ServoPrediction.ipynb

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    {
      "cell_type": "markdown",
      "source": [
        "# **SERVO PREDICTION WITH LINEAR REGRESSION **"
      ],
      "metadata": {
        "id": "3EY4S5TxY5_c"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "DATA SOURCE:'https://github.com/YBIFoundation/Dataset/raw/main/Servo%20Mechanism.csv"
      ],
      "metadata": {
        "id": "-G-oKxtMZXO0"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**IMPORT LIBRARY**"
      ],
      "metadata": {
        "id": "7ekD9rOFZfEH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ],
      "metadata": {
        "id": "28kqUs5PZWeX"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**IMPORT DATA**"
      ],
      "metadata": {
        "id": "jkQenpcmZu7x"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "i_0CaHueY5Dx"
      },
      "outputs": [],
      "source": [
        "servo=pd.read_csv('https://github.com/YBIFoundation/Dataset/raw/main/Servo%20Mechanism.csv')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**DESCRIBE DATA**"
      ],
      "metadata": {
        "id": "XAGcWqaGariM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "servo.head()"
      ],
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          "height": 206
        },
        "id": "_CXSBw3LaNRZ",
        "outputId": "5262ef96-166f-4428-ee8a-38fa2919dc3d"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Motor Screw  Pgain  Vgain  Class\n",
              "0     E     E      5      4      4\n",
              "1     B     D      6      5     11\n",
              "2     D     D      4      3      6\n",
              "3     B     A      3      2     48\n",
              "4     D     B      6      5      6"
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              "type": "dataframe",
              "variable_name": "servo",
              "summary": "{\n  \"name\": \"servo\",\n  \"rows\": 167,\n  \"fields\": [\n    {\n      \"column\": \"Motor\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"B\",\n          \"A\",\n          \"D\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Screw\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"D\",\n          \"C\",\n          \"A\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pgain\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 3,\n        \"max\": 6,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          6,\n          3,\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Vgain\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 5,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          5,\n          1,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Class\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13,\n        \"min\": 1,\n        \"max\": 51,\n        \"num_unique_values\": 51,\n        \"samples\": [\n          32,\n          21,\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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          "metadata": {},
          "execution_count": 21
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      "execution_count": 22,
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 167 entries, 0 to 166\n",
            "Data columns (total 5 columns):\n",
            " #   Column  Non-Null Count  Dtype \n",
            "---  ------  --------------  ----- \n",
            " 0   Motor   167 non-null    object\n",
            " 1   Screw   167 non-null    object\n",
            " 2   Pgain   167 non-null    int64 \n",
            " 3   Vgain   167 non-null    int64 \n",
            " 4   Class   167 non-null    int64 \n",
            "dtypes: int64(3), object(2)\n",
            "memory usage: 6.6+ KB\n"
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      "execution_count": 23,
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            "text/plain": [
              "            Pgain       Vgain       Class\n",
              "count  167.000000  167.000000  167.000000\n",
              "mean     4.155689    2.538922   21.173653\n",
              "std      1.017770    1.369850   13.908038\n",
              "min      3.000000    1.000000    1.000000\n",
              "25%      3.000000    1.000000   10.500000\n",
              "50%      4.000000    2.000000   18.000000\n",
              "75%      5.000000    4.000000   33.500000\n",
              "max      6.000000    5.000000   51.000000"
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              "      <td>4.155689</td>\n",
              "      <td>2.538922</td>\n",
              "      <td>21.173653</td>\n",
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              "      <th>std</th>\n",
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              "      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-7a9ffea3-89e9-41dd-9102-9229ab9bacdf 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-7a9ffea3-89e9-41dd-9102-9229ab9bacdf');\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-24fa4cee-d3c5-4b10-9ded-c798fc0ad36c\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-24fa4cee-d3c5-4b10-9ded-c798fc0ad36c')\"\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-24fa4cee-d3c5-4b10-9ded-c798fc0ad36c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"servo\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Pgain\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 57.740561948664485,\n        \"min\": 1.0177701110003587,\n        \"max\": 167.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          167.0,\n          4.155688622754491,\n          5.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Vgain\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 58.207164595320016,\n        \"min\": 1.0,\n        \"max\": 167.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          167.0,\n          2.538922155688623,\n          4.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Class\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 53.719912103701525,\n        \"min\": 1.0,\n        \"max\": 167.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          21.17365269461078,\n          18.0,\n          167.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**DATA VISUALIZATION**"
      ],
      "metadata": {
        "id": "mRwWsjnMogQD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "servo.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B5AW_nSsamFc",
        "outputId": "cac23876-7bfd-493e-95a0-23187aaa2e76"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Motor', 'Screw', 'Pgain', 'Vgain', 'Class'], dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "servo.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dxUddJO0n_bi",
        "outputId": "fd72c0b2-5f36-42eb-8e50-822e42461490"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(167, 5)"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "servo['Motor'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lEuSBUEpoHvu",
        "outputId": "9f9df701-3113-4b47-a5f5-0f709761463f"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Motor\n",
              "C    40\n",
              "B    36\n",
              "A    36\n",
              "E    33\n",
              "D    22\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "servo['Screw'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qyi3EzfsoRAA",
        "outputId": "a5aea00c-e53a-49c9-b8b8-542d35c4317d"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Screw\n",
              "A    42\n",
              "B    35\n",
              "C    31\n",
              "D    30\n",
              "E    29\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "servo.replace({'Motor':{'A':0,'B':1,'C':2,'D':3,'E':4}},inplace=True)"
      ],
      "metadata": {
        "id": "C5N99AGwolbf"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "servo.replace({'Screw':{'A':0,'B':1,'C':2,'D':3,'E':4}},inplace=True)"
      ],
      "metadata": {
        "id": "GHPR4CWWpLaq"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y=servo['Class']\n"
      ],
      "metadata": {
        "id": "ls2vef65ph8J"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AIMQ8BmyxABi",
        "outputId": "a4530e33-3537-47aa-c89a-660141c9d03e"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(167,)"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sGTuI9U6xCzK",
        "outputId": "5656d9fc-2a1f-408a-e5cf-208fd5c06624"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       4\n",
              "1      11\n",
              "2       6\n",
              "3      48\n",
              "4       6\n",
              "       ..\n",
              "162    44\n",
              "163    40\n",
              "164    25\n",
              "165    44\n",
              "166    20\n",
              "Name: Class, Length: 167, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X=servo[['Motor', 'Screw','Pgain', 'Vgain']]"
      ],
      "metadata": {
        "id": "gHKs2HEHxI-v"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X=servo.drop(['Class'],axis=1)"
      ],
      "metadata": {
        "id": "bEEJOHzVxrhY"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "HhcSFV9XxyiC",
        "outputId": "371c424e-140b-4fcb-f82d-0bcd18f3a6b1"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Motor  Screw  Pgain  Vgain\n",
              "0        4      4      5      4\n",
              "1        1      3      6      5\n",
              "2        3      3      4      3\n",
              "3        1      0      3      2\n",
              "4        3      1      6      5\n",
              "..     ...    ...    ...    ...\n",
              "162      1      2      3      2\n",
              "163      1      4      3      1\n",
              "164      2      3      4      3\n",
              "165      0      1      3      2\n",
              "166      0      0      6      5\n",
              "\n",
              "[167 rows x 4 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e5f070a9-ac6a-41b9-a813-dcd9d7c73854\" 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",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Motor</th>\n",
              "      <th>Screw</th>\n",
              "      <th>Pgain</th>\n",
              "      <th>Vgain</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
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              "      <td>5</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
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              "    <tr>\n",
              "      <th>162</th>\n",
              "      <td>1</td>\n",
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              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>163</th>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>164</th>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>165</th>\n",
              "      <td>0</td>\n",
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              "      <td>2</td>\n",
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              "    <tr>\n",
              "      <th>166</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>167 rows × 4 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
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              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
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              "    }\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-e5f070a9-ac6a-41b9-a813-dcd9d7c73854 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-e5f070a9-ac6a-41b9-a813-dcd9d7c73854');\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-93380616-9442-41bb-a788-0b5d582e7725\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-93380616-9442-41bb-a788-0b5d582e7725')\"\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-93380616-9442-41bb-a788-0b5d582e7725 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_ba192339-1938-4f4a-aa9d-129486408382\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate: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",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('X')\"\n",
              "            title=\"Generate code using this dataframe.\"\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",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_ba192339-1938-4f4a-aa9d-129486408382 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('X');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "X",
              "summary": "{\n  \"name\": \"X\",\n  \"rows\": 167,\n  \"fields\": [\n    {\n      \"column\": \"Motor\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 4,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          1,\n          0,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Screw\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 4,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          3,\n          2,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pgain\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 3,\n        \"max\": 6,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          6,\n          3,\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Vgain\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 5,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          5,\n          1,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Na7B1RPnx1Kz",
        "outputId": "5d820ec8-971c-449f-fc63-29073a928f44"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(167, 4)"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Train Test Split**"
      ],
      "metadata": {
        "id": "x4LIwn6SzB3I"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "metadata": {
        "id": "XMQXPvWux7MO"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.3,random_state=2529)"
      ],
      "metadata": {
        "id": "P2RwuR77yisi"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.shape,X_test.shape,y_train.shape,y_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sTSqXOhky2hC",
        "outputId": "bdf3130a-00dc-49f9-97bf-3886b4f4d2b3"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((116, 4), (51, 4), (116,), (51,))"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Modeling**"
      ],
      "metadata": {
        "id": "A6ZhN8km1u4r"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "lr=LinearRegression()\n"
      ],
      "metadata": {
        "id": "3UpVp4Z61xCR"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lr.fit(X_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "6u2NAv8b19lx",
        "outputId": "61eaca05-7d65-4e71-fe45-84b812131db2"
      },
      "execution_count": 43,
      "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": 43
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Prediction**"
      ],
      "metadata": {
        "id": "axC_BG4h2iy5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred=lr.predict(X_test)"
      ],
      "metadata": {
        "id": "BBIHfJ-92k_0"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rqAYyioz3Cm7",
        "outputId": "f7edb5a5-3ae0-4431-a4d1-17639f920e89"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(51,)"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bU9wtCEY7wYM",
        "outputId": "846b20c6-08ff-41c4-febc-979e6015c2ac"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([24.55945258, 30.98765106, 18.54485477, 25.51524243, 38.56082023,\n",
              "       23.52007775, 11.61947065, 20.03335614, 40.60404401, 41.7009556 ,\n",
              "       13.66269443, 26.01242807, 16.50163099, 16.54663453, 21.92598051,\n",
              "       22.52570646, -5.46449561, 30.68912392, 32.7323477 ,  1.41282941,\n",
              "       33.97718702, 31.63543611, 33.52806048, 30.04133887, 19.38557109,\n",
              "        6.49364826, 28.5528375 , 17.04382017, 25.06611589,  3.50411229,\n",
              "       30.59606128, 23.67067716, 35.72188367, 32.08456265, 12.46018697,\n",
              "        3.6547117 , 23.47201865, 33.03087484, 17.49294672, 37.61450804,\n",
              "       27.54898855, 22.07657992, 11.51387478,  9.470651  , 30.53852451,\n",
              "       28.64590014, 33.67865989,  4.60102388, 24.1198037 , 21.13026773,\n",
              "       25.71390094])"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Model Evaluation"
      ],
      "metadata": {
        "id": "ys6piNwq71Lt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_squared_error,mean_absolute_error,r2_score"
      ],
      "metadata": {
        "id": "fAef3Rwp9Dje"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mean_squared_error(y_test,y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "12iMwyXy9W29",
        "outputId": "3acb7ebc-0436-4569-9588-12508d391813"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "66.03589175595563"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mean_absolute_error(y_test,y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tUscjHX49cw8",
        "outputId": "2266ca0f-e7cd-4135-de4a-f9c5fb141b85"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "7.190539677251235"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "r2_score(y_test,y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O2geyvsG9ox9",
        "outputId": "eb1e6279-502e-4135-abd9-abd67b7b6317"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6807245170563927"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.scatter(y_test,y_pred)\n",
        "plt.xlabel('Actual')\n",
        "plt.ylabel('Predicted')\n",
        "plt.title(\"Actual vs Predicted\")\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "6Ip_0pIB9ydf",
        "outputId": "1345ba9a-b116-4186-924d-7bc8c7d65d7d"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "SvUl1MkL_44J"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "UZUewH1C_8NL"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}