Task1_Prediction_using_Supervised.ipynb

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        "<a href=\"https://colab.research.google.com/github/vaishnavirk0411/Sparks_Foundation_Modules/blob/main/Task1_Prediction_using_Supervised.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "# **Author :** **Vaishnavi Kulkarni**\n",
        "\n",
        "# **Task 1 : Prediction using Supervised Machine Learning**\n",
        "\n",
        "***GRIP @ The Sparks Foundation***\n",
        "\n",
        "In this regression task I tried to predict the percentage of marks that a student is expected to score based upon the number of hours they studied.\n",
        "\n",
        "This is a simple linear regression task as it involves just two variables."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y27skMQTtmfV"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
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        },
        "id": "J7Asbb5xtpJs",
        "outputId": "9b3b1207-d38a-4c33-c533-ff6d160ec29c"
      },
      "source": [
        "student_data = pd.read_csv(\"http://bit.ly/w-data\")\n",
        "student_data"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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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>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",
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            ],
            "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"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
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        "outputId": "6d479de8-60f8-4eed-c2a4-7957eeab8cf4"
      },
      "source": [
        "student_data.info()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 25 entries, 0 to 24\n",
            "Data columns (total 2 columns):\n",
            " #   Column  Non-Null Count  Dtype  \n",
            "---  ------  --------------  -----  \n",
            " 0   Hours   25 non-null     float64\n",
            " 1   Scores  25 non-null     int64  \n",
            "dtypes: float64(1), int64(1)\n",
            "memory usage: 528.0 bytes\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
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        "colab": {
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        "id": "jxCMeJh4t6BS",
        "outputId": "2ab22e91-fbec-49ab-cc8c-a6f4747e0827"
      },
      "source": [
        "student_data.describe()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "</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": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GnxVuG5y3cAn"
      },
      "source": [
        "# **The relation between Independent and Target Variable**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "id": "ATqSS8ybuIgZ",
        "outputId": "f4cda78f-e273-477b-8e2e-a8eb2e452caa"
      },
      "source": [
        "plt.xlabel('Hours',fontsize=15)\n",
        "plt.ylabel('Scores',fontsize=15)\n",
        "plt.title('Hours studied vs Score', fontsize=10)\n",
        "plt.scatter(student_data.Hours,student_data.Scores,color='red')\n",
        "plt.show()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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NNYA80juQnwSupBgbuM/27cDtownE9lLgOYFQtBIiIqIGI+omsv0UcAfwgrENJyIi6tDMAPLJwCcl/dVYBRMREfVopoT1J4CtgaWS7gUeYMAloLb3aWFsERFRkWaSwbLyERERXaaZEtbvGMtAIiKiPs20DJ4maWtgK+Ah2w+2NqSIiKhaU/MZSHqLpOUUdwjfCqwpy1D/7ZhEF1GXzC8c48yIWwaSjgUWUNx9/DmKAeRpwFuA8yVNsH3+mEQZUaXMLxzjUDMlrJcB19p+9yDvfR3Y3/aeLY5vSLkDOcZMSktHF2tFCeudKWY0G8zF5fsRnS+lpWMcaiYZPMDgZSQo1z+w4eFEtIGUlo5xqJlk8C3gU5I+IWl3SVMk7SbpExTzH39zbEKMqFhKS8c41MylpZ8GNqaYjObUhvV/BL5Qvh/R+foHiU8+uegamjGjSAQZPI4uNuIB5Kd3kKYAewLTgdXAMtsPj0Fsw8oAckRE8za0hPXTyi/+a1oSVUREtIURjxlIOk3Svw/x3tfL6S8jIqIDNTOAfCxDtwiuoZgSc70krZB0k6SlknrLdVtJWijpjvJ5ShNxRUTEBmomGWwH3DvEe/eV74/UQbZnN/RbnQQssr0LsKh8HRERFWkmGdwP7D3Ee3sDfRsQx1HAOeXyOcDRG3CsiIhoUjPJ4EKKmc5e17hS0muB/wuMtC6RgSskLZZUFnxhmu3V5fL9FDWPnkPSPEm9knr7+jYk90RERKNmrib6JDAb+J6kBykuK51OUcr6CoqEMBL7275X0rbAQkm3Nr5p25IGvd7V9nxgPhSXljYRe0REDKOZyW0eBw6XdARwEMUUmA9S9PUvbOI495bPayRdAuwDPCBpuu3VkqZTlMiOiIiKjOY+gx8DPx7NySRNBjay/Ydy+XCKO5cvA+YCny+fLx3N8SMiYnRGO9PZJOCdwO4Uffz/YXuQmr/PMQ24RFL/ub9t+3JJvwQulPROYCXw5tHEFRERozPsALKkL0q6fcC65wNLgC9TTGzzSeBXknZd38ls32X7peXjxbZPK9c/aPsQ27vYPtT2Q6P+iyKqllnRogus72qig4BzB6z7MLAr8C7b21DcX7CCkQ8gR3SP/lnRVq4E+5lZ0ZIQosOsLxnMAhYPWPdG4Bbb3wSw3Qd8Ediv5dFFtLuTT35mesx+a9cW6yM6yPqSwUTg8f4XkrYCXgRcOWC7FcALWhpZRCfIrGjRJdaXDG4HDmx4fWT5PPBqom2B9PPH+JNZ0aJLrO9qoq8A35C0BcW0lh8A7qa4yazR4cCy1ocX0eZOO60YI2jsKsqsaNGBhk0Gts8ubwJ7L7AlxVVE77X9RP82kqZS1BY6dfCjRHSxzIoWXaLpmc7aRWY6i4ho3lAznTVTqC4iIrpUkkFERCQZREREkkFERJBkEBERJBnEaKU4W0RXGVUJ6xjn+ouz9d9o1V+cDXJ9fUSHSssgmtepxdnSmokYUloG0bxOLM6W1kzEsGppGUiaIOkGSd8vX+8k6TpJd0q6QNImdcQVI9SJxdk6tTUTUZG6uomOB5Y3vD4d+JLtnYGHKabUjHZ12mlFMbZG7V6crRNbMxEVqjwZSNoBeB1wZvlawMHAReUm5wBHVx1XNGHOHJg/H2bOBKl4nj+/vbtbOrE1E1GhOloGXwY+CjxVvt4aeMT2uvL1KmD7GuKKZsyZAytWwFNPFc/tnAigM1szERWqNBlIOhJYY3vgVJoj3X+epF5JvX19fS2OLrpaJ7ZmIipUaQlrSZ8D3g6sAzYF/gK4BDgCeIHtdZL2BT5l+4jhjpUS1hERzWuLEta2/8H2DrZnAccAV9qeA1wFvKncbC5waZVxRUSMd+1y09nHgA9KupNiDOGsmuOJiBhXarvpzPbVwNXl8l3APnXFEhEx3rVLyyAiImqUZBAREUkGERGRZBARESQZRN1SVjqiLaSEddQnZaUj2kZaBlGflJWOaBtJBlGflJWOaBtJBlGflJWOaBtJBlGflJWOaBtJBlGflJWOaBu5mijqNWdOvvwj2kBaBhERkWQQERFJBhERQZJBRESQZBAREVScDCRtKul6Sb+SdLOkU8v1O0m6TtKdki6QtEmVcXW04Qq91VUELsXnIjpO1ZeW/gk42PajkjYGrpX0I+CDwJdsny/p68A7ga9VHFvnGa7QG9RTBC7F5yI6kmzXc2JpEnAt8B7gB8ALbK+TtC/wKdtHDLd/T0+Pe3t7K4i0jc2aVXzZDjRzZvE81HsrVtQT01ieNyJGRNJi2z0D11c+ZiBpgqSlwBpgIfBr4BHb68pNVgHbD7HvPEm9knr7+vqqCbidDVfora4icCk+F9GRKk8Gtp+0PRvYAdgH2L2Jfefb7rHdM3Xq1DGLsWMMV+itriJwKT4X0ZFqu5rI9iPAVcC+wJaS+scvdgDurSuujjJcobe6isCl+FxER6r6aqKpkrYslzcDDgOWUySFN5WbzQUurTKujjVcobe6isCl+FxER6p0AFnSS4BzgAkUiehC25+W9ELgfGAr4Abgbbb/NNyxMoAcEdG8thhAtn2j7b1sv8T2nrY/Xa6/y/Y+tne2/bfrSwRRg9w7ENHVUsI61i/3DkR0vZSjiPXLxPURXS/JINYv9w5EdL0kg1i/3DsQ0fWSDLpZqwZ9c+9ARNdLMuhW/YO+K1eC/cyg72gSQu4diOh6SQZVqOOyzFYP+s6ZUxSae+qp4jmJIKKr5NLSsVbXZZkZ9I2IJqRlMNbquiwzg74R0YQkg7FW1y/0DPpGRBOSDMZaXb/QM+gbEU1IMhhrdf5Cz6BvRIxQksFYyy/0iOgAuZqoCv3zC0REtKm0DDpdSktHRAukZdDJUlo6Ilqk6mkvd5R0laRbJN0s6fhy/VaSFkq6o3yeUmVcI9KOv8BTWjoiWqTqbqJ1wIds7wG8EnivpD2Ak4BFtncBFpWv20cr6/y0Uu4yjogWqXray9W2l5TLfwCWA9sDR1HMjUz5fHSVca1Xu/4Cz13GEdEitQ0gS5oF7AVcB0yzvbp8635g2hD7zJPUK6m3r6+vkjiB9v0FnruMI6JFakkGkjYHLgZOsP37xvdsG/Bg+9meb7vHds/UqVMriLTUrr/Acw9DRLRI5clA0sYUiWCB7e+Wqx+QNL18fzqwpuq4htXOv8Bzl3FEtEDVVxMJOAtYbvuMhrcuA+aWy3OBS6uMa73yCzwiupyKXpmKTibtD1wD3AQ8Va7+OMW4wYXADGAl8GbbDw13rJ6eHvf29o5htBER3UfSYts9A9dXfTXRtbZl+yW2Z5ePH9p+0PYhtnexfej6EsGoteO9AhERbWD83IGcu3UjIoY0fmoTteu9AhERbWD8JIN2vVcgIqINjJ9k0K73CkREtIHxkwza+V6BiIiajZ9kkHsFIiKGNH6uJoLMOBYRMYTx0zKIiIghJRlERESSQUREJBlERARJBhERQcVVS1tJUh9FhdOR2Ab47RiGM1rtGFc7xgSJqxntGBO0Z1ztGBOMbVwzbT9ndrCOTQbNkNQ7WMnWurVjXO0YEySuZrRjTNCecbVjTFBPXOkmioiIJIOIiBg/yWB+3QEMoR3jaseYIHE1ox1jgvaMqx1jghriGhdjBhERMbzx0jKIiIhhJBlERER3JwNJ35S0RtKyumPpJ2lHSVdJukXSzZKOrzsmAEmbSrpe0q/KuE6tO6Z+kiZIukHS9+uOpZ+kFZJukrRUUm/d8fSTtKWkiyTdKmm5pH1rjme38jPqf/xe0gl1xtRP0onlv/Vlks6TtGkbxHR8Gc/NVX9OXT1mIOlVwKPAf9jes+54ACRNB6bbXiLp+cBi4Gjbt9Qcl4DJth+VtDFwLXC87V/UGReApA8CPcBf2D6y7nigSAZAj+22umFJ0jnANbbPlLQJMMn2I3XHBUVSB+4FXmF7pDeMjlUs21P8G9/D9h8lXQj80PbZNca0J3A+sA/wZ+By4N2276zi/F3dMrD9U+ChuuNoZHu17SXl8h+A5cD29UYFLjxavty4fNT+S0HSDsDrgDPrjqXdSdoCeBVwFoDtP7dLIigdAvy67kTQYCKwmaSJwCTgvprjeRFwne21ttcBPwHeUNXJuzoZtDtJs4C9gOvqjaRQdscsBdYAC223Q1xfBj4KPFV3IAMYuELSYknz6g6mtBPQB3yr7FY7U9LkuoNqcAxwXt1BANi+F/gCcA+wGvid7SvqjYplwAGStpY0CXgtsGNVJ08yqImkzYGLgRNs/77ueABsP2l7NrADsE/ZbK2NpCOBNbYX1xnHEPa3vTfwGuC9ZZdk3SYCewNfs70X8BhwUr0hFcouq9cD36k7FgBJU4CjKBLodsBkSW+rMybby4HTgSsouoiWAk9Wdf4kgxqUffIXAwtsf7fueAYquxauAl5dcyj7Aa8v++fPBw6WdG69IRXKX5bYXgNcQtHPW7dVwKqGFt1FFMmhHbwGWGL7gboDKR0K3G27z/YTwHeBv645JmyfZftltl8FPAzcXtW5kwwqVg7UngUst31G3fH0kzRV0pbl8mbAYcCtdcZk+x9s72B7FkUXw5W2a/31BiBpcjn4T9kNczhFE79Wtu8HfiNpt3LVIUCtFyY0OJY26SIq3QO8UtKk8v/JQyjG72oladvyeQbFeMG3qzr3xKpOVAdJ5wEHAttIWgWcYvuseqNiP+DtwE1l/zzAx23/sMaYAKYD55RXfGwEXGi7bS7lbDPTgEuK7xAmAt+2fXm9IT3t/cCCslvmLuAdNcfTnzAPA/6+7lj62b5O0kXAEmAdcAPtUZriYklbA08A763yAoCuvrQ0IiJGJt1EERGRZBAREUkGERFBkkFERJBkEBERJBlEIOlTkgYtOCfp7HaqShoxVpIMIiIiySCiHZRFAjepO44Yv5IMIpogabakRZLWSnpY0gJJ0xreP1CSBxb5k3R1ecdr/+uzJfVKOlrSzcDjwCvKyWnOlHSfpMcl3SPpG9X9hTFedXU5iohmlHXtn7O64f2pwNUUNWzeCmwOfB5YKKnH9p+bPOUs4J+ATwP3A3cDZ1AUTDuxXLcjxRwFEWMqySCi0F8PZjD9JbQ/VD4f0V92XNIdwC+AN9J8IbatgUNt99eoQtI+wFdtX9CwXVtUao3ulmQQUfgdRVnjgU6hKOIHRZnqKxrnnygLnq0A9qf5ZHBvYyIoLQU+IulJ4L9tV1bCOMa3jBlEFNbZ7h34AB5s2GY6MFg9/geArUZxzsGO9T7gv4BPArdJukPSMaM4dkRTkgwiRm41sO0g66fxzFzbj5fPA68MmjLIfs8pGWz7EdsfsP0C4KUUU6IukLTH6EKOGJkkg4iRuw44on9iGwBJL6cYCL62XLWqfH5RwzY7Ars3ezLbNwIfofj/tOn9I5qRMYOIkTsDeA/wY0mn88zVRDdRTGOK7VXlHcufkbSW4ov84zzTchiWpGspptFcRtFyeBfFXMbXt/ZPiXi2tAwiRsh2H3AQRVfQecBXgWuAwwZcVnosxbSK5wKfpbh09LYRnubnwHEU8xdfCGwDvMb2quF2ithQmeksIiLSMoiIiCSDiIggySAiIkgyiIgIkgwiIoIkg4iIIMkgIiJIMoiICOD/A880qAd82yf/AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KrcbV_8c37wY"
      },
      "source": [
        "**Observation:** \n",
        "**Number of Hours studied is linearly related to the Scores of the student.**\n",
        "\n",
        "# **# Preparing Data and splitting into train and test sets.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mAnUDWlYL-oq"
      },
      "source": [
        "X = student_data.iloc[:,:-1].values\n",
        "Y = student_data.iloc[:,1].values"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6bkvMv4puQkn"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train,X_test,Y_train,Y_test = train_test_split(X,Y,random_state = 0,test_size=0.2)\n"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "23TU2ZmmuV6j",
        "outputId": "0bfed2ff-8783-4543-80a4-bd69124e2052"
      },
      "source": [
        "print(\"X train.shape =\", X_train.shape)\n",
        "print(\"Y train.shape =\", Y_train.shape)\n",
        "print(\"X test.shape  =\", X_test.shape)\n",
        "print(\"Y test.shape  =\", Y_test.shape)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "X train.shape = (20, 1)\n",
            "Y train.shape = (20,)\n",
            "X test.shape  = (5, 1)\n",
            "Y test.shape  = (5,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-AV9CwNG4g2g"
      },
      "source": [
        "# **Training the Linear Regression Model.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J9N3ezHxuZG3"
      },
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "linreg=LinearRegression()"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A9yQTxCcudVC",
        "outputId": "90c74158-ef8e-49c1-be91-809510ddc2c1"
      },
      "source": [
        "##Fitting Training Data\n",
        "linreg.fit(X_train,Y_train)\n",
        "print(\"Training our algorithm is finished\")"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training our algorithm is finished\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X3yslBa6ugcA",
        "outputId": "db4df0af-82d6-4b33-9d4d-ebdd188db9b6"
      },
      "source": [
        "print(\"B0 =\",linreg.intercept_,\"\\nB1 =\",linreg.coef_)## B0 is Intercept & Slope of the line is B1.,\""
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "B0 = 2.018160041434662 \n",
            "B1 = [9.91065648]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NHX-GnSF7cJ3"
      },
      "source": [
        "**Plotting the REGRESSION LINE**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BMaqTSTZujxn"
      },
      "source": [
        "Y0 = linreg.intercept_ + linreg.coef_*X_train"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D1UhTZIY7qyc"
      },
      "source": [
        "**Plotting on training data**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "id": "GXQio8KTunfI",
        "outputId": "7aef9cd0-4f12-47f6-ec7b-9250a1a5ec8a"
      },
      "source": [
        "plt.scatter(X_train,Y_train,color='red',marker='.')\n",
        "plt.plot(X_train,Y0,color='black')\n",
        "plt.xlabel(\"Hours\",fontsize=15)\n",
        "plt.ylabel(\"Scores\",fontsize=15)\n",
        "plt.title(\"Regression line(Train set)\",fontsize=10)\n",
        "plt.show()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RhrEL9PZ5BDq"
      },
      "source": [
        "# **Test Data**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rNalIRY1uqIA",
        "outputId": "2aaaed8f-939d-404e-ed00-2da24607f0a6"
      },
      "source": [
        "Y_pred=linreg.predict(X_test)##predicting the Scores for test data\n",
        "print(Y_pred)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[16.88414476 33.73226078 75.357018   26.79480124 60.49103328]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0_JTgiPRutWn",
        "outputId": "7cdcfe65-bd4f-4126-830d-3ece9cecf176"
      },
      "source": [
        "#now print the Y_test.\n",
        "Y_test"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([20, 27, 69, 30, 62])"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "id": "kvzsTj2Buvte",
        "outputId": "3e48c20c-cc8e-4d0f-8951-49aa3a89deb3"
      },
      "source": [
        "#plotting line on test data\n",
        "plt.plot(X_test,Y_pred,color='black')\n",
        "plt.scatter(X_test,Y_test,color='red')\n",
        "plt.xlabel(\"Hours\",fontsize=15)\n",
        "plt.ylabel(\"Scores\",fontsize=15)\n",
        "plt.title(\"Regression line(Test set)\",fontsize=10)\n",
        "plt.show()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EviPMSEB5Lq4"
      },
      "source": [
        "# **Comparing Actual vs Predicted Scores**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "id": "uK-pCL1euyBm",
        "outputId": "efc3c205-cb5e-41d1-c3fa-1f7817fe5684"
      },
      "source": [
        "Y_test1 = list(Y_test)\n",
        "prediction=list(Y_pred)\n",
        "df_compare = pd.DataFrame({ 'Actual':Y_test1,'Result':prediction})\n",
        "df_compare"
      ],
      "execution_count": 17,
      "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>Result</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     Result\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": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jca1ezpi5Um4"
      },
      "source": [
        "# **ACCURACY OF THE MODEL**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OVyBRzanu0x2",
        "outputId": "f3233c4a-e597-4f80-ec1b-09bf28695f41"
      },
      "source": [
        "from sklearn import metrics\n",
        "metrics.r2_score(Y_test,Y_pred)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9454906892105354"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RG94okLJ5bj7"
      },
      "source": [
        "**Observation:Above 94% percentage indicates that it is a good model.**\n",
        "\n",
        "# **PREDICTING THE ERROR**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vp5fiQpDu5Tw"
      },
      "source": [
        "from sklearn.metrics import mean_squared_error,mean_absolute_error"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r5DudEAdu7_-",
        "outputId": "99a0093c-a37b-4be3-ad78-417c7970d4ca"
      },
      "source": [
        "MSE = metrics.mean_squared_error(Y_test,Y_pred)\n",
        "root_E = np.sqrt(metrics.mean_squared_error(Y_test,Y_pred))\n",
        "Abs_E = np.sqrt(metrics.mean_squared_error(Y_test,Y_pred))\n",
        "print(\"Mean Squared Error      = \",MSE)\n",
        "print(\"Root Mean Squared Error = \",root_E)\n",
        "print(\"Mean Absolute Error     = \",Abs_E)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Squared Error      =  21.598769307217456\n",
            "Root Mean Squared Error =  4.647447612100373\n",
            "Mean Absolute Error     =  4.647447612100373\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S23qk0t052Ps"
      },
      "source": [
        "# **CONCLUSION BY PREDICTING THE SCORE**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6rdjGrxuu-mc",
        "outputId": "348b29e5-05c3-4389-dc40-a8ab3ffc736e"
      },
      "source": [
        "Prediction_score = linreg.predict([[9.25]])\n",
        "print(\"predicted score for a student studying 9.25 hours :\",Prediction_score)"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "predicted score for a student studying 9.25 hours : [93.69173249]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m7Y60Q0PMRdL"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}