ml_SVMR.ipynb

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        "<a href=\"https://colab.research.google.com/github/naidu199/Machine_Learn/blob/main/ml_SVMR.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "metadata": {
        "id": "pFEwL7KPNxEp"
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      "source": [
        "#support vector machine(Regression)\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df=pd.read_csv(\"https://raw.githubusercontent.com/nandu19k/Support-Vector-Regression/master/Data.csv\")"
      ],
      "metadata": {
        "id": "XPULFbtFOEu-"
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      "execution_count": 133,
      "outputs": []
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        "df.head()"
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            "text/plain": [
              "      AT      V       AP     RH      PE\n",
              "0  14.96  41.76  1024.07  73.17  463.26\n",
              "1  25.18  62.96  1020.04  59.08  444.37\n",
              "2   5.11  39.40  1012.16  92.14  488.56\n",
              "3  20.86  57.32  1010.24  76.64  446.48\n",
              "4  10.82  37.50  1009.23  96.62  473.90"
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              "      <th>0</th>\n",
              "      <td>14.96</td>\n",
              "      <td>41.76</td>\n",
              "      <td>1024.07</td>\n",
              "      <td>73.17</td>\n",
              "      <td>463.26</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>25.18</td>\n",
              "      <td>62.96</td>\n",
              "      <td>1020.04</td>\n",
              "      <td>59.08</td>\n",
              "      <td>444.37</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5.11</td>\n",
              "      <td>39.40</td>\n",
              "      <td>1012.16</td>\n",
              "      <td>92.14</td>\n",
              "      <td>488.56</td>\n",
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              "      <th>3</th>\n",
              "      <td>20.86</td>\n",
              "      <td>57.32</td>\n",
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              "      <td>76.64</td>\n",
              "      <td>446.48</td>\n",
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              "      <td>10.82</td>\n",
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          },
          "metadata": {},
          "execution_count": 134
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Cgx-P4IfPPLh",
        "outputId": "f734027f-722b-4b1d-d499-8d6c82e8cfd4"
      },
      "execution_count": 135,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "AT    0\n",
              "V     0\n",
              "AP    0\n",
              "RH    0\n",
              "PE    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 135
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X=df.iloc[:,:-1].values\n",
        "Y=df.iloc[:,-1:].values\n",
        "Y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MyVLiQnNPhYm",
        "outputId": "b497726d-ebb9-43f1-cfd8-eec5cb0932f7"
      },
      "execution_count": 136,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[463.26],\n",
              "       [444.37],\n",
              "       [488.56],\n",
              "       ...,\n",
              "       [429.57],\n",
              "       [435.74],\n",
              "       [453.28]])"
            ]
          },
          "metadata": {},
          "execution_count": 136
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#data spliting for training and testing\n",
        "from sklearn.model_selection import train_test_split\n",
        "xtrain,xtest,ytrain,ytest = train_test_split(X,Y,test_size=0.22,random_state=0)\n"
      ],
      "metadata": {
        "id": "kdwIW48OQTSB"
      },
      "execution_count": 137,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#feature scaling\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "sc=StandardScaler()\n",
        "xtrain=sc.fit_transform(xtrain)\n",
        "xtest=sc.transform(xtest)\n",
        "ytrain=sc.fit_transform(ytrain)\n",
        "# ytrain"
      ],
      "metadata": {
        "id": "E0ha0iDjQkbv"
      },
      "execution_count": 138,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.svm import SVR\n",
        "reg=SVR(kernel='rbf')\n",
        "reg.fit(xtrain,ytrain)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 129
        },
        "id": "s2wX16KBRo-d",
        "outputId": "2b18cf44-cba4-4acb-b41c-4fabb37d67d3"
      },
      "execution_count": 139,
      "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()"
            ],
            "text/html": [
              "<style>#sk-container-id-11 {color: black;background-color: white;}#sk-container-id-11 pre{padding: 0;}#sk-container-id-11 div.sk-toggleable {background-color: white;}#sk-container-id-11 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-11 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-11 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-11 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-11 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-11 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-11 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-11 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-11 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 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-11 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-11 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-11 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-11 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 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-11 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-11 div.sk-item {position: relative;z-index: 1;}#sk-container-id-11 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-11 div.sk-item::before, #sk-container-id-11 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-11 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-11 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-11 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-11 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-11 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-11 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-11 div.sk-label-container {text-align: center;}#sk-container-id-11 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-11 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-11\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVR()</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-11\" type=\"checkbox\" checked><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVR</label><div class=\"sk-toggleable__content\"><pre>SVR()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 139
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ypred=reg.predict(xtest)\n",
        "ypred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AbxewDzbUPtI",
        "outputId": "c2e1bd10-2a54-45f8-cfee-d4229c07d236"
      },
      "execution_count": 140,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([-1.18863324,  0.21476506,  0.4079334 , ..., -1.21496278,\n",
              "        1.06710521,  1.0370534 ])"
            ]
          },
          "metadata": {},
          "execution_count": 140
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# ypred=sc.inverse_transform(ypred)"
      ],
      "metadata": {
        "id": "172uDUDbV0qE"
      },
      "execution_count": 141,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ypred = sc.inverse_transform(ypred)\n",
        "ypred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 408
        },
        "id": "lqtvozAIX5OY",
        "outputId": "ae7e0350-76ec-4ee4-8c3c-86375a48e8bd"
      },
      "execution_count": 142,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-142-c848f6725bdc>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mypred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minverse_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mypred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mypred\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/preprocessing/_data.py\u001b[0m in \u001b[0;36minverse_transform\u001b[0;34m(self, X, copy)\u001b[0m\n\u001b[1;32m   1032\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1033\u001b[0m         \u001b[0mcopy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcopy\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcopy\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1034\u001b[0;31m         X = check_array(\n\u001b[0m\u001b[1;32m   1035\u001b[0m             \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1036\u001b[0m             \u001b[0maccept_sparse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"csr\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator, input_name)\u001b[0m\n\u001b[1;32m    900\u001b[0m             \u001b[0;31m# If input is 1D raise error\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    901\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 902\u001b[0;31m                 raise ValueError(\n\u001b[0m\u001b[1;32m    903\u001b[0m                     \u001b[0;34m\"Expected 2D array, got 1D array instead:\\narray={}.\\n\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    904\u001b[0m                     \u001b[0;34m\"Reshape your data either using array.reshape(-1, 1) if \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Expected 2D array, got 1D array instead:\narray=[-1.18863324  0.21476506  0.4079334  ... -1.21496278  1.06710521\n  1.0370534 ].\nReshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample."
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import r2_score\n",
        "r2_score(ytest,ypred)"
      ],
      "metadata": {
        "id": "8uhz83biYdnw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "_ZtW2BHMY1kW"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}