level1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "954ee57c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as pl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "17dde68b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(\"Book2.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "99d364f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>HBPM</th>\n",
       "      <th>Age</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Level_of_Stress</th>\n",
       "      <th>Spo2 level % (Random)</th>\n",
       "      <th>Normal Condition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ann</td>\n",
       "      <td>111</td>\n",
       "      <td>34</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>95</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Sam</td>\n",
       "      <td>87</td>\n",
       "      <td>54</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>92</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Matt</td>\n",
       "      <td>100</td>\n",
       "      <td>70</td>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>92</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Matt</td>\n",
       "      <td>90</td>\n",
       "      <td>71</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>97</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>George</td>\n",
       "      <td>100</td>\n",
       "      <td>52</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>92</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name  HBPM  Age  BMI  Sex  Level_of_Stress  Spo2 level % (Random)  \\\n",
       "0     Ann   111   34   23    1                2                     95   \n",
       "1     Sam    87   54   33    1                3                     92   \n",
       "2    Matt   100   70   49    0                2                     92   \n",
       "3    Matt    90   71   50    0                1                     97   \n",
       "4  George   100   52   19    0                2                     92   \n",
       "\n",
       "   Normal Condition  \n",
       "0             False  \n",
       "1             False  \n",
       "2             False  \n",
       "3              True  \n",
       "4             False  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f2332467",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(799, 8)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7f47c713",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name                     0\n",
       "HBPM                     0\n",
       "Age                      0\n",
       "BMI                      0\n",
       "Sex                      0\n",
       "Level_of_Stress          0\n",
       "Spo2 level % (Random)    0\n",
       "Normal Condition         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1b298c73",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name                     object\n",
       "HBPM                      int64\n",
       "Age                       int64\n",
       "BMI                       int64\n",
       "Sex                       int64\n",
       "Level_of_Stress           int64\n",
       "Spo2 level % (Random)     int64\n",
       "Normal Condition           bool\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5c071e71",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "fb60d01f",
   "metadata": {},
   "outputs": [],
   "source": [
    "lb=LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "708a3a47",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Normal Condition']=lb.fit_transform(df['Normal Condition'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5388069e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name                     object\n",
       "HBPM                      int64\n",
       "Age                       int64\n",
       "BMI                       int64\n",
       "Sex                       int64\n",
       "Level_of_Stress           int64\n",
       "Spo2 level % (Random)     int64\n",
       "Normal Condition          int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "033eb49a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>HBPM</th>\n",
       "      <th>Age</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Level_of_Stress</th>\n",
       "      <th>Spo2 level % (Random)</th>\n",
       "      <th>Normal Condition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ann</td>\n",
       "      <td>111</td>\n",
       "      <td>34</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>95</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Sam</td>\n",
       "      <td>87</td>\n",
       "      <td>54</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>92</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Matt</td>\n",
       "      <td>100</td>\n",
       "      <td>70</td>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>92</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Matt</td>\n",
       "      <td>90</td>\n",
       "      <td>71</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>97</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>George</td>\n",
       "      <td>100</td>\n",
       "      <td>52</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>92</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Matt</td>\n",
       "      <td>75</td>\n",
       "      <td>23</td>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>96</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ram</td>\n",
       "      <td>90</td>\n",
       "      <td>43</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>95</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Matt</td>\n",
       "      <td>65</td>\n",
       "      <td>48</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>92</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>yugal</td>\n",
       "      <td>100</td>\n",
       "      <td>72</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>96</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>aman</td>\n",
       "      <td>65</td>\n",
       "      <td>40</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>98</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name  HBPM  Age  BMI  Sex  Level_of_Stress  Spo2 level % (Random)  \\\n",
       "0     Ann   111   34   23    1                2                     95   \n",
       "1     Sam    87   54   33    1                3                     92   \n",
       "2    Matt   100   70   49    0                2                     92   \n",
       "3    Matt    90   71   50    0                1                     97   \n",
       "4  George   100   52   19    0                2                     92   \n",
       "5    Matt    75   23   48    0                3                     96   \n",
       "6     ram    90   43   41    1                3                     95   \n",
       "7    Matt    65   48   20    0                2                     92   \n",
       "8   yugal   100   72   44    0                3                     96   \n",
       "9    aman    65   40   44    0                2                     98   \n",
       "\n",
       "   Normal Condition  \n",
       "0                 0  \n",
       "1                 0  \n",
       "2                 0  \n",
       "3                 1  \n",
       "4                 0  \n",
       "5                 1  \n",
       "6                 1  \n",
       "7                 0  \n",
       "8                 1  \n",
       "9                 1  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2a36296e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5bfd4a29",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>HBPM</th>\n",
       "      <th>Age</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Level_of_Stress</th>\n",
       "      <th>Spo2 level % (Random)</th>\n",
       "      <th>Normal Condition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>794</th>\n",
       "      <td>George</td>\n",
       "      <td>107</td>\n",
       "      <td>25</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>92</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>795</th>\n",
       "      <td>rohit</td>\n",
       "      <td>99</td>\n",
       "      <td>31</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>98</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>796</th>\n",
       "      <td>rohit</td>\n",
       "      <td>72</td>\n",
       "      <td>49</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>92</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>797</th>\n",
       "      <td>Sam</td>\n",
       "      <td>111</td>\n",
       "      <td>61</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>92</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>798</th>\n",
       "      <td>ram</td>\n",
       "      <td>100</td>\n",
       "      <td>29</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>98</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Name  HBPM  Age  BMI  Sex  Level_of_Stress  Spo2 level % (Random)  \\\n",
       "794  George   107   25   15    1                1                     92   \n",
       "795   rohit    99   31   25    0                1                     98   \n",
       "796   rohit    72   49   18    0                1                     92   \n",
       "797     Sam   111   61   29    0                1                     92   \n",
       "798     ram   100   29   18    1                2                     98   \n",
       "\n",
       "     Normal Condition  \n",
       "794                 0  \n",
       "795                 1  \n",
       "796                 0  \n",
       "797                 0  \n",
       "798                 1  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "81e52d50",
   "metadata": {},
   "outputs": [],
   "source": [
    "x=df[['HBPM','Spo2 level % (Random)']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e76b964f",
   "metadata": {},
   "outputs": [],
   "source": [
    "y=df['Normal Condition']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "446dcb2a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>HBPM</th>\n",
       "      <th>Spo2 level % (Random)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>111</td>\n",
       "      <td>95</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>87</td>\n",
       "      <td>92</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>90</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>794</th>\n",
       "      <td>107</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>795</th>\n",
       "      <td>99</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>796</th>\n",
       "      <td>72</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>797</th>\n",
       "      <td>111</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>798</th>\n",
       "      <td>100</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>799 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     HBPM  Spo2 level % (Random)\n",
       "0     111                     95\n",
       "1      87                     92\n",
       "2     100                     92\n",
       "3      90                     97\n",
       "4     100                     92\n",
       "..    ...                    ...\n",
       "794   107                     92\n",
       "795    99                     98\n",
       "796    72                     92\n",
       "797   111                     92\n",
       "798   100                     98\n",
       "\n",
       "[799 rows x 2 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "0c43369c",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test = train_test_split(x,y,train_size=0.75)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "61c64010",
   "metadata": {},
   "outputs": [],
   "source": [
    "#from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "bc940c92",
   "metadata": {},
   "outputs": [],
   "source": [
    "#m1=LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "90ea6c9e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#m1.fit(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "b4ed0073",
   "metadata": {},
   "outputs": [],
   "source": [
    "#m1.score(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "60136328",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "44901001",
   "metadata": {},
   "outputs": [],
   "source": [
    "m2=LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c3128b7f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m2.fit(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "daf9948f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.899874843554443"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m2.score(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "15e7d81d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ravib\\anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([1], dtype=int64)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m2.predict([[50,97]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "9a391787",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "normal_count = df['Normal Condition'].value_counts()[0]\n",
    "abnormal_count = df['Normal Condition'].value_counts()[1]\n",
    "\n",
    "# Create a pie chart\n",
    "labels = ['Normal Condition', 'Abnormal Condition']\n",
    "sizes = [normal_count, abnormal_count]\n",
    "colors = ['#99ff99','#ff9999']\n",
    "explode = (0, 0.1)\n",
    "fig1, ax1 = plt.subplots()\n",
    "ax1.pie(sizes, explode=explode, labels=labels, colors=colors, autopct='%1.1f%%', shadow=True, startangle=90)\n",
    "ax1.axis('equal')\n",
    "plt.title('Pie chart of Normal and Abnormal conditions')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "89b1b1e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=m2.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "78488b2a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0,\n",
       "       1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1,\n",
       "       0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1,\n",
       "       1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1,\n",
       "       1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1,\n",
       "       0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0,\n",
       "       1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1,\n",
       "       1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0,\n",
       "       1, 0], dtype=int64)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "1bc6dbdc",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ravib\\anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([1], dtype=int64)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m2.predict([[89,99]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "dbef452b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ravib\\anaconda3\\lib\\site-packages\\sklearn\\base.py:450: 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": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m2.predict([[65,90]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "11e546e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix,classification_report,accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "7d53ba38",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 72  12]\n",
      " [  0 116]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.86      0.92        84\n",
      "           1       0.91      1.00      0.95       116\n",
      "\n",
      "    accuracy                           0.94       200\n",
      "   macro avg       0.95      0.93      0.94       200\n",
      "weighted avg       0.95      0.94      0.94       200\n",
      "\n"
     ]
    }
   ],
   "source": [
    "cm_rf = confusion_matrix(y_test,y_pred)\n",
    "print(cm_rf)\n",
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "aabd4810",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#input_data=(65,96)\n",
    "#input_data_as_numpy_array=np.asarray(input_data)\n",
    "#input_data_reshaped=input_data_as_numpy_array.reshape(1,-1)\n",
    "#prediction=m2.predict(input_data_reshaped)\n",
    "#if(prediction[0]==0):\n",
    "#    print(\"The Person is not in Normal Condition may visit doctor\")\n",
    "#else:\n",
    "#    print(\"The Person is in Normal Condition\")\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Get the data\n",
    "x = df['HBPM']\n",
    "y = df['Spo2 level % (Random)']\n",
    "colors = df['Normal Condition']\n",
    "\n",
    "# Create the scatter plot\n",
    "plt.scatter(x, y, c=colors, cmap='viridis')\n",
    "plt.xlabel('Heartbeat')\n",
    "plt.ylabel('Spo2 Level')\n",
    "plt.colorbar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d67e5b46",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "# assume that `m2` is a pre-trained model\n",
    "\n",
    "# prompt the user to enter heartbeat and spo2 level\n",
    "input_data = input(\"Enter user heartbeat and spo2 level separated by a comma: \")\n",
    "try:\n",
    "    heartbeat, spo2 = input_data.split(\",\")\n",
    "except ValueError:\n",
    "    print(\"Invalid input. Please enter the values separated by a comma.\")\n",
    "\n",
    "# convert input data to a numpy array and reshape it\n",
    "input_data_as_numpy_array = np.asarray([float(heartbeat), float(spo2)])\n",
    "input_data_reshaped = input_data_as_numpy_array.reshape(1, -1)\n",
    "\n",
    "# make a prediction using the pre-trained model\n",
    "prediction = m2.predict(input_data_reshaped)\n",
    "\n",
    "# print the prediction result\n",
    "if prediction[0] == 0:\n",
    "    print(\"The person is not in normal condition and may need to visit a doctor.\")\n",
    "else:\n",
    "    print(\"The person is in normal condition.\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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