TitanicProjectExample.ipynb

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  "metadata": {
    "colab": {
      "name": "TitanicProjectExample",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyMZtqlxLRiESJQnkbVDoO2U",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/carlacotas/data-science-kaggle-Titanic-Project/blob/main/TitanicProjectExample.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tZ-o2QRD9uqB"
      },
      "source": [
        "# several helpful packages to load\n",
        "\n",
        "import numpy as np # linear algebra\n",
        "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
        "import seaborn as sns \n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6TVpe7hY0JBQ"
      },
      "source": [
        "# **kaggle 'Competitions': Titanic Project**\n",
        "\n",
        "This notebook contains the solution for the [Titanic ML competition on Kaggle]( https://www.kaggle.com/c/titanic) following the steps presented by Ken Jee [Titanic Project Example](https://www.kaggle.com/kenjee/titanic-project-example)\n",
        "\n",
        "\n",
        "**Main objective:** use machine learning to create a model that predicts which passengers survived the Titanic shipwreck.\n",
        "\n",
        "**Main steps**\n",
        "\n",
        "1. Understand the shape of the data (Histograms, box plots, etc.)\n",
        "2. Data Cleaning\n",
        "3. Data Exploration\n",
        "4. Feature Engineering\n",
        "5. Data Preprocessing for Model\n",
        "6. Basic Model Building\n",
        "7. Model Tuning\n",
        "8. Ensemble Modle Building\n",
        "9. Results\n",
        "\n",
        "> Note: It is very important to know all the information needed to get ready to start working through this problem. Thus, **Section 1. Data information** is composed with information provided in the [Titanic ML competition on Kaggle]( https://www.kaggle.com/c/titanic). The goal is to have access to all the information and solution in one single notebook. \n",
        "\n",
        "</br>\n",
        "</br>\n",
        "\n",
        "## **1. Data information**\n",
        "\n",
        "\n",
        "### **1.1 The Challenge**\n",
        "The sinking of the Titanic is one of the most infamous shipwrecks in history.\n",
        "\n",
        "On April 15, 1912, during her maiden voyage, the widely considered “unsinkable” RMS Titanic sank after colliding with an iceberg. Unfortunately, there weren’t enough lifeboats for everyone onboard, resulting in the death of 1502 out of 2224 passengers and crew.\n",
        "\n",
        "While there was some element of luck involved in surviving, it seems some groups of people were more likely to survive than others.\n",
        "\n",
        "In this challenge, we ask you to build a predictive model that answers the question: “what sorts of people were more likely to survive?” using passenger data (ie name, age, gender, socio-economic class, etc).\n",
        "\n",
        "</br>\n",
        "\n",
        "### **1.2 What Data Will I Use in This Competition?**\n",
        "In this competition, you’ll gain access to two similar datasets that include passenger information like name, age, gender, socio-economic class, etc. One dataset is titled `train.csv` and the other is titled `test.csv`.\n",
        "\n",
        "`train.csv` will contain the details of a subset of the passengers on board (891 to be exact) and importantly, will reveal whether they survived or not, also known as the “ground truth”.\n",
        "\n",
        "The `test.csv` dataset contains similar information but does not disclose the “ground truth” for each passenger. It’s your job to predict these outcomes.\n",
        "\n",
        "Using the patterns you find in the `train.csv` data, predict whether the other 418 passengers on board (found in `test.csv`) survived.\n",
        "\n",
        "The **training set** should be used to build your machine learning models. For the training set, we provide the outcome (also known as the “ground truth”) for each passenger. Your model will be based on “features” like passengers’ gender and class. You can also use feature engineering to create new features.\n",
        "\n",
        "The **test set** should be used to see how well your model performs on unseen data. For the test set, we do not provide the ground truth for each passenger. It is your job to predict these outcomes. For each passenger in the test set, use the model you trained to predict whether or not they survived the sinking of the Titanic.\n",
        "\n",
        "</br>\n",
        "\n",
        "### **1.3 Data Dictionary**\n",
        "\n",
        "![Titanic.JPG](data:image/jpeg;base64,/9j/4AAQSkZJRgABAQEAYABgAAD/4RCWRXhpZgAATU0AKgAAAAgABAE7AAIAAAAMAAAISodpAAQAAAABAAAIVpydAAEAAAAYAAAQduocAAcAAAgMAAAAPgAAAAAc6gAAAAgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAGNhcmxhIGNvdGFzAAAB6hwABwAACAwAAAhoAAAAABzqAAAACAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA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        "\n",
        "#### **1.3.1 Variable Notes**\n",
        "\n",
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AZblQk7+Uu6VRwAxxyPY1BHrNnNqUVlbyLO0sUkgeJgyjYyBgSD1y44+tWri6t7SMPdTxwISFDSOFBPpk0dAMy68NafJo76dYW9vp8TzRzMLeBVBKOrcgYznbir1npen6fFJFYWNtbRynMiQxKgc9OQBzUk95bWy7rm4ihUDOZHCjHTPNV59WtrfUrezkJDTwSTrJkbAqFQcnP8AtigBbTRtMsObHTrS2OGH7mBU4OMjgd9o/IelFpoul6e++x020tm5G6GBUPOM9B3wPyFWzIgdULKGYEqueSB1/nSNNEm/dIi+WMvlh8o9T6UAVbXR9NsCTp9ha2bnd88ECIRuxk8DvgZ9cD0rOsfDckGpR3V5cWsghZnQW1kIGkcqV3SEMd5wT0CjnpWrd3i21u8igSuq7hGJFUsM4zliBjn1qpdeItPtoNSkEvmtpkZkuIoyCwAXccDP+TQFr6F5LK1jjgSO2hVLb/UKsYAi4I+UduCRx2NRNpOnPFcxtYWpS7bdcKYVxMfVhj5j9aktr23u932eeORkwHVXBKE9iB0NOnu7e12fariKHedq+Y4XcfQZ60Bcig0uwtreO3t7G2ihjfzEjSJQqv8A3gAOD70trYx2k93MhJe7l82Qn1CqoH0woqw7rGheRgqqMlmOABUcV1BcPIsE8crRHbIEcEofQ46UAQtpOnPqIv2sLVrxelwYV8wcY+9jPTinnT7IzTzGzgMtyoSd/KXdKo4AY45HsaWK+tJkkaG5hkWI4kKSAhPr6UqXtrIZRHcwsYf9btkB8vv83p+NAGbdeGdPk0v7BYW1vYQm4inZYIFUMUdWwQMdduKu22k6dZW0lvZ2FrbwS58yKKFVV8jHIAweOKkW+tX83Zcwt5PEuJAfL+vp+NSmRA6oWUMwJVc8kDr/ADoArW2k6dZWsltZ2FrBBL/rIooVVX4xyAMHjikg0fTLW0Nrbadaw25cOYY4FVCwIIOAMZBAOfYVJf31vpthPe3sgjggQu7HsAKqvr+nx3cULzoqS27XCzs6iPaGC43Z65YUBsrl8QxCdpxGglZQjSbRuKgkgZ9Bk8e5p9RTXVvb2/n3E8UUPH7x3Crz05NJLeW0EYknuIo0K7gzuACOOcntyPzoAmoqL7Xb/avs3nxeft3eVvG7HrjriqNhrkGoaje28Gzy7Ntjy+apy2ATgDthup7g0AadFQxXtrNb+fDcwyQ52+YkgK5zjGfrTW1GyWFJmvLcRSNtRzKu1jnGAc8nNAFiikZgqlmICgZJJ6VFFd208rxwXEUkiYLojglc9MgdKAJqKhS8tpZZI47iF5Iv9YiuCU+o7U37fZiKSX7VB5cWPMfzBhMjPJ7daALFFULnWbO0ns0lkGy7DlJgw8sBV3Ek56Yq7FLHPEskLrJGwyrocgj1BoAdRVbUL6HTbGS6ud2xMDaoyzMTgKB3JJAH1qDT9W+23MtrcWc9lcxKshinKEshyAwKsQeQQecj8sgGhRWa+tQjxAmkxBHm8vzJCZVGwc4AHUnjp6Vdiu7eeSRIbiKR4jiRUcEofQjtR0uBLRUUN1BcMywTxylQCwRwcA8jOPWs6bxLp0VrcTxy+eLa4S3lWIglWdlUd+mXHP1oA1qKigure6Rntp4plVirNG4YAjqDjvTYr21nhaaG5hkiQkM6SAqp9CaAJ6KrHUrEKWN5b7RJ5ZPmrgP/AHevX2qzQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAVi+IvsoFs1/Z3skSsxFzYiQyW7Y44i+fB5HGRxzxW1RQBx3+nXFlb3F9FeXmnWuo+Yqz258+SARkKzRBQx2yHIG3dhQcZ6xz2T3spmtbCZLCfVbZ1he3ZMhRiSQoQCqngcgfdz3rtaKO3y/C3+Qnqrf11/zOOuNHxb+IGi07942oRSW5WHkgLCdy8dA245HfPvVe/sZmvtStrpbnzbu53wvBppmLLxsYS/dQrj+IjGM9+e5oo6p+Vv6+4b1MjxHA82mw/uGuYYrmOS4hRdxkjByfl/iwcHHfGOayb+3tdUgt2sNJnSFtUt2m820aMSherlGAOAMAkgdO4rraKVvzv+X+QnqreVjj9W02YPqa2lnIEkv7CVRFEcNh03sMDnAXk+3NQ21tLaSRXWo2NxLaRanfO0YtmkZS8h8uTYASRjcMgH72enNdtRQtP69P8AIq+lv66/5nCQ6a1xPDt0qaK1bxB54ilgIAj+zEBypHC7vXoTjg8VJq9jINS1aK7inZLzaLfyNNNwWQRqoUOOIyG3EbiAM5z1x29FPpb+tkv0C/8AXzb/AFOLutHkls/F5lsnmnljUW7tDlpCtsmCvqd47d639ctZb3wjf20cRlnmspEVCOWcocDnvmtWijpYalZp9v8Agf5HNWVul34msrwWUgt00wqjTWzR+W/mDjDAFWxn3xWZbpczatpp+xSQSxag8k0UGmtEkQZZMsZSMPnIyVODnpXcUUdv663ISsmv62scpZ6def221g0LpZ6a0txazMvyM0o+QA99m6QY7fLVDRbGVZNLt5Uuo7uyVvOC6cY1B2ENumPDhic/KSScHscd1RS6WH1uee2+k6m+iWkMVrOko8PrCytGV+bepaM5xhiARgkVZaylvoL97CO5dvsflPF/Zxs1lG4HZ82CzbQwGOBuPPNdzRT/AOD+Lf8AmHW/9dP8jlbEQXXjW0u9O0y4trdNPmikmktHgXdviKphgDkAHtjrjODi1qnl2viJL3UbSa6tDaeVEYrZ5/KfdlsqoJG4becY+XGeRXQUUPp5frf/ADA4/RdIlXVtO+3WJ8mOyuNiyR5WENMDHGewIQ4x7Go9O0eSW50u1urOVbRbG+t5FaMhUVpUCqfTKjgegrtKbJGJYmjfdtcFTtYqcH0I5H4Uf8H8b/5gtJX/AK6f5HN+Flubu5mu9QDebZJ/ZyM38ZQ/vJB7Mdv/AHzSeINOnudaghit3ltNUjFtesFyqKjb/m9AymRfxFdDaWkFjapb2kYjiToo9zknPck8knrU1D13A4R9Nv5fDWrme1uGuIo4rCBShLSxxN98AddxYn8BVvWLWQ2/iy3ispmnu7QvAY7diJR5QUgMBjduH3c574rsKKTV3djj7rujAg08WvifTXtrTyYRp0scjRx7VB3RbVJHGfvYB9/eqviLzH1aSJ7M+W9psjmi05rh5SScx78FYwPl+8MHOc8Gupopy95W/rW/+Yloc1d291L8LZLYwzPdtpOwxbCZC/lYxjrnPaor6xls9Qim03TFkxo9wjRiLCSODHsRsf8AAsD611VFNu7b/rr/AJhH3Ul2/wCB/keez21zcw6gbS2uHRtDuIAE0x7VS/y7UVGG4nk4Bz3x3q7rukyJaKNPsHBfQruFxDCeWKx7EOB1zuwOvWu1opPVf15/5lRfK0+3/A/yOZbRYn1XTYhZbLaXTJ7e5KR4XB8rCsfX72M+/vUOh3UkcV7q2s7wNLhNlv253+WcySADruIXj/ZrqpIxLE0b7trgqdrFTg+hHI/CoItOtILAWUcCC3H/ACzPIPOcnPUk8knqad9b/wBdf82T0S/r+tit4itpL7wvqdtBF5ss1pKsaY+8xQ4H51l21rFfeItNujYv9mTTZFBntmTY5dOCGAKnAPB7ZrpqKS3/AK7NfqD1Vv66f5HCrZXVpb6VLNFLHa2y3MWwWLXBiYy/IfLHIGwEAgcD2NWNN0k/2roTPazyW0Ud64NxbbBEXdCo287OM7QcHHYYNdlRQC0OFisJxd/ZLsXIu/7RNwHh04tkebuD+eflA24BGcgZXHQVcudMla11nbZOyNqkUrxrHgzwqIi4H94cNx3wRXXUULT+vT/ITV/69f8AM4bV7T+1Y76bTNOuFtJTZxuj2rxGZluAWPlsA2FTqxHT6Vc1m1a219ppIiLJ7RYYRFpzXQU7mLrtUHbkFecYOMdhXW0UraWKvp/Xl/kYsdu1r4HaB/NzHYsuJgA4Gw4BAJGR061iaOI7mHw+dK02e3mtId9w8lu0a7DCRs8wjD7nKn5SemTXZSxJPC8Uq7kkUqw9QeDRBDHbW8cEC7I4kCIuegAwBT6t97fr/mLt8/0OEtIrme605kspYWjgnSaKHTHt44C0Z+Tcwy3zAcgkEgHuKvwWC2Gh+HJLnTpGtraJWuYI7cuySGLAcxgEkg5B4JBbPauvoo/4H4X/AMxW1/ry/wAjlTZW97quiTW2lSw2a3FxKyTW5UKShw5U/cyeQCAc9s1LFplydHv7W1tCudQkeOL7ZLZDaWzkPGCwGSTwMGulopW1v/XT/IZyr6TqEOgSBrQ+dFdQ3KxDU570yLG6sQGmAIOAcAcZxV3T5jqviJtRht7mG2htfIVrmB4Wd2cMcK4DYG0c4wc8dK3aKq/9fKwmrq39b3MC+sJ5tc1CS0j8uWTTBHDcFOBJl8fN6jIqhZxRT3ekppmmXFk9jG63RktWiCoYyvl7iAHJfaflJHy59K66ipSsrf11/wAym7u/9dP8jF8H2X2DwhpkL232aYW0fmoU2MGxzuHXOfWsPUrBpdN1uxWwmaSXU7ebaLZiskRkiyQ2MMBtbIB474rtqKpu8ub+t7/oLt95yOs6Vcve6wmm2pRJrC3GEjwspWWTeo6AsU4xnuKo3dm97pesSWUV3I72HklBpjWwkOcgBWwzMOegwM9a7yikEtf69P8AI5ifRIH17VP+Jahgk0qONCIflZt0uVHGM42+/StXR7h/sNlaXENws62UUkjvEQucYK7um7I5HWtKij+vz/zB6u/9dP8AIKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigD/9k=)\n",
        "\n",
        "\n",
        "</br>\n",
        "\n",
        "## **2. Understand the shape of the data (Histograms, box plots, etc.)**\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ysn7-S92-HgD"
      },
      "source": [
        "\n",
        ">The first analysis is always made working with the Training set. Only after this first analysis, it comes the final step which is to make predictions based on the test set.\n",
        "\n",
        "The first thing to do is to import the data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 157
        },
        "id": "zoSvc-46-EME",
        "outputId": "0273e09d-ebc6-498e-89c4-0eb553e3771b"
      },
      "source": [
        "# reading files from pc in google colab\n",
        "from google.colab import files\n",
        "import io\n",
        "uploaded = files.upload()\n",
        "\n",
        "# import the data\n",
        "training = pd.read_csv('train.csv')\n",
        "test = pd.read_csv('test.csv')\n",
        "\n",
        "training['train_test'] = 1\n",
        "test['train_test'] = 0\n",
        "all_data = pd.concat([training,test])\n",
        "\n",
        "%matplotlib inline\n",
        "all_data.columns\n",
        "\n",
        "\n"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-5d52aed4-898a-4c94-9803-55366c688991\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-5d52aed4-898a-4c94-9803-55366c688991\">\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": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving test.csv to test (1).csv\n",
            "Saving train.csv to train (1).csv\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n",
              "       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked', 'train_test'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ESLuS7ya2GVa"
      },
      "source": [
        "### **2.1 Data Exploration**\n",
        "\n",
        "First, we should look at our data to understand the data types and null counts to think about how to manage that data/information"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bKWLxNPH28Jt",
        "outputId": "f5279c2e-4bf3-46f6-fb1e-bf43bbf3d9f0"
      },
      "source": [
        "training.info()"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 891 entries, 0 to 890\n",
            "Data columns (total 13 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",
            " 12  train_test   891 non-null    int64  \n",
            "dtypes: float64(2), int64(6), object(5)\n",
            "memory usage: 90.6+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "WeiMrKHCkcjS",
        "outputId": "8e74bf5c-e428-4bae-f271-e058ccf256ea"
      },
      "source": [
        "training.head()"
      ],
      "execution_count": 41,
      "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",
              "      <th>train_test</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",
              "      <td>1</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",
              "      <td>1</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",
              "      <td>1</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",
              "      <td>1</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",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   PassengerId  Survived  Pclass  ... Cabin Embarked  train_test\n",
              "0            1         0       3  ...   NaN        S           1\n",
              "1            2         1       1  ...   C85        C           1\n",
              "2            3         1       3  ...   NaN        S           1\n",
              "3            4         1       1  ...  C123        S           1\n",
              "4            5         0       3  ...   NaN        S           1\n",
              "\n",
              "[5 rows x 13 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "L4dWL0oXkk_x",
        "outputId": "e39dff47-a761-480a-b607-2af8bf67c2df"
      },
      "source": [
        "training.tail(5)"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "      <th>train_test</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>886</th>\n",
              "      <td>887</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>Montvila, Rev. Juozas</td>\n",
              "      <td>male</td>\n",
              "      <td>27.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>211536</td>\n",
              "      <td>13.00</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>887</th>\n",
              "      <td>888</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Graham, Miss. Margaret Edith</td>\n",
              "      <td>female</td>\n",
              "      <td>19.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>112053</td>\n",
              "      <td>30.00</td>\n",
              "      <td>B42</td>\n",
              "      <td>S</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>888</th>\n",
              "      <td>889</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
              "      <td>female</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>W./C. 6607</td>\n",
              "      <td>23.45</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>889</th>\n",
              "      <td>890</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Behr, Mr. Karl Howell</td>\n",
              "      <td>male</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>111369</td>\n",
              "      <td>30.00</td>\n",
              "      <td>C148</td>\n",
              "      <td>C</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>890</th>\n",
              "      <td>891</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Dooley, Mr. Patrick</td>\n",
              "      <td>male</td>\n",
              "      <td>32.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>370376</td>\n",
              "      <td>7.75</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Q</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     PassengerId  Survived  Pclass  ... Cabin Embarked  train_test\n",
              "886          887         0       2  ...   NaN        S           1\n",
              "887          888         1       1  ...   B42        S           1\n",
              "888          889         0       3  ...   NaN        S           1\n",
              "889          890         1       1  ...  C148        C           1\n",
              "890          891         0       3  ...   NaN        Q           1\n",
              "\n",
              "[5 rows x 13 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HVwmP8S33fP5"
      },
      "source": [
        "Also, it is important to get some information about descriptive statistics"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "39e2dgGX39wN",
        "outputId": "4ae14be9-e5f4-42c1-a3ed-6b507f1eb2c0"
      },
      "source": [
        "training.describe()"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "        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>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Fare</th>\n",
              "      <th>train_test</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>891.000000</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>714.000000</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>891.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>446.000000</td>\n",
              "      <td>0.383838</td>\n",
              "      <td>2.308642</td>\n",
              "      <td>29.699118</td>\n",
              "      <td>0.523008</td>\n",
              "      <td>0.381594</td>\n",
              "      <td>32.204208</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>257.353842</td>\n",
              "      <td>0.486592</td>\n",
              "      <td>0.836071</td>\n",
              "      <td>14.526497</td>\n",
              "      <td>1.102743</td>\n",
              "      <td>0.806057</td>\n",
              "      <td>49.693429</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.420000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>223.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>20.125000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>7.910400</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>446.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>14.454200</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>668.500000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>38.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>31.000000</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>891.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>512.329200</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       PassengerId    Survived      Pclass  ...       Parch        Fare  train_test\n",
              "count   891.000000  891.000000  891.000000  ...  891.000000  891.000000       891.0\n",
              "mean    446.000000    0.383838    2.308642  ...    0.381594   32.204208         1.0\n",
              "std     257.353842    0.486592    0.836071  ...    0.806057   49.693429         0.0\n",
              "min       1.000000    0.000000    1.000000  ...    0.000000    0.000000         1.0\n",
              "25%     223.500000    0.000000    2.000000  ...    0.000000    7.910400         1.0\n",
              "50%     446.000000    0.000000    3.000000  ...    0.000000   14.454200         1.0\n",
              "75%     668.500000    1.000000    3.000000  ...    0.000000   31.000000         1.0\n",
              "max     891.000000    1.000000    3.000000  ...    6.000000  512.329200         1.0\n",
              "\n",
              "[8 rows x 8 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aoP_hlsP4S3B"
      },
      "source": [
        "It is very advantageous for the data analysis, if we can look to the columns and the data presented for each column. Thus, it is possible to separate numeric and categorical values  "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NrL4XZih5FbD"
      },
      "source": [
        "#quick way to see all the columns name, and, separate numeric and categorical colummns\n",
        "training.describe().columns\n",
        "df_num = training[['Age','SibSp','Parch','Fare']] #numerical variables\n",
        "df_cat = training[['Survived','Pclass','Sex','Ticket','Cabin','Embarked']] #categorical variables\n",
        "\n",
        "\n",
        "# ***a little data remimder ***\n",
        "#Age   - age in years\n",
        "#SibSp - # of siblings / spouses aboard the Titanic\n",
        "#Parch - # of parents / children aboard the Titanic\n",
        "#Fare  - Passenger fare\n"
      ],
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qjumOk1S5jw6"
      },
      "source": [
        "#### **2.1.1 Distributions**\n",
        "\n",
        "Now, it's time to get some visuals from **numerical variables** to start thinking on the data from a critical point of view\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "htrNa4ol6mo8",
        "outputId": "15012186-0cea-4463-c4bc-1b5eed676129"
      },
      "source": [
        "#distributions for all numerical variables \n",
        "for i in df_num.columns:\n",
        "    plt.hist(df_num[i])\n",
        "    plt.title(i)\n",
        "    plt.show()"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jaSVF1GO8slK"
      },
      "source": [
        "From the histograms above, it is easily seen that only 'Age' seems to follow a normal distribution. For 'SibSp', 'Parch' and 'Fare' perhaps it can be useful to normalise them."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 371
        },
        "id": "MiMSUGty9iZ8",
        "outputId": "21db643e-b39d-42f7-affa-5aaea9304f98"
      },
      "source": [
        "print(df_num.corr())\n",
        "sns.heatmap(df_num.corr())"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "            Age     SibSp     Parch      Fare\n",
            "Age    1.000000 -0.308247 -0.189119  0.096067\n",
            "SibSp -0.308247  1.000000  0.414838  0.159651\n",
            "Parch -0.189119  0.414838  1.000000  0.216225\n",
            "Fare   0.096067  0.159651  0.216225  1.000000\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fac80471810>"
            ]
          },
          "metadata": {},
          "execution_count": 46
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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vAHcCBwKfAO4jm6vUzKxS6jBcrmhinhYRFwHPR8T1EfF+wL1lM6uciTQq4/n0z4cl/SHwEPCK9oRkZjZ+dShlFE3M/yBpb+BvgM8DewEfaVtUZmbjNFDpvnAxjSbK353sceyDyH62+6KIWNSJwMzMxmMi9JgvIStj3Ai8A5gHfKjdQZmZjVfUvccMzIuIwwAkXQT8uP0hmZmN30ToMQ/d9Bv6xdg2h2Nm1poqD4MrqlFiPlzSU2ldwB5pe+jHWPdqa3RmZk3q/bTcIDFHRF+nAjEzK8POGqRm/zyUmdXKRLj517KlUw9p9yl6Rv9TRR+0rL9ph6/odgiVsewnK7sdQq2UefNP0mLgs0AfcGFEfGrY6y8BLgWOBh4DTo2I+1o9rzOFmdVKNPG/saRfbzqfXUOFl0maN6zZ6cCvIuIg4Dzg02VcgxOzmdXKYBNLAwuArRGxLSKeA9YAS4a1WUL2vAfAFcBbVcLwNSdmM6uVgYjCSwMzgQdy29vTvhHbRMRO4ElgWqvX4MRsZrXSzLSfkpZL2pBblnc7fvCoDDOrmWZGZUTEKmDVKC8/CMzObc9K+0Zqs13SZGBvspuALXGP2cxqpcQa83pgrqQDJU0BlgL9w9r0A6el9VOAH0Q0rpE04h6zmdVKWY9kp2kozgCuIhsud3FEbJK0EtgQEf3ARcBqSVuBx8mSd8ucmM2sVsp8wCQi1gJrh+1bkVv/LfBfSjth4sRsZrVSYLRF5Tkxm1mtTITZ5czMespEmI/ZzKyneBIjM7OKcSnDzKxiShhG3HVOzGZWKwPuMZuZVYtLGWZmFeNShplZxbjHbGZWMR4uZ2ZWMX4k28ysYlzKMDOrGCdmM7OK8agMM7OKcY/ZzKxiJtSoDElvBOZGxL9Kmg5MjYh72xeamVnzBqL3J/4slJglfRyYDxwM/CuwG/Bl4A3tC83MrHl1qDEX/ZXsdwMnAjsAIuIhYM/RGktaLmmDpA23/npr61GamRU0SBReqqpoYn4u/SR3AEh66ViNI2JVRMyPiPlH7nlQqzGamRUWTfyvqorWmL8u6QvAyyR9AHg/8MX2hWVmNj6DNShlNEzMkgRcBhwCPEVWZ14REd9rc2xmZk2rck+4qIaJOSJC0tqIOAxwMjazSqvDqIyiNeZbJP3ntkZiZlaCwYjCS1UVTczHADdJukfSRkm3S9rYzsDMzMajUzf/JL1C0vck3Z3++fIR2hwh6SZJm1LuPLXIsYve/PuDpiI2M+uSDvaEzwKuiYhPSTorbf/dsDa/Ad4bEXdL2h+4WdJVEfHEWAcu1GOOiPsj4n7gGbIhcy8MnTMzq5IODpdbAlyS1i8B/vh3Yom4KyLuTusPAY8A0xsduFBilnSipLuBe4HrgfuA7xR5r5lZJw3EQOEl/zBcWpY3caoZEfFwWv85MGOsxpIWAFOAexoduGgp4xzgWOD7EXGkpEXAnxZ8r5lZxzTzSHZErAJWjfa6pO8D+47w0seGHSckjXpiSfsBq4HTIhoPGymamJ+PiMckTZI0KSKulfR/C77XzKxjynzUOiJOGO01Sb+QtF9EPJwS7yOjtNsL+DfgYxGxrsh5i47KeELSVOAG4CuSPkuaN8PMrEoiovDSon7gtLR+GnDl8AaSpgDfBC6NiCuKHnjMxCzpVWl1CdndxY8A3yWrkbyr6EnMzDqlg+OYPwW8Ld1/OyFtI2m+pAtTm/cAbwbeJ+m2tBzR6MCNShnfAo6KiB2SvhERJ7PrLqSZWeV06pHsiHgMeOsI+zcAf5HWv0w2RXJTGiVm5db/U7MHNzPrtDo8kt0oMcco62ZmlVSHifIbJebDJT1F1nPeI62TtiMi9mprdGZmTaryHBhFjZmYI6KvU4GYmZVhIvSYzcx6SpV/MqooJ2YzqxX3mM3MKmYijMowM+sptb/5Z2bWa1zKMDOrmAnxY6xmZr3EPWYzs4qpQ41ZdfjrUoSk5WlS7AnPn8Uu/ix28WdRHUXnY66DZn4ypu78Weziz2IXfxYVMZESs5lZT3BiNjOrmImUmF0728WfxS7+LHbxZ1ERE+bmn5lZr5hIPWYzs57gxGxmVjG1ScyS/lhSSDqk27F0kqSPSdokaWP6Bd5jJF0oaV56/elR3nespB+l9/xU0tkdDbwNJA2k67lD0uWSfq/F482RdEdZ8XVL7nMZWuZ0OyYbW21qzJIuA/YHfhARH+92PJ0g6TjgXGBhRDwraR9gSkQ8lGvzdERMHeG9W4D3RMRPJPUBB0fE5o4F3wb5a5X0FeDmiDi3wPsmR8TOEfbPAb4dEYeWHWsnjfbvQIP3iCw/9P4cmj2oFj1mSVOBNwKnA0vTvkmS/lnSnZK+J2mtpFPSa0dLul7SzZKukrRfF8NvxX7AoxHxLEBEPBoRD0m6TtL8oUaSzku96mskTU+7Xwk8nN43MJSUJZ0tabWkmyTdLekDHb6mstwIHCTpXembwa2Svi9pBrzoOn8IrJY0Q9I3Jf0kLa9Px+mT9MX0+V0taY+uXVFJJE1N/y7cIul2SUvS/jmStki6FLgDmC3po5LWp29kn+hu5BNHLRIzsAT4bkTcBTwm6WjgJGAOMA/4M+A4AEm7AZ8HTomIo4GLgU92I+gSXE32H89d6Y/Q8SO0eSmwISJeC1wPDH2bOA/YkpLRX0raPfee1wFvIfvMVkjav43XUDpJk4F3ALcD/w4cGxFHAmuAv801nQecEBHLgM8B10fE4cBRwKbUZi5wfvr8ngBO7sxVlGqPXBnjm8BvgXdHxFHAIuAzqYcM2fX+c7reg9P2AuAI4GhJb+5C/BNOXSYxWgZ8Nq2vSduTgcvTV7GfS7o2vX4wcCjwvfTvYh+p59hrIuLp9EfoTWT/gV0m6axhzQaBy9L6l4H/l967Mn3dfzvwX8k+s4Wp3ZUR8QzwTPrcFgDfaue1lGQPSbel9RuBi8j+/74sfSuaAtyba9+frhOyP0TvhewbBPCkpJcD90bE0DFvJvtj32ueiYgjhjZS5+QfU5IdBGYCM9LL90fEurT+9rTcmrankiXqGzoS9QTW84lZ0ivI/qM6TFKQJdoAvjnaW4BNEXFch0Jsq5RErgOuk3Q7cFqjt+Teew9wgaQvAr+UNG14m1G2q+pFCQhA0ueBcyOiX9JC4OzcyzsKHPPZ3PoA0POlDOBPgOnA0RHxvKT7gKFvTPnPRMA/RcQXOhzfhFeHUsYpwOqIOCAi5kTEbLJe0ePAyanWPINdvcEtwPR04wxJu0l6bTcCb5WkgyXNze06Arh/WLNJZJ8RZD3jf0/v/cNhX18HyL6qAyyRtHtK1AuB9W0Iv1P2Bh5M62P90boG+CCApD5Je7c7sC7aG3gkJeVFwAGjtLsKeH+6h4OkmZJe2akgJ7I6JOZl/G7v+BvAvsB2YDPZV/hbgCcj4jmyRPVpST8BbgNeT2+aClwiabOkjWQ107OHtdkBLFA27OstwMq0/8/Iasy3AauBP0m9b4CNwLXAOuCc/CiPHnQ2cLmkm4FHx2j3IWBR+tZxM9lnWVdfAeana30vcOdIjSLiauCrwE2p7RXAnh2LcgKrzXC5kUiamuqw04AfA2+IiJ93O64qUzae+emI+D/djsVsour5GnMD35b0MrKbPuc4KZtZL6h1j9nMrBfVocZsZlYrTsxmZhXjxGxmVjFOzGZmFePEbGZWMf8fsqJP5CT1zO8AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QmfWP7m6QY0y"
      },
      "source": [
        "Here, we can see that 'Parch' and 'SibSp' are the most correlated variables, which means that families were traveling together aboard the Titanic. For the numerical variables, it is important to look at the survival rate across those variables. Thus, it can be answered the question: how it is related the survival rate across some groups of passenger?\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "id": "QBJg3-UBRxGZ",
        "outputId": "2c7c11d9-a4ea-43d6-bd4b-b32babe1c479"
      },
      "source": [
        "# survival rate across the numerical variables Age, SibSp, Parch, and Fare \n",
        "pd.pivot_table(training, index = 'Survived', values = ['Age','SibSp','Parch','Fare'])\n",
        "\n",
        "#Little remimder, survived: 0(No) and 1(Yes)\n"
      ],
      "execution_count": 47,
      "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>Age</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Parch</th>\n",
              "      <th>SibSp</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Survived</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>30.626179</td>\n",
              "      <td>22.117887</td>\n",
              "      <td>0.329690</td>\n",
              "      <td>0.553734</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>28.343690</td>\n",
              "      <td>48.395408</td>\n",
              "      <td>0.464912</td>\n",
              "      <td>0.473684</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                Age       Fare     Parch     SibSp\n",
              "Survived                                          \n",
              "0         30.626179  22.117887  0.329690  0.553734\n",
              "1         28.343690  48.395408  0.464912  0.473684"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5HWqWcSiTleW"
      },
      "source": [
        "From the survival rate results presented above, we can start to conclude that:\n",
        "- younger people may have high chance to survive\n",
        "- people that paid more also have more chance to survive\n",
        "- kids with parents on board may have a high chance to survive\n",
        "- people with siblings on board may have low chance to survive"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b-BQM_54W9Xg"
      },
      "source": [
        "The next question is: what about **categorical variables**?\n",
        "Now, it's time to get some visuals from **categorical variables** to add more knowledge about data to continue thinking from a critical point of view"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "rCCcdnOcXb75",
        "outputId": "b7870dcd-977e-47b4-e4d1-02cff46cfef3"
      },
      "source": [
        "#distributions for all categorical variables \n",
        "for i in df_cat.columns:\n",
        "    sns.barplot(df_cat[i].value_counts().index,df_cat[i].value_counts()).set_title(i)\n",
        "    plt.show()\\"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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AgbzojwFyMPvVcF23dfpqblTdw6XmRtUdqub8ADU2zBCHAe8ETh6ZP6lvkyRpTYYIq/8FnJbkKUmOBM4H/niAupKkKTHxw4BVtTvJJcCHgU3A1VV18yqrXXuA5U4D/u8BrvtobERdt3X6am5U3cOl5kbV3ahtnSoTv8BCkqRHyztYSJKaZ1hJkpq36jmrJG8E/gXdZZELwGur6pP9Y/8FuAf4PuDlE+ynJGkYC3Q7Mg/380f24+qHe4GvA38N/GC/fAG7geOB24F/XlXfTPIrwE/3628G/jEwU1X3JPnXwEX9up8FXlNV/7DfXlXVfgfgOcDfAEf18ycATxx5/DxgF93tlOaB/0f35bf7+6EcHBwcHDZ0WBjTNj/S/hBd0MzTBdHD/fSXga8Bj/TL/B7wh33bY+kuGnl6nwVvA+7upy8D/tOYPHkR8LF++knA3wFH9/PvBV69Uh6tdhjwRGBXVT0EUFW7ququkccDbAW+v9+4f6C788S9wF2sTa0yL0k6cKNfFl78/XoEe7+ofP/I/Fb2BtmWfnoTXXC9mW7P6Ci6QLuFLnQAzgS+1U+/HXjJmH5cALxrZH4zcHSSzcAxrJIZq4XVR4CTk/yfJG9J8kPLHj+RLnG/r9/QTcDj6b74e+oqz71oeThtxLfaJWlajZ7uGff79fEj7ZvowmgzcCtwXP/YA8CfAE/t208EngHMJLmN7sYPr+qf4+vAd40WSHIMcDbwfoCquhP4beArdHtq36qqj6y0ESuGVVU9APwA3T375oD3JHn1ssXu6jdsM92u5FeAbwNfWum5x/Rhqm8VIkkNGt1ZCN0e1a5+/vl0wQXd7/Of6Zd5DF3oXFpV76yq7wEeBH4DoLrjest3Ql4E/FVV3QOQ5Dvobmj+FOCJwLFJLlypo6teDVhV81V1fVW9CbgEeNnIwzfThdR9fec+BzyTLlm/c7XnXmbTOpeXJB24xfNVCyNtRwDf0U8fyd6M+CG6Gzt8le60zzuqavTmDXfR3anohCQnAncvq3U+Sw8B/ijwd1U1V1WP0N0I4rkrdXbFsErytCSnjTSdQXfsctHH6M5TbaVL3BngP9Mdx1wprB5e4TFJ0sHzyJi2ojsSdhTd7+7FPaGH6Q7L7e7Hc3RHveaBj9MF2zeq6vIkpyZZPHz4t3RXAn6Dbg/sjxYLJTmOLuz2tNEdgXt2kmP65ziL7vDifq14B4skP0B3Bcjxfee/AMxW1a4kvw7sBG6ku1RxU7/BnnOSpEPfLrpzVvN0h/4Wf78/RJcF30kXhN/sx+mX/zLdpeuLh/xeTfdnos4fffIkvwa8gi5bPg1ctHgx31grXSp4sAe6YNuyrO064MRH8ZxHATsPYL3r6E4S7unTsukLgTeu9Nx0N+n9b6v1Zfl20911/oUHsu10e7H3Af/xQLa/X34OePYqy11IdwnqhcBlfdtVwI/0/b9mpO3Z49Yd7V/f7+vo9ryvA948uj7whP4DOwtcTfe/rCXbNvq8I6/FZ+n+x7f4fu3ZNmAb3fnTfwU8OLLe1f02XLbC9u/pz8h7+DPLt2vMeh8Fbtrf6zkyv7jdFwKf6of9fhboLvN9w/KfmZF+vhX4rX57b1/+2Rp9n/rX64r1/qyMvOZ73r+1/DzSnfu4dWR7vw2cPmbdJZ8llv48Ln6Olrwv42ou+yyN+yzu83topc/9uDr9a/BkuivgThzX/1Vezycv6/M1/Wdg7Ou6jvdqn9dnWgbvDShJap63W5IkNc+wkiQ1z7CSJDXPsJIkNc+wkiQ1z7CSJDXv/wP5dKZ0J5R3YAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jyIWzjm8YPQ8"
      },
      "source": [
        "All the histograms have useful information; however, 'Ticket' and 'Cabin' histograms are not very legible -> **Note:** further work, *to do some feature engineering!*\n",
        "\n",
        "**And, what about how it is related the survival rate across these categorical variables?**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kGROD-iKZdfz",
        "outputId": "55e6b602-0262-4ad3-9d12-712f8e797388"
      },
      "source": [
        "# survival rate across all the categorical variables\n",
        "print(pd.pivot_table(training, index = 'Survived', columns = 'Pclass', values = 'Ticket' ,aggfunc ='count'))\n",
        "print()\n",
        "print(pd.pivot_table(training, index = 'Survived', columns = 'Sex', values = 'Ticket' ,aggfunc ='count'))\n",
        "print()\n",
        "print(pd.pivot_table(training, index = 'Survived', columns = 'Embarked', values = 'Ticket' ,aggfunc ='count'))"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Pclass      1   2    3\n",
            "Survived              \n",
            "0          80  97  372\n",
            "1         136  87  119\n",
            "\n",
            "Sex       female  male\n",
            "Survived              \n",
            "0             81   468\n",
            "1            233   109\n",
            "\n",
            "Embarked   C   Q    S\n",
            "Survived             \n",
            "0         75  47  427\n",
            "1         93  30  217\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k3hd815BZ1C0"
      },
      "source": [
        "it is evident that:\n",
        "- people from 1st class survived more than people on the 3rd class\n",
        "- women survived more than men\n",
        "- Passengers that embarked from C(Cherbourg) maybe have a little more chance to survive"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ncyiLJoebmwW"
      },
      "source": [
        "#### **2.1.2 Feature Engineering**\n",
        "\n",
        "In this step, the main objective is to get more information to complement the histograms visualisation for the categorical variables `Cabin` and `Ticket`. Moreover, as indicated in the problem challenge ([Titanic ML competition on Kaggle]( https://www.kaggle.com/c/titanic)), we can also use [feature engineering]( https://triangleinequality.wordpress.com/2013/09/08/basic-feature-engineering-with-the-titanic-data/) to create new features. \n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FqqjDm0ZlDK_"
      },
      "source": [
        "#### ***Cabin***"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "Y-utLr_pXHoU",
        "outputId": "53c960fe-5527-4de0-d13e-17d4a063fda2"
      },
      "source": [
        "#quick way to see the first 5 rows from the categorical variables to see how a cabin number looks like\n",
        "df_cat.head()\n"
      ],
      "execution_count": 50,
      "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>Ticket</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>A/5 21171</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>PC 17599</td>\n",
              "      <td>C85</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>STON/O2. 3101282</td>\n",
              "      <td>NaN</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>113803</td>\n",
              "      <td>C123</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>373450</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Survived  Pclass     Sex            Ticket Cabin Embarked\n",
              "0         0       3    male         A/5 21171   NaN        S\n",
              "1         1       1  female          PC 17599   C85        C\n",
              "2         1       3  female  STON/O2. 3101282   NaN        S\n",
              "3         1       1  female            113803  C123        S\n",
              "4         0       3    male            373450   NaN        S"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "3-dmELmSYxaz",
        "outputId": "b9e110aa-202f-47c6-b95b-6a60927a212f"
      },
      "source": [
        "#quick way to see the last 5 rows from the categorical variables\n",
        "df_cat.tail(5)"
      ],
      "execution_count": 51,
      "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>Ticket</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>886</th>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>male</td>\n",
              "      <td>211536</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>887</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>112053</td>\n",
              "      <td>B42</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>888</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>female</td>\n",
              "      <td>W./C. 6607</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>889</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>male</td>\n",
              "      <td>111369</td>\n",
              "      <td>C148</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>890</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>370376</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Q</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     Survived  Pclass     Sex      Ticket Cabin Embarked\n",
              "886         0       2    male      211536   NaN        S\n",
              "887         1       1  female      112053   B42        S\n",
              "888         0       3  female  W./C. 6607   NaN        S\n",
              "889         1       1    male      111369  C148        C\n",
              "890         0       3    male      370376   NaN        Q"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pdhcwEPoY3dY"
      },
      "source": [
        "> It looks like that the Cabin number are only for the 1st class. \n",
        "\n",
        "\n",
        "**Was it possible that a passenger have multiple Cabins?**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q6WQo_ZiXvc5",
        "outputId": "8b44b2ad-ada9-46c9-a5cd-17113ba738d1"
      },
      "source": [
        "#well, first we can investigate further if one passenger had multiple Cabins\n",
        "df_cat.Cabin\n",
        "training['cabin_multiple'] = training.Cabin.apply(lambda x: 0 if pd.isna(x) else len(x.split(' ')))\n",
        "# after looking at this, we may want to look at cabin by letter or by number. Let's create some categories for this \n",
        "# letters \n",
        "# multiple letters \n",
        "training['cabin_multiple'].value_counts()"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    687\n",
              "1    180\n",
              "2     16\n",
              "3      6\n",
              "4      2\n",
              "Name: cabin_multiple, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E0_rp681Zq_V"
      },
      "source": [
        "**And, what about how it is related the survival rate if passenger had multiple Cabins?**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "id": "czAyDDPPaD79",
        "outputId": "86f2434a-7d0f-4362-db2a-7f82c6d831a1"
      },
      "source": [
        "pd.pivot_table(training, index = 'Survived', columns = 'cabin_multiple', values = 'Ticket' ,aggfunc ='count')"
      ],
      "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>cabin_multiple</th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Survived</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>481.0</td>\n",
              "      <td>58.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>206.0</td>\n",
              "      <td>122.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "cabin_multiple      0      1    2    3    4\n",
              "Survived                                   \n",
              "0               481.0   58.0  7.0  3.0  NaN\n",
              "1               206.0  122.0  9.0  3.0  2.0"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6hgygmeuaWdC"
      },
      "source": [
        "It is clear that:\n",
        "- passengers with only one Cabin have low chance to survive (maybe travelling alone and not in the prioritory group)\n",
        "- passengers with two or three cabins survived more than passengers with only on Cabin (maybe passengers travelling in family, the prioritory by group was followed, kids and mothers first) "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7tB8WCEqb2yS"
      },
      "source": [
        "**Now, what about the Cabin letter and the survival rate?**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "id": "hbUTNBDdb99r",
        "outputId": "77b3506a-0e02-40ee-a7c9-35a411a958d6"
      },
      "source": [
        "#creates categories based on the cabin letter (n stands for null)\n",
        "#in this case we will treat null values like it's own category\n",
        "training['cabin_adv'] = training.Cabin.apply(lambda x: str(x)[0])\n",
        "\n",
        "#comparing survival rate by cabin\n",
        "print(training.cabin_adv.value_counts())\n",
        "pd.pivot_table(training,index='Survived',columns='cabin_adv', values = 'Name', aggfunc='count')"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "n    687\n",
            "C     59\n",
            "B     47\n",
            "D     33\n",
            "E     32\n",
            "A     15\n",
            "F     13\n",
            "G      4\n",
            "T      1\n",
            "Name: cabin_adv, dtype: int64\n"
          ]
        },
        {
          "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>cabin_adv</th>\n",
              "      <th>A</th>\n",
              "      <th>B</th>\n",
              "      <th>C</th>\n",
              "      <th>D</th>\n",
              "      <th>E</th>\n",
              "      <th>F</th>\n",
              "      <th>G</th>\n",
              "      <th>T</th>\n",
              "      <th>n</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Survived</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>8.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>481.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>7.0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>206.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "cabin_adv    A     B     C     D     E    F    G    T      n\n",
              "Survived                                                    \n",
              "0          8.0  12.0  24.0   8.0   8.0  5.0  2.0  1.0  481.0\n",
              "1          7.0  35.0  35.0  25.0  24.0  8.0  2.0  NaN  206.0"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S1pZdfqTdAWQ"
      },
      "source": [
        "It can be concluded that:\n",
        "- Passengers from cabins with letters have more chance to survive\n",
        "- Maybe it is a coincidence, and it should/can be investigated further, but how it is the null cabin letter related with passenger with only one cabin"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_9nbG108js12"
      },
      "source": [
        "#### ***Tickets***"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K_DzoH1LePFH"
      },
      "source": [
        "**Is there any combination from the ticket information and the survival rate?**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "fqu_r_43e9uz",
        "outputId": "9828ff46-960f-4ca1-a0cb-237a07f922cf"
      },
      "source": [
        "#a quick recap about the first 5 rows from the categorical variables to see how ticket looks like\n",
        "df_cat.head()\n",
        "\n"
      ],
      "execution_count": 55,
      "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>Ticket</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>A/5 21171</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>PC 17599</td>\n",
              "      <td>C85</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>STON/O2. 3101282</td>\n",
              "      <td>NaN</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>113803</td>\n",
              "      <td>C123</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>373450</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Survived  Pclass     Sex            Ticket Cabin Embarked\n",
              "0         0       3    male         A/5 21171   NaN        S\n",
              "1         1       1  female          PC 17599   C85        C\n",
              "2         1       3  female  STON/O2. 3101282   NaN        S\n",
              "3         1       1  female            113803  C123        S\n",
              "4         0       3    male            373450   NaN        S"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PyIt1ocTfTqK",
        "outputId": "224f24a7-7fb9-4003-86a3-a6809f939f1e"
      },
      "source": [
        "#separate the ticket by letters and numbers\n",
        "training['numeric_ticket'] = training.Ticket.apply(lambda x: 1 if x.isnumeric() else 0)\n",
        "training['ticket_letters'] = training.Ticket.apply(lambda x: ''.join(x.split(' ')[:-1]).replace('.','').replace('/','').lower() if len(x.split(' ')[:-1]) >0 else 0)\n",
        "\n",
        "print('number of tickets with only numbers:')\n",
        "training['numeric_ticket'].value_counts()\n"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "number of tickets with only numbers:\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    661\n",
              "0    230\n",
              "Name: numeric_ticket, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "52hmlXTogKvm",
        "outputId": "11c53448-d024-41ee-9c55-5554b23bac75"
      },
      "source": [
        "print('number of ticket with letters:')\n",
        "training['ticket_letters'].value_counts()"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "number of ticket with letters:\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0            665\n",
              "pc            60\n",
              "ca            41\n",
              "a5            21\n",
              "stono2        18\n",
              "sotonoq       15\n",
              "scparis       11\n",
              "wc            10\n",
              "a4             7\n",
              "soc            6\n",
              "fcc            5\n",
              "c              5\n",
              "wep            3\n",
              "sopp           3\n",
              "pp             3\n",
              "ppp            2\n",
              "scah           2\n",
              "sotono2        2\n",
              "swpp           2\n",
              "scow           1\n",
              "sp             1\n",
              "scahbasle      1\n",
              "sc             1\n",
              "as             1\n",
              "sop            1\n",
              "casoton        1\n",
              "fa             1\n",
              "fc             1\n",
              "sca4           1\n",
              "Name: ticket_letters, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "id": "E-ZNVbHYh-5_",
        "outputId": "ae7c6130-5bc9-4f69-de70-fad328515670"
      },
      "source": [
        "#difference from tickets if they are only numeric in the survival rate \n",
        "pd.pivot_table(training,index='Survived',columns='numeric_ticket', values = 'Ticket', aggfunc='count')"
      ],
      "execution_count": 58,
      "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>numeric_ticket</th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Survived</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>142</td>\n",
              "      <td>407</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>88</td>\n",
              "      <td>254</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "numeric_ticket    0    1\n",
              "Survived                \n",
              "0               142  407\n",
              "1                88  254"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "id": "1QtqFce9iPKw",
        "outputId": "3e1fb76b-af87-49fd-ca52-4d7eb297cd2b"
      },
      "source": [
        "#difference from tickets if they have letters in the survival rate \n",
        "pd.pivot_table(training,index='Survived',columns='ticket_letters', values = 'Ticket', aggfunc='count')"
      ],
      "execution_count": 59,
      "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>ticket_letters</th>\n",
              "      <th>0</th>\n",
              "      <th>a4</th>\n",
              "      <th>a5</th>\n",
              "      <th>as</th>\n",
              "      <th>c</th>\n",
              "      <th>ca</th>\n",
              "      <th>casoton</th>\n",
              "      <th>fa</th>\n",
              "      <th>fc</th>\n",
              "      <th>fcc</th>\n",
              "      <th>pc</th>\n",
              "      <th>pp</th>\n",
              "      <th>ppp</th>\n",
              "      <th>sc</th>\n",
              "      <th>sca4</th>\n",
              "      <th>scah</th>\n",
              "      <th>scahbasle</th>\n",
              "      <th>scow</th>\n",
              "      <th>scparis</th>\n",
              "      <th>soc</th>\n",
              "      <th>sop</th>\n",
              "      <th>sopp</th>\n",
              "      <th>sotono2</th>\n",
              "      <th>sotonoq</th>\n",
              "      <th>sp</th>\n",
              "      <th>stono2</th>\n",
              "      <th>swpp</th>\n",
              "      <th>wc</th>\n",
              "      <th>wep</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Survived</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>410.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>9.0</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>255.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.0</td>\n",
              "      <td>39.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "ticket_letters      0   a4    a5   as    c  ...   sp  stono2  swpp   wc  wep\n",
              "Survived                                    ...                             \n",
              "0               410.0  7.0  19.0  1.0  3.0  ...  1.0    10.0   NaN  9.0  2.0\n",
              "1               255.0  NaN   2.0  NaN  2.0  ...  NaN     8.0   2.0  1.0  1.0\n",
              "\n",
              "[2 rows x 29 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PnaeZjH-it9H"
      },
      "source": [
        "It is a little difficult to get some conclusions about how the survival rate can be related with the tickets letters. The meaning of the letters is very important for this analysis and without this meaning it is hard to get some conclusions."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XdY452g7dd27"
      },
      "source": [
        "#### ***Person Title***"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7PdRa9Wudy9z"
      },
      "source": [
        "**Is the survival rate related with the person's title?**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D-x1-v5ReI5O",
        "outputId": "615288a1-9a66-4225-ae6b-e83065c1db48"
      },
      "source": [
        "#a quick recap about the first 5 rows from the Name to see how it looks like\n",
        "training['Name'].head()"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0                              Braund, Mr. Owen Harris\n",
              "1    Cumings, Mrs. John Bradley (Florence Briggs Th...\n",
              "2                               Heikkinen, Miss. Laina\n",
              "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)\n",
              "4                             Allen, Mr. William Henry\n",
              "Name: Name, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3yjfAS-2fmhV"
      },
      "source": [
        "#person's title - Mr., Mrs., Miss., etc\n",
        "training.Name.head(50)\n",
        "training['name_title'] = training.Name.apply(lambda x: x.split(',')[1].split('.')[0].strip())\n"
      ],
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "id": "-rDDnIHggPYi",
        "outputId": "1ab511f1-3ecf-4c5f-e3d4-cd5aea93e088"
      },
      "source": [
        "#comparing survival rate by person's title\n",
        "print(training['name_title'].value_counts())\n",
        "pd.pivot_table(training,index='Survived',columns='name_title', values = 'Name', aggfunc='count')"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mr              517\n",
            "Miss            182\n",
            "Mrs             125\n",
            "Master           40\n",
            "Dr                7\n",
            "Rev               6\n",
            "Major             2\n",
            "Mlle              2\n",
            "Col               2\n",
            "the Countess      1\n",
            "Lady              1\n",
            "Capt              1\n",
            "Don               1\n",
            "Ms                1\n",
            "Mme               1\n",
            "Sir               1\n",
            "Jonkheer          1\n",
            "Name: name_title, dtype: int64\n"
          ]
        },
        {
          "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>name_title</th>\n",
              "      <th>Capt</th>\n",
              "      <th>Col</th>\n",
              "      <th>Don</th>\n",
              "      <th>Dr</th>\n",
              "      <th>Jonkheer</th>\n",
              "      <th>Lady</th>\n",
              "      <th>Major</th>\n",
              "      <th>Master</th>\n",
              "      <th>Miss</th>\n",
              "      <th>Mlle</th>\n",
              "      <th>Mme</th>\n",
              "      <th>Mr</th>\n",
              "      <th>Mrs</th>\n",
              "      <th>Ms</th>\n",
              "      <th>Rev</th>\n",
              "      <th>Sir</th>\n",
              "      <th>the Countess</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Survived</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>436.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>23.0</td>\n",
              "      <td>127.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>81.0</td>\n",
              "      <td>99.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "name_title  Capt  Col  Don   Dr  Jonkheer  ...   Mrs   Ms  Rev  Sir  the Countess\n",
              "Survived                                   ...                                   \n",
              "0            1.0  1.0  1.0  4.0       1.0  ...  26.0  NaN  6.0  NaN           NaN\n",
              "1            NaN  1.0  NaN  3.0       NaN  ...  99.0  1.0  NaN  1.0           1.0\n",
              "\n",
              "[2 rows x 17 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C8d1rUaohTfA"
      },
      "source": [
        "The survival rate taking into account the person's title clearly corroborate the conclusion from the numerical variables: women and children were to board lifeboats first during the emergency."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KZwWGW1ViyBa"
      },
      "source": [
        "## **3. Data Preprocessing**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P3Od9Rg-jCKZ"
      },
      "source": [
        "Now that the some knowledge from data were obtained, it's time to prepare data for the model and analysis. This means that it is only of interest to have data with non-null values, only relevant variables should be included in the model, and transformed variables if needed for the model development. Moreover, the most important thing is that to have the same variables in the training and test set."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M-uRdfjwlI7D"
      },
      "source": [
        "### **3.1 Categorical variables for both training and test sets**\n",
        "\n",
        "On the data preprocessing, the first step is to create the same categorical variables that were created in Section 2 for both training and test sets."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GNtO0pJIl0IB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d1c7bfc2-29be-4d32-a435-da830e53ba6d"
      },
      "source": [
        "#in Section 2, it were created the categorical variables: cabin_multiple, cabin_adv, numeric_ticket, ticket_letters and name_title\n",
        "#here, we will create the same variables, but for the training and test sets\n",
        "#please, note that this is not a good practice for real DS projects\n",
        "all_data['cabin_multiple'] = all_data.Cabin.apply(lambda x: 0 if pd.isna(x) else len(x.split(' ')))\n",
        "all_data['cabin_adv'] = all_data.Cabin.apply(lambda x: str(x)[0])\n",
        "all_data['numeric_ticket'] = all_data.Ticket.apply(lambda x: 1 if x.isnumeric() else 0)\n",
        "all_data['ticket_letters'] = all_data.Ticket.apply(lambda x: ''.join(x.split(' ')[:-1]).replace('.','').replace('/','').lower() if len(x.split(' ')[:-1]) >0 else 0)\n",
        "all_data['name_title'] = all_data.Name.apply(lambda x: x.split(',')[1].split('.')[0].strip())\n",
        "\n",
        "all_data.info()"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 1309 entries, 0 to 417\n",
            "Data columns (total 18 columns):\n",
            " #   Column          Non-Null Count  Dtype  \n",
            "---  ------          --------------  -----  \n",
            " 0   PassengerId     1309 non-null   int64  \n",
            " 1   Survived        891 non-null    float64\n",
            " 2   Pclass          1309 non-null   int64  \n",
            " 3   Name            1309 non-null   object \n",
            " 4   Sex             1309 non-null   object \n",
            " 5   Age             1046 non-null   float64\n",
            " 6   SibSp           1309 non-null   int64  \n",
            " 7   Parch           1309 non-null   int64  \n",
            " 8   Ticket          1309 non-null   object \n",
            " 9   Fare            1308 non-null   float64\n",
            " 10  Cabin           295 non-null    object \n",
            " 11  Embarked        1307 non-null   object \n",
            " 12  train_test      1309 non-null   int64  \n",
            " 13  cabin_multiple  1309 non-null   int64  \n",
            " 14  cabin_adv       1309 non-null   object \n",
            " 15  numeric_ticket  1309 non-null   int64  \n",
            " 16  ticket_letters  1309 non-null   object \n",
            " 17  name_title      1309 non-null   object \n",
            "dtypes: float64(3), int64(7), object(8)\n",
            "memory usage: 194.3+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YtQXuTPso8KK"
      },
      "source": [
        "### **3.2 Nulls**\n",
        "\n",
        "From Section 2.1, there are some variables with Null elements: `Age`, `Cabin` and `Embarked`. The data knowledge that we already have shows us that  `Age`and `Embarked` should be included in our model. Thus, the columns with null elements from `Embarked` can be removed because there is no additional information. However, the null elements for `Age` can be replaced with the mean value. \n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AT1cfTyLqiLX",
        "outputId": "e95d238a-3202-49e4-c405-0d9290aa761a"
      },
      "source": [
        "#Replace NaN elements of 'Age' with mean.\n",
        "all_data.Age = all_data.Age.fillna(training.Age.mean())\n",
        "#all_data.Age = all_data.Age.fillna(training.Age.median())\n",
        "all_data.Fare = all_data.Fare.fillna(training.Fare.mean())\n",
        "#all_data.Fare = all_data.Fare.fillna(training.Fare.median())\n",
        "\n",
        "#Remove missing values on 'embarked'\n",
        "all_data.dropna(subset=['Embarked'],inplace = True)\n",
        "\n",
        "all_data.info()"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 1307 entries, 0 to 417\n",
            "Data columns (total 18 columns):\n",
            " #   Column          Non-Null Count  Dtype  \n",
            "---  ------          --------------  -----  \n",
            " 0   PassengerId     1307 non-null   int64  \n",
            " 1   Survived        889 non-null    float64\n",
            " 2   Pclass          1307 non-null   int64  \n",
            " 3   Name            1307 non-null   object \n",
            " 4   Sex             1307 non-null   object \n",
            " 5   Age             1307 non-null   float64\n",
            " 6   SibSp           1307 non-null   int64  \n",
            " 7   Parch           1307 non-null   int64  \n",
            " 8   Ticket          1307 non-null   object \n",
            " 9   Fare            1307 non-null   float64\n",
            " 10  Cabin           293 non-null    object \n",
            " 11  Embarked        1307 non-null   object \n",
            " 12  train_test      1307 non-null   int64  \n",
            " 13  cabin_multiple  1307 non-null   int64  \n",
            " 14  cabin_adv       1307 non-null   object \n",
            " 15  numeric_ticket  1307 non-null   int64  \n",
            " 16  ticket_letters  1307 non-null   object \n",
            " 17  name_title      1307 non-null   object \n",
            "dtypes: float64(3), int64(7), object(8)\n",
            "memory usage: 194.0+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Jz7gyFmTu4ua"
      },
      "source": [
        "### **3.3 Normalize data**\n",
        "\n",
        "From Section 2.1.1, it was concluded that it can be useful to normalise some numerical variables. Thus, `Fare` and `SibSp` can be normalised (`SibSp` is higly correlated with `Parch`) using log norm.  \n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "1PsEKOvPwEOQ",
        "outputId": "64020578-e9ec-48e7-baf9-ee52e892c136"
      },
      "source": [
        "from matplotlib.patches import Rectangle\n",
        "\n",
        "# log norm of fare\n",
        "all_data['norm_fare'] = np.log(all_data.Fare+1)\n",
        "all_data['norm_fare'].hist()\n",
        "\n",
        "#log norm of sibsp\n",
        "all_data['norm_sibsp'] = np.log(all_data.SibSp+1)\n",
        "all_data['norm_sibsp'].hist()\n",
        "\n",
        "\n",
        "cmap = plt.get_cmap('jet')\n",
        "medium = cmap(0.25)\n",
        "high = cmap(0.8)\n",
        "\n",
        "handles = [Rectangle((0,0),1,1,color=c,ec=\"k\") for c in [medium ,high]]\n",
        "labels= [\"norm_fare\",\"norm_sibsp\"]\n",
        "plt.legend(handles, labels)\n"
      ],
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fac80b68190>"
            ]
          },
          "metadata": {},
          "execution_count": 87
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oai962b801lZ"
      },
      "source": [
        "The normalized `Fare` is more similar to a normal distribution; however, the normalized `SibSp` is still not following a normal distribution.\n",
        "So, it makes sense to use the normalised `Fare` data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sk7u72Qq1kz4"
      },
      "source": [
        "# converted fare to category for pd.get_dummies()\n",
        "all_data.Pclass = all_data.Pclass.astype(str)\n",
        "\n",
        "#created dummy variables from categories, including 'Pclass'\n",
        "all_dummies = pd.get_dummies(all_data[['Pclass','Sex','Age','SibSp','Parch','norm_fare','Embarked','cabin_adv','cabin_multiple','numeric_ticket','name_title','train_test']])\n",
        "\n"
      ],
      "execution_count": 88,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oAZIfO6D2MTX"
      },
      "source": [
        "The last step is related with split the data into training and test sets again."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I7P-1t5X2NHF",
        "outputId": "4fd53fef-6ac5-411d-88d7-15a3568d5241"
      },
      "source": [
        "#Split to training and test stes\n",
        "X_train = all_dummies[all_dummies.train_test == 1].drop(['train_test'], axis =1)\n",
        "X_test = all_dummies[all_dummies.train_test == 0].drop(['train_test'], axis =1)\n",
        "\n",
        "\n",
        "y_train = all_data[all_data.train_test==1].Survived\n",
        "y_train.shape"
      ],
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(889,)"
            ]
          },
          "metadata": {},
          "execution_count": 89
        }
      ]
    }
  ]
}