Untitled17.ipynb

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      "name": "Untitled17.ipynb",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_SmKSHSucCFN"
      },
      "source": [
        "#  GRIP - GRADUATE ROTATIONAL INTERNSHIP PROGRAM\n",
        "## THE SPARK FOUNDATION\n",
        "### TASK 1 SPARK FOUNDATION :: PREDICTION USING SUPERVISED ML\n",
        "\n",
        "### AUTHOR : MITALI MATHUR\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cQfog8rKdWt7"
      },
      "source": [
        "IMPORTING LIBRARIES"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5OVxm4tfcBG-"
      },
      "source": [
        "#importing libraries\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt  \n",
        "%matplotlib inline"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kAJ93Oy3dZ9G"
      },
      "source": [
        "IMPORTING DATASET"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4wuWDeZjdVyW"
      },
      "source": [
        "## importing data set\n",
        "url = \"http://bit.ly/w-data\"\n",
        "data = pd.read_csv(url)\n"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 205
        },
        "id": "yxUIjsHpdez-",
        "outputId": "d687ade1-9981-4918-ba1a-c2c32144330c"
      },
      "source": [
        "data.head()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<div>\n",
              "<style scoped>\n",
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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>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": 4
        }
      ]
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        "id": "bMiiDfgldwmW",
        "outputId": "0047ddd0-480a-4857-d3a3-433c00acda15"
      },
      "source": [
        "data.tail()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Hours</th>\n",
              "      <th>Scores</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\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",
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            "text/plain": [
              "    Hours  Scores\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": {
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          "execution_count": 5
        }
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      "cell_type": "code",
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          "height": 299
        },
        "id": "9TNa535Qdx_q",
        "outputId": "16962327-e06e-4a64-d499-b5d4837a9fb8"
      },
      "source": [
        "data.describe()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <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",
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            ],
            "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": []
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          "execution_count": 6
        }
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        "id": "72t20pm9dz-2",
        "outputId": "82a01c3d-8c7e-409f-f4d0-942ec5cb2857"
      },
      "source": [
        "data.info()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "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"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1g8ibYMed1kk",
        "outputId": "6fc7e0f2-de65-474d-a1c3-ea1765ee2f0a"
      },
      "source": [
        "data.duplicated()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0     False\n",
              "1     False\n",
              "2     False\n",
              "3     False\n",
              "4     False\n",
              "5     False\n",
              "6     False\n",
              "7     False\n",
              "8     False\n",
              "9     False\n",
              "10    False\n",
              "11    False\n",
              "12    False\n",
              "13    False\n",
              "14    False\n",
              "15    False\n",
              "16    False\n",
              "17    False\n",
              "18    False\n",
              "19    False\n",
              "20    False\n",
              "21    False\n",
              "22    False\n",
              "23    False\n",
              "24    False\n",
              "dtype: bool"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
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        },
        "id": "Iq73AlKfd7xk",
        "outputId": "2fcb9163-fd78-462c-cf60-39ec13aa8f93"
      },
      "source": [
        "data.isnull()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Hours</th>\n",
              "      <th>Scores</th>\n",
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              "      <th>1</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
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              "      <th>2</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Hours  Scores\n",
              "0   False   False\n",
              "1   False   False\n",
              "2   False   False\n",
              "3   False   False\n",
              "4   False   False\n",
              "5   False   False\n",
              "6   False   False\n",
              "7   False   False\n",
              "8   False   False\n",
              "9   False   False\n",
              "10  False   False\n",
              "11  False   False\n",
              "12  False   False\n",
              "13  False   False\n",
              "14  False   False\n",
              "15  False   False\n",
              "16  False   False\n",
              "17  False   False\n",
              "18  False   False\n",
              "19  False   False\n",
              "20  False   False\n",
              "21  False   False\n",
              "22  False   False\n",
              "23  False   False\n",
              "24  False   False"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TLxfzDwDd-uN",
        "outputId": "09f94948-d311-47f6-a7ed-2a7bf864eb8d"
      },
      "source": [
        "data.isnull().sum()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Hours     0\n",
              "Scores    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sXmgsXmZeJKB"
      },
      "source": [
        "Plotting distribution of scores\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "G2AIofgoeCaq",
        "outputId": "1e5fce4f-0eda-46c1-ca9a-fdd855a7648f"
      },
      "source": [
        "# Plotting distribution of scores\n",
        "data.plot(x='Hours', y='Scores', style='o')  \n",
        "plt.title('Hours vs Percentage')  \n",
        "plt.xlabel('Hours Studied')  \n",
        "plt.ylabel('Percentage Score')  \n",
        "plt.show()"
      ],
      "execution_count": 11,
      "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": "MZKx7DAjeQrF"
      },
      "source": [
        "preparing the data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ea26SbLseH7w"
      },
      "source": [
        "X = data.iloc[:, :-1].values  \n",
        "y = data.iloc[:, 1].values  "
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hCTbcJXPeVvO"
      },
      "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=0.2, random_state=0) "
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z_rGSYeJebXC"
      },
      "source": [
        "TRAINING ALGORITHM"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FU48LM5heYD1",
        "outputId": "214e72da-78fc-46f6-ecdc-feb9f2157abd"
      },
      "source": [
        "from sklearn.linear_model import LinearRegression  \n",
        "reg = LinearRegression()  \n",
        "reg.fit(X_train, y_train) \n"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KXdtwyhpeiFR"
      },
      "source": [
        " Plotting the regression line"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "KwyxOR7weajO",
        "outputId": "7b5d700e-22cd-441b-c5d3-e34b131bc788"
      },
      "source": [
        "# Plotting the regression line\n",
        "line = reg.coef_*X+reg.intercept_\n",
        "plt.scatter(X, y)\n",
        "plt.plot(X, line);\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": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ItbMTGonepD7"
      },
      "source": [
        "MAKING PREDICTIONS"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WAQ1qVhTegvs",
        "outputId": "f7ad71f5-9de4-44d9-9d4a-e48680403df0"
      },
      "source": [
        "print(X_test) \n",
        "y_pred = reg.predict(X_test) "
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[1.5]\n",
            " [3.2]\n",
            " [7.4]\n",
            " [2.5]\n",
            " [5.9]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eKX5VkbueojA"
      },
      "source": [
        "# Comparing Actual vs Predicted\n",
        "df = pd.DataFrame({'Actual': y_test, 'Predicted': y_pred})  \n"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 205
        },
        "id": "rq3VO3Wke1os",
        "outputId": "c0891e18-287d-4092-8472-1900a966cf4b"
      },
      "source": [
        "df"
      ],
      "execution_count": 19,
      "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>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  Predicted\n",
              "0      20  16.884145\n",
              "1      27  33.732261\n",
              "2      69  75.357018\n",
              "3      30  26.794801\n",
              "4      62  60.491033"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p0MY0ziFe49_"
      },
      "source": [
        "EVALUATING MODEL"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TEXPeSkqe2hk",
        "outputId": "843a4b01-401f-4218-d9c6-9adabcf81115"
      },
      "source": [
        "from sklearn import metrics  \n",
        "print('Mean Absolute Error:', metrics.mean_absolute_error(y_test, y_pred)) "
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean Absolute Error: 4.183859899002982\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s0rWt1G6e8pJ"
      },
      "source": [
        "COMPLETED..."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nq8k_dxxe764"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}