Example.ipynb

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    "colab": {
      "name": "Example.ipynb",
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      "collapsed_sections": [],
      "toc_visible": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_cEMXjCoaCsr",
        "colab_type": "text"
      },
      "source": [
        "#  Titanic Survival Prediction at Beginner Level"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Azy9juwQ3m3d",
        "colab_type": "text"
      },
      "source": [
        "I have putforth a solution with guidance from various notebooks, blogs and ofcourse stack overflow website. Please feel free to glance over the analysis and do recommend any suggestions and queries."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y0DVNakgCSTf",
        "colab_type": "text"
      },
      "source": [
        "A Kaggle Competition use machine learning to create a model that predicts which passengers survived the Titanic shipwreck."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9cHmnR0zC_KC",
        "colab_type": "text"
      },
      "source": [
        "**The Challenge**\n",
        "\n",
        "* The sinking of the Titanic is one of the most infamous shipwrecks in history.\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."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LxXX52WYD-hh",
        "colab_type": "text"
      },
      "source": [
        "**Contents**\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XJ57HG_53Ywz",
        "colab_type": "text"
      },
      "source": [
        "1. Import Libraries\n",
        "2. Explore Dataset\n",
        "4. Data Visualization\n",
        "5. Cleaning Data and Dealing with Missing Data\n",
        "6. Add New Features\n",
        "6. Building Models\n",
        "7. Choosing the best model\n",
        "8. Create Submission File"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AmgAvQ-U4xrj",
        "colab_type": "text"
      },
      "source": [
        "## Import Libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2O44y1BrA170",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        },
        "outputId": "36abd1b3-f3e0-4978-cf08-5080adb6bc00"
      },
      "source": [
        "# data analysis and wrangling\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "# data visualization\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Machine Learning Algorithms\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier\n"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rNHvy7zYBaj9",
        "colab_type": "text"
      },
      "source": [
        "Upload necessary csv files."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kB8OX_yaCmdW",
        "colab_type": "code",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 106
        },
        "outputId": "3d8fbd08-1fc8-4160-ee92-9810b9825103"
      },
      "source": [
        "#Upload CSV files\n",
        "from google.colab import files\n",
        "uploaded = files.upload()"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-33bdaf23-5aa3-4045-ad81-4680a78caf59\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-33bdaf23-5aa3-4045-ad81-4680a78caf59\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving test.csv to test.csv\n",
            "Saving train.csv to train.csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BrKSMJueBbKQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_df = pd.read_csv(\"train.csv\")\n",
        "test_df = pd.read_csv(\"test.csv\")"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wBORAHFkBp8U",
        "colab_type": "text"
      },
      "source": [
        "Assign it to respective DataFrame and check its dimensions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qHO9QKjyRjbE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "91c34178-10d8-4b5f-91fa-857dd1eaf962"
      },
      "source": [
        "print(\"Dimensions of train: {}\".format(train_df.shape))\n",
        "print(\"Dimensions of test: {}\".format(test_df.shape))"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Dimensions of train: (891, 12)\n",
            "Dimensions of test: (418, 11)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Jkxx0Za45vJJ",
        "colab_type": "text"
      },
      "source": [
        "We can see that in train_df we are provided with the Survived Column, whereas test_df doesnt give us survival column"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MJ8OoJGaaTU8",
        "colab_type": "text"
      },
      "source": [
        "## Exploratory Data Analysis"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UeKu_mzzBcpe",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        },
        "outputId": "a83344b3-5012-4e76-e45f-3ab911dca1ca"
      },
      "source": [
        "#Data Exploration\n",
        "train_df.info()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 891 entries, 0 to 890\n",
            "Data columns (total 12 columns):\n",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   PassengerId  891 non-null    int64  \n",
            " 1   Survived     891 non-null    int64  \n",
            " 2   Pclass       891 non-null    int64  \n",
            " 3   Name         891 non-null    object \n",
            " 4   Sex          891 non-null    object \n",
            " 5   Age          714 non-null    float64\n",
            " 6   SibSp        891 non-null    int64  \n",
            " 7   Parch        891 non-null    int64  \n",
            " 8   Ticket       891 non-null    object \n",
            " 9   Fare         891 non-null    float64\n",
            " 10  Cabin        204 non-null    object \n",
            " 11  Embarked     889 non-null    object \n",
            "dtypes: float64(2), int64(5), object(5)\n",
            "memory usage: 83.7+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zL35DjRb6DNA",
        "colab_type": "text"
      },
      "source": [
        "A Broader explanation of all the column names and its meaning is being given [here](https://www.kaggle.com/c/titanic/data) in tabular format."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NWTtStux6n5X",
        "colab_type": "text"
      },
      "source": [
        "The training-set has 891 examples and 11 features + the target variable (survived). 2 of the features are floats, 5 are integers and 5 are objects"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s1Zsq2__MJq_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 495
        },
        "outputId": "2b350085-d08d-4d01-bf87-bc80e9e81cf8"
      },
      "source": [
        "train_df.describe(include='all')"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "    </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>891</td>\n",
              "      <td>891</td>\n",
              "      <td>714.000000</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>891</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>204</td>\n",
              "      <td>889</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unique</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>891</td>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>681</td>\n",
              "      <td>NaN</td>\n",
              "      <td>147</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>top</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Meek, Mrs. Thomas (Annie Louise Rowley)</td>\n",
              "      <td>male</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>347082</td>\n",
              "      <td>NaN</td>\n",
              "      <td>G6</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freq</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>577</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>7</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4</td>\n",
              "      <td>644</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>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>29.699118</td>\n",
              "      <td>0.523008</td>\n",
              "      <td>0.381594</td>\n",
              "      <td>NaN</td>\n",
              "      <td>32.204208</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</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>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>14.526497</td>\n",
              "      <td>1.102743</td>\n",
              "      <td>0.806057</td>\n",
              "      <td>NaN</td>\n",
              "      <td>49.693429</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</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>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.420000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</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>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>20.125000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>7.910400</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</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>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>14.454200</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</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>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>38.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>31.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</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>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>512.329200</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        PassengerId    Survived      Pclass  ...        Fare Cabin  Embarked\n",
              "count    891.000000  891.000000  891.000000  ...  891.000000   204       889\n",
              "unique          NaN         NaN         NaN  ...         NaN   147         3\n",
              "top             NaN         NaN         NaN  ...         NaN    G6         S\n",
              "freq            NaN         NaN         NaN  ...         NaN     4       644\n",
              "mean     446.000000    0.383838    2.308642  ...   32.204208   NaN       NaN\n",
              "std      257.353842    0.486592    0.836071  ...   49.693429   NaN       NaN\n",
              "min        1.000000    0.000000    1.000000  ...    0.000000   NaN       NaN\n",
              "25%      223.500000    0.000000    2.000000  ...    7.910400   NaN       NaN\n",
              "50%      446.000000    0.000000    3.000000  ...   14.454200   NaN       NaN\n",
              "75%      668.500000    1.000000    3.000000  ...   31.000000   NaN       NaN\n",
              "max      891.000000    1.000000    3.000000  ...  512.329200   NaN       NaN\n",
              "\n",
              "[11 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pL3DT3kM6-YD",
        "colab_type": "text"
      },
      "source": [
        "* It can be observed that 38% survived with a mean age of 29 years. \n",
        "* Also that Age column is continuous ranging from 0.4 to 80.\n",
        "* Some Columns have missing values like Cabin, Ticket."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9aC5KSW91gGA",
        "colab_type": "text"
      },
      "source": [
        "Let us check first few rows in train.csv. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iPVp0AOYMJ5S",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 461
        },
        "outputId": "896a2f56-148c-4e3a-c3d6-dd95ee7d08e8"
      },
      "source": [
        "train_df.head(10)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "    </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",
              "    <tr>\n",
              "      <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",
              "    </tr>\n",
              "    <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",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>6</td>\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",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>McCarthy, Mr. Timothy J</td>\n",
              "      <td>male</td>\n",
              "      <td>54.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>17463</td>\n",
              "      <td>51.8625</td>\n",
              "      <td>E46</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Palsson, Master. Gosta Leonard</td>\n",
              "      <td>male</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>349909</td>\n",
              "      <td>21.0750</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>9</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</td>\n",
              "      <td>female</td>\n",
              "      <td>27.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>347742</td>\n",
              "      <td>11.1333</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>10</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>Nasser, Mrs. Nicholas (Adele Achem)</td>\n",
              "      <td>female</td>\n",
              "      <td>14.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>237736</td>\n",
              "      <td>30.0708</td>\n",
              "      <td>NaN</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   PassengerId  Survived  Pclass  ...     Fare Cabin  Embarked\n",
              "0            1         0       3  ...   7.2500   NaN         S\n",
              "1            2         1       1  ...  71.2833   C85         C\n",
              "2            3         1       3  ...   7.9250   NaN         S\n",
              "3            4         1       1  ...  53.1000  C123         S\n",
              "4            5         0       3  ...   8.0500   NaN         S\n",
              "5            6         0       3  ...   8.4583   NaN         Q\n",
              "6            7         0       1  ...  51.8625   E46         S\n",
              "7            8         0       3  ...  21.0750   NaN         S\n",
              "8            9         1       3  ...  11.1333   NaN         S\n",
              "9           10         1       2  ...  30.0708   NaN         C\n",
              "\n",
              "[10 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3g0rVUP_MJ8s",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "26374d00-8d83-47e1-efbe-2e0a1a66292b"
      },
      "source": [
        "#From train dataset it is observed that it contains different types of data such as numeric data, categorical, continous, missing/NaN values, which need to be taken care of\n",
        "train_df.columns.values "
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n",
              "       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fmdcUdU87Raw",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "1. Numerical Features: Age (Continuous), Fare (Continuous), SibSp (Discrete), Parch (Discrete)\n",
        "2. Categorical Features: Survived, Sex, Embarked, Pclass \n",
        "3. \n",
        "Alphanumeric Features: Ticket, Cabin\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XGnKZnY07JaD",
        "colab_type": "text"
      },
      "source": [
        "As we can see, Cabin, Age , Embarked and Fare have missing data in them. We will find and fill missing values later in the notebook"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "peGlGnS9785O",
        "colab_type": "text"
      },
      "source": [
        "## Data Visualization"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5St1_XLxNjha",
        "colab_type": "text"
      },
      "source": [
        "### 1. Survived "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "88TbZIgeM3Vl",
        "colab_type": "text"
      },
      "source": [
        "* How many People Survived on the RMS Titanic?\n",
        "\n",
        "For this let us look at the 'Survived Column' in our dataset. Use countplot to give a count of total survived people."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "50XtvOFgqyna",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 339
        },
        "outputId": "1d2dedce-1cc0-4b27-8d13-d193ef0f940e"
      },
      "source": [
        "sns.set(style=\"darkgrid\")\n",
        "plt.figure(figsize = (8, 5))\n",
        "ax= sns.countplot(x='Survived', hue=\"Survived\", data=train_df)\n"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WJknj46wNy9f",
        "colab_type": "text"
      },
      "source": [
        "From the plot it is clear that survival chances of people on Titanic were quite low."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HEXnKWH8NbB2",
        "colab_type": "text"
      },
      "source": [
        "### 2. Sex \n",
        "\n",
        "Consists of No of Female and Male passengers"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QiB06ET_NuFI",
        "colab_type": "text"
      },
      "source": [
        "* Which Gender outlived the other?\n",
        "* Who had more chance of survival?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3iwuTyz8rozI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 339
        },
        "outputId": "6f87c070-a6da-46f1-b743-87d697106b38"
      },
      "source": [
        "plt.figure(figsize = (8, 5))\n",
        "ax= sns.countplot(x='Sex', hue=\"Survived\", data=train_df)\n"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "80phIYyQOSpu",
        "colab_type": "text"
      },
      "source": [
        "It was observed that Females were mostly rescued than Male passengers. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "56TIWjzGOg1O",
        "colab_type": "text"
      },
      "source": [
        "### 3. Pclass \n",
        "\n",
        "Consists of three classes according to socio-economic status\n",
        "1st = Upper\n",
        "2nd = Middle\n",
        "3rd = Lower\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AhJy2DmNOqHy",
        "colab_type": "text"
      },
      "source": [
        "* Which class had higher survival ?\n",
        "* Did the people of higher socio-economic make it alive or otherwise?\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9WTv__5vFOJI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#By using heatmap\n",
        "group = train_df.groupby(['Pclass','Survived'])\n",
        "pclass_survived = group.size().unstack()"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yeGXexPlHTF2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "outputId": "43ba2b74-c669-4bd5-c6a9-ac8bd8b8db68"
      },
      "source": [
        "# Heatmap - Color encoded 2D representation of data. \n",
        "sns.heatmap(pclass_survived, annot = True, fmt =\"d\")"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f17f9f61320>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PLtagLl-PaNb",
        "colab_type": "text"
      },
      "source": [
        "The heatmap tells us that Class 1 passengers have a higher survival chance compared to classes 2 and 3. It implies that Pclass contributes a lot to a passenger’s survival rate."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CfSqpZMfaR2D",
        "colab_type": "text"
      },
      "source": [
        "### 4. Sex vs Age with respect to Survived Column"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-fkbWnbUHTTj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "outputId": "b93b3d7d-c92a-43cb-c176-799501fc9ce2"
      },
      "source": [
        "# Violinplot Displays distribution of data  \n",
        "# across all levels of a category. \n",
        "sns.violinplot(x =\"Sex\", y =\"Age\", hue =\"Survived\",  \n",
        "data = train_df, split = True)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f17f8f17b70>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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8wIkTx6isKG9w09F5IQY1LQK07Nz5sxiFJMji8OFUVDp/LvdV1bO5L8181Hz66RLMZpPrivNQLgsFs9nM9OnTGThwILfffjtTp9YuS5uZmcno0aMZNGgQo0eP5tSpU64qyWvs3fs7CqXqogvg/ZXOEXpyc3PFnAXB5Wpqav5YkiXyss9TKxUMb+1PUVER33672kXVeS6XhcJbb72FTqdj06ZNrF27lmeffRaA6dOnM2bMGDZt2sSYMWOYNm2aq0ryCpIkkbxvD0qfCBSqi7fLXk7HMD16tZIff9zshOoE4dIOHUrBarGg9ov5y+fGB2rpFmUgafMPnDhxzAXVeS6XhEJVVRVr1qzh2WefrdsPuFmzZhQVFZGWlsbw4cMBGD58OGlpaRQXO363I2915kwWJcVFaC4yYa0htCoFnSP0JCfvFiM8BJf6/fddKNV6VL7hDXr+gHg/AvVqlixZKOYuXAWXhMKZM2cICgri/fff54477uCBBx5g79695OTkEBERgUpVu+SCSqUiPDycnJwcV5TlFfbvTwZA1YhRR//rxmgfkCQ2b/7BUWUJwmVVVlayb18yKv9YFIqGfU3p1EpGtPYjPz+fr79e6eQKPZdLFsSz2WycOXOGa665hokTJ5KSksLjjz/OvHnzHHL80FA/hxzHEx08uA+VTxhKtf6KjxGkV9ExTM+2n7fw97+PITAw0IEVCkJ927cnYbNZ0QW1atTrWgXruCHKwJYtm+jbtzfXXXedkyr0XC4JhaioKNRqdV0z0XXXXUdwcDB6vZ68vDxsNhsqlQqbzUZ+fj5RUY3rEC0qqsRuF6Nj/ldRUSGZmZnowjtf9bF6x/pyqKCIFStWcs89YxxQnSBcnNVqZc2a71H5hKPSBzf69f1b+pNRZuXtOXN45dW38PHxdUKVTZtSqbjkxbRLmo9CQkLo0aMHO3fuBGpHHBUVFREfH0/79u1Zt24dAOvWraN9+/aEhIS4oiyPl5y8BwC1/1931P2VMB81ncL0/Lhlo+hbEJxq167tlJQUoQ1td0Wv16oU3NHWn7KyMpYv/9jB1Xk+l40+mjlzJosWLeL222/n+eefZ/bs2QQEBDBjxgxWrFjBoEGDWLFiBTNnznRVSR5vz57dqPRBKLX+DjnerXF+2O02MexPcBqz2cy3336FyhCK6gqGUJ8X46+hTwsfdu/+ld9+2+nACj2fyzbZiY2NZfny5fUeT0hIYPVq8SXjaEVFhWRkHEMb1slhxwzWq+gRZWDXzl/o128ALVsmOOzYggCwYcP3lJWVYIjrVzdS8Ur1ivXlRImFzz5bSuvWbWnWrHE7kHkrMaPZQ52/OtIExDn0uLe08MVXq2LF8mXY7d61CbrgXOfOnWX9+u9RB8Sh9mnYMNTLUSkU3JEYgGSt4aOPFojPawOJUPBAkiSxbdtPqHzCUWodOzJLr1YyIN6XzFMn2bbtR4ceW/BeVquVRYvfB6UGXcT1DjtusF7FkFZ+HD9+jA0bvnfYcT2ZCAUPlJaWSmFhPppGDudrqGvD9bQM0rJ69ZeUlIiJhsLVW7lyBWfPnEYb2e2qhk9fzHXhejo007FmzVdkZWU69NieSISCB0pK+gGlxoDav3EL4DWUQlG71ozNUsOnny4Ri+UJV2XHjm1s3boZTUjiFc+8vxyFQsGw1gH4ahR8tHgBFkuNw8/hSUQoeJizZ09z8OAB1IEJKJQqp50n1KCmX5wPBw8eYNeu7U47j+DZDh8+xCeffITaNwJduPMmmvlolIxs4092TjbffvuV087jCUQoeJi1a9egUGnQhrRx+rl6RPvQIlDL559/QmFhgdPPJ3iW48fTmT9/LgptAPqYXg1ezuJKtQ7W0TXSwKZN6zl58oRTz9WUiVDwIKdPn2LPnt/QBLVBodI5/XxKhYK/tQ3Abqlh6ZIPxOgOocGOH09n7tw3sSl06GP7XNEKvldiQEs//LUqPl76IVar1SXnbGpEKHgISZL4z3++RKnSXvFM0CtxfnRH+rGjbNy4zmXnFZqu1NSDzJnzb6xo0Mf2Rak2uOzcerWSYQl+ZOdks3nzBpedtykRoeAhDh06QFraITShHVCotC49d+cIPdc00/HNN/8hM/OkS88tNC07d/7Cu+/Oxq7yQd/iNpQaH5fXkBiqIzFUx9rvvxGj5y5ChIIHsFgsfP75p6h0AWhCWrv8/AqFgttbB+CvVbLow/cwGo0ur0Fwb3a7nTVrvmLp0g9RGMIwtOjn8KGnjTGopR82q4Wvv14lWw3uSoSCB9i4cR0FBflow69HoXDeiKPLMWiU3NHWn4LCfFasWCZLDYJ7MplMfPDBPL7//hvUgS0xxN7i8rvZ/xViUNMj2sCvu7Zz+nSWrLW4GxEKTVxubg7fr/0WtX8sar8rX0DMEeICtfSJ9eXXX3eIYaoCUPv5fOWVqSQn70UXfj36qBtku3D5X72a+6LTKPn2G3G38GciFJowu93OJ598hCQp0UV0kbscoHZtpLhALcs/W0purthBz5slJ+9h5szJ5BUUYmjRB21o4lUvcudIBo2Sm6MNpBw8QGZmhtzluA0RCk3Ytm1bOXbsKJrw61BqXDeC43KUfyxCppRsfPjBPCwWi9wlCS5mtVpZtepzFix4B6vSF0P8QNS+kXKXdVE9on0waFR8//03cpfiNkQoNFGFhQWsWvU5at8INIHOWePoSgXqVIxs48fpM6dZvfoLucsRXKi4uIg33nyFTZvWowluU9uhrHHfnc90aiU3RutJSdlPVtYpuctxCyIUmiC73c7HyxZjtdnRRd3gVrfk57UL1dMjunav3P37k+UuR3CBw4cPMX36y5w6dQp99E3oI7s6dakVR7kh2ge9WsnateJuAUQoNEk///wjR48cRhN2nVtfhQ1o6U+Un4aPl34gtvD0YHa7ne+//4a3334Dk02NIW4AmkDH7uPhTAa1kh7RBvbt2yvuFhCh0OTk5eX+0WwUiSbIvXc+UysV3NUuAIvZxIcfvCeWFfBA1dXVzJ//NmvWfIU6oAWGuP4odQFyl9VoN8XU9i189dWXXr/qrwiFJsRut/PRkg+w2XHbZqP/FWpQM7y1HycyjrNmjVid0pPk5eUy65UppBxMQRfRBX30jSiULtvh16H0aiW3xBo4fPgQKSn75S5HViIUmpANG77nZMZxtBFdZFke4EpdG26gS6SBDRu+58CBfXKXIzjAsWNHmfXKVAqLSjDE9kUb0rZJXKRcTvcoH8J8NKxY/rFXz8oXodBEnDp1svYW3T8WtYP3XXaFIQm1/QtLPlpAfn6e3OUIVyE5eQ9vzXmdGpsKQ1x/1L5Xv5+yO1ArFdzexo+SkmK+/PIzucuRjQiFJsBsNvHhh++jUOnRR3VrkldkGqWCu9sFYLeaeX/+25jNJrlLEq7Ab7/tZOHCd0ETiKHFbSi1/nKX5FAtArT0ivVlx45tbN/+s9zlyEKEQhPwxRefkZ+fizaqh0v2SXCWEIOauxIDOJd9lo8/XuT1HXpNzd69u/noo4UoDWEYWtyKQt10P4uXc2ucLy2DtHz22RLS0lLlLsflGhwKGRkZLFiwgJkzZ9b9fPToUacVJtT69dcdbN/+M9rQa1D7RshdzlVrHazjtjg/9uzZzXfffS13OUIDHTt2lEWLFqA0NMMQ27vJdig3hFKh4J72gYTqVbw//21OnDgmd0ku1aBQ+OGHHxg7dix5eXl89913QO1QtDfeeMOpxXm7s2fP8MknS1D5hKEN6yh3OQ7Ts7kP10fo+f77b9i58xe5yxH+QklJMfPnzwW1AUNMLxRK1+ySJieDWsnYDoH4quy8Ped1jhw5LHdJLtOgUHjvvfdYtmwZs2bNQqWqnaHYrl07cafgRNXVVcyfPxcbKvTRNzt9/1pXUigUDGsdQMsgLcuWLebgQe8eAujO7HY7ixcvoNpoQhfT22ObjC4mQKfioU6BBGok5s79t9f0MTTom6a4uJjExESAuk5OhULRJDs8mwKr1cr7C96lsLAAXfTNbrPYnSOplQpGtw8kwkfFggXvcuyYuMBwR9u3/0x6+hG04dejaoKT0q6Wv1bFP64NIs5fzbJli/nyy+UePwmzQaHQoUOHumaj89avX8+1117rlKK8mSRJfPbZUo4eOYwusjtqnzC5S3IavVrJ/R2CCNTAO++84XVtt+7OaKxm9eovUfmEoQlyr0UXXcnwx+f0hmgDSUk/8MYbMyksLJC7LKdpUChMnjyZd999l7Fjx1JdXc24ceOYN28eL7/8cqNP+P7775OYmMixY7VfAAcOHGDEiBEMGjSIhx9+mKIi714j56uvVrJjxza0zTqgCWopdzlO56dV8veOgfip7Mx9+98cP54ud0nCH378cTPV1VXowjt7fauASqlgaEIAd7cL5NzpTKZNm8jOnb945Ai6BoVCQkICP/zwA2PGjGH8+PHccccdrF27lvj4+Ead7PDhwxw4cICYmBigtr3ypZdeYtq0aWzatIlu3boxZ86cRr8JT7FmzVf88MNaNEGt0TbznI7lvxKgU/FgxyB8VTbenvM6hw8fkrskr2e1WklK2oTKNwqVIVTuctxGhzA9j18fQrjOztKlH/L++3MpKSmRuyyHanDvpcFgYOjQoTzyyCMMGzYMX9/Grc5ZU1PDrFmzmDFjRt1jqamp6HQ6unXrBsC9997Lxo0bG3VcTyBJEt9+u7puD1tdZBevuzIL0Kn4R6cgQnQw793Z7N27W+6SvNqBA8lUVJShDWkjdyluJ1iv4qFOQQxo6cehlH1MmfwCv/zyE3a7Xe7SHKJBg43HjBlz0S8prVZLZGQkAwYMoF+/fpc9xrx58xgxYgTNmzeveywnJ4fo6Oi6n0NCQrDb7ZSWlhIUFNTQ99Ck2e12vvjiU7ZuTUIT2LLJLHTnDH7a2juGL9LK+GDhPEbf+wADBw6RuyyvtHPndpQaH1RuumOa3JQKBT2b+9IuVMfa4xV88slH7NyxjQf+Po7mzWPlLu+qNCgUbrjhBtasWcOoUaOIiooiJyeH7777juHDhyNJEv/3f//HuHHjePTRRy/6+v3795OamsqLL77o0OLPCw31c8pxnc1sNvPOO++wc+dONCHt0IVfJ0sgSJKE3VK7AFix0YokSbIFk0Gj5O8dg/gmvYyVK5dTVVXKuHHj6oZCC85XUVHBoUMpqILaeNRQaGcINaj5e6cgUvJMJJ3KYOaMlxk5ahT33nsvBkPTHDXYoFDYuXMnS5cuJSHhv+v333777UyaNInVq1czcOBAnn/++UuGwp49e8jIyOC2224DIDc3l3HjxvHAAw+QnZ1d97zi4mKUSmWj7xKKiiqx25tWh09ZWSnz5s3h1KmT6MI7ow1tJ1stltITSJZKALIrrezNMdI9Wr5VWDUqBXe3D2TzyUrWrl1LVtYZHnvsaQyGprMybFP2888/Yrfb0Ae0kLuUJkGpUHB9pIG2oTq2ZFbwzTffsHXrVu677+9069bDLe/8lUrFJS+mG3QZcPLkSWJjL7wliomJITMzE4Brr732sqOG/vnPf7Jjxw62bt3K1q1biYyMZOnSpTzyyCOYTCb27t0LwMqVKxk8eHCD3lRTlpFxnOkz/o+s06fRN+8layAAWCuyL/g5vdgsUyX/pVQoGJzgz7DW/qQeOsDrr02noCBf7rK8ws6d21HpAlHqg+UupUnx1SgZ2TaQcdcFo7dW8cEH7/H2nNfJycn+6xe7kQaFQvfu3Xn55ZfJysrCbDaTlZXF5MmT6dq1KwDp6emEhTV+PL1SqWT27NnMnDmTgQMHsmfPHl544YVGH6epkCSJn37awr//PYsqoxVD3G1o/Jv/9QudXtiFk3EsbnTX1T3Kh/s7BFFUkMOsWZM5ejRN7pI82tmzp8nIOIY6MN4tr3CbgtgALY92DmZIgj8njx9h2rSJfP31Ssxm+S+2GkIhNWCgbWlpKTNnziQpKQmr1YparWbgwIFMnTqV4OBgTp48SVVVFZ06dXJFzfU0heYjo7GaTz9dyu+//4raLwp99E0oVFq5ywKgOutHbNX/nYwTF6jhH9eGyFhRfYXVVlYeKaPYZOf++x/i1lv7y12SR1qy5AN+/W0Xvgkj3GZJC0mSqEz/CiQbg/WffYsAACAASURBVFr6cWOMT5MJrMoaG0mZlaTkmwgNDeX++/9B585d5C7rss1HDQqF8+x2O8XFxRQWFvLdd9+xdu1aduzY4bBCr5S7h0JWViYLF75HQUE+2rBOaEPbu9WHuimEAoDJauero2WcKKmhX78B3HvvA6jVnrtap6udO3eWadMmog5uiz7iernLqVNTchxzbnLdz8MS/GXt87oSp8pqWJ9RSUGVha5db2Ds2IcIDJRvhOXlQqHBf1HFxcWsXbuWNWvWcPToUbp168bkyZMdVqQnstvtbN68ga++WoVCpcMQdytqH8/YpUoOerWSMR2CSMqsZOvWJHJyzvHUU8/h49O4OTNCfXa7nU8+WYJCpUUbeo3c5VzgYn1eTS0U4gO1PNY5mF1nq/ll/x6OpB1i9L0P0KtXH7e6QIS/CAWLxcLWrVv59ttv2bFjBy1atGDYsGGcO3eOd999l9BQMdPxUkpKilmy5EOOHElF7d8cfVT3Jr1BjrtQKhQMauVPuK+adUeP8Nqr0xj/3ETCwkTYXo31678nI+MY+qgbULpJs1EdN+7zagy1UsEtLXy5ppmOtScqWLZsMfuSf+fBhx4lKMh9OvUv29Hcs2dPpk2bRsuWLVm1ahUbNmzgqaeeQqt1j7Zwd7V3726mTJnA0fSj6CK7o4/pKQLBwa6PMDC2YxAlRXm8+spUTp06KXdJTdb+/cl8++1q1AFxqAM9f70tuTXzUfNgpyAGtfLjcGoKU6dOcKvl4y8bComJiVRUVJCSksKhQ4coKytzVV1NktFYzZIlH7Bw4TxqMODTchDa4AS3uz30FC2DtIy7NhiV1cjsN18hPf2I3CU1OUeOHGbhB/NQGYJr72bFZ9UllAoFN8X48tj1IfhRw7vvvsV//vM5NptN7tIuHwrLly8nKSmJnj178vHHH9OzZ08ef/xxqqurPX5N8cY6duwoU6dOYteuHWibdcAQ53mbmrujMB81/7g2qG6VVbGYXsMdOJDMO+/MBrUvhuZ9PHqLTXcV5qPmkeuC6RZpYOPG9bzzzptUVlbKWtNfzlOIiYnhqaeeYvPmzXzyySeEhYWhVCoZMWIEs2fPdkWNbs1qtfLNN//hzTdfoazShE/cbejCOonlAVwosG4xPQXz35tDRsYJuUtya7XzZZKYP/8dJE0A+thb3Wb4qTfSqBQMbxPAiDYBpB85zKuvTpF1v4ZGDUk9z2w2k5SUxJo1a1iyZIkz6moUuYak5ufn8eGH8zl16iTqwJboI7qgUDW9/WubypDUv1JRY2PZwVLMCi2Tp7xCZGSU3CW5HYulhhUrPmH79p9R+0Wjj7nJ7fdc9pTPZ0NkldXw5ZEy9D7+vPDiZGJinDO51WHzFNyVHKHw6687+PTTpVhtEtrI7mgCmu7KiJ70R1dstLIkpZTQyBimTHkFjca9v/BcKTv7HB988B7nzp1BG3oN2rBOTaIPwZM+nw2RV2Vh+eEylDpfXn55BhERjl+p9qrXPhL+y2w2s3TpIj76aCE2dQCGloOadCB4mhCDmpFt/Dlz5jRff71S7nLcwvnlVWbOnExOXgGG2FvQhV/bJALBG0X4avh7hyCspiremv0qZWWlLj2/CIVGyMk5x6xZU9i5s3a7TEOLW1FqxMQpd5MYqqNrpIEtWzaRn58ndzmyKikpZu7cN1i+/GMkbQiG+EGo/aL/+oWCrMJ91Yy9JpCK8lLmz38bi6XGZecWodBAycl7mDVrKnkFRRhi+4rOZDfXp4UvSiTWrVsjdymykCSJ7dt/ZvLkl0g7cgRdRFf0sX1QaprmGv/eKNpfw9/a+nPyZAZffbXKZecV32p/wW638+23q1mw4B2sSl8M8QNR+4ndqNxdgE5FxzAdyXt3e8w2iQ1VVFTI3LlvsGzZYixK/9r5MiFtRHNRE3RNMz03RBtISvrBZcOtRShchtls5sMP57N27bdoAltiaNEPpaZprbnizRKCtBhNJrKyTsldiktIksS2bVuZMmXCH3cHXWqbOMV8mSZtQLw/oT5qln+21CXNSCIULqGyspK35rzO3r270YV3rt07WemZW0JKNgt6vZ4RI0ag1+sxWz3jyrqZT+1krOLiS28A5SmKi4uYO/cNPv10CVZ1AD4tB6MNaSvuDjyARqVgaCs/8gvy2bJlk9PPJ6YwXkRJSQlz5rxObm4O+pieHj+6SLJbGDhkII8++mhtW/Tm9XKX5BDnRymrVJ597fP777/yySdLMNdY0EV0RRPcWoSBh0kI1tE6WMuG9d/Rt29/p+7/LELhf5SVlfHm7FcoKChEH9sHtW+E3CU5nUKpYfPmzUiSRFJSEkFqz/hCKaiuXYolONgzV/M1m82sWLGMnTt/QWUIxafljaKpyIP1jfNjyYFidu78hf79BzntPJ59CdVI1dVVzJ796h+BcItXBAKAQqXBZDKxdu1aTCYTOrVnfCyOFZsJ8PcnNtbzNqDPz8/j1VensXPnL2hDrxFrbXmB5v4amgdo2frjJpw559gz/vodwGq18v6Cd2ubjJr3FpvhNHEF1VaOFpm5oUdPlErP+pifOHGMmbOmkJOX/6eJaJ71Hv/MU/u8rsT14Tpy83I5ffqU087huZ+kRvrmm1UcPXIYXWQ3r7lD8FSSJLH5ZCVarY7hw0fJXY5DpaamMPut16ixKTHE9feKiWiS3cLAgbV9XgMGDMBkbfIr81yx9s30KICUFOftvyD6FKhd9nrjxg1oghLQBLWSuxzhKu06V83xEjP33juWgIAAuctxmBMnjvHe/LlIKr/aiWhqvdwluYSn9nldCR+Nkkg/jVP3DvH6OwVJkvjii89QaX3QRXSWuxzhKh0vNrPlVCVdu97AgAFD5C7HYUpLS5g3bw6SUu9VgQCe2+d1pcJ9VORkn3Xa8b37XxdIS0vl9OlTqEOucfslhIXLO1FiZtWRMpo3b8HDDz/mUcMyP/10CdVGI7qY3l4VCEJ9AToVZeXlTju+14dCSso+FEo1msB4uUsRrkJ6kZmVaWVExcTy0kuTnTqO29UyMk6QkrIfTWhHVDrPaQ4TroyC2hYOZy3f4vV9CqdOZaLUB3nsbGVPJ0kSu7ONbMqsIK5FPM+/8DJ+fp41NPOXX7aiVGnQBreWuxTBDZTX2AjwD3DaqDqvDwWNVgtNf58hr2S1S2w8WcHeHCNdru/Go/98Ep3O85pWjh8/hsIQ1iR39RMcS5IkzlTYaB7vvLk3Xh8KEeERpKenI9lqUKi0cpcjNFCZ2cbqI+WcrahhyJDbufPO0R43H+G8iooKlBoxTFqAc5VWiqotDO16g9PO4Zl/RY1w660DsNss1BQ5b4iX4FjHi80sOlBCYY2CJ58cz9133+exgQBg8DEg2UxylyHITJIkfjxViZ+vLz163Oy083juX1IDxca24Oabe1NTdISa0gy5yxEuw2qX2JxZweeHSwluFsnUaa/RrZvzrpjcRds2idiNBUiS987kFeC37GoyS2sYOepufHyct4S/S5qPSkpKmDBhAqdPn0ar1RIXF8esWbMICQnhwIEDTJs2DbPZTExMDG+99Rahoa5dwOzBBx+hvLyM1NQ9SFYT2tD2Hr1sQFNUbLTydXo55yos9O17G/fe+wBarXc0991ww43s2rUda9kpMbnSSx0pNJGUWUmXLt3p12+AU8/lkm8+hULBI488wqZNm1i7di2xsbHMmTMHu93OSy+9xLRp09i0aRPdunVjzpw5rijpAhqNhqeeeo7u3W+kpuAQxqwfsZudNw7Y7SguvDbQKN1rfP/BfCOLDpRQYlXz5JPP8ve/j/OaQADo1Kkz8fGtqCk8hN0qmpG8zYE8I6uPlhEf34px4x53+vwbl4RCUFAQPXr0qPu5c+fOZGdnk5qaik6no1u3bgDce++9bNy40RUl1aPT6XjiiWd47LGn0WCkKnMjppw92C1VstTjSmr/C9fPSQzRyVTJhcw2O9+ml/FNejmxcQnMmPkG3br1+OsXehiFQsFDDz2Kwm7BnLNbNCN5CatdYkNGOWuOldO2bXteeNE1829cPvrIbrfz5Zdf0q9fP3JycoiO/u8XUkhICHa7ndLSUoKCghp8zNBQP4fVN3z4IG6+uTv/+c9/2LhxI9XlmagDW6ENbovSQycOaYJaU1OUjmSpJNpPTbco+Sd+5VZZ+OpoBUVGK6NHj+a+++5DpfLeuSRhYR157LF/snDhQsy5yegiu3nUjG3hQjmVFr4/UUFOhYWRI0fy4IMPotG4Zkiyy0PhlVdewcfHh7Fjx5KUlOSQYxYVVWK3O3KugYY777yfvn0HsXbtt+zYsY2qkhOofSNQB7VG7R/jUX0OCoUCpcaAzVJJiEEt+5fNvlwjGzIq8PHz58UX/0X79h0oLq6WtSZ30K1bL4YNO8f69d+BQoEuoqvs/18JjmW22vn5dBW/ZVfj5+vP008/TZcu3SktNQGOazpUKhWXvJh2aSi8+eabZGVl8eGHH6JUKomKiiI7O7vu98XFxSiVykbdJThTaGgzHnroUUaNuovt23/mp59+pPTcTpQaH9QBLVAHxKHUBYk/TAex2iV+yKggOdfINe078Og/nyYwMFDustzKHXfcg81mY+PGdbX7DHjw3uHexGqXSM4x8svZaqpqbPTp04+77roXX1/HtYI0lMtCYe7cuaSmprJ48eK6TsKOHTtiMpnYu3cv3bp1Y+XKlQwePNhVJTVYUFAwt9/+N4YOHcHBgwfYtu1HUlMPUlN0FJU+EJV/CzQBcSi1rv8/0FNU1thZdaSMM+WePxntaigUCu6++z58fX35+utVGK3V6GN6oVS7Rz+Q0DhWu8TBfBPbz1ZTYrSSmNiee+4ZQ8uWCbLVpJCcua/bH44fP87w4cOJj49Hr69dhqB58+YsWLCAffv2MX369AuGpDZr1qxRx3d889Ffq6goZ+/e3ezatZOMjGMAqAwhqPyao/Fv3uT6H6qzfsRWXUDHMD13tXPt1XmR0crnh8uosCp45JEn6N79Rpeev6n6/fdf+WjJB6DUoYvuicoQIndJTlF9ehu2qpy6n1sHaxnbMVjGiq5ejU1if66RXdlGykxWWsS24M677qNjx2td0vJwueYjl4SCs8kRCn9WWFjAnj2/sXfv72Rm1k6AU+kCUfk3R+0Xg1If7PZNTHKFQnalhRWHy1Bq9Dw7fgIJCW1cdm5PkJl5kvnvz6WsrAxdRFePnMdQU3Icc25y3c/DEvzpHu28yVvOVFFj4/dsI8m5JqotNlontGH47X+jU6frXPodIULBhYqLi9i3by979+7m+PF0JElCpfVF6RuN2i8GlW8YCoX7tQHLEQq5lRY+TS3Dxz+QF1+aQkREpEvO62nKy8v48MP5HD2ahjqwJfrIriiUnrOsmSRJVKZ/BZKNQS39uDHGx+0vsv7X2QoLv2dXc7jAjF2S6Hx9VwYNGkbbtu1kqUeEgkzKy8tJSdnH/v3JpB4+iNViQanSoPSNQu3fHLVvlNusfOnqUCgyWll6sBSdjz+TXp5BWFi408/pyex2O9999zVr165BpQ9EF30TKp3ndNJXpH8D9hpevimsyey8ZrFLpBaY2JNjJLvCgk6no3fvvvTvP5jwcHkXOHSb0UfeJiAggN69+9K7d1/MZjNpaans35/M/gPJVJ07jUKhROkTjtovBrV/DEpN07wlbiyzzc6qI+UoNHomTJwqAsEBlEolf/vb3bRpk8iixQuoPpWELrKb2DxKBkVGK3tzjBzIN2O02IiKjGLsyMHcdFOvJrH5kwgFF9HpdFx/fVeuv74rdrudjIzj7N+/l+TkvRTkJWPOS0ZlCEXtH4vav7lHj2Raf6KCgmorzz//IhERUXKX41E6dryWWTPf4MMP3+P48d+wVeeji+jiUc1J7sgmSRwvNrMnx0hGSQ1KpZLrr+9Gv34DaNfumibV3CWaj2QmSRI5Odns27eHPXt/58zpU8CfRjIFxKLUOn8nMVc1H50sMfNZaikjRtzBqFF3Oe083s5ms7FmzWrWr/8elSEEffTNTfpCw12bjyprbOzLNZGcZ6LMZCUoKIi+ffvTu/etBAe77wgp0XzkxhQKBdHRMURHxzB8+CgKCvJJTv6dPXt2k5l5kJqCg6gMzVAHxKEOaNGkx6PbJYmNmVU0C23GsGEj5C7Ho6lUKu68814SEtqy+KOFGLOS0EXdiNpP3Jk5Qk6lhd/OVZNaaMZml2jf/hrG9htE585dmvxyLCIU3ExYWDiDBw9n8ODhFBcXsXv3Lnbu3E52djLm/P2ofSPRBCWg8otqckttnCqtIb/KwqNj7kGj8Z5VTuXUuXMXZkx/jfnz53LuzC/owq9DE5LYpJoz3IVdkjhWbObXc0ayymrQabX06duf224bSFRUjNzlOYwIBTcWEhLKkCG3M2TI7Zw5c5pff93Brl3bKT+7HZXWF1VgApqgVijVTWNf4v15JnwMBq/YGMedhIdHMGXKTJYuXcTevbuxm8vRRXV1y6HR7sgmSRwuMLHjrJH8KguhISHcc88QbrmlLz4+vnKX53AiFJqI2NgWxMaO4Y477uHAgWR+/DGJ9PSDWApTUQe2RNvsGpQa9/6AZpVbufb6G8Rdggx0Oj1PPPEM3333Nd9//w12axWGmF5uMyTaHUmSxOFCM1uzqig2WomOiubRMaO44YabmnwT0eWIUGhi1Go13br1oFu3HmRnn2PLlo388svPVJdlunU4VFnslJuttGjRUu5SvJZCoWDUqLsID4/g448XYTy9FX3zW1Bq3H+YpKtlldWwKbOS7AoLMTHNefpvd9O5c1evWI9LhEITFh0dw9//Po5hw0ayYcP3bPvlJ6rLs9A264gmpK1b9TlU1tRuDOPqrVaF+m6+uTcBAYG8//47mM5sRd+8T5MemeRIJqudpMxKknONBAcHM27caG66qZdXhMF53vNOPVhoaDMeeOBh3vj3XK679lrM+QcwnkrCZiqVu7Q69j9GPnvybXdT0rHjtbz00v+hUdownd6KvaZC7pJkd6a8hoX7StiXZ2Lw4GG8/vpceva8xasCAUQoeJRmzcL4179e4KmnnsNXa8eYtQVL+Rm5ywJAq6od7WI0GmWuRDgvIaENkyZORadRYDz9k1cHw75cI58cKkUXEMyUKbO455770ema7vDvqyFCwcMoFAq6du3OjBmvEx8Xh+ncTmqKj8ldFgHa2juE4uIimSsR/qxFizgmTZqKQafC5KXBkJxTzffHy0ls14GpU1+TdS8DdyBCwUMFBQUzadJUru/SDXPePixlWbLWo1EpCNSrOXfurKx1CPU1b96CCS9NRqdRYDrzM/aaSrlLcpljxWbWZVTQqeO1jB8/AT8/0bciQsGDaTRaHn/sadq0ScScsxubuUzWeqJ9VWSePCFrDcLFxca2YMKEyWhUUm0wWDx/T2yj1c7aExXERDfnyaeeQ60W425AhILH02i0PPXUePQGA+bcPci51FWMv4aCwgLKyuQNJ+HiWrSI58UXJqFWWP8IBs/u/9l1tppKs41/PPy41/YfXIwIBS8QEBDIvaPvx1ZdiK0yW7Y64gJrJ0odP54uWw3C5bVq1ZoXXpiESjLXBoPVJHdJTmG1S+zLM3Fd5y60bOl5u9VdDREKXuKmm3oRFByCpUS+L+QoPw1qpYKMjOOy1SD8tdat2/LccxNQ2o21nc9Wz7tjOFtuoarGRs+efeQuxe2IUPASarWavn36Ya3Kl61ZQK1UEOWnJuOEuFNwd4mJ7Xn++Yn/DQYP62PILKtBoVDQvn0HuUtxOyIUvEi3bj0AsFbKNwIo0rd2BJIHbOPh8RIT2/Piiy+jpsbjJrhlV1qIiozCx8c7djtsDBEKXiQqKprg4BBsVXmy1RBiUGE0maio8JwvGE/Wpk0iEyZMQa8BY9aP2IzFcpfkELlVduLiRV/CxYhQ8CIKhYJrrumI3Vgo25W6r6b2I1ddXSXL+YXGa9myFZMnzyQwwBfj6a1YK87JXdJVKTfbqDBbiYsTizNejAgFL9OmTSJ2qwlJpqYA5R+bu9hsNlnOL1yZyMgopk19hdjmzTGe3UFNUXqTbQLMLK0BapvHhPpEKHiZNm3aAmA1Fshy/mpL7Wqpoi236QkMDOLll6fRpUs3zPn7MeX8jmRveuF+uNBMQEAAsbEt5C7FLYlQ8DKRkdH4+vljq5YnFIqMVnRaLUFB7rupuXBpOp2eJ598lhEj7sBalonx9I/YLU2nKbDEZON4iZmePft43eqnDSX+VbyMQqGgfbtrkKrzXX77L0kSGaVWWrZqLfYIbsKUSiWjRt3Fv/71Amp7NcZTm7FWyDcpsjF+yqpErVJz220D5S7FbYlQ8EIdO16LzVKN3cVrIeVWWSmstnDDDTe59LyCc1x/fVdmzHiN6MgIjGd/wZx/AEmyy13WJR0vNnMw38TAQUMJCRGbPV2KW4RCZmYmo0ePZtCgQYwePZpTp07JXZJH69SpMwDWSteOItl2ugq9Tke3bje49LyC80RERDF16iz69OlHTdFRjFk/uuUqq+VmG9+dqCA6KpoRI+6Quxy35hahMH36dMaMGcOmTZsYM2YM06ZNk7skjxYcHEzLlgnYKlw3iS2rrIajRWYGD7kdPz9/l51XcD6NRsuDDz7CE088g0aqxnhqk+xLtf9ZjU3iy7QyLKh4/Iln0Gi0cpfk1mQPhaKiItLS0hg+fDgAw4cPJy0tjeJiz5gk46569LgJm6kEu7nc6ecyWe2sOV5BaEgIAwcOdfr5BHl0734js2a9QXx8PKbsXzFm/4Zkszjk2ArNlY1Ws9gkvkwrJbfKyuOPP0vz5mLE0V+RfQHxnJwcIiIi6vbuValUhIeHk5OTQ0hISIOOERoqNsZorMGD+7Nq1edYyrPQhXVCE9gKe3Whw88jSRJrj5dTarLx5sxJxMaGOfwcgvsIC/Nn7ttvsXLlSlatWoXRVIQu6kZUhqtrw1dqfLCZG7fnuMUmsepIKafKahg//jn69bvlqmrwFrKHgiMUFVVitzfNiTTy0dK+fUfST5xEatYRTVBLanL3OPwsP2ZVcrjQzJ133kuzZs0pKBDLW3iDgQNHEB/flkWLFlCS9SPaZp3QhrZz2agzs83OyrQyTpXW8OBDj9KpU3fx2fsTpVJxyYtp2ZuPoqKiyMvLq5vharPZyM/PJyoqSubKPF/Pnr2x1VRhq853yvF3Z1ez40w1ffr0Y+jQ251yDsF9tW3bjlmz/k23rt2pKUhx2f4M1RY7y1PLOFVmYdwjT3DLLbc6/ZyeRPZQCA0NpX379qxbtw6AdevW0b59+wY3HQlXrkuX7uh0eixlpxx+7N3Z1fyQUUHnzl0ZO/YfYl6Cl/L19eOJJ57hwQcfAXMxxlObsDpxQcaKGhufHiolt8rGk0+O5+abezvtXJ5K9lAAmDFjBitWrGDQoEGsWLGCmTNnyl2SV9DpdHTv3gN75Vkku9Vhx/31XFVdIDz55LN1/UWCd1IoFPTp049p014hLCQI4+mfMRcdcfjkyVKTjWUHSym1KHh2/AS6du3u0ON7C7foU0hISGD16tVyl+GVevS4mR07tmGtzLnqY0mSxE9ZVfxypoouXbrz+OP/EpuhC3WaN2/B9Omv8fGyRezdsxu7sQh99I0olFf/GSkx2fjkUCkWhYYXXpxI69ZtHVCxd3KLOwVBPu3aXYOvrx/WijNXdRy7JLH+RAW/nKmid+++PPHEMyIQhHr0ej1PPP4M99xzP7bKc7WT3a5yV7fSukDQ8tKEKSIQrpIIBS+nUqno3LkL9uo84Mpu5y12if8cKWNvrpEhQ27noYceFU1GwiUpFAoGDx7Gs8++hFoyYsragu0Kl1ypqrGz/HAZNah5acJksUeCA4hQEOjUqTN2q/mK2niNVjvLU0s5WmTmvvse4O677xOdykKDXHttZ15+eTq+Bg2mrB+xGYsa9XqrXeLLI2WUW+DZ8RNEIDiICAWBdu2ubLORihobnxws5Vyljccee5oBA4Y4uDLB07VoEceUKbMIDg7EdObnRgXDhowKzpbX8OijT9G2bTsnVuldRCgIBAQE0iwsvFGvKTHZ+PiPkR7jx0+gR4+bnVSd4OmaNQvj5UnTCAkOxnT2lwY1JR0uMLEv18iwYSPFAosOJkJBACC+EbfehdVWlh0qpUahZcLEqXTo0MmJlQneICQklJde+j98DDrMZ3+57CS3aoud9RmVxMfFM3LknS6s0juIUBAAiI6OadDzCqutfJJaiqTWM2HiNFq2THByZYK3CAsL5/nnJqCw12DK/vWSezNsO12F0WrnoX88Jka4OYEIBQGAiIjIv3xOicnGZ4fLUGh8mDhputjjVnC4+PhWPPDAP7BV5VFTlF7v95UWO8m5Rnr16kOLFnEyVOj5RCgIAH+5E1VVTe0oI6tCwwsv/l+D7ywEobF69epD167dsRQeqre0+55sIza7xNChI2SqzvOJUBAAiImJRX2JuQVWu8SqI2VUWOC55yeJKzTBqRQKBWPHPoxWq8Wcf+CC3x0sMHFNh04NurMVrowIBQEAPz8/gi9xt/BDRgWny2sY98gTJCS0cXFlgjcKDAxk+PCRWCuzLximWm2xiz2+nUyEgnBZhwtMJOcaGTp0hPhjFFyqX78B6PUGaoqOXvB4x47XyVSRdxChIFxSRY2NdRmVxMe3ZNSou+QuR/AyBoMPvXv3xVZ5DslqBuDuu8cQHBwsc2WeTYSCcElJmZVY7PDPfz4thv4Jsujbtx96gx4spYSHRzJwoJg172ziL124qLPlFg7mmxg2bASRkWIXPEEeUVExLHh/idxleBVxpyBc1LYzVfj6+jJs2Ci5SxEEwYVEKAj15FVZOV5sZsCAIej1ernLEQTBhUQoCPXszzOiUqno12+A3KUIguBiok9BuMDxYjM2Ca7r3A0/P3+5yxEEwcVEKAh1Bg0ezpEjqSiVnmVmXQAAB0BJREFUSoYMEcsICII3UkhXst2WmykqqsRub/JvQxAEwSWUSgWhoX4X/52LaxEEQRDcmAgFQRAEoY4IBUEQBKGOCAVBEAShjggFQRAEoY4IBUEQBKGOR8xTUCoVcpcgCILQZFzuO9Mj5ikIgiAIjiGajwRBEIQ6IhQEQRCEOiIUBEEQhDoiFARBEIQ6IhQEQRCEOiIUBEEQhDoiFARBEIQ6IhQEQRCEOiIUBEEQhDoiFITLmj9/Pm+++abcZQhN2JYtWxgyZAijRo3i5MmTTj3XpEmTWLFihVPP4ek8Yu0jQRDc18qVK3nmmWcYMmSI3KUIDSBCwYMlJiYyfvx4tmzZQmlpKa+++iq7du1i+/btWK1W5s2bR0JCAgUFBTz//PNUVVVhNpvp06cPEyZMuOgxFy9ezObNm7HZbERERPDKK68QFhbm4ncmNBWvv/46ycnJZGZm8sUXX/Diiy8yZ84cqqqqAHjmmWfo27cvZ8+e5c477+See+5h+/btmEwm5syZw8qVK0lJSUGv17Nw4ULCwsJIT09n5syZGI1GzGYz99xzDw899FC9c9fU1PDOO++wZ88eampqSExMZMaMGfj6+rr4X6GJkQSP1bZtW2nFihWSJEnShg0bpM6dO0tbt26VJEmSFi9eLL3wwguSJEmSyWSSKisrJUmSpJqaGumBBx6Qtm3bJkmSJL333nvSG2+8IUmSJK1Zs0aaMmWKZLPZJEmSpM8//1x6/vnnXfqehKZn7Nix0tatW6WysjJp5MiRUl5eniRJkpSXlyf17t1bKisrk86cOSO1bdtW+umnnyRJkqSPPvpI6tq1q5SWliZJkiRNnz5dmjt3riRJklRRUSGZzWZJkiSpsrJSGjJkiHTixAlJkiRp4sSJ0vLlyyVJkqQFCxZICxYsqKtj9uzZdccQLk3cKXi487fsHTp0AODWW28FoGPHjiQlJQFgs9mYPXs2+/fvR5IkCgsLOXr0KLfccssFx9q6dSupqan87W9/q3udn5+fq96K0MTt37+fs2fP8uijj9Y9plAoyMrKIjg4GB8fH/r27QvUfl4jIyNp37593c+7du0CwGQyMWPGDNLT01EoFOTn53P06FESEhIuON/WrVuprKxk06ZNQO2dQ7t27VzwTps2EQoeTqfTAaBUKtFqtXWPK5VKrFYrAMuWLaO8vJzVq1ej0+mYOnUqZrO53rEkSeKJJ57grrvuck3xgkeRJInExEQ+//zzer87e/Zsvc/nn39WqVTYbDYA5s6dS1hYGG+88QZqtZqHH374kp/X6dOnc9NNNznh3XguMfpIoKKigrCwMHQ6HXl5ef/f3v2EwrvFcRx/TyNZzCQplJRZWwiNhURmo4mYZGoUK/KnSJGhSBJSs7CZ/C1moSllR6zIbLCYjVlNNKGIhUQzSpR7F1NP6c693fG7/fx+3c9r+Tynp3NWn3PO8zzfw8HBQcp2DoeDYDDI8/MzkJx5RaPRn9lV+Y2VlZVxfX3N6empcS0SifBHmke6xONxCgoKyMjI4Pz8nHA4nLKdw+EgEAjw+voKQCKRIBaLfX0A/xNaKQgdHR0MDg7S2NhIfn7+386sXC4XT09PtLe3A8mZWFtbm5bk8q9kZ2ezuLiIz+djbm6O9/d3ioqKWF5eTus5fX19eL1etre3sdls2O32lO26u7vx+/20trZiMpkwmUz09/f/ZZtJPtPJayIiYtD2kYiIGBQKIiJiUCiIiIhBoSAiIgaFgoiIGBQKIiJiUCiIfEE4HMbj8VBRUUFlZSUej4dIJPLd3RL5Yfp5TSRNiUSC3t5epqamcDqdvL+/Ew6HP5VlEPldaaUgkqbLy0sAGhsbMZvNZGVlUV1dbfzZvb29jdPpxG6309nZye3tLZAsO+52u42aU8FgkIaGhpR1e0S+i0JBJE02mw2z2czo6CihUMioBQXJU8ZWVlbw+/2cnJxQUVHB8PAwAF1dXWRmZrK0tMTV1RULCwv4fD6jaKHIr0BlLkS+IBaLsba2xvHxMQ8PD9TU1DAzM8PY2Bj19fW43W4APj4+KCsrY29vj8LCQm5ubmhpaSE3NxeXy0VPT883j0TkM4WCyA+KxWKMjIxQXFxMNBrl7u4Os9ls3H97eyMQCFBeXg7AwMAAoVCI4+NjnUchvxyFgsh/YHNzk62tLfLy8mhubqapqSllu6OjIyYmJigpKSE/P5/p6emf3FORf6Z3CiJpisVirK+vc39/D8Dd3R27u7uUlpbi8XhYXV3l4uICSNb+39/fB+Dx8ZGJiQlmZ2eZn5/n8PCQUCj0beMQSUWfpIqkyWKxcHZ2xsbGBvF4HKvVSl1dHV6vF4vFwsvLC0NDQ9ze3mK1WqmqqsLpdDI5OYnD4aC2thaA2dlZxsfH2dnZIScn55tHJZKk7SMRETFo+0hERAwKBRERMSgURETEoFAQERGDQkFERAwKBRERMSgURETEoFAQERGDQkFERAx/AsEJzGIKyf/cAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3jU6XQLAaCjU",
        "colab_type": "text"
      },
      "source": [
        "This graph gives a summary of the age range of men, women and children who were saved. The survival rate is –\n",
        "\n",
        "* Good for children.\n",
        "* High for women in the age range 20-50.\n",
        "* Less for men as the age increases."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QEDDOndPaZ37",
        "colab_type": "text"
      },
      "source": [
        "### 5. Embarked\n",
        "\n",
        "C = Cherbourg, Q = Queenstown, S = Southampton"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5M7RmHiqaiYB",
        "colab_type": "text"
      },
      "source": [
        "* Which port has maximum boarding?\n",
        "* Does this correlate with survival?\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "clNjXN8BIjR4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 319
        },
        "outputId": "a261e509-70b9-4e6c-faa0-acb5507b3fb6"
      },
      "source": [
        "sns.catplot(x ='Embarked', hue ='Survived',  \n",
        "kind ='count', col ='Pclass', data = train_df) "
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f17fad36080>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1141.55x360 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1DPuC3oka_DD",
        "colab_type": "text"
      },
      "source": [
        "### 6. Family.\n",
        "\n",
        "In our dataset we had SipSp and Parch columns which told us about various family relations. So I thought it would be better to combine them into a single column named Family and get insights."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oRoN9PL1bBhM",
        "colab_type": "text"
      },
      "source": [
        "* What is the optimal Family size to survive the catastrophe?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z4QzGc1wltG5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Sibsp + Parch + Family, make them into Family COlumn\n",
        "all_df = [train_df, test_df]\n",
        "\n",
        "for i in all_df:\n",
        "  i['Family'] = i['SibSp'] + i['Parch'] + 1\n"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gKbYAZlw0P4u",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 436
        },
        "outputId": "34411e34-bd62-49d0-ccba-b70e9c611f8e"
      },
      "source": [
        "# Factorplot for Family_Size \n",
        "sns.factorplot(x ='Family', y ='Survived', data = train_df)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-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"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f17f65c08d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j9NCCeqFb9bb",
        "colab_type": "text"
      },
      "source": [
        "If the family size is greater than 5, chances of survival decreases considerably"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AGpX1CrNbpTZ",
        "colab_type": "text"
      },
      "source": [
        "### 7. IsAlone \n",
        "\n",
        " It can be possible that some passengers might be travelling alone. So it is better to add IsAlone column."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5CjRqwPd0NVn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "all_df = [train_df, test_df]\n",
        "\n",
        "for data in all_df:\n",
        "  data['IsAlone'] = 0\n",
        "  data.loc[data['Family'] == 1, 'IsAlone'] = 1"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dc_7BB3orpAW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 436
        },
        "outputId": "30adf1a0-6d7e-4a66-bf8e-975d0029289e"
      },
      "source": [
        "# Factorplot for Alone \n",
        "sns.factorplot(x ='IsAlone', y ='Survived', data = train_df)\n"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-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"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f17f64ae828>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zWQObPrWcIvq",
        "colab_type": "text"
      },
      "source": [
        "If a passenger is alone, the survival rate is less"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YrN4_TBtcT2p",
        "colab_type": "text"
      },
      "source": [
        "### 8. Fare\n",
        "\n",
        "As we have seen from data exploration that Fare is a continous column and also has some missing values. Hence, we wil group Fare values in an assigned group and visualize it."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "So5zQBwXmDOC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#So we will fill whose empty rows before doing computation.\n",
        "\n",
        "all_df = [train_df, test_df]\n",
        "\n",
        "for data in all_df:\n",
        "  data['Fare'] = data['Fare'].fillna(data['Fare'].median())\n"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w3v4C2PMvY1L",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "outputId": "696c64f6-47b2-4503-a78c-8cb7eb7c8197"
      },
      "source": [
        "# Barplot - Shows approximate values based  \n",
        "# on the height of bars. \n",
        "sns.barplot(x ='category_fare', y ='Survived',  data = train_df) "
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f17f64989b0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SoWUn0NUec-d",
        "colab_type": "text"
      },
      "source": [
        " It can be concluded that if a passenger paid a higher fare, the survival rate is more."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0jKsoaKMcFas",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "cb1ef5d9-1900-450c-8024-2a7c20930ce4"
      },
      "source": [
        "print( train_df[[\"Sex\",\"Survived\"]].groupby([\"Sex\"], as_index = False).mean() )"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "      Sex  Survived\n",
            "0  female  0.742038\n",
            "1    male  0.188908\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0rowJ_n9b6OU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "6e8cd5c6-fd83-4c83-fd09-3669cb87ee73"
      },
      "source": [
        "print( train_df[[\"Pclass\",\"Survived\"]].groupby([\"Pclass\"], as_index = False).mean() )"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "   Pclass  Survived\n",
            "0       1  0.629630\n",
            "1       2  0.472826\n",
            "2       3  0.242363\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "05PIQtKIls9Z",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "c7ab1d84-965c-46a0-9d47-9d55069daedf"
      },
      "source": [
        "print( train_df[[\"Embarked\",\"Survived\"]].groupby([\"Embarked\"], as_index = False).mean() )"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "  Embarked  Survived\n",
            "0        C  0.553571\n",
            "1        Q  0.389610\n",
            "2        S  0.336957\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6BD2SJ3QvCAB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        },
        "outputId": "9c2dd1e1-18f3-4880-9761-5b50db23f2dd"
      },
      "source": [
        "print( train_df[[\"Family\",\"Survived\"]].groupby([\"Family\"], as_index = False).mean() )"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "   Family  Survived\n",
            "0       1  0.303538\n",
            "1       2  0.552795\n",
            "2       3  0.578431\n",
            "3       4  0.724138\n",
            "4       5  0.200000\n",
            "5       6  0.136364\n",
            "6       7  0.333333\n",
            "7       8  0.000000\n",
            "8      11  0.000000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VMQyz3teltLF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 111
        },
        "outputId": "49b65ebd-11e5-47d3-f4db-9d5bf796f5ec"
      },
      "source": [
        "train_df[['IsAlone', 'Survived']].groupby(['IsAlone'], as_index=False).mean()"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>IsAlone</th>\n",
              "      <th>Survived</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>0.505650</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>0.303538</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   IsAlone  Survived\n",
              "0        0  0.505650\n",
              "1        1  0.303538"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yKa92RHNigfP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        },
        "outputId": "05655650-33d1-46b0-c6bb-21808c92fdb0"
      },
      "source": [
        "train_df['category_fare'] = pd.qcut(train_df['Fare'], 4)\n",
        "print( train_df[[\"category_fare\",\"Survived\"]].groupby([\"category_fare\"], as_index = False).mean() )"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "     category_fare  Survived\n",
            "0   (-0.001, 7.91]  0.197309\n",
            "1   (7.91, 14.454]  0.303571\n",
            "2   (14.454, 31.0]  0.454955\n",
            "3  (31.0, 512.329]  0.581081\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4Pv97FvJt9Cy",
        "colab_type": "text"
      },
      "source": [
        "## Cleaning the Data and Dealing with Missing Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZsiQi6aufV2W",
        "colab_type": "text"
      },
      "source": [
        "It can be seen that, Age, Cabin and Embarked have some NaN values\n",
        "Cabin has more than 75% missing values, so it would be wise to drop it."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4v3D84o2OMpC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_df = train_df.drop(['Cabin'], axis = 1)\n",
        "test_df = test_df.drop(['Cabin'], axis = 1 )"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9XCWh8tiffmC",
        "colab_type": "text"
      },
      "source": [
        "we can also drop the Ticket feature since it's unlikely to yield any useful information"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ie4QAqo0UkkG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_df = train_df.drop(['Ticket'], axis = 1)\n",
        "test_df = test_df.drop(['Ticket'], axis = 1)"
      ],
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jJzxyNeHf0gp",
        "colab_type": "text"
      },
      "source": [
        "### 1. Clean Embarked\n",
        "\n",
        "Let us check the counts of total passengers boarded from respective ports."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UE0hKE80Uknu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        },
        "outputId": "5852b72c-cf9d-4677-b381-0307db59744a"
      },
      "source": [
        "print(\"Southampton(S):\")\n",
        "southampton = train_df[train_df['Embarked'] == 'S'].shape[0]\n",
        "print(southampton)\n",
        "\n",
        "print(\"Cherbourg(C):\")\n",
        "cherbourg = train_df[train_df['Embarked'] == 'C'].shape[0]\n",
        "print(cherbourg)\n",
        "\n",
        "print(\"Queenstown(Q):\")\n",
        "queenstown = train_df[train_df['Embarked'] == 'Q'].shape[0]\n",
        "print(queenstown)"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Southampton(S):\n",
            "644\n",
            "Cherbourg(C):\n",
            "168\n",
            "Queenstown(Q):\n",
            "77\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_oDwzP1UgFwh",
        "colab_type": "text"
      },
      "source": [
        "It can be observed that most people boarded from Southampton. \n",
        "\n",
        "Replacing the missing values in the Embarked feature with 'S'."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sm_crFU5Yhbn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_df = train_df.fillna({\"Embarked\": \"S\"})"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KEdyWYX5gefR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "e70d27d1-1724-4f5f-b685-56006ea5cd85"
      },
      "source": [
        "train_df.Embarked.isnull().sum()"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OOZ0SK7JgjV4",
        "colab_type": "text"
      },
      "source": [
        "### 2. Get Title from Name\n",
        "\n",
        "Looking at the Name Column we can see many things. It contains the name of the passenger with its respective title.\n",
        "\n",
        "For Example, Master title has Age mean of 5 years.\n",
        "\n",
        "So let us extract the 'title' feature from the Name."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EZWqsWGLYiGO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "all_df = [train_df, test_df]\n",
        "titles = {'Mr':1, 'Miss':2, 'Mrs':3, 'Master':4,'Rare':5}\n",
        "\n",
        "for i in all_df:\n",
        "  i['Title'] = i.Name.str.extract(' ([A-Za-z]+)\\.', expand=False)\n",
        "  i['Title'] = i['Title'].replace(['Lady','Countess','Capt','Col','Don','Dr','Major', 'Rev','Sir','Jonkheer','Dona'], 'Rare')\n",
        "  i['Title'] = i['Title'].replace('Mlle', 'Miss')\n",
        "  i['Title'] = i['Title'].replace('Ms', 'Miss')\n",
        "  i['Title'] = i['Title'].replace('Mme', 'Mrs')\n",
        "  # convert titles into numbers\n",
        "  i['Title'] = i['Title'].map(titles)\n",
        "  # filling NaN with 0, to get safe\n",
        "  i['Title'] = i['Title'].fillna(0)\n",
        "\n"
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c6XGvyzlYwzQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_df = train_df.drop(['Name'], axis=1)\n",
        "test_df = test_df.drop(['Name'], axis=1)"
      ],
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gs5PuWhRYw3N",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "20111b6f-c2b2-4ef5-e487-d64a9e5b802a"
      },
      "source": [
        "train_df[['Title', 'Survived']].groupby(['Title'], as_index=False).mean()"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Title</th>\n",
              "      <th>Survived</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0.156673</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>0.702703</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>0.793651</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>0.575000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>0.347826</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Title  Survived\n",
              "0      1  0.156673\n",
              "1      2  0.702703\n",
              "2      3  0.793651\n",
              "3      4  0.575000\n",
              "4      5  0.347826"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k-tkQahzh-9u",
        "colab_type": "text"
      },
      "source": [
        "Numeric data is always easy to handle rather than string data. So I have converted the titles 'Mr, Miss, Rare' to integer data types for ease of use."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VT6api_hi_St",
        "colab_type": "text"
      },
      "source": [
        "### 3. Clean Age\n",
        "\n",
        "From Data Exploration Age had 177 missing entries so fare. So I have replaced it with average mean across all the rows. Also need to convert the Age column from float to integer"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vwKFf1cPYiLu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "all_df = [train_df, test_df]\n",
        "\n",
        "for i in all_df:\n",
        "  i['Age'] = i['Age'].replace(np.NaN, i['Age'].mean())"
      ],
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oPsWhnwji-K_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        },
        "outputId": "d875a9d6-0195-4429-fe53-5ee1301be8d0"
      },
      "source": [
        "train_df.Age.describe()"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    891.000000\n",
              "mean      29.699118\n",
              "std       13.002015\n",
              "min        0.420000\n",
              "25%       22.000000\n",
              "50%       29.699118\n",
              "75%       35.000000\n",
              "max       80.000000\n",
              "Name: Age, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ATnqXe-xjrcz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "all_df = [train_df, test_df]\n",
        "\n",
        "for data in all_df:\n",
        "  data['Age'] = data['Age'].astype(int)\n",
        "  data.loc[data['Age'] <= 16, 'Age']                        = 0\n",
        "  data.loc[(data['Age'] > 16) & (data['Age'] <= 32), 'Age'] = 1\n",
        "  data.loc[(data['Age'] > 32) & (data['Age'] <= 48), 'Age'] = 2\n",
        "  data.loc[(data['Age'] > 48) & (data['Age'] <= 64), 'Age'] = 3\n",
        "  data.loc[data['Age'] > 64, 'Age']                        = 4"
      ],
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ATAmMWhBj1AK",
        "colab_type": "text"
      },
      "source": [
        "Here I have grouped together Age ranges and assigned a number to it."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t4G9AmN-jrgs",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        },
        "outputId": "b2b83d74-9fb2-4c1f-fc44-7533bfc787c0"
      },
      "source": [
        "#Let us see its distribution\n",
        "train_df['Age'].value_counts()"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    525\n",
              "2    186\n",
              "0    100\n",
              "3     69\n",
              "4     11\n",
              "Name: Age, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qLGIv41SkJHF",
        "colab_type": "text"
      },
      "source": [
        "### 4. Handle Fare\n",
        "\n",
        "I had already created category_fare to bin Fare ranges together.  First we will convert it from float into integer."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f0IexQtDi-Ha",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Mapping Fare\n",
        "all_df = [train_df, test_df]\n",
        "\n",
        "for i in all_df:\n",
        "  i.loc[i['Fare'] <= 7.91, 'Fare'] = 0\n",
        "  i.loc[(i['Fare'] > 7.91) & (i['Fare'] <= 14.45), 'Fare'] = 1\n",
        "  i.loc[(i['Fare'] > 14.45) & (i['Fare'] <= 31), 'Fare'] = 2\n",
        "  i.loc[i['Fare'] > 31, 'Fare'] = 3\n",
        "  i['Fare'] = i['Fare'].astype(int)"
      ],
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HrcDQUlGYiQt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_df =train_df.drop(['category_fare'], axis =1)"
      ],
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8L3RRw05kH6e",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6pBS1Lkqmixg",
        "colab_type": "text"
      },
      "source": [
        "Now, if we look at dataset I can observe some categorical columns which could be easily converted to numeric values. \n",
        "\n",
        "For example, 'Sex' and 'Embarked'"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zl3rYBlDYiKR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Convert Sex column to Numerics\n",
        "#Mapping Sex Column\n",
        "all_df = [train_df, test_df]\n",
        "gender = {\"male\":0, \"female\":1}\n",
        "\n",
        "for i in all_df:\n",
        "  i[\"Sex\"] = i[\"Sex\"].map(gender).astype(int)"
      ],
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_cKzrRpY4bBC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Convert all Port names to Numeric Form\n",
        "#Mapping Embarked\n",
        "all_df = [train_df, test_df]\n",
        "port = {'S':0, 'C':1, 'Q':2}\n",
        "\n",
        "for i in all_df:\n",
        "  i['Embarked'] = i['Embarked'].map(port)"
      ],
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0_rv7X_-lsZH",
        "colab_type": "text"
      },
      "source": [
        "## Add New Features\n",
        "\n",
        "As seen earlier, I have created a 'Title' column extracted from Name column. Let me create one more column before moving on to model building."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DLAPIGW1mIEe",
        "colab_type": "text"
      },
      "source": [
        "### Age_Class "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mqHleIF32W46",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data = [train_df, test_df]\n",
        "for dataset in data:\n",
        "    dataset['Age_Class']= dataset['Age']* dataset['Pclass']"
      ],
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Pkb--hePM42U",
        "colab_type": "text"
      },
      "source": [
        "## Building Models\n",
        "\n",
        "Before using any Machine Learning Algorithm it would better if I eliminate some columns which would not be that useful."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SQ3moQDyngbs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_df = train_df.drop(['SibSp','Parch'], axis = 1)\n",
        "test_df = test_df.drop(['SibSp','Parch'], axis = 1)"
      ],
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AgRUPP_xM2zV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train = train_df.drop(['PassengerId', 'Survived'], axis= 1)\n",
        "y_train = train_df['Survived']"
      ],
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5ZfMvymSNlYn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_test = test_df.drop(['PassengerId'], axis = 1)"
      ],
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pEcoVbfQocbN",
        "colab_type": "text"
      },
      "source": [
        "### 1. Logistic Regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vO5uJJxTNljj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "b4945a95-3f06-4737-cebf-778084d1558c"
      },
      "source": [
        "#Logistic Regression\n",
        "logreg = LogisticRegression()\n",
        "logreg.fit(X_train, y_train)\n",
        "y_pred = logreg.predict(X_test)\n",
        "acc_log = round(logreg.score(X_train, y_train) * 100, 2)\n",
        "print(\"Logistic Regression Accuracy:\", acc_log)"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Logistic Regression Accuracy: 81.48\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gIz6q6aEohKo",
        "colab_type": "text"
      },
      "source": [
        "### 2. KNN"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "svVuppY-Nluf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "0e7af647-d021-4d7e-ab95-14cc29c420e6"
      },
      "source": [
        "#KNN\n",
        "knn = KNeighborsClassifier()\n",
        "knn.fit(X_train, y_train)\n",
        "y_pred = knn.predict(X_test)\n",
        "acc_knn = round(knn.score(X_train, y_train) * 100, 2)\n",
        "print(\"KNN Accuracy:\",acc_knn)"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "KNN Accuracy: 85.63\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3TJfVRePokA_",
        "colab_type": "text"
      },
      "source": [
        "### 3. Decision Tree"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VfkrGJ9ZNlzl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "46946b40-204e-420b-a702-5dbb05c731b9"
      },
      "source": [
        "#Decision Tree\n",
        "dt = DecisionTreeClassifier()\n",
        "dt.fit(X_train, y_train)\n",
        "y_pred = dt.predict(X_test)\n",
        "acc_dt = round(dt.score(X_train, y_train) * 100, 2)\n",
        "print(\"Decision Tree Accuracy:\", acc_dt)"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Decision Tree Accuracy: 88.78\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A8CdfgMXon2n",
        "colab_type": "text"
      },
      "source": [
        "### 4. Random Forest"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YqtRIVKVNlry",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "2c6c882a-1072-44f8-ccef-a61d47deb9c4"
      },
      "source": [
        "#Random Forest\n",
        "rf = RandomForestClassifier()\n",
        "rf.fit(X_train, y_train)\n",
        "y_pred = rf.predict(X_test)\n",
        "acc_rf = round(rf.score(X_train, y_train) * 100, 2)\n",
        "print(\"Random Forest Accuracy:\", acc_rf)"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Random Forest Accuracy: 88.78\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MvFSa_y0UEf9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "models = pd.DataFrame({\n",
        "    'Model' : ['Logistic Regression', 'KNN', 'Random Forest','Decision Tree'],\n",
        "    'Score' : [acc_log, acc_knn, acc_dt, acc_rf]\n",
        "})"
      ],
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jajai8EZUEkT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "outputId": "34588c96-c77f-44b8-bfd7-1b4b2278a59e"
      },
      "source": [
        "models.sort_values(by = 'Score', ascending= False)"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Model</th>\n",
              "      <th>Score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Random Forest</td>\n",
              "      <td>88.78</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Decision Tree</td>\n",
              "      <td>88.78</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>KNN</td>\n",
              "      <td>85.63</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Logistic Regression</td>\n",
              "      <td>81.48</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 Model  Score\n",
              "2        Random Forest  88.78\n",
              "3        Decision Tree  88.78\n",
              "1                  KNN  85.63\n",
              "0  Logistic Regression  81.48"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gLxgsGZbotSW",
        "colab_type": "text"
      },
      "source": [
        "It can be observed that Random Forest performed excellently"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5WH7DoRyoxag",
        "colab_type": "text"
      },
      "source": [
        "### K-Fold Cross Validation to validate a models performance"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qQhnyW8UZmV8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "cbd7c9e4-abe3-4d89-cf5a-97f46053bfc2"
      },
      "source": [
        "from sklearn.model_selection import cross_val_score\n",
        "rf = RandomForestClassifier(n_estimators=100)\n",
        "scores = cross_val_score(rf, X_train, y_train, cv=10, scoring = 'accuracy')\n",
        "\n",
        "print(\"Scores:\", scores)\n",
        "print(\"Mean:\", scores.mean())\n",
        "print(\"Standard Deviation:\", scores.std())"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Scores: [0.75555556 0.86516854 0.73033708 0.84269663 0.84269663 0.82022472\n",
            " 0.83146067 0.7752809  0.86516854 0.84269663]\n",
            "Mean: 0.8171285892634206\n",
            "Standard Deviation: 0.044572498546778555\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bOiQoe8I2eML",
        "colab_type": "text"
      },
      "source": [
        "Here it is seen that I was provided with 10 different cross validators which each gave different scores.\n",
        "\n",
        "Mean represented the best out of 10 score which was 81.71%\n",
        "\n",
        "Also the standard deviation said that the accuracy can be in the range of +4 or -4."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SJsL9x-Of-l7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "outputId": "da1dab47-ff00-4f36-a415-b929f286544c"
      },
      "source": [
        "importances = pd.DataFrame({'feature':X_train.columns,'importance':np.round(rf.feature_importances_,2)})\n",
        "importances = importances.sort_values('importance',ascending=False).set_index('feature')\n",
        "importances.head(15)"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>importance</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>feature</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Sex</th>\n",
              "      <td>0.24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Title</th>\n",
              "      <td>0.22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Family</th>\n",
              "      <td>0.13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pclass</th>\n",
              "      <td>0.10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fare</th>\n",
              "      <td>0.10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Age_Class</th>\n",
              "      <td>0.09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Embarked</th>\n",
              "      <td>0.06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Age</th>\n",
              "      <td>0.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>IsAlone</th>\n",
              "      <td>0.02</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           importance\n",
              "feature              \n",
              "Sex              0.24\n",
              "Title            0.22\n",
              "Family           0.13\n",
              "Pclass           0.10\n",
              "Fare             0.10\n",
              "Age_Class        0.09\n",
              "Embarked         0.06\n",
              "Age              0.04\n",
              "IsAlone          0.02"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gln5wtrQ3D7Z",
        "colab_type": "text"
      },
      "source": [
        "This Dataframe holds all the importance of various columns in train.csv.\n",
        "\n",
        "'Sex' and 'Title' top the list whereas 'Age' and 'IsAlone' are not that necessary for evaluation.\n",
        "\n",
        "Further step would be to drop 'IsAlone' and train the model again"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WUdB_pUSo_EQ",
        "colab_type": "text"
      },
      "source": [
        "Confusion Matrix\n",
        "\n",
        "A confusion matrix is a table that is often used to describe the performance of a classification model (or “classifier”) on a set of test data for which the true values are known"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pbnlPpyC2Ke0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "6dd778eb-a1e3-4cfb-c12f-3c4a332f7557"
      },
      "source": [
        "from sklearn.model_selection import cross_val_predict\n",
        "from sklearn.metrics import confusion_matrix\n",
        "predictions = cross_val_predict(rf, X_train, y_train, cv=3)\n",
        "confusion_matrix(y_train, predictions)"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[484,  65],\n",
              "       [102, 240]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EDEddVuupDZJ",
        "colab_type": "text"
      },
      "source": [
        "Precision and Recall Scores\n",
        "\n",
        "Precision refers to the percentage of your results which are relevant, recall refers to the percentage of total relevant results correctly classified by your algorithm."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jWFwnRcT2Rv9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "29616a8d-0e60-42b7-ec14-3b4a4fbd9e51"
      },
      "source": [
        "from sklearn.metrics import precision_score, recall_score\n",
        "\n",
        "print(\"Precision:\", precision_score(y_train, predictions))\n",
        "print(\"Recall:\",recall_score(y_train, predictions))"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Precision: 0.7868852459016393\n",
            "Recall: 0.7017543859649122\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jMiOPa5UpHit",
        "colab_type": "text"
      },
      "source": [
        "F1 Score\n",
        "\n",
        "F1 score, is a measure of a model’s accuracy on a dataset. It is used to evaluate binary classification systems, which classify examples into ‘positive’ or ‘negative’."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ptlK-QmG3FoH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "44e21728-0ebd-481b-b4d5-84a6fd12b627"
      },
      "source": [
        "from sklearn.metrics import f1_score\n",
        "f1_score(y_train, predictions)"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7418856259659969"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xAwEdi3m3JFJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kfZkR3os3n7g",
        "colab_type": "text"
      },
      "source": [
        "## Creating Submission File"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BeW8TZ_l3sM8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Create csv file to save results\n",
        "submission = pd.DataFrame({\n",
        "    'PassengerId' : test_df['PassengerId'],\n",
        "    'Survived': y_pred\n",
        "})\n",
        "\n",
        "submission.to_csv('submission.csv', index = False)"
      ],
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gsnhxROTA1da",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}