Logistic Regression (Multiclass Classification).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7892b0ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings as wr\n",
    "wr.filterwarnings ('ignore')\n",
    "\n",
    "from sklearn.datasets import load_digits\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "digits = load_digits()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "75786031",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['DESCR', 'data', 'feature_names', 'frame', 'images', 'target', 'target_names']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(digits)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b6c910a1",
   "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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "digits.data[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1081e826",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1d435109f00>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZoAAAGkCAYAAAAIduO+AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAYhUlEQVR4nO3df2zUhf3H8dfRrgfD9vghhXYcLSqKgK1AgbDqBEFIgwT2ByMEswqbi+SYYGNi+s9gWcaxP7bhNlKBsULiGLhlRecGHTBbssyOUtIENEFQfhwidC5yV5rtML3P96/1uw5o+zn65sPn+nwkn8w7P8e9Ylyffq7XXsBxHEcAABgZ5PUAAEBmIzQAAFOEBgBgitAAAEwRGgCAKUIDADBFaAAApggNAMAUoQEAmCI0AABTGROarVu3qri4WIMHD9asWbN07Ngxryf16ujRo1q8eLEKCwsVCAS0f/9+ryf1STQa1YwZM5Sbm6v8/HwtXbpUp0+f9npWn9TU1KikpER5eXnKy8vT7NmzdeDAAa9nubZ582YFAgGtX7/e6ym92rhxowKBQLdj4sSJXs/qk08++UTPPfecRo4cqSFDhuixxx7T8ePHvZ7Vq+Li4pv+mQcCAUUiEU/2ZERo9u3bp6qqKm3YsEEnTpxQaWmpFi5cqLa2Nq+n9aijo0OlpaXaunWr11NcaWxsVCQSUVNTkw4dOqQvvvhCCxYsUEdHh9fTejV27Fht3rxZLS0tOn78uJ5++mktWbJE77//vtfT+qy5uVnbtm1TSUmJ11P6bPLkyfr000+7jr/+9a9eT+rV559/rvLycn3pS1/SgQMH9MEHH+jHP/6xhg8f7vW0XjU3N3f7533o0CFJ0rJly7wZ5GSAmTNnOpFIpOt2Z2enU1hY6ESjUQ9XuSPJqaur83pGWtra2hxJTmNjo9dT0jJ8+HDnl7/8pdcz+qS9vd2ZMGGCc+jQIeepp55y1q1b5/WkXm3YsMEpLS31eoZrr776qvPEE094PaNfrFu3znnwwQedVCrlyfP7/ormxo0bamlp0fz587vuGzRokObPn6/33nvPw2UDRzwelySNGDHC4yXudHZ2au/evero6NDs2bO9ntMnkUhEixYt6vbvux+cOXNGhYWFeuCBB7Ry5UpdvHjR60m9evvtt1VWVqZly5YpPz9fU6dO1Y4dO7ye5dqNGzf0xhtvaPXq1QoEAp5s8H1oPvvsM3V2dmr06NHd7h89erSuXLni0aqBI5VKaf369SovL9eUKVO8ntMnJ0+e1H333adgMKgXX3xRdXV1mjRpktezerV3716dOHFC0WjU6ymuzJo1S7t27dLBgwdVU1Ojc+fO6cknn1R7e7vX03r08ccfq6amRhMmTFB9fb3WrFmjl156Sbt37/Z6miv79+/XtWvX9Pzzz3u2IduzZ0ZGiEQiOnXqlC9ec/+PRx55RK2trYrH4/rd736nyspKNTY23tOxicViWrdunQ4dOqTBgwd7PceVioqKrr8uKSnRrFmzVFRUpDfffFPf+ta3PFzWs1QqpbKyMm3atEmSNHXqVJ06dUqvv/66KisrPV7Xdzt37lRFRYUKCws92+D7K5r7779fWVlZunr1arf7r169qjFjxni0amBYu3at3nnnHb377rsaO3as13P6LCcnRw899JCmT5+uaDSq0tJSvfbaa17P6lFLS4va2to0bdo0ZWdnKzs7W42NjfrZz36m7OxsdXZ2ej2xz4YNG6aHH35YZ8+e9XpKjwoKCm76j49HH33UFy/7/ceFCxd0+PBhffvb3/Z0h+9Dk5OTo+nTp+vIkSNd96VSKR05csQ3r7v7jeM4Wrt2rerq6vSXv/xF48eP93rSHUmlUkomk17P6NG8efN08uRJtba2dh1lZWVauXKlWltblZWV5fXEPrt+/bo++ugjFRQUeD2lR+Xl5Te9bf/DDz9UUVGRR4vcq62tVX5+vhYtWuTpjox46ayqqkqVlZUqKyvTzJkztWXLFnV0dGjVqlVeT+vR9evXu/1X3blz59Ta2qoRI0Zo3LhxHi7rWSQS0Z49e/TWW28pNze363thoVBIQ4YM8Xhdz6qrq1VRUaFx48apvb1de/bsUUNDg+rr672e1qPc3Nybvgc2dOhQjRw58p7/3tgrr7yixYsXq6ioSJcvX9aGDRuUlZWlFStWeD2tRy+//LK++tWvatOmTfrGN76hY8eOafv27dq+fbvX0/oklUqptrZWlZWVys72+Eu9J+91M/Dzn//cGTdunJOTk+PMnDnTaWpq8npSr959911H0k1HZWWl19N6dKvNkpza2lqvp/Vq9erVTlFRkZOTk+OMGjXKmTdvnvPnP//Z61lp8cvbm5cvX+4UFBQ4OTk5zle+8hVn+fLlztmzZ72e1Sd/+MMfnClTpjjBYNCZOHGis337dq8n9Vl9fb0jyTl9+rTXU5yA4ziON4kDAAwEvv8eDQDg3kZoAACmCA0AwBShAQCYIjQAAFOEBgBgKqNCk0wmtXHjxnv+p7z/l193S/7d7tfdkn+3+3W35N/t98rujPo5mkQioVAopHg8rry8PK/n9Jlfd0v+3e7X3ZJ/t/t1t+Tf7ffK7oy6ogEA3HsIDQDA1F3/TWupVEqXL19Wbm5uv3/aWyKR6Pa/fuHX3ZJ/t/t1t+Tf7X7dLfl3u/Vux3HU3t6uwsJCDRp0++uWu/49mkuXLikcDt/NpwQAGIrFYj1+JtVdv6LJzc29208JSX/84x+9npAWP33I1P9as2aN1xOAu6K3r+t3PTT9/XIZ+mbo0KFeT0jLvf75NgB6/7rOmwEAAKYIDQDAFKEBAJgiNAAAU4QGAGCK0AAATBEaAIApQgMAMEVoAACmCA0AwBShAQCYIjQAAFOEBgBgitAAAEwRGgCAKUIDADCVVmi2bt2q4uJiDR48WLNmzdKxY8f6excAIEO4Ds2+fftUVVWlDRs26MSJEyotLdXChQvV1tZmsQ8A4HOuQ/OTn/xEL7zwglatWqVJkybp9ddf15e//GX96le/stgHAPA5V6G5ceOGWlpaNH/+/P//AwYN0vz58/Xee+/d8jHJZFKJRKLbAQAYOFyF5rPPPlNnZ6dGjx7d7f7Ro0frypUrt3xMNBpVKBTqOsLhcPprAQC+Y/6us+rqasXj8a4jFotZPyUA4B6S7ebk+++/X1lZWbp69Wq3+69evaoxY8bc8jHBYFDBYDD9hQAAX3N1RZOTk6Pp06fryJEjXfelUikdOXJEs2fP7vdxAAD/c3VFI0lVVVWqrKxUWVmZZs6cqS1btqijo0OrVq2y2AcA8DnXoVm+fLn+8Y9/6Hvf+56uXLmixx9/XAcPHrzpDQIAAEhphEaS1q5dq7Vr1/b3FgBABuJ3nQEATBEaAIApQgMAMEVoAACmCA0AwBShAQCYIjQAAFOEBgBgitAAAEwRGgCAKUIDADBFaAAApggNAMAUoQEAmCI0AABThAYAYCrgOI5zN58wkUgoFArdzaeEpPPnz3s9IS1FRUVeTxhwLly44PWEtBUXF3s9YUCKx+PKy8u77d/nigYAYIrQAABMERoAgClCAwAwRWgAAKYIDQDAFKEBAJgiNAAAU4QGAGCK0AAATBEaAIApQgMAMEVoAACmCA0AwBShAQCYIjQAAFOEBgBgitAAAEwRGgCAKdehOXr0qBYvXqzCwkIFAgHt37/fYBYAIFO4Dk1HR4dKS0u1detWiz0AgAyT7fYBFRUVqqiosNgCAMhArkPjVjKZVDKZ7LqdSCSsnxIAcA8xfzNANBpVKBTqOsLhsPVTAgDuIeahqa6uVjwe7zpisZj1UwIA7iHmL50Fg0EFg0HrpwEA3KP4ORoAgCnXVzTXr1/X2bNnu26fO3dOra2tGjFihMaNG9ev4wAA/uc6NMePH9fcuXO7bldVVUmSKisrtWvXrn4bBgDIDK5DM2fOHDmOY7EFAJCB+B4NAMAUoQEAmCI0AABThAYAYIrQAABMERoAgClCAwAwRWgAAKYIDQDAFKEBAJgiNAAAU4QGAGCK0AAATBEaAIApQgMAMEVoAACmXH/wGfzp2rVrXk9IS1FRkdcT0haPx72ekJaGhgavJ6Rt2LBhXk9Ii1///9lXXNEAAEwRGgCAKUIDADBFaAAApggNAMAUoQEAmCI0AABThAYAYIrQAABMERoAgClCAwAwRWgAAKYIDQDAFKEBAJgiNAAAU4QGAGCK0AAATBEaAIApV6GJRqOaMWOGcnNzlZ+fr6VLl+r06dNW2wAAGcBVaBobGxWJRNTU1KRDhw7piy++0IIFC9TR0WG1DwDgc9luTj548GC327t27VJ+fr5aWlr0ta99rV+HAQAyg6vQ/K94PC5JGjFixG3PSSaTSiaTXbcTicSdPCUAwGfSfjNAKpXS+vXrVV5erilTptz2vGg0qlAo1HWEw+F0nxIA4ENphyYSiejUqVPau3dvj+dVV1crHo93HbFYLN2nBAD4UFovna1du1bvvPOOjh49qrFjx/Z4bjAYVDAYTGscAMD/XIXGcRx997vfVV1dnRoaGjR+/HirXQCADOEqNJFIRHv27NFbb72l3NxcXblyRZIUCoU0ZMgQk4EAAH9z9T2ampoaxeNxzZkzRwUFBV3Hvn37rPYBAHzO9UtnAAC4we86AwCYIjQAAFOEBgBgitAAAEwRGgCAKUIDADBFaAAApggNAMAUoQEAmCI0AABThAYAYIrQAABMERoAgClCAwAwRWgAAKYIDQDAlKsPPoN/nT9/3usJaSktLfV6QtpCoZDXE9LS2trq9YS0Xbt2zesJuAWuaAAApggNAMAUoQEAmCI0AABThAYAYIrQAABMERoAgClCAwAwRWgAAKYIDQDAFKEBAJgiNAAAU4QGAGCK0AAATBEaAIApQgMAMEVoAACmCA0AwBShAQCYchWampoalZSUKC8vT3l5eZo9e7YOHDhgtQ0AkAFchWbs2LHavHmzWlpadPz4cT399NNasmSJ3n//fat9AACfy3Zz8uLFi7vd/uEPf6iamho1NTVp8uTJ/ToMAJAZXIXmv3V2duq3v/2tOjo6NHv27Nuel0wmlUwmu24nEol0nxIA4EOu3wxw8uRJ3XfffQoGg3rxxRdVV1enSZMm3fb8aDSqUCjUdYTD4TsaDADwF9eheeSRR9Ta2qq///3vWrNmjSorK/XBBx/c9vzq6mrF4/GuIxaL3dFgAIC/uH7pLCcnRw899JAkafr06WpubtZrr72mbdu23fL8YDCoYDB4ZysBAL51xz9Hk0qlun0PBgCA/+bqiqa6uloVFRUaN26c2tvbtWfPHjU0NKi+vt5qHwDA51yFpq2tTd/85jf16aefKhQKqaSkRPX19XrmmWes9gEAfM5VaHbu3Gm1AwCQofhdZwAAU4QGAGCK0AAATBEaAIApQgMAMEVoAACmCA0AwBShAQCYIjQAAFOEBgBgitAAAEwRGgCAKUIDADBFaAAApggNAMAUoQEAmHL1wWfwr6VLl3o9IS1z5szxekLaHn/8ca8npOWnP/2p1xMGnC1btng9wRRXNAAAU4QGAGCK0AAATBEaAIApQgMAMEVoAACmCA0AwBShAQCYIjQAAFOEBgBgitAAAEwRGgCAKUIDADBFaAAApggNAMAUoQEAmCI0AABThAYAYOqOQrN582YFAgGtX7++n+YAADJN2qFpbm7Wtm3bVFJS0p97AAAZJq3QXL9+XStXrtSOHTs0fPjw/t4EAMggaYUmEolo0aJFmj9/fq/nJpNJJRKJbgcAYODIdvuAvXv36sSJE2pubu7T+dFoVN///vddDwMAZAZXVzSxWEzr1q3Tr3/9aw0ePLhPj6murlY8Hu86YrFYWkMBAP7k6oqmpaVFbW1tmjZtWtd9nZ2dOnr0qH7xi18omUwqKyur22OCwaCCwWD/rAUA+I6r0MybN08nT57sdt+qVas0ceJEvfrqqzdFBgAAV6HJzc3VlClTut03dOhQjRw58qb7AQCQ+M0AAABjrt919r8aGhr6YQYAIFNxRQMAMEVoAACmCA0AwBShAQCYIjQAAFOEBgBgitAAAEwRGgCAKUIDADBFaAAApggNAMAUoQEAmCI0AABThAYAYIrQAABMERoAgKk7/uAzwBIfrAc3iouLvZ6AW+CKBgBgitAAAEwRGgCAKUIDADBFaAAApggNAMAUoQEAmCI0AABThAYAYIrQAABMERoAgClCAwAwRWgAAKYIDQDAFKEBAJgiNAAAU4QGAGCK0AAATBEaAIApV6HZuHGjAoFAt2PixIlW2wAAGSDb7QMmT56sw4cP//8fkO36jwAADCCuK5Gdna0xY8ZYbAEAZCDX36M5c+aMCgsL9cADD2jlypW6ePFij+cnk0klEoluBwBg4HAVmlmzZmnXrl06ePCgampqdO7cOT355JNqb2+/7WOi0ahCoVDXEQ6H73g0AMA/XIWmoqJCy5YtU0lJiRYuXKg//elPunbtmt58883bPqa6ulrxeLzriMVidzwaAOAfd/Sd/GHDhunhhx/W2bNnb3tOMBhUMBi8k6cBAPjYHf0czfXr1/XRRx+poKCgv/YAADKMq9C88soramxs1Pnz5/W3v/1NX//615WVlaUVK1ZY7QMA+Jyrl84uXbqkFStW6J///KdGjRqlJ554Qk1NTRo1apTVPgCAz7kKzd69e612AAAyFL/rDABgitAAAEwRGgCAKUIDADBFaAAApggNAMAUoQEAmCI0AABThAYAYIrQAABMERoAgClCAwAwRWgAAKYIDQDAFKEBAJgiNAAAU64++Az+tWTJEq8npCUej3s9IW0bN270esKAs3//fq8n4Ba4ogEAmCI0AABThAYAYIrQAABMERoAgClCAwAwRWgAAKYIDQDAFKEBAJgiNAAAU4QGAGCK0AAATBEaAIApQgMAMEVoAACmCA0AwBShAQCYIjQAAFOuQ/PJJ5/oueee08iRIzVkyBA99thjOn78uMU2AEAGyHZz8ueff67y8nLNnTtXBw4c0KhRo3TmzBkNHz7cah8AwOdcheZHP/qRwuGwamtru+4bP358v48CAGQOVy+dvf322yorK9OyZcuUn5+vqVOnaseOHT0+JplMKpFIdDsAAAOHq9B8/PHHqqmp0YQJE1RfX681a9bopZde0u7du2/7mGg0qlAo1HWEw+E7Hg0A8A9XoUmlUpo2bZo2bdqkqVOn6jvf+Y5eeOEFvf7667d9THV1teLxeNcRi8XueDQAwD9chaagoECTJk3qdt+jjz6qixcv3vYxwWBQeXl53Q4AwMDhKjTl5eU6ffp0t/s+/PBDFRUV9esoAEDmcBWal19+WU1NTdq0aZPOnj2rPXv2aPv27YpEIlb7AAA+5yo0M2bMUF1dnX7zm99oypQp+sEPfqAtW7Zo5cqVVvsAAD7n6udoJOnZZ5/Vs88+a7EFAJCB+F1nAABThAYAYIrQAABMERoAgClCAwAwRWgAAKYIDQDAFKEBAJgiNAAAU4QGAGCK0AAATBEaAIApQgMAMEVoAACmCA0AwBShAQCYcv3BZ/CnuXPnej0hLevWrfN6woCze/duryekraGhwesJuAWuaAAApggNAMAUoQEAmCI0AABThAYAYIrQAABMERoAgClCAwAwRWgAAKYIDQDAFKEBAJgiNAAAU4QGAGCK0AAATBEaAIApQgMAMEVoAACmCA0AwBShAQCYchWa4uJiBQKBm45IJGK1DwDgc9luTm5ublZnZ2fX7VOnTumZZ57RsmXL+n0YACAzuArNqFGjut3evHmzHnzwQT311FP9OgoAkDlchea/3bhxQ2+88YaqqqoUCARue14ymVQymey6nUgk0n1KAIAPpf1mgP379+vatWt6/vnnezwvGo0qFAp1HeFwON2nBAD4UNqh2blzpyoqKlRYWNjjedXV1YrH411HLBZL9ykBAD6U1ktnFy5c0OHDh/X73/++13ODwaCCwWA6TwMAyABpXdHU1tYqPz9fixYt6u89AIAM4zo0qVRKtbW1qqysVHZ22u8lAAAMEK5Dc/jwYV28eFGrV6+22AMAyDCuL0kWLFggx3EstgAAMhC/6wwAYIrQAABMERoAgClCAwAwRWgAAKYIDQDAFKEBAJgiNAAAU4QGAGCK0AAATBEaAIApQgMAMEVoAACmCA0AwBShAQCYuusfkcln2Xjj3//+t9cT0pJIJLyeMOD861//8noCfKa3r+sB5y5/5b906ZLC4fDdfEoAgKFYLKaxY8fe9u/f9dCkUildvnxZubm5CgQC/fpnJxIJhcNhxWIx5eXl9eufbcmvuyX/bvfrbsm/2/26W/LvduvdjuOovb1dhYWFGjTo9t+JuesvnQ0aNKjH8vWHvLw8X/3L8B9+3S35d7tfd0v+3e7X3ZJ/t1vuDoVCvZ7DmwEAAKYIDQDAVEaFJhgMasOGDQoGg15PccWvuyX/bvfrbsm/2/26W/Lv9ntl911/MwAAYGDJqCsaAMC9h9AAAEwRGgCAKUIDADBFaAAApggNAMAUoQEAmCI0AABT/wcQNUN2lZPH1AAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 480x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.gray()\n",
    "plt.matshow(digits.images[5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "666388da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 480x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 480x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 480x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 480x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "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": 6,
   "id": "6330bdba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "digits.target[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b4cdb81d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split as tts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "df1da909",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = tts(digits.data, digits.target, test_size=0.2, random_state=42)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "491ac5bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "model = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "43d5aa91",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>LogisticRegression()</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\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "99411ed1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9694444444444444"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a6731346",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(digits.data[0:5])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16f74c86",
   "metadata": {},
   "source": [
    "## Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9c29fdd2",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predicted = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "186cf5b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[33,  0,  0,  0,  0,  0,  0,  0,  0,  0],\n",
       "       [ 0, 28,  0,  0,  0,  0,  0,  0,  0,  0],\n",
       "       [ 0,  0, 33,  0,  0,  0,  0,  0,  0,  0],\n",
       "       [ 0,  0,  0, 33,  0,  1,  0,  0,  0,  0],\n",
       "       [ 0,  1,  0,  0, 44,  0,  1,  0,  0,  0],\n",
       "       [ 0,  0,  1,  0,  0, 44,  1,  0,  0,  1],\n",
       "       [ 0,  0,  0,  0,  0,  1, 34,  0,  0,  0],\n",
       "       [ 0,  0,  0,  0,  0,  1,  0, 33,  0,  0],\n",
       "       [ 0,  0,  0,  0,  0,  1,  0,  0, 29,  0],\n",
       "       [ 0,  0,  0,  1,  0,  0,  0,  0,  1, 38]], dtype=int64)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "cm = confusion_matrix(y_test, y_predicted)\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6f341cb2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(95.72222222222221, 0.5, 'Truth')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "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": "markdown",
   "id": "77b44428",
   "metadata": {},
   "source": [
    "## Try with Iris Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c740dd84",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.datasets import load_iris\n",
    "\n",
    "# Load the iris dataset\n",
    "iris = load_iris()\n",
    "\n",
    "# Create a DataFrame with column names\n",
    "df = pd.DataFrame(data=iris.data, columns=iris.feature_names)\n",
    "\n",
    "# Add the target column to the DataFrame\n",
    "df['target'] = iris.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "6ce1b9da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
       "0                5.1               3.5                1.4               0.2   \n",
       "1                4.9               3.0                1.4               0.2   \n",
       "2                4.7               3.2                1.3               0.2   \n",
       "3                4.6               3.1                1.5               0.2   \n",
       "4                5.0               3.6                1.4               0.2   \n",
       "\n",
       "   target  \n",
       "0       0  \n",
       "1       0  \n",
       "2       0  \n",
       "3       0  \n",
       "4       0  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "fcc21472",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    50\n",
       "1    50\n",
       "2    50\n",
       "Name: target, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.target.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "411c61ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop('target', axis = 1)\n",
    "y = df[['target']]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9c83bde",
   "metadata": {},
   "source": [
    "#### Scaling "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "8c1e18e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "# Create an instance of the MinMaxScaler\n",
    "scaler = MinMaxScaler()\n",
    "\n",
    "# Apply min-max scaling to your features\n",
    "X_scaled = scaler.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "71a70cba",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = tts(X_scaled, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c914c9be",
   "metadata": {},
   "outputs": [],
   "source": [
    "model2 = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "4246f065",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>LogisticRegression()</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\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model2.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2ad17788",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9666666666666667"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model2.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "627c5f44",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model2.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "9ae102b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[10,  0,  0],\n",
       "       [ 0,  8,  1],\n",
       "       [ 0,  0, 11]], dtype=int64)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm2 = confusion_matrix(y_test, y_pred)\n",
    "cm2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "58459703",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(95.72222222222221, 0.5, 'Truth')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10,7))\n",
    "sn.heatmap(cm2, annot=True)\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('Truth')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fa346022",
   "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": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}