digit_classify.ipynb

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    "colab": {
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      "authorship_tag": "ABX9TyNAJZjtc38SUXRxiPTLgzZc",
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      "name": "python3",
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      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
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        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Mathurshab2210/Ml_with_Mathur/blob/main/digit_classify.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": "z-Qys0-qQfWx"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "from sklearn.datasets import load_digits\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "digits=load_digits()"
      ],
      "metadata": {
        "id": "KYNDFkGrSjeo"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "digits"
      ],
      "metadata": {
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          "base_uri": "https://localhost:8080/"
        },
        "id": "6W-hZpElSnfo",
        "outputId": "a3e80769-abad-4311-9b5a-d93f2f7de193"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              " 'images': array([[[ 0.,  0.,  5., ...,  1.,  0.,  0.],\n",
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              " 'DESCR': \".. _digits_dataset:\\n\\nOptical recognition of handwritten digits dataset\\n--------------------------------------------------\\n\\n**Data Set Characteristics:**\\n\\n    :Number of Instances: 1797\\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\"}"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.gray()\n",
        "for i in range(4):\n",
        "  plt.matshow(digits.images[i])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "qaCNFOvtSqip",
        "outputId": "40b40eb6-b4f6-41ca-fc6f-c2f469156c23"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 288x288 with 1 Axes>"
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              "<Figure size 288x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 288x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 288x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "digits.target[:4]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sRdGDseTS_79",
        "outputId": "6310b518-ec85-408b-cc01-6056f6934e42"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 2, 3])"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df=pd.DataFrame(digits.data)"
      ],
      "metadata": {
        "id": "m06sRZ5fTdKm"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dir(digits)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WjtwpFEkTn__",
        "outputId": "44570a13-ff7c-4810-fa7b-158ea73c8c30"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['DESCR', 'data', 'feature_names', 'frame', 'images', 'target', 'target_names']"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['target']=digits.target"
      ],
      "metadata": {
        "id": "66l9UrQWUFrO"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 236
        },
        "id": "1jcyHpTkUQBM",
        "outputId": "f3361781-4a56-4da2-833a-b052883d5e91"
      },
      "execution_count": 15,
      "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]"
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              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-646c386c-2a83-4c7e-8cf5-32e2e95c6bb7 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-646c386c-2a83-4c7e-8cf5-32e2e95c6bb7');\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": [
        "x=df.drop('target',axis=1)\n",
        "y=df['target']"
      ],
      "metadata": {
        "id": "EPoF0IvzUU_D"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "metadata": {
        "id": "bRB1RwY8Uju-"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.2)"
      ],
      "metadata": {
        "id": "sWe0QZBqUt9Q"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IfjtIrTaU-Cq",
        "outputId": "228bf5ba-6699-416c-c590-b25d59591d59"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1437, 64)"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier"
      ],
      "metadata": {
        "id": "4h6JJ9tLVGDU"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model=RandomForestClassifier(n_estimators=150)"
      ],
      "metadata": {
        "id": "kRYnM5GTVSok"
      },
      "execution_count": 100,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(x_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7lSKgxsOVgl5",
        "outputId": "f7157e44-d50b-47cd-fed3-bd245b95e91b"
      },
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(n_estimators=150)"
            ]
          },
          "metadata": {},
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score"
      ],
      "metadata": {
        "id": "nkrVZwydVloO"
      },
      "execution_count": 102,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_pred_acc=model.predict(x_train)"
      ],
      "metadata": {
        "id": "8uLJ4ocPVyx9"
      },
      "execution_count": 103,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "score=accuracy_score(x_pred_acc,y_train)"
      ],
      "metadata": {
        "id": "QoD3XHL6V3TB"
      },
      "execution_count": 104,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "score"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n4t4gWTGV8o4",
        "outputId": "56cdec59-dd76-4d44-ed58-c096ec17d672"
      },
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {},
          "execution_count": 105
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.score(x_test,y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IIw1a0FtV93N",
        "outputId": "06cc5865-84dc-4e7c-c9e1-10929419e9d2"
      },
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9694444444444444"
            ]
          },
          "metadata": {},
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.predict(x_test[2:3])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tPBECunzWMVo",
        "outputId": "c7f777d9-dca7-4542-b7bc-057217a78433"
      },
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([4])"
            ]
          },
          "metadata": {},
          "execution_count": 107
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_test[2:3]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yXW3LFoxXUG8",
        "outputId": "c250dc35-13a3-4540-8003-ac494ccd0648"
      },
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "873    4\n",
              "Name: target, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 108
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "oIcseLHnXWs1"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}