StudentMarkPrediction.ipynb

{
  "nbformat": 4,
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  "metadata": {
    "colab": {
      "name": "StudentMarkPrediction",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 73
        },
        "id": "eghyf_ea2Brc",
        "outputId": "26d4d023-4ccf-457a-fbce-c3a0c6168ed3"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "\n",
        "from google.colab import files\n",
        "uploaded = files.upload()\n",
        "\n"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-822e6a7d-d1df-4136-8c83-2a794c35fe64\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-822e6a7d-d1df-4136-8c83-2a794c35fe64\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving student_scores.csv to student_scores (2).csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "id": "MdGgEhM-Ykqe",
        "outputId": "d1054f59-c0fc-4815-fb4f-3c17c97a7c3f"
      },
      "source": [
        "import io\n",
        "data = pd.read_csv(io.BytesIO(uploaded['student_scores.csv']))\n",
        "#to display the datasheet, only shows 5 rows by default\n",
        "data.head()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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</th>\n",
              "      <th>Scores</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2.5</td>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5.1</td>\n",
              "      <td>47</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3.2</td>\n",
              "      <td>27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>8.5</td>\n",
              "      <td>75</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3.5</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Hours  Scores\n",
              "0    2.5      21\n",
              "1    5.1      47\n",
              "2    3.2      27\n",
              "3    8.5      75\n",
              "4    3.5      30"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "id": "mXpo1GWsaXPB",
        "outputId": "1f313886-ac9d-4737-8ba9-d7e3f7aa5f89"
      },
      "source": [
        "#default statistical calculation\n",
        "data.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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</th>\n",
              "      <th>Scores</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>25.000000</td>\n",
              "      <td>25.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>5.012000</td>\n",
              "      <td>51.480000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>2.525094</td>\n",
              "      <td>25.286887</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.100000</td>\n",
              "      <td>17.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2.700000</td>\n",
              "      <td>30.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>4.800000</td>\n",
              "      <td>47.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>7.400000</td>\n",
              "      <td>75.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>9.200000</td>\n",
              "      <td>95.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           Hours     Scores\n",
              "count  25.000000  25.000000\n",
              "mean    5.012000  51.480000\n",
              "std     2.525094  25.286887\n",
              "min     1.100000  17.000000\n",
              "25%     2.700000  30.000000\n",
              "50%     4.800000  47.000000\n",
              "75%     7.400000  75.000000\n",
              "max     9.200000  95.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "7Ze0sBWtbrEY",
        "outputId": "393e0483-f43a-423a-8acf-a760645a56dc"
      },
      "source": [
        "data.plot(x=\"Hours\", y=\"Scores\", style = \"*\")\n",
        "plt.title(\"Student Marks Prediciton\")\n",
        "plt.xlabel(\"Hours\")\n",
        "plt.ylabel(\"Percentage\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7xrwbnvvevh7",
        "outputId": "2750bdcf-df29-4477-a9fb-a46cbe3c4868"
      },
      "source": [
        "#predict data\n",
        "X=data.iloc[:, :-1].values   #hour which is independant  \n",
        "Y=data.iloc[:,1].values   #score which is dependant\n",
        "\n",
        "#training & testing models\n",
        "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=0)     #test_size is amt of data for testing between 0 & 1\n",
        "\n",
        "#LR model finds the best optimal value for intercept & slope\n",
        "from sklearn.linear_model import LinearRegression\n",
        "regressor = LinearRegression()\n",
        "regressor.fit(X_train, Y_train)\n",
        "print(regressor.intercept_)\n",
        "print(regressor.coef_)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2.018160041434662\n",
            "[9.91065648]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "id": "Z9EECWmukSuu",
        "outputId": "ee01e08f-b7fe-4f9e-dd91-2c9bbcdf7c2e"
      },
      "source": [
        "#creating prediction\n",
        "y_pred=regressor.predict(X_test)\n",
        "df = pd.DataFrame({'Actual':Y_test, 'Predicted':y_pred})\n",
        "df\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Actual</th>\n",
              "      <th>Predicted</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>20</td>\n",
              "      <td>16.884145</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>27</td>\n",
              "      <td>33.732261</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>69</td>\n",
              "      <td>75.357018</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>30</td>\n",
              "      <td>26.794801</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>62</td>\n",
              "      <td>60.491033</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Actual  Predicted\n",
              "0      20  16.884145\n",
              "1      27  33.732261\n",
              "2      69  75.357018\n",
              "3      30  26.794801\n",
              "4      62  60.491033"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    }
  ]
}