Titanic Survival Prediction.ipynb

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  "metadata": {
    "colab": {
      "name": "Titanic Survival Prediction .ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c29PiG2BuxOM",
        "colab_type": "text"
      },
      "source": [
        "# Importing important libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-kXoMJvyryIk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8Xb-OmXuu7A2",
        "colab_type": "text"
      },
      "source": [
        "# Reading the dataset\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BdZVRZt-u39A",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 343
        },
        "outputId": "a5a9409c-02ed-468c-8d1a-d0812f5a7560"
      },
      "source": [
        "url = \"https://raw.github.com/mattdelhey/kaggle-titanic/master/Data/train.csv\"\n",
        "titanic = pd.read_csv(url) \n",
        "titanic.head(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<div>\n",
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              "    .dataframe thead th {\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>survived</th>\n",
              "      <th>pclass</th>\n",
              "      <th>name</th>\n",
              "      <th>sex</th>\n",
              "      <th>age</th>\n",
              "      <th>sibsp</th>\n",
              "      <th>parch</th>\n",
              "      <th>ticket</th>\n",
              "      <th>fare</th>\n",
              "      <th>cabin</th>\n",
              "      <th>embarked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Braund, Mr. Owen Harris</td>\n",
              "      <td>male</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>A/5 21171</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
              "      <td>female</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17599</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C85</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Heikkinen, Miss. Laina</td>\n",
              "      <td>female</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>STON/O2. 3101282</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
              "      <td>female</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>113803</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>C123</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Allen, Mr. William Henry</td>\n",
              "      <td>male</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>373450</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Moran, Mr. James</td>\n",
              "      <td>male</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>330877</td>\n",
              "      <td>8.4583</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Q</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>McCarthy, Mr. Timothy J</td>\n",
              "      <td>male</td>\n",
              "      <td>54.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>17463</td>\n",
              "      <td>51.8625</td>\n",
              "      <td>E46</td>\n",
              "      <td>S</td>\n",
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              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Palsson, Master. Gosta Leonard</td>\n",
              "      <td>male</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>349909</td>\n",
              "      <td>21.0750</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</td>\n",
              "      <td>female</td>\n",
              "      <td>27.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>347742</td>\n",
              "      <td>11.1333</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>Nasser, Mrs. Nicholas (Adele Achem)</td>\n",
              "      <td>female</td>\n",
              "      <td>14.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>237736</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
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              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   survived  pclass  ... cabin embarked\n",
              "0         0       3  ...   NaN        S\n",
              "1         1       1  ...   C85        C\n",
              "2         1       3  ...   NaN        S\n",
              "3         1       1  ...  C123        S\n",
              "4         0       3  ...   NaN        S\n",
              "5         0       3  ...   NaN        Q\n",
              "6         0       1  ...   E46        S\n",
              "7         0       3  ...   NaN        S\n",
              "8         1       3  ...   NaN        S\n",
              "9         1       2  ...   NaN        C\n",
              "\n",
              "[10 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c92Yo-gmvGbi",
        "colab_type": "text"
      },
      "source": [
        "# Data Analysis"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iXIpMuU1vAWd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "9989d2c6-ef84-45f3-cbd6-f1e7b0a4b381"
      },
      "source": [
        "titanic.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(891, 11)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PWFQW4UUvLGU",
        "colab_type": "text"
      },
      "source": [
        "There are **891** rows & **11 columns**. This means there are 891 datapoints in the dataset & 11 features."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PmqvkkcWvKJT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        },
        "outputId": "6ff8afc7-15c4-49e9-8572-500cee407ceb"
      },
      "source": [
        "titanic.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['survived', 'pclass', 'name', 'sex', 'age', 'sibsp', 'parch', 'ticket',\n",
              "       'fare', 'cabin', 'embarked'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xB1c8nwpvrGc",
        "colab_type": "text"
      },
      "source": [
        "Out of these features, the feature **'survived' is the target feature**. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h5CaCdvlvoAi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 319
        },
        "outputId": "0752b93b-87c6-4c07-c200-d50f94bbe34a"
      },
      "source": [
        "titanic.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 891 entries, 0 to 890\n",
            "Data columns (total 11 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   survived  891 non-null    int64  \n",
            " 1   pclass    891 non-null    int64  \n",
            " 2   name      891 non-null    object \n",
            " 3   sex       891 non-null    object \n",
            " 4   age       714 non-null    float64\n",
            " 5   sibsp     891 non-null    int64  \n",
            " 6   parch     891 non-null    int64  \n",
            " 7   ticket    891 non-null    object \n",
            " 8   fare      891 non-null    float64\n",
            " 9   cabin     204 non-null    object \n",
            " 10  embarked  889 non-null    object \n",
            "dtypes: float64(2), int64(4), object(5)\n",
            "memory usage: 76.7+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q7Ys_pTKwA7A",
        "colab_type": "text"
      },
      "source": [
        "*  There are **5 object fields** which needs to be encoded. \n",
        "\n",
        "*  'age', 'cabin' & embarked has some **missing values**\n",
        "\n",
        "\n",
        "So I need to know how many Nan values are there in each columns."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6jFzc5dAwl-o",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 218
        },
        "outputId": "44ee85a2-a815-4da5-d596-141cafe97242"
      },
      "source": [
        "titanic.isna().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "survived      0\n",
              "pclass        0\n",
              "name          0\n",
              "sex           0\n",
              "age         177\n",
              "sibsp         0\n",
              "parch         0\n",
              "ticket        0\n",
              "fare          0\n",
              "cabin       687\n",
              "embarked      2\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Jmthd8eIxBx6",
        "colab_type": "text"
      },
      "source": [
        "# Data Visualization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qOSgi-m-w3pC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 613
        },
        "outputId": "e0ceefe3-4f5c-4820-b270-100f8676b3d2"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "plt.figure(figsize=(10,10))\n",
        "sns.heatmap(titanic.corr(), annot=True, linewidths=0.5, fmt= '.3f')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f4d723b7208>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xezPNknAxP7c",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "outputId": "2c87b678-8a05-4ac5-cb60-e979eb515bbb"
      },
      "source": [
        "titanic.corr()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>survived</th>\n",
              "      <th>pclass</th>\n",
              "      <th>age</th>\n",
              "      <th>sibsp</th>\n",
              "      <th>parch</th>\n",
              "      <th>fare</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>survived</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.338481</td>\n",
              "      <td>-0.077221</td>\n",
              "      <td>-0.035322</td>\n",
              "      <td>0.081629</td>\n",
              "      <td>0.257307</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>pclass</th>\n",
              "      <td>-0.338481</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.369226</td>\n",
              "      <td>0.083081</td>\n",
              "      <td>0.018443</td>\n",
              "      <td>-0.549500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>age</th>\n",
              "      <td>-0.077221</td>\n",
              "      <td>-0.369226</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.308247</td>\n",
              "      <td>-0.189119</td>\n",
              "      <td>0.096067</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sibsp</th>\n",
              "      <td>-0.035322</td>\n",
              "      <td>0.083081</td>\n",
              "      <td>-0.308247</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.414838</td>\n",
              "      <td>0.159651</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>parch</th>\n",
              "      <td>0.081629</td>\n",
              "      <td>0.018443</td>\n",
              "      <td>-0.189119</td>\n",
              "      <td>0.414838</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.216225</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fare</th>\n",
              "      <td>0.257307</td>\n",
              "      <td>-0.549500</td>\n",
              "      <td>0.096067</td>\n",
              "      <td>0.159651</td>\n",
              "      <td>0.216225</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          survived    pclass       age     sibsp     parch      fare\n",
              "survived  1.000000 -0.338481 -0.077221 -0.035322  0.081629  0.257307\n",
              "pclass   -0.338481  1.000000 -0.369226  0.083081  0.018443 -0.549500\n",
              "age      -0.077221 -0.369226  1.000000 -0.308247 -0.189119  0.096067\n",
              "sibsp    -0.035322  0.083081 -0.308247  1.000000  0.414838  0.159651\n",
              "parch     0.081629  0.018443 -0.189119  0.414838  1.000000  0.216225\n",
              "fare      0.257307 -0.549500  0.096067  0.159651  0.216225  1.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xkyG53d1yM0w",
        "colab_type": "text"
      },
      "source": [
        "By the previous knowledge we have, let's create a new feature telling **whether the passenger is man, woman or a child.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LRwejFqrxSs4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def woman_child_or_man(passenger):\n",
        "    age, sex = passenger\n",
        "    if age < 16:\n",
        "        return \"child\"\n",
        "    else:\n",
        "        return dict(male=\"man\", female=\"woman\")[sex]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nHeMKAqqxtR_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "outputId": "89d20b72-71d7-4029-957f-bd901ef8dbdb"
      },
      "source": [
        "titanic[\"who\"] = titanic[[\"age\", \"sex\"]].apply(woman_child_or_man, axis=1)\n",
        "titanic.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>survived</th>\n",
              "      <th>pclass</th>\n",
              "      <th>name</th>\n",
              "      <th>sex</th>\n",
              "      <th>age</th>\n",
              "      <th>sibsp</th>\n",
              "      <th>parch</th>\n",
              "      <th>ticket</th>\n",
              "      <th>fare</th>\n",
              "      <th>cabin</th>\n",
              "      <th>embarked</th>\n",
              "      <th>who</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Braund, Mr. Owen Harris</td>\n",
              "      <td>male</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>A/5 21171</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>man</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
              "      <td>female</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17599</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C85</td>\n",
              "      <td>C</td>\n",
              "      <td>woman</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Heikkinen, Miss. Laina</td>\n",
              "      <td>female</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>STON/O2. 3101282</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>woman</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
              "      <td>female</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>113803</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>C123</td>\n",
              "      <td>S</td>\n",
              "      <td>woman</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Allen, Mr. William Henry</td>\n",
              "      <td>male</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>373450</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>man</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   survived  pclass  ... embarked    who\n",
              "0         0       3  ...        S    man\n",
              "1         1       1  ...        C  woman\n",
              "2         1       3  ...        S  woman\n",
              "3         1       1  ...        S  woman\n",
              "4         0       3  ...        S    man\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b4ZYpEvf0RXl",
        "colab_type": "text"
      },
      "source": [
        "We will create another feature to see wether a person was an adult male or not."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RC2seGyaxyoh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 246
        },
        "outputId": "d8bbb5cd-61c5-44be-fa90-b329118c760e"
      },
      "source": [
        "titanic[\"adult_male\"] = titanic.who == \"man\"\n",
        "titanic.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>survived</th>\n",
              "      <th>pclass</th>\n",
              "      <th>name</th>\n",
              "      <th>sex</th>\n",
              "      <th>age</th>\n",
              "      <th>sibsp</th>\n",
              "      <th>parch</th>\n",
              "      <th>ticket</th>\n",
              "      <th>fare</th>\n",
              "      <th>cabin</th>\n",
              "      <th>embarked</th>\n",
              "      <th>who</th>\n",
              "      <th>adult_male</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Braund, Mr. Owen Harris</td>\n",
              "      <td>male</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>A/5 21171</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>man</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
              "      <td>female</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17599</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C85</td>\n",
              "      <td>C</td>\n",
              "      <td>woman</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Heikkinen, Miss. Laina</td>\n",
              "      <td>female</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>STON/O2. 3101282</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>woman</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
              "      <td>female</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>113803</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>C123</td>\n",
              "      <td>S</td>\n",
              "      <td>woman</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Allen, Mr. William Henry</td>\n",
              "      <td>male</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>373450</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>man</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   survived  pclass  ...    who adult_male\n",
              "0         0       3  ...    man       True\n",
              "1         1       1  ...  woman      False\n",
              "2         1       3  ...  woman      False\n",
              "3         1       1  ...  woman      False\n",
              "4         0       3  ...    man       True\n",
              "\n",
              "[5 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IPORNJXp0MBd",
        "colab_type": "text"
      },
      "source": [
        "We can have another feature with the deck information."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LjIUqg6jx9oT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 246
        },
        "outputId": "3b12f7c9-391a-4ccf-8a7c-781845b4a29b"
      },
      "source": [
        "titanic[\"deck\"] = titanic.cabin.str[0]\n",
        "titanic.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>survived</th>\n",
              "      <th>pclass</th>\n",
              "      <th>name</th>\n",
              "      <th>sex</th>\n",
              "      <th>age</th>\n",
              "      <th>sibsp</th>\n",
              "      <th>parch</th>\n",
              "      <th>ticket</th>\n",
              "      <th>fare</th>\n",
              "      <th>cabin</th>\n",
              "      <th>embarked</th>\n",
              "      <th>who</th>\n",
              "      <th>adult_male</th>\n",
              "      <th>deck</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Braund, Mr. Owen Harris</td>\n",
              "      <td>male</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>A/5 21171</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>man</td>\n",
              "      <td>True</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
              "      <td>female</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17599</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C85</td>\n",
              "      <td>C</td>\n",
              "      <td>woman</td>\n",
              "      <td>False</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Heikkinen, Miss. Laina</td>\n",
              "      <td>female</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>STON/O2. 3101282</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>woman</td>\n",
              "      <td>False</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
              "      <td>female</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>113803</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>C123</td>\n",
              "      <td>S</td>\n",
              "      <td>woman</td>\n",
              "      <td>False</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Allen, Mr. William Henry</td>\n",
              "      <td>male</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>373450</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>man</td>\n",
              "      <td>True</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   survived  pclass  ... adult_male deck\n",
              "0         0       3  ...       True  NaN\n",
              "1         1       1  ...      False    C\n",
              "2         1       3  ...      False  NaN\n",
              "3         1       1  ...      False    C\n",
              "4         0       3  ...       True  NaN\n",
              "\n",
              "[5 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P98XMiVPz4ft",
        "colab_type": "text"
      },
      "source": [
        "Now one more feature can be created, whether the passenger was alone or not. So let's do this."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ppOf9NMz0AvF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 246
        },
        "outputId": "809e0014-fd62-41ce-88f8-9ba07a82a58d"
      },
      "source": [
        "titanic[\"alone\"] = ~(titanic.parch + titanic.sibsp).astype(bool)\n",
        "titanic.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "\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>survived</th>\n",
              "      <th>pclass</th>\n",
              "      <th>name</th>\n",
              "      <th>sex</th>\n",
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              "      <th>sibsp</th>\n",
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              "      <th>fare</th>\n",
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              "      <th>who</th>\n",
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              "      <th>deck</th>\n",
              "      <th>alone</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Braund, Mr. Owen Harris</td>\n",
              "      <td>male</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>A/5 21171</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>man</td>\n",
              "      <td>True</td>\n",
              "      <td>NaN</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
              "      <td>female</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17599</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C85</td>\n",
              "      <td>C</td>\n",
              "      <td>woman</td>\n",
              "      <td>False</td>\n",
              "      <td>C</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Heikkinen, Miss. Laina</td>\n",
              "      <td>female</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>STON/O2. 3101282</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>woman</td>\n",
              "      <td>False</td>\n",
              "      <td>NaN</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
              "      <td>female</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>113803</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>C123</td>\n",
              "      <td>S</td>\n",
              "      <td>woman</td>\n",
              "      <td>False</td>\n",
              "      <td>C</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Allen, Mr. William Henry</td>\n",
              "      <td>male</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>373450</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>man</td>\n",
              "      <td>True</td>\n",
              "      <td>NaN</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   survived  pclass  ... deck  alone\n",
              "0         0       3  ...  NaN  False\n",
              "1         1       1  ...    C  False\n",
              "2         1       3  ...  NaN   True\n",
              "3         1       1  ...    C  False\n",
              "4         0       3  ...  NaN   True\n",
              "\n",
              "[5 rows x 15 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mQJaUBF31g_Q",
        "colab_type": "text"
      },
      "source": [
        "Now let's try to look at the trends in different feature."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m155JX5K0Bpt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "outputId": "2d011a83-09ed-4965-c6bd-63ef5ab81e53"
      },
      "source": [
        "sns.factorplot(\"pclass\", \"survived\", data=titanic).set(ylim=(0, 1))\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/categorical.py:3666: UserWarning: The `factorplot` function has been renamed to `catplot`. The original name will be removed in a future release. Please update your code. Note that the default `kind` in `factorplot` (`'point'`) has changed `'strip'` in `catplot`.\n",
            "  warnings.warn(msg)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f4d722b2be0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "apn3iKAK1mYm",
        "colab_type": "text"
      },
      "source": [
        "From here we see that if a passenger travelled in 1st class, the survival rate is highest and equal to 0.63. If a passenger travelled in 2nd class, the survival rate is medium and equal to 0.5. If a passenger travelled in 3rd class, the survival rate is lowest and equal to 0.3"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dMIbVdvy2EDC",
        "colab_type": "text"
      },
      "source": [
        "Let's see how the above case is dependent on the **sex of the passenger.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WdsDQ5WP0Goe",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 439
        },
        "outputId": "6a4dda02-30a2-43d1-c001-180f4764cd94"
      },
      "source": [
        "sns.factorplot(\"pclass\", \"survived\", data=titanic, hue=\"sex\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/categorical.py:3666: UserWarning: The `factorplot` function has been renamed to `catplot`. The original name will be removed in a future release. Please update your code. Note that the default `kind` in `factorplot` (`'point'`) has changed `'strip'` in `catplot`.\n",
            "  warnings.warn(msg)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f4d722c5a58>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 64
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 430.5x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CSl5Gqnj2U6z",
        "colab_type": "text"
      },
      "source": [
        "It;s pretty clear that the surviavl of female passengers is much more than the male passengers. From here we see that if a passenger travelled in 1st class and was female then their survival chance is most. On the other hand, if a passenger travelled in 3rd class amd was male then their survival chance is least. So we can combine these two features to **create new feature**."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VMdQjxmU5L3U",
        "colab_type": "text"
      },
      "source": [
        "Let's have a similar observation with the features **'class' & 'who'**  "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fWpCtlDr2Qvb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 439
        },
        "outputId": "5f116831-142e-45ac-cc17-0b2d9c827b85"
      },
      "source": [
        "sns.factorplot(\"pclass\", \"survived\", data=titanic, hue=\"who\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/categorical.py:3666: UserWarning: The `factorplot` function has been renamed to `catplot`. The original name will be removed in a future release. Please update your code. Note that the default `kind` in `factorplot` (`'point'`) has changed `'strip'` in `catplot`.\n",
            "  warnings.warn(msg)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f4d721eaef0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 65
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432.25x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XNdb0jKk5wHZ",
        "colab_type": "text"
      },
      "source": [
        "From here also we can have similar observation. We get 9 cases from here and we will be building a feature based on it in a while."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZOWG9wDf6c0Y",
        "colab_type": "text"
      },
      "source": [
        "Let's try to find the trends with **the feature 'alone' & 'adult_male'**."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oGXCl_9D-cff",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 439
        },
        "outputId": "3f30c824-116a-47c9-89ae-5a5678cf1826"
      },
      "source": [
        "sns.factorplot(\"alone\", \"survived\", data=titanic, hue=\"sex\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/categorical.py:3666: UserWarning: The `factorplot` function has been renamed to `catplot`. The original name will be removed in a future release. Please update your code. Note that the default `kind` in `factorplot` (`'point'`) has changed `'strip'` in `catplot`.\n",
            "  warnings.warn(msg)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f4d72164e10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 66
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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2qjsK/G93X9PoeXoQTO0ZD3QC5rr7r8+mpqYk+5nRlxO+7wrcRrDEhIiInLsHODWIEn0KuJ8gQM7WhcAdwKcJwugeYArBpqZ/Hf78j4X70F0H/CPBv/OJ/oZgz6NPm1kf4F0z+10y+9slI9luuvcanXrDzN5tiwJERISHkrh+LmG0yd1XA5hZCbDI3T1cyGA4kAv8xMxGEfSCdWriOW4AbjGzkzcnXYGhtNEO3cl20/VLOMwCigiKFxGRczekhetDz/H5jyd8X59wXE+QA48Br7r7J8NuvSVNPIcBtzXuvmsryXbTvUeQlkawEuxmWk5yERFJzjbOHEhbI/75ucD28PsHm2mzEPiCmX0hvKua6O7L26qAZId2/xVwqbuPAJ4hmGt0pK2KEBHp4H50jtfP1ePAN8xsOc3fpDxG0H23Kuzqe6wtC0hqCwkzW+Xul5jZlLCAfwa+5u5XtGUxydAWEiKSJpLeQmL4wy9lA/NpehDD88CsTB/eneyd0cm/hJuAp9z9JUDLA4mItIEwaO4kGO32BkG33RvhccYHESR/Z/Qbgv7E64FJBOPQ33X3CdGWdzrdGYlImtDmeq2Q7J3RLIIPrz7u7tVAP+ArkVUlIiIdSrLzjI4Q9FuePN4J7IyqKBER6ViS3nZcREQkKgojERGJncJIRCTDmdkXzazUzH4W0fPPTVgm6KwkuwKDiIhEaW5us1tIMHf/uQ7t/hPgOncvP8fniYzCSEQkbkEQNbuFBHNz72Tu/rPdQuLfgQuA/zazecBIGm0DYWYPArcCPYBRBAsbdCZYLfw48Al332tmnwVmh9fWA/eHA9wSf95I4Akgj2Clns+6e1lLdaqbTkQkfslsIXFW3P1zwA7gWoKwWezuk8Pjb4X7FEEQUJ8CLgf+ATji7hOBt8L6AJ5398vDOaalNL1G6ZPAF9z9MoLth76fTJ2RhpGZzTCzNWa23swePkO728zMzawoynpERFJUMltItIUbgIfNbAXBytwnt4GAYNXug+5eCewHXgzPn9xmAmC8mf0h3HriXuCixCc3s54Ed3O/DH/GD4Dzkikssm46M8smuFW7HigHlprZAnf/oFG7XsCfAu9EVYuISIqLeguJk5rcBsLMrqDlbSYAngZudfeVYdfe1EbPnwVUu/ulrS0syjujycB6d9/o7jXAPGBmE+0eA/4JOBZhLSIiqWxbC9fbaguJk9tAGICZTWzl43sBO82sEx9uV97A3Q8Am8zsjvD5zcySWjYuyjAazKl/weXhuQZmNgkYEi682iwzm21mxWZWXFlZ2faViojEq722kDjXbSD+lqAX6w2guUEJ9wIPmdlKoISmb0JOk9RCqWfDzG4HZrj7Z8Lj+4Er3H1OeJwFLAYedPfNZrYE+LK7n3EVVC2UKiJpIvmFUufmtriFRBsM705pUd4ZbefUftACPtxJEILbvfHAEjPbDFwJLNAgBhHpcIKgaXYLiUwPIoj2zigHWAtMJwihpcA97l7STPsl6M5IRDKHtpBohcjujNy9FphD8IFZKTDf3UvM7FEzuyWqnysiIuknsjujqOjOSETShO6MWkErMIiISOwURiIiEjuFkYiIxE5hJCIisVMYiYhI7BRGIiISO4WRiIjETmEkIiKxUxiJiEjsFEYiIhI7hZGIiMROYSQiIrHLabmJpKP7f/QO5fuOUtC3G888dEXc5YiInJHCKEOV7zvKpqrDcZchIpIUddOJiEjsFEYiIhI7hZGIiMROYSQiIrFTGImISOwURiIiEjuFkYiIxE5hJCIisVMYiYhI7BRGIiISO4WRiIjETmEkIiKxUxiJiEjsFEYiIhI7hZGIiMQu0jAysxlmtsbM1pvZw01c/5yZrTazFWb2upmNi7IeERFJTZGFkZllA08ANwLjgLubCJufu/vF7n4p8DjwnajqERGR1BXlndFkYL27b3T3GmAeMDOxgbsfSDjsAXiE9YiISIqKctvxwcC2hONy4IrGjczs88CXgM7AtKaeyMxmA7MBhg4d2uaFiohIvGIfwODuT7j7SOCvgEeaafOkuxe5e1FeXl77FigiIpGLMoy2A0MSjgvCc82ZB9waYT0iIpKiogyjpcAoMxthZp2Bu4AFiQ3MbFTC4U3AugjrERGRFBXZZ0buXmtmc4CFQDbwY3cvMbNHgWJ3XwDMMbPrgBPAPuCPoqpHRERSV5QDGHD33wK/bXTuawnf/2mUP19ERNJD7AMYREREFEYZpraunvlLt7Gj+igAO6qPMn/pNurqNYVLRFKXwiiD1NbVM+fny/nLX63ieG09AMdr6/nLX63i8z9bRm1dfcwViog0TWGUQZ5ftp2XS3Y1ee3lkl08v/xMI+tFROKjMMogvyjedsbr85ee+bqISFwURhlkZ/g5UXNKdx7gzQ1VnFB3nYikmEiHdkv7Oq9PN3bsP9bs9cM1ddzz1Dv06pLD1WPymDYmn6lj8ujfs0s7VikicjqFUQa5s2gI723Z12K7g8dreWnVTl5atRMzmDikD9PHDmRaYT6Fg3phZu1QrYjIh8w9vYb8FhUVeXFxcdxlpKS6eufzP1vW5CCGG8YO5L6rhrFkTSWLynazZc+RJp/j/NyuTBubz/TCgVw1sj9dO2VHXbZIptL/1bWCwijD1NbV8/zy7fztC+9zvLaeLjlZPHbreG6bVEB2VvC74e5srDrM4tIKFpXtZunmfU3OQ+raKYspFw5gWmFw1zQot2t7vxyRdKYwagWFUYa69p+XsKnqMCMG9ODVL089Y9v9R0/w2tpKFpdV8OqaCqqPnGiy3UXn92Z6YT7XFuYzoaAPWVn6XRM5A/2CtII+MxJyu3Xi5gnnc/OE86mrd5Zv3ceisgpeLaugbNfBhnYlOw5QsuMA/7p4PQN6dmbqmHymF+YzZdQAenXtFOMrEJF0pzCSU2RnGUXD+1E0vB9/NaOQ8n1HeLWsgkVlFby5YQ814coOVYdqeO69cp57r5xO2cYVI/ozrTCf6WPzGda/R8yvQkTSjcJIzqigb3fuv2o49181nCM1tbyxfg+Ly3azqLSCioPHAThR57y+vorX11fx6G8+YGRej4bReZcN60unbE1nE5EzUxhJ0rp3zuH6cQO5ftxA3J2SHQdYVFrB4rLdrCzf39BuQ+VhNlRu5MnXNtK7aw7XhN1514zOo2+PzjG+AhFJVQojOStmxvjBuYwfnMufXjeKioPHWFIWDBv/w7oqjtTUAXDgWC0vrtzBiyt3kGVw2bC+DaPzRg/sqTlNIgIojKSN5PfqyqzLhzDr8iEcr63jnY17WVwWDB3ftjdYpqjeYenmfSzdvI9/ermMwX26MX1sPtMK87nyAs1pEunIFEbS5rrkZHP16DyuHp3H3908jg2Vh1hUGgyCeG/Lh3Oatlcf5advbeGnb22hW6dspowa0DB0fGBvzWkS6UgURhIpM+PC/F5cmN+L/3PNSKqP1PD7cE7TkjWV7D8azGk6eqKOVz7YzSsf7Abg4sG5DaPzxp+fqzlNIhlOYSTtqk/3zsy8dDAzLx1MbV09y7ZWs6hsN4tLK1hXcaih3ert+1m9fT/fXbSOvF5dmDYmn2lj85ly4QB6dNHbViTT6LdaYpOTncXkEf2YPKIfX71xLFv3HAmGjZdV8M7GvdSEW11UHjzOL4q38YvibXTOzuLKkf2ZNiaP6WMHMqRf95hfhYi0BS0HlKFasxxQKjp0vJbX11WxuGw3i8sqqTp0vMl2o/J7NizsOmloH3I0p0lSh/qWW0F3RpKSenbJYcb4QcwYP4j6emf19v0sKgvmNL2//UBDu3UVh1hXcYgf/H4jud06MXVMHtMK85k6Op/c7lqiSCRdKIwk5WVlGROG9GHCkD586frR7D5wrGGJotfXVXH0RDCnaf/RE/x6xQ5+vWIH2VnGZcP6Mj0cBDEyT3OaRFKZwkjSzsDeXblr8lDumjyUYyfqeHvjnmBOU2kF28Ot1+vqnXc37eXdTXv5xn+XMbRf94bReZNH9KNLjuY0iaQShZGkta6dspk6Jp+pY/L5+1uctbsPNYzOW7Z1Hye3adq69whPv7mZp9/cTI/O2XxsVNidV5hHfi/NaRKJm8JIMoaZMWZQL8YM6sWfTL2QvYdr+P3a4I7p92srOXisFoDDNXW8XLKrYUfcCQW5TCscyPSx+Vx0fm9154nEQGEkGatfj858cmIBn5xYwIm6eoo37wtH51WwofJwQ7uV5ftZWb6ff/ndWgb27sK0wnymFQ7koxf2p3tn/YqItAf9pkmH0Ck7i6tG9ueqkf35m5vGsbnqMIvLKlhcVsE7m/Zwoi7oz9t94DjPvruNZ9/dRuecLD4ysn/DEkUFfTWnSSQqCiPpkIYP6MGnp4zg01NGcPDYCV5fV9Wwu+2ewzUA1NTWs2RNJUvWVMKvSygc1KthEMSlQ/qSrSWKRNpMpGFkZjOA7wLZwA/d/ZuNrn8J+AxQC1QCn3b3LVHWJNJYr66duPHi87jx4vOor3dWllc3jM77YOeHc5rKdh2kbNdBvr9kA327d+LaMcEd09Wj88jtpjlNIucisjAys2zgCeB6oBxYamYL3P2DhGbLgSJ3P2Jmfww8DtwZVU0iLcnKMiYO7cvEoX35ixvGsHP/0aA7r7SC19dXcTzcdn3fkRM8v3w7zy/fTnaWcfnwvkwvHMi0sflcMKCHBkGItFKUd0aTgfXuvhHAzOYBM4GGMHL3VxPavw3cF2E9HUpB326n/Cln57zcbtx7xTDuvWIYR2vqeGtjFYtKg+68HfuPAcGcprc37uXtjXv5h9+WMrx/94bReZcP70fnHC1RJNKSyNamM7PbgRnu/pnw+H7gCnef00z77wG73P3rTVybDcwGGDp06GVbtqgnT+Ll7pTtOhh25+1m+bZqmvpV6tklh6tHD2Ba4UCmjsljQM8u7V+sxEW3x62QEmFkZvcBc4Br3L3pFTFDWihVUtGeQ8dZsibYp+m1tZUcPF57WhszmFDQh+mFwXYY487TnKYMp/+4rRBlGF0FzHX3j4fHXwVw9280ancd8G8EQVTR0vMqjCTV1dTWU7x5L4vCu6bNe4402e683K5cW5jP9MJ8PjJyAN06a4miDKMwaoUowygHWAtMB7YDS4F73L0koc1E4DmCO6h1yTyvwkjSzcbKQw2j85Zu3ktt/em/c11ysvjohQPCCbf5nN8n9T7ru/9H71C+7ygFfbvxzENXxF1OOlAYtUJkAxjcvdbM5gALCYZ2/9jdS8zsUaDY3RcA3wJ6Ar8Muyu2uvstUdUkEocL8npyQV5PPvOxC9h/9AR/WPfhtut7wzlNx2vrGybhAow9r3dDd96Egj4pMaepfN9RNlUdbrmhyFnQ5noiMamrd1Zsqw52ty2toGzXwSbb9e/Rmaljgsm2Hxs1gF5d45nTlO4bNsYg/v+DSCNagUEkJif3XLpsWF++8vFCtlefnNO0mzc27KEmnNO053ANv1pWzq+WlZOTZUwe0S9cCWIgIwb0iPlViLQNhZFIihjcpxv3XzmM+68cxpGaWt5cv6dhd9vdB4JBprX1zpsb9vDmhj18/aVSLhjQI/icKZzT1EnbrkuaUhiJpKDunXO4btxArhs3EPfxlOw4EAyCKKtg5bbqhnYbqw6z8fVN/PD1TfTqksPVY/KYXhjs79SvR+cYX4FI6yiMRFKcmTF+cC7jB+fyxemjqDx4nFfXBKtAvLa2ksM1wbbrB4/X8tKqnby0aidmMGlo34aFXccM7KU5TZLSFEYiaSavVxdmFQ1hVtEQjtfWsXTTPhaFgyC27g3mNLnDe1v28d6WfXxr4RoG9+nWMGz8qpH96dpJc5oktSiMRNJYl5xspowawJRRA/ja/xrHhsrDDaPzirfsoy6c07S9+ijPvL2FZ97eQtdOWUy5MFiiaFphPoNyte26xE9hJJIhzIwL83tyYX5PZl89kv1HTvD7dZUsLt3NkrWVVB85AcCxE/X8rrSC35UGc5ouOv/knKaBXDI4l6wUmNMkHY/mGYl0ALV19SzfVs2i0mB03trdh5psN6BnZ64N5zRNGZVHzy451NbV8/yy7fztr9/neG09XXKyeGzmeG67rCAlJuOmMP3ltILCSKQD2rb3SMOKD05/HsEAAAZcSURBVG9t2ENNXf1pbTplB3Oa9hyqaXJC7oyLBvG9eyaSo+HkzVEYtYLCSKSDO3y8ljfWVzUMHa88eMaF80/x+O2XMKtoSITVpTWFUSvoMyORDq5HlxxuuGgQN1w0iPp6p2THARaV7WZxWQWryvef8bHzl25TGEmb0P21iDTIyjIuLsjlz64bzYI5UxjY68ybAe6oPtpOlUmmUxiJSLMK+nU/4/VU3OpC0pPCSESadWcLXXCzLlcXnbQNhZGINOu2ywqYcdGgJq/NuGgQt00qaOeKJFMpjESkWdlZxvfumcjjt19Cl5zgn4suOVk8fvslPHHvJM0zkjajMBKRM8rJzmJW0ZCGz4fO79ONWUVDFETSphRGIiISO4WRiIjETmEkIiKxUxiJiEjsFEYiIhI7hZGIiMROYSQiIrFTGImISOy0hYSIJKWgb7dT/hRpSwojEUnKMw9dEXcJksHUTSciIrFTGImISOwiDSMzm2Fma8xsvZk93MT1q81smZnVmtntUdYiIiKpK7IwMrNs4AngRmAccLeZjWvUbCvwIPDzqOoQEZHUF+UAhsnAenffCGBm84CZwAcnG7j75vBafYR1iIhIiouym24wsC3huDw812pmNtvMis2suLKysk2KExGR1JEWAxjc/Ul3L3L3ory8vLjLERGRNhZlGG0HhiQcF4TnREREThFlGC0FRpnZCDPrDNwFLIjw54mISJqKLIzcvRaYAywESoH57l5iZo+a2S0AZna5mZUDdwA/MLOSqOoREZHUZe4edw2tUlRU5MXFxXGXISLSEou7gHSSdmFkZpXAlrjrSBMDgKq4i5CMovdU8qrcfUbcRaSLtAsjSZ6ZFbt7Udx1SObQe0qikhZDu0VEJLMpjEREJHYKo8z2ZNwFSMbRe0oioc+MREQkdrozEhGR2CmMREQkdlFuISFtzMzqgNUJp249uQ1HE20PuXvPdilM0pqZ9QcWhYeDgDrg5PL4k929JpbCpEPRZ0ZppDUBozCSs2Fmc4FD7v7PCedywuW9RCKjbro0ZmY9zWxRuHX7ajOb2USb88zsNTNbYWbvm9nHwvM3mNlb4WN/aWYKLmlgZk+b2b+b2TvA42Y218y+nHD9fTMbHn5/n5m9G77HfhDu8izSKgqj9NIt/IVfYWb/BRwDPunuk4BrgW+bWeP1sO4BFrr7pcAEYIWZDQAeAa4LH1sMfKn9XoakiQLgI+7e7HvDzMYCdwIfDd9jdcC97VSfZBB9ZpRejoa/8ACYWSfgH83saqCeYCfdgcCuhMcsBX4ctn3B3VeY2TXAOOCNMLs6A2+102uQ9PFLd69roc104DJgafhe6gZURF2YZB6FUXq7F8gDLnP3E2a2Geia2MDdXwvD6ibgaTP7DrAPeMXd727vgiWtHE74vpZTe1JOvs8M+Im7f7XdqpKMpG669JYLVIRBdC0wrHEDMxsG7Hb3p4AfApOAt4GPmtmFYZseZja6HeuW9LOZ4L2DmU0CRoTnFwG3m1l+eK1f+J4TaRXdGaW3nwEvmtlqgs99yppoMxX4ipmdAA4BD7h7pZk9CDxrZl3Cdo8Aa6MvWdLUr4AHwg0w3yF8r7j7B2b2CPA/ZpYFnAA+j7Z5kVbS0G4REYmduulERCR2CiMREYmdwkhERGKnMBIRkdgpjEREJHYKI+kQzGxzuAySiKQghZGIiMROYSQZx8xeMLP3zKzEzGY3cf1L4arT75vZn4XnhptZqZk9FT7uf8ysW3htpJm9HD7nH8yssL1fk0im06RXyThm1s/d94ZhshS4BngPKCJYMulp4EqCddXeAe4jWK9vPVAULiY7H1jg7v9pZouAz7n7OjO7AviGu09r9xcmksG0HJBkoi+a2SfD74cAoxKuTQH+y90PA5jZ88DHgAXAJndfEbZ7Dxge7vP0EeCXCbtzdEFE2pTCSDKKmU0FrgOucvcjZraERiuZn8HxhO/rCLZDyAKqE7fuEJG2p8+MJNPkAvvCICok6I5L9AfgVjPrbmY9gE+G55rk7geATWZ2B4AFJkRUu0iHpTCSTPMykGNmpcA3CbbLaODuywg+M3qX4POiH7r78hae817gITNbCZQAp23vLiLnRgMYREQkdrozEhGR2CmMREQkdgojERGJncJIRERipzASEZHYKYxERCR2CiMREYnd/wfLrsw+NH4xDwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 430.5x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bfEDPeOR-scj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 439
        },
        "outputId": "78326f61-aeb8-45af-8a9d-2bdf371b649f"
      },
      "source": [
        "sns.factorplot(\"adult_male\", \"survived\", data=titanic, hue=\"sex\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/categorical.py:3666: UserWarning: The `factorplot` function has been renamed to `catplot`. The original name will be removed in a future release. Please update your code. Note that the default `kind` in `factorplot` (`'point'`) has changed `'strip'` in `catplot`.\n",
            "  warnings.warn(msg)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f4d72130358>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 67
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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4MAr4vrv/C/gniUN4weQkM8jdLzCzE0iczv2AmTUFHnH3X8SaTtLCcU3rM2N0Hlc/MI8NH+xl8Yad3DAlnynX96NRvaQ+YiJSDdx9Lok55Mr6bDnrupR63s2xhUpS0tcZuftWd7+HxA1Sl5BoXBEAWjdrwIzRebRp3gCA/PU7GDk1n30HiwMnE5F0kOx1Rl3MbJyZLSdxmuD/EZ2RIXJI2xYNmTm6P62b1Qdg3roPGP1QAfsLVUgiUrlk94wmATuAL7r7+e7+J3d/L8ZckqbaH5sopOOa1APg32u387VpCzlQpEISkYpVWUZmlg285e5/dPctNZBJ0lyHlo2YMbo/LRsnCumVNdv45vRFHCwqCZxMRFJVlWXk7sVAOzOrWwN5JEOcclxjZozO49hGiY/Ni2+8x7dmLqKwWIUkIp+U9HxGwKtm9jMzu/nQI85gkv5OPb4J00fl0bxh4vKFuSvf5buzllCkQhKRMpIto/8Af43GNyn1EKlUl9ZNmT4yj6b1E6d4P7P8Hb7/6FKKSzxwMhGpipmdb2Z/rYn3SvY6o5/HHUQyV/c2zZg2Mo9hE+ez+0ARTy3ZQk5WFndd2YOsLAsdTyQldLj1mRxgODCSxD3lNgIPAlPX/+aSjD8DKNlTu18ys3+UfcQdTjJHz3bNmXJDPxrVzQbg8UWb+MlfllOiPSSRQ0X0CInyOZtEGZ0dLc+Oth8RM+tgZm+Y2RQzW2NmD5vZhWb2qpm9aWb9osdrZrbYzP7v0NQSZV6nkZlNMrMF0bhLjzRTeZI9TPcD4IfR42ckLnotqM4gkvn6nNSCydf3o0GdRCHNyt/I2DkrcFchSa03HLi8gm2XA9ce5eufAvwPcHr0GAKcQ+Lv9p8AbwDnuntvEjc0+FU5r/FT4B/u3g+4ALjLzBodZa7Dkj1MV/YO3a+a2YLqCiG1R7+OxzBpRF+un7KA/YUlTJ+3gTrZWYz9clfMdMhOaq2RSWyffBSv/5a7Lwcws5XAi+7u0Y0MOgDNgKlm1hlwoLybpn4BGGhmP4iW6wPt+e/sDkcl2cN0x5R6tDSzASTCi3xqZ3U6lgnDc6mbk/j4TX51Pb9+7g3tIUlt1q6K7e2P8vVLz35ZUmq5hMROyR3AS9GEfF8hUTRlGXBFqeko2rt7tRQRJH+YbiH/ndPo/4CbqbrJRSp0budWPHBtH+pmJz6C4/+5jrvmrlYhSW21sYrtG2J+/2bA5ujnERWMmQt8y6JDGGbWuzoDJFtGPwJ6uXtHYBqwh8SETCJH7ILTjuP+oWeSE51Rd//L/+EPf38zcCqRIB48yu1H607g12a2mIq/vrmDxOG7ZdGhvjuqM0CyZXSbu39oZucAnwMmAn+qziBSO13Y9XjuHdKb7KiQ/vjim9z7DxWS1DpTgScq2PYE8NCRvrC7r48Ovx1aHuHuj5Xe5u6vufup7t7b3W9z9w7R9pfd/cvRz/vc/WvRbLHdDq2vLsmW0aFz3C8BJrj7M4BuDyTVYkD31vzxml4cuuTod39bwwOv/CdsKJEaFF1HdDVwA/AqicN2r0bLg2rDdUbJnru+2cweAC4iMWNgPT7FXEgiVflyjxMpKna+N3sJ7vDr594gJzuLked0DB1NpEas/80lRSTOmDuas+bSVrKFMojEl1dfdPedwDEkrjkSqTaX9W7DnVf04NAZ3nf89XUeem19yEgiUkOSKiN33+vuT7j7m9HyO+7+t6qeZ2YDzGy1ma01s1srGXeFmbmZ5SYfXTLRVbnt+NVXzzi8PPaplcxcEPeJRCISWmyH2qJ5kO4DLga6AoPNrGs545oA3wHmx5VF0svgfu2549Juh5d/8pflPFpQ1ZmvIpLO4vzepx+w1t3XuftBYBZQ3r2M7gB+C+yPMYukmWvP6sDYLyf+38Udbnl8GX9ZvClwKhGJS5xl1IaPX8i1KVp3mJmdCbSLzs4T+ZgbzunIT750OpAopO/PXsrTSzXZsMinZWbfNrNVZvZwTK8/rtRtgo7IEd8J9miZWRZwNxVf7Vt67BhgDED79kd7VwxJJ2M+24nCYueuuaspcfjuI0uok20M6N46dDSR6jWuWYVTSDBu19Ge2v1N4EJ3T9nDC3HuGW3m4/dbast/bzcBicn5ugMvm9l6oD8wp7yTGNx9vLvnuntuq1atYowsqejGC07huxd2BqC4xLlpxmL+/vq7gVOJVKNEEVU4hUS0/YiY2Z+Bk4HnzOyn5U0DYWYjzOxJM3vBzNab2U3RjN6LzWyemR0TjRttZvlmttTMHjezhuW8Xycze97MFprZv8zs9GRyxllG+UBnM+toZnWBa4A5hza6+y53b+nuHaKrfecBA91dU1PIJ3zn85258YJOABSVON98eBEvrX4vcCqRahPbFBLu/nVgC4lpHxpR8TQQ3aP36gv8EtgbTSnxWpQP4Al37+vuPUncrbu8e5SOB77l7n1ITFFxfzI5Yysjdy8CbiJxfdIqYLa7rzSz281sYFzvK5nJzPjBF05jzGdPBuBgcQlfm7aQf725LXAykWqRzBQS1eELwK1mtgR4mf9OAwGJu3bvdvdtwC7g6Wj9oWkmALpHezvLgaHAf097BcysMYk9ukej93gASOqYeqzfGbn7s8CzZdaNrWDs+XFmkfRnZvz44tMpLC5h8qvrOVhUwqipBUy+vi9nd2oZOp7I0Yh7ColDDk0DsfpjK83yqHqaCYApwGXuvtTMRgDnl3n9LGCnu/f6tMF0Sx9JK2bG2C93ZVj/xO/mgaISRk4pYMFbHwROJnJUamoKiaOdBqIJ8I6Z1SGxZ/Qx7v4h8JaZXRW9vplZz2ReWGUkacfMuH1gd67pm/ifyX2FxVw/eQEL394ROJnIEaupKSSOdhqIn5G4QcGrJKYqL89QYKSZLQVWUv71pZ9g6TaZWW5urhcU6ByHqlzwu5d5a/seOrZsxEs/OD90nFiUlDi3PL6MxxYmzlZtUi+HaaPy6NWueeBkIkDikFhyxjXLBmZT/kkMTwCDquH07pSmPSNJW1lZxm+v6MFlvU4EYPeBIoY/OJ8Vm3cFTibyKSWKpsIpJDK9iCDgRa8i1SE7y/jdVT0pLHGeWfYOH+4vYtiD85k5uj9dWjcNHU8keeN2aQoJkXSWk53FH67uxRe7HQ/Azr2FDJ04nzXv7g6cTESSpTLKUG1bNKBjy0a0bdEgdJQaUSc7i/8dfCYXdjkOgA/2HGTIhPmsfe+jwMlEJBk6gUEyyoGiYsY8tJBX1iQuhj2uST0e+dpZdGzZqIpnilS75E9gEO0ZSWapl5PNA9f24ZxTEhfBvrf7AEMmzGPD+3sDJxORyqiMJOPUr5PNhOG59D/5GADe2bWfwRPmsWmHCkkkVamMJCM1qJvNg9f1pW+HFgBs3rmPIRPm886ufYGTiUh5VEaSsRrVy2Hy9f3o3T5xEeyGD/YyZMJ83v1QkwqLpBqVkWS0xvVymHpDP3q0bQbAW9v3MGTCPLbtPlDFM0WkJqmMJOM1rV+HaTfk0TW6CPY/2/YwdOI83v9IhSSSKlRGUis0a1iHh0flcfoJTQBY8+5HDJ04nx17DgZOJiKgMpJapEWjukwflUfn4xoD8MbW3Vw7aT679hUGTiYiKiOpVVo2rsfDo/M4OboIdsXmDxk+aQEf7lchiYSkMpJa57gm9Zkxuj8nHdsQgKUbd3L95Hw+OlAUOJlI7aUyklrphGb1mTm6P+2OSdy7b+HbO7hhcj57D6qQREJQGUmtdWLzBswY1Z82zROFtGD9B4ycUsC+gxk/dYxIylEZSa3W7piGzBidxwlN6wPw2rr3GTOtgP2FKiSRmqQyklrvpGMbMWN0Hq2a1APgX29u5xvTF3KgSIUkUlNURiLAya0aM3N0Hi0b1wXgpdXbuPHhxRwsKgmcTKR2UBmJRE45rgkPj+pPi4Z1APj7qnf5zqzFFBWrkETipjISKeW0E5owfVQezRokCum5FVv53uylKiSRmKmMRMrodmIzpo/Mo0n9HACeXrqFWx5bRnFJes2KLJJOVEYi5TijbTMeuqEfjeslCumJxZu59fFllKiQRGKhMhKpQO/2LZhyfV8a1s0G4NGFm/jpkytwVyGJVDeVkUglcjscw+QRfalfJ/GrMnPBBsbNWalCEqlmKiORKuSdfCwPXteXejmJX5epr73NL55ZpUISqUYqI5EkfOaUlowfnkvd7MSvzIP/fovfPP+GCkmkmqiMRJJ03qmt+PO1Z1In2wB44JV1/P6FNYFTiWQGlZHIp/C504/nviFnkpOVKKR7/rGWe158M3AqkfSnMhL5lL7Q7QTuGdyb7KiQ7n5hDfe/vDZwKpH0FmsZmdkAM1ttZmvN7NZytn/dzJab2RIz+7eZdY0zj0h1+dIZrbl7UE+iPuLO51cz8V/rwoYSSWOxlZGZZQP3ARcDXYHB5ZTNDHc/w917AXcCd8eVR6S6XdqrDb+7qicWFdIvnlnFlFffChtKJE3FuWfUD1jr7uvc/SAwC7i09AB3/7DUYiNApyZJWrn8zLb89vIeh5fHPf060+e9HTCRSHrKifG12wAbSy1vAvLKDjKzG4GbgbrA58p7ITMbA4wBaN++fbUHFTkag/q2o7CkhJ/+ZQUAtz25gjrZxtV99VkVSVbwExjc/T537wT8CLitgjHj3T3X3XNbtWpVswFFkjA07yR+PrDb4eVbn1jO4ws3BUwkkl7iLKPNQLtSy22jdRWZBVwWYx6RWF13dgduu6QLAO7ww8eW8tSSyj7yInJInGWUD3Q2s45mVhe4BphTeoCZdS61eAmgCzYkrY0692R+NOB0AEocbp69lGeWvRM4lUjqi+07I3cvMrObgLlANjDJ3Vea2e1AgbvPAW4yswuBQmAHcF1ceURqyjfO70RhcQl3v7CG4hLnO7MWk5NtfLHbCaGjiaQsS7d7a+Xm5npBQUHoGCJVuvtvq7nnH4mLYetkG38e1ofPdzk+cCqpQRY6QDoJfgKDSKb63kWn8vXzOgFQWOx8Y/oiXlmzLXAqkdSkMhKJiZnxowGnMeqcjgAcLC5hzEMFvLp2e+BkIqlHZSQSIzPjp5d04bqzTgLgQFEJI6fmM2/d+4GTiaQWlZFIzMyMcQO7MSQvcRHs/sISbpiST8H6DwInE0kdKiORGmBm/OLS7gzKbQvA3oPFjJicz+INOwInE0kNKiORGpKVZfz68h5c3rsNAB8dKGL4pAUs27QzcDKR8FRGIjUoO8u466qefKXniQDs3l/EtQ8uYOWWXYGTiYSlMhKpYdlZxu8H9eTi7omLYHftK2TYxPm8sfXDKp4pkrlURiIB5GRncc/g3lzUNXER7I69hQydMJ83390dOJlIGCojkUDqZGdx75DeXHBa4k707+85yJCJ81m37aPAyURqnspIJKB6Odn8aVgfzu3cEoBtuw8wZMJ83n5/T+BkIjVLZSQSWP062UwYnsvZnY4FYOuH+xk8fh4bP9gbOJlIzVEZiaSA+nWymXhdLv06HgPAll37GTxhHlt27gucTKRmqIxEUkTDujlMGtGXPie1AGDTjn0MnjCPrbv2B04mEj+VkUgKaVwvhynX96Vnu+YAvP3+XoZMmMd7u1VIktlURiIppkn9Ojx0Qz+6t2kKwLrtexg6YT7bPzoQOJlIfFRGIimoWYM6TB+ZR5fWiUJ6872PGDZxPh/sORg4mUg8VEYiKap5w7pMH9mPU49vDMAbW3czbOJ8du5VIUnmURmJpLBjG9fj4VH96dSqEQCvv/MhwyctYNe+wsDJRKqXykgkxbVqUo+Zo/vTsWWikJZt2sWIyQvYvV+FJJlDZSSSBo5rWp8Zo/Nof0xDABZv2MkNU/LZc6AocDKR6qEyEkkTrZs1YMboPNo0bwBA/vodjJyaz76DxYGTiRw9lZFIGmnboiEzR/endbP6AMxb9wGjHypgf6EKSdKbykgkzbQ/NlFIxzWpB8C/127na9MWcqBIhSTpS2UkkoY6tGzEjNH9adk4UUivrNnGN6cv4mBRSeBkIkdGZSSSpk45rjEzRudxbKO6ALz4xnt8a+YiCotVSJJ+VEYiaezU45swfVQezRvWAWDuynf57qwlFKmQJM2ojETSXJfWTZk+Mo+m9XMAeGb5Owmy/LgAAAmxSURBVHz/0aUUl3jgZCLJUxmJZIDubZoxbWQeTeolCumpJVu45bFllKiQJE2ojEQyRM92zZlyQz8a1c0G4PFFm/jJX5arkCQtqIxEMkifk1ow+fp+NKiTKKRZ+RsZO2cF7iokSW0qI5EM06/jMUwa0Zf6dRK/3tPnbeD2v76uQpKUpjISyUBndTqWCcNzqZuT+BWf/Op6fv3cGyokSVmxlpGZDTCz1Wa21sxuLWf7zWb2upktM7MXzeykOPOI1Cbndm7FA9f2oW524td8/D/Xcdfc1SokSUmxlZGZZQP3ARcDXYHBZta1zLDFQK679wAeA+6MK49IbXTBacdx/9AzyckyAO5/+T/84e9vBk4l8klx7hn1A9a6+zp3PwjMAi4tPcDdX3L3vdHiPKBtjHlEaqULux7PvUN6kx0V0h9ffJN7/6FCktQSZxm1ATaWWt4UravISOC58jaY2RgzKzCzgm3btlVjRJHaYUD31vzxml5EfcTv/raGB175T9hQIqXkhA4AYGbDgFzgvPK2u/t4YDxAbm6uDniLHIEv9ziRomLne7OX4A6/fu4NcrKzGHlOx6Sef+2D89m0Yx9tWzRg2si8mNNKbRNnGW0G2pVabhut+xgzuxD4KXCeux+IMY9IrXdZ7zYUFpdwy+PLcIc7/vo6dbKN4Wd1qPK5m3bs463te+IPKbVSnIfp8oHOZtbRzOoC1wBzSg8ws97AA8BAd38vxiwiErkqtx2/+uoZh5fHPrWSmQs2BEwkEmMZuXsRcBMwF1gFzHb3lWZ2u5kNjIbdBTQGHjWzJWY2p4KXE5FqNLhfe+64tNvh5Z/8ZTmPFmys5Bki8Yr1OyN3fxZ4tsy6saV+vjDO9xeRil17VgcKiz26OwPc8vgycrKNr/bWSa1S83QHBpFa7IZzOvKTL50OgDt8f/ZSnl66JXAqqY1URiK13JjPduKHXzwNgBKH7z6yhOdXvBM4ldQ2KiMR4cYLTuG7F3YGoLjEuWnGYv7++ruBU0ltojISEQC+8/nO3HhBJwCKSpxvPryIl1brJFepGSojEQHAzPjBF05jzGdPBuBgcQlfm7aQf72pu55I/FRGInKYmfHji0/n+s90AOBgUQmjphbwq2dWsWXnPgC27NzH7PyNFGsGWalGlm63k8/NzfWCgoLQMUQymrvzs6dWMH1exRfDDuh2AvcO6U1Otv6ftgIWOkA60adIRD7BzLh9YHf6dTimwjHPr9zKE4s/cYcvkSOiMhKRcmVlGUUlJZWOmZ2vuzZI9VAZiUiFtu7aX+n2Q98jiRwtlZGIVKh18waVbj+xiu0iyVIZiUiFrs5tV+n2QX0r3y6SLJWRiFToij5tGdDthHK3Deh2AlecqZuqSvVQGYlIhbKzjHuH9ObOK3tQLyfx10W9nCzuvLIH9w09k+wsnb0s1UNlJCKVysnOYlBuu8PfD53YvAGDctupiKRaqYxERCQ4lZGIiASnMhIRkeBURiIiEpzKSEREglMZiYhIcCojEREJTmUkIiLB5YQOICLpoW2LBh/7p0h1UhmJSFKmjcwLHUEymA7TiYhIcCojEREJTmUkIiLBqYxERCQ4lZGIiASnMhIRkeBURiIiEpzKSEREglMZiYhIcCojEREJTmUkIiLBmbuHzvCpmNk24O3QOdJES2B76BCSUfSZSt52dx8QOkS6SLsykuSZWYG754bOIZlDnymJiw7TiYhIcCojEREJTmWU2caHDiAZR58piYW+MxIRkeC0ZyQiIsGpjEREJLic0AEkeWZWDCwvteoyd19fwdiP3L1xjQSTtGZmxwIvRosnAMXAtmi5n7sfDBJMahV9Z5RGPk3BqIzkSJjZOOAjd/9dqXU57l4ULpXUBjpMl8bMrLGZvWhmi8xsuZldWs6Y1mb2TzNbYmYrzOzcaP0XzOy16LmPmpmKSw4zsylm9mczmw/caWbjzOwHpbavMLMO0c/DzGxB9Bl7wMyyA8WWNKYySi8Nol/4JWb2F2A/8FV3PxO4APgfM7MyzxkCzHX3XkBPYImZtQRuAy6MnlsA3FxzfwxJE22Bs929ws+GmXUBrgY+E33GioGhNZRPMoi+M0ov+6JfeADMrA7wKzP7LFACtAGOB7aWek4+MCka+6S7LzGz84CuwKtRd9UFXquhP4Okj0fdvbiKMZ8H+gD50WepAfBe3MEk86iM0ttQoBXQx90LzWw9UL/0AHf/Z1RWlwBTzOxuYAfwgrsPrunAklb2lPq5iI8fSTn0OTNgqrv/uMZSSUbSYbr01gx4LyqiC4CTyg4ws5OAd919AjAROBOYB3zGzE6JxjQys1NrMLekn/UkPjuY2ZlAx2j9i8CVZnZctO2Y6DMn8qlozyi9PQw8bWbLSXzv80Y5Y84HfmhmhcBHwHB332ZmI4CZZlYvGncbsCb+yJKmHgeGm9lKYD7RZ8XdXzez24C/mVkWUAjciKZ5kU9Jp3aLiEhwOkwnIiLBqYxERCQ4lZGIiASnMhIRkeBURiIiEpzKSEREglMZSdoysxFmdm8VYzqY2Yro515m9qWYM00xsyvjfA+RTKQyktqkFxBrGYnIkVEZScoysyfNbKGZrTSzMdG6681sjZktAD5TauzH9kjM7KMyr1UXuB24Orrr+dUVvOc4M5tqZv8ys7fN7HIzuzOaouP56IazmNlYM8uPplIYX87d0jGzPmb2SvRnmGtmravlX4xIBlIZSSq7wd37ALnAt82sDfBzEiV0Dok7jyclmq10LPCIu/dy90cqGd4J+BwwEJgOvOTuZwD7SNxwFuBed+/r7t1J3Kn6y6VfICqt/wWujP4Mk4BfJptXpLbRvekklX3bzL4a/dwOuBZ42d23AZjZI0AcN3h9Lrr57HIgG3g+Wr8c6BD9fIGZ3QI0BI4BVgJPl3qN04DuwAvRTlM28E4MWUUygspIUpKZnQ9cCJzl7nvN7GUSN4KtaG/o8BQH0Q076x7F2x8AcPcSMyv0/97AsQTIMbP6wP1ArrtvjKbqrl/mNQxY6e5nHUUOkVpDh+kkVTUDdkRFdDrQn8ThsPPM7NjoMNhVpcavJzHJGyQOr9Up5zV3A02qIduh4tkeTdde3tlzq4FWZnYWJA7bmVm3anhvkYykMpJU9TyJvZBVwG9IzMH0DjCOxKy0rwKrSo2fQKKolgJn8fGJ4Q55Ceha2QkMyXD3ndH7rQDmkphNt+yYgyRK6rdRpiXA2Uf6niKZTlNIiIhIcNozEhGR4HQCg9RKZnY98J0yq1919xtD5BGp7XSYTkREgtNhOhERCU5lJCIiwamMREQkOJWRiIgE9/8BbEpS9z0iMCgAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 430.5x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_deIKVcp57jP",
        "colab_type": "text"
      },
      "source": [
        "Now let's see what effect does the feature **'deck'** has."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wk-iz28v5myt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "aa361d1a-8507-4088-e03e-fe3b5a042618"
      },
      "source": [
        "sns.barplot(\"deck\", \"survived\", data=titanic,order=['A','B','C','D','E','F','G'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f4d72024438>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 68
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r_xp1GQ06TLb",
        "colab_type": "text"
      },
      "source": [
        "Now let's try to combine 3 features together."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2vXNivF76Cin",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "outputId": "731b46e3-6bd0-47ab-84a4-bd5e4f6c41aa"
      },
      "source": [
        "sns.factorplot(\"alone\", \"survived\", data=titanic, hue=\"sex\",col=\"pclass\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/categorical.py:3666: UserWarning: The `factorplot` function has been renamed to `catplot`. The original name will be removed in a future release. Please update your code. Note that the default `kind` in `factorplot` (`'point'`) has changed `'strip'` in `catplot`.\n",
            "  warnings.warn(msg)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f4d7204a940>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 69
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1150.5x360 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BpLM5JwG-4dG",
        "colab_type": "text"
      },
      "source": [
        "# Data Preprocessing"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BFZDO8Jt_Iu1",
        "colab_type": "text"
      },
      "source": [
        "Let's have the object fields encoded."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nlHRO8s06OmW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 246
        },
        "outputId": "64cb8186-c384-422e-82f4-19b17be3feb0"
      },
      "source": [
        "#encoding deck\n",
        "\n",
        "dk = {\"A\": 1, \"B\": 2, \"C\": 3, \"D\": 4, \"E\": 5, \"F\": 6, \"G\": 7}\n",
        "titanic['deck']=titanic.deck.map(dk)\n",
        "titanic.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>survived</th>\n",
              "      <th>pclass</th>\n",
              "      <th>name</th>\n",
              "      <th>sex</th>\n",
              "      <th>age</th>\n",
              "      <th>sibsp</th>\n",
              "      <th>parch</th>\n",
              "      <th>ticket</th>\n",
              "      <th>fare</th>\n",
              "      <th>cabin</th>\n",
              "      <th>embarked</th>\n",
              "      <th>who</th>\n",
              "      <th>adult_male</th>\n",
              "      <th>deck</th>\n",
              "      <th>alone</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Braund, Mr. Owen Harris</td>\n",
              "      <td>male</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>A/5 21171</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>man</td>\n",
              "      <td>True</td>\n",
              "      <td>NaN</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
              "      <td>female</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17599</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C85</td>\n",
              "      <td>C</td>\n",
              "      <td>woman</td>\n",
              "      <td>False</td>\n",
              "      <td>3.0</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Heikkinen, Miss. Laina</td>\n",
              "      <td>female</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>STON/O2. 3101282</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>woman</td>\n",
              "      <td>False</td>\n",
              "      <td>NaN</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
              "      <td>female</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>113803</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>C123</td>\n",
              "      <td>S</td>\n",
              "      <td>woman</td>\n",
              "      <td>False</td>\n",
              "      <td>3.0</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Allen, Mr. William Henry</td>\n",
              "      <td>male</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>373450</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>man</td>\n",
              "      <td>True</td>\n",
              "      <td>NaN</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   survived  pclass  ... deck  alone\n",
              "0         0       3  ...  NaN  False\n",
              "1         1       1  ...  3.0  False\n",
              "2         1       3  ...  NaN   True\n",
              "3         1       1  ...  3.0  False\n",
              "4         0       3  ...  NaN   True\n",
              "\n",
              "[5 rows x 15 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2Mfx_3oa_ppH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        },
        "outputId": "b6223d65-024b-45e9-b338-0905a8d07913"
      },
      "source": [
        "# encoding embarked\n",
        "\n",
        "\n",
        "titanic['embarked'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "S    644\n",
              "C    168\n",
              "Q     77\n",
              "Name: embarked, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nOADubwe_ZIA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 246
        },
        "outputId": "062c7d07-f3f0-4c23-a4a6-f5dae8d624c3"
      },
      "source": [
        "e = {'S':3,'Q':2, 'C':1}\n",
        "titanic['embarked']=titanic.embarked.map(e)\n",
        "titanic.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\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>survived</th>\n",
              "      <th>pclass</th>\n",
              "      <th>name</th>\n",
              "      <th>sex</th>\n",
              "      <th>age</th>\n",
              "      <th>sibsp</th>\n",
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              "      <th>embarked</th>\n",
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              "      <th>alone</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Braund, Mr. Owen Harris</td>\n",
              "      <td>male</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>A/5 21171</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>man</td>\n",
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              "      <td>False</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
              "      <td>female</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17599</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C85</td>\n",
              "      <td>1.0</td>\n",
              "      <td>woman</td>\n",
              "      <td>False</td>\n",
              "      <td>3.0</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Heikkinen, Miss. Laina</td>\n",
              "      <td>female</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>STON/O2. 3101282</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.0</td>\n",
              "      <td>woman</td>\n",
              "      <td>False</td>\n",
              "      <td>NaN</td>\n",
              "      <td>True</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
              "      <td>female</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>113803</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>C123</td>\n",
              "      <td>3.0</td>\n",
              "      <td>woman</td>\n",
              "      <td>False</td>\n",
              "      <td>3.0</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Allen, Mr. William Henry</td>\n",
              "      <td>male</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>373450</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.0</td>\n",
              "      <td>man</td>\n",
              "      <td>True</td>\n",
              "      <td>NaN</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   survived  pclass  ... deck  alone\n",
              "0         0       3  ...  NaN  False\n",
              "1         1       1  ...  3.0  False\n",
              "2         1       3  ...  NaN   True\n",
              "3         1       1  ...  3.0  False\n",
              "4         0       3  ...  NaN   True\n",
              "\n",
              "[5 rows x 15 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xUj1AlQzAGTM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 246
        },
        "outputId": "5df527b5-c272-4065-fac7-8bde00ed19dc"
      },
      "source": [
        "# encoding gender\n",
        "\n",
        "genders = {\"male\": 0, \"female\": 1}\n",
        "titanic['sex'] = titanic['sex'].map(genders)\n",
        "titanic.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>survived</th>\n",
              "      <th>pclass</th>\n",
              "      <th>name</th>\n",
              "      <th>sex</th>\n",
              "      <th>age</th>\n",
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              "      <th>alone</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Braund, Mr. Owen Harris</td>\n",
              "      <td>0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>A/5 21171</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.0</td>\n",
              "      <td>man</td>\n",
              "      <td>True</td>\n",
              "      <td>NaN</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
              "      <td>1</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17599</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C85</td>\n",
              "      <td>1.0</td>\n",
              "      <td>woman</td>\n",
              "      <td>False</td>\n",
              "      <td>3.0</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Heikkinen, Miss. Laina</td>\n",
              "      <td>1</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>STON/O2. 3101282</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.0</td>\n",
              "      <td>woman</td>\n",
              "      <td>False</td>\n",
              "      <td>NaN</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
              "      <td>1</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>113803</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>C123</td>\n",
              "      <td>3.0</td>\n",
              "      <td>woman</td>\n",
              "      <td>False</td>\n",
              "      <td>3.0</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Allen, Mr. William Henry</td>\n",
              "      <td>0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>373450</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.0</td>\n",
              "      <td>man</td>\n",
              "      <td>True</td>\n",
              "      <td>NaN</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   survived  pclass  ... deck  alone\n",
              "0         0       3  ...  NaN  False\n",
              "1         1       1  ...  3.0  False\n",
              "2         1       3  ...  NaN   True\n",
              "3         1       1  ...  3.0  False\n",
              "4         0       3  ...  NaN   True\n",
              "\n",
              "[5 rows x 15 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tSizNzLAAPS6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#encoding who\n",
        "\n",
        "wh = {'child':3,'woman':2, 'man':1}\n",
        "titanic['who']=titanic.who.map(wh)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V5PENJzKAU4Q",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 246
        },
        "outputId": "935ad191-7c1c-449a-fd81-e4499be86d9a"
      },
      "source": [
        "titanic.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
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              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
              "      <td>1</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17599</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C85</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2</td>\n",
              "      <td>False</td>\n",
              "      <td>3.0</td>\n",
              "      <td>False</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Heikkinen, Miss. Laina</td>\n",
              "      <td>1</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>STON/O2. 3101282</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2</td>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
              "      <td>1</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>113803</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>C123</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2</td>\n",
              "      <td>False</td>\n",
              "      <td>3.0</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Allen, Mr. William Henry</td>\n",
              "      <td>0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>373450</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>NaN</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   survived  pclass  ... deck  alone\n",
              "0         0       3  ...  NaN  False\n",
              "1         1       1  ...  3.0  False\n",
              "2         1       3  ...  NaN   True\n",
              "3         1       1  ...  3.0  False\n",
              "4         0       3  ...  NaN   True\n",
              "\n",
              "[5 rows x 15 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PdBESBiJA1fB",
        "colab_type": "text"
      },
      "source": [
        "Now we need to impute the **Missing Values**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zr4guzZ2BfrF",
        "colab_type": "text"
      },
      "source": [
        "There are alot of missing values in deck. So we will simply fill it with **0**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5MMFgt7qAciv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 246
        },
        "outputId": "ba1d81aa-7184-4952-c9b2-c9811e53bbbd"
      },
      "source": [
        "#imputing deck\n",
        "titanic['deck']=titanic['deck'].fillna(0)\n",
        "titanic.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\n",
              "\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>survived</th>\n",
              "      <th>pclass</th>\n",
              "      <th>name</th>\n",
              "      <th>sex</th>\n",
              "      <th>age</th>\n",
              "      <th>sibsp</th>\n",
              "      <th>parch</th>\n",
              "      <th>ticket</th>\n",
              "      <th>fare</th>\n",
              "      <th>cabin</th>\n",
              "      <th>embarked</th>\n",
              "      <th>who</th>\n",
              "      <th>adult_male</th>\n",
              "      <th>deck</th>\n",
              "      <th>alone</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Braund, Mr. Owen Harris</td>\n",
              "      <td>0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>A/5 21171</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>0.0</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
              "      <td>1</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17599</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C85</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2</td>\n",
              "      <td>False</td>\n",
              "      <td>3.0</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Heikkinen, Miss. Laina</td>\n",
              "      <td>1</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>STON/O2. 3101282</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2</td>\n",
              "      <td>False</td>\n",
              "      <td>0.0</td>\n",
              "      <td>True</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
              "      <td>1</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>113803</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>C123</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2</td>\n",
              "      <td>False</td>\n",
              "      <td>3.0</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Allen, Mr. William Henry</td>\n",
              "      <td>0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>373450</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>0.0</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   survived  pclass  ... deck  alone\n",
              "0         0       3  ...  0.0  False\n",
              "1         1       1  ...  3.0  False\n",
              "2         1       3  ...  0.0   True\n",
              "3         1       1  ...  3.0  False\n",
              "4         0       3  ...  0.0   True\n",
              "\n",
              "[5 rows x 15 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3EqJIuNZBybS",
        "colab_type": "text"
      },
      "source": [
        "There are only 2 missing vaues in 'embarked'. So we will find out which of the values in embarked has **maximum occurence** and fill the missing values with **that value**."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Eem95ZtcBHO3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        },
        "outputId": "b8d847cd-ae49-4f4c-a6ed-f4a28c22cf77"
      },
      "source": [
        "#imputing embarked\n",
        "\n",
        "titanic['embarked'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3.0    644\n",
              "1.0    168\n",
              "2.0     77\n",
              "Name: embarked, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0i12cqpmBPN-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 246
        },
        "outputId": "6fdac589-ee6c-4bfd-d3ba-c318ecd3ac93"
      },
      "source": [
        "titanic['embarked']=titanic['embarked'].fillna('3.0')\n",
        "titanic.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>survived</th>\n",
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              "      <th>alone</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Braund, Mr. Owen Harris</td>\n",
              "      <td>0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
              "      <td>1</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17599</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C85</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>False</td>\n",
              "      <td>3.0</td>\n",
              "      <td>False</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Heikkinen, Miss. Laina</td>\n",
              "      <td>1</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>STON/O2. 3101282</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>False</td>\n",
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              "    <tr>\n",
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              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
              "      <td>1</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>113803</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>C123</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>False</td>\n",
              "      <td>3.0</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Allen, Mr. William Henry</td>\n",
              "      <td>0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>373450</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   survived  pclass  ... deck  alone\n",
              "0         0       3  ...  0.0  False\n",
              "1         1       1  ...  3.0  False\n",
              "2         1       3  ...  0.0   True\n",
              "3         1       1  ...  3.0  False\n",
              "4         0       3  ...  0.0   True\n",
              "\n",
              "[5 rows x 15 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y21lkD1ZCQ7E",
        "colab_type": "text"
      },
      "source": [
        "Now we will impute the missing values in **'age'**."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1KkSk3J-BUWl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "51d83da2-0b86-45ad-c5e3-05b2ba44d6fe"
      },
      "source": [
        "#imputing age\n",
        "\n",
        "m=titanic['age'].mean()\n",
        "m\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "29.69911764705882"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OIbn8JYsGqjR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 246
        },
        "outputId": "cda48c73-056e-4c10-d2b9-3884cdb57ca0"
      },
      "source": [
        "titanic['age']=titanic['age'].fillna(m)\n",
        "titanic.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>2</th>\n",
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              "      <td>1</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>2</td>\n",
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              "      <td>Allen, Mr. William Henry</td>\n",
              "      <td>0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>373450</td>\n",
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              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   survived  pclass  ... deck  alone\n",
              "0         0       3  ...  0.0  False\n",
              "1         1       1  ...  3.0  False\n",
              "2         1       3  ...  0.0   True\n",
              "3         1       1  ...  3.0  False\n",
              "4         0       3  ...  0.0   True\n",
              "\n",
              "[5 rows x 15 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mIDuSpxPHAwT",
        "colab_type": "text"
      },
      "source": [
        "# Adding New Features"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x3dXBIXbG18O",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def process_family(parameters):\n",
        "     \n",
        "    x,y=parameters\n",
        "    \n",
        "    # introducing a new feature : the size of families (including the passenger)\n",
        "    family_size = x+ y + 1\n",
        "    \n",
        "    if (family_size==1):\n",
        "      return 1 # for singleton\n",
        "    elif(2<= family_size <= 4 ):\n",
        "      return 2 #for small family\n",
        "    else:\n",
        "      return 3 #for big family "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sBEBYrWNHMxL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 262
        },
        "outputId": "421d31e9-1bef-4d1b-b32a-91a2c613dbca"
      },
      "source": [
        "titanic['FAM_SIZE']= titanic[['parch','sibsp']].apply(process_family, axis=1)\n",
        "titanic.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>survived</th>\n",
              "      <th>pclass</th>\n",
              "      <th>name</th>\n",
              "      <th>sex</th>\n",
              "      <th>age</th>\n",
              "      <th>sibsp</th>\n",
              "      <th>parch</th>\n",
              "      <th>ticket</th>\n",
              "      <th>fare</th>\n",
              "      <th>cabin</th>\n",
              "      <th>embarked</th>\n",
              "      <th>who</th>\n",
              "      <th>adult_male</th>\n",
              "      <th>deck</th>\n",
              "      <th>alone</th>\n",
              "      <th>FAM_SIZE</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Braund, Mr. Owen Harris</td>\n",
              "      <td>0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>A/5 21171</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>0.0</td>\n",
              "      <td>False</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
              "      <td>1</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17599</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C85</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>False</td>\n",
              "      <td>3.0</td>\n",
              "      <td>False</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Heikkinen, Miss. Laina</td>\n",
              "      <td>1</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>STON/O2. 3101282</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>False</td>\n",
              "      <td>0.0</td>\n",
              "      <td>True</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
              "      <td>1</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>113803</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>C123</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>False</td>\n",
              "      <td>3.0</td>\n",
              "      <td>False</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Allen, Mr. William Henry</td>\n",
              "      <td>0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>373450</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>0.0</td>\n",
              "      <td>True</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   survived  pclass  ...  alone  FAM_SIZE\n",
              "0         0       3  ...  False         2\n",
              "1         1       1  ...  False         2\n",
              "2         1       3  ...   True         1\n",
              "3         1       1  ...  False         2\n",
              "4         0       3  ...   True         1\n",
              "\n",
              "[5 rows x 16 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dp_7HPNcHOv_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# to get title from the name.\n",
        "\n",
        "titles = set()\n",
        "for name in titanic['name']:\n",
        "    titles.add(name.split(',')[1].split('.')[0].strip())"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eiIr98SYHWua",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "outputId": "3812ade1-b686-403c-ff88-0af810fe6db0"
      },
      "source": [
        "titles #all the salutations present in my dataset."
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Capt',\n",
              " 'Col',\n",
              " 'Don',\n",
              " 'Dr',\n",
              " 'Jonkheer',\n",
              " 'Lady',\n",
              " 'Major',\n",
              " 'Master',\n",
              " 'Miss',\n",
              " 'Mlle',\n",
              " 'Mme',\n",
              " 'Mr',\n",
              " 'Mrs',\n",
              " 'Ms',\n",
              " 'Rev',\n",
              " 'Sir',\n",
              " 'the Countess'}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jj9bsp2LHYjm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "a6a77fd7-1148-4401-c256-48a114e73d5f"
      },
      "source": [
        "len(titles)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "17"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0sqIBnclHbVK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Title_Dictionary = {\n",
        "    \"Capt\": \"Officer\",\n",
        "    \"Col\": \"Officer\",\n",
        "    \"Major\": \"Officer\",\n",
        "    \"Jonkheer\": \"Royalty\",\n",
        "    \"Don\": \"Royalty\",\n",
        "    \"Sir\" : \"Royalty\",\n",
        "    \"Dr\": \"Officer\",\n",
        "    \"Rev\": \"Officer\",\n",
        "    \"the Countess\":\"Royalty\",\n",
        "    \"Mme\": \"Mrs\",\n",
        "    \"Mlle\": \"Miss\",\n",
        "    \"Ms\": \"Mrs\",\n",
        "    \"Mr\" : \"Mr\",\n",
        "    \"Mrs\" : \"Mrs\",\n",
        "    \"Miss\" : \"Miss\",\n",
        "    \"Master\" : \"Master\",\n",
        "    \"Lady\" : \"Royalty\"\n",
        "}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ryWoGhk3Hete",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def get_titles():\n",
        "    # we extract the title from each name\n",
        "    titanic['title'] = titanic['name'].map(lambda name:name.split(',')[1].split('.')[0].strip())\n",
        "    \n",
        "    # a map of more aggregated title\n",
        "    # we map each title\n",
        "    titanic['title'] = titanic.title.map(Title_Dictionary)\n",
        "    return titanic"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FOA5PuwdHmvu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 330
        },
        "outputId": "6aeca439-10b3-4956-8292-174a701053f1"
      },
      "source": [
        "titanic = get_titles()\n",
        "titanic.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>survived</th>\n",
              "      <th>pclass</th>\n",
              "      <th>name</th>\n",
              "      <th>sex</th>\n",
              "      <th>age</th>\n",
              "      <th>sibsp</th>\n",
              "      <th>parch</th>\n",
              "      <th>ticket</th>\n",
              "      <th>fare</th>\n",
              "      <th>cabin</th>\n",
              "      <th>embarked</th>\n",
              "      <th>who</th>\n",
              "      <th>adult_male</th>\n",
              "      <th>deck</th>\n",
              "      <th>alone</th>\n",
              "      <th>FAM_SIZE</th>\n",
              "      <th>title</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Braund, Mr. Owen Harris</td>\n",
              "      <td>0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>A/5 21171</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>0.0</td>\n",
              "      <td>False</td>\n",
              "      <td>2</td>\n",
              "      <td>Mr</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
              "      <td>1</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17599</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C85</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>False</td>\n",
              "      <td>3.0</td>\n",
              "      <td>False</td>\n",
              "      <td>2</td>\n",
              "      <td>Mrs</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Heikkinen, Miss. Laina</td>\n",
              "      <td>1</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>STON/O2. 3101282</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>False</td>\n",
              "      <td>0.0</td>\n",
              "      <td>True</td>\n",
              "      <td>1</td>\n",
              "      <td>Miss</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
              "      <td>1</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>113803</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>C123</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>False</td>\n",
              "      <td>3.0</td>\n",
              "      <td>False</td>\n",
              "      <td>2</td>\n",
              "      <td>Mrs</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Allen, Mr. William Henry</td>\n",
              "      <td>0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>373450</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>0.0</td>\n",
              "      <td>True</td>\n",
              "      <td>1</td>\n",
              "      <td>Mr</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   survived  pclass  ... FAM_SIZE  title\n",
              "0         0       3  ...        2     Mr\n",
              "1         1       1  ...        2    Mrs\n",
              "2         1       3  ...        1   Miss\n",
              "3         1       1  ...        2    Mrs\n",
              "4         0       3  ...        1     Mr\n",
              "\n",
              "[5 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0aeVgTMbH3Bp",
        "colab_type": "text"
      },
      "source": [
        "Now we need to encode these titles. Right now I will use one-hot encoding with this."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SI1271H-HqGY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 484
        },
        "outputId": "5fde9779-d21e-4e82-81f7-53b28f4f5214"
      },
      "source": [
        "titles_dummies = pd.get_dummies(titanic['title'], prefix='title')\n",
        "titanic = pd.concat([titanic, titles_dummies], axis=1)\n",
        "titanic.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>survived</th>\n",
              "      <th>pclass</th>\n",
              "      <th>name</th>\n",
              "      <th>sex</th>\n",
              "      <th>age</th>\n",
              "      <th>sibsp</th>\n",
              "      <th>parch</th>\n",
              "      <th>ticket</th>\n",
              "      <th>fare</th>\n",
              "      <th>cabin</th>\n",
              "      <th>embarked</th>\n",
              "      <th>who</th>\n",
              "      <th>adult_male</th>\n",
              "      <th>deck</th>\n",
              "      <th>alone</th>\n",
              "      <th>FAM_SIZE</th>\n",
              "      <th>title</th>\n",
              "      <th>title_Master</th>\n",
              "      <th>title_Miss</th>\n",
              "      <th>title_Mr</th>\n",
              "      <th>title_Mrs</th>\n",
              "      <th>title_Officer</th>\n",
              "      <th>title_Royalty</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Braund, Mr. Owen Harris</td>\n",
              "      <td>0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>A/5 21171</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>0.0</td>\n",
              "      <td>False</td>\n",
              "      <td>2</td>\n",
              "      <td>Mr</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
              "      <td>1</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17599</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C85</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>False</td>\n",
              "      <td>3.0</td>\n",
              "      <td>False</td>\n",
              "      <td>2</td>\n",
              "      <td>Mrs</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Heikkinen, Miss. Laina</td>\n",
              "      <td>1</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>STON/O2. 3101282</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>False</td>\n",
              "      <td>0.0</td>\n",
              "      <td>True</td>\n",
              "      <td>1</td>\n",
              "      <td>Miss</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
              "      <td>1</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>113803</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>C123</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>False</td>\n",
              "      <td>3.0</td>\n",
              "      <td>False</td>\n",
              "      <td>2</td>\n",
              "      <td>Mrs</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Allen, Mr. William Henry</td>\n",
              "      <td>0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>373450</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>0.0</td>\n",
              "      <td>True</td>\n",
              "      <td>1</td>\n",
              "      <td>Mr</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   survived  pclass  ... title_Officer  title_Royalty\n",
              "0         0       3  ...             0              0\n",
              "1         1       1  ...             0              0\n",
              "2         1       3  ...             0              0\n",
              "3         1       1  ...             0              0\n",
              "4         0       3  ...             0              0\n",
              "\n",
              "[5 rows x 23 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JGX79S9BIUiB",
        "colab_type": "text"
      },
      "source": [
        "And finally the Feature that we observed during the visualization."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8TZUIJ35IDDK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def new_fe(parameters):\n",
        "  p,w=parameters\n",
        "  \n",
        "  if (p==1):\n",
        "    if (w==1):\n",
        "      return 1\n",
        "    elif (w==2):\n",
        "      return 2\n",
        "    elif (w==3):\n",
        "      return 3\n",
        "  elif (p==2):\n",
        "    if (w==1):\n",
        "      return 4\n",
        "    elif (w==2):\n",
        "      return 5\n",
        "    elif (w==3):\n",
        "      return 6\n",
        "  elif (p==3):\n",
        "    if (w==1):\n",
        "      return 7\n",
        "    elif (w==2):\n",
        "      return 8\n",
        "    elif (w==3):\n",
        "      return 9"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3Zm6wGgBJaFh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 484
        },
        "outputId": "b9f579f4-c4d9-484c-8615-0b7c9afcbeee"
      },
      "source": [
        "titanic['pcl_wh']= titanic[['pclass','who']].apply(new_fe, axis=1)\n",
        "titanic.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>survived</th>\n",
              "      <th>pclass</th>\n",
              "      <th>name</th>\n",
              "      <th>sex</th>\n",
              "      <th>age</th>\n",
              "      <th>sibsp</th>\n",
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              "      <th>embarked</th>\n",
              "      <th>who</th>\n",
              "      <th>adult_male</th>\n",
              "      <th>deck</th>\n",
              "      <th>alone</th>\n",
              "      <th>FAM_SIZE</th>\n",
              "      <th>title</th>\n",
              "      <th>title_Master</th>\n",
              "      <th>title_Miss</th>\n",
              "      <th>title_Mr</th>\n",
              "      <th>title_Mrs</th>\n",
              "      <th>title_Officer</th>\n",
              "      <th>title_Royalty</th>\n",
              "      <th>pcl_wh</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Braund, Mr. Owen Harris</td>\n",
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              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>A/5 21171</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
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              "      <td>2</td>\n",
              "      <td>Mr</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
              "      <td>1</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17599</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C85</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>False</td>\n",
              "      <td>3.0</td>\n",
              "      <td>False</td>\n",
              "      <td>2</td>\n",
              "      <td>Mrs</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Heikkinen, Miss. Laina</td>\n",
              "      <td>1</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>STON/O2. 3101282</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>False</td>\n",
              "      <td>0.0</td>\n",
              "      <td>True</td>\n",
              "      <td>1</td>\n",
              "      <td>Miss</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
              "      <td>1</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>113803</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>C123</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>False</td>\n",
              "      <td>3.0</td>\n",
              "      <td>False</td>\n",
              "      <td>2</td>\n",
              "      <td>Mrs</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Allen, Mr. William Henry</td>\n",
              "      <td>0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>373450</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>0.0</td>\n",
              "      <td>True</td>\n",
              "      <td>1</td>\n",
              "      <td>Mr</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   survived  pclass  ... title_Royalty  pcl_wh\n",
              "0         0       3  ...             0       7\n",
              "1         1       1  ...             0       2\n",
              "2         1       3  ...             0       8\n",
              "3         1       1  ...             0       2\n",
              "4         0       3  ...             0       7\n",
              "\n",
              "[5 rows x 24 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jCpP4FZgKfki",
        "colab_type": "text"
      },
      "source": [
        "Now we will drop all the features which I don't want."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YEVJGSKwKUxS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 101
        },
        "outputId": "56900c18-5f53-4eb7-f38f-cc79e1d733da"
      },
      "source": [
        "titanic.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['survived', 'pclass', 'name', 'sex', 'age', 'sibsp', 'parch', 'ticket',\n",
              "       'fare', 'cabin', 'embarked', 'who', 'adult_male', 'deck', 'alone',\n",
              "       'FAM_SIZE', 'title', 'title_Master', 'title_Miss', 'title_Mr',\n",
              "       'title_Mrs', 'title_Officer', 'title_Royalty', 'pcl_wh'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y_tvIyhYKlWO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 215
        },
        "outputId": "4496b366-2e9f-47b9-bf6e-90f4778d764e"
      },
      "source": [
        "drop_list=['name','ticket','fare', 'cabin','title']\n",
        "titanic = titanic.drop(drop_list, axis=1)\n",
        "titanic.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    .dataframe tbody tr th {\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>survived</th>\n",
              "      <th>pclass</th>\n",
              "      <th>sex</th>\n",
              "      <th>age</th>\n",
              "      <th>sibsp</th>\n",
              "      <th>parch</th>\n",
              "      <th>embarked</th>\n",
              "      <th>who</th>\n",
              "      <th>adult_male</th>\n",
              "      <th>deck</th>\n",
              "      <th>alone</th>\n",
              "      <th>FAM_SIZE</th>\n",
              "      <th>title_Master</th>\n",
              "      <th>title_Miss</th>\n",
              "      <th>title_Mr</th>\n",
              "      <th>title_Mrs</th>\n",
              "      <th>title_Officer</th>\n",
              "      <th>title_Royalty</th>\n",
              "      <th>pcl_wh</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
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              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>False</td>\n",
              "      <td>3.0</td>\n",
              "      <td>False</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>False</td>\n",
              "      <td>0.0</td>\n",
              "      <td>True</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>False</td>\n",
              "      <td>3.0</td>\n",
              "      <td>False</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>0.0</td>\n",
              "      <td>True</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   survived  pclass  sex   age  ...  title_Mrs  title_Officer title_Royalty  pcl_wh\n",
              "0         0       3    0  22.0  ...          0              0             0       7\n",
              "1         1       1    1  38.0  ...          1              0             0       2\n",
              "2         1       3    1  26.0  ...          0              0             0       8\n",
              "3         1       1    1  35.0  ...          1              0             0       2\n",
              "4         0       3    0  35.0  ...          0              0             0       7\n",
              "\n",
              "[5 rows x 19 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rv3LPjFvLPgl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "e840f57f-2441-4114-ac61-590cdbbabc61"
      },
      "source": [
        "plt.figure(figsize=(20,20))\n",
        "sns.heatmap(titanic.corr(), annot=True, linewidths=0.5, fmt= '.3f')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f4d72061630>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 94
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x1440 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Mo5llYFwufni",
        "colab_type": "text"
      },
      "source": [
        "# Build the Models"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qOUII3fBu-ZY",
        "colab_type": "text"
      },
      "source": [
        "The first task will be to **split the dataset** into train set and test set."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K7TpuKz-ttDZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train = titanic.drop(\"survived\", axis=1)\n",
        "Y_train = titanic[\"survived\"]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rSzY7JyevBH6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# splitting data in training set(70%) and test set(30%).\n",
        "x_train, x_test, y_train, y_test = train_test_split(X_train, Y_train, test_size=0.3)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M7DJ1GSmwDrB",
        "colab_type": "text"
      },
      "source": [
        "## Logistic Regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NEUJ9JskvK_p",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 151
        },
        "outputId": "b23b3a59-f267-4476-a875-4da48a119359"
      },
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "lr = LogisticRegression() #create the object of the model\n",
        "lr = lr.fit(x_train,y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sXy1m0_Sw6I2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        },
        "outputId": "e38c4469-8d0f-4818-acc1-4c2047e0b62f"
      },
      "source": [
        "from sklearn.metrics import accuracy_score, confusion_matrix,precision_score,recall_score,f1_score\n",
        "\n",
        "act = accuracy_score(y_train,lr.predict(x_train))\n",
        "print('Training Accuracy is: ',(act*100))\n",
        "p = precision_score(y_train,lr.predict(x_train))\n",
        "print('Training Precision is: ',(p*100))\n",
        "r = recall_score(y_train,lr.predict(x_train))\n",
        "print('Training Recall is: ',(r*100))\n",
        "f = f1_score(y_train,lr.predict(x_train))\n",
        "print('Training F1 Score is: ',(f*100))\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training Accuracy is:  84.43017656500803\n",
            "Training Precision is:  80.54298642533936\n",
            "Training Recall is:  76.72413793103449\n",
            "Training F1 Score is:  78.58719646799118\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AAESUcAkvZoh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        },
        "outputId": "628568b7-75f0-4c55-ff6e-33e3a3a032f4"
      },
      "source": [
        "act = accuracy_score(y_test,lr.predict(x_test))\n",
        "print('Test Accuracy is: ',(act*100))\n",
        "p = precision_score(y_test,lr.predict(x_test))\n",
        "print('Test Precision is: ',(p*100))\n",
        "r = recall_score(y_test,lr.predict(x_test))\n",
        "print('Test Recall is: ',(r*100))\n",
        "f = f1_score(y_test,lr.predict(x_test))\n",
        "print('Test F1 Score is: ',(f*100))\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Test Accuracy is:  82.46268656716418\n",
            "Test Precision is:  79.43925233644859\n",
            "Test Recall is:  77.27272727272727\n",
            "Test F1 Score is:  78.34101382488478\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CBtSEfJk3At8",
        "colab_type": "text"
      },
      "source": [
        "## Random Forest Classifier"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VeKalFUn3MPR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "\n",
        "\n",
        "rf = RandomForestClassifier(criterion = \"gini\", \n",
        "                                       min_samples_leaf = 3, \n",
        "                                       min_samples_split = 10,   \n",
        "                                       n_estimators=100, \n",
        "                                       max_features=0.5, \n",
        "                                       oob_score=True, \n",
        "                                       random_state=1, \n",
        "                                       n_jobs=-1)\n",
        "rf = rf.fit(x_train,y_train)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0UwcWQx03Yn3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        },
        "outputId": "25667fed-aa5b-4c14-cc2d-b6a8efbf5be6"
      },
      "source": [
        "act = accuracy_score(y_train,rf.predict(x_train))\n",
        "print('Training Accuracy is: ',(act*100))\n",
        "p = precision_score(y_train,rf.predict(x_train))\n",
        "print('Training Precision is: ',(p*100))\n",
        "r = recall_score(y_train,rf.predict(x_train))\n",
        "print('Training Recall is: ',(r*100))\n",
        "f = f1_score(y_train,rf.predict(x_train))\n",
        "print('Training F1 Score is: ',(f*100))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training Accuracy is:  87.96147672552166\n",
            "Training Precision is:  87.20379146919431\n",
            "Training Recall is:  79.3103448275862\n",
            "Training F1 Score is:  83.06997742663657\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZnmZsPiU3Yac",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        },
        "outputId": "4bccf783-ab54-4178-959d-d59a3e34c2f5"
      },
      "source": [
        "act = accuracy_score(y_test,rf.predict(x_test))\n",
        "print('Test Accuracy is: ',(act*100))\n",
        "p = precision_score(y_test,rf.predict(x_test))\n",
        "print('Test Precision is: ',(p*100))\n",
        "r = recall_score(y_test,rf.predict(x_test))\n",
        "print('Test Recall is: ',(r*100))\n",
        "f = f1_score(y_test,rf.predict(x_test))\n",
        "print('Test F1 Score is: ',(f*100))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Test Accuracy is:  81.34328358208955\n",
            "Test Precision is:  80.61224489795919\n",
            "Test Recall is:  71.81818181818181\n",
            "Test F1 Score is:  75.96153846153845\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N0pnebpNyKZG",
        "colab_type": "text"
      },
      "source": [
        "## Decision Tree Classifier"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c7r8ZExHvolc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "\n",
        "dt = DecisionTreeClassifier()\n",
        "dt=dt.fit(x_train, y_train)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cxObawiNyd-U",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        },
        "outputId": "88ab6842-9c77-4cd7-b2eb-ef7ee4806c77"
      },
      "source": [
        "act = accuracy_score(y_train,dt.predict(x_train))\n",
        "print('Training Accuracy is: ',(act*100))\n",
        "p = precision_score(y_train,dt.predict(x_train))\n",
        "print('Training Precision is: ',(p*100))\n",
        "r = recall_score(y_train,dt.predict(x_train))\n",
        "print('Training Recall is: ',(r*100))\n",
        "f = f1_score(y_train,dt.predict(x_train))\n",
        "print('Training F1 Score is: ',(f*100))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training Accuracy is:  95.6661316211878\n",
            "Training Precision is:  98.12206572769952\n",
            "Training Recall is:  90.08620689655173\n",
            "Training F1 Score is:  93.93258426966293\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WBFZ9HYj2vNY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        },
        "outputId": "bcd3181e-9b2e-4faa-b7d3-449d15e345bd"
      },
      "source": [
        "act = accuracy_score(y_test,dt.predict(x_test))\n",
        "print('Test Accuracy is: ',(act*100))\n",
        "p = precision_score(y_test,dt.predict(x_test))\n",
        "print('Test Precision is: ',(p*100))\n",
        "r = recall_score(y_test,dt.predict(x_test))\n",
        "print('Test Recall is: ',(r*100))\n",
        "f = f1_score(y_test,dt.predict(x_test))\n",
        "print('Test F1 Score is: ',(f*100))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Test Accuracy is:  75.0\n",
            "Test Precision is:  70.09345794392523\n",
            "Test Recall is:  68.18181818181817\n",
            "Test F1 Score is:  69.12442396313364\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K9DrjPA8314r",
        "colab_type": "text"
      },
      "source": [
        "Since Random Forest Classifier performs the best, so that will be chosen as the final model."
      ]
    }
  ]
}