FastAI_2022_Linear_Models_and_Frameworks.ipynb

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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/bachaudhry/Alien_Invasion/blob/master/FastAI_2022_Linear_Models_and_Frameworks.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Building a Linear Model and Neural Network from Scratch - Review**"
      ],
      "metadata": {
        "id": "o3JLZbOXDod7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install kaggle"
      ],
      "metadata": {
        "colab": {
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        },
        "id": "dAzeZ-i93WQP",
        "outputId": "71ef70a1-731e-40b7-8502-66d3ddde6d36"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: kaggle in /usr/local/lib/python3.10/dist-packages (1.5.16)\n",
            "Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.10/dist-packages (from kaggle) (1.16.0)\n",
            "Requirement already satisfied: certifi in /usr/local/lib/python3.10/dist-packages (from kaggle) (2024.2.2)\n",
            "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.10/dist-packages (from kaggle) (2.8.2)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from kaggle) (2.31.0)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from kaggle) (4.66.2)\n",
            "Requirement already satisfied: python-slugify in /usr/local/lib/python3.10/dist-packages (from kaggle) (8.0.4)\n",
            "Requirement already satisfied: urllib3 in /usr/local/lib/python3.10/dist-packages (from kaggle) (2.0.7)\n",
            "Requirement already satisfied: bleach in /usr/local/lib/python3.10/dist-packages (from kaggle) (6.1.0)\n",
            "Requirement already satisfied: webencodings in /usr/local/lib/python3.10/dist-packages (from bleach->kaggle) (0.5.1)\n",
            "Requirement already satisfied: text-unidecode>=1.3 in /usr/local/lib/python3.10/dist-packages (from python-slugify->kaggle) (1.3)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->kaggle) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->kaggle) (3.6)\n"
          ]
        }
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    {
      "cell_type": "code",
      "source": [
        "# Setup\n",
        "import os\n",
        "import torch\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from pathlib import Path\n",
        "\n",
        "# NB options\n",
        "np.set_printoptions(linewidth=120)\n",
        "torch.set_printoptions(linewidth=140, sci_mode=False, edgeitems=7)\n",
        "pd.set_option('display.width', 140)\n",
        "# Kaggle\n",
        "iskaggle = os.environ.get('KAGGLE_KERNEL_RUN_TYPE', '')\n",
        "# Using Kaggle API key and creating a path for credentials to be stored\n",
        "# This step will need to be repeated thanks to Google's non persistent storage\n",
        "creds = '{\"username\":\"bachaudhry\",\"key\":\"f8e0ee26f34cb0d3e4eccd774de62f63\"}'\n",
        "cred_path = Path('~/.kaggle/kaggle.json').expanduser()\n",
        "if not cred_path.exists():\n",
        "  cred_path.parent.mkdir(exist_ok=True)\n",
        "  cred_path.write_text(creds)\n",
        "  cred_path.chmod(0o600)\n"
      ],
      "metadata": {
        "id": "GMJoyA9Rz-4k"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "if iskaggle:\n",
        "  path = Path('../input/titanic')\n",
        "else:\n",
        "  path = Path('titanic')\n",
        "  if not path.exists():\n",
        "    import zipfile, kaggle\n",
        "    kaggle.api.competition_download_cli(str(path))\n",
        "    zipfile.ZipFile(f'{path}.zip').extractall(path)"
      ],
      "metadata": {
        "id": "2WBV_oPTz_kP"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Loading and Cleaning the Data"
      ],
      "metadata": {
        "id": "sMSKbHAD5uop"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(path/'train.csv')\n",
        "df.head()"
      ],
      "metadata": {
        "id": "j3pN3gizz_tX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "1cc0c6c2-d316-4a1e-d719-42ba51dad2bb"
      },
      "execution_count": 11,
      "outputs": [
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              "   PassengerId  Survived  Pclass                                               Name     Sex   Age  SibSp  Parch            Ticket  \\\n",
              "0            1         0       3                            Braund, Mr. Owen Harris    male  22.0      1      0         A/5 21171   \n",
              "1            2         1       1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1      0          PC 17599   \n",
              "2            3         1       3                             Heikkinen, Miss. Laina  female  26.0      0      0  STON/O2. 3101282   \n",
              "3            4         1       1       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1      0            113803   \n",
              "4            5         0       3                           Allen, Mr. William Henry    male  35.0      0      0            373450   \n",
              "\n",
              "      Fare Cabin Embarked  \n",
              "0   7.2500   NaN        S  \n",
              "1  71.2833   C85        C  \n",
              "2   7.9250   NaN        S  \n",
              "3  53.1000  C123        S  \n",
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              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 891,\n  \"fields\": [\n    {\n      \"column\": \"PassengerId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 257,\n        \"min\": 1,\n        \"max\": 891,\n        \"num_unique_values\": 891,\n        \"samples\": [\n          710,\n          440,\n          841\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Survived\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          3,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 891,\n        \"samples\": [\n          \"Moubarek, Master. Halim Gonios (\\\"William George\\\")\",\n          \"Kvillner, Mr. Johan Henrik Johannesson\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"female\",\n          \"male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.526497332334042,\n        \"min\": 0.42,\n        \"max\": 80.0,\n        \"num_unique_values\": 88,\n        \"samples\": [\n          0.75,\n          22.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SibSp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 8,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Parch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 6,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ticket\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 681,\n        \"samples\": [\n          \"11774\",\n          \"248740\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 49.6934285971809,\n        \"min\": 0.0,\n        \"max\": 512.3292,\n        \"num_unique_values\": 248,\n        \"samples\": [\n          11.2417,\n          51.8625\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cabin\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 147,\n        \"samples\": [\n          \"D45\",\n          \"B49\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Embarked\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"S\",\n          \"C\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "id": "Pia3c0AIz_0y",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1b9f371f-3714-40e9-cacd-a4c7a7ad06e1"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isna().sum()"
      ],
      "metadata": {
        "id": "DMcviInr0D33",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2d0ae1fb-c74f-4019-c729-1c3715d84f03"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PassengerId      0\n",
              "Survived         0\n",
              "Pclass           0\n",
              "Name             0\n",
              "Sex              0\n",
              "Age            177\n",
              "SibSp            0\n",
              "Parch            0\n",
              "Ticket           0\n",
              "Fare             0\n",
              "Cabin          687\n",
              "Embarked         2\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe(include=['object', 'number'])"
      ],
      "metadata": {
        "id": "9KPqgQAr0EfF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "outputId": "9df1e895-f06e-49a6-c323-8fe8789931e8"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        PassengerId    Survived      Pclass                     Name   Sex         Age       SibSp       Parch  Ticket        Fare  \\\n",
              "count    891.000000  891.000000  891.000000                      891   891  714.000000  891.000000  891.000000     891  891.000000   \n",
              "unique          NaN         NaN         NaN                      891     2         NaN         NaN         NaN     681         NaN   \n",
              "top             NaN         NaN         NaN  Braund, Mr. Owen Harris  male         NaN         NaN         NaN  347082         NaN   \n",
              "freq            NaN         NaN         NaN                        1   577         NaN         NaN         NaN       7         NaN   \n",
              "mean     446.000000    0.383838    2.308642                      NaN   NaN   29.699118    0.523008    0.381594     NaN   32.204208   \n",
              "std      257.353842    0.486592    0.836071                      NaN   NaN   14.526497    1.102743    0.806057     NaN   49.693429   \n",
              "min        1.000000    0.000000    1.000000                      NaN   NaN    0.420000    0.000000    0.000000     NaN    0.000000   \n",
              "25%      223.500000    0.000000    2.000000                      NaN   NaN   20.125000    0.000000    0.000000     NaN    7.910400   \n",
              "50%      446.000000    0.000000    3.000000                      NaN   NaN   28.000000    0.000000    0.000000     NaN   14.454200   \n",
              "75%      668.500000    1.000000    3.000000                      NaN   NaN   38.000000    1.000000    0.000000     NaN   31.000000   \n",
              "max      891.000000    1.000000    3.000000                      NaN   NaN   80.000000    8.000000    6.000000     NaN  512.329200   \n",
              "\n",
              "          Cabin Embarked  \n",
              "count       204      889  \n",
              "unique      147        3  \n",
              "top     B96 B98        S  \n",
              "freq          4      644  \n",
              "mean        NaN      NaN  \n",
              "std         NaN      NaN  \n",
              "min         NaN      NaN  \n",
              "25%         NaN      NaN  \n",
              "50%         NaN      NaN  \n",
              "75%         NaN      NaN  \n",
              "max         NaN      NaN  "
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              "      <td>32.204208</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>14.526497</td>\n",
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              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-12bf9c73-3a5f-416c-80c7-711db2d050db button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 11,\n  \"fields\": [\n    {\n      \"column\": \"PassengerId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 320.8159711429856,\n        \"min\": 1.0,\n        \"max\": 891.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          891.0,\n          446.0,\n          668.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Survived\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 314.87136618745586,\n        \"min\": 0.0,\n        \"max\": 891.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.3838383838383838,\n          1.0,\n          0.4865924542648575\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 314.2523437079694,\n        \"min\": 0.836071240977049,\n        \"max\": 891.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          891.0,\n          2.308641975308642,\n          3.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"891\",\n          \"Braund, Mr. Owen Harris\",\n          \"1\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2,\n          \"577\",\n          \"891\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 242.9056731818781,\n        \"min\": 0.42,\n        \"max\": 714.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          29.69911764705882,\n          28.0,\n          714.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SibSp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 314.4908277465442,\n        \"min\": 0.0,\n        \"max\": 891.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          891.0,\n          0.5230078563411896,\n          8.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Parch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 314.65971717879,\n        \"min\": 0.0,\n        \"max\": 891.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.38159371492704824,\n          6.0,\n          0.8060572211299483\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ticket\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          681,\n          \"7\",\n          \"891\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 330.6256632228577,\n        \"min\": 0.0,\n        \"max\": 891.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          32.204207968574636,\n          14.4542,\n          891.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cabin\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          147,\n          \"4\",\n          \"204\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Embarked\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          3,\n          \"644\",\n          \"889\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Get modes for all columnns for imputation\n",
        "modes = df.mode().iloc[0]\n",
        "modes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VyTm3Jz71_Gr",
        "outputId": "82edf0f1-202a-4180-df60-0244549acc0f"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PassengerId                      1\n",
              "Survived                       0.0\n",
              "Pclass                         3.0\n",
              "Name           Abbing, Mr. Anthony\n",
              "Sex                           male\n",
              "Age                           24.0\n",
              "SibSp                          0.0\n",
              "Parch                          0.0\n",
              "Ticket                        1601\n",
              "Fare                          8.05\n",
              "Cabin                      B96 B98\n",
              "Embarked                         S\n",
              "Name: 0, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.fillna(modes, inplace=True)"
      ],
      "metadata": {
        "id": "qLAzEsQ47PUQ"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XzGOUnts7QUg",
        "outputId": "5347f148-8fca-4dd0-9b45-f34a0097cf7e"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PassengerId    0\n",
              "Survived       0\n",
              "Pclass         0\n",
              "Name           0\n",
              "Sex            0\n",
              "Age            0\n",
              "SibSp          0\n",
              "Parch          0\n",
              "Ticket         0\n",
              "Fare           0\n",
              "Cabin          0\n",
              "Embarked       0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plotting key distributions\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "# Create grid fo subplots\n",
        "fig, axes =  plt.subplots(nrows=1, ncols=3, figsize=(15,5))\n",
        "#\n",
        "sns.histplot(df, x='Fare', color='black', bins=20, ax=axes[0])\n",
        "sns.histplot(df, x='Age',  color='black', bins=20, ax=axes[1])\n",
        "sns.histplot(df, x='SibSp',color='black', bins=20, ax=axes[2])\n",
        "\n",
        "plt.show();"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        },
        "id": "iCx2H7-h7Qfd",
        "outputId": "e7d65688-0cd0-4165-d41a-3ebb3e4555c1"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x500 with 3 Axes>"
            ],
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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "dbE_f_Zd7Rzz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "DBWO22DF7R8R"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "VSDvAvrZ7SDk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "UushBbbi7SKY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "90m6au9x7SRK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "zzrjdcvd7SXJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "-Ftjc5dt7Sfi"
      },
      "execution_count": null,
      "outputs": []
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      "cell_type": "code",
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      "metadata": {
        "id": "uXD5XA_C7Smm"
      },
      "execution_count": null,
      "outputs": []
    }
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}