Support_Vector_Machine_(SVM)_HAAI.ipynb

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      "cell_type": "markdown",
      "source": [
        "Let's apply the SVM classifier to a dataset. At first, we are importing the necessary python packages."
      ],
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      "source": [
        "# importing required libraries\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's download the iris dataset from its weblink as follows:"
      ],
      "metadata": {
        "id": "Z7MahXea_c2G"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "path = \"https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data\""
      ],
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        "id": "pj6czdap9NuD"
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      "execution_count": 2,
      "outputs": []
    },
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      "cell_type": "markdown",
      "source": [
        "Now, assign the column names to the dataset as follows:"
      ],
      "metadata": {
        "id": "er21sfqe_mAX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "headernames = ['sepal-length', 'sepal-width', 'petal-length', 'petal-width', 'Class']\n"
      ],
      "metadata": {
        "id": "8V08mqsJZlBB"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Next, we have to read the dataset using the Pandas data frame as follows:"
      ],
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        "id": "7fcTLczy_uPL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_csv(path, names=headernames)\n",
        "data.shape"
      ],
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      "execution_count": 4,
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            "text/plain": [
              "(150, 5)"
            ]
          },
          "metadata": {},
          "execution_count": 4
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      "cell_type": "code",
      "source": [
        "data.head()\n",
        ""
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      "execution_count": 5,
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              "   sepal-length  sepal-width  petal-length  petal-width        Class\n",
              "0           5.1          3.5           1.4          0.2  Iris-setosa\n",
              "1           4.9          3.0           1.4          0.2  Iris-setosa\n",
              "2           4.7          3.2           1.3          0.2  Iris-setosa\n",
              "3           4.6          3.1           1.5          0.2  Iris-setosa\n",
              "4           5.0          3.6           1.4          0.2  Iris-setosa"
            ],
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          "metadata": {},
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    {
      "cell_type": "markdown",
      "source": [
        "Let's implement the very basic step of data preprocessing."
      ],
      "metadata": {
        "id": "R0nZSNmI_43W"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = data.iloc[:, :-1].values\n",
        "y = data.iloc[:, 4].values"
      ],
      "metadata": {
        "id": "KA6JYD2H92Wp"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Here, we are dividing the data into train and test splits, with 70% of the training data and 30% of the testing data."
      ],
      "metadata": {
        "id": "q81cma-S_9ty"
      }
    },
    {
      "cell_type": "code",
      "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=0.30)"
      ],
      "metadata": {
        "id": "O9vMcRbAbFH-"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's do the data scaling."
      ],
      "metadata": {
        "id": "YdqteUsfAC6M"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "scaler = StandardScaler()\n",
        "scaler.fit(X_train)\n",
        "X_train = scaler.transform(X_train)\n",
        "X_test = scaler.transform(X_test)"
      ],
      "metadata": {
        "id": "tQK-c3U0bNwX"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now, we are training the model with the help of the SVM as follows:"
      ],
      "metadata": {
        "id": "D6Ttu1jxALAo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# import support vector classifier\n",
        "# \"Support Vector Classifier\"\n",
        "from sklearn.svm import SVC\n",
        "clf = SVC(kernel='linear')\n",
        "\n",
        "# fitting x samples and y classes\n",
        "clf.fit(X_train, y_train)\n"
      ],
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      },
      "execution_count": 11,
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC(kernel='linear')"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 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-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-1 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-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-1 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-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 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-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 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-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 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-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(kernel=&#x27;linear&#x27;)</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-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(kernel=&#x27;linear&#x27;)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now, we have to make the prediction, which can be done in the following way:"
      ],
      "metadata": {
        "id": "mNQJia49AVS6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = clf.predict(X_test)"
      ],
      "metadata": {
        "id": "8wg58uoebvAA"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's print the results as follows:"
      ],
      "metadata": {
        "id": "Da8RLlPvAcvy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score\n",
        "result = confusion_matrix(y_test, y_pred)\n",
        "sns.heatmap(result,\n",
        "            annot=True,\n",
        "            fmt='g',\n",
        "            xticklabels=['Setosa','Versicolor', 'Virginica'],\n",
        "            yticklabels=['Setosa','Versicolor', 'Virginica'])\n",
        "plt.ylabel('Prediction',fontsize=13)\n",
        "plt.xlabel('Actual',fontsize=13)\n",
        "plt.title('Confusion Matrix',fontsize=17)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 481
        },
        "id": "IeM18X6acAjo",
        "outputId": "87ef3d22-6a72-4ae1-ad72-84d9878cce6a"
      },
      "execution_count": 13,
      "outputs": [
        {
          "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": [
        "result1 = classification_report(y_test, y_pred)\n",
        "print(\"Classification Report:\",)\n",
        "print (result1)\n",
        "result2 = accuracy_score(y_test,y_pred)\n",
        "print(\"Accuracy:\",result2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N7HeHX78-jHw",
        "outputId": "2ebf1552-164e-4f69-8708-584af8d03c80"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Classification Report:\n",
            "                 precision    recall  f1-score   support\n",
            "\n",
            "    Iris-setosa       1.00      1.00      1.00        11\n",
            "Iris-versicolor       0.95      0.95      0.95        19\n",
            " Iris-virginica       0.93      0.93      0.93        15\n",
            "\n",
            "       accuracy                           0.96        45\n",
            "      macro avg       0.96      0.96      0.96        45\n",
            "   weighted avg       0.96      0.96      0.96        45\n",
            "\n",
            "Accuracy: 0.9555555555555556\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We have achieved an accuracy of 95.56% (approximately) using SVM."
      ],
      "metadata": {
        "id": "SiGNG4N5BMlU"
      }
    }
  ]
}