Titanic_EDA.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Titanic_EDA.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [
        "Le9ixpctgct8",
        "E3W3Opjpgkze",
        "B8_PBOUlh3TK"
      ],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/KMayank29/Machine-Learning/blob/master/Titanic_EDA.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "id": "t5e_0vNO1wx1",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# TITANIC   \n",
        "\n"
      ]
    },
    {
      "metadata": {
        "id": "IFTaffcl1-nR",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "**Kaggle API set**"
      ]
    },
    {
      "metadata": {
        "id": "AqCunsZGb_VX",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "!pip install -q kaggle # installing kaggale "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "DSWNArRxcnzY",
        "colab_type": "code",
        "outputId": "29c5fde7-4e28-43f1-e90a-3ea999c8ddce",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": "Ly8gQ29weXJpZ2h0IDIwMTcgR29vZ2xlIExMQwovLwovLyBMaWNlbnNlZCB1bmRlciB0aGUgQXBhY2hlIExpY2Vuc2UsIFZlcnNpb24gMi4wICh0aGUgIkxpY2Vuc2UiKTsKLy8geW91IG1heSBub3QgdXNlIHRoaXMgZmlsZSBleGNlcHQgaW4gY29tcGxpYW5jZSB3aXRoIHRoZSBMaWNlbnNlLgovLyBZb3UgbWF5IG9idGFpbiBhIGNvcHkgb2YgdGhlIExpY2Vuc2UgYXQKLy8KLy8gICAgICBodHRwOi8vd3d3LmFwYWNoZS5vcmcvbGljZW5zZXMvTElDRU5TRS0yLjAKLy8KLy8gVW5sZXNzIHJlcXVpcmVkIGJ5IGFwcGxpY2FibGUgbGF3IG9yIGFncmVlZCB0byBpbiB3cml0aW5nLCBzb2Z0d2FyZQovLyBkaXN0cmlidXRlZCB1bmRlciB0aGUgTGljZW5zZSBpcyBkaXN0cmlidXRlZCBvbiBhbiAiQVMgSVMiIEJBU0lTLAovLyBXSVRIT1VUIFdBUlJBTlRJRVMgT1IgQ09ORElUSU9OUyBPRiBBTlkgS0lORCwgZWl0aGVyIGV4cHJlc3Mgb3IgaW1wbGllZC4KLy8gU2VlIHRoZSBMaWNlbnNlIGZvciB0aGUgc3BlY2lmaWMgbGFuZ3VhZ2UgZ292ZXJuaW5nIHBlcm1pc3Npb25zIGFuZAovLyBsaW1pdGF0aW9ucyB1bmRlciB0aGUgTGljZW5zZS4KCi8qKgogKiBAZmlsZW92ZXJ2aWV3IEhlbHBlcnMgZm9yIGdvb2dsZS5jb2xhYiBQeXRob24gbW9kdWxlLgogKi8KKGZ1bmN0aW9uKHNjb3BlKSB7CmZ1bmN0aW9uIHNwYW4odGV4dCwgc3R5bGVBdHRyaWJ1dGVzID0ge30pIHsKICBjb25zdCBlbGVtZW50ID0gZG9jdW1lbnQuY3JlYXRlRWxlbWVudCgnc3BhbicpOwogIGVsZW1lbnQudGV4dENvbnRlbnQgPSB0ZXh0OwogIGZvciAoY29uc3Qga2V5IG9mIE9iamVjdC5rZXlzKHN0eWxlQXR0cmlidXRlcykpIHsKICAgIGVsZW1lbnQuc3R5bGVba2V5XSA9IHN0eWxlQXR0cmlidXRlc1trZXldOwogIH0KICByZXR1cm4gZWxlbWVudDsKfQoKLy8gTWF4IG51bWJlciBvZiBieXRlcyB3aGljaCB3aWxsIGJlIHVwbG9hZGVkIGF0IGEgdGltZS4KY29uc3QgTUFYX1BBWUxPQURfU0laRSA9IDEwMCAqIDEwMjQ7Ci8vIE1heCBhbW91bnQgb2YgdGltZSB0byBibG9jayB3YWl0aW5nIGZvciB0aGUgdXNlci4KY29uc3QgRklMRV9DSEFOR0VfVElNRU9VVF9NUyA9IDMwICogMTAwMDsKCmZ1bmN0aW9uIF91cGxvYWRGaWxlcyhpbnB1dElkLCBvdXRwdXRJZCkgewogIGNvbnN0IHN0ZXBzID0gdXBsb2FkRmlsZXNTdGVwKGlucHV0SWQsIG91dHB1dElkKTsKICBjb25zdCBvdXRwdXRFbGVtZW50ID0gZG9jdW1lbnQuZ2V0RWxlbWVudEJ5SWQob3V0cHV0SWQpOwogIC8vIENhY2hlIHN0ZXBzIG9uIHRoZSBvdXRwdXRFbGVtZW50IHRvIG1ha2UgaXQgYXZhaWxhYmxlIGZvciB0aGUgbmV4dCBjYWxsCiAgLy8gdG8gdXBsb2FkRmlsZXNDb250aW51ZSBmcm9tIFB5dGhvbi4KICBvdXRwdXRFbGVtZW50LnN0ZXBzID0gc3RlcHM7CgogIHJldHVybiBfdXBsb2FkRmlsZXNDb250aW51ZShvdXRwdXRJZCk7Cn0KCi8vIFRoaXMgaXMgcm91Z2hseSBhbiBhc3luYyBnZW5lcmF0b3IgKG5vdCBzdXBwb3J0ZWQgaW4gdGhlIGJyb3dzZXIgeWV0KSwKLy8gd2hlcmUgdGhlcmUgYXJlIG11bHRpcGxlIGFzeW5jaHJvbm91cyBzdGVwcyBhbmQgdGhlIFB5dGhvbiBzaWRlIGlzIGdvaW5nCi8vIHRvIHBvbGwgZm9yIGNvbXBsZXRpb24gb2YgZWFjaCBzdGVwLgovLyBUaGlzIHVzZXMgYSBQcm9taXNlIHRvIGJsb2NrIHRoZSBweXRob24gc2lkZSBvbiBjb21wbGV0aW9uIG9mIGVhY2ggc3RlcCwKLy8gdGhlbiBwYXNzZXMgdGhlIHJlc3VsdCBvZiB0aGUgcHJldmlvdXMgc3RlcCBhcyB0aGUgaW5wdXQgdG8gdGhlIG5leHQgc3RlcC4KZnVuY3Rpb24gX3VwbG9hZEZpbGVzQ29udGludWUob3V0cHV0SWQpIHsKICBjb25zdCBvdXRwdXRFbGVtZW50ID0gZG9jdW1lbnQuZ2V0RWxlbWVudEJ5SWQob3V0cHV0SWQpOwogIGNvbnN0IHN0ZXBzID0gb3V0cHV0RWxlbWVudC5zdGVwczsKCiAgY29uc3QgbmV4dCA9IHN0ZXBzLm5leHQob3V0cHV0RWxlbWVudC5sYXN0UHJvbWlzZVZhbHVlKTsKICByZXR1cm4gUHJvbWlzZS5yZXNvbHZlKG5leHQudmFsdWUucHJvbWlzZSkudGhlbigodmFsdWUpID0+IHsKICAgIC8vIENhY2hlIHRoZSBsYXN0IHByb21pc2UgdmFsdWUgdG8gbWFrZSBpdCBhdmFpbGFibGUgdG8gdGhlIG5leHQKICAgIC8vIHN0ZXAgb2YgdGhlIGdlbmVyYXRvci4KICAgIG91dHB1dEVsZW1lbnQubGFzdFByb21pc2VWYWx1ZSA9IHZhbHVlOwogICAgcmV0dXJuIG5leHQudmFsdWUucmVzcG9uc2U7CiAgfSk7Cn0KCi8qKgogKiBHZW5lcmF0b3IgZnVuY3Rpb24gd2hpY2ggaXMgY2FsbGVkIGJldHdlZW4gZWFjaCBhc3luYyBzdGVwIG9mIHRoZSB1cGxvYWQKICogcHJvY2Vzcy4KICogQHBhcmFtIHtzdHJpbmd9IGlucHV0SWQgRWxlbWVudCBJRCBvZiB0aGUgaW5wdXQgZmlsZSBwaWNrZXIgZWxlbWVudC4KICogQHBhcmFtIHtzdHJpbmd9IG91dHB1dElkIEVsZW1lbnQgSUQgb2YgdGhlIG91dHB1dCBkaXNwbGF5LgogKiBAcmV0dXJuIHshSXRlcmFibGU8IU9iamVjdD59IEl0ZXJhYmxlIG9mIG5leHQgc3RlcHMuCiAqLwpmdW5jdGlvbiogdXBsb2FkRmlsZXNTdGVwKGlucHV0SWQsIG91dHB1dElkKSB7CiAgY29uc3QgaW5wdXRFbGVtZW50ID0gZG9jdW1lbnQuZ2V0RWxlbWVudEJ5SWQoaW5wdXRJZCk7CiAgaW5wdXRFbGVtZW50LmRpc2FibGVkID0gZmFsc2U7CgogIGNvbnN0IG91dHB1dEVsZW1lbnQgPSBkb2N1bWVudC5nZXRFbGVtZW50QnlJZChvdXRwdXRJZCk7CiAgb3V0cHV0RWxlbWVudC5pbm5lckhUTUwgPSAnJzsKCiAgY29uc3QgcGlja2VkUHJvbWlzZSA9IG5ldyBQcm9taXNlKChyZXNvbHZlKSA9PiB7CiAgICBpbnB1dEVsZW1lbnQuYWRkRXZlbnRMaXN0ZW5lcignY2hhbmdlJywgKGUpID0+IHsKICAgICAgcmVzb2x2ZShlLnRhcmdldC5maWxlcyk7CiAgICB9KTsKICB9KTsKCiAgY29uc3QgY2FuY2VsID0gZG9jdW1lbnQuY3JlYXRlRWxlbWVudCgnYnV0dG9uJyk7CiAgaW5wdXRFbGVtZW50LnBhcmVudEVsZW1lbnQuYXBwZW5kQ2hpbGQoY2FuY2VsKTsKICBjYW5jZWwudGV4dENvbnRlbnQgPSAnQ2FuY2VsIHVwbG9hZCc7CiAgY29uc3QgY2FuY2VsUHJvbWlzZSA9IG5ldyBQcm9taXNlKChyZXNvbHZlKSA9PiB7CiAgICBjYW5jZWwub25jbGljayA9ICgpID0+IHsKICAgICAgcmVzb2x2ZShudWxsKTsKICAgIH07CiAgfSk7CgogIC8vIENhbmNlbCB1cGxvYWQgaWYgdXNlciBoYXNuJ3QgcGlja2VkIGFueXRoaW5nIGluIHRpbWVvdXQuCiAgY29uc3QgdGltZW91dFByb21pc2UgPSBuZXcgUHJvbWlzZSgocmVzb2x2ZSkgPT4gewogICAgc2V0VGltZW91dCgoKSA9PiB7CiAgICAgIHJlc29sdmUobnVsbCk7CiAgICB9LCBGSUxFX0NIQU5HRV9USU1FT1VUX01TKTsKICB9KTsKCiAgLy8gV2FpdCBmb3IgdGhlIHVzZXIgdG8gcGljayB0aGUgZmlsZXMuCiAgY29uc3QgZmlsZXMgPSB5aWVsZCB7CiAgICBwcm9taXNlOiBQcm9taXNlLnJhY2UoW3BpY2tlZFByb21pc2UsIHRpbWVvdXRQcm9taXNlLCBjYW5jZWxQcm9taXNlXSksCiAgICByZXNwb25zZTogewogICAgICBhY3Rpb246ICdzdGFydGluZycsCiAgICB9CiAgfTsKCiAgaWYgKCFmaWxlcykgewogICAgcmV0dXJuIHsKICAgICAgcmVzcG9uc2U6IHsKICAgICAgICBhY3Rpb246ICdjb21wbGV0ZScsCiAgICAgIH0KICAgIH07CiAgfQoKICBjYW5jZWwucmVtb3ZlKCk7CgogIC8vIERpc2FibGUgdGhlIGlucHV0IGVsZW1lbnQgc2luY2UgZnVydGhlciBwaWNrcyBhcmUgbm90IGFsbG93ZWQuCiAgaW5wdXRFbGVtZW50LmRpc2FibGVkID0gdHJ1ZTsKCiAgZm9yIChjb25zdCBmaWxlIG9mIGZpbGVzKSB7CiAgICBjb25zdCBsaSA9IGRvY3VtZW50LmNyZWF0ZUVsZW1lbnQoJ2xpJyk7CiAgICBsaS5hcHBlbmQoc3BhbihmaWxlLm5hbWUsIHtmb250V2VpZ2h0OiAnYm9sZCd9KSk7CiAgICBsaS5hcHBlbmQoc3BhbigKICAgICAgICBgKCR7ZmlsZS50eXBlIHx8ICduL2EnfSkgLSAke2ZpbGUuc2l6ZX0gYnl0ZXMsIGAgKwogICAgICAgIGBsYXN0IG1vZGlmaWVkOiAkewogICAgICAgICAgICBmaWxlLmxhc3RNb2RpZmllZERhdGUgPyBmaWxlLmxhc3RNb2RpZmllZERhdGUudG9Mb2NhbGVEYXRlU3RyaW5nKCkgOgogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAnbi9hJ30gLSBgKSk7CiAgICBjb25zdCBwZXJjZW50ID0gc3BhbignMCUgZG9uZScpOwogICAgbGkuYXBwZW5kQ2hpbGQocGVyY2VudCk7CgogICAgb3V0cHV0RWxlbWVudC5hcHBlbmRDaGlsZChsaSk7CgogICAgY29uc3QgZmlsZURhdGFQcm9taXNlID0gbmV3IFByb21pc2UoKHJlc29sdmUpID0+IHsKICAgICAgY29uc3QgcmVhZGVyID0gbmV3IEZpbGVSZWFkZXIoKTsKICAgICAgcmVhZGVyLm9ubG9hZCA9IChlKSA9PiB7CiAgICAgICAgcmVzb2x2ZShlLnRhcmdldC5yZXN1bHQpOwogICAgICB9OwogICAgICByZWFkZXIucmVhZEFzQXJyYXlCdWZmZXIoZmlsZSk7CiAgICB9KTsKICAgIC8vIFdhaXQgZm9yIHRoZSBkYXRhIHRvIGJlIHJlYWR5LgogICAgbGV0IGZpbGVEYXRhID0geWllbGQgewogICAgICBwcm9taXNlOiBmaWxlRGF0YVByb21pc2UsCiAgICAgIHJlc3BvbnNlOiB7CiAgICAgICAgYWN0aW9uOiAnY29udGludWUnLAogICAgICB9CiAgICB9OwoKICAgIC8vIFVzZSBhIGNodW5rZWQgc2VuZGluZyB0byBhdm9pZCBtZXNzYWdlIHNpemUgbGltaXRzLiBTZWUgYi82MjExNTY2MC4KICAgIGxldCBwb3NpdGlvbiA9IDA7CiAgICB3aGlsZSAocG9zaXRpb24gPCBmaWxlRGF0YS5ieXRlTGVuZ3RoKSB7CiAgICAgIGNvbnN0IGxlbmd0aCA9IE1hdGgubWluKGZpbGVEYXRhLmJ5dGVMZW5ndGggLSBwb3NpdGlvbiwgTUFYX1BBWUxPQURfU0laRSk7CiAgICAgIGNvbnN0IGNodW5rID0gbmV3IFVpbnQ4QXJyYXkoZmlsZURhdGEsIHBvc2l0aW9uLCBsZW5ndGgpOwogICAgICBwb3NpdGlvbiArPSBsZW5ndGg7CgogICAgICBjb25zdCBiYXNlNjQgPSBidG9hKFN0cmluZy5mcm9tQ2hhckNvZGUuYXBwbHkobnVsbCwgY2h1bmspKTsKICAgICAgeWllbGQgewogICAgICAgIHJlc3BvbnNlOiB7CiAgICAgICAgICBhY3Rpb246ICdhcHBlbmQnLAogICAgICAgICAgZmlsZTogZmlsZS5uYW1lLAogICAgICAgICAgZGF0YTogYmFzZTY0LAogICAgICAgIH0sCiAgICAgIH07CiAgICAgIHBlcmNlbnQudGV4dENvbnRlbnQgPQogICAgICAgICAgYCR7TWF0aC5yb3VuZCgocG9zaXRpb24gLyBmaWxlRGF0YS5ieXRlTGVuZ3RoKSAqIDEwMCl9JSBkb25lYDsKICAgIH0KICB9CgogIC8vIEFsbCBkb25lLgogIHlpZWxkIHsKICAgIHJlc3BvbnNlOiB7CiAgICAgIGFjdGlvbjogJ2NvbXBsZXRlJywKICAgIH0KICB9Owp9CgpzY29wZS5nb29nbGUgPSBzY29wZS5nb29nbGUgfHwge307CnNjb3BlLmdvb2dsZS5jb2xhYiA9IHNjb3BlLmdvb2dsZS5jb2xhYiB8fCB7fTsKc2NvcGUuZ29vZ2xlLmNvbGFiLl9maWxlcyA9IHsKICBfdXBsb2FkRmlsZXMsCiAgX3VwbG9hZEZpbGVzQ29udGludWUsCn07Cn0pKHNlbGYpOwo=",
              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 74
        }
      },
      "cell_type": "code",
      "source": [
        "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-68ba2a6d-dfa5-4975-8aec-1bb02ad4f01f\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-68ba2a6d-dfa5-4975-8aec-1bb02ad4f01f\">\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 kaggle.json to kaggle.json\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "pBI-Lqj9cqtF",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Creating directory\n",
        "!mkdir -p ~/.kaggle\n",
        "!cp kaggle.json ~/.kaggle/"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "BJc1oJ3cc2b9",
        "colab_type": "code",
        "outputId": "3d485c67-8d4c-4400-ba87-d1971541d0e7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 214
        }
      },
      "cell_type": "code",
      "source": [
        "## Fetch Titanic data set using Kaggle API\n",
        "\n",
        "! kaggle competitions download -c titanic"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Warning: Your Kaggle API key is readable by other users on this system! To fix this, you can run 'chmod 600 /root/.kaggle/kaggle.json'\n",
            "Downloading train.csv to /content\n",
            "\r  0% 0.00/59.8k [00:00<?, ?B/s]\n",
            "\r100% 59.8k/59.8k [00:00<00:00, 50.7MB/s]\n",
            "Downloading test.csv to /content\n",
            "\r  0% 0.00/28.0k [00:00<?, ?B/s]\n",
            "100% 28.0k/28.0k [00:00<00:00, 29.7MB/s]\n",
            "Downloading gender_submission.csv to /content\n",
            "  0% 0.00/3.18k [00:00<?, ?B/s]\n",
            "100% 3.18k/3.18k [00:00<00:00, 2.23MB/s]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "nTrZ_XxE2hK9",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Import important modules"
      ]
    },
    {
      "metadata": {
        "id": "vzLPbtUAfgtW",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "REj8CzzzfQIB",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Know my Data\n",
        "\n",
        "We load the data and inspect its shape. This gives us info about the number of rows and columns the data table has.  \n",
        "\n",
        "We check for null values in the data. Some columns like name and others are dropped for  further processes."
      ]
    },
    {
      "metadata": {
        "id": "48b87vsQMcjD",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def load_data(path):\n",
        "  \n",
        "  data = pd.read_csv(path)\n",
        "  \n",
        "  return data"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "GdqVKudeP20l",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 247
        },
        "outputId": "75aaf60f-d99d-4072-efad-84a728a56668"
      },
      "cell_type": "code",
      "source": [
        "path_tr = '/content/train.csv'\n",
        "\n",
        "path_test = '/content/test.csv'\n",
        "\n",
        "train_orig = load_data(path_tr)\n",
        "test_orig = load_data(path_test)\n",
        "\n",
        "train_orig.head() # See first five data points or observations"
      ],
      "execution_count": 8,
      "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",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   PassengerId  Survived  Pclass  \\\n",
              "0            1         0       3   \n",
              "1            2         1       1   \n",
              "2            3         1       3   \n",
              "3            4         1       1   \n",
              "4            5         0       3   \n",
              "\n",
              "                                                Name     Sex   Age  SibSp  \\\n",
              "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
              "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
              "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
              "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
              "4                           Allen, Mr. William Henry    male  35.0      0   \n",
              "\n",
              "   Parch            Ticket     Fare Cabin Embarked  \n",
              "0      0         A/5 21171   7.2500   NaN        S  \n",
              "1      0          PC 17599  71.2833   C85        C  \n",
              "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
              "3      0            113803  53.1000  C123        S  \n",
              "4      0            373450   8.0500   NaN        S  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "metadata": {
        "id": "dhf2E82XRjoE",
        "colab_type": "code",
        "outputId": "cf360f73-f382-4af9-df0f-b6621b036008",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        }
      },
      "cell_type": "code",
      "source": [
        "## Inspect the number of features and observations in the training data set\n",
        "\n",
        "print('the number of features/columns:', train_orig.shape[1] )\n",
        "print('-------------------------------------------------------')\n",
        "print('the number of training examples:', train_orig.shape[0])"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "the number of features/columns: 12\n",
            "-------------------------------------------------------\n",
            "the number of training examples: 891\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "URsdVbK_RzXl",
        "colab_type": "code",
        "outputId": "6201545f-e884-45a2-ff08-dbe15eecbc5c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        }
      },
      "cell_type": "code",
      "source": [
        "## Store the column names\n",
        "\n",
        "col_names = train_orig.columns\n",
        "print(col_names)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n",
            "       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],\n",
            "      dtype='object')\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "o44h3McFgWlR",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Check for Null values"
      ]
    },
    {
      "metadata": {
        "id": "nQ0zeYyVV2rI",
        "colab_type": "code",
        "outputId": "043d13e5-f1e1-43a3-dee4-99d2887bc124",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        }
      },
      "cell_type": "code",
      "source": [
        "# Check for null values\n",
        "\n",
        "null_val = train_orig.isnull().sum()\n",
        "\n",
        "null_val_per = np.divide(null_val, train_orig.shape[0]) * 100\n",
        "\n",
        "print(null_val_per)\n",
        "print('-------------------')\n",
        "print(type(null_val))"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "PassengerId     0.000000\n",
            "Survived        0.000000\n",
            "Pclass          0.000000\n",
            "Name            0.000000\n",
            "Sex             0.000000\n",
            "Age            19.865320\n",
            "SibSp           0.000000\n",
            "Parch           0.000000\n",
            "Ticket          0.000000\n",
            "Fare            0.000000\n",
            "Cabin          77.104377\n",
            "Embarked        0.224467\n",
            "dtype: float64\n",
            "-------------------\n",
            "<class 'pandas.core.series.Series'>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "Le9ixpctgct8",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Drop the columns"
      ]
    },
    {
      "metadata": {
        "id": "FKjpME1YgBdm",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "\"\"\"\n",
        "There are more than 77% data missing in cabin feature.\n",
        "We delete 'cabin', 'PassengerId', 'Name'\n",
        "\n",
        "\"\"\"\n",
        "\n",
        "def del_feat(x):\n",
        "  \n",
        "  x = x.drop(['PassengerId','Name','Cabin'], axis = 1)\n",
        "  \n",
        "  return x"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "AIevlm7kZ51P",
        "colab_type": "code",
        "outputId": "ac3e5696-dbeb-4601-a06c-c364997d84bc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        }
      },
      "cell_type": "code",
      "source": [
        "train = del_feat(train_orig)\n",
        "train.head()"
      ],
      "execution_count": 14,
      "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>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Embarked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>3</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>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</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>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>3</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>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1</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>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>3</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>S</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Survived  Pclass     Sex   Age  SibSp  Parch            Ticket     Fare  \\\n",
              "0         0       3    male  22.0      1      0         A/5 21171   7.2500   \n",
              "1         1       1  female  38.0      1      0          PC 17599  71.2833   \n",
              "2         1       3  female  26.0      0      0  STON/O2. 3101282   7.9250   \n",
              "3         1       1  female  35.0      1      0            113803  53.1000   \n",
              "4         0       3    male  35.0      0      0            373450   8.0500   \n",
              "\n",
              "  Embarked  \n",
              "0        S  \n",
              "1        C  \n",
              "2        S  \n",
              "3        S  \n",
              "4        S  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "metadata": {
        "id": "E3W3Opjpgkze",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Check for data imbalance \n",
        "\n",
        "We inspect the target variable value counts"
      ]
    },
    {
      "metadata": {
        "id": "OfxKwpXka5jA",
        "colab_type": "code",
        "outputId": "40318627-823e-49c9-c8b9-99ebf7134c1c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        }
      },
      "cell_type": "code",
      "source": [
        "survived = train['Survived'].value_counts(normalize= True)*100\n",
        "print(\"The percentage of people who survived is {}% and who didn't survive {}%\".format(survived[1].round(2), survived[0].round(2)))\n",
        "print('------------')\n",
        "print(type(survived))"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The percentage of people who survived is 38.38% and who didn't survive 61.62%\n",
            "------------\n",
            "<class 'pandas.core.series.Series'>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "B8_PBOUlh3TK",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Missing value imputation"
      ]
    },
    {
      "metadata": {
        "id": "xhvBASJ4c6x_",
        "colab_type": "code",
        "outputId": "8bcc780d-04fa-46c0-fc1e-e88fdbca10da",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 378
        }
      },
      "cell_type": "code",
      "source": [
        "# We visualize 'Embarked' feature before filling the null values\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "\n",
        "fig, [ax1, ax2] = plt.subplots(1,2)\n",
        "sns.countplot(x = train.Embarked, hue = train.Survived, data = train, ax = ax1)\n",
        "sns.countplot(x = train.Embarked, data = train, ax = ax2)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f058738c2b0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfIAAAFYCAYAAACoFn5YAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAJgYQQ4AgIkR5AAAmNj/B2EBBixoGXs8AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 576x396 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "YSeHivCcfkr1",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Methods to locate missing values \n",
        "\n",
        "There are different ways we can locate the exact positon of missing values.\n",
        "\n",
        "1) One is **np.where(df['column'].isnull())** that returns the indices and using **df.loc[]** we locate the missing values.  \n",
        "\n",
        "2) Another method is by using callable function in **df.loc[callable]** we get the datafram directly containing missing value for particular column"
      ]
    },
    {
      "metadata": {
        "id": "cXW9X1yIgpZt",
        "colab_type": "code",
        "outputId": "12154025-8e05-452f-efd5-e34f8de03003",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "# Use np.where()\n",
        "\n",
        "ind = np.where(train['Embarked'].isnull())\n",
        "ind"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([ 61, 829]),)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "metadata": {
        "id": "Cm13xNEVVKtb",
        "colab_type": "code",
        "outputId": "bde788b6-7f3b-46c8-ca28-c4f2d9aab864",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 141
        }
      },
      "cell_type": "code",
      "source": [
        "print(train.loc[[61,829], :])\n",
        "print('-----------------------------------------------------------')\n",
        "print(train.loc[[61, 829], ['Survived','Age','Pclass']])"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "     Survived  Pclass     Sex   Age  SibSp  Parch  Ticket  Fare Embarked\n",
            "61          1       1  female  38.0      0      0  113572  80.0      NaN\n",
            "829         1       1  female  62.0      0      0  113572  80.0      NaN\n",
            "-----------------------------------------------------------\n",
            "     Survived   Age  Pclass\n",
            "61          1  38.0       1\n",
            "829         1  62.0       1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "zyHdt3kZbDmw",
        "colab_type": "code",
        "outputId": "0edc205a-dc44-4c5d-962e-45253e55b813",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 107
        }
      },
      "cell_type": "code",
      "source": [
        "# Use callable function in df.loc()\n",
        "\n",
        "train.loc[lambda x : x['Embarked'].isnull()]"
      ],
      "execution_count": 19,
      "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>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Embarked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>61</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>38.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>113572</td>\n",
              "      <td>80.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>829</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>62.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>113572</td>\n",
              "      <td>80.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     Survived  Pclass     Sex   Age  SibSp  Parch  Ticket  Fare Embarked\n",
              "61          1       1  female  38.0      0      0  113572  80.0      NaN\n",
              "829         1       1  female  62.0      0      0  113572  80.0      NaN"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "metadata": {
        "id": "HyFibOpjh8Nf",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "We impute the missing values in Embarked feature with 'S' because the frequency of S is maximum"
      ]
    },
    {
      "metadata": {
        "id": "Bk5Sh_XvbflH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 107
        },
        "outputId": "78177d1c-e1e0-4da4-9117-fe4bbe5799ce"
      },
      "cell_type": "code",
      "source": [
        "train.loc[lambda x : x['Embarked'].isnull(),'Embarked'] = 'S'\n",
        "\n",
        "train.loc[[61,829]]\n"
      ],
      "execution_count": 20,
      "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>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Embarked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>61</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>38.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>113572</td>\n",
              "      <td>80.0</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>829</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>62.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>113572</td>\n",
              "      <td>80.0</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     Survived  Pclass     Sex   Age  SibSp  Parch  Ticket  Fare Embarked\n",
              "61          1       1  female  38.0      0      0  113572  80.0        S\n",
              "829         1       1  female  62.0      0      0  113572  80.0        S"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "metadata": {
        "id": "G9EQfCh4j2tj",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Imputaion for feature 'Age'"
      ]
    },
    {
      "metadata": {
        "id": "uuj4huv_lAy3",
        "colab_type": "code",
        "outputId": "08be9456-2dca-4df4-9c77-7bb50cc82e6b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 538
        }
      },
      "cell_type": "code",
      "source": [
        "print(train['Age'].describe())\n",
        "\n",
        "fig1,ax1 = plt.subplots(1, 1)\n",
        "\n",
        "sns.boxplot(x = train['Survived'], y = train['Age'], data = train, ax = ax1)"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "count    714.000000\n",
            "mean      29.699118\n",
            "std       14.526497\n",
            "min        0.420000\n",
            "25%       20.125000\n",
            "50%       28.000000\n",
            "75%       38.000000\n",
            "max       80.000000\n",
            "Name: Age, dtype: float64\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f0587e75518>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAe0AAAFYCAYAAAB+s6Q9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "bp6LuohMn0pg",
        "colab_type": "code",
        "outputId": "c7e050cd-c039-4da8-840c-11b4a87d8f1e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 393
        }
      },
      "cell_type": "code",
      "source": [
        "plt.hist(train['Age'], bins = 8, edgecolor = 'black', density = True)\n",
        "plt.title('Age distribution')\n",
        "plt.xlabel('Age')\n",
        "plt.ylabel('Number of people')"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Number of people')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfsAAAFnCAYAAAChL+DqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nwOAIewAADO7/AVBzm+Ru0R4yAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "dfkm-G3twZCj",
        "colab_type": "code",
        "outputId": "c0680f65-f93f-49e1-ea3e-e55597320efc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        }
      },
      "cell_type": "code",
      "source": [
        "train.loc[lambda x : x['Age'].isnull(),'Survived'].value_counts(normalize = True)"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0.706215\n",
              "1    0.293785\n",
              "Name: Survived, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "metadata": {
        "id": "SJtAa6BJnVud",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "\"\"\"\n",
        "We drop the corresponding rows containing 'Null values' in feature 'Age'\n",
        "\n",
        "\"\"\"\n",
        "\n",
        "def drop_row(x):\n",
        "  \n",
        "  x = x.dropna(axis=0)\n",
        "  \n",
        "  return x"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "aXV3kl1Q3TUs",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "train = drop_row(train)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "gQs-10aC3pTB",
        "colab_type": "code",
        "outputId": "4e4a2d4e-b959-403a-a1c1-6fa41d84e022",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        }
      },
      "cell_type": "code",
      "source": [
        "train.info()"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 714 entries, 0 to 890\n",
            "Data columns (total 9 columns):\n",
            "Survived    714 non-null int64\n",
            "Pclass      714 non-null int64\n",
            "Sex         714 non-null object\n",
            "Age         714 non-null float64\n",
            "SibSp       714 non-null int64\n",
            "Parch       714 non-null int64\n",
            "Ticket      714 non-null object\n",
            "Fare        714 non-null float64\n",
            "Embarked    714 non-null object\n",
            "dtypes: float64(2), int64(4), object(3)\n",
            "memory usage: 55.8+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "sYKMFmhJ6shn",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Drop Ticket column\n",
        "\n",
        "train = train.drop('Ticket', axis=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "820fqk6P7GRH",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Visualize Features w.r.t Target Respone"
      ]
    },
    {
      "metadata": {
        "id": "-aTzA1amozPn",
        "colab_type": "code",
        "outputId": "e3f668e4-784c-44c8-bf2f-ca7a77cf79d1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        }
      },
      "cell_type": "code",
      "source": [
        "# Pclass - Survived\n",
        "\n",
        "fig2, [ax1, ax2, ax3] = plt.subplots(1,3, figsize=(10,5))\n",
        "\n",
        "sns.countplot(x = train['Pclass'], data = train, ax = ax1)\n",
        "\n",
        "sns.countplot(x = train['Pclass'], hue = 'Survived', data=train, ax = ax2)\n",
        "\n",
        "sns.countplot(x = train['Pclass'], hue = 'Sex', data = train, ax = ax3)"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f05849a1710>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmIAAAE9CAYAAACoZg5ZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n+f6vuhcAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 720x360 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "z5dhR0VoASbK",
        "colab_type": "code",
        "outputId": "93c832c2-bea1-4ee9-98dd-bf8a228cc3cf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 378
        }
      },
      "cell_type": "code",
      "source": [
        "# Sex - Survived\n",
        "\n",
        "fig4, [ax1, ax2] = plt.subplots(1,2)\n",
        "\n",
        "sns.countplot(x = 'Sex', data = train, ax = ax1)\n",
        "\n",
        "sns.countplot(x = 'Sex', hue = 'Survived', data = train, ax = ax2)"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f05848f9da0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfIAAAFYCAYAAACoFn5YAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nv8dF8LBjZQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 576x396 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "_piT88c-shCw",
        "colab_type": "code",
        "outputId": "1ba7f0c4-69f0-4ae1-e8ac-61563b1bfa46",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 378
        }
      },
      "cell_type": "code",
      "source": [
        "# Embarked - Survived\n",
        "\n",
        "fig5, [ax1, ax2] = plt.subplots(1,2)\n",
        "\n",
        "sns.countplot(x = 'Embarked', data = train, ax = ax1)\n",
        "\n",
        "sns.countplot(x = 'Embarked', hue = 'Survived', data = train, ax = ax2)"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f05848187b8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfIAAAFYCAYAAACoFn5YAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nMmINAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 576x396 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "A3gPNdHxEbFh",
        "colab_type": "code",
        "outputId": "b65c4fd0-87b0-4569-c430-a73d6997cbde",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 378
        }
      },
      "cell_type": "code",
      "source": [
        "# Age - Survived\n",
        "\n",
        "fig6, [ax1, ax2] = plt.subplots(1,2)\n",
        "\n",
        "sns.distplot(train['Age'], bins = 7, norm_hist = True, ax = ax1)\n",
        "\n",
        "sns.boxplot(x = 'Survived', y = 'Age', data = train, ax = ax2)"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f05847c4c88>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAe0AAAFYCAYAAAB+s6Q9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nkoSKtoiISJL4BwHUQD5eP9BHAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 576x396 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "EdsgPy-KKRD4",
        "colab_type": "code",
        "outputId": "0ce58678-68c1-4363-d5e2-752df3d6dbdb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 378
        }
      },
      "cell_type": "code",
      "source": [
        "# Fare - Survived\n",
        "\n",
        "fig7, [ax1, ax2] = plt.subplots(1,2)\n",
        "\n",
        "sns.distplot(train['Fare'], norm_hist = True, ax = ax1)\n",
        "\n",
        "\n",
        "sns.boxplot(x = 'Survived', y = 'Fare', data = train, ax = ax2)"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f05846a1780>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAe0AAAFYCAYAAAB+s6Q9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrKBKAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 576x396 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "3JeXOik4hZsi",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "In **Age** feature we have outliers which might be fatal for ML modeling"
      ]
    },
    {
      "metadata": {
        "id": "FxElySavhyCF",
        "colab_type": "code",
        "outputId": "e8e9ce16-5bed-4a7a-a754-4a522bd14c01",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 475
        }
      },
      "cell_type": "code",
      "source": [
        "# SibSp - Survived\n",
        "\n",
        "print(train['SibSp'].value_counts(normalize = True)*100)\n",
        "\n",
        "fig7, [ax1,ax2] = plt.subplots(1,2, figsize = (10,5))\n",
        "\n",
        "sns.countplot(x = 'SibSp', data = train, ax = ax1)\n",
        "\n",
        "sns.countplot(x = 'SibSp', hue = 'Survived', data = train,ax = ax2)"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0    65.966387\n",
            "1    25.630252\n",
            "2     3.501401\n",
            "4     2.521008\n",
            "3     1.680672\n",
            "5     0.700280\n",
            "Name: SibSp, dtype: float64\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f0584553fd0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmIAAAE9CAYAAACoZg5ZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIgYAAGAIRQwAAMCQ/w+MhUFlVmw14AAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 720x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "o0XP1oxRjopH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 495
        },
        "outputId": "2a01ebd6-00cd-4d20-f389-f842b599be57"
      },
      "cell_type": "code",
      "source": [
        "# Parch - Survived\n",
        "\n",
        "print(train['Parch'].value_counts(normalize = True)*100)\n",
        "\n",
        "fig8, [ax1,ax2] = plt.subplots(1,2, figsize = (10,5))\n",
        "\n",
        "sns.countplot(x = 'Parch', data = train, ax = ax1)\n",
        "\n",
        "sns.countplot(x = 'Parch', hue = 'Survived', data = train,ax = ax2)"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0    72.969188\n",
            "1    15.406162\n",
            "2     9.523810\n",
            "5     0.700280\n",
            "3     0.700280\n",
            "4     0.560224\n",
            "6     0.140056\n",
            "Name: Parch, dtype: float64\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f05844a3898>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmIAAAE/CAYAAADlrq9SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nKgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 720x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "k5t0weA2lyWp",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Feature Engineering"
      ]
    },
    {
      "metadata": {
        "id": "t_VP_JcOlxqx",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "\"\"\"\n",
        "\n",
        "We combine 'SibSp' and 'Parch' to form a single feature 'IsAlone'.\n",
        "IsAlone is a binary categorical feature which says if the person was \n",
        "travelling alone\n",
        "\n",
        "\"\"\"\n",
        "\n",
        "train['IsAlone'] = train['SibSp'] + train['Parch']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "TqQgdOtHmcgA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        },
        "outputId": "ee7d67fe-448b-484d-cf18-03b1766da8eb"
      },
      "cell_type": "code",
      "source": [
        "train.loc[lambda x : x['IsAlone'] > 0, 'IsAlone'] = 1\n",
        "\n",
        "print(train['IsAlone'].value_counts(normalize = True) * 100)"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0    56.582633\n",
            "1    43.417367\n",
            "Name: IsAlone, dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "yIBtmo13m__Z",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 378
        },
        "outputId": "f8fa904c-cbf1-41d6-8697-ce0b99b6218f"
      },
      "cell_type": "code",
      "source": [
        "# Visualize 'IsAlone' feature\n",
        "\n",
        "fig8, [ax1, ax2] = plt.subplots(1,2)\n",
        "\n",
        "sns.countplot(x = 'IsAlone', data = train, ax = ax1)\n",
        "\n",
        "sns.countplot(x = 'IsAlone', data = train, hue = 'Survived', ax = ax2)"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f0584402940>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfIAAAFYCAYAAACoFn5YAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
            "text/plain": [
              "<Figure size 576x396 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "fIkegXKqoRex",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Drop SibSp and Parch"
      ]
    },
    {
      "metadata": {
        "id": "shic7OU0oPmj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        },
        "outputId": "7a84a1a0-44c6-401f-90e7-5999cedf79d5"
      },
      "cell_type": "code",
      "source": [
        "train = train.drop(['SibSp','Parch'],axis = 1)\n",
        "train.head()"
      ],
      "execution_count": 38,
      "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>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Embarked</th>\n",
              "      <th>IsAlone</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>22.0</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>S</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>38.0</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>female</td>\n",
              "      <td>26.0</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>S</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>35.0</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>S</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>35.0</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>S</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Survived  Pclass     Sex   Age     Fare Embarked  IsAlone\n",
              "0         0       3    male  22.0   7.2500        S        1\n",
              "1         1       1  female  38.0  71.2833        C        1\n",
              "2         1       3  female  26.0   7.9250        S        0\n",
              "3         1       1  female  35.0  53.1000        S        1\n",
              "4         0       3    male  35.0   8.0500        S        0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "metadata": {
        "id": "yyGLn8hTxIFE",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Prepare the Test Set"
      ]
    },
    {
      "metadata": {
        "id": "2AUXmRsHhvXf",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "\"\"\"\"\n",
        "\n",
        "1. del_feat(x) ~ PassengerId, Name, 'Cabin'\n",
        "2. drop_row(x) ~ NA for Age\n",
        "3. Ticket\n",
        "\n",
        "\"\"\"\n",
        "\n",
        "test = del_feat(test_orig)\n",
        "\n",
        "test = test.drop('Ticket', axis = 1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "QEo5wTV8yhNj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 230
        },
        "outputId": "e98c9c8b-0656-40ff-c4d5-e022d63900fa"
      },
      "cell_type": "code",
      "source": [
        "test.info()"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 418 entries, 0 to 417\n",
            "Data columns (total 7 columns):\n",
            "Pclass      418 non-null int64\n",
            "Sex         418 non-null object\n",
            "Age         332 non-null float64\n",
            "SibSp       418 non-null int64\n",
            "Parch       418 non-null int64\n",
            "Fare        417 non-null float64\n",
            "Embarked    418 non-null object\n",
            "dtypes: float64(2), int64(3), object(2)\n",
            "memory usage: 22.9+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "FiUjqTjs32Mo",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Impute the missing values in Age feature"
      ]
    },
    {
      "metadata": {
        "id": "Twefs1UO4MI7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 176
        },
        "outputId": "46aad526-f180-4bd1-d812-dc5fcf2fdd42"
      },
      "cell_type": "code",
      "source": [
        "test['Age'].describe()"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    332.000000\n",
              "mean      30.272590\n",
              "std       14.181209\n",
              "min        0.170000\n",
              "25%       21.000000\n",
              "50%       27.000000\n",
              "75%       39.000000\n",
              "max       76.000000\n",
              "Name: Age, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "metadata": {
        "id": "N9771Ga9yjkT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 230
        },
        "outputId": "330a8444-d7a2-4cb2-b153-081c9f40e2c6"
      },
      "cell_type": "code",
      "source": [
        "np.random.seed(0)\n",
        "\n",
        "test.loc[lambda x : x['Age'].isnull(), 'Age'] = np.random.randint(30-14, 30+14)\n",
        "\n",
        "test.loc[lambda x : x['Fare'].isnull(), 'Fare'] = test['Fare'].mean()\n",
        "\n",
        "\n",
        "test.info()"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 418 entries, 0 to 417\n",
            "Data columns (total 7 columns):\n",
            "Pclass      418 non-null int64\n",
            "Sex         418 non-null object\n",
            "Age         418 non-null float64\n",
            "SibSp       418 non-null int64\n",
            "Parch       418 non-null int64\n",
            "Fare        418 non-null float64\n",
            "Embarked    418 non-null object\n",
            "dtypes: float64(2), int64(3), object(2)\n",
            "memory usage: 22.9+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "K9rlQSQP85Xc",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Save the data for ML modeling"
      ]
    },
    {
      "metadata": {
        "id": "Gnlv1Zy7yzRX",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "train.to_csv('/content/train_data.csv', index = False)\n",
        "\n",
        "test.to_csv('/content/test_data.csv',index = False)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "VYtr8XLf-P5P",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from google.colab import files"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "MlUCBCrT-unH",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "files.download('/content/train_data.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Gr_xGYgg-2Z8",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "files.download('/content/test_data.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "fHu9mrJo-9so",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}