Support_vector_machine.ipynb

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      "name": "Support_vector_machine.ipynb",
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      "authorship_tag": "ABX9TyOjW2abS/dVN5/2bO29bDYa",
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      "name": "python3",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Chandradithya8/Machine-Learning/blob/master/Support_vector_machine.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OHlifR_ihTuh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "from sklearn.datasets import load_iris\n",
        "iris=load_iris()"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6ZNWJmXSkZHr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "97605290-3a6c-475c-8d70-5aa7a0c79427"
      },
      "source": [
        "dir(iris)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['DESCR', 'data', 'feature_names', 'filename', 'target', 'target_names']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jLV9RaaKkq_k",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 90
        },
        "outputId": "0e975203-d1ed-4619-8d33-a860bf271447"
      },
      "source": [
        "iris.feature_names"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['sepal length (cm)',\n",
              " 'sepal width (cm)',\n",
              " 'petal length (cm)',\n",
              " 'petal width (cm)']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hrirB0Wvk5b8",
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          "height": 198
        },
        "outputId": "c426ecd0-5d56-497f-acbc-f80836535c52"
      },
      "source": [
        "df=pd.DataFrame(iris.data,columns=iris.feature_names)\n",
        "df.head()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <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",
              "    </tr>\n",
              "  </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",
              "    </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",
              "    </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",
              "    </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",
              "    </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",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "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"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ErpAS30QlKa7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        },
        "outputId": "ea80d032-6a1f-401e-d9d0-50c5e5f38328"
      },
      "source": [
        "df['target']=iris.target\n",
        "df.head()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <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",
              "    </tr>\n",
              "  </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</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</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</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</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</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   sepal length (cm)  sepal width (cm)  ...  petal width (cm)  target\n",
              "0                5.1               3.5  ...               0.2       0\n",
              "1                4.9               3.0  ...               0.2       0\n",
              "2                4.7               3.2  ...               0.2       0\n",
              "3                4.6               3.1  ...               0.2       0\n",
              "4                5.0               3.6  ...               0.2       0\n",
              "\n",
              "[5 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Dnm8d0XTlYvy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "c043b8d1-c47a-4904-efee-7ce5f58702b3"
      },
      "source": [
        "iris.target_names # 0,1,2 = 'setosa', 'versicolor', 'virginica'"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['setosa', 'versicolor', 'virginica'], dtype='<U10')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4VQr-M3_luQL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        },
        "outputId": "08e9fbc4-5c34-4f89-8660-3d6d4ea44dc6"
      },
      "source": [
        "df['flower_name']=df.target.apply(lambda x:iris.target_names[x])\n",
        "df.head()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe thead th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <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",
              "      <th>flower_name</th>\n",
              "    </tr>\n",
              "  </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</td>\n",
              "      <td>setosa</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</td>\n",
              "      <td>setosa</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</td>\n",
              "      <td>setosa</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</td>\n",
              "      <td>setosa</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</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   sepal length (cm)  sepal width (cm)  ...  target  flower_name\n",
              "0                5.1               3.5  ...       0       setosa\n",
              "1                4.9               3.0  ...       0       setosa\n",
              "2                4.7               3.2  ...       0       setosa\n",
              "3                4.6               3.1  ...       0       setosa\n",
              "4                5.0               3.6  ...       0       setosa\n",
              "\n",
              "[5 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SS-BeuItnPNG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df0=df[df.target==0]\n",
        "df1=df[df.target==1]\n",
        "df2=df[df.target==2]"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kDlagjtPpfxk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "outputId": "250138e3-218c-4a68-9aa4-e397d4db97d7"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.scatter(df0['sepal length (cm)'],df0['sepal width (cm)'],color='blue',marker='+')\n",
        "plt.scatter(df1['sepal length (cm)'],df1['sepal width (cm)'],color='red',marker='+')"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f61491e50b8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        },
        {
          "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": "1e_rZrLUqM-X",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "335ab3b4-b69a-4505-97f0-a0de19d4d00f"
      },
      "source": [
        "plt.scatter(df0['petal length (cm)'],df0['petal width (cm)'],color='blue',marker='+')\n",
        "plt.scatter(df1['petal length (cm)'],df1['petal width (cm)'],color='red',marker='+')"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f614913d5c0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        },
        {
          "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": "tJybWK_Eqk3f",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        },
        "outputId": "e321e20d-8d3e-47db-d3f7-1252192ccf32"
      },
      "source": [
        "x=df.drop(['target','flower_name'],axis='columns')\n",
        "x.head()"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <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",
              "    </tr>\n",
              "  </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",
              "    </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",
              "    </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",
              "    </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",
              "    </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",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "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"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3f39KsAcrPBV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 126
        },
        "outputId": "1514cdd8-2d78-487e-fe64-7f85c7845f2d"
      },
      "source": [
        "y=df['target']\n",
        "y.head()"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0\n",
              "1    0\n",
              "2    0\n",
              "3    0\n",
              "4    0\n",
              "Name: target, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TZ3DZ_d7rU0u",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.2)"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "whMD51_proz8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.svm import SVC\n",
        "model=SVC()"
      ],
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e3f3GxO-seEx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 90
        },
        "outputId": "f5de715f-c805-44d8-b329-ec93317363f4"
      },
      "source": [
        "model.fit(x_train,y_train)"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC(C=1.0, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,\n",
              "    decision_function_shape='ovr', degree=3, gamma='scale', kernel='rbf',\n",
              "    max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
              "    tol=0.001, verbose=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hM8yKYhrso-L",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "e19ad5c3-adb0-49c4-cc8f-f1d480e82263"
      },
      "source": [
        "model.score(x_test,y_test)"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9666666666666667"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    }
  ]
}