38.K_Nearest_Neighbors_Regression_.ipynb

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      "name": "38.K-Nearest Neighbors Regression .ipynb",
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      "name": "python3",
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      "cell_type": "code",
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        "outputId": "6d1a8c0c-149c-4b5c-a1f4-20547b916e39"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "datas = pd.read_csv('LinearData.csv')\n",
        "datas"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>X</th>\n",
              "      <th>Y</th>\n",
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              "      <th>0</th>\n",
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              "      <td>2068</td>\n",
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              "      <td>2871</td>\n",
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              "      <td>3972</td>\n",
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              "      <td>7936</td>\n",
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              "      <td>8577</td>\n",
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              "      <th>9</th>\n",
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              "      <td>10950</td>\n",
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              "      <td>11077</td>\n",
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              "      <td>12468</td>\n",
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              "      <td>13780</td>\n",
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              "      <th>13</th>\n",
              "      <td>14</td>\n",
              "      <td>13888</td>\n",
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              "      <th>14</th>\n",
              "      <td>15</td>\n",
              "      <td>14325</td>\n",
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              "      <td>16</td>\n",
              "      <td>14496</td>\n",
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              "      <td>17</td>\n",
              "      <td>16116</td>\n",
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              "      <td>18</td>\n",
              "      <td>17064</td>\n",
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              "      <th>18</th>\n",
              "      <td>19</td>\n",
              "      <td>20387</td>\n",
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              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>20</td>\n",
              "      <td>18620</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>21</td>\n",
              "      <td>20517</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>22</td>\n",
              "      <td>22264</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>23</td>\n",
              "      <td>22770</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>24</td>\n",
              "      <td>25344</td>\n",
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              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>25</td>\n",
              "      <td>23450</td>\n",
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              "      <th>25</th>\n",
              "      <td>26</td>\n",
              "      <td>28522</td>\n",
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              "      <th>26</th>\n",
              "      <td>27</td>\n",
              "      <td>25920</td>\n",
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              "      <td>28</td>\n",
              "      <td>30072</td>\n",
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              "      <td>29</td>\n",
              "      <td>31813</td>\n",
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              "      <th>29</th>\n",
              "      <td>30</td>\n",
              "      <td>32370</td>\n",
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            "text/plain": [
              "     X      Y\n",
              "0    1    953\n",
              "1    2   2068\n",
              "2    3   2871\n",
              "3    4   3972\n",
              "4    5   5240\n",
              "5    6   5610\n",
              "6    7   7196\n",
              "7    8   7936\n",
              "8    9   8577\n",
              "9   10  10950\n",
              "10  11  11077\n",
              "11  12  12468\n",
              "12  13  13780\n",
              "13  14  13888\n",
              "14  15  14325\n",
              "15  16  14496\n",
              "16  17  16116\n",
              "17  18  17064\n",
              "18  19  20387\n",
              "19  20  18620\n",
              "20  21  20517\n",
              "21  22  22264\n",
              "22  23  22770\n",
              "23  24  25344\n",
              "24  25  23450\n",
              "25  26  28522\n",
              "26  27  25920\n",
              "27  28  30072\n",
              "28  29  31813\n",
              "29  30  32370"
            ]
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        }
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      "cell_type": "code",
      "metadata": {
        "id": "ipTJm_kbwnOJ"
      },
      "source": [
        "X = datas.iloc[:, :-1].values\n",
        "y = datas.iloc[:, -1].values"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4_1YE6Y-wr6Z",
        "outputId": "23cd6bb0-8aac-448d-8493-6e68fc6af5f0"
      },
      "source": [
        "X"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 1],\n",
              "       [ 2],\n",
              "       [ 3],\n",
              "       [ 4],\n",
              "       [ 5],\n",
              "       [ 6],\n",
              "       [ 7],\n",
              "       [ 8],\n",
              "       [ 9],\n",
              "       [10],\n",
              "       [11],\n",
              "       [12],\n",
              "       [13],\n",
              "       [14],\n",
              "       [15],\n",
              "       [16],\n",
              "       [17],\n",
              "       [18],\n",
              "       [19],\n",
              "       [20],\n",
              "       [21],\n",
              "       [22],\n",
              "       [23],\n",
              "       [24],\n",
              "       [25],\n",
              "       [26],\n",
              "       [27],\n",
              "       [28],\n",
              "       [29],\n",
              "       [30]])"
            ]
          },
          "metadata": {
            "tags": []
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          "execution_count": 3
        }
      ]
    },
    {
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        },
        "id": "A0a9m2DewwWS",
        "outputId": "f194492d-baa4-4852-b090-2459908b1bcf"
      },
      "source": [
        "y"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([  953,  2068,  2871,  3972,  5240,  5610,  7196,  7936,  8577,\n",
              "       10950, 11077, 12468, 13780, 13888, 14325, 14496, 16116, 17064,\n",
              "       20387, 18620, 20517, 22264, 22770, 25344, 23450, 28522, 25920,\n",
              "       30072, 31813, 32370])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4GBhRFq6xC7d"
      },
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cQq7ZxVRwwyj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "75c1ee0c-83a6-427e-9163-08c7c8b69c10"
      },
      "source": [
        "clf =  KNeighborsClassifier()\n",
        "clf.fit(X, y)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
              "                     metric_params=None, n_jobs=None, n_neighbors=5, p=2,\n",
              "                     weights='uniform')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7vZB-6cvw2G8",
        "outputId": "c15eb892-de69-4869-f4f2-26c9fbc76eb6"
      },
      "source": [
        "clf.predict(X)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([  953,   953,   953,  2068,  2871,  3972,  5240,  5610,  7196,\n",
              "        7936,  8577, 10950, 11077, 12468, 13780, 13888, 14325, 14496,\n",
              "       16116, 17064, 18620, 18620, 20517, 22264, 22770, 23450, 23450,\n",
              "       25920, 25920, 25920])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8SOcQ_b1xUKH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "outputId": "8b820399-dd5a-44c2-f6e9-c054a9dae6ee"
      },
      "source": [
        "plt.scatter(X, y, color = 'green')\n",
        "plt.plot(X,clf.predict(X),color='red')\n",
        "plt.title('Regressor')\n",
        "plt.show()"
      ],
      "execution_count": 8,
      "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": "code",
      "metadata": {
        "id": "ebrQ3wAq_Sms"
      },
      "source": [
        ""
      ],
      "execution_count": 8,
      "outputs": []
    }
  ]
}