Medium_pima_indian.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing the libraries\n",
    "import sklearn\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "#reading the .csv file \n",
    "data = pd.read_csv(\"pima-indians-diabetes.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>6</th>\n",
       "      <th>148</th>\n",
       "      <th>72</th>\n",
       "      <th>35</th>\n",
       "      <th>0</th>\n",
       "      <th>33.6</th>\n",
       "      <th>0.627</th>\n",
       "      <th>50</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>116</td>\n",
       "      <td>74</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25.6</td>\n",
       "      <td>0.201</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   6  148  72  35    0  33.6  0.627  50  1\n",
       "0  1   85  66  29    0  26.6  0.351  31  0\n",
       "1  8  183  64   0    0  23.3  0.672  32  1\n",
       "2  1   89  66  23   94  28.1  0.167  21  0\n",
       "3  0  137  40  35  168  43.1  2.288  33  1\n",
       "4  5  116  74   0    0  25.6  0.201  30  0"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#preview of the data.\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pregnancies</th>\n",
       "      <th>glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>116</td>\n",
       "      <td>74</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25.6</td>\n",
       "      <td>0.201</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pregnancies  glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            1       85             66             29        0  26.6   \n",
       "1            8      183             64              0        0  23.3   \n",
       "2            1       89             66             23       94  28.1   \n",
       "3            0      137             40             35      168  43.1   \n",
       "4            5      116             74              0        0  25.6   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  Class  \n",
       "0                     0.351   31      0  \n",
       "1                     0.672   32      1  \n",
       "2                     0.167   21      0  \n",
       "3                     2.288   33      1  \n",
       "4                     0.201   30      0  "
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#renaming the columns\n",
    "data.columns=['pregnancies','glucose','BloodPressure','SkinThickness',\n",
    "              'Insulin','BMI', 'DiabetesPedigreeFunction', 'Age', 'Class']\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
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2/zpY3CTXiIjoKaPpcy2JdPoQp8wHNmrZfilwb5tzrrH9HNV613dQJdvrB7tomoUjIqK3dLZZ+Hpgc0mbSFqeannQCwac81/A3gCS1qZqJr5rqIsmuQ5C0qfKkOvZkmZJ2kXSvPKLHXju74a51s/KNe6U9Eh5PkvS7kNc8y2DDQkvxydLumXJ3l1ERO9y38gfw17LXgB8GLgYuA04x/ZcScdJeks57WLgQUm3ApcDH7f94FDXTbNwG5J2A94M7GD7mZL8lh/sfNu7D3U9228v190LOMr2m1tiDfaaC3jht6eIiOjwBE22LwQuHLDvmJbnBo4sjxFJzbW99YEHbD8DYPsB28+3wUtaSdJFkv6xbD9efu4laYak8yTdLulMDZY9F/cRSTdKmiNpq3KtQyV9pzxfr9R+by6PxZK5pE0l3SRpp/K6n5by/UHSl1vOe4Okq0uscyWtWvYfX26Oni3pq2Xf/pJuKfGuXJpfZkREJ3nByB9NSXJt7xJgI0m/l3SipNe0HFsV+AXwY9snt3nt9sDhwNbApsAeI4j3gO0dgJOAo9ocPwG4wva2wA7A3P4DkrYEzgfeZ7u/c3074N3Aq4B3S9qo1L4/DbyuxJoJHClpLeDtwCttbwN8vlzjGOBvS8z+ppHFSJomaaakmc8teGwEbzMiYul1slm4Lkmubdh+HNiRavj2/cBPJB1aDv8cOM32Dwd5+XW259vuA2YBk0cQ8qfl5w2DnL8PVeLF9kLbj5T965TyHGx7Vsv5l9l+xPbTwK3AxsCuVAn/t5JmAYeU/Y8CTwOnSHoH8GS5xm+B00vtfGK7Qtuebnuq7amTllttBG8zImLp9UJyTZ/rIGwvBGYAMyTNoUpGUCWdN0r6sdsvKvhMy/OFjOx33P+akZ7f7xGqm5/3oKU2O0gZBFxq+8CBF5G0M/BaqlFyHwb2sf1BSbsAfwfMkrTdcB34ERHd0GTSHKnUXNuQtGWZS7LfdsAfy/NjgAeBE7tYpMuAfy5lmyhp9bL/WeBtwHslvWeYa1wD7CHp5eU6K0vaovS7rlE69A+neq9I2sz2taVT/wEWvw8sIqI51sgfDUlybW9V4Iz+QT5UzanHthw/HFixdbBQzT4G7F1q0DcAr+w/YPsJqpHNR0gaOB8mLefdDxwKnFXe0zXAVsBqwC/LviuAI8pLvlIGWN0CXAl0f8HWiIg2eqFZWO1bNiNGZ9WVN+n6H1JTi6XvMmnYBTFqMb4WS1+hkbg/W6mZT+OT7/1t12M2uVj65fMvXaoq5b277z3iz5sNfnd5I9XX9LlGRERPcYPNvSOV5BoRET2lFwY0JblGRERPcV9qrhERER3VC0OFklwjIqKnpOYaERHRYX0Lk1xjnJg4ofu3TP8TG3Q9JsCZC5uZqKqJ22K2u/lrXY8JcNTUTzYSt6mP7PVWWbPrMdeeuHLXY3ZKaq4REREdlltxIiIiOiy34kRERHRYX2quERERndW3cOxPi5/kGhERPSX3uUZERHRYRgtHRER0WPpcIyIiOqwXbsUZ+73C8TxJj3f4epPLYuhImirphE5ePyKiDvbIH01JzTUAsD0TmNl0OSIihrOwb+zXC8d+CeMFJO0laYak8yTdLulMSSrHjpd0q6TZkr5a9p0uab+W17+gBlyu+cvy/FhJp5YYd0n6aLfeW0TEcFJzjTptD7wSuBf4LbCHpFuBtwNb2bakpZmwdCtgb2A14A5JJ9l+rvUESdOAaQArLr82y09afSnCRUSMTC8MaErNtXddZ3u+7T5gFjAZeBR4GjhF0juAJ5fi+r+y/YztB4D7gPUGnmB7uu2ptqcmsUZEt9ga8aMpSa6965mW5wuB5WwvAHYGzgfeBlxUji+g/FuX5uPll+T6S1vgiIhO6LNG/GhKkusyRNKqwBq2LwQOB7Yrh+YBO5bnbwUmdb90ERGd4VE8mpLayLJlNeDnklakWpryiLL/5LL/OuAy4ImGyhcRsdR6YbRwkmsPsb1q+TkDmNGy/8Mtp+3c5nV/AXZt2XV02T8PmDLwmraPHfD6KUtb9oiITun0inOS9gW+BUwETrF9/CDn7QecC+xUbl8c1NhP/xERES2MRvwYjqSJwHeBNwJbAwdK2rrNeasBHwWuHUkZk1wjIqKn9HnkjxHYGbjT9l22nwXOphqbMtDngC9T3ZExrCTXiIjoKX1oxI8R2BC4p2V7ftn3PEnbAxvZ/uVIy5g+14iI6CkLR5Y0gcUnuymm257eekqblz1f55U0AfgGcOhoypjkGhERPWUkfanPn1sl0ulDnDIf2Khl+6VUM9/1W41q4OeMMsvsS4ALJL1lqEFNSa4REdFTOjxa+Hpgc0mbAH8CDgDe03/Q9iPA2v3bkmYARw03WjjJNTpi41XX7XrMuZMWdj0mwL59L24k7sMLFnQ95lFTP9n1mABfnfnFRuK+d8cjG4m7/oprdT3mq/tW63rMTulkcrW9QNKHgYupbsU51fZcSccBM21fsCTXTXKNiIieMppm4RFdr5rV7sIB+44Z5Ny9RnLNJNeIiOgpfWN/UZwk14iI6C2jGS3clCTXiIjoKZ2e/rAOSa4REdFT+pSaa0REREc1uZTcSCW5RkRET0mzcERERIct6IFm4UzcPw5IWihplqSbJd0oafeyf7IkS/pcy7lrS3pO0nfK9rGSjmqq7BERA3kUj6YkuY4PT9nezva2VAul/0fLsbuAN7ds7w/M7WbhIiJGo08jfzQlyXX8WR14uGX7KeA2SVPL9ruBc7peqoiIEeobxaMp6XMdH1aSNAtYEVgf2GfA8bOBAyT9H7CQakWIDbpbxIiIkemF0cKpuY4P/c3CWwH7Aj+UFhsRcBHweuBA4CcjvaikaZJmSpr50JN/6WyJIyIGkWbhGHNsX021fNI6LfueBW4A/gU4fxTXmm57qu2pa628XsfLGhHRzoJRPJqSZuFxRtJWVMsqPQis3HLoa8AVth9UDwxzj4jxyz3wEZXkOj7097kCCDjE9sLWJGp7LhklHBE9IJNIxJhge+Ig++cBU9rsPx04vTw/tr6SRUSMXpJrREREh/XCaOEk14iI6ClZLD0iIqLDmhwFPFJJrhER0VPSLBwREdFhaRaOiIjosIwWjnHjoWcf63rMeSs81fWYAJcveHj4k2qw40rdnwWrqQrCe3c8spG4P7zh643E3e1Vh3Q95uyJz3Q9ZqekWTgiIqLDFvRAek1yjYiInjL2U2uSa0RE9Jj0uUZERHRYRgtHRER0WF8PNAwnuUZERE8Z+6k1yTUiInpML4wWntB0ASIiIkbDo3iMhKR9Jd0h6U5Jn2hz/EhJt0qaLekySRsPd81hk6ukhZJmSZor6eYSZEI5NlXSCcO8/lBJ3xkuzoDXfHI05w947emS7i5lvlHSbqN8/ePl5waSzlvScowi3rGS/lTKO0vS8R2+/tskbd2yfZyk13UyRkREN/WN4jEcSROB7wJvBLYGDmz9zCxuAqba3gY4D/jycNcdSbPwU7a3K4VYF/gxsAbwGdszgZkjuMZofRL44lK8/uO2z5P0BuD7wDajvYDte4H9RvMaSRNtLxxtLOAbtr+6BK8bibcBvwRuBbB9TE1xIiK6osMDmnYG7rR9F4Cks4G3Uj4zAWxf3nL+NcDBw110VM3Ctu8DpgEfVmUvSb8sBdpZ0u8k3VR+btny0o0kXVSq3Z/p3ynpYEnXlRrb9yVNLDW3lcq+M4c4b2Kppd4iaY6kI9oU+Urg5eUam5Uy3CDpN5K2Kvs3kXS1pOslfa6lbJMl3VKeryzpnNIk8BNJ10qaWo49XmqD1wK7SdpR0hUlzsWS1h8q/mAkzZO0dnk+VdKM8vxYSadKmiHpLkkfbXnNe0sZb5b0I0m7A28BvlJ+d5uV39l+5fzXln+vOeWaK7TE/myp+c8ZrqwREd3U4WbhDYF7Wrbnl32D+QDw38NddNR9riW7TwDWHXDoduDVtrcHjmHxmufOwEHAdsD+JVm8Ang3sEepGS8EDrL9CUpt2fZBg51XrrWh7Sm2XwWc1qa4fw/MKc+nAx+xvSNwFHBi2f8t4CTbOwH/N8jb/hDwcGkS+BywY8uxVYBbbO8CXAt8G9ivxDkV+MIw8QGOaGkW/ttBytBqK+BvqX6vn5E0SdIrgU8B+9jeFviY7d8BF1DV5Lez/T/9F5C0InA68O7y+1sO+OeWGA/Y3gE4qZQ3ImJMGE2zsKRpkma2PKYNuFy7u2bb5mVJBwNTga8MV8YlHS3crjBrAGdI2rwUbFLLsUttP1gK91NgT6r1bncErpcEsBJwX5vrvnaQ834BbCrp28CvgEtaXvMVSZ8G7gc+IGlVYHfg3HINgBXKzz2Ad5bnPwK+1KYMe1IlYWzfIml2y7GFwPnl+ZbAFODSEmci8Odh4sPom4V/ZfsZ4BlJ9wHrAfsA59l+oJTzoWGusSVwt+3fl+0zgMOAb5btn5afNwDvaHeB8kc6DWDNlddnlRXWGsVbiIhYMgtH0SxsezpV5WYw84GNWrZfCtw78CRVY1U+BbymfP4OadTJVdKmVAnlPuAVLYc+B1xu++2SJgMzWo4N/E2YKkGfYfvo4UIOdp6kbalqcIcB7wLeXw593PZ5LeetDvy1v++4jeH+pYaaD+Tpln5WAXNtLzaIagTx21nAopaFFQcca/2HXUj17yhGd/vXcHOc9Mfov/4LtP7RvnStKWN/bHxELBM63Od6PbC5pE2APwEHAO9pPUHS9lTjd/Yt3aPDGlWzsKR1gO8B37E98N2tUQoGcOiAY6+XtJaklagG2PwWuAzYT9UgKcrx/uHNz0nqr/m2Pa/0R06wfT7w78AOg5Xb9qPA3ZL2L9dQScyUshxQnh80yCWuokreqBpF9qpBzrsDWEdlhHJ/c+0w8Qczj0XNz+8c4rx+lwHvkvTiEqO/GvkYsFqb828HJkt6edn+B+CKEcSJiGhUJ/tcbS8APgxcDNwGnGN7bhlL85Zy2leAValaH2dJumC4646k5rqSpFlUzbwLqJpO2y16+GWqZuEjgV8POHZVed3LgR+XUcaUpttLVN3a8xxVDfSPVLWh2ZJuLP2u7c57Cjit7AMYrgZ8EHBSudYk4GzgZuBjwI8lfYxFzbsDnVje22yqIdmzgUcGnmT72TJY6ARJa1D9fr8JzB0i/mA+C/xA1W1J1w7z3ih/DF8ArpC0sJTz0BLn5DLwab+W85+W9D6qP5blqL69fW+4OBERTev09Ie2LwQuHLDvmJbno759US+sgMZAqu6DmlQS0mZUtcQtbD/bcNHGjCaahfdY7eXDn1SDu59raLH05cfPYukPN/S/1nhaLH37FV7S9Zj9Tp537lL9af3j5P1H/HmztLGWVKY/HJmVgctLU7WAf05ijYhoxmgGNDUlyXUEbD9GNfw6IiIa5iTXiIiIzspi6RERER3W1wNjhZJcIyKip4z91JrkGhERPabTt+LUIck1OuL6rdfpeszT71m16zEBtlx+lUbifvHeGV2Pud4qa3Y9JsD6KzYzlWYTt8QAXD3njK7H/OzUT3c9ZqdktHBERESHpeYaERHRYbkVJyIiosNyK05ERESH9cK0vUmuERHRU9LnGhER0WEZLRwREdFhqblGRER0WC/0uU4Y/pRYFkh6uyRL2qrpskRELI2+UTyakuQ6fhwIXAUc0HRBIiKWhkfxX1OSXMcBSasCewAfoCRXSRMknShprqRfSrpQ0n7l2I6SrpB0g6SLJa3fYPEjIhaz0H0jfjQlfa7jw9uAi2z/XtJDknYANgUmA68C1gVuA06VNAn4NvBW2/dLejfwBeD9zRQ9ImJxGdAUY8WBwDfL87PL9iTgXNt9wP9Jurwc3xKYAlwqCWAi8Od2F5U0DZgG8OXNtuDgl2xQ2xuIiOiX6Q+jcZJeDOwDTJFkqmRp4GeDvQSYa3u34a5tezowHeDPe+499v/aI2KZ0AuLpafPddm3H/BD2xvbnmx7I+Bu4AHgnaXvdT1gr3L+HcA6knYDkDRJ0iubKHhERDsexaMpqbku+w4Ejh+w73zgFcB84Bbg98C1wCO2ny0Dm06QtAbV38g3gbndK3JExODS5xqNs71Xm30nQDWK2Pbjpen4OmBOOT4LeHU3yxkRMW0HOtkAAB0DSURBVFJNjgIeqSTX8e2XktYElgc+Z/v/mi5QRMRwUnONMa1drTYiYqzLaOGIiIgO64W5hZNcIyKip6RZOCIiosN6YUBT7nONiIie0umJ+yXtK+kOSXdK+kSb4ytI+kk5fq2kycNdM8k1IiJ6Sp894sdwJE0Evgu8EdgaOFDS1gNO+wDwsO2XA98AvjTcddMsHB3xzfkv6XrMgyc80vWYAIc9+2Qjcf9m3YH/v9dv7Ykrdz0mwKv7Vmsk7uyJzzQS97NTP931mJ+Z+fmux+yUDo8W3hm40/ZdAJLOBt4K3NpyzluBY8vz84DvSJKHGFmVmmtERPSUTtZcgQ2Be1q255d9bc+xvQB4BHjxUBdNco2IiJ4ymj5XSdMkzWx5TBtwObUNMfpzFpNm4YiI6CmjGS3cunrXIOYDG7VsvxS4d5Bz5ktaDlgDeGiouKm5RkRET+lws/D1wOaSNpG0PHAAcMGAcy4ADinP9wN+PVR/K6TmGhERPaaTA5psL5D0YeBiqvWuT7U9V9JxwEzbFwA/AH4k6U6qGusBw103yTUiInqKOzyJhO0LgQsH7Dum5fnTwP6juWaSa0RE9JRMfxgREdFhvTBxfwY0LSMkvUTS2ZL+R9Ktki6UtIWkW5ouW0REJy1034gfTUnNdRkgScDPgDNsH1D2bQes12jBIiJqMMJRwI1KzXXZsDfwnO3v9e+wPYuWWUckTZb0G0k3lsfuZf/6kq6UNEvSLZL+RtJESaeX7TmSjuj+W4qIaK/TE/fXITXXZcMU4IZhzrkPeL3tpyVtDpwFTAXeA1xs+wtlAuuVge2ADW1PAZC0ZrsLlplOpgG8Ya2pbLfayzvyZiIihpI+1xhLJgEnS5oDnEu1+gNUN1C/T9KxwKtsPwbcBWwq6duS9gUebXdB29NtT7U9NYk1IrqlD4/40ZQk12XDXGDHYc45AvgLsC1VjXV5ANtXAq8G/kR1k/R7bT9czpsBHAacUk+xIyJGb2Ff34gfTUlyXTb8GlhB0j/275C0E7BxyzlrAH92dff1P1DNRIKkjYH7bJ9MNQvJDpLWBibYPh/4d2CH7ryNiIjh2R7xoynpc10G2LaktwPflPQJ4GlgHnB4y2knAudL2h+4HHii7N8L+Lik54DHgfdSLa90mqT+L19H1/4mIiJGKJNIRNfYvhd4V5tDU8rxPwDbtOw/uuw/AzijzetSW42IMakXBjQluUZERE/phftck1wjIqKnNHn/6kgluUZERE9pchTwSCW5RkRET0nNNSIiosMyoCkiIqLDeiG5qhcKGcs2SdNsT1/WYybush13PL3XJuP2iszQFGPBtHESM3GX7bjj6b02GbcnJLlGRER0WJJrREREhyW5xljQRL9NU31Fibvsxh1P77XJuD0hA5oiIiI6LDXXiIiIDktyjYiI6LAk14iIiA5Lco0YByS9SNI2w58ZEZ2QAU3RCElfBj4PPAVcBGwLHG77P2uMuQVwErCe7Skl2bzF9ufritlw3BnAW6imOZ0F3A9cYfvIOuOW2BOB9WiZYtX2/9YUa8j3Y/vrdcQtsbcAPg5szOLvdZ8aY64HfBHYwPYbJW0N7Gb7B3XFLHE/BpwGPAacAmwPfML2JXXG7VWpuUZT3mD7UeDNwHyg/0OqTicDRwPPAdieDRxQc8wm465RfsfvAE6zvSPwurqDSvoI8BfgUuBX5fHLGkOuNsyjTucCNwKfpvr77X/U6XTgYmCDsv174PCaYwK8v/w9vQFYB3gfcHwX4vakTNwfTZlUfr4JOMv2Q5Lqjrmy7esGxFlQd9AG4y4naX3gXcCnuhCv38eALW0/2I1gtj/bjTiDWGD7pC7HXNv2OZKOBrC9QNLCLsTt/wN+E9WXtZvVhf9pe1WSazTlF5Jup2oW/pCkdYCna475gKTNoFoMUtJ+wJ9rjtlk3OOoajhX2b5e0qbAH7oQ9x7gkS7EAUDSCUMdt/3RGsP/QtKHgJ8Bz7TEfKjGmE9IejGL/p52pTu/7xskXQJsAhwtaTVg7K9a3pD0uUZjJL0IeNT2QkmrAKvZ/r8a421KNavM7sDDwN3Awbbn1RVziLgH2f5jnXGbIukHwJZUzcGtCaeWvk9JzwK3AOcA97KohtUf94w64pbYd7fZbdub1hhzB+DbwBSq970OsF/pbqiNpAnAdsBdtv8qaS3gpXXH7VWpuUYjJK0MHAa8jGp1jQ2oPpBr65uzfRfwupLIJ9h+rK5Y/coH0lTbXY1bYnd90Fjxv+WxfHnUbX1gf+DdVM3tPwHOt/1w3YFtb1J3jDYxb5T0Gqr/XwTcYfu5LoTeDZhl+wlJBwM7AN/qQtyelJprNELST4AbgPeWEbQrAVfb3q7GmK2jHU+m+nCofbSjpCttv7rOGIPEnWV7O0lvB94GHAFcbnvbbpelWyRtCBwIHAn8m+0f1RxvEvDPQP+/7wzg+3UmO0nvaLP7EWCO7ftqjDub6gvaNsCPgB8A77D9mrpi9rLUXKMpm9l+t6QDAWw/1YXBEe+3/S1JfwusSzXa8TSg7lsJLpV0FFWN6on+nTX3y0GXB41J+qbtwyX9gtIf2Mr2W2oLzvPNpQcCrwf+m+rLW91Oovo9n1i2/6Hs+381xvwAVS3y8rK9F3ANsIWk42r8QrHAtiW9FfiW7R9IOqSmWD0vyTWa8myprfYPytiMlv65mjQ12vH95edhLfsM1NYvV3R70Fj/h/pXa4zxApI+S3VL123A2cDRtrsxGhtgpwEtAb+WdHPNMfuAV9j+Czx/3+tJwC7AlSz6d+i0x8oI5YOBV5d7mScN85pxK83C0QhJr6e6N3BrqprjHsChtmfUGPM0YEOq0Y7bAhOBGeX+z2VStweNNUFSH3AX1ZcIWFRrFtXgotpmppJ0I7C/7f8p25sC59neocaYc2y/qmVbVE3CUyTdZHv7muK+BHgPcL3t30h6GbCX7R/WEa/XJblGY8rtBLtSfQheY/uBmuMNHO34YmDDLoyyfG+7/XV/KJVBY0cCL7M9TdLmVPef1jJoTNIc2jQH96sryUnaeKjjdY7KlvRaqq6Fu6j+jjcG3mf78iFfuHQxT6QaCHhu2fVOqolYPg780vbedcWOkUtyja6StJXt20v/2AvYvrHm+G9h0eCTK2z/os54Jea3WzZXBF4L3Gh7v5rjdnXQWJNJrk1Z1gYedBc+4CStwKKRu7fbrrV7o9RU3wHsWXY9CKxv+7DBX9WRuLtS3QL0CqpR4BOBx22vUWfcXpU+1+i2I6luvflam2MG6pyT9XhgJ+DMsuujkna3fXRdMQFsf2RAOdagvn6xVl0dNNbUfbvlQ/944CHgc1S/27WBCZLea/uiGmLuY/vXbUbubiYJ2z/tdMx+ZVDR/1D1sb6L6r7p8+uK1+I7VNN2ngtMBd4LbN6FuD0pyTW6yva08rOJpqs3AdvZ7gOQdAZwE9W8v930JN35UGpi0BiSHmNR8/DyVINenrC9ek0hvwN8ElgD+DXwRtvXSNoKOIvqHt9Oe02J9fdtjhnoeHItiwQcQDUi+kGq0efq5v9Ltu+UNNH2QuA0Sb/rVuxek+QajZB0GHCm7b+W7RcBB9o+cehXLrU1qWo4UH0Y127ArSkTqAZxndOF0J+hSiwbSTqTMmis7qC2F5ssX9LbgJ1rDLlc/73K5VaUa0o5bq+rom77M+XpcbYXm6VJUl0TS9wO/Ab4e9t3llhH1BSrnSclLQ/MKhOU/BlYpYvxe0r6XKMR/RMcDNhX20jHcv0DqZoPL6fqH3s11W0bZ9cVs8Rtvcl+AfBH2/PrjNkSu6uDxoYoxzW2d63p2jf2j85tfd5uu87YLftuqGMEepkM5ACqaTQvorrt6JRuzRJV+tTvo2qJOILqy+mJ/Yk+FpeaazRlgiT1Dzgp98zVOlWe7bNUrXG6E1Wy+bcu3ZYyE3jKdl9p2ttB0l+6NGXdilTzGS8HbF36A6+sM+CAfsgJVP1zdX6L31bSo1T/piuV55TtFesIWJqcXwmsMeD9rl5XTNs/A35Wbqnqn3FrPUknAT+re6axlj71p4AmVyLqCam5RiMkfQWYDHyP6oP3g8A9tv+lxphvB35t+5GyvSbVfXr/VVfMEucG4G+AF1HNpDMTeNL2QTXH/RLVfLtzWbR6ibswU9JpLZsLgHnAyXVOzddtZZait1EtRn9By6HHgLNtd6UvUtXk+fsD73ZNC7Q3dYtVr0tyjUaUe07/ieq2FFFNJHFKGShRV8yuN0WXGDfa3kHVIuIr2f5yl+LeAWxT960h45mk3Wxf3XQ56lTuj16PainBVhsD96ZZuL0JTRcgxifbfbZPsr2f7Xfa/n6dibVo9/feja4RSdoNOIhqGbZuxb2LBqank/RlSatLmiTpMkkPqFpFZVn0wdICAlQD8ySd2mSBavANqlm+/tj6oBr1/o2GyzZmpc81GiFpD+BYqm+/y7Foqro659udKenrwHepmrk+Qncmdz+c6nafn9meW6bIq20GnxZPUo3svIzF11Wtc/FwgDfY/tfSDD+fqtnycqDupe6asE3/iHcA2w9LqrVFogGT281iZnumpMndL05vSHKNpvyAakDGDUDdNdZ+HwH+nXJ/IFVTdK2z2gDYvgK4Ap5vDn+gCwkOqr7AC4Y9q/O6uhpPwyZIepHL2rGlD3RZ+1wdaoDWSl0rRY9Z1v4Ionc8Yvu/uxnQ9hPAJ7oZE0DSj6kGbC2k+jKxhqSv2/5KzaFvsb1YzVxSu0kPOq3bq/E06WvA7ySdV7b3B77QYHnqcL2kf7R9cutOSR+gOy0/PSkDmqIRZSrCiVQz2bQ2WdY2t7Cky2m/zmhtUy6WuP2Llh8E7Aj8G3BD3aMsVa3YcojtOWX7QOBw27vUGbfEal2NZ2Vg9S7d9tR1kl4J7E3VGnKZ7VsbLlJHqVrS7mfAsyxKplOpbp17+7L677q0UnONpvR/wE9t2Vfr3MLAUS3PV6RaTaQb635OkjSJ6taN79h+TlI3vtXuB5xXkvqeVHPBvqELcaGa3H2ypNbPmGV1abLbWXQvMZJeZvt/my1S57haN3Z3SXsDU8ruX9n+dYPFGvNSc41xTdIVtl8z/JlLFeOjVLXVm4G/o1ou7D9t/02dcUvsLYD/orqN4m22nxrmJZ2I+SNgM2AWi/rT3aV+5q4qt1d9BvgL1XutfQ3Z6A1JrtEYSX9HNcvN8wMmbB9XY7y1WjYnUDXRnmB7y7piDlGW5WzXUmtuc9P/usAjlOb3LjRH3wZs7XHw4SLpTmAX2w82XZYYW9IsHI2Q9D1gZaq+qlOomjCvqznsDVRJR1TNwXcDH6g5Zn+f1ReBDWy/UdLWwG5UI6br8OaarjtStwAvoZrYfVl3D9UXl4jFpOYajZA02/Y2LT9XBX5qu1t9gl0j6b+B04BP2d629EPeZPtVNcfdFZhr+7GyvRpVjfLamuNeDmxH9WWpdbBardMuNkHSD6gWSv8Vi7/XrzdWqBgTUnONpvT3/T0paQOq9SlrWd1DL1zQejGucWHrYm3b50g6usRbIKkb9/aeBLSu2PJEm311OLbm648l/1sey1PzwhPRW5Jcoym/LNPGfQW4kaq59pSaYg22oLWoaWHrAZ4oS7/1rwC0K91pSlRrv2dZlaf2/+fLpBnjgu2sDhNtpVk4GidpBWDF/tVqaozzLyxKqpTnj1Ddczqrxrg7AN+muo3hFmAdYL92U8p1OO5PgRlUtVWADwF7235bTfEeo/3qKf0jaFevI26Tmrp3Osa+JNdojKTdqZade742Zbu2eyHLTElTqaYEFNVtMdcDWwHn2v5yDTEnUC1Wfh1V35yAO7qxlqukdYETqO4dNnAZ1SQSy8zSb02T1Loo+vP3Ttv+14aKFGNEkms0ool7ISVdDLzT9uNle1XgPODtVLXXrWuKe7Xt3eq4dow93bh3Osa+9LlGU6bS/XshX0Y1hVu/54CNbT8lqc41Ty+R9E6q0dC1v19J/1rWjP027Zssl7nJHJoyyL3TL2moODGGJLlGU5q4F/LHwDWSfl62/x44S9IqQJ3zwR4JrAIskPQ09fdB3lZ+zqzp+rFII/dOx9iXZuFoRFP3QpY+sj2pPgyvsp0EFBEdl+QajZDUtk9qWbqNowwo+iTwcmA2cLztR7sYfwuqxQoms/igsYxkXUqSvmj7k+X5621f2nSZYmxJco2oiaSLqJoNr6SaknA124d2Mf7NwPcYsCD9wDVeY/Qk3Wh7h4HPI/qlzzUaMcg9kY9Q9RP+i+27ul+qjnuJ7U+V5xeX9VW7aYHtk4Y/LSI6Lck1mvJ14F6qQUYCDqAa4HQHcCqwV2Ml6xyVRcP7J62Y2Lpt+6GagvaPYP2FpA9RLXTd2q9dS9xxZl1JR1L9W/Y/f17mFo40C0cjJF1re5cB+66xvaukm21v21TZOkXSPKCPRcm1lW1vWlPcu1l8JqquxB1PJH1mqOOZFjFSc42m9El6F9UkDlAtOddvmfjGZ3tyQ6HfY/vqhmKPC0meMZzUXKMRkjYFvkW1rqmBa4AjgD8BO9q+qsHidUSZU3hQtmvpg80Am+4pI7JPAtazPUXSNsBbbH++4aJFw5JcI2pS7uWFas7ZqcDNVE212wDX2t6zprg32d6+jmvH4iRdAXwc+H7/71zSLbanNFuyaFqahaMR4+Ebv+29ASSdDUyzPadsT6G6/7Qum0i6YIhyLXOLljdoZdvXSYt1by9oqjAxdiS5RlNOpnzjB7A9u6xas8wk1xZb9SdWANu3SNquxnj3A1+r8fqxyAOSNmPRWr370d0pPWOMSnKNpoynb/y3SToF+E+qD+GDWTT/bx0eW5ZmuhrjDgOmA1tJ+hPV3MIHN1ukGAuSXKMp4+kb//uAfwY+VravZNEC5nWYV+O1o0WZ7OR1ZfGHCbYfa7pMMTZkQFM0oowWng7sDjxM9Y3/INt/bLRgNZG0PNVi6aZLi6WXuF1dkH68kbQe8EVgA9tvlLQ1sJvtHzRctGhYkmt0naQJwH62zxkP3/gl7QWcQVWjFLARcIjtK2uO2/UF6ccbSf8NnAZ8yva2kpYDbrL9qoaLFg1Lco1GSLrS9qubLkc3SLqBamKHO8r2FsBZtnesOe5tdH9B+nFF0vW2d2q9/UnSLNt1DliLHjCh6QLEuHWppKMkbSRprf5H04WqyaT+xApg+/fApC7E7V+QPurzhKQXs2jswK5UC1DEOJeaazSiZf7bxSyL895KOpXqvf6o7DoIWM72+2qO28iC9ONJmYXr28AUqi8z61B1ecxutGDRuCTXaISklYAPAXtSJZ7fAN+z/VSjBauBpBWobtnYk6rP9UrgRNvPDPnCpY+7zC9I36QydmBXqi8vW1L923ZtsFqMbUmu0QhJ5wCPAmeWXQcCa9p+V3Olqk9To4WjXpKutr1b0+WIsSf3uUZTthywrNzlkm5urDQ1ajdaWFJto4UlXWV7zzYL0otqtPDqdcQdpy6R9E7gpxk4Fq2SXKMpN0na1fY1AJJ2AX7bcJnq8jXgDQNHCwO1jBbuXxDA9mp1XD8WcySwCrBA0tPkC0wUGS0cTdkF+J2keWVR8auB10iaI2lZGwzSyGhhSR9os+/4uuOOJ7ZXsz3B9vK2Vy/bSayRmms0Zt+mC9BFMyX9gMVHC9/Qhbj7SXra9pkAkk6kWv4uOmSQNXsfAf5oe1mdKztGIAOaImrW4GjhlYALgFOBNwIP2T68zpjjjaRrgB2A/lWPXkW1bu+LgQ/avqSpskWzklwjljEDJuNYDfg5cBVwDIDth5oo17KorNX7Odtzy/bWVEspfo5qkFNmahqnklwjaiJpDm0myuhne5ua4vZP0KEBP/vjLnMTdTSl3VSH/fsyDeL4lj7XiPq8uaG47wbusf1nAEmHAO+kuhXo2IbKtKy6Q9JJwNll+93A70tXQO5lHsdSc43oIklrAw/WeU+kpBuB19l+SNKrqT74P0I1FeIrbO9XV+zxZsBMY6Jqfj8ReBpY2fbjDRYvGpTkGlGTMon78cBDVH1wPwLWproF7r22L6op7s39E3RI+i5wv+1jy3aaKiO6IM3CEfX5DvBJYA3g18AbbV8jaSuqSSRqSa7AREnLlVtBXgtMazmW/+c7QNI5tt81WL96Xf3p0TvyP1pEfZbrvxVD0nH9s1HZvl1SnXHPAq6Q9ADwFNWiCEh6OVkOrVM+Vn421a8eY1ySa0R9+lqeD1ztp7b+GNtfkHQZsD5wSUv/7gSqvtdYSv2DxWz/semyxNiUPteImkhaCDxBNdBlJeDJ/kPAira7sWB61KDNogiLyRSIkZprRE1sT2y6DFGP/kURJB0H/B/VYDVRTW2ZBRMiNdeIiCUl6Vrbuwy3L8afrIoTEbHkFko6SNJESRMkHQQsbLpQ0bwk14iIJfce4F3AX8pj/7Ivxrk0C0dERHRYaq4REUtI0haSLpN0S9neRtKnmy5XNC/JNSJiyZ0MHE2ZpN/2bOCARksUY0KSa0TEklvZ9nUD9i1opCQxpiS5RkQsuQckbUaZUELSfsCfmy1SjAUZ0BQRsYQkbQpMB3YHHgbuBg7KtIiR5BoRsZQkrQJMsP1Y02WJsSHNwhERoyRpF0k3S3pc0tXAy5JYo1WSa0TE6H0XOAp4MfB14JvNFifGmiTXiIjRm2D7UtvP2D4XWKfpAsXYklVxIiJGb01J7xhs2/ZPGyhTjCEZ0BQRMUqSThvisG2/v2uFiTEpyTUiIqLD0ucaEbGEJH1M0uqqnCLpRklvaLpc0bwk14iIJfd+248CbwDWBd4HHN9skWIsSHKNiFhyKj/fBJxm++aWfTGOJblGRCy5GyRdQpVcL5a0GtDXcJliDMiApoiIJSRpArAdcJftv0p6MbBhWXouxrHUXCMilpyBrYGPlu1VgBWbK06MFam5RkQsIUknUTUD72P7FZJeBFxie6eGixYNywxNERFLbhfbO0i6CcD2w5KWb7pQ0bw0C0dELLnnJE1k0WLp65ABTUGSa0TE0jgB+BmwrqQvAFcB/9FskWIsSJ9rRMRSkLQV8Fqq+1svs31bw0WKMSDJNSJiCUn6ke1/GG5fjD9pFo6IWHKvbN0o/a87NlSWGEOSXCMiRknS0ZIeA7aR9Kikx8r2fcDPGy5ejAFpFo6IWEKS/sP20U2XI8aeJNeIiCVUpj98D7CJ7c9J2ghY3/Z1DRctGpbkGhGxhDJDUwwmMzRFRCy5zNAUbWVAU0TEkssMTdFWkmtExJLrn6FpvZYZmr7YbJFiLEifa0TEUmiZoQng15mhKSB9rhERS2tloL9peKWGyxJjRJqFIyKWkKRjgDOAtYC1gdMkfbrZUsVYkGbhiIglJOk2YHvbT5ftlYAbbb+i2ZJF01JzjYhYcvOAFVu2VwD+p5mixFiSPteIiFGS9G2qPtZngLmSLi3br6caMRzjXJqFIyJGSdIhQx23fUa3yhJjU5JrREREh6VZOCJiCUnaHPgPYGta+l5tb9pYoWJMyICmiIgldxpwErAA2Bv4IfCjRksUY0KSa0TEklvJ9mVUXWx/tH0ssE/DZYoxIM3CERFL7umypusfJH0Y+BOwbsNlijEgA5oiIpaQpJ2A24A1gc8BawBftn1NowWLxiW5RkREdFiahSMiRknSN20fLukXlLVcW9l+SwPFijEkyTUiYvT6RwR/tdFSxJiVZuGIiKUgaR0A2/c3XZYYO3IrTkTEKKlyrKQHgNuB30u6vyxBF5HkGhGxBA4H9gB2sv1i2y8CdgH2kHREs0WLsSDNwhERoyTpJuD1th8YsH8d4BLb2zdTshgrUnONiBi9SQMTKzzf7zqpgfLEGJPkGhExes8u4bEYJ9IsHBExSpIWAk+0OwSsaDu113EuyTUiIqLD0iwcERHRYUmuERERHZbkGhER0WFJrhERER2W5BoREdFh/x+fBy8qEaEqVwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting a heatmap which shows the relation between the variables.\n",
    "sns.heatmap(data.corr())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#dividing data into those having glucose level above 150 and those having levels lower than 150.\n",
    "data_gh = data[data['glucose'] >= 150]\n",
    "data_lh = data[data['glucose'] < 150]\n",
    "#plotting a bar graph for the people with high glucose.\n",
    "data_gh.Class.value_counts().plot(kind='bar')\n",
    "plt.title('diabetes among people with high glucose')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting a bar graph for people less glucose.\n",
    "data_lh.Class.value_counts().plot(kind='bar')\n",
    "plt.title('diabetes among people with low glucose')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#comparing diabetes level among people who have daistolic BP above 90 and those who have daistolic BP lower than 90.\n",
    "data_hb = data[data['BloodPressure'] >= 90]\n",
    "data_lb = data[data['BloodPressure'] < 90]\n",
    "data_hb.Class.value_counts().plot(kind='bar')\n",
    "plt.title('diabetes among people with High BP') \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#comparing diabetes level among people who have daistolic BP above 90 and those who have daistolic BP lower than 90.\n",
    "data_hb.Class.value_counts().plot(kind='bar')\n",
    "plt.title('diabetes among people with High BP')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing the standard scaler\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "#dividing the data into features and prediction column.\n",
    "X = scaler.fit_transform(data.drop(columns='Class'))\n",
    "y = data.Class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing train test split.\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y,train_size=0.33, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing the logisticRegression model from scikit learn.\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "reg = LogisticRegression()\n",
    "#fitting the model\n",
    "model = reg.fit(X_train, y_train)\n",
    "#predicting the output\n",
    "pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "75.68093385214007"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#importing the accuracy score.\n",
    "from sklearn.metrics import accuracy_score\n",
    "accuracy_score(y_test, pred)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "73.92996108949417"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Using min max scaler to check if our model improves.\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scaler1 = MinMaxScaler()\n",
    "x = scaler1.fit_transform(data.drop(columns='Class'))\n",
    "Y = data.Class\n",
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_test, Y_train, Y_test = train_test_split(x,Y,train_size=0.33, random_state=42)\n",
    "model1 = reg.fit(x_train, Y_train)\n",
    "pred1 = model1.predict(x_test)\n",
    "from sklearn.metrics import accuracy_score\n",
    "accuracy_score(Y_test, pred1)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing k-nearest-neighbors.\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "neighbour_classifier = KNeighborsClassifier(n_neighbors=2)\n",
    "#training our model\n",
    "mod = neighbour_classifier.fit(X_train, y_train)\n",
    "#predicting an output.\n",
    "predictions = mod.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "68.8715953307393"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#calculating the score of our knn classifier.\n",
    "y_test = np.array(y_test).reshape(-1,1)\n",
    "cross_val_score(mod, X_test, y_test.ravel(),cv=5)\n",
    "accuracy_score(predictions, y_test)*100"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}