ML_sk.ipynb

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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ]
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      "source": [
        "heart_disease = pd.read_csv('https://raw.githubusercontent.com/mrdbourke/zero-to-mastery-ml/master/data/heart-disease.csv')\n",
        "heart_disease.head()"
      ],
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              "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
              "0   63    1   3       145   233    1        0      150      0      2.3      0   \n",
              "1   37    1   2       130   250    0        1      187      0      3.5      0   \n",
              "2   41    0   1       130   204    0        0      172      0      1.4      2   \n",
              "3   56    1   1       120   236    0        1      178      0      0.8      2   \n",
              "4   57    0   0       120   354    0        1      163      1      0.6      2   \n",
              "\n",
              "   ca  thal  target  \n",
              "0   0     1       1  \n",
              "1   0     2       1  \n",
              "2   0     2       1  \n",
              "3   0     2       1  \n",
              "4   0     2       1  "
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      "source": [
        "### Three of the main steps you'll often have to take are:\n",
        "\n",
        "\n",
        "#### 1. Splitting the data into features (usually X) and labels (usually y).\n",
        "#### 2. Splitting the data into training and testing sets (and possibly a validation set).\n",
        "#### 3. Filling (also called imputing) or disregarding missing values.\n",
        "#### 4. Converting non-numerical values to numerical values (also call feature encoding)."
      ],
      "metadata": {
        "id": "KXAHvzVagkpc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x = heart_disease.drop(\"target\", axis = 1)\n",
        "y = heart_disease[\"target\"]"
      ],
      "metadata": {
        "id": "p4du3uBogXqL"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.2)\n",
        "\n",
        "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
      ],
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        "id": "aHXnK5_shKrr",
        "outputId": "a275dc51-1844-4b46-84ea-1472fe2e2f2a"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((242, 13), (61, 13), (242,), (61,))"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
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    {
      "cell_type": "code",
      "source": [
        "# 1. Make sure that the data is all numerical\n",
        "\n",
        "car_sales = pd.read_csv('https://raw.githubusercontent.com/mrdbourke/zero-to-mastery-ml/master/data/car-sales-extended.csv')\n",
        "car_sales.head()"
      ],
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        {
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            "text/plain": [
              "     Make Colour  Odometer (KM)  Doors  Price\n",
              "0   Honda  White          35431      4  15323\n",
              "1     BMW   Blue         192714      5  19943\n",
              "2   Honda  White          84714      4  28343\n",
              "3  Toyota  White         154365      4  13434\n",
              "4  Nissan   Blue         181577      3  14043"
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            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "car_sales.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-kP-pe3ZkCdw",
        "outputId": "762ec14d-2a18-4e2f-f403-57026c01445a"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Make             object\n",
              "Colour           object\n",
              "Odometer (KM)     int64\n",
              "Doors             int64\n",
              "Price             int64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Splitting the data\n",
        "\n",
        "x = car_sales.drop(\"Price\", axis = 1)\n",
        "y = car_sales[\"Price\"]\n",
        "\n",
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.2)"
      ],
      "metadata": {
        "id": "PG4ELPxHkReG"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Turn the categories into variables\n",
        "\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "from sklearn.compose import ColumnTransformer\n",
        "\n",
        "categorical_features = [\"Make\", \"Colour\", \"Doors\"]\n",
        "\n",
        "one_hot = OneHotEncoder()\n",
        "transformer = ColumnTransformer([(\"one_hot\",\n",
        "                                   one_hot,\n",
        "                                   categorical_features)],\n",
        "                                   remainder = \"passthrough\")\n",
        "\n",
        "transformed_x = transformer.fit_transform(x)\n",
        "\n",
        "pd.DataFrame(transformed_x)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "M7PaIWeGAQPE",
        "outputId": "2e785c40-9e28-402d-f00a-14ae3de5372d"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
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              "\n",
              "[1000 rows x 13 columns]"
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              "<p>1000 rows × 13 columns</p>\n",
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              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-92b177b1-d17d-4e31-821a-a64d1b642215 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-92b177b1-d17d-4e31-821a-a64d1b642215');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-64adf834-06f1-484e-9baf-15f14ade88e2\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-64adf834-06f1-484e-9baf-15f14ade88e2')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .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-64adf834-06f1-484e-9baf-15f14ade88e2 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dummies = pd.get_dummies(car_sales[[\"Make\", \"Colour\", \"Doors\"]])\n",
        "dummies"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 439
        },
        "id": "ZsOa0wsGDPmg",
        "outputId": "bbf18c3a-6f52-4b90-8231-c38189bcb5e3"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Doors  Make_BMW  Make_Honda  Make_Nissan  Make_Toyota  Colour_Black  \\\n",
              "0        4         0           1            0            0             0   \n",
              "1        5         1           0            0            0             0   \n",
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              "..     ...       ...         ...          ...          ...           ...   \n",
              "995      4         0           0            0            1             1   \n",
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              "998      4         0           1            0            0             0   \n",
              "999      4         0           0            0            1             0   \n",
              "\n",
              "     Colour_Blue  Colour_Green  Colour_Red  Colour_White  \n",
              "0              0             0           0             1  \n",
              "1              1             0           0             0  \n",
              "2              0             0           0             1  \n",
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              "..           ...           ...         ...           ...  \n",
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              "997            1             0           0             0  \n",
              "998            0             0           0             1  \n",
              "999            1             0           0             0  \n",
              "\n",
              "[1000 rows x 10 columns]"
            ],
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              "\n",
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              "\n",
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              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "\n",
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              "        document.querySelector('#df-7cbe41f0-09db-4ba7-9a4f-c5f9fa0f278a button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
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              "        const dataTable =\n",
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              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .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-5df7f2c9-d070-4b43-87ed-86f643a6e979 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.random.seed(42)\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "\n",
        "x_train, x_test, y_train, y_test = train_test_split(transformed_x, y, test_size = 0.2)\n",
        "\n",
        "model = RandomForestRegressor()\n",
        "\n",
        "model.fit(x_train, y_train)\n",
        "model.score(x_test, y_test)"
      ],
      "metadata": {
        "id": "W9km59Svkrnm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5e126856-8644-49bd-a162-19dd3f17ab11"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.3235867221569877"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Handling missing values in the data"
      ],
      "metadata": {
        "id": "X558p6vDEfOl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "car_sales_missing = pd.read_csv('https://raw.githubusercontent.com/mrdbourke/zero-to-mastery-ml/master/data/car-sales-extended-missing-data.csv')\n",
        "car_sales_missing.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "OPoNbCUAKsHQ",
        "outputId": "d07b5f07-c587-4131-83b8-70f8146654cb"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Make Colour  Odometer (KM)  Doors    Price\n",
              "0   Honda  White        35431.0    4.0  15323.0\n",
              "1     BMW   Blue       192714.0    5.0  19943.0\n",
              "2   Honda  White        84714.0    4.0  28343.0\n",
              "3  Toyota  White       154365.0    4.0  13434.0\n",
              "4  Nissan   Blue       181577.0    3.0  14043.0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f0aec7e6-54f7-4c79-b78d-602e1fddf953\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Make</th>\n",
              "      <th>Colour</th>\n",
              "      <th>Odometer (KM)</th>\n",
              "      <th>Doors</th>\n",
              "      <th>Price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Honda</td>\n",
              "      <td>White</td>\n",
              "      <td>35431.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>15323.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>BMW</td>\n",
              "      <td>Blue</td>\n",
              "      <td>192714.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>19943.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Honda</td>\n",
              "      <td>White</td>\n",
              "      <td>84714.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>28343.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Toyota</td>\n",
              "      <td>White</td>\n",
              "      <td>154365.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>13434.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Nissan</td>\n",
              "      <td>Blue</td>\n",
              "      <td>181577.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>14043.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f0aec7e6-54f7-4c79-b78d-602e1fddf953')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-f0aec7e6-54f7-4c79-b78d-602e1fddf953 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-f0aec7e6-54f7-4c79-b78d-602e1fddf953');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-6cf86366-6496-48b9-a16f-a17c157859b0\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-6cf86366-6496-48b9-a16f-a17c157859b0')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .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-6cf86366-6496-48b9-a16f-a17c157859b0 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create x and y\n",
        "\n",
        "x = car_sales_missing.drop(\"Price\", axis = 1)\n",
        "y = car_sales_missing[\"Price\"]"
      ],
      "metadata": {
        "id": "BUzbv_JZD5pu"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Turn the categories into variables\n",
        "\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "from sklearn.compose import ColumnTransformer\n",
        "\n",
        "categorical_features = [\"Make\", \"Colour\", \"Doors\"]\n",
        "\n",
        "one_hot = OneHotEncoder()\n",
        "transformer = ColumnTransformer([(\"one_hot\",\n",
        "                                   one_hot,\n",
        "                                   categorical_features)],\n",
        "                                   remainder = \"passthrough\")\n",
        "\n",
        "transformed_x = transformer.fit_transform(x)\n",
        "\n",
        "# pd.DataFrame(transformed_x)"
      ],
      "metadata": {
        "id": "ZRgtQ5l-JudQ"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# The data still has missing values\n",
        "# Onehotencoder was upgraded\n",
        "# Still fill missing values\n",
        "\n",
        "car_sales_missing[\"Make\"].fillna(\"missing\", inplace = True)\n",
        "\n",
        "car_sales_missing[\"Colour\"].fillna(\"missing\", inplace = True)\n",
        "\n",
        "car_sales_missing[\"Doors\"].fillna(4, inplace = True)\n",
        "\n",
        "car_sales_missing[\"Odometer (KM)\"].fillna(car_sales_missing[\"Odometer (KM)\"].mean(),\n",
        "                                          inplace = True)\n"
      ],
      "metadata": {
        "id": "-ZYQaAQ4J4iu"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "car_sales_missing.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sdYDEZzsMCFv",
        "outputId": "63ddda44-5038-445b-b6b8-13875964b527"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Make              0\n",
              "Colour            0\n",
              "Odometer (KM)     0\n",
              "Doors             0\n",
              "Price            50\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Remove the rows with null values\n",
        "\n",
        "car_sales_missing.dropna(inplace = True)"
      ],
      "metadata": {
        "id": "_9CYumypMMux"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "car_sales_missing.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M0qAWyC_M2x-",
        "outputId": "209fb652-d276-4201-90d6-1cca6d8c75a1"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Make             0\n",
              "Colour           0\n",
              "Odometer (KM)    0\n",
              "Doors            0\n",
              "Price            0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "car_sales_missing.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XF8v6DlaM6Wl",
        "outputId": "89f2182f-ba72-4b70-b549-bc9739ebe08a"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(950, 5)"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = car_sales_missing.drop(\"Price\", axis = 1)\n",
        "y = car_sales_missing[\"Price\"]"
      ],
      "metadata": {
        "id": "VskN8SfEM9k6"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Turn the categories into variables\n",
        "\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "from sklearn.compose import ColumnTransformer\n",
        "\n",
        "categorical_features = [\"Make\", \"Colour\", \"Doors\"]\n",
        "\n",
        "one_hot = OneHotEncoder()\n",
        "transformer = ColumnTransformer([(\"one_hot\",\n",
        "                                   one_hot,\n",
        "                                   categorical_features)],\n",
        "                                   remainder = \"passthrough\")\n",
        "\n",
        "transformed_x = transformer.fit_transform(car_sales_missing)\n",
        "\n",
        "transformed_x"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-gWsemN1NH_x",
        "outputId": "1544e609-dab5-4f6a-d32b-77bb00d79259"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.00000e+00, 1.00000e+00, 0.00000e+00, ..., 0.00000e+00,\n",
              "        3.54310e+04, 1.53230e+04],\n",
              "       [1.00000e+00, 0.00000e+00, 0.00000e+00, ..., 1.00000e+00,\n",
              "        1.92714e+05, 1.99430e+04],\n",
              "       [0.00000e+00, 1.00000e+00, 0.00000e+00, ..., 0.00000e+00,\n",
              "        8.47140e+04, 2.83430e+04],\n",
              "       ...,\n",
              "       [0.00000e+00, 0.00000e+00, 1.00000e+00, ..., 0.00000e+00,\n",
              "        6.66040e+04, 3.15700e+04],\n",
              "       [0.00000e+00, 1.00000e+00, 0.00000e+00, ..., 0.00000e+00,\n",
              "        2.15883e+05, 4.00100e+03],\n",
              "       [0.00000e+00, 0.00000e+00, 0.00000e+00, ..., 0.00000e+00,\n",
              "        2.48360e+05, 1.27320e+04]])"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Fill missing values with Scikit learn ( IMPORTANT !!! )"
      ],
      "metadata": {
        "id": "JJHvIbGoNuqp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "car_sales_missing = pd.read_csv('https://raw.githubusercontent.com/mrdbourke/zero-to-mastery-ml/master/data/car-sales-extended-missing-data.csv')\n",
        "car_sales_missing.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "AYkP1h15NSoE",
        "outputId": "b718024e-b0d3-4bae-d99a-07428a6ea2b2"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Make Colour  Odometer (KM)  Doors    Price\n",
              "0   Honda  White        35431.0    4.0  15323.0\n",
              "1     BMW   Blue       192714.0    5.0  19943.0\n",
              "2   Honda  White        84714.0    4.0  28343.0\n",
              "3  Toyota  White       154365.0    4.0  13434.0\n",
              "4  Nissan   Blue       181577.0    3.0  14043.0"
            ],
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              "\n",
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              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "    </div>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "car_sales_missing.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AxAi-DPiUrR9",
        "outputId": "bb866171-81bc-419e-aaeb-e239babb02d4"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Make             49\n",
              "Colour           50\n",
              "Odometer (KM)    50\n",
              "Doors            50\n",
              "Price            50\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "car_sales_missing.dropna(subset = [\"Price\"], inplace = True)\n",
        "car_sales_missing.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KAZutK0eUxg-",
        "outputId": "4d9e0467-31d1-422f-b10c-63d0014f1e31"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Make             47\n",
              "Colour           46\n",
              "Odometer (KM)    48\n",
              "Doors            47\n",
              "Price             0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = car_sales_missing.drop(\"Price\", axis = 1)\n",
        "y = car_sales_missing[\"Price\"]"
      ],
      "metadata": {
        "id": "ZmhjvGiHVHHi"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.2)"
      ],
      "metadata": {
        "id": "NqwZqxaadSXF"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.impute import SimpleImputer\n",
        "from sklearn.compose import ColumnTransformer\n",
        "\n",
        "# Fill categorical features with \"missing\"\n",
        "# Fill numerical features with mean\n",
        "\n",
        "cat_imputer = SimpleImputer(strategy = \"constant\",\n",
        "                            fill_value = \"missing\")\n",
        "\n",
        "door_imputer = SimpleImputer(strategy = \"constant\",\n",
        "                             fill_value = 4)\n",
        "\n",
        "num_imputer = SimpleImputer(strategy = \"mean\")\n",
        "\n",
        "# Define the cols to be worked upon\n",
        "\n",
        "cat_features = [\"Make\", \"Colour\"]\n",
        "door_features = [\"Doors\"]\n",
        "num_features = [\"Odometer (KM)\"]\n",
        "\n",
        "# Create an imputer (Fills the missing data)\n",
        "\n",
        "imputer = ColumnTransformer([\n",
        "             (\"cat_imputer\", cat_imputer, cat_features),\n",
        "             (\"door_imputer\", door_imputer, door_features),\n",
        "             (\"num_imputer\", num_imputer, num_features) ])\n",
        "\n",
        "\n",
        "#  Transform the data\n",
        "\n",
        "filled_x_train = imputer.fit_transform(x_train)\n",
        "\n",
        "filled_x_test = imputer.transform(x_test)\n",
        "\n",
        "\n",
        "# Get our transformed data array's back into DataFrame's\n",
        "filled_x_train_df = pd.DataFrame(filled_x_train,\n",
        "                                 columns=[\"Make\", \"Colour\", \"Doors\", \"Odometer (KM)\"])\n",
        "\n",
        "filled_x_test_df = pd.DataFrame(filled_x_test,\n",
        "                                columns=[\"Make\", \"Colour\", \"Doors\", \"Odometer (KM)\"])\n",
        "\n",
        "# Check missing data in training set\n",
        "filled_x_train_df.isna().sum()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_8aCysx-VQ2k",
        "outputId": "cff1fe34-2027-4c1a-d9be-d82d503551c2"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Make             0\n",
              "Colour           0\n",
              "Doors            0\n",
              "Odometer (KM)    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "filled_x_train_df"
      ],
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        "id": "NSVcAiIeaaPy",
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      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Make Colour Doors  Odometer (KM)\n",
              "0     Toyota  Black   4.0        86696.0\n",
              "1     Toyota  Black   4.0        20714.0\n",
              "2     Toyota  White   4.0       134415.0\n",
              "3     Toyota  Green   4.0  130710.230137\n",
              "4      Honda  White   4.0       145850.0\n",
              "..       ...    ...   ...            ...\n",
              "755   Nissan   Blue   4.0       111256.0\n",
              "756    Honda  White   4.0       246079.0\n",
              "757    Honda   Blue   4.0       224900.0\n",
              "758  missing  White   3.0        19482.0\n",
              "759    Honda   Blue   4.0       199833.0\n",
              "\n",
              "[760 rows x 4 columns]"
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              "      <td>Toyota</td>\n",
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              "      <td>4.0</td>\n",
              "      <td>86696.0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Toyota</td>\n",
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              "      <td>20714.0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Toyota</td>\n",
              "      <td>White</td>\n",
              "      <td>4.0</td>\n",
              "      <td>134415.0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Toyota</td>\n",
              "      <td>Green</td>\n",
              "      <td>4.0</td>\n",
              "      <td>130710.230137</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Honda</td>\n",
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              "      <td>4.0</td>\n",
              "      <td>145850.0</td>\n",
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              "      <th>755</th>\n",
              "      <td>Nissan</td>\n",
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              "      <td>111256.0</td>\n",
              "    </tr>\n",
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              "      <th>756</th>\n",
              "      <td>Honda</td>\n",
              "      <td>White</td>\n",
              "      <td>4.0</td>\n",
              "      <td>246079.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>757</th>\n",
              "      <td>Honda</td>\n",
              "      <td>Blue</td>\n",
              "      <td>4.0</td>\n",
              "      <td>224900.0</td>\n",
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              "    <tr>\n",
              "      <th>758</th>\n",
              "      <td>missing</td>\n",
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              "      <td>3.0</td>\n",
              "      <td>19482.0</td>\n",
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              "    <tr>\n",
              "      <th>759</th>\n",
              "      <td>Honda</td>\n",
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              "      <td>4.0</td>\n",
              "      <td>199833.0</td>\n",
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              "        document.querySelector('#df-86f64871-d9ac-4e33-96d0-e42e087c9fcd button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-86f64871-d9ac-4e33-96d0-e42e087c9fcd');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "      --bg-color: #E8F0FE;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
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              "\n",
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              "    width: 32px;\n",
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              "    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",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
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              "\n",
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              "      border-color: transparent;\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "  }\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "      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",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "filled_x_test_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "H5ZUbsR7dnHX",
        "outputId": "b680fd19-ad06-4e5a-d197-0a902b58bbe8"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Make   Colour Doors Odometer (KM)\n",
              "0     Toyota      Red   4.0      133195.0\n",
              "1        BMW    White   5.0       37513.0\n",
              "2     Toyota    White   4.0       28809.0\n",
              "3     Nissan    White   4.0       82429.0\n",
              "4     Nissan    White   4.0      234161.0\n",
              "..       ...      ...   ...           ...\n",
              "185  missing    White   4.0      128072.0\n",
              "186   Toyota    White   4.0      188338.0\n",
              "187   Toyota      Red   4.0      241987.0\n",
              "188    Honda  missing   4.0      150582.0\n",
              "189   Nissan    White   4.0      129188.0\n",
              "\n",
              "[190 rows x 4 columns]"
            ],
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              "      <th>1</th>\n",
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              "      <td>37513.0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Toyota</td>\n",
              "      <td>White</td>\n",
              "      <td>4.0</td>\n",
              "      <td>28809.0</td>\n",
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              "      <td>234161.0</td>\n",
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              "    <tr>\n",
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              "      <td>Toyota</td>\n",
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              "      <td>188338.0</td>\n",
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              "    <tr>\n",
              "      <th>187</th>\n",
              "      <td>Toyota</td>\n",
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              "    <tr>\n",
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              "      <td>Nissan</td>\n",
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              "      <td>4.0</td>\n",
              "      <td>129188.0</td>\n",
              "    </tr>\n",
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              "<p>190 rows × 4 columns</p>\n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-635b15f7-e37d-47bd-9954-bbff9fc8f326 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-635b15f7-e37d-47bd-9954-bbff9fc8f326');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "\n",
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              "\n",
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              "  .colab-df-quickchart-complete:disabled,\n",
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              "    border-bottom-color: var(--fill-color);\n",
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              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
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              "\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",
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              "        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",
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              "    (() => {\n",
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              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Turn the categories into variables\n",
        "\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "from sklearn.compose import ColumnTransformer\n",
        "\n",
        "categorical_features = [\"Make\", \"Colour\", \"Doors\"]\n",
        "\n",
        "one_hot = OneHotEncoder()\n",
        "transformer = ColumnTransformer([(\"one_hot\",\n",
        "                                   one_hot,\n",
        "                                   categorical_features)],\n",
        "                                   remainder = \"passthrough\",\n",
        "                                   sparse_threshold = 0)\n",
        "\n",
        "\n",
        "# Fill train and test values separately\n",
        "\n",
        "transformed_x_train = transformer.fit_transform(filled_x_train_df)\n",
        "\n",
        "transformed_x_test = transformer.fit_transform(filled_x_test_df)"
      ],
      "metadata": {
        "id": "Vu11ShPbdpPI"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "transformed_x_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y8UaVpzprZvn",
        "outputId": "cff7f86b-bee3-4ed7-f961-fc63adf23404"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.0, 0.0, 0.0, ..., 1.0, 0.0, 86696.0],\n",
              "       [0.0, 0.0, 0.0, ..., 1.0, 0.0, 20714.0],\n",
              "       [0.0, 0.0, 0.0, ..., 1.0, 0.0, 134415.0],\n",
              "       ...,\n",
              "       [0.0, 1.0, 0.0, ..., 1.0, 0.0, 224900.0],\n",
              "       [0.0, 0.0, 0.0, ..., 0.0, 0.0, 19482.0],\n",
              "       [0.0, 1.0, 0.0, ..., 1.0, 0.0, 199833.0]], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "transformed_x_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FLWS8COOrg9j",
        "outputId": "64d93ecf-ee2b-4537-fdf1-89b8c916e574"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.0, 0.0, 0.0, ..., 1.0, 0.0, 133195.0],\n",
              "       [1.0, 0.0, 0.0, ..., 0.0, 1.0, 37513.0],\n",
              "       [0.0, 0.0, 0.0, ..., 1.0, 0.0, 28809.0],\n",
              "       ...,\n",
              "       [0.0, 0.0, 0.0, ..., 1.0, 0.0, 241987.0],\n",
              "       [0.0, 1.0, 0.0, ..., 1.0, 0.0, 150582.0],\n",
              "       [0.0, 0.0, 1.0, ..., 1.0, 0.0, 129188.0]], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Now we've transformed X, let's see if we can fit a model\n",
        "\n",
        "np.random.seed(42)\n",
        "\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "\n",
        "model = RandomForestRegressor()\n",
        "\n",
        "model.fit(transformed_x_train, y_train)\n",
        "model.score(transformed_x_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4WK-PiQIr7x7",
        "outputId": "be9d39f1-efbb-4e2d-b59b-6719c3a78c18"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.09497633411335504"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Choosing the right model\n",
        "\n",
        "Some things to note:\n",
        "\n",
        "* Scikit-Learn refers to machine learning models and\n",
        "algorithms as estimators.\n",
        "* Classification problem - predicting a category (heart disease or not).\n",
        "* Sometimes you'll see clf (short for classifier) used as a classification estimator instance's variable name.\n",
        "Regression problem - predicting a number (selling price of a car).\n",
        "* Unsupervised problem (data with no labels) - clustering (grouping unlabelled samples with other similar unlabelled samples)."
      ],
      "metadata": {
        "id": "2lTYpMROunWM"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Picking model for regression -> California housing dataset"
      ],
      "metadata": {
        "id": "VFOsq3A-Zguk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.datasets import fetch_california_housing\n",
        "housing = fetch_california_housing()"
      ],
      "metadata": {
        "id": "9ty6ujvlscfA"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "housing_df = pd.DataFrame(housing[\"data\"], columns = housing[\"feature_names\"])\n",
        "housing_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "aMgYwOMnZ3XV",
        "outputId": "5c8aaec8-f548-446a-fa8e-6af68ecf397b"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   MedInc  HouseAge  AveRooms  AveBedrms  Population  AveOccup  Latitude  \\\n",
              "0  8.3252      41.0  6.984127   1.023810       322.0  2.555556     37.88   \n",
              "1  8.3014      21.0  6.238137   0.971880      2401.0  2.109842     37.86   \n",
              "2  7.2574      52.0  8.288136   1.073446       496.0  2.802260     37.85   \n",
              "3  5.6431      52.0  5.817352   1.073059       558.0  2.547945     37.85   \n",
              "4  3.8462      52.0  6.281853   1.081081       565.0  2.181467     37.85   \n",
              "\n",
              "   Longitude  \n",
              "0    -122.23  \n",
              "1    -122.22  \n",
              "2    -122.24  \n",
              "3    -122.25  \n",
              "4    -122.25  "
            ],
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              "\n",
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              "      <th>MedInc</th>\n",
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              "      <th>2</th>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3.8462</td>\n",
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              "      --hover-fill-color: #174EA6;\n",
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              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "  }\n",
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              "\n",
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              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
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              "      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-f3a1970e-7ca0-4118-b7a0-bef12b1a8709 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "housing_df[\"target\"] = housing[\"target\"]\n",
        "housing_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "E9kagsG-bHUz",
        "outputId": "9da3b7ac-d8ff-4812-a195-7dab35bea2b3"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   MedInc  HouseAge  AveRooms  AveBedrms  Population  AveOccup  Latitude  \\\n",
              "0  8.3252      41.0  6.984127   1.023810       322.0  2.555556     37.88   \n",
              "1  8.3014      21.0  6.238137   0.971880      2401.0  2.109842     37.86   \n",
              "2  7.2574      52.0  8.288136   1.073446       496.0  2.802260     37.85   \n",
              "3  5.6431      52.0  5.817352   1.073059       558.0  2.547945     37.85   \n",
              "4  3.8462      52.0  6.281853   1.081081       565.0  2.181467     37.85   \n",
              "\n",
              "   Longitude  target  \n",
              "0    -122.23   4.526  \n",
              "1    -122.22   3.585  \n",
              "2    -122.24   3.521  \n",
              "3    -122.25   3.413  \n",
              "4    -122.25   3.422  "
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-e4afa730-11ca-4494-8dd0-aea3b9b4f571');\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
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              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    }\n",
              "  }\n",
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              "\n",
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              "    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-b110f3b3-980f-46ba-b44e-5a46465ffe72 button');\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# housing_df.drop(\"MedHouseVal\", axis = 1, inplace = True)\n",
        "housing_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "fDc9NHBjcIlI",
        "outputId": "50af4644-f0d9-4bda-c99b-220168cfaf19"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   MedInc  HouseAge  AveRooms  AveBedrms  Population  AveOccup  Latitude  \\\n",
              "0  8.3252      41.0  6.984127   1.023810       322.0  2.555556     37.88   \n",
              "1  8.3014      21.0  6.238137   0.971880      2401.0  2.109842     37.86   \n",
              "2  7.2574      52.0  8.288136   1.073446       496.0  2.802260     37.85   \n",
              "3  5.6431      52.0  5.817352   1.073059       558.0  2.547945     37.85   \n",
              "4  3.8462      52.0  6.281853   1.081081       565.0  2.181467     37.85   \n",
              "\n",
              "   Longitude  target  \n",
              "0    -122.23   4.526  \n",
              "1    -122.22   3.585  \n",
              "2    -122.24   3.521  \n",
              "3    -122.25   3.413  \n",
              "4    -122.25   3.422  "
            ],
            "text/html": [
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>MedInc</th>\n",
              "      <th>HouseAge</th>\n",
              "      <th>AveRooms</th>\n",
              "      <th>AveBedrms</th>\n",
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              "      <td>41.0</td>\n",
              "      <td>6.984127</td>\n",
              "      <td>1.023810</td>\n",
              "      <td>322.0</td>\n",
              "      <td>2.555556</td>\n",
              "      <td>37.88</td>\n",
              "      <td>-122.23</td>\n",
              "      <td>4.526</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>8.3014</td>\n",
              "      <td>21.0</td>\n",
              "      <td>6.238137</td>\n",
              "      <td>0.971880</td>\n",
              "      <td>2401.0</td>\n",
              "      <td>2.109842</td>\n",
              "      <td>37.86</td>\n",
              "      <td>-122.22</td>\n",
              "      <td>3.585</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7.2574</td>\n",
              "      <td>52.0</td>\n",
              "      <td>8.288136</td>\n",
              "      <td>1.073446</td>\n",
              "      <td>496.0</td>\n",
              "      <td>2.802260</td>\n",
              "      <td>37.85</td>\n",
              "      <td>-122.24</td>\n",
              "      <td>3.521</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5.6431</td>\n",
              "      <td>52.0</td>\n",
              "      <td>5.817352</td>\n",
              "      <td>1.073059</td>\n",
              "      <td>558.0</td>\n",
              "      <td>2.547945</td>\n",
              "      <td>37.85</td>\n",
              "      <td>-122.25</td>\n",
              "      <td>3.413</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3.8462</td>\n",
              "      <td>52.0</td>\n",
              "      <td>6.281853</td>\n",
              "      <td>1.081081</td>\n",
              "      <td>565.0</td>\n",
              "      <td>2.181467</td>\n",
              "      <td>37.85</td>\n",
              "      <td>-122.25</td>\n",
              "      <td>3.422</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "    }\n",
              "  </style>\n",
              "\n",
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              "      const buttonEl =\n",
              "        document.querySelector('#df-87a37a96-4c35-4ca1-8434-8d96ff80b7f9 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-87a37a96-4c35-4ca1-8434-8d96ff80b7f9');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "            title=\"Suggest charts.\"\n",
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              "\n",
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              "\n",
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              "    fill: var(--fill-color);\n",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    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",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "    }\n",
              "    90% {\n",
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              "      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-33d4682d-f22a-4f31-a673-b720a42d69b0 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "housing_df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ziw8bSVecPhX",
        "outputId": "49d91603-59c6-40c4-8fc3-b72c872475d9"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(20640, 9)"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import Ridge\n",
        "from sklearn.linear_model import Lasso\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.tree import DecisionTreeRegressor\n",
        "from sklearn.linear_model import LinearRegression\n",
        "\n",
        "np.random.seed(42)\n",
        "\n",
        "x = housing_df.drop(\"target\", axis=1)\n",
        "y = housing_df[\"target\"]\n",
        "\n",
        "\n",
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.2)\n",
        "\n",
        "\n",
        "models = {\n",
        "    'Linear Regression': LinearRegression(),\n",
        "    'Ridge Regression' : Ridge(),\n",
        "    'Lasso Regression' : Lasso(),\n",
        "    'Decision Tree': DecisionTreeRegressor(),\n",
        "    'Random Forest': RandomForestRegressor(),\n",
        "}\n",
        "\n",
        "for name, model in models.items():\n",
        "   model.fit(x_train, y_train)\n",
        "   scores = model.score(x_train, y_train)\n",
        "   print(f\"Model : {model} , Acuuracy : {scores}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8PGL-VWoeVB7",
        "outputId": "e79e088b-87d2-4306-fe93-19634faf54ba"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model : LinearRegression() , Acuuracy : 0.6125511913966952\n",
            "Model : Ridge() , Acuuracy : 0.6125511245209703\n",
            "Model : Lasso() , Acuuracy : 0.29040715589885424\n",
            "Model : DecisionTreeRegressor() , Acuuracy : 1.0\n",
            "Model : RandomForestRegressor() , Acuuracy : 0.9736913566055061\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Picking model for classification -> Iris dataset"
      ],
      "metadata": {
        "id": "Z1SMnKWiBJg3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "heart_disease = pd.read_csv('https://raw.githubusercontent.com/mrdbourke/zero-to-mastery-ml/master/data/heart-disease.csv')\n",
        "heart_disease.head()"
      ],
      "metadata": {
        "id": "S-YwsnJve-sH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "b2800d33-33c1-459d-99ea-b994d3a904f8"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
              "0   63    1   3       145   233    1        0      150      0      2.3      0   \n",
              "1   37    1   2       130   250    0        1      187      0      3.5      0   \n",
              "2   41    0   1       130   204    0        0      172      0      1.4      2   \n",
              "3   56    1   1       120   236    0        1      178      0      0.8      2   \n",
              "4   57    0   0       120   354    0        1      163      1      0.6      2   \n",
              "\n",
              "   ca  thal  target  \n",
              "0   0     1       1  \n",
              "1   0     2       1  \n",
              "2   0     2       1  \n",
              "3   0     2       1  \n",
              "4   0     2       1  "
            ],
            "text/html": [
              "\n",
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              "    <div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>cp</th>\n",
              "      <th>trestbps</th>\n",
              "      <th>chol</th>\n",
              "      <th>fbs</th>\n",
              "      <th>restecg</th>\n",
              "      <th>thalach</th>\n",
              "      <th>exang</th>\n",
              "      <th>oldpeak</th>\n",
              "      <th>slope</th>\n",
              "      <th>ca</th>\n",
              "      <th>thal</th>\n",
              "      <th>target</th>\n",
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              "      <th>0</th>\n",
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              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-6b22f61d-5e17-4bce-8fe4-daadde9e81b7 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-6b22f61d-5e17-4bce-8fe4-daadde9e81b7');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e1e49d8c-61ff-40dc-9f8a-0817bf8c62f1')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .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-e1e49d8c-61ff-40dc-9f8a-0817bf8c62f1 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "heart_disease.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MLbWLLHFB2ec",
        "outputId": "0a716806-aa72-4da8-dd93-e446c61ab75b"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(303, 14)"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Trying out linear SVC acc to the map\n",
        "\n",
        "from sklearn.svm import LinearSVC\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "np.random.seed(42)\n",
        "\n",
        "x = heart_disease.drop(\"target\", axis = 1)\n",
        "y = heart_disease[\"target\"]\n",
        "\n",
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.2)\n",
        "\n",
        "model = LinearSVC(max_iter = 2000)\n",
        "\n",
        "model.fit(x_train, y_train)\n",
        "\n",
        "model.score(x_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JWTZPz76B8hT",
        "outputId": "02baf6c4-1105-4d71-b17a-f353f911e16f"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/svm/_base.py:1244: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8852459016393442"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "heart_disease[\"target\"].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IMgWjTw0DCjg",
        "outputId": "1a0c0609-6356-4d82-a8d2-27bd3eb52b1d"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    165\n",
              "0    138\n",
              "Name: target, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "\n",
        "np.random.seed(42)\n",
        "\n",
        "x = heart_disease.drop(\"target\", axis = 1)\n",
        "y = heart_disease[\"target\"]\n",
        "\n",
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.2)\n",
        "\n",
        "\n",
        "models = {\n",
        "    'Logistic Regression': LogisticRegression(max_iter = 200),\n",
        "    'Decision Tree': DecisionTreeClassifier(),\n",
        "    'Random Forest': RandomForestClassifier(),\n",
        "}\n",
        "\n",
        "for name, model in models.items():\n",
        "   model.fit(x_train, y_train)\n",
        "   scores = model.score(x_train, y_train)\n",
        "   print(f\"Model : {model} , Acuuracy : {scores}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fB9KWpAmDiDw",
        "outputId": "3b085947-349c-4148-c420-016e334e4bda"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model : LogisticRegression(max_iter=200) , Acuuracy : 0.8512396694214877\n",
            "Model : DecisionTreeClassifier() , Acuuracy : 1.0\n",
            "Model : RandomForestClassifier() , Acuuracy : 1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### A little tidbit for modelling in machine learning is:\n",
        "\n",
        "* If you have structured data (tables, spreadsheets or dataframes), use ensemble methods, such as, a Random Forest.\n",
        "* If you have unstructured data (text, images, audio, things not in tables), use deep learning or transfer learning (see the ZTM TensorFlow and PyTorch courses for more on deep learning).\n"
      ],
      "metadata": {
        "id": "0hX5_76bF0zc"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Ways to make predictions"
      ],
      "metadata": {
        "id": "IVxg3n66IrNE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "np.random.seed(42)\n",
        "\n",
        "x = heart_disease.drop(\"target\", axis = 1)\n",
        "y = heart_disease[\"target\"]\n",
        "\n",
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.2)\n",
        "\n",
        "clf = RandomForestClassifier(n_estimators = 100)\n",
        "\n",
        "clf.fit(x_train, y_train)\n",
        "\n",
        "clf.predict(x_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c6e_eANXEEqv",
        "outputId": "189fba98-d0b3-444d-8fa4-9d960292a9bb"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0,\n",
              "       1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_preds = clf.predict(x_test)\n",
        "np.mean(y_preds == y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7fxcyBE2JVev",
        "outputId": "82eba733-683b-4b8c-c99a-bb4f40a99a69"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8524590163934426"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "clf.score(x_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7U2-g8MpJgzT",
        "outputId": "17047cac-8ce9-4a69-fd1b-3f15a1067441"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8524590163934426"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Evaluate the model"
      ],
      "metadata": {
        "id": "T5O1BzjPJ52M"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "accuracy_score(y_test, y_preds)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lGeLrLg3Josz",
        "outputId": "6b516000-f7e3-46fe-f5b8-da9081ead797"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8524590163934426"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Make predictions with predict_proba()\n",
        "#### returns the probabilities of a classification label"
      ],
      "metadata": {
        "id": "_YvcW8xRNtdG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "clf.predict_proba(x_test[:5])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6QajiSR1KDP9",
        "outputId": "78141244-82cf-4b65-8497-ed39888d24d3"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.89, 0.11],\n",
              "       [0.49, 0.51],\n",
              "       [0.43, 0.57],\n",
              "       [0.84, 0.16],\n",
              "       [0.18, 0.82]])"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "clf.predict(x_test[:5])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-D25cUhXKelg",
        "outputId": "52563fc7-c87e-4c12-845a-77b2c39510af"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 1, 0, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Using predict() for regression problems"
      ],
      "metadata": {
        "id": "NleCtCgVLmLh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "housing_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "9hXuPQVOKrQH",
        "outputId": "fff3b4d8-7b3f-4916-b3cc-b021e6231110"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   MedInc  HouseAge  AveRooms  AveBedrms  Population  AveOccup  Latitude  \\\n",
              "0  8.3252      41.0  6.984127   1.023810       322.0  2.555556     37.88   \n",
              "1  8.3014      21.0  6.238137   0.971880      2401.0  2.109842     37.86   \n",
              "2  7.2574      52.0  8.288136   1.073446       496.0  2.802260     37.85   \n",
              "3  5.6431      52.0  5.817352   1.073059       558.0  2.547945     37.85   \n",
              "4  3.8462      52.0  6.281853   1.081081       565.0  2.181467     37.85   \n",
              "\n",
              "   Longitude  target  \n",
              "0    -122.23   4.526  \n",
              "1    -122.22   3.585  \n",
              "2    -122.24   3.521  \n",
              "3    -122.25   3.413  \n",
              "4    -122.25   3.422  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f6e2501d-a15e-4e56-996e-088ad781a8be\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .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>MedInc</th>\n",
              "      <th>HouseAge</th>\n",
              "      <th>AveRooms</th>\n",
              "      <th>AveBedrms</th>\n",
              "      <th>Population</th>\n",
              "      <th>AveOccup</th>\n",
              "      <th>Latitude</th>\n",
              "      <th>Longitude</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>8.3252</td>\n",
              "      <td>41.0</td>\n",
              "      <td>6.984127</td>\n",
              "      <td>1.023810</td>\n",
              "      <td>322.0</td>\n",
              "      <td>2.555556</td>\n",
              "      <td>37.88</td>\n",
              "      <td>-122.23</td>\n",
              "      <td>4.526</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>8.3014</td>\n",
              "      <td>21.0</td>\n",
              "      <td>6.238137</td>\n",
              "      <td>0.971880</td>\n",
              "      <td>2401.0</td>\n",
              "      <td>2.109842</td>\n",
              "      <td>37.86</td>\n",
              "      <td>-122.22</td>\n",
              "      <td>3.585</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7.2574</td>\n",
              "      <td>52.0</td>\n",
              "      <td>8.288136</td>\n",
              "      <td>1.073446</td>\n",
              "      <td>496.0</td>\n",
              "      <td>2.802260</td>\n",
              "      <td>37.85</td>\n",
              "      <td>-122.24</td>\n",
              "      <td>3.521</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5.6431</td>\n",
              "      <td>52.0</td>\n",
              "      <td>5.817352</td>\n",
              "      <td>1.073059</td>\n",
              "      <td>558.0</td>\n",
              "      <td>2.547945</td>\n",
              "      <td>37.85</td>\n",
              "      <td>-122.25</td>\n",
              "      <td>3.413</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3.8462</td>\n",
              "      <td>52.0</td>\n",
              "      <td>6.281853</td>\n",
              "      <td>1.081081</td>\n",
              "      <td>565.0</td>\n",
              "      <td>2.181467</td>\n",
              "      <td>37.85</td>\n",
              "      <td>-122.25</td>\n",
              "      <td>3.422</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f6e2501d-a15e-4e56-996e-088ad781a8be')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-f6e2501d-a15e-4e56-996e-088ad781a8be button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-f6e2501d-a15e-4e56-996e-088ad781a8be');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-9252f9ee-50c8-41f3-bc6e-868256922e28\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-9252f9ee-50c8-41f3-bc6e-868256922e28')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .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-9252f9ee-50c8-41f3-bc6e-868256922e28 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestRegressor\n",
        "\n",
        "np.random.seed(42)\n",
        "\n",
        "x = housing_df.drop(\"target\", axis = 1)\n",
        "y = housing_df[\"target\"]\n",
        "\n",
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.2)\n",
        "\n",
        "model = RandomForestRegressor(n_estimators = 100)\n",
        "\n",
        "model.fit(x_train, y_train)\n",
        "\n",
        "# model.score(x_test, y_test)\n",
        "\n",
        "y_preds = model.predict(x_test)"
      ],
      "metadata": {
        "id": "aJV2tPcdLsjp"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_preds[:10]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xzEEDfJRO1dU",
        "outputId": "37eb243b-4c48-4a15-b162-7960c7637e20"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.49384  , 0.75494  , 4.9285964, 2.54029  , 2.33176  , 1.6549701,\n",
              "       2.34323  , 1.66182  , 2.47489  , 4.8344779])"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.array(y_test[:10])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "81VgFALtPPJq",
        "outputId": "91117897-491a-45b1-976f-7941cd29ad58"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.477  , 0.458  , 5.00001, 2.186  , 2.78   , 1.587  , 1.982  ,\n",
              "       1.575  , 3.4    , 4.466  ])"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_absolute_error\n",
        "\n",
        "mean_absolute_error(y_test, y_preds)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N20abvRBPQ_8",
        "outputId": "bcb8c155-d466-4cb6-e8de-739c6c0e181e"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.3265721842781009"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Evaluating a model"
      ],
      "metadata": {
        "id": "RT2GmZ9OCnlW"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Scikit-Learn implements 3 different methods of evaluating models.\n",
        "\n",
        "* The score() method. Calling score() on a model instance will return a metric assosciated with the type of model you're using. The metric depends on which model you're using.\n",
        "* The scoring parameter. This parameter can be passed to methods such as cross_val_score() or GridSearchCV() to tell Scikit-Learn to use a specific type of scoring metric.\n",
        "* Problem-specific metric functions available in sklearn.metrics. Similar to how the scoring parameter can be passed different scoring functions, Scikit-Learn implements these as stand alone functions."
      ],
      "metadata": {
        "id": "82NWnQBwCsOu"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Evaluating using score method"
      ],
      "metadata": {
        "id": "f8wK4FAeFjHx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "np.random.seed(42)\n",
        "\n",
        "x = heart_disease.drop(\"target\", axis = 1)\n",
        "y = heart_disease[\"target\"]\n",
        "\n",
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.2)\n",
        "\n",
        "clf = RandomForestClassifier(n_estimators = 100)\n",
        "\n",
        "clf.fit(x_train, y_train)\n",
        "\n",
        "clf.score(x_test, y_test)"
      ],
      "metadata": {
        "id": "VpNHx6vPPmYp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "528c64b6-375c-4123-b163-0b3906c55db6"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8524590163934426"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestRegressor\n",
        "\n",
        "np.random.seed(42)\n",
        "\n",
        "x = housing_df.drop(\"target\", axis = 1)\n",
        "y = housing_df[\"target\"]\n",
        "\n",
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.2)\n",
        "\n",
        "model = RandomForestRegressor(n_estimators = 100)\n",
        "\n",
        "model.fit(x_train, y_train)\n",
        "\n",
        "model.score(x_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PT6EO7aiDOQ5",
        "outputId": "efaddaec-0d38-43c4-c7f5-83cbaae706c1"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8066196804802649"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Evaluating using scoring parameter"
      ],
      "metadata": {
        "id": "weV0DHX9Fe3j"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "np.random.seed(42)\n",
        "\n",
        "x = heart_disease.drop(\"target\", axis = 1)\n",
        "y = heart_disease[\"target\"]\n",
        "\n",
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.2)\n",
        "\n",
        "clf = RandomForestClassifier(n_estimators = 100)\n",
        "\n",
        "clf.fit(x_train, y_train)\n",
        "\n",
        "clf.score(x_test, y_test)"
      ],
      "metadata": {
        "id": "n07aSmvUDynw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7ff2bd94-6ac5-4a34-adba-18af99dc7776"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8524590163934426"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cross_val_score(clf, x, y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-l09SDa7Hb_f",
        "outputId": "ed828110-9096-4e99-d43c-3b0d7260f934"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.81967213, 0.86885246, 0.81967213, 0.78333333, 0.76666667])"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Scoring parameter set to none by default\n",
        "\n",
        "cross_val_score(clf, x, y, cv = 5, scoring = None)"
      ],
      "metadata": {
        "id": "K_csb0ozHlnY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a27f8548-a933-47c0-a2ab-9d1a1641b82f"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.83606557, 0.8852459 , 0.7704918 , 0.8       , 0.8       ])"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Evaluating a classification model\n",
        "* Accuracy\n",
        "* Area under ROC curve\n",
        "* Confusion matrix\n",
        "* Classification report"
      ],
      "metadata": {
        "id": "LvZdvCgtNCSC"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Accuracy"
      ],
      "metadata": {
        "id": "6Ewa-QYpNlwn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "np.random.seed(42)\n",
        "\n",
        "x = heart_disease.drop(\"target\", axis = 1)\n",
        "y = heart_disease[\"target\"]\n",
        "\n",
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.2)\n",
        "\n",
        "clf = RandomForestClassifier(n_estimators = 100)\n",
        "\n",
        "clf.fit(x_train, y_train)\n",
        "\n",
        "cross_val_score(clf, x, y, cv = 5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WmNwht-vMJSP",
        "outputId": "8145b2e2-e4d8-4ab9-9e3b-3190b5798680"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.81967213, 0.86885246, 0.81967213, 0.78333333, 0.76666667])"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Heart Disease Classifier Accuracy: {clf.score(x_test, y_test) * 100:.2f}%\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ei0FpwG9Sdy3",
        "outputId": "c5457db7-f592-4393-e0ba-279bd7695cd4"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Heart Disease Classifier Accuracy: 85.25%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Area under ROC curve\n",
        "* Receiver operating characteristic curve\n",
        "* ROC curves are a comparison of true postive rate (tpr) versus false positive rate (fpr).\n",
        "\n",
        "For clarity:\n",
        "\n",
        "* True positive = model predicts 1 when truth is 1\n",
        "* False positive = model predicts 1 when truth is 0\n",
        "* True negative = model predicts 0 when truth is 0\n",
        "* False negative = model predicts 0 when truth is 1"
      ],
      "metadata": {
        "id": "0zSXo4C3Txuo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import roc_curve\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "np.random.seed(42)\n",
        "\n",
        "x = heart_disease.drop(\"target\", axis = 1)\n",
        "y = heart_disease[\"target\"]\n",
        "\n",
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.2)\n",
        "\n",
        "clf = RandomForestClassifier(n_estimators = 100)\n",
        "\n",
        "clf.fit(x_train, y_train)\n",
        "\n",
        "y_probs = clf.predict_proba(x_test)"
      ],
      "metadata": {
        "id": "gjWPtwvfTsfX"
      },
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_probs[:5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vaQ7tyBGU9G2",
        "outputId": "76154d61-1a50-40ec-b362-e3e0f6f1191f"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.89, 0.11],\n",
              "       [0.49, 0.51],\n",
              "       [0.43, 0.57],\n",
              "       [0.84, 0.16],\n",
              "       [0.18, 0.82]])"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_probs_positives = y_probs[:, 1]\n",
        "y_probs_positives[:10]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_nWGK5WjU-0c",
        "outputId": "b3963cc3-34c8-493d-e818-429db649592a"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.11, 0.51, 0.57, 0.16, 0.82, 0.86, 0.64, 0.05, 0.01, 0.53])"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fpr, tpr, thresholds = roc_curve(y_test, y_probs_positives)"
      ],
      "metadata": {
        "id": "2vHx56QGVGU_"
      },
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "def plot_roc_curve(fpr, tpr):\n",
        "   \"\"\"\n",
        "   plots ROC curves\n",
        "   \"\"\"\n",
        "   # Plot ROC curve\n",
        "   plt.plot(fpr, tpr, color = \"orange\", label = \"ROC\")\n",
        "\n",
        "   # Plot line with no predictive power (baseline)\n",
        "   plt.plot([0, 1], [0, 1], color = \"darkblue\", linestyle = \"--\", label = \"Guessing\")\n",
        "\n",
        "   # customise the plot\n",
        "   plt.xlabel(\"False positive rate (fpr)\")\n",
        "   plt.ylabel(\"True positive rate(tpr)\")\n",
        "   plt.title(\"ROC curve\")\n",
        "   plt.legend()\n",
        "   plt.show()\n",
        "\n",
        "plot_roc_curve(fpr, tpr)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "dCJMze39Vp6g",
        "outputId": "aa936ab9-3643-48f8-d198-4adfa39324bd"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import roc_auc_score\n",
        "\n",
        "roc_auc_score(y_test, y_probs_positives)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x-yEZDW0XRNQ",
        "outputId": "0129e76a-d90e-4bb4-f43d-603d5e12b9f8"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9304956896551724"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Confusion matrix\n",
        "\n",
        "* A confusion matrix is a quick way to compare the labels a model predicts and the actual labels it was supposed to predict.\n",
        "\n",
        "* In essence, giving you an idea of where the model is getting confused."
      ],
      "metadata": {
        "id": "VwGgXtpHeaVx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "y_preds = clf.predict(x_test)\n",
        "\n",
        "confusion_matrix(y_test, y_preds)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BEBcPtZud3p8",
        "outputId": "da8d78e2-b6fe-4ac6-e2f0-9d82770226f4"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[24,  5],\n",
              "       [ 4, 28]])"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.crosstab(y_test,\n",
        "            y_preds,\n",
        "            rownames=[\"Actual Label\"],\n",
        "            colnames=[\"Predicted Label\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "id": "vyJsYajuez8F",
        "outputId": "68222cbe-b976-4318-b885-e419042d2474"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Predicted Label   0   1\n",
              "Actual Label           \n",
              "0                24   5\n",
              "1                 4  28"
            ],
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              "    <div>\n",
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              "                                                    [key], {});\n",
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              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .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-59bc7174-d87b-4bde-b215-36c5db69ac16 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "\n",
        "sns.set(font_scale = 1.5)\n",
        "\n",
        "conf_mat = confusion_matrix(y_test, y_preds)\n",
        "\n",
        "sns.heatmap(conf_mat)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 459
        },
        "id": "voajjR7sfWTt",
        "outputId": "7c79880f-a118-4e39-ac94-b960c4f9a3cf"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 70
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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LiSeemD/5kz/JhAkTejWmltr2viquj2y49+t9PQRoSkPPndXXQ4Cm8+Y7zzf+GX/w/xW5z14/eLjIfZqFigYAFNAMFY1mZDIoANAwKhoAUEIT7gzaDAQNACigJmhU0joBABpGRQMASlDRqCRoAEABWifVtE4AgIZR0QCAElQ0KgkaAFCA1kk1rRMAoGFUNACgABWNaoIGABQgaFQTNACghFpLX4+gKZmjAQA0jIoGABSgdVJN0ACAAmrdWidVtE4AgIZR0QCAArROqgkaAFBAzaqTSlonAEDDqGgAQAFaJ9UEDQAowKqTalonAEDDqGgAQAG1Wl+PoDkJGgBQgNZJNUEDAAoQNKqZowEANIyKBgAUYI5GNUEDAArQOqmmdQIANIyKBgAU4F0n1QQNACjAFuTVtE4AgIZR0QCAArq1TioJGgBQgDka1bROAICGUdEAgALso1FN0ACAAuwMWk3QAIACVDSqmaMBADSMigYAFGB5azVBAwAKsLy1mtYJANAwKhoAUIBVJ9UEDQAowByNalonAEDDqGgAQAEmg1YTNACgAHM0qmmdAAANo6IBAAWYDFrtIxs0Pnn2LX09BGhKG1b/rK+HAB9L5mhU+8gGDQBoJioa1czRAAAaRkUDAAqw6KSaoAEABWidVNM6AQAaRkUDAAqw6qSaoAEABXT39QCalKABADuR1157LQsWLMjSpUvz9NNPZ/ny5eno6Mj48eNzxx13bPO6z372s1m1atX73nvJkiUZMGBAXeMRNACggFqao3UyZ86cXHfddb2+/rd/+7fT3t5eeaylpf5/RkEDAArobpL1re3t7TnuuOMyZsyYjBkzJsuWLcuMGTM+9PVf+9rXcswxxxQbj6ABADuRKVOmZMqUKT3fX3nllT4cjaABAEV0N0nrpNkIGgBQQLPM0dhed911V773ve9l48aN2XfffXP00Ufn9NNP3+a8jQ8iaABAAaWWt06cOPF9j8+bN6/Qk6r9+7//+1bff/SjH+Wmm27Kt771rRx//PF130/QAAAyfvz4TJgwIWPGjMnQoUOzadOmLFq0KN/+9rezbNmyXHjhhfnnf/7njB49uq77ChoAUECp1kmjKxbbcv3112/1/ROf+EROOumkHHvssTn77LPzzDPP5Jvf/GZmzZpV13296wQACugu9Gk2u+66ay677LIkyWOPPZZ169bVdb2gAQC8ryOPPDJJ0t3dnV/+8pd1Xat1AgAFNGM1opTW1taev7u6uuq6VtAAgAJ2luWtVX7xi1/0/L3//vvXda3WCQDwvmbOnJkkOeiggzJ48OC6rlXRAIACuj/CBY3bbrstbW1tOe2007LXXnv1/P7mm2/mxhtvzIMPPpgkufTSS+u+t6ABAAU0yxbka9asyeTJk3u+d3Z2JkmeeOKJrV6WNnXq1EybNi1J8utf/zrf//73c+2112bYsGHZe++9s3Hjxrz44ovZvHlzdtlll1x++eU55ZRT6h6PoAEAO5Gurq6sXbv2Pb9v3rx5q983btzY8/ekSZOSJEuWLMnq1avz7LPPpl+/fhk+fHjGjx+fs88+O4cddlivxiNoAEABTfKW+AwfPjz//d//Xdc148aNy7hx4xoyHkEDAArYmZe3bg9BAwAK6G5pjjkazcbyVgCgYVQ0AKCAZpmj0WwEDQAowByNalonAEDDqGgAQAEf5Z1BG0nQAIACmmVn0GajdQIANIyKBgAUYNVJNUEDAAowR6Oa1gkA0DAqGgBQgH00qgkaAFCAORrVBA0AKMAcjWrmaAAADaOiAQAFmKNRTdAAgAIEjWpaJwBAw6hoAEABNZNBKwkaAFCA1kk1rRMAoGFUNACgABWNaoIGABRgZ9BqWicAQMOoaABAAbYgryZoAEAB5mhUEzQAoABBo5o5GgBAw6hoAEABVp1UEzQAoACTQatpnQAADaOiAQAFmAxaTdAAgALM0aimdQIANIyKBgAU0K2mUUnQAIACzNGopnUCADSMigYAFKBxUk3QAIACtE6qCRoAUICdQauZowEANIyKBgAUYHlrNUEDAAoQM6ppnQAADaOiAQAFWHVSTdAAgALM0aimdQIANIyKBgAUoJ5RTdAAgALM0aimdQIANIyKBgAUYDJoNUEDAAoQM6oJGgBQgDka1czRAAAaRkUDAAqoaZ5UEjQAoACtk2paJwBAw6hoAEABlrdWEzQAoIBmiRmvvfZaFixYkKVLl+bpp5/O8uXL09HRkfHjx+eOO+5432s3bdqU2bNn51//9V/z8ssvp7W1NYceemi+9KUv5Xd/93d7NR5BAwB2InPmzMl1111X93UdHR0577zzsmjRovTr1y8HHXRQNmzYkMcffzyPP/54pk2blr/4i7+o+76CBgAU0Cytk/b29hx33HEZM2ZMxowZk2XLlmXGjBkfeN03v/nNLFq0KMOHD8/MmTPz6U9/Okkyb968XHbZZZk5c2aOPPLIfPazn61rPIIGABTQLKtOpkyZkilTpvR8f+WVVz7wmtdffz133XVXkuTaa6/tCRlJMnHixEydOjUzZszIzTffXHfQsOoEAD7m5s+fn02bNmXkyJGZMGHCe47/4R/+YZLkmWeeycsvv1zXvQUNACigVuh/fWHx4sVJkqOOOqry+ODBgzN8+PCtzv2wdnjr5LbbbsuGDRvyp3/6pzv60QDQMKVaJxMnTnzf4/PmzSv0pP9n5cqVSZIRI0Zs85wRI0bkV7/6VVasWFHXvXd4RWPmzJn5zne+s6MfCwAN9VGuaKxbty5JMnDgwG2es+XYW2+9Vde9TQYFgCbSiIrFB+no6EiStLa2bvOctra2JMnGjRvruregAQAFNMuqk94YMGBAkt9s2LUtnZ2dSZJdd921rnv3Kmicc845vbksSfLOO+/0+loAaFbdtebYR6M39thjjyT/r4VSZcuxLed+WL0KGo8//nhaWlpS6+W/1JaWll5dBwCUN3LkyDzxxBN56aWXtnnOlmWtI0eOrOvevQoaAwYMSGdnZy666KLss88+dV17ww031N3fAYBm99GtZyTjxo3LD3/4wzzxxBOVx1955ZX86le/6jm3Hr0KGocddlieeuqpjBo1Kp/73Ofqunb69OmCBgA7nWbZgrw3Jk6cmL/7u7/LypUrs3Dhwvds2rVl19BRo0blwAMPrOvevVreevjhhydJnn766d5cDgA0kX333TdnnnlmkuSqq67Kiy++2HNs/vz5+ad/+qckycUXX1z3vXtV0RgzZkxqtVqWLl1a97W9ndcBAM2sr/bA+L/WrFmTyZMn93zfslrkiSeeyDHHHNPz+9SpUzNt2rSe71/5ylfyzDPP5Mknn8xpp52Wgw8+OO+++27P3Izzzz+/7i5G0sugcfzxx+erX/1q2tvb6772gQceSFdXV28eCwBNq1mWt3Z1dWXt2rXv+X3z5s1b/f5/pzHsuuuu+f73v59Zs2bl3/7t37Jy5cq0trZm/Pjx+eIXv5hTTjmlV+NpqX1ESwz924b19RCgKW1Y/bO+HgI0ndZ9P/3BJ22nMw+cXOQ+d790f5H7NAsbdgFAAR/lyaCNJGgAQAHNMkej2QgaAFBAs8zRaDY7/O2tAMDHh4oGABTwEV1b0XCCBgAUYDJoNa0TAKBhVDQAoACTQasJGgBQgOWt1bROAICGUdEAgAJMBq0maABAAZa3VtM6AQAaRkUDAAqw6qSaoAEABVh1Uk3QAIACTAatZo4GANAwKhoAUIBVJ9UEDQAoQOukmtYJANAwKhoAUIBVJ9UEDQAooNscjUpaJwBAw6hoAEAB6hnVBA0AKMCqk2qCBgAUIGhUM0cDAGgYFQ0AKMDOoNUEDQAoQOukmtYJANAwKhoAUICdQasJGgBQgDka1bROAICGUdEAgAJMBq0maABAAVon1bROAICGUdEAgAK0TqoJGgBQgOWt1QQNACig2xyNSuZoAAANo6IBAAVonVQTNACgAK2TalonAEDDqGgAQAFaJ9UEDQAoQOukmtYJANAwKhoAUIDWSTVBAwAK0DqppnUCADSMigYAFKB1Uk3QAIACarXuvh5CUxI0AKAAr4mvZo4GANAwKhoAUEDNqpNKggYAFKB1Uk3rBABoGBUNAChA66SaoAEABdgZtJrWCQDQMCoaAFBAs+wMOn369Nx8883ve87f/u3f5qyzztoh4xE0AKCAZpujsc8+++TAAw+sPDZo0KAdNg5BAwB2QieeeGKuv/76vh6GoAEAJdhHo5qgAQAFNFvrpFkIGgBQQLMtb3322WdzxRVX5LXXXsvuu++eQw45JJMmTcrBBx+8Q8chaABAE5k4ceL7Hp83b96Hus/y5cuzfPnynu/z58/Pd7/73Zxzzjn5q7/6q/Tr12+7xvlhCRoAUECztE7222+/XHrppTnhhBMyfPjwtLe3Z8WKFbnzzjtz1113Zfbs2enfv3/+8i//coeMp6XWLP9m6tS/bVhfDwGa0obVP+vrIUDTad330w1/xsD23ypyn3XvvFDkPlVmzpyZG264If3798+DDz6Y4cOHN+xZW9gZFAA+Js4///zst99+2bx5c+bPn79DniloAEABtVqtyKeR+vXrlyOOOCJJ8tJLLzX0WVuYowEABTTbqpNtaW1tTZJs3rx5hzxPRQMAPkaee+65JMn++++/Q56nogEABTTLS9Xez8MPP9wTNI4//vgd8kwVDQAooLtWK/LZHs8991z++q//Os8+++zWY+vuzo9+9KNcccUVSZKTTjopY8eO3a5nfVgqGgCwk9i8eXPuvvvu3H333dlzzz0zdOjQ9OvXLy+//HLWrVuXJDn66KPzD//wDztsTIIGABTQDNtSDRs2LJdddlkWL16cF154IS+99FI6OzszcODAnHjiiTnttNNy2mmn7bBdQRMbdsFOx4Zd8F47YsOuAbseUOQ+HRt/WeQ+zUJFAwAK+Ij+d3vDmQwKADSMigYAFKCiUU3QAIACxIxqWicAQMN8ZFedAADNT0UDAGgYQQMAaBhBAwBoGEEDAGgYQQMAaBhBAwBoGEEDAGgYQQMAaBhBAwBoGEEDAGgYQQMAaBhBAwBoGEEDAGgYQQMAaJj+fT0AProWLlyY22+/PU899VTefffdDB06NKeeemouuOCC7Lbbbn09PNihXnvttSxYsCBLly7N008/neXLl6ejoyPjx4/PHXfc0dfDgz4jaNArd9xxR6699trUarXsv//+GTJkSJ5//vnccsst+clPfpI777wze+65Z18PE3aYOXPm5LrrruvrYUDTETSo29KlS/ONb3wjSXLNNdfkjDPOSEtLS1555ZVceOGFeeaZZ3L11Vdn+vTpfTxS2HHa29tz3HHHZcyYMRkzZkyWLVuWGTNm9PWwoM8JGtRtxowZ6e7uzuTJk3PmmWf2/D548OD84z/+Y37v934vP/nJT/Lss8/m0EMP7cORwo4zZcqUTJkypef7K6+80oejgeZhMih1Wb9+fX72s58lSc4444z3HB85cmQmTJiQJJk7d+4OHRsAzUfQoC7Lly9PZ2dn2traMnbs2MpzjjrqqCTJU089tSOHBkATEjSoy4oVK5IkQ4cOTWtra+U5I0aM2OpcAD6+BA3qsm7duiTJwIEDt3nOlmNbzgXg40vQoC4dHR1Jss1qRpK0tbVtdS4AH1+CBnUZMGBAkmTTpk3bPKezs3OrcwH4+BI0qMuHaYt8mPYKAB8PggZ1GTlyZJJk9erV26xqvPzyy1udC8DHl6BBXQ477LC0trams7MzS5YsqTxn0aJFSZJx48btwJEB0IwEDerS3t6ez3zmM0mSe+655z3HV65cmYULFyZJTj311B06NgCaj6BB3S666KK0tLTkgQceyN13351arZYkefXVV3P55Zenu7s7n/vc52w/DkBaalv+XwLqMGvWrFx//fWp1WoZMmRI9tprrzz//PPp7OzMpz71qdx5553Ze++9+3qYsMOsWbMmkydP7vne2dmZd999N/379097e3vP71OnTs20adP6YITQN7xUjV4599xzc8ghh+R73/telixZkv/5n//J0KFDc+qpp+aCCy7I7rvv3tdDhB2qq6sra9eufc/vmzdv3ur3jRs37rhBQRNQ0QAAGsYcDQCgYQQNAKBhBA0AoGEEDQCgYQQNAKBhBA0AoGEEDQCgYQQNAKBhBA0AoGEEDQCgYQQNAKBhBA0AoGEEDQCgYf5/tWXhD9rx9rUAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import ConfusionMatrixDisplay\n",
        "\n",
        "ConfusionMatrixDisplay.from_estimator(clf, x, y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 495
        },
        "id": "6oscHrnZgCrG",
        "outputId": "7f6174de-ab78-4c63-97e0-014b0ed797f4"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f6bf941ae30>"
            ]
          },
          "metadata": {},
          "execution_count": 71
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Classification Report\n",
        "\n",
        "* A classification report is more so a collection of metrics rather than a single one."
      ],
      "metadata": {
        "id": "1XnPLldBiroY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report\n",
        "\n",
        "print(classification_report(y_test, y_preds))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9aTrQsPeiTAl",
        "outputId": "b8426d5a-c6ec-4ba3-f3d1-e5390c784b3d"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.86      0.83      0.84        29\n",
            "           1       0.85      0.88      0.86        32\n",
            "\n",
            "    accuracy                           0.85        61\n",
            "   macro avg       0.85      0.85      0.85        61\n",
            "weighted avg       0.85      0.85      0.85        61\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Evaluating a regression model\n",
        "\n",
        "* R^2 (pronounced r-squared) or coefficient of determination - Compares your models predictions to the mean of the targets. Values can range from negative infinity (a very poor model) to 1. For example, if all your model does is predict the mean of the targets, its R^2 value would be 0. And if your model perfectly predicts a range of numbers it's R^2 value would be 1. Higher is better.\n",
        "* Mean absolute error (MAE) - The average of the absolute differences between predictions and actual values. It gives you an idea of how wrong your predictions were. Lower is better.\n",
        "* Mean squared error (MSE) - The average squared differences between predictions and actual values. Squaring the errors removes negative errors. It also amplifies outliers (samples which have larger errors). Lower is better.\n"
      ],
      "metadata": {
        "id": "_hGN_SzJqNqC"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### R squared error"
      ],
      "metadata": {
        "id": "5LMOVhFarKMV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestRegressor\n",
        "\n",
        "np.random.seed(42)\n",
        "\n",
        "x = housing_df.drop(\"target\", axis = 1)\n",
        "y = housing_df[\"target\"]\n",
        "\n",
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.2)\n",
        "\n",
        "model = RandomForestRegressor(n_estimators = 100)\n",
        "\n",
        "model.fit(x_train, y_train)\n",
        "\n",
        "model.score(x_test, y_test)"
      ],
      "metadata": {
        "id": "0r5_avw3jEE5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6a6aeaf5-1cbe-45c5-cef7-fd3e9f2fd9a7"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8066196804802649"
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import r2_score\n",
        "\n",
        "y_test_mean = np.full(len(y_test), y_test.mean())\n",
        "\n",
        "r2_score(y_true = y_test, y_pred = y_test_mean)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hkwUk65prIic",
        "outputId": "9cb377e8-dc00-4586-8c1a-dcb09a95612d"
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Mean Absolute error (MAE)"
      ],
      "metadata": {
        "id": "Skr0VNvAr4m2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_absolute_error\n",
        "\n",
        "y_preds = model.predict(x_test)\n",
        "\n",
        "mean_absolute_error(y_test, y_preds)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9S0wyvrkruci",
        "outputId": "dadbd296-63da-42b4-ef1d-6773146e2c91"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.3265721842781009"
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Mean Squared error (MSE)"
      ],
      "metadata": {
        "id": "qwXRc7V5ssSm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_squared_error\n",
        "\n",
        "mean_squared_error(y_test, y_preds)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yLl5e_92sOEF",
        "outputId": "695abde1-7bc1-425e-800b-37fc2184a0f1"
      },
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.2534073069137548"
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Classification functions\n",
        "\n",
        "For:\n",
        "\n",
        "* Accuracy we can use sklearn.metrics.accuracy_score\n",
        "* Precision we can use sklearn.metrics.precision_score\n",
        "* Recall we can use sklearn.metrics.recall_score\n",
        "* F1 we can use sklearn.metrics.f1_score"
      ],
      "metadata": {
        "id": "8dlJTuKquoRV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "np.random.seed(42)\n",
        "\n",
        "x = heart_disease.drop(\"target\", axis=1)\n",
        "y = heart_disease[\"target\"]\n",
        "\n",
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2)\n",
        "\n",
        "clf = RandomForestClassifier(n_estimators = 100)\n",
        "\n",
        "clf.fit(x_train, y_train)\n",
        "\n",
        "y_preds = clf.predict(x_test)\n",
        "\n",
        "print(\"Classifier metrics on the test set:\")\n",
        "print(f\"Accuracy: {accuracy_score(y_test, y_preds) * 100:.2f}%\")\n",
        "print(f\"Precision: {precision_score(y_test, y_preds):.2f}%\")\n",
        "print(f\"Recall: {recall_score(y_test, y_preds):.2f}%\")\n",
        "print(f\"F1: {f1_score(y_test, y_preds):.2f}%\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HxL_mCFNs2Fv",
        "outputId": "a87111ba-bf56-4e0f-e47c-49a3d136c24f"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Classifier metrics on the test set:\n",
            "Accuracy: 85.25%\n",
            "Precision: 0.85%\n",
            "Recall: 0.88%\n",
            "F1: 0.86%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can use a similar setup for our regression problem, just with different methods.\n",
        "\n",
        "For:\n",
        "\n",
        "* R^2 we can use sklearn.metrics.r2_score\n",
        "* MAE (mean absolute error) we can use sklearn.metrics.mean_absolute_error\n",
        "* MSE (mean squared error) we can use sklearn.metrics.mean_squared_error"
      ],
      "metadata": {
        "id": "RVhZS7SYu7g9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import r2_score, mean_absolute_error, mean_squared_error\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "np.random.seed(42)\n",
        "\n",
        "x = housing_df.drop(\"target\", axis=1)\n",
        "y = housing_df[\"target\"]\n",
        "\n",
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.2)\n",
        "\n",
        "model = RandomForestRegressor(n_estimators=100, n_jobs = -1)\n",
        "\n",
        "model.fit(x_train, y_train)\n",
        "\n",
        "y_preds = model.predict(x_test)\n",
        "\n",
        "print(\"Regression model metrics on the test set:\")\n",
        "print(f\"R^2: {r2_score(y_test, y_preds):.2f}\")\n",
        "print(f\"MAE: {mean_absolute_error(y_test, y_preds):.2f}\")\n",
        "print(f\"MSE: {mean_squared_error(y_test, y_preds):.2f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3F1XHrqLu17E",
        "outputId": "1ef5f3ce-b866-490d-8c32-a8f518597929"
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Regression model metrics on the test set:\n",
            "R^2: 0.81\n",
            "MAE: 0.33\n",
            "MSE: 0.25\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Improving a model"
      ],
      "metadata": {
        "id": "49CaidOgvnRe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "clf = RandomForestClassifier()\n",
        "\n",
        "clf.get_params()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UzQiry1zvLMY",
        "outputId": "73bd7642-8ddc-48ae-dfb4-a9e0ecd723de"
      },
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'bootstrap': True,\n",
              " 'ccp_alpha': 0.0,\n",
              " 'class_weight': None,\n",
              " 'criterion': 'gini',\n",
              " 'max_depth': None,\n",
              " 'max_features': 'sqrt',\n",
              " 'max_leaf_nodes': None,\n",
              " 'max_samples': None,\n",
              " 'min_impurity_decrease': 0.0,\n",
              " 'min_samples_leaf': 1,\n",
              " 'min_samples_split': 2,\n",
              " 'min_weight_fraction_leaf': 0.0,\n",
              " 'n_estimators': 100,\n",
              " 'n_jobs': None,\n",
              " 'oob_score': False,\n",
              " 'random_state': None,\n",
              " 'verbose': 0,\n",
              " 'warm_start': False}"
            ]
          },
          "metadata": {},
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "To get familar with hyparameter tuning, we'll take our RandomForestClassifier and adjust its hyperparameters in 3 ways.\n",
        "\n",
        "* By hand\n",
        "* Randomly with sklearn.model_selection.RandomizedSearchCV\n",
        "* Exhaustively with sklearn.model_selection.GridSearchCV"
      ],
      "metadata": {
        "id": "5oInT239wzAu"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Tuning by hand"
      ],
      "metadata": {
        "id": "23C9zKMhxBBU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def evaluate_preds(y_true, y_preds):\n",
        "   accuracy = accuracy_score(y_true, y_preds)\n",
        "   precision = precision_score(y_true, y_preds)\n",
        "   recall = recall_score(y_true, y_preds)\n",
        "   f1 = f1_score(y_true, y_preds)\n",
        "\n",
        "   metric_dict = {\"accuracy\": round(accuracy, 2),\n",
        "                   \"precision\": round(precision, 2),\n",
        "                   \"recall\": round(recall, 2),\n",
        "                   \"f1\": round(f1, 2)}\n",
        "   print(f\"Accuracy: {accuracy * 100:.2f}%\")\n",
        "   print(f\"Precision: {precision:.2f}\")\n",
        "   print(f\"Recall: {recall:.2f}\")\n",
        "   print(f\"F1 score: {f1:.2f}\")\n",
        "   return metric_dict"
      ],
      "metadata": {
        "id": "i_bmllWbwgqS"
      },
      "execution_count": 80,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "# Shuffle the data\n",
        "heart_disease_shuffled = heart_disease.sample(frac = 1)\n",
        "\n",
        "x = heart_disease_shuffled.drop(\"target\", axis = 1)\n",
        "y = heart_disease_shuffled[\"target\"]\n",
        "\n",
        "train_split = round(0.7 * len(heart_disease_shuffled))\n",
        "valid_split = round(train_split + 0.15 * len(heart_disease_shuffled))\n",
        "\n",
        "x_train, y_train = x[:train_split], y[:train_split]\n",
        "x_valid, y_valid = x[train_split:valid_split], y[train_split:valid_split]\n",
        "x_test, y_test = x[valid_split:], y[valid_split:]\n",
        "\n",
        "clf = RandomForestClassifier()\n",
        "clf.fit(x_train, y_train)\n",
        "\n",
        "# Make baseline predictions\n",
        "y_preds = clf.predict(x_valid)\n",
        "\n",
        "# Evaluate the classifier on validation set\n",
        "baseline_metrics = evaluate_preds(y_valid, y_preds)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aVjyJW9J2Nv_",
        "outputId": "2b615467-8864-4586-95d7-69c35b82a750"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 75.56%\n",
            "Precision: 0.69\n",
            "Recall: 0.86\n",
            "F1 score: 0.77\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Tuning hyperparameter\n",
        "np.random.seed(42)\n",
        "\n",
        "clf2 = RandomForestClassifier(n_estimators = 100)\n",
        "clf2.fit(x_train, y_train)\n",
        "\n",
        "y_preds_2 = clf2.predict(x_valid)\n",
        "\n",
        "clf2_metrics = evaluate_preds(y_valid, y_preds_2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Sbsipdzw3sO8",
        "outputId": "1e2d4703-530f-48e1-90bc-d1578093bd6a"
      },
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 75.56%\n",
            "Precision: 0.68\n",
            "Recall: 0.90\n",
            "F1 score: 0.78\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Randomised Search CV"
      ],
      "metadata": {
        "id": "Qlb_uFAE-rCz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import RandomizedSearchCV\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "grid = { \"n_estimators\" : [10, 100, 200, 300, 500, 1000],\n",
        "         \"max_depth\" : [None, 5, 10, 20 , 30],\n",
        "         \"max_features\" : [\"auto\", \"sqrt\"],\n",
        "         \"min_samples_split\" : [2, 4, 6],\n",
        "         \"min_samples_leaf\" : [1, 2, 4] }\n",
        "\n",
        "np.random.seed(42)\n",
        "\n",
        "x = heart_disease_shuffled.drop(\"target\", axis = 1)\n",
        "y = heart_disease_shuffled[\"target\"]\n",
        "\n",
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.2)\n",
        "\n",
        "clf = RandomForestClassifier(n_jobs = -1)\n",
        "clf.fit(x_train, y_train)\n",
        "\n",
        "# Setup the RandomisedSearchCV\n",
        "\n",
        "rs_clf = RandomizedSearchCV(estimator = clf,\n",
        "                            param_distributions = grid,\n",
        "                            n_iter = 10,\n",
        "                            cv = 5,\n",
        "                            verbose = 0)\n",
        "\n",
        "rs_clf.fit(x_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 115
        },
        "id": "IMlCFgfd9duY",
        "outputId": "f7323ef5-b980-45bb-bfc3-7df72a423dea"
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomizedSearchCV(cv=5, estimator=RandomForestClassifier(n_jobs=-1),\n",
              "                   param_distributions={'max_depth': [None, 5, 10, 20, 30],\n",
              "                                        'max_features': ['auto', 'sqrt'],\n",
              "                                        'min_samples_leaf': [1, 2, 4],\n",
              "                                        'min_samples_split': [2, 4, 6],\n",
              "                                        'n_estimators': [10, 100, 200, 300, 500,\n",
              "                                                         1000]})"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomizedSearchCV(cv=5, estimator=RandomForestClassifier(n_jobs=-1),\n",
              "                   param_distributions={&#x27;max_depth&#x27;: [None, 5, 10, 20, 30],\n",
              "                                        &#x27;max_features&#x27;: [&#x27;auto&#x27;, &#x27;sqrt&#x27;],\n",
              "                                        &#x27;min_samples_leaf&#x27;: [1, 2, 4],\n",
              "                                        &#x27;min_samples_split&#x27;: [2, 4, 6],\n",
              "                                        &#x27;n_estimators&#x27;: [10, 100, 200, 300, 500,\n",
              "                                                         1000]})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomizedSearchCV</label><div class=\"sk-toggleable__content\"><pre>RandomizedSearchCV(cv=5, estimator=RandomForestClassifier(n_jobs=-1),\n",
              "                   param_distributions={&#x27;max_depth&#x27;: [None, 5, 10, 20, 30],\n",
              "                                        &#x27;max_features&#x27;: [&#x27;auto&#x27;, &#x27;sqrt&#x27;],\n",
              "                                        &#x27;min_samples_leaf&#x27;: [1, 2, 4],\n",
              "                                        &#x27;min_samples_split&#x27;: [2, 4, 6],\n",
              "                                        &#x27;n_estimators&#x27;: [10, 100, 200, 300, 500,\n",
              "                                                         1000]})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(n_jobs=-1)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(n_jobs=-1)</pre></div></div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rs_clf.best_params_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0QDXVv-GBIB9",
        "outputId": "911f4674-d378-4e11-ac45-df18d8a72195"
      },
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'n_estimators': 200,\n",
              " 'min_samples_split': 6,\n",
              " 'min_samples_leaf': 2,\n",
              " 'max_features': 'sqrt',\n",
              " 'max_depth': None}"
            ]
          },
          "metadata": {},
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Make predictions with the best parameters\n",
        "\n",
        "rs_y_preds = rs_clf.predict(x_test)\n",
        "\n",
        "rs_metrics = evaluate_preds(y_test, rs_y_preds)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NU6u5onDBhtD",
        "outputId": "fb880e7d-5706-47a8-c4f1-6c63f8bbac0c"
      },
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 75.41%\n",
            "Precision: 0.81\n",
            "Recall: 0.78\n",
            "F1 score: 0.79\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Grid Search CV"
      ],
      "metadata": {
        "id": "JePCV8cYCVhf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "grid"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0N9qHlNnCKif",
        "outputId": "07cf0b3f-0028-4969-e7ef-c68087f689b6"
      },
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'n_estimators': [10, 100, 200, 300, 500, 1000],\n",
              " 'max_depth': [None, 5, 10, 20, 30],\n",
              " 'max_features': ['auto', 'sqrt'],\n",
              " 'min_samples_split': [2, 4, 6],\n",
              " 'min_samples_leaf': [1, 2, 4]}"
            ]
          },
          "metadata": {},
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "grid_2 = {'n_estimators': [100, 200, 300],\n",
        "          'max_depth': [None],\n",
        "          'max_features': ['auto', 'sqrt'],\n",
        "          'min_samples_split': [6],\n",
        "          'min_samples_leaf': [1, 2] }"
      ],
      "metadata": {
        "id": "faP2OjmdCi4L"
      },
      "execution_count": 87,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "np.random.seed(42)\n",
        "\n",
        "x = heart_disease_shuffled.drop(\"target\", axis = 1)\n",
        "y = heart_disease_shuffled[\"target\"]\n",
        "\n",
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.2)\n",
        "\n",
        "clf = RandomForestClassifier(n_jobs = -1)\n",
        "clf.fit(x_train, y_train)\n",
        "\n",
        "# Setup the GridSearchCV\n",
        "\n",
        "gs_clf = GridSearchCV(estimator = clf,\n",
        "                      param_grid = grid_2,\n",
        "                      cv = 5,\n",
        "                      verbose = 0)\n",
        "\n",
        "gs_clf.fit(x_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 115
        },
        "id": "2dl_jR10E0QI",
        "outputId": "1919254b-2bff-4aba-f3ad-1a5105837cd2"
      },
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=5, estimator=RandomForestClassifier(n_jobs=-1),\n",
              "             param_grid={'max_depth': [None], 'max_features': ['auto', 'sqrt'],\n",
              "                         'min_samples_leaf': [1, 2], 'min_samples_split': [6],\n",
              "                         'n_estimators': [100, 200, 300]})"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=RandomForestClassifier(n_jobs=-1),\n",
              "             param_grid={&#x27;max_depth&#x27;: [None], &#x27;max_features&#x27;: [&#x27;auto&#x27;, &#x27;sqrt&#x27;],\n",
              "                         &#x27;min_samples_leaf&#x27;: [1, 2], &#x27;min_samples_split&#x27;: [6],\n",
              "                         &#x27;n_estimators&#x27;: [100, 200, 300]})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5, estimator=RandomForestClassifier(n_jobs=-1),\n",
              "             param_grid={&#x27;max_depth&#x27;: [None], &#x27;max_features&#x27;: [&#x27;auto&#x27;, &#x27;sqrt&#x27;],\n",
              "                         &#x27;min_samples_leaf&#x27;: [1, 2], &#x27;min_samples_split&#x27;: [6],\n",
              "                         &#x27;n_estimators&#x27;: [100, 200, 300]})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(n_jobs=-1)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(n_jobs=-1)</pre></div></div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "gs_clf.best_params_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dQk3u3YaFQqm",
        "outputId": "e57a0f7a-21f8-4b9e-b305-fba62c89137b"
      },
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'max_depth': None,\n",
              " 'max_features': 'sqrt',\n",
              " 'min_samples_leaf': 2,\n",
              " 'min_samples_split': 6,\n",
              " 'n_estimators': 300}"
            ]
          },
          "metadata": {},
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "gs_y_preds = gs_clf.predict(x_test)\n",
        "\n",
        "gs_metrics = evaluate_preds(y_test, gs_y_preds)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LT7VbRo9FjmV",
        "outputId": "27e19f69-1276-4c43-ee03-5f9ac91eb93e"
      },
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 77.05%\n",
            "Precision: 0.83\n",
            "Recall: 0.78\n",
            "F1 score: 0.81\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Lets compare both metrics\n",
        "\n",
        "compare_metrics = pd.DataFrame({\"baseline\": baseline_metrics,\n",
        "                                \"clf_2\": clf2_metrics,\n",
        "                                \"random search\": rs_metrics,\n",
        "                                \"grid search\": gs_metrics} )\n",
        "\n",
        "compare_metrics.plot.bar(figsize=(10, 4));"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 461
        },
        "id": "Y3wQ1w3fFyIU",
        "outputId": "d80710f7-6c87-4d1c-e52e-c3fb01027d27"
      },
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Saving and loading a model\n",
        "\n",
        "* We saw right at the start, one way to save a model is using Python's pickle module.\n",
        "\n",
        "* We'll use pickle's dump() method and pass it our model, gs_clf, along with the open() function containing a string for the filename we want to save our model as, along with the \"wb\" string which stands for \"write binary\", which is the file type open() will write our model as."
      ],
      "metadata": {
        "id": "CsN8N4S8JR58"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pickle\n",
        "\n",
        "# Saving an existing model to a file\n",
        "\n",
        "best_model = 'gs_clf_random_forest.pkl'\n",
        "pickle.dump(gs_clf, open(best_model, \"wb\"))"
      ],
      "metadata": {
        "id": "nemWIlXJGF1r"
      },
      "execution_count": 92,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Loading a saved model\n",
        "\n",
        "best_model_loaded = pickle.load(open(best_model, \"rb\"))"
      ],
      "metadata": {
        "id": "dULU6X6SKnit"
      },
      "execution_count": 94,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Putting it all together !!!"
      ],
      "metadata": {
        "id": "aS4oP3qpPGOe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_csv('https://raw.githubusercontent.com/mrdbourke/zero-to-mastery-ml/master/data/car-sales-extended-missing-data.csv')\n",
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "BtdV6alSPE0F",
        "outputId": "bc43707d-e4da-483c-ad8c-1b430b42bd53"
      },
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Make Colour  Odometer (KM)  Doors    Price\n",
              "0   Honda  White        35431.0    4.0  15323.0\n",
              "1     BMW   Blue       192714.0    5.0  19943.0\n",
              "2   Honda  White        84714.0    4.0  28343.0\n",
              "3  Toyota  White       154365.0    4.0  13434.0\n",
              "4  Nissan   Blue       181577.0    3.0  14043.0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a15fa26c-b055-49e2-88c2-3de6de1a3df4\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Make</th>\n",
              "      <th>Colour</th>\n",
              "      <th>Odometer (KM)</th>\n",
              "      <th>Doors</th>\n",
              "      <th>Price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Honda</td>\n",
              "      <td>White</td>\n",
              "      <td>35431.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>15323.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>BMW</td>\n",
              "      <td>Blue</td>\n",
              "      <td>192714.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>19943.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Honda</td>\n",
              "      <td>White</td>\n",
              "      <td>84714.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>28343.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Toyota</td>\n",
              "      <td>White</td>\n",
              "      <td>154365.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>13434.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Nissan</td>\n",
              "      <td>Blue</td>\n",
              "      <td>181577.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>14043.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a15fa26c-b055-49e2-88c2-3de6de1a3df4')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-a15fa26c-b055-49e2-88c2-3de6de1a3df4 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-a15fa26c-b055-49e2-88c2-3de6de1a3df4');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-343b0fd5-ce9e-43bc-ba87-600e4649b731\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-343b0fd5-ce9e-43bc-ba87-600e4649b731')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .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-343b0fd5-ce9e-43bc-ba87-600e4649b731 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 95
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BuEMv3q3P7WW",
        "outputId": "35bb40cd-6ed9-40ae-c870-24c923bd21d5"
      },
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Make              object\n",
              "Colour            object\n",
              "Odometer (KM)    float64\n",
              "Doors            float64\n",
              "Price            float64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ObZB03raP_nK",
        "outputId": "c1c8b1fb-65da-40e0-db55-91abd81ebbc8"
      },
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Make             49\n",
              "Colour           50\n",
              "Odometer (KM)    50\n",
              "Doors            50\n",
              "Price            50\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We'll build a Pipeline to do so.\n",
        "\n",
        "* Pipeline's main input parameter is steps which is a list of tuples ([(step_name, action_to_take)]) of the step name, plus the action you'd like it to perform.\n",
        "\n",
        "In our case, you could think of the steps as:\n",
        "\n",
        "* Fill missing data\n",
        "* Convert data to numbers\n",
        "* Build a model on the data"
      ],
      "metadata": {
        "id": "Q7DNw9o9SkGd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.impute import SimpleImputer\n",
        "from sklearn.compose import ColumnTransformer\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "np.random.seed(42)\n",
        "\n",
        "\n",
        "data.dropna(subset = [\"Price\"], inplace = True)\n",
        "\n",
        "\n",
        "# Define different features and transformer pipelines\n",
        "\n",
        "categorical_features = [\"Make\", \"Colour\"]\n",
        "categorical_transformer = Pipeline(steps = [\n",
        "    (\"imputer \", SimpleImputer(strategy = \"constant\", fill_value = \"missing\")),\n",
        "    (\"one_hot\", OneHotEncoder(handle_unknown = \"ignore\")) ])\n",
        "\n",
        "\n",
        "door_feature = [\"Doors\"]\n",
        "door_transformer = Pipeline(steps = [\n",
        "    (\"imputer\", SimpleImputer(strategy = \"constant\", fill_value = 4)) ])\n",
        "\n",
        "\n",
        "numeric_features = [\"Odometer (KM)\"]\n",
        "numeric_transformer = Pipeline(steps = [\n",
        "    (\"imputer\", SimpleImputer(strategy = \"mean\"))\n",
        "])\n",
        "\n",
        "\n",
        "# Setup preprocessing steps (fill missing values, then convert to numbers)\n",
        "\n",
        "preprocessor = ColumnTransformer(\n",
        "    transformers = [\n",
        "        (\"cat\", categorical_transformer, categorical_features),\n",
        "        (\"doors\", door_transformer, door_feature),\n",
        "        (\"num\", numeric_transformer, numeric_features) ])\n",
        "\n",
        "\n",
        "# Create a preprocessing and modelling pipeline\n",
        "\n",
        "model = Pipeline(steps = [(\"preprocessor\", preprocessor),\n",
        "                          (\"model\", RandomForestRegressor())])\n",
        "\n",
        "\n",
        "# Splitting the data into training and testing sets\n",
        "\n",
        "x = data.drop(\"Price\", axis = 1)\n",
        "y = data[\"Price\"]\n",
        "\n",
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.2)\n",
        "\n",
        "\n",
        "# Fit and score the model\n",
        "\n",
        "model.fit(x_train, y_train)\n",
        "model.score(x_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_UwG4gqRQDz9",
        "outputId": "66ec9f9e-d6b3-47e2-97c9-76b55c506bff"
      },
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.22188417408787875"
            ]
          },
          "metadata": {},
          "execution_count": 100
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### It's also possible to GridSearchCV or RandomizedSearchCV with a Pipeline."
      ],
      "metadata": {
        "id": "-NV9WFVVX9se"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Using grid search with pipeline\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "# note the double underscore after each prefix \"preprocessor__\"\n",
        "pipe_grid = {\n",
        "    \"preprocessor__num__imputer__strategy\": [\"mean\", \"median\"],\n",
        "    \"model__n_estimators\": [100, 1000],\n",
        "    \"model__max_depth\": [None, 5],\n",
        "    \"model__max_features\": [\"sqrt\"],\n",
        "    \"model__min_samples_split\": [2, 4]\n",
        "}\n",
        "\n",
        "gs_model = GridSearchCV(model, pipe_grid, cv = 5, verbose = 0)\n",
        "gs_model.fit(x_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 247
        },
        "id": "6xnDnR3PXHGD",
        "outputId": "f870caea-fefd-432d-c543-2febe12992a2"
      },
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=5,\n",
              "             estimator=Pipeline(steps=[('preprocessor',\n",
              "                                        ColumnTransformer(transformers=[('cat',\n",
              "                                                                         Pipeline(steps=[('imputer ',\n",
              "                                                                                          SimpleImputer(fill_value='missing',\n",
              "                                                                                                        strategy='constant')),\n",
              "                                                                                         ('one_hot',\n",
              "                                                                                          OneHotEncoder(handle_unknown='ignore'))]),\n",
              "                                                                         ['Make',\n",
              "                                                                          'Colour']),\n",
              "                                                                        ('doors',\n",
              "                                                                         Pipeline(steps=[('imputer',\n",
              "                                                                                          SimpleImputer(fill_value=4,\n",
              "                                                                                                        strategy='constant'))]),\n",
              "                                                                         ['Doors']),\n",
              "                                                                        ('num',\n",
              "                                                                         Pipeline(steps=[('imputer',\n",
              "                                                                                          SimpleImputer())]),\n",
              "                                                                         ['Odometer '\n",
              "                                                                          '(KM)'])])),\n",
              "                                       ('model', RandomForestRegressor())]),\n",
              "             param_grid={'model__max_depth': [None, 5],\n",
              "                         'model__max_features': ['sqrt'],\n",
              "                         'model__min_samples_split': [2, 4],\n",
              "                         'model__n_estimators': [100, 1000],\n",
              "                         'preprocessor__num__imputer__strategy': ['mean',\n",
              "                                                                  'median']})"
            ],
            "text/html": [
              "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5,\n",
              "             estimator=Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
              "                                        ColumnTransformer(transformers=[(&#x27;cat&#x27;,\n",
              "                                                                         Pipeline(steps=[(&#x27;imputer &#x27;,\n",
              "                                                                                          SimpleImputer(fill_value=&#x27;missing&#x27;,\n",
              "                                                                                                        strategy=&#x27;constant&#x27;)),\n",
              "                                                                                         (&#x27;one_hot&#x27;,\n",
              "                                                                                          OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))]),\n",
              "                                                                         [&#x27;Make&#x27;,\n",
              "                                                                          &#x27;Colour&#x27;]),\n",
              "                                                                        (&#x27;doors&#x27;,\n",
              "                                                                         Pipeline(steps=[(&#x27;imputer&#x27;,\n",
              "                                                                                          SimpleImputer(fill_value=4,\n",
              "                                                                                                        strategy=&#x27;constant&#x27;))]),\n",
              "                                                                         [&#x27;Doors&#x27;]),\n",
              "                                                                        (&#x27;num&#x27;,\n",
              "                                                                         Pipeline(steps=[(&#x27;imputer&#x27;,\n",
              "                                                                                          SimpleImputer())]),\n",
              "                                                                         [&#x27;Odometer &#x27;\n",
              "                                                                          &#x27;(KM)&#x27;])])),\n",
              "                                       (&#x27;model&#x27;, RandomForestRegressor())]),\n",
              "             param_grid={&#x27;model__max_depth&#x27;: [None, 5],\n",
              "                         &#x27;model__max_features&#x27;: [&#x27;sqrt&#x27;],\n",
              "                         &#x27;model__min_samples_split&#x27;: [2, 4],\n",
              "                         &#x27;model__n_estimators&#x27;: [100, 1000],\n",
              "                         &#x27;preprocessor__num__imputer__strategy&#x27;: [&#x27;mean&#x27;,\n",
              "                                                                  &#x27;median&#x27;]})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5,\n",
              "             estimator=Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
              "                                        ColumnTransformer(transformers=[(&#x27;cat&#x27;,\n",
              "                                                                         Pipeline(steps=[(&#x27;imputer &#x27;,\n",
              "                                                                                          SimpleImputer(fill_value=&#x27;missing&#x27;,\n",
              "                                                                                                        strategy=&#x27;constant&#x27;)),\n",
              "                                                                                         (&#x27;one_hot&#x27;,\n",
              "                                                                                          OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))]),\n",
              "                                                                         [&#x27;Make&#x27;,\n",
              "                                                                          &#x27;Colour&#x27;]),\n",
              "                                                                        (&#x27;doors&#x27;,\n",
              "                                                                         Pipeline(steps=[(&#x27;imputer&#x27;,\n",
              "                                                                                          SimpleImputer(fill_value=4,\n",
              "                                                                                                        strategy=&#x27;constant&#x27;))]),\n",
              "                                                                         [&#x27;Doors&#x27;]),\n",
              "                                                                        (&#x27;num&#x27;,\n",
              "                                                                         Pipeline(steps=[(&#x27;imputer&#x27;,\n",
              "                                                                                          SimpleImputer())]),\n",
              "                                                                         [&#x27;Odometer &#x27;\n",
              "                                                                          &#x27;(KM)&#x27;])])),\n",
              "                                       (&#x27;model&#x27;, RandomForestRegressor())]),\n",
              "             param_grid={&#x27;model__max_depth&#x27;: [None, 5],\n",
              "                         &#x27;model__max_features&#x27;: [&#x27;sqrt&#x27;],\n",
              "                         &#x27;model__min_samples_split&#x27;: [2, 4],\n",
              "                         &#x27;model__n_estimators&#x27;: [100, 1000],\n",
              "                         &#x27;preprocessor__num__imputer__strategy&#x27;: [&#x27;mean&#x27;,\n",
              "                                                                  &#x27;median&#x27;]})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
              "                 ColumnTransformer(transformers=[(&#x27;cat&#x27;,\n",
              "                                                  Pipeline(steps=[(&#x27;imputer &#x27;,\n",
              "                                                                   SimpleImputer(fill_value=&#x27;missing&#x27;,\n",
              "                                                                                 strategy=&#x27;constant&#x27;)),\n",
              "                                                                  (&#x27;one_hot&#x27;,\n",
              "                                                                   OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))]),\n",
              "                                                  [&#x27;Make&#x27;, &#x27;Colour&#x27;]),\n",
              "                                                 (&#x27;doors&#x27;,\n",
              "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
              "                                                                   SimpleImputer(fill_value=4,\n",
              "                                                                                 strategy=&#x27;constant&#x27;))]),\n",
              "                                                  [&#x27;Doors&#x27;]),\n",
              "                                                 (&#x27;num&#x27;,\n",
              "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
              "                                                                   SimpleImputer())]),\n",
              "                                                  [&#x27;Odometer (KM)&#x27;])])),\n",
              "                (&#x27;model&#x27;, RandomForestRegressor())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">preprocessor: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(transformers=[(&#x27;cat&#x27;,\n",
              "                                 Pipeline(steps=[(&#x27;imputer &#x27;,\n",
              "                                                  SimpleImputer(fill_value=&#x27;missing&#x27;,\n",
              "                                                                strategy=&#x27;constant&#x27;)),\n",
              "                                                 (&#x27;one_hot&#x27;,\n",
              "                                                  OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))]),\n",
              "                                 [&#x27;Make&#x27;, &#x27;Colour&#x27;]),\n",
              "                                (&#x27;doors&#x27;,\n",
              "                                 Pipeline(steps=[(&#x27;imputer&#x27;,\n",
              "                                                  SimpleImputer(fill_value=4,\n",
              "                                                                strategy=&#x27;constant&#x27;))]),\n",
              "                                 [&#x27;Doors&#x27;]),\n",
              "                                (&#x27;num&#x27;,\n",
              "                                 Pipeline(steps=[(&#x27;imputer&#x27;, SimpleImputer())]),\n",
              "                                 [&#x27;Odometer (KM)&#x27;])])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">cat</label><div class=\"sk-toggleable__content\"><pre>[&#x27;Make&#x27;, &#x27;Colour&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(fill_value=&#x27;missing&#x27;, strategy=&#x27;constant&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" ><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">doors</label><div class=\"sk-toggleable__content\"><pre>[&#x27;Doors&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" ><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(fill_value=4, strategy=&#x27;constant&#x27;)</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-15\" type=\"checkbox\" ><label for=\"sk-estimator-id-15\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">num</label><div class=\"sk-toggleable__content\"><pre>[&#x27;Odometer (KM)&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-16\" type=\"checkbox\" ><label for=\"sk-estimator-id-16\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer()</pre></div></div></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-17\" type=\"checkbox\" ><label for=\"sk-estimator-id-17\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor()</pre></div></div></div></div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Score the best model\n",
        "gs_model.score(x_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qgIOk2JPZ2cl",
        "outputId": "4de9aa6d-3aeb-4244-d9c7-fd40d279ab84"
      },
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.2848784564026805"
            ]
          },
          "metadata": {},
          "execution_count": 103
        }
      ]
    }
  ]
}