notebooks/supervised/classification/knn/digit_recognition.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8eb3f8c0-31e3-4fbc-86c5-35cd40563c3e",
   "metadata": {},
   "source": [
    "# Import libraries\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3374acc8-d198-4b97-89f5-5e5ad588be07",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import datasets, linear_model, neighbors, metrics\n",
    "from sklearn.model_selection import train_test_split\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a613526-005d-4b46-91c5-81eb433fbc18",
   "metadata": {},
   "source": [
    "# Data processing and splitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d1070358-3ffc-4e00-91ce-d15e0a360377",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load digit data and assign features and labels\n",
    "X_digits, y_digits = datasets.load_digits(return_X_y=True)\n",
    "\n",
    "# Normalize data by dividing each value in the X_digits matrix by its maximum value\n",
    "X_digits = X_digits / X_digits.max()\n",
    "\n",
    "# Split data into training and test sets (9|1 ratio)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_digits, y_digits, test_size=0.1, train_size=0.9, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1f59abf-ab27-42f3-867a-51bbbff3b64a",
   "metadata": {},
   "source": [
    "# Train models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "76cb76d5-108a-4972-9ca2-eb2883d699b6",
   "metadata": {},
   "outputs": [
    {
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       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
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       "\n",
       "div.sk-parallel-item,\n",
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       "\n",
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       "\n",
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       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
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       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
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       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
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       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
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       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
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       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
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       "\n",
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       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(max_iter=1000)</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 fitted 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 fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(max_iter=1000)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression(max_iter=1000)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Initialize K-NN and Logistic Regression models\n",
    "knn = neighbors.KNeighborsClassifier()\n",
    "logistic = linear_model.LogisticRegression(max_iter=1000)\n",
    "\n",
    "# Train K-NN model\n",
    "knn.fit(X_train, y_train)\n",
    "\n",
    "# Train Logistic Regression model\n",
    "logistic.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9e8c0a4-dea7-49cd-8245-d0ce56608590",
   "metadata": {},
   "source": [
    "# Evaluate models"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b73c50e5-af81-4953-8ffb-a91c63c1a08c",
   "metadata": {},
   "source": [
    "## Evaluate results on the test set for each model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "33a18c8a-9ab1-46fa-91ec-a069855e2046",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of K-NN: 0.988889\n",
      "Accuracy of Logistic Regression: 0.977778\n"
     ]
    }
   ],
   "source": [
    "# Print the accuracy of K-NN and Logistic Regression models\n",
    "print(\"Accuracy of K-NN: %f\" % knn.score(X_test, y_test))\n",
    "print(\"Accuracy of Logistic Regression: %f\" % logistic.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f8fee12-52fe-4b99-9f92-6fb966299c6c",
   "metadata": {},
   "source": [
    "## Analyze results and visualize"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b11133e-41cc-4967-b708-31e03a1eb209",
   "metadata": {},
   "source": [
    "### Confusion Matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6956b1c9-f4f5-46f8-876e-42824b655f7e",
   "metadata": {},
   "source": [
    "#### K-NN Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1b269287-b99b-4622-a0e3-9856c527809d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAx0AAADSCAYAAAAvz61vAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8hTgPZAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAZbElEQVR4nO3de1BU5x3G8WcRUEAEajASo4Cpmo4JpRrTId6wgkYiQlpi1bQVqymTVgV6sZ2kFYgmdhKnYq211qaQSbXNmCaQGFuFFIyXaccK2MFcaikYja3RKKZeGkXe/sEsdeW24HldL9/PDH+4nPOcs7i/ZR/O2bMuY4wRAAAAAFji5+sdAAAAAHBzo3QAAAAAsIrSAQAAAMAqSgcAAAAAqygdAAAAAKyidAAAAACwitIBAAAAwCpKBwAAAACrKB0AAAAArLrhSkdMTIwyMzNb/11ZWSmXy6XKykrHtuFyuZSfn+9Yng179+7VAw88oJCQELlcLtXU1Ph6l3AdYU5aMCfoDHPSgjlBV5iVFszK1elW6SguLpbL5Wr96tOnj4YPH66FCxfq2LFjtvbRiq1bt173D+6OXLx4UY888ohOnjypVatW6cUXX1R0dHS7y7qfGF5++WWP2y9cuKDp06fLz89Pv/71rztd1+Vyad++fW2+n5mZqb59+3rclpiYKJfLpdTU1DbLNzQ0yOVyaeXKld7e1XbV1dVpzpw5GjBggIKCgjRs2DA9+eSTV5XpJObk+nArz8k//vEPZWRkKCIiQsHBwRo3bpwqKip6nGcDc3J9uFXn5OjRo/rKV76iESNGKDQ0VOHh4br//vv1wgsvyBjTo0xbmJXrw606K/n5+R6Pvyu/du/e7XWWf0924KmnnlJsbKz++9//ateuXVq3bp22bt2q2tpaBQcH9ySyxyZMmKDz588rMDCwW+tt3bpVa9eubffBf/78efn79+hHc03U1dXp0KFD2rBhgxYsWNDt9S9evKiMjAxt3bpVGzZs0Ne//vUu18nPz9frr7/u9Ta2bNmiffv2afTo0d3ev87U1NQoMTFRgwYN0ne+8x31799f77//vg4fPuzodpzAnPjWrTonhw8fVkJCgnr16qXvfe97CgkJUVFRkaZMmaI333xTEyZMcGxbTmBOfOtWnZMTJ07oyJEjysjI0JAhQ3Tx4kWVlZUpMzNT7733np555hnHtuUUZsW3btVZ+eIXv6hPf/rTbW5/4okndObMGY0ZM8brrB79706bNk333XefJGnBggXq37+/fvKTn6i0tFSzZ89ud52zZ88qJCSkJ5vrlJ+fn/r06eNoptN5Tvvwww8lSeHh4d1e9+LFi5o5c6a2bNmi9evXa/78+V2uEx8fry1btqiqqkqjRo3qcvkhQ4boP//5jwoKCvTaa691ex870tzcrK9+9au6++67VVFRoaCgIMeybWBOfOtWnZMf//jHamxsVG1trUaMGCFJeuyxx3T33XcrNze33b+c+RJz4lu36pzExcW1OTVo4cKFSk1N1U9/+lMtW7ZMvXr1cmx7TmBWfOtWnpW4uDiP2w4fPqwjR45owYIF3Sqejryn4wtf+IIkqb6+XtL/D/3U1dUpJSVFoaGhevTRRyW1vHAsLCzUyJEj1adPH91+++3KysrSqVOnPDKNMVq+fLnuvPNOBQcHa9KkSTpw4ECbbXd0XuFf/vIXpaSkKCIiQiEhIYqLi9Pq1atb92/t2rWS5HGIyK298wqrq6s1bdo09evXT3379tXkyZP15z//2WMZ9yHQ3bt369vf/rYiIyMVEhKihx9+WMePH/fqZ/mnP/1J48ePV0hIiMLDw5WWlqZ33nmn9fuZmZmaOHGiJOmRRx6Ry+VSYmKiV9lNTU2aNWuWSktLtW7dOj322GNerbdo0SJFRER4fUg0NDRUubm5ev3111VVVdXl8nV1daqrq+tyue3bt6u2tlZ5eXkKCgrSuXPndOnSJa/26XrAnLRgTlrYmpOdO3fqc5/7XGvhkKTg4GDNmDFDVVVVOnjwoFf75yvMSQvmpIWtOelITEyMzp07pwsXLvQ441phVlowKy2u5az89re/lTGm9fHlLUeOY7l3un///q23NTU1aerUqRo3bpxWrlzZeugvKytLxcXFmjdvnhYvXqz6+nr97Gc/U3V1tXbv3q2AgABJ0tKlS7V8+XKlpKQoJSVFVVVVmjJlildPBGVlZZo+fbqioqKUnZ2tgQMH6p133tGWLVuUnZ2trKwsHT16VGVlZXrxxRe7zDtw4IDGjx+vfv36acmSJQoICND69euVmJioHTt26POf/7zH8u4HSl5enhoaGlRYWKiFCxfqpZde6nQ75eXlmjZtmoYOHar8/HydP39ea9as0dixY1VVVaWYmBhlZWVp0KBBeuaZZ7R48WKNGTNGt99+e5f3oampSbNnz9arr76qtWvXKisrq8t13Pr166fc3FwtXbrU68adnZ2tVatWKT8/v8vGPXnyZEkt5x12pry8XJLUu3dv3Xfffdq3b58CAwP18MMP6+c//7k+9alPeXeHfIQ5YU6uZGNOPvnkE0VERLS53f3Y2rdvn4YNG9blvvkKc8KcXMnGnLidP39eZ8+e1ZkzZ7Rjxw4VFRUpISHhuj+SLjErzEpbNmflchs3btTgwYO7f7qu6YaioiIjyZSXl5vjx4+bw4cPm9/97nemf//+JigoyBw5csQYY8zcuXONJPODH/zAY/2dO3caSWbjxo0et//xj3/0uP3DDz80gYGB5qGHHjLNzc2tyz3xxBNGkpk7d27rbRUVFUaSqaioMMYY09TUZGJjY010dLQ5deqUx3Yuz/rWt75lOrr7kkxeXl7rv9PT001gYKCpq6trve3o0aMmNDTUTJgwoc3PJykpyWNbubm5plevXqaxsbHd7bnFx8ebAQMGmI8++qj1tv379xs/Pz/zta99rc193rx5c6d5ly8bHR1tJJm1a9d2uU5722lsbDQRERFmxowZrd+fO3euCQkJ8Vhn4sSJZuTIkcYYYwoKCowks2/fPmOMMfX19UaSee655zzWiY6ONtHR0V3uz4wZM4wk079/f/Poo4+al19+2fzoRz8y/v7+5oEHHvD4mfsSc8Kc+HJOUlNTTXh4uPn44489bk9ISDCSzMqVK72+bzYxJ8yJL+fEbcWKFUZS69fkyZPN+++/7/X61wKzwqxcD7PiVltbaySZJUuWdHvdHp1elZSUpMjISA0ePFizZs1S37599eqrr2rQoEEeyz3++OMe/968ebPCwsKUnJysEydOtH6NHj1affv2bb26Snl5uS5cuKBFixZ5HHrLycnpct+qq6tVX1+vnJycNufdXZ7lrUuXLmn79u1KT0/X0KFDW2+PiorSnDlztGvXLn388cce63zjG9/w2Nb48eN16dIlHTp0qMPt/Otf/1JNTY0yMzM9/mIfFxen5ORkbd26tdv7frljx47J399fsbGxPVo/LCxMOTk5eu2111RdXe3VOtnZ2YqIiFBBQUGnyzU0NHjVtM+cOSNJGjNmjH7zm9/oS1/6kp566iktW7ZMe/bs0ZtvvunVfl0rzAlz4g2n5+Txxx9XY2OjvvzlL6u6ulp///vflZOTo7/+9a+SWv6yez1hTpgTbzg9J26zZ89WWVmZNm3apDlz5ki6/mbEjVlhVrxha1bcNm7cKEndPrVK6uF7OtauXauysjJVVFTo7bff1j//+U9NnTrVYxl/f3/deeedHrcdPHhQp0+f1oABAxQZGenxdebMmdY36bgfIFeeAhAZGdnuaQOXcx9uvOeee3py19o4fvy4zp0753F+tNtnPvMZNTc3t7ly0pAhQzz+7d7nK8+dvJz7Pne0nRMnTujs2bPd3n+3Z599VkOGDFFGRkaby5v9+9//9vjq6Ak3Oztb4eHhXp9f2JNh6Yz7cPeVb5hz/6LYs2fPVW/DScxJC+akc07PybRp07RmzRq99dZbGjVqlEaMGKE33nhDTz/9tCS1udSirzEnLZiTzjk9J27R0dFKSkrS7NmztXHjRg0dOlRJSUnXZfFgVlowK52zNStSy3t+Nm3apHvuuafNm8u90aP3dNx///2tV1DoSO/eveXn59lpmpubNWDAgNaWdKXIyMie7M51p6MrXhgfXvs7KipKZWVlGjdunB566CHt2LFDn/3sZ1u/d7mioiKPDwFycz+Q8/Pzu9W4V61apYKCAhUWFl7Vfbjjjjskqc15lAMGDJDU+ROLLzAnnWNO/s/JOZFarsIzb948/e1vf1NgYKDi4+P1/PPPS5KGDx9+1flOYk46x5z8n9Nz0p6MjAxt2LBBb731VpsX9L7GrHSOWfk/W7Oye/duHTp0SCtWrOjR+tf0gsh33XWXysvLNXbs2E7fpOX+sJWDBw96HFY7fvx4ly8s77rrLklSbW2tkpKSOlzO28N9kZGRCg4O1nvvvdfme++++678/Pw0ePBgr7I6477PHW3ntttuu+rL3g0dOlTbtm3TxIkTNXXqVO3cuVPDhg1TWVmZx3IjR47sMCMnJ0eFhYUqKCjw6rJxlw/L3Llzr2r/R48erQ0bNuiDDz7wuP3o0aOSbp4nTuakY8yJ90JCQpSQkND67/LycgUFBWns2LGO5Psac9Ix5qTn3H9tPn36tJV8X2BWOsasdM/GjRvlcrlazzDpLkcumeutmTNn6tKlS1q2bFmb7zU1NamxsVFSy3mLAQEBWrNmjUdD9aatjRo1SrGxsSosLGzNc7s8y/0gunKZK/Xq1UtTpkxRaWmpx7lvx44d06ZNmzRu3Dj169evy/3qSlRUlOLj4/XCCy947FNtba22b9+ulJSUq96GJN1777164403dObMGSUnJ+uDDz5QUlKSx9eV7fty7gdyaWmpampqvNqm+xzPp556qt3ve3vZtrS0NPXu3VtFRUVqbm5uvf1Xv/qVJCk5Odmr/bneMScdY056dnnDPXv26JVXXtH8+fMVFhbWo4zrDXPSMeak6znp6FKqzz//vFwul1dXCrpRMCsdY1a8/51y8eJFbd68WePGjWtzKpu3rumRjokTJyorK0srVqxQTU2NpkyZooCAAB08eFCbN2/W6tWrlZGRocjISH33u9/VihUrNH36dKWkpKi6ulp/+MMfdNttt3W6DT8/P61bt06pqamKj4/XvHnzFBUVpXfffVcHDhzQtm3bJKn10xoXL16sqVOnqlevXpo1a1a7mcuXL289PPbNb35T/v7+Wr9+vT755BM9++yzjv18nnvuOU2bNk0JCQmaP39+62XbwsLCvD6XzxsJCQl65ZVXlJqaquTkZO3cudPjkntdcR+2279/v1d/AQgLC1N2dnaHb2ry9rJtAwcO1JNPPqmlS5fqwQcfVHp6uvbv368NGzZo9uzZ3fpUzOsZc9I55qSh05xDhw5p5syZmjFjhgYOHKgDBw7oF7/4heLi4q7LT1nuKeakc8xJQ6c5Tz/9tHbv3q0HH3xQQ4YM0cmTJ/X73/9ee/fu1aJFi9r9BOYbFbPSOWalwavtb9u2TR999FGP3kDeqjuXunJflmzv3r2dLtfe5bwu98tf/tKMHj3aBAUFmdDQUHPvvfeaJUuWmKNHj7Yuc+nSJVNQUGCioqJMUFCQSUxMNLW1tSY6OrrTy7a57dq1yyQnJ5vQ0FATEhJi4uLizJo1a1q/39TUZBYtWmQiIyONy+XyuISbrrhsmzHGVFVVmalTp5q+ffua4OBgM2nSJLNnzx6vfj4d7WN7ysvLzdixY01QUJDp16+fSU1NNW+//Xa7ed25bFt7y7700kvGz8/PjBkzps3lNbtaNy8vz0jq9LJtlzt16pQJCwu76su2NTc3mzVr1pjhw4ebgIAAM3jwYPPDH/7QXLhwwav1rwXmhDlx88WcnDx50qSlpZmBAweawMBAExsba77//e+3u+++xJwwJ26+mJPt27eb6dOnmzvuuMMEBASY0NBQM3bsWFNUVHTdXH7djVlhVtx89drLGGNmzZplAgICPC4t3F0uY3z4DhsAAAAAN71r+p4OAAAAALceSgcAAAAAqygdAAAAAKyidAAAAACwitIBAAAAwCpKBwAAAACrKB0AAAAArLqmn0h+IyssLHQ8s6SkxPFMSaqsrLSSi5tLenq645mlpaWOZ+bl5TmeKcnRT5rFzcvG48TG75P4+HjHMyU79z8xMdHxTPiet59s3R2ZmZmOZ95IbMx1Tk6O45kxMTFeLceRDgAAAABWUToAAAAAWEXpAAAAAGAVpQMAAACAVZQOAAAAAFZROgAAAABYRekAAAAAYBWlAwAAAIBVlA4AAAAAVlE6AAAAAFhF6QAAAABgFaUDAAAAgFWUDgAAAABWUToAAAAAWEXpAAAAAGAVpQMAAACAVZQOAAAAAFZROgAAAABYRekAAAAAYJW/r3fAhsrKSsczc3NzHc/My8tzPBM3p+LiYsczS0tLHc9MS0tzPLOkpMTxTElqbGx0PLOwsNDxTHjPxmPaxuzZeEzbeuzV1NQ4npmYmOh4JnzPxmPQxuMvPj7e8UzJzu+U1atXO545adIkxzNjYmK8Wo4jHQAAAACsonQAAAAAsIrSAQAAAMAqSgcAAAAAqygdAAAAAKyidAAAAACwitIBAAAAwCpKBwAAAACrKB0AAAAArKJ0AAAAALCK0gEAAADAKkoHAAAAAKsoHQAAAACsonQAAAAAsIrSAQAAAMAqSgcAAAAAqygdAAAAAKyidAAAAACwitIBAAAAwCpKBwAAAACrXMYY4+udcFpiYqLjmeHh4Y5nlpSUOJ6Jm1NhYaHjmbm5uY5n2ng6ycnJcTxTklavXu145k34dAoLGhsbHc+Mj493PFOy83vK1r7Ct2y89rKRmZ+f73imrdyGhgbHM4uLix3P9BZHOgAAAABYRekAAAAAYBWlAwAAAIBVlA4AAAAAVlE6AAAAAFhF6QAAAABgFaUDAAAAgFWUDgAAAABWUToAAAAAWEXpAAAAAGAVpQMAAACAVZQOAAAAAFZROgAAAABYRekAAAAAYBWlAwAAAIBVlA4AAAAAVlE6AAAAAFhF6QAAAABgFaUDAAAAgFWUDgAAAABWuYwxxtc74bTw8HDHM9PT0x3PjImJcTzTVq6N+2/j/+lm1dDQ4HhmfHz8DZG5Y8cOxzNtqa6udjzTxs8U3rtRZs/W82lNTY3jmTz3+15jY6PjmREREY5n2nhOtSUxMdHxTBvzZ+u1pzc40gEAAADAKkoHAAAAAKsoHQAAAACsonQAAAAAsIrSAQAAAMAqSgcAAAAAqygdAAAAAKyidAAAAACwitIBAAAAwCpKBwAAAACrKB0AAAAArKJ0AAAAALCK0gEAAADAKkoHAAAAAKsoHQAAAACsonQAAAAAsIrSAQAAAMAqSgcAAAAAqygdAAAAAKyidAAAAACwymWMMb7eCaeFh4c7nnn69GnHM/Py8hzPlKSGhgbHM2NiYhzPzM/PdzwT3istLXU8s76+3vFMW3Jzcx3PrKiocDwzMTHR8Uz4lo3nvpKSEsczJTvP/bb2Fd5rbGx0PNPGa4/4+HjHM209p6anpzuemZOT43imL3GkAwAAAIBVlA4AAAAAVlE6AAAAAFhF6QAAAABgFaUDAAAAgFWUDgAAAABWUToAAAAAWEXpAAAAAGAVpQMAAACAVZQOAAAAAFZROgAAAABYRekAAAAAYBWlAwAAAIBVlA4AAAAAVlE6AAAAAFhF6QAAAABgFaUDAAAAgFWUDgAAAABWUToAAAAAWEXpAAAAAGAVpQMAAACAVS5jjPH1TjgtJibG8cycnJwbItOWxMRExzMrKysdz8TNp7S01Epuenq645klJSWOZ6alpTmeCXgrPj7e8cyCggLHM5mTm5ON1wm2XnvV1NRYyb2ZcKQDAAAAgFWUDgAAAABWUToAAAAAWEXpAAAAAGAVpQMAAACAVZQOAAAAAFZROgAAAABYRekAAAAAYBWlAwAAAIBVlA4AAAAAVlE6AAAAAFhF6QAAAABgFaUDAAAAgFWUDgAAAABWUToAAAAAWEXpAAAAAGAVpQMAAACAVZQOAAAAAFZROgAAAABYRekAAAAAYJW/r3fAhszMTMczKysrHc/MyclxPFOSSktLHc/csWOH45kNDQ2OZ8bExDieebOqqalxPNPGnOTm5jqeKUlpaWk3RCZ8KzEx0fHM4uJixzMbGxsdz5Sk8PBwxzPr6+sdz8TNycbrJBszDe9wpAMAAACAVZQOAAAAAFZROgAAAABYRekAAAAAYBWlAwAAAIBVlA4AAAAAVlE6AAAAAFhF6QAAAABgFaUDAAAAgFWUDgAAAABWUToAAAAAWEXpAAAAAGAVpQMAAACAVZQOAAAAAFZROgAAAABYRekAAAAAYBWlAwAAAIBVlA4AAAAAVlE6AAAAAFhF6QAAAABglb+vd8CG/Px8xzNzcnIcz4yJiXE805aSkhLHM2+k+38zyszMdDxz//79jmempaU5nilJxcXFVnJxc2lsbHQ8MzY21vHMsLAwxzMlO7/7bGTC92pqahzPbGhocDzTxu8+eIcjHQAAAACsonQAAAAAsIrSAQAAAMAqSgcAAAAAqygdAAAAAKyidAAAAACwitIBAAAAwCpKBwAAAACrKB0AAAAArKJ0AAAAALCK0gEAAADAKkoHAAAAAKsoHQAAAACsonQAAAAAsIrSAQAAAMAqSgcAAAAAqygdAAAAAKyidAAAAACwitIBAAAAwCpKBwAAAACrXMYY4+udAAAAAHDz4kgHAAAAAKsoHQAAAACsonQAAAAAsIrSAQAAAMAqSgcAAAAAqygdAAAAAKyidAAAAACwitIBAAAAwCpKBwAAAACr/gcilSkQImGWaAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x300 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "knn_predicted = knn.predict(X_test)\n",
    "\n",
    "# Create subplot to visualize samples and predictions of K-NN for the first 4 test samples\n",
    "_, axes = plt.subplots(nrows=1, ncols=4, figsize=(10, 3))\n",
    "\n",
    "# Loop through samples and corresponding predictions of K-NN\n",
    "for ax, image, prediction in zip(axes, X_test, knn_predicted):\n",
    "    ax.set_axis_off()  # Turn off the axes of the subplot\n",
    "    image = image.reshape(8, 8)  # Reshape the sample to its original size 8x8\n",
    "    ax.imshow(image, cmap=plt.cm.gray_r, interpolation=\"nearest\")  # Display the image in grayscale\n",
    "    ax.set_title(f\"Prediction of K-NN: {prediction}\")  # Set the title with K-NN's prediction\n",
    "    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b38c8df-e3e7-4c65-a3cb-7497d6bcc5ad",
   "metadata": {},
   "source": [
    "**Confusion Matrix**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d398feec-53e2-43af-9046-83fa3ca19a75",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix:\n",
      "[[17  0  0  0  0  0  0  0  0  0]\n",
      " [ 0 11  0  0  0  0  0  0  0  0]\n",
      " [ 0  0 17  0  0  0  0  0  0  0]\n",
      " [ 0  0  0 17  0  0  0  0  0  0]\n",
      " [ 0  0  0  0 25  0  0  0  0  0]\n",
      " [ 0  0  0  0  0 22  0  0  0  0]\n",
      " [ 0  0  0  0  0  0 19  0  0  0]\n",
      " [ 0  0  0  0  0  0  0 18  0  1]\n",
      " [ 0  0  0  0  0  0  0  0  8  0]\n",
      " [ 0  0  0  0  1  0  0  0  0 24]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create and display confusion matrix\n",
    "disp = metrics.ConfusionMatrixDisplay.from_predictions(y_test, knn_predicted)\n",
    "disp.figure_.suptitle(\"Confusion Matrix\")\n",
    "print(f\"Confusion matrix:\\n{disp.confusion_matrix}\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85b55b81-0536-4a7d-887d-496e3b93bbbc",
   "metadata": {},
   "source": [
    "#### Logistic Regression Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4c763fea-82d1-47a1-9970-fbc883f44c95",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x300 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "logistic_predicted = logistic.predict(X_test)\n",
    "\n",
    "# Create subplot to visualize samples and predictions of Logistic Regression for the first 4 test samples\n",
    "_, axes = plt.subplots(nrows=1, ncols=4, figsize=(10, 3))\n",
    "\n",
    "# Loop through samples and corresponding predictions of Logistic Regression\n",
    "for ax, image, prediction in zip(axes, X_test, logistic_predicted):\n",
    "    ax.set_axis_off()  # Turn off the axes of the subplot\n",
    "    image = image.reshape(8, 8)  # Reshape the sample to its original size 8x8\n",
    "    ax.imshow(image, cmap=plt.cm.gray_r, interpolation=\"nearest\")  # Display the image in grayscale\n",
    "    ax.set_title(f\"Prediction of LR: {prediction}\")  # Set the title with Logistic Regression's prediction\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "366d3792-f97a-4244-91a0-591d97c648b7",
   "metadata": {},
   "source": [
    "**Confusion Matrix**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1384d2e7-6517-4d27-b38f-198daf0ef8d5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix:\n",
      "[[17  0  0  0  0  0  0  0  0  0]\n",
      " [ 0 11  0  0  0  0  0  0  0  0]\n",
      " [ 0  0 17  0  0  0  0  0  0  0]\n",
      " [ 0  0  0 15  0  1  0  0  1  0]\n",
      " [ 0  0  0  0 25  0  0  0  0  0]\n",
      " [ 0  0  0  0  0 22  0  0  0  0]\n",
      " [ 0  0  0  0  0  0 19  0  0  0]\n",
      " [ 0  0  0  0  0  0  0 18  0  1]\n",
      " [ 0  0  0  0  0  0  0  0  8  0]\n",
      " [ 0  0  0  0  0  0  0  0  1 24]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create and display confusion matrix\n",
    "disp = metrics.ConfusionMatrixDisplay.from_predictions(y_test, logistic_predicted)\n",
    "disp.figure_.suptitle(\"Confusion Matrix\")\n",
    "print(f\"Confusion matrix:\\n{disp.confusion_matrix}\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f038ce61-7f09-4993-97ed-82e5bb08b0b3",
   "metadata": {},
   "source": [
    "### Some samples misclassified by one model but correctly classified by the other (and vice versa)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5ef4bc25-4e25-407e-b6b1-50b6f6546da9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of samples where K-NN predicted incorrectly but Logistic Regression predicted correctly: 0\n",
      "Number of samples where Logistic Regression predicted incorrectly but K-NN predicted correctly: 2\n"
     ]
    }
   ],
   "source": [
    "# Find samples where K-NN predicted incorrectly but Logistic Regression predicted correctly\n",
    "incorrect_knn_correct_logistic = np.where((knn_predicted != y_test) & (logistic_predicted == y_test))[0]\n",
    "\n",
    "# Find samples where Logistic Regression predicted incorrectly but K-NN predicted correctly\n",
    "incorrect_logistic_correct_knn = np.where((logistic_predicted != y_test) & (knn_predicted == y_test))[0]\n",
    "\n",
    "# Create DataFrame for samples where K-NN predicted incorrectly but Logistic Regression predicted correctly\n",
    "df_incorrect_knn_correct_logistic = pd.DataFrame({\n",
    "    'K-NN predicted': knn_predicted[incorrect_knn_correct_logistic],\n",
    "    'Logistic Regression predicted': logistic_predicted[incorrect_knn_correct_logistic],\n",
    "    'Actual': y_test[incorrect_knn_correct_logistic]\n",
    "})\n",
    "\n",
    "# Create DataFrame for samples where Logistic Regression predicted incorrectly but K-NN predicted correctly\n",
    "df_incorrect_logistic_correct_knn = pd.DataFrame({\n",
    "    'K-NN predicted': knn_predicted[incorrect_logistic_correct_knn],\n",
    "    'Logistic Regression predicted': logistic_predicted[incorrect_logistic_correct_knn],\n",
    "    'Actual': y_test[incorrect_logistic_correct_knn]\n",
    "})\n",
    "\n",
    "print(\"Number of samples where K-NN predicted incorrectly but Logistic Regression predicted correctly:\", len(df_incorrect_knn_correct_logistic))\n",
    "print(\"Number of samples where Logistic Regression predicted incorrectly but K-NN predicted correctly:\", len(df_incorrect_logistic_correct_knn))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "268c431c-b03e-4759-aa3e-596d179ca034",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Empty DataFrame\n",
      "Columns: [K-NN predicted, Logistic Regression predicted, Actual]\n",
      "Index: []\n"
     ]
    }
   ],
   "source": [
    "# Samples where K-NN predicted incorrectly but Logistic Regression predicted correctly\n",
    "print(df_incorrect_knn_correct_logistic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d865a038-dc74-4ca2-ad80-0f3546e949d3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   K-NN predicted  Logistic Regression predicted  Actual\n",
      "0               3                              5       3\n",
      "1               3                              8       3\n"
     ]
    }
   ],
   "source": [
    "# Samples where Logistic Regression predicted incorrectly but K-NN predicted correctly\n",
    "print(df_incorrect_logistic_correct_knn)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df757216-0636-475f-a45a-1ff830cd4b5a",
   "metadata": {},
   "source": [
    "### Some samples misclassified by both models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "539f07de-48f1-43b8-830e-90b925dc7cae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of samples where both models predicted incorrectly: 2\n",
      "\n",
      "Samples where both models predicted incorrectly:\n",
      "K-NN predicted: 9, Logistic Regression predicted: 9, Actual: 7\n",
      "K-NN predicted: 4, Logistic Regression predicted: 8, Actual: 9\n"
     ]
    }
   ],
   "source": [
    "# Find samples where both models predicted incorrectly\n",
    "incorrect_both_models = np.where((knn_predicted != y_test) & (logistic_predicted != y_test))[0]\n",
    "\n",
    "print(\"Number of samples where both models predicted incorrectly:\", len(incorrect_both_models))\n",
    "\n",
    "print(\"\\nSamples where both models predicted incorrectly:\")\n",
    "for idx in incorrect_both_models[:5]:  # Display the first 5 samples\n",
    "    print(f\"K-NN predicted: {knn_predicted[idx]}, Logistic Regression predicted: {logistic_predicted[idx]}, Actual: {y_test[idx]}\")"
   ]
  }
 ],
 "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": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}