load_digits.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_digits\n",
    "digits= load_digits()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".. _digits_dataset:\n",
      "\n",
      "Optical recognition of handwritten digits dataset\n",
      "--------------------------------------------------\n",
      "\n",
      "**Data Set Characteristics:**\n",
      "\n",
      ":Number of Instances: 1797\n",
      ":Number of Attributes: 64\n",
      ":Attribute Information: 8x8 image of integer pixels in the range 0..16.\n",
      ":Missing Attribute Values: None\n",
      ":Creator: E. Alpaydin (alpaydin '@' boun.edu.tr)\n",
      ":Date: July; 1998\n",
      "\n",
      "This is a copy of the test set of the UCI ML hand-written digits datasets\n",
      "https://archive.ics.uci.edu/ml/datasets/Optical+Recognition+of+Handwritten+Digits\n",
      "\n",
      "The data set contains images of hand-written digits: 10 classes where\n",
      "each class refers to a digit.\n",
      "\n",
      "Preprocessing programs made available by NIST were used to extract\n",
      "normalized bitmaps of handwritten digits from a preprinted form. From a\n",
      "total of 43 people, 30 contributed to the training set and different 13\n",
      "to the test set. 32x32 bitmaps are divided into nonoverlapping blocks of\n",
      "4x4 and the number of on pixels are counted in each block. This generates\n",
      "an input matrix of 8x8 where each element is an integer in the range\n",
      "0..16. This reduces dimensionality and gives invariance to small\n",
      "distortions.\n",
      "\n",
      "For info on NIST preprocessing routines, see M. D. Garris, J. L. Blue, G.\n",
      "T. Candela, D. L. Dimmick, J. Geist, P. J. Grother, S. A. Janet, and C.\n",
      "L. Wilson, NIST Form-Based Handprint Recognition System, NISTIR 5469,\n",
      "1994.\n",
      "\n",
      "|details-start|\n",
      "**References**\n",
      "|details-split|\n",
      "\n",
      "- C. Kaynak (1995) Methods of Combining Multiple Classifiers and Their\n",
      "  Applications to Handwritten Digit Recognition, MSc Thesis, Institute of\n",
      "  Graduate Studies in Science and Engineering, Bogazici University.\n",
      "- E. Alpaydin, C. Kaynak (1998) Cascading Classifiers, Kybernetika.\n",
      "- Ken Tang and Ponnuthurai N. Suganthan and Xi Yao and A. Kai Qin.\n",
      "  Linear dimensionalityreduction using relevance weighted LDA. School of\n",
      "  Electrical and Electronic Engineering Nanyang Technological University.\n",
      "  2005.\n",
      "- Claudio Gentile. A New Approximate Maximal Margin Classification\n",
      "  Algorithm. NIPS. 2000.\n",
      "\n",
      "|details-end|\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(digits.DESCR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1797,)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "digits.target.shape "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1797, 64)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "digits.data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1797, 8, 8)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "digits.images.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "x = digits.images[1500]\n",
    "\n",
    "print(digits.target[1500])\n",
    "plt.gray()\n",
    "plt.imshow(x)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preprocess"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(digits.data, digits.target, test_size=0.3)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.,  0.,  6., 16., 16., 12.,  3.,  0.,  0.,  0., 13., 12., 10.,\n",
       "       16.,  2.,  0.,  0.,  1., 16.,  3., 10., 11.,  0.,  0.,  0.,  1.,\n",
       "        7.,  1., 16.,  3.,  0.,  0.,  0.,  0.,  0.,  7., 15.,  4.,  1.,\n",
       "        0.,  0.,  0., 10., 16., 16., 16.,  4.,  0.,  0.,  0.,  2., 16.,\n",
       "        8.,  3.,  0.,  0.,  0.,  0.,  6., 16.,  3.,  0.,  0.,  0.])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_test = scaler.transform(X_test)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.        , 0.        , 0.375     , 1.        , 1.        ,\n",
       "       0.75      , 0.1875    , 0.        , 0.        , 0.        ,\n",
       "       0.8125    , 0.75      , 0.625     , 1.        , 0.125     ,\n",
       "       0.        , 0.        , 0.0625    , 1.        , 0.1875    ,\n",
       "       0.625     , 0.6875    , 0.        , 0.        , 0.        ,\n",
       "       0.06666667, 0.4375    , 0.0625    , 1.        , 0.1875    ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.4375    , 0.9375    , 0.25      , 0.07142857, 0.        ,\n",
       "       0.        , 0.        , 0.625     , 1.        , 1.        ,\n",
       "       1.        , 0.25      , 0.        , 0.        , 0.        ,\n",
       "       0.125     , 1.        , 0.5       , 0.1875    , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.375     , 1.        ,\n",
       "       0.1875    , 0.        , 0.        , 0.        ])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "\n",
    "pca = PCA(n_components=32)\n",
    "X_train = pca.fit_transform(X_train)\n",
    "X_test = pca.transform(X_test)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1257, 32), (540, 32))"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# performance metric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score, precision_score, recall_score\n",
    "\n",
    "def calculate_metrics(y_train, y_test, y_pred_train, y_pred_test):\n",
    "    acc_train = accuracy_score(y_true= y_train, y_pred= y_pred_train)\n",
    "    acc_test = accuracy_score(y_true=y_test, y_pred=y_pred_test)\n",
    "\n",
    "    p = precision_score(y_true=y_test, y_pred=y_pred_test, average='weighted')\n",
    "    r = recall_score(y_true=y_test, y_pred=y_pred_test, average='weighted')\n",
    "\n",
    "    print(f\"acc train: {acc_train} - acc test: {acc_test} - precision: {p} - recall: {r}\")\n",
    "    \n",
    "    return acc_train, acc_test, p, r  \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# classification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Random forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "acc train: 1.0 - acc test: 0.9796296296296296 - precision: 0.9799063435464553 - recall: 0.9796296296296296\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "rf = RandomForestClassifier(max_depth=128, n_estimators=256)\n",
    "rf.fit(X_train, y_train)\n",
    "\n",
    "y_pred_train = rf.predict(X_train)\n",
    "y_pred_test = rf.predict(X_test)\n",
    "\n",
    "acc_train_rf, acc_test_rf, p_rf, r_rf= calculate_metrics(y_train, y_test, y_pred_train, y_pred_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-4 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-4 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-4 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 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",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-4 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-4 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-4 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-4 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-4 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-4 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\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-4 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-4 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-4 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-4 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-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(kernel=&#x27;poly&#x27;)</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-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;SVC<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.svm.SVC.html\">?<span>Documentation for SVC</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>SVC(kernel=&#x27;poly&#x27;)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "SVC(kernel='poly')"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "\n",
    "svm = SVC(kernel='poly')\n",
    "\n",
    "svm.fit(X_train, y_train)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "acc train: 0.9984089101034208 - acc test: 0.9888888888888889 - precision: 0.9896255532261823 - recall: 0.9888888888888889\n"
     ]
    }
   ],
   "source": [
    "y_pred_train = svm.predict(X_train)\n",
    "y_pred_test = svm.predict(X_test)\n",
    "\n",
    "acc_train_svm, acc_test_svm, p_svm, r_svm = calculate_metrics(y_train, y_test, y_pred_train, y_pred_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. ANN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neural_network import MLPClassifier\n",
    "\n",
    "ann = MLPClassifier(hidden_layer_sizes=256,batch_size=64, solver='adam',learning_rate='adaptive')\n",
    "ann.fit(X_train, y_train)\n",
    "\n",
    "y_pred_train = ann.predict(X_train)\n",
    "y_pred_test = ann.predict(X_test)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "acc train: 1.0 - acc test: 0.9851851851851852 - precision: 0.985630917636097 - recall: 0.9851851851851852\n"
     ]
    }
   ],
   "source": [
    "y_pred_train = ann.predict(X_train)\n",
    "y_pred_test = ann.predict(X_test)\n",
    "\n",
    "acc_train_ann, acc_test_ann, p_ann, r_ann = calculate_metrics(y_train, y_test, y_pred_train, y_pred_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-5 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-5 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-5 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 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",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-5 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-5 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-5 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-5 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-5 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-5 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\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-5 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-5 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-5 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-5 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-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier(n_neighbors=8)</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-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;KNeighborsClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.neighbors.KNeighborsClassifier.html\">?<span>Documentation for KNeighborsClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>KNeighborsClassifier(n_neighbors=8)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=8)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "knn = KNeighborsClassifier(n_neighbors=8)\n",
    "knn.fit(X_train, y_train)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "acc train: 0.9864757358790772 - acc test: 0.9851851851851852 - precision: 0.9858392487157797 - recall: 0.9851851851851852\n"
     ]
    }
   ],
   "source": [
    "y_pred_train = knn.predict(X_train)\n",
    "y_pred_test = knn.predict(X_test)\n",
    "\n",
    "acc_train_knn, acc_test_knn, p_knn, r_knn = calculate_metrics(y_train, y_test, y_pred_train, y_pred_test)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "acc_train = [acc_train_knn, acc_train_rf, acc_train_svm, acc_train_ann]\n",
    "title = ['KNN', 'RF', 'SVM', 'ANN']\n",
    "colors = ['black', 'red', 'yellow', 'orange']\n",
    "\n",
    "plt.bar(title, acc_train, color=colors)\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "acc_test = [acc_test_knn, acc_test_rf, acc_test_svm, acc_test_ann]\n",
    "title = ['KNN', 'RF', 'SVM', 'ANN']\n",
    "colors = ['black', 'red', 'yellow', 'orange']\n",
    "\n",
    "plt.bar(title, acc_test, color=colors)\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "p = [p_knn, p_rf, p_svm, p_ann]\n",
    "title = ['KNN', 'RF', 'SVM', 'ANN']\n",
    "colors = ['black', 'red', 'yellow', 'orange']\n",
    "\n",
    "plt.bar(title, p, color=colors)\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "r = [r_knn, r_rf, r_svm, r_ann]\n",
    "title = ['KNN', 'RF', 'SVM', 'ANN']\n",
    "colors = ['black', 'red', 'yellow', 'orange']\n",
    "\n",
    "plt.bar(title, r, color=colors)\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Learning_venv",
   "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": "3.12.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}