From 6880f58c580e6311e6c38e9dd2c54676e48c8ad4 Mon Sep 17 00:00:00 2001
From: prernasharma28 <159785240+prernasharma28@users.noreply.github.com>
Date: Wed, 17 Apr 2024 18:31:08 +0530
Subject: [PATCH] Add files via upload

---
 Final_NB_Heart_Attack.ipynb | 4554 +++++++++++++++++++++++++++++++++++
 1 file changed, 4554 insertions(+)
 create mode 100644 Final_NB_Heart_Attack.ipynb

diff --git a/Final_NB_Heart_Attack.ipynb b/Final_NB_Heart_Attack.ipynb
new file mode 100644
index 0000000..404c820
--- /dev/null
+++ b/Final_NB_Heart_Attack.ipynb
@@ -0,0 +1,4554 @@
+{
+ "cells": [
+  {
+   "attachments": {
+    "Heart.webp": {
+     "image/webp": 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fgSY30+6SXLPIFK0ZH84DhgznVnfNdOygM8EDsR3MEBua7pONzH1l5r2WSBFrs1I8rHpV5IWoEPS3yOaFifrbtP86MdxIkzecVs1SIXle+TAc0vbeYXuRLBZ1BX/Zx5jS9xiXmSsoLWtDmSkEHKfhQWJeJvRml8pWDob7Ls4Mr1MidBv25ymAdaLlvHX1ARYm9htTEBRs2+EnY+rX60sK8jqarm6rPPSmlOwCgzNnD7C/noD+EyIJaMYXBqQ7xFgdN8jXUf02hXJo4X88D38ms4eLEwh0UyhBnEB4JGf0Ds0/3akyPVkeTHBC+HbzcLVHTFHreUf1zhYuI/nBC68VwIQkUjfm7MIcenYvLw5Fw6Hb+7VSR5djcQPm/uvRjlXb+nlNAYBTEZxCD5rCBZiAwXlWH1lP51znnH8aFTQtbb/0reHLPn2kgt3754pFYF/LGPBOhg6QQS9iVkPVrvFuqBkHlpO9UuaUBznCvts2fkRb8qs26zQgQ1NNX0ggwPjWFZGnRLtOBhRLswvp4k6lXRp7c9j1COTT/l4zESKO/XVFo/zVcVesjgo1Bn/nTp+63l/SZ/3UqEn453m0Qn4GP9cA+OZvT6IsG6+AjsY/Umn/dGSt764v7p1sJNEGvVqaytXjC1N+eXfZiL6Sg7aQ7xVDsZES9dNNaQ7AvaafaPMyx3r+dg8FNqY3yFr6UHaeca5i1CMp+/PdvgTHIYKFjt60Ya6P98ob4IC4LZ1iEhO82vD//8Y4ftoRegvCm+SFJDa6w/zSUMnP+eakR/8+10uNhgfA+7JLX3yEpgfKkE53xP0ylAVw+XnRHFb71aRwnJY25t1WWhAiRs7zCq/vYMNwB7PHfKjZxdPGzWRSxawj8ClMz0EP+fWxxwEv9tTjk24WX/UKOj810c7QXCWuMNhn2/zrT2AHpSj3lvu7uzBmfT3DY4uWap7k2ilTbNkMYwutDCWfgMwuWL+7M/h0OpwD+0AQKBBUz/7/5dOorRqr83wcH1H02mMu1zbtwVm3brfhq6BuL9Q7f2cgRXS3h2kyCcrP9YZ2jGmyefF2FB+MFYgdczVdfYxaxqPVhgpQCftGMiqLo7U/IedipSoRmlL3RnvjTTdiOWUZY3J6CXsShjeN9uhNhmjtN7eFOMtlHBzrnnjCdRIlrsnG1R++M6LfegwoI6YI90q+/Y9RgGkptJYGZcOnOvDfGzj/KxvMN2+dHJBw4pGkV4MIGMknIbUw+eaUNbEKPu5VWsM4C8TLCL46p4QA/uwQhF5fRRzFm/SfeDRFM/lPtjyxMyDBZnorVrIIUHCqwQ88Usc4yLI18lAgws3Vr/WggFMtF7e6RZ9a4V/B6vlF/HqHCwWCv0P7dJc/gk3EfhENhPNt/y5sM6dTLUbSMtWJQ/rm8EUe4AgMVMl8GV+pfP1sBmGaCQMe4Pa4JXFCfA9TL3Nmcw/DJtwLHJEKqw98anHdSKRltkIH0FAvlVyJwUn6w/pnGM5CgMiPlfE4sdikE+H8d/7ep2fHHkxPPAzlqRuavVI34bkvb02wwHnrPtFJfUxCBzJ556kfhCnR0Ze1sYQpmiMDYw4b1fNPGDuFjv1UH4eYoJIKwaduXBFzkFHY4Ov9K9655VnQ5NzUmn6LHgQb70OI9IsMmbbMEux1lQzafAIDrzPd/2k8BpJPaoaBcfDbvJe/QseoKt3QGzKXYCeorOBLPw+fyjnQLJRXsAPE6BbeW4IdSPQbqzxkt8M+uQZMnGfDBjr/JEJ+92DbOCqHRKBdBZ+RgkWSRYzEwLxMOG6jnICn5Fb1LF+Oa8GG37jEvDJ6UN36Lo/Qtbt7gL0txX9TaLvwylJRVh/EWbWeCWMkilcjgY+12JRx9daKYxEG12Jha7BtZCYkEXB76Dvg30ZfayKdjqE/d86OUkMmhoX4z9EjusrKudBBZkHTQfH2mLKu3RUE6MYxN5EFz+XnXfCvU8HPX0/v676nJihtaB9ZTS1JEJ5DXhggJxgnDpLYWIWIROSDKmcbj0S0V7G2NyoPYnpym1QmxvBl3VrLV+gnsontma3gvwiyAjgBI6h+sW58PBYMK0A9pHa79x6DXUPKvUIstuk2Ap3tPaMLoExwngqNlBHyzUYWI2f11jT6EfIsTLTXgSRUZVdN81G0HFlYiKmYpcb/kGm/kCZmRvEZoz7Gh5QdgwJEQ7qM/x13yJFVqc/eZAQdmFm4QkiD/ob/31pLC8M/d8HVQLfOBQClKbVxqIXhNeIKX34+M3W8kBUmHB5l52kdNtmGt10OLXm3TRutx7kATgtOiWgZfRoZzo9DtBtdcQo2vmDQfDynL2QWVrLUDMjPe1iRjq72EQV9HoMlQDR4h5JxkIgaqywal17ZOEJYSKaGC6zdHhOZTusw0TnsUugs1ppn9f759a+Pry0p3uySMxAaz3nUJ7DIty/59RBx9EZxc5wVMarTF0kNjQXMxS/49o69JNpINpnon0/qRkl3W5faaOAsxIulIfEaGq4qo4To7UuLv3Zc49+Mpkp1OVfWpHqI7Kf/kiBN2WC3T84EDazI21BnYUaadQcyWRrmN4n6pbEp804q5GNtPnaalyXdl/Sf1EbjOmPcb6ZVM88qcsxolU2LnjcJTshtdLs0TSC30m1haMST4YeuEGygP+wmMJQSJi5d+vNtt+kM8xVqYvt3PXLyM6eLQA+u936xbPFyPQDbNGP9m1L3zSs6zrCJkxCIORyE8bqUazKRGABjramr9/GTFQGY5bmHWlayaJERZhLjQUX0KchtvA7WWoQPM7ST4AIxvIWyVNW4vACQZ/itIv97oKjYlHtL4MGWu88hwIF9MRfFw1xParT28imCtxA995I4eZCZC9+QwI/L/gWE5S1viVF4TneTgHG1xiJ1X3EL2fKldfuxWBu06ZeFiqlZmZuqK80fw/aAn2rO1dHdFGi2EqiaC5lCER4A40TliA7XY7sB5zU2vF1rO/3rRY65WB51SLhowKM5OH2AT+IMdFgH5/ApdrER2yTFDWaeAn4hn0NkeYW9luqJI9fmLNR2LPNCmcdUbbUk1WBXXIqifO4yBXoohc1Ig/IgTylfOl+Eo8jDHze6zvI6Wr+hcifzvnepTbqUiSZ6ephVMS6aLWyYmIPqglh3pBHr7aE+rqoOvbe4erdFzSQy+9yulKu5m8PEh+wx8LHlbzRH0+WTr4sNoLjFgt+vETS+68deR62B7h4Q15zA1e2flKMa4IxOcKXHvXp66rpYLTKpbmztlUl2xB65C15yrIvUopLi8biTDL8L+N/6iRPkFR2oqhfQFPcNr5OnXKqxFG4c0hpGS+GhBnj2ty0lvdVqjCsJeejBPYDIe255f+nAMUxmw0qQSJuNHEvuqc62/IW9GUa5e6qi86ZVqnX3DMDLO+BZfEzwIM1MU390o2TvitvwTVfDWfvyV//57NhKbdsUnsBA+eanjuNprz/lJobae65+hf28qYoeD/TxFfn00Y0rSx2J20Snll1NNPQ65SrZGt5PiCtRuuqqFeUrn/JaF2Wn5d3j0J5B9X4IYfTCbWHQw8GkwsBas6BRomfw8KUl+G1cdnMVz7tJHdYEd2M3hvyfjOnrin7/3NXNratbUDgzsF/aKFVAMKh+6p0t3mCrO5LuXppT9mPpSpeZ4wGvcAW82xnzU3TgLPsu1D1BAvjaVI6xUdmnUgEefZdW0O0gQMT25uKruWOuLbnDhKNuLvUqpCVJUb4a9zNT+nkD0ILS2FZwylvwFz/f3tCfEdJ94ElSSMfyJBZ+VKfT5TJYPkiozpFhpUE68kNp8t/KLqWPX+BVOTGz5twFxCFLJltYk5wWsU/R99DHzExH8/fTVx+WiPdO3UVEKyb8vA0cEtmyrI/esXZAFqLOqcfC9K0whKnludMA+Cj2p5Gkxp4bDVN3HW2ymQQJmcCQqB7qPJ+u4kYVjg22V0Q7RWjXBokko9JPjR1SOgE2FMg50+Lv2vpylCREJ3tWH70oNK7v9LnfLei+Fm2zyVqq3AmnM4jkuC/dRSoc9hSC4qNTUvI3vxDfog+UaJN4xgDyGnY/Fkz2lE9aWjQn6BDNON9x8irSADu7YnpjxPvoKlKsotNCASgEfW7FGOr4ZvVvCWZmC7DP4c9KofMy/bX12CR+/xFnRkSFFFQ27N2uPkdoum4g2QlrivdSi+s1yViVh96l1a+c0CI1hDXRAZz4WpQi1zbSX3uie3wJExR2LsQXfp9qPnZMcqNFZihEdrlEabEvz+R+x1CRB2HpNpxkBzBQrdqRFn+yGnZtfrWeWjy/feod/q190Ub8LZjegWh2UAxwxNo95FPnElu9HvrW7rMCfM4ZgPmUPyInJMWpRyPnjNZxSbPHX5UoL3l9HeN5EkA70mpCbBgcBRDKrOoHk0JbO2Q/0BMkSJihFnXwCIfFdvTCkOYay2mHHHO5rL4TJEMJN7wdvC+QJ6b5lQ9xrBsK3YNW0+y4nzAUDM5u1YIGiEYL/pDD9CLbc9lGYAX0AWFDqZETJr8pGjgK7w6wLtnwTrANJoOAJJwXlgHofL2A3m8KNkReKtAjZfNvPYnLSAM+j05KmZb77xvoDyWbxEbeC3khPi/8b3IACJT7lNsfQ+1IZf9EXumCAP9sV78aZVgOkkDhJfhNQEmvwkoRU68Pymm8eC4xsYPp77XnZbhJO27+yAM7GR85GGTMFOcR9vl1dPbIhniMOOERlDJuzxRUVGs2s1Z8oM93ZmMGfdk/3Zqj51+AmvWy2dq4XW9F2eHii6WrczqbJGTfILOnVvDJkXYl3J2mVp7IKR1CMD2auQCrxM10Gcrd8zaJbq425DSEG7zCAKsI2V2Gp3y1Htl2tJ+cks3kvyxRi/dVnvVdhTOTDEiVT3v9xSeQq9n56xl4Sa/i+W3TW8gbo+RrPaYArQnwjYcxgzc1iTzTxm0J/nPs0ZC+96AfO+I7aiOuUoFp2Ea+GthjmAHJLtj4Evxppoj7nqI2AS7mecRMvH9akBxhp1ZyH4SGXn86mEsIT1V/KY4KuHNLBTsZzGNDNw2f5e1yI/LIV6CEhSlCURCx1ny+59qQpd10dn0fl6zKdCV4iyH/Hk19o2R7d/ycGz9EQhZUvW4lGAh9VIdHiq31bS+dG4y65eGt5XCwB1t+1SYx6mlnCAq+WoQ2PCGd5BFQcZMDO2I8Xkze8xN9bPa0hK0Qp1+dyKnWJ9HrdKEeOm0mGqNlARs69c8/9UjrEWeIp6STDQVNHXW3eKoNjvDaXGq6pOmuwylqhY6eXSoQnGy4RIhapuOKoZ27smKslaVc4JES+AHrFfgmI7kUTn5UHierk3lqZnts7p9cGvqYSn+9lv80PNUuq4nIg+XCr5Vyfq8E1cl6z2WG3IeVNK6MXRk3y00S0Z/K1+YWdd12yyJuYyCOeRky/mqyXlB07Wi1bdqjL9Pbtxvp7AJSQGcd3rkB6RrmQadnu2WuP4hlsnNZKDYhlmSR3DfD/Sw3JWf/ta+Wx2DJGk0Plev45NIt7CdB/D9fkruaLHcWVqLjxtKCtp04mnAeP/+UGHHlzxu3Zs99bOENg329YhBAfoESsnqzNhN/6KE4q67LqRixDaMlSCLUkKeV2iLHGjMZEqAlCHi45vDxnhw51b5g1qVk9GhRMfgJJmjuYYDkhFd/rtQpUfOOYN3owGQD572BZ0p1NVaTJk2z/idwjAuubZzV+Xso19ofHc22e6dwQ9MSjUvntz+lE5LLsB6XEeFDY2EhtEYIT2QCa3tMFu53DjYataUMfuXy/k4RoPPQYId7idP8O3VHeHP8m6cP2jNdm5f2g50NVhxJTA7QaHsqVRVJ6AMS22BAYKkCb9zMNELfXsUuLrHiBHyXlHvqvU+4daPIACMh5WQWLQtgGLGqgg0wdES/bqCncS0HWRuYv+n/rJjDSz6wbFD4yxat2oyldqUP1gjkq7XLFQHS6f/UByVyQx/3UUGgMVplTVmN159+ZWQjnsHgqELwNXELenCwLE2dPdWFJ49r8DcbkQWNHL2YMZRspP69GqKhpwKfRfgBvAkC2U5WRisneKfQh6K5QCt4MVsojUNNy2WFtbyVyEKfdfKode6F6cgh0YrhRvL8NqZgx3R4E/9SEXka3b0lUwy9qLrmmU3fGaWmCdUmirRMu3AB/c28zAkvbkhRz/ftmjpw34l34z8QyG19iksW5Q4r+cQNrGCiI21NQX9+ISEw9pwG2qyUYsSAzFqI/iOyDY1XfyNvMV1PfOzTREyFxw237OrJa2WZFen4DNS+KECmmKQ/RpUNY5AGk0Zs+ixZQy7o3ojVTkrcLInBrOUIM9Hi+uD+mrucCN4D7L79U8+hqk7jWuwTEzTQ+W5DQTBncZVfqBis/6VmwHvvtdbYkXf/2MORJ+jrXR57S27pWdV319qTtwGA6hhZqcb+N19wBIpV143dvJ64MtHmm7TQgE0ecFuWhxS6LwvADMpqAPIHOpNFOtgNba0aLYn+pKxuUkdPPrjomnX2J5i6Sf67M7p1puk/x82T1qPlvC8fDROXsU3ERnMMYQpSFPRH4rLI+T155+c/hTdpfzq1B+ycqPiV97EpILBQRI+0yrrgmNRCmTDuvwDVX7NLJIXAg7DQ2ZCsQTHTq+4e7qLCR17Y5PGqjkdg4la1fukESzBlMVUtQZhv8oF8GTuIyobnl/++0/qze/WsvVx/QkuRtSXea6/a+GLUgTvGE1AF5APpg8olcJ0UjwrJGAcczIYBGO355B34yhWR0igGdDQGvI1fWu3xKVnSaXLNwMJ5qmc+lZsj5I/OIjAcuZjK79Ta8K10M/WjjuG4Hwoms6QVELlDV7TPj8Q0hNfGbUPjogml8sPt3HIJix9f9fKKLfN75i/5PllqTjOz3MP8osRj27AheJOnQ4wFaebke67esAMeioFqI12I5rfQAMTu6XQHyqQoqafTY86BjxLXQEzSp29YwPI31wWlpakav+SeqeXjELUocQ0/H5bL1fSTtvDk3c6NaJQDL8EM37TfpzfvRNQoLrUKhOpNo+aI1hA7bPDMA2CF4kN8FK2y7IH6B7/hlD00ANWdSkL0VLuImt1BiqPDG90/xNM/zMpKBIPBpBOn+9c10GthYbrcHUNlBZckqjuYAFfOg9BF7hllajKnkN0g1nGQ9beDis35SbSTI2JuzktmNgdEbiPinkIJXks5GsP87QRM1xbnjzylrUntcKh45Q9JLVi33k/WgSk/0oyXiI3vUaeLJanYhUw/5J+Tx6Ft0Cc0NslrVKxs9dyZ0R6B/vOvzxOeK9OsUBLwcymed3woxtrQTk1QnRTESTeO2JVUK8XyTtbGu3+Up+3i9odb5P4qgpaK06LqyKc9KoPjL080P/wOfI4nDRMW2PRi1pXNjD+3ExuFdaSoexXRJ07yX961e3ChStSJ1WnWiAuonyw+rco4zVKdfgF5zk/7bJxQz6S1rVDFZ24Z7vhH+XEAsoHcCNMXRjHAYBZ00mgZezmnvNjru6Iadn4cgcyCIaK1XarXWWqmq/GhT1vQ3GRccDydXMW+0wAnIbIX9c2vhuZeJte2vHBqVfzIp+/kLB8AtClH9kj3TV91sBpEMoBDjKBbOg3nC8YqOxm6nA2/K9zMKxyORSwtuhoLy5wj/2T6hyn/hMne4vjwtTTsuuVlbndygE7Qja3JIwT3fawG4aMbuOEgC8op8QaLFYtXhBP2EMp59SEH5RgYvu6PtXTIPSyZqu2FtTfwtAIev5og0EU0uPcHre+j4TiAV8up5mT50nUudR6FHnKHgMhhB4gPchBYPCbJEfKtRNPOAVfRPEn+dgTN8KSThVL5G0yLnboB/Vt2YqcZZanOeudY8mu7dN5H4umlZFBrH/Q2clffp/9dHwvxHBcqAk4/1o+vsVCTs1H/oqGCjEnJEnuXA3fQxZeNEnmEUPgAK+ARWZ5AVY/ExzkkhjJvexbEIFsMO6J2arR4HrhZknacRvIPSODmI/pz3mXzC/Wen8Vc9oZKOI4WCC9d8K3rdBluXLCbd/+629atMW0/lBjnjuVar6hUR9JTfLSZdqL0okN6ZTEm1KIB8+P2QqJL4DmdIrPOYWiL/SGfjNtTS18Ld4zNoJ4BdgpJG7DxecfuTpujpihpeJIN82kl8xCCvJPqxaZ5zoeDY4AW672OZcCntcZvPUhiwhx/KsrrMqDyJyR5J59AlJfjmy7m4dD3euVxEDLXVlXUPFlaa1CoKjsQbYon9howMe5dYWM1xveqZX8Jyzr28ToOvPe++IoMwCXBiR0LcQCUojBKF9dsdBuNT+Mzhqz4iYzIlcPytFMX5t6YsIpImiEckYxMPBweaupkigt+fyqigI2FMH+rUD0eJSHy1W9FxAw9IReYBsWkyfw4dVGJb0lBYuuCkvNGG5qk/kgpjuj6V1jwz2D/WEBWhmoFgPlKvxkqf7TjHSsH4s1LZXTslEckLC3yhDbqLziUuLBXyDNo10O71rx/vK01piB8W2reL8JdvYzhMrDUD5ShqWByV2YbCWbfzq69052U1Gvg+bEKFQawWAIfikfiKhMeKnEur+MfXefY+qJYxUl4BYsZsvs08wDMgdjJBy1i9mtnVgtJxUsEM7Htr7oJiUAR3DzYJ/DevzVUP8xhMmJUWEA5ofHp+NMH7gNuVMGl8/MVj16dkSgVaeLVuWOFr7kGG3F/szoArypZHK0iIMapYgOyPCOuSFvS4bWGLLvZKVfb5rNIScXvF9cp9k74Ahp4b2NSmSVh9mj5RTiwM3LzcT49CsMaXtZoUEpv671qF+iRmBmcsD25at86pLWrF7slBBPg1grpvZ5MnGXcrF1/RZLEPTDFHRzUVR+Ep8xHLxFbEEbKWi4qv/R3LQWQoxE3rg4mMigBYQAVOiUWu5V7piYePxVn301Y4WcgL5jDNMeDeYrpvJYqnsVqJjAG5yndHStOojpGZfXtoclNvzgJfR/xhGZ7cK8TYVHAm99vp07APHq81irfd4pzIDbgN52fgD46tE2Jb7PXnlm0zRjGmXSc5OFCsFtzGWZDPDNwpAnNV/oZuODQNgikHp6UIexSSY75o274Z5GiQv0XGHSncXJ4eRx+9kWhfvtqsWpynHIH3ZKHfx8OVJ1CfR6Ba0UqkZB68vwgAQOLHkJe3wfe7RNd0U47GiYcz1JlBv6h4i5EFH7z0ECOnNAZk3/x5i9ooQ5ICrqBUwXuTa3k6CrLCKjyO0FdJX1Qc5nMTgdo/31eFrRB/DehA3fOClCwr4Y8/8RYSDBx5m4zKsf+nGUQL4h453Bzjtwc3IYWc4+SDIHYOemvWeDx2Vg3nHsz2jnmkD1y6b82p2mQAFFycNFemCi+1pPL0PzUGXXNvZF0yxt18LiKKH6cxH9xqtuxq6ZvZo63qdpxKNMy+1RpkS50orQHM1EDnTimbGFCaMRZpcZ4B4nr3rylchSbleI2QfMwdkJwbOzBIEtTFL+kIIuXBz8j/lgAc50z4injntb3VnwK/BS/P1XI+QncN5f4SzKOVyGZnJCpBwWAxg7vq9miA0ZJTSGysHZ3tXUH78HYjMfDO7joanFQ0ddkXNvMWZ/AFGW+V7P5TYgjEhrPFN53CRIKS0dvVuGLUPaLJpRMKn9ZcrVmr3xTnew0AfFNh6pUnrfk1dcmlBMBYaftfuvbOoc+IOt1snU9C3GDmiYYfxoYiTKm31Id/164Ur5Y5lHud7mvlNZTLMNp0x0l+oBk8gJURUEriBuijAKDoO8l6n7QYa2KwxieIGxEPwyBO9GhmkR9lXHnFKNVIwCqSEG4M+BfiV8iifHjkAM6uWob2ldv+EFT3E7bC+JsuAyMjF3czZvo0oxBtSDif2VTXsOsuIP/JKLDdzH5BXE7gaOJygUhhJmPEv/BZjFtgwEPDAkU5/bufQbjMSyiLtRHrxi4FYAV3yB96qzBYUDQCDqPUIfH7bODTTPrCRx5zvO2RxFs75llZIOOx9k6VHiqrY7cAQ6/qL9/PUq1ElMgeY+HCHwhds1awCZ3WdMKWLg4pVdfm7HSX8FSfk0z3R7LvTCdzTboAaOdDbq8UMtrtxasfZY4lTzIrw8L4ukoDurmIUoDDvkk/F3PBhrq+m6fqmJgKZUWn6TOe3tg+YPDt+dGpboTDhGmScBl2ISS3RFqumMkaxX6hznAjGALcQw/fb/mKdh8CnbX50jAWhcWJ6ceDCs15e9uoTjR4Q9o1ZnnqYbabcgS+lEERPeWerD3/jbCiQ6/X3Yy41GFAsKP7HZWTy+QJlvNfC8lYbJ5Y+/ByLzvO6oPLLgXD0Vrb/PdOljfOyvy8G9xSZ9p8ioSq+ovK323Z587BFpVML0iWstCUmFKbdd1WT4/ZnEjLPNKHM9gBRRzXpMWNJ1P4+PsVMnjAfsCOITOLq57EEP7oVgqPjb/HqLWay0OalpfOlJXD6gYdqRxeGF09ilMlIX5b7o2D/YNd6LZ6eOzGGedlWQ4FVhW38uPjqgP38spsfJIhD4aPtaST6ly1P362NQVsaqWeA+2RI9suG89pSAtlGZQP1OnfCZOP9PtBOVQ5GbyVo8uOrbi1Yb+H/8ng829STm93hAJ1k/O50M4AVvbAsJX2oeg4myaabcJY61tVX50nG4ZGwkU5wldzrQWdo4TKE2zVYCvEMWcztoGFI3WB9qCkGCZ6S+bsR313a78UMK6MlktjedJQ0I+Gaiai8DX4WDbZhxn027s7EC6h+ElnYqXTuZYZ9MqSPobsW2POwhDGU7wpzCZL6nwXEEqCZ6/HbXBFyemdE9iwimJjYEZaj/FMCdbs6+1SBSYpi91Iul5tacBoirinKCoFBAP8QEIOsD3mq0kBgJZ1RP4h23/9N+ZEqC3N16rBvwh6C80zaoBT8sxWA7sTAuq1jY+LY2w30YTPMx3IixkDytcfM+E9AxNJivD7nb5wCYBHBVcu3fDBL0BX8DOXGM+4M6FVFghiwokXSzpTDFS/JqBV/YcYzi9dYe6J8ODdfdezG/awQ1zriVGWc+El0AmDgq5cnsft54Wd3wX26LBRoQmZX1yJD4OICDAMnRRUGiyw6p6de5i/axOdzALrV/Fx8e+lhy3QYhYvROpqbW72ApFHRhQgsoNUq/g/dBKdaI+W/1IYvmpaVlgpszaDOOlZCEKbyDWeh1tJJ9VQ4ntN/rx9QJJKFpUMsE8FmMEUpyKm3eSGj3OpYmqDELUOhmVE+f7fwKGZTSltGvtkPLgztFxNhvG7+OflBvZtTfa/pvgZH5bQn+XeYoTFFsl/W9zBvylM1TNxJSyt16kyyRrbY/v3PjqQdikq3vTlpCCbD1O4ONko+jwcSiZvEylpRmAMxpdCIAqQotPL5eENnHzo+xx6gCXzFGaxvh5Dlo2SkEpMvZz1r3+5OscGgup2zWKgvMw+YtMeiSbgCgAa6/4cGRiIasl90CohvEn4gJKoeLzheJJSKZWRwcZcmz828Y24Ksop1QMwvD2LWmzrOcfJQSHbeD38Km1HGQl9PMmj/WO1F/Wj3FpGQa5bOIa0FdNQb6YoDkvoh0XOYtELayQAVybtBciEiN461gqbp1Gt44IvG+iWOMHksaWxvXU4iJVVw47eMBNLFjygMm1hLq7sB+ne5H0D45Iulsjx7Jdf/+8OhjgBLbyCSTQf62kHKc/Ne2PEPhJ8uhFMy5U41HA5lsnSPOr+hbC012uAh23bmsd7Fo/ylIFT8ctFfbePiqSgaC0gOa4jmEKHe4OA/R9Yf/FGYmq9Ei+ujPBLyMRcMG8KaJIQqQY5atTbZ37LBc7DeeOaCA5k0zFclsekpacSVWfrASQgBF/+vih1lwpYZ1FJowXuNdN3850Sm6cgSYl11lQ3zikgGP02WVxAv8DOMbhZIS730UOL7KGtXgkxFgc/YfzaOMFkRNHi3GFsISxpcKEZgedh02fWf8c6G7Mjvb2vh3XCKuSO09pxGXideOY8f60Jx/Zb+8WjWMNRuEs7/OX7gMceDF3QzvnDH1rL2DmgzI4wXt3debj6lh+Y4dwmS2MnHtwHojyd2RRDF9r+K3fA6nKCkOtUOkuJyOoUz1m/Q2K6rY7ix1Y/pXC5Bv531MBO3NdtfGh6pZ8jHEAjr/WUU2OyKdq8d7QaHMvWdK6LajzQWdir2wvINEHCysdCo631mzcQ0UG4DdeiWH72QStVO9DuvbvQtGlmQCXBfiBCe4e8SLnkV63T0ybz9CYO1+8opa3eG6wB4vpliMK47xpOQq5eaCT40VNaj/cbImoOPF72msx2Z6OGABBQIhqLfelEVbQdb2aOq/DKI7FmkM4DItyzyx44M1UPCowlbP6zJVNx4KAZ+GOohfJqFgR1j95qhAO1HQmQJ/idFceOqZB9NxdAEAkwgmne7FcYgQ+Y1ZpdXFZCIf9lq2j5r5LpDjgyOnDAzz//v00Wna3dEkXLrVGZyyPtKky49/h2Os/p85sf/RuHAWKCk/9ghO+/gw1NWBcwP3nZ5AXcvFaYq5YD5oETA3JR5hvhiR4kndB4ye5y++g3fTX24YAh/MROhNfMb4P1tgp/hc55JbQYn436eYlumrx+a1Un7nh9C5X3g97RmH5g226b+6fKWKUDwc57teMMTbhk6Xl58qRhC5IRCm1ecWMrMg43eSc5vcVDgH7mbuelQVLpG9j4yPvZS7tTZy2Xf1STdT3qejGnIgXG+NqJJxsnYTXATsXsj0z5z+4QvwLTHZTjPkCOznTvs7lkle2vkTE9YrQtNZFkWbFowyxkPNPSmpkOUh/29d0K+vFgqa+UPySbzphHtlsrPjxfa1diYs15lAoQi+dVmjJRQ9MnV4Rwk5duWYAX/8Ft2q0LzOkWoJpdOdBlNbNsGFfZt4K8bu06TEDOpAIfpVteuqoRfZ2fXiY9IeiOwnsS4bwOcJV3Veyou22SXSNcPX1tjOThPVaFnrFvWxkrWnvr8J+WQVBnXYj5Ow9kazTWrYN8ebr9IzWTUc9/X3ueB1wXEgP7x2UASZGYvy78EObZZbTV+F0LaX3fDdrbCyUpVwNMIjjeg5MFSYkuwpYMzOjp1jTG1yK86B2A7e4PBoYy3NPZCZtprzrbHVXbDSHF8+w9ZYixKIPqgUt3zzQCe+XJlLiN5LcKWQMQE09mODplp/G+vD3QRCERwf1J3U5/fT/5Exes7RUW0GEI8LhhWL1XUwaaGL8Xg1Kw/z3uHiqZg1XF9mbuiK5MeDtO9VvEm8zTfxDUV2Qnxv1Th2VYFYsU0PCzEBMaKu2ql335j6Rcm9DOdD6Sk1C5yocp/rfu4FE03cO32InNfEso4vfIZOnzn8Eu9o1RIf0+63xCnrYz16PLGA9eLZtoMKpp2qjELSIJmhtRzjQfxwgBgb9SSjcbuV8Z7/Gmac5sIXkYf7JVD8H+z6PdBaH62RYS252tgqf9JbfMvNljICcdl+dGEMzo4dfiSbQo1HE4ctI2vTc2W/DepMGqc001A73AVdgrUwnNeyZ4OWCSz43zEgBfWuHCJeUdvUuE0gh9CvMKNPbMd4c3g0lSRluvD5dN+rWzxL1LibHt82NucwXZunO+sTRm2mGNoi6INKG3VgCBYvDKhswf6tMSzI+tjctKsEueonZF5fwo1GxvAQJti2ah3149bVonWl48xwKpJsGHzZH947UtQ4yQudv4qw6w8PVAcxorfqllGzYUNpBOW0L7RnV4c8PLnYh1wAAeuspf5450xe/kxZrxHEXLbsYKnbrAkN8drJWUUOhFfPxVnfLXJjhMLuw+xcOtgr+h6V9FieuKlJjZICJM8ewNasW7htozz8uXM6JtMKC1gGPVLSBCr5riuZgcMuABv30mJHnHDSDIVHpL6HOLUyPKr4N9BIGpR1cagXmHiBwJ9nbi8+fIUZxJdBRHMPHs06BCfGaVbvs2TrR1gZWHqj2rLT/kMQ3zV6ffS9Ynkjez3nGE94t7SGdXLFVkdmaK2bkmAQgGk/l6IfTgg/TwIhFLCJWQYELrAsMHkWwKKE3iqUMCgtCYramWk2awQQq/GlRvJC9rNq+A7CzyqSy2CGK0iVjpVL0OtxMFKq71UA5dmeY0srMxXacw6AhzBZwUQWeAhgkrrB/1+bjnuKkqFGU7xRLqdmsuQW/JW1L4d3IXGEyD/YyciFiE66gHq0xltc9MUItX8sg5pnYeTi95YtCccDvrmnw8D9CthgNrF8rbtCiZ4Upr7KBrumwH5X3tNUP75eEspvbOBPnkmX6dnaoRijPK7RFcEaurjediX5G/9WZsDIrKJCkvNaNrQ88JHFwiYeCtnBgGAd9M8R5qOUJdr27+YkegSJ9Ni+w6PF99csUzc1gMdRc1HYKl5rXqPkZ3tdUarKwJnOg/Amkjg2N/6wv6iLnfLKovI5+X6hbapklkShskuBXRnZ3Pxn7Or2wV6/r7Yu5hmjm9mcbZ9Q90OGKGLcor56r/bl+pfxd0urvn/UtCj+JVRdWIjdeA0X+OKWg6CjnVhysmKlgD1oOJwJCk3vftvpG+OgLVVMEgRpMLpNFQeNY/WvIpxVHWGMwaUaO+DWD7V5B96rP2jJ4BLEyOa9iRKEj1PKtgeoMhiVR9o6MFC/tzjU99zRSu7AOxqEhHWMIkMdJgJER6/GXxA4GdeF+PUJC9rkVwhKOJUrnaY6wc1qxg8yL26xZ+F3TAJc5IDA5wcMAnZZIDOTueAvizxSDdHN+pl7KOKF6GrLIkIPIA0StxK/N51WZDs0rigB8NJLwytVM4+OxzxLdELFY+EcjOZTKlIaD28Sbo0Hy0L3JfBFjovpKgZOcCVvqmeFTLIR7woA2eC/xcIF2dAmardnLB4oHnTB51pI1MvvDnROn4eMDqo8X8JlLJ/40bx1QsKJA2F3zhinhNWJXmaEOaIpF3wV470ly0rhgVzNfHb/WQ2+V21rRp8Hk3Eq6+tG1kEMYmgj0+0YoDCe5rQn+tSpTKRFA7AXxvRfaMC3g+YY/L9xGaXu7O5npdSpFjMgO2mVjVOqgwj1kruOQbiBFkxdaS3sGxuAlhYSlhMncalY6aPpbZJdN4d3OshKcWQqyakyQqwFi4fwSbDE18kVmywlqOW1loMXpBL286v61VoQ3hIgr3RPH9hry8pK02nfN9mR6RM0jjSa4o9Sb91T7ktNQer0DGw6wDDUa5A4eI+EAkvNbOMcM35px+Gd7r2Nn3oMoQ7xD1heYHQyHNuAKvGhE5ekhSYZmSX9Xr/WlLL2nKWAgXLGPA+8roa3PdHx5MBmi0/iqLLjefOBzYoWN7f28O6XtLCFRU9t15G1xOix/Gle9gGDG8YbCY6Ii7IV2HztdKr1YzbP3Upzu5//lHoUjlefNiqsuxe5eSRr6DNtmU+tiZZfVJHcv1QLep/Ewwin7CmAUj8bKk9YTlZoJEku5/EkeCQgcNSa0dNZ3+IY3SlCRryQrEpg7BoqO2anpKnQgLO8762XMbj+5JwbpVAtuBoBACot3A6nYV5BpkkC5cXcAs27lWcLR1p4+Ye7RZrW7xIKG7SI8UTgrKKiBF4r4EL+WD50QQP5Wd8M2H/CDs2BJcZ7FUKuh648fI9HksxeqzQ5ty7l7qrHzLOkRqlDbu3g8iNmv7e3WHEPHAaHROzGlK+QGSKwe8ZGyKF/Gd6E1pYZqj44FZUD45GTz/916ILciNgclZ0IoNmwcgzjF+eoAf70rKMaby4TKkG5ada5vwHc0RqCukCjVa3s/lKH5ywYr3M02qqqAezVN+34MHNwWIS9SLfIEQD4QmKREmC6gY3bSNG4Ad+ypJe7ePF752MzH8n/6tsTIFBeUQ65CPI8JL5ChLYSRybaDl28vo3aXTrdxMJGKgjJQSRQN0jCfickhcqAsLsxR84WhgWFcja3iRLsc/t8TkBbOKfNBo7opjOObXKLmWwWzhMeh6DeZi0LKpmEWenIyowL5imlH8BIh/MpEBEPZe39zfzbD8ubYTcXqd32hcjHTkiCP0Ob3t243UN/Dgj0rPoYHwW/jjjyWOVB+Hx4u8qTigtfdR5SioHKyTxDqo8WNjx7Y3eRCGkzfKACmJrygl2lfYtdLyiEZ45ij897lSFo5qO51bKESCvB0QsuDInAFNv6kgUOBOI6oUvT+kstUrbHvaTybf9FBSdhor2TQY8BL3TD77k5FKDoi0JVVYcqn5DxBE+wRR9UrpmkX7jR9P7Wf6yk+cbYRdoiTe6f1xKEp5mblTcHe2oxrX5D9jk4TLY8OxIpPFN7asS7MpUz7P+pMK/oG7sxxXIRGQxEQQsja/vNoxshbTtbUKDPbum5kA3Hm5vFkAoRQgJ/8SEX6NZkrG2bWQT44mn3mf7NZGEo7SLQ0iYiBfEY3FyFE15VlGCUjaKPRr/gX0WMLvfLhcR9u4qT26TGdZh8EhMS4qinKf8c1RGHUVrhbtxqqoEitk239DMYVbsEvScg0ozdxtZm8ldkLPpA1gqmjY4gRjs0YcvfWeVJpHiW1/Rc6GrN487slVkZhqJ4UbZCeOPmBfdUuJLdw2rmY23jbM3p8du34MEilt4d9DHL4QjkIlAlbPlCPMjB/+rQnf5O0qYvVGPIusoU/v+R2sQ++QBMixMRzUckvaOMwCd1oe77ZEQu4SaiXYRa9AL5hgn0zOhYIPQiAg2avYonAk60yHhSNILcsuoQGBxwgHoMeXAWDlwXX68/s0BiQhFVniNILDUKDcGBzkDD/KvMf3CfWDHBHIR56HOt+aM7FzAvNJymPlK5tWNWWgNGXB+HydGLUulymS0ZtZnKpvcpZ7QohsNPX3wgKVSNK/g3KNl2kUEfKiLTGwSD4gW8ju7a9saqMUA6vrN3SNRBVScP1JW6qRvOeLJh+h35jlS3c4WY3KyyYKqFlbALJY8KoN9uo3C8l58RPqzvxUOqdNPqHla/bHPedPOiOvYj4hkiFkHaRSuLGMysmtciH9XntLz68RIqBNALOOz8RQ43VUzEqxgsAHQSdq2uNds37CFhXEWnhoB5VV8JVNFJg0GNblPlpKrI0rXqC1Lbs28hzXpcIzuCzWWGvUybc2+JI/yZQL1GYS72Sm3WIjzSF6mVpwy0K+gorCwN18z2+sIPqi/SFUMW09MNWDDE2Vhrq3dcwWhKGq1T4q5WQXwoiEhkMMBAFkpywQGkF0IUiach2F7DFGDBnQPpEwNovwDB1hONFzM5waAOY0zm43UYThefFalRBiW9/YMuga3YndxE54PcqwfC47DGXgQpE9dw9cmfXNilryQ/bhxjFTKVJtuLKv0Tzka8ybqSSSoU3nn9UkTQ0Mk1BKkvlunop6+Su01Oxu8ieopJN7NhzdkZNDYlLI8UPnmqtGKFHKzp71bRxgxB/dUlZuyhWzxS154bWEsO+JvRflhlm+Kk1nYalpu7qHSEdwYllvZSGF+gT8hHar+G0xs5oN7f1EfyZo5K1uc+/5/EAICRCh6uLaqBEgGp8TRFoab45wGIvzDDpsRMxlqNGphu6cGIgnGdKxsXDEfa2RSk6/YH+bidycpmC2FFOtU6VVS16SVDr/TfvlSTajH+EW0WsXKlyLgw2iY8Oye/hbSSRczW56KjFG2po0l0g7tt++bwN2IHvrBnZ3sjvzuX80U/D5iCRMDzPz5w3cTHc1VCndUXXrl+32haaStgfcOUfHDCfrIGAbS/8LYdritqb15iHP/93tKt28Ce7SojUPP0LN8GfDjq5BglLIsp+scGNiJFhjhyV8VVVltnOyAcUBOzbmTDcA6J33xJRx9uPxockO3sJ+MT8KLoRa2AwuPIBPg7yHFRN9WQ4hBEAmaUNvB29DgoDYc+li7FYWam7ICFHOoJVH5J8cX1vuoCWhMgjvoWhxPcW34o95YNY/ud7ywP871wrlpLJDiKZUxnM88RfOkePRjmufa9+BDirrYnud302bNXLZYmkke+lVOjFPV4UHwj5pMCKgJ1sZZZcsKSWqWxAzLkH1UjH72UZNo3jL/DjNQNB7Ay02DFfkmYZ0FJuSzMkoFalMKViJOEQWuERMbQT8nC5U7/xI2wWbaxhto/nX3TP/INqRkeUag32s+jRuswHjtCH5nZsL85fx1xxS2qeanKajxPBXNOhWvs1ssgyQNFmiuxwihVVEhEb1JiAdiv6qfy80SIpHEwWGOKil5iScR2OWys5n52Zd4XThpAXXJ+gXmNftO+ARWFTMkKLizHY2D6oW/2+zw9/V6IDNXe9AZudFEA2kQV0k4X+p06Jvh3f6dyhhBHnFrXOgzskiHIZGA6ArUFfpd4qNAf9d3HZoj8XobdNCZmHNDW4PjMtG7Y5f+i5j4PCitSzhNPmkA+VwacanopX3zo9mvUxJhI+lreNPXzDrPK7/JTrdaN31l11nJQFjAw+q/mVH/GityJgLvDbzmsEg0B52uYEqJA5JkfODT62P/S0XRJNsPgXa/ufkJPwn0DHZUXsZRaTTgr7G4qomNKxxiPORox4UPB1Om93PF+3iFv+lgjlvnLc1H+U+Imglc7Sf5MIFjrevRWJBtFGWFQM0U2LCwMk+ZTs1WtmMG0JEuX7fcN8UqtYy3tQqY6yPnHaNf+koakK5adMyx+/v1LU1v1pwnCHtKXuCaDSprZzVS6oNg0K795FnukjCLLxglYaY21q4VU5P6CD7kLd2AQDapm3zJPxAduBaRlRqhSchM7sahESxhlOiB6dcNJPL73Nb6/xi/dFmWI78GauTMVsb51Zc09zzDPoDz3M2x2Lb6zAjde3sTlPgo1FhYB1UX4GZNMyF6X4Y0gCdykNb79sO/77SZGj2GqkmuPt6x4ax2G6q/7uOYPskbvS091VwbgJrjAsoAZZ2G048FOrB0cUND5Q79C0HYDE30kj1uytWVkXZrxzRdirh6olAFGT5cI9J9ivL/Gqg3hJsRic8yxIuJqReFotyd6CeStzZyWBV+3cAvjOm2xcmTx06tyMmVQEWIHVAGHo/N5CHeXz3z/SGOezUb6SPSswXgDRDPWDa2dSm23PxK7AaEAu5tlq12zgOZVDAQ69Lk41/8i87hetrd4Pt74b2CH3fK5uD4k8qOndndniI5N1GkD/U2jvUoGAyNK+jBSDeRH1XoQ3v1aVFIqVMvbM0iqeeLz4tJ/SBPvepn2QBATo9PeLgbo27tzObMkm1rqHQbd+t+K+9YHNwBH5/bzOclw9u3rGCbMdaR5vwRo1YZNFX4HbIW2zrzwKmKhHhdAxuo9Svq1f8IqKUovInraaf8fiNFjXm4yI1awtcyD/A8oZA9bfjwpXw+JyoQQlX6HSkOJXqzJCJEzz6EpBPaqfMUOYgITnAdZqCF4SVF5A14yzaU/M4thhkup3I8T9Gal8N38akww3HrJD+vNFsWUuvjUQ7uIheku2lQ3a6ERMgk1/Yzaxt+OF8K5dpMLeS8qOJjjo9fd3/Xug09Bw/Mm05JD7zq3YFlx31wdwNfI7tbo4V4ar6M0E8mwrUZHlt0vzw6qumK6hABO8UvPM8G4l9M5iN4L3Zi/Pd8SZL08dYGcOv2/CT7ReHVgPsVSeqsLM6ufIVSJ2/zN1/5ijgxrYQBiOmr4Wwo95gjAceIHNgwuvk7aki8qbu/zTjDF5qRp6wpyVn/clc1nhgBS5qNGK2pCFIMSW9zL7x7fjztR8ywiKwlkqMPySg9j4E9wlP7wVO7HFWukveYFE+TmjF+VKLuUXdrsDaPluSCtvualjXyjVAU22bRLOxqcm9cy7pu4Sk/PYNNexjlNAjYjpxHQzONgLqkuSGsToKFAPb6NFMjrHoaMIGzME1bOoFpmHjLkHvnJjvY+Ittyro1ybW4fPc0laGnNe+VGCYCCdaI40X/gEfUhsa/cWgCJuIeAmEcWFCi6BWzn/+m41kDkdupzN/aAKYBVP7GH+DD42SYeO492mhAyfhxhz+goWnHp9UFzzIjeG5yYcd92CT/ERZhtWMeCa0g4wDGNXDoEayIU01vnH7yxWF2TC/Z/qLKilkGbndkWrnTaFy6AT2AzaAKKjVjl2Pw2SPPA9MI5m7pnm1GaH68y5ANN7UQGnAMRkFZHPwmvyq9P90y95LR9kIMU6ZBismMrL8NbeMPO/1g/vDaBzdwP0BC/jcd/pu0XN8ZlqITvcn8cVFjP+vNiw4UTEwx4LClttK6NLzIwBqHLGUhvXW+52whH3dojIOP6OViX0skX3Xe34xh1lR1UOZhJmYc02Y45Cs98Yc0wBvZ4rLDxEg12wZlWa1sW8LrY/kZs1mPpxM4w+RkPzN11Dz97FflqL0eCJMMm3VAz9yXtaUs1K6qZHtxFHg1Hf7BkWhGg/tjqJARU17uH+rB9MNQmT3N8FVe5ikzw05DtPtSksgiFMdg4rsL8oQHUoww3CU2SRX2eNwwdpW4r84mTrx9bPFiC75gph4FSfPd5gS667DYTJw+zFoT4r4rgqnXBF3ATF2BLc86SL0d+5RtCd2SRpOewSRbUe9NWAC804Sixeo6skm69pjUXyFlAwo9tW7jqyn/PAB6mKLen+N1y1RfkTjWrphQ8UAUYCMf+N44cMrospJ37qc50IUVW13kE4f3S9ScgqPcN6lbKPcZk3I0RM1v2cOU/oErZyLYGyxxTBysqZ9VHrA41/F62eL3SsMF6F+HxTcYxN84xgChdqS/n05FaFn+c7R46ru1XtKrgWTSpLmXw83yzLQr9d1yZYDNqf9W2ZkCl6t0tQei9yVQxSJ4I8EzF62aOhDzHxscU3KuAfhOoHySumo2bH1hJVXnSEV5TiGFiimhTJg1cBN+2+4f6g0E52stoegs4ZDaX/9oMuB9SVIn6WLqMmsrI77vAuba4RaDsG5f9/VhQ/ruG0MSoNwuWyhjoJXTAfWdQOverTpy0/EHqmn7CfkWg1tTHJovdbo6wi1UJbIkSpywmiJt6aNtLXFfVWKMLWLcp/zpfIzImY+rpDl+YN7YfB9UFnVCY4m6CMIzThLfLTcRz2J9EtRt6g3Ta9hbSi7pFzyQJZP3sV7/nN6tBnQJ3ACM4k7p7vLnUncbb0IMJPvPd0zhE6XhGRDajeA8hpUpOLK4tu8OYN95M3eOzSFaN4Qs5DDTduMUFHC3Txf0AW9s1i5Sbm8rdW7yLSZT8aOTdXbi+U6CFRsxUSTvH29Xk4eSMXEfxLGfq64Ev5oNMt3rio1w/p50PwUMr4xYk7xp0psqM1HLIJbUcjqK1L0bdVPQAXxO85DgxhK2ng6Yu1n6jEL9rLwERb9DHrn2jB9oc8Bxkc/8I7E6WdBpMigHVCi3uYX29GtGJQa533oOj7HJYQgEZycQj2Qsi+nIV5x9dECN79jsaAmKQoQZ+Fragd7RccfK5+81I+Ju6SohmGK/mF2OwZC08xkG04hZbDGt+FdsFtK69RSU+4v6Vf1xG2ebk1lVetgwzOOu9VNXnTsaJ6oi1XCU+ZQ/OfrQ+kiF5EGyEWw/5mp2+kRzdpgx+SVY+DTvGEGJsSGBgVnOLdTLvWSYU0IafVs1DkY1YQXG+otgb1+2p5uNyzbjH3Qy9BokJGWZyatWjesPTZcblYTc0yqQGFWEh8lh62DQoVf9GHBTkFRG0oetf85WQY6goCbi5ZZXkKWeHyMS0uzxXVBqfT7T/EMS/QRUko+WQAIxKInvhx7ZbBjeZZymB2ev+3P6q5GjpU2zBD/bU5OCPoBnsjlkVOdQblKYCHmdNRC0qVabyw0e7A3eMswOrqfvns4bDEycRPH6rTz8AdIETzbVfJtsW+K+4o2rncIUQqSEXVY7Yeq2pSh2Q3GUtjhT7u3oAtqYNApxcvfE4HMRz4DDqYzdRopbHPL2+/O4cb4oYedzyeg5Zukk4RAu/oxrZPMjVsGq0/Sul9NCiiVeZwktovYaCp880k9UPJsPb8ZSaW37V489QXEdgJ4pQnPCnKlrOFskMerJNkGTGBeiSagWMrpl9D0bR7ULzFuoGMRRkXO72yWr32OqHgv9lE6kLfdcmCchiiHSJXSORvd/j3JcR2bhxgbAv2dJC9FOtrj0/W0PMP4rjd45LOw4ZJpuhC0Z0u390b2VM5F4ns0MEF7jLa24UH47sDNEFGAujR9Z5yLcm6oXTZNZyixR95PecRSwpDWlxr/WvUjGExU39fItaW8nawy3Q+NH6e7TrwhgG9JfCQZKafMzQqLI6dqDqOmr+l+JLL8xrdbfL36cybeoK+/9ZmkHvgLlH8hDnq74aDPs9fNkRvJrr46SMNw9nfBCN9D20S7oo74cCgzhoRBdty83yEnn+5+E+8x49+VivyMtRt5zwlVObPjTu2V+zUCzzIE/hDZh2kWDeCBK1+n4KdPoQP1EAEKPwyDl8Blm87c30OIh7NG/hivB/5V2UPpIvBsOS520449IFHUwxSTsBbMoAi0pB26HKeMnklNZZ9XB8JzVOd8yvoZTYINfbd0FsDeFy1XrYPGAHKbT9Yew/kpIkmHt6/9AWnHxFGWtIPaKgHAVCA6X+ri0MAnhykd3km/He8XmjLHSNPircUWcOHrcPvbHSjF+Q7v92f1HuKsxHVk0kiGqVDUFGOjdDg9iTaWM0N/BVLUMlbP9bH9QfHVzW3W0RB6A69QnIeidiGmFOsIECJvSCEV8o4Ksi/BYPXayAnAuqPLEBiO9a31Rge5Wz++u+HYzW7inicJZvcvCqlNzcRilFEWp+t3JhGNn1lj1souZbrPscXRvWyc0JKVSdJWRlyyQYhs3B5Fur8iKWeX7QqsyWTE7807SsgfPtxVlFPOiDuKDHjMejGVnaBsyjWZEC1UdIg/tlcGrpeIjwYGbLu0NgMwxLqbbp/csgxlN7kj1QAq4/5s8picXsYRSuWblnRMDfpBcVZzY4vq96A5b908BDn/GLAFowmCdgH/g71t/KcIQTmLkN7PBnT9ECuvXN6L13KCNizd2RH4izk2ctaq9MvauvIXLEVWLMlfqI0Ylo1L9ANL8NGMlx1H+QyTj3ATOdsNBik+3B3dFaEke8zxTcaA4alWlUrvflUBS91drSWq3RYnYcSlqaoljUDFhXT+msZnMaelLt1SY9NTqg/POIgCuUFUKe1slIKcNZDAljFmBcdDUlriDRJUJQHjjkJdRM62eDjG7nuDh23r2giACJGmZgzfsnMtzflvdWIppOrnyzUntMKxg3yLQ9lT3Xa7hH/LA8caPh1TmhxpxJUhP+tfyYara2+ALkFASS7kPafnPS0DjNLjHDWgHuCuvQlgNBP8u6AC4xug5ZuVMjgpXZYNeJlb/Qig9aTme149se+ogh0RKa298UfW53oYa6zJioU4zcpYQPn3qIab+btyHykOGFCdQDcA417FIQF1JtXK9I/lS55AHlHTwChaVf0pRBmKIKQydcugbjmkH3i/LU3ub3o3dLKrX9/F/+a67BRXpREc+HrmEVq590JYGyCA6m2M569WB9Ovg19MXN0+ptbHeMrbSyCCD0Y842BDvxTyp8Oo5WGCQJVnYpzFuUirJmdDeo21fg5UnjOFdMWW9hIq0xpWPWj4w3/AxpG9Nh9OVT2Zvzt3vTuQa79a4OAphc1mFDhTbut3GeuCnmpfscRNdEqh+1EPEzNqYMOcMqvsQcNNANan8ndrABOKQR4wnsiHpoy2s4+Y96vYJLRMuXZpWLyR7c5ZqT9jXNJ7R5GrbXO1akzY/28i2uKuTT8SovxMHeLM1Tib03Lb0C3S77uxDoXGaU3iMBHBr6Ommnggvw0hknnZDY9x+I2cSW0YM5GT5CfT9segU45QaibkLPp7LNd96R5jJ+orz0vrVaLqwC2EkIftf7D4Tup3UIb3kQOk4Af3tu4MdS4VCjEL7kMXxmpc6egXFa7GiRF7+aq0M7ulxxT4nhwpJ9zNiTznyjYxTVgIUwWxqZTpkTyK4UwLcD2WGonliW3mwYeNOcW4z+eL7cFNDiZpeECEuAZgRdBWC0XLZm/ZpqazrPQN5OP2tRQu5okgkIwnXQhWoTN7m0Ln973yFrVqgRR02WVOhvW/YYG8u9LHqw8TWjY3eFtZeR+1ignveIsZvaGmKiXryok7newbsLpvUpL17VZI44isjmnfUXbBuBkAAqACimKB7IZdAuwDFFX9Hl2tOb7o6QW9GoNtP5wKUyCHHccmw+rvjHAEgn1VUOs9aKGKLMfjnQgoKnF6QeOQeugoB2QSmcLGfqxSAVguY2WwLn2v9gFUAAfRibehKY9wUDDdZElq5o8xhhUA2TrOdJhwXDN8d12RMVuM8RPj8FAvAG4aDRyp8F31aDPDa7hZ123wJRC4Rh1hFGFrSZA2zB8PMQSnYjYbIRjv39CTCCo6VgnWcAb7+8v2D0LrKd1XKFQ3XQ3TkG7dAbs8ueswWG50p1ggQcgcJEP5R4LnQ5Eb4gNgD7J8p91XLt7lR/Ym5mYXDNs4pxV0N/ExAZ+mXeTxJSknDYDELyljPm9XCTbATB0IlY/ljmuZmY1i3QNfrg/jqLP98EDDUQF9U6XbAX02v/wAvV2u1gNMnBX1vVIjT4tiU0U3IunCxFeiBCmkEt5/3CHRcQHCPqousHUinPw7XIvG/BtewOUzFoVu5dwLVYn2B4zNVkHZmVtAEHmzwMQBVs1Ttr87HJJDdz5iMpyqzw45TXUAT888HOzjCZqdA2W7QbFQQ/N11O0hIlsQjXFJJLhusbjbnU9sN9g2xRaNN8oFcCBT1kxzkPQmWBXNL55n/OWT+nLWw6zjRU5S0VkTIsSJeztevlpcwSbxRAiHLyg6BWnOvglyOawM63o1v6E38ExmcTH5WnxPLZmLYB67UJViUij8AkyRb2oBdCNer5X37i+Iv/IdrpkvA95dChdsVvtio8vvxRNTU9vhXcmN5Jiz2m4Y5KFPoU352hlnfI+4jq5jdU9X8128BmCZE0gT0X+SVmZ11Im3opuBGYIE9SLRFwmnLncECv83jV8ZdEDWNg+DBypR5XPgJjcMCe+gDqHlFiYsAQRYNzAvtokVqFKf7+VpNfN7vWY4jNk/AiaZNgNL4D/CcjuF9+14oCr8HlU+aXodpC+BXjwa8o3chqYQpun3+XnQ2c66PG1tJBq6ejZbVU4RvOj4BgDJbnfcqrYuNvb7UagcVRfbfi5jb+vVfL8TBQalVov3YVdN3p9Us8HkoG0YdhDsm1iq7FoNy744MJuI3YOpyC2wuZhFMWBSftBzKf2tw8SY6D6AZuLhvwkEH3LCOxbi8suNFqSSFoJmGYj0bSS/jnLYLcH0qbFenCQoguBEDMqJzGzW5JwHZxIAxINs2ZQ3oNGz+gMV6xicDRNVY9smBKWK+E/rVarSxnUnWyCPGG4Rzqx+vENUD/8n7M1I1nsMDnRjQ9wRRTZ0TQ42F8zntWYeU3g/J3/pRAlmrwVwP7PvRq/HMAUZrkRSZuQYgyFV/6ARqxFA027YNQwOl+o8fZ7plATJrrC49FofdTkwlCGmqKx5DgfjGjbIGhAVDFFa4N0UnkSe4BmhkxdVeb79R9dsMmCPg3dslW9l1cszot+az+YiqfdvMqXM5rYaLy8OGfk5pYSGNiDqq0YK4+t/o1PkBMmhgT/ckCopOPHaQ1wcHaPzcpCREsNScAVJOnE2rKu1sXozwWuABctIvQqumqNH+UYomneIKYu/7C5zc2pH+xQEPYs3hlNDu/JYU0Xmiiv9xmZmp9khAU6UwMJU6yJW2nxaDCUdGcBn5xVLxXCRfWDTBuBHenaNXXdQvcopAB7DTZjfvxUUV5y1+mMd14gGhmwarUi+fzchZpvsdwVDDM5Ya2/Jy1sA55qiTE9dw2TRLcLdEfRtHmHCTAAEG9/Hbk14xXwRWILxvRJLdR3ABX6BmjxYFuQOopy4MJ965r9iUuvByXgs1WeGZySq2i1vw2V00Bi07litCr9XUjZCrprp5kavx/3/AxrGEtqVNu7PEMLf7XqSRpPDjclrueGn5pc9b5NxRIpVn2Slkv0iA+403hEDw4xTmDvSrRuvawh5ib63aZoqnQxCs60YDh9ncVKQR6yQZzq6p/Hva58tdI01rcv7jFzHu4oRQYmaPmGtLuMjiRCzQOIloGr2LCwIkVNHp93GfZoKBDthGL6a3NQMFN5bqoU6HjI2fi9VX5VXj4wQvNRjBrIKvHpApRDHVdjmKI3m1T3bRdbhy+lq6bk/mjiFdUc70KJ51K5SoGUYM/F26CxqEF7lYhNNK3TstQoDvZp5prATSPeZUU62/G8N2OOpyNYtWxeEgspGJJYob5bsDmtk83J0c37aDrn8um8mM5fiiEHiALV6PuaZAAYtB6jEmN+IQY3ZGJxmDn+YuqfVLLzRXl7vBhlkZ4Ia5yhSREGCAgzjttc1Wocu4da7KMGWOW0avYsCIWJOETwI7OQEv0A5dHfhPt8V3Z0y88Qwy1oM0taHA7aFkw/KfGODXt01vzAYROgguzDzV4n+H8bPiyBNcaOf97LWiEPrcvf5TqCLN3DJY/UpgTvYQDshW793wC7Vxpy84pjE9TPhV61Xs22PoK/YBm4B0m1tKx2V6J7Rvb2+DVzJHVoKRsf1ocSBA8t7Hmg0S3TBc1aR09WZa7HYLbNVIDFTZsY7nb8nkBzNvfoKYWAsk2yTll2ZCjns1PTs2Vi1W61MPsyTQPRBENyzTmPQn6NorQqbxkI1LTzVB3WQiKc3sPtmVPN2Z9s7HiPep7HNymd/SX59U6yCVfU8/ounmmH1bKUmRIVpOQPkztt1EX4dyStQoQrl5jA2KQM0wTReeeZV+ZBhS6d+b7RYSjpiLB2ufg4edtSM3E0hmy6MlAEe0z1mfd48gzghD1iCGW3iCapvHJr6oSG2adqSVs7prTuVwCZs/r/TdM8dCJ7ARA2DRllELdzIdsTgpHdAPo4jWr7VYLxAVVEUxpkBk38J/5A5QvCM4214JeFGpKrFn/yicI+CIewtin/qu5o6hqQ/dFSd4o9oRzaPHQc3JPiptquJnJGdBaJJru2+TObKTjCVUl+PIBs70G1ZCJj0svZfNwzB1t36yHzpl6urUZ8Tih3Eg8y11zw0ui38KdoG6/+1rTh+vlzCVxE5PRiiEDDjyy5VAsFYOzB+D25Ysq0iKlb7H91DhnervDFCgxvryNNW2/1RSdsbWxw3ZNw7JaZuuRj+n4QBwxVtVIcvF2SmECpO9QRyRGVFfodkrhq9OaSbZkw6ZZ6QVIyb0iNJTePxFA5JqD9FxIeU0sMfUWTUr7UAGuypHcnspEgP4JRtAlcWLtTnQwIaSfkDg1OX3XuvN9toZ2jV9X7A/etsHzF1NTcBdDN7OZl+ppVurQFXCGT2V5J8nxUgwJDOy/HCK1MKWBEC62qEZYZ9aZ0VjAmRgTDQ6DcUU9y1Lt73eyDXuRrUCXHQk0w2JtueP8EqT1kGvFf9/TX5lezfxz5HZHYq2Aip3s7P/p0x4n2t1QRuUpP1dH6DbdZ5UPwQ4g29dbjDqxeG6ehr4fTpndYhtOdopgU1nE+WaOc1ZWqxNAhyjNL+toIwE5Y2ZGT21p9HiHpOB+7Ct20XkH14ODgBt0m4AYpSwiGNYaMj20lSGbE3/iW1cS8+VsgUBjO+l5veKGJK1uwq5498oszkd2yK3EYTIvq5x/1A8ClZ5DpPvBGnq/sz3w6F0IiYAeiJuU/wryVp+0Wkr7zKG+79G0p9cRMtbESXlTW7l+Mz41UkYFNSBx4bWniQS8WU4Gd+vdtHzxGx7sGEpdJpMxPSk+wRLgCXmqXF7rqT2kpVYYweAjh/unK6pBe468G5oP/stcW4sffqAiu80+uPVXvkKYVcjsGEHh4A1Rpg3PGXwZww8yJzYCEPWT69zrM6lTmeDfl5zgiNJhjBmBW2yE6d/BDCsehU7eJAC1LP+rHrXeBilZxFDKR5GYKIuYN9yHgDQI0NELiibOQ4R53yo/Gey05LTSRt9dyzeKya0UQ/P936PQcqVMGawLA2+TGBVDJSs6nH4dHkVpK0RPcSnsamJJ4ITPNlDAPTrdc6npVrxuWN4iG3ULWKb6BcKeORaS8xGmetNsAjYe++KDu4JaYxgOCYwBXzUPH48+yzW3Y6DH1NX6Lrg+gu2JIlX65mQfAdBybn3gzAcQDBIn6/c4TYwVgSsGoGylvlzPEvqiwkhgxntSxlCv7Nj6Ezfngtel3uf/fw3+bxofxHxM+LtpdlsmZnT5qOcafml9hLYjto0Lphcqt27yZmce9FaC08dZDdMCXxr/ElaQG2+wkVt0gyCUaI1lpmDu1YUTgq8oE8tpDAhkynqg0Oh+l6/Dv4R4Z+5LGcW/jzyhJfTWz1hJPH99xivY0EIf6YsjuTL+zvMo4RL5miWJ4K7vdANu88yK9X4D/kcv/6EQSJ4/ItG9iZXy5MGF/yf4Qa0VfqLrSEge+dlpfS7zrbZp4RQKiri/z9xhGyzsfdQtgVhEB0Kf5CUsspCkhDv/zXi5gwdwEbNWiRFKWxMhSrLe6xuQ8e/df4GxyVey73uqMgaeWg41BcdXBJ59rBNPxgskGYxSVPBGeJPSgucqCmLb5Q7YbbhdDjWR1g+2yyxANqZB/Bweiv6WIAJuxjqtVllCd3CTOONCfr4cz2g1KAuPdisUor5JTtD5vYQ6+U14GW8nzXj6qmKTSmhNg9L/L17wtT3O7NsTmSlRuI8306AhFM9HC6wdG0QTa1GE32JgHNoxwtG9SqVLiq1IHDZbsrYGZudYge/3dwCyR/JnzkigbavcdfcIjha8dekXK/P0sTgHxK7htSsU63XcNNKtZEc99q/2evk/iYxpb5nK8FgbMA35D7xgOkiUpJwAycCDFFtKitssB/7Ngftd15p/eAA5e6oY2rm/KXsepe948yQOBLvAuIPLLpvu+7Imf6pzjJySn5Jt22aMYgMGm6Bd35VnLIFekg4fzvxUDvh4l5tNfIk3ganzCavY3lu6b5diSXK+oR81ZA+tPj85c4/TODp3vpjbGx6JtB6V9pGLSkvyr1rYjvWCRlnc3ToCdvKBWSiNDK7j49OZXB54w/p3TVeN9U39UUPhErqVnc4GUZI9fJCF7JhvZjS93mmLI9+J2DVmujPw8NqmIRfCwSoB2qcNZZjjWLld7rLGxM/M2OSQgHb17aRO5J19op8ntw0fyrDDk0ltAssvYOJ/d1DvUqR8G5/lFyM0UA+HmM8I/vp3u+7RRgFQfg52aMT1e6PE4Ss2Y37Oq8+d8dI/poegEksbacAOXw18oOcqDb1WAsrx5yRV27E0fPtz2YX+RwdOmeGsvwHix/AccZ5qIj6KvJL9kHzLzboKjUcTktgyI5dJDhpNIrRgfr1NMS3l+hDtm/8Tcv7U1+aH6tmyvrX3+Cg41SlcDnMHi6rK1vg8qfiiKyfn5LU9s5N/Gm7sOo8xk9LJzd9CLdoLclRSmJHfsrk5Be5SPga0L1KulMY5UAtquVzLXayqlMKAun/0grL1Kqpp31yuskLEhUrRNdhu+sa7YohbH9AMLxy/1lgJRwm/dyRoMmQ4wkBFLiymzWdjyxsS8/+s0DxRH99tj4cSmVLnL0wceLvlw59jTICP/z3eC6HDjuVCJr9jFnDwmpi2Yz26B6NOFP6bXEjqtHysJ0w0p+vA/4SmGnF7HZSgXnmZT1a3ujp7mA86UVlgSKq6st7cpQmUNNs45qFuTyCt593YAkVvD1OVwU5UG4/5sOmNHsp4bberDfx1VbR8FFw2NEwV60+gNcaoCBJn6v6yLnVK4bj7pyD2teXq8EZ7eRS2e9DhRWTbEPIuxKjZrX45ub++3knsl3lhE/7zpfqU8jfCgeAE/I2MPAhASIAmWM1ue+pQCrVDWIXIfT9iRGfeILBlC53463wRcQ65Wlbxl9U46L5/7xWJWAaGvRjA/6rNl+WN5ZU1oDGnZn94BptbqH5RXMMlmTUbLKwlQHZeiauK0MQ04aDK+FrXHeFUPTT8pZN4IMHSmWCPXmP0bkpcGGVKnglYpWgCL/xTM4iJycsNFMg6T0uLoXWTn8CdGKKPk5t/hpdl9VDC9A8lfBRWI9Y93YT7pJqAmDk+BfdpwAX5PGQzaos9pzWi61H9wQCNHl61xrdOZRrSewZ73/j2xjO/itWsVg5s1m75j9fzDiEliWlcAY3V0zCR9Ynq7L+wZJ9onYcPi3Ac1W/URnDuVS3u+8AfGw8YgSt5xlh2Ve/W4oVOQEo24sOi0zqmT5k5BtYQgbsn7WNt9ttUgDpbw4Co2NIMtizTm1IZNbsRJtWEKXaV8Iv9Xe2ZS/HBLEsXzW5fI5SHk/tPo3Rp/CrDAr70EIE08BxlASO2C1AdvazrLrzLsp49Qlgp4aXSWy7i2F6lOi8UQ67pPtHN1prbmbnhYN9CbGh601lTWBNbs674w9Vap6KYINm1wmQN/J8LjshNc4DpL3ndw6e51UeFemYAJ+o2qHSfJ2j8+cPARvfB3qessDBOUr18FwCnxYO8DF6a7rYgGcwyX0X6xDvUWP+ZY7TwBkBqgZ3hz6+dsL/zEGbNXrshcbOlZeRlOY6YBajN1tfjSvzE+TRLbG3W4XdjoJwyTzkmLxMUs50/r4gyDfilh9MZh6wWaByUSTrBlP6hrzwY4WGnshzVn7lFpBuELKciQ6xGmW9gzSJHr8ZTqXrEaf6B2Q3t3p/EskhBAUVK7RLJHDWg2h+aVtPclORjhF9ZDU5C6xPs5MKC+68Up+1s3ShjanHVCGJl7NsiADnTrPbCPdSOgdmL1oPP7Wj72ufAXzk8ZM5NyLKzsb4YC1DM9NuDiMscRxSggtiYj0lnvKlVMWPLv5TtwgxKPCETfZ/joFeb/QW7H/Nx/4DFMAvVxiD9s0knqa9VcInny34Ss21Jzvfb2eZfSW5e7YepEmUmnJi8RT5a/2bgDilIIL8EHpTDPa7yi0J6pssbjYXTfTtbE0p19pcBix5J8I2xVkf4gZP5UrD5wwP4qtP7CZDlPM/MgpZ3GerlQNOkM/JcGdnt6vibL/BMv7TR6U0bOEO8z7FgUfL/vl9nq5/rfQNT4MJy4X1p15SqLsnWLQV0R3uE9d4YIF5sKvImzvdqf+6XZyOz0TNSlLTn4YkomGPCUa5U73SRbI1a5AX7tpQBP2FaqlBdc+JonP3WiY20CSVN8392GA2C9Zslby6PS8z/7HFyxLvNOyY/6R/jfW8Q99wx/TXspalPT3C3J2iHuY8A3oV6TxYgpXxH3tiI2lgdeo3iMnt2ElJ3zfacBFYSNntdFJQZAQM1D7L3vNS7DKT9QuHgrijXustpSi2d70ESiNLgx/+5KmX3KJfgM3oAbbVQlYc2zAhHn7cJOW1dwtc/y1FDSS8vOZVhjVbNtNKAKHgUrYwRXA+KIYForXGUesVk8NHq+GyzMrnFasfAz9JXlYPEAIisGZoJ3fryJiNX5rCI2WV2QSBUkeyw+1oWli2SuHy6LjZ8BJXOnULBqWRPu9T0cKlGSg6eu6BOAEBXUvQevGrJgs1S1IAp5w8OZZWN3YCsR+aRItkT2ZB7pxcQwivpoOdY2Rc0b2qUh2UaK8GrwgdOJBNhGvAjiC9cN6i1UONgJp5AOq3V7PHDXvGdpFObBADJLLduFhy5vGtyg26s1E8HFzxvZ7hivSzXvb8JlVlW2Y0RTiPhZf6lYo4j5wLVM4z4KhKVdKqhWvy8VTkf5b0okbUzAKtdb0egIy/RS8l71IdLnO2nVXrs4x/P4+jcDtaCjQNCGXB11M1dvM/dijfKd4JEQIMoi+/h6Q3WMRIXmUbM/Fi8YC+pIAL0LtASDAAqy71M27576l93io3A9Hrz57ScYY9WF9aPXJiAuGe5UnpmkzHNq98mKRvJZoi3KDJChsu1QWS73wPD318pJ6gNKoVvNkXWwEuXNpQOiC4k5Pj3hrYMTBu10MQzRVRvSy9N/NO5R/oWivWMKR6elz5wBoHVezkZbCpxE0j6w7HFkbSyhD5n+XI3uRPLkEIOX/xT3+p6vOtta1jj7m+Y2KFwE5RfNBrfL4tCVBCe8g7tyMcfwegEHXyh/AXamQKfVEF3BNdsELXN5UMeLswy5aioIy1QJpLO495x0Bwc32HOZRBkpRxBg2BUWvGD4Do1UCAi1uthOjkwRDO3qNcGJ2TC9JNdIwGbwjDiSP+rL7lFeyS70SjhzAaBGx75J1Lm6XD75W/+Umf6+fe2Y0v8xZEONgVp1OXJDTvhhjtf3Ec6L5jx2Z52ZJ/D0P1TZ+gTcO2aFuD5rYXiS9zirEHwN/hrSShz8/6/1+klUg9ACIN5Ao5LJLa45iz2mUQgvX8P++7nsqrgzODheEE5CMyok8Xxal0S4xVtBC69NYf59JjcdYQzyMxNiAQWY1KHpHJWfAKthftZxhyLqvDFOvfF54NM7lX3Zct8j9zt89P2Ljw7NsndRWwtqXRDFvhIqUhnhoT0ptSFqjxgFRRKFUr6ft0NJ48EPaNVri0fWVj+uYnfVrXD5rgW+xkJs4leF5vF3xd+OGuYFFpppjGi63CdbXd0jwZrzlyUn08lkzKj/UHCSC4bbd7Pdc6U/nmGE6x/A2Lfo2ig4CKgv7i4wqk0tM7Gn7E3c/Z84e3fe9kaP8jyEbt9JbtGQsKU8CWraNsZXcFEgCDaCK9K9LR6le8U4FX/Mlk4x0AAA96BKxuWdRqPnm1MXZzoRClE+HYDcODH5gKQvNgQi3bgkdNbI6LI9QfSoQoCHgfS8hHRO3SomHVTk9HHxyvhbfw+gqR6sp9r2xk3vM47NE02Gdlsb3/1HXqwBdHm3ieKDq+jMKUQlOIb37+fKPxDHvpA9dnEC0OeFs9IMogLHq6m3Xax/IxqgiFTq6zAaB08KjpQPhzweIdWlp0aL8Q7MNgbEV4mAgGBRSnKeftKOoiH/v2VUaxaAM+eNtUBnTx4cW3eCkgq0y9GqlPm+lzwthxIcRetfC1Q/cHMW/Hg+7qzhbYGPy1RcEAMZegZJyOZAC1OofcHTj1QkjTI1ZdK4s9f0pmzGYMfuVJgLHCq6kGlY4+U0hdkFfdR1ktmS3e8KhFADI0o5ZYvVRVz4ZY/p+ZN/Z6XRs/tYrnN/VuC1aB1GKU4gb4Jqszgzgu7YL0ISZ0Ls+nbvaonCOIjtQsbQPCyPxZAWit3gMtfiekg68xgPG9Evd8N3qUCXjDaVPFHz+H+lfMaEporyjHGiiNXE8OOXaNHh97Kdb1n4k5d4MujQ+BniOYmGiMV/Hge4IZTiFpM8C46f8ivqX57mGpR1WXUtM8cOoLUwbExuisYr0oT4f+/zRynuOgxhSmApc9X2HDDvILMhIoJ3r08yAyPFZpUc3Js4BxKX1hyetSbOaXvazINCkS3BK6KsbtWAk84+PNGFgtheWE+9Uf5NO9e/M2bv1zMGHDaLqRhhcwxwbUY1PD5OSk3iXDYW1tmOQ7ZkWcFuZP2GZIQRDNaefIBPh/t1D2Yl9hCyITDMI8NAM6FwAuAy0bT+L1AtsmZWp6uXt32uCdf4iRgkt0XP/YsAAY3nVvIOfll+yfYVcMCmxEKQzTwjQfAmQcD8afLh/beXTWNgWAU6JdhulWyliMJlhrNzRk19HYDDa8kWTMAJ+LIabAaX0tS34h33iIoU0ldz2yHY5rV+4Nh03WeE70IL8SHTvhz5VsE9qTd5FfxJT13b6vOFW2oIqTF3ERHvw8KCBfy+Zb0/gjs3hGUYjLvLTIjapVV5TaUScIytVXEK0h2LUpDCY/93NW4atfDesG0t9CuWgAe3g3ad3yEe680U5c1kpdcYf/kuZyMyx5D00tqOkCGG8RLk+BbKvc2HDlNusjJ7RgrwfECbYEoa+sh329WPSAHxqYb7RW/WX+B4ELZMwhZO4+3txMrHmAMyOROgWtlxlZoZH6F4VbShx38dYFBaXa1Q00MFwS8zMVH/L8MEo4UHC7ojF4Tq+J2AvQvS1QGkC0+rP9OTTGo3kI/y4RyVErfy3brhV8APnNlJNVGUDvbvehKUgEjlbYY1QtQeN1ZUgxg+GtzFchdzKDOyg6qkET0h1gdnfXm5Q7xEGqqFRoW8+vVG8GXqPSV05Y4STUYls3gcPNL/Udyh3mqlZ/KHpMxY0/LMoM7zUuWwFXK4JQfmg2PLAijAxk2HxKmJGbmSUHJj8Aj2Fzgg48UNADod1bCFu/KIUnfdCqfC3Fe0HJUNNVizVIH8uJFiXBHZU9v8n2WDjVlBpZQiSYt0BGx1oeF3TgCRp0rKG9HTWI3Qeww2p5FOjIYgEN9UTVRjB2cAgBFCAEsCRPJ3YqAt7sdyY9fwUHODtS7UbDlgECm5qewjtdJcl/cfBPD00uHHJz+H4K3FhhWzqqLuWBkQhQq6xv/XeImpDJEatF40jezsj/2ZPcSGBLP4b9eitCKawVT7SJe2t/qDn3hgnn+gExXIVyEDkQ6iAYKWQBX6yAuHhUwuT01KMiFNAKDqddZtfwBCFtD4JrAJdsd2hACEIUnZuwAW6aJZ34+9baiZQozNuMfcJNq84Ag5U0oVgKMX/YnfXrSDbrwFO0lwsq0RE+Jf3al4el2mOIYA0IMrRQeA1kxgCN06bHOkCgIMqvXh+ymvSAJYwO84iMyoWyjMohLAAJw3AAAAAArU9iH3gvwbI47orIAD0pvb/o479yrs4YZkqtosOwSPVRx6/W1KBQpHPrbpD3f8457HAAAna7kccOUgBZvNay9AAAA="
+    }
+   },
+   "cell_type": "markdown",
+   "id": "a6fae6a3",
+   "metadata": {},
+   "source": [
+    "![Heart.webp](attachment:Heart.webp)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "25ea44f2",
+   "metadata": {},
+   "source": [
+    "# <span style='color:green;font-size:40px'>Objective:<span/>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "26d1d1c5",
+   "metadata": {},
+   "source": [
+    "<span style=\"font-weight:bold\">Developing a AIML model to predict heart attack risk based on medical records and lifestyle data and personalized prevention to help patients avoid heart attacks and achieve better health outcomes.<span/>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fb50dd70",
+   "metadata": {},
+   "source": [
+    "<span style=\"font-weight:bold;font-size:20px;\">Importing Libraries<span/>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "bb93d100",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "import warnings\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3325a3b9",
+   "metadata": {},
+   "source": [
+    "<span style=\"font-weight:bold;font-size:20px;\">Importing Dataset<span/>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "9f73fb3c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv(r\"heart.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "7a1b636f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "     age  sex  cp  trtbps  chol  fbs  restecg  thalachh  exng  oldpeak  slp  caa  thall  output\n",
+      "0     63    1   3     145   233    1        0       150     0      2.3    0    0      1       1\n",
+      "1     37    1   2     130   250    0        1       187     0      3.5    0    0      2       1\n",
+      "2     41    0   1     130   204    0        0       172     0      1.4    2    0      2       1\n",
+      "3     56    1   1     120   236    0        1       178     0      0.8    2    0      2       1\n",
+      "4     57    0   0     120   354    0        1       163     1      0.6    2    0      2       1\n",
+      "5     57    1   0     140   192    0        1       148     0      0.4    1    0      1       1\n",
+      "6     56    0   1     140   294    0        0       153     0      1.3    1    0      2       1\n",
+      "7     44    1   1     120   263    0        1       173     0      0.0    2    0      3       1\n",
+      "8     52    1   2     172   199    1        1       162     0      0.5    2    0      3       1\n",
+      "9     57    1   2     150   168    0        1       174     0      1.6    2    0      2       1\n",
+      "10    54    1   0     140   239    0        1       160     0      1.2    2    0      2       1\n",
+      "11    48    0   2     130   275    0        1       139     0      0.2    2    0      2       1\n",
+      "12    49    1   1     130   266    0        1       171     0      0.6    2    0      2       1\n",
+      "13    64    1   3     110   211    0        0       144     1      1.8    1    0      2       1\n",
+      "14    58    0   3     150   283    1        0       162     0      1.0    2    0      2       1\n",
+      "15    50    0   2     120   219    0        1       158     0      1.6    1    0      2       1\n",
+      "16    58    0   2     120   340    0        1       172     0      0.0    2    0      2       1\n",
+      "17    66    0   3     150   226    0        1       114     0      2.6    0    0      2       1\n",
+      "18    43    1   0     150   247    0        1       171     0      1.5    2    0      2       1\n",
+      "19    69    0   3     140   239    0        1       151     0      1.8    2    2      2       1\n",
+      "20    59    1   0     135   234    0        1       161     0      0.5    1    0      3       1\n",
+      "21    44    1   2     130   233    0        1       179     1      0.4    2    0      2       1\n",
+      "22    42    1   0     140   226    0        1       178     0      0.0    2    0      2       1\n",
+      "23    61    1   2     150   243    1        1       137     1      1.0    1    0      2       1\n",
+      "24    40    1   3     140   199    0        1       178     1      1.4    2    0      3       1\n",
+      "25    71    0   1     160   302    0        1       162     0      0.4    2    2      2       1\n",
+      "26    59    1   2     150   212    1        1       157     0      1.6    2    0      2       1\n",
+      "27    51    1   2     110   175    0        1       123     0      0.6    2    0      2       1\n",
+      "28    65    0   2     140   417    1        0       157     0      0.8    2    1      2       1\n",
+      "29    53    1   2     130   197    1        0       152     0      1.2    0    0      2       1\n",
+      "30    41    0   1     105   198    0        1       168     0      0.0    2    1      2       1\n",
+      "31    65    1   0     120   177    0        1       140     0      0.4    2    0      3       1\n",
+      "32    44    1   1     130   219    0        0       188     0      0.0    2    0      2       1\n",
+      "33    54    1   2     125   273    0        0       152     0      0.5    0    1      2       1\n",
+      "34    51    1   3     125   213    0        0       125     1      1.4    2    1      2       1\n",
+      "35    46    0   2     142   177    0        0       160     1      1.4    0    0      2       1\n",
+      "36    54    0   2     135   304    1        1       170     0      0.0    2    0      2       1\n",
+      "37    54    1   2     150   232    0        0       165     0      1.6    2    0      3       1\n",
+      "38    65    0   2     155   269    0        1       148     0      0.8    2    0      2       1\n",
+      "39    65    0   2     160   360    0        0       151     0      0.8    2    0      2       1\n",
+      "40    51    0   2     140   308    0        0       142     0      1.5    2    1      2       1\n",
+      "41    48    1   1     130   245    0        0       180     0      0.2    1    0      2       1\n",
+      "42    45    1   0     104   208    0        0       148     1      3.0    1    0      2       1\n",
+      "43    53    0   0     130   264    0        0       143     0      0.4    1    0      2       1\n",
+      "44    39    1   2     140   321    0        0       182     0      0.0    2    0      2       1\n",
+      "45    52    1   1     120   325    0        1       172     0      0.2    2    0      2       1\n",
+      "46    44    1   2     140   235    0        0       180     0      0.0    2    0      2       1\n",
+      "47    47    1   2     138   257    0        0       156     0      0.0    2    0      2       1\n",
+      "48    53    0   2     128   216    0        0       115     0      0.0    2    0      0       1\n",
+      "49    53    0   0     138   234    0        0       160     0      0.0    2    0      2       1\n",
+      "50    51    0   2     130   256    0        0       149     0      0.5    2    0      2       1\n",
+      "51    66    1   0     120   302    0        0       151     0      0.4    1    0      2       1\n",
+      "52    62    1   2     130   231    0        1       146     0      1.8    1    3      3       1\n",
+      "53    44    0   2     108   141    0        1       175     0      0.6    1    0      2       1\n",
+      "54    63    0   2     135   252    0        0       172     0      0.0    2    0      2       1\n",
+      "55    52    1   1     134   201    0        1       158     0      0.8    2    1      2       1\n",
+      "56    48    1   0     122   222    0        0       186     0      0.0    2    0      2       1\n",
+      "57    45    1   0     115   260    0        0       185     0      0.0    2    0      2       1\n",
+      "58    34    1   3     118   182    0        0       174     0      0.0    2    0      2       1\n",
+      "59    57    0   0     128   303    0        0       159     0      0.0    2    1      2       1\n",
+      "60    71    0   2     110   265    1        0       130     0      0.0    2    1      2       1\n",
+      "61    54    1   1     108   309    0        1       156     0      0.0    2    0      3       1\n",
+      "62    52    1   3     118   186    0        0       190     0      0.0    1    0      1       1\n",
+      "63    41    1   1     135   203    0        1       132     0      0.0    1    0      1       1\n",
+      "64    58    1   2     140   211    1        0       165     0      0.0    2    0      2       1\n",
+      "65    35    0   0     138   183    0        1       182     0      1.4    2    0      2       1\n",
+      "66    51    1   2     100   222    0        1       143     1      1.2    1    0      2       1\n",
+      "67    45    0   1     130   234    0        0       175     0      0.6    1    0      2       1\n",
+      "68    44    1   1     120   220    0        1       170     0      0.0    2    0      2       1\n",
+      "69    62    0   0     124   209    0        1       163     0      0.0    2    0      2       1\n",
+      "70    54    1   2     120   258    0        0       147     0      0.4    1    0      3       1\n",
+      "71    51    1   2      94   227    0        1       154     1      0.0    2    1      3       1\n",
+      "72    29    1   1     130   204    0        0       202     0      0.0    2    0      2       1\n",
+      "73    51    1   0     140   261    0        0       186     1      0.0    2    0      2       1\n",
+      "74    43    0   2     122   213    0        1       165     0      0.2    1    0      2       1\n",
+      "75    55    0   1     135   250    0        0       161     0      1.4    1    0      2       1\n",
+      "76    51    1   2     125   245    1        0       166     0      2.4    1    0      2       1\n",
+      "77    59    1   1     140   221    0        1       164     1      0.0    2    0      2       1\n",
+      "78    52    1   1     128   205    1        1       184     0      0.0    2    0      2       1\n",
+      "79    58    1   2     105   240    0        0       154     1      0.6    1    0      3       1\n",
+      "80    41    1   2     112   250    0        1       179     0      0.0    2    0      2       1\n",
+      "81    45    1   1     128   308    0        0       170     0      0.0    2    0      2       1\n",
+      "82    60    0   2     102   318    0        1       160     0      0.0    2    1      2       1\n",
+      "83    52    1   3     152   298    1        1       178     0      1.2    1    0      3       1\n",
+      "84    42    0   0     102   265    0        0       122     0      0.6    1    0      2       1\n",
+      "85    67    0   2     115   564    0        0       160     0      1.6    1    0      3       1\n",
+      "86    68    1   2     118   277    0        1       151     0      1.0    2    1      3       1\n",
+      "87    46    1   1     101   197    1        1       156     0      0.0    2    0      3       1\n",
+      "88    54    0   2     110   214    0        1       158     0      1.6    1    0      2       1\n",
+      "89    58    0   0     100   248    0        0       122     0      1.0    1    0      2       1\n",
+      "90    48    1   2     124   255    1        1       175     0      0.0    2    2      2       1\n",
+      "91    57    1   0     132   207    0        1       168     1      0.0    2    0      3       1\n",
+      "92    52    1   2     138   223    0        1       169     0      0.0    2    4      2       1\n",
+      "93    54    0   1     132   288    1        0       159     1      0.0    2    1      2       1\n",
+      "94    45    0   1     112   160    0        1       138     0      0.0    1    0      2       1\n",
+      "95    53    1   0     142   226    0        0       111     1      0.0    2    0      3       1\n",
+      "96    62    0   0     140   394    0        0       157     0      1.2    1    0      2       1\n",
+      "97    52    1   0     108   233    1        1       147     0      0.1    2    3      3       1\n",
+      "98    43    1   2     130   315    0        1       162     0      1.9    2    1      2       1\n",
+      "99    53    1   2     130   246    1        0       173     0      0.0    2    3      2       1\n",
+      "100   42    1   3     148   244    0        0       178     0      0.8    2    2      2       1\n",
+      "101   59    1   3     178   270    0        0       145     0      4.2    0    0      3       1\n",
+      "102   63    0   1     140   195    0        1       179     0      0.0    2    2      2       1\n",
+      "103   42    1   2     120   240    1        1       194     0      0.8    0    0      3       1\n",
+      "104   50    1   2     129   196    0        1       163     0      0.0    2    0      2       1\n",
+      "105   68    0   2     120   211    0        0       115     0      1.5    1    0      2       1\n",
+      "106   69    1   3     160   234    1        0       131     0      0.1    1    1      2       1\n",
+      "107   45    0   0     138   236    0        0       152     1      0.2    1    0      2       1\n",
+      "108   50    0   1     120   244    0        1       162     0      1.1    2    0      2       1\n",
+      "109   50    0   0     110   254    0        0       159     0      0.0    2    0      2       1\n",
+      "110   64    0   0     180   325    0        1       154     1      0.0    2    0      2       1\n",
+      "111   57    1   2     150   126    1        1       173     0      0.2    2    1      3       1\n",
+      "112   64    0   2     140   313    0        1       133     0      0.2    2    0      3       1\n",
+      "113   43    1   0     110   211    0        1       161     0      0.0    2    0      3       1\n",
+      "114   55    1   1     130   262    0        1       155     0      0.0    2    0      2       1\n",
+      "115   37    0   2     120   215    0        1       170     0      0.0    2    0      2       1\n",
+      "116   41    1   2     130   214    0        0       168     0      2.0    1    0      2       1\n",
+      "117   56    1   3     120   193    0        0       162     0      1.9    1    0      3       1\n",
+      "118   46    0   1     105   204    0        1       172     0      0.0    2    0      2       1\n",
+      "119   46    0   0     138   243    0        0       152     1      0.0    1    0      2       1\n",
+      "120   64    0   0     130   303    0        1       122     0      2.0    1    2      2       1\n",
+      "121   59    1   0     138   271    0        0       182     0      0.0    2    0      2       1\n",
+      "122   41    0   2     112   268    0        0       172     1      0.0    2    0      2       1\n",
+      "123   54    0   2     108   267    0        0       167     0      0.0    2    0      2       1\n",
+      "124   39    0   2      94   199    0        1       179     0      0.0    2    0      2       1\n",
+      "125   34    0   1     118   210    0        1       192     0      0.7    2    0      2       1\n",
+      "126   47    1   0     112   204    0        1       143     0      0.1    2    0      2       1\n",
+      "127   67    0   2     152   277    0        1       172     0      0.0    2    1      2       1\n",
+      "128   52    0   2     136   196    0        0       169     0      0.1    1    0      2       1\n",
+      "129   74    0   1     120   269    0        0       121     1      0.2    2    1      2       1\n",
+      "130   54    0   2     160   201    0        1       163     0      0.0    2    1      2       1\n",
+      "131   49    0   1     134   271    0        1       162     0      0.0    1    0      2       1\n",
+      "132   42    1   1     120   295    0        1       162     0      0.0    2    0      2       1\n",
+      "133   41    1   1     110   235    0        1       153     0      0.0    2    0      2       1\n",
+      "134   41    0   1     126   306    0        1       163     0      0.0    2    0      2       1\n",
+      "135   49    0   0     130   269    0        1       163     0      0.0    2    0      2       1\n",
+      "136   60    0   2     120   178    1        1        96     0      0.0    2    0      2       1\n",
+      "137   62    1   1     128   208    1        0       140     0      0.0    2    0      2       1\n",
+      "138   57    1   0     110   201    0        1       126     1      1.5    1    0      1       1\n",
+      "139   64    1   0     128   263    0        1       105     1      0.2    1    1      3       1\n",
+      "140   51    0   2     120   295    0        0       157     0      0.6    2    0      2       1\n",
+      "141   43    1   0     115   303    0        1       181     0      1.2    1    0      2       1\n",
+      "142   42    0   2     120   209    0        1       173     0      0.0    1    0      2       1\n",
+      "143   67    0   0     106   223    0        1       142     0      0.3    2    2      2       1\n",
+      "144   76    0   2     140   197    0        2       116     0      1.1    1    0      2       1\n",
+      "145   70    1   1     156   245    0        0       143     0      0.0    2    0      2       1\n",
+      "146   44    0   2     118   242    0        1       149     0      0.3    1    1      2       1\n",
+      "147   60    0   3     150   240    0        1       171     0      0.9    2    0      2       1\n",
+      "148   44    1   2     120   226    0        1       169     0      0.0    2    0      2       1\n",
+      "149   42    1   2     130   180    0        1       150     0      0.0    2    0      2       1\n",
+      "150   66    1   0     160   228    0        0       138     0      2.3    2    0      1       1\n",
+      "151   71    0   0     112   149    0        1       125     0      1.6    1    0      2       1\n",
+      "152   64    1   3     170   227    0        0       155     0      0.6    1    0      3       1\n",
+      "153   66    0   2     146   278    0        0       152     0      0.0    1    1      2       1\n",
+      "154   39    0   2     138   220    0        1       152     0      0.0    1    0      2       1\n",
+      "155   58    0   0     130   197    0        1       131     0      0.6    1    0      2       1\n",
+      "156   47    1   2     130   253    0        1       179     0      0.0    2    0      2       1\n",
+      "157   35    1   1     122   192    0        1       174     0      0.0    2    0      2       1\n",
+      "158   58    1   1     125   220    0        1       144     0      0.4    1    4      3       1\n",
+      "159   56    1   1     130   221    0        0       163     0      0.0    2    0      3       1\n",
+      "160   56    1   1     120   240    0        1       169     0      0.0    0    0      2       1\n",
+      "161   55    0   1     132   342    0        1       166     0      1.2    2    0      2       1\n",
+      "162   41    1   1     120   157    0        1       182     0      0.0    2    0      2       1\n",
+      "163   38    1   2     138   175    0        1       173     0      0.0    2    4      2       1\n",
+      "164   38    1   2     138   175    0        1       173     0      0.0    2    4      2       1\n",
+      "165   67    1   0     160   286    0        0       108     1      1.5    1    3      2       0\n",
+      "166   67    1   0     120   229    0        0       129     1      2.6    1    2      3       0\n",
+      "167   62    0   0     140   268    0        0       160     0      3.6    0    2      2       0\n",
+      "168   63    1   0     130   254    0        0       147     0      1.4    1    1      3       0\n",
+      "169   53    1   0     140   203    1        0       155     1      3.1    0    0      3       0\n",
+      "170   56    1   2     130   256    1        0       142     1      0.6    1    1      1       0\n",
+      "171   48    1   1     110   229    0        1       168     0      1.0    0    0      3       0\n",
+      "172   58    1   1     120   284    0        0       160     0      1.8    1    0      2       0\n",
+      "173   58    1   2     132   224    0        0       173     0      3.2    2    2      3       0\n",
+      "174   60    1   0     130   206    0        0       132     1      2.4    1    2      3       0\n",
+      "175   40    1   0     110   167    0        0       114     1      2.0    1    0      3       0\n",
+      "176   60    1   0     117   230    1        1       160     1      1.4    2    2      3       0\n",
+      "177   64    1   2     140   335    0        1       158     0      0.0    2    0      2       0\n",
+      "178   43    1   0     120   177    0        0       120     1      2.5    1    0      3       0\n",
+      "179   57    1   0     150   276    0        0       112     1      0.6    1    1      1       0\n",
+      "180   55    1   0     132   353    0        1       132     1      1.2    1    1      3       0\n",
+      "181   65    0   0     150   225    0        0       114     0      1.0    1    3      3       0\n",
+      "182   61    0   0     130   330    0        0       169     0      0.0    2    0      2       0\n",
+      "183   58    1   2     112   230    0        0       165     0      2.5    1    1      3       0\n",
+      "184   50    1   0     150   243    0        0       128     0      2.6    1    0      3       0\n",
+      "185   44    1   0     112   290    0        0       153     0      0.0    2    1      2       0\n",
+      "186   60    1   0     130   253    0        1       144     1      1.4    2    1      3       0\n",
+      "187   54    1   0     124   266    0        0       109     1      2.2    1    1      3       0\n",
+      "188   50    1   2     140   233    0        1       163     0      0.6    1    1      3       0\n",
+      "189   41    1   0     110   172    0        0       158     0      0.0    2    0      3       0\n",
+      "190   51    0   0     130   305    0        1       142     1      1.2    1    0      3       0\n",
+      "191   58    1   0     128   216    0        0       131     1      2.2    1    3      3       0\n",
+      "192   54    1   0     120   188    0        1       113     0      1.4    1    1      3       0\n",
+      "193   60    1   0     145   282    0        0       142     1      2.8    1    2      3       0\n",
+      "194   60    1   2     140   185    0        0       155     0      3.0    1    0      2       0\n",
+      "195   59    1   0     170   326    0        0       140     1      3.4    0    0      3       0\n",
+      "196   46    1   2     150   231    0        1       147     0      3.6    1    0      2       0\n",
+      "197   67    1   0     125   254    1        1       163     0      0.2    1    2      3       0\n",
+      "198   62    1   0     120   267    0        1        99     1      1.8    1    2      3       0\n",
+      "199   65    1   0     110   248    0        0       158     0      0.6    2    2      1       0\n",
+      "200   44    1   0     110   197    0        0       177     0      0.0    2    1      2       0\n",
+      "201   60    1   0     125   258    0        0       141     1      2.8    1    1      3       0\n",
+      "202   58    1   0     150   270    0        0       111     1      0.8    2    0      3       0\n",
+      "203   68    1   2     180   274    1        0       150     1      1.6    1    0      3       0\n",
+      "204   62    0   0     160   164    0        0       145     0      6.2    0    3      3       0\n",
+      "205   52    1   0     128   255    0        1       161     1      0.0    2    1      3       0\n",
+      "206   59    1   0     110   239    0        0       142     1      1.2    1    1      3       0\n",
+      "207   60    0   0     150   258    0        0       157     0      2.6    1    2      3       0\n",
+      "208   49    1   2     120   188    0        1       139     0      2.0    1    3      3       0\n",
+      "209   59    1   0     140   177    0        1       162     1      0.0    2    1      3       0\n",
+      "210   57    1   2     128   229    0        0       150     0      0.4    1    1      3       0\n",
+      "211   61    1   0     120   260    0        1       140     1      3.6    1    1      3       0\n",
+      "212   39    1   0     118   219    0        1       140     0      1.2    1    0      3       0\n",
+      "213   61    0   0     145   307    0        0       146     1      1.0    1    0      3       0\n",
+      "214   56    1   0     125   249    1        0       144     1      1.2    1    1      2       0\n",
+      "215   43    0   0     132   341    1        0       136     1      3.0    1    0      3       0\n",
+      "216   62    0   2     130   263    0        1        97     0      1.2    1    1      3       0\n",
+      "217   63    1   0     130   330    1        0       132     1      1.8    2    3      3       0\n",
+      "218   65    1   0     135   254    0        0       127     0      2.8    1    1      3       0\n",
+      "219   48    1   0     130   256    1        0       150     1      0.0    2    2      3       0\n",
+      "220   63    0   0     150   407    0        0       154     0      4.0    1    3      3       0\n",
+      "221   55    1   0     140   217    0        1       111     1      5.6    0    0      3       0\n",
+      "222   65    1   3     138   282    1        0       174     0      1.4    1    1      2       0\n",
+      "223   56    0   0     200   288    1        0       133     1      4.0    0    2      3       0\n",
+      "224   54    1   0     110   239    0        1       126     1      2.8    1    1      3       0\n",
+      "225   70    1   0     145   174    0        1       125     1      2.6    0    0      3       0\n",
+      "226   62    1   1     120   281    0        0       103     0      1.4    1    1      3       0\n",
+      "227   35    1   0     120   198    0        1       130     1      1.6    1    0      3       0\n",
+      "228   59    1   3     170   288    0        0       159     0      0.2    1    0      3       0\n",
+      "229   64    1   2     125   309    0        1       131     1      1.8    1    0      3       0\n",
+      "230   47    1   2     108   243    0        1       152     0      0.0    2    0      2       0\n",
+      "231   57    1   0     165   289    1        0       124     0      1.0    1    3      3       0\n",
+      "232   55    1   0     160   289    0        0       145     1      0.8    1    1      3       0\n",
+      "233   64    1   0     120   246    0        0        96     1      2.2    0    1      2       0\n",
+      "234   70    1   0     130   322    0        0       109     0      2.4    1    3      2       0\n",
+      "235   51    1   0     140   299    0        1       173     1      1.6    2    0      3       0\n",
+      "236   58    1   0     125   300    0        0       171     0      0.0    2    2      3       0\n",
+      "237   60    1   0     140   293    0        0       170     0      1.2    1    2      3       0\n",
+      "238   77    1   0     125   304    0        0       162     1      0.0    2    3      2       0\n",
+      "239   35    1   0     126   282    0        0       156     1      0.0    2    0      3       0\n",
+      "240   70    1   2     160   269    0        1       112     1      2.9    1    1      3       0\n",
+      "241   59    0   0     174   249    0        1       143     1      0.0    1    0      2       0\n",
+      "242   64    1   0     145   212    0        0       132     0      2.0    1    2      1       0\n",
+      "243   57    1   0     152   274    0        1        88     1      1.2    1    1      3       0\n",
+      "244   56    1   0     132   184    0        0       105     1      2.1    1    1      1       0\n",
+      "245   48    1   0     124   274    0        0       166     0      0.5    1    0      3       0\n",
+      "246   56    0   0     134   409    0        0       150     1      1.9    1    2      3       0\n",
+      "247   66    1   1     160   246    0        1       120     1      0.0    1    3      1       0\n",
+      "248   54    1   1     192   283    0        0       195     0      0.0    2    1      3       0\n",
+      "249   69    1   2     140   254    0        0       146     0      2.0    1    3      3       0\n",
+      "250   51    1   0     140   298    0        1       122     1      4.2    1    3      3       0\n",
+      "251   43    1   0     132   247    1        0       143     1      0.1    1    4      3       0\n",
+      "252   62    0   0     138   294    1        1       106     0      1.9    1    3      2       0\n",
+      "253   67    1   0     100   299    0        0       125     1      0.9    1    2      2       0\n",
+      "254   59    1   3     160   273    0        0       125     0      0.0    2    0      2       0\n",
+      "255   45    1   0     142   309    0        0       147     1      0.0    1    3      3       0\n",
+      "256   58    1   0     128   259    0        0       130     1      3.0    1    2      3       0\n",
+      "257   50    1   0     144   200    0        0       126     1      0.9    1    0      3       0\n",
+      "258   62    0   0     150   244    0        1       154     1      1.4    1    0      2       0\n",
+      "259   38    1   3     120   231    0        1       182     1      3.8    1    0      3       0\n",
+      "260   66    0   0     178   228    1        1       165     1      1.0    1    2      3       0\n",
+      "261   52    1   0     112   230    0        1       160     0      0.0    2    1      2       0\n",
+      "262   53    1   0     123   282    0        1        95     1      2.0    1    2      3       0\n",
+      "263   63    0   0     108   269    0        1       169     1      1.8    1    2      2       0\n",
+      "264   54    1   0     110   206    0        0       108     1      0.0    1    1      2       0\n",
+      "265   66    1   0     112   212    0        0       132     1      0.1    2    1      2       0\n",
+      "266   55    0   0     180   327    0        2       117     1      3.4    1    0      2       0\n",
+      "267   49    1   2     118   149    0        0       126     0      0.8    2    3      2       0\n",
+      "268   54    1   0     122   286    0        0       116     1      3.2    1    2      2       0\n",
+      "269   56    1   0     130   283    1        0       103     1      1.6    0    0      3       0\n",
+      "270   46    1   0     120   249    0        0       144     0      0.8    2    0      3       0\n",
+      "271   61    1   3     134   234    0        1       145     0      2.6    1    2      2       0\n",
+      "272   67    1   0     120   237    0        1        71     0      1.0    1    0      2       0\n",
+      "273   58    1   0     100   234    0        1       156     0      0.1    2    1      3       0\n",
+      "274   47    1   0     110   275    0        0       118     1      1.0    1    1      2       0\n",
+      "275   52    1   0     125   212    0        1       168     0      1.0    2    2      3       0\n",
+      "276   58    1   0     146   218    0        1       105     0      2.0    1    1      3       0\n",
+      "277   57    1   1     124   261    0        1       141     0      0.3    2    0      3       0\n",
+      "278   58    0   1     136   319    1        0       152     0      0.0    2    2      2       0\n",
+      "279   61    1   0     138   166    0        0       125     1      3.6    1    1      2       0\n",
+      "280   42    1   0     136   315    0        1       125     1      1.8    1    0      1       0\n",
+      "281   52    1   0     128   204    1        1       156     1      1.0    1    0      0       0\n",
+      "282   59    1   2     126   218    1        1       134     0      2.2    1    1      1       0\n",
+      "283   40    1   0     152   223    0        1       181     0      0.0    2    0      3       0\n",
+      "284   61    1   0     140   207    0        0       138     1      1.9    2    1      3       0\n",
+      "285   46    1   0     140   311    0        1       120     1      1.8    1    2      3       0\n",
+      "286   59    1   3     134   204    0        1       162     0      0.8    2    2      2       0\n",
+      "287   57    1   1     154   232    0        0       164     0      0.0    2    1      2       0\n",
+      "288   57    1   0     110   335    0        1       143     1      3.0    1    1      3       0\n",
+      "289   55    0   0     128   205    0        2       130     1      2.0    1    1      3       0\n",
+      "290   61    1   0     148   203    0        1       161     0      0.0    2    1      3       0\n",
+      "291   58    1   0     114   318    0        2       140     0      4.4    0    3      1       0\n",
+      "292   58    0   0     170   225    1        0       146     1      2.8    1    2      1       0\n",
+      "293   67    1   2     152   212    0        0       150     0      0.8    1    0      3       0\n",
+      "294   44    1   0     120   169    0        1       144     1      2.8    0    0      1       0\n",
+      "295   63    1   0     140   187    0        0       144     1      4.0    2    2      3       0\n",
+      "296   63    0   0     124   197    0        1       136     1      0.0    1    0      2       0\n",
+      "297   59    1   0     164   176    1        0        90     0      1.0    1    2      1       0\n",
+      "298   57    0   0     140   241    0        1       123     1      0.2    1    0      3       0\n",
+      "299   45    1   3     110   264    0        1       132     0      1.2    1    0      3       0\n",
+      "300   68    1   0     144   193    1        1       141     0      3.4    1    2      3       0\n",
+      "301   57    1   0     130   131    0        1       115     1      1.2    1    1      3       0\n",
+      "302   57    0   1     130   236    0        0       174     0      0.0    1    1      2       0\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df.to_string())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "48d69dbb",
+   "metadata": {},
+   "source": [
+    "<span style=\"font-weight:bold;font-size:20px;\">Understanding Dataset</span>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e587dab9",
+   "metadata": {},
+   "source": [
+    "First:Shape of the Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "01641429",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The shape of the data is  (303, 14)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"The shape of the data is \",df.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b0168755",
+   "metadata": {},
+   "source": [
+    "Preview of the first 5 rows of the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "dbda7562",
+   "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>sex</th>\n",
+       "      <th>cp</th>\n",
+       "      <th>trtbps</th>\n",
+       "      <th>chol</th>\n",
+       "      <th>fbs</th>\n",
+       "      <th>restecg</th>\n",
+       "      <th>thalachh</th>\n",
+       "      <th>exng</th>\n",
+       "      <th>oldpeak</th>\n",
+       "      <th>slp</th>\n",
+       "      <th>caa</th>\n",
+       "      <th>thall</th>\n",
+       "      <th>output</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>63</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>145</td>\n",
+       "      <td>233</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>150</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2.3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>37</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>130</td>\n",
+       "      <td>250</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>187</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>41</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>130</td>\n",
+       "      <td>204</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>172</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>56</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>120</td>\n",
+       "      <td>236</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>178</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.8</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>57</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>120</td>\n",
+       "      <td>354</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>163</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.6</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   age  sex  cp  trtbps  chol  fbs  restecg  thalachh  exng  oldpeak  slp  \\\n",
+       "0   63    1   3     145   233    1        0       150     0      2.3    0   \n",
+       "1   37    1   2     130   250    0        1       187     0      3.5    0   \n",
+       "2   41    0   1     130   204    0        0       172     0      1.4    2   \n",
+       "3   56    1   1     120   236    0        1       178     0      0.8    2   \n",
+       "4   57    0   0     120   354    0        1       163     1      0.6    2   \n",
+       "\n",
+       "   caa  thall  output  \n",
+       "0    0      1       1  \n",
+       "1    0      2       1  \n",
+       "2    0      2       1  \n",
+       "3    0      2       1  \n",
+       "4    0      2       1  "
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1c5f2746",
+   "metadata": {},
+   "source": [
+    "Checking the number of unique values in each column"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "125d4f1b",
+   "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>unique count</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>age</th>\n",
+       "      <td>41</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sex</th>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>cp</th>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>trtbps</th>\n",
+       "      <td>49</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>chol</th>\n",
+       "      <td>152</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fbs</th>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>restecg</th>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>thalachh</th>\n",
+       "      <td>91</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>exng</th>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>oldpeak</th>\n",
+       "      <td>40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>slp</th>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>caa</th>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>thall</th>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>output</th>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          unique count\n",
+       "age                 41\n",
+       "sex                  2\n",
+       "cp                   4\n",
+       "trtbps              49\n",
+       "chol               152\n",
+       "fbs                  2\n",
+       "restecg              3\n",
+       "thalachh            91\n",
+       "exng                 2\n",
+       "oldpeak             40\n",
+       "slp                  3\n",
+       "caa                  5\n",
+       "thall                4\n",
+       "output               2"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dict = {}\n",
+    "for i in list(df.columns):\n",
+    "    dict[i] = df[i].value_counts().shape[0]\n",
+    "\n",
+    "pd.DataFrame(dict,index=[\"unique count\"]).transpose()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "29f22331",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['age', 'sex', 'cp', 'trtbps', 'chol', 'fbs', 'restecg', 'thalachh',\n",
+       "       'exng', 'oldpeak', 'slp', 'caa', 'thall', 'output'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.columns"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "acc2fdd2",
+   "metadata": {},
+   "source": [
+    "age - Age of the patient.\n",
+    "\n",
+    "sex - Sex of the patient.\n",
+    "\n",
+    "cp - Chest pain type:\n",
+    "        0 = Typical Angina:Cause by reduce blood flow to the heart(emotional stress,cold temperature),\n",
+    "        1 = Atypical Angina:Like a Typical Angina but have diff characteristics(eating heavy meal), \n",
+    "        2 = Non-anginal Pain:Chest pain not cause by reduce blood flow to the heart,\n",
+    "        3 = Asymptomatic:Have some virus but not develop any symptoms.\n",
+    "\n",
+    "trtbps - Resting blood pressure (in mm Hg):Measurement of blood pressure while a person is at rest.\n",
+    "\n",
+    "chol - Cholestoral in mg/dl fetched via BMI sensor:produce hormones,Vitamin D,other substance that help to digest food\n",
+    "\n",
+    "fbs - (fasting blood sugar > 120 mg/dl):Measurement of glucose concentration in the blood after a period of fasting.\n",
+    "        1 = True:fbp is greater then 120mg/dl,\n",
+    "        0 = False:fbp is less then 120mg/dl.\n",
+    "\n",
+    "restecg - Resting electrocardiographic results:Medical test that measure the electrical activity of the heart while patient is at rest.\n",
+    "         0 = Normal,\n",
+    "         1 = ST-T wave normality,\n",
+    "         2 = Left ventricular hypertrophy.\n",
+    "\n",
+    "thalachh - Maximum heart rate achieved during exercise.\n",
+    "\n",
+    "oldpeak - Previous peak\n",
+    "\n",
+    "slp - Slope\n",
+    "\n",
+    "caa - Number of major vessels:Blocks the o^2 supply.\n",
+    "\n",
+    "thall - Thalium Stress Test result ~ (0,3)\n",
+    "\n",
+    "exng - Exercise induced angina ~ \n",
+    "       1 = Yes(Chestpain occur due to physical activity,\n",
+    "       0 = No\n",
+    "               \n",
+    "output - Target variable"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "288dfd1a",
+   "metadata": {},
+   "source": [
+    "Separating the columns in categorical and continuous"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "e09288d8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The categorial cols are :  ['sex', 'exng', 'caa', 'cp', 'fbs', 'restecg', 'slp', 'thall']\n",
+      "The continuous cols are :  ['age', 'trtbps', 'chol', 'thalachh', 'oldpeak']\n",
+      "The target variable is :   ['output']\n"
+     ]
+    }
+   ],
+   "source": [
+    "cat_cols = ['sex','exng','caa','cp','fbs','restecg','slp','thall']\n",
+    "con_cols = [\"age\",\"trtbps\",\"chol\",\"thalachh\",\"oldpeak\"]\n",
+    "target_col = [\"output\"]\n",
+    "print(\"The categorial cols are : \", cat_cols)\n",
+    "print(\"The continuous cols are : \", con_cols)\n",
+    "print(\"The target variable is :  \", target_col)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b001b3af",
+   "metadata": {},
+   "source": [
+    "Summary statistics"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "a83beb0e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>count</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>min</th>\n",
+       "      <th>25%</th>\n",
+       "      <th>50%</th>\n",
+       "      <th>75%</th>\n",
+       "      <th>max</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>age</th>\n",
+       "      <td>303.0</td>\n",
+       "      <td>54.366337</td>\n",
+       "      <td>9.082101</td>\n",
+       "      <td>29.0</td>\n",
+       "      <td>47.5</td>\n",
+       "      <td>55.0</td>\n",
+       "      <td>61.0</td>\n",
+       "      <td>77.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>trtbps</th>\n",
+       "      <td>303.0</td>\n",
+       "      <td>131.623762</td>\n",
+       "      <td>17.538143</td>\n",
+       "      <td>94.0</td>\n",
+       "      <td>120.0</td>\n",
+       "      <td>130.0</td>\n",
+       "      <td>140.0</td>\n",
+       "      <td>200.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>chol</th>\n",
+       "      <td>303.0</td>\n",
+       "      <td>246.264026</td>\n",
+       "      <td>51.830751</td>\n",
+       "      <td>126.0</td>\n",
+       "      <td>211.0</td>\n",
+       "      <td>240.0</td>\n",
+       "      <td>274.5</td>\n",
+       "      <td>564.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>thalachh</th>\n",
+       "      <td>303.0</td>\n",
+       "      <td>149.646865</td>\n",
+       "      <td>22.905161</td>\n",
+       "      <td>71.0</td>\n",
+       "      <td>133.5</td>\n",
+       "      <td>153.0</td>\n",
+       "      <td>166.0</td>\n",
+       "      <td>202.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>oldpeak</th>\n",
+       "      <td>303.0</td>\n",
+       "      <td>1.039604</td>\n",
+       "      <td>1.161075</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.8</td>\n",
+       "      <td>1.6</td>\n",
+       "      <td>6.2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          count        mean        std    min    25%    50%    75%    max\n",
+       "age       303.0   54.366337   9.082101   29.0   47.5   55.0   61.0   77.0\n",
+       "trtbps    303.0  131.623762  17.538143   94.0  120.0  130.0  140.0  200.0\n",
+       "chol      303.0  246.264026  51.830751  126.0  211.0  240.0  274.5  564.0\n",
+       "thalachh  303.0  149.646865  22.905161   71.0  133.5  153.0  166.0  202.0\n",
+       "oldpeak   303.0    1.039604   1.161075    0.0    0.0    0.8    1.6    6.2"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[con_cols].describe().transpose()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "19da872f",
+   "metadata": {},
+   "source": [
+    "Missing values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "71e3ae49",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "age         0\n",
+       "sex         0\n",
+       "cp          0\n",
+       "trtbps      0\n",
+       "chol        0\n",
+       "fbs         0\n",
+       "restecg     0\n",
+       "thalachh    0\n",
+       "exng        0\n",
+       "oldpeak     0\n",
+       "slp         0\n",
+       "caa         0\n",
+       "thall       0\n",
+       "output      0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "548a2527",
+   "metadata": {},
+   "source": [
+    "<span style=\"background-color: yellow;\">There are no NaN values in the data.</span>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "97347c7e",
+   "metadata": {},
+   "source": [
+    "<span style=\"font-weight:bold;font-size:20px;\">Univariate analysis :It a statistical method used to analyze a single variable in isolation.<span/>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "51508e91",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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8Qyw2dr5SNmwyxMaMnSSLJdsQCw0d8avxszVm7KRfjb/Jpca/1vmZE5dkiM2JSyrW92f4i+GG7bycPEWHRxti6anpSoxPNMQS4xOVnppuiEWHRysvJ89lxy/O9ycqNtbJAeAsXraCZFtpB8nIOK4Hu4zXzu8/UO27Q5Vv+cS+r2r1vyrvxMelPQRTYq9S2imxVq8gJ2Xi+rxtqWanAFRYFy9eUq/HwtW92/0a87cnJEmNmg3VutVRCgjw1dGjuerUdYL27oor1XGo74WcccsDNb6QM2r81uxqTsjEPbTxyzE7BTjJ0aO5OnosV/fdW1+nTp3V/e1G6bNPpmj+wtXy9b1FEyf0VWRUgvLyTpX6tjZ4Dneql47Wu8Ubs8ook/I14IEaDr+n1Isy8tsrfntlHN9zf3tV3PPP+HAFNptNQ4a/rSaNa9mbCZLUp3eQFixaLUlasGi1Hu3d3qwUAQAlxExjAM5SqhkK5fXbK4nfYF2NGQrOwwwF4No2btqpDp3Hq0XzuqpU6UrvefrroWrXtrH6PjNd/ztsUe1a/vp4yWT5+pZu4V3qeyFmKDgXMxScixkK7qk8ZhrDM7hTvWSGQvGVeIYCv70CAPf1wJ+by1aQrB/SP9C2tBhtS4tRzx5tVa3arVqzKlL7d8/VmlWRpW4mAADMU54zjd1tpiXj//74FXkdmOKMX5rzEx+zVCfzjeuPfDA91rB9Mv+U4mOWGmJ7tu/V2hXrDbG1K9Zrz/a95Tq+ozONSzxDoTx/eyXxG6yrMUPBeZihAJiP+l6IGQrOxQwF52KGgnspz5nG8AzuVC+ZoVB8PiU92C+/vbqWNasiSzosAAAAgDL0RzONJ07oy0xjAMVS4oYCAAAAANez6ZtdWrR4jVo0r6tWrUdKujLTeOL4vur7zHTNnb/KPtMYjnOXmWLM5EVx0FAAAAAAPAgzjQE4S6kfGwkAAAAAADwPDQUAAAAAAOAwGgoAAAAAAMBhNBQAAAAAAIDDaCgAAAAAAACH0VAAAAAAAAAOo6EAAAAAAAAcRkMBAAAAAAA4jIYCAAAAAABwGA0FAAAAAADgMBoKAAAAAADAYTQUAAAAAACAw2goAAAAAAAAh9FQAAAAAAAADqOhAAAAAAAAHEZDAQAAAAAAOMzH7AQAV7c1u5rZKVQYbfxyzE4BAAAAQDlhhgIAAAAAAHBYmTUUVq5KU6NmQ1W/yWBFRiWU1WEAAOWM+g4A7osaD8ARZXLLg9Vq1ciXYvRV0nQFBvqpTfuX1KdXOzVtWqcsDgcAKCfUdwBwX86s8ScitpRBhuWvelhbs1MAKrQymaGwZes+1b+7hu66K0CVK1+nfn07atny1LI4FACgHFHfAcB9UeMBOKpMGgqZmdmqFehv3w6s6afMLBZrAwBXR30HAPdFjQfgqDK55cFmKxrz8jJuz4lL0py4lZKkc+cv6Ibrq5RFKk5lyf5J/n63mZ2GtCze7AycosKcTzfhCufTz+9WrVwxzew0UArFqe8SNb7E3KS+SxXkfLoJVzmX1HjX52o1vlz+blTQuuwqdaEseOpnL4/PPfN39v1WjS+ThkJgoJ8OH7HYt49kZqtGgPHResOH9tTwoT3L4vBlpnXQKKWlvmt2Gm6D8+lcnE+Uh+LUd4kaD86nM3EuUV5crcZ78t8NPrvnffaK+rnL5JaHNq0bav+BLB08eEwFBRe1NGG9+vQKKotDAQDKEfUdANwXNR6Ao8pkhoKPj7fef/t5dX/kFVkvWzU49CE1a8YK4ADg6qjvAOC+qPEAHFUmDQVJ6tmjrXr2cK/HrAwf+rDZKbgVzqdzcT5RXtyxvkv8HXI2zqfzcC5Rnlypxnvy3w0+u+epqJ/by1aQfI3lVwAAAAAAAH5bmayhAAAAAAAA3BsNhWJauSpNjZoNVf0mgxUZlWB2Oi5t8LBoVa/ZT81bPWd2Ki7v8GGLOnd7WU1aDFezliP0znufmZ0S4HKo785DfXcuajxwbZ5ctz21znpyPTx/vkBt//SSWt7/gpq1HKHwqYvMTsmAWx6KwWq1qmGzofoqaboCA/3Upv1LWrLoZTVtyiI1JZGyYYduvvkGPTvoTe3cNsvsdFza0aO5OnosV/fdW1+nTp3V/e1G6bNPpvC9CRQT9d25qO/ORY0HivL0uu2pddaT66HNZtOZM+d188036OLFS3qg0zi9Ez1CQe2amJ2aJGYoFMuWrftU/+4auuuuAFWufJ369e2oZctTzU7LZT3YoYV8q95idhpuISDAV/fdW1+SdMstN6pJ41rKzMoxOSvAdVDfnYv67lzUeKAoT6/bnlpnPbkeenl56eabb5AkXbx4SRcvXpKXl5fJWRWioVAMmZnZqhXob98OrOnnMd/AcB0ZGcf1/fYf1a5tI7NTAVwG9R2ughoPXEHdhifWQ6vVqlatR6p6zf7q1uVetWvb2OyU7GgoFIPtGjeFVKCmEKDTp8/pyaen6e03R+jWW28yOx3AZVDf4Qqo8UAh6rZn89R66O3trW1pMTpycJG2pO3Tzp0ZZqdkR0OhGAID/XT4iMW+fSQzWzUCqpmYEVDo4sVLevLpaRrQv7OeePzPZqcDuBTqOyo6ajxgRN32XNRD6fbbb1anB+/Ryi/TzE7FjoZCMbRp3VD7D2Tp4MFjKii4qKUJ69WnV5DZaQGy2WwaMvxtNWlcS2P+9oTZ6QAuh/qOiowaDxRF3fZMnlwPLZZ85eefliSdO3dBq9d+r8aNapmcVSGe8lBMSclb9Lexc2S9bNXg0Ic0Oay/2Sm5rP7BkVqX8oOys0/qjjtu19RXQzRkUHez03JJGzftVIfO49WieV1VqnSlPzj99VD17NHW5MwA10F9dx7qu3NR44Fr8+S67al11pPr4Q8/HFTokDdltV7W5cs29X2qg159ZYDZadnRUAAAAAAAAA7jlgcAAAAAAOAwGgoAAAAAAMBhNBQAAAAAAIDDaCgAAAAAAACH0VAAAAAAAAAOo6EAAAAAAAAcRkMBAAAAAAA4jIYCAAAAAABw2P8D16oJe6aqFlIAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 1296x1080 with 9 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure(figsize=(18,15))\n",
+    "gs = fig.add_gridspec(3,3)\n",
+    "gs.update(wspace=0.5, hspace=0.25)\n",
+    "ax0 = fig.add_subplot(gs[0,0])\n",
+    "ax1 = fig.add_subplot(gs[0,1])\n",
+    "ax2 = fig.add_subplot(gs[0,2])\n",
+    "ax3 = fig.add_subplot(gs[1,0])\n",
+    "ax4 = fig.add_subplot(gs[1,1])\n",
+    "ax5 = fig.add_subplot(gs[1,2])\n",
+    "ax6 = fig.add_subplot(gs[2,0])\n",
+    "ax7 = fig.add_subplot(gs[2,1])\n",
+    "ax8 = fig.add_subplot(gs[2,2])\n",
+    "background_color = \"#FFFAB7\"\n",
+    "color_palette = [\"#FA7070\", \"#FEFDED\", \"#C6EBC5\", \"#A1C398\", \"#da8829\"]\n",
+    "fig.patch.set_facecolor(background_color) \n",
+    "ax0.set_facecolor(background_color) \n",
+    "ax1.set_facecolor(background_color) \n",
+    "ax2.set_facecolor(background_color) \n",
+    "ax3.set_facecolor(background_color) \n",
+    "ax4.set_facecolor(background_color) \n",
+    "ax5.set_facecolor(background_color) \n",
+    "ax6.set_facecolor(background_color) \n",
+    "ax7.set_facecolor(background_color) \n",
+    "ax8.set_facecolor(background_color) \n",
+    "# Title of the plot\n",
+    "ax0.spines[\"bottom\"].set_visible(False)\n",
+    "ax0.spines[\"left\"].set_visible(False)\n",
+    "ax0.spines[\"top\"].set_visible(False)\n",
+    "ax0.spines[\"right\"].set_visible(False)\n",
+    "ax0.tick_params(left=False, bottom=False)\n",
+    "ax0.set_xticklabels([])\n",
+    "ax0.set_yticklabels([])\n",
+    "ax0.text(0.5,0.5,\n",
+    "         'Count plot for various\\n categorical features\\n_________________',\n",
+    "         horizontalalignment='center',\n",
+    "         verticalalignment='center',\n",
+    "         fontsize=20, fontweight='bold',\n",
+    "         fontfamily='serif',\n",
+    "         color=\"#000000\")\n",
+    "# Sex count\n",
+    "ax1.text(0.4, 220, 'Sex', fontsize=14, fontweight='bold', fontfamily='serif', color=\"#000000\")\n",
+    "ax1.grid(color='#000000', linestyle=':', axis='y', zorder=0,  dashes=(1,5))\n",
+    "sns.countplot(ax=ax1,data=df,x='sex',palette=color_palette)\n",
+    "ax1.set_xlabel(\"\")\n",
+    "ax1.set_ylabel(\"\")\n",
+    "# Exng count\n",
+    "ax2.text(0.3, 220, 'Exng', fontsize=14, fontweight='bold', fontfamily='serif', color=\"#000000\")\n",
+    "ax2.grid(color='#000000', linestyle=':', axis='y', zorder=0,  dashes=(1,5))\n",
+    "sns.countplot(ax=ax2,data=df,x='exng',palette=color_palette)\n",
+    "ax2.set_xlabel(\"\")\n",
+    "ax2.set_ylabel(\"\")\n",
+    "\n",
+    "# Caa count\n",
+    "ax3.text(1.5, 200, 'Caa', fontsize=14, fontweight='bold', fontfamily='serif', color=\"#000000\")\n",
+    "ax3.grid(color='#000000', linestyle=':', axis='y', zorder=0,  dashes=(1,5))\n",
+    "sns.countplot(ax=ax3,data=df,x='caa',palette=color_palette)\n",
+    "ax3.set_xlabel(\"\")\n",
+    "ax3.set_ylabel(\"\")\n",
+    "\n",
+    "# Cp count\n",
+    "ax4.text(1.5, 162, 'Cp', fontsize=14, fontweight='bold', fontfamily='serif', color=\"#000000\")\n",
+    "ax4.grid(color='#000000', linestyle=':', axis='y', zorder=0,  dashes=(1,5))\n",
+    "sns.countplot(ax=ax4,data=df,x='cp',palette=color_palette)\n",
+    "ax4.set_xlabel(\"\")\n",
+    "ax4.set_ylabel(\"\")\n",
+    "\n",
+    "# Fbs count\n",
+    "ax5.text(0.5, 290, 'Fbs', fontsize=14, fontweight='bold', fontfamily='serif', color=\"#000000\")\n",
+    "ax5.grid(color='#000000', linestyle=':', axis='y', zorder=0,  dashes=(1,5))\n",
+    "sns.countplot(ax=ax5,data=df,x='fbs',palette=color_palette)\n",
+    "ax5.set_xlabel(\"\")\n",
+    "ax5.set_ylabel(\"\")\n",
+    "\n",
+    "# Restecg count\n",
+    "ax6.text(0.75, 165, 'Restecg', fontsize=14, fontweight='bold', fontfamily='serif', color=\"#000000\")\n",
+    "ax6.grid(color='#000000', linestyle=':', axis='y', zorder=0,  dashes=(1,5))\n",
+    "sns.countplot(ax=ax6,data=df,x='restecg',palette=color_palette)\n",
+    "ax6.set_xlabel(\"\")\n",
+    "ax6.set_ylabel(\"\")\n",
+    "\n",
+    "# Slp count\n",
+    "ax7.text(0.85, 155, 'Slp', fontsize=14, fontweight='bold', fontfamily='serif', color=\"#000000\")\n",
+    "ax7.grid(color='#000000', linestyle=':', axis='y', zorder=0,  dashes=(1,5))\n",
+    "sns.countplot(ax=ax7,data=df,x='slp',palette=color_palette)\n",
+    "ax7.set_xlabel(\"\")\n",
+    "ax7.set_ylabel(\"\")\n",
+    "\n",
+    "# Thall count\n",
+    "ax8.text(1.2, 180, 'Thall', fontsize=14, fontweight='bold', fontfamily='serif', color=\"#000000\")\n",
+    "ax8.grid(color='#000000', linestyle=':', axis='y', zorder=0,  dashes=(1,5))\n",
+    "sns.countplot(ax=ax8,data=df,x='thall',palette=color_palette)\n",
+    "ax8.set_xlabel(\"\")\n",
+    "ax8.set_ylabel(\"\")\n",
+    "\n",
+    "for s in [\"top\",\"right\",\"left\"]:\n",
+    "    ax1.spines[s].set_visible(False)\n",
+    "    ax2.spines[s].set_visible(False)\n",
+    "    ax3.spines[s].set_visible(False)\n",
+    "    ax4.spines[s].set_visible(False)\n",
+    "    ax5.spines[s].set_visible(False)\n",
+    "    ax6.spines[s].set_visible(False)\n",
+    "    ax7.spines[s].set_visible(False)\n",
+    "    ax8.spines[s].set_visible(False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "0ae535c7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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IoinjOoz4n0pQ1iwR2Qm4BPiG6ygi7fQe3r7qv5JoKroOI/6lEpS2eTO2Xwb8GE1aK7XpOeDnJJpecR1E/EklKK1LRI7GK8B1XUcRWUt5vKvP/JZE03LHWcRnVILyVYnIlsCNwHjHSUTK7T3gRBJNKddBxD9UguJJRLrgne5wFjrfT4Ltn8DJJJrmug4i7qkEBRKRMcCfgG1cRxGpksXA2cD1OnAm3FSCYZaI9ME76vOn6MAXCadpwM9INL3qOoi4oRIMq0TkG8BtwCDXUUQcy+KdTnGZRoXhoxIMm0SkEfg93hRHGv2JfGkKcCSJpvdcB5HqUQmGSSKyBXAHMMJ1FBGfWgycRKJpkusgUh0qwbBIRI4BrgF6uo4iUgP+ChxPommJ6yBSWSrBoPMOfrkB+JHjJCK15m3gUBJNL7gOIpWjEgyyRGQ03jvaIa6jiNSoHHAmcAWJJr1YBlCd6wBSIYnIWXg7+lWAIp3XiHf5wHtIRHq5DiPlp5Fg0CQiPYBbgYNdRxEJmJnAASSa3nUdRMpHJRgkicjGwL3AcMdJRIJqIXAwiabHXAeR8tDm0KDwLn32PCpAkUpaF/gviciproNIeWgkGASJyE+BPwJdXEcRCZGJwHGawb62qQRrWSLSAFwJnOQ6ikhIPQccSKJpnusg0jkqwVqViPQD7gL2ch1FJOTm4hXhNNdBpONUgrUoEdkEeBjY3HUUEQFgOXAQiaYHXQeRjtGBMbUmERkKPI0KUMRPegD/JhE5zHUQ6RiVYC1JREYBT6Lpj0T8qBG4nURE++hriEqwViQi+wCPAn1dRxGRNhngWhKRhOsg0j7aJ1gLEpGD8K4BqlMgRGrHdcDJuuaov2kk6HfeOYB/RwUoUmtOwts82ug6iLRNJehnicivgT+j35NIrToM74CZHq6DSOu0OdSvEpGL8KZwEZHa9ySwH4mm5a6DyFdphOFHichvUQGKBMlY4F4Ska6ug8hXaSToN4nIL4HLXccQkYq4D/gBiaa86yDiUQn6SSLyc+AG1zFEpKL+DhxGoqnoOohoc6h/JCJHANe7jiEiFfcj4GYSEeM6iKgE/SERORBvWhb9PkTC4Si88wjFMb3oupaIfBO4E6h3HUVEquoEEpFLXIcIO5WgS4nIWOBf6ER4kbA6Q5dYc0sHxriSiAwHpgC9XUcREeeOI9F0o+sQYaQSdCER2QhvRmrNBiEiAHngWySaHnMdJGxUgtWWiPTCmw9wR9dRRMRXPgd2I9H0husgYaJ9gtWUiNTjHQSjAhSRlvoA/yER6ec6SJioBKvo6cLQs4GY6xwi4lubAfeQiOhguSpRCVZJNJ469ojcWeffUxgz2Vq0DVpE2jIW+JPrEGGhfYJVEI2n9gAeAxoBflz/8NQLGibuYAyaXkVE2nIWiaaLXYcIOpVghUXjqY2BF4ABzb8/0rzx+p1dfte33hTXd5NMRHzOAgeRaLrHdZAgUwlWUDSe6gn8jzYOhNmABR8/2vWMRT3Nym2qm0xcW5m3jL11GZkC5Itw0DYNnL9nN/4xK0diSobX5xeZ9rOejBj09QsJvflZgR/dveKLr99dVOSCPbty6qiu/OaRlTz4dp5h69dz24HdAZj0SpaFKyynjNIsPjVoObAriaaZroMElfYJVtYNrOZI0Hn0W39EZsKQ94rrTa1iJvGBrvXw+JE9eeW4Xkz/eU8eeifP1A/zDF2vjnsO7s7YIW1fRW+r/vVMP64X04/rxYvH9qRHo+HArRtpWml55sMCrx7fi4K1zPikwIqcZeIrOU4YqeMsalQP4C4SkZ6ugwSVSrBCovHU/wFHrOl+K+jaY1z2yl0fLuw8ueKhxDeMMfTq4k0ikCtCrgAG2GZAPVv1b/9lZB+bU2CzdesY0qeOOgPZgsVay4ocNNbDpc9kOXmXLjTWa8KCGrYN8EfXIYJKJVgB0XhqCzr0n9aYY3Onj784d+j/rCVTsWDiK4WiZdgNS1nv0iXss2kDu27U0OFl3Dkzx6FDGwHo3dXwg20aGX7jMjbpU0ekq+H5uQUO2Lqx3NGl+o4kEfk/1yGCSPsEyywaT3UBngV26szjx9TNmHFbY3L9OmMHrPneEgSfr7Qc+PflXLtfN4au540Cx09cxmX7dmt1n+Aq2YJl0OVLmXVCTwb2+vr72Z/et4ITR3bhxXkFHn4nzw4D6zlnrPYL1rBlwAhdUaa8NBIsvz/QyQIEeLq4/fbjs1dkVtgus8uYSXysTzfD+CENPPR2vkOPe3B2np02qGu1AF+eVwBgy3513PZKjrt+2IOZnxaYvaBQlsziRE+8/YPdXAcJEpVgGUXjqRhw6tou5307cKMRmQkbzLXrTlv7VOJH85cV+XyltxVmRc7y6Jw8W/fv2J/j35ptCm3p3CcyXLBnV3JFKJQ29tQZWJ5bq9ji3vbA1a5DBIlKsEyi8dQgvNnhy2IZ3XuNzlwz4qnC9lPKtUzxj3lLLXv+ZRk7TFjKyD8vY59NG/j2lo386/UcG12xhGc/LBC7YznfvH0ZAHOXFNn/r8u/ePzynOWRdwt8f5uvl+C9b+QYOaieQb3r6NPNsNtG9Ww/YSnGwI7ra+7mADiWROQQ1yGCQvsEyyAaT9UBjwJ7VmL5pzX846mT6/81yhh0hIOIACwBdiLR9LbrILVOI8HyOJMKFSDAlfkf7nFs7pezipZFlVqHiNSU3sBEEhG9hq8lPYFrKRpP7QokKr2eR4ojhu2TvXRxxjbMqfS6RKQmjAZOdB2i1mlz6FqIxlONwEvA0Gqtcx2WNj3W9Yy3B5imnau1ThHxrWXAUBJNaddBapVGgmvn11SxAAEW0ysyKnPdji8Ut3yymusVEV/qiaZdWisaCXZS6aowrwLOztk5t2HSlGPqHxxjDDrkTyTcjiHRdKvrELVII8HOuxGHBQhwYf7H407OnfSytTS5zCEizl1BIrKB6xC1SCXYCdF46igqeDRoR9xf3H3E/tmLP8vZ+vddZxERZ/oA17sOUYu0ObSDovHUAOB1oJ/rLM31ZfHCx7v+6oO+ZmmbUzeJSOD9iETTXa5D1BKNBDvuSnxWgACLWGfdXTLXbzOrOORp11lExJlrSUR89/rkZyrBDojGU/sCh7vO0ZYcDV1i2YvH3Jnfc4q1FF3nEZGqWw/4nesQtUSbQ9spGk/1AGYCm7jO0h4H1z8x7Q8Nf97WGHq5ziIiVVUAhpFomuk6SC3QSLD9zqJGChDgrsKeuxyYveCjvK37yHUWEamqeuBy1yFqhUaC7RCNpzYEZgPdXWfpqPVYNP+xrr/6tLdZsZ3rLCJSVTESTQ+4DuF3Ggm2z/nUYAECfErfASMyEzZ7uzjoGddZRKSqLicRaXAdwu9UgmsQjae2BY5ynWNtZOjSbe/sZbvfXxg12Vo09BcJh62B41yH8DuV4JpdDMG4LNkvciePvyD/46nWssJ1FhGpigSJSB/XIfxMJbga0XhqDPBd1znK6dbCfrsdkj1nTsGaT1xnEZGK6wec5zqEn6kEV+8S1wEq4Tm77bZjM1cVl9uub7jOIiIVdxKJyBauQ/iVSrAN0Xjq+8BurnNUykcM2GBEZsLg94sDprrOIiIV1QgkXYfwK50i0YpoPNWAd2L8Vq6zVJ61NzVeNmXv+pfHu04iIhVjgZ1INE13HcRvNBJs3U8IRQECGPPT3BnjL80d/LS1ZF2nEZGKMMBvXYfwI40EW4jGU92Bd4H1XWeptrF1r7w6sfGSQXXG9nedRUTKTqPBVmgk+HVHE8ICBHiyuOMOe2UvW7HSNr7tOouIlJ1Gg61QCTYTjafqgF+6zuFS2m4weERmwnof274vuM4iIuVjLUufKOzYZ/P4v4e6zuInKsGv+j6wmesQri2lxzq7Z64d/kxh2ymus4jI2rGWpocKI6bslLkhe3TuN+PzNJzpOpOfaJ9gM9F46jlgF9c5/OSMhjufOqH+vt2MQdcgFKkhRWsW3lfcbca5uaOHLaFnpNlNBWDLdDL2rqtsfqKRYEk0nhqHCvBrLs0fssdxuVNnFC2fu84iImtWsGb+nfk9p2yfuanLqbmTxrUoQPAuA3mGi2x+pJFgSTSeSgH7u87hV1uaD+b8p8vZpovJR11nEZGvy9u6eZMK+8z+Q/6QkSvpuqZZbzJANJ2MfVyNbH6mkSAQjae2A/ZzncPP3rKDNxmZub7PAtv7ZddZRORLOVv/4fX57z61TWZiv/PzR45tRwECdAVOqXS2WqAS9PwK7/BhWY0mevXZJXP99tOLmz3lOotI2GVtw5zLcwf9b+vMxPUvyR+yR46GLh1cxE+j8VRHHxM4oS/B0qzxh7nOUSsK1Dd8L3vhHn/J7zvFWgqu84iEzUrb+Pbvcoc/s1Vm4pBrC98fXaC+swet9QcOKme2WhT6EsTbJBD6d0Md9dv8UeNOy53wsrUsdp1FJAyW265vnJM7eurWmYmb3VSI7W6pK8frd+gn3Q31gTGlS6TNBfo4jlKztjNz3r63y3ldG01hsOssIkG0xHaflcgdufyfxbEjK7SK7dLJ2GsVWrbvhX0keCAqwLUyy26y+ajMdd0/tz1fdZ1FJEgW2V6vnJA95aXtMzdvV8ECBPh5BZfte2EfCT4C7O06RxA0ks/e3+XsaVvXfTDGdRaRWjbfRl76de7Y+ieKw3es0io/Bwalk7EVVVqfr4R2JBiNp4YAe7nOERQ5Grp8K/uHMXcXxk62lvC+sxLppLm23/OHZc+aNTIzYacqFiB4W8MOqeL6fCXMl8I6khC/CaiUX+WOG/9S/eZTf99wy/bG0NN1HhE/sxb7vl3vuVNzJ/Z92W5RyU2ea3IccKvD9TsTys2h0XjKAO8Am7jOElQ7mbfeuKvLBZEGU9zAdRYRv7GWwtt2w+dOyZ048DUb9ctF+3dKJ2OhuxhGWEdC41ABVtRLdsutx2SuqVtqu4X2qDORlqwlN6s45Ol9s5d8sE/20t19VIAQ0tMlwlqCx7gOEAYfs+7AEZkJm7xbXP9Z11lEXLKWzEvFzZ8cl73yk1j24jGz7UZR15lacVg0nurhOkS1ha4Eo/HUOsAPXOcIi5V07b5X9vJRDxR20dyEEjrWsuLZwjZTRmeuWfj97AVj37cDN3KdaTV6EcJrKIeuBIGDgdC923HLmBNyp467MHfEM9ay0nUakUorzeI+eWTm+qWH5s4dN5f+tbJvPHSXUQvdgTHReOp/wO6uc4TV7nUzZ01qvHhAvbHruc4iUm7W0vTf4siX47mf7vg5vfu6ztMJS4EB6WQsNG9WQzUSLJ0bqAJ06Jni0O3GZa/KL7dd3nSdRaRcitYs/Fdh9OQdMn/muNxp42u0AMHbJPot1yGqKVQlCHzXdQCBD+2AQSMzEzb8yPab5jqLyNooWDP/b/k9pwzN3Nz1tNyJ41uZxb0WhWqTqEpQnFhG915jMlePmFzYQQfMSM3J27p5t+a/+eR2mVt6nZn/2bjldAvShSG+E42nuroOUS2h2SdYOir0M6DRdRb5qlPq//n0qQ3/3MUYTWkl/paz9R/8qRBLX5U/aNdOTGJbS76bTsbudx2iGsJ02bT9UAH60tWFH4yZYTd55abGywfXGbuu6zwiLWVtw5xr89+be33hgF0L1Idh2rAfAqEowTCNBP+KZpD3tU3M3Pcf6nJmrqvJ+ekqGhJiK23j25flD/705sJ+o8o0iW2t+BwYmE7Gsq6DVFoofqnReKqBEJ4EWmvm2EEbj8hc3/9T2+cF11kk3JbZrq+fnTvmuTLP4l5L+hCSaebC8ovdA6jVQ5ZDZQk9I6My1w2fVtxKB8xI1S223Wf+Mnvc89tlbt3mr4W9dwVjXGdy6PuuA1RDWEpQR4XWkCJ19QdnfzvuT/n9n7SWvOs8EnyLbK/px2dPeXmHzM1D76nsLO61JBQjwVDsE4zGU+8Am7rOIR23f91zL/2x8erNjCEI51+JzziYxb3WbJZOxt51HaKSAj8SjMZTQ1EB1qwHirvu9K1scmHW1r/nOosEh8NZ3GvNnq4DVFrgSxD4pusAsnbetBtvskvm+nUW2t7TXWeR2mUtNl0c+OyBmfPf3D1z7chnikO3c52pBuzlOkClhaEE93AdQNbe5/Tuu0vmj9u9WtzkKddZpLZYS2F2ccNnYtmL3h2fvXK3l+0WW7nOVEMCPxIM/D7BaDw1H+jvOoeUz+8abp5yeP1jexgTijdx0knWknvNDnnu1NyJG/l0EttasW06GXvddYhKCfQVY6Lx1FaoAAPnnPxPxk23m0+7tOHGbYyht+s84i/WknnZbj7t1NyJm7xvB45xnScA9gQCW4JBfyetP4CAurswbpcDshd+nLd1H7rOIv7gzeK+7ZOlWdz38Pks7rUk0PsFVYJSs161m20xKnNd18W2xwzXWcSdr87ifs7YGprFvVaMj8ZTgb1oQNBLcLTrAFJZn9FnwIjMhC3fKm74P9dZpLqspenBwsgpwzM35o7O/Wb8Z/QZ4DpTQPUDdnAdolICW4LReGo9YAvXOaTysjR23Td76eh/FUZPtpZgH+klq2Zxn7JD5s8cnzttXA3P4l5LAnuUaJAPjNGm0JA5LXfi+Jfqt5h6QcPEHYyhh+s8Ul4Fa+bfVRj/2oX5H49YTrdxrvOETGAvJRfkEtSm0BCaVNh31BvFjV+/s8vv+tab4vqu88jay9u6ebcV9n3rD/lDds3QReXnxjDXASolsOcJRuOp54BdXOcQNzZgwcePdj1jUU+zchvXWaRzQjSLey0oAL3SydhK10HKLZD7BKPxVDdguOsc4s48+q0/IjNhyHvF9aa6ziIdk7UNcy7L/fDprTMTN7g0f8geKkBfqAe2dx2iEgJZgsC2QKPrEOLWCrr2GJe9cteHCztPdp1F1mylbZx9Ye6IZ7bKTBxyXeHAMQXqg7y7phYNcx2gEoJagoF8xyKdYcyxudPHX5w79H/WknGdRr5ume36+lm5Y6ZunZm4+c2F/cM4i3utGOY6QCUE9Z2WSlC+4sbCd0bPstEZtzUm168zVueT+cBi231mInfkCk1iWzMCOeVUIA+MicZTD6EplKQVG5tPPvxvl9+s6G6yOofUkUW21/Qzcz8pPlTcdSfXWaRDlgCRdDIWqNII6mYHjQSlVe/bgRuNyExYf65dd5rrLGHzqY28eHT2jFeGZ/40TAVYk3oDm7kOUW6BK8FoPNUXGOQ6h/jXMrr3Hp25ZsTThaFTXGcJg49sv2mHZM+etUtmws6axb3mDXMdoNwCV4KAJsyUNbLU1R2RO2vctfnvPWUtOdd5gqb5LO6jM9fuMrW4nWZxD4ZhrgOUWxAPjFEJSrtdnj94j1eLm06/sfGKIXUGXYNyLVlLYbbdcOqpuRPXf81Gd3OdR8oucBefCOJIcEvXAaS2PFIcMWyf7KWLM7ZhjusstcpacjOL0af2zl764b7ZS0e/ZqOB23ckAAxxHaDcgliCGglKh71jNxwyMnP9uvNt5EXXWWqJtWReKm7+1LjslZ98O3vRHu/YDQP3IilfsbHrAOWmEhQpWUyvyKjMdTu+UNzySddZ/E6zuIfWgGg81d11iHIKYglu4jqA1K4C9Q0HZRNjb87vN8VaCq7z+I21LH28MEyzuIdboEaDgSrBaDzVG+jpOofUvgvzPx53cu6kl62lyXUWP2g+i/sxuV9rFvdwC1QJBu3oUL0rlbK5v7j7iLezG75zX5dzmhpNIVB/+O1VtGbhv4u7zzgvd9SwJfTUXH4CASvBQI0EAU2iKmX1uh2y2a6Z63p+bnu+4jpLNRWs+fSO/F5ThmZu7npa7sRxS+gZcZ1JfEMl6GMqQSm7hUT6jcxM2Oa14sZPu85SaXlbN++W/Lee3DZz6zpn5X86bjndtHtBWgrUEcAqQZF2yNHQZf9scsyd+fGTraXoOk+55Wz9B3/MH/DUNpmJ/S7I/9/YDF26uc4kvhWokWDQ9gmqBKWi4vljx0+3mz93ccNNQ42p/YOwsrZhzjX5Az+aUPjuqAL1g13nkZqgEvQxlaBU3J2FvXZ9o7jxm3d3SfRuMMWavFj7Sts4+9L8j+bfUvjWKEudTiuSjgjUOaHaHCrSCdPt5lvtnrm2cYntPst1lo7QLO5SBl2j8VTNbwVZJWh/ADpFQqrmU/oOGJGZsNnbxUHPuM6yJott95mnZY9/frvMrdvcUdh7FBjjOpPUtN6uA5RL0EpQI0Gpqgxduu2dvWz3+wujJluL72bcXmh7vXJc9pSXdsjcPPRfxT1Gus4jgRGYEgzMPsFoPFUH6CoW4sQvciePf6l+i2fPa5g0zBicX1vxUxt58de5nzdOLg7TJLZSCSpBH+oO1LsOIeF1a2G/3V4rDnntji6/71dv7EAXGT6y/aadnjuu59Tidju7WL+ERmBKMEibQ7u6DiDynN1227GZq4rLbdc3qrXOVbO4fy9zgWZxl2pRCfqQSlB84SMGbDAiM2Hw+8UBUyu5HmspvFXc8JlY9qJ3x2ev3G263VzTiEm1qAR9SCUovrGcbj3HZq/a9bHC8MnlXnZpFvenS7O4765Z3MUBlaAPqQTFZ4z5Se6M8Zflfvi0tWTXdmnWknmxuMWTpVncx2gWd3EoMCUYpANjVILiS9cVDhwz027y6i2Nl25YZ2y/jj7eWlY8W9x22um547eaR7+xlcgo0kGBKUGNBEWqYHJx2A57ZS9bvtI2vt3ex1jLklWzuB+WO2fcPPrpPFjxC5WgD3VxHUBkddJ2g8EjMhPW+9j2fWF197OWpgcKu0wenrkxr1ncxad6uA5QLkEqQY0ExfeW0mOd3TPXDn+msO2UlrcVrVlwT2HM5O0zN5kTcqeO/5zefV1kFGmHgusA5aISFKmyInX1h+XOGffH/HefspZ8s1ncu/0yd8L4pfRYx3VGkTXIuw5QLjowRsSRS/OH7PFQYZfZb9mNBmfoMs51HpEOyLkOUC5BKsEg/SwSEjPsplu4ziDSCYEZCQZpc2jGdQARkZBQCfrQctcBRERCQiXoQypBEZHqUAn60DLXAUREQiIwB8YEqQQ1EhQRqQ6NBH1IJSgiUh0qQR9SCYqIVIdK0IdUgiIi1RGYYzACU4LpZCxLgN6diIj42ELXAcolMCVYssJ1ABGREFjgOkC5BK0EAzNEFxHxMZWgTy1yHUBEJAS0OdSnPnIdQEQk4CwqQd/60HUAEZGAW5hOxjSprk9pJCgiUlkfuw5QTkErQY0ERUQqSyXoYxoJiohU1jzXAcopaCWokaCISGVpJOhjGgmKiFSWRoI+Nh/Iug4hIhJg77gOUE6BKsF0MmaBua5ziIgE2JuuA5RToEqwRPsFRUQqI49Ggr73gesAIiIBNSedjOVchyinIJbgG64DiIgEVKA2hUIwS3CG6wAiIgGlEqwBKkERkcpQCdaAd4HlrkOIiASQStDv0slYEZjlOoeISACpBGuENomKiJRXUzoZ+8R1iHILagnOdB1ARCRgAjcKhOCWoEaCIiLlFcjdTCpBERFpj6muA1RCIEuwtN16vuscIiIB8qzrAJUQyBIs0WhQRKQ8FqPNoTXnBdcBREQC4vnS6WeBE+QSfNJ1ABGRgAjkplAIdgk+DQTynYuISJWpBGtNOhlrAqa7ziEiUuMsAT0yFAJcgiXaJCoisnZmp5Oxha5DVErQS3CK6wAiIjUusJtCIfgl+BTeUF5ERDpHJVir0snYAnQdURGRtaESrHHaJCoi0jnzCPiFR8JQgjo4RkSkcx5IJ2OB3qWkEhQRkbakXAeotMCXYOli2oG85p2ISAVlgUdch6i0wJdgyb9dBxARqTFPppOxpa5DVFpYSvAe1wFERGpM4DeFQkhKMJ2MvQi87zqHiEgNUQkGzL2uA4iI1IjZ6WRstusQ1RCmEtQmURGR9gnFKBDCVYJPA5+5DiEiUgNUgkGTTsYKwH2uc4iI+NwSQnR+dWhKsESbREVEVu/edDKWdR2iWsJWgo/ivcsREZHW3eY6QDWFqgTTyVgGeNB1DhERn/oQeNx1iGoKVQmW3O06gIiIT/01nYwVXYeopjCW4P3A565DiIj4UKg2hUIISzCdjK0E/uY6h4iIz7yYTsZecx2i2kJXgiW3uA4gIuIzoRsFQkhLMJ2MvUDAZ0sWEemAHHCH6xAuhLIES251HUBExCceTCdjobyiVphL8DYg4zqEiIgPhHJTKIS4BNPJ2ALgn65ziIg4thDvqPlQCm0JltzgOoCIiGM3hekyaS2FugTTydhTwCzXOUREHMkB17gO4VKoS7DkRtcBREQc+Uc6GfvIdQiXVILeDmFdVFtEwugK1wFcC30JppOxJjQaFJHweTKdjL3oOoRroS/BkivQ6RIiEi6hHwWCShCAdDI2D/iL6xwiIlUymxCfFtGcSvBLlwAF1yFERKrgqrBNmdQWlWBJOhl7B7jLdQ4RkQpbCEx0HcIvVIJflXQdQESkwm5MJ2PLXYfwC5VgM+lk7FUg5TqHiEiFrASudR3CT1SCX3eR6wAiIhVyXelAQClRCbaQTsaeAZ50nUNEpMwWo10+X6MSbN3FrgOIiJTZZaXZc6QZlWAr0snYQ8D/XOcQESmTT4ErXYfwI5Vg204HrOsQIiJl8Pt0MrbUdQg/Ugm2IZ2MPQf83XUOEZG19B6aO7VNKsHVOxNdU1REalsizJPmrolKcDXSyVgauNp1DhGRTnodmOQ6hJ+pBNfsIuAz1yFERDrhnHQypmsir4ZKcA1K8w0mXOcQEemgaelk7B7XIfxOJdg+NwJvuA4hItJOReBE1yFqgUqwHdLJWB74tescIiLtNCGdjL3gOkQtUAm2UzoZux94wnUOEZE1mAec5TpErVAJdswpQM51CBGR1fhlOhlb7DpErVAJdkA6GZsB/MF1DhGRNjycTsbudB2ilqgEO+5C4DXXIUREWliJDobpMJVgB5WuvPATvKOvRET84uJ0Mva26xC1RiXYCelkbCqanVlE/ONNNFdgp6gEO+9sYI7rECIiwAm6PmjnqAQ7KZ2MLQN+5jqHiITe7elk7HHXIWqVSnAtpJOxx4BbXOcQkdD6EDjZdYhaphJce6fjnZwqIlJNFjgqnYwtch2klhlrNXn62orGUwcA97rOIeVTXLmUBQ9eQ/az9wHov/8pmIauLPjvH7GFLKaunnX3OZ6ug7b6yuNsPsvHd/wGm89BsUiPrUbTZ4/DAVg0+VZWvPsiXdbbhP7fPh2ApTMfp7hyCeuMOKC6P6AEwdXpZOxU1yFqnUaCZZBOxv4N3Ow6h5TPwsf+RLdNd2bDn93AoGOupbHfYBZNvpU+ow9l0NHX0mfM4SyafOvXH1jfyMBDLmLQMdexwdHXsGLOi2Q+eoNiZhmZj15n0DHXYW2R7Pw0xVyGZTMfpffwWPV/QKl1s4C46xBBoBIsn18AM12HkLVXzCxn5Qez6LXDvgCY+kbquvXybssu/+I+9b36fe2xxhjqunQHwBbzUCyAMYDBFvJYa7F5byS5eNo99N75u5j6hur8YBIUWeCIdDK20nWQINDm0DKKxlNbAy8APV1nkc7LfvIuC/57LY39Nib76Ry6rr85fb9xLIXF8/nkrvMAC7bI+kdcRkNkva893hYLzPvLqeQXzaP3TjH6jj8agKbn7mbZa1PoNmRH1tnl+yx86FrWO+i3Vf7pJABOTSdjV7sOERQqwTKLxlM/Bm5znUM6LzNvNh9POp31j7iUroO2YuGjN1LXpQfF7HK6Dh5Kz61Gs+z1p1j6ykMMPOT3bS6nuHIpn/7r96y798/pMiD6ldsWPHgNvXeKkfn4bVbOeZnG9aL02f2QCv9kEgD3p5Ox77oOESTaHFpm6WRsEjptoqY19O5Pfe/+Xxz00mOr0WQ/eYelMx6jx5a7e9/begyZeW+tdjl13XrRbfD2rHj3pa98P/vJO956+m7IspmPM+B7cXLz3yO38KMK/DQSIB8BR7sOETQqwco4Ce0frFn1vfrSsE5/cgs+BGDle6/Q2H9j6nutS+aDGV9+r++grz22sLyJ4sqlABRzGVa+N53Gfht95T6fP3U7kTGHQzEPtnQJWlOHzWcq+FNJjSsCh6eTsQWugwSNNodWiPYP1rbsJ++y4KFrsIU8DX3Wp9/+p5L77D0WPfonbLGAaejCuvueQNf1Nye/ZAELHrqGgT88n+ync/gsdaVXbrZIj633oM/oQ79Y7vK3niX76Rz6jDkMgEWP38yKOS/RuF6UAd85w9WPK/53djoZu8h1iCBSCVaQ9g+KSBncmU7GDl3z3aQztDm0grR/UETW0kvAMa5DBJlKsPJOAl50HUJEas7HwAHpZGyF6yBBphKssNJ/4O8AH7jOIiI1IwMcmE7GPnQdJOhUglWQTsbmATFgiessIlITjitN3i0VphKsknQyNgP4IZB3nUVEfO3KdDI20XWIsFAJVlE6Gfsv3jVGRURa8xCgc2WqSKdIOBCNpy7Dm4dQRGSVN4Fd08lYk+sgYaKRoBtnAPe4DiEivvEp8B0VYPVpJOhINJ7qDkwGdnEcRUTc+hwYn07GXnEdJIxUgg5F46mBwHPAENdZRMSJZcA+6WTsWddBwkqbQx1KJ2OfAN8EPnGdRUSqLoN3MrwK0CGVoGPpZOxNYC9gvussIlI1eeCQdDL2mOsgYacS9IF0MvYa8A3gM9dZRKTiLHBMOhm713UQUQn6Rulk+r2Bha6ziEhFnVS6uL74gErQR0pHh+0NLHKdRUQq4qx0Mna96xDyJZWgz6STsZeBfQGdLyQSLMl0Mnax6xDyVSpBH0onYy/gHTW62HUWESmL89LJ2JmuQ8jX6TxBH4vGU7sD/wV6uc4iIp1igZPTydh1roNI61SCPheNp8YA9wN9HEcRkY7JA0elk7G/ug4ibVMJ1oBoPDUUeBDYyHUWEWmXlcAP08nYf1wHkdVTCdaIaDy1EV4RDnWdRURWazHexbCfdB1E1kwHxtSIdDL2ITAG76LbIuJP84E9VYC1QyVYQ0rTrHwL+LvrLCLyNR8Ae6STsZdcB5H2UwnWmHQylgEOBa50nUVEvvA6MLp0LWCpIdonWMOi8dRpwOWAcZ1FJMT+AxyeTsZ0Xm8N0kiwhqWTsSuBQ/CmZBGR6rsIbzokFWCN0kgwAErnEt4NDHSdRSQklgNHp5Oxu1wHkbWjEgyIaDw1CPgnMMp1FpGASwPfK13wXmqcNocGRDoZmwuMA/7kOotIgE0GRqoAg0MjwQCKxlM/Ba4DurrOIhIgfwROTSdjeddBpHxUggEVjad2Av4BbOo6i0iNywInppOxm1wHkfJTCQZYNJ6KALcCB7rOIlKj3sQ7/eFF10GkMlSCIRCNp04FLgEaHUcRqSUTgF+lk7HlroNI5agEQyIaT+0KTAK2cJ1FxOc+BX6iGSDCQUeHhkQ6GXsO2BG4Gm+iTxH5ugeAHVSA4aGRYAhF46mxePsKddCMiGcFcEY6Gfuj6yBSXSrBkIrGUz3x9hMej649KuE2HTgsnYy97jqIVJ9KMOSi8dRewC3AENdZRKqsgHcB+nPTyVjWdRhxQyUoROOp3sBlwLGus4hUyVTg+HQyNt11EHFLJShfiMZT+wI3AYNdZxGpkIVAHLgpnYzpxU9UgvJVpX2FZwG/BLo5jiNSLhaYCPw6nYx95jiL+IhKUFoVjac2wdtE+n3XWUTW0gzghHQy9rTrIOI/KkFZrdKBM1cB2zuOItJRS4EEcLUuei1tUQnKGkXjqXrg58CFwLqO44i0x93Aaelk7EPXQcTfVILSbtF4al3gfOA4oMFxHJHWPAacXbpCksgaqQSlw6Lx1HbAFcC+rrOIlDyLV35PuA4itUUlKJ0WjadGA+ehMhR3XgXOSSdj97sOIrVJJShrrTRDxXnA/q6zSGi8BfwW+LvO95O1oRKUsonGUyPwyvA7rrNIYL0PXABMTCdjBddhpPapBKXsovHUMLwy/B66OLeUx1t4+6EnppOxjOswEhwqQamYaDy1PXAu8AM0d6V0zlN4F7m+T5s9pRJUglJx0Xgqinee4U+AAW7TSA3IA/cAl6eTsWmuw0iwqQSlaqLxVBfgILw5DMc4jiP+8ynwJ+CGdDL2keswEg4qQXGitKn0eOAIoLfjOOLW88C1wF3a3yfVphIUp0pzGR6BV4i6Pml4fAj8Fbg9nYzNdB1GwkslKL4RjafGAIfjzVyxnuM4Un6LgX8Ck4Ap6WSs6DiPiEpQ/Kd0we7xwMF4hdjfaSBZG3ngIeB2vCM8VzjOI/IVKkHxtWg81QDsiVeIBwL93CaSdrDANLziu1OT2IqfqQSlZpQK8Rt4hfg9NK2TnzQBjwAPAA+lk7F5jvOItItKUGpSqRB3BfbGK8ZRQKPTUOHzCvBg6eMZTVwrtUglKIEQjad6AmPxCvEbwI7okm3lthh4lC9HezqXT2qeSlACKRpP9Qf2wivEvYFN3SaqSe8AzzX7eCmdjOXcRhIpL5WghEI0nlofGA7sVPo8HBVjc5/jHczyRenpgBYJA5WghFY0nuoDDOPLUtwJ2Bqod5eq4op40xG9Vfp4AZgKvKULVEsYqQRFmonGU92BocDmwGZ4o8VVnzekdvYzzsMrudl8WXizgXd0aTKRL6kERdqpdAHwDYHBwEalz4OBgUAEWKfZ53WAnpSvNC3eJsvPgPmlz5+18vVHwOx0Mra0TOsVCTSVoEiFROOpOr4sxOYf9UChnR85YCGwQDOpi5SfSlBEREJLs32LiEhoqQRFRCS0VIIiIhJaKkEREQktlaCIiISWSlBEREJLJSgiIqGlEhQRkdBSCYqISGipBEVEJLRUghIIxhhrjJnU7OsGY8x8Y8x/1vC48Wu6j4gEl0pQgmIZMNQY07309T54MyqIiLRJJShB8iAQK/37UOBvq24wxuxijHnGGPNy6fNWLR9sjOlpjLnFGPN86X4HVCm3iDiiEpQguRM4xBjTDdgBeK7ZbW8AY621w4HzgItaefzZwOPW2pHAnsClxpieFc4sIg41uA4gUi7W2leNMVG8UeADLW6OAH8xxmyBN0FtYyuL2Bf4rjHmV6WvuwEbA69XJrGIuKYSlKC5D7gMGA/0a/b9C4EnrLUHlopyciuPNcAPrLVvVjijiPiENodK0NwCXGCtndHi+xG+PFDmqDYe+1/gF8YYA2CMGV6RhCLiGypBCRRr7YfW2qtbuekS4GJjzP+A+jYefiHeZtJXjTEzS1+LSIAZa63rDCIiIk5oJCgiIqGlEhQRkdBSCYqISGipBEVEJLRUgiIiEloqQRERCS2VoIiIhJZKUEREQkslKCIioaUSFBGR0FIJiohIaKkERUQktFSCIiISWipBEREJLZWgiIiElkpQRERCSyUoIiKhpRIUEZHQUgmKiEhoqQRFRCS0VIIiIhJaKkEREQktlaCIiISWSlBEREJLJSgiIqGlEhQRkdBSCYqISGipBEVEJLRUgiIiEloqQRERCa3/B9KDJ8xp2S/zAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 576x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "\n",
+    "# Assuming your dataset is stored in a DataFrame named 'df'\n",
+    "data = {\n",
+    "    'Male': df[df['sex'] == 1].shape[0],\n",
+    "    'Female': df[df['sex'] == 0].shape[0]\n",
+    "}\n",
+    "\n",
+    "# Plotting the pie chart\n",
+    "plt.figure(figsize=(8, 8))\n",
+    "plt.pie(data.values(), labels=data.keys(), autopct='%1.1f%%', startangle=140)\n",
+    "plt.title('Distribution of Male and Female Individuals')\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6015f9bb",
+   "metadata": {},
+   "source": [
+    "<span style=\"background-color: yellow;\">\n",
+    "The data consists of more than twice the number of people with sex = 1 than sex = 0.\n",
+    "<span/>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "9cc700e4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x720 with 8 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Read the dataset\n",
+    "data = pd.read_csv('heart.csv')\n",
+    "\n",
+    "# Define the columns for which we want to create pie charts\n",
+    "columns = ['sex', 'cp', 'fbs', 'restecg', 'exng', 'slp', 'caa', 'thall']\n",
+    "\n",
+    "# Set up subplots for pie charts\n",
+    "fig, axs = plt.subplots(nrows=2, ncols=4, figsize=(20, 10))\n",
+    "\n",
+    "# Flatten the axes for easier iteration\n",
+    "axs = axs.flatten()\n",
+    "\n",
+    "# Define custom colors\n",
+    "colors = ['#ff9999','#66b3ff','#99ff99','#ffcc99']\n",
+    "\n",
+    "# Iterate over the columns and create pie charts\n",
+    "for i, col in enumerate(columns):\n",
+    "    # Calculate value counts for the current column\n",
+    "    counts = data[col].value_counts()\n",
+    "    \n",
+    "    # Plot the pie chart\n",
+    "    axs[i].pie(counts, labels=counts.index, autopct='%1.1f%%', startangle=90, colors=colors, shadow=True)\n",
+    "    axs[i].set_title(col, fontsize=14, fontweight='bold')\n",
+    "\n",
+    "# Adjust layout\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "21c44e61",
+   "metadata": {},
+   "source": [
+    "<span style=\"font-weight:bold;font-size:20px;\">Di<span style=\"font-weight:bold;font-size:20px;\">stribution Analysis<span/>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "966f2e48",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1152x720 with 16 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#visualize all features to see distributions\n",
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "df.hist(bins=15, figsize=(16,10)) #figsize = (width, height)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "212661db",
+   "metadata": {},
+   "source": [
+    "<span style=\"background-color: yellow;\">\n",
+    "Scales for the features are different, will require feature scaling.<br/>\n",
+    "After visualizing the feature distributions, it's clear several numeric features are categorical (i.e. discrete)<br/>\n",
+    "Categorical Features: sex, cp, fbs, restecg, exng, slp, caa, thall, and output.<br/>\n",
+    "Continuous Features: age, trtbps, chol, thalachh, oldpeak.<br/>\n",
+    "<span/>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7ce0e205",
+   "metadata": {},
+   "source": [
+    "<span style=\"font-weight:bold;font-size:20px;\">Boxen plot of continuous features<span/>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "08063444",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1296x1152 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure(figsize=(18,16))\n",
+    "gs = fig.add_gridspec(2,3)\n",
+    "gs.update(wspace=0.3, hspace=0.15)\n",
+    "ax0 = fig.add_subplot(gs[0,0])\n",
+    "ax1 = fig.add_subplot(gs[0,1])\n",
+    "ax2 = fig.add_subplot(gs[0,2])\n",
+    "ax3 = fig.add_subplot(gs[1,0])\n",
+    "ax4 = fig.add_subplot(gs[1,1])\n",
+    "ax5 = fig.add_subplot(gs[1,2])\n",
+    "\n",
+    "background_color = \"#FFFAB7\"\n",
+    "color_palette = [\"#FA7070\", \"#FEFDED\", \"#C6EBC5\", \"#A1C398\", \"#da8829\"]\n",
+    "fig.patch.set_facecolor(background_color) \n",
+    "ax0.set_facecolor(background_color) \n",
+    "ax1.set_facecolor(background_color) \n",
+    "ax2.set_facecolor(background_color) \n",
+    "ax3.set_facecolor(background_color) \n",
+    "ax4.set_facecolor(background_color) \n",
+    "ax5.set_facecolor(background_color) \n",
+    "# Title of the plot\n",
+    "ax0.spines[\"bottom\"].set_visible(False)\n",
+    "ax0.spines[\"left\"].set_visible(False)\n",
+    "ax0.spines[\"top\"].set_visible(False)\n",
+    "ax0.spines[\"right\"].set_visible(False)\n",
+    "ax0.tick_params(left=False, bottom=False)\n",
+    "ax0.set_xticklabels([])\n",
+    "ax0.set_yticklabels([])\n",
+    "ax0.text(0.5,0.5,\n",
+    "         'Boxen plot for various\\n continuous features\\n_________________',\n",
+    "         horizontalalignment='center',\n",
+    "         verticalalignment='center',\n",
+    "         fontsize=18, fontweight='bold',\n",
+    "         fontfamily='serif',\n",
+    "         color=\"#000000\")\n",
+    "# Age \n",
+    "ax1.text(-0.05, 81, 'Age', fontsize=14, fontweight='bold', fontfamily='serif', color=\"#000000\")\n",
+    "ax1.grid(color='#000000', linestyle=':', axis='y', zorder=0,  dashes=(1,5))\n",
+    "sns.boxenplot(ax=ax1,y=df['age'],palette=[\"#800000\"],width=0.6)\n",
+    "ax1.set_xlabel(\"\")\n",
+    "ax1.set_ylabel(\"\")\n",
+    "\n",
+    "# Trtbps \n",
+    "ax2.text(-0.05, 208, 'Trtbps', fontsize=14, fontweight='bold', fontfamily='serif', color=\"#000000\")\n",
+    "ax2.grid(color='#000000', linestyle=':', axis='y', zorder=0,  dashes=(1,5))\n",
+    "sns.boxenplot(ax=ax2,y=df['trtbps'],palette=[\"#8000ff\"],width=0.6)\n",
+    "ax2.set_xlabel(\"\")\n",
+    "ax2.set_ylabel(\"\")\n",
+    "\n",
+    "# Chol \n",
+    "ax3.text(-0.05, 600, 'Chol', fontsize=14, fontweight='bold', fontfamily='serif', color=\"#000000\")\n",
+    "ax3.grid(color='#000000', linestyle=':', axis='y', zorder=0,  dashes=(1,5))\n",
+    "sns.boxenplot(ax=ax3,y=df['chol'],palette=[\"#6aac90\"],width=0.6)\n",
+    "ax3.set_xlabel(\"\")\n",
+    "ax3.set_ylabel(\"\")\n",
+    "\n",
+    "# Thalachh \n",
+    "ax4.text(-0.09, 210, 'Thalachh', fontsize=14, fontweight='bold', fontfamily='serif', color=\"#000000\")\n",
+    "ax4.grid(color='#000000', linestyle=':', axis='y', zorder=0,  dashes=(1,5))\n",
+    "sns.boxenplot(ax=ax4,y=df['thalachh'],palette=[\"#5833ff\"],width=0.6)\n",
+    "ax4.set_xlabel(\"\")\n",
+    "ax4.set_ylabel(\"\")\n",
+    "\n",
+    "# oldpeak \n",
+    "ax5.text(-0.1, 6.6, 'Oldpeak', fontsize=14, fontweight='bold', fontfamily='serif', color=\"#000000\")\n",
+    "ax5.grid(color='#000000', linestyle=':', axis='y', zorder=0,  dashes=(1,5))\n",
+    "sns.boxenplot(ax=ax5,y=df['oldpeak'],palette=[\"#da8829\"],width=0.6)\n",
+    "ax5.set_xlabel(\"\")\n",
+    "ax5.set_ylabel(\"\")\n",
+    "\n",
+    "for s in [\"top\",\"right\",\"left\"]:\n",
+    "    ax1.spines[s].set_visible(False)\n",
+    "    ax2.spines[s].set_visible(False)\n",
+    "    ax3.spines[s].set_visible(False)\n",
+    "    ax4.spines[s].set_visible(False)\n",
+    "    ax5.spines[s].set_visible(False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0e9fadce",
+   "metadata": {},
+   "source": [
+    "<span style=\"background-color: yellow;\">There are certain outliers in all the continuous features.</span>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "eca19fe0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "col_num = ['age', 'trtbps', 'chol', 'thalachh', 'oldpeak']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "1eb5d3b0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for i in col_num:\n",
+    "    Q1 = df[i].quantile(0.25) \n",
+    "    Q3 = df[i].quantile(0.75)\n",
+    "    IQR = Q3-Q1 \n",
+    "    LowerBound = Q1 - (1.5 * IQR) \n",
+    "    UpperBound = Q3 + (1.5 * IQR) \n",
+    "    \n",
+    "    df = df[(df[i] >= LowerBound)&(df[i] <= UpperBound)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "58fade87",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x360 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "background_color = \"#FFFAB7\"\n",
+    "color_palette = [\"#FA7070\", \"#5833ff\",\"#da8829\", \"#A1C398\", \"#FDD835\"]\n",
+    "# col_num = ['age', 'trtbps', 'chol', 'thalachh', 'oldpeak']\n",
+    "\n",
+    "plt.figure(figsize=(10, 5))\n",
+    "plt.gcf().set_facecolor(background_color)  # Set background color\n",
+    "\n",
+    "for i in range(len(col_num)):\n",
+    "    plt.subplot(1, len(col_num), i + 1)\n",
+    "    sns.boxplot(y=df[col_num[i]], color=color_palette[i])\n",
+    "    plt.tight_layout()\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "01cf87af",
+   "metadata": {},
+   "source": [
+    "<span style=\"background-color: yellow;\">Removing outlier from continuous features.</span>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "c1847344",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1296x504 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure(figsize=(18,7))\n",
+    "gs = fig.add_gridspec(1,2)\n",
+    "gs.update(wspace=0.3, hspace=0.15)\n",
+    "ax0 = fig.add_subplot(gs[0,0])\n",
+    "ax1 = fig.add_subplot(gs[0,1])\n",
+    "\n",
+    "background_color = \"#FFFAB7\"\n",
+    "color_palette = [\"#FA7070\", \"#FEFDED\", \"#C6EBC5\", \"#A1C398\", \"#da8829\"]\n",
+    "fig.patch.set_facecolor(background_color) \n",
+    "ax0.set_facecolor(background_color) \n",
+    "ax1.set_facecolor(background_color) \n",
+    "\n",
+    "# Title of the plot\n",
+    "ax0.text(0.5,0.5,\"Count of the target\\n___________\",\n",
+    "        horizontalalignment = 'center',\n",
+    "        verticalalignment = 'center',\n",
+    "        fontsize = 18,\n",
+    "        fontweight='bold',\n",
+    "        fontfamily='serif',\n",
+    "        color='#000000')\n",
+    "\n",
+    "ax0.set_xticklabels([])\n",
+    "ax0.set_yticklabels([])\n",
+    "ax0.tick_params(left=False, bottom=False)\n",
+    "# Target Count\n",
+    "ax1.text(0.35,177,\"Output\",fontsize=14, fontweight='bold', fontfamily='serif', color=\"#000000\")\n",
+    "ax1.grid(color='#000000', linestyle=':', axis='y', zorder=0,  dashes=(1,5))\n",
+    "sns.countplot(ax=ax1, data=df, x = 'output',palette = color_palette)\n",
+    "ax1.set_xlabel(\"\")\n",
+    "ax1.set_ylabel(\"\")\n",
+    "ax1.set_xticklabels([\"Low chances of attack(0)\",\"High chances of attack(1)\"])\n",
+    "\n",
+    "ax0.spines[\"top\"].set_visible(False)\n",
+    "ax0.spines[\"left\"].set_visible(False)\n",
+    "ax0.spines[\"bottom\"].set_visible(False)\n",
+    "ax0.spines[\"right\"].set_visible(False)\n",
+    "ax1.spines[\"top\"].set_visible(False)\n",
+    "ax1.spines[\"left\"].set_visible(False)\n",
+    "ax1.spines[\"right\"].set_visible(False)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ad1f2e27",
+   "metadata": {},
+   "source": [
+    "<span style=\"background-color: yellow;\">\n",
+    "Total count of low chances of attack:approx(125)<br/>\n",
+    "Total count of High chances of attack:159\n",
+    "</span>\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "c70b759b",
+   "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": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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8YGOSfcDVwHlJzmTqT6GngN8EqKqHk9wCPAK8Bny0ql5fgbL7di5wKfBQd+4P4PeBa4FbklwOPAP8EqzZfpirDy5ZR2NhE7A9Ux/Y8ibglqq6I8k9rJ9xMFcf/M1qGwdeSi9JjfIUiiQ1ygCXpEYZ4JLUKANckhplgEtSowxwSWqUAS5Jjfo/+mTe8Frd8hwAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "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": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "i = ['age', 'trtbps', 'chol','thalachh','oldpeak']\n",
+    "for j in i:\n",
+    "    plt.hist(df[j],bins = 60, color = 'slateblue')\n",
+    "    plt.title(j)\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "14d1eabd",
+   "metadata": {},
+   "source": [
+    "<span style=\"font-weight:bold;font-size:20px;\">Bivariate Analysis: It involves examining the relationship between two variables<span/>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "347f65c8",
+   "metadata": {},
+   "source": [
+    "<span style=\"font-weight:bold;font-size:20px;\">Correlation matrix of continuous features<span/>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "c8533b3f",
+   "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>trtbps</th>\n",
+       "      <th>chol</th>\n",
+       "      <th>thalachh</th>\n",
+       "      <th>oldpeak</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>age</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.277741</td>\n",
+       "      <td>0.178688</td>\n",
+       "      <td>-0.414400</td>\n",
+       "      <td>0.213528</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>trtbps</th>\n",
+       "      <td>0.277741</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.111825</td>\n",
+       "      <td>-0.069547</td>\n",
+       "      <td>0.142423</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>chol</th>\n",
+       "      <td>0.178688</td>\n",
+       "      <td>0.111825</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.025388</td>\n",
+       "      <td>-0.004568</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>thalachh</th>\n",
+       "      <td>-0.414400</td>\n",
+       "      <td>-0.069547</td>\n",
+       "      <td>-0.025388</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.343370</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>oldpeak</th>\n",
+       "      <td>0.213528</td>\n",
+       "      <td>0.142423</td>\n",
+       "      <td>-0.004568</td>\n",
+       "      <td>-0.343370</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               age    trtbps      chol  thalachh   oldpeak\n",
+       "age       1.000000  0.277741  0.178688 -0.414400  0.213528\n",
+       "trtbps    0.277741  1.000000  0.111825 -0.069547  0.142423\n",
+       "chol      0.178688  0.111825  1.000000 -0.025388 -0.004568\n",
+       "thalachh -0.414400 -0.069547 -0.025388  1.000000 -0.343370\n",
+       "oldpeak   0.213528  0.142423 -0.004568 -0.343370  1.000000"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_corr = df[con_cols].corr().transpose()\n",
+    "df_corr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "5a7e832e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure(figsize=(10,10))\n",
+    "gs = fig.add_gridspec(1,1)\n",
+    "gs.update(wspace=0.3, hspace=0.15)\n",
+    "# ax0 = fig.add_subplot(gs[0,0])\n",
+    "\n",
+    "color_palette = [\"#5833ff\",\"#da8829\"]\n",
+    "mask = np.triu(np.ones_like(df_corr))\n",
+    "ax0.text(1.5,-0.1,\"Correlation Matrix\",fontsize=22, fontweight='bold', fontfamily='serif', color=\"#000000\")\n",
+    "df_corr = df[con_cols].corr().transpose()\n",
+    "sns.heatmap(df_corr,mask=mask,fmt=\".1f\",annot=True,cmap='YlGnBu')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d87f038f",
+   "metadata": {},
+   "source": [
+    "<span style=\"background-color: yellow;\">\n",
+    "Here,thalachh(Maximum heart rate achieve during exercise) with age is negative correlated,i.e they have inversely relation.<br/>\n",
+    "Here.trbbps(Resting blood pressure)with age is positive correlated.i.e they have directly relation.\n",
+    "</span>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dbee8425",
+   "metadata": {},
+   "source": [
+    "<span style=\"font-weight:bold;font-size:20px;\">Correlation<span/>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "b68898f0",
+   "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>sex</th>\n",
+       "      <th>cp</th>\n",
+       "      <th>trtbps</th>\n",
+       "      <th>chol</th>\n",
+       "      <th>fbs</th>\n",
+       "      <th>restecg</th>\n",
+       "      <th>thalachh</th>\n",
+       "      <th>exng</th>\n",
+       "      <th>oldpeak</th>\n",
+       "      <th>slp</th>\n",
+       "      <th>caa</th>\n",
+       "      <th>thall</th>\n",
+       "      <th>output</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>age</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.067761</td>\n",
+       "      <td>-0.064336</td>\n",
+       "      <td>0.277741</td>\n",
+       "      <td>0.178688</td>\n",
+       "      <td>0.108777</td>\n",
+       "      <td>-0.115474</td>\n",
+       "      <td>-0.414400</td>\n",
+       "      <td>0.097342</td>\n",
+       "      <td>0.213528</td>\n",
+       "      <td>-0.153942</td>\n",
+       "      <td>0.292386</td>\n",
+       "      <td>0.058585</td>\n",
+       "      <td>-0.226382</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sex</th>\n",
+       "      <td>-0.067761</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.088865</td>\n",
+       "      <td>0.008780</td>\n",
+       "      <td>-0.126743</td>\n",
+       "      <td>0.065687</td>\n",
+       "      <td>-0.080962</td>\n",
+       "      <td>-0.040530</td>\n",
+       "      <td>0.180735</td>\n",
+       "      <td>0.154856</td>\n",
+       "      <td>-0.057669</td>\n",
+       "      <td>0.154794</td>\n",
+       "      <td>0.241863</td>\n",
+       "      <td>-0.316165</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>cp</th>\n",
+       "      <td>-0.064336</td>\n",
+       "      <td>-0.088865</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.084889</td>\n",
+       "      <td>-0.085120</td>\n",
+       "      <td>0.082816</td>\n",
+       "      <td>0.083788</td>\n",
+       "      <td>0.288116</td>\n",
+       "      <td>-0.389993</td>\n",
+       "      <td>-0.125400</td>\n",
+       "      <td>0.098872</td>\n",
+       "      <td>-0.156713</td>\n",
+       "      <td>-0.180510</td>\n",
+       "      <td>0.418107</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>trtbps</th>\n",
+       "      <td>0.277741</td>\n",
+       "      <td>0.008780</td>\n",
+       "      <td>0.084889</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.111825</td>\n",
+       "      <td>0.126368</td>\n",
+       "      <td>-0.137224</td>\n",
+       "      <td>-0.069547</td>\n",
+       "      <td>0.000455</td>\n",
+       "      <td>0.142423</td>\n",
+       "      <td>-0.081073</td>\n",
+       "      <td>0.109499</td>\n",
+       "      <td>-0.011922</td>\n",
+       "      <td>-0.113745</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>chol</th>\n",
+       "      <td>0.178688</td>\n",
+       "      <td>-0.126743</td>\n",
+       "      <td>-0.085120</td>\n",
+       "      <td>0.111825</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.015158</td>\n",
+       "      <td>-0.146415</td>\n",
+       "      <td>-0.025388</td>\n",
+       "      <td>0.079792</td>\n",
+       "      <td>-0.004568</td>\n",
+       "      <td>0.034071</td>\n",
+       "      <td>0.061745</td>\n",
+       "      <td>0.093842</td>\n",
+       "      <td>-0.109764</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fbs</th>\n",
+       "      <td>0.108777</td>\n",
+       "      <td>0.065687</td>\n",
+       "      <td>0.082816</td>\n",
+       "      <td>0.126368</td>\n",
+       "      <td>0.015158</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.078602</td>\n",
+       "      <td>-0.024885</td>\n",
+       "      <td>0.007048</td>\n",
+       "      <td>0.016368</td>\n",
+       "      <td>-0.070795</td>\n",
+       "      <td>0.148502</td>\n",
+       "      <td>-0.063728</td>\n",
+       "      <td>-0.028433</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>restecg</th>\n",
+       "      <td>-0.115474</td>\n",
+       "      <td>-0.080962</td>\n",
+       "      <td>0.083788</td>\n",
+       "      <td>-0.137224</td>\n",
+       "      <td>-0.146415</td>\n",
+       "      <td>-0.078602</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.092574</td>\n",
+       "      <td>-0.106425</td>\n",
+       "      <td>-0.092015</td>\n",
+       "      <td>0.114613</td>\n",
+       "      <td>-0.078471</td>\n",
+       "      <td>0.033026</td>\n",
+       "      <td>0.173856</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>thalachh</th>\n",
+       "      <td>-0.414400</td>\n",
+       "      <td>-0.040530</td>\n",
+       "      <td>0.288116</td>\n",
+       "      <td>-0.069547</td>\n",
+       "      <td>-0.025388</td>\n",
+       "      <td>-0.024885</td>\n",
+       "      <td>0.092574</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.389143</td>\n",
+       "      <td>-0.343370</td>\n",
+       "      <td>0.378621</td>\n",
+       "      <td>-0.236812</td>\n",
+       "      <td>-0.114326</td>\n",
+       "      <td>0.424381</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>exng</th>\n",
+       "      <td>0.097342</td>\n",
+       "      <td>0.180735</td>\n",
+       "      <td>-0.389993</td>\n",
+       "      <td>0.000455</td>\n",
+       "      <td>0.079792</td>\n",
+       "      <td>0.007048</td>\n",
+       "      <td>-0.106425</td>\n",
+       "      <td>-0.389143</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.320105</td>\n",
+       "      <td>-0.261445</td>\n",
+       "      <td>0.123218</td>\n",
+       "      <td>0.203725</td>\n",
+       "      <td>-0.432780</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>oldpeak</th>\n",
+       "      <td>0.213528</td>\n",
+       "      <td>0.154856</td>\n",
+       "      <td>-0.125400</td>\n",
+       "      <td>0.142423</td>\n",
+       "      <td>-0.004568</td>\n",
+       "      <td>0.016368</td>\n",
+       "      <td>-0.092015</td>\n",
+       "      <td>-0.343370</td>\n",
+       "      <td>0.320105</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.526623</td>\n",
+       "      <td>0.164554</td>\n",
+       "      <td>0.191045</td>\n",
+       "      <td>-0.435748</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>slp</th>\n",
+       "      <td>-0.153942</td>\n",
+       "      <td>-0.057669</td>\n",
+       "      <td>0.098872</td>\n",
+       "      <td>-0.081073</td>\n",
+       "      <td>0.034071</td>\n",
+       "      <td>-0.070795</td>\n",
+       "      <td>0.114613</td>\n",
+       "      <td>0.378621</td>\n",
+       "      <td>-0.261445</td>\n",
+       "      <td>-0.526623</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.043186</td>\n",
+       "      <td>-0.087894</td>\n",
+       "      <td>0.328483</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>caa</th>\n",
+       "      <td>0.292386</td>\n",
+       "      <td>0.154794</td>\n",
+       "      <td>-0.156713</td>\n",
+       "      <td>0.109499</td>\n",
+       "      <td>0.061745</td>\n",
+       "      <td>0.148502</td>\n",
+       "      <td>-0.078471</td>\n",
+       "      <td>-0.236812</td>\n",
+       "      <td>0.123218</td>\n",
+       "      <td>0.164554</td>\n",
+       "      <td>-0.043186</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.147651</td>\n",
+       "      <td>-0.379219</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>thall</th>\n",
+       "      <td>0.058585</td>\n",
+       "      <td>0.241863</td>\n",
+       "      <td>-0.180510</td>\n",
+       "      <td>-0.011922</td>\n",
+       "      <td>0.093842</td>\n",
+       "      <td>-0.063728</td>\n",
+       "      <td>0.033026</td>\n",
+       "      <td>-0.114326</td>\n",
+       "      <td>0.203725</td>\n",
+       "      <td>0.191045</td>\n",
+       "      <td>-0.087894</td>\n",
+       "      <td>0.147651</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.350042</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>output</th>\n",
+       "      <td>-0.226382</td>\n",
+       "      <td>-0.316165</td>\n",
+       "      <td>0.418107</td>\n",
+       "      <td>-0.113745</td>\n",
+       "      <td>-0.109764</td>\n",
+       "      <td>-0.028433</td>\n",
+       "      <td>0.173856</td>\n",
+       "      <td>0.424381</td>\n",
+       "      <td>-0.432780</td>\n",
+       "      <td>-0.435748</td>\n",
+       "      <td>0.328483</td>\n",
+       "      <td>-0.379219</td>\n",
+       "      <td>-0.350042</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               age       sex        cp    trtbps      chol       fbs  \\\n",
+       "age       1.000000 -0.067761 -0.064336  0.277741  0.178688  0.108777   \n",
+       "sex      -0.067761  1.000000 -0.088865  0.008780 -0.126743  0.065687   \n",
+       "cp       -0.064336 -0.088865  1.000000  0.084889 -0.085120  0.082816   \n",
+       "trtbps    0.277741  0.008780  0.084889  1.000000  0.111825  0.126368   \n",
+       "chol      0.178688 -0.126743 -0.085120  0.111825  1.000000  0.015158   \n",
+       "fbs       0.108777  0.065687  0.082816  0.126368  0.015158  1.000000   \n",
+       "restecg  -0.115474 -0.080962  0.083788 -0.137224 -0.146415 -0.078602   \n",
+       "thalachh -0.414400 -0.040530  0.288116 -0.069547 -0.025388 -0.024885   \n",
+       "exng      0.097342  0.180735 -0.389993  0.000455  0.079792  0.007048   \n",
+       "oldpeak   0.213528  0.154856 -0.125400  0.142423 -0.004568  0.016368   \n",
+       "slp      -0.153942 -0.057669  0.098872 -0.081073  0.034071 -0.070795   \n",
+       "caa       0.292386  0.154794 -0.156713  0.109499  0.061745  0.148502   \n",
+       "thall     0.058585  0.241863 -0.180510 -0.011922  0.093842 -0.063728   \n",
+       "output   -0.226382 -0.316165  0.418107 -0.113745 -0.109764 -0.028433   \n",
+       "\n",
+       "           restecg  thalachh      exng   oldpeak       slp       caa  \\\n",
+       "age      -0.115474 -0.414400  0.097342  0.213528 -0.153942  0.292386   \n",
+       "sex      -0.080962 -0.040530  0.180735  0.154856 -0.057669  0.154794   \n",
+       "cp        0.083788  0.288116 -0.389993 -0.125400  0.098872 -0.156713   \n",
+       "trtbps   -0.137224 -0.069547  0.000455  0.142423 -0.081073  0.109499   \n",
+       "chol     -0.146415 -0.025388  0.079792 -0.004568  0.034071  0.061745   \n",
+       "fbs      -0.078602 -0.024885  0.007048  0.016368 -0.070795  0.148502   \n",
+       "restecg   1.000000  0.092574 -0.106425 -0.092015  0.114613 -0.078471   \n",
+       "thalachh  0.092574  1.000000 -0.389143 -0.343370  0.378621 -0.236812   \n",
+       "exng     -0.106425 -0.389143  1.000000  0.320105 -0.261445  0.123218   \n",
+       "oldpeak  -0.092015 -0.343370  0.320105  1.000000 -0.526623  0.164554   \n",
+       "slp       0.114613  0.378621 -0.261445 -0.526623  1.000000 -0.043186   \n",
+       "caa      -0.078471 -0.236812  0.123218  0.164554 -0.043186  1.000000   \n",
+       "thall     0.033026 -0.114326  0.203725  0.191045 -0.087894  0.147651   \n",
+       "output    0.173856  0.424381 -0.432780 -0.435748  0.328483 -0.379219   \n",
+       "\n",
+       "             thall    output  \n",
+       "age       0.058585 -0.226382  \n",
+       "sex       0.241863 -0.316165  \n",
+       "cp       -0.180510  0.418107  \n",
+       "trtbps   -0.011922 -0.113745  \n",
+       "chol      0.093842 -0.109764  \n",
+       "fbs      -0.063728 -0.028433  \n",
+       "restecg   0.033026  0.173856  \n",
+       "thalachh -0.114326  0.424381  \n",
+       "exng      0.203725 -0.432780  \n",
+       "oldpeak   0.191045 -0.435748  \n",
+       "slp      -0.087894  0.328483  \n",
+       "caa       0.147651 -0.379219  \n",
+       "thall     1.000000 -0.350042  \n",
+       "output   -0.350042  1.000000  "
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "corr = df.corr()\n",
+    "corr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "a63bd9f4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x720 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(15, 10))\n",
+    "sns.heatmap(corr,annot=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2e53a417",
+   "metadata": {},
+   "source": [
+    "<span style=\"background-color: yellow;\">\n",
+    "Some features exhibit strong positive correlations, such as:<br/>\n",
+    "thalachh (maximum heart rate achieved) and output (presence of heart disease) with a correlation coefficient of 0.42.<br/>\n",
+    "cp (chest pain type) and output with a correlation coefficient of 0.42.<br/>\n",
+    "<span/>\n",
+    "<span style=\"background-color: orange;\">\n",
+    "There are also some negative correlations, such as between thalachh and age (-0.41), indicating that as age increases, maximum heart rate achieved tends to decrease.\n",
+    "<span/>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "047de5db",
+   "metadata": {},
+   "source": [
+    "<span style=\"font-weight:bold;font-size:20px;\">Scatterplot heatmap of dataframe<span/>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "fe460747",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 864x864 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 781.625x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure(figsize=(12,12))\n",
+    "corr_mat = df.corr().stack().reset_index(name=\"correlation\")\n",
+    "g = sns.relplot(\n",
+    "    data=corr_mat,\n",
+    "    x=\"level_0\", y=\"level_1\", hue=\"correlation\", size=\"correlation\",\n",
+    "    palette=\"YlGnBu\", hue_norm=(-1, 1), edgecolor=\".7\",\n",
+    "    height=10, sizes=(50, 250), size_norm=(-.2, .8),\n",
+    ")\n",
+    "g.set(xlabel=\"features on X\", ylabel=\"featurs on Y\", aspect=\"equal\")\n",
+    "g.fig.suptitle('Scatterplot heatmap',fontsize=22, fontweight='bold', fontfamily='serif', color=\"#000000\")\n",
+    "g.despine(left=True, bottom=True)\n",
+    "g.ax.margins(.02)\n",
+    "for label in g.ax.get_xticklabels():\n",
+    "    label.set_rotation(90)\n",
+    "# for artist in g.legend.legendHandles:\n",
+    "#     artist.set_edgecolor(\".7\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "c5b2a32f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1296x1296 with 10 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure(figsize=(18,18))\n",
+    "gs = fig.add_gridspec(5,2)\n",
+    "gs.update(wspace=0.5, hspace=0.5)\n",
+    "ax0 = fig.add_subplot(gs[0,0])\n",
+    "ax1 = fig.add_subplot(gs[0,1])\n",
+    "ax2 = fig.add_subplot(gs[1,0])\n",
+    "ax3 = fig.add_subplot(gs[1,1])\n",
+    "ax4 = fig.add_subplot(gs[2,0])\n",
+    "ax5 = fig.add_subplot(gs[2,1])\n",
+    "ax6 = fig.add_subplot(gs[3,0])\n",
+    "ax7 = fig.add_subplot(gs[3,1])\n",
+    "ax8 = fig.add_subplot(gs[4,0])\n",
+    "ax9 = fig.add_subplot(gs[4,1])\n",
+    "\n",
+    "background_color = \"#FFFAB7\"\n",
+    "color_palette = [\"#FA7070\", \"#FEFDED\", \"#C6EBC5\", \"#A1C398\", \"#da8829\"]\n",
+    "fig.patch.set_facecolor(background_color) \n",
+    "ax0.set_facecolor(background_color) \n",
+    "ax1.set_facecolor(background_color) \n",
+    "ax2.set_facecolor(background_color)\n",
+    "ax3.set_facecolor(background_color)\n",
+    "ax4.set_facecolor(background_color)\n",
+    "ax5.set_facecolor(background_color) \n",
+    "ax6.set_facecolor(background_color) \n",
+    "ax7.set_facecolor(background_color)\n",
+    "ax8.set_facecolor(background_color)\n",
+    "ax9.set_facecolor(background_color)\n",
+    "\n",
+    "# Age title\n",
+    "ax0.text(0.5,0.5,\"Distribution of age\\naccording to\\n target variable\\n___________\",\n",
+    "        horizontalalignment = 'center',\n",
+    "        verticalalignment = 'center',\n",
+    "        fontsize = 18,\n",
+    "        fontweight='bold',\n",
+    "        fontfamily='serif',\n",
+    "        color='#000000')\n",
+    "ax0.spines[\"bottom\"].set_visible(False)\n",
+    "ax0.set_xticklabels([])\n",
+    "ax0.set_yticklabels([])\n",
+    "ax0.tick_params(left=False, bottom=False)\n",
+    "\n",
+    "# Age\n",
+    "ax1.grid(color='#000000', linestyle=':', axis='y', zorder=0,  dashes=(1,5))\n",
+    "sns.kdeplot(ax=ax1, data=df, x='age',hue=\"output\", fill=True,palette=[\"#FF004D\",\"#074173\"], alpha=.5, linewidth=0)\n",
+    "ax1.set_xlabel(\"\")\n",
+    "ax1.set_ylabel(\"\")\n",
+    "\n",
+    "# TrTbps title\n",
+    "ax2.text(0.5,0.5,\"Distribution of trtbps\\naccording to\\n target variable\\n___________\",\n",
+    "        horizontalalignment = 'center',\n",
+    "        verticalalignment = 'center',\n",
+    "        fontsize = 18,\n",
+    "        fontweight='bold',\n",
+    "        fontfamily='serif',\n",
+    "        color='#000000')\n",
+    "ax2.spines[\"bottom\"].set_visible(False)\n",
+    "ax2.set_xticklabels([])\n",
+    "ax2.set_yticklabels([])\n",
+    "ax2.tick_params(left=False, bottom=False)\n",
+    "\n",
+    "# TrTbps\n",
+    "ax3.grid(color='#000000', linestyle=':', axis='y', zorder=0,  dashes=(1,5))\n",
+    "sns.kdeplot(ax=ax3, data=df, x='trtbps',hue=\"output\", fill=True,palette=[\"#FF004D\",\"#074173\"], alpha=.5, linewidth=0)\n",
+    "ax3.set_xlabel(\"\")\n",
+    "ax3.set_ylabel(\"\")\n",
+    "\n",
+    "# Chol title\n",
+    "ax4.text(0.5,0.5,\"Distribution of chol\\naccording to\\n target variable\\n___________\",\n",
+    "        horizontalalignment = 'center',\n",
+    "        verticalalignment = 'center',\n",
+    "        fontsize = 18,\n",
+    "        fontweight='bold',\n",
+    "        fontfamily='serif',\n",
+    "        color='#000000')\n",
+    "ax4.spines[\"bottom\"].set_visible(False)\n",
+    "ax4.set_xticklabels([])\n",
+    "ax4.set_yticklabels([])\n",
+    "ax4.tick_params(left=False, bottom=False)\n",
+    "\n",
+    "# Chol\n",
+    "ax5.grid(color='#000000', linestyle=':', axis='y', zorder=0,  dashes=(1,5))\n",
+    "sns.kdeplot(ax=ax5, data=df, x='chol',hue=\"output\", fill=True,palette=[\"#FF004D\",\"#074173\"], alpha=.5, linewidth=0)\n",
+    "ax5.set_xlabel(\"\")\n",
+    "ax5.set_ylabel(\"\")\n",
+    "\n",
+    "# Thalachh title\n",
+    "ax6.text(0.5,0.5,\"Distribution of thalachh\\naccording to\\n target variable\\n___________\",\n",
+    "        horizontalalignment = 'center',\n",
+    "        verticalalignment = 'center',\n",
+    "        fontsize = 18,\n",
+    "        fontweight='bold',\n",
+    "        fontfamily='serif',\n",
+    "        color='#000000')\n",
+    "ax6.spines[\"bottom\"].set_visible(False)\n",
+    "ax6.set_xticklabels([])\n",
+    "ax6.set_yticklabels([])\n",
+    "ax6.tick_params(left=False, bottom=False)\n",
+    "\n",
+    "# Thalachh\n",
+    "ax7.grid(color='#000000', linestyle=':', axis='y', zorder=0,  dashes=(1,5))\n",
+    "sns.kdeplot(ax=ax7, data=df, x='thalachh',hue=\"output\", fill=True,palette=[\"#FF004D\",\"#074173\"], alpha=.5, linewidth=0)\n",
+    "ax7.set_xlabel(\"\")\n",
+    "ax7.set_ylabel(\"\")\n",
+    "\n",
+    "# Oldpeak title\n",
+    "ax8.text(0.5,0.5,\"Distribution of oldpeak\\naccording to\\n target variable\\n___________\",\n",
+    "        horizontalalignment = 'center',\n",
+    "        verticalalignment = 'center',\n",
+    "        fontsize = 18,\n",
+    "        fontweight='bold',\n",
+    "        fontfamily='serif',\n",
+    "        color='#000000')\n",
+    "ax8.spines[\"bottom\"].set_visible(False)\n",
+    "ax8.set_xticklabels([])\n",
+    "ax8.set_yticklabels([])\n",
+    "ax8.tick_params(left=False, bottom=False)\n",
+    "\n",
+    "# Oldpeak\n",
+    "ax9.grid(color='#000000', linestyle=':', axis='y', zorder=0,  dashes=(1,5))\n",
+    "sns.kdeplot(ax=ax9, data=df, x='oldpeak',hue=\"output\", fill=True,palette=[\"#FF004D\",\"#074173\"], alpha=.5, linewidth=0)\n",
+    "ax9.set_xlabel(\"\")\n",
+    "ax9.set_ylabel(\"\")\n",
+    "\n",
+    "for i in [\"top\",\"left\",\"right\"]:\n",
+    "        ax0.spines[i].set_visible(False)\n",
+    "        ax1.spines[i].set_visible(False)\n",
+    "        ax2.spines[i].set_visible(False)\n",
+    "        ax3.spines[i].set_visible(False)\n",
+    "        ax4.spines[i].set_visible(False)\n",
+    "        ax5.spines[i].set_visible(False)\n",
+    "        ax6.spines[i].set_visible(False)\n",
+    "        ax7.spines[i].set_visible(False)\n",
+    "        ax8.spines[i].set_visible(False)\n",
+    "        ax9.spines[i].set_visible(False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "834769a0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1296x1440 with 12 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure(figsize=(18,20))\n",
+    "gs = fig.add_gridspec(6,2)\n",
+    "gs.update(wspace=0.5, hspace=0.5)\n",
+    "ax0 = fig.add_subplot(gs[0,0])\n",
+    "ax1 = fig.add_subplot(gs[0,1])\n",
+    "ax2 = fig.add_subplot(gs[1,0])\n",
+    "ax3 = fig.add_subplot(gs[1,1])\n",
+    "ax4 = fig.add_subplot(gs[2,0])\n",
+    "ax5 = fig.add_subplot(gs[2,1])\n",
+    "ax6 = fig.add_subplot(gs[3,0])\n",
+    "ax7 = fig.add_subplot(gs[3,1])\n",
+    "ax8 = fig.add_subplot(gs[4,0])\n",
+    "ax9 = fig.add_subplot(gs[4,1])\n",
+    "ax10 = fig.add_subplot(gs[5,0])\n",
+    "ax11 = fig.add_subplot(gs[5,1])\n",
+    "\n",
+    "background_color = \"#FFFAB7\"\n",
+    "color_palette = [\"#FA7070\", \"#FEFDED\", \"#C6EBC5\", \"#A1C398\", \"#da8829\"]\n",
+    "fig.patch.set_facecolor(background_color) \n",
+    "ax0.set_facecolor(background_color) \n",
+    "ax1.set_facecolor(background_color) \n",
+    "ax2.set_facecolor(background_color)\n",
+    "ax3.set_facecolor(background_color)\n",
+    "ax4.set_facecolor(background_color)\n",
+    "ax5.set_facecolor(background_color) \n",
+    "ax6.set_facecolor(background_color) \n",
+    "ax7.set_facecolor(background_color)\n",
+    "ax8.set_facecolor(background_color)\n",
+    "ax9.set_facecolor(background_color)\n",
+    "ax10.set_facecolor(background_color)\n",
+    "ax11.set_facecolor(background_color)\n",
+    "\n",
+    "# Cp title\n",
+    "# 0 = Typical Angina, 1 = Atypical Angina, 2 = Non-anginal Pain, 3 = Asymptomatic\n",
+    "ax0.text(0.5,0.5,\"Chest pain\\ndistribution\\n__________\",\n",
+    "        horizontalalignment = 'center',\n",
+    "        verticalalignment = 'center',\n",
+    "        fontsize = 18,\n",
+    "        fontweight='bold',\n",
+    "        fontfamily='serif',\n",
+    "        color='#000000')\n",
+    "ax0.spines[\"bottom\"].set_visible(False)\n",
+    "ax0.set_xticklabels([])\n",
+    "ax0.set_yticklabels([])\n",
+    "ax0.tick_params(left=False, bottom=False)\n",
+    "ax0.text(1,.5,\"0 - Typical Angina\\n1 - Atypical Angina\\n2 - Non-anginal Pain\\n3 - Asymptomatic\",\n",
+    "        horizontalalignment = 'center',\n",
+    "         verticalalignment = 'center',\n",
+    "        fontsize = 14\n",
+    "        )\n",
+    "\n",
+    "# Cp\n",
+    "ax1.grid(color='#000000', linestyle=':', axis='y', zorder=0,  dashes=(1,5))\n",
+    "sns.kdeplot(ax=ax1, data=df, x='cp',hue=\"output\", fill=True,palette=[\"#8000ff\",\"#da8829\"], alpha=.5, linewidth=0)\n",
+    "ax1.set_xlabel(\"\")\n",
+    "ax1.set_ylabel(\"\")\n",
+    "\n",
+    "# Caa title\n",
+    "ax2.text(0.5,0.5,\"Number of\\nmajor vessels\\n___________\",\n",
+    "        horizontalalignment = 'center',\n",
+    "        verticalalignment = 'center',\n",
+    "        fontsize = 18,\n",
+    "        fontweight='bold',\n",
+    "        fontfamily='serif',\n",
+    "        color='#000000')\n",
+    "ax2.text(1,.5,\"0 vessels\\n1 vessel\\n2 vessels\\n3 vessels\\n4vessels\",\n",
+    "        horizontalalignment = 'center',\n",
+    "         verticalalignment = 'center',\n",
+    "        fontsize = 14\n",
+    "        )\n",
+    "\n",
+    "ax2.spines[\"bottom\"].set_visible(False)\n",
+    "ax2.set_xticklabels([])\n",
+    "ax2.set_yticklabels([])\n",
+    "ax2.tick_params(left=False, bottom=False)\n",
+    "\n",
+    "# Caa\n",
+    "ax3.grid(color='#000000', linestyle=':', axis='y', zorder=0,  dashes=(1,5))\n",
+    "sns.kdeplot(ax=ax3, data=df, x='caa',hue=\"output\", fill=True,palette=[\"#8000ff\",\"#da8829\"], alpha=.5, linewidth=0)\n",
+    "ax3.set_xlabel(\"\")\n",
+    "ax3.set_ylabel(\"\")\n",
+    "\n",
+    "# Sex title\n",
+    "ax4.text(0.5,0.5,\"Heart Attack\\naccording to\\nsex\\n______\",\n",
+    "        horizontalalignment = 'center',\n",
+    "        verticalalignment = 'center',\n",
+    "        fontsize = 18,\n",
+    "        fontweight='bold',\n",
+    "        fontfamily='serif',\n",
+    "        color='#000000')\n",
+    "ax4.text(1,.5,\"0 - Female\\n1 - Male\",\n",
+    "        horizontalalignment = 'center',\n",
+    "         verticalalignment = 'center',\n",
+    "        fontsize = 14\n",
+    "        )\n",
+    "ax4.spines[\"bottom\"].set_visible(False)\n",
+    "ax4.set_xticklabels([])\n",
+    "ax4.set_yticklabels([])\n",
+    "ax4.tick_params(left=False, bottom=False)\n",
+    "\n",
+    "# Sex\n",
+    "ax5.grid(color='#000000', linestyle=':', axis='y', zorder=0,  dashes=(1,5))\n",
+    "sns.countplot(ax=ax5,data=df,x='sex',palette=[\"#8000ff\",\"#da8829\"], hue='output')\n",
+    "ax5.set_xlabel(\"\")\n",
+    "ax5.set_ylabel(\"\")\n",
+    "\n",
+    "# Thall title\n",
+    "ax6.text(0.5,0.5,\"Distribution of thall\\naccording to\\n target variable\\n___________\",\n",
+    "        horizontalalignment = 'center',\n",
+    "        verticalalignment = 'center',\n",
+    "        fontsize = 18,\n",
+    "        fontweight='bold',\n",
+    "        fontfamily='serif',\n",
+    "        color='#000000')\n",
+    "ax6.text(1,.5,\"Thalium Stress\\nTest Result\\n0, 1, 2, 3\",\n",
+    "        horizontalalignment = 'center',\n",
+    "         verticalalignment = 'center',\n",
+    "        fontsize = 14\n",
+    "        )\n",
+    "ax6.spines[\"bottom\"].set_visible(False)\n",
+    "ax6.set_xticklabels([])\n",
+    "ax6.set_yticklabels([])\n",
+    "ax6.tick_params(left=False, bottom=False)\n",
+    "\n",
+    "# Thall\n",
+    "ax7.grid(color='#000000', linestyle=':', axis='y', zorder=0,  dashes=(1,5))\n",
+    "sns.kdeplot(ax=ax7, data=df, x='thall',hue=\"output\", fill=True,palette=[\"#8000ff\",\"#da8829\"], alpha=.5, linewidth=0)\n",
+    "ax7.set_xlabel(\"\")\n",
+    "ax7.set_ylabel(\"\")\n",
+    "\n",
+    "# Thalachh title\n",
+    "ax8.text(0.5,0.5,\"Boxen plot of\\nthalachh wrt\\noutcome\\n_______\",\n",
+    "        horizontalalignment = 'center',\n",
+    "        verticalalignment = 'center',\n",
+    "        fontsize = 18,\n",
+    "        fontweight='bold',\n",
+    "        fontfamily='serif',\n",
+    "        color='#000000')\n",
+    "ax8.text(1,.5,\"Maximum heart\\nrate achieved\",\n",
+    "        horizontalalignment = 'center',\n",
+    "         verticalalignment = 'center',\n",
+    "        fontsize = 14\n",
+    "        )\n",
+    "\n",
+    "ax8.spines[\"bottom\"].set_visible(False)\n",
+    "ax8.set_xticklabels([])\n",
+    "ax8.set_yticklabels([])\n",
+    "ax8.tick_params(left=False, bottom=False)\n",
+    "\n",
+    "# Thalachh\n",
+    "ax9.grid(color='#000000', linestyle=':', axis='y', zorder=0,  dashes=(1,5))\n",
+    "sns.boxenplot(ax=ax9, data=df,x='output',y='thalachh',palette=[\"#8000ff\",\"#da8829\"])\n",
+    "ax9.set_xlabel(\"\")\n",
+    "ax9.set_ylabel(\"\")\n",
+    "\n",
+    "\n",
+    "\n",
+    "# Exng title\n",
+    "ax10.text(0.5,0.5,\"Strip Plot of\\nexng vs age\\n______\",\n",
+    "        horizontalalignment = 'center',\n",
+    "        verticalalignment = 'center',\n",
+    "        fontsize = 18,\n",
+    "        fontweight='bold',\n",
+    "        fontfamily='serif',\n",
+    "        color='#000000')\n",
+    "ax10.text(1,.5,\"Exercise induced\\nangina\\n0 - No\\n1 - Yes\",\n",
+    "        horizontalalignment = 'center',\n",
+    "         verticalalignment = 'center',\n",
+    "        fontsize = 14\n",
+    "        )\n",
+    "ax10.spines[\"bottom\"].set_visible(False)\n",
+    "ax10.set_xticklabels([])\n",
+    "ax10.set_yticklabels([])\n",
+    "ax10.tick_params(left=False, bottom=False)\n",
+    "\n",
+    "\n",
+    "# Exng\n",
+    "ax11.grid(color='#000000', linestyle=':', axis='y', zorder=0,  dashes=(1,5))\n",
+    "sns.stripplot(ax=ax11, data=df,x='exng',y='age',hue='output',palette=[\"#8000ff\",\"#da8829\"])\n",
+    "ax9.set_xlabel(\"\")\n",
+    "ax9.set_ylabel(\"\")\n",
+    "\n",
+    "for i in [\"top\",\"left\",\"right\"]:\n",
+    "    ax0.spines[i].set_visible(False)\n",
+    "    ax1.spines[i].set_visible(False)\n",
+    "    ax2.spines[i].set_visible(False)\n",
+    "    ax3.spines[i].set_visible(False)\n",
+    "    ax4.spines[i].set_visible(False)\n",
+    "    ax5.spines[i].set_visible(False)\n",
+    "    ax6.spines[i].set_visible(False)\n",
+    "    ax7.spines[i].set_visible(False)\n",
+    "    ax8.spines[i].set_visible(False)\n",
+    "    ax9.spines[i].set_visible(False)\n",
+    "    ax10.spines[i].set_visible(False)\n",
+    "    ax11.spines[i].set_visible(False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6778f004",
+   "metadata": {},
+   "source": [
+    "Conclusions:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8a57fbc5",
+   "metadata": {},
+   "source": [
+    "<span style=\"background-color: yellow;\">\n",
+    "1.There are no NaN values in the data.<br/>\n",
+    "2.The data consists of more than twice the number of people with sex = 1 than sex = 0.<br/>\n",
+    "3.The scatterplot heatmap matrix suggests that there might be some correlation between output and cp, thalachh and slp.<br>\n",
+    "4.It is intuitive that elder people might have higher chances of heart attack but according to the distribution plot of age wrt output, it is evident that this isn't the case.\n",
+    "According to the distribution plot of thalachh wrt output, people with higher maximum heart rate achieved have higher chances of heart attack.<br/>\n",
+    "According to the distribution plot of oldpeak wrt output, people with lower pevious peak achieved have higher chances of heart attack.<br/>\n",
+    "5.Res:\n",
+    "People with Non-Anginal chest pain, that is with cp = 2 have higher chances of heart attack.\n",
+    "People with 0 major vessels, that is with caa = 0 have high chance of heart attack.\n",
+    "People with sex = 1 have higher chance of heart attack.\n",
+    "People with thall = 2 have much higher chance of heart attack.\n",
+    "People with no exercise induced angina, that is with exng = 0 have higher chance of heart attack.\n",
+    "<span/>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2c6aa22b",
+   "metadata": {},
+   "source": [
+    "<span style=\"font-weight:bold;font-size:20px;\">Feature Scaling:<span/>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a45aef2a",
+   "metadata": {},
+   "source": [
+    "Instead of just looking at the average (or mean) and the spread (or standard deviation) of the data[standard scalar], it uses the median (the middle value) and the interquartile range (the range between the 25th and 75th percentiles) to scale the data. This means it's less likely to be thrown off by unusually large or small numbers."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "17fd0b38",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import RobustScaler"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "054c8cac",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# creating a copy of df\n",
+    "df1 = df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "1b2e14a4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# define the columns to be encoded and scaled\n",
+    "cat_cols = ['sex','exng','caa','cp','fbs','restecg'\n",
+    "            ,'slp','thall']\n",
+    "con_cols = [\"age\",\"trtbps\",\"chol\",\"thalachh\",\"oldpeak\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "25aeb295",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "     age  trtbps  chol  thalachh  oldpeak  output  sex_1  exng_1  caa_1  \\\n",
+      "0     63     145   233       150      2.3       1      1       0      0   \n",
+      "1     37     130   250       187      3.5       1      1       0      0   \n",
+      "2     41     130   204       172      1.4       1      0       0      0   \n",
+      "3     56     120   236       178      0.8       1      1       0      0   \n",
+      "4     57     120   354       163      0.6       1      0       1      0   \n",
+      "..   ...     ...   ...       ...      ...     ...    ...     ...    ...   \n",
+      "298   57     140   241       123      0.2       0      0       1      0   \n",
+      "299   45     110   264       132      1.2       0      1       0      0   \n",
+      "300   68     144   193       141      3.4       0      1       0      0   \n",
+      "301   57     130   131       115      1.2       0      1       1      1   \n",
+      "302   57     130   236       174      0.0       0      0       0      1   \n",
+      "\n",
+      "     caa_2  ...  cp_2  cp_3  fbs_1  restecg_1  restecg_2  slp_1  slp_2  \\\n",
+      "0        0  ...     0     1      1          0          0      0      0   \n",
+      "1        0  ...     1     0      0          1          0      0      0   \n",
+      "2        0  ...     0     0      0          0          0      0      1   \n",
+      "3        0  ...     0     0      0          1          0      0      1   \n",
+      "4        0  ...     0     0      0          1          0      0      1   \n",
+      "..     ...  ...   ...   ...    ...        ...        ...    ...    ...   \n",
+      "298      0  ...     0     0      0          1          0      1      0   \n",
+      "299      0  ...     0     1      0          1          0      1      0   \n",
+      "300      1  ...     0     0      1          1          0      1      0   \n",
+      "301      0  ...     0     0      0          1          0      1      0   \n",
+      "302      0  ...     0     0      0          0          0      1      0   \n",
+      "\n",
+      "     thall_1  thall_2  thall_3  \n",
+      "0          1        0        0  \n",
+      "1          0        1        0  \n",
+      "2          0        1        0  \n",
+      "3          0        1        0  \n",
+      "4          0        1        0  \n",
+      "..       ...      ...      ...  \n",
+      "298        0        0        1  \n",
+      "299        0        0        1  \n",
+      "300        0        0        1  \n",
+      "301        0        0        1  \n",
+      "302        0        1        0  \n",
+      "\n",
+      "[284 rows x 23 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# encoding the categorical columns  #drop_first will drop the first column of categorial column\n",
+    "df1 = pd.get_dummies(df1, columns = cat_cols, drop_first = True)\n",
+    "print(df1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "109a1fdd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# defining the features and target\n",
+    "X = df1.drop(['output'],axis=1)\n",
+    "y = df1[['output']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "d82f75b8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The first 5 rows of X are\n"
+     ]
+    },
+    {
+     "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>trtbps</th>\n",
+       "      <th>chol</th>\n",
+       "      <th>thalachh</th>\n",
+       "      <th>oldpeak</th>\n",
+       "      <th>sex_1</th>\n",
+       "      <th>exng_1</th>\n",
+       "      <th>caa_1</th>\n",
+       "      <th>caa_2</th>\n",
+       "      <th>caa_3</th>\n",
+       "      <th>...</th>\n",
+       "      <th>cp_2</th>\n",
+       "      <th>cp_3</th>\n",
+       "      <th>fbs_1</th>\n",
+       "      <th>restecg_1</th>\n",
+       "      <th>restecg_2</th>\n",
+       "      <th>slp_1</th>\n",
+       "      <th>slp_2</th>\n",
+       "      <th>thall_1</th>\n",
+       "      <th>thall_2</th>\n",
+       "      <th>thall_3</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.603774</td>\n",
+       "      <td>0.75</td>\n",
+       "      <td>-0.099174</td>\n",
+       "      <td>-0.087591</td>\n",
+       "      <td>1.0625</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>-1.358491</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.181818</td>\n",
+       "      <td>0.992701</td>\n",
+       "      <td>1.8125</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>-1.056604</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>-0.578512</td>\n",
+       "      <td>0.554745</td>\n",
+       "      <td>0.5000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.075472</td>\n",
+       "      <td>-0.50</td>\n",
+       "      <td>-0.049587</td>\n",
+       "      <td>0.729927</td>\n",
+       "      <td>0.1250</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.150943</td>\n",
+       "      <td>-0.50</td>\n",
+       "      <td>1.900826</td>\n",
+       "      <td>0.291971</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 22 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        age  trtbps      chol  thalachh  oldpeak  sex_1  exng_1  caa_1  caa_2  \\\n",
+       "0  0.603774    0.75 -0.099174 -0.087591   1.0625      1       0      0      0   \n",
+       "1 -1.358491    0.00  0.181818  0.992701   1.8125      1       0      0      0   \n",
+       "2 -1.056604    0.00 -0.578512  0.554745   0.5000      0       0      0      0   \n",
+       "3  0.075472   -0.50 -0.049587  0.729927   0.1250      1       0      0      0   \n",
+       "4  0.150943   -0.50  1.900826  0.291971   0.0000      0       1      0      0   \n",
+       "\n",
+       "   caa_3  ...  cp_2  cp_3  fbs_1  restecg_1  restecg_2  slp_1  slp_2  thall_1  \\\n",
+       "0      0  ...     0     1      1          0          0      0      0        1   \n",
+       "1      0  ...     1     0      0          1          0      0      0        0   \n",
+       "2      0  ...     0     0      0          0          0      0      1        0   \n",
+       "3      0  ...     0     0      0          1          0      0      1        0   \n",
+       "4      0  ...     0     0      0          1          0      0      1        0   \n",
+       "\n",
+       "   thall_2  thall_3  \n",
+       "0        0        0  \n",
+       "1        1        0  \n",
+       "2        1        0  \n",
+       "3        1        0  \n",
+       "4        1        0  \n",
+       "\n",
+       "[5 rows x 22 columns]"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# instantiating the scaler\n",
+    "scaler = RobustScaler()\n",
+    "\n",
+    "# scaling the continuous featuree\n",
+    "X[con_cols] = scaler.fit_transform(X[con_cols])\n",
+    "print(\"The first 5 rows of X are\")\n",
+    "X.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "72746946",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# pip install imbalanced-learn"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "54d68ebd",
+   "metadata": {},
+   "source": [
+    "<span style=\"font-weight:bold;font-size:20px;\">Train and test split:\n",
+    "Test-20%\n",
+    "Training-80%<span/>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "95c171da",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The shape of X_train is       (227, 22)\n",
+      "The shape of X_test is        (57, 22)\n",
+      "The shape of y_train is       (227, 1)\n",
+      "The shape of y_test is        (57, 1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "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, random_state = 42)\n",
+    "print(\"The shape of X_train is      \", X_train.shape)\n",
+    "print(\"The shape of X_test is       \",X_test.shape)\n",
+    "print(\"The shape of y_train is      \",y_train.shape)\n",
+    "print(\"The shape of y_test is       \",y_test.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7ee1d7b6",
+   "metadata": {},
+   "source": [
+    "<span style=\"font-weight:bold;font-size:20px;\">MODELING<span/>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "777183d1",
+   "metadata": {},
+   "source": [
+    "<span style=\"font-weight:bold;font-size:20px;\">LOGISTIC REGRESSION<span/>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "b514cb40",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The test accuracy score of Logistric Regression is  0.8771929824561403\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.metrics import accuracy_score\n",
+    "# instantiating the object\n",
+    "logreg = LogisticRegression()\n",
+    "\n",
+    "# fitting the object\n",
+    "logreg.fit(X_train, y_train)\n",
+    "\n",
+    "# calculating the probabilities\n",
+    "y_pred_proba = logreg.predict_proba(X_test)\n",
+    "\n",
+    "# finding the predicted valued\n",
+    "y_pred = np.argmax(y_pred_proba,axis=1)\n",
+    "\n",
+    "# printing the test accuracy\n",
+    "print(\"The test accuracy score of Logistric Regression is \", accuracy_score(y_test, y_pred))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d3145e29",
+   "metadata": {},
+   "source": [
+    "<span style=\"font-weight:bold;font-size:20px;\">SMOTE(Synthetic minority over-sampling technique)<span/>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "b4ddd995",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from imblearn.over_sampling import SMOTE\n",
+    "#Initialize smote\n",
+    "smote=SMOTE(random_state=1)\n",
+    "#apply smote to data\n",
+    "x_resampled,y_resampled=smote.fit_resample(X_train,y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "5babf9cd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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+       "\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>trtbps</th>\n",
+       "      <th>chol</th>\n",
+       "      <th>thalachh</th>\n",
+       "      <th>oldpeak</th>\n",
+       "      <th>sex_1</th>\n",
+       "      <th>exng_1</th>\n",
+       "      <th>caa_1</th>\n",
+       "      <th>caa_2</th>\n",
+       "      <th>caa_3</th>\n",
+       "      <th>...</th>\n",
+       "      <th>cp_2</th>\n",
+       "      <th>cp_3</th>\n",
+       "      <th>fbs_1</th>\n",
+       "      <th>restecg_1</th>\n",
+       "      <th>restecg_2</th>\n",
+       "      <th>slp_1</th>\n",
+       "      <th>slp_2</th>\n",
+       "      <th>thall_1</th>\n",
+       "      <th>thall_2</th>\n",
+       "      <th>thall_3</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.301887</td>\n",
+       "      <td>0.200000</td>\n",
+       "      <td>-0.578512</td>\n",
+       "      <td>0.262774</td>\n",
+       "      <td>0.125000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.301887</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.446281</td>\n",
+       "      <td>0.116788</td>\n",
+       "      <td>0.625000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.226415</td>\n",
+       "      <td>-1.500000</td>\n",
+       "      <td>0.148760</td>\n",
+       "      <td>-0.905109</td>\n",
+       "      <td>0.250000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.830189</td>\n",
+       "      <td>1.500000</td>\n",
+       "      <td>-0.181818</td>\n",
+       "      <td>-0.437956</td>\n",
+       "      <td>1.062500</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.905660</td>\n",
+       "      <td>-1.200000</td>\n",
+       "      <td>-0.264463</td>\n",
+       "      <td>-0.321168</td>\n",
+       "      <td>-0.187500</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>251</th>\n",
+       "      <td>-1.320864</td>\n",
+       "      <td>-0.624928</td>\n",
+       "      <td>-0.405116</td>\n",
+       "      <td>-0.656943</td>\n",
+       "      <td>0.562536</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>252</th>\n",
+       "      <td>0.001681</td>\n",
+       "      <td>0.087747</td>\n",
+       "      <td>1.880983</td>\n",
+       "      <td>-0.609561</td>\n",
+       "      <td>0.387531</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>253</th>\n",
+       "      <td>-0.435485</td>\n",
+       "      <td>-0.438876</td>\n",
+       "      <td>-0.612783</td>\n",
+       "      <td>0.364428</td>\n",
+       "      <td>0.092604</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>254</th>\n",
+       "      <td>-1.297778</td>\n",
+       "      <td>0.024355</td>\n",
+       "      <td>0.835901</td>\n",
+       "      <td>0.013898</td>\n",
+       "      <td>-0.375000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>255</th>\n",
+       "      <td>0.524256</td>\n",
+       "      <td>-0.105360</td>\n",
+       "      <td>0.352423</td>\n",
+       "      <td>-0.095201</td>\n",
+       "      <td>0.552680</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>256 rows × 22 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          age    trtbps      chol  thalachh   oldpeak  sex_1  exng_1  caa_1  \\\n",
+       "0    0.301887  0.200000 -0.578512  0.262774  0.125000      1       0      0   \n",
+       "1    0.301887  1.000000 -0.446281  0.116788  0.625000      1       0      0   \n",
+       "2    0.226415 -1.500000  0.148760 -0.905109  0.250000      0       0      0   \n",
+       "3    0.830189  1.500000 -0.181818 -0.437956  1.062500      1       0      0   \n",
+       "4    0.905660 -1.200000 -0.264463 -0.321168 -0.187500      0       0      0   \n",
+       "..        ...       ...       ...       ...       ...    ...     ...    ...   \n",
+       "251 -1.320864 -0.624928 -0.405116 -0.656943  0.562536      1       0      0   \n",
+       "252  0.001681  0.087747  1.880983 -0.609561  0.387531      1       1      1   \n",
+       "253 -0.435485 -0.438876 -0.612783  0.364428  0.092604      1       0      0   \n",
+       "254 -1.297778  0.024355  0.835901  0.013898 -0.375000      1       1      0   \n",
+       "255  0.524256 -0.105360  0.352423 -0.095201  0.552680      1       0      0   \n",
+       "\n",
+       "     caa_2  caa_3  ...  cp_2  cp_3  fbs_1  restecg_1  restecg_2  slp_1  slp_2  \\\n",
+       "0        1      0  ...     0     1      0          1          0      0      1   \n",
+       "1        0      0  ...     1     0      1          1          0      0      1   \n",
+       "2        0      0  ...     0     0      0          0          0      1      0   \n",
+       "3        0      0  ...     0     0      0          0          0      0      1   \n",
+       "4        1      0  ...     0     0      0          1          0      0      1   \n",
+       "..     ...    ...  ...   ...   ...    ...        ...        ...    ...    ...   \n",
+       "251      0      0  ...     0     0      0          1          0      1      0   \n",
+       "252      0      0  ...     0     0      0          1          0      1      0   \n",
+       "253      0      0  ...     0     0      0          0          0      0      1   \n",
+       "254      0      0  ...     0     0      0          0          0      0      0   \n",
+       "255      0      0  ...     0     0      0          0          0      1      0   \n",
+       "\n",
+       "     thall_1  thall_2  thall_3  \n",
+       "0          0        1        0  \n",
+       "1          0        1        0  \n",
+       "2          0        1        0  \n",
+       "3          1        0        0  \n",
+       "4          0        1        0  \n",
+       "..       ...      ...      ...  \n",
+       "251        0        0        1  \n",
+       "252        0        0        1  \n",
+       "253        0        0        1  \n",
+       "254        0        0        1  \n",
+       "255        0        0        0  \n",
+       "\n",
+       "[256 rows x 22 columns]"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_resampled"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "219e112b",
+   "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>output</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>251</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>252</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>253</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>254</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>255</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>256 rows × 1 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     output\n",
+       "0         0\n",
+       "1         1\n",
+       "2         1\n",
+       "3         1\n",
+       "4         1\n",
+       "..      ...\n",
+       "251       0\n",
+       "252       0\n",
+       "253       0\n",
+       "254       0\n",
+       "255       0\n",
+       "\n",
+       "[256 rows x 1 columns]"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_resampled"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "50bd417c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.linear_model import LogisticRegression\n",
+    "logreg=LogisticRegression()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "e078a562",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LogisticRegression()"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#fitting the object\n",
+    "logreg.fit(x_resampled,y_resampled)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "18fc73d5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Get feature names from your dataset\n",
+    "feature_names = x_resampled.columns\n",
+    "\n",
+    "# Extract coefficients and their absolute values\n",
+    "coefficients = logreg.coef_[0]\n",
+    "absolute_coefficients = np.abs(coefficients)\n",
+    "\n",
+    "# Create a DataFrame with feature names and their absolute coefficients\n",
+    "imp_feature = pd.DataFrame({'Feature': feature_names, 'Coefficient': absolute_coefficients})\n",
+    "\n",
+    "# Sort features by absolute coefficient value to get a clearer visualization\n",
+    "imp_feature = imp_feature.sort_values(by='Coefficient', ascending=False)\n",
+    "\n",
+    "# Define colors for the bar plot\n",
+    "colors = ['red', 'green', 'blue', 'black', 'yellow', 'magenta', 'cyan']\n",
+    "\n",
+    "# Plotting the coefficients\n",
+    "plt.figure(figsize=(10, 6))  # Adjust figure size as needed\n",
+    "plt.barh(imp_feature['Feature'], imp_feature['Coefficient'], color=colors)\n",
+    "plt.title(\"Barplot Representing Feature Importance (Absolute Coefficients)\")\n",
+    "plt.xlabel(\"Absolute Coefficient Value\")\n",
+    "plt.ylabel(\"Features\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "9dca78af",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0.25405672, 0.74594328],\n",
+       "       [0.79948772, 0.20051228],\n",
+       "       [0.01945218, 0.98054782],\n",
+       "       [0.97843743, 0.02156257],\n",
+       "       [0.1565778 , 0.8434222 ],\n",
+       "       [0.96798821, 0.03201179],\n",
+       "       [0.64224985, 0.35775015],\n",
+       "       [0.83221046, 0.16778954],\n",
+       "       [0.14316409, 0.85683591],\n",
+       "       [0.88830785, 0.11169215],\n",
+       "       [0.22256858, 0.77743142],\n",
+       "       [0.95514289, 0.04485711],\n",
+       "       [0.04166444, 0.95833556],\n",
+       "       [0.98570592, 0.01429408],\n",
+       "       [0.47503896, 0.52496104],\n",
+       "       [0.74878857, 0.25121143],\n",
+       "       [0.94937178, 0.05062822],\n",
+       "       [0.11300504, 0.88699496],\n",
+       "       [0.13268481, 0.86731519],\n",
+       "       [0.12936525, 0.87063475],\n",
+       "       [0.01376266, 0.98623734],\n",
+       "       [0.14252603, 0.85747397],\n",
+       "       [0.61866004, 0.38133996],\n",
+       "       [0.79788277, 0.20211723],\n",
+       "       [0.06068087, 0.93931913],\n",
+       "       [0.66984112, 0.33015888],\n",
+       "       [0.63697947, 0.36302053],\n",
+       "       [0.69532319, 0.30467681],\n",
+       "       [0.66379286, 0.33620714],\n",
+       "       [0.14561393, 0.85438607],\n",
+       "       [0.54342568, 0.45657432],\n",
+       "       [0.01945218, 0.98054782],\n",
+       "       [0.78553762, 0.21446238],\n",
+       "       [0.13857041, 0.86142959],\n",
+       "       [0.29427202, 0.70572798],\n",
+       "       [0.74083495, 0.25916505],\n",
+       "       [0.08141645, 0.91858355],\n",
+       "       [0.47634356, 0.52365644],\n",
+       "       [0.6108104 , 0.3891896 ],\n",
+       "       [0.92125502, 0.07874498],\n",
+       "       [0.13143711, 0.86856289],\n",
+       "       [0.26125023, 0.73874977],\n",
+       "       [0.27349893, 0.72650107],\n",
+       "       [0.7225691 , 0.2774309 ],\n",
+       "       [0.29557328, 0.70442672],\n",
+       "       [0.75826376, 0.24173624],\n",
+       "       [0.79268772, 0.20731228],\n",
+       "       [0.26682519, 0.73317481],\n",
+       "       [0.34839875, 0.65160125],\n",
+       "       [0.97507975, 0.02492025],\n",
+       "       [0.93726732, 0.06273268],\n",
+       "       [0.94523505, 0.05476495],\n",
+       "       [0.90920021, 0.09079979],\n",
+       "       [0.41211002, 0.58788998],\n",
+       "       [0.15793274, 0.84206726],\n",
+       "       [0.55089634, 0.44910366],\n",
+       "       [0.92453929, 0.07546071]])"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# calculating the probabilities\n",
+    "y_pred_proba = logreg.predict_proba(X_test)\n",
+    "y_pred_proba"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "9e4c0fdb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1,\n",
+       "       0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0,\n",
+       "       1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0], dtype=int64)"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# finding the predicted valued:argmax= argument of the maximum  is used to find the index of the maximum value in an array or sequence.\n",
+    "y_pred = np.argmax(y_pred_proba,axis=1)\n",
+    "y_pred"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "7195efa6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The test accuracy score of Logistric Regression is  0.8947368421052632\n"
+     ]
+    }
+   ],
+   "source": [
+    "# printing the test accuracy\n",
+    "from sklearn.metrics import accuracy_score\n",
+    "res1=accuracy_score(y_test, y_pred)\n",
+    "print(\"The test accuracy score of Logistric Regression is \", accuracy_score(y_test, y_pred))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "97ce0677",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[25,  1],\n",
+       "       [ 5, 26]], dtype=int64)"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# import the metrics class\n",
+    "from sklearn import metrics\n",
+    "\n",
+    "cnf_matrix = metrics.confusion_matrix(y_test, y_pred)\n",
+    "cnf_matrix"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c9f71d84",
+   "metadata": {},
+   "source": [
+    " The dimension of this matrix is 2*2 because this model is binary classification. You have two classes 0 and 1. Diagonal values represent accurate predictions, while non-diagonal elements are inaccurate predictions. In the output, 24 and 26 are actual predictions, and 2 and 5 are incorrect predictions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "6f9fc390",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 257.44, 'Predicted label')"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# import required modules\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "class_names=[0,1] # name  of classes\n",
+    "fig, ax = plt.subplots()\n",
+    "tick_marks = np.arange(len(class_names))\n",
+    "plt.xticks(tick_marks, class_names)\n",
+    "plt.yticks(tick_marks, class_names)\n",
+    "# create heatmap\n",
+    "sns.heatmap(pd.DataFrame(cnf_matrix), annot=True, cmap=\"YlGnBu\" ,fmt='g')\n",
+    "ax.xaxis.set_label_position(\"top\")\n",
+    "plt.tight_layout()\n",
+    "plt.title('Confusion matrix', y=1.1)\n",
+    "plt.ylabel('Actual label')\n",
+    "plt.xlabel('Predicted label')\n",
+    "\n",
+    "#Text(0.5,257.44,'Predicted label');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "854a77a1",
+   "metadata": {},
+   "source": [
+    "True Positives (TP): The number of instances correctly predicted as positive:26\n",
+    "True Negatives (TN): The number of instances correctly predicted as negative:25 "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "1702784b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "          No       0.83      0.96      0.89        26\n",
+      "         Yes       0.96      0.84      0.90        31\n",
+      "\n",
+      "    accuracy                           0.89        57\n",
+      "   macro avg       0.90      0.90      0.89        57\n",
+      "weighted avg       0.90      0.89      0.89        57\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import classification_report\n",
+    "target_names = ['No','Yes']\n",
+    "print(classification_report(y_test, y_pred, target_names=target_names))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "55c8ed2a",
+   "metadata": {
+    "scrolled": true
+   },
+   "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": [
+    "from sklearn.metrics import  roc_curve\n",
+    "# calculating the probabilities\n",
+    "y_pred_prob = logreg.predict_proba(X_test)[:,1]\n",
+    "\n",
+    "# instantiating the roc_cruve\n",
+    "fpr,tpr,threshols=roc_curve(y_test,y_pred_prob)\n",
+    "\n",
+    "# plotting the curve\n",
+    "plt.plot([0,1],[0,1],\"k--\",'r+')\n",
+    "plt.plot(fpr,tpr,label='Logistic Regression')\n",
+    "plt.xlabel(\"False Positive Rate\")\n",
+    "plt.ylabel(\"True Positive Rate\")\n",
+    "plt.title(\"Logistric Regression ROC Curve\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4ab6cdc3",
+   "metadata": {},
+   "source": [
+    "<span style=\"font-weight:bold;font-size:20px;\">SUPPORT VECTOR MACHINE<span/>\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "36b36408",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The test accuracy score of SVM is  0.8596491228070176\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.svm import SVC\n",
+    "# instantiating the object and fitting\n",
+    "clf = SVC(kernel='linear', C=1, random_state=2).fit(x_resampled,y_resampled)\n",
+    "\n",
+    "# predicting the values\n",
+    "y_pred = clf.predict(X_test)\n",
+    "\n",
+    "res2=accuracy_score(y_test, y_pred)\n",
+    "# printing the test accuracy\n",
+    "print(\"The test accuracy score of SVM is \", accuracy_score(y_test, y_pred))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "ee8632cf",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[23,  3],\n",
+       "       [ 5, 26]], dtype=int64)"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# import the metrics class\n",
+    "from sklearn import metrics\n",
+    "\n",
+    "cnf_matrix = metrics.confusion_matrix(y_test, y_pred)\n",
+    "cnf_matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "ed6f7180",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 257.44, 'Predicted label')"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# import required modules\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "class_names=[0,1] # name  of classes\n",
+    "fig, ax = plt.subplots()\n",
+    "tick_marks = np.arange(len(class_names))\n",
+    "plt.xticks(tick_marks, class_names)\n",
+    "plt.yticks(tick_marks, class_names)\n",
+    "# create heatmap\n",
+    "sns.heatmap(pd.DataFrame(cnf_matrix), annot=True, cmap=\"YlGnBu\" ,fmt='g')\n",
+    "ax.xaxis.set_label_position(\"top\")\n",
+    "plt.tight_layout()\n",
+    "plt.title('Confusion matrix', y=1.1)\n",
+    "plt.ylabel('Actual label')\n",
+    "plt.xlabel('Predicted label')\n",
+    "\n",
+    "#Text(0.5,257.44,'Predicted label');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ed31787f-7e53-4f74-bb1d-57f17ec084b8",
+   "metadata": {},
+   "source": [
+    "DECISION TREE "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "23984e27-aedf-41c1-b94f-8e05426ba71a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The test accuracy score of Decision Tree is  0.7894736842105263\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.tree import DecisionTreeClassifier\n",
+    "# instantiating the object\n",
+    "dt = DecisionTreeClassifier(random_state = 42)\n",
+    "\n",
+    "# fitting the model\n",
+    "dt.fit(x_resampled,y_resampled)\n",
+    "\n",
+    "# calculating the predictions\n",
+    "y_pred = dt.predict(X_test)\n",
+    "\n",
+    "res3=accuracy_score(y_test, y_pred)\n",
+    "# printing the test accuracy\n",
+    "print(\"The test accuracy score of Decision Tree is \", accuracy_score(y_test, y_pred))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "7bdbe024",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[20,  6],\n",
+       "       [ 6, 25]], dtype=int64)"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# import the metrics class\n",
+    "from sklearn import metrics\n",
+    "\n",
+    "cnf_matrix = metrics.confusion_matrix(y_test, y_pred)\n",
+    "cnf_matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "3c067707",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 257.44, 'Predicted label')"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# import required modules\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "class_names=[0,1] # name  of classes\n",
+    "fig, ax = plt.subplots()\n",
+    "tick_marks = np.arange(len(class_names))\n",
+    "plt.xticks(tick_marks, class_names)\n",
+    "plt.yticks(tick_marks, class_names)\n",
+    "# create heatmap\n",
+    "sns.heatmap(pd.DataFrame(cnf_matrix), annot=True, cmap=\"YlGnBu\" ,fmt='g')\n",
+    "ax.xaxis.set_label_position(\"top\")\n",
+    "plt.tight_layout()\n",
+    "plt.title('Confusion matrix', y=1.1)\n",
+    "plt.ylabel('Actual label')\n",
+    "plt.xlabel('Predicted label')\n",
+    "\n",
+    "#Text(0.5,257.44,'Predicted label');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ebbc8cf0-ebd5-4cc1-ba2b-c2516e079db6",
+   "metadata": {},
+   "source": [
+    "Random Forest"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "f2102339-8e24-466c-8a5d-51aed68eb184",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Best Hyperparameters: {'max_depth': None, 'min_samples_leaf': 1, 'min_samples_split': 5, 'n_estimators': 100}\n",
+      "The test accuracy score of Random Forest is  0.8070175438596491\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "from sklearn.model_selection import GridSearchCV\n",
+    "from sklearn.metrics import accuracy_score\n",
+    "\n",
+    "# Instantiate RandomForestClassifier\n",
+    "rf = RandomForestClassifier(random_state=42)\n",
+    "\n",
+    "# Define the hyperparameters grid for tuning\n",
+    "param_grid = {\n",
+    "    'n_estimators': [100, 200, 300],\n",
+    "    'max_depth': [None, 10, 20],\n",
+    "    'min_samples_split': [2, 5, 10],\n",
+    "    'min_samples_leaf': [1, 2, 4]\n",
+    "}\n",
+    "\n",
+    "# Instantiate GridSearchCV to find the best hyperparameters\n",
+    "grid_search = GridSearchCV(rf, param_grid, cv=5, scoring='accuracy')\n",
+    "\n",
+    "# Fit the grid search to the data\n",
+    "grid_search.fit(x_resampled,y_resampled)\n",
+    "\n",
+    "# Get the best hyperparameters\n",
+    "best_params = grid_search.best_params_\n",
+    "print(\"Best Hyperparameters:\", best_params)\n",
+    "\n",
+    "# Use the best hyperparameters to train the model\n",
+    "best_rf = RandomForestClassifier(random_state=42, **best_params)\n",
+    "best_rf.fit(x_resampled,y_resampled)\n",
+    "\n",
+    "# Make predictions\n",
+    "y_pred = best_rf.predict(X_test)\n",
+    "\n",
+    "\n",
+    "res4=accuracy_score(y_test, y_pred)\n",
+    "# Print the test accuracy\n",
+    "print(\"The test accuracy score of Random Forest is \", accuracy_score(y_test, y_pred))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "043f66e5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[22,  4],\n",
+       "       [ 7, 24]], dtype=int64)"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# import the metrics class\n",
+    "from sklearn import metrics\n",
+    "\n",
+    "cnf_matrix = metrics.confusion_matrix(y_test, y_pred)\n",
+    "cnf_matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "447755b9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 257.44, 'Predicted label')"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# import required modules\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "class_names=[0,1] # name  of classes\n",
+    "fig, ax = plt.subplots()\n",
+    "tick_marks = np.arange(len(class_names))\n",
+    "plt.xticks(tick_marks, class_names)\n",
+    "plt.yticks(tick_marks, class_names)\n",
+    "# create heatmap\n",
+    "sns.heatmap(pd.DataFrame(cnf_matrix), annot=True, cmap=\"YlGnBu\" ,fmt='g')\n",
+    "ax.xaxis.set_label_position(\"top\")\n",
+    "plt.tight_layout()\n",
+    "plt.title('Confusion matrix', y=1.1)\n",
+    "plt.ylabel('Actual label')\n",
+    "plt.xlabel('Predicted label')\n",
+    "\n",
+    "#Text(0.5,257.44,'Predicted label');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "075879dd-e8f5-478b-9c1b-f04f31c19449",
+   "metadata": {},
+   "source": [
+    "Gradient Boosting Classifier - without tuning "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "efc7e2e4-bc0a-4c55-9e70-087925c8c5f8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The test accuracy score of Gradient Boosting Classifier is  0.8596491228070176\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.ensemble import GradientBoostingClassifier\n",
+    "# instantiate the classifier\n",
+    "gbt = GradientBoostingClassifier(n_estimators = 300,max_depth=1,subsample=0.8,max_features=0.2,random_state=42)\n",
+    "\n",
+    "# fitting the model\n",
+    "gbt.fit(x_resampled,y_resampled)\n",
+    "\n",
+    "res5=accuracy_score(y_test, y_pred)\n",
+    "# predicting values\n",
+    "y_pred = gbt.predict(X_test)\n",
+    "print(\"The test accuracy score of Gradient Boosting Classifier is \", accuracy_score(y_test, y_pred))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "2b303780",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[25,  1],\n",
+       "       [ 7, 24]], dtype=int64)"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# import the metrics class\n",
+    "from sklearn import metrics\n",
+    "\n",
+    "cnf_matrix = metrics.confusion_matrix(y_test, y_pred)\n",
+    "cnf_matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "id": "474c92e9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 257.44, 'Predicted label')"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZsAAAE9CAYAAADd3c8LAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAaqklEQVR4nO3deZRkdX338fenGdkRGNYBJG4gwQ0NImIkKHkUYxJERQU0LuhoDBoRFTA+ImpMjsZdVIZNjIpoAEXwARRF3FAWR3YBleDAwCCLDAQVmO/zR93GdtLT3dXdt6q65v06556puvfW7357mNMffsu9lapCkqQ2jfS7AEnS8DNsJEmtM2wkSa0zbCRJrTNsJEmtM2wkSa0zbDTQkqyT5OtJfpvkKzNo54Ak58xmbf2S5BlJft7vOqRuxPtsNBuS7A+8BdgBWA4sBv61qr4/w3ZfDrwR2K2q7p9pnYMuSQHbVdV1/a5Fmk32bDRjSd4CfBR4P7AFsC3wKWDvWWj+z4BrVoegmYok8/pdgzQdho1mJMmGwHuAf6qqU6vqnqq6r6q+XlVva85ZK8lHk9zUbB9NslZzbI8kS5IckmRZkqVJXtUcOxJ4F/CSJHcnOTDJu5N8fsz1H56kRn8JJ3llkl8mWZ7kV0kOGLP/+2M+t1uSC5vhuQuT7Dbm2HlJ3pvkB0075yTZdBU//2j9bx9T//OT/E2Sa5LcnuQdY87fJcmPktzZnPvJJGs2x85vTvtZ8/O+ZEz7hya5GThhdF/zmUc113hy836rJL9JssdM/rtKs82w0Uw9DVgbOG2Cc/4F2BXYCXgisAvwzjHHtwQ2BLYGDgSOSrJxVR1Bp7d0clWtX1XHTVRIkvWAjwPPraoNgN3oDOetfN584Mzm3E2ADwNnJtlkzGn7A68CNgfWBN46waW3pPN3sDWdcDwGeBnwF8AzgHcleWRz7gPAwcCmdP7u9gTeAFBVuzfnPLH5eU8e0/58Or28hWMvXFW/AA4FvpBkXeAE4LNVdd4E9Uo9Z9hopjYBfjPJMNcBwHuqallV3QocCbx8zPH7muP3VdU3gLuBx0yznhXA45KsU1VLq+qKcc55HnBtVf1nVd1fVScBVwN/N+acE6rqmqq6F/gynaBclfvozE/dB3yJTpB8rKqWN9e/AngCQFVdXFUXNNe9Hjga+Ksp/ExHVNXvm3r+RFUdA1wL/BhYQCfcpYFi2GimbgM2nWQuYSvgv8e8/+9m34NtrBRW/wOs320hVXUP8BLg9cDSJGcm2WEK9YzWtPWY9zd3Uc9tVfVA83o0DG4Zc/ze0c8n2T7JGUluTnIXnZ7buEN0Y9xaVb+b5JxjgMcBn6iq309yrtRzho1m6kfA74DnT3DOTXSGgEZt2+ybjnuAdce833Lswao6u6r+D53/w7+azi/hyeoZrenGadbUjU/TqWu7qnoo8A4gk3xmwiWjSdans0DjOODdzTChNFAMG81IVf2WzjzFUc3E+LpJHpLkuUk+0Jx2EvDOJJs1E+3vAj6/qjYnsRjYPcm2zeKEw0cPJNkiyd83cze/pzMc98A4bXwD2D7J/knmJXkJsCNwxjRr6sYGwF3A3U2v6x9XOn4L8Mj/9amJfQy4uKpeQ2cu6jMzrlKaZYaNZqyqPkznHpt3ArcCvwYOAr7anPI+4CLgUuAy4JJm33Su9U3g5Kati/nTgBgBDqHTc7mdzlzIG8Zp4zbgb5tzbwPeDvxtVf1mOjV16a10Fh8sp9PrOnml4+8GTmxWq714ssaS7A3sRWfoEDr/HZ48ugpPGhTe1ClJap09G0lS6wwbSVLrDBtJUusMG0lS6wwbSVLrDBtJUusMG0lS6wwbSVLrDBtJUusMG0lS6wwbSVLrDBtJUusMG0lS6wwbSVLrDBtJUusMG0lS6wwbSVLrDBtJUusMG/VNkgeSLE5yeZKvJFl3Bm19NsmLmtfHJtlxgnP3SLLbNK5xfZJNp7p/pXPu7vJa707y1m5rlAaVYaN+ureqdqqqxwF/AF4/9mCSNabTaFW9pqqunOCUPYCuw0bS9Bk2GhTfAx7d9Dq+k+SLwGVJ1kjywSQXJrk0yesA0vHJJFcmORPYfLShJOcl2bl5vVeSS5L8LMm5SR5OJ9QObnpVz0iyWZJTmmtcmOTpzWc3SXJOkp8mORrIZD9Ekq8muTjJFUkWrnTsQ00t5ybZrNn3qCRnNZ/5XpIdZuVvUxow8/pdgJRkHvBc4Kxm1y7A46rqV80v7N9W1VOSrAX8IMk5wJOAxwCPB7YArgSOX6ndzYBjgN2btuZX1e1JPgPcXVX/0Zz3ReAjVfX9JNsCZwN/DhwBfL+q3pPkecCfhMcqvLq5xjrAhUlOqarbgPWAS6rqkCTvato+CFgEvL6qrk3yVOBTwLOm8dcoDTTDRv20TpLFzevvAcfRGd76SVX9qtn/bOAJo/MxwIbAdsDuwElV9QBwU5Jvj9P+rsD5o21V1e2rqOOvgR2TBzsuD02yQXONFzSfPTPJHVP4md6UZJ/m9cOaWm8DVgAnN/s/D5yaZP3m5/3KmGuvNYVrSHOOYaN+ureqdhq7o/mle8/YXcAbq+rslc77G6AmaT9TOAc6w8lPq6p7x6llKp8fPX8POsH1tKr6nyTnAWuv4vRqrnvnyn8H0jByzkaD7mzgH5M8BCDJ9knWA84HXtrM6SwAnjnOZ38E/FWSRzSfnd/sXw5sMOa8c+gMadGct1Pz8nzggGbfc4GNJ6l1Q+COJmh2oNOzGjUCjPbO9qczPHcX8Ksk+zbXSJInTnINaU4ybDTojqUzH3NJksuBo+n0yE8DrgUuAz4NfHflD1bVrXTmWU5N8jP+OIz1dWCf0QUCwJuAnZsFCFfyx1VxRwK7J7mEznDeDZPUehYwL8mlwHuBC8Ycuwd4bJKL6czJvKfZfwBwYFPfFcDeU/g7keacVE15lECStJpJ8jDgc8CWdOYeF1XVx5K8G3gtcGtz6juq6hurbMewkSStSjNMvaCqLmkWzlwMPB94MWNWdU7GBQKSpFWqqqXA0ub18iRXAVt3287A9mzW2Xa/wSxMQ+3eG47sdwla7Ww/6c3C3ej2d+fvfv2l1/Gn95AtqqpF453b3BR9PvA44C3AK4G7gIuAQ6pqlbcHuEBAkoZIMtLVVlWLqmrnMduqgmZ94BTgzc1Kyk8DjwJ2otPz+dBEdTmMJklDJC30IZpbD04BvlBVpwJU1S1jjh8DnDFRG4aNJA2RZHbDJp27m48DrqqqD4/Zv6CZzwHYB7h8onYMG0kaIrMdNsDTgZfTeTDu4mbfO4D9mhugC7geeN1EjRg2kjRExjxnb1ZU1fcZ/4nnq7ynZjyGjSQNlcFc92XYSNIQaWEYbVYYNpI0RAwbSVLr2lj6PBsMG0kaIvZsJEmtM2wkSa0zbCRJrcu4t8T0n2EjSUPEno0kqXWGjSSpdYaNJKkHDBtJUsvs2UiSWmfYSJJa5+NqJEmtGxlZo98ljMuwkaQh4jCaJKl1DqNJklpnz0aS1DrDRpLUOofRJEnts2cjSWqbw2iSpNYlfp+NJKllztlIklrnMJokqX0Oo0mSWjeYHRvDRpKGij0bSVLrDBtJUuscRpMkta3s2UiSWjeYWWPYSNJQGRnMtDFsJGmYOIwmSWrdYGaNYSNJQ8VhNElS6xxGkyS1bg3DRpLUtsHMGsNGkoaJN3VKkto3oAsEBvQpOpKkaUmX22TNJQ9L8p0kVyW5Isk/N/vnJ/lmkmubPzeeqB3DRpKGSdLdNrn7gUOq6s+BXYF/SrIjcBhwblVtB5zbvF8lw0aShslIutsmUVVLq+qS5vVy4Cpga2Bv4MTmtBOB50/UjnM2kjRMWpyySfJw4EnAj4EtqmopdAIpyeYTfdaejSQNky6H0ZIsTHLRmG3h+M1mfeAU4M1VdVe3ZdmzkaRh0uXS56paBCyauMk8hE7QfKGqTm1235JkQdOrWQAsm6gNezaSNExGutwmkSTAccBVVfXhMYdOB17RvH4F8LWJ2rFnI0nDZPZv6nw68HLgsiSLm33vAP4d+HKSA4EbgH0nasSwkaRhMstZU1Xfn6DVPafajmEzh22zYD7HfuQNbLHZRqyo4vgvnstRx5/Fvxz8Ql6937O49bbOHN4RHziZs7+zuL/FaigdfvjHOO+8C9lkkw0544yj+l2OgBrQJwgYNnPY/Q+s4LD3fZ7Fl1/P+uutzQ/PfD/nfu8yAD5x7Df46KIz+1yhht0LXrAnL3vZ8zj00I/0uxSN8tlomm03L7uTm5fdCcDd9/yOq6+7ka22nN/forRaecpTHseSJbf0uwyNNZhZ017YJNmBzh2mWwMF3AScXlVXtXXN1dm222zKTo99OBf+9DqetvP2vP4Vz2H/F+7OJZf+ksPe93nu/O09/S5RUi8M6DBaK0ufkxwKfIlOxv4EuLB5fVKSCZ+fo+6tt+5anHT0wbztyM+x/O57OeY/v8WOz/hnnrrXYdy87A7+/Z0v63eJknpl9p+NNiva6tkcCDy2qu4buzPJh4Er6CyZ+1+aO1cXAszbeGfmrf/olsobHvPmrcFJRx/Myaf9gK+ddSEAy37z2wePH3/Stzn1hLf3qzxJvTaYHZvWbupcAWw1zv4FzbFxVdWiqtq5qnY2aKbmMx9cyM+vu4mPH/uNB/dtuflGD77e+zlP4cqf/7oPlUnqi1l+EOdsaatn82bg3CTXAqO/6bYFHg0c1NI1Vzu7PeUxHPDC3bnsqhu44P/9G9BZ5vzivXfjCTv+GVXw30tu5Y2HH9vnSjWs3vKWD/KTn1zGHXfcxe67v5I3vnF/9t332f0ua/U2oHM2qap2Gk5GgF3oLBAIsAS4sKoemMrn19l2v3YKkyZw7w1H9rsErXa2n9V0eORrvtLV785fHrtvT9KptdVoVbUCuKCt9iVJ4xjQno332UjSMPGmTklS6+YZNpKkttmzkSS1zjkbSVLbyp6NJKl1A/r9y4aNJA0Th9EkSa1zGE2S1Dp7NpKk1g1m1hg2kjRMyp6NJKl1ho0kqXUuEJAktc77bCRJrbNnI0lqnXM2kqTWGTaSpLb5IE5JUvtcICBJap09G0lS65yzkSS1zrCRJLVuMLPGsJGkYeKDOCVJ7VtjMJejGTaSNEwGs2Nj2EjSMBkZzI6NYSNJw2RAb7MxbCRpmMy5sEmyHKjRt82f1byuqnpoy7VJkrqUAU2bVYZNVW3Qy0IkSTM3oFkztUe2JfnLJK9qXm+a5BHtliVJmo6ku61XJg2bJEcAhwKHN7vWBD7fZlGSpOnJSHfbpO0lxydZluTyMfveneTGJIub7W8ma2cqPZt9gL8H7gGoqpsAh9gkaQC10LP5LLDXOPs/UlU7Nds3JmtkKqvR/lBVlaQ6P0jWm1J5kqSem+2n1VTV+UkePtN2ptKz+XKSo4GNkrwW+BZwzEwvLEmafT2cszkoyaXNMNvGk508adhU1X8A/wWcAmwPvKuqPjGjEiVJreg2bJIsTHLRmG3hFC7zaeBRwE7AUuBDk31gqjd1XgasQ+c+m8um+BlJUo91e59NVS0CFnX5mVvGXO8Y4IzJPjOV1WivAX4CvAB4EXBBkld3U5gkqTdmezXauNdIFox5uw9w+arOHTWVns3bgCdV1W3NRTYBfggcP50iJUntme17Z5KcBOwBbJpkCXAEsEeSneiMdl0PvG6ydqYSNkuA5WPeLwd+3V25kqRemO2wqar9xtl9XLftTPRstLc0L28Efpzka3RSbG86w2qSpAEzqI+rmahnM3rj5i+abdTX2itHkjQTA/qt0BM+iPPIXhYiSZq5udizASDJZsDbgccCa4/ur6pntViXJGkaBjVsprLw7QvA1cAjgCPprDy4sMWaJEnTlJF0tfXKVMJmk6o6Drivqr5bVa8Gdm25LknSNAzqVwxMZenzfc2fS5M8D7gJ2Ka9kiRJ0zUyzRs12zaVsHlfkg2BQ4BPAA8FDm61KknStMy51Wijqmr0mTe/BZ7ZbjmSpJkY1AUCE93U+Qk6N3GOq6re1EpFkqRpm+7zzto2Uc/mop5VIUmaFXOuZ1NVJ/ayEEnSzHX7FQO9MtXvs5EkzQEDmjWGjSQNE8NGktS6ORc2/V6N9u2LXtZm89K45m/38X6XoNXM7dd+clbbm4v32bgaTZLmmDkXNq5Gk6S5ZySrHJDqq6l+xcChwI74FQOSNNAGtWcz1a8YuAq/YkCSBt5Il1sv65qMXzEgSXPESKqrrVf8igFJGiKDOozmVwxI0hAZ0Odw+hUDkjRM5mzPJskJjHNzZzN3I0kaIJmrS5+BM8a8XhvYh868jSRpwMzZnk1VnTL2fZKTgG+1VpEkadrm7JzNOLYDtp3tQiRJMzeXnyCwnD+ds7mZzhMFJEkDZi4Po23Qi0IkSTM3b0DDZtLhvSTnTmWfJKn/5twTBJKsDawLbJpkY2A0Lx8KbNWD2iRJXZqLw2ivA95MJ1gu5o9hcxdwVLtlSZKmY86tRquqjwEfS/LGqvpED2uSJE3ToK5Gm0oIrkiy0eibJBsneUN7JUmSpmsk3W09q2sK57y2qu4cfVNVdwCvba0iSdK0DWrYTOWmzpEkqaoCSLIGsGa7ZUmSpmPOzdmMcTbw5SSfoXNz5+uBs1qtSpI0LYM6ZzOVsDkUWAj8I50VaecAx7RZlCRpegZ16fOkPa6qWlFVn6mqF1XVC4Er6HyJmiRpwIx0ufXKlB7EmWQnYD/gJcCvgFNbrEmSNE2D2rOZ6AkC2wMvpRMytwEnA6kqv61TkgbUoH552kS9qKuBPYG/q6q/bG7sfKA3ZUmSpmO2lz4nOT7JsiSXj9k3P8k3k1zb/LnxpHVNcOyFdL5O4DtJjkmyJ398ZI0kaQC1MGfzWWCvlfYdBpxbVdsB5zbvJ61rXFV1WlW9BNgBOA84GNgiyaeTPHtqNUqSemm2n/pcVecDt6+0e2/gxOb1icDzJ61rChe6p6q+UFV/C2wDLGYKKSZJ6r0ePUFgi6paCtD8ufmkdXXTelXdXlVHV9WzplmgJKlF3YZNkoVJLhqzLWyjriktfZYkzQ1rdHl+VS0CFnX5sVuSLKiqpUkWAMsm+8CgPkZHkjQNPfqmztOBVzSvXwF8bbIP2LORpCEy2zd1JjkJ2IPOtzYvAY4A/p3OMzMPBG4A9p2sHcNGkobIbIdNVe23ikN7dtOOYSNJQ2SNAb0b0rCRpCEyb2QwH1dj2EjSEJlzD+KUJM093S597hXDRpKGiD0bSVLr5vLXQkuS5ghXo0mSWucwmiSpdYaNJKl1ho0kqXVruEBAktS2QX2Uv2EjSUPEYTRJUusMG0lS65yzkSS1zp6NJKl1ho0kqXWGjSSpdT4bTZLUOp/6LElqnTd1qnVLb1jGp4743IPvb73pNvY5cC+e8+K/6mNVGjZbb7kRn/rgP7DFZg9lxYrixJN/wNEnnvfg8YMO3JP3HLYPj97lUG6/457+Fbqacs5GrVuw7ea894S3ArDigRW8+QVH8he7P77PVWnY3P/ACv7vv53KpVcuYf311uLbpx3KeT+4mp9fdzNbb7kRezx9B3594+39LnO1NahzNoPa49IMXXnxtWy+1SZsuuX8fpeiIXPLrXdx6ZVLALj7nt9zzS9uZsEWGwHwr//yQo74wFepGsx5g9XBSKqrrVfs2QypH5/7U3b96yf1uwwNuYdtPZ8n7LgNF//sevZ61uNZesudXHH1jf0ua7U2b0C7ED0vK8mrJji2MMlFSS766ufO6mVZQ+X+++7npz+4gqc8c6d+l6Ihtt66a3LiJ1/DO/71FO6//wEOecNzeP9Hz+x3Wau9kS63XtbVa0eu6kBVLaqqnatq5+f/w169rGmoXHrB1fzZ9luz4fwN+l2KhtS8eSOc+MnX8l+nX8QZ5/yMh2+7Gdtuswnf+/rhLP7OkWy15Uac99VD2XxT/w32WtLd1iutDKMluXRVh4At2rim/uiCb13Crns+ud9laIh9/P0HcM0vbuZTJ3wbgKuuuYnH7Hr4g8cXf+dInvWCD7garQ8GdH1Aa3M2WwDPAe5YaX+AH7Z0TQG//90fuOKia3jl2/btdykaUk/9i0fy0n2eyhVX38h3Tz8MgPd+6HS+9d0r+1yZoLe9lW60FTZnAOtX1eKVDyQ5r6VrClhr7TU56sz39bsMDbEfX/xL5m930ITn7PTMI3pUjVY2oOsD2gmbqjpwgmP7t3FNSRLEx9VIkto2oKNoho0kDZPVbc5GktQHA5o1ho0kDRMfxClJat2AZo1hI0nDxDkbSVLrBjRrDBtJGiaGjSSpdS4QkCS1bkCzxrCRpGHi42okSa1ro2eT5HpgOfAAcH9V7dxtG4aNJA2RFpc+P7OqfjPdDxs2kjREBvUrBga1LknSNLT0tdAFnJPk4iQLp1OXPRtJGiLdjqI14TE2QBZV1aKVTnt6Vd2UZHPgm0murqrzu7mOYSNJQ6Tb+2yaYFk5XFY+56bmz2VJTgN2AboKG4fRJGmIjKS7bTJJ1kuywehr4NnA5d3WZc9GkoZIC4vRtgBOS2eCZx7wxao6q9tGDBtJGiKzfVNnVf0SeOJM2zFsJGmI+LgaSVLr/D4bSVLrBjRrDBtJGiaDusTYsJGkIeIwmiSpBwYzbQwbSRoiMWwkSW1LBnPWxrCRpKFiz0aS1DKH0SRJPWDYSJJa5pyNJKkH7NlIklrmnI0kqXWGjSSpB5yzkSS1LAP6cDTDRpKGimEjSWqZczaSpB5wzkaS1LIRb+qUJLXPYTRJUsviMJokqX32bCRJLfM+G0lSDxg2kqSWOWcjSeoBezaSpJb5BAFJUutcICBJ6gHnbCRJLXMYTZLUA4aNJKllztlIknrAORtJUssGdc4mVdXvGjTLkiysqkX9rkOrD//NaTKD2d/STC3sdwFa7fhvThMybCRJrTNsJEmtM2yGk2Pn6jX/zWlCLhCQJLXOno0kqXWGzRBJsleSnye5Lslh/a5Hwy/J8UmWJbm837VosBk2QyLJGsBRwHOBHYH9kuzY36q0GvgssFe/i9DgM2yGxy7AdVX1y6r6A/AlYO8+16QhV1XnA7f3uw4NPsNmeGwN/HrM+yXNPknqO8NmeIz3QCSXGkoaCIbN8FgCPGzM+22Am/pUiyT9CcNmeFwIbJfkEUnWBF4KnN7nmiQJMGyGRlXdDxwEnA1cBXy5qq7ob1UadklOAn4EPCbJkiQH9rsmDSafICBJap09G0lS6wwbSVLrDBtJUusMG0lS6wwbSVLrDBtJUusMG0lS6wwbSVLr/j8e+tTw/gT7RQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# import required modules\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "class_names=[0,1] # name  of classes\n",
+    "fig, ax = plt.subplots()\n",
+    "tick_marks = np.arange(len(class_names))\n",
+    "plt.xticks(tick_marks, class_names)\n",
+    "plt.yticks(tick_marks, class_names)\n",
+    "# create heatmap\n",
+    "sns.heatmap(pd.DataFrame(cnf_matrix), annot=True, cmap=\"YlGnBu\" ,fmt='g')\n",
+    "ax.xaxis.set_label_position(\"top\")\n",
+    "plt.tight_layout()\n",
+    "plt.title('Confusion matrix', y=1.1)\n",
+    "plt.ylabel('Actual label')\n",
+    "plt.xlabel('Predicted label')\n",
+    "\n",
+    "#Text(0.5,257.44,'Predicted label');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cc231c61",
+   "metadata": {},
+   "source": [
+    "<span style=\"font-weight:bold;font-size:20px;\">MODEL EVALUATION<span/>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "id": "6c2e9763",
+   "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>Model</th>\n",
+       "      <th>Accuracy</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Logistic Regression</td>\n",
+       "      <td>89.473684</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Support Vector Machine</td>\n",
+       "      <td>85.964912</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Decision Tree</td>\n",
+       "      <td>78.947368</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Random Forest</td>\n",
+       "      <td>80.701754</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Gradient Boost</td>\n",
+       "      <td>80.701754</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                    Model   Accuracy\n",
+       "0     Logistic Regression  89.473684\n",
+       "1  Support Vector Machine  85.964912\n",
+       "2           Decision Tree  78.947368\n",
+       "3           Random Forest  80.701754\n",
+       "4          Gradient Boost  80.701754"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model_ev = pd.DataFrame({'Model': ['Logistic Regression','Support Vector Machine','Decision Tree','Random Forest','Gradient Boost'], 'Accuracy': [\n",
+    "                    res1*100,res2*100,res3*100,res4*100,res5*100]})\n",
+    "model_ev"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "id": "f1b8b0a7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "colors = ['red','green','blue','gold','silver','yellow','orange',]\n",
+    "plt.figure(figsize=(12,5))\n",
+    "plt.title(\"barplot Represent Accuracy of different models\")\n",
+    "plt.xlabel(\"Accuracy %\")\n",
+    "plt.ylabel(\"Algorithms\")\n",
+    "plt.bar(model_ev['Model'],model_ev['Accuracy'],color = colors)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "092f7481",
+   "metadata": {},
+   "source": [
+    "<span style=\"font-weight:bold;font-size:20px;\">CONCLUSION:<span/>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "af93a8d7",
+   "metadata": {},
+   "source": [
+    "1) Logistic Regression gives the best Accuracy compared to other models.\n",
+    "\n",
+    "2) Exercise induced angina,Chest pain is major symptoms of heart attack."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}