Data_exploration_using_titanic_dataset.ipynb

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        "<a href=\"https://colab.research.google.com/github/Sukhman723/my_projects/blob/main/google_colab/Data_exploration_using_titanic_dataset.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "code",
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      "source": [
        "# Pseudo code\n",
        "# load the data\n",
        "# create plots\n",
        "# generate frequence tables\n",
        "# remove duplicates\n",
        "# recognize and treat missing values\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
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      "source": [
        "#__Import relevent modules and *load* data__ "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0oCMTesGWp60"
      },
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import numpy as np"
      ],
      "execution_count": null,
      "outputs": []
    },
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      "source": [
        "# import data\n",
        "train_full = pd.read_csv('train.csv')\n",
        "#test_full = pd.read_csv('test.csv')\n",
        "train_full"
      ],
      "execution_count": null,
      "outputs": [
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              "     PassengerId  Survived  Pclass  \\\n",
              "0              1         0       3   \n",
              "1              2         1       1   \n",
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              "888          889         0       3   \n",
              "889          890         1       1   \n",
              "890          891         0       3   \n",
              "\n",
              "                                                  Name     Sex   Age  SibSp  \\\n",
              "0                              Braund, Mr. Owen Harris    male  22.0      1   \n",
              "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
              "2                               Heikkinen, Miss. Laina  female  26.0      0   \n",
              "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
              "4                             Allen, Mr. William Henry    male  35.0      0   \n",
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              "889                              Behr, Mr. Karl Howell    male  26.0      0   \n",
              "890                                Dooley, Mr. Patrick    male  32.0      0   \n",
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              "     Parch            Ticket     Fare Cabin Embarked  \n",
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          "metadata": {},
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    {
      "cell_type": "code",
      "metadata": {
        "id": "3CvqyxyZ60yq"
      },
      "source": [
        "#test_full"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kyI1jrQf6FF8"
      },
      "source": [
        "#__Variable Identification__ :\n",
        "##Types of variable:\n",
        "---\n",
        "* Predictor variable: PassengerId...\n",
        "* Target Variables: Survived\n",
        "\n",
        "##DataType of Variables:\n",
        "---\n",
        "* character: name, sex, ticket, cabin, embarked\n",
        "* Numeric: PassengerId, Survived, pclass, age, subsp, parch, face\n",
        "\n",
        "##Variable Category:\n",
        "---\n",
        "* categorical: survived, sex \n",
        "* continuous: all the others"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6OqhrerhKq0T"
      },
      "source": [
        "#__Univariate Analysis__ :\n",
        "---\n",
        "Explore variables one by one. Methods to explore variables vary with variables types:\n",
        "##Continuous Variables :\n",
        "Find central tendency and spread of variable using following ways:\n",
        "\n",
        "* *__Central Tendency__*: Mean (find mean)\n",
        "\n",
        "* *__Spread of varaible__*: standard_deviation\n",
        "\n",
        "We can use 'box plot' and 'Histogram' to visualize the data. \n",
        "\n",
        "##Categorical Variables :\n",
        "We'll use frequency table (.value_counts()) to understand the distribution of each category. We can use 'bar chart' for visualizing. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T_dz35IJp9PL",
        "outputId": "b96a22f4-7304-43e1-d915-776621fb42ba"
      },
      "source": [
        "# For categorical variables\n",
        "print(train_full['Survived'].value_counts())\n",
        "print(train_full['Sex'].value_counts())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0    549\n",
            "1    342\n",
            "Name: Survived, dtype: int64\n",
            "male      577\n",
            "female    314\n",
            "Name: Sex, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
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          "height": 54
        },
        "id": "LnqiSVMAsmHf",
        "outputId": "18a7d7ca-fe6b-423d-ffb9-9d66d2be0d37"
      },
      "source": [
        "# For categorical variables\n",
        "# visualizing using bar chart\n",
        "sns.countplot(x='Sex', data=train_full)\n",
        "plt.title('Examining individually')\n",
        "plt.ylabel('counts')\n",
        "plt.xlabel('Sex')\n",
        "plt.xticks(rotation = 360)\n",
        "plt.show()\n",
        "# no outliers detected"
      ],
      "execution_count": null,
      "outputs": [
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            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sehDwvdnWZyH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "f5855c10-5e29-4c58-d8dd-ec24d10eff64"
      },
      "source": [
        "# for categorical variables\n",
        "# plot for character data type---no other method (such as boxplot) can plot figure for character variables\n",
        "sns.countplot(x='Embarked', data=train_full)\n",
        "plt.title('Examining individually')\n",
        "plt.ylabel('counts')\n",
        "plt.xlabel('Embarked')\n",
        "plt.xticks(rotation = 360)\n",
        "plt.show();\n",
        "\n",
        "# no-outliers detected"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "id": "wAvSWFjMNxFZ",
        "outputId": "6944a527-a34e-4975-e757-056e7047eb66"
      },
      "source": [
        "# For continuous variables\n",
        "train_full.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
              "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
              "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
              "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
              "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
              "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
              "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
              "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
              "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
              "\n",
              "            Parch        Fare  \n",
              "count  891.000000  891.000000  \n",
              "mean     0.381594   32.204208  \n",
              "std      0.806057   49.693429  \n",
              "min      0.000000    0.000000  \n",
              "25%      0.000000    7.910400  \n",
              "50%      0.000000   14.454200  \n",
              "75%      0.000000   31.000000  \n",
              "max      6.000000  512.329200  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-04cdd311-39e5-4e3c-9e4c-8360f143f1ca\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
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              "\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>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Fare</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>891.000000</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>714.000000</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>891.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>446.000000</td>\n",
              "      <td>0.383838</td>\n",
              "      <td>2.308642</td>\n",
              "      <td>29.699118</td>\n",
              "      <td>0.523008</td>\n",
              "      <td>0.381594</td>\n",
              "      <td>32.204208</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>257.353842</td>\n",
              "      <td>0.486592</td>\n",
              "      <td>0.836071</td>\n",
              "      <td>14.526497</td>\n",
              "      <td>1.102743</td>\n",
              "      <td>0.806057</td>\n",
              "      <td>49.693429</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.420000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>223.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>20.125000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>7.910400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>446.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>14.454200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>668.500000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>38.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>31.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>891.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>512.329200</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "      display:flex;\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-04cdd311-39e5-4e3c-9e4c-8360f143f1ca');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "id": "ZxHLNZ_iUJ2r",
        "outputId": "baf7bce3-8de3-4a2e-bd60-2f23b2c33d2b"
      },
      "source": [
        "# For continuous variables\n",
        "# create a figure where you plot will reside\n",
        "fig = plt.figure()\n",
        "# how many plots you wanna create\n",
        "ax = fig.add_subplot(1,1,1)\n",
        "# define variables that will show up on plot\n",
        "ax.hist(train_full['Pclass']) # for Histogram\n",
        "\n",
        "# labels and titles\n",
        "plt.title('embarked figure')\n",
        "plt.xlabel('Pclass')\n",
        "plt.ylabel('count')\n",
        "plt.show()\n",
        "# no outliers detected"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "id": "_4-B23ZGUDaG",
        "outputId": "28ecfd38-1784-4493-f8cb-847429638d63"
      },
      "source": [
        "# for continous variables\n",
        "# create a figure where you plot will reside\n",
        "fig = plt.figure()\n",
        "# how many plots you wanna create\n",
        "ax = fig.add_subplot(1,1,1)\n",
        "# define variables that will show up on plot\n",
        "ax.hist(train_full['Age']) # for Histogram\n",
        "\n",
        "# labels and titles\n",
        "plt.title('Age figure')\n",
        "plt.xlabel('Age')\n",
        "plt.ylabel('count')\n",
        "plt.show()\n",
        "#no outliers detected"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "id": "nOopP6XVT63q",
        "outputId": "6d800d79-7906-46a6-ea68-a58463d3a5b0"
      },
      "source": [
        "# for continuous variables\n",
        "# create a figure where you plot will reside\n",
        "fig = plt.figure()\n",
        "# how many plots you wanna create\n",
        "ax = fig.add_subplot(1,1,1)\n",
        "# define variables that will show up on plot\n",
        "ax.hist(train_full['SibSp']) # for Histogram\n",
        "\n",
        "# labels and titles\n",
        "plt.title('SibSp figure')\n",
        "plt.xlabel('SibSp')\n",
        "plt.ylabel('count')\n",
        "plt.show()\n",
        "# only one or two people brought eight siblings/wifes with them--- \n",
        "# ---outliers detected"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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qV7+SzWbryDwAgAhl/OLDrbfeqg8++KAjswAAIpjxGYbX69XUqVOVlpamPn36NBnj3VMAcPEzLozvfe97+t73vteRWQAAEcy4MKZOndqROQAAEc64MM4uEdKc66+/vl3CAAAil3FhnF0i5Kxjx47p1KlTSkhI0DvvvNPuwQAAkcW4MM4uEXKWz+fTihUrWHwQAC4RbV7Tw+FwaPLkyXr55ZfbMw8AIEIZn2E0Z8eOHcYf5HvkkUd08OBB2e12denSRbNnz1ZKSooqKiqUk5Oj2tpaOZ1OFRQUaMCAAZLU6hgAILSMC+Pmm29uUg4nTpxQY2Oj8vPzjeYXFBSoe/fukqQ///nPys3NVVFRkfLz85Wdna2f/OQn2rRpk/Ly8vTKK69IUqtjAIDQMi6MhQsXNrnduXNnDRw4UN26dTOaf7YsJKm+vl42m03V1dXatWtX4Gtes7KyNG/ePNXU1MiyrBbH4uLiTGMDANqJcWH86Ec/knRmafOjR4+qT58+QS9rPnPmTO3YsUOWZenll1+Wx+NRQkKCHA6HpDOvi8THx8vj8ciyrBbHKAwACD3jwqivr9fcuXO1efNmnT59WlFRUfrxj3+sWbNmNTl7aM2CBQskSRs3blRhYaGmTZvWttRBcLvdbZqXlpbWzknMlZeXf6vxcCFXcMgVHHIFpyNyGRfG/PnzdeLECRUXFyspKUmHDh3S888/r/nz56ugoCCog44bN055eXnq27evjhw5Ip/PJ4fDIZ/Pp6qqKrlcLlmW1eJYMFJTUxUTExPUnHBrrazKy8vDWmYtIVdwyBUccgXn2+Tyer0tPtE2vqb0/vvvq7CwUAMHDlR0dLQGDhyoZ599Vu+///4F5zY0NMjj8QRul5aWqmfPnurdu7dSUlJUUlIiSSopKVFKSori4uJaHQMAhJ7xGUZMTIxqamqUlJQU2Hbs2DFFR0dfcO6JEyc0bdo0nThxQna7XT179tTKlStls9k0Z84c5eTkaPny5erRo0eTs5XWxgAAoWVcGHfffbcmTpyoBx54QImJiaqsrNTatWv105/+9IJz+/Tpoz/96U/NjiUnJ2v9+vVBjwEAQsu4MKZMmaKEhAQVFxerqqpK8fHxeuihh4wKAwDw3Wf8GsaCBQs0cOBArV27Vps3b9batWuVnJwceOcTAODiZlwYJSUlSk1NbbItNTU18KI0AODiZlwYNptNfr+/yTafz3feNgDAxcm4MNLT07VkyZJAQfj9fi1dulTp6ekdFg4AEDmC+gKlhx9+WMOGDVNiYqI8Ho8uu+wyrVy5siPzAQAihHFh9O3bV0VFRfrnP/8pj8cjl8ula665Juj1pAAA301BfR+G3W7XkCFDNGTIkI7KAwCIUJweAACMUBgAACMUBgDACIUBADBCYQAAjFAYAAAjFAYAwAiFAQAwQmEAAIxQGAAAIxQGAMAIhQEAMEJhAACMUBgAACMUBgDACIUBADBCYQAAjFAYAAAjISmMY8eOadKkSRo5cqTGjBmjqVOnqqamRpL0ySefaOzYsRo5cqQmTpyo6urqwLzWxi5Wjad8rY6npaWF7dgALm1Bfad3W9lsNj300EMaOnSoJKmgoECLFi3S/Pnz9cQTT+jZZ59Venq6li9frkWLFunZZ5+V3+9vcexiFt3JoTG/2RSWYxf/9idhOS6A74aQnGE4nc5AWUjSkCFDVFlZKbfbrZiYGKWnp0uS7rnnHr399tuS1OoYACD0QnKGcS6/36/XXntNGRkZ8ng8SkxMDIzFxcXJ7/ertra21TGn02l8PLfb3aacHXnpJ5KVl5eHZW5HIldwyBWcSylXyAtj3rx56tKli372s59p27ZtHX681NRUxcTEdPhxLhZtLcry8vKILFlyBYdcwbkYc3m93hafaIe0MAoKCrR//36tXLlSdrtdLpdLlZWVgfGamhrZ7XY5nc5WxwAAoReyt9UuXrxYbrdby5YtU3R0tKQzz/5PnjypsrIySdLrr7+uUaNGXXAMABB6ITnD2Lt3r1atWqUBAwbonnvukST169dPy5YtU2FhofLz8+X1epWUlKSFCxdKkux2e4tjAIDQC0lhfP/739fu3bubHbvuuutUXFwc9BgAILT4pDcAwAiFAQAwQmEAAIxQGAAAIxQGAMAIhQEAMEJhAACMUBgAACMUBgDACIUBADBCYQAAjFAYAAAjFAYAwAiFAQAwQmEAAIxQGAAAIxQGAMAIhQEAMEJhAACMUBgAACMUBgDACIUBADBCYQAAjFAYAAAjFAYAwEhICqOgoEAZGRkaNGiQ9uzZE9heUVGh8ePHa+TIkRo/fry++uorozEAQOiFpDAyMzO1bt06JSUlNdmen5+v7OxsbdmyRdnZ2crLyzMaAwCEXkgKIz09XS6Xq8m26upq7dq1S1lZWZKkrKws7dq1SzU1Na2OAQDCIypcB/Z4PEpISJDD4ZAkORwOxcfHy+PxyLKsFsfi4uKCOo7b7W5TvrS0tDbN+64rLy8Py9yORK7gkCs4l1KusBVGqKSmpiomJibcMb4z2lqU5eXlEVmy5AoOuYJzMebyer0tPtEOW2G4XC4dOXJEPp9PDodDPp9PVVVVcrlcsiyrxTEAQHiE7W21vXv3VkpKikpKSiRJJSUlSklJUVxcXKtjAIDwCMkZxvz587V161YdPXpUP//5z+V0OvXWW29pzpw5ysnJ0fLly9WjRw8VFBQE5rQ2BgAIvZAUxqxZszRr1qzzticnJ2v9+vXNzmltDAAQenzSGwBghMIAABihMAAARigMAIARCgMAYITCAAAYoTAAAEYoDACAEQoDAGCEwgAAGKEwAABGKAwAgBEKAwBghMIAABihMAAARigMAIARCgMAYITCAAAYoTAAAEYoDAQ0nvK1eW5aWlpYjgsgdKLCHQCRI7qTQ2N+synkxy3+7U9CfkwAweMMAwBghMIAwuCqlB+E7dhcAkRbcUkKYdd4yqfoTo4O2feFXlvpyGO3pmuX2LBc/pO4BIi2ozAQduF67UTiwRMIRsRfkqqoqND48eM1cuRIjR8/Xl999VW4IwHAJSniCyM/P1/Z2dnasmWLsrOzlZeXF+5IAGAkXK8XddRrZBF9Saq6ulq7du3SmjVrJElZWVmaN2+eampqFBcX1+pcy7IkSY2NjW0+vrNr6K9te73esBw3nMcO9+8cLuH6nesbvlGnqOaPnZqa2mH/JqdO+1o8rolw/q1ac6Fc9+X9vxAl+a+XZ97a5n+vs4+ZZx9Dz2WzmtsaIdxut2bMmKG33norsG306NFauHChfvCD1hu0rq5Oe/bs6eiIAHBRuvLKK9W9e/cm2yL6DOPb6Nq1q6688kp16tRJNpst3HEA4DvBsiydOnVKXbt2PW8sogvD5XLpyJEj8vl8cjgc8vl8qqqqksvluuBcu91+XjsCAC4sNja22e0R/aJ37969lZKSopKSEklSSUmJUlJSLvj6BQCg/UX0axiStG/fPuXk5Og///mPevTooYKCAl1xxRXhjgUAl5yILwwAQGSI6EtSAIDIQWEAAIxQGAAAIxQGAMBIRH8OIxwqKiqUk5Oj2tpaOZ1OFRQUaMCAAeGOpYKCAm3ZskWHDh1ScXGxrrzyynBH0rFjx/Tkk0/q66+/VnR0tC6//HLNnTs3It72/Mgjj+jgwYOy2+3q0qWLZs+erZSUlHDHCnjxxRe1dOnSiPlbSlJGRoaio6MVExMjSZo+fbqGDx8e1kxer1fPPPOM/va3vykmJkZDhgzRvHnzwppJkg4ePKhf/vKXgdt1dXWqr6/X3//+9zCmOuPdd9/VkiVLZFmWLMvS1KlTddttt7XPzi00cd9991kbN260LMuyNm7caN13331hTnTGRx99ZFVWVlq33HKLtXv37nDHsSzLso4dO2Z9+OGHgdvPPfec9dRTT4Ux0X/95z//Cfy8bds2a9y4cWFM05Tb7bYefPDBiPpbWpYVcXksy7LmzZtnLViwwPL7/ZZlWda///3vMCdq3vz5862nn3463DEsv99vpaenB/6OX3zxhTVkyBDL5/O1y/65JHWOs4sdZmVlSTqz2OGuXbtUU1MT5mRSenq60SfcQ8npdGro0KGB20OGDFFlZWUYE/3XuZ/yr6+vj5jlYRobGzV37lzNmTMn3FEiXkNDgzZu3Khp06YF/n59+vQJc6rzNTY2qri4WHfddVe4o0g6s8pFXV2dpDNnPvHx8bLb2+ehnktS5/B4PEpISJDDcWZFTYfDofj4eHk8noi4zBLJ/H6/XnvtNWVkZIQ7SsDMmTO1Y8cOWZall19+OdxxJElLlizR2LFj1a9fv3BHadb06dNlWZbS0tL061//Wj169AhblgMHDsjpdOrFF1/Uzp071bVrV02bNk3p6elhy9Sc0tJSJSQkXHBB1FCw2Wx64YUX9Mgjj6hLly5qaGjQSy+91G775wwD7WLevHnq0qWLfvazn4U7SsCCBQv0l7/8Rb/61a9UWFgY7jj6+OOP5Xa7lZ2dHe4ozVq3bp3efPNNbdiwQZZlae7cuWHN4/P5dODAAV199dV64403NH36dD366KOqr68Pa67/tWHDhog5uzh9+rRWrVql5cuX691339WKFSv0+OOPq6GhoV32T2Gc49zFDiUFtdjhpaygoED79+/XCy+80G6nvu1p3Lhx2rlzp44dOxbWHB999JH27dunzMxMZWRk6PDhw3rwwQf1wQcfhDXXWWf/O4+OjlZ2drb+8Y9/hD1PVFRU4BLx4MGD1atXL1VUVIQ117mOHDmijz76SGPGjAl3FEnSF198oaqqqsB32aelpalz587at29fu+w/8v7vDiMWOwze4sWL5Xa7tWzZMkVHR4c7jqQz1749Hk/gdmlpqXr27Cmn0xnGVNIvfvELffDBByotLVVpaan69u2r1atXa9iwYWHNJUnffPNN4Lq3ZVnavHlz2N9VFhcXp6FDh2rHjh2SzryDsbq6WpdffnlYc52rqKhIN998s3r16hXuKJKkvn376vDhw/rXv/4l6cxafNXV1fq///u/dtk/a0n9j0hd7HD+/PnaunWrjh49ql69esnpdDb5Yqlw2Lt3r7KysjRgwIDAcsj9+vXTsmXLwprr6NGjeuSRR3TixAnZ7Xb17NlTM2bMiIhrzOfKyMjQypUrI+JttQcOHNCjjz4qn88nv9+v5ORkzZo1S/Hx8WHPlZubq9raWkVFRenxxx/XzTffHNZM5xo5cqRmzpypm266KdxRAt588039/ve/D7xR4LHHHtOIESPaZd8UBgDACJekAABGKAwAgBEKAwBghMIAABihMAAARigMoJ29+eabmjhxYuD2oEGDtH///jAmAtoHa0kBbVRWVqZFixZp7969cjgcuuKKK5Sbm6uxY8dq7NixRvtobGzU4sWLtXnzZtXV1alXr17KzMzUzJkzOzg9EDwKA2iD+vp6TZ48WXPmzNHtt9+uU6dOqaysLOhPu7/00ktyu91av3694uPjdejQIZWVlXVQauDb4ZIU0AZn1zPKysqSw+FQbGyshg0bpquuukpvvPGG7r333ib33759uzIzMzV06FAVFBTI7/dLkj777DONGDFCCQkJstls6tevn8aNGxeYl5GRoVWrVmn06NH64Q9/qKeeekperzd0vyhwDgoDaIOBAwfK4XBoxowZ2r59u44fP97q/bdt26YNGzaoqKhIpaWl2rBhg6QzC+qtXbtW69at0+7du9XcwgvFxcVavXq1tm3bpoqKCi1fvrxDfifgQigMoA26deumV199VTabTbNnz9b111+vyZMn6+jRo83ef9KkSXI6nUpMTNT9998fWODy4Ycf1qRJkwJfwDN8+HAVFRU1mTthwgS5XC45nU5NmTIl7GuI4dJFYQBtlJycrOeee07vvfeeiouLVVVVpWeeeabZ+567RH5SUpKqqqoknfmSrgkTJuj1119XWVmZpkyZotzc3CbLUZ87NzExMTAXCDUKA2gHycnJuvPOO7V3795mx89dbr2ysrLZVWBjY2M1YcIE9ejRQ19++WVQc4FQoDCANti3b5/+8Ic/6PDhw5LOPKiXlJRo8ODBzd5/9erVOn78uDwej1555RWNHj1akrR27Vrt3LlTJ0+e1BbSGrAAAADQSURBVOnTp1VUVKSGhgZdffXVgbmvvvqqDh8+rNraWq1cuTIwFwg13lYLtEG3bt306aefas2aNaqrq1P37t11yy236Mknn9TWrVvPu39mZqbuvPNO1dfX64477tDdd98tSercuXPgGwttNpsGDBigpUuXqn///oG5WVlZmjhxoqqqqpSZmakpU6aE7PcEzsX3YQARLCMjQ/Pnz9cNN9wQ7igAl6QAAGYoDACAES5JAQCMcIYBADBCYQAAjFAYAAAjFAYAwAiFAQAwQmEAAIz8f/1g9M1n5iTeAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "id": "PnEoJWpITW7t",
        "outputId": "4ac2d2e2-3f2a-42ab-8e6b-33ac33e6a666"
      },
      "source": [
        "# for continuous variables\n",
        "# create a figure where you plot will reside\n",
        "fig = plt.figure()\n",
        "# how many plots you wanna create\n",
        "ax = fig.add_subplot(1,1,1)\n",
        "\n",
        "ax.hist(train_full['Parch']) # for Histogram\n",
        "\n",
        "# labels and titles\n",
        "plt.title('Parch figure')\n",
        "plt.xlabel('Parch')\n",
        "plt.ylabel('count')\n",
        "plt.show()\n",
        "# no outliers detected"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YnFManU_-b4k",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "49a907a4-eb7e-40cf-efb3-3a4b8fe68d10"
      },
      "source": [
        "# for continuous variables\n",
        "# for numeric data representation\n",
        "#import matplotlib.pyplot as plt\n",
        "# create a figure where you plot will reside\n",
        "fig = plt.figure()\n",
        "# how many plots you wanna create\n",
        "ax = fig.add_subplot(1,1,1)\n",
        "# define variables that will show up on plot\n",
        "ax.hist(train_full['Fare']) # for Histogram\n",
        "\n",
        "# labels and titles\n",
        "plt.title('embarked figure')\n",
        "plt.xlabel('Fare')\n",
        "plt.ylabel('count')\n",
        "plt.show()\n",
        "# more than 700 people paid less than 40; only one or two person\n",
        "# paid more than 200 which makes it the best candidate for an outlier\n",
        "# outliers detected"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6jjgaex-SoTo"
      },
      "source": [
        "#plt.hist(train_full['Fare'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iYKUf6_bJtP6"
      },
      "source": [
        "#plt.boxplot(train_full['Fare'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yzPKIX23ZBGO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6a205022-7ea0-414a-8ad5-70e92e0bda3d"
      },
      "source": [
        "# type of variables in a dataset\n",
        "train_full.dtypes"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PassengerId      int64\n",
              "Survived         int64\n",
              "Pclass           int64\n",
              "Name            object\n",
              "Sex             object\n",
              "Age            float64\n",
              "SibSp            int64\n",
              "Parch            int64\n",
              "Ticket          object\n",
              "Fare           float64\n",
              "Cabin           object\n",
              "Embarked        object\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m72IbNxxuj86"
      },
      "source": [
        "#__Bi-variate Analysis__ :\n",
        "Bi-variate means to find out the relationship between two variables. The combination we can perform are:\n",
        "##Continuous & Continuous:\n",
        "Use scatter plot. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "665wxzoWG4wH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "9741844c-ceee-47a6-c307-ee1a6d288977"
      },
      "source": [
        "# to find relationships between two variables\n",
        "import seaborn as sns\n",
        "\n",
        "sns.set_theme(style=\"whitegrid\")\n",
        "\n",
        "#tips = sns.load_dataset(\"tips\")\n",
        "\n",
        "ax = sns.barplot(x=\"Age\", y=\"Embarked\", data=train_full)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYAAAAEMCAYAAADNtWEcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAWi0lEQVR4nO3dfXAU9eHH8c/l4Q6SAJHHhIeC1jkb4MdTeHIogljkwVhBRmyLoTwNP2CKgqYIaAGpFkIsioCVVvSnLbaDBY7yVC0g/MHUCjhMoFBTGcCaQBDDUxLI0+3vD+VKDIELZnfv+L5fM5m53cvtfrKzc598d+92PZZlWQIAGCfG7QAAAHdQAABgKAoAAAxFAQCAoSgAADBUnNsBwhEMBlVSUqL4+Hh5PB634wBAVLAsSxUVFUpMTFRMTM3/96OiAEpKSpSXl+d2DACISn6/X40aNaoxPyoKID4+XtJXf4TX63U5Td0cOnRInTt3djvGTYnW7OR2FrmdVZfc5eXlysvLC72HflNUFMCVwz5er1c+n8/lNHUXjZmviNbs5HYWuZ1V19y1HTrnJDAAGIoCAABDUQAAYCgKAAAMRQHYLD093e0INy1as5PbWdGQu7yiyu0IESkqPgV0xROLNupCaYXbMQBEmXeWjHE7QkRiBAAAhqIAAMBQFAAAGIoCAABDUQAAYCgKAAAMRQEAgKEoAAAwFAUAAIaiAADAUBQAABiKAgAAQ1EAAGAoCgAADEUBAIChKAAAMBQFAACGogAAwFAUAAAYigIAAENRAABgKAoAAAxFAQCAoeKcXuG2bdu0atUqWZalsrIyderUSb/+9a+djgEAxnO0AE6fPq3nnntOGzZsUGpqqizL0pEjR5yMAAD4mqOHgM6cOaO4uDglJydLkjwejzp27OhkBADA1xwdAXzve99Tly5dNHDgQPXp00c9evTQQw89pNtuu83JGABuAWXn/6Pigo9lVVXc8HfHjn2v5uvLyuTz+WrMT0hI0Lhx49S3b996yRnJHC2AmJgYvfrqq8rLy9PevXu1fft2rV69Wps2bQqNCgAgHCWnDqqy9Muwfjc//0Kdlr127VoKwC5+v19+v19jxozR8OHD9dFHH+n+++93IwqAKJWY8j8qLqgIawSQ0rxRjXnXGwGMHj26XjJGOkcLoLCwUAUFBerevbsk6dSpUyoqKlLbtm2djAHgFuBr0k6+Ju3C+t23l4ypMW///v1KT0+v71hRxdECqKys1PLly5Wfn68GDRooGAxqxowZnAgGABc4WgBt2rTRG2+84eQqAQC14JvAAGAoCgAADEUBAIChKAAAMBQFAACGogAAwFAUAAAYigIAAENRAABgKAoAAAxFAQCAoSgAADAUBQAAhqIAAMBQFAAAGIoCAABDUQAAYCgKAAAMRQEAgKEoAAAwFAUAAIaiAADAUBQAABiKAgAAQ8W5HaAuls15SD6fz+0YAKJMeUWVvPGxbseIOIwAbLZ//363I9y0aM1ObmdFQ27e/K+NAgAAQ1EAAGAoCgAADEUBAIChKAAAMBQFAACGogAAwFAUAAAYigIAAENRAABgKAoAAAxFAQCAoSgAADAUBWCz9PR0tyPctGjNTm5n3aq5g5UVDiVxT1TdD+DQqtnS5YtuxwBggPRZr7sdwXbXLYABAwbI4/HccCG7du2qrzwAAIdctwBycnJCjw8ePKhAIKDMzEy1bt1aBQUF+sMf/qARI0bYHhIAUP+uWwC9e/cOPV64cKFWr16tVq1ahebdc889mjRpkiZMmGBfQgCALcI+CXz69GklJCRUm5eQkKDCwsJ6DwUAsF/YJ4EHDRqkqVOnaurUqUpJSdHJkye1atUqDRo0yM58AACbhF0Azz33nJYvX6758+fr9OnTatmypYYOHaqf/exnduYDANgk7ALw+XzKyspSVlaWnXkAAA6p0/cA9uzZoy1btqioqEivvfaaDh48qOLiYt1999125QMA2CTsk8C///3vtWDBAnXo0EF79+6VJDVo0EDLli2zLRwAwD5hF8Bbb72lN998U5MnT1ZMzFcvu+OOO3Ts2DHbwgEA7BN2AZSUlCg1NVWSQt8OrqysVHx8vD3JAAC2CrsAevXqpd/+9rfV5r399tvq06dPvYcCANgv7JPAzz77rKZMmaJ3331XJSUlGjJkiBITE7Vq1So78wEAbBJ2ATRt2lTr1q3TwYMHlZ+fr9TUVHXp0kXFxcV25gMA2CTsQ0BPPvmkPB6PunTpomHDhqlbt266cOGCfvrTn9qZDwBgk7ALID4+XnPmzAlNFxUVKTMzUwMGDLAlGADAXmEXQHZ2toqKivSrX/1KX3zxhR577DENGzZMM2bMsDMfAMAmYRdAXFycXnnlFR05ckQZGRkaNWqUpk2bZmc2AICNrnsS+Oc//3mNO4IlJSUpNjZWn3zyiWbNmiVJWrJkiX0JAQC2uG4BtG/f/przO3XqZEsYAIBzrlsAVy71XFVVpQ0bNujBBx+Uz+dzJBgAwF5hnQOIjY3V4sWLefMHgFtI2CeB7733Xu3cudPOLAAAB4X9TeCysjI9/vjj6t69u1JSUqqdHA73JHBFRYVeffVVbd26VV6vV7Gxserbt6+eeuopLioHAA4LuwD8fr/8fv+3WtmcOXNUVlamdevWKSkpSZWVlVq3bp3Ky8spAABwWNgF8G3v/Xv8+HFt375du3fvVlJS0lcrj4vTo48++q2WCwC4OXW6JWR5ebmOHTums2fPyrKs0Pxwbgl5+PBhtW/fXk2aNKl7SgC4ypEvSvX+p2dVVhm0bR2+Q2NtW/YVCQkJGjdunPr27Wv7uq4l7ALYt2+fZsyYofLychUXFyspKUklJSVKSUnRjh077MwIANXsPnZe+RfK7V1Jab69y//a2rVrI78AFi1apEmTJmncuHHq1auXPvroI61YsUINGzYM6/UdO3bUiRMndP78eUYBAL6VAbc3UVlV0N4RwG2tbFv2FQkJCRo9erTt66lN2AVw/PhxjR1bfUg0efJk3XfffZo4ceINX9+hQwcNGjRI8+bN0wsvvKCkpCRVVVVp/fr1Gj58uBITE+ueHoCR0lokKK1Fgq3rSJ/1uq3LjwRhF0CjRo1UXFysxo0bq0WLFvr000+VnJys0tLSsFe2ePFirVy5UqNGjVJ8fLyCwaAGDBggr9d7U+EBADcv7AIYPHiwdu/erQcffFCjRo3S2LFjFRcXpyFDhoS9Mq/Xq5kzZ2rmzJk3FRYAUH/CLoBnnnkm9HjixInq2rWrSkpK1L9/f1uCAQDsVaePgUpSYWGhCgsL1a5dO7VqZf9JEgCAPcIugIKCAmVlZenAgQNq0qSJzp8/r27duiknJ0dt2rSxMyMAwAZhXwzu6aefVqdOnbRv3z79/e9/1969e9W5c2fNnj3bznwAAJuEPQL45z//qTfeeCN0zZ7ExERlZWWpT58+toUDANgn7BFAt27dlJubW23eoUOH1L1793oPBQCw33VHAMuWLQs9bteunSZPnqyBAwcqJSVFp06d0u7du5WRkWF7SABA/btuAZw6dara9P333y9JKioqktfr1eDBg1VWVmZfOgCAba5bAIsWLXIqBwDAYXX6HsClS5d04sSJGpd/6NGjR72GAgDYL+wCCAQCWrhwoeLj49WgQYPQfI/Ho127dtmRDQBgo7ALICcnR8uXL1e/fv3szAMAcEjYHwONj49X79697cwCAHBQ2AXwxBNPaPHixSoqKrIzDwDAIWEfAurQoYNeeeUVvfPOO6F5lmXJ4/HoyJEjtoQDANgn7AKYNWuWHnroIQ0fPrzaSWAAQHQKuwDOnTunJ554Qh6Px848AACHhH0O4OGHH9bGjRvtzAIAcFDYI4Dc3FytWbNGv/nNb9S8efNqz61Zs6begwEA7BV2AYwePVqjR4+uMZ9DQgAQnW54COj555+XJI0cOVIjR45UZWVl6PHIkSO1Y8cO20MCAOrfDQtg/fr11aZzcnKqTe/Zs6d+EwEAHHHDArAsq07TAIDocMMC+OYx/htNAwCiww1PAldVVenDDz8M/adfWVlZbToYDNqb8Cqd/3exfD6fY+sDYK5gZYVi4uLdjmGrGxZAs2bNNHfu3NB0cnJytemmTZvak+wWsX//fqWnp7sd46ZEa3ZyO+tWzX2rv/lLYRTAzp07ncgBAHBY2N8EBgDcWigAADAUBQAAhqIAAMBQFAAAGIoCAABDUQAAYCgKAAAMRQEAgKEoAAAwFAUAAIaiAGwWjRfJuiJas5PbWW7kLq+scHydt6Kw7wkcCbLefU4XK0rcjgHAZf83fpnbEW4JjAAAwFAUAAAYigIAAENRAABgKAoAAAxFAQCAoSgAADAUBQAAhqIAAMBQFAAAGIoCAABDUQAAYCgKAAAMRQEAgKEoAAAwFAUAAIaiAADAUBQAABiKAgAAQ1EAAGAoCgAADEUBAIChKAAAMFScUysqLy/X0qVLtX37dsXFxcnn82nKlCkaNmyYUxEAAFdxrAAWLFig0tJSbdmyRT6fT3l5eZo4caKSk5N19913OxUDAPA1RwogPz9f27Zt0wcffCCfzydJ8vv9mjp1qlasWEEBAIALHCmAvLw8fec731FycnK1+d26ddNLL73kRATAKJc+v6jzuYWyKoJuR7HF2A/GfutllJWVhf4hjWQJCQkaN26c+vbtW+/LdqQALMuq9TmPx+NEBMAoFw9/oYqiy27HsE3+xXy3Izhq7dq10VsAfr9fn332mc6dO1dtFHDgwAF1797diQiAURp1bKFg5a07AmjVuMW3XkY0jQBGjx5ty7IdKYC2bdtq6NChWrBggbKzs0Mngd966y29/PLLTkQAjNKwbSM1bNvI7Ri2+b/xy771Mvbv36/09PR6SBO9HPsU0Pz587V06VINHz5cHo9HhYWFWrt2rdLS0pyKAAC4imNfBGvQoIHmzp2rHTt2aNu2bcrIyNCSJUtUVlbmVAQAwFUcGwFcLT4+XosWLXJj1QCAr3EpCAAwFAUAAIaiAADAUBQAABiKAgAAQ1EAAGAoCgAADEUBAIChKAAAMBQFAACGogAAwFAUAAAYigIAAENRAABgKAoAAAxFAQCAoSgAADAUBQAAhqIAAMBQFAAAGIoCAABDUQAAYCgKAAAMFed2gLp48ZH58vl8bscA4LLyygp54+LdjhH1GAHYbP/+/W5HuGnRmp3cznIjN2/+9YMCAABDUQAAYCgKAAAMRQEAgKGi4lNAlmVJksrLy11OcnPKysrcjnDTojU7uZ1FbmeFm/vKe+aV99Bv8li1PRNBLl68qLy8PLdjAEBU8vv9atSoUY35UVEAwWBQJSUlio+Pl8fjcTsOAEQFy7JUUVGhxMRExcTUPOIfFQUAAKh/nAQGAENRAABgKAoAAAxFAQCAoSgAADAUBQAAhqIAAMBQUXEpiGPHjmn27Nk6d+6ckpOTlZ2drQ4dOrgd64YGDRokr9cbuolNVlaW+vfv73KqmrKzs/Xee+8pPz9fmzZtkt/vlxT527223JG+3c+ePatZs2bps88+k9frVfv27bVw4UI1bdpUBw4c0Lx581RWVqY2bdooJydHzZo1czuypOvnvuuuu+T3+0NfNlqyZInuuusulxP/17Rp0/T5558rJiZGCQkJ+sUvfqG0tLSI38dry11v+7gVBTIzM61AIGBZlmUFAgErMzPT5UThuffee61PPvnE7Rg3tHfvXqugoKBG3kjf7rXljvTtfvbsWevDDz8MTS9evNiaM2eOVVVVZf3gBz+w9u7da1mWZa1cudKaPXu2WzFrqC23ZVmW3++3iouL3Yp2QxcuXAg9/tvf/maNGDHCsqzI38dry11f+3jEHwL68ssvdfjwYWVkZEiSMjIydPjwYRUVFbmc7NbRs2dPpaamVpsXDdv9WrmjQXJysvr06ROa7tatmwoKCnTo0CH5fD717NlTkvSjH/1If/3rX92KWUNtuaPB1dfBKS4ulsfjiYp9/Fq561PEHwI6efKkWrVqpdjYWElSbGysWrZsqZMnT6pp06Yup7uxrKwsWZal9PR0Pfnkk2rcuLHbkcLCdndGMBjUH//4Rw0aNEgnT55U69atQ881bdpUwWAwdHgiklyd+4rMzExVVVXpnnvu0fTp0+X1el1MWNMzzzyjPXv2yLIsvf7661Gzj38z9xX1sY9H/Aggmq1Zs0Z/+ctftG7dOlmWpYULF7odyQjRtN1/+ctfKiEhQY899pjbUerkm7l37dql9evXa82aNfr000+1cuVKlxPW9MILL2jXrl2aOXOmlixZ4nacsF0rd33t4xFfAKmpqSosLFRVVZUkqaqqSqdPn46Kof+VjF6vVz/5yU/08ccfu5wofGx3+2VnZ+vEiRN6+eWXFRMTo9TU1GqHVIqKihQTExNx//1/M7f0322elJSkRx55JGK3uSSNGDFC//jHP5SSkhJV+/iV3GfPnq23fTziC6BZs2ZKS0vT5s2bJUmbN29WWlpaRA3RrqW0tFQXL16U9NUlWbdu3aq0tDSXU4WP7W6vpUuX6tChQ1q5cmXoUEnnzp11+fJl7du3T5L0pz/9SUOHDnUzZg3Xyn3+/HldvnxZklRZWan33nsvorZ5SUmJTp48GZreuXOnmjRpEvH7eG25fT5fve3jUXE56KNHj2r27Nm6cOGCGjdurOzsbN1xxx1ux7qu//znP5o+fbqqqqoUDAb13e9+V88++6xatmzpdrQann/+eb3//vs6c+aMbrvtNiUnJ2vLli0Rv92vlfu1116L+O3+73//WxkZGerQoYMaNGggSWrbtq1Wrlypjz/+WPPnz6/2MdDmzZu7nPgrteWeNGmS5s2bJ4/Ho8rKSnXv3l1z585VYmKiy4m/cubMGU2bNk2XLl1STEyMmjRpoqefflqdOnWK6H28ttyNGzeut308KgoAAFD/Iv4QEADAHhQAABiKAgAAQ1EAAGAoCgAADEUBAIChKADgBjIzM9WrVy+Vl5e7HQWoVxQAcB2ff/659u3bJ4/Hox07drgdB6hXFABwHYFAQF27dtXIkSMVCARC88+ePaspU6aoR48eGjVqlF566SX9+Mc/Dj1/9OhRjR8/Xr1799aQIUO0detWN+ID1xXxl4MG3LRx40aNGzdOXbt21aOPPqozZ86oefPmWrhwoRo2bKg9e/YoPz9fEydODF3KubS0VBMmTNDjjz+u3/3ud8rLy9P48ePl9/t15513uvwXAf/FCACoxb59+1RQUKBhw4apc+fOateunTZv3qyqqiq9//77mj59uho2bKg777xTI0aMCL1u165datOmjUaNGqW4uDh17NhRQ4YMiaibuwASIwCgVoFAQP369QtdHTIjI0MbNmzQAw88oMrKymqXDb76cX5+vnJzc0N39pK+utTwD3/4Q+fCA2GgAIBruHz5srZt26ZgMKh+/fpJksrLy3XhwgV9+eWXiouL06lTp3T77bdLUrXL9qampqpXr1568803XckOhItDQMA1bN++XbGxsdqyZYsCgYACgYC2bt2qnj17KhAIaPDgwVqxYoUuXbqko0ePauPGjaHXDhw4UMePH1cgEFBFRYUqKiqUm5uro0ePuvgXATVRAMA1bNiwQQ8//LBat26tFi1ahH7GjBmjTZs2ad68ebp48aL69eunWbNm6YEHHgjdICUpKUmrV6/W1q1b1b9/f33/+9/Xiy++yPcIEHG4HwBQD3JycnTmzBllZ2e7HQUIGyMA4CYcPXpU//rXv2RZlnJzc/XnP/9ZgwcPdjsWUCecBAZuQklJiZ566imdPn1azZo104QJE3Tfffe5HQuoEw4BAYChOAQEAIaiAADAUBQAABiKAgAAQ1EAAGAoCgAADPX/xmK8C7827HcAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "id": "Unncp30ZxANm",
        "outputId": "dea727b8-76f8-48f6-cf42-35f5ad229051"
      },
      "source": [
        "#scatter plot PassengerId-Pclass relations. \n",
        "#Plots in matplotlib reside within a figure object, use plt.figure to create new figure \n",
        "fig=plt.figure()\n",
        "\n",
        "#Create one or more subplots using add_subplot, because you can't create blank figure\n",
        "ax = fig.add_subplot(1,1,1)\n",
        "\n",
        "#Variable\n",
        "ax.scatter(train_full['PassengerId'],train_full['Pclass'])\n",
        "\n",
        "#Labels and Title\n",
        "plt.title('PassengerId and Pclass distribution')\n",
        "plt.xlabel('PassengerId')\n",
        "plt.ylabel('Pclass')\n",
        "plt.show()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "id": "ysRh7IDJ3Dlg",
        "outputId": "fb05929e-7a75-4b70-8e84-df0669313fd0"
      },
      "source": [
        "#Pclass-Age relationships\n",
        "fig=plt.figure()\n",
        "\n",
        "#Create one or more subplots using add_subplot, because you can't create blank figure\n",
        "ax = fig.add_subplot(1,1,1)\n",
        "\n",
        "#Variable\n",
        "ax.scatter(train_full['Age'],train_full['Fare'])\n",
        "\n",
        "#Labels and Tit\n",
        "plt.title('Age and Fare distribution')\n",
        "plt.xlabel('Age')\n",
        "plt.ylabel('Fare')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pS-aBdMf6yH7"
      },
      "source": [
        "#import numpy as np \n",
        "#from random import sample\n",
        "#import pandas as pd\n",
        "# select random samples to understand data\n",
        "#rindex = np.array(sample(range(len(train_full)),5))\n",
        "#dfr = train_full.PassengerId[rindex]\n",
        "#print(dfr)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 576
        },
        "id": "yUiboNQfJIWZ",
        "outputId": "abcd68f0-0014-4a3f-efc2-7cfe13f89e22"
      },
      "source": [
        "# choose 7 samples from dataframe randomly\n",
        "train_full_sampled = train_full.sample(n=7)\n",
        "train_full_sampled"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     PassengerId  Survived  Pclass                                  Name  \\\n",
              "82            83         1       3        McDermott, Miss. Brigdet Delia   \n",
              "885          886         0       3  Rice, Mrs. William (Margaret Norton)   \n",
              "514          515         0       3                     Coleff, Mr. Satio   \n",
              "508          509         0       3              Olsen, Mr. Henry Margido   \n",
              "572          573         1       1      Flynn, Mr. John Irwin (\"Irving\")   \n",
              "805          806         0       3             Johansson, Mr. Karl Johan   \n",
              "521          522         0       3                       Vovk, Mr. Janko   \n",
              "\n",
              "        Sex   Age  SibSp  Parch    Ticket     Fare Cabin Embarked  \n",
              "82   female   NaN      0      0    330932   7.7875   NaN        Q  \n",
              "885  female  39.0      0      5    382652  29.1250   NaN        Q  \n",
              "514    male  24.0      0      0    349209   7.4958   NaN        S  \n",
              "508    male  28.0      0      0    C 4001  22.5250   NaN        S  \n",
              "572    male  36.0      0      0  PC 17474  26.3875   E25        S  \n",
              "805    male  31.0      0      0    347063   7.7750   NaN        S  \n",
              "521    male  22.0      0      0    349252   7.8958   NaN        S  "
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              "      <td>McDermott, Miss. Brigdet Delia</td>\n",
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              "      <td>Coleff, Mr. Satio</td>\n",
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              "      <td>509</td>\n",
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              "      <td>Olsen, Mr. Henry Margido</td>\n",
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              "      <td>Flynn, Mr. John Irwin (\"Irving\")</td>\n",
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              "      <td>36.0</td>\n",
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              "      <td>PC 17474</td>\n",
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              "                                                     [key], {});\n",
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            ]
          },
          "metadata": {},
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P9Nc3oN2KbP6",
        "outputId": "ebffb954-5959-4202-8fde-7facf010b8d7"
      },
      "source": [
        "train_full_sampled['Survived']"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "82     1\n",
              "885    0\n",
              "514    0\n",
              "508    0\n",
              "572    1\n",
              "805    0\n",
              "521    0\n",
              "Name: Survived, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "id": "GEClm3sOCMRn",
        "outputId": "b0f84126-9477-45e3-c3b6-21e5809afe31"
      },
      "source": [
        "# stacked bar chart of categorical columns relationships (Age-Survived relationship)\n",
        "# importing package\n",
        "import matplotlib.pyplot as plt\n",
        "  \n",
        "# create data\n",
        "x = ['A','B','C','D','E','F','G']\n",
        "#y1 = [10, 20, 10, 30]\n",
        "y1 = train_full_sampled['Age']\n",
        "#y2 = [2, 5, 1, 3,4,3,10]\n",
        "y2 = train_full_sampled['Survived']\n",
        "# plot bars in stack manner\n",
        "plt.bar(x, y1, color='r')\n",
        "plt.bar(x, y2, bottom=y1, color='b')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "otZJFdptNCB6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "outputId": "65939171-a1b1-495e-f204-ab8dee1c0fb1"
      },
      "source": [
        "train_d = train_full.groupby(['Sex'])\n",
        "train_d.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       PassengerId                                                             \\\n",
              "             count        mean         std  min     25%    50%     75%    max   \n",
              "Sex                                                                             \n",
              "female       314.0  431.028662  256.846324  2.0  231.75  414.5  641.25  889.0   \n",
              "male         577.0  454.147314  257.486139  1.0  222.00  464.0  680.00  891.0   \n",
              "\n",
              "       Survived            ... Parch        Fare                              \\\n",
              "          count      mean  ...   75%  max  count       mean        std   min   \n",
              "Sex                        ...                                                 \n",
              "female    314.0  0.742038  ...   1.0  6.0  314.0  44.479818  57.997698  6.75   \n",
              "male      577.0  0.188908  ...   0.0  5.0  577.0  25.523893  43.138263  0.00   \n",
              "\n",
              "                                          \n",
              "              25%   50%    75%       max  \n",
              "Sex                                       \n",
              "female  12.071875  23.0  55.00  512.3292  \n",
              "male     7.895800  10.5  26.55  512.3292  \n",
              "\n",
              "[2 rows x 56 columns]"
            ],
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              "      <th>Sex</th>\n",
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              "      <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",
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              "      <th>female</th>\n",
              "      <td>314.0</td>\n",
              "      <td>431.028662</td>\n",
              "      <td>256.846324</td>\n",
              "      <td>2.0</td>\n",
              "      <td>231.75</td>\n",
              "      <td>414.5</td>\n",
              "      <td>641.25</td>\n",
              "      <td>889.0</td>\n",
              "      <td>314.0</td>\n",
              "      <td>0.742038</td>\n",
              "      <td>...</td>\n",
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              "      <td>6.0</td>\n",
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              "      <td>44.479818</td>\n",
              "      <td>57.997698</td>\n",
              "      <td>6.75</td>\n",
              "      <td>12.071875</td>\n",
              "      <td>23.0</td>\n",
              "      <td>55.00</td>\n",
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              "    <tr>\n",
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              "      <td>454.147314</td>\n",
              "      <td>257.486139</td>\n",
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              "      <td>222.00</td>\n",
              "      <td>464.0</td>\n",
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              "      <td>577.0</td>\n",
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              "      <td>10.5</td>\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
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          },
          "metadata": {},
          "execution_count": 88
        }
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    {
      "cell_type": "code",
      "metadata": {
        "id": "f6cL81huSUU-",
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          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5610d99f-d826-4865-f3c3-e1dd6f95e94d"
      },
      "source": [
        "isnull = train_full.isnull()\n",
        "#isnull\n",
        "train_full[isnull]\n",
        "# use .any() function to either false or true on each column to see whether they contain atleast one NAN value. \n",
        "train_full.isnull().any()\n",
        "# output says either false (means no NAN values in that column) or true ( means atleast one NAN value)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PassengerId    False\n",
              "Survived       False\n",
              "Pclass         False\n",
              "Name           False\n",
              "Sex            False\n",
              "Age             True\n",
              "SibSp          False\n",
              "Parch          False\n",
              "Ticket         False\n",
              "Fare           False\n",
              "Cabin           True\n",
              "Embarked        True\n",
              "dtype: bool"
            ]
          },
          "metadata": {},
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ll3DN4wjUQ40",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 818
        },
        "outputId": "a6752dbb-f9f1-4437-a59b-da42e1573069"
      },
      "source": [
        "# creating bool series True for NaN values \n",
        "bool_series = pd.isnull(train_full['Age']) \n",
        "# NAN columns: age,cabin,Embarked(only two NAN traps(at index 61,829))\n",
        "# filtering data \n",
        "# displaying data having 'column' = NaN \n",
        "train_full[bool_series]\n",
        "# as around 80% of the values in 'Cabin' column are NAN we can just delete this column as it already do not contain any valuable information. "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     PassengerId  Survived  Pclass                                      Name  \\\n",
              "5              6         0       3                          Moran, Mr. James   \n",
              "17            18         1       2              Williams, Mr. Charles Eugene   \n",
              "19            20         1       3                   Masselmani, Mrs. Fatima   \n",
              "26            27         0       3                   Emir, Mr. Farred Chehab   \n",
              "28            29         1       3             O'Dwyer, Miss. Ellen \"Nellie\"   \n",
              "..           ...       ...     ...                                       ...   \n",
              "859          860         0       3                          Razi, Mr. Raihed   \n",
              "863          864         0       3         Sage, Miss. Dorothy Edith \"Dolly\"   \n",
              "868          869         0       3               van Melkebeke, Mr. Philemon   \n",
              "878          879         0       3                        Laleff, Mr. Kristo   \n",
              "888          889         0       3  Johnston, Miss. Catherine Helen \"Carrie\"   \n",
              "\n",
              "        Sex  Age  SibSp  Parch      Ticket     Fare Cabin Embarked  \n",
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              "17     male  NaN      0      0      244373  13.0000   NaN        S  \n",
              "19   female  NaN      0      0        2649   7.2250   NaN        C  \n",
              "26     male  NaN      0      0        2631   7.2250   NaN        C  \n",
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              "..      ...  ...    ...    ...         ...      ...   ...      ...  \n",
              "859    male  NaN      0      0        2629   7.2292   NaN        C  \n",
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              "868    male  NaN      0      0      345777   9.5000   NaN        S  \n",
              "878    male  NaN      0      0      349217   7.8958   NaN        S  \n",
              "888  female  NaN      1      2  W./C. 6607  23.4500   NaN        S  \n",
              "\n",
              "[177 rows x 12 columns]"
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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            ]
          },
          "metadata": {},
          "execution_count": 90
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      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "azAIXWaeDml6"
      },
      "source": [
        "# __Delete variables__:\n",
        "Delete only those variables where NAN values are >= 50% of total number of values. \n",
        "For example, in our case, it is column 'Cabin' which contains more than 50% of the NAN values. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Cnjl0FsCgoSf"
      },
      "source": [
        "#delete 'Cabin' column\n",
        "#treat 'age', 'Embarked' normally \n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 531
        },
        "id": "YgcftlTkg1Rd",
        "outputId": "2e554647-46cc-4e11-df11-06e07133e812"
      },
      "source": [
        "train_full_d = train_full.drop('Cabin', axis=1)\n",
        "train_full_d.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   PassengerId  Survived  Pclass  \\\n",
              "0            1         0       3   \n",
              "1            2         1       1   \n",
              "2            3         1       3   \n",
              "3            4         1       1   \n",
              "4            5         0       3   \n",
              "\n",
              "                                                Name     Sex   Age  SibSp  \\\n",
              "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
              "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
              "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
              "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
              "4                           Allen, Mr. William Henry    male  35.0      0   \n",
              "\n",
              "   Parch            Ticket     Fare Embarked  \n",
              "0      0         A/5 21171   7.2500        S  \n",
              "1      0          PC 17599  71.2833        C  \n",
              "2      0  STON/O2. 3101282   7.9250        S  \n",
              "3      0            113803  53.1000        S  \n",
              "4      0            373450   8.0500        S  "
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              "    <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>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Heikkinen, Miss. Laina</td>\n",
              "      <td>female</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>STON/O2. 3101282</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
              "      <td>female</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>113803</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>S</td>\n",
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              "      <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>S</td>\n",
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              "          const element = document.querySelector('#df-29651f9c-f4b7-48a7-9337-847396fcbff5');\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9fJjWwEUEnCs"
      },
      "source": [
        "#__Impute Values__:\n",
        "Variables that contain few NAN values must be treated with imputation (filling NAN traps). We can use `SimpleImputer` model to first train (fit) on our data and then transform our values. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "id": "gqnoGc7zp-dZ",
        "outputId": "2611680b-483f-49ef-c9eb-1eef9754781d"
      },
      "source": [
        "# for now remove categorical columns (simplicity)\n",
        "train_full_dEo = train_full_d.select_dtypes(exclude=['object'])\n",
        "train_full_dEo.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   PassengerId  Survived  Pclass   Age  SibSp  Parch     Fare\n",
              "0            1         0       3  22.0      1      0   7.2500\n",
              "1            2         1       1  38.0      1      0  71.2833\n",
              "2            3         1       3  26.0      0      0   7.9250\n",
              "3            4         1       1  35.0      1      0  53.1000\n",
              "4            5         0       3  35.0      0      0   8.0500"
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              "\n",
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              "\n",
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              "          const element = document.querySelector('#df-9e64333b-e9ab-4bae-b113-bd8d779fb555');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qkXaznKzmF--",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "outputId": "09a2c010-d059-46b5-9563-98ab32515f77"
      },
      "source": [
        "# impute values (in this case only numeric values)\n",
        "from sklearn.impute import SimpleImputer\n",
        "\n",
        "# Imputation\n",
        "my_imputer = SimpleImputer() # calling \n",
        "# fit on our model and transform\n",
        "imputed_X_train_full = pd.DataFrame(my_imputer.fit_transform(train_full_dEo))\n",
        "\n",
        "imputed_X_train_full.head()\n",
        "imputed_X_train_full.columns = train_full_dEo.columns\n",
        "# see if there any NAN trap left in 'age' column.\n",
        "#imputed_X_train_full['Age'].isnull().any()\n",
        "imputed_X_train_full "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     PassengerId  Survived  Pclass        Age  SibSp  Parch     Fare\n",
              "0            1.0       0.0     3.0  22.000000    1.0    0.0   7.2500\n",
              "1            2.0       1.0     1.0  38.000000    1.0    0.0  71.2833\n",
              "2            3.0       1.0     3.0  26.000000    0.0    0.0   7.9250\n",
              "3            4.0       1.0     1.0  35.000000    1.0    0.0  53.1000\n",
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              "..           ...       ...     ...        ...    ...    ...      ...\n",
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              "888        889.0       0.0     3.0  29.699118    1.0    2.0  23.4500\n",
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              "890        891.0       0.0     3.0  32.000000    0.0    0.0   7.7500\n",
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              "      <td>889.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>29.699118</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>23.4500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>889</th>\n",
              "      <td>890.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>26.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>30.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>890</th>\n",
              "      <td>891.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.7500</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>891 rows × 7 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-742fb90b-f220-4dc7-a4df-ed61e24b40ca')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
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              "      padding: 0 0 0 0;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-742fb90b-f220-4dc7-a4df-ed61e24b40ca button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-742fb90b-f220-4dc7-a4df-ed61e24b40ca');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "id": "0H4WpufLDrkl",
        "outputId": "8338eff7-d739-464d-ba05-cb930f497537"
      },
      "source": [
        "imputed_X_train_full.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
              "count   891.000000  891.000000  891.000000  891.000000  891.000000   \n",
              "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
              "std     257.353842    0.486592    0.836071   13.002015    1.102743   \n",
              "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
              "25%     223.500000    0.000000    2.000000   22.000000    0.000000   \n",
              "50%     446.000000    0.000000    3.000000   29.699118    0.000000   \n",
              "75%     668.500000    1.000000    3.000000   35.000000    1.000000   \n",
              "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
              "\n",
              "            Parch        Fare  \n",
              "count  891.000000  891.000000  \n",
              "mean     0.381594   32.204208  \n",
              "std      0.806057   49.693429  \n",
              "min      0.000000    0.000000  \n",
              "25%      0.000000    7.910400  \n",
              "50%      0.000000   14.454200  \n",
              "75%      0.000000   31.000000  \n",
              "max      6.000000  512.329200  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e36b2e05-0912-41ec-bbfc-bca68ac4fa67\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Fare</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>891.000000</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>891.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>446.000000</td>\n",
              "      <td>0.383838</td>\n",
              "      <td>2.308642</td>\n",
              "      <td>29.699118</td>\n",
              "      <td>0.523008</td>\n",
              "      <td>0.381594</td>\n",
              "      <td>32.204208</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>257.353842</td>\n",
              "      <td>0.486592</td>\n",
              "      <td>0.836071</td>\n",
              "      <td>13.002015</td>\n",
              "      <td>1.102743</td>\n",
              "      <td>0.806057</td>\n",
              "      <td>49.693429</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.420000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>223.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>7.910400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>446.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>29.699118</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>14.454200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>668.500000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>35.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>31.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>891.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>512.329200</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e36b2e05-0912-41ec-bbfc-bca68ac4fa67')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
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              "\n",
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              "\n",
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              "\n",
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              "\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e36b2e05-0912-41ec-bbfc-bca68ac4fa67 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-e36b2e05-0912-41ec-bbfc-bca68ac4fa67');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 95
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qx3KW1LeFk2F"
      },
      "source": [
        "#__Outliers__:\n",
        "---\n",
        "###Detect outliers:\n",
        "We can use `boxplot` to detect outliers.\n",
        "###Treat Outliers:\n",
        "`IQR` is the best method to solve outliers. \n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 635
        },
        "id": "uQC4xViwF2CT",
        "outputId": "99e421af-80c0-45ed-9e49-b6335c07f806"
      },
      "source": [
        "# Box Plot\n",
        "import seaborn as sns\n",
        "sns.boxplot(imputed_X_train_full['Parch'])\n",
        "# outliers detected\n",
        "outliers_indexs = np.where(imputed_X_train_full['Parch'] > 0)\n",
        "outliers_indexs"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. 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": "execute_result",
          "data": {
            "text/plain": [
              "(array([  7,   8,  10,  13,  16,  24,  25,  27,  43,  50,  54,  58,  59,\n",
              "         63,  65,  68,  71,  78,  86,  88,  93,  97,  98, 102, 118, 119,\n",
              "        124, 128, 136, 140, 145, 147, 148, 153, 155, 159, 160, 164, 165,\n",
              "        166, 167, 171, 172, 175, 176, 180, 182, 183, 184, 188, 193, 197,\n",
              "        201, 205, 229, 233, 237, 247, 248, 251, 254, 255, 259, 261, 262,\n",
              "        266, 268, 272, 273, 278, 279, 297, 299, 305, 311, 312, 314, 318,\n",
              "        319, 323, 324, 328, 329, 332, 340, 341, 348, 352, 356, 360, 362,\n",
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              "        435, 436, 437, 438, 440, 445, 446, 448, 450, 469, 472, 479, 480,\n",
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              "        659, 670, 678, 679, 683, 684, 685, 686, 689, 691, 698, 702, 709,\n",
              "        720, 736, 742, 745, 746, 750, 751, 754, 755, 763, 774, 779, 783,\n",
              "        787, 788, 792, 799, 801, 802, 803, 813, 817, 819, 820, 823, 824,\n",
              "        827, 831, 835, 846, 848, 850, 852, 853, 855, 856, 858, 863, 869,\n",
              "        871, 879, 880, 885, 888]),)"
            ]
          },
          "metadata": {},
          "execution_count": 96
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "id": "VXKZ82t3FE8k",
        "outputId": "3c11a0dd-a1e4-45b4-94f1-ad61cd58b0f4"
      },
      "source": [
        "# Box Plot\n",
        "import seaborn as sns\n",
        "sns.boxplot(imputed_X_train_full['SibSp'])\n",
        "# outliers detected"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. 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": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa91b249650>"
            ]
          },
          "metadata": {},
          "execution_count": 97
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "id": "Z6o_glY_EW9Q",
        "outputId": "25194ef8-7b92-4a64-94d2-88363c53b54f"
      },
      "source": [
        "# Box Plot\n",
        "import seaborn as sns\n",
        "sns.boxplot(imputed_X_train_full['Pclass'])\n",
        "# no outliers detected"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. 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": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa91b5a9950>"
            ]
          },
          "metadata": {},
          "execution_count": 98
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "id": "XLB3M8RGENg-",
        "outputId": "e78982da-fa71-41b2-ea29-0d690d8833f5"
      },
      "source": [
        "# Box Plot\n",
        "import seaborn as sns\n",
        "sns.boxplot(imputed_X_train_full['Fare'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. 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": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa9112604d0>"
            ]
          },
          "metadata": {},
          "execution_count": 99
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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333/X9OnTdddddykmJkaSNGTIEOXn5+vQoUN6/fXXVV9fr8GDB+vdd9/V7bffLkkh13VVlZWVWrJkierq6uRyudSvXz8tX75c9913X7c8t8253W6tX79eI0aM6HbnVZLKy8v14osvyuPxqKmpSffcc49effVVDRgwoNPny+fpAoAhfpAGAIaILgAYIroAYIjoAoAhogsAhoguABgK+2cvANfL7XarsrLS97kGkrRjxw4NHDiwE48KCC+iiy5l/fr1evjhh695P+fqJ+bJ5eIPb+ja+ApFl3Xx4kUtWrRI48eP19ixY7Vo0SKdPXvWtz4rK0tr1qxRZmamxowZo/LycpWUlOiZZ57RuHHjlJKSooKCgk6cAdAa0UWX1dTUpCeeeEK7du3Srl271Lt371afFPX1119r5cqVOnTokBISEjR//nxNnz5dP/30k9asWaM33nhDf/zxRyfNAGiN2wvoUl544QXfPd1x48Zp3bp1vnWLFy/W008/7bd9enq6hg8fLknau3evBg8erFmzZkmSRo0apZSUFO3YsUNLly41mgEQGtFFl5Kfn++7p1tXV6fXXntNe/fu1cWLFyVd/WQwj8fjC3Pzj1T866+/dOzYMT344IO+xzwej2bMmGE4AyA0oosu69NPP1VZWZm2bNmiO+64Q8ePH1daWpqaf0ZTRESE7/eJiYkaO3asPvvss844XKBduKeLLqumpka9e/fWrbfeqgsXLmjt2rUht580aZJOnjypbdu26cqVK7py5YqOHTumkpISoyMG2kZ00WXNmzdP9fX1Gj9+vDIyMjRx4sSQ28fFxWnDhg0qKCjQxIkTNWHCBOXl5amhocHoiIG28Xm6AGCIK10AMER0AcAQ0QUAQ0QXAAwRXQAwRHQBwBDRBQBDRBcADBFdADD0f37o0TD4M1/6AAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "id": "DvfNUD0tDjJn",
        "outputId": "9e023bab-5575-4295-88f1-728b5a10faa6"
      },
      "source": [
        "# Box Plot\n",
        "import seaborn as sns\n",
        "sns.boxplot(imputed_X_train_full['Age'])\n",
        "# outliers detected"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. 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": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa9111dc590>"
            ]
          },
          "metadata": {},
          "execution_count": 100
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cRDIDyFguV5f",
        "outputId": "d68d5473-64fd-4f52-b4b0-3c0366396836"
      },
      "source": [
        "# Importing\n",
        "import sklearn\n",
        "#from sklearn.datasets import load_boston\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "# Load the dataset\n",
        "#bos_hou = load_boston()\n",
        "\n",
        "# Create the dataframe\n",
        "#column_name = bos_hou.feature_names\n",
        "#df_boston = pd.DataFrame(bos_hou.data)\n",
        "#df_boston.columns = column_name\n",
        "#df_boston.head()\n",
        "\n",
        "''' Detection '''\n",
        "# IQR \n",
        "# calculate 25th and 75th percentile --- means calculate \n",
        "# those numbers below which 25% and 75% values can be found.\n",
        "Q1 = np.percentile(imputed_X_train_full['Fare'], 25,\n",
        "\t\t\t\tinterpolation = 'midpoint')\n",
        "\n",
        "Q3 = np.percentile(imputed_X_train_full['Fare'], 75,\n",
        "\t\t\t\tinterpolation = 'midpoint')\n",
        "IQR = Q3 - Q1\n",
        "\n",
        "print(\"Old Shape: \", imputed_X_train_full.shape)\n",
        "\n",
        "# Upper bound\n",
        "upper = np.where(imputed_X_train_full['Fare'] >= (Q3+1.5*IQR))\n",
        "print('upper[0]',upper[0])\n",
        "# Lower bound\n",
        "lower = np.where(imputed_X_train_full['Fare'] <= (Q1-1.5*IQR))\n",
        "print(upper)\n",
        "''' Removing the Outliers '''\n",
        "imputed_X_train_full.drop(upper[0], inplace = True) # upper[0] because .drop function only accept list-like inputs\n",
        "imputed_X_train_full.drop(lower[0], inplace = True) # inplace means not to produce another copy of dataframe but drop\n",
        "\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t# rows or columns from the original dataframe. \n",
        "\n",
        "print(\"New Shape: \", imputed_X_train_full.shape)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Old Shape:  (891, 7)\n",
            "upper[0] [  1  27  31  34  52  61  62  72  88 102 118 120 124 139 151 159 180 195\n",
            " 201 215 218 224 230 245 256 257 258 262 268 269 275 290 291 297 299 305\n",
            " 306 307 310 311 318 319 324 325 332 334 336 337 341 366 369 373 375 377\n",
            " 380 385 390 393 412 435 438 445 453 484 486 496 498 504 505 520 527 537\n",
            " 540 544 550 557 558 581 585 587 591 609 627 641 645 655 659 660 665 679\n",
            " 681 689 698 700 708 716 730 737 741 742 745 759 763 765 779 789 792 802\n",
            " 820 829 835 846 849 856 863 879]\n",
            "(array([  1,  27,  31,  34,  52,  61,  62,  72,  88, 102, 118, 120, 124,\n",
            "       139, 151, 159, 180, 195, 201, 215, 218, 224, 230, 245, 256, 257,\n",
            "       258, 262, 268, 269, 275, 290, 291, 297, 299, 305, 306, 307, 310,\n",
            "       311, 318, 319, 324, 325, 332, 334, 336, 337, 341, 366, 369, 373,\n",
            "       375, 377, 380, 385, 390, 393, 412, 435, 438, 445, 453, 484, 486,\n",
            "       496, 498, 504, 505, 520, 527, 537, 540, 544, 550, 557, 558, 581,\n",
            "       585, 587, 591, 609, 627, 641, 645, 655, 659, 660, 665, 679, 681,\n",
            "       689, 698, 700, 708, 716, 730, 737, 741, 742, 745, 759, 763, 765,\n",
            "       779, 789, 792, 802, 820, 829, 835, 846, 849, 856, 863, 879]),)\n",
            "New Shape:  (775, 7)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "p4WTPvHFJa3v",
        "outputId": "201344ae-9062-438c-f0ec-0216c5d3ebc8"
      },
      "source": [
        "imputed_X_train_full"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     PassengerId  Survived  Pclass        Age  SibSp  Parch     Fare\n",
              "0            1.0       0.0     3.0  22.000000    1.0    0.0   7.2500\n",
              "2            3.0       1.0     3.0  26.000000    0.0    0.0   7.9250\n",
              "3            4.0       1.0     1.0  35.000000    1.0    0.0  53.1000\n",
              "4            5.0       0.0     3.0  35.000000    0.0    0.0   8.0500\n",
              "5            6.0       0.0     3.0  29.699118    0.0    0.0   8.4583\n",
              "..           ...       ...     ...        ...    ...    ...      ...\n",
              "886        887.0       0.0     2.0  27.000000    0.0    0.0  13.0000\n",
              "887        888.0       1.0     1.0  19.000000    0.0    0.0  30.0000\n",
              "888        889.0       0.0     3.0  29.699118    1.0    2.0  23.4500\n",
              "889        890.0       1.0     1.0  26.000000    0.0    0.0  30.0000\n",
              "890        891.0       0.0     3.0  32.000000    0.0    0.0   7.7500\n",
              "\n",
              "[775 rows x 7 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4855cde1-83e0-42cb-beb8-4cb8e3bec374\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Fare</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.2500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>26.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.9250</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>35.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>53.1000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>35.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.0500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>6.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>29.699118</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.4583</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>886</th>\n",
              "      <td>887.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>27.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>13.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>887</th>\n",
              "      <td>888.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>19.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>30.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>888</th>\n",
              "      <td>889.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>29.699118</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>23.4500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>889</th>\n",
              "      <td>890.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>26.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>30.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>890</th>\n",
              "      <td>891.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.7500</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>775 rows × 7 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4855cde1-83e0-42cb-beb8-4cb8e3bec374')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-4855cde1-83e0-42cb-beb8-4cb8e3bec374 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-4855cde1-83e0-42cb-beb8-4cb8e3bec374');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 102
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-vKJZP_xQIdu"
      },
      "source": [
        "#__Model__:\n",
        "* Choose (we'll choose XGBoost) or create your own model using keras\n",
        "* Train model\n",
        "* Validate on validation dataset\n",
        "* Finally predict"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DZIdU8f7QuKI"
      },
      "source": [
        "#imputed_X_train_full.isnull().any()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iAjcVluSjLpP"
      },
      "source": [
        "imputed_y_train_full = imputed_X_train_full['Survived']\n",
        "imputed_X_train_full_c = imputed_X_train_full.drop('Survived', axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Pa6IS7BDkHq9"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "# Divide data into training and validation subsets\n",
        "X_train_full, X_valid_full, y_train, y_valid = train_test_split(imputed_X_train_full_c,\n",
        "                                                                imputed_y_train_full, \n",
        "                                                                train_size=0.8,\n",
        "                                                                test_size=0.2, random_state=0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "knQrrbCYRCUU",
        "outputId": "c8e042eb-a6a6-4df5-cd6d-fa6ed9eebad5"
      },
      "source": [
        "# number of rows we have\n",
        "len(X_train_full)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "620"
            ]
          },
          "metadata": {},
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZYnPOGpehHRy",
        "outputId": "eb87fa37-7703-446e-8d13-5412ba7bf438"
      },
      "source": [
        "from sklearn.ensemble import RandomForestRegressor\n",
        "model = RandomForestRegressor(n_estimators=100, \n",
        "                              random_state=0)\n",
        "# fit model on training data\n",
        "model.fit(X_train_full,y_train)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestRegressor(random_state=0)"
            ]
          },
          "metadata": {},
          "execution_count": 107
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JT4ntV7xrvzm",
        "outputId": "7928dcbd-8415-4524-f3d6-c88e61ca0886"
      },
      "source": [
        "import xgboost as xgb\n",
        "xgb_model = xgb.XGBRegressor(n_estimators=500,\n",
        "                             learning_rate=0.01,\n",
        "                             )\n",
        "xgb_model.fit(X_train_full,y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[01:45:57] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBRegressor(learning_rate=0.01, n_estimators=500)"
            ]
          },
          "metadata": {},
          "execution_count": 108
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9qP4vbWslQVH",
        "outputId": "63f98dc0-dca4-45c7-c691-8792855e289f"
      },
      "source": [
        "# predict on validation data\n",
        "predics = xgb_model.predict(X_valid_full)\n",
        "predics"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 0.23124042,  0.24067071,  0.19476825,  0.16496071,  0.16065148,\n",
              "        0.20899642,  0.3366978 ,  0.43277982,  0.78087103,  0.23173484,\n",
              "        0.2083106 ,  0.15936333,  0.1644333 ,  0.17286083,  0.17277351,\n",
              "        0.7778661 ,  0.217626  ,  0.20049366,  0.30498895,  0.17135534,\n",
              "        0.79239154,  0.23657045,  0.3039712 ,  0.32958633,  0.34686777,\n",
              "        0.1599063 ,  0.29856142,  0.31057268,  0.18911609,  0.6058535 ,\n",
              "        0.2109859 ,  0.14601055,  0.15484545,  0.10265031,  0.3268684 ,\n",
              "        0.1468746 ,  0.29856142,  0.20176739,  0.09928137,  0.42826495,\n",
              "        0.1468746 ,  0.0770852 ,  0.16256091,  0.52571404,  0.39744365,\n",
              "        0.13244027,  0.27396417,  0.40766644,  0.11645511,  0.6812619 ,\n",
              "        0.45281202,  0.3555282 ,  0.5491256 ,  0.11307359,  0.3285781 ,\n",
              "        0.38897842,  0.18963617,  0.1995686 ,  0.23173484,  0.196244  ,\n",
              "        0.22475138,  0.16242853,  0.14522782,  0.22720793,  0.54443645,\n",
              "        0.22720793,  0.29856142,  0.26282492,  0.57302594,  0.19658929,\n",
              "        0.16173214,  0.15478212,  0.29209188,  1.0321107 ,  0.58469033,\n",
              "        0.5189039 ,  0.21551281,  0.89468807,  0.5014415 ,  0.92222273,\n",
              "        0.39594364,  0.38951558,  0.11336014,  0.23173484,  0.4593629 ,\n",
              "        0.15587768,  0.17064393,  0.3370887 ,  0.27546865,  0.2869274 ,\n",
              "        0.3670119 ,  0.4107253 ,  0.11720768,  0.18045622,  0.02471653,\n",
              "        0.37784958,  0.46715888,  0.8156051 ,  0.12716648,  0.33355907,\n",
              "        0.81755036,  0.9384763 ,  0.2592466 ,  0.22151092, -0.01540393,\n",
              "        0.19371486,  0.18404865,  0.26646855,  0.3503353 ,  0.11888254,\n",
              "        0.8567257 ,  0.38979465,  0.35230663,  0.46097106,  0.1981498 ,\n",
              "        0.4417423 ,  0.481002  ,  0.3468463 ,  0.18973038,  0.18554533,\n",
              "        0.33217728,  0.13616803,  0.24122083,  0.1723032 ,  0.6386419 ,\n",
              "        0.15397051,  0.11884603,  0.47791952,  0.22475138,  0.50189584,\n",
              "        0.43550634,  0.20899642,  0.2871597 ,  0.24325621,  0.66478425,\n",
              "        0.16286314,  0.268381  ,  0.37837455,  0.25567108,  0.38217738,\n",
              "        0.19658929,  0.5003456 ,  0.17896092,  0.5002045 ,  0.07748055,\n",
              "        0.27664706,  0.14001751,  0.50826216,  0.34716314,  0.22362748,\n",
              "        0.4290173 ,  0.21585876,  0.29856142,  0.16070107,  0.10258701],\n",
              "      dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 109
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yKMTPYWPnRF_",
        "outputId": "a984aa1a-8d4b-4b5d-beb7-be8f85e28c51"
      },
      "source": [
        "# mean_absolute_error\n",
        "from sklearn.metrics import mean_absolute_error\n",
        "mean_absolute_error(y_valid,predics)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.3862145577707598"
            ]
          },
          "metadata": {},
          "execution_count": 110
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "id": "T3osCPy5l9Bf",
        "outputId": "039ffb3a-50ab-40c2-be5a-0a70eb95c04e"
      },
      "source": [
        "X_valid_full.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     PassengerId  Pclass        Age  SibSp  Parch     Fare\n",
              "428        429.0     3.0  29.699118    0.0    0.0   7.7500\n",
              "702        703.0     3.0  18.000000    0.0    1.0  14.4542\n",
              "464        465.0     3.0  29.699118    0.0    0.0   8.0500\n",
              "15          16.0     2.0  55.000000    0.0    0.0  16.0000\n",
              "832        833.0     3.0  29.699118    0.0    0.0   7.2292"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-61d888b5-c077-454e-912b-15254a01d42d\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
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              "        vertical-align: middle;\n",
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Fare</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>428</th>\n",
              "      <td>429.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>29.699118</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.7500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>702</th>\n",
              "      <td>703.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>14.4542</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>464</th>\n",
              "      <td>465.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>29.699118</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.0500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>16.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>55.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>16.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>832</th>\n",
              "      <td>833.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>29.699118</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.2292</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "              style=\"display:none;\">\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
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              "          document.querySelector('#df-61d888b5-c077-454e-912b-15254a01d42d button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-61d888b5-c077-454e-912b-15254a01d42d');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
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              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 111
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W8gyR31HluF-"
      },
      "source": [
        "output = pd.DataFrame({'Id': X_valid_full.PassengerId,\n",
        "                      'Age': X_valid_full.Age,\n",
        "                      'Survived%': predics})\n",
        "output.to_csv('Titanic_survival_XGB.csv', index=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "OUn5f1YCmqT6",
        "outputId": "b366f695-1a40-4c0d-d335-37508859ea3c"
      },
      "source": [
        "output"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Id        Age  Survived%\n",
              "428  429.0  29.699118   0.231240\n",
              "702  703.0  18.000000   0.240671\n",
              "464  465.0  29.699118   0.194768\n",
              "15    16.0  55.000000   0.164961\n",
              "832  833.0  29.699118   0.160651\n",
              "..     ...        ...        ...\n",
              "547  548.0  29.699118   0.429017\n",
              "560  561.0  29.699118   0.215859\n",
              "246  247.0  25.000000   0.298561\n",
              "677  678.0  18.000000   0.160701\n",
              "661  662.0  40.000000   0.102587\n",
              "\n",
              "[155 rows x 3 columns]"
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              "<p>155 rows × 3 columns</p>\n",
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              "\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-2cd3b2a8-67a4-47f2-a5ec-8773d2c89784 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-2cd3b2a8-67a4-47f2-a5ec-8773d2c89784');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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          },
          "metadata": {},
          "execution_count": 113
        }
      ]
    }
  ]
}