student_performance_project_in_shoolini_university.ipynb

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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/mahirmsb25/First-Sem-Project-/blob/main/student_performance_project_in_shoolini_university.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Import library"
      ],
      "metadata": {
        "id": "ed0x6lZLFnWu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z61a-oxt_gd-",
        "outputId": "fb129283-8c9a-48a7-bcf0-9128624e5d0f"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "QUqmievMFNz8"
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      "outputs": [],
      "source": [
        "import pandas as pd #data manipulation\n",
        "import numpy as np #mathematics\n",
        "import matplotlib.pyplot as plt # data visualization\n",
        "import seaborn as sns # data visualization\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Import the data"
      ],
      "metadata": {
        "id": "MWO7MFIyG85Q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(\"/content/drive/MyDrive/Data/Student_Performance.csv\")"
      ],
      "metadata": {
        "id": "6sESAPwHG47B"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
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          "height": 423
        },
        "id": "k2T7L7PHIUWo",
        "outputId": "0ec6f866-efb7-46a4-e23d-148a3e5738de"
      },
      "execution_count": 7,
      "outputs": [
        {
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            "text/plain": [
              "      Hours Studied  Previous Scores Extracurricular Activities  Sleep Hours  \\\n",
              "0                 7               99                        Yes            9   \n",
              "1                 4               82                         No            4   \n",
              "2                 8               51                        Yes            7   \n",
              "3                 5               52                        Yes            5   \n",
              "4                 7               75                         No            8   \n",
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              "9995              1               49                        Yes            4   \n",
              "9996              7               64                        Yes            8   \n",
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              "\n",
              "      Sample Question Papers Practiced  Performance Index  \n",
              "0                                    1               91.0  \n",
              "1                                    2               65.0  \n",
              "2                                    2               45.0  \n",
              "3                                    2               36.0  \n",
              "4                                    5               66.0  \n",
              "...                                ...                ...  \n",
              "9995                                 2               23.0  \n",
              "9996                                 5               58.0  \n",
              "9997                                 5               74.0  \n",
              "9998                                 0               95.0  \n",
              "9999                                 1               64.0  \n",
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              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 10000,\n  \"fields\": [\n    {\n      \"column\": \"Hours Studied\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 1,\n        \"max\": 9,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          1,\n          4,\n          6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Previous Scores\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17,\n        \"min\": 40,\n        \"max\": 99,\n        \"num_unique_values\": 60,\n        \"samples\": [\n          99,\n          78,\n          93\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Extracurricular Activities\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"No\",\n          \"Yes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sleep Hours\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 4,\n        \"max\": 9,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          9,\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sample Question Papers Practiced\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 0,\n        \"max\": 9,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          9,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Performance Index\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19.212557799463323,\n        \"min\": 10.0,\n        \"max\": 100.0,\n        \"num_unique_values\": 91,\n        \"samples\": [\n          41.0,\n          49.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###find the number of rows and columns"
      ],
      "metadata": {
        "id": "zaHw334sIh5m"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ncubJ_slIVE5",
        "outputId": "dff7a404-ff38-4f74-8df8-7732084104fa"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10000, 6)"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###find the data from the top"
      ],
      "metadata": {
        "id": "_wkZpZvMI7LQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.head(2)# by default five rows extract"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "h_KxlZg_IorV",
        "outputId": "af457697-74fc-4df1-c118-69b13d2f9803"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Hours Studied  Previous Scores Extracurricular Activities  Sleep Hours  \\\n",
              "0              7               99                        Yes            9   \n",
              "1              4               82                         No            4   \n",
              "\n",
              "   Sample Question Papers Practiced  Performance Index  \n",
              "0                                 1               91.0  \n",
              "1                                 2               65.0  "
            ],
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 10000,\n  \"fields\": [\n    {\n      \"column\": \"Hours Studied\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 1,\n        \"max\": 9,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          1,\n          4,\n          6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Previous Scores\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17,\n        \"min\": 40,\n        \"max\": 99,\n        \"num_unique_values\": 60,\n        \"samples\": [\n          99,\n          78,\n          93\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Extracurricular Activities\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"No\",\n          \"Yes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sleep Hours\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 4,\n        \"max\": 9,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          9,\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sample Question Papers Practiced\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 0,\n        \"max\": 9,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          9,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Performance Index\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19.212557799463323,\n        \"min\": 10.0,\n        \"max\": 100.0,\n        \"num_unique_values\": 91,\n        \"samples\": [\n          41.0,\n          49.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###find the rows from bottom"
      ],
      "metadata": {
        "id": "lB9A3-5PJeeB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.tail(4) #by default five rows extract"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "VQIEPifQJi42",
        "outputId": "a15e99bd-6148-41b9-c7e1-3b18979597b3"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Hours Studied  Previous Scores Extracurricular Activities  Sleep Hours  \\\n",
              "9996              7               64                        Yes            8   \n",
              "9997              6               83                        Yes            8   \n",
              "9998              9               97                        Yes            7   \n",
              "9999              7               74                         No            8   \n",
              "\n",
              "      Sample Question Papers Practiced  Performance Index  \n",
              "9996                                 5               58.0  \n",
              "9997                                 5               74.0  \n",
              "9998                                 0               95.0  \n",
              "9999                                 1               64.0  "
            ],
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              "    .colab-df-convert:hover {\n",
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              "      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-1dda7f4b-5e66-40e8-a712-a414670f9400 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-1dda7f4b-5e66-40e8-a712-a414670f9400');\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-010b1f3e-2174-43ec-bd73-4a32315e9cb4\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-010b1f3e-2174-43ec-bd73-4a32315e9cb4')\"\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-010b1f3e-2174-43ec-bd73-4a32315e9cb4 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\": 4,\n  \"fields\": [\n    {\n      \"column\": \"Hours Studied\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 6,\n        \"max\": 9,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          7,\n          6,\n          9\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Previous Scores\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14,\n        \"min\": 64,\n        \"max\": 97,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          83,\n          74,\n          64\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Extracurricular Activities\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"No\",\n          \"Yes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sleep Hours\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 7,\n        \"max\": 8,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          7,\n          8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sample Question Papers Practiced\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 0,\n        \"max\": 5,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          5,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Performance Index\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16.235249715767644,\n        \"min\": 58.0,\n        \"max\": 95.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          74.0,\n          64.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Find the number of rows randomly"
      ],
      "metadata": {
        "id": "uiJODJapMzeB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.sample(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "FQN1BHrZM5Qs",
        "outputId": "df0a4c64-29d7-4523-fc68-3b45228b5f18"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Hours Studied  Previous Scores Extracurricular Activities  Sleep Hours  \\\n",
              "7771              3               54                         No            7   \n",
              "3159              4               75                         No            8   \n",
              "7904              4               58                        Yes            7   \n",
              "9231              4               99                         No            6   \n",
              "4461              2               80                         No            8   \n",
              "\n",
              "      Sample Question Papers Practiced  Performance Index  \n",
              "7771                                 6               32.0  \n",
              "3159                                 6               60.0  \n",
              "7904                                 2               46.0  \n",
              "9231                                 6               82.0  \n",
              "4461                                 8               58.0  "
            ],
            "text/html": [
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              "      <td>60.0</td>\n",
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              "    <tr>\n",
              "      <th>7904</th>\n",
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              "      <td>46.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9231</th>\n",
              "      <td>4</td>\n",
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              "      <td>6</td>\n",
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              "    <tr>\n",
              "      <th>4461</th>\n",
              "      <td>2</td>\n",
              "      <td>80</td>\n",
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              "      <td>8</td>\n",
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              "      <td>58.0</td>\n",
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              "  </tbody>\n",
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              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-958fe43f-f159-4b7c-a665-0da4685fec3c')\"\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",
              "    }\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-958fe43f-f159-4b7c-a665-0da4685fec3c 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-958fe43f-f159-4b7c-a665-0da4685fec3c');\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-efbbaf0c-18cc-4b14-9b87-55ef776ef23f\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-efbbaf0c-18cc-4b14-9b87-55ef776ef23f')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
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              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\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",
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              "      --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",
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              "  }\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-efbbaf0c-18cc-4b14-9b87-55ef776ef23f 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\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Hours Studied\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 2,\n        \"max\": 4,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          3,\n          4,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Previous Scores\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 18,\n        \"min\": 54,\n        \"max\": 99,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          75,\n          80,\n          58\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Extracurricular Activities\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Yes\",\n          \"No\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sleep Hours\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 6,\n        \"max\": 8,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          7,\n          8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sample Question Papers Practiced\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 2,\n        \"max\": 8,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          6,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Performance Index\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 18.51485889765299,\n        \"min\": 32.0,\n        \"max\": 82.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          60.0,\n          58.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###find columns only"
      ],
      "metadata": {
        "id": "Zj5JwAqHNOsV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wgSdX6J2NT7v",
        "outputId": "aae758df-3a2f-44fd-a07a-63a601eb0361"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Hours Studied', 'Previous Scores', 'Extracurricular Activities',\n",
              "       'Sleep Hours', 'Sample Question Papers Practiced', 'Performance Index'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###find the null value in the data frame"
      ],
      "metadata": {
        "id": "pjHmF22gNb2a"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "id": "1JwUXpePNVPn",
        "outputId": "b1671f13-c67a-4fbd-ab8d-82c6ec43be4d"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Hours Studied                       0\n",
              "Previous Scores                     0\n",
              "Extracurricular Activities          0\n",
              "Sleep Hours                         0\n",
              "Sample Question Papers Practiced    0\n",
              "Performance Index                   0\n",
              "dtype: int64"
            ],
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              "</div><br><label><b>dtype:</b> int64</label>"
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          "metadata": {},
          "execution_count": 13
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###find the duplicates value"
      ],
      "metadata": {
        "id": "3okjtY3OOWLp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.duplicated().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OrmsjlPrNjmT",
        "outputId": "a8bb73d6-2407-4630-b7d2-67ada114e1ea"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "127"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Drop the duplicate value"
      ],
      "metadata": {
        "id": "egYNEKVIOxxN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.drop_duplicates(inplace = True) #inplace = True means drop the data permanently\n"
      ],
      "metadata": {
        "id": "YZ45Y4_pOc46"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.duplicated().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c9KJLOYFPOQv",
        "outputId": "fcb5611d-e366-4d26-aa65-c56a868543f1"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vXUJRANoPRAT",
        "outputId": "8f42e30c-918a-498c-d3ba-9de19fc2e6d7"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Hours Studied', 'Previous Scores', 'Extracurricular Activities',\n",
              "       'Sleep Hours', 'Sample Question Papers Practiced', 'Performance Index'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"Extracurricular Activities\"].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2sVuf_SPPzDx",
        "outputId": "f1c38245-76ff-48e7-8449-63f6192c9cfb"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Yes', 'No'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#df['Extracurricular Activities']  = df[\"Extracurricular Activities\"].map({\"Yes\": 1, \"No\": 0})"
      ],
      "metadata": {
        "id": "U6l7m422QA_o"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "ws1DNVHXRYld",
        "outputId": "116efa46-031d-4b82-c359-65281d52b854"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Hours Studied  Previous Scores Extracurricular Activities  Sleep Hours  \\\n",
              "0              7               99                        Yes            9   \n",
              "1              4               82                         No            4   \n",
              "2              8               51                        Yes            7   \n",
              "3              5               52                        Yes            5   \n",
              "4              7               75                         No            8   \n",
              "\n",
              "   Sample Question Papers Practiced  Performance Index  \n",
              "0                                 1               91.0  \n",
              "1                                 2               65.0  \n",
              "2                                 2               45.0  \n",
              "3                                 2               36.0  \n",
              "4                                 5               66.0  "
            ],
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } 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-797feae3-a76f-4de8-8545-6ab966163e4f 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",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 9873,\n  \"fields\": [\n    {\n      \"column\": \"Hours Studied\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 1,\n        \"max\": 9,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          1,\n          4,\n          6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Previous Scores\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17,\n        \"min\": 40,\n        \"max\": 99,\n        \"num_unique_values\": 60,\n        \"samples\": [\n          99,\n          78,\n          93\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Extracurricular Activities\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"No\",\n          \"Yes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sleep Hours\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 4,\n        \"max\": 9,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          9,\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sample Question Papers Practiced\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 0,\n        \"max\": 9,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          9,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Performance Index\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19.208570054616885,\n        \"min\": 10.0,\n        \"max\": 100.0,\n        \"num_unique_values\": 91,\n        \"samples\": [\n          41.0,\n          49.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Data Visualization"
      ],
      "metadata": {
        "id": "0s0pSGEOTIA4"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Exploratory data analysis"
      ],
      "metadata": {
        "id": "hCyVwya-TPC0"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "1. Univariate analysis\n",
        "\n",
        "Analysis on single column"
      ],
      "metadata": {
        "id": "JEhtA4HHTXlW"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Numerical variable"
      ],
      "metadata": {
        "id": "bB8ftyMe3j_c"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#find outlier\n",
        "sns.boxplot(df['Hours Studied'])\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "scetHMtiRaVm",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 411
        },
        "outputId": "ebc0982f-61aa-49f7-84a6-a8c4060f19bb"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(df[\"Previous Scores\"])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 411
        },
        "id": "aYoWqSCm20b2",
        "outputId": "3313658d-b2a9-4280-8160-bd5f3837d543"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.histplot(df[\"Previous Scores\"])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "YF76NBKs2_DZ",
        "outputId": "7077b6fb-6bd8-4396-c378-7f9e8d88c12a"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.histplot(df[\"Hours Studied\"])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "2DrQTk463t4S",
        "outputId": "0a56c849-051a-4955-e8e8-3794a89a1f81"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.histplot(df[\"Sample Question Papers Practiced\"])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "M8lyV8TG4AVN",
        "outputId": "4aebf09d-f659-4948-8caa-8b59bcb37a0b"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.histplot(df['Performance Index'])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "ycyuJ_3v4HeY",
        "outputId": "dc23d67d-054a-4460-e1c2-0de898d4be89"
      },
      "execution_count": 26,
      "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": [
        "###categorical data"
      ],
      "metadata": {
        "id": "KCdk92fx4fIG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(x = df[\"Extracurricular Activities\"])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "aQJl3YBo4Zkc",
        "outputId": "fbd18e09-011b-48aa-fb00-69bf23046568"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.pie(x = df[\"Extracurricular Activities\"].value_counts().values,\n",
        "        labels = df[\"Extracurricular Activities\"].value_counts().index, autopct='%1.1f%%')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        },
        "id": "kZg-5kyq5QCp",
        "outputId": "31173bac-b443-4d6c-bbe9-2d3434e51d5c"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1ctqgv3V5fFg",
        "outputId": "28c6da61-8945-4966-d5b2-776126aca3c3"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Hours Studied', 'Previous Scores', 'Extracurricular Activities',\n",
              "       'Sleep Hours', 'Sample Question Papers Practiced', 'Performance Index'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Bivariate analysis"
      ],
      "metadata": {
        "id": "odobL7Mo5XIL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.scatterplot(x = df[\"Hours Studied\"], y = df[\"Performance Index\"])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "tzAJkEaS5hWB",
        "outputId": "be900cad-38cc-495f-9d57-9b54e55d0302"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.scatterplot(x = df[\"Previous Scores\"], y = df[\"Performance Index\"])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "3awkoq3B5tzF",
        "outputId": "061c090b-3b15-4920-e15b-bdd45ca2c8d6"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.scatterplot(x = df[\"Sample Question Papers Practiced\"], y = df[\"Performance Index\"])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "yrTm2pFh7Bj_",
        "outputId": "d7d50836-b08c-4d91-fbfd-3c88f7dbaaf3"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(x = df[\"Extracurricular Activities\"], y = df[\"Performance Index\"])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "J-R6OYp07P7l",
        "outputId": "cd77dbdf-4e8d-4763-ee60-825e30807e55"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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LF+rKlSuSpHPnzik3N9eh4X788Uc1b95cZcuW1aZNm/Ttt99q+vTpqlChgnWbadOmadasWVqwYIFSU1Pl5+enNm3aKC8vz6FZAADA3cnub1A+ffq02rZtq4yMDOXn5+vxxx+Xv7+/pk6dqvz8fC1YsMBh4aZOnaqwsDAlJSVZxyIjI60/G4ahGTNmaNSoUerQoYMk6b333lNwcLA2bNig7t2733K/+fn5ys/Pty5zAVMAAMzL7iM7AwcOVOPGjfXjjz/K19fXOt6pUydt27bNoeE2btyoxo0b65lnnlGVKlXUqFEjLV682Lo+PT1dmZmZ1ouTSlJgYKCaNGmilJSUYvebmJiowMBA6y0sLMyhuQEAgPuwu+zs2rVLo0aNkpeXl8149erV9d///tdhwSTp5MmTmj9/vqKiorRlyxa98sorev3117V8+XJJUmZmpiQpODjY5n7BwcHWdbcSHx+v7Oxs6+3MmTMOzQ0AANyH3aexioqKbnlJiLNnz8rf398hoX79WI0bN9bkyZMlSY0aNdLhw4e1YMECxcXF3fZ+vb295e3t7aiYAADAjdl9ZKd169aaMWOGddlisSg3N1djxoxRu3btHJlNVatWVd26dW3G6tSpo4yMDElSSEiIJCkrK8tmm6ysLOs6AADw52Z32Zk+fbqSk5NVt25d5eXl6dlnn7Wewpo6dapDwzVv3lxHjx61GTt27Jj10hWRkZEKCQmxmSuUk5Oj1NRUNW3a1KFZAADA3cnu01jVqlXTwYMH9eGHH+rgwYPKzc1Vnz59FBsbazNh2REGDx6sZs2aafLkyeratav27t2rRYsWadGiRZJ+Pqo0aNAgTZw4UVFRUYqMjFRCQoJCQ0PVsWNHh2YBAAB3J7vLjiR5enoqNjZWsbGxjs5j48EHH9T69esVHx+v8ePHKzIyUjNmzLB53OHDh+vq1avq16+fLl++rIcfflibN2+Wj4+PU7MBAIC7g91lJzExUcHBwerdu7fN+NKlS/X9999rxIgRDgsnSe3bt1f79u2LXW+xWDR+/HiNHz/eoY8LAADMwe45OwsXLlTt2rVvGq9Xr55Dv1AQAADAEewuO5mZmapatepN45UrV9b58+cdEgoAAMBR7C47YWFhSk5Ovmk8OTlZoaGhDgkFAADgKHbP2enbt68GDRqkgoICtWzZUpK0bds2DR8+XEOHDnV4QAAAgNKwu+y88cYb+uGHH/Tqq6/q+vXrkiQfHx+NGDFC8fHxDg8IAABQGnaXHYvFoqlTpyohIUFHjhyRr6+voqKiuPwCAABwS7f1PTuSVL58eT344IOOzAIAAOBwdpedq1evasqUKdq2bZsuXLigoqIim/UnT550WDgAAIDSsrvsvPjii9qxY4eee+45Va1aVRaLxRm5AAAAHMLusrNp0yZ98sknat68uTPyAAAAOJTdZadChQqqWLGiM7KYnsdPl10dAXArZnpPmOm5AI7gTu8Ju8vOhAkTNHr0aC1fvlzlypVzRibT8k3f6eoIAJyE9zfgvuwuO9OnT9eJEycUHBys6tWrq2zZsjbrDxw44LBwZvNTZAsV+Qa5OgbgNjx+umyaksD7G7DlTu9vu8tOx44dnRDjz6HIN0hFfv/j6hgAnID3N+C+7C47Y8aMcUYOAAAAp7D7QqAAAAB3E7uP7BQWFuqdd97R6tWrlZGRYb0+1i8uXbrksHAAAAClZfeRnXHjxuntt99Wt27dlJ2drSFDhqhz587y8PDQ2LFjnRARAADg9tlddlasWKHFixdr6NCh8vT0VI8ePfTuu+9q9OjR2rNnjzMyAgAA3Da7y05mZqaio6Ml/Xwx0OzsbElS+/bt9cknnzg2HQAAQCnZXXaqVaum8+fPS5Luvfde/fvf/5Yk7du3T97e3o5NBwAAUEp2l51OnTpp27ZtkqTXXntNCQkJioqK0vPPP6/evXs7PCAAAEBp2P1prClTplh/7tatm8LDw5WSkqKoqCg99dRTDg0HAABQWnaXnd9q2rSpmjZt6ogsAAAADndbZefcuXPavXu3Lly4oKKiIpt1r7/+ukOCAQAAOILdZWfZsmV66aWX5OXlpUqVKslisVjXWSwWyg4AAHArdpedhIQEjR49WvHx8fLw4GoTAADAvdndVq5du6bu3btTdAAAwF3B7sbSp08frVmzxhlZAAAAHM7u01iJiYlq3769Nm/erOjoaJUtW9Zm/dtvv+2wcAAAAKV1W2Vny5YtqlWrliTdNEEZAADAndhddqZPn66lS5eqV69eTogDAADgWHbP2fH29lbz5s2dkQUAAMDh7C47AwcO1OzZs52RBQAAwOHsPo21d+9effbZZ/r4449Vr169myYor1u3zmHhAAAASsvushMUFKTOnTs7IwsAAIDD2VV2bty4occee0ytW7dWSEiIszIBAAA4jF1zdjw9PfXyyy8rPz/fWXkAAAAcyu4Jyn/961/11VdfOSMLAACAw9k9Z+fVV1/V0KFDdfbsWT3wwAPy8/OzWV+/fn2HhQMAACgtu8tO9+7dJUmvv/66dcxiscgwDFksFhUWFjouHQAAQCnZXXbS09OdkQMAAMAp7C47ERERzsgBAADgFHaXHUk6ceKEZsyYoSNHjkiS6tatq4EDB+ree+91aDgAAIDSsvvTWFu2bFHdunW1d+9e1a9fX/Xr11dqaqrq1aunrVu3OiMjAADAbbP7yM7IkSM1ePBgTZky5abxESNG6PHHH3dYOAAAgNKy+8jOkSNH1KdPn5vGe/furW+//dYhoQAAABzF7rJTuXJlpaWl3TSelpamKlWqOCITAACAw9h9Gqtv377q16+fTp48qWbNmkmSkpOTNXXqVA0ZMsThAQEAAErD7rKTkJAgf39/TZ8+XfHx8ZKk0NBQjR071uaLBgEAANxBiU5jbdy4UQUFBZJ+/rbkwYMH6+zZs8rOzlZ2drbOnj2rgQMHymKxODUsAACAvUpUdjp16qTLly9LksqUKaMLFy5Ikvz9/eXv7++0cAAAAKVVorJTuXJl7dmzR5Ks18ACAAC4G5Rozs7LL7+sDh06yGKxyGKxKCQkpNhtuRAoAABwJyUqO2PHjlX37t313Xff6emnn1ZSUpKCgoKcHA0AAKD0SvxprNq1a6tWrVqKi4tTly5dVL58eWfmAgAAcAi7vlTQMAytWLFC58+fd1YeAAAAh7Kr7Hh4eCgqKko//PCDs/IAAAA4lN2Xi5gyZYreeOMNHT582Bl5AAAAHMrub1B+/vnnde3aNTVo0EBeXl7y9fW1WX/p0iWHhQMAACgtu8vOjBkznBADAADAOewuO3Fxcc7IAQAA4BR2lx1JOnHihJKSknTixAnNnDlTVapU0aZNmxQeHq569eo5OqNpeORluzoC4FZ4TwC4E+wuOzt27NATTzyh5s2ba+fOnZo0aZKqVKmigwcPasmSJfroo4+ckVPSz5Oj4+PjNXDgQOvptLy8PA0dOlSrVq1Sfn6+2rRpo3nz5ik4ONhpOewVGBiosl7e0skdro4CuJ2yXt4KDAx0dQwAJmZ32Rk5cqQmTpyoIUOG2FwEtGXLlpozZ45Dw/3avn37tHDhQtWvX99mfPDgwfrkk0+0Zs0aBQYGasCAAercubOSk5OdlsVewcHB+tf77yk7m//FuoPTp09r0qRJeuuttxQREeHqOH96gYGBbvWfEwDmY3fZOXTokFauXHnTeJUqVXTx4kWHhPqt3NxcxcbGavHixZo4caJ1PDs7W0uWLNHKlSvVsmVLSVJSUpLq1KmjPXv26KGHHnJKntsRHBzMP+huJiIiQjVr1nR1DACAk9lddoKCgnT+/HlFRkbajH/11Vf6y1/+4rBgv9a/f389+eSTiomJsSk7+/fvV0FBgWJiYqxjtWvXVnh4uFJSUootO/n5+crPz7cu5+TkOCU3gD8P5h8BttzpPWF32enevbtGjBihNWvWyGKxqKioSMnJyRo2bJief/55hwdctWqVDhw4oH379t20LjMzU15eXjddlDQ4OFiZmZnF7jMxMVHjxo1zdFQAf0LMyQOK5y5z8uwuO5MnT1b//v0VFhamwsJC1a1bV4WFhXr22Wc1atQoh4Y7c+aMBg4cqK1bt8rHx8dh+42Pj9eQIUOsyzk5OQoLC3PY/gH8eTAnz70wJ8+9uMucPLvLjpeXlxYvXqzRo0fr0KFDys3NVaNGjRQVFeXwcPv379eFCxd0//33W8cKCwu1c+dOzZkzR1u2bNH169d1+fJlm6M7WVlZCgkJKXa/3t7e8vb2dnheAH9OzMlzP8zJw6+VuOwUFRXpH//4hzZu3Kjr16+rVatWGjNmzE2Xi3CkVq1a6dChQzZjL7zwgmrXrq0RI0YoLCxMZcuW1bZt29SlSxdJ0tGjR5WRkaGmTZs6LRcAALh7lLjsTJo0SWPHjlVMTIx8fX01c+ZMXbhwQUuXLnVaOH9/f9133302Y35+fqpUqZJ1vE+fPhoyZIgqVqyogIAAvfbaa2ratKlbfRILAAC4TonLznvvvad58+bppZdekiR9+umnevLJJ/Xuu+/Kw8Pui6c7zDvvvCMPDw916dLF5ksFAQAAJDvKTkZGhtq1a2ddjomJkcVi0blz51StWjWnhLuV7du32yz7+Pho7ty5mjt37h3LAAAA7h4lPiRz48aNmz4RVbZsWRUUFDg8FAAAgKOU+MiOYRjq1auXzaeY8vLy9PLLL8vPz886tm7dOscmBAAAKIUSl524uLibxnr27OnQMAAAAI5W4rKTlJTkzBwAAABO4bqPUQEAANwBlB0AAGBqlB0AAGBqlB0AAGBqlB0AAGBqlB0AAGBqlB0AAGBqlB0AAGBqlB0AAGBqlB0AAGBqlB0AAGBqlB0AAGBqlB0AAGBqlB0AAGBqlB0AAGBqlB0AAGBqlB0AAGBqlB0AAGBqlB0AAGBqlB0AAGBqlB0AAGBqlB0AAGBqlB0AAGBqlB0AAGBqlB0AAGBqlB0AAGBqlB0AAGBqlB0AAGBqlB0AAGBqlB0AAGBqlB0AAGBqlB0AAGBqlB0AAGBqlB0AAGBqlB0AAGBqlB0AAGBqlB0AAGBqlB0AAGBqlB0AAGBqlB0AAGBqlB0AAGBqlB0AAGBqlB0AAGBqlB0AAGBqlB0AAGBqlB0AAGBqlB0AAGBqlB0AAGBqlB0AAGBqlB0AAGBqlB0AAGBqlB0AAGBqlB0AAGBqlB0AAGBqlB0AAGBqlB0AAGBqlB0AAGBqlB0AAGBqlB0AAGBqlB0AAGBqbl12EhMT9eCDD8rf319VqlRRx44ddfToUZtt8vLy1L9/f1WqVEnly5dXly5dlJWV5aLEAADA3bh12dmxY4f69++vPXv2aOvWrSooKFDr1q119epV6zaDBw/W//3f/2nNmjXasWOHzp07p86dO7swNQAAcCeerg7wezZv3myzvGzZMlWpUkX79+9XixYtlJ2drSVLlmjlypVq2bKlJCkpKUl16tTRnj179NBDD7kiNgAAcCNufWTnt7KzsyVJFStWlCTt379fBQUFiomJsW5Tu3ZthYeHKyUlpdj95OfnKycnx+YGAADM6a4pO0VFRRo0aJCaN2+u++67T5KUmZkpLy8vBQUF2WwbHByszMzMYveVmJiowMBA6y0sLMyZ0QEAgAvdNWWnf//+Onz4sFatWlXqfcXHxys7O9t6O3PmjAMSAgAAd+TWc3Z+MWDAAH388cfauXOnqlWrZh0PCQnR9evXdfnyZZujO1lZWQoJCSl2f97e3vL29nZmZAAA4Cbc+siOYRgaMGCA1q9fr88++0yRkZE26x944AGVLVtW27Zts44dPXpUGRkZatq06Z2OCwAA3JBbH9np37+/Vq5cqf/93/+Vv7+/dR5OYGCgfH19FRgYqD59+mjIkCGqWLGiAgIC9Nprr6lp06Z8EgsAAEhy87Izf/58SdKjjz5qM56UlKRevXpJkt555x15eHioS5cuys/PV5s2bTRv3rw7nBQAALgrty47hmH84TY+Pj6aO3eu5s6dewcSAQCAu41bz9kBAAAoLcoOAAAwNcoOAAAwNcoOAAAwNcoOAAAwNcoOAAAwNcoOAAAwNcoOAAAwNcoOAAAwNcoOAAAwNcoOAAAwNcoOAAAwNcoOAAAwNcoOAAAwNcoOAAAwNcoOAAAwNcoOAAAwNcoOAAAwNcoOAAAwNcoOAAAwNcoOAAAwNcoOAAAwNcoOAAAwNcoOAAAwNcoOAAAwNU9XB8DdIy8vTxkZGa6OUWqnT5+2+fNuFh4eLh8fH1fHAAC3RtlBiWVkZKhfv36ujuEwkyZNcnWEUlu0aJFq1qzp6hgA4NYoOyix8PBwLVq0yNUx8Cvh4eGujgAAbo+ygxLz8fHhKAIA4K5D2QEAMCfPDTEnz3EoOwAA5uS5IebkOQ5lBwDAnDw3xJw8x6HsAACYkwdT40sFAQCAqVF2AACAqVF2AACAqVF2AACAqVF2AACAqVF2AACAqVF2AACAqVF2AACAqVF2AACAqVF2AACAqVF2AACAqVF2AACAqVF2AACAqXHVc0mGYUiScnJyXJwEAACU1C+/t3/5PV4cyo6kK1euSJLCwsJcnAQAANjrypUrCgwMLHa9xfijOvQnUFRUpHPnzsnf318Wi8XVceBkOTk5CgsL05kzZxQQEODqOAAciPf3n4thGLpy5YpCQ0Pl4VH8zByO7Ejy8PBQtWrVXB0Dd1hAQAD/GAImxfv7z+P3juj8ggnKAADA1Cg7AADA1Cg7+NPx9vbWmDFj5O3t7eooAByM9zduhQnKAADA1DiyAwAATI2yAwAATI2yAwAATI2yAwAATI2yA9MwDEMxMTFq06bNTevmzZunoKAgnT171gXJADhSr169ZLFYNGXKFJvxDRs28C34uCXKDkzDYrEoKSlJqampWrhwoXU8PT1dw4cP1+zZs/mmbMAkfHx8NHXqVP3444+ujoK7AGUHphIWFqaZM2dq2LBhSk9Pl2EY6tOnj1q3bq1GjRrpiSeeUPny5RUcHKznnntOFy9etN73o48+UnR0tHx9fVWpUiXFxMTo6tWrLnw2AIoTExOjkJAQJSYmFrvN2rVrVa9ePXl7e6t69eqaPn36HUwId0LZgenExcWpVatW6t27t+bMmaPDhw9r4cKFatmypRo1aqQvv/xSmzdvVlZWlrp27SpJOn/+vHr06KHevXvryJEj2r59uzp37iy+hgpwT2XKlNHkyZM1e/bsW56e3r9/v7p27aru3bvr0KFDGjt2rBISErRs2bI7HxYux5cKwpQuXLigevXq6dKlS1q7dq0OHz6sXbt2acuWLdZtzp49q7CwMB09elS5ubl64IEHdOrUKUVERLgwOYA/0qtXL12+fFkbNmxQ06ZNVbduXS1ZskQbNmxQp06dZBiGYmNj9f333+vf//639X7Dhw/XJ598om+++caF6eEKHNmBKVWpUkUvvfSS6tSpo44dO+rgwYP6/PPPVb58eeutdu3akqQTJ06oQYMGatWqlaKjo/XMM89o8eLFzAUA7gJTp07V8uXLdeTIEZvxI0eOqHnz5jZjzZs31/Hjx1VYWHgnI8INUHZgWp6envL09JQk5ebm6qmnnlJaWprN7fjx42rRooXKlCmjrVu3atOmTapbt65mz56tWrVqKT093cXPAsDvadGihdq0aaP4+HhXR4Eb83R1AOBOuP/++7V27VpVr17dWoB+y2KxqHnz5mrevLlGjx6tiIgIrV+/XkOGDLnDaQHYY8qUKWrYsKFq1aplHatTp46Sk5NttktOTlbNmjVVpkyZOx0RLsaRHfwp9O/fX5cuXVKPHj20b98+nThxQlu2bNELL7ygwsJCpaamavLkyfryyy+VkZGhdevW6fvvv1edOnVcHR3AH4iOjlZsbKxmzZplHRs6dKi2bdumCRMm6NixY1q+fLnmzJmjYcOGuTApXIWygz+F0NBQJScnq7CwUK1bt1Z0dLQGDRqkoKAgeXh4KCAgQDt37lS7du1Us2ZNjRo1StOnT9cTTzzh6ugASmD8+PEqKiqyLt9///1avXq1Vq1apfvuu0+jR4/W+PHj1atXL9eFhMvwaSwAAGBqHNkBAACmRtkBAACmRtkBAACmRtkBAACmRtkBAACmRtkBAACmRtkBAACmRtkBAACmRtkB4Fa2b98ui8Wiy5cvO2yfFotFGzZscNj+7gR7M1evXl0zZsz43W3Gjh2rhg0blioXcDei7ABuplevXrJYLDfd2rZtW+J9PProoxo0aJDzQjpRs2bNdP78eQUGBro6il1q164tb29vZWZm2nW/4grI+fPn7bpcyb59+9SvXz/r8q3K0rBhw7Rt2za78gFmQNkB3FDbtm11/vx5m9sHH3zg0McwDEM3btxw6D5L6vr167ccLygokJeXl0JCQmSxWO5wquIVl/cXu3fv1k8//aS///3vWr58uUMeMyQkRN7e3iXevnLlyipXrtzvblO+fHlVqlSptNGAuw5lB3BD3t7eCgkJsblVqFBB0s+neby8vLRr1y7r9tOmTVOVKlWUlZWlXr16aceOHZo5c6b1qNCpU6esp4c2bdqkBx54QN7e3tq9e7dOnDihDh06KDg4WOXLl9eDDz6oTz/91CZPfn6+RowYobCwMHl7e6tGjRpasmSJJGnZsmUKCgqy2X7Dhg02ZeWXoxfvvvuuIiMj5ePjI+nnow/z58/X008/LT8/P02aNOmWp7GSk5P16KOPqly5cqpQoYLatGmjH3/8UdKtT980bNhQY8eOLfb1HTFihGrWrKly5crpnnvuUUJCggoKCv4wb3GWLFmiZ599Vs8995yWLl160/qzZ8+qR48eqlixovz8/NS4cWOlpqZq2bJlGjdunA4ePGj9u1q2bJn1tfnlyEyzZs00YsQIm31+//33Klu2rHbu3HnT61C9enVJUqdOnWSxWKzLtzqK9O6776pOnTry8fFR7dq1NW/ePOu669eva8CAAapatap8fHwUERGhxMTE330tAHfk6eoAAOzzyymq5557TgcPHtTJkyeVkJCgNWvWKDg4WDNnztSxY8d03333afz48ZJ+/l//qVOnJEkjR47UP//5T91zzz2qUKGCzpw5o3bt2mnSpEny9vbWe++9p6eeekpHjx5VeHi4JOn5559XSkqKZs2apQYNGig9PV0XL160K/d3332ntWvXat26dSpTpox1fOzYsZoyZYpmzJghT09PnTx50uZ+aWlpatWqlXr37q2ZM2fK09NTn3/+uQoLC2/7NfT399eyZcsUGhqqQ4cOqW/fvvL399fw4cP/MO9vXblyRWvWrFFqaqpq166t7Oxs7dq1S3/7298kSbm5uXrkkUf0l7/8RRs3blRISIgOHDigoqIidevWTYcPH9bmzZutBfNWp+9iY2M1bdo0TZkyxVoiP/zwQ4WGhlof59f27dunKlWqKCkpSW3bti02/4oVKzR69GjNmTNHjRo10ldffaW+ffvKz89PcXFxmjVrljZu3KjVq1crPDxcZ86c0ZkzZ0r+QgNugrIDuKGPP/5Y5cuXtxl788039eabb0qSJk6cqK1bt6pfv346fPiw4uLi9PTTT0v6+Zell5eXypUrp5CQkJv2PX78eD3++OPW5YoVK6pBgwbW5QkTJmj9+vXauHGjBgwYoGPHjmn16tXaunWrYmJiJEn33HOP3c/p+vXreu+991S5cmWb8WeffVYvvPCCdfm3ZWfatGlq3LixzRGHevXq2f34vzZq1Cjrz9WrV9ewYcO0atUqm7JTXN7fWrVqlaKioqyZunfvriVLllhLyMqVK/X9999r3759qlixoiSpRo0a1vuXL19enp6et/y7+kXXrl01aNAg7d6922a/PXr0uOXpvl8yBwUF/e5+x4wZo+nTp6tz586SpMjISH377bdauHCh4uLilJGRoaioKD388MOyWCyKiIj43dcCcFeUHcANPfbYY5o/f77N2C+/KCXJy8tLK1asUP369RUREaF33nmnxPtu3LixzXJubq7Gjh2rTz75ROfPn9eNGzf0008/KSMjQ9LPR1bKlCmjRx55pBTPSIqIiLhlcfhtnt9KS0vTM888U6rH/q0PP/xQs2bN0okTJ5Sbm6sbN24oICCgRHl/a+nSperZs6d1uWfPnnrkkUc0e/Zs+fv7Ky0tTY0aNbL5+7NX5cqV1bp1a61YsUJ/+9vflJ6erpSUFC1cuPC293n16lWdOHFCffr0Ud++fa3jN27csB5d6tWrlx5//HHVqlVLbdu2Vfv27dW6devbfkzAVZizA7ghPz8/1ahRw+b221+WX3zxhSTp0qVLunTpkl37/rVhw4Zp/fr1mjx5snbt2qW0tDRFR0dbJ+X6+vr+7v48PDxkGIbN2K/nvxT3uH80/gtHPf4vUlJSFBsbq3bt2unjjz/WV199pbfeeuumSch/lEuSvv32W+3Zs0fDhw+Xp6enPD099dBDD+natWtatWpVifKXVGxsrD766CMVFBRo5cqVio6OVnR09G3vLzc3V5K0ePFipaWlWW+HDx/Wnj17JEn333+/0tPTNWHCBP3000/q2rWr/v73vzvk+QB3EmUHuAudOHFCgwcP1uLFi9WkSRPFxcWpqKjIut7Ly6vEc1qSk5PVq1cvderUSdHR0QoJCbHO75Gk6OhoFRUVaceOHbe8f+XKlXXlyhVdvXrVOpaWlnZbz+tW6tev/7sfl65cubLOnz9vXc7JyVF6enqx23/xxReKiIjQW2+9pcaNGysqKkqnT5++rWxLlixRixYtdPDgQZvCMGTIEOsE7vr16ystLa3YQlrSv6sOHTooLy9Pmzdv1sqVKxUbG/u725ctW/Z39xscHKzQ0FCdPHnypmIdGRlp3S4gIEDdunXT4sWL9eGHH2rt2rV2lWvAHVB2ADeUn5+vzMxMm9svE4ILCwvVs2dPtWnTRi+88IKSkpL09ddfa/r06db7V69eXampqTp16pQuXrxoU4R+KyoqSuvWrVNaWpoOHjyoZ5991mb76tWrKy4uTr1799aGDRuUnp6u7du3a/Xq1ZKkJk2aqFy5cnrzzTd14sQJrVy50vqJIkeIj4/Xvn379Oqrr+rrr7/Wf/7zH82fP9/6erRs2VLvv/++du3apUOHDikuLu53JxRHRUUpIyNDq1at0okTJzRr1iytX7/e7lwFBQV6//331aNHD9133302txdffFGpqan65ptv1KNHD4WEhKhjx45KTk7WyZMntXbtWqWkpEj6+fVNT09XWlqaLl68qPz8/Fs+np+fnzp27KiEhAQdOXJEPXr0+N181atX17Zt25SZmWn95NpvjRs3TomJiZo1a5aOHTumQ4cOKSkpSW+//bYk6e2339YHH3yg//znPzp27JjWrFmjkJCQmz59B7g9A4BbiYuLMyTddKtVq5ZhGIYxbtw4o2rVqsbFixet91m7dq3h5eVlpKWlGYZhGEePHjUeeughw9fX15BkpKenG59//rkhyfjxxx9tHi89Pd147LHHDF9fXyMsLMyYM2eO8cgjjxgDBw60bvPTTz8ZgwcPNqpWrWp4eXkZNWrUMJYuXWpdv379eqNGjRqGr6+v0b59e2PRokXGr/95GTNmjNGgQYObnqskY/369TZjt8q5fft2o1mzZoa3t7cRFBRktGnTxro+Ozvb6NatmxEQEGCEhYUZy5YtMxo0aGCMGTOm2Md54403jEqVKhnly5c3unXrZrzzzjtGYGDgH+b9tY8++sjw8PAwMjMzb7m+Tp06xuDBgw3DMIxTp04ZXbp0MQICAoxy5coZjRs3NlJTUw3DMIy8vDyjS5cuRlBQkCHJSEpKKva1+X//7/8ZkowWLVrc9HgRERHGO++8Y13euHGjUaNGDcPT09OIiIgo9nmtWLHCaNiwoeHl5WVUqFDBaNGihbFu3TrDMAxj0aJFRsOGDQ0/Pz8jICDAaNWqlXHgwIHffV0Ad2QxjN+c7AYAADARTmMBAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABT+/9DNaI+IkhohwAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.barplot(x = df[\"Extracurricular Activities\"], y = df[\"Performance Index\"])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "ih9mBKej76Hn",
        "outputId": "1c943310-2f65-4449-c25e-d1a404e4c203"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Extracurricular Activities'] = df['Extracurricular Activities'].map({'Yes':1, 'No':0})"
      ],
      "metadata": {
        "id": "EFG1dF3N8hPF"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
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      "source": [
        "df"
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        "id": "R4xDDCiF8zEa",
        "outputId": "948754b8-0741-4c70-93c6-301c42b290a5"
      },
      "execution_count": 36,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "      Hours Studied  Previous Scores  Extracurricular Activities  Sleep Hours  \\\n",
              "0                 7               99                           1            9   \n",
              "1                 4               82                           0            4   \n",
              "2                 8               51                           1            7   \n",
              "3                 5               52                           1            5   \n",
              "4                 7               75                           0            8   \n",
              "...             ...              ...                         ...          ...   \n",
              "9995              1               49                           1            4   \n",
              "9996              7               64                           1            8   \n",
              "9997              6               83                           1            8   \n",
              "9998              9               97                           1            7   \n",
              "9999              7               74                           0            8   \n",
              "\n",
              "      Sample Question Papers Practiced  Performance Index  \n",
              "0                                    1               91.0  \n",
              "1                                    2               65.0  \n",
              "2                                    2               45.0  \n",
              "3                                    2               36.0  \n",
              "4                                    5               66.0  \n",
              "...                                ...                ...  \n",
              "9995                                 2               23.0  \n",
              "9996                                 5               58.0  \n",
              "9997                                 5               74.0  \n",
              "9998                                 0               95.0  \n",
              "9999                                 1               64.0  \n",
              "\n",
              "[9873 rows x 6 columns]"
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              "      <th>9998</th>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 9873,\n  \"fields\": [\n    {\n      \"column\": \"Hours Studied\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 1,\n        \"max\": 9,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          1,\n          4,\n          6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Previous Scores\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17,\n        \"min\": 40,\n        \"max\": 99,\n        \"num_unique_values\": 60,\n        \"samples\": [\n          99,\n          78,\n          93\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Extracurricular Activities\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sleep Hours\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 4,\n        \"max\": 9,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          9,\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sample Question Papers Practiced\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 0,\n        \"max\": 9,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          9,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Performance Index\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19.208570054616885,\n        \"min\": 10.0,\n        \"max\": 100.0,\n        \"num_unique_values\": 91,\n        \"samples\": [\n          41.0,\n          49.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Multivariate analysis"
      ],
      "metadata": {
        "id": "7HCBomj483OU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(df.corr(), annot=True )\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 660
        },
        "id": "byxKL_yu8znQ",
        "outputId": "6e02a39a-dddb-478b-9734-bf7d125d4f65"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "In our dataset correlation between Hours studied and previous scores with performance index good, other columns coorelation not good so we not use these columns"
      ],
      "metadata": {
        "id": "9mYQBEir9XCo"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Find the independent and dependent feature"
      ],
      "metadata": {
        "id": "EDTWyQTR9yzc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.head(2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "txJ52Rjq9-_Y",
        "outputId": "f6007275-acf1-4b4a-c2f9-a510fd96b9c6"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Hours Studied  Previous Scores  Extracurricular Activities  Sleep Hours  \\\n",
              "0              7               99                           1            9   \n",
              "1              4               82                           0            4   \n",
              "\n",
              "   Sample Question Papers Practiced  Performance Index  \n",
              "0                                 1               91.0  \n",
              "1                                 2               65.0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-661a754c-ca16-4804-ad57-427242afe290\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Hours Studied</th>\n",
              "      <th>Previous Scores</th>\n",
              "      <th>Extracurricular Activities</th>\n",
              "      <th>Sleep Hours</th>\n",
              "      <th>Sample Question Papers Practiced</th>\n",
              "      <th>Performance Index</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>7</td>\n",
              "      <td>99</td>\n",
              "      <td>1</td>\n",
              "      <td>9</td>\n",
              "      <td>1</td>\n",
              "      <td>91.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4</td>\n",
              "      <td>82</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>65.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-661a754c-ca16-4804-ad57-427242afe290')\"\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-661a754c-ca16-4804-ad57-427242afe290 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-661a754c-ca16-4804-ad57-427242afe290');\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-e9e72ffa-2706-4bac-9c49-1bd65949d40e\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e9e72ffa-2706-4bac-9c49-1bd65949d40e')\"\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-e9e72ffa-2706-4bac-9c49-1bd65949d40e 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",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 9873,\n  \"fields\": [\n    {\n      \"column\": \"Hours Studied\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 1,\n        \"max\": 9,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          1,\n          4,\n          6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Previous Scores\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17,\n        \"min\": 40,\n        \"max\": 99,\n        \"num_unique_values\": 60,\n        \"samples\": [\n          99,\n          78,\n          93\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Extracurricular Activities\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sleep Hours\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 4,\n        \"max\": 9,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          9,\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sample Question Papers Practiced\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 0,\n        \"max\": 9,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          9,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Performance Index\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19.208570054616885,\n        \"min\": 10.0,\n        \"max\": 100.0,\n        \"num_unique_values\": 91,\n        \"samples\": [\n          41.0,\n          49.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = df.iloc[:, :2]\n",
        "x"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "EuOMKEyX9xr-",
        "outputId": "e3f4b1c8-d74d-4ba5-f07d-48e571e39bdc"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Hours Studied  Previous Scores\n",
              "0                 7               99\n",
              "1                 4               82\n",
              "2                 8               51\n",
              "3                 5               52\n",
              "4                 7               75\n",
              "...             ...              ...\n",
              "9995              1               49\n",
              "9996              7               64\n",
              "9997              6               83\n",
              "9998              9               97\n",
              "9999              7               74\n",
              "\n",
              "[9873 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-662bda18-6f09-4299-84d5-2fc740c64115\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Hours Studied</th>\n",
              "      <th>Previous Scores</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>7</td>\n",
              "      <td>99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4</td>\n",
              "      <td>82</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>8</td>\n",
              "      <td>51</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5</td>\n",
              "      <td>52</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>7</td>\n",
              "      <td>75</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9995</th>\n",
              "      <td>1</td>\n",
              "      <td>49</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9996</th>\n",
              "      <td>7</td>\n",
              "      <td>64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9997</th>\n",
              "      <td>6</td>\n",
              "      <td>83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9998</th>\n",
              "      <td>9</td>\n",
              "      <td>97</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9999</th>\n",
              "      <td>7</td>\n",
              "      <td>74</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>9873 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-662bda18-6f09-4299-84d5-2fc740c64115')\"\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-662bda18-6f09-4299-84d5-2fc740c64115 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-662bda18-6f09-4299-84d5-2fc740c64115');\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-58deb9b6-eb80-4a6f-b9a0-9b0f9d200070\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-58deb9b6-eb80-4a6f-b9a0-9b0f9d200070')\"\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-58deb9b6-eb80-4a6f-b9a0-9b0f9d200070 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_7f966179-94b7-4d6b-9f37-9f11b9121ac1\">\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_7f966179-94b7-4d6b-9f37-9f11b9121ac1 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\": 9873,\n  \"fields\": [\n    {\n      \"column\": \"Hours Studied\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 1,\n        \"max\": 9,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          1,\n          4,\n          6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Previous Scores\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17,\n        \"min\": 40,\n        \"max\": 99,\n        \"num_unique_values\": 60,\n        \"samples\": [\n          99,\n          78,\n          93\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y = df.iloc[:, -1]\n",
        "y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "id": "fG6CbBqd-GKI",
        "outputId": "829e0af1-8c34-40d0-e23a-87f70ee3675f"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       91.0\n",
              "1       65.0\n",
              "2       45.0\n",
              "3       36.0\n",
              "4       66.0\n",
              "        ... \n",
              "9995    23.0\n",
              "9996    58.0\n",
              "9997    74.0\n",
              "9998    95.0\n",
              "9999    64.0\n",
              "Name: Performance Index, Length: 9873, dtype: float64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Performance Index</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>91.0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>65.0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>45.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>36.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>66.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9995</th>\n",
              "      <td>23.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9996</th>\n",
              "      <td>58.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9997</th>\n",
              "      <td>74.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9998</th>\n",
              "      <td>95.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9999</th>\n",
              "      <td>64.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>9873 rows × 1 columns</p>\n",
              "</div><br><label><b>dtype:</b> float64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###split the data into train and test"
      ],
      "metadata": {
        "id": "xhtng8eL-Rzg"
      }
    },
    {
      "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 = 42)\n",
        "x_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "kikKAqr4-MZQ",
        "outputId": "6f940786-13dc-4b52-c3e0-e92c2a40e90e"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Hours Studied  Previous Scores\n",
              "8591              5               71\n",
              "9120              2               57\n",
              "4112              1               49\n",
              "811               3               58\n",
              "6848              3               80\n",
              "...             ...              ...\n",
              "5771              2               46\n",
              "5221              9               60\n",
              "5424              5               95\n",
              "860               1               47\n",
              "7337              9               84\n",
              "\n",
              "[7898 rows x 2 columns]"
            ],
            "text/html": [
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "      <td>3</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <th>5771</th>\n",
              "      <td>2</td>\n",
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              "      <th>5221</th>\n",
              "      <td>9</td>\n",
              "      <td>60</td>\n",
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              "    <tr>\n",
              "      <th>5424</th>\n",
              "      <td>5</td>\n",
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              "      <th>7337</th>\n",
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              "    </tr>\n",
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              "</table>\n",
              "<p>7898 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-f46a6362-940b-4581-ad7d-b9959e6d4c3f')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\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",
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              "      display: none;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-f46a6362-940b-4581-ad7d-b9959e6d4c3f 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-f46a6362-940b-4581-ad7d-b9959e6d4c3f');\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-3d75b71d-78d4-4ad3-8698-999abd393b2c\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-3d75b71d-78d4-4ad3-8698-999abd393b2c')\"\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",
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              "      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",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-3d75b71d-78d4-4ad3-8698-999abd393b2c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_d06a1c4a-a6fe-4577-b928-b7eaa387a55a\">\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_train')\"\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_d06a1c4a-a6fe-4577-b928-b7eaa387a55a 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_train');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "x_train",
              "summary": "{\n  \"name\": \"x_train\",\n  \"rows\": 7898,\n  \"fields\": [\n    {\n      \"column\": \"Hours Studied\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 1,\n        \"max\": 9,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          8,\n          2,\n          6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Previous Scores\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17,\n        \"min\": 40,\n        \"max\": 99,\n        \"num_unique_values\": 60,\n        \"samples\": [\n          71,\n          55,\n          62\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Import and train the model"
      ],
      "metadata": {
        "id": "tup-efDp-xSx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "model = LinearRegression()\n",
        "model.fit(x_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "j1VhQDbD-skR",
        "outputId": "9d4e640a-9025-40eb-bcfb-3cd5d59fec26"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: black;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-1 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-1 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: block;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-1 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-1 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 1ex;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-1 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</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 fitted 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 fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LinearRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LinearRegression()</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###After training model return coefficient and intercept"
      ],
      "metadata": {
        "id": "ZOiYYRVj_HED"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "coefficient = model.coef_\n",
        "intercept = model.intercept_\n",
        "print(coefficient)\n",
        "print(intercept)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aWqhjzar_UyC",
        "outputId": "26416a2d-4a35-43fb-94b5-6793f7e22391"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[2.85506398 1.01875676]\n",
            "-29.782105901801103\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "hour_study = 6\n",
        "previous_score = 86\n",
        "performance = coefficient[0]*hour_study + coefficient[1]*previous_score + intercept\n",
        "print(performance)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nESO4IJJ_jDD",
        "outputId": "5907be02-e7b9-408b-b2a8-d9db27c825d5"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "74.961359009364\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###prediction the model"
      ],
      "metadata": {
        "id": "HuPXVN2yALZi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = model.predict(x_test)\n",
        "print(y_pred), print(y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "afKZa8sEAJoE",
        "outputId": "3237f34f-98a2-4daf-a471-7e96eb9fa06b"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[46.83858242 81.89145001 61.71752118 ... 76.78494094 85.96647704\n",
            " 34.81470763]\n",
            "6099    47.0\n",
            "106     76.0\n",
            "9265    62.0\n",
            "4707    23.0\n",
            "2155    76.0\n",
            "        ... \n",
            "8732    19.0\n",
            "3112    39.0\n",
            "5297    77.0\n",
            "6116    88.0\n",
            "5088    34.0\n",
            "Name: Performance Index, Length: 1975, dtype: float64\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(None, None)"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Find the performance of model"
      ],
      "metadata": {
        "id": "98zNuQ9SAqJ-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import r2_score\n",
        "print(r2_score(y_test, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c909do65AYHQ",
        "outputId": "72b69bf0-005e-4077-9aa2-9353c764857d"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9850233951895029\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Find the error of the model"
      ],
      "metadata": {
        "id": "EpQZS09QA94a"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_squared_error, mean_absolute_error\n",
        "print(mean_squared_error(y_test, y_pred))\n",
        "print(mean_absolute_error(y_test, y_pred))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HBJE1wvdBEQh",
        "outputId": "7d1eb9c6-a0f2-433d-ff80-14f2152a3862"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "5.573764139536916\n",
            "1.8798308295705541\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "OwUhtHYEB08k"
      },
      "execution_count": 47,
      "outputs": []
    }
  ]
}