project 36 SVR.ipynb

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      "source": [
        "# Support Vector Regression (SVR)\n",
        "\n",
        "It is a machine learning technique used for regression tasks.\n",
        "\n",
        "It is a variant of Support Vector Machines (SVM) and is designed to predict continuous numeric values.\n",
        "\n",
        "\n",
        "SVR is a regression technique that seeks to find a regression model with a margin around the predicted values, allowing for a balance between fitting the data and avoiding overfitting.\n",
        "\n",
        "It is particularly useful when dealing with non-linear relationships and can be adapted to various problem domains through the choice of kernel functions."
      ],
      "metadata": {
        "id": "BjxTstiDXzvJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "from sklearn.svm import SVR\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "AzkT9OzOYKd7"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "us = pd.read_csv(\"US Christmas Tree Sales.csv\")\n",
        "us.head(4)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "RAlEsu10YXOE",
        "outputId": "e4cd9c11-265e-4a65-e020-53db7a27c6eb"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   index  Year Type of tree  Number of trees sold  Average Tree Price  \\\n",
              "0      0  2010    Real tree              27000000               36.12   \n",
              "1      1  2011    Real tree              30800000               34.87   \n",
              "2      2  2012    Real tree              24500000               40.30   \n",
              "3      3  2013    Real tree              33020000               35.30   \n",
              "\n",
              "        Sales  \n",
              "0   975240000  \n",
              "1  1073996000  \n",
              "2   987350000  \n",
              "3  1165606000  "
            ],
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              "\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>index</th>\n",
              "      <th>Year</th>\n",
              "      <th>Type of tree</th>\n",
              "      <th>Number of trees sold</th>\n",
              "      <th>Average Tree Price</th>\n",
              "      <th>Sales</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>2010</td>\n",
              "      <td>Real tree</td>\n",
              "      <td>27000000</td>\n",
              "      <td>36.12</td>\n",
              "      <td>975240000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>2011</td>\n",
              "      <td>Real tree</td>\n",
              "      <td>30800000</td>\n",
              "      <td>34.87</td>\n",
              "      <td>1073996000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>2012</td>\n",
              "      <td>Real tree</td>\n",
              "      <td>24500000</td>\n",
              "      <td>40.30</td>\n",
              "      <td>987350000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>2013</td>\n",
              "      <td>Real tree</td>\n",
              "      <td>33020000</td>\n",
              "      <td>35.30</td>\n",
              "      <td>1165606000</td>\n",
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              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = us[['Average Tree Price']]\n",
        "y = us[['Sales']]"
      ],
      "metadata": {
        "id": "QFSFE9CBZNfl"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "svr_rbf = SVR(kernel='rbf', C=100, gamma=0.1)\n",
        "\n",
        "#y_1d = np.ravel(y)\n",
        "svr_rbf.fit(X, y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 129
        },
        "id": "Zq_F1sjEZJar",
        "outputId": "1d031e8c-7e69-4b5b-c99b-2b118e8ca044"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVR(C=100, gamma=0.1)"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVR(C=100, gamma=0.1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVR</label><div class=\"sk-toggleable__content\"><pre>SVR(C=100, gamma=0.1)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = svr_rbf.predict(X_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xemPNy7obkV5",
        "outputId": "1fd82f44-5b78-46da-9dfb-248e3b4ac576"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but SVR was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(9,4))\n",
        "\n",
        "plt.scatter(X, y, color='brown', label='data')\n",
        "\n",
        "plt.plot(X_test, y_pred, color='green', lw=2, label='RBF model')\n",
        "\n",
        "plt.xlabel('Data')\n",
        "plt.ylabel('Target')\n",
        "plt.title('Support Vector Regression')\n",
        "\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        },
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        "outputId": "bafe2de8-06f9-4f30-8ff7-94421ff958f5"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 900x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}