Task_01_Sparks.ipynb

{
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  "metadata": {
    "colab": {
      "name": "Task-01-Sparks.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyO3oWNQCWWrL/dI2gu6OMQh",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/parnika-dotcom/Sparks_Tasks/blob/main/Task_01_Sparks.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rX9iReMbprN_"
      },
      "source": [
        "## **Data Science and Business Analytics Intern**\r\n",
        "\r\n",
        "##**The Sparks Foundation**\r\n",
        "\r\n",
        "**Task1:- Prediction using Supervised Machine Learning**\r\n",
        "\r\n",
        "***Name: Parnika Sharma***"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WPuxmOsRsPzf"
      },
      "source": [
        "**Aim:-To Predict the Percentage of a student based on the number of hours of study hours** "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uIQ-Rx0xzRpA"
      },
      "source": [
        "**Import the Required Libraries**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Uflp5kk9zSU5"
      },
      "source": [
        "import pandas as pd\r\n",
        "import numpy as np  \r\n",
        "import matplotlib.pyplot as plt  "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tMUc1SSqz02d"
      },
      "source": [
        "**Reading Data from Remote Link**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x5BBgcbAzx_A"
      },
      "source": [
        "df=pd.read_csv(\"https://raw.githubusercontent.com/AdiPersonalWorks/Random/master/student_scores%20-%20student_scores.csv\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ei2TZBRY2CYA"
      },
      "source": [
        "**Data Cleaning and Preparation**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 194
        },
        "id": "ZMy3QQMV2AYN",
        "outputId": "f191b88d-dfe1-4d95-8aa8-407a851db427"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Hours</th>\n",
              "      <th>Scores</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2.5</td>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5.1</td>\n",
              "      <td>47</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3.2</td>\n",
              "      <td>27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>8.5</td>\n",
              "      <td>75</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3.5</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Hours  Scores\n",
              "0    2.5      21\n",
              "1    5.1      47\n",
              "2    3.2      27\n",
              "3    8.5      75\n",
              "4    3.5      30"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-TK384ys2K2u",
        "outputId": "34ac0203-35e1-427d-88ec-80f4aeaa3a49"
      },
      "source": [
        "df.dtypes"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Hours     float64\n",
              "Scores      int64\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "reTzwkDJ2UtY"
      },
      "source": [
        "Here only two types of Data are available."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JtrtCzcl2eQI",
        "outputId": "00a139a7-8eb8-4dbb-dfd4-73bc13cfb00b"
      },
      "source": [
        "df.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Hours     0\n",
              "Scores    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xnnSeiaM2pbH"
      },
      "source": [
        "Since, there is no null data here, we conitnue without making any changes!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "FmCUdO5e2wQl",
        "outputId": "6d0934d6-5d88-413d-9c81-32de790bf70b"
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Hours</th>\n",
              "      <th>Scores</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>25.000000</td>\n",
              "      <td>25.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>5.012000</td>\n",
              "      <td>51.480000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>2.525094</td>\n",
              "      <td>25.286887</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.100000</td>\n",
              "      <td>17.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2.700000</td>\n",
              "      <td>30.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>4.800000</td>\n",
              "      <td>47.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>7.400000</td>\n",
              "      <td>75.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>9.200000</td>\n",
              "      <td>95.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           Hours     Scores\n",
              "count  25.000000  25.000000\n",
              "mean    5.012000  51.480000\n",
              "std     2.525094  25.286887\n",
              "min     1.100000  17.000000\n",
              "25%     2.700000  30.000000\n",
              "50%     4.800000  47.000000\n",
              "75%     7.400000  75.000000\n",
              "max     9.200000  95.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0A5bpDG523Yx"
      },
      "source": [
        "**Checking Outliers**\r\n",
        "\r\n",
        "\r\n",
        "We will be using boxpot to check the presence of outliers\r\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "wpvXe62O267t",
        "outputId": "5a073925-05c8-4e5b-b328-4ec89c9102d5"
      },
      "source": [
        "df['Scores'].plot.box()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f2cdff09a50>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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PJT0zz8C1Ujwf+Ey3Vr0fuA/YBFzRbT8WeAJ4G/B3wGe7s/T9wPu7d67M/p5/AXwK+HaS5wHfY/BOl4uA9yV5EtgLfGLUP5w0jH/ElKRGuYQiSY0y4JLUKAMuSY0y4JLUKAMuSY0y4JLUKAMuSY36P+bMwY+O8v0kAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "Nx9Hc8CD3g7y",
        "outputId": "ecad2f5d-8106-4952-b8ab-974966c05208"
      },
      "source": [
        "df['Hours'].plot.box()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f2cdfdcb1d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R5prnV6E3oED"
      },
      "source": [
        "From the above two plots, we conclude that there are no outliers present in the data since, no data point is located outside the whiskers of the box plot."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xtUiMqvi4u1o"
      },
      "source": [
        "**Data Analysis and Visualization**\r\n",
        "\r\n",
        "\r\n",
        "Analysis of Data using Graphs\r\n",
        "\r\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "EwLl58qA4TKv",
        "outputId": "23c4b345-7f27-4846-eb09-15f615db5bfb"
      },
      "source": [
        "df.plot.scatter('Hours','Scores', color='r')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f2cdf909450>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WQfqIiIY6Aga"
      },
      "source": [
        "From the above graph, we infer that there is positive linear relationship between the two variables which can also be confirmed by finding the correlation between the two."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 106
        },
        "id": "ODxq11hd57pv",
        "outputId": "91e17618-99ac-436c-b67e-b29916f7cbd8"
      },
      "source": [
        "df.corr()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Hours</th>\n",
              "      <th>Scores</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Hours</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.976191</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Scores</th>\n",
              "      <td>0.976191</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           Hours    Scores\n",
              "Hours   1.000000  0.976191\n",
              "Scores  0.976191  1.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-NA7wIP_6knk"
      },
      "source": [
        "**Model Building**\r\n",
        "\r\n",
        "Here, we will be diving the data into two categcategories i.e Train data and Test data and then we will perform simple linear regression for the problem.\r\n",
        "\r\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xv9jU4SI6myr"
      },
      "source": [
        "x=df.drop('Scores',axis=1)\r\n",
        "y=df['Scores']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qCb-KDvw7k2k",
        "outputId": "2fa38e67-a10f-41ce-b6e5-4c621668c691"
      },
      "source": [
        "x.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(25, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Sv6LeZ_k7nWg",
        "outputId": "86facf6e-b35f-4b02-f40b-aa019828aa26"
      },
      "source": [
        "y.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(25,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FzpuVhjw7qNb"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\r\n",
        "train_x, test_x, train_y, test_y=train_test_split(x,y, random_state=0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zfl_X0er8Ji9"
      },
      "source": [
        "**Training The Model**\r\n",
        "\r\n",
        "Fit the Data in the Linear Regression Model\r\n",
        "\r\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q8Zhb0JN8dOn",
        "outputId": "aa8f6fb8-427e-49be-c791-fee54f93d411"
      },
      "source": [
        "from sklearn.linear_model import LinearRegression\r\n",
        "lreg=LinearRegression()\r\n",
        "lreg.fit(train_x, train_y)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5caIy1ts8-33"
      },
      "source": [
        "**Visualization of the Model**\r\n",
        "\r\n",
        "The regression line has the equation b0 + b1x\r\n",
        "\r\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "Y-x1hHgV9EJg",
        "outputId": "e6291aa2-ef56-4c8e-eacd-82989a5ca71d"
      },
      "source": [
        "reg_line = lreg.intercept_ + lreg.coef_*x\r\n",
        "plt.scatter(train_x, train_y, color ='g')\r\n",
        "plt.plot(x,reg_line)\r\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "guz05Nv8-yLg"
      },
      "source": [
        "**Prediction**\r\n",
        "\r\n",
        "After Training and Visualization of the Model, we make Predictions!\r\n",
        "\r\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KU9bKMwt-kYO",
        "outputId": "ad415096-06c2-4d02-c5a3-e08926e594db"
      },
      "source": [
        "pred = lreg. predict(test_x)\r\n",
        "pred"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([16.84472176, 33.74557494, 75.50062397, 26.7864001 , 60.58810646,\n",
              "       39.71058194, 20.8213931 ])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bhdvxA4J_R3a",
        "outputId": "4c46e1eb-e907-4c2b-b49a-47718376f614"
      },
      "source": [
        "test_y"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5     20\n",
              "2     27\n",
              "19    69\n",
              "16    30\n",
              "11    62\n",
              "22    35\n",
              "17    24\n",
              "Name: Scores, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 252
        },
        "id": "H6Gk-BX-_tKP",
        "outputId": "896cf920-24f3-457d-b992-c2e941eea086"
      },
      "source": [
        "data = pd. DataFrame({'Actual Score' :np.array(test_y),'Predected Score':pred})\r\n",
        "data"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Actual Score</th>\n",
              "      <th>Predected Score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>20</td>\n",
              "      <td>16.844722</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>27</td>\n",
              "      <td>33.745575</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>69</td>\n",
              "      <td>75.500624</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>30</td>\n",
              "      <td>26.786400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>62</td>\n",
              "      <td>60.588106</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>35</td>\n",
              "      <td>39.710582</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>24</td>\n",
              "      <td>20.821393</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Actual Score  Predected Score\n",
              "0            20        16.844722\n",
              "1            27        33.745575\n",
              "2            69        75.500624\n",
              "3            30        26.786400\n",
              "4            62        60.588106\n",
              "5            35        39.710582\n",
              "6            24        20.821393"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zDLUuWCpA8nf"
      },
      "source": [
        "**Prediction of score made by the student if he/she studies for 9.25 hours**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oVSP4sUwBlS-",
        "outputId": "72801463-b278-43db-a4c4-f11c43e4f727"
      },
      "source": [
        "time=9.25\r\n",
        "pred_score=lreg.predict([[time]])\r\n",
        "pred_score"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([93.89272889])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KMSjkcdbCA18"
      },
      "source": [
        "So, here we see that the student will score appropriately 93.9 percent marks if he studies for 9.25 hours a day"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fZw7LupjCTuY"
      },
      "source": [
        "**Model Evaluation**\r\n",
        "\r\n",
        "We test the Model\r\n",
        "\r\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1xRO_uh0CRXE",
        "outputId": "171d8a0f-bb8a-4547-9ac4-d4bd1401e436"
      },
      "source": [
        "lreg.score(test_x,test_y)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9367661043365056"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m1TIW_hmDTc0",
        "outputId": "760dc575-6c7f-4859-a233-ca6a1610c1c7"
      },
      "source": [
        "lreg.score(train_x,train_y)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9484509249326872"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ug8Bdxb0Dbjn"
      },
      "source": [
        "Here, since the r^2 value of the training and the test data are almost equal, we can say that our model is quite perfect.\r\n",
        "\r\n",
        "We check mean square error since, less error means our model is well fitted.\r\n",
        "\r\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5k2jg-w1DaNc",
        "outputId": "d35e31f8-9d75-4328-fd85-a0c633fb97d8"
      },
      "source": [
        "from sklearn import metrics\r\n",
        "metrics.mean_squared_error(test_y,pred)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "20.33292367497996"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MBdsqkPXFk0V"
      },
      "source": [
        "## **RESULT: For studying 9.25 hours per day a student is likely to score 94 percentage of marks(approximately).**"
      ]
    }
  ]
}