZBPm6qUC6VOuKbv6KKapZtPMyiNekcK650WuvdMYgZiXFc36c9nnXf56or4bunYe0LYNgdx3xC4IZ/Qe+J6o4Xafn0j9xFFMSLiIiIiNTx8Y5sHnl7O1abI3y4pmdbXryr/9k3uzsXBfHSAlXV2FjxQzbzV6eScrzUaS061JepI2K5PSEaf2/dgN3cKYhvOaw1dlbtzObVpDT2Hi12WusQ7MOUEbHcMTiaIB/P0wuZG+GDWY73sZN6joMb/g0BEY1UuYi4gYJ4F1EQLyIiIiLyI2sP5jHztS2UVjlmXA+MacOCyQmE+Hld2BMpiJcW5ESZlTc2ZLBkXQa5JVVOa/2iQ5iRGMe1l7W9+DtIpMlREN/yGIbB2kP5zEtO5bvajcpPCvD24M5B0UwZGUvHEF/HQWs5fP0UbJh7+kS/cLjxP9BrXCNWLiKNSEG8iyiIFxERERGpx+7sIu5ftOlU4Ng1MoClUwfT4WQ4cT4UxEsLkJFfxoLVaby7OYuKatup4yYTjOnZlpmj4hgY00b7KbRACuJbtpTjJcxPTuXDH7JP3QUGYDGbuKFPe2YkxtEnKthxMH01fPgAFGacfoI+t8F1/wS/0EauXERcTG/oLqIgXkRERETkLDILyrlv4UbS8soAaBfkw9Jpg+nWNvD8nkBBvDRjWzIKmJeUxud7jlH310YfTzO3DYxm6shYYsP93VeguJyC+NYhp6SSpWszeH1DBoXl1U5rQ+NCmZEYx+jukZiry+DLJ2DzwtMnBLSDcS9At2sbuWoRcSEF8S6iIF5ERERE5BzyS6uYungT27OKAMcmlAvuH8SgzufRAaggXpoZm93gi93HeDU5lR8OFzqthQd4MXlYZ+4ZGkOo/wWOaZJmSUF861JurWH5liwWrE4jI7/caa1LhD/TE+O4uX9HfA5/DysehOIjp0/ody+M/Tv4BDdy1SLiAgriXURBvIiIiIjITyirqmH2G1tJSnHM0/X2MPPCXf259rJ2536ggnhpJsqqanh3cyYL16RzuMA5gOsaGcCMxFjG9+uIj+dFbFoszZaC+NbJZjf4cs9x5iWnsiXjhNNamL8X9w3rzKT+IYQm/wm2vX56MSgKxr8IXa5q3IJFpKEpiHcRBfEiIiIiIueh2mbnseU7eP8HRweg2QR/mdCbe4bEnP1BCuKlicsprmTx2nTe2HCYogrnkRTDu4QxIzGOK7pFYDbrd/LWSEG8bMk4wfzkVD7ffQx7nfjI28PMxIFRPBR1kPbfPwalx08vJkyFMX8B74DGL1hEGoLe9F3Ew90FiIiIiIg0B54WM8/d1peIQG9eSUrFbsDjH+wip7iKX18Tr40qpVnZf6yEecmprNh2hGrb6XTNw2zixsvbMz0xjt4dNWJCpLUbGNOGgTEDOZxfzsI1aby9KZOKahtVNXbe3HCYtzZ6MS7+ZZ6IWER42krHgzYvhINfw4Q50Hmkez8BEZEmREG8iIiIiMh5MptN/P76nkQEevPXVXsBeP7rA+SUVPHXCb2xqGtYmjDDMFh9MI95yWmnxiydFOjtwV1DOnH/8M50CPF1U4Ui0lR1CvPjT+Mu49fXxPPGhsMsWZtOTkkVhgErUipYwZ3MiujNw1Uv420tgMIMWHwjDJ0NVz0BXn7u/hRERNxOQbyIiIiIyAWanhhHRKA3j767nWqbwVsbD5NfWsULd/XXDG1pcqw1dj7ans285FT2HStxWusY4suUEZ25Y1A0gT6ebqpQRJqLED8vfjG6K9MTY1m5LZv5yWnsP+74vvJybm/e5W8857eU0fZ1gAHr58CBL2DCXIge7N7iRUTcTEG8iIiIiMhFGN+vI6H+Xsx6bQtlVhtf7DnOpAUbmH/fIIL9FGiK+xWVV/PmxsMsXpvG8eIqp7U+HYOZMSqO63u3w8NidlOFItJceXtYuC0hmlsHRpF0II/5yakkH8gjn2CmlD/IOPNA/uK1iGDKIP8gLLwWhv8SRv8BPLzdXb6IiFsoiBcRERERuUiJ8REsmzmMKYs3kldqZVP6CW57ZS1/Htebb/YdZ3pJJW2Byho7Pu4uVloMwzBYl5rPih+yKaywEh8ZyJ2Do4lq4xj9kFlQzoLVabyzOZNyq83psdf0jGR6YhxDYkO1r4GIXDKTycQV3SK4olsEe7KLmb86lY+2Z7PSNpz1lT152nM+V1t+AMMOa/4LKZ/DzXM54dmO/Z/NxevYVuwWHyyX3USfq+7Cw9PL3Z+StBCHU7aR/c2reBenY/WNIGTYZLonXOXusqSVMxmG8dNnNSEJCQnG5s2b3V2GiIiIiMgpGfll3LdwIxn55U7Hv/F6hDjzMVLt7Xh90Ac8cWNPhZ9ySWx2g0fe2caKbdlOxz0tJn51dTx7j5bw6a6j2Ov8muflYWbigCimjYyla2RAI1cszdno574jLa+M2HB/vn30SneXI83EsaJKlqxL5431GRRXVnOb5Xue8HiNIFMFAHbMWLHgQ7XT4/Z6Xkb0Q6sICGrjhqqlJdnw9j9I2PM0FpNz5rk+8naGzHoFk1l3gv0E/bDqIi79yjOZTGNNJtN+k8l00GQy/V896yaTyfRC7foOk8k0wJX1iIiIiIi4QkyYP8tnDScuwv+s5yxck3ZGeCpyoRasTq3366jaZvDcFyms2nk6hA/19+JXV8ez9v+u4ulb+iiEF5FG0S7Yh8fG9mDd76/myZsuY13QdVxb9U+SbH0AMGM/I4Q3DOhZvZs9i37hjpKlBUnZ+h1D9v79jBDeMGBozjts+fgVN1Um4sLRNCaTyQK8BIwBsoBNJpNppWEYe+qcdh0QX/vfEGBu7UcRERERkWYlItCbyzoEkZpbdtZz/vTRbr7Zl9OIVUnLYvDlnp/++okL92daYiwTB0Rp82ARcRt/bw+mjIhl0tAYPt99nH8lxZF7/BkmWpLPOPfkzWIDCz5h67M3YTdrrxW5OB1Kd9Z7/OTXWNCOhTBudiNWJHKaK2fEDwYOGoaRCmAymZYB44G6Qfx4YKnhmI+z3mQyhZhMpvaGYRx1YV0iIiIiIi5xrhAeoLC8mpXb1RUvrtOnYxArfjESs1l3lYtI0+BhMXPD5e25vk87vnm+LRSe/VyLyWBAWVLjFSetTnR1urtLkFbMlaNpOgKZdf6eVXvsQs/BZDLNNJlMm00m0+bc3NwGL1REREREpCEE+aiDT9yrS0SAQngRaZJMJhN+weHuLkNauRKTxrSJ+7iyI76+n/5+vDPs+ZyDYRivAq+CY7PWSy9NRERERKThjevXgXWp+af+nmVEgL32IzB5WAy/HdvDXeVJC/DY8u2s2nnsrOs39e3QiNX8//buP9avur7j+PNFb38XKVQtSOuApNaSKoU2WMCRYNko1WAkkjHZCEowuM3K3KJzW0x00alzyjYnxDBkmwQzQRYoESFC5xb5YYtgiwUyxbpatGil8kso9L0/vge9lrby43t6es99PpJv7vd7vienrzbv3J77uuf7Oeq7OQdO/bWv0os15/Vvg40XU/WrpUJG+9/9jmD2yq8Rf6GoF2j9qotYet/Hdztj35t9Ci/f+7EkoN0ifhMwd9TrOcDOn8N9LvtIkiRJY8LpxxzKlWs3sXbjzwA4e/sHfvne3IOmsnLZPGZMbvMUXH33/uULuOV7W9n66JPPeu/kBS/npPnWCxqefz/XW7hpuObOO4pbDz6LpT+6/JfbnilMn6gRtp/yMfafeVCHCTXWHX36BWz4+2tZsP07z3pvc2Yz7/S/7iCVNNDm0jTfBOYlOTzJJOBM4Jqd9rkGODsDS4Ftrg8vSZKksWryyAT+7R3Hct5vH85Lpow02/bjjMVzuOr845k1Y3LHCTXWvXLWNK7+o+N542sPYaS5YvSlMyZzwcnz+OxZi12WRtI+73Xv/Ay3HflXbMrBwKCE//aUxWx881UseN0pHafTWDd5yjReufIr3HrwWWxjOgC/qIncPnMFk867kVmz53ScUONZBvdJbengyQrgQmACcGlVfSTJ+QBVdXGSAJ8BlgOPAW+vqjV7OuaSJUtqzZo97iJJkiR17qmnd/DQ49vZf8oIk0cmdB1HPfSL7U/zyBNPceC0SUywgJc0xtSOHTz00x8zacpUpu8/s+s46qGntj/Jtq1bmHHAQUyeMq3rOGOJJxUtabWIb4NFvCRJkiRJkiS1wiK+JW0uTSNJkiRJkiRJ0rhnES9JkiRJkiRJUoss4iVJkiRJkiRJapFFvCRJkiRJkiRJLbKIlyRJkiRJkiSpRRbxkiRJkiRJkiS1yCJekiRJkiRJkqQWWcRLkiRJkiRJktQii3hJkiRJkiRJklpkES9JkiRJkiRJUoss4iVJkiRJkiRJalGqqusMz0uSB4GNXecYY14K/KTrEOot50ttc8bUNmdMbXPG1CbnS21zxtQ2Z0xtc8aen59U1fKuQ/TRmCvi9fwlWVNVS7rOoX5yvtQ2Z0xtc8bUNmdMbXK+1DZnTG1zxtQ2Z0z7CpemkSRJkiRJkiSpRRbxkiRJkiRJkiS1yCJ+fPhc1wHUa86X2uaMqW3OmNrmjKlNzpfa5oypbc6Y2uaMaZ/gGvGSJEmSJEmSJLXIK+IlSZIkSZIkSWqRRbwkSZIkSZIkSS2yiO+xJJcm2ZJkfddZ1D9J5ia5OcmGJHcneU/XmdQvSaYkuT3JXc2MfajrTOqfJBOSfCvJqq6zqH+SfD/JuiR3JlnTdR71T5KZSa5Mck9zTnZc15nUH0nmN9+/nnn8PMkFXedSfyT50+Y8f32SK5JM6TqTxrZd9WBJzmjmbEeSJV3mkyzi++0yYHnXIdRbTwF/VlULgKXAHyc5suNM6pcngDdU1VHAImB5kqUdZ1L/vAfY0HUI9dpJVbWoqvzBT234B+D6qno1cBR+P9MQVdW9zfevRcBi4DHg6o5jqSeSHAqsBJZU1UJgAnBmt6nUA5fx7B5sPXA68PW9nkbaiUV8j1XV14GtXedQP1XVA1V1R/P8YQY/+B3abSr1SQ080ryc2Dy8w7iGJskc4I3AJV1nkaTnK8lLgBOBfwGoqier6qFuU6nHlgHfraqNXQdRr4wAU5OMANOAzR3n0Ri3qx6sqjZU1b0dRZJ+jUW8pBctyWHA0cBt3SZR3zTLhtwJbAFurCpnTMN0IfA+YEfXQdRbBdyQZG2Sd3YdRr1zBPAg8Plmia1LkkzvOpR660zgiq5DqD+q6ofAJ4EfAA8A26rqhm5TSVK7LOIlvShJZgBXARdU1c+7zqN+qaqnm49DzwGOTbKw60zqhyRvArZU1dqus6jXTqiqY4BTGSzhdmLXgdQrI8AxwEVVdTTwKPAX3UZSHyWZBJwGfKnrLOqPJAcCbwYOB14BTE/yB92mkqR2WcRLesGSTGRQwl9eVV/uOo/6q/mo/Wq874WG5wTgtCTfB74IvCHJF7qNpL6pqs3N1y0M1lU+tttE6plNwKZRnxa7kkExLw3bqcAdVfXjroOoV04G7q+qB6tqO/Bl4PiOM0lSqyziJb0gScJgTdINVfWprvOof5K8LMnM5vlUBifr93SbSn1RVR+oqjlVdRiDj9vfVFVehaWhSTI9yf7PPAd+l8HNwqShqKofAf+XZH6zaRnwnQ4jqb9+H5el0fD9AFiaZFrzs+UyvOG0pJ6ziO+xJFcAtwDzk2xKcm7XmdQrJwB/yOAq0jubx4quQ6lXDgFuTvJt4JsM1ohf1XEmSXquZgP/k+Qu4Hbguqq6vuNM6p93A5c3/1cuAj7acR71TJJpwO8wuFpZGprm0zxXAncA6xj0U5/rNJTGvF31YEnekmQTcBxwXZKvdptS41mqqusMkiRJkiRJkiT1llfES5IkSZIkSZLUIot4SZIkSZIkSZJaZBEvSZIkSZIkSVKLLOIlSZIkSZIkSWqRRbwkSZIkSZIkSS2yiJckSZIkSZIkqUUW8ZIkSRp3kqxMsiHJ5S/yOB9OcpI29WMAAALZSURBVHLzfHWSJUPKd0mSI4dxLEmSJEndS1V1nUGSJEnaq5LcA5xaVfcP8ZirgT+vqjXDOqYkSZKkfvCKeEmSJI0rSS4GjgCuSfL+JN9I8q3m6/xmn3OS/GeSa5Pcn+RPkry32e/WJAc1+12W5K07Hf/cJJ8e9fq8JJ/aTZbpSa5LcleS9Ul+r9m+OsmSJKclubN53Jvk/ub9xUn+K8naJF9Nckg7/1qSJEmShsEiXpIkSeNKVZ0PbAZOAi4CTqyqo4EPAh8dtetC4G3AscBHgMea/W4Bzt7DH/FF4LQkE5vXbwc+v5t9lwObq+qoqloIXL9T1muqalFVLQLuAj7ZHPefgLdW1WLg0iafJEmSpH3USNcBJEmSpA4dAPxrknlAARNHvXdzVT0MPJxkG3Bts30d8NrdHbCqHk1yE/CmJBuAiVW1bje7r2NQrn8cWFVV/72rnZK8D3i8qv45yUIGvyS4MQnABOCB5/j3lSRJktQBi3hJkiSNZ3/DoHB/S5LDgNWj3nti1PMdo17v4DefR18C/CVwD7u/Gp6qui/JYmAF8LdJbqiqD4/eJ8ky4AzgxGc2AXdX1XG/IYMkSZKkfYRFvCRJksazA4AfNs/PGdZBq+q2JHOBY9jD1fNJXgFsraovJHlk5wxJfgv4LLC8qh5vNt8LvCzJcVV1S7NUzauq6u5h5ZckSZI0XBbxkiRJGs8+wWBpmvcCNw352P8BLKqqn+1hn9cAf5dkB7AdeNdO758DzAKubpah2VxVK5obxP5jkgMYnNNfCFjES5IkSfuoVFXXGSRJkqTeSbIK+HRVfa3rLJIkSZK6tV/XASRJkqQ+STIzyX0Mbq5qCS9JkiTJK+IlSZKktiWZBeyqlF9WVT/d23kkSZIk7V0W8ZIkSZIkSZIktcilaSRJkiRJkiRJapFFvCRJkiRJkiRJLbKIlyRJkiRJkiSpRRbxkiRJkiRJkiS16P8B0xoNpBeSM44AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1510.5x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# To get the full family size of a person, added siblings and parch.\n",
    "#fig, axes = plt.subplots(nrows=1, ncols=1,figsize=(15, 5))\n",
    "titanic_train['family_size'] = titanic_train['SibSp'] + titanic_train['Parch'] + 1 \n",
    "_ = sns.factorplot('family_size','Survived', hue = 'Gender', data=titanic_train, aspect = 4)\n",
    "#separate for male and female\n",
    "_ = sns.factorplot('SibSp','Survived',data=titanic_train,aspect=4)\n",
    "#all passengers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "nesnayJzPK4f"
   },
   "source": [
    "- Assumption: the less people was in your family the faster you were to get to the boat. The more people they are the more managment is required. However, if you had no family members you might wanted to help others and therefore sacrifice\n",
    "- The females traveling with up to 2 more family members had a higher chance to survive. However, a high variation of survival rate appears once family size exceeds 4 as mothers/daughters would search longer for the members and therefore the chanes for survival decrease\n",
    "- Alone men might want to sacrifice and help other people to survive"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "zI5hX-v9PK4h"
   },
   "source": [
    "# General overview of all variables vs survival"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 626
    },
    "colab_type": "code",
    "id": "CQJLXcYiPK4k",
    "outputId": "f48bb8a1-7386-4bd2-c537-3e920a3c9550"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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r6mTgVcDFQw+UbGuuybT30KFDR5tdkjSPhRaX7kyyE3g+cH+HeSRJk+mox4m+a2mcCWwEzk6ycZZ+7wCuXry4kqQl1ubzxAxwYt/2OuDgbJ2r6lrgaUlOGLJvZ1VNV9X0mjVrjiK2JGkhFlpcegbw/9Kbznpnkku9E4MkqU+bcWLea2k0LgDeB9yzmIElSUuqzTixB9iQZH2S4+jdXW5Xf4ckT09zlfAkzwWOA7606OklSXNaUHGpqh6oqvdW1b8BngM8Abim02SSpInRcpwYdi2Ntf0dkqwFXg3sWMS4kqQl1macqKrDwPn0Zq7eBry3qvYn2Z5ke9PtNcAtSfbRmw37ur4LfEuSlshCL+hNkhcDr6O3fGEP8CNdhZIkTZ4W48RCrqXx68Bbq+qhgdtXD37vbcA2gKmpqYVGliQtoTafJ6pqN7B7oG1H3+N30Fs6LUkaoQUVl5LcCewD3gv8bFV9tdNUkqSJ0nKcWMi1NKaBK5vC0gnAWUkOV9UH+jtV1U56dyFienraM9aSNGb8PCFJy9tCZy6dUlVf6TSJJGmStRknvnEtDeAL9K6lcU5/h6paf+RxkncDfzpYWJIkTQQ/T0jSMjZncSnJz1XVfwF+Ocmw237+VGfJJElj71jGiao6nOTItTRWAVccuZZGs9/rLEnShPPzhCStDPPNXLqt+e/eroNIkibSMY0T811LY6D99W2+hyRppPw8IUkrwJzFpar6YPPwpqr66yXII0maII4TkqS5OE5I0srwqAX2e2eSzya5OMkzO00kSZpEjhOSpLk4TkjSMrag4lJVvQQ4AzgE7Exyc5Jf7DKYJGlyOE5IkubiOCFJy9tCZy5RVX9XVb8JbKd3G9GLOkslSZo4jhOSpLk4TkjS8rWg4lKSf5nkbUluAS4FPgms6zSZJGliOE5IkubiOCFJy9t8d4s74v8B3gP8QFUd7DCPJGkyOU5IkubiOCFJy9i8M5eSrAL+Z1X9xtEOBEk2J7k9yYEkFw7ZvyXJTUn2Jdmb5EVHc3xJ0ugdyzghSVr+HCckafmbt7hUVQ8BT0py3NEcuBlELgPOBDYCZyfZONDto8ApVXUq8G+Bdx3N95AkjV7bcUKStDI4TkjS8rfQZXGfB65Lsgv46pHGqnrnHM/ZBByoqjsAklwJbAFu7Xv+/X39jwdqgXkkSeOlzTghSVo5HCckaRlbaHHpYPP1KODxC3zOWuDuvu0Z4PTBTkleDfwK8GTgFQs8tiRpvLQZJyRJK4fjhCQtYwsqLlXVf25x7Aw71JBjvx94f5LvAy4Gvv8RB0q2AdsApqamWkSRJHWp5TghSVohHCckaXlbUHEpyV8wvDD00jmeNgOc2Le9jt7ZiqGq6tokT0tyQlXdO7BvJ7ATYHp62qVzkjRmWo4TkqQVwnFCkpa3hS6Le0vf48cArwEOz/OcPcCGJOuBLwBbgXP6OyR5Or07R1SS5wLHAV9aYCZJ0vhoM05IklYOxwlJWsYWuizu+oGm65JcM89zDic5H7gaWAVcUVX7k2xv9u+gN6icl+SfgQeA11WVM5MkacK0GSckSSuH44SWk2u+78WjjjDUi6/1n5RGZ6HL4r69b/NRwDTwlPmeV1W7gd0DbTv6Hr8DeMeCkkqSxlbbcUKStDI4TkjS8rbQZXHX88010oeBvwXe0EUgSdJEcpyQJM3FcUKSlrE5i0tJTgPurqr1zfaP01vK9rfArZ2nkySNNccJSdJcHCckaWV41Dz7/2/gQYAk3wf8CvC7wH00d2+TJK1ojhOSpLk4TkjSCjDfsrhVVfXl5vHrgJ1V9T7gfUn2dRtNkjQBHCckSXNxnJCkFWC+mUurkhwpQL0M+FjfvoVer0mStHw5TkiS5uI4IUkrwHx/0N8DXJPkXuAB4BMASZ5ObyqrJGllc5yQJM3FcUKSVoA5i0tV9ctJPgp8J/CRqjpyh4dHARd0HU6SNN4cJyRJc3GckKSVYd6pqFX16SFtn+smjiRp0jhOSJLm4jghScvffNdckiSpM0k2J7k9yYEkFw7ZvyXJTUn2Jdmb5EWjyClJkiRpdl5ET5I0EklWAZcBLwdmgD1JdlXVrX3dPgrsqqpK8mzgvcDJS59WkiRJ0mycuSRJGpVNwIGquqOqHgSuBLb0d6iq+/uuz3E8UEiSJEkaKxaXJEmjsha4u297pml7mCSvTvJZ4M+Af7tE2SRJkiQtkMUlSdKoZEjbI2YmVdX7q+pk4FXAxUMPlGxrrsm099ChQ4scU5IkSdJcLC5JkkZlBjixb3sdcHC2zlV1LfC0JCcM2bezqqaranrNmjWLn1SSJEnSrCwuSZJGZQ+wIcn6JMcBW4Fd/R2SPD1JmsfPBY4DvrTkSSVJkiTNyuKSJGkkquowcD5wNXAb8N6q2p9ke5LtTbfXALck2UfvznKv67vAtyRpmUuyOcntSQ4kuXDI/nOT3NR8fTLJKaPIKUkr3epRB5AkrVxVtRvYPdC2o+/xO4B3LHUuSdLoJVlF78TCy+ktpd6TZFdV3drX7U7gxVX190nOBHYCpy99Wkla2SwuSTpmL/ytF446wrJ13QXXjTqCJEmjsgk4UFV3ACS5EtgCfKO4VFWf7Ov/aXrX75MkLTGXxUmSJEkaR2uBu/u2Z5q22bwB+FCniSRJQzlzSZIkSdI4ypC2odfdS/ISesWlF82yfxuwDWBqamqx8kmSGs5ckiRJkjSOZoAT+7bXAQcHOyV5NvAuYEtVDb2jaFXtrKrpqppes2ZNJ2ElaSWzuCRJkiRpHO0BNiRZn+Q4YCuwq79DkingKuDHqupzI8goScJlcZIkSZLGUFUdTnI+cDWwCriiqvYn2d7s3wFcBDwJuDwJwOGqmh5VZklaqSwuSZIkSRpLVbUb2D3QtqPv8RuBNy51LknSw7ksTpIkSZIkSa1ZXJIkSZIkSVJrFpckSZIkSZLUmsUlSZIkSZIktWZxSZIkSZIkSa11WlxKsjnJ7UkOJLlwyP5zk9zUfH0yySld5pEkSZIkSdLi6qy4lGQVcBlwJrARODvJxoFudwIvrqpnAxcDO7vKI0mSJEmSpMXX5cylTcCBqrqjqh4ErgS29Heoqk9W1d83m58G1nWYR5IkSZIkSYusy+LSWuDuvu2Zpm02bwA+1GEeSZIkSZIkLbLVHR47Q9pqaMfkJfSKSy+aZf82YBvA1NTUYuWTJEmSJEnSMeqyuDQDnNi3vQ44ONgpybOBdwFnVtWXhh2oqnbSXI9penp6aIFKkiRJkiTN7tL/8MFRRxjq/F/7oVFH0DHqclncHmBDkvVJjgO2Arv6OySZAq4CfqyqPtdhFkmSJEmSJHWgs5lLVXU4yfnA1cAq4Iqq2p9ke7N/B3AR8CTg8iQAh6tquqtMkiRJkiRJWlxdLoujqnYDuwfadvQ9fiPwxi4zSJIkSZIkqTtdLouTJEmSJEnSMmdxSZIkSZIkSa1ZXJIkjUySzUluT3IgyYVD9p+b5Kbm65NJThlFTkmSJEmzs7gkSRqJJKuAy4AzgY3A2Uk2DnS7E3hxVT0buBjYubQpJUmSJM3H4pIkaVQ2AQeq6o6qehC4EtjS36GqPllVf99sfhpYt8QZJUmSJM3D4pIkaVTWAnf3bc80bbN5A/ChThNJkiRJOmqrRx1AkrRiZUhbDe2YvIRecelFs+zfBmwDmJqaWqx8kiRJkhbAmUuSpFGZAU7s214HHBzslOTZwLuALVX1pWEHqqqdVTVdVdNr1qzpJKwkSZKk4SwuSZJGZQ+wIcn6JMcBW4Fd/R2STAFXAT9WVZ8bQUZJkiRJ83BZnCRpJKrqcJLzgauBVcAVVbU/yfZm/w7gIuBJwOVJAA5X1XSXuZ73s7/X5eE1Ya6/5LxRR5AkSRp7FpckSSNTVbuB3QNtO/oevxF441LnkiRJkrRwLouTJEmSJElSaxaXJEmSJEmS1JrFJUmSJEmSJLVmcUmSJEmSJEmtWVySJEmSJElSaxaXJEmSJEmS1JrFJUmSJEmSJLVmcUmSJEmSJEmtWVySJEmSJElSaxaXJEmSJEmS1JrFJUmSJEljKcnmJLcnOZDkwiH7T07yqSRfS/KWUWSUJMHqUQeQJEmSpEFJVgGXAS8HZoA9SXZV1a193b4M/BTwqhFElCQ1nLkkSZIkaRxtAg5U1R1V9SBwJbClv0NV3VNVe4B/HkVASVKPxSVJkiRJ42gtcHff9kzTJkkaMxaXJEmSJI2jDGmrVgdKtiXZm2TvoUOHjjGWJGmQxSVJkiRJ42gGOLFvex1wsM2BqmpnVU1X1fSaNWsWJZwk6ZssLkmSJEkaR3uADUnWJzkO2ArsGnEmSdIQnRaXvHWoJEmSpDaq6jBwPnA1cBvw3qran2R7ku0ASZ6SZAb498AvJplJ8oTRpZaklWl1Vwf21qGSJEmSjkVV7QZ2D7Tt6Hv8d/SWy0mSRqjLmUveOlSSJEmSJGmZ67K45K1DJUmSJEmSlrkui0veOlSSJEmSJGmZ67K45K1DJUmSJEmSlrkui0veOlSSNCfvKipJkiRNvs7uFldVh5McuXXoKuCKI7cObfbvSPIUYC/wBODrSX4G2FhVX+kqlyRpPHhXUUmSJGl56Ky4BN46VJI0p2/cVRQgyZG7in6juFRV9wD3JHnFaCJKkiRJmk+Xy+IkSZqLdxWVJEmSlgGLS5KkUfGuopIkSdIyYHFJkjQq3lVUkiRJWgYsLkmSRsW7ikqSJEnLQKcX9JYkaTbeVVSSJElaHiwuSZJGxruKSpIkSZPPZXGSJEmSJElqzeKSJEmSJEmSWrO4JEmSJEmSpNYsLkmSJEmSJKk1i0uSJEmSJElqzeKSJEmSJEmSWrO4JEmSJEmSpNYsLkmSJEmSJKk1i0uSJEmSJElqzeKSJEmSJEmSWrO4JEmSJEmSpNYsLkmSJEmSJKk1i0uSJEmSJElqzeKSJEmSJEmSWrO4JEmSJEmSpNYsLkmSJEmSJKk1i0uSJEmSJElqzeKSJEmSJEmSWrO4JEmSJEmSpNYsLkmSJEmSJKk1i0uSJEmSJElqzeKSJEmSJEmSWuu0uJRkc5LbkxxIcuGQ/Unym83+m5I8t8s8kqTx4jghSZqL44QkTYbOiktJVgGXAWcCG4Gzk2wc6HYmsKH52gb8dld5JEnjxXFCkjQXxwlJmhxdzlzaBByoqjuq6kHgSmDLQJ8twO9Vz6eBJyb5zg4zSZLGh+OEJGkujhOSNCG6LC6tBe7u255p2o62jyRpeXKckCTNxXFCkibE6g6PnSFt1aIPSbbRm+YKcH+S248x27g6Abh31CEWKr/646OOMA4m6nfGLw37J7ciTczvLT91VL+zp3aVoyOOE+NtYv6ddMmxbmz4eoTFGMcdJ3q6HCcW7bU6gr8/i/fvbOnfcy7e//eje++1GBbv/3smN/sF71yMoxyVSR5Xuszeepzosrg0A5zYt70OONiiD1W1E9i52AHHTZK9VTU96hxaOH9nk8nf29hwnBhj/jvROPH1uGJN3Dgxya9Vs4+G2UfD7Iuvy2Vxe4ANSdYnOQ7YCuwa6LMLOK+5y8Pzgfuq6osdZpIkjQ/HCUnSXBwnJGlCdDZzqaoOJzkfuBpYBVxRVfuTbG/27wB2A2cBB4B/An6iqzySpPHiOCFJmovjhCRNji6XxVFVu+n9we9v29H3uIA3d5lhwrikY/L4O5tM/t7GhOPEWPPficaJr8cVagLHiUl+rZp9NMw+GmZfZOn9PZYkSZIkSZKOXpfXXJIkSZIkSdIyZ3FpDCS5Isk9SW4ZdRYtTJITk/xFktuS7E/y06POpLkleUySzyS5sfmd/edRZ5ImSZIzkvzpqHNoMiX5qWbM/MOOjv+2JG/p4tjSQiXZnOT2JAeSXDjqPAs1yZ9FJvk9+aS/N02yKslfT9p7gyR/m+TmJPuS7B11nqOR5P9oXiu3JHlPkseMOlM/i0vj4d3A5lGH0FE5DPyHqvqXwPOBNyfZOOJMmtvXgJdW1SnAqcDm5q4ykqTu/e/AWVV17qiDSF1Isgq4DDgT2AicPUHvDd/N5H4WmeT35JP+3vSngdtGHaKll1TVqVU1PeogC5VkLfBTwHRVPYveTQ62jjbVw1lcGgNVdS3w5VHn0MJV1Rer6uM1+FwAACAASURBVIbm8T/S+8O6drSpNJfqub/ZfHTz5UXntKIkOSnJZ5O8qznr9YdJvj/JdUn+Jsmm5uuTzdnITyZ5xpDjHN+c6d7T9Nsyip9HkyHJDuC7gF1JfmHYayfJ65N8IMkHk9yZ5Pwk/77p8+kk3970+8nmuTcmeV+Sxw35fk9L8uEk1yf5RJKTl/Yn1gq1CThQVXdU1YPAlcBE/G2c5M8ik/yefJLfmyZZB7wCeNeos6wwq4HHJlkNPA44OOI8D2NxSTpGSU4CngP81WiTaD7N9N19wD3An1eVvzOtRE8HfgN4NnAycA7wIuAtwH8EPgt8X1U9B7gI+L+GHOMXgI9V1WnAS4BLkhy/BNk1gapqO703wC8Bjmf2186z6L0eNwG/DPxT8zr8FHBe0+eqqjqtOdN/G/CGId9yJ3BBVT2P3uv68m5+Mulh1gJ3923PMCFFjuViEt+TT/B7018Hfg74+qiDtFDAR5oTENtGHWahquoLwK8CdwFfBO6rqo+MNtXDrR51AGmSJfkW4H3Az1TVV0adR3OrqoeAU5M8EXh/kmdV1cRdX0A6RndW1c0ASfYDH62qSnIzcBLwrcDvJtlA7w3Yo4cc4weAf913jZvHAFNM7vR4LZ3ZXjsAf9HMPPjHJPcBH2zab6ZXDAV4VpL/E3gi8C3A1f0Hb8blfwX8UZIjzf9bFz+INCBD2iZiFspyMKnvySfxvWmSVwL3VNX1Sc4YdZ4WXlhVB5M8GfjzJJ9tZu+NtSTfRm825HrgH+iNcz9aVX8w2mTfZHFJainJo+kNYn9YVVeNOo8Wrqr+IcnH6V1fYKwHcKkDX+t7/PW+7a/Te19wMb0P+a9uzgJ/fMgxArymqm7vLqaWqaGvnSSnM/9rE3rXhnlVVd2Y5PXAGQPHfxTwD1V16uLGluY1A5zYt72OMVuyslwth/fkE/be9IX0ThKcRe8EwROS/EFV/eiIcy1IVR1s/ntPkvfTmy079sUl4PvpnSA8BJDkKnonU8amuOSyOKmF9E6H/jfgtqp656jzaH5J1jRnhUjyWHp/oD872lTSWPpW4AvN49fP0udq4ILmbyFJnrMEubQ8HOtr5/HAF5sPk4+4OHgzY+HOJD/cHD9JTjnGzNJC7AE2JFmf5Dh6F9rdNeJMy94kvyef1PemVfXzVbWuqk6i9zr/2KQUlpprRj7+yGN6s2nHvZh3xF3A85M8rnndv4wxmzFucWkMJHkPvesJPCPJTJJh1w/QeHkh8GPAS9O7jeW+pnqv8fWdwF8kuYneG8A/r6qJunWqtET+C/ArSa6jdyeSYS6mt1zupvRuXX3xUoXTxDvW185/onc9lT9n9g9h5wJvSHIjsJ8JuaiyJltVHQbOp1dAvQ14b1XtH22qhZnwzyKT/J7c96ZL7zuAv2zGh88Af1ZVHx5xpgVprsf1x8AN9JaLP4reNQbHRqpcCixJkiRJkqR2nLkkSZIkSZKk1iwuSZIkSZIkqTWLS5IkSZIkSWrN4pIkSZIkSZJas7gkSZIkSZKk1iwuSUCSh5pbl96S5I+SPG6Ovm9L8palzCdJWnpJviPJf09yR5Lrk3wqyasX4bhnJPF205K0DCX5hST7k9zUfL44Pcm7kmxs9t8/y/Oen+SvmufcluRtSxpcOkarRx1AGhMPVNWpAEn+ENgOvHO0kSRJo5IkwAeA362qc5q2pwL/egRZVlfV4aX+vpKko5PkBcArgedW1deSnAAcV1VvXMDTfxf4kaq6Mckq4BldZpUWmzOXpEf6BPB0gCTnNWcdbkzy+4Mdk/xkkj3N/vcdmfGU5IebWVA3Jrm2aXtmks80ZyNuSrJhSX8qSdLReCnwYFXtONJQVZ+vqt9KsirJJc3f/5uS/Dv4xoykjyf54ySfTfKHTZGKJJubtr8E/s2RYyY5PskVzbH+OsmWpv31zUzaDwIfWdKfXJLU1ncC91bV1wCq6t6qOtiMDdNHOiX5tSQ3JPlokjVN85OBLzbPe6iqbm36vi3J7yf5WJK/SfKTS/wzSQticUnqk2Q1cCZwc5JnAr8AvLSqTgF+eshTrqqq05r9twFvaNovAn6waT9ylns78BvNDKlpYKbDH0WSdGyeCdwwy743APdV1WnAacBPJlnf7HsO8DPARuC7gBcmeQzwO8APAd8LPKXvWL8AfKw51kuAS5Ic3+x7AfDjVfXSxfuxJEkd+ghwYpLPJbk8yYuH9DkeuKGqngtcA/xS0/5fgduTvD/Jv2vGjiOeDbyC3rhwUZJ/0eHPILVicUnqeWySfcBe4C7gv9E7a/3HVXUvQFV9ecjznpXkE0luBs6l92EE4Drg3c2ZhVVN26eA/5jkrcBTq+qB7n4cSdJiSnJZMxt1D/ADwHnNuPFXwJOAI7NRP1NVM1X1dWAfcBJwMnBnVf1NVRXwB32H/gHgwuZYHwceA0w1+/58lrFHkjSGqup+4HnANuAQ8D+SvH6g29eB/9E8/gPgRc1z307vBPRHgHOAD/c950+q6oHmc8lfAJu6+hmktrzmktTzjWsuHdEsZah5nvdu4FXN2ujXA2cAVNX2JKfTO8OwL8mpVfXfk/xV03Z1kjdW1ccW+eeQJC2O/cBrjmxU1Zuba2ccOQlxQVVd3f+EJGcAX+treohvvteabTwJ8Jqqun3gWKcDXz2WH0CStPSq6iF6Jws+3pyA/vH5ntL33P8J/HaS3wEOJXnSYJ9ZtqWRc+aSNLuPAj9y5I96km8f0ufxwBeTPJrezCWavk+rqr+qqouAe+lNj/0u4I6q+k1gF73prZKk8fQx4DFJ3tTXduROolcDb2r+9pPku/uWsg3zWWB9kqc122f37bsauKDv2kzPWZT0kqQll+QZA9dVPRX4/EC3RwGvbR6fA/xl89xXHBkL6M2GfQj4h2Z7S5LHNJ9LzgD2dBBfOibOXJJmUVX7k/wycE2Sh4C/Bl4/0O0/0VsS8XngZnrFJuhdM2MDvTPSHwVuBC4EfjTJPwN/B7y98x9CktRKVVWSVwH/NcnP0Vve8FXgrcAf0VvudkPzQeAQ8Ko5jvX/JdkG/FmSe+l9kHhWs/ti4NeBm5pj/S29Ow1JkibPtwC/leSJwGHgAL0lcn/c1+erwDOTXA/cB7yuaf8xemPOPzXPPbeqHmrqTZ8B/ozesumLq+rgUvww0tFIb+m/JEmSJEkaJ0neBtxfVb866izSXFwWJ0mSJEmSpNacuSRJkiRJkqTWnLkkSZIkSZKk1iwuSZIkSZIkqTWLS5IkSZIkSWrN4pIkSZIkSZJas7gkSZIkSZKk1iwuSZIkSZIkqTWLS5IkSZIkSWrN4pIkSZIkSZJas7gkSZIkSZKk1iwuSZIkSZIkqTWLS5IkSZIkSWrN4pIkSZIkSZJas7gkSZIkSZKk1iwuSZIkSZIkqTWLS5IkSZLGTpIrktyT5JZZ9ifJbyY5kOSmJM9d6oySpB6LS5IkSZLG0buBzXPsPxPY0HxtA357CTJJkoawuCRJkiRp7FTVtcCX5+iyBfi96vk08MQk37k06SRJ/SwuSZIkSZpEa4G7+7ZnmjZJ0hJbPeoAR+uEE06ok046adQxJGnsXH/99fdW1ZpR5xg1xwlJGm4ZjhMZ0lZDOybb6C2d4/jjj3/eySef3GUuSZpIxzJOTFxx6aSTTmLv3r2jjiFJYyfJ50edYRw4TkjScMtwnJgBTuzbXgccHNaxqnYCOwGmp6fLcUKSHulYxgmXxUmSJEmaRLuA85q7xj0fuK+qvjjqUJK0Ek3czCVJkiRJy1+S9wBnACckmQF+CXg0QFXtAHYDZwEHgH8CfmI0SSVJFpckSZIkjZ2qOnue/QW8eYniSJLm4LI4SZIkSZIktWZxSZIkSZIkSa11WlxKsjnJ7UkOJLlwlj5nJNmXZH+Sa7rMI0mSJEmSpMXV2TWXkqwCLgNeTu82oXuS7KqqW/v6PBG4HNhcVXcleXJXeSRJkiRJkrT4upy5tAk4UFV3VNWDwJXAloE+5wBXVdVdAFV1T4d5JEmSJEmStMi6LC6tBe7u255p2vp9N/BtST6e5Pok53WYR5IkSZIkSYuss2VxQIa01ZDv/zzgZcBjgU8l+XRVfe5hB0q2AdsApqamOoiqleaa73vxqCM8wouv9ZJjkjSfu97+PaOOsKxNXXTzqCNIkqQJ1OXMpRngxL7tdcDBIX0+XFVfrap7gWuBUwYPVFU7q2q6qqbXrFnTWWBJkiRJkiQdnS6LS3uADUnWJzkO2ArsGujzJ8D3Jlmd5HHA6cBtHWaSJEmSJEnSIupsWVxVHU5yPnA1sAq4oqr2J9ne7N9RVbcl+TBwE/B14F1VdUtXmSRJkiRJkrS4urzmElW1G9g90LZjYPsS4JIuc0iSJEmSJKkbXS6LkyRJkiRJ0jJncUmSJEmSJEmtWVySJEmSJElSaxaXJEmSJEmS1JrFJUmSJEmSJLVmcUmSJEmSJEmtWVySJEmSJElSaxaXJEmSJEmS1JrFJUmSJEmSJLVmcUmSJEmSJEmtWVySJEmSJElSaxaXJEmSJEmS1JrFJUmSJEmSJLVmcUmSJEmSJEmtWVySJI1Mks1Jbk9yIMmFQ/afkeS+JPuar4tGkVOSJEnS7FaPOsBKd9fbv2fUER5h6qKbRx1B0gqQZBVwGfByYAbYk2RXVd060PUTVfXKJQ8oSZIkaUGcuSRJGpVNwIGquqOqHgSuBLaMOJMkSZKko2RxSZI0KmuBu/u2Z5q2QS9IcmOSDyV55tJEkyRJkrRQLouTJI1KhrTVwPYNwFOr6v4kZwEfADY84kDJNmAbwNTU1GLnlCRJkjQHZy5JkkZlBjixb3sdcLC/Q1V9parubx7vBh6d5ITBA1XVzqqarqrpNWvWdJlZkiRJ0gCLS5KkUdkDbEiyPslxwFZgV3+HJE9JkubxJnrj1peWPKkkSZKkWbksTpI0ElV1OMn5wNXAKuCKqtqfZHuzfwfwWuBNSQ4DDwBbq2pw6ZwkSZKkEbK4JEkamWap2+6Bth19jy8FLl3qXJIkSZIWzmVxkiRJkiRJas3ikiRJkiRJklqzuCRJkiRJkqTWLC5JkiRJkiSpNYtLkiRJkiRJas3ikiRJkiRJklqzuCRJkiRpLCXZnOT2JAeSXDhk/7cm+WCSG5PsT/ITo8gpSSudxSVJkiRJYyfJKuAy4ExgI3B2ko0D3d4M3FpVpwBnAL+W5LglDSpJ6ra4tIAzDWckuS/Jvubroi7zSJIkSZoYm4ADVXVHVT0IXAlsGehTwOOTBPgW4MvA4aWNKUla3dWB+840vByYAfYk2VVVtw50/URVvbKrHJIkSZIm0lrg7r7tGeD0gT6XAruAg8DjgddV1deXJp4k6YguZy4t5EyDJEmSJA2TIW01sP2DwD7gXwCnApcmecIjDpRsS7I3yd5Dhw4tflJJWuG6LC4NO9Owdki/FzQX4PtQkmd2mEeSJEnS5JgBTuzbXkdvhlK/nwCuqp4DwJ3AyYMHqqqdVTVdVdNr1qzpLLAkrVSdLYtjYWcabgCeWlX3JzkL+ACw4REHSrYB2wCmpqYWO6daeuFvvXDUER7huguuG3UESZIkLY49wIYk64EvAFuBcwb63AW8DPhEku8AngHcsaQpJUmdzlya90xDVX2lqu5vHu8GHp3khMEDeaZBkiRJWlmq6jBwPnA1cBvw3qran2R7ku1Nt4uBf5XkZuCjwFur6t7RJJaklavLmUvznmlI8hTgf1VVJdlEr9j1pQ4zSZIkSZoQzQno3QNtO/oeHwR+YKlzSZIerrPiUlUdTnLkTMMq4IojZxqa/TuA1wJvSnIYeADYWlWDS+ckSZIkSZI0prqcubSQMw2X0rt9qP7/9u4/zq67rvP4693Eyq8iLs0C5odmIVCj29YyLbAgUARt0TVWqv0liMLmkcfSArv+oCqLq112rSgP1LbEWGpFWQJSChFjq4sWkFJJqrUl/WVIKwmFJUWWXxZK0s/+cU/K7XQyc+dkzpy5M6/n4zGP3HPO9955z7mT+733M+f7/UqSJEmSJI2hLudckiRJkiRJ0iJncUmSJEmSJEmtWVySJEmSJElSaxaXJEmSJEmS1JrFJUmSJEmSJLVmcUmSJEmSJEmtWVySJEmSJElSaxaXJEmSJEmS1JrFJUmSJEmSJLVmcUmSJEmSJEmtWVySJEmSJElSaxaXJEmSJEmS1JrFJUmSJEmSJLVmcUmSJEmSJEmtWVySJEmSJElSaxaXJEmSJEmS1JrFJUmSJEmSJLVmcUmS1JskpyW5I8nuJBdO0+7kJAeTnDmf+SRJkiTNzOKSJKkXSZYBlwKnA+uBc5KsP0y7i4Fr5zehJEmSpFFYXJIk9eUUYHdV7amq+4GtwIYp2l0AXAV8bj7DSZIkSRqNxSVJUl9WAnuHtvc1+x6UZCVwBrB5HnNJkiRJmgWLS5KkvmSKfTVp+y3A66rq4LQPlGxMsjPJzv37989ZQEmSJEkzW953AEnSkrUPWD20vQq4Z1KbCWBrEoBjgRcnOVBV7xtuVFVbgC0AExMTkwtUkiRJkjpkcUmS1JcdwLoka4FPA2cD5w43qKq1h24nuRL4wOTCkiRJkqR+WVySJPWiqg4kOZ/BKnDLgCuqaleSTc1x51mSJEmSxoDFJUlSb6pqO7B90r4pi0pV9fL5yCRp7j37957dd4RF66MXfLTvCJIkOaG3JEmSJEmS2rO4JEmSJEmSpNYsLkmSJEmSJKk1i0uSJEmSJElqrdPiUpLTktyRZHeSC6dpd3KSg0nO7DKPJEmSJEmS5lZnxaUky4BLgdOB9cA5SdYfpt3FDJailiRJkiRJ0hjp8sqlU4DdVbWnqu4HtgIbpmh3AXAV8LkOs0iSJEmSJKkDXRaXVgJ7h7b3NfselGQlcAawucMckiRJkiRJ6kiXxaVMsa8mbb8FeF1VHZz2gZKNSXYm2bl///45CyhJkiRJkqQjs7zDx94HrB7aXgXcM6nNBLA1CcCxwIuTHKiq9w03qqotwBaAiYmJyQUqSZIkSZIk9aTL4tIOYF2StcCngbOBc4cbVNXaQ7eTXAl8YHJhSZIkSZIkSQtXZ8WlqjqQ5HwGq8AtA66oql1JNjXHnWdJkiRJkiRpzHV55RJVtR3YPmnflEWlqnp5l1kkSZIkSZI097qc0FuSJEmSWktyWpI7kuxOcuFh2jw/yU1JdiX50HxnlCR1fOWSJEmSJLWRZBlwKfAiBosF7UiyrapuHWrzOOAy4LSq+lSSf9tPWkla2rxySZIkSdJCdAqwu6r2VNX9wFZgw6Q25wLvrapPAVTV5+Y5oyQJi0uSJEmSFqaVwN6h7X3NvmFPBb49yXVJbkzysnlLJ0l6kMPiJEmSJC1EmWJfTdpeDjwd+AHgkcDHktxQVXc+5IGSjcBGgDVr1nQQVZKWNq9ckiRJkrQQ7QNWD22vAu6Zos01VfXVqroX+DBwwuQHqqotVTVRVRMrVqzoLLAkLVUWlyRJkiQtRDuAdUnWJjkaOBvYNqnN+4HvT7I8yaOAZwC3zXNOSVryHBYnSZIkacGpqgNJzgeuBZYBV1TVriSbmuObq+q2JNcANwMPAJdX1Sf6Sy1JS5PFJUmSJEkLUlVtB7ZP2rd50vabgDfNZy5J0kNNW1xK8mUePmneg6rqsXOeSJI0Nsa9n3j6L7y97wiL2o1vctEmSZKkpWDa4lJVHQOQ5NeBzwJ/zGDVhvOAYzpPJ0la0OwnJEmSJI06ofcPVdVlVfXlqvpSVb0VeEmXwSRJY8V+QpIkSVqiRi0uHUxyXpJlSY5Kch5wsMtgkqSxYj8hSZIkLVGjFpfOBX4S+L/N1080+yRJAvsJSZIkackaabW4qrob2NBtFEnSuLKfkCRJkpauka5cSvLUJB9M8olm+/gkr+82miRpXNhPSJIkSUvXqMPi/gD4JeAbAFV1M3B2V6EkSWPHfkKSJElaokYaFgc8qqo+nmR434EO8rTy9F94e98RHubGN72s7wiSNJ8WdD8hSZIkqTujXrl0b5InAwWQ5EzgM52lkiSNG/sJSZIkaYka9cqlVwFbgOOSfBq4Czivs1SSpHFjPyFJkiQtUaMWl/65ql6Y5NHAUVX15S5DSZLGTqt+IslpwO8Ay4DLq+o3Jh3fAFwEPMBgmN1rq+pv5za6JEmSpCMx6rC4u5JsAZ4JfKXDPJKk8TTrfiLJMuBS4HRgPXBOkvWTmn0QOKGqTgR+Frh87iJLkiRJmgujFpeeBvwfBsMe7kpySZLndBdLkjRm2vQTpwC7q2pPVd0PbAU2DDeoqq9UVTWbj6aZ00mSJEnSwjFScamq7quqd1fVjwPfBzwW+FCnySRJY6NlP7ES2Du0va/Z9xBJzkhyO/DnDK5ekiRJkrSAjHrlEkmel+Qy4O+BRwA/2VkqSdLYadFPZIp9D7syqaqurqrjgB9jMP/SVN97Y5KdSXbu379/lsklSZIkHYmRJvROchdwE/Bu4Beq6qudppIkjZWW/cQ+YPXQ9irgnsM1rqoPJ3lykmOr6t5Jx7YwWK2OiYkJh85JkiRJ82jU1eJOqKovdZpEkjTO2vQTO4B1SdYCnwbOBs4dbpDkKcAnq6qSnAQcDXx+LgJLkiRJmhvTFpeS/GJV/SbwxiRTDVV4dWfJJEkL3pH0E1V1IMn5wLXAMuCKqtqVZFNzfDPwEuBlSb4B3AecNTTBtyRJkqQFYKYrl25r/t3ZdRBJ0lg6on6iqrYD2yft2zx0+2Lg4tbpJEmSJHVu2uJSVf1Zc/PmqvqHecgjSRoj9hOSJEmSRl0t7s1Jbk9yUZLvGfXBk5yW5I4ku5NcOMXxDUluTnJTs8rPc0ZOLklaSFr1E5IkSZLG30jFpao6FXg+sB/YkuSWJK+f7j5JlgGXAqcD64Fzkqyf1OyDDCaBPRH4WeDy2cWXJC0EbfoJSZIkSYvDqFcuUVWfrarfBTYxWG76DTPc5RRgd1Xtqar7ga3AhkmP+ZWhiVkfDThJqySNqRb9hCRJkqRFYKTiUpLvTvLfk3wCuAS4Hlg1w91WAnuHtvc1+yY/9hlJbgf+nMHVS5KkMdOyn5AkSZK0CMy0Wtwhfwi8E/jBqrpnxPtkin1TLVN9NXB1kucCFwEvfNgDJRuBjQBr1qwZ8dtLkuZRm35CkiRJ0iIw45VLzdxJn6yq35nlB4Z9wOqh7VXAYe9fVR8Gnpzk2CmObamqiaqaWLFixSwiSJK6dgT9hCRJkqRFYMbiUlUdBB6f5OhZPvYOYF2Stc19zwa2DTdI8pQkaW6fBBwNfH6W30eS1KMj6CckSZIkLQKjDov7Z+CjSbYBXz20s6refLg7VNWBJOcD1wLLgCuqaleSTc3xzcBLgJcl+QZwH3DW0ATfkqTxMet+QpIkSdLiMGpx6Z7m6yjgmFEfvKq2A9sn7ds8dPti4OJRH0+StGC16ickSZIkjb+RiktV9WtdB5EkjS/7CUmSJGnpGqm4lORvmHqltxfMeSJJ0tixn5AkSZKWrlGHxf380O1HMJgr6cDcx5EkjSn7CUmSJGmJGnVY3I2Tdn00yYc6yCNJGkP2E5IkSdLSNeqwuH8ztHkUMAE8sZNEkqSxYz8hSZIkLV2jDou7kW/OpXEAuBt4RReBJEljyX5CkiRJWqKmLS4lORnYW1Vrm+2fZjCPxt3ArZ2nkyQtaPYTkiRJko6a4fjvA/cDJHku8L+APwK+CGzpNpokaQzYT0iSJElL3EzD4pZV1b80t88CtlTVVcBVSW7qNpokaQzYT0iSJElL3ExXLi1LcqgA9QPAXw8dG3W+JknS4mU/IUmSJC1xMxWX3gl8KMn7gfuAjwAkeQqDIQ+SpKXNfkKS1JkkpyW5I8nuJBdO0+7kJAeTnDmf+SRJA9P+Vbmq3pjkg8CTgL+sqkMrAR0FXNB1OEnSwmY/IUnqSpJlwKXAi4B9wI4k26rq1inaXQxcO/8pJUkwwpCFqrphin13dhNHkjRu7CckSR05BdhdVXsAkmwFNvDw1UgvAK4CTp7feJKkQ2YaFidJkiRJfVgJ7B3a3tfse1CSlcAZwOZ5zCVJmsTikiRJkqSFKFPsq0nbbwFeV1UHp32gZGOSnUl27t+/f84CSpIGXMlHkiRJ0kK0D1g9tL0KuGdSmwlgaxKAY4EXJzlQVe8bblRVW4AtABMTE5MLVJKkI2RxSZIkSdJCtANYl2Qt8GngbODc4QZVtfbQ7SRXAh+YXFiSJHXP4pIkSZKkBaeqDiQ5n8EqcMuAK6pqV5JNzXHnWZKkBcLikiRJkqQFqaq2A9sn7ZuyqFRVL5+PTJKkh3NCb0mSJEmSJLVmcUmSJEmSJEmtWVySJEmSJElSaxaXJEmSJEmS1JrFJUmSJEmSJLVmcUmS1JskpyW5I8nuJBdOcfy8JDc3X9cnOaGPnJIkSZIOz+KSJKkXSZYBlwKnA+uBc5Ksn9TsLuB5VXU8cBGwZX5TSpIkSZqJxSVJUl9OAXZX1Z6quh/YCmwYblBV11fVF5rNG4BV85xRkiRJ0gwsLkmS+rIS2Du0va/ZdzivAP6i00SSJEmSZm153wEkSUtWpthXUzZMTmVQXHrOYY5vBDYCrFmzZq7ySZIkSRqBVy5JkvqyD1g9tL0KuGdyoyTHA5cDG6rq81M9UFVtqaqJqppYsWJFJ2ElSZIkTa3T4pKrAEmSprEDWJdkbZKjgbOBbcMNkqwB3gu8tKru7CGjJEmSpBl0NixuaBWgFzH46/SOJNuq6tahZodWAfpCktMZrAL0jK4ySZIWjqo6kOR84FpgGXBFVe1Ksqk5vhl4A/B44LIkAAeqaqKvzJIkSZIerss5lx5cBQggyaFVgB4sLlXV9UPtXQVIkpaYqtoObJ+0b/PQ7VcCr5zvXJIkSZJG1+WwOFcBkiRJkiRJWuS6vHLJVYAkPeiNP3Vm3xGm9Ct/8p6+I0iSJEnSWOvyyiVXAZIkSZIkSVrk5ZIrqQAAEaRJREFUuiwuuQqQJEmSJEnSItfZsDhXAZIkSZIkSVr8upxzyVWAJEmSJEmSFrkuh8VJkiRJkiRpkbO4JEmSJEmSpNYsLkmSJEmSJKk1i0uSJEmSJElqzeKSJEmSJEmSWrO4JEmSJEmSpNYsLkmSJEmSJKk1i0uSJEmSJElqzeKSJEmSJEmSWrO4JEmSJEmSpNYsLkmSJEmSJKk1i0uSJEmSJElqzeKSJEmSJEmSWrO4JEmSJEmSpNYsLkmSJEmSJKk1i0uSJEmSJElqzeKSJEmSJEmSWrO4JEmSJEmSpNYsLkmSJEmSJKk1i0uSJEmSJElqzeKSJEmSpAUpyWlJ7kiyO8mFUxw/L8nNzdf1SU7oI6ckLXUWlyRJkiQtOEmWAZcCpwPrgXOSrJ/U7C7geVV1PHARsGV+U0qSwOKSJEmSpIXpFGB3Ve2pqvuBrcCG4QZVdX1VfaHZvAFYNc8ZJUlYXJIkSZK0MK0E9g5t72v2Hc4rgL/oNJEkaUrL+w4gSZIkSVPIFPtqyobJqQyKS885zPGNwEaANWvWzFU+SVLDK5ckSZIkLUT7gNVD26uAeyY3SnI8cDmwoao+P9UDVdWWqpqoqokVK1Z0ElaSljKLS5IkSZIWoh3AuiRrkxwNnA1sG26QZA3wXuClVXVnDxklSVhckiT1aIQlpo9L8rEkX0/y831klCT1o6oOAOcD1wK3Ae+uql1JNiXZ1DR7A/B44LIkNyXZ2VNcSVrSnHNJktSLoSWmX8Rg6MOOJNuq6tahZv8CvBr4sR4iSpJ6VlXbge2T9m0euv1K4JXznUuS9FBeuSRJ6ssoS0x/rqp2AN/oI6AkSZKkmVlckiT1ZbZLTEuSJElagDotLjmXhiRpGiMvMT3jAyUbk+xMsnP//v1HGEuSJEnSbHRWXBqaS+N0YD1wTpL1k5odmkvjt7rKIUlasEZaYnoULjEtSZIk9afLK5ecS0OSNJ0Zl5iWJEmStPB1uVrcVHNpPKPD7ydJGiNVdSDJoSWmlwFXHFpiujm+OckTgZ3AY4EHkrwWWF9VX+otuCRJkqSH6LK4NKdzaQAbAdasWXMkmSRJC8gIS0x/lsFwOUmSJEkLVJfD4pxLQ5IkSZIkaZHrsrjkXBqSJEmSJEmLXGfD4pxLQ5IkSZIkafHrcs4l59KQJEmSJEla5LocFidJkiRJkqRFzuKSJEmSJEmSWrO4JEmSJEmSpNYsLkmSJEmSJKk1i0uSJEmSJElqzeKSJEmSJEmSWrO4JEmSJEmSpNYsLkmSJEmSJKk1i0uSJEmSJElqzeKSJEmSJEmSWrO4JEmSJEmSpNYsLkmSJEmSJKk1i0uSJEmSJElqzeKSJEmSJEmSWrO4JEmSJEmSpNYsLkmSJEmSJKk1i0uSJEmSJElqzeKSJEmSJEmSWrO4JEmSJEmSpNYsLkmSJEmSJKk1i0uSJEmSJElqzeKSJEmSJEmSWrO4JEmSJEmSpNYsLkmSJEmSJKk1i0uSJEmSJElqzeKSJEmSJEmSWrO4JEmSJEmSpNYsLkmSJEmSJKk1i0uSJEmSJElqzeKSJEmSJEmSWuu0uJTktCR3JNmd5MIpjifJ7zbHb05yUpd5JEkLi/2EJGk69hOSNB46Ky4lWQZcCpwOrAfOSbJ+UrPTgXXN10bgrV3lkSQtLPYTkqTp2E9I0vjo8sqlU4DdVbWnqu4HtgIbJrXZALy9Bm4AHpfkSR1mkiQtHPYTkqTp2E9I0pjosri0Etg7tL2v2TfbNpKkxcl+QpI0HfsJSRoTyzt87Eyxr1q0IclGBpe5AnwlyR1HmG06xwL3HumD5Ld+eg6izNqcZOdXp3paOjU3uYG8enyzkzHOPv/mLPvr37Gozvt3dvS4XRnXfqJPY/X/tqe+cKEZq+esh/cAC9XYPG+zfO9jPzHw9SSfOMJsi8HY/J53yHMw4HkY8DzA09rescvi0j5g9dD2KuCeFm2oqi3AlrkOOJUkO6tqYj6+11wb1+zjmhvM3hezLxpj2U/0yd+f8eNzNp583haMTvoJn98Bz4Pn4BDPw4DnYXAO2t63y2FxO4B1SdYmORo4G9g2qc024GXNKg/PBL5YVZ/pMJMkaeGwn5AkTcd+QpLGRGdXLlXVgSTnA9cCy4ArqmpXkk3N8c3AduDFwG7gX4Gf6SqPJGlhsZ+QJE3HfkKSxkeXw+Koqu0MXvCH920eul3Aq7rM0MI4D6sY1+zjmhvM3hezLxJj2k/0yd+f8eNzNp583haIjvoJn98Bz4Pn4BDPw4Dn4QjOQQavx5IkSZIkSdLsdTnnkiRJkiRJkhY5i0uNJKcluSPJ7iQX9p1nVEmuSPK5cVxONcnqJH+T5LYku5K8pu9Mo0ryiCQfT/KPTfZf6zvTbCRZluQfknyg7yyzleTuJLckuelIVjPoQ5LHJXlPktub3/tn9Z1J4yHJrzSvNTc3v/vP6DuTppfkiUm2JvlkkluTbE/y1L5z6fCSrEry/iT/lGRPkkuSfGvfudTeTO/vm0nAf7c5fnOSk/rI2aURzsF5zc9+c5Lrk5zQR86ujfpZL8nJSQ4mOXM+882XUc5Dkuc37zV2JfnQfGfs2gj/J74tyZ8Nfc5bdPO4zVRDaPvaaHGJwQdt4FLgdGA9cE6S9f2mGtmVwGl9h2jpAPBzVfXdwDOBV43Ref868IKqOgE4ETitWaFkXLwGuK3vEEfg1Ko6cQyXCv0d4JqqOg44gfF+DjRPmiLkjwAnVdXxwAuBvf2m0nSSBLgauK6qnlxV64FfBp7QbzIdTvOcvRd4X1WtA9YBjwR+s9dgam3E9/enM3iu1wEbgbfOa8iOjXgO7gKe1/QvF7EI55wZ9bNe0+5iBhPILzqjnIckjwMuA360qr4H+Il5D9qhEX8XXgXc2nzOez7w2xmsVrmYXMn0NYRWr40WlwZOAXZX1Z6quh/YCmzoOdNIqurDwL/0naONqvpMVf19c/vLDD5or+w31Whq4CvN5rc0X2MxgVmSVcAPA5f3nWUpSfJY4LnA2wCq6v6q+n/9ptKYeBJwb1V9HaCq7q2qe3rOpOmdCnxj0qTDN1XVR3rMpOm9APhaVf0hQFUdBP4LgyXuH9NrMrU1yvv7DcDbm/d1NwCPS/Kk+Q7aoRnPQVVdX1VfaDZvAFbNc8b5MOpnvQuAq4DPzWe4eTTKeTgXeG9VfQqgqhbbuRjlHBRwTPNHh8cw+Kx9YH5jdmuEGkKr10aLSwMreehfgfcxJkWOxSLJdwHfB/xdv0lG1wwtu4lBB/RXVTUu2d8C/CLwQN9BWirgL5PcmGRj32Fm4d8B+4E/bIYkXp7k0X2H0lj4S2B1kjuTXJbkeX0H0oy+F7ix7xCale9h0nNWVV8C7gae0kcgHbFR3t8v9s8As/35XgH8RaeJ+jHjeUiyEjgD2MziNcrvw1OBb09yXfNe+2Xzlm5+jHIOLgG+G7gHuAV4TVWN6+emtlq9NlpcGsgU+8biKpTFoPmL4FXAa5s3cmOhqg5W1YkM/sJzSpLv7TvTTJL8CPC5qhrnDz3PrqqTGFyu+aokz+070IiWAycBb62q7wO+CozN/G7qT3OV5NMZXJa8H3hXkpf3GkpafMLU7/2meo+o8TDK+/vF/hlg5J8vyakMikuv6zRRP0Y5D28BXtdctbhYjXIeljN4z/HDwA8B/22RzRc4yjn4IeAm4DsYTH9ySTMCYSlp9dpocWlgH7B6aHsVg0qlOpbkWxgUlt5RVe/tO08bzdCm6xiPua+eDfxokrsZXAb6giR/0m+k2Tk0HKi5TPdqBpe3joN9wL6hK9zew6DYJM2oKWZfV1W/CpwPvKTvTJrWLgZvzjU+dgEPmcev+TDxBOCOXhLpSI3y/n6xfwYY6edLcjyD6RI2VNXn5ynbfBrlPEwAW5v3yGcClyX5sfmJN29G/T9xTVV9taruBT7MYJ7QxWKUc/AzDIYGVlXtZjAv2XHzlG+haPXaaHFpYAewLsnaZrKus4FtPWda9JpxrG8DbquqN/edZzaSrGgmvCPJIxlMsHt7v6lmVlW/VFWrquq7GPye/3VV/VTPsUaW5NFJjjl0G/hBYCxWSqyqzwJ7kzyt2fUDwK09RtKYSPK0JOuGdp0I/HNfeTSSvwa+Ncl/OrSjWYHIIY0L1weBRx0aAtJM+vrbwCVVdV+vydTWKO/vtzGYVyvNwixfrKrPzHfQDs14DpKsYTCZ/Uur6s4eMs6HGc9DVa2tqu9q3iO/B/jPVfW++Y/aqVH+T7wf+P4ky5M8CngGi2sBmlHOwacYvE8nyROApwF75jVl/1q9Ni7vPtfCV1UHkpzPYGWAZcAVVbWr51gjSfJOBrPYH5tkH/CrVfW2flON7NnAS4FbmrmLAH65qrb3mGlUTwL+qHnzeRTw7qr6QM+ZloInAFcP6pIsB/53VV3Tb6RZuQB4R9OZ7WHwlxFpJo8Bfq8paB8AdjMYIqcFqqoqyRnAWzJY5vhrDObueW2vwXRYQ8/ZpUn+G7ACeFdVvbHnaGrpcO/vk2xqjm8GtgMvZvC6+q8ssn55xHPwBuDxDK7UATgwhqvxTmvE87DojXIequq2JNcANzOYn/XyqhqLP+SOYsTfhYuAK5PcwmB42Ouaq7gWjalqCAwWqDqi18ZULaZhxZIkSdKRSfIfgHcCPz7m8xRKkjQvLC5JkiRJkiSpNedckiRJkiRJUmsWlyRJkiRJktSaxSVJkiRJkiS1ZnFJkiRJkiRJrVlckhpJDia5KcknkvxpkkfNwWO+PMklc5FPkjT3hl77D31dOIv7Pj/JB47w+1+XpNWy33Px/SVJkubC8r4DSAvIfVV1IkCSdwCbgDePcscky6rqYJfhJEmdePC1f74lWdbH95UkSZprXrkkTe0jwFMAkrwvyY1JdiXZeKhBkq8k+fUkfwc8K8nJSa5P8o9JPp7kmKbpdyS5Jsk/JfnNHn4WSdIsJbk7yf9M8rEkO5OclOTaJJ9Msmmo6WOTXJ3k1iSbkxzV3P+tzf12Jfm1SY/7hiR/C/zE0P6jkvxRkv/RbP9g873/vrma9jHN/tOS3N7c/8fn5WRIkiTNwOKSNEmS5cDpwC3Nrp+tqqcDE8Crkzy+2f9o4BNV9Qzg48C7gNdU1QnAC4H7mnYnAmcB/x44K8nq+flJJEkjeOSkYXFnDR3bW1XPYvAHhyuBM4FnAr8+1OYU4OcYvMY/mW8WfH6lqiaA44HnJTl+6D5fq6rnVNXWZns58A7gzqp6fZJjgdcDL6yqk4CdwH9N8gjgD4D/CHw/8MQ5OgeSJElHxGFx0jc9MslNze2PAG9rbr86yRnN7dXAOuDzwEHgqmb/04DPVNUOgKr6EkASgA9W1Reb7VuB7wT2dvujSJJGNN2wuG3Nv7cAj6mqLwNfTvK1JI9rjn28qvYAJHkn8BzgPcBPNle7LgeeBKwHbm7u865J3+f3gXdX1Rub7Wc27T/a9CNHAx8DjgPuqqp/ar7fnwAbkSRJ6pnFJembHvYBI8nzGVyF9Kyq+tck1wGPaA5/bWiepQB1mMf9+tDtg/j/TpLGxaHX7wd46Gv5A3zztXzya38lWQv8PHByVX0hyZV8s+8A+Oqk+1wPnJrkt6vqawz6lL+qqnOGGyU5cYrvJ0mS1DuHxUnT+zbgC01h6TgGf02eyu0M5lY6GSDJMc3wOknS4nZKkrXNXEtnAX8LPJZBAemLSZ7AYKj1dN4GbAf+tOk7bgCeneTQ3H+PSvJUBn3N2iRPbu53zpSPJkmSNM/88CtN7xpgU5KbgTsYvOF/mKq6v5mn4/eSPJLBfEsvnL+YkqSWhodEA1xTVRfO4v4fA36DwZxLHwaurqoHkvwDsAvYA3x0pgepqjcn+Tbgj4HzgJcD70zyrU2T11fVnc1Quz9Pci+DQtb3ziKrJElSJ1Ll1dWSJEmSJElqx2FxkiRJkiRJas3ikiRJkiRJklqzuCRJkiRJkqTWLC5JkiRJkiSpNYtLkiRJkiRJas3ikiRJkiRJklqzuCRJkiRJkqTWLC5JkiRJkiSptf8PfyM4inZ8EDQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x720 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plain_features = ['Pclass', 'Gender', 'SibSp', 'Parch', 'Embarked']\n",
    "fig, ax = plt.subplots(nrows = 2, ncols = 3 ,figsize=(20,10))\n",
    "start = 0\n",
    "for j in range(2):\n",
    "    for i in range(3):\n",
    "        if start == len(plain_features):\n",
    "            break\n",
    "        sns.barplot(x=plain_features[start], y='Survived', data=titanic_train, ax=ax[j,i], ci = False)\n",
    "        start += 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "VQ7iwNHVPK4o"
   },
   "source": [
    "Observations in a Nutshell for all features:\n",
    "- Gender: The chance of survival for women is high as compared to men.\n",
    "\n",
    "- Pclass:There is a visible trend that being a 1st class passenger gives you better chances of survival. The survival rate for Pclass3 is very low. For women, the chance of survival from Pclass1 is almost 1 and is high too for those from Pclass2. Money Wins!!!.\n",
    "\n",
    "- Age: Children less than 5-10 years do have a high chance of survival.Also all old passengers survived(>=80). Most of the passengers between age group 15 to 35 died.\n",
    "\n",
    "- Embarked: This is a very interesting feature. The chances of survival at C looks even though the majority of Pclass1 passengers boarded at S. Passengers at Q were all from Pclass3.\n",
    "\n",
    "- Parch+SibSp: Having 1-2 siblings,spouse on board or 1-3 count of parents/children shows a greater probablity of survival rather than being alone or having a large family travelling with you"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "TtbMIaG8PK4p"
   },
   "source": [
    "# Data Preprocessing\n",
    "## Missing Value Imputation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import tree, model_selection\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 269
    },
    "colab_type": "code",
    "id": "VRQ5tt6APK4p",
    "outputId": "3e86f786-c286-4674-b6e5-617223b5c164"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Survived         0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Gender           0\n",
       "Age            177\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             0\n",
       "Cabin          687\n",
       "Embarked         2\n",
       "Fare_cat         0\n",
       "family_size      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 123
    },
    "colab_type": "code",
    "id": "6sCQ1SKoPK4v",
    "outputId": "57b28081-67cc-42f0-e3a4-057d536bc48c"
   },
   "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>Name</th>\n",
       "      <th>Gender</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>Fare_cat</th>\n",
       "      <th>family_size</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>Icard, Miss. Amelie</td>\n",
       "      <td>female</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>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>829</th>\n",
       "      <td>830</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Stone, Mrs. George Nelson (Martha Evelyn)</td>\n",
       "      <td>female</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>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass                                       Name  \\\n",
       "61            62         1       1                        Icard, Miss. Amelie   \n",
       "829          830         1       1  Stone, Mrs. George Nelson (Martha Evelyn)   \n",
       "\n",
       "     Gender   Age  SibSp  Parch  Ticket  Fare Cabin Embarked  Fare_cat  \\\n",
       "61   female  38.0      0      0  113572  80.0   B28      NaN         3   \n",
       "829  female  62.0      0      0  113572  80.0   B28      NaN         3   \n",
       "\n",
       "     family_size  \n",
       "61             1  \n",
       "829            1  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_train[titanic_train['Embarked'].isnull()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Csd8c66rPK4x"
   },
   "source": [
    "PassengerId 62 and 830 have missing embarked values\n",
    "\n",
    "Both have Passenger class 1 and fare $80.\n",
    "\n",
    "plot a graph to visualize and try to guess from where they embarked"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 296
    },
    "colab_type": "code",
    "id": "navcRQkmPK4x",
    "outputId": "660fc6f4-3327-4fdc-af75-401c6c0f5dc7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2e01227b080>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=\"Embarked\", y=\"Fare\", hue=\"Pclass\", data=titanic_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "hlc2V_5PPK4z"
   },
   "outputs": [],
   "source": [
    "titanic_train[\"Embarked\"] = titanic_train[\"Embarked\"].fillna('C')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "A6J-K3slPK42"
   },
   "source": [
    "For 1st class median line is coming around fare $80 for embarked value 'C'. So we can replace NA values in Embarked column with 'C'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "eRZDLGJ8PK42"
   },
   "source": [
    "## Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "FbZ8okTTPK47"
   },
   "outputs": [],
   "source": [
    "# Preprocessing data\n",
    "titanic_train_encoded = pd.get_dummies(titanic_train, columns = ['Embarked','Gender'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 319
    },
    "colab_type": "code",
    "id": "YymhFHIkPK49",
    "outputId": "a074ee1a-adfe-4f66-eec8-5985a94bebd5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId        int64\n",
       "Survived           int64\n",
       "Pclass             int64\n",
       "Name              object\n",
       "Age              float64\n",
       "SibSp              int64\n",
       "Parch              int64\n",
       "Ticket            object\n",
       "Fare             float64\n",
       "Cabin             object\n",
       "Fare_cat           int64\n",
       "family_size        int64\n",
       "Embarked_C         uint8\n",
       "Embarked_Q         uint8\n",
       "Embarked_S         uint8\n",
       "Gender_female      uint8\n",
       "Gender_male        uint8\n",
       "dtype: object"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_train_encoded.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "lOfahcjNPK5A"
   },
   "outputs": [],
   "source": [
    "X = titanic_train_encoded.drop(['PassengerId','Survived', 'Cabin', 'Name', 'Age', 'Ticket', ], axis = 1)\n",
    "y = titanic_train_encoded['Survived']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 195
    },
    "colab_type": "code",
    "id": "m63wpwfqPK5C",
    "outputId": "6159a910-0faa-41ef-fa0d-426a8cf95276"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Fare_cat</th>\n",
       "      <th>family_size</th>\n",
       "      <th>Embarked_C</th>\n",
       "      <th>Embarked_Q</th>\n",
       "      <th>Embarked_S</th>\n",
       "      <th>Gender_female</th>\n",
       "      <th>Gender_male</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
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       "      <td>7.2500</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
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       "      <th>1</th>\n",
       "      <td>1</td>\n",
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       "      <td>71.2833</td>\n",
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       "      <td>0</td>\n",
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       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
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       "      <td>7.9250</td>\n",
       "      <td>1</td>\n",
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       "      <th>3</th>\n",
       "      <td>1</td>\n",
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       "      <td>53.1000</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass  SibSp  Parch     Fare  Fare_cat  family_size  Embarked_C  \\\n",
       "0       3      1      0   7.2500         0            2           0   \n",
       "1       1      1      0  71.2833         3            2           1   \n",
       "2       3      0      0   7.9250         1            1           0   \n",
       "3       1      1      0  53.1000         3            2           0   \n",
       "4       3      0      0   8.0500         1            1           0   \n",
       "\n",
       "   Embarked_Q  Embarked_S  Gender_female  Gender_male  \n",
       "0           0           1              0            1  \n",
       "1           0           0              1            0  \n",
       "2           0           1              1            0  \n",
       "3           0           1              1            0  \n",
       "4           0           1              0            1  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 118
    },
    "colab_type": "code",
    "id": "uLDNYfNiPK5D",
    "outputId": "311a087d-afcd-4f7e-affa-17f863d5b170"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    1\n",
       "2    1\n",
       "3    1\n",
       "4    0\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "KohK_CQ7PK5F"
   },
   "outputs": [],
   "source": [
    "# Dividing into train and test dataset\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, stratify = y, random_state = 10 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 118
    },
    "colab_type": "code",
    "id": "ipe-iu1XPK5L",
    "outputId": "8ed4b714-2119-47e2-c90f-c59e83d1063c"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Softwares\\lib\\site-packages\\ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "#Baseline Model - As deduced from EDA, most passengers died. Make all passengers as died.\n",
    "X_test['y_pred_bas'] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "kqFgvNnWPK5N",
    "outputId": "1a2e4fde-5a79-484a-d4c3-4819cab49203"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6143497757847534"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test, X_test.y_pred_bas)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "9N9cc_z0PK5P"
   },
   "source": [
    "# Model Building"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 118
    },
    "colab_type": "code",
    "id": "Zh3ZkW6xPK5Q",
    "outputId": "84dff782-6410-487c-8865-e58131dc8cf1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                       max_depth=None, max_features=None, 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, presort='deprecated',\n",
       "                       random_state=None, splitter='best')"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Make Decision Tree Model\n",
    "dt = tree.DecisionTreeClassifier()\n",
    "dt.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 84
    },
    "colab_type": "code",
    "id": "LOyGdJJnWolp",
    "outputId": "5f7d5e67-2c38-4cc1-f159-78e8a303182e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Pclass', 'SibSp', 'Parch', 'Fare', 'Fare_cat', 'family_size',\n",
       "       'Embarked_C', 'Embarked_Q', 'Embarked_S', 'Gender_female',\n",
       "       'Gender_male', 'y_pred_bas'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Bt5-_0IhPK5S"
   },
   "outputs": [],
   "source": [
    "y_pred = dt.predict(X_test.drop('y_pred_bas', axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 202
    },
    "colab_type": "code",
    "id": "hX1FPM56PK5U",
    "outputId": "afaf2021-55a5-4178-87bd-c60dbe2eba9a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1,\n",
       "       1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0,\n",
       "       1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0,\n",
       "       0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1,\n",
       "       1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0,\n",
       "       0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1,\n",
       "       0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,\n",
       "       0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0,\n",
       "       1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1,\n",
       "       0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0,\n",
       "       1, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "a-9OiSa4PK5X",
    "outputId": "cffc4c41-ab9b-42ba-8e2a-c66957935f32"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8071748878923767"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_pred, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "sYbo7WdOPK5Z"
   },
   "outputs": [],
   "source": [
    "#Before Actually applying to the test data, you need to check how your model is performing. So we keep some data for validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 50
    },
    "colab_type": "code",
    "id": "X3QWZzu4PK5c",
    "outputId": "e449e9bb-2367-4ef6-9dbb-d3031a95e4b3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross Validation Score :  0.7708502939846225\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:    0.0s finished\n"
     ]
    }
   ],
   "source": [
    "cv_scores = model_selection.cross_val_score(dt, X_train, y_train, cv=10, verbose=1)\n",
    "print ('Cross Validation Score : ',cv_scores.mean() )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "GxPK-qOHPK5f",
    "outputId": "f2b8fc68-2b31-4864-c194-cd859f39c543"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy :  0.9281437125748503\n"
     ]
    }
   ],
   "source": [
    "print('Training Accuracy : ', dt.score(X_train,y_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 50
    },
    "colab_type": "code",
    "id": "3pZI7TBBPK5k",
    "outputId": "601409e7-e2d6-4d18-83b9-3f54cb67dce7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[111,  17],\n",
       "       [ 26,  69]], dtype=int64)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_pred, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ML resource links\n",
    "- [Machine learning course in Coursera](https://www.coursera.org/learn/machine-learning)\n",
    "- [Google's ML crash course](https://developers.google.com/machine-learning/crash-course/)\n",
    "- [Learning Path : Your mentor to become a machine learning expert - Analytics Vidhya](https://www.analyticsvidhya.com/learning-path-learn-machine-learning/)\n",
    "- [Kaggle's Micro-Courses](https://www.kaggle.com/learn/overview)\n",
    "- [Scikit-Learn tutorials](https://scikit-learn.org/stable/tutorial/index.html)\n",
    "\n",
    "# Top blog posts\n",
    "- [Data Science Central](https://www.datasciencecentral.com/)\n",
    "- [Analytics Vidhya](https://www.analyticsvidhya.com/)\n",
    "- [Machine Learning Mastery](https://machinelearningmastery.com/start-here/)\n",
    "- [DeepMind](https://deepmind.com/blog), [Google AI](https://ai.googleblog.com/), [OpenAI](https://openai.com/blog/)\n",
    "- [Medium articles](https://medium.com/topic/machine-learning)\n",
    "- [Other blogs](https://www.springboard.com/blog/machine-learning-blog/)\n",
    "\n",
    "# Competitions\n",
    "- [Kaggle](https://www.kaggle.com/competitions)\n",
    "- [DataHack Analytics Vidhya](https://datahack.analyticsvidhya.com/)\n",
    "- [Other platforms](https://towardsdatascience.com/10-data-science-competitions-for-you-to-hone-your-skills-for-2020-32d87ee19cc9)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The End!"
   ]
  }
 ],
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  "colab": {
   "name": "Titanic Survival Prediction-v1.ipynb",
   "provenance": [],
   "toc_visible": true
  },
  "kernelspec": {
   "display_name": "Python 3",
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  "language_info": {
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