Prediction_using_Supervised_ML.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "#Predict the percentage of an student based on the no. of study hours.\n",
        "\n",
        "Simple Linear Regression\n",
        "\n",
        "What will be predicted score if a student studies for 9.25 hrs/ day?"
      ],
      "metadata": {
        "id": "q2q4vtTsxfF6"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "i1fxTcf3uXdi"
      },
      "outputs": [],
      "source": [
        "#importing all the libraries.\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#importing and reaading the dataset\n",
        "dataset = pd.read_csv(\"Students_scores.csv\")"
      ],
      "metadata": {
        "id": "SL4DIDKJvUZ3"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#the first five values in the dataset\n",
        "dataset.head(25)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 833
        },
        "id": "eQz_CDutwJcD",
        "outputId": "05e0ea6e-ca7c-4470-d90d-5c6f85c78f16"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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\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\n",
              "10    7.7      85\n",
              "11    5.9      62\n",
              "12    4.5      41\n",
              "13    3.3      42\n",
              "14    1.1      17\n",
              "15    8.9      95\n",
              "16    2.5      30\n",
              "17    1.9      24\n",
              "18    6.1      67\n",
              "19    7.4      69\n",
              "20    2.7      30\n",
              "21    4.8      54\n",
              "22    3.8      35\n",
              "23    6.9      76\n",
              "24    7.8      86"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ae770cec-b813-4389-80e7-d6b0a14b2c67\">\n",
              "    <div 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</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",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1.5</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>9.2</td>\n",
              "      <td>88</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>5.5</td>\n",
              "      <td>60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>8.3</td>\n",
              "      <td>81</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2.7</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>7.7</td>\n",
              "      <td>85</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>5.9</td>\n",
              "      <td>62</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>4.5</td>\n",
              "      <td>41</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>3.3</td>\n",
              "      <td>42</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>1.1</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>8.9</td>\n",
              "      <td>95</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>2.5</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>1.9</td>\n",
              "      <td>24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>6.1</td>\n",
              "      <td>67</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>7.4</td>\n",
              "      <td>69</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>2.7</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>4.8</td>\n",
              "      <td>54</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>3.8</td>\n",
              "      <td>35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>6.9</td>\n",
              "      <td>76</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>7.8</td>\n",
              "      <td>86</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ae770cec-b813-4389-80e7-d6b0a14b2c67')\"\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",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-ae770cec-b813-4389-80e7-d6b0a14b2c67 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-ae770cec-b813-4389-80e7-d6b0a14b2c67');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#number of rows and columns\n",
        "dataset.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZZHYcXhRwMz3",
        "outputId": "cf32889e-4c84-4faa-cac8-1b916d78fac4"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(25, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "4M4r1neuwP6C",
        "outputId": "3ec9358e-03f6-415a-9e6e-a34ccadd6df3"
      },
      "execution_count": 7,
      "outputs": [
        {
          "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",
              "max     9.200000  95.000000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-7dc4ad2c-f8d3-4f8e-a835-0ce1d1863160\">\n",
              "    <div 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</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>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7dc4ad2c-f8d3-4f8e-a835-0ce1d1863160')\"\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",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-7dc4ad2c-f8d3-4f8e-a835-0ce1d1863160 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-7dc4ad2c-f8d3-4f8e-a835-0ce1d1863160');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Visualization"
      ],
      "metadata": {
        "id": "NfaqWiKQxWvC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Hours Vs Percentage of Scores\n",
        "plt.scatter(dataset['Hours'], dataset['Scores'])\n",
        "plt.title('Hours vs Percentage')\n",
        "plt.xlabel('Studied Hours')\n",
        "plt.ylabel('Scores')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "TfuiX_H3wUAw",
        "outputId": "4245e122-90a4-41df-d39c-fbed9d4ddc98"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Train-Test Split"
      ],
      "metadata": {
        "id": "6P7hlA19xSyq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#X will take all the values except for the last column which is our dependent variable (target variable)\n",
        "X = dataset.iloc[:, :-1].values\n",
        "y = dataset.iloc[:, -1].values"
      ],
      "metadata": {
        "id": "yErj7JkswXqY"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "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 = 0.3, random_state = 0)"
      ],
      "metadata": {
        "id": "YSDpcEjYwagD"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Training the Simple Linear Regression model on the Training set"
      ],
      "metadata": {
        "id": "DqPoJO-axNom"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "regressor = LinearRegression()\n",
        "regressor.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "clPE7flBwdQt",
        "outputId": "5c48e3a2-b0f3-4b66-c97f-55fe509e937e"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plotting the regression line\n",
        "line = regressor.coef_*X+regressor.intercept_\n",
        "\n",
        "# Plotting for the test data\n",
        "plt.scatter(X, y)\n",
        "plt.plot(X, line,color = 'red');\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "YWxlG7Jlwgfr",
        "outputId": "07afd7e1-8dcc-4316-dd01-be5832dec532"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Predicting the Test set results\n",
        "y_pred = regressor.predict(X_test)\n",
        "print(y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Azlbp8xmwjcT",
        "outputId": "e7d1dffe-acfd-4604-e0c0-4d03a05ee241"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[17.05366541 33.69422878 74.80620886 26.8422321  60.12335883 39.56736879\n",
            " 20.96909209 78.72163554]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Visualising the Training set results\n",
        "plt.scatter(X_train, y_train, color = 'yellow')\n",
        "plt.plot(X_train, regressor.predict(X_train), color = 'blue')\n",
        "plt.title('Hours vs. Percentage (Training set)')\n",
        "plt.xlabel('Hours studied')\n",
        "plt.ylabel('Percentage of marks')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "bCxSWPNtwmnf",
        "outputId": "65e59a2d-a335-4ecc-e479-c114d0d6b091"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAEWCAYAAABhffzLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nO3deZxcVZ3//9c7C0vCGgjIFsImgrgAAWFARUBlB9EZRgKC4DgossiooPBD/Dm4K8IoYiRC0LAruyIZNtmMQEACJBKWQICEbUgIYcn2+f5xbtHV1dvt7rq1vp+PRz267qlb936quvtTpz733nMUEZiZWfsYUu8AzMystpz4zczajBO/mVmbceI3M2szTvxmZm3Gid/MrM048Zs1GUl3Sdq2ytscL+mmaq9bL5L2l3RZveNoVE78DU7SbEl7VrQdKenOesVUJEkXSlos6XVJ/ydpiqT31DuuEkm7SXq2jvvfH1gYEQ9IOi97n17P3rMlZct/7s92I2JyRHyi2uvWgqSxkkLSsFJbRFwHvFfS++sYWsNy4rd3lP/j1NmPImIVYEPgReDC/m6ggV5LtR0D/A4gIo6JiFWy9+p7wGWl5YjYu/SEFn4v+nIJ8MV6B9GInPhbgKStJN0mab6kRyQdUPbYbZK+ULbc6dtC1lM6VtIsYJaSsyS9KOk1SdMlbdPNPg+RdF9F21clXZvd30fSo5IWSnpO0tf6+7oi4g3gYmCbbJvrS/qDpJckPSXp+LJ9nyHpSkm/l/QacKSkUZIukPS8pFclXV22/n6SHszes7vLe4bZt6yvSXpI0gJJl0laSdJI4M/A+mU96/Ul7SjpnmxbcyX9QtIKZdv7hKR/Zts6V9LtFb+ToyTNyGL8i6SNu3s/sm3uDtze13uXvYaTJT0ELJI0TNIpkp7IfiePSvpU2frd/V0cI2lW9rp+KUkDWHeopJ9Kejn7nX2lsndeEffJ2d/Lwuw92yNrH1IW/yuSLpc0KnvaX7Of87Pfyc7Z8m3Avn29V20pInxr4BswG9izou1I4M7s/nDgceBbQCkxLAS2zB6/DfhCd8/NlgOYAowCVgY+CdwPrAEI2ApYr5u4RmT72aKs7V7g37P7c4EPZ/fXBLbL+XovBP47u78KKfHfQeqk3A+cnr3OTYEngU9m654BLAEOytZdGbgBuCzb/3Dgo9m625K+SXwIGAockb3PK5a9538H1s/elxnAMdljuwHPVsS8PbATMAwYm61/YvbY2sBrwMHZ4ydkcX4he/zA7Pe3Vfb4acDdPbw37wUW9fDYGcDvK/5uHgQ2AlbO2v41e01DgEOARaXfbQ9/F9dnfwdjgJeAvQaw7jHAo6Rvb2sC/5utP6yb17AlMAdYP1seC2yW3T8B+Fu2nRWBXwOXlK3XZZvZ7y6A1er9f9xot7oH4Fsfv6D0D/w6ML/s9gYdif/DwDxgSNlzLgHOyO7fRt+Jf/ey5d2Bx7JENqSP2H4PnJ7d34L0QTAiW34G+M/+/tOREv9b2eucB1wLbEZK0s9UrPtN4ILs/hnAX8seWw9YDqzZzT5+BXy3ou2fdHwwzAYOK3vsR8B52f3dqEj83Wz/ROCq7P7ngHvKHlOW3EqJ/8/A0WWPD8l+vxt3s91dgHk97PMMuib+o/qI80HgwF7+LnYtW74cOGUA694C/GfZY3vSc+LfnPSBvCcwvOKxGcAeFb/fJXR82HaX+Idn7WOK+v9s1ptLPc3hoIhYo3QDvlz22PrAnIhYXtb2NLBBP7Y/p3QnIm4BfgH8EnhR0gRJq/XwvIuBz2b3DwWujlSeAfg0sA/wdFba2Lm7DfTgJ9lrfVdEHBARTwAbk0os80s30recdbt7HaSe7v9FxKvdbH9j4L8qtrUR6b0smVd2/w3St49uSXq3pOslzcvKTN8j9fTJtln+/gZQfnB4Y+Dssjj+j/Th0N3v71Vg1Z7i6Eb5+4Gkz5WVt+aTSmhrd/9UoB/vQS/rdnr9lTGVi4jHSR+aZ5D+9i6VVPqdbAxcVRb7DGAZnX//lUrv1fxe1mlLTvzN73lgI0nlv8sxwHPZ/UWkskzJu7rZRqchWiPinIjYHtgaeDfw9R72PQUYLemDpA+Ai8u2cW9EHAisA1xN6gUOxhzgqfIPwIhYNSL26eF1zAFGSVqjh22dWbGtERFxSY44uhvO9lfATFLZazXSB5Kyx+aSyhMAZLXvDcueO4fUIy6PZeWIuLub/TyebSLvh/o7sWbHDX4DfAVYK+tAPFwWZ1E6vX7SB2yPIuLiiNiVlOgD+GH20Bxg74r3aaWIeI7ufyeQymezI+K1wb2E1uPE3/ymknpY35A0XNJuwP7ApdnjDwIHSxohaXPg6N42JmkHSR+SNJz0ofEWqWTSRUQsAa4Afkyqp07JtrGC0rneq2frvNbTNvrh78DC7ODfytlBw20k7dBDbHNJZZRzJa2ZvTcfyR7+DXBM9jolaaSkfSXl6U2/AKwlafWytlVJr/F1pVNPv1T22A3A+yQdlB3QPJbOH77nAd+U9F4ASatL+tceXtNiUo38oznirDSSlCBfyvbzebKD5gW7HDhB0gbZh/DJPa0oaUtJu0takfR39yYdfzfnAWeWDnxLGi3pwOyxl7L1Nq3Y5EdJfwNWwYm/yWXJYH9gb+Bl4FzgcxExM1vlLGAxKWFNAib3scnVSInxVVLJ6BVSYu/JxaSa7BURsbSs/XBgdlb6OAYYDyBpTHbmxZjcLxKIiGXAfsAHgadIr/V8YPVennY4qQ48k1Q7PjHb1n3Af5BKWq+SetJH5oxjJukYypNZ2WF94GukUtdC0nt3Wdn6L5MOqv6I9F5uDdwHvJ09fhWpV3tp9l49TPpd9uTX2evql4h4FPgpcA/pb+F9wF393c4A/Aa4CXgIeAD4E7CUVKaptCLwA9Lvdh7p2+I3s8fOJh3vuUnSQtKB3g/BO2d/nQnclf1Odsqe81nS+2UVlB0EMbMayEpyzwLjI+LWAW7jLuArEfFAVYOrAUl7kw6Ud3vKahX3sz9weET8W5H7aVZO/GYFk/RJUknuTdLxkmOBTSPizboGVgOSVgY+Rur1rwv8AfhbRJxY18DanEs9ZsXbGXiCVMLYn3SWVssn/YyA75BKag+QzsY5va4RmXv8Zmbtxj1+M7M20xSDN6299toxduzYeodhZtZU7r///pcjYnRle1Mk/rFjx3Lffff1vaKZmb1D0tPdtbvUY2bWZpz4zczajBO/mVmbceI3M2szTvxmZm3Gid/MrCYmk+aMGZL97Gu8xOI0xemcZmbNbTJp3vfSPEVP0zEP/PiaR+Mev5lZ4U6lI+mXvJG1154Tv5lZ4Z7pZ3uxnPjNzArX07xD/ZqPqGqc+M3MCncmnae+Jls+sw6xOPGbmdXAeGACaQ55ZT8nUI8Du+CzeszMamQ89Ur0ldzjNzNrQAsXwiOPFLNtJ34zswbz/e/DaqvBNtvAokXV375LPWZmDeLJJ2GzzTqWTzwRRo6s/n6c+M3M6iwCDj4Yrr66o+2FF2CddYrZn0s9ZmZ1dOutMGRIR9K/4IL0QVBU0gf3+M3M6uLNN2HsWHjxxbS8+ebpYO4KKxS/b/f4zcxq7Je/hBEjOpL+3XfDrFm1SfrgHr+ZWc08/jhssUXH8uc+B5Mm1T4OJ34zsxqQOi/PmQMbblifWFzqMTMr0DnndE76226bDt72nvSLnbTFPX4zswK88UbXc/BfeQVGjerrmcVP2uIev5lZlX38452T/imnpF5+30kfajFpi3v8ZmZVMnMmbLVV57Zly9J5+vkVP2mLe/xmZlUgdU76N96Yevn9S/pQi0lbnPjNzAZh8uTOB29XXDEl/E9+cqBbLH7SFpd6zMwGYMmSrhdcPfssbLDBYLdcOoB7Kqm8M4aU9Ks3lr97/GbWhIo93bEvhx/eOekfdVTq5Q8+6ZeMB2YDy7Of1Z3AxT1+M2syxZ/u2JOHH4b3va9z25IlMKzJMql7/GbWZIo/3bE7Uuekf+mlqZffbEkfnPjNrOkUf7pjubPP7jrcQgQcckghu6uJJvysMrP2NoZU3umuvXqWLevam7/nHthpp6rupi7c4zezJlP86Y7vf3/XpB/RGkkfnPjNrOmMByYAGwPKfk6gGgd2n3kmlXWmT+9oe+21lPRbiRO/mTWh6p/uKMHGG3csl07RXHXVQW+64bjGb2Zt7ZJL4NBDO7e1Wg+/khO/mbWl7sbRueEG2Gef+sRTSy71mFnb2X//rkk/opT063tVcC24x29mbeOVV2DttTu3vfACrLNOaal+VwXXUqE9fklflfSIpIclXSJpJUmbSJoq6XFJl0mq0bzyZtbOpM5Jf889Uy+/I+lDva4KrrXCEr+kDYDjgXERsQ0wFPh34IfAWRGxOfAqcHRRMZiZTZnS9crb5ctTe1e1vSq4Xoqu8Q8DVpY0jHSFxVxgd+DK7PFJwEEFx2BmbUqCT3yiY/nCC1Mvv/KDoEPxk6A0gsISf0Q8B/yE9FE5F1gA3A/Mj4il2WrPAt0OZCrpi5Luk3TfSy+9VFSYZtaCjj22+/F1jjiir2cWf1VwIyiy1LMmcCCwCbA+MBLYK+/zI2JCRIyLiHGjR48uKEozayWLFqWEf+65HW1PPNGf8/KLuyq4kRR5Vs+ewFMR8RKApD8CuwBrSBqW9fo3BJ4rMAYzaxOVPfzNN4dZswaypfG0WqKvVGSN/xlgJ0kjJAnYA3gUuBX4TLbOEcA1BcZgZi3uvvu6Jv0lSwaa9NtDkTX+qaSDuNOA6dm+JgAnAydJehxYC5hYVAxm1tok2GGHjuUf/rB5J0eppULfnoj4NvDtiuYngR2L3K+ZtbbDDoPJFRfUtvr4OtXkz0UzaxqLF8OKK3Zue+AB+OAH6xNPs/JYPWY2CLUb10bqmvQjnPQHwonfzAaoNK7N00DQMa5NdZP/Aw90PXi7cKFLO4PhxG9mA1T8uDYSbLddx/JHP5oS/iqrVG0XbcmJ38wGqLhxbU47rfsrb2+7bdCbHqTWGLLZB3fNbIDGkMo73bUPzPLlMHRo57ZLL4VDDhnwJquodYZsdo/fzAaouuPaSF2TfkSjJH1opSGb+0z8kkZKGpLdf7ekAyQNLz40M2ts1RnXZvbsrmWdefMa8eBt6wzZnKfU81fgw9mgazcB9wKH0GzfbcysAIMb16Yy4a+3Hjz//OAiKk71S1v1kqfUo4h4AzgYODci/hV4b7FhmVkrO++87idHadykD600ZHOuxC9pZ9LH+g1Z29Be1jcz65EEX/pSx/JZZ/U1OUqjaJ0hm/OUek4AvglcFRGPSNqUNMKmmVlu73531xEzG6+O35fWGLI5T+KfExEHlBYi4klJvyswJjNrIa+80nmSc4DHHoMttqhPPJav1HNlNnE6AJI+Cvy2uJDMrFVIXZN+hJN+veVJ/McAV0t6l6R9gHOAfYoNy8ya2TXXdK3ZL13ajKWd1tRnqSci7pV0POlUzreAPUvTKZqZVapM+F/9KvzsZ/WJxbrXY+KXdB1pyL2SEcACYKIkyuv+Zmb77Qc33NC5zT38xtRbj/8nNYvCzJrWokVdR8ucOhV29Dx7DavHxB8Rt0saCvxvRHyshjGZWZPo7tx79/IbX68HdyNiGbBc0uo1isfMmsCtt3ZN+m++6aTfLPKcx/86MF3SFGBRqTEiji8sKjNrWJUJf9dd4Y476hOLDUyexP/H7GZmbezQQ+GSSzq3uYffnPKczjmpFoGYWWNauhSGVwzEfuWV8OlP1yceG7w84/FvIelKSY9KerJ0q0VwZpZXMVMCSl2TfoSTfrPLc+XuBcCvgKXAx4CLgN8XGZSZ9UdpSsCnSZfelKYEHHjynz69ay3/5Zdd2mkVeRL/yhFxM2lc/qcj4gxg32LDMrP8qjsloATvf3/H8tprp4S/1loDjc8aTZ7E/3Y29eIsSV+R9Clglb6eZGa1Up0pAU89tWsvPwJe8gAtLSdP4j+BNFzD8cD2wOHAEUUGZWb90dPUf/mmBCxNgvK973W0nX22yzqtLNcgbdnd14HPFxuOmfXfmaSafnm5J9+UgL7ytj3lOatnnKSrJE2T9FDpVovgzCyP/k8J+OyzXZP+k0866beLPBdwTQa+DkwHlhcbjpkNTP4pAd3Ltzw1/pci4tqIeCo7q+fpiHi68MjMrKp+/euuSX/5cif9dpSnx/9tSecDNwNvlxojwsM4mDUJT45i5fIk/s8D7wGG01HqCTx+j1nDGzsWnq74fu4evuVJ/DtExJaFR2JmVTN/Pqy5Zue2+++H7barTzzWWPIk/rslbR0RjxYejZkNmg/eWl/yHNzdCXhQ0j+zUzmn+3ROs8Zz3XVdk/7ixU761lWeHv9ehUdhZoNSmfAPOACuuaY+sVjjy3Plrk/dNGtQn/wk3HRT5zb38K0veUo9ZtZg3n479fLLk/6f/+ykb/n0mPglrTjYjUtaI5vEZaakGZJ2ljRK0hRJs7Kfa/a9JbNWUJ3JUiRYaaXObRGwl4uyllNvPf57ACT9bhDbPxu4MSLeA3wAmAGcAtwcEVuQLgo7ZRDbN2sSg58sZerUrrX8BQvcy7f+663Gv4KkQ4F/kXRw5YN9XbkraXXgI8CR2fqLgcWSDgR2y1abBNwGnNzfwM2aS2+TpfQ9xk5lwt9yS5g5s0qhWdvpLfEfQ/qLXAPYv+KxPFfubgK8BFwg6QPA/aSx/deNiLnZOvOAdbt7sqQvkrpEjBmTb1xxs8Y1sMlSjj0Wzj23c5t7+DZYPSb+iLgTuFPSfRExcYDb3g44LiKmSjqbirJORISkbv+MI2ICaWxZxo0b5z91a3JjSOWd7tq7Wr4chg7t3DZxIhx1VNUDszaU5zz+30k6nlS2AbgdOC8ilvTxvGeBZyNiarZ8JSnxvyBpvYiYK2k94MWBBG7WXPJPluIrb61oeU7nPJc05eK52W074Fd9PSki5gFzJJXG+dkDeBS4lo6pG48AfJmJtYG+J0t5/PGuSf+555z0rfryDtL2gbLlWyT9I+f2jwMmS1oBeJI00ucQ4HJJR5O++/5bfwI2a149T5biXr7VUp7Ev0zSZhHxBICkTYFleTYeEQ8C47p5aI/8IZq1rh//GL7xjc5ty5d3/0FgVi15Ev/XgVslPUnHd1RPum42SJXJ/TvfgdNPr08s1l7yjNVzs6QtgFKt/p8R8XZvzzGzno0cCW9UnNLvso7VUq6xeiLi7Yh4KLs56ZsNwPPPp15+edJ/9FEnfau9PKUeMxskH7y1RuLROc0K9ItfdE36S5Y46Vt99Zn4lRwm6fRseYykHYsPzaxWqjNqZiUJjjuuY3nXXVPCH+bv2VZnef4EzwWWA7sD/z+wEPgDsEOBcZnVSGnUzFLhvTRqJuQZPK07K62Uxssv5x6+NZI8pZ4PRcSxwFsAEfEqsEKhUZnVTG+jZvbPwoWpl1+e9K+/3knfGk+eHv8SSUNJI3IiaTTpG4BZCxjYqJmVfPDWmkmeHv85wFXAOpLOBO4EvldoVGY109OQ3/mGAr/mmq5J//XXnfStseW5gGuypPtJwywIOCgiZhQemVlN5B81s1Jlwl99dZg/v4qhmRUkz1k9o0hDJ18CXEwaVnl40YGZ1Ubfo2ZW2nHHrkk/wknfmkeeUs800kxajwGzsvuzJU2TtH2RwZnVxnhgNunQ1Wx6SvpLlqSEf++9HW3nneeyjjWfPAd3pwBXRsRfACR9Avg0cAHpVM8PFReeWWPwwVtrJXl6/DuVkj5ARNwE7BwRfwNWLCwyswbw9793Tfrz5jnpW3PL0+OfK+lk4NJs+RBSnX8oPq3TWph7+daq8vT4DwU2BK7ObmOytqF49ixrQUcc0f3BWyd9axV5Tud8mTSFYncer244ZvUTAUMqukInnww/+EF94jErSp+JP7tS9xvAe4GVSu0RsXuBcZnVlMs61k7ylHomAzOBTYDvkM53u7e3J5g1iyef7Jr0Z8xw0rfWlufg7loRMVHSCRFxO3C7JCd+a3ru5Vu7ytPjX5L9nCtpX0nbAqMKjMmsUN/9btekv3y5k761jzyJ/78lrQ78F/A14HzgxEKjMiuIBKef3rH8qU+lhN9d7797xUzaYlZLeUo9r0bEAmAB8DEASbsUGpVZlVWnrFP9SVvM6iFPj/9/craZNZxXXuma9G+/faBlnepN2mJWTz32+CXtDPwLMFrSSWUPrUa6eMusoVX/4G11Jm0xq7feevwrAKuQPhxWLbu9Bnym+NDMBuaKK7om/bfeqsbB28FN2mLWKHrs8ZedunlhRDxdw5jMBqwy4W+5JcycWa2tD3zSFrNGkufg7oqSJpBOYXhnfV+5a41k++1h2rTObdU/PbN0APdUUnlnDCnp+8CuNZc8if8K4DzSaZzLig3HrH/efhtWWqlz21VXwUEHFbXH8TjRW7PLk/iXRsSvCo/ErJ985a3ZwOQ5nfM6SV+WtJ6kUaVb4ZGZ9eDOO7sm/fnznfTN8srT4z8i+/n1srYANq1+OGa9q0z4q60GCxbUJxazZpVnPP5NahGIWW/Gj4eLL+7c5h6+2cD0WeqRNELSadmZPUjaQtJ+xYdmjaN+49MsX556+eVJ/5xznPTNBiNPqecC4H7SVbwAz5HO9Lm+qKCskdRvfBofvDUrRp6Du5tFxI/IhmeOiDeA3GMZWrOr/fg0M2d2TfrPPOOkb1YteRL/Ykkrkw7oImkz4O1Co7IGUtvxaSTYaqvObRGw0Ub93ZKHTzbrSZ7E/23gRmAjSZOBm0lz8FpbqM34NKefXs3JUUrlqadJ/ZVSecrJ3wxyJP6ImAIcDBwJXAKMi4jb8u5A0lBJD0i6PlveRNJUSY9LukzSCgML3WrjTNJ4NOWqOz6NlGbFKjnppP5OjlLJwyeb9SbPWT2fIl29e0NEXA8sldSfC+JPAGaULf8QOCsiNgdeBY7uT8BWa+OBCcDGpEM7G2fLgz+wK3VN7hHw058OdssePtmsN7lKPdkMXABExHxS+adPkjYE9iWN84MkAbsDV2arTAIKG1XFqmU8MBtYnv0cXNKfN69rwv/HP6p58NbDJ5v1Js/pnN19OOR5HsDPSccDVs2W1wLmR8TSbPlZYIPunijpi2TnDY4Z43/YVlGbUzQ9fLJZb/L0+O+T9DNJm2W3n5HO6+9VdpHXixHR57rdiYgJETEuIsaNHj16IJuwBvLb33ZN+kuXFnWKZnHlKbNWkKfnfhzw/wGXkU6RmAIcm+N5uwAHSNoHWIk0ZePZwBqShmW9/g1JF4RZC6tM+PvvD9deW/RePXyyWU96TfyShgLXR8TH+rvhiPgm8M1sO7sBX4uI8ZKuIE3deClpALhr+rttaw5jx8LTFXO3+SIss/rrtdQTEcuA5ZJWr+I+TwZOkvQ4qeY/sYrbtgawaFHq5Zcn/SlTnPTNGkWeUs/rwHRJU4BFpcaIOD7vTrLz/m/L7j8J7NivKK1peHwds8aXJ/H/MbuZ9egvf4G99urctmgRjKi89svM6i7PePyTsrF6xkTEP2sQkzWZyl7+FlvAY4/VJxYz61ueK3f3Bx4kjdeDpA9KKvycDGt8e+/d/ZW3TvpmjS3PefxnkGry8wEi4kE87WJbW7o0Jfwbb+xomzTJtXyzZpGnxr8kIhaoc9dueUHxWIPzwVuz5penx/+IpEOBodm0i/8D3F1wXNZgHniga9J/4QUnfbNmlCfxHwe8lzT5ysXAAuDEIoOyxiLBdtt1bouAddapTzxmNjg9lnokrQQcA2wOTAd2LhtczdrAT34CX/965zb38M2aX281/kmkeXbvAPYGtsI9/bYQAUMqvguefz4c7ZkTzFpCb4l/64h4H4CkicDfaxOS1dPo0fDyy53b3Ms3ay291fiXlO64xNP65s5NtfzypP/MM076Zq2otx7/ByS9lt0XsHK2LCAiYrXCo7OaqDxbZ/hwWLy4PrGYWfF67PFHxNCIWC27rRoRw8ruO+m3gIsv7pr0ly930jdrdXlO57QWJMH4snlKzjgjlXW6u0DLzFpL3rlzrUXssgvcXXH5nev4Zu3FPf6WMRkYS/qVjs2WOyxcmHrz5Un/oYeqkfR736+ZNR73+FvCZOCLwBvZ8tPZMsD4AsfX6X2/ZtaY3ONvCafSkXxL3uDmm2/okvQXL65maaf7/aZ2M2tU7vG3hGe6tEids/tRR8HEqs9u3HW/vbebWSNwj78ljHnn3hFHXNgl6UcUkfQ77zdfu5k1Aif+lnAmy5atghRcdNER77TeeuuUgs/YOROonFR3RNZuZo3Kib8FfPnL4xk2bGGntojJ7Lbbxwve83hgArAx6YLujbNlH9g1a2Su8TexefNgvfU6t739NqywAtQu+Y6v4b7MrBrc429Sa67ZOen/9replp+SvplZz9zjbzK33AJ77NG5zVfemll/uMffJCJgv/06J/3p0530zaz/nPibwK23phmxbrghLX/4wynhb7NNfeMys+bkxN/A3noL1l0Xdt89LW+6aTp4+9e/dre2x8wxs3yc+BvUuefCyivDiy+m5bvugiee6OngbWnMnKeBoGPMHCd/M+vKB3cbzPPPwwYbdCwfdhhcdFFf4+T3NmaOT7U0s87c428gn/9856T/zDPwu9/lmRzFY+aYWX5O/A1g6tSU3C+8MC3//Ofp4O1GG+XdgsfMMbP8XOqpoyVL0pk5jz2WlkeNgjlzYETl8Dd9OpPO4+KDx8wxs564x18nF12UDtSWkv6UKfDKKwNJ+uAxc8ysP9zjr7GXX4bRozuW99sPrr22GpOce8wcM8vHPf4aOumkzkl/1iy47rpqJH0zs/yc+Gtg+vSU3M86Ky1/97vp4O3mm9c3LjNrT078BVq2DHbeGd7//rQ8ZAgsWACnnTbQLfrqXDMbPCf+glx1FQwbBn/7W1q++ur0QbDaagPdoq/ONbPqKCzxS9pI0q2SHpX0iKQTsvZRkqZImpX9XLOoGOphwYJU1jn44LS8664p4R944GC33NvVuWZm+RXZ418K/FdEbA3sBBwraWvgFODmiNgCuDlbbgnf+Q6ssUbH8vTpcMcdqcQzeL4618yqo7DEHxFzI2Jadn8hMAPYADgQmJStNgk4qKgYamXWrAdCaV8AAAmeSURBVNTLP+OMtPy1rxUxbLKvzjWz6qjJefySxgLbAlOBdSNibvbQPGDdHp7zRVIRmzFjGjO5RcA++8CNN3a0vfwyrLVWEXvz1blmVh2FH9yVtArwB+DEiHit/LGICNKRyi4iYkJEjIuIcaPLT35vEDfdlEo4paQ/eXL6ICgm6YOvzjWzaim0xy9pOCnpT46IP2bNL0haLyLmSloPeLHIGKrtjTfSJOevZR9h22wD06bB8OG12LuvzjWzwSvyrB4BE4EZEfGzsoeuBY7I7h8BXFNUDNX285/DyJEdSf/ee9MB3NokfTOz6iiyx78LcDgwXdKDWdu3gB8Al0s6mnQy+r8VGENVzJkD5YcZ/uM/YMKE+sVjZjYYhSX+iLiTVIzuzh5F7bfDZNI57s+Qznw5k/6WSSLSDFgXX9zR9vzzqdRjZtasWvTK3cFf5XrXXengbSnpn3de+iBw0jezZteiwzIPfA7axYthyy1h9uy0vOGG8PjjsOKK1Y/SzKweWrTHP7CrXCdOTAm+lPRvuy3V9530zayVtGiPfwypvNNde1cvvADvelfH8mc+A5df7nHyzaw1tWiP/0zSVa3lur/K9dhjOyf9p56CK65w0jez1tWiib/vq1ynTUvJ/dxz0/KPfpQO3o4dW+tYzcxqq0VLPdDTVa5Ll8IOO8CD2ZUFI0akUs8qq9Q2OjOzemnRHn/3LrssXWVbSvp/+hMsWuSkb2btpYV7/B1efRVGjepY3nNP+MtfqjVOvplZc2n51Petb3VO+jNmwJQpTvpm1r5aOv0ddxx8//vp/qmnpoO373lPfWMyM6u3li717LNPqudfd13nKRHNzNpZSyf+vfdONzMz69DSpR4zM+vKid/MrM048ZuZtRknfjOzNuPEb2bWZpz4zczajBO/mVmbceI3M2szioh6x9AnSS/R/ZRa3VkbeLnAcAaqEeNqxJjAcfVHI8YEjRlXI8YExca1cUSMrmxsisTfH5Lui4hx9Y6jUiPG1YgxgePqj0aMCRozrkaMCeoTl0s9ZmZtxonfzKzNtGLin1DvAHrQiHE1YkzguPqjEWOCxoyrEWOCOsTVcjV+MzPrXSv2+M3MrBdO/GZmbaZlEr+k30p6UdLD9Y6lRNJGkm6V9KikRySdUO+YACStJOnvkv6RxfWdesdUImmopAckXV/vWEokzZY0XdKDku6rdzwlktaQdKWkmZJmSNq5zvFsmb1Hpdtrkk6sZ0wlkr6a/a0/LOkSSSs1QEwnZPE8Uuv3qWVq/JI+ArwOXBQR29Q7HgBJ6wHrRcQ0SasC9wMHRcSjdY5LwMiIeF3ScOBO4ISI+Fs94wKQdBIwDlgtIvardzyQEj8wLiIa6uIfSZOAOyLifEkrACMiYn6944L0AQ48B3woIvJefFlULBuQ/sa3jog3JV0O/CkiLqxjTNsAlwI7AouBG4FjIuLxWuy/ZXr8EfFX4P/qHUe5iJgbEdOy+wuBGcAG9Y0KInk9Wxye3ereA5C0IbAvcH69Y2l0klYHPgJMBIiIxY2S9DN7AE/UO+mXGQasLGkYMAJ4vs7xbAVMjYg3ImIpcDtwcK123jKJv9FJGgtsC0ytbyRJVlJ5EHgRmBIRjRDXz4FvAMvrHUiFAG6SdL+kL9Y7mMwmwEvABVlp7HxJI+sdVJl/By6pdxAAEfEc8BPgGWAusCAibqpvVDwMfFjSWpJGAPsAG9Vq5078NSBpFeAPwIkR8Vq94wGIiGUR8UFgQ2DH7Ktn3UjaD3gxIu6vZxw92DUitgP2Bo7Nyor1NgzYDvhVRGwLLAJOqW9ISVZ2OgC4ot6xAEhaEziQ9GG5PjBS0mH1jCkiZgA/BG4ilXkeBJbVav9O/AXLauh/ACZHxB/rHU+lrDxwK7BXnUPZBTggq6dfCuwu6ff1DSnJeoxExIvAVaS6bL09Czxb9k3tStIHQSPYG5gWES/UO5DMnsBTEfFSRCwB/gj8S51jIiImRsT2EfER4FXgsVrt24m/QNlB1InAjIj4Wb3jKZE0WtIa2f2VgY8DM+sZU0R8MyI2jIixpDLBLRFR114ZgKSR2YF5slLKJ0hf0+sqIuYBcyRtmTXtAdT1pIEyn6VByjyZZ4CdJI3I/if3IB1vqytJ62Q/x5Dq+xfXat/DarWjokm6BNgNWFvSs8C3I2JifaNiF+BwYHpWTwf4VkT8qY4xAawHTMrOvBgCXB4RDXP6ZINZF7gq5QuGARdHxI31DekdxwGTs9LKk8Dn6xxP6cPx48B/1juWkoiYKulKYBqwFHiAxhi+4Q+S1gKWAMfW8uB8y5zOaWZm+bjUY2bWZpz4zczajBO/mVmbceI3M2szTvxmZm3Gid+akqTXK5aPlPSLesWTxbCbpH5fGCTpQkmfye6fL2nrfu7Tp+Jav7TMefxm1SBpWDZo1kDsRhoh9u6B7j8ivjDQ55rl5R6/tRxJYyXdIukhSTdnV0Z26llny69nP3eTdIeka4FHs6t1b8jmK3hY0iHd7OP4bJ6FhyRdmg3Cdwzw1Wws+g/3sj9J+oWkf0r6X2CdsnVukzQuu/8JSfdImibpimzMJyTtpTQG/zRqOKKjtQ73+K1ZrVx2NTTAKODa7P7/AJMiYpKko4BzgIP62N52wDYR8ZSkTwPPR8S+8M4QyJVOATaJiLclrRER8yWdB7weET/Jnnd0D/v6FLAlsDXpyuBHgd+WryBpbeA0YM+IWCTpZOAkST8CfgPsDjwOXNbH6zLrwj1+a1ZvRsQHSzfg9LLHdqZj3JPfAbvm2N7fI+Kp7P504OOSfijpwxGxoJv1HyINl3AYaRiA/vgIcEk2QurzwC3drLMT6YPhruwD7ghgY+A9pAHHZkW67L4hBrKz5uLEb+1kKdnfvKQhwApljy0q3YmIx0jfAKYD/y2p/EOlZF/gl9l692YTfPRnf30RaZ6E0ofb1hHR0zcIs35x4rdWdDdphE+A8cAd2f3ZwPbZ/QNIM491IWl94I2I+D3wYyqGO86S+EYRcStwMrA6sAqwEFi1bNWe9vdX4JBsMpz1gI91E8bfgF0kbZ7tc6Skd5NGUR0rabNsvc92/xaY9cw1fmtFx5Fmpvo6aZaq0qiVvwGukfQP0uQXi3p4/vuAH0taTho58UsVjw8Ffp/V/gWck9X4rwOulHRgFkNP+7uKVKN/lDRk8D2VAUTES5KOBC6RtGLWfFpEPKY0C9gNkt4gfaitWvl8s954dE4zszbjUo+ZWZtx4jczazNO/GZmbcaJ38yszTjxm5m1GSd+M7M248RvZtZm/h/jwaCJ81xRpAAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Visualising the Test set results\n",
        "plt.scatter(X_test, y_test, color = 'yellow')\n",
        "plt.plot(X_train, regressor.predict(X_train), color = 'blue')\n",
        "plt.title('Hours vs. Percentage (Test set)')\n",
        "plt.xlabel('Hours studied')\n",
        "plt.ylabel('Percentage of marks')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "jupVJOO9wqTY",
        "outputId": "424ed126-5e6d-4de0-ae93-41db5133b65b"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Comparing the actual values with the predicted ones.\n",
        "dataset = pd.DataFrame({'Actual': y_test, 'Predicted': y_pred})  \n",
        "dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "D0H9oOk_wtUG",
        "outputId": "8cee0c1a-0e0a-4b98-ed84-d65162800f18"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Actual  Predicted\n",
              "0      20  17.053665\n",
              "1      27  33.694229\n",
              "2      69  74.806209\n",
              "3      30  26.842232\n",
              "4      62  60.123359\n",
              "5      35  39.567369\n",
              "6      24  20.969092\n",
              "7      86  78.721636"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-61c6fb13-70d1-4edb-9267-732e4f511549\">\n",
              "    <div 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>Actual</th>\n",
              "      <th>Predicted</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>20</td>\n",
              "      <td>17.053665</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>27</td>\n",
              "      <td>33.694229</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>69</td>\n",
              "      <td>74.806209</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>30</td>\n",
              "      <td>26.842232</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>62</td>\n",
              "      <td>60.123359</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>35</td>\n",
              "      <td>39.567369</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>24</td>\n",
              "      <td>20.969092</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>86</td>\n",
              "      <td>78.721636</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-61c6fb13-70d1-4edb-9267-732e4f511549')\"\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",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-61c6fb13-70d1-4edb-9267-732e4f511549 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-61c6fb13-70d1-4edb-9267-732e4f511549');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#predicting the score \n",
        "dataset = np.array(9.25)\n",
        "dataset = dataset.reshape(-1, 1)\n",
        "pred = regressor.predict(dataset)\n",
        "print(\"If the student studies for 9.25 hours/day, the score is {}.\".format(pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5PusSCqjwwmE",
        "outputId": "3df6942b-bf71-4492-a1df-5f7ae09b2fad"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "If the student studies for 9.25 hours/day, the score is [92.91505723].\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Error Metrics"
      ],
      "metadata": {
        "id": "yyei4G0fxDLh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import metrics  \n",
        "print('Mean Absolute Error:', metrics.mean_absolute_error(y_test, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5arFr8ahwzPf",
        "outputId": "a17a091f-0b23-49fb-8b86-984390009d38"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Absolute Error: 4.419727808027651\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import r2_score\n",
        "print(\"The R-Square of the model is: \",r2_score(y_test,y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Kpixk5Tqw2qa",
        "outputId": "73ba08be-7744-4fa0-d3c5-8c58a018e3b2"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The R-Square of the model is:  0.9568211104435257\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Conclusion:\n",
        "\n",
        "We used a Linear Regression Model to predict the score of a student if he/she studies for 9.25 hours/day and the Predicted Score came out to be 92.91."
      ],
      "metadata": {
        "id": "_dbAwPmpw6Kk"
      }
    }
  ]
}