MNISTClassificatoinSVM.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/jamal221/Machine_Learning_Project/blob/main/MNISTClassificatoinSVM.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "VVAqpGkTX1Do"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn import datasets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "hI7R7vNKX1D2"
      },
      "outputs": [],
      "source": [
        "data = datasets.load_digits()\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "type(data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wQG3-xoIH4-I",
        "outputId": "d98a906d-95e9-4b89-c4bd-3ddbef57d16f"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "sklearn.utils._bunch.Bunch"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "id": "J3DGaBawX1D4",
        "outputId": "7d97f59e-7c56-4589-fc7d-573c930f642b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7e5a3526ffa0>"
            ]
          },
          "metadata": {},
          "execution_count": 4
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.subplot()\n",
        "plt.imshow(data.images[11], cmap=plt.cm.gray_r)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.target[11]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K5LIdcxpIcU4",
        "outputId": "c76ea41a-9fc4-405b-d4a8-b6f09d37954f"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "counter=0\n",
        "dict1=dict()\n",
        "nums=data.target\n",
        "nums=set(nums)\n",
        "print(nums)\n",
        "for i in range(len(data.images)):\n",
        "  if data.target[i]==0:\n",
        "     counter+=1\n",
        "dict1[0]=counter\n",
        "print (dict1)\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bJWX18RkI7Nh",
        "outputId": "85447a0e-9373-46ab-86df-789dcb4e6d0f"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{0, 1, 2, 3, 4, 5, 6, 7, 8, 9}\n",
            "{0: 178}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# counet=0\n",
        "dict2=dict()\n",
        "nums=data.target\n",
        "nums=set(nums)\n",
        "nums=list(nums)\n",
        "# print(nums)\n",
        "for i in nums:\n",
        "  count=0\n",
        "  for j in range(len(data.images)):\n",
        "    if  data.target[j]==i:\n",
        "      count+=1\n",
        "  dict2[i]=count\n",
        "print(dict2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kcKcUoyC2B2T",
        "outputId": "0cf7ab39-25a3-48ed-ac63-26a28ed51159"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{0: 178, 1: 182, 2: 177, 3: 183, 4: 181, 5: 182, 6: 181, 7: 179, 8: 174, 9: 180}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "WsTMWR0TX1D7"
      },
      "outputs": [],
      "source": [
        "images_and_labels = list(zip(data.images, data.target))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "g_Kf6yHUX1D9",
        "outputId": "89bf5917-f4a8-407c-842c-3cc107808a99",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([[ 0.,  0.,  0., 10., 15.,  0.,  0.,  0.],\n",
              "        [ 0.,  0., 11., 15.,  3.,  0.,  0.,  0.],\n",
              "        [ 0.,  7., 15.,  4.,  0.,  0.,  0.,  0.],\n",
              "        [ 0., 12., 11.,  1.,  3.,  8.,  2.,  0.],\n",
              "        [ 0.,  4., 12., 15., 15., 16.,  9.,  0.],\n",
              "        [ 0.,  0.,  0.,  8., 16.,  8.,  2.,  0.],\n",
              "        [ 0.,  0.,  0., 10., 12.,  0.,  0.,  0.],\n",
              "        [ 0.,  0.,  0., 12.,  9.,  0.,  0.,  0.]]),\n",
              " 4)"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "images_and_labels[110]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "W3BgTJ-HX1D-",
        "outputId": "00ccf680-5d13-4fcb-acf5-d2e9552949a5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 345
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 9 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "for i, (image, label) in enumerate(images_and_labels[125:134]):\n",
        "    plt.subplot(2, 7, i+1)\n",
        "    plt.imshow(image, cmap=plt.cm.gray_r)\n",
        "    plt.title(label)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "U3-V3D9sX1EA",
        "outputId": "6f269daf-87a4-4667-924c-90fae3efda70",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1797, 8, 8)"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "np.shape(data.images)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "EZmTz10jX1EC",
        "outputId": "c6cff9fa-1f59-4e16-a021-ebbf81c3087d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1797"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "\n",
        "len(data.images)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "-3HbC_o6X1EE"
      },
      "outputs": [],
      "source": [
        "X = data.images.reshape((len(data.images), -1))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "LzRn5K1KX1EG",
        "outputId": "ec291ae2-1918-418e-867b-6a2239a72bce",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1797, 64)"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "np.shape(X)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "9jZ6dmzAX1EH",
        "outputId": "09e42885-1385-45e3-c1ca-17b5dd518671",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0.,  0.,  5., ...,  0.,  0.,  0.],\n",
              "       [ 0.,  0.,  0., ..., 10.,  0.,  0.],\n",
              "       [ 0.,  0.,  0., ..., 16.,  9.,  0.],\n",
              "       ...,\n",
              "       [ 0.,  0.,  1., ...,  6.,  0.,  0.],\n",
              "       [ 0.,  0.,  2., ..., 12.,  0.,  0.],\n",
              "       [ 0.,  0., 10., ..., 12.,  1.,  0.]])"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "X"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "_mo-sMvSX1EJ"
      },
      "outputs": [],
      "source": [
        "y = data.target"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "O9SmbRZYX1EK",
        "outputId": "c61aef1f-ea14-4ef2-c498-274986f6394c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1797"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "len(y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "Kf_5NLaEX1EL",
        "outputId": "70bb3e90-1965-4d45-bb52-617934a6761a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "sklearn.svm._classes.SVC"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "from sklearn.svm import SVC\n",
        "SVC"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "qDCkKzypX1EM"
      },
      "outputs": [],
      "source": [
        "svm_classifier = SVC()\n",
        "#SVC() is Constractor of SVC class"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "JqCqdF8-X1EM",
        "outputId": "a502bea7-dcee-41dc-8ded-8a5aa1531627",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC()"
            ],
            "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()</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()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "svm_classifier.fit(X, y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "F08r6ZqrX1EN",
        "outputId": "877f18cf-8f35-48cd-84b5-bc2d36ba4009",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 470
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, '1')"
            ]
          },
          "metadata": {},
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.subplot()\n",
        "plt.imshow(data.images[200], cmap=plt.cm.Greys_r)\n",
        "plt.title(data.target[200])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "feJJyFXdX1EP",
        "outputId": "75a19af2-3548-47d9-8b99-0541e694e1a2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1])"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "svm_classifier.predict([X[200]])"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Train_mount=int(len(data.images)*.76)\n",
        "Train_mount"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1xDvP_LZxei5",
        "outputId": "0a274f9b-b752-4835-df43-7e4b4f799e97"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1365"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Test_mount=len(data.images)-Train_mount\n",
        "Test_mount"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2_jSd3Fvx6zb",
        "outputId": "5ee01269-6ff2-4a9b-d67e-00a2bc07765c"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "432"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "9iEaxQXiX1EW",
        "outputId": "84ff9196-800e-4435-f9db-0586b7a85dfd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC()"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ],
      "source": [
        "svm_new = SVC()\n",
        "svm_new.fit(X[:Train_mount], y[:Train_mount])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "Ih7tyBEfX1EX"
      },
      "outputs": [],
      "source": [
        "pridcted_image=svm_new.predict(X[Train_mount:])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "wEdcqVVDX1EX"
      },
      "outputs": [],
      "source": [
        "Real_image=y[Train_mount:]"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "correct_val=0\n",
        "for i in range(Test_mount):\n",
        "  if pridcted_image[i]==Real_image[i]:\n",
        "    correct_val+=1\n",
        "print ((correct_val/Test_mount)*100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZyDnCTh6yeFS",
        "outputId": "d3ac40ed-3c57-43ce-cb6a-1da019e44fcd"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "95.13888888888889\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "corrected_val_list=list()\n",
        "for i in range(1,100):\n",
        "  prc=i/100\n",
        "  # print (prc)\n",
        "  Train_mount=int(len(data.images)*prc)\n",
        "  Test_mount=len(data.images)-Train_mount\n",
        "  svm_new = SVC()\n",
        "  svm_new.fit(X[:Train_mount], y[:Train_mount])\n",
        "  pridcted_image=svm_new.predict(X[Train_mount:])\n",
        "  Real_image=y[Train_mount:]\n",
        "  correct_val=0\n",
        "  for j in range(Test_mount):\n",
        "    if pridcted_image[j]==Real_image[j]:\n",
        "      correct_val+=1\n",
        "  corrected_val_list.append((correct_val/Test_mount)*100)\n"
      ],
      "metadata": {
        "id": "sodDrsWD1q1q"
      },
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(corrected_val_list)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ydt8RmrY8cEu",
        "outputId": "71f1e976-2a68-4e96-fc37-a83d754e1910"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[60.0, 77.1282633371169, 79.0137614678899, 80.41714947856316, 82.08430913348947, 82.89940828402366, 81.39952153110048, 81.01571946795647, 81.54034229828851, 81.21137206427689, 82.125, 81.79519595448798, 81.52173913043478, 81.63001293661061, 83.57329842931938, 84.63576158940397, 86.72922252010724, 87.38127544097694, 88.66758241758241, 89.221140472879, 90.0, 90.5848787446505, 91.61849710982659, 91.65446559297217, 92.80415430267063, 93.15789473684211, 92.98780487804879, 93.19938176197836, 93.96551724137932, 94.51510333863276, 94.75806451612904, 94.92635024549918, 94.93355481727575, 94.69250210614996, 95.03849443969204, 95.04778453518679, 94.88084730803178, 94.97757847533632, 95.07748404740201, 95.08804448563485, 95.19321394910462, 95.30201342281879, 95.1219512195122, 95.63058589870904, 95.75328614762387, 95.88053553038105, 95.90766002098636, 95.8288770053476, 95.85605234460196, 96.1067853170189, 96.14074914869465, 96.29200463499421, 96.09467455621302, 96.13059250302297, 96.04449938195303, 95.95448798988622, 95.98965071151359, 96.15894039735099, 95.79375848032564, 95.6884561891516, 95.72039942938659, 95.60761346998537, 95.78947368421052, 95.5177743431221, 95.23052464228935, 95.41734860883797, 95.95959595959596, 95.65972222222221, 95.51971326164875, 95.37037037037037, 95.01915708812261, 95.03968253968253, 95.47325102880659, 94.87179487179486, 94.88888888888889, 95.13888888888889, 94.92753623188406, 94.6969696969697, 94.17989417989418, 94.16666666666667, 93.85964912280701, 93.51851851851852, 93.13725490196079, 93.05555555555556, 92.5925925925926, 92.06349206349206, 92.3076923076923, 92.12962962962963, 93.93939393939394, 95.0, 95.06172839506173, 94.44444444444444, 96.03174603174604, 96.29629629629629, 97.77777777777777, 97.22222222222221, 100.0, 100.0, 100.0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(corrected_val_list, color='green', marker='o', linestyle='dashed',linewidth=.5, markersize=3)\n",
        "plt.grid()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "cxCcd_-po-uE",
        "outputId": "2432bf6b-9078-49b4-ac56-5910d5b1b6dc"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "svm_new.predict([X[948]])"
      ],
      "metadata": {
        "id": "nnvpJJlYJF1Q",
        "outputId": "f405279c-579e-49da-8b4e-302691d78ac2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([7])"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.target[9]"
      ],
      "metadata": {
        "id": "Qj4yFUnEJWcA",
        "outputId": "b5605873-58d2-4541-91a6-5f3568731e2a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "id": "xJeitmnhX1EY"
      },
      "outputs": [],
      "source": [
        "from sklearn import metrics"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "id": "Nvl9QqDuX1EZ",
        "outputId": "b4084e7e-6228-40b9-c2a1-01c82a081920",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00        79\n",
            "           1       0.99      1.00      0.99        80\n",
            "           2       1.00      1.00      1.00        77\n",
            "           3       0.99      1.00      0.99        79\n",
            "           4       1.00      1.00      1.00        83\n",
            "           5       0.99      1.00      0.99        82\n",
            "           6       1.00      1.00      1.00        80\n",
            "           7       1.00      1.00      1.00        80\n",
            "           8       1.00      0.99      0.99        76\n",
            "           9       1.00      0.98      0.99        81\n",
            "\n",
            "    accuracy                           1.00       797\n",
            "   macro avg       1.00      1.00      1.00       797\n",
            "weighted avg       1.00      1.00      1.00       797\n",
            "\n"
          ]
        }
      ],
      "source": [
        "pridect_val = svm_new.predict(X[1000:])\n",
        "real_val = y[1000:]\n",
        "print(metrics.classification_report(real_val, pridect_val))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "metadata": {
        "id": "IGmy9L-1X1EZ",
        "outputId": "b0491192-a019-4ce6-f9f8-50fec6424fd8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[79  0  0  0  0  0  0  0  0  0]\n",
            " [ 0 80  0  0  0  0  0  0  0  0]\n",
            " [ 0  0 77  0  0  0  0  0  0  0]\n",
            " [ 0  0  0 79  0  0  0  0  0  0]\n",
            " [ 0  0  0  0 83  0  0  0  0  0]\n",
            " [ 0  0  0  0  0 82  0  0  0  0]\n",
            " [ 0  0  0  0  0  0 80  0  0  0]\n",
            " [ 0  0  0  0  0  0  0 80  0  0]\n",
            " [ 0  1  0  0  0  0  0  0 75  0]\n",
            " [ 0  0  0  1  0  1  0  0  0 79]]\n"
          ]
        }
      ],
      "source": [
        "print(metrics.confusion_matrix(real_val, pridect_val))"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.6"
    },
    "colab": {
      "provenance": [],
      "include_colab_link": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}