Top_6_Percent_titanic_1.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3",
      "language": "python"
    },
    "colab": {
      "name": "Top 6 Percent_titanic-1.ipynb",
      "provenance": [],
      "include_colab_link": true
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/GeorgeEngland/Kaggle-Notebooks/blob/main/Top_6_Percent_titanic_1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
        "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
        "trusted": true,
        "id": "O9LZrb5wzWrP",
        "outputId": "eec0d46c-3171-45d9-9370-98020c586462"
      },
      "source": [
        "# This Python 3 environment comes with many helpful analytics libraries installed\n",
        "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
        "# For example, here's several helpful packages to load\n",
        "\n",
        "import numpy as np # linear algebra\n",
        "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
        "\n",
        "# Input data files are available in the read-only \"../input/\" directory\n",
        "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
        "\n",
        "import os\n",
        "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
        "    for filename in filenames:\n",
        "        print(os.path.join(dirname, filename))\n",
        "\n",
        "# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
        "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/kaggle/input/titanic/train.csv\n",
            "/kaggle/input/titanic/test.csv\n",
            "/kaggle/input/titanic/gender_submission.csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a",
        "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
        "trusted": true,
        "id": "khLkIfOdzWrb"
      },
      "source": [
        "train_data = pd.read_csv(\"/kaggle/input/titanic/train.csv\")\n",
        "test_data = pd.read_csv(\"/kaggle/input/titanic/test.csv\")\n",
        "\n",
        "train = train_data.copy().iloc[0:800]\n",
        "val = train_data.copy().iloc[800:]\n",
        "test = test_data.copy()\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "A9SdxCk4zWrg"
      },
      "source": [
        "train.drop(['PassengerId'],axis=1,inplace=True)\n",
        "test.drop(['PassengerId'],axis=1,inplace=True)\n",
        "val.drop(['PassengerId'],axis=1,inplace=True)\n",
        "pred = train_data.iloc[0:800]['Survived']\n",
        "pred_val = train_data.iloc[800:]['Survived']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "sxssJWkIzWrg"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "ngkOWpJqzWrh",
        "outputId": "58687ccf-2ffa-4e30-f912-5599d73a828f"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "plt.figure(figsize=(8, 5))\n",
        "sns.set_style('whitegrid')\n",
        "sns.countplot(x = 'Sex',hue='Survived',data=train_data,palette='rainbow')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f834f4c8ed0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 256
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "PyAE4YrAzWrh"
      },
      "source": [
        "sex1 = pd.get_dummies(train['Sex'])\n",
        "sexVal = pd.get_dummies(val['Sex'])\n",
        "sex2 = pd.get_dummies(test['Sex'])\n",
        "\n",
        "train.drop(['Sex'],axis=1,inplace=True)\n",
        "test.drop(['Sex'],axis=1,inplace=True)\n",
        "val.drop(['Sex'],axis=1, inplace=True)\n",
        "\n",
        "train = pd.concat([train,sex1],axis=1)\n",
        "test = pd.concat([test,sex2],axis=1)\n",
        "val = pd.concat([val,sexVal],axis=1)\n",
        "\n",
        "val.drop(['female'],axis=1,inplace=True) \n",
        "train.drop(['female'],axis=1,inplace=True) \n",
        "test.drop(['female'],axis=1,inplace=True) "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "BeclsD_bzWri",
        "outputId": "c4979fa5-5f8c-4ca1-df3f-b2618e155799"
      },
      "source": [
        "test.head(5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Pclass                                          Name   Age  SibSp  Parch  \\\n",
              "0       3                              Kelly, Mr. James  34.5      0      0   \n",
              "1       3              Wilkes, Mrs. James (Ellen Needs)  47.0      1      0   \n",
              "2       2                     Myles, Mr. Thomas Francis  62.0      0      0   \n",
              "3       3                              Wirz, Mr. Albert  27.0      0      0   \n",
              "4       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)  22.0      1      1   \n",
              "\n",
              "    Ticket     Fare Cabin Embarked  male  \n",
              "0   330911   7.8292   NaN        Q     1  \n",
              "1   363272   7.0000   NaN        S     0  \n",
              "2   240276   9.6875   NaN        Q     1  \n",
              "3   315154   8.6625   NaN        S     1  \n",
              "4  3101298  12.2875   NaN        S     0  "
            ],
            "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>Pclass</th>\n",
              "      <th>Name</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>male</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3</td>\n",
              "      <td>Kelly, Mr. James</td>\n",
              "      <td>34.5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>330911</td>\n",
              "      <td>7.8292</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Q</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3</td>\n",
              "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
              "      <td>47.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>363272</td>\n",
              "      <td>7.0000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>Myles, Mr. Thomas Francis</td>\n",
              "      <td>62.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>240276</td>\n",
              "      <td>9.6875</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Q</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>Wirz, Mr. Albert</td>\n",
              "      <td>27.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>315154</td>\n",
              "      <td>8.6625</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3</td>\n",
              "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3101298</td>\n",
              "      <td>12.2875</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 258
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "bi73fwZqzWri",
        "outputId": "3228af0a-eea7-4e46-d8dd-ea42afe45c0c"
      },
      "source": [
        "plt.figure(figsize=(10, 9))\n",
        "sns.boxplot(x='Pclass',y='Age',data=train_data,palette='rainbow')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f834f4a68d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 259
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x648 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "8Fjac_mBzWri",
        "outputId": "2ba29a39-d613-43df-8f62-92fabb98fa00"
      },
      "source": [
        "plt.figure(figsize=(16, 5))\n",
        "for x in [1,2,3]:    ## for 3 classes\n",
        "    sns.kdeplot(data=train_data.Age[train_data.Pclass == x],cut = 0, clip=(0,2000))\n",
        "    \n",
        "plt.title(\"Age vs Pclass\")\n",
        "plt.legend((\"1st\",\"2nd\",\"3rd\"))\n",
        "plt.xlabel(\"Age\")\n",
        "plt.ylabel(\"Density\")\n",
        "train[\"Age\"].fillna(train['Age'].describe().loc[['50%']][0], inplace = True) \n",
        "test[\"Age\"].fillna(test['Age'].describe().loc[['50%']][0], inplace = True) \n",
        "val[\"Age\"].fillna(val['Age'].describe().loc[['50%']][0], inplace = True) "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "YBDjW0VSzWri"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "UBKXOQQuzWrj"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "YATMPF5szWrj"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "BBr4l08UzWrj"
      },
      "source": [
        "test[\"Fare\"].fillna(test['Fare'].describe().loc[['50%']][0], inplace = True) \n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "-8aNM3cUzWrj"
      },
      "source": [
        "train[\"Embarked\"].fillna(\"S\", inplace = True) \n",
        "val[\"Embarked\"].fillna(\"S\", inplace = True) \n",
        "test[\"Embarked\"].fillna(\"S\", inplace = True) "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "_PKmtD76zWrj"
      },
      "source": [
        "embark1 = pd.get_dummies(train['Embarked'])\n",
        "embarkval = pd.get_dummies(val['Embarked'])\n",
        "embark2 = pd.get_dummies(test['Embarked'])\n",
        "\n",
        "train.drop(['Embarked'],axis=1,inplace=True)\n",
        "val.drop(['Embarked'],axis=1,inplace=True)\n",
        "test.drop(['Embarked'],axis=1,inplace=True)\n",
        "\n",
        "train = pd.concat([train,embark1],axis=1)\n",
        "val = pd.concat([val,embarkval],axis=1)\n",
        "test = pd.concat([test,embark2],axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "v5gim7QSzWrk"
      },
      "source": [
        "def fam(x):\n",
        "    if  (x['SibSp'] + x['Parch'])  > 0:\n",
        "        return 1\n",
        "    else:\n",
        "        return 0\n",
        "\n",
        "train['Family'] = train.apply(fam, axis = 1)\n",
        "test['Family'] = test.apply(fam, axis = 1)\n",
        "val['Family'] = val.apply(fam, axis = 1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "nhCEVaLTzWrk"
      },
      "source": [
        "train = train.drop(['SibSp','Parch'],axis=1)\n",
        "val = val.drop(['SibSp','Parch'],axis=1)\n",
        "test = test.drop(['SibSp','Parch'],axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "U8o0VcuQzWrk",
        "outputId": "0dc2aed2-3662-4dab-b8e9-ce53c55c2591"
      },
      "source": [
        "#train[\"Cabin\"] = pd.Series([i[0] if not pd.isnull(i) else 'X' for i in train['Cabin'] ])\n",
        "#test[\"Cabin\"] = pd.Series([i[0] if not pd.isnull(i) else 'X' for i in test['Cabin'] ])\n",
        "#val[\"Cabin\"] = pd.Series([i[0] if not pd.isnull(i) else 'X' for i in val['Cabin'] ])\n",
        "val['Cabin'] = pd.Series([i[0] if not pd.isnull(i) else 'X' for i in val['Cabin'] ])\n",
        "\n",
        "val\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Survived  Pclass                                         Name    Age  \\\n",
              "800         0       2                         Ponesell, Mr. Martin  34.00   \n",
              "801         1       2  Collyer, Mrs. Harvey (Charlotte Annie Tate)  31.00   \n",
              "802         1       1          Carter, Master. William Thornton II  11.00   \n",
              "803         1       3              Thomas, Master. Assad Alexander   0.42   \n",
              "804         1       3                      Hedman, Mr. Oskar Arvid  27.00   \n",
              "..        ...     ...                                          ...    ...   \n",
              "886         0       2                        Montvila, Rev. Juozas  27.00   \n",
              "887         1       1                 Graham, Miss. Margaret Edith  19.00   \n",
              "888         0       3     Johnston, Miss. Catherine Helen \"Carrie\"  27.00   \n",
              "889         1       1                        Behr, Mr. Karl Howell  26.00   \n",
              "890         0       3                          Dooley, Mr. Patrick  32.00   \n",
              "\n",
              "         Ticket      Fare Cabin  male  C  Q  S  Family  \n",
              "800      250647   13.0000   NaN     1  0  0  1       0  \n",
              "801  C.A. 31921   26.2500   NaN     0  0  0  1       1  \n",
              "802      113760  120.0000   NaN     1  0  0  1       1  \n",
              "803        2625    8.5167   NaN     1  1  0  0       1  \n",
              "804      347089    6.9750   NaN     1  0  0  1       0  \n",
              "..          ...       ...   ...   ... .. .. ..     ...  \n",
              "886      211536   13.0000   NaN     1  0  0  1       0  \n",
              "887      112053   30.0000   NaN     0  0  0  1       0  \n",
              "888  W./C. 6607   23.4500   NaN     0  0  0  1       1  \n",
              "889      111369   30.0000   NaN     1  1  0  0       0  \n",
              "890      370376    7.7500   NaN     1  0  1  0       0  \n",
              "\n",
              "[91 rows x 12 columns]"
            ],
            "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>Age</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>male</th>\n",
              "      <th>C</th>\n",
              "      <th>Q</th>\n",
              "      <th>S</th>\n",
              "      <th>Family</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>800</th>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>Ponesell, Mr. Martin</td>\n",
              "      <td>34.00</td>\n",
              "      <td>250647</td>\n",
              "      <td>13.0000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>801</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>Collyer, Mrs. Harvey (Charlotte Annie Tate)</td>\n",
              "      <td>31.00</td>\n",
              "      <td>C.A. 31921</td>\n",
              "      <td>26.2500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>802</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Carter, Master. William Thornton II</td>\n",
              "      <td>11.00</td>\n",
              "      <td>113760</td>\n",
              "      <td>120.0000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>803</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Thomas, Master. Assad Alexander</td>\n",
              "      <td>0.42</td>\n",
              "      <td>2625</td>\n",
              "      <td>8.5167</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>804</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Hedman, Mr. Oskar Arvid</td>\n",
              "      <td>27.00</td>\n",
              "      <td>347089</td>\n",
              "      <td>6.9750</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>886</th>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>Montvila, Rev. Juozas</td>\n",
              "      <td>27.00</td>\n",
              "      <td>211536</td>\n",
              "      <td>13.0000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>887</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Graham, Miss. Margaret Edith</td>\n",
              "      <td>19.00</td>\n",
              "      <td>112053</td>\n",
              "      <td>30.0000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>888</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
              "      <td>27.00</td>\n",
              "      <td>W./C. 6607</td>\n",
              "      <td>23.4500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>889</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Behr, Mr. Karl Howell</td>\n",
              "      <td>26.00</td>\n",
              "      <td>111369</td>\n",
              "      <td>30.0000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>890</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Dooley, Mr. Patrick</td>\n",
              "      <td>32.00</td>\n",
              "      <td>370376</td>\n",
              "      <td>7.7500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>91 rows × 12 columns</p>\n",
              "</div>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 266
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "Wmfwv_6szWrk",
        "outputId": "5c0e3f51-4289-46de-9268-4d67ce8e95c0"
      },
      "source": [
        "plt.figure(figsize=(8, 5))\n",
        "g = sns.catplot(y=\"Survived\",x=\"Cabin\",data=val,kind=\"bar\",order=['A','B','C','D','E','F','G','X'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x360 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "-_DN4mgszWrl",
        "outputId": "d5e51fe4-42fb-4132-f731-ec8e333334bf"
      },
      "source": [
        "train[\"Cabin\"] = train[\"Cabin\"].map({\"X\":0, \"A\":1, \"B\" : 2 , \"C\":3, \"D\":4, \"E\":5, \"F\":6, \"G\":7,\"T\":0})\n",
        "train[\"Cabin\"] = train[\"Cabin\"].astype(int)\n",
        "test[\"Cabin\"] = test[\"Cabin\"].map({\"X\":0, \"A\":1, \"B\" : 2 , \"C\":3, \"D\":4, \"E\":5, \"F\":6, \"G\":7,\"T\":0})\n",
        "test[\"Cabin\"] = test[\"Cabin\"].astype(int)\n",
        "val[\"Cabin\"] = val[\"Cabin\"].map({\"X\":0, \"A\":1, \"B\" : 2 , \"C\":3, \"D\":4, \"E\":5, \"F\":6, \"G\":7,\"T\":0})\n",
        "val[\"Cabin\"] = val[\"Cabin\"].astype(int)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "Cannot convert non-finite values (NA or inf) to integer",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-268-86519a8a8d5e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mtrain\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Cabin\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Cabin\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m\"X\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"A\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"B\"\u001b[0m \u001b[0;34m:\u001b[0m \u001b[0;36m2\u001b[0m \u001b[0;34m,\u001b[0m \u001b[0;34m\"C\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"D\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"E\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"F\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"G\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m7\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\"T\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mtrain\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Cabin\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Cabin\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mtest\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Cabin\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtest\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Cabin\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m\"X\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"A\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"B\"\u001b[0m \u001b[0;34m:\u001b[0m \u001b[0;36m2\u001b[0m \u001b[0;34m,\u001b[0m \u001b[0;34m\"C\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"D\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"E\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"F\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"G\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m7\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\"T\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mtest\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Cabin\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtest\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Cabin\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mval\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Cabin\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mval\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Cabin\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m\"X\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"A\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"B\"\u001b[0m \u001b[0;34m:\u001b[0m \u001b[0;36m2\u001b[0m \u001b[0;34m,\u001b[0m \u001b[0;34m\"C\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"D\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"E\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"F\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"G\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m7\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\"T\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mastype\u001b[0;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[1;32m   5546\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5547\u001b[0m             \u001b[0;31m# else, only a single dtype is given\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5548\u001b[0;31m             \u001b[0mnew_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mgr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5549\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_constructor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__finalize__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"astype\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5550\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/pandas/core/internals/managers.py\u001b[0m in \u001b[0;36mastype\u001b[0;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[1;32m    602\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"raise\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    603\u001b[0m     ) -> \"BlockManager\":\n\u001b[0;32m--> 604\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"astype\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    605\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    606\u001b[0m     def convert(\n",
            "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/pandas/core/internals/managers.py\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self, f, align_keys, **kwargs)\u001b[0m\n\u001b[1;32m    407\u001b[0m                 \u001b[0mapplied\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    408\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 409\u001b[0;31m                 \u001b[0mapplied\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    410\u001b[0m             \u001b[0mresult_blocks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_extend_blocks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mapplied\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult_blocks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    411\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/pandas/core/internals/blocks.py\u001b[0m in \u001b[0;36mastype\u001b[0;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[1;32m    593\u001b[0m             \u001b[0mvals1d\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    594\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 595\u001b[0;31m                 \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mastype_nansafe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvals1d\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    596\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mValueError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    597\u001b[0m                 \u001b[0;31m# e.g. astype_nansafe can fail on object-dtype of strings\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/pandas/core/dtypes/cast.py\u001b[0m in \u001b[0;36mastype_nansafe\u001b[0;34m(arr, dtype, copy, skipna)\u001b[0m\n\u001b[1;32m    966\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    967\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misfinite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 968\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Cannot convert non-finite values (NA or inf) to integer\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    969\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    970\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mis_object_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Cannot convert non-finite values (NA or inf) to integer"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "hpB0OFUIzWrl"
      },
      "source": [
        "train_title = [i.split(\",\")[1].split(\".\")[0].strip() for i in train[\"Name\"]]\n",
        "train[\"Title\"] = pd.Series(train_title)\n",
        "test_title = [i.split(\",\")[1].split(\".\")[0].strip() for i in test[\"Name\"]]\n",
        "test[\"Title\"] = pd.Series(test_title)\n",
        "val_title = [i.split(\",\")[1].split(\".\")[0].strip() for i in val[\"Name\"]]\n",
        "val[\"Title\"] = pd.Series(val_title)\n",
        "\n",
        "train = train.drop(['Name'], axis = 1)\n",
        "test = test.drop(['Name'], axis = 1)\n",
        "val = val.drop(['Name'],axis=1)\n",
        "\n",
        "train = train.drop(['Cabin'], axis = 1)\n",
        "test = test.drop(['Cabin'], axis = 1)\n",
        "val = val.drop(['Cabin'],axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "NAo965NkzWrl",
        "outputId": "3f799d38-59f2-4ca7-b918-cb0b280dae6a"
      },
      "source": [
        "plt.figure(figsize=(14, 6))\n",
        "g = sns.countplot(x=\"Title\",hue='Survived',data=train)\n",
        "g = plt.setp(g.get_xticklabels(), rotation=45) \n",
        "val"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Survived  Pclass    Age      Ticket      Fare  male  C  Q  S  Family  \\\n",
              "800         0       2  34.00      250647   13.0000     1  0  0  1       0   \n",
              "801         1       2  31.00  C.A. 31921   26.2500     0  0  0  1       1   \n",
              "802         1       1  11.00      113760  120.0000     1  0  0  1       1   \n",
              "803         1       3   0.42        2625    8.5167     1  1  0  0       1   \n",
              "804         1       3  27.00      347089    6.9750     1  0  0  1       0   \n",
              "..        ...     ...    ...         ...       ...   ... .. .. ..     ...   \n",
              "886         0       2  27.00      211536   13.0000     1  0  0  1       0   \n",
              "887         1       1  19.00      112053   30.0000     0  0  0  1       0   \n",
              "888         0       3  27.00  W./C. 6607   23.4500     0  0  0  1       1   \n",
              "889         1       1  26.00      111369   30.0000     1  1  0  0       0   \n",
              "890         0       3  32.00      370376    7.7500     1  0  1  0       0   \n",
              "\n",
              "    Title  \n",
              "800   NaN  \n",
              "801   NaN  \n",
              "802   NaN  \n",
              "803   NaN  \n",
              "804   NaN  \n",
              "..    ...  \n",
              "886   NaN  \n",
              "887   NaN  \n",
              "888   NaN  \n",
              "889   NaN  \n",
              "890   NaN  \n",
              "\n",
              "[91 rows x 11 columns]"
            ],
            "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>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>male</th>\n",
              "      <th>C</th>\n",
              "      <th>Q</th>\n",
              "      <th>S</th>\n",
              "      <th>Family</th>\n",
              "      <th>Title</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>800</th>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>34.00</td>\n",
              "      <td>250647</td>\n",
              "      <td>13.0000</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>801</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>31.00</td>\n",
              "      <td>C.A. 31921</td>\n",
              "      <td>26.2500</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>802</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>11.00</td>\n",
              "      <td>113760</td>\n",
              "      <td>120.0000</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>803</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0.42</td>\n",
              "      <td>2625</td>\n",
              "      <td>8.5167</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>804</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>27.00</td>\n",
              "      <td>347089</td>\n",
              "      <td>6.9750</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>886</th>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>27.00</td>\n",
              "      <td>211536</td>\n",
              "      <td>13.0000</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>887</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>19.00</td>\n",
              "      <td>112053</td>\n",
              "      <td>30.0000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>888</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>27.00</td>\n",
              "      <td>W./C. 6607</td>\n",
              "      <td>23.4500</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>889</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>26.00</td>\n",
              "      <td>111369</td>\n",
              "      <td>30.0000</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>890</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>32.00</td>\n",
              "      <td>370376</td>\n",
              "      <td>7.7500</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>91 rows × 11 columns</p>\n",
              "</div>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 270
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1008x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "LcMDERSbzWrl",
        "outputId": "fd6005b0-874e-41d3-beed-3104c7556c9e"
      },
      "source": [
        "train[\"Title\"] = train[\"Title\"].replace(['Lady', 'the Countess','Countess','Capt', 'Col','Don', 'Dr', 'Major', 'Rev', 'Sir', 'Jonkheer', 'Dona'], 'Rare')\n",
        "train[\"Title\"] = train[\"Title\"].map({\"Master\":0, \"Miss\":1, \"Ms\" : 1 , \"Mme\":1, \"Mlle\":1, \"Mrs\":1, \"Mr\":2, \"Rare\":3})\n",
        "train[\"Title\"] = train[\"Title\"].astype(int)\n",
        "test[\"Title\"] = test[\"Title\"].replace(['Lady', 'the Countess','Countess','Capt', 'Col','Don', 'Dr', 'Major', 'Rev', 'Sir', 'Jonkheer', 'Dona'], 'Rare')\n",
        "test[\"Title\"] = test[\"Title\"].map({\"Master\":0, \"Miss\":1, \"Ms\" : 1 , \"Mme\":1, \"Mlle\":1, \"Mrs\":1, \"Mr\":2, \"Rare\":3})\n",
        "test[\"Title\"] = test[\"Title\"].astype(int)\n",
        "val[\"Title\"] = val[\"Title\"].replace(['Lady', 'the Countess','Countess','Capt', 'Col','Don', 'Dr', 'Major', 'Rev', 'Sir', 'Jonkheer', 'Dona'], 'Rare')\n",
        "val[\"Title\"] = val[\"Title\"].map({\"Master\":0, \"Miss\":1, \"Ms\" : 1 , \"Mme\":1, \"Mlle\":1, \"Mrs\":1, \"Mr\":2, \"Rare\":3})\n",
        "val[\"Title\"] = val[\"Title\"].astype(int)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "Cannot convert non-finite values (NA or inf) to integer",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-271-cdef2f320600>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0mval\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Title\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mval\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Title\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreplace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Lady'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'the Countess'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'Countess'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'Capt'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Col'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'Don'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Dr'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Major'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Rev'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Sir'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Jonkheer'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Dona'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Rare'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0mval\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Title\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mval\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Title\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m\"Master\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"Miss\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"Ms\"\u001b[0m \u001b[0;34m:\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;34m,\u001b[0m \u001b[0;34m\"Mme\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"Mlle\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"Mrs\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"Mr\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"Rare\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0mval\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Title\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mval\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Title\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mastype\u001b[0;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[1;32m   5546\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5547\u001b[0m             \u001b[0;31m# else, only a single dtype is given\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5548\u001b[0;31m             \u001b[0mnew_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mgr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5549\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_constructor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__finalize__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"astype\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5550\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/pandas/core/internals/managers.py\u001b[0m in \u001b[0;36mastype\u001b[0;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[1;32m    602\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"raise\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    603\u001b[0m     ) -> \"BlockManager\":\n\u001b[0;32m--> 604\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"astype\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    605\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    606\u001b[0m     def convert(\n",
            "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/pandas/core/internals/managers.py\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self, f, align_keys, **kwargs)\u001b[0m\n\u001b[1;32m    407\u001b[0m                 \u001b[0mapplied\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    408\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 409\u001b[0;31m                 \u001b[0mapplied\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    410\u001b[0m             \u001b[0mresult_blocks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_extend_blocks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mapplied\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult_blocks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    411\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/pandas/core/internals/blocks.py\u001b[0m in \u001b[0;36mastype\u001b[0;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[1;32m    593\u001b[0m             \u001b[0mvals1d\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    594\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 595\u001b[0;31m                 \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mastype_nansafe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvals1d\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    596\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mValueError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    597\u001b[0m                 \u001b[0;31m# e.g. astype_nansafe can fail on object-dtype of strings\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/pandas/core/dtypes/cast.py\u001b[0m in \u001b[0;36mastype_nansafe\u001b[0;34m(arr, dtype, copy, skipna)\u001b[0m\n\u001b[1;32m    966\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    967\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misfinite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 968\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Cannot convert non-finite values (NA or inf) to integer\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    969\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    970\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mis_object_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Cannot convert non-finite values (NA or inf) to integer"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "MwFeb4pezWrm"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "YVCF-KctzWrm"
      },
      "source": [
        "\n",
        "train.drop(['Ticket'],axis=1,inplace=True)\n",
        "test.drop(['Ticket'],axis=1,inplace=True)\n",
        "val.drop(['Ticket'],axis=1,inplace=True)\n",
        "\n",
        "train.drop(['Title'],axis=1,inplace=True)\n",
        "test.drop(['Title'],axis=1,inplace=True)\n",
        "val.drop(['Title'],axis=1,inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "4DWQ7CvKzWrm",
        "outputId": "d42555ce-dbfd-4ac5-acd8-010619efa896"
      },
      "source": [
        "train.drop(['Survived'],axis=1,inplace=True)\n",
        "val.drop(['Survived'],axis=1,inplace=True)\n",
        "\n",
        "train.head(5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Pclass   Age     Fare  male  C  Q  S  Family\n",
              "0       3  22.0   7.2500     1  0  0  1       1\n",
              "1       1  38.0  71.2833     0  1  0  0       1\n",
              "2       3  26.0   7.9250     0  0  0  1       0\n",
              "3       1  35.0  53.1000     0  0  0  1       1\n",
              "4       3  35.0   8.0500     1  0  0  1       0"
            ],
            "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>Pclass</th>\n",
              "      <th>Age</th>\n",
              "      <th>Fare</th>\n",
              "      <th>male</th>\n",
              "      <th>C</th>\n",
              "      <th>Q</th>\n",
              "      <th>S</th>\n",
              "      <th>Family</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3</td>\n",
              "      <td>22.0</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>38.0</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>26.0</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>35.0</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3</td>\n",
              "      <td>35.0</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 273
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "g-NqJqxqzWrn",
        "outputId": "993dae22-fdd4-4208-8889-38f1970cc1da"
      },
      "source": [
        "print(train.isnull().sum())\n",
        "print(val.isnull().sum())\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Pclass    0\n",
            "Age       0\n",
            "Fare      0\n",
            "male      0\n",
            "C         0\n",
            "Q         0\n",
            "S         0\n",
            "Family    0\n",
            "dtype: int64\n",
            "Pclass    0\n",
            "Age       0\n",
            "Fare      0\n",
            "male      0\n",
            "C         0\n",
            "Q         0\n",
            "S         0\n",
            "Family    0\n",
            "dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "n6o6gYwxzWrn"
      },
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "sc = StandardScaler()\n",
        "train2 = sc.fit_transform(train)\n",
        "val = sc.fit_transform(val)\n",
        "test2 = sc.transform(test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "kUWxyxzvzWrn"
      },
      "source": [
        "#val = train2[800:]\n",
        "#val_pred = pred[800:]\n",
        "\n",
        "#train2 = train2[:800]\n",
        "#pred = train2[:800]\n",
        "\n",
        "###train = train[:800]\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "tD4lrEo2zWrn",
        "outputId": "1be9e70c-7611-42d8-8451-34eef63ff761"
      },
      "source": [
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn import svm\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier\n",
        "import xgboost as xgb\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "from sklearn import metrics\n",
        "from sklearn.model_selection import KFold, cross_val_score\n",
        "\n",
        "KFold_Score = pd.DataFrame()\n",
        "classifiers = ['Linear SVM', 'Radial SVM', 'LogisticRegression', \n",
        "               'RandomForestClassifier', 'AdaBoostClassifier', \n",
        "               'XGBoostClassifier', 'KNeighborsClassifier','GradientBoostingClassifier']\n",
        "models = [svm.SVC(kernel='linear'),\n",
        "          svm.SVC(kernel='rbf'),\n",
        "          LogisticRegression(max_iter = 1000),\n",
        "          RandomForestClassifier(n_estimators=200, random_state=0),\n",
        "          AdaBoostClassifier(random_state = 0),\n",
        "          xgb.XGBClassifier(n_estimators=100),\n",
        "          KNeighborsClassifier(),\n",
        "          GradientBoostingClassifier(random_state=0)\n",
        "         ]\n",
        "j = 0\n",
        "for i in models:\n",
        "    model = i\n",
        "    cv = KFold(n_splits=5, random_state=0, shuffle=True)\n",
        "    print(train.shape,pred.shape)\n",
        "    KFold_Score[classifiers[j]] = (cross_val_score(model, train, np.ravel(pred), scoring = 'accuracy', cv=cv))\n",
        "    j = j+1\n",
        "    "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(800, 8) (800,)\n",
            "(800, 8) (800,)\n",
            "(800, 8) (800,)\n",
            "(800, 8) (800,)\n",
            "(800, 8) (800,)\n",
            "(800, 8) (800,)\n",
            "(800, 8) (800,)\n",
            "(800, 8) (800,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "qJo2vpRCzWro",
        "outputId": "7623754d-0f95-455f-b2e5-1245657232a5"
      },
      "source": [
        "mean = pd.DataFrame(KFold_Score.mean(), index= classifiers)\n",
        "KFold_Score = pd.concat([KFold_Score,mean.T])\n",
        "KFold_Score.index=['Fold 1','Fold 2','Fold 3','Fold 4','Fold 5','Mean']\n",
        "KFold_Score.T.sort_values(by=['Mean'], ascending = True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                             Fold 1   Fold 2   Fold 3   Fold 4   Fold 5  \\\n",
              "KNeighborsClassifier        0.65625  0.60000  0.69375  0.68125  0.67500   \n",
              "Radial SVM                  0.71250  0.62500  0.68125  0.71875  0.61875   \n",
              "LogisticRegression          0.76250  0.74375  0.83125  0.80000  0.77500   \n",
              "Linear SVM                  0.76875  0.76875  0.82500  0.80000  0.78125   \n",
              "RandomForestClassifier      0.85000  0.73125  0.80625  0.80625  0.81250   \n",
              "AdaBoostClassifier          0.82500  0.75625  0.84375  0.78750  0.80000   \n",
              "XGBoostClassifier           0.86250  0.75000  0.85000  0.79375  0.79375   \n",
              "GradientBoostingClassifier  0.86250  0.78125  0.83750  0.85000  0.80000   \n",
              "\n",
              "                               Mean  \n",
              "KNeighborsClassifier        0.66125  \n",
              "Radial SVM                  0.67125  \n",
              "LogisticRegression          0.78250  \n",
              "Linear SVM                  0.78875  \n",
              "RandomForestClassifier      0.80125  \n",
              "AdaBoostClassifier          0.80250  \n",
              "XGBoostClassifier           0.81000  \n",
              "GradientBoostingClassifier  0.82625  "
            ],
            "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>Fold 1</th>\n",
              "      <th>Fold 2</th>\n",
              "      <th>Fold 3</th>\n",
              "      <th>Fold 4</th>\n",
              "      <th>Fold 5</th>\n",
              "      <th>Mean</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>KNeighborsClassifier</th>\n",
              "      <td>0.65625</td>\n",
              "      <td>0.60000</td>\n",
              "      <td>0.69375</td>\n",
              "      <td>0.68125</td>\n",
              "      <td>0.67500</td>\n",
              "      <td>0.66125</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Radial SVM</th>\n",
              "      <td>0.71250</td>\n",
              "      <td>0.62500</td>\n",
              "      <td>0.68125</td>\n",
              "      <td>0.71875</td>\n",
              "      <td>0.61875</td>\n",
              "      <td>0.67125</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LogisticRegression</th>\n",
              "      <td>0.76250</td>\n",
              "      <td>0.74375</td>\n",
              "      <td>0.83125</td>\n",
              "      <td>0.80000</td>\n",
              "      <td>0.77500</td>\n",
              "      <td>0.78250</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Linear SVM</th>\n",
              "      <td>0.76875</td>\n",
              "      <td>0.76875</td>\n",
              "      <td>0.82500</td>\n",
              "      <td>0.80000</td>\n",
              "      <td>0.78125</td>\n",
              "      <td>0.78875</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RandomForestClassifier</th>\n",
              "      <td>0.85000</td>\n",
              "      <td>0.73125</td>\n",
              "      <td>0.80625</td>\n",
              "      <td>0.80625</td>\n",
              "      <td>0.81250</td>\n",
              "      <td>0.80125</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AdaBoostClassifier</th>\n",
              "      <td>0.82500</td>\n",
              "      <td>0.75625</td>\n",
              "      <td>0.84375</td>\n",
              "      <td>0.78750</td>\n",
              "      <td>0.80000</td>\n",
              "      <td>0.80250</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>XGBoostClassifier</th>\n",
              "      <td>0.86250</td>\n",
              "      <td>0.75000</td>\n",
              "      <td>0.85000</td>\n",
              "      <td>0.79375</td>\n",
              "      <td>0.79375</td>\n",
              "      <td>0.81000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GradientBoostingClassifier</th>\n",
              "      <td>0.86250</td>\n",
              "      <td>0.78125</td>\n",
              "      <td>0.83750</td>\n",
              "      <td>0.85000</td>\n",
              "      <td>0.80000</td>\n",
              "      <td>0.82625</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 278
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "qZHEsM_0zWro"
      },
      "source": [
        "rfc = RandomForestClassifier(random_state=0)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "REbABhtqzWro"
      },
      "source": [
        "param_grid = { \n",
        "    'n_estimators': [ 200,300],\n",
        "    'max_features': ['auto', 'sqrt'],\n",
        "    'max_depth' : [6,7,8],\n",
        "    'criterion' :['gini', 'entropy']\n",
        "}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "sbHCEfdVzWro",
        "outputId": "c8032bd8-a548-4902-e6e3-0b8743e4ee5a"
      },
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "CV_rfc = GridSearchCV(estimator=rfc, param_grid=param_grid, cv= 5)\n",
        "CV_rfc.fit(train2,pred )\n",
        "CV_rfc.best_params_"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'criterion': 'gini',\n",
              " 'max_depth': 8,\n",
              " 'max_features': 'auto',\n",
              " 'n_estimators': 300}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 281
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "2nxNL2GozWro",
        "outputId": "adbd658f-c4da-49dc-b6d9-358545d16070"
      },
      "source": [
        "rfc1=RandomForestClassifier(random_state=0, n_estimators= 300, criterion = 'gini',max_features = 'auto',max_depth = 8)\n",
        "rfc1.fit(train2, pred)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(max_depth=7, n_estimators=300, random_state=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 298
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "G3y-8POazWrs",
        "outputId": "13936881-cf67-4d2a-c6bb-c791688f7373"
      },
      "source": [
        "pred2= rfc1.predict(test2)\n",
        "from sklearn.metrics import accuracy_score\n",
        "val_pred = rfc1.predict(val)\n",
        "accuracy_score(val_pred,train_data.iloc[800:]['Survived'])\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8131868131868132"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 299
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "214FJEL5zWrs"
      },
      "source": [
        "pred_test = pred2\n",
        "output = pd.DataFrame({'PassengerId': test_data.PassengerId, 'Survived': pred_test})\n",
        "output.to_csv('./submission.csv', index=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "r_BokJRazWrs",
        "outputId": "efcd49eb-f323-450f-9ab3-d7cbb384ee6d"
      },
      "source": [
        "print(np.array(train_data['Survived']))\n",
        "val"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0 1 1 1 0 0 0 0 1 1 1 1 0 0 0 1 0 1 0 1 0 1 1 1 0 1 0 0 1 0 0 1 1 0 0 0 1\n",
            " 0 0 1 0 0 0 1 1 0 0 1 0 0 0 0 1 1 0 1 1 0 1 0 0 1 0 0 0 1 1 0 1 0 0 0 0 0\n",
            " 1 0 0 0 1 1 0 1 1 0 1 1 0 0 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 1 1 0 1 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 1 1 0 0 0 0 1 0 0 1 0 0 0 0 1 1 0 0 0 1 0\n",
            " 0 0 0 1 0 0 0 0 1 0 0 0 0 1 0 0 0 1 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 1\n",
            " 0 1 1 0 0 1 0 1 1 1 1 0 0 1 0 0 0 0 0 1 0 0 1 1 1 0 1 0 0 0 1 1 0 1 0 1 0\n",
            " 0 0 1 0 1 0 0 0 1 0 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0 1 1 0 0 0 0 0 0 1 1 1 1\n",
            " 1 0 1 0 0 0 0 0 1 1 1 0 1 1 0 1 1 0 0 0 1 0 0 0 1 0 0 1 0 1 1 1 1 0 0 0 0\n",
            " 0 0 1 1 1 1 0 1 0 1 1 1 0 1 1 1 0 0 0 1 1 0 1 1 0 0 1 1 0 1 0 1 1 1 1 0 0\n",
            " 0 1 0 0 1 1 0 1 1 0 0 0 1 1 1 1 0 0 0 0 0 0 0 1 0 1 1 0 0 0 0 0 0 1 1 1 1\n",
            " 1 0 0 0 0 1 1 0 0 0 1 1 0 1 0 0 0 1 0 1 1 1 0 1 1 0 0 0 0 1 1 0 0 0 0 0 0\n",
            " 1 0 0 0 0 1 0 1 0 1 1 0 0 0 0 0 0 0 0 1 1 0 1 1 1 1 0 0 1 0 1 0 0 1 0 0 1\n",
            " 1 1 1 1 1 1 0 0 0 1 0 1 0 1 1 0 1 0 0 0 0 0 0 0 0 1 0 0 1 1 0 0 0 0 0 1 0\n",
            " 0 0 1 1 0 1 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 1 1 0 1 1 0 1 1 0 0 1 0\n",
            " 1 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 1 0 1 0 1 1 0 0 1 0 0 1 1 0 1 1 0 0 1 1\n",
            " 0 1 0 1 1 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 1 1 0 1 1 1 0 0 0 1 0 1 0 0 0 1\n",
            " 0 0 0 0 1 0 0 1 1 0 0 0 1 0 0 1 1 1 0 0 1 0 0 1 0 0 1 0 0 1 1 0 0 0 0 1 0\n",
            " 0 1 0 1 0 0 1 0 0 0 0 0 1 0 1 1 1 0 1 0 1 0 1 0 1 0 0 0 0 0 0 1 0 0 0 1 0\n",
            " 0 0 0 1 1 0 0 1 0 0 0 1 0 1 0 1 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 1 0 0 1 1 0\n",
            " 0 0 0 1 1 1 1 1 0 1 0 0 0 1 1 0 0 1 0 0 0 1 0 1 1 0 0 1 0 0 0 0 0 0 1 0 0\n",
            " 1 0 1 0 1 0 0 1 0 0 1 1 0 0 1 1 0 0 0 1 0 0 1 1 0 1 0 0 0 0 0 0 0 0 1 0 0\n",
            " 1 0 1 1 1 0 0 0 0 1 0 1 0 0 0 0 0 0 0 1 1 0 0 0 1 1 1 1 0 0 0 0 1 0 0 0 0\n",
            " 0 0 0 0 0 0 1 1 0 1 0 0 0 1 1 1 1 1 0 0 0 1 0 0 1 1 0 0 1 0 0 0 0 0 0 1 0\n",
            " 0 0 1 0 1 1 1 1 0 0 0 1 0 0 1 1 0 0 1 0 1 0 0 1 1 0 0 0 1 1 0 0 0 0 0 0 1\n",
            " 0 1 0]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([], shape=(0, 10), dtype=float64)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "rhoW6os2zWrs"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "cGwUE_WNzWrs"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "zs-3lv0ozWrt"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}