Log_reg.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>Unnamed: 0</th>\n",
       "      <th>cgpa</th>\n",
       "      <th>iq</th>\n",
       "      <th>placement</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>6.8</td>\n",
       "      <td>123.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>5.9</td>\n",
       "      <td>106.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>5.3</td>\n",
       "      <td>121.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>7.4</td>\n",
       "      <td>132.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>5.8</td>\n",
       "      <td>142.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  cgpa     iq  placement\n",
       "0           0   6.8  123.0          1\n",
       "1           1   5.9  106.0          0\n",
       "2           2   5.3  121.0          0\n",
       "3           3   7.4  132.0          1\n",
       "4           4   5.8  142.0          0"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('placement.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>cgpa</th>\n",
       "      <th>iq</th>\n",
       "      <th>placement</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6.8</td>\n",
       "      <td>123.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.9</td>\n",
       "      <td>106.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.3</td>\n",
       "      <td>121.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.4</td>\n",
       "      <td>132.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.8</td>\n",
       "      <td>142.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>4.3</td>\n",
       "      <td>200.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>4.4</td>\n",
       "      <td>42.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>6.7</td>\n",
       "      <td>182.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>6.3</td>\n",
       "      <td>103.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>6.2</td>\n",
       "      <td>113.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    cgpa     iq  placement\n",
       "0    6.8  123.0          1\n",
       "1    5.9  106.0          0\n",
       "2    5.3  121.0          0\n",
       "3    7.4  132.0          1\n",
       "4    5.8  142.0          0\n",
       "..   ...    ...        ...\n",
       "95   4.3  200.0          0\n",
       "96   4.4   42.0          0\n",
       "97   6.7  182.0          1\n",
       "98   6.3  103.0          1\n",
       "99   6.2  113.0          1\n",
       "\n",
       "[100 rows x 3 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.iloc[: , 1:]\n",
    "df"
   ]
  },
  {
   "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": [
    "plt.scatter(df['cgpa'],df['iq'],c=df['placement'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df.iloc[:,0:2]\n",
    "y=df.iloc[:,-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>cgpa</th>\n",
       "      <th>iq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6.8</td>\n",
       "      <td>123.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.9</td>\n",
       "      <td>106.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.3</td>\n",
       "      <td>121.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.4</td>\n",
       "      <td>132.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.8</td>\n",
       "      <td>142.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   cgpa     iq\n",
       "0   6.8  123.0\n",
       "1   5.9  106.0\n",
       "2   5.3  121.0\n",
       "3   7.4  132.0\n",
       "4   5.8  142.0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.head()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    0\n",
       "2    0\n",
       "3    1\n",
       "4    0\n",
       "Name: placement, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "x_train, x_test, y_train, y_test = train_test_split(x,y,test_size=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>cgpa</th>\n",
       "      <th>iq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>6.0</td>\n",
       "      <td>102.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>5.3</td>\n",
       "      <td>114.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>6.8</td>\n",
       "      <td>112.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>6.9</td>\n",
       "      <td>139.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>4.9</td>\n",
       "      <td>151.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>5.4</td>\n",
       "      <td>139.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>7.3</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5.7</td>\n",
       "      <td>143.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>6.2</td>\n",
       "      <td>113.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>6.7</td>\n",
       "      <td>154.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>90 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    cgpa     iq\n",
       "62   6.0  102.0\n",
       "46   5.3  114.0\n",
       "93   6.8  112.0\n",
       "60   6.9  139.0\n",
       "89   4.9  151.0\n",
       "..   ...    ...\n",
       "12   5.4  139.0\n",
       "77   7.3   50.0\n",
       "6    5.7  143.0\n",
       "99   6.2  113.0\n",
       "74   6.7  154.0\n",
       "\n",
       "[90 rows x 2 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "62    0\n",
       "46    0\n",
       "93    1\n",
       "60    1\n",
       "89    0\n",
       "     ..\n",
       "12    0\n",
       "77    1\n",
       "6     0\n",
       "99    1\n",
       "74    1\n",
       "Name: placement, Length: 90, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.00403821, -0.48903672],\n",
       "       [-0.63198029, -0.19397546],\n",
       "       [ 0.7309165 , -0.24315234],\n",
       "       [ 0.82177628,  0.4207355 ],\n",
       "       [-0.99541943,  0.71579677],\n",
       "       [-1.8131575 , -0.5382136 ],\n",
       "       [ 1.91209371,  1.08462335],\n",
       "       [ 0.45833714, -2.08728523],\n",
       "       [-0.90455964, -0.75950955],\n",
       "       [-1.8131575 , -1.15292456],\n",
       "       [ 1.18521542, -0.14479858],\n",
       "       [ 0.64005671,  1.47803836],\n",
       "       [-0.17768136,  0.49450082],\n",
       "       [ 1.36693499,  0.88791584],\n",
       "       [-0.99541943,  1.8222765 ],\n",
       "       [ 0.27661757, -0.46444828],\n",
       "       [ 0.7309165 ,  0.46991238],\n",
       "       [ 0.91263607,  1.89604182],\n",
       "       [-0.99541943,  0.81415052],\n",
       "       [-0.72284007,  0.96168115],\n",
       "       [-0.90455964, -1.44798583],\n",
       "       [ 0.54919693, -0.04644483],\n",
       "       [ 0.36747735,  0.46991238],\n",
       "       [ 1.45779478, -0.80868642],\n",
       "       [-0.72284007,  2.51075278],\n",
       "       [-1.08627921, -0.24315234],\n",
       "       [ 0.094898  ,  0.83873896],\n",
       "       [-2.26745642,  2.73204873],\n",
       "       [ 0.82177628,  0.39614707],\n",
       "       [ 1.00349585, -1.81681241],\n",
       "       [ 0.36747735, -0.14479858],\n",
       "       [-1.26799878,  0.59285457],\n",
       "       [-0.72284007, -0.29232921],\n",
       "       [ 0.91263607, -1.42339739],\n",
       "       [ 0.00403821,  0.05190893],\n",
       "       [-0.26854114,  0.44532394],\n",
       "       [ 0.91263607,  1.30591929],\n",
       "       [-0.72284007,  0.24861643],\n",
       "       [ 0.7309165 , -0.78409799],\n",
       "       [ 1.63951435, -0.19397546],\n",
       "       [-0.5411205 , -0.36609453],\n",
       "       [ 0.094898  , -1.39880895],\n",
       "       [-0.81369986,  0.15026268],\n",
       "       [-0.63198029, -0.02185639],\n",
       "       [-1.44971835,  0.66661989],\n",
       "       [ 1.91209371,  0.66661989],\n",
       "       [ 0.45833714,  0.93709271],\n",
       "       [-1.177139  , -0.8578633 ],\n",
       "       [-0.99541943,  0.29779331],\n",
       "       [ 0.094898  , -1.00539393],\n",
       "       [ 1.82123392, -1.05457081],\n",
       "       [ 1.36693499,  0.19943956],\n",
       "       [-1.44971835, -1.96434304],\n",
       "       [-1.08627921,  0.46991238],\n",
       "       [-1.08627921,  1.01085803],\n",
       "       [-1.08627921,  1.15838866],\n",
       "       [ 0.27661757,  0.15026268],\n",
       "       [-0.90455964, -0.09562171],\n",
       "       [ 2.27553285, -0.04644483],\n",
       "       [ 0.45833714, -0.78409799],\n",
       "       [ 1.45779478,  0.15026268],\n",
       "       [ 0.54919693,  0.39614707],\n",
       "       [ 1.18521542,  0.37155863],\n",
       "       [-1.177139  , -1.71845865],\n",
       "       [ 0.82177628, -0.63656735],\n",
       "       [ 0.45833714,  0.19943956],\n",
       "       [-0.81369986, -1.37422051],\n",
       "       [ 0.094898  , -0.46444828],\n",
       "       [ 1.18521542, -0.88245174],\n",
       "       [-1.177139  , -0.02185639],\n",
       "       [-0.99541943, -0.04644483],\n",
       "       [ 0.094898  ,  0.24861643],\n",
       "       [ 1.27607521,  0.24861643],\n",
       "       [-0.99541943, -1.4971627 ],\n",
       "       [-0.17768136,  1.08462335],\n",
       "       [ 0.00403821,  0.66661989],\n",
       "       [ 0.91263607,  0.4207355 ],\n",
       "       [-0.81369986,  1.33050773],\n",
       "       [-0.72284007, -0.78409799],\n",
       "       [ 0.00403821, -1.89057772],\n",
       "       [ 1.36693499, -1.4971627 ],\n",
       "       [ 0.00403821, -1.37422051],\n",
       "       [-0.5411205 ,  0.32238175],\n",
       "       [-1.54057814,  1.92063026],\n",
       "       [-0.08682157, -0.39068297],\n",
       "       [-0.5411205 ,  0.4207355 ],\n",
       "       [ 1.18521542, -1.76763553],\n",
       "       [-0.26854114,  0.51908926],\n",
       "       [ 0.18575778, -0.2185639 ],\n",
       "       [ 0.64005671,  0.78956208]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train =scaler.fit_transform(x_train)\n",
    "x_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.26854114,  1.15838866],\n",
       "       [ 0.91263607, -0.24315234],\n",
       "       [ 0.7309165 ,  0.02732049],\n",
       "       [ 2.09381328,  1.13380022],\n",
       "       [ 0.27661757,  0.12567424],\n",
       "       [-2.44917599,  1.5026268 ],\n",
       "       [ 1.00349585,  0.71579677],\n",
       "       [-1.90401728, -0.31691765],\n",
       "       [-0.5411205 , -0.19397546],\n",
       "       [-0.26854114,  0.24861643]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test = scaler.transform(x_test)\n",
    "x_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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/1.5/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 1, 1, 1, 0, 1, 0, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = clf.predict(x_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "84    0\n",
       "29    1\n",
       "0     1\n",
       "53    1\n",
       "70    1\n",
       "17    0\n",
       "21    1\n",
       "31    0\n",
       "41    0\n",
       "87    1\n",
       "Name: placement, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mlxtend.plotting import plot_decision_regions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 23,
     "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": [
    "plot_decision_regions(x_train, y_train.values, clf=clf , legend=2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump(clf,open('model.pkl','wb'))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}