SVM_Image_Classification.ipynb

{
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  "metadata": {
    "colab": {
      "name": "SVM - Image Classification.ipynb",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Vaibhavyadav350/AI-Learnings/blob/main/SVM_Image_Classification.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5W8zTuNh6RdH"
      },
      "outputs": [],
      "source": [
        "from sklearn.datasets import load_digits"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "digits = load_digits()"
      ],
      "metadata": {
        "id": "YIR3YJvE7hk5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "type(digits)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UnDDPmq27kgv",
        "outputId": "28d94c5f-0cae-4034-b4dd-a3a0d3ec33fe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "sklearn.utils.Bunch"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "digits.keys()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AAxAYzrh7mAx",
        "outputId": "20565e9d-c37b-4230-e1d8-3b373a694ffd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['data', 'target', 'frame', 'feature_names', 'target_names', 'images', 'DESCR'])"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "digits.target"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-EstB5n37qDT",
        "outputId": "e7ab03cc-a75a-4a2c-9aa5-cddfcd8b6657"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 2, ..., 8, 9, 8])"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "digits.data[0]\n",
        "# Vector Format"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "413I6AOm7zp_",
        "outputId": "00d971ff-b926-4ce2-fe31-ebe5ef3d8b24"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 0.,  0.,  5., 13.,  9.,  1.,  0.,  0.,  0.,  0., 13., 15., 10.,\n",
              "       15.,  5.,  0.,  0.,  3., 15.,  2.,  0., 11.,  8.,  0.,  0.,  4.,\n",
              "       12.,  0.,  0.,  8.,  8.,  0.,  0.,  5.,  8.,  0.,  0.,  9.,  8.,\n",
              "        0.,  0.,  4., 11.,  0.,  1., 12.,  7.,  0.,  0.,  2., 14.,  5.,\n",
              "       10., 12.,  0.,  0.,  0.,  0.,  6., 13., 10.,  0.,  0.,  0.])"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "digits.images[0]\n",
        "# Matrix Format"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cBrCopvO743K",
        "outputId": "a12bc59b-8a79-4a9b-85ac-62c0464f6760"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0.,  0.,  5., 13.,  9.,  1.,  0.,  0.],\n",
              "       [ 0.,  0., 13., 15., 10., 15.,  5.,  0.],\n",
              "       [ 0.,  3., 15.,  2.,  0., 11.,  8.,  0.],\n",
              "       [ 0.,  4., 12.,  0.,  0.,  8.,  8.,  0.],\n",
              "       [ 0.,  5.,  8.,  0.,  0.,  9.,  8.,  0.],\n",
              "       [ 0.,  4., 11.,  0.,  1., 12.,  7.,  0.],\n",
              "       [ 0.,  2., 14.,  5., 10., 12.,  0.,  0.],\n",
              "       [ 0.,  0.,  6., 13., 10.,  0.,  0.,  0.]])"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.imshow(digits.images[16],cmap='gray')\n",
        "plt.title(digits.target[16])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "4hvUSoXj8wbv",
        "outputId": "b9812640-8065-4079-9a23-e71d91ee1022"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Image is represented in matrix(original form)\n",
        "# Vector = Flatten(Matrix)"
      ],
      "metadata": {
        "id": "1Sx7UNcd8BVR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "df = pd.DataFrame(digits.data)\n",
        "df['Target'] = digits.target\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "id": "3KPqFQKr-Lqq",
        "outputId": "736c2c93-05c7-4148-f8d9-fe927ed5bc29"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     0    1    2     3     4     5    6    7    8    9  ...   55   56   57  \\\n",
              "0  0.0  0.0  5.0  13.0   9.0   1.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0   \n",
              "1  0.0  0.0  0.0  12.0  13.0   5.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0   \n",
              "2  0.0  0.0  0.0   4.0  15.0  12.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0   \n",
              "3  0.0  0.0  7.0  15.0  13.0   1.0  0.0  0.0  0.0  8.0  ...  0.0  0.0  0.0   \n",
              "4  0.0  0.0  0.0   1.0  11.0   0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0   \n",
              "\n",
              "    58    59    60    61   62   63  Target  \n",
              "0  6.0  13.0  10.0   0.0  0.0  0.0       0  \n",
              "1  0.0  11.0  16.0  10.0  0.0  0.0       1  \n",
              "2  0.0   3.0  11.0  16.0  9.0  0.0       2  \n",
              "3  7.0  13.0  13.0   9.0  0.0  0.0       3  \n",
              "4  0.0   2.0  16.0   4.0  0.0  0.0       4  \n",
              "\n",
              "[5 rows x 65 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8430da7a-3cd6-4227-bde3-97e00760f5f6\">\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
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              "      <th>5</th>\n",
              "      <th>6</th>\n",
              "      <th>7</th>\n",
              "      <th>8</th>\n",
              "      <th>9</th>\n",
              "      <th>...</th>\n",
              "      <th>55</th>\n",
              "      <th>56</th>\n",
              "      <th>57</th>\n",
              "      <th>58</th>\n",
              "      <th>59</th>\n",
              "      <th>60</th>\n",
              "      <th>61</th>\n",
              "      <th>62</th>\n",
              "      <th>63</th>\n",
              "      <th>Target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.0</td>\n",
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              "      <td>5.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>1.0</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>10.0</td>\n",
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              "      <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",
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              "      <td>7.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>13.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>...</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>...</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",
              "<p>5 rows × 65 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8430da7a-3cd6-4227-bde3-97e00760f5f6')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-8430da7a-3cd6-4227-bde3-97e00760f5f6 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-8430da7a-3cd6-4227-bde3-97e00760f5f6');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lGzgMfyD_ABs",
        "outputId": "320cd64e-195a-4e48-c097-34f8569c4992"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1797, 65)"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# splitting the data into train and test\n",
        "from sklearn.model_selection import train_test_split\n",
        "x_train,x_test,y_train,y_test = train_test_split(digits.data,digits.target,random_state=42)"
      ],
      "metadata": {
        "id": "q2kG2du3CjKH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.svm import SVC\n",
        "model = SVC()\n",
        "model.fit(x_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GR2S0NAH_Lwi",
        "outputId": "fe758bc5-be49-4d26-ec76-3bb83604cff3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC()"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = model.predict(x_test)"
      ],
      "metadata": {
        "id": "Xd9RezOqChn1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-PTRqMdpCyU5",
        "outputId": "77e31e13-bcab-492c-bb52-8e747e27aec6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([6, 9, 3, 7, 2, 1, 5, 2, 5, 2, 1, 9, 4, 0, 4, 2, 3, 7, 8, 8, 4, 3,\n",
              "       9, 7, 5, 6, 3, 5, 6, 3, 4, 9, 1, 4, 4, 6, 9, 4, 7, 6, 6, 9, 1, 3,\n",
              "       6, 1, 3, 0, 6, 5, 5, 1, 9, 5, 6, 0, 9, 0, 0, 1, 0, 4, 5, 2, 4, 5,\n",
              "       7, 0, 7, 5, 9, 5, 5, 4, 7, 0, 4, 5, 5, 9, 9, 0, 2, 3, 8, 0, 6, 4,\n",
              "       4, 9, 1, 2, 8, 3, 5, 2, 9, 0, 4, 4, 4, 3, 5, 3, 1, 3, 5, 9, 4, 2,\n",
              "       7, 7, 4, 4, 1, 9, 2, 7, 9, 7, 2, 6, 9, 4, 0, 7, 2, 7, 5, 8, 7, 5,\n",
              "       7, 9, 0, 6, 6, 4, 2, 8, 0, 9, 4, 6, 9, 9, 6, 9, 0, 3, 5, 6, 6, 0,\n",
              "       6, 4, 3, 9, 3, 7, 7, 2, 9, 0, 4, 5, 3, 6, 5, 9, 9, 8, 4, 2, 1, 3,\n",
              "       7, 7, 2, 2, 3, 9, 8, 0, 3, 2, 2, 5, 6, 9, 9, 4, 1, 5, 4, 2, 3, 6,\n",
              "       4, 8, 5, 9, 5, 7, 8, 9, 4, 8, 1, 5, 4, 4, 9, 6, 1, 8, 6, 0, 4, 5,\n",
              "       2, 7, 4, 6, 4, 5, 6, 0, 3, 2, 3, 6, 7, 1, 5, 1, 4, 7, 6, 8, 8, 5,\n",
              "       5, 1, 6, 2, 8, 8, 9, 5, 7, 6, 2, 2, 2, 3, 4, 8, 8, 3, 6, 0, 9, 7,\n",
              "       7, 0, 1, 0, 4, 5, 1, 5, 3, 6, 0, 4, 1, 0, 0, 3, 6, 5, 9, 7, 3, 5,\n",
              "       5, 9, 9, 8, 5, 3, 3, 2, 0, 5, 8, 3, 4, 0, 2, 4, 6, 4, 3, 4, 5, 0,\n",
              "       5, 2, 1, 3, 1, 4, 1, 1, 7, 0, 1, 5, 2, 1, 2, 8, 7, 0, 6, 4, 8, 8,\n",
              "       5, 1, 8, 4, 5, 8, 7, 9, 8, 6, 0, 6, 2, 0, 7, 9, 8, 9, 5, 2, 7, 7,\n",
              "       1, 8, 7, 4, 3, 8, 3, 5, 6, 0, 0, 3, 0, 5, 0, 0, 4, 1, 2, 8, 4, 5,\n",
              "       9, 6, 3, 1, 8, 8, 4, 2, 3, 8, 9, 8, 8, 5, 0, 6, 3, 3, 7, 1, 6, 4,\n",
              "       1, 2, 1, 1, 6, 4, 7, 4, 8, 3, 4, 0, 5, 1, 9, 4, 5, 7, 6, 3, 7, 0,\n",
              "       5, 9, 7, 5, 9, 7, 4, 2, 1, 9, 0, 7, 5, 8, 3, 6, 3, 9, 6, 9, 5, 0,\n",
              "       1, 5, 5, 8, 3, 3, 6, 2, 6, 5])"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cEBuyK2RCzQ1",
        "outputId": "8b4de396-9a81-4831-980d-9ad10141d9ac"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([6, 9, 3, 7, 2, 1, 5, 2, 5, 2, 1, 9, 4, 0, 4, 2, 3, 7, 8, 8, 4, 3,\n",
              "       9, 7, 5, 6, 3, 5, 6, 3, 4, 9, 1, 4, 4, 6, 9, 4, 7, 6, 6, 9, 1, 3,\n",
              "       6, 1, 3, 0, 6, 5, 5, 1, 9, 5, 6, 0, 9, 0, 0, 1, 0, 4, 5, 2, 4, 5,\n",
              "       7, 0, 7, 5, 9, 5, 5, 4, 7, 0, 4, 5, 5, 9, 9, 0, 2, 3, 8, 0, 6, 4,\n",
              "       4, 9, 1, 2, 8, 3, 5, 2, 9, 0, 4, 4, 4, 3, 5, 3, 1, 3, 5, 9, 4, 2,\n",
              "       7, 7, 4, 4, 1, 9, 2, 7, 8, 7, 2, 6, 9, 4, 0, 7, 2, 7, 5, 8, 7, 5,\n",
              "       7, 7, 0, 6, 6, 4, 2, 8, 0, 9, 4, 6, 9, 9, 6, 9, 0, 3, 5, 6, 6, 0,\n",
              "       6, 4, 3, 9, 3, 9, 7, 2, 9, 0, 4, 5, 3, 6, 5, 9, 9, 8, 4, 2, 1, 3,\n",
              "       7, 7, 2, 2, 3, 9, 8, 0, 3, 2, 2, 5, 6, 9, 9, 4, 1, 5, 4, 2, 3, 6,\n",
              "       4, 8, 5, 9, 5, 7, 8, 9, 4, 8, 1, 5, 4, 4, 9, 6, 1, 8, 6, 0, 4, 5,\n",
              "       2, 7, 4, 6, 4, 5, 6, 0, 3, 2, 3, 6, 7, 1, 5, 1, 4, 7, 6, 8, 8, 5,\n",
              "       5, 1, 6, 2, 8, 8, 9, 9, 7, 6, 2, 2, 2, 3, 4, 8, 8, 3, 6, 0, 9, 7,\n",
              "       7, 0, 1, 0, 4, 5, 1, 5, 3, 6, 0, 4, 1, 0, 0, 3, 6, 5, 9, 7, 3, 5,\n",
              "       5, 9, 9, 8, 5, 3, 3, 2, 0, 5, 8, 3, 4, 0, 2, 4, 6, 4, 3, 4, 5, 0,\n",
              "       5, 2, 1, 3, 1, 4, 1, 1, 7, 0, 1, 5, 2, 1, 2, 8, 7, 0, 6, 4, 8, 8,\n",
              "       5, 1, 8, 4, 5, 8, 7, 9, 8, 5, 0, 6, 2, 0, 7, 9, 8, 9, 5, 2, 7, 7,\n",
              "       1, 8, 7, 4, 3, 8, 3, 5, 6, 0, 0, 3, 0, 5, 0, 0, 4, 1, 2, 8, 4, 5,\n",
              "       9, 6, 3, 1, 8, 8, 4, 2, 3, 8, 9, 8, 8, 5, 0, 6, 3, 3, 7, 1, 6, 4,\n",
              "       1, 2, 1, 1, 6, 4, 7, 4, 8, 3, 4, 0, 5, 1, 9, 4, 5, 7, 6, 3, 7, 0,\n",
              "       5, 9, 7, 5, 9, 7, 4, 2, 1, 9, 0, 7, 5, 3, 3, 6, 3, 9, 6, 9, 5, 0,\n",
              "       1, 5, 5, 8, 3, 3, 6, 2, 6, 5])"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score,confusion_matrix\n",
        "confusion_matrix(y_pred,y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JFVA2FcsC1Tq",
        "outputId": "b2e38d4a-ce0d-438d-dd82-92dc2469732d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[43,  0,  0,  0,  0,  0,  0,  0,  0,  0],\n",
              "       [ 0, 37,  0,  0,  0,  0,  0,  0,  0,  0],\n",
              "       [ 0,  0, 38,  0,  0,  0,  0,  0,  0,  0],\n",
              "       [ 0,  0,  0, 45,  0,  0,  0,  0,  0,  0],\n",
              "       [ 0,  0,  0,  0, 55,  0,  0,  0,  0,  0],\n",
              "       [ 0,  0,  0,  0,  0, 58,  0,  0,  0,  1],\n",
              "       [ 0,  0,  0,  0,  0,  1, 45,  0,  0,  0],\n",
              "       [ 0,  0,  0,  0,  0,  0,  0, 40,  0,  1],\n",
              "       [ 0,  0,  0,  1,  0,  0,  0,  0, 37,  0],\n",
              "       [ 0,  0,  0,  0,  0,  0,  0,  1,  1, 46]])"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy_score(y_pred,y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_sFLvAUKC4_p",
        "outputId": "0fca714b-3a0c-4763-a130-0fad5e4894c5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9866666666666667"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "ixQmZRp2C74q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "dyxEbwLFDRz4"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}