linear_regression.ipynb

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        "id": "AP950fTc0YVQ"
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      "source": [
        "import numpy as np\n",
        "import pandas as pd"
      ]
    },
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      "source": [
        "df = pd.read_csv('placement.csv')\n",
        "df"
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              "     cgpa  package\n",
              "0    6.89     3.26\n",
              "1    5.12     1.98\n",
              "2    7.82     3.25\n",
              "3    7.42     3.67\n",
              "4    6.94     3.57\n",
              "..    ...      ...\n",
              "195  6.93     2.46\n",
              "196  5.89     2.57\n",
              "197  7.21     3.24\n",
              "198  7.63     3.96\n",
              "199  6.22     2.33\n",
              "\n",
              "[200 rows x 2 columns]"
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        "y= df.iloc[:,-1]\n",
        "X = df.iloc[:,0:1]"
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      "execution_count": 43,
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          "data": {
            "text/plain": [
              "     cgpa  package\n",
              "0    6.89     3.26\n",
              "1    5.12     1.98\n",
              "2    7.82     3.25\n",
              "3    7.42     3.67\n",
              "4    6.94     3.57\n",
              "..    ...      ...\n",
              "195  6.93     2.46\n",
              "196  5.89     2.57\n",
              "197  7.21     3.24\n",
              "198  7.63     3.96\n",
              "199  6.22     2.33\n",
              "\n",
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              "      <th>199</th>\n",
              "      <td>6.22</td>\n",
              "      <td>2.33</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>200 rows × 2 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fc598b98-cc8c-4148-a4d1-8b1477b2a198')\"\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-fc598b98-cc8c-4148-a4d1-8b1477b2a198 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-fc598b98-cc8c-4148-a4d1-8b1477b2a198');\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-6b4cae01-af90-4d00-8f37-ec57321c88d9\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-6b4cae01-af90-4d00-8f37-ec57321c88d9')\"\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-6b4cae01-af90-4d00-8f37-ec57321c88d9 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 200,\n  \"fields\": [\n    {\n      \"column\": \"cgpa\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0694086798895526,\n        \"min\": 4.26,\n        \"max\": 9.58,\n        \"num_unique_values\": 163,\n        \"samples\": [\n          8.71,\n          6.37,\n          7.94\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"package\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.6916442411250215,\n        \"min\": 1.37,\n        \"max\": 4.62,\n        \"num_unique_values\": 138,\n        \"samples\": [\n          3.76,\n          3.13,\n          2.89\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.scatter(df['cgpa'],df['package'])\n",
        "plt.xlabel('CGPA')\n",
        "plt.ylabel('package')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "omB2oqCbJSIy",
        "outputId": "f65e18d6-af66-465e-c09f-e30edd047f7f"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'package')"
            ]
          },
          "metadata": {},
          "execution_count": 44
        },
        {
          "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.model_selection import train_test_split\n",
        "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=2)\n",
        "from sklearn.linear_model import LinearRegression"
      ],
      "metadata": {
        "id": "-QQYg-XmLdtp"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lr=LinearRegression()"
      ],
      "metadata": {
        "id": "tdJespzCMK2Z"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qXSfxR2dOZoa",
        "outputId": "94e5bef8-cb9e-42a3-9081-c956a93368b4"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "112    4.10\n",
              "29     3.49\n",
              "182    2.08\n",
              "199    2.33\n",
              "193    1.94\n",
              "85     1.48\n",
              "10     1.86\n",
              "54     3.09\n",
              "115    4.21\n",
              "35     2.87\n",
              "12     3.65\n",
              "92     4.00\n",
              "13     2.89\n",
              "126    2.60\n",
              "174    2.99\n",
              "2      3.25\n",
              "44     1.86\n",
              "3      3.67\n",
              "113    2.37\n",
              "14     3.42\n",
              "23     2.48\n",
              "25     3.65\n",
              "6      2.60\n",
              "134    2.83\n",
              "165    4.08\n",
              "173    2.56\n",
              "45     3.58\n",
              "65     3.81\n",
              "48     4.09\n",
              "122    2.01\n",
              "178    3.63\n",
              "64     2.92\n",
              "9      3.51\n",
              "57     1.94\n",
              "78     2.21\n",
              "71     3.34\n",
              "128    3.34\n",
              "176    3.23\n",
              "131    2.01\n",
              "53     2.61\n",
              "Name: package, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "0APIpVKjMoLS",
        "outputId": "a2370f69-b0da-4f8d-b0c9-153ae3747342"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     cgpa\n",
              "112  8.58\n",
              "29   7.15\n",
              "182  5.88\n",
              "199  6.22\n",
              "193  4.57\n",
              "85   4.79\n",
              "10   5.32\n",
              "54   6.86\n",
              "115  8.35\n",
              "35   6.87\n",
              "12   8.94\n",
              "92   7.90\n",
              "13   6.93\n",
              "126  5.91\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "X_test",
              "summary": "{\n  \"name\": \"X_test\",\n  \"rows\": 40,\n  \"fields\": [\n    {\n      \"column\": \"cgpa\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0883838583316656,\n        \"min\": 4.57,\n        \"max\": 8.94,\n        \"num_unique_values\": 40,\n        \"samples\": [\n          7.73,\n          5.09,\n          7.82\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lr.fit(X_train,y_train)"
      ],
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          "height": 74
        },
        "id": "jNoDS0l5MZI0",
        "outputId": "ebb8dacf-73ec-4603-b45e-dbb827e7036d"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ],
            "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>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-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" 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": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lr.predict(X_test.iloc[0].values.reshape(1,1))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aFS1djz2PRjD",
        "outputId": "60310169-23da-44d8-b75b-043cc321fd9e"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([3.89111601])"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.scatter(df['cgpa'],df['package'])\n",
        "plt.xlabel('CGPA')\n",
        "plt.ylabel('package')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "1vHZ2e_BUU67",
        "outputId": "8057fbdf-5561-448b-f954-0e47e43ff5bd"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'package')"
            ]
          },
          "metadata": {},
          "execution_count": 54
        },
        {
          "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": [
        "import matplotlib.pyplot as plt\n",
        "plt.scatter(df['cgpa'],df['package'])\n",
        "plt.plot(X_train,lr.predict(X_train),color = 'red')\n",
        "plt.xlabel('CGPA')\n",
        "plt.ylabel('package')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "2oZ640h1UdbP",
        "outputId": "cf8696a6-30d0-4560-cd55-c755745a1970"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'package')"
            ]
          },
          "metadata": {},
          "execution_count": 57
        },
        {
          "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": [
        "m = lr.coef_\n",
        "lr.intercept_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p3WiP2UoXp1h",
        "outputId": "36c03e15-9ebc-47dd-b3eb-1451df53ee59"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-0.8961119222429144"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    }
  ]
}