Assignment_7.ipynb

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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Assignment_7.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyM4Ms6Ys64kSjGvjYxFxjdR",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Jagannath8/GoeduHub-Training/blob/main/Assignment_7.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G4qNELR2SGpQ"
      },
      "source": [
        "# **GO_STP_579 -> Jagannath Pal**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eYnariSUSQC0"
      },
      "source": [
        "# ***Assignment_7***\n",
        "\n",
        "---\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T061INKoSh1D"
      },
      "source": [
        "Prediction using Supervised Machine Learning using Simple Linear Regression\n",
        "\n",
        "In this task we have to find the students scores based on their study hours. This is a simple Regression problem type because it has only two variables.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gVZScQbmsBic"
      },
      "source": [
        "# Importing Libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NsaJUHi9SDH3"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mS91gEs1sFeT"
      },
      "source": [
        "# Reading the Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "du2eHl6sVrHo",
        "outputId": "590826cc-4600-4aa9-ac2a-f30776e79da3"
      },
      "source": [
        "dataset = pd.read_csv(\"/content/StudentHoursScores.csv\")\n",
        "print(dataset)"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "    Hours  Scores\n",
            "0     7.7      79\n",
            "1     5.9      60\n",
            "2     4.5      45\n",
            "3     3.3      33\n",
            "4     1.1      12\n",
            "5     8.9      87\n",
            "6     2.5      21\n",
            "7     1.9      19\n",
            "8     2.7      29\n",
            "9     8.3      81\n",
            "10    5.5      58\n",
            "11    9.2      88\n",
            "12    1.5      14\n",
            "13    3.5      34\n",
            "14    8.5      85\n",
            "15    3.2      32\n",
            "16    6.5      66\n",
            "17    2.5      21\n",
            "18    9.6      96\n",
            "19    4.3      42\n",
            "20    4.1      40\n",
            "21    3.0      30\n",
            "22    2.6      25\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "0xfkK3BzWqqi",
        "outputId": "437aa190-e878-490c-dca1-c42e57616862"
      },
      "source": [
        "print(type(dataset))"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "as285oiVXGan",
        "outputId": "c169191b-0de0-408d-87e9-9c8e882e666c"
      },
      "source": [
        "dataset.columns"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Hours', 'Scores'], dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "GmckPOqgXK5w",
        "outputId": "b29b3041-1548-44f4-82e0-96dd6b435f1e"
      },
      "source": [
        "dataset.shape"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(23, 2)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "o2TmYTtmXNmX",
        "outputId": "1e19356f-ff42-4213-d0ad-8f86aad7d055"
      },
      "source": [
        "dataset.info()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 23 entries, 0 to 22\n",
            "Data columns (total 2 columns):\n",
            " #   Column  Non-Null Count  Dtype  \n",
            "---  ------  --------------  -----  \n",
            " 0   Hours   23 non-null     float64\n",
            " 1   Scores  23 non-null     int64  \n",
            "dtypes: float64(1), int64(1)\n",
            "memory usage: 496.0 bytes\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "ujGBmOCcXRYe",
        "outputId": "2c399191-11a5-4579-b658-3723bc7fc8f1"
      },
      "source": [
        "dataset.min()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Hours      1.1\n",
              "Scores    12.0\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "MGaArqnaXUJH",
        "outputId": "ff4ba7c1-56c4-40e1-85ac-e28201425700"
      },
      "source": [
        "dataset.max()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Hours      9.6\n",
              "Scores    96.0\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "id": "SvrvWnApXXYz",
        "outputId": "4e8f6b38-687a-4d28-c1cf-11b7484a4227"
      },
      "source": [
        "dataset.describe()"
      ],
      "execution_count": 9,
      "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>23.000000</td>\n",
              "      <td>23.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>4.817391</td>\n",
              "      <td>47.695652</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>2.709688</td>\n",
              "      <td>27.103228</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.100000</td>\n",
              "      <td>12.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2.650000</td>\n",
              "      <td>27.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>4.100000</td>\n",
              "      <td>40.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>7.100000</td>\n",
              "      <td>72.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>9.600000</td>\n",
              "      <td>96.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           Hours     Scores\n",
              "count  23.000000  23.000000\n",
              "mean    4.817391  47.695652\n",
              "std     2.709688  27.103228\n",
              "min     1.100000  12.000000\n",
              "25%     2.650000  27.000000\n",
              "50%     4.100000  40.000000\n",
              "75%     7.100000  72.500000\n",
              "max     9.600000  96.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 106
        },
        "id": "a1_jRk3tXbwP",
        "outputId": "93473943-19a4-44a3-abf7-ca83dbe45093"
      },
      "source": [
        "dataset.corr()"
      ],
      "execution_count": 10,
      "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>0.997656</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Scores</th>\n",
              "      <td>0.997656</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           Hours    Scores\n",
              "Hours   1.000000  0.997656\n",
              "Scores  0.997656  1.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rDOa_D4_XfS2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "7551e7fe-2c45-4ede-fb36-950c63cdda0d"
      },
      "source": [
        "dataset.nunique()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Hours     22\n",
              "Scores    22\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b2lO6NrojR6R"
      },
      "source": [
        "# Visualization using scatter plots"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "cSmWCAf6czqU",
        "outputId": "2161add1-eadd-45ee-d89a-999cf5bad7b0"
      },
      "source": [
        "plt.xlabel(\"Scores\")\n",
        "plt.ylabel(\"Study Hours\")\n",
        "plt.title(\"Visualization\")\n",
        "plt.scatter(x, y, color=\"red\", marker=\"*\")\n",
        "plt.show()"
      ],
      "execution_count": 33,
      "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": "xtrbobbDjXEo"
      },
      "source": [
        "# Spliting of dataset in testing and training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9OZiAkQXdmW2"
      },
      "source": [
        "x = dataset.iloc[:,:-1].values\n",
        "y = dataset.iloc[:,1].values\n",
        "\n",
        "import sklearn\n",
        "from sklearn.model_selection import train_test_split\n",
        "xtrain, xtest, ytrain, ytest = train_test_split(x, y, test_size=1/3, random_state=1)"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jSdDKwOOjdlf"
      },
      "source": [
        "# Creating simple linear model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tvde2ldld2dI"
      },
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "model = LinearRegression()\n",
        "mymodel = model.fit(xtrain, ytrain)"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K7mBO13pjjDq"
      },
      "source": [
        "# Prediction"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "uxM9n2q1eE4A",
        "outputId": "08f14ee8-59c8-4799-a2ff-c82ac5714520"
      },
      "source": [
        "y_pred = mymodel.predict(xtest)\n",
        "y_pred"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([40.50038393, 24.63061172, 32.56549782, 34.54921935, 42.48410545,\n",
              "       64.30504223, 54.3864346 , 10.74456104])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "keuiIBwleNLt",
        "outputId": "183e8882-7b80-4aec-f8a7-3299f93e4f31"
      },
      "source": [
        "ytest"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([40, 21, 33, 34, 42, 66, 58, 12])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "cSDwnkKqhtFs",
        "outputId": "32e16cc6-89c5-4ee1-9ec4-31104de9f377"
      },
      "source": [
        "#Error\n",
        "e = y_pred-ytest\n",
        "e"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 0.50038393,  3.63061172, -0.43450218,  0.54921935,  0.48410545,\n",
              "       -1.69495777, -3.6135654 , -1.25543896])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "G63ZND8ReO1o",
        "outputId": "db9aa43f-c1a7-481e-a918-beb11a739da8"
      },
      "source": [
        "mymodel.predict([[6]])"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([59.34573842])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "WvAKgmjnem4V",
        "outputId": "5e678307-9eb9-433a-ee74-8ca386a394dd"
      },
      "source": [
        "mymodel.coef_"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([9.91860763])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "WvCKAGYBer_t",
        "outputId": "93d0c8aa-1fb4-4793-ac81-bbbff0a45d8f"
      },
      "source": [
        "mymodel.intercept_"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-0.16590734578961275"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "TqYtK8tte9Bc",
        "outputId": "a67dc39d-b007-4e75-92d9-0edb81cf4f27"
      },
      "source": [
        "9.9186*6 + (-0.16590)"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "59.3457"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "id": "Awf_7T6lfaZu",
        "outputId": "e8ed2238-4b72-4380-9c4c-9c00a9a1eee7"
      },
      "source": [
        "plt.scatter(xtrain, ytrain, color='m')\n",
        "plt.plot(xtrain, mymodel.predict(xtrain), color='y')\n",
        "plt.title(\"Best Fit Line for Training Data\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "No handles with labels found to put in legend.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "id": "ld6PpAu0gij8",
        "outputId": "e264b062-b33b-49cc-e008-210cc11523c7"
      },
      "source": [
        "plt.scatter(xtest, ytest, color='red')\n",
        "plt.plot(xtrain, mymodel.predict(xtrain), 'g--')\n",
        "plt.title(\"Best Fit Line for Testing Data\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "No handles with labels found to put in legend.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "id": "ycIz-f7YluWR",
        "outputId": "fb2bf934-f56e-4651-dbf6-23102e5f6ba5"
      },
      "source": [
        "dataset_comparison = pd.DataFrame({\"Actual\" : y_pred , \"Predicted\" : ytest})\n",
        "dataset_comparison\n"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Actual</th>\n",
              "      <th>Predicted</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>40.500384</td>\n",
              "      <td>40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>24.630612</td>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>32.565498</td>\n",
              "      <td>33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>34.549219</td>\n",
              "      <td>34</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>42.484105</td>\n",
              "      <td>42</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>64.305042</td>\n",
              "      <td>66</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>54.386435</td>\n",
              "      <td>58</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>10.744561</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      Actual  Predicted\n",
              "0  40.500384         40\n",
              "1  24.630612         21\n",
              "2  32.565498         33\n",
              "3  34.549219         34\n",
              "4  42.484105         42\n",
              "5  64.305042         66\n",
              "6  54.386435         58\n",
              "7  10.744561         12"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "DWVOXJh6nLuX",
        "outputId": "0670f1b1-5770-4ad5-bc21-582477c04f35"
      },
      "source": [
        "# Accuracy Score\n",
        "import sklearn.metrics as metrics\n",
        "metrics.r2_score(ytest, y_pred)"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9856694359125361"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "6mF4lSBgn_oI",
        "outputId": "4e1ed247-c142-450e-c00a-a9fce3b556dd"
      },
      "source": [
        "mean_square_error = metrics.mean_squared_error(y_pred, ytest)\n",
        "print(mean_square_error)"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "3.9579226689064453\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m7WbgoxnjsSp"
      },
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    }
  ]
}