34.PLSRegression.ipynb

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    "colab": {
      "name": "34.PLSRegression.ipynb",
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      "name": "python3",
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      "name": "python"
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    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 990
        },
        "id": "M83cKR2MtPZ3",
        "outputId": "264c0ae7-dba6-4e49-b6af-ea6d308424d4"
      },
      "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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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>X</th>\n",
              "      <th>Y</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>953</td>\n",
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              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>2068</td>\n",
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              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>2871</td>\n",
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              "      <td>4</td>\n",
              "      <td>3972</td>\n",
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              "      <td>5240</td>\n",
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              "      <td>8</td>\n",
              "      <td>7936</td>\n",
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              "      <th>8</th>\n",
              "      <td>9</td>\n",
              "      <td>8577</td>\n",
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              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>10</td>\n",
              "      <td>10950</td>\n",
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              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>11</td>\n",
              "      <td>11077</td>\n",
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              "      <th>11</th>\n",
              "      <td>12</td>\n",
              "      <td>12468</td>\n",
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              "      <th>12</th>\n",
              "      <td>13</td>\n",
              "      <td>13780</td>\n",
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              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>14</td>\n",
              "      <td>13888</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>15</td>\n",
              "      <td>14325</td>\n",
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              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>16</td>\n",
              "      <td>14496</td>\n",
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              "      <th>16</th>\n",
              "      <td>17</td>\n",
              "      <td>16116</td>\n",
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              "      <th>17</th>\n",
              "      <td>18</td>\n",
              "      <td>17064</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>19</td>\n",
              "      <td>20387</td>\n",
              "    </tr>\n",
              "    <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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>25</td>\n",
              "      <td>23450</td>\n",
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              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>26</td>\n",
              "      <td>28522</td>\n",
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              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>27</td>\n",
              "      <td>25920</td>\n",
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              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>28</td>\n",
              "      <td>30072</td>\n",
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              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>29</td>\n",
              "      <td>31813</td>\n",
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              "    <tr>\n",
              "      <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": "dd12a90b-9222-4805-facf-c17a5bb19b12"
      },
      "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": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
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        },
        "id": "A0a9m2DewwWS",
        "outputId": "096f4b4a-f66d-4b43-81a5-9e7bf004ef5e"
      },
      "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.decomposition import PCA\n",
        "from sklearn.cross_decomposition import PLSRegression"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cQq7ZxVRwwyj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2f21c88b-f529-4d75-b134-5f2644b49c1d"
      },
      "source": [
        "clf =  PLSRegression(n_components=1)\n",
        "clf.fit(X, y)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PLSRegression(copy=True, max_iter=500, n_components=1, scale=True, tol=1e-06)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7vZB-6cvw2G8",
        "outputId": "89fa3d2a-f235-42cc-e19a-ac1ef173bfb4"
      },
      "source": [
        "clf.predict(X)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[  566.22795699],\n",
              "       [ 1609.09959214],\n",
              "       [ 2651.97122729],\n",
              "       [ 3694.84286244],\n",
              "       [ 4737.71449759],\n",
              "       [ 5780.58613274],\n",
              "       [ 6823.45776789],\n",
              "       [ 7866.32940304],\n",
              "       [ 8909.20103819],\n",
              "       [ 9952.07267334],\n",
              "       [10994.94430849],\n",
              "       [12037.81594364],\n",
              "       [13080.68757879],\n",
              "       [14123.55921394],\n",
              "       [15166.43084909],\n",
              "       [16209.30248424],\n",
              "       [17252.17411939],\n",
              "       [18295.04575454],\n",
              "       [19337.91738969],\n",
              "       [20380.78902484],\n",
              "       [21423.66065999],\n",
              "       [22466.53229514],\n",
              "       [23509.40393029],\n",
              "       [24552.27556544],\n",
              "       [25595.14720059],\n",
              "       [26638.01883574],\n",
              "       [27680.89047089],\n",
              "       [28723.76210604],\n",
              "       [29766.63374119],\n",
              "       [30809.50537634]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8SOcQ_b1xUKH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "outputId": "97abcd03-8c40-43f5-fbc5-df9d0e9bae97"
      },
      "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": 12,
      "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": null,
      "outputs": []
    }
  ]
}