Task1_student_score.ipynb

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        "<a href=\"https://colab.research.google.com/github/Yashkaka/Student_Marks_prediction/blob/main/Task1_student_score.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "cell_type": "markdown",
      "source": [
        "## **Task 1 : Prediction using Supervised Model(Linear Regression)**\n",
        "### **Author: Yash Kaka**"
      ],
      "metadata": {
        "id": "ZSkv_wNn8_kA"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Perform exploratory Data Analysis on Dataset \"students\" to predict the percentage of the marks of the students based on the numbers of hours they studied.**"
      ],
      "metadata": {
        "id": "PUB17rfMAJEl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# importing the needed libraries\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.metrics import mean_absolute_error\n",
        "from sklearn import metrics\n"
      ],
      "metadata": {
        "id": "6K2wIsDCBcVV"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#reading the data\n",
        "data=pd.read_csv(\"http://bit.ly/w-data\")\n",
        "print(\"data imported succesfully\")"
      ],
      "metadata": {
        "colab": {
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        },
        "id": "ywbJK9ijDE2N",
        "outputId": "5c7de14e-28e6-451c-9b94-f0559a4346dc"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "data imported succesfully\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "(data.head(10))"
      ],
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          "height": 363
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      "execution_count": 5,
      "outputs": [
        {
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              "   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\n",
              "5    1.5      20\n",
              "6    9.2      88\n",
              "7    5.5      60\n",
              "8    8.3      81\n",
              "9    2.7      25"
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              "      <td>9.2</td>\n",
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    {
      "cell_type": "code",
      "source": [
        "#get more understanding of the dataset\n",
        "data.describe()"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
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          "output_type": "execute_result",
          "data": {
            "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",
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              "      <th>mean</th>\n",
              "      <td>5.012000</td>\n",
              "      <td>51.480000</td>\n",
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              "    <tr>\n",
              "      <th>std</th>\n",
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              "      <td>25.286887</td>\n",
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              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.100000</td>\n",
              "      <td>17.000000</td>\n",
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              "      <th>25%</th>\n",
              "      <td>2.700000</td>\n",
              "      <td>30.000000</td>\n",
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              "      <th>50%</th>\n",
              "      <td>4.800000</td>\n",
              "      <td>47.000000</td>\n",
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              "      <th>75%</th>\n",
              "      <td>7.400000</td>\n",
              "      <td>75.000000</td>\n",
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              "      <th>max</th>\n",
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              "                                                     [key], {});\n",
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              "\n",
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          "metadata": {},
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    },
    {
      "cell_type": "code",
      "source": [
        "data.isnull().sum()\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hqauKGiOESMI",
        "outputId": "bf4e0dda-dd5e-4733-e649-b43c7a116b8d"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Hours     0\n",
              "Scores    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**There is no null values in the dataset**"
      ],
      "metadata": {
        "id": "sLa10w_YE3Or"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.set_style('darkgrid')\n",
        "sns.scatterplot(data=data,x=\"Hours\",y=\"Scores\")\n",
        "plt.title(\"Marks VS Study Hours\",size=15)\n",
        "plt.xlabel(\"Hours Studied\",size=10)\n",
        "plt.ylabel(\"Marks Percentage\",size=10)\n",
        "plt.show()"
      ],
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      "execution_count": 31,
      "outputs": [
        {
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              "<Figure size 432x288 with 1 Axes>"
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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Preparing the Data**"
      ],
      "metadata": {
        "id": "bd5iWS16T9l9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# defining x and y from data\n",
        "X=data.iloc[:,:-1].values\n",
        "Y=data.iloc[:,1].values"
      ],
      "metadata": {
        "id": "faS9B88eU1IA"
      },
      "execution_count": 91,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Splitting the data into 2\n",
        "X_train,X_test,Y_train,Y_test = train_test_split(X,Y,test_size=0.2,random_state=0)"
      ],
      "metadata": {
        "id": "96GU3vOYWTXf"
      },
      "execution_count": 92,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Fitting the data into model**"
      ],
      "metadata": {
        "id": "PDf-2J5rZqg9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "regression=LinearRegression()\n",
        "regression.fit(X_train,Y_train)\n",
        "print(\"training Complete\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tHSnfmuUZu1U",
        "outputId": "68af4908-041d-4df8-a62d-b90897a55d74"
      },
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "training Complete\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.regplot(x = \"Hours\",y = \"Scores\", ci = None,data = data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "JRzjUkKDawPs",
        "outputId": "e1d6d0c6-4fa6-4c7e-efc4-c30678cf157a"
      },
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8928405950>"
            ]
          },
          "metadata": {},
          "execution_count": 94
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Predicting the percentage of marks**"
      ],
      "metadata": {
        "id": "0-8JB0F8ckG8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(X_test)\n",
        "pred_Y=regression.predict(X_test)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sTW4ozVbcqmi",
        "outputId": "8332b628-c63a-4628-d845-d4c02e6016e2"
      },
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[1.5]\n",
            " [3.2]\n",
            " [7.4]\n",
            " [2.5]\n",
            " [5.9]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "prediction=pd.DataFrame({'Hours':[i[0] for i in X_test],'predicted Marks':[j for j in pred_Y]})\n",
        "prediction"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "PGE6Vj7P65V_",
        "outputId": "99446506-e487-4738-f851-1fa9fab310ce"
      },
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Hours  predicted Marks\n",
              "0    1.5        16.884145\n",
              "1    3.2        33.732261\n",
              "2    7.4        75.357018\n",
              "3    2.5        26.794801\n",
              "4    5.9        60.491033"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-602cfdda-e9aa-42f6-a267-face613dead8\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Hours</th>\n",
              "      <th>predicted Marks</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.5</td>\n",
              "      <td>16.884145</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3.2</td>\n",
              "      <td>33.732261</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7.4</td>\n",
              "      <td>75.357018</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2.5</td>\n",
              "      <td>26.794801</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.9</td>\n",
              "      <td>60.491033</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-602cfdda-e9aa-42f6-a267-face613dead8')\"\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 0 24 24\"\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-602cfdda-e9aa-42f6-a267-face613dead8');\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",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 102
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Comparing the data with predicted marks**"
      ],
      "metadata": {
        "id": "BYEetn8V77R6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.DataFrame({'Actual Marks': Y_test, 'Predicted Marks':  pred_Y})  \n",
        "df "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "FCk7UG1L5_te",
        "outputId": "6d0cdb0d-f503-46db-f327-4dbb763cba1d"
      },
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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"
            ],
            "text/html": [
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              "<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",
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              "      <th>0</th>\n",
              "      <td>20</td>\n",
              "      <td>16.884145</td>\n",
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              "      <th>1</th>\n",
              "      <td>27</td>\n",
              "      <td>33.732261</td>\n",
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              "    <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",
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              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "    }\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",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-df08c53a-e46d-4a0a-9699-adbf32df2d71 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-df08c53a-e46d-4a0a-9699-adbf32df2d71');\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",
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              "          dataTable['output_type'] = 'display_data';\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 98
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Evaluating the method**"
      ],
      "metadata": {
        "id": "eHpv6Obt8hAa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#calculating the mean absolute error\n",
        "\n",
        "print(\"Mean absolute error :\",mean_absolute_error(Y_test,pred_Y))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lw-Bxt4O8gjD",
        "outputId": "b64d8e3c-b6f7-4bb7-8895-17fa6ed51446"
      },
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean absolute error : 4.183859899002982\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**what will be the score of the student if he/she studies for 9.5 hrs/day?**"
      ],
      "metadata": {
        "id": "MEGaBEnd9i1V"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "hours= [9.25]\n",
        "sol = regression.predict([hours])\n",
        "print(\"No of Hours = {}\".format(hours[0]))\n",
        "print(\"Predicted Score = {}\".format(sol[0]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QHfQC6rx9Xjc",
        "outputId": "cc0c75e3-5add-4c39-925e-a1501701b402"
      },
      "execution_count": 127,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "No of Hours = 9.25\n",
            "Predicted Score = 93.69173248737539\n"
          ]
        }
      ]
    }
  ]
}