Healthcare(Diabetes) capstone Project For Simplilearn.ipynb

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   "source": [
    "**Problem Statement**\n",
    "* NIDDK (National Institute of Diabetes and Digestive and Kidney Diseases) research creates knowledge about and treatments for the most chronic, costly, and consequential diseases.\n",
    "\n",
    "* The dataset used in this project is originally from NIDDK. The objective is to predict whether or not a patient has diabetes, based on certain diagnostic measurements included in the dataset.\n",
    "\n",
    "* Build a model to accurately predict whether the patients in the dataset have diabetes or not."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "1a0ab848",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import style\n",
    "import seaborn as sns  \n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "26903a29",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"F:/Simply learn/Datascience Capstone/Healthcare Dataset/Project 2/Healthcare - Diabetes/health care diabetes.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "807894ae",
   "metadata": {},
   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
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       "      <th>0</th>\n",
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       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
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       "      <td>23.3</td>\n",
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       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
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       "      <th>4</th>\n",
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       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
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       "  </tbody>\n",
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      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            6      148             72             35        0  33.6   \n",
       "1            1       85             66             29        0  26.6   \n",
       "2            8      183             64              0        0  23.3   \n",
       "3            1       89             66             23       94  28.1   \n",
       "4            0      137             40             35      168  43.1   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                     0.627   50        1  \n",
       "1                     0.351   31        0  \n",
       "2                     0.672   32        1  \n",
       "3                     0.167   21        0  \n",
       "4                     2.288   33        1  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ead681c1",
   "metadata": {},
   "source": [
    "# Week 1\n",
    "### Checking For Null Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "cd1e97c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pregnancies                 False\n",
       "Glucose                     False\n",
       "BloodPressure               False\n",
       "SkinThickness               False\n",
       "Insulin                     False\n",
       "BMI                         False\n",
       "DiabetesPedigreeFunction    False\n",
       "Age                         False\n",
       "Outcome                     False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().any()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a8a8778",
   "metadata": {},
   "source": [
    "#### There are no Missing Values in This Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "36abea4e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 768 entries, 0 to 767\n",
      "Data columns (total 9 columns):\n",
      " #   Column                    Non-Null Count  Dtype  \n",
      "---  ------                    --------------  -----  \n",
      " 0   Pregnancies               768 non-null    int64  \n",
      " 1   Glucose                   768 non-null    int64  \n",
      " 2   BloodPressure             768 non-null    int64  \n",
      " 3   SkinThickness             768 non-null    int64  \n",
      " 4   Insulin                   768 non-null    int64  \n",
      " 5   BMI                       768 non-null    float64\n",
      " 6   DiabetesPedigreeFunction  768 non-null    float64\n",
      " 7   Age                       768 non-null    int64  \n",
      " 8   Outcome                   768 non-null    int64  \n",
      "dtypes: float64(2), int64(7)\n",
      "memory usage: 54.1 KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a34017ea",
   "metadata": {},
   "outputs": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
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       "      <td>31.0</td>\n",
       "      <td>0.248</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2</td>\n",
       "      <td>197</td>\n",
       "      <td>70</td>\n",
       "      <td>45</td>\n",
       "      <td>543</td>\n",
       "      <td>30.5</td>\n",
       "      <td>0.158</td>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
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      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            6      148             72             35        0  33.6   \n",
       "2            8      183             64              0        0  23.3   \n",
       "4            0      137             40             35      168  43.1   \n",
       "6            3       78             50             32       88  31.0   \n",
       "8            2      197             70             45      543  30.5   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                     0.627   50        1  \n",
       "2                     0.672   32        1  \n",
       "4                     2.288   33        1  \n",
       "6                     0.248   26        1  \n",
       "8                     0.158   53        1  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Positive = data[data['Outcome']==1]\n",
    "Positive.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3022cf8",
   "metadata": {},
   "source": [
    "## Checking the balance of the data by plotting the count of outcomes by their value. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "cffc38c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Outcome', ylabel='count'>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    " sns.countplot(x='Outcome',data=data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27bc22ec",
   "metadata": {},
   "source": [
    "### By observing above plot the  Number of Non Diabetec patients is twice of the Diabetec patients Counts , Hence  Data is Not Balanced."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30065c3e",
   "metadata": {},
   "source": [
    "# Data Distribution of Glucose,BloodPressure,SkinThickness,Insulin,BMI\tusing Histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "bfeaae52",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "99     17\n",
       "100    17\n",
       "111    14\n",
       "129    14\n",
       "125    14\n",
       "106    14\n",
       "112    13\n",
       "Name: Glucose, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Glucose'].value_counts().head(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "673b8490",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  5.,   0.,   4.,  32., 156., 211., 163.,  95.,  56.,  46.]),\n",
       " array([  0. ,  19.9,  39.8,  59.7,  79.6,  99.5, 119.4, 139.3, 159.2,\n",
       "        179.1, 199. ]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(data['Glucose'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40e9116f",
   "metadata": {},
   "source": [
    "\n",
    "### By looking at the Above Plot  We Observe that Glucose Variable Value are left Skewed and Major values are Accumulated between 75 to 150."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "71c8cf11",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "70    57\n",
       "74    52\n",
       "78    45\n",
       "68    45\n",
       "72    44\n",
       "64    43\n",
       "80    40\n",
       "Name: BloodPressure, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['BloodPressure'].value_counts().head(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "ed713487",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 35.,   1.,   2.,  13., 107., 261., 243.,  87.,  14.,   5.]),\n",
       " array([  0. ,  12.2,  24.4,  36.6,  48.8,  61. ,  73.2,  85.4,  97.6,\n",
       "        109.8, 122. ]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(data['BloodPressure'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e616ffd5",
   "metadata": {},
   "source": [
    "### By looking at the Above Plot  We Observe Blood Pressure  Variable most Values are accummulated between 50 to 100.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "b7f87c07",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     227\n",
       "32     31\n",
       "30     27\n",
       "27     23\n",
       "23     22\n",
       "33     20\n",
       "28     20\n",
       "Name: SkinThickness, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['SkinThickness'].value_counts().head(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "f683d3c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([231., 107., 165., 175.,  78.,   9.,   2.,   0.,   0.,   1.]),\n",
       " array([ 0. ,  9.9, 19.8, 29.7, 39.6, 49.5, 59.4, 69.3, 79.2, 89.1, 99. ]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(data['SkinThickness'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65934e08",
   "metadata": {},
   "source": [
    "### By looking at the Above Plot  We Observe SkinThickness  Variable Values are Right Skewed.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "f53cbdb8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      374\n",
       "105     11\n",
       "130      9\n",
       "140      9\n",
       "120      8\n",
       "94       7\n",
       "180      7\n",
       "Name: Insulin, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Insulin'].value_counts().head(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "361b5c33",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([487., 155.,  70.,  30.,   8.,   9.,   5.,   1.,   2.,   1.]),\n",
       " array([  0. ,  84.6, 169.2, 253.8, 338.4, 423. , 507.6, 592.2, 676.8,\n",
       "        761.4, 846. ]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(data['Insulin'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "107f2ede",
   "metadata": {},
   "source": [
    "### By looking at the Above Plot  We Observe Insulin Variable Values are Right Skewed.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "68b1321b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32.0    13\n",
       "31.6    12\n",
       "31.2    12\n",
       "0.0     11\n",
       "32.4    10\n",
       "33.3    10\n",
       "30.1     9\n",
       "Name: BMI, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['BMI'].value_counts().head(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "cc1bf2c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 11.,   0.,  15., 156., 268., 224.,  78.,  12.,   3.,   1.]),\n",
       " array([ 0.  ,  6.71, 13.42, 20.13, 26.84, 33.55, 40.26, 46.97, 53.68,\n",
       "        60.39, 67.1 ]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(data['BMI'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c85ff7bf",
   "metadata": {},
   "source": [
    "### By looking at the Above Plot  We Observe BMI  Variable Most Values are Accumulated Between 20 to 40"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "71300f14",
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Pregnancies</th>\n",
       "      <td>768.0</td>\n",
       "      <td>3.845052</td>\n",
       "      <td>3.369578</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>6.00000</td>\n",
       "      <td>17.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Glucose</th>\n",
       "      <td>768.0</td>\n",
       "      <td>120.894531</td>\n",
       "      <td>31.972618</td>\n",
       "      <td>0.000</td>\n",
       "      <td>99.00000</td>\n",
       "      <td>117.0000</td>\n",
       "      <td>140.25000</td>\n",
       "      <td>199.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BloodPressure</th>\n",
       "      <td>768.0</td>\n",
       "      <td>69.105469</td>\n",
       "      <td>19.355807</td>\n",
       "      <td>0.000</td>\n",
       "      <td>62.00000</td>\n",
       "      <td>72.0000</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>122.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SkinThickness</th>\n",
       "      <td>768.0</td>\n",
       "      <td>20.536458</td>\n",
       "      <td>15.952218</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>23.0000</td>\n",
       "      <td>32.00000</td>\n",
       "      <td>99.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Insulin</th>\n",
       "      <td>768.0</td>\n",
       "      <td>79.799479</td>\n",
       "      <td>115.244002</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>30.5000</td>\n",
       "      <td>127.25000</td>\n",
       "      <td>846.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BMI</th>\n",
       "      <td>768.0</td>\n",
       "      <td>31.992578</td>\n",
       "      <td>7.884160</td>\n",
       "      <td>0.000</td>\n",
       "      <td>27.30000</td>\n",
       "      <td>32.0000</td>\n",
       "      <td>36.60000</td>\n",
       "      <td>67.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <td>768.0</td>\n",
       "      <td>0.471876</td>\n",
       "      <td>0.331329</td>\n",
       "      <td>0.078</td>\n",
       "      <td>0.24375</td>\n",
       "      <td>0.3725</td>\n",
       "      <td>0.62625</td>\n",
       "      <td>2.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>768.0</td>\n",
       "      <td>33.240885</td>\n",
       "      <td>11.760232</td>\n",
       "      <td>21.000</td>\n",
       "      <td>24.00000</td>\n",
       "      <td>29.0000</td>\n",
       "      <td>41.00000</td>\n",
       "      <td>81.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Outcome</th>\n",
       "      <td>768.0</td>\n",
       "      <td>0.348958</td>\n",
       "      <td>0.476951</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          count        mean         std     min       25%  \\\n",
       "Pregnancies               768.0    3.845052    3.369578   0.000   1.00000   \n",
       "Glucose                   768.0  120.894531   31.972618   0.000  99.00000   \n",
       "BloodPressure             768.0   69.105469   19.355807   0.000  62.00000   \n",
       "SkinThickness             768.0   20.536458   15.952218   0.000   0.00000   \n",
       "Insulin                   768.0   79.799479  115.244002   0.000   0.00000   \n",
       "BMI                       768.0   31.992578    7.884160   0.000  27.30000   \n",
       "DiabetesPedigreeFunction  768.0    0.471876    0.331329   0.078   0.24375   \n",
       "Age                       768.0   33.240885   11.760232  21.000  24.00000   \n",
       "Outcome                   768.0    0.348958    0.476951   0.000   0.00000   \n",
       "\n",
       "                               50%        75%     max  \n",
       "Pregnancies                 3.0000    6.00000   17.00  \n",
       "Glucose                   117.0000  140.25000  199.00  \n",
       "BloodPressure              72.0000   80.00000  122.00  \n",
       "SkinThickness              23.0000   32.00000   99.00  \n",
       "Insulin                    30.5000  127.25000  846.00  \n",
       "BMI                        32.0000   36.60000   67.10  \n",
       "DiabetesPedigreeFunction    0.3725    0.62625    2.42  \n",
       "Age                        29.0000   41.00000   81.00  \n",
       "Outcome                     0.0000    1.00000    1.00  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe().transpose()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8500a821",
   "metadata": {},
   "source": [
    "# Week 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "9f5221d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 2.,  0.,  0.,  0.,  0.,  0.,  3., 13., 38., 61., 61., 36., 27.,\n",
       "        14.,  7.,  3.,  1.,  1.,  0.,  1.]),\n",
       " array([ 0.   ,  3.355,  6.71 , 10.065, 13.42 , 16.775, 20.13 , 23.485,\n",
       "        26.84 , 30.195, 33.55 , 36.905, 40.26 , 43.615, 46.97 , 50.325,\n",
       "        53.68 , 57.035, 60.39 , 63.745, 67.1  ]),\n",
       " [<matplotlib.patches.Polygon at 0x1cc99103a30>])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(Positive['BMI'],histtype='stepfilled',bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "7ca79074",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32.9    8\n",
       "31.6    7\n",
       "33.3    6\n",
       "31.2    5\n",
       "30.5    5\n",
       "32.0    5\n",
       "34.3    4\n",
       "Name: BMI, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Positive['BMI'].value_counts().head(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "1a9e7139",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 2.,  0.,  0.,  0.,  0.,  0.,  0.,  1.,  4.,  9., 28., 26., 36.,\n",
       "        27., 29., 22., 24., 21., 25., 14.]),\n",
       " array([  0.  ,   9.95,  19.9 ,  29.85,  39.8 ,  49.75,  59.7 ,  69.65,\n",
       "         79.6 ,  89.55,  99.5 , 109.45, 119.4 , 129.35, 139.3 , 149.25,\n",
       "        159.2 , 169.15, 179.1 , 189.05, 199.  ]),\n",
       " [<matplotlib.patches.Polygon at 0x1cc9916fbb0>])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(Positive['Glucose'],histtype='stepfilled',bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "0a7753f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "125    7\n",
       "128    6\n",
       "129    6\n",
       "115    6\n",
       "158    6\n",
       "146    5\n",
       "124    5\n",
       "Name: Glucose, dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Positive['Glucose'].value_counts().head(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "de40a9e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([16.,  0.,  0.,  0.,  0.,  1.,  0.,  1.,  6.,  6., 19., 37., 56.,\n",
       "        36., 41., 31.,  7.,  4.,  4.,  3.]),\n",
       " array([  0. ,   5.7,  11.4,  17.1,  22.8,  28.5,  34.2,  39.9,  45.6,\n",
       "         51.3,  57. ,  62.7,  68.4,  74.1,  79.8,  85.5,  91.2,  96.9,\n",
       "        102.6, 108.3, 114. ]),\n",
       " [<matplotlib.patches.Polygon at 0x1cc991e1b50>])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(Positive['BloodPressure'],histtype='stepfilled',bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "3eb35638",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "70    23\n",
       "76    18\n",
       "78    17\n",
       "74    17\n",
       "72    16\n",
       "0     16\n",
       "80    13\n",
       "Name: BloodPressure, dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Positive['BloodPressure'].value_counts().head(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "8ccbb938",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([88.,  1.,  4., 10., 18., 30., 41., 34., 23., 15.,  1.,  1.,  1.,\n",
       "         0.,  0.,  0.,  0.,  0.,  0.,  1.]),\n",
       " array([ 0.  ,  4.95,  9.9 , 14.85, 19.8 , 24.75, 29.7 , 34.65, 39.6 ,\n",
       "        44.55, 49.5 , 54.45, 59.4 , 64.35, 69.3 , 74.25, 79.2 , 84.15,\n",
       "        89.1 , 94.05, 99.  ]),\n",
       " [<matplotlib.patches.Polygon at 0x1cc9924fb50>])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(Positive['SkinThickness'],histtype='stepfilled',bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "f12c1796",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     88\n",
       "32    14\n",
       "30     9\n",
       "33     9\n",
       "39     8\n",
       "37     8\n",
       "36     8\n",
       "Name: SkinThickness, dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Positive['SkinThickness'].value_counts().head(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "1eaf9a3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([141.,   6.,  23.,  33.,  24.,  12.,   7.,   7.,   2.,   1.,   1.,\n",
       "          5.,   3.,   1.,   1.,   0.,   0.,   0.,   0.,   1.]),\n",
       " array([  0. ,  42.3,  84.6, 126.9, 169.2, 211.5, 253.8, 296.1, 338.4,\n",
       "        380.7, 423. , 465.3, 507.6, 549.9, 592.2, 634.5, 676.8, 719.1,\n",
       "        761.4, 803.7, 846. ]),\n",
       " [<matplotlib.patches.Polygon at 0x1cc992b6610>])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD4CAYAAAAXUaZHAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAPaUlEQVR4nO3df6hfd33H8edria22nTQlNyUmYTeO4BZlo+XSqQWRxdKyStN/CpFVMtcRBp1Tt+EShfWvQtzE6R9TCG01YNcSakeCMmeIigxm622rs2msyYxLr43NdeKPOaimvvfH9+i+3t3k3vs99+bmfvJ8QDjnfM459/vOm3tf38Pnnu+5qSokSW35teUuQJK0+Ax3SWqQ4S5JDTLcJalBhrskNWj1chcAsHbt2hofH1/uMiRpRXniiSe+V1Vjs+27KMJ9fHycycnJ5S5DklaUJP95rn1Oy0hSgwx3SWqQ4S5JDTLcJalBhrskNchwl6QGGe6S1KA5wz3JA0nOJHl6ln1/laSSrB0a25PkRJJnk9y82AVLkuY2nyv3TwC3zBxMsgm4CTg1NLYV2AG8tjvno0lWLUqlkqR5m/MTqlX1pSTjs+z6e+C9wMGhse3Aw1X1InAyyQngBuDfFqHWcxrf/ZmRz/323lsXsRJJujiMNOee5DbgO1X1tRm7NgDPDW1PdWOzfY1dSSaTTE5PT49ShiTpHBYc7kmuAN4P/M1su2cZm/Xv+FXVvqqaqKqJsbFZn3sjSRrRKA8O+01gM/C1JAAbgSeT3MDgSn3T0LEbgef7FilJWpgFX7lX1deral1VjVfVOINAv76qvgscAnYkuTzJZmAL8PiiVixJmtN8boV8iMEvRF+TZCrJXec6tqqOAgeAZ4DPAndX1UuLVawkaX7mc7fM2+bYPz5j+17g3n5lSZL68BOqktQgw12SGmS4S1KDDHdJapDhLkkNMtwlqUGGuyQ1yHCXpAYZ7pLUIMNdkhpkuEtSgwx3SWqQ4S5JDTLcJalBhrskNchwl6QGGe6S1CDDXZIaZLhLUoMMd0lq0JzhnuSBJGeSPD009ndJvpHk35P8U5Krh/btSXIiybNJbl6iuiVJ5zGfK/dPALfMGDsMvK6qfgf4JrAHIMlWYAfw2u6cjyZZtWjVSpLmZc5wr6ovAd+fMfa5qjrbbX4Z2NitbwcerqoXq+okcAK4YRHrlSTNw2LMuf8x8M/d+gbguaF9U92YJOkC6hXuSd4PnAUe/MXQLIfVOc7dlWQyyeT09HSfMiRJM4wc7kl2Am8F/rCqfhHgU8CmocM2As/Pdn5V7auqiaqaGBsbG7UMSdIsRgr3JLcAfw3cVlX/M7TrELAjyeVJNgNbgMf7lylJWojVcx2Q5CHgzcDaJFPAPQzujrkcOJwE4MtV9adVdTTJAeAZBtM1d1fVS0tVvCRpdnOGe1W9bZbh+89z/L3AvX2KkiT14ydUJalBhrskNchwl6QGGe6S1CDDXZIaZLhLUoMMd0lqkOEuSQ0y3CWpQYa7JDXIcJekBhnuktQgw12SGmS4S1KDDHdJapDhLkkNMtwlqUGGuyQ1yHCXpAYZ7pLUIMNdkho0Z7gneSDJmSRPD41dk+RwkuPdcs3Qvj1JTiR5NsnNS1W4JOnc5nPl/gnglhlju4EjVbUFONJtk2QrsAN4bXfOR5OsWrRqJUnzMme4V9WXgO/PGN4O7O/W9wO3D40/XFUvVtVJ4ARww+KUKkmar1Hn3K+tqtMA3XJdN74BeG7ouKlu7P9JsivJZJLJ6enpEcuQJM1msX+hmlnGarYDq2pfVU1U1cTY2NgilyFJl7ZRw/2FJOsBuuWZbnwK2DR03Ebg+dHLkySNYtRwPwTs7NZ3AgeHxnckuTzJZmAL8Hi/EiVJC7V6rgOSPAS8GVibZAq4B9gLHEhyF3AKuAOgqo4mOQA8A5wF7q6ql5aodknSOcwZ7lX1tnPs2naO4+8F7u1TlCSpHz+hKkkNMtwlqUGGuyQ1yHCXpAYZ7pLUIMNdkhpkuEtSgwx3SWqQ4S5JDTLcJalBhrskNchwl6QGGe6S1CDDXZIaZLhLUoMMd0lqkOEuSQ0y3CWpQYa7JDXIcJekBvUK9yTvSXI0ydNJHkry8iTXJDmc5Hi3XLNYxUqS5mfkcE+yAfhzYKKqXgesAnYAu4EjVbUFONJtS5IuoL7TMquBVyRZDVwBPA9sB/Z3+/cDt/d8DUnSAo0c7lX1HeCDwCngNPDDqvoccG1Vne6OOQ2sm+38JLuSTCaZnJ6eHrUMSdIs+kzLrGFwlb4ZeBVwZZI753t+Ve2rqomqmhgbGxu1DEnSLPpMy7wFOFlV01X1M+BR4I3AC0nWA3TLM/3LlCQtRJ9wPwW8PskVSQJsA44Bh4Cd3TE7gYP9SpQkLdTqUU+sqseSPAI8CZwFngL2AVcBB5LcxeAN4I7FKFSSNH8jhztAVd0D3DNj+EUGV/GSpGXiJ1QlqUGGuyQ1yHCXpAYZ7pLUIMNdkhpkuEtSgwx3SWqQ4S5JDTLcJalBhrskNchwl6QGGe6S1CDDXZIaZLhLUoMMd0lqkOEuSQ0y3CWpQYa7JDXIcJekBhnuktQgw12SGtQr3JNcneSRJN9IcizJG5Jck+RwkuPdcs1iFStJmp++V+4fAT5bVb8F/C5wDNgNHKmqLcCRbluSdAGNHO5JXgm8CbgfoKp+WlU/ALYD+7vD9gO39ytRkrRQfa7cXw1MAx9P8lSS+5JcCVxbVacBuuW62U5OsivJZJLJ6enpHmVIkmbqE+6rgeuBj1XVdcBPWMAUTFXtq6qJqpoYGxvrUYYkaaY+4T4FTFXVY932IwzC/oUk6wG65Zl+JUqSFmrkcK+q7wLPJXlNN7QNeAY4BOzsxnYCB3tVKElasNU9z38n8GCSy4BvAe9g8IZxIMldwCngjp6vIUlaoF7hXlVfBSZm2bWtz9eVJPXjJ1QlqUGGuyQ1yHCXpAYZ7pLUIMNdkhpkuEtSgwx3SWqQ4S5JDTLcJalBhrskNchwl6QGGe6S1CDDXZIaZLhLUoMMd0lqkOEuSQ0y3CWpQYa7JDXIcJekBhnuktSg3uGeZFWSp5J8utu+JsnhJMe75Zr+ZUqSFmIxrtzfBRwb2t4NHKmqLcCRbluSdAH1CvckG4FbgfuGhrcD+7v1/cDtfV5DkrRwfa/cPwy8F/j50Ni1VXUaoFuum+3EJLuSTCaZnJ6e7lmGJGnYyOGe5K3Amap6YpTzq2pfVU1U1cTY2NioZUiSZrG6x7k3Arcl+QPg5cArk3wSeCHJ+qo6nWQ9cGYxCpUkzd/IV+5VtaeqNlbVOLAD+HxV3QkcAnZ2h+0EDvauUpK0IEtxn/te4KYkx4Gbum1J0gXUZ1rml6rqi8AXu/X/ArYtxteVJI3GT6hKUoMMd0lqkOEuSQ0y3CWpQYvyC1Ut3Pjuz4x87rf33rqIlUhqkVfuktQgw12SGmS4S1KDDHdJapDhLkkNMtwlqUGGuyQ1yHCXpAYZ7pLUIMNdkhpkuEtSgwx3SWqQ4S5JDfKpkCtQnydKgk+VlC4FhnsPfUNWkpbKyNMySTYl+UKSY0mOJnlXN35NksNJjnfLNYtXriRpPvrMuZ8F/rKqfht4PXB3kq3AbuBIVW0BjnTbkqQLaORwr6rTVfVkt/5j4BiwAdgO7O8O2w/c3rNGSdICLcrdMknGgeuAx4Brq+o0DN4AgHXnOGdXkskkk9PT04tRhiSp0zvck1wFfAp4d1X9aL7nVdW+qpqoqomxsbG+ZUiShvQK9yQvYxDsD1bVo93wC0nWd/vXA2f6lShJWqg+d8sEuB84VlUfGtp1CNjZre8EDo5eniRpFH3uc78ReDvw9SRf7cbeB+wFDiS5CzgF3NGrQknSgo0c7lX1r0DOsXvbqF9XktSfz5aRpAYZ7pLUIMNdkhpkuEtSgwx3SWqQ4S5JDfJ57pegPs+h9w99SCuDV+6S1CDDXZIaZLhLUoMMd0lqkOEuSQ0y3CWpQYa7JDXI+9y1IH3uke/D++ulhbnkw325wkqSltIlH+7SUvLTwFouzrlLUoMMd0lqkNMy0kXKKR314ZW7JDVoya7ck9wCfARYBdxXVXuX6rXUvr53NXklq0vNkoR7klXAPwA3AVPAV5IcqqpnluL1pLlcare8Luf/91J8I70Yp9CWalrmBuBEVX2rqn4KPAxsX6LXkiTNsFTTMhuA54a2p4DfGz4gyS5gV7f530me7fF6a4Hv9Ti/Zfbm/OzP+S24P/nAElVycer9/dOzX79xrh1LFe6ZZax+ZaNqH7BvUV4smayqicX4Wq2xN+dnf87P/pzfxdyfpZqWmQI2DW1vBJ5foteSJM2wVOH+FWBLks1JLgN2AIeW6LUkSTMsybRMVZ1N8mfAvzC4FfKBqjq6FK/VWZTpnUbZm/OzP+dnf87vou1PqmruoyRJK4qfUJWkBhnuktSgFR3uSW5J8mySE0l2L3c9yyHJpiRfSHIsydEk7+rGr0lyOMnxbrlm6Jw9Xc+eTXLz8lV/YSRZleSpJJ/utu3NkCRXJ3kkyTe676M32KOBJO/pfq6eTvJQkpevmN5U1Yr8x+AXtf8BvBq4DPgasHW561qGPqwHru/Wfx34JrAV+Ftgdze+G/hAt76169XlwOauh6uW+/+xxD36C+AfgU932/bmV/uzH/iTbv0y4Gp7VDD4MOZJ4BXd9gHgj1ZKb1bylbuPOACq6nRVPdmt/xg4xuCbcjuDH1q65e3d+nbg4ap6sapOAicY9LJJSTYCtwL3DQ3bm06SVwJvAu4HqKqfVtUPsEe/sBp4RZLVwBUMPq+zInqzksN9tkccbFimWi4KScaB64DHgGur6jQM3gCAdd1hl1rfPgy8F/j50Ji9+T+vBqaBj3dTV/cluRJ7RFV9B/ggcAo4Dfywqj7HCunNSg73OR9xcClJchXwKeDdVfWj8x06y1iTfUvyVuBMVT0x31NmGWuyN0NWA9cDH6uq64CfMJhqOJdLpkfdXPp2BlMsrwKuTHLn+U6ZZWzZerOSw91HHHSSvIxBsD9YVY92wy8kWd/tXw+c6cYvpb7dCNyW5NsMpu1+P8knsTfDpoCpqnqs236EQdjbI3gLcLKqpqvqZ8CjwBtZIb1ZyeHuIw6AJGEwX3qsqj40tOsQsLNb3wkcHBrfkeTyJJuBLcDjF6reC6mq9lTVxqoaZ/D98fmquhN780tV9V3guSSv6Ya2Ac9gj2AwHfP6JFd0P2fbGPxOa0X0ZsX+DdW68I84uFjdCLwd+HqSr3Zj7wP2AgeS3MXgm/QOgKo6muQAgx/gs8DdVfXSBa96edmbX/VO4MHuIulbwDsYXPhd0j2qqseSPAI8yeD/+hSDxw1cxQrojY8fkKQGreRpGUnSORjuktQgw12SGmS4S1KDDHdJapDhLkkNMtwlqUH/C4RyD/y717CUAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(Positive['Insulin'],histtype='stepfilled',bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "7d8b4dde",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      138\n",
       "130      6\n",
       "180      4\n",
       "175      3\n",
       "156      3\n",
       "185      2\n",
       "194      2\n",
       "Name: Insulin, dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Positive['Insulin'].value_counts().head(7)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d13966af",
   "metadata": {},
   "source": [
    "## Creating Scatter charts to Understand the Relationship of the Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "dd5027a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "BloodPressure = Positive['BloodPressure']\n",
    "Glucose = Positive['Glucose']\n",
    "SkinThickness = Positive['SkinThickness']\n",
    "Insulin = Positive['Insulin']\n",
    "BMI = Positive['BMI']\n",
    "\n",
    "\n",
    "plt.scatter(BloodPressure, Glucose, color=['b'])\n",
    "plt.xlabel('BloodPressure')\n",
    "plt.ylabel('Glucose')\n",
    "plt.title('BloodPressure & Glucose')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "46674a85",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g =sns.scatterplot(x= \"Glucose\" ,y= \"BloodPressure\",\n",
    "              hue=\"Outcome\",\n",
    "              data=data);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "2d90b7ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "B =sns.scatterplot(x= \"BMI\" ,y= \"Insulin\",\n",
    "              hue=\"Outcome\",\n",
    "              data=data);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "9717e484",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "S =sns.scatterplot(x= \"SkinThickness\" ,y= \"Insulin\",\n",
    "              hue=\"Outcome\",\n",
    "              data=data);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "28c5c4eb",
   "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>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Pregnancies</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.129459</td>\n",
       "      <td>0.141282</td>\n",
       "      <td>-0.081672</td>\n",
       "      <td>-0.073535</td>\n",
       "      <td>0.017683</td>\n",
       "      <td>-0.033523</td>\n",
       "      <td>0.544341</td>\n",
       "      <td>0.221898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Glucose</th>\n",
       "      <td>0.129459</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.152590</td>\n",
       "      <td>0.057328</td>\n",
       "      <td>0.331357</td>\n",
       "      <td>0.221071</td>\n",
       "      <td>0.137337</td>\n",
       "      <td>0.263514</td>\n",
       "      <td>0.466581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BloodPressure</th>\n",
       "      <td>0.141282</td>\n",
       "      <td>0.152590</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.207371</td>\n",
       "      <td>0.088933</td>\n",
       "      <td>0.281805</td>\n",
       "      <td>0.041265</td>\n",
       "      <td>0.239528</td>\n",
       "      <td>0.065068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SkinThickness</th>\n",
       "      <td>-0.081672</td>\n",
       "      <td>0.057328</td>\n",
       "      <td>0.207371</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.436783</td>\n",
       "      <td>0.392573</td>\n",
       "      <td>0.183928</td>\n",
       "      <td>-0.113970</td>\n",
       "      <td>0.074752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Insulin</th>\n",
       "      <td>-0.073535</td>\n",
       "      <td>0.331357</td>\n",
       "      <td>0.088933</td>\n",
       "      <td>0.436783</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.197859</td>\n",
       "      <td>0.185071</td>\n",
       "      <td>-0.042163</td>\n",
       "      <td>0.130548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BMI</th>\n",
       "      <td>0.017683</td>\n",
       "      <td>0.221071</td>\n",
       "      <td>0.281805</td>\n",
       "      <td>0.392573</td>\n",
       "      <td>0.197859</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.140647</td>\n",
       "      <td>0.036242</td>\n",
       "      <td>0.292695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <td>-0.033523</td>\n",
       "      <td>0.137337</td>\n",
       "      <td>0.041265</td>\n",
       "      <td>0.183928</td>\n",
       "      <td>0.185071</td>\n",
       "      <td>0.140647</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.033561</td>\n",
       "      <td>0.173844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>0.544341</td>\n",
       "      <td>0.263514</td>\n",
       "      <td>0.239528</td>\n",
       "      <td>-0.113970</td>\n",
       "      <td>-0.042163</td>\n",
       "      <td>0.036242</td>\n",
       "      <td>0.033561</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.238356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Outcome</th>\n",
       "      <td>0.221898</td>\n",
       "      <td>0.466581</td>\n",
       "      <td>0.065068</td>\n",
       "      <td>0.074752</td>\n",
       "      <td>0.130548</td>\n",
       "      <td>0.292695</td>\n",
       "      <td>0.173844</td>\n",
       "      <td>0.238356</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Pregnancies   Glucose  BloodPressure  SkinThickness  \\\n",
       "Pregnancies                  1.000000  0.129459       0.141282      -0.081672   \n",
       "Glucose                      0.129459  1.000000       0.152590       0.057328   \n",
       "BloodPressure                0.141282  0.152590       1.000000       0.207371   \n",
       "SkinThickness               -0.081672  0.057328       0.207371       1.000000   \n",
       "Insulin                     -0.073535  0.331357       0.088933       0.436783   \n",
       "BMI                          0.017683  0.221071       0.281805       0.392573   \n",
       "DiabetesPedigreeFunction    -0.033523  0.137337       0.041265       0.183928   \n",
       "Age                          0.544341  0.263514       0.239528      -0.113970   \n",
       "Outcome                      0.221898  0.466581       0.065068       0.074752   \n",
       "\n",
       "                           Insulin       BMI  DiabetesPedigreeFunction  \\\n",
       "Pregnancies              -0.073535  0.017683                 -0.033523   \n",
       "Glucose                   0.331357  0.221071                  0.137337   \n",
       "BloodPressure             0.088933  0.281805                  0.041265   \n",
       "SkinThickness             0.436783  0.392573                  0.183928   \n",
       "Insulin                   1.000000  0.197859                  0.185071   \n",
       "BMI                       0.197859  1.000000                  0.140647   \n",
       "DiabetesPedigreeFunction  0.185071  0.140647                  1.000000   \n",
       "Age                      -0.042163  0.036242                  0.033561   \n",
       "Outcome                   0.130548  0.292695                  0.173844   \n",
       "\n",
       "                               Age   Outcome  \n",
       "Pregnancies               0.544341  0.221898  \n",
       "Glucose                   0.263514  0.466581  \n",
       "BloodPressure             0.239528  0.065068  \n",
       "SkinThickness            -0.113970  0.074752  \n",
       "Insulin                  -0.042163  0.130548  \n",
       "BMI                       0.036242  0.292695  \n",
       "DiabetesPedigreeFunction  0.033561  0.173844  \n",
       "Age                       1.000000  0.238356  \n",
       "Outcome                   0.238356  1.000000  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### correlation matrix\n",
    "data.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a35b20cb",
   "metadata": {},
   "source": [
    "## Correlation Analysis Using Heat map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "b7584ac9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=(14,7))\n",
    "sns.heatmap(data.corr())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9eef614e",
   "metadata": {},
   "source": [
    "## There is No MultiColinearity Between variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "79fe6c99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=(14,7))\n",
    "sns.heatmap(data.corr(),annot=True,cmap='viridis')  ### gives correlation value"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64b96258",
   "metadata": {},
   "source": [
    "# Beacuse it is Binary Classification Problem so We can use Logistic Rergression model\n",
    "## MODEL BULIDING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "d34c9e81",
   "metadata": {},
   "outputs": [],
   "source": [
    "features = data.iloc[:,[0,1,2,3,4,5,6,7]].values\n",
    "label = data.iloc[:,8].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "76765a4f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Train test split\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,y_train,y_test = train_test_split(features,\n",
    "                                                label,\n",
    "                                                test_size=0.2,\n",
    "                                                random_state =10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "dfe31f09",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Create model\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "model = LogisticRegression()\n",
    "model.fit(X_train,y_train) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "4194f23e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7719869706840391\n",
      "0.7662337662337663\n"
     ]
    }
   ],
   "source": [
    "print(model.score(X_train,y_train))\n",
    "print(model.score(X_test,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "879e3058",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[446,  54],\n",
       "       [122, 146]], dtype=int64)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "cm = confusion_matrix(label,model.predict(features))\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "471a5280",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.79      0.89      0.84       500\n",
      "           1       0.73      0.54      0.62       268\n",
      "\n",
      "    accuracy                           0.77       768\n",
      "   macro avg       0.76      0.72      0.73       768\n",
      "weighted avg       0.77      0.77      0.76       768\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(label,model.predict(features)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "930ea4f6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC: 0.837\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1cc99741280>]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Preparing ROC Curve (Receiver Operating Characteristics Curve)\n",
    "from sklearn.metrics import roc_curve\n",
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "# predict probabilities\n",
    "probs = model.predict_proba(features)\n",
    "# keep probabilities for the positive outcome only\n",
    "probs = probs[:, 1]\n",
    "# calculate AUC\n",
    "auc = roc_auc_score(label, probs)\n",
    "print('AUC: %.3f' % auc)\n",
    "# calculate roc curve\n",
    "fpr, tpr, thresholds = roc_curve(label, probs)\n",
    "# plot no skill\n",
    "plt.plot([0, 1], [0, 1], linestyle='--')\n",
    "# plot the roc curve for the model\n",
    "plt.plot(fpr, tpr, marker='.')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "ed82450c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(max_depth=5)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Applying Decission Tree Classifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "model3 = DecisionTreeClassifier(max_depth=5)\n",
    "model3.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "1eaaba56",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8289902280130294"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model3.score(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "1fd093b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7727272727272727"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model3.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "d3b52194",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(n_estimators=11)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Applying Random Forest\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "model4 = RandomForestClassifier(n_estimators=11)\n",
    "model4.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "fd8975fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.990228013029316"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model4.score(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "750cbccd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7467532467532467"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model4.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "d76e9140",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(gamma='auto')"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Support Vector Classifier\n",
    "\n",
    "from sklearn.svm import SVC \n",
    "model5 = SVC(kernel='rbf',\n",
    "           gamma='auto')\n",
    "model5.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "635a5081",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model5.score(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "327342f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6168831168831169"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model5.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "384777f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6948051948051948"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Applying K-NN\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "model6 = KNeighborsClassifier(n_neighbors=7,\n",
    "                             metric='minkowski',\n",
    "                             p = 2)\n",
    "model6.fit(X_train,y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "c90357b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6948051948051948"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model6.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "29f5ea61",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC: 0.836\n",
      "True Positive Rate - [0.         0.06716418 0.23880597 0.44776119 0.60074627 0.75373134\n",
      " 0.88059701 0.98507463 1.        ], False Positive Rate - [0.    0.    0.02  0.056 0.12  0.248 0.428 0.668 1.   ] Thresholds - [2.         1.         0.85714286 0.71428571 0.57142857 0.42857143\n",
      " 0.28571429 0.14285714 0.        ]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'True Positive Rate')"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Preparing ROC Curve (Receiver Operating Characteristics Curve)\n",
    "from sklearn.metrics import roc_curve\n",
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "# predict probabilities\n",
    "probs = model6.predict_proba(features)\n",
    "# keep probabilities for the positive outcome only\n",
    "probs = probs[:, 1]\n",
    "# calculate AUC\n",
    "auc = roc_auc_score(label, probs)\n",
    "print('AUC: %.3f' % auc)\n",
    "# calculate roc curve\n",
    "fpr, tpr, thresholds = roc_curve(label, probs)\n",
    "print(\"True Positive Rate - {}, False Positive Rate - {} Thresholds - {}\".format(tpr,fpr,thresholds))\n",
    "# plot no skill\n",
    "plt.plot([0, 1], [0, 1], linestyle='--')\n",
    "# plot the roc curve for the model\n",
    "plt.plot(fpr, tpr, marker='.')\n",
    "plt.xlabel(\"False Positive Rate\")\n",
    "plt.ylabel(\"True Positive Rate\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "4ae2bba9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "f1=0.624 auc=0.726 ap=0.727\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1cc9adb16d0>]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Precision Recall Curve for Logistic Regression\n",
    "\n",
    "from sklearn.metrics import precision_recall_curve\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import auc\n",
    "from sklearn.metrics import average_precision_score\n",
    "# predict probabilities\n",
    "probs = model.predict_proba(features)\n",
    "# keep probabilities for the positive outcome only\n",
    "probs = probs[:, 1]\n",
    "# predict class values\n",
    "yhat = model.predict(features)\n",
    "# calculate precision-recall curve\n",
    "precision, recall, thresholds = precision_recall_curve(label, probs)\n",
    "# calculate F1 score\n",
    "f1 = f1_score(label, yhat)\n",
    "# calculate precision-recall AUC\n",
    "auc = auc(recall, precision)\n",
    "# calculate average precision score\n",
    "ap = average_precision_score(label, probs)\n",
    "print('f1=%.3f auc=%.3f ap=%.3f' % (f1, auc, ap))\n",
    "# plot no skill\n",
    "plt.plot([0, 1], [0.5, 0.5], linestyle='--')\n",
    "# plot the precision-recall curve for the model\n",
    "plt.plot(recall, precision, marker='.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "90d3b486",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "f1=0.658 auc=0.752 ap=0.709\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1cc9ae8ad00>]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Precision Recall Curve for KNN\n",
    "\n",
    "from sklearn.metrics import precision_recall_curve\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import auc\n",
    "from sklearn.metrics import average_precision_score\n",
    "# predict probabilities\n",
    "probs = model6.predict_proba(features)\n",
    "# keep probabilities for the positive outcome only\n",
    "probs = probs[:, 1]\n",
    "# predict class values\n",
    "yhat = model6.predict(features)\n",
    "# calculate precision-recall curve\n",
    "precision, recall, thresholds = precision_recall_curve(label, probs)\n",
    "# calculate F1 score\n",
    "f1 = f1_score(label, yhat)\n",
    "# calculate precision-recall AUC\n",
    "auc = auc(recall, precision)\n",
    "# calculate average precision score\n",
    "ap = average_precision_score(label, probs)\n",
    "print('f1=%.3f auc=%.3f ap=%.3f' % (f1, auc, ap))\n",
    "# plot no skill\n",
    "plt.plot([0, 1], [0.5, 0.5], linestyle='--')\n",
    "# plot the precision-recall curve for the model\n",
    "plt.plot(recall, precision, marker='.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "e6f6e06b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "f1=0.714 auc=0.815 ap=0.768\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1cc9aec9520>]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Precision Recall Curve for Decission Tree Classifier\n",
    "\n",
    "from sklearn.metrics import precision_recall_curve\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import auc\n",
    "from sklearn.metrics import average_precision_score\n",
    "# predict probabilities\n",
    "probs = model3.predict_proba(features)\n",
    "# keep probabilities for the positive outcome only\n",
    "probs = probs[:, 1]\n",
    "# predict class values\n",
    "yhat = model3.predict(features)\n",
    "# calculate precision-recall curve\n",
    "precision, recall, thresholds = precision_recall_curve(label, probs)\n",
    "# calculate F1 score\n",
    "f1 = f1_score(label, yhat)\n",
    "# calculate precision-recall AUC\n",
    "auc = auc(recall, precision)\n",
    "# calculate average precision score\n",
    "ap = average_precision_score(label, probs)\n",
    "print('f1=%.3f auc=%.3f ap=%.3f' % (f1, auc, ap))\n",
    "# plot no skill\n",
    "plt.plot([0, 1], [0.5, 0.5], linestyle='--')\n",
    "# plot the precision-recall curve for the model\n",
    "plt.plot(recall, precision, marker='.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "133af268",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "0a9351fe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "f1=0.914 auc=0.971 ap=0.964\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1cc9af2c580>]"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Precision Recall Curve for Random Forest\n",
    "\n",
    "from sklearn.metrics import precision_recall_curve\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import auc\n",
    "from sklearn.metrics import average_precision_score\n",
    "# predict probabilities\n",
    "probs = model4.predict_proba(features)\n",
    "# keep probabilities for the positive outcome only\n",
    "probs = probs[:, 1]\n",
    "# predict class values\n",
    "yhat = model4.predict(features)\n",
    "# calculate precision-recall curve\n",
    "precision, recall, thresholds = precision_recall_curve(label, probs)\n",
    "# calculate F1 score\n",
    "f1 = f1_score(label, yhat)\n",
    "# calculate precision-recall AUC\n",
    "auc = auc(recall, precision)\n",
    "# calculate average precision score\n",
    "ap = average_precision_score(label, probs)\n",
    "print('f1=%.3f auc=%.3f ap=%.3f' % (f1, auc, ap))\n",
    "# plot no skill\n",
    "plt.plot([0, 1], [0.5, 0.5], linestyle='--')\n",
    "# plot the precision-recall curve for the model\n",
    "plt.plot(recall, precision, marker='.')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}