Logistic_Regression_multiclass.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "20ce4be9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline \n",
    "from sklearn.datasets import load_digits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1cf09c86",
   "metadata": {},
   "outputs": [],
   "source": [
    "digits = load_digits()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7ec518bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['DESCR', 'data', 'feature_names', 'frame', 'images', 'target', 'target_names']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(digits)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b2f2da93",
   "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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "digits.data[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ec8fca38",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 480x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 480x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZoAAAGkCAYAAAAIduO+AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAYlUlEQVR4nO3df2yUhR3H8c9B7aHYHoIU23BAg0R+FAq2zBVw/gCbNEgg25guyMqYyzoLgo2Jq/4B+8WxP7bgwmxWRqrEYcky+bFsgCWD4mK60cbODg3CQHsorIHIHXTZEdtnf3mxYn88R788PNf3K3my3e05n0+I4b2nvfYCjuM4AgDAyDCvBwAA0huhAQCYIjQAAFOEBgBgitAAAEwRGgCAKUIDADBFaAAApggNAMAUoQEAmEqb0Lz00kvKz8/XiBEjVFRUpDfffNPrSf06evSolixZory8PAUCAe3Zs8frSQMSiUQ0d+5cZWVlKScnR8uWLdOJEye8njUgNTU1mjVrlrKzs5Wdna2SkhLt37/f61muRSIRBQIBrV+/3usp/dq4caMCgUCP46677vJ61oB89NFHeuKJJzRmzBjddtttmj17tlpaWrye1a9JkyZd82ceCARUWVnpyZ60CM2uXbu0fv16vfDCC3r77bd1//33q6ysTO3t7V5P61NnZ6cKCwu1detWr6e40tjYqMrKSjU1NamhoUGffvqpSktL1dnZ6fW0fo0fP16bN29Wc3Ozmpub9fDDD2vp0qU6fvy419MG7NixY6qtrdWsWbO8njJgM2bM0Llz55JHW1ub15P69cknn2j+/Pm65ZZbtH//fr377rv65S9/qVGjRnk9rV/Hjh3r8efd0NAgSVq+fLk3g5w08JWvfMWpqKjo8dzUqVOdH/3oRx4tck+Ss3v3bq9npKSjo8OR5DQ2Nno9JSV33HGH87vf/c7rGQNy+fJlZ8qUKU5DQ4PzwAMPOOvWrfN6Ur82bNjgFBYWej3Dteeee85ZsGCB1zMGxbp165zJkyc73d3dnlzf93c0V69eVUtLi0pLS3s8X1paqrfeesujVUNLLBaTJI0ePdrjJe50dXWpvr5enZ2dKikp8XrOgFRWVmrx4sVatGiR11NcOXnypPLy8pSfn6/HH39cp0+f9npSv/bt26fi4mItX75cOTk5mjNnjrZt2+b1LNeuXr2qV199VatXr1YgEPBkg+9Dc+HCBXV1dWncuHE9nh83bpzOnz/v0aqhw3EcVVVVacGCBSooKPB6zoC0tbXp9ttvVzAYVEVFhXbv3q3p06d7Patf9fX1amlpUSQS8XqKK/fdd5927NihgwcPatu2bTp//rzmzZunixcvej2tT6dPn1ZNTY2mTJmigwcPqqKiQk8//bR27Njh9TRX9uzZo0uXLmnVqlWebcjw7MqD7IuldhzHs3oPJWvWrNE777yjv/3tb15PGbB77rlHra2tunTpkv74xz+qvLxcjY2NN3VsotGo1q1bpzfeeEMjRozweo4rZWVlyf8+c+ZMlZSUaPLkyXrllVdUVVXl4bK+dXd3q7i4WJs2bZIkzZkzR8ePH1dNTY2+853veLxu4LZv366ysjLl5eV5tsH3dzR33nmnhg8ffs3dS0dHxzV3ORhca9eu1b59+3T48GGNHz/e6zkDlpmZqbvvvlvFxcWKRCIqLCzUiy++6PWsPrW0tKijo0NFRUXKyMhQRkaGGhsb9etf/1oZGRnq6uryeuKAjRw5UjNnztTJkye9ntKn3Nzca/7Px7Rp0276Nxl93ocffqhDhw7pySef9HSH70OTmZmpoqKi5LsqPtPQ0KB58+Z5tCq9OY6jNWvW6PXXX9df//pX5efnez3pujiOo0Qi4fWMPi1cuFBtbW1qbW1NHsXFxVqxYoVaW1s1fPhwrycOWCKR0Hvvvafc3Fyvp/Rp/vz517xt//3339fEiRM9WuReXV2dcnJytHjxYk93pMWXzqqqqrRy5UoVFxerpKREtbW1am9vV0VFhdfT+nTlyhWdOnUq+fjMmTNqbW3V6NGjNWHCBA+X9a2yslI7d+7U3r17lZWVlbybDIVCuvXWWz1e17fnn39eZWVlCofDunz5surr63XkyBEdOHDA62l9ysrKuuZ7YCNHjtSYMWNu+u+NPfvss1qyZIkmTJigjo4O/exnP1M8Hld5ebnX0/r0zDPPaN68edq0aZO+9a1v6R//+Idqa2tVW1vr9bQB6e7uVl1dncrLy5WR4fFf9Z68183Ab37zG2fixIlOZmamc++99/rirbaHDx92JF1zlJeXez2tT1+2WZJTV1fn9bR+rV69OvnvydixY52FCxc6b7zxhtezUuKXtzc/9thjTm5urnPLLbc4eXl5zte//nXn+PHjXs8akD/96U9OQUGBEwwGnalTpzq1tbVeTxqwgwcPOpKcEydOeD3FCTiO43iTOADAUOD779EAAG5uhAYAYIrQAABMERoAgClCAwAwRWgAAKbSKjSJREIbN2686X/K+4v8ulvy73a/7pb8u92vuyX/br9ZdqfVz9HE43GFQiHFYjFlZ2d7PWfA/Lpb8u92v+6W/Lvdr7sl/26/WXan1R0NAODmQ2gAAKZu+G9a6+7u1scff6ysrKxB/7yYeDze4z/9wq+7Jf9u9+tuyb/b/bpb8u92692O4+jy5cvKy8vTsGG937fc8O/RnD17VuFw+EZeEgBgKBqN9vmZVDf8jiYrK+tGXxI+tmDBAq8npGznzp1eT0hJW1ub1xNS5vXnrgxV/f29fsNDw8crww3PP0fjOvjp3UmfN3LkSK8nwGf6+3udNwMAAEwRGgCAKUIDADBFaAAApggNAMAUoQEAmCI0AABThAYAYIrQAABMERoAgClCAwAwRWgAAKYIDQDAFKEBAJgiNAAAU4QGAGAqpdC89NJLys/P14gRI1RUVKQ333xzsHcBANKE69Ds2rVL69ev1wsvvKC3335b999/v8rKytTe3m6xDwDgc65D86tf/Urf+9739OSTT2ratGnasmWLwuGwampqLPYBAHzOVWiuXr2qlpYWlZaW9ni+tLRUb7311pe+JpFIKB6P9zgAAEOHq9BcuHBBXV1dGjduXI/nx40bp/Pnz3/payKRiEKhUPIIh8OprwUA+E5KbwYIBAI9HjuOc81zn6murlYsFkse0Wg0lUsCAHwqw83Jd955p4YPH37N3UtHR8c1dzmfCQaDCgaDqS8EAPiaqzuazMxMFRUVqaGhocfzDQ0Nmjdv3qAOAwCkB1d3NJJUVVWllStXqri4WCUlJaqtrVV7e7sqKios9gEAfM51aB577DFdvHhRP/nJT3Tu3DkVFBToL3/5iyZOnGixDwDgc65DI0lPPfWUnnrqqcHeAgBIQ/yuMwCAKUIDADBFaAAApggNAMAUoQEAmCI0AABThAYAYIrQAABMERoAgClCAwAwRWgAAKYIDQDAFKEBAJgiNAAAU4QGAGCK0AAATKX0wWfwn9mzZ3s9ISWHDx/2ekLKYrGY1xNSMmnSJK8nIM1wRwMAMEVoAACmCA0AwBShAQCYIjQAAFOEBgBgitAAAEwRGgCAKUIDADBFaAAApggNAMAUoQEAmCI0AABThAYAYIrQAABMERoAgClCAwAwRWgAAKYIDQDAlOvQHD16VEuWLFFeXp4CgYD27NljMAsAkC5ch6azs1OFhYXaunWrxR4AQJrJcPuCsrIylZWVWWwBAKQh16FxK5FIKJFIJB/H43HrSwIAbiLmbwaIRCIKhULJIxwOW18SAHATMQ9NdXW1YrFY8ohGo9aXBADcRMy/dBYMBhUMBq0vAwC4SfFzNAAAU67vaK5cuaJTp04lH585c0atra0aPXq0JkyYMKjjAAD+5zo0zc3Neuihh5KPq6qqJEnl5eV6+eWXB20YACA9uA7Ngw8+KMdxLLYAANIQ36MBAJgiNAAAU4QGAGCK0AAATBEaAIApQgMAMEVoAACmCA0AwBShAQCYIjQAAFOEBgBgitAAAEwRGgCAKUIDADBFaAAApggNAMCU6w8+gz8tW7bM6wkp+ec//+n1hJTt2bPH6wkp2bBhg9cTkGa4owEAmCI0AABThAYAYIrQAABMERoAgClCAwAwRWgAAKYIDQDAFKEBAJgiNAAAU4QGAGCK0AAATBEaAIApQgMAMEVoAACmCA0AwBShAQCYIjQAAFOuQhOJRDR37lxlZWUpJydHy5Yt04kTJ6y2AQDSgKvQNDY2qrKyUk1NTWpoaNCnn36q0tJSdXZ2Wu0DAPhchpuTDxw40ONxXV2dcnJy1NLSoq997WuDOgwAkB5cheaLYrGYJGn06NG9npNIJJRIJJKP4/H49VwSAOAzKb8ZwHEcVVVVacGCBSooKOj1vEgkolAolDzC4XCqlwQA+FDKoVmzZo3eeecdvfbaa32eV11drVgsljyi0WiqlwQA+FBKXzpbu3at9u3bp6NHj2r8+PF9nhsMBhUMBlMaBwDwP1ehcRxHa9eu1e7du3XkyBHl5+db7QIApAlXoamsrNTOnTu1d+9eZWVl6fz585KkUCikW2+91WQgAMDfXH2PpqamRrFYTA8++KByc3OTx65du6z2AQB8zvWXzgAAcIPfdQYAMEVoAACmCA0AwBShAQCYIjQAAFOEBgBgitAAAEwRGgCAKUIDADBFaAAApggNAMAUoQEAmCI0AABThAYAYIrQAABMERoAgClXH3wG/9qyZYvXE1LywQcfeD0hZX79M9+7d6/XE5BmuKMBAJgiNAAAU4QGAGCK0AAATBEaAIApQgMAMEVoAACmCA0AwBShAQCYIjQAAFOEBgBgitAAAEwRGgCAKUIDADBFaAAApggNAMAUoQEAmCI0AABThAYAYMpVaGpqajRr1ixlZ2crOztbJSUl2r9/v9U2AEAacBWa8ePHa/PmzWpublZzc7MefvhhLV26VMePH7faBwDwuQw3Jy9ZsqTH45///OeqqalRU1OTZsyYMajDAADpwVVoPq+rq0t/+MMf1NnZqZKSkl7PSyQSSiQSycfxeDzVSwIAfMj1mwHa2tp0++23KxgMqqKiQrt379b06dN7PT8SiSgUCiWPcDh8XYMBAP7iOjT33HOPWltb1dTUpB/+8IcqLy/Xu+++2+v51dXVisViySMajV7XYACAv7j+0llmZqbuvvtuSVJxcbGOHTumF198Ub/97W+/9PxgMKhgMHh9KwEAvnXdP0fjOE6P78EAAPB5ru5onn/+eZWVlSkcDuvy5cuqr6/XkSNHdODAAat9AACfcxWa//znP1q5cqXOnTunUCikWbNm6cCBA3rkkUes9gEAfM5VaLZv3261AwCQpvhdZwAAU4QGAGCK0AAATBEaAIApQgMAMEVoAACmCA0AwBShAQCYIjQAAFOEBgBgitAAAEwRGgCAKUIDADBFaAAApggNAMAUoQEAmHL1wWdD3ahRo7yekLL169d7PSEly5Yt83rCkLNq1SqvJyDNcEcDADBFaAAApggNAMAUoQEAmCI0AABThAYAYIrQAABMERoAgClCAwAwRWgAAKYIDQDAFKEBAJgiNAAAU4QGAGCK0AAATBEaAIApQgMAMEVoAACmris0kUhEgUDAtx8TDACwl3Jojh07ptraWs2aNWsw9wAA0kxKobly5YpWrFihbdu26Y477hjsTQCANJJSaCorK7V48WItWrSo33MTiYTi8XiPAwAwdGS4fUF9fb1aWlrU3Nw8oPMjkYh+/OMfux4GAEgPru5ootGo1q1bp9///vcaMWLEgF5TXV2tWCyWPKLRaEpDAQD+5OqOpqWlRR0dHSoqKko+19XVpaNHj2rr1q1KJBIaPnx4j9cEg0EFg8HBWQsA8B1XoVm4cKHa2tp6PPfd735XU6dO1XPPPXdNZAAAcBWarKwsFRQU9Hhu5MiRGjNmzDXPAwAg8ZsBAADGXL/r7IuOHDkyCDMAAOmKOxoAgClCAwAwRWgAAKYIDQDAFKEBAJgiNAAAU4QGAGCK0AAATBEaAIApQgMAMEVoAACmCA0AwBShAQCYIjQAAFOEBgBgitAAAExd9wefDSUbN270ekLK1q1b5/WEIWfZsmVeT0jJpUuXvJ6ANMMdDQDAFKEBAJgiNAAAU4QGAGCK0AAATBEaAIApQgMAMEVoAACmCA0AwBShAQCYIjQAAFOEBgBgitAAAEwRGgCAKUIDADBFaAAApggNAMAUoQEAmCI0AABTrkKzceNGBQKBHsddd91ltQ0AkAYy3L5gxowZOnToUPLx8OHDB3UQACC9uA5NRkYGdzEAgAFz/T2akydPKi8vT/n5+Xr88cd1+vTpPs9PJBKKx+M9DgDA0OEqNPfdd5927NihgwcPatu2bTp//rzmzZunixcv9vqaSCSiUCiUPMLh8HWPBgD4h6vQlJWV6Rvf+IZmzpypRYsW6c9//rMk6ZVXXun1NdXV1YrFYskjGo1e32IAgK+4/h7N540cOVIzZ87UyZMnez0nGAwqGAxez2UAAD52XT9Hk0gk9N577yk3N3ew9gAA0oyr0Dz77LNqbGzUmTNn9Pe//13f/OY3FY/HVV5ebrUPAOBzrr50dvbsWX3729/WhQsXNHbsWH31q19VU1OTJk6caLUPAOBzrkJTX19vtQMAkKb4XWcAAFOEBgBgitAAAEwRGgCAKUIDADBFaAAApggNAMAUoQEAmCI0AABThAYAYIrQAABMERoAgClCAwAwRWgAAKYIDQDAFKEBAJgKOI7j3MgLxuNxhUKhG3nJQTN79myvJ6Ts5Zdf9npCSgoLC72eMOTs3bvX6wkpq6ur83pCSvz8Zy5JsVhM2dnZvf7v3NEAAEwRGgCAKUIDADBFaAAApggNAMAUoQEAmCI0AABThAYAYIrQAABMERoAgClCAwAwRWgAAKYIDQDAFKEBAJgiNAAAU4QGAGCK0AAATBEaAIAp16H56KOP9MQTT2jMmDG67bbbNHv2bLW0tFhsAwCkgQw3J3/yySeaP3++HnroIe3fv185OTn697//rVGjRhnNAwD4navQ/OIXv1A4HFZdXV3yuUmTJg32JgBAGnH1pbN9+/apuLhYy5cvV05OjubMmaNt27b1+ZpEIqF4PN7jAAAMHa5Cc/r0adXU1GjKlCk6ePCgKioq9PTTT2vHjh29viYSiSgUCiWPcDh83aMBAP7hKjTd3d269957tWnTJs2ZM0c/+MEP9P3vf181NTW9vqa6ulqxWCx5RKPR6x4NAPAPV6HJzc3V9OnTezw3bdo0tbe39/qaYDCo7OzsHgcAYOhwFZr58+frxIkTPZ57//33NXHixEEdBQBIH65C88wzz6ipqUmbNm3SqVOntHPnTtXW1qqystJqHwDA51yFZu7cudq9e7dee+01FRQU6Kc//am2bNmiFStWWO0DAPicq5+jkaRHH31Ujz76qMUWAEAa4nedAQBMERoAgClCAwAwRWgAAKYIDQDAFKEBAJgiNAAAU4QGAGCK0AAATBEaAIApQgMAMEVoAACmCA0AwBShAQCYIjQAAFOEBgBgyvUHnw1lra2tXk9I2ezZs72ekBK/7pakjRs3ej0hJUuXLvV6Qso++OADryekZO/evV5PMMUdDQDAFKEBAJgiNAAAU4QGAGCK0AAATBEaAIApQgMAMEVoAACmCA0AwBShAQCYIjQAAFOEBgBgitAAAEwRGgCAKUIDADBFaAAApggNAMAUoQEAmCI0AABTrkIzadIkBQKBa47KykqrfQAAn8twc/KxY8fU1dWVfPyvf/1LjzzyiJYvXz7owwAA6cFVaMaOHdvj8ebNmzV58mQ98MADgzoKAJA+XIXm865evapXX31VVVVVCgQCvZ6XSCSUSCSSj+PxeKqXBAD4UMpvBtizZ48uXbqkVatW9XleJBJRKBRKHuFwONVLAgB8KOXQbN++XWVlZcrLy+vzvOrqasViseQRjUZTvSQAwIdS+tLZhx9+qEOHDun111/v99xgMKhgMJjKZQAAaSClO5q6ujrl5ORo8eLFg70HAJBmXIemu7tbdXV1Ki8vV0ZGyu8lAAAMEa5Dc+jQIbW3t2v16tUWewAAacb1LUlpaakcx7HYAgBIQ/yuMwCAKUIDADBFaAAApggNAMAUoQEAmCI0AABThAYAYIrQAABMERoAgClCAwAwRWgAAKYIDQDAFKEBAJgiNAAAU4QGAGDqhn9EJp9lAze6urq8npCy//73v15PSEk8Hvd6Qsr+97//eT1hSOrv7/WAc4P/5j979qzC4fCNvCQAwFA0GtX48eN7/d9veGi6u7v18ccfKysrS4FAYFD/2fF4XOFwWNFoVNnZ2YP6z7bk192Sf7f7dbfk3+1+3S35d7v1bsdxdPnyZeXl5WnYsN6/E3PDv3Q2bNiwPss3GLKzs331L8Nn/Lpb8u92v+6W/Lvdr7sl/2633B0Khfo9hzcDAABMERoAgKm0Ck0wGNSGDRsUDAa9nuKKX3dL/t3u192Sf7f7dbfk3+03y+4b/mYAAMDQklZ3NACAmw+hAQCYIjQAAFOEBgBgitAAAEwRGgCAKUIDADBFaAAApv4PS/lMVa4QCyoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 480x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 480x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 480x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.gray()\n",
    "for i in range(5):\n",
    "    plt.matshow(digits.images[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ea822c3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "digits.target[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ed381c80",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8f9bd693",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(digits.data, digits.target, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f3e599c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1437"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len (X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "fe32ec0e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "360"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len (X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "aa3e679f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "model = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "39492b0e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\nisch\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "1853a1f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9583333333333334"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "3466cdf6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1efb48ea7f0>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 480x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.matshow(digits.images[67])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "af683611",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "digits.target[67]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d433b30d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict([digits.data[67]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "6e5599fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(digits.data[:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "f8e105a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[39,  0,  0,  0,  0,  0,  0,  0,  0,  0],\n",
       "       [ 0, 30,  0,  1,  0,  0,  0,  0,  0,  0],\n",
       "       [ 0,  0, 33,  0,  0,  0,  0,  0,  0,  0],\n",
       "       [ 0,  0,  1, 36,  0,  1,  0,  0,  1,  1],\n",
       "       [ 0,  2,  0,  0, 31,  0,  0,  1,  0,  0],\n",
       "       [ 0,  1,  0,  0,  0, 41,  0,  0,  0,  0],\n",
       "       [ 0,  1,  0,  0,  0,  0, 42,  0,  0,  0],\n",
       "       [ 0,  0,  0,  0,  0,  0,  0, 34,  0,  0],\n",
       "       [ 0,  1,  0,  1,  0,  0,  0,  0, 29,  2],\n",
       "       [ 0,  0,  0,  0,  0,  0,  0,  0,  1, 30]], dtype=int64)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predicted = model.predict(X_test)\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "cm = confusion_matrix(y_test, y_predicted)\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "83672a30",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(95.72222222222221, 0.5, 'Truth')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1000x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sn\n",
    "plt.figure(figsize = (10,7))\n",
    "sn.heatmap(cm, annot=True)\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('Truth')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e3ea13f8",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}