Mini Project - Diabetic Nephropathy Risk Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Project on Disease Prediction using Machine Learning\n",
    "\n",
    "##### ML algorithms used:-\n",
    "- Linear regression\n",
    "- Logistic Regression\n",
    "- Decision Tree\n",
    "- K-Means Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50, 17)"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "sns.set(color_codes=True)\n",
    "%matplotlib inline\n",
    "\n",
    "data_frame = pd.read_csv(\"proj-data.csv\")\n",
    "data_frame.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CORRELATION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_corr(data_frame, size=25):\n",
    "    \"\"\"Function plots a graphical correlation matrix for each pair of columns in the dataframe.\n",
    "    Input:\n",
    "        data_frame: pandas DataFrame\n",
    "        size: vertical and horizontal size of the plot\n",
    "    Displays:\n",
    "        matrix of correlation between columns.\n",
    "        Blue-cyan-yellow-red-darkred => less to more correlated\n",
    "        0 ------------------>  1\n",
    "        Expect a dark-red line running from top left to bottom right\n",
    "    \"\"\"\n",
    "    corr = data_frame.corr()\n",
    "    fig, ax = plt.subplots(figsize=(size, size))\n",
    "    ax.matshow(corr)\n",
    "    plt.xticks(range(len(corr.columns)), corr.columns)\n",
    "    plt.yticks(range(len(corr.columns)), corr.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Blood_Pressure</th>\n",
       "      <th>Blood_Sugar</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>Diabetes_Outcome</th>\n",
       "      <th>Blood_urea_nitrogen_level</th>\n",
       "      <th>Uric_acid</th>\n",
       "      <th>Creatinine_level</th>\n",
       "      <th>Kidney_test_outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.005466</td>\n",
       "      <td>0.363865</td>\n",
       "      <td>0.063184</td>\n",
       "      <td>-0.098943</td>\n",
       "      <td>0.343225</td>\n",
       "      <td>0.199776</td>\n",
       "      <td>0.222279</td>\n",
       "      <td>-0.075619</td>\n",
       "      <td>-0.023118</td>\n",
       "      <td>0.132242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BMI</th>\n",
       "      <td>0.005466</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.295682</td>\n",
       "      <td>-0.170338</td>\n",
       "      <td>0.221143</td>\n",
       "      <td>0.206208</td>\n",
       "      <td>-0.033682</td>\n",
       "      <td>0.204468</td>\n",
       "      <td>0.054969</td>\n",
       "      <td>0.265964</td>\n",
       "      <td>0.090342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Blood_Pressure</th>\n",
       "      <td>0.363865</td>\n",
       "      <td>0.295682</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.029250</td>\n",
       "      <td>0.085568</td>\n",
       "      <td>0.121625</td>\n",
       "      <td>-0.127110</td>\n",
       "      <td>0.403617</td>\n",
       "      <td>0.021752</td>\n",
       "      <td>0.104475</td>\n",
       "      <td>0.073826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Blood_Sugar</th>\n",
       "      <td>0.063184</td>\n",
       "      <td>-0.170338</td>\n",
       "      <td>-0.029250</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.012649</td>\n",
       "      <td>0.139285</td>\n",
       "      <td>0.080412</td>\n",
       "      <td>0.181586</td>\n",
       "      <td>0.248508</td>\n",
       "      <td>0.237710</td>\n",
       "      <td>0.190894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Insulin</th>\n",
       "      <td>-0.098943</td>\n",
       "      <td>0.221143</td>\n",
       "      <td>0.085568</td>\n",
       "      <td>0.012649</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.257081</td>\n",
       "      <td>-0.234672</td>\n",
       "      <td>0.052369</td>\n",
       "      <td>-0.098805</td>\n",
       "      <td>0.001285</td>\n",
       "      <td>0.104956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Glucose</th>\n",
       "      <td>0.343225</td>\n",
       "      <td>0.206208</td>\n",
       "      <td>0.121625</td>\n",
       "      <td>0.139285</td>\n",
       "      <td>0.257081</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.477320</td>\n",
       "      <td>0.099939</td>\n",
       "      <td>-0.026066</td>\n",
       "      <td>0.151775</td>\n",
       "      <td>0.104187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Diabetes_Outcome</th>\n",
       "      <td>0.199776</td>\n",
       "      <td>-0.033682</td>\n",
       "      <td>-0.127110</td>\n",
       "      <td>0.080412</td>\n",
       "      <td>-0.234672</td>\n",
       "      <td>0.477320</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.053565</td>\n",
       "      <td>-0.074559</td>\n",
       "      <td>0.015724</td>\n",
       "      <td>0.065530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Blood_urea_nitrogen_level</th>\n",
       "      <td>0.222279</td>\n",
       "      <td>0.204468</td>\n",
       "      <td>0.403617</td>\n",
       "      <td>0.181586</td>\n",
       "      <td>0.052369</td>\n",
       "      <td>0.099939</td>\n",
       "      <td>-0.053565</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.480021</td>\n",
       "      <td>0.610877</td>\n",
       "      <td>0.689735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uric_acid</th>\n",
       "      <td>-0.075619</td>\n",
       "      <td>0.054969</td>\n",
       "      <td>0.021752</td>\n",
       "      <td>0.248508</td>\n",
       "      <td>-0.098805</td>\n",
       "      <td>-0.026066</td>\n",
       "      <td>-0.074559</td>\n",
       "      <td>0.480021</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.725949</td>\n",
       "      <td>0.698722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Creatinine_level</th>\n",
       "      <td>-0.023118</td>\n",
       "      <td>0.265964</td>\n",
       "      <td>0.104475</td>\n",
       "      <td>0.237710</td>\n",
       "      <td>0.001285</td>\n",
       "      <td>0.151775</td>\n",
       "      <td>0.015724</td>\n",
       "      <td>0.610877</td>\n",
       "      <td>0.725949</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.708877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kidney_test_outcome</th>\n",
       "      <td>0.132242</td>\n",
       "      <td>0.090342</td>\n",
       "      <td>0.073826</td>\n",
       "      <td>0.190894</td>\n",
       "      <td>0.104956</td>\n",
       "      <td>0.104187</td>\n",
       "      <td>0.065530</td>\n",
       "      <td>0.689735</td>\n",
       "      <td>0.698722</td>\n",
       "      <td>0.708877</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                Age       BMI  Blood_Pressure  Blood_Sugar  \\\n",
       "Age                        1.000000  0.005466        0.363865     0.063184   \n",
       "BMI                        0.005466  1.000000        0.295682    -0.170338   \n",
       "Blood_Pressure             0.363865  0.295682        1.000000    -0.029250   \n",
       "Blood_Sugar                0.063184 -0.170338       -0.029250     1.000000   \n",
       "Insulin                   -0.098943  0.221143        0.085568     0.012649   \n",
       "Glucose                    0.343225  0.206208        0.121625     0.139285   \n",
       "Diabetes_Outcome           0.199776 -0.033682       -0.127110     0.080412   \n",
       "Blood_urea_nitrogen_level  0.222279  0.204468        0.403617     0.181586   \n",
       "Uric_acid                 -0.075619  0.054969        0.021752     0.248508   \n",
       "Creatinine_level          -0.023118  0.265964        0.104475     0.237710   \n",
       "Kidney_test_outcome        0.132242  0.090342        0.073826     0.190894   \n",
       "\n",
       "                            Insulin   Glucose  Diabetes_Outcome  \\\n",
       "Age                       -0.098943  0.343225          0.199776   \n",
       "BMI                        0.221143  0.206208         -0.033682   \n",
       "Blood_Pressure             0.085568  0.121625         -0.127110   \n",
       "Blood_Sugar                0.012649  0.139285          0.080412   \n",
       "Insulin                    1.000000  0.257081         -0.234672   \n",
       "Glucose                    0.257081  1.000000          0.477320   \n",
       "Diabetes_Outcome          -0.234672  0.477320          1.000000   \n",
       "Blood_urea_nitrogen_level  0.052369  0.099939         -0.053565   \n",
       "Uric_acid                 -0.098805 -0.026066         -0.074559   \n",
       "Creatinine_level           0.001285  0.151775          0.015724   \n",
       "Kidney_test_outcome        0.104956  0.104187          0.065530   \n",
       "\n",
       "                           Blood_urea_nitrogen_level  Uric_acid  \\\n",
       "Age                                         0.222279  -0.075619   \n",
       "BMI                                         0.204468   0.054969   \n",
       "Blood_Pressure                              0.403617   0.021752   \n",
       "Blood_Sugar                                 0.181586   0.248508   \n",
       "Insulin                                     0.052369  -0.098805   \n",
       "Glucose                                     0.099939  -0.026066   \n",
       "Diabetes_Outcome                           -0.053565  -0.074559   \n",
       "Blood_urea_nitrogen_level                   1.000000   0.480021   \n",
       "Uric_acid                                   0.480021   1.000000   \n",
       "Creatinine_level                            0.610877   0.725949   \n",
       "Kidney_test_outcome                         0.689735   0.698722   \n",
       "\n",
       "                           Creatinine_level  Kidney_test_outcome  \n",
       "Age                               -0.023118             0.132242  \n",
       "BMI                                0.265964             0.090342  \n",
       "Blood_Pressure                     0.104475             0.073826  \n",
       "Blood_Sugar                        0.237710             0.190894  \n",
       "Insulin                            0.001285             0.104956  \n",
       "Glucose                            0.151775             0.104187  \n",
       "Diabetes_Outcome                   0.015724             0.065530  \n",
       "Blood_urea_nitrogen_level          0.610877             0.689735  \n",
       "Uric_acid                          0.725949             0.698722  \n",
       "Creatinine_level                   1.000000             0.708877  \n",
       "Kidney_test_outcome                0.708877             1.000000  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_frame.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x1800 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#correlation\n",
    "plot_corr(data_frame)\n",
    "plt.savefig('corr.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of True cases:  20 (40.00%)\n",
      "Number of False cases: 30 (60.00%)\n"
     ]
    }
   ],
   "source": [
    "#true cases\n",
    "num_obs = len(data_frame)\n",
    "num_true = len(data_frame.loc[data_frame['Diabetes_Outcome'] == 1])\n",
    "num_false = len(data_frame.loc[data_frame['Diabetes_Outcome'] == 0])\n",
    "print(\"Number of True cases:  {0} ({1:2.2f}%)\".format(num_true, ((1.00 * num_true)/(1.0 * num_obs)) * 100))\n",
    "print(\"Number of False cases: {0} ({1:2.2f}%)\".format(num_false, (( 1.0 * num_false)/(1.0 * num_obs)) * 100))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### HISTOGRAMS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Serial_ID</th>\n",
       "      <th>Patient_Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Blood_group</th>\n",
       "      <th>Age</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Blood_Pressure</th>\n",
       "      <th>Blood_Sugar</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>Diabetes_Outcome</th>\n",
       "      <th>Blood_urea_nitrogen_level</th>\n",
       "      <th>Uric_acid</th>\n",
       "      <th>Creatinine_level</th>\n",
       "      <th>Kidney_test_outcome</th>\n",
       "      <th>Drug_Diabetes</th>\n",
       "      <th>Drug_kidney</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A001</td>\n",
       "      <td>Ananya Mohan</td>\n",
       "      <td>Female</td>\n",
       "      <td>A-</td>\n",
       "      <td>47</td>\n",
       "      <td>33.6</td>\n",
       "      <td>62</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>138</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>5.5</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0</td>\n",
       "      <td>Precose</td>\n",
       "      <td>Enalapril</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A002</td>\n",
       "      <td>Dasari K</td>\n",
       "      <td>Male</td>\n",
       "      <td>A-</td>\n",
       "      <td>23</td>\n",
       "      <td>38.2</td>\n",
       "      <td>82</td>\n",
       "      <td>52</td>\n",
       "      <td>125</td>\n",
       "      <td>84</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0</td>\n",
       "      <td>Glyset</td>\n",
       "      <td>Enalapril</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A003</td>\n",
       "      <td>Karan Jadeja</td>\n",
       "      <td>Male</td>\n",
       "      <td>AB+</td>\n",
       "      <td>31</td>\n",
       "      <td>44.2</td>\n",
       "      <td>0</td>\n",
       "      <td>99</td>\n",
       "      <td>0</td>\n",
       "      <td>145</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>3.4</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0</td>\n",
       "      <td>Precose</td>\n",
       "      <td>Enalapril</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A004</td>\n",
       "      <td>Himanshu Soni</td>\n",
       "      <td>Male</td>\n",
       "      <td>A+</td>\n",
       "      <td>24</td>\n",
       "      <td>42.3</td>\n",
       "      <td>68</td>\n",
       "      <td>110</td>\n",
       "      <td>250</td>\n",
       "      <td>135</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>4.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0</td>\n",
       "      <td>Precose</td>\n",
       "      <td>Ramipril</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A005</td>\n",
       "      <td>Moti Lal</td>\n",
       "      <td>Male</td>\n",
       "      <td>O+</td>\n",
       "      <td>21</td>\n",
       "      <td>40.7</td>\n",
       "      <td>62</td>\n",
       "      <td>155</td>\n",
       "      <td>480</td>\n",
       "      <td>139</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>6.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1</td>\n",
       "      <td>Precose</td>\n",
       "      <td>Captopril</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Serial_ID   Patient_Name     Sex Blood_group  Age   BMI  Blood_Pressure  \\\n",
       "0      A001   Ananya Mohan  Female          A-   47  33.6              62   \n",
       "1      A002       Dasari K    Male          A-   23  38.2              82   \n",
       "2      A003   Karan Jadeja    Male         AB+   31  44.2               0   \n",
       "3      A004  Himanshu Soni    Male          A+   24  42.3              68   \n",
       "4      A005       Moti Lal    Male          O+   21  40.7              62   \n",
       "\n",
       "   Blood_Sugar  Insulin  Glucose  Diabetes_Outcome  Blood_urea_nitrogen_level  \\\n",
       "0           80        0      138                 1                         20   \n",
       "1           52      125       84                 0                         20   \n",
       "2           99        0      145                 1                          7   \n",
       "3          110      250      135                 0                         15   \n",
       "4          155      480      139                 0                         25   \n",
       "\n",
       "   Uric_acid  Creatinine_level  Kidney_test_outcome Drug_Diabetes Drug_kidney  \n",
       "0        5.5               1.2                    0       Precose   Enalapril  \n",
       "1        1.8               1.3                    0        Glyset   Enalapril  \n",
       "2        3.4               0.9                    0       Precose   Enalapril  \n",
       "3        4.5               0.4                    0       Precose    Ramipril  \n",
       "4        6.7               1.5                    1       Precose   Captopril  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv(\"proj-data.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Blood_Pressure</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>Blood_urea_nitrogen_level</th>\n",
       "      <th>Uric_acid</th>\n",
       "      <th>Creatinine_level</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>50.00000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>36.30000</td>\n",
       "      <td>32.064000</td>\n",
       "      <td>68.940000</td>\n",
       "      <td>66.360000</td>\n",
       "      <td>122.800000</td>\n",
       "      <td>23.180000</td>\n",
       "      <td>5.232000</td>\n",
       "      <td>1.34600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>13.21139</td>\n",
       "      <td>8.178787</td>\n",
       "      <td>17.779155</td>\n",
       "      <td>101.080845</td>\n",
       "      <td>36.027767</td>\n",
       "      <td>8.160857</td>\n",
       "      <td>2.179257</td>\n",
       "      <td>0.73435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>21.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.700000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>25.00000</td>\n",
       "      <td>26.775000</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>91.750000</td>\n",
       "      <td>16.500000</td>\n",
       "      <td>3.250000</td>\n",
       "      <td>0.90000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>33.00000</td>\n",
       "      <td>32.200000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>114.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>5.650000</td>\n",
       "      <td>1.30000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>45.00000</td>\n",
       "      <td>37.275000</td>\n",
       "      <td>77.500000</td>\n",
       "      <td>101.250000</td>\n",
       "      <td>145.750000</td>\n",
       "      <td>29.750000</td>\n",
       "      <td>6.700000</td>\n",
       "      <td>1.57500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>67.00000</td>\n",
       "      <td>49.100000</td>\n",
       "      <td>110.000000</td>\n",
       "      <td>480.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>8.900000</td>\n",
       "      <td>3.60000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Age        BMI  Blood_Pressure     Insulin     Glucose  \\\n",
       "count  50.00000  50.000000       50.000000   50.000000   50.000000   \n",
       "mean   36.30000  32.064000       68.940000   66.360000  122.800000   \n",
       "std    13.21139   8.178787       17.779155  101.080845   36.027767   \n",
       "min    21.00000   0.000000        0.000000    0.000000   71.000000   \n",
       "25%    25.00000  26.775000       64.000000    0.000000   91.750000   \n",
       "50%    33.00000  32.200000       70.000000    0.000000  114.000000   \n",
       "75%    45.00000  37.275000       77.500000  101.250000  145.750000   \n",
       "max    67.00000  49.100000      110.000000  480.000000  195.000000   \n",
       "\n",
       "       Blood_urea_nitrogen_level  Uric_acid  Creatinine_level  \n",
       "count                  50.000000  50.000000          50.00000  \n",
       "mean                   23.180000   5.232000           1.34600  \n",
       "std                     8.160857   2.179257           0.73435  \n",
       "min                     5.000000   1.700000           0.00000  \n",
       "25%                    16.500000   3.250000           0.90000  \n",
       "50%                    24.000000   5.650000           1.30000  \n",
       "75%                    29.750000   6.700000           1.57500  \n",
       "max                    38.000000   8.900000           3.60000  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "msm= df[['Age','BMI','Blood_Pressure','Insulin','Glucose','Blood_urea_nitrogen_level','Uric_acid','Creatinine_level']]\n",
    "msm.describe()#it presents the mean,standard deviation,min value,max value of the dataset of patient parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#BMI\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import pylab as pl\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "\n",
    "BMI= df[['BMI',]]\n",
    "BMI.hist(figsize=(8,8))\n",
    "plt.title(\"Histogram of BMI\")\n",
    "plt.show()\n",
    "plt.savefig('histoBMI.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#blood pressure\n",
    "bp= df[['Blood_Pressure']]\n",
    "bp.hist(figsize=(8,8))\n",
    "plt.title(\"Histogram of Blood_Pressure\")\n",
    "plt.show()\n",
    "plt.savefig('HistoBP.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Insulin\n",
    "I= df[['Insulin']]\n",
    "I.hist(figsize=(8,8))\n",
    "plt.title(\"Histogram of Insulin\")\n",
    "plt.show()\n",
    "plt.savefig('HistoInsulin.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Glucose\n",
    "G= df[['Glucose']]\n",
    "G.hist(figsize=(8,8))\n",
    "plt.title(\"Histogram of Glucose\")\n",
    "plt.show()\n",
    "plt.savefig('HistoGlucose.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Blood Urea Nitrogen Level\n",
    "bun= df[['Blood_urea_nitrogen_level']]\n",
    "bun.hist(figsize=(8,8))\n",
    "plt.title(\"Histogram of Blood_urea_nitrogen_level\")\n",
    "plt.show()\n",
    "plt.savefig('HistoBloodUreaNitroLv.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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4p44xfmPeQwHAsvE8XABosJqTppIkY4yLk1w8t0kAYIlZ4QJAA8EFgAaCCwANBBcAGgguADQQXABoILgA0EBwAaCB4AJAA8EFgAaCCwANBBcAGgguADQQXABoILgA0EBwAaCB4AJAA8EFgAaCCwANBBcAGgguADQQXABoILgA0EBwAaCB4AJAA8EFgAaCCwANBBcAGgguADQQXABoILgA0EBwAaCB4AJAA8EFgAaCCwANBBcAGgguADQQXABoILgA0EBwAaDB5kUPAMDabLntcTn2mMV8G19Z2bKQ2z2cCS7AYerYYzZn67mXLHqM/brsojMWPcK64pAyADQQXABoILgA0EBwAaCB4AJAA8EFgAaCCwANBBcAGgguADQQXABoILgA0EBwAaCB4AJAA8EFgAaCCwANBBcAGgguADQQXABoILgA0EBwAaCB4AJAA8EFgAaCCwANBBcAGgguADQQXABoILgA0EBwAaCB4AJAA8EFgAaCCwANBBcAGgguADQQXABoILgA0EBwAaCB4AJAA8EFgAaCCwANBBcAGgguADTYvJoLVdXzkzxu+uq7xxi/Ob+RAGD5HHSFW1WnJXlEkvsmOTnJv6iqM+c9GAAsk9WscL+Z5NwxxvYkqaovJrnLXKcCgCVz0OCOMT6/+/9VdY9MDi2fstobOPHEE9Y2GaxzKytbDsttszrbb9pxSPeD+/Dwsb/7atb34aoew02SqrpXkncnedYY48urvd61116XnTt3rWW2W/AJzHpyzTXb5rLdlZUth7xtXyuH7uijNmXruZcseowDuuyiMxY9wlLY19fbWr4ON27ccMBF5qrOUq6qU5JcmeTZY4w33aoJAICDr3Cr6s5J3pnkrDHG++c/EgAsn9UcUj4vybFJXlFVu9/2ujHG6+Y2FQAsmdWcNPWMJM9omAUAlpbfNAUADQQXABoILgA0EFwAaCC4ANBAcAGggeACQAPBBYAGggsADQQXABoILgA0EFwAaCC4ANBAcAGggeACQAPBBYAGggsADQQXABoILgA0EFwAaCC4ANBAcAGggeACQAPBBYAGggsADQQXABoILgA0EFwAaCC4ANBAcAGggeACQAPBBYAGggsADQQXABoILgA0EFwAaCC4ANBAcAGggeACQAPBBYAGmxc9AByOtt+0IysrW+a2/XluG1gMwYU1OPqoTdl67iWLHmO/LrvojEWPAOzFIWUAaCC4ANBAcAGggeACQAPBBYAGggsADQQXABoILgA0EFwAaCC4ANBAcAGggeACQAPBBYAGggsADQQXABoILgA0EFwAaCC4ANBAcAGggeACQAPBBYAGggsADQQXABoILgA0EFwAaCC4ANBAcAGggeACQAPBBYAGggsADQQXABoILgA0EFwAaCC4ANBAcAGggeACQAPBBYAGggsADQQXABoILgA0EFwAaLB5NReqql9M8twkRyX5vTHGa+Y6FQAsmYOucKvqx5O8KMmDk5yc5KlV9VPzHgwAlslqVrinJXn/GOPvkqSq3pbksUkuOMj1NiXJxo0bDmnAvd3p9sfNdHvzsN5nXO/zJWachfU+X7L+Z1zv8yXrf8b1Pl+y/07d2n7tcflN+3r/hl27dh1wA1X1nCTHjzGeO339KUkeOMZ46kFu+8FJPnSrpgWAw99Dknx47zeuZoW7McmeVd6QZOcqrveJ6Y1+M8mOVVweAA5nm5L8aCb9u4XVBPfrmYRztx9J8n9Xcb0bs4/CA8AS+8r+3rGa4L4vyQuqaiXJ9Ul+IcnBDicDAHs46FnKY4xvJDk/yQeSXJXkj8YYH5/3YACwTA560hQAcOj8pikAaCC4ANBAcAGggeACQAPBBYAGq/prQcugqp6f5HHTV989xvjNRc4zD1V1QSa/53pXktePMV6x4JHmoqpenuSOY4xzFj3LrFXVB5LcKclN0zc9bYzxlwscaaaqamuS5yc5PskVY4xnLHikmZn+2tun7/Gmn0zyB2OMp+/nKoelqnpCkudMX718jHHeIueZtap6dpInZvLLm946xnjRrLZ9RDwtqKpOS/LCJA/LJEbvTfKfxxjvWOhgM1RVD83krzqdmsmfUfxCkn8zxhiLnGvWqurhSf44kx+azlnwODNVVRsy+c1udx1j3LzoeWatqu6Wye9X/+kk/y/J+5O8eIxx+UIHm4OquleSdyZ50BjjW4ueZ1aq6jaZfI7eM8nfJ/lIkvPHGO9b6GAzMm3FKzL5WwDXJ3lHkovHGG+fxfaPlEPK30xy7hhj+xjjpiRfTHKXBc80U2OMv0jysOk36jtlcvTi+sVONVtVdYdMfqh48aJnmZOa/ntFVV1dVUu1MkpyZiYrhq9Pvw7PSrI0q/e9vDbJby1TbKc2ZdKN4zP5wf6oJDcsdKLZum+SPxtjfHeMsSOTxdnPzWrjR0RwxxifH2P8zySpqntkcmj5PYudavbGGDdV1QszWd1emeQbCx5p1n4/k9969u1FDzInt8/kfjszycOT/EpV/exiR5qpuyfZVFWXVtVVSX41S3hfTldJx40x/mTRs8zaGGNbkucl+VImK93/k+Sji5xpxj6d5JFVdYeqOjbJ6Zn8/YCZOCKCu9v0MM+fJ3nWGOPLi55nHsYYz0+ykuTOSX55wePMzPTxsa+NMa5c9CzzMsb42Bjjl8YY35mujF6f5NGLnmuGNmfy97WfnORBmRxaPnuhE83H0zI5LLl0quqkJE9KctckP5bJX4Jbmsdwp99fLk7ywUxWtx9Osn1W2z9igltVp2Syenj2GONNi55n1qrqn1bVyUkyxvhekrcnOWmxU83UWUkeMV0ZXZDk9Kp65YJnmqmqevD0MerdNuQHJ08tg79J8r4xxjVjjBsyeXzsgQueaaaq6ugkD01y6aJnmZNHJrlyjPG3Y4wbM4nTqQudaIaqakuSPx1jnDTGODWTE6f2+9d/bq0j4izlqrpzJicwnDXGeP+i55mTuyV5YVU9OJMTw85I8obFjjQ7Y4x/OLRaVeckOXWM8RuLm2gubpfkgqr6V5k8NnZ2kl9Z7Egz9a4kb6qq2yXZluRRmXxdLpOTkvz1GGOpzp/Yw9VJLqyq45N8L8nW7Odvvx6mfjLJm6vq/pk8Tv3k6ctMHCkr3POSHJvkFVV11fRlmb6RZYzxniTvTvJXST6V5KNjjD9e7FTcGmOMd+WH78M3jDE+ttipZmf69KYLMzlM94UkX03yxoUONXt3y+SxzaU0xrgiyVsy+fz8TCY/GL5koUPN0BjjM0n+NJN9+3iS3xtjfGRW2z8inhYEAIt2pKxwAWChBBcAGgguADQQXABoILgA0EBwAaCB4AJAg/8Py5nlPRfVmXcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Uric acid\n",
    "ua= df[['Uric_acid']]\n",
    "ua.hist(figsize=(8,8))\n",
    "plt.title(\"Histogram of Uric_acid\")\n",
    "plt.show()\n",
    "plt.savefig('HistoUricAcid.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Creatinine level\n",
    "c= df[['Creatinine_level']]\n",
    "c.hist(figsize=(8,8))\n",
    "plt.title(\"Histogram of Creatinine level\")\n",
    "plt.show()\n",
    "plt.savefig('HistoCreatinineLv.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### BAR GRAPH"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table=pd.read_csv('proj-data.csv')\n",
    "table.head()\n",
    "\n",
    "plt.bar(x=np.arange(1,51),height=table['Blood_Sugar'])\n",
    "\n",
    "plt.title(\"Bar graph of Patient age wise Blood Sugar levels\")\n",
    "\n",
    "\"\"\"\n",
    "Give the x axis some labels across the tick marks.\n",
    "Argument one is the position for each label\n",
    "Argument two is the label values and the final one is to rotate our labels\n",
    "\"\"\"\n",
    "plt.xticks(np.arange(1,51), table['Age'], rotation=90)\n",
    "\n",
    "plt.xlabel(\"Age\")\n",
    "plt.ylabel(\"Blood_Sugar\")\n",
    "\n",
    "plt.show()\n",
    "plt.savefig('BarBloodSugar-age.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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d+ftxYFyZaRARkWL0pLKIiABqEEREJFGDICIigBoEERFJ1CCIiAigBkFERJLSX10hIrKgGTlqOMOGzju8traOZOb7HzDtnRmFlqm8EqOzZRpNDYKISIMNG7pQ1Zfe1XsTUXeWaTR1GYmICKAGQUREEjUIIiICqEEQEZFEDYKIiABqEEREJFGDICIigBoEERFJ1CCIiAhQ50llM5sGzK0yqQmY6+6jSkuViEgf6M4rJ3ozTWW/0qLeqys+0fC1iYj0Y/3h9RF5vZmmmg2Cu79c+dvMdgDWBX4KjHf3S0pIi4iI9KFOX25nZkcAnwdWAE4FjjWz1dz9xALLHgvslH5e7+6HVZm+J/BWCjrX3c/sQvpFRKRBigwqfwPYGnjX3acCnwJ27mwhM9sS+AKwHnF1McbMvpqbbSzwDXdfN/1TYyAi0keKNAiz3f39yg93fxuYXWC514GD3X2Wu88GngFWzM0zFjjSzJ4wszPMbFjRhIuISGMV+R7CK2b2ZWCumQ0FDgFe7mQZ3P3pyt9mtjrRdbRJJmwE8ChwKPA8MAk4BjiqaOJHjx5RdNa6KiP3/T28P6ZJees8vD+mSXnrPDxrscUXYcjCLR2WmTV7TqfLdbauIusuGlcjFGkQDgIuBNYG3gXuo0CXUYWZfRy4HjjU3Z+rhLv7dKIrqjLfBGAiXWgQpk6dTnt7tTtji2ttHcmUKfOP1/e38P6YJuWt8/D+mCblrXZ4rYPskIVbqt7p01X5dRVZd5G4impubqp7Il2kQXjX3bcws0WAFncvnAoz2wSYDPzA3S/NTVsR2NLdJ6agJop1RYmISAmKjCG8aGa/A9bvYmOwAnAVsHO+MUhmAL8ws1XMrAk4ELiyaPwiItJYRa4QVgG+CUwws8WA84AL3H1KJ8sdAgwDTjGzStg5wHbAj939ITPbD7gWGALcDUzoehZERKQROm0Q3P2/xIH8HDNbB/g18L/Ewb7ect8Hvl9l0jmZeSYTXUoiItLHilwhYGbrA7sDOwIPpv9FRKSL8u8m6ut3JWUVeVL5CWBR4LfAGHd/rfRUiYgMUvl3E/X1u5KyilwhHOzut5SeEhER6VNFGoT7zOwMYE2iq+hnRCMxvdSUiYhIrypy2+mvgP8CHwFmAqOA35SZKBER6X1FGoT13P0o4p1G7wG7EC+rExGRQaRIg5B/YUcL0F5CWkREpA8VaRDuMrOTgOFmthVwBXBHqakSEZFeV6RBOByYTowj/AR4gngKWUREBpEiTyrPBk5M/4AP32D6dM2FRERkwClyhVDNvQ1NhYiI9LnuNghNDU2FiIj0ue42CD37Ko2IiPQ73W0QRERkkKk5qGxm06h+JdAELFJaikREpE/Uu8voE72WChER6XM1GwR3f7k3EyIiIn1LYwgiIgKoQRARkaRwg2Bmi5eZEBER6VtFPqFpwJXAYmY2DrgV+Kq7P1tg2WOBndLP6939sNz0dYHziG8s3AXs7+4fdC0LIiLSCEWuEP4P+D7wb3f/V/rd6QdyzGxL4AvAesT3E8aY2Vdzs10EHOTubcTtrPt0Ie0iItJARRqE0dlvKrv7WcQZfWdeJz61OSu9IO8ZYMXKRDNbCRju7veloEnEJzpFRKQPFPmm8lwzG0Z6SM3MliE+klOXu3/4NlQzW53oOtokM8tyRKNR8TqwfIH0fGj06BFdmb2m1taRAyK8P6ZJees8vD+maUHP26zZcxiycEuH8Fmz898Ca7zulG9X4+qJIg3CWcBNwNJm9jPgm8BJRVeQXpV9PXCouz+XmdRMxyehm+jil9imTp1Oe3vPXqvU2jqSKVOm9fvw/pgm5a3z8P6YJuUtfm978NUd5rl2wvj5lmu0KVOmVT2Q1wrvLK6uam5uqnsiXeR7CBPN7AVga2BhYJ9sF1I9ZrYJMBn4gbtfmpv8KrBs5vcywGtF4hURkcYretvpvcDPgZ8CD5vZkp0tYGYrAFcBO1dpDCpPQs9MjQbArsCNBdMjIiINVuS20/2BU4EhKaiJ6OrpbBzhEGAYcErcuQrAOcB2wI/d/SFgF+BcMxsFPAKc3tUMiIhIYxQZQzgU2MTdH+lKxO7+feJ21bxzMvM8DozrSrwiIlKOIg3CG11tDEREyjBy1HCGDY3DVmUQdub7HzDtnRl9maxBo0iDcLOZHQBcA3xY6u7+ZmmpEhGpYtjQhareHdT1+22kmiINwhHAUODMTFiRMQQRERlAitx2Orw3EiIiIn2r3ic0v+XuF5nZD6tNd/dTykuWiIj0tnpXCKun/z/ZGwkREZG+Ve8Tmsem//foveSIiEhfKfJg2kbEE8pLEg+lAeDua5eYLhER6WVF7jL6NfFq6kfo+DI6EREZRIo0CB9oAFlEZPAr8nK7p8xMA8siIoNckSuEjxFvOH2Zjk8qawxBRGQQKdIgHFV6KkREpM/V7DIyszXSn9Nq/BMRkUGk3hXCycA2xBfP8uYSXUkiIjJI1HswbZv056fd/dXstPSdZBERGUTqvcuo8pnM681sM+Z9KW0IcdWwRo1FRURkAKrXZXQJ8Pn099RM+AfAH0tL0SCnD3z0D4NhO+Tz0N30D4aykMao12W0FYCZTXT3PXsvSYObPvDRPwyG7ZDPQ3fTPxjKQhqjyPcQ9kzdR4sS3UYtwGrufkuRFZjZKOAeYBt3fyk37VhgT+CtFHSuu5+JiIj0uiIvtzseODL9/IAYQ/gbBV6LbWYbAucCbTVmGQt8w93vLZRaEREpTZFXV3wbWJEYN1gd2B14umD8+wAHAq/VmD4WONLMnjCzM8xsWMF4RUSkwYo0CP9299eBZ4B13P1CCn40x933dve/VJtmZiOAR4FDgfWBxYFjCqVaREQarsirK2ab2aqAA582s5uAHp/Ju/t0YOvKbzObAEykC6/KGD16RE+TAcy7s6KR4bNmz2HIwi0dwmfNnlMoDbXiLyutjQqvl+dq4ZWw3kxTtfXWWrZR6elJXPn0dpaHfHzdSWu1eXq7TvbmdutNjUxbGfkp0iD8DPgNsB1wItGFdF1PV2xmKwJbuvvEFNQEzO5KHFOnTqe9vWefaGhtHcmUKfPfT9HT8NbWkVXv3Kilsmyt+MtMa6PC6+W5Wnh+2d5IU2W9tXamzrZDV8N7GletMq2Vh+6UaXfLoivr6E6aytxufdU41NtuXU1TrW1ST3NzU90T6SJ3GV1HagDMbF1iHOGJLqdkfjOAX5jZ7cBLxFjDlQ2IV0REuqHTMQQzG2FmZ5rZbURX0XeIW1C7xcxuMLOx7j4F2A+4luiOagImdDdeERHpmSJdRqcDrwMfAWYCo4gupJ2LrsTdV878vXXm78lUf3meiIj0siJ3Ga3n7kcBs939PWAXYN1ykyUiUtzIUcM/7INvbR1Ja+tIRo4a3sepGniKXCHkb41pAdpLSIuISLfo9RuNUeQK4S4zOwkYbmZbAVcAt5ebLBER6W1FGoTDgenAf4GfEHcYHVpmokREpPcV6TI6wd1/RDyDICIig1SRK4RtOp9FREQGuiJXCP8ws5uBu4muIwDc/ZTSUtVHsh8KgZ59dGQwq/VBlZ7EU4mrXnkP5u1TK29lraPIdiv7wzmDeXsOVEUahDfT/6tkwnr2voh+SncqFFOrnBoVT63yHszbp1Fl2sh1lF3eg3l7DlRFXl2xR+VvMxvq7u+XmyQREekLNRsEMxtCfNzmKnevvGNosplNAfZx98Zez4qISJ+qN6h8AvGair9mwvYDlgCOKzFNIiLSB+o1CNsAO7v7vysB7v4vYDfgq2UnTEREele9MYRZ7j7fcL+7v2NmA3ocoTfu6Ogrg+HOja5un4GU5/54N9FgprLomnoNwhwzG+nuHQb9zWwksHC5ySpXb9zR0VcGw50b/e1umEbqj3cTDWYqi66p12V0CXCemX347YP093noldUiIoNOvSuE04BzgDfM7Gmi8VgT+D0x4CwiIoNIzQbB3duBfc3sJ8AY4pXX97v7672VOBER6T1FHkx7GXi5F9IiIiJ9qMjL7UREZAGgBkFERIBiL7frNjMbBdwDbOPuL+WmrUvcsTQKuAvYX6/DEBHpO6VdIZjZhsQrs9tqzHIRcJC7twFNwD5lpUVERDpXZpfRPsCBwGv5CWa2EjDc3e9LQZOAHUtMi4iIdKK0LiN33xvAzKpNXg7I3r76OrB8V9cxevSIbqWtqyqPvNf63Vl4V9dRL57urruneehJ3vpKd/JSZDv0NN5G6MvtVHZdGkx1MK+ReSujPEodQ6ijmY4f2WkinnPokqlTp9Pe3vVv9XS1IKdMmfcShNbWkR1+1wrv7jpqxV903bXW25081AofKDvmlCnTqqa1VnhlGvS8jGpNa1TZdSdvjdKoulRvG/RV3srWyLzVqm/1NDc31T2R7qu7jF4Fls38XoYqXUsiItJ7+qRBSA+7zTSzTVLQrsCNfZEWEREJvdogmNkNZjY2/dwFONXMngVGAKf3ZlpERKSj0scQ3H3lzN9bZ/5+HBhX9vpFRKSYvhpUHnDyH9rorx9g6Y7BnLey1foAy4JYfgPpQ0VSnRqEgvIf2uivH2DpjsGct7INpI/zlE1lMfDpXUYiIgKoQRARkUQNgoiIAGoQREQk0aDyAkJ3gIhIZ9QgLCB0B4iIdEZdRiIiAqhBEBGRRA2CiIgAahBERCTRoPIAVeuuIRGR7lKDMEDVumtIRKS71GUkIiKAGgQREUnUIIiICKAxhH5Pg8ci0lvUIPRzGjwWkd6iLiMREQFKvkIws52Bo4GFgdPc/czc9GOBPYG3UtC5+XlERKR3lNYgmNlHgZ8AY4D3gXvM7HZ3/1tmtrHAN9z93rLSISIixZTZZbQl8Gd3f9Pd3wX+COyQm2cscKSZPWFmZ5jZsBLTIyIidZTZZbQc8Hrm9+vAuMoPMxsBPAocCjwPTAKOAY4quoLRo0c0Ip2dam0dWSi81nxFLLb4IgxZuKVDPLNmz+l2fEXVy9us2XP6JE2NUnS71ZrWneUrstuzskwjy64naWv0ujtbZ0/nL7rcQNDIvJVRHmU2CM3A3MzvJqC98sPdpwNbV36b2QRgIl1oEKZOnU57+9zOZ8zpakFOmTKt6jJTpsz7vExr68j5fnfFkIVb+uRuonp5a20dOaDvcOosb7WWgfm3Z0XR7Vz29uxO3hq57opseXRWpl2Zv6/yVrZG5q1a/exMc3NT3RPpMruMXgWWzfxeBnit8sPMVjSzPTPTm4DZJaZHRETqKPMK4VbgODNrBd4Ftgf2zUyfAfzCzG4HXgIOBK4sMT0iIlJHaVcI7v4vovvnduAx4GJ3f8DMbjCzse4+BdgPuBZw4gphQlnpERGR+kp9DsHdLwYuzoVtnfl7MjC5zDSIiEgxenVFD2XfNVQZFNK7hgauWttz2jsz+jJZIr1CDUIP6V1Dg0ut7dn1+zlEBh69y0hERAA1CCIikqhBEBERQA2CiIgkGlQWKUB3k8mCQA2CSAG6m0wWBOoyEhERQA2CiIgkahBERATQGIKIdFF2gB1ikF0D7IODGgQR6RINsA9e6jISERFADYKIiCRqEEREBFCDICIiiRoEEREB1CCIiEiiBkFERICSn0Mws52Bo4GFgdPc/czc9HWB84BRwF3A/u6uJ1xERPpAaVcIZvZR4CfApsC6wL5mtlZutouAg9y9DWgC9ikrPSIiUl+ZVwhbAn929zcBzOyPwA7ACen3SsBwd78vzT8JOB44u0DcLQDNzU3dTtzSSwwvHF5ZT35arfDurKOvwpW3/pHWroYPpLzl99Ps74Get66GNzJv3Tn+ZZZpqTa9ae7cuV2OtAgz+xGwqLsfnX7vDYxz933T742AX7r7pun3asAN6WqhM5sCfykl4SIig9+ngbvzgWVeITQD2damCWjvwvR6HiQy9DowpwdpFBFZkLQAyxLH0PmU2SC8Shy0K5YBXstNX7bO9Hrep0rrJiIinXqh1oQybzu9FdjCzFrNbBFge+BPlYnu/jIw08w2SUG7AjeWmB4REamjtAbB3f8FHAXcDjwGXOzuD5jZDWY2Ns22C3CqmT0LjABOLys9IiJSX2mDyiIiMrDoSWUREQHUIIiISKIGQUREADUIIiKSqEEQERFADYKIiCSlvv56oDCzLepeggsAABFWSURBVIG3iecljgPWJp6EnuDuDXk1hpltDsxw93vN7GBgM+Lx8Z+7+6zcvNcAP3D3fzRgvdXy9gGwI7H9jwa2BmYDVwMzgeWBq9z9L5l4TgD+BVyZ4jsCGAc8nPLxlbRcO/HE+X/c/Yi07F6ZdTzu7j9L4Vtkwq909/t7mt/eYGbj3f3q9Hc2b1e6+x/6MF3V6tjDwDvAR5l/mx7n7scVjPsAdz/bzIbSsc5cCTwKvEnHOvYmsJe7zylSRp3VeTPbCrjf3d82s92IuvcoUYfzdXJ1YLy7P1skbzXWN8HdD64xrUvb38wWAg4EVqTgNujOMo2wwDyHYGZPAPu5+7258JOATYDFiAPZ/wMuJTbGTOBX1eJz97sycVxC/be07g98jPguxIvEQXMSsB2wJnAO8EfgVOCzwGopLWcBp7v77Dr5uiTFfzywAlEpL8rk7dvAlFzefgdckqJYHJhAvEvq0vT/r4GDgHPd/acprjeJJ8m/R+z0I4DLiLfXfhQ4jHi3VBPxGpIzgXOB6cQrTP4vTZsEnJHSsz9wfgr/NvA8sFRafhbxiP3jwJ+rZL0F2CpNz5bdg8AhlbfsdsbMmonXru+U8lFp0G5MedgZmJFbx7JAWyqLbN4OB/5OfOMja+30/xPV0lClLu1PHNzOJMrvVGAD4nUvZwL/rRJNrTo2gSiratv0EXdfP7fuXYHd6uT5eDrWmUnAR6hdx6bmyug7wCLA13N5W5Uadd7MTgPWS8scCGxINAJHpTg2oWOdnEyc9ByfjcvMRgGHpLydA1yY8jaNeFnmu5ny3A64BjDiOJCtG2sAq6T1Z/O2byrrIXSsw4sDLwNPVtkGfwf2Zn6HpbhurbJMh+3WSAtSg/AqcdZyJ3C8u/8nhT8FfBJYkth4S7p7u5ndCnwuzZ99z+y6xEv5HsuEjSUq4GJV5oeo8KOAocA/geXcfbaZTSLOrP8CjAbuJw6itxJndm8RFfYsYiebRMcXAlbWPZOozIcQB5JH3X3flLdZwOdzeXuE6C5sAtZz9/ZUFo8DQ919DTNrTen4rbufZmYz3H14mu9RYH13n2tmDsxx9w7fukjrGJ7Wv6G7z8yEL0rslFu4+9QUfgbx5PrOxGvSHyd2vnPT/HfnynUNYoe5P1d2xwErE41OVuUVKX/Nhe+R4j2ajg3arsQr3B8BhuXWcQtwL3Emms3bbUSduSOX1nWoXjdq1aXZxEHqk8S2f5E4uF5DHIjuongdexzA3dfJblNgfFpX9iVnY4l6VK1cs3nO1pmniLP0Tahdx7Jl9KeU1pVzebsVeJa4Cv2wzrv7y2b2NLB2utp4BNjI3d83syeBhdx9zVydfJRodO6n4/7zf0SDvQixbc8mGsrLiH3kMOJKA+JE5x8pD0fRsW5cCdyUtk82b78iGtN8HZ4EnOTuJ1bZr97JlHd2m24APODum1dZ5lF3X48SLEhdRv8mKu2RwFNmdiXzzoiHuvtUMzukUtGJM8ZXiC+9XVOJxMwOIs4Ef0dU5iZih9kPOC0/f1rmKeKAMII4uI0izpzGEBXtK8Cr7r5xmv8NYAV339bMVifOYG8BWonKvF9u3QB7u/udZnYDcJ2ZTUjhzVXyNpp4S+yTxJnZcyl8KNGw4e5TzGxr4K9mNgWYY2Yfd/eniR13BeLA8wHzN1IQZ0XvEWe1o4hGK7uOqcRLCis+A7zu7jea2U3AXe6+acrPf6pshyfcfW0za8mV3YxUrpvRcQf7NLAScQadDV8PuMLdsy9LfB6428xmuvuqVdYxnWiQXs7lbadUJvm0Lky8wqVoXYLYntPMbC133zXNvxZxwOpKHWsh7efZbUo0WptUWXczsCfRAGfzPJu40nmGjnVmYWB2lTo2kqhjL+TKaDngjSp5+3/A0u6+RbbOm9kwYAlgaWJfeSXl733ipGLxFG+2Ti4EvOfuu+X2n5WBpdx9YzP7l7ufmpbdxsyeB74B/Mjd7zCzHwDLuvsadPR8ulregKgn2bxtDrxWpQ4fB3wLOLHKfvU8sZ/k68aTwLb57ZaWKe0sfoEaVHb3Ge5+DHHp+yTR8q8ETDezFnc/D8DMNibO0H8GbJyL4wyiq2JPYCV3vwOY5u5/Js4KOsyf/JzY8I8TZyE3m9kviB3rWmB9YDEzWznNv3D6h7s/5+6Hpe9ErEacuXZYN3HZ7JU8Al8jzoCeB9aokreRRL/yBsQlO2a2B3FZvETq26+8j+qLwE9Tem6x+NDRLOD+1KguBbSa2S1m9jszu8DMbiZ24OHEQefstI6vpTwvR1yKn5HCNyfOPK9P+V+WaPgg6uirVcq13czaqpTdIcRZ7t3uvkfm32pEN1CHcOLANl93n8X3OubUWMcjaVvk8/YIcFI+ranLYs8q4VXrUsrvkDTbo2ZWORtcg9imXaljI4Elq2zTzxFdIPl1TyMOvPk8/4E4O87XmeWIOpOvY8sQjVO+jNqIg3M+b8OJA2O+zq8HHAs8aGYnE43XnWZ2KnGC1FqlThpx4peP6yngYjPbERhhZmNSulYn9qEvA99J62kBppnZBlXK+jmi8cnnzYirAehYh38NrFxjv1qTKnWDuJp5JLfMVpllSrEgdRnVvMwys6OIA+QN7v6CmS1PnG19FZjo7q9ZfOBnbeKAcpmZDSEajBWBtdz942Y2Pv2+wd1fyMT/E+B/iUq2IdEPOp7ow9ycOOgdSOzUTxKXuRcAP/WOg2gPufuk/LqJyn8W8B2fN9i1DHAd0S2xYiYPGwIvZvojF3X3d83sfOJAshdxFn9xJQ9mNjKl72/E2ddqzBuQ/hkxGL04cXBoI87OzyF20hWBG939OTP7BPAJ4qzqTmJgewmiS+XtlLZ7gE8RXV8PArel+J/LlquZfYHoapiRK7txwMnAMHc/ObedlwV2yYZbfNf7QqJb6HXi7GtZ4qzvTOKkIb99xqXyvpg4cG9OnF3ekaZXqwNnEmex+fB9iYNIdnt+LeV5OtEnvxnR1dFKXIW+XyWeWnXsuDR/rW16Sm7d/0OMgVTL8zHufmGVOjMamJ6vY8QYxHu5MnqKOBjm87Y80ZjWytt5xP64F9G9MoWo36sQZ/4ziJOWN4gut5WYf999Fdgm5e1gYvzqPWLs5Vvu/ieLmzAuI+re74h6WK1unJ9+Z+v2V1L53cr8dfj+VI7VtsFk4N38cYboSpzh7q9kwh8grnJOowQLUoOwjrs/ngubQFSescSl8I7EgORFZvY/xM7wBrFBVwSuIHay/wD3ATcQFfKbRAUdT1SGD+NJ63nP3RcxswOZN5DaTPQ3PkDsJNczbwB2W2AjohslO4j2JeLS/ubsut19LzP7LnCRu7+VydsrRKV8P5eHI4mdMVse2xH9naPTPPk8/DuVUWVgbxxwVfp7BeKAnl3HIcQZ6I25cn0kre985h9Uvpo4ODyY8j6OGLBcNL99UppeIAb9s2X3FPAZd/9Ndjt7lTtGsuFmtiLRoDUTd8fslcLbmHewOybl/x9Ew9ZSJc9z07bJ5vnnxGDqBVXy8BywQWr49ya2+93u/lszW595je/BxOD6ejXKolYdO5wYi7qlRvmdxvx16Syi8cqW6zeJs37PFOF2xFXJWmkd2bL4HnGVOI3595+7U9zZvN1OnLwUyVulwToik7edgIML7Lt3u/uJKd5hKe9Luvu6Kf79gInMq5PnEWM3H9YNYl8fk8oon9Z/ESdDl6aGYijR0G1F9WPDq8SJU74u1Su/v7r7CZRgQWoQJlYJ3o7YyFe7+x7p0vFm4qznx0Qf6HjgaaJVnpkuww8gzuqyO/2TRF/q+tl43P3yTIV+jI4DqacwrwJm43oaeD/FlR1EO4m44yA//0Tm3RWRz9u1KS/ZPJwCfJc4gGUH0ZqJs8BJVfIwAxjh1Qf2FiYq/FNAa1rHU8Rg8zq5cv0RsbM15cricOBE4kwsuwOcBJzs7sfn4hlDlYNsnbKo3HV0d8Hwa4gD3dLMv7OeRJxZT8yVa608/xj4wN3Xy4VvQhxgVk952ZBoZH9EHAgqt+E2EScJVetqJ3XsaaDF40aBIuVXtfFK3Sjfp3qdOZ44KOfLoj2lOxs+KZXvVd3I26Mpb5XvtNfKW619N7/uyrYmbe/1c2X3uzT9itz8tY4Ztcq03rFhBnGV/JGC5TcEeNDd16EEC9IYwpvE5eLjRHfFnURf83vpb9z9uTTPr4j+z3aPD/mc7OlOAuKe43+6+3eJM6cTU59k5SDXIR4z2wxosri9MT+Q+gXg5SpxvUcaQ2DeIBopzn9Umf9NoourWt7+XCUPRxB3c3wjrf+CFMd/ie6TanloJw6O+TQtRFT2l4nnNirraGLeYGa2XBcnzqLzZbErMQD5GeJsbxt3P4sYJPxalXh2Ap7vQlm8QJzhFg2/kxhTWatKml4BvlKlXGvleUTKcz58PPCcu7+Rwsa7+9nA5UR/dCVNd1CnrnZSx0jbrmj5fRl4IR/u7ocALzF/nXkHOL9KWTQTB+t8+JtEl2F38vYmcTbdWd5q7bv5dd+V1j0j/Z0vu6kprqLHjFplWu/YAHHyV7T8oMSbgRaYBiFV6G8yf4U+F9jLzMal+Z4mWvelgbUtBsuOg+h2IvobL0vzZivDg8DqVeK5jDi4/5P5B1JXJfpZ83FdEbPMN4i2KnFmmp//OqrvrBNT3vJ5uDtNyw+iXQ7cUSMPUH1gbylgSJV1LEYM3OXjWp7oGqhWFpdV2QEmA6tViWfFtEyhsnD3LxF90EXDLyAOdtV21itSmormuZWoS9XKYrUU5yvMG4Q8nqgvRetqvTq2OLBoF8qvauOVDl7TmL/OTE51oVpZLFolfFPgopLzVmvfza97Ulr388T4Sz7+TxPjREXTWqtM6x0bmrpYfncTA/ylWGAaBAB3v41chXb344mBt2mZ+f5K3NFxpXd8UnkmsQNsWWXjfjnNk49nDHCGuy8PbEH0L0KciUwGPlclrv9J63qNGJC7N8X7O2DPGhVr6Sp5+zFwdJU8HOvuJ7j7m+6+E9EX+kadshhD7ASfrpKmLwP7VFnHPsTdE/m4ViW+jJcviytTWeR3gC8SDVU+nl8Rd4cVKos0z3zbv144tQ92XyD6iIvmeU1ifCQfvi9x9ptvZO8jumOK1tV6dWzfGmmqVX61Gq/LgFWr1JladWxvYLcadW/3kvNWa9+tte7P1Yj/WHffrQtprVWm9Y4N53Sj/EoZPwBg7ty5C+S/tra2vdva2m7u5rJbtLW1rZkLW6Gtre20suMqMn9P8tbH2+Qzud/W1tb2pZ6UXa2yKBre1TR1I8+rtLW1/bCtre2stra237S1tZ3Q1tY2rkhaG7DuWuU3uVH1u0Aa+qyudnXdReZv5LGhL/4tMIPKIiJS3wLVZSQiIrWpQRAREWDBepeRSMNYvJ/on8Bj6U4lkQFPVwgi3fM14tUCY82stHfLiPQmXSGIdM8BxNtyXyCe4N0fwMyOIJ4+nkY87PQVd185PWF6EvGeqhbi4y7fc/d3+iDtIlXpCkGkiyxeQ70R8XzEBcBuZjba4qteuxNvBB1DvGm04gjitd9jPF478Brxvh2RfkNXCCJddwBwncc7b6aa2YvEQ1LLAJe7+9vw4RtOt0jLbEM8Wft5M4N4uO/fvZ1wkXrUIIh0gZktSrx36X0zeykFjyJeOlj54FJF9inTFuD77n5jimcE8VplkX5DXUYiXbML8dKz5dx9ZXdfmXi/1Qjio0rbm9liad69mPd1q5uAg8xsSHpR27nEdwhE+g01CCJdcwBwSvYdM6mL6HTiHVTnAvea2UPEC8reS7OdSLx071HiQ0NNxHcARPoNvbpCpEHMbCywsbufnn7/kPgI+9f7NmUixWgMQaRx/g4cbvFZzLnEg2v79m2SRIrTFYKIiAAaQxARkUQNgoiIAGoQREQkUYMgIiKAGgQREUn+P0NKV2kfcgjcAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table=pd.read_csv('proj-data.csv')\n",
    "table.head()\n",
    "\n",
    "plt.bar(x=np.arange(1,51),height=table['Creatinine_level'])\n",
    "plt.title(\"Bar graph of Patient age wise Creatinine levels\")\n",
    "\"\"\"\n",
    "Give the x axis some labels across the tick marks.\n",
    "Argument one is the position for each label\n",
    "Argument two is the label values and the final one is to rotate our labels\n",
    "\"\"\"\n",
    "plt.xticks(np.arange(1,51), table['Age'], rotation=90)\n",
    "\n",
    "plt.xlabel(\"Age\")\n",
    "plt.ylabel(\"Creatinine Level\")\n",
    "\n",
    "plt.show()\n",
    "plt.savefig('BarCreatinineLv-age.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table=pd.read_csv('proj-data.csv')\n",
    "table.head()\n",
    "\n",
    "plt.bar(x=np.arange(1,51),height=table['Blood_Pressure'])\n",
    "plt.title(\"Bar graph of Patient age wise Blood Sugar levels\")\n",
    "\"\"\"\n",
    "Give the x axis some labels across the tick marks.\n",
    "Argument one is the position for each label\n",
    "Argument two is the label values and the final one is to rotate our labels\n",
    "\"\"\"\n",
    "plt.xticks(np.arange(1,51), table['Age'], rotation=90)\n",
    "\n",
    "plt.xlabel(\"Age\")\n",
    "plt.ylabel(\"Blood Pressuer\")\n",
    "\n",
    "plt.show()\n",
    "plt.savefig('BarBP-age.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LINEAR REGRESSION"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "FOR DIABETES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "dataset = pd.read_csv('proj-data.csv')\n",
    "X = dataset.iloc[:, [4]].values\n",
    "y = dataset.iloc[:, 7].values\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "x_train,x_test,y_train, y_test = train_test_split(X,y,test_size=0.2)\n",
    "\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "regr = LinearRegression()\n",
    "\n",
    "regr.fit(x_train, y_train)\n",
    "\n",
    "y_pred = regr.predict(x_test)\n",
    "\n",
    "plt.scatter(x_train, y_train, color = 'blue')\n",
    "plt.plot(x_test, y_pred, color = 'black', linewidth = 2)\n",
    "plt.title('Truth or Bluff (Linear Regression)')\n",
    "plt.xlabel('Blood Sugar')\n",
    "plt.ylabel('Diabetes Outcome')\n",
    "plt.xticks(())\n",
    "plt.yticks(())\n",
    "plt.show()\n",
    "plt.savefig('linear-regr-BloodSugar-DiabetOut.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100.79302935013986\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "\"\\nAround 0.001 is great, 1.0 - 2.0 means you should tune your model\\ngreater than that means if tuning doesn't work, try another model\\n\""
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import math\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "rmse = math.sqrt(mean_squared_error(x_test, y_pred))\n",
    "print(rmse)\n",
    "\"\"\"\n",
    "Around 0.001 is great, 1.0 - 2.0 means you should tune your model\n",
    "greater than that means if tuning doesn't work, try another model\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LOGISTIC REGRESSION"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "FOR DIABETES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "feature_col_names = ['Blood_Sugar', 'Insulin', 'Glucose']\n",
    "predicted_class_names = ['Diabetes_Outcome']\n",
    "\n",
    "X = data_frame[feature_col_names].values\n",
    "y = data_frame[predicted_class_names].values\n",
    "split_test_size = 0.30\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=split_test_size, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\allan\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "dataset = pd.read_csv('proj-data.csv')\n",
    "X = dataset.iloc[:, [7, 9]].values\n",
    "y = dataset.iloc[:, 10].values\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()\n",
    "X_train = sc.fit_transform(X_train)\n",
    "X_test = sc.transform(X_test)\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "classifier = LogisticRegression(random_state = 0)\n",
    "classifier.fit(X_train, y_train)\n",
    "\n",
    "y_pred = classifier.predict(X_test)\n",
    "\n",
    "from sklearn.metrics import confusion_matrix\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "from matplotlib.colors import ListedColormap\n",
    "X_set, y_set = X_train, y_train\n",
    "X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),\n",
    "                     np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))\n",
    "plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),\n",
    "             alpha = 0.75, cmap = ListedColormap(('red', 'green')))\n",
    "plt.xlim(X1.min(), X1.max())\n",
    "plt.ylim(X2.min(), X2.max())\n",
    "for i, j in enumerate(np.unique(y_set)):\n",
    "    plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],\n",
    "                c = ListedColormap(('blue', 'yellow'))(i), label = j)\n",
    "plt.title('Logistic Regression (Training set)')\n",
    "plt.xlabel('Attributes')\n",
    "plt.ylabel('Outcome')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "plt.savefig('LogisticDiabetesTraining-Attribute-Outcome.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.colors import ListedColormap\n",
    "X_set, y_set = X_test, y_test\n",
    "X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),\n",
    "                     np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))\n",
    "plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),\n",
    "             alpha = 0.75, cmap = ListedColormap(('blue', 'yellow')))\n",
    "plt.xlim(X1.min(), X1.max())\n",
    "plt.ylim(X2.min(), X2.max())\n",
    "for i, j in enumerate(np.unique(y_set)):\n",
    "    plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],c = ListedColormap(('black', 'white'))(i), label = j)\n",
    "plt.title('Logistic Regression (Test set)')\n",
    "plt.xlabel('Attributes')\n",
    "plt.ylabel('Outcome')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "plt.savefig('LogisticDiabetesTesting-Attribute-outcome.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5384615384615384"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "FOR KIDNEY"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\allan\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "dataset = pd.read_csv('proj-data.csv')\n",
    "X = dataset.iloc[:, [11, 13]].values\n",
    "y = dataset.iloc[:, 14].values\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()\n",
    "X_train = sc.fit_transform(X_train)\n",
    "X_test = sc.transform(X_test)\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "classifier = LogisticRegression(random_state = 0)\n",
    "classifier.fit(X_train, y_train)\n",
    "\n",
    "y_pred = classifier.predict(X_test)\n",
    "\n",
    "from sklearn.metrics import confusion_matrix\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "from matplotlib.colors import ListedColormap\n",
    "X_set, y_set = X_train, y_train\n",
    "X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),\n",
    "                     np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))\n",
    "plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),\n",
    "             alpha = 0.75, cmap = ListedColormap(('red', 'green')))\n",
    "plt.xlim(X1.min(), X1.max())\n",
    "plt.ylim(X2.min(), X2.max())\n",
    "for i, j in enumerate(np.unique(y_set)):\n",
    "    plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1], c = ListedColormap(('blue', 'yellow'))(i), label = j)\n",
    "plt.title('Logistic Regression (Training set)')\n",
    "plt.xlabel('Attributes')\n",
    "plt.ylabel('kidney function test')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "plt.savefig('LogisticKidneyTraining-Attribute-KidneyFuncTest.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.colors import ListedColormap\n",
    "X_set, y_set = X_test, y_test\n",
    "X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),\n",
    "                     np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))\n",
    "plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),\n",
    "             alpha = 0.75, cmap = ListedColormap(('blue', 'yellow')))\n",
    "plt.xlim(X1.min(), X1.max())\n",
    "plt.ylim(X2.min(), X2.max())\n",
    "for i, j in enumerate(np.unique(y_set)):\n",
    "    plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1], c = ListedColormap(('black', 'white'))(i), label = j)\n",
    "plt.title('Logistic Regression (Test set)')\n",
    "plt.xlabel('Attributes')\n",
    "plt.ylabel('Kidney Function test')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "plt.savefig('LogisticKidneyTest-Attribute-KidneyFuncTest.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9230769230769231"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DECISION TREE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "FOR DIABETES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='entropy', max_depth=4,\n",
       "                       max_features=None, max_leaf_nodes=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, presort=False,\n",
       "                       random_state=None, splitter='best')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_col_names = [ 'Blood_Sugar', 'Insulin', 'Glucose']\n",
    "predicted_class_names = ['Diabetes_Outcome']\n",
    "X = data_frame[feature_col_names].values     # predictor feature columns (8 X m)\n",
    "y = data_frame[predicted_class_names].values # predicted class (1=true, 0=false) column (1 X m)\n",
    "split_test_size = 0.30\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_trainset, X_testset, y_trainset, y_testset = train_test_split(X, y, test_size=0.2, random_state=3)\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "clf=DecisionTreeClassifier(criterion=\"entropy\", max_depth =4)\n",
    "clf.fit(X,y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\allan\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\externals\\six.py:31: DeprecationWarning: The module is deprecated in version 0.21 and will be removed in version 0.23 since we've dropped support for Python 2.7. Please rely on the official version of six (https://pypi.org/project/six/).\n",
      "  \"(https://pypi.org/project/six/).\", DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DecisionTrees's Accuracy:  1.0\n"
     ]
    }
   ],
   "source": [
    "from sklearn.externals.six import StringIO\n",
    "from IPython.display import Image\n",
    "from sklearn import tree\n",
    "import pydotplus\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "clf=DecisionTreeClassifier()\n",
    "clf.fit(X,y)\n",
    "y_pred=clf.predict(X_testset)\n",
    "\n",
    "from sklearn import metrics\n",
    "import matplotlib.pyplot as plt\n",
    "print(\"DecisionTrees's Accuracy: \", metrics.accuracy_score(y_testset, y_pred))\n",
    "dot_data=StringIO()\n",
    "tree.export_graphviz(clf,out_file=dot_data,feature_names=feature_col_names)\n",
    "graph=pydotplus.graph_from_dot_data(dot_data.getvalue())\n",
    "graph.write_png('DecisionTreeDIABETES.png')\n",
    "Image=(graph.create_png())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "FOR KIDNEY"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DecisionTrees's Accuracy:  1.0\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "feature_col_names = [ 'Blood_urea_nitrogen_level', 'Uric_acid', 'Creatinine_level']\n",
    "predicted_class_names = ['Kidney_test_outcome']\n",
    "\n",
    "X = data_frame[feature_col_names].values\n",
    "y = data_frame[predicted_class_names].values\n",
    "split_test_size = 0.30\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_trainset, X_testset, y_trainset, y_testset = train_test_split(X, y, test_size=0.2, random_state=3)\n",
    "\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "clf=DecisionTreeClassifier(criterion=\"entropy\", max_depth =4)\n",
    "clf.fit(X,y)\n",
    "\n",
    "from sklearn.externals.six import StringIO\n",
    "from IPython.display import Image\n",
    "from sklearn import tree\n",
    "import pydotplus\n",
    "\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "clf=DecisionTreeClassifier()\n",
    "clf.fit(X,y)\n",
    "y_pred=clf.predict(X_testset)\n",
    "\n",
    "from sklearn import metrics\n",
    "import matplotlib.pyplot as plt\n",
    "print(\"DecisionTrees's Accuracy: \", metrics.accuracy_score(y_testset, y_pred))\n",
    "\n",
    "dot_data=StringIO()\n",
    "tree.export_graphviz(clf,out_file=dot_data,feature_names=feature_col_names)\n",
    "graph=pydotplus.graph_from_dot_data(dot_data.getvalue())\n",
    "graph.write_png('DecisionTreeKIDNEY.png')\n",
    "Image=(graph.create_png())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>Serial_ID</th>\n",
       "      <th>Patient_Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Blood_group</th>\n",
       "      <th>Age</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Blood_Pressure</th>\n",
       "      <th>Blood_Sugar</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>Diabetes_Outcome</th>\n",
       "      <th>Blood_urea_nitrogen_level</th>\n",
       "      <th>Uric_acid</th>\n",
       "      <th>Creatinine_level</th>\n",
       "      <th>Kidney_test_outcome</th>\n",
       "      <th>Drug_Diabetes</th>\n",
       "      <th>Drug_kidney</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A001</td>\n",
       "      <td>Ananya Mohan</td>\n",
       "      <td>Female</td>\n",
       "      <td>A-</td>\n",
       "      <td>47</td>\n",
       "      <td>33.6</td>\n",
       "      <td>62</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>138</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>5.5</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0</td>\n",
       "      <td>Precose</td>\n",
       "      <td>Enalapril</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A002</td>\n",
       "      <td>Dasari K</td>\n",
       "      <td>Male</td>\n",
       "      <td>A-</td>\n",
       "      <td>23</td>\n",
       "      <td>38.2</td>\n",
       "      <td>82</td>\n",
       "      <td>52</td>\n",
       "      <td>125</td>\n",
       "      <td>84</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0</td>\n",
       "      <td>Glyset</td>\n",
       "      <td>Enalapril</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A003</td>\n",
       "      <td>Karan Jadeja</td>\n",
       "      <td>Male</td>\n",
       "      <td>AB+</td>\n",
       "      <td>31</td>\n",
       "      <td>44.2</td>\n",
       "      <td>0</td>\n",
       "      <td>99</td>\n",
       "      <td>0</td>\n",
       "      <td>145</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>3.4</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0</td>\n",
       "      <td>Precose</td>\n",
       "      <td>Enalapril</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A004</td>\n",
       "      <td>Himanshu Soni</td>\n",
       "      <td>Male</td>\n",
       "      <td>A+</td>\n",
       "      <td>24</td>\n",
       "      <td>42.3</td>\n",
       "      <td>68</td>\n",
       "      <td>110</td>\n",
       "      <td>250</td>\n",
       "      <td>135</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>4.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0</td>\n",
       "      <td>Precose</td>\n",
       "      <td>Ramipril</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A005</td>\n",
       "      <td>Moti Lal</td>\n",
       "      <td>Male</td>\n",
       "      <td>O+</td>\n",
       "      <td>21</td>\n",
       "      <td>40.7</td>\n",
       "      <td>62</td>\n",
       "      <td>155</td>\n",
       "      <td>480</td>\n",
       "      <td>139</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>6.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1</td>\n",
       "      <td>Precose</td>\n",
       "      <td>Captopril</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Serial_ID   Patient_Name     Sex Blood_group  Age   BMI  Blood_Pressure  \\\n",
       "0      A001   Ananya Mohan  Female          A-   47  33.6              62   \n",
       "1      A002       Dasari K    Male          A-   23  38.2              82   \n",
       "2      A003   Karan Jadeja    Male         AB+   31  44.2               0   \n",
       "3      A004  Himanshu Soni    Male          A+   24  42.3              68   \n",
       "4      A005       Moti Lal    Male          O+   21  40.7              62   \n",
       "\n",
       "   Blood_Sugar  Insulin  Glucose  Diabetes_Outcome  Blood_urea_nitrogen_level  \\\n",
       "0           80        0      138                 1                         20   \n",
       "1           52      125       84                 0                         20   \n",
       "2           99        0      145                 1                          7   \n",
       "3          110      250      135                 0                         15   \n",
       "4          155      480      139                 0                         25   \n",
       "\n",
       "   Uric_acid  Creatinine_level  Kidney_test_outcome Drug_Diabetes Drug_kidney  \n",
       "0        5.5               1.2                    0       Precose   Enalapril  \n",
       "1        1.8               1.3                    0        Glyset   Enalapril  \n",
       "2        3.4               0.9                    0       Precose   Enalapril  \n",
       "3        4.5               0.4                    0       Precose    Ramipril  \n",
       "4        6.7               1.5                    1       Precose   Captopril  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import pylab as pl\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "\n",
    "df = pd.read_csv(\"proj-data.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Actoplus' 'Glyset' 'Glyset' 'Actoplus' 'Glyset' 'Glyset' 'Precose'\n",
      " 'Precose' 'Precose' 'Glyset']\n",
      "12    Actoplus\n",
      "39      Glyset\n",
      "9       Glyset\n",
      "47    Actoplus\n",
      "31      Glyset\n",
      "28      Glyset\n",
      "13     Precose\n",
      "48      Glyset\n",
      "45     Precose\n",
      "6       Glyset\n",
      "Name: Drug_Diabetes, dtype: object\n",
      "DecisionTrees's Accuracy:  0.9\n"
     ]
    }
   ],
   "source": [
    "import numpy as np \n",
    "import pandas as pd\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "X =df[['Age', 'Sex','Diabetes_Outcome']].values\n",
    "d={'Male':1,'Female':0}\n",
    "df['Sex']=df['Sex'].map(d)\n",
    "X =df[['Age', 'Sex', 'Glucose','Diabetes_Outcome']].values\n",
    "y = df[\"Drug_Diabetes\"]\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_trainset, X_testset, y_trainset, y_testset = train_test_split(X, y, test_size=0.2, random_state=3)\n",
    "drugTree = DecisionTreeClassifier(criterion=\"entropy\", max_depth =4)\n",
    "drugTree # it shows the default parameters\n",
    "drugTree.fit(X_trainset,y_trainset)\n",
    "predTree = drugTree.predict(X_testset)\n",
    "print (predTree [0:50])\n",
    "print (y_testset [0:50])\n",
    "\n",
    "from sklearn import metrics\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "print(\"DecisionTrees's Accuracy: \", metrics.accuracy_score(y_testset, predTree))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
       "                       max_features=None, max_leaf_nodes=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, presort=False,\n",
       "                       random_state=None, splitter='best')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.externals.six import StringIO\n",
    "import pydotplus\n",
    "import matplotlib.image as mpimg\n",
    "from sklearn import tree\n",
    "%matplotlib inline \n",
    "\n",
    "X =df[['Age', 'Glucose','Sex', 'Diabetes_Outcome']]\n",
    "X[0:50]\n",
    "feature=list(X)\n",
    "feature\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "y=df['Drug_Diabetes']\n",
    "X=df[feature]\n",
    "clf=tree.DecisionTreeClassifier()\n",
    "clf.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.externals.six import StringIO\n",
    "from IPython.display import Image\n",
    "from sklearn import tree\n",
    "import pydotplus\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "clf=tree.DecisionTreeClassifier()\n",
    "clf.fit(X,y)\n",
    "dot_data=StringIO()\n",
    "tree.export_graphviz(clf,out_file=dot_data,feature_names=feature)\n",
    "graph=pydotplus.graph_from_dot_data(dot_data.getvalue())\n",
    "graph.write_png('Drug-Diabetes.png')\n",
    "Image=(graph.create_png())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>Serial_ID</th>\n",
       "      <th>Patient_Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Blood_group</th>\n",
       "      <th>Age</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Blood_Pressure</th>\n",
       "      <th>Blood_Sugar</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>Diabetes_Outcome</th>\n",
       "      <th>Blood_urea_nitrogen_level</th>\n",
       "      <th>Uric_acid</th>\n",
       "      <th>Creatinine_level</th>\n",
       "      <th>Kidney_test_outcome</th>\n",
       "      <th>Drug_Diabetes</th>\n",
       "      <th>Drug_kidney</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A001</td>\n",
       "      <td>Ananya Mohan</td>\n",
       "      <td>Female</td>\n",
       "      <td>A-</td>\n",
       "      <td>47</td>\n",
       "      <td>33.6</td>\n",
       "      <td>62</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>138</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>5.5</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0</td>\n",
       "      <td>Precose</td>\n",
       "      <td>Enalapril</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A002</td>\n",
       "      <td>Dasari K</td>\n",
       "      <td>Male</td>\n",
       "      <td>A-</td>\n",
       "      <td>23</td>\n",
       "      <td>38.2</td>\n",
       "      <td>82</td>\n",
       "      <td>52</td>\n",
       "      <td>125</td>\n",
       "      <td>84</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0</td>\n",
       "      <td>Glyset</td>\n",
       "      <td>Enalapril</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A003</td>\n",
       "      <td>Karan Jadeja</td>\n",
       "      <td>Male</td>\n",
       "      <td>AB+</td>\n",
       "      <td>31</td>\n",
       "      <td>44.2</td>\n",
       "      <td>0</td>\n",
       "      <td>99</td>\n",
       "      <td>0</td>\n",
       "      <td>145</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>3.4</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0</td>\n",
       "      <td>Precose</td>\n",
       "      <td>Enalapril</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A004</td>\n",
       "      <td>Himanshu Soni</td>\n",
       "      <td>Male</td>\n",
       "      <td>A+</td>\n",
       "      <td>24</td>\n",
       "      <td>42.3</td>\n",
       "      <td>68</td>\n",
       "      <td>110</td>\n",
       "      <td>250</td>\n",
       "      <td>135</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>4.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0</td>\n",
       "      <td>Precose</td>\n",
       "      <td>Ramipril</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A005</td>\n",
       "      <td>Moti Lal</td>\n",
       "      <td>Male</td>\n",
       "      <td>O+</td>\n",
       "      <td>21</td>\n",
       "      <td>40.7</td>\n",
       "      <td>62</td>\n",
       "      <td>155</td>\n",
       "      <td>480</td>\n",
       "      <td>139</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>6.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1</td>\n",
       "      <td>Precose</td>\n",
       "      <td>Captopril</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Serial_ID   Patient_Name     Sex Blood_group  Age   BMI  Blood_Pressure  \\\n",
       "0      A001   Ananya Mohan  Female          A-   47  33.6              62   \n",
       "1      A002       Dasari K    Male          A-   23  38.2              82   \n",
       "2      A003   Karan Jadeja    Male         AB+   31  44.2               0   \n",
       "3      A004  Himanshu Soni    Male          A+   24  42.3              68   \n",
       "4      A005       Moti Lal    Male          O+   21  40.7              62   \n",
       "\n",
       "   Blood_Sugar  Insulin  Glucose  Diabetes_Outcome  Blood_urea_nitrogen_level  \\\n",
       "0           80        0      138                 1                         20   \n",
       "1           52      125       84                 0                         20   \n",
       "2           99        0      145                 1                          7   \n",
       "3          110      250      135                 0                         15   \n",
       "4          155      480      139                 0                         25   \n",
       "\n",
       "   Uric_acid  Creatinine_level  Kidney_test_outcome Drug_Diabetes Drug_kidney  \n",
       "0        5.5               1.2                    0       Precose   Enalapril  \n",
       "1        1.8               1.3                    0        Glyset   Enalapril  \n",
       "2        3.4               0.9                    0       Precose   Enalapril  \n",
       "3        4.5               0.4                    0       Precose    Ramipril  \n",
       "4        6.7               1.5                    1       Precose   Captopril  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import pylab as pl\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "\n",
    "df = pd.read_csv(\"proj-data.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='entropy', max_depth=4,\n",
       "                       max_features=None, max_leaf_nodes=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, presort=False,\n",
       "                       random_state=None, splitter='best')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np \n",
    "import pandas as pd\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "X =df[['Age', 'Sex','Diabetes_Outcome']].values\n",
    "d={'Male':1,'Female':0}\n",
    "df['Sex']=df['Sex'].map(d)\n",
    "\n",
    "X =df[['Age', 'Sex', 'Glucose','Diabetes_Outcome']].values\n",
    "y = df[\"Drug_kidney\"]\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_trainset, X_testset, y_trainset, y_testset = train_test_split(X, y, test_size=0.2, random_state=3)\n",
    "\n",
    "drugTree = DecisionTreeClassifier(criterion=\"entropy\", max_depth =4)\n",
    "drugTree # it shows the default parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='entropy', max_depth=4,\n",
       "                       max_features=None, max_leaf_nodes=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, presort=False,\n",
       "                       random_state=None, splitter='best')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drugTree.fit(X_trainset,y_trainset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Enalapril' 'Enalapril' 'Enalapril' 'Enalapril' 'Enalapril' 'Enalapril'\n",
      " 'Captopril' 'Enalapril' 'Enalapril' 'Enalapril']\n",
      "12     Ramipril\n",
      "39    Captopril\n",
      "9     Enalapril\n",
      "47    Captopril\n",
      "31    Enalapril\n",
      "28    Enalapril\n",
      "13    Enalapril\n",
      "48    Captopril\n",
      "45    Captopril\n",
      "6     Captopril\n",
      "Name: Drug_kidney, dtype: object\n"
     ]
    }
   ],
   "source": [
    "predTree = drugTree.predict(X_testset)\n",
    "print (predTree [0:50])\n",
    "print (y_testset [0:50])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DecisionTrees's Accuracy:  0.3\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "print(\"DecisionTrees's Accuracy: \", metrics.accuracy_score(y_testset, predTree))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
       "                       max_features=None, max_leaf_nodes=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, presort=False,\n",
       "                       random_state=None, splitter='best')"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.externals.six import StringIO\n",
    "import pydotplus\n",
    "import matplotlib.image as mpimg\n",
    "from sklearn import tree\n",
    "%matplotlib inline \n",
    "\n",
    "X =df[['Age', 'Glucose','Sex', 'Diabetes_Outcome']]\n",
    "X[0:50]\n",
    "feature=list(X)\n",
    "feature\n",
    "\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "y=df['Drug_kidney']\n",
    "X=df[feature]\n",
    "clf=tree.DecisionTreeClassifier()\n",
    "clf.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.externals.six import StringIO\n",
    "from IPython.display import Image\n",
    "from sklearn import tree\n",
    "import pydotplus\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "clf=tree.DecisionTreeClassifier()\n",
    "clf.fit(X,y)\n",
    "dot_data=StringIO()\n",
    "tree.export_graphviz(clf,out_file=dot_data,feature_names=feature)\n",
    "graph=pydotplus.graph_from_dot_data(dot_data.getvalue())\n",
    "graph.write_png('Drug-Kidney.png')\n",
    "Image=(graph.create_png())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### K-Means"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "FOR DIABETES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "%matplotlib inline\n",
    "\n",
    "dataset = pd.read_csv('proj-data.csv')\n",
    "X = dataset.iloc[:, [7, 9]].values\n",
    "y = dataset.iloc[:, 10].values\n",
    "\"\"\"from sklearn.cross_validation import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)\"\"\"\n",
    "\n",
    "\"\"\"from sklearn.preprocessing import StandardScaler\n",
    "sc_X = StandardScaler()\n",
    "X_train = sc_X.fit_transform(X_train)\n",
    "X_test = sc_X.transform(X_test)\n",
    "sc_y = StandardScaler()\n",
    "y_train = sc_y.fit_transform(y_train)\"\"\"\n",
    "\n",
    "from sklearn.cluster import KMeans\n",
    "\n",
    "wcss = []\n",
    "for i in range(1, 11):\n",
    "    kmeans = KMeans(n_clusters = i, init = 'k-means++', random_state = 42)\n",
    "    kmeans.fit(X)\n",
    "    wcss.append(kmeans.inertia_)\n",
    "plt.plot(range(1, 11), wcss)\n",
    "plt.title('The Elbow Method')\n",
    "plt.xlabel('Attributes')\n",
    "plt.ylabel('Diabetes Outcome')\n",
    "plt.show()\n",
    "plt.savefig('KmeansTraining-Diabetes.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "kmeans = KMeans(n_clusters = 5, init = 'k-means++', random_state = 42)\n",
    "y_kmeans = kmeans.fit_predict(X)\n",
    "\n",
    "plt.scatter(X[y_kmeans == 0, 0], X[y_kmeans == 0, 1], s = 100, c = 'red', label = 'Cluster 1')\n",
    "plt.scatter(X[y_kmeans == 1, 0], X[y_kmeans == 1, 1], s = 100, c = 'blue', label = 'Cluster 2')\n",
    "plt.scatter(X[y_kmeans == 2, 0], X[y_kmeans == 2, 1], s = 100, c = 'green', label = 'Cluster 3')\n",
    "plt.scatter(X[y_kmeans == 3, 0], X[y_kmeans == 3, 1], s = 100, c = 'cyan', label = 'Cluster 4')\n",
    "plt.scatter(X[y_kmeans == 4, 0], X[y_kmeans == 4, 1], s = 100, c = 'magenta', label = 'Cluster 5')\n",
    "plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1], s = 300, c = 'yellow', label = 'Centroids')\n",
    "plt.title('Clusters of Diabetes dataset')\n",
    "plt.xlabel('Attributes')\n",
    "plt.ylabel('Diabetes Outcome')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "plt.savefig('Kmeans-Visual-Diabetes.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.42\n"
     ]
    }
   ],
   "source": [
    "correct = 0\n",
    "for i in range(len(X)):\n",
    "    predict_me = np.array(X[i].astype(float))\n",
    "    predict_me = predict_me.reshape(-1, len(predict_me))\n",
    "    prediction = kmeans.predict(predict_me)\n",
    "    if prediction[0] == y[i]:\n",
    "        correct += 1\n",
    "\n",
    "print(correct/len(X))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "FOR KIDNEY"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "%matplotlib inline\n",
    "\n",
    "dataset = pd.read_csv('proj-data.csv')\n",
    "X = dataset.iloc[:, [11, 13]].values\n",
    "y = dataset.iloc[:, 14].values\n",
    "\n",
    "\"\"\"from sklearn.cross_validation import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)\"\"\"\n",
    "\n",
    "\"\"\"from sklearn.preprocessing import StandardScaler\n",
    "sc_X = StandardScaler()\n",
    "X_train = sc_X.fit_transform(X_train)\n",
    "X_test = sc_X.transform(X_test)\n",
    "sc_y = StandardScaler()\n",
    "y_train = sc_y.fit_transform(y_train)\"\"\"\n",
    "\n",
    "from sklearn.cluster import KMeans\n",
    "wcss = []\n",
    "for i in range(1, 11):\n",
    "    kmeans = KMeans(n_clusters = i, init = 'k-means++', random_state = 42)\n",
    "    kmeans.fit(X)\n",
    "    wcss.append(kmeans.inertia_)\n",
    "plt.plot(range(1, 11), wcss)\n",
    "plt.title('The Elbow Method')\n",
    "plt.xlabel('Attributes')\n",
    "plt.ylabel('Diabetes Outcome')\n",
    "plt.show()\n",
    "plt.savefig('KmeansTraining-Kidney.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "kmeans = KMeans(n_clusters = 5, init = 'k-means++', random_state = 42)\n",
    "y_kmeans = kmeans.fit_predict(X)\n",
    "\n",
    "plt.scatter(X[y_kmeans == 0, 0], X[y_kmeans == 0, 1], s = 100, c = 'red', label = 'Cluster 1')\n",
    "plt.scatter(X[y_kmeans == 1, 0], X[y_kmeans == 1, 1], s = 100, c = 'blue', label = 'Cluster 2')\n",
    "plt.scatter(X[y_kmeans == 2, 0], X[y_kmeans == 2, 1], s = 100, c = 'green', label = 'Cluster 3')\n",
    "plt.scatter(X[y_kmeans == 3, 0], X[y_kmeans == 3, 1], s = 100, c = 'cyan', label = 'Cluster 4')\n",
    "plt.scatter(X[y_kmeans == 4, 0], X[y_kmeans == 4, 1], s = 100, c = 'magenta', label = 'Cluster 5')\n",
    "plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1], s = 300, c = 'yellow', label = 'Centroids')\n",
    "plt.title('Clusters of Kidney dataset ')\n",
    "plt.xlabel('Attributes')\n",
    "plt.ylabel('Kidney Outcome')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "plt.savefig('KmeansVisual-Kidney.png')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.06\n"
     ]
    }
   ],
   "source": [
    "correct = 0\n",
    "for i in range(len(X)):\n",
    "    predict_me = np.array(X[i].astype(float))\n",
    "    predict_me = predict_me.reshape(-1, len(predict_me))\n",
    "    prediction = kmeans.predict(predict_me)\n",
    "    if prediction[0] == y[i]:\n",
    "        correct += 1\n",
    "\n",
    "print(correct/len(X))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}