confusion matrix plot via seaborn for SO

confusion_matrix_plot_via_seaborn_for_SO.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "cb800acd-a6e3-498a-8d60-5d61379435ae",
   "metadata": {},
   "source": [
    "### For SO https://stackoverflow.com/a/79406713/8508004 \"Why values in the second row of the confusion matrix plot are missing?\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5791b352-a986-4254-8f4a-ebeff2155b57",
   "metadata": {},
   "outputs": [],
   "source": [
    "%pip install scikit-learn -q"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0e861040-4a80-4ec0-80d2-ae4b27768fc8",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test = [0]*122 + [0]*27 + [1]*40 + [1]*42\n",
    "y_predicted = [0]*122 + [1]*27 + [0]*40 + [1]*42"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a7d7caba-4059-4019-9999-083226bf1680",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[122  27]\n",
      " [ 40  42]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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kZKTGjRunPn36KCIiIss5ZIeIiAgNGzZMjz/+uFauXOlwMUC69bUZP368hg8fng0ZOi8yMlIvv/yy+vTpo3Xr1jk0SZqcnKzFixerW7du+vbbb7OcS1xcnIYOHaqRI0faVQyQbv08/f777+rSpYt2796d5RzuZF5eXho4cKDpcytWrNCNGzdyJI8zZ87o3nvv1dChQ7VkyRKnigGStHHjRg0YMEDDhw/X9evXs5zXxYsX9eijj+r333936H0uSdu2bdPDDz+cI++xS5cu6fHHH9fYsWPt/h2TkJCgadOm6dFHH9XFixeznMO5c+fUp08fvfrqqw4VA6RbnykTJkxQly5dtHHjRqdzuHDhgh599FGNHj3a6WKAJJ08eVJTpkzRzp07nR7DUUuXLlXHjh31yy+/OFUMkG4Vhf755x+HCxEAACB7URAAAMBNpNf+wNFJquTkZA0bNky//PKLK9LSkiVL9Mgjj2jPnj1ZGufSpUt66qmnNGfOHKfHOHLkiB5//HFDGw17bd++Xf369VNMTIzTOWSHvXv3qmfPnvlyE+lDhw7pgQce0MKFC7M0TkJCgsaOHau33nrL4YnWFLGxsXryySe1bNkyp86/dOmS+vXr5/Yb6Hbu3FkFCxY0xOPj47M0OeuI69evu/TneMGCBerdu7dTBdgU165d04ABAxwumt4uIiJCgwYN0pkzZ5weIzPnz5/XI488ovDwcKfOP3TokHr37p1u2zF77N27Vw8++KC2bNni9BiSdPbsWQ0cOFCzZ892+NwLFy6ob9++Tn8dctOCBQv00ksv6dq1a7mdCgAAyAbsIQAAgJtI7w7XIkWKODTOZ599puXLl9vELBaLateurcqVK6tYsWLy9PTU5cuXtXv37gwnNxcuXKhXXnkl3bvVCxYsqLp16+quu+5SYGCgrFarLl++rPDwcNM7Pm/evKk33nhDwcHBatmypUOvKyIiQk8//bQuX75s+nxKL+6KFSsqKChIkZGROnXqlDZt2qTExMTU4w4dOqRXXnlFNWrUcOj62WXnzp168sknM5xcK1KkiOrXr68SJUooKChIiYmJunbtmk6fPq3du3dnaWIuOx0+fFj9+vVLd1WGh4eHqlevrrvvvluBgYHy8fFRRESEDh06pB07dpi+73799VcFBgY6vArCarVq+PDh2r59e7rHVKtWTTVq1FDJkiUVFxen8+fPa9OmTTaTbrGxsXr22Wf15ptvOnT9/CQgIEANGzbUunXrDM/9+++/Dv9su5Knp6cqVqyoKlWqKDAwUAEBAfLy8lJ0dLQuXryo3bt3pzvZvnv3br322muaMmWKU9d+8803deTIEdPnKlasqB49eqhFixYqVaqU/P39dfHiRR09elRLly7VokWLUu/yvnLlikaOHOlUDpmJj4/XU089ZfgaBAcHKywsTCVKlFBAQIAiIyN18OBBbdu2zfTn8OTJk/roo4/07rvvOpzDyZMn9cQTT6S7obLFYlG9evUUGhqqYsWKKTo6WmfOnNGmTZtM74RPSkrSm2++KT8/P3Xs2NHuPF5//XWdOHHC9DkvLy/VqlUr9feJn5+fbty4oejoaF25ckUHDx7UyZMnlZycbPf1XOXUqVN6/fXX0/29XLRoUdWsWVPly5dXQECAvL29U/cTOHXqlA4ePJhnV8oBAIBbKAgAAOAGzp8/n26rjaJFi9o9zrFjx7R169bUx97e3urfv7+eeOIJFStWzPScQ4cOmfY83rFjh0aNGmU66VC1alU9/fTT6tChQ7orG44cOaLx48frr7/+soknJyfr9ddf1/z58xUUFGT3a/vggw9MJ/I8PDw0YMAADRw40PQ1RkZGatasWZo8eXJqYWDlypU6deqU3dfOLufPn9ezzz6b7oR+s2bNNHjwYNWvX1+enp6mx1itVh04cEALFy7UwoULM7yz+Pb+9qdPn1abNm0Mx6xYsUJly5Z18JUYRUZGavDgwaYTT8HBwerfv78eeeSR1J78aUVERGjq1KmaOXOmTUFHkqZNm6ZmzZo51B//999/16pVq0yfa9GihV599VVVqVLF8FxCQoKWLl2qDz74QFeuXJF0q9XJZ599Zve186PGjRubFgT27t2b47mULl1abdu21X/+8x/VrVs3003YDxw4oFmzZum3334zTOiuWrVKs2bNUu/evR3KYfny5aYrTzw9PfX8889r0KBB8vb2tnmubNmyKlu2rFq0aKHBgwfrtdde07///ivpVmEl7fGu8PHHH+vw4cOpj++55x69/PLLatSokSwWi+H4Cxcu6MMPPzRd4fPbb7/p0UcfVc2aNe2+/s2bN/Xqq6+aFgMsFosee+wxPfPMM6b75sTFxWnu3LkaN26cYmNjbZ5LTk7W6NGjVa9ePdNz01q1apXp+zcwMFDPP/+8evXqlek+CXFxcfrnn3+0YsUKLV++3Om2VY76+OOPTf++UL9+fb3wwgtq3LixzebUZk6fPq1Vq1ZpxYoVObaqBwAA2I+WQQAAuIH0/kFepEiRdCdMzVy6dCl1Aj84OFi///67XnnllXSLAZJUpUoVw8R8fHy8hg8froSEBMPxTz75pObOnatu3bqlWwyQpNDQUH355ZcaPXq0YXLi8uXLGjNmjN2va8OGDfrzzz8NcV9fX3377bcaOXJkuq8xZYLnl19+sflaOtt2yFWsVqtGjRpluuLBz89PEydO1PTp0xUWFpZuMUC6NYlWvXp1DR8+XH/99Zc+/vhjhYaGZmfqdnnnnXdMiy7NmjXTggUL9Mwzz2T43g4KCtLIkSM1Y8YMw3HJyckaOXKk4uPj7colIiJCn3zyielzI0aM0NSpU02LAdKtVl5du3bVggULVKtWrdR4br9/clv16tVN41lpl+OoOnXqaMKECVq5cqXefPNNNWrUKNNigHRrJch///tf/fDDD6bvwSlTpigpKcnuPBISEtK9U/7dd9/V888/n+nkfrly5fTNN9/YFLnSFsJc4fZiwODBg/XLL7+ocePGpsUASSpZsqQ+/fRTDRgwwPBcyma8jpg2bZrpKp0CBQpo6tSpGjNmTLoT+n5+furdu7fmz5+vu+66y/B8VFSURo0aZVdLsQULFhhiKb8z+/XrZ9emyX5+fmrbtq0++OADrV69Wq+99ppDRW5nxMTEmBY2u3Tpoh9++EFNmzbNtBgg3SpG9e3bV999950WLlyoXr16ZUe6AADASRQEAABwAz/99JNpvEmTJulO1GTE19dXM2bMSHfSLjNfffWV6WTuyy+/rJEjR8rLy/5FjL179zZtf/HXX3+l214jLbMNDz08PPTZZ5+pSZMmdo1Rs2ZNTZ482a4Jw5ywaNEibdiwwRAPCAjQd999p7Zt2zo8pqenp7p166a3337bBRk6b8OGDVq0aJEh3qJFC3399dcOTZrVr19fU6dONUyonjt3TvPmzbNrjEmTJpnekfz000/r6aeftmuMoKAgTZ06VeXLl7fr+PwuvaLT5cuXs2UiO62KFSvqt99+03/+8x+7JkDNNGjQQNOnTze8ty5evOjQPhNLly413aC6X79+euCBB+wex8fHR5MmTVLx4sXtPsdZgwcP1ksvvWT38a+88oqqVq1qiC9atMi0cGwmKipKkydPNsQ9PDz06aefqnnz5naNExISomnTppl+nTZs2KA1a9ZkOsb69esNsZdfflnlypWzK4e0ChUqpAEDBqhu3bpOnW+vLVu2GL7efn5+eueddzIsHGckNDQ021pUAQAA51AQAAAgn5s3b166mxred999To354osvqnLlyk6dGxkZqe+//94Qb9++vZ555hmnxhwwYIDuvfdem5jVatWMGTMyPXfHjh3av3+/If7AAw+oVatWDuVRv359Pfnkkw6dkx2sVqsmTJhg+tyYMWOyfVIpu33++eeGWEhIiD777DOnJq3q1Kmj5557zhCfOXNmpufeuHHDdHVJaGiohg0b5lAeQUFB+u9//+vQOflVepPWVqtVFy9ezPbrO1sESKtWrVrq27evIW72nknPb7/9ZogFBQXphRdecDgff39/jRgxwuHzHFG7dm0NHTrUoXO8vLw0ePBgQzwqKsruVSFz5swxXdXz0EMPOVwALVeunN544w3T53744YcMz71x44bp5tGO/j7JDefOnTPEGjRoYNeKBgAAcOegIAAAQD62evXqdFvnFCtWTF27dnV4zOLFi5tOcNnrjz/+MGzc6O3trVdeecXpMSWZTuj++eefmd5NbDbZVrBgQYcnc1MMGjQowxZKOWHjxo06duyYIX7//fc79T3PS/bv32/aEuSll15SoUKFnB63X79+CggIsIkdOXIkw02CpVt3b9++KXCKESNGOLTSJUXTpk11//33O3xefuPj42P4fqSIiYnJ4Wyy5uGHHzbEduzYYde5Fy5c0ObNmw3xRx55JN2vT2a6deumkiVLOnWuPYYOHerUe79169YqWLCgIZ7RxvQprFar6Uo4f39/pz/LO3bsqIYNGxri69atS3ezYEnpbmZcuHBhp/LISWafZUWKFMmFTAAAQHaiIAAAQD506tQpjRkzRoMHD9b169dNj3nmmWfk5+fn8NjdunVzarInhVlv5TZt2jjdSiFFWFiYQkJCbGJxcXGZTial3ZRYujUx5eykvp+fX65Pui9evNg0/tRTT+VwJq43f/58Q6xYsWLq3Llzlsb19/dXu3btDPEtW7ZkeN7y5csNseLFi2fpbmCzCWR3lF77rfQ2SM+rKlasqBIlStjEIiIi7Np4fPv27aY967t06eJ0Ph4eHurUqZPT52ckJCRELVq0cOpcX19f07027NlP4+jRozp+/Lgh3qFDhyz13X/ssccMMavVqtWrV6d7TnqFSXtb2OUms78T3L4vBAAAyB+c/9c8AADIUf/++68mTZqU7vOxsbGKiIjQ7t27dejQoQw3Prz//vvVu3dvp/LIykRnSn5ptW/f3ukxbxcWFqYzZ87YxLZt26Y6deqYHn/q1CnTuzmzOlnWpUsXffvtt1kaIyvWrl1riFWuXNn0btc7jdlra9eunUtavDRs2FBz5syxiWW2QmDnzp2GWIcOHbKUT/PmzVWkSBHTu3XdSXob5drbUz4vKV68uKHV0eHDhzMthO7atcsQK126tNMt21K0aNEiWz6jMtpA2B6hoaGG12zPz0F6bfGyWpxt27at/Pz8DKvaMlrh4e/vb/rz+8UXX2jixIkua0eVHcqUKWOI7d+/X0uXLnXZ72kAAJD7KAgAAHCH2LRpkzZt2pTlcerVq6fx48c71WvdYrGoRo0aTl97586dSk5ONsTr16/v9Ji3q1ChgiG2d+/edI83m2xzRT41atSQr6+vaT/r7HblyhWdPXvWEG/atGmO5+JqsbGxpv3E69Wr55LxHX3/XL582bTndlbfP97e3qpdu7bWrVuXpXHudOlN/Pv4+ORwJkZnzpzR4cOHde3aNcXExCg2NjbD9mRmhUd7Jrr37NljiFWrVs2hXM24YgwztWrVytL5Zm11oqOjMz3PbILey8sryz+LBQsWVO3atQ2/e9MrQKQICwszrB5auXKlBg0apNdeey3dTbNzW1hYmDw8PAy/p4cPH65jx46pX79+Tq0sBAAAeQsFAQAA3ISnp6eeeuopDR06NN07bzNTokSJLG0ueODAAUPMz89PpUqVcnrM2wUGBhpiERERDuVTvHhxBQcHZykPDw8PValSxfTu8eyW3gac6a2SuJMcPHjQdOVLpUqVXDJ+0aJFDTFH3z+SVL169SznUr16dbcvCKTXGii9VkLZKTo6WkuXLtWiRYu0c+dOuyap7RkzM5cuXTLEsro6QJKCg4NVtGhR081vszpuVpj9fomNjc30PLPPvUqVKrmkeHT33XcbCgJnzpxRbGxsuu2BHn74YdN2YmvXrtW6devUqFEjtW/fXi1btlTZsmWznKOrFClSRO3btze0nUtMTNT48eM1depUtW/fXu3atVOjRo2ytG8LAADIPRQEAADI5/z8/NS5c2f16dMnyxOVWd0UMW07H+lWn//sultVkqKiotJ9zmwyzOwucWdUqFAhVwoC58+fN42b9ea+05itfJCkBx98MNuueePGDd24ccN0Ejq9zUNd8R666667sjzGnSwhISHdzYNzcnPW69eva8qUKfrmm29cvneBPQUBs2NctclrkSJFXF4QyOr3xqydjtmqsrTMVltUrFgxS7mkSK/gGBUVle6EeMuWLdWiRQv9/fffhuesVqvNir+QkBCFhYWpfv36atSokcvydtawYcO0du1a05+/mJgYzZ49W7Nnz5aXl5dq1aql+vXrq0GDBmrUqNEdsXEyAACgIAAAQL7h6+srf39/FS5cWKVLl1atWrVUu3ZtNWnSJEt39d8uq+NcvnzZJXk4IqO2HGaTbXnla+Ws9Cb4AgICcjgT1zO7WzonXLt2zbAprGRebPLz83OqHVda7n7n7YULF0zjHh4eKl68eI7kcOTIEQ0aNEinT5/OlvFv3ryZ6TFmn1Guem9kx2eUK977zsjOz/L0PjuvXbum0qVLp3veuHHjNHDgwEwLw2fOnNGZM2c0b948SVLJkiXVvHlzde3aNct7Mjjjrrvu0ueff66hQ4ca9k64XVJSksLDwxUeHq5vvvlGHh4eqlGjhtq2bauuXbvmqZUPAADAFgUBAADuEEOGDNHQoUNzNQdnWw2luH79uosysV9Gfb3NJnSzexIpu6X3Nc4Pd27mxvtHSr+XfXZO1uaHAk5WHDlyxDRerFgxeXll/z9hjhw5on79+uVKEfN2Zu89V+2hkBf2YnAVs8Jvdhd3M1p9Jt36zJ01a5bGjx+vmTNnZvi76HYXLlzQ77//rt9//13lypXT888/r27duuVosaVZs2b67bffNHr0aG3dutWuc5KTk7V7927t3r1bn3/+uVq1aqVhw4a5pIUaAABwLQoCAAAgx9hzRyyyR07fZZodeP+4j/3795vGs7O9WIqbN2/qpZdeSrcYUKJECTVp0kT169dXuXLlVLp0aRUtWlQ+Pj4qUKCAaeG0b9++2rx5s8O5+Pv7G1pT2dNT3x6uGievctVnXlbG8fHx0ciRI9W7d2/NmjVL8+fPd2il06lTpzRq1Cj9/PPP+uyzzzJckeBqlStX1o8//qg1a9bo119/1Zo1a+wualitVq1atUqrV6/WM888oxdffNG0HRQAAMgdFAQAAECOMevD7u/vr4EDB2bbNTO6M97sLuz0+pY7yhWbjjqjYMGCpvGoqKg7/q7z9DaTHTBggMv6qptJb2xnN0C1R269f/KKtJu4pqhZs2a2X/uXX34x3TA6KChIb775ptq3b+/wKgV7J1LTCggIyLaCgKs+6/KCwoULGwo42f1Z7siqq7Jly2rkyJEaMWKEdu3apQ0bNmjz5s0KDw/PsC1PivDwcD300EP6/fffVapUKbuv6wotW7ZUy5YtFRUVpU2bNmnjxo3avHmzDh06ZLrJ++2sVqumTJmi8+fP68MPP8yhjAEAQGYoCAAAgBxjNoFitVr13HPP5UI25vm4ahIptybbihYtahqPiopSSEhIDmfjWulNzHfr1i1HJorTMnv/xMXF6ebNm1lu75Hf797OSFRUlLZs2WL6XOPGjbP9+r/++qshVrRoUc2ePVtlypRxasz0NqDOjNl77Ny5c06NdbukpKRc25MjO5gVBFxVVEvvs9yZIqSnp6fuuece3XPPPXr22WeVlJSkvXv3avPmzdqwYYM2bdqUbvHo0qVLGjZsmH7++WeHr+sKhQsXVrt27dSuXTtJt9o0bdmyRVu2bNHq1at19OjRdM+dN2+eGjRooIcffjin0gUAABlg3R4AAMgxZpNpsbGxTt89m1WBgYGG2IkTJ1wytqvGcVR6LSUOHz6cw5m4XnqvzdnJ1qwye/9IrvneHz9+PMtj3Knmz59v2ju/UKFCCgsLy9Zrnzt3Tvv27TPEX331VaeLAVL6m31npmLFioaY2eoFRx07dizdvTHuRGaT8676GTp27Jhp3BX7snh5ealOnTp66qmnNH36dG3cuFHvvPOO6fddkrZv3641a9Zk+bquUKRIEbVp00YjR47U4sWLtXjxYg0YMCDdVWpffvllrv2uBwAAtigIAACAHBMaGmoaz+jOwuxUtWpVQ+zSpUuKiIjI0rjJyck6dOhQlsZwltlrkqSdO3fmcCauV7lyZdN4Xnr/SOn3v3eEK8a4EyUlJembb74xfa59+/ZZ3tg8M3v27DHEChQooA4dOjg95tmzZ50uWtWuXdsQO3jwYJY32M4Pnwe3q1KliiF29OhRlxQ9zApEISEhLttA/Hb+/v569NFHNX/+/HTvpl+yZInLr+sKlSpV0muvvaYFCxaoXLlyhucvXLig7du350JmAAAgLQoCAAAgx9xzzz2mcWc223SFOnXqmMa3bt2apXH37t2r+Pj4LI3hrKCgIJUtW9YQ37BhQ47mkd4Gkpn1nM5IqVKlVLJkSUM8t94/JUqUMO3nvW3btiyNm5iYqF27dmVpjDvVd999p9OnT5s+9/jjj2f79c02Ei5btqz8/PycHjMrk6BmBYHr169r9erVTo8pSYsWLcrS+XmN2e+WxMREhYeHZ2ncGzdumP4spve7zFW8vb01ZswY05UCWf18yW7lypXT2LFjTZ+jIAAAQN5AQQAAAOSYChUq6K677jLEly1blvPJSCpfvrxp25esTpYtWLAgS+dnVfPmzQ2xQ4cOZbnQ4Yj0JlBv3LiRpXFbtmxpiG3YsCHX9mwwKyotXbpUycnJTo+5du1aXbt2LStp3ZF2796tL774wvS55s2bm06Ou5rZnfxZbQ3zxx9/OH1u7dq1TT+j5s6d6/SY58+f18aNG50+Py9Kb4I+q5/FK1asMP1sqVu3bpbGtYeXl5c6duxoiF+5ciXbr51VDRs2NG3xZlZwAwAAOY+CAAAAyFFdunQxxDZv3pxrdw62adPGEFu1apXTExfx8fG5XhDo3LmzaXz69Ok5lkN67TSy2u/f7P0THR2tH374IUvjOsvs/XPx4sUs3cFttqltfnf48GENHjzYtGDk5eWlUaNG5Ugevr6+hlhW3rPHjh3T33//7fT5Pj4+6t69uyG+evVqpyf1P/74YyUlJTmdU15UqVIl02Lz4sWLs1RcM9vA12KxqFWrVk6P6QizFUh3yt4Pd3LuAADkdxQEAABAjnrkkUfk4+NjiL/77rtZ7ovtjIceesgQi4+P12effebUeF999ZUuXbqUxayyJiwszLSn9ooVK7Rw4cIcycHb29v0zub0Nui0V+PGjU1790+bNi3LYzujQ4cOpneQf/LJJ05Num7YsEGrVq1yRWp3jL/++kuPPfZYuj83L730Urr7R7haUFCQIXbixAmn7sq2Wq0aPXp0ltpkSUq3l/zbb7/tcLFi+fLluV6wzA4Wi0WPPfaYIR4VFZXuqpPMLFu2TJs2bTLEmzVrpgoVKjg1pqPMCtMlSpTIkWtn1Z2cOwAA+R0FAQAAkKNKlCihPn36GOJ79uzRyJEjXXbnamJioulmkGnVq1fPdIJ59uzZDt/lvX379nQ3RM1pQ4cONY2/8847Odafvlq1aobYmjVrsjzu8OHDDbHo6GgNHjw4yxtC386ejVcLFiyobt26GeJHjhxxuKgUERGh0aNHO3TOnezAgQN6/vnnNWTIEEVFRZke07FjRw0cODDHcqpRo4YhlpycrBkzZjg81oQJE1yyv0XlypXVtWtXQ/zYsWN65pln7L4Dfv369Xr55ZeznE9e1atXL9MVHj/99JPDqzTOnj2rd9991/Q5s99ftzt48KBLipPJycmm7fTMVkK4ytq1a12y/83u3bt16tQpQ9xsTwQAAJDzKAgAAIAc9/zzz6tcuXKG+JIlS9S3b1+dP3/e6bGvXbum6dOnq23btvr666/tOmfkyJGGWHJysoYNG2Z6h6iZvXv3ptvyJDe0b9/edC+Ba9euqX///k7dhX7z5k398ccfevvtt+063qzP9vLly/X77787fO3btWrVSh06dDDEjx8/rh49emjLli1Oj52QkKBFixapV69eevbZZ+0659lnn1WRIkUM8alTp2ratGl2jREREaFBgwbp5MmTDuV7J7FarTpy5IhmzJihRx99VN26ddPy5cvTPb5t27b6+OOPZbFYcizH0NBQ097n06dP19q1a+0aIzk5WZ988okmTpzosrxef/110xU34eHh6ty5sxYsWJBuO5YLFy7o//7v/zRw4MDUz6eCBQuafgbfyQoXLmz6M3vz5k29+OKLdrdYOnv2rJ588kldvHjR8FzTpk1N9zG53e7du9WpUycNHTo0S/u2jBs3Tvv37zfE27dv7/SYmfn666/VqlUrffHFF6av3x5XrlzRa6+9ZogXLFgw068dAADIGV65nQAAAHA//v7++vTTT9WvXz/D3Yjbtm1Thw4d1KtXLz3++ON2tQo5deqUNm7cqKVLl2rjxo1KTEx0KJ9mzZqpS5cuhlYa8fHxGjBggAYMGKCBAweqWLFihnOvXbumH374QZMnT7a5bpUqVXTo0CGH8nC19957Tw888IChFUtsbKwGDx6s5s2ba/DgwapXr548PT1Nx7BarTpw4IAWLVqkBQsW6MyZM2rUqJFd1+/WrZuhKGO1WvXGG29o8uTJql+/vkqVKqWCBQsaJn0LFy6c4Z24//3vf7V//34dP37cJn7hwgX16dNHrVq10hNPPKEGDRrIyyvjv/JGRERo27ZtWrZsmVauXKno6GhJMv1+mylWrJhGjBiht956y/Dcxx9/rM2bN+vVV181fS8nJCRo2bJl+uCDD2xabOSF909m/vjjj3QnPJOTk5WYmKjY2FhdvnxZ586d06FDhxQbG5vpuBaLRc8884xefPFFeXjk/P1Ljz/+uMaNG2cTS0xM1LPPPqvBgwerf//+CggIMJx38+ZNbdiwQZ988onN6iR/f38VK1bM8F51RFBQkMaMGaOXXnrJ8NylS5c0fPhwFSlSRE2aNFHJkiVVqFAhXblyRUeOHNH27dsNm1y/9NJLWrFiheEu7pwsvmSHp556SqtWrTLsSRMXF6cnnnhCffv21cCBA1WyZEnDuXFxcZo3b57GjRtnupFwQECAxo4da9fXKOXu/mXLlikkJESdOnVSmzZtVKNGDRUoUCDDc8PDwzVhwgStW7fO8Fzx4sVNNxp2pcjISE2cOFGTJ09Ww4YN1alTJ917772ZtkmKj4/XwoUL9cUXX+jChQuG53v16pXu3jIAACBnURAAAAC5ok6dOvr88881dOhQw1318fHxmjVrlmbNmqXg4GDdc889Kl68uAIDA+Xh4aHo6GhFRUXp3Llz2r9/f7rtRhzxxhtvaNu2bTp79qxNPDk5Wd98842+//57hYWF6a677lJQUJAiIyN18uRJbdq0yVCAaN26tWrUqJHrE7olS5bUlClTNGDAgNRJ7tutXbtWa9euVWBgoBo0aKDixYsrKChIiYmJunbtmk6fPq3du3c7/fWtUqWKWrVqZdp66fTp0zp9+nS654aEhGRYEChSpIimT5+ufv366cyZMzbPWa1WrVq1SqtWrZKfn5/q1q2rkJAQFSlSRL6+voqJiVFUVJSuXLmi/fv3m05eOeqhhx7S8uXLTVsirVmzRmvWrNHdd9+tu+++WyVKlND169d17tw5bdq0ydAHvnTp0ho2bJief/75LOeVnWbPnu3yMatWrarRo0crLCzM5WPbq0+fPpo5c6bhDunExERNmDBB06dPV/369RUaGqqAgABFRUXpwoUL2rx5s65evWoYb/To0fr999+zVBCQpE6dOunMmTP65JNPTJ+/du2ali5dmuk4PXr00IABA7RixQrDc97e3lnKMbd5enrqww8/1MMPP2z4uUpOTtb333+vmTNnqkGDBqpYsaKCg4MVExOjM2fOaOPGjYqLizMd18PDQ++++67pRrmZOXPmjKZOnaqpU6fKy8tLVapUUaVKlVSkSBEVKVJEFotFsbGxqZ+3GX0evf322/L393c4B2ckJydr8+bNqW2vihQpoho1aqhUqVIqUqSIAgIClJCQoMjISB07dkw7d+5Mdx+g0qVLm7Z6AwAAuYOCAAAAyDUtW7bUN998oxdeeCHdTTuvXLliOnHlakFBQfrqq6/Uv39/0z70N2/e1MaNGzNtO1GlShV99NFH+u6777IpU8fUqlVL3333nZ599tl0W0BERkZm29f4/fff10MPPWSYtHeFsmXL6qefftJzzz2n3bt3mx4TFxenDRs2uPzaaVksFn366ad68skntWPHDtNj9u3bl+m+FoUKFdLkyZNdUuS6k9x9990aOHCgOnXqlO5qlZzi5+eniRMnqk+fPqYtwOLi4rRu3TrTO7jTGjJkiLp3757lNlkpnn76aQUGBurdd991qj3ZY489lrqSxWzyO7O71+8EFSpU0DfffKOnnnrK9LM8OTlZ//77r/7991+7xvPy8tLbb7/tkjvzk5KS7PocSMtiseiNN95Q27Zts5yDs65du+bUZ2mpUqU0ffr0HCtkAACAzLGHAAAAyFUNGzbUn3/+adoTPiuKFCmi+vXrO3RO1apV9eOPPyo0NNSpa9arV08zZswwbSeSm2rVqqW5c+eqTZs2LhnPkbYiwcHBmjNnjrp06ZItE70lS5bUzz//rGeffdalk5ne3t667777HDrH399f3377rdNf5+LFi2vGjBm6++67nTr/TmKxWFS1alUNGjRIc+fO1bx589S1a9dcLwakqFOnjiZNmmTat98e3t7eevPNN9Pd3DsrHnroIc2ZM8d0j5D03HXXXZo0aZLefvvt1K+xWdHJ2deb19SsWVOzZ89WgwYNsjRO6dKlNW3aND300EN2n+PqtkvFixfX559/rr59+7p03JzQtGlT/fTTT07/TgUAANmDFQIAACDXFStWTJ9//rnCw8P17bffasWKFQ7vAyDdmsxq3LixOnbsqDZt2sjHx8fhMSpWrKjZs2dr8uTJmjFjhmGPAzNFihTRU089pSeffDLTfvW5pVixYpo0aZLWr1+vqVOnauPGjbJarQ6NUaVKFT3wwAPq2bOnQ+cFBgZq3LhxGjlypJYsWaLdu3frwIEDunLlimJjYxUfH+9wLrfz9vbWsGHD9PDDD+u7777T3LlznbrD3sfHR/Xr11e7du3UuXNnFS1a1OExChUqpEmTJmnOnDn67LPP7GpH5O3tre7du2vEiBFOXTOvsVgs8vb2lo+Pj3x9fRUUFKRixYqpTJkyqlSpkkJDQ3XPPfeYbsSclzRr1kyzZ8/Whx9+qL/++suu96jFYtF9992nkSNHqmrVqtmWW+XKlTVt2jQdPHhQixYt0tatW3X06FFdu3ZN0q33Yfny5VW7dm3df//9uu+++2z2Y7BaraarskqUKJFtOee0MmXKaNasWfrjjz/01Vdf6ejRo3afGxgYqN69e+vJJ590+M727t27q3z58lq9erXWrFmjgwcPOvX5Vrx4cfXq1UuDBg3KsbvrJ06cqLVr12rVqlX6559/0l25l5l69eppwIABLi/0AwAA17BYs/KvLwAA4HKnT5/W3LlzDfFGjRqpcePGuZBRzouOjtbatWu1bds2HThwQGfOnNHVq1d148YNeXl5qVChQvL391epUqVsJhhr1arl0k1Ir127pgULFmj9+vU6cOCALl++rMTERPn5+alUqVKqVq2amjdvrnbt2t1xmyWePXtWq1at0rZt23TkyBGdO3dOsbGxslqt8vPzU6FChRQSEqLQ0FDVrFlT9913n8qWLZvbadslMTExtff13r17derUKV2+fDm1uJPy/ilWrJgqVqyoSpUqqVatWmrQoIEKFizo0jxWrFihVatWac+ePTp79qzi4+NVsGBBBQcHq3LlymrSpIk6dOjgVG9y5JwjR47or7/+0ubNm3X8+PHUzyNfX18VK1ZMlSpVUoMGDdSmTRtVrFjRcH7K9/52RYsWVVBQUE69BBuHDx9W586dDfHVq1erdOnSuZBR9gsPD9eaNWu0c+dOHT9+XBEREbp+/bq8vb1VuHBhlS1bVrVq1VKTJk3UokULpwrKZqKiorR9+3bt2rVLx48f18mTJ3XhwgXFxsYqLi5O3t7e8vf3V0BAgCpVqqS7775bDRo0UOPGjXN9xczx48cVHh6uffv26cSJEzp9+rQiIiIUFxenhIQE+fr6yt/fX8HBwapWrZpq1Kihli1bqnz58rmaNwAAyBgFAQAAAABwIz/++KPeeecdm1hgYKA2bdqUSxkBAAAgp7CHAAAAAAC4kV9++cUQa9iwYS5kAgAAgJxGQQAAAAAA3MTy5cu1f/9+Q9xVm44DAAAgb6NlEAAAAAC4gTNnzqhXr16KjIy0iRcqVEh///13jm1eCwAAgNzDCgEAAAAAyOOOHTumcePG6eLFi06dv3PnTj322GOGYoAkPfrooxQDAAAA3AQrBAAAAAAgj9u3b5969Oghb29vtW7dWu3bt1fLli0zncjfv3+/vvvuOy1YsECJiYmG54sXL67FixcrICAgu1IHAABAHuKV2wkAAAAAAOyTmJiopUuXaunSpfLw8FCFChVUvXp1BQcHKyAgQFarVdeuXdPFixcVHh6uK1eupDuWp6enPv74Y4oBAAAAboSCAAAAAADcgZKTk3Xs2DEdO3bM4XP9/Pw0btw4NW3aNBsyAwAAQF5FQQAAAAAA3EjZsmX1xRdfqGbNmrmdCgAAAHIYewgAAAAAQB4XExOj7777TkuXLtXBgwedGiMkJESDBw9Wz5495e3t7eIMAQAAcCegIAAAAAAAd5Bz585p+/bt2rlzp44fP64zZ84oIiJCcXFxunHjhgoUKKDAwEAVKVJEZcqUUVhYmBo1aqS7775bHh4euZ0+AAAAchEFAQAAAAAAAAAA3AC3hwAAAAAAAAAA4AYoCAAAAAAAAAAA4AYoCAAAAAAAAAAA4AYoCAAAAAAAAAAA4AYoCAAAAAAAAAAA4AYoCAAAAAAAAAAA4AYoCAAAAAAAAAAA4AYoCAAAAAAAAAAA4AYoCAAAAAAAAAAA4AYoCAAAAAAAAAAA4AYoCAAAAAAAAAAA4AYoCAAAAAAAAAAA4AYoCAAAAAAAAAAA4AYoCAAA/l97diAAAAAAIMjfepBLIwAAAAAGhAAAAAAAAAwIAQAAAAAAGBACAAAAAAAwIAQAAAAAAGBACAAAAAAAwIAQAAAAAACAASEAAAAAAAADQgAAAAAAAAaEAAAAAAAADAgBAAAAAAAYEAIAAAAAADAgBAAAAAAAYEAIAAAAAADAgBAAAAAAAIABIQAAAAAAAAMBWYR3ATGTeygAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 900x900 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "conf_matrix = confusion_matrix(y_test, y_predicted)\n",
    "print(conf_matrix)\n",
    "\n",
    "plt.figure(figsize=(3, 3), dpi=300)\n",
    "# Scale up the size of all text\n",
    "sns.set(font_scale = 1.1)\n",
    " \n",
    "ax = sns.heatmap(conf_matrix, annot=True, fmt='d',)\n",
    " \n",
    "# set x-axis label and ticks. \n",
    "ax.set_xlabel(\"Predicted Diagnosis\", fontsize=14, labelpad=20)\n",
    "ax.xaxis.set_ticklabels(['Negative', 'Positive'])\n",
    " \n",
    "# set y-axis label and ticks\n",
    "ax.set_ylabel(\"Actual Diagnosis\", fontsize=14, labelpad=20)\n",
    "ax.yaxis.set_ticklabels(['Negative', 'Positive'])\n",
    " \n",
    "# set plot title\n",
    "ax.set_title(\"Confusion Matrix for the Diabetes Detection Model\", fontsize=14, pad=20)\n",
    " \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "96b13877-ce93-42da-af75-c0bfed9a7370",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Package                   Version\n",
      "------------------------- --------------\n",
      "alembic                   1.13.2\n",
      "annotated-types           0.7.0\n",
      "anyio                     4.4.0\n",
      "argon2-cffi               23.1.0\n",
      "argon2-cffi-bindings      21.2.0\n",
      "arrow                     1.3.0\n",
      "asttokens                 2.4.1\n",
      "async_generator           1.10\n",
      "async-lru                 2.0.4\n",
      "attrs                     24.2.0\n",
      "Babel                     2.14.0\n",
      "beautifulsoup4            4.12.3\n",
      "bleach                    6.1.0\n",
      "blinker                   1.8.2\n",
      "Brotli                    1.1.0\n",
      "cached-property           1.5.2\n",
      "certifi                   2024.7.4\n",
      "certipy                   0.1.3\n",
      "cffi                      1.17.0\n",
      "charset-normalizer        3.3.2\n",
      "click                     8.1.8\n",
      "cloudpickle               3.1.1\n",
      "colorcet                  3.1.0\n",
      "colorlover                0.3.0\n",
      "comm                      0.2.2\n",
      "contourpy                 1.3.1\n",
      "cryptography              43.0.0\n",
      "cufflinks                 0.17.3\n",
      "cycler                    0.12.1\n",
      "dask                      2025.1.0\n",
      "datashader                0.16.3\n",
      "debugpy                   1.8.5\n",
      "decorator                 5.1.1\n",
      "defusedxml                0.7.1\n",
      "entrypoints               0.4\n",
      "exceptiongroup            1.2.2\n",
      "executing                 2.0.1\n",
      "fastjsonschema            2.20.0\n",
      "fonttools                 4.55.4\n",
      "fqdn                      1.5.1\n",
      "fsspec                    2024.12.0\n",
      "greenlet                  3.0.3\n",
      "h11                       0.14.0\n",
      "h2                        4.1.0\n",
      "hpack                     4.0.0\n",
      "httpcore                  1.0.5\n",
      "httpx                     0.27.2\n",
      "hyperframe                6.0.1\n",
      "idna                      3.8\n",
      "imageio                   2.37.0\n",
      "importlib_metadata        8.4.0\n",
      "importlib_resources       6.4.4\n",
      "ipykernel                 6.29.5\n",
      "ipympl                    0.9.6\n",
      "ipysigma                  0.24.4\n",
      "ipython                   8.26.0\n",
      "ipywidgets                8.1.5\n",
      "isoduration               20.11.0\n",
      "jedi                      0.19.1\n",
      "Jinja2                    3.1.4\n",
      "joblib                    1.4.2\n",
      "json5                     0.9.25\n",
      "jsonpointer               3.0.0\n",
      "jsonschema                4.23.0\n",
      "jsonschema-specifications 2023.12.1\n",
      "jupyter_client            8.6.2\n",
      "jupyter_core              5.7.2\n",
      "jupyter-events            0.10.0\n",
      "jupyter-lsp               2.2.5\n",
      "jupyter_offlinenotebook   0.3.1\n",
      "jupyter-resource-usage    1.1.0\n",
      "jupyter_server            2.14.2\n",
      "jupyter_server_terminals  0.5.3\n",
      "jupyterhub                5.1.0\n",
      "jupyterlab                4.2.5\n",
      "jupyterlab_pygments       0.3.0\n",
      "jupyterlab_server         2.27.3\n",
      "jupyterlab_widgets        3.0.13\n",
      "kiwisolver                1.4.8\n",
      "lazy_loader               0.4\n",
      "llvmlite                  0.44.0\n",
      "locket                    1.0.0\n",
      "Mako                      1.3.5\n",
      "MarkupSafe                2.1.5\n",
      "matplotlib                3.10.0\n",
      "matplotlib-inline         0.1.7\n",
      "mistune                   3.0.2\n",
      "multipledispatch          1.0.0\n",
      "nbclient                  0.10.0\n",
      "nbconvert                 7.16.4\n",
      "nbformat                  5.10.4\n",
      "nbgitpuller               1.2.1\n",
      "nest_asyncio              1.6.0\n",
      "networkx                  3.4.2\n",
      "notebook                  7.2.2\n",
      "notebook_shim             0.2.4\n",
      "numba                     0.61.0\n",
      "numpy                     2.1.3\n",
      "oauthlib                  3.2.2\n",
      "overrides                 7.7.0\n",
      "packaging                 24.1\n",
      "pamela                    1.2.0\n",
      "pandas                    2.2.3\n",
      "pandocfilters             1.5.0\n",
      "param                     2.2.0\n",
      "parso                     0.8.4\n",
      "partd                     1.4.2\n",
      "patsy                     1.0.1\n",
      "pexpect                   4.9.0\n",
      "pickleshare               0.7.5\n",
      "pillow                    11.1.0\n",
      "pip                       24.2\n",
      "pkgutil_resolve_name      1.3.10\n",
      "platformdirs              4.2.2\n",
      "plotly                    5.24.1\n",
      "plotly-geo                1.0.0\n",
      "prometheus_client         0.20.0\n",
      "prompt_toolkit            3.0.47\n",
      "psutil                    5.9.8\n",
      "ptyprocess                0.7.0\n",
      "pure_eval                 0.2.3\n",
      "pyarrow                   19.0.0\n",
      "pycparser                 2.22\n",
      "pyct                      0.5.0\n",
      "pydantic                  2.8.2\n",
      "pydantic_core             2.20.1\n",
      "Pygments                  2.18.0\n",
      "PyJWT                     2.9.0\n",
      "pyOpenSSL                 24.2.1\n",
      "pyparsing                 3.2.1\n",
      "PySocks                   1.7.1\n",
      "python-dateutil           2.9.0\n",
      "python-json-logger        2.0.7\n",
      "pytz                      2024.1\n",
      "PyYAML                    6.0.2\n",
      "pyzmq                     26.2.0\n",
      "referencing               0.35.1\n",
      "requests                  2.32.3\n",
      "rfc3339-validator         0.1.4\n",
      "rfc3986-validator         0.1.1\n",
      "rpds-py                   0.20.0\n",
      "scikit-image              0.25.0\n",
      "scikit-learn              1.6.1\n",
      "scipy                     1.15.1\n",
      "seaborn                   0.13.2\n",
      "Send2Trash                1.8.3\n",
      "setuptools                72.2.0\n",
      "six                       1.16.0\n",
      "sniffio                   1.3.1\n",
      "soupsieve                 2.5\n",
      "SQLAlchemy                2.0.32\n",
      "stack-data                0.6.2\n",
      "statsmodels               0.14.4\n",
      "tenacity                  9.0.0\n",
      "terminado                 0.18.1\n",
      "threadpoolctl             3.5.0\n",
      "tifffile                  2025.1.10\n",
      "tinycss2                  1.3.0\n",
      "tomli                     2.0.1\n",
      "toolz                     1.0.0\n",
      "tornado                   6.4.1\n",
      "traitlets                 5.14.3\n",
      "types-python-dateutil     2.9.0.20240821\n",
      "typing_extensions         4.12.2\n",
      "typing-utils              0.1.0\n",
      "tzdata                    2025.1\n",
      "uri-template              1.3.0\n",
      "urllib3                   2.2.2\n",
      "voila                     0.5.8\n",
      "wcwidth                   0.2.13\n",
      "webcolors                 24.8.0\n",
      "webencodings              0.5.1\n",
      "websocket-client          1.8.0\n",
      "websockets                14.2\n",
      "wheel                     0.44.0\n",
      "widgetsnbextension        4.0.13\n",
      "xarray                    2025.1.1\n",
      "xlrd                      2.0.1\n",
      "zipp                      3.20.1\n",
      "zstandard                 0.23.0\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%pip list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9990ecc7-ff49-4efc-acdf-5a926f99ddf4",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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