logistic.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from ydata_profiling import ProfileReport\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\"\"\"\n",
    "    methods:\n",
    "        show\n",
    "        sig\n",
    "        decision_boundary\n",
    "        predicted_proba\n",
    "        predict\n",
    "        logistic_cost\n",
    "        fit\n",
    "    \n",
    "    attributes:\n",
    "        t0 (bias)\n",
    "        t (array of coefs)\n",
    "        learning_rate\n",
    "        threshold\n",
    "\"\"\"\n",
    "class LogisticRegression:\n",
    "\n",
    "    def __init__(self, learning_rate, t0, t, threshold):\n",
    "        self.t0 = float(t0)\n",
    "        self.t = np.array(t, dtype=np.float64)\n",
    "        self.threshold = threshold\n",
    "        self.learning_rate = learning_rate\n",
    "\n",
    "\n",
    "    def show(self):\n",
    "        print(f\"##############################################\\nparams: {self.t0}, {self.t}\\nlearning rate: {self.learning_rate}\\n##############################################\")\n",
    "\n",
    "    def sig(self, value):\n",
    "        return (1/(1+np.exp(-1 * self.decision_boundary(value))))\n",
    "\n",
    "    \"\"\"\n",
    "        input: array of n values\n",
    "        output: theta0 + theta1 * input1 + ... + thetan * inputn  ( z = theta0 + theta1*x1 + theta2*x2 )\n",
    "    \"\"\"\n",
    "    def decision_boundary(self, input):\n",
    "        return (self.t0 + np.dot(input, self.t))\n",
    "    \n",
    "    def predicted_proba(self, input):\n",
    "        return self.sig(input)\n",
    "\n",
    "    def predict(self, input):\n",
    "        return [1 if x >= self.threshold else 0 for x in self.sig(input)]\n",
    "        \n",
    "        \n",
    "    \"\"\"\n",
    "        cost = -y*log(ŷ)-(1-y)*log(1-ŷ)\n",
    "\n",
    "    \"\"\"\n",
    "    def logistic_cost(self, samples, correct_classes):\n",
    "\n",
    "        m=correct_classes.shape[0]\n",
    "\n",
    "        predictions = np.array(self.predicted_proba(samples))\n",
    "        \n",
    "        pos = correct_classes >= self.threshold\n",
    "        neg = correct_classes < self.threshold\n",
    "\n",
    "        pos_cost = np.log2(predictions[pos])\n",
    "        neg_cost = np.log2(1 - predictions[neg])\n",
    "\n",
    "        cost = (-1/m)*(np.sum(neg_cost) + np.sum(pos_cost))\n",
    "        \n",
    "        return cost\n",
    "\n",
    "\n",
    "\n",
    "    def fit(self, samples, correct_classes, epochs):\n",
    "        m = correct_classes.shape[0]\n",
    "\n",
    "        for _ in range(epochs):\n",
    "            predictions = np.array(self.predict(samples))\n",
    "            upd_t0 = self.t0 - self.learning_rate *(1/m) * np.sum(predictions - correct_classes)\n",
    "            self.t0 = float(\"%.4f\" % upd_t0)\n",
    "            upd_t = self.t - self.learning_rate * (1/m) * np.dot((predictions - correct_classes), samples)\n",
    "            self.t = np.round(upd_t, decimals=4)\n",
    "        \n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "d = np.array([\n",
    "        [2, 1, 0],\n",
    "        [4, 1.5, 1],\n",
    "        [3, 1, 0],\n",
    "        [3.5, 0.5, 1],\n",
    "        [2, 0.5, 0],\n",
    "        [5.5, 1, 1],\n",
    "        [1, 1, 0]\n",
    "    ])\n",
    "\n",
    "dataset = pd.DataFrame(d, columns=['age', 'cholesterol', 'stroke'])\n",
    "\n",
    "\n",
    "samples = dataset[['age', 'cholesterol']]\n",
    "classes = dataset['stroke']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "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>age</th>\n",
       "      <th>cholesterol</th>\n",
       "      <th>stroke</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  cholesterol  stroke\n",
       "0  2.0          1.0     0.0\n",
       "1  4.0          1.5     1.0\n",
       "2  3.0          1.0     0.0\n",
       "3  3.5          0.5     1.0\n",
       "4  2.0          0.5     0.0"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7 entries, 0 to 6\n",
      "Data columns (total 3 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   age          7 non-null      float64\n",
      " 1   cholesterol  7 non-null      float64\n",
      " 2   stroke       7 non-null      float64\n",
      "dtypes: float64(3)\n",
      "memory usage: 296.0 bytes\n"
     ]
    }
   ],
   "source": [
    "dataset.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "\n",
    "ax.scatter(samples.loc[classes == 0, 'age'], samples.loc[classes == 0, 'cholesterol'], classes[classes == 0], c='blue', label='No Stroke')\n",
    "ax.scatter(samples.loc[classes == 1, 'age'], samples.loc[classes == 1, 'cholesterol'], classes[classes == 1], c='red', label='Stroke')\n",
    "\n",
    "ax.set_xlabel('Age')\n",
    "ax.set_ylabel('Cholesterol')\n",
    "ax.set_zlabel('Stroke')\n",
    "\n",
    "\n",
    "ax.set_title('3D Scatter Plot')\n",
    "\n",
    "\n",
    "ax.legend()\n",
    "\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "134d82e175c44b77a4ba1142d8e4a770",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Summarize dataset:   0%|          | 0/5 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "90bb3d1cd96246ce881571059766e15c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generate report structure:   0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2b409ab0da124269a88dae11db5bd174",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Render HTML:   0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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GradientType=0);background-repeat:repeat-x;border-color:#dcdcdc;-webkit-box-shadow:inset 0 1px 3px rgba(0,0,0,.05),0 1px 0 rgba(255,255,255,.1);box-shadow:inset 0 1px 3px rgba(0,0,0,.05),0 1px 0 rgba(255,255,255,.1)}\n",
       "/*# sourceMappingURL=bootstrap-theme.min.css.map */        &lt;/style&gt;&lt;style&gt;body {\n",
       "  padding-top: 80px;\n",
       "}\n",
       ".content .container-fluid{\n",
       "    margin-left: 20px !important;\n",
       "    margin-right: 20px !important;\n",
       "    margin-bottom: 20px;\n",
       "}\n",
       "\n",
       ".page-header{\n",
       "    border:0 !important;\n",
       "}\n",
       "\n",
       ".row.variable, .section-items &gt; .row {\n",
       "    border: 1px solid #e1e1e8;\n",
       "    border-top: hidden;\n",
       "}\n",
       "\n",
       ".row.spacing {\n",
       "    padding: 2em 1em;\n",
       "}\n",
       "\n",
       ".row.header {\n",
       "    border-bottom: 1px solid #e1e1e8;\n",
       "    /*background-color: #f5f5f5;*/\n",
       "    padding-left: 2em;\n",
       "}\n",
       "\n",
       "\n",
       ".dl-horizontal dt {\n",
       "    text-align: left;\n",
       "    padding-right: 1em;\n",
       "    white-space: normal;\n",
       "}\n",
       "\n",
       ".dl-horizontal dd {\n",
       "    margin-left: 0;\n",
       "}\n",
       "\n",
       ".col-md-12 {\n",
       "    padding-left: 2em;\n",
       "}\n",
       "\n",
       ".indent {\n",
       "    margin-left: 1em;\n",
       "}\n",
       "\n",
       ".center-img {\n",
       "    margin-left: auto !important;\n",
       "    margin-right: auto !important;\n",
       "    display: block;\n",
       "}\n",
       "\n",
       "/* Table example_values */\n",
       "table.example_values {\n",
       "    border: 0;\n",
       "}\n",
       "\n",
       ".example_values th {\n",
       "    border: 0;\n",
       "    padding: 0;\n",
       "    color: #555;\n",
       "    font-weight: 600;\n",
       "}\n",
       "\n",
       ".example_values tr, .example_values td {\n",
       "    border: 0;\n",
       "    padding: 0;\n",
       "    color: #555;\n",
       "}\n",
       "\n",
       "table.correlation-table {\n",
       "    width: 98%  /* To avoid an unnecessary overflow. */\n",
       "}\n",
       "\n",
       "/* STATS */\n",
       "table.stats, table.sample, table.duplicate, table.correlation-table{\n",
       "    border: 0;\n",
       "}\n",
       "\n",
       ".stats tr, .sample tr, .duplicate tr, .correlation-table tr {\n",
       "    border: 0;\n",
       "}\n",
       "\n",
       ".stats th, .stats td{\n",
       "    color: #555;\n",
       "    border: 0;\n",
       "}\n",
       "\n",
       ".stats th {\n",
       "    padding: 0 2em 0 0;\n",
       "    font-weight: 600;\n",
       "}\n",
       "\n",
       ".stats td {\n",
       "    padding: 1px;\n",
       "}\n",
       "\n",
       "\n",
       "/* Sample table */\n",
       "table.sample, table.duplicate, table.correlation-table {\n",
       "    margin-bottom: 2em;\n",
       "    margin-left: 1em;\n",
       "}\n",
       "\n",
       ".sample td, .sample th, .duplicate td, .duplicate th, .correlation-table td, .correlation-table th {\n",
       "    padding: 0.5em;\n",
       "    white-space: nowrap;\n",
       "    border: 0;\n",
       "\n",
       "}\n",
       "\n",
       ".sample thead, .duplicate thead, .correlation-table thead {\n",
       "    border-top: 0;\n",
       "    border-bottom: 2px solid #ddd;\n",
       "}\n",
       "\n",
       ".sample td, .duplicate td {\n",
       "    width: 100%;\n",
       "} \n",
       "\n",
       "/* There is no good solution available to make the divs equal height and then center ... */\n",
       ".histogram {\n",
       "    margin-top: 3em;\n",
       "}\n",
       "\n",
       "/* Freq table */\n",
       "table.freq {\n",
       "    margin-bottom: 2em;\n",
       "    border: 0;\n",
       "}\n",
       "\n",
       "table.freq th, table.freq tr, table.freq td {\n",
       "    border: 0;\n",
       "    padding: 0;\n",
       "}\n",
       "\n",
       ".freq thead {\n",
       "    font-weight: 600;\n",
       "    white-space: nowrap;\n",
       "    overflow: hidden;\n",
       "    text-overflow: ellipsis;\n",
       "\n",
       "}\n",
       "\n",
       "/* Freq mini */\n",
       ".freq.mini td {\n",
       "    width: 50%;\n",
       "    padding: 1px;\n",
       "    font-size: 12px;\n",
       "\n",
       "}\n",
       "\n",
       "table.freq.mini {\n",
       "    width: 100%;\n",
       "}\n",
       "\n",
       ".freq.mini th {\n",
       "    overflow: hidden;\n",
       "    text-overflow: ellipsis;\n",
       "    white-space: nowrap;\n",
       "    max-width: 5em;\n",
       "    font-weight: 400;\n",
       "    text-align: right;\n",
       "    padding-right: 0.5em;\n",
       "}\n",
       "\n",
       "/* Message classes */\n",
       ".missing {\n",
       "    color: #a94442;\n",
       "}\n",
       "\n",
       ".alert, .alert &gt; th, .alert &gt; td {\n",
       "    color: #a94442;\n",
       "}\n",
       "\n",
       ".ignore {\n",
       "    opacity: 0.4;\n",
       "}\n",
       "\n",
       "/* Bars in tables */\n",
       ".freq.table{\n",
       "    table-layout: fixed;\n",
       "}\n",
       "\n",
       ".freq:not(.mini) tr td:nth-child(1), .freq:not(.mini) tr th:nth-child(1){\n",
       "    width: auto;\n",
       "    max-width: none;\n",
       "\n",
       "    white-space: nowrap;\n",
       "    overflow: hidden;\n",
       "    text-overflow: ellipsis;\n",
       "}\n",
       "\n",
       ".freq:not(.mini) tr td:nth-child(2), .freq:not(.mini) tr td:nth-child(3), .freq:not(.mini) tr th:nth-child(2), .freq:not(.mini) tr th:nth-child(3){\n",
       "    width: 100px;\n",
       "    text-align: right;\n",
       "}\n",
       ".freq:not(.mini) tr td:nth-child(4), .freq:not(.mini) tr th:nth-child(4){\n",
       "    width:200px;\n",
       "}\n",
       "\n",
       ".freq .bar {\n",
       "    float: left;\n",
       "    width: 0;\n",
       "    height: 100%;\n",
       "    line-height: 20px;\n",
       "    color: #fff;\n",
       "    text-align: center;\n",
       "    border-radius: 3px;\n",
       "    margin-right: 4px;\n",
       "}\n",
       "\n",
       ".other .bar {\n",
       "    background-color: #999;\n",
       "}\n",
       "\n",
       ".missing .bar {\n",
       "    background-color: #a94442;\n",
       "}\n",
       "\n",
       ".tooltip-inner {\n",
       "    width: 100%;\n",
       "    white-space: nowrap;\n",
       "    text-align: left;\n",
       "}\n",
       "\n",
       ".extrapadding {\n",
       "    padding: 2em;\n",
       "}\n",
       "\n",
       ".variable .h4 {\n",
       "    text-overflow: ellipsis;\n",
       "    display:inline-block;\n",
       "    width: calc(90%); /* The trick is here! */\n",
       "    overflow:hidden;\n",
       "}\n",
       "\n",
       ".variable.ignore .h4{\n",
       "    text-decoration: line-through;\n",
       "}\n",
       "\n",
       ".table-responsive{\n",
       "    overflow: scroll;\n",
       "    width: 100%;\n",
       "    overflow-y: hidden;\n",
       "}\n",
       ".img-responsive{\n",
       "    max-width: 99%;\n",
       "    min-width: 99%;\n",
       "}\n",
       ".footer-text{\n",
       "    padding:20px;\n",
       "}\n",
       "\n",
       "table.list-warnings td{\n",
       "    padding-right:10px;\n",
       "}\n",
       "\n",
       "a.anchor-pos {\n",
       "    display: block;\n",
       "    position: relative;\n",
       "    top: -70px;\n",
       "    visibility: hidden;\n",
       "}\n",
       "\n",
       "a.anchor-pos-variable{\n",
       "    /*top: -70px;*/\n",
       "}\n",
       "\n",
       "#sample-container, #duplicate-container {\n",
       "    overflow: auto;\n",
       "    width: 100%;\n",
       "    overflow-y: hidden;\n",
       "}\n",
       "\n",
       "#correlation-table-container {\n",
       "    max-height: 400px;\n",
       "    overflow: auto;\n",
       "}\n",
       "\n",
       "#overview-content td, #overview-content th{\n",
       "    border-top: 0;\n",
       "    line-height: 1;\n",
       "}\n",
       "\n",
       ".variable-description{\n",
       "    color: #777;\n",
       "    font-size: 10pt;\n",
       "    margin-top: 10px;\n",
       "    font-style: italic;\n",
       "}\n",
       "\n",
       "select.multiple{\n",
       "    width: 180px;\n",
       "    height: 500px;\n",
       "    margin: 10px 0;\n",
       "}\n",
       "\n",
       ".named-list-item{\n",
       "    padding: 1em;\n",
       "}\n",
       "\n",
       "/* not printing tabs */\n",
       "@media print {\n",
       "    .tab-content &gt; .tab-pane, .collapse {\n",
       "        display: block !important;\n",
       "        opacity: 1 !important;\n",
       "        visibility: visible !important;\n",
       "        /*page-break-after: always;*/\n",
       "        page-break-after: right;\n",
       "        page-break-before: avoid;\n",
       "    }\n",
       "\n",
       "    .nav-pills, .nav-tabs, button[data-toggle=&quot;collapse&quot;], .mini, .col-sm-3 img {\n",
       "        display:none !important;\n",
       "    }\n",
       "\n",
       "    a[download=&quot;config.yml&quot;]:after {\n",
       "        content: none !important;\n",
       "    }\n",
       "\n",
       "    .row {\n",
       "        border: 0 !important;\n",
       "    }\n",
       "}\n",
       "\n",
       ".text-placeholder {\n",
       "    display: inline-block;\n",
       "    background-color: #444;\n",
       "    height: 12px;\n",
       "    border-radius: 100px;\n",
       "    margin: 5px 0;\n",
       "    min-width: 200px;\n",
       "    opacity: .1;\n",
       "}\n",
       "\n",
       "    .freq.table-0 .bar {\n",
       "        background-color: #377eb8;\n",
       "    }\n",
       "    .freq.table-1 .bar {\n",
       "        background-color: #e41a1c;\n",
       "    }\n",
       "&lt;/style&gt;&lt;/head&gt;&lt;body&gt;&lt;a class=anchor-pos id=top&gt;&lt;/a&gt;&lt;nav class=&quot;navbar navbar-default navbar-fixed-top&quot;&gt;&lt;div class=container-fluid&gt;&lt;div class=navbar-header&gt;&lt;button type=button class=&quot;navbar-toggle collapsed&quot; data-toggle=collapse data-target=#navbar aria-expanded=false aria-controls=navbar&gt;&lt;span class=sr-only&gt;Toggle navigation&lt;/span&gt;&lt;span class=icon-bar&gt;&lt;/span&gt;&lt;span class=icon-bar&gt;&lt;/span&gt;&lt;span class=icon-bar&gt;&lt;/span&gt;&lt;/button&gt;&lt;a class=&quot;navbar-brand anchor&quot; href=#top&gt;Pandas Profiling Report&lt;/a&gt;&lt;/div&gt;&lt;div id=navbar class=&quot;navbar-collapse collapse&quot;&gt;&lt;ul class=&quot;nav navbar-nav navbar-right&quot;&gt;&lt;li&gt;&lt;a href=#overview&gt;Overview&lt;/a&gt;&lt;/li&gt;&lt;li&gt;&lt;a href=#variables-dropdown&gt;Variables&lt;/a&gt;&lt;/li&gt;&lt;li&gt;&lt;a href=#interactions&gt;Interactions&lt;/a&gt;&lt;/li&gt;&lt;li&gt;&lt;a href=#correlations_tab&gt;Correlations&lt;/a&gt;&lt;/li&gt;&lt;li&gt;&lt;a href=#missing&gt;Missing values&lt;/a&gt;&lt;/li&gt;&lt;li&gt;&lt;a href=#sample&gt;Sample&lt;/a&gt;&lt;/li&gt;&lt;/ul&gt;&lt;/div&gt;&lt;/div&gt;&lt;/nav&gt;&lt;div class=content&gt;&lt;div class=container&gt;&lt;div class=&quot;row header&quot;&gt;&lt;a class=anchor-pos id=overview&gt;&lt;/a&gt;&lt;h1 class=page-header&gt;Overview&lt;/h1&gt;&lt;/div&gt;&lt;div class=section-items&gt;&lt;div class=&quot;row spacing&quot;&gt;&lt;ul class=&quot;nav nav-pills&quot; role=tablist&gt;&lt;li role=presentation class=active&gt;&lt;a href=#overview-dataset_overview aria-controls=overview-dataset_overview role=tab data-toggle=tab&gt;Overview&lt;/a&gt;&lt;/li&gt;&lt;li role=presentation&gt;&lt;a href=#overview-reproduction aria-controls=overview-reproduction role=tab data-toggle=tab&gt;Reproduction&lt;/a&gt;&lt;/li&gt;&lt;/ul&gt;&lt;div class=tab-content style=&quot;padding-top: 10px;&quot;&gt;&lt;div role=tabpanel class=&quot;tab-pane col-sm-12 active&quot; id=overview-dataset_overview&gt;&lt;div class=col-sm-6&gt;&lt;p class=h4&gt;Dataset statistics&lt;/p&gt;&lt;table class=&quot;table table-condensed stats&quot;&gt;&lt;tbody&gt;&lt;tr&gt;&lt;th&gt;Number of variables&lt;/th&gt;&lt;td style=&quot;white-space:pre white-space:nowrap&quot;&gt;3&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Number of observations&lt;/th&gt;&lt;td style=&quot;white-space:pre white-space:nowrap&quot;&gt;7&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Missing cells&lt;/th&gt;&lt;td style=&quot;white-space:pre white-space:nowrap&quot;&gt;0&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Missing cells (%)&lt;/th&gt;&lt;td style=&quot;white-space:pre white-space:nowrap&quot;&gt;0.0%&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Duplicate rows&lt;/th&gt;&lt;td style=&quot;white-space:pre white-space:nowrap&quot;&gt;0&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Duplicate rows (%)&lt;/th&gt;&lt;td style=&quot;white-space:pre white-space:nowrap&quot;&gt;0.0%&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Total size in memory&lt;/th&gt;&lt;td style=&quot;white-space:pre white-space:nowrap&quot;&gt;296.0 B&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Average record size in memory&lt;/th&gt;&lt;td style=&quot;white-space:pre white-space:nowrap&quot;&gt;42.3 B&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;/div&gt;&lt;div class=col-sm-6&gt;&lt;p class=h4&gt;Variable types&lt;/p&gt;&lt;table class=&quot;table table-condensed stats&quot;&gt;&lt;tbody&gt;&lt;tr&gt;&lt;th&gt;Numeric&lt;/th&gt;&lt;td style=&quot;white-space:pre white-space:nowrap&quot;&gt;1&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Categorical&lt;/th&gt;&lt;td style=&quot;white-space:pre white-space:nowrap&quot;&gt;2&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;/div&gt;&lt;/div&gt;&lt;div role=tabpanel class=&quot;tab-pane col-sm-12&quot; id=overview-reproduction&gt;&lt;div class=col-sm-12&gt;&lt;p class=h4&gt;Reproduction&lt;/p&gt;&lt;table class=&quot;table table-condensed stats&quot;&gt;&lt;tbody&gt;&lt;tr&gt;&lt;th&gt;Analysis started&lt;/th&gt;&lt;td style=&quot;white-space:pre white-space:nowrap&quot;&gt;2023-12-20 11:38:26.470644&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Analysis finished&lt;/th&gt;&lt;td style=&quot;white-space:pre white-space:nowrap&quot;&gt;2023-12-20 11:38:28.567507&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Duration&lt;/th&gt;&lt;td style=&quot;white-space:pre white-space:nowrap&quot;&gt;2.1 seconds&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Software version&lt;/th&gt;&lt;td style=&quot;white-space:pre white-space:nowrap&quot;&gt;&lt;a href=https://github.com/ydataai/ydata-profiling&gt;ydata-profiling vv4.6.3&lt;/a&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Download configuration&lt;/th&gt;&lt;td style=&quot;white-space:pre white-space:nowrap&quot;&gt;&lt;a download=config.json href=&quot;data:text/plain;charset=utf-8,%7B%22title%22%3A%20%22Pandas%20Profiling%20Report%22%2C%20%22dataset%22%3A%20%7B%22description%22%3A%20%22%22%2C%20%22creator%22%3A%20%22%22%2C%20%22author%22%3A%20%22%22%2C%20%22copyright_holder%22%3A%20%22%22%2C%20%22copyright_year%22%3A%20%22%22%2C%20%22url%22%3A%20%22%22%7D%2C%20%22variables%22%3A%20%7B%22descriptions%22%3A%20%7B%7D%7D%2C%20%22infer_dtypes%22%3A%20true%2C%20%22show_variable_description%22%3A%20true%2C%20%22pool_size%22%3A%200%2C%20%22progress_bar%22%3A%20true%2C%20%22vars%22%3A%20%7B%22num%22%3A%20%7B%22quantiles%22%3A%20%5B0.05%2C%200.25%2C%200.5%2C%200.75%2C%200.95%5D%2C%20%22skewness_threshold%22%3A%2020%2C%20%22low_categorical_threshold%22%3A%205%2C%20%22chi_squared_threshold%22%3A%200.999%7D%2C%20%22text%22%3A%20%7B%22length%22%3A%20true%2C%20%22words%22%3A%20true%2C%20%22characters%22%3A%20true%2C%20%22redact%22%3A%20false%7D%2C%20%22cat%22%3A%20%7B%22length%22%3A%20true%2C%20%22characters%22%3A%20true%2C%20%22words%22%3A%20true%2C%20%22cardinality_threshold%22%3A%2050%2C%20%22percentage_cat_threshold%22%3A%200.5%2C%20%22imbalance_threshold%22%3A%200.5%2C%20%22n_obs%22%3A%205%2C%20%22chi_squared_threshold%22%3A%200.999%2C%20%22coerce_str_to_date%22%3A%20false%2C%20%22redact%22%3A%20false%2C%20%22histogram_largest%22%3A%2050%2C%20%22stop_words%22%3A%20%5B%5D%7D%2C%20%22image%22%3A%20%7B%22active%22%3A%20false%2C%20%22exif%22%3A%20true%2C%20%22hash%22%3A%20true%7D%2C%20%22bool%22%3A%20%7B%22n_obs%22%3A%203%2C%20%22imbalance_threshold%22%3A%200.5%2C%20%22mappings%22%3A%20%7B%22t%22%3A%20true%2C%20%22f%22%3A%20false%2C%20%22yes%22%3A%20true%2C%20%22no%22%3A%20false%2C%20%22y%22%3A%20true%2C%20%22n%22%3A%20false%2C%20%22true%22%3A%20true%2C%20%22false%22%3A%20false%7D%7D%2C%20%22path%22%3A%20%7B%22active%22%3A%20false%7D%2C%20%22file%22%3A%20%7B%22active%22%3A%20false%7D%2C%20%22url%22%3A%20%7B%22active%22%3A%20false%7D%2C%20%22timeseries%22%3A%20%7B%22active%22%3A%20false%2C%20%22sortby%22%3A%20null%2C%20%22autocorrelation%22%3A%200.7%2C%20%22lags%22%3A%20%5B1%2C%207%2C%2012%2C%2024%2C%2030%5D%2C%20%22significance%22%3A%200.05%2C%20%22pacf_acf_lag%22%3A%20100%7D%7D%2C%20%22sort%22%3A%20null%2C%20%22missing_diagrams%22%3A%20%7B%22bar%22%3A%20true%2C%20%22matrix%22%3A%20true%2C%20%22heatmap%22%3A%20true%7D%2C%20%22correlation_table%22%3A%20true%2C%20%22correlations%22%3A%20%7B%22auto%22%3A%20%7B%22key%22%3A%20%22auto%22%2C%20%22calculate%22%3A%20true%2C%20%22warn_high_correlations%22%3A%2010%2C%20%22threshold%22%3A%200.5%2C%20%22n_bins%22%3A%2010%7D%2C%20%22spearman%22%3A%20%7B%22key%22%3A%20%22spearman%22%2C%20%22calculate%22%3A%20false%2C%20%22warn_high_correlations%22%3A%2010%2C%20%22threshold%22%3A%200.5%2C%20%22n_bins%22%3A%2010%7D%2C%20%22pearson%22%3A%20%7B%22key%22%3A%20%22pearson%22%2C%20%22calculate%22%3A%20false%2C%20%22warn_high_correlations%22%3A%2010%2C%20%22threshold%22%3A%200.5%2C%20%22n_bins%22%3A%2010%7D%2C%20%22phi_k%22%3A%20%7B%22key%22%3A%20%22phi_k%22%2C%20%22calculate%22%3A%20false%2C%20%22warn_high_correlations%22%3A%2010%2C%20%22threshold%22%3A%200.5%2C%20%22n_bins%22%3A%2010%7D%2C%20%22cramers%22%3A%20%7B%22key%22%3A%20%22cramers%22%2C%20%22calculate%22%3A%20false%2C%20%22warn_high_correlations%22%3A%2010%2C%20%22threshold%22%3A%200.5%2C%20%22n_bins%22%3A%2010%7D%2C%20%22kendall%22%3A%20%7B%22key%22%3A%20%22kendall%22%2C%20%22calculate%22%3A%20false%2C%20%22warn_high_correlations%22%3A%2010%2C%20%22threshold%22%3A%200.5%2C%20%22n_bins%22%3A%2010%7D%7D%2C%20%22interactions%22%3A%20%7B%22continuous%22%3A%20true%2C%20%22targets%22%3A%20%5B%5D%7D%2C%20%22categorical_maximum_correlation_distinct%22%3A%20100%2C%20%22memory_deep%22%3A%20false%2C%20%22plot%22%3A%20%7B%22missing%22%3A%20%7B%22force_labels%22%3A%20true%2C%20%22cmap%22%3A%20%22RdBu%22%7D%2C%20%22image_format%22%3A%20%22svg%22%2C%20%22correlation%22%3A%20%7B%22cmap%22%3A%20%22RdBu%22%2C%20%22bad%22%3A%20%22%23000000%22%7D%2C%20%22dpi%22%3A%20800%2C%20%22histogram%22%3A%20%7B%22bins%22%3A%2050%2C%20%22max_bins%22%3A%20250%2C%20%22x_axis_labels%22%3A%20true%2C%20%22density%22%3A%20false%7D%2C%20%22scatter_threshold%22%3A%201000%2C%20%22cat_freq%22%3A%20%7B%22show%22%3A%20true%2C%20%22type%22%3A%20%22bar%22%2C%20%22max_unique%22%3A%2010%2C%20%22colors%22%3A%20null%7D%7D%2C%20%22duplicates%22%3A%20%7B%22head%22%3A%2010%2C%20%22key%22%3A%20%22%23%20duplicates%22%7D%2C%20%22samples%22%3A%20%7B%22head%22%3A%2010%2C%20%22tail%22%3A%2010%2C%20%22random%22%3A%200%7D%2C%20%22reject_variables%22%3A%20true%2C%20%22n_obs_unique%22%3A%2010%2C%20%22n_freq_table_max%22%3A%2010%2C%20%22n_extreme_obs%22%3A%2010%2C%20%22report%22%3A%20%7B%22precision%22%3A%208%7D%2C%20%22html%22%3A%20%7B%22style%22%3A%20%7B%22primary_colors%22%3A%20%5B%22%23377eb8%22%2C%20%22%23e41a1c%22%2C%20%22%234daf4a%22%5D%2C%20%22logo%22%3A%20%22%22%2C%20%22theme%22%3A%20null%7D%2C%20%22navbar_show%22%3A%20true%2C%20%22minify_html%22%3A%20true%2C%20%22use_local_assets%22%3A%20true%2C%20%22inline%22%3A%20true%2C%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class=&quot;row header&quot;&gt;&lt;a class=anchor-pos id=variables-dropdown&gt;&lt;/a&gt;&lt;h1 class=page-header&gt;Variables&lt;/h1&gt;&lt;/div&gt;&lt;div class=section-items&gt;&lt;div class=&quot;row spacing&quot;&gt;&lt;select name=Variables id=variables-dropdown class=dropdown-toggle&gt;&lt;option value&gt;Select Columns&lt;/option&gt;&lt;option value=age&gt;age&lt;/option&gt;&lt;option value=cholesterol&gt;cholesterol&lt;/option&gt;&lt;option value=stroke&gt;stroke&lt;/option&gt;&lt;/select&gt;&lt;/div&gt;&lt;div class=&quot;row spacing&quot;&gt;&lt;a class=&quot;anchor-pos anchor-pos-variable&quot; id=pp_var_8538662089341378362&gt;&lt;/a&gt;&lt;div class=variable&gt;&lt;div class=col-sm-12&gt;&lt;p class=h4 title=age&gt;&lt;a href=#pp_var_8538662089341378362&gt;age&lt;/a&gt;&lt;br&gt;&lt;small&gt;Real number (&amp;Ropf;)&lt;/small&gt;&lt;/p&gt;&lt;p class=variable-description&gt;&lt;/p&gt;&lt;/div&gt;&lt;div class=col-sm-4&gt;&lt;table class=&quot;table table-condensed 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14.3% &lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=3&gt; 3 &lt;/td&gt;&lt;td&gt;1&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:50.0%&gt; 14.3% &lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=3.5&gt; 3.5 &lt;/td&gt;&lt;td&gt;1&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:50.0%&gt; 14.3% &lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=5.5&gt; 5.5 &lt;/td&gt;&lt;td&gt;1&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:50.0%&gt; 14.3% &lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=1&gt; 1 &lt;/td&gt;&lt;td&gt;1&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:50.0%&gt; 14.3% &lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;/div&gt;&lt;div role=tabpanel class=&quot;tab-pane col-sm-12&quot; id=8538662089341378362bottom-8538662089341378362extreme_values&gt;&lt;div class=&quot;row spacing&quot;&gt;&lt;ul class=&quot;nav nav-tabs&quot; role=tablist&gt;&lt;li role=presentation class=active&gt;&lt;a href=#8538662089341378362extreme_values-8538662089341378362firstn aria-controls=8538662089341378362extreme_values-8538662089341378362firstn role=tab data-toggle=tab&gt;Minimum 10 values&lt;/a&gt;&lt;/li&gt;&lt;li role=presentation&gt;&lt;a href=#8538662089341378362extreme_values-8538662089341378362lastn aria-controls=8538662089341378362extreme_values-8538662089341378362lastn role=tab data-toggle=tab&gt;Maximum 10 values&lt;/a&gt;&lt;/li&gt;&lt;/ul&gt;&lt;div class=tab-content&gt;&lt;div role=tabpanel class=&quot;tab-pane col-sm-12 active&quot; id=8538662089341378362extreme_values-8538662089341378362firstn&gt;&lt;table class=&quot;freq table table-hover table-striped table-0&quot;&gt;&lt;thead&gt;&lt;tr&gt;&lt;td&gt;Value&lt;/td&gt;&lt;td&gt;Count&lt;/td&gt;&lt;td&gt;Frequency (%)&lt;/td&gt;&lt;/tr&gt;&lt;/thead&gt;&lt;tbody&gt;&lt;tr class&gt;&lt;td title=1&gt; 1 &lt;/td&gt;&lt;td&gt;1&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:50.0%&gt; 14.3% &lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=2&gt; 2 &lt;/td&gt;&lt;td&gt;2&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:100.0%&gt; 28.6% &lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=3&gt; 3 &lt;/td&gt;&lt;td&gt;1&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:50.0%&gt; 14.3% &lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=3.5&gt; 3.5 &lt;/td&gt;&lt;td&gt;1&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:50.0%&gt; 14.3% &lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=4&gt; 4 &lt;/td&gt;&lt;td&gt;1&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:50.0%&gt; 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14.3% &lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=3&gt; 3 &lt;/td&gt;&lt;td&gt;1&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:50.0%&gt; 14.3% &lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=2&gt; 2 &lt;/td&gt;&lt;td&gt;2&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:100.0%&gt; 28.6% &lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=1&gt; 1 &lt;/td&gt;&lt;td&gt;1&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:50.0%&gt; 14.3% &lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;div class=&quot;row spacing&quot;&gt;&lt;a class=&quot;anchor-pos anchor-pos-variable&quot; id=pp_var_8012943184765047294&gt;&lt;/a&gt;&lt;div class=variable&gt;&lt;div class=col-sm-12&gt;&lt;p class=h4 title=cholesterol&gt;&lt;a href=#pp_var_8012943184765047294&gt;cholesterol&lt;/a&gt;&lt;br&gt;&lt;small&gt;Categorical&lt;/small&gt;&lt;/p&gt;&lt;p class=variable-description&gt;&lt;/p&gt;&lt;/div&gt;&lt;div class=col-sm-6&gt;&lt;table class=&quot;table table-condensed stats&quot;&gt;&lt;tbody&gt;&lt;tr&gt;&lt;th&gt;Distinct&lt;/th&gt;&lt;td style=&quot;white-space:pre white-space:nowrap&quot;&gt;3&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Distinct (%)&lt;/th&gt;&lt;td style=&quot;white-space:pre white-space:nowrap&quot;&gt;42.9%&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Missing&lt;/th&gt;&lt;td style=&quot;white-space:pre white-space:nowrap&quot;&gt;0&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Missing (%)&lt;/th&gt;&lt;td style=&quot;white-space:pre white-space:nowrap&quot;&gt;0.0%&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Memory size&lt;/th&gt;&lt;td style=&quot;white-space:pre white-space:nowrap&quot;&gt;184.0 B&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;/div&gt;&lt;div class=col-sm-6&gt;&lt;div class=row id=minifreqtable&gt;&lt;table class=&quot;mini freq table-0&quot;&gt;&lt;tr class&gt;&lt;th width=10%&gt; 1.0 &lt;/th&gt;&lt;td width=10%&gt;&lt;div class=bar style=width:100.0% data-toggle=tooltip data-placement=center data-html=true data-delay=500&gt; 4&amp;nbsp; &lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;th width=10%&gt; 0.5 &lt;/th&gt;&lt;td width=10%&gt;&lt;div class=bar style=width:50.0% data-toggle=tooltip data-placement=center data-html=true data-delay=500&gt; 2&amp;nbsp; &lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;th width=10%&gt; 1.5 &lt;/th&gt;&lt;td width=10%&gt;&lt;div class=bar style=width:25.0% data-toggle=tooltip data-placement=center data-html=true data-delay=500&gt; 1&amp;nbsp; &lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/table&gt;&lt;/div&gt;&lt;/div&gt;&lt;div class=&quot;col-sm-12 text-right&quot;&gt;&lt;button class=&quot;btn btn-default btn-sm&quot; data-toggle=collapse data-target=&quot;#bottom-8012943184765047294, #minifreqtable8012943184765047294&quot; aria-expanded=true aria-controls=collapseExample&gt; More details &lt;/button&gt;&lt;/div&gt;&lt;div id=bottom-8012943184765047294 class=collapse&gt;&lt;div class=&quot;row spacing&quot;&gt;&lt;ul class=&quot;nav nav-tabs&quot; role=tablist&gt;&lt;li role=presentation class=active&gt;&lt;a href=#8012943184765047294bottom-8012943184765047294overview aria-controls=8012943184765047294bottom-8012943184765047294overview role=tab data-toggle=tab&gt;Overview&lt;/a&gt;&lt;/li&gt;&lt;li role=presentation&gt;&lt;a href=#8012943184765047294bottom-8012943184765047294string aria-controls=8012943184765047294bottom-8012943184765047294string role=tab data-toggle=tab&gt;Categories&lt;/a&gt;&lt;/li&gt;&lt;li role=presentation&gt;&lt;a href=#8012943184765047294bottom-8012943184765047294word aria-controls=8012943184765047294bottom-8012943184765047294word role=tab data-toggle=tab&gt;Words&lt;/a&gt;&lt;/li&gt;&lt;li role=presentation&gt;&lt;a href=#8012943184765047294bottom-8012943184765047294characters aria-controls=8012943184765047294bottom-8012943184765047294characters role=tab data-toggle=tab&gt;Characters&lt;/a&gt;&lt;/li&gt;&lt;/ul&gt;&lt;div class=tab-content&gt;&lt;div role=tabpanel class=&quot;tab-pane col-sm-12 active&quot; id=8012943184765047294bottom-8012943184765047294overview&gt;&lt;div class=row&gt;&lt;div class=col-sm-3&gt;&lt;p class=h4&gt;Length&lt;/p&gt;&lt;table class=&quot;table table-condensed stats&quot;&gt;&lt;tbody&gt;&lt;tr&gt;&lt;th&gt;Max length&lt;/th&gt;&lt;td style=&quot;white-space:pre white-space:nowrap&quot;&gt;3&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Median length&lt;/th&gt;&lt;td style=&quot;white-space:pre white-space:nowrap&quot;&gt;3&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Mean length&lt;/th&gt;&lt;td style=&quot;white-space:pre white-space:nowrap&quot;&gt;3&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Min length&lt;/th&gt;&lt;td style=&quot;white-space:pre white-space:nowrap&quot;&gt;3&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;/div&gt;&lt;div class=col-sm-3&gt;&lt;p class=h4&gt;Characters and Unicode&lt;/p&gt;&lt;table class=&quot;table table-condensed stats&quot;&gt;&lt;tbody&gt;&lt;tr&gt;&lt;th&gt;Total characters&lt;/th&gt;&lt;td style=&quot;white-space:pre white-space:nowrap&quot;&gt;21&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Distinct characters&lt;/th&gt;&lt;td style=&quot;white-space:pre white-space:nowrap&quot;&gt;4&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Distinct categories&lt;/th&gt;&lt;td style=&quot;white-space:pre white-space:nowrap&quot;&gt;2 &lt;a title=&quot;Unicode categories (click for more information)&quot; href=https://en.wikipedia.org/wiki/Unicode_character_property#General_Category&gt;&lt;span class=&quot;badge pull-right&quot; style=color:#fff;background-color:#337ab7; title=&quot;Unicode categories (click for more information)&quot;&gt;?&lt;/span&gt;&lt;/a&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Distinct scripts&lt;/th&gt;&lt;td style=&quot;white-space:pre white-space:nowrap&quot;&gt;1 &lt;a title=&quot;Unicode scripts (click for more information)&quot; href=https://en.wikipedia.org/wiki/Script_(Unicode)#List_of_scripts_in_Unicode&gt;&lt;span class=&quot;badge pull-right&quot; style=color:#fff;background-color:#337ab7; title=&quot;Unicode scripts (click for more information)&quot;&gt;?&lt;/span&gt;&lt;/a&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Distinct blocks&lt;/th&gt;&lt;td style=&quot;white-space:pre white-space:nowrap&quot;&gt;1 &lt;a title=&quot;Unicode blocks (click for more information)&quot; href=https://en.wikipedia.org/wiki/Unicode_block&gt;&lt;span class=&quot;badge pull-right&quot; style=color:#fff;background-color:#337ab7; title=&quot;Unicode blocks (click for more information)&quot;&gt;?&lt;/span&gt;&lt;/a&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;div class=&quot;caption text-center text-muted&quot;&gt; The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables. &lt;/div&gt;&lt;/div&gt;&lt;div class=col-sm-3&gt;&lt;p class=h4&gt;Unique&lt;/p&gt;&lt;table class=&quot;table table-condensed stats&quot;&gt;&lt;tbody&gt;&lt;tr&gt;&lt;th&gt;Unique&lt;/th&gt;&lt;td style=&quot;white-space:pre white-space:nowrap&quot;&gt;1 &lt;span class=&quot;badge pull-right&quot; style=color:#fff;background-color:#337ab7; title=&quot;The number of unique values (all values that occur exactly once in the dataset).&quot;&gt;?&lt;/span&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Unique (%)&lt;/th&gt;&lt;td style=&quot;white-space:pre white-space:nowrap&quot;&gt;14.3%&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;/div&gt;&lt;div class=col-sm-3&gt;&lt;p class=h4&gt;Sample&lt;/p&gt;&lt;table class=&quot;table table-condensed stats&quot;&gt;&lt;tbody&gt;&lt;tr&gt;&lt;th&gt;1st row&lt;/th&gt;&lt;td style=&quot;white-space:pre white-space:nowrap&quot;&gt;1.0&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;2nd row&lt;/th&gt;&lt;td style=&quot;white-space:pre white-space:nowrap&quot;&gt;1.5&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;3rd row&lt;/th&gt;&lt;td style=&quot;white-space:pre white-space:nowrap&quot;&gt;1.0&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;4th row&lt;/th&gt;&lt;td style=&quot;white-space:pre white-space:nowrap&quot;&gt;0.5&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;5th row&lt;/th&gt;&lt;td style=&quot;white-space:pre white-space:nowrap&quot;&gt;0.5&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;div role=tabpanel class=&quot;tab-pane col-sm-12&quot; id=8012943184765047294bottom-8012943184765047294string&gt;&lt;div class=row&gt;&lt;div class=col-sm-12&gt;&lt;h4 class=indent&gt;Common Values&lt;/h4&gt;&lt;table class=&quot;freq table table-hover table-striped table-0&quot;&gt;&lt;thead&gt;&lt;tr&gt;&lt;td&gt;Value&lt;/td&gt;&lt;td&gt;Count&lt;/td&gt;&lt;td&gt;Frequency (%)&lt;/td&gt;&lt;/tr&gt;&lt;/thead&gt;&lt;tbody&gt;&lt;tr class&gt;&lt;td title=1.0&gt; 1.0 &lt;/td&gt;&lt;td&gt;4&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:100.0%&gt; 57.1% &lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=0.5&gt; 0.5 &lt;/td&gt;&lt;td&gt;2&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:50.0%&gt; 28.6% &lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=1.5&gt; 1.5 &lt;/td&gt;&lt;td&gt;1&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:25.0%&gt; &amp;nbsp; &lt;/div&gt; 14.3% &lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;/div&gt;&lt;/div&gt;&lt;div class=row&gt;&lt;div class=col-sm-12&gt;&lt;h4 class=indent&gt;Length&lt;/h4&gt;&lt;?xml version=&quot;1.0&quot; 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id=8012943184765047294unicode-8012943184765047294characters&gt;&lt;div class=&quot;row named-list-item&quot;&gt;&lt;h4&gt;Most occurring characters&lt;/h4&gt;&lt;table class=&quot;freq table table-hover table-striped table-0&quot;&gt;&lt;thead&gt;&lt;tr&gt;&lt;td&gt;Value&lt;/td&gt;&lt;td&gt;Count&lt;/td&gt;&lt;td&gt;Frequency (%)&lt;/td&gt;&lt;/tr&gt;&lt;/thead&gt;&lt;tbody&gt;&lt;tr class&gt;&lt;td title=.&gt; . &lt;/td&gt;&lt;td&gt;7&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:100.0%&gt; 33.3% &lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=0&gt; 0 &lt;/td&gt;&lt;td&gt;6&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:85.7%&gt; 28.6% &lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=1&gt; 1 &lt;/td&gt;&lt;td&gt;5&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:71.4%&gt; 23.8% &lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=5&gt; 5 &lt;/td&gt;&lt;td&gt;3&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:42.9%&gt; 14.3% &lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;/div&gt;&lt;/div&gt;&lt;div role=tabpanel class=&quot;tab-pane col-sm-12&quot; id=8012943184765047294unicode-8012943184765047294categories&gt;&lt;div class=&quot;row named-list-item&quot;&gt;&lt;h4&gt;Most occurring categories&lt;/h4&gt;&lt;table class=&quot;freq table table-hover table-striped table-0&quot;&gt;&lt;thead&gt;&lt;tr&gt;&lt;td&gt;Value&lt;/td&gt;&lt;td&gt;Count&lt;/td&gt;&lt;td&gt;Frequency (%)&lt;/td&gt;&lt;/tr&gt;&lt;/thead&gt;&lt;tbody&gt;&lt;tr class&gt;&lt;td title=&quot;Decimal Number&quot;&gt; Decimal Number &lt;/td&gt;&lt;td&gt;14&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:100.0%&gt; 66.7% &lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=&quot;Other Punctuation&quot;&gt; Other Punctuation &lt;/td&gt;&lt;td&gt;7&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:50.0%&gt; 33.3% &lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;/div&gt;&lt;div class=&quot;row named-list-item&quot;&gt;&lt;h4&gt;Most frequent character per category&lt;/h4&gt;&lt;div class=row&gt;&lt;div class=col-sm-12&gt;&lt;h5&gt;&lt;em&gt;Decimal Number&lt;/em&gt;&lt;/h5&gt;&lt;table class=&quot;freq table table-hover table-striped table-0&quot;&gt;&lt;thead&gt;&lt;tr&gt;&lt;td&gt;Value&lt;/td&gt;&lt;td&gt;Count&lt;/td&gt;&lt;td&gt;Frequency (%)&lt;/td&gt;&lt;/tr&gt;&lt;/thead&gt;&lt;tbody&gt;&lt;tr class&gt;&lt;td title=0&gt; 0 &lt;/td&gt;&lt;td&gt;6&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:100.0%&gt; 42.9% &lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=1&gt; 1 &lt;/td&gt;&lt;td&gt;5&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:83.3%&gt; 35.7% &lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=5&gt; 5 &lt;/td&gt;&lt;td&gt;3&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:50.0%&gt; 21.4% &lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;/div&gt;&lt;/div&gt;&lt;div class=row&gt;&lt;div class=col-sm-12&gt;&lt;h5&gt;&lt;em&gt;Other Punctuation&lt;/em&gt;&lt;/h5&gt;&lt;table class=&quot;freq table table-hover table-striped table-0&quot;&gt;&lt;thead&gt;&lt;tr&gt;&lt;td&gt;Value&lt;/td&gt;&lt;td&gt;Count&lt;/td&gt;&lt;td&gt;Frequency (%)&lt;/td&gt;&lt;/tr&gt;&lt;/thead&gt;&lt;tbody&gt;&lt;tr class&gt;&lt;td title=.&gt; . &lt;/td&gt;&lt;td&gt;7&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:100.0%&gt; 100.0% &lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;div role=tabpanel class=&quot;tab-pane col-sm-12&quot; id=8012943184765047294unicode-8012943184765047294scripts&gt;&lt;div class=&quot;row named-list-item&quot;&gt;&lt;h4&gt;Most occurring scripts&lt;/h4&gt;&lt;table class=&quot;freq table table-hover table-striped table-0&quot;&gt;&lt;thead&gt;&lt;tr&gt;&lt;td&gt;Value&lt;/td&gt;&lt;td&gt;Count&lt;/td&gt;&lt;td&gt;Frequency (%)&lt;/td&gt;&lt;/tr&gt;&lt;/thead&gt;&lt;tbody&gt;&lt;tr class&gt;&lt;td title=Common&gt; Common &lt;/td&gt;&lt;td&gt;21&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:100.0%&gt; 100.0% &lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;/div&gt;&lt;div class=&quot;row named-list-item&quot;&gt;&lt;h4&gt;Most frequent character per script&lt;/h4&gt;&lt;div class=row&gt;&lt;div class=col-sm-12&gt;&lt;h5&gt;&lt;em&gt;Common&lt;/em&gt;&lt;/h5&gt;&lt;table class=&quot;freq table table-hover table-striped table-0&quot;&gt;&lt;thead&gt;&lt;tr&gt;&lt;td&gt;Value&lt;/td&gt;&lt;td&gt;Count&lt;/td&gt;&lt;td&gt;Frequency (%)&lt;/td&gt;&lt;/tr&gt;&lt;/thead&gt;&lt;tbody&gt;&lt;tr class&gt;&lt;td title=.&gt; . &lt;/td&gt;&lt;td&gt;7&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:100.0%&gt; 33.3% &lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=0&gt; 0 &lt;/td&gt;&lt;td&gt;6&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:85.7%&gt; 28.6% &lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=1&gt; 1 &lt;/td&gt;&lt;td&gt;5&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:71.4%&gt; 23.8% &lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=5&gt; 5 &lt;/td&gt;&lt;td&gt;3&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:42.9%&gt; 14.3% &lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;div role=tabpanel class=&quot;tab-pane col-sm-12&quot; id=8012943184765047294unicode-8012943184765047294blocks&gt;&lt;div class=&quot;row named-list-item&quot;&gt;&lt;h4&gt;Most occurring blocks&lt;/h4&gt;&lt;table class=&quot;freq table table-hover table-striped table-0&quot;&gt;&lt;thead&gt;&lt;tr&gt;&lt;td&gt;Value&lt;/td&gt;&lt;td&gt;Count&lt;/td&gt;&lt;td&gt;Frequency (%)&lt;/td&gt;&lt;/tr&gt;&lt;/thead&gt;&lt;tbody&gt;&lt;tr class&gt;&lt;td title=ASCII&gt; ASCII &lt;/td&gt;&lt;td&gt;21&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:100.0%&gt; 100.0% &lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;/div&gt;&lt;div class=&quot;row named-list-item&quot;&gt;&lt;h4&gt;Most frequent character per block&lt;/h4&gt;&lt;div class=row&gt;&lt;div class=col-sm-12&gt;&lt;h5&gt;&lt;em&gt;ASCII&lt;/em&gt;&lt;/h5&gt;&lt;table class=&quot;freq table table-hover table-striped table-0&quot;&gt;&lt;thead&gt;&lt;tr&gt;&lt;td&gt;Value&lt;/td&gt;&lt;td&gt;Count&lt;/td&gt;&lt;td&gt;Frequency (%)&lt;/td&gt;&lt;/tr&gt;&lt;/thead&gt;&lt;tbody&gt;&lt;tr class&gt;&lt;td title=.&gt; . &lt;/td&gt;&lt;td&gt;7&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:100.0%&gt; 33.3% &lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=0&gt; 0 &lt;/td&gt;&lt;td&gt;6&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:85.7%&gt; 28.6% &lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=1&gt; 1 &lt;/td&gt;&lt;td&gt;5&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:71.4%&gt; 23.8% &lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=5&gt; 5 &lt;/td&gt;&lt;td&gt;3&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:42.9%&gt; 14.3% &lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;div class=&quot;row spacing&quot;&gt;&lt;a class=&quot;anchor-pos anchor-pos-variable&quot; id=pp_var_6339942047070694394&gt;&lt;/a&gt;&lt;div class=variable&gt;&lt;div class=col-sm-12&gt;&lt;p class=h4 title=stroke&gt;&lt;a href=#pp_var_6339942047070694394&gt;stroke&lt;/a&gt;&lt;br&gt;&lt;small&gt;Categorical&lt;/small&gt;&lt;/p&gt;&lt;p class=variable-description&gt;&lt;/p&gt;&lt;/div&gt;&lt;div class=col-sm-6&gt;&lt;table class=&quot;table table-condensed stats&quot;&gt;&lt;tbody&gt;&lt;tr&gt;&lt;th&gt;Distinct&lt;/th&gt;&lt;td style=&quot;white-space:pre white-space:nowrap&quot;&gt;2&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Distinct (%)&lt;/th&gt;&lt;td style=&quot;white-space:pre white-space:nowrap&quot;&gt;28.6%&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Missing&lt;/th&gt;&lt;td style=&quot;white-space:pre white-space:nowrap&quot;&gt;0&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Missing (%)&lt;/th&gt;&lt;td style=&quot;white-space:pre white-space:nowrap&quot;&gt;0.0%&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Memory size&lt;/th&gt;&lt;td style=&quot;white-space:pre white-space:nowrap&quot;&gt;184.0 B&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;/div&gt;&lt;div class=col-sm-6&gt;&lt;div class=row id=minifreqtable&gt;&lt;table class=&quot;mini freq table-0&quot;&gt;&lt;tr class&gt;&lt;th width=10%&gt; 0.0 &lt;/th&gt;&lt;td width=10%&gt;&lt;div class=bar style=width:100.0% data-toggle=tooltip data-placement=center data-html=true data-delay=500&gt; 4&amp;nbsp; &lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;th width=10%&gt; 1.0 &lt;/th&gt;&lt;td width=10%&gt;&lt;div class=bar style=width:75.0% data-toggle=tooltip data-placement=center data-html=true data-delay=500&gt; 3&amp;nbsp; &lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/table&gt;&lt;/div&gt;&lt;/div&gt;&lt;div class=&quot;col-sm-12 text-right&quot;&gt;&lt;button class=&quot;btn btn-default btn-sm&quot; data-toggle=collapse data-target=&quot;#bottom-6339942047070694394, #minifreqtable6339942047070694394&quot; aria-expanded=true aria-controls=collapseExample&gt; More details &lt;/button&gt;&lt;/div&gt;&lt;div id=bottom-6339942047070694394 class=collapse&gt;&lt;div class=&quot;row spacing&quot;&gt;&lt;ul class=&quot;nav nav-tabs&quot; role=tablist&gt;&lt;li role=presentation class=active&gt;&lt;a href=#6339942047070694394bottom-6339942047070694394overview aria-controls=6339942047070694394bottom-6339942047070694394overview role=tab data-toggle=tab&gt;Overview&lt;/a&gt;&lt;/li&gt;&lt;li role=presentation&gt;&lt;a href=#6339942047070694394bottom-6339942047070694394string aria-controls=6339942047070694394bottom-6339942047070694394string role=tab data-toggle=tab&gt;Categories&lt;/a&gt;&lt;/li&gt;&lt;li role=presentation&gt;&lt;a href=#6339942047070694394bottom-6339942047070694394word aria-controls=6339942047070694394bottom-6339942047070694394word role=tab data-toggle=tab&gt;Words&lt;/a&gt;&lt;/li&gt;&lt;li role=presentation&gt;&lt;a href=#6339942047070694394bottom-6339942047070694394characters aria-controls=6339942047070694394bottom-6339942047070694394characters role=tab data-toggle=tab&gt;Characters&lt;/a&gt;&lt;/li&gt;&lt;/ul&gt;&lt;div class=tab-content&gt;&lt;div role=tabpanel class=&quot;tab-pane col-sm-12 active&quot; id=6339942047070694394bottom-6339942047070694394overview&gt;&lt;div class=row&gt;&lt;div class=col-sm-3&gt;&lt;p class=h4&gt;Length&lt;/p&gt;&lt;table class=&quot;table table-condensed stats&quot;&gt;&lt;tbody&gt;&lt;tr&gt;&lt;th&gt;Max length&lt;/th&gt;&lt;td style=&quot;white-space:pre white-space:nowrap&quot;&gt;3&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Median length&lt;/th&gt;&lt;td style=&quot;white-space:pre white-space:nowrap&quot;&gt;3&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Mean length&lt;/th&gt;&lt;td style=&quot;white-space:pre white-space:nowrap&quot;&gt;3&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Min length&lt;/th&gt;&lt;td style=&quot;white-space:pre white-space:nowrap&quot;&gt;3&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;/div&gt;&lt;div class=col-sm-3&gt;&lt;p class=h4&gt;Characters and Unicode&lt;/p&gt;&lt;table class=&quot;table table-condensed stats&quot;&gt;&lt;tbody&gt;&lt;tr&gt;&lt;th&gt;Total characters&lt;/th&gt;&lt;td style=&quot;white-space:pre white-space:nowrap&quot;&gt;21&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Distinct characters&lt;/th&gt;&lt;td style=&quot;white-space:pre white-space:nowrap&quot;&gt;3&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Distinct categories&lt;/th&gt;&lt;td style=&quot;white-space:pre white-space:nowrap&quot;&gt;2 &lt;a title=&quot;Unicode categories (click for more information)&quot; href=https://en.wikipedia.org/wiki/Unicode_character_property#General_Category&gt;&lt;span class=&quot;badge pull-right&quot; style=color:#fff;background-color:#337ab7; title=&quot;Unicode categories (click for more information)&quot;&gt;?&lt;/span&gt;&lt;/a&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Distinct scripts&lt;/th&gt;&lt;td style=&quot;white-space:pre white-space:nowrap&quot;&gt;1 &lt;a title=&quot;Unicode scripts (click for more information)&quot; href=https://en.wikipedia.org/wiki/Script_(Unicode)#List_of_scripts_in_Unicode&gt;&lt;span class=&quot;badge pull-right&quot; style=color:#fff;background-color:#337ab7; title=&quot;Unicode scripts (click for more information)&quot;&gt;?&lt;/span&gt;&lt;/a&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Distinct blocks&lt;/th&gt;&lt;td style=&quot;white-space:pre white-space:nowrap&quot;&gt;1 &lt;a title=&quot;Unicode blocks (click for more information)&quot; href=https://en.wikipedia.org/wiki/Unicode_block&gt;&lt;span class=&quot;badge pull-right&quot; style=color:#fff;background-color:#337ab7; title=&quot;Unicode blocks (click for more information)&quot;&gt;?&lt;/span&gt;&lt;/a&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;div class=&quot;caption text-center text-muted&quot;&gt; The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables. &lt;/div&gt;&lt;/div&gt;&lt;div class=col-sm-3&gt;&lt;p class=h4&gt;Unique&lt;/p&gt;&lt;table class=&quot;table table-condensed stats&quot;&gt;&lt;tbody&gt;&lt;tr&gt;&lt;th&gt;Unique&lt;/th&gt;&lt;td style=&quot;white-space:pre white-space:nowrap&quot;&gt;0 &lt;span class=&quot;badge pull-right&quot; style=color:#fff;background-color:#337ab7; title=&quot;The number of unique values (all values that occur exactly once in the dataset).&quot;&gt;?&lt;/span&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Unique (%)&lt;/th&gt;&lt;td style=&quot;white-space:pre white-space:nowrap&quot;&gt;0.0%&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;/div&gt;&lt;div class=col-sm-3&gt;&lt;p class=h4&gt;Sample&lt;/p&gt;&lt;table class=&quot;table table-condensed stats&quot;&gt;&lt;tbody&gt;&lt;tr&gt;&lt;th&gt;1st row&lt;/th&gt;&lt;td style=&quot;white-space:pre white-space:nowrap&quot;&gt;0.0&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;2nd row&lt;/th&gt;&lt;td style=&quot;white-space:pre white-space:nowrap&quot;&gt;1.0&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;3rd row&lt;/th&gt;&lt;td style=&quot;white-space:pre white-space:nowrap&quot;&gt;0.0&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;4th row&lt;/th&gt;&lt;td style=&quot;white-space:pre white-space:nowrap&quot;&gt;1.0&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;5th row&lt;/th&gt;&lt;td style=&quot;white-space:pre white-space:nowrap&quot;&gt;0.0&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;div role=tabpanel class=&quot;tab-pane col-sm-12&quot; 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id=6339942047070694394unicode-6339942047070694394characters&gt;&lt;div class=&quot;row named-list-item&quot;&gt;&lt;h4&gt;Most occurring characters&lt;/h4&gt;&lt;table class=&quot;freq table table-hover table-striped table-0&quot;&gt;&lt;thead&gt;&lt;tr&gt;&lt;td&gt;Value&lt;/td&gt;&lt;td&gt;Count&lt;/td&gt;&lt;td&gt;Frequency (%)&lt;/td&gt;&lt;/tr&gt;&lt;/thead&gt;&lt;tbody&gt;&lt;tr class&gt;&lt;td title=0&gt; 0 &lt;/td&gt;&lt;td&gt;11&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:100.0%&gt; 52.4% &lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=.&gt; . &lt;/td&gt;&lt;td&gt;7&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:63.6%&gt; 33.3% &lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=1&gt; 1 &lt;/td&gt;&lt;td&gt;3&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:27.3%&gt; &amp;nbsp; &lt;/div&gt; 14.3% &lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;/div&gt;&lt;/div&gt;&lt;div role=tabpanel class=&quot;tab-pane col-sm-12&quot; id=6339942047070694394unicode-6339942047070694394categories&gt;&lt;div class=&quot;row named-list-item&quot;&gt;&lt;h4&gt;Most occurring categories&lt;/h4&gt;&lt;table class=&quot;freq table table-hover table-striped table-0&quot;&gt;&lt;thead&gt;&lt;tr&gt;&lt;td&gt;Value&lt;/td&gt;&lt;td&gt;Count&lt;/td&gt;&lt;td&gt;Frequency (%)&lt;/td&gt;&lt;/tr&gt;&lt;/thead&gt;&lt;tbody&gt;&lt;tr class&gt;&lt;td title=&quot;Decimal Number&quot;&gt; 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       "\n",
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       "\n",
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      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
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    {
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    }
   ],
   "source": [
    "\n",
    "ProfileReport(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "##############################################\n",
      "params: 0.0, [0. 0.]\n",
      "learning rate: 0.001\n",
      "##############################################\n",
      "[0.5 0.5 0.5 0.5 0.5 0.5 0.5]\n",
      "1.0\n",
      "##############################################\n",
      "params: -2.5515, [ 1.1062 -0.9061]\n",
      "learning rate: 0.001\n",
      "##############################################\n",
      "[0.22353215 0.62577883 0.46530584 0.70415233 0.31170721 0.93254772\n",
      " 0.08695456]\n",
      "0.4602042191360741\n"
     ]
    }
   ],
   "source": [
    "model = LogisticRegression(0.001, 0, [0,0], 0.5)\n",
    "model.show()\n",
    "print(model.predict(samples))\n",
    "print(model.logistic_cost(samples=samples, correct_classes=classes))\n",
    "\n",
    "model.fit(samples, classes, 100000)\n",
    "model.show()\n",
    "print(model.predict(samples))\n",
    "print(model.logistic_cost(samples=samples, correct_classes=classes))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.22353215 0.62577883 0.46530584 0.70415233 0.31170721 0.93254772\n",
      " 0.08695456]\n"
     ]
    }
   ],
   "source": [
    "print(model.predict(samples))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.22353215 0.62577883 0.46530584 0.70415233 0.31170721 0.93254772\n",
      " 0.08695456]\n"
     ]
    }
   ],
   "source": [
    "print(model.predict(samples))\n"
   ]
  }
 ],
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