Assignment_Task_12.ipynb

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      "name": "Assignment / Task - 12.ipynb",
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        "<a href=\"https://colab.research.google.com/github/rammya29/Goeduhub-STP/blob/main/Assignment_Task_12.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "#Assignment / Task - 12"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TQ6edO4HxC9u"
      },
      "source": [
        "Train SVM classifier using sklearn digits dataset (i.e. from sklearn.datasets import load_digits) and then,\n",
        "\n",
        "1. Measure accuracy of your model using different kernels such as rbf and linear.\n",
        "2. Tune your model further using regularization and gamma parameters and try to come up with highest accurancy score\n",
        "3. Use 80% of samples as training data size"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZBKJwmhCrSA8"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import sklearn\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.datasets import load_digits\n",
        "import seaborn as sns"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
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          "base_uri": "https://localhost:8080/"
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        "id": "hS9BPeY_xdIN",
        "outputId": "e4e524f5-7b5a-46fa-dc3f-1776d3ccfcf1"
      },
      "source": [
        "digits=load_digits()\n",
        "print(digits)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'data': array([[ 0.,  0.,  5., ...,  0.,  0.,  0.],\n",
            "       [ 0.,  0.,  0., ..., 10.,  0.,  0.],\n",
            "       [ 0.,  0.,  0., ..., 16.,  9.,  0.],\n",
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            "       [ 0.,  0.,  2., ..., 12.,  0.,  0.],\n",
            "       [ 0.,  0., 10., ..., 12.,  1.,  0.]]), 'target': array([0, 1, 2, ..., 8, 9, 8]), 'target_names': array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]), 'images': array([[[ 0.,  0.,  5., ...,  1.,  0.,  0.],\n",
            "        [ 0.,  0., 13., ..., 15.,  5.,  0.],\n",
            "        [ 0.,  3., 15., ..., 11.,  8.,  0.],\n",
            "        ...,\n",
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            "        [ 0.,  0., 13., ...,  2.,  1.,  0.],\n",
            "        [ 0.,  0., 16., ..., 16.,  5.,  0.],\n",
            "        ...,\n",
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            "\n",
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            "        ...,\n",
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            "\n",
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            "        ...,\n",
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            "        [ 0.,  8., 16., ..., 16.,  8.,  0.],\n",
            "        [ 0.,  1.,  8., ..., 12.,  1.,  0.]]]), 'DESCR': \".. _digits_dataset:\\n\\nOptical recognition of handwritten digits dataset\\n--------------------------------------------------\\n\\n**Data Set Characteristics:**\\n\\n    :Number of Instances: 5620\\n    :Number of Attributes: 64\\n    :Attribute Information: 8x8 image of integer pixels in the range 0..16.\\n    :Missing Attribute Values: None\\n    :Creator: E. Alpaydin (alpaydin '@' boun.edu.tr)\\n    :Date: July; 1998\\n\\nThis is a copy of the test set of the UCI ML hand-written digits datasets\\nhttps://archive.ics.uci.edu/ml/datasets/Optical+Recognition+of+Handwritten+Digits\\n\\nThe data set contains images of hand-written digits: 10 classes where\\neach class refers to a digit.\\n\\nPreprocessing programs made available by NIST were used to extract\\nnormalized bitmaps of handwritten digits from a preprinted form. From a\\ntotal of 43 people, 30 contributed to the training set and different 13\\nto the test set. 32x32 bitmaps are divided into nonoverlapping blocks of\\n4x4 and the number of on pixels are counted in each block. This generates\\nan input matrix of 8x8 where each element is an integer in the range\\n0..16. This reduces dimensionality and gives invariance to small\\ndistortions.\\n\\nFor info on NIST preprocessing routines, see M. D. Garris, J. L. Blue, G.\\nT. Candela, D. L. Dimmick, J. Geist, P. J. Grother, S. A. Janet, and C.\\nL. Wilson, NIST Form-Based Handprint Recognition System, NISTIR 5469,\\n1994.\\n\\n.. topic:: References\\n\\n  - C. Kaynak (1995) Methods of Combining Multiple Classifiers and Their\\n    Applications to Handwritten Digit Recognition, MSc Thesis, Institute of\\n    Graduate Studies in Science and Engineering, Bogazici University.\\n  - E. Alpaydin, C. Kaynak (1998) Cascading Classifiers, Kybernetika.\\n  - Ken Tang and Ponnuthurai N. Suganthan and Xi Yao and A. Kai Qin.\\n    Linear dimensionalityreduction using relevance weighted LDA. School of\\n    Electrical and Electronic Engineering Nanyang Technological University.\\n    2005.\\n  - Claudio Gentile. A New Approximate Maximal Margin Classification\\n    Algorithm. NIPS. 2000.\"}\n"
          ],
          "name": "stdout"
        }
      ]
    },
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        "id": "AA0aOwcVxf-P",
        "outputId": "3ccba874-2748-40c9-a16a-f6db93ff3ed4"
      },
      "source": [
        "digits.keys()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['data', 'target', 'target_names', 'images', 'DESCR'])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
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        "outputId": "caa37214-b7e1-48e2-ea28-27d6ee99d33c"
      },
      "source": [
        "df=pd.DataFrame(digits.data)\n",
        "print(df.head())"
      ],
      "execution_count": null,
      "outputs": [
        {
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          "text": [
            "    0    1    2     3     4     5    6   ...   57   58    59    60    61   62   63\n",
            "0  0.0  0.0  5.0  13.0   9.0   1.0  0.0  ...  0.0  6.0  13.0  10.0   0.0  0.0  0.0\n",
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            "2  0.0  0.0  0.0   4.0  15.0  12.0  0.0  ...  0.0  0.0   3.0  11.0  16.0  9.0  0.0\n",
            "3  0.0  0.0  7.0  15.0  13.0   1.0  0.0  ...  0.0  7.0  13.0  13.0   9.0  0.0  0.0\n",
            "4  0.0  0.0  0.0   1.0  11.0   0.0  0.0  ...  0.0  0.0   2.0  16.0   4.0  0.0  0.0\n",
            "\n",
            "[5 rows x 64 columns]\n"
          ],
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        "outputId": "b7f60c10-c41c-4947-8871-a23dd61fdab6"
      },
      "source": [
        "print(df.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(1797, 64)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
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      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "dn7TSRpCxmTD",
        "outputId": "d90b21f6-4196-482b-9674-f8646e8b480f"
      },
      "source": [
        "df.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RangeIndex(start=0, stop=64, step=1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
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    },
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        "outputId": "5cd9b41d-e02c-46d5-bbc4-96c567b6cb41"
      },
      "source": [
        "df.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0     0\n",
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              "     ..\n",
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        "id": "FBA2_Wvlxq_E",
        "outputId": "26a3f436-305a-4761-bd09-58df8cc5dec9"
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      "source": [
        "df['target']=digits.target\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     0    1    2     3     4     5  ...    59    60    61   62   63  target\n",
              "0  0.0  0.0  5.0  13.0   9.0   1.0  ...  13.0  10.0   0.0  0.0  0.0       0\n",
              "1  0.0  0.0  0.0  12.0  13.0   5.0  ...  11.0  16.0  10.0  0.0  0.0       1\n",
              "2  0.0  0.0  0.0   4.0  15.0  12.0  ...   3.0  11.0  16.0  9.0  0.0       2\n",
              "3  0.0  0.0  7.0  15.0  13.0   1.0  ...  13.0  13.0   9.0  0.0  0.0       3\n",
              "4  0.0  0.0  0.0   1.0  11.0   0.0  ...   2.0  16.0   4.0  0.0  0.0       4\n",
              "\n",
              "[5 rows x 65 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j65Mv_G8xs1S",
        "outputId": "67c8d64f-588f-4fda-b32a-4798b5275d4e"
      },
      "source": [
        "print(digits.data.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(1797, 64)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P1cat3jFxwk-",
        "outputId": "0b259bca-fb99-4cd2-af4b-5f3dda45512e"
      },
      "source": [
        "print(digits.target.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(1797,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_j4WPDwfxyGI",
        "outputId": "d447bdc1-464b-47f7-ade0-cb3a427ed033"
      },
      "source": [
        "df.target"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       0\n",
              "1       1\n",
              "2       2\n",
              "3       3\n",
              "4       4\n",
              "       ..\n",
              "1792    9\n",
              "1793    0\n",
              "1794    8\n",
              "1795    9\n",
              "1796    8\n",
              "Name: target, Length: 1797, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qZ4PCXVyxzVG",
        "outputId": "6603e721-4f8e-4e1d-e850-de552504c4ab"
      },
      "source": [
        "df.values"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0.,  0.,  5., ...,  0.,  0.,  0.],\n",
              "       [ 0.,  0.,  0., ...,  0.,  0.,  1.],\n",
              "       [ 0.,  0.,  0., ...,  9.,  0.,  2.],\n",
              "       ...,\n",
              "       [ 0.,  0.,  1., ...,  0.,  0.,  8.],\n",
              "       [ 0.,  0.,  2., ...,  0.,  0.,  9.],\n",
              "       [ 0.,  0., 10., ...,  1.,  0.,  8.]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6GcMsbKqxz-Z"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "x=df.drop(['target'],axis='columns')\n",
        "y=df.target\n",
        "x_train,x_test,y_train,y_test= train_test_split(x,y,test_size=0.2,random_state=12)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7SAoFlgfx2jS",
        "outputId": "3c0dc8d6-e229-4d52-fd42-229fc267dd7c"
      },
      "source": [
        "print(len(x_train))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1437\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u6xpKGjGx6BZ",
        "outputId": "e1be55ca-a76e-4c2b-98aa-8eb5acd30135"
      },
      "source": [
        "print(len(x_test))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "360\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OIE1WU13yDDu",
        "outputId": "98ef977e-30ba-470e-b3fd-2b0bff37b80e"
      },
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.svm import SVC\n",
        "\n",
        "model1=SVC(kernel='rbf',random_state=0, probability=True)\n",
        "model1.fit(x_train,y_train)\n",
        "y_pred_1=model1.predict(x_test)\n",
        "print(\"Model Score of Kernal(rbf) :\", model1.score(x_test,y_test))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model Score of Kernal(rbf) : 0.9916666666666667\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QO7NiMNWyfre",
        "outputId": "10676a23-6336-4545-cced-d0fb3ca5b9c1"
      },
      "source": [
        "model2=SVC(kernel='linear',random_state=0, probability=True)\n",
        "model2.fit(x_train,y_train)\n",
        "y_pred_2=model2.predict(x_test)\n",
        "print(\"Model Score of Kernal(linear) :\", model2.score(x_test,y_test))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model Score of Kernal(linear) : 0.975\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rJoimkF6yjOY",
        "outputId": "7979ceea-b484-47d4-b71b-bd9532d31a95"
      },
      "source": [
        "model3=SVC(kernel='poly',random_state=0, probability=True)\n",
        "model3.fit(x_train,y_train)\n",
        "y_pred_3=model3.predict(x_test)\n",
        "print(\"Model Score of Kernal(poly) :\", model3.score(x_test,y_test))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model Score of Kernal(poly) : 0.9944444444444445\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KCuqovtqymu7",
        "outputId": "5a16dcd7-0f91-4108-f360-e7d5d323dec6"
      },
      "source": [
        "accuracy=accuracy_score(y_test,y_pred_3)\n",
        "print('ACCURACY is',accuracy)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "ACCURACY is 0.9944444444444445\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wVHL0tOdyo_X",
        "outputId": "29755702-ae02-4216-eddb-d9d7a711570b"
      },
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "cm=np.array(confusion_matrix(y_test,y_pred_3))\n",
        "cm"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[37,  0,  0,  0,  0,  0,  0,  0,  0,  0],\n",
              "       [ 0, 32,  0,  0,  0,  0,  0,  0,  0,  0],\n",
              "       [ 0,  0, 38,  0,  0,  0,  0,  0,  0,  0],\n",
              "       [ 0,  0,  0, 43,  0,  0,  0,  0,  0,  0],\n",
              "       [ 0,  0,  0,  0, 39,  0,  0,  0,  0,  0],\n",
              "       [ 0,  0,  0,  0,  0, 32,  0,  0,  0,  2],\n",
              "       [ 0,  0,  0,  0,  0,  0, 29,  0,  0,  0],\n",
              "       [ 0,  0,  0,  0,  0,  0,  0, 42,  0,  0],\n",
              "       [ 0,  0,  0,  0,  0,  0,  0,  0, 32,  0],\n",
              "       [ 0,  0,  0,  0,  0,  0,  0,  0,  0, 34]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ffGNxcQwyrXP",
        "outputId": "4b37887c-5ac1-4ff0-b8ac-bcb33518d643"
      },
      "source": [
        "from sklearn.metrics import mean_squared_error\n",
        "mse=mean_squared_error(y_test,y_pred_3)\n",
        "mse"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.08888888888888889"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1xsZL1-gyujI",
        "outputId": "819492de-da8f-4d5e-9994-ccecfecab81b"
      },
      "source": [
        "model1_C=SVC(C=3)\n",
        "model1_C.fit(x_train,y_train)\n",
        "model1_C.score(x_test,y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9944444444444445"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9mwoF7s_yxtX",
        "outputId": "2ee8dae4-48e0-4a9f-9958-a6d5b170947e"
      },
      "source": [
        "model2_C=SVC(C=3)\n",
        "model2_C.fit(x_train,y_train)\n",
        "model2_C.score(x_test,y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9944444444444445"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9N-MvP97y02I",
        "outputId": "d7b9561d-1378-4214-e4b2-2f2d859f5fcf"
      },
      "source": [
        "model3_C=SVC(C=3)\n",
        "model3_C.fit(x_train,y_train)\n",
        "model3_C.score(x_test,y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9944444444444445"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 309
        },
        "id": "zNsSA92Sy3Kc",
        "outputId": "42b21380-92e2-46c4-ecc5-130ba112acad"
      },
      "source": [
        "plt.figure(figsize=(5,5))\n",
        "sns.heatmap(cm, annot=True, fmt=\".2f\", linewidths=.5, square = True, cmap = 'Blues_r')\n",
        "plt.ylabel('Actual label')\n",
        "plt.xlabel('Predicted label')\n",
        "A=f'Accuracy Score :{accuracy:.2f}'\n",
        "plt.title(A)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Gi7x4sDKzxh-"
      },
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fHxyg6BCzTc-"
      },
      "source": [
        "##END OF ASSIGNMENT"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cDcbeQZIzWYx"
      },
      "source": [
        "**Author** : Rammya Dharshini K\n",
        "\n",
        "**Date** : 15-06-2021\n",
        "\n",
        "**Reg.ID** : GO_STP_1613"
      ]
    }
  ]
}