ml_SVMC.ipynb

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        "<a href=\"https://colab.research.google.com/github/naidu199/Machine_Learn/blob/main/ml_SVMC.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "I4NOjJDksAc_"
      },
      "outputs": [],
      "source": [
        "#support vector machine (classification)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "metadata": {
        "id": "byz5w0Yws8-y"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.datasets import load_iris\n",
        "dataset=load_iris()"
      ],
      "metadata": {
        "id": "UVRrXvQCsMNA"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.keys()"
      ],
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        },
        "id": "arw49tFvtPJb",
        "outputId": "9b8023a2-1dcc-4373-b9a8-c5e391deb30b"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['data', 'target', 'frame', 'target_names', 'DESCR', 'feature_names', 'filename', 'data_module'])"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df=pd.DataFrame(np.c_[dataset['data'],dataset['target']] , columns=(np.append(dataset['feature_names'],['target'])))"
      ],
      "metadata": {
        "id": "yRxn5hhCsk-L"
      },
      "execution_count": 18,
      "outputs": []
    },
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      "source": [
        "df"
      ],
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        "id": "qkmLZccZtfD4",
        "outputId": "390e28c3-ef7f-4212-dcf1-49c80ce65e8d"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
              "0                  5.1               3.5                1.4               0.2   \n",
              "1                  4.9               3.0                1.4               0.2   \n",
              "2                  4.7               3.2                1.3               0.2   \n",
              "3                  4.6               3.1                1.5               0.2   \n",
              "4                  5.0               3.6                1.4               0.2   \n",
              "..                 ...               ...                ...               ...   \n",
              "145                6.7               3.0                5.2               2.3   \n",
              "146                6.3               2.5                5.0               1.9   \n",
              "147                6.5               3.0                5.2               2.0   \n",
              "148                6.2               3.4                5.4               2.3   \n",
              "149                5.9               3.0                5.1               1.8   \n",
              "\n",
              "     target  \n",
              "0       0.0  \n",
              "1       0.0  \n",
              "2       0.0  \n",
              "3       0.0  \n",
              "4       0.0  \n",
              "..      ...  \n",
              "145     2.0  \n",
              "146     2.0  \n",
              "147     2.0  \n",
              "148     2.0  \n",
              "149     2.0  \n",
              "\n",
              "[150 rows x 5 columns]"
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sepal length (cm)</th>\n",
              "      <th>sepal width (cm)</th>\n",
              "      <th>petal length (cm)</th>\n",
              "      <th>petal width (cm)</th>\n",
              "      <th>target</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.6</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>145</th>\n",
              "      <td>6.7</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>2.3</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>146</th>\n",
              "      <td>6.3</td>\n",
              "      <td>2.5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.9</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>147</th>\n",
              "      <td>6.5</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148</th>\n",
              "      <td>6.2</td>\n",
              "      <td>3.4</td>\n",
              "      <td>5.4</td>\n",
              "      <td>2.3</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>149</th>\n",
              "      <td>5.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.1</td>\n",
              "      <td>1.8</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>150 rows × 5 columns</p>\n",
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          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MT_EA4xDuny8",
        "outputId": "08c518c0-66e1-4afb-b386-4eadfe42690a"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "sepal length (cm)    0\n",
              "sepal width (cm)     0\n",
              "petal length (cm)    0\n",
              "petal width (cm)     0\n",
              "target               0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X=df.iloc[:,:-1].values\n",
        "Y=df.iloc[:,-1].values"
      ],
      "metadata": {
        "id": "gjlYOS-Nusev"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "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)"
      ],
      "metadata": {
        "id": "PomBPbUlu89n"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "sc=StandardScaler()\n",
        "xtrain=sc.fit_transform(xtrain)\n",
        "xtest=sc.transform(xtest)"
      ],
      "metadata": {
        "id": "DROQn2gfvRJK"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.svm import SVC\n",
        "svc=SVC(kernel='rbf')\n",
        "svc.fit(xtrain,ytrain)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "id": "bKtr_0Gdviyk",
        "outputId": "d0302b5e-6693-465b-d466-08a4532bfcd1"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC()"
            ],
            "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>SVC()</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\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ypred=svc.predict(xtest)\n",
        "ypred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p2UogC0dv501",
        "outputId": "804a9566-08f2-4da1-e705-4685de702d49"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([2., 1., 0., 2., 0., 2., 0., 1., 1., 1., 2., 1., 1., 1., 1., 0., 1.,\n",
              "       1., 0., 0., 2., 1., 0., 0., 2., 0., 0., 1., 1., 0., 2., 1., 0.])"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "cm=confusion_matrix(ytest,ypred)"
      ],
      "metadata": {
        "id": "AG-oRo8CwS0Z"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(cm,annot=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "IhCw-ppVwjph",
        "outputId": "db2bd65c-a572-4763-f3da-de4a722756e2"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 35
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "accuracy_score(ytest,ypred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y-phoAF0wyr3",
        "outputId": "e7d7e785-a8f3-4736-9d24-4ded0c8bc39d"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Af1FkRbjxGTq"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}