PCA digit.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e2533416",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_digits\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e75afc44",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "        [ 0.,  0.,  2., ..., 12.,  0.,  0.],\n",
       "        [ 0.,  0., 10., ..., 12.,  1.,  0.]]),\n",
       " 'target': array([0, 1, 2, ..., 8, 9, 8]),\n",
       " 'frame': None,\n",
       " 'feature_names': ['pixel_0_0',\n",
       "  'pixel_0_1',\n",
       "  'pixel_0_2',\n",
       "  'pixel_0_3',\n",
       "  'pixel_0_4',\n",
       "  'pixel_0_5',\n",
       "  'pixel_0_6',\n",
       "  'pixel_0_7',\n",
       "  'pixel_1_0',\n",
       "  'pixel_1_1',\n",
       "  'pixel_1_2',\n",
       "  'pixel_1_3',\n",
       "  'pixel_1_4',\n",
       "  'pixel_1_5',\n",
       "  'pixel_1_6',\n",
       "  'pixel_1_7',\n",
       "  'pixel_2_0',\n",
       "  'pixel_2_1',\n",
       "  'pixel_2_2',\n",
       "  'pixel_2_3',\n",
       "  'pixel_2_4',\n",
       "  'pixel_2_5',\n",
       "  'pixel_2_6',\n",
       "  'pixel_2_7',\n",
       "  'pixel_3_0',\n",
       "  'pixel_3_1',\n",
       "  'pixel_3_2',\n",
       "  'pixel_3_3',\n",
       "  'pixel_3_4',\n",
       "  'pixel_3_5',\n",
       "  'pixel_3_6',\n",
       "  'pixel_3_7',\n",
       "  'pixel_4_0',\n",
       "  'pixel_4_1',\n",
       "  'pixel_4_2',\n",
       "  'pixel_4_3',\n",
       "  'pixel_4_4',\n",
       "  'pixel_4_5',\n",
       "  'pixel_4_6',\n",
       "  'pixel_4_7',\n",
       "  'pixel_5_0',\n",
       "  'pixel_5_1',\n",
       "  'pixel_5_2',\n",
       "  'pixel_5_3',\n",
       "  'pixel_5_4',\n",
       "  'pixel_5_5',\n",
       "  'pixel_5_6',\n",
       "  'pixel_5_7',\n",
       "  'pixel_6_0',\n",
       "  'pixel_6_1',\n",
       "  'pixel_6_2',\n",
       "  'pixel_6_3',\n",
       "  'pixel_6_4',\n",
       "  'pixel_6_5',\n",
       "  'pixel_6_6',\n",
       "  'pixel_6_7',\n",
       "  'pixel_7_0',\n",
       "  'pixel_7_1',\n",
       "  'pixel_7_2',\n",
       "  'pixel_7_3',\n",
       "  'pixel_7_4',\n",
       "  'pixel_7_5',\n",
       "  'pixel_7_6',\n",
       "  'pixel_7_7'],\n",
       " 'target_names': array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]),\n",
       " '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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       " \n",
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       "         ...,\n",
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       " \n",
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       "         ...,\n",
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       "         [ 0.,  3., 13., ..., 11.,  5.,  0.],\n",
       "         [ 0.,  0.,  0., ..., 16.,  9.,  0.]],\n",
       " \n",
       "        ...,\n",
       " \n",
       "        [[ 0.,  0.,  1., ...,  1.,  0.,  0.],\n",
       "         [ 0.,  0., 13., ...,  2.,  1.,  0.],\n",
       "         [ 0.,  0., 16., ..., 16.,  5.,  0.],\n",
       "         ...,\n",
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       "         [ 0.,  0.,  2., ...,  6.,  0.,  0.]],\n",
       " \n",
       "        [[ 0.,  0.,  2., ...,  0.,  0.,  0.],\n",
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       "         [ 0.,  4., 16., ..., 16.,  7.,  0.],\n",
       "         ...,\n",
       "         [ 0.,  0.,  0., ..., 16.,  2.,  0.],\n",
       "         [ 0.,  0.,  4., ..., 16.,  2.,  0.],\n",
       "         [ 0.,  0.,  5., ..., 12.,  0.,  0.]],\n",
       " \n",
       "        [[ 0.,  0., 10., ...,  1.,  0.,  0.],\n",
       "         [ 0.,  2., 16., ...,  1.,  0.,  0.],\n",
       "         [ 0.,  0., 15., ..., 15.,  0.,  0.],\n",
       "         ...,\n",
       "         [ 0.,  4., 16., ..., 16.,  6.,  0.],\n",
       "         [ 0.,  8., 16., ..., 16.,  8.,  0.],\n",
       "         [ 0.,  1.,  8., ..., 12.,  1.,  0.]]]),\n",
       " '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\"}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = load_digits()\n",
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ed9b7fb0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['data', 'target', 'frame', 'feature_names', 'target_names', 'images', 'DESCR'])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = load_digits()\n",
    "dataset.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "32306ab0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1797, 64)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8f9605ea",
   "metadata": {},
   "outputs": [
    {
     "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.])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.data[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "82f48932",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.,  0., 10., 14.,  8.,  1.,  0.,  0.,  0.,  2., 16., 14.,  6.,\n",
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       "        5., 16., 16., 10.,  0.,  0.,  0.,  0., 12., 15., 15., 12.,  0.,\n",
       "        0.,  0.,  4., 16.,  6.,  4., 16.,  6.,  0.,  0.,  8., 16., 10.,\n",
       "        8., 16.,  8.,  0.,  0.,  1.,  8., 12., 14., 12.,  1.,  0.])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.data[1796]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7c05c181",
   "metadata": {},
   "outputs": [
    {
     "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",
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       "       [ 0.,  5.,  8.,  0.,  0.,  9.,  8.,  0.],\n",
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       "       [ 0.,  2., 14.,  5., 10., 12.,  0.,  0.],\n",
       "       [ 0.,  0.,  6., 13., 10.,  0.,  0.,  0.]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.data[0].reshape(8,8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b51a9c0a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x22a4d66ed10>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.gray()\n",
    "plt.matshow(dataset.data[0].reshape(8,8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f66ba040",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x22a4d7ab700>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.matshow(dataset.data[9].reshape(8,8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "81dff9b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "np.unique(dataset.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "1d1d7514",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.target[1500]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "fd9dcc77",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  </tbody>\n",
       "</table>\n",
       "<p>1797 rows × 64 columns</p>\n",
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      ],
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       "3     0.0  0.0   7.0  15.0  13.0   1.0  0.0  0.0  0.0  8.0  ...  9.0  0.0   \n",
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       "1792  0.0  0.0   4.0  10.0  13.0   6.0  0.0  0.0  0.0  1.0  ...  4.0  0.0   \n",
       "1793  0.0  0.0   6.0  16.0  13.0  11.0  1.0  0.0  0.0  0.0  ...  1.0  0.0   \n",
       "1794  0.0  0.0   1.0  11.0  15.0   1.0  0.0  0.0  0.0  0.0  ...  0.0  0.0   \n",
       "1795  0.0  0.0   2.0  10.0   7.0   0.0  0.0  0.0  0.0  0.0  ...  2.0  0.0   \n",
       "1796  0.0  0.0  10.0  14.0   8.0   1.0  0.0  0.0  0.0  2.0  ...  8.0  0.0   \n",
       "\n",
       "       56   57   58    59    60    61   62   63  \n",
       "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   3.0  11.0  16.0  9.0  0.0  \n",
       "3     0.0  0.0  7.0  13.0  13.0   9.0  0.0  0.0  \n",
       "4     0.0  0.0  0.0   2.0  16.0   4.0  0.0  0.0  \n",
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       "1792  0.0  0.0  2.0  14.0  15.0   9.0  0.0  0.0  \n",
       "1793  0.0  0.0  6.0  16.0  14.0   6.0  0.0  0.0  \n",
       "1794  0.0  0.0  2.0   9.0  13.0   6.0  0.0  0.0  \n",
       "1795  0.0  0.0  5.0  12.0  16.0  12.0  0.0  0.0  \n",
       "1796  0.0  1.0  8.0  12.0  14.0  12.0  1.0  0.0  \n",
       "\n",
       "[1797 rows x 64 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(dataset.data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "185a5980",
   "metadata": {},
   "outputs": [
    {
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       "[5 rows x 64 columns]"
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     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(dataset.data, columns=dataset.feature_names)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "aba56294",
   "metadata": {},
   "outputs": [
    {
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       "      <td>11.848080</td>\n",
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       "      <td>0.005565</td>\n",
       "      <td>1.993879</td>\n",
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       "      <td>5.557596</td>\n",
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       "      <td>11.809126</td>\n",
       "      <td>6.764051</td>\n",
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       "      <td>4.287388</td>\n",
       "      <td>5.666418</td>\n",
       "      <td>3.325775</td>\n",
       "      <td>1.037383</td>\n",
       "      <td>0.094222</td>\n",
       "      <td>3.196160</td>\n",
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       "      <td>4.919406</td>\n",
       "      <td>0.984401</td>\n",
       "      <td>0.023590</td>\n",
       "      <td>0.934302</td>\n",
       "      <td>5.103019</td>\n",
       "      <td>4.374694</td>\n",
       "      <td>4.933947</td>\n",
       "      <td>5.900623</td>\n",
       "      <td>4.090548</td>\n",
       "      <td>1.860122</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>10.000000</td>\n",
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       "      <td>13.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>4.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>16.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>16.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>16.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 64 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       pixel_0_0    pixel_0_1    pixel_0_2    pixel_0_3    pixel_0_4  \\\n",
       "count     1797.0  1797.000000  1797.000000  1797.000000  1797.000000   \n",
       "mean         0.0     0.303840     5.204786    11.835838    11.848080   \n",
       "std          0.0     0.907192     4.754826     4.248842     4.287388   \n",
       "min          0.0     0.000000     0.000000     0.000000     0.000000   \n",
       "25%          0.0     0.000000     1.000000    10.000000    10.000000   \n",
       "50%          0.0     0.000000     4.000000    13.000000    13.000000   \n",
       "75%          0.0     0.000000     9.000000    15.000000    15.000000   \n",
       "max          0.0     8.000000    16.000000    16.000000    16.000000   \n",
       "\n",
       "         pixel_0_5    pixel_0_6    pixel_0_7    pixel_1_0    pixel_1_1  ...  \\\n",
       "count  1797.000000  1797.000000  1797.000000  1797.000000  1797.000000  ...   \n",
       "mean      5.781859     1.362270     0.129661     0.005565     1.993879  ...   \n",
       "std       5.666418     3.325775     1.037383     0.094222     3.196160  ...   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000  ...   \n",
       "25%       0.000000     0.000000     0.000000     0.000000     0.000000  ...   \n",
       "50%       4.000000     0.000000     0.000000     0.000000     0.000000  ...   \n",
       "75%      11.000000     0.000000     0.000000     0.000000     3.000000  ...   \n",
       "max      16.000000    16.000000    15.000000     2.000000    16.000000  ...   \n",
       "\n",
       "         pixel_6_6    pixel_6_7    pixel_7_0    pixel_7_1    pixel_7_2  \\\n",
       "count  1797.000000  1797.000000  1797.000000  1797.000000  1797.000000   \n",
       "mean      3.725097     0.206455     0.000556     0.279354     5.557596   \n",
       "std       4.919406     0.984401     0.023590     0.934302     5.103019   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.000000     0.000000     0.000000     0.000000     1.000000   \n",
       "50%       1.000000     0.000000     0.000000     0.000000     4.000000   \n",
       "75%       7.000000     0.000000     0.000000     0.000000    10.000000   \n",
       "max      16.000000    13.000000     1.000000     9.000000    16.000000   \n",
       "\n",
       "         pixel_7_3    pixel_7_4    pixel_7_5    pixel_7_6    pixel_7_7  \n",
       "count  1797.000000  1797.000000  1797.000000  1797.000000  1797.000000  \n",
       "mean     12.089037    11.809126     6.764051     2.067891     0.364496  \n",
       "std       4.374694     4.933947     5.900623     4.090548     1.860122  \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000  \n",
       "25%      11.000000    10.000000     0.000000     0.000000     0.000000  \n",
       "50%      13.000000    14.000000     6.000000     0.000000     0.000000  \n",
       "75%      16.000000    16.000000    12.000000     2.000000     0.000000  \n",
       "max      16.000000    16.000000    16.000000    16.000000    16.000000  \n",
       "\n",
       "[8 rows x 64 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "56876c8a",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df\n",
    "y = dataset.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "11eefe3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.        , -0.33501649, -0.04308102, ..., -1.14664746,\n",
       "        -0.5056698 , -0.19600752],\n",
       "       [ 0.        , -0.33501649, -1.09493684, ...,  0.54856067,\n",
       "        -0.5056698 , -0.19600752],\n",
       "       [ 0.        , -0.33501649, -1.09493684, ...,  1.56568555,\n",
       "         1.6951369 , -0.19600752],\n",
       "       ...,\n",
       "       [ 0.        , -0.33501649, -0.88456568, ..., -0.12952258,\n",
       "        -0.5056698 , -0.19600752],\n",
       "       [ 0.        , -0.33501649, -0.67419451, ...,  0.8876023 ,\n",
       "        -0.5056698 , -0.19600752],\n",
       "       [ 0.        , -0.33501649,  1.00877481, ...,  0.8876023 ,\n",
       "        -0.26113572, -0.19600752]])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(X)\n",
    "X_scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "3bdd5122",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ec232fc2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9722222222222222"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "model = LogisticRegression()\n",
    "model.fit(X_train, y_train)\n",
    "model.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "36442814",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
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       "</table>\n",
       "<p>1797 rows × 64 columns</p>\n",
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       "1796        0.0        0.0        0.0        2.0  ...        8.0        0.0   \n",
       "\n",
       "      pixel_7_0  pixel_7_1  pixel_7_2  pixel_7_3  pixel_7_4  pixel_7_5  \\\n",
       "0           0.0        0.0        6.0       13.0       10.0        0.0   \n",
       "1           0.0        0.0        0.0       11.0       16.0       10.0   \n",
       "2           0.0        0.0        0.0        3.0       11.0       16.0   \n",
       "3           0.0        0.0        7.0       13.0       13.0        9.0   \n",
       "4           0.0        0.0        0.0        2.0       16.0        4.0   \n",
       "...         ...        ...        ...        ...        ...        ...   \n",
       "1792        0.0        0.0        2.0       14.0       15.0        9.0   \n",
       "1793        0.0        0.0        6.0       16.0       14.0        6.0   \n",
       "1794        0.0        0.0        2.0        9.0       13.0        6.0   \n",
       "1795        0.0        0.0        5.0       12.0       16.0       12.0   \n",
       "1796        0.0        1.0        8.0       12.0       14.0       12.0   \n",
       "\n",
       "      pixel_7_6  pixel_7_7  \n",
       "0           0.0        0.0  \n",
       "1           0.0        0.0  \n",
       "2           9.0        0.0  \n",
       "3           0.0        0.0  \n",
       "4           0.0        0.0  \n",
       "...         ...        ...  \n",
       "1792        0.0        0.0  \n",
       "1793        0.0        0.0  \n",
       "1794        0.0        0.0  \n",
       "1795        0.0        0.0  \n",
       "1796        1.0        0.0  \n",
       "\n",
       "[1797 rows x 64 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "46082429",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1797, 29)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "\n",
    "pca = PCA(0.95)\n",
    "X_pca = pca.fit_transform(X)\n",
    "X_pca.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "08feb7de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ -1.25946645,  21.27488348,  -9.46305462, ...,   3.67072108,\n",
       "         -0.9436689 ,  -1.13250195],\n",
       "       [  7.9576113 , -20.76869896,   4.43950604, ...,   2.18261819,\n",
       "         -0.51022719,   2.31354911],\n",
       "       [  6.99192297,  -9.95598641,   2.95855808, ...,   4.22882114,\n",
       "          2.1576573 ,   0.8379578 ],\n",
       "       ...,\n",
       "       [ 10.8012837 ,  -6.96025223,   5.59955453, ...,  -3.56866194,\n",
       "          1.82444444,   3.53885886],\n",
       "       [ -4.87210009,  12.42395362, -10.17086635, ...,   3.25330054,\n",
       "          0.95484174,  -0.93895602],\n",
       "       [ -0.34438963,   6.36554919,  10.77370849, ...,  -3.01636722,\n",
       "          1.29752723,   2.58810313]])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_pca"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "05a9418f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.14890594, 0.13618771, 0.11794594, 0.08409979, 0.05782415,\n",
       "       0.0491691 , 0.04315987, 0.03661373, 0.03353248, 0.03078806,\n",
       "       0.02372341, 0.02272697, 0.01821863, 0.01773855, 0.01467101,\n",
       "       0.01409716, 0.01318589, 0.01248138, 0.01017718, 0.00905617,\n",
       "       0.00889538, 0.00797123, 0.00767493, 0.00722904, 0.00695889,\n",
       "       0.00596081, 0.00575615, 0.00515158, 0.0048954 ])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.explained_variance_ratio_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "7524a30d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "29"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.n_components_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1e486a49",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_pca, X_test_pca, y_train, y_test = train_test_split(X_pca, y, test_size=0.2, random_state=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "6a8a8c77",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9694444444444444"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "model = LogisticRegression(max_iter=1000)\n",
    "model.fit(X_train_pca, y_train)\n",
    "model.score(X_test_pca, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "54bd7964",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1797, 2)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca = PCA(n_components=2)\n",
    "X_pca = pca.fit_transform(X)\n",
    "X_pca.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "47505686",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ -1.25947872,  21.27490618],\n",
       "       [  7.95762056, -20.76872864],\n",
       "       [  6.99194661,  -9.95602946],\n",
       "       ...,\n",
       "       [ 10.80125833,  -6.9601931 ],\n",
       "       [ -4.87213416,  12.42403679],\n",
       "       [ -0.34442441,   6.36563665]])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_pca"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "9f2ae1bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.14890594, 0.13618771])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.explained_variance_ratio_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "66788e99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6083333333333333"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_pca, X_test_pca, y_train, y_test = train_test_split(X_pca, y, test_size=0.2, random_state=30)\n",
    "\n",
    "model = LogisticRegression(max_iter=1000)\n",
    "model.fit(X_train_pca, y_train)\n",
    "model.score(X_test_pca, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "094606e6",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "codemirror_mode": {
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