Result_Analysis.ipynb

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      "source": [
        "Import the libraries\n"
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        "id": "CWA3a3ErPUfq"
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      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Import the dataset"
      ],
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        "id": "7MsRBYxdQRz9"
      }
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      "cell_type": "code",
      "source": [
        "data = pd.read_csv('/content/result_student.csv')\n",
        "data"
      ],
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            "text/plain": [
              "    index Student_id  SGPA\n",
              "0       0   20dcs001  9.17\n",
              "1       1   20dcs002  6.78\n",
              "2       2   20dcs003  8.74\n",
              "3       3   20dcs004  7.00\n",
              "4       4   20dcs005  9.52\n",
              "5       5   20dcs006  8.35\n",
              "6       6   20dcs007  9.43\n",
              "7       7   20dcs008  9.87\n",
              "8       8   20dcs009  9.87\n",
              "9       9   20dcs010  7.17\n",
              "10     10   20dcs011  9.52\n",
              "11     11   20dcs012  8.26\n",
              "12     12   20dcs013  8.87\n",
              "13     13   20dcs015  9.48\n",
              "14     14   20dcs016  8.65\n",
              "15     15   20dcs017  8.17\n",
              "16     16   20dcs018  9.57\n",
              "17     17   20dcs019  7.39\n",
              "18     18   20dcs020  9.91"
            ],
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              "      <th>Student_id</th>\n",
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              "      <td>0</td>\n",
              "      <td>20dcs001</td>\n",
              "      <td>9.17</td>\n",
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              "      <td>20dcs002</td>\n",
              "      <td>6.78</td>\n",
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              "      <td>5</td>\n",
              "      <td>20dcs006</td>\n",
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              "      <th>6</th>\n",
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              "      <td>20dcs007</td>\n",
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              "      <td>9.87</td>\n",
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              "      <th>8</th>\n",
              "      <td>8</td>\n",
              "      <td>20dcs009</td>\n",
              "      <td>9.87</td>\n",
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              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>9</td>\n",
              "      <td>20dcs010</td>\n",
              "      <td>7.17</td>\n",
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              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>10</td>\n",
              "      <td>20dcs011</td>\n",
              "      <td>9.52</td>\n",
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              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>11</td>\n",
              "      <td>20dcs012</td>\n",
              "      <td>8.26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>12</td>\n",
              "      <td>20dcs013</td>\n",
              "      <td>8.87</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>13</td>\n",
              "      <td>20dcs015</td>\n",
              "      <td>9.48</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>14</td>\n",
              "      <td>20dcs016</td>\n",
              "      <td>8.65</td>\n",
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              "      <th>15</th>\n",
              "      <td>15</td>\n",
              "      <td>20dcs017</td>\n",
              "      <td>8.17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>16</td>\n",
              "      <td>20dcs018</td>\n",
              "      <td>9.57</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>17</td>\n",
              "      <td>20dcs019</td>\n",
              "      <td>7.39</td>\n",
              "    </tr>\n",
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              "      <th>18</th>\n",
              "      <td>18</td>\n",
              "      <td>20dcs020</td>\n",
              "      <td>9.91</td>\n",
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          },
          "metadata": {},
          "execution_count": 3
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    {
      "cell_type": "markdown",
      "source": [
        "Calculating Average SGPA"
      ],
      "metadata": {
        "id": "8KWKtSzIQWIN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data['Average_SGPA'] = data.groupby('Student_id')['SGPA'].transform('mean')"
      ],
      "metadata": {
        "id": "OGV7i90lPjo3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Calculating highest 3 and lowest 3 student"
      ],
      "metadata": {
        "id": "mGndzYWFQkXV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "top_3_highest = data.sort_values(by='Average_SGPA', ascending=False).head(3)\n",
        "top_3_lowest = data.sort_values(by='Average_SGPA').head(3)"
      ],
      "metadata": {
        "id": "GJa5UTqYPuuU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Highest 3 student (top-3)"
      ],
      "metadata": {
        "id": "PSouaqtkQmc_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Top 3 Students with Highest Average SGPA:\")\n",
        "print(top_3_highest[['Student_id', 'Average_SGPA']])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jTZB89r1Pxit",
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Top 3 Students with Highest Average SGPA:\n",
            "   Student_id  Average_SGPA\n",
            "18   20dcs020          9.91\n",
            "8    20dcs009          9.87\n",
            "7    20dcs008          9.87\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Lowest - 3"
      ],
      "metadata": {
        "id": "SO5uUpDIQrlS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nTop 3 Students with Lowest Average SGPA:\")\n",
        "print(top_3_lowest[['Student_id', 'Average_SGPA']])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yeHTzaaiP0R4",
        "outputId": "46f6ab1d-5c25-47d1-bd35-6506e912b308"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Top 3 Students with Lowest Average SGPA:\n",
            "  Student_id  Average_SGPA\n",
            "1   20dcs002          6.78\n",
            "3   20dcs004          7.00\n",
            "9   20dcs010          7.17\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Data visualisation"
      ],
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    {
      "cell_type": "code",
      "source": [
        "plt.bar(top_3_highest['Student_id'], top_3_highest['Average_SGPA'], color='green', label='Top 3 Highest')\n",
        "plt.bar(top_3_lowest['Student_id'], top_3_lowest['Average_SGPA'], color='red', label='Top 3 Lowest')\n",
        "plt.xlabel('Student ID')\n",
        "plt.ylabel('Average SGPA')\n",
        "plt.title('Top 3 Students with Highest and Lowest Average SGPA')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "ZL0rp5PUP5nU",
        "outputId": "d47c0c85-140d-43f8-e848-eab7b72ccc61"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Top 3 Students with Highest and Lowest Average SGPA')"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "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": [],
      "metadata": {
        "id": "2KBKE74qQB09"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}