logistic regression binary.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1bce2ce5-387c-4a92-ae6b-b0d2e8e354d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "23001907-4f5c-43dd-a12b-ddcf53c4d240",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>bought_insurance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25</td>\n",
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       "      <th>2</th>\n",
       "      <td>47</td>\n",
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       "      <th>3</th>\n",
       "      <td>52</td>\n",
       "      <td>0</td>\n",
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       "      <th>4</th>\n",
       "      <td>46</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>55</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>62</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>61</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>49</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>55</td>\n",
       "      <td>1</td>\n",
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       "      <th>16</th>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
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       "      <th>17</th>\n",
       "      <td>58</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>54</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age  bought_insurance\n",
       "0    22                 0\n",
       "1    25                 0\n",
       "2    47                 1\n",
       "3    52                 0\n",
       "4    46                 1\n",
       "5    56                 1\n",
       "6    55                 0\n",
       "7    60                 1\n",
       "8    62                 1\n",
       "9    61                 1\n",
       "10   18                 0\n",
       "11   28                 0\n",
       "12   27                 0\n",
       "13   29                 0\n",
       "14   49                 1\n",
       "15   55                 1\n",
       "16   25                 1\n",
       "17   58                 1\n",
       "18   19                 0\n",
       "19   18                 0\n",
       "20   21                 0\n",
       "21   26                 0\n",
       "22   40                 1\n",
       "23   45                 1\n",
       "24   50                 1\n",
       "25   54                 1\n",
       "26   23                 0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv(\"insurance_data.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d60941e5-0cac-4740-aeb3-28a1c58d4cb6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1f2aae669c0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "plt.scatter(df.age,df.bought_insurance,marker='+',color='blue')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f6b4eea8-e023-431b-b92a-eac932e48a26",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(27, 2)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8a0a1487-c678-4017-9049-1d78eee45dee",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "413e3414-20eb-4595-9f13-4a46984873e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# x must be a 2 dimention array\n",
    "X_train, X_test, y_train, y_test=train_test_split(df[['age']],df.bought_insurance,test_size=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "5cd4b3ba-e9a7-4170-9bcf-96f51aa580a7",
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age\n",
       "23   45\n",
       "14   49\n",
       "2    47"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "d4e2c4fe-c0ec-4232-8b65-08872a5e2de0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "7cc63d22-68c0-4584-8d54-ebfdb066c4f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "model=LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "d0d4a5a6-1b8e-45e4-8448-6d00d3b746d1",
   "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>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e9c4e7cb-dc1c-40e9-bfef-4d3fb1b4adf0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1], dtype=int64)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "f3c72bca-6423-4b21-af50-b6045d5b25bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "e11412b7-1ae3-40bc-bda2-97ede238005a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Alamin\\anaconda3\\Lib\\site-packages\\sklearn\\base.py:493: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([0], dtype=int64)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict([[27]])"
   ]
  }
 ],
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