code-fasl-5.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "درخت تصمیم:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris = load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = iris.data[: , 2:]\n",
    "y = iris.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\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-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 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-1 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-1 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-1 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-1 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-1 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-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 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-1 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-1 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-1 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-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 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-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 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-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 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-1 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-1 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-1 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-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",
       "}\n",
       "\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",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 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-1 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-1 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-1 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-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\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",
       "}\n",
       "\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",
       "/* 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-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>DecisionTreeClassifier(max_depth=2, random_state=42)</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;DecisionTreeClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.tree.DecisionTreeClassifier.html\">?<span>Documentation for DecisionTreeClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeClassifier(max_depth=2, random_state=42)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier(max_depth=2, random_state=42)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree = DecisionTreeClassifier(max_depth= 2, random_state= 42)\n",
    "tree.fit(x, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ترسیم درخت:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import plot_tree\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.4, 0.8333333333333334, 'sepal length (cm) <= 2.45\\ngini = 0.667\\nsamples = 150\\nvalue = [50, 50, 50]\\nclass = 0'),\n",
       " Text(0.2, 0.5, 'gini = 0.0\\nsamples = 50\\nvalue = [50, 0, 0]\\nclass = 0'),\n",
       " Text(0.30000000000000004, 0.6666666666666667, 'True  '),\n",
       " Text(0.6, 0.5, 'sepal width (cm) <= 1.75\\ngini = 0.5\\nsamples = 100\\nvalue = [0, 50, 50]\\nclass = 1'),\n",
       " Text(0.5, 0.6666666666666667, '  False'),\n",
       " Text(0.4, 0.16666666666666666, 'gini = 0.168\\nsamples = 54\\nvalue = [0, 49, 5]\\nclass = 1'),\n",
       " Text(0.8, 0.16666666666666666, 'gini = 0.043\\nsamples = 46\\nvalue = [0, 1, 45]\\nclass = 2')]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.tree import plot_tree\n",
    "plt.figure(figsize= (10, 8))\n",
    "plot_tree(tree, feature_names= iris.feature_names, class_names= ['0', '1', '2'], filled= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\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-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 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-2 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-2 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-2 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-2 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-2 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-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 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-2 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-2 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-2 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-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 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-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 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-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 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-2 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-2 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-2 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-2 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-2 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-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 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-2 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-2 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-2 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-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 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-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 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-2 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-2 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-2 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-2 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-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeRegressor(max_depth=2, random_state=42)</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-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;DecisionTreeRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.tree.DecisionTreeRegressor.html\">?<span>Documentation for DecisionTreeRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeRegressor(max_depth=2, random_state=42)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeRegressor(max_depth=2, random_state=42)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor \n",
    "tree_reg = DecisionTreeRegressor(max_depth= 2, random_state= 42)\n",
    "tree_reg.fit(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.4, 0.8333333333333334, 'sepal length (cm) <= 2.45\\nsquared_error = 0.667\\nsamples = 150\\nvalue = 1.0'),\n",
       " Text(0.2, 0.5, 'squared_error = 0.0\\nsamples = 50\\nvalue = 0.0'),\n",
       " Text(0.30000000000000004, 0.6666666666666667, 'True  '),\n",
       " Text(0.6, 0.5, 'sepal width (cm) <= 1.75\\nsquared_error = 0.25\\nsamples = 100\\nvalue = 1.5'),\n",
       " Text(0.5, 0.6666666666666667, '  False'),\n",
       " Text(0.4, 0.16666666666666666, 'squared_error = 0.084\\nsamples = 54\\nvalue = 1.093'),\n",
       " Text(0.8, 0.16666666666666666, 'squared_error = 0.021\\nsamples = 46\\nvalue = 1.978')]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize= (10, 8))\n",
    "plot_tree(tree_reg, feature_names= iris.feature_names, class_names= ['0', '1', '2'], filled= True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---------------------------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "svm:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "from sklearn.pipeline import Pipeline \n",
    "from sklearn.preprocessing import StandardScaler \n",
    "from sklearn.svm import LinearSVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris = datasets.load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = iris['data'][:, (2, 3)]\n",
    "y = (iris['target'] == 2).astype(np.float64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "svm_clf = Pipeline([(\"scaler\", StandardScaler()), (\"linear_svc\", LinearSVC(C= 1, loss= 'hinge', random_state= 8)),  ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {\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-3 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 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-3 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-3 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-3 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-3 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-3 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-3 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 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-3 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-3 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-3 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-3 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-3 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-3 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 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-3 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 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-3 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-3 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-3 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-3 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-3 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-3 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-3 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-3 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-3 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-3 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-3 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-3 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-3 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-3 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 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-3 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-3 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-3 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-3 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-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
       "                (&#x27;linear_svc&#x27;, LinearSVC(C=1, loss=&#x27;hinge&#x27;, random_state=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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;Pipeline<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
       "                (&#x27;linear_svc&#x27;, LinearSVC(C=1, loss=&#x27;hinge&#x27;, random_state=8))])</pre></div> </div></div><div class=\"sk-serial\"><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\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;StandardScaler<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>StandardScaler()</pre></div> </div></div><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\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;LinearSVC<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.svm.LinearSVC.html\">?<span>Documentation for LinearSVC</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>LinearSVC(C=1, loss=&#x27;hinge&#x27;, random_state=8)</pre></div> </div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('scaler', StandardScaler()),\n",
       "                ('linear_svc', LinearSVC(C=1, loss='hinge', random_state=8))])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_clf.fit(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_clf.predict([[5.5, 1.7]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "دسته بندی با ماشین بردار پشتیبان غیرخطی:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import make_moons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "x, y = make_moons(n_samples= 100, noise= 0.15, random_state= 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_dataset(x, y, axes):\n",
    "    plt.plot(x[:, 0] [y == 0], x[:, 1] [y == 0], \"bs\")\n",
    "    plt.plot(x[:, 0] [y == 1], x[:, 1] [y == 1], \"g^\")\n",
    "    plt.axis(axes)\n",
    "    plt.grid(True, which= 'both')\n",
    "    plt.xlabel(r\"$x_1$\", fontsize= 20)\n",
    "    plt.ylabel(r\"$x_2$\", fontsize= 20, rotation= 0)\n",
    "    plot_dataset(x, y, [-1.5, 2.5, -1, 1.5])\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Ozvrovz3bcv3e9V09MFLnkFttY602+jbGvTzezOeyu6ysLC3atEi7j+zWok2LNONjM1wT5lLNrj/fTpJo+7k+6AAYuKKCIhUVFFldRkJ6L4sPSWR5vJnP5QRV+6os2zcJMIvjVl198MEH2rlzp3bu3ClJ2rdvn3bu3Cmfz2dtYQCSZtiwzn1yosnNlcKNRvdeFh+SyPJ4M5/L7gzDUMWmiq6Vdk5bYQeEOC7obN++XWVlZSor65z4N2/ePJWVlWnRokUWVwakl1Qury4ulvbskWprI9/27Om8rrtQD4wnwq86jzwxv3mb+VxOsPP9naptrLVk3yTATI4bupo8ebJrfpEATtX7zKupY6YmfcimuLhvkOmt9+rT9kC7fM0+BRUMe31QQe1v2a/2QHufidS9j5xoD7Srvimx53IawzD0ZOOTaTNEB3dzXNBxsuqGat2x7g49dMVDmlYyzepygIRZeeZVPLxZXtXMrul3InW4kNP3yAmvlF8jDep8rpwc6bnnep6vFe65nKhqX5X2fri3z/3de3Xs+O8NhEPQSREr/gIGksHKM68SkchE6ohHTrQUdd4ktUsqlHShyw4SDc3NibRvUmiIzq7/3kBvjpuj41Th/gIGutuwb4MjjhSw8syrVGlstLoC67QH2rW/ZX/YkCP1HKIDnIAenRRw2l/ASD3DMLTglQW27/FLh+XVPp/0la9YXYV1vFlebb15q56vfF6TJk1SVphNjNwyRIf0QNBJge69ORLj3OgrtMJFsvecl96v5RA3vaabmqT2NO+sKMovUmleqcpGlkXcpK33ZO3ehg3rf/J4sjAfEt0xdJVkbjn1GcnTfYWLZN/XRrotr0ZkocnaEyZEvo0d23ldqvWeD8nrEQSdJEunDcaQmNAKF7vPeYlnqTbcLeJk7W5OnIje45MszIdEbwxdJVH3v4DDvTmwegGhFS69XyN2nPOS6FLt/jDMALMwHxLhEHSSaCCblSE9VNZX9jggMsSuc17MPvPKydsu5OR0zkOBfTAfEuEQdJIoWX8Bwx3o8XPOxoPhPPecdZNt0Vc6rAhEYgg6SebkU58Rm0SHXtK9x8/pwwyFLtso0OnSYUUgEkPQAQZgIEMvoR6/xpZGbdmyJeyeJW7u8bPrMEPopPRok21zcxm2shN6RxENQQcYgIEOvRQVFGlk3kg15jVG3bNkIOy434mdhxlCJ6Xbrc0QWbr3jiI6gg6QICcMvYQ/nLKn3NzON/ZUvnHbfZghlpPS05nder2YD4loCDpAguw69NJdPPudpOqNnWEG57NjrxfzIREJQQdIgJ2HXuyOYQZ3oNcLTkHQARJg96EXO2OYAUAqEXSAOKXj0IvZE5oZZgCQKgQdIE5uH3rpHWoaG6WvfCX6id5WTGgGgFgQdIA4uXnoJZZVWuGkekIzAMSKoAMkINahl1iGfOy0w24sq7QAwEkIOkCSxLqHzeuvJ68Gu+13AjiVHTfeRGwIOkCShO0dKamWrrhDWveQ1DBNJ05IR44krwY77ncCOI1dN95EbAg6QMoY0tRyaXhd538bpkpK/qos9jsBBsaOG28idgQdIFVKK6WzPtp756yazo/r2WsH1ok0HNPRIdXXF8jnk0pLU18XYCaCDpAShvSFhVIwU/IEOv/7hYVS/XSrC0Oaij4cky1psn7wA4PhGDiex+oCgLQQ6s3xfHRchCdwslfHBcye0FzdUK1xy8epuqHavCdFD7ENx2REnd8FOAFBB0i6br053X3Uq2MYhjVlhRFapRVNTo704otSbe3Jm5l/9RuGofIN5aprqlP5hvKUtA/BCnAvhq4AE1U3VOuOdXfooSse0hBN67yz+9yc7j7q1dnWVKXRqS0zolSt0oq2VPfVQyfPEUvFuWG9g9XUMVNdc3QHAIIOYJreb5i/nz5V3lyp7QsLpaBH8oQ5MiLo0SP7KnTPqAWpLziCZK/Sij43xJBmL5QKO+cypeI0+O4HtHIgK+A+DF0BJun9hrnbX6ld/2jXkDG+8CFHkjxBHTrxjjqMjhRWaq2oc0N6zWXqfhp8iJnDTKEDWjMzOocVQ8EqNFzmtiEtn0/asaPzVldndTXOEcuQLhtv2hc9OoAJur9hBoyTPRHbvrFNO78Z/Vys03NO19+2/C2F1dpVr5VpH+neqyPJ1GGm7uFU6hmsppdOd9WQVqLnmIGNN50uoaDT2tqqkpISHTp0SGPGjNGePXuUnZ3d57oPP/xQU6dO1datW+X1elVZWalLL710wEUDdhPtDXPGOTMinotV3VCtrz37NV1/2vW6Ulemqlx7ijCXqXevjlnDTL3DaUj3Xh03DWlxjtnAsPGmcyU0dDVo0CCVl5dLkvbt26fVq1f3uSYYDOprX/uatm7dKo/HoyeeeIKQA1fqPfwR0nsYJNzjyjeUa/eR3Xr84OO2Wn2VeqHenPC/kjLk0YI/LtCCjQsiDjPFKxROu4cc6WSw+vbL3zbta4XjhGGx3FyD4Rg4XsJzdObMmaPij+LtvffeK7/f3+Pzd955p9auXStJ+ulPf6qrr756AGUC9tXfG2b3+SXhHidJez/cq6p9VUmv1bYy26WCyHOZDAVV/896bT+4vaud+2vfaELh1BPhV2CGMvTG0Tf6fK3l6yu75riEbj5f3F/ekiX0sXriCWnbNr8efPAVvf56B70YcLyEg47X69WiRYskSW+99ZZWrlzZ9bkHH3xQy5YtkyR973vf07e//e0BlgnYU39vmB55wvYE9O4F8sijik0VtnrDS6mAV/p1jbSiNuzt8UnbVVxQHHevWSTtgXb5mn0KKlKwCvN8wUx967mFmjDB0IQJ6rqNHRt/2Am30ssuPv5xqaxMKi1tJuTAFQY0GXnWrFn6yU9+ojfeeEM/+tGPdPPNN2vt2rW66667JEnXX3+9fvKTn5hSKGBH/b1hBhXU/pb9ag+0y5vl7bq/95yeoIKqbaw1fR5ItP1qJJtNoGwp6ryFccy/Xn9976997u89FypW3iyvamaHnyT+6v5X9a113+r7oO67WXc7oyzewxwjTVxP5hJ6IJ0NKOhkZmbqnnvu0XXXXae3335b3/zmN/X4451zDaZMmaJVq1bxgwtXi/aGGTJi0IgeIae/SbBmveHFssomN9fcXY1jEVqqG/vEWEMP7+7sNQsXKEO9ZqFVWbEqKijqM0ncMAzNeXFOxK+loKfbGWWJ/Rv1N3EdgLkGvI/Of/zHf+hTn/qUJOmRRx5RW1ubzj//fK1du1Y5OTlhH3PgwAH9/Oc/1/Tp01VcXKycnByNHDlS//7v/65t27YNtCQgpYoKinRh4YURb6PyR/W4PtE5PfGK7Syj6D0+yRBaqltb2zkfpF+Z7Xr3w9h6zQaqvx46eYJS/v7OOUUJSHTiOoDEDXgfnYyMDM2ePVtz586VJI0cOVLr1q1TQUFBxMcsW7ZM9913n0pLSzV9+nQNHz5cb775pp5//nk9//zzWrNmja699tqBlgYkrPtRDtNKppn2vN3n9PTXO+Hm3tC4luoGvHr80hqNLOm/16z3ooh49e6hq6uTbrih10WtIzrnFCWgd29OSCp6dWLpSWPTu+gcNRSMLgMOOm+++aYqKiq6Pm5tbZXXG/2XwEUXXaRXXnlFn//853vc/6c//UlTp07VbbfdppkzZ/b7PEhvyQwjydooLp45Pe8d9Lr+l2qsb76fLCpScWH4+Ttm6zGk1fjRzQTJCrmx/hzEs+ndAPOiK9l1KBj9G1DQOXTokC6//HI1NTVp6NChOnLkiN5//30tXbpUDz74YMTHfeUrXwl7/7/8y79oypQpqqys1K5du/TpT396IOXBxZIZRpJ59lG4OT0dHR3asmWLJk2apKysLI0YNELvHfSmxS/VdNpxNtGJ69HE+3PApneJi2comDa2l4SDTmtrq774xS+qoaFBgwcPVlVVle655x49//zz+uUvf6l58+bprLPOivt5QzssZ2VxOgUiS1YYScWKmN6TYP1+vxrzGlU2sqzr9b9jb/r8Uk2XN99EJq73hwNJgf4llCY6Ojp0zTXXaPv27crKytIzzzyjsrIyLV68WC+88IJOnDihe+65RytWrIjreX0+n6qrq1VYWKjzzz8/kdKQBpIZRtJ9RQxzEHoye15LuJVeiWKZOhCbhILOnDlztG7dOknSihUrNGNG5xvA+PHjddVVV+m5557TY489pu9///sqLS2N6Tn9fr9uvPFGtbW16b777lNmZmb/D0JaSlYYSdWyb7tiDkJfdh5aS/dQDsQq7uXlixcv1qOPPipJqqio0C233NLn8xkZGero6OjaObk/wWBQs2bN0ubNmzV79mzdeOON8ZaFNJHM5bmpWvadKqHeiGi690bYdTm61YqLpQsvjHyzIuSwTB2IXVxB59FHH9WSJUskSbfccosWL17c55rzzz+/61yrp556Srt27Yr6nMFgULfccovWrFmjG264Qb/61a/iKQlpJllhJNGjHOys+341kW7p1DvjJm4L5UAyxRx0XnrpJc2ZM0eSNGPGjKjzbyoqKuTxeBQMBrVgwYKI1wWDQd18881avXq1rr/+eq1atUoez4D3MIRLxXIQ48ynZqqqPv7DMeNZEeMkduyNwMC4MZQDyRTzHJ0rr7wy5s24PvGJTygQCES9JhRyfvvb3+raa6/V448/zrwcRBXLQYwnAid094a7Na1kWlzzaZKxIgZIhmQsU0f/2HDRuSxZwx0arvrtb3+ra665Rk888QQhB/2K9SDGRA/HNHNFzEDxSxWREMqtYeeJ6YjOkqBzzz33aPXq1Ro8eLDOPfdc/fCHP+xzzcyZM7vO0AJCoh3E6KZltvxSRTR2CuXpJF32fHIbS4LOW2+9JUn64IMPdO+994a95uyzz44YdJYvX677779f7777rsaPH69ly5bpoosuSlK1sDu3LrPllyoADJwlM39XrVolwzCi3mbNmhX2sU8//bTmzZuniooK7dixQ+PHj9eMGTN06NCh1H4TsAWW2Zon3uXoAOAEjlvi9NOf/lSzZ8/WzTffrHHjxulXv/qV8vLy9Nhjj1ldGj5S3VCtccvHqbqhOulfi2W25mE5OgA3ctSBUu3t7aqtrdXdd9/ddZ/H49G0adO0devWsI9pa2tTW1tb18ctLS2SOndijnUVGfoKtV3vNjQMQ/Or56uuqU7zq+fr1VmvJm2ejGEY+sEffxD1NOgf/PEHmlI8xdZzdSK1pRUKCztv0digzIjs1JZORjuah7Y0T6Jt6Kig09TUpEAgoDPOOKPH/WeccYZ2794d9jFLly7t2uSwu40bNyovLy8pdaaTqqqee9a81vKaahtrJXWufvrR0z9SWX5ZUr62P+hX/eH6qMts6w/X6w//+wdle7KTUoOZerclEmfHtvzr+3/Vb975jWaPmq3xp463upyY2LEdnYq2HLjjx48n9DhHBZ1E3H333Zo3b17Xxy0tLSoqKtKUKVM0dOhQCytzNr/fr6qqKl122WVdJ24bhqH/XvXfPVY/vfjhiyq/tjxpPSoXXnqhmo5HXpo0PG+4RuWPSsrXNku4tkRiktGWG/Zt0Hcqv6OfTf+Zpo6ZmtBzhH423ml7R384/gfN/4/5tu9l5DVpDtrSPEeOHEnocY4KOsOGDVNmZqbee++9Hve/9957GjlyZNjHeL1eeb1995PIzs7mRWeC7u24fu/6rt4cqXOeTG1jrTb6NiZt9VPJ0BKVDC1JynOnGq9J85jVloZhaNGmRdp9ZLcWbVqkGR+bkVBA6f6zkeyfCTPxmjQPbTlwibafoyYj5+TkaMKECdqwYUPXfcFgUBs2bNDEiRMtrAysfoIbdd+6INHJ7b1/NviZAFLLUUFHkubNm6ff/OY3Wr16terq6nTbbbeptbVVN998s9WlpTVWP8FtzAoovX82Qj8TP/7zj02vGUBfjgs61157rR544AEtWrRIn/rUp7Rz5069/PLLfSYoI3U4ZBBuFCmgxBPaI/V0StIPN/9QwWD4ifQAzOO4oCNJt99+u95++221tbVp27Ztuvjii60uKa259eRvpC+zhmIj9XRK0nH/cS3dstSUegFE5qjJyLAnDhlMH9UN1bpj3R166IqHNK1kmtXlJE3vY0VC4jlepHtPZ6Q/An605Ue6e9Ld8ngc+Tcn4AgEHZiCQwbdzzAMlW8oV11Tnco3lGvqmKm2XiKdqP4CSmgotr9DY/vr6ZQ6e3X+983/1ZfGfsmU2gH0RdABEJNwK5ASWSLt89n7VPZ4hmKj9VJ27+k0DENfX/t17W7a3eN5PfLovzf/t/713H91ZWg0i91fM7A3gg6AfnWfsxLaDDKWXo3efD5p7FjpxInI1+TmJn6m1oZ9G3R73e36zcd/o8vPvTz+J5C5Q7Ghns71e9frH03/6PP5oIIDCo3pINmvGbgfQQdAv3rPWYlnrkp3TU3R37Ckzs83NcX/pmUYhha8skDvtL2jBa8sSHhzP8ncoVizhsLSVTJfM0gPzIADEJVTNoOsrK/ssfuwXfZuYlUiYC16dIA0Feu8BzNWICWzPsm8obVkYFUiYC2CDpCGYp33sHu3NcMujY3S5z4X+7yMWIbWrFwaz6pEwDoEHcCBQm/aCyY8pPNy+r5pNzZKx45Jp50mFRb2fXxjY2zzHhoPmbMCKV7HjsU+L6OoqGdvTkj3Xh1JabE0HkBfBB3AYbrvZ3PDynIZv54qKb437ZycGK/LtP+wSyxDa5JMWRoPwHkIOoDDdH9jN86skUorpfr43rTb45j3audhl1hWNC3YuEAyZMv5OwCSj1VXgIP0WQEVzJS+sFCSPVY+9WfYsM65NdHk5nYOucXCH+x/aK3+aL22N24f0OGcsE6sr5lhw1JTD5yHHh3AQfoM03gC0lmJ9epYobi4cwJxf6upon2+u95Dax0dHdqyZYsmTZqkrKwsGYahW/9wq1raWiLO36FXx95ifc2whw4iIegADtF7CXWXUK9O/XTFO1fHCsXF/b8pxRp0pJ5Da36/X415jSobWabs7Gyt37tef33vr30eM9Cl8elyuKldxPKaASJh6ApwiFBvTo+QI/Xs1UGX7vN3wgktjY93w8Peh5vaZcNEAOERdJC2qhuqNW75OFU3VFtdSr/6e9NW0BP3XJ3+Vl5ZOe/BjHkZydqRONzhpgDsi6ErpKXef5XbfV+V/t605QlK+fulzHYpENtS7+eeC7/HToiV8x7MmJeRjB2J7bwDM4DwCDpIS+H+Krfzvirh3rQbG6WvfKXbUvHWETGHnNxc6fzzYw8y8RzHYBYz5mWYvTTerMNNAaQOQQdpx6l/lfd50y6U3twcPoD0tzNyPMEk1uMiQscxuFWkyeBOef0A6Yqgg7Tjpr/KU7Eapakp9uMY7Bh0zOqNsupwUwADQ9BBWnHjX+UsdY7MrN6oWHZgdurrxymsGD6FOxB0kFbc9le50yZVp5pZvVHxrOCy8twvt2L4FANB0EHacONf5U6bVO1UyVjBhdg5ffgU1iLoIG247a9yp06qdio7H24KIDKCDmwnWXNO7PxX+V/f/6vmr5ivZVcui/l7dtOkagBIFoIObCXZc07s+Fe5YRh6/ODj2vvh3pi/ZzdOqgaAZOAICNhKOm6vX7WvSns/3Csp9u850rlX3Xt1zGLGcQwAYBV6dGAb6TjnxDAMVWyq6JogHcv3nOpJ1WYcxwAAViHowDbScc5JZX2lahtruz6O5Xu2YlJ1KjYmTIZQb1R/y5Lj7Y1iTxfAOQg6sIV0nHOS6Pds50nVdpOM3ij2dEm9ZAVWpAeCDmzBbRv5xWIg37MdJ1Xbldm9UezpknoMn2IgCDqwnBs38utPOn7PwEA4dfgU1iPowHLJmHNi9zkUbtu80M16v5bq6qyrBUD8CDqwnNlzTpwwh6L799zR0aEtW7Zo0qRJyso6+SPJPBvrxfJaAmBvBB3YgplzTpwyhyL0Pfv9fjXmNapsZJmys7OtKwh9xPJaAmBvbBgIwBLVDdUat3ycqhuqrS4FgIsRdACkXO+jPgzDsLokAC5F0AGQcm4/6oM9XQD7IOgASIpIQ1PdN0qUTm6Q6ORenSeekGprT97YLBCwD4IO4ABOm88SbWiq94GkyTiINNU+/nHpwgtP3tIx5DjtNYr0QdABbM6J81kiDU317s0JcUOvTjpz4msU6YOgA9cJnYsTjZPmUDhtPku0oanevTkhdu3VSfVryeeTduyIfPP5zPk6ZnPaaxTphX104DpuOhen98GfTjjkNNIp9Ov3rteiVxY56tiLVL6WnLDRZThOfI0ivRB04EpuORcnUmiw6yGn0U5kX7Bxgd5pecdxx16k6rXklI0ue3PaaxTph6AD2FS00GDXv5ijnche21irx2c+rnEjxkV8PMdeOIsTX6NIP8zRAWzKafNZup/IHo5HHj30fw+pbGSZLiy8MOxtVP6oFFftPqlc/eS01yjSE0EHsKFYQoPdVinFcyI7kiOVq5+c+BpFemLoCrCheEKDXYZ6zD6FHvELt/opWfNknPgaRXoi6AA25NTQYOYp9IhPqlc/OfU1ivRD0AFsitCAeFix+onXKJzAUXN07r33Xl1yySXKy8vTaaedZnU5AGCagWxOyI7TQGSO6tFpb2/XNddco4kTJ+rRRx+1uhwAMM1ANieMtqyfPW2Q7hwVdJYsWSJJWrVqlbWFAEASJLI5YffVT07ZcRpIJUcFHQAI8fn69n50dEj19QV67TVp5Eh77SCcLKx+AqJzfdBpa2tTW1tb18ctLS2SJL/fL7/fb1VZjhdqO9pw4GjL+Pl80ic/maUTJ3r3UGRLmixJys019PrrHa4POx559OrNr6rpeOQxr+F5w+UxPDG/xnhNmoe2NE+ibWh50Jk/f77uu+++qNfU1dXpvPPOS+j5ly5d2jXk1d3GjRuVl5eX0HPipKqqKqtLcA3aMnb19QU6cWJy1GtOnMjQCy/8WaWlzakpysYa1ai/6W9xP47XpHloy4E7fvx4Qo/LMCyejn/48GEdOXIk6jUlJSXKycnp+njVqlW68847dezYsX6fP1yPTlFRkRobGzV06NCE6053fr9fVVVVuuyyy5SdnW11OY5GW8bvtdekiy/uv622bfOrrCwFBbkMr0nz0JbmOXLkiAoLC9Xc3Kz8/PyYH2d5j87w4cM1fPjwpD2/1+uV19t3XDo7O5sXnQloR/PQlrHLivE3V1ZWtmjSxPGaNA9tOXCJtp/lQScePp9PR48elc/nUyAQ0M6dOyVJ55xzjgYPHmxtcQAAwHYctWHgokWLVFZWpoqKCn3wwQcqKytTWVmZtm/fbnVpAGxo2+HUneQNwJ4cFXRWrVolwzD63CZPnmx1aQBsx9D/q0vNSd4A7MtRQQcA+iipluaO6/xvd6WV+kdzz5O8AaQfgg4Axzl5LpQhTS2Xhtd1/lehXhtDGVNPnv3EmU9A+iLoAElU3cAckWQInQu1bF2ldNZHZzydVaNfvPiSHnzwFf3ixZdknFmjgBGQ1PPMJwDphaADJIlhGCrfwByRZCkqMvTb/T17bX57oEJjxvxTvz1QkZYneft80o4dJ28Pr69WyYPj9PD6au3Y0fl5IN04ank54CTdT5TmBGnz9T6xO2AEVNtYq2eNZ1X7bm2f66Od5B3u3KzuIp0abic+nzR2rHTiROgeQ5pdLp1Vp28+Wy79ZqpyczO0Z4/9vxfATAQdIAlCJ0pnZmQqYAS6ehM4Qdocvds3xCOPnnnvmbhO8u4bEPrKzZXtA0JTU6/vobTnsJ5KK3Wifoaamuz9fQBmY+gKSIJQbwNzRJKjd/uGBBVUm9EW00neIX0CQhgnTkTv8bEfQ/rCQin40fBdMLPzY7l32A6IhB4dwGSRehvo1TFHqH0j9dpI0seHfVyPX/V42HYeMWiEvFl9j4Vxle69OZLkCXT16kgMnyK9EHQAk/WeOxISbY4IYtceaJev2Rcx5EjS0Q+P6pMjPun+QBNWt94cT7cer496dQxjuiSCNtIHQQeIU3VDte5Yd4ceuuIhTSuZ1uNz/fU2hJsjgvh4s7yqmV2jw8cP9/lcR0eHtmzZoqumX5WmIUd9e3NCPurV2Xq4UhPo1UEaIegAcei9ZHzqmKk9Akt/vQ3d54ik7RuxCYoKilRUUNTnfr/fr8a8Ro3KH2VBVXYQ6s3xSJ4wr8GgRw/vXqi5MwjaSB8EHSAO/S0Zj9bbEJIWc0Rgjcx2qcAXPuRIkieo904QtJFeCDpAjGJdMh6ptwFIpmHDpNxsr078ukYaFD5o5+RI/7uOoI30QtABYhRugzomFztf6Nys/vbRGTYsdTUlInQsRlNTkaTwQdspGx86ffNG2AtBB4hBKpeM84s+tU4GhMjXOKXNi4udUWckbtm8EfZC0AFikKol4/yit4bTA4JbxLN5I/9eiBU7IwP96L5kPJzQknEzDot05y691uIEeSC9EXSAfsSzZBz2wgnyABi6AvrBkvHYRNtI0SqcIA+AoAPEgCXj0fW3kaJVNXGCPACCDuBAdXV977NyZZAde07YDgCARNABHOmGG/reZ9VqLDv2nHCCPIAQJiMDLmHVaqxQz0koUHTvObFK1b6qHjWF2KE2RBbavDEaJ2zeCHuhRwewkVh26bUTO/acGIahik0VnCDvQG7avBH2QdABbCTSL/q6uvDDVVZL1UaK8egwOrS/ZT8nyDsUmzfCbAQdwGac8ou++0aKduo5yfZka+vNW3Ws/VjEa9gOAEgfBB0ACYlnI8VUh4qi/CKVZJek9GsCsCeCDoCEsJEiACcg6ABIGBspArA7lpcDDsCyWwBIDD06gAOw7BYAEkPQARzCKauxAMBOGLoCAACuRdABAACuRdABAACuRdABAACuxWRkAIiDz8fqN8BJCDoAECOfTxo7Nvrp8rm5nVsBEHYAe2DoCgBi1NQUPeRInZ+P1uMDILUIOgAAwLUIOgAAwLUIOgAAwLUIOgAAwLUIOgAAwLUIOgAAwLXYRwcAuom2IWBjo+T1Sm1tkR+fm9u5aSAAeyDoAMBHYtkQ0OuVXnxRKiwM/3l2RgbshaADAB+JZUPAtrbOkHPhhampCcDAEHQApL3QcFVdndWVADAbQQdAWotluAqAc7HqCkBaqG6o1rjl41TdUN3j/liGqwA4l2OCzltvvaVbb71VY8aM0SmnnKLS0lJVVFSovb3d6tIA2JxhGCrfUK66pjqVbyiXYRhWlwQgRRwzdLV7924Fg0GtWLFC55xzjl5//XXNnj1bra2teuCBB6wuD4CNVdZXquZgjSSp5mCNKusrNeOcGRZXBSAVHBN0Lr/8cl1++eVdH5eUlGjPnj16+OGHCToAIjIMQws3LlRmRqYCRkCZGZlauHGhppdOV0ZGhtXlAUgyxwSdcJqbmzVkyJCo17S1tamt2+5eLS0tkiS/3y+/35/U+tws1Ha04cDRluYJ15aVDSd7cyQpYARUc7BGL73xkqaXTFdHhyRlx/w1cnMNFRR0yM3/XLwmzUNbmifRNswwHDpYvXfvXk2YMEEPPPCAZs+eHfG6xYsXa8mSJX3uX7NmjfLy8pJZIgCLGYahu964Sw0fNiioYNf9HnlUckqJ7j/3fjU0nKbvfndyv8/1ne9s16hRHyg/v13Dh3+YxKoBhHP8+HF99atfVXNzs/Lz82N+nOVBZ/78+brvvvuiXlNXV6fzzjuv6+MDBw7o85//vCZPnqxHHnkk6mPD9egUFRWpsbFRQ4cOHVjxaczv96uqqkqXXXaZsrNj/2sYfdGW5undlpUNlfrXp/414vUvXveihjdP18UX99/u27b5VVZmZrX2xWvSPLSleY4cOaLCwsK4g47lQ1ff/e53NWvWrKjXlJSUdP3/wYMHNWXKFF1yySX69a9/3e/ze71eeb3ePvdnZ2fzojMB7Wge2tI82dnZysrK0pLNS+SRp0dvTohHHi3ZvES/n36lcnOjLzHPzZVGjsxWuv3z8Jo0D205cIm2n+VBZ/jw4Ro+fHhM1x44cEBTpkzRhAkTtHLlSnk8jlkdDyDF2gPt8jX7woYcSQoqqP0t+zXyrHbt2eONeJCnxPlVgJNZHnRideDAAU2ePFmjR4/WAw88oMOHD3d9buTIkRZWBsCOvFle1cyu0eHjhyNeM2LQCHmzvCouJsgAbuWYoFNVVaW9e/dq7969GjVqVI/POXQ+NYAkKyooUlFBkdVlALCQY8Z+Zs2aJcMwwt4AAADCcUzQAQAAiBdBBwAAuBZBBwAAuBZBBwAAuBZBBwAAuBZBBwAAuBZBBwAAuBZBBwAAuBZBBwAAuBZBBwAAuBZBBwAAuBZBBwAAuBZBBwAAuBZBBwAAuBZBBwAAuBZBBwAAuBZBBwAAuBZBBwAAuBZBBwAAuBZBBwAAuBZBBwAAuBZBBwAAuBZBBwAAuBZBBwAAuBZBBwAAuBZBBwAAuBZBBwAAuBZBBwAAuBZBBwAAuBZBBwAAuBZBBwAAuBZBBwAAuBZBBwAAuBZBBwAAuBZBBwAAuBZBBwAAuBZBBwAAuBZBBwAAuBZBBwAAuBZBBwAAuBZBBwAAuBZBBwAAuBZBBwAAuBZBBwAAuBZBBwAAuBZBBwAAuBZBBwAAuBZBBwAAuBZBBwAAuBZBBwAAuBZBBwAAuJajgs6Xv/xlFRcXKzc3V4WFhbrxxht18OBBq8sCAAA25aigM2XKFP3ud7/Tnj179Oyzz6q+vl5XX3211WUBAACbyrK6gHh85zvf6fr/0aNHa/78+Zo5c6b8fr+ys7MtrAwAANiRo4JOd0ePHtWTTz6pSy65JGrIaWtrU1tbW9fHzc3NXY9H4vx+v44fP64jR44QMgeItjQPbWkO2tE8tKV5Qu/bhmHE90DDYb7//e8beXl5hiTjs5/9rNHU1BT1+oqKCkMSN27cuHHjxs0Ft/r6+rhyQ4ZhxBuNzDV//nzdd999Ua+pq6vTeeedJ0lqamrS0aNH9fbbb2vJkiUqKCjQiy++qIyMjLCP7d2jc+zYMY0ePVo+n08FBQXmfSNppqWlRUVFRdq/f7/y8/OtLsfRaEvz0JbmoB3NQ1uap7m5WcXFxfrnP/+p0047LebHWR50Dh8+rCNHjkS9pqSkRDk5OX3uf+edd1RUVKRXX31VEydOjOnrtbS0qKCgQM3NzbzoBoB2NA9taR7a0hy0o3loS/Mk2paWz9EZPny4hg8fntBjg8GgJPXosQEAAAixPOjEatu2baqpqdGkSZN0+umnq76+XgsXLlRpaWnMvTkAACC9OGYfnby8PD333HOaOnWqxo4dq1tvvVUXXHCBNm3aJK/XG/PzeL1eVVRUxPUY9EU7moe2NA9taQ7a0Ty0pXkSbUvL5+gAAAAki2N6dAAAAOJF0AEAAK5F0AEAAK5F0AEAAK6V1kHny1/+soqLi5Wbm6vCwkLdeOONOnjwoNVlOcpbb72lW2+9VWPGjNEpp5yi0tJSVVRUqL293erSHOnee+/VJZdcory8vLh2/oS0fPlynX322crNzdXFF1+s//u//7O6JMfZvHmzvvSlL+nMM89URkaGnn/+eatLcqSlS5fqM5/5jE499VSNGDFCM2fO1J49e6wuy5EefvhhXXDBBcrPz1d+fr4mTpyodevWxfUcaR10pkyZot/97nfas2ePnn32WdXX1+vqq6+2uixH2b17t4LBoFasWKG///3v+tnPfqZf/epXKi8vt7o0R2pvb9c111yj2267zepSHOXpp5/WvHnzVFFRoR07dmj8+PGaMWOGDh06ZHVpjtLa2qrx48dr+fLlVpfiaJs2bdLcuXP1l7/8RVVVVfL7/Zo+fbpaW1utLs1xRo0apR//+Meqra3V9u3b9YUvfEH/9m//pr///e+xP0liR2u60wsvvGBkZGQY7e3tVpfiaD/5yU+MMWPGWF2Go61cudIoKCiwugzHuOiii4y5c+d2fRwIBIwzzzzTWLp0qYVVOZskY+3atVaX4QqHDh0yJBmbNm2yuhRXOP30041HHnkk5uvTukenu6NHj+rJJ5/UJZdcouzsbKvLcbTm5mYNGTLE6jKQJtrb21VbW6tp06Z13efxeDRt2jRt3brVwsqATs3NzZLE78UBCgQCeuqpp9Ta2hrXiQhpH3T+67/+S4MGDdLQoUPl8/n0wgsvWF2So+3du1fLli3Tf/7nf1pdCtJEU1OTAoGAzjjjjB73n3HGGXr33XctqgroFAwGdeedd+pzn/ucPvnJT1pdjiPt2rVLgwcPltfr1Zw5c7R27VqNGzcu5se7LujMnz9fGRkZUW+7d+/uuv6uu+7Sa6+9psrKSmVmZurrX/+6DDaLjrsdJenAgQO6/PLLdc0112j27NkWVW4/ibQlAHeYO3euXn/9dT311FNWl+JYY8eO1c6dO7Vt2zbddtttuummm/SPf/wj5se77giIw4cP68iRI1GvKSkpUU5OTp/733nnHRUVFenVV19N+4NC423HgwcPavLkyfrsZz+rVatWyeNxXYZOWCKvyVWrVunOO+/UsWPHklyd87W3tysvL0/PPPOMZs6c2XX/TTfdpGPHjtFLm6CMjAytXbu2R5siPrfffrteeOEFbd68WWPGjLG6HNeYNm2aSktLtWLFipiud8zp5bEaPny4hg8fntBjg8GgJKmtrc3MkhwpnnY8cOCApkyZogkTJmjlypWEnF4G8ppE/3JycjRhwgRt2LCh6005GAxqw4YNuv32260tDmnJMAx961vf0tq1a/XKK68QckwWDAbjep92XdCJ1bZt21RTU6NJkybp9NNPV319vRYuXKjS0tK0782Jx4EDBzR58mSNHj1aDzzwgA4fPtz1uZEjR1pYmTP5fD4dPXpUPp9PgUBAO3fulCSdc845Gjx4sLXF2di8efN000036dOf/rQuuugi/fznP1dra6tuvvlmq0tzlA8++EB79+7t+njfvn3auXOnhgwZouLiYgsrc5a5c+dqzZo1euGFF3Tqqad2zRUrKCjQKaecYnF1znL33XfriiuuUHFxsd5//32tWbNGr7zyitavXx/7kyRp9Zft/e1vfzOmTJliDBkyxPB6vcbZZ59tzJkzx3jnnXesLs1RVq5caUgKe0P8brrpprBtuXHjRqtLs71ly5YZxcXFRk5OjnHRRRcZf/nLX6wuyXE2btwY9vV30003WV2ao0T6nbhy5UqrS3OcW265xRg9erSRk5NjDB8+3Jg6dapRWVkZ13O4bo4OAABACJMpAACAaxF0AACAaxF0AACAaxF0AACAaxF0AACAaxF0AACAaxF0AACAaxF0AACAaxF0AACAaxF0AACAaxF0AACAaxF0AACAaxF0ANhaa2urzjjjDGVkZKikpER+vz/sdR9++KEuueQSZWRkKDc3V5s3b05xpQDsiKADwNYGDRqk8vJySdK+ffu0evXqPtcEg0F97Wtf09atW+XxePTEE0/o0ksvTXWpAGwowzAMw+oiACCatrY2nXvuufL5fDr77LP1xhtvKDs7u+vzd9xxh5YtWyZJ+vnPf65vf/vbVpUKwGbo0QFge16vV4sWLZIkvfXWW1q5cmXX5x588MGukPO9732PkAOgB3p0ADhCIBDQuHHj9MYbb2j06NF68803tXbtWl133XUyDEPXX3+9nnzySWVkZFhdKgAbIegAcIynn35a1113nSTpG9/4hh5//HG1tbVpypQpevnll5WTk2NxhQDshqADwDEMw9CFF16onTt3dt13/vnn609/+pMKCgoiPu6JJ57Qn/70J9XW1mrXrl1qb2/XypUrNWvWrOQXDcBSzNEB4BgZGRmaPXt218cjR47UunXrooYcSVqwYIF+/etf6+2331ZhYWGyywRgIwQdAI7x5ptvqqKiouvj1tZWeb3efh/3yCOP6K233tLhw4c1Z86cZJYIwGYIOgAc4dChQ7r88svV1NSkoUOHSpLef/99LV26tN/HTps2TaNHj052iQBsiKADwPZaW1v1xS9+UQ0NDRo8eLCqqqo0c+ZMSdIvf/lLHThwwNoCAdgWQQeArXV0dOiaa67R9u3blZWVpWeeeUZlZWVavHixMjIydOLECd1zzz1WlwnApgg6AGxtzpw5WrdunSRpxYoVmjFjhiRp/PjxuuqqqyRJjz32mOrr6y2rEYB9EXQA2NbixYv16KOPSpIqKip0yy239Pl8RkaGOjo6unZOBoDuCDoAbOnRRx/VkiVLJEm33HKLFi9e3Oea888/X1dffbUk6amnntKuXbtSWSIAByDoALCdl156qWsZ+IwZM7RixYqI11ZUVMjj8SgYDGrBggWpKhGAQ2RZXQAA9HbllVfK7/fHdO0nPvEJBQKBJFcEwKno0QEAAK7FWVcAXO+RRx7Rli1bJEm7du3Sjh079LnPfU7nnHOOJGnSpEn6xje+YWWJAJKEoSsArrdlyxatXr26x31//vOf9ec//7nrY4IO4E706AAAANdijg4AAHAtgg4AAHAtgg4AAHAtgg4AAHAtgg4AAHAtgg4AAHAtgg4AAHAtgg4AAHAtgg4AAHAtgg4AAHAtgg4AAHAtgg4AAHCt/w9ndzGuEUx0vQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def plot_dataset(x, y, axes):\n",
    "    plt.plot(x[:, 0][y == 0], x[:, 1][y == 0], \"bs\")  \n",
    "    plt.plot(x[:, 0][y == 1], x[:, 1][y == 1], \"g^\")  \n",
    "    plt.axis(axes)\n",
    "    plt.grid(True, which='both')\n",
    "    plt.xlabel(r\"$x_1$\", fontsize=20)\n",
    "    plt.ylabel(r\"$x_2$\", fontsize=20, rotation=0)\n",
    "    plt.show()\n",
    "\n",
    "# Example usage\n",
    "np.random.seed(42)\n",
    "x = np.random.randn(100, 2)  \n",
    "y = np.random.randint(0, 2, size=100)  \n",
    "\n",
    "plot_dataset(x, y, [-3, 3, -3, 3])  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline \n",
    "from sklearn.preprocessing import PolynomialFeatures \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "polynomial_svm_clf = Pipeline([(\"poly_features\", PolynomialFeatures(degree= 3)), \n",
    "                               (\"scaler\", StandardScaler()), \n",
    "                               (\"svm_clf\", LinearSVC(C= 10, loss='hinge', random_state= 42))])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\kimia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1235: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "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>Pipeline(steps=[(&#x27;poly_features&#x27;, PolynomialFeatures(degree=3)),\n",
       "                (&#x27;scaler&#x27;, StandardScaler()),\n",
       "                (&#x27;svm_clf&#x27;, LinearSVC(C=10, loss=&#x27;hinge&#x27;, random_state=42))])</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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;Pipeline<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;poly_features&#x27;, PolynomialFeatures(degree=3)),\n",
       "                (&#x27;scaler&#x27;, StandardScaler()),\n",
       "                (&#x27;svm_clf&#x27;, LinearSVC(C=10, loss=&#x27;hinge&#x27;, random_state=42))])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;PolynomialFeatures<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.PolynomialFeatures.html\">?<span>Documentation for PolynomialFeatures</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>PolynomialFeatures(degree=3)</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;StandardScaler<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>StandardScaler()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;LinearSVC<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.svm.LinearSVC.html\">?<span>Documentation for LinearSVC</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>LinearSVC(C=10, loss=&#x27;hinge&#x27;, random_state=42)</pre></div> </div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('poly_features', PolynomialFeatures(degree=3)),\n",
       "                ('scaler', StandardScaler()),\n",
       "                ('svm_clf', LinearSVC(C=10, loss='hinge', random_state=42))])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "polynomial_svm_clf.fit(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_predictions(clf, axes):\n",
    "    x0s = np.linspace(axes[0], axes[1], 100)\n",
    "    x1s = np.linspace(axes[2], axes[3], 100)\n",
    "    x0, x1 = np.meshgrid(x0s, x1s)\n",
    "    x = np.c_[x0.ravel(), x1.ravel()]\n",
    "    y_pred = clf.predict(x).reshape(x0.shape)\n",
    "    y_decision = lf.decision_function(x).reshape(x0.shape)\n",
    "    plt.contourf(x0, x1, y_pred, cmap= plt.cm.brg, alpha= 0.2)\n",
    "    plt.contourf(x0, x1, y_decision, cmap= plt.cm.brg, alpha= 0.1)\n",
    "    plot_predictions(polynomial_svm_clf, [-1.5, 2.5, -1, 1.5])\n",
    "    plot_dataset(x, y, [-1.5, 2.5, -1, 1.5])\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "def plot_predictions(clf, axes):\n",
    "    x0s = np.linspace(axes[0], axes[1], 100)\n",
    "    x1s = np.linspace(axes[2], axes[3], 100)\n",
    "    x0, x1 = np.meshgrid(x0s, x1s)\n",
    "    x = np.c_[x0.ravel(), x1.ravel()]\n",
    "    y_pred = clf.predict(x).reshape(x0.shape)\n",
    "    \n",
    "    \n",
    "    try:\n",
    "        y_decision = clf.decision_function(x).reshape(x0.shape)\n",
    "        plt.contourf(x0, x1, y_decision, cmap=plt.cm.brg, alpha=0.1)\n",
    "    except AttributeError:\n",
    "        pass\n",
    "\n",
    "    plt.contourf(x0, x1, y_pred, cmap=plt.cm.brg, alpha=0.2)\n",
    "\n",
    "def plot_dataset(x, y, axes):\n",
    "    plt.plot(x[:, 0][y == 0], x[:, 1][y == 0], \"bs\")  \n",
    "    plt.plot(x[:, 0][y == 1], x[:, 1][y == 1], \"g^\")  \n",
    "    plt.axis(axes)\n",
    "    plt.grid(True, which='both')\n",
    "    plt.xlabel(r\"$x_1$\", fontsize=20)\n",
    "    plt.ylabel(r\"$x_2$\", fontsize=20, rotation=0)\n",
    "\n",
    "\n",
    "np.random.seed(42)\n",
    "x = np.random.randn(100, 2)  \n",
    "y = (x[:, 0] ** 2 + x[:, 1] ** 2 < 1).astype(int)  \n",
    "\n",
    "\n",
    "polynomial_svm_clf = SVC(kernel=\"poly\", degree=3, coef0=1, C=5)\n",
    "polynomial_svm_clf.fit(x, y)\n",
    "\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "plot_predictions(polynomial_svm_clf, [-2, 2, -2, 2])\n",
    "plot_dataset(x, y, [-2, 2, -2, 2])\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "هسته ی چند جمله ای :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
       "                (&#x27;svm_clf&#x27;, SVC(C=5, coef0=1, 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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;Pipeline<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
       "                (&#x27;svm_clf&#x27;, SVC(C=5, coef0=1, kernel=&#x27;poly&#x27;))])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;StandardScaler<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>StandardScaler()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;SVC<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.svm.SVC.html\">?<span>Documentation for SVC</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>SVC(C=5, coef0=1, kernel=&#x27;poly&#x27;)</pre></div> </div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('scaler', StandardScaler()),\n",
       "                ('svm_clf', SVC(C=5, coef0=1, kernel='poly'))])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poly_kernel_svm_clf = Pipeline([(\"scaler\", StandardScaler()),\n",
    "                                (\"svm_clf\", SVC(kernel= \"poly\", degree= 3, coef0= 1, C= 5))])\n",
    "\n",
    "\n",
    "poly_kernel_svm_clf.fit(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-6 {\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-6 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 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-6 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-6 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-6 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-6 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-6 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-6 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-6 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-6 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-6 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-6 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-6 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-6 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-6 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 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-6 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 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-6 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-6 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-6 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-6 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-6 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-6 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-6 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-6 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-6 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-6 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-6 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-6 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-6 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-6 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 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-6 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-6 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-6 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-6 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-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
       "                (&#x27;svm_clf&#x27;, SVC(C=5, coef0=100, degree=10, 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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" ><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;Pipeline<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
       "                (&#x27;svm_clf&#x27;, SVC(C=5, coef0=100, degree=10, kernel=&#x27;poly&#x27;))])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" ><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;StandardScaler<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>StandardScaler()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-15\" type=\"checkbox\" ><label for=\"sk-estimator-id-15\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;SVC<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.svm.SVC.html\">?<span>Documentation for SVC</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>SVC(C=5, coef0=100, degree=10, kernel=&#x27;poly&#x27;)</pre></div> </div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('scaler', StandardScaler()),\n",
       "                ('svm_clf', SVC(C=5, coef0=100, degree=10, kernel='poly'))])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poly100_kernel_svm_clf = Pipeline([(\"scaler\", StandardScaler()), \n",
    "                                    (\"svm_clf\", SVC(kernel= \"poly\", degree= 10, coef0= 100, C= 5))])\n",
    "\n",
    "\n",
    "poly100_kernel_svm_clf.fit(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(121)\n",
    "plot_predictions(poly_kernel_svm_clf, [-1.5, 2.5, -1, 1.5])\n",
    "plot_dataset(x, y, [-1.5, 2.5, -1, 1.5])\n",
    "plt.title(r\"$d= 3, r= 1, C=5$\", fontsize= 18)\n",
    "plt.subplot(122)\n",
    "\n",
    "plot_predictions(poly100_kernel_svm_clf, [-1.5, 2.5, -1, 1.5])\n",
    "plot_dataset(x, y, [-1.5, 2.5, -1, 1.5])\n",
    "plt.title(r\"$d= 10, r= 100, C=5$\", fontsize= 18)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "------------------------------------------------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "دسته بندی در پایتون:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import datasets \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris = datasets.load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = pd.DataFrame(iris.data, columns= iris.feature_names)\n",
    "y = pd.DataFrame(iris.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)\n",
       "0                5.1               3.5                1.4               0.2\n",
       "1                4.9               3.0                1.4               0.2\n",
       "2                4.7               3.2                1.3               0.2\n",
       "3                4.6               3.1                1.5               0.2\n",
       "4                5.0               3.6                1.4               0.2"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.843333</td>\n",
       "      <td>3.057333</td>\n",
       "      <td>3.758000</td>\n",
       "      <td>1.199333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.828066</td>\n",
       "      <td>0.435866</td>\n",
       "      <td>1.765298</td>\n",
       "      <td>0.762238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.300000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.100000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>0.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.800000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.350000</td>\n",
       "      <td>1.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.400000</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>1.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.900000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>2.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       sepal length (cm)  sepal width (cm)  petal length (cm)  \\\n",
       "count         150.000000        150.000000         150.000000   \n",
       "mean            5.843333          3.057333           3.758000   \n",
       "std             0.828066          0.435866           1.765298   \n",
       "min             4.300000          2.000000           1.000000   \n",
       "25%             5.100000          2.800000           1.600000   \n",
       "50%             5.800000          3.000000           4.350000   \n",
       "75%             6.400000          3.300000           5.100000   \n",
       "max             7.900000          4.400000           6.900000   \n",
       "\n",
       "       petal width (cm)  \n",
       "count        150.000000  \n",
       "mean           1.199333  \n",
       "std            0.762238  \n",
       "min            0.100000  \n",
       "25%            0.300000  \n",
       "50%            1.300000  \n",
       "75%            1.800000  \n",
       "max            2.500000  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0\n",
       "0    50\n",
       "1    50\n",
       "2    50\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size= 0.25, random_state= 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "دسته بندی با استفاده از درخت تصمیم:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "tree =  DecisionTreeClassifier(random_state= 2, criterion= 'entropy', max_depth= 5, \n",
    "                               min_samples_split= 4, min_samples_leaf= 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-7 {\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-7 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 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-7 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-7 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-7 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-7 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-7 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-7 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-7 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-7 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-7 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-7 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-7 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-7 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-7 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 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-7 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 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-7 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-7 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-7 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-7 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-7 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-7 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-7 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-7 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-7 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-7 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-7 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-7 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-7 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-7 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 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-7 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-7 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-7 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-7 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-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(criterion=&#x27;entropy&#x27;, max_depth=5, min_samples_leaf=3,\n",
       "                       min_samples_split=4, random_state=2)</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-16\" type=\"checkbox\" checked><label for=\"sk-estimator-id-16\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;DecisionTreeClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.tree.DecisionTreeClassifier.html\">?<span>Documentation for DecisionTreeClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeClassifier(criterion=&#x27;entropy&#x27;, max_depth=5, min_samples_leaf=3,\n",
       "                       min_samples_split=4, random_state=2)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier(criterion='entropy', max_depth=5, min_samples_leaf=3,\n",
       "                       min_samples_split=4, random_state=2)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "tree_pred = tree.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import f1_score, confusion_matrix, classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score: 0.9736842105263158\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy Score:\" ,accuracy_score(y_test, tree_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "confusion matrix:  \n",
      " [[16  0  0]\n",
      " [ 0 10  1]\n",
      " [ 0  0 11]]\n"
     ]
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test, tree_pred)\n",
    "print(\"confusion matrix:  \\n\", cm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00        16\n",
      "           1       1.00      0.91      0.95        11\n",
      "           2       0.92      1.00      0.96        11\n",
      "\n",
      "    accuracy                           0.97        38\n",
      "   macro avg       0.97      0.97      0.97        38\n",
      "weighted avg       0.98      0.97      0.97        38\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, tree_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import ConfusionMatrixDisplay\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=[0, 1, 2])\n",
    "disp.plot(cmap=plt.cm.Blues)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.375, 0.875, 'petal width (cm) <= 0.8\\nentropy = 1.582\\nsamples = 112\\nvalue = [34, 39, 39]\\nclass = 1'),\n",
       " Text(0.25, 0.625, 'entropy = 0.0\\nsamples = 34\\nvalue = [34, 0, 0]\\nclass = 0'),\n",
       " Text(0.3125, 0.75, 'True  '),\n",
       " Text(0.5, 0.625, 'petal width (cm) <= 1.65\\nentropy = 1.0\\nsamples = 78\\nvalue = [0, 39, 39]\\nclass = 1'),\n",
       " Text(0.4375, 0.75, '  False'),\n",
       " Text(0.25, 0.375, 'petal length (cm) <= 4.95\\nentropy = 0.378\\nsamples = 41\\nvalue = [0, 38, 3]\\nclass = 1'),\n",
       " Text(0.125, 0.125, 'entropy = 0.0\\nsamples = 37\\nvalue = [0, 37, 0]\\nclass = 1'),\n",
       " Text(0.375, 0.125, 'entropy = 0.811\\nsamples = 4\\nvalue = [0, 1, 3]\\nclass = 2'),\n",
       " Text(0.75, 0.375, 'petal length (cm) <= 4.85\\nentropy = 0.179\\nsamples = 37\\nvalue = [0, 1, 36]\\nclass = 2'),\n",
       " Text(0.625, 0.125, 'entropy = 0.918\\nsamples = 3\\nvalue = [0, 1, 2]\\nclass = 2'),\n",
       " Text(0.875, 0.125, 'entropy = 0.0\\nsamples = 34\\nvalue = [0, 0, 34]\\nclass = 2')]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.tree import plot_tree\n",
    "plt.figure(figsize= (10 ,8))\n",
    "plot_tree(tree, feature_names= iris.feature_names, class_names= ['0', '1', '2'], filled= True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ":دسته بندی با استفاده از \n",
    "GaussianNB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\kimia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\utils\\validation.py:1339: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "gnb = GaussianNB()\n",
    "gnb.fit(x_train, y_train)\n",
    "gnb_pred = gnb.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score: 0.9736842105263158\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy Score:\" , accuracy_score(y_test, gnb_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "confusion matrix:  \n",
      " [[16  0  0]\n",
      " [ 0 10  1]\n",
      " [ 0  0 11]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00        16\n",
      "           1       1.00      0.91      0.95        11\n",
      "           2       0.92      1.00      0.96        11\n",
      "\n",
      "    accuracy                           0.97        38\n",
      "   macro avg       0.97      0.97      0.97        38\n",
      "weighted avg       0.98      0.97      0.97        38\n",
      "\n"
     ]
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test, gnb_pred)\n",
    "print(\"confusion matrix:  \\n\", cm)\n",
    "print(classification_report(y_test, gnb_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "دسته بندی با استفاده از K-NN :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\kimia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\neighbors\\_classification.py:238: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n"
     ]
    }
   ],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors= 5)\n",
    "knn.fit(x_train, y_train)\n",
    "knn_pred = knn.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score: 1.0\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy Score:\", accuracy_score(y_test, knn_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "confusion matrix:  \n",
      " [[16  0  0]\n",
      " [ 0 11  0]\n",
      " [ 0  0 11]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00        16\n",
      "           1       1.00      1.00      1.00        11\n",
      "           2       1.00      1.00      1.00        11\n",
      "\n",
      "    accuracy                           1.00        38\n",
      "   macro avg       1.00      1.00      1.00        38\n",
      "weighted avg       1.00      1.00      1.00        38\n",
      "\n"
     ]
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test, knn_pred)\n",
    "print(\"confusion matrix:  \\n\", cm)\n",
    "print(classification_report(y_test, knn_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "دسته بندی با استفاده از رگرسیون:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score: 0.9736842105263158\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\kimia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\utils\\validation.py:1339: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    }
   ],
   "source": [
    "lr = LogisticRegression()\n",
    "lr.fit(x_train, y_train)\n",
    "lr_pred = lr.predict(x_test)\n",
    "print(\"Accuracy Score:\", accuracy_score(y_test, lr_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "confusion matrix:  \n",
      " [[16  0  0]\n",
      " [ 0 10  1]\n",
      " [ 0  0 11]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00        16\n",
      "           1       1.00      0.91      0.95        11\n",
      "           2       0.92      1.00      0.96        11\n",
      "\n",
      "    accuracy                           0.97        38\n",
      "   macro avg       0.97      0.97      0.97        38\n",
      "weighted avg       0.98      0.97      0.97        38\n",
      "\n"
     ]
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test, lr_pred)\n",
    "print(\"confusion matrix:  \\n\", cm)\n",
    "print(classification_report(y_test, lr_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "دسته بندی با استفاده از شبکه عصبی:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neural_network import MLPClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "nn = MLPClassifier(random_state= 1, learning_rate_init= 0.01,\n",
    "                   learning_rate= 'invscaling', max_iter= 300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\kimia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:1105: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score: 1.0\n"
     ]
    }
   ],
   "source": [
    "nn.fit(x_train, y_train)\n",
    "nn_pred = nn.predict(x_test)\n",
    "print(\"Accuracy Score:\", accuracy_score(y_test, nn_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "confusion matrix:  \n",
      " [[16  0  0]\n",
      " [ 0 11  0]\n",
      " [ 0  0 11]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00        16\n",
      "           1       1.00      1.00      1.00        11\n",
      "           2       1.00      1.00      1.00        11\n",
      "\n",
      "    accuracy                           1.00        38\n",
      "   macro avg       1.00      1.00      1.00        38\n",
      "weighted avg       1.00      1.00      1.00        38\n",
      "\n"
     ]
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test, nn_pred)\n",
    "print(\"confusion matrix:  \\n\", cm)\n",
    "print(classification_report(y_test, nn_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "دسته بندی با استفاده از ماشین بردار پشتیبان:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\kimia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\utils\\validation.py:1339: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    }
   ],
   "source": [
    "svc = SVC(kernel= 'poly', degree= 4)\n",
    "svc.fit(x_train, y_train)\n",
    "svc_pred = svc.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score: 1.0\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy Score:\", accuracy_score(y_test, svc_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "confusion matrix:  \n",
      " [[16  0  0]\n",
      " [ 0 11  0]\n",
      " [ 0  0 11]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00        16\n",
      "           1       1.00      1.00      1.00        11\n",
      "           2       1.00      1.00      1.00        11\n",
      "\n",
      "    accuracy                           1.00        38\n",
      "   macro avg       1.00      1.00      1.00        38\n",
      "weighted avg       1.00      1.00      1.00        38\n",
      "\n"
     ]
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test, svc_pred)\n",
    "print(\"confusion matrix:  \\n\", cm)\n",
    "print(classification_report(y_test, svc_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---------------------------------------------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "پیش بینی در پایتون:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = pd.read_csv('petrol_consumption.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Petrol_tax</th>\n",
       "      <th>Average_income</th>\n",
       "      <th>Paved_Highways</th>\n",
       "      <th>Population_Driver_licence(%)</th>\n",
       "      <th>Petrol_Consumption</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9.0</td>\n",
       "      <td>3571</td>\n",
       "      <td>1976</td>\n",
       "      <td>0.525</td>\n",
       "      <td>541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9.0</td>\n",
       "      <td>4092</td>\n",
       "      <td>1250</td>\n",
       "      <td>0.572</td>\n",
       "      <td>524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9.0</td>\n",
       "      <td>3865</td>\n",
       "      <td>1586</td>\n",
       "      <td>0.580</td>\n",
       "      <td>561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.5</td>\n",
       "      <td>4870</td>\n",
       "      <td>2351</td>\n",
       "      <td>0.529</td>\n",
       "      <td>414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.0</td>\n",
       "      <td>4399</td>\n",
       "      <td>431</td>\n",
       "      <td>0.544</td>\n",
       "      <td>410</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Petrol_tax  Average_income  Paved_Highways  Population_Driver_licence(%)  \\\n",
       "0         9.0            3571            1976                         0.525   \n",
       "1         9.0            4092            1250                         0.572   \n",
       "2         9.0            3865            1586                         0.580   \n",
       "3         7.5            4870            2351                         0.529   \n",
       "4         8.0            4399             431                         0.544   \n",
       "\n",
       "   Petrol_Consumption  \n",
       "0                 541  \n",
       "1                 524  \n",
       "2                 561  \n",
       "3                 414  \n",
       "4                 410  "
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = dataset.iloc[:, 0:4].values\n",
    "y = dataset.iloc[:, 4].values\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size= 0.2, random_state= 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "sc = StandardScaler()\n",
    "x_train = sc.fit_transform(x_train)\n",
    "x_test = sc.transform(x_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "پیش بینی با استفاده از درخت تصمیم:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import metrics\n",
    "from sklearn.tree import DecisionTreeRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "tree = DecisionTreeRegressor(random_state= 0, criterion= 'squared_error',\n",
    "                             max_depth= 3, min_samples_split= 3)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "tree.fit(x_train, y_train)\n",
    "y_pred = tree.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAS:  56.86819444444444\n"
     ]
    }
   ],
   "source": [
    "print('MAS: ', metrics.mean_absolute_error(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE:  4890.384031828704\n"
     ]
    }
   ],
   "source": [
    "print('MSE: ', metrics.mean_squared_error(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE:  69.93128078212713\n"
     ]
    }
   ],
   "source": [
    "print('RMSE: ', np.sqrt(metrics.mean_squared_error(y_test, y_pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "پیش بینی با استفاده از K-NN:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "knn = KNeighborsRegressor(n_neighbors= 5, weights= 'uniform')\n",
    "knn.fit(x_train, y_train)\n",
    "y_pred = knn.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAS:  64.12000000000002\n"
     ]
    }
   ],
   "source": [
    "print('MAS: ', metrics.mean_absolute_error(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE:  5069.816000000002\n"
     ]
    }
   ],
   "source": [
    "print('MSE: ', metrics.mean_squared_error(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE:  71.20264040047954\n"
     ]
    }
   ],
   "source": [
    "print('RMSE: ', np.sqrt(metrics.mean_squared_error(y_test, y_pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "پیش بینی با استفاده از شبکه ی عصبی:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neural_network import MLPRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "mlp = MLPRegressor(random_state= 5, hidden_layer_sizes= (20),\n",
    "                   max_iter= 150, learning_rate_init= 0.01,\n",
    "                   activation= 'relu', learning_rate= 'invscaling',\n",
    "                   momentum= 0.4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\kimia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "mlp.fit(x_train, y_train)\n",
    "y_pred = mlp.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAS:  438.2922937882228\n"
     ]
    }
   ],
   "source": [
    "print('MAS: ', metrics.mean_absolute_error(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE:  196856.75787258116\n"
     ]
    }
   ],
   "source": [
    "print('MSE: ', metrics.mean_squared_error(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE:  443.68542670746035\n"
     ]
    }
   ],
   "source": [
    "print('RMSE: ', np.sqrt(metrics.mean_squared_error(y_test, y_pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "پیش بینی با استفاده از رگرسیون:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = LinearRegression()\n",
    "lr.fit(x_train, y_train)\n",
    "y_pred = lr.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAS:  56.822247478964734\n"
     ]
    }
   ],
   "source": [
    "print('MAS: ', metrics.mean_absolute_error(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE:  4666.34478758837\n"
     ]
    }
   ],
   "source": [
    "print('MSE: ', metrics.mean_squared_error(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE:  68.31064915215175\n"
     ]
    }
   ],
   "source": [
    "print('RMSE: ', np.sqrt(metrics.mean_squared_error(y_test, y_pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "پیش بینی با استفاده از ماشین بردار پشتیبان:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "svr = SVR(kernel= 'rbf', degree= 3, C= 0.9)\n",
    "svr.fit(x_train, y_train)\n",
    "y_pred = svr.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAS:  55.072436818828656\n"
     ]
    }
   ],
   "source": [
    "print('MAS: ', metrics.mean_absolute_error(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE:  5850.014047574007\n"
     ]
    }
   ],
   "source": [
    "print('MSE: ', metrics.mean_squared_error(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE:  76.48538453570072\n"
     ]
    }
   ],
   "source": [
    "print('RMSE: ', np.sqrt(metrics.mean_squared_error(y_test, y_pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----------------------------------------------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "قوانین انجمنی\n",
    "| Association rules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defaulting to user installation because normal site-packages is not writeable\n",
      "Collecting mlxtend\n",
      "  Using cached mlxtend-0.23.3-py3-none-any.whl.metadata (7.3 kB)\n",
      "Requirement already satisfied: scipy>=1.2.1 in d:\\program\\lib\\site-packages (from mlxtend) (1.13.1)\n",
      "Requirement already satisfied: numpy>=1.16.2 in d:\\program\\lib\\site-packages (from mlxtend) (1.26.4)\n",
      "Requirement already satisfied: pandas>=0.24.2 in d:\\program\\lib\\site-packages (from mlxtend) (2.2.2)\n",
      "Requirement already satisfied: scikit-learn>=1.3.1 in d:\\program\\lib\\site-packages (from mlxtend) (1.4.2)\n",
      "Requirement already satisfied: matplotlib>=3.0.0 in d:\\program\\lib\\site-packages (from mlxtend) (3.8.4)\n",
      "Requirement already satisfied: joblib>=0.13.2 in d:\\program\\lib\\site-packages (from mlxtend) (1.4.2)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in d:\\program\\lib\\site-packages (from matplotlib>=3.0.0->mlxtend) (1.2.0)\n",
      "Requirement already satisfied: cycler>=0.10 in d:\\program\\lib\\site-packages (from matplotlib>=3.0.0->mlxtend) (0.11.0)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in d:\\program\\lib\\site-packages (from matplotlib>=3.0.0->mlxtend) (4.51.0)\n",
      "Requirement already satisfied: kiwisolver>=1.3.1 in d:\\program\\lib\\site-packages (from matplotlib>=3.0.0->mlxtend) (1.4.4)\n",
      "Requirement already satisfied: packaging>=20.0 in c:\\users\\kimia\\appdata\\roaming\\python\\python312\\site-packages (from matplotlib>=3.0.0->mlxtend) (24.1)\n",
      "Requirement already satisfied: pillow>=8 in d:\\program\\lib\\site-packages (from matplotlib>=3.0.0->mlxtend) (10.3.0)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in d:\\program\\lib\\site-packages (from matplotlib>=3.0.0->mlxtend) (3.0.9)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\kimia\\appdata\\roaming\\python\\python312\\site-packages (from matplotlib>=3.0.0->mlxtend) (2.9.0.post0)\n",
      "Requirement already satisfied: pytz>=2020.1 in d:\\program\\lib\\site-packages (from pandas>=0.24.2->mlxtend) (2024.1)\n",
      "Requirement already satisfied: tzdata>=2022.7 in d:\\program\\lib\\site-packages (from pandas>=0.24.2->mlxtend) (2023.3)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in d:\\program\\lib\\site-packages (from scikit-learn>=1.3.1->mlxtend) (2.2.0)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\kimia\\appdata\\roaming\\python\\python312\\site-packages (from python-dateutil>=2.7->matplotlib>=3.0.0->mlxtend) (1.16.0)\n",
      "Using cached mlxtend-0.23.3-py3-none-any.whl (1.4 MB)\n",
      "Installing collected packages: mlxtend\n",
      "Successfully installed mlxtend-0.23.3\n"
     ]
    }
   ],
   "source": [
    "! pip install mlxtend\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defaulting to user installation because normal site-packages is not writeable\n",
      "Requirement already satisfied: mlxtend in c:\\users\\kimia\\appdata\\roaming\\python\\python312\\site-packages (0.23.3)\n",
      "Requirement already satisfied: scipy>=1.2.1 in d:\\program\\lib\\site-packages (from mlxtend) (1.13.1)\n",
      "Requirement already satisfied: numpy>=1.16.2 in d:\\program\\lib\\site-packages (from mlxtend) (1.26.4)\n",
      "Requirement already satisfied: pandas>=0.24.2 in d:\\program\\lib\\site-packages (from mlxtend) (2.2.2)\n",
      "Requirement already satisfied: scikit-learn>=1.3.1 in d:\\program\\lib\\site-packages (from mlxtend) (1.4.2)\n",
      "Requirement already satisfied: matplotlib>=3.0.0 in d:\\program\\lib\\site-packages (from mlxtend) (3.8.4)\n",
      "Requirement already satisfied: joblib>=0.13.2 in d:\\program\\lib\\site-packages (from mlxtend) (1.4.2)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in d:\\program\\lib\\site-packages (from matplotlib>=3.0.0->mlxtend) (1.2.0)\n",
      "Requirement already satisfied: cycler>=0.10 in d:\\program\\lib\\site-packages (from matplotlib>=3.0.0->mlxtend) (0.11.0)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in d:\\program\\lib\\site-packages (from matplotlib>=3.0.0->mlxtend) (4.51.0)\n",
      "Requirement already satisfied: kiwisolver>=1.3.1 in d:\\program\\lib\\site-packages (from matplotlib>=3.0.0->mlxtend) (1.4.4)\n",
      "Requirement already satisfied: packaging>=20.0 in c:\\users\\kimia\\appdata\\roaming\\python\\python312\\site-packages (from matplotlib>=3.0.0->mlxtend) (24.1)\n",
      "Requirement already satisfied: pillow>=8 in d:\\program\\lib\\site-packages (from matplotlib>=3.0.0->mlxtend) (10.3.0)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in d:\\program\\lib\\site-packages (from matplotlib>=3.0.0->mlxtend) (3.0.9)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\kimia\\appdata\\roaming\\python\\python312\\site-packages (from matplotlib>=3.0.0->mlxtend) (2.9.0.post0)\n",
      "Requirement already satisfied: pytz>=2020.1 in d:\\program\\lib\\site-packages (from pandas>=0.24.2->mlxtend) (2024.1)\n",
      "Requirement already satisfied: tzdata>=2022.7 in d:\\program\\lib\\site-packages (from pandas>=0.24.2->mlxtend) (2023.3)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in d:\\program\\lib\\site-packages (from scikit-learn>=1.3.1->mlxtend) (2.2.0)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\kimia\\appdata\\roaming\\python\\python312\\site-packages (from python-dateutil>=2.7->matplotlib>=3.0.0->mlxtend) (1.16.0)\n"
     ]
    }
   ],
   "source": [
    "! pip install --upgrade mlxtend\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from mlxtend.preprocessing import TransactionEncoder\n",
    "from mlxtend.frequent_patterns import apriori\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = [['milk', 'onion', 'nutmeg', 'kidney beans', 'eggs', 'yogurt'],\n",
    "           ['dill', 'onion', 'nutmeg', 'kidney beans', 'eggs', 'yogurt'],\n",
    "           ['milk', 'apple', 'kidney beans', 'eggs'],\n",
    "           ['milk', 'unicorn', 'corn', 'kidney beans', 'yogurt'],\n",
    "           ['corn', 'onion', 'onion', 'kidney beans', 'ice cream', 'eggs']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>support</th>\n",
       "      <th>itemsets</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.8</td>\n",
       "      <td>(eggs)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>(kidney beans)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.6</td>\n",
       "      <td>(milk)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.6</td>\n",
       "      <td>(onion)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.6</td>\n",
       "      <td>(yogurt)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.8</td>\n",
       "      <td>(eggs, kidney beans)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.6</td>\n",
       "      <td>(onion, eggs)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.6</td>\n",
       "      <td>(kidney beans, milk)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.6</td>\n",
       "      <td>(onion, kidney beans)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.6</td>\n",
       "      <td>(kidney beans, yogurt)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.6</td>\n",
       "      <td>(onion, kidney beans, eggs)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    support                     itemsets\n",
       "0       0.8                       (eggs)\n",
       "1       1.0               (kidney beans)\n",
       "2       0.6                       (milk)\n",
       "3       0.6                      (onion)\n",
       "4       0.6                     (yogurt)\n",
       "5       0.8         (eggs, kidney beans)\n",
       "6       0.6                (onion, eggs)\n",
       "7       0.6         (kidney beans, milk)\n",
       "8       0.6        (onion, kidney beans)\n",
       "9       0.6       (kidney beans, yogurt)\n",
       "10      0.6  (onion, kidney beans, eggs)"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "te = TransactionEncoder()\n",
    "te_ary = te.fit(dataset).transform(dataset)\n",
    "df = pd.DataFrame(te_ary, columns= te.columns_)\n",
    "frequent_itemsets = apriori(df, min_support=0.6, use_colnames= True)\n",
    "frequent_itemsets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mlxtend.frequent_patterns import association_rules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\kimia\\AppData\\Roaming\\Python\\Python312\\site-packages\\mlxtend\\frequent_patterns\\association_rules.py:182: RuntimeWarning: invalid value encountered in divide\n",
      "  cert_metric = np.where(certainty_denom == 0, 0, certainty_num / certainty_denom)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>antecedents</th>\n",
       "      <th>consequents</th>\n",
       "      <th>antecedent support</th>\n",
       "      <th>consequent support</th>\n",
       "      <th>support</th>\n",
       "      <th>confidence</th>\n",
       "      <th>lift</th>\n",
       "      <th>representativity</th>\n",
       "      <th>leverage</th>\n",
       "      <th>conviction</th>\n",
       "      <th>zhangs_metric</th>\n",
       "      <th>jaccard</th>\n",
       "      <th>certainty</th>\n",
       "      <th>kulczynski</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(eggs)</td>\n",
       "      <td>(kidney beans)</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(kidney beans)</td>\n",
       "      <td>(eggs)</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.80</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(onion)</td>\n",
       "      <td>(eggs)</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.25</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.12</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(eggs)</td>\n",
       "      <td>(onion)</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1.25</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.12</td>\n",
       "      <td>1.6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.375</td>\n",
       "      <td>0.875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(milk)</td>\n",
       "      <td>(kidney beans)</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>(onion)</td>\n",
       "      <td>(kidney beans)</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>(yogurt)</td>\n",
       "      <td>(kidney beans)</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>(onion, kidney beans)</td>\n",
       "      <td>(eggs)</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.25</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.12</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>(onion, eggs)</td>\n",
       "      <td>(kidney beans)</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>(eggs, kidney beans)</td>\n",
       "      <td>(onion)</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1.25</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.12</td>\n",
       "      <td>1.6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.375</td>\n",
       "      <td>0.875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>(onion)</td>\n",
       "      <td>(eggs, kidney beans)</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.25</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.12</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>(eggs)</td>\n",
       "      <td>(onion, kidney beans)</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1.25</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.12</td>\n",
       "      <td>1.6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.375</td>\n",
       "      <td>0.875</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              antecedents            consequents  antecedent support  \\\n",
       "0                  (eggs)         (kidney beans)                 0.8   \n",
       "1          (kidney beans)                 (eggs)                 1.0   \n",
       "2                 (onion)                 (eggs)                 0.6   \n",
       "3                  (eggs)                (onion)                 0.8   \n",
       "4                  (milk)         (kidney beans)                 0.6   \n",
       "5                 (onion)         (kidney beans)                 0.6   \n",
       "6                (yogurt)         (kidney beans)                 0.6   \n",
       "7   (onion, kidney beans)                 (eggs)                 0.6   \n",
       "8           (onion, eggs)         (kidney beans)                 0.6   \n",
       "9    (eggs, kidney beans)                (onion)                 0.8   \n",
       "10                (onion)   (eggs, kidney beans)                 0.6   \n",
       "11                 (eggs)  (onion, kidney beans)                 0.8   \n",
       "\n",
       "    consequent support  support  confidence  lift  representativity  leverage  \\\n",
       "0                  1.0      0.8        1.00  1.00               1.0      0.00   \n",
       "1                  0.8      0.8        0.80  1.00               1.0      0.00   \n",
       "2                  0.8      0.6        1.00  1.25               1.0      0.12   \n",
       "3                  0.6      0.6        0.75  1.25               1.0      0.12   \n",
       "4                  1.0      0.6        1.00  1.00               1.0      0.00   \n",
       "5                  1.0      0.6        1.00  1.00               1.0      0.00   \n",
       "6                  1.0      0.6        1.00  1.00               1.0      0.00   \n",
       "7                  0.8      0.6        1.00  1.25               1.0      0.12   \n",
       "8                  1.0      0.6        1.00  1.00               1.0      0.00   \n",
       "9                  0.6      0.6        0.75  1.25               1.0      0.12   \n",
       "10                 0.8      0.6        1.00  1.25               1.0      0.12   \n",
       "11                 0.6      0.6        0.75  1.25               1.0      0.12   \n",
       "\n",
       "    conviction  zhangs_metric  jaccard  certainty  kulczynski  \n",
       "0          inf            0.0     0.80      0.000       0.900  \n",
       "1          1.0            0.0     0.80      0.000       0.900  \n",
       "2          inf            0.5     0.75      1.000       0.875  \n",
       "3          1.6            1.0     0.75      0.375       0.875  \n",
       "4          inf            0.0     0.60      0.000       0.800  \n",
       "5          inf            0.0     0.60      0.000       0.800  \n",
       "6          inf            0.0     0.60      0.000       0.800  \n",
       "7          inf            0.5     0.75      1.000       0.875  \n",
       "8          inf            0.0     0.60      0.000       0.800  \n",
       "9          1.6            1.0     0.75      0.375       0.875  \n",
       "10         inf            0.5     0.75      1.000       0.875  \n",
       "11         1.6            1.0     0.75      0.375       0.875  "
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frequent_itemsets = apriori(df, min_support=0.6, use_colnames=True)\n",
    "\n",
    "num_itemsets = frequent_itemsets['itemsets'].apply(len)\n",
    "\n",
    "\n",
    "rules = association_rules(frequent_itemsets, metric=\"confidence\", min_threshold=0.7, num_itemsets=num_itemsets)\n",
    "rules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>apple</th>\n",
       "      <th>corn</th>\n",
       "      <th>dill</th>\n",
       "      <th>eggs</th>\n",
       "      <th>ice cream</th>\n",
       "      <th>kidney beans</th>\n",
       "      <th>milk</th>\n",
       "      <th>nutmeg</th>\n",
       "      <th>onion</th>\n",
       "      <th>unicorn</th>\n",
       "      <th>yogurt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   apple   corn   dill   eggs  ice cream  kidney beans   milk  nutmeg  onion  \\\n",
       "0  False  False  False   True      False          True   True    True   True   \n",
       "1  False  False   True   True      False          True  False    True   True   \n",
       "2   True  False  False   True      False          True   True   False  False   \n",
       "3  False   True  False  False      False          True   True   False  False   \n",
       "4  False   True  False   True       True          True  False   False   True   \n",
       "\n",
       "   unicorn  yogurt  \n",
       "0    False    True  \n",
       "1    False    True  \n",
       "2    False   False  \n",
       "3     True    True  \n",
       "4    False   False  "
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = [['milk', 'onion', 'nutmeg', 'kidney beans', 'eggs', 'yogurt'],\n",
    "           ['dill', 'onion', 'nutmeg', 'kidney beans', 'eggs', 'yogurt'],\n",
    "           ['milk', 'apple', 'kidney beans', 'eggs'],\n",
    "           ['milk', 'unicorn', 'corn', 'kidney beans', 'yogurt'],\n",
    "           ['corn', 'onion', 'onion', 'kidney beans', 'ice cream', 'eggs']]\n",
    "\n",
    "\n",
    "\n",
    "import pandas as pd \n",
    "from mlxtend.preprocessing import TransactionEncoder\n",
    "\n",
    "te = TransactionEncoder()\n",
    "te_ary = te.fit(dataset).transform(dataset)\n",
    "df = pd.DataFrame(te_ary, columns= te.columns_)\n",
    "df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mlxtend.frequent_patterns import fpgrowth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>support</th>\n",
       "      <th>itemsets</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>(5)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.8</td>\n",
       "      <td>(3)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.6</td>\n",
       "      <td>(10)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.6</td>\n",
       "      <td>(6)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.6</td>\n",
       "      <td>(8)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.8</td>\n",
       "      <td>(3, 5)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.6</td>\n",
       "      <td>(10, 5)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.6</td>\n",
       "      <td>(5, 6)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.6</td>\n",
       "      <td>(8, 3)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.6</td>\n",
       "      <td>(8, 5)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.6</td>\n",
       "      <td>(8, 3, 5)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    support   itemsets\n",
       "0       1.0        (5)\n",
       "1       0.8        (3)\n",
       "2       0.6       (10)\n",
       "3       0.6        (6)\n",
       "4       0.6        (8)\n",
       "5       0.8     (3, 5)\n",
       "6       0.6    (10, 5)\n",
       "7       0.6     (5, 6)\n",
       "8       0.6     (8, 3)\n",
       "9       0.6     (8, 5)\n",
       "10      0.6  (8, 3, 5)"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fpgrowth(df, min_support= 0.6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>support</th>\n",
       "      <th>itemsets</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>(kidney beans)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.8</td>\n",
       "      <td>(eggs)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.6</td>\n",
       "      <td>(yogurt)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.6</td>\n",
       "      <td>(milk)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.6</td>\n",
       "      <td>(onion)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.8</td>\n",
       "      <td>(eggs, kidney beans)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.6</td>\n",
       "      <td>(kidney beans, yogurt)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.6</td>\n",
       "      <td>(kidney beans, milk)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.6</td>\n",
       "      <td>(onion, eggs)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.6</td>\n",
       "      <td>(onion, kidney beans)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.6</td>\n",
       "      <td>(onion, kidney beans, eggs)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    support                     itemsets\n",
       "0       1.0               (kidney beans)\n",
       "1       0.8                       (eggs)\n",
       "2       0.6                     (yogurt)\n",
       "3       0.6                       (milk)\n",
       "4       0.6                      (onion)\n",
       "5       0.8         (eggs, kidney beans)\n",
       "6       0.6       (kidney beans, yogurt)\n",
       "7       0.6         (kidney beans, milk)\n",
       "8       0.6                (onion, eggs)\n",
       "9       0.6        (onion, kidney beans)\n",
       "10      0.6  (onion, kidney beans, eggs)"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fpgrowth(df, min_support= 0.6, use_colnames= True)"
   ]
  }
 ],
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