1  {
   2   "cells": [
   3    {
   4     "cell_type": "code",
   5     "execution_count": 1,
   6     "id": "42103e7f",
   7     "metadata": {},
   8     "outputs": [],
   9     "source": [
  10      "from sklearn.datasets import fetch_openml\n",
  11      "mnist= fetch_openml('mnist_784')"
  12     ]
  13    },
  14    {
  15     "cell_type": "code",
  16     "execution_count": 2,
  17     "id": "e7885600",
  18     "metadata": {},
  19     "outputs": [],
  20     "source": [
  21      "import numpy as np"
  22     ]
  23    },
  24    {
  25     "cell_type": "code",
  26     "execution_count": 42,
  27     "id": "9ab4b76b",
  28     "metadata": {},
  29     "outputs": [],
  30     "source": [
  31      "size=3000\n",
  32      "x , y = mnist['data'][size:2*size].to_numpy(), mnist['target'][size:2*size].to_numpy()\n",
  33      "train_size= int(size*0.8)\n",
  34      "x_train , y_train= x[:train_size].astype(np.int16), y[:train_size].astype(np.int16)\n",
  35      "x_test, y_test = x[train_size:].astype(np.int16), y[train_size:].astype(np.int16)"
  36     ]
  37    },
  38    {
  39     "cell_type": "code",
  40     "execution_count": 43,
  41     "id": "9df67d03",
  42     "metadata": {},
  43     "outputs": [],
  44     "source": [
  45      "from sklearn.linear_model import LinearRegression\n",
  46      "clfr= LinearRegression()"
  47     ]
  48    },
  49    {
  50     "cell_type": "code",
  51     "execution_count": 44,
  52     "id": "63e05c24",
  53     "metadata": {},
  54     "outputs": [
  55      {
  56       "data": {
  57        "text/plain": [
  58         "LinearRegression()"
  59        ]
  60       },
  61       "execution_count": 44,
  62       "metadata": {},
  63       "output_type": "execute_result"
  64      }
  65     ],
  66     "source": [
  67      "clfr.fit(x_train, y_train)"
  68     ]
  69    },
  70    {
  71     "cell_type": "code",
  72     "execution_count": 45,
  73     "id": "c6526d4e",
  74     "metadata": {},
  75     "outputs": [],
  76     "source": [
  77      "%matplotlib inline\n",
  78      "import matplotlib\n",
  79      "import matplotlib.pyplot as plt\n",
  80      "def draw(to_predict):\n",
  81      "    some_dig= x[to_predict]\n",
  82      "\n",
  83      "    some_dig_img= some_dig.reshape(28,28)\n",
  84      "    plt.imshow(some_dig_img)\n",
  85      "    "
  86     ]
  87    },
  88    {
  89     "cell_type": "code",
  90     "execution_count": 46,
  91     "id": "e0b89418",
  92     "metadata": {},
  93     "outputs": [],
  94     "source": [
  95      "def predict(to_predict):\n",
  96      "    draw(to_predict)\n",
  97      "    pre= clfr.predict([list(x[to_predict])])\n",
  98      "    return pre"
  99     ]
 100    },
 101    {
 102     "cell_type": "markdown",
 103     "id": "f71bba48",
 104     "metadata": {},
 105     "source": []
 106    },
 107    {
 108     "cell_type": "code",
 109     "execution_count": 57,
 110     "id": "926f7fff",
 111     "metadata": {},
 112     "outputs": [
 113      {
 114       "data": {
 115        "text/plain": [
 116         "6.125877097447713"
 117        ]
 118       },
 119       "execution_count": 57,
 120       "metadata": {},
 121       "output_type": "execute_result"
 122      },
 123      {
 124       "data": {
 125        "image/png": "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\n",
 126        "text/plain": [
 127         "<Figure size 432x288 with 1 Axes>"
 128        ]
 129       },
 130       "metadata": {
 131        "needs_background": "light"
 132       },
 133       "output_type": "display_data"
 134      }
 135     ],
 136     "source": [
 137      "predict(2490)[0]"
 138     ]
 139    },
 140    {
 141     "cell_type": "code",
 142     "execution_count": null,
 143     "id": "79b39530",
 144     "metadata": {},
 145     "outputs": [],
 146     "source": []
 147    }
 148   ],
 149   "metadata": {
 150    "kernelspec": {
 151     "display_name": "Python 3",
 152     "language": "python",
 153     "name": "python3"
 154    },
 155    "language_info": {
 156     "codemirror_mode": {
 157      "name": "ipython",
 158      "version": 3
 159     },
 160     "file_extension": ".py",
 161     "mimetype": "text/x-python",
 162     "name": "python",
 163     "nbconvert_exporter": "python",
 164     "pygments_lexer": "ipython3",
 165     "version": "3.8.8"
 166    }
 167   },
 168   "nbformat": 4,
 169   "nbformat_minor": 5
 170  }
 171