Fish_Weight_Prediction.ipynb

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        "# Fish Weight Prediction\n",
        "\n",
        "With a dataset of fish species, with some of it characteristic like it vertical, diagonal, length, height, and width. We will try to predict the weight of the fish based on their characteristic. We will use Linear Regression Method to see whether the weight of the fish related to their characteristic.\n",
        "\n",
        "* Species: Species name of fish\n",
        "* Weight: Weight of fish in gram\n",
        "* Length1: Vertical length in cm\n",
        "* Length2: Diagonal length in cm\n",
        "* Length3: Cross length in cm\n",
        "* Height: Height in cm\n",
        "* Width: Diagonal width in cm\n"
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      "source": [
        "# Step 1 : import library\n",
        "import pandas as pd"
      ]
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      "source": [
        "# Step 2 : import data\n",
        "fish = pd.read_csv('https://github.com/ybifoundation/Dataset/raw/main/Fish.csv')"
      ],
      "metadata": {
        "id": "OPhqD8Ur8JCq"
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      "execution_count": 3,
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      "source": [
        "fish.head()"
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              "   Category Species  Weight   Height   Width  Length1  Length2  Length3\n",
              "0         1   Bream   242.0  11.5200  4.0200     23.2     25.4     30.0\n",
              "1         1   Bream   290.0  12.4800  4.3056     24.0     26.3     31.2\n",
              "2         1   Bream   340.0  12.3778  4.6961     23.9     26.5     31.1\n",
              "3         1   Bream   363.0  12.7300  4.4555     26.3     29.0     33.5\n",
              "4         1   Bream   430.0  12.4440  5.1340     26.5     29.0     34.0"
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Category</th>\n",
              "      <th>Weight</th>\n",
              "      <th>Height</th>\n",
              "      <th>Width</th>\n",
              "      <th>Length1</th>\n",
              "      <th>Length2</th>\n",
              "      <th>Length3</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>159.000000</td>\n",
              "      <td>159.000000</td>\n",
              "      <td>159.000000</td>\n",
              "      <td>159.000000</td>\n",
              "      <td>159.000000</td>\n",
              "      <td>159.000000</td>\n",
              "      <td>159.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>3.264151</td>\n",
              "      <td>398.326415</td>\n",
              "      <td>8.970994</td>\n",
              "      <td>4.417486</td>\n",
              "      <td>26.247170</td>\n",
              "      <td>28.415723</td>\n",
              "      <td>31.227044</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.704249</td>\n",
              "      <td>357.978317</td>\n",
              "      <td>4.286208</td>\n",
              "      <td>1.685804</td>\n",
              "      <td>9.996441</td>\n",
              "      <td>10.716328</td>\n",
              "      <td>11.610246</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.728400</td>\n",
              "      <td>1.047600</td>\n",
              "      <td>7.500000</td>\n",
              "      <td>8.400000</td>\n",
              "      <td>8.800000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>120.000000</td>\n",
              "      <td>5.944800</td>\n",
              "      <td>3.385650</td>\n",
              "      <td>19.050000</td>\n",
              "      <td>21.000000</td>\n",
              "      <td>23.150000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>273.000000</td>\n",
              "      <td>7.786000</td>\n",
              "      <td>4.248500</td>\n",
              "      <td>25.200000</td>\n",
              "      <td>27.300000</td>\n",
              "      <td>29.400000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>4.500000</td>\n",
              "      <td>650.000000</td>\n",
              "      <td>12.365900</td>\n",
              "      <td>5.584500</td>\n",
              "      <td>32.700000</td>\n",
              "      <td>35.500000</td>\n",
              "      <td>39.650000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>7.000000</td>\n",
              "      <td>1650.000000</td>\n",
              "      <td>18.957000</td>\n",
              "      <td>8.142000</td>\n",
              "      <td>59.000000</td>\n",
              "      <td>63.400000</td>\n",
              "      <td>68.000000</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-449b7608-b4eb-4c10-b09b-541f0ec82e94')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
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              "  </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",
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              "\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",
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              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-449b7608-b4eb-4c10-b09b-541f0ec82e94 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-449b7608-b4eb-4c10-b09b-541f0ec82e94');\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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              "        docLink.innerHTML = docLinkHtml;\n",
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              "\n",
              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-bbc31f70-3a0e-4cfc-8adf-def315dab9e6')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "    </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-bbc31f70-3a0e-4cfc-8adf-def315dab9e6 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"fish\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Category\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 55.112173408540684,\n        \"min\": 1.0,\n        \"max\": 159.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          3.2641509433962264,\n          3.0,\n          159.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Weight\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 523.52371054653,\n        \"min\": 0.0,\n        \"max\": 1650.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          398.3264150943396,\n          273.0,\n          159.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Height\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 53.44397133500888,\n        \"min\": 1.7284,\n        \"max\": 159.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          8.970993710691824,\n          7.786,\n          159.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Width\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 54.81966876572199,\n        \"min\": 1.0476,\n        \"max\": 159.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          4.417485534591195,\n          4.2485,\n          159.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Length1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 49.76394993452418,\n        \"min\": 7.5,\n        \"max\": 159.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          26.247169811320756,\n          25.2,\n          159.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Length2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 49.4240506754142,\n        \"min\": 8.4,\n        \"max\": 159.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          28.415723270440253,\n          27.3,\n          159.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Length3\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 49.09863853684949,\n        \"min\": 8.8,\n        \"max\": 159.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          31.227044025157234,\n          29.4,\n          159.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 3 : define target (y) and features (X)\n",
        "fish.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wdLN-yOp9J0a",
        "outputId": "e0ab5c1f-5adb-4476-c4bf-757d44f1ca06"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Category', 'Species', 'Weight', 'Height', 'Width', 'Length1',\n",
              "       'Length2', 'Length3'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y = fish['Weight']\n",
        "X = fish[['Category','Height', 'Width', 'Length1',\n",
        "       'Length2', 'Length3']]"
      ],
      "metadata": {
        "id": "wV_W1vgv9Lkg"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 4 : train test split\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X,y, train_size=0.7, random_state=2529)"
      ],
      "metadata": {
        "id": "Ld8NYgLg9YKI"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# check shape of train and test sample\n",
        "X_train.shape, X_test.shape, y_train.shape, y_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "REmjnoNr9ZMv",
        "outputId": "fea17af1-beb4-4509-a9e4-c1e061ed3be5"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((111, 6), (48, 6), (111,), (48,))"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 5 : select model\n",
        "from sklearn.linear_model import LinearRegression\n",
        "model = LinearRegression()"
      ],
      "metadata": {
        "id": "C2MITPrl9aUZ"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 6 : train or fit model\n",
        "model.fit(X_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "F0aBAFq79bqK",
        "outputId": "4fd25166-b803-4e2d-9110-e78ba8a08cb2"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ],
            "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>LinearRegression()</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\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.intercept_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H1muowvU9cp-",
        "outputId": "229ab953-0318-40cd-8d8f-e87bd3029714"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-684.4235918478537"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.coef_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CCSgzNN29dzB",
        "outputId": "bd639dcd-1ea5-405e-adb5-183411424787"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 35.19634977,  52.19372157, -37.13869125,  11.2218449 ,\n",
              "        78.11233002, -59.11783139])"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 7 : predict model\n",
        "y_pred = model.predict(X_test)\n",
        "y_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5DAkiTHX9e7e",
        "outputId": "e9878b9d-35cd-4a8d-e527-9eb2141c161d"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 475.93351307,  525.81910195,   77.63275849,  881.10235121,\n",
              "        160.9685664 ,  255.94371856,  361.87029932,  358.87068094,\n",
              "        499.83411068, -150.07834151, -115.91810869,  428.65470115,\n",
              "        114.67533404,  812.51385122,  586.5071178 ,  273.38510858,\n",
              "        579.63900729,  225.18126845,  639.26068037,   85.00820599,\n",
              "        136.92159041,  -87.7778087 ,  629.97231046,  732.63097812,\n",
              "        859.8720695 , -166.76928607,  342.04209934,  722.92198147,\n",
              "        321.44827179,  787.98248357,  486.93194673,  541.89982795,\n",
              "        376.74813045,  624.81211202, -170.11945033,  917.76513801,\n",
              "        792.26439518,  -21.15655005,  300.24921659,  914.07325473,\n",
              "        621.05636286,  934.17373986,  676.85479574,  653.92304403,\n",
              "        615.51226767,  336.61090622,  505.75519147,  -33.53283763])"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 8 : model accuracy\n",
        "from sklearn.metrics import mean_absolute_error, r2_score\n",
        "mean_absolute_error(y_test,y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xIW3XSia9gdT",
        "outputId": "2c73aa28-21ad-4104-b833-ec354f6ac21d"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "99.58910366731813"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "r2_score(y_test,y_pred)"
      ],
      "metadata": {
        "id": "SfjHoVHA9ih-",
        "outputId": "ef2f157c-b3f6-4e51-867b-7a6be7e4de14",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.83982461599445"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "JTEep3kp9j_X"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}