titanic_fastai.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 5,
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.6"
    },
    "toc": {
      "base_numbering": 1,
      "nav_menu": {},
      "number_sections": true,
      "sideBar": true,
      "skip_h1_title": false,
      "title_cell": "Table of Contents",
      "title_sidebar": "Contents",
      "toc_cell": false,
      "toc_position": {},
      "toc_section_display": true,
      "toc_window_display": false
    },
    "colab": {
      "name": "titanic_rf.ipynb",
      "provenance": [],
      "include_colab_link": true
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/addicted-ai/kaggle_practice/blob/main/titanic_fastai.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H9XERJaP0x_R"
      },
      "source": [
        "# supressing warnings\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")"
      ],
      "id": "H9XERJaP0x_R",
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "218ebb8d"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "%matplotlib inline"
      ],
      "id": "218ebb8d",
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OZbqtHv8zb9v",
        "outputId": "f631e034-66f1-4c61-88ea-e206fe13561a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# Installing fastbook for fastai & importing\n",
        "!pip install -Uqq fastbook\n",
        "import fastbook\n",
        "fastbook.setup_book()"
      ],
      "id": "OZbqtHv8zb9v",
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[K     |████████████████████████████████| 720 kB 4.3 MB/s \n",
            "\u001b[K     |████████████████████████████████| 188 kB 31.9 MB/s \n",
            "\u001b[K     |████████████████████████████████| 46 kB 3.9 MB/s \n",
            "\u001b[K     |████████████████████████████████| 1.2 MB 44.6 MB/s \n",
            "\u001b[K     |████████████████████████████████| 56 kB 4.2 MB/s \n",
            "\u001b[K     |████████████████████████████████| 51 kB 307 kB/s \n",
            "\u001b[?25hMounted at /content/gdrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iqQt74BEzceG"
      },
      "source": [
        "# Deep Learing Libraries\n",
        "from fastbook import *\n",
        "from fastai.tabular.all import *\n",
        "import torch.nn as nn"
      ],
      "id": "iqQt74BEzceG",
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_wkwHs3lLLXu"
      },
      "source": [
        "# Reading the dataset\n",
        "df = pd.read_csv('https://raw.githubusercontent.com/addicted-ai/kaggle_practice/main/dataset/titanic/train.csv')\n",
        "test = pd.read_csv('https://raw.githubusercontent.com/addicted-ai/kaggle_practice/main/dataset/titanic/test.csv')"
      ],
      "id": "_wkwHs3lLLXu",
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "Er6D8jCKLNeA",
        "outputId": "3be2712a-5d92-4e23-9f71-e2e4f66d0083"
      },
      "source": [
        "# data to be used for training & with labeled dependent variable\n",
        "df.head(4)"
      ],
      "id": "Er6D8jCKLNeA",
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Braund, Mr. Owen Harris</td>\n",
              "      <td>male</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>A/5 21171</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Cumings, Mrs. John Bradley (Florence Briggs Thayer)</td>\n",
              "      <td>female</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17599</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C85</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Heikkinen, Miss. Laina</td>\n",
              "      <td>female</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>STON/O2. 3101282</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
              "      <td>female</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>113803</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>C123</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   PassengerId  Survived  Pclass  \\\n",
              "0            1         0       3   \n",
              "1            2         1       1   \n",
              "2            3         1       3   \n",
              "3            4         1       1   \n",
              "\n",
              "                                                  Name     Sex   Age  SibSp  \\\n",
              "0                              Braund, Mr. Owen Harris    male  22.0      1   \n",
              "1  Cumings, Mrs. John Bradley (Florence Briggs Thayer)  female  38.0      1   \n",
              "2                               Heikkinen, Miss. Laina  female  26.0      0   \n",
              "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
              "\n",
              "   Parch            Ticket     Fare Cabin Embarked  \n",
              "0      0         A/5 21171   7.2500   NaN        S  \n",
              "1      0          PC 17599  71.2833   C85        C  \n",
              "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
              "3      0            113803  53.1000  C123        S  "
            ]
          },
          "metadata": {},
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "mJ4Yc5S8LOpT",
        "outputId": "53f731d2-2572-4e3a-c965-f6f370446716"
      },
      "source": [
        "# test data that i have to get prediction & submit\n",
        "test.head(4)"
      ],
      "id": "mJ4Yc5S8LOpT",
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>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>892</td>\n",
              "      <td>3</td>\n",
              "      <td>Kelly, Mr. James</td>\n",
              "      <td>male</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>893</td>\n",
              "      <td>3</td>\n",
              "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
              "      <td>female</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>894</td>\n",
              "      <td>2</td>\n",
              "      <td>Myles, Mr. Thomas Francis</td>\n",
              "      <td>male</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>895</td>\n",
              "      <td>3</td>\n",
              "      <td>Wirz, Mr. Albert</td>\n",
              "      <td>male</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",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   PassengerId  Pclass                              Name     Sex   Age  SibSp  \\\n",
              "0          892       3                  Kelly, Mr. James    male  34.5      0   \n",
              "1          893       3  Wilkes, Mrs. James (Ellen Needs)  female  47.0      1   \n",
              "2          894       2         Myles, Mr. Thomas Francis    male  62.0      0   \n",
              "3          895       3                  Wirz, Mr. Albert    male  27.0      0   \n",
              "\n",
              "   Parch  Ticket    Fare Cabin Embarked  \n",
              "0      0  330911  7.8292   NaN        Q  \n",
              "1      0  363272  7.0000   NaN        S  \n",
              "2      0  240276  9.6875   NaN        Q  \n",
              "3      0  315154  8.6625   NaN        S  "
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w9cjNwcSLP4n",
        "outputId": "853ee300-157f-448d-8075-c8b611dace63"
      },
      "source": [
        "df.info()"
      ],
      "id": "w9cjNwcSLP4n",
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 891 entries, 0 to 890\n",
            "Data columns (total 12 columns):\n",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   PassengerId  891 non-null    int64  \n",
            " 1   Survived     891 non-null    int64  \n",
            " 2   Pclass       891 non-null    int64  \n",
            " 3   Name         891 non-null    object \n",
            " 4   Sex          891 non-null    object \n",
            " 5   Age          714 non-null    float64\n",
            " 6   SibSp        891 non-null    int64  \n",
            " 7   Parch        891 non-null    int64  \n",
            " 8   Ticket       891 non-null    object \n",
            " 9   Fare         891 non-null    float64\n",
            " 10  Cabin        204 non-null    object \n",
            " 11  Embarked     889 non-null    object \n",
            "dtypes: float64(2), int64(5), object(5)\n",
            "memory usage: 83.7+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GfcydDBEUFo1"
      },
      "source": [
        "- Categorical columns are `Name`, `Sex`, `Ticket`, `Cabin`, `Embarked`.\n",
        "- Name column can't be used for model."
      ],
      "id": "GfcydDBEUFo1"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hEXjWoCNi5Yu",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8480526b-e696-4ef2-b5cd-30ee31a6a8fe"
      },
      "source": [
        "test.info()"
      ],
      "id": "hEXjWoCNi5Yu",
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 418 entries, 0 to 417\n",
            "Data columns (total 11 columns):\n",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   PassengerId  418 non-null    int64  \n",
            " 1   Pclass       418 non-null    int64  \n",
            " 2   Name         418 non-null    object \n",
            " 3   Sex          418 non-null    object \n",
            " 4   Age          332 non-null    float64\n",
            " 5   SibSp        418 non-null    int64  \n",
            " 6   Parch        418 non-null    int64  \n",
            " 7   Ticket       418 non-null    object \n",
            " 8   Fare         417 non-null    float64\n",
            " 9   Cabin        91 non-null     object \n",
            " 10  Embarked     418 non-null    object \n",
            "dtypes: float64(2), int64(4), object(5)\n",
            "memory usage: 36.0+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9mHo-ClHUs1k",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 818
        },
        "outputId": "45cb8d44-30b7-4d0e-e059-02ade76e3514"
      },
      "source": [
        "cat_col = ['Sex', 'Ticket', 'Cabin', 'Embarked']\n",
        "for i in cat_col:\n",
        "  print(i,':')\n",
        "  display(df[i].value_counts(dropna=False))\n",
        "  print('________\\n')"
      ],
      "id": "9mHo-ClHUs1k",
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Sex :\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "male      577\n",
              "female    314\n",
              "Name: Sex, dtype: int64"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "text": [
            "________\n",
            "\n",
            "Ticket :\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "CA. 2343             7\n",
              "347082               7\n",
              "1601                 7\n",
              "347088               6\n",
              "3101295              6\n",
              "                    ..\n",
              "STON/O 2. 3101289    1\n",
              "2620                 1\n",
              "343275               1\n",
              "315096               1\n",
              "233639               1\n",
              "Name: Ticket, Length: 681, dtype: int64"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "text": [
            "________\n",
            "\n",
            "Cabin :\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "NaN            687\n",
              "G6               4\n",
              "C23 C25 C27      4\n",
              "B96 B98          4\n",
              "F2               3\n",
              "              ... \n",
              "B94              1\n",
              "B86              1\n",
              "C82              1\n",
              "C106             1\n",
              "F G63            1\n",
              "Name: Cabin, Length: 148, dtype: int64"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "text": [
            "________\n",
            "\n",
            "Embarked :\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "S      644\n",
              "C      168\n",
              "Q       77\n",
              "NaN      2\n",
              "Name: Embarked, dtype: int64"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "text": [
            "________\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H-MQ-hwcTYeE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 90
        },
        "outputId": "54934409-1d81-458f-e372-7413dd100b52"
      },
      "source": [
        "print('No of Unique values in Cabin:')\n",
        "display(df['Cabin'].nunique())\n",
        "print('No of Unique values in Ticket:')\n",
        "display(df['Ticket'].nunique())"
      ],
      "id": "H-MQ-hwcTYeE",
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "No of Unique values in Cabin:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "147"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "text": [
            "No of Unique values in Ticket:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "681"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3vn4qbM7V6Dj"
      },
      "source": [
        "- Both Ticket & Cabin have very high no of level. We can't use them for training.\n",
        "- 'Age' Columns seems to have ~20% NaN values. We can drop it.\n",
        "- 'Embarked` column has 2 NaN values. We can impute NaN with mode of column."
      ],
      "id": "3vn4qbM7V6Dj"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PXr07MYZX6bz"
      },
      "source": [
        "df['Embarked'] = df['Embarked'].replace(np.nan, df['Embarked'].mode()[0])"
      ],
      "id": "PXr07MYZX6bz",
      "execution_count": 78,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2Y2hfnL1jNIU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "adc74b87-e724-438f-dc3c-071577883c49"
      },
      "source": [
        "df['Fare'].describe(percentiles=[0.1, 0.25, 0.5, 0.75, .8, 0.9, 0.95, 0.97, 0.99, 1])"
      ],
      "id": "2Y2hfnL1jNIU",
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    891.000000\n",
              "mean      32.204208\n",
              "std       49.693429\n",
              "min        0.000000\n",
              "10%        7.550000\n",
              "25%        7.910400\n",
              "50%       14.454200\n",
              "75%       31.000000\n",
              "80%       39.687500\n",
              "90%       77.958300\n",
              "95%      112.079150\n",
              "97%      151.550000\n",
              "99%      249.006220\n",
              "100%     512.329200\n",
              "max      512.329200\n",
              "Name: Fare, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "04mFh8NnjTQ1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bd932658-f123-48ba-c00d-06d875246580"
      },
      "source": [
        "df['Fare'].median()"
      ],
      "id": "04mFh8NnjTQ1",
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "14.4542"
            ]
          },
          "metadata": {},
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_LaHMaFVjEbF"
      },
      "source": [
        "test['Fare'] = test['Fare'].replace(np.nan, df['Fare'].median())"
      ],
      "id": "_LaHMaFVjEbF",
      "execution_count": 81,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IMJuj23lWvyA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7ecc5f1b-b801-4d7a-bec7-80c32dcf38d0"
      },
      "source": [
        "df.columns"
      ],
      "id": "IMJuj23lWvyA",
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n",
              "       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mei_qlEMQpRU"
      },
      "source": [
        "features = ['Pclass', 'Sex', 'SibSp', 'Parch', 'Fare', 'Embarked', 'Survived']\n",
        "df = df[features]\n",
        "test = test[['Pclass', 'Sex', 'SibSp', 'Parch', 'Fare', 'Embarked']]"
      ],
      "id": "mei_qlEMQpRU",
      "execution_count": 83,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RUniAgCNzx1d",
        "outputId": "564e84d4-ec5f-4dbb-df59-3c76048d172b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "splits = RandomSplitter(valid_pct=0.2, seed=0.42)(range_of(df))\n",
        "splits[0],splits[1]"
      ],
      "id": "RUniAgCNzx1d",
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((#713) [673,88,154,99,112,717,56,29,159,433...],\n",
              " (#178) [890,70,352,819,208,78,373,578,416,630...])"
            ]
          },
          "metadata": {},
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hgCoK8Fzz9SU"
      },
      "source": [
        "# setting Dependent variable\n",
        "dep_var = 'Survived'\n",
        "# getting continuous variable & categorical\n",
        "cont_nn,cat_nn = cont_cat_split(df, max_card=20, dep_var=dep_var)"
      ],
      "id": "hgCoK8Fzz9SU",
      "execution_count": 85,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X62NufZV0DfZ"
      },
      "source": [
        "# We will apply FillMissing & scale train data by Normalizing(MinMax Scaler) it\n",
        "procs_nn = [Normalize]\n",
        "\n",
        "# Creating a TabularPandas object\n",
        "to_nn = TabularPandas(df, procs=procs_nn, cont_names=cont_nn,\n",
        "                      y_names=dep_var, splits=splits)\n",
        "dls = to_nn.dataloaders(bs=64)"
      ],
      "id": "X62NufZV0DfZ",
      "execution_count": 86,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wM4MSQPL0U5i",
        "outputId": "a2021f88-269d-41dc-f25d-3ef6b05beab2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        }
      },
      "source": [
        "# show a single batch\n",
        "dls.show_batch()"
      ],
      "id": "wM4MSQPL0U5i",
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Fare</th>\n",
              "      <th>Survived</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>52.000001</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>15.500000</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>14.499999</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>19.258300</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>6.750001</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>7.250000</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>7.887499</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>9.825000</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>7.895800</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>31.275000</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JgO9luFA0W6P"
      },
      "source": [
        "learn = None\n",
        "learn = tabular_learner(dls, y_range=(0,1), layers=[62,32,8], \n",
        "                        n_out=1, metrics=accuracy)"
      ],
      "id": "JgO9luFA0W6P",
      "execution_count": 88,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fXaEllfT1SIL"
      },
      "source": [
        "try:\n",
        "    lr\n",
        "except NameError:\n",
        "    lr = learn.lr_find()[0]"
      ],
      "id": "fXaEllfT1SIL",
      "execution_count": 89,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qtEHv1mO1Tku"
      },
      "source": [
        "learn.fit(500, lr)"
      ],
      "id": "qtEHv1mO1Tku",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3xRIf5rD1V0G"
      },
      "source": [
        "learn.show_results()"
      ],
      "id": "3xRIf5rD1V0G",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0p1jolFJ1ol2"
      },
      "source": [
        "dl = learn.dls.test_dl(test)\n",
        "learn.get_preds(dl=dl)"
      ],
      "id": "0p1jolFJ1ol2",
      "execution_count": null,
      "outputs": []
    }
  ]
}