35.DecisionTreeRegressor.ipynb

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      "name": "35.DecisionTreeRegressor.ipynb",
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      "name": "python3",
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      "name": "python"
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      "cell_type": "code",
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        "outputId": "c9fce5d6-bafd-469f-8d6f-7ce6970d55a8"
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      "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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              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>953</td>\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>9</td>\n",
              "      <td>8577</td>\n",
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              "      <th>9</th>\n",
              "      <td>10</td>\n",
              "      <td>10950</td>\n",
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              "      <th>10</th>\n",
              "      <td>11</td>\n",
              "      <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>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",
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              "    <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",
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              "    <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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              "      <td>25920</td>\n",
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              "      <td>30072</td>\n",
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              "      <th>28</th>\n",
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              "      <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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      "metadata": {
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      "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": "4e3a8142-8f26-40f5-d7f2-469e3e457a21"
      },
      "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
        }
      ]
    },
    {
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        },
        "id": "A0a9m2DewwWS",
        "outputId": "304ceed0-0240-4653-9f20-33d86723a6b8"
      },
      "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.tree import DecisionTreeRegressor"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cQq7ZxVRwwyj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1a7251f1-60f2-40c3-8ac8-4e7c14778db7"
      },
      "source": [
        "clf =  DecisionTreeRegressor(max_depth=50)\n",
        "clf.fit(X, y)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeRegressor(ccp_alpha=0.0, criterion='mse', max_depth=50,\n",
              "                      max_features=None, max_leaf_nodes=None,\n",
              "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                      min_samples_leaf=1, min_samples_split=2,\n",
              "                      min_weight_fraction_leaf=0.0, presort='deprecated',\n",
              "                      random_state=None, splitter='best')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7vZB-6cvw2G8",
        "outputId": "5073fa62-6e76-43de-d901-deda38426c7d"
      },
      "source": [
        "clf.predict(X)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([  953.,  2068.,  2871.,  3972.,  5240.,  5610.,  7196.,  7936.,\n",
              "        8577., 10950., 11077., 12468., 13780., 13888., 14325., 14496.,\n",
              "       16116., 17064., 20387., 18620., 20517., 22264., 22770., 25344.,\n",
              "       23450., 28522., 25920., 30072., 31813., 32370.])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8SOcQ_b1xUKH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "outputId": "a129e84c-9fb0-4b08-e1a6-502f4a02b197"
      },
      "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": []
    }
  ]
}