50.ADA_Regression.ipynb

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    "colab": {
      "name": "50.Linear Regression.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
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    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 990
        },
        "id": "M83cKR2MtPZ3",
        "outputId": "422f6bd9-152f-4b43-ba4e-8247d52bb69f"
      },
      "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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>953</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>2068</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>2871</td>\n",
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              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>3972</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>5240</td>\n",
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              "      <th>5</th>\n",
              "      <td>6</td>\n",
              "      <td>5610</td>\n",
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              "      <th>6</th>\n",
              "      <td>7</td>\n",
              "      <td>7196</td>\n",
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              "      <th>7</th>\n",
              "      <td>8</td>\n",
              "      <td>7936</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>9</td>\n",
              "      <td>8577</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>10</td>\n",
              "      <td>10950</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>11</td>\n",
              "      <td>11077</td>\n",
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              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>12</td>\n",
              "      <td>12468</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>13</td>\n",
              "      <td>13780</td>\n",
              "    </tr>\n",
              "    <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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>16</td>\n",
              "      <td>14496</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>17</td>\n",
              "      <td>16116</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>26</td>\n",
              "      <td>28522</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>27</td>\n",
              "      <td>25920</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>28</td>\n",
              "      <td>30072</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>29</td>\n",
              "      <td>31813</td>\n",
              "    </tr>\n",
              "    <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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        }
      ]
    },
    {
      "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": "a173bbf0-ce8a-443a-8634-0214c3d67d76"
      },
      "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": "d801a9ee-b416-4b50-ca91-29458a9b058c"
      },
      "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\n",
        "from sklearn.ensemble import AdaBoostRegressor"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cQq7ZxVRwwyj",
        "outputId": "c765b34a-1ce3-4088-fc0e-897eb0b6ab93"
      },
      "source": [
        "Dec = DecisionTreeRegressor(max_depth=4)\n",
        "\n",
        "Ada = AdaBoostRegressor(DecisionTreeRegressor(max_depth=4), n_estimators=300)\n",
        "\n",
        "Dec.fit(X, y)\n",
        "Ada.fit(X, y)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "AdaBoostRegressor(base_estimator=DecisionTreeRegressor(ccp_alpha=0.0,\n",
              "                                                       criterion='mse',\n",
              "                                                       max_depth=4,\n",
              "                                                       max_features=None,\n",
              "                                                       max_leaf_nodes=None,\n",
              "                                                       min_impurity_decrease=0.0,\n",
              "                                                       min_impurity_split=None,\n",
              "                                                       min_samples_leaf=1,\n",
              "                                                       min_samples_split=2,\n",
              "                                                       min_weight_fraction_leaf=0.0,\n",
              "                                                       presort='deprecated',\n",
              "                                                       random_state=None,\n",
              "                                                       splitter='best'),\n",
              "                  learning_rate=1.0, loss='linear', n_estimators=300,\n",
              "                  random_state=None)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "7vZB-6cvw2G8",
        "outputId": "0581aaa7-d28a-4308-a801-b415e08acd80"
      },
      "source": [
        "plt.scatter(X, y, color = 'green')\n",
        "plt.plot(X,Dec.predict(X),color='red')\n",
        "plt.plot(X,Ada.predict(X),color='blue')\n",
        "plt.title('Polynomial Regression')\n",
        "plt.show()"
      ],
      "execution_count": 7,
      "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": "8SOcQ_b1xUKH"
      },
      "source": [
        ""
      ],
      "execution_count": 7,
      "outputs": []
    }
  ]
}